google research articles

Reference management. Clean and simple.

Google Scholar: the ultimate guide

What is Google Scholar?

Why is google scholar better than google for finding research papers, the google scholar search results page, the first two lines: core bibliographic information, quick full text-access options, "cited by" count and other useful links, tips for searching google scholar, 1. google scholar searches are not case sensitive, 2. use keywords instead of full sentences, 3. use quotes to search for an exact match, 3. add the year to the search phrase to get articles published in a particular year, 4. use the side bar controls to adjust your search result, 5. use boolean operator to better control your searches, google scholar advanced search interface, customizing search preferences and options, using the "my library" feature in google scholar, the scope and limitations of google scholar, alternatives to google scholar, country-specific google scholar sites, frequently asked questions about google scholar, related articles.

Google Scholar (GS) is a free academic search engine that can be thought of as the academic version of Google. Rather than searching all of the indexed information on the web, it searches repositories of:

• universities
• scholarly websites

This is generally a smaller subset of the pool that Google searches. It's all done automatically, but most of the search results tend to be reliable scholarly sources.

However, Google is typically less careful about what it includes in search results than more curated, subscription-based academic databases like Scopus and Web of Science . As a result, it is important to take some time to assess the credibility of the resources linked through Google Scholar.

➡️ Take a look at our guide on the best academic databases .

One advantage of using Google Scholar is that the interface is comforting and familiar to anyone who uses Google. This lowers the learning curve of finding scholarly information .

There are a number of useful differences from a regular Google search. Google Scholar allows you to:

• copy a formatted citation in different styles including MLA and APA
• export bibliographic data (BibTeX, RIS) to use with reference management software
• explore other works have cited the listed work
• easily find full text versions of the article

Although it is free to search in Google Scholar, most of the content is not freely available. Google does its best to find copies of restricted articles in public repositories. If you are at an academic or research institution, you can also set up a library connection that allows you to see items that are available through your institution.

The Google Scholar results page differs from the Google results page in a few key ways. The search result page is, however, different and it is worth being familiar with the different pieces of information that are shown. Let's have a look at the results for the search term "machine learning.”

• The first line of each result provides the title of the document (e.g. of an article, book, chapter, or report).
• The second line provides the bibliographic information about the document, in order: the author(s), the journal or book it appears in, the year of publication, and the publisher.

Clicking on the title link will bring you to the publisher’s page where you may be able to access more information about the document. This includes the abstract and options to download the PDF.

To the far right of the entry are more direct options for obtaining the full text of the document. In this example, Google has also located a publicly available PDF of the document hosted at umich.edu . Note, that it's not guaranteed that it is the version of the article that was finally published in the journal.

Below the text snippet/abstract you can find a number of useful links.

• Cited by : the cited by link will show other articles that have cited this resource. That is a super useful feature that can help you in many ways. First, it is a good way to track the more recent research that has referenced this article, and second the fact that other researches cited this document lends greater credibility to it. But be aware that there is a lag in publication type. Therefore, an article published in 2017 will not have an extensive number of cited by results. It takes a minimum of 6 months for most articles to get published, so even if an article was using the source, the more recent article has not been published yet.
• Versions : this link will display other versions of the article or other databases where the article may be found, some of which may offer free access to the article.
• Quotation mark icon : this will display a popup with commonly used citation formats such as MLA, APA, Chicago, Harvard, and Vancouver that may be copied and pasted. Note, however, that the Google Scholar citation data is sometimes incomplete and so it is often a good idea to check this data at the source. The "cite" popup also includes links for exporting the citation data as BibTeX or RIS files that any major reference manager can import.

Pro tip: Use a reference manager like Paperpile to keep track of all your sources. Paperpile integrates with Google Scholar and many popular academic research engines and databases, so you can save references and PDFs directly to your library using the Paperpile buttons and later cite them in thousands of citation styles:

Although Google Scholar limits each search to a maximum of 1,000 results , it's still too much to explore, and you need an effective way of locating the relevant articles. Here’s a list of pro tips that will help you save time and search more effectively.

You don’t need to worry about case sensitivity when you’re using Google scholar. In other words, a search for "Machine Learning" will produce the same results as a search for "machine learning.”

Let's say your research topic is about self driving cars. For a regular Google search we might enter something like " what is the current state of the technology used for self driving cars ". In Google Scholar, you will see less than ideal results for this query .

The trick is to build a list of keywords and perform searches for them like self-driving cars, autonomous vehicles, or driverless cars. Google Scholar will assist you on that: if you start typing in the search field you will see related queries suggested by Scholar!

If you put your search phrase into quotes you can search for exact matches of that phrase in the title and the body text of the document. Without quotes, Google Scholar will treat each word separately.

This means that if you search national parks , the words will not necessarily appear together. Grouped words and exact phrases should be enclosed in quotation marks.

A search using “self-driving cars 2015,” for example, will return articles or books published in 2015.

Using the options in the left hand panel you can further restrict the search results by limiting the years covered by the search, the inclusion or exclude of patents, and you can sort the results by relevance or by date.

Searches are not case sensitive, however, there are a number of Boolean operators you can use to control the search and these must be capitalized.

• AND requires both of the words or phrases on either side to be somewhere in the record.
• NOT can be placed in front of a word or phrases to exclude results which include them.
• OR will give equal weight to results which match just one of the words or phrases on either side.

➡️ Read more about how to efficiently search online databases for academic research .

In case you got overwhelmed by the above options, here’s some illustrative examples:

Tip: Use the advanced search features in Google Scholar to narrow down your search results.

You can gain even more fine-grained control over your search by using the advanced search feature. This feature is available by clicking on the hamburger menu in the upper left and selecting the "Advanced search" menu item.

Adjusting the Google Scholar settings is not necessary for getting good results, but offers some additional customization, including the ability to enable the above-mentioned library integrations.

The settings menu is found in the hamburger menu located in the top left of the Google Scholar page. The settings are divided into five sections:

• Collections to search: by default Google scholar searches articles and includes patents, but this default can be changed if you are not interested in patents or if you wish to search case law instead.
• Bibliographic manager: you can export relevant citation data via the “Bibliography manager” subsection.
• Languages: if you wish for results to return only articles written in a specific subset of languages, you can define that here.
• Library links: as noted, Google Scholar allows you to get the Full Text of articles through your institution’s subscriptions, where available. Search for, and add, your institution here to have the relevant link included in your search results.
• Button: the Scholar Button is a Chrome extension which adds a dropdown search box to your toolbar. This allows you to search Google Scholar from any website. Moreover, if you have any text selected on the page and then click the button it will display results from a search on those words when clicked.

When signed in, Google Scholar adds some simple tools for keeping track of and organizing the articles you find. These can be useful if you are not using a full academic reference manager.

All the search results include a “save” button at the end of the bottom row of links, clicking this will add it to your "My Library".

To help you provide some structure, you can create and apply labels to the items in your library. Appended labels will appear at the end of the article titles. For example, the following article has been assigned a “RNA” label:

Within your Google Scholar library, you can also edit the metadata associated with titles. This will often be necessary as Google Scholar citation data is often faulty.

There is no official statement about how big the Scholar search index is, but unofficial estimates are in the range of about 160 million , and it is supposed to continue to grow by several million each year.

Yet, Google Scholar does not return all resources that you may get in search at you local library catalog. For example, a library database could return podcasts, videos, articles, statistics, or special collections. For now, Google Scholar has only the following publication types:

• Journal articles : articles published in journals. It's a mixture of articles from peer reviewed journals, predatory journals and pre-print archives.
• Books : links to the Google limited version of the text, when possible.
• Book chapters : chapters within a book, sometimes they are also electronically available.
• Book reviews : reviews of books, but it is not always apparent that it is a review from the search result.
• Conference proceedings : papers written as part of a conference, typically used as part of presentation at the conference.
• Court opinions .
• Patents : Google Scholar only searches patents if the option is selected in the search settings described above.

The information in Google Scholar is not cataloged by professionals. The quality of the metadata will depend heavily on the source that Google Scholar is pulling the information from. This is a much different process to how information is collected and indexed in scholarly databases such as Scopus or Web of Science .

➡️ Visit our list of the best academic databases .

Google Scholar is by far the most frequently used academic search engine , but it is not the only one. Other academic search engines include:

• Science.gov
• Semantic Scholar
• scholar.google.fr : Sur les épaules d'un géant
• scholar.google.es (Google Académico): A hombros de gigantes
• scholar.google.pt (Google Académico): Sobre os ombros de gigantes
• scholar.google.de : Auf den Schultern von Riesen

➡️ Once you’ve found some research, it’s time to read it. Take a look at our guide on how to read a scientific paper .

No. Google Scholar is a bibliographic search engine rather than a bibliographic database. In order to qualify as a database Google Scholar would need to have stable identifiers for its records.

No. Google Scholar is an academic search engine, but the records found in Google Scholar are scholarly sources.

No. Google Scholar collects research papers from all over the web, including grey literature and non-peer reviewed papers and reports.

Google Scholar does not provide any full text content itself, but links to the full text article on the publisher page, which can either be open access or paywalled content. Google Scholar tries to provide links to free versions, when possible.

The easiest way to access Google scholar is by using The Google Scholar Button. This is a browser extension that allows you easily access Google Scholar from any web page. You can install it from the Chrome Webstore .

Stand on the shoulders of giants

Google Scholar provides a simple way to broadly search for scholarly literature. From one place, you can search across many disciplines and sources: articles, theses, books, abstracts and court opinions, from academic publishers, professional societies, online repositories, universities and other web sites. Google Scholar helps you find relevant work across the world of scholarly research.

How are documents ranked?

Google Scholar aims to rank documents the way researchers do, weighing the full text of each document, where it was published, who it was written by, as well as how often and how recently it has been cited in other scholarly literature.

Features of Google Scholar

• Search all scholarly literature from one convenient place
• Explore related works, citations, authors, and publications
• Locate the complete document through your library or on the web
• Keep up with recent developments in any area of research
• Check who's citing your publications, create a public author profile

Disclaimer: Legal opinions in Google Scholar are provided for informational purposes only and should not be relied on as a substitute for legal advice from a licensed lawyer. Google does not warrant that the information is complete or accurate.

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Google Research, 2022 & beyond: Natural sciences

It's an incredibly exciting time to be a scientist. With the amazing advances in machine learning (ML) and quantum computing, we now have powerful new tools that enable us to act on our curiosity, collaborate in new ways, and radically accelerate progress toward breakthrough scientific discoveries.

Since joining Google Research eight years ago, I’ve had the privilege of being part of a community of talented researchers fascinated by applying cutting-edge computing to push the boundaries of what is possible in applied science. Our teams are exploring topics across the physical and natural sciences. So, for this year’s blog post I want to focus on high-impact advances we’ve made recently in the fields of biology and physics, from helping to organize the world’s protein and genomics information to benefit people's lives to improving our understanding of the nature of the universe with quantum computers. We are inspired by the great potential of this work.

Using machine learning to unlock mysteries in biology

Many of our researchers are fascinated by the extraordinary complexity of biology, from the mysteries of the brain, to the potential of proteins, and to the genome, which encodes the very language of life. We’ve been working alongside scientists from other leading organizations around the world to tackle important challenges in the fields of connectomics , protein function prediction , and genomics , and to make our innovations accessible and useful to the greater scientific community.

Neurobiology

One exciting application of our Google-developed ML methods was to explore how information travels through the neuronal pathways in the brains of zebrafish , which provides insight into how the fish engage in social behavior like swarming. In collaboration with researchers from the Max Planck Institute for Biological Intelligence , we were able to computationally reconstruct a portion of zebrafish brains imaged with 3D electron microscopy — an exciting advance in the use of imaging and computational pipelines to map out the neuronal circuitry in small brains, and another step forward in our long-standing contributions to the field of connectomics.

The technical advances necessary for this work will have applications even beyond neuroscience. For example, to address the difficulty of working with such large connectomics datasets, we developed and released TensorStore , an open-source C++ and Python software library designed for storage and manipulation of n -dimensional data. We look forward to seeing the ways it is used in other fields for the storage of large datasets.

We're also using ML to shed light on how human brains perform remarkable feats like language by comparing human language processing and autoregressive deep language models (DLMs). For this study, a collaboration with colleagues at Princeton University and New York University Grossman School of Medicine , participants listened to a 30-minute podcast while their brain activity was recorded using electrocorticography . The recordings suggested that the human brain and DLMs share computational principles for processing language, including continuous next-word prediction, reliance on contextual embeddings, and calculation of post-onset surprise based on word match (we can measure how surprised the human brain is by the word, and correlate that surprise signal with how well the word is predicted by the DLM). These results provide new insights into language processing in the human brain, and suggest that DLMs can be used to reveal valuable insights about the neural basis of language.

Biochemistry

ML has also allowed us to make significant advances in understanding biological sequences. In 2022, we leveraged recent advances in deep learning to accurately predict protein function from raw amino acid sequences. We also worked in close collaboration with the European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) to carefully assess model performance and add hundreds of millions of functional annotations to the public protein databases UniProt , Pfam/InterPro , and MGnify . Human annotation of protein databases can be a laborious and slow process and our ML methods enabled a giant leap forward — for example, increasing the number of Pfam annotations by a larger number than all other efforts during the past decade combined. The millions of scientists worldwide who access these databases each year can now use our annotations for their research.

Although the first draft of the human genome was released in 2003, it was incomplete and had many gaps due to technical limitations in the sequencing technologies. In 2022 we celebrated the remarkable achievements of the Telomere-2-Telomere (T2T) Consortium in resolving these previously unavailable regions — including five full chromosome arms and nearly 200 million base pairs of novel DNA sequences — which are interesting and important for questions of human biology, evolution, and disease. Our open source genomics variant caller, DeepVariant , was one of the tools used by the T2T Consortium to prepare their release of a complete 3.055 billion base pair sequence of a human genome . The T2T Consortium is also using our newer open source method DeepConsensus , which provides on-device error correction for Pacific Biosciences long-read sequencing instruments, in their latest research toward comprehensive pan-genome resources that can represent the breadth of human genetic diversity.

Using quantum computing for new physics discoveries

When it comes to making scientific discoveries, quantum computing is still in its infancy, but has a lot of potential. We’re exploring ways of advancing the capabilities of quantum computing so that it can become a tool for scientific discovery and breakthroughs. In collaboration with physicists from around the world, we are also starting to use our existing quantum computers to create interesting new experiments in physics.

As an example of such experiments, consider the problem where a sensor measures something, and a computer then processes the data from the sensor. Traditionally, this means the sensor’s data is processed as classical information on our computers. Instead, one idea in quantum computing is to directly process quantum data from sensors. Feeding data from quantum sensors directly to quantum algorithms without going through classical measurements may provide a large advantage. In a recent Science paper written in collaboration with researchers from multiple universities, we show that quantum computing can extract information from exponentially fewer experiments than classical computing, as long as the quantum computer is coupled directly to the quantum sensors and is running a learning algorithm. This “ quantum machine learning ” can yield an exponential advantage in dataset size, even with today’s noisy intermediate-scale quantum computers. Because experimental data is often the limiting factor in scientific discovery, quantum ML has the potential to unlock the vast power of quantum computers for scientists. Even better, the insights from this work are also applicable to learning on the output of quantum computations, such as the output of quantum simulations that may otherwise be difficult to extract.

Even without quantum ML, a powerful application of quantum computers is to experimentally explore quantum systems that would be otherwise impossible to observe or simulate. In 2022, the Quantum AI team used this approach to observe the first experimental evidence of multiple microwave photons in a bound state using superconducting qubits . Photons typically do not interact with one another, and require an additional element of non-linearity to cause them to interact. The results of our quantum computer simulations of these interactions surprised us — we thought the existence of these bound states relied on fragile conditions, but instead we found that they were robust even to relatively strong perturbations that we applied.

Given the initial successes we have had in applying quantum computing to make physics breakthroughs, we are hopeful about the possibility of this technology to enable future groundbreaking discoveries that could have as significant a societal impact as the creation of transistors or GPS . The future of quantum computing as a scientific tool is exciting!

Acknowledgements

I would like to thank everyone who worked hard on the advances described in this post, including the Google Applied Sciences, Quantum AI, Genomics and Brain teams and their collaborators across Google Research and externally. Finally, I would like to thank the many Googlers who provided feedback in the writing of this post, including Lizzie Dorfman, Erica Brand, Elise Kleeman, Abe Asfaw, Viren Jain, Lucy Colwell, Andrew Carroll, Ariel Goldstein and Charina Chou.

Google Research, 2022 & beyond

This was the seventh blog post in the “Google Research, 2022 & Beyond” series. Other posts in this series are listed in the table below:

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In-Datacenter Performance Analysis of a Tensor Processing Unit

Norman P. Jouppi, Cliff Young , Nishant Patil, David Patterson , Gaurav Agrawal, Raminder Bajwa, Sarah Bates, Suresh Bhatia, Nan Boden, Al Borchers, Rick Boyle, Pierre-luc Cantin, Clifford Chao, Chris Clark, Jeremy Coriell, Mike Daley, Matt Dau, Jeffrey Dean , Ben Gelb, Tara Vazir Ghaemmaghami, Rajendra Gottipati, William Gulland, Robert Hagmann, C. Richard Ho, Doug Hogberg, John Hu, Robert Hundt , Dan Hurt, Julian Ibarz , Aaron Jaffey, Alek Jaworski, Alexander Kaplan, Harshit Khaitan, Andy Koch, Naveen Kumar, Steve Lacy, James Laudon , James Law, Diemthu Le, Chris Leary, Zhuyuan Liu, Kyle Lucke, Alan Lundin, Gordon MacKean, Adriana Maggiore, Maire Mahony, Kieran Miller, Rahul Nagarajan, Ravi Narayanaswami, Ray Ni, Kathy Nix, Thomas Norrie, Mark Omernick, Narayana Penukonda, Andy Phelps, Jonathan Ross

ISCA (2017) (to appear)

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Jyrki Alakuijala , Robert Obryk, Ostap Stoliarchuk, Zoltan Szabadka, Lode Vandevenne, Jan Wassenberg

arXiv (2017)

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Yonghui Wu , Mike Schuster , Zhifeng Chen , Quoc V. Le , Mohammad Norouzi , Wolfgang Macherey , Maxim Krikun, Yuan Cao, Qin Gao , Klaus Macherey , Jeff Klingner , Apurva Shah, Melvin Johnson , Xiaobing Liu , Łukasz Kaiser , Stephan Gouws , Yoshikiyo Kato , Taku Kudo, Hideto Kazawa, Keith Stevens , George Kurian, Nishant Patil, Wei Wang, Cliff Young , Jason Smith, Jason Riesa , Alex Rudnick , Oriol Vinyals , Greg Corrado , Macduff Hughes, Jeffrey Dean

CoRR, vol. abs/1609.08144 (2016)

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

Varun Gulshan , Lily Peng , Marc Coram, Martin C Stumpe , Derek Wu , Arunachalam Narayanaswamy , Subhashini Venugopalan, Kasumi Widner , Tom Madams , Jorge Cuadros, Ramasamy Kim, Rajiv Raman, Philip Q Nelson , Jessica Mega, Dale Webster

JAMA (2016)

Digitized Adiabatic Quantum Computing with a Superconducting Circuit

Rami Barends , Alireza Shabani , Lucas Lamata, Julian Kelly , Antonio Mezzacapo, Urtzi Las Heras, Ryan Babbush , Austin Fowler , Brooks Campbell, Yu Chen, Zijun Chen, Ben Chiaro, Andrew Dunsworth, Evan Jeffrey , Erik Lucero , Anthony Megrant, Josh Mutus , Matthew Neeley , Charles Neill, Peter O'Malley, Chris Quintana , Enrique Solano, Ted White , Jim Wenner, Amit Vainsencher , Daniel Sank , Pedram Roushan, Hartmut Neven , John Martinis

Nature, vol. 534 (2016), pp. 222-226

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I'm at Google because that's where the data is, and the means to use it. This makes it possible to do great work at scale. In the end, the reason for doing the work is to create something useful that helps people, and Google makes it easy for researchers to roll out products that will help hundreds of millions of people.

The raw computation power available at Google is just incredible - we can do research on a scale that was unimaginable to me in academia, and work on problems that no one else is even thinking of.

Using Google for Research

• Google Search
• Google Scholar
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What is Google Scholar?

Google Scholar searches for scholarly literature in a simple, familiar way. You can search across many disciplines and sources at once to find articles, books, theses, court opinions, and content from academic publishers, professional societies, some academic web sites, and more. See the Google Scholar inclusion guidelines for more about what’s in Google Scholar.

Advanced Search Tips

For more precise searching, use Google's  Advanced Scholar Search Page

• To pull up the Advanced Scholar Search menu, go to the regular Google Scholar search page.
• In the upper left corner of the page, press the button made of three horizontal lines to open a new menu. 
• Advanced Search should be the second to last option in the newly-opened menu.

Or, try these tips:

Find content by an author:.

• Add the author's name to the search, or
• Use the "author:" operator (eg. aphasia author:jones finds articles about aphasia written by people named Jones)

Search for a phrase:

• Use "quotation marks" to find phrases (eg. "allegory of the cave" plato republic finds articles about Plato's cave allegory in The Republic )

Search by words in the title:

• Use the "intitle:" operator (eg. intitle:fellini finds articles with Fellini in the title]

Setting "Library Links" Preferences in Google Scholar

1. go to scholar.google.com , and click on the menu button (3 horizontal bars) in the upper left-hand corner of the screen..

2. In the menu that appears, click "Settings"

3. Click "Library links" in the left-hand menu. 

4. Search for NYU, and select only  "New York University Libraries - View via NYU Libraries" then click "Save".

5. Conduct a new search in Google Scholar. Click the "Check via NYU Libraries" link under each search result to get NYU Libraries-subscribed access to the article. If you are off campus, you will be prompted to log in with your NetID and password before being granted access to the full-text.

6. If you encounter a search result without a "Check via NYU Libraries" link underneath it, click on the "double arrow" button below the result, and the link should appear.

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Google Scholar Search Strategies

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Using Google Scholar for Research

Google Scholar is a powerful tool for researchers and students alike to access peer-reviewed papers. With Scholar, you are able to not only search for an article, author or journal of interest, you can also save and organize these articles, create email alerts, export citations and more. Below you will find some basic search tips that will prove useful.

This page also includes information on Google Scholar Library - a resource that allows you to save, organize and manage citations - as well as information on citing a paper on Google Scholar.

Search Tips

• Locate Full Text
• Sort by Date
• Related Articles
• Court Opinions
• Email Alerts
• Advanced Search

Abstracts are freely available for most of the articles and UMass Lowell holds many subscriptions to journals and online resources. The first step is make sure you are affiliated with the UML Library on and off campus by Managing your Settings, under Library Links. 

When searching in Google Scholar here are a few things to try to get full text:

• click a library link, e.g., "Full-text @ UML Library", to the right of the search result;
• click a link labeled [PDF] to the right of the search result;
• click "All versions" under the search result and check out the alternative sources;
• click "More" under the search result to see if there's an option for full-text;
• click "Related articles" or "Cited by" under the search result to explore similar articles.

Your search results are normally sorted by relevance, not by date. To find newer articles, try the following options in the left sidebar:

• click "Sort by date" to show just the new additions, sorted by date;  If you use this feature a lot, you may also find it useful to setup email alerts to have new results automatically sent to you.
• click the envelope icon to have new results periodically delivered by email.

Note: On smaller screens that don't show the sidebar, these options are available in the dropdown menu labeled "Any time" right below the search button .

The Related Articles option under the search result can be a useful tool when performing research on a specific topic. 

After clicking you will see articles from the same authors and with the same keywords.

You can select the jurisdiction from either the search results page or the home page as well; simply click "select courts". You can also refine your search by state courts or federal courts. 

To quickly search a frequently used selection of courts, bookmark a search results page with the desired selection. 

 How do I sign up for email alerts?

Do a search for the topic of interest, e.g., "M Theory"; click the envelope icon in the sidebar of the search  results page; enter your email address, and click " Create alert ". Google will periodically email you newly published papers that match your search criteria. You can use any email address for this; it does not need to be a Google Account. 

If you want to get alerts from new articles published in a specific journal; type in the name of this journal in the search bar and create an alert like you would a keyword. 

How do I get notified of new papers published by my colleagues, advisors or professors?

First, do a search for your their name, and see if they have a Citations profile. If they do, click on it, and click the "Follow new articles" link in the right sidebar under the search box.

If they don't have a profile, do a search by author, e.g., [author:s-hawking], and click on the mighty envelope in the left sidebar of the search results page. If you find that several different people share the same name, you may need to add co-author names or topical keywords to limit results to the author you wish to follow.

How do I change my alerts?

If you created alerts using a Google account, you can manage them all on the "Alerts" page . 

From here you can create, edit or delete alerts. Select cancel under the actions column to unsubscribe from an alert. 

This will pop-open the advanced search menu

Here you can search specific words/phrases as well as for author, title and journal. You can also limit your search results by date.

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The Google Scholar Features Every Student Should Know

From magazine articles to peer-reviewed papers and case laws, Google Scholar can provide cutting-edge research for free. It's one of Google's lesser-known search tools—but it's invaluable if you need to cite data from trusted sources. 

Google Scholar isn't perfect as an academic search engine—you'll need to know a few tips to help you get the most out of it.

Use Google Scholar's Advanced Search

With some practice, Google Scholar's Advanced Search filters will become your go-to search tool. 

Click the hamburger menu;  Advanced Search  is the second-to-last option.

The Advanced Search box gives you nine filters to search with—their functions are self-explanatory.

You can search for specific terms, find exact phrases, and also search with synonyms. 

For more targeted results, combine these options with filters like publication date, time range, or author. 

Note:  Search results are usually sorted by relevance, not by date. Use the date filters on the results page or select  Sort by date , if that's what you're looking for. Scholar supports all the regular and advanced Boolean search operators, just like Google's regular search. 

Browse articles in your area of interest

You can go to the Google Scholar Metrics page, search through the  top 100 publications , and then drill down to the ones cited most. Then, instead of conducting a random search, you can take a bird's-eye view of the developments in your area of specialization. 

Open the sidebar via the hamburger menu and click on  Metrics . 

Select the dropdown next to  Categories  and then the dropdown for  Subcategories  if you want to drill down deeper.  

Go back to find the fundamental research

When we use Google Search, we are interested in the latest. In Google Scholar, we can go back in time with the date filters (or use a custom date range) to search for foundational research in any field. 

Tip:  Try searching with Chrome's incognito mode and see if it gives you slightly different, non-personalized results that aren't based on your search history. 

Search for experts

 It helps to know who the more influential voices are in your field. You can enter a search term in Google Scholar and find the most cited papers. But first, open the sidebar (select the hamburger icon next to the Google Scholar logo) and select  Profiles . 

The search page updates with the authors' public profiles while retaining what you had initially searched for. Use this information to learn more about their work in your field. 

Tip:  Search for the authors you want more of on YouTube, other academic journals, or social profiles to follow their latest work and lectures. 

Stay updated with Google Scholar Alerts

If you are a heavy Google Scholar user, set up alerts to stay on top of the latest developments. You'll receive emails whenever a new academic topic is published. 

Log into your Google account. 

Click the hamburger menu to open the sidebar. 

Select  Alerts  to open a new page. 

Click the red  Create alert  button and insert the keywords for which Google Scholar should look. 

Select  Update results  to get a preview of the results for the query you used.

Select  Create Alert  to set up the alert. You can set as many alerts as you want. 

Tip:  You can also set up an alert for a particular query from the search results page by clicking on  Create alert .  

Install the Google Scholar button

The Google Scholar button is a Chrome extension that gives you ready access to Google Scholar search results without copying and pasting. You can look up academic articles from any webpage you are on without leaving it. 

For instance, highlight any keyword, topic heading, or citation on any webpage. Click the Google Scholar button, and the relevant results appear in a window. 

You can also use the search field in the Google Scholar window to directly type in a search query.

Learn any subject with Google Scholar

Performing basic research is a fundamental soft skill that can help you develop critical thinking skills. Google Scholar gives you an idea of the broader research in any field. With the right keywords, you can then dive deeper into the results.

Explore the  Top publications  page and the most cited papers to see emerging trends in any field. Use the language filter on the right to read or translate papers in other languages.

Click on the  Cited by  link in the search results to see if fresher research has built upon the material you are reading about.

Click on  All versions  under the search result and see if the alternative sources have the entire paper instead of an abstract. 

Check out  Related articles  and  Related searches  to cross-pollinate your learning with similar ideas from adjacent fields.

Use Google Scholar's  My library  feature to curate articles for later. For instance, click  Save  to read a paper later and use the  Reading list  label. 

Google recommends that if you're new to a subject, picking up the terminology from secondary sources may be helpful. For example, a Wikipedia article on "intravenous feeding" might suggest a Scholar search for "hyperalimentation."

Google Scholar offers  Library links and Library Search  to access any electronic and print resources in a library connected to Google Scholar results. 

Have fun on Google Scholar

Yes, Google Scholar is a serious search engine. But you can still use it to search for quirky topics and see the results it throws up. As a Star Trek fan, I often go there to search for stuff a fellow fan or academic might have written. And 148,000 results suggest that the iconic show is a scholarly subject for many. Try it with your favorite show or movie. 

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Finding and Reading Journal Articles

• Journal Articles: Why You Use Them

Why are articles so important to research?

• Subject Databases: Organizing Research Conversations
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• Sources in the Disciplines
• Reading in the Disciplines

Journal articles are the academic's stock in trade, t he basic means of communicating research findings to an audience of one’s peers. That holds true across the disciplinary spectrum, so no matter where you land as a concentrator, you can expect to rely on them heavily. 

Regardless of the discipline, moreover,  journal articles perform an important knowledge-updating function .

Textbooks and handbooks and manuals will have a secondary function for chemists and physicists and biologists, of course. But in the sciences, articles are the standard and  preferred publication form. 

In the social sciences and humanities , where knowledge develops a little less rapidly or is driven less by issues of time-sensitivity , journal articles and books are more often used together.

Not all important and influential ideas warrant book-length studies, and some inquiry is just better suited to the size and scope and concentrated discussion that the article format offers.

Journal articles sometimes just present the most  appropriate  solution for communicating findings or making a convincing argument.  A 20-page article may perfectly fit a researcher's needs.  Sustaining that argument for 200 pages might be unnecessary -- or impossible.

The quality of a research article and the legitimacy of its findings are verified by other scholars, prior to publication, through a rigorous evaluation method called peer-review . This seal of approval by other scholars doesn't mean that an article is the best, or truest, or last word on a topic. If that were the case, research on lots of things would cease. Peer review simply means other experts believe the methods, the evidence, the conclusions of an article have met important standards of legitimacy, reliability, and intellectual honesty.

Searching the journal literature is part of being a responsible researcher at any level: professor, grad student, concentrator, first-year. Knowing why academic articles matter will help you make good decisions about what you find -- and what you choose to rely on in your work.

Think of journal articles as the way you tap into the ongoing scholarly conversation , as a way of testing the currency of  a finding, analysis, or argumentative position, and a way of bolstering the authority (or plausibility) of explanations you'll offer in the papers and projects you'll complete at Harvard. 

• Next: Subject Databases: Organizing Research Conversations >>

Except where otherwise noted, this work is subject to a Creative Commons Attribution 4.0 International License , which allows anyone to share and adapt our material as long as proper attribution is given. For details and exceptions, see the Harvard Library Copyright Policy ©2021 Presidents and Fellows of Harvard College.

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Apple admits to using Google Tensor hardware to train Apple Intelligence

The research paper, called "Apple Intelligence Foundation Language Models" is pretty technical, and details the already-known sources of the language model at the core of the company's new technology. However, a quote buried inside the paper hints that Apple may have been using Google hardware in early development.

In the paper, it says that Apple's Foundation Model (AFM) and the server technology that drives it, were initially built on "v4 and v5p Cloud TPU clusters" using Apple software . There is a great deal of information in the research about how that's done, and what data sources they used to train.

A CNBC report on Monday suggests that Apple rented time on existing Google-hosted clusters, but the research doesn't directly support that, nor say anything about Google or Nvidia at all. What's more likely is that Apple bought the hardware outright from the company, and used it within its own data centers.

The model's initial training being performed on Google-designed hardware ultimately doesn't mean much in the long-run. Apple has been said to have its own hardware derived from Apple Silicon in its data centers to process Apple Intelligence queries.

Said to be called " Project ACDC ," Apple is reportedly planning to optimize AI applications within its data centers.

Apple is significantly increasing its investment in the artificial intelligence sector, planning to allocate over $5 billion to AI server enhancements over the next two years. The company aims to match the technological capabilities of industry leaders like Microsoft and Meta by acquiring tens of thousands of AI server units, likely driven by Project ACDC.

Apple has also acquired firms in Canada and France that both work on compressing data used in AI queries to data centers.

26 Comments

What's more likely is that Apple bought the hardware outright from the company, and used it within its own data centers. Is there any source for this claim?  Wall street has been asking Google to sell TPU hardware basically forever but as far as anyone knows it's never happened.

Hreb said: What's more likely is that Apple bought the hardware outright from the company, and used it within its own data centers.

Is there any source for this claim?  Wall street has been asking Google to sell TPU hardware basically forever but as far as anyone knows it's never happened.

If Google permitted Apple to buy their TPU hardware it would be a first. They've been very protective of it, and allowing a potential competitor to pick it apart to better understand how Google arrived at the chip design seems unlikely.  EDIT: Apple seems to have more than a passing interest in using Google Tensor hardware for their intelligence features.  "Apple's engineers said in the paper it would be possible to make even larger, more sophisticated models with Google's chips, than the two models it discussed in the paper"

What's the big deal? Do whatever you need to move forward. Microsoft used PowerMac G5's when developing the first Xbox.

“Admits” is such an unnecessary loaded word here. 😂 “Reveals” would be more appropriate haha. I think it’s no surprise given that Chris Lattner went to Google and worked on TensorFlow and created Swift for TensorFlow so….seems logical to me to tap into the leading APIs for machine learning.

leehericks said: “Admits” is such an unnecessary loaded word here. 😂 “Reveals” would be more appropriate haha. I think it’s no surprise given that Chris Lattner went to Google and worked on TensorFlow and created Swift for TensorFlow so….seems logical to me to tap into the leading APIs for machine learning. He left Google 5 years ago didn't he?  https://www.reddit.com/r/swift/comments/1b85ucx/an_interested_read_on_why_google_engineers_chose/

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Trusting Google to deliver best search results can hurt your wallet, study finds

By Megan Cerullo

Edited By Anne Marie Lee

Updated on: July 30, 2024 / 1:19 PM EDT / CBS News

Google's search engine is a common place for users to start their hunt for everything from the best  sunscreens to the top finance and budgeting tools that will stretch their dollars the furthest. 

But Google's search hub doesn't always deliver the most accurate or useful results for financial products, according to a new study from personal finance website WalletHub. Far from showing searchers top-notch results, the search engine often yields responses that can cost people $202 on average, and up to more than $1,000 when looking for certain types of credit cards, the study found.

WalletHub evaluated Google's results for commonly queried credit card and banking terms, and conducted a survey in which it asked consumers about how useful, accurate and aligned with their searches the results they received were. 

"Consumers are putting a lot of trust in Google and its top results," WalletHub CEO Odysseas Papadimitriou told CBS MoneyWatch. "So what we asked was, 'Is Google really doing its job and serving the best results?'" 

WalletHub analysts evaluated results for credit card and banking-related terms including "best airline credit card," "best no interest credit cards," "best jumbo money market rates," "best CD rates," and more commonly searched terms. 

Costly search results

WalletHub analyzed search results to determine their cost to consumers. For example, when searching for the "best credit cards for bad credit," the first nonsponsored hit directs users to Mastercard's website, where they are exclusively shown Mastercard products. This alone does consumers a disservice, according to Papadimitriou, because it eliminates card alternatives from competitors like Visa and Discover. 

"The result that ranks first for 'credit cards for bad credit' is from one of the biggest financial brands in the world," Papadimitriou said. "When you go to that page, it doesn't include cards from competitors that might be superior to Mastercard's own offerings."

"People expect Google to put the best result first, that Google is doing the work for you and putting the best information forward. But what we found is happening is Google blindly follows the biggest brand, and is shortchanging consumers," he said.

He added that the cards Mastercard lists on its site aren't even necessarily the best. 

"They just give you some credit cards for bad credit. They don't even pretend to be serving what you ask for," Papadimitriou said.  

Among the most costly credit card search terms, "best credit cards to build credit," ranked highest, costing consumers who selected one of the top products appearing in Google search results $1,095, according to WalletHub. Choosing one of the top results for the banking search term "best jumbo money market rates," could cost consumers, $1,347, the most of any search term in the study.

Google said its results satisfy users, and that it is constantly upgrading its search engine. 

"Our research shows that search satisfies the overwhelming majority of user needs for people around the world, and we launch thousands of improvements every year to make Search even better for people," Google said in a statement to CBS MoneyWatch. "Our systems aim to connect people with content that is helpful and original, from a diverse range of sites across the web." 

Shortchanging consumers

Take another term, like "best savings account" — based on how much interest it yields. In this case, Google's search results could cost consumers if, for example, Google's top hit offers 4.5% but the best account on the market offers more.

"So they trust Google and proceed and sign up for the 4.5% account, when they could have gotten 5.5% That's how they are being shortchanged," Papadimitiou said. 

Big brand bias 

Seventy-five percent of consumers surveyed said they believe Google favors big brands in search results. Other drawbacks to Google search results, according to WalletHub, included:

• Only 41% of results met searchers' intent
• 34% of results only showed advertisers
• 58% of results weren't transparent
• 63% of survey respondents said they believed Google search results were superior last year

"I think that the takeaway here is people should not trust Google blindly; it has a lot of biases," Papadimitriou said. 

Megan Cerullo is a New York-based reporter for CBS MoneyWatch covering small business, workplace, health care, consumer spending and personal finance topics. She regularly appears on CBS News 24/7 to discuss her reporting.

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Carbon removal experts: Technology could blunt climate mitigation

By Corbin Hiar | 08/01/2024 06:58 AM EDT

Researchers who study pulling carbon from the sky say it could discourage efforts to prevent the gas from being released in the first place.

A carbon removal plant is pictured in Hellisheidi, Iceland. Halldor Kolbeins/AFP via Getty Images

Efforts to filter carbon dioxide from the air and oceans will rapidly expand in the coming decades — but not enough to reach net-zero emissions globally, according to a landmark survey of carbon removal experts.

That means global warming would be unlikely to remain below the 2-degrees-Celsius limit set by the Paris Agreement, which would require the world to offset more emissions than it produces by midcentury, the New York University School of Law study found.

Climate scientists have warned that temperature increases beyond 2 C could lead to increasingly severe weather events, wildfires, droughts and rising seas, with significant harms to the agricultural systems that sustain human populations.

The study , published Wednesday by the law school’s Institute for Policy Integrity, is likely the largest expert survey of its kind. It was based on responses from nearly 700 carbon removal academics, developers, regulators and analysts who have published scientific papers on the topic in the past five years.

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Apple appears to have just shunned Nvidia again

• It appears that Apple opted for Google's TPU chips over Nvidia's for its AI development.
• An Apple research paper highlights the use of TPUv5p chips from Google for iPhone AI.
• Nvidia chips remain in high demand, and the company charges toward a $4 trillion valuation.

It looks like Apple has confirmed it's using Google's chips for Apple Intelligence — not Nvidia's.

There was no mention of Nvidia in an Apple research paper published on Monday that discussed foundation language models developed to power Apple Intelligence features. In fact, the tech giant wrote that it used TPUv4 and TPUv5p chips, Google's tensing processing units, to train its artificial intelligence tools.

Nvidia chips, which use graphics processing units for AI processing, are in high demand in the tech industry. Companies like Meta spend big to amass large volumes of them to train their models. According to a Mizuho Securities estimate, Nvidia contro ls more than 70% of the AI chips market , and the chips can cost tens of thousands of dollars to buy.

Google uses its own TPU chips instead and rents them out through its cloud service to clients like Apple.

While Apple didn't explicitly say that no Nvidia chips were used in the hardware and software infrastructure of its AI features, it has been somewhat public about its work with Google to train its upcoming Apple Intelligence.

In Monday's research paper, Apple wrote that it used 2,048 TPUv5p chips (made by Google) to build the AI model that will reportedly operate on iPhones and other Apple hardware.

Apple, Microsoft, Nvidia and Alphabet are among the top companies in the world by market cap . Nvidia's success in the AI chip market has helped drive it to a more than $2 billion valuation in the last few years.

Watch: An AI expert discusses the hardware and infrastructure needed to properly run and train AI models

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