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GUIDELINES to Fairfying Data Easy Guide for Glams and Researchers in the Digital Humanities

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GUIDELINES to Fairfying Data Easy Guide for Glams and Researchers in the Digital Humanities GUIDELINES to FAIRfying data Easy guide for GLAMs and researchers in the Digital Humanities Franco Niccolucci, PIN Sara Di Giorgio, ICCU Colour vision, Paul Grigg, Wellcome Collection, United Kingdom, CC BY HOUSEKEEPING Please make sure your microphone is muted when you don’t speak - Thank you! Any questions that you have you can add them in the chat or to this document https://bit.ly/3gxed44 MentiMeter interactions - https://www.menti.com/ The recording, list of resources, and relevant documents will be made available on the event page on Europeana Pro https://bit.ly/3eveWky SPEAKERS Sara Di Giorgio, Project Manager ICCU, Cultura Italia Franco Niccolucci, director of the VAST-LAB laboratory at PIN, Prato, Italy Rob Davies - Europeana Common Culture, Cyprus Institute of Technology Henning Scholz - Data Publishing Services, Europeana FAIR DATA PRINCIPLES The FAIR data principles concern data organization. They aim at improving data discovery, access and re-use by humans and directly by computational systems with little or no human intervention - the so-called machine-actionability. The principles focus on four aspects: Findability, Accessibility, Interoperability and Re-usability. ORIGINS OF FAIR ● The FAIR principles originate from a workshop held in Leiden in 2014 and from previous discussions on open science ● A 2016 paper published by a large grouping of scientists formulated them in a precise and formal way ● Supported by the EU Commission ● Implemented through the recommendations of international projects and organizations like RDA Guidelines on FAIR data management https://www.force11.org/ G8 Science Ministers Statement (2013) WHY FAIR DATA? The FAIR data principles are about data stewardship and re-use ● Open Science: foster collaboration & cross-fertilization ● Saving money: 10 000 M€/year ● Saving time: no double work, 80% time spent in searching data ● Machine-actionability Image by OpenAire ● Widely adopted: UNESCO, G7, EU, national governments (DE, NL, FR), LIBER, ... WHY GOING FAIR? Adopting the FAIR principles has many benefits for researchers: ● Encouraging scientific enquiry and debate ● Promoting innovation and potential new data uses ● Fostering new collaborations between data users and data creators ● Maximising transparency and accountability ● Enabling scrutiny of research methods and findings ● Reducing the cost of duplicating data collection ● Increasing the impact and visibility of research ● Getting credit for research outputs ● Increase citation rates ● Last but not least, it is mandatory for many funders PARTHENOS GUIDELINES 20 GUIDELINES TO FAIRify DATA MANAGEMENT AND MAKE DATA REUSABLE https://zenodo.org/record/2668479#.XsfJiWgzZPY PREREQUISITE INVEST IN PEOPLE AND INFRASTRUCTURE ● Best practices ● Training ● Implementation of technical infrastructures How to make data FINDABLE? ● Use persistent identifiers (IDs) ● Cite research data ● Use persistent author identifiers (IDs) ● Use a standardized metadata schema Guidelines - FINDABLE Focus: Metadata Schema and Identifiers. Humanists work with a variety of sources (from archives, museums, or surveys) and each of them requires a specific metadata standard; Main message: make use of the right fields to describe the right research object; Identifiers also play an essential role for the humanities: they need to be as unique and persistent as possible. How to make data ACCESSIBLE? ● Choose a trustworthy repository ● Clearly state accessibility ● Use a data embargo when needed ● Use standardised exchange protocols Guidelines - ACCESSIBLE Focus: where the researcher’s data are stored (trustworthy), how they can be retrieved (protocols), and stating if and how much they are accessible; Main message: your research (meta)data can be as standardized as possible, but if they can’t be accessed, they cannot be consulted and cited, reused, etc. Access does not mean Open Access: the rule is “As open as possible, as closed as necessary” How to make data INTEROPERABLE? ● Establish well documented APIs ● Use open, well-defined vocabularies ● Document metadata models ● Use community data standards ● Enhance data quality ● Use future-proof file formats Guidelines - INTEROPERABLE Focus: making the researcher’s data able to dialogue with other (researchers’) data; Main message: use of standards, vocabularies/ ontologies, APIs, support the researchers who want to connect their data with other data and enable machine-actionable interoperability How to make data REUSABLE? ● Document data ● Follow naming conventions ● Use common file formats ● Maintain data integrity ● Use licence that allow reuse Guidelines - REUSABLE Focus:being able to integrate other researchers’ data into your own research and vice versa; Main message: not only data, but also their documentation and reuse licences are important. Humanities data are all about interpretation, but aim to the higher data integration. PARTHENOS GUIDELINES The PARTHENOS high-level guides offer common guidelines to build bridges between different - although tightly interrelated - fields and stakeholders within the Cultural heritage and the Humanities sectors by the harmonization of policy definition and their implementation. PARTHENOS is an FP7 project funded by the European Commission. It provided integrated facilities and resources concerning all aspects of data management for the Humanities and Cultural Heritage https://zenodo.org/record/2668479#.XsfJiWgzZPY PARTHENOS GUIDELINES The PARTHENOS Guidelines are also available in French, German, Greek, Hungarian and Italian. If you want to learn more visit PARTHENOS Training modules at http://training.parthenos-project.eu/sample-page/ manage-improve-and-open-up-your-research-and -data/ Mentimeter Questions Thank you! Franco Niccolucci, PIN [email protected] Sara Di Giorgio, ICCU europeana.eu [email protected] @EuropeanaEU The Chinese Market, 1767 - 1769, Rijksmuseum, Netherlands, Public domain.
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