1. Digital Preservation DAVID GIARETTA (APA) FIRST PRELIDA WORKSHOP, TIRRENIA, JUNE 25TH-- ‐ 27TH,2013

2. Outline  Fundamental demands  Fundamental concepts  Trust  OAIS and Linked Data

3. Fundamental demands

4. Preservation and value  Why pays?  Why?  What to preserve?  What value?

5. Examples  Books  Web  Science data  What are the differences?

6. Value RIDING THE WAVE

7. Vision 2030 (2) Researchers and practitioners from any discipline are able to find, access and process the data they need. They can be confident in their ability to use and understand data and they can evaluate the degree to which the data can be trusted.  Create a robust, reliable, flexible, green, evolvable data framework with appropriate governance and long-term funding schemes to key services such as Persistent Identification and registries of metadata.  Propose a directive demanding that data descriptions and provenance are associated with public (and other) data.  Create a directive to set up a unified authentication and authorisation system.  Set Grand Challenges to aggregate domains.  Provide “forums” to define strategies at disciplinary and cross-disciplinary levels for metadata definition. IMPACT IF ACHIEVED  Dramatic progress in the efficiency of the scientific process, and rapid advances in our understanding of our complex world, enabling the best brains to thrive wherever they are.

8. Vision 2030 (3) Producers of data benefit from opening it to broad access and prefer to deposit their data with confidence in reliable repositories. A framework of repositories work to international standards, to ensure they are trustworthy.  Propose reliable metrics to assess the quality and impact of datasets. All agencies should recognise high quality data publication in career advancement.  Create instruments so long-term (rolling) EU and national funding is available for the maintenance and curation of significant datasets.  Help create and support international audit and certification processes.  Link funding of repositories at EU and national level to their evaluation.  Create the discipline of data scientist, to ensure curation and quality in all aspects of the system. IMPACT IF ACHIEVED  Data-rich society with information that can be used for new and unexpected purposes.  Trustworthy information is useable now and for future generations.

9. Vision 2030 (4) Public funding rises, because funding bodies have confidence that their investments in research are paying back extra dividends to society, through increased use and re-use of publicly generated data.  EU and national agencies mandate that data management plans be created. IMPACT IF ACHIEVED  Funders have a strategic view of the value of data produced.

10. Vision 2030 (6) The public has access and can make creative use of the huge amount of data available; it can also contribute to the data store and enrich it. All can be adequately educated and prepared to benefit from this abundance of information.  Create non-specialist as well as specialist data access, visualisation, mining and research environments.  Create annotation services to collect views and derived results.  Create data recommender systems.  Embed data science in all training and academic qualifications.  Integrate into gaming and social networks IMPACT IF ACHIEVED  Citizens get a better awareness of and confidence in sciences, and can play an active role in evidence based decision making and can question statements made in the media.

11. Vision 2030 (7) Policy makers can make decisions based on solid evidence, and can monitor the impacts of these decisions. Government becomes more trustworthy.  Policy makers are able to make decisions based on solid evidence, and can monitor the impacts of these decisions. Government becomes more trustworthy. IMPACT IF ACHIEVED  Policy decisions are evidence-based to bridge the gap between society and decision-making, and increase public confidence in political decisions.

12. Fundamental concepts OAIS

13. Digital Preservation… Easy to do… …as long as you can provide money forever Easy to test claims about repositories… …as long as you live a long time

14. Preservation techniques For each technique look for evidence – what evidence? must at least make sure we consider different types of data ◦rendered vs non-rendered ◦composite vs simple ◦dynamic vs static ◦active vs passive must look at all types of threats

15. Threats Things change…… ◦Hardware ◦Software ◦Environment ◦Tacit knowledge Things become unfamiliar

16. Problems when preserving data Preserve? Preserve what? For how long? How to test? Which people? Which organisations? How well? Metadata? – What kind? How much?

17. “A fundamental characteristic of our age is the raising tide of data – global, diverse, valuable and complex. In the realm of science, this is both an opportunity and a challenge.” Report of the High-Level Group on Scientific Data, October 2010 “Riding the Wave: how Europe can gain from the raising tide of scientific data” raising tide of data… Requirements Who pays? Why?

18. Raising tide of data…

19. Opportunities

20. Data contains numbers etc – need meaning 20

21. ... to be combined and processed to get this 21 Level 2Level 0Level 1 Processing Processing/ combining

22. Preserving digitally encoded information Ensure that digitally encoded information are understandable and usable over the long term ◦Long term could start at just a few years ◦Chain of preservation Need to do something because things become “unfamiliar” over time But the same techniques enable use of data which is “unfamiliar” right now

23. Preservation Planning Data Management Data Management Archival Storage Archival Storage Access Ingest SIP Descriptive Information AIP queries query responses orders DIP MANAGEMENT Administration Lots of useful terminology

24. Key OAIS Concepts Claiming “This is being preserved” is untestable ◦Essentially meaningless ◦Except “BIT PRESERVATION” How can we make it testable? ◦Claim to be able to continue to “do something” with it ◦Understand/use ◦Need Representation Information Still meaningless… ◦Things are too interrelated ◦Representation Information potentially unlimited ◦Need to define a Designated Community – those we guarantee can understand – so we can test

25. OAIS Information model: Representation Information The Information Model is key Recursion ends at KNOWLEDGEBASE of the DESIGNATED COMMUNITY (this knowledge will change over time and region) Does not demand that ALL Representation Information be collected at once. A process which can be tested

26. Dictionary specification XML GOCE N1 file description Representation Network GOCE Level 1 (N1 File Format) GOCE Level 0 Processor Algorithm GOCE N1 file Dictionary GOCE N1 file standard PDF standard PDF software

27. Archival Information Package Preservation Description Information Preservation Description Information Content Information further described by Package Description Packaging Information derived from described by delimited by identifies Data Object Data Object Representation Information Representation Information Physical Object Digital Object Structure Information Semantic Information Reference Information Provenance Information Context Information Fixity Information Other Representation Information Interpreted using Bit adds meaning to Access Rights Information Interpreted using 1 * 1 1...*

28. Representation Information Representation Information Provenance has

29. When things changes We need to: ◦Know something has changed ◦Identify the implications of that change ◦Decide on the best course of action for preservation ◦What RepInfo we need to fill the gaps ◦Created by someone else or creating a new one ◦If transformed: how to maintain data authenticity ◦Alternatively: hand it over to another repository ◦Make sure data continues to be usable

30. Transformation Change the format e.g. ◦Word  PDF/A ◦PDF/A does not support macros ◦GIF  JPEG2000 ◦Resolution/ colour depth……. ◦Excel table  FITS file ◦NB FITS does not support formulae ◦Old EO or proprietary format  HDF ◦Certainly need to change STRUCTURE RepInfo ◦May need to change SEMANTIC RepInfo Transformational Information Properties

31. Hand-over Preservation requires funding Funding for a dataset (or a repository) may stop Need to be ready to hand over everything needed for preservation ◦OAIS (ISO 14721) defines “Archival Information Package (AIP). ◦Issues: ◦Storage naming conventions ◦Representation Information ◦Provenance ◦Identifiers ◦….

32. ThreatRequirement for solution Users may be unable to understand or use the data e.g. the semantics, format, processes or algorithms involved Ability to create and maintain adequate Representation Information Non-maintainability of essential hardware, software or support environment may make the information inaccessible Ability to share information about the availability of hardware and software and their replacements/substitutes The chain of evidence may be lost and there may be lack of certainty of provenance or authenticity Ability to bring together evidence from diverse sources about the Authenticity of a digital object Access and use restrictions may make it difficult to reuse data, or alternatively may not be respected in future Ability to deal with Digital Rights correctly in a changing and evolving environment Loss of ability to identify the location of data An ID resolver which is really persistent The current custodian of the data, whether an organisation or project, may cease to exist at some point in the future Brokering of organisations to hold data and the ability to package together the information needed to transfer information between organisations ready for long term preservation The ones we trust to look after the digital holdings may let us down Certification process so that one can have confidence about whom to trust to preserve data holdings over the long term RepInfo toolkit, Packager and Registry – to create and store Representation Information. In addition the Orchestration Manager and Knowledge Gap Manager help to ensure that the RepInfo is adequate. Registry and Orchestration Manager to exchange information about the obsolescence of hardware and software, amongst other changes. The Representation Information will include such things as software source code and emulators. Authenticity toolkit will allow one to capture evidence from many sources which may be used to judge Authenticity. Packaging toolkit to package access rights policy into AIP Persistent Identifier system: such a system will allow objects to be located over time. Orchestration Manager will, amongst other things, allow the exchange of information about datasets which need to be passed from one curator to another. Certification toolkit to help repository manager capture evidence for ISO 16363 Audit and Certification

33. Infrastructure support SCIDIP-ES ◦Converting CASPAR prototypes into robust services

34. Trust

35. Vision 2030 (2) Researchers and practitioners from any discipline are able to find, access and process the data they need. They can be confident in their ability to use and understand data and they can evaluate the degree to which the data can be trusted.  Create a robust, reliable, flexible, green, evolvable data framework with appropriate governance and long-term funding schemes to key services such as Persistent Identification and registries of metadata.  Propose a directive demanding that data descriptions and provenance are associated with public (and other) data.  Create a directive to set up a unified authentication and authorisation system.  Set Grand Challenges to aggregate domains.  Provide “forums” to define strategies at disciplinary and cross-disciplinary levels for metadata definition. IMPACT IF ACHIEVED  Dramatic progress in the efficiency of the scientific process, and rapid advances in our understanding of our complex world, enabling the best brains to thrive wherever they are.

36. Trust IssueVision 2030ShortMediumLong authenticity of data Scientists can establish the authenticity of the data they use ● Standardised system for provenance and related evidence in repositories. ● Standardised way to capture reputation of data producers and holders ● Adoption of machine readable provenance in major repositories ● Capture of reputation of producers and holders (see Social networking) ● 80% of repositories of scientific data have adequate machine readable evidence ● Automated ways to evaluate evidence of authenticity validity of data Users and systems will be able to evaluate the reputation of the data they use. Annotation system for datasets, with efforts to formalise annotation language Ranking system to allow systems to produce rankings of levels of trust (akin to Page rank but based on reputation rather than links) Systems can choose datasets which are most trustworthy and can evaluate the risks involved in using less trusted data. certification of repositories People can make a judgement about which repositories can be trusted International system of repository certification created Certification demanded by EU and national funders 80% of major repositories of scientific data are certified global trust issues Users can deal with the global datasets with the same confidence as European sources Discussions with US, China, etc MOU with international agencies on common standards International agreement so that users have evidence of authenticity for world-wide scientific data Complexity of the system People can trust that the ever more complex tangle of systems are doing the right thing Simplify interfaces and entanglement. Move towards autonomic, self- configuring, self-healing, self- optimising and self-protecting systems, with appropriate monitoring. Systems have survived many generations of changes in technologies and architectures.

37. Reality check What could jeopardise the vision Counter by: Lack of long term investment in critical components such as persistent identification Identify new funding mechanisms Identify new sources of funding Identify risks and benefits associated with digitally encoded information Lack of preparation Ensure the required research is done in advance Lack of willingness to co- operate across disciplines/ funders/ nations Apply subsidiarity principle so we do not step on researchers’ toes Take advantage of growing need of integration: within and across disciplines Lack of published data Provide ways for data producers to benefit from publishing their data Lack of trust Need ways of managing reputations Need ways of auditing and certifying repositories Need quality, impact, and trust metrics for datasets Not enough data experts Need to train data scientists and to make researchers aware of the importance of sharing their data The infrastructure is not used Work closely with real users and build according to their requirements Make data use interesting – for example integrating into games Use “data recommender” systems i.e. “you may also be interested in...” Too complex to work Do not aim for a single top down system Ensure effective governance and maintenance system (c.f. IETF) Lack of coherent data description allowing re-use of data Provide “forums” to define strategies at disciplinary and cross-disciplinary levels for metadata definition From Riding the Wave

38. Trust issues  Has it been preserved properly?  Is it of high quality?  Has it been changed in some way?  Does the pointer get me to the right object?

39. Has it been preserved properly?  Can the repository be trusted?  Certification of various kinds  ISO16363 certification should be available soon  Judged on the basis of evidence collected and examined

40. Is it of good quality? More than one in ten scientists and doctors claim to have witnessed colleagues deliberately fabricating data in order to get their research published, a new poll has revealed. The survey of almost 2,800 experts in Britain also found six per cent knew of possible research misconduct at their own institution that has not been properly investigated. The poll for the hugely-respected British Medical Journal (BMJ) http://www.dailymail.co.uk/sciencetech/article- 2085814/Scientists-falsify-data-research-published- whistleblowers-bullied-keeping-quiet-claim-colleagues.html

41. Dirk Smeesters had spent several years of his career as a social psychologist at Erasmus University in Rotterdam studying how consumers behaved in different situations. Did colour have an effect on what they bought? How did death-related stories in the media affect how people picked products? And was it better to use supermodels in cosmetics adverts than average-looking women? The questions are certainly intriguing, but unfortunately for anyone wanting truthful answers, some of Smeesters' work turned out to be fraudulent. The psychologist, who admitted "massaging" the data in some of his papers, resigned from his position in June after being investigated by his university, which had been tipped off by Uri Simonsohn from the University of Pennsylvania in Philadelphia. Simonsohn carried out an independent analysis of the data and was suspicious of how perfect many of Smeesters' results seemed when, statistically speaking, there should have been more variation in his measurements. Dutch psychologist Diederik Stapel. He was found to have fabricated data for years and published it in at least 30 peer-reviewed papers, including a report in the journal Science about how untidy environments may encourage discrimination. http://www.guardian.co.uk/science/2012/sep/13/scientific-research-fraud- bad-practice

42. Peer review of data ….is difficult

43. Lessons from APARSEN Data Quality Cost Models for preservation Preservation tools Preservation services

44. Has it been changed in some way? OAIS defines Authenticity as: The degree to which a person (or system) regards an object as what it is purported to be. Authenticity is judged on the basis of evidence. Need to capture evidence –what evidence?

45. Authenticity evidence Authenticity Model Provenance capture ◦How to deal with combinations of data ◦How to deal with changes Security and tampering with logs

46. OAIS and Linked Data

47. Linked Open Data: Issues  Links – just another dataset?  Or do we have to view as part of a huge “cloud”  is that cloud just another dataset?  Is it just like archiving snapshots of the Web?  Snapshots? But at different times across the cloud  HTTP URIs – how persistent?  HTTP – how persistent?  RDF – how persistent?  What do the links mean?

48. OAIS-related issues  Designated community  Representation Information  Provenance  Rights  Authenticity  Trustability  Is it easier to “poison” the system?

49. OAIS / Linked Data questions  Can OAIS concepts be applied to the preservation of Linked Data?  Do existing concepts apply?  Are new concepts needed?  What new terminology is needed?

50. END QUESTIONS?