Preparation for long-term preservation

What should I keep in mind when preparing data for preservation and re-use?

Preparing data for preservation and reuse is not a stage, but an ongoing part of the research process

• Archives and repositories require clarity on who owns data and that permission for preservation and re-use is granted
• Data containing direct or a number of significant indirect identifiers will not be accepted unless anonymised or removed
• Data requires good explanatory contextual material and information to be accepted into an archive or repository  
• Converting or migrating data to make the data preservable for the long term

Data Ownership  

Is it clear who owns the data? Archives and repositories are unable to accept data where ownership is unclear or permission for preservation and re-use is not given. Clarify ownership early in the research in case there is a problem. If data cannot be shared because of ownership issues, the research funder must be informed during the funding application stage. Funders will hold principal investigators liable where data is unable to be shared because ownership rights were not resolved or permission to deposit data had been not sought.

Sensitivity

Preparation of sensitive data involves potentially removing sensitive data, anonymising it, and matching the data to an appropriate license or level of controlled access in the archive or repository.

Personal data should never be included in a data set, unless a respondent has given specific consent to do so, ideally in writing. Archives and repositories will not accept variables that can directly identify an individual or can indirectly identify them in either isolation or by linking to another publically available data set -- unless there is a compelling reason why data cannot be anonymised or removed.

Funders will treat data where consent for a poorly worded or unnecessarily restrictive process of obtaining consent has prevented preservation and sharing as a Research Data Management failure.

Documentation and Metadata

Research data has many re-use purposes: teaching, replicating existing research findings, repurposing for new research questions, re-used as part of larger data sets comparing, or combining data from different sources. Therefore, it is difficult to know for what it is going to be used. However, we can reasonably guess at what re-users will want to do. Most likely, discover, integrate, and aggregate data. Here good quality documentation and metadata enhance the value of the data, aid its discoverability, and facilitates wider re-use.

Much of what counts as preparation includes generating documentation and metadata during the research itself, where the documentation could have elements of the following

Reasons data was collected

• Aims and objectives of the project, these are often outlined in funding proposals or end of award reports.
• Data collection methods and procedures
• Definition of the universe of analysis and sample framework, notes on instruments used to collect data and analyse data, plus information on the conditions of data collection.

Data collection tools

• Copy of the questionnaire(s), prompts, and/or interview schedule(s).

Database scheme and data structure

• Variable labels and descriptions, an outline of relationships within the dataset.

Coding schemes

• Definition of coding conventions used – including information on missing data, categories, classifications, acronyms and annotations.

Data modifications

• Anonymisation work undertaken. Specification of any weighting used, identification of derived variables and the syntax used to create them, output files, and subsequent modifications to the original data.

Quality control measures

• Details on activities undertaken to verify and clean the data, an outline of formatting applied to the data, an explanation of file naming conventions, and if needed, a statement on known problems with the data.

Simple things can help: check spellings, aim for standardised vocabularies, and avoid acronyms. Finally, as a test of readiness for reuse and archiving, could someone familiar with the field understand data without having to ask questions of the original data creator?

Converting or Migrating Files