An overview

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) 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%.

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.