Find research datasets worth reusing

opencc-by-4.0May 2021View details →

COVID-19 Vaccine Tweets in Turkish

This dataset contains the Turkish tweets that are collected with the keyword vaccine, sinovac and biontech in Turkish by using Twitter Academic API. The new versions will be more up-to-date and will be classified monthly folders. You can visit the github repository  of the project: <a href="https://github.com/burakozturan/tria-covid19">https://github.com/burakozturan/Turkish-Vaccine-Tweets</a>

opencc-by-4.0Mar 2024View details →

Replication data for: Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study

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. 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.

opencc-by-4.0Jan 2023View details →

VaxxHesitancy: A Dataset for Studying Hesitancy Towards COVID-19 Vaccination on Twitter

We create a publicly available dataset of over 3,100 COVID-19 vaccine-related tweets labeled as one of four stance categories: pro-vaxx, anti-vaxx, vaxx-hesitant, or irrelevant. *** Please use the V2 version. *** We split our dataset into two separate files: (1) VaccineHesitancy_train_v2.csv (Single + Double annotated) (2) VaccineHesitancy_test.csv (Double annotated) We present the details of this dataset here: VaxxHesitancy: A Dataset for Studying Hesitancy Towards COVID-19 Vaccination on Twitter (ICWSM 2023) Our Pre-trained model (GateNLP/covid-vaccine-twitter-bert) : https://huggingface.co/GateNLP/covid-vaccine-twitter-bert Paper: https://ojs.aaai.org/index.php/ICWSM/article/view/22213/21992   <pre>@inproceedings{mu2023vaxxhesitancy, title={VaxxHesitancy: A Dataset for Studying Hesitancy Towards COVID-19 Vaccination on Twitter}, author={Mu, Yida and Jin, Mali and Grimshaw, Charlie and Scarton, Carolina and Bontcheva, Kalina and Song, Xingyi}, booktitle={Proceedings of the International AAAI Conference on Web and Social Media}, volume={17}, pages={1052--1062}, year={2023} } </pre>

opencc-by-4.0Jun 2023View details →

Visualizing the Impact of COVID-19 and the Vaccination Data in 2021

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. 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. 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. 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). 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. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. References: 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 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 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

opencc-by-4.0Mar 2023View details →

Impact of vaccinations, boosters and lockdowns on COVID-19 waves in French Polynesia

COVID-19 case, hospitalisation, death, seroprevalence, vaccination and population data, and age-dependent contact rate, severe burden risk and vaccine effectiveness parameter estimates, required to fit model and run simulations in article "Impact of vaccinations, boosters and lockdowns on COVID-19 waves in French Polynesia"

opencc-by-4.0Oct 2021View details →

COVID-19 vaccination data in Israel by age over time until August 2021

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.

opencc-by-4.0Dec 2021View details →

Covid-19 Worldwide Data 2021 Cases and Vaccination

This dataset includes information about active cases, accumulative cases, accumulative deaths, daily information, vaccination classified by country of year 2021.

opencc-by-4.0Jan 2022View details →

A Multilingual Dataset of COVID-19 Vaccination Attitudes on Twitter

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). 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.

opencc-by-4.0Oct 2022View details →

Risk and symptoms of COVID-19 in health professionals according to baseline immune status and booster vaccination during the Delta and Omicron waves in Switzerland – a multicentre cohort study

For details, see publication

opencc-by-4.0Jun 2024View details →

COVID-19 Vaccines Database: 2020-2022

The attached databases were generated and used in for the analysis of the research article 'Which roads lead to access? A global landscape of six COVID-19 vaccine innovation models'. They contain data related to COVID-19 vaccines' registration status, prices, production, purchases, deliveries, and investments between 2020 and 2022. These databases were compiled by collecting and revising data from two sources: UNICEF's COVID Market Dasboard, and the COVID-19 vaccine R&D investments tracker from the Geneva Graduate Institute's Global Health Centre: https://www.knowledgeportalia.org/covid-19-vaccine-r-d-funding.

opencc-by-4.0Aug 2021View details →

Datasets Cured and Enriched with Provenance from the National Vaccination Campaign Against COVID-19

The COVID-19 pandemic is a global threat. If, on the one hand, weaccount for many losses, on the other hand, the generation of datasets and ur-gent analytical demands has accelerated. Among the combat strategies, vacci-nation and data-centered epidemiological investigations stand out. This datasetpaper presents the process of building cured and annotated datasets with prove-nance metadata. The main dataset is based on the registration data of the Vacci-nation Campaign against COVID-19 in Brazil. The dataset contains thousandsof records processed up to March 2021. The data were analyzed, investigated,treated and cross-checked with other sources, in order to correct and comple-ment them, resulting in cured datasets and aligned to the FAIR principles.

opencc-by-4.0Aug 2021View details →

Proactive COVID-19 testing in a partially vaccinated population.

Complete simulation-generated datasets analyzed in McGee et al. (2021) Proactive COVID-19 testing in a partially vaccinated population. medRxiv 2021.08.15.21262095. Data is uploaded in comma-separated .csv files which have been compressed using gzip. Descriptions of data columns can be found in the column_descriptions.csv file.

opencc-by-4.0Feb 2023View details →

Database Fear of COVID-19 and Vaccine Attitudes Examination Scale (VAX) in Spain

This dataset contains data collected between November 15, 2021 and March 7, 2022, and between December 1, 2022 and February 6, 2023. It contains demographic variables, data on vaccinated people, responses to the items on the Fear of COVID-19 Scale, and to the items on the Vaccination Attitudes Examination (VAX) scale are included. Although the language of the open answers is Spanish, the name of the variables and the value labels are written in English to facilitate their understanding. Data and codebooks are provided in csv format, following the FAIR principles. Three files are provided: 1. Database Fear of COVID-19 and VAX, with the data related to sample characteristics and the answers to the items of the questionnaires. 2. Database codebook of variables, with information of the labels of the variables of the Database file. 3. Variable values codebook, with the labels of the values of the variables in the Database file.

opencc-by-4.0Jul 2023View details →

Let's talk about COVID-19 vaccination: relevance of conversations about COVID-19 vaccination and information sources on vaccination intention in Switzerland

Data to replicate the publication "Let's talk about COVID-19 vaccination: relevance of conversations about COVID-19 vaccination and information sources on vaccination intention in Switzerland?". This publication examines how public information sources and conversations about COVID-19 are associated with COVID-19 vaccination intention. Multivariable logistic regression and mediation analysis using generalized structural equation modeling were applied.

opencc-by-4.0Aug 2023View details →

Covid-19 Vaccine Monitoring project (CVM)-Electronic Health Record data sources Codelist

This is the code list that was used to identify outcomes and covariates (those tagged as in narrow) in electronic health records of participating data sources in the the CVM study which was addressing the following questions   1) To create and assess readiness of electronic health record data sources for rapid evaluation of safety signals by  <ul> <li> Providing an overview of the methods for identification of COVID-19 vaccine exposure in the data sources  </li> <li> Monitoring the number of individuals exposed to any COVID-19 vaccine and to compare this to COVID-19 vaccine exposure (benchmark: ECDC vaccine tracker)1   </li> <li> Generation of updated background rates for AESIs  </li> </ul> 2) To conduct rapid safety assessment studies using electronic healthcare records and support EMA safety assessments.   The protocol for this study is publicly available www.encepp.eu/encepp/viewResource.htm?id=42637. The report with results using the code list is publicly available on Zenodo as well.

opencc-by-4.0Mar 2023View details →

Characterization of the anti-spike IgG immune response to COVID-19 vaccines in people with a wide variety of immunodeficiencies

Participants submitted saliva using the OME-505 collection device (OMNIgene Oral, Ottawa, Canada) every two weeks from vaccination through six months post-dose 3 to detect breakthrough SARS-CoV-2 infections. Viral RNA was extracted using the NucliSENS easyMag automated extraction system from 200ul of saliva in stabilizing solution and eluted in a total volume of 50ul. First strand cDNA synthesis was performed from 5ul of eluted RNA using SuperScript IV VILO Master Mix (Thermo Fisher). Positive specimens were then sequenced. Multiplex tiled amplicon libraries were prepared using the Midnight panel and Rapid barcoding kit RBK-004 (Oxford Nanopore technologies) using previously published protocol. Twelve sample pooled libraries were sequenced on a GridION X5 nanopore sequencer using Flongle adapters. After sequencing, raw data were processed using interARTIC to generate consensus sequences and variant calls. SARS-CoV-2 lineages were determined using these consensus sequences and the NextClade and Pangolin platforms.

opencc-by-4.0Dec 2021View details →

The knowledge, attitudes and perceptions towards the Covid-19 vaccine among dental staff at the University of the Western Cape, South Africa

Background: Despite the well-known increased risk of exposure to the Covid-19 virus in a dental setting, vaccination rates among staff members are low. This study was aimed at understanding the knowledge, attitudes and perceptions of staff towards the Covid-19 vaccine. This information, as well as the possible associations to demographic profiles, are necessary for authorities to adequately address specific concerns and uncertainties; (2) Methods: A descriptive cross-sectional study was conducted by means of an anonymous, online, validated questionnaire.; (3) Results: 105 staff members participated. Majority of staff have received the Covid-19 vaccine but stated that they would not take the booster vaccination. Significant associations between the level of education and the knowledge, attitudes and perceptions of staff were found.; (4) Conclusions: Majority of the staff members had a positive attitude towards the Covid-19 vaccine. However, specific concerns and uncertainties were identified and will need to be addressed in order to improve vaccination rates among staff members.

opencc-by-4.0Jan 2022View details →

Dataset from "Merging Digital Humanities and Discourse Analysis in the Study of COVID-19 Vaccine Distribution in Norwegian Newspapers" (Sverdljuk et al. 2022)

Contains URNs (identifiers) for the newspapers used in the corpus study "Merging Digital Humanities and Discourse Analysis in the Study of COVID-19 Vaccine Distribution in Norwegian Newspapers". For each subcorpus there is an Excel file containing references to the objects used, together with basic metadata. The corpus definitions can be used in various webapps of the DH-LAB at the National Library of Norway, e.g.: <a href="https://beta.nb.no/dhlab/concordances/">https://beta.nb.no/dhlab/concordances/</a> <a href="https://beta.nb.no/dhlab/collocations/">https://beta.nb.no/dhlab/collocations/</a> See more at <a href="https://www.nb.no/dh-lab/">https://www.nb.no/dh-lab/</a>

opencc-by-4.0Feb 2022View details →

Repeated information of benefits reduces COVID-19 vaccination hesitancy: Experimental evidence from Germany

This replication package contains the raw data and code to replicate the findings reported in the paper. The data and code are licensed under a Creative Commons Attribution 4.0 International Public License. See LICENSE.txt for details. Software requirements All analysis were done in Stata version 16: <ul> <li>Add-on packages are included in scripts/libraries/stata and do not need to be installed by user. The names, installation sources, and installation dates of these packages are available in scripts/libraries/stata/stata.trk.</li> </ul> Instructions <ol> <li>Save the folder ‘replication_PLOS’ to your local drive.</li> <li>Open the master script ‘run.do’ and change the global pointing to the working direction (line 20) to the location where you save the folder on your local drive</li> <li>Run the master script ‘run.do’ to replicate the analysis and generate all tables and figures reported in the paper and supplementary online materials</li> </ol> Datasets <ul> <li>Wave 1 – Survey experiment: ‘wave1_survey_experiment_raw.dta’</li> <li>Wave 2 – Follow-up Survey: ‘wave2_follow_up_raw.dta'</li> <li>Map: shape-files ‘plz2stellig.shp’ ‘OSM_PLZ.shp’, area codes ‘Postleitzahlengebiete-_OSM.csv’_, (all links to the sources can be found in the script ‘04_figure2_germany_map.do’)</li> <li>Pretest: ‘pre-test_corona_raw.dta’</li> <li>For Appendix S7: ‘alter_geschlecht_zensus_det.xlsx’, ‘vaccination_landkreis_raw.dta’, ‘census2020_age_gender.csv’ (all links to the sources can be found in the script ‘06_AppendixS7.do’)</li> <li>For Appendix S10: ‘vaccination_landkreis_raw.dta’ (all links to the sources can be found in the script ‘07_AppendixS10.do’)</li> </ul> Descriptions of scripts 1_1_clean_wave1.do This script processes the raw data from wave 1, the survey experiment 1_2_clean_wave2.do This script processes the raw data from wave 2, the follow-up survey 1_3_merge_generate.do This script creates the datasets used in the main analysis and for robustness checks by merging the cleaned data from wave 1 and 2, tests the exclusion criteria and creates additional variables 02_analysis.do This script estimates regression models in Stata, creates figures and tables, saving them to results/figures and results/tables 03_robustness_checks_no_exclusion.do This script runs the main analysis using the dataset without applying the exclusion criteria. Results are saved in results/tables 04_figure2_germany_map.do This script creates Figure 2 in the main manuscript using publicly available data on vaccination numbers in Germany. 05_figureS1_dogmatism_scale.do This script creates Figure S1 using data from a pretest to adjust the dogmatism scale. 06_AppendixS7.do This script creates the figures and tables provided in Appendix S7 on the representativity of our sample compared to the German average using publicly available data about the age distribution in Germany. 07_AppendixS10.do This script creates the figures and tables provided in Appendix S10 on the external validity of vaccination rates in our sample using publicly available data on vaccination numbers in Germany.