Research Data Management

Research Data Management (RDM)

What is RDM?

Planning, Data Collection, Management and Analysis, Preservation and Sharing

Research data management (RDM) is a term that describes the organization, storage, preservation and sharing of data collected and used in a research project.​​​​​​

You can ask yourself if people can find, access and understand your data. Did you

• explain your data?
• store it safely?
• open it (if possible)?​​​​​​

Why do we need RDM?

 

Our role is to offer support and guidance during the different research phases; to interface between policymakers, researchers and IT specialists and to provide practical approaches to support you in making your data more FAIR and Open.

Research data management helps you achieve the following;

1. Accountability and Integrity
2. Ensuring data is preserved 
3. Minimizing risk for data loss
4. Ensuring data is findable and easily discoverable, increased time-saving. 
5. Data can be used and reused, avoid duplicated effort
6. Encouraging collaboration, research is more global requiring collaborative data sharing! 
7. Minimizes time and effort preparing for publication and open data requirements  
8. Increased recognition, citation, and scholarly impact
9. Ensuring intellectual property rights are preserved
10. Compliance with funders requirements and institutional policies
11. Ensuring public funds are well spent and utilized maximizing their value 
12. To ensure scientists have what they need to do good science and to create good quality data 
13. Helps you answer the big question “I have big data now what can I do with it?”

Data & Metadata:

What is data?

 

Any type of information that is collected, observed, or created, in the context of research, as such, data can be;

• Primary- Raw from measurements or instruments
• Secondary- Processed from secondary analysis and interpretations.
• Published- final format available for use and reuse
• Metadata- data about your data 

What is Metadata?

 

Metadata is independent data that contain structured information about other data, i.e. Data about data. In other words, it is the data that provides essential context and relevant information about how the data was created, stored and shared. 

For instance, metadata can describe various aspects related to an experimental procedure such as; who carried out the experiment, which parameters were chosen, what type of equipment was used, the output and results, how the results were analysed, shared and used. 

• It ensures reliability, accessibility and discoverability
• Increases the value of your data
• Reduces duplication efforts
• Allows us to track people,institutions or publications associated with the original research 
• Enables researchers to quickly assess the quality and relevance
• Metadata is frequently required for depositing data in repositories.

Metadata is independent data that contain structured information about other data, i.e. Data about data. In other words, it is the data that provides essential context and relevant information about how the data was created, stored and shared. 

For instance, metadata can describe various aspects related to an experimental procedure such as; who carried out the experiment, which parameters were chosen, what type of equipment was used, the output and results, how the results were analysed, shared and used. 

• It ensures reliability, accessibility and discoverability
• Increases the value of your data
• Reduces duplication efforts
• Allows us to track people,institutions or publications associated with the original research 
• Enables researchers to quickly assess the quality and relevance
• Metadata is frequently required for depositing data in repositories.

What are the different types of metadata?

 

Descriptive metadata: Information outlining basic facts necessary for discovery and identification, i.e. title, authors, keywords and abstract,

Structural metadata:  Information regarding the structure (organisation and relationship) of data and underlying items. For instance, it could be a description of enclosed files and scripts, how they are organized and structured and how they are related and where they can be found i.e. DOI

Administrative metadata:  Information that describes the technical information and information regarding the management of the data including, licensing and copyright permissions, technical requirements, file formats, provenance (i.e. history of ownership, who owns the data and where did it come from), access and sharing controls and permissions, quality controls and integrity checks. 

Descriptive metadata: Information outlining basic facts necessary for discovery and identification, i.e. title, authors, keywords and abstract,

Structural metadata:  Information regarding the structure (organisation and relationship) of data and underlying items. For instance, it could be a description of enclosed files and scripts, how they are organized and structured and how they are related and where they can be found i.e. DOI

Administrative metadata:  Information that describes the technical information and information regarding the management of the data including, licensing and copyright permissions, technical requirements, file formats, provenance (i.e. history of ownership, who owns the data and where did it come from), access and sharing controls and permissions, quality controls and integrity checks. 

What are Metadata standards (also known as Schemas)?

 

• Metadata standards enable the structuring of metadata and enhance its interoperability, by using common terms and definitions, to provide consistency and accuracy to data documentation.
• Metadata standards offer technical standards that ensure units of measurement, time, are entered in controlled formats, i.e. the date and time formats 
• The standards can be discipline-specific or general such as Dublin Core, DataCite Metadata Schema, Data Documentation Initiative (DDI) and International Standards Organisation (ISO).
• Example of metadata standards and tools for lab-based research: 
  • ISA framework and tools: https://isa-tools.org/
  • Minimum Information for Biological and Biomedical Investigations: https://fairsharing.org/collection/MIBBI
• Examples of metadata standards for software:
  • CodeMeta https://codemeta.github.io/
  • The Software Ontology http://theswo.sourceforge.net/
  • PROV Ontology (PROV-O) https://www.w3.org/TR/prov-o/ 

Which file format should I use for my Metadata?

 

• A text or Html document.
• An XML document linked to data files
• Information embedded in an XML data file

XML (eXtensible Mark-up Language) files include key data and metadata documentation that is interoperable for web browsers and analysis engines which in turn enables field-specific searching.

• A text or Html document.
• An XML document linked to data files
• Information embedded in an XML data file

XML (eXtensible Mark-up Language) files include key data and metadata documentation that is interoperable for web browsers and analysis engines which in turn enables field-specific searching.

How to capture metadata? 

 

• Use ELN to record your work 
• Use versioning controls to track history, progress and changes in a descriptive manner 
• Use metadata standards
• Use README files

• Use ELN to record your work 
• Use versioning controls to track history, progress and changes in a descriptive manner 
• Use metadata standards
• Use README files

What is open data?

 

Open data is data that can be freely used, re-used and redistributed by anyone - subject only, at most, to the requirement to attribute and share-alike.

The Open Data Handbook- Open Knowledge Foundation

Open data is data that can be freely used, re-used and redistributed by anyone - subject only, at most, to the requirement to attribute and share-alike.

The Open Data Handbook- Open Knowledge Foundation

What is FAIR data?

 

FAIR is a set of principles to define the best practices for data and software to facilitate discovery, access and reuse by humans and machines.

FAIR stands for:​​​

Findable: Your data should be findable, by you and others.

Accessible: Your data should be accessible for both humans and machines, i.e. retrievable and understandable

Interoperable: Machines and humans can interpret and use the data in different settings.

Reusable: The ultimate goal of FAIR is to advance the reuse of data. Everything you’ve done so far ultimately leads to this point, ensuring the data can be reused by others.

FAIR data summary​​​​​

1. Deposit your data where others can find it, keep in mind where your peers can find it, i.e. field-specific repository and give it a stable unique identifier (PID).
2. Make your data & metadata accessible via standard means such as http/API.
3. Create metadata and explain in detail what this data is about, never assume people know!
4. Deposit metadata with PID and make it available with/out data i.e. in case data itself is heavily protected.
5. Include information on ownership and provenance.
6. Outline what the reusers of your data are/not allowed to do, use clear license. Commonly used licenses like MIT or Creative Commons (keep in mind funders requirements).
7. Specify access conditions, if authentication or authorization is required.
8. Describe your data in a standardized fashion using agreed terminology and vocabulary.  
9. Share the data in preferred & open file formats.
10. Start the process early on!

You can find more details in our training material on the basics of Research Data Management: https://zenodo.org/record/4562630

FAIR is a set of principles to define the best practices for data and software to facilitate discovery, access and reuse by humans and machines.

FAIR stands for:​​​

Findable: Your data should be findable, by you and others.

Accessible: Your data should be accessible for both humans and machines, i.e. retrievable and understandable

Interoperable: Machines and humans can interpret and use the data in different settings.

Reusable: The ultimate goal of FAIR is to advance the reuse of data. Everything you’ve done so far ultimately leads to this point, ensuring the data can be reused by others.

FAIR data summary​​​​​

1. Deposit your data where others can find it, keep in mind where your peers can find it, i.e. field-specific repository and give it a stable unique identifier (PID).
2. Make your data & metadata accessible via standard means such as http/API.
3. Create metadata and explain in detail what this data is about, never assume people know!
4. Deposit metadata with PID and make it available with/out data i.e. in case data itself is heavily protected.
5. Include information on ownership and provenance.
6. Outline what the reusers of your data are/not allowed to do, use clear license. Commonly used licenses like MIT or Creative Commons (keep in mind funders requirements).
7. Specify access conditions, if authentication or authorization is required.
8. Describe your data in a standardized fashion using agreed terminology and vocabulary.  
9. Share the data in preferred & open file formats.
10. Start the process early on!

You can find more details in our training material on the basics of Research Data Management: https://zenodo.org/record/4562630

Electronic Lab Notebook (ELN)

What is an ELN?

 

An ELN is a computer program designed to replace paper laboratory notebooks. 

Centralized ELN solution at MDC

 

MDC has a subscription to RSpace ELN. One can test and get to know the software by logging into the https://rspace-test.mdc-berlin.net/.  However, when familiar with the system, one should switch to the production version of the software https://rspace.mdc-berlin.net. For further information, please visit our Electronic Lab Notebook page.

FAQ about ELN at MDC

 

Please visit our Electronic Lab Notebook page for the FAQs.

What are 'proprietary' and 'open' formats?

 

Proprietary formats are file formats that usually can be viewed only in the software/tool which created the files. This software uses its own proprietary format to save and read the file. Only the company itself or licensees may use it. The description of the format is confidential or unpublished and the company/organization has the right to change it at any time. 
In contrast, an open format is a file format that is published and free to be used by everybody.

Which formats should I use?

 

The choice of file formats might be different dependent on which phase of the research data life cycle you are, for instance; when sharing data externally, it is more favourable to convert to an open file format, however if you are in the process of analyzing the data, you might be required to use file formats compatible with the software used.

Consider the following:

• Choose standard file formats most commonly used in your field.
• Convert data to a standard format.
• Choose a format that is required for data deposition i.e. repository requirements, archival compression.
• Consider exporting or converting from original format to a more open/preferred format but keep in mind that some data might be lost or altered during the process e.g., text formatting in documents, decimal point formatting, date and time values.
• Keep in mind there are no standard preferred file formats, and none are perfect, but consider choosing open formats that are most applicable for your use and field.
• When archiving data, combine the whole project (i.e., raw data, analysis, documentation, code and software) in one package.
• For software consider the use of containers to enable interoperability and long-term re-use.

For more information, you can visit the Organizing your data link under RDM tab.

Data Protection

What is personal and sensitive data?

 

GDPR defines personal data in Article 4(1) as, any information relating to an identified or identifiable natural person (‘data subject’); an identifiable natural person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person.

According to Article 4(13), (14) and (15) and Article 9 and Recitals (51) to (56) of the GDPR the following personal data is considered ‘sensitive’ and is subject to specific processing conditions:

• personal data revealing racial or ethnic origin, political opinions, religious or philosophical beliefs;
• trade-union membership;
• genetic data, biometric data processed solely to identify a human being;
• health-related data;
• data concerning a person’s sex life or sexual orientation.

GDPR defines personal data in Article 4(1) as, any information relating to an identified or identifiable natural person (‘data subject’); an identifiable natural person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person.

According to Article 4(13), (14) and (15) and Article 9 and Recitals (51) to (56) of the GDPR the following personal data is considered ‘sensitive’ and is subject to specific processing conditions:

• personal data revealing racial or ethnic origin, political opinions, religious or philosophical beliefs;
• trade-union membership;
• genetic data, biometric data processed solely to identify a human being;
• health-related data;
• data concerning a person’s sex life or sexual orientation.

My data is sensitive. How do I share or publish it?

 

Sensitive data can only be shared or published if the researcher 

• have the right to publish the data
• took freely given consents for data sharing from the participants before
• got ethical approval for data publication

and by making the data anonymised and by licensing for reuse and attribution.

Otherwise, the researcher can publish the non-identifiable metadata of sensitive/personal data.

Sensitive data can only be shared or published if the researcher 

• have the right to publish the data
• took freely given consents for data sharing from the participants before
• got ethical approval for data publication

and by making the data anonymised and by licensing for reuse and attribution.

Otherwise, the researcher can publish the non-identifiable metadata of sensitive/personal data.