Scientific Committee on Antarctic Research Data Management Plans Amsterdam 8 th September 2009

Scientific Committee on Antarctic Research Data management can be an after-thought in conducting science. Even when it is considered, it is often poorly specified, and hence under-resourced. Sometimes its deliberately left out of proposals and planning because funding sources dont want to pay for data management and it inflates the size of the grant being requested. Sometimes scientists just dont know what is best practise in this space, or where to get help. What Problem Are We Addressing ?

Scientific Committee on Antarctic Research Insufficient people, technology, skills to address data management during project lifecycle. Instead of a project-based approach – each collaborator does their own thing (end up with islands of data): –similar datasets are unable to be integrated; unnecessary reformatting and data manipulation occurs; data access and distribution is difficult; data versioning is uncoordinated or absent; quality is difficult to ascertain; no archiving for re-use. Project is completed – scientists do what is in their own best interest to publish and move on to next project. –Data probably not broadly discoverable, accessible or re-usable – except by the researcher who captured the data. What Is The Outcome Of Poor Planning ?

Scientific Committee on Antarctic Research Partial Solution – Data Management Plans SCAR Rules Of Procedure –Scientific Programme Planning Groups – have data management planning in their instructions. SCAR Strategy recommends Data Management Planning. Draft SCAR Data Policy – encourages all SCAR sponsored projects to have data plans.

Scientific Committee on Antarctic Research What is a Data Management Plan Describes data flows from capture through to publication and archival. Sets out process for managing data, metadata and products. Data Management Plan Spells out management actions: Track data provenance Adequate data annotation Provide for data discovery Minimises data copies managed - versioning Minimises need for data transformations Protects and secures data from accidental release/loss Provides for data access Provides for long-term re-use

Scientific Committee on Antarctic Research Plan Components Plan objectives/deliverables/timelines Data capture (data types, volumes, sampling protocols, data flows) Post capture data processing ( calibration, validation, filtering, specimen identification ) Data storage ( required systems, sample tracking and access methods ) Data analysis/product generation ( provenance tracking ) Data publishing ( methods, systems, IP, citation norms ) Data archiving (storage to permit re-use) Resources and skills

Scientific Committee on Antarctic Research Training Session 1 – Breakout Groups Group 1 –In your country what are the hardest things to overcome in setting up a Data Centre ? Group 2 –What difficulties do you expect to encounter in requiring scientists in your country to prepare data management plans ? In Plenary –What things can we do to overcome the obstacles we have identified ?

Scientific Committee on Antarctic Research Training Session 1 – Breakout Groups Group 1 –Identifying the right agency politically –Getting resources –Dont know where to put initial effort –Dont know what technologies to use –Dont have the right skills Group 2 –No policy framework in place –No way to police requirement –Dont have contact with scientists –Dont know how to help scientists if they seek assistance –National funders wont fund data management