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.
9,674
datasets available to search
ShareScore release 0.7.1
Dataset results
9,674 results for “COVID-19”
Spatiotemporal Prediction of COVID-19 Cases using Inter- and Intra-County Proxies of Human Interactions (dataset)
<p>This repository contains data (features) necessary to run STXGB model and accompanies the paper titled "Spatiotemporal Prediction of COVID-19 Cases using Inter- and Intra-County Proxies of Human Interactions".</p> <p> </p> <p>STXGB is a spatiotemporal autoregressive model that predicts county-level new cases of COVID-19 in the coterminous US in 1- to 4-week prediction horizons using spatiotemporal lags of infection rates, human interactions, human mobility, and socioeconomic composition of counties as predictive features.</p>
A Competing Compound For Remdesivir and Molnupiravir In Inhibiting RdRp Protein of COVID-19
<p>According to a docking study conducted with the online tool [1][2], the compound Nicotinate mononucleotide with the formula C11H15NO9P+ could inhibit RNA-dependent RNA polymerase (RdRp (RTP site)) protein in COVID-19 coronavirus, with a Score Value of -9.4 (kcal / mol). This is better than the amount for the Remdesivir molecule (-9.2 (kcal / mol)) and after MW correction, it is also better than Molnupiravir, to inhibit the same protein. Since inhibition of this protein plays a key role in the inhibitory function of Remdesivir against COVID-19 virus [3], we can see the Nicotinate mononucleotide compound as an alternative to Remdesivir in inhibiting this coronavirus. Since the molecular weight of Nicotinate mononucleotide (336 g / mol) is approximately half that of Remdesivir (603 g / mol) and Molnupiravir-active-form (499.16 g / mol), it is better in terms of both protein adhesion and absorption capacity. Since the main precursor of Nicotinate mononucleotide, Trigonelline alkaloid, is a naturally occurring plant secondary metabolite, and Nicotinate mononucleotide itself is present in mammalian biomolecular pathways, it is likely to be more available, more cost-effective, and more non-toxic and be better than Remdesivir and Molnupiravir. Please see added data.</p>
Brazilian Portuguese COVID-19 Tweets
<p><strong>Brazilian Portuguese symptoms about COVID-19:</strong></p> <ul> <li><strong>Source</strong>: Twitter</li> <li><strong>Start</strong>: 2019-01-01 (January 1st)</li> <li><strong>End</strong>: 2021-09-30 (September 30th)</li> <li><strong>Tweets</strong>: 13,859,059 <ul> <li>Year 2019 [full year]: 4,043,958 obs. of 26 variables (Brazil_Portuguese_COVID19_Tweets2019.csv)</li> <li>Year 2020 [full year]: 6,155,844 obs. of 26 variables (Brazil_Portuguese_COVID19_Tweets2020.csv)</li> <li>Year 2021 [Q1 - Q3]: 3,659,257 obs. of 26 variables (Brazil_Portuguese_COVID19_Tweets2021.csv)</li> </ul> </li> </ul> <p><strong>Search terms (56 symptoms keywords about COVID-19):</strong></p> <p><strong>(1)</strong> adinamia, <strong>(2)</strong> ageusia, <strong>(3)</strong> anosmia, <strong>(4)</strong> boca azulada, <strong>(5)</strong> calafrio, <strong>(6)</strong> cansaço, <strong>(7) </strong>cefaleia, <strong>(8)</strong> cianose, <strong>(9)</strong> coloração azulada no rosto, <strong>(10)</strong> congestão nasal, <strong>(11)</strong> conjuntivite, <strong>(12) </strong>coriza, <strong>(13)</strong> desconforto respiratório, <strong>(14)</strong> diarreia, <strong>(15)</strong> dificuldade para respirar, <strong>(16)</strong> diminuição do apetite, <strong>(17)</strong> dispneia, <strong>(18)</strong> distúrbio gustativo, <strong>(19)</strong> distúrbio olfativo, <strong>(20)</strong> dor abdominal, <strong>(21)</strong> dor de cabeça, <strong>(22)</strong> dor de garganta, <strong>(23)</strong> dor no corpo, <strong>(24)</strong> dor no peito, <strong>(25) </strong>dor persistente no tórax, <strong>(26) </strong>erupção cutânea na pele, <strong>(27)</strong> fadiga, <strong>(28)</strong> falta de ar, <strong>(29)</strong> febre, <strong>(30)</strong> gripe, <strong>(31)</strong> hiporexia, <strong>(32)</strong> inapetência, <strong>(33)</strong> infecção respiratória, <strong>(34)</strong> lábio azulado, <strong>(35)</strong> mialgia, <strong>(36)</strong> nariz entupido, <strong>(37) </strong>náusea, <strong>(38)</strong> obstrução nasal, <strong>(39)</strong> perda de apetite, <strong>(40)</strong> perda do olfato, <strong>(41)</strong> perda do paladar, <strong>(42)</strong> pneumonia, <strong>(43)</strong> pressão no peito, <strong>(44)</strong> pressão no tórax, <strong>(45)</strong> prostração, <strong>(46)</strong> quadro gripal, <strong>(47)</strong> quadro respiratório, <strong>(48)</strong> queda da saturação, <strong>(49)</strong> resfriado, <strong>(50)</strong> rosto azulado, <strong>(51)</strong> saturação baixa, <strong>(52)</strong> saturação de o2 menor que 95%, <strong>(53)</strong> síndrome respiratória aguda grave, <strong>(54) </strong>srag, <strong>(55)</strong> tosse, <strong>(56)</strong> vômito.</p> <p><strong>Variables:</strong></p> <pre><code>Variable str Description ---------------------------------------------------------------------------------- id (integer64) - Tweet identifier conversation_id (integer64) - Tweet conversation identifier date (POSIXct) - Tweet created date (format: YYYY-MM-DD hh:mm:ss) tweet (chr) - Symptoms mention about COVID-19 language (chr) - Tweet language: Portuguese hashtags (chr) - Sign (#) used to identify specific topic user_id (integer64) - User identifier username (chr) - Twitter user name link (chr) - Tweet url urls (chr) - External urls from tweet photos (chr) - Photos posted in message (link) video (int) - Video posted in message (1=True;0=False) thumbnail (chr) - Thumbnail posted in message retweet (logi) - Message reposted by another user nlikes (int) - Number of tweet likes nreplies (in) - Number of tweet replies nretweets (int) - Number of tweet retweets Near (logi) - Near a certain City (Example: London) geo (logi) - Geo coordinates (lat,lon,km/mi.) user_rt_id (logi) - User retweet identifier user_rt (logi) - Retweet user retweet_id (logi) - Retweet identifier reply_to (chr) - Answer to someone retweet_date (logi) - Retweet created date (format: YYYY-MM-DD hh:mm:ss) symptoms (chr) - Symptoms mentioned nsymptoms (int) - Number of symptons mentioned </code></pre> <p><em>str: Compactly Display the Structure of an Arbitrary R Object</em></p>
Italian COVID-19 Integrated Surveillance Dataset (v42.0.0)
<p><strong>Abstract</strong></p> <p>COVID-19 integrated surveillance data provided by the <a href="http://www.iss.it/">Italian National Institute of Health</a> and processed via <a href="https://github.com/InPhyT/UnrollingAverages.jl">UnrollingAverages.jl</a> to deconvolve the weekly simple moving averages.</p> <p><strong>Overview</strong> </p> <p>Every week the National Institute for Nuclear Physics (<a href="https://home.infn.it/it/">INFN</a>) imports an anonymous individual-level dataset from the Italian National Institute of Health (<a href="https://www.iss.it/">ISS</a>) and converts it into an incidence time series data organized by date of event and disaggregated by sex, age and administrative level with a consolidation period of approximately two weeks. The information available to the <a href="https://home.infn.it/it/">INFN</a> is summarised in the following <a href="https://covid19.infn.it/iss/campi-iss.pdf">meta-table</a>.</p> <p><strong>Output Data </strong></p> <p>The output data has been stored <a href="https://github.com/InPhyT/COVID19-Italy-Integrated-Surveillance-Data/tree/main/3_output/data">here</a> and contain the following information:</p> <ul> <li>Reconstructed daily time series of <strong>confirmed cases by date of diagnosis</strong> stratified by sex and age at the regional level;</li> <li>Reconstructed daily time series of <strong>symptomatic cases by date of symptoms onset</strong> stratified by sex and age at the regional level;</li> <li>Reconstructed daily time series of <strong>ordinary hospital admissions</strong> by date of admission stratified by sex and age at the regional level;</li> <li>Reconstructed daily time series of <strong>intensive hospital admissions</strong> by date of admission stratified by sex and age at the regional level;</li> <li>Reconstructed daily time series of <strong>deceased cases by date of death</strong> stratified by sex and age at the regional level.</li> </ul>
Experience of COVID-19 disease and fear of the SARS-CoV-2 virus among Polish students
<p>The deposited files contain a database related to the study of the fear of COVID-19 among Polish students and a code book. It is connected with the article titled <em>Experience of COVID-19 disease and fear of the SARS-CoV-2 virus among Polish students</em></p>
Social contact data before and during COVID-19 in China
<p>Social contact data for Wuhan and Shanghai, China before and during the COVID-19 outbreak.<br> Changelog for Version 2:<br> - Contact data from outbreak and baseline merged. A variable to distinguish the two ("collection_period") is added to "contact_extra".<br> <br> For problems with the dataset, please contact:</p> <table> <tbody> <tr> <td> </td> <td>socialcontactdata@gmail.com</td> </tr> </tbody> </table>
Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 1 FOV1
<p><strong>Image-based data set of a post-mortem lung sample from a non-COVID-related pneumonia donor (CONTROL CASE 1, FOV1)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
Aggregated number of officially recorded COVID-19 deaths in 2020 in Poland by county (powiat) with indication of sources
<p>This is a complimentary dataset for the article "Deaths during the first year of the COVID‑19 pandemic: insights from regional patterns in Germany and Poland" by Myck, Oczkowska, Garten, Krol, Brandt in BMC Public Health (DOI: 10.1186/s12889-022-14909-9).</p>
Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 2 FOV2
<p><strong>Image-based data set of a post-mortem lung sample from a non-COVID-19-related pneumonia donor (CONTROL CASE 2 FOV2)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 2 FOV1
<p><strong>Image-based data set of a post-mortem lung sample from a non-COVID-19-related pneumonia donor (CONTROL CASE 2 FOV1)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 3 FOV2
<p><strong>Image-based data set of a post-mortem lung sample from a non-COVID-19-related pneumonia donor (CONTROL CASE 3 FOV2)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - ACUTE CASE 2 FOV3
<p><strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (ACUTE CASE 2 FOV3)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - ACUTE CASE 1 FOV2
<p><strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (ACUTE CASE 1 FOV2)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - CONROL CASE 3 FOV1
<p><strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (CONTROL CASE 3 FOV1)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - PROLONGED CASE 2 FOV2
<p><strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (PROLONGED CASE 2 FOV2)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - ACUTE CASE 1 FOV1
<p><strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (ACUTE CASE 1 FOV1)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - PROLONGED CASE 4 FOV1
<p><strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (PROLONGED CASE 4 FOV1)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 1 FOV3
<p><strong>Image-based data set of a post-mortem lung sample from a non-COVID-related pneumonia donor (CONTROL CASE 1 FOV3)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - CHRONIC CASE 1 FOV3
<p><strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (CHRONIC CASE 1 FOV3)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - PROLONGED CASE 3 FOV1
<p><strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (PROLONGED CASE 3 FOV1)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</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.