A Data Curation Framework: Data Curation and Research Support Services
- 1. A Data Curation Framework Data Curation and Research Support Services Research Support Community Day 2016
- 3. University library research support ● Information literacy, e.g. information seeking, literature searches ● Collection access, e.g. acquisition, document supply, digitisation ● Scholarship, e.g. citation management, open access publishing ● Data literacy, e.g. research data management planning ● Mix of generic and domain services ● Access to resources ● Access to scholarly skills development
- 4. University library research support for DH ● Digital literacy, e.g. training in use of tools like Omeka ● Technical literacy, e.g. training in web publishing and site management ● Project management support, e.g. planning, technical development ● Repackaging digitised collection material as data ● Infrastructure literacy, e.g. training in use of infrastructure ● Mix of generic and domain services ● Access to scholarly skills development ● Access to infrastructure
- 5. Data Curation Framework ● Data curation framework developed for liaison and engagement, i.e. it is a tool to highlight areas to tackle and where collaboration may occur ● Align eResearch service providers’ efforts with university research support service development, and change as services evolve Identify where: ● support enables researchers to undertake eResearch, i.e. within university or national infrastructure ● generic versus specialised support services are provided, i.e. within university or by third party providers
- 6. Framework: 4 components Data processing Data storage Data archiving Research Data Management Data services Research Project Management
- 7. Framework: data processing ● Improve discoverability and reuse of collected data through repackaging or transforming data ready for publishing or transit.
- 8. Framework: data storage ● Enable data transit to and from international, national and local data storage services.
- 9. Framework: data archiving ● Enable data archiving practices to be embedded into data curation and research project activities.
- 10. Framework: data services ● Provide data and services for researchers to exploit for data intensive research projects.
- 11. Capability development Service/skills development: ● technical processing ● resource analysis and management ● archival processing Capability Areas: ● data access ● data integration ● data interoperability (to support data and publication discoverability)
- 12. Technical processing ● Selection and reuse of appropriate universal, domain specific or archival ontologies, schemas and vocabularies. ● Crosswalking techniques, and data and metadata structuring methods. For the improvement of reuse, access to and discoverability of data.
- 13. Resource analysis and management Analysis of: ● data types and storage options ● researcher requirements For the provision of appropriate storage and access to data.
- 14. Archiving processing Selection of: ● backup or export regimes ● tools and services ● procedures to capture and retain data To improve immediate and ongoing access to data.
- 16. Examples ● Data derived from digitised material ● Data derived from descriptive metadata ● Bulk data for web download ● Bulk data for remote access ● File network as data backup ● Repository as data archive ● Project support for eResearch ● Data with research value
- 17. Data derived from digitised material Digitisation activities commonly conducted to provide access to library resources may be extended with data processing activities to enable the digital material to be treated as a data source by a researcher. An example output could be the OCR text made available as a corpus.
- 18. Data derived from descriptive metadata Metadata describing unique materials is commonly treated as a series of catalogue records for display. Metadata may be transformed and published as a dataset. An example output could be the metadata made available in its local schema, crosswalked into a different format, or structured as linked open data.
- 19. Bulk data for web download Digital material that is within a lower file size range and non-sensitive can be packaged up with guidelines for reuse and published openly online. An example output would be a set of steps and a template for file arrangements and instructions aimed at data users.
- 20. Bulk data for remote access Digital material that is too large to download over the web with standard internet speeds or sensitive can be packaged up for transfer to shared and secure storage services. There may also be a need for the data to be near to applications in the cloud or high performance computing services. An example output would be a set of steps and a template for setting access arrangements, file arrangements and instructions aimed at data users.
- 21. File network as data backup Retention copies of data may need to be maintained in file networks as a short term solution for the purposes of data backup. These storage services may be provided institutionally, through third parties or on national infrastructure. An example output would be guidance information on the services available to researchers, including costs, security concerns, and backup routines.
- 22. Repository as data archive Archival copies of data may need to be maintained in repositories as a long term solution for preservation. These repository services may be provided institutionally, through third parties or on national infrastructure. An example output would be guidance information on the services available to researchers, including costs, security concerns, and preservation routines.
- 23. Project support for eResearch Research support services have been focused on provision of access to information as a scholarly resource and support the generation of research publications. An example output would be a list of services already available and the identification of gaps in service where capacity may be developed to support data intensive research projects.
- 24. Data with research value Data has research value at different points in its generation and ongoing retention. An example output would be a list of collected data available to make accessible, the selection criteria for prioritising a data collection for enhancing its accessibility because of its research value, and the rationale for the techniques (e.g. web publishing, remote storage, repackaging etc) employed to support accessibility.