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.
10,623
datasets available to search
ShareScore release 0.7.1
Dataset results
10,623 results for “COVID”
Keyword frequencies in popular tech media during the COVID-19 pandemic (01.2020-06.2020)
<p>Sources: </p> <ul> <li>Euractiv</li> <li>The Conversation</li> <li>Politico Europe </li> <li>IEEE Spectrum </li> <li>Techforge </li> <li>Fastcompany </li> <li>The Guardian (Tech) </li> <li>Arstechnica </li> <li>Reuters </li> <li>Gizmodo </li> <li>ZDNet </li> <li>The Register </li> <li>The Verge </li> <li>TechCrunch </li> </ul> <p>Methodology is modified relative to the regular trend analysis due to the short period of analysis (weekly freqiencies)</p> <ul> <li>Frequency of appearances for all unigrams and bigrams in the texts</li> <li>Frequency: number of appearances of every term divided by the number of all terms (for every week) </li> <li>Several media sources: all articles are treated equally</li> <li>Average monthly change in the analised term's frequency is calculated by OLS regressions</li> <li>The dependent variable of the estimation is the frequency index, while the number of weeks since the beginning of the analysed period (January 2020) is the independent variable</li> <li>The regression coefficient (referred to as coef) shows by how much on average the analysed expression’s frequency changed with every observed week (marginal change of the frequency), revealing which keywords had the biggest weekly growth</li> </ul> <p>Columns</p> <p>freq_2020_weeks (e.g. freq_2020_ww0): the average frequency of the term</p> <p>coef: the regression coefficient</p> <p>coef_norm: the regression coefficient divided by the mean frequency of the keyword</p>
Students' perceived obstacles with Forced Online Distance Learning during the CoVID-19 outbreak and their preferences to continue with the introduced teaching methods after the reopening of the University of Maribor [Project documentation]
<p>The outbreak of COVID -19 forced most universities into distance education. Three didacticians and researchers from the University of Maribor, Slovenia: Kosta Dolenc, Mateja Ploj Virtič and Andrej Šorgo formed a self-initiated initiative project group during the COVID -19 epidemic and started the first project with the working title: The Side Effects of Forced Online Distance Education (FODE).</p> <p>The aim of the second study, conducted during the first wave of the epidemic in March 2020, was to investigate the response of university students to the new situation. The project documentation provided for the Forced Online Distance Learning (FODL) consists of:</p> <ul> <li>abstract,</li> <li>instrument,</li> <li>copy of the descriptive statistics,</li> <li>and SPSS dataset.</li> </ul>
Forced Continuance Intention Model of Distance Online Teaching during CoVID-19 outbreak at University of Maribor, Slovenia [Project documentation]
<p>The outbreak of COVID -19 forced most universities into distance education. Three didacticians and researchers from the University of Maribor, Slovenia: Kosta Dolenc, Mateja Ploj Virtič and Andrej Šorgo formed a self-initiated initiative project group during the COVID -19 epidemic and started the project with the working title: The Side Effects of Forced Online Distance Education (FODE).</p> <p>The aim of the first study, conducted during the first wave of the epidemic in March 2020, was to investigate the response of university teachers to the new situation. The project documentation provided for the Forced Online Distance Teaching (FODT) consist of:</p> <ul> <li>abstract,</li> <li>instrument,</li> <li>copy of the descriptive statistics, and</li> <li>SPSS dataset.</li> </ul>
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>
A Multilingual Dataset of COVID-19 Vaccination Attitudes on Twitter
<p>This dataset consists of the IDs of 2,198,090 tweets collected from Western Europe, of which 17,934 are annotated with labels indicating the originators' affective vaccination stances, including Positive (PO), Negative (NG), Positive but dissatisfaction (PD), Neutral (NE) and Off-topic (OT).</p> <p>all_tweets.txt contains all the ids of the collected tweets, annotated_tweets.txt contains the ids of the annotated tweets and the categories they are annotated to.</p> <p> </p>
Covid-19 et Intention d'utiliser les chèques psycho par les étudiants : la prise en compte du contexte dans la théorie du comportement planifié
<p>Cette base de données est issue d’une enquête quantitative par questionnaire (nombre d’observations = 460, période : mars 2021). Elle est construite sur la base de la théorie du comportement planifié. La variable finale que le modèle cherche à expliquer (la variable dépendante) est l’intention d’utiliser les chèques psycho, dispositif proposé par le gouvernement français en février 2021, par les étudiants. Ces chèques permettent aux étudiants de bénéficier de 3 consultations auprès d’un psychologue conventionné, pour un montant total de 96 €.</p> <p>Les objectifs (et les utilisations possibles) de cette BDD sont autant orientés vers les besoins des acteurs (notamment ceux en charge de la mise en œuvre du dispositif « chèque psycho » pour les étudiants ou de dispositifs similaires) que des chercheurs.</p> <p>L’objectif opérationnel est de mesurer (statistiques descriptives) et de comprendre (statistiques explicatives) les facteurs qui conduisent des étudiants à envisager d’utiliser ce dispositif ; et donc à pouvoir comprendre comment agir pour optimiser cette utilisation. L’extension récente (juin 2021) de ce dispositif aux enfants et adolescents de 3 à 17 ans (dispositif « PsyEnfantAdo ») induit un deuxième objectif opérationnel : la possibilité pour les acteurs en charge de ce nouveau dispositif de s’inspirer de la méthodologie présentée dans cette base pour piloter au mieux ce projet.</p> <p>L’objectif théorique réside a) dans la prise en compte de l’impact d’un contexte (le vécu des étudiants durant la pandémie de la Covid-19 (t leur antécédents au niveau psychologique dans la théorie du comportement planifié (TCP) et b) dans la confirmation de la place de l’identité personnelle en tant que mesure alternative de l’intention comportementale (et non en tant que variable explicative de cette intention). Les chercheurs pourront également utiliser cette base dans des méta-analyses sur la TCP, la prise en compte du contexte dans la compréhension de l’intention comportementale et les impacts de la Covid-19.</p>
E-commerce et covid 19 en France. Une application de la théorie du comportement planifié
<p>Cette base de données est issue d’une enquête quantitative par questionnaire (n= 378, année 2021). Elle est construite sur la base de la théorie du comportement planifié. La variable dépendante est l’achat de biens et/ou de services par internet.</p> <p><em>Contenu de la base de données</em></p> <p>Le questionnaire comprend les mesures suivantes : 2 variables de segmentation (piratage et type d’achat), le comportement de l’individu (2 items : fréquence et récence), l’impact du Covid-19 sur la fréquence d’achat (1 item), l’intention comportementale (2 items dont 1 d’identité personnelle), les croyances sur les bénéfices attendus (11 items), l’attitude (3 items), les croyances sur les freins perçus (13 items), la perception de contrôle sur le comportement (2 items), les normes descriptives (2 items), les normes injonctives (2 items), 3 variables de signalétique (sexe, âge et CSP). L’administration étant réalisée en ligne, la base de données comprend également une variable « temps de saisie » du questionnaire qui pourra servir à épurer la base. Toutes les variables à échelle sont mesurées en 6 points.</p>
Investigating the effects of COVID‑19 lockdown on Italian children and adolescents with and without neurodevelopmental disorders: a cross‑sectional study - DATASET
<p>Dataset to support the findings in the journal paper titled "Investigating the effects of COVID‑19 lockdown on Italian children and adolescents with and without neurodevelopmental disorders: a cross‑sectional study".</p> <p>Each row is a different subject.</p> <p>Each column represents an answer to the questionnaire. For single choice questions, the answer was reported as-is (Italian). For multiple choice questions, the alternatives where splitted in several columns and the answer was coded as 0/1 (one hot encoding). For the "school" column, 2=primary school, 3=middle school, 4=high school. For the "school.class" column, classes from 4 to 8 belong to primary school, from first to fifth grade; classes from 9 to 11 belong to middle school, from first to third grade; classes from 12 to 16 belong to high school, from first to fifth grade.</p>
Dataset for evaluation of unrealistic optimism in time of pandemic COVID-19 on a Polish sample
<p>This dataset contains the data used in a project called "Unrealistic optimism in the eye of the storm. Positive bias towards the consequences of COVID-19 during the second and third waves of the pandemic. ". The project concerns the occurrence of a cognitive bias - unrealistic optimism - with regard to contracting the coronavirus. The following information are attached to the dataset: codebooks with variables' names; analysis codes to replicate our results; supplementary materials with the description of procedures. </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>
Modeling robust COVID-19 intensive care unit occupancy thresholds for imposing mitigation to prevent exceeding capacities
<p>Simulation output files for 'Modeling robust COVID-19 intensive care unit occupancy thresholds for imposing mitigation to prevent exceeding capacities'.</p> <p>Simulating COVID-19 transmission and hospital burden to assess at which intensive care unit (ICU) occupancies mitigation, that reduces transmission, needs to be triggered to avoid exceeding ICU capacity limits, using the city of Chicago, Illinois as an example.</p> <p>Manuscript is under review for scientific publication, (see <a href="https://www.medrxiv.org/content/10.1101/2021.06.27.21259530v1">preprint on medRxiv</a>) and scripts are available from the GitHub repository at https://github.com/numalariamodeling/ICUtrigger_covid_chicago_paper_2021. </p> <p>Simulation output files uploaded per scenario including projected COVIID-19 transmission and burden trajectories for Chicago city for March 2020 to May 2021 per day.</p> <p>Simulation scenarios:</p> <p><reopening % above ICU capacity>_<delay after reaching ICU threshold>_<%mitigation>_<common simulation name> i.e. `50perc_1daysdelay_pr6_triggeredrollback_reopen`</p> <ul> <li>`emodl` file <ul> <li>required file for COVID-19 transmission model in the <a href="https://docs.idmod.org/projects/cms/en/latest/index.html">Compartmental Modeling Software</a> (see <a href="https://github.com/numalariamodeling/ICUtrigger_covid_chicago_paper_2021">GitHub repository</a> for details)</li> </ul> </li> <li>sampled_parameters.csv <ul> <li>simulation input and scenario parameters, (nrow=4400, 400 unique parameter combinations * 11 scenario values)</li> </ul> </li> <li>rt_trajectoriescovidregion_11.csv <ul> <li>estimated reproductive numbers per trajectory for complete timeline per day</li> </ul> </li> <li>trajectoriesDat_region_11_traces.csv <ul> <li>filtered to include top 100 trajectories fitted to ICU data</li> </ul> </li> <li>trajectoriesDat_region_trimfut.csv <ul> <li>truncated to only include projections after September 1st 2020</li> </ul> </li> </ul> <p>The folder `mainfigures_csvs.zip` includes processed simulation output data for the publication figures.</p>
Deciphering the Neurosensory Olfactory Pathway and Associated Neo-Immunometabolic Vulnerabilities Implicated in COVID-Associated Mucormycosis (CAM) and COVID-19 in a Diabetes Backdrop—A Novel Perspective
<p>Raw data files of transcriptomic profiling experiments, which form the basis for our publication (https://www.mdpi.com/2673-4540/3/1/13).</p>
Where2Test Saxony-Czechia COVID-19 new cases dataset
<p>Data in the repository were used in the study "Fine-scale variation in the effect of national border on COVID-19 spread: A case study of the Saxon-Czech border region", published in <a href="https://www.sciencedirect.com/journal/spatial-and-spatio-temporal-epidemiology">Spatial and Spatio-temporal Epidemiology</a>.</p> <p>This repository consists of two files:</p> <p><strong>saxony-westczechia_cases7</strong></p> <p>Weekly numbers of new COVID-19 cases in all municipalities in Saxony and Northwestern Czechia (Liberec, Ústí nad Labem, and Karlovy Vary regions) in the first half of 2021. Data are extracted from the websites <a href="https://www.coronavirus.sachsen.de">coronavirus.sachsen</a> and <a href="https://onemocneni-aktualne.mzcr.cz/covid-19">onemocneni-aktualne.mzcr.cz/covid-19</a>. The missing values were interpolated, and daily values were recalculated to weekly values.</p> <p><strong>municipalities</strong></p> <p>The second file consists of a list of all municipalities with their names, geometries, and population values. For Germany, we used the dataset <a href="https://hub.arcgis.com/datasets/esri-de-content::gemeindegrenzen-2018-mit-einwohnerzahl/about">"Gemeindegrenzen 2018 mit Einwohnerzahl"</a> (© GeoBasis-DE / BKG, Statistisches Bundesamt (Destatis) (2020), <a href="http://www.govdata.de/dl-de/by-2-0">dl-de/by-2-0</a>) as a source of geometries and population sizes of the municipalities (“<em>Gemeinde”</em>) in Saxony. Czech population numbers on the municipality level ("obec") were taken from the <a href="https://www.czso.cz/csu/czso/population-of-municipalities-1-january-2021">Czech Statistical Office</a>, while the geometries were obtained from <a href="https://www.cuzk.cz/ruian/RUIAN.aspx">RÚIAN</a> (@<a href="http://geoportal.cuzk.cz">Czech Office for Surveying, Mapping and Cadastre</a>, 2021). To keep the same geometry detail on both sides of the borders, we applied the Douglas-Peucker simplification algorithm implemented in the Python library <a href="https://github.com/mattijn/topojson">TopoJSON</a>.</p>
Data from: Elevated fires during COVID-19 lockdown and the vulnerability of protected areas
<p><strong>Related article:</strong> Johanna Eklund, Julia P G Jones, Matti Räsänen, Jonas Geldmann, Ari-Pekka Jokinen, Adam Pellegrini, Domoina Rakotobe, O. Sarobidy Rakotonarivo, Tuuli Toivonen, and Andrew Balmford. Elevated fires during COVID-19 lockdown and the vulnerability of protected areas. Nature Sustainability (2022) https://doi.org/10.1038/s41893-022-00884-x.</p> <p><strong>In this dataset:</strong></p> <p>This dataset contains information about monthly fire incidence and precipitation for the protected areas of Madagascar from January 2012 to December 2020. The fire data is sourced from NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product and the precipitation data from the Global Precipitation Measurement (GPM) mission (for years 2016-2020) and its predecessor The Tropical Rainfall Measuring Mission (TRMM) (for years 2011-2015) at spatial resolution 10 km. The fire and precipitation data was overlayed with the protected area polygons of the June 2020 release of the World Database of Protected Areas. For sources and more details on how the data was compiled see the related article. The data can be used to inspect temporal dynamics of wildfires inside protected areas and for informing adaptive protected area management and planning.</p> <p><strong>Please cite this dataset as:</strong></p> <p>Johanna Eklund, Julia P G Jones, Matti Räsänen, Jonas Geldmann, Ari-Pekka Jokinen, Adam Pellegrini, Domoina Rakotobe, O. Sarobidy Rakotonarivo, Tuuli Toivonen, and Andrew Balmford. Elevated fires during COVID-19 lockdown and the vulnerability of protected areas. Nature Sustainability (2022) https://doi.org/10.1038/s41893-022-00884-x.</p> <p><strong>Column names</strong></p> <p>NAME: Name of protected area</p> <p>Fires_sum: Number of observed fires (VIIRS)</p> <p>Month: Month</p> <p>Year: Year</p> <p>Precipitation: Precipitation (mm)</p> <p>Plag_1:Plag_12: Precipitation during previous month; 2 months ago; 3 months ago…12 months ago</p> <p>YEAR_CREAT: Year of establishment of protected area</p> <p>Biome: Biome</p> <p>REP_AREA: Area of protected area (km<sup>2</sup>)</p> <p>Fires_per_km2: Fires per km<sup>2</sup></p> <p>Prec_acc_12m: Accumulated precipitation during the last 12 months</p> <p>fBiome: Biome as factor</p> <p>fNAME: Name as factor</p> <p>sPrecipitation: Precipitation (scaled; see Methods section of article)</p> <p>sPlag_1: Precipitation in previous month (scaled; see Methods section of article)</p> <p>sPrec_acc_12m: Accumulated precipitation during the last 12 months (scaled; see Methods section of article)</p> <p>Pred_Zinb_1a: Predicted fires (see Methods section of article)</p> <p>Diff_Zinb_1a: Difference: Observed fires - predicted fires</p> <p>Year_pred: Year for prediction</p> <p><strong>License</strong><br> Creative Commons Attribution 4.0 International.</p>
PANACEA dataset - Heterogeneous COVID-19 Claims
<p>The peer-reviewed publication for this dataset has been presented in the 2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), and can be accessed here: https://arxiv.org/abs/2205.02596. Please cite this when using the dataset.</p> <p> </p> <p>This dataset contains a heterogeneous set of True and False COVID claims and online sources of information for each claim.</p> <p> </p> <p>The claims have been obtained from online fact-checking sources, existing datasets and research challenges. It combines different data sources with different foci, thus enabling a comprehensive approach that combines different media (Twitter, Facebook, general websites, academia), information domains (health, scholar, media), information types (news, claims) and applications (information retrieval, veracity evaluation).</p> <p> </p> <p>The processing of the claims included an extensive de-duplication process eliminating repeated or very similar claims. The dataset is presented in a LARGE and a SMALL version, accounting for different degrees of similarity between the remaining claims (excluding respectively claims with a 90% and 99% probability of being similar, as obtained through the MonoT5 model). The similarity of claims was analysed using BM25 (Robertson et al., 1995; Crestani et al., 1998; Robertson and Zaragoza, 2009) with MonoT5 re-ranking (Nogueira et al., 2020), and BERTScore (Zhang et al., 2019).</p> <p> </p> <p>The processing of the content also involved removing claims making only a direct reference to existing content in other media (audio, video, photos); automatically obtained content not representing claims; and entries with claims or fact-checking sources in languages other than English.</p> <p> </p> <p>The claims were analysed to identify types of claims that may be of particular interest, either for inclusion or exclusion depending on the type of analysis. The following types were identified: (1) Multimodal; (2) Social media references; (3) Claims including questions; (4) Claims including numerical content; (5) Named entities, including: PERSON − People, including fictional; ORGANIZATION − Companies, agencies, institutions, etc.; GPE − Countries, cities, states; FACILITY − Buildings, highways, etc. These entities have been detected using a RoBERTa base English model (Liu et al., 2019) trained on the OntoNotes Release 5.0 dataset (Weischedel et al., 2013) using Spacy.</p> <p> </p> <p>The original labels for the claims have been reviewed and homogenised from the different criteria used by each original fact-checker into the final True and False labels.</p> <p> </p> <p>The data sources used are:</p> <p>- The CoronaVirusFacts/DatosCoronaVirus Alliance Database. https://www.poynter.org/ifcn-covid-19-misinformation/</p> <p>- CoAID dataset (Cui and Lee, 2020) https://github.com/cuilimeng/CoAID</p> <p>- MM-COVID (Li et al., 2020) https://github.com/bigheiniu/MM-COVID</p> <p>- CovidLies (Hossain et al., 2020) https://github.com/ucinlp/covid19-data</p> <p>- TREC Health Misinformation track https://trec-health-misinfo.github.io/</p> <p>- TREC COVID challenge (Voorhees et al., 2021; Roberts et al., 2020) https://ir.nist.gov/covidSubmit/data.html</p> <p> </p> <p>The LARGE dataset contains 5,143 claims (1,810 False and 3,333 True), and the SMALL version 1,709 claims (477 False and 1,232 True).</p> <p> </p> <p>The entries in the dataset contain the following information:</p> <p>- Claim. Text of the claim.</p> <p>- Claim label. The labels are: False, and True.</p> <p>- Claim source. The sources include mostly fact-checking websites, health information websites, health clinics, public institutions sites, and peer-reviewed scientific journals.</p> <p>- Original information source. Information about which general information source was used to obtain the claim.</p> <p>- Claim type. The different types, previously explained, are: Multimodal, Social Media, Questions, Numerical, and Named Entities.</p> <p> </p> <p>Funding. This work was supported by the UK Engineering and Physical Sciences Research Council (grant no. EP/V048597/1, EP/T017112/1). ML and YH are supported by Turing AI Fellowships funded by the UK Research and Innovation (grant no. EP/V030302/1, EP/V020579/1).</p> <p> </p> <p>References</p> <p>- Arana-Catania M., Kochkina E., Zubiaga A., Liakata M., Procter R., He Y.. Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims. NAACL 2022 https://arxiv.org/abs/2205.02596</p> <p>- Stephen E Robertson, Steve Walker, Susan Jones, Micheline M Hancock-Beaulieu, Mike Gatford, et al. 1995. Okapi at trec-3. Nist Special Publication Sp,109:109.</p> <p>- Fabio Crestani, Mounia Lalmas, Cornelis J Van Rijsbergen, and Iain Campbell. 1998. “is this document relevant?. . . probably” a survey of probabilistic models in information retrieval. ACM Computing Surveys (CSUR), 30(4):528–552.</p> <p>- Stephen Robertson and Hugo Zaragoza. 2009. The probabilistic relevance framework: BM25 and beyond. Now Publishers Inc.</p> <p>- Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, and Jimmy Lin. 2020. Document ranking with a pre-trained sequence-to-sequence model. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings, pages 708–718.</p> <p>- Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019. Bertscore: Evaluating text generation with bert. In International Conference on Learning Representations.</p> <p>- Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.</p> <p>- Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, et al. 2013. Ontonotes release 5.0 ldc2013t19. Linguistic Data Consortium, Philadelphia, PA, 23.</p> <p>- Limeng Cui and Dongwon Lee. 2020. Coaid: Covid-19 healthcare misinformation dataset. arXiv preprint arXiv:2006.00885.</p> <p>- Yichuan Li, Bohan Jiang, Kai Shu, and Huan Liu. 2020. Mm-covid: A multilingual and multimodal data repository for combating covid-19 disinformation.</p> <p>- Tamanna Hossain, Robert L. Logan IV, Arjuna Ugarte, Yoshitomo Matsubara, Sean Young, and Sameer Singh. 2020. COVIDLies: Detecting COVID-19 misinformation on social media. In Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, Online. Association for Computational Linguistics.</p> <p>- Ellen Voorhees, Tasmeer Alam, Steven Bedrick, Dina Demner-Fushman, William R Hersh, Kyle Lo, Kirk Roberts, Ian Soboroff, and Lucy Lu Wang. 2021. Trec-covid: constructing a pandemic information retrieval test collection. In ACM SIGIR Forum, volume 54, pages 1–12. ACM New York, NY, USA.</p>
Dataset: Characterizing Anti-Asian Rhetoric During The COVID-19 Pandemic: A Sentiment Analysis Case Study on Twitter
<p>This is the dataset, trained model, and software companion for the paper titled: Characterizing Anti-Asian Rhetoric During The COVID-19 Pandemic: A Sentiment Analysis Case Study on Twitter accepted for the Workshop on Data for the Wellbeing of Most Vulnerable of the ICWSM 2022 conference.</p> <p>The COVID-19 pandemic has shown a measurable increase in the usage of sinophobic comments or terms on online social media platforms. In the United States, Asian Americans have been primarily targeted by violence and hate speech stemming from negative sentiments about the origins of the novel SARS-CoV-2 virus. While most published research focuses on extracting these sentiments from social media data, it does not connect the specific news events during the pandemic with changes in negative sentiment on social media platforms. In this work we combine and enhance publicly available resources with our own manually annotated set of tweets to create machine learning classification models to characterize the sinophobic behavior. We then applied our classifier to a pre-filtered longitudinal dataset spanning two years of pandemic related tweets and overlay our findings with relevant news events.</p>
Sharing research data and findings relevant to the novel coronavirus (COVID-19) outbreak - Literature sources
<p>The spreadsheet in the present dataset (CSV format) includes the sources considered during the literature review stage for the report: From intent to impact: Investigating the effects of open sharing commitments. Please note that not all sources in this deposit have been referenced in the above-mentioned report and that the report may include additional sources</p>
Sharing research data and findings relevant to the novel coronavirus (COVID-19) outbreak - Survey responses
<p>The spreadsheets in the present dataset (CSV format) include the anonymised responses to our online survey of signatories of the Joint Statement on open research and data sharing. Responses have been split into quantitative responses (i.e., closed survey questions) and qualitative responses (i.e., free text survey questions).</p> <p>This data has been used to inform our final report, which is available in our <a href="https://zenodo.org/communities/data-sharing-in-public-health-emergencies">Zenodo Project Community</a>.</p>
Sharing research data and findings relevant to the novel coronavirus (COVID-19) outbreak - Thematic coding of qualitative research findings
<p>The spreadsheet in the present dataset (CSV format) includes the anonymised thematic coding that has been applied to our interview and literature review findings to inform the preparation of the report: From intent to impact: Investigating the effects of open sharing commitments.</p> <p>The thematic coding has been applied by using <a href="https://www.qsrinternational.com/nvivo-qualitative-data-analysis-software/home">NVivo</a>, a professional qualitative analysis software, and then exported in spreadsheet form for public sharing.</p> <p>Find out more about this project in our dedicated <a href="https://zenodo.org/communities/data-sharing-in-public-health-emergencies">Zenodo project community</a>.</p>
Exames de Pacientes no Diagnóstico do Covid-19
<p>O conjunto de dados usado neste estudo, foi fornecido pelo <strong><em>Hospital Adventista de Manaus</em></strong>, situado na 32<sup>a</sup> posição no Ranking Nacional dos Hospitais em Todo o Brasil para o ano de 2020, o Quadro 1 apresenta uma lista completa dos atributos do conjunto de dados.</p> <p>Quadro 1 - Lista dos atributos do dataset</p> <table> <tbody> <tr> <td><strong>Atributo</strong></td> <td><strong>Descrição</strong></td> <td><strong>Natureza</strong></td> </tr> <tr> <td>idade</td> <td>Idade do Paciente</td> <td>Numérico</td> </tr> <tr> <td>rt_pcr</td> <td>Exame Covid-19</td> <td>Categórico</td> </tr> <tr> <td>leucócitos</td> <td>Exame Laboratoriais</td> <td>Numérico</td> </tr> <tr> <td>basofilos</td> <td>Exame Laboratoriais</td> <td>Numérico</td> </tr> <tr> <td>creatinina</td> <td>Exame Laboratoriais</td> <td>Numérico</td> </tr> <tr> <td>proteina_c</td> <td>Exame Laboratoriais</td> <td>Numérico</td> </tr> <tr> <td>hemoglobina</td> <td>Exame Laboratoriais</td> <td>Numérico</td> </tr> </tbody> </table> <p>As amostras são de pacientes que foram admitidos para internação com suspeita de Convid-19 e foram submetidos a exames que seguem o protocolo estabelecido de diagnóstico adotado naquela instituição, além do exame <strong>rt_pcr <em>(covid-19)</em></strong>, também sistematicamente eram realizados outros exames que dão suporte ao diagnóstico do Covid-19.</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.