The Data Management Skills Support Initiative: Synthesising Postgraduate Training in Research Data Management

This paper will describe the efforts and findings of the JISC Data Management Skills Support Initiative (‘DaMSSI’). DaMSSI was co-funded by the JISC Managing Research Data programme and the Research Information Network (RIN), in partnership with the Digital Curation Centre, to review, synthesise and augment the training offerings of the JISC Research Data Management Training Materials (‘RDMTrain’) projects.DaMSSI tested the effectiveness of the Society of College, National and University Libraries’ Seven Pillars of Information Literacy model (SCONUL, 2011), and Vitae’s Researcher Development Framework (‘Vitae RDF’) for consistently describing research data management (‘RDM’) skills and skills development paths in UK HEI postgraduate courses.With the collaboration of the RDMTrain projects, we mapped individual course modules to these two models and identified basic generic data management skills alongside discipline-specific requirements. A synthesis of the training outputs of the projects was then carried out, which further investigated the generic versus discipline-specific considerations and other successful approaches to training that had been identified as a result of the projects’ work. In addition we produced a series of career profiles to help illustrate the fact that data management is an essential component – in obvious and not-so-obvious ways – of a wide range of professions.We found that both models had potential for consistently and coherently describing data management skills training and embedding this within broader institutional postgraduate curricula. However, we feel that additional discipline-specific references to data management skills could also be beneficial for effective use of these models. Our synthesis work identified that the majority of core skills were generic across disciplines at the postgraduate level, with the discipline-specific approach showing its value in engaging the audience and providing context for the generic principles.Findings were fed back to SCONUL and Vitae to help in the refinement of their respective models, and we are working with a number of other projects, such as the DCC and the EC-funded Digital Curator Vocational Education Europe (DigCurV2) initiative, to investigate ways to take forward the training profiling work we have begun.

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Researcher training in spreadsheet curation

International Journal of Digital Curation ◽

10.2218/ijdc.v12i2.507 ◽

2018 ◽

Vol 12 (2) ◽

pp. 116-124

Author(s):

Gene Lyddon Melzack

Keyword(s):

Data Management ◽

Management Training ◽

Skills Training ◽

Research Data ◽

Practical Skills ◽

Research Data Management ◽

Qualitative Feedback ◽

Hands On ◽

Software Skills ◽

Data Documentation

Spreadsheets are commonly used across most academic discplines, however their use has been associated with a number of issues that affect the accuracy and integrity of research data. In 2016, new training on spreadsheet curation was introduced at the University of Sydney to address a gap between practical software skills training and generalised research data management training. The approach to spreadsheet curation behind the training was defined and the training's distinction from other spreadsheet curation training offering described.\parThe uptake of and feedback on the training were evaluated. Training attendance was analysed by discipline and by role. Quantitative and qualitative feedback were analysed and discussed. Feedback revealed that many attendees had been expecting and desired practical spreadsheet software skills training. Issues relating to whether or not practical skills training should and can be integrated with curation training were discussed. While attendees were found to be predominantly from science disciplines, qualitative feedback suggests that humanities attendees have specific needs in relation to managing data with spreadsheets that are currently not being met. Feedback also suggested that some attendees would prefer the curation training to be delivered as a longer, more in depth, hands on workshop.\parThe impact of the training was measured using data collected from the University's Research Data Management Planning (RDMP) tool and the Sydney eScholarship Repository. RDMP descriptions of spreadsheet data and records of tabular datasets published in the repository were analysed and assessed for quality and for accompanying data documentation. No significant improvements in data documentation or quality were found, however it is likely too soon after the launch of the training program to have seen much in the way of impact.\parIdentified next steps include clarifying the marketing material promoting to the training to better communicate the curation focus, investigating the needs of humanities researchers working with qualitative data in spreadsheets, and incorporating new material into the training in order to address those needs. Integrating curation training with practical skills training and modifying the training to be more hands on are changes that may be considered in future, but will not be implemented at this stage.

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Teaching Research Data Management

Innovations in Teaching & Learning Conference Proceedings ◽

10.13021/g8fc73 ◽

2014 ◽

Vol 6 ◽

Author(s):

Wendy Mann

Keyword(s):

Graduate Students ◽

Data Management ◽

Management Training ◽

Boot Camp ◽

Research Data ◽

University Libraries ◽

Management Skills ◽

Data Services ◽

Research Data Management ◽

The University

In January 2014, Mason Libraries along with four other libraries in Virginia used 4-VA telepresence technology to teach a two-day boot camp to graduate students on research data management. Our workshop generated greater than expected interest from graduate students here at Mason. One lesson learned is that there appears to be a gap in the curriculum across all participating schools regarding the teaching of data management skills to budding researchers. This session will be an overview of the learning outcomes from the Data Management Boot Camp, how the University Libraries' Data Services carries out research data management training, and discussion of future plans for helping researchers organize and manage their data.

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NFDI4Chem - Towards a National Research Data Infrastructure for Chemistry in Germany

Research Ideas and Outcomes ◽

10.3897/rio.6.e55852 ◽

2020 ◽

Vol 6 ◽

Cited By ~ 3

Author(s):

Christoph Steinbeck ◽

Oliver Koepler ◽

Felix Bach ◽

Sonja Herres-Pawlis ◽

Nicole Jung ◽

...

Keyword(s):

Data Management ◽

Data Science ◽

Open Data ◽

Research Data ◽

Data Standards ◽

Data Repositories ◽

Data Infrastructure ◽

Research Data Management ◽

Wide Range ◽

Chemistry Community

The vision of NFDI4Chem is the digitalisation of all key steps in chemical research to support scientists in their efforts to collect, store, process, analyse, disclose and re-use research data. Measures to promote Open Science and Research Data Management (RDM) in agreement with the FAIR data principles are fundamental aims of NFDI4Chem to serve the chemistry community with a holistic concept for access to research data. To this end, the overarching objective is the development and maintenance of a national research data infrastructure for the research domain of chemistry in Germany, and to enable innovative and easy to use services and novel scientific approaches based on re-use of research data. NFDI4Chem intends to represent all disciplines of chemistry in academia. We aim to collaborate closely with thematically related consortia. In the initial phase, NFDI4Chem focuses on data related to molecules and reactions including data for their experimental and theoretical characterisation. This overarching goal is achieved by working towards a number of key objectives: Key Objective 1: Establish a virtual environment of federated repositories for storing, disclosing, searching and re-using research data across distributed data sources. Connect existing data repositories and, based on a requirements analysis, establish domain-specific research data repositories for the national research community, and link them to international repositories. Key Objective 2: Initiate international community processes to establish minimum information (MI) standards for data and machine-readable metadata as well as open data standards in key areas of chemistry. Identify and recommend open data standards in key areas of chemistry, in order to support the FAIR principles for research data. Finally, develop standards, if there is a lack. Key Objective 3: Foster cultural and digital change towards Smart Laboratory Environments by promoting the use of digital tools in all stages of research and promote subsequent Research Data Management (RDM) at all levels of academia, beginning in undergraduate studies curricula. Key Objective 4: Engage with the chemistry community in Germany through a wide range of measures to create awareness for and foster the adoption of FAIR data management. Initiate processes to integrate RDM and data science into curricula. Offer a wide range of training opportunities for researchers. Key Objective 5: Explore synergies with other consortia and promote cross-cutting development within the NFDI. Key Objective 6: Provide a legally reliable framework of policies and guidelines for FAIR and open RDM.

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Research Data Management Training for Geographers: First Impressions

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi5040040 ◽

2016 ◽

Vol 5 (4) ◽

pp. 40 ◽

Cited By ~ 1

Author(s):

Kerstin Helbig

Keyword(s):

Data Management ◽

Management Training ◽

Research Data ◽

First Impressions ◽

Research Data Management

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Towards a Specialized Environmental Data Portal: Challenges and Opportunities

10.5194/egusphere-egu2020-13237 ◽

2020 ◽

Author(s):

Ionut Iosifescu-Enescu ◽

Gian-Kasper Plattner ◽

Dominik Haas-Artho ◽

David Hanimann ◽

Konrad Steffen

Keyword(s):

Data Management ◽

Scientific Information ◽

Central Element ◽

Research Data ◽

Environmental Data ◽

Research Data Management ◽

Environmental Domain ◽

Federal Institute ◽

Wide Range ◽

Data Portal

EnviDat &#8211; www.envidat.ch &#8211; is the institutional Environmental Data portal of the Swiss Federal Institute for Forest, Snow and Landscape Research WSL. Launched in 2012 as a small project to explore possible solutions for a generic WSL-wide data portal, it has since evolved into a strategic initiative at the institutional level tackling issues in the broad areas of Open Research Data and Research Data Management. EnviDat demonstrates our commitment to accessible research data in order to advance environmental science.EnviDat actively implements the FAIR (Findability, Accessibility, Interoperability and Reusability) principles. Core EnviDat research data management services include the registration, integration and hosting of quality-controlled, publication-ready data from a wide range of terrestrial environmental systems, in order to provide unified access to WSL&#8217;s environmental monitoring and research data. The registration of research data in EnviDat results in the formal publication with permanent identifiers (EnviDat own PIDs as well as DOIs) and the assignment of appropriate citation information.Innovative EnviDat features that contribute to the global system of modern documentation and exchange of scientific information include: (i) a DataCRediT mechanism designed for specifying data authorship (Collection, Validation, Curation, Software, Publication, Supervision), (ii) the ability to enhance published research data with additional resources, such as model codes and software, (iii) in-depth documentation of data provenance, e.g., through a dataset description as well as related publications and datasets, (iv) unambiguous and persistent identifiers for authors (ORCIDs) and, in the medium-term, (v) a decentralized &#8220;peer-review&#8221; data publication process for safeguarding the quality of available datasets in EnviDat.More recently, the EnviDat development has been moving beyond the set of core features expected from a research data management portal with a built-in publishing repository. This evolution is driven by the diverse set of researchers&#8217; requirements for a specialized environmental data portal that formally cuts across the five WSL research themes forest, landscape, biodiversity, natural hazards, and snow and ice, and that concerns all research units and central IT services.Examples of such recent requirements for EnviDat include: (i) immediate access to data collected by automatic measurements stations, (ii) metadata and data visualization on charts and maps, with geoservices for large geodatasets, and (iii) progress towards linked open data (LOD) with curated vocabularies and semantics for the environmental domain.There are many challenges associated with the developments mentioned above. However, they also represent opportunities for further improving the exchange of scientific information in the environmental domain. Especially geospatial technologies have the potential to become a central element for any specialized environmental data portal, triggering the convergence between publishing repositories and geoportals. Ultimately, these new requirements demonstrate the raised expectations that institutions and researchers have towards the future capabilities of research data portals and repositories in the environmental domain. With EnviDat, we are ready to take up these challenges over the years to come.

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Outside the R1: Equitable data management at the undergraduate level

IASSIST Quarterly ◽

10.29173/iq1011 ◽

2021 ◽

Vol 45 (2) ◽

Author(s):

Elizabeth Blackwood

Keyword(s):

Data Management ◽

Management Practices ◽

Undergraduate Curriculum ◽

Research Data ◽

Management Skills ◽

State University System ◽

Final Project ◽

Research Data Management ◽

Management Plans ◽

Regional Universities

Universities within the California State University System are given the mandate to teach the students of the state, as is the case with many regional, public universities. This mandate places teaching first; however, research and scholarship are still required activities for reaching retention, tenure, and promotion, as well as important skills for students to practice. Data management instruction for both faculty and undergraduates is often omitted at these institutions, which fall outside of the R1 designation. This happens for a variety of reasons, including personnel and resource limitations. Such limitations disproportionately burden students from underrepresented populations, who are more heavily represented at these institutions. These students have pathways to graduate school and the digital economy, like their counterparts at R1s; thus, they are also in need of research data management skills. This paper describes and provides a scalable, low-resource model for data management instruction from the university library and integrated into a department’s capstone or final project curriculum. In the case study, students and their instructors participated in workshops and submitted data management plans as a requirement of their final project. The analysis will analyze the results of the project and focus on the broader implications of integrating research data management into undergraduate curriculum at public, regional universities. By working with faculty to integrate data management practices into their curricula, librarians reach both students and faculty members with best practices for research data management. This work also contributes to a more equitable and sustainable research landscape.

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Towards a Unified University Infrastructure: The Data Management Roll-Out at the University of Oxford

International Journal of Digital Curation ◽

10.2218/ijdc.v8i2.287 ◽

2013 ◽

Vol 8 (2) ◽

pp. 235-246 ◽

Cited By ~ 6

Author(s):

James A. J. Wilson ◽

Paul Jeffreys

Keyword(s):

Data Management ◽

Research Data ◽

Management Policy ◽

Research Information ◽

Institutional Level ◽

Research Data Management ◽

University Of Oxford ◽

The University ◽

And Training ◽

Roll Out

Since presenting a paper at the International Digital Curation Conference 2010 conference entitled ‘An Institutional Approach to Developing Research Data Management Infrastructure’, the University of Oxford has come a long way in developing research data management (RDM) policy, tools and training to address the various phases of the research data lifecycle. Work has now begun on integrating these various elements into a unified infrastructure for the whole university, under the aegis of the Data Management Roll-out at Oxford (Damaro) Project.This paper will explain the process and motivation behind the project, and describes our vision for the future. It will also introduce the new tools and processes created by the university to tie the individual RDM components together. Chief among these is the ‘DataFinder’ – a hierarchically-structured metadata cataloguing system which will enable researchers to search for and locate research datasets hosted in a variety of different datastores from institutional repositories, through Web 2 services, to filing cabinets standing in department offices. DataFinder will be able to pull and associate research metadata from research information databases and data management plans, and is intended to be CERIF compatible. DataFinder is being designed so that it can be deployed at different levels within different contexts, with higher-level instances harvesting information from lower-level instances enabling, for example, an academic department to deploy one instance of DataFinder, which can then be harvested by another at an institutional level, which can then in turn be harvested by another at a national level.The paper will also consider the requirements of embedding tools and training within an institution and address the difficulties of ensuring the sustainability of an RDM infrastructure at a time when funding for such endeavours is limited. Our research shows that researchers (and indeed departments) are at present not exposed to the true costs of their (often suboptimal) data management solutions, whereas when data management services are centrally provided the full costs are visible and off-putting. There is, therefore, the need to sell the benefits of centrally-provided infrastructure to researchers. Furthermore, there is a distinction between training and services that can be most effectively provided at the institutional level, and those which need to be provided at the divisional or departmental level in order to be relevant and applicable to researchers. This is being addressed in principle by Oxford’s research data management policy, and in practice by the planning and piloting aspects of the Damaro Project.

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Research Data Management at 9 Universities in Baden-Wuerttemberg, Germany. The Results from the Final Report of the bwFDM Communities Project

Septentrio Conference Series ◽

10.7557/5.3665 ◽

2015 ◽

Author(s):

Karlheinz Pappenberger

Keyword(s):

Data Management ◽

Research Data ◽

The State ◽

Final Report ◽

Full Time ◽

It Service ◽

Research Data Management ◽

User Stories ◽

Wide Range ◽

The Status

See video of the presentation.On 17th July 2015 the Ministry of Science, Research and the Arts for Baden-Wuerttemberg, Germany, invited national experts to the presentation of the final report of the ‘bwFDM communities’ project. This 18 month project was launched at the beginning of 2014 to evaluate the needs of services and the support that libraries and IT service centres should offer researchers in the area of research data management. Full-time key project staff had been established at all 9 universities in the state of Baden-Wuerttemberg to conduct semi-structured personal interviews of all research groups working with research data (in a broad sense including all areas of science, social science and humanities) and to document them in the form of user stories. 627 interviews have been conducted and more than 2,500 user stories could be extracted, showing the wide range of needs and wishes articulated by researchers. On this basis issues of importance and requirements had be identified, categorised in 18 different groups and finalised into an analysis of the status quo and recommendations for concrete action plans. The results cover the areas ‘general requirements and policy framework’, ‘data collection and data sharing’, ‘technical framework and virtual research environments’, ‘preservation’, ‘IT infrastructure and IT support’, ‘licencing’ and ‘Open Science’.The presentation will give an overview of the project results and will highlight the roles libraries and IT service centres are expected to play from the researcher´s point of view.As the final report to the Ministry contributes to a comprehensive research data management strategy for the State of Baden-Wuerttemberg, the presentation will also point out the status of the federal strategy in RDM.

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