Detailed Academy data management plan guidelines and best practices in DMPTuuli

Data management plan questions, Academy guidelines and best practices

These detailed guidelines are the same as those published in DMPTuuli that concern calls by the Academy of Finland.

  1. General description of data
  2. Documentation and quality
  3. Storage and backup
  4. Ethics and legal compliance
  5. Data sharing and long-term preservation

1. General description of data

Academy of Finland guidelines:

The qualities of data and the choice of file formats support researchers’ research activities and collaboration with other scholars. Both are important information regarding the best practices in opening and sharing research data.

Use standardised or validated protocols of data collection and standard data types to ensure data sharing and reuse. The types of data to be stored and archived depend on the type of research and the scientific discipline. Details on the data collection and analyses should be described in the research plan.

DMPTuuli guidelines:

1.1 What kinds of data are collected or reused?

Briefly describe your research data. Explain what kinds of data you are collecting or producing. Outline how the data will be collected: e.g. via surveys, interviews, laboratory experiments or observations. Moreover, explain what kinds of existing data you will reuse.

Briefly describe what types of data will be used and are expected to be produced: e.g. texts, images, photographs, statistics, physical samples or codes.

Tips for best practices:

  • Describe your data in such a way that you can refer to them later in the plan. Your answer to this question forms the basis of the whole plan.
  • Explain your methods in more detail in the research plan.
  • By reusing data produced by you or others, you will avoid duplicating work already done.

1.2 What file formats will the data be in?

File format is a primary factor in the accessibility and reusability of your data in the future. List the file formats the data will be stored in. Note that a file format used during the project might not be the one most suitable for long-term preservation and reuse.

Tips for best practices:

  • List the file formats that the data will be in: e.g. .csv, .txt, .docx, .xslx, .tif.
  • When listing the data formats you will be using, make sure to include any software necessary to view the data.
  • Favour software and formats based on open standards to enable data reuse, interoperability and sharing.

2. Documentation and quality

Academy of Finland guidelines:

Standardised data documentation and quality measures of data throughout the whole research project create effective links between the particular study and the scientific community, especially to enable the validation of results presented in scientific publications and the reusability of shared data. The data produced or used in the project need to be discoverable, identifiable and locatable with metadata.

DMPTuuli guidelines:

2.1 How will the data be documented?

Data documentation enables datasets and files to be discovered, used and properly cited. Metadata are essentially information regarding the data, such as where, when, why and how the data were collected, processed and interpreted. Metadata may also contain details about experiments, analytical methods, and research context.

Metadata elements can include descriptive metadata that enable indexing, discovery and retrieval (e.g. keywords); technical metadata that describe how datasets were produced and structured and how they should be used (e.g. file naming); and rights to metadata that define who owns and can access the data, and who has the right to manage them.

Tips for best practices:

  • Consider how the data will be organised during the project. Describe, for instance, your file naming conventions, version control and folder structure.
  • Identify the types of information that should be captured to enable a researcher like you to discover, access, interpret, use and cite your data.
  • Repositories for long-term preservation often require the use of a specific metadata standard. Check whether a discipline/community- or repository-based metadata schema or standard (i.e. preferred sets of metadata elements) exists that can be adopted.
  • The national service for publishing metadata is the Etsin research data finder, which contains metadata of datasets.

2.2 How will the consistency and quality of data be controlled and documented?

Data quality control ensures that no data will be lost or accidentally changed during the research process. Quality control of data is an integral part of all research and takes place during data collection, data entry or digitisation and data checking.

Tips for best practices:

  • Explain how the data collection methods used will affect the quality of data. You can provide evidence of data quality by documenting in detail how the data is collected.
  • Quality control measures can include, for instance, using standardised methods and protocols for capturing observations, alongside recording forms with clear instructions, taking multiple measurements, observations or samples, and calibrating instruments.

3. Storage and backup

Academy of Finland guidelines:

Arrangements for storage and backup are important themes during the research process, especially if the amount of data is exceptionally large or the various data collected create a complex material. Applicants should describe their plans for securely and reliably storing data during the whole life cycle of the research project.

DMPTuuli guidelines:

3.1 How will the data be stored and backed up?

Describe where you will store and back up your data during your research project. Methods for preserving and sharing your data after your research project has ended are explained in more detail in section 5.

Consider who will be responsible for backup and recovery. If there are several researchers involved, create a plan with your collaborators and ensure safe transfer between participants.

Tips for best practices:

  • The use of a safe and secure storage provided and maintained by your organisation’s IT support is preferable.
  • If you use commercial cloud services (e.g. Google Drive), make sure not to store or share unencrypted personal or sensitive data with them.

3.2 How will you control access to keep the data secure?

It is vital to consider data security issues, especially if your data are sensitive (e.g. personal data, politically sensitive information or trade secrets).

Describe who has access to your data and what they are authorised to do with the data. Who will be responsible for access control?

Tips for best practices:

  • Access controls should always be proportionate to the kind of data and level of confidentiality involved.
  • Please note that there may be institutional data security policies that you are required to adhere to.

4. Ethics and legal compliance

Academy of Finland guidelines:

Ethical questions and intellectual property rights are key issues regarding the limitations on storing and opening research data. The Academy of Finland aims to maximise access to data and the reuse of data, but reminds that research data should be closed when necessary. Researchers need to find a balance between openness, privacy concerns, commercialisation and IPRs.

Make the necessary plans and arrangements to solve possible ethical or legal issues that could affect data sharing. Details of the ethical issues, ethical committee statements and use of laboratory animals should be described in the research plan. Here, describe only ethical aspects of data management.

DMPTuuli guidelines:

4.1 How will ethical issues be managed?

Describe how you will maintain high ethical standards and comply with relevant legislation. Ethical issues must be considered throughout the whole research life cycle, from planning to publication as well as in paving the way for future reuse.

For example, following the guidelines regarding informing research participants is considered an ethical requirement for most research. Moreover, if you handle personal or sensitive information, describe how you will ensure privacy protection and data anonymisation.

Tips for best practices:

  • Check your institutional ethical guidelines and security policy and prepare to follow the instructions that are given in these guidelines.
  • Check whether an ethical review is required for your research project.
  • If your research is to be reviewed by an ethical committee, outline in your data management plan how you will comply with the protocol (i.e. how to remove personal or sensitive information from your data before sharing to ensure privacy protection; or how you will use restricted access procedures).
  • See e.g. the Finnish Advisory Board on Research Integrity for more information about the responsible conduct of research.
  • See e.g. the European Code of Conduct for Research Integrity.
  • See e.g. the General Data Protection Regulation.

4.2 How will ownership, copyright and Intellectual Property Right (IPR) issues be managed?

Describe who will own the data and who can issue permissions to reuse them. If you use research material or data collected or produced by a third party, consider the copyright issues and potential licences that may affect data distribution. These issues should be solved already at the planning stage of the research project. If ownership issues have not been considered early enough in the research life cycle, sharing and reusing the data may become impossible.

Tips for best practices:

  • Check your organisational data policy for ownership guidelines.
  • Also, consider the funder’s policy on copyrights or IPR.
  • It is recommended to make all research data, code and software created within a research project available for reuse, for example under Creative Commons, GNU, MIT or another relevant licence. The recommended CC licence according to open science principles is CC BY.

5. Data sharing and long-term preservation

Academy of Finland guidelines:

Research data are important outputs of the public research funding provided by the Academy of Finland. Therefore, open access to all publicly funded data is the default policy. Access and sharing of data helps increase the scope and outcomes of scientific discoveries, often beyond the initial boundaries of the original research project. Open data compilations are also merits for the scholars and the research team that have collected, stored and opened them.

Applicants should describe their plans for preserving the data after the project as well as specify the intended established and safe data repositories, data archives or databases.

DMPTuuli guidelines:

5.1 How, when, where and to whom will the data be made available?

Describe whether you will share all or only parts of your data, and for how long the data will be made available. If your data or parts of your data cannot be shared, explain why. Valid explanations might include confidentiality, trade secrets or ownership issues (licence, copyright). Sometimes data cannot be shared due to the unreasonable effort required for its sharing (e.g. legacy data or large volumes of analogue data).

Tips for best practices:

  • Consider data sharing both during and after the research.
  • The openness and sharing of research data promote reuse.
  • When sharing your data, it is recommended that the data be made available for reuse, for example under Creative Commons or another relevant licence. The recommended CC licence for open science is the CC BY licence.
  • Use persistent identifiers (PID) to enable access to the data via a persistent link (e.g. DOI, URN).

5.2 How and where will the data with long-term value be made available?

The aim of long-term preservation is to store and keep data usable and comprehensible for dozens or even hundreds of years. Data selected for long-term preservation will be submitted to a data repository or data archive. Long-term preservation will ensure your data can be found, understood, accessed and used in the future, even for generations.

Tips for best practices:

  • Briefly describe what data to preserve and for how long – as well as what data to dispose of after the project.
  • Remember to check funder, disciplinary or national recommendations for data repositories, data archives or data banks.
  • Use persistent identifiers (PID) to enable access to the data via a persistent link (e.g. DOI, URN, Handle).

5.3 Have you estimated costs in time and effort to prepare the data for preservation and sharing?

Tips for best practices:

  • Remember to mention that you will specify your data management costs in the budget.
  • Will you need to hire experts help to manage, preserve and share the data?
  • Do you have sufficient storage space, or will you need to include charges for additional services? Consider the additional computational facilities and resources that need to be accessed, and what the costs associated will amount to.
  • How will responsibilities for data management and costs be split across partner sites in collaborative research projects?

Updated for the September 2017 call on 14 June 2017.

Last modified 14 Jun 2017
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