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Welkom op de werkconferentie research data in kaart

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1 Welkom op de werkconferentie research data in kaart

2 Programma 14.00 Welkom door Natalia Grygierczyk
14.05 Opening door Gerard Meijer 14.15 Plenair deel geleid door dagvoorzitter Ron Dekker (directeur NWO) met schets van best practices uit de verschillende vakgebieden 15.15 Koffie en thee 15.30 Parallelle werkgroepsessies 16.30 Panelgesprek geleid door Ron Dekker 17.20 Afsluiting door Natalia Grygierczyk 17.30 Borrel

3 - Research Data Management - 3TU.Datacentrum
Jeroen Rombouts Nijmegen, 19 juni 2013 Datacentrum: Dataintelligence: Data:

4 Reasons Ethics Codes of conduct. KNAW Cie. Schuyt “Zorgvuldig en Integer omgaan met wetensch. Onderzoeks gegevens”  VSNU: “Education”, NWO: “Data Management Plans”, Univ.: “Inst. Policies”, … Efficiency No ‘refunding’, growing ‘base of data’ for research. International ‘competition’: EU: H2020, UK, Aus., US. Reputation Enhance reputation by data production and sharing. Avoid damage by transparency.

5 One size fits no-one! More differences!! Data: types & sizes, sources, contextual information, … Culture: openness, competition, standardization, personal attitude, … Context: policies, regulation, tools & infrastructure, … RD Management elements IP & Open Access, Privacy, Copyright, Licensing, Funding, Incentives, Citation, Data capture, Training, Review, Security, Sharing, Ethics, Policies, Preservation, Software & Dynamic data, Valorization, … 3TU.Datacentrum

6 Stakeholders Users: Data-producers & Data-consumers
Researchers, (N)GO’s, Industry, Citizens, … Infrastructure providers Research infrastructure, RDM infrastructure, … Service providers Data-repositories, Publishers, Persist. ID Providers, Add value to data & infrastructure. Funders Research Institutes (Univ.), Research Councils, EC, … Facilitate, Incentives, …?

7 Lifecycle phases Before During After
How will data be stored, shared, versioned, …? Is valuable data already available? How much data will be collected? Funder requirements? During What if … data (or documentation) is lost, accessed without permission, …? How is data-quality assured? How is data transported? After Which data is valuable to others, for how long and what documentation is required? Who will control access?

8 3TU.Datacentrum Goal: Make valuable data (re)usable, discoverable and accessible on long term. Open to all ! Experiences: Lot of data ‘lost’ and valuable data not used. Started project 2008: Find and meet needs of researchers… Limited resources for data management Prevent data loss Efficiency Safe storage of valuable data Efficient data distribution Data collection as asset Trustworthy benchmark data Register/Link Share data Publish data (with article) Easy access outside institute 8

9 Geoscience Data Journal (Wiley)
3TU.Datacentrum tools Data-labs Collaboration platforms to support ongoing research. Support exchange of research objects (data, models & tools) and e.g. visualization, early review. Data-archive Multi disciplinary, multi institutional data archive to ‘freeze’ research data and data descriptions for future use, data publication and citation. Data-services Training, advice and hands on support for data-model, DMP, ... Data-R&D Raise awareness, develop licensing, training and technology, etc. For and with researchers. Geoscience Data Journal (Wiley) RD-NL

10 For your sessions… What are urgent issues? What are barriers?
Who are (or should be) involved? In which phases of research? “Never, ever, think outside the box.” ;-) TRUST Trust your own data enough to share Trust other people to take care of your data Trust data collected by others Trust other people to use ‘your’ data correctly

11 Van Research Data Management tot Duurzame Beschikbaarstelling De Universiteiten en DANS
Peter Doorn Ingrid Dillo Directeur DANS Hoofd Beleid & Communicatie Werkconferentie Research Data in Kaart Woensdag 19 juni 2013, 14:00-17:15 Villa Klein Heumen, Nijmegen

12 Contents About DANS Trust, data fraud and responsible data management
a few words about the Schuyt report Roles and responsibilities: Federated data infrastructure Front and Back-office functions Dataverse Research Data Netherlands and collaboration with Universities

13 Mission: promote permanent access to digital research information
What is DANS? First predecessor dates back to 1964 (Steinmetz Foundation), Historical Data Archive 1989 Institute of Dutch Academy and Research Funding Organisation (KNAW & NWO) since 2005 Mission: promote permanent access to digital research information

14 Data Seal of Approval EASY: Electronic Archiving System for self-deposit Persistent Identifier URN:NBN resolver NARCIS: Gateway to scholarly information In the Netherlands

15 September 2011: Diederik Stapel, Social Psychology
November 2011: Don Poldermans, Cardiovascular Medicine Niederlande Renommierter Psychologe gesteht Fälschungen June 2012: Dirk Smeesters, Experimental Social Psychology October 2012: Mart Bax, Cultural Anthropology

16 The KNAW “Schuyt report” on data practices
94 interviews with researchers Variation across and within disciplines Pattern: data management in small-scale research more risky than in big science Risk: missing checks and balances, especially in phase after granting research proposal and before publication Peer pressure is an important control mechanism

17 Dutch Academy (KNAW) endorses Schuyt recommendations
Hans Clevers, President KNAW: “Maximum access to data promotes the pre-eminently scientific approach whereby researchers check one another’s findings and build critically on one another’s work” The Academy supports the free movement of data and results. Taking into account variations across and within scientific disciplines, free availability of data should be the default. We do not need additional rules or codes of conduct, but should focus on revitalising existing rules and making these better known. Examination of data management practices should become an integral part of official research evaluations.

18 The federated data infrastructure: a collaborative framework
E-Science / E-Humanities Research Gov’t. agencies Private sector Research Gov’t. agencies Private sector Data Providers Data Users } University Libraries / local data facilities Research Infrastructures NWO Gebieden Front office Data-Research Acquisition, services, support, training, consultation Data curation/stewardship, management, archiving Back office } Systems and infrastructure Basic Infra-structure Storage Cloud/Grid, processing, backup facilities Based on: Riding the wave: How Europe can gain from the rising tide of scientific data - Final report of the High Level Expert Group on Scientific Data. A submission to the European Commission, October 2010

19 Front Office – Back Office Model
Universities (libraries, local data centers) Disciplinary research infrastructures (ESFRI/National Roadmap) Possibly: NWO areas (data contracts for funded projects) Back office DANS (humanities, social sciences) 3TU.Datacentrum (technical sciences) Basic Technical Infrastructure SURFsara Target

20 Roles and responsibilities: Front Office
Focus supporting researchers at the home institution Research Data Management: awareness raising, information, training Offer Virtual Research Environments (research tools, data storage during research; Sharepoint, Dataverse; transfer for long-term archiving in trusted digital repository) Liaising with back offices Data acquisition

21 What is Dataverse? Dataverse is a ‘virtual web archive’
contains ‘data studies’ customized and managed by its administrator or owner collaboration between the Harvard-MIT Data Center (now part of Institute for Quantitative Social Science) and Harvard University Library open-source application for publishing, referencing, extracting and analyzing research data

22 Dutch Data Verse Network (DVN)

23 Research Data Netherlands
Mission: the promotion of sustained access and responsible re-use of digital research data Building on existing cooperation DANS-3TU.Datacentrum (D4L training, Dutch Data Prize) Expanding cooperation of back-office function Stepping stone to federated data infrastructure: open to other trusted digital repositories

24 Front-office / Back-office: huidige situatie
Universiteit DANS 3TU.DC LEI FO/BO ism. UB FO/BO ism. UB, overleg LDE, Data-lab RUG Verkenning tijdens bijeenkomsten UU Besprekingen over Dutch DVN 3TU's zijn partner in Dutch DVN UvA Backup-archivering UB VU Oriënterende gesprekken TUD RDN met 3TU.DC Partner 3TU.DC EUR RDM Beleid (projectleider DANS) RDM Beleid, overleg LDE WU Archivering data promotieonderzoek Speciale collectie PhD's, project collectie, samenwerkingsgesprekken RU Verkenning tijdens bijeenkomst TiU Backoffice functies repository TU/e UT UM - (nog geen contact)

25 Conclusions Sharing data increases the transparency of research, therefore reducing the risk of fraud Funders/Universities should require that project proposals contain a data management plan, and that such plans contain a section on accessibility of data after publication of the results DANS (and 3TU.DC) offer back-office services complementary to Research Data Management at Universities (Libraries)

26 Thank you for your attention

27 Werkconferentie Radboud
Research Data Uninet Visit Werkconferentie Radboud Dr. Maurice Bouwhuis Relations and Innovation Manager Dr. Anwar Osseyran Managing Director

28 SARA part of the ‘SURF family’
The mission of SURFsara is 2-fold: 1. Supporting Education and R&D in The Netherlands with High Performance Computing services [HPC for Science & Education] 2. Support “early adoption” of pre-competitive ICT technologies [HPC for the Knowledge Economy] On 1/1/2013 SARA became part of the SURF organization 28

29 e-Infrastructure en Innovatie met ICT
To fulfill that mission and make research and Innovation with advanced ICT possible they provide high end ICT services and support to many different scientific disciplines. These range from from physics (experiments at CERN), chemistry, climate studie and astronomy (LOFAR) through the Life Sciences (NBIC) to arts and the humanities (CLARIN, Meertens Institute). The wide range of users is matched by the wide range of ICT services, covering the whole IT spectrum from the national supercomputer (now Huygens and later this year Cartesius) and other High Performance Compute clusters to the storage of large amounts of research data and visualization services. It is not enough to just make these infrastructures and systems available, SURFsara’s support and development teams work together with the researchers in order to ensure efficient and easy use of the systems, improve and optimize applications and guide new users through unknown territory .

30 Large-scale data != new

31 Data-centric e-infrastructure
National Facilities Render cluster Lightpath Science Results/ Input transfer Visualisation Data Storage Services Long-term preservation Reliability Big Data High-end Simulation Capability compute Hadoop Statistics Security Backup Privacy Performance Redundancy Ensemble Calculations Parameter Studies Cloud/ Capacity compute Cloud/ Capacity compute

32 domain of globally referable data
Data Life Cycle Data Stage-Out EUDAT acquisition generation description data enrichment processing reduction analysis domain of globally referable data temporary data referable data global registration citable publication registration Safe Replication Data Stage-In preservation EPIC PID

33 Communities ↔ Data Centers
Trend: Communities Collaboration in dealing with data Requirements, service cases Technology appraisal & matching Service provision

34 Sector & cross-Sector collaboration

35 The Importance of Community Clouds
“Overall we expect to see the growth of Community Clouds in the near future” Sustainable Secure Fit like a glove

36 Take to discussions Deal with the data Possible questions
Collaborate and Unite Think Big, Start Small, Act Now Possible questions What do you expect from your funders (university/NWO/EC/…) w.r.t. research data

37 To Finish with…

38 Experiences with data-sharing Maroeska Rovers


40 Data checking: Pattern of randomisation Radiotherapy vs Chemotherapy in Multiple Myeloma
Number of patients randomised Chemotherapy Radiotherapy

41 Number of randomisations
Data checking: Weekday randomised Post-operative radiotherapy in lung cancer 12 10 This example has an unusually high number of weekend randomisations, and quite big imbalances between the number of randomisations by arm. When it was brought to the attention of the trialist they discovered problems with data collection and data entry procedures. They went back to individual patient records and ensured that the correct appropriate information was supplied 8 Number of randomisations 6 4 2 FRIDAY SUNDAY MONDAY TUESDAY THURSDAY SATURDAY WEDNESDAY

42 Data checking: original trial
Try to reproduce results of original publication Of all 5 IPDMAs I have done all had problems with reproducing all results Contact PI of original study to solve disagreements


44 Datamanagement bij klinisch onderzoek
Dyonne van Duren RU Werkconferentie research data in kaart 19 juni 2013

45 RU Werkconferentie research data in kaart 19 juni 2013
Wet- en regelgeving WMO GCP NFU Good Clinical Practice (geneesmiddelenonderzoek) Kwaliteitsborging Mensgebonden Onderzoek 2.0 Code goed gedrag, Code goed gebruik lichaamsmateriaal, WBP, Verklaring van Helsinki RU Werkconferentie research data in kaart 19 juni 2013

46 RU Werkconferentie research data in kaart 19 juni 2013
GCP en DM Systeem Data privacy Authenticatie (wie) -> beveiligingssysteem ter voorkoming van inzage door onbevoegden Autorisatie (wat) -> Welke personen zijn bevoegd om veranderingen aan te brengen Beveilig je MS Excel of Access gegevens met een wachtwoord RU Werkconferentie research data in kaart 19 juni 2013

47 RU Werkconferentie research data in kaart 19 juni 2013
GCP en DM: Systeem Systeem continuïteit Gevalideerd systeem Het systeem dient overeen te stemmen met de door de verrichter opgestelde eisen voor volledigheid, nauwgezetheid, betrouwbaarheid en consistent functioneren zoals bedoeld. Deze validatie dient gedocumenteerd te zijn. Back-ups (data) Er dient een adequaat reservebestand van de gegevens te worden bijgehouden SOPs (Standard Operating Procedures) voor het gebruik van het systeem dienen te worden bijgehouden. RU Werkconferentie research data in kaart 19 juni 2013

48 RU Werkconferentie research data in kaart 19 juni 2013
GCP en DM: Systeem Gebruik een systeem met audit trail (Wie, Wat, Wanneer, Waarom) Het systeem dient veranderingen van gegevens mogelijk te maken op een dusdanige wijze dat deze veranderingen van gegevens worden gedocumenteerd en dat de oorspronkelijke ingevoerde gegevens niet worden gewist. Het systeem dient bij te houden wie, wat, wanneer, waarom….. Versiebeheer of track changes is echt iets anders!…. Voer kwaliteitchecks uit Wanneer: tijdens, na, hoe vaak? Wat: validiteit, volledigheid, consistentie RU Werkconferentie research data in kaart 19 juni 2013

49 Data Management proces
Data Ontwerp Data Management Plan eCRF ontwerp Go-Live, training. Data Verzamelen Gegevens invoeren Batch-gewijs gegevens laden Data Validatie Monitoring Afsluiting Data Database Lock Aanleveren van uiteindelijke gegevens voor analyse RU Werkconferentie research data in kaart 19 juni 2013

50 RU Werkconferentie research data in kaart 19 juni 2013
Excel SPSS Acces SQL Macro® Veiligheid (data) authenticatie autorisatie Continuiteit (systeem) bereikbaarheid back-ups Herleidbaarheid/ reproduceerbaarheid data trail/log dictionary: type dictionary: codering Kwaliteitscheck tijdens invoer na invoer Structuur herhaalde gegevens Gebruik gemak; design kosten RU Werkconferentie research data in kaart 19 juni 2013

51 RU Werkconferentie research data in kaart 19 juni 2013
Data Management proces Studieprotocol, -opzet Onderzoeker Data Management Proefpersonen Batch loads, bv. lab data, vragenlijsten Onderzoeker Queries Dossier/bron-document Bron eCRF via www MACRO studie database DB lock transfer ruwe data voor analyse en archivering RU Werkconferentie research data in kaart 19 juni 2013

52 Project Management PaNaMa
Beheer van Onderzoek: Documenten Vastlegging wijze van data-toegang, -opslag en -archivering Voortgang (Medisch Ethische) toetsing Projectafspraken, bv. met andere afdelingen Financiële gegevens: begroting, offertes, facturen Voorspelling te verwachten project resultaat Medewerkers op projecten Patiënten inclusie bij klinische trials Standaardoverzichten (urenregistratie) RU Werkconferentie research data in kaart 19 juni 2013

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