The document emphasizes the importance of research data management in enhancing reproducibility in scientific research, which is crucial for validating findings and methods. It outlines key aspects of research data, including planning for data sharing to facilitate future use, and adherence to the FAIR principles: Findable, Accessible, Interoperable, and Reusable. The conclusion stresses the need for careful planning and documentation throughout the research process to ensure effective data stewardship.

1. Research Data Management and Reproducibility
Aim:- To highlight how research data management can
help with reproducibility.
Judith Carr, Research Data Manager
Co-ordinator - Gary Jeffers, Research Data Officer
Photo by Aron Visuals on Unsplash

2. Reproducibility is defined as “obtaining consistent results
using the same input data, computational steps, methods,
and code, and conditions of analysis” (National
Academies of Sciences, Engineering, and Medicine, 2019.
Reproducibility and Replicability in Science. Washington,
D.C.: The National Academies Press. https://doi.org).
Why be reproducible ? to show your results are correct and enable
others to make use of your methods
Reproducibility is a core principle of scientific progress.
Scientific claims should not gain credence because of the
status or authority of their originator but by the replicability
of their supporting evidence." - Open Science
Collaboration
Replicability means obtaining consistent results
across studies aimed at answering the same
scientific question using different data

3. “an explicit process covering the
creation and stewardship of research
materials to enable their use for as long
as they retain value.”
Research data are
Research data management is
Any recorded information necessary to
support or validate a research project’s
observations, findings or outputs,
regardless of format
What is Research Data????

4. Research Data isn’t just
• Your results
• Your figures
• Your conclusions
Research Data is much more!
What
When
Where
Who
How
Which
Why

5. To Illustrate
Metadata and sharing Covid-19 research
Schriml, L.M., Chuvochina, M., Davies, N. et al. COVID-19
pandemic reveals the peril of ignoring metadata standards. Sci
Data 7, 188 (2020). https://doi.org/10.1038/s41597-020-0524-5
Prof Bill Greenhalf UoL video https://www.liverpool.ac.uk/library/research-data-
management/reproducibility-and-ukrn/
https://youtu.be/FpCrY7x5nEE

6. cea + from The Netherlands [CC BY 2.0]
• Don’t drown in data/information
• Don’t rely on your memory
• Avoids repetitive reading, testing, analysing
• Helps you find your data/information
• Helps you to explain what you have done
• Helps when collaborating – ask management questions
first
• Versioning, shows progress, thought process,
development
• No one size fits all
Planning

7. Photo by Derick McKinney on Unsplash
Not the most exciting part of research!
• For some might be as simple as filing, learning
data descriptions or metadata vocabulary
• It might mean a lot of conversations about
what, how and where data is collected
• If you start out with a plan, then you avoid
delays further down the line. Plan to share as
well
www.Liverpool.ac.uk/rdm

8. Planning can also include how you are going to
share and make data open?
Planning to share is an ingredient to
making your research reproducible.
https://www.youtube.com/watch?v=N2zK3sAtr-
4&ab_channel=NYUHealthSciencesLibrary

9. SangyaPundir •CC BY-SA 4.0
https://www.youtube.com/watch?v=5OeCrQE3HhE&ab_chann
el=MaastrichtUniversity

10. FINDABLE:-
Easy to find by both humans and computer systems – persistent
identifier, metadata in registered or searchable resource, metadata
must include the persistent identifier, minimum standards of ‘rich’
metadata.
ACCESSIBLE:- Data stored for long term so can be accessed and or downloaded,
with appropriate licence. Even if data not available metadata
should be. Free and universally implementable
INTEROPERABLE:-
Ready to be combined with other datasets by humans as well as
computer systems. Data and metadata use a formal, accessible, shared
and broadly applicable language for knowledge representation
REUSABLE:-
Clear and accessible data usage licence,
detailed provenance and domain relevant
community standards.
https://www.go-fair.org/fair-principles/ file:///C:/Users/carrjc/Desktop/open%20research%20webpage%202020/workshop/Parthenos%20IPERI
ON%20E-RIHS%20Workshop%20Crete%20FAIR%20Principles.pdf

11. To conclude
PLAN from the beginning, be flexible but note down changes and
why. Plan to share, think about what you would need to know if
you wanted to use your own research data in years to come
DMP online use this resource, use funder templates, ask
questions of your collaborators at the beginning
Metadata ask those questions, who, what, where, why, when,
which – have readme files and protocols, whatever helps
FAIR might not mean open but consider openness and
transparency within team and with collaborators.

12. Webpages:- www.Liverpool.ac.uk/rdm
Thank You – Any questions???????