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.
8,537
datasets available to search
ShareScore release 0.9.0
Dataset results
8,537 results for “Openness”
◂Fig. 1 Morphology of thecate and coccoid cells, with labelled thecal plates. a–c, i, m Light microscopy, d–h, k–l scanning electron microscopy. a Ventral view of strain GeoM*788; b dorsal view of strain GeoM*793; c apical view of strain GeoK*044; d ventral view of strain GeoK*037; e dorsal view of strain GeoM*788; f apical view of strain GeoK*024, with the dehiscence of epithecal opening indicated by a blue line; g antapical view of strain GeoK*044; h leftlateral view of strain GeoM*866; i motile cell of strain GeoK*037; k–m coccoid cells showing variability in shape and size of strains k GeoM*866, l GeoM*793 and m GeoK*024. Abbreviations: n′: apical plate, n′′: precingular plate, n′′′: postcingular plate, n′′′′: antapical plate, na: anterior intercalary plate, nC: cingular plate, Sa: anterior sulcal plate, Sd: right sulcal plate, Sp: posterior sulcal plate. Ss: left sulcal plate. Scale bar: 10 µm. UA: 15 kV in Morphological and molecular variability of Peridinium volzii Lemmerm. (Peridiniaceae, Dinophyceae) and its relevance for infraspecific taxonomy
◂Fig. 1 Morphology of thecate and coccoid cells, with labelled thecal plates. a–c, i, m Light microscopy, d–h, k–l scanning electron microscopy. a Ventral view of strain GeoM*788; b dorsal view of strain GeoM*793; c apical view of strain GeoK*044; d ventral view of strain GeoK*037; e dorsal view of strain GeoM*788; f apical view of strain GeoK*024, with the dehiscence of epithecal opening indicated by a blue line; g antapical view of strain GeoK*044; h leftlateral view of strain GeoM*866; i motile cell of strain GeoK*037; k–m coccoid cells showing variability in shape and size of strains k GeoM*866, l GeoM*793 and m GeoK*024. Abbreviations: n′: apical plate, n′′: precingular plate, n′′′: postcingular plate, n′′′′: antapical plate, na: anterior intercalary plate, nC: cingular plate, Sa: anterior sulcal plate, Sd: right sulcal plate, Sp: posterior sulcal plate. Ss: left sulcal plate. Scale bar: 10 µm. UA: 15 kV
CVEfixes Dataset: Automatically Collected Vulnerabilities and Their Fixes from Open-Source Software
<p><em>CVEfixes</em> is a comprehensive vulnerability dataset that is automatically collected and curated from Common Vulnerabilities and Exposures (CVE) records in the public <a href="https://nvd.nist.gov/">U.S. National Vulnerability Database (NVD)</a>. The goal is to support data-driven security research based on source code and source code metrics related to fixes for CVEs in the NVD by providing detailed information at different interlinked levels of abstraction, such as the commit-, file-, and method level, as well as the repository- and CVE level.</p> <p>This release, v1.0.8, covers all published CVEs up to 23 July 2024. All open-source projects that were reported in CVE records in the NVD in this time frame _and_ had publicly available git repositories were fetched and considered for the construction of this vulnerability dataset. The dataset is organized as a relational database and covers 12107 vulnerability fixing commits in 4249 open source projects for a total of 11873 CVEs in 272 different Common Weakness Enumeration (CWE) types. The dataset includes the source code before and after changing 51342 files and 138974 functions. The collection took 48 hours with 4 workers (AMD EPYC Genoa-X 9684X).</p> <p>This repository includes the SQL dump of the dataset, as well as the JSON for the CVEs and XML of the CWEs at the time of collection. The complete process has been documented in the paper <em>"CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Software"</em>, which is published in the Proceedings of the 17th International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE '21). You will find a copy of the paper in the Doc folder. </p> <p><em><strong>Citation and Zenodo links</strong></em></p> <p>Please cite this work by referring to the published paper:</p> <ul> <li>Guru Bhandari, Amara Naseer, and Leon Moonen. 2021. CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Software. In Proceedings of the 17th International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE '21). ACM, 10 pages. <a href="https://doi.org/10.1145/3475960.3475985">https://doi.org/10.1145/3475960.3475985</a></li> </ul> <pre><code>@inproceedings{bhandari2021:cvefixes, title = {{CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Software}}, booktitle = {{Proceedings of the 17th International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE '21)}}, author = {Bhandari, Guru and Naseer, Amara and Moonen, Leon}, year = {2021}, pages = {10}, publisher = {{ACM}}, doi = {10.1145/3475960.3475985}, copyright = {Open Access}, isbn = {978-1-4503-8680-7}, language = {en} }</code></pre> <p>The dataset has been released on Zenodo with DOI:<a href="https://doi.org/10.5281/zenodo.4476563">10.5281/zenodo.4476563</a>. The GitHub repository containing the code to automatically collect the dataset can be found at <a href="https://github.com/secureIT-project/CVEfixes">https://github.com/secureIT-project/CVEfixes</a>, released with DOI:<a href="https://doi.org/10.5281/zenodo.5111494">10.5281/zenodo.5111494</a>.</p>
Books per Publisher: Data from the Directory of Open Access Books (DOAB)
<p>Quantitative information on publishers and published books from the Directory of Open Access Books (DOAB).</p>
Belmont Forum Open Data Survey 2014
<p>Belmont Forum Open Data Survey, September -- November 2014, by the Open Data working group of the E-Infrastructure and Data Management Collaborative Research Action (http://www.bfe-inf.org/).</p> <p>For the publication of the survey data free text answers have either be lightly edited (question 4, other), or are provided in separate files (questions 8, 15, 16, 17). </p> <p>Publication of the results is under preparation (manuscript submitted). </p>
On the Popularity of Modern Open Source Software
<p><strong>This dataset contains the data analyzed on the paper:</strong></p> <p>Hudson Borges and Marco Tulio Valente. <em>On the Popularity of Modern Open Source Software</em>. Submitted to Journal of Systems and Software (JSS), 2018.</p> <p><strong>Files:</strong></p> <ul> <li><em>cdf.csv</em>: Cumulative distribution function data.</li> <li><em>contributos.csv</em>: List of contributors of the repositories.</li> <li><em>developers_perceptions.csv</em>: Survey of Developers' Perceptions on Growth Patterns.</li> <li><em>factors.[activity,owner,repository].csv</em>: Additional information of the analyzed repositories and their owners.</li> <li><em>growth_patterns.zip</em>: A compressed file containing the output of the KSC algorithm (time series clusters).</li> <li><em>motivations_for_starring.csv</em>: Developers' motivations for starring projects.</li> <li><em>owners.csv</em>: Information of the repositories' owners.</li> <li><em>releases.csv</em>: Releases considered in the study (i.e., major and minor releases only).</li> <li><em>repositories.csv</em>: Information of the analyzed repositories (e.g., stars, forks, owner, creation date, etc.).</li> <li><em>timeseries.json</em>: File containing the number of stars gained by week for each repository since their creation.</li> </ul>
Open Access Tutorial
<p>Das Video ist in einem Werkstattprojekt (Forschendes Lernen) zum Thema Open Access mit Studierenden der FH Potsdam am Fachbereich Informationswissenschaften entstanden.</p> <p>Die Aufgabenstellung war es, ein Video-Tutorial zu Open Access zu produzieren, welches die Entwicklung, ebenso wie Vor- und Nachteile von Open Access erläutert und aufgrund der Ausführungen zur Open Access Publikation motiviert. </p> <p>Das Video gibt einen Überblick über die Geschichte des wissenschaftlichen Publizierens und die Ursprünge der Open Access-Bewegung. Daran anknüpfend werden verschiedene Strategien vorgestellt: der goldene und der grüne Weg. Die Vor- und Nachteile von Open Access werden insbesondere in Hinblick auf Article Processing Charges diskutiert.</p> <p>Umgesetzt wurde das Video mit Simpleshow: https://www.mysimpleshow.com/de/ </p> <p>Urheber des Videos: Studierende Fachhochschule Potsdam, Fachbereich 5 Informationswissenschaften<br> (https://www.fh-potsdam.de/studieren/fachbereiche/informationswissenschaften/)</p> <p>Werkstattleitung: Prof. Dr. jur. Ellen Euler,LL.M. zusammen mit Tutorin: Dorothea Strecker</p>
Numbers of Articles, Books and Dissertation theses indexed in BASE and percentages of items published Open Access, under Creative Commons Licenses and under Open Licenses (2013-2017)
<p>A look at the data provided by the Open Access search engine BASE (http://base-search.net) shows that the Open Science compliance among dissertation theses stagnates. BASE knows three categories of accessibility: Open Access, Unknown, Non-Open Access. In the following tables and graphs, figures reported as "Open Access" have been categorised by BASE as Open Access. The tables and graphics show data from BASE (as of 06.03.2018) as follows:</p> <p>a) Indexed theses, books and journal articles</p> <p>b) Indexed theses, books and journal articles published by Open Access</p> <p>c) indexed theses, books and journal articles under Creative Commons licenses.</p> <p>d) indexed theses, books and journal articles, which are published under open licenses in the sense of the Open License, i. e. reflect terms of use of the Open Source.</p> <p> </p> <p>Although doctoral theses already had a high share of open access by 2013 (43%), by 2017 it had risen by only 5% (2017:48%). At the same time, the proportion of books published in open access rose by 14% (from 20% to 34%) and articles by 17% from 44% (2013) to 61% (2017). The same effect can be seen in the proportion of CC-licensed items: Their share rose by 4% (from 9% to 13%) for doctoral theses, by 9% for books (from 4% to 13%) and 8% for articles (from 10% to 18%) between 2013 and 2017. However, the share of openly licensed items is most pronounced: it did not increase for doctoral theses, but remained at 2% between 2013 and 2017; in the same period it increased by 5% (from 1% to 6%) for books, and by 5% (from 5% to 10%) for articles.</p>
Résultats du sondage institutionnel sur l'Open Access à l'Université de Lausanne
<p>Ces données contiennent les réponses anonymisées des 796 chercheur-e-s de l'institution qui ont complété le sondage institutionnel sur l'Open Access 2017.</p>
Categories of Datasets Offered by Current Open Data Portals
<p>This dataset lists categories of open datasets from 40 European open data catalogs. The 40 European data catalogues were taken from four countries (France, Germany, Spain, and the United Kingdom) at a rate of 10 per country. These categories are an indicator of the topics of interests in the respective countries (back in 2016), the types of questions data publishers assume users will ask, and ultimately the types of questions citizens can ask.</p>
University of Lausanne Institutional Open Access Survey Results
<p>This dataset contains the anonymized responses of 796 researchers from the institution who completed the 2017 institutional survey on Open Access.</p>
Open-Source Shared Memory implementation of the HPCG benchmark: analysis, improvements and evaluation on Cavium ThunderX2
<p>This archive contains the output files used to gerenate tables and plots in the paper "Open-Source Shared Memory implementation of the HPCG benchmark: analysis, improvements and evaluation on Cavium ThunderX2" submitted to the 9th IEEE International Workshop on "Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems" (PMBS18) held as part of ACM/IEEE Supercomputing 2018 (SC18), Dallas, TX, USA.</p>
The landscape of open access in Africa
<p>This project is a continuation from this previous project stored here (https://zenodo.org/record/817600#.W5DGy359hyU). It was based on the following keywords:</p> <p>"Open Access" OR "Institutional Repository" OR "Self-archiving" OR "Green OA" OR "Gold OA" AND Africa</p> <p>We used Web of Science database provided by The University of Sydney in Sep 6 2018. There were 3986 docs, but it decreased to 3976, presumably due to bad records when we imported it to Jabref Ref Manager. We export the dataset in to CSV and RIS for easy use.</p>
Ecosystem-Level Determinants of Sustained Activity in Open-Source Projects: A Case Study of the PyPI Ecosystem
<pre><em>Replication pack, FSE2018 submission #164: </em><em>------------------------------------------ </em></pre> <pre><strong>**</strong>Working title:<strong>** </strong>Ecosystem-Level Factors Affecting the Survival of Open-Source Projects: A Case Study of the PyPI Ecosystem <strong>**</strong>Note:<strong>** </strong>link to data artifacts is already included in the paper. Link to the code will be included in the Camera Ready version as well. <em>Content description </em><em>=================== </em> <strong>- **</strong>ghd-0.1.0.zip<strong>** </strong>- the code archive. This code produces the dataset files described below <strong>- **</strong>settings.py<strong>** </strong>- settings template for the code archive. <strong>- **</strong>dataset_minimal_Jan_2018.zip<strong>** </strong>- the minimally sufficient version of the dataset. This dataset only includes stats aggregated by the ecosystem (PyPI) <strong>- **</strong>dataset_full_Jan_2018.tgz<strong>** </strong>- full version of the dataset, including project-level statistics. It is ~34Gb unpacked. This dataset still doesn't include PyPI packages themselves, which take around 2TB. <strong>- **</strong>build_model.r, helpers.r<strong>** </strong>- R files to process the survival data (`survival_data.csv` in <strong>**</strong>dataset_minimal_Jan_2018.zip<strong>**</strong>, `common.cache/survival_data.pypi_2008_2017-12_6.csv` in <strong>**</strong>dataset_full_Jan_2018.tgz<strong>**</strong>) <strong>- **</strong>Interview protocol.pdf<strong>** </strong>- approximate protocol used for semistructured interviews. <strong>- </strong>LICENSE - text of GPL v3, under which this dataset is published <strong>- </strong>INSTALL.md - replication guide (~2 pages)</pre> <pre><em>Replication guide </em><em>================= </em> <em>Step 0 - prerequisites </em><em>---------------------- </em> <strong>- </strong>Unix-compatible OS (Linux or OS X) <strong>- </strong>Python interpreter (2.7 was used; Python 3 compatibility is highly likely) <strong>- </strong>R 3.4 or higher (3.4.4 was used, 3.2 is known to be incompatible) Depending on detalization level (see Step 2 for more details): <strong>- </strong>up to 2Tb of disk space (see Step 2 detalization levels) <strong>- </strong>at least 16Gb of RAM (64 preferable) <strong>- </strong>few hours to few month of processing time <em>Step 1 - software </em><em>---------------- </em> <strong>- </strong>unpack <strong>**</strong>ghd-0.1.0.zip<strong>**</strong>, or clone from gitlab: git clone https://gitlab.com/user2589/ghd.git git checkout 0.1.0 `cd` into the extracted folder. All commands below assume it as a current directory. <strong>- </strong>copy `settings.py` into the extracted folder. Edit the file: <strong> * </strong>set `DATASET_PATH` to some newly created folder path <strong> * </strong>add at least one GitHub API token to `SCRAPER_GITHUB_API_TOKENS` <strong>- </strong>install docker. For Ubuntu Linux, the command is `sudo apt-get install docker-compose` <strong>- </strong>install libarchive and headers: `sudo apt-get install libarchive-dev` <strong>- </strong>(optional) to replicate on NPM, install yajl: `sudo apt-get install yajl-tools` Without this dependency, you might get an error on the next step, but it's safe to ignore. <strong>- </strong>install Python libraries: `pip install --user -r requirements.txt` . <strong>- </strong>disable all APIs except GitHub (Bitbucket and Gitlab support were not yet implemented when this study was in progress): edit `scraper/init.py`, comment out everything except GitHub support in `PROVIDERS`. <em>Step 2 - obtaining the dataset </em><em>----------------------------- </em> The ultimate goal of this step is to get output of the Python function `common.utils.survival_data()` and save it into a CSV file: # copy and paste into a Python console from common import utils survival_data = utils.survival_data('pypi', '2008', smoothing=6) survival_data.to_csv('survival_data.csv') Since full replication will take several months, here are some ways to speedup the process: <em>####Option 2.a, difficulty level: easiest </em> Just use the precomputed data. Step 1 is not necessary under this scenario. <strong>- </strong>extract <strong>**</strong>dataset_minimal_Jan_2018.zip<strong>** </strong><strong>- </strong>get `survival_data.csv`, go to the next step <em>####Option 2.b, difficulty level: easy </em> Use precomputed longitudinal feature values to build the final table. The whole process will take 15..30 minutes. <strong>- </strong>create a folder `<DATASET_PATH>/common.cache`, where `<DATASET_PATH>` is the value of the variable `DATASET_PATH` in `settings.py` <strong>- </strong>extract <strong>**</strong>dataset_minimal_Jan_2018<strong>** </strong>to the newly created folder <strong>- </strong>rename files: mv backporting.csv monthly_data.pypi_backporting.csv mv cc_degree.csv monthly_data.pypi_cc_degree.csv mv commercial.csv monthly_data.pypi_commercial.csv mv commits.csv monthly_data.pypi_commits.csv mv contributors.csv monthly_data.pypi_contributors.csv mv dc_katz.csv monthly_data.pypi_dc_katz.csv mv downstreams.csv monthly_data.pypi_downstreams.csv mv d_upstreams.csv monthly_data.pypi_d_upstreams.csv mv github_user_info.csv user_info.pypi.csv mv issues.csv monthly_data.pypi_issues.csv mv non_dev_issues.csv monthly_data.pypi_non_dev_issues.csv mv non_dev_submitters.csv monthly_data.pypi_non_dev_submitters mv package_urls.csv package_urls.pypi.csv mv q90.csv monthly_data.pypi_q90.csv # raw_dependencies.csv is not required # raw_packages_info.csv is not required # Feel free to read README.md for more details about the data mv submitters.csv monthly_data.pypi_submitters.csv # In this scenario we'll generate a new survival_data.csv mv university.csv monthly_data.pypi_university.csv mv upstreams.csv monthly_data.pypi_upstreams.csv <strong>- </strong>edit `common/decorators.py`, set `DEFAULT_EXPIRY` to some higher value, e.g. `DEFAULT_EXPIRY = float('inf') # cache never expires` Then, use the Python code above to obtain `survival_data.csv`. <em>####Option 2.c, difficulty level: medium </em> Use pre-downloaded raw data to build longitudinal feature values, and then the dataset. Despite most of the data is cached, some functions will pull up updates which might take anywhere from days to couple weeks to run. <strong>- </strong>Download <strong>**</strong>dataset_full_Jan_2018.tgz<strong>** </strong>(5.4Gb compressed, 34Gb unpacked). <strong>- </strong>edit `common/decorators.py`, set `DEFAULT_EXPIRY` to some higher value, e.g. `DEFAULT_EXPIRY = float('inf') # cache never expires` <strong>- </strong>extract the content of this archive into `<DATASET_PATH>`. <strong>- </strong>clean up `<DATASET_PATH>/common.cache` (otherwise you'll get Step 2.a. You can reproduce Step 2.b by deleting only `survival_data.pypi_2008_2017-12_6.csv`) Run the Python code above to obtain `survival_data.csv`. <em>####Option 2.d, difficulty level: hard </em> Build the dataset from scratch. Although most of the processing is parallelized, it will take at least couple months on a reasonably powerful server (32 cores, 512G of RAM, 2Tb+ of HDD space in our setup). <strong>- </strong>ensure the `<DATASET_PATH>` is empty <strong>- </strong>add more GitHub tokens (borrow from your coworkers) to `settings.py`. Run the Python code above to obtain `survival_data.csv`. <em>Step 3 - run the regression </em><em>--------------------------- </em> install R libraries: install.packages(c("htmlTable", "OIsurv", "survival", "car", "survminer", "ggplot2", "sqldf", "pscl", "texreg", "xtable")) Use `build_model.r` (e.g. in RStudio) and produced `survival_data.csv` to build the regressions used in the paper. This process takes at least 16Gb of RAM and takes few hours to run due to the gigantic size of the dataset. </pre>
Verlage und Open Access. Ergebnisse einer Umfrage unter wissenschaftlichen Verlagen in Deutschland, Österreich und der Schweiz.
<p>Im Rahmen einer Umfrage zum Thema „Verlage und Open Access“ kontaktierten wir im Mai/Juni 2018 82 wissenschaftliche Verlage in Deutschland, Österreich und der Schweiz, um einen Überblick über deren Einschätzungen bezüglich Open Access, über Erfahrungen, Angebote und Überlegungen zu erhalten. Ziel war es, die Positionen wissenschaftlicher Verlage besser kennen zu lernen und damit einen Beitrag zu einem besseren Austausch zwischen Verlagen, Bibliotheken und anderen Stakeholdern zu leisten.</p> <p>Dazu wurden in 33 Fragen die Nachfrage von AutorInnen bezüglich Open-Access-Angeboten, Herausforderungen und Chancen durch Open Access aus Verlagssicht und der Investitionsbedarf für eine Umstellung auf Open Access ebenso erhoben wie die Einstellung der Verlage zur Zweitveröffentlichung wissenschaftlicher Publikationen, Einschätzungen zu Entwicklungen im Bereich des wissenschaftlichen Publizierens und die mögliche zukünftige Rolle von Verlagen.</p> <p>In May and June 2018 we contacted 82 scholarly publishers in Germany, Austria and Switzerland for a survey on the subject of "Publishers and Open Access" in order to obtain an overview of their opionins on Open Access, their experiences, offers and considerations. Our aim was to find out more about the positions of scholarly publishers and thus contribute to a better exchange between publishers, libraries and other stakeholders.</p> <p>To this end, we asked 33 questions on the demand of authors for Open Access offers, the challenges and opportunities related to Open Access from the publishers' point of view and the investment requirements for a Transition to Open Access, as well as on the attitude of publishers to the self-archiving of scholarly publications, assessments of developments in the field of scholarly publishing and the possible future role of publishers.</p>
TU Delft Open Science MOOC survey 2018
<p>The dataset contains a survey resultas conducted by the Open Science MOCC in Assignment 1. The results include the researchers' opinions regarding the Open Science topics.</p>
Improving Open Source Face Detection by Combining an Adapted Cascade Classification Pipeline and Active Learning
<p>The <em><strong>EAVISE Open Source Face Detection Dataset</strong></em> consists of several items that were used to generate the improved frontal face detection model using LBP features and AdaBoost for OpenCV3.2.</p> <ul> <li>The annotations of the FDDB dataset, converted to the OpenCV format for doing a correct evaluation.</li> <li>The final trained model (IterativeHardPositives+ model) which is included in the OpenCV 3.2 framework.</li> </ul>
Guidelines for Open Peer Review Implementation
<p>Background data and documents for submitted article "<strong>Guidelines for open peer review implementation</strong>", created as a result of a workshop organised by the OpenUP project 27th March 2018 in London, UK.</p> <p>Article abstract: Open peer review (OPR) is moving into the mainstream, but it is often poorly understood and surveys of researcher attitudes show important barriers to implementation. As more journals move to implement and experiment with the myriad of innovations covered by this term, there is a clear need for best practice guidelines to guide implementation. This brief article aims to address this knowledge gap, reporting work based on an interactive stakeholder workshop to create best-practice guidelines for editors and journals who wish to transition to OPR. Although the advice is aimed mainly at editors and publishers of scientific journals, since this is the area in which OPR is at its most mature, many of the principles may also be applicable for the implementation of OPR in other areas (e.g., books, conference submissions).</p> <p>Documents contained here:</p> <p>- Pre-workshop informal data-gathering</p> <p>- Workshop agenda and attendee list</p> <p>- Minutes from the Workshop session on implementation guidelines</p> <p>- Slides used to introduce the theme and begin discussion</p> <p> </p>
Molecular Biology Open Access Pubmed Word and Sentence Representations
<p><strong>Natural Language Embeddings about Molecular Biology</strong></p> <p>This dataset is concerned with developing a tailored training data set for word and sentence embedding based on biomedical text that has some component associated with molecular work (as opposed to the other range of work indexed in PubMed like non molecular clinical work, studies of human behavior, etc). </p> <p><strong>Raw Data</strong></p> <p>In order to develop natural language embeddings (for words and sentences), we queried PMC and MEDLINE for molecular papers only by using high-level MeSH terms to restrict interest to papers with a molecular focus. We used the following MeSH terms:</p> <ul> <li>Cells [A11]</li> <li>Multiprotein Complexes [D05.500]</li> <li>Protein Aggregates [D05.875]</li> <li>Hormones [D06]</li> <li>Enzymes and Coenzymes [D08]</li> <li>Carbohydrates [D08]</li> <li>Lipids [D10]</li> <li>Amino Acids, Peptides and Proteins [D12]</li> <li>Nucleic Acids, Nucleotides and Nucleosides [D13]</li> <li>Biological Factors [D23]</li> <li>Pharmaceutical Preparations [D26]</li> <li>Metabolism [G03]</li> <li>Genetic Phenomena [G06]</li> </ul> <p>Queries for these terms use the following string:</p> <blockquote> <p>"cells"[MeSH Terms] OR "Multiprotein Complexes"[mh] OR "Protein Aggregates"[mh] OR "Hormones, Hormone Substitutes, and Hormone Antagonists"[mh] OR "Enzymes and Coenzymes"[mh] OR "Carbohydrates"[mh] OR "Lipids"[mh] OR "Amino Acids, Peptides, and Proteins"[mh] OR "Nucleic Acids, Nucleotides, and Nucleosides"[mh] OR "Biological Factors"[mh] OR "Pharmaceutical Preparations"[mh] OR "Metabolism"[mh] OR "Cell Physiological Phenomena"[mh] OR "Genetic Phenomena"[mh]</p> </blockquote> <p>PubMed returns 11,447,521 abstracts. PMC returns, 1,720,266 documents, 509,722 of these are open access. We downloaded, parsed and concatenated 403,825 PMC open access documents into a single file `molecular_oa_pmc.tsv`. This is a 33GB TSV file with the following columns:</p> <ul> <li>File:Paragraph - a unique identifier for each paragraph</li> <li>SentenceId - the local number of the sentence in the document</li> <li>Sentence Text - tokenized text of the sentence (based on <a href="https://github.com/ClearTK/cleartk/blob/master/cleartk-token/src/main/java/org/cleartk/token/tokenizer/TokenAnnotator.java">ClearTk's TokenAnnotator.java</a>)</li> <li>Codes - <code>exLink</code> for the presence of a citation, <code>inLink</code> for the presence of link to a Figure</li> <li>Figures - Figure codes</li> <li>Headings - High level section of the paper</li> <li>Offset_Begin - offset of the start of the sentence within the paper</li> <li>Offset_End - offset of the start of the sentence within the paper</li> </ul> <p>We repeated the same process for PubMed abstracts to generate a 3.6G file (`molecular_oa_medline.tsv`) with three columns:</p> <ol> <li>Pubmed ID</li> <li>A Boolean value indicating whether the article is a review</li> <li>Text</li> </ol> <p>We concatenated the text columns of these two files into a single 30GB file (`molecular_oa.txt`) where each line is a single sentence and the text is fully tokenized. </p> <p>These three files are archived in `molecular_oa_raw_text.tar.gz`.</p> <p><strong>Fasttext Embedding</strong></p> <p>We trained a fasttext model on the raw training data (https://fasttext.cc/) using the standard `skipgram` parameter. A gzipped copy of the word embeddings is included in `fasttext.model.vec.gz` </p> <p><strong>GloVe Embedding</strong></p> <p>We trained GloVe models on the raw training data (https://nlp.stanford.edu/projects/glove/). A gzipped copy of the best performing word embeddings is included in `bio_GloVe_300.tar.gz` </p>
Reverse flip open access journals
<p>This dataset provides data on 152 scholarly journals that have been identified to have "reverse flipped", i.e. at some point changed their publishing model from open access publishing to toll-access (incl. hybrid open access). Appended is a brief documentation covering the contents of the various data points. A description of the data collection method and analysis are aimed to be made available through a journal article publication during 2019.</p>
ASN 2016 evaluation with open data
<p>This publication contains several datasets that have been used in the paper "Crowdsourcing open citations with CROCI – An analysis of the current status of open citations, and a proposal" submitted to the <a href="https://www.issi2019.org/">17th International Conference on Scientometrics and Bibliometrics (ISSI 2019)</a>, available at <a href="https://arxiv.org/abs/1902.03287">https://arxiv.org/abs/1902.03287</a>.</p> <p>Additional information about the analyses described in the paper, including the code and the data we have used to compute all the figures, is available at <a href="https://github.com/sosgang/asn2016-issi2019">https://github.com/sosgang/asn2016-issi2019</a>. The datasets contain the following information.</p> <p><strong>[asncv | dblp | dblp_asncv]-data.csv:</strong> these CSV files contains all the final data used for the experiment with three conditions as described in the paper. The columns of the CSV file are the following ones (each row represents a particular candidate's application):</p> <ul> <li><em>level:</em> the professor level (1: Full Professor, 2: Associate Professor) to which the candidate wants to get the habilitation;</li> <li><em>session_date:</em> the date of the session for submitting the application;</li> <li><em>journal_number_open:</em> the number of journal article published by the candidate according to the open data available;</li> <li><em>citation_number_open:</em> the number of citations received by candidate's articles according to the open data available;</li> <li><em>h_index_open:</em> the h-index of the candidate according to the open data available;</li> <li><em>journal_number_real:</em> the number of journal article published by the candidate according to the official ASN 2016 data;</li> <li><em>citation_number_real:</em> the number of citations received by candidate's articles according to the official ASN 2016 data;</li> <li><em>h_index_real:</em> the h-index of the candidate according to the the official ASN 2016 data.</li> </ul> <p>All the other CSV files are intermediated data used to calculate the aforementioned one.</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.