Learning Outcomes

By the end of this section, you should be able to:

  • Understand and describe the FAIR Principles and what they are used for
  • Understand how Cultural Heritage Institutions and projects cooperate to manage data

Introduction to the module

Depending on what discipline or sector you work in, you may not even think you have data.  Part of this may be a semantic issue: the word data seems to be able to stand for a huge variety of things these days, digital and analogue, machine created or human created, highly structured or utterly unstructured.  Data, it seems, is in the eye of the beholder, meaning perhaps ‘research inputs’ or perhaps merely ‘stuff.’ Humanists don’t tend to use the word data, however, though they do use a lot of referents that would be seen as data by others.

What is Data, Anyway?

None of this is to say that no attempts have been made to define the word data: much to the contrary, many scholars of science have written about data, and the definitions are myriad.  Within the discourse of users of data (eg. computer scientists) the word moves fluidly between referents such as those given above.  For the humanist, primary sources, secondary sources, theoretical texts, methodological tools, digital tools, notes, annotations, references … all of these comprise research data in the humanities.

How does humanities data tend to be different?

There are problems with sharing and managing the humanistic data, however.  First of all, much of it is not digital.  Humanists still tend to gravitate toward multimodal knowledge creation systems, hybrid digital and technical worlds that resist norms of deposit and reuse.  Second, the semiotic systems of humanities data can be quite personal and individual: we prepare our sources to be useful for us, and what works for our research questions and personal epistemic instruments may not work at all for anyone else.  Finally, and perhaps most importantly, cultural data is seldom if ever ‘raw,’ and seldom, if ever, under the sole ownership of the researcher him or herself.  The records of human activity and creativity belong to everyone and no one, they are often preserved and curated by dedicated public institutions or private publishers.  Whatever humanities data is, it is not simple!

Why would I want or need to manage, improve or open up my data?

The time step of humanities research is slow, and this can lead to systemic inefficiencies.  If we could protect early stage insight in the humanities, would we reuse them sooner? Would we find more effective ways to apply humanities knowledge to contemporary problems, and give access to citizens (who may not read our books or articles) to our insights? Opening up our data could open up many opportunities for using and reusing it, for collaborating, informing and increasing the impact of our work.

But there is a further incentive as well.  At a European level, support for data sharing across the disciplines is gaining momentum, and will likely become policy for publicly funded research in the near future.  Though the challenges may be great, we cannot afford to allow the humanities disciplines be left out of EU policies for research.  At the same time, the mechanisms for management and sharing of data that work for physics won’t work for the humanities and cultural data.  New ways must be found, and it is the goal of this module to give its users a basis for understanding the issues and opportunities.

Cooperation in the Management of Data

Infrastructures, cultural heritage institutions, data libraries and researchers need to work together to create and sustain the evolving system for research data sharing.  Some of the tools we use for this exist as standards (allowing disparate data to be described similarly, and potentially used as a common resource on that basis).  To support the more active use of appropriate standards between researchers and source providers, the PARTHENOS project is building a Standardisation Survival Kit (to be launched shortly).   But there are also active collaboration platforms you can engage with, such as the Data Reuse Charter initiative (which you can learn more about here in the section on Research Infrastructures and Data Policy at the end of this module).

Christine Borgman “Big Data, Little Data, No Data: Scholarship in the Networked World” 2015, MIT Press, London https://mitpress.mit.edu/big-data-little-data-no-data

Sahle, Kronenwett: Jenseits der Daten: Überlegungen zu Datenzentren für die Geisteswissenschaften am Beispiel des Kölner ‘Data Center for the Humanities’, http://libreas.eu/ausgabe23/09sahle/ (German / Deutsch)

Science Europe Guidance Document “Presenting a Framework for Discipline-specific Research Data Management” January 2018  http://www.scienceeurope.org/wp-content/uploads/2018/01/SE_Guidance_Document_RDMPs.pdf (English)

Website: Research Data Management E-Learning platform, http://www.researchdatamanagement.ch/ (Deutsch / Francais)

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