Datamanagementplan

En datamanagementplan er et dokument, der beskriver, hvordan du indsamler, håndterer, dokumenterer, opbevarer, deler og arkiverer forskningsdata igennem hele forskningsprocessen. Den er projektets fælles referenceramme for alle spørgsmål, der har med projektets data at gøre.

Alle projekter som falder ind under SDUs ”Open science policy”, skal have en DMP. Denne skal udfærdiges ved projektopstart, og opdateres når det er nødvendigt. Det er den projektansvarlige der er ansvarlig for udfærdigelsen af DMP’en, evt. bistået af vejledere.    

DMP’en skal dokumentere almindelige forskningsmæssige standarder for håndtering og kontrol af alle slags data, samt overholde ”responsible conduct of research”.

En DMP skal fungere som en praktisk guide som, konkret, kan anvendes igennem forskningsprojektet til at skabe transparens i forskningsprocessen, og skal derfor opdateres når der tages beslutninger som ikke tidligere er beskrevet, dvs. at DMP’en ikke er en protokol, men snarere en praktisk manual til en gennemsigtig forskningsproces.

Din datamanagementplan bør omfatte en række vigtige elementer:

Når du har styr på ovenstående elementer i din datamanagementplan, opnår du følgende:

• Du gør dine data mere værdifulde og brugbare for andre forskere

Et forskningsprojekt strækker sig typisk over flere år, og der vil uvægerligt ske ændringer undervejs, når de oprindelige planer implementeres i virkeligheden, eller når eksterne rammer ændrer sig. For at datamanagementplanen skal bevare sin værdi, er det vigtigt, at du tænker på den som et levende dokument, som du løbende skal justere.

En række af de punkter, der skal besvares i din datamanagementplan, kan du blive klogere på ved at læse nogle af de øvrige afsnit her i OPEN Forskerguide. Derudover findes der efterhånden mange ressourcer, hvor du kan lære mere om datamanagement og datamanagementplaner samt online-skabeloner og værktøjer til at lave din egen plan.

Selv om det måske kan virke uoverskueligt at gå i gang med en datamanagementplan, så vil du senere i dit projekt få stor glæde af at have gjort dig disse tanker på forhånd og i det lange løb spare både tid og bekymringer.

Data Management Plan

Title

Position, Name, title

Affiliation

Date the first version of the DMP was completed

Date the DMP was last changed

1. Administrative Data

Project Description 
Questions to consider
•    What is the nature of your research project?
•    What research questions are you addressing?
•    For what purpose are the data being collected or created?
Guidance:
•    Briefly summarize the type of study (or studies) to help others understand the purposes for which the data are being collected or created. 
•    “Documentation on study level”: Frame of the study (including surroundings/environment (e.g. special guidelines/instruction that may affect the study, special considerations in relation to data collection/creation, time/period, place/site)

Related Policies 
Questions to consider:
•    Are you aware under which data protection policy your data belong? 
•    Are there any existing procedures that you will base your approach on?
•    Does your department/group have data management guidelines?
•    Does your institution have a data protection or security policy that you will follow?
•    Does your institution have a Research Data Management (RDM) policy?
•    Does your funder have a Research Data Management policy?
•    Are there any formal standards that you will adopt?
•    Considerations/focus on FAIR principles? 
Guidance:
•    List any other relevant funder, institutional, departmental or group policies on data management, data sharing and data security. Some of the information you give in the remainder of the DMP will be determined by the content of other policies. If so, point/link to them here (there are definitely some).
•    Write the permissions your study needs and the approval number(s) (journal number(s)) 

2. Data Collection

What data will you collect or create?
Questions to consider:
•    What type, format and volume of data?
•    Are there any existing data that you can reuse?
Guidance:
•    Give a brief description of the data, including any existing data or third-party sources that will be used, in each case noting its content, type and coverage.     

How will the data be collected or created?
Questions to Consider:
•    What standards or methodologies will you use to collect or create data (e.g. frame for questionnaire data collection)?
•    How will you structure your data? (including a figure showing how data are combined)
•    How will you name your folders and files?
•    How will you handle versioning?
•    What quality assurance processes will you adopt?

3. Documentation and Metadata

What documentation and metadata will accompany the data?
Questions to consider:
•    What information is needed for the data to be to be read and interpreted in the future?
•    How will you capture / create this documentation and metadata?
•    Including your codebook and figure showing a participation/data flowchart 
Guidance:
•    Describe the types of documentation that will accompany the data to help secondary users to understand and reuse it. Explain how the consistency and quality of data collection will be controlled and documented. This may include processes such as calibration, repeat samples or measurements, standardized data capture or recording, data entry validation, peer review of data or representation with controlled vocabularies.

What kind of data control will you do on your data? 
Questions to consider:
•    Description of how data control will be done and how you will ensure high data quality.
•    Including:
•    Which requirements do you pose on the values of categorical variables, and how do you check these?
•    In which intervals are your numerical variables allowed to lie, and how do you check this?
•    Which assumptions do you impose on the order of dates/times and the lengths of time intervals?
•    Which missing data do you expect and how will you handle them? 
Guidance:
•    Describe which assumptions your data should fulfill, and how you plan to check these assumptions, both for each individual variable and for important relationships between variables.
•    Describe the expected reasons for missing data, the type of missing (MCAR, MAR, MNAR) you expect and how you plan to handle missing data in each relevant variable, specifying if you exclude observations, impute values, or apply other handling strategies.

4. Ethics and Legal Compliance

How will you manage any ethical issues?
Questions to consider:
•    Have you gained consent for data preservation and sharing?
•    How will you protect the identity of participants if required? e.g. via pseudomisation
•    How will sensitive data be handled to ensure it is stored and transferred securely (including how you will work with your data)?

How will you manage copyright and Intellectual Property Rights (IPR) issues?
Questions to consider:
•    Who owns the data?
•    Are there any restrictions on the reuse of third-party data?
Guidance:
•    State who will own the data. For multi-partner projects, IPR ownership may be worth covering in a consortium agreement. Consider any relevant funder, institutional, departmental or group policies on copyright or IPR. Also consider permissions to reuse third-party data and any restrictions needed on data sharing.

5. Patient involvement

How will you handle patient involvement?
Questions to consider:
•    Will you include patients, caregivers or relatives?
•    Other relevant groups?
•    How will you include them?
Guidance:
•    State who will be included. How will they be included? What are their tasks?
•    Consider and describe their inclusion in your study and the expected outcome 

6. Storage and Backup

How will the data be stored and backed up during the research?
Questions to consider:
•    How will you storage data?
•    How will the data/files be backed up?
•    Who will be responsible for backup?

How will you manage access?
Questions to consider:
•    How will you control access to keep the data secure?
•    How will you ensure that collaborators can access your data securely?
•    If creating or collecting data in the field how will you ensure its safe transfer into your main secured systems?

7. Long-term preservation

What is the long-term preservation plan for the dataset?
Questions to consider:
•    Will the data be archived? 
•    Will all data be archived? 

8. Data Sharing

How will you share the data?
Questions to consider:
•    With whom will you share the data, and under what conditions?
•    Will you share metadata?
•    When will you make the data available?
•    What software to use?
•    Consider how people might acknowledge the reuse of your data?
•    Will you pursue getting a persistent identifier (PID) for your data?

Are any restrictions on data sharing required?
Questions to consider:
•    For how long do you need exclusive use of the data and why?
•    How long will the data be retained and preserved?
•    Consider how people might acknowledge the reuse of your data?
•    Co-authorship

9. Responsibilities and Resources

Who will be responsible for data management?
Questions to consider:
•    Who is responsible for implementing the DMP, and ensuring it is reviewed and revised?
•    Who will be responsible for each data management activity?
•    How will responsibilities be split across partner sites in collaborative research projects?
•    Will data ownership and responsibilities be part of any consortium agreement
or contract agreed between partners?
Guidance:
•    Outline the roles and responsibilities for all activities e.g. data capture, metadata production, data quality, storage and backup, data archiving & data sharing. Consider who will be responsible for ensuring relevant policies will be respected. Individuals should be named where possible.

What resources will you require to deliver your plan?
Questions to consider:
•    Is additional specialist expertise (research support or training for existing staff) required?
•    Do you require hardware or software that is additional or exceptional to existing institutional provision?