Key Points

Introduction to Data Curation

  • Data curation makes data understandable, trustworthy and reusable over the long term by people outside the project
  • Curation complements data management (during the project) and data cleaning (fixing errors)
  • The curation lifecycle runs from creation through appraisal, description, ingest, preservation, access and reuse
  • Funders and federal policy increasingly require data underlying publications to be shared
  • FAIR data is findable, accessible, interoperable and reusable, which does not always mean open

Metadata

  • Metadata is structured information that lets others find, understand, judge and cite a dataset
  • Metadata can be descriptive, administrative, structural or technical; provenance records where data came from and what was done to it
  • Geospatial metadata must include the spatial and temporal extent, coordinate reference system, resolution or accuracy, lineage and license
  • ISO 19115 is the main geospatial standard; DataCite, Dublin Core, DDI and STAC serve other needs
  • QGIS has an ISO-based metadata editor; save the metadata with the data

Documentation

  • Documentation is the human-readable explanation that lets others use data correctly
  • Every dataset needs a README covering what, who, when, where, files, CRS, sources, methods, limitations, citation and license
  • A codebook or data dictionary describes each variable’s meaning, type, units, values and missing-value codes
  • Record provenance: precise sources, every processing step, software versions, and the reasons for decisions
  • Cite datasets with creator, year, title, version, publisher and a persistent identifier or URL with access date

Preservation

  • Digital data is threatened by media failure, silent corruption, format obsolescence, loss of context and organizational change
  • Preserve data in open, widely used, well-documented, lossless formats such as GeoPackage, GeoTIFF and CSV
  • Checksums provide fixity: proof that a file has not changed
  • Deposit data in a trustworthy repository that assigns persistent identifiers and has a preservation commitment
  • Very large data needs research storage, transfer services such as Globus, and cloud-optimized formats

Sharing and Publishing Data

  • Share data through a repository rather than on request; choose disciplinary, institutional or general-purpose repositories to suit the data
  • PURR is Purdue’s repository; ICPSR is the main archive for social science data
  • Persistent identifiers (DOIs for data, ORCID iDs for people) keep data findable and creators credited
  • Choose a clear license (often CC0 or CC BY), and keep the licenses of data you reuse, such as ODbL for OpenStreetMap
  • Data can be open, embargoed, controlled or metadata-only; apply both FAIR and CARE principles

Ethics and Sensitive Location Data

  • Locations can identify people even without names, especially combined with other attributes or small counts
  • Aggregation, geomasking, coordinate coarsening, suppression and controlled access protect locations, each with a trade-off in precision
  • Some places need protection even when no person is involved: archaeological sites, sacred places, endangered species
  • Plan protection before collecting data: IRB review, informed consent, data use agreements and secure storage
  • Document how locations were protected, and consider who could be harmed by a map