Potential re-use of research data is one of important aspects of long-term curation. Access to data is predefined by given licences.
Conditions of use
Conditions of use and access categories depend on the license of the deposited study. Data can be accessible with registration or without it. Access depends on user categories and purposes of use.
Examples
- DANS: Conditions of use (pdf)
- GESIS. Usage regulations (pdf)
- ICPSR: Standard terms of use
- ADP: Restrictions and terms of use
- GESIS: Secure data center
Training
- CESSDA: User Guide on Digital Preservation: Access and Reuse (pdf)
- DASISH: Access policies and data sharing training
- CESSDA SaW / NSD: webinar on access policies and regulations
- UKDS: The 5 Safes of secure access to confidential data
Licensing
Different licenses are available for sharing data and defining terms of use.
Documentation
- Creative Commons: Legally share your work
- Open Data Commons: licenses
- Open Definition: Guide to open data licensing
Training
- Ball, A. (2014). ‘How to License Research Data’. DCC How-to Guides. Edinburgh: Digital Curation Centre.
- CESSDA SaW: Webinar Access Policies and Usage Regulations: Licenses
Data sharing
"Data sharing is the practice of making data used for scholarly research available to other investigators. Replication has a long history in science. Many funding agencies, institutions, and publication venues have policies regarding data sharing because transparency and openness are considered by many to be part of the scientific method". (Wikipedia, 2016)
Documentation
- NYU: Data Sharing and Management Snafu in 3 Short Acts
- Teperek, M. Building a collaborative RDM community: tips and tricks
Training
- EDINA and Data Library, University of Edinburgh: Short video, part of the Research Data MANTRA (online course)
- DANS: Sharing data: good for science, good for you
- ICPSR/DANS: Preparing data for sharing (pdf): Guide to social science data archiving, Dans Data guide 8 (2010)
Data citation
“Citing data supports the discovery and reuse of data, leading to better science through the validation of results” according to the special interest group on citation (IASSIST). To enable citing publications and data sets, data sets should have persistent identifiers. Many repositories provide identifiers that enable citation.
Documentation
- IASSIST: Special Interest Group on Data Citation
- IASSIST:Data Citation Resources
- DCC: Ball, A. & Duke, M. (2015). ‘How to Cite Datasets and Link to Publications’. DCC How-to Guides. Edinburgh: Digital Curation Centre.
- DataCite: Locate, identify, and cite research data
- LIBER/DataCite: case study (pdf)
- FORCE11: joint declaration of citation principles
Examples
- UKDA: How to cite data
- FSD: Citing Archival data
- Elsevier journal: citation guidelines
- DANS: Data citation and persistent identifiers
Secondary analyses
"Secondary analysis involves the use of existing data, collected for the purposes of a prior study, in order to pursue a research interest which is distinct from that of the original work; this may be a new research question or an alternative perspective on the original question" (Hinds, Vogel and Clarke-Steffen 1997, Szabo and Strang 1997).
Training
- EDINA and Data Library, University of Edinburgh: Short video on secondary data analysis. Part of the Research Data MANTRA (online course)
Examples
- UKDS: Reusing quantitative and qualitative data (also mentioning the Secondary Data Analysis Initiative from the ESRC)
- SSDAN: Social Science Data Analysis Network in the US
- StatLine: electronic databank of Statistics Netherlands