Breakout 1 Socio-legal etc.

1. Breakout 1 Socio-legal etc. • Every discipline wil be different & each datacentre will have diff answers to questions. Use questionnaire and send to data centres. • Promote use & interop thro metadata standards resource discovery standards should be promtoed & dev by learned societies/ (membership arms) subject communities by disciplines (not data curators). Bottom up rather than top down. (not JISC!) • Education – recognise v wide range of understaing amongst disciplines re value of data curation centres/IRs/archives – need go out and prmote why they exist and why should be used. Focus at p/g community. • Political issue every research council should havce a written data policy, written, disseminated and policed. Some have them but not policed, need more meatier than 7-8 words. • Legal issues – value of JISC legal centre but like clairty of law where law existis re use of digital objects, IP etc need clarity of law and guidance on how best to interpret it. Model licences ffro use, interprtation, confidentiality, disclosure. • Academics & data centre people need to b told differences between data banks/data centres etc and IRs. IRs have not had enough institutional buy-in. like to know/JISC to investigate why subject repositoires more successful than IRs. JISC policy should reflect what is happening on ground. – controversial! • Comment – clarity of law is not easy – hope for clarity of assretians to uphold. Need straightforward answers to straightforward questions! • JISC should help sell IRs better

2. Breakout 2 Technical etc. • Need to define what is meant by semantics of structured data and publish guidelines at levels of metadata, classiffication/subject areas/factial names/agreed conventions layerd on top e.g identifiers. Ie application profiles – who should be keeper of those definitions eg registries – who funds and owns them ? • Scientists concentrate on narrow areas but connections are to toher areas • Timeseries are dufferent – how discover and use? More diffciult to defind discovery metadata for time series. Data might not be logically the same. • Data curation responsility at institutional level/data centre – data curation requires specialisms and data cnetres could feed this expertise back to institutions – need flow of expertisefrom DCs to institutions • Mixed economy re org reposnsibility is inevitable some federation will be there – how to express quality – role for provenance audit as a means to express quality also ranking and annotation – liked levels 1-3 way of thinking about annotations • Curation of data is of more ineterst to scientists than interoperability re marketing/selling it.

3. Breakout 3 R&R • Individual scientists to deposit data using domain standards of an acceptable quality • Reuser should acknowledge where dat came from and if approp to imporive the quality of the data. • Institution should have policies that mandata dposit in an appopiate place not neces IR • Publishers/journals/editors should mandate open deposit of data. • Curators who collect describe and connect data, idea of community proxy role – (NSF ref) try to understand ontology of the domain and efine standards for domain working in and with the scientists • Funders should enforce their data deposit policies where possiblem recognise emerging need for new infrastructure and provide aprop funding for new infrsstructure and for their resulting actions • Feed back views on data to the data centre manager (not snivel in public meetings). • Click use licence – says if you enmhance the data you must give it back for us ESDS rule. But how police that policy by data centre? Versioning an issue here • Good enough versus completely comprehensive descriptions (Graham C) • Who is reposnsible for ownership of the data to do changes? If mu;tiple versions not necessarily last one is best – look at annotation model • Competeitve views re sabotage other groups work is possible. • Who checks provence of anyrthing new? Curators?