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15 results for “Scholarly articles”
Scholarly journals publishing articles by family and community physicians in Brazil, up to December 2018
<p>This is the dataset of manuscript titled "In which journals do family and community physicians in Brazil publish? The <em>Trajetórias MFC</em> project". There are two spreadsheets: the dataset proper and the data dictionary. See the manuscript for background.</p> <p>All spreadsheets are in the CSV (comma-separated values) format, delimited with semicolons and encoded in UTF-8 with the byte-order mark (BOM). The spreadsheets can be opened with desktop or Web application software (LibreOffice Calc, Microsoft Excel, Google Sheets) or with statistical software such as R.</p> <p>A <a href="https://zenodo.org/record/3905255">previous version</a> of this dataset was used in a <a href="https://doi.org/10.1101/19005744">preprint</a>. This version should be cited by an upcoming article.</p> <p>See also the <a href="https://doi.org/10.1136/fmch-2020-000321">article</a>, <a href="https://doi.org/10.5281/zenodo.3376310">dataset</a> and <a href="https://doi.org/10.5281/zenodo.3381576">supplementary table</a> for an earlier milestone, about the postgraduate education of family and community physicians in Brazil.</p>
Dataset for Creating a Scholarly Knowledge Graph from Survey Article Tables
<ul> <li><strong>Selected papers.csv</strong><br> This file lists all selected survey papers used to create the knowledge graph. The file contains paper titles, table references (of the tables that are extracted), sources and the reference to the survey paper (either a DOI, or a full textual reference)<br> </li> <li><strong>ORKG comparisons.csv</strong><br> All comparisons imported in the ORKG are listed in this file. Per survey paper, multiple tables could be extracted, and therefore multiple comparisons are created. The files lists the internal IDs and the URL to the comparisons. <br> </li> <li><strong>Ingested papers.csv</strong><br> A full list of all individual papers extracted from the survey articles. The file contains paper titles and their respective URL in the ORKG. </li> </ul>
Yearly pageviews of English Wikipedia articles with potential links to green open access scholarly articles
<p>Number of visits in 2019 for a sample of 23462 English Wikipedia articles which contain references to academic sources which have a green open access copy available but not yet used. The consultation statistics were retrieved from the Wikimedia pageviews API using the Python client (script also included). The sample was selected among articles which in April 2020 had at least one citation of an academic paper (using the "cite journal" template) for which OAbot (through Unpaywall data) had found a green open access URL to add (gratis open access, not necessarily libre open access). Data shows that the top 1 % most visited articles received 30 % of the visits: over 500 million in the year, corresponding to 1 million potential citation link clicks to distribute across all references assuming a 0.2 % click-through rate per Piccardi et al. (2020).</p>
Scholarly articles written by women extracted from Indexes of Archaeological Papers (1891-1907) and Gomme's Index of Archaeological Papers 1665-1890
<p>The dataset `List-of-Women-in-Archaeological-Indexes_cleaned.tsv` contains scholarly articles written by women extracted from annual <em>Indexes of Archaeological Papers</em> published between 1891 and 1907 inclusive and George Laurence Gomme's <a href="https://archive.org/details/indexofarchaeolo00gommrich/page/n5/mode/2up"><em>Index of Archaeological Papers 1665-1890</em></a>, referred to hereafter as the source datasets. These Indexes were published in London, initially by the Congress of Archaeological Societies directly, and from 1898 by Archibald Constable & Co. The Indexes were sent to Societies subscribing to the Congress, but could also be acquired separately. A list of indexes consulted in available on our <a href="https://www.zotero.org/groups/4509023/beyond_notability/collections/FDKSUL8J">Zotero library</a>.</p> <p>The dataset is published in .tsv and .xslx formats.</p> <p>This is v2 of the dataset, including some cleaned publication titles and the addition of the socities that published each journal (v2.1 fixes a faulty dataset export in v2).</p>
Machine Learning Articles Extracted from Google Scholar
<p>This dataset was created as part of a web scraping practice aimed at capturing academic information from Google Scholar. It contains data on <em>Machine Learning</em> research articles, including the article's title, authors, summary, direct link, citation count, and APA reference. This dataset was collected using Python and Selenium to develop skills in web scraping tools for extracting data from websites with dynamic content.</p> <p>The dataset was generated specifically as part of an academic exercise to learn and apply web scraping techniques, without a deep analysis intent for the data obtained. This dataset is intended as a resource for learning and evaluating the methods used in web data collection.</p> <p><strong>Included Fields</strong>:</p> <ul> <li><strong>title</strong>: Title of the research article.</li> <li><strong>link</strong>: Direct link to the article.</li> <li><strong>authors</strong>: Names of the article’s authors.</li> <li><strong>description</strong>: Summary or brief description of the article.</li> <li><strong>citations</strong>: Number of times the article has been cited on Google Scholar.</li> <li><strong>APA_citation</strong>: APA-formatted citation of the article.</li> </ul> <p>This dataset was created solely for educational purposes and to demonstrate the application of web scraping techniques in a controlled environment.</p>
Raw and aggregated data for the study introduced in the article "An analysis of citing and referencing habits across all scholarly disciplines: approaches and trends in bibliographic metadata errors"
<p>This dataset contains all the raw data and aggregated data subject of the study introduced in the article "An analysis of citing and referencing habits across all scholarly disciplines: approaches and trends in bibliographic metadata errors". The study is based on the bibliographic and citation data contained in 729 articles published in 147 journals in 27 subject areas. The articles contained a total amount of 34,140 bibliographic references and 55,100 mentions and quotations overall.</p> <p>The dataset is composed of a series of files:</p> <ul> <li>the files "subject_area_<discipline-name>.csv" contain the raw data of the articles published in the journals of all the disciplines considered in the study;</li> <li>the file "article_data_summary.csv" contains the aggregated data created considering the raw data in the previous files, which have been used to creating all the tables and figures in the article;</li> <li>the file "starred_metadata_set.csv" contains information about the most used subset of bibliographic metadata;</li> <li>the file "journals_selection.csv" contains information about all the journals selected for the study.</li> </ul>
List of articles resulting from the Google Scholar search "graph based author name disambiguation" published after 1/1/2021
<p>This dataset contains the list of articles resulting from the Google Scholar search “graph based author name disambiguation” published after 1/1/2021. The list is provided for reproducibility of the survey article “Graph-based Methods for Author Name Disambiguation: A Survey” and it was obtained using the following Python script available at <a href="https://github.com/WittmannF/sort-google-scholar">https://github.com/WittmannF/sort-google-scholar</a>:</p> <blockquote> <p>$ python sortgs.py --kw “graph based author name disambiguation” --startyear 2021</p> </blockquote> <p>The command returned the CSV file that contains the first 94 publications matching the query (articles with corrupted metadata have been excluded), each with metadata about Title, Number of Citations, and Rank. The CSV contains a column that specified which articles have been eventually selected for the survey.</p>
Data set of the article: Language Bias in the Google Scholar Ranking Algorithm
<p>Data of investigation published in the article Cristòfol Rovira; Lluís Codina; Carlos Lopezosa Language Bias in the Google Scholar Ranking Algorithm. Future Internet, 2021, 13.</p> <p><strong>Abstract: </strong>The visibility of academic articles or conference papers depends on their being easily found in academic search engines, above all in Google Scholar. To enhance this visibility, search engine optimization (SEO) has been applied in recent years to academic search engines in order to optimize documents and, thereby, ensure they are better ranked in search pages (i.e., academic search engine optimization or ASEO). To achieve this degree of optimization, we first need to further our understanding of Google Scholar’s relevance ranking algorithm, so that, based on this knowledge, we can highlight or improve those characteristics that academic documents already present and which are taken into account by the algorithm. This study seeks to advance our knowledge in this line of research by determining whether the language in which a document is published is a positioning factor in the Google Scholar relevance ranking algorithm. Here, we employ a reverse engineering research methodology based on a statistical analysis that uses Spearman’s correlation coefficient. The results obtained point to a bias in multilingual searches conducted in Google Scholar with documents published in languages other than in English being systematically relegated to positions that make them virtually invisible. This finding has important repercussions, both for conducting searches and for optimizing positioning in Google Scholar, being especially critical for articles on subjects that are expressed in the same way in English and other languages, the case, for example, of trademarks, chemical compounds, industrial products, acronyms, drugs, diseases, etc.</p>
Dataset of first appearances of the scholarly bibliographic references on English Wikipedia articles as of 1 March 2017 and as of 1 October 2021
<p><strong>Abstract</strong></p> <p>We developed a methodology to detect the oldest scholarly reference added to Wikipedia articles by which a certain paper is uniquely identifiable as the "first appearance of the scholarly reference." We identified the first appearances of 923,894 scholarly references (611,119 unique DOIs) in180,795 unique pages on English Wikipedia as of March 1, 2017, and stored them in the dataset. Moreover, we assessed the precision of the dataset, which was and it was a high precision regardless of the research field. In this version, it is available not only the dataset of English Wikipedia as of March 1, 2017, but also English Wikipedia as of October 1, 2021, generated by using the same methodology.</p> <p> </p> <p><strong>Data Records</strong></p> <p>The data format of the dataset is JSON lines, where each line is a single record. In this dataset, we detected the first appearance of each scholarly reference added to Wikipedia articles. If there are multiple references corresponding to the same paper on the same page, only the oldest one is collected. Sample of the record is the following.</p> <ul> <li>doi -- DOI corresponding to the paper (String), e.g., "10.1006/anbe.1996.0497"</li> <li>paper_type -- Document type of the paper (String), e.g., "journal-article"</li> <li>paper_container_title -- Journal title, book title, or proceedings title (Array of String), e.g., ["Animal Behaviour"]</li> <li>paper_publisher -- Publisher name (String), e.g., "Elsevier BV"</li> <li>paper_title -- Paper title (Array of String), e.g., ["Push or pull: an experimental study on imitation in marmosets"]</li> <li>paper_published_year -- Published year (String), e.g., "1997"</li> <li>paper_issue -- Issue number (String), e.g., "4"</li> <li>paper_volume -- Volume number (String), e.g., "54"</li> <li>paper_page -- Page numbers (String), e.g., "817-831"</li> <li>paper_author -- Authors information consisted of given and family names, sequences (order in author names), and affiliations (Array of JSON), e.g., [{"given":"THOMAS", "family":"BUGNYAR", "sequence":"first", "affiliation":[]}, {"given":"LUDWIG", "family":"HUBER", "sequence":"additional", "affiliation":[]}]</li> <li>issn -- ISSN related to the paper (Array of String), e.g., ["0003-3472"]</li> <li>research_field -- Research fields from ESI categories (Array of String), e.g., ["PLANT & ANIMAL SCIENCE"]</li> <li>page_id -- Page id (String), e.g., "577858"</li> <li>page_title -- Page title (String), e.g., "Imitation"</li> <li>revision_id -- Revision id (String), e.g., "203309031"</li> <li>revision_timestamp -- Revision timestamp (String), e.g., "2008-04-04 15:54:09 UTC"</li> <li>revision_comment -- Revision comment (edit summary) (String), e.g., "/* Animal Behaviour */"</li> <li>editor_name -- Wikipedia editor's name (String), e.g., "Nicemr"</li> <li>editor_type -- Type of the editor (String), e.g., "User"</li> </ul> <p><strong>References</strong></p> <ul> <li>Kikkawa, J., Takaku, M. & Yoshikane, F. "Dataset of first appearances of the scholarly bibliographic references on Wikipedia articles", Scientific Data, Vol. 9, Article number 85, pp. 1-11, 2022. <a href="https://doi.org/10.1038/s41597-022-01190-z">https://doi.org/10.1038/s41597-022-01190-z</a>.</li> </ul> <p><strong>FUNDING</strong></p> <ul> <li>JSPS KAKENHI Grant Number <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-20K12543">JP20K12543</a></li> <li>JSPS KAKENHI Grant Number <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-21K21303">JP21K21303</a></li> </ul>
Data for "Researchers and their data. A study based on the use of the word data in scholarly articles"
<p><em>Data</em> is one of the most used terms in scientific vocabulary. This article focusses on the relationship between data and research by analyzing the contexts of occurrence of the word <em>data</em> in a corpus of 72,471 research articles (1980-2012) from two distinct fields (Social sciences, Physical sciences). The aim is to shed light on the issues raised by research on data, namely the difficulty of defining what is considered as data, the transformations that data undergo during the research process and how they gain value for researchers who hold them. Relying on the distribution of occurrences throughout the texts and over time, it demonstrates that the word <em>data </em>mostly occurs at the beginning and at the end of research articles. Adjectives and verbs accompanying the noun <em>data</em> turn out to be even more important than <em>data</em> itself in specifying data. The increase in the use of possessive pronouns at the end of the articles reveals that authors tend to claim ownership of their data at the very end of the research process. Our research demonstrates that even if data handling operations are increasingly frequent, they are still described with imprecise verbs that do not reflect the complexity of these transformations.</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>
LG-covid19-HOTP: Literature Graph of Scholarly Articles Relevant to COVID-19 Study
<p>Parallel to the dataset <a href="http://dx.doi.org/10.5281/zenodo.3727291">CORD-19</a> of scholarly articles, we provide the literature graph <a href="https://lg-covid-19-hotp.cs.duke.edu"> LG-covid19-HOTP</a> composed of not only articles (graph nodes) that are relevant to the study of coronavirus, but also in and out citation links (directed graph edges) to base navigation and search among the articles. The article records are related and connected, not isolated. The graph has been updated weekly since March 26, 2020. The current graph includes <strong>42,279</strong> hot-off-the-press (HOTP) articles since January 2020. It contains <strong>485,097</strong> articles and <strong>4,259,944</strong> links. The link-to-node ratio is remarkably higher than some other existing literature graphs. In addition to the dataset we provide more functionalities at <a href="https://lg-covid-19-hotp.cs.duke.edu">lg-covid-19-hotp.cs.duke.edu</a> such as new articles, weekly meta-data analysis in terms of publication growth over time, ranking by citation, and statistical near-neighbor embedding maps by similarity in co-citation, and similarity in co-reference. Since April 11, we have enabled a novel functionality - self-navigated surf-search over the maps. At the site we also take courtesy input of COVID-19 articles that are missing from the current collection.</p>
Data for article: Normative drift and self-correction and self-regulation in scholarly publishing: The case of scholarly books published by academics at Makerere University
<p>University academics face multiple pressures to publish. These pressures emanate from contexts with different, often competing, social norms which result in academics publishing for reasons that may run counter to accepted scientific practice. This paper asks what decisions are being taken by academics when it comes to their choice of scholarly book publisher. An analysis of books selected from more than 2,500 self-reported scholarly publications produced by academics at Makerere University in Uganda from 2011 to 2017, reveals that 31 scholarly books were published. Of these books, more than half (54%) were published by publishers that do not follow accepted scholarly publishing practice. Findings also show that there was a sharp decline in books published with suspect publishers in the second half of the seven-year period. The article discusses possible reasons for the selection of suspect publishers and considers four factors that may account for the observable decline: (1) a cyclical downturn; (2) improved research management and reporting; (3) explication of the norms of science; and (4) self-correction.</p> <p><strong>METHODS</strong></p> <p>The specific interest of this study is the publication of scholarly books by academics at Makerere University. </p> <p>The sources of the publications data were the lists of research outputs published in Makerere University’s annual reports from 2011 to 2017. Digital copies of the annual reports in PDF format were downloaded from the university’s website. Data were scraped from the annual report PDFs by exporting the publication lists from the PDFs to MSWord, removing unnecessary line breaks and spacing to create a single publication per line, and copying the publication lists into MSExcel for cleaning and analysis. Data were cleaned to remove (1) duplicate entries (e.g. identical publications in the same annual report or a publication listed as ‘under review’ or ‘in press’ in one year and listed again as a publication in a following year); (2) publications listed as ‘submitted’, unpublished masters and PhD theses, speeches, lectures, responses or retractions; (3) incomplete entries (e.g. no article, book or journal title); and (4) publications prior to 2011 (e.g. a 2008 publication listed in the 2011 annual report).</p> <p>The cleaned data were then coded for ‘year of publication’ (2011 to 2017 inclusive), ‘college’ (any of the colleges at Makerere University to which the authors were affiliated), and ‘publication type’ (book chapter, journal article, edited volume, working paper, policy brief, monograph, conference paper, textbook, report, popular article, poster presentation, newsletter, essay, catalogue, poetry, handbook). Books or parts of books (i.e. ‘book chapter’, ‘edited volume’, ‘monograph’, ‘textbook’, ‘catalogue’, ‘poetry’ and ‘handbook’) were further coded for ‘publisher’ (name); ‘publisher location’ (city, country as indicated on the imprint page of the publication); and ‘URL’ (online reference to publisher or publication). In some cases, publications were listed by type in the annual reports. Where publication lists were not presented in this way, indicators such as ‘journal of’, ‘volume’, ‘issue’ and ISSNs were used to identify journals; conference names, ‘proceedings of’, dates and cities were used to identify conference papers and presentations; ‘news’, ‘magazine’, ISSNs and the absence of ‘journal of’ were used to identify periodicals; and place of publication, publisher and ISBNs were used to identify books. Where these indicators were insufficient to determine a publication type, an online search was done to find the missing data. </p> <p>A limitation of the adopted method is that it relies on self-reporting by the university colleges and the list can therefore not be assumed to be comprehensive. Without knowledge of the reporting guidelines, it is also not possible to know whether all colleges and schools used the same criteria when selecting what to submit for reporting purposes. For example, some colleges may have decided to include working papers, policy briefs or papers presented at conferences, while others may have decided not to do so. </p>
Number of scholarly articles by country
<p>Set of queried data related to scholarly articles in Wikidata and shows the number of articles per country.</p>
KWX: scholarly articles with keywords from arXiv
<p>A dataset based on <a href="https://www.kaggle.com/datasets/Cornell-University/arxiv">arXiv Dataset</a>, where keywords of papers are added in this dataset.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.