GSH-course Research Data Management (RDM) Expert Centre Research Data

INTRODUCTION Welcome & introduce yourself Structure of the course: focus on data life cycle Before the break: -Creating data -Processing data -Analysing data After the break: -Preserving data -Giving access to data -Reusing data Don’t hesitate to ask questions & give feedback!

WHY RESEARCH DATA MANAGEMENT Save time Increase efficiency Keep your data safe Share and reuse your data Link publication and data set Transparency and integrity Funder, journal and university policies International laws and guidelines Data is research output!

FUNDER & JOURNAL POLICIES Horizon Open research data pilot, including data management plan NWO (& data archive DANS) -Data management paragraph & pilot for data management plan ZonMw -Pilot “Toegang tot data”, including data management plan and data paragraph Journals can have a policy regarding research data availability, e.g. Plos, Nature

RADBOUD UNIVERSITY POLICY Data are stored at the latest at the time of publication of the research Data must be accompanied by the information necessary for understanding and potential reuse of the data Data management plan is recommended The minimum term of storage is ten years Responsibility: researcher & director of the research institute Details are worked out by the research institutes Radboudumc has more stringent guidelines for research that falls under WMO (medical research involving human subjects act)

RADBOUD UNIVERSITY PILOTS The University will set preconditions (infrastructure, support) Expert Centre Research Data: developing a support service at the University Library regarding research data management (DMP, storage during and after your research, legal and ethical advice, training etc.) Research data services (RDS): linking existing infrastructures for data storage for the long tail data (Radboud Repository, Metis, DANS / 3TU.DC) (RIS interface)RIS interface Repository: building a repository for big data at the Donders Institute for Brain, Cognition and Behaviour Digital Research Environment: developing a system at the UMC which manages the data from start to end of a research project.

WHAT IS DATA Types of research data Qualitative Quantitative Text Questionnaires Images Videos Audio files Architecture Interviews Observations Patient data Measurements Lab results Everything a researcher needs to answer his/her research question! Notes Analysis scheme

DATA LIFE CYCLE Source: UK data archive

DATA MANAGEMENT PLAN: FUNCTIONS You think and decide timely about research data management issues Use it as a dynamic document (mention date / version) Use it as a discussion document Useful in meetings for monitoring progress of your research For WMO, investigator initiated studies within Radboudumc, a DMP is obligatory for obtaining RvB approval Several formats (funders, university, research institute, online tools)fundersuniversity

CREATING DATA

The process starts by deciding what kind of data you need in order to answer your research question. This can go three ways: Use existing data  Find out if you need permission to use the data and what your rights are concerning long term storage Collect your own data  Closely document the collection process Use a combination

CREATING DATA Before you will start collecting your data, think about: Privacy and legal issues. -Do you need informed consent forms? -Do you need approval of the ethics committee? -Find out if you need permission to use the data -Find out what your rights are concerning long-term storage of the data Documentation -Describe the collection process as detailed as possible Practical issues -Storage space -Folder structure -Versioning -Etc. Write a data management plan!

PROCESSING DATA

A lot of your data management choices depend on the kind of data you collected. So before you start processing your data, find out to which category your data belong. Within Radboud University policy three levels are used: Critical data: contains information that enables the identification of an individual; Sensitive data: are competition-sensitive or confidential; Standard data: neither critical nor sensitive. PROCESSING DATA: LEVELS OF DATA

When you are working with critical data, your first step is anonymising or pseudonymising your data. NB If this will complicate your research, you can work with the original data. However, be sure to take appropriate safety measures. Some tips for anonymising data: Make sure that your key file is stored seperately from your anonymised file. Anonymisation is not only about removing names. Any form of identification should be made impossible. PROCESSING DATA: ANONYMISATION

PROCESSING DATA: STORAGE Decide where to store your data Paper documents: Closet, shelf, drawer, desk etc? Do I need a lock? Do I have enough space? Digital documents: The university network Storing possibilities within the research institute Surfdrive USB-stick or laptop Google docs or Dropbox The kind of data you collected determines which storage location you can use.

Classification/ CriticalSensitiveStandard Facility Designated RU storage Suitable Mobile media (usb/laptop) Not permittedEncrypted only FileSender Not permittedPermitted*Permitted Edugroups Not permittedPermitted*Permitted SURFdrive Not permittedPermitted*Permitted * Encryption recommended Critical = personal data; Sensitive= competition-sensitive or confidential PROCESSING DATA: STORAGE

Think about a good backup strategy. Paper documents: Make copies and store them separated from your originals. Scan important papers and safe them on the network drive. Digital documents: Backup your files regularly, preferably at a fixed moment. Consider where to save your backed up files. Consider what to back up: files and/or folders or also software? Make sure that the backed up file is the same as the original. Think about long term storage. Think about safety and privacy (pseudonimisation). A backup is a backup! PROCESSING DATA: BACKUPS

PROCESSING DATA: STORAGE Decide how to store your data Tips for filing paper documents: -Keep your filing system simple (alphabetical, numerical, thematic, type). -Make sure you have enough space. -Make sure everything is kept safe. -Think about the long term (will you understand your filling system ten years from now?) -Make a content file and give every document a code.

Tips for filing digital documents: Don’t use long and complicated file names and make them meaningful. Use folders to create shorter file names. Mention which version is concerned. Preferably: don’t go deeper than three or four levels. Make categories which are logical for your research. Separate ongoing and complete work. Don’t Dataset1.xls Maaikedataset5version10.xls Do Mortalitygirls xls Mortalitygirls _v01.xls PROCESSING DATA: FILING

Versioning, a few tips: A good versioning strategy depends on who is using the files and where they are stored. Make sure that if information in one file is altered, the information in related files is also altered. Decide how many versions you want to save, which versions to keep and for how long. Identify milestone versions and a raw data version, which can never be altered or deleted. Record the changes that are made in a new version and the status of the version. For more tips, read “Managing and sharing data” from the UK data archive page PROCESSING DATA: VERSIONING

PROCESSED DATA: VERSIONING

After you have decided where and how to store your data, you can enter, digitise, translate, transcribe, clean, validate, check and anonymise your data. It is very important to describe this process for yourself and others: (Lab)journal/methodology Start from the beginning of the data collection process Record all your steps and choices Record the reasons behind your choices Codebooks The manual for your dataset What does the variable name mean? What do the variable values mean? Work in progress PROCESSING DATA: DOCUMENTATION

PROCESSING DATA: SHARING Will you share your data with others during your research? Think about: Who should have access to the data? Which access level do you assign? Especially important when you are working with critical or sensitive data. When you are working together with others on the data, make sure that you have agreements on: Versioning Documentation Safe storage Etc. Fill in a data management plan together!

ANALYSING DATA