Data curation is enabling more complete and high

quality data-driven models for knowledge

organisations.

eScience projects are the key innovators while

Biomedical and Media companies are the early

adopters.

Pre-competitive economic models can support the

creation of curation infrastructures.

Curation at scale requires blending of automated

curation platforms with large numbers of data curators.

Improvement of human-data interaction is needed.

Standards and models needed to reduce data curation

effort.

Interviews with domain experts, sector

case studies and literature analysis.

Focus on ,

and .

Five main categories of analysis:

Figure: The long tail of data variety and data curation

scalability.

Provide a for the future of data

curation.

Distributed data generation.

Data quality issues.

Increasing data variety and volume.

Data curation activities as a fundamental

process for coping with the

.

Project co-funded by the European Commission within the

7th Framework Program (Grant Agreement No. 318062).