Title: Data Management
Course: MSc level academic and professional (information) skills > Section: Part 4. Data management and ethics

Introduction and learning objectives

In this course you will learn basic knowledge about the importance of data (management) in a reasearch project and why a data management plan (DMP) is important. You will learn about the general concepts of data management, such as FAIR data, and basic knowledge about the quality of data. Furthermore you will learn how to collect and store your data systematically during your research and basic knowledge about management of data for verification and reuse after research.

After this you will be able to start writing your template DMP.

Source image: wikipedia

Why research data management?

"Good scientific practice is one of the main reasons to manage research data during, and archive the data after the project. It is important to keep available the raw, processed and/or analyzed data, as well as the documentation necessary for understanding the data and the way it was collected, processed and analyzed. This plays an important role in accountability issues and allows the researcher to reuse his/her own data or to return to earlier stages of the research process when needed. As a result, research will be increasingly reproducible and verifiable.

Proper RDM makes science more transparent and improves scientific integrity and societal trust”.

From: UT/ITC Data Policy

Click the picture below for a video about the importance of data managment

1. Access, Re-use & Recognition

- Facilitating future research by allowing others to build on or add to your research data.
- Increased citations of research data and of publications based on that data.

2. Efficiency

- Increasing your research efficiency by saving time and resources.
- Preventing duplication of effort by enabling others to use your data.

3. Quality & Security

- Ensuring the integrity and reproducibility of your research.
- Ensuring that research data and records are accurate, complete, authentic and reliable.
- Enhancing data security and minimizing the risk of data loss.

4. Compliance

- Meeting legal obligations, restrictions and codes of conduct.
- Meeting the University's Research Data Management policy requirements (if they are available)
- Meeting funding body grant requirements.
- Meeting publisher requirements for data access.

What is research data?

“Research data means data in the form of facts, observations, images, computer program results, recordings, measurements or experiences on which an argument, theory, test or hypothesis, or another research output is based. Data may be numerical, descriptive, visual or tactile. It may be raw, cleaned or processed, and may be held in any format or media.”

From: University of Southampton

1. “That which is collected, observed, or created for purposes of analysing to produce original research results.” (University of Nottingham)

2. “Any and all complex data entities from observations, experiments, simulations, models, and higher order assemblies, along with the associated documentation needed to describe and interpret the data” (National Science Foundation)

3. “We do not have a set definition for research data – the material that researchers use as data is so varied across disciplines and research methods that to define one would inevitably leave out something important!” (UK DataCite)

4. Research data comes in a variety of formats that can be read with various types of software.

5. Research data is collected, observed or created for the purpose of analysis to produce and validate original research results.

6. Research data is defined as recorded factual material commonly retained by and accepted in the scientific community as necessary to validate research findings.

Research data types MSc research

Data types in Earth Science can range from

- data measured and collected in the field,
- data collected through interviews or questionnaires,
- (experimental) data generated in geoscience labs,
- satellite data products, and retrievals,
- data from public databases,
- data from simulation models,
- data from space crafts including drones, and
- cadastral and property data utilized for urban and land use planning etc.etc.etc.

Categorisation research data

Research data can be categorized in different ways. These are criteria for data type distinction:

- Character: qualitative – quantitative
- Source: primary – secondary
- Creation: observational – experimental – simulation – derived or compiled
- Phase: raw – prepared – analysed – published

Difference primary and secondary data

Primary data | Secondary data
- Collected from the field by researcher | - Gathered and recorded external to the project
- Collected for specific purpose of the research | - Collected for another, often general purpose
- Collected for the first time | - Re-used, usually in a different context
- Current studies as well as for future studies | - Less cost, time and effort

Case

Dr. Grant Nicoll is collecting data for his research as a post-doctoral researcher in the School of GeoSciences at the University of Edinburgh.

This research involves the use of seismic data acquired by the University of Edinburgh from the British Geological Survey (and previously worked on by them) – his current study requires these data to be interpreted in Petrel (a seismic interpretation package) to create a set of surfaces – these will then be used to create a 3D layered grid model in MPath (a fluid flow modelling package) on which fluid flow modelling will be performed.

Dr. Grant Nicoll has not used either software package before although some training has been provided by the University – a brief handover of data has also been performed by another post-doctoral researcher who has previously worked on these data in MPath – they are about to leave the University.

Seismic data are loaded into Petrel and correlated with well horizon depths and well logs from oil company reports but some horizons don’t match up.

What information would the researcher need to fix the problem?

Questions case

Question the researcher could ask to help solve his problem are:

1. Who worked on these data before and what did they do to it in terms of processing?

2. Are there any records available of the processing steps undertaken – notes, logs, workflows? The researcher needs to know who did what and where everything (i.e. data, files, reports, workflows, etc.) can be found and that everything is up to date.

3. Is there anyone else available in the university who has used either piece of software before to offer assistance?

4. If there is a problem, how easy is it going to be to fix it? Are additional resources, software, etc. likely to be required?

What can we learn from this case?

- Always check where the data came from, who did what to the data, how suitable are they for the current research and if unsuitable, how easy is it to effect a remedy!

- Also, make sure that you document your own data through the course of your project so that data will be usable without any problems in future

- If a data management plan would have existed all the questions could have been answered.

Discover / finding data

How can you discover and reuse existing or previously collected datasets?

1. Discipline-specific data repositories:

- https://www.re3data.org

You can search or browse by subject, content type or country and filter the results by criteria for choosing a data repository as described above.

- https://datacatalogue.cessda.eu

Social science data archives.

2. More general repositories:

- DANS repository

https://easy.dans.knaw.nl/ui/home

3.  Via your supervisor, colleagues etc.

Organizing data

As soon as you have collected your data it is really important to have a systematic way of storing your data. Think about:

- Designing an appropriate data file structure, naming, documenting and organizing your data files within suitable folder structures.

- Thinking of ways to categorize data.
- Developing a file naming strategy.
- Designing a comprehensive folder structure.

See three examples below:

Example1 | Example 2
Example 3 (with thanks to Wageningen University) |

Tips data organization

- The folder structure used for the data and the file names for the data files should be logical.

- Make sure to use consistent file names. When you use a date in the file name, choose a notation (for instance, YYYYMMDD or yymmdd).

- Do not use strange characters like ?\!@*%{[<> in the file name.

- Use traceable file names, such as Project_Instrument_locatie_YYYYMMDD.ext.

- Make sure to only use each file once in the folder structure. If you store a file in more than one place, several versions of the same file can unwillingly be created.

- Think about version management.

Data documentation

When you want to store your data for reuse other researchers need to know what kind of data you have stored. This information is what we call metadata.

- The term metadata refers to information used to describe data. It is data about data. Metadata adds value to the data

- Descriptive metadata are essential for the preservation, retrieval and re-use of datasets

- Metadata can include keywords, field of science classification codes, the DOIs of related papers, the responsible researchers but also e.g. the codes for the grants that supported the research.

- For scientific data, metadata provides the context needed to make sense of what would otherwise be a collection of random numbers

Describe how you are going to document your data collection process, what the resulting data files comprise and how they will be processed further. Think about:

- Content (what does your dataset contain)

- Context (who, what, why,where and how will the data be collected and analyzed)

- Process (are there specific processes and does it make send to organize your notes according the processes?)

Metadata standards

There are different metadata standards. You have metadata specific for the Earth Sciences, but also more general metadata standards.

- Metadata In the Earth Sciences:

http://www.dcc.ac.uk/resources/subject-areas/earth-science

Various disciplines apply their own metadata schedules and standards. Which metadata fields are mandatory or desirable may vary per file.

- Metadata general:

http://dublincore.org/documents/dces/

Both DANS Easy use Dublin Core Metadata Initiative DCMI as the standard for metadata. Dublin Core is easy to use and it is used worldwide. As a result, it is easy to link metadata to other files and automatically search through them, which increases the familiarity of the data. The underlying data itself, however, cannot be searched.

Digital Curation Centre - gives different metadata standards in the Earth Sciences

Dublin Core

Data Protection Regulation in Ethiopia

When working with data you are often required to abide certain rules or laws. These ususally cover subjects like sensitive data, personal data, and privacy and certain safeguards that need to be used in order to protect the data.

In Europe there is a special law for data protection, the GDPR. Ethiopia does not have one specific law focussed on data protection, but does have rules in the Constitution that regulate subjects relevant for data privacy and protection. There are, for example, articles in the Constitution that regulate the use of personal data and privacy, like the Mass Media Proclamation and the Civil Code.

For more information about laws and data protection you can take a look at the following website: https://www.dataguidance.com/notes/ethiopia-data-protection-overview

Laws can sometimes be confusing and difficult to understand. Important to keep in mind when you work with data is to always think about who is allowed to have access to the data and if personal and/or sensitive data is involved. Always be careful with your data, so people and companies know they can safely share information with you and don't need to worry about their privacy or sensitive data falling into the wrong hands.

Processing personal data in your research

The data subject has given consent to the processing of his or her personal data for one or more specific purposes. Personal data can be anonymized irreversibly. After that it is no longer possible to trace the data back to persons. If the process is not irreversible, we speak about pseudonymization. Pseudonymization is a security measure. In addition to pseudonymization, encryption also a security measure for working with personal data.

Informed consent procedure

When working with personal data a good practice is to use an informed consent procedure. In this procedure you inform the subject that is participating in your research, about the research. What is the research about, what is it's purpuse, what type of intervention is used, how did you select the participant and what is the procedure of the research?
What you tell the participant exactly depends on the nature of your research. You don't want to give the participant information that might have an influence on the outcome of your research.

After you've informed the participant, he/she signs an informed consent form. This states that the participant is informed about the research and is willing to participate.

Personal data in your research

Access to your data

Process data during research

If you start processing your data it is very important that you control the process and that you've described the different steps for yourself. This way you know what you need to do, but also what you have done if you need to look back to your data later on. So what kind of steps did you take, how did you process it and what is finally the analysis.

- Prepare your data files for analysis and data sharing. Throughout the different phases of your project, your data files will be edited numerous times. During this process, it is crucial to maintain the authenticity of research information contained in the data and prevent it from loss or deterioration. Think about version control.

- Be able to manage the integrity and authenticity of your data during the research process;

- Understand the importance of a systematic approach to data quality.

Data access during and after research

- Who needs to have access to the data (during your research)?
- Who controls data access?
- How can you make the data available after your research: open or under restrictions?
- How will the data be licensed for reuse?
- Are there any limitations on the access of your data that you agreed upon with third parties? Did you sign agreements? Can the data be reused (e.g. secondary data)? Did you ask your participants about reuse of the data?

Sharing your data

When doing research you will probably share data in one or more phases of your research. It's important to think about who you will be sharing your data with and how. It can also be beneficial to share your data after your research is done.

During your research
- With others involved in your research: fellow students, researchers, supervisors, etc.

After your research
- Scientific integrity: validating results by replication requires data reusing. This will result in data-driven science (big data, long tale data).

- Data are unique / not easily repeatable (long term observational data).

Benefits for researchers:
- Increases your visibility.

- Enhances the trustworthiness of your research.

Citation of data is becoming increasingly important. Scientific communication was up til now really only about articles. Nowadays, there is a little shift going on from articles to data. So it is really important as a researcher to store your data and to have an unique identifier to this data so it is clear that that data was collected, processed and/or analyzed by you.

You will also need to go back to society with your research to show what you have been doing and that your research is really valid in order to promote and retain trust.

FAIR data

Below you see a overview of the meaning of FAIR data.
From: Liber Association of European Research Libraries

To be findable:

F1. (meta)data are assigned a globally unique and eternally persistent identifier.
F2. data are described with rich metadata.
F3. (meta)data are registered or indexed in a searchable resource.
F4. metadata specify the data identifier.

To be accesible:

A1. (meta)data are retrievable by their identifier using a standardized communications protocol.
A1.1 the protocol is open, free and universally implementable.
A1.2. the protocol allows for an authentication and authorization procedure, where necessary.
A2. metadata are accessible, even when the data are no longer available.

To be interoperable:

I1. (meta)data use a formal, accessible, shared and broadly applicable language for knowledge representation.
I2. (meta)data use vocabularies that follow FAIR principles.
I3. (meta)data include qualified references to other (meta)data.

To be re-usable:

R1. meta(data) have a plurality of accurate and relevant attributes.
R1.1. (meta)data are released with a clear and accessible data usage license.
R1.2. (meta)data are associated with their provenance.
R1.3. (meta)data meet domain-relevant community standards.

From: https://www.force11.org/group/fairgroup/fairprinciples

Data management plan

https://youtu.be/gYDb-GP1CA4

- A data management plan is a project document which tells the story of your research data. It outlines what research data were collected, how they were collected and what you will do with your data during and after your research.

- Thinking in advance about how data are to be collected, stored, described, and archived and how access, sharing, reuse and linking to publications will be realised.

- Part of good research design

From: Data Management Plans as Infrastructure: The Planning or the Plan? – Kevin Ashley (DCC)

Data management plan (DMP); Thinking in advance

1. Data Management Plans help to critically think about how research data will be handled, and to establish good data practices.

2. A Data Management Plan (DMP) is a tool that makes you think carefully about your data collection process. Investing time in thinking about managing your research data at the start of your project helps to you to:

- Keep track of your research progress more efficiently.

- Easily find and understand the data you created earlier.

- Prepare your research data for future use (e.g., data publication, verification purposes or reuse of your data by others).

- Comply with ethical guidelines, and institutional, funder and journal requirements.

Example questions DMP

Summary:

A DMP contains information about:

- General information (e.g. personalia, dates, supervisors)
- Data collection (e.g. types of data, format, software)
- Data management (e.g. tools, informed consent, version control, data encryption)
- Data storage and back-up (e.g. storage medium, back-up location)
- Data documentation (e.g. standards to be used for documentation and metadata)
- Data access (e.g. during and after research, access criteria, privacy issues)

Summary

Data management:

- Important for the quality, reliability, replicability and verification of scientific research.

- Protection against physical loss and destruction, against intellectual loss and irretrievability, against unauthorized use.

- Sharing your data during and after the project.

- Good start with data management is a data management plan DMP.

*With thanks to Marga Koelen (University of Twente) for (part of) the content.