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
datasets available to search
ShareScore release 0.9.0
Dataset results
8 results for “microdata”
Microdata from the METEOR survey for nurses
<p>These are anonymized original data on 1351 European hospital nurses collected during the survey carried out within the project “Mental Health: focus on Retention of Healthcare Workers” (METEOR), funded by the European Union - European Health and Digital Executive Agency in 2020 as part of the 3rd EU Health Program <span> (Grant Agreement No: 101018310). </span>These data were analyzed in Maniscalco et al. (2024). Further details about the METEOR project are available at <a href="https://meteorproject.eu/">https://meteorproject.eu/.</a></p> <p> </p> <p><strong>Reference</strong></p> <p>1. Maniscalco L, Enea M, de Vries N, Mazzucco W, Boone A, Lavreysen O, et al. Intention to leave, depersonalisation and job satisfaction in physicians and nurses: a cross-sectional study in Europe. Sci Rep. 2024;14(1):2312. <a href="https://doi.org/10.1038/s41598-024-52887-7">https://doi.org/10.1038/s41598-024-52887-7</a></p>
Microdata from the METEOR survey for physicians
<p>These are anonymized original data on 375 European hospital physicians collected during the survey carried out within the project “Mental Health: focus on Retention of Healthcare Workers” (METEOR), funded by the European Health and Digital Executive Agency in 2020 as part of the 3rd EU Health Program (<a href="https://meteorproject.eu/">https://meteorproject.eu/</a>). These data were analyzed in Maniscalco et al. (2024).</p> <p> </p> <p><strong>Reference</strong></p> <p>1. Maniscalco L, Enea M, de Vries N, Mazzucco W, Boone A, Lavreysen O, et al. Intention to leave, depersonalisation and job satisfaction in physicians and nurses: a cross-sectional study in Europe. Sci Rep. 2024;14(1):2312. <a href="https://doi.org/10.1038/s41598-024-52887-7">https://doi.org/10.1038/s41598-024-52887-7</a></p> <p> </p>
Microdata used to construct the Causal Diagrams to model investment decisions related to the energy transition
<ul> <li><strong>Name</strong>: Microdata used to construct the Causal Diagrams to model investment decisions related to the energy transition</li> <li><strong>Summary</strong>: This dataset contains answers from a panel of experts to build a) a taxonomy of determinants that explain the investment decision making on assets related to the energy transition, b) the individual contributions when sorting the taxonomy of determinantes on the different stages of the transtheoretical model for different archetypes of persons and c) the causal diagrams agreed between the different groups of experts.</li> <li><strong>License</strong>: cc-BY-SA</li> <li><strong>Acknowledge</strong>: These data have been collected in the framework of the WHY project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 891943.</li> <li><strong>Disclaimer</strong>: The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the Executive Agency for Small and Medium-sized Enterprises (EASME) or the European commission (Ec). EASME or the Ec are not responsible for any use that may be made of the information contained therein.</li> <li><strong>Collection Date</strong>: 22/07/2022</li> <li><strong>Publication Date</strong>: 01/06/2024</li> <li><strong>DOI</strong>: 10.5281/zenodo.11234441</li> <li><strong>Other repositories:</strong></li> <li><strong>Author</strong>: University of Deusto</li> <li><strong>Objective of collection</strong>: This data was originally collected to build a set of causal diagrams of the .</li> <li><strong>Description:</strong> <br> <ul> <li><strong>Scenarios: </strong>This dataset contains the description of 20 different scenarios used in this research activity. </li> <li><strong>File 1 - individual reasons to be coded<br></strong>This dataset compiles the reasons given by experts of different panels of the Intrinsic and Extrinsic Determinants, and the Barriers and potential Rebound effects of citizens towards a set of 20 different scenarios. The file contains the following sheets:<br> <ul> <li><strong>Methodology</strong>: Methodology followed by the coders.</li> <li><strong>Help</strong>: Short summary of the Social Cognitive Theor and Self Determination Theory used for coding. </li> <li><strong>Glossary</strong>: Glossary of terms build by the experts coding the answers. </li> <li><strong>Appliances/Flexibility/Buildings/Mobility</strong>: The contributions of each expert, the code provided by the two researchers and the consensus achived. </li> <li><strong>Summary</strong>: Assesment of the results.</li> </ul> </li> <li><strong>File 2 - individual microdata to sort determinants into causal threads from experts</strong>This dataset includes the individual sortings made by the experts of the taxonomy of determinantes into each one of the stages of the transtheoretical model. The file includes one sheet per expert where he/she has sort each determinant for each arquetype into the stage he/she thinks is more relevant to advance to the next step of the TTM. </li> <li><strong>File 3 - collective microdata to sort determinants into causal threads from EU and LATAM experts</strong> <p>This dataset compiles the results, stage by stage, of the consensus reached by each panel regarding the determining factors that make up each of the archetypes in the contexts of Europe (EU) and Latin America (LATAM). And in which stage of the change of the Transtheoretical Model (TTM) the factors should appears.</p> <ul> <li> <p><strong>Stage 1</strong>: The panels reached a consensus on the factors that describe each of the archetypes in their context. In the case of Latin America, for the panels of some countries, the existence of all eight archetypes was not evident. The number of archetypes analysed by each panel is indicated in parentheses in the following list:</p> <ul> <li> <p><strong>European panels</strong>: Group – F (8), Group–A (8). Group–FF (8), Group–M (4)</p> </li> <li> <p><strong>Latin America panels</strong>: Group-MX (5), Group-CO (8), Group-CL (7), Group-SV (7)</p> </li> </ul> </li> </ul> <ul> <li> <p><strong>Stage 2</strong>: For each of the eight archetypes, the results of the consensus for each panel are consolidated in the tabs indicated in the list below. The column on the far right shows the weights (percentage) of each factor in each stage of the TTM: Archetype-EarlyAdopter, Archetype-Uninterested, Archetype-HomoEconomicus, Archetype-Fearful, Archetype-Stubborn, Archetype-Influencer, Archetype-Careful and Archetype-Activist.</p> </li> <li> <p><strong>Stage3</strong>: In the "<em>Archetypes - Consensus Results</em>" tab, the weights of the factors for each archetype are consolidated. The far-right column calculates the average weight of each factor at each stage of the TTM (Transtheoretical Model of Change).</p> </li> <li> <p><strong>Stage 4</strong>. In the “EU vs Latam - split context” sheet, it is presented a comparative assessment between the European and Latin American results. The comparison has four tables:</p> <ul> <li> <p><em>Table (s)</em>: Difference and Agreements between both context: European & Latin American Archetypes. The table highlights the regions of determinants that mark the differences between both contexts for each archetype. If a determinant is identified by both contexts (EU, Latam), it is considered an agreement and allocated to the early TTM stage. The remaining determinants highlight the differences between the two contexts. European (-1) & Latin American (1) Archetypes FINAL Consensus (0) on TTM Stages.</p> </li> <li> <p><em>Table (t)</em>: This table shows the difference (E, L) and agreements (X) between both context: European (E) & Latin American (L) Archetypes.</p> </li> <li> <p><em>Table (t.1)</em>: This table shows just the <strong>agreements</strong> (X) between both context: European & Latin American Archetypes.</p> </li> <li> <p><em>Table (t.2)</em>: Show the difference between both context: European (E) & Latin American Archetypes (L).</p> </li> <li> <p><em>Table (t.3)</em>: This table shows the differences (E, L) and agreements (X) between both contexts: European (E) & Latin American (L) archetypes. In this table, the main regions of factors for each archetype are coloured to highlight the set of factors that make the main differences.</p> </li> </ul> </li> </ul> </li> </ul> </li> <li><strong>5 star</strong>: ⭐⭐⭐</li> <li><strong>Preprocessing steps:</strong> Data transcription from written documents and oral discussions.</li> <li><strong>Reuse:</strong> NA</li> <li><strong>Update policy:</strong> No more updates are planned.</li> <li><strong>Ethics and legal aspects:</strong> Names of the persons involved have been removed. </li> <li><strong>Technical aspects</strong>: </li> <li><strong>Other:</strong></li> </ul>
Open microdata registers from the Brazilian COVID-19 vaccination campaign
<p>This repository provides the Brazilian Ministry of Health’s (MoH) open microdata registers from the national COVID-19 vaccination campaign used in the paper <em>Age reporting for the oldest old in the Brazilian COVID-19 vaccination database: what can we learn from it?</em></p> <p>The dataset was downloaded on 14 March 2022 and was initially available at openDataSus <a href="https://opendatasus.saude.gov.br/dataset/covid-19-vacinacao">Campanha Nacional de Vacinação contra Covid-19</a> under a <a href="http://creativecommons.org/licenses/by/4.0/?ref=chooser-v1">CC BY 4.0</a> license.</p> <p>The reproducible <em><code>R</code></em> code and other datasets for the paper are available on GitHub at <a href="https://github.com/demographyandme/covid-19-datasus-vacina">Age reporting for the oldest old in the Brazilian COVID-19 vaccination database: what can we learn from it?</a> </p>
Microdata on vector abundance and IRS quality assurance (Estimating the impact of indoor residual spraying on sandfly abundance and incidence of visceral leishmaniasis in India from 2016 to 2022: an interrupted time-series analysis and modelling study)
<p>This repository contains the microdata on vector abundance and quality assurance of indoor residual spraying (IRS) that was used to estimate the impact of IRS on sandfly abundance and incidence of visceral leishmaniasis (VL) in India, as described in the paper "Estimating the impact of indoor residual spraying on sandfly abundance and incidence of visceral leishmaniasis in India from 2016 to 2022: an interrupted time-series analysis and modelling study" by Coffeng et al (<a href="https://doi.org/10.1016/S1473-3099(24)00420-1">https://doi.org/10.1016/S1473-3099(24)00420-1</a>). These data were collected as part of a BMGF-funded project led by dr. Michael Coleman at the Liverpool School for Tropical Medicine, as described in an earlier paper by Deb et al (<a href="https://doi.org/10.1371/journal.pntd.0009101">https://doi.org/10.1371/journal.pntd.0009101</a>).</p> <p>This repository does not include microdata on VL cases as these are owned by India's National Center for Vector Borne Disease Control (NCVBDC, <a href="https://ncvbdc.mohfw.gov.in/" target="_blank" rel="nofollow noreferrer noopener">https://ncvbdc.mohfw.gov.in/</a>).</p>
European Quality of Government Index (EQI) microdata
<p>EQI microdata and codebook for 2010 and 2013 rounds</p>
Archive of Census Related Products (ACRP): 1990 Public Use Microdata Sample Areas (PUMA) Boundary Files
The 1990 Public Use Microdata Sample Areas (PUMA) Boundary Files portion of the Archive of Census Related Products (ACRP) consists of 5% sample (apuma) and 1% sample (bpuma) areas for the mapping of 1990 PUMS data covering the continental United States, Alaska, and Hawaii. These boundary files are created based on equivalency files generated by the Geographic Correspondence Engine (GeoCorr). A national census tract to PUMA geography correspondence file is used in merging the two files resulting in the PUMA geographies. An additional file is also available consisting of geographic centroids for the PUMA coverages calculated by UIC (Urban Information Center/Office of Computing, University of Missouri). This portion of the ACRP is produced by the Center for International Earth Science Information Network (CIESIN).
Public Use Microdata Samples (PUMS)
The Public Use Microdata Samples (PUMS) are computer-accessible files containing records for a sample of housing Units, with information on the characteristics of each housing Unit and the people in it for 1940-1990. Within the limits of sample size and geographical detail, these files allow users to prepare virtually any tabulations they require. Each datafile is documented in a codebook containing a data dictionary and supporting appendix information. Electronic versions for the codebooks are only available for the 1980 and 1990 datafiles. Identifying information has been removed to protect the confidentiality of the respondents. PUMS is produced by the United States Census Bureau (USCB) and is distributed by USCB, Inter-university Consortium for Political and Social Research (ICPSR), and Columbia University Center for International Earth Science Information Network (CIESIN).
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.