The Red Queen in the Repository

Abstract

One of the grand curation challenges is to secure metadata quality in the ever-changing environment of metadata standards and file formats. As the Red Queen tells Alice in Through the Looking-Glass: “Now, here, you see, it takes all the running you can do, to keep in the same place.” That is, there is some “running” needed to keep metadata records in a research data repository fit for long-term use and put in place. One of the main tools of adaptation and keeping pace with the evolution of new standards, formats – and versions of standards in this ever-changing environment are validation schemas. Validation schemas are mainly seen as methods of checking data quality and fitness for use, but are also important for long-term preservation. We might like to think that our present (meta)data standards and formats are made for eternity, but in reality we know that standards evolve, formats change (some even become obsolete with time), and so do our needs for storage, searching and future dissemination for re-use. Eventually, we come to a point where transformation of our archival records and migration to other formats will be necessary. This could also mean that even if the AIPs, the Archival Information Packages stay the same in storage, the DIPs, the Dissemination Information Packages that we want to extract from the archive are subject to change of format. Further, in order for archival information packages to be self-sustainable, as required in the OAIS model, it is important to take interdependencies between individual files in the information packages into account. This should be done already by the time of ingest and validation of the SIPs, the Submission Information Packages, and along the line at different points of necessary transformation/migration (from SIP to AIP, from AIP to DIP etc.), in order to counter obsolescence. This paper investigates possible validation errors and missing elements in metadata records from three general purpose, multidisciplinary research data repositories – Figshare, Harvard’s Dataverse and Zenodo, and explores the potential effects of these errors on future transformation to AIPs and migration to other formats within a digital archive.