Metadata standards

Metadata refers to "data about the data" or information that may not constitute the data itself but provides an understanding of different aspects of the data. For instance, keywords associated with a dataset or the date on which a dataset was uploaded to a repository can be considered part of the metadata along the data. There are different types of metadata depending on their goal. A data dictionary, as described below, can be considered descriptive metadata that provides definitions and other elements for the content of a dataset. The citation of a dataset (similar to the citation of a paper) provides referencing metadata, and a data reuse license may provide legal metadata. Using standardized metadata increases the Findability and Interoperability of the data resources. The ODCs utilize the following standards.

  • Data citation and DOI standards. A Digital Object Identifier accompanies datasets published through the ODCs (DOI) generated using the DataCite standards. This allows for cross-referencing and tracking published datasets and provides a standardized way to cite them.

  • ORCID. The ODCs support the Open Researcher and Contributor ID or ORCID, a researcher global standard identifier. Users can link their ODCs accounts and profiles to ORCID and use it for identification.

  • RRIDs. The ODCs support the use of Research Resource Identifiers or RRIDs, a standard identification number for the catalog of scientific tools and resources.

    • ODC-SCI: SCR_016673

    • ODC-TBI: SCR_021736

  • Creative Commons License. All datasets published on the ODC are under the Creative Commons Attribution License (CC-BY 4.0).

  • ODC Data dictionary. A data dictionary or codebook provides information about the dataset variables. It is one of the most important pieces of information to include with a dataset for anyone who wants to interpret and reuse the data.

  • ODC narrative summary (abstract). ODC offers a metadata narrative and summary where data owners can provide information about the dataset. This information is unique to each dataset and is essential for archiving, interpretability, and reuse.

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