Professionalising data stewardship in the Netherlands. Competences, training and education. Dutch roadmap towards national implementation of FAIR data stewardship

This is the end report of the NPOS-F project “Professionalising data stewardship”, part of the NPOS FAIR data programme line

Defining a lingua franca for the ELIXIR/GOBLET e-learning ecosystem

Today, the term e-learning is widely used in a variety of contexts. Many of us are familiar with concepts like Massive Open Online Courses (MOOCs), such as those provided by Coursera, Udacity, edX or MIT OpenCourseWare; Content- or Learning-Management Systems (CMSs, LMSs), like Moodle or Blackboard; Virtual Learning Environments (VLEs), like EMBER; problem and tutorial portals, such as Rosalind and Train online; repositories for uploading and hosting Educational Resources (ERs), such as that provided by GOBLET; ER aggregators that harvest and disseminate training information, such as TeSS; and so on. Resources like these are seamlessly discussed under the umbrella term ‘e-learning’; however, they are heterogeneous and do not mean the same thing from user, developer, trainer or content-provider perspectives. Confusion can therefore arise if those using the term generically have not grasped each other’s specific meanings. In the context of ELIXIR, this situation came to the fore in preparations for the EXCELERATE project, whose training programme (WP11) includes an explicit e-learning subtask (11.1.3) as part of the commitment to build an ELIXIR training infrastructure. The ultimate deliverable of the subtask is to develop an ELIXIR e-learning policy and deploy e-learning platform(s) at the ELIXIR level. But what does an ‘e-learning platform for ELIXIR’ mean? Is it an instance of a MOOC platform, a customised LMS, a bespoke VLE, an ER repository, an aggregator, or something else? Clarification of this question was important, because each of the possible answers has very different implementation and resource requirements. To try to address this question, and to facilitate communication both within ELIXIR, and between the ELIXIR and GOBLET trainer communities, an initial workshop – Defining an e-learning lingua franca – was held in Ljubljana (SI), 15-17 September 2015. At one level, the aim was to develop an overview of some of the e-learning approaches and applications developed or used by representatives from ELIXIR, GOBLET and other organisations – participants were therefore asked briefly to describe the systems they’d developed, to allow them, and their respective strengths and weaknesses, to be compared. At another level, the aim of the workshop was to try to converge on a common e-learning ‘language’, to help the ELIXIR and GOBLET trainer communities to communicate more coherently. To broaden the picture and gain a wider understanding of the challenges, a follow-up event was held during the GOBLET Annual General Meeting in Cape Town (ZA), 18-20 November 2015. Here, the outcomes of the first meeting were further discussed and refined. Gaining a clearer view of the current e-learning landscape, and consensus on what we collectively mean by e-learning, were the necessary first steps towards formulating appropriate e-learning strategies for ELIXIR and GOBLET, and suggesting solutions that can feasibly be implemented. In the event, the issues were more deep-rooted than we expected, and many additional discussions were needed. This paper reflects the results of those discussions and of the deliberations of the workshop participants

Bioinformatics in the Netherlands: The value of a nationwide community

This review provides a historical overview of the inception and development of bioinformatics research in the Netherlands. Rooted in theoretical biology by foundational figures such as Paulien Hogeweg (at Utrecht University since the 1970s), the developments leading to organizational structures supporting a relatively large Dutch bioinformatics community will be reviewed. We will show that the most valuable resource that we have built over these years is the close-knit national expert community that is well engaged in basic and translational life science research programmes. The Dutch bioinformatics community is accustomed to facing the ever-changing landscape of data challenges and working towards solutions together. In addition, this community is the stable factor on the road towards sustainability, especially in times where existing funding models are challenged and change rapidly

Bioinformatics in the Netherlands: The value of a nationwide community

This review provides a historical overview of the inception and development of bioinformatics research in the Netherlands. Rooted in theoretical biology by foundational figures such as Paulien Hogeweg (at Utrecht University since the 1970s), the developments leading to organizational structures supporting a relatively large Dutch bioinformatics community will be reviewed. We will show that the most valuable resource that we have built over these years is the close-knit national expert community that is well engaged in basic and translational life science research programmes. The Dutch bioinformatics community is accustomed to facing the ever-changing landscape of data challenges and working towards solutions together. In addition, this community is the stable factor on the road towards sustainability, especially in times where existing funding models are challenged and change rapidly.Pattern Recognition and Bioinformatic

The FAIR Funder pilot programme to make it easy for funders to require and for grantees to produce FAIR Data

There is a growing acknowledgement in the scientific community of the importance of making experimental data machine findable, accessible, interoperable, and reusable (FAIR). Recognizing that high quality metadata are essential to make datasets FAIR, members of the GO FAIR Initiative and the Research Data Alliance (RDA) have initiated a series of workshops to encourage the creation of Metadata for Machines (M4M), enabling any self-identified stakeholder to define and promote the reuse of standardized, comprehensive machine-actionable metadata. The funders of scientific research recognize that they have an important role to play in ensuring that experimental results are FAIR, and that high quality metadata and careful planning for FAIR data stewardship are central to these goals. We describe the outcome of a recent M4M workshop that has led to a pilot programme involving two national science funders, the Health Research Board of Ireland (HRB) and the Netherlands Organisation for Health Research and Development (ZonMW). These funding organizations will explore new technologies to define at the time that a request for proposals is issued the minimal set of machine-actionable metadata that they would like investigators to use to annotate their datasets, to enable investigators to create such metadata to help make their data FAIR, and to develop data-stewardship plans that ensure that experimental data will be managed appropriately abiding by the FAIR principles. The FAIR Funders design envisions a data-management workflow having seven essential stages, where solution providers are openly invited to participate. The initial pilot programme will launch using existing computer-based tools of those who attended the M4M Workshop

The FAIR Funder pilot programme to make it easy for funders to require and for grantees to produce FAIR Data

There is a growing acknowledgement in the scientific community of the importance of making experimental data machine findable, accessible, interoperable, and reusable (FAIR). Recognizing that high quality metadata are essential to make datasets FAIR, members of the GO FAIR Initiative and the Research Data Alliance (RDA) have initiated a series of workshops to encourage the creation of Metadata for Machines (M4M), enabling any self-identified stakeholder to define and promote the reuse of standardized, comprehensive machine-actionable metadata. The funders of scientific research recognize that they have an important role to play in ensuring that experimental results are FAIR, and that high quality metadata and careful planning for FAIR data stewardship are central to these goals. We describe the outcome of a recent M4M workshop that has led to a pilot programme involving two national science funders, the Health Research Board of Ireland (HRB) and the Netherlands Organisation for Health Research and Development (ZonMW). These funding organizations will explore new technologies to define at the time that a request for proposals is issued the minimal set of machine-actionable metadata that they would like investigators to use to annotate their datasets, to enable investigators to create such metadata to help make their data FAIR, and to develop data-stewardship plans that ensure that experimental data will be managed appropriately abiding by the FAIR principles. The FAIR Funders design envisions a data-management workflow having seven essential stages, where solution providers are openly invited to participate. The initial pilot programme will launch using existing computer-based tools of those who attended the M4M Workshop