An overview

Over the last few years, the notion that good data management is an important part of scientific practice has increasingly found widespread acceptance | Van Berchum & Grootveld, 2017

You are about to dive into the course contents of Essentials 4 Data Support. First of all, we would like to give you a little context. You will be entering a dynamic area of research support. An area that is constantly on the move, both nationally and internationally.

Once upon a time ...

When the first Essentials 4 Data Support course was held in 2014, the emphasis was mainly on awareness that data management is important. Meanwhile, about five years and 300 students later, we can state that this research support area is becoming increasingly mature. That the emphasis is no longer on the why but on the how. How can you best support researchers with data management? How do you organise support, which tools are needed, which services are essential, which networks are necessary and which qualities and skills are required? And how do you make sure you don't lose sight of the researcher's perspective? The various roles and tasks that data supporters can fulfil in this regard are beginning to crystallise (Verheul, et.al., 2019).

The compass on which scientific research is based is one of integrity and reproducibility. How do we organise the scientific process in such a way that it leads to reliable and usable scientific insights? In recent years, many manifestos, statements and recommendations in the field of research, research data, open science, scientific communication and publication have been drawn up by the research community itself (Bosman and Kramer, 2015 & 2018) and researching research - metascience - has become a separate scientific discipline (Ionnadis, 2015; Munafò, 2017). 

In the slideshow below, a dozen important statements scroll by. A more extensive list is also available (Bosman and Kramer, 2018). The terms used are explained in more detail in the following sections.

A taste of the future

The call to make research data open and reusable is increasingly being heard. A concrete tool for preparing data for reuse, including by computers, is facilitating FAIR data (Findable, Accessible, Interoperable, Reusable) in a FAIR data infrastructure. The pursuit of FAIR data is not only good for promoting transparency and reproducibility. If we fail to facilitate FAIR data, it would cost us a huge amount in lost opportunities for efficiency and innovation (European Commission, 2019).

FAIR data are building blocks - a kind of interoperable machine-readable Lego blocks - that can be combined. And the countless possibilities for new combinations can lead to new discoveries and innovations taking place at the boundaries of disciplines. All the more so because FAIR is an enabler of artificial intelligence (Wise, 2019), the intelligence with which machines, software and devices independently learn and solve problems. 

Against the background of the technological possibilities, there is a social debate about how we can best value researchers for their efforts to make data FAIR. In the Netherlands, the Nationaal Plan Open Science (2017), signed by, among others, the Dutch research funder NWO, the Association of Universities of Applied Sciences and the VSNU, is committed to appreciating researchers for their contributions to making their data FAIR. The signing of the DORA statement by the Dutch research funders NWO, ZonMW and KNAW (NWO, 2019) also symbolises the transition to a research ecosystem in which research data count for the full 100%.

FAIR is part of a broader movement which is changing the way science is done, with the emergence of data stewardship and a growing momentum in favour of openness | European Commission, 2019

The data infrastructure that has developed over the past decades and that will develop over the coming decades, supports the movement towards a FAIR data infrastructure. The data infrastructure that makes it possible to store, publish, share, combine and (re)use research data for the longer term has various layers and phases. During Essentials 4 Data Support we focus on the measures to prepare research data during all these phases for an ethical and reproducible existence and a future that is as reusable as possible.

The illustration below gives a taste of the measures we are talking about. Perhaps the terms used sound like abracadabra in your ears at the moment but we assure you one thing:

When you have finished the course, the illustration below will no longer hold any secrets for you.

Sources

Click to open/close

Bosman, J., Kramer, B. (2015). From Budapest via San Francisco to The Hague: a bird's eye view of two dozen scholarly communication charters.From Budapest via San Francisco to The Hague: a bird's eye view of two dozen scholarly communication charters. Journal of Brief Ideas. 8 oct 2015.https://doi.org/10.5281/zenodo.31943

Bosman, J., Kramer. B., e.a. (2018). SC-SCWG Charters - draft list. http://tinyurl.com/scholcomm-charters

Budroni, P., Hanslik, S., Austrian Federal Ministry for Education, Science and Research. (2018). The Vienna Declaration on the European Open Science Cloud. https://eosc-launch.eu/fileadmin/user_upload/k_eosc_launch/EOSC_Vienna_Declaration_2018.pdf

CoAlitoin S. (2018). Plan S https://www.coalition-s.org/

DORA (2012). The Declaration on Research Assessment (DORA). https://sfdora.org/

European Commission (2019). Cost-benefit analysis for FAIR research data. Cost of not having FAIR research data - Study. https://publications.europa.eu/en/publication-detail/-/publication/d375368c-1a0a-11e9-8d04-01aa75ed71a1

FORCE 11 (2014a). Data citation principles. https://www.force11.org/datacitationprinciples

FORCE 11 (2014b). The FAIR data principles. https://www.force11.org/group/fairgroup/fairprinciples

FORCE 11 (2017). Principles of the Scholarly Commons. https://www.force11.org/scholarly-commons/principles

Ioannidis, J.P.A., Fanelli, D., Dunne, D.D., Goodman, S.N. (2015). Meta-research: Evaluation and Improvement of Research Methods and Practices. PLOS Biology 13(10): e1002264. https://doi.org/10.1371/journal.pbio.1002264

Kraker, P., Dörler, D., Ferus, A., Gutounig, R., Heigl, F., Kaier, C., … Wandl-Vogt, E. (2016, June 15). The Vienna Principles: A Vision for Scholarly Communication in the 21st Century. Zenodo. https://doi.org/10.5281/zenodo.55597

Leiden Manifesto (2015). Leiden Manifesto for Research Metrics. http://www.leidenmanifesto.org/

Max Planck Gesellschaft (2003). Berlin Declaration on Open Access. https://openaccess.mpg.de/Berlin-Declaration

Munafò, M. R., Nosek, B. A., Bishop, D. V. M., Button, K. S., Chambers, C. D., Percie Du Sert, N., ... Ioannidis, J. P. A. (2017). A manifesto for reproducible science. Nature Human Behaviour, 1(1), [0021]. https://doi.org/10.1038/s41562-016-0021

OECD (2007). OECD Principles and Guidelines for Access to Research Data from Public Funding. http://www.oecd.org/science/inno/38500813.pdf

Nationaal Plan Open Science. DANS, De Jonge Akademie, DTL, GO FAIR, KB, KNAW, LCRDM, Netherlands eScience Center, NFU, NWO, PNN, SURF, 4TU.Centre for Research Data, UKB, VH, VSNU, ZonMw. (2017). https://www.openscience.nl/het-nationaal-platform-open-science/nationaal-plan-open-science

NWO (2019, 18 april). KNAW, NWO and ZonMw to sign DORA declaration [Newsitem]. https://www.nwo.nl/en/news-and-events/news/2019/04/knaw-nwo-and-zonmw-to-sign-dora-declaration.html

TOP Guidelines Committee (2015). Promoting an open research culture. 10.1126/science.aab2374 

The Hague Declaration (2015). The Hague declaration on knowledge discovery in the digital age. https://thehaguedeclaration.com/the-hague-declaration-on-knowledge-discovery-in-the-digital-age/

Van Berchum, M. & Grootveld, M. (2017). Research data management. An overview of recent developments in the Netherlands. http://hdl.handle.net/20.500.11755/a9539a60-ecef-4e62-a998-0fda190b303b

Verheul, I., Imming, M., Ringersma, J., Mordant, A., Ploeg, J.L., Pronk, M. (2019, April 16). Datastewardship op de kaart: Een verkenning van taken en rollen in Nederlandse onderzoeksinstellingen. Zenodo. http://doi.org/10.5281/zenodo.2642066 (Also see https://www.lcrdm.nl/en/guidelines-data-stewardship for more LCRDM-information about data stewardship)

Wise, et al. (2019). Implementation and relevance of FAIR data principles in biopharmaceutical R&D. Drug Discovery Today, 24, pp. 933-938. https://doi.org/10.1016/j.drudis.2019.01.008