Librarians’ Perspectives on the Factors Influencing Research Data Management Programs

Authors: Ixchel M. Faniel, Lynn Silipigni Connaway

This qualitative research study examines librarians’ research data management (RDM) experiences, specifically the factors that influence their ability to support researchers’ needs.

Findings from interviews with 36 academic library professionals in the United States identify 5 factors of influence: 1) technical resources; 2) human resources; 3) researchers’ perceptions about the library; 4) leadership support; and 5) communication, coordination, and collaboration. Findings show different aspects of these factors facilitate or constrain RDM activity. The implications of these factors on librarians’ continued work in RDM are considered.

URL : Librarians’ Perspectives on the Factors Influencing Research Data Management Programs

DOI : https://doi.org/10.5860/crl.79.1.100

Creating a Community of Data Champions

Authors : Rosie Higman, Marta Teperek, Danny Kingsley

Research Data Management (RDM) presents an unusual challenge for service providers in Higher Education. There is increased awareness of the need for training in this area but the nature of the discipline-specific practices involved make it difficult to provide training across a multi-disciplinary organisation.

Whilst most UK universities now have a research data team of some description, they are often small and rarely have the resources necessary to provide targeted training to the different disciplines and research career stages that they are increasingly expected to support.

This practice paper describes the approach taken at the University of Cambridge to address this problem by creating a community of Data Champions. This collaborative initiative, working with researchers to provide training and advocacy for good RDM practice, allows for more discipline-specific training to be given, researchers to be credited for their expertise and creates an opportunity for those interested in RDM to exchange knowledge with others.

The ‘community of practice’ model has been used in many sectors, including Higher Education, to facilitate collaboration across organisational units and this initiative will adopt some of the same principles to improve communication across a decentralised institution.

The Data Champions initiative at Cambridge was launched in September 2016 and this paper reports on the early months, plans for building the community in the future and the possible risks associated with this approach to providing RDM services.

URL : Creating a Community of Data Champions

DOI : https://doi.org/10.2218/ijdc.v12i2.562

Scaling Research Data Management Services Along the Maturity Spectrum: Three Institutional Perspectives

Authors : Cinthya Ippoliti, Amy Koshoffer, Renaine Julian, Micah Vandegrift, Devin Soper, Sophie Meridien

Research data services promise to advance many academic libraries’ strategic goals of becoming partners in the research process and integrating library services with modern research workflows. Academic librarians are well positioned to make an impact in this space due to their expertise in managing, curating, and preserving digital information, and a history of engaging with scholarly communications writ large.

Some academic libraries have quickly developed infrastructure and support for every activity ranging from data storage and curation to project management and collaboration, while others are just beginning to think about addressing the data needs of their researchers.

Regardless of which end of the spectrum they identify with, libraries are still seeking to understand the research landscape and define their role in the process.

This article seeks to blend both a general perspective regarding these issues with actual case studies derived from three institutions, University of Cincinnati, Oklahoma State University, and Florida State University, all of which are at different levels of implementation, maturity, and campus involvement.

URL : Scaling Research Data Management Services Along the Maturity Spectrum: Three Institutional Perspectives

DOI : https://dx.doi.org/10.17605/OSF.IO/WZ8FN

 

Evaluating the Effectiveness of Data Management Training: DataONE’s Survey Instrument

Authors : Chung-Yi Hou, Heather Soyka, Vivian Hutchison, Isis Sema, Chris Allen, Amber Budden

Effective management is a key component for preparing data to be retained for future long term access, use, and reuse by a broader community. Developing the skills to plan and perform data management tasks is important for individuals and institutions.

Teaching data literacy skills may also help to mitigate the impact of data deluge and other effects of being overexposed to and overwhelmed by data.

The process of learning how to manage data effectively for the entire research data lifecycle can be complex. There are often multiple stages involved within a lifecycle for managing data, and each stage may require specific knowledge, expertise, and resources.

Additionally, although a range of organizations offers data management education and training resources, it can often be difficult to assess how effective the resources are for educating users to meet their data management requirements.

In the case of Data Observation Network for Earth (DataONE), DataONE’s extensive collaboration with individuals and organizations has informed the development of multiple educational resources. Through these interactions, DataONE understands that the process of creating and maintaining educational materials that remain responsive to community needs is reliant on careful evaluations.

Therefore, the impetus for a comprehensive, customizable Education EVAluation instrument (EEVA) is grounded in the need for tools to assess and improve current and future training and educational resources for research data management.

In this paper, the authors outline and provide context for the background and motivations that led to creating EEVA for evaluating the effectiveness of data management educational resources. The paper details the process and results of the current version of EEVA.

Finally, the paper highlights the key features, potential uses, and the next steps in order to improve future extensions and revisions of EEVA.

URL : Evaluating the Effectiveness of Data Management Training: DataONE’s Survey Instrument

DOI : https://doi.org/10.2218/ijdc.v12i2.508

Documentation and Visualisation of Workflows for Effective Communication, Collaboration and Publication @ Source

Authors : Cerys Willoughby, Jeremy G. Frey

Workflows processing data from research activities and driving in silico experiments are becoming an increasingly important method for conducting scientific research. Workflows have the advantage that not only can they be automated and used to process data repeatedly, but they can also be reused – in part or whole – enabling them to be evolved for use in new experiments.

A number of studies have investigated strategies for storing and sharing workflows for the benefit of reuse. These have revealed that simply storing workflows in repositories without additional context does not enable workflows to be successfully reused.

These studies have investigated what additional resources are needed to facilitate users of workflows and in particular to add provenance traces and to make workflows and their resources machine-readable.

These additions also include adding metadata for curation, annotations for comprehension, and including data sets to provide additional context to the workflow. Ultimately though, these mechanisms still rely on researchers having access to the software to view and run the workflows.

We argue that there are situations where researchers may want to understand a workflow that goes beyond what provenance traces provide and without having to run the workflow directly; there are many situations in which it can be difficult or impossible to run the original workflow.

To that end, we have investigated the creation of an interactive workflow visualization that captures the flow chart element of the workflow with additional context including annotations, descriptions, parameters, metadata and input, intermediate, and results data that can be added to the record of a workflow experiment to enhance both curation and add value to enable reuse.

We have created interactive workflow visualisations for the popular workflow creation tool KNIME, which does not provide users with an in-built function to extract provenance information that can otherwise only be viewed through the tool itself.

Making use of the strengths of KNIME for adding documentation and user-defined metadata we can extract and create a visualisation and curation package that encourages and enhances curation@source, facilitating effective communication, collaboration, and reuse of workflows.

URL : Documentation and Visualisation of Workflows for Effective Communication, Collaboration and Publication @ Source

DOI : https://doi.org/10.2218/ijdc.v12i1.532

Research Data Management Instruction for Digital Humanities

Author : Willow Dressel

eScience related library services at Princeton University started in response to the National Science Foundation’s (NSF) data management plan requirements, and grew to encompass a range of services including data management plan consultation, assistance with depositing into a disciplinary or institutional repository, and research data management instruction.

These services were initially directed at science and engineering disciplines on campus, but the eScience Librarian soon realized the relevance of research data management instruction for humanities disciplines with digital approaches.

Applicability to the digital humanities was initially recognized by discovery of related efforts from the history department’s Information Technology (IT) manager in the form of a graduate-student workshop on file and digital-asset management concepts.

Seeing the common ground these activities shared with research data management, a collaboration was formed between the history department’s IT Manager and the eScience Librarian to provide a research data management overview to the entire campus community.

The eScience Librarian was then invited to participate in the history department’s graduate student file and digital asset management workshop to provide an overview of other research data management concepts. Based on the success of the collaboration with the history department IT, the eScience Librarian offered to develop a workshop for the newly formed Center for Digital Humanities at Princeton.

To develop the workshop, background research on digital humanities curation was performed revealing similarities and differences between digital humanities curation and research data management in the sciences. These similarities and differences, workshop results, and areas of further study are discussed.

URL : Research Data Management Instruction for Digital Humanities

DOI : https://doi.org/10.7191/jeslib.2017.1115

Versioned data: why it is needed and how it can be achieved (easily and cheaply)

Authors : Daniel S. Falster, Richard G. FitzJohn, Matthew W. Pennell, William K. Cornwell

The sharing and re-use of data has become a cornerstone of modern science. Multiple platforms now allow quick and easy data sharing. So far, however, data publishing models have not accommodated on-going scientific improvements in data: for many problems, datasets continue to grow with time — more records are added, errors fixed, and new data structures are created. In other words, datasets, like scientific knowledge, advance with time.

We therefore suggest that many datasets would be usefully published as a series of versions, with a simple naming system to allow users to perceive the type of change between versions. In this article, we argue for adopting the paradigm and processes for versioned data, analogous to software versioning.

We also introduce a system called Versioned Data Delivery and present tools for creating, archiving, and distributing versioned data easily, quickly, and cheaply. These new tools allow for individual research groups to shift from a static model of data curation to a dynamic and versioned model that more naturally matches the scientific process.

URL : Versioned data: why it is needed and how it can be achieved (easily and cheaply)

DOI : https://doi.org/10.7287/peerj.preprints.3401v1