Find research datasets worth reusing

opencc-by-4.0Sep 2024View details →

3C : Cardiac-CT-Covid19

The 3C (Cardiac-CT-COVID-19) database offers CT scans of patients diagnosed with COVID-19, encompassing both individuals with and without cardiac complications (49 females, 58 males, aged between 8 and 89 years). Image interpretation was carried out by two experienced radiologists. The dataset also provides information on the severity of each cardiac condition, making it a valuable resource for examining the impact of COVID-19 on the cardiovascular system.

opencc-by-4.0Aug 2021View details →

COVID19 Flow-Maps Daily Cases Reports

This repository contains COVID-19 data for Spain, including daily cases at the level of autonomous communities as well as provinces, and higher spatial resolution for several autonomous communities (eight out of the nineteen autonomous communities publish reports with local daily COVID-19 cases at the level of municipalities or Basic Health Areas). Each record has an identifier, the associated date, the corresponding identifier of the layer and code of the region and a set of COVID-19 related fields, which include the number of new cases (daily incidence) and total cases. <ul> <li> Autonomous Communities: ES.covid_cca </li> <li> Provinces: ES.covid_cpro </li> <li> Higher spatial resolution: <ul> <li> Principado de Asturias: 03.covid_cumun </li> <li> Cantabria: 06.covid_cumun </li> <li> Castilla y Leon: 07.covid_abs </li> <li> Cataluña/Catalunya: 09.covid_abs </li> <li> Comunitat Valenciana: 10.covid_cumun </li> <li> Comunidad de Madrid: 13.covid_abs </li> <li> Comunidad Foral de Navarra: 15.covid_abs </li> <li> País Vasco/Euskadi: 16.covid_abs </li> </ul> </li> </ul> For information about data sources, visit: <a href="https://flowmaps.life.bsc.es/flowboard/data">https://flowmaps.life.bsc.es/flowboard/data</a>

opencc-by-4.0Aug 2021View details →

COVID19 Flow-Maps Mobility-Associated-Risk

The Mobility Associated Risk The Mobility Associated Risk is a risk score combines mobility and COVID-19 incidence to estimate how many cases could theoretically be exported/imported between different origin-destination pairs of regions. For more information about how the MAR is calculated visit: <a href="https://flowmaps.life.bsc.es/flowboard/board_what_is_risk#what_is_risk">https://flowmaps.life.bsc.es/flowboard/board_what_is_risk#what_is_risk</a> Dashboard The Mobility Associated Risk combines mobility and COVID-19 incidence to estimate how many cases could theoretically be exported/imported between different origin-destination pairs of regions. For more information about how the MAR is calculated visit: <a href="https://flowmaps.life.bsc.es/flowboard/board_what_is_risk#what_is_risk">https://flowmaps.life.bsc.es/flowboard/board_what_is_risk#what_is_risk</a> Dashboard <a href="https://flowmaps.life.bsc.es/flowboard/">https://flowmaps.life.bsc.es/flowboard/</a>

opencc-by-4.0Aug 2021View details →

COVID19 Flow-Maps Population data

Daily population and trips per person data from Spain 2020-2021 This repository contains daily population records based on a study conducted by the MITMA, that analysed the mobility and distribution of the population in Spain from February 14th 2020 to May 9th 2021. The study is based on a sample of more than 13 million anonymised mobile phone lines provided by a single mobile operator whose subscribers are evenly distributed. For more information on the data visit: <a href="https://www.mitma.gob.es/ministerio/covid-19/evolucion-movilidad-big-data">https://www.mitma.gob.es/ministerio/covid-19/evolucion-movilidad-big-data</a> Data provided by MITMA is related to the layer mitma_mov. For the rest of the layers, the population was estimated using the population grid from GEOSTAT: <a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/population-distribution-demography/geostat">https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/population-distribution-demography/geostat</a>

Figshare44/100

UnityMol COVID19 Spike protein 360 video

A 360 degree video with a camera path through the COVID19 spike protein-ACE2 complex (example 1 of our paper on biorxiv), illustrating the use of specific cameras to export enriched media. NB: not all video players allow you to experience the 360 degree navigability. The youtube link should work fine.

opencc-by-4.0Dec 2019View details →

COVID19 - Clinical Trials

The objective of the project is to extract a set of clinical trials carried out for Covid-19 disease around the world in order to carry out statistical and data mining studies . The <a href="https://clinicaltrials.gov/">ClinicalTrials.gov</a> website is a database of privately and publicly clinical trials conducted around the world. The URL of the project to extract the data is the GitHub <a href="https://github.com/jordi-puig/web-scraping">repository</a>.

opencc-by-4.0Dec 2020View details →

Supplementary Data – Indian Covid19 Cases at Different Temperature

Supplimentary Datasets of A STATEWISE STATISTICAL ANALYSIS ON COVID19 CASES IN INDIA AT DIFFERENT TEMPERATURE derived from Curve Expert 1.4 and Excell Spreadsheet .

opencc-by-4.0Nov 2021View details →

The SPIN covid19 RMRIO dataset: Global trade network data for the years 2016-2026 reflecting macroeconomic effects of the covid19 pandemic - B. Data for 2020 - 2026 - Covid scenario

opencc-by-4.0Nov 2021View details →

The SPIN covid19 RMRIO dataset: Global trade network data for the years 2016-2026 reflecting macroeconomic effects of the covid19 pandemic - A. Code and data for 2016-2019

The SPIN covid19 RMRIO dataset is a time series of MRIO tables covering years from 2016-2026 on a yearly basis. The dataset covers 163 sectors in 155 countries. This repository includes data for years from 2016 to 2019 (hist scenario) and the corresponding labels. Data for years 2020 to 2026 are stored in the corresponding repositories: <ul> <li>covid: <a href="https://doi.org/10.5281/zenodo.5713825">10.5281/zenodo.5713825</a></li> <li>counterfactual: <a href="https://doi.org/10.5281/zenodo.5713839">10.5281/zenodo.5713839</a></li> </ul> Tables are generated using the <a href="https://github.com/TBeaufils/SPIN">SPIN method</a>, based on the <a href="https://doi.org/10.5281/ZENODO.3993659">RMRIO tables</a> for the year 2015, GDP, imports and exports data from the <a href="https://data.imf.org/?sk=4c514d48-b6ba-49ed-8ab9-52b0c1a0179b">International Financial Statistics</a> (IFS) and the World Economic Outlooks (WEO) of <a href="https://www.imf.org/en/Publications/WEO/weo-database/2019/October">October 2019</a> and <a href="https://www.imf.org/en/Publications/WEO/weo-database/2021/April">April 2021</a>. From 2020 to 2026, the dataset includes two diverging scenarios. The covid scenario is in line with April 2021 WEO's data and includes the macroeconomic effects of Covid 19. The counterfactual scenario is in line with October 2019 WEO's data and simulates the global economy without Covid 19. Tables from 2016 to 2019 are labelled as hist. The Projections folder includes the generated tables for years from 2016 to 2019 (hist scenario) and the corresponding labels. The Sources folder contains the data records from the IFS and WEO databases. The Method data contains the data files used to generate the tables with the SPIN method and the following Python scripts: <ul> <li>SPIN_covid19_MRIO_files_preparation.py generates the data files from the source data.</li> <li>SPIN_covid19_RMRIO runs.py is the command to run the SPIN method and generate the dataset.</li> <li>figures.py is a script to produce figures reflecting the consistency of the projected tables and the evolution of macroeconomic figures in the 2016-2026 period for a selection of countries.</li> </ul> All tables are labelled in 2015 US$ and valued in basic prices.

opencc-by-4.0Nov 2021View details →

The SPIN covid19 RMRIO dataset: Global trade network data for the years 2016-2026 reflecting macroeconomic effects of the covid19 pandemic - C. Data for 2020 - 2026 - Counterfactual scenario

zenodo40/100

Daily aerosol emissions changes in 2020 due to Covid19: modified SSP2-4.5 to account for sector activity level

Daily aerosol emissions estimates for 2020, modified by the country-specific impacts of COVID-19 lockdown.  This repository holds the netcdf files for aerosol and precursor emissions projected by the scenario SSP2-4.5, from the Scenario4MIPs database (<a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>), modified by the country and sector activity levels associated with lockdown for 2020, with observation-based data up until the 5th of July and a fixed estimate thereafter. This is the daily equivalent of <a href="https://zenodo.org/record/3951601#.XxYBsihKhPY">https://zenodo.org/record/3951601#.XxYBsihKhPY</a>.  Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) <a href="http://constrain-eu.org/">http://constrain-eu.org/</a>  see <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a> for more details.

opencc-by-4.0Jul 2020View details →

zenodo40/100

COVID19 & PTSD treatment seeking veterans

Data set on longitudinal research (2 time points). Sociodemographic data; COVID-19 related data, PCL-5, Brief Cope. Number of participants 132

opencc-by-4.0Dec 2021View details →

zenodo40/100

Core bibliometric Covid19 and comparable research dataset and code for the study "From intent to impact: Investigating the effects of open sharing commitments"

This document provides the underlying dataset for the bibliometric component for the 2022 study "From intent to impact: Investigating the effects of open sharing commitments" by Research Consulting and Science-Metrix. Before reproducing the study findings or re-using the underlying datasets for other purposes, please cautiously review their limitations in the study's technical annex and main report, available at: https://zenodo.org/communities/data-sharing-in-public-health-emergencies/  Particularly, note that there is an error rate in attribution of signatory status to journal publications and preprints; in their location within specific thematic disease-based areas; or computing of dimension such as identification of data availability statement sections; identification of data depisition mentions within data availability statement sections; or matching of preprints and journal publications. These error rates are expected and have been estimated, please consult the technical report for full details.   Definition of data fields is provided is the table below: <table> <tbody> <tr> <td>Column name </td> <td>Definition</td> </tr> <tr> <td>document_type</td> <td>preprint or journal publication</td> </tr> <tr> <td>doi</td> <td>digital object identifier</td> </tr> <tr> <td>arxiv_id</td> <td>arXiv preprint server's unique identifier for its preprints</td> </tr> <tr> <td>ssrn_id</td> <td>SSRN preprint server's unique identifier for its preprints. Note that some of these IDs are contained within the DOIs also assigned to some (but not all) SSRN preprints , in the form of "10.2139/ssrn." + 'ssrn_id'</td> </tr> <tr> <td>coalesce_id</td> <td>coalesce function applied to the DOI, arxiv_id and ssrn_id. Redundant for journal publications.</td> </tr> <tr> <td>preprint_server</td> <td>Preprint platform on which a preprint has been published, restricted to arXiv, bioRxiv, medRxiv and SSRN for this study.</td> </tr> <tr> <td>journal_title</td> <td>Publishing journal name in the case of a journal publication.</td> </tr> <tr> <td>year</td> <td>The set is restricted to 2020 and 2021 for Covid19 preprints and journal publications. HVRD journal publications restricted to 2018-2019. HVRD preprints were restricted to 2020-2021 instead, to compensate for the lac of year-normalization for preprints, and generally better control findings against the launch of medRxiv in 2019.</td> </tr> <tr> <td>publication_title</td> <td>Title of the individual journal publication or preprint, not that of the publishing journal or preprint server.</td> </tr> <tr> <td>authors</td> <td>First 100 researchers that appear as authors of a preprint or journal publication. These are not parsed and provided for qualitative validation or  assessments rather than for further quantitative treatment.</td> </tr> <tr> <td>Covid19</td> <td>Journal publications or preprints are coded 1 if they has been identified as falling into this thematic area through our queries (see the technical annex), 0 otherwise</td> </tr> <tr> <td>HVRD</td> <td>Human viral respiratory disease, the thematic area considered to be the closest to Covid19. Journal publications or preprints are coded 1 if they has been identified as falling into this thematic area through our queries (see the technical annex), 0 otherwise</td> </tr> <tr> <td>Journal_sig</td> <td>Journal publications where the publishing journal and/or its publishing house are Joint Statement signatories. Coded as 1 if they are signatories, 0 if not signatory, null if status could not be determined due to insufficient metadata. Not that all preprint servers included in this study are Joint Statement signatories. This category was fully removed from the models for preprints, rather than all preprints being assigned automatic signatory status.</td> </tr> <tr> <td>RPO_sig</td> <td>Journal publications and preprints where at least one author is affiliated with at least one research performing organization that is a Joint Statement signatory. Coded as 1 ifor signatory, 0 if not signatory, null if status could not be determined due to insufficient metadata.</td> </tr> <tr> <td>Funder_sig</td> <td>Journal publications and preprints where at least one funder supporting the research is a Joint Statement signatory. Coded as 1 ifor signatory, 0 if not signatory, null if status could not be determined due to insufficient metadata. Although funding is attributed to researchers rather than publications, funding metadata is more readily available at the second level. This approach also captures the flexible usage of financial resources that researchers may make accross mulitple concurrently ongoing research projects.</td> </tr> <tr> <td>overton_norm</td> <td>Year and subfield-normalized binary score of whether the journal publications has been cited by one or more policy-related documents from the Overton database. Null scores for journal publications not covered by the database.</td> </tr> <tr> <td>overton</td> <td>Normalizations being unable for preprints, binary score of whether the preprint has been cited by one or more policy-ralated documents from the Overton database. Null scores for preprints not covered by the database.</td> </tr> <tr> <td>daswriting_binary</td> <td>Binary score capturing identification of a data availability statement in the journal publication or preprint using the queries presented in the technical annex. Null scores are for publications and preprints where records of full texts were unavailable for text mining, or were this analysis could not be performed due to licensing restrictions. </td> </tr> <tr> <td>deposition_binary</td> <td>Binary score capturing identification of a data availability statement and data deposition mention therein in the journal publication or preprint using the queries presented in the technical annex. Null scores are for publications and preprints where records of full texts were unavailable for text mining, or were this analysis could not be performed due to licensing restrictions. </td> </tr> <tr> <td>is_oa</td> <td>Binary score capturing OA or free-to-read (also so-calleod "bronze OA" and "green OA") status of journal publications. Unpaywall categories have been used in a mutually exclusive implementation, with the best (gold > hybrid>bronze>green) possible applicable category being retained. Null scores for journal publications not covered in our Unpaywall dataset. Scores of 0 denote journal publications not available under an OA or free-to-read category.</td> </tr> <tr> <td>is_gold</td> <td>as above</td> </tr> <tr> <td>is_hybrid</td> <td>as above</td> </tr> <tr> <td>is_bronze</td> <td>as above</td> </tr> <tr> <td>is_green</td> <td>as above</td> </tr> <tr> <td>matched_journal_binary</td> <td>For preprints, whether one or more matching journal publications could be identified using the queries identified in the technical, or preprint servers' own lists of preprint-journal publication matches. Null scores for preprints with insufficient metadata information to perform the matching operation.</td> </tr> <tr> <td>matched_journal_doi</td> <td>For those preprints with or more matching journal publications, the DOI(s) of the matching journal publication(s). Note that some of the maching journal publications identified do not have DOIs.</td> </tr> <tr> <td>matched_preprint_binary</td> <td>For journal publications, whether one or more matching preceding preprints could be identified using the queries identified in the technical annex, or preprint servers' own lists of preprint-journal publication matches. Null scores for journal publications without sufficient metadata to run the analysis.</td> </tr> <tr> <td>matched_preprint_id</td> <td>For those journal publications preceded with one or more arXiv, bioRxiv, medRxiv or SSRN preprints, the DOI(s), arXiv ID and/or SSRN ID of the matching preprint(s). </td> </tr> <tr> <td>hasdoi</td> <td>Only journal publications with DOIs were retained in the core quantitative analyses.</td> </tr> <tr> <td>hasacknowledgements</td> <td>Only journal publications with funding acknowledgements (to determine funding-based signatory status) were retained in the core quantitative analyses.</td> </tr> <tr> <td>funder_array</td> <td>Array (but cast as string) of names of the funders on the basis of whose idenitification signatory status has been attributed, where relevant. Null if non-signatory or unknown signatory status.</td> </tr> <tr> <td>RPO_array</td> <td>Array (but cast as string) of names of the research performing organizations on the basis of whose idenitification signatory status has been attributed, where relevant. Null if non-signatory or unknown signatory status.</td> </tr> <tr> <td>DAS_excerpt</td> <td>Journal publication or preprint text excerpt on which succesful identifcation of data availability statements and/or data deposition mentions have been made. Null both where the query could not be run at all, or where the query was negative.</td> </tr> <tr> <td>big5</td> <td>Journal publication published in a journal owned by one of the following five publishing houses: Elsevier, Sage, Springer Nature, Taylor-Francis, Wiley.</td> </tr> <tr> <td>LMIC</td> <td>Journal publication whose authors include at least one researcher affiliated with at least one institution located in a lower-middle income country as defined by the World Bank</td> </tr> <tr> <td>LIC</td> <td>Journal publication whose authors include at least one researcher affiliated with at least one institution located in a low income country as defined by the World Bank</td> </tr> <tr> <td>SouthNorth</td> <td>Journal publication whose authors include at least one researcher affiliated with at least one institution located in a upper-middle income country, a lower-middle income country, or a low income country as defined by the World Bank; as well as at least one researcher affiliated with at least one institution located in a high income country. For the purpose of this indicator, Sicnece-Metrix exceptionally includes China and Bulgaria in the list of high income countries.</td> </tr> <tr> <td>DID_allauthors_OR</td> <td>Journal publication is included in the difference-in-difference model defining signatory publication as EITHER holding journal-based signatory status OR funding-based signatory status, and where no filter has been applied to control for author-level biases.</td> </tr> <tr> <td>DID_authorcontrol_OR</td> <td>Journal publication is included in the difference-in-difference model defining signatory publication as EITHER holding journal-based signatory status OR funding-based signatory status, and where a filter has been applied to control for author-level biases.</td> </tr> <tr> <td>DID_authorcontrol_AND</td> <td>Journal publication is included in the difference-in-difference model defining signatory publication as holding journal-based signatory status AND funding-based signatory status, and where a filter has been applied to control for author-level biases.</td> </tr> <tr> <td>DID_allauthors_AND</td> <td>Journal publication is included in the difference-in-difference model defining signatory publication as holding journal-based signatory status AND funding-based signatory status, and where no filter has been applied to control for author-level biases.</td> </tr> <tr> <td>Preprint_authorcontrol</td> <td>Preprint is included in the the analytical breakdowns where a filter has been applied to control for author-level biases. Note that authors have been kept constant in preprints on the basis of their belonging to all analytical breakdowns in journal publications rather than in preprint-based groups.</td> </tr> </tbody> </table>

opencc-by-4.0Jun 2022View details →

dryad40/100

CovidCounties is an interactive real time tracker of the COVID19 pandemic at the level of US counties

Open the record for dataset details and reuse information.

publicNov 2020View details →

SUPPLIMENTARY Dataset - A STUDY ON STATISTICAL MODELLING ON RECOVERY INFORMATICS OF COVID19

Collection of data – a set of data of number of days with seven days interval vs number of recovered cases with effect from 7th September – 25th October ,2020 .Active Covid cases with respect to Sept,7 to Oct ,25 -2020 in India . Retrieved from -Sharpest weekly fall as cases down 16%, toll 19% | Man hits docs over Covid death, -Oct 26, 2020, ethealthworld.com. ( data report ) .The above dataset is converted in terms of number of Recovered Covid-cases with respect .Data Informatics Tool : Standard Statistical Software Curve Expert v.1.4 .

Comparativa de l'evolució de la incidència de la Covid19-Sars2 a l'Estat Espanyol respecte del conjunt d'Europa

Amb la finalitat de poder fer una comparativa temporal de la incidència de la Covid19-Sars2 a  l’Estat Espanyol respecte del conjunt dels països d’Europa, hem recollit les dades diàries del 20 d’Octubre reportades a ambdós territoris pel que fa a: <ul> <li> Total cases (Número total de casos): sumatori de casos de positius acumulats. </li> <li> New Cases (Casos nous): número diari de casos positius reportats. </li> <li> Total Deaths (Número total de morts): sumatori de defuncions per Covid19 acumulades. </li> <li> New Deaths (Morts noves): número diari de defuncions per Covid19 reportats. </li> <li> Total Recovered (Número total de recuperats): sumatori de casos de pacients recuperats. </li> <li> New Recovered (Recuperats nous): número diari de casos de pacients recuperats. </li> <li> Active Cases (Casos actius): sumatori del número total de casos actius de Covid19. </li> <li> Serious, Critical (Casos crítics): sumatori del total de malalts de Covid19 en UCIs. </li> </ul>

Contagios y mortalidad de COVID19 por país

<pre>The motivation of this dataset's publication is strictly academical. Extraction of data on mortality, recovery and contagion of COVID19 in all countries of the world downloaded from the Google website. Its objective is merely informative: to provide a source of up-to-date and real data on the evolution of the pandemic to the global population.</pre> The fields are: <ul> <li>Currently infected (Positives)</li> <li>Currently infected, per million inhabitants (Positives Per Million)</li> <li>Contagious and recovered (Recovered)</li> <li>Infected and deceased (Dead)</li> </ul> These data have been collected since May 2020. In addition: <ul> <li>Only people who have been tested and tested positive are included in contagion cases. The rules and availability of the test vary by country. Some areas may not have data because it has not been published or because the data is not recent.</li> <li>The data originally comes from Wikipedia and the cases are constantly updated with resources from around the world, essentially relying on the World Health Organization website, where daily reports on the situation are published.</li> <li>The collection and display of the data on the website is the responsibility of Google.</li> </ul>

opencc-by-4.0Jan 2021View details →

Covid19 case data December 2020 - The Netherlands

This data is the daily case data on covid-19 cases in the Netherlands, published by the RIVM (national public health agency). The data covers all case data of December 2020.

opencc-by-4.0Jan 2021View details →

Covid19 case data September 2020 - The Netherlands

This data is the daily case data on covid-19 cases in the Netherlands, published by the RIVM (national public health agency). The data covers all case data of September 2020.