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Welkom op de werkconferentie research data in kaart.

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Presentatie over: "Welkom op de werkconferentie research data in kaart."— Transcript van de presentatie:

1 Welkom op de werkconferentie research data in kaart

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

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! 3TU.Datacentrum5 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, …

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 Before 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? Lifecycle phases

8 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… Trustworthy benchmark data Safe storage of valuable data Prevent data loss Share data Register/Link Publish data (with article) Limited resources for data management Efficient data distribution Efficiency Data collection as asset Easy access outside institute

9 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.” ;-) “Never, ever, think outside the box.” ;-)

11 Data Archiving and Networked Services DANS is an institute of KNAW en NWO Van Research Data Management tot Duurzame Beschikbaarstelling De Universiteiten en DANS Peter DoornIngrid Dillo Directeur DANSHoofd 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 What is DANS? Institute of Dutch Academy and Research F unding O rganisation (KNAW & NWO) since 2005 First predecessor dates back to 1964 (Steinmetz Foundation), Historical Data Archive 1989 Mission: promote permanent access to digital research information

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

15 Niederlande Renommierter Psychologe gesteht Fälschungen September 2011: Diederik Stapel, Social Psychology November 2011: Don Poldermans, Cardiovascular Medicine 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 /Internet_KNAW/publicaties/ pdf/ pdf

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 E-Science / E-Humanities Research Gov’t. agencies Private sector University Libraries / local data facilities Research Infrastructures NWO Gebieden Acquisition, services, support, training, consultation Data curation/stewardship, management, archiving Systems and infrastructure Storage Cloud/Grid, processing, backup facilities Research Gov’t. agencies Private sector Front office Basic Infra- structure Data-Research The federated data infrastructure: a collaborative framework Data Providers Data Users 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 Back office

19 Front Office – Back Office Model Front office – 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 UniversiteitDANS3TU.DC LEIFO/BO ism. UBFO/BO ism. UB, overleg LDE, Data-lab RUGVerkenning tijdens bijeenkomsten UUBesprekingen over Dutch DVN3TU's zijn partner in Dutch DVN UvABackup-archivering UB VUOriënterende gesprekken TUDRDN met 3TU.DCPartner 3TU.DC EURRDM Beleid (projectleider DANS)RDM Beleid, overleg LDE WU Archivering data promotieonderzoek Speciale collectie PhD's, project collectie, samenwerkingsgesprekken RUVerkenning tijdens bijeenkomst TiUBackoffice functies repository TU/eRDN met 3TU.DCPartner 3TU.DC UTRDN met 3TU.DCPartner 3TU.DC 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 Data Archiving and Networked Services DANS is an institute of KNAW en NWO Thank you for your attention

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

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

29 e-Infrastructure en Innovatie met ICT

30 Large-scale data != new

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

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

33 Communities ↔ Data Centers

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

41 SATURDAY FRIDAY THURSDAY WEDNESDAY TUESDAY MONDAY SUNDAY Number of randomisations Data checking: Weekday randomised Post-operative radiotherapy in lung cancer

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 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 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 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 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 Data Afsluiting Database Lock Aanleveren van uiteindelijke gegevens voor analyse RU Werkconferentie research data in kaart 19 juni 2013

50 ExcelSPSSAccesSQLMacro® Veiligheid (data)authenticatie autorisatie Continuiteit (systeem) bereikbaarheid back-ups Herleidbaarheid/ reproduceerbaarheid data trail/log dictionary: type dictionary: codering Kwaliteitschecktijdens invoer na invoer Structuurherhaalde gegevens Gebruikgemak; design kosten

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

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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