Research Data Management, Data Management Plans and Open Data 21 November 2018

RDM Definition Research data management (RDM) encompasses the control of data inputs, the use of data, the protection of data, and the creation of data outputs. RDM covers the description of data and tools; the storage of data during analysis; the provision of clear and accurate metadata; the preservation of data; and - where possible - making research data outputs available to other researchers as open data.

Agenda Open Data and Open Science Launch of European Open Science Cloud (EOSC, Fri. 23 Nov. ‘18) Horizon 2020 data requirements Data management plans (DMPs) The DMP-online tool Data management during the research cycle Data inputs Data protection and database copyright Data security during the research project Assigning metadata to research data outputs Preparing datasets for preservation and sharing in EUI ResData Open data and embargos

Open access Open data Open science

‘Open Science’ ‘Open science’ refers to a culture of collaboration, sharing and openness – made possible by digital innovation, open access to publications, open research data, open source software, open educational resources, and citizen science.

Cadmus ResData Open science

EUI ResData Data elaboration Library Data Portal Data discovery Original data Data elaboration EUI ResData

March 2018. EC Implementation Roadmap for the. European Open March 2018 EC Implementation Roadmap for the European Open Science Cloud EOSC is “EOSC “is a data infrastructure commons, serving the needs of scientists…federating existing resources across national data centres, European e-infrastructures and research infrastructures.” Roadmap includes “measures taken under Horizon 2020 Programme to start implementing the EOSC”

Data in EOSC “The objective of the EOSC is to give the Union a global lead in research data management and ensure that European scientists reap the full benefits of data-driven science.” Commission “foresees setting up a European Data Infrastructure, underpinning high-capacity cloud solutions with super-computing capacity…”

Components of EOSC (i) an architecture to address fragmentation by federating existing infrastructures (ii) the enhancement of data management tools to make research data FAIR: findable, accessible, interoperable and re-usable (iii) services to meet user needs (iv) interfaces and mechanisms to access EOSC (v) rules of participation for scientists and administrators (vi) governance of EOSC – which will consist of institutional, advisory and executive layers.

EC open science strategy 25 April 2018: revised recommendations on access to, and preservation of, scientific information 28/29 May: EU Competitiveness Council discussion of the European Open Science Cloud roadmap – intended by EC to produce a high-level “political endorsement” 11 June: an EOSC ‘coalition of doers’ summit 23 November 2018: launch of the EOSC governance structure and publication of new EC FAIR Data action plan, to make research data findable, accessible, interoperable and re-usable

Research Data Cycle

Data in EU Horizon 2020

Data Management Plans Data management plans are short documents, normally required by science funding agencies, providing information on: -   How data is generated and/or sourced -   How data is used, elaborated and organised -   How data, and data subjects, are protected -   How data, tools, &c. are described and documented -   How data is stored and secured during the research project -   How data authorship and credit are assigned -   How data is preserved -   How, whether, and under what terms, research data outputs can be shared.

DMP-online tool

Data Protection Special terms and conditions apply to access and use of qualitative and micro-socioeconomic data, reflecting the sensitive nature of data observations about human subjects, families and households. Persons, families and households cannot be identifiable in any dataset. The collection and use of observations relating to ethnicity, health, orientation, religion, biometrics &c. are subject to data protection laws.

Folders, files, variables, format and versioning The structure of the dataset should be carefully considered at the start of the project Dataset design varies by conventions of discipline and sub-discipline, medium, types of variables, units of analysis, relationship between data elements, and whether or not the dataset is part of a series Clear and consistent metadata for folders, files, variables and versioning, helps facilitate future data retrieval, reuse and replicability File names should be standardised, eg: date, version Variables (age, country &c.) should be clearly tagged, avoiding special characters and spaces Temporary identifiers should be removed from the schema Different file versions should be systematically named, using a standardised date system (YYYY-MM-DD) or version numbering.

Documentation and codebooks Clear and accurate documentation should be provided about the purpose and context of the research project, and about the research data output. Good documentation makes datasets findable, accessible, interoperable and re-usable - FAIR data principles. Documentation should include a description of folders, files, variables, versioning, and – where applicable – information about problematic values, missing observations, weightings &c.. Codebooks, questionnaires and data dictionaries should be included. A concise note on methodology, or methodologies, should be given.

Security and backup during the research project During the research project it is important to keep data secure at all times. Use a desktop computer for data elaboration, and make regular backups on the EUI network server, or on a safely-secured external memory device to facilitate recovery. In accordance with contractual agreements, micro-socioeconomic data at the EUI can only be accessed and elaborated on a desktop computer, after completing the required Library registration protocols. 

Data protection, informed consent of data subjects OECD guidelines recommend: “The default position should be that personal data is not collected, processed or shared without informed consent.”  The mode of consent obtained from subjects depends on the nature of the research project, the kind of data collected, and how and when the data will be used. Where possible, it is recommended to obtain written consent, using a template appropriate to the discipline or sub-discipline in which the research is being conducted.

Anonymisation Dataset creators are responsible for the anonymisation of sensitive data observations. Anonymisation techniques include: partial data removal pseudonymisation aggregation banding.

RDM and open data Research Data Management helps determine: Whether, when, how, where and under what terms, research data outputs – especially ‘mixed’ data outputs – can be shared as open data.

Open Data A growing trend among scholars, government agencies and international organisations to share data outputs, codebooks and software.

Metadata Can be a ‘Checklist’ for Open Data Title, names of creator(s) Description – ‘data abstract’ Source(s) Creation date Spatial / temporal coverage Format Location of data Access status and embargo Licence Funding Statement Related publications

Research Data Cycle

Data Services homepage econlibrary@eui.eu Support Data Services homepage econlibrary@eui.eu Badia Library Office (entry-floor, BF-085) Weekday mornings and Tuesday & Thursday afternoons Departmental Information Desk, VLF (2nd Floor, Library) Monday, Wednesday & Friday afternoons Weekly Data eBulletin

Research Data Management, Data Management Plans and Open Data 21 November 2018