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219 results for “wikipedia”
Polifonia_Corpus_Wikipedia_Annotation_EN
<p>Polifonia_Corpus_Wikipedia_Annotation_EN</p>
Wikipedia Category Granularity (WikiGrain) data
<p>The "Wikipedia Category Granularity (WikiGrain)" data consists of three files that contain information about articles of the English-language version of Wikipedia (https://en.wikipedia.org).</p> <p>The data has been generated from the database dump dated 20 October 2016 provided by the Wikimedia foundation licensed under the GNU Free Documentation License (GFDL) and the Creative Commons Attribution-Share-Alike 3.0 License. </p> <p>WikiGrain provides information on all 5,006,601 Wikipedia articles (that is, pages in Namespace 0 that are not redirects) that are assigned to at least one category.</p> <p>The WikiGrain Data is analyzed in the paper</p> <p>Jürgen Lerner and Alessandro Lomi: <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0190674"><strong>Knowledge categorization affects popularity and quality of Wikipedia articles</strong></a>. <em>PLoS ONE</em>, 13(1):e0190674, 2018.</p> <p>===============================================================<br> Individual files (tables in comma-separated-values-format):</p> <p>---------------------------------------------------------------<br> * article_info.csv contains the following variables:</p> <p>- "id"<br> (integer) Unique identifier for articles; identical with the page_id in the Wikipedia database.</p> <p>- "granularity"<br> (decimal) The granularity of an article A is defined to be the average (mean) granularity of the categories of A, where the granularity of a category C is the shortest path distance in the parent-child subcategory network from the root category (Category:Articles) to C. Higher granularity values indicate articles whose topics are less general, narrower, more specific. </p> <p>- "is.FA"<br> (boolean) True ('1') if the article is a featured article; false ('0') else.</p> <p>- "is.FA.or.GA"<br> (boolean) True ('1') if the article is a featured article or a good article; false ('0') else.</p> <p>- "is.top.importance"<br> (boolean) True ('1') if the article is listed as a top importance article by at least one WikiProject; false ('0') else.</p> <p>- "number.of.revisions"<br> (integer) Number of times a new version of the article has been uploaded.</p> <p><br> ---------------------------------------------------------------<br> * article_to_tlc.csv<br> is a list of links from articles to the closest top-level categories (TLC) they are contained in. We say that an article A is a member of a TLC C if A is in a category that is a descendant of C and the distance from C to A (measured by the number of parent-child category links) is minimal over all TLC. An article can thus be member of several TLC.<br> The file contains the following variables:</p> <p>- "id"<br> (integer) Unique identifier for articles; identical with the page_id in the Wikipedia database.</p> <p>- "id.of.tlc"<br> (integer) Unique identifier for TLC in which the article is contained; identical with the page_id in the Wikipedia database. </p> <p>- "title.of.tlc"<br> (string) Title of the TLC in which the article is contained.</p> <p>---------------------------------------------------------------<br> * article_info_normalized.csv<br> contains more variables associated with articles than article_info.csv. All variables, except "id" and "is.FA" are normalized to standard deviation equal to one. Variables whose name has prefix "log1p." have been transformed by the mapping x --> log(1+x) to make distributions that are skewed to the right 'more normal'. <br> The file contains the following variables:</p> <p>- "id"<br> Article id.</p> <p>- "is.FA"<br> Boolean indicator for whether the article is featured.</p> <p>- "log1p.length"<br> Length measured by the number of bytes.</p> <p>- "age"<br> Age measured by the time since the first edit.</p> <p>- "log1p.number.of.edits"<br> Number of times a new version of the article has been uploaded.</p> <p>- "log1p.number.of.reverts"<br> Number of times a revision has been reverted to a previous one.</p> <p>- "log1p.number.of.contributors"<br> Number of unique contributors to the article.</p> <p>- "number.of.characters.per.word"<br> Average number of characters per word (one component of 'reading complexity').</p> <p>- "number.of.words.per.sentence"<br> Average number of words per sentence (second component of 'reading complexity').</p> <p>- "number.of.level.1.sections"<br> Number of first level sections in the article.</p> <p>- "number.of.level.2.sections"<br> Number of second level sections in the article.</p> <p>- "number.of.categories"<br> Number of categories the article is in.</p> <p>- "log1p.average.size.of.categories"<br> Average size of the categories the article is in.</p> <p>- "log1p.number.of.intra.wiki.links"<br> Number of links to pages in the English-language version of Wikipedia.</p> <p>- "log1p.number.of.external.references"<br> Number of external references given in the article.</p> <p>- "log1p.number.of.images"<br> Number of images in the article.</p> <p>- "log1p.number.of.templates"<br> Number of templates that the article uses.</p> <p>- "log1p.number.of.inter.language.links"<br> Number of links to articles in different language edition of Wikipedia.</p> <p>- "granularity"<br> As in article_info.csv (but normalized to standard deviation one).<br> </p>
Extracted external domains from Wikipedia dump - 20/03/2024
<p>This dataset contains 6,459,779 distinct domains derived from the external links section of Wikipedia pages.</p> <p>The external links section of a page such as <a href="https://en.wikipedia.org/wiki/OpenWeb" target="_blank" rel="noopener">OpenWeb</a> contains only one <a href="https://www.openweb.com/" target="_blank" rel="noopener">link</a>.</p> <p>The primary objective of assembling this dataset is to improve content prioritization and filtering in web crawling techniques.</p> <p>The dataset is structured as a text file, with each line representing a distinct domain.<br><br><br></p>
Wikipedia-Index-2024-03-20
<p>The English Wikipedia of March 2024 (<a href="https://dumps.wikimedia.org/enwiki/20240320/">enwiki-20240320-pages-articles-multistream.xml</a>), parsed using <a href="https://github.com/daveshap/PlainTextWikipedia">PlainTextWikipedia</a>, segmented by headline (wikipedia-segments.ndjson.gz) and indexed with ElasticSearch 8.13.2. The unzipped elasticsearch-wikipedia-index directory can be directly mounted in docker.elastic.co/elasticsearch/elasticsearch:8.13.2 at /usr/share/elasticsearch/data.</p>
Processed Wikipedia Dataset
<p>We extract a subset of about 1,000,000 documents of Wikipedia 2020 and extract the keywords of them. The <code>wiki_kws_dict.pkl</code> is a map which maps each keyword to its total counts in files and query trend. The <code>wiki_doc_0.pkl</code> contains lists of keywords of each document. These two datasets can be loaded by the pickle package with python.</p>
Video of Using LOD to crowdsource Dutch WW2 underground newspapers on Wikipedia, SWIB2016, Bonn, 29-11-2016
<p><strong> </strong> Video registration of presentation about <a title="File:Using LOD to crowdsource Dutch WW2 underground newspapers on Wikipedia, SWIB2016, 29-11-2016.pdf" href="https://commons.wikimedia.org/wiki/File:Using_LOD_to_crowdsource_Dutch_WW2_underground_newspapers_on_Wikipedia,_SWIB2016,_29-11-2016.pdf">Using Linked Open Data to crowdsource Dutch WW2 underground newspapers on Wikipedia</a>, <a href="https://swib.org/swib16/programme.html" rel="nofollow">SWIB2016</a>, 29-11-2016 in Bonn.</p> <p>The video describes a project <a title="nl:Wikipedia:Wikiproject/Verzetskranten" href="https://nl.wikipedia.org/wiki/Wikipedia:Wikiproject/Verzetskranten">to systematically describe and interlink 1,300 Dutch underground newspapers from World War 2</a> on Wikipedia using linked open data.</p> <p>The project extracts contextual information about the newspapers from a book, converts it to structured data, and generates Wikipedia stubs linked to metadata, full texts, and each other.</p> <p>Volunteers are expanding the stubs into full articles, improving access to information about this historical period.</p> <div> <h2>Abstract</h2> </div> <p>During the second World War some 1.300 illegal newspapers were issued by the Dutch resistance. Right after the war as many of these newspapers as possible were physically preserved by Dutch memory institutions. They were described in formal library catalogues that were digitized and brought online in the 1990s. In 2010 the national collection of underground newspapers - some 200.000 pages - was full-text digitized in Delpher, the national aggregator for historical full-texts. Having created online metadata and full-texts for these publications, the third pillar <em>context</em> was still missing, making it hard for people to understand the historic background of the newspapers. We are currently running a project to tackle this contextual problem. We started by extracting contextual entries from a hard-copy standard work on Dutch illegal press and combined these with data from the library catalogue and Delpher into a central LOD triple store. We then created links between historically related newspapers and used Named Entity Recognition to find persons, organisations and places related to the newspapers. We further semantically enriched the data using DBPedia. Next, using an article template to ensure uniformity and consistency, we generated 1.300 Wikipedia article stubs from the database. Finally, we sought collaboration with the Dutch Wikipedia volunteer community to extend these stubs into full encyclopedic articles. In this way we can give every newspaper its own Wikipedia article, making these WW2 materials much more visible to the Dutch public, over 80% of whom uses Wikipedia. At the same time the triple store can serve as a source for alternative applications, like data visualizations. This will enable us to visualize connections and networks between underground newspapers, as they developed over time between 1940 and 1945.</p> <div> <h2>Presentation slides</h2> </div> <ul> <li><a title="File:Using LOD to crowdsource Dutch WW2 underground newspapers on Wikipedia, SWIB2016, 29-11-2016.pdf" href="https://commons.wikimedia.org/wiki/File:Using_LOD_to_crowdsource_Dutch_WW2_underground_newspapers_on_Wikipedia,_SWIB2016,_29-11-2016.pdf">Using LOD to crowdsource Dutch WW2 underground newspapers on Wikipedia</a></li> <li><a href="https://swib.org/swib16/slides/janssen_using_lod.pdf" rel="nofollow">https://swib.org/swib16/slides/janssen_using_lod.pdf</a></li> <li><a href="https://doi.org/10.5281/zenodo.13132987" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13132987</a></li> </ul>
word2vec model trained on lemmatized French Wikipedia 2018
<p>The files presented are trained <a href="https://en.wikipedia.org/wiki/Word2vec">word2vec</a> models for French.</p> <p><strong>Corpus</strong></p> <p>The base corpus used for training is a dump of the French Wikipedia performed on 20 October 2018. The corpus was then processed to remove, as much as possible, the Mediawiki syntax, links, etc... Note that this is not perfect, but hopefully has little consequence on the weights calculated.</p> <p>The corpus was then POS-tagged and lemmatized with <a href="http://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/">TreeTagger</a>. The list of tags can be found <a href="http://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/data/french-tagset.html">here</a>. During POS-tagging, each word is replaced with its lemma and is replaced with the syntax `[lemma]_[tag]`. For instance, the sentence "Il a sauté dans sa voiture" will produce this output:</p> <pre><code>il_PRO:PER avoir_VER:pres sauter_VER:pper dans_PRP son_DET:POS voiture_NOM</code></pre> <p><strong>Word2vec training</strong></p> <p>The training was performed using the <a href="https://pypi.org/project/gensim/">gensim</a> Python module (v3.5.0). The skip-grams method was used, with a size of 500, a window size of 5 and a minimum count of 5. This is how the model creation was invoked:</p> <pre><code class="language-python">model = Word2Vec(size=500, window=5, min_count=5, workers=workers, sg=1)</code></pre> <p>Two versions of the model were trained:</p> <ul> <li>frwiki-20181020.treetag.2__2019-01-24_10.41__.s500_w5_skip.word2vec.bin<br> No extra processing was performed on the corpus before training.</li> <li>frwiki-20181020.treetag.2.ngram-pass2__2019-04-08_09.02__.s500_w5_skip.word2vec.bin<br> Where two passes of the 2-gram detection were ran before training. This allows detection of 2-, 3- and 4-grams in the vocabulary.</li> </ul> <p>The files are in the word2vec binary format, so can be used either with the original <a href="https://github.com/tmikolov/word2vec">C implementation of word2vec</a>, or with the Python gensim version (and possibly other libraries that support that format).</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
PAN Wikipedia Quality Flaw Corpus 2012 (PAN-WQF-12)
<p>The PAN Wikipedia Quality Flaw Corpus 2012, PAN-WQF-12, provides human-labeled English Wikipedia articles that contain specific quality flaws.</p> <p>The corpus comprises 1,592,226 articles extracted from the English Wikipedia snapshot from January 4th, 2012. A subset of 208,228 articles is labled with ten specific quality flaws, which are listed in the following table. The labeling is based on human-defined cleanup tags. In addition, the corpus comprises 1,383,998 articles that have not been tagged with any cleanup tag.</p>
PAN Wikipedia Vandalism Corpus 2010 (PAN-WVC-10)
<p>The PAN Wikipedia Vvandalism Ccorpus 2010 (PAN-WVC-10) is a corpus for the evaluation of automatic vandalism detectors for Wikipedia. For research purposes the corpus can be used free of charge.</p> <p>This corpus is supplemented by the <a href="https://doi.org/10.5281/zenodo.3342157">PAN-WVC-11</a>, which features additional edits in English, Spanish and German. Both corpora should be used to get more representative results.</p> <p>As part of our research on automatic vandalism detection we have compiled a corpus of vandalism cases found in Wikipedia. The corpus compiles 32452 edits on 28468 Wikipedia articles, among which 2391 vandalism edits have been identified. To annotate the corpus we have used Amazon's Mechanical Turk; 753 workers have been recruited who cast more than 150000 votes on the edits, so that each edit was reviewed by at least 3 annotators. The achieved level of agreement was analyzed in order to label an edit as "regular" or "vandalism."</p>
Webis Wikipedia Vandalism Corpus (Webis-WVC-07)
<p>This corpus is outdated. Please use its successors <a href="https://doi.org/10.5281/zenodo.3341488">PAN-WVC-10</a> and <a href="https://doi.org/10.5281/zenodo.3342157">PAN-WVC-11</a>.</p> <p>The Webis Wikipedia Vandalism Corpus (Webis-WVC-07) is a corpus for the evaluation of automatic vandalism detection algorithms for Wikipedia. For research purposes the corpus can be used free of charge.</p> <p>The corpus is the first standardized test collection for the comparison of vandalism detection algorithms. It comprises 940 edits from which 301 are marked as vandalism by human evaluators.</p>
PAN Wikipedia Vandalism Corpus 2011 (PAN-WVC-11)
<p>The PAN Wikipedia Vandalism Corpus 2011 (PAN-WVC-11) is a corpus for the evaluation of automatic vandalism detectors for Wikipedia. For research purposes the corpus can be used free of charge.</p> <p>This corpus supplements the <a href="https://doi.org/10.5281/zenodo.3341488">PAN-WVC-10</a>, which features only English edits. Both corpora should be used to get more representative results.</p> <p>The corpus compiles 29949 edits on 24351 Wikipedia articles, among which 2813 vandalism edits have been identified. The corpus features 9985 English edits, 9990 German edits, and 9974 Spanish edits. To annotate the corpus we have used Amazon's Mechanical Turk; each edit was presented to a number of annotators who were asked to decide whether it is vandalism or regular, and the agreement of the annotators was analyzed in order to label an edit.</p>
Most popular scholarly works in the English Wikipedia and their transition to open access
<p>Following the release of "The future of OA" by Piwowar, Priem, Orr (2019), interest has grown on how to accelerate the share of scholarly works consultations which meet an open access record.</p> <p>Based on download patterns for over 23 million DOIs in 2017, released by Elbakyan (2018), we found that the 1 million most downloaded DOIs accounted for over 30 % of the total downloads. Of these 1 million DOIs, over 50 thousands (5 %) were previously identified as cited on the English Wikipedia and not open access (Leva 2018). Of these, 2440 DOIs are now open access according to the Unpaywall API as of 2019-10-25: a list of the corresponding OA URL and host type is enclosed, showing that 34 % became OA at the publisher while 66 % were made OA by a repository. The newly OA works were hosted at over 400 domains of which over 300 repositories, but the top 10 repositories accounted for a large portion of the works, with the top 3 repositories accounting for over 40 % of the newly found green open access DOIs.</p> <p>Part of the newly OA works were just false negatives in Unpaywall in 2018, but a small manual sample shows that most are truly new deposits. Works from 2017 can be expected to be over-represented in the sample given that they were probably the most popular downloads of 2017 and could have been under embargo in 2018 when the previous measure of open access status was made.</p>
Wikipedia: Wikipedia for Fungi (Mycological characteristics) (879) DwCA
Wikipedia is a multilingual, web-based, free-content encyclopedia project supported by the Wikimedia Foundation and based on a model of openly editable content. EOL harvests articles from wikipedia that are indexed as species or higher taxa.<p></p>
WordNet–Wikipedia–Wiktionary alignment
<p>This distribution contains the three-way alignments between WordNet 3.0, the English edition of Wikipedia, and the English edition of Wiktionary, as described in the LREC 2014 paper by Tristan Miller and Iryna Gurevych (see below).</p> <p><strong>Format</strong></p> <p>Here you will find two tab-delimited text files, <code>alignment_3way.tsv</code> and <code>alignment_3way_conjoint.tsv</code>. The first of these contains the full alignment of WordNet, Wikipedia, and Wiktionary, except for the unaligned singleton senses. The second file contains the conjoint alignment of WordNet, Wikipedia, and Wiktionary.</p> <p>The format of both files is the same: each line consists of a tab-delimited list of “sense” identifiers which refer to the same concept. Identifiers for Wiktionary are prefixed with a <code>#</code> character, and take the form of the unique sense identifier generated by the <a href="https://dkpro.github.io/dkpro-jwktl/">JWKTL library</a> for a 3 April 2010 dump of the English edition of Wiktionary. Identifiers for Wikipedia are prefixed with a <code>%</code> character, and take the form of the article title (with underscores replacing spaces) as found in a 22 August 2009 snapshot of the English edition of Wikipedia. Identifiers for WordNet are prefixed with a <code>=</code> character and take the form of a synset offset, followed by a hyphen (<code>-</code>), followed by a part of speech label (<code>a</code>, <code>n</code>, <code>r</code>, or <code>v</code>, for adjectives, nouns, adverbs, and verbs, respectively).</p> <p><strong>Citing this resource</strong></p> <p>If you use this resource in your own work, please cite the following paper:</p> <p>Tristan Miller and Iryna Gurevych. <a href="http://www.lrec-conf.org/proceedings/lrec2014/pdf/4_Paper.pdf">WordNet–Wikipedia–Wiktionary: Construction of a three-way alignment</a>. In Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asunción Moreno, Jan Odijk, and Stelios Piperidis, editors, <em>Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014)</em>, pages 2094–2100. European Language Resources Association, May 2014. ISBN 978-2-9517408-8-4.</p> <p>You can use the following BibTeX entry:</p> <pre>@inproceedings{miller2014wordnet, author = {Tristan Miller and Iryna Gurevych}, title = {{WordNet}--{Wikipedia}--{Wiktionary}: Construction of a Three-way Alignment}, booktitle = {Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014)}, year = 2014, editor = {Nicoletta Calzolari and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asunci{\'{o}}n Moreno and Jan Odijk and Stelios Piperidis}, pages = {2094--2100}, month = may, publisher = {European Language Resources Association}, pdf = {http://www.lrec-conf.org/proceedings/lrec2014/pdf/4_Paper.pdf}, isbn = {978-2-9517408-8-4}, }</pre>
Relations from Italian Wikipedia using Unsupervised Information Extraction
<p>This dataset contains relations extracted from the Italian Wikipedia by the WikiOIE framework.<br> WikiOIE is based on UDPipe and the Universal Dependencies project for text processing.<br> It easily allows customizing the information extraction (IE) approach to automatically extract triples (subject, predicate, object).<br> This dataset contains relations extracted by two unsupervised IE methods. The former (<strong>simple</strong>) is based only on PoS-tag patterns; the latter (<strong>simpledep</strong>) also uses syntactic dependencies. <br> The extraction process is provided in JSON format.</p> <p>More information and the Java code are available here https://github.com/pippokill/WikiOIE</p> <p>Pierluigi Cassotti, Lucia Siciliani, Pierpaolo Basile,Marco de Gemmis, and Pasquale Lops. 2021. Extracting relations from Italian Wikipedia using unsupervised information extraction. In Proceedings of the 11th Italian Information Retrieval Workshop 2021 (IIR 2021). CEUR-WS.</p>
Dataset for Named Entity Recognition and Entity Linking from Greek Wikipedia Events
<p>An automated benchmark dataset for (Named Entity Recognition) NER and (Named Entity Linking) NEL tools, based on Greek Wikipedia events pages.</p> <p>Note: This data includes data from the following sources:<br> - Wikipedia el.wikipedia.org</p> <p><strong>Description</strong></p> <p>The dataset is provided in the form of three JSON-formatted subsets i.e., train, validation and test in an analogy of 70-20-10. The current version of the dataset contains 18,617 events annotated with 40,798 entity mentions and 36,189 links to elWikipedia (and wikidata ids). The dataset contains annotations belonging to 8 entity types: person, organization, location, gpe, event, facility, product and work of art.</p> <table> <caption>Overall dataset statistics</caption> <thead> <tr> <th scope="col"> </th> <th scope="col">Docs</th> <th scope="col">Tokens</th> <th scope="col">Sentences</th> <th scope="col">Surface Mentions</th> <th scope="col">Valid Links</th> <th scope="col">Red Links</th> </tr> </thead> <tbody> <tr> <td><strong>Train</strong></td> <td>13,031</td> <td>332,077</td> <td>16,927</td> <td>28,593</td> <td>25,365</td> <td>3,228</td> </tr> <tr> <td><strong>Validation</strong></td> <td>3,722</td> <td>94,746</td> <td>4,844</td> <td>8,168</td> <td>7,240</td> <td>928</td> </tr> <tr> <td><strong>Test</strong></td> <td>1,862</td> <td>47,450</td> <td>2,427</td> <td>4,037</td> <td>3,584</td> <td>453</td> </tr> <tr> <td><strong>Total</strong></td> <td>18,617</td> <td>474,361</td> <td>24,200</td> <td>40,798</td> <td>36,189</td> <td>4,609</td> </tr> </tbody> </table> <p><strong>Example</strong></p> <p>A record example is given below.</p> <p>{</p> <p>"json_file": "February 2012_39_0 events",<br> "text": "Sudan and South Sudan sign non-aggression pact.",<br> "ground_truth_mentions": [<br> {"start": 0, "end": 4, "surface_mention": "Sudan", "mention_type": "GPE"},<br> {"start": 10, "end": 20, "surface_mention": "South Sudan", "mention_type": "GPE"}<br> ],<br> "ground_truth_links": [<br> {"enwiki": "Sudan","wikidata": "Q1049"},<br> {"enwiki": "South_Sudan", "wikidata": "Q958"}<br> ]<br> }</p> <p><strong>Code</strong></p> <p><a href="https://gitlab.isl.ics.forth.gr/debatelab/elwiki_events_benchmark">https://gitlab.isl.ics.forth.gr/debatelab/elwiki_events_benchmark</a></p> <p><strong>Acknowledgments</strong></p> <p>This work has received funding from the Hellenic Foundation for Research and Innovation (HFRI) and the General Secretariat for Research and Technology (GSRT), under grant agreement No 4195.</p>
Wikipedia Academic Disciplines Network
<p>This network is a result of scraping Wikipedia (using <a href="https://scrapy.org/">scrapy</a>), starting at the <a href="https://en.wikipedia.org/wiki/Outline_of_academic_disciplines">Outline of Academic Disciplines</a> and following every link until a page is reached (i.e. if an outline is reached, all the links there are followed, when a page is reached, the links there are recorded, but not followed). Each page/article visited is recorded as a node. If one page/article links to another, the nodes are connected with an edge. The edges are weighted by the number of links between the pages. An example of a weight greater than 1 is if two pages both link to each other, or if there are multiple links to the same page in an article. </p> <p>This process resulted in 640,031 nodes and 4,503,438 edges. The network is undirected, so each edge only appears once. </p> <p> </p>
Webis Wikipedia Innovation History 2023
<p>History sections of science and technology articles on Wikipedia extracted from the Wikimedia dump from 1 January 2022. Articles retrieved using Wikipedia's category network. History sections extracted using a combination of section-heading-based heuristics and classifiers trained on articles with designated history sections.</p> <p>If you use this corpus, please cite the following paper:</p> <p>Wolfgang Kircheis, Marion Schmidt, Arno Simons, Benno Stein, and Martin Potthast. <a href="https://webis.de/downloads/publications/papers/kircheis_2023.pdf">Mining the History Sections of Wikipedia Articles on Science and Technology</a>. In 23rd ACM/IEEE Joint Conference on Digital Libraries (JCDL 2023), June 2023. [<a href="https://github.com/webis-de/JCDL-23">code</a>] [<a href="http://wiki-sci-tech-corpus.web.webis.de">corpus-viewer</a>]</p> <pre><code>@InProceedings{kircheis:2023, author = {Wolfgang Kircheis and Marion Schmidt and Arno Simons and Benno Stein and Martin Potthast}, booktitle = {23rd {ACM/IEEE} Joint Conference on Digital Libraries (JCDL 2023)}, codeurl = {https://github.com/webis-de/JCDL-23}, keywords = {nlp, natural language processing}, month = jun, title = {{Mining the History Sections of Wikipedia Articles on Science and Technology}}, year = 2023 }</code></pre>
Dataset of Bibliometric and Wikipedia Indicators of Universities
<p>This dataset is related to the paper: "From academic to media capital: To what extent does the scientific reputation of universities translate into Wikipedia attention?" It contains for each university different bibliometric and Wikipedia indicators.</p> <table align="center"> <thead> <tr> <th scope="col">General</th> <th scope="col">Bibliometrics</th> <th scope="col">Wikipedia</th> </tr> </thead> <tbody> <tr> <td>id</td> <td>impact_P</td> <td>langlinks</td> </tr> <tr> <td>grid_id</td> <td>impact_P_Bio_Health</td> <td>total_views</td> </tr> <tr> <td>short_name</td> <td>impact_P_Life_Earth</td> <td>local_views</td> </tr> <tr> <td>full_name</td> <td>impact_P_Math_Comp</td> <td>int_views</td> </tr> <tr> <td>Country</td> <td>impact_P_Phy_Eng</td> <td>rest_views</td> </tr> <tr> <td>established_year</td> <td>impact_P_Soc_Hum</td> <td>editors</td> </tr> <tr> <td>age</td> <td>P_top10</td> <td>revisions</td> </tr> <tr> <td>Continent</td> <td>collab_P</td> <td>words</td> </tr> <tr> <td> </td> <td>P_int_collab</td> <td>unique_references</td> </tr> <tr> <td> </td> <td>P_OA</td> <td>characters</td> </tr> <tr> <td> </td> <td>TCS</td> <td>sections</td> </tr> <tr> <td> </td> <td>TNCS</td> <td> </td> </tr> <tr> <td> </td> <td>P_industry_collab</td> <td> </td> </tr> <tr> <td> </td> <td>PA_F_MF</td> <td> </td> </tr> <tr> <td> </td> <td>dimensions_wos</td> <td> </td> </tr> <tr> <td> </td> <td>topic_specialisation</td> <td> </td> </tr> <tr> <td> </td> <td>topic_specialisation_bio</td> <td> </td> </tr> <tr> <td> </td> <td>topic_specialisation_eng</td> <td> </td> </tr> <tr> <td> </td> <td>gini_production</td> <td> </td> </tr> </tbody> </table> <p> </p>
Database of Wikipedia Education Programs from Brazil, 2011-2020
<p>Database of Wikipedia Education Programs from Brazil, 2011-2020, as used in:<br> * Davis, L.L., Sigalov, S.E., Maljković, F., Peschanski, J.A. (2023). The Wikipedia Education Program as Open Educational Practice: Global Stories. In: Olivier, J., Rambow, A. (eds) Open Educational Resources in Higher Education. Future Education and Learning Spaces. Springer, Singapore. https://doi-org.wikipedialibrary.idm.oclc.org/10.1007/978-981-19-8590-4_12<br> * Peschanski, J.A. (2023). "Wikipedia Education Program como prática educacional aberta: histórias globais". In: 23ª OFICINA WIKIMEDIA & EDUCAÇÃO. https://pt.wikiversity.org/wiki/Oficinas_Wikimedia_%26_Educa%C3%A7%C3%A3o/Oficina_23</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.