Responding to COVID-19: Weaver Library is open Sunday through Thursday 9am-9pm, and Friday and Saturday 9am-5pm. You can also contact us by chat, text, and email during those hours. We’re providing limited services in the Main Library lobby. The Health Sciences Library is open to Health Sciences affiliates.Learn more about access during COVID-19.
While Scopus is the closest equivalent to Web of Science, there are a number of other databases and indexers that you can use to discover scholarly content.
Google Scholar has many advantages over Web of Science and Scopus, such as:
Includes all types of documents - e.g., tutorials, posters, presentations
Finds more citations in most subject areas
Includes book coverage via Google Books and free online publications.
More international and interdisciplinary coverage
Google Scholar can be optimized for off-campus use. Follow these configuration steps to access UA-licensed content from off-campus:
In Google Scholar, open the "hamburger" menu in the far upper left next to the Google Scholar logo and select "Settings.
From the settings menu, select "Library links".
Type "University of Arizona" in box and select "University of Arizona - Full-Text @ UofA Libraries". Then click Save.
When searching, articles for which we have full text access will display with a link "Full-Text @ UofA Libraries"; select this link to access it. (Please note that this link may be hidden in the smaller links below the citation, and you may need to click on the double arrow to the right of them to find it.)
To export citations to a bibliography manager, such as RefWorks or EndNote, go to the Settings menu, click on "search results", select the desired manager in the drop-down menu next to "show links to import citations into", and Save.
To display Web of Science citations counts, use this link to access Google Scholar.
The Lens is an open global cyberinfrastructure that aims to make the innovation system more efficient, fair, transparent, and inclusive. It combines both global patent and scholarly knowledge into a public resource. Lens serves over 200 million scholarly records, compiled and harmonized from Microsoft Academic, PubMed and Crossref, enhanced with UnPaywall open access information, CORE full text, and links to ORCID. All Lens data is fully open, shareable, and reusable.
Lens.org offers robust discovery, analytics and management tools, including APIs for scholarly works, patents, and patent sequence data, Users can create their own accounts, enabling them to search, analyze, and receive updates within a private and secure online platform. Users can create/save queries, create collections, customize data analysis and visualizations, create interactive reports, and download up to 50,000 records at a time. Applications within Lens.org include:
PatCite – enables discovering linkages between patents and scholarship
In4M – maps and shows how scholars and their institutions have influenced other research and societal outcomes
Collections – allows users to create, publish, and share curated collections of patents and scholarly works
PatSeq – allows exploration and analysis of biological sequences in patents
Lens.org is a project of Cambia, an independent non-profit based in Australia.
Microsoft Academic (MA) is a semantic search engine powered by Microsoft Academic Graph (MAG) and Microsoft Academic Knowledge Exploration Service (MAKES) APIs. Microsoft Academic (MA) uses advances in machine learning, semantic inference, and knowledge discovery to help you explore scholarly information in more powerful ways than mere keyword matching. Though e the online search options are not as robust as some other platforms, MA has strong coverage across the disciplines.
MA uses artificial intelligence (AI) to scan and extract knowledge from all scholarly publications discovered and indexed by Bing. Bing indexes data from a variety of sources ranging from publisher sites to individual authors' personal homepages. MA's AI agent then cleans and organizes these data into a graph database we call the Microsoft Academic Graph (MAG).
MA extracts information about publication venues such as journals automatically from publications and their metadata. MA indexes journals in a variety of disciplines. Journals are more likely to be included in MA if they are easily discoverable on the Web.
MA extracts author affiliation information automatically from publications and their metadata. Authors’ affiliations are listed as they appeared at the time of publication. Authors who have had multiple affiliations may therefore have multiple profiles. Authors can consolidate their publications under one profile by using the claim feature.
MA identifies individual authors using a variety of signals such as name, affiliation, co-authors, and publication topics. It uses large scale entity linking to extract more than 200 million unique authors from 62 million names.
MA gathers publications from across the web and partnered sources and these are the heart of the MAG. Data from these publications is extracted to generate and inform other entities in the graph (like Authors for instance). Publications can be claimed by Microsoft Academic users to build a profile and improve the quality of the graph as well.
MA extracts information about publication venues such as conferences automatically from conference proceedings and their metadata. MA indexes conferences that publish proceedings and therefore have discoverable publications on the Web. See a full calendar of conference and their important date on your personal home page once you sign in.
MA tags publications with fields of study, or topics, using artificial intelligence and semantic understanding of content. Topics are organized in a non-mutually exclusive hierarchy with 19 top-level fields of study.
Unpaywall is an open database of over 26 million free scholarly articles harvested from over 50,000 publishers. Unpaywall has a free browser extension for Firefox and Chrome. You can also access the Unpaywall data for research using the REST API, the R API wrapper, the Simple Query Tool, or download the whole dataset.