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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 .

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How to Find Sources | Scholarly Articles, Books, Etc.

Published on June 13, 2022 by Eoghan Ryan . Revised on May 31, 2023.

It’s important to know how to find relevant sources when writing a  research paper , literature review , or systematic review .

The types of sources you need will depend on the stage you are at in the research process , but all sources that you use should be credible , up to date, and relevant to your research topic.

There are three main places to look for sources to use in your research:

Research databases

• Your institution’s library
• Other online resources

Table of contents

Library resources, other online sources, other interesting articles, frequently asked questions about finding sources.

You can search for scholarly sources online using databases and search engines like Google Scholar . These provide a range of search functions that can help you to find the most relevant sources.

If you are searching for a specific article or book, include the title or the author’s name. Alternatively, if you’re just looking for sources related to your research problem , you can search using keywords. In this case, it’s important to have a clear understanding of the scope of your project and of the most relevant keywords.

Databases can be general (interdisciplinary) or subject-specific.

• You can use subject-specific databases to ensure that the results are relevant to your field.
• When using a general database or search engine, you can still filter results by selecting specific subjects or disciplines.

Example: JSTOR discipline search filter

Check the table below to find a database that’s relevant to your research.

Google Scholar

To get started, you might also try Google Scholar , an academic search engine that can help you find relevant books and articles. Its “Cited by” function lets you see the number of times a source has been cited. This can tell you something about a source’s credibility and importance to the field.

Example: Google Scholar “Cited by” function

Boolean operators

Boolean operators can also help to narrow or expand your search.

Boolean operators are words and symbols like AND , OR , and NOT that you can use to include or exclude keywords to refine your results. For example, a search for “Nietzsche NOT nihilism” will provide results that include the word “Nietzsche” but exclude results that contain the word “nihilism.”

Many databases and search engines have an advanced search function that allows you to refine results in a similar way without typing the Boolean operators manually.

Example: Project Muse advanced search

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You can find helpful print sources in your institution’s library. These include:

• Journal articles
• Encyclopedias
• Newspapers and magazines

Make sure that the sources you consult are appropriate to your research.

You can find these sources using your institution’s library database. This will allow you to explore the library’s catalog and to search relevant keywords. You can refine your results using Boolean operators .

Once you have found a relevant print source in the library:

• Consider what books are beside it. This can be a great way to find related sources, especially when you’ve found a secondary or tertiary source instead of a primary source .
• Consult the index and bibliography to find the bibliographic information of other relevant sources.

You can consult popular online sources to learn more about your topic. These include:

• Crowdsourced encyclopedias like Wikipedia

You can find these sources using search engines. To refine your search, use Boolean operators in combination with relevant keywords.

However, exercise caution when using online sources. Consider what kinds of sources are appropriate for your research and make sure the sites are credible .

Look for sites with trusted domain extensions:

• URLs that end with .edu are educational resources.
• URLs that end with .gov are government-related resources.
• DOIs often indicate that an article is published in a peer-reviewed , scientific article.

Other sites can still be used, but you should evaluate them carefully and consider alternatives.

If you want to know more about ChatGPT, AI tools , citation , and plagiarism , make sure to check out some of our other articles with explanations and examples.

• ChatGPT vs human editor
• ChatGPT citations
• Is ChatGPT trustworthy?
• Using ChatGPT for your studies
• What is ChatGPT?
• Chicago style
• Paraphrasing

 Plagiarism

• Types of plagiarism
• Self-plagiarism
• Avoiding plagiarism
• Academic integrity
• Consequences of plagiarism
• Common knowledge

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The AI-powered Citation Checker helps you avoid common mistakes such as:

• Missing commas and periods
• Incorrect usage of “et al.”
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• Missing reference entries

You can find sources online using databases and search engines like Google Scholar . Use Boolean operators or advanced search functions to narrow or expand your search.

For print sources, you can use your institution’s library database. This will allow you to explore the library’s catalog and to search relevant keywords.

It is important to find credible sources and use those that you can be sure are sufficiently scholarly .

• Consult your institute’s library to find out what books, journals, research databases, and other types of sources they provide access to.
• Look for books published by respected academic publishing houses and university presses, as these are typically considered trustworthy sources.
• Look for journals that use a peer review process. This means that experts in the field assess the quality and credibility of an article before it is published.

When searching for sources in databases, think of specific keywords that are relevant to your topic , and consider variations on them or synonyms that might be relevant.

Once you have a clear idea of your research parameters and key terms, choose a database that is relevant to your research (e.g., Medline, JSTOR, Project MUSE).

Find out if the database has a “subject search” option. This can help to refine your search. Use Boolean operators to combine your keywords, exclude specific search terms, and search exact phrases to find the most relevant sources.

There are many types of sources commonly used in research. These include:

You’ll likely use a variety of these sources throughout the research process , and the kinds of sources you use will depend on your research topic and goals.

Scholarly sources are written by experts in their field and are typically subjected to peer review . They are intended for a scholarly audience, include a full bibliography, and use scholarly or technical language. For these reasons, they are typically considered credible sources .

Popular sources like magazines and news articles are typically written by journalists. These types of sources usually don’t include a bibliography and are written for a popular, rather than academic, audience. They are not always reliable and may be written from a biased or uninformed perspective, but they can still be cited in some contexts.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Ryan, E. (2023, May 31). How to Find Sources | Scholarly Articles, Books, Etc.. Scribbr. Retrieved April 9, 2024, from https://www.scribbr.com/working-with-sources/finding-sources/

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Citation Databases

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Introduction to Google Scholar

Google Scholar is the largest citation search-base in the world, though it suffers from some difficulties it is the most widely used of all such search engines. The reason for this is that author profiles in Google Scholar tend to index the entirety of an author’s output. The database is maintained by a powerful algorithm which searches the entire internet for citations, documents, and other research output. However, not all of the material which can be found in Google Scholar is peer-reviewed so remember that it is up to you to be a critical consumer of information. Google Scholar cannot sort by research field or type, browse by title, and cannot limit search results (except for by year).

Uses for Researchers

There are many use cases of Google Scholar for researchers, the most obvious cases are literature reviews and making note of research trends. Searching Google Scholar is easy, and by making use of some of the tips below you will be able to narrow your search results.

Search Tips

Here are some easy search tips for using Google Scholar.

Opening Advanced search: to open advanced search navigate to the three lines at the top left of the google scholar home page, click on that and then click on advanced search.

Using “Boolean Operators” such as ‘AND’ as well as ‘OR’ can allow you to search for things which include specific words

Using the command “intitle:” we can do an in title search, so for example “Intitle: “Romanitas”     Roman”' will search for articles with “Romanitas” in the title and the word “Roman” in the text of the article. This can help you to find articles with specific content.

Cited by References search is a powerful tool which allows you to search for articles that have cited a particular article. Search for an article and then click “cited by” under the entry.

Setting library links can be done by clicking “settings”, clicking on “library links” and then searching for “Purdue”, and finally clicking “save”. This will allow you to access materials you find on Google Scholar directly from your searches

Metrics in Google Scholar

Google Scholar maintains researcher level profiles which can be created or claimed. If you are already a prolific author, you might have an algorithmically generated profile already. These can be claimed, but others will find that they can create a profile themselves. Either way, once you have done this you can begin to access personalized metrics such as h-index and citations.

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Research: How to Close the Gender Gap in Startup Financing

• Malin Malmström,
• Barbara Burkhard,
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Three ways policymakers, financiers, and other stakeholders can mitigate gender bias in entrepreneurial funding.

A global analysis of previous research over the last three decades shows that women entrepreneurs face a higher rate of business loan denials and increased interest rates in loan decisions made by commercial bankers. Interestingly, the data also reveals that the formal and informal standing of women in a particular society can provide clues to some of the true hurdles to positive change. This article reviews these hurdles, and offers three recommendations for change.

Gender disparities persist in entrepreneurship and statistics reveal the severity of the issue. Globally, only one in three businesses is owned by women . In 2019, the share of startups with at least one female founding member was a mere 20% .

• MM Malin Malmström is a professor of entrepreneurship and innovation at Luleå University of Technology, and a director of the research center Sustainable Finance Lab in Sweden.
• BB Barbara Burkhard is a postdoctoral researcher of entrepreneurship at the Institute of Responsible Innovation at the University of St.Gallen.
• CS Charlotta Sirén is an associate professor of management at the Institute of Responsible Innovation at the University of St.Gallen.
• DS Dean Shepherd is a professor of entrepreneurship, management, and organization at The Mendoza College of Business, University of Notre Dame.
• JW Joakim Wincent is a professor of entrepreneurship and management at the Hanken School of Economics and the Global Center for Entrepreneurship and Innovation at the University of St.Gallen.

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What every researcher should know about searching – clarified concepts, search advice, and an agenda to improve finding in academia

Michael gusenbauer.

1 Department of Strategic Management, Marketing and Tourism, University of Innsbruck, Innsbruck Austria

2 Chair for Strategy and Organization, Technical University of Munich, Munich Germany

Neal R. Haddaway

3 Mercator Research Institute on Global Commons and Climate Change, Berlin Germany

4 Stockholm Environmental Institute, Stockholm Sweden

5 Africa Centre for Evidence, University of Johannesburg, Johannesburg South Africa

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Data sharing is not applicable to this article as no new data were created or analysed.

We researchers have taken searching for information for granted for far too long. The COVID‐19 pandemic shows us the boundaries of academic searching capabilities, both in terms of our know‐how and of the systems we have. With hundreds of studies published daily on COVID‐19, for example, we struggle to find, stay up‐to‐date, and synthesize information—all hampering evidence‐informed decision making. This COVID‐19 information crisis is indicative of the broader problem of information overloaded academic research. To improve our finding capabilities, we urgently need to improve how we search and the systems we use.

We respond to Klopfenstein and Dampier ( Res Syn Meth . 2020) who commented on our 2020 paper and proposed a way of improving PubMed's and Google Scholar's search functionalities. Our response puts their commentary in a larger frame and suggests how we can improve academic searching altogether. We urge that researchers need to understand that search skills require dedicated education and training. Better and more efficient searching requires an initial understanding of the different goals that define the way searching needs to be conducted. We explain the main types of searching that we academics routinely engage in; distinguishing lookup, exploratory, and systematic searching. These three types must be conducted using different search methods (heuristics) and using search systems with specific capabilities. To improve academic searching, we introduce the “Search Triangle” model emphasizing the importance of matching goals, heuristics, and systems. Further, we suggest an urgently needed agenda toward search literacy as the norm in academic research and fit‐for‐purpose search systems.

What is already known?

• To stay up‐to‐date, we researchers would need to read hundreds of research papers a day(!). Particularly, the avalanche of COVID‐19 papers exemplifies how we are chronically information overloaded.
• Evidence synthesis is more important than ever, yet we lack the knowledge and systems to effectively and efficiently identify the evidence bases for systematic reviews.

What is new?

• We claim that research discovery needs an urgent overhaul. Only with awareness of the basic concepts of academic searching, we can know how to make our search routines and systems fit‐for‐purpose.
• Our commentary clarifies these search concepts to point out the particularities of lookup, exploratory, and systematic searching. The “Search Triangle” model emphasizes that efficient and effective search only works when goals, systems, and heuristics are well matched.

Potential impact for RSM readers outside the authors' field

• Awareness for the importance of search literacy and search education is needed across disciplines.
• Better search skills not only help in research, but anywhere online.

We thank Klopfenstein and Dampier 1 for their comment on our paper and for acknowledging the need to improve both PubMed and Google Scholar with functionalities that each is currently missing. We welcome increased scrutiny of the functionality of search systems and assessing whether these are truly fit‐for‐purpose as we struggle with information overload, particularly in times of crises like the current COVID‐19 pandemic. We are also very happy to see increased research attention on the systems that we use on a day‐to‐day basis for research discovery: functionalities that have remained unquestioned by those of us who are not information specialists for too long.

Indeed, we were overwhelmed by the substantial attention given to our paper 2 (it currently has an Altmetric score of well above 300) and the positive comments we have received. This demonstrates the need for further scrutiny and improvement to academic search. It shows that researchers want to know more about the limitations of the systems they use to discover research, which limitations they must account for, and how to match their search strategies with each system. These decisions concerning the design of search strategies profoundly affect the resultant evidence that researchers identify, what they (often unknowingly) fail to identify, and what conclusions they draw based on the emergent evidence. 3

In this article, we go beyond our original article and put the work of Klopfenstein and Dampier 1 in a larger frame to discuss the kind of agenda setting needed to overhaul academic searching, and how this might be achieved by the research community.

1. SEARCHING AND BIAS IN TIME OF A GLOBAL CRISIS

The importance of effective and efficient identification of academic publications (hereafter referred to as searching ) has become particularly evident in the current COVID‐19 pandemic: This pandemic is not only a medical crisis, but also an information crisis—not because there is no information on COVID‐19, but because there is more than we can handle. Recently, a Lancet editorial called this an “infodemic” and a “major threat to public health.” 4 According to Semantic Scholar, more than 211 000 scientific articles exist to date on COVID‐19 across all disciplines a —almost all published in 2020. The National Institute of Health (NIH)'s isearch COVID‐19 Portfolio , an expert‐curated data collection, lists 60 297 medical COVID‐19 publications, whereas 79% were listed between May and August 2020 b —amounting to an average daily(!) publication rate of almost 400 publications for medicine alone. This incredible avalanche of evidence is more than any individual can process. For any particular intervention (eg, mask‐wearing), one can find a confusing and conflicting set of studies purportedly demonstrating evidence for and against (eg, face masks for the public during the COVID‐19 crisis 5 ). Thus, the way we can process and make sense of this overabundance of evidence is one of our greatest challenges the current infodemic shows us.

Currently there is overwhelming research attention trying to solve these information challenges in a diverse suite of innovative ways, each aiming to make COVID‐19‐related information readily discoverable and analyzable. On the one hand, there are dozens of new AI‐ or expert‐curated repositories: for example, NIH LitCovid , NIH isearch , OPENICPSR COVID‐19 data repository , WHO COVID‐19 database (also linking to many other repositories) , and the Center for Disease Control and Prevention (CDC) giving an overview of various repositories. On the other hand, there are new tools for visualization, access, categorization, and analysis of COVID‐19 information (eg, SciSight or CoVis ), some of them via crowdsourced idea contests (eg, Kaggle ) or hackathons organized by institutions around the globe. This host of new initiatives is important means to fight the COVID‐19 infodemic with improved information access and analysis. However, we argue that the information overload problem is exacerbated by the insufficient nature of the search systems we must use to find relevant information. If the systems and practices we have in place—to discover, analyze, and evaluate evidence—were fit‐for‐purpose, we would not need to battle COVID‐19 with context‐specific fixes that do only little in battling infodemics in all the other contexts. We advocate that fixing existing search systems and practices is at least as important as building new resources on top. This means raising researchers' awareness and understanding about the objectives of searching, along with improving search heuristics and the search systems that make the avalanche of evidence accessible. Klopfenstein and Dampier 1 provide a good example of how best practices can be adopted across platforms and how researchers across disciplines can influence search system providers in how their systems should be improved.

One of the most critical factors that can easily limit the quality of our work is the belief that how we search academically is perfectly fine. 6 , 7 It is the belief that the systems we use on a daily basis and the habits we have developed throughout our careers are adequate to find effectively and efficiently. However, searching—one of the central elements of research work—needs trained skills, careful thought, and planning. We need to understand that where and how we search greatly impacts what we find and miss, what we conclude, and what we suggest for evidence‐informed decision making. Improving academic searching helps to improve the quality of science and helps fighting so‐called infodemics. Thus, much can be gained if we improve day‐to‐day academic searching for the millions of researchers worldwide.

We argue that the COVID‐19 pandemic is an important time to consider how to improve academic searching altogether. In this text, we clarify some important concepts of academic searching that are the subject of frequent misunderstanding, we introduce the “Search Triangle”—a user‐centric search model to understand the key characteristics of academic searching, and we explore why and how we need to overhaul academic searching to better inform decision making (Box 1 ).

How we expend much effort to get around a terrible searching environment

COVID‐19 exemplifies an information crisis, with researchers building workarounds to cope with the insufficiencies of established search systems.

In theory, research on COVID‐19 could be readily identified by any user searching a database for “COVID‐19” and finding all relevant studies. However, several problems make this difficult, for example: (a) authors describe the concept using different terms; (b) many databases typically index records (and allow searches) based only on titles, abstracts, and keywords, missing potentially relevant terms in the full texts; (c) no single database catalogues all research; (d) poor search literacy in the research community means that errors or inefficiencies in searching are common; (e) paywalls restrict users' access to search facilities and the underlying research articles.

A suite of systems has been built to identify and assemble COVID‐19 relevant research to overcome these problems, making use of artificial intelligence (including machine learning), expert curation and screening for relevant information, and temporarily making resources Open Access.

These are admirable, but necessary only because accurate and efficient identification of (free‐to‐access) relevant research across comprehensive free‐to‐use databases does not exist.

2. UNDERSTANDING ACADEMIC SEARCHING—THE DIFFERENT SEARCH TYPES: LOOKUP, EXPLORATORY, SYSTEMATIC

As Klopfenstein and Dampier 1 point out, Google Scholar is by far the most commonly used resource by researchers. 8 This is not a coincidence—it allows straightforward, user‐friendly access to its vast database of research records. 9 However, Google Scholar also shows us beautifully how a system can be perfectly suited for one type of search, while failing miserably for another. On the one hand it is very capable for targeted searches aimed at finding specific research articles, 10 but has severe limitations in systematic searches (eg, a lack of transparency and reproducibility). 2 , 11 Most academics are unaware of the different types of searching that they use on a day‐to‐day basis. 12 They use the systems they know and to which they are accustomed in ways for which they were never designed. The result is substantially biased, nontransparent, and irreproducible research studies. As researchers, we must start understanding the basic types of searching we engage in and how the objectives behind each search type (why we search) should determine the search methods—that is, system choice (where we search) and search heuristics (how we search).

There is much we can learn about searching from the information retrieval and information science literature: substantial efforts have been made to determine the types of searching at various level of granularity and the capabilities required by search systems. This discipline broadly distinguishes lookup and exploratory searching as the two key search types. 13 , 14 Lookup searches—also called “known item searches” or “navigational searches”—are conducted with a clear goal in mind and “yield precise results with minimal need for result set examination and item comparison.” 14 (p. 42) Here, the search process should be swift and efficient so as not to disturb the user's workflow. However, lookup searches can also be used by researchers or decision‐makers for cherry picking. From the avalanche of studies, it is relatively easy to select evidence that supports a pre‐held belief or dogma that portrays a biased picture of reality. Sometimes, this cherry picking is deliberate; selecting whichever study provides support for an argument or decision that has already been made (ie, post hoc evidence use). And sometimes it is unintentional: when the first evidence encountered is assumed to be representative. In general, users want efficient and convenient information retrieval, particularly in lookup searches 15 , 16 —the first result that fits typically satisfies the information need. 17 However, as researchers or decision‐makers we should explore the available evidence in the least biased way or, better still, to additionally search systematically to have all available evidence for a specific topic (including the counter‐evidence to one cherry‐picked paper). Only then, we can be sure that our conclusions and decisions are sufficiently evidence‐informed.

As many topics are complex and require in‐depth understanding, and we cannot always trust anecdotal evidence (see lookup searches), we need exploratory searches to enrich our understanding. In exploratory searches, the search goal is somewhat abstract. 18 It is a desire to better understand the nature of a topic, and the path to reaching this goal is not always apparent. Exploratory searching is a process characterized by learning 19 where users aim to be exposed to a multitude of different, sometimes contradicting knowledge sources to build their mental models on a topic. Users “submit a tentative query to navigate proximal to relevant documents in the collection, then explore the environment to better understand how to exploit it, selectively seeking and passively obtaining cues about their next steps.” 20 (p. 38) The heuristics that users employ and their ultimate goals change throughout the session as they make sense of the information, linking it to and adapting their mental models iteratively. 21 A single search session might exclusively consist of lookup or exploratory searches, or might alter the two with mixed episodes of lookup (eg, fact checking, navigation) and exploratory searches (eg, discovery and learning). In exploratory searches, the search process often spans multiple sessions (ie, days, weeks, months) or media (eg, search, videos, offline conversations) where users engage with one or more systems, take notes, and save results to knowledge management systems. Users will often stop searching when they believe they have reached their goal (the information need is met) or when they conclude it cannot be reached with the resources available. 17

While both lookup and exploratory searches are established concepts in information retrieval, they do not cover systematic searches —which we claimed in our paper 2 is a distinct third search type with unique heuristics and requirements. Evidence synthesis, in the form of systematic reviews (including meta‐analyses) and systematic maps, has introduced many disciplines to the concept of systematic searches , with the goal to (a) identify all relevant records (within the resource constraints) in a (b) transparent and (c) reproducible manner. 2 None of these three systematic search goals is shared by lookup or exploratory searches. Systematic searching is similar to lookup searching in that the search goal is known, yet the level of rigor in planning and reporting and the sophistication in the search scope are unmatched making it a distinct type of search activity. One key aspect of systematic searching is that the methods used to search should be a priori and developed through careful planning, ideally involving information retrieval experts. 22

There are presently significant misunderstandings within the research community regarding what systematic searches should and should not entail. These misunderstandings have led to criticism of the systematic review method (compared to narrative reviews) which we find are unfounded—at least in view of the literature search phase that identifies the corpus of evidence for subsequent synthesis. A major criticism is that systematic reviews would not entail “hermeneutic circles” of iterative learning about a research concept, so that researchers would not include and reflect upon findings throughout the search process. 19 , 23 In practice, however, systematic searches should always be preceded by a thorough exploratory search phase, which in systematic reviews is called “scoping.” In this initial phase, the researchers use exploratory searches to familiarize themselves with the review topic: they extend their knowledge of concepts and language and define inclusion/exclusion criteria. 24 Only then do they compose a systematic search strategy that aims to identify all available, relevant records on the topic in a transparent and reproducible manner (ie, well reported in the final manuscript). We agree that, when an initial scoping phase is missing, this may limit the validity of a systematic review greatly, since key terms and concepts may have been omitted or misunderstood, even by experts. Thus, for systematic reviews it is essential that systematic searches are preceded by a thorough exploratory search phase.

It is important to note that systematic searches do not themselves entail a learning process. They should be predefined, protocol‐driven, structured means of systematically searching, and extracting all potentially relevant bibliographic records. The search area is specified by these search steps (mostly through the use of building blocks and snowballing heuristics—see Table ​ Table1) 1 ) and lays out all records for subsequent review of relevance/eligibility. In systematic searching, the “hermeneutic circle” of understanding should be well advanced (though it probably will never be finished). Thus, in systematic reviews using the building blocks heuristic (connecting concepts via Boolean operators) only the final iteration of the search string is truly systematic and must be transparently documented in detail. It is typically at this point that the researchers stop exploring for the purpose of improving the search area. While exploratory searches ( scoping ) might use the same heuristics (see Table ​ Table1), 1 ), these initial searches are iterative and incrementally improve the search area used for the systematic review. Hence, one of the main advantages of systematic reviews is that they include both an exploratory and a systematic search, upon which the subsequent synthesis is based. Unlike in narrative reviews that often rely on exploratory searching alone, the systematic search phase in systematic reviews aims to maximize comprehensiveness and full transparency and reproducibility.

Academic search types: Their goals, use cases, dominant heuristics, and key requirements to search systems

To date, systematic searching and its unique requirements have not been described by the information science literature. The influential work of Marchionini 14 that distinguishes between lookup and exploratory searching lists synthesis work as part of exploratory search and fails to capture the nature of systematic searches (as employed in systematic reviews). To help distinguishing the three search types, we define and summarize them and add associated use cases and heuristics in Table ​ Table1 1 .

3. CONDUCTING ACADEMIC SEARCHING—THE “SEARCH TRIANGLE”

We contend that good academic searching starts with users thoughtfully establishing what their search goals are: that is, what they want to know/find. Given their search goals, search‐literate users know which type of search they need to engage in and can thus then select appropriate heuristics and search systems . Whether users are search literate, that is, are able to optimally match heuristics and search systems to their (evolving) search goals, determines the effectiveness and efficiency of finding and learning. We maintain that researchers—and indeed all information seekers—should understand the following three points that span a “Search Triangle” (see Figure ​ Figure1 1 ):

• The users ' goals : what needs to be accomplished with the search task? For lookup searches, the goal is rapid and efficient identification of an artifact where the search area is already well known to users; for exploratory searches, the goal is learning about one or multiple concepts or about an evidence base; for systematic searches, the goal is the identification and extraction of all available records on an already well understood (scoped) topic.
• The appropriate heuristics : how can the search be best conducted? The user must ask which (set of) heuristics best attain the search goal. While simple lookup searches come relatively intuitively with user‐friendly search systems like Google Scholar, 17 the users' considerations of appropriate heuristics become important for effective explorative searches and particularly for systematic searches. Some of the most popular search heuristics described in information science literature (see Table ​ Table1) 1 ) are most specific first , wayfinding , snowballing (or citation chasing/chaining , pearl growing) , (post‐query) filtering , successive fraction , building blocks ( via Boolean operators) , or handsearching . 2 , 17 , 25 , 26 , 32 It is important to note that no single heuristic is associated with a single search type. Rather, the choice of appropriate heuristics depends on the particular nature of the search goal and the options at hand, given a particular search system. For example, while building blocks are primarily used in systematic searching, they might also be used in particular types or phases of exploratory searching. Snowballing, for example, is used both in exploratory and systematic searching—yet with a different level of attention to rigor, transparency, and reproducibility.
• The appropriate systems : which (set of) search system(s) best supports the required search type and the suitable search heuristics? It is important to know what can and cannot be accomplished, given the functional capabilities of a particular search system: eg, of the 28 systems analyzed in our paper 2 only half can be recommended as stand‐alone systems in systematic searches. The selection of search systems, among the dozens available, defines what users will find. The search and retrieval capabilities are defined by the implicit characteristics of the search system in terms of functionality and coverage. It cannot be emphasized enough that no single search system is like the other and that each system is more or less adequate for specific search types (lookup/exploratory/systematic) in terms of coverage and supported heuristics.

The “Search Triangle”: efficient and effective search only works when all three (search goals, search systems, and search heuristics) are matched well [Colour figure can be viewed at wileyonlinelibrary.com ]

4. IMPROVING ACADEMIC SEARCHING—SETTING AN AGENDA AND CALLS TO ACTION

To improve academic searching, we suggest an agenda that is rooted in three areas: (a) more awareness for the intricacies of academic searching; (b) better search education; and (c) pressure on search system providers to ensure their services are fit‐for‐purpose. We suggest key points that we believe the scholarly community must tackle, also jointly with institutions, publishing bodies, and search system providers.

4.1. More awareness for the intricacies of academic searching

Improving our search practice starts by creating awareness that search literacy is a crucial skill that does not come naturally through extensive computer and internet use, but needs to be trained in search education as part of research training. 33 , 34 Particularly, in the context of systematic reviews we must understand the two consecutive, yet distinct phases: exploratory searching and systematic searching. Too often, researchers skip the exploratory scoping phase and jump straight into systematic searching, while they still are (un/consciously) unsure about the meaning and language of central concepts.

Search literacy becomes increasingly needed as the number of search systems increases and the functionality they offer is diversified and continually updated, making them more or less (or not at all) suitable for specific search types. In recent years, we have seen the introduction of numerous new systems (eg, Microsoft Academic, Dimensions.ai, Meta, The Lens, Semantic Scholar) and techniques (eg, personalized or AI‐based search results) in academic search. Researchers must understand that these systems are all different and that system choice will heavily affect (or bias) what they will find. At the moment, the algorithms of so‐called semantic search systems (eg, Google Scholar or Semantic Scholar) and the precise methods of how they select and rank what is shown on the results page are unknown. However, there is evidence 6 , 35 that these opaque algorithmic decisions influence how we researchers conduct science—what we find, what we cite, how we argue, what we conclude. The academic community needs to be aware of these biases, and equip itself with the know‐how to avoid basing entire research projects (particularly systematic reviews) on potentially biased evidence bases (eg, Burivalova et al 36 ).

We currently see an alarming absence of awareness for search system choice. This is evident in the many publications that confuse search system types 37 : foremost platforms used to access databases (such as Web of Science) and the databases themselves (such as Science Citations Index Expanded). These types are confused not only by research users more generally, but also by experts in the field of Scientometrics and others, where researchers specifically research these systems. This lack of awareness illustrates how urgently we need to start understanding academic search: the search types, the heuristics, and the search systems—to find more, faster, and with less bias.

Call to action : We must raise awareness across research communities—among students, educators, journal editors, university teaching boards, and interest organizations—of the intricacies of academic searching and how it can be improved. Organizations like the Collaboration for Environmental Evidence, 38 Campbell Collaboration, 28 and Cochrane 39 can play important roles in creating awareness for the intricacies of academic search by updating their guidance to include more nuanced academic search advice. Additionally, academic journals must ensure that editors and peer‐reviewers are aware of the importance of robust search methods to encourage more rigor in academic searching (even more so as evidence synthesis become increasingly valued and prevalent). Only with this awareness, we can adequately link search goals to appropriate heuristics and systems to perform “good science”:

• It starts with the users ' goals : Raising awareness so users understand what goals they want to reach with their searching and with which (implicit) scientific standards the specific search types (lookup/exploratory/systematic) are associated.
• Search types : Raising awareness that searching is not always a quick “just Google (Scholar) it,” but in fact can be described by a “Search Triangle” that needs a matching of search goals/types with heuristics and systems (see Figure ​ Figure1 1 ).
• Search heuristics : Raising awareness that we could use better methods in searching databases and should be designing our searches around suitable heuristics that allow us meeting our diverse search goals.
• Search systems : Raising awareness that search systems are all different, not only in coverage, but also in the functions they offer and (equally important) they do not offer. It is also vital to understand that searches can be biased through the use of algorithms to adjust the order of records in search results. 40 In the context of systematic reviews, ensuring transparent and adequate reporting of which systems are searched must be a key responsibility of research authors, editors, and peer‐reviewers. Systems to support reporting of this level of detail are available (eg, PRISMA‐S 41 ) and should be adapted to all forms of research involving searching, not just systematic reviews.

4.2. Better search education: Toward search literacy as the norm

To build search literacy that enables quick choices of both heuristics and systems given an imminent information need involves more than the day‐to‐day search experience we researchers have at hand. Instead, it requires targeted search education . Such education has been shown to significantly improve search quality. 32 , 42 Without anchoring search education in research curricula, much scholarly search effort will remain wasted. 43 , 44

Call to action : we must make search literacy a priority in research education:

• What needs to be taught? Since many researchers think their current search practices and systems suffice, we need to raise awareness about problems associated with search illiteracy 45 in combination with showing better ways of searching. The teaching objective should be to improve knowledge and skills on how to effectively and efficiently find, evaluate, manage, and use information. Taught concepts should include matching: (a) user goals/search types, (b) search heuristics, and (c) search systems. Among others, this includes awareness for the importance of adequate language to describe concepts, the ability to formulate comprehensive, yet precise search strings and the skills to search the most suitable systems.
• Who teaches it? University libraries can play a key role in making emergent and established researchers and professionals search literate. 46 In times where fewer people visit physical libraries, more advice is required in the online realm. The freed‐up resources of librarians and information specialists might be used to teach new formats to students and scholars about search.
• How can it be taught? Search literacy can be taught as stand‐alone course or extend existing teaching concepts on digital literacy or information literacy, particularly also in courses on evidence‐based research. 47 , 48 , 49 As many institutions lack libraries—particularly the ones from resource‐constrained environments—education should also be freely and easily accessible to all (ie, Open Education). Perhaps this could be organized most impactfully as self‐paced online training or freely licensed teaching materials that can be used and adapted by trainers across the world.

4.3. Toward fit‐for‐purpose search systems

No two‐search systems are identical, and none is perfect. The reason for the great popularity of some systems is not because of their adequacy for each of the three search types we describe, 2 but rather because of their ease of use in day‐to‐day research practices. In the last decade, the tremendous success of Google Scholar has shown that users generally want to search intuitively, with as little effort as possible. 17

In terms of functionality, two broad types of search systems exist at present: the traditional “comprehensive‐transparent” (eg, ProQuest, PubMed, Web of Science) and the newer “efficient‐slick” (eg, Google Scholar, Semantic Scholar). The first type allows users to specify their search to the greatest detail, while the second identifies relevant results quickly. The most popular systems are efficient‐slick, while it seems the traditional systems have focused on new features rather than low latency and accessibility. The mission statements of some popular and newly created semantic systems—including Microsoft Academic, Semantic Scholar, and Meta—can be summarized with: simpler and more efficient searching , faster results . Their aim is the fast satisfaction of researchers' information needs, without detours.

While this increase in search efficiency is generally positive, it comes at a cost. We see two fundamental problems: first, in these semantic search systems it is opaque algorithms that decide about the “right” information that is shown (either absolutely or by order). We currently have neither insight, nor control over these decisions. This is particularly problematic for systematic searching, where our study has shown that all semantic search systems in our sample failed to meet the requirements. 2 Second, we must stay alert as these efficient‐slick systems aim at transforming ‘inefficient’ exploratory searching into ‘efficient’ lookup searching (eg, through presentation of pre‐selected cues). This means exploratory searching (and thus learning) might be more and more crippled toward quick, unconsciously biased lookup searching (cherry picking) that users more and more expect when engaging with online systems. 50 To be innovative as an academic it is essential to build own mental models, to connect disconnected threads that have not been connected before—by neither machine nor human. If we reduce these “hermeneutic circles” for the sake of efficiency, we must be aware of the drawbacks. It clearly makes a difference if users are efficient in finding information on for example “the capital of Kiribati” or to which president to vote in the next election. While the first should be efficient (lookup), the latter should largely remain exploratory where users are presented with a balanced information diet. We must be careful and stay alert with systems that give us readymade answers. We must question the algorithms (AI, machine learning) and behavioral data that are used to create relevance rankings and thereby determine what researchers get to see and what not. 6 Unfortunately, it seems as if the greatest level of effort of many search systems does not go into what researchers need to accomplish in all their search tasks, but rather in making users satisfied (and not smarter) sooner.

We researchers need the best of both worlds to ensure the best research outcomes: we need efficient‐slick and comprehensive‐transparent. We claim that, at present, systems could do much more in different areas than fine‐tuning for the sake of efficient lookup searching—particularly in the realm of evidence synthesis.

Call to action :

• Greater transparency : Search functionalities – that is, what can(not) be done with a search system (see our paper 2 for details of how this can be quantified) need to become transparent. This can only be done through an independent assessment of the claims of search system providers—our study, for example, has shown that one out of four systems promoted the functioning of search options (ie, Boolean search) that we found was flawed. 2 Additionally, we need clarity in the algorithms that semantic search systems use to fine‐tune their search results to reflect on how this impacts research work. With transparency, users can make informed choices on which systems to choose and systems can benchmark to compete for users, all driving a healthy competition toward better options of search facilities.
• Toward fit‐for‐purpose—matching requirements with technical possibilities : Some of the limitations we academics are confronted with when using search systems exist because of a lack of communication between the technical (what is possible) and the applied (what is needed). We believe the tools and features of search systems would greatly benefit from effective guidance and feedback from the research community (besides the user testing, etc. they are already doing). By establishing clear rules (similar to what systematic search needs to fulfill), we can help to direct the improvement of search systems, and thereby improving access to future‐proof search functionalities. Here we need to involve information technology research methods that have a long history in investigating the performance of particular search features or technologies (eg, reinforcement learning, 51 interactive intent modeling, 52 query expansion 53 ). We do not need to reinvent the wheel, yet we need to improve communication between library science/evidence‐based research methodologists (the applied) and information technology research, and importantly: the search systems we use on a daily basis (the technical). Klopfenstein and Dampier 1 demonstrate that: first, there is much room for improvement of search system workflows, features, and supported heuristics. Second, cross‐database integration might make sense to combine strengths of different databases (the coverage of Google Scholar and the specialized features of PubMed). Third, transparent comparison of features across search systems can be key to improve the systems we have. To improve our systems, we need an understanding of the exact requirements systems need to have for specific search types. The academic community should rally around these definitions and search types and demand clarity on which systems are best suited for which type of searching.
• Organize change : To see real improvements in academic searching, we must coordinate around the issue of fit‐for‐purpose research discovery. Without organized pressure this will remain a top‐down decision process, where search organizations continue deciding on what systems we use without hearing the requirements of the academic community. The popular example is Google Scholar that has refrained from improving transparency despite the many calls from, for example, the Scientometrics community in recent years. 9 , 11 , 54 , 55 COVID‐19 has shown us that positive change is possible if the pressure and a sense of urgency is great enough: for example, search systems and publishing houses have met criticism of impeding efficient, Open Science by temporarily making COVID‐19 literature Open Access. 56 Thus, we need to decide how to organize the academic community to put pressure on search system providers to design their systems in such a way that supports the three different types of searching. Such demands for improvements are warranted and should be heard particularly by the systems we are (collectively) paying for through subscription fees. As a consequence, a great amount of effort (and thereby public money) could be saved if deliberately imposed barriers (such as view and download limits, paywall barriers, or data access restrictions) were to be removed and search functionalities improved.

5. CONCLUSION

The tremendous thirst for information on COVID‐19 by policy makers, managers, and the general public has triggered an avalanche of research. While this ever‐growing evidence base shows the academic system's capabilities to produce evidence rapidly and on tremendous scale, it has also triggered a COVID‐19 infodemic. The information overloaded researchers found across subjects and disciplines highlight the vital need to improve research discovery. Newly developed COVID‐19‐specific tools and repositories are certainly helpful, yet we also must carefully evaluate what these new technologies promise and why current systems are not already adequate. To fight the COVID‐19 infodemic—and in fact all infodemics—we argue it is essential to foremost fix how we search for scholarly evidence on a daily basis. This not only has the potential to improve search literacy across academic disciplines, but may also have spillover effects to a broader audience by educating students, organizations, and institutions.

Currently, we are at an exciting point in the development of informatics: an avalanche of research publications is being catalogued more comprehensively by an expanding suite of different bibliographic databases and research platforms (interesting developments include Dimensions.ai and The Lens). Intelligent research discovery systems make it easier than ever to identify research that is relevant to us. 9 However, it has been shown how relevance rankings direct science, a phenomenon that is aggravated with new the technologies of artificial intelligence and machine learning that introduce black‐box relevance rankings and auto‐suggestions to the daily scientific enterprise of millions of scholars. Before we have fully understood the cost of such efficient systems, we need to be cautious for how we use them. Without full understanding of the different types of searching and their requirements, users of search systems are increasingly at risk of identifying a biased or unrepresentative set of search results. 6 We must improve our understanding of the intricacies of searching and ensure search systems are specifically designed to tackle all modes of searching: only then can we conduct research with a more balanced information diet and make sure the evidence bases on which decisions are based are fit‐for‐purpose.

We currently see the greatest search issues in systematic searching : both in terms of the inadequate systems we have at hand and the uneducated researchers that use them. If the available search systems were specifically tailored to the needs of search‐literate researchers, the evidence we could produce would be of significantly greater validity and at significantly lower cost. Facilitating and thus accelerating the creation of systematic reviews could particularly help in times of crises—such as we experience today with COVID‐19.

We hope the clarification of academic search concepts, the advice in form of the “Search Triangle” model and our calls to action will help improving academic search. We hope our work informs decision making in academic searching and might prove useful in structuring and conducting search education toward search literacy as a methodical skill every academic exhibits and cherishes.

CONFLICT OF INTEREST

The author reported no conflict of interest.

a Searching for “COVID‐19,” a suggested keyword by Semantic Scholar ( https://www.semanticscholar.org/search?q=COVID-19&sort=year ), accessed on 1 September 2020.

b Isearch was accessed with a blank query to access all records on the database. ( https://icite.od.nih.gov/covid19/search/#search:searchId=5f4dff240e329a34eac4e89f ), 60 297 records as of 3 August 2020, 47 514 between 1 May and 31 August 2020, accessed on 1 September 2020.

DATA AVAILABILITY STATEMENT

UC experts present at national neurology conference

University of Cincinnati researchers will present abstracts at the American Academy of Neurology annual meeting 2024, April 13-18 in Denver, Colorado.

Two-component treatment leads to improvement for patients

Hani Kushlaf, MD. Photo/University of Cincinnati.

Late-onset Pompe disease (LOPD) is a rare, inherited genetic disease caused by the accumulation of glycogen, the body’s stored form of glucose, in muscles and other organs. Left untreated, the muscle weakness it causes can lead to the loss of the ability to walk and breathing impairment.

A research team led by UC’s Hani Kushlaf, MD, looked at the effect of a two-component enzyme replacement therapy (ERT) of drugs cipaglucosidase and miglustat (cipa+mig) compared to a single ERT drug, alglucosidase alfa (alg) and a placebo. UC researchers participated in the Phase 3 trials that led to the Food and Drug Administration approval of both ERT regimens.

“The research question was to look at the magnitude and practical significance of the effect of cip+mig versus alg using patient data from the PROPEL trial on outcomes that included motor function, pulmonary function, muscle strength, biomarkers and patient- and physician-reported quality of life,” said Kushlaf, associate professor and director of Neuromuscular Research and the Neuromuscular Disorders Division in UC’s Department of Neurology & Rehabilitation Medicine in the College of Medicine.

Patients who switched to the dual ERT regimen experienced improvement or stability across the measured outcomes with no worsening of outcomes. Those who remained on the single ERT drug plus placebo experienced worsening or stability across the measured outcomes.

“This analysis highlights the potential of cipaglucosidase+miglustat to become an important treatment option for patients with LOPD, including patients already on enzyme replacement therapy,” Kushlaf said.

This research was sponsored by Amicus Therapeutics, Inc.

Research team learns more about events following immunotherapy treatment

Luca Marsili, MD, PhD. Photo/University of Cincinnati.

Immune checkpoint inhibitors (ICIs), an immunotherapy that activates the body’s immune system to fight cancer cells, has revolutionized cancer treatment. But while boosting anti-tumor immunity, the treatments may cause severe neurological-immune related adverse events. 

“These neurological-immune-related adverse events include meningitis, encephalitis, demyelinating diseases, vasculitis, neuropathy, neuromuscular junction disorders and myopathy,” said Luca Marsili, MD, PhD, movement disorder fellow in the Department of Neurology and Rehabilitation Medicine in the University of Cincinnati College of Medicine.  

Marsili said the frequency of these adverse events, and the best way to manage them, is still largely unknown. 

A team led by Marsili and Alberto Espay, MD, reviewed reported neurological-immune-related adverse events in patients treated with immune checkpoint inhibitors at UC from 2011-2023. They found the adverse events are rare, affecting 28 patients out of 1,677 treated, or 1.66%. 

The adverse events were most often associated with melanoma treatment with pembrolizumab, a common immunotherapy treatment. 

“The adverse events were most expressed as peripheral neuropathies and encephalitis, manifesting early during treatment within a mean of 2.3 months after ICI initiation,” Marsili said. “Most ICIs, 68%, were discontinued, and in only 10.7% of cases they were restarted without complications.” 

Moving forward, the team said further research is needed to determine clinical susceptibility factors and appropriate timing of restarting ICI treatment after discontinuing due to an adverse event. They are also planning to do more detailed demographic and clinical comparisons of the 28 patients identified to have adverse events to see if there are any predictive factors like tumor type, age, sex or ethnicity. 

“This study is part of a broader project in collaboration with the University of Udine in Italy and with the Department of Internal Medicine at UC,” Marsili said. “We would like to gather a high number of participants to assess incidence/prevalence of these adverse events and also to raise awareness among neurologists on how to treat/manage them.”

Safe, effective treatment for Parkinson’s

Alberto Espay, MD. Photo/University of Cincinnati.

Alberto Espay, MD, will present findings recently published in the Lancet Neurology journal that found Parkinson’s disease medication delivered through an infusion pump is safe and effective at reducing symptoms for longer periods of time. 

Parkinson’s symptoms such as tremors, slowness and stiffness are caused by low levels of dopamine in the body. For decades, doctors have treated Parkinson’s by giving patients levodopa, the inactive substance in the brain that once converted makes dopamine.  

“Levodopa is a replacement strategy. We all make levodopa, but Parkinson's patients make less of it,” said Espay, co-principal investigator of the trial, the James J. and Joan A. Gardner Family Center for Parkinson’s Disease Research Endowed Chair in UC’s Department of Neurology and Rehabilitation Medicine and a physician at the UC Gardner Neuroscience Institute.  

Levodopa is most commonly administered orally, but this trial tested continuous, 24-hour levodopa delivery through a subcutaneous infusion pump. A total of 381 patients with Parkinson’s disease in 16 countries enrolled in the trial and were randomized to receive levodopa through the infusion pump or through traditional oral medication. 

The researchers found levodopa delivered through the infusion pump was safe and led to almost two hours a day (1.72) of additional “on time,” or the time when the medication is working and symptoms are lessened, compared to taking levodopa orally. 

“Once approved, this will become an important treatment strategy to consider for patients with Parkinson’s disease experiencing motor fluctuations not adequately controlled with medication,” he said. “Future studies will need to determine the durability of the long-term benefits and whether any safety issues could emerge, as well as how it might compare with deep brain stimulation.”

Impact Lives Here

The University of Cincinnati is leading public urban universities into a new era of innovation and impact. Our faculty, staff and students are saving lives, changing outcomes and bending the future in our city's direction.  Next Lives Here.

UC research being presented at AAN includes:

• Kushlaf presenting “ Effect Size Analysis of Cipaglucosidase Alfa Plus Miglustat Versus Alglucosidase Alfa in ERT-experienced Adults with Late-onset Pompe Disease in PROPEL .”
• Marsili and Espay presenting “ Neurological Immune-related Adverse Events of Immune Checkpoint Inhibitors: A Single-center Retrospective Study. ” 
• Espay presenting “ Efficacy of ND0612, a 24-hour Subcutaneous Levodopa/Carbidopa Infusion for People with Parkinson’s Disease Experiencing Motor Fluctuations: Subgroup-analyses from a Randomized, Controlled Phase 3 Study .”
• Stacie Demel, DO, PhD, a physician-researcher at the UC Gardner Neuroscience Institute and associate professor of clinical neurology and rehabilitation medicine in UC’s College of Medicine, presenting “ Methylation Patterns Differ Between ICH Cases and Controls .”
• Yang Yu, MD, UC medical resident/fellow, presenting “ Multiple Sclerosis in a Patient with Friedreich's Ataxia .”
• Rhonna Shatz, DO, adjunct associate professor, division director for behavioral neurology, and the Bob and Sandy Heimann Endowed Chair in Research and Education in Alzheimer’s Disease in the UC College of Medicine, presenting “ Identifying a Relationship Between Executive Dysfunction, Poor Sleep Hygiene/Sleep Apnea, and Ventriculomegaly in Cancer-related Cognitive Impairment (CRCI) ”

Featured illustration at top of brain. Photo/iStock/Onurdongel.

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Ted Hesson is an immigration reporter for Reuters, based in Washington, D.C. His work focuses on the policy and politics of immigration, asylum and border security. Prior to joining Reuters in 2019, Ted worked for the news outlet POLITICO, where he also covered immigration. His articles have appeared in POLITICO Magazine, The Atlantic and VICE News, among other publications. Ted holds a master's degree from the Columbia University Graduate School of Journalism and bachelor's degree from Boston College.

Mica Rosenberg leads the immigration team at Reuters, reporting her own projects while helping edit and coordinate cross-border coverage. An investigation she published with colleagues into child labor in the United States – exposing migrant children manufacturing car parts and working in chicken processing in Alabama – was a finalist for the Pulitzer Prize and won a George Polk award among other honors. She was a foreign correspondent reporting from nearly a dozen countries across Latin America and also covered legal affairs and white-collar crime in New York. She completed a Knight Bagehot Fellowship in business journalism and earned a master’s from Columbia’s School of International and Public Affairs. She is originally from New Mexico and is based in Brooklyn.

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PricingService.ai Democratizes Dynamic Pricing in the Hospitality Industry

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His academic and professional journey is rooted in the study and application of pricing optimization, a field he has contributed to since his doctoral studies. Starting with revenue management for airlines and the hotel industry, he expanded his expertise into consulting for various sectors, including e-commerce and ridesharing, showing the versatility and applicability of pricing optimization across industries.

A pivotal moment in his career came with his work for DiDi, a leading ride-hailing company in China, which illuminated the broader potential applications of pricing optimization. He observed that while industries like airlines had been pioneers in pricing optimization since the 1970s, sectors such as the hotel industry lagged behind, adhering to outdated practices despite the critical role of pricing optimization in their operations.

How Pricingservice.ai Levels the Playing Field with AI-Driven Pricing Optimization

Identifying a significant gap in the market, Dan saw an opportunity to bring advanced, AI-driven pricing optimization to independent hotels, which represent about two-thirds of the market yet lack the sophisticated pricing strategies of their franchised counterparts. Many of these hotels, he noted, rely on rudimentary pricing due to the prohibitive costs of acquiring and maintaining advanced pricing systems.

To address this challenge, he envisioned a subscription-based service offering cutting-edge, AI-powered pricing optimization delivered via the cloud. This innovative approach led to the creation of pricingservice.ai, which democratizes access to advanced pricing strategies, making them accessible and affordable even for smaller, independent hotels. By leveraging cloud technology, the service eliminates the need for hotels to hire dedicated personnel or manage complex software systems, significantly reducing costs and leveling the playing field in the hospitality industry.

In 2021, Dan co-founded Pricingservice.ai and secured its first customer in December of the same year, with the company officially forming in 2023. The co-founder's background as an Associate Dean of Research and Academics at Leeds, specializing in advanced data analysis and holding a keen interest in addressing substantive business problems through research, played an important role in the venture's creation. The motivation to create a real-world impact led to collaboration with two other co-founders: Professor David Li at the City University of Hong Kong and Matt Schwartz, the Chief Technology Officer at SageHospitality. Their collective expertise and the recognition of an opportunity in the hotel industry were instrumental in launching this innovative pricing service.

Historically, setting the right prices for hotel rooms was a grueling task, burdened by the need to consider countless variables ranging from event schedules to weather patterns. The use of AI in this domain has revolutionized the process, enabling automated systems to make pricing decisions with minimal human intervention. PricingService.ai's system exemplifies this shift by integrating with leading property management systems, such as those provided by Oracle, to offer dynamic pricing updates. This integration allows the system to adjust hotel room prices every few hours based on a comprehensive analysis of data, including historic booking patterns, upcoming events, competitor pricing, and even weather forecasts. This level of responsiveness was previously unimaginable, as manual adjustments in response to rapid market changes, like a surge in demand due to a Taylor Swift concert, would be too slow and impractical.

The system was developed from scratch, and incorporates custom algorithms designed to optimize hotel pricing effectively. This AI-driven tool not only enhances the efficiency of pricing strategies but also ensures that hotels can respond to market dynamics swiftly, maximizing revenue and staying competitive in a fast-paced industry. PricingService.ai is not just about leveraging AI for better pricing decisions; it's also a symbol of redefining the future of hotel revenue management by making it more adaptive, intelligent, and efficient. Through their innovative use of AI, the founders envision a new paradigm for the hotel industry, where data-driven insights lead to smarter business decisions and ultimately, greater success in a highly competitive marketplace.

A hot topic in today’s daily conversations, Dan stresses the importance of Artificial Intelligence in our day-to-day lives, and viewing it as a tool rather than the enemy, or something that may “replace” jobs in the future: 

“We use what we have for the people, not against the people. The idea is to get that idea back, man working seamlessly with machines.” 

Transforming Research into a Revenue Boost

Starting a business can happen by chance, not just by planning. Dan’s story is one of a researcher who didn't set out to be a business owner, but ended up becoming one because of his desire to make his research useful to others. His work led to creating a tool that helps small hotels set their prices using advanced technology, a task usually too expensive for them to manage on their own.

The idea came to life after noticing a lot of interest from tech companies in his research. These companies saw value in his work and even offered him jobs, but he chose to focus on helping smaller hotels instead. He wanted to use his skills to make a real difference, allowing these smaller players to compete by offering them a low-cost, high-tech solution for setting their room prices.

Dan notes that one of the best parts of this adventure has been seeing how much his tool has helped. For example, a small hotel in Yorktown, Virginia, managed to increase its revenue by 25% thanks to the tool. This hotel used to charge $199 for rooms in July, but now they can charge up to $450, all because the tool helps them set the best prices without needing to hire extra staff.

His story shows that starting a business isn't just about wanting to be an entrepreneur. It's also about wanting to do something meaningful with what you know and can do. Helping others succeed and making a positive impact can be the most rewarding parts of being in business.

The business was recognized with a EX20 People’s Choice Award at HITEC Toronto in June 2023, the largest hospitality technology conference in the world. More recently, PricingService.ai participated in both NVC Deep Tech Competition and NVC General Competition at CU Boulder and is the only team that is a finalist in both competitions. The final showcase for NVC General Competition will be on April 17 at the Boulder Theater.  

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Bringing chemistry to medicine to redefine the undruggable

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Chemical approaches, such as those that leverage induced proximity, targeted degradation, synthetic gene regulators or protein design offer opportunities to therapeutically target cellular processes that have long been thought of as undruggable. We report on the progress and the potential for transformative collaborations between fields discussed at the 2023 Bringing Chemistry to Medicine symposium at St. Jude Children’s Research Hospital.

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