On a Quest for Cultural Change – Surveying Research Data Management Practices at Delft University of Technology

Authors : Heather Andrews Mancilla, Marta Teperek, Jasper van Dijck, Kees den Heijer, Robbert Eggermont, Esther Plomp, Yasemin Turkyilmaz-van der Velden, Shalini Kurapati

The Data Stewardship project is a new initiative from the Delft University of Technology (TU Delft) in the Netherlands. Its aim is to create mature working practices and policies regarding research data management across all TU Delft faculties.

The novelty of this project relies on having a dedicated person, the so-called ‘Data Steward’, embedded in each faculty to approach research data management from a more discipline-specific perspective. It is within this framework that a research data management survey was carried out at the faculties that had a Data Steward in place by July 2018.

The goal was to get an overview of the general data management practices, and use its results as a benchmark for the project. The total response rate was 11 to 37% depending on the faculty.

Overall, the results show similar trends in all faculties, and indicate lack of awareness regarding different data management topics such as automatic data backups, data ownership, relevance of data management plans, awareness of FAIR data principles and usage of research data repositories.

The results also show great interest towards data management, as more than ~80% of the respondents in each faculty claimed to be interested in data management training and wished to see the summary of survey results.

Thus, the survey helped identified the topics the Data Stewardship project is currently focusing on, by carrying out awareness campaigns and providing training at both university and faculty levels.

URL : On a Quest for Cultural Change – Surveying Research Data Management Practices at Delft University of Technology

Skills, Standards, and Sapp Nelson’s Matrix: Evaluating Research Data Management Workshop Offerings

Authors : Philip Espinola Coombs, Christine Malinowski, Amy Nurnberger


To evaluate library workshops on their coverage of data management topics.


We used a modified version of Sapp Nelson’s Competency Matrix for Data Management Skills, a matrix of learning goals organized by data management competency and complexity level, against which we compared our educational materials: slide decks and worksheets.

We examined each of the educational materials against the 333 learning objectives in our modified version of the Matrix to determine which of the learning objectives applied.


We found it necessary to change certain elements of the Matrix’s structure to increase its clarity and functionality: reinterpreting the “behaviors,” shifting the organization from the three domains of Bloom’s taxonomy to increasing complexity solely within the cognitive domain, as well as creating a comprehensive identifier schema.

We appreciated the Matrix for its specificity of learning objectives, its organizational structure, the comprehensive range of competencies included, and its ease of use. On the whole, the Matrix is a useful instrument for the assessment of data management programming.

URL : Skills, Standards, and Sapp Nelson’s Matrix: Evaluating Research Data Management Workshop Offerings

Alternative location : https://escholarship.umassmed.edu/jeslib/vol8/iss1/6/

Establishing, Developing, and Sustaining a Community of Data Champions

Authors : James L. Savage, Lauren Cadwallader

Supporting good practice in Research Data Management (RDM) is challenging for higher education institutions, in part because of the diversity of research practices and data types across disciplines.

While centralised research data support units now exist in many universities, these typically possess neither the discipline-specific expertise nor the resources to offer appropriate targeted training and support within every academic unit.

One solution to this problem is to identify suitable individuals with discipline-specific expertise that are already embedded within each unit, and empower these individuals to advocate for good RDM and to deliver support locally.

This article focuses on an ongoing example of this approach: the Data Champion Programme at the University of Cambridge, UK.

We describe how the Data Champion programme was established; the programme’s reach, impact, strengths and weaknesses after two years of operation; and our anticipated challenges and planned strategies for maintaining the programme over the medium- and long-term.

URL : Establishing, Developing, and Sustaining a Community of Data Champions

DOI : http://doi.org/10.5334/dsj-2019-023

Data Sharing Practices among Researchers at South African Universities

Authors : Siviwe Bangani, Mathew Moyo

Research data management practices have gained momentum the world over. This is due to increased demands by governments and other funding agencies to have research data archived and shared as widely as possible.

This paper sought to establish the data sharing practices of researchers in South Africa. The study further sought to establish the level of collaboration among researchers in sharing research data at the university level.

The outcomes of the survey will help the researchers to develop appropriate data literacy awareness programmes meant to stimulate growth in data sharing practices for the benefit of research, not only in South Africa, but the world at large.

A survey research method was used to gather data from willing public universities in South Africa. A similar study was conducted in other countries such as the United Kingdom, France and Turkey but the Researchers believe that circumstances in the developed world may differ with the South African research environment, hence the current study.

The major finding of this study was that most researchers preferred to use data produced by others but less keen on sharing their own data.

This study is the first of its kind in South Africa which investigates data sharing practices of researchers from multi-disciplinary fields at the university level and will contribute immensely to the growing body of literature in the area of research data management.

URL : Data Sharing Practices among Researchers at South African Universities

DOI : http://doi.org/10.5334/dsj-2019-028

A model for initiating research data management services at academic libraries

Authors : Kevin B. Read, Jessica Koos, Rebekah S. Miller, Cathryn F. Miller, Gesina A. Phillips, Laurel Scheinfeld, Alisa Surkis


Librarians developed a pilot program to provide training, resources, strategies, and support for medical libraries seeking to establish research data management (RDM) services. Participants were required to complete eight educational modules to provide the necessary background in RDM.

Each participating institution was then required to use two of the following three elements: (1) a template and strategies for data interviews, (2) a teaching tool kit to teach an introductory RDM class, or (3) strategies for hosting a data class series.

Case Presentation

Six libraries participated in the pilot, with between two and eight librarians participating from each institution. Librarians from each institution completed the online training modules.

Each institution conducted between six and fifteen data interviews, which helped build connections with researchers, and taught between one and five introductory RDM classes.

All classes received very positive evaluations from attendees. Two libraries conducted a data series, with one bringing in instructors from outside the library.


The pilot program proved successful in helping participating librarians learn about and engage with their research communities, jump-start their teaching of RDM, and develop institutional partnerships around RDM services.

The practical, hands-on approach of this pilot proved to be successful in helping libraries with different environments establish RDM services.

The success of this pilot provides a proven path forward for libraries that are developing data services at their own institutions.

URL : A model for initiating research data management services at academic libraries

Alternative location : http://jmla.pitt.edu/ojs/jmla/article/view/545

Developing a research data policy framework for all journals and publishers

Authors : Iain Hrynaszkiewicz​, Natasha Simons​, Azhar Hussain​,​ Simon Goudie

More journals and publishers – and funding agencies and institutions – are introducing research data policies. But as the prevalence of policies increases, there is potential to confuse researchers and support staff with numerous or conflicting policy requirements.

We define and describe 14 features of journal research data policies and arrange these into a set of six standard policy types or tiers, which can be adopted by journals and publishers to promote data sharing in a way that encourages good practice and is appropriate for their audience’s perceived needs.

Policy features include coverage of topics such as data citation, data repositories, data availability statements, data standards and formats, and peer review of research data.

These policy features and types have been created by reviewing the policies of multiple scholarly publishers, which collectively publish more than 10,000 journals, and through discussions and consensus building with multiple stakeholders in research data policy via the Data Policy Standardisation and Implementation Interest Group of the Research Data Alliance.

Implementation guidelines for the standard research data policies for journals and publishers are also provided, along with template policy texts which can be implemented by journals in their Information for Authors and publishing workflows.

We conclude with a call for collaboration across the scholarly publishing and wider research community to drive further implementation and adoption of consistent research data policies.

URL : Developing a research data policy framework for all journals and publishers

Alternative location : https://figshare.com/articles/Developing_a_research_data_policy_framework_for_all_journals_and_publishers/8223365/1

A Generic Research Data Infrastructure for Long Tail Research Data Management

Authors : Atif Latif, Fidan Limani, Klaus Tochtermann

The advent of data intensive science has fueled the generation of digital scientific data. Undoubtedly, digital research data plays a pivotal role in transparency and re-producibility of scientific results as well as in steering the innovation in a research process.

However, the main challenges for science policy and infrastructure projects are to develop practices and solutions for research data management which in compliance with good scientific standards make the research data discoverable, citeble and accessible for society potential reuse.

GeRDI – the Generic Research Data (RD) Infrastructure – is such a research data management initiative which targets long tail content that stems from research communities belonging to different domain and research practices.

It provides a generic and open software which connects research data infrastructures of communities to enable the investigation of multidisciplinary research questions.

URL : A Generic Research Data Infrastructure for Long Tail Research Data Management

DOI : http://doi.org/10.5334/dsj-2019-017