1. Data Management Plans based on digital workflow models: A report on a DMP project within the frame of e-Infrastructures Austria Paolo Budroni, Barbara Sánchez Solís University of Vienna

2. Budroni, Sánchez 2 e-Infrastructures Austria – London June 2015 WORLDS OF DATA : ABOUT RESEARCH DATA ABOUT DATA

3. Budroni, Sánchez 3 e-Infrastructures Austria – London June 2015 The LERU Roadmap for Research Data presents the following sets this context (*) : “Research data, from the point of view of the institution with a responsibility for managing the data, includes: All data which is created by researchers in the course of their work, and for which the institution has a curational responsibility for at least as long as the code and relevant archives/ record keeping acts require, and third-party data which have originated within the institution or come from elsewhere.” (*) LERU Research Data Workingg Group, Advice Paper No. 14 – December 2014

4. Budroni, Sánchez 4 e-Infrastructures Austria – London June 2015 The Australian Griffith University presents the following definition (*) : “Research data are factual records, which may take the form of numbers, symbols, text, images or sounds, which are used as primary sources for research, which are commonly accepted in the research community as necessary to validate research findings.” (*) Ingrid Dillo – Data Archiving and Networked Services (DANS), Certification as a means of providing trust, Florence, Fondazione Rinascimento Digitale, 2012

5. Budroni, Sánchez 5 e-Infrastructures Austria – London June 2015 The University of Minnesota about research data (*) : Research data are data in any format or medium that relate to or support research, scholarship, or artistic activity. They can be classified as: Raw or primary data: information recorded as notes, images, video footage, paper surveys, computer files, etc. Processed data: analyses, descriptions, and conclusions prepared as reports or papers Published data: information distributed to people beyond those involved in data acquisition and administration According to this logic we identify:  Different levels (or “layers”) of processing data This has an impact on the digital workflow model (*) Ingrid Dillo – Data Archiving and Networked Services (DANS), Certification as a means of providing trust, Florence, Fondazione Rinascimento Digitale, 2012

6. World of data Raw data (primary data) World of data Raw data (primary data) Processed Data Negative Results Processed Data Negative Results Processed Data Inconclusive Results Processed Data Inconclusive Results Processed Data Shared Data Shared Data Processed Data Positive results Shared Data Shared Data Shared Data Shared Data Pub. Data OA Pub. Data Released Data Released Data Different levels of processing of data Model for digital archiving Ensuring legal and ethical compliance is key issue in this context

7. Budroni, Sánchez 7 e-Infrastructures Austria – London June 2015 Levels / layers in processing of data Raw data, primary data Processed Data (positive, negative and inconclusive results) Shared Data and Published Data Open Access Published Data

8. Budroni, Sánchez 8 e-Infrastructures Austria – London June 2015 Open Data Shared Data and Reused Data Published Data Restricted Data  Strata of research data

9. Budroni, Sánchez 9 e-Infrastructures Austria – London June 2015 THE DIGITAL WORKFLOW

10. Budroni, Sánchez 10 e-Infrastructures Austria – London June 2015 Any conceptual model of a digital workflow for the creation of a reference architecture for the permanent provision of research data should include aspects that factor in the requirements of at least four of five stakeholders identified in this process. These are: a) The research community (Data Supplier), b) Repository Management (with special attention to technical requirements), c) The Institution (research facility), d) Funding Bodies (research sponsors including research services), and finally e) The qualified public (among whom are also parts of the research community). Setup of a reference architecture for research data management

11. Budroni, Sánchez 11 e-Infrastructures Austria – London June 2015 WORKFLOW MODEL SEGMENTS

12. Budroni, Sánchez 12 e-Infrastructures Austria – London June 2015 Digital workflow model Preingest Ingest Rep-Management Re-Use archives Ensuring legal and ethical compliance is key issue in this context

13. Metadata File License Persistent Identifier # External Systems All types of data Processes DATA PROVIDERS DMP – along all processes Ontology of Objects Different Types COMMON LEGAL SPACE

14. Budroni, Sánchez 14 e-Infrastructures Austria – London June 2015 S ETUP OF A REFERENCE ARCHITECTURE FOR RESEARCH DATA MANAGEMENT ROLE MODELS

15. Budroni, Sánchez 15 e-Infrastructures Austria – London June 2015 However, an additional perspective exists that provides not only a division of technical processes. This broader view is created by the distinction between the respective areas of responsibility in the processing of data and the underlying process-oriented and managed activities. Thus, there are also definitions in roles and responsibilities. In essence, this is the view from the management level. What roles and responsibilities should and will be observed by the main digital workflow actors? a) For Preingest, the lead in the role of Responsibility is taken by the Content Provider (usually also the “data supplier”) b) When it comes to Ingest, responsibility for results lie initially with the Content Supplier, then when the data packet is handed over, responsibility for results is transferred via process management to Repository Management. c) Management competencies are clearly distributed; here the responsibility lies in the management of work processes in Repository Management d) Regarding re-use, responsibility is transferred following data modeling and transfer of content to the Content Supplier (in his role as Contents Owner). Four roles result from the role model: Quality Assurance: The content provider (Data Supplier) initially secures the quality of the content Safeguarding: Repository Management stores the data (chronologically as well), first always in the format in which it was delivered Ensuring legal security: The Institution handles this Observance of social responsibility: The research sponsor assumes this function. An additional actor is thereby created Roles: models

16. Budroni, Sánchez 16 e-Infrastructures Austria – London June 2015 FOUR MAIN ACTORS INVOLVED IN THE MGMT OF RESEARCH DATA THE STAKEHOLDERS Research Libraries IT-Services Researchers Funders / Funding Services

17. Budroni, Sánchez 17 e-Infrastructures Austria – London June 2015 FOUR MAIN ACTORS ARE INVOLVED IN THE MGMT OF RESEARCH DATA FOUR STAKEHOLDERS Research Libraries IT-Services ResearchersFunders / Funding services Data Management Plans

18. Budroni, Sánchez 18 e-Infrastructures Austria – London June 2015 Different views – different roles, responsibilities, competencies ARCHIVESAFEGUARDING CONTENT SUPPLIER FUNDING BODY QUALITY SOCIAL RESPONSIBILITY ROLERESPONSIBILITY INSTITUTION LEGAL SECURITY

19. Budroni, Sánchez 19 e-Infrastructures Austria – London June 2015 PUBLISHER? SAFEGUARDING ? ARCHIVESAFEGUARDING CONTENT SUPPLIER FUNDING BODY QUALITY SOCIAL RESPONSIBILITY ROLERESPONSIBILITY INSTITUTION LEGAL SECURITY Different views – different roles, responsibilities, competencies

20. Budroni, Sánchez 20 e-Infrastructures Austria – London June 2015 HARMONIZED SETUP OF REPOSITORIES THE PROJECT E-INFRASTRUCTURES AUSTRIA

21. Fact Sheet Start: 1 January 2014 End: 31 December 2016 Project coordinator: University of Vienna Project partners: 25 Partner Institutions Client: Federal Ministry of Science, Research and Economics

22. 22 Paolo Budroni - e-Infrastructures Austria – 3 rd Liber Workshop on Digital Curation – Vienna 19-20 May 2014 Project A Common goals: Increased visibility, standardized repository landscape, harmonization of policies, and, where appropriate, joint visualization and representation surface time Construction of (institutional) repositories and publication services Support of the project partners Types: „Papers“, Pictures (Formats: mainly PDF, JPG ) Project BProject C 1 3 2 4 Docking to publication services Designing an Austrian repository or multiple repositories for further digital objects (research data, e-learning content and multimedia content Development of services in addition to project A Phase 1: Models and workflows for reseacrh data Phase 2: Start of construction of the repository Phase 3: Multimedia and streaming services Structure of the knowledge network e- Infrastructures Austria Transdisciplinary collaboration in the context of Work Package clusters involving as many stakeholders as possible (scientists, editors and so on) Preparation for Horizon 2020 Examples: Definition of policies, metadata, interoperability … Research Data The three sub-projects

23. Budroni, Sánchez 23 e-Infrastructures Austria – London June 2015 WORK PACKAGes – 12 fields of action

24. Budroni, Sánchez 24 e-Infrastructures Austria – London June 2015 DMP PILOT ON A NATIONAL LEVEL

25. Budroni, Sánchez 25 e-Infrastructures Austria – London June 2015 The main purpose of a DMP is to define and offer a model in order to define a comprehensive data management plan that can be used by Content providers (even at the stage of a project proposal), Technical management, Repository management and Reusing parties Elements of a Data Management Plans Introduction and Context Data Types, Formats, Standards and Capture Methods Ethical and Privacy Issues Access, Data Sharing and Reuse Short-Term Storage and Data Management Deposit and Long-Term Preservation Resourcing Statement of Agreement Annexes Example 1: DCC (Digital Curation Center) http://www.dcc.ac.uk/resources/data-management-planshttp://www.dcc.ac.uk/resources/data-management-plans Example 2. DINI/nestor-Workshop "Datenmanagementpläne und ihre Bedeutung im Forschungsdatenmanagement" http://www.forschungsdaten.org/index.php/DINI-nestor-WS2http://www.forschungsdaten.org/index.php/DINI-nestor-WS2 Xample 3: Anleitung zur Erstellung eines DMPs http://phaidra.univie.ac.at/o:367863 (enthält eine Liste an Quellen)http://phaidra.univie.ac.at/o:367863 DMP - GENERAL

26. Budroni, Sánchez 26 e-Infrastructures Austria – London June 2015 Premises Univ. of Vienna: Document on the creation of DMPs  Reasons for DMPs  Consideration of different project stages (processes)  Forms of DMPs  Checklist 5 workshops in different partner institutions  Target groups: librarians, IT, research support

27. Budroni, Sánchez 27 e-Infrastructures Austria – London June 2015 Project appliance Content provider/ owner of the object Funding agency Repository management Service providing institution Reusing parties Anonymous user > One user, different roles < DMP - Different Points of Views

28. Budroni, Sánchez 28 e-Infrastructures Austria – London June 2015 DMP pilot on national level DMP focussed on Horizon 2020 Based on DCC checklist for a Data Management Plan, v4.0 Customised to actual institution Goal: Align DMP with existing local infrastructures and services Procedure  Run pilot with 7 different faculties at University of Vienna  Optimisation according to feedback  Extend pilot to other institutions in Austria 5 workshops in different partner institutions  Target groups: librarians, IT, research support

29. Budroni, Sánchez 29 e-Infrastructures Austria – London June 2015

30. Budroni, Sánchez 30 e-Infrastructures Austria – London June 2015

31. Budroni, Sánchez 31 e-Infrastructures Austria – London June 2015 Characteristics Execution on a national level (20 Austrian universities) Support of dialogue with different scientific communities Common approach may lead to  Development of national services in non-technical areas  Pooling of competencies at a national level  Development of systems apart from DMPonline Administration of data stays with institutions Frame/design of DMP can be changed (e.g. inclusion of text modules, drop down menus etc.) Extraction of data can be optimised DMP requires constant adaptation and adjustment Archiving strategies involve more parties than originally assumed (planning of resources) Business re-engineering in research support services:  Data security management, storage, metadata modelling, ethical and legal issues New job profiles: data scientists, data librarian

32. Budroni, Sánchez 32 e-Infrastructures Austria – London June 2015 First Conclusions Extension of language area DMP requires constant adaptation and adjustment Archiving strategies involve more parties than originally assumed (planning of resources) Business re-engineering in research support services  Data security management, storage, metadata modelling, ethical and legal issues New job profiles: data scientists, data librarian Further support services required

33. Budroni, Sánchez 33 e-Infrastructures Austria – London June 2015 Challenges Who is the owner of DMPs? Who administrates DMPs? Who reviews DMPs (institutional/national/European level?) What about projects beyond H2020? What about existing projects and the preservation of the data?

34. Budroni, Sánchez 34 e-Infrastructures Austria – London June 2015 University of Vienna Library and Archive Services Paolo Budroni, paolo.budroni@univie.ac.at paolo.budroni@univie.ac.at Barbara Sánchez Solís, barbara.sanchez.solis@univie.ac.at barbara.sanchez.solis@univie.ac.at Computer Centre Raman Ganguly, raman.ganguly@univie.ac.at raman.ganguly@univie.ac.at José Luis Preza, jose.luis.preza@univie.ac.at jose.luis.preza@univie.ac.at office@e-infrastructures.atoffice@e-infrastructures.at www.e-infrastructures.atwww.e-infrastructures.at e-Infrastructures Austria Thanks!