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6 results for “Microsoft Academic”
EMAKG: an enriched version of the Microsoft Academic Knowledge Graph
<p>The <strong>Enhanced MAKG</strong> (<strong>EMAKG</strong>) provides an updated and enriched version of the Microsoft Academic Knowledge Graph (MAKG).</p> <p>The EMAKG is a large dataset of <strong>scientific publications</strong> and related entities such as <strong>authors</strong>, <strong>affiliations</strong>, <strong>venues</strong>, and <strong>fields of study</strong>. Data includes <strong>authors' careers and networks of collaborations</strong>, <strong>linguistics features</strong>, together with <strong>worldwide yearly authors' stocks and flows</strong>.</p> <p>The EMAKG data is mainly based on the MAKG - <strong>Version 2020-06-19 (March 25, 2021)</strong>.</p> <p>Methods: <a href="https://github.com/LauraPollacci/EMAKG">https://github.com/LauraPollacci/EMAKG</a></p> <p><em>Version 0.0 (reduced version)</em><br> Version 0.0 provides a set of EMAKG subsets, some of which are in abridged form:</p> <p>01.AffiliationsGeo: Affiliations subset.<br> 03.ConferenceInstances: Conferences subset.<br> 04.Conference Series: ConferenceSeries subset. <br> 05.Journals: Journals subset. <br> 06.24.PaperAuthorAffiliations_Disambiguated: Relationships between papers and disambiguated authors. <br> 09.PaperResources: URLs and resources of publications.<br> 10.Papers: Papers subset.<br> 12.EntityRelatedEntities: Connections between entities.<br> 13.FieldOfStudyChildren: Field of study kinship relations.<br> 14.FieldOfStudyExtendedAttributes: Fields of study co-references between different datasets.<br> 15.FieldsOfStudy: Fields of study subset.<br> 16.PaperFieldsOfStudy: Relationships between papers and fields of study.<br> 18.RelatedFieldOfStudy: Relationships between symptoms, medical treatments, disease causes and fields of study.<br> 19.PaperCitationContexts: Contexts of citations in CiTo.<br> 20.AbstractsProcessed_Chunk0-14: Chunk of processed abstracts.<br> 22.FieldOfStudyLabeled: Tags and scores of fields of studies.<br> 23.Authors_disambiguated: Disambiguated authors subset.<br> 24.PaperAuthorAffiliation_Disambiguated: Relationships between papers, disambiguated authors and affiliations.<br> 25.AuthorORCID: Authors' ORCIDs.<br> 26.AuthorCareer: Authors' yearly publications.<br> 27.AuthorYearLocation: Authors' yearly locations.<br> 28.AuthorEgoNetworks_2000-2014: Authors' ego networks from 2000 to 2014.<br> 29.CountryAnnualFlowsAggregated: Flows aggregated by country and year.<br> 30.FlowsAnnual: Annual country to country flows.<br> 31.StocksAnnual: Annual stocks aggregated by country.<br> 32.PaperFieldsOfStudyLabeled: Publications tagged with fields of studies.<br> 33.Authors_disambiguated_Hindex: H-index of disambiguated authors.</p>
Data set of the article: Ranking by relevance and citation counts, a comparative study: Google Scholar, Microsoft Academic, WoS and Scopus
<p>Data of investigation published in the article "Ranking by relevance and citation counts, a comparative study: Google Scholar, Microsoft Academic, WoS and Scopus".</p> <p>Abstract of the article:</p> <p>Search engine optimization (SEO) constitutes the set of methods designed to increase the visibility of, and the number of visits to, a web page by means of its ranking on the search engine results pages. Recently, SEO has also been applied to academic databases and search engines, in a trend that is in constant growth. This new approach, known as academic SEO (ASEO), has generated a field of study with considerable future growth potential due to the impact of open science. The study reported here forms part of this new field of analysis. The ranking of results is a key aspect in any information system since it determines the way in which these results are presented to the user. The aim of this study is to analyse and compare the relevance ranking algorithms employed by various academic platforms to identify the importance of citations received in their algorithms. Specifically, we analyse two search engines and two bibliographic databases: Google Scholar and Microsoft Academic, on the one hand, and Web of Science and Scopus, on the other. A reverse engineering methodology is employed based on the statistical analysis of Spearman’s correlation coefficients. The results indicate that the ranking algorithms used by Google Scholar and Microsoft are the two that are most heavily influenced by citations received. Indeed, citation counts are clearly the main SEO factor in these academic search engines. An unexpected finding is that, at certain points in time, WoS used citations received as a key ranking factor, despite the fact that WoS support documents claim this factor does not intervene.</p>
Large-scale comparison of bibliographic data sources: Scopus, Web of Science, Dimensions, Crossref, and Microsoft Academic
<p>This data set contains supplementary material for the paper 'Large-scale comparison of bibliographic data sources: Scopus, Web of Science, Dimensions, Crossref, and Microsoft Academic' by Martijn Visser, Nees Jan van Eck, and Ludo Waltman. The data set provides the statistics presented in the figures in the paper.</p>
Microsoft Academic Graph
<p>This is the Microsoft Academic Graph data from 2021-09-13. To get this, you'd normally jump through these hoops: <a href="https://docs.microsoft.com/en-us/academic-services/graph/get-started-setup-provisioning">https://docs.microsoft.com/en-us/academic-services/graph/get-started-setup-provisioning</a></p> <p>As required by <a href="https://opendatacommons.org/licenses/by/1.0/">ODC-BY</a>, I acknowledge Microsoft Academic using the URI <a href="https://aka.ms/msracad">https://aka.ms/msracad</a>.</p> <p>You can find out more about the data schema of the Microsoft Academic Graph at: <a href="https://web.archive.org/web/20220218202531/https://docs.microsoft.com/en-us/academic-services/graph/reference-data-schema">https://web.archive.org/web/20220218202531/https://docs.microsoft.com/en-us/academic-services/graph/reference-data-schema</a><br> Since Microsoft docs are covered by <a href="https://docs.microsoft.com/en-us/legal/termsofuse">different licensing terms</a>, the documentation cannot be provided along with the data.</p> <p>There were no changes to the files except compressing them with zstd (-T8 -19). This results in a smaller packed size, but still more data than the previous version.</p> <p>The compressed files will expand to the following sizes (output of zstd -l):</p> <pre><code>Compressed Uncompressed Ratio Filename 1.39 MiB 5.30 MiB 3.822 Affiliations.txt.zst 4.45 MiB 15.7 MiB 3.518 AuthorExtendedAttributes.txt.zst 4.29 GiB 17.4 GiB 4.052 Authors.txt.zst 575 KiB 2.55 MiB 4.530 ConferenceInstances.txt.zst 126 KiB 453 KiB 3.598 ConferenceSeries.txt.zst 1.56 MiB 5.96 MiB 3.820 Journals.txt.zst 12.5 GiB 51.7 GiB 4.137 PaperAuthorAffiliations.txt.zst 687 MiB 2.76 GiB 4.116 PaperExtendedAttributes.txt.zst 7.32 GiB 40.5 GiB 5.530 PaperReferences.txt.zst 1.23 MiB 9.72 MiB 7.894 PaperResources.txt.zst 18.5 GiB 72.0 GiB 3.898 Papers.txt.zst 5.59 GiB 34.7 GiB 6.203 PaperUrls.txt.zst -------------------------------------------------- 48.9 GiB 219 GiB 4.484 XXH64 12 files </code></pre> <p>This data is not the whole set what you get to download, there is much more (roughly 160GiB compressed), but the upload quota only permits this much. The additional data is retained and you may ask for it. The additional data is huge, so be prepared to provide sftp, rsync or similar access to drop the files in.</p> <p>If you want to <strong>donate an update</strong> but lack the bandwidth to download and repack the set, feel free to contact me (details via my <a href="https://orcid.org/0000-0001-8453-5026">ORCiD page</a>), once you have gone through the provisioning steps. I'll either grab the set directly from the azure storage (you might have to give me access rights) or provide an sftp/rsync drop for you to dump the data in.</p> <p>The data for version 2021-09-13 was kindly contributed by <a href="https://orcid.org/ 0000-0002-6021-804X">Rudolf Siegel</a></p>
Microsoft Academic data set for RPYS analysis
<p>Microsoft Academic data set for RPYS analysis, used in "Discovering seminal works with marker papers", see https://arxiv.org/abs/1901.07352</p> <p>Please see also:</p> <p>https://aka.ms/msracad</p> <p>Arnab Sinha, Zhihong Shen, Yang Song, Hao Ma, Darrin Eide, Bo-June (Paul) Hsu, and Kuansan Wang.<br> 2015. An Overview of Microsoft Academic Service (MA) and Applications. In Proceedings of the 24th<br> International Conference on World Wide Web (WWW '15 Companion). ACM, New York, NY, USA,<br> 243-246. DOI=http://dx.doi.org/10.1145/2740908.2742839</p>
Microsoft Academic Graph dataset specific to 8 journals
<p>Partial Microsoft Academic Graph dataset including all publications related to a given journal.</p>
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