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1,010 results for “governance”
User-centered Usability Analysis of 41 Open Government Data Portals
<p>The data were collected during the user-centered analysis of usability of 41 open government data portals including EU27, applying a common methodology to them, considering aspects such as specification of open data set, feedback and requests, further broken down into 14 sub-criteria. Each aspect was assessed using a three-level Likert scale (fulfilled - 3, partially fulfilled - 2, and unfulfilled – 1), that belongs to the acceptability tasks. This dataset summarises a total of 1640 protocols obtained during the analysis of the selected portals carried out by 40 participants, who were selected on a voluntary basis. This is complemented with 4 summaries of these protocols, which include calculated average scores by category, aspect and country. These data allow comparative analysis of the national open data portals, help to find the key challenges that can negatively impact users’ experience, and identifies portals that can be considered as an example for the less successful open data portals.</p>
Experimental data and software for: Defaults: a double-edged sword in governing common resources
<p>Experimental data and software for the paper: <strong>Defaults: a double-edged sword in governing common resources</strong></p> <p>The experiment consisted in three treatments of the Common Pool Resource Dilemma, where three default interventions were applied: pro-social, self-serving and no default. Plus, the participants had to complete an SVO task and a Risk assessment task.</p> <h4>Description of the data and file structure</h4> <p>In the file called <code>all_participants.csv</code> is the full dataset of all participants that took part of the experiment. This includes participants who will end up excluded and dropouts.</p> <p>The experimental data files come in two formats: wide and long. The wide version, called <code>data_wide_format.csv</code> contains one row per participant and a column for all the fields, including rounds from 1 to 10 of the CPR task. Also, this file includes all demographic information of the participants, times and payments. The ID shown is generated internally and has no relationship with the participants' Prolific ID.</p> <p>The long version, called <code>data_long_format.csv</code>, contains 10 rows per participant, and columns for the extraction and other variables necessary for analysis. This version contains the necessary data to reproduce all the figures and statistics detailed in the main manuscript.</p> <p>In both of the previous files, the participants taken into account were the ones who completed the whole experiment. Those who did not complete the comprehension test, dropped out or did not sign the Informed Consent Form were excluded from the experimental data used. More details in the Methods below.</p> <p>In the file <code>default_opinions.csv</code>, we manually classified the responses by participants to whether they were influenced by the default presented.</p> <p>The file "<code>Instructions of the experiment.pdf</code>" contains the instructions of the experiment as shown to participants, also screenshots of the platform.</p>
An Integrated Usability Framework for Evaluating Open Government Data Portals and Analysis of EU and GCC OGD Portals
<p><span>This dataset contains data collected during a study (<em><strong>"<a href="https://arxiv.org/ftp/arxiv/papers/2403/2403.08451.pdf">An Integrated Usability Framework for Evaluating Open Government Data Portals: Comparative Analysis of EU and GCC Countries</a>"</strong></em>) conducted by Fillip Molodtsov and Anastasija Nikiforova (University of Tartu).</span></p> <p><span> </span><span>It being made public both to act as supplementary data for the paper and in order for other researchers to use these data in their own work potentially contributing to the improvement of current data ecosystems and develop user-friendly, collaborative, robust, and sustainable open data portals.</span></p> <p><span>***Purpose of the study***</span></p> <p><span>This paper develops an integrated framework for evaluating OGD portal effectiveness that accommodates user diversity (regardless of their data literacy and language), evaluates collaboration and participation, and the ability of users to explore and understand the data provided through them. </span></p> <p><span>The framework is validated by applying it to 33 national portals across European Union (EU) and Gulf Cooperation Council (GCC) countries, as a result of which we rank OGD portals, identify some good practices that lower-performing portals can learn from, and common shortcomings.</span></p> <p><span>***Methodology***</span></p> <p><span>(1) systematic literature review to establish a knowledge base and identify frameworks have been used to evaluate OGD portals, we conducted a systematic literature review - Dataset_ Usability_Framework_SLR;</span></p> <p><span>(2) development of the Integrated Usability Framework for Evaluating Open Government Data Portals, which content is based on the outputs of the first step, along with selected articles of experts in portal design, and an exploratory assessment of the French, Irish, Estonian and Spanish portals - Dataset_Integrated_Usability_Framework;</span></p> <p><span>(3) data collection, that is a completion of the protocol developed in the previous step by analysing 34 national OGD portals of the EU and GCC countries. When all individual protocols were collected, the total score are calculated using the weighting system. The average scores are calculated for the EU and GCC. The portals are ranked. The top portals (best performers) are determined for each dimension - Dataset_EU_GCC_OGDportal_Usability_results_clustering.</span></p> <p><span>(4) identification of relationships and patterns among different portals based on their performance metrics as a result of the cluster analysis. By calculating the average dimensional scores of portals from both types of clusters, their performance across multiple dimensions is evaluated - Dataset_EU_GCC_OGDportal_Usability_results_clustering.</span></p> <p> </p> <p><strong><em><span>For more details see Molodtsov, F., Nikiforova, A. (2024). “An Integrated Usability Framework for Evaluating Open Government Data Portals: Comparative Analysis of EU and GCC Countries”. In Proceedings of the 25th Annual International Conference on Digital Government Research (DGO 2024), June 11--14, 2024, Taipei, Taiwan, 10.1145/3657054.3657159</span></em></strong></p> <p><span>***Format of the file***</span></p> <p><span>.xls, .csv</span></p> <p><span>***Licenses or restrictions***</span></p> <p><span>CC-BY</span></p>
Local Governance in Ukraine during the full-scale Russian invasion. – Merged data from online surveys of local self-government authorities by the Congress of Local and Regional Authorities of the Council of Europe in 2022 and Kyiv School of Economics in 2024.
The dataset includes responses from two waves of online surveys targeting local self-government representatives in Ukraine, with a focus on crisis governance during the ongoing Russian war. The first wave was conducted from August 30 to September 20, 2022, by the Congress of Local and Regional Authorities of the Council of Europe, yielding 241 responses (16% of all Ukrainian local communities). The second wave was conducted by Kyiv School of Economics from January 1 to March 12, 2024, with 181 responses (14% of government-controlled municipalities). Data formats include CSV and SAV files, along with an XSL codebook for both waves. The merged dataset comprises 442 responses from small, medium, and large municipalities under varied security conditions, with a total file size of approximately 4 MB.
Smarter open government data for Society 5.0: analysis of 51 OGD portals
<p>This dataset contains data collected during a study <a href="https://doi.org/10.3390/s21155204">"Smarter open government data for Society 5.0: are your open data smart enough"</a> (<em>Sensors</em>. 2021; 21(15):5204) conducted by Anastasija Nikiforova (University of Latvia).<br> It being made public both to act as supplementary data for "Smarter open government data for Society 5.0: are your open data smart enough" paper and in order for other researchers to use these data in their own work.</p> <p>The data in this dataset were collected in the result of the inspection of 60 countries and their OGD portals (total of 51 OGD portal in May 2021) to find out whether they meet the trends of Society 5.0 and Industry 4.0 obtained by conducting an analysis of relevant OGD portals.</p> <p>Each portal has been studied starting with a search for a data set of interest, i.e. “real-time”, “sensor” and “covid-19”, follwing by asking a list of additional questions.<br> These questions were formulated on the basis of combination of (1) crucial open (government) data-related aspects, including open data principles, success factors, recent studies on the topic, PSI Directive etc., (2) trends and features of Society 5.0 and Industry 4.0, (3) elements of the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use Model (UTAUT).</p> <p>The method used belongs to typical / daily tasks of open data portals sometimes called “usability test” – keywords related to a research question are used to filter data sets, i.e. “real-time”, “real time” and “real time”, “sensor”, covid”, “covid-19”, “corona”, “coronavirus”, “virus”. In most cases, “real-time”, “sensor” and “covid” keywords were sufficient.<br> The examination of the respective aspects for less user-friendly portals was adapted to particular case based on the portal or data set specifics, by checking:<br> 1. are the open data related to the topic under question ({sensor; real-time; Covid-19}) published, i.e. available?<br> 2. are these data available in a machine-readable format?<br> 3. are these data current, i.e. regularly updated? Where the criteria on the currency depends on the nature of data, i.e. Covid-19 data on the number of cases per day is expected to be updated daily, which won’t be sufficient for real-time data as the title supposes etc.<br> 4. is API ensured for these data? having most importance for real-time and sensor data;<br> 5. have they been published in a timely manner? which was verified mainly for Covid-19 related data. The timeliness is assessed by comparing the dates of the first case identified in a given country and the first release of open data on this topic.<br> 6. what is the total number of available data sets?<br> 7. does the open government data portal provides use-cases / showcases? <br> 8. does the open government portal provide an opportunity to gain insight into the popularity of the data, i.e. does the portal provide statistics of this nature, such as the number of views, downloads, reuses, rating etc.?<br> 9. is there an opportunity to provide a feedback, comment, suggestion or complaint?<br> 10. (9a) is the artifact, i.e. feedback, comment, suggestion or complaint, visible to other users?</p> <p>***Format of the file***<br> .xls, .ods, .csv (for the first spreadsheet only)</p> <p>***Licenses or restrictions***<br> CC-BY</p> <p>For more info, see README.txt</p>
Survey: Data Governance in Colombian Enterprises
<p>The purpose of this survey is to analyze the perception of Data Governance (DG) by Information Technology (IT) professionals in Colombian companies, in order to understand the knowledge about the importance and prospects, as well as the potential challenges in adopting good data governance practices. Its design is based on studies carried out from surveys such as Modern Data Governance (Russom, 2020) to IT professionals and consultants of companies mainly in the United States and Canada, with another smaller percentage in companies in Asia, Europe and Latin America.</p>
Transparency in agricultural land lease by local government
<p>In this research, the focus was on analysing transparency aspect of government and public administration, i.e. how transparent tenders for the allocation and disposition of state-owned agricultural land are conducted. The main objective of this work was to investigate and critically examine the practices of publishing tenders for the lease of state agricultural land in the local units of six selected counties in the Republic of Croatia.</p>
Network properties data and code used in "Ecological plasticity governs ecosystem services in multilayer networks".
<p>Code and network properties data used in the analyses presented in "Ecological plasticity governs ecosystem services in multilayer networks". Further information can be requested of the author David A. Bohan (David.Bohan@inrae.fr).</p>
Dataset from: A quiet public? Procedural justice in Portuguese wind energy governance
<p>This dataset accompanies a journal article related with public participation in wind and solar energy in Portugal. It contains a database of web scraped public consultation processes related with wind power plants and decentralized solar power plants until 2023. It also contains the R Markdown files that were used to analyze the scraped data. The results of this analyzes, and their discussion, can be found in the associated article.</p>
Identifying patterns and recommendations of and for sustainable open data initiatives: a benchmarking-driven analysis of open government data initiatives among European countries
<p>This dataset contains data collected during a study <a href="https://www.sciencedirect.com/science/article/pii/S0740624X23000989"><em><strong>"Identifying patterns and recommendations of and for sustainable open data initiatives: a benchmarking-driven analysis of open government data initiatives among European countries"</strong></em></a> conducted by <em>Martin Lnenicka (University of Pardubice, Pardubice, Czech Republic), Anastasija Nikiforova (University of Tartu, Tartu, Estonia), Mariusz Luterek (University of Warsaw, Warsaw, Poland), Petar Milic (University of Pristina - Kosovska Mitrovica, Kosovska Mitrovica, Serbia), Daniel Rudmark (University of Gothenburg and RISE Research Institutes of Sweden, Gothenburg, Sweden), Sebastian Neumaier (St. Pölten University of Applied Sciences, Austria), Caterina Santoro (KU Leuven, Leuven, Belgium), Cesar Casiano Flores (University of Twente, Twente, the Netherlands), Marijn Janssen (Delft University of Technology, Delft, the Netherlands), Manuel Pedro Rodríguez Bolívar (University of Granada, Granada, Spain).</em></p> <p>It is being made public both to act as supplementary data for "<em>Identifying patterns and recommendations of and for sustainable open data initiatives: a benchmarking-driven analysis of open government data initiatives among European countries</em>", Government Information Quarterly*, and in order for other researchers to use these data in their own work. </p> <p>***Methodology***</p> <p>The paper focuses on benchmarking of open data initiatives over the years and attempts to identify patterns observed among European countries that could lead to disparities in the development, growth, and sustainability of open data ecosystems. </p> <p>This study examines existing benchmarks, indices, and rankings of open (government) data initiatives to find the contexts by which these initiatives are shaped, both of which then outline a protocol to determine the patterns. The composite benchmarks-driven analytical protocol is used as an instrument to examine the understanding, effects, and expert opinions concerning the development patterns and current state of open data ecosystems implemented in eight European countries - Austria, Belgium, Czech Republic, Italy, Latvia, Poland, Serbia, Sweden. 3-round Delphi method is applied to identify, reach a consensus, and validate the observed development patterns and their effects that could lead to disparities and divides. Specifically, this study conducts a comparative analysis of different patterns of open (government) data initiatives and their effects in the eight selected countries using six open data benchmarks, two e-government reports (57 editions in total), and other relevant resources, covering the period of 2013–2022.</p> <p>***Description of the data in this data set***</p> <p>The file "OpenDataIndex_<em>2013_</em>2022" collects an overview of 27 editions of 6 open data indices - for all countries they cover, providing respective ranks and values for these countries. These indices are:</p> <p>1) Global Open Data Index (GODI) (4 editions)</p> <p>2) Open Data Maturity Report (ODMR) (8 editions)</p> <p>3) Open Data Inventory (ODIN) (6 editions)</p> <p>4) Open Data Barometer (ODB) (5 editions)</p> <p>5) Open, Useful and Re-usable data (OURdata) Index (3 editions)</p> <p>6) Open Government Development Index (OGDI) (2 editions)</p> <p>These data shapes the third context - open data indices and rankings. The second sheet of this file covers countries covered by this study, namely, Austria, Belgium, Czech Republic, Italy, Latvia, Poland, Serbia, Sweden. It serves the basis for Section 4.2 of the paper.</p> <p>Based on the analysis of selected countries, incl. the analysis of their specifics and performance over the years in the indices and benchmarks, covering 57 editions of OGD-oriented reports and indices and e-government-related reports (2013-2022) that shaped a protocol (see paper, Annex 1), 102 patterns that may lead to disparities and divides in the development and benchmarking of ODEs were identified, which after the assessment by expert panel were reduced to a final number of 94 patterns representing four contexts, from which the recommendations defined in the paper were obtained. These patterns are available in the file "OGDdevelopmentPatterns". The first sheet contains the list of patterns, while the second sheet - the list of patterns and their effect as assessed by expert panel.</p> <p>***Format of the file***<br>.xls, .csv (for the first spreadsheet only)</p> <p>***Licenses or restrictions***<br>CC-BY</p> <p> </p> <p>For more info, see README.txt<br> </p>
Nordic forest governance database
<p>An open-access database of forest policy regulation focusing on Norway, Sweden, Finland and Denmark from the 19th century up to today. The resulting open-access database indicates date of introduction, summarizes the key policy objective, provides links to regulations and has been double-checked by national experts. The resulting database contains a comprehensive collection of public policies and private initiatives addressing forest management in Finland, Sweden, Norway, Denmark, and supranational policies of the EU. It includes the original title, the year of implementation, a short description of the policy, whether it has direct or indirect effect on forest management, and information and links to the original text or sources. The database is an open-access resource available to the community, and intended to be a living document which can be further complemented and updated.</p> <p> </p>
Public opinion poll "War, Peace, Victory and the Future" – National face-to-face opinion poll representative of the population in government-controlled territories of Ukraine on the war-related issues (June 2023)
The face-to-face survey was conducted by the Ilko Kucheriv Democratic Initiatives Foundation in cooperation with the Centre for Political Sociology from 5 to 15 June 2023. A total of 2,001 respondents aged 18 or older took part in the survey in Vinnytsia, Volyn, Dnipropetrovsk, Zhytomyr, Zakarpattia, Zaporizhzhia, Ivano-Frankivsk, Kyiv, Kirovohrad, Lviv, Mykolaiiv, Odesa, Poltava, Rivne, Sumy, Ternopil, Kharkiv, Kherson, Khmelnytskyi, Cherkasy, Chernihiv, and Chernivtsi regions, and the city of Kyiv (in Zaporizhzhia, Kharkiv, and Kherson regions – only in the territories controlled by Ukraine and not affected by hostilities). The sampling technique used in the survey is multi-stage, with a random selection of localities in the first stage and a quota-based selection of respondents in the final stage. The random selection is representative of the demographic structure of the adult population in the areas covered by the survey at the beginning of 2022. The maximum sampling error shall not exceed 2.3%. At the same time, it is necessary to take into account systematic deviations in the sample caused by the forced migration of millions of citizens due to the Russian-Ukrainian war. COMPOSITION OF MACRO-REGIONS: West – Volyn, Zakarpattia, Ivano-Frankivsk, Lviv, Rivne, Ternopil, and Chernivtsi regions; Center – Vinnytsia, Zhytomyr, Kyiv, Kirovohrad, Poltava, Sumy, Khmelnytskyi, Cherkasy, and Chernihiv regions, and the city of Kyiv; South – Zaporizhzhia, Mykolaiiv, Kherson, and Odesa regions; East – Dnipropetrovsk and Kharkiv regions. This dataset contains the original survey data. The SPSS file (.sav) is the original file. It has been exported to an Excel file. The content of the corresponding XLSX file should be identical to the original SAV file. The SAV file contains the questions and answer options of the original questionnaire in Ukrainian. The original questionnaire and an English translation have also been included in this data collection as separate PDF files. In addition, the dataset includes a file of "selected findings", which documents some of the key findings of the survey in the form of analytical summaries and descriptive statistics. The report was prepared by the civil society organisation OPORA.
Environment's Share in Total Government Budget Allocations for R&D
<p>Government Budget Allocations for R&D (GBARD). GBARD data are measuring government support to research and development (R&D) activities, and thereby provide information about the priority Governments give to different public R&D funding activities.</p> <p>GBARD data are compiled using the guidelines laid out in the OECD Guidelines for collecting and reporting data on research and experimental development - Frascati Manual, OECD, 2015 (See related identifiers). </p> <p>GBARD data are broken down by:</p> <p> - Socio-economic objectives (SEOs) in accordance to the Nomenclature for the analysis and comparison of scientific programmes and budget.</p> <p>This dataset calculates the share of the Environment objective compared to the total allocations.</p> <p><br> The source (raw) dataset released by Eurostat: <a href="http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=gba_nabsfin07&lang=en">GBARD by socioeconomic objectives (NABS 2007)[gba_nabsfin07]</a></p>
Government Budget Allocations for R&D in Environment
<p>Government Budget Allocations for R&D (GBARD). GBARD data are measuring government support to research and development (R&D) activities, and thereby provide information about the priority Governments give to different public R&D funding activities.</p> <p>GBARD data are compiled using the guidelines laid out in the OECD Guidelines for collecting and reporting data on research and experimental development - Frascati Manual, OECD, 2015 (See related identifiers). </p> <p>GBARD data are broken down by:</p> <p> - Socio-economic objectives (SEOs) in accordance to the Nomenclature for the analysis and comparison of scientific programmes and budget. This dataset uses the Environment objective.<br> <br> The missing data are approximated, forecasted and backcasted by country. In our version of the dataset, 40% more countries, and a 23% larger dataset can be used for supervised and unsupervised learning models, such as machine learning, that require complete datasets compared to the source dataset released by Eurostat: <a href="http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=gba_nabsfin07&lang=en">GBARD by socioeconomic objectives (NABS 2007)[gba_nabsfin07]</a></p>
Data of Female members of National Federations Sport Governing Boards. Database GESPORT Project.
<p>This database has been built by the authors. The data has been collected from the websites of the national federations of Italy, Portugal, Turkey, Spain and the United Kingdom in 2018.</p> <p>With the support of the European Commission. Erasmus+ Project. "Corporate governance in sport organizations: a gendered approach". Project Reference -EPP-1-2017-1-ES-SPO-SCP</p>
Dynamic full-field imaging of rupture radiation: Material contrast governs source mechanism
<p>Datasets related to the research article 'Dynamic full-field imaging of rupture radiation: Material contrast governs source mechanism'. <br> A readme with the necessary Matlab code to load the data is included.<br> Tested on Matlab2020b</p> <p>For the analytic rupture radiation simulation code please check the linked github repository.</p>
Digital government HRM literature review
<p>Digital government HRM index</p> <p> </p> <p>Data collected and processed as part of the ODDEA (Overcoming Digital Divide Between Europe and Southeast Asia) EU research project (<em>Project ID: HORIZON MSCA-SE 101086381)</em></p>
Data for: Open Infrastructure Governance: Current structures, nomenclature, composition, and service trends, 2024 State of Open Infrastructure Report
<p>The purpose of the analysis based on these data was to<span> record information about community governance groups for open infrastructures, focused primarily on the individuals and institutions that serve in these groups. The data were summarized and reported in the “2024 State of Open Infrastructure Report” section “Open infrastructure governance: Current structures, nomenclature, composition, and trends.” The full report is available at <a href="The%20data%20were%20summarized%20and%20reported%20in%20the%20&ldquo;2024%20State%20of%20Open%20Infrastructure%20Report&rdquo;%20section%20&ldquo;Open%20infrastructure%20governance:%20Current%20structures,%20nomenclature,%20composition,%20and%20trends,&rdquo;%20available%20at%20https:/doi.org/10.5281/zenodo.10934089.">https://doi.org/10.5281/zenodo.10934089</a>.</span></p> <p><span>A readme is provided with the dataset with additional detail.</span></p>
Data and Code for the paper 'Indication of long-range correlations governing city size'
<div> <h1>Summary</h1> <p>This repository contains the preprocessed data necessary for constructing the city network as described in the related paper, as well as the code to do the Shortest-path Fluctuation Analysis (SFA) on those networks.</p> <div> <h2>Under the <code>data</code> folder</h2> <ul> <li> <p>In the <code>Node_list</code> folder, each <em><code>CSV</code></em> file has the name convention like <code>AL_1000_node_list.csv</code>, for example, this means the table contains the list of <strong>nodes</strong> that make up the network for <strong>Austria</strong> (as country code, <code>AL</code>) at the spatial clustering distance threshold of <strong>1000</strong>m. The table has 3 columns with column names, and without row names.</p> <ul> <li> <p>Each row in the table is a record of one node, the <strong><em>node ID</em></strong> is the <code>row_id</code> of the record in the table, the first record has a row_id of <strong>0</strong>.</p> </li> <li> <p>The <strong>first</strong> column in the table is the <em><code>X</code></em> coordinate of the mass center of the node.</p> </li> <li> <p>The <strong>second</strong> column in the table is the <em><code>Y</code></em> coordinate of the mass center of the node.</p> </li> <li> <p>The <strong>third</strong> column in the table is the <strong>Size</strong> of the node, i.e., the number of pixels of this city.</p> </li> </ul> </li> <li> <p>In the <code>Edge_list</code> folder, each <em><code>TXT</code></em> file has the name convention like <code>AL_1000_edges.txt</code>, for example, this means the table contains the list of <strong>edges</strong> that make up the network for <strong>Austria</strong> (as country code, <code>AL</code>) at the spatial clustering distance threshold of <strong>1000</strong>m. The table has 2 columns without column name, without row name.</p> <ul> <li> <p>Each row in the table is a record of one edge that is composed of a pair of nodes, the <strong><em>node ID</em></strong> is the <code>row_id</code> of that node in the <strong>node_list</strong> table.</p> </li> <li> <p>The <strong>first</strong> column in the table is the <strong><em>ID</em></strong> of the node on one side of an edge.</p> </li> <li> <p>The <strong>second</strong> column in the table is the <strong><em>ID</em></strong> of the node on the other side of an edge.</p> </li> </ul> </li> </ul> </div> <div> <h2>Under the <code>code</code> folder</h2> <ul> <li> <p>The <code>SFA_args_LSPT_out.cpp</code> file contains the <strong>C++</strong> code to do the SFA calculation, detailed information can be found in the documentation of the code file.</p> </li> <li> <p>The <code>SFA_config.ini</code> file contains some configuration parameters for running the compiled program, details can also be found in the file. This file has to be put in the same folder as the compiled executable file.</p> </li> </ul> </div> </div>
Resource Metadata Harvested from Government and Research Open Data Portals
<p>This dataset consists of resource metadata harvested from the APIs of hundreds of government and research data portals from all over the world. This dataset was harvested between the 13<sup>th</sup> and 15<sup>th</sup> of September 2018. The metadata harvested from these portals was translated to a single metadata format (see <em>metadata_format.odt</em>). An overview of all harvested domains is given in <em>portal_list.txt</em>.</p> <p>The harvested data is divided into five gzipped json-lines files, based on the ‘type’ of the resource that is derived from the data of the APIs:</p> <ul> <li><em>dataset_metadata.jsonl.gz</em>: Resources classified as a Dataset, or subsets of dataset (e.g. Dataset:Image and Dataset:Audio) [6 246 250 resources]</li> <li><em>document_metadata.jsonl.gz</em>: Resources classified as a Document, or subset of document (e.g. Document:Paper:Conference and Document:Book) [15 626 541 resources]</li> <li><em>software_metadata.jsonl.gz</em>: Resources classified as Sofware (including Software:Model) [42 036 resources]</li> <li><em>service_metadata.jsonl.gz</em>: Resources classified as a service (e.g. WMS, APIs) [1257 resources]</li> <li><em>other_metadata.jsonl.gz</em>: Resources of which the ‘type’ could not be determined from the data the API returned. This set still contains many datasets [1 502 979 resources]</li> </ul>
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.