1. From Data to Capabilities
Medha Devare

2. or Combined, Depersonalized
Modified from:
Scale of Data
Shared Data
Little Data
Aggregated
Data
or Combined, Depersonalized
BIG DATA pool

3. How? Support best practices in data generation and management
Enable data discovery, interoperability, reuse
…to allow aggregation, combining, creation of Big Data pool

4. Why?

6. Data discovery++
Findable; Accessible; Interoperable; Reusable

7. BIG DATA HIGHWAY?

8. Data quality

9. Data quality Choose a module Trial, demo set-up Land preparation
Residue, mulch
Seed treatment
Planting, transplanting, sowing
Water, irrigation
Nutrients
Biotic stress
Abiotic stress
Soil
Weather
Harvest
Post-harvest
Socioeconomic
Choose a module

10. Interoperability
Syntactic interoperability: machines communicate and exchange data
Semantic interoperability: ascribe meaning to and automatically interpret data (ontologies, common vocabularies, etc)

11. Interoperability – Ontologies

12. Interoperability – Linked Open Data
Linking Open Data cloud diagram 2017, by Andrejs Abele, John P. McCrae, Paul Buitelaar, Anja Jentzsch and Richard Cyganiak.
Interoperability –
Linked Open Data

13. Interoperability – Linked Open Data via RDF
Country (Tanzania)
Village (Long)
is part of
CIAT
TZ Met
collected by
collected by
Dataset 3
(weather)
collected from
Dataset 1
(C, N in topsoil)
collected from
collected from
is part of
is part of
IFPRI
N content – plot 1
Dataset 2
(maize yields)
collected by
N content – plot 2
is part of
is part of
Maize kg/ha – HH 1
Maize kg/ha – HH 2