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8,537 results for “OpenITI”
ENLIGHT Open Science Surveys datasets
<p>This data was collected in the context of Open Science surveys of the <a href="https://enlight-eu.org/" rel="nofollow">ENLIGHT</a> European university alliance.</p> <p>ENLIGHT RISE collected data from its partner universities:</p> <ul> <li> <p>Information on research data management (RDM) policies, support services and other activities was collected in November 2021. The dataset contains information from 9 partner universities.</p> </li> <li> <p>Information on Open Science (OS) activities, infrastructure and support, skills and knowledge, community activities, policy, recognition and rewards, and environment was collected from 11 December 2021 until 31 January 2022. The dataset contains information from 9 partner universities.</p> </li> <li> <p>Updated information on OS and RDM policies, support and other activities was collected in February/March 2024. Moreover, views on joint achievements and possible future actions were investigated. The 2024 dataset contains information from 10 universities (one additional partner joined on 1 December 2023).</p> </li> </ul>
Raw Data for Mapping Repositories and their Institutional Open Science Policies in Asia
<p>Persistent Identifiers (PIDs), particularly Digital Object Identifiers (DOIs), are crucial for establishing a robust and globally accessible research infrastructure. In Asia, a diverse array of research outputs and resources are produced and published in repositories. However, a significant number of these repositories, and outputs remain undiscoverable in global registries and aggregators. <br><br>These three datasets provides comprehensive information on the adoption of repositories, Open Access mandates, and DOIs adoption in Asian countries. It includes detailed records from different registry sources and repository platforms.<br><br>You can read the full report titled 'Mapping Repositories and their Institutional Open Science Policies in Asia' at <a href="https://doi.org/10.5281/zenodo.12566244">https://doi.org/10.5281/zenodo.12566244</a></p>
Dataset for: Evaluating phylogenetic methods for quantifying risks and opportunities presented by forks in open source software (master dissertation).
<p>This is the data for my master dissertation [1]. If you wish to get a copy, download it from Zenodo and open docs/master.pdf.</p> <p>Data acquisition and encoding techniques are described in paragraph 3.1.1 (table 3.1).</p> <p>The data is described in more detail in paragraph 4.1 (table 4.2).</p> <p>* fork1_all.csv: MySQL server / MariaDB server<br> * fork2_all.csv: Linux kernel / Android kernel<br> * fork3_all.csv: Apache OpenOffice / LibreOffice</p> <p>==Cite==<br> [1] A. Ortiz-Troncoso. Evaluating phylogenetic methods for quantifying risks and opportunities presented<br> by forks in open source software (master dissertation). Zenodo, 2018. doi: http://doi.org/10.5281/zenodo.1158292</p>
Figures in Scientific Open Access Publications - Underlying Data
<p>This publication contains data for a statistical analysis of an OA article corpus. The underlying dataset consists of over 1 million open access articles from different publishers (Copernicus: 9592; Springer:78418; Hindawi: 147848; Frontiers: 57621; PMC (aggregator): 747839)</p>
CrowdTruth Corpus for Open Domain Relation Extraction from Sentences
<p>This repository contains a ground truth corpus for open domain relation extraction from sentences, acquired with crowdsourcing and processed with <strong><a href="http://crowdtruth.org/">CrowdTruth</a></strong> metrics that capture ambiguity in annotations by measuring inter-annotator disagreement.</p> <p>The dataset contains annotations for 4,100 sentences sampled from Angeli et al. (1) and Riedel et al. (2), over 16 relations, with each sentence annotated by 15 workers. The sentences have been pre-processed with Distant Supervision (3) using the Freebase knowledge base, in order to identify the term pairs in each sentence that are likely to express a relation. The crowdsourced data was collected from <a href="http://figure-eight.com/">Figure Eight</a> and <a href="https://www.mturk.com/">Amazon Mechanical Turk</a>.</p> <p>This corpus has been discussed in the following papers:</p> <ul> <li>Anca Dumitrache, Lora Aroyo and Chris Welty: <strong><a href="https://arxiv.org/abs/1809.00537">Crowdsourcing Semantic Label Propagation in Relation Classification</a></strong>. <a href="http://fever.ai/">FEVER</a> Workshop at <a href="http://emnlp2018.org/">EMNLP 2018</a>.</li> <li>Anca Dumitrache, Lora Aroyo and Chris Welty: <strong><a href="https://arxiv.org/abs/1711.05186">False Positive and Cross-relation Signals in Distant Supervision Data</a></strong>. <a href="http://www.akbc.ws/">AKBC</a> Workshop at <a href="http://nips.cc/">NIPS 2017</a>.</li> <li>Anca Dumitrache, Lora Aroyo and Chris Welty: <strong><a href="http://crowdtruth.org/wp-content/uploads/2017/03/collint17-open-domain.pdf">Disagreement in Crowdsourcing and Active Learning for Better Distant Supervision Quality</a></strong>. <a href="http://collectiveintelligenceconference.org/">Collective Intelligence 2017</a>.</li> </ul> <p>Sentence-level data is available in file: <code>|--data/output/aggregated_sentences.csv</code></p> <p>Worker-level data is available in file: <code>|--data/output/aggregated_workers.csv</code></p> <p>Raw crowdsourcig data is available in folder: <code>|--data/input/</code></p> <p>Results of the relation classification model are available in folder: <code>|--data/model_results/</code></p> <p> </p> <p>References</p> <p>(1) Angeli, Gabor, et al. "Combining distant and partial supervision for relation extraction." Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). 2014.</p> <p>(2) Riedel, Sebastian, et al. "Relation extraction with matrix factorization and universal schemas." Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL). 2013.</p> <p>(3) Mintz, Mike, et al. "Distant supervision for relation extraction without labeled data." Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2-Volume 2. Association for Computational Linguistics, 2009.</p>
Built Open spaces
<p>ESM data subset, generated by extracting band 30 as buildings with the next information:</p> <p>gid integer geom geometry(Polygon,EPSG:3035) albedo real transmissivity real vegetation_shadow real run_off_coefficient real building_shadow smallint hillshade_building real</p> <p>This data is an input for local effects calculation.</p>
Types, open citations, closed citations, publishers, and participation reports of Crossref entities
<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://opencitations.wordpress.com/2019/02/07/crowdsourcing-open-citations-with-croci/">https://opencitations.wordpress.com/2019/02/07/crowdsourcing-open-citations-with-croci/</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 as a Jupyter notebook at <a href="https://github.com/sosgang/pushing-open-citations-issi2019/blob/master/script/croci_nb.ipynb">https://github.com/sosgang/pushing-open-citations-issi2019/blob/master/script/croci_nb.ipynb</a>. The datasets contain the following information.</p> <p><strong>non_open.zip:</strong> it is a zipped (~5 GB unzipped) CSV file containing the numbers of open citations and closed citations received by the entities in the Crossref dump used in our computation, dated October 2018. All the entity types retrieved from Crossref were aligned to one of following five categories: journal, book, proceedings, dataset, other. The open CC0 citation data we used came from the CSV dump of <a href="https://doi.org/10.6084/m9.figshare.6741422.v3">most recent release of COCI dated 12 November 2018</a>. The number of closed citations was calculated by subtracting the number of open citations to each entity available within COCI from the value “is-referenced-by-count” available in the Crossref metadata for that particular cited entity, which reports all the DOI-to-DOI citation links that point to the cited entity from within the whole Crossref database (including those present in the Crossref ‘closed’ dataset).</p> <p>The columns of the CSV file are the following ones:</p> <ul> <li><em>doi:</em> the DOI of the publication in Crossref;</li> <li><em>type:</em> the type of the publication as indicated in Crossref;</li> <li><em>cited_by:</em> the number of open citations received by the publication according to COCI;</li> <li><em>non_open:</em> the number of closed citations received by the publication according to Crossref + COCI.</li> </ul> <p><strong>croci_types.csv:</strong> it is a CSV file that contains the numbers of open citations and closed citations received by the entities in the Crossref dump used in our computation, as collected in the previous CSV file, alligned in five classes depening on the entity types retrieved from Crossref: <em>journal</em> (Crossref types: journal-article, journal-issue, journal-volume, journal), <em>book</em> (Crossref types: book, book-chapter, book-section, monograph, book track, book-part, book-set, reference-book, dissertation, book series, edited book), <em>proceedings</em> (Crossref types: proceedings-article, proceedings, proceedings-series), <em>dataset</em> (Crossref types: dataset), <em>other</em> (Crossref types: other, report, peer review, reference-entry, component, report-series, standard, posted-content, standard-series).</p> <p>The columns of the CSV file are the following ones:</p> <ul> <li><em>type:</em> the type publication between "journal", "book", "proceedings", "dataset", "other";</li> <li><em>label:</em> the label assigned to the type for visualisation purposes;</li> <li><em>coci_open_cit</em>: the number of open citations received by the publication type according to COCI;</li> <li><em>crossref_close_cit:</em> the number of closed citations received by the publication according to Crossref + COCI.</li> </ul> <p><strong>publishers_cits.csv:</strong> it is a CSV file that contains the top twenty publishers that received the greatest number of open citations. The columns of the CSV file are the following ones:</p> <ul> <li><em>publisher</em>: the name of the publisher;</li> <li><em>doi_prefix</em>: the list of DOI prefixes used assigned by the publisher;</li> <li><em>coci_open_cit</em>: the number of open citations received by the publications of the publisher according to COCI;</li> <li><em>crossref_close_cit</em>: the number of closed citations received by the publications of the publishers according to Crossref + COCI;</li> <li><em>total_cit</em>: the total number of citations received by the publications of the publisher (= <em>coci_open_cit</em> + <em>crossref_close_cit</em>).</li> </ul> <p><strong>20publishers_cr.csv: </strong>it is a CSV file that contains the numbers of the contributions to open citations made by the twenty publishers introduced in the previous CSV file as of 24 January 2018, according to the data available through the Crossref API. The counts listed in this file refers to the number of publications for which each publisher has submitted metadata to Crossref that include the publication’s reference list. The categories 'closed', 'limited' and 'open' refer to publications for which the reference lists are not visible to anyone outside the Crossref Cited-by membership, are visible only to them and to Crossref Metadata Plus members, or are visible to all, respectively. In addition, the file also record the total number of publications for which the publisher has submitted metadata to Crossref, whether or not those metadata include the reference lists of those publications.</p> <p>The columns of the CSV file are the following ones:</p> <ul> <li><em>publisher: </em>the name of the publisher;</li> <li><em>open: </em>the number of publications in Crossref with an 'open' visibility for their reference lists;</li> <li><em>limited: </em>the number of publications in Crossref with an 'limited' visibility for their reference lists;</li> <li><em>closed: </em>the number of publications in Crossref with an 'closed' visibility for their reference lists;</li> <li><em>overall_deposited:</em> the overall number of publications for which the publisher has submitted metadata to Crossref.</li> </ul>
Aversive imagery causes de novo fear conditioning (Open Data and Open Materials)
<p><strong>Open Data and Open Materials of: Mueller, E. M., Sperl, M. F. J., & Panitz, C. (2019). Aversive imagery causes de novo fear conditioning. <em>Psychological Science</em>, <em>30</em>(7), 1001–1015.</strong></p> <p>In classical fear conditioning, neutral conditioned stimuli (CS) that have been paired with aversive physical unconditioned stimuli eventually trigger fear responses. Here, we test whether aversive mental images systematically paired with a CS may also cause de novo fear learning in the absence of any external aversive stimulation. In two experiments, <em>N</em>=45 and <em>N</em>=41 participants were first trained to produce aversive, neutral, or no imagery in response to one of three different visual imagery cues. In a subsequent imagery-based differential conditioning paradigm, each of the three cues systematically co-terminated with one of three different neutral faces. Although the face that was paired with the aversive imagery cue was never paired with aversive external stimuli or threat-related instructions, participants rated it as more arousing, unpleasant, and threatening and displayed relative fear bradycardia and fear-potentiated startle. These results could be relevant for the development of fear and related disorders without trauma.</p>
Datensatz zu: Fachgesellschaften und Open Access in Deutschland – eine Analyse zur Herausgabe von Zeitschriften
<p>In der Debatte um die Open-Access-Transformation wird auch die Rolle wissenschaftlicher Fachgesellschaften diskutiert. Bisher gab es keine systematische Erhebung zum Einfluss von Fachgesellschaften auf das Publikationssystem. Dieser unbefriedigende Forschungsstand führte dazu, dass das Potenzial dieses wichtigen Akteurs bei der Open-Access-Transformation bisher weitgehende unbeachtet blieb und möglichen Barrieren auf Seiten der Fachgesellschaften nicht adressiert wurden. Im Rahmen des Projekts „Options4OA“ wurden darum Publikations- und Open-Access-Aktivitäten deutscher Fachgesellschaften untersucht.</p> <p>Vorliegender Datensatz dokumentiert die dem Poster zugrundeliegenden Forschungsdaten in drei Datensätzen.</p> <p>Diese Datensätze beschreiben 182 Zeitschriften, die wissenschaftliche Fachgesellschaften, die in Deutschland angesiedelt sind, veröffentlichen. Neben allgemeinen Metadaten zu den Zeitschriften und den herausgebenden Fachgesellschaften finden sich in den Datensätzen Informationen zum Open-Access-Status der Zeitschriften und den Open-Access-Publikationsgebühren. Auch sind die Zeitschriften den Notationen der Fachsystematik der Deutschen Forschungsgemeinschaft (DFG) zugeordnet.</p> <p>Das Vorhaben wurde vom Bundesministerium für Bildung und Forschung (BMBF) im Rahmen des Projektes „Options4OA” gefördert (Förderkennzeichen: 16OA034).</p> <p>Weitere Informationen unter: <a href="https://os.helmholtz.de/projekte/options4oa/">https://os.helmholtz.de/projekte/options4oa/</a></p>
Open Access in developing countries – attitudes and experiences of researchers Dataset
<p>A survey was conducted of 507 researchers from the developing world and connected to INASP’s AuthorAID project to ascertain experiences and attitudes to Open Access publishing. This file is the raw output from the survey, with names and email addresses removed to preserve anonymity. </p>
Dataset: Open access potential and uptake in the context of Plan S - a partial gap analysis
<p>Dataset belonging to the report: <a href="https://doi.org/10.5281/zenodo.3543000">Open access potential and uptake in the context of Plan S - a partial gap analysis</a></p> <p> </p> <p>On the report: </p> <p>The analysis presented in the report, carried out by Utrecht University Library, aims to provide cOAlition S, an international group of research funding organizations, with initial quantitative and descriptive data on the availability and usage of various open access options in different fields and subdisciplines, and, as far as possible, their compliance with Plan S requirements.</p> <p>Plan S, launched in September 2018, aims to accelerate a transition to full and immediate Open Access. In the guidance to implementation, released in November 2018 and updated in May 2019, a gap analysis of Open Access journals/platforms was announced. Its goal was to inform Coalition S funders on the Open Access options per field and identify fields where there is a need to increase the share of Open Access journals/platforms. </p> <p>The report should be seen as a first step: an exploration in methodology as much as in results. Subsequent interpretation (e.g. on fields where funder investment/action is needed) and decisions on next steps (e.g. on more complete and longitudinal monitoring of Plan S-compliant venues) is intentionally left to cOAlition S and its members. </p> <p> </p> <p><em>This work was commissioned on behalf of cOAlition S by the Dutch Research Council (NWO), a member of cOAlition S. Bianca Kramer and Jeroen Bosman of Utrecht University Library were appointed to lead the project.</em></p>
Github commit data for the article "Beyond Zipf's law: Exploring the discrete generalized beta distribution in open-source repositories"
<p><span>This dataframe corresponds to the data used in the Nowak's et al. 2024 article "Beyond Zipf’s law: Exploring the discrete generalized beta distribution in open-source repositories" (see reference below).</span></p> <p><span>It consists of the distirbutions of number of commits per user across a number of GitHub repositories. <br><br>There are three columns:</span></p> <ul> <li><span>repository: the repository name</span></li> <li><span># of commits: the number of commits of a given individual</span></li> <li><span>rank: the user rank in the repository (by decreasing number of commits)<br><br></span></li> </ul> <p><strong><span>Reference:</span></strong></p> <p><span>Nowak, P., Santolini, M., Singh, C., Siudem, G., & Tupikina, L. (2024). Beyond Zipf’s law: Exploring the discrete generalized beta distribution in open-source repositories. <em>Physica A: Statistical Mechanics and Its Applications</em>, <em>649</em>, 129927. <a href="https://doi.org/10.1016/j.physa.2024.129927">https://doi.org/10.1016/j.physa.2024.129927</a></span></p>
The Open Review–Based (ORB) dataset: Towards Automatic Assessment of Scientific Papers and Experiment Proposals in High–Energy Physics
<p>We introduce the new comprehensive Open Review–Based dataset (ORB); it includes a curated list of more than 62,000 scientific papers, publications and submitted preprints with their more than 157,000 reviews and final decisions. We gather this information from three peer-reviewed sources: the OpenReview.net, SciPost.org and PeerJ.com websites.</p>
Raw Data for Mapping Repositories and their Institutional Open Science Policies in the Middle East and North Africa (MENA)
<div> <p>Persistent Identifiers (PIDs), particularly Digital Object Identifiers (DOIs), are crucial for establishing a robust and globally accessible research infrastructure. In the Middle East and North Africa (MENA) region, a diverse array of research outputs and resources are produced and published in repositories. However, a significant number of these repositories, and outputs remain undiscoverable in global registries and aggregators. <br><br>These three datasets provides comprehensive information on the adoption of repositories, Open Access mandates, and DOIs adoption in MENA countries. It includes detailed records from different registry sources and repository platforms.<br><br>You can read the full report titled 'Mapping Repositories and their Institutional Open Science Policies in MENA' at <a href="https://doi.org/10.5281/zenodo.11370031">https://doi.org/10.5281/zenodo.11370031</a></p> </div>
EuDML zbMATH Open fulltext links subset (preview version)
<p>Lists all links from zbMATH Open documents that have fulltext links and an EuDML identifier. See <a href="https://doi.org/10.5281/zenodo.8021789">10.5281/zenodo.8021788</a> for more fulltext links.</p> <p>The meaning of the fields is:</p> <ul> <li><strong>eudml_id </strong>Unique identifier from EuDML. Prefix with <code>https://eudml.org/doc/</code> to visit additional information on the article. For example, <code>116878</code> is associated with <a href="https://eudml.org/doc/116878">https://eudml.org/doc/116878</a></li> <li><strong>zbmath_id</strong> Unique identifier from zbMATH Open. Prefix with <code>https://zbmath.org/</code> to visit additional information on the article. For example, <code>5224712</code> is associated with <a href="https://zbmath.org/5224712">https://zbmath.org/5224712</a></li> <li><strong>link</strong> link to the fulltext. Note that those links are not fully reliable. We estimate a success rate of 90%</li> </ul>
[review paper] Sustaining the 'Frozen Footprints' of Scholarly Communication through Open Citations_Dataset
<p>This dataset belongs to the review titled <em>Sustaining the ‘Frozen Footprints’ of Scholarly Communication through Open Citations</em>. The review explores the developments in the open citations movement, the OpenCitations infrastructure, and the Initiative for Open Citations (I4OC), providing a comprehensive overview of key milestones and initiatives.</p> <p>The dataset includes bibliographic and citation data for 174 scholarly outputs and 149 blogposts analyzed in the review. These outputs were drawn from a range of sources, including journal articles, conference proceedings, and other scholarly materials. The data has been curated to adhere to open citation principles, ensuring it is structured, separable, and openly accessible. It is provided in accordance with the licenses and terms of use of the original databases.</p> <p>Researchers, practitioners, and policymakers can use this dataset to explore the evolution of open citations and to further their understanding of the connections between scholarly works in the field of open research.</p>
NWO and ZonMw Open Access Monitor 2023 dataset
<p>This is the full dataset of the NWO and ZonMw Open Access Monitor 2023 report and accompanying Appendix 'Estimating Costs of Open Access Publishing'. </p> <p>DOI of NWO and ZonMw Open Access Monitor report: <a href="https://doi.org/10.5281/zenodo.12685800">https://doi.org/10.5281/zenodo.12685800 </a><br>DOI to Appendix ‘Estimating costs of open access publishing’: <a href="https://doi.org/10.5281/zenodo.13885012">https://doi.org/10.5281/zenodo.13885012</a></p> <p> </p>
5D-NP-FABTECH_ALD - Open Dataset for: "ZnO vapor phase infiltration into photo-patternable polyacrylate networks for the microfabrication of hybrid organic-inorganic structures"
<p>This is the open dataset for the paper: "L. Demelius, L. Zhang, A. M. Coclite and M. D. Losego, ZnO vapor phase infiltration into photo-patternable polyacrylate networks for the microfabrication of hybrid organic–inorganic structures, <em>Mater. Adv.</em>, 2024, <strong>5</strong>, 8464–8474."</p> <p>This includes the supplementary information and all the source material that was used for the paper preparation.</p> <p>For each folder (sub-dataset), there exists a corresponding readme file describing the content and including material.</p>
Testing whole slide image for OpenPhi - Open Pathology Interface
<p>An anonymous whole slide image in Philips iSyntax format for running software tests on OpenPhi - Open Pathology Interface (https://zenodo.org/record/4680748#.YNnBxDqxXJU). See the repository (https://gitlab.com/BioimageInformaticsGroup/openphi/) for up to date information.</p>
Indicative distribution map for Ecosystem Functional Group FM1.2 Permanently open riverine estuaries and bays
<p>This archive contains indicative distribution maps and profiles for <strong>FM1.2 Permanently open riverine estuaries and bays</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</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.