1. Building a Strategy for Data Management and Preservation to Support Agricultural Research
Marcos C. Visoli , Debora P. Drucker, Patricia R. Bello Bertin
SciDadaCon, September 2016
Denver, USA

2. 1. A few words about ‘Embrapa’
Founded on April 26, 1973
Public research organization (Brazilian Ministry of Agriculture, Livestock, and Food Supply)
Wide, geographically-distributed research organization)
Population: 200 million people
Main exporter: coffee, sugar, orange juice, sugarcane ethanol, beef, chicken and soybean

3. 2. The challenge of developing a data management program
EPISTEMOLOGICAL: Complexity of research practice, wide range of knowledge domains and specialisms (scientific data emerge from heterogeneous sources and techniques)
TECHNOLOGICAL: specificities of datasets, integrating databases
INSTITUTIONAL: research units’ geographical distribution, divisional autonomy, inertia and absence of an institutional strategy/policy
CULTURAL/SOCIAL:
competition for research funds and for priority
in scientific discovery
reward system (individual contributions)
legal and ethical concerns
Skepticism, unawareness and lack of training
Nature: 461 (7261), 2010

4. 3. The overall approach
1st Workshop: Campinas, SP
6th Workshop: Fortaleza, CE
5th Workshop: Manaus, AM
4th Workshop: Bento Gonçalves, RS
3rd Workshop: Brasília, DF
2nd Workshop: Rio de Janeiro, RJ
Premise: participatory and collaborative process, taking into account the multiple perspectives of all relevant actors
Six regional workshops: to raise awareness and discuss the main concepts and principles of research data management (two representatives of each of Embrapa’s 46 Research Units took part in the workshops) -

5. 3. The overall approach Interviews and survey:
to map existing experiences and achieve a better understanding of current research data management practices across the organization (electronic questionnaire covering research data practices throughout its lifecycle: collect, assure, describe, discover, preserve, integrate, analyze) -

6. 3. Preliminary results and ways forward
Ongoing RDM efforts (best practices):
Platform ALELO: genetic resources’ data
AGRITEMPO: agro-meteorological data
ATAS ELETRONICAS: digital lab-books
GeoInfo: Spatial Data Infrastruture
DATAEXP: infrastructure for data processing, storage and preservation
SIEXP: Experimental Data System (...among others)
Well-structured IT and IM areas (professionals, infrastructure)
Specialists in quantitative methods and computer science, to ensure best quality research data and analysis

7. 3. Preliminary results and ways forward
Still a reality...

8. Research Data Management
4. Developing the institutional strategy
Web survey and regional workshops provided material upon which a roadmap for an institutional research data management is being designed
Standards, interoperability
Data (context, documentation)
Software and hardware infrastructure
Institutional policies, norms
People (training, communication)
Research Data Management
- Policies for access, sharing and re-use

9. 5. Some preliminary considerations
To adopt well-established and widely-used standards that allow data interoperability
To prevent duplication of efforts
Intellectual property and copyright issues:
Who owns the copyright? (absence of na institutional policy, embargos on data due to patents, politics, or journal requirements)
How should data be cited when used?
The overall strategy contributed to raise awareness on the subject, while providing an opportunity to discuss the main issues, principles and foundations of research data management
Embrapa’s experience in building a corporate research data management program provides a useful analytical framework that can be easily adapted for other organizations and contexts

10. MANAGE RESEARCH DATA Thanks!
“The important thing in science is not so much to obtain new facts as to discover new ways of thinking about them.”
AND
MANAGE
YOUR
RESEARCH
DATA
William Bragg, quoted in Reif and Larkin (1991, p.739)
Thanks!