This document provides a comprehensive overview of research data management, focusing on the usability and accessibility of data. It discusses effective data organization, metadata requirements, the importance of documentation for data provenance, and outlines the functionalities of DataverseNL for data sharing and storage. Key lessons emphasize the significance of clear variable names, data formats, and user roles in a collaborative environment for effective data management.

1. Research data management
PROOF Advanced course Information Literacy and
Research Data Management
TU/e, 12-11-2015
l.osinski@tue.nl, TU/e IEC/Library
Available under CC BY-SA 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 & you distribute
the adapted material under the same license as the
original

2. Topics part Research data management
1. Usable data (tabular data)
2. Accessible data (DataverseNL)

3. Topic #1
1. Usable data (tabular data)
2. Accessible data (DataverseNL)

4. What is the nature of the “unusual episode” to which this table refers?

6. Raw data:
https://www.amstat.org/publica
tions/jse/datasets/titanic.dat.txt
Documentation of the data:
https://www.amstat.org/publica
tions/jse/datasets/titanic.txt
 Size (number of observations
and variables)
 Description
 Provenance
 Variable descriptions
Based on:
The "Unusual Episode" Data
Revisited / by Robert J. MacG.
Dawson, in: Journal of Statistics
Education vol. 3(1995), issue 3

7. Morphological Measurements
of Galapagos Finches
http://dx.doi.org/10.5061/dry
ad.152
 Use of standard names
(taxonomy, species)
 Variable names clear
enough? WingL must be
wing length but what is
N.Ubkl?
Based on:
Looking after datasets / by
Antony Unwin, 01-09-2015,
http://blog.revolutionanalytics
.com/2015/09/looking-after-
datasets.html

8. Air crashes
http://bit.ly/KIB_PlaneTruth
 meaning of px?
 basis for visualizations
Ecological datasets:
http://esapubs.org/archive/ec
ol/E090/118/
 excellent metadata
including project
description, experimental
design and license
information (copyright)
Sample datasets:
http://dx.doi.org/10.6084/m9.
figshare.1314459

9. Heart rate changes… / by
Daniel Lakens,
http://dx.doi.org/10.4121/uui
d:ab52261c-206b-4bed-a59d-
026a16c04144
 Excel-file
 No documentation
Proteomic Analysis in Type 2
Diabetes Patients … / by Maria
A. Sleddering , Albert J.
Markvoort et. al.,
http://dx.doi.org/10.1371/jou
rnal.pone.0112835
 Word.doc

10. to allow your data to be easily:
 imported by data management systems;
 analyzed by analysis software, and ;
 combined with other data (interoperability)
make sure that:
 each row represents a single observation (record) and each column a single
variable or type of measurement (field)
 every cell should contain only a single value
 there should be only one column for each type of information
Cross-tab structure / contingency table: different columns contain measurements
of the same variable: easier to read but difficult to add data (columns) to the
records (rows). See Titanic table versus Titanic raw data
Lessons learned
table structure

11.  columns: use clear, descriptive variable names, avoid special characters (can
cause problems with some software)
 rows: if possible, use standard names within cells (derived from a taxonomy for
example)
 missing data / null values: best option: use a blank
Lessons learned
columns (variables) and rows (records)

12.  size of the data set: number of observations and variables
 explanation of the variables
 description of the data: what’s included and excluded, known problems or
inconsistencies in the data, units of measurement
 provenance (origin) of the data, data manipulation steps
a simple readme file can be enough (see documentation titanic dataset)
Lessons learned
intelligibility: documentation

13.  if possible use a non-proprietary (open) file format (are easier to use in a variety
of software), like csv for tabular data
 if possible, take the preferred formats of a data archive in account
http://datacentrum.3tu.nl/fileadmin/editor_upload/File_formats/Digital_Preser
vation_Support_levels.pdf
Lessons learned
long term availability

14. Excel
 data provenance and documentation of data processing is bad
OpenRefine
 runs on your computer (not in the cloud), inside the Firefox browser (not in IE),
no web connection is needed
 working with OpenRefine: http://www.datacarpentry.org/OpenRefine-
ecology/01-working-with-openrefine.html
 captures all steps done to your raw data ; original dataset is not modified ; steps
are easily reversed ;
Tools
for working with messy data

15. Topic #2
1. Usable data (tabular data)
2. Accessible data (DataverseNL)

16.  Test environment: Go to: https://act.dataverse.nl/
 [ Actual website: https://www.dataverse.nl ]
 Click ‘Log in’ (at the top right)
 Select SURFconext in the Please select your institution list and click Continue.
 Select Eindhoven University of Technology and log on with your TU/e username
and password
 When asked for it, give permission to share your data by answering Yes or click
this Tab
 When asked to create an account, answer Yes or click this Tab.
 When you succeeded to create an account, your username is: @[prefix of your
email address]
DataverseNL
log in | creating an account

17. Storage and backup of data through DANS [Dutch Archiving and Networking
Services]
 Data transfer: up to 2 Gb per dataset
 Via 3TU.Datacentrum: up to 50 Gb free
DataverseNL
storage and backup of data

18.  Organization of data in Dataverse  [Dataverse]  Dataset  (Data)file
 Before uploading, you have to describe your data (‘metadata’)
+ Discovery metadata
+ Formal metadata (for citation)
+ Substantial metadata (for discovery)
+ Metadata on data collection and methodology
+ …
 Version control of datasets, not of (data) files!
DataverseNL
organization and description of your data

19. Read-, edit- and access rights by assigning roles to registered users
A role defines the permissions you have
 Access restricted site: reading rights only (downloading datafiles)
 Contributor: the previous plus creating and editing own Studies
 Contributor +: all the previous plus editing all Studies in a Dataverse
 Curator: all the previous plus publishing (‘releasing’) Studies & assigning access rights to
Studies
 Admin: all the previous plus assigning roles to users in a Dataverse & creating external user
accounts
Access rights to specified groups at Dataverse, Study and data file level
 ‘Unreleashed’ Study; only visible to persons who have access rights to that Study
 ‘Released’ Study: default Public ; after that access can be restricted (‘restricted access’)
 Access rights = 1reading/downloading data files ; 2edit rights = editing metadata, adding or
deleting data files [defined by a role]
DataverseNL
access control by assigning roles and access rights to users #1

20. DataverseNL
access control by assigning roles and access rights to users #2

21. DataverseNL
recognition for and collaborating on your data
 Persistent identifier (DOI)
 Assigning roles (with edit-rights) to users
 [ Jointly / online analysis of data (Stata, SPSS, GraphML) ]

22.  Registering via SURFconext
+ At start you only have a user account ( your email address)  then Curator
may assign you reading rights or Admin a particular role (with rights)
+ ‘External’ persons can use DataverseNL but cannot create an account
themselves  Admin has to do this
 A Dataverse or Study that has not been released, is only visible to persons that
have rights to that Dataverse or Study
 A Dataverse or Study that has been released with full restriction of access, is still
accessible to persons that have rights to that Dataverse or Study
 Non released Studies do not have version control
 Contributor cannot release own Studies / assigning access rights  Admin or
Curator has to do this after a request
 When assigning rights (Permissions), do not forget to Save changes
DataverseNL
practical

23. More sharing or collaboration platforms