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Research Data Management: What, When, Why, How, and Where?

Introduction to Research Data Management for Social Scientists, GESIS Cologne, August 7-8, 2014
by

CESSDA Training

on 7 August 2014

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Transcript of Research Data Management: What, When, Why, How, and Where?

Dark Data
What is Research Data Management?
practices, policies, and procedures to...
When do you do Research Data Management?
You are always doing Research Data Management...
Why do Research Data Management?
You should...and you must
Research Data Management
What, when, why, how, and where?
Re-usable Data
Data hidden to other potential users and therefore likely to be underutilized and lost.
Poorly described
Vulnerable storage
Problems with consent and anonymisation
Problems with ownership
It's about looking after your data
(the stuff you analyze)
It.
protect
validate
describe
allow you to use it or others to re-use
Our attitude is that data is knowledge awaiting discovery.

We want to raise the level of accessible, re-usable, citable data.
In fact...
You are probably doing Research Data Management...
Without realizing it is Research Data Management.
Good science
replication, reliability
Good Politics
Maximizing taxpayer value to the fullest possible extent
Data re-use
National Institute of Health, 2003
National Science Foundation, 2011
Institutional specific approaches
Economic and Social Research Council, 2000
Research Councils UK, 2011
Deutsche Forschungsgemeinschaft (DFG), 1998
Empfehlung der 16. Mitgliederversammlung der HRK Management von Forschungsdaten (2014)
"It can be a chore, but good research data management is like good dental hygiene. It is healthy, attractive and makes people want to get to know you. "
Reviewing existing data sources for potential re-use, replication, enhancement, and methodologies
Describe data to be collected:
types, formats, standards, documentation, methods, ownership, consent, responsibilities, platforms for working storage, back-up, quality assurance, long-term preservation plans
Implementing methods of collection, description, assurance of data quality through checks and inspections
Working from copies not the raw data.
Keep copies of syntax files and all analysis steps.
data preparation for re-use
registration with DOI
long-term preservation
dissemination
provision of tools and standards
publishing research results through information systems
Full transcript