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335 results for “covid-19 data”
Data and code release for Carleton, Cornetet, Huybers, Meng & Proctor (PNAS, 2020), "Global evidence for ultraviolet radiation decreasing COVID-19 growth rates"
<p>This upload contains all replication material for "Global evidence for ultraviolet radiation decreasing COVID-19 growth rates" (PNAS, 2020). Please note that previous versions of this upload provided data and code for the pre-print version of the article, which changed somewhat through the peer review process. </p> <p><strong>Authors:</strong> Tamma Carleton, Jules Cornetet, Peter Huybers, Kyle C. Meng, Jonathan Proctor.</p> <p><strong>Code is located within CCHMP_covid_climate_code_release.zip</strong>, and is written in R, Stata, and Matlab. The working directory should be set to the repository folder at the top of each script (all other filepaths are relative).</p> <p>Please find the code needed to replicate the main findings of the paper described below:</p> <ul> <li>Plots of data: R and Stata scripts to make figures 1B, 2A/B/C, S1, S2, and S3, can be found within “code/analysis/data_plots/”.</li> <li>Regression analysis: Stata scripts to run the distributed lag regressions and plot the results in figures 2, 3C, S5, S6, S7, S8, S10, and S14, as well as Table S1, can be found within “code/analysis/regressions/”. R scripts for data analysis and plotting for figures 3A/B and S9 are also within "code/analysis/regressions/".</li> <li>Seasonal simulations: R and Stata scripts to replicate the seasonal simulation shown in figures 4, S4 and S11 can be found within “code/analysis/seasonal_sim/”.</li> <li>SEIR simulations: Matlab scripts to replicate the SEIR simulations shown in figures S12 and S13 can be found within “code/analysis/SEIR/”.</li> </ul> <p><strong>Data are located within CCHMP_covid_climate_data_release.zip.</strong></p>
Data for "The effects of weather and mobility on respiratory viruses dynamics before and during the COVID-19 pandemic in the USA and Canada".
<p>Epidemiological and mobility data analysed in the paper "The effects of weather and mobility on respiratory viruses dynamics before and during the COVID-19 pandemic in the USA and Canada".</p>
DATA SET: Peripheral microcirculatory alterations are associated with the severity of acute respiratory distress syndrome in COVID-19 patients admitted to intermediate respiratory and intensive care units
<p>This repository contains the data sets of the article:</p> <p>Mesquida, J., Caballer, A., Cortese, L. <em>et al.</em> Peripheral microcirculatory alterations are associated with the severity of acute respiratory distress syndrome in COVID-19 patients admitted to intermediate respiratory and intensive care units. <em>Crit Care</em> <strong>25, </strong>381 (2021). https://doi.org/10.1186/s13054-021-03803-2</p>
A blood atlas of COVID-19 defines hallmarks of disease severity and specificity: Associated data
<p>This dataset contains raw and processed data from the COvid-19 Multi-omics Blood ATlas (COMBAT) consortium. Data are divided into 26 datasets representing anonymised raw and processed data from deep immune phenotyping of peripheral blood from COVID-19 patients. </p> <p>In addition to the data listed below, some datasets are available through other repositories: </p> <ul> <li> <p>Proteomics data (CBD-KEY-PROTEOMICS) is available at PRIDE</p> <ul> <li> <p>Accession number: PDX023175</p> </li> <li> <p>Contact: Roman Fischer</p> </li> </ul> </li> </ul> <ul> <li> <p>Genetic data and detailed clinical information are available via a data access agreement through EGA</p> <ul> <li> <p>Study accession: EGAS00001005493 </p> </li> </ul> </li> </ul> <p>For further information regarding specific datasets, please contact the individuals listed in Dataset_descriptions.pdf through <a href="mailto:contact@combat.ox.ac.uk">contact@combat.ox.ac.uk</a>. </p>
Data from: "Alteration of the gut microbiota's composition and metabolic output correlates with COVID-19-like severity in obese NASH hamsters"
<p>This dataset contains all data collected and used for the publication : "Alteration of the gut microbiota’s composition and metabolic output correlates with COVID-19-like severity in obese NASH hamsters". Besides the Readme, it contains 11 files.</p> <p><br> Excel files with classification (i.e. genes according to their fold induction or repression) are provided. Data include different conditions with varying number of samples per group. Data are structured according to employed methods and then stratify the data obtained within the individual work packages.</p>
FolkArtiNet: Folk music groups: their artistic practice and infrastructural needs in the COVID-19 era and beyond - survey data
<p> The online survey was one of three methods used for collecting information about the infrastructural needs of the folk music groups. It included a series of questions about different areas of artistic activity, such as working on repertoire, collaboration among group members, storage and sharing of data, and organization of artistic events. The survey data includes all questions and answers in csv format.</p>
Replication data for: Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study
<p>This is the replication data for the scientific paper titled "Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study" accepted for publication in the Journal of Medical Internet Research (JMIR). For details on how to use the data files, please consider the "supplementary_material.R" file or the "supplementary_material.pdf" where the variables of interest and R code are presented.</p> <p>For the code to run correctly, have the files "multilevel_labels.R" and "glm_labels.R" in the same working directory as the .R or .Rmd script. They are executed in the background, applying modifications to labels inside the regression tables. </p> <p> </p>
TomoBreast randomized clinical trial's lung-heart outcomes and mortality through the 2020 COVID-19 pandemic: data and software
<p>Dataset and R script to reproduce the analyses of the manuscript:</p> <p>Vinh-Hung V, Gorobets O, Adriaenssens N, Van Parijs H, Storme G, Verellen D, Nguyen NP, Magne N, De Ridder M.</p> <p><strong>Lung-heart outcomes and mortality through the 2020 COVID-19 pandemic in a prospective cohort of breast cancer radiotherapy patients.</strong></p> <p>Cancers 2022; 14(24):6241. https:// doi.org/10.3390/cancers14246241</p> <p>https://www.mdpi.com/2072-6694/14/24/6241</p> <p>PubMed: PMID: 36551726</p> <p>PMCID: PMC9777311</p> <p>Info on the variables in file "aelq6_public.R"</p> <p>reproduced in "aelq_2_3_readme.txt":</p> <p>"aelq2_base2.txt" = baseline characteristics.</p> <p>"aelq3.txt" = longitudinal maesurements.</p> <p>Variables in "aelq2_base2.txt":</p> <p>"<strong>aelq2_base2.txt</strong>" = baseline characteristics. <br># Age at randomization, years. <br># RTdose: cf TomoBreast papers. <br># 51 Gy = hypofractionated, simultaneous integrated boost<br># 42 Gy = hypofractionated, no boost, mastectomy cases only<br># 50 Gy = conventional, no boost, mastectomy cases only<br># 66 Gy = conventional, sequential boost<br># Weight kg, Height cm, <br># Detection 1=found by screening (senology follow-up/controle)<br># 2=found by symptoms (pain, palpable)<br># 9=unknown<br># Smoker 0= Not smoker<br># 1= Smoker<br># 2=ex-smoker<br># Mastectomy (and other binary coded) 1= yes<br># chemosched 0=none<br># 1= planned after RT (sequential)<br># 2= prior to RT and is finished (sequential)<br># 3= chemo is on-going or is planned to start with RT (concomitant)<br># hormonetherapy 0=no<br># 1=tamoxifen (nolvadex)<br># 2=Femara (Letrozole)<br># 3=zoladex<br># 4=tamoxifen + zoladex<br># Laterality 1,=Right, 2=Left, 3=Bilateral<br># LengthFU: length of follow-up, days from randomization</p> <p>"<strong>aelq3.txt</strong>" = longitudinal maesurements.<br># "Nr" = Case ID<br># "Time" in days from origin (origin =date of randomization), <br># if negative =before randomization<br># "KPS" "Weight" <br># "Died" "LocalRec" "Metast" "NewPrim" = binary code, 0=no, 1=yes<br># "fAEBreast" "fAEHeart" "fAELung" "fAEOther" <br># fAE = freedom from breast, heart, lung, other adverse event score<br># "LVEF2" = ejection fraction, %<br># "MacIver" = estimated cardiac strain</p> <p># the following are pulmonary function tests, untransformed units<br># "FVC", "FEV1", "PEF", "VC", "TLC", "RV", "FRC", "Raw", "sRaw", "DLCO",<br># "VA", "PF"</p> <p># "fDY", "fFA", "fPA" = freedom from dyspnea, from fatigue, from pain<br># range 0 to 100 (best)<br># see papers:</p> <p># Van Parijs, H.; Vinh-Hung, V.; Fontaine, C.; Storme, G.; Verschraegen, C.;<br># Nguyen, D.M.; Adriaenssens, N.; Nguyen, N.P.; Gorobets, O.; De Ridder, M.<br># Cardiopulmonary-related patient-reported outcomes in a randomized clinical<br># trial of radiation therapy for breast cancer. BMC Cancer 2021, 21, 1177,<br># doi:10.1186/s12885-021-08916-z.</p> <p># preprint:<br># Van Parijs, H.; Cecilia-Joseph, E.; Gorobets, O.; Storme, G.; <br># Adriaenssens, N.; Heyndrickx, B.; Verschraegen, C.; Nguyen, N.P.;<br># De Ridder, M.; Vinh-Hung, V. Lung-heart toxicity in a randomized <br># clinical trial of hypofractionated image guided radiation therapy for<br># breast cancer. Preprints 2022, 202212, 0214.<br># https://doi.org/10.20944/preprints202212.0214.v1</p> <p># <br># "Year" = year of the observation<br># example: randomized 1/1/2011, measurement done 1/31/2011, time = 30 days,<br># Year =2011<br>#<br> </p>
Visualizing the Impact of COVID-19 and the Vaccination Data in 2021
<p>COVID-19 has been a hot topic in recent years. While numerous visualizations have showcased the distribution of COVID-19 cases and deaths, few demonstrate the temporal relationships between cases, deaths, and COVID-19 vaccinations. Our visualization aims to fill this gap by showcasing the temporal evolution of COVID-19 cases, deaths, and vaccinations in the U.S. while also comparing them geographically by U.S. states.</p> <p>Our dataset was obtained from two different organizations: the New York Times and Our World in Data. The New York Times dataset focuses on COVID-19 cases and deaths within each county/state of the US in 2021, while the dataset from Our World in Data contains information on the various vaccination data of each state throughout the year. We chose to focus on 2021 since that is when the first data was collected for the us_state_vaccinations.csv, in addition to the reason that the majority of vaccination data from 2022 are not as consistent and missing a lot.</p> <p>Our visualizations are targeted towards individuals who want to learn more about the timeline of COVID-19 cases, deaths, and vaccinations data and the complex relationships among them. This includes public health officials who need to make informed decisions regarding interventions to mitigate the spread of COVID-19, journalists and media organizations who want to report accurate information about the pandemic to the public, and the general public who are interested in understanding the impact of COVID-19 on their local communities.</p> <p>We implemented our visualizations using Python's Altair and Streamlit libraries, using drop-down selection bars, time/date sliders, multi-select widgets, and various types of linked views. These interactive features are implemented using built-in functions from the Streamlit and Altair libraries, including st.selectbox, st.multiselect, st.slider, alt.selection_interval, and alt.selection_single. The details of the code that we wrote to implement these visualizations can be found on our project's GitHub page (https://github.com/Tony-Xiayi-Ding/COVID-19-Visualizations).</p> <p>Our visualizations showed that the temporal evolution of COVID-19 cases and deaths exhibited a striking similarity, with a rather consistent trend over time. Additionally, the cases and deaths count generally remained at much slower increasing rates during seasons with higher temperatures and at much higher increasing rates during colder months, highlighting the complex interplay of demographic and seasonal factors in shaping the pandemic in the U.S. Moreover, the overall trend for case fatality rate was decreasing for most states, and states that were close to each other shared similar trends of case fatality rate. Furthermore, states with higher average temperatures shared similar trends in case fatality rates that were quite different from those states that were relatively colder. Lastly, as total vaccinations per hundred increased over time, the relative case fatality rate dropped, and coastal states were found to have slightly higher total vaccinations per hundred values, potentially due to their higher population densities and that the residents in those states are more aware of the importance of getting vaccinated due to their elevated chances of contracting COVID-19.</p> <p>The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.</p> <p>References:</p> <p>1. New York Times. (2022). Covid-19-data/US-counties-2021.csv. GitHub. Retrieved February 12, 2023, from https://github.com/nytimes/covid-19-data/blob/master/us-counties-2021.csv</p> <p>2. Our World in Data. (2023). Covid-19-data/US_state_vaccinations.CSV. GitHub. Retrieved February 12, 2023, from https://github.com/owid/covid-19-data/blob/master/public/data/vaccinations/us_state_vaccinations.csv</p> <p>3. U.S. Department of Health and Human Services. (2023). What is a FIPS code and why do I need one? National Institutes of Health. Retrieved February 12, 2023, from https://nitaac.nih.gov/resources/frequently-asked-questions/what-fips-code-and-why-do-i-need-one</p>
PANDEM-2 European COVID-19 training data set
<p>The PANDEM-2 COVID-19 European training dataset is a large collection of time series either of real or realistic synthetic (generated) data and indicators associated with the European pandemic response to the COVID-19 pandemic. It is intended to be used for training in pandemic management.</p> <p> </p> <p>This dataset is the result of a data gathering requirement process for pandemic management involving feedback and inputs from several public health and first responder professionals as well as researchers and military personnel directly involved in the European COVID-19 pandemic response. This work is part of the PANDEM-2 project funded by the <em>Horizon 2020 Secure Societies</em> program. To collect this data, an open source software was developed named PANDEM-Source allowing reproducibility and customisation of this dataset. </p> <p> </p> <p>The dataset includes indicators for cases, deaths, hospitalisation, testing and laboratory data including pathogen genomic information, vaccination, non-pharmaceutical interventions, participatory surveillance, social media, flights resources (human and material such as beds or vaccines), and contact tracing activities. When no open available data was found, realistic synthetic data and indicators were generated with the goal of producing a data set to be used for pandemic management training. </p> <p>The project received funding from the European Union’s Horizon 2020 Research and Innovation programme under the Grant Agreement No. 883285. The material presented and views expressed here are the responsibility of the author(s) only. The EU Commission takes no responsibility for any use made of the information set out.<br> References</p> <p> </p> <p> </p>
Adjoint-based Data Assimilation of an Epidemiology Model for the Covid-19 Pandemic in 2020 --- Data Files
<p>New data on github:</p> <p>https://github.com/sesterhenn/Corona-DataAssimilation</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p>doi://10.5281/zenodo.3732292</p> <p>https://zenodo.org/record/3733244</p>
Coronavirus COVID-19 (2019-nCoV) Data Repository for Africa
<p>The purpose of this repository is to collate data on the ongoing coronavirus pandemic in Africa. Our goal is to record detailed information on each reported case in every African country. We want to build a line list – a table summarizing information about people who are infected, dead, or recovered. The table for each African country would include demographic, location, and symptom (where available) information for each reported case. The data will be obtained from official sources (e.g., WHO, departments of health, CDC etc.) and unofficial sources (e.g., news). Such a dataset has many uses, including studying the spread of COVID-19 across Africa and assessing similarities and differences to what’s being observed in other regions of the world.</p> <p>See the repo here <a href="https://github.com/dsfsi/covid19africa">https://github.com/dsfsi/covid19africa</a></p>
CanESM5 data for CCCma COVID-19 climate scenarios
<p>This data is associated with the publication:<br> <br> <strong> Quantifying the Influence of COVID-19 Emission Reductions on Climate</strong></p> <p><br> John C. Fyfe, Viatcheslav V. Kharin, Neil Swart, Gregory M. Flato, Michael<br> Sigmond and Nathan Gillett<br> <br> Canadian Centre for Climate Modelling and Analysis, Environment and Climate<br> Change Canada, Victoria, British Columbia, V8W 2Y2, Canada.<br> <br> The data includes the monthly CO2 emissions used to drive CanESM5, and also the<br> monthly CO2 concentrations, and Global Mean Screen Temperatures resulting from<br> the model simulations. Using this data, Figure 1 of the paper can be completely<br> reproduced.</p> <p> </p> <p>The organisation of the data is described in the readme.txt file. All contents are</p> <p>collected into a tar archive.<br> <br> </p>
COVID-19 Mobility Data Aggregator
<p><strong>Description</strong></p> <p>This repository includes:<br> 1) Data scraper of Google, Apple and Waze Mobility data<br> 2) Preprocessed mobility reports in different formats<br> 3) Merged mobility reports in summary files</p> <p><strong>About data</strong></p> <p>About <a href="https://www.google.com/covid19/mobility/">Google COVID-19 Community Mobility Reports</a></p> <p>About <a href="https://www.apple.com/covid19/mobility">Apple COVID-19 Mobility Trends Reports</a></p> <p>About <a href="https://www.waze.com/covid19">Waze COVID-19 local driving trends</a></p> <p><strong>Description of data files</strong></p> <p><em><strong>Google reports (located in google_reports directory):</strong></em></p> <p>The raw report in ZIP format: Global_Mobility_Report.zip<br> Data for the worldwide (only 1st level of subregions): mobility_report_countries (CSV and Excel formats available)<br> Data for Brazil: mobility_report_brazil (CSV and Excel formats available)<br> Data for Europe: mobility_report_europe (CSV and Excel formats available)<br> Data for Asia + Africa: mobility_report_asia_africa (CSV and Excel formats available)<br> Data for North and South America + Oceania (Brazil and US excluded): mobility_report_america_oceania (CSV and Excel formats available)</p> <p><em><strong>Apple reports (located in apple_reports directory):</strong></em></p> <p>Raw report: applemobilitytrends.csv<br> Data for the worldwide: apple_mobility_report (Google Sheets, CSV and Excel formats available)<br> Data for the US: apple_mobility_report_US (CSV and Excel formats available)</p> <p><em><strong>Waze reports (located in waze_reports directory):</strong></em></p> <p>Raw CSV files: Waze_Country-Level_Data.csv, Waze_City-Level_Data.csv<br> Preprocessed report: waze_mobility (Google Sheets, CSV and Excel formats available)</p> <p><em><strong>Summary reports (located in summary_reports directory)</strong></em></p> <p>These are merged Apple and Google reports.</p> <p>Report by regions: summary_report_regions (CSV and Excel formats available)<br> Report by countries: summary_report_countries (Google Sheets, CSV and Excel formats available)<br> Report for the US: summary_report_US (CSV and Excel formats available)</p> <p><strong>License</strong></p> <p>See LICENSE.txt</p> <p><strong>Credits</strong></p> <p>If you use this dataset, please also cite the original data sources:</p> <p>1. Google LLC <em>"Google COVID-19 Community Mobility Reports"</em>. https://www.google.com/covid19/mobility/ Accessed: <date></p> <p>2. Apple Inc. "<em>Apple COVID-19 Mobility Trends Reports"</em>. https://www.apple.com/covid19/mobility Accessed: <date></p> <p>3. Waze Ltd "<em>Waze COVID-19 Impact Dashboard". </em>https://www.waze.com/covid19 Accessed: <date></p>
Data from: The impact of human mobility networks on the global spread of COVID-19
<p>This is empirical dataset from the paper "The impact of human mobility networks on the global spread of COVID-19". Specifically, the dataset includes several files: (a) the COVID-19 network - an origin/destination matrix (i.e., "covid_network.csv"); (b) the common language network - edgelist format (i.e. "edge_list_comlang.csv"); (c) the same continent network - edgelist format (i.e., "edge_list_continent.csv"; (d) the contiguity network (i.e., "edge_list_contig.csv"); (e) the migration network - edgelist format (i.e., "edge_list_migration_in.csv"; (f) the tourism network - edgelist format (i.e., edge_list_tourism_in.csv"); (g) the list of nodes (countries) corresponding to files (b)-(e) (i.e., "nodes.csv"). Additionally, we uploaded the Rcode used in the paper (i.e. "code"), as a .pdf file format, the data source for the figures included in the paper (i.e., "covid_network_matrix.csv", "matrix_migration_out.csv", "matrix_tourism.csv" - Figure 1; "Fig_2_a_matrix_comlang.csv", Fig_2_b_matrix_contig.csv", "Fig_2_c_matrix_continent.csv" - Figure 2; "Fig_3.graphmlz - Figure 3; Fig_4.graphmlz - Figure 4) and the "global network of COVID-19 onset" (an individual-level data) (i.e., "global_covid_network.csv"). </p> <p>For details, please, see the Methods section of the paper: The impact of human mobility networks on the global spread of COVID-19 (Hancean, M.-G., Slavinec, M., Perc, M). </p> <p> </p> <p> </p> <p> </p>
Artificial COVID-19 Cases in Paris and Geographic Data Useful for Geomasking
<p>Artificial dataset of addresses of COVID-19 cases in Paris. The dataset was created to test geomasking techniques to be used on the real data collected by the French health administration. The dataset was used in the paper "Geographically Masking Addresses to Study COVID-19 Clusters" by Walid Houfaf-Khoufaf and Guillaume Touya. The dataset contains the following files:</p> <ul> <li>roads_paris_IGN.shp contains the road lines from IGN France in Paris;</li> <li>buildings_paris_IGN.shp contains the building polygons from IGN France in Paris (useful to aggregate points to building groups);</li> <li>faces.shp contains the blocks built from the roads (useful to aggregate points to blocks);</li> <li>ban_75.shp contains all the address points in Paris from the open BAN database.</li> <li>artificial_COVID_cases.csv contains the artificial COVID cases generated from 3 months in 2020 in the Paris area.</li> </ul> <p> </p>
COVID-19 vaccination data in Israel by age over time until August 2021
<p>COVID-19 vaccination data in Israel processed to show vaccination by age over time. These datasets are derived from publicly available Ministry of Health data, but processed for analytics about uptake in different age groups over time. They cover the mass vaccination campaign for COVID-19 until August 2021. The campaign consisted of the administration of multiple doses of the Pfizer vaccine.</p>
Data for: COVID-19 patents/patent applications (Jan. 2020 – Oct. 2021)
<p>This dataset contains information regarding both applications and granted patents on COVID-19 disease</p> <p>Derwent Innovation database was used for data mining (accessed on Nov. 20, 2021).</p> <p>The patent search was carried out on by means of a precise set of keywords and performed in the title/abstract/claims search field.</p> <p>6,148 Inpadoc patent families were retrieved. </p> <p>The <em>XLS file</em> contains information related to Title, Abstract - DWPI , First Claim, Priority Number, Priority Date, Application Number, Application Date, Publication Number, Publication Date, IPC – Current, CPC – Current, Assignee/Applicant, Optimized Assignee, INPADOC Family Members. </p> <p>The top countries/regions are China (3,271), WO (1,057), India (487), United States (455).</p> <p>The top IPC codes are listed in the following table:</p> <p> </p> <table align="center"> <tbody> <tr> <td> <p><strong>IPC</strong></p> </td> <td> <p><strong>Definition</strong></p> </td> <td> <p><strong>No. of patents/applications</strong></p> </td> </tr> <tr> <td> <p>A61P 31/14</p> </td> <td> <p><em>Antivirals for RNA viruses</em></p> </td> <td> <p>1565</p> </td> </tr> <tr> <td> <p>G01N 33/569</p> </td> <td> <p><em>Biological material •• Chemical analysis of biological material ••• Immunoassay; Biospecific binding assay; Materials therefor •••• for microorganisms</em></p> </td> <td> <p>783</p> </td> </tr> <tr> <td> <p>C12Q 1/70</p> </td> <td> <p><em>Measuring or testing processes • involving virus or bacteriophage</em></p> </td> <td> <p>642</p> </td> </tr> <tr> <td> <p>A61P 11/00</p> </td> <td> <p><em>Drugs for disorders of the respiratory system</em></p> </td> <td> <p>582</p> </td> </tr> <tr> <td> <p>A61K 39/215</p> </td> <td> <p><em>Medicinal preparations containing antigens or antibodies • Viral antigens •• Coronaviridae, e.g., avian infectious bronchitis virus</em></p> </td> <td> <p>377</p> </td> </tr> </tbody> </table> <p><strong>Value of the dataset</strong>: prior art searches; patent landscape analysis </p> <p><strong>Steps to reproduce data</strong>: </p> <p>CTB=("covid-19" OR "covid 19" OR "covid19" ADJ "SARS-CoV-2" OR "SARS-CoV2" OR "sarscov2" ADJ "2019 ncov" OR "2019-nCoV" OR "2019nCoV" ADJ "covid-2019" OR "covid 2019" OR "COVID2019" OR "severe acute respiratory syndrome coronavirus 2" OR "2019 novel coronavirus" OR "coronavirus disease 2019" OR "novel corona virus" OR "novel coronavirus" OR "new corona virus" OR "new coronavirus" OR "Wuhan coronavirus")</p> <p>CTB=title/abstract/claims</p>
Covid-19 Worldwide Data 2021 Cases and Vaccination
<p>This dataset includes information about active cases, accumulative cases, accumulative deaths, daily information, vaccination classified by country of year 2021. </p>
Data for "Paris Agreement requires substantial, broad, and sustained policy efforts beyond COVID-19 recovery packages"
<p>This dataset contains the underlying data for the following publication: Tanaka, K., C. Azar, O. Boucher, P. Ciais, Y. Gaucher, D. J. A. Johansson (2022) Paris Agreement requires substantial, broad, and sustained policy efforts beyond COVID-19 public stimulus packages. <em>Climatic Change</em> <strong>172, </strong>1 (2022). https://doi.org/10.1007/s10584-022-03355-6</p> <p>Earlier manuscripts were published as a preprint. https://arxiv.org/abs/2104.08342</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.