Writing a Data Management Plan

Introduction
What is Data?
Introduction
What Is the Typical Lifecycle of Research Data?
What Is Research Data Management (RDM)?
What Are the Benefits of RDM?
Questionnaire – Rate Your Data Management
FAIR Data
FAIR Data – Introduction
Exercise: Identify FAIR data
How to Achieve FAIR Data
OPEN Data
FAIR vs OPEN
Open Science
Research Data Frameworks
University Policies
Funder Requirements
Funder Requirements – Introduction
European Commission – Horizon 2020
FWO
The Data Management Plan (DMP)
DMP – Introduction
Questionnaire – Your Main Reason for Writing a DMP
RDM Support
Contact Details
1. Preparing a DMP
Tools
Tools – Introduction
DMPOnline
7 Main Themes
Theme 1 Data Description
Theme 2 Ethical and Legal Issues
Theme 3 Data Documentation and Metadata
Theme 4 Data Storage and Backup During the Project
Theme 5 Data Preservation after the End of the Project
Theme 6 Data Sharing and Reuse
Theme 7 Responsibilities
What is DMPOnline?
VIB Forms
General Information & Datasets Information
Data Types & Data Purpose
Ethical and Legal Issues
Making Data Findable
Documentation and Metadata
Data Storage & Backup During the Project
Data Access: for Whom and under Which Conditions?
Data Preservation after the End of the Project
Responsibilities
Ready to Submit?
Data Collection
The Data Flow
Discover Data
Assess the Usefulness of Existing Data
Data Collection – Introduction
Training Video: Current Ways of Finding Existing Data
Copyright and Intellectual Property Rights (IPR) Issues
About Data Ownership
Confidential or Privacy-Sensitive Data
Licences
About Licences
Software Licences
Training Video: Providing Licences to meet requirements for reuse of datasets and software
Costs Involved with Managing Your Data
Costs – introduction
Data Management Cost Guide
2. Documenting and Organizing Data
Data Documentation
Data Documentation – Introduction
Why Document Your Data?
Examples of Data Documentation
Different Levels of Data Documentation
Metadata
Data Standards
Exercise: Data Standards
Organizing
Research and Lab Notebooks
File Formats
File Naming
Version Control
Folder Structure
Good File Management
Benefits of Good File Management
Do’s and Don’ts
Batch Renaming of Automatically Generated Files
Documenting & Organizing – References
Documenting & Organizing – References
3. Storing Data and Data Security
Data Storage
Data Storage – Introduction
Things That Can Go Wrong
Questions to Consider
Shared or Individual Data
Access Management
External/Cloud Storage
Single or Multiple Storage
Additional or Tailored Services
Store Your Masterfile
Backups
Questionnaire: How Much Data Would You Lose if Your Laptop Got Stolen?
Data Security
Data Security- Introduction
Data Breaches
Prevent Unauthorised Access
Questionnaire: Prevent Unauthorised Access
Legal Agreements and Contracts
Exercise: When to Use Which Legal Contract?
Privacy
Privacy in a Nutshell
Privacy-sensitive Data
Six Principles from the European General Data Protection Regulation
Privacy by Design
Exercise: Which Data Breach Is Breached?
Storing Personal Data
Cases on How to Make Personal Data Accessible
Informed Consent
Storing Data & Data Security – References
Storing Data & Data Security – References
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Metadata

Writing a Data Management Plan Data Documentation Metadata

Metadata is data about data. Consequently, it allows for very structured documentation, with descriptors facilitating cataloguing data and data discovery. Metadata is intended for machine-reading, and thanks to metadata you can browse sites like Zenodo using filters like Access Right and File Type.

Watch this web lecture to learn about the different types of metadata and how metadata can help make your research data better findable. You are pointed to useful sources for metadata standards.

Metadata Schemas and Standards

There is generally no need to generate machine-readable metadata yourself. Metadata often follows a certain schema, which is nothing but a predefined set of elements. There are also metadata standards, which in some cases are domain-specific. An example of such a standard is the DublinCore schema, which is a set of 15 metadata elements applicable to a wide range of datatypes

Exercise: Different Types of Metadata

Metadata for Different Disciplines

Different disciplines like biology, earth sciences, physical sciences and social sciences and humanities have their own standards. By choosing a well-supported standard, you will maximise the chance that your data can be (re)used and understood by other researchers.

Controlled Vocabulary

You are advised to use a ‘controlled’ vocabulary when you collaborate with others or when you regularly do the same type of research, A ‘controlled’ vocabulary uses predefined and authorised terms. This avoids that the same concept is given different names and ensures consistency. Hence it makes it easier to find and understand data.

Without you knowing, you may already use a generally accepted controlled vocabulary, for instance:

  • Latin names for plants in plant studies
  • Chemical names in chemistry
  • Entry terms for genes
  • Latin names for diseases
  • Mesh terms for medical terms in general
  • Standard units for your results

If there is no vocabulary available, you can consider making a custom list within your research team.

Improve a Record Description

Units are important, and metadata field names need explanations in for instance a codebook or another document explaining names. If metadata field names are from a standard, these are explained in the standard. This saves you time and you can simply refer to the standard in your data.

Metadata for Software

Metadata might include information like the source code, location, contributors, licence, version, identifier, references and how to cite the software (Jiménez 2017).

Useful links to metadata standards:

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