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89 results for “Authorship”
Connecting U.S. Supreme Court Case Information and Opinion Authorship (SCDB) to Full Case Text Data (CAP), 1791-2011
<p>This dataset was constructed to connect the rich metadata created by the Supreme Court Database (SCDB) to the Caselaw Access Project (CAP) full-text court opinion data. Since the SCDB includes only substantive opinions, it is necessarily a subset of the full range of opinions available through CAP.</p> <p>There are two parts to this data: the map connecting each SCDB ID to its corresponding CAP case number, and a more advanced (but error-prone) version in which the authorship of each opinion text identified for the case in CAP is attributed to the Justice who wrote it. Each of these data products have been hand-corrected to the best of this author's ability.</p> <p><strong>SCDB-CAP map</strong></p> <p>The SCDB->CAP map began as a relatively straightforward automated matching process, based on the US Reports citation for each case as expressed in both SCDB and CAP. Slightly over 80% of SCDB entries found a single CAP data match this way. From there, the data was entirely hand-corrected, with non-matches or duplicate matches individually investigated and manually corrected.</p> <p>Some SCDB entries simply could not be matched to an appropriate CAP text. Initially, the entirety of US Reports volume 44 was missing, but with the help of CAP staff, the volume was located as having been filed in the New York jurisdiction rather that the United States jurisdiction. The case numbers were then added to the map, but until the volume is relocated to the United States jurisdiction, it may be necessary to also incorporate the New York jurisdiction in full text analysis so that the cases from volume 44 can be searched. 108 more missing cases are from US Reports volume 131, which was a "catch up" volume published in the 19th century. These catch-up cases, many heard by the Supreme Court decades prior, were numbered with lowercase roman numerals instead of the ordinary numbers, which is almost certainly why CAP's software dismissed the catch-up section as prefatory material. Many of the rest of the errors seem largely to be examples where the SCDB project recognized a separate court action that CAP did not. Perhaps most of these seem to have been later rehearings for a case previously decided, which in the 19th century particularly were commonly reported out at the end of the first decision text. While SCDB sometimes gave these subsequent but related actions a separate SCDB entry, CAP seems to have largely incorporated them as part of the text of the main case. Additionally, there were a few that simply could not be found, despite a careful look through each database as well as the original US Reports and sometimes adjacent volumes. Finally, the cases were only matched up through the 2011 court term. After the 2011 term, the mismatches between CAP and SCDB were extensive and frequently seemed impossible to resolve.</p> <p>Even so, with the manual correction, the overall error rate is low. Of 28,304 cases, only 191 do not have a match, and of those, 108 are contained within the vol. 131 "catch up" volume. Since most of the rest are extremely short subsequent actions that were separately noted by SCDB, the effect of these non-matched cases would seem to be small in most cases.</p> <p>The typical use case would be that the researcher would generate some kind of results based on searching in the CAP full text, then could use the CAP ID to look up the SCDB ID in the map. With the SCDB ID, of course, the rich metadata from the SCDB can then be connected to each result as needed.</p> <p><strong>Opinion authorship</strong></p> <p>Being able to use the rich metadata of SCDB in conjunction with a case's full text is exciting, but it immediately prompts a further question -- what if the texts could be attributed directly to the Justices who authored them? SCDB produces its data in two forms; one is "case centered," where each record represents one case, and the other is "justice centered," in which each record is the vote of one Justice in one case. CAP, in turn, breaks the total text of the case into distinct opinions, and tries to attribute those opinions to their authors by scraping a string of text from the raw input. Therefore, the challenge was to connect these two sources at the opinion level.</p> <p>Connecting the opinions, like connecting the cases, involved an initial match by machines, followed by manual correction and revision. In this case, the scope of the manual effort was much larger than that posed by the case-level connection, and more errors were noted in both SCDB and CAP.</p> <p>The matching process involved a number of steps. First a list of opinions was generated from the CAP data, then matched to SCDB using the SCDB-CAP connector data described above. (Thus, a case without a CAP match in the SCDB-CAP data will not appear in the opinion author data either.) CAP opinions were numbered in the order they were encountered in each CAP case JSON object, and these numbers are used to distinguish the opinions.</p> <p>Next, a round of automatic matching was performed. If there was only one opinion, and only one author listed in the SCDB data, then the majority opinion author (as listed in SCDB) was safely assumed to be the author. If there was no author listed in SCDB, "percuriam" was recorded as the author in this data. If there were exactly two opinions and two authors, the process was also straightforward, as the SCDB-identified majority opinion author was assigned to opinion 1, and the remaining author assigned opinion 2.</p> <p>Subsequently, cases with more than two opinions were processed. A potential match (i.e. a "guess") for each opinion in a given case was created by listing each Justice identified by SCDB as having written an opinion in the case. These guesses were then parsed using a semi-automatic procedure with Levenshtein distance fuzzy name matching. With sufficiently conservative parameters, a successful fuzzy match meant that the non-successful guesses for that opinion could be deleted. These sorted guesses were then reviewed manually. Particular care was also taken for any opinion that contained authored opinions by Justices who had similar names (for example, Clark and Black differ by only a single letter). These sorts of cases, as well as instances of co-authorship, were identified and fixed manually.</p> <p>Those opinions whose authorship could not be matched then were fixed by hand. These included some where the CAP author strings were more complicated than SCDB's strict interpretation; others where the OCR in CAP which contained the Justice name was especially bad; and a number of others where "Mr. Chief Justice" couldn't be directly matched with an author name by the machine. After this light manual correction, almost 500 opinions with substantial errors remained to be individually investigated in depth, by examining the CAP record, the SCDB record, and images of the US Reports for that case. For these last tough customers, errors in the source data were commonly the cause of matching problems. Typically these were of three kinds: examples where CAP should have split the text but didn't (e.g. 2 opinions together in one opinion entry in CAP); examples where SCDB either did not identify or mis-identified an author (such as attributing it to Swayne when it was written by Miller); and examples of non-valid opinions (such as where CAP mistakenly split the opinion too early, leaving an opinion fragment).</p> <p>For these errors, a system of codes was created in the author field to signal the error type so that researchers can be suitably cautious. The error code is always at the beginning of the field and is followed by a comma and the names of each author, separated by a comma with no space to facilitate parsing. Note also that co-authors are listed as comma-separated names in this same field with no error code. Researchers will probably want to disaggregate this field to create duplicate records with each individual author for most purposes. The justice number field also contains information about all justices authoring the opinion but the error codes have been omitted here.</p> <ul> <li>!C -- error: multiple opinion texts combined (i.e. CAP splitting error)</li> <li>!X -- error: unattributed or misattributed opinion (not listed in SCDB as writer)</li> <li>!D -- error: extra opinion that should be deleted, i.e. not a valid opinion</li> <li>!W -- error: listed as Writers by SCDB, but should be co-authors</li> </ul> <p> </p> <p><strong>Data file structure</strong></p> <p>"scdb_cap-051820.tsv" is a Tab-separated data file containing 5 columns: SCDB ID, CAP ID, US Reports citation, case date, and case name (the latter three from the SCDB data).</p> <p>"scdb-cap-opinion-authorship_051920.tsv" is a Tab-separated data file containing seven columns: SCDB ID, CAP ID, US Reports citation, case name, opinion number in the case, opinion author, and SCDB justice ID. See above for caveats about disaggregating and error codes in fields six and seven.</p> <p><strong>Errors</strong></p> <p>It is likely that errors remain in this data, and it is also hoped that some of the errors beyond the author's immediate control might be fixed in the upstream data so that they can be corrected here. Authors would be grateful for error reports, and also reports of errors fixed, if any.</p> <p> </p>
Open Science and Authorship of Supplementary Material for the MES research community
<p>This spreadsheet contains the data and the results from the analysis described in the paper "Open Science and Authorship of Supplementary Material. Evidence from a Research Community." being accepted at STI 2022.</p>
Dataset for "Authorship concentration in health sciences journals from Latin America and the Caribbean"
<p>Authorship concentration indexes and other data for journals in the LILACS (Latin American and the Caribbean Literature on Health Sciences) bibliographic database, from 2015 to 2019 (FONTENELLE, 2022). These data are read and created by <a href="https://doi.org/10.5281/zenodo.6127497">analytic code in Zenodo</a>.</p> <ul> <li><em>authorship_concentration.csv</em> - dataset derived in Fontenelle (2022) from raw data exported from LILACS. This is the main file, and it's CC-BY because other researchers might have curated the raw data differently and thus derived different data. CSV file encoded with ASCII.</li> <li><em>authorship_concentration_datadictionary.csv</em> - data dictionary for the previous file. This file is actually CC0. CSV file encoded with ASCII.</li> <li><em>journals.csv</em> - dataset about the journals indexed in LILACS between 2015 and 2019. As a result of simply converting and filtering the original TITLE database, this is actually CC0 by the Pan American Health Organization (PAHO). CSV file encoded with UTF-8.</li> <li><em>journal_subjects.csv</em> - DeCS descriptors for the journals identified by the ISSN. CC0 by the Pan American Health Organization (PAHO), as above. CS file encoded with ASCII.</li> </ul>
Dataset for "AGU Publications updates authorship policy to foster better equity and transparency in global research collaboration"
<p>Dataset supporting "AGU Publications updates authorship policy to foster better equity and transparency in global research collaboration." </p> <p>This file provides summary data for new submissions for "Global Biogeochemical Cycles" (GBC) and "Journal of Geophysical Research: Biogeosciences" (JGR: Biogeo) from 2012 through 2023 including International Collaboration Status (whether more than one country was represented on the author list), Research4Life Author Status (whether any author was from a country on the Research4Life eligibility list [https://www.research4life.org/access/eligibility/]), and Research4Life Abstract Status (whether the submission abstract referenced a country on the Research4Life eligibility list). </p> <p>Summary data for all AGU journals combined are provided for years 2012 and 2023, including whether more than one country was represented by the author list and whether any author was from a country on the Research4Life eligibility list. </p> <p>Summary data are presented in compliance with AGU's Privacy Policy, https://www.agu.org/Privacy-Policy</p>
Original dataset for "A validation of co-authorship credit models with empirical data from the contributions of PhD candidates"
<p><strong>Publication reference:</strong><br> Donner, P. (2020). A validation of co-authorship credit models with empirical data from the contributions of PhD candidates. Quantitative Science Studies, v. 1, i. 2, p. 551-564. <a href="https://doi.org/10.1162/qss_a_00048">https://doi.org/10.1162/qss_a_00048</a>.</p> <p> </p> <p>The file contains one row per authorship contribution statement. Rows of publications and theses are grouped.</p> <p><strong>Description of columns:</strong></p> <p>dissertation_id - an integer identifying each dissertation thesis</p> <p>university - university at which the dissertation thesis was written and PhD degree conferred</p> <p>year - publication year of the dissertation thesis</p> <p>author - dissertation thesis author name</p> <p>title - dissertation thesis title</p> <p>subject - the field of research</p> <p>publication_id - an integer identifying each publication; publication associated with more than one thesis have the same id across theses</p> <p>reference - bibliographic reference for the publication associated with the thesis</p> <p>author_count - number of authors of the publication</p> <p>author_position - position in the author byline of the credited author</p> <p>credit - claimed credit of the author in percent</p> <p>corresponding_author - flag for whether the publication author of this row is a orresponding author</p>
Authorship Identification of SOurce COde 2020 (AI-SOCO)
<p>General authorship identification is essential to the detection of undesirable deception of others' content misuse or exposing the owners of some anonymous hurtful content. This is done by revealing the author of that content. <strong>A</strong>uthorship <strong>I</strong>dentification of <strong>SO</strong>urce <strong>CO</strong>de (AI-SOCO) focuses on uncovering the author who wrote some piece of code. This facilitates solving issues related to cheating in academic, work and open source environments. Also, it can be helpful in detecting the authors of malware softwares over the world.</p> <p>The detection of cheating in academic communities is significant to properly address the contribution of each researcher. Also, in work environments, credit sometimes goes to people that did not deserve it. Such issues of plagiarism could arise in open source projects that are available on public platforms. Similarly, this could be used in public or private online coding contests whether done in coding interviews or in official coding training contests to detect the cheating of applicants or contestants. A system like this could also play a big role in detecting the source of anonymous malicious softwares.</p> <p>The dataset is composed of source codes collected from the open submissions in the <a href="http://www.google.com/url?q=http%3A%2F%2Fcodeforces.com%2F&sa=D&sntz=1&usg=AFQjCNHKGPIjzjl6ujCm0t4EU_waJWvU-Q">Codeforces</a> online judge. Codeforces is an online judge for hosting competitive programming contests such that each contest consists of multiple problems to be solved by the participants. A Codeforces participant can solve a problem by writing a solution for it using any of the available programming languages on the website, and then submitting the solution through the website. The solution's result can be correct (accepted) or incorrect (wrong answer, time limit exceeded, etc.).</p> <p>In our dataset, we selected 1,000 users and collected 100 source codes from each one. So, the total number of source codes is 100,000. All collected source codes are correct, bug-free, compile-ready and written using the C++ programming language using different versions. For each user, all collected source codes are from unique problems.</p> <p>Given the pre-defined set of source codes and their authors, the task is to build a system to determine which one of these authors wrote a given unseen before source code.</p> <p>Dataset website: https://sites.google.com/view/ai-soco-2020.</p>
Data on authorship issues in Cochrane manuscripts
<p><span>Anonymized data on authorship issues in Cochrane manuscripts after inplementation of an authorship declaration form in Cochrane Colorectal Group.</span></p>
Supplementary material of the manuscript "Beyond authorship: Analyzing disciplinary patterns of contribution statements using the CRediT taxonomy"
<p>Supplementary material of the manuscript "Beyond authorship: Analyzing disciplinary patterns of contribution statements using the CRediT taxonomy". In this research article we present the first cross-disciplinary descriptive analysis on the use of contribution statements. Our main objective is to obtain further insight on contributions by a variety of fields (Multidisciplinary, Health, Life, Physical and Social Sciences) from the largest dataset used up to now. We examine more than 700,000 articles published between 2017 and 2024 in Elsevier and PLOS journals, in combination with bibliometric data extracted from the Scopus database. The descriptive analysis of the dataset focuses on the overall coverage of the merged data, the distribution of authorship and disciplines at paper level, and the interactions between contribution statements, author order and disciplines. Our two main findings indicate that, on the one hand, looking at contributions and authorship order can enrich the way we understand science as a social endeavor. On the other hand, delving deeper into contributorship differences by field is key. We underscore the value of the CRediT taxonomy in unveiling nuanced research dynamics and offering a more equitable framework for evaluation.</p>
False Authorship: Methods and materials package
<p>This package contains Python, shell, awk scripts, and data used to obtain the curated table and excerpt associated with the above named article.</p> <h2>Data Contents</h2> <p>The following data files are included. * README.md: This file * article-details.xlsx: Curated table with details of published articles in Microsoft Excel file format * index.html: HTML document with * links to GIJIR materials saved in the Internet Archive * a list of all the GIJIR articles’ citation data according to Crossref and links to each article’s locally available landing page, full-text PDF, plus links to Crossref metadata and the article via DOI and original journal URL. (Note that non-local, non-archived links may rot over time.) * ybs-works.json: Results of Crossref query to obtain all the publisher’s works made on 2024-09-22 * ChatGPT: Prompts and responses associated with the generation of a fake article in one of the journal’s topics. * global-us/metadata/: Article metadata as HTML files collected on 2024-09-10 * global-us/global-us.mellbaou.com/index.php/global/article/download/: A copy of the journal’s article PDFs as crawled on 2024-09-10 * spinellis business - Google Scholar.pdf: Printout of a Google Search query for the terms <em>spinellis business</em> made on 2025-02-06.</p> <h2>Executable Contents</h2> <p>The following programs and scripts are used to obtain the above contents.</p> <ul> <li>Makefile: Commands that orchestrate the articles’ analysis</li> <li>get-metadata.sh: Obtain article metadata pages from the journal’s web site</li> <li>apply-to-pdfs.sh: Apply the specified Python script to all article PDFs</li> <li>extract-citations-emails.py: Extract number of probable in-text citations and corresponding author email from article PDF</li> <li>extract-doi-affiliations.py: Extract article DOI and affiliations from an article’s metadata</li> <li>extract-all-doi-affiliations.sh: Extract article DOI and affiliations from all articles’ metadata</li> <li>emails-to-csv.awk: Convert emails and article numbers to CSV with URL for sending emails</li> </ul>
Figure 2 in The taxonomic catalog of the Brazilian fauna: Dermaptera and Phasmatodea (Insecta), with commentaries on species list, types, authorship and distribution
Figure 2. Data on species authorship of Dermaptera: (A) demography in relation to nationality; (B) ranking of authors who described the highest numbers of earwig species, from first to last: Carlos Moreira in first place; Malcom Burr and Carl August Dohrn tied in second; Alan Brindle in third; Jean Guillaume Audinet-Serville, Alfredo Borelli, Samuel Scudder, Carl Stål, and Henrik Steinmann tied in fourth; Auguste de Bormans, William Kirby, and Joaquim Machado Filho tied in fifth.
Figure 5 in The taxonomic catalog of the Brazilian fauna: Dermaptera and Phasmatodea (Insecta), with commentaries on species list, types, authorship and distribution
Figure 5. Distribution of the publications of Phasmatodea species across time. Time gap* = period of time that had no descriptions of species; species cluster** = high amounts of species described clustered in a short period of time, between time gaps.
Figure 4 in The taxonomic catalog of the Brazilian fauna: Dermaptera and Phasmatodea (Insecta), with commentaries on species list, types, authorship and distribution
Figure 4. Distribution of the publications of Dermaptera species across time. Time gap* = period of time that had no descriptions of species.
Figure 3 in The taxonomic catalog of the Brazilian fauna: Dermaptera and Phasmatodea (Insecta), with commentaries on species list, types, authorship and distribution
Figure 3. Data on species authorship of Phasmatodea: (A) demography in relation to nationality; (B) ranking of authors who described the highest numbers of walking-sticks species, from first to last: Ludwig Redtenbacher, Salvador de Toledo Piza Jr., John O. Westwood, Karl Brunner-von Wattenwyl, and Raphael A. Heleodoro.
Categorization of articles 2017 with authorship of Pontificia Universidad Católica de Chile, through the SDGs
<p>The dataset comprises a single list of publications exported from Web of Science (Clarivate Analytics) and Scopus (Elsevier) databases, to which a process was applied that eliminated duplicate records. 2,379 scientific publications in English or Spanish of the "Article" type from the year 2017, with authorship associated with the Pontificia Universidad Católica de Chile, were considered.</p> <p>In addition, the classification process carried out by the team of specialists that considered three consecutive milestones is included: establishment of the reading level applied to each publication record; assignment of one of the 18 categories identified in the information analysis, which include the 17 SDGs and the option "Unclassified" and one of the 169 subcategories corresponding to the goals; and, finally, the status of the review process carried out.</p>
Colombia researchers co-authorship network and adjacency matrix.
<p>OpenAlex subset of papers published by researchers affiliated with an institution in Colombia.</p> <p>* gml file containing the coauthorship network with the metadata: Researcher name, one affiliation, list of OpenAlex concepts of level 0.</p> <p>* Adjacency matrix of type scipy sparse saved in a npz file</p> <p>*npz file with the concepts the author ar marked with</p> <p>*npz file with a map of 200 words and the affiliation each author reports</p> <p>*npz file with a dict that has the mappings from numbers to string for each variable (X with words and affiliations and Z with concepts)</p>
The Authorship of Stephen King's Books Written Under the Pseudonym "Richard Bachman": A Stylometric Analysis (data)
<p>This data accompanies a paper for the 2nd Annual Conference for Computational Literary Studies: "The Authorship of Stephen King’s Books Written Under the Pseudonym 'Richard Bachman': A Stylometric Analysis".</p> <p><strong>Abstract</strong>:</p> <p>Between 1977 and 1984, Stephen King published five novels under the pseudonym “Richard Bachman”. Reviewers noted similarities between King’s and Bachman’s writing styles when <em>Thinner </em>(1984) was published, ultimately leading to King’s unmasking. We investigate, using the Juola protocol, whether computational techniques can correctly identify King as the author of the Bachman books out of a selection of contemporary candidate authors – Dean Koontz, Peter Straub, and Thomas Harris. We also perform a post-hoc analysis of the use of pop-culture references and brand names in Bachman, King, Koontz, Straub, and Harris novels, based on comments in reviews of Bachman and King novels. The references extracted from the Bachman books occurred significantly more often in King’s texts than in the others’, showing that attentive readers could have “heard King’s voice” in the Bachman books through what a reviewer denigratingly called King’s “compulsion to list brand-name products and his affinity for pop-cult teenage junk”. These results contribute to the vexed issue of explainability, which is a recurrent challenge in author identification for literary texts.</p> <p> </p> <p>Below is a description of each file in this repository:</p> <p><strong>bachman_segments_features_array_1000token_segments.csv</strong>, <strong>bachman_segments_features_array_5000token_segments.csv</strong>, and <strong>bachman_segments_features_array_10000token_segments.csv</strong> contain the feature spaces created by vectorizing 1,000-, 5,000-, and 10,000-token segments of Bachman, King, Koontz, and Straub books. Each row of the csv files contains the vectorized segment, the segment's author, the book the segment was drawn from, the book's publication date, and the segment number. </p> <p> </p> <p><strong>bachman_segments_author_candidate_cosine_distances_1000token_segments.csv</strong>, <strong>bachman_segments_author_candidate_cosine_distances_5000token_segments.csv</strong>,and <strong>bachman_segments_author_candidate_cosine_distances_10000token_segments.csv </strong>contain the Bachman segment number, bootstrap iteration number (from 0 and 9,999), the distractor author of the randomly-sampled segment, and the cosine distance between the Bachman segment vector and the distractor author's randomly-sampled segment vector (calculated using the data stored in the bachman_segments_features_array_1000token_segments.csv, bachman_segments_features_array_5000token_segments.csv, and bachman_segments_features_array_10000token_segments.csv files).</p> <p> </p> <p><strong>bachman_segments_author_candidate_ranks_1000token_segments.csv</strong>, <strong>bachman_segments_author_candidate_ranks_5000token_segments.csv</strong>,and <strong>bachman_segments_author_candidate_ranks_10000token_segments.csv </strong>contain the same columns as the 3 files described in the previous paragraph, but the cosine distance between Bachman segment and distractor author segment is converted to a ranking. For each bootstrap iteration there are 4 (one for each candidate author) rows containing the distance ranking between the Bachman segment and a candidate author segment. In a particular bootstrap iteration, if a King segment had the smallest cosine distance to a Bachman segment, King has the ranking "1", and if a Koontz segment had the second smallest distance to a Bachman segment, Koontz has the ranking "2", and so on. </p> <p> </p> <p><strong>predicted_author_candidate_raw_counts_1000token_segments.csv</strong>,<strong> predicted_author_candidate_raw_counts_5000token_segments.csv</strong>, and<strong> predicted_author_candidate_raw_counts_10000token_segments.csv </strong>contain the total number of times King, Straub, Harris, and Koontz segments received a certain distance ranking in the files described in the previous paragraph. </p> <p> </p> <p><strong>predicted_author_candidate_proportions_1000token_segments.csv</strong>, <strong>predicted_author_candidate_proportions_5000token_segments.csv</strong>, and and <strong>predicted_author_candidate_proportions_10000token_segments.csv</strong> contain a Bachman book title, and percentage of that book's segments that received the distance rankings 1-4 of each author. For example, in <strong>predicted_author_candidate_proportions_10000token_segments.csv, </strong><em>The Long Walk</em>'s segments were ranked as most similar (rank= "1") to King segments in 73.3% of bootstrap iterations. </p> <p> </p> <p><strong>pop_culture_refs_counts_books_10000token_segments.csv </strong>contains the author and book title of a randomly-sampled 10,000-token segment from the aforementioned book, the iteration (from 0 to 99), and the number of pop culture references found in the segment that match those extracted from Bachman books.</p> <p> </p> <p> </p> <p> </p>
Data from: International authorship and collaboration across bioRxiv preprints
<p>Data and supplementary tables for <a href="https://doi.org/10.7554/eLife.58496">"International authorship and collaboration across bioRxiv preprints,"</a> a paper first posted to <a href="https://doi.org/10.1101/2020.04.25.060756">bioRxiv</a> and now published in <em>eLife</em>.</p> <ul> <li><strong>"reproduce.md"</strong> includes all R code used to generate figures and perform analyses described in the paper.</li> <li><strong>"biorxiv_countries.postgres.backup"</strong> is a database snapshot that can be loaded into a PostgreSQL database to access all data collected and used in the study.</li> <li><strong>"schema.pdf"</strong> describes each field in each table of the database.</li> <li><strong>"manual_edits.sql"</strong> describes all corrections made to the automated inference of the country-level affiliations inferred for all authors.</li> <li><strong>"affiliation_corrections.csv"</strong> lists every unique affiliation string that was re-categorized after institutional corrections. The consequences of the corrections described in "manual_edits.sql."</li> <li><strong>"institution_corrections_summary.csv"</strong> summarizes "affiliation_corrections.csv" by listing each "before" and "after" correction one time. It is important to note that each before/after pair does not necessarily indicate that <em>every</em> affiliation string from the "before" institution was reassigned to the "after" institution, just that at least one affiliation string was switched from one to the other. <ul> <li>Note that the final two "corrections" files describe steps taken to correct the institution-level associations between authors and countries. The final set of corrections assigned authors to countries <strong>using heuristics that did not take institution-level accuracy into account</strong>.</li> </ul> </li> </ul> <p><strong>Version history:</strong></p> <ul> <li><strong>1.0.0: </strong>New files uploaded reflecting substantial corrections to the data, mostly linked to classification of authors and preprints previously without a country classification. (26 Jun 2020)</li> <li><strong>0.2.1:</strong> Added "schema.pdf" file, previously only in the manuscript.</li> <li><strong>0.2.0:</strong> Added new files "affiliation_corrections.csv" and "institution_corrections_summary.csv"</li> <li><strong>0.1.1: </strong>Database snapshot added.</li> <li><strong>0.1.0: </strong>First version with supplementary tables added.</li> </ul>
University of Turin IRIS-registered Publications for co-authorship networks
<p>Data extracted from the University of Turin (UNITO) IRIS publication database (available at <a href="https://iris.unito.it/">www.iris.unito.it</a>) regarding publication authorship.<br>This data was generated in order to produce co-authorship networks of authors inside of UNITO.<br><br>The JSON-formatted dataset includes data regarding UNITO affiliated authors that have published from the year 2012 to roughly September 2023, along with a list of all of their publications in the same time period.The dataset encompasses 15807 authors and 66313 articles.<br><br>The JSON has the following structure:</p><ul><li>`authors` (list): A list of objects representing authors, each with the following structure:<ul><li>`name` (string): The author's given name, all in lowercase letters. This field is always populated;</li><li>`surname` (string): The author's family name, all in lowercase letters. This field is always populated;</li><li>`affiliation` (string): The string "university of turin";</li><li>`department` (string or NULL): Empty (`null`) or with a string indicating the author's <strong>current</strong> (as of data collection, roughly September 2023) work department;</li><li>`id` (string): Unique UUID4 of the author.</li></ul></li><li>`papers` (list): A list of object representing published articles, each with the following structure:<ul><li>`id` (string): Unique IRIS ID of the publication. This field is always populated;</li><li>`title` (string): Title of the publication. This field is always populated;</li><li>`year` (numeric): Year of the article's publication date. This field is always populated;</li><li>`authors` (list): A non-empty list of strings. Each string is one ID of one of the authors in the `authors` list.</li></ul></li></ul><p>The data was kindly provided by the IRIS office in September 2023 and preprocessed by Luca Visentin to a digestible JSON.</p>
Network visualisation of co-authorship analysis of countries
<p>VOSviewer mapping shows network visualisation of co-authorship analysis of countries for <span>focusing on MICP research in the context of hydrodynamics (1999-2024).</span></p>
International comparison of cross-disciplinary integration in industry 4.0: A co-authorship analysis using academic literature databases
<p>In innovation strategy, a type of Schumpeterian competitive strategy in business administration, "intra-individual diversity" has attracted attention as one factor for creating innovation. In this study, we redefine "framework for identifying researchers' areas of expertise" as "a framework for quantifying intra-individual diversity among researchers. Note that diversity here refers to authorship of articles in multiple research fields. The application of this framework then made it possible to visualize organizational diversity by accumulating the intra-individual diversity of researchers and to discuss the innovation strategy of the organization. The analysis in this study discusses how countries are promoting research on the topics of artificial intelligence (AI), big data, and Internet of Things (IoT) technologies, which are at the core of Industry 4.0, from an innovation perspective. Note that Industry 4.0 is a technological framework that aims to "improve the efficiency of all social systems," "create new industries," and "increase intellectual productivity." For the analysis, we used 19-year bibliographic data (2000–2018) from the top 20 countries in terms of the number of papers in AI, big data, and IoT technologies. As the results, this study classified the styles of cross-disciplinary fusion into four patterns in AI and three patterns in big data. This study did not consider the results in IoT because of only small differences between countries. Furthermore, regional differences in the style of cross-disciplinary fusion were also observed, and the global innovation patterns in Industry 4.0 were classified into seven categories. In Europe and North America, the cross-disciplinary integration style was similar to that between the United States, Germany, the Netherlands, Spain, England, Italy, Canada, and France. In Asia, the cross-disciplinary fusion style was similar between China, Japan, and South Korea.</p>
ScienceDex guides
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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.