Guides, Examples and Vocabularies
FSD's Guidelines in DMPTuuli
DMPTuuli is a data management planning tool for Finnish research organisations. It provides templates and guidance for making a data management plan (DMP).
Use DMPTuuli to check the requirements and guidance given by different research funders and research organisations. The tool will help you to create your own DMP according to predefined templates, requirements and guidance that pertain to your research.
The following recommendations are FSD's DMPTuuli guidelines for research projects planning to archive their data to the FSD.
» General description of data
» Ethical and legal compliance
» Documentation and metadata
» Storage and backup during the research project
» Opening, publishing and archiving the data after the research project
» Data management resources
General description of data
Describe briefly what kind of data will be collected and the collection method used. Outline the type(s) of data (e.g. survey, interview, observation, face-to-face focus group, self-administered writings/diaries, photographs, news articles etc.) and estimate the foreseeable amount/volume of each data type. Describe also any existing data you will reuse.
- By reusing data, produced by you or others, you will avoid doing the same work twice.
- Check Aila Data Service Portal to browse and search for available data.
File format is a primary factor in accessing and reusing your data in the future. List the file formats you use for saving the data, such as CSV, TXT or MP3.
- Check the digital file formats used at the FSD for different types of data.
- Be aware that some statistical formats impose restrictions on the handling of missing information, the number of variables, the length of variable names, and characters allowed in variable and value labels.
- Do not include content or structural information in data files by formatting the text (i.e. using doc-format for bolding or italicization) because formatting disappears in conversions to other formats. If formatting is indispensable to understand the data, use the PDF/A file format.
Describe procedures for ensuring data quality during the project. Technical and content decisions made at data entry stage influence the quality of data. Solutions chosen for post-collection processing also have an impact on data quality.
- Always test the technical instruments and equipment before collecting data.
- Systematic and consistent naming of data files facilitates data management during research as well as data archiving and reuse.
- Check the FSD's guidelines for Naming and managing quantitative data files and the guidelines for Naming qualitative files.
- Be systematic and consistent when transcribing data even if you feel that you will only need parts of the data.
- When recoding variables, use statistical software and, if possible, consider recoding the variables by using syntax.
- Be consistent in determining values for missing data and 'can't say' type of responses.
- Check the guidelines for Processing Quantitative Data and the guidelines for Processing Qualitative Data Files.
Ethical and legal compliance
Ethics and privacy
All human subject research must adhere to research ethical principles and data protection legislation. It is essential to carefully plan the information sheets, the procedures of obtaining informed consent from participants, and the anonymisation of data. It is also very important to inform research participants about the fact that the data will be archived for further use.
- Carefully read the Ethical principles of research in the humanities and social and behavioral sciences. If you are conducting medical research, consult the guidelines of your regional ethics committee and the guidelines of the National Committee on Medical Research Ethics.
- Check if your research needs to be reviewed by an ethics committee; see the guidelines of ethical review of the Finnish Advisory Board on research Integrity.
- If your research is to be reviewed by an ethics committee, include a sample information sheet, a consent form for research participants, and a data anonymisation plan in your request for review. Also include your plan to archive the data in the request.
- Read through the guidelines for Informing Research Participants.
- Read through the guidelines for Anonymisation and Personal Data.
Intellectual Property Rights
When data are collected and created by researchers, original creators retain the ownership, copyright and related intellectual rights to the deposited dataset, while the archive has the right to preserve and disseminate the data. The terms and conditions for the deposition and reuse of the data are specified in the deposit agreement. The FSD requires that authors are specified for each archived dataset. Authors are the persons responsible for the substantive and intellectual content of the data.
The FSD has an agreement with the Kopiosto Copyright Society, which is an umbrella organisation for associations representing performing artists, authors and publishers. The agreement allows FSD to archive material collected by researchers for their research but created by others. This agreement applies to digital or digitalised newspaper and magazine material as well as photographs, but not to audiovisual material or music.
- Make a separate agreement concerning research data with the members of the project. See the general guidelines for agreements.
- When collecting newspaper articles or published photographs for analyses, make a separate document including all the bibliographic information of collected items. For an example, see the guidelines for data collected from periodicals.
- When collecting copyright-protected data (e.g. photographs, poems, diaries) directly from research participants, make an agreement concerning the transfer of copyright. See the guidelines.
Documentation and metadata
Detailed documentation of data contains information on the topic and authors/creators of the data, sampling and data collection methods, and target population and geographic coverage of the data.
The Archive will perform the final documentation of archived datasets by using the DDI metadata standard.
Tips for best practices with quantitative data:
- Create a detailed document that explains terms, variable names and labels, and abbreviations used.
- Save one blank copy of the questionnaire. If you create a web-based/computer-assisted questionnaire, save the questions and response alternatives in a text file in the order they are presented to the respondents.
- If the data are created by using source information (books, registers etc.), list the data sources, describe their selection method and define the variables that are used.
Tips for best practices with qualitative data:
- Separately preserve all the documents that refer to or have affected the data. These may include interview questions, writing instructions, transcription methods used and information sheets given to research participants.
- Documentation of each data unit (e.g. an interview, a text written by a participant, a newspaper article) is necessary to enable archiving and reuse of the data. See the guidelines for Documenting background information.
- See the guidelines for Documenting newspaper articles.
Storage and backup during the research project
Check and follow the data security guidelines of your organisation.
See also FSD's general guidelines on Data Security.
Opening, publishing and archiving the data after the research project
If you plan to share your data already during the primary research, explain to whom and how you will give the access to your data. If your data are to be archived at FSD, you can use FSD's services and data catalogue to inform others about your data already during the primary research. To protect your original research questions/analyses, you can evaluate each data request separately and permit only data use that suits your research project.
Explain if your data or part of it cannot be shared and give your reasons for this. The reasons might include confidentiality issues, trade secrets or contracts (e.g. restricted registries used as data). Sometimes data cannot be shared due to unreasonable effort required by data sharing (e.g. legacy data or large volumes of analogical data). A research group can also reserve the right to publish the most essential results from the data they have gathered and the sharing of data may be done after the main results are published.
- Mention if you plan to share your data with other researchers already during the primary research (How? When? With whom?)
- Make a plan and timetable for removing identifiers and parts of data that are not suitable for long term-preservation and sharing.
- If you do not intend to share your data during the original research project, mention which parts of your data will be disseminated by the FSD after the completion of the project.
- Check what kinds of data FSD accepts.
Long term preservation
Data selected for long-term preservation can be deposited at the FSD, which provides services for archiving, preserving and disseminating data. The FSD will evaluate the collected data paying attention to the legal constraints and technical prerequisites before making the final selection of data files to be curated, documented and preserved for further use. The FSD processes datasets for reuse and takes care of access applications and delivery.
Reasons to use FSD's free of charge archive services:
- The FSD is a trusted repository: it has received the international Data Seal of Approval (DSA) certificate in 2014 and the CoreTrustSeal certificate in 2017.
- The FSD assigns persistent URN identifiers (PIDs) to all archived datasets.
- The FSD is FAIR compliant.
Links for further information
- FSD's archiving services
- Guidelines for depositing data to FSD (including examples of data suitable for archiving)
- Deposition agreement (PDF)
- Aila Data Service
Data management resources
Consider the data-related costs, as well as the time and effort needed. The FSD will cover long-term storage costs and the effort of making a detailed DDI documentation of your data. Your budget needs to cover the collection, processing, basic documentation and anonymisation of your data - even if you archive your data at the FSD.
- Glance through the core sections of the Data management guidelines (e.g. Processing Quantitative Data Files, Processing Qualitative Data Files, Anonymisation and Personal Data)
- Evaluate the costs, as well as the time and effort needed, and make an itemized list of data-related costs
- Use the data management costing tool (DOCX) developed by UK Data Service for research data that are preserved beyond primary research for sharing.