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12 results for “Covid19 Dataset”

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zenodo44/100

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 →

zenodo44/100

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 →

zenodo44/100

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

opencc-by-4.0Nov 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 →

zenodo36/100

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 .

opencc-by-4.0Nov 2020View details →

zenodo36/100

U-10: United-10 COVID19 CT Dataset

This dataset supports the research detailed in the pre-print "<a href="https://arxiv.org/abs/2308.09730">Virtual Imaging Trials Improved the Transparency and Reliability of AI Systems in COVID-19 Imaging</a>." The study employs both clinical and simulated CT data to evaluate AI models for COVID-19 diagnosis. By leveraging the Virtual Imaging Trials (VIT) framework, the research addresses reproducibility and generalizability issues prevalent in medical imaging AI models. The dataset includes: <ul> <li>Clinical CT Data: Drawn from 10 publicly available datasets, comprising over 12,000 volumes. These datasets span diverse populations, imaging protocols, and scanner configurations. Each of the 10 zip files contains pre-processed CT TFRecords (Train/Validation/Test)  used in the study. Detailed information about the data sources, pre-processing steps, and the inclusion and exclusion criteria can be found in the manuscript (<a href="https://arxiv.org/abs/2308.09730">https://arxiv.org/abs/2308.09730</a>).</li> <li>Simulated CT Data: Generated using computational anatomical phantoms from the XCAT model and imaged with the DukeSim simulation framework. This synthetic dataset allows controlled experiments that isolate the effects of imaging physics and patient-specific factors. Can be available upon request through Center For virtual Imaging Trial Portal at <a href="https://cvit.duke.edu/">https://cvit.duke.edu/</a></li> </ul> The accompanying study analyzes the performance of lightweight convolutional neural networks on both real and synthetic data, comparing results across multiple internal and external validation scenarios. Insights into factors such as infection severity, imaging dose, and modality type are explored. For further details, visit our Project Page: <a href="https://fitushar.github.io/ReviCOVID.github.io/">https://fitushar.github.io/ReviCOVID.github.io/</a> The full source code is available on gitHub and GitLab GitHub: <a href="https://github.com/fitushar/CVIT_ReviCOVID19">https://github.com/fitushar/CVIT_ReviCOVID19</a> GitLab : <a href="https://gitlab.oit.duke.edu/cvit-public/cvit_revicovid19">https://gitlab.oit.duke.edu/cvit-public/cvit_revicovid19</a> Citation:  When using this dataset, please cite the manuscript () and the original data-source. Tushar et al., "Virtual Imaging Trials Improved the Transparency and Reliability of AI Systems in COVID-19 Imaging", arXiv:2308.09730. Contact:  fakrulislam.tushar@duke.edu

opencc-by-4.0Nov 2024View details →

zenodo36/100

Multimodal dataset: Protein Function Prediction using STRING data & COVID19 Mortality Model by EI

The PFP.zip file contains 1. 5 well-formated GO terms dataset for EI, 2. STRING data 3. GO term annotation. The last two could be merged by the 'generate_data.py' script in https://github.com/GauravPandeyLab/ensemble_integration The covid19_model_built.zip contained the EI model built based on the COVID-19 Mortality dataset, the detail of usage are here:.

opencc-by-4.0Jan 2022View details →

zenodo36/100

Covid19-twitter dataset

Here, we provide the tab-separated values (tsv) version of the dataset. The first line contains the column names and each subsequent line contains features of an instance of the dataset. Features are separated by a tab character ("/t").

opencc-by-4.0Feb 2024View details →

zenodo32/100

DATASET THE COVID19 ECONOMIC PROSPECTS 2020 & 2021 of IFM

Dataset in excel of main macroeconomic indicators growth from 2017 to 2021 for near 200 countries and  according to IMF data. It allows us to quickly assess  the impact of the COVID19 in the global economic It includes: real GDP growth, GDP per capita, inflation, unemployment rate, general government net lending /borrowing.

opencc-by-4.0Apr 2020View details →

zenodo32/100

Dataset_covid19

Este dataset es construido usando tecnicas de Web Scraping del sitio web Worldometers, el archivo corresponde al dia 3 de noviembre con las estadisticas del dia.

opencc-by-4.0Nov 2020View details →

zenodo32/100

The COVID19 concentration datasets

Open the record for dataset details and reuse information.

opencc-by-4.0Oct 2023View details →

zenodo20/100

Covid19 X-ray Dataset

Covid19 X-ray dataset. 25 Covid19 positiv and 25 healthy images, a total of 50 images.

opencc-by-4.0Aug 2020View details →

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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Allen Brain Atlas

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allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

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abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

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neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record