Workshop 15th March 2010 10 a.m. e-Science Institute, 15 South College Street, Edinburgh
Data-Intensive Research: how should we improve our ability to use data
Various speakers
- Web page
- http://wikis.nesc.ac.uk/escienvoy/Data-Intensive_Research:_how_should_we_improve_our_ability_to_use_data
- Submitter
- Nicki Lewin
The use of data in research is growing rapidly; the digital revolution generates more and more data, and policies encourage more data to be published. Expectations for openness, repeatability and evidence quality increase the data-use imperative.
Data use includes the data-curation lifecycle from creation or collection, through cleaning, integration, analysis, annotation, citation and presentation to preservation or discard. It includes finding data, developing an understanding of a body of data (often drawn from multiple sources), working out how to get evidence from data or what the newly available collections of data may now enable, deciding how to extract evidence from data possibly in combination with other data and presenting results so that evidence is understood, trusted and used.
There are many challenges in the wide range of data uses; for example, coping with complexity, with variable or poor data quality, with high volumes, with sophisticated analytic and presentation requests, with high data rates, with heterogeneity, with user numbers and diversity and so on. These challenges are addressed through multiple forms of iteration, progressively discovering and understanding data, progressively developing and understanding analytic methods, progressively refining the processes used to obtain particular forms of evidence, progressively improving the software, progressively adapting the computational platforms, and so on. Communities of data users build expertise around data and as they do they change requirements, patterns of use and modes of acceptable behaviour.
Data-intensive research is both research in any domain that has to pay serious attention to the ways in which it uses data in order to succeed, and research that improves our ability to use data. These co-evolve.
Monday’s programme explores that co-evolution.
Space is limited to 100 participants, so please register quickly if you are interested. If you are interested in staying beyond Monday, please email me (mpa@nesc.ac.uk).