Numerous aspects of research data management should be considered and addressed at the beginning of the research. Use this checklist to follow best practices for your data management plan.
- Who is responsible for which aspects of data management?
- Are new skills required for certain activities?
- Will you need additional resources to manage data such as staff, time or hardware?
- Have you accounted for the cost of long-term data preservation and access?
- Will others be able to understand, re-use and benefit from your data?
- Are the abbreviations, codes and variables of your structured data self-explanatory?
- Which contextual documentation explains what your data means, how it was collected, and the methods used to create it?
- How will you label and organise data, records and files?
- How will you ensure that your data is consistently catalogued?
- Do you use standardized and consistent procedures to collect, process, transcribe, check, validate and verify data, such as standard protocols, templates or input forms?
- Which data formats will you use? Do formats and software enable the sharing and long-term sustainability of data, such as non-proprietary software or software based on open standards?
- How do you ensure that no data, annotations, or internal metadata will be lost or changed when converting data between different formats?
- Are your digital and non-digital data stored in multiple secure locations?
- If data is stored in multiple locations, how will you track versions?
- Do you need to ensure the secure storage of personal or sensitive information? How will you protect your data?
- When collecting data with mobile devices, how will you transfer and store the data?
- Are your files backed up regularly and are backups stored safely?
- How will you know which version of your data files is the master?
- Who has access to which data during and after the search? Is there a need for access restrictions? How will they be managed in the long term?
- How long will you save your data and will you need to select what data should be kept and what data should be destroyed?
Confidentiality, ethics and consent
- Does your data contain sensitive or confidential information? If so, have you discussed data sharing with the respondents from whom you collected the data?
- Do you have written consent from respondents to share data beyond your research?
- Do you need to anonymize data, such as removing personal information, during the research process or in preparation to share data?
- Who owns the copyright to your data? Could there be joint copyright?
- What type of license is appropriate for sharing your data, and what restrictions may apply to reuse?
- If you’re reusing other researchers’ data sources, have you considered how to share that data, such as negotiating a new license?
- How and where will you preserve your research data for the long term?
- Do you intend to make your data available for sharing? How will you choose which data should be shared and which (possibly) not?
- When will you make your data available for re-use? Will you require an embargo period?
- Are data quality assurance processes described?
Making data accessible
- How will you make your data accessible to future users (e.g. by deposition in a repository)?
- Have you explored appropriate arrangements with the identified repository?
- Will the data produced and/or used in the project discoverable with metadata, identifiable and locatable by means of a standard identification mechanism (e.g. persistent and unique identifiers such as Digital Object Identifiers)?
Making data interoperable
- Are the data produced in the project interoperable, that is allowing data exchange and re-use between researchers, institutions, organisations, countries, etc. (i.e. adhering to standards for formats, as much as possible compliant with available (open) software applications, and in particular facilitating re-combinations with different datasets from different origins)?