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Caspar J. van Lissa 4/1/2020

COVID-19 Metadata

A collection of relevant country/city level metadata about the COVID-19 pandemic, made interoperable for secondary analysis. Curated by Data scientists Against Corona, collaborators: Caspar van Lissa, Tim Draws, Andrii Grygoryshyn, Konstantin Tomić, and Malte Lüken.

Available data sets

The following data sets have been processed:

CategoryInformationSourceURLProgressFolderLicenseReference
MobilityGoogle mobility dataGooglehttps://www.google.com/covid19/mobility/Donegoogle_mobility
Risk levelHospital data per countryWHO Health workforce/facilities databasehttps://apps.who.int/gho/data/node.main.HWFDoneWHO_OECD
Risk levelHealth infrastructure per country dataOECD Health care resources databasehttps://stats.oecd.org/index.aspx?queryid=30183DoneWHO_OECD
PoliciesGovernment effectivenessWorldwide Governance Indicatorswww.govindicators.orgDoneWB_GOVCC-BY 3.0
PoliciesCOVID-19 specific regulation policiesOxford Tracker for Regulation Policieshttps://www.bsg.ox.ac.uk/research/research-projects/oxford-covid-19-government-response-trackerDoneOx_CGRTCC-BY 4.0Hale, Thomas and Samuel Webster (2020)
PreparednessGlobal Health Security IndexNuclear Threat Initiativehttps://www.ghsindex.org/DoneGHSCC BY-NC-ND�4.0
COVID19Number of cases and fatalitiesCSSE Global Caseshttps://systems.jhu.edu/DoneCSSECopyright (academic use permitted)<a href = "https://doi.org/10.1016/S1473-3099(20)30120-1">Dong, Du, & Gardner, 2020</a>
EconomicWorld Development IndicatorsWorld Bankhttps://datacatalog.worldbank.org/dataset/world-development-indicatorsDoneWB_WDICC-BY 4.0
ResponseNumber of testsOur world in dataOWID_Tests
EconomicDoing BusinessWorld Bankhttps://s3.amazonaws.com/datascope-ast-datasets-nov29/datasets/435/data.csvWB_BUSINESSCC-BY 4.0
MobilityLogistics Performance IndexWorld Bankhttps://s3.amazonaws.com/datascope-ast-datasets-nov29/datasets/50/data.csvWB_LOGISTICSCC-BY 4.0
Failed States IndexWorld Bankhttps://s3.amazonaws.com/datascope-ast-datasets-nov29/datasets/97/data.csvWB_FAILEDCC-BY 4.0
Freedom HouseWorld Bankhttps://s3.amazonaws.com/datascope-ast-datasets-nov29/datasets/997/data.csvWB_FREEDOMCC-BY 4.0
Global Indicators of Regulatory GovernanceWorld Bankhttps://s3.amazonaws.com/datascope-ast-datasets-nov29/datasets/50/data.csvWB_GOVERNANCECC-BY 4.0
Institutional Profiles DatabaseWorld Bankhttps://s3.amazonaws.com/datascope-ast-datasets-nov29/datasets/999/data.csvWB_INSTITUTIONALCC-BY 4.0
Worldwide Buresucracy IndicatorsWorld Bankhttps://s3.amazonaws.com/datascope-ast-datasets-nov29/datasets/4127/data.csvWB_BUREAUCRACYCC-BY 4.0
United Nations Conference on Trade and DevelopmentWorld Bankhttps://s3.amazonaws.com/datascope-ast-datasets-nov29/datasets/513/data.csvWB_TRADE_DEVCC-BY 4.0
Press Freedom Index by Reporters without BordersWorld Bankhttps://s3.amazonaws.com/datascope-ast-datasets-nov29/datasets/1000/data.csvWB_PRESS_FREECC-BY 4.0
Education StatisticsWorld Bankhttps://s3.amazonaws.com/datascope-ast-datasets-nov29/datasets/748/data.csvWB_EDUCATIONCC-BY 4.0
Gender StatisticsWorld Bankhttps://s3.amazonaws.com/datascope-ast-datasets-nov29/datasets/747/data.csvWB_GENDERCC-BY 4.0
Travel & Tourism CompetitivenessWorld Bankhttps://s3.amazonaws.com/datascope-ast-datasets-nov29/datasets/78/data.csvWB_TOURISMCC-BY 4.0
World Travel & Tourism CounsilWorld Bankhttps://s3.amazonaws.com/datascope-ast-datasets-nov29/datasets/79/data.csvWB_WTTCCC-BY 4.0
Poverty ans Equity DataWorld Bankhttps://s3.amazonaws.com/datascope-ast-datasets-nov29/datasets/3755/data.csvWB_POV_EQUITYCC-BY 4.0

Folder structure:

FolderDescriptionPermissions
dataMetadata sources in .csv format (intermediate formats are acceptable until they can be made tidy).Do not edit
scripts(R)-scriptsHuman editable
docDocumentation for your contribution, ideally in Rmarkdown format. Rmarkdown can contain code chunks. Elaborate functions should be relegated to the ‘scripts’ folder.Human editable

How to use

Fork or clone this repository (for GitHub beginners: You can also click the green button that says “Clone or download”, and download a .zip). All data are in the /data folder. Some data are rarely updated (e.g., annual data), and some are updated daily. To ensure that you have access to the latest data for frequently updated sources, run the R-script in the run_me.R file, in the main folder.

Standards for data

Every source is condensed into one data file in .csv format, according to these specifications:

Standards for data dictionary

A data_dictionary.csv is available for each data set, unless the file contents are immediately clear from the file. This data dictionary includes:

Any other important information per variable can be included in this dictionary, such as sources, weights, etc.

News

The following issues are ongoing:

License

This project is under a GNU GPL v3 open source license (see the LICENSE file). Individual data sources have different licenses; always check the license before publishing based on these data.

Contributing and Contact Information

This project is open for collaborators with valuable expertise. Contribute by:

By participating in this project, you agree to abide by the Contributor Code of Conduct v2.0.

A WORCS Project

This project is based on the Workflow for Open Reproducible Code in Science (WORCS). For more details, please read the preprint at https://osf.io/zcvbs/.

WORCS - steps to follow for each project

Study design phase

  1. Create a new Private repository on github, copy the https:// link to clipboard
    The link should look something like https://github.com/yourname/yourrepo.git
  2. In Rstudio, click File > New Project > New directory > WORCS Project Template
    1. Paste the GitHub Repository address in the textbox
    2. Keep the checkbox for renv checked if you want to document all dependencies (recommended)
    3. Select a preregistration template
  3. Write the preregistration .Rmd
  4. In the top-right corner of Rstudio, select the Git tab, select the checkboxes next to all files, and click the Commit button. Write an informative message for the commit, e.g., “Preregistration”, again click Commit, and then click the green Push arrow to send your commit to GitHub
  5. Go to the GitHub repository for this project, and tag the Commit as a preregistration
  6. Optional: Render the preregistration to PDF, and upload it to AsPredicted.org or OSF.io as an attachment
  7. Optional: Add study Materials (to which you own the rights) to the repository. It is possible to solicit feedback (by opening a GitHub Issue) and acknowledge outside contributions (by accepting Pull requests)

Data analysis phase

  1. Read the data into R, and document this procedure in prepare_data.R
  2. Use open_data() or closed_data() to store the data
  3. Write the manuscript in Manuscript.Rmd, using code chunks to perform the analyses.
  4. Regularly commit your progress to the Git repository; ideally, after completing each small and clearly defined task. Use informative commit messages. Push the commits to GitHub.
  5. Cite essential references with one at-symbol ([@essentialref2020]), and non-essential references with a double at-symbol ([@@nonessential2020]).

Submission phase

  1. To save the state of the project library (all packages used), call renv::snapshot(). This updates the lockfile, renv.lock.
  2. To render the paper with essential citations only for submission, change the line knit: worcs::cite_all to knit: worcs::cite_essential. Then, press the Knit button to generate a PDF

Publication phase

  1. Make the GitHub repository public
  2. Create an OSF project; although you may have already done this in Step 6.
  3. Connect your GitHub repository to the OSF project
  4. Add an Open Science statement to the manuscript, with a link to the OSF project
  5. Optional: Publish preprint in a not-for-profit preprint repository such as PsyArchiv, and connect it to your existing OSF project
    • Check Sherpa Romeo to be sure that your intended outlet allows the publication of preprints; many journals do, nowadays - and if they do not, it is worth considering other outlets.