academic honesty in coursework policy 2015

This is CS50x 2020, an older version of the course. See cs50.harvard.edu/x for the latest!

Academic Honesty

This course’s philosophy on academic honesty is best stated as “be reasonable.” The course recognizes that interactions with classmates and others can facilitate mastery of the course’s material. However, there remains a line between enlisting the help of another and submitting the work of another. The course’s policy characterizes both sides of that line.

Or read this paper for context.

The essence of all work that you submit to this course must be your own. Unless otherwise specified, collaboration on assessments (e.g., assignments, labs, problem sets, projects, quizzes, or tests) is not permitted except to the extent that you may ask classmates and others for help so long as that help does not reduce to another doing your work for you. Generally speaking, when asking for help, you may show your work to others, but you may not view theirs, so long as you and they respect this policy’s other constraints.

Regret clause. If you commit some act that is not reasonable but bring it to the attention of the course’s heads by emailing [email protected] within 72 hours, the course may impose local sanctions that may include an unsatisfactory or failing grade for work submitted, but the course will not refer the matter for further disciplinary action except in cases of repeated acts.

Below are rules of thumb that (inexhaustively) characterize acts that the course considers reasonable and not reasonable. If in doubt as to whether some act is reasonable, do not commit it until you solicit and receive approval in writing from the course’s heads. Acts considered not reasonable by the course are handled harshly. If the course refers some matter for disciplinary action and the outcome is punitive, the course reserves the right to impose local sanctions on top of that outcome that may include an unsatisfactory or failing grade for work submitted or for the course itself. The course ordinarily recommends exclusion (i.e., required withdrawal) from the course itself.

• Communicating with classmates about assessments in English (or some other spoken language), and properly citing those discussions.
• Discussing the course’s material with others in order to understand it better.
• Helping a classmate identify a bug in their code, as by viewing, compiling, or running their code after you have submitted that portion of the pset yourself.
• Incorporating a few lines of code that you find online or elsewhere into your own code, provided that those lines are not themselves solutions to assigned work and that you cite the lines’ origins.
• Sending or showing code that you’ve written to someone, possibly a classmate, so that they might help you identify and fix a bug.
• Submitting the same or similar work to this course that you have submitted previously to this course.
• Turning to the web or elsewhere for instruction beyond the course’s own, for references, and for solutions to technical difficulties, but not for outright solutions to assigned work.
• Whiteboarding solutions with others using diagrams or pseudocode but not actual code.
• Working with (and even paying) a tutor to help you with the course, provided the tutor does not do your work for you.

Not Reasonable

• Accessing a solution to some assessement prior to (re-)submitting your own.
• Accessing or attempting to access, without permission, an account not your own.
• Asking a classmate to see their solution to some assessment before (re-)submitting your own.
• Discovering but failing to disclose to the course’s heads bugs in the course’s software that affect scores.
• Decompiling, deobfuscating, or disassembling the staff’s solutions.
• Failing to cite (as with comments) the origins of code or techniques that you discover outside of the course’s own lessons and integrate into your own work, even while respecting this policy’s other constraints.
• Giving or showing to a classmate a solution to an assessement when it is they, and not you, who is struggling to solve it.
• Manipulating or attempting to manipulate scores artificially, as by exploiting bugs or formulas in the course’s software.
• Paying or offering to pay an individual for work that you may submit as (part of) your own.
• Providing or making available solutions to assessments to individuals who might take this course in the future.
• Searching for or soliciting outright solutions to assessments online or elsewhere.
• Splitting an assessment’s workload with another individual and combining your work.
• Submitting (after possibly modifying) the work of another individual beyond the few lines allowed herein.
• Submitting the same or similar work to this course that you have submitted or will submit to another.
• Submitting work to this course that you intend to use outside of the course (e.g., for a job) without prior approval from the course’s heads.
• Viewing another’s solution to an assessment and basing your own solution on it.

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Promoting Academic Integrity 

While it is each student’s responsibility to understand and abide by university standards towards individual work and academic integrity, instructors can help students understand their responsibilities through frank classroom conversations that go beyond policy language to shared values. By creating a learning environment that stimulates engagement and designing assessments that are authentic, instructors can minimize the incidence of academic dishonesty.

Academic dishonesty often takes place because students are overwhelmed with the assignments and they don’t have enough time to complete them. So, in addition to being clear about expectations and responsibilities related to academic integrity, instructors should also invite students to  plan accordingly and communicate with them in the event of an emergency. Instructors can arrange extensions and offer solutions in case that students have an emergency. Communication between instructors and students is vital to avoid bad practices and contribute to hold on to the academic integrity values. 

The guidance and strategies included in this resource are applicable to courses in any modality (in-person, online, and hybrid) and includes a discussion of addressing generative Artificial Intelligence (AI) tools like ChatGPT with students. 

On this page:

What is academic integrity, why does academic dishonesty occur, strategies for promoting academic integrity, academic integrity in the age of artificial intelligence, columbia university resources.

• References and Additional Resources
• Acknowledgment

Cite this resource: Columbia Center for Teaching and Learning (2020). Promoting Academic Integrity. Columbia University. Retrieved [today’s date] from https://ctl.columbia.edu/resources-and-technology/resources/academic-integrity/

According to the  International Center for Academic Integrity , academic integrity is “a commitment, even in the face of adversity, to six fundamental values: honesty, trust, fairness, respect, responsibility, and courage.” We commit to these values to honor the intellectual efforts of the global academic community, of which Columbia University is an integral part.

Academic dishonesty in the classroom occurs when one or more values of academic integrity are violated. While some cases of academic dishonesty are committed intentionally, other cases may be a reflection of something deeper that a student is experiencing, such as language or cultural misunderstandings, insufficient or misguided preparation for exams or papers, a lack of confidence in their ability to learn the subject, or perception that course policies are unfair (Bernard and Keith-Spiegel, 2002).

Some other reasons why students may commit academic dishonesty include:

• Cultural or regional differences in what comprises academic dishonesty
• Lack or poor understanding on how to cite sources correctly
• Misunderstanding directions and/or expectations
• Poor time management, procrastination, or disorganization
• Feeling disconnected from the course, subject, instructor, or material
• Fear of failure or lack of confidence in one’s ability
• Anxiety, depression, other mental health problems
• Peer/family pressure to meet unrealistic expectations

Understanding some of these common reasons can help instructors intentionally design their courses and assessments to pre-empt, and hopefully avoid, instances of academic dishonesty. As Thomas Keith states in “Combating Academic Dishonesty, Part 1 – Understanding the Problem.” faculty and administrators should direct their steps towards a “thoughtful, compassionate pedagogy.”

The CTL is here to help!

The CTL can help you think through your course policies and ways to create community, design course assessments, and set up CourseWorks to promote academic integrity. Email [email protected] to schedule your 1-1 consultation .

In his research on cheating in the college classroom, James Lang argues that “the amount of cheating that takes place on our campuses may well depend on the structures of the learning environment” (Lang, 2013a; Lang, 2013b). Instructors have agency in shaping the classroom learning experience; thus, instances of academic dishonesty can be mitigated by efforts to design a supportive, learning-oriented environment (Bertam, 2017 and 2008).

Understanding Student’s Perceptions about Cheating 

It is important to know how students understand critical concepts related to academic integrity such as: cheating, transparency, attribution, intellectual property, etc. As much as they know and understand these concepts, they will be able to show good academic integrity practices.

1. Acknowledge the importance of the research process, not only the outcome, during student learning.

Although the research process is slow and arduous, students should understand the value of the different processes involved during academic writing: investigation, reading, drafting, revising, editing and proof-reading. For Natalie Wexler, using generative Artificial Intelligence tools like ChatGPT as a substitute of writing itself is beyond cheating, an act of self cheating: “The process of writing itself can and should deepen that knowledge and possibly spark new insights” (“‘ Bots’ Can Write Good Essays, But That Doesn’t Make Writing Obsolete” ).

Ways to understand the value of writing their own work without external help, either from external sources, peers or AI, hinge on prioritizing the process over the product:

• Asking students to present drafts of their work and receive feedback can help students to gain confidence to continue researching and writing.
• Allowing students the freedom to choose or change their research topic can increase their investment in an assignment, which can motivate them to conduct their own writing and research rather than relying on AI tools. 

2. Create a supportive learning environment

When students feel supported in a course and connected to instructors and/or TAs and their peers, they may be more comfortable asking for help when they don’t understand course material or if they have fallen behind with an assignment.

Ways to support student learning include:

• Convey confidence  in your students’ ability to succeed in your course from day one of the course (this may ease student anxiety or  imposter syndrome ) and through timely and regular feedback on what they are doing well and areas they can improve on. 
• Explain the relevance  of the course to students; tell them why it is important that they actually learn the material and develop the skills for themselves. Invite students to connect the course to their goals, studies, or intended career trajectories. Research shows that students’ motivation to learn can help deter instances of academic dishonesty (Lang, 2013a). 
• Teach important skills  such as taking notes, summarizing arguments, and citing sources. Students may not have developed these skills, or they may bring bad habits from previous learning experiences. Have students practice these skills through exercises (Gonzalez, 2017). 
• Provide students multiple opportunities to practice challenging skills  and receive immediate feedback in class (e.g., polls, writing activities, “boardwork”). These frequent low-stakes assessments across the semester can “[improve] students’ metacognitive awareness of their learning in the course” (Lang, 2013a, pp. 145). 
• Help students manage their time  on course tasks by scheduling regular check-ins to reduce students’ last minute efforts or frantic emails about assignment requirements. Establish weekly online office hours and/or be open to appointments outside of standard working hours. This is especially important if students are learning in different time zones. Normalize the use of campus resources and academic support resources that can help address issues or anxieties they may be facing.  (See the Columbia University Resources section below for a list of support resources.)
• Provide lists of approved websites and resources  that can be used for additional help or research. This is especially important if on-campus materials are not available to online learners. Articulate permitted online “study” resources to be used as learning tools (and not cheating aids – see McKenzie, 2018) and how to cite those in homework, writing assignments or problem sets. 
• Encourage TAs (if applicable) to establish good relationships  with students and to check-in with you about concerns they may have about students in the course. (Explore the  Working with TAs Online  resource to learn more about partnering with TAs.)

3. Clarify expectations and establish shared values

In addition to including Columbia’s  academic integrity policy  on syllabi, go a step further by creating space in the classroom to discuss your expectations regarding academic integrity and what that looks like in your course context. After all, “what reduces cheating on an honor code campus is not the code itself, but  the dialogue about academic honesty that the code inspires. ” (Lang, 2013a, pp. 172)

Ways to cultivate a shared sense of responsibility for upholding academic integrity include: 

• Ask students to identify goals and expectations  around academic integrity in relation to course learning objectives. 
• Communicate your expectations  and explain your rationale for course policies on artificial intelligence tools, collaborative assignments, late work, proctored exams, missed tests, attendance, extra credit, the use of plagiarism detection software or proctoring software, etc. It will make a difference to take the time at the beginning of the course to explain differences between quoting, summarizing and paraphrasing. Providing examples of good and bad quotation/paraphrasing will help students to know what constitutes good academic writing. 
• Define and provide examples  for what constitutes plagiarism or other forms of academic dishonesty in your course.
• Invite students to generate ideas  for responding to scenarios where they may be pressured to violate the values of academic integrity (e.g.: a friend asks to see their homework, or a friend suggests using chat apps during exams), so students are prepared to react with integrity when suddenly faced with these situations. 
• State clearly when collaboration and group learning is permitted  and when independent work is expected. Collaboration and group work provide great opportunities to build student-student rapport and classroom community, but at the same time, it can lead students to fall into academic misconduct due to unintended collaboration/failure to safeguard their work.
• Discuss the ethical, academic, and legal repercussions  of posting class recordings, notes and/or class materials online (e.g., to sites such as Chegg, GitHub, CourseHero – see Lederman, 2020).
• Partner with TAs  (if applicable) and clarify your expectations of them, how they can help promote shared values around academic integrity, and what they should do in cases of suspected cheating or classroom difficulties

4. Design assessments to maximize learning and minimize pressure

High stakes course assessments can be a source of student anxiety. Creating multiple opportunities for students to demonstrate their learning, and spreading assessments  throughout  the semester can lessen student stress and keep the focus on student learning (see  Darby, 2020  for strategies on assessing students online). As Lang explains, “The more assessments you provide, the less pressure you put on students to do well on any single assignment or exam. If you maintain a clear and consistent academic integrity policy, and ensure that all students caught cheating receive an immediate and substantive penalty, the benefit of cheating on any one assessment will be small, while the potential consequences will be high” (Lang, 2013a and Lang, 2013c). For support with creating online exams, please please refer to our  Creating Online Exams resource .

Ways to enhance one’s assessment approach:

• Design assignments  based on authentic problems in your discipline. Ask students to  apply  course concepts and materials to a problem or concept. 
• Structure assignments into smaller parts  (“scaffolding”) that will be submitted and checked throughout the semester. This scaffolding can also help students learn how to tackle large projects by breaking down the tasks. 
• Break up a single high-stakes exam  into smaller, weekly tests. This can help distribute the weight of grades, and will lessen the pressure students feel when an exam accounts for a large portion of their grade. 
• Give students options  in how their learning is assessed and/or invite students to present their learning in creative ways (e.g., as a poster, video, story, art project, presentation, or oral exam).
• Provide feedback prior to grading  student work. Give students the opportunity to implement the feedback. The revision process encourages student learning, while also lowering the anxiety around any one assignment. 
• Utilize multiple low-stakes assignments  that prepare students for high-stakes assignments or exams to reduce anxiety (e.g., in-class activities, in-class or online discussions)
• Create grading rubrics and share them  with your students and TAs (if applicable) so that expectations are clear, to guide student work, and aid with the feedback process.  
• Use individual student portfolio folders  and provide tailored feedback to students throughout the semester. This can help foster positive relationships, as well as allow you to watch students’ progress on drafts and outlines. You can also ask students to describe how their drafts have changed and offer rationales for those decisions.
• For exams , consider refreshing tests every term, both in terms of organization and content. Additionally, ground your assignments by having students draw connections between course content and the unique experience of your course in terms of time (unique to the semester), place (unique to campus, local community, etc. ), personal (specific student experiences), and interdisciplinary opportunities (other courses students have taken, co-curricular activities, campus events, etc.). (Lang, 2013a, pp. 77).

Since its release, ChatGPT has raised concern in universities across the country about the opportunity it presents for students to cheat and appropriate AI ideas, texts, and even code as their own work. However, there are also potential positive uses of this tool in the learning process–including as a tool for teachers to rely on when creating assessments or working with repetitive and time-consuming tasks.

Possible Advantages of ChatGPT

Due to the novelty of this tool, the possible advantages that might present in the teaching-learning process should be under the control of each instructor since they know exactly what they expect from students’ work. 

Prof. Ethan Mollick teaches innovation and entrepreneurship at the Wharton School of the University of Pennsylvania, and has been openly sharing on his Twitter account his journey incorporating ChatGPT into his classes. Prof. Mollick advises his students to experiment with this tool, trying and retrying prompts. He recognizes the importance of acknowledging its limits and the risks of violating academic honesty guidelines if the use of this tool is not stated at the end of the assignment.

Prof. Mollick uncovers four possible uses of this AI tool, ranging from using ChatGPT as an all-knowing intern, as a game designer, as an assistant to launch a business, or even to “hallucinate” together ( “Four Paths to the Revelation” ). For Prof. Mollick, ChatGPT is a useful technology to craft initial ideas, as long as the prompts are given within a specific field, include proper context, step-by-step directions and have the proper changes and edits.

Resources for faculty: 

• Academic Integrity Best Practices for Faculty (Columbia College & School of Engineering and Applied Sciences)
• Faculty Statement on Academic Integrity (Columbia College)
• FAQs: Academic Integrity from Columbia Student Conduct and Community Standards 
• Ombuds Office for assistance with academic dishonesty issues. 
• Columbia Center of Artificial Intelligence Technology

Resources for students: 

• Policies from Columbia Student Conduct and Community Standards
• Understanding the Academic Integrity Policy (Columbia College & School of Engineering and Applied Sciences)

Student support resources:

• Maximizing Student Learning Online (Columbia Online)
• Center for Student Advising Tutoring Service (Berick Center for Student Advising)
• Help Rooms and Private Tutors by Department (Berick Center for Student Advising
• Peer Academic Skills Consultants (Berick Center for Student Advising)
• Academic Resource Center (ARC) for School of General Studies
• Center for Engaged Pedagogy (Barnard College)
• Writing Center (for Columbia undergraduate and graduate students)
• Counseling and Psychological Services
• Disability Services

For graduate students: 

• Writing Studio (Graduate School of Arts and Sciences)
• Student Center (Graduate School of Arts and Sciences)
• Teachers College

Columbia University Information Technology (CUIT) CUIT’s Academic Services provides services that can be used by instructors in their courses such as Turnitin , a plagiarism detection service and online proctoring services such as Proctorio , a remote proctoring service that monitors students taking virtual exams through CourseWorks. 

Center for Teaching and Learning (CTL) The CTL can help you think through your course policies, ways to create community, design course assessments, and setting up CourseWorks to promote integrity, among other teaching and learning facets. To schedule a one-on-one consultation, please contact the CTL at [email protected] . 

References 

Bernard, W. Jr. and Keith-Spiegel, P. (2002).  Academic Dishonesty: An Educator’s Guide . Mahwah, NJ: Psychology Press.

Bertram Gallant, T. (2017).  Academic Integrity as a Teaching and Learning Issue: From Theory to Practice .  Theory Into Practice,  56(2), 88-94.

Bertram Gallant, T. (Ed.). (2008).  Academic Integrity in the Twenty-First Century: A Teaching and Learning Imperative .  ASHE Higher Education Report . 33(5), 1-143. 

Columbia Center for Teaching and Learning (2020).  Creating Online Exams . 

Columbia Center for Teaching and Learning (2020).  Working with TAs online . 

Darby, F. (2020).  7 Ways to Assess Students Online and Minimize Cheating .  The Chronicle of Higher Education.  

Gonzalez, J. (2017, February).  Teaching Students to Avoid Plagiarism . Cult of Pedagogy, 26.

International Center for Academic Integrity (2023).  Fundamental Values of Academic Integrity .

International Center on Academic Integrity (2023).  https://academicintegrity.org/

Keith, T. Combating Academic Dishonesty, Part 1 – Understanding the Problem. The University of Chicago. (2022, Feb 16).

Lang, J.M. (2013a).  Cheating Lessons: Learning from Academic Dishonesty . Harvard University Press.

Lang, J. M. (2013b).  Cheating Lessons, Part 1 .  The Chronicle of Higher Education. 

Lang, J. M. (2013c).  Cheating Lessons, Part 2 .  The Chronicle of Higher Education. 

Lederman, D. (2020, February 19).  Course Hero Woos Professors . Inside Higher Ed. 

McKenzie, L. (2018, May 8).  Learning Tool or Cheating Aid?   Inside Higher Ed.

Marche, S. (2022, Dec 6). The College Essay is Dead. The Atlantic.

Mollick, E. (2023, Jan 17). All my Classes Suddenly Became AI Classes. One Useful Thing.

Mollick, Ethan. (2022, Dic 8). Four Paths to the Revelation. One Useful Thing.

Wexler, N. Bots’ Can Write Good Essays, But That Doesn’t Make Writing Obsolete. Minding the Gap.

Additional Resources

Bretag, T. (Ed.). (2016). Handbook of Academic Integrity. Singapore: Springer Publishing.

Ormand, C. (2017 March 6).  SAGE Musings: Minimizing and Dealing with Academic Dishonesty . SAGE 2YC: 2YC Faculty as Agents of Change.

WCET (2009).  Best Practice Strategies to Promote Academic Integrity in Online Education .

Thomas, K.  (2022 February 16). Combating Academic Dishonesty, Part 1 – Understanding the Problem. The University of Chicago. Academic Technology Solutions.

______. (2022 February 25). Combating Academic Dishonesty, Part 2: Small Steps to Discourage Academic Dishonesty. The University of Chicago. Academic Technology Solutions.

______.  (2022 April 28). Combating Academic Dishonesty, Part 3: Towards a Pedagogy of Academic Integrity. The University of Chicago. Academic Technology Solutions.

______.  (2022 June 7). Combating Academic Dishonesty, Part 4: Library Services to Support Academic Honesty. The University of Chicago. Academic Technology Solutions.

Acknowledgement

This resource was adapted from the faculty booklet  Promoting Academic Integrity & Preventing Academic Dishonesty: Best Practices at Columbia University  developed by Victoria Malaney Brown, Director of Academic Integrity at Columbia College and Columbia Engineering, Abigail MacBain and Ramón Flores Pinedo, PhD students in GSAS. We would like to thank them for their extensive support in creating this academic integrity resource.

Want to communicate your expectations around AI tools?

See the CTL’s resource “Considerations for AI Tools in the Classroom.”

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Academic Honesty and Stanford's Honor Code

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At Stanford University, the Honor Code is an undertaking of the Stanford academic community, individually and collectively, to uphold a culture of academic integrity in the classroom. Here we offer background information about academic integrity issues in higher education and pedagogic strategies to support and promote academic integrity.

A revised Honor Code was approved by the university community in Spring 2023 , effective September 1, 2023. 

The Office of Community Standards (OCS) oversees the Honor Code and alleged student violations. For policies and guidance about Stanford’s Honor Code and the student accountability process please visit the Office of Community Standards website . 

What is academic integrity?

The term academic integrity generally means a commitment to a set of fundamental values that support research, learning, scholarship, and service in academia.

At Stanford, the Honor Code is the university's statement on academic integrity, first written by students in 1921. It articulates university expectations of students and faculty in establishing and maintaining the highest standards in academic work.

The Stanford Honor Code

The revised Honor Code (effective September 1, 2023) has been clarified to encourage clear communication between faculty, instructors, and students.

Stanford’s Honor Code has three components:

• Students will support this culture of academic honesty by neither giving nor accepting unpermitted academic aid in any work that serves as a component of grading or evaluation.
• Instructors will support this culture of academic honesty by providing clear guidance, both in their course syllabi and in response to student questions, on what constitutes permitted and unpermitted aid. Instructors will also not take unusual or unreasonable precautions to prevent academic dishonesty.
• Students and instructors will also cultivate an environment conducive to academic integrity. While instructors set academic requirements, the Honor Code is a community undertaking that requires students and instructors to work together to ensure conditions that support academic integrity.

Practices for supporting academic integrity and student learning

Here are some practices that instructors and students in instructional roles can use to promote a learning environment that supports academic integrity and works to uphold the Honor Code.

Many strategies that help students abide by the Honor Code also enhance their learning. While there are many reasons why students, intentionally or unintentionally, might violate the Honor Code, this will likely hinder their learning and compromise their academic experience and, potentially, their future careers.

For details of these and similar strategies, see “ Teaching strategies to support the Honor Code and student learning ”.

Decide on your course policies

What constitutes permitted and unpermitted aid might be different for a course you are leading than for other courses. Depending on your course goals, this might include how to collect and cite research, student collaboration and group work, approved tools, exam protocols, and so on. Be thoughtful as you decide what is appropriate based on the goals of the course, the requirements of the discipline, your teaching philosophy, and the needs of your students.

Guidance on Generative AI

The Office of Community Standards’ (OCS) guidance on generative AI tools states that the use of generative AI tools, like chatbots, image generators, and code generators, is treated analogously to assistance from another person. In particular, using generative AI tools to substantially complete an assignment or exam (e.g. by entering exam or assignment questions) is not permitted. Individual course instructors are free to set their own policies regulating the use of generative AI tools in their courses, including allowing or disallowing some or all uses of such tools.

These pedagogic strategies for adapting to generative AI chatbots can help you determine how to best address generative AI in your course.

Clearly communicate expectations and policies to students and across instructional teams

Discuss the Honor Code and your own individual course policy on academic integrity in your course syllabus. The CTL course syllabus template contains samples of how to do this.

Consider providing examples of what is permissible or not, and review these expectations on the first day of class and before each assignment and assessment. Be prepared to respond to student questions.

Giving a quiz graded on completion to help students identify what is and is not plagiarism can be helpful to check for understanding.

Frame assessments as part of the learning process

Some students may view good grades—or simply submitting a finished assignment—as an end in itself, and so value the outcome more than the learning process. Others might see assessments as unfair or busy work, intended to create high pressure situations, or a way to rank students against each other, rather than to support their learning.

To provide students with a sense of purpose and fairness in assessment, it can be motivating for students to understand the purpose behind the design of the assessment and what you expect to see. Using rubrics for feedback and assessment can make grading more transparent and consistent, so students can demonstrate their learning.

Assessments can be opportunities for students to get valuable feedback, reflect on their learning strategies, and practice important skills. Explain how your assessments support the learning process and encourage them to do their best and be honest, so that the assessment can accurately inform you if they need more help.

Well-designed assessments can reveal where you might improve the course, or areas where students need more support, such as misconceptions they might pick up.

Design assessments that encourage students to demonstrate individual learning

Rather than just ask for an answer to a question that could be provided by another source, ask students to explain how they arrived at that answer. This can give you (and your students) more information to help identify where their gaps in understanding are and requires a more considered and unique response from each student.

Assessments can also be designed to encourage students to demonstrate reasoning and originality . For example:

• Include opportunities for students to demonstrate their problem-solving and reflect on their processes, such as project or problem-based assessments.
• Where suitable, include opportunities for students to demonstrate their originality, such as in personal response papers and creative work.
• Create assessments that require synthesis and critical analysis, such as combining sources and approaches, which also encourage higher-order thinking.
• Get students to show their drafting and editing process alongside finished work.
• Encourage students to think about and respond to contemporary issues or recent questions in the field, where there is less chance an answer that they can copy already exists.

Address the importance of integrity in your field

You can play a valuable role in discussing with students the value of academic integrity in the field of study. What happens if a researcher plagiarizes another scholar’s work? What are the consequences of misrepresenting one’s ideas or falsifying data?

Work with them to foster the habits of academic integrity, such as accurately noting and acknowledging research sources, and being transparent about their methods and sources.

Foster well-being and belonging

Although there are many reasons students may violate the Honor Code, addressing the reasons that students may feel pressured to complete assessments or perform well every time, or other stresses that can affect academic performance, may help mitigate some of these factors.

Consider these optional assessment strategies:

• Frequent and low-stakes assessments : Administering multiple low-stakes assessments reduces the overall weight and stress students associate with each assessment. Students may feel less pressure to take extreme measures to get every answer correct because an incorrect answer will not impact their grades as much. More frequent assessments also provide students with increased opportunities to practice and get feedback on their performance. 
• Consider instituting exam or assignment resubmissions : Consider allowing students opportunities to earn points back on questions that they missed. This can be particularly important if you must include an assessment in your course that is worth a large percentage of a student’s grade, but is helpful in any type of assessment to encourage reflection and growth in student learning. This practice not only incentivizes students to learn from their mistakes and fill in their gaps in understanding but also reduces the stress associated with the assessment. This technique also places value on student learning rather than student performance, as students are rewarded for improvement.
• Provide flexibility in final grade components : If an instructor offers a greater number of assessments during the quarter, more flexibility can be given to calculating a student’s final grade. Flexibility can be automatically built into a grading scheme for all students at the start of the quarter by allowing students to drop a number or percentage of assessments. Such flexibility also can assist students who face unexpected difficulties during the quarter without requiring them to disclose details to instructors.

Instructors can also support students’ sense of self-efficacy by regularly encouraging them to connect with various learning programs on campus, such as academic skills coaching , subject matter tutors , and writing tutors .

To mitigate student concerns about the consequences of poor performance, remind students of helpful policies about taking an incomplete or withdrawal . 

Consider short and synchronous assessments

Reducing the overall length of an assessment can make it less feasible for students to receive unpermitted help from websites or other sources. Synchronous assessments also remove the temptation or pressure for students to share assessment content with other students completing the assessment at a later time.

Work together with students

The third part of the Honor Code states that both parties must cooperate to establish optimal conditions. Trust between students and faculty is key. Communicate with your students about the Honor Code and consider working with them to adjust as needed your assessments, rubrics, and grading policies over time.

You might invite students into a dialogue about the purpose and uses of AI, collaboration, primary and secondary sources, and so on. Crafting a collaboration and resource policy together can be a valuable learning experience to reflect with students on the purpose of the course, be transparent about learning objectives and pedagogical choices, and encourage buy-in and community-building within the class.

By connecting to student interests and sharing your passion for the subject, students can become more intrinsically motivated to learn for the sake of learning, rather than learning for the sake of a grade (e.g., to perform on a test). This resource on promoting intrinsic motivation has strategies to help you.

What about plagiarism detection tools?

Tools such as Turnitin, Unicheck, and Plagiarism Checker from Grammarly typically compare uploaded student work to a database of other works to detect matches and help determine the originality and sources of the student work. Some plagiarism detection tools also leverage AI technology and can detect AI-generated text to varying degrees of accuracy, but this technology is new and not reliable.

Instructors may use plagiarism detection tools with clear advance notice. Students must be informed that their assignments will be checked with this technology.

See Tips for Faculty & Teaching Assistants on the OCS website for current policy guidance on plagiarism detection tools and the Honor Code.

See also Guidance on technology tools for academic integrity for a more detailed discussion.

What about proctoring exams?

The Honor Code has been clarified to encourage clear communication between faculty, instructors, and students. The revised Honor Code applies to cases filed after September 1, 2023 .

The approved proposal to update the university’s Honor Code includes the addition of new text to improve clarity and to launch an Academic Integrity Working Group (AIWG) to evaluate equitable practices for proctoring in-person examinations through a multi-year study.

The AIWG proctoring study

While the Honor Code no longer explicitly prohibits proctoring, such practices, defined as the reasonable supervision of exams by an exam administrator, are still prohibited unless done as a part of the Academic Integrity Working Group pilot program. 

The AIWG will begin its work during the 2023–24 academic year. The study is expected to span two to four academic years. During this time, proctoring will be limited to the few courses that are part of the study. Unless your course is part of the study, proctoring will remain forbidden, and there is no need to adjust your syllabi for proctoring at this time.

• Honor Code , Office of Community Standards (September 2023)
• Interpretations of the Honor Code , Office of Community Standards (Spring 2023)
• What is Plagiarism? , Office of Community Standards
• Tips for Faculty & Teaching Assistants , Office of Community Standards
• Exams and the Honor Code , Office of Community Standards
• Teaching strategies to support the Honor Code and student learning , Teaching Commons (2020)
• Filing an Honor Code concern , Office of Community Standards
• Guidance on technology tools for academic integrity , Teaching Commons

Beck, Victoria. 2014. “ Testing a Model to Predict Online Cheating—Much Ado about Nothing. ” Active Learning in Higher Education 15 (1): 65–75.

Carl Wieman Science Education Initiative. April 2015. Assessments That Support Student Learning .

G. Gibbs and C. Simpson. 2004. “ Conditions Under Which Assessment Supports Student Learning ,” Learning and Teaching in Higher Education , V. 1, pp. 3-31

International Center for Academic Integrity (ICAI). 2021. The Fundamental Values of Academic Integrity . (3rd ed.).

Lang, James M. May 28, 2013. “ Cheating Lessons, Part 1. ” The Chronicle of Higher Education .

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Academic Honesty

Academic integrity.

Auburn University is dedicated to honesty and strong moral behavior in academics. Cheating and plagiarism are expressly prohibited by the Auburn University Academic Honesty Code.

Students who attend Auburn are expected to attain high competency and deep understanding in their areas of study. While developing skills and knowledge, it is essential that Auburn students commit themselves to core principles and behaviors consistent with academic and personal integrity:

Honesty – Upholding trust and honesty by doing your own academic work and not cheating.

Fairness – Following correct academic procedures and practices as stated in course guidelines and as defined by Auburn University.

Respect – Growing as a student by facing academic challenges and interacting productively with instructors.

Responsibility – Being accountable for and accepting responsibility for class assignments and personal academic development.

Examples of Academic Dishonesty

Plagiarism or using the words or ideas of another as if they were one’s own without giving the author or creator credit through proper documentation or recognition, as through the use of footnotes.

Using unauthorized sources in preparation of your work.

Copying from another student’s exam, paper, or assignment.

• Use of materials not authorized during a test; e.g., electronic devices, notes, textbook, notes written on any part of your body or clothing including hats and shoes.

Submitting a paper, report, examination, or any class assignment which has been altered or corrected, in part or in whole, for reevaluation or re-grading without the consent of the instructor.

Serving as or enlisting the assistance of another as a substitute in the taking of examinations.

Enlisting the assistance of another to write a paper or writing a paper for someone.

Altering or misusing a document for academic purposes. This would include university forms and doctor’s excuses.

Selling, giving, lending, or otherwise furnishing to any other person any material (homework assignments, tests, etc.), whether electronically or otherwise which can be shown to contain the questions or answers to any examination scheduled to be given at some subsequent date in any course of study, excluding questions and answers from tests previously administered and returned to a student by the instructor.

Altering or attempting to alter an assigned grade on any official Auburn University record.

Instructors may delineate other actions that they consider a violation of the Academic Honesty Code in a written course syllabus.

Examples of Academic Dishonesty in an Online Environment

In addition to the examples above, academic dishonesty in an online environment also includes, but is not limited to:

The use of “cheat sheets”.

The use of Google Search.

The use of Google Translate.

Having someone else do your homework or take a test for you or doing the same for another student.

The use of smartphones by storing exam related information on the device.

Copying test questions for distribution to others.

Hiding flashcards underneath the keyboard.

Posting answers on walls.

Tips for Avoiding Academic Dishonesty

Be aware of what constitutes plagiarism and cheating.

If you are unclear as to what could be considered academic dishonesty, consult your instructor.

Read the course syllabus to ensure that you understand assignment instructions and course policies.

Allow adequate time to study for exams and to write papers.

Do not lend completed assignments to others.

Be cautious when sharing a computer with others.

Make careful notes and use appropriate research citations to avoid plagiarism.

Only submit your own work. Never turn in work that was prepared by others.

All sanctions assigned for violations of the Academic Honesty Code are reported to the Office of the Provost and to the dean of the college or school in which the student is registered. The following sanctions may be imposed for violation of the Student Academic Honesty Code:

A zero grade on the examination, project, paper, etc., in which the violation occurs.

A grade of F in the course in which the violation occurs. The notation “assigned for academic dishonesty” may be placed on the academic transcript for a designated length of time.

Suspension from Auburn University for one or more semesters. During suspension, the student will not be allowed to take any courses at Auburn University either in residence or by correspondence. Auburn University will not accept any course credits earned at another institution during suspension.

Expulsion from Auburn University.

Sanctions are listed in Chapter 1202 of the Student Academic Honesty code.

Student Rights

If a student violates the Academic Honesty Code, he or she can expect the following:

Written notice of any official charge of academic dishonesty.

Option 1: A facilitated meeting to resolve the charge of academic dishonesty. The right to request a meeting at which any charges of academic dishonesty can be discussed and resolved with the instructor of the course in which the alleged violation occurs and a faculty member of the Academic Honesty Committee who shall serve as a Facilitator. The student will be notified of his or her right to request a facilitated meeting within fifteen (15) working days of the detection of the alleged violation. The student will have five (5) days after notification of the violation to indicate his or her intention to attend a Facilitated Meeting. In order for a Facilitated Meeting to occur, the instructor of the course in question must also request to engage in the Facilitated Meeting.

Option 2: Resolution of academic honesty violations by a hearing process before the full Academic Honesty Committee.

To present witnesses and evidence before the Academic Honesty Committee.

The right to appeal to the President of Auburn University a finding of a violation and recommended sanction by the Committee.

Student Rights are listed in Chapter 1206 and 1208 of the Student Academic Honesty Code.

Quick Links

Student Academic Honesty Code

Resources for Faculty

Academic Honesty On-line Reporting Form

Questions regarding Auburn University’s Academic Honesty Code should be directed to:

Dr. Jim Ryan Office of the Provost [email protected]

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• Academic Policies

Academic Honesty Policy

Date of Current Revision :  December 2015

Primary Responsible Officer :  Associate Vice Provost Student Engagement

Honesty in all endeavors is essential to the functioning of society. Honesty in the classroom among students and between students and faculty is a matter that should concern everyone in the university community. Indeed, academic honesty is one of the most important qualities influencing the character and image of an educational institution. As higher education is challenged to improve the quality of its programs, there is great value in emphasizing academic standards and integrity.

A. University Responsibility: It is the university's responsibility to provide an educational process that informs both students and faculty of their rights and responsibilities regarding such important matters as cheating, plagiarism, and professional ethics. Most of what is considered unethical or dishonest behavior can be avoided if faculty and students clearly understand both what constitutes these practices and their consequences. The university community should also be aware of the procedures to be followed should a breach of academic honesty occur.

B. Student Responsibilities. Students must be aware that the consequences of violating standards of academic honesty are extremely serious and costly and may result in the loss of academic and career opportunities. Students found to have committed violations against academic honesty face removal from university classes and degree programs, and/or suspension while remaining fully responsible for payment of current and any past due tuition and fees.

To that end, the following Procedures for Enforcement of the University's Academic Honesty Policy shall be followed to ensure that constitutionally required due process safeguards are extended to an accused student.

II. Procedures for Enforcement of Central Missouri's Academic Honesty Policy.

A. Defining Offenses Against Academic Honesty

A violation against academic honesty committed by a student is any act, which would deceive, cheat, or defraud so as to promote or enhance one's academic standing. Academic dishonesty also includes knowingly or actively assisting any person in the commission of an offense of academic dishonesty.

Examples of offenses against academic honesty include, but are not limited to, the following:

• Plagiarism - Plagiarism is defined as the borrowing of ideas, opinions, examples, key words, phrases, sentences, paragraphs, or even structure from another person's work, including work written or produced by others without proper acknowledgment. "Work" is defined as theses, drafts, completed essays, examinations, quizzes, projects, assignments, presentations, or any other form of communication, be it on the Internet or in any other medium or media. "Proper acknowledgment" is defined as the use of quotation marks or indenting plus documentation for directly quoted work and specific, clearly articulated citation for paraphrased or otherwise borrowed material.

• Cheating - Includes, but is not limited to, those activities where a student (either on campus or on-line):

(a) Obtains or attempts to obtain the pre-knowledge content of an examination;

(b) Copies someone else's work;

(c) works in a group when the student has been told to work individually;

(d) uses unauthorized reference material in an examination;

(e) Has someone else taken an examination.

(f) Has someone else complete course work and/or an examination using a student's secure login and passcode.

• Breach of Standards of Professional Ethics - In certain degree programs, students will be instructed on and provided with that particular profession's code of ethics (e.g. The American Nurses Associations Code for Nurses). Under some circumstances, if a student is found to have violated that professional code, that violation may be considered a breach of the Academic Honesty Policy.

B. Reporting Violations of the Academic Honesty Policy

If a faculty member believes that a student has committed a violation of the Academic Honesty Policy with regard to an examination or other assigned work (laboratory assignment, term paper, etc.), the faculty member shall preserve any evidence (e.g. plagiarized article, examination or other material) which substantiates that a violation has occurred. Within one week of the incident, the faculty member will schedule a private conference with the student, advise him/her that the faculty member believes a violation of this Policy occurred, and allow the student to provide his/her side of the story or otherwise offer an explanation. Upon consideration of the information, if any, provided by the student, the faculty member shall make an independent determination within five calendar days whether a violation of the Academic Honesty Policy has occurred. If the faculty member is unable, for whatever reason, to contact the student within a five-class day period, the incident will be reported to the faculty member's department chair for further action.

(1) In the event the faculty member finds no violation of the Academic Honesty Policy has occurred, the faculty member shall notify the student, in writing, of this finding.

(2) In the event the faculty member determines a violation of the Academic Honesty Policy has occurred, he/she shall notify the student, within five class days, in writing, of this finding. The written notification shall contain a statement of finding and shall specify the provision of the policy violated and, consistent with the severity of the violation, shall indicate which of the following action(s) he/she shall take:

a. Give the student an opportunity to resubmit the assignment or be retested to make up the work or test where the violation occurred;

b. Assign a grade of "F" to the assignment or examination affected by the violation;

c. Assign a grade of "F" for the course;

d. Recommend to the Office of the Vice Provost for Student Experience and Engagement, and the Vice Provost for Academic Programs and Services if the student is enrolled for graduate credit, that the student be dis-enrolled from class;

e. Recommend to the Department Chair and the Vice Provost for Student Experience and Engagement, and the Vice Provost for Academic Programs and Services if the student is enrolled for graduate credit, that the student be removed from the degree program; or

f. Recommend to the Office of the Vice Provost for Student Experience and Engagement that the student be suspended from the university.

(3) In the event the faculty member determines a violation of the Academic Honesty Policy has occurred, the faculty member will complete an Academic Alert Form. When the faculty submits an Academic Alert Form, the faculty will receive an email confirmation. A member of the Academic Alert team will attempt to follow up and provide updates on the progress/results.

(4) The faculty member shall keep the evidence and a copy of all summaries and documentation on file in the event the student wishes to appeal the faculty member's decision. The faculty member may interview other students and members of the university community to ascertain the pertinent facts and circumstances and may request written statements from them. However, the anonymity of witnesses or witness statements cannot be guaranteed.

(5) In the event that the student does not appeal the faculty member's decision within ten class days of notification, the faculty member's decision shall become final and the recommended action shall take place.

(6) A student charged with a violation of this policy shall not be barred from participating in and attending classes, or from taking quizzes, tests and/or final examinations during the ten-day period described in paragraph (4) (above) and/or during the appeal process.

C. Student Appeal Process

In the event a student charged with a violation of this policy disagrees with the faculty member's decision and wishes to appeal, the student is responsible for notifying the appropriate parties where he/she may be reached for purposes of appeal and must follow the following process:

Level 1 of The Appeal Process

Within five (5) class days of receipt of the faculty member's decision, the student should schedule a meeting with the faculty member's department chair. The chair will review the faculty member's documentation and evidence, review the circumstances with the student, and if possible, consult the faculty member. The chair will determine within five (5) class days of the meeting an appropriate action which may include, but is not limited to, endorsing, modifying, or overturning the faculty member's original decision, or he/she may determine an alternate course of action.

The chair shall communicate his/her decision to the student, faculty member, and the Office of the Vice Provost for Student Experience and Engagement, and prepare a report of the evidence and reasons for making this decision.

Level 2 of The Appeal Process

In the event the student disagrees with the chair's actions, he/she may request a meeting with the college dean, to be scheduled within five (5) class days of receipt of the chair's decision. The dean will discuss the facts and circumstances of the violation with the student, and other involved parties, on a collective or individual basis depending on the circumstances. The dean may also interview witnesses and undertake further investigative activities if he/she believes the circumstances merit further action. The dean shall complete his/her meetings and investigation and issue a finding within fifteen (15) class days of receipt of the student's appeal. In the event the dean is unable to accommodate this time frame, the student and other affected parties will be notified of this fact and the anticipated length of time needed to render a decision. The dean will notify the student, faculty member, chair, and Vice Provost for Student Experience and Engagement of his/her findings, and his/her intended course of action in writing.

Level 3 of The Appeal Process

If the student disagrees with the dean's decision, the student may request, within five (5) class days of receipt of notification of the dean's decision, a meeting with the Provost. The Provost will consider all the evidence on the record and shall decide, within ten (10) class days to take one of the following actions:

(1) Uphold one or all of the previous decisions.

(2) Overturn the decisions outright and make an alternate resolution.

(3) Refer the matter to a university grievance committee. The Provost will appoint a committee of two students and two faculty members to review the matter within fifteen class days of the Provost's referral. The committee will make its recommendation to the Provost within five (5) days of completing its work. In the event of a tie vote of the committee, the Provost shall cast the deciding vote. The Provost shall immediately, upon receipt of the committee's recommendation, notify the student of the grievance committee's decision in writing.

The Provost's decision is final and binding on all parties, and once communicated, shall be placed in full force and effect immediately.

Questions concerning this policy or other issues related to academic honesty should be addressed to the Office of the Provost or the Office of Student Experience and Engagement.

Revision History:

Established and archived prior to 1999

Updated by Vice Provost for Academic Programs and Services May 30, 2013.

Updated to include Academic Alert System. Approved by Vice Provost for Academic Programs and Services December 2015.

Edited for web links, formatting and plain language. Approved December 2015.

Previously annotated as II.A. Academic Honest Policy. Renamed Academic Honest Policy for alphabetical listing, grammar and spell check,  and transitioned into policy library April 2017.

social-section

Table of Contents

• Fabrication or Falsification
• Other Academic Misconduct
• Applicable Actions
• Shared Responsibility Policy Statement
• Faculty Academic Integrity
• Dean of Students Office
• Honor Code Office
• Office of the Associate Academic Vice President – Undergraduate Studies

Related Policies

• Academic Freedom Policy
• Aims of a BYU Education
• Church Educational System Honor Code
• Copyright Policy
• Honor Code Investigation and Administrative Review Process
• Personnel Conduct Policy

Contents, Related Policies, Applicability ▾

The first injunction of the Honor Code is the call to “be honest.” Students come to the university not only to improve their minds, gain knowledge, and develop skills that will assist them in their life’s work, but also to build character. “President David O. McKay taught that character is the highest aim of education.” (See Aims of a BYU Education .) It is the purpose of the BYU Academic Honesty Policy to assist in fulfilling that aim.

BYU students should seek to be totally honest in their dealings with others. They should complete their own work and be evaluated based upon that work. They should avoid academic dishonesty and misconduct in all its forms, including but not limited to plagiarism, fabrication or falsification, cheating, and other academic misconduct.

Intentional plagiarism is a form of intellectual theft that violates widely recognized principles of academic integrity as well as the Honor Code. Such plagiarism may subject the student to appropriate disciplinary action administered through the university Honor Code Office, in addition to academic sanctions that may be applied by an instructor. Inadvertent plagiarism, which may not be a violation of the Honor Code, is nevertheless a form of intellectual carelessness that is unacceptable in the academic community. Plagiarism of any kind is completely contrary to the established practices of higher education where all members of the university are expected to acknowledge the original intellectual work of others that is included in their own work. In some cases, plagiarism may also involve violations of copyright law.

Intentional Plagiarism— Intentional plagiarism is the deliberate act of representing the words, ideas, or data of another as one’s own without providing proper attribution to the author through quotation, reference, or footnote.

Inadvertent Plagiarism —Inadvertent plagiarism involves the inappropriate, but nondeliberate, use of another’s words, ideas, or data without proper attribution. Inadvertent plagiarism usually results from an ignorant failure to follow established rules for documenting sources or from simply not being sufficiently careful in research and writing. Although not a violation of the Honor Code, inadvertent plagiarism is a form of academic misconduct for which an instructor can impose appropriate academic sanctions. Students who are in doubt as to whether they are providing proper attribution have the responsibility to consult with their instructor and obtain guidance.

Examples of plagiarism include:

Direct Plagiarism —The verbatim copying of an original source without acknowledging the source.

Paraphrased Plagiarism —the paraphrasing, without acknowledgement, of ideas from another that the reader might mistake for the author’s own.

Plagiarism Mosaic —The borrowing of words, ideas, or data from an original source and blending this original material with one’s own without acknowledging the source.

Insufficient Acknowledgement —The partial or incomplete attribution of words, ideas, or data from an original source.

Plagiarism may occur with respect to unpublished as well as published material. Copying another student’s work and submitting it as one’s own individual work without proper attribution is a serious form of plagiarism.

Fabrication or falsification is a form of dishonesty where a student invents or distorts the origin or content of information used as authority. Examples include:

• Citing a source that does not exist.
• Attributing to a source ideas and information that are not included in the source.
• Citing a source for a proposition that it does not support.
• Citing a source in a bibliography when the source was neither consulted nor cited in the body of the paper.
• Intentionally distorting the meaning or applicability of data.
• Inventing data or statistical results to support conclusions.

Cheating is a form of dishonesty where a student attempts to give the appearance of a level of knowledge or skill that the student has not obtained. Examples include:

• Copying from another person’s work during an examination or while completing an assignment.
• Allowing someone to copy during an examination or while completing an assignment.
• Using unauthorized materials during an examination or while completing an assignment.
• Collaborating on an examination or assignment without authorization.
• Taking an examination or completing an assignment for another, or permitting another to take an examination or to complete an assignment in place of the student.

Academic misconduct includes other academically dishonest, deceitful, or inappropriate acts that are intentionally committed. Examples of such acts include but are not limited to:

• Inappropriately providing or receiving information or academic work so as to gain unfair advantage over others.
• Planning with another to commit any act of academic dishonesty.
• Attempting to gain an unfair academic advantage for oneself or another by bribery or by any act of offering, giving, receiving, or soliciting anything of value to another for such purpose.
• Changing or altering grades or other official educational records.
• Obtaining or providing to another an unadministered test or answers to an unadministered test.
• Breaking and entering into a building or office for the purpose of obtaining an unauthorized test.
• Continuing work on an examination or assignment after the allocated time has elapsed.
• Submitting the same work for more than one class without disclosure and approval.

Faculty are responsible to establish and communicate to students their expectations of behavior with respect to academic honesty and the student’s conduct in the course. Responsible instructors will investigate alleged academic dishonesty, determine the facts, and take appropriate action. In a case where academic dishonesty is determined to have occurred, the instructor must notify the Honor Code Office of the incident as a means of encouraging behavior change and discouraging repeated violations. In addition, the instructor shall consult with the department chair concerning disciplinary actions to be taken. If the incident of academic dishonesty involves the violation of a public law, such as breaking and entering into an office or stealing an examination, the act should also be reported to appropriate law enforcement officials. If an affected student disagrees with the determination or action and is unable to resolve the matter to the mutual satisfaction of the student and the instructor, the student may have the matter reviewed through the university’s Student Academic Grievance Procedure.

A wide range of possible actions exists for cases of academic dishonesty. Instructors should take actions that are appropriate under the circumstances and should attempt to reach an understanding with the affected student on the imposition of an appropriate action. In some cases, the department, the college, or the university may also take actions independent of the instructor. Examples of possible actions include but are not limited to the following:

For instructors (in consultation with the department chair):

• Reprimanding the student orally or in writing.
• Requiring work affected by the academic dishonesty to be redone.
• Administering a lower or failing grade on the affected assignment or test.
• Administering a lower or failing grade for the course (even if the student withdraws from the course).
• Removing the student from the course.

For departments and colleges:

• After consulting with the Honor Code Office, dismissing the student from the program, department, or college.
• Recommending probation, suspension, or dismissal from the university.

For the university:

The university may elect to discipline a student for academic dishonesty in addition to, or independently from, discipline imposed by a faculty member, a department, or a college. University discipline may be administered through the Honor Code Office or through the Dean of Student’s Office. The Honor Code Office will maintain a record of all violations of this Academic Honesty Policy reported to it by the faculty. The university may elect to place an affected student on probation, or to suspend or dismiss the student, and to place a temporary or permanent notation on the student’s permanent academic transcript indicating that he or she was suspended or dismissed due to academic misconduct.

The university may report an incident of academic misconduct to appropriate law enforcement officials and may pursue the prosecution of an affected student if the act in question involves the commission of a crime.

Students are responsible not only to adhere to the Honor Code requirement to be honest but also to assist other students in fulfilling their commitment to be honest.

The substantive standards of academic honesty stated in this policy apply a fortiori to faculty. Indeed, all members of the BYU community are expected to act according to the highest principles of academic integrity.

A large number of publications and policies of colleges and universities were reviewed in creating BYU’s Academic Honesty Policy. Some of the content and structure of this policy were adapted from the following sources:

• “Academic Honesty,” a brochure produced by the Office of Judicial Affairs, University of Florida.
• “Academic Honesty and Dishonesty,” a brochure produced by the Office of the Dean of Students, University of Delaware.
• “Academic Honesty and Dishonesty,” a brochure produced by the Dean of Students Office, Louisiana State University.
• “A Statement on Plagiarism,” a committee report from the October 1994 Conference on the Center for Academic Integrity, Tom Langhorne, Binghamton University (chair).
• “Definition of Plagiarism,” by Harold C. Martin, taken from The Logic and Rhetoric of Exposition, by Harold C. Martin, Richard M. Ohmann, and James H. Wheatly, 3rd ed. (New York: Holt, Rinehart and Winston, 1969).
• Legal Aspects of Plagiarism, by Ralph D. Mawdsley (Topeka, Kansas: National Organization on Legal Problems of Education, 1985).
• “Plagiarism—The Do’s and Don’ts,” a brochure produced by the Office of Student Judicial Affairs of the University of California—Davis.

For questions about this policy, contact the responsible office(s) listed above.

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A Positive Approach to Academic Integrity

In 2017, 83 Ohio State students were reported for using an app called GroupMe to share quiz questions and answers (Bever, 2017). At universities across the nation, students have cheated using various apps and technology. Increased access to technology tools does provide additional avenues for cheating, but the availability of these new tools has not led to more cheating (see Lang, 2013). 

Still, preventing academic misconduct is a topic that weighs on many instructors’ minds. We want students to learn and to come by their degrees honestly. The good news is that the educator’s role in academic honesty does not always have to be punitive or after-the-fact. Proactively promoting academic integrity in positive ways can reduce the likelihood that students will commit misconduct.

In the United States, public attitudes about academic misconduct range from mild irritation at the existence of cheating to the moral outrage one might show toward hard criminal offenses. In an effort to reduce cheating, instructors often implement defensive measures. For example, using a digital plagiarism detector such as  Turnitin  is meant to deter students from plagiarizing in their writing and to catch the ones who do so. Setting time limits for synchronous online exams is a common tactic for reducing the time available for students to use the textbook or a website like  Chegg  to solve their problems for them. 

But telling students not to cheat—and what will happen to them if they do—only goes so far in deterring academic misconduct.  

Underneath those dos and don’ts are implicit values present in the American system of higher education. What if we openly communicated those values instead? 

What do we value? 

The concept of academic integrity is often taught with a focus on academic misconduct and how not to misbehave. Students navigate through college trying not to break the rules. Underneath those rules lie traits that are valued in our education system, and in scholarly work. For example, we trust that a student who can explain a concept in their own words rather than quoting a text has truly learned that concept. We also value original thought and the individual voice in scholarly conversation. We place importance on respecting what writers and researchers contribute to the conversation, and on distinguishing who said what.  

For a brief history on the development of intellectual property, see Bloch, 2012, Chapter 2. 

Do students understand what academic integrity is? 

Bretag and colleagues (2014) discuss two main types of research into academic integrity: student self-reports about their cheating behaviors and research on students’ understanding of academic integrity. Based on surveys of students at multiple institutions, they found that students had some idea of what academic integrity is but did not feel they received enough support for how to practice it effectively, beyond the generic information provided early in their college careers. In one of the surveys, students indicated that instructors’ expectations varied and that conventions were not uniform across courses, and that knowing what happens when you commit academic misconduct is not helpful. 

Learning disciplinary practices 

Nelms (2015) points out that many students plagiarize unintentionally on their way to becoming more expert in their fields. As novice students learn to use the language of their disciplines, they may begin by imitating the language that they are reading. He provides a positive view of plagiarism as an opportunity to help students develop their own voices and learn to participate in scholarly conversation. By viewing students first as learners, it is possible to create penalties that are educational rather than punitive (Morris, 2016).  

English language learners 

It’s especially critical to support English language learners writing in a non-native language to understand academic integrity expectations. Rhetorical styles and conventions vary around the world. Students who were not educated in the United States may have learned practices surrounding academic integrity that do not align with the Western conventions of incorporating and citing scholarly work, and therefore face a steeper learning curve. 

Explore  resources for supporting international students with writing  from Writing Across the Curriculum. 

The learning environment 

In  Cheating Lessons  (2013), James Lang examines how features of a learning environment might lead to increased academic misconduct. He argues that instructors can influence these features directly. They are (p. 35): 

Emphasis on performance : Students who are more concerned with doing well on a test than with learning are more likely to cheat on that test. If an instructor overemphasizes grades, the focus on performance can put pressure on students and become a dominant feature of the learning environment. 

High stakes : If a student’s grade is determined by one or two assessments, such as a midterm (at 50% of the grade) and a final (the other 50% of the grade), cheating is more likely. In such a class, students are not receiving regular feedback on their work, and only have two chances to demonstrate their learning. 

Extrinsic motivation for success : Many students are motivated by grades or other extrinsic motivators, such as pressure from parents. However, students who are motivated by grades or other extrinsic rewards are not necessarily only motivated by extrinsic rewards. 

Low expectation of success on the part of the student : A student who does not believe they have the necessary knowledge and skills to successfully complete an assessment are more likely to resort to cheating. 

In the next section, we’ll discuss how to address each of these characteristics so we can take a positive, rather than punitive, approach to teaching about academic integrity. 

In Practice

 From explicit communication to assessment design to student support, the strategies below will help you proactively promote academic integrity in your courses. 

Be transparent about expectations

Good course design, coupled with transparency, can go a long way to reducing academic misconduct. Explicitly communicate to students your expectations for the course, for individual assessments and assignments, and for academic honesty and other behaviors you want them to demonstrate. Include language in your syllabus around academic integrity and discuss openly what that means and looks like at the start of term. Ensure students understand both the university expectations for academic integrity and the specific expectations for your course.  

Align all assessments and assignments to learning outcomes and communicate that alignment clearly to students. Address any specific academic integrity expectations for a given assignment or assessment in the instructions. For example, make clear how resources should be used and cited, what types of collaboration are allowed or encouraged, how previous student work can be repurposed (if at all), and whether a quiz or test is “open book” or “open notes.” These clarifications will help students understand why their work matters, how it fits in the broader context of your course, and what they need to do to be successful while maintaining academic integrity.

Syllabus Language 

See this  sample syllabus statement for academic integrity and misconduct  and the additional considerations in the  Online and Hybrid Syllabus template  provided by the Office of Distance Education and eLearning. 

Communicate values 

Support students to understand the values and communication conventions within your discipline. While an introductory composition course may help them learn fundamental concepts or habits, that is just the beginning. Explain to students that they are participating in a scholarly conversation—just as they would with their friends, they should respect the ideas that everyone contributes, including their own. Openly encouraging them to find their own voices as distinct from others can reduce the likelihood of plagiarism. 

Beyond scholarly conversation, Lang suggests that educators must explain to students the importance of creating original work in their discipline. 

"… I think these two questions are ones that students might pose to faculty in any discipline: how do I produce my own work in this discipline, and why does it matter that I produce my own work? Those two general questions, it seems to me, are ones that each discipline—and perhaps even each faculty member and each course—has to answer distinctively. And those two questions, it also seems to me, can help form the basis for the more substantial conversation you have with your students about academic honesty and dishonesty in your courses, in addition to the general conversation they might be having through educational campaigns on campus."  (Lang, 2013, p. 194) 

Lang teaches literature, so for him, original work means creating meaningful connections to other works, or to current events in the world. He reminds readers that building these connections leads to deeper learning as students create a more sophisticated mental network (Bransford, et al., 2000, Chapter 2).  

If you teach in another discipline, your approach will be different. In the experimental sciences, for example, we often begin by replicating an experiment, fully or partially. We build on or extend it to test another hypothesis or look at the same hypothesis under different conditions. We get a result, we interpret the result, and this prompts more questions and hypotheses. In the sciences, we have a responsibility to be honest and accurate about those results (Committee on Science Engineering, and Public Policy, 1995). 

Teach for mastery to de-emphasize performance  

Students develop mastery when they acquire a set of skills, practice integrating those skills, and then know when to apply them (Ambrose, et al., 2010). They need opportunities to practice skills in isolation and in combination, and you should evaluate them in both situations. If students are weaker in some skills, provide additional support, perhaps in the form of tutorials or additional practice outside of class.  

Build in opportunities for students to apply important skills in different contexts. Some students excel with certain types of assessments and not others. Providing multiple opportunities—and options—for assessment allows students a variety of ways to demonstrate their knowledge and skills. 

Lower the stakes 

Among your assessments should be many lower stakes opportunities. For example, rather than giving one midterm and one final, include multiple exams or quizzes that are worth fewer points overall. Your students will benefit from the testing effect; Karpicke and Roedinger (2008) demonstrated that the more frequently students were tested on information, the more likely they were to retain that information.  

Plan ways for students to practice for graded assessments during class time or through ungraded asynchronous activities. Autograded  quizzes in Carmen  that present a random set of questions aligned to appropriate learning outcomes make it possible for students to take a quiz as many times as needed until they get the answers right. Shorter assignments that are worth just a few points can help students practice—and get feedback on—what they need to do for a bigger project. 

Scaffolding  assignments is another way you can lower the stakes. Break a larger project or paper into manageable pieces and ask students to show their progress on each piece, so you can see how their work unfolds over time. You will get a sense of which students need more support earlier in the semester, preventing unpleasant surprises later. 

Foster intrinsic motivation 

According to Bain (2004), “People learn best when they ask an important question that they care about answering.” Connecting your course material to students’ interests and personal lives beyond your class can increase their investment. For example, a freshman statistics seminar at Carnegie Mellon University, Statistics of Sexual Orientation, included rigorous statistical analysis while also dealing with theories about the LGBT population.     The following are additional techniques for fostering student motivation (Ambrose et al., 2010). 

Integrate real–world, authentic tasks so students can see the relevance of what they are learning. 

Connect your course content to other courses students are taking or will take so they understand its place in the larger context of their educations. 

Demonstrate how learned skills will be useful in students’ future professional lives. 

Build students’ self-efficacy 

In a chapter on student motivation, Ambrose et al. (2010) describe two parts to self-efficacy. First, students must believe they know what they need to know in order to succeed at a given task. Second, they must believe, when they begin that task, that they will succeed. Even if students have the necessary knowledge and skills, they may feel rushed on the task, that the instructor will not grade fairly, that other members in a group project will hinder their progress, or simply that they will not succeed. Imposter syndrome and stereotype threat can also affect students’ self-efficacy. 

Lang (2013) and Ambrose et al. (2010) describe a variety of strategies for supporting student self-efficacy. One important strategy is to help students develop  metacognition . Students who have an awareness of how they learn tend to be more successful learners. There are a variety of ways to support metacognitive thinking. For example, in STEM courses, separate problem-solving strategies from the actual computation to help students categorize problems into types and see deeper patterns. Ask students to review their graded work and reflect upon study strategies that worked or didn’t work for them (see “ exam wrappers ”). Explicitly guiding students to identify and leverage behaviors they can control, such as study strategies and time management, can increase their success. Sharing recommended study strategies and resources with students can give them options they may not have considered. 

Dive deeper into strategies for  Designing Assessments of Student Learning  and  Supporting Student Learning in Your Course . 

Learning for Mastery

Building a question bank, student tips for preserving academic integrity.

By taking proactive approaches, you can make the shift from the defensive prevention of cheating to the creation of an environment in which students are less likely to cheat in the first place.  

Key strategies for promoting academic integrity include: 

Focus on positive messages rather than fear or the threat of punishment . Emphasizing the consequences of academic misconduct does not support students to understand why academic integrity matters. 

Use good course design to reduce the chances of academic misconduct . Intentionally align assignments to learning outcomes and clearly communicate that alignment to students. 

Provide transparent and explicit instruction and support around academic integrity . Students come to college with diverse backgrounds and values around appropriate academic behavior. Openly discuss what academic integrity looks like at the university and in the context of your course. 

Explain the values and discourse of your discipline . Provide positive examples of how students can enact those values. This is a crucial piece of helping students see themselves as participants in the scholarly conversation of your discipline. 

Teach for mastery and lower the stakes . Focusing on learning over grades and allowing students many opportunities to practice—and make mistakes—will lessen the anxiety around performance on bigger exams or projects. 

Foster intrinsic motivation and help build students’ self-efficacy . Authentic assignments connected to student interests and a balance of challenge and support will keep students motivated. 

You may do everything you can to proactively promote academic integrity but still encounter the occasional student who cheats. In the event that you need to report academic misconduct, consult these  resources from the Office of Academic Affairs  to familiarize yourself with your responsibilities and the university procedure. 

• Academic Integrity and Misconduct (website)
• Academic Integrity in Online Courses (workshop recording)
• Cheating Lessons: Learning from Academic Dishonesty (e-book)
• Instructor Resources for Choosing and Using Sources (website)
• International Center for Academic Integrity (website)
• Plagiarism, Intellectual Property and the Teaching of L2 Writing (book)
• Setting up Question Banks in Carmen (help article)
• Using question banks to randomize exam questions in Carmen (help article)

Learning Opportunities

Ambrose, S.A., Bridges, M.W., DiPietro, M., Lovett, M. C., Norman, M.K. (2010).   How learning works: Seven research-based principles for smart teaching . San Francisco: Jossey-Bass. 

Bain, K. (2004).  What the best college teachers do . Cambridge, MA: Harvard University Press. 

Bever, L. (2017). Dozens of Ohio State students accused of cheating ring that used group-messaging app.  The Washington Post , 13 Nov. 2017.  https://www.washingtonpost.com/news/grade-point/wp/2017/11/13/dozens-of-ohio-state-students-accused-in-cheating-ring-using-group-messaging-app/

Bransford, J.D., Brown, A.L., and Cocking, R.R. (Eds.). (2000).  How people learn: Brain, mind, experience, and school . Washington, D.C.: The National Academies Press. 

Bretag, T., Mahmud, S., Wallace, M., Walker, R., McGowan, U., East, J., Green, M., Partridge, L., & James, C. (2014). ‘Teach us how to do it properly!’ An Australian academic integrity survey.  Studies in Higher Education   37 (7): 1150---1169. 

Bloch, Joel. (2012).  Plagiarism, intellectual property and the teaching of L2 writing . Bristol, UK: Multilingual Matters. 

Committee on Science, Engineering, and Public Policy. (1995).  On being a scientist: Responsible conduct in research . 2nd edition. Washington DC: National Academy Press.  https://www.ncbi.nlm.nih.gov/books/NBK232224/   

DiPietro, M. (2009, Fall). Diversity content as a gateway to deeper learning: the statistics of sexual orientation.  Diversity & Democracy   12  (3).  https://www.aacu.org/publications-research/periodicals/diversity-content-gateway-deeper-learning-statistics-sexual

Karpicke, J.K. and Roediger, H. L. (2008). The critical importance of retrieval for learning.  Science   319 : 966-968. 

Lang, James M. (2013). Cheating lessons: learning from academic dishonesty . Cambridge: Harvard University Press. 

Morris. E.J. (2016). Academic integrity: A teaching and learning approach. Chapter 70 (pp. 1038-1051) in Bretag, T. (Ed.).  Handbook of Academic Integrity . Singapore: Springer Science and Business Media. 

Nelms, G. (2015, July 20).  Why plagiarism doesn’t bother me at all: A research-based overview of plagiarism as an educational opportunity . Teaching and Learning in Higher Ed.  https://teachingandlearninginhighered.org/2015/07/20/plagiarism-doesnt-bother-me-at-all-research/

Tatum, H. and Schwartz, B.M. (2017). Honor codes: Evidence based strategies for improving academic integrity.  Theory into Practice   56 :129-135. 

Related Teaching Topics

Shaping a positive learning environment, designing assessments of student learning, strategies and tools for academic integrity in online environments, related toolsets, carmencanvas, search for resources.

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• Academic Honesty Policy

Templates for Courses

Simple model, from an english course.

Academic honesty: All assignments and activities associated with this course must be performed in accordance with the University of Rochester's Academic Honesty Policy .

Model for Collaborative Work in Laboratory or Problem Sets, from a Computer Science Course

Academic honesty: www.rochester.edu/college/honesty   www.rochester.edu/college/honesty/policy

Homework collaboration: You may discuss homework problems with others, but you must not retain written notes from your conversations with other students, or share data via computer files to be used in completing your homework. Your written work must be completed without reference to such notes, with the exception of class and recitation notes, which may be retained in written form. [NOTE: some instructors require students to report the names of those with whom they discussed an assignment.]

General rule: When in doubt, cite.

Writing-Intensive Model from a Humanities Course

Academic honesty: Students and faculty at the University must agree to adhere to high standards of academic honesty in all of the work that we do. First-year students read and sign an academic honesty policy statement to indicate that they understand the general principles upon which our work is based. The College Board on Academic Honesty website gives further information on our policies and procedures: www.rochester.edu/college/honesty .

In this course the following additional requirements are in effect: You are encouraged to discuss course readings and assignments with your fellow students. However, all written work must be done independently and not in collaboration with another. In order to make appropriate help available for your essays, I encourage you to consult with me and with the College Writing, Speaking, and Argument Program. The term research paper will require citations and “Works Cited” following the MLA format.

Lab Manual Model

There are extensive guidelines for the boundaries of academic honesty at the University of Rochester ( www.rochester.edu/college/honesty/ ), and this was a point of emphasis during your orientation. In this class, you are held to this mutual agreement. Nevertheless, there are some unique features of laboratory courses that deserve emphasis so the expectations are clear for all of us. This lab is a collaborative exercise in learning, as is most of science. Almost all of the projects are done in pairs and we encourage students to freely and openly discuss findings, interpretation of results, etc. However, the written lab report is an individual product. No two lab reports should have matching sections, paragraphs, or sentences, even among lab partners. Also not acceptable are matching sections among students that are not lab partners but in the same class, in different classes, or in different years.

Figures and graphs may vary from the rules regarding text. Depending on the assignment, the same figure may be used by the entire class. In other assignments, construction of your own figures may be part of the lesson. Your teaching assistant will make this clear in class.

An important part of academic integrity is distinguishing clearly when information is original and when it is derived from another source. Consider this problem carefully in both written reports and oral presentations. Examination of the scientific papers we read in class will give you a good sense of when and how to correctly cite a reference in a sentence. Oral presentations follow the same general rules as written documents but differ in some specifics. For example, often in a talk, one will show data from other papers as background (papers usually discuss other results but rarely show the data). In a case when a slide presents data from another study, the reference should be shown somewhere on the slide. Using multiple slides from a presentation made by another person or group is always unacceptable. Just as for written studies, oral presentations must show an independent contribution by the presenter.

(Courtesy of Robert Minckley)

Collaboration Model

Rules for Collaboration and Use of Sources:

Our rules about the ways in which you may collaborate with other students in preparing these assignments are extremely strict and different from many other classes, so pay attention:

• You may verbally discuss any aspects of any assignment (including ideas about how to do it well) with anyone face to face or via phone or video call.
• You may NOT discuss any aspect of any assignment with anyone, other than the instructor and TAs for the course, via email, text message, chat/IM, online discussion forum, social media post, or any other written means.
• When you verbally discuss (face to face, on the phone, via video call) any aspect of any assignment with anyone other than the course instructor or TAs, you may NOT take notes on or in any way record any aspect or portion of your conversation.
• You may discuss any aspect of any assignment via any means with the course instructor and TAs, and you are welcome to take notes on the basis of these interactions.

The intent of these rules is to help you share ideas with other students that can help you to do the assignments well, while preventing you from substituting (accidentally or intentionally) the words of other students for your own in your written work.

As for the use of sources, the only written source you may ever use language from in writing an article brief or critique is the article itself. You should always quote any verbatim passages from the article you use.

When we grade assignments, we will check for overlap in the wording used between assignments submitted by different students. If we suspect that you have violated the rules on collaboration or use of sources, we will report this as a violation of the College's policy on academic honesty, and this can result in severe sanctions.

(Courtesy of Stuart Jordan)

Classroom Honesty Powerpoint

This one-slide PowerPoint prepared by Professor John Werren can be used as is or modified to show in class and/or to post to Blackboard.

Guidelines for Group Projects and Reports

These “ Academic Honesty Guidelines for Group Projects and Group Reports ” can be used as is or modified to fit your specific assignments.

Use in any format (class handout, post to Blackboard, incorporate into a PowerPoint presentation).

More Information

For more information about academic honesty in the classroom contact the board chair .

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• University-Wide Policies

Academic Dishonesty Policy

Policy Statement

It is important for students to understand that the University faculty and staff value student honesty and integrity as explained in this policy.

Purpose/Scope

Context of policy for students: Academic dishonesty, “cheating” and other forms of misrepresenting others’ work as your own, such as plagiarism, are considered serious breaches of academic integrity and are major violations of the standards of ethical behavior that the University expects from all its students. When detected, as it often is, academic dishonesty can result in a range of disciplinary actions including failure on an assignment, failure of a course, or even Conduct Dismissal from the University. Records of disciplinary actions for dishonesty are kept and conduct dismissals are noted on University transcripts. The best rule is to assume that instructors expect all work (exams, papers, projects, etc.) submitted for grading to be entirely your own, done without collaboration. If the instructor allows or desires collaboration, you should assume that the instructor will make that clear in the assignment. If the instructor has not explicitly stated that collaboration is permitted, all work submitted should be entirely your own.

Applicability

There is no applicability provided for this policy at this time

Definitions

Violations of the Student Academic Dishonesty Policy refer to actions related to the standards of honesty required in submission and evaluation of coursework in any undergraduate or graduate course bearing SUNY Brockport credit. These violations include, but are not limited to the following:

A. Plagiarism — presenting as one’s own, the exact words of another, not properly indicated by quotation marks, paraphrased text too similar to the original, ideas, or creative products of another without providing an adequate standard form of documentation to identify the source — such as footnotes, endnotes, or bibliographic documentation. Students are advised to scrupulously acknowledge and properly cite all sources to give appropriate credit for borrowed materials.

B. Fabricating facts, data, statistics, or other forms of evidence included in papers, laboratory experiments, theses, or other assignments.

C. Presenting someone else’s examination results, paper, computer work, or other material as one’s own work. This includes work done as part of group/team effort unless collaboration has been specifically approved by the instructor for any particular assignment. Students should always assume that any out-of-class assignments or take home examinations are to be done individually and without help or collaboration unless the instructor specifically states otherwise. Students should not generalize from one assignment to another as instructors may permit collaboration on some assignments but not on others.

D. Representing one’s own performance as another’s or knowingly allowing such misrepresentation to occur, e.g., signing another student into class; taking an exam for another student; writing or attempting to write an examination, paper, computer work, or other material for another student.

E. Buying and selling, or sharing of examinations or assignments; being in possession of examinations or answers to examinations without the instructor’s permission.

F. Using “cheat sheets,” looking onto another’s paper, talking to someone other than the instructor or proctor during an examination, or using any other method of communication (e.g. cell phones, text messaging) during an examination without the instructor’s permission.

G. Failing to follow the rules of conduct for taking an examination as stipulated by the instructor prior to the examination or as stated in a written course syllabus.

H. Presenting work for a current course (e.g. papers, projects, research) that is substantially the same as a previous submission for another course without obtaining the current instructor’s prior consent to do so. When the courses are taught in the same semester, informing and obtaining prior approval of both course instructors is required to avoid a possible dishonesty charge.

Policy Procedures

Note 1: Published divisional, departmental, unit, and/or individual program policies or individual instructor’s course policies may address additional violations but must not be in conflict with this University policy.

Note 2: Faculty may require students to use software (e.g. SafeAssign) for detecting textual similarities to existing documents. Such software compares submitted student text to previously published documents from a large number of different sources. When similarities between the submitted text and an existing document are found, the software identifies those similarities for student’s and/or the instructor’s review. This software is best used to assist students in learning how to properly cite textual resources. However, such detection may lead to charges of plagiarism if the matched text in the student’s written work has not been properly cited to identify the original source.

I. Bringing Charges of Academic Dishonesty against a Student

The instructor in charge of a course in which an act of academic dishonesty is alleged is responsible for investigating any personally observed, discovered or reported instances of academic dishonesty.

A. The course instructor, any student, or any University employee who has personally witnessed or has knowledge of an act of academic dishonesty can bring a charge of academic dishonesty against a student.

B. All charges of academic dishonesty that do not originate with the course instructor must be made in writing by a signed complainant and delivered to the instructor in charge of the course in which the alleged act of dishonesty occurred. Anonymous accusations are not acceptable. If the course instructor does not personally witness the alleged act of academic dishonesty, evidence in addition to the testimony of the claimant is required to support the charge of academic dishonesty.

C. If the instructor concludes that a violation of the Student Academic Dishonesty Policy has occurred, the instructor must immediately file a Report of Academic Dishonesty form, along with all documentation, with the department chairperson and, within five business days, present the student with a copy of the form, either in person or by certified, restricted delivery mail. The form will state the sanctions the instructor intends to apply to the student. This form also informs the student of her/his appeal rights.

II. Procedures for Investigating and Adjudicating Academic Dishonesty

A. The Course Instructor’s Role

• The course instructor is responsible for investigating any personally observed, discovered or reported instances of alleged academic dishonesty, and for making a determination of guilt or innocence based on that investigation, and notifying the student and the department chair.
• The student has ten (10) business days from receipt of her/his copy of the Report of Academic Dishonesty form to notify the school dean in writing if s/he wishes to appeal the case to the school dean’s level.
• If necessary, when the instructor discovers suspected dishonesty after the semester has ended and is considering imposing a grade penalty, s/he should submit an “I” on the final course grade sheet and note whatever “alternative grade” s/he believes is justified on an Incomplete Contract (I Contract) that states that an academic dishonesty charge is pending. After the instructor’s investigation is complete, the “I” can be changed to a letter grade.

B. The Department Chairperson’s Role

The chairperson’s responsibility is to consult with the instructor to make certain that University policy has been followed with respect to the charges, evidence considered, sanctions applied, and notification of appeal rights. This is a review; not an appeal step.

• The department chairperson will review the case within three (3) days after receiving the Report of Academic Dishonesty from the course instructor.
• The chairperson may consult with the instructor if s/he finds any problems with the instructor’s procedure in the matter, and attempt to resolve these problems.
• Upon concluding her/his review, the chairperson will send a copy of the Report of Academic Dishonesty through the school dean to the Office of the Provost.
• If the student notifies the school dean of her/his wish to file an appeal of the charges or the sanctions applied (see Dean’s Level Dishonesty Appeal Hearing and Procedures the dean may ask the chairperson to forward a copy of the Report of Academic Dishonesty form to the dean, along with any supporting documentation that the course instructor has supplied.

III. Departmental Sanctions for a First Academic Dishonesty Defense

A. Instructor’s sanctions. After concluding that a student is guilty of academic dishonesty (in accordance with the procedures described in Section III of this policy), the instructor may at her/his discretion apply the following sanctions:

• Assign a lowered grade (including an “E” grade) for the particular test or assignment in which the offense occurred, and/or
• Assign a lowered grade (including an “E”) for the entire course in which the offense occurred.

B. Other Departmental sanctions. After being notified that a violation of the Academic Dishonesty Policy has been confirmed, the chairperson of the department in which the offense occurred may have cause to begin an action to dismiss the student from a departmental major or other departmental program.

• Dismissal from a program would normally only take place if the student, through the confirmed act of dishonesty, has also violated an existing, written, and published department/program policy designed to enforce a system of professional ethics.
• After receipt of the Report of Academic Dishonesty form, the department chairperson must communicate a dismissal from program action to the student in writing stating the cause or reason for the action and notifying the student of her/his appeal rights. Such dismissals will be done in compliance with published departmental procedures on dismissal from program. Dismissals from program are only done after consultation of department chairperson with the school dean. An appeal of the dishonesty charge would be normally have to be completed prior to adjudicating any appeal of a dismissal from program action.

C. The determination of academic dishonesty, and/or the grade sanctions imposed by the instructor, and the dismissal from program can be appealed to the school dean (see Dean’s Level Dishonesty Appeal Hearing and Procedures).

IV. University-wide Sanction for a Second Academic Dishonesty Offense

A. A second confirmed violation of the Student Academic Dishonesty Policy may result in the student’s dismissal from the University. This applies when the second dishonesty charge is filed in a later semester after the first charge. At the discretion of the Provost, when more than one dishonesty charge is filed in the same semester and there are no charges from a prior semester, the student will be strongly warned but not dismissed from the University.

B. The process for dismissal will begin when the second Report of Academic Dishonesty form is presented to the Provost’s office file. This will initiate a letter to the student (copy to the dean and chair) informing her/him that a second dishonesty charge has been filed, that this could lead to Conduct Dismissal, and that s/he may wish to file an appeal with the school dean’s office within the required time frame. If an appeal is not filed or a subsequently filed appeal is unsuccessful, the Provost will order the student’s dismissal from the University. This will be a Conduct Dismissal.

C. The appeal of the impending Conduct Dismissal for dishonesty will usually be a dean’s level appeal of the second dishonesty charge. However, the Provost and the President of the University always have the right to review any charge that would result in a student’s dismissal.

D. Students under threat of dismissal for dishonesty who do not appeal or whose appeal is not successful will be separated immediately from the University (except as described in Dean’s Level Dishonesty Appeal Hearings and Procedures) and be assigned a failing grade for the course in question. Such students will receive whatever grades (or within deadlines, drops or “Ws”) are appropriate for Conduct Dismissal at that point in the semester for any other courses. Dismissed students will remain liable for all charges incurred for the semester. A Conduct Dismissal will be recorded on the student’s transcript.

V. Dean’s Level Dishonesty Appeal Hearings and Procedures

A student may appeal the course instructor’s determination of academic dishonesty, the chair’s review, or any sanctions imposed, to the dean (or designee) of the school in which the alleged offense occurred. If a student files an appeal of “dismissal from program” with the dean within the required time limit, the dismissal will be deferred until the dean acts on the dishonesty appeal. If the appeal is denied, the dismissal will take place immediately thereafter. All appeals must be based on one of the following grounds:

• The student asserts that s/he can provide evidence to disprove the instructor’s charge.
• The student asserts that the instructor’s or department’s imposed sanction is grossly inappropriate to the proven or admitted offense.

A. The student must request an appeal in writing to the school dean within the specified time limit (see Procedures for Investigating and Adjudicating Academic Dishonesty). The appeal letter must specify the ground(s) on which the appeal is based (see above).

B. Within three days of receiving the student’s written appeal, the dean will obtain and examine all documentation related to the instructor’s charges and the chairperson’s review and determine whether a hearing is merited. The dean may summarily deny the appeal if s/he feels the evidence in support of the charge and/or assigned sanction is compelling and/or that the grounds for appeal are not present.

C. Within three days of receiving the request for appeal, the dean will notify the student in writing of her/his decision on whether to hold a hearing.. If there is to be a hearing, the dean’s office will contact the student to schedule a meeting date and time.

D. At the hearing, the dean (or designee) will accept relevant evidence and testimony from both sides. The appeal hearing will include the dean (or designee), the course instructor, the student and any witnesses requested by either the instructor or the student. The dean (or designee) will hear all statements, examine the evidence, and adjudicate the claim of academic dishonesty. The dean also has the option of appointing a committee of three faculty members of her/his choice to conduct the hearing and make a recommendation to the dean on the matter.

E. If the dean finds that the charge of academic dishonesty is not substantiated, any sanctions that have been applied will be removed. The student will continue in the course (and/or program) without penalty. If the semester has ended, the dean will inform the faculty member of her/his responsibility to determine what the final grade should be and to submit a revised grade through the dean as appropriate. All copies of the Report of Academic Dishonesty form relating to the alleged incident will be removed from departmental, Provost’s, and student conduct coordinator’s files.

F. If the dean finds that the academic dishonesty charge is substantiated, s/he may review the sanctions previously recommended by the course instructor (or department chair) and allow these sanctions to stand or change them. The dean will inform the student of her/his decision in writing within three (3) days of the date the hearing is held and a record of this decision will be filed with the provost. Any pending dismissal from program will be imposed upon denial of the appeal.

VI. Student Rights

Students have the following rights in regard to an instructor’s investigation of charges and all appeal hearings regarding academic dishonesty charges:

A. The right to a written notice of the nature of the charges and to be informed of one’s rights prior to any hearing or investigation of the charges.

B. The right to continue in a course until the process for confirming academic dishonesty is completed (including appeals).

C. The right to receive, upon request and in advance, a list of the witnesses who will appear to give evidence in any hearing or investigation of the charges. The provision of such a list of witnesses shall not preclude the testimony of witnesses who were unknown at the time of such a request.

D. The right to bring witnesses to give evidence on the part of the student and to examine any witnesses brought by the instructor. Only the accused student may present the student’s case and examine witnesses. Postponement of a scheduled dean’s hearing may be allowed on the basis of unavailability of important witnesses but only if the cause for unavailability is reasonable.

E. Members of the University community or parents or guardians may be present for “moral support” but cannot actively participate in the hearing.

F. In accordance with the Family Education Rights and Privacy Act of 1974, the University may release information pertaining to individual judicial cases to appropriate University personnel and to parents of students who are dependent. With those exceptions, information from a student’s judicial file will not be made available to anyone other than the student without the student’s written consent except in compliance with a lawfully issued subpoena or court order or in the event of a health or safety emergency.

VII. General Policies Governing Dishonesty Actions and Hearings

A. A student who withdraws from the course (or the University) after being charged with academic dishonesty will not be exempt from the Student Academic Dishonesty Policy. The normal process will be followed and the accused student will receive due notice of any hearings and his/her right to respond.

B. When there is insufficient time to hold a hearing on a dishonesty charge before a semester ends, a hearing will be held as soon after the semester ends as is feasible. In such circumstances, a student who leaves the Brockport area is responsible for transportation and other expenses related to her/his right to be present at the hearing as scheduled.

C. Failure to appear in response to the charge(s) on the date set for a dean’s appeal hearing, unless there is a continuance for good cause approved by the dean prior to the hearing, shall be deemed an admission of the facts as stated in the instructor’s charges. By failure to appear, the student also forfeits any right to further appeal of these charges.

D. All hearings are closed to the public.

E. An academic dishonesty hearing or examination of charges shall not be bound by technical rules of evidence, but may include testimony or evidence that is relevant and material to the issues presented by the charge(s) and which will contribute to a full and fair disposition of the charge(s).

F. Cameras or recording equipment are not permitted in a hearing or examination of charges.

G. The student’s academic dishonesty record may be used in any future judicial proceedings or readmission decisions involving the student.

H. The term “days” used in this code with reference to scheduling and notification means days on which the administrative offices of the University are officially open for business.

I. When necessary, the University reserves the right to extend the time periods set for actions under this policy and such extensions

will not invalidate the charges and sanctions. However, actions on the part of the University will be carried out with all due haste. With good reason, students charged may also request extension of deadlines by written request to the school dean.

J. The president of the University always has the right to grant clemency or pardon in regard to any sanction assigned under this policy.

VIII. Summary of actions and deadlines under this policy:

Links to Related Procedures and Information

Report of Academic Dishonesty Form

Contact Information

There is no contact information for this policy at this time.

History (in descending order)

There are no approvals for this policy at this time.

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Academic Integrity at Georgia State

Academic integrity, academic integrity resources, policies & procedures, georgia state policy on academic honesty, policy on academic honesty.

As members of the academic community, students are expected to recognize and uphold standards of intellectual and academic integrity. The Policy on Academic Honesty assumes as a basic and minimum standard of conduct in academic matters that students be honest and that they submit for credit only the products of their own efforts. The ideals of scholarship and the need for fairness require that all dishonest work be rejected as a basis for academic credit. They also require that students refrain from any and all forms of dishonorable or unethical conduct related to their academic work.

The policy represents a core value of the university, and all members of the university community are responsible for abiding by its tenets. Lack of knowledge of this policy is not an acceptable defense to any charge of academic dishonesty. Members of the academic community, students, faculty and staff, are expected to report violations of these standards of academic conduct in accordance with the procedures articulated in this Policy.

Georgia State Student Code of Conduct

Student code of conduct.

The university has established the policies and procedures that comprise the Student Code of Conduct (the “Code”) to both promote the university mission and protect the rights of Students, faculty and staff. The official university rules and regulations are contained in the Georgia State University General Catalog and the student handbook.The most current version of the Code may be found online at: codeofconduct.gsu.edu. In the event of a conflict between the Code and other university policies, the most current version of the Code governs.

Student conduct is governed by the Code, university policy and applicable law. Students involved in criminal matters may be sanctioned by the university in addition to any sanctions that may be imposed by a court of law. However, the relationship a Student has with the state or federal court system does not alter the Student’s relationship with the university unless the Student is also found responsible for violating university Policy.

Dean Certification Letter – Contents and Process

Dean’s certification procedure.

A Dean’s Certification is a letter commonly requested by third parties for a variety of reasons (e.g., transferring to another institution, admission to graduate/professional programs, state bar associations, government agencies, and for certain forms of employment) to determine whether a student (past or current) has a disciplinary record(s) on file with the Office of the Dean of Students.

A request for a Dean’s Certification Letter is submitted online through the Dean of Students website at deanofstudents.gsu.edu and is typically completed by the student who as part of the request process gives consent for GSU to release the information to a third party. However, some requests are received directly from third parties using uploaded verification forms that include the student’s signature granting permission for the release of information.

For purposes of Dean’s Certification, the following information will be released upon request:

• All Code of Conduct violations for which a student has been found responsible, all disciplinary sanctions issued except as noted below, and a statement indicating whether the student is/is not in good conduct standing.
• If no applicable conduct history exists, the Dean’s Certification letter will indicate that the student has no disciplinary history and is in good conduct standing.

Information regarding the following matters will not be included in Dean’s Certification letters:

• Academic honesty violations in which the outcome was limited to an academic penalty (i.e., no disciplinary penalty was issued).
• Violations processed as University Housing Community Living Standard violations.
• Conduct matters resolved through alternative resolution options, including mediation, informal resolution, and restorative justice.
• Conduct matters for which a student is afforded Amnesty as defined in the Code of Conduct.
• Conduct records which have expired per applicable law or that are no longer maintained per University System of Georgia record retention policies.

Homework Help Websites

There is a growing number of websites purporting to provide homework help to students. These sites have a reputation for helping students cheat and for building their knowledge base from content contributed by students. Content may include exams, quizzes, lecture notes, homework assignments, etc. Because faculty and instructors own the rights to their course materials in most cases, only they can request takedown of their specific materials found on most of these sites. These types of sites are technically compliant with the law, but don’t offer a way to block content on a broader level, e.g., such as by course name.

Georgia State University Legal Affairs has provided takedown instructions for several sites. Download the PDFs by selecting the link below.

As a reminder, the following Student Code of Conduct Statement regarding sharing or posting course materials including audio recordings of lectures was approved by the Georgia State University Senate on August 21, 2020:

The selling, sharing, publishing, presenting, or distributing of instructor-prepared course lecture notes, videos, audio recordings, or any other instructor-produced materials from any course for any commercial purpose is strictly prohibited unless explicit written permission is granted in advance by the course instructor. This includes posting any materials on websites such as Chegg, Course Hero, OneClass, Stuvia, StuDocu and other similar sites. Unauthorized sale or commercial distribution of such material is a violation of the instructor’s intellectual property and the privacy rights of students attending the class, and is prohibited.

What is Academic Integrity? Is it Different Than Academic Dishonesty? (Without Quiz)

This student-oriented video explains the importance of academic integrity for student success. It does NOT include the embedded quiz – for that version, please see the dropdown section below this one.

The link to add in your syllabus is: https://vimeo.com/486904319/26bcd7dd2f

Academic Integrity Video, With Quiz

This version of the video requires students to log in to Mediaspace with their campus ID and password to view the video and complete the embedded quiz.

Direct link:  https://mediaspace.gsu.edu/media/Academic+Honesty+Ethics+and+Integrity+-+Quiz/1_qte94uxn

Syllabus Statements

Statement and recommendations, sample statement.

Academic dishonesty is a serious violation of the trust upon which the success of our University depends. Cheating and plagiarism can not only result in a poor grade and penalties from the University, but it can cause your mentors and peers to mistrust you and could keep you from developing the habits to make you a successful student and a successful worker in your future career. The University’s policy on academic honesty is published in the Student Handbook,  https://deanofstudents.gsu.edu/files/2019/07/Academic-Honesty-Policy.pdf , and includes dishonest actions such as cheating, plagiarism and facilitating academic dishonesty. Please be aware that violations of this policy will result in a grade of “F” for the assignment or the course. If you have any questions about the policy or are unsure if something you’re about to do counts as academic dishonesty, please come to my office hours and we can discuss it.

Recommendations

• Let your students know that violations of the academic honesty policy that end up their records appear on background checks if an employer contacts the university. Consider including this statement: “If you are found responsible for violating the Academic Honesty Policy, this could become a part of your permanent academic record that may become available to future employers.”
• Make it explicitly clear in your syllabus that sharing information/cheating via group messaging apps such as GroupMe or Slack is a violation of the Academic Honesty Policy.
• Respondus Lockdown Browser Options – Be clear on your syllabus about the importance of the testing environment. Include ways a student can express concerns such as needing accommodations due to the technology before the assignment.
• When using proctoring services, be clear with students how an external proctoring company (such as ProctorU) determines suspicions of academic honesty and what students can expect when they do.
• Consider Using Turnitin, available through iCollege.  Make students aware of the plagiarism detection software and how you plan to use it in your class. Turnitin allows students to view the originality score helps them to better understand plagiarism.  When you allow unlimited submissions, students have the opportunity to make changes to their original document.

Alternative Syllabus Statements

1) The following statement was approved by the GSU Faculty Affairs Committee (8/21/2020) regarding students posting, selling, or sharing information:

2) Cheating or plagiarizing may seem like easy short-term solutions when you haven’t studied or aren’t prepared for an assignment, but will hurt you in the long-term because you aren’t learning what you need to know and are negatively impacting your personal integrity and future course or job performance.   If you cheat or plagiarize you will receive an “F” for the assignment and have your name submitted to the appropriate Department Chair or Dean . Absolutely no cell phones maybe be used during quizzes or exams. You may not access any other browser windows during online quizzes or exams. Sharing quiz or text answers via group messaging apps is considered cheating. Please read the   GSU Student Code of Conduct.  All members of the university are responsible for abiding by the GSU tenets on Academic Honesty.  Lack of knowledge of this policy is NOT an acceptable defense to any violation of academic honesty.  All members of the Georgia State community are expected to report violations of these standards of academic conduct to the appropriate authorities.  The procedures for such reporting are on file in the offices of the deans of each college, the dean of students, and the provost.  Please read the University Policy on Academic Honesty, page 23, ( https://deanofstudents.gsu.edu/files/2021/02/Academic-Honesty-Polidy.pdf .

3) To learn and grow intellectually you need to take responsibility for your own work and be aware and honest as you complete your assignments. Taking ideas or words from others — plagiarizing — is dishonest and will result in a failing grade on the paper or assignment and possibly other disciplinary actions.  If you are unsure about what constitutes plagiarism, ask me or consult the University Policy on Academic Honesty, page 23, ( https://deanofstudents.gsu.edu/files/2021/02/Academic-Honesty-Polidy.pdf .

Download these statements and recommendations by selecting the link below (PDF opens in new window).

Sample Course Materials from GSU 1010-PCO 1020

Access sample course assignments and other materials on ethics and integrity from a first-year experience course at Georgia State (GSU 1010/PCO 1020).

Journal Articles/Research

Find a wealth of journal articles and research related to academic integrity by selecting the button below, which will take you to a page of listings with abstracts, citations, and links to articles.

NOTE: You may need your Georgia State Campus ID and password to access many of these articles, especially if you are not on campus.

Chronicle of Higher Education

Students cheat. how much does it matter as the pandemic continues, the debate grows louder., students cheat. how much does it matter as the pandemic continues, the debate grows louder..

Beckie Supiano Oct. 21, 2020

In interviews with faculty around the United States, the author discusses academic integrity and dishonesty during the pressures of the COVID-19 pandemic, different approaches faculty have taken, risk factors for academic dishonesty, and potential approaches and models.

7 Ways to Assess Students Online and Minimize Cheating

Flower Darby Sept. 24, 2020

The author, a long-time instructor and instructional designer, provides seven recommendations to both meaningfully assess student learning and foster academic integrity.

Chronicle of Higher Education: On-Demand Webinar

Academic integrity online - webinar recording, academic integrity online (on-demand webinar).

The Chronicle of Higher Education Recorded March 18, 2021

Amid the many challenges presented by higher ed, one of the biggest is the issue of academic integrity in online learning. How prevalent have cheating and plagiarism been during the pandemic? And what can colleges do to ensure students’ work is honest and fair?

To find out, The Chronicle brought bring together an expert panel for a discussion of academic integrity as it stands now. Subject addressed included:

• What practices and policies should colleges use to build a culture of academic integrity online?
• How can faculty members set fair expectations and rules early in a course without being overly focused on “catching cheaters”?
• What role should online proctoring play?

Click the button to watch on demand.

International Center for Academic Integrity

Integrity matters blog, integrity matters: a blog of the international center for academic integrity.

International Center for Academic Integrity (ICAI)

This blog of the ICAI includes helpful insights from faculty and other academic professionals on issues related to academic integrity, including the prevention of academic dishonesty, insights on academic integrity during the COVID-19 pandemic, and how to promote and manage academic integrity at colleges and universities.

Send this to a friend

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Success in college, as in other aspects of life, demands absolute honesty at all times. Sierra College expects that students, as well as faculty, will observe the principles of ethical conduct in their treatment of fellow members of the academic community and in their accomplishment of academic work. Students are responsible for familiarizing themselves with these principles as they pertain to each course in which they enroll. When completing assignments, students should be careful to follow the principles of ethical conduct. Students who are uncertain about the ethics involved in particular courses or assignments should make it a point to talk with instructors. Proven misconduct or violation of these principles will be disciplined as set forth in this policy.

The instructor has absolute authority over issuing the final course grade (Education Code, Section 76224).

It is important to remember that the principles of academic honesty in no way restrict free inquiry and the open exchange of diverse, and sometimes unpopular, ideas. These the College encourages, for they are vital to learning and the pursuit of reason and truth.

Students who fail to comply with this policy will be subject to disciplinary action as described in Administrative Procedure 5515 .

Reference: Board Policies Chapter 5

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Academic Integrity

The importance of academic integrity .

Academic integrity speaks directly to student honesty, responsibility and respect for scholarship. Academic assignments and tests help students learn course content, while grades show how fully this goal is achieved. Coursework and associated grades should be the result of the student's own understanding of academic content, as well as demonstrated effort and achievement.

How does WPI support Academic Integrity? 

As a community of scholars, WPI prides itself on maintaining the highest level of academic integrity. WPI articulates this core value through the  Student Code of Conduct . Students, faculty and administrative staff are expected to be familiar with the tenets of the Academic Honesty Policy as well as with the associated procedures for determining and responding to acts of academic dishonesty.

Toward this end, the  Dean of Students Office , which is charged with developing and articulating policies and procedures associated with WPI's conduct system, has developed this web site as a resource for faculty and students to:

• Learn about What Constitutes Academic Dishonesty ; 
• Look at Academic Integrity Resolution Forms and Faculty & Student Resources 
• Consider Practices that Discourage Academic Dishonesty in the Classroom .

This webpage adapts information from the University of Oklahoma, Norman Campus, Harvard University, and other colleges and universities.

	University of Houston
	Aug 09, 2024   2024-2025 Academic Calendar     2024-2025 Academic Calendar Policies  > Academic Regulations > Dropping Courses Students are expected to commit themselves to courses as early as possible in order to succeed in their courses. The last day to drop or withdraw from a course without receiving a grade is the Official Reporting Day (ORD). Please see the academic calendar    for the exact date. Before dropping courses: Students receiving financial aid should consult with a financial aid advisor All F-1 and J-1 international students must consult with an advisor in the International Student and Scholar Services Office Athletes must consult with the Associate Director of Athletics for Student-Athlete Services Beginning in Fall term 2007, all students (current, transfer, and first time in college students) are permitted a total of six Ws (withdrawals), whether student or instructor initiated. Ws may be used at any time during their college career to drop a course up through the last day to drop a course or withdraw from all courses. When these six Ws have been used, the student must complete all subsequent courses. When enrollment in a course requires concurrent enrollment in another class (e.g., lecture/lab combination), the dropping of such a course combination whether for credit or not will count as one withdrawal if dropped within the same term. The academic department offering the course must verify the concurrent enrollment requirement. The last day to drop a course with a W is near the end of a term. The specific term deadline is posted on the academic calendar   . Through the last day to drop a course with a grade of W, enrollment in a course may be terminated in any one of the ways listed below. Termination of enrollment does not entitle the student to receive a refund of tuition and fees if the drop date is after the refund date. Should an attempted drop result in exceeding the six W limit, the student will remain enrolled in the course and the instructor will assign the grade earned, which may be an F. Undergraduate students who wish to drop a course must do so online by logging in to their myUH account at https://my.uh.edu . Lack of prerequisites or corequisites for the course listed in the current catalog. Students who enroll in a course for which they are not eligible and remain in the course knowingly misrepresent their academic records or achievements as they pertain to course prerequisites or corequisites and are in violation of the university’s Academic Honesty Policy. Excessive absences Causes that tend to disrupt the academic process (except those actions involving academic honesty, which come under the jurisdiction of the academic honesty policy). Disruptive behavior includes the use of or the failure to deactivate cell phones, pagers, and other electronic devices likely to disrupt the classroom. Students may appeal such a decision in writing within 30 days through the office of the dean of the college in which the course is taught. After the last day for dropping courses, undergraduate students may be dropped from a course with a W, only for rare, urgent, substantiated, nonacademic reasons. Students wishing to initiate such actions must submit the request in writing to the Senior Vice President for Academic Affairs (or designated representative) with accompanying documentation. Students have 90 days after the posting of a grade to initiate this action. Until a decision on this request has been made, the instructor should assign whatever grade is appropriate other than an Incomplete. The review procedure will be the same as that applied for consideration of medical and administrative withdrawals. The student and instructor will be notified in writing of the final decision. The effective date recorded for termination of enrollment for all matters relating to University of Houston records will be the date the student drops the course through his/her myUH account at https://my.uh.edu or the date the properly approved enrollment change request form is processed by the Office of the University Registrar in the Welcome Center. Students are responsible for verifying that they have been dropped from a course by logging in to their myUH account at https://my.uh.edu or at the Office of the University Registrar in the Welcome Center. Students may not receive a W for a course in which they have been found guilty of a violation of the Academic Honesty Policy. If a W is received prior to a guilty finding, the student will become liable for the Academic Honesty penalty, which may be a grade of F. Term withdrawals (dropping to zero credit hours) do not count toward the limit of six Ws. Texas Education Code §51.907 provides that, except for several specific instances of good cause, undergraduate students who enrolled for the first time in a Texas public institution of higher education in Fall 2007 or after will be limited to a total of six dropped courses during their entire undergraduate career. This statute applies to courses dropped at public institutions of higher education in Texas including community and technical colleges, health science centers that offer undergraduate programs, and universities. Courses dropped at independent/private institutions, or at colleges and universities outside of Texas, do not count against the student’s six drop limit. Students may also refer to the Texas Administrative Code §4.10 . Pardon Our Interruption As you were browsing something about your browser made us think you were a bot. There are a few reasons this might happen: You've disabled JavaScript in your web browser. You're a power user moving through this website with super-human speed. You've disabled cookies in your web browser. A third-party browser plugin, such as Ghostery or NoScript, is preventing JavaScript from running. Additional information is available in this support article . To regain access, please make sure that cookies and JavaScript are enabled before reloading the page. Promoting Data Sharing: The Moral Obligations of Public Funding Agencies Original Research/Scholarship Open access Published: 06 August 2024 Volume 30 , article number  35 , ( 2024 ) Cite this article You have full access to this open access article Christian Wendelborn   ORCID: orcid.org/0000-0002-8012-1835 1   nAff2 , Michael Anger   ORCID: orcid.org/0000-0002-9328-510X 1 & Christoph Schickhardt   ORCID: orcid.org/0000-0003-2038-1456 1   106 Accesses Explore all metrics Sharing research data has great potential to benefit science and society. However, data sharing is still not common practice. Since public research funding agencies have a particular impact on research and researchers, the question arises: Are public funding agencies morally obligated to promote data sharing? We argue from a research ethics perspective that public funding agencies have several pro tanto obligations requiring them to promote data sharing. However, there are also pro tanto obligations that speak against promoting data sharing in general as well as with regard to particular instruments of such promotion. We examine and weigh these obligations and conclude that all things considered funders ought to promote the sharing of data. Even the instrument of mandatory data sharing policies can be justified under certain conditions. Similar content being viewed by others Preparedness for Research Data Sharing: A Study of University Researchers in Three European Countries Data Decisions and Ethics: The Case of Stakeholder-Engaged Research Openness in Big Data and Data Repositories Avoid common mistakes on your manuscript. Introduction The potential benefits of sharing research data for science and society have been widely acknowledged and emphasised. Some disciplines or sub-disciplines have a longstanding tradition and well established practices of data sharing, for instance, astrophysics, climate research and biomedical genomic research. However, despite various efforts to promote and encourage data sharing, for instance by scientific journals, it is still not common practice in most fields of the sciences. As public funding agencies have considerable influence on both the scientific communities as well as the individual researchers, the question arises whether they are morally obligated to promote data sharing. In order to answer this question, we examine the following more specific three questions from the perspective of research ethics: Do public funders have general pro tanto moral obligations that require them to promote data sharing? Do public funders have general pro tanto moral obligations that speak against promoting data sharing? What pro tanto moral obligations have to be considered in the particular case of using mandatory data sharing policies, i.e., policies that require researchers to share data? Answering these questions is a desideratum of (bio)ethical research on issues of data sharing. Although it is stated that individual researchers have a scientific responsibility (Bauchner et al., 2016 ; Fischer & Zigmond, 2010 ) and even a moral obligation to share data (Schickhardt et al., 2016 ), the moral responsibilities and obligations of public funding agencies in matters of data sharing have not been discussed systematically and explicitly from the perspective of research ethics. While it is common to postulate that funders “should” encourage data sharing or that it is their “responsibility” to do so, we want to carry out an in-depth ethical analysis of funders’ moral obligations. In doing so, we also contribute to an analysis of what funders are generally morally obligated to – another question that has thus far been rather neglected in research ethics and discussed primarily in terms of priority-setting and with regard to the general obligation to benefit society (Pierson & Millum, 2018 ; Pratt & Hyder, 2017 , 2019 ). Thus, we will provide a broader analysis of general moral obligations of funders and evaluate what they imply with regard to promoting data sharing in particular. We proceed as follows: After some preliminary remarks in Sect. " Preliminary Remarks ", we provide a brief review of empirical data on the current status quo of data sharing in Sect. " The Current State of Data Sharing and of Promoting Data Sharing ". In Sect. " The Moral Obligations of Funders and the Promotion of Data Sharing ", we set out that funders have three general moral pro tanto obligations that require them to promote data sharing. In Sect. " Further Relevant Moral Obligations ", we examine two pro tanto obligations that both speak in favour of and against promoting data sharing. We conclude Sect. " Further Relevant Moral Obligations " by weighing all pro tanto obligations. In Sect. " Mandatory Data Sharing Policies and Academic Freedom ", we ethically assess the specific instrument of promoting data sharing by way of mandatory policies with regards to academic freedom. We conclude and summarise our arguments in Sect. " Summary and Conclusion ". Preliminary Remarks In the following, we use the term “research data” and “data” as referring to digital data that is collected and/or generated during a research project. We use the term “data sharing” as referring to the act of making data available for other researchers – either for the purpose of transparency of studies and replication of published research results or for the purpose of other researchers using the data for their own research questions and projects (secondary research use). Footnote 1 Data sharing is increasingly supposed to meet the requirements of the FAIR principles, i.e., data should be findable, accessible, interoperable and re-usable (Wilkinson et al., 2016 ). Data can be shared in various ways, for example via open access or restricted or controlled access, and by using Data Use and Access Committees or data sharing licenses. Footnote 2 Restricted or controlled access comes, for instance, with additional data protection requirements when personal data are involved. Data sharing activities (and data sharing policies by funders) must comply with the applicable local laws and regulations. In EU countries, the possibilities for international sharing of non-anonymous data are dependent on the EU GDPR, making personal data sharing difficult between EU countries and the US, for example. As to legal challenges to international data sharing raised by local laws, there are possible legal approaches (contracts) and technical solutions such as code-to-data approaches, when the data remains at the location of the data producer or the repository and is only analysed there on behalf of the other researcher. Footnote 3 We define public funding agencies, following the European Commission Joint Research Centre (2017), as organisational entities that distribute public funding for research on behalf of either regional, national, or transnational governments. The definition covers both i) funding agencies operating at arm’s length from the public administration and enjoying relative autonomy from the government and ii) ministries and offices within the government that fund research projects. The definition comprises of centralised and non-discipline specific agencies such as the German Research Foundation (Deutsche Forschungsgemeinschaft), de-centralised and discipline specific agencies such as the National Institutes of Health in the US or the UK Research Councils, as well as international funding agencies and bodies such as the European Commission. When we speak of research funding, we refer to funders who grant funds to individual researchers or groups of researchers (collaborative projects or research consortia). Against the background of the existing organisation of the (academic) science system with its systematic competition between researchers and the importance of scientific publications, we assume that funded researchers use the funding to seek and publish new findings and that they do so in a somehow exclusive way that does not involve the immediate disclosure of all data and results. The tendencies of competition, exclusive use of data and the pursuit of (more or less) exclusive first scientific publications of previously unknown research results are the reasons why funders' policies on sharing research data and overcoming data secrecy are important, at least at some point in the project and research cycle. Traditionally, research projects funded in this way tend to be hypothesis driven. However, as research methods, the nature of projects and the associated research funding evolve rapidly and potentially change in the era of Big Data and AI, the boundaries are blurring, and some things may change. There might be more scientific community-led research projects that are designed to be less exclusive and competitive, with community participation, immediate disclosure, and data sharing at the forefront from the start. A historical example is the Human Genome Project. Funding of such community-led research projects is not the focus of our paper, but community-led research is worth mentioning and discussing in further research. As public funders are public (or even state) institutions and spend public money that they receive from the government, their moral obligations are related to their public and therefore political character. Our analysis of the moral obligations assumes a liberal-democratic and rights-based normative-ethical framework. To put it simply, public institutions are normatively conceived as "by the people, for the people and of the people", and citizens, including researchers, have fundamental liberal rights vis-à-vis the state and public institutions, especially negative rights that protect them from state interference. These moral rights, which play an important role in our analysis, include academic freedom and the rights to privacy and informational self-determination. We confine our analysis in this article only to the promotion of data sharing within academic science and exclude the question of the promotion of data sharing from publicly funded academic science with private for-profit companies. We do not limit our argument to funders that focus on a particular scientific discipline (for instance, biomedical funders), as we believe that the pro tanto obligations we will attribute to funders do not depend on the specific characteristics of particular scientific disciplines. However, we think that when applying our framework in practice, context factors that depend on the features of a certain discipline or a specific research project need to be taken into account. Some of the following arguments for the moral pro tanto obligations of public funders can be translated mutatis mutandis to private funders, but not all of them can. Particularly those arguments that refer to the special status of public funders as public institutions that spend public money and have particular responsibilities towards the public and the rights of citizens cannot be applied to private funders. The obligations of private funders call for a separate analysis in a separate paper. This paper presents an ethical analysis of the moral obligations of funders and is not concerned with legal rights and obligations that pertain to funders in a particular national state or legal area such as the European Union. We assume that the moral obligations presented below do not conflict with the legal requirements of (public) funders in any legal context. However, our claims that funders have a moral obligation to promote data sharing and that they should also implement mandatory data sharing policies under certain circumstances have implications for the revision of (templates for) future legally binding funding contracts between funders and funded researchers. In this respect our ethical analysis has legal implications. We take a pro tanto obligation as an obligation that has some weight in determining what an actor morally ought to do all things considered (Dabbagh, 2018 ). Suppose I promise my friend to visit her tonight; however, my daughter is sick, and I ought to stay with her. I have then two pro tanto obligations that prescribe conflicting actions. To find out what I am obligated to do all things considered , I must find out which of the two obligations weighs heavier. Footnote 4 Therefore, when we examine pro tanto obligations that require the promotion of data sharing, these obligations must be weighed against other pro tanto obligations that speak against such promotion. The Current State of Data Sharing and of Promoting Data Sharing As to the current state of data sharing, there are differences across scientific disciplines (Tedersoo et al., 2021a , 2021b ). Some disciplines, such as astrophysics, climate research or genomic research, have a long history of data sharing. For instance, genomics research paved the way with the important and pioneering Fort Lauderdale (Fort Lauderdale Agreement, 2003 ) and Bermuda principles (First International Strategy Meeting on Human Genome Sequencing, 1996 ) on data sharing (Kaye et al., 2009 ) within the revolutionary and community driven Human Genome Project and has created a genomic commons, i.e., openly available data bases for genetic and genomic driven biomedical research (Contreras & Knoppers, 2018 ; National Cancer Institute; National Library of Medicine). With the exception of some more advanced scientific disciplines or sub-disciplines, the sharing of research data for purposes of transparency and secondary use still remains the exception rather than the norm in most fields and disciplines of the sciences (Danchev et al., 2021 ; Gabelica et al., 2022 ; Naudet et al., 2021 ; Ohmann et al., 2021 ; Thelwall et al., 2020 ; Watson, 2022 ; Strcic et al., 2022 ; Gorman, 2020 ; Towse et al., 2021 ). While there is an increased awareness of the benefits and importance of data sharing in all of the sciences and although various initiatives of funders and journals promote data sharing, for instance through data sharing policies, data sharing is still not common practice. Several studies report rather low rates of compliance with data sharing expectations or requirements of funders and journals (Couture et al., 2018 ; Federer et al., 2018 ; Gabelica et al., 2022 ; Naudet et al., 2018 , 2021 ; Danchev et al., 2021 ). Studies also report a gap between high in-principle support for data sharing, and low in-practice intention (Tan et al., 2021 ). It is frequently emphasised that funders should improve and intensify their current efforts to promote data sharing. Some see the need to create incentives, for example by including a record of past data sharing as an additional criterion for the reviews of grant applications (Perrier et al., 2020 ; Terry et al., 2018 ). Since the majority of funders’ data sharing policies do not strictly require the sharing of data (Ohmann et al., 2021 ), some authors call for stronger policies with strict requirements for data sharing (Couture et al., 2018 ; Naudet et al., 2021 ; Ohmann et al., 2021 ; Sim et al., 2020 ; Stewart et al., 2022 ; Tedersoo et al., 2021a , 2021b ) Footnote 5 and contest the lack of monitoring and enforcing compliance (Couture et al., 2018 ; Kozlov, 2022 ). However, as a series of interviews shows, funders struggle to implement data sharing requirements, incentives, monitoring, and sanctions for non-compliance for various reasons (Anger et al., 2022 , 2024 ). In consideration of the foregoing and from the perspective of research ethics, the question arises whether public funders are morally obligated to promote data sharing. To answer this question, in the next section we set out a description and analysis of funders' general moral obligations and their relevance for data sharing. The Moral Obligations of Funders and the Promotion of Data Sharing We will argue that funding agencies have several general moral pro tanto obligations requiring them to promote data sharing: The obligation to benefit society, the obligation to promote scientific progress as such and the obligation to promote scientific integrity. Our methodological approach consists of first introducing and explaining the individual moral obligations in order to then briefly justify them with reference to plausible and, for the most part, generally shared fundamental considerations, values or norms. The Obligation to Benefit Society Publicly funded research should benefit society, or, as it is sometimes put, it should have social value. Footnote 6 As a requirement for public funders, this means funders should base their decisions on considerations of social value. Barsdorf and Millum ( 2017 ) argue that funders ought to consider the social value in particular in their priority-setting, i.e., when setting goals and priorities for the research they fund. We extend the obligation to promote social value to all decisions and actions of public funding agencies. Footnote 7 Benefitting society or social value is sometimes conceptualised in terms of well-being. The concept of well-being is notoriously controversial in philosophy (as it relates to the complicated and controversial topic of the “good life”). In research ethics, the benefits at stake in the social value obligation are sometimes framed more pragmatically, for example when Resnik ( 2018b ) (following Kitcher 2001 ) states that benefits are “practical applications in technology, industry, medicine, engineering, criminal justice, the military, and public policy”, and that these applications “can also produce economic growth and prosperity”. We limit our conception of social value (benefit) to a more basic understanding (which does not include potentially problematic or controversial elements such as military and economic growth): We understand it in terms of the basic goods of health and wealth (housing, food, employment, income, etc.), infrastructure development (for communications, travel, etc.), and environmental protection (as natural resources). What are the justifying reasons for this obligation? First of all, it must be pointed out that the obligation can be understood in different ways, depending on whether the population to be benefited is the local or the global population. Barsdorf and Millum ( 2017 ), for instance, argue that for health research the social value obligation of funders is towards the global and not the local (national) population of the funders’ country. In the literature, this question (local vs. global) is controversial. In general, the controversial positions on this question also depend on the justification one is willing to accept for the obligation. For instance, if one justifies the obligation as owed to the citizens as tax payers who finance the state and the public funder via taxes, then it is rather obvious to understand social value as benefit for the national tax paying population. In contrast, if one considers the social value obligation of funders as owed to all humans all over the world, it suggests itself to understand the social value broadly in terms of global benefit for all humans. Such a global understanding of the social value obligation could be justified with considerations of beneficence towards every human being or with a universalistic-egalitarian account of human rights. Global understandings of the obligation are likely to give priority to poor populations of the global South. We deem a combination of a local and a global understanding as being the most plausible one: funders have a primary obligation to foster social value on the national level, and an additional (weaker) social value obligation on a global level. But even this combined view raises questions and cannot be elaborated here. Most importantly for the purpose of our paper, we believe that the question concerning the understanding of the social value obligation(s) of funders (towards national vs global population or both) is not relevant for our question about the promotion of data sharing by funders. At first glance it might seem that a local reading of the social value obligation suggests that funders should promote sharing of research data only among local/national researchers. However, the contrary is much more plausible, at least for the academic sciences. Most fields of modern academic scientific research are international endeavours and advancements are achieved through multiple and interacting contributions from scientists from different countries. In most disciplines, there is no such thing as a „national current state of scientific progress “. As for sharing research data from the academic and publicly financed sciences with private for-profit companies, it might be plausible to assume that sharing data only with national companies is more likely to benefit the national population than sharing data with for-profit companies from abroad. However, this assumption can also be challenged, for example, in light of the rapid and effective development of vaccines during the covid pandemic. Most importantly, the sharing of research data from the publicly funded academic sciences with private for-profit companies is a very specific topic that we do not address in this paper. Footnote 8 As far as sharing of research data between academic researchers is concerned, it is plausible to assume: The more data are shared on a national and international level, and the more science advances – which in almost all scientific disciplines occurs as an international advancement -, the more likely national populations will benefit. A last and more specific reason for funders’ obligation to foster social benefit is the following, which applies only to research involving humans or animals: If funders fund research that exposes animals and humans to risks and burdens, the funding can only be justified if the potential benefits for society are maximised (National Commission for the Protection of Human Subjects of Biomedical & Behavioral Research, 1978 ; World Medical Association, 2013 ). Footnote 9 The concept of social value refers to (classical and much debated) questions of distributive justice: Of all persons concerned, who should benefit how much ? Following Barsdorf and Millum, we think the obligation to benefit society, i.e., the social value obligation, should be understood according to a prioritarian account of social value. On a prioritarian account, benefits should be distributed such that the distribution (expectedly) maximises “weighted well-being” (or in our terms “weighted social benefit”), i.e. the well-being of the worse off gets some priority in the distribution of benefits. Let's put this in the following proposition and call it the social value obligation for public funders: Funders have a pro tanto obligation to align their decisions and actions in such a way that the research they fund maximises weighted social benefit. Footnote 10 Now, what is the relevance of the social value obligation for matters of promoting data sharing? We develop our answer step by step: First step . Data sharing has the potential to optimise research in terms of i) progressiveness, ii) cost and iii) quality (Fischer & Zigmond, 2010 ; Sardanelli et al., 2018 ). Ad i) The sharing of research data accelerates research, enables more cooperation and collaboration between researchers and disciplines, allows for the integration and pooling of data from disparate sources into large data sets, and bears the potential for innovative research, meta-analyses and new lines of inquiry that can lead to better diagnoses and treatments. Ad ii) It reduces costs and is efficient as reusing the data increases the value of the initial investment. Ad iii) It allows research findings to be verified or reproduced based on the original data and thus increases the quality of research and potentially reduces “research waste” (i.e., research with questionably quality). Second step . Given this efficiency-, quality- and progress-enhancing potential of data sharing, it is rational to assume that the following holds true: A world in which funded researchers share their data is better in terms of social value than a world in which funded researchers do not share their data. Notice that this holds true only under the following conditions: a) Funders must set research funding priorities according to the social value obligation. It is plausible to assume that only the sharing of data from research projects that were selected according to the right priorities (expectedly) maximises weighted social benefit. b) The funding of secondary use and decisions on data access for secondary use must be aligned to the social value obligation as well. Footnote 11 Third step . From the claim that a world in which funded researchers share their data is better in terms of social value it does not directly follow that funding agencies are obligated to promote a world in which researchers share their data, for two reasons: If there are alternative actions than promoting data sharing that lead to a larger increase in weighed social benefit and that cannot (for cost or other reasons) be taken together with promoting data sharing, then these alternative actions should be taken. For instance, perhaps an initiative to promote translational biomedical research increases weighed social benefit more than the promotion of data sharing and the funder's budget can only finance one of the two initiatives. Realising a world in which researchers share data comes with costs, for instance for warranting long-term storage and data availability or for incentivising data sharing. Hence, it may be that the means to realise a data sharing-world are so costly that they cancel out the benefits data sharing brings, so that realising this world does not maximise weighted social benefit and ought not to be done. However, we think that both possibilities are very unlikely. Ad 1. We deem it highly unlikely that there are alternatives that are incompatible with promoting data sharing and more efficient in terms of social value. Ad 2. We think that the means to realise a world in which researchers share their data are not so costly that they cancel out the benefits. For instance, incentivising data sharing or making data sharing mandatory are means that can be expected to promote data sharing without being too costly. Footnote 12 Therefore, we conclude: To fulfil the social value obligation, funders pro tanto ought to promote data sharing. Footnote 13 This conclusion leaves open which specific means of promotion funders are required to take. Since there are many ways of promoting data sharing, some of which are cheaper, some of which are more effective, the social value obligation – in principle – requires a specific means of promotion. For example, incentivising data sharing (for instance, through data sharing prizes or other forms of recognition) might be cheaper but less effective, whereas mandatory policies in combination with monitoring and sanctioning might be more expensive but lead to a greater extent of data sharing. It is an empirical question which of these different means (or combination of means) maximises weighted social benefit (for each situation of each individual funder). We cannot answer this question here. For now, we confine ourselves to the conclusion that the social value-obligation pro tanto requires funders to promote data sharing and leave it open which specific means of promotion they ought to apply. Footnote 14 The Obligation to Promote Scientific Progress In addition to the social value obligation, public funding agencies have a pro tanto obligation to promote scientific progress. Since scientific progress is likely to increase the social value of scientific research, one reason for funders’ obligation to promote scientific progress is the already discussed social value obligation. However, beyond social value there are also other reasons for the obligation to promote scientific progress and these reasons ground an independent obligation to promote scientific progress. In the following, we focus on these reasons that justify the obligation to foster scientific progress independently from social value. In democratic countries, public funders have an obligation to promote scientific progress, i.e., the growth of (significant) scientific knowledge and understanding, Footnote 15 because it is their mandate to support a science system that is geared towards producing scientific knowledge (independently of considerations of social benefits). In most democratic countries this mandate is institutionalised on a constitutional level. In this sense, funders owe this obligation to the (democratic) public and the citizens. There is a set of further reasons that justify the obligation of funders to support the scientific system and foster scientific progress with considerations of the value of scientific knowledge and progress. The value of science and scientific progress touches on complex questions about whether knowledge is valuable in itself and/or (only) insofar as it is somehow conducive to realising other values or ends. We do not want to take a position here on the hotly contested question about whether scientific knowledge (or progress) are intrinsically valuable (end in itself). Footnote 16 We just want to point to the aspects of knowledge that make knowledge instrumentally valuable apart from its instrumental value for the benefits of society. i) Scientific knowledge can be instrumentally valuable when it satisfies “human curiosity” (Kitcher, 2001 ) and the desire for a practically disinterested understanding of the natural world. ii) Scientific knowledge is a precondition and a contributory factor for the ability and freedom of “pursuing our own good in our own way” (Mill, 2008 ) and making reflective decisions about the goals of our own lives. By expanding our understanding of the world and our place in it, scientific progress can contribute to the exercise of this elementary freedom and can thus be seen as valuable for a self-governed and autonomous life (Kitcher, 2001 ; Wilholt, 2012 ). iii) scientific knowledge and progress is valuable for a functioning democracy insofar as (growth of) knowledge is a requirement for processes of informed deliberation, opinion-forming and decision-making (Brown & Guston, 2009 ). Now, this set of three reasons (i-iii) could be understood as reflecting not only the values and interests of the citizens (or tax payers) of the funder’s country, but also the values and interests of all people all over the world. Although it is plausible to some extent that the three reasons also reflect values or interests of people around the world, we do not think that this can establish a relationship in terms of strong moral rights and obligations between the global population and the local funder. Due to the rather loose relationship between persons in each country of the world on the one hand and the local state and funder on the other hand, only rather weak reasons for funders to promote scientific progress could result from the global understanding of the three reasons. So far, we have argued that the obligation of funders to promote scientific progress is primarily owed to the public and the citizens (and rather weakly to the global population). But of course the question arises whether funders owe the promotion of scientific progress also to scientists or the scientific community. We think that this is the case. Scientists have the professional obligation to strive for scientific knowledge and progress. To fulfil this professional obligation, they depend on the scientific system in which funders play an important role. Scientists need a functional system that is designed to enable and promote scientific progress. Therefore, it is plausible that funders owe the obligation to promote scientific progress to the scientists as well. We take the scientific progress obligation as follows: Funders have a pro tanto obligation to align their decisions and actions such that the research they fund maximises scientific progress. What relevance does this obligation have when discussing funders’ role in promoting data sharing? First and in general terms, this obligation to maximise scientific progress does not necessarily require funders to exercise intensive control and strong intervention in science. Keeping funders largely out of the methodological and content-related decisions of researchers is plausibly conducive to a functioning and progress-making scientific system. However, specific measures or interventions on the part of funders (for instance through policies) might have the potential to promote scientific progress. The promotion of data sharing plausibly is such an intervention: As we argued in Sect. " The Obligation to Benefit Society ", a scientific system in which researchers share their data can be expected to be a more efficient, effective, and innovative scientific system, and this means that it is also a better system in terms of scientific progress than a system in which researchers do not share data. Funders can contribute to realising such a system through various means (such as, for instance, data sharing policies) and thus promoting scientific progress. However, as it is the case of the social value obligation as well, it does not follow directly that funders are obligated to promote data sharing. This depends on whether there are other means than promoting data sharing which are more conducive to scientific progress (and which cannot be taken together with the promotion of data sharing). Again (as with the social value obligation), this is an empirical question that we cannot answer here. Nonetheless, we think it is plausible to assume that promoting data sharing is an effective and efficient means to promoting scientific progress and that it is rather unlikely there are other more efficient and effective actions or means, which, at the same time, are incompatible (for cost or other reasons) with the promotion of data sharing. Footnote 17 Accordingly, to fulfil their moral obligation to use the resources at their disposal to maximise scientific progress requires them to promote data sharing. The Obligation to Promote the Epistemic Integrity of Research Public funding agencies have an obligation to promote the integrity of the research they fund—a view which is widely held (Bouter, 2016 , 2018 , 2020 ; Mejlgaard et al., 2020 ; Titus & Bosch, 2010 ), but not systematically developed and justified. To give a more detailed account of this obligation, we start with clarifying the concept of research integrity. Research integrity relates to a set of professional norms and obligations that morally regulate and prescribe how researchers ought to conduct research. These norms and obligations can be differentiated between epistemic and socio-moral norms and obligations . Footnote 18 Epistemic norms or obligations are grounded in the goals or nature of science (Resnik, 1998 ), i.e., (roughly) the goals to obtain knowledge and understanding through reliable methods of inquiry. These obligations prohibit misconduct that is problematic from the point of view of epistemic rationality . Epistemic obligations are, for instance, the obligation not to fabricate, falsify, or misrepresent data. Epistemic obligations form what one might call epistemic research integrity . We take epistemic research integrity to be mainly about avoiding practices that lead to deception, inaccuracy, and imprecision in research and (the presentation) of research results. We thus follow Winter and Kosolosky ( 2013 ), who explicate the notion of epistemic research integrity by drawing on the property of deceptiveness and “define the epistemic integrity of a practice as a function of the degree to which the statements resulting from this practice are deceptive.” Socio-moral obligations result from the fact that research can negatively affect the rights and interests of individuals or groups outside science. Such non-epistemic obligations take into account general responsibilities and potential effects of science for society and humanity and comprises, for example, obligations to obtain consent and to minimise risks for participants and third parties. These socio-moral obligations constitute what one might call socio-moral research integrity . In the following, we focus only on epistemic research integrity and investigate whether funders’ obligation to promote epistemic research integrity implies that they ought to promote data sharing. We briefly address the relationship between data sharing and socio-moral research integrity in Sect. " Further Relevant Moral Obligations ". The promotion of epistemic research integrity is required by the two above mentioned obligations of funders to promote social value and scientific progress since epistemic integrity arguably furthers social value and scientific progress or is even a prerequisite for them. Now, the goal of this section is to show that there are reasons independent from social value and scientific progress that ground or justify an obligation of funders to maximize epistemic research integrity. There are two reasons for this as an independent obligation in its own right: Funders should promote the epistemic integrity of research for two reasons. 1. As public funders are either governmental institutions or at least spend public money, they should ensure that the activities they finance abide by professional norms and standards. Funders are not supposed to spend public money on activities where “anything goes” but rather fund activities and work that are lege artis . This is owed to the citizens and taxpayers and required by the recognition of the value of a rules-based scientific system. 2. Funders must guarantee a fair and rule-based research environment and competition. This is primarily owed to the scientists, among other things, to protect the honest and bona fide researchers against unfair and dishonest competitors. In the following, we take the obligation of funders to promote epistemic research integrity as follows: Funders have a pro tanto obligation to align their decisions and actions such that they maximise the epistemic integrity of research. What does the obligation to promote epistemic research integrity imply for the question of whether funders ought to promote data sharing? To answer this question, we must investigate whether data sharing is required by epistemic research integrity. To begin, we must differentiate between two different perspectives on epistemic research integrity. One perspective can be labelled as normative - philosophical and takes research integrity as a set of philosophically justified norms. The other perspective can be labelled as the community consensus perspective and takes research integrity as a set of norms that are agreed on and prescribed by the scientific community and that are codified in statements and codes of conduct by scientific societies and associations. These two perspectives usually do not display great discrepancies in terms of concrete norms of research integrity, but in principle they are not necessarily congruent. For reasons of space, we cannot give a systematic answer to the question of which of the two perspectives takes normative priority when they have conflicting norms and prescriptions. However, in the following we first examine the relationship between epistemic integrity and data sharing from a philosophical perspective and then describe how this relationship is treated in relevant codes of conduct and guidelines on research integrity. We will show that the two perspectives converge to some extent, and where they do not clearly converge, we will explain what this means for funders. We will do this in turn for data sharing for transparency (A.) and data sharing for secondary use (B.). Footnote 19 A. Epistemic Integrity and Data Sharing for Transparency 1. Philosophical perspective : Philosophers of science consider practices that enable “each scientist to scrutinize the work of others in his field, to verify and replicate results [and that make] it more likely that flaws will be uncovered” (Haack, 2007 ) to be prescribed by an important epistemic norm. The pertinent norm here is what David Resnik calls the “principle of openness” (Resnik, 1998 ) or what Susan Haack calls the epistemic norm of “evidence-sharing” (Haack, 2007 ). According to this understanding, practices of evidence-sharing enable collective efforts of communicating, reviewing, critiquing, and reproducing the evidence claimed by researchers as supporting their scientific claims and research results, i.e., evidence “which includes the methodology applied, the data acquired, and the process of methodology implementation, data analysis and outcome interpretation” (Munafò et al., 2017 ). Footnote 20 The sharing of evidence is a necessary condition for science as rational communication and argumentation and a requirement for efforts of reviewing and assessing scientific claims. Evidence-sharing can thus be understood as part of an organized skepticism Footnote 21 that increases the credibility of scientific claims and characterises (the ideal of) modern science as a specific social and cooperative enterprise. Following Winter and Koslovsky (2013), the principle of openness and the norm of evidence-sharing can be understood as prescribing practices that prevent and guard against deceptiveness. One of these practices is arguably data transparency, i.e., transparency with respect to data on which an already published scientific paper is based. We want to explicate at least two reasons for why data transparency is an important norm of evidence-sharing and openness. Data sharing as a prerequisite for replication . It is widely agreed that replication studies have epistemic value and are an essential and important part of scientific practice at least in a substantial part of the quantitative empirical sciences. Even those who caution against the crisis narrative in connection with failed replications or even doubt the epistemic value of replications for all disciplines (Leonelli, 2018 ) agree with this proposition. However, a precondition and minimal requirement for conducting replication studies is that the original studies can be (computationally or analytically) reproduced , that is, the published findings can be reproduced when the reported analyses are repeated upon the raw data (Hardwicke et al., 2021 ; Nuijten et al., 2018 ; Peels & Bouter, 2021 ). If a result cannot be reproduced, there is no need to even attempt a replication – since something with the analysis or the data must have gone wrong. Therefore, if we agree that efforts to replicate should be enabled and encouraged (due to its important epistemic value for research), then we must also recognise the importance of data transparency. Data sharing as means for preventing and detecting breaches of epistemic integrity. Although the empirical evidence about the prevalence of scientific misconduct and questionable research practices (QRP) should be handled with care, studies suggest that it is non-negligible. For instance, a survey among researchers in The Netherlands found that “over the last three years one in two researchers engaged frequently in at least one QRP, while one in twelve reported having falsified or fabricated their research at least once” – with the highest prevalence estimate for fabrication and falsification in the life and medical sciences (Gopalakrishna et al., 2022 ). Similarly worrisome results with regard to different forms of questionable research practices or misconduct are reported in (Boutron & Ravaud, 2018 ; John et al., 2012 ; Kaiser et al., 2021 ). Footnote 22 Additionally, we think that it is not entirely unreasonable to assume that the widespread lack of transparency (particularly the much-reported difficulties of obtaining data even after personal requests) is at least somewhat indicative of a non-negligible prevalence of scientific misconduct and questionable research (data) practices. Footnote 23 The possibility of keeping data opaque enables misconduct or at least makes it more difficult to detect it. As data transparency makes it easier to detect (at least some forms of) fraud and questionable research practices and can function as a deterrent (Fischer & Zigmond, 2010 ; Gopalakrishna et al., 2022 ; Hedrick, 1988 ; Winter & Kosolosky, 2013 ), we argue that data sharing for transparency can help prevent and detect unethical scientific practices. Since data transparency is a prerequisite for reproducibility and a means for preventing and detecting misconduct and questionable research practices, we conclude that there are good (normative-philosophical) arguments for taking data sharing for transparency as an important requirement of epistemic research integrity. 2. The community consensus perspective: The scientific community also sees data sharing as an important part of epistemic integrity (All European Academies ALLEA, 2017 ; Deutsche Forschungsgemeinschaft (DFG), 2019 ; Kretser et al., 2019 ; National Academies Press (US), 2017 ; Netherlands Code of Conduct for Research Integrity, 2018 ; Resnik & Shamoo, 2011 ; World Conference on Research Integrity, 2010 ). However, most of these guidelines and codes of conduct do not explicitly differentiate between epistemic and socio-moral integrity of research and many do not clearly differentiate between the purposes of data sharing (i.e., the purposes of transparency and secondary use). Therefore, we must deduce from the context what the respective statements refer to. We cannot do this in a systematic way here. But our impression is that many documents emphasise the values of transparency and honesty and explicitly or implicitly refer to these values when they state the importance of data sharing for research integrity. It thus seems there is a (international and trans-disciplinary) consensus that data sharing for purposes of transparency is a part of epistemic integrity. For example, the Netherland Code of Conduct explicitly connects data availability with the value of transparency, and the German DFG also explicitly refers to data sharing for the purpose of confirmability (“Nachvollziehbarkeit”). Hence, both perspectives—the normative-philosophical and the community consensus perspectives—support the proposition that data sharing for transparency is an important component of epistemic research integrity. B. Epistemic Integrity and Data Sharing for Secondary Use Philosophical perspective : While data sharing for transparency clearly falls within the scope of epistemic research integrity, the same cannot be said about data sharing for secondary use. Since we follow Winter and Kosolosky ( 2013 ) and “define the epistemic integrity of a practice as a function of the degree to which the statements resulting from this practice are deceptive”, we believe that data sharing for secondary use is not part of epistemic research integrity. Although one might argue that secondary use of data has the potential to correct for misleading or deceptive statements from original studies, we think that the main importance of sharing data for secondary use is that it promotes scientific progress and social value. Data sharing for secondary use is of rather secondary importance when it comes to correcting misleading scientific statements or results. It does not seem to be a strict requirement of epistemic integrity but more of a supererogatory practice. Therefore, from a philosophical perspective, the promotion of data sharing for secondary use is not required by the obligation to promote epistemic research integrity Community consensus perspective : Only a few guidelines and codes of conduct explicitly state that data sharing for secondary use is a requirement of research integrity (for instance, DFG, 2019 ). Many do not mention data sharing for secondary use explicitly, and some do not even seem to consider it implicitly. Thus, there does not appear to be a clear and unambiguous international consensus on the relationship between data sharing for secondary use and epistemic integrity. And since most of these documents do not differentiate explicitly between epistemic and socio-moral integrity, it is not clear whether data sharing for secondary use is considered as important from an epistemic perspective or from a non-epistemic, socio-moral perspective. Footnote 24 Therefore, from a community consensus perspective there is no clear consensus that data sharing for secondary use is a requirement of (epistemic) research integrity. From this perspective then, the obligation of funders to promote epistemic research integrity does not require the promotion of data sharing for secondary use. However, if there are specific disciplinary or national communities that explicitly take data sharing for secondary use as part of research integrity, those funders for whom this consensus is pertinent might have a reason to promote this kind of data sharing with reference to the obligation to promote research integrity. This holds true even though from a philosophical perspective data sharing for secondary use is not a part of epistemic research integrity: If the pertinent community takes data sharing for secondary use as part of (epistemic) integrity, funders might take this as a reason to promote it. Footnote 25 Therefore, and to conclude this whole Sect. " The Obligation to Promote the Epistemic Integrity of Research " about research integrity: Since funders have the obligation to promote epistemic research integrity, and since data sharing for transparency is an important part of epistemic research integrity, funders pro tanto ought to promote data sharing for transparency. From a philosophical perspective, epistemic research integrity does not require data sharing for secondary use, and from a community consensus perspective it is clearly considered as part of epistemic integrity only in a few cases of specific scientific communities. Therefore, a universal obligation for funders to promote data sharing for secondary use cannot be derived from considerations of epistemic research integrity. Further Relevant Moral Obligations In this section, we present two further obligations that partially speak in favour of funders promoting data sharing and partially against it. After presenting these various obligations in the following, we will close the section by weighing all pertinent obligations of funders and come to an all things considered judgement. Funders have a pro tanto obligation to respect the rights of individuals and to not harm human or non-human beings, which includes the obligation to not induce, cause or increase risks of harm and of rights violations. This includes the obligation to respect privacy and informational autonomy of data subjects and not to induce, cause or increase informational risks or harms This obligation is part of the obligation to promote the socio-moral integrity of funded research and it speaks both in favour and against the promotion of data sharing: As data sharing reduces the need for ever-new data collection, data sharing also reduces the amount and frequency of research procedures in interventional and non-interventional studies that carry risks for participants (Fischer & Zigmond, 2010 ). Hence, in this regard the obligation to respect the rights of persons and to not harm anybody speaks in favour of funders’ promoting data sharing. The sharing of research data and its ensuing secondary use increases informational risks for data subjects. Prima facie , this speaks against the promotion of data sharing. However, if subjects are informed about these risks and give consent to the usage of their data despite these risks, this increase of informational risks does not represent an infringement of the obligation not to harm. Volenti non fit inuria . Thus, the risks do not speak against funders promoting data sharing if consent is obtained in funded research. Of course, this argument raises the question of a model that offers research subjects appropriate information and opportunities to consent or reject consent and, at the same time, allows for data sharing without causing unreasonable practical burdens or hurdles (Manson, 2019 ; Mikkelsen et al., 2019 ; Ploug & Holm, 2016 ). We deem that broad consent, if combined with a normative and technical governance framework and data protection measures, is an appropriate information and consent model. In order to meet their obligation to respect the rights of data subjects, funders should thus recommend that broad consent be embedded in appropriate normative and technical governance frameworks. Irrespective of the question of informed consent, informational risks exist due to data misuse and data breaches. Erlich & Narayanan, 2014 ; Hayden, 2013 ; Homer et al., 2008 ; Levy et al., 2007 have shown how different techniques could be used for breaching (particularly genetic) privacy. These risks pro tanto speak against the promotion of the sharing of personal data. The pooling of data from different sources and the use of big data methods enables predictions about sensitive information regarding persons or groups other than the original data subjects (Mühlhoff, 2021 ). Some authors warn that this increases risks of stigmatisation and discrimination of marginalised groups (Favaretto et al., 2019 ; Reed-Berendt et al., 2022 ; Xafis et al., 2019 ). Promoting and accelerating data sharing and secondary use expand the opportunities for pooling and big data and thus might increase these risks. Thus, in this regard the obligation to minimise risks of harm speaks against the promotion of data sharing. Funders also have a pro tanto obligation to increase public trust in science and research funding. This obligation partly speaks in favour and partially speaks against the promotion of data sharing. On the one hand, as data sharing promotes transparency and accountability, it can increase and consolidate public trust and confidence in science and research funding. Hence, in this respect funders ought to promote data sharing in order to promote public trust. On the other hand, since promoting data sharing increases risks for privacy and creates challenges for informational self-determination, concerns about these risks and challenges might reduce trust in the research system (Platt et al., 2018 ; Ploug, 2020 ). Hence, in this respect funders ought not to promote data sharing in order to promote or maintain public trust. However, the extent to which the two obligations (not to harm and respect rights and to foster public trust) speak against the promotion of data sharing can be significantly reduced. In fact, funding agencies can and should do various things to minimise or prevent the pertaining risks: They should fund technological as well as ethical, legal, and social research (ELSA-research) on practical solutions for data security and privacy protection with a particular view on problems and risks resulting from big data and machine learning. Funders should promote research on data augmentation and synthetic data as potential approaches to handle limitations to data sharing due to risks for data subjects. They should finance and promote data infrastructures and archives or repositories that can guarantee data privacy and security and require funded researchers use these trusted repositories. Funders should fund the development and implementation of data access committees that take into account the aforementioned risks resulting from secondary use. Funders should support data stewardship infrastructures that convey “a fiduciary (or trust) relation” that also takes into consideration the rights of patients and participants (Rosenbaum, 2010 ). Funders should develop principles and provide best practices that support and enable researchers to provide appropriate forms of consent with regard to data sharing. They should create a framework for protecting the privacy of research participants that provides guidance on how participant information and (broad) consent forms are to be designed. Funders should provide standards and best practice for contracts between data producers, repositories, and data re-users with special attention to data protection and security. Footnote 26 In all of the aforementioned measures, the participation and inclusion of patient representatives should be promoted and enabled. Footnote 27 Funders should require researchers to reflect upon and identify potential risks (early in the process) by creating a data management plan, elaborating how they address and intent to deal with or avoid these risks. If the pertaining risks are addressed and thus comparatively small, the trust obligation and the not to harm obligation rather speak in favour of the promotion of data sharing or at least have no significant weight against data sharing. Even if they keep having some limited weight against data sharing, they are outweighed by the obligations in favour of promoting data sharing, i.e., the obligations of social value and scientific progress. Footnote 28 Of course, the more funders encourage and press researchers to share persona-related (non-anonymous) data, the more they are responsible for the impact of their policies on data subjects and the more they have to support researchers in protecting data subjects’ informational rights and privacy and this increases the financial and administrative costs and burdens for funders. However, we do not think that this outweighs the benefits in terms of social value and scientific progress. The main conclusion of Sects. " The Moral Obligations of Funders and the Promotion of Data Sharing " and " Further Relevant Moral Obligations " is thus: Although there are two pro tanto obligations that speak against the promotion of data sharing by public funders, the pro tanto obligations in favour of the promotion weigh heavier (provided that the mentioned risk reducing measures are implemented). Public funders thus have an all things considered obligation to promote the sharing of data of funded researchers. Footnote 29 Mandatory Data Sharing Policies and Academic Freedom Up to this point, we have not directly commented which means funders ought to use to promote data sharing. As we said, it is an empirical question what follows from the pro tanto obligations of funders to promote data sharing in terms of specific means to promote it. However, in the following we want to examine a question with regard to a specific means of promoting data sharing: Mandatory Data Sharing Policies . Funders are increasingly advised to adopt policies that require data sharing (Sim et al., 2020 ). The NIH as a major funder has been setting standards in implementing such policies for years now and has recently implemented a new mandatory data sharing policy (Kozlov, 2022 ; National Institutes of Health, 2022 ). We find it plausible that such policies are comparatively effective and efficient. Mandatory data sharing policies can be designed at least with two different objectives: 1. They can require only the sharing of data that is the evidential basis for an already published paper and only for purposes of transparency and confirmability. 2. Or they can additionally require the sharing of data (either publication-related or all data that are generated during a research project) for purposes of secondary use. As mandatory data sharing policies of public funders restrict the individual freedom of funded researchers (at least if these are dependent on third-party funding), the question arises whether such policies conflict with academic freedom . Do data sharing requirements implemented by public funders infringe on the academic freedom of individual researchers? Footnote 30 To answer this question, we have to clarify what academic freedom is and what it protects. From a philosophical perspective, Footnote 31 academic freedom is first and foremost the negative right of individual researchers against external intervention in their scientific work and decision-making. Academic freedom mainly concerns the freedom to choose research questions, theories, and methodologies as well as publication venues independently from outside intervention, in particular state intervention . This negative right of freedom from intervention of researchers thus corresponds to the negative duty of the state not to intervene. As public funders are (semi-)governmental institutions whose funding comes predominantly from government budgets and on whose boards government representatives participate in decision-making, the following holds: Public funders have the negative obligation to respect the negative right to academic freedom of researchers. The question now is whether mandatory data sharing policies violate the negative right of researchers to academic freedom? To answer this question, we must determine in more detail the scope of protection of academic freedom. From our perspective, the scope of protection of academic freedom includes only actions of researchers that are not violations of crucial and basic norms of epistemic research integrity. Such crucial and basic norms determine fundamental requirements of science and research as a specific kind of rational practice and communication. For instance, researchers that engage in data fabrication or falsification fail to meet such fundamental requirements. They thus violate crucial and basic norms of research integrity and engage in behaviour that is not protected by academic freedom. Hence, we must answer the following questions: Is the omission (or refusal) to share data that are the evidential basis of published research results for purposes of transparency and reproducibility a violation of fundamental requirements of scientific work and communication? Is the omission (or refusal) to share data (either publication-related or all data that are generated during a research project) for purposes of secondary use a failure to meet such fundamental requirements? Ad 1 : We believe that not sharing data that underlies research results (a published paper) for purposes of transparency is a violation of the fundamental requirements of scientific work and communication. This is clearly the case from the philosophical perspective on research integrity. Although not sharing data that underlies a published paper for transparency seems to be a less severe scientific misconduct as data fabrication or falsification, it clearly runs counter to one of the basic requirements of scientific communication and (collective) truth-seeking: To make one’s own scientific work transparent and reproducible. There is no reasonable justification for why researchers should be generally free to avoid that their published (!) work can be reviewed in all its parts. Footnote 32 We believe the philosophical perspective is backed by the perspective of the consensus of the scientific community. The community recognises data sharing for transparency as a key requirement of epistemic research integrity. Almost all codes of conduct and guidelines on research integrity emphasise the close relation between honesty, reproducibility, and data transparency (see Sect. " The Obligation to Promote the Epistemic Integrity of Research ". A). Therefore, research without sharing data for transparency is not protected by academic freedom. Thus, mandatory policies that require data sharing for transparency do not infringe on the right to academic freedom of individual researchers. Ad 2 : In Sect. " The Obligation to Promote the Epistemic Integrity of Research ", we have already noted that neither in the community consensus nor in the normative-philosophical perspective data sharing for secondary use is a requirement of epistemic integrity. This means that the freedom to share or not to share data for secondary use is within the scope of protection of academic freedom. However, data sharing requirements for secondary use of public funders are not necessarily an infringement of academic freedom. First, it depends on how much researchers must rely on third-party funding in their research. If they have access to basic financial resources of their institutions and are not dependent on applying for additional public funding, then such requirements are not a restriction of their academic freedom. Second, data sharing requirements of public funders that enjoy relative autonomy from government and whose decisions are essentially made by scientists themselves do not represent state coercion but rather self-determination of the scientific community. However, academic freedom does not only protect individuals against state intervention but also against infringements through (parts of) the scientific community. Thus, data sharing requirements of public funders with state autonomy (and also those without state autonomy) do represent an infringement of academic freedom (at least for researchers that depend on their funding), though not in the classical sense of state infringements. It must be noted, however, that this infringement of academic freedom is a fairly small one. The freedom to share or not to share data for secondary use does not belong to the core of academic freedom. The core arguably is the freedom to “follow a line of research where it leads” (Russell, 1993 ), i.e., the freedom to choose research questions, theories, and methodologies as well as publication venues independently from outside intervention. Nonetheless, it is an infringement, but we believe it can be mitigated by the following measures: Funders can a) offer the possibility for a justified exception from data sharing requirements (for instance, for reasons of data protection or dual use risks), b) allow for an embargo period in which the funded and data producing researcher has the exclusive privilege to use their data, c) consider discipline-specific standards for data management and sharing, and d) compensate for burden and costs financially (for instance, for fees of repositories for long-term storage or for data protection measures) and through investments in and supply of technical and administrative support (for instance, digital privacy and security safeguarding solutions and best practices). If funders implement measures like these, the infringement of academic freedom through mandatory data sharing policies becomes so small that it can be justified with reference to the other pro tanto obligations of funders, namely the obligations with respect to social value, scientific progress, and the minimisation of harm. Footnote 33 However, the justifiability of the infringement on academic freedom through mandatory data sharing policies is dependent on a further condition: Mandatory policies can only be justified, if there are no measures of promoting data sharing that are more effective and less invasive in terms of academic freedom. A last word on the implications of the diagnosis that policies that require data sharing for secondary use infringe on the academic freedom of researchers: If public funders infringe on the academic freedom of researchers with reference to the benefits of data sharing, they have the responsibility to ensure that these benefits are realised. This requires two things of them: 1. Since the benefits of data sharing only materialise if reproduction and replication as well as secondary use are actually carried out, funders should fund appropriate projects. They should finance and reward reproduction and replication studies and set up a funding programme for secondary research. 2. Funders should fund research and monitoring on whether their own initiatives to promote data sharing are i) effective in terms of actual data sharing and ii) actually lead to the hoped-for benefits. Summary and Conclusion In this paper, we investigated the question whether public funders have a moral obligation to promote the sharing of research data generated in funded research projects. More specifically, we asked which of funders general moral obligations speak in favour of and which of these obligations speak against the promotion of data sharing. We draw the following conclusions: First, public funders have several general pro tanto obligations that (under certain conditions) require funders to promote data sharing. The main obligations are the social value-, scientific progress- and epistemic research integrity-obligation. Second, in the assessment of pro tanto obligations against promoting data sharing, we argued that – provided that funders take measures to minimise the risks for research subjects and third parties – the obligations in favour of promoting data sharing outweigh the obligations against. Therefore, we concluded with respect to our overall research question that public funders ought to promote data sharing all things considered . With respect to our third specific research question whether mandatory data sharing policies are an ethically justifiable means of promoting data sharing, we argued: First, the scope of protection of academic freedom does not cover the omission or refusal to share data for purposes of transparency. Requirements to share data for the purpose of transparency therefore do not violate academic freedom. Second, the scope of protection does cover the omission or refusal to share data for secondary use, therefore requirements to share data for secondary use violate academic freedom to a small extent (at least for researchers that are dependent on public funding). However, such requirements and thus the violation of academic freedom can be justified with reference to the other pro tanto obligations that public funders have. Sometimes research data can only be re-used when research methodologies that have been used to collect, generate and analyse the data (questionnaires, analytical codes, etc.) are shared as well (Goldacre et al., 2019 ). Thus, sharing these methodologies and other intermediary resources might equally be important as sharing the data themselves. However, due to some disanalogies between data and those resources (most saliently the fact that some of the latter can be seen as intellectual property), we confine our discussion here to research data. The Creative Commons set of licenses are the most commonly used for sharing research data. These licenses are designed to be open, which means that data can be freely reused without requiring explicit permission as long as the terms of the license are adhered to. Such licences can be a good and efficient way of reducing costs and burdens for data sharing, while they may have limited applicability in cases of person-related data or for researchers who wish to retain control over the subsequent use of the data they produce. If regulatory considerations limit the sharing of data generally or on an international level, the generation of synthetic data can be an alternative. However, (sharing of) synthetic data can only complement but not fully replace (the sharing of) non-synthetic data. Pro tanto obligations are what David Ross ( 1930 ) called prima facie obligations. In line with the established terminology “ pro tanto and all things considered moral reasons” (Alvarez, 2016 ), we chose to deviate from Ross’ terminology, see also Hurtig ( 2007 ) and Dabbagh ( 2018 ). Similar claims are made with regard to journal policies in Federer et al. ( 2018 ) and Gabelica et al. ( 2019 ). For the debate about the social value requirement, see Barsdorf and Millum ( 2017 ), Pierson and Millum ( 2018 ), Resnik ( 2018a , 2018b ), Wendler and Rid ( 2017 ), Wertheimer ( 2015 ). See also Bierer et al. ( 2018 ). For further discussions on this topic see Winkler et al. ( 2023 ). Notice that the references only state that research must have sufficient benefits for society in order to be justified if it exposes participants to risks. However, we find this implausible and believe that it has to maximise benefits. For it seems questionable to choose project A over the alternatively fundable projects B and C, if it can be expected that either project B or C have more social benefit than A. Notice that this obligation does not require a short-sighted restriction to immediate benefits and “mere” application-oriented research but will plausibly take into account basic research that enables long-term fruitful and sustainable research by exploring fundamental causal mechanisms. Otherwise, maximisation would hardly be possible. These conditions also secure that data sharing of funded projects does not facilitate the exploitation or extraction of resources from the underprivileged to the privileged or to private corporations and does not promote epistemically biased research. See Leonelli ( 2023 ) for examples of such detrimental effects of data sharing. This issue of the cost–benefit balance of promoting data sharing is also pertinent for all other obligations we will discuss below. We will not mention it again though and assume for the rest of the paper that the benefits of promoting data sharing are greater than the costs. Bierer et al. ( 2018 ) also argue that funders ought to promote data sharing in order to advance the social value of research. Notice that this obligation might be stronger or weaker for particular research fields or specific data. For instance, the social value of sharing particular health data in a pandemic or biomedical data in general is presumably bigger than the social value of the sharing of archaeological data about a particular Egyptian pharaoh. We believe that on any other plausible account of social value, i.e., on any plausible distributive principle funders ought to promote data sharing and fund research that has social value. For instance, a utilitarian account of social value will give us the same conclusion. On the notion of scientific progress and “significant” knowledge, see Bird ( 2007 ), Kitcher ( 2001 ), Niiniluoto ( 2019 ). For the view that scientific knowledge has intrinsic value, see for instance Schwartz ( 2020 ). What holds for the social value obligation also holds for the obligation to promote scientific progress: Depending on the particular research field and the particular data the obligation to promote data sharing in order to promote scientific progress is stronger or weaker. The sharing of particular (kinds of) data might bear more potential to promote scientific progress while the sharing of other (kinds of) data might bear less potential. For different terminologies for both kinds of obligations, for instance internal vs. external norms, see Resnik ( 1996 ) and Reydon ( 2013 ). For an attempt to differentiate the justificatory grounds for the various kinds of obligations of scientists see Resnik ( 1998 ). For a legal analysis of the relation between (semi-)governmental promotion of data sharing and good scientific practice in the context of German constitutional law see Fehling and Tormin ( 2021 ). Strictly speaking, evidence is that which confirms or disconfirms a scientific claim, i.e., data. A methodology or an analysis is not evidence in this sense. However, we stick to the understanding of Munafò above because the sharing of evidence in his sense is required by the norm of evidence-sharing. At least we think that Haack has this in mind. Robert Merton ( 1942 /1973) famously introduced this term in his description of “the normative structure” and the “ethos of science”, see also Ziman ( 2009 ). Although Fanelli ( 2018 ) doubts that misconduct has a major impact on the scientific literature, she agrees that it is non-negligible. For instance, Tsuyoshi Miyakawa ( 2020 ) reports the results of analyses on the manuscripts that he has handled as Editor-in-Chief of Molecular Brain as showing that “more than 97% of the 41 manuscripts did not present the raw data supporting their results when requested by an editor, suggesting a possibility that the raw data did not exist from the beginning, at least in some portions of these cases”. The DFG Guideline ( 2019 ) is arguably a guideline exclusively for epistemic research integrity, and it is thus reasonable to assume that the explicit inclusion of data sharing for secondary use means that it is considered to be an epistemically required practice. However, the ALLEA code (ALLEA 2017 ), as some other codes, is not exclusively focused on epistemic integrity as it includes socio-moral obligations (for instance, to respect the needs and right of study participants). Its statement that data should be as open as possible, as closed as necessary can be understood as including data sharing for secondary use, but it remains open whether this is taken to be a requirement of epistemic integrity. It could be case that the justification for data sharing for secondary use is mainly seen in its benefit for society and scientific progress. If this is the reason why data sharing for secondary use is included in research integrity, then the research integrity obligation adds nothing to the social value and scientific progress obligation with respect to data sharing for secondary use – which we already discussed in Sect. " The Obligation to Benefit Society " and " The Obligation to Promote Scientific Progress ". Only in cases in which there are strong philosophical or ethical reasons that speak against the community consensus, funders might not be allowed to follow this consensus. However, we believe this is not the case for the issue of data sharing for secondary use. There have been intense and broad research and debates on ethical, legal, and social issues of privacy and data protection and other informational aspects of research subject protection in biomedical data intense research and data sharing for 10–15 years. Following the increasing activities of genomic data sharing, approaches and best practices have been developed to address challenges concerning data protection, privacy, and informational rights and autonomy. See for instance the GA4GH and its “Regulatory and Ethics Work Stream” ( https://www.ga4gh.org/how-we-work/workstreams/ ) that provides standard solutions for genetic data sharing and a framework for responsible sharing of genomic and health-related data ( https://www.ga4gh.org/genomic-data-toolkit/regulatory-ethics-toolkit/framework-for-responsible-sharing-of-genomic-and-health-related-data/ ) or the European Genome Archive (EGA) which also provides best practices for genetic data sharing. We develop a systematic approach to funders' responsibilities for the protection and participation of data subjects from a legal and ethical perspective in Fehling et al. ( 2023 ). Since the pertaining risks are mainly associated with data sharing for secondary use, and since data sharing for secondary use is not a requirement of research integrity, the weighing of obligations here must exclude the obligation to promote research integrity and focus only on scientific progress and social value. Of course, we cannot exclude the possibility of very specific cases in certain areas of research where there are additional reasons against the promotion of data sharing which override the pro tanto obligation that speak in favour of promoting data sharing. For example, sharing huge amounts of high quality data used to develop machine learning programs in biomedicine with a Russian research institute closely linked to the Russian military complex might bear the risk of harmful consequences for society. Our all things considered claim should thus be understood as not applying to such special cases. For the possibility of such cases see footnote 11 and the reference to Leonelli ( 2023 ). How differently the relation between academic freedom and data sharing requirements is perceived by German funders as compared to non-German funders is examined in more detail in Anger et al. ( 2024 ). The following is a philosophical and not a legal analysis. For a legal analysis of the possibilities and limits of (semi-)governmental promotion of data sharing in the German context see Overkamp and Tormin ( 2022 ) and for the German and European context with a side glance at US constitutional law see Fehling and Tormin ( 2021 ). Of course, there can be specific reasons in a particular case not to make data transparent for confirmation efforts (such as, for instance, privacy concerns). However, our point is that besides such special circumstances, there is no reason for why researchers ought to generally be free to refuse to make their data available for confirmation. Of course, this depends on how strong these pro tanto obligations are with respect to particular (kinds of) data. As we explained in footnotes 13 and 17 the weight of these obligations depends on how much the sharing of particular data from a particular research field contributes to social value and scientific progress. We believe, however, that for the most part of the sciences the sharing of research data is so valuable in theses respects that an infringement of academic freedom can be justified. All European Academies (ALLEA) (2017). The European Code of Conduct for Research Integrity. Retrieved 25 February 2022 https://allea.org/code-of-conduct/ . Alvarez, M. (2016). Reasons for action: Justification, motivation, explanation. In E. N. Zalta (Ed.). The Stanford encyclopedia of philosophy (Winter 2017 edition). Retrieved June 14, 2022, from https://plato.stanford.edu/archives/win2017/entries/reasons-just-vs-expl/ . Anger, M., Wendelborn, C., & Schickhardt, C. (2024). German funders’ data sharing policies—A qualitative interview study. PLoS ONE, 19 (2), e0296956. https://doi.org/10.1371/journal.pone.0296956 Article   Google Scholar   Anger, M., Wendelborn, C., Winkler, E. C., & Schickhardt, C. (2022). Neither carrots nor sticks? Challenges surrounding data sharing from the perspective of research funding agencies—A qualitative expert interview study. PLoS ONE, 17 (9), e0273259. https://doi.org/10.1371/journal.pone.0273259 Barsdorf, N., & Millum, J. (2017). The social value of health research and the worst off. Bioethics, 31 (2), 105–115. https://doi.org/10.1111/bioe.12320 Bauchner, H., Golub, R. M., & Fontanarosa, P. B. (2016). Data sharing: An ethical and scientific imperative. JAMA, 315 (12), 1237–1239. https://doi.org/10.1001/jama.2016.2420 Begley, C. G., & Ioannidis, J. P. A. (2015). Reproducibility in science: Improving the standard for basic and preclinical research. Circulation Research, 116 (1), 116–126. https://doi.org/10.1161/CIRCRESAHA.114.303819 Bierer, B. E., Strauss, D. H., White, S. A., & Zarin, D. A. (2018). Universal funder responsibilities that advance social value. The American Journal of Bioethics AJOB, 18 (11), 30–32. https://doi.org/10.1080/15265161.2018.1523498 Bird, A. (2007). What is scientific progress? Noûs, 41 (1), 64–89. https://doi.org/10.1111/j.1468-0068.2007.00638.x Bouter, L. (2016). What funding agencies and journals can do to prevent sloppy science. Retrieved June 14, 2022, from https://www.euroscientist.com/what-funding-agencies-and-journals-can-do-to-prevent-sloppy-science/ . Bouter, L. (2020). What research institutions can do to foster research integrity. Science and Engineering Ethics, 26 (4), 2363–2369. https://doi.org/10.1007/s11948-020-00178-5 Bouter, L. M. (2018). Fostering responsible research practices is a shared responsibility of multiple stakeholders. Journal of Clinical Epidemiology, 96 , 143–146. https://doi.org/10.1016/j.jclinepi.2017.12.016 Boutron, I., & Ravaud, P. (2018). Misrepresentation and distortion of research in biomedical literature. Proceedings of the National Academy of Sciences of the United States of America, 115 (11), 2613–2619. https://doi.org/10.1073/pnas.1710755115 Brock, D. W. (2012). Priority to the worse off in health care resource prioritization. In R. Rhodes, M. Battin, & A. Silvers (Eds.), Medicine and social justice: Essays on the distribution of health care (pp. 155–164). Oxford University Press. Chapter   Google Scholar   Brown, M. B., & Guston, D. H. (2009). Science, democracy, and the right to research. Science and Engineering Ethics, 15 (3), 351–366. https://doi.org/10.1007/s11948-009-9135-4 Burton, P. R., Banner, N., Elliot, M. J., Knoppers, B. M., & Banks, J. (2017). Policies and strategies to facilitate secondary use of research data in the health sciences. International Journal of Epidemiology, 46 (6), 1729–1733. https://doi.org/10.1093/ije/dyx195 Chan, A.-W., Song, F., Vickers, A., Jefferson, T., Dickersin, K., Gøtzsche, P. C., Krumholz, H. M., Ghersi, D., & van der Worp, H. B. (2014). Increasing value and reducing waste: Addressing inaccessible research. The Lancet, 383 (9913), 257–266. https://doi.org/10.1016/S0140-6736(13)62296-5 Contreras, J., & Knoppers, B. M. (2018). The genomic commons. Annual Review of Genomics and Human Genetics, 19 , 429–453. Couture, J. L., Blake, R. E., McDonald, G., & Ward, C. L. (2018). A funder-imposed data publication requirement seldom inspired data sharing. PLOS ONE , 13 (7). https://doi.org/10.1371/journal.pone.0199789 . Dabbagh, H. (2018). The problem of explanation and reason-giving account of pro tanto duties in the Rossian ethical framework. Public Reason, 10 (1), 69–80. Google Scholar   Danchev, Valentin, Min, Yan, Borghi, John, Baiocchi, Mike, & Ioannidis, John P. A. (2021). Evaluation of data sharing after implementation of the International Committee of Medical Journal Editors Data Sharing Statement Requirement. JAMA Network Open , 4 (1), e2033972. https://doi.org/10.1001/jamanetworkopen.2020.33972 Deutsche Forschungsgemeinschaft (DFG) (2019). Leitlinien zur Sicherung guter wissenschaftlicher Praxis: Kodex. Retrieved 25 February 2022 https://doi.org/10.5281/zenodo.3923602 . Digital Science Report (2019). State of Open Data 2019. A selection of analyses and articles about open data, curated by Figshare. figshare. https://doi.org/10.6084/M9.FIGSHARE.10011788.V2 . Eckert, Ester M., Di Cesare, Andrea, Fontaneto, Diego, Berendonk, Thomas U., Bürgmann, Helmut, Cytryn, Eddie et al. (2020). Every fifth published metagenome is not available to science. PLOS Biology 18 (4), e3000698. https://doi.org/10.1371/journal.pbio.3000698 . Erlich, Y., & Narayanan, A. (2014). Routes for breaching and protecting genetic privacy. Nature Reviews Genetics, 15 , 409–421. https://doi.org/10.1038/nrg3723 Errington, T. M., Denis, A., Perfito, N., Iorns, E., & Nosek, B. A. (2021). Challenges for assessing replicability in preclinical cancer biology. eLife , 10 . https://doi.org/10.7554/eLife.67995 . European Commission. Joint Research Centre. (2017). Analysis of national public research funding (PREF). In Handbook for data collection and indicators production . Publications Office. https://doi.org/10.2760/849945 Fanelli, D. (2018). Opinion: Is science really facing a reproducibility crisis, and do we need it to? Proceedings of the National Academy of Sciences of the United States of America, 115 (11), 2628–2631. https://doi.org/10.1073/pnas.1708272114 Favaretto, M., Clercq, E. de, & Elger, B. S. (2019). Big Data and discrimination: Perils, promises and solutions. A systematic review. Journal of Big Data , 6 (1). https://doi.org/10.1186/s40537-019-0177-4 . Federer, L. M., Belter, C. W., Joubert, D. J., Livinski, A., Lu, Y.-L., Snyders, L. N., & Thompson, H. (2018). Data sharing in PLOS ONE: An analysis of data availability statements. PLOS ONE , 13 (5). https://doi.org/10.1371/journal.pone.0194768 . Fehling, M., & Tormin, M. (2021). Das Teilen von Forschungsdaten zwischen Wissenschaftsfreiheit und guter wissenschaftlicher Praxis. Wissenschaftsrecht, 54 (3–4), 281. https://doi.org/10.1628/wissr-2021-0022 Fehling, M., Tormin, M., Wendelborn, C., & Schickhardt, C. (2023). Forschungsförderorganisationen in der Verantwortung zwischen Data Sharing und dem Schutz von Datensubjekten. Medizinrecht, 41 (11), 869–878. https://doi.org/10.1007/s00350-023-6599-1 First International Strategy Meeting on Human Genome Sequencing (1996): Bermuda principles. http://web.ornl.gov/sci/techresources/Human_Genome/research/bermuda.shtml#1 . Accessed 29 July 2023 Fischer, B. A., & Zigmond, M. J. (2010). The essential nature of sharing in science. Science and Engineering Ethics, 16 (4), 783–799. https://doi.org/10.1007/s11948-010-9239-x Fort Lauderdale Agreement (2003). Sharing data from large-scale biological research projects: A system of tripartite responsibility. http://www.genome.gov/Pages/Research/WellcomeReport0303.pdf . Accessed 29 July 2023 Gabelica, M., Cavar, J., & Puljak, L. (2019). Authors of trials from high-ranking anesthesiology journals were not willing to share raw data. Journal of Clinical Epidemiology, 109 , 111–116. https://doi.org/10.1016/j.jclinepi.2019.01.012 Gabelica, M., Bojčić, R., & Puljak, L. (2022). Many researchers were not compliant with their published data sharing statement: A mixed-methods study. Journal of Clinical Epidemiology, 150 , 33–41. https://doi.org/10.1016/j.jclinepi.2022.05.019 Glasziou, P., Altman, D. G., Bossuyt, P., Boutron, I., Clarke, M., Julious, S., Michie, S., Moher, D., & Wager, E. (2014). Reducing waste from incomplete or unusable reports of biomedical research. The Lancet, 383 (9913), 267–276. https://doi.org/10.1016/S0140-6736(13)62228-X Goldacre, B., Morton, C. E., & DeVito, N. J. (2019). Why researchers should share their analytic code. BMJ (Clinical Research ed.), 367 , l6365. https://doi.org/10.1136/bmj.l6365 Gopalakrishna, G., Riet, G. ter, Vink, G., Stoop, I., Wicherts, J. M., & Bouter, L. M. (2022). Prevalence of questionable research practices, research misconduct and their potential explanatory factors: A survey among academic researchers in the Netherlands. PLOS ONE , 17 (2). https://doi.org/10.1371/journal.pone.0263023 . Gorman, D. M. (2020). Availability of research data in high-impact addiction journals with data sharing policies. Science and Engineering Ethics , 26 (3), S. 1625–1632. https://doi.org/10.1007/s11948-020-00203-7 . Haack, S. (2007). The integrity of science: What it means, why it matters. Contrastes: Revista International de Filosofia 12 , S. 5–26. Online verfügbar unter https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1105831 , zuletzt geprüft am 25. Februar 2022. Hardwicke, T. E., Bohn, M., MacDonald, K., Hembacher, E., Nuijten, M. B., Peloquin, B. N., deMayo, B. E., Long, B., Yoon, E. J., & Frank, M. C. (2021). Analytic reproducibility in articles receiving open data badges at the Journal Psychological Science: An observational study. Royal Society Open Science , 8 (1). https://doi.org/10.1098/rsos.201494 . Hardwicke, T. E., Mathur, M. B., MacDonald, K., Nilsonne, G., Banks, G. C., Kidwell, M. C., Hofelich Mohr, A., Clayton, E., Yoon, E. J., Henry Tessler, M., Lenne, R. L., Altman, S., Long, B., & Frank, M. C. (2018). Data availability, reusability, and analytic reproducibility: Evaluating the impact of a mandatory open data policy at the journal Cognition. Royal Society Open Science , 5 (8). https://doi.org/10.1098/rsos.180448 . Hayden, E. C. (2013). Privacy protections: The genome hacker. Nature 497 (7448), S. 172–174. https://doi.org/10.1038/497172a . Hedrick, T. E. (1988). Justifications for the sharing of social science data. Law and Human Behavior, 12 (2), 163–171. https://doi.org/10.1007/BF01073124 Herlitz, A. (2018). Health, priority to the worse off, and time. Medicine, Health Care, and Philosophy, 21 (4), 517–527. https://doi.org/10.1007/s11019-018-9825-2 Homer, N., Szelinger, S., Redman, M., Duggan, D., Tembe, W., & Muehling, J. et al. (2008). Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays. PLoS Genetics , 4 (8), e1000167. https://doi.org/10.1371/journal.pgen.1000167 . Hurtig, K. (2007). On prima facie obligations and nonmonotonicity. Journal of Philosophical Logic, 36 (5), 599–604. Iqbal, S. A., Wallach, J. D., Khoury, M. J., Schully, S. D., & Ioannidis, J. P. A. (2016). Reproducible research practices and transparency across the biomedical literature. PLOS Biology , 14 (1). https://doi.org/10.1371/journal.pbio.1002333 . John, L. K., Loewenstein, G., & Prelec, D. (2012). Measuring the prevalence of questionable research practices with incentives for truth telling. Psychological Science, 23 (5), 524–532. https://doi.org/10.1177/0956797611430953 Kaiser, M., Drivdal, L., Hjellbrekke, J., Ingierd, H., & Rekdal, O. B. (2021). Questionable research practices and misconduct among Norwegian researchers. Science and Engineering Ethics , 28 (1). https://doi.org/10.1007/s11948-021-00351-4 . Kaye, J., Heeney, C., Hawkins, N., de Vries, J., & Boddington, P. (2009). Data sharing in genomics—Re-shaping scientific practice. Nature Reviews Genetics, 10 (5), 331–335. https://doi.org/10.1038/nrg2573 Kitcher, P. (2001). Science, truth, and democracy . Oxford University Press. https://doi.org/10.1093/0195145836.001.0001 Kozlov, M. (2022). NIH issues a seismic mandate: Share data publicly. Nature . https://doi.org/10.1038/d41586-022-00402-1 Kretser, A., Murphy, D., Bertuzzi, S., Abraham, T., Allison, D. B., Boor, K. J., Dwyer, J., Grantham, A., Harris, L. J., Hollander, R., Jacobs-Young, C., Rovito, S., Vafiadis, D., Woteki, C., Wyndham, J., & Yada, R. (2019). Scientific Integrity principles and best practices: Recommendations from a scientific integrity consortium. Science and Engineering Ethics, 25 (2), 327–355. https://doi.org/10.1007/s11948-019-00094-3 Leonelli, S. (2018). Rethinking reproducibility as a criterion for research quality. In L. Fiorito (Ed.), Including a symposium on the work of Mary Morgan: Curiosity, imagination, and surprise (pp. 129–146). Emerald Publishing Limited. Leonelli, S. (2023). Philosophy of open science . Cambridge University Press. https://doi.org/10.1017/9781009416368 Levy, S., Sutton, G., Ng, P. C., Feuk, L., Halpern, A. L., Walenz, B. P. et al. (2007): The diploid genome sequence of an individual human. PLoS Biology 5 (10), e254. https://doi.org/10.1371/journal.pbio.0050254 . Manson, N. C. (2019). The biobank consent debate: Why ‘meta-consent’ is not the solution? Journal of Medical Ethics, 45 (5), 291–294. https://doi.org/10.1136/medethics-2018-105007 Mejlgaard, N., Bouter, L. M., Gaskell, G., Kavouras, P., Allum, N., Bendtsen, A.-K., Charitidis, C. A., Claesen, N., Dierickx, K., Domaradzka, A., Reyes Elizondo, A., Foeger, N., Hiney, M., Kaltenbrunner, W., Labib, K., Marušić, A., Sørensen, M. P., Ravn, T., Ščepanović, R. … Veltri, G. A. (2020). Research integrity: Nine ways to move from talk to walk. Nature , 586 (7829), 358–360. https://doi.org/10.1038/d41586-020-02847-8 . Merton, R. (Ed.) (1942/1973). The sociology of science: Theoretical and empirical investigations . The University of Chicago Press. Mikkelsen, R. B., Gjerris, M., Waldemar, G., & Sandøe, P. (2019). Broad consent for biobanks is best—provided it is also deep. BMC Medical Ethics, 20 (1), 71. https://doi.org/10.1186/s12910-019-0414-6 Mill, J. S. (2008). On liberty and other essays . Oxford University Press. Miyakawa, T. (2020). No raw data, no science: Another possible source of the reproducibility crisis. Molecular Brain , 13 (1). https://doi.org/10.1186/s13041-020-0552-2 . Mühlhoff, R. (2021). Predictive privacy: Towards an applied ethics of data analytics. Ethics and Information Technology, 23 (4), 675–690. https://doi.org/10.1007/s10676-021-09606-x Munafò, M. R., Nosek, B. A., Bishop, D. V. M., Button, K. S., Chambers, C. D., Du Sert, N. P., Simonsohn, U., Wagenmakers, E.-J., Ware, J. J., & Ioannidis, J. P. A. (2017). A manifesto for reproducible science. Nature Human Behaviour , 1 . https://doi.org/10.1038/s41562-016-0021 . National Academies Press (US) (2017). Fostering integrity in research . https://doi.org/10.17226/21896 . National Cancer Institute (n.d.). Genomic data commons, accessed 27 July 2023, https://gdc.cancer.gov/ National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research (1978). The Belmont report: Ethical principles and guidelines for the protection of human subjects of research. DHEW Pub , No (OS) 78–0014. US Govt Print Office. National Institutes of Health (2022). NIH Data Sharing Policy 2023. Retrieved 23 June 2022 https://sharing.nih.gov/data-management-and-sharing-policy/about-data-management-sharing-policy/data-management-and-sharing-policy-overview . National Library of Medicine (n.d.). ClinVar, accessed 27 July 2023, https://www.ncbi.nlm.nih.gov/clinvar/ Naudet, F., Sakarovitch, C., Janiaud, P., Cristea, I., Fanelli, D., Moher, D., & Ioannidis, J. P. A. (2018). Data sharing and reanalysis of randomized controlled trials in leading biomedical journals with a full data sharing policy: Survey of studies published in The BMJ and PLOS Medicine. BMJ , 360 . https://doi.org/10.1136/bmj.k400 . Naudet, F., Siebert, M., Pellen, C., Gaba, J., Axfors, C., Cristea, I., Danchev, V., Mansmann, U., Ohmann, C., Wallach, J. D., Moher, D., & Ioannidis, J. P. A. (2021). Medical journal requirements for clinical trial data sharing: Ripe for improvement. PLOS Medicine , 18 (10). https://doi.org/10.1371/journal.pmed.1003844 . Netherlands Code of Conduct for Research Integrity (2018). Neylon, C. (2017). Compliance culture or culture change? The role of funders in improving data management and sharing practice amongst researchers. Research Ideas and Outcomes, 3 , e21705. https://doi.org/10.3897/rio.3.e21705 Niiniluoto, I. (2019). Scientific progress. In E. N. Zalta (Ed.). The Stanford encyclopedia of philosophy (Winter 2019 edition). Retrieved June 14, 2022, from https://plato.stanford.edu/archives/win2019/entries/scientific-progress/ . Nuijten, M. B., Bakker, M., Maassen, E., & Wicherts, J. M. (2018). Verify original results through reanalysis before replicating. Behavioral and Brain Sciences , 41 . https://doi.org/10.1017/S0140525X18000791 . Ohmann, C., Moher, D., Siebert, M., Motschall, E., & Naudet, F. (2021). Status, use and impact of sharing individual participant data from clinical trials: A scoping review. BMJ Open , 11 (8). https://doi.org/10.1136/bmjopen-2021-049228 . Ottersen, T. (2013). Lifetime QALY prioritarianism in priority setting. Journal of Medical Ethics, 39 (3), 175–180. https://doi.org/10.1136/medethics-2012-100740 Overkamp, P., & Tormin, M. (2022). Staatliche Steuerungsmöglichkeiten zur Förderung des Teilens von Forschungsdaten. Ordnungen der Wissenschaft, 1 , 39–54. Peels, R. (2019). Replicability and replication in the humanities. Research Integrity and Peer Review , 4 . https://doi.org/10.1186/s41073-018-0060-4 . Peels, R., & Bouter, L. (2021). Replication and trustworthiness. Accountability in Research . https://doi.org/10.1080/08989621.2021.1963708 Perrier, L., Blondal, E., & MacDonald, H. (2020). The views, perspectives, and experiences of academic researchers with data sharing and reuse: A meta-synthesis. PLOS ONE , 15 (2). https://doi.org/10.1371/journal.pone.0229182 . Persad, G. (2019). Justice and public health. In A. C. Mastroianni, J. P. Kahn, & N. E. Kass (Eds.), The Oxford handbook of public health ethics (pp. 32–46). Oxford University Press. Pierson, L., & Millum, J. (2018). Health research priority setting: The duties of individual funders. The American Journal of Bioethics, 18 (11), 6–17. https://doi.org/10.1080/15265161.2018.1523490 Platt, J. E., Jacobson, P. D., & Kardia, S. L. R. (2018). Public trust in health information sharing: A measure of system trust. Health Services Research, 53 (2), 824–845. https://doi.org/10.1111/1475-6773.12654 Ploug, T. (2020). In defence of informed consent for health record research—Why arguments from ‘easy rescue’, ‘no harm’ and ‘consent bias’ fail. BMC Medical Ethics, 21 (1), 75. https://doi.org/10.1186/s12910-020-00519-w Ploug, T., & Holm, S. (2016). Meta consent—A flexible solution to the problem of secondary use of health data. Bioethics, 30 (9), 721–732. https://doi.org/10.1111/bioe.12286 Powell, Kendall (2021). The broken promise that undermines human genome research. Nature 590 (7845), S. 198–201. https://doi.org/10.1038/d41586-021-00331-5 . Pratt, B., & Hyder, A. A. (2017). Fair resource allocation to health research: Priority topics for bioethics scholarship. Bioethics, 31 (6), 454–466. https://doi.org/10.1111/bioe.12350 Pratt, B., & Hyder, A. A. (2019). Ethical responsibilities of health research funders to advance global health justice. Global Public Health, 14 (1), 80–90. https://doi.org/10.1080/17441692.2018.1471148 Rauh, S., Torgerson, T., Johnson, A. L., Pollard, J., Tritz, D., & Vassar, M. (2020). Reproducible and transparent research practices in published neurology research. Research Integrity and Peer Review , 5 . https://doi.org/10.1186/s41073-020-0091-5 . Reed-Berendt, R., Dove, E. S., & Pareek, M. (2022). The ethical implications of big data research in public health: “Big Data Ethics by Design” in the UK-REACH study. Ethics and Human Research, 44 (1), 2–17. https://doi.org/10.1002/eahr.500111 Resnik, D. (1996). Review: Ethics of scientific research by Shrader-Frechette, Kristin. Noûs , 30 (1), 133–143. https://doi.org/10.2307/2216307 . Resnik, D. B. (1998). The ethics of science: An introduction. Philosophical issues in science. Routledge. Resnik, D. B. (2018a). Difficulties with applying a strong social value requirement to clinical research. The Hastings Center Report, 48 (6), 35–37. https://doi.org/10.1002/hast.936 Resnik, D. B. (2018b). Examining the social benefits principle in research with human participants. Health Care Analysis, 26 (1), 66–80. https://doi.org/10.1007/s10728-016-0326-2 Resnik, D. B., & Shamoo, A. E. (2011). The singapore statement on research integrity. Accountability in Research, 18 (2), 71–75. https://doi.org/10.1080/08989621.2011.557296 Reydon, T. (2013). Wissenschaftsethik: Eine Einführung. UTB Philosophie, Naturwissenschaften , 4032. Ulmer. Rosenbaum, S. (2010). Data governance and stewardship: Designing data stewardship entities and advancing data access. Health Services Research, 45 (5 Pt 2), 1442–1455. https://doi.org/10.1111/j.1475-6773.2010.01140.x Ross, W. D. (1930). The right and the good . Clarendon. Russell, C. (1993). Academic freedom. (1 st edition). Routledge. Sardanelli, F., Alì, M., Hunink, M. G., Houssami, N., Sconfienza, L. M., & Di Leo, G. (2018). To share or not to share? Expected pros and cons of data sharing in radiological research. European Radiology, 28 (6), 2328–2335. https://doi.org/10.1007/s00330-017-5165-5 Schickhardt, C., Hosley, N., & Winkler, E. C. (2016). Researchers’ duty to share pre-publication data: From the prima facie duty to practice. In B. D. Mittelstadt & L. Floridi (Eds.), The ethics of biomedical big data (pp. 309–337). Springer. Schwartz, J. S. J. (2020). The value of science in space exploration . Oxford University Press. https://doi.org/10.1093/oso/9780190069063.001.0001 Sen, A. (2002). Why health equity? Health Economics, 11 (8), 659–666. https://doi.org/10.1002/hec.762 Sim, I., Stebbins, M., Bierer, B. E., Butte, A. J., Drazen, J., Dzau, V., Hernandez, A. F., Krumholz, H. M., Lo, B., Munos, B., Perakslis, E., Rockhold, F., Ross, J. S., Terry, S. F., Yamamoto, K. R., Zarin, D. A., & Li, R. (2020). Time for NIH to lead on data sharing. Science, 367 (6484), 1308–1309. https://doi.org/10.1126/science.aba4456 Stewart, S. L. K., Pennington, C. R., da Silva, G. R., Ballou, N., Butler, J., Dienes, Z., Jay, C., Rossit, S., & Samara, A. (2022). Reforms to improve reproducibility and quality must be coordinated across the research ecosystem: The view from the UKRN local network leads. BMC Research Notes , 15 (1). https://doi.org/10.1186/s13104-022-05949-w . Strcic, Josip, Civljak, Antonia, Glozinic, Terezija, Pacheco, Rafael Leite, Brkovic, Tonci, & Puljak, Livia (2022): Open data and data sharing in articles about COVID-19 published in preprint servers medRxiv and bioRxiv. Scientometrics 127 (5), S. 2791–2802. https://doi.org/10.1007/s11192-022-04346-1 . Tan, Aidan Christopher, Askie, Lisa M., Hunter, Kylie Elizabeth, Barba, Angie, Simes, Robert John, & Seidler, Anna Lene (2021): Data sharing-trialists' plans at registration, attitudes, barriers and facilitators: A cohort study and cross-sectional survey. Research Synthesis Methods,1 2 (5), S. 641–657. https://doi.org/10.1002/jrsm.1500 Tedersoo, Leho, Küngas, Rainer, Oras, Ester, Köster, Kajar, Eenmaa, Helen, Leijen, Äli et al. (2021). Data sharing practices and data availability upon request differ across scientific disciplines. Scientific Data , 8 (1), Artikel 192. https://doi.org/10.1038/s41597-021-00981-0 . Tedersoo, L., Küngas, R., Oras, E., Köster, K., Eenmaa, H., Leijen, Ä., Pedaste, M., Raju, M., Astapova, A., Lukner, H., Kogermann, K., & Sepp, T. (2021). Data sharing practices and data availability upon request differ across scientific disciplines. Scientific Data , 8 (1). https://doi.org/10.1038/s41597-021-00981-0 . Terry, R. F., Littler, K., & Olliaro, P. L. (2018). Sharing health research data - the role of funders in improving the impact. F1000Research , 7 . https://doi.org/10.12688/f1000research.16523.2 . Thelwall, M., Munafò, M., Mas-Bleda, A., Stuart, E., Makita, M., Weigert, V., Keene, C., Khan, N., Drax, K., & Kousha, K. (2020). Is useful research data usually shared? An investigation of genome-wide association study summary statistics. PLOS ONE , 15 (2). https://doi.org/10.1371/journal.pone.0229578 . Titus, S., & Bosch, X. (2010). Tie funding to research integrity. Nature, 466 (7305), 436–437. https://doi.org/10.1038/466436a Towse, John N., Ellis, David A., & Towse, Andrea S. (2021). Opening Pandora's Box: Peeking inside psychology's data sharing practices, and seven recommendations for change. Behavior Research Methods 53 (4), S. 1455–1468. https://doi.org/10.3758/s13428-020-01486-1 . Watson, Clare (2022). Many researchers say they'll share data - but don't. Nature 606 (7916), S. 853. https://doi.org/10.1038/d41586-022-01692-1 . Wendler, D., & Rid, A. (2017). In defense of a social value requirement for clinical research. Bioethics, 31 (2), 77–86. https://doi.org/10.1111/bioe.12325 Wertheimer, A. (2015). The social value requirement reconsidered. Bioethics, 29 (5), 301–308. https://doi.org/10.1111/bioe.12128 Wilholt, T. (2010). Scientific freedom: Its grounds and their limitations. Studies in History and Philosophy of Science Part A, 41 (2), 174–181. https://doi.org/10.1016/j.shpsa.2010.03.003 Wilholt, T. (2012). Die Freiheit der Forschung: Begründungen und Begrenzungen . Suhrkamp. Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., & Finkers, R. … Mons, B. (2016). The FAIR guiding principles for scientific data management and stewardship. Scientific Data , 3 . https://doi.org/10.1038/sdata.2016.18 . Winkler, E. C., Jungkunz, M., Thorogood, A. et al. (2023). Patient data for commercial companies? An ethical framework for sharing patients’ data with for-profit companies for research . Journal of Medical Ethics. https://doi.org/10.1136/jme-2022-108781 de Winter, J., & Kosolosky, L. (2013). The epistemic integrity of scientific research. Science and Engineering Ethics, 19 (3), 757–774. https://doi.org/10.1007/s11948-012-9394-3 World Conference on Research Integrity (2010). Singapore Statement on Research Integrity. Retrieved 25 February 2022 https://wcrif.org/guidance/singapore-statement . World Medical Association. (2013). World medical association declaration of Helsinki: Ethical principles for medical research involving human subjects. JAMA, 310 (20), 2191–2194. https://doi.org/10.1001/jama.2013.281053 Xafis, V., Schaefer, G. O., Labude, M. K., Brassington, I., Ballantyne, A., Lim, H. Y., Lipworth, W., Lysaght, T., Stewart, C., Sun, S., Laurie, G. T., & Tai, E. S. (2019). An ethics framework for big data in health and research. Asian Bioethics Review, 11 (3), 227–254. https://doi.org/10.1007/s41649-019-00099-x Ziman, J. (2009). Real science. Cambridge University Press. https://doi.org/10.1017/CBO9780511541391 Download references Acknowledgements The authors would like to thank the following individuals and groups for their contributions to this project: our partners within the joint research project DATABLIC, Prof. Dr. Michael Fehling and Miriam Tormin (Bucerius Law School, Hamburg), Prof. Dr. Christiane Schwieren and Tamás Olah (University of Heidelberg); all members of the Section Translational Medical Ethics at the National Center for Tumour Diseases, Heidelberg, especially the head of section Prof. Dr. Dr. Eva Winkler; Maya Doering for assistence with literature review and formatting.   The work on this article has been funded by the German Ministry for Education and Research (Bundesministerium für Bildung und Forschung, funding reference no. 01GP1904A) as part of the joint research project DATABLIC. The funder had no role in research design, analysis, decision to publish, or preparation of the manuscript. Author information Christian Wendelborn Present address: University of Konstanz, Konstanz, Germany Authors and Affiliations Section for Translational Medical Ethics, German Cancer Research Center (DKFZ), National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg, Germany Christian Wendelborn, Michael Anger & Christoph Schickhardt You can also search for this author in PubMed   Google Scholar Contributions Conceptualization: CW and CS. Methodology: CW and CS. Ethical analysis and investigation: CW and CS. Writing—original draft preparation: CW. Writing—review and editing: MA and CS. Supervision: CS. Project proposal and successful application: CS    Corresponding author Correspondence to Christian Wendelborn . Ethics declarations Conflict of interest. The authors declare that no competing interests exist. Additional information Publisher's note. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 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Promoting Data Sharing: The Moral Obligations of Public Funding Agencies. Sci Eng Ethics 30 , 35 (2024). https://doi.org/10.1007/s11948-024-00491-3 Download citation Received : 21 October 2022 Accepted : 08 June 2024 Published : 06 August 2024 DOI : https://doi.org/10.1007/s11948-024-00491-3 Share this article Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative Data sharing Epistemic integrity Funding agencies Moral obligations Research integrity Scientific progress Scientific freedom Social value Find a journal Publish with us Track your research 443 IMAGES Academic Honesty Posters ACADEMIC HONESTY POLICY UNIVERSITY OF HOUSTON Academic Honesty Policy and Procedures by Mark Aaron Academic Honesty Policy 2015-2016 Academic Honesty Policy Academic Honesty Policy COMMENTS Academic Honesty in Coursework Policy 2015 Name of policy This is the Academic Honesty in Coursework Policy 2015. 1 Commencement . This policy commences on 1 January 2016 . 2 Policy is binding . Except to the extent that a contrary intention is expressed, this policy binds the University, staff, affiliates and students. 3 Statement of intent . This policy: Academic integrity The former policy and associated procedures, which were superseded in February 2023, are the Academic Honesty in Coursework Policy 2015 (pdf, 417KB) and the Academic Honesty Procedures 2016 (pdf, 438KB). These will apply for any breaches related to submissions prior to 20 February 2023 Academic Honesty Procedures 2016 These procedures are to give effect to the Academic Honesty in Coursework Policy 2015 and the Research Code of Conduct 2019 (jointly, "the policies"). non-award students, exchange students and study abroad students enrolled in a unit of study at the University. These procedures commence on 26 August 2016. Academic Honesty Policy The current Academic Honesty Policy was approved by Faculty Council in May of 2015. Revisions were approved by the Steering Committee of Faculty Council on January 18, 2016; November 1, 2016; February 21, 2017; December 19, 2017; and March 30, 2018. Additional revisions were approved by the full Faculty Council on November 2, 2020; May 6, 2021 ... Academic Honesty This course's philosophy on academic honesty is best stated as "be reasonable.". The course recognizes that interactions with classmates and others can facilitate mastery of the course's material. However, there remains a line between enlisting the help of another and submitting the work of another. The course's policy characterizes ... Promoting Academic Integrity According to the International Center for Academic Integrity, academic integrity is "a commitment, even in the face of adversity, to six fundamental values: honesty, trust, fairness, respect, responsibility, and courage.". We commit to these values to honor the intellectual efforts of the global academic community, of which Columbia ... Academic Honesty and Stanford's Honor Code Stanford's Honor Code has three components: Students will support this culture of academic honesty by neither giving nor accepting unpermitted academic aid in any work that serves as a component of grading or evaluation. Instructors will support this culture of academic honesty by providing clear guidance, both in their course syllabi and in ... Academic Dishonesty Student In order for a Facilitated Meeting to occur, the instructor of the course in question must also request to engage in the Facilitated Meeting. Option 2: Resolution of academic honesty violations by a hearing process before the full Academic Honesty Committee. To present witnesses and evidence before the Academic Honesty Committee. Academic Honesty Policy Renamed Academic Honest Policy for alphabetical listing, grammar and spell check, and transitioned into policy library April 2017. Honesty in all endeavors is essential to the functioning of society. Honesty in the classroom among students and between students and faculty is a matter that should concern everyone in the university community. PDF 5 Academic honesty policies When you graduate, you will be leaving with more than just a diploma. If you embrace the principles underlying the academic honesty rules, you will also be leaving as a better-rounded individual who has a sense of integrity, both in the classroom and beyond. Discussion questions. 1. We have rules in many aspects of life. Academic Honesty Policy Academic Honesty Policy. The first injunction of the Honor Code is the call to "be honest.". Students come to the university not only to improve their minds, gain knowledge, and develop skills that will assist them in their life's work, but also to build character. "President David O. McKay taught that character is the highest aim of ... PDF Academic Honesty Policy University of Houston Jurisdiction. Academic Research 743-9222 or. olarship be1.03 College with Jurisdiction. ic jurisdiction jurisdiction academic is determined honesty matters rests by the c. urse of the University Houston. jurisdiction. If th. college with jurisdiction dishonesty occurs. If of of a student than that of students and be adetermined particular ... PDF Academic Honesty Policy Student Responsibility: It is the duty of the student to practice and preserve academic honesty. Each student is responsible for knowing the specific policies that govern academic conduct for the program(s) and course(s) in which they are enrolled, as well as the appeals process for adjudicating such policies. If the student has any doubt about ... Academic policy updates in 2023 If you are being investigated for an academic integrity breach related to a unit of study in 2022, the case will be managed under the former Academic Honesty in Coursework Policy 2015 and Academic Honesty Procedures 2016. A Positive Approach to Academic Integrity The concept of academic integrity is often taught with a focus on academic misconduct and how not to misbehave. Students navigate through college trying not to break the rules. Underneath those rules lie traits that are valued in our education system, and in scholarly work. For example, we trust that a student who can explain a concept in their ... Templates for Courses : Academic Honesty Policy : Academic Honesty Academic honesty: Students and faculty at the University must agree to adhere to high standards of academic honesty in all of the work that we do. First-year students read and sign an academic honesty policy statement to indicate that they understand the general principles upon which our work is based. The College Board on Academic Honesty ... Academic Dishonesty Policy: SUNY Brockport Violations of the Student Academic Dishonesty Policy refer to actions related to the standards of honesty required in submission and evaluation of coursework in any undergraduate or graduate course bearing SUNY Brockport credit. ... The instructor in charge of a course in which an act of academic dishonesty is alleged is responsible for ... Academic Integrity at Georgia State Policy on Academic Honesty. As members of the academic community, students are expected to recognize and uphold standards of intellectual and academic integrity. The Policy on Academic Honesty assumes as a basic and minimum standard of conduct in academic matters that students be honest and that they submit for credit only the products of their ... Honesty in Academic Work < Sierra College Success in college, as in other aspects of life, demands absolute honesty at all times. Sierra College expects that students, as well as faculty, will observe the principles of ethical conduct in their treatment of fellow members of the academic community and in their accomplishment of academic work. Students are responsible for familiarizing ... Academic Integrity The Importance of Academic Integrity Academic integrity speaks directly to student honesty, responsibility and respect for scholarship. Academic assignments and tests help students learn course content, while grades show how fully this goal is achieved. Coursework and associated grades should be the result of the student's own understanding of academic content, as well as demonstrated effort ... Academic Integrity Policy 2022 Part 1Preliminary. 1Name of policy. This is the Academic Integrity Policy 2022. 2Commencement. This policy commences on 20 February 2023. 3Policy is binding. Except to the extent that a contrary intention is expressed, this policy binds the University, staff, students and affiliates. 4Statement of intent. Dropping Courses The academic department offering the course must verify the concurrent enrollment requirement. ... Students may not receive a W for a course in which they have been found guilty of a violation of the Academic Honesty Policy. If a W is received prior to a guilty finding, the student will become liable for the Academic Honesty penalty, which may ... Academic Honesty Procedures 2016: Guidelines and Application for Academic Honesty Procedures 2016 Page 2 of 25 PART 2 - DEFINITIONS 3 Interpretation Words and phrases used in these procedures and not otherwise defined in this document have the meanings they have in the policies. Note: see clause 6 of each policy. academic dishonesty has the meaning given in clause 7 of the Academic Honesty in Coursework Policy 2015. ... Academic Integrity Policy 2022 scholarship. The University is committed to academic excellence and integrity as the cornerstones of scholastic achievement and quality assurance. (2) Academic integrity requires: (a) scrupulous ethical behaviour from individuals; (b) a collective culture that champions academic honesty, which is fostered by all staff, affiliates and students; Promoting Data Sharing: The Moral Obligations of Public Funding 1. In the following, we use the term "research data" and "data" as referring to digital data that is collected and/or generated during a research project. We use the term "data sharing" as referring to the act of making data available for other researchers - either for the purpose of transparency of studies and replication of published research results or for the purpose of other ... essay homework paper phd project resume review speech study thesis