We are announcing available support for FAIR data management in DLN projects (including partner projects). For submission use the on-line form linked below. Deadline: 30. November.
There is an increased attention towards FAIR research results generated in public financed research projects. As a result, research projects need to integrate data management. Following up on the trends in the European funding agencies, the RCN now demands all new projects to make and follow a data management plan (DMP).
DLN supports data management according to the FAIR principles (Findable, Accessible, Interoperable and Re-usable). To better support each project we strongly advice project leaders to appoint a person to be responsible for data management in the project. Many of you have done this already. These persons will be our contact points towards the project data management, and they will be offered extra training and support.
We have set aside resources to support projects on data management and welcome applications from the research projects (including partner projects). The limit for applied support is set to 50 000 NOK/application. However, this number will depend on the total amounts of applications or if several projects apply together.
Examples of what we can support:
- Collaborative developments between two or more projects. This could be developments of templates for data management, standard operating procedures, best practices, exchange of know how (meetings/workshops on data management).
- Competence development of the appointed responsible data manager.
- Manual help on FAIR data management. Resources for such support will likely have to involve several projects combined, but can be applied for separately.
The following requirements must be fulfilled or included to get the funding:
- A responsible person (named) must be appointed for data management in the project.
- Include a description of the current status and practice of data management in the project(s). How project participants will work to facilitate FAIR data management.
- What challenges needs to be solved on data management.
- Tentative budget.
- A brief data management plan must be written for the project and attached (see suggested resources and checklist below).
We welcome applications from single projects, however, if needed we will prioritize collaborative efforts between projects.
NB: Use the following on-line form for the application. The most updated data management plan should be uploaded as an attachment (pdf only). After completing the activity a brief report stating the use of the funding should be sent to the WG4 coordinator (email@example.com). Please do not hesitate to contact us (Fatemeh or Rune) if you plan to apply.
Data Management Plan
Helpful resources for making data management plans can be found at the site of national infrastructures:
FAIR data management plan checklist
Issues to be addressed
1. Data summary
- What is the purpose of the data collection/generation and its relation to the objectives of the project?
- What types and formats of data will the project generate/collect?
- Will you re-use any existing data and how?
- What is the origin of the data?
- What is the expected size of the data? (if known)
- To whom might it be useful ('data utility')?
2. FAIR Data
2.1. Making data findable, including provisions for metadata
- Are 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)?
- What naming conventions do you follow?
- Will search keywords be provided that optimize possibilities for re-use?
- What metadata will be created? In case metadata standards do not exist in your discipline, please outline what type of metadata will be created and how.
2.2. Making data openly accessible
- Which data produced and/or used in the project will be made openly available as the default? If certain datasets cannot be shared (or need to be shared under restrictions), explain why, clearly separating legal and contractual reasons from voluntary restrictions.
- How will the data be made accessible (e.g. by deposition in a repository)?
- What methods or software tools are needed to access the data?
- Is documentation about the software needed to access the data included?
- Is it possible to include the relevant software (e.g. in open source code)?
- Where will the data and associated metadata, documentation and code be deposited? Preference should be given to certified repositories, which support open access where possible.
- Have you explored appropriate arrangements with the identified repository? If there are restrictions on use, how will access be provided? Is there a need for a data access committee? Are there well-described conditions for access (i.e. a machine readable license)? How will the identity of the person accessing the data be ascertained?
2.3. 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)?
- What data and metadata vocabularies, standards or methodologies will you follow to make your data interoperable?
- Will you be using standard vocabularies for all data types present in your data set, to allow inter-disciplinary interoperability?
- In case it is unavoidable that you use uncommon or generate project specific ontologies or vocabularies, will you provide mappings to more commonly used ontologies?
2.4. Increase data re-use (through clarifying licences)
- How will the data be licensed to permit the widest re-use possible? (e.g. http://docs.seek4science.org/help/user-guide/licenses.html)
- Are the data produced and/or used in the project useable by third parties, in particular after the end of the project? If the re-use of some data is restricted, explain why.
- How long is it intended that the data remains re-usable?
- Are data quality assurance processes described? Further to the FAIR principles, DMPs should also address:
- When will the data be made available for re-use? If an embargo is sought to give time to publish or seek patents, specify why and how long this will apply, bearing in mind that research data should be made available as soon as possible.
3. Allocation of resources
- What are the costs for making data FAIR in your project?
- How will these be covered?
- Who will be responsible for data management in your
- Are the resources for long term preservation discussed (costs and potential value, who decides and how what data will be kept and for how long)?
4. Data security
- What provisions are in place for data security (including data recovery as well as secure storage and transfer of sensitive data)?
- Is the data safely stored in certified repositories for long- term preservation and curation?
5. Ethical aspects
- Are there any ethical or legal issues that can have an impact on data sharing? These can also be discussed in the context of the ethics review. If relevant, include references to ethics deliverables and ethics chapter in the Description of the Action (DoA).
- Is informed consent for data sharing and long-term preservation included in questionnaires dealing with personal data?
- Do you make use of other national/funder/sectorial/departmental procedures for data management? If yes, which ones?