Your answer to following question forms a general structure for your data management plan. Categorise your data in such a way that you can refer to it later in the plan. For example,
The categorisation follows the license policy of your data sets. For example, briefly describe according to which license you are entitled to (re)use the data.
In DMP, describe the required disk space - not how many informants were participating the project. A rough estimation of the size of the data is sufficient - e.g. less than 100 Gb, approx. 1 Tb, or several petabytes.
Choose file formats according to long-term access if possible and use formats which are in common use by the research community. Favor following properties:
You may have to choose certain formats during data collection and analyses, and others for long term preservation. The formats you choose can depend on how you plan to analyse your data or software compatibility. You may need to convert your data files to a preservation file format at some point of your research.
Some preferred file formats for long term preservation:
Some research data are still gathered and handled non-digitally. Examples of such data might include e.g. paper-based data, biospecimen, fossil specimen, art samples, artefacts or other concrete objects that either cannot be converted to digital form at all or such processing would require too much labor or other resources to be feasible.
Regardless of whether research data are digital or non-digital, proper data management is always crucial. Non-digital data require different approaches, methods and tools for preservation and management than digital data. Non-digital research data might require e.g. filing cabinets, archive-friendly filing systems, physical storage solutions, specific climate conditions and other special tools and instructions for preservation.
Metadata production is a key element in non-digital research data management. Different types of metadata (e.g. descriptive, structural, process-related, administrative etc.) are required to ensure the proper care and preservation of non-digital research data. The principles of data documentation will be explained in Metadata & Documentation -section. In addition to this, Tampere University Archives also give further instructions on metadata guidelines regarding non-digitally preserved research data.
For more information and advice, please contact:
Specify all dataset types that contain personal, sensitive or confidential data. Identifying the sensitive components of research data is particularly important, as the planning of data management focuses on the identification and management of related risks. If you work with personal data, specify the party serving as the controller.
Sensitive data is information that could cause damage if made public:
Personal information includes all identifiers from which the person is identifiable directly or indirectly.
Data Service at Tampere higher education community helps you manage your research data. Our service comprehends library, IT-support, research and innovation services, document management and law department together with Tietoarkisto (FSD).
We offer service, tools and training for gathering, documenting, storing and sharing the data. Our service supports the implementation of FAIR in all phases of your research project. You can contact us at email@example.com