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what after DOBES & CLARIN? PowerPoint Presentation
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what after DOBES & CLARIN?

what after DOBES & CLARIN?

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what after DOBES & CLARIN?

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  1. Data Practices and RDA AgreementsPeter WittenburgMax Planck Data and Compute Center (Munich)Former: MPI for Psycholinguistics (Nijmegen)

  2. what after DOBES & CLARIN? • since ~2 years retired from MPI Nijmegen • in DOBES & CLARIN we created exemplary infrastructures with proper data organizations • this was very well perceived by others • thus we got invited • to give advice to the EC and member states • to participate in the European EUDAT data infrastructure • to participate in the worldwide Research Data Alliance

  3. is there a problem? • data as an issue • many publications about data deluge • many publications with the question whether we are fit for the challenges of “data science” • many discussions about Big Data • many stories about inefficiencies and being excluded (projects, researchers, industry) • in the last 2 years: • about 50 interviews in many departments/institutes (different disciplines, DOBES, CLARIN) in EUDAT, RDA-EU etc. • about 80 intensive interactions at scientific meetings • in DFT Working group about 20 data models • workshop with leading scientists 2010/2011

  4. which conclusions from interviews? • Data Management and Processing is too time consuming and costly due to the heterogeneity in how data is organized in particular with respect to logical information. Researchers see the need to change habitudes, but do not have agreed suggestions for solutions. many researchers stick to file systems – often at its limits • Federating data including logical layer information which is relevant for tracing provenance, for understanding creation context, for checking identity and integrity, etc. is so costly that in practice it is not done, although most data professionals understand that this practice cannot be continued like this. • DM and DP is not ready for Big Data due to the lack of usage of automated procedures incorporating proper data organization mechanisms. too many ad hoc scripts without proper documentation around

  5. which conclusions from interviews? • Due to lack of software that is supporting proper data organizations we continue to create legacy data that cannot be integrated easily into our growing domain of accessible data. most disciplines have massive problem with legacy, we still create legacy • When we only consider that for example one of the key biologists in a large research institute is spending 75% of his time for manual data management, we can estimate how much money and human capital is wasted by the way we are doing. huge waste of money and skills

  6. some more conclusions from interviews? • Need “chains of trust” in our increasingly anonymous data domain • one corner stone is trusted and certified repositories • another one is PID association incl. state information • change the way we publish/cite data so that creators are acknowledged • We need to start with training our young people in how to overcome this situation. • of course still hard work on changing data sharing culture

  7. one concrete example • a great online database with brilliant data (about 40 TB) created by excellent and engaged researchers • some data is in file system • some derived data is in mySQL • metadata (descriptions about data content) is in a CMS • identified are data items by URLs (or are they cool URIs?) • relational metadata is stored in Python scripts • how do you want to replicate such data at low costs? • how do you want to federate such data at low costs? • how do you want to scale up such a solution? • how do you want to certify the repository? • what when the script expert leaves the institution?

  8. are there trends? current practice is different: many collections have their own organization, own query system, own management principles, etc.

  9. can we learn from Internet? ~1960 TCP/IP 1986-1989 what happened in all these years? • many ideas, much testing, many proprietary solutions, many complete and “closed” solutions • an abstraction towards a unifying protocol + free a lot of time

  10. is there an equivalence in data? • can we agree on simple concepts ... • such as an API for PID systems and simple protocols • it would simplify our software • could use PID attributes (identity, integrity, rights, MD ref, data ref, etc.) • can again focus on other aspects Internet Domain nodes with IP numbers packages being exchanged standardized protocols Data Domain objects with PID numbers objects being exchanged standardized protocols what

  11. here is a role for RDA – Research Data Alliance • How to tackle big challenges if data is highly fragmented by disciplines, domains and countries? RDAis building bridges to overcome the hurdles for easy data access, data sharing and interoperability by facilitating collaboration between experts from all over the world belonging to different disciplines and organisations

  12. Who supports RDA?(RDA Colloquium) Global initiative with the support & funding of European Commission, Australian National Data Service and US National Science Foundation other countries to join soon

  13. Why do we need RDA? Have already so many important initiatives and organizations ... Many work on policy level which was & is so important such as CODATA and WDS. Others work on standards in different but related areas (ISO, IETF, W3C, IEEE, etc.) • … wanted to have an initiative that acts like early Internet initiative in a bottom-up way to overcome concrete barriers in short time frames. • real progress in practice is so slow!!!

  14. RDA – How does it work? Experts and Data practitioners come together in RDAWorking and Interest Groups to overcome concrete hurdles Working together by f2f & virtual meetings and on the collaborative web platform

  15. The RDA Engine – Working & Interest Groups • 12 Working groups including Community Capability Model, Data Citation, Data Foundation and Terminology, Data Type Registries... • 27 Interest groups including Agricultural Data Interoperability, Big Data Analytics... • including joint groups with CODATA and WDS

  16. Examples from WGs Data Foundation & Terminology • What is a suitable data organization? What is the core of it? • 21 models evaluated and defining a common ground. • at the core is a Digital Object having a PID and metadata • a PID record is like a passport – has a number, fingerprint, etc. • a world-wide registration system is in place (incl. an API)

  17. Examples from WGs PID information types WG • Persistent identifiers (PID) are the core of proper data management and access • let’s look at passports: a number is not sufficient ... • …. first solution for standardized PID info types • … will design and implement an API for interaction with typed information • Automated data management across disciplines and repositories can highly benefit from standardized types (DOI is one specific form of PID used for published data sets to make data citable)

  18. Examples from WGs Data Type Registry WG • There are so many data types in use, and new ones are continuously defined in science – innovation is a must • The result is that often researchers see interesting data, but don’t know how to open, process or visualize the data • … implementing a type registry for data, which explains how to open, visualize and process the data • In 2014 a first implementation for a type registry is expected to be there • You could build your own – all must be open

  19. RDA Interest Groups • An Interest Group (IGs) can be established prior to a Working Group for community discussion of issues and areas that facilitate data-driven research. • IGs are longer-term groups defining common issues and interests.

  20. RDA – classification (check web site) RDA WGs cross-disciplinary infrastructural (7) non-infra (6) discipline-specific (1) infra: DFT, DTR, PIT, PP, MDR, Data Citation, MDR Interoperability non-infra: Data Categories & Codes, Certification, Data Publication (4) discipline: Wheat Interoperability RDA IGs cross-disciplinary infrastructural (10) non-infra (6) discipline-specific (7) infra: Semantics, PIDs, MD, Provenance, Context, BD Analytics, Longtail D, Brokering, FIM, Preservation, Data Fabric non-infra: publishing, legal Interoperability, Clouds in Dev. Countries, community capability, engagement discipline: agriculture, toxic genomics, structural bio, biodiversity, marine data, urban data, data in History and ethnography

  21. Working & interest groups get together and hold face-to-face discussions • New groups proposals & Birds of a Feather • RDA member networking • Co-locatedevents RDA Plenary Meetings … • Plenary 1 – 18- 20 March 2013 • Gothenburg, Sweden • 240 participants • 3 WG, 9 IG • Plenary 2 - 18-20 September 2013 in Washington, DC, USA • 380 participants • 6 WG, 17 IG, 5 BOF • Plenary 3 - 26-28 March 2014 in Dublin, Ireland • 490 participants • 16 WG, 35 IG and 20 BOF meetings • 10 co-located workshops & meetings • Plenary 4 - 22-24 September 2014 , Amsterdam, Netherlands • come and join the work

  22. RDA Members – who’s engaged? ~1694 members from 73 countries Afghanistan Argentina Armenia Australia Austria Belgium Bolivia Botswana Brazil Bulgaria Canada China Congo {Democratic Rep} Costa Rica Croatia Cuba Cyprus Czech Republic Denmark Estonia Finland France Germany Ghana Greece Hungary Iceland India Ireland {Republic} Israel Italy Japan Kenya Korea South Lithuania Malaysia Mexico Mozambique Nepal Netherlands New Zealand Niger Nigeria Norway Pakistan Palestine Philippines Poland Portugal Qatar Romania Russian Federation Senegal Serbia Singapore Slovenia South Africa Spain Sudan Sweden Switzerland Taiwan Tanzania Turkey Ukraine United Arab Emirates United Kingdom United States Uruguay Vatican City Venezuela

  23. RDA MEMBERS – what type of organisations…?

  24. Become a member … • Member benefits: join and form Working & Interest Groups, participate in RDA elections, contribute to discussions & debates, comment on emerging groups, attend plenaries, news & updates, etc. • Register to the on-line community and become a Member of RDA - open & free • • Countries can become organisational members Openness Consensus Balance Harmonization Community Driven Non-Profit

  25. The European plug-in to RDA … RDA Europe, the European plug-in to the global RDA, supports RDA global and brings European voice to the table • RDA Europe Forum – strategic advice • RDA Europe Science Workshops – interaction & feedback from target audience • RDA Europe national & pan-European outreach – to engage new members & disseminate outputs • RDA Europe policy report – to support European policy-makers & funders

  26. All the links …. RDA Collaborative Web Platform Interaction with RDA RDA Europe - | Twitter - @resdatall Facebook - LinkedIn - SlideShare -

  27. Thank you! RDA Collaborative Web Platform Interaction with RDA

  28. do you need to bother? • but ... • you will have to create a Data Management Plan, since funders will not accept current attitudes anymore • you will need to make your data visible (metadata), accessible (repository) and re-usable (metadata) • you will need to demonstrate trustfulness of your data (identity, integrity, authenticity, provenance) • trusted repositories will be the anchor of our future data domain and they will establish requirements to be auditable • most results are not re-producible (some surveys: reproducibility for articles published in scientific journals is as low as 10-30%) ... researchers hate the “overhead” involved.

  29. Trends relevant for DFT • we had store and organization together (FS, Fedora, DB, etc) • see a split in function to cope with amounts and complexity • on the one side we see Clouds etc. solving the amount problems • on the other side heterogeneity in the area of the logical layer logical layer components physical layer (FS, Cloud)

  30. What is Research Data Alliance about? There will be many bridges ... • … building the social and technical bridges that enable global open sharing of data.

  31. Can your Organisation become a member? • … include R&D agencies, for-profit companies and non-profit foundations, community organizations, institutions, etc.(Annual membership fee based on size of organisation (# persons)) Why should it become a member? • Affiliation with likeminded organisationsto coordinate efforts in mutual areas of interest & to avoid unnecessary duplication ...

  32. Strong engagement and impact-Bottom-up meeting top-down “Knowledge is the engine of our economy. And data is its fuel.”(Neelie Kroes, Vice-President of the European Commission) Community engagement through the researchers global involvement in the working and interest group activities

  33. Community-Driven RDA Groups by Focus Community Needs - focused • Community Capability Model IG • Engagement IG • Clouds in Developing Countries IG Domain Science - focused • Toxicogenomics Interoperability IG • Structural Biology IG • Biodiversity Data Integration IG • Agricultural Data Interoperability IG • Digital History and Ethnography IG • Defining Urban Data Exchange for Science IG • Marine Data Harmonization IG • Materials Data Management IG Reference and Sharing - focused • Data Citation IG • Data Categories and Codes WG • Legal Interoperability IG Data Stewardship - focused • Research Data Provenance IG • Certification of Digital Repositories IG • Preservation e-infrastructure • Long-tail of Research Data IG • Publishing Data IG • Domain Repositories IG • Global Registry of Trusted Data Repositories and Services IG Base Infrastructure - focused • Data Foundations and Terminology WG • Metadata Standards WG • Practical Policy WG • PID Information Types WG • Data Type Registries WG • Metadata IG • Big Data Analytics IG • Data Brokering IG

  34. How can you become a member? • Register to the on-line community and become a Member of RDA. • No fees involved for individual participation. • Membership is open to any individual who subscribes to the RDA Guiding Principles. • As a Member one may join and form Working and Interest Groups and participate in RDA elections. •

  35. RDA Working Groups • Form the Foundation for RDA Community Impact! • … envisioned as accelerants to data sharing practice and infrastructure in the short-term with the overarching goal of advancing global data-driven discovery and innovation • RDA Working Group profile: • Short-term: 12-18 months • Focused efforts with specific actions adopted by specific communities • International participation • Open, voluntary, consensus-driven • Complementary to effective efforts elsewhere • Outcomes / deliverables: • New data standards or harmonization of existing standards. • Greater data sharing, exchange, interoperability, usability and re-usability. • Greater discoverability of research data sets. • Better management, stewardship, and preservation of research data.

  36. high volume vs. complex data “Most of the bytes are at the high end, but most of the datasets are at the low end”– Jim Gray mostly “regular” data collection data Volume Orphan data (B. Heidorn) Rank frequency of datatype

  37. do you need to bother? Time of publication • if you just work in your own domain it may still work • still a difference between regular and complex data • everywhere the problem when people leave Specific details General details Retirement or career change Information Content Accident Data Entropy Bill Michener DataOne Death Time (Michener et al. 1997)

  38. are there commonalities? often all in file system data one can work with organized data scientific data fabric sharable & re-usable data often a lot of copying in file system

  39. RDA Outputs .. What’s coming in 2014 (1/2) • Data Type Registries WG • Defining a system of data type registries • Defining a formal model for describing types and building a working model of a registry. • To be adopted by CNRI, International DOI Foundation, and used by the Deep Carbon Observatory and others • (working in conjunction with PID group) • Scheduled to complete Summer, 2014 • Persistent Identifier Information Types • Defining a minimal set of types that must be associated with a PID (e.g. checksum, author). Specifying an API for interaction with PID types • Adopted and used by Data Conservancy and DKRZ • (working in conjunction with DTR group) • Scheduled to complete Summer, 2014 • Metadata Standards • Creating use cases and prototype directory of current metadata standards from starting point of DCC directory and stakeholder contributions. • To be hosted and used by JISC, DataOneand others • Scheduled to complete Fall, 2014

  40. RDA Outputs .. What’s coming in 2014 (2/2) • Language Codes • Operationalization of ISO language categories for repositories • Adopted and used by the Language Archive, PARADISEC • Proposal of data categories associated with the CMDI schema as ISO standards. • Scheduled to complete Fall, 2014 • Data Foundations and Terminology • Defining a common vocabulary for data terms based on existing models. • Creating  formal definitions in a structured vocabulary too which also  provides an open registry  for data terms. • (active input from all RDA WGs) • Tested and adopted by EUDAT, DKRZ, Deep Carbon Observatory, CLARIN, EPOS, and others • Scheduled to complete Summer, 2014 • Practical Code policies (rules) • Survey of policies in production use across data management centers. Test bed of machine-actionable policies (IRODS, DataVerse, dCache) at RENCI, DataNet Federation Consortium, CESNET, Odum Institute. • Deployment of 5 policy sets (integrity, access control, replication, provenance / event tracking, publication ) on test beds. Publication of standard policies for use as starter kits. • Scheduled to complete Summer, 2014