Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
89
datasets available to search
ShareScore release 0.9.0
Dataset results
89 results for “Authorship”
Open access and international co-authorship: a longitudinal study of the United Arab Emirates research output
<p>Enriched data from Scopus used for the article: Open access and international co-authorship: a longitudinal study of the United Arab Emirates research output</p>
Data from: Gender disparity in physician authorship among commentary articles in high impact medical journals
Open the record for dataset details and reuse information.
Research collaboration patterns in sustainable mining – a co-authorship analysis of publications
<p>This dataset includes data related to 4220 articles on sustainable mining published from 1983 to 2018. The Scopus database was selected as a data source. Detailed data applies to co-authored articles. The number of authors and affiliations (country, institution, sector) were taken into account. Data has been cleaned in terms of names of institutions and countries.<br> In given sets the following data were included:<br> - Distribution of articles in sustainable mining from 1983 to 2018<br> - Distribution of joint articles and the types of joint articles from 1983 to 2018<br> - Team size in terms of the number of authors of articles in sustainable mining from 1983 to 2018<br> - Team size in terms of the number of authors' institutions in articles in sustainable mining from 1983 to 2018<br> - Team size in terms of the number of authors' countries in articles in sustainable mining from 1983 to 2018</p> <p><strong>Please note, the field separator used in these files is a semicolon, while the decimal separator is a comma. Each of the files has two header lines.</strong></p>
Data from: The co-authorship networks of the most productive European researchers
<p>This individual-level data-set describes the most productive European Union (EU) researchers (in terms of articles), during 2007 - 2018, irrespective of their research field. Specifically, in the data-set file, i.e. "iconic_5000", we profile the most productive 4,588 EU researchers using the following variables: number of papers; number of citations; repeated collaborations; number of co-authors; number of co-authors from the same country (as the author), from the same city, from the same institution and from different countries; geographical dispersion (number of unique countries wherein co-authors are based in), star (the largest number of articles published by one of an author's collaborators), godfather (the largest number of citations received by one of an author's collaborators), co-authors' citations and co-authors' papers. Variables are yearly measured.</p>
FIGURE 1 in Note on authorship and lectotypification of Scutellaria glechomoides (Lamiaceae) from Iran
FIGURE 1. Lectotype of Scutellaria glechomoides (K).
FIGURE 3 in Authorship and date of publication of the name Scarabaeus stercorosus (currently Anoplotrupes stercorosus) (Coleoptera: Scarabaeoidea: Geotrupidae)
FIGURE 3. Scriba's (1791) article: description of Scarabaeus stercorosus (pages 250–251).
FIGURE 2 in Authorship and date of publication of the name Scarabaeus stercorosus (currently Anoplotrupes stercorosus) (Coleoptera: Scarabaeoidea: Geotrupidae)
FIGURE 2. Scriba's (1791) article: first lines (page 244).
PAN22 Authorship Analysis: Style Change Detection
<p>This is the dataset for the <a href="https://pan.webis.de/clef22/pan22-web/style-change-detection.html">Style Change Detection</a> task of PAN 2022.</p> <p><strong>Task</strong></p> <p>The goal of the style change detection task is to identify text positions within a given multi-author document at which the author switches. Hence, a fundamental question is the following: If multiple authors have written a text together, can we find evidence for this fact; i.e., do we have a means to detect variations in the writing style? Answering this question belongs to the most difficult and most interesting challenges in author identification: Style change detection is the only means to detect plagiarism in a document if no comparison texts are given; likewise, style change detection can help to uncover gift authorships, to verify a claimed authorship, or to develop new technology for writing support.</p> <p>Previous editions of the Style Change Detection task aim at e.g., detecting whether a document is single- or multi-authored (<a href="https://pan.webis.de/clef18/pan18-web/style-change-detection.html">2018</a>), the actual number of authors within a document (<a href="https://pan.webis.de/clef19/pan19-web/style-change-detection.html">2019</a>), whether there was a style change between two consecutive paragraphs (<a href="https://pan.webis.de/clef20/pan20-web/style-change-detection.html">2020</a>, <a href="https://pan.webis.de/clef21/pan21-web/style-change-detection.html">2021</a>) and where the actual style changes were located (<a href="https://pan.webis.de/clef21/pan21-web/style-change-detection.html">2021</a>). Based on the progress made towards this goal in previous years, we again extend the set of challenges to likewise entice novices and experts:</p> <p>Given a document, we ask participants to solve the following three tasks:</p> <ul> <li><strong>[Task1] Style Change Basic:</strong> for a text written by two authors that contains a single style change only, find the position of this change (i.e., cut the text into the two authors’ texts on the paragraph-level),</li> <li><strong>[Task2] Style Change Advanced:</strong> for a text written by two or more authors, find all positions of writing style change (i.e., assign all paragraphs of the text uniquely to some author out of the number of authors assumed for the multi-author document)</li> <li><strong>[Task3] Style Change Real-World:</strong> for a text written by two or more authors, find all positions of writing style change, where style changes now not only occur between paragraphs, but at the sentence level.</li> </ul> <p>All documents are provided in English and may contain an arbitrary number of style changes, resulting from at most five different authors.</p> <p><strong>Data</strong></p> <p>To develop and then test your algorithms, three datasets including ground truth information are provided (<em>dataset1</em> for task 1, <em>dataset2</em> for task 2, and <em>dataset3</em> for task 3).</p> <p>Each dataset is split into three parts:</p> <ol> <li><em>training set:</em> Contains 70% of the whole dataset and includes ground truth data. Use this set to develop and train your models.</li> <li><em>validation set:</em> Contains 15% of the whole dataset and includes ground truth data. Use this set to evaluate and optimize your models.</li> <li><em>test set:</em> Contains 15% of the whole dataset, no ground truth data is given. This set is used for evaluation (see later).</li> </ol> <p>You are free to use additional external data for training your models. However, we ask you to make the additional data utilized freely available under a suitable license.</p> <p><strong>Input Format</strong></p> <p>The datasets are based on user posts from various sites of the StackExchange network, covering different topics. We refer to each input problem (i.e., the document for which to detect style changes) by an ID, which is subsequently also used to identify the submitted solution to this input problem. We provide one folder for train, validation, and test data for each dataset, respectively.</p> <p>For each problem instance <code>X</code> (i.e., each input document), two files are provided:</p> <ol> <li><code>problem-X.txt</code> contains the actual text, where paragraphs are denoted by <code>\n</code> for tasks 1 and 2. For task 3, we provide one sentence per paragraph (again, split by <code>\n</code>).</li> <li><code>truth-problem-X.json</code> contains the ground truth, i.e., the correct solution in JSON format. An example file is listed in the following (note that we list keys for the three tasks here): <pre><code>{ "authors": NUMBER_OF_AUTHORS, "site": SOURCE_SITE, "changes": RESULT_ARRAY_TASK1 or RESULT_ARRAY_TASK3, "paragraph-authors": RESULT_ARRAY_TASK2 }</code></pre> <p>The result for task 1 (key "changes") is represented as an array, holding a binary for each pair of consecutive paragraphs within the document (0 if there was no style change, 1 if there was a style change). For task 2 (key "paragraph-authors"), the result is the order of authors contained in the document (e.g., <code>[1, 2, 1]</code> for a two-author document), where the first author is "1", the second author appearing in the document is referred to as "2", etc. Furthermore, we provide the total number of authors and the Stackoverflow site the texts were extracted from (i.e., topic). The result for task 3 (key "changes") is similarly structured as the results array for task 1. However, for task 3, the <code>changes</code> array holds a binary for each pair of consecutive <em>sentences</em> and they may be multiple style changes in the document.</p> <p>An example of a multi-author document with a style change between the third and fourth paragraph (or sentence for task 3) could be described as follows (we only list the relevant key/value pairs here):</p> <pre><code>{ "changes": [0,0,1,...], "paragraph-authors": [1,1,1,2,...] }</code></pre> <p> </p> </li> </ol> <p><strong>Output Format</strong></p> <p>To evaluate the solutions for the tasks, the results have to be stored in a single file for each of the input documents and each of the datasets. Please note that we require a solution file to be generated for each input problem for each dataset. The data structure during the evaluation phase will be similar to that in the training phase, with the exception that the ground truth files are missing.</p> <p>For each given problem <code>problem-X.txt</code>, your software should output the missing solution file <code>solution-problem-X.json</code>, containing a JSON object holding the solution to the respective task. The solution for tasks 1 and 3 is an array containing a binary value for each pair of consecutive paragraphs (task 1) or sentences (task 3). For task 2, the solution is an array containing the order of authors contained in the document (as in the truth files).</p> <p>An example solution file for tasks 1 and 3 is featured in the following (note again that for task 1, changes are captured on the paragraph level, whereas for task 3, changes are captured on the sentence level):</p> <pre><code>{ "changes": [0,0,1,0,0,...] }</code></pre> <p>For task 2, the solution file looks as follows:</p> <pre><code>{ "paragraph-authors": [1,1,2,2,3,2,...] }</code></pre> <p> </p>
Trends in authorship demographics for manuscripts published in Endocrine journals: A 70-year analysis
<p><em><span>Background</span> </em></p> <p><span>Over the previous few decades, demographics, gender, and the amount of papers published have all changed considerably. One of the fields of medicine that has yet to be extensively investigated is endocrinology.</span> </p> <p><em><span>Material and Methods</span> </em></p> <p><span>Journal of Endocrinology and General & Comparative Endocrinology are two landmark journals that publish articles from around the world. We examined each decade during the 70-year period from 1961 to 2021. Funding source, first author – last author gender, their demographics and proportion of papers with at least one female author were the parameters considered while studying each publication. We predicted that the number of female authors per paper would increase with time, as would the range of degrees held by the authors, demographical variations in authorship, and the funding source. Our goal was also to determine the distribution of female first authors and senior authors in endocrinology journals over a 70-year period, as well as to check the gender combinations using the Punnett square. </span> </p> <p><em><span>Results</span> </em></p> <p><span>Female initial authors rose from 7% to 29.6% (p<0.0006) between 1961 and 2021, whereas female senior authors rose from 15.6% to 22.2%. Despite women's small contributions to first and senior authors, female participation rose from 17.48% (25/143) to 70% (170/250) between 1961 and 2021. Male-Female and Female-Male combinations rose with Chi-Square = 124.6, (p<0.0001). Europe and the Americas had the most female academic medical contributors (p<0.0001) Regardless of author status, female participation rose from 17.48% in 1961 to 68% in 2021.</span> </p> <p><em><span>Conclusion </span> </em></p> <p><span>In papers published in endocrinology journals, there was a rising trend in female contributions to academic medicine. Even with the large growth of female endocrinologists, there is still a disparity in why the increase in female authors is comparably fewer.</span> </p>
FIGURE 1 in Taxonomic status of Parapercis elongata (Teleostei: Pinguipedidae), with comments on its authorship
FIGURE 1. Drawing of Parapercis elongata, shown as Parapercis sp. by Fourmanoir (1965).
Testing for the Presence of Authorship Bias in Peer Review
ClinicalTrials.gov study NCT02739737. IPD Sharing: NO. Countries: 0. Publications: 6.
Data from: Gender differences in patterns of authorship do not affect peer review outcomes at an ecology journal
Open the record for dataset details and reuse information.
Data from: Patterns of authorship in ecology and evolution: first, last and corresponding authorship vary with gender and geography
Open the record for dataset details and reuse information.
Trends in authorship demographics for manuscripts published in Endocrine journals: A 70-year analysis
Open the record for dataset details and reuse information.
PAN19 Authorship Analysis: Celebrity Profiling
<p><strong>Paper:</strong> https://webis.de/publications.html?q=wiegmann_2019a</p><p><strong>Source Dataset:</strong> https://files.webis.de/data-in-progress/data-research/social-media-analysis/acl19-celebrity-profiling/</p><p> </p><p>Celebrities are among the most prolific users of social media, promoting their personas and rallying followers. This activity is closely tied to genuine writing samples, rendering them worthy research subjects in many respects, not least author profiling.</p><p>The Celebrity Profiling task this year is to predict four traits of a celebrity from their social media communication. The traits are the degree of fame, occupation, age, and gender. The social media communication is given as the teaser messages from past tweets. The goal is to develop a piece of software which predicts celebrity traits from the teaser history.</p><p>The training dataset contains two files: a feeds.ndjson as input and a labels.ndjson as output. Each file lists all celebrities as JSON objects, one per line and identified by the id key.</p><p>The input file contains the cid and a list of all teaser messages for each celebrity.</p><p>{"id": 1234, "text": ["a tweet", "another tweet", ...]}</p><p>The output file contains the cid and a value for each trait for each celebrity from the input file.</p><p>{"id": 1234, "fame": "star", "occupation": "sports", "gender": "female", "birthyear": 2002}</p><p>The following values are possible for each of the traits:</p><p>fame := {rising, star, superstar} occupation := {sports, performer, creator, politics, manager, science, professional, religious} birthyear := {1940, ..., 2012} gender := {male, female, nonbinary}</p><p> </p>
Dataset for Ray et al. "Giving and Getting Credit for Authorship of Research Publications"
<p>Survey questionnaire and dataset for Ray et al. "Giving and Getting Credit for Authorship of Research Publications"</p>
Raw genderized data for 'examining gender imbalance in chemistry authorship'
<p>Raw genderized data for 'examining gender imbalance in chemistry authorship'</p>
Authorship information of open educational resources derived from peer-reviewed scientific literature
<p>This dataset evaluates the authorship of open educational resources available at 5 STEM resource web sites (EcoEd Digital Library, CourseSource, Teaching Issues and Experiments in Ecology, Teach the Earth, and the National Center for Case Study Teaching in Science). Using 20 exercises from each site, it evaluates the proportion of exercises that draw on peer-reviewed scientific studies; of those, it also evaluates the proportion in which any author of the teaching exercise overlaps with any author of a scientific article from which the open educational resource is derived. The overall goal is to evaluate overlap in authorship between scientific publications and associated open educational resources. The first tab shows how the studies were selected and the second tab presents authorship data for each study.</p>
Patient Preferences in Empathetic Communication by AI vs Human Authorship
ClinicalTrials.gov study NCT06832891. IPD Sharing: NO. Countries: 1. Publications: 0.
Authorship information of open educational resources derived from peer-reviewed scientific literature
Open the record for dataset details and reuse information.
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.