Good (enough) research data management practices

Information Expertise Center / Library
Leon Osinski, IEC
Course for 0HV90 OGO Quantitative research, 20-11-2019
Good (enough) research data management practices
Available under CC BY-SA 4.0 license, which permits copying and redistributing the
material in any medium or format for non-commercial purposes & adapting the material
for any purpose, provided the original author and source are credited & you distribute the
adapted material under the same license as the original

Recommendations*
1. Save your raw data
2. Backup your data
3. Organize your data
4. Make your data human friendly
5. Make your data machine friendly
6. Archive and preserve your data
*Based upon Eugene Barsky (2017), Good enough research data management: a very brief guide.
https://researchdata.library.ubc.ca/files/files/2017/07/GoodEnoughResearchDataManagement_V1.1_20170705.pdf
Good (enough) research data management practices: course for 0HV90, 20-11-20192

Keep your raw or original data raw
1. Save your raw data read-only in its original format in a separate folder
2. Make a working copy of your raw data (input data, used for processing or
analysis)
+ This version can be identical to the original version. In some cases it will be a
modified version. For example, modifications required to allow your software
to read the file or removing explanatory notes from a table
+ The original and working copy of a data file should be given different names
+ The changes you make to your original data files should be described in a
Readme file
3. Keep the metadata of the original data files (if obtained from others) in a
separate folder
2. Backup your data
3

Backup your data
During research:
 General backup rule: save 3 copies of your data, on 2 different devices, and 1 copy
off site
 By using the local ICT infrastructure (university network servers (home drive) and
OneDrive (Office365)) this general rule is met. Moreover, your data is also stored
securely (with controlled access)
 For sharing data with (project)partners use SURFdrive (on request for students)
 For sending files use SURFfilesender
 Do not store or share sensitive data on a commercial cloud (Dropbox, Google
Drive, WeTransfer)
2. Backup your data
4

Organize your data
TIER documentation protocol : guiding principles
1. keep your raw or original data read-only in its original format in a separate folder
2. keep the working copy of your raw data (used for processing and analysis) in a
separate folder
3. keep the command files (files containing code written in the syntax of the
(statistical) software you use for the study) apart from the data
4. keep the analysis files (the fully cleaned and processed data files that you use to
generate the results reported in your paper) in a separate folder
5. store the metadata (codebook, description of variables, etc.) in a separate folder,
apart from the data itself
2. Backup your data
5

Organize your data
1. Main project folder (name of your research project/working title of your thesis or paper)
1.1. Original data and metadata
1.1.1. Original data
1.1.2. Metadata (if the original data has been obtained from others)
1.2. Processing and analysis files
1.2.1. Importable data files (a working copy of the original data)
1.2.2. Command files (files containing the code)
1.2.3. Analysis files (the fully cleaned cleaned and processed data files)
1.3. Documents (codebook, readme file, final paper)
1.4. Literature
2. Backup your data
6

Organize your data
File naming
Good file names are human readable:
 meaningful (use descriptive names that contain info on content)
 consistent (use file-naming conventions)
 unique (distinguishes a file from files with similar subjects as well as different versions of
the file)
and machine readable/searchable
 avoid using special characters in file names
 use “_” underscore to delimit units in names
 use “- “ hyphen to delimit names for readability
 include dates (format YYYYMMDD) and a version number on file names
2. Backup your data

Make your data human friendly
Describe your data in such a way that your data is understandable for humans
 Describe your variables and give them clear descriptive names
 Use standard names for nominal/categorical data within cells
 Document your data by adding a readme file that at least mentions
+ the size of the dataset (number of variables and observations)
+ information about the variables and its measurement units (codebook)
+ what’s included and excluded in the dataset (why data are missing)
+ a description of each step of how the data is collected (study design) and processed
(provenance)
+ an explanation of the structure and naming of the files when your data consists of
multiple files organized in a folder
2. Backup your data
8

Make your data machine friendly
Ensure that your data can be easily processed, found and reused by computers
 Tidy your (tabular) data
 Convert your data to open, non-proprietary formats
 Describe your dataset with a metadata standard for discovery (title, creators,
description, etc. of the dataset)
 Add a user license to your data.
2. Backup your data
9

Tidy your (tabular) data
 Each variable you measure is in one column
 Column headers are variable names
 Store units as metadata in their own column
 Each observation is in a different row
 Each cell contains only one piece of information
2. Backup your data
10

Tidy data Messy data
2. Backup your data
11

Use open non-proprietary formats
 With open non-proprietary data formats it is best ensured that the data will
remain usable and ‘legible’ for computers in the future
 Are easy to use in a variety of software, like .csv for tabular data
 Check the data formats that are supported by a data archive like
4TU.ResearchData
2. Backup your data
12

Add metadata for discovery
 Creator ; title ; short description + key words ; date(s) of data collection ;
publication year ; related publications ; DOI ; etc.
 When uploading your data in a data archive like 4TU.ResearchData, you will be
asked to enter these metadata.
 A DOI - a number that uniquely identifies your dataset regardless of where it is on
the internet (URL) - is assigned by the data archive
2. Backup your data
13

Add a user license
With a user license you make clear in advance what other people under what
conditions are allowed to do with your data
 Creative Commons license for data sets
 GNU General Public License (GPL) for software
 License selector ; Choose an open source license
2. Backup your data
14

Archive and preserve your data
Submit your final datasets to a data repository your data are archived and preserved
for the long term and your data will be published and made available to others
 Choose a repository where other researchers of your discipline are sharing their
data
 If not available use a general repository that at least assigns a DOI, that requires
that you provide adequate metadata and where you can select a user license
+ 4TU.Centre for Research Data, DANS, Zenodo
+ ARCHIE (TU/e, HTI)
2. Backup your data
15

FAIR research data management
If you follow these good data management practices, you data will be FAIR, that is:
Findable, Accessible, Interoperable, Reusable.

Furthermore, these good data management practices
 reduce the risk of data loss
 improve your research workflow
 can help you get recognized for your work
 can lead to novel insights because data can easily be combined
 promote scientific integrity and quality of data (combat scientific fraud)
 reduce the need for duplication of research and data collection
 put public-funded research results in the public sphere
 promote collaboration as your data is findable and usable for other researchers
 businesses and other organisations can also profit from research (data)
 contribute to better and more efficient science overall as your research results are more
accessible and usable

Support
 General: rdmsupport@tue.nl
 4TU.Centre for Research data: researchdata@4tu.nl ; l.osinski@tue.nl ;
m.j.h.ollers@tue.nl
 Data Coach (website): https://www.tue.nl/datacoach
 Working with data (website): https://intranet.tue.nl/en/university/digital-
university/data-stewardship/working-with-data/
 Open Science Community Eindhoven: https://osceindhoven.github.io/
 Online course Research data management basics (online training)
 PROOF course Open Science (with a part on research data management) (training)

Recommended reading on ‘good data practices’
1. Eugene Barsky (2017), Good enough research data management: a very brief guide.
https://researchdata.library.ubc.ca/files/files/2017/07/GoodEnoughResearchDataManagement_V1.1_201
70705.pdf
2. Wilson G, Bryan J, Cranston K, Kitzes J, Nederbragt L, Teal TK (2017) Good enough practices in scientific
computing. PLOS Computational Biology, 13(6): e1005510. https://doi.org/10.1371/journal.pcbi.1005510
3. Broman, K.W., Woo, K.H., Data organization in spreadsheets, in: The American Statistician.
https://doi.org/10.1080/00031305.2017.1375989
4. Ellis SE, Leek JT. (2017) How to share data for collaboration. PeerJ reprints : e3139v1
https://doi.org/10.7287/peerj.preprints.3139v1

Useful references
Save your raw data and backup your data
1. Storage, back up of data: https://www.ukdataservice.ac.uk/manage-data/store
2. University network servers: https://intranet.tue.nl/en/university/services/ict-services/ict-service-catalog/management-
services/data-management-storage/ (TU/e intranet)
3. OneDrive: https://intranet.tue.nl/en/university/services/01-01-1970-information-management-services/help-and-
support/manuals/user-support-systems/office-365/manual-transition-to-office-365/migration-to-office-365/step-4-put-
onedrive-into-operation/faqs-onedrive/how-do-i-use-onedrive/
4. SURFdrive: https://www.surfdrive.nl/
5. SURFdrive (at TU/e): https://intranet.tue.nl/en/university/services/ict-services/ict-service-catalog/management-
services/data-management-surfdrive (TU/e intranet)

Useful references
Organize your data
6. Organization of your data in folders according to the TIER documentation protocol: https://www.projecttier.org/tier-
protocol/specifications/#overview-of-the-documentation
7. File naming: Best practices for file naming (Stanford University Libraries) and
http://www2.stat.duke.edu/~rcs46/lectures_2015/01-markdown-git/slides/naming-slides/naming-slides.pdf
8. Readme file: https://researchdata.4tu.nl/fileadmin/user_upload/Documenten/Guidelines_for_creating_a_README_file.pdf
based upon Cornell University’s Guide to writing ‘readme’ style metadata:
https://data.research.cornell.edu/content/readme

Useful references
9. Tidy data: https://www.jstatsoft.org/article/view/v059i10
10. OpenRefine (tool for tidying data): http://openrefine.org
11. TidyR (R package for tidying data) : http://tidyr.tidyverse.org/
12. PROOF course Practical data analysis using R for researchers: https://intranet.tue.nl/en/university/services/service-for-
personnel-and-organization/human-resource-management/professional-development/proof-training-program/research-
skills/practical-data-analysis-using-r-for-researchers/
13. Licensing your data with 4TU.ResearchData: https://researchdata.4tu.nl/en/use-4turesearchdata/archive-research-data/upload-
your-data-in-our-data-archive/licencing/
14. Creative Commons licenses: https://creativecommons.org/
15. GNU General Public License: https://www.gnu.org/licenses/gpl-3.0.en.html
16. License selector: https://ufal.github.io/public-license-selector/
17. Choose an open source license: https://choosealicense.com/
18. Preferred data formats of 4TU.ResearchData: http://researchdata.4tu.nl/en/publishing-research/data-description-and-formats/
19. DataCite metadata schema for discovery: https://schema.datacite.org/

Useful references
20. Research data catalogue Re3data.org: https://www.re3data.org/
21. Publishing data: 4TU.Centre for Research Data: https://researchdata.4tu.nl/en/
22. Self upload 4TU.ResearchData: https://data.4tu.nl/account/login/?next=/upload/
23. Publishing data: Zenodo: http://www.zenodo.org/
24. Publishing data: DANS: http://www.dans.knaw.nl/en

1
Research data management basics in a nutshell1
by Leon Osinski, Eindhoven University of Technology & 4TU.Centre for Research Data, November 2019
6 recommendations
2. Backup your data
Save your raw data
1. Keep your raw data raw by saving your raw data read-only in its original format in a separate folder
2. Make a working copy of your raw data (input data, used for processing or analysis)
+ This version can be identical to the original version. In some cases, it will be a modified version.
For example, modifications required to allow your software to read the file or removing
explanatory notes from a table
+ The original and working copy of a data file should be given different names
+ The changes you make to your original data files should be described in a Readme file
3. If the original data file has been obtained from others, keep the metadata of it in a separate folder.
Backup your data
During research:
1. Save 3 copies of your data, on 2 different devices, and 1 copy off site
2. By using the local ICT infrastructure (university network servers (home drives) and OneDrive
(Office365)) this general rule is met. Moreover, your data is also stored securely (controlled access)
3. For sharing data with (project)partners use SURFdrive (on request for students)
4. For sending files use SURFfilesender
5. Do not store or share sensitive data on a commercial cloud (Dropbox, Google Drive, WeTransfer)
Organize your data
1. Main project folder (name of your research project/working title of your thesis or paper)
1.1. Original data and metadata
1.1.1.Original data
1.1.2.Metadata (if the original data has been obtained from others)
1.2. Processing and analysis files
1.2.1.Importable data files (a working copy of the original data)
1.2.2.Command files (files containing code written in the syntax of the statistical software)
1.2.3.Analysis files (the fully cleaned and processed data files used to generate the results)
1.3. Documents (codebook, readme file, final paper)
1.4. Literature
1
Based upon Eugene Barsky (2017), Good enough research data management: a very brief guide.
https://researchdata.library.ubc.ca/files/files/2017/07/GoodEnoughResearchDataManagement_V1.1_2
0170705.pdf

2
Describe your data in such a way that your data is understandable for humans
1. Describe your variables and give them clear descriptive names
2. Use standard names for nominal/categorical data within cells
3. Document your data by adding a readme file that at least mentions
+ the size of the dataset (number of variables and observations)
+ information about the variables and its measurement units (codebook)
+ what’s included and excluded in the dataset (why data are missing)
+ a description of each step of how the data is collected (study design) and processed
(provenance)
+ an explanation of the structure and naming of the files when your data consists of multiple files
organized in a folder
Ensure that your data can be easily processed, found and reused by computers
1. Tidy your (tabular) data
+ Each variable you measure is in one column
+ Column headers are variable names
+ Store units as metadata in their own column
+ Each observation is in a different row
+ Each cell contains only one piece of information
2. Convert your data to open, non-proprietary formats
3. Describe your dataset with a metadata standard for discovery (title, creators, description, etc. of the
dataset)
4. Add a user license to your data.
Submit your final datasets to a data repository where your data are archived and preserved for the long
term and your data will be published and made available to others
1. Choose a repository where other researchers of your discipline are sharing their data
2. If not available use a general repository that at least assigns a persistent identifier to your data (a
DOI), that requires that you provide adequate metadata and where you can select a user license of
choice
+ 4TU.Centre for Research Data, DANS, Zenodo
Support
1. General RDM support: rdmsupport@tue.nl
2. 4TU.Centre for Research data: researchdata@4tu.nl ; l.osinski@tue.nl ; m.j.h.ollers@tue.nl
3. Website Data Coach: https://www.tue.nl/datacoach
4. Website Working with data: https://intranet.tue.nl/en/university/digital-university/data-
stewardship/working-with-data/
5. Training Online course Research data management basics
6. Training PROOF course Open Science (with a part on research data management)
7. Training Electronic lab notebook (ELN)
Available under CC BY license, which permits copying and redistributing the material in any medium or format &
adapting the material for any purpose, provided the original author and source are credited

Good (enough) research data management practices

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