scholarly journals The History, Advocacy and Efficacy of Data Management Plans

2018 ◽  
Author(s):  
Nicholas Smale ◽  
Kathryn Unsworth ◽  
Gareth Denyer ◽  
Daniel Barr

AbstractData management plans (DMPs) have increasingly been encouraged as a key component of institutional and funding body policy. Although DMPs necessarily place administrative burden on researchers, proponents claim that DMPs have myriad benefits, including enhanced research data quality, increased rates of data sharing, and institutional planning and compliance benefits.In this manuscript, we explore the international history of DMPs and describe institutional and funding body DMP policy. We find that economic and societal benefits from presumed increased rates of data sharing was the original driver of mandating DMPs by funding bodies. Today, 86% of UK Research Councils and 63% of US funding bodies require submission of a DMP with funding applications. Given that no major Australian funding bodies require DMP submission, it is of note that 37% of Australian universities have taken the initiative to internally mandate DMPs.Institutions both within Australia and internationally frequently promote the professional benefits of DMP use, and endorse DMPs as ‘best practice’. We analyse one such typical DMP implementation at a major Australian institution, finding that DMPs have low levels of apparent translational value. Indeed, an extensive literature review suggests there is very limited published systematic evidence that DMP use has any tangible benefit for researchers, institutions or funding bodies.We are therefore led to question why DMPs have become the go-to tool for research data professionals and advocates of good data practice. By delineating multiple use-cases and highlighting the need for DMPs to be fit for intended purpose, we question the view that a good DMP is necessarily that which encompasses the entire data lifecycle of a project. Finally, we summarise recent developments in the DMP landscape, and note a positive shift towards evidence-based research management through more researcher-centric, educative, and integrated DMP services.

1970 ◽  
Vol 15 (1) ◽  
pp. 30
Author(s):  
Nicholas Andrew Smale ◽  
Kathryn Unsworth ◽  
Gareth Denyer ◽  
Elise Magatova ◽  
Daniel Barr

Data management plans (DMPs) have increasingly been encouraged as a key component of institutional and funding body policy. Although DMPs necessarily place administrative burden on researchers, proponents claim that DMPs have myriad benefits, including enhanced research data quality, increased rates of data sharing, and institutional planning and compliance benefits. In this article, we explore the international history of DMPs and describe institutional and funding body DMP policy. We find that economic and societal benefits from presumed increased rates of data sharing was the original driver of mandating DMPs by funding bodies. Today, 86% of UK Research Councils and 63% of US funding bodies require submission of a DMP with funding applications. Given that no major Australian funding bodies require DMP submission, it is of note that 37% of Australian universities have taken the initiative to internally mandate DMPs. Institutions both within Australia and internationally frequently promote the professional benefits of DMP use, and endorse DMPs as ‘best practice’. We analyse one such typical DMP implementation at a major Australian institution, finding that DMPs have low levels of apparent translational value. Indeed, an extensive literature review suggests there is very limited published systematic evidence that DMP use has any tangible benefit for researchers, institutions or funding bodies. We are therefore led to question why DMPs have become the go-to tool for research data professionals and advocates of good data practice. By delineating multiple use-cases and highlighting the need for DMPs to be fit for intended purpose, we question the view that a good DMP is necessarily that which encompasses the entire data lifecycle of a project. Finally, we summarise recent developments in the DMP landscape, and note a positive shift towards evidence-based research management through more researcher-centric, educative, and integrated DMP services.


Author(s):  
Florencia Grattarola ◽  
Daniel Pincheira-Donoso

Data-sharing has become a key component in the modern scientific era of large-scale research, with numerous advantages for both data collectors and users. However, data-sharing in Uruguay remains neglected given that major public sources of biodiversity information (government and academia) are not open-access. As a consequence, the patterns and drivers of biodiversity in this country remain poorly understood and so does our ability to manage and conserve its biodiversity. To overcome this critical gap, collaborative strategies are needed to communicate the importance and benefits of data openness, exchange and provide technical tools and training on all aspects of data management, sharing practices, focus on incentives, and motivation structures for data-holders. Here, we introduce the Biodiversidata initiative (www.biodiversidata.org) – a novel Uruguayan Consortium of Biodiversity Data. Biodiversidata is a collaboration among experts with the aim of improving the country’s biodiversity knowledge and the open-access of the vast resources they generate. Biodiversidata aims to collate the first comprehensive open-access database on Uruguay's whole biodiversity, to support advancements in scientific research and conservation actions. Currently, Biodiversidata consists of over 30 experts from across national and international institutions, studying diverse biodiversity groups. After less than two years, we have collected, curated and standardised a dataset of ~70,000 records of primary biodiversity data of tetrapod species – the first and most comprehensive open biodiversity database ever gathered for Uruguay to date. However, the process is hampered by multiple challenges: the lack of support for sampling of specimens and maintenance of collections has contributed to the situation were data are often perceived as personal property rather than collective resources; institutions have no plans or strategies directed to digitisation of their collections which actually places biodiversity data in Uruguay ‘at risk’ of being lost; the scarce governmental and academic incentive structures towards open scientific research relegates data-sharing to a personal decision; although scientists individually are willing to share their research data, the lack of data management plans within their research groups hampers the capacity to digitise the data and thus, to make them available; former initiatives aimed to create comprehensive biodiversity databases did not consider the balance between openness and gain for researchers, setting the subject of data-sharing more of an obligation than a path of promotion, which impacted negatively in the perception of scientist to open their data. the lack of support for sampling of specimens and maintenance of collections has contributed to the situation were data are often perceived as personal property rather than collective resources; institutions have no plans or strategies directed to digitisation of their collections which actually places biodiversity data in Uruguay ‘at risk’ of being lost; the scarce governmental and academic incentive structures towards open scientific research relegates data-sharing to a personal decision; although scientists individually are willing to share their research data, the lack of data management plans within their research groups hampers the capacity to digitise the data and thus, to make them available; former initiatives aimed to create comprehensive biodiversity databases did not consider the balance between openness and gain for researchers, setting the subject of data-sharing more of an obligation than a path of promotion, which impacted negatively in the perception of scientist to open their data. To overcome some of these challenges, we decided to direct Biodiversidata to individual researchers/experts and not institutions. We called them with the plan of collecting the maximum possible amount of data from vertebrate, invertebrate and plant species, use it to collaboratively generate impactful scientific research. An important aspect was that we requested data only to fit the premise of being primary biodiversity data (i.e., data records that document the occurrence of a species in space and time). This meant cleaning and standardising very heterogeneous information, from a variety of source types and formats, including updating scientific names and georeferentiating sampling locations. However, centralising the cleaning process allowed researchers to send their raw records without spending time cleaning them themselves and, as a consequence, enlarged the amount of data being collated. Collectively, Biodiversidata’s approach towards changing the culture of data-sharing practices has relied on the reinforcement of a scientific collaboration culture that benefits not only researchers at the individual level, but the progress of larger-scale issues as a whole. There is a long way to go on the subject of open research data in Uruguay, though, aiming strategies to people, capitalising data management and progressing with step-by-step rewards, is already showing some preliminary encouraging results.


2019 ◽  
Vol 39 (06) ◽  
pp. 322-328
Author(s):  
Cynthia Hudson Vitale ◽  
Heather Moulaison Sandy

With increasing world-wide emphasis on providing access to research data, data management plans (DMPs) have emerged as the expected way for researchers to formalise and communicate their intentions to stakeholders, including to their funders. This review paper focuses on a thematic analysis and presentation of empirical research on DMPs, a literature that is surprisingly limited, likely due to the young age of the field. Research shows that, despite the benefits associated with data sharing, DMPs have potential that is not being realised to the fullest. Researchers in scholarly communication and information science primarily have evaluated DMPs using text analysis methodologies, often supplementing them with surveys or interviews. Future study, especially in areas of machine-actionable DMPs is promising; such research is needed to further explore how DMPs can best be utilised to support data sharing.


2018 ◽  
Vol 12 (2) ◽  
pp. 107-115 ◽  
Author(s):  
Minna Ahokas ◽  
Mari Elisa Kuusniemi ◽  
Jari Friman

Many research funders have requirements for data sharing and data management plans (DMP). DMP tools are services built to help researchers to create data management plans fitting their needs and based on funder and/or organisation guidelines. Project Tuuli (2015–2017) has provided DMPTuuli, a data management planning tool for Finnish researchers and research organisations offering DMP templates and guidance. In this paper we describe how project has helped both Finnish researchers and research organisations adopt research data management best practices. As a result of the project we have also created a national Tuuli network. With growing competence and collaboration of the network, the project has reached most of its goals. The project has also actively promoted DMP support and training in Finnish research organisations.


GigaScience ◽  
2020 ◽  
Vol 9 (10) ◽  
Author(s):  
Daniel Arend ◽  
Patrick König ◽  
Astrid Junker ◽  
Uwe Scholz ◽  
Matthias Lange

Abstract Background The FAIR data principle as a commitment to support long-term research data management is widely accepted in the scientific community. Although the ELIXIR Core Data Resources and other established infrastructures provide comprehensive and long-term stable services and platforms for FAIR data management, a large quantity of research data is still hidden or at risk of getting lost. Currently, high-throughput plant genomics and phenomics technologies are producing research data in abundance, the storage of which is not covered by established core databases. This concerns the data volume, e.g., time series of images or high-resolution hyper-spectral data; the quality of data formatting and annotation, e.g., with regard to structure and annotation specifications of core databases; uncovered data domains; or organizational constraints prohibiting primary data storage outside institional boundaries. Results To share these potentially dark data in a FAIR way and master these challenges the ELIXIR Germany/de.NBI service Plant Genomic and Phenomics Research Data Repository (PGP) implements a “bring the infrastructure to the data” approach, which allows research data to be kept in place and wrapped in a FAIR-aware software infrastructure. This article presents new features of the e!DAL infrastructure software and the PGP repository as a best practice on how to easily set up FAIR-compliant and intuitive research data services. Furthermore, the integration of the ELIXIR Authentication and Authorization Infrastructure (AAI) and data discovery services are introduced as means to lower technical barriers and to increase the visibility of research data. Conclusion The e!DAL software matured to a powerful and FAIR-compliant infrastructure, while keeping the focus on flexible setup and integration into existing infrastructures and into the daily research process.


2020 ◽  
Vol 2 (4) ◽  
pp. 554-568
Author(s):  
Chris Graf ◽  
Dave Flanagan ◽  
Lisa Wylie ◽  
Deirdre Silver

Data availability statements can provide useful information about how researchers actually share research data. We used unsupervised machine learning to analyze 124,000 data availability statements submitted by research authors to 176 Wiley journals between 2013 and 2019. We categorized the data availability statements, and looked at trends over time. We found expected increases in the number of data availability statements submitted over time, and marked increases that correlate with policy changes made by journals. Our open data challenge becomes to use what we have learned to present researchers with relevant and easy options that help them to share and make an impact with new research data.


Author(s):  
Susanne Blumesberger ◽  
Nikos Gänsdorfer ◽  
Raman Ganguly ◽  
Eva Gergely ◽  
Alexander Gruber ◽  
...  

This article gives an overview of the FAIR Data Austria project objectives and current results. In collaboration with our project partners, we work on the development and establishment of tools for managing the lifecycle of research data, including machine-actionable Data Management Plans (maDMPs), repositories for long-term archiving of research results, RDM training and support services, models, and profiles for Data Stewards and FAIR Office Austria.


2017 ◽  
Vol 78 (5) ◽  
pp. 274 ◽  
Author(s):  
Sarah Barbrow ◽  
Denise Brush ◽  
Julie Goldman

Research in many academic fields today generates large amounts of data. These data not only must be processed and analyzed by the researchers, but also managed throughout the data life cycle. Recently, some academic libraries have begun to offer research data management (RDM) services to their communities. Often, this service starts with helping faculty write data management plans, now required by many federal granting agencies. Libraries with more developed services may work with researchers as they decide how to archive and share data once the grant work is complete.


2018 ◽  
Vol 4 (1) ◽  
pp. 68-75 ◽  
Author(s):  
H. Spallek ◽  
S.M. Weinberg ◽  
M. Manz ◽  
S. Nanayakkara ◽  
X. Zhou ◽  
...  

Introduction: Increasing attention is being given to the roles of data management and data sharing in the advancement of research. This study was undertaken to explore opinions and past experiences of established dental researchers as related to data sharing and data management. Methods: Researchers were recruited from the International Association for Dental Research scientific groups to complete a survey consisting of Likert-type, multiple-choice, and open-ended questions. Results: All 42 respondents indicated that data sharing should be promoted and facilitated, but many indicated reservations or concerns about the proper use of data and the protection of research subjects. Many had used data from data repositories and received requests for data originating from their studies. Opinions varied regarding restrictions such as requirements to share data and the time limits of investigator rights to keep data. Respondents also varied in their methods of data management and storage, with younger respondents and those with higher direct costs of their research tending to use dedicated experts to manage their data. Discussion: The expressed respondent support for research data sharing, with the noted concerns, complements the idea of developing managed data clearinghouses capable of promoting, managing, and overseeing the data-sharing process. Knowledge Transfer Statement: Researchers can use the results of this study to evaluate and improve management and sharing of research data. By encouraging and facilitating the data-sharing process, research can advance more efficiently, and research findings can be implemented into practice more rapidly to improve patient care and the overall oral health of populations.


2020 ◽  
Vol 6 ◽  
Author(s):  
Mareike Petersen ◽  
Bianca Pramann ◽  
Ralf Toepfer ◽  
Janna Neumann ◽  
Harry Enke ◽  
...  

This report describes the results of a workshop on research data management (RDM) that took place in June 2019. More than 50 experts from 46 different non-university institutes covering all Leibniz Sections participated. The aim of the workshop was the intra- and transdisciplinary exchange among RDM experts of different institutions and sections within the Leibniz Association on current questions and challenges but also on experiences and activities with respect to RDM. The event was structured in inspiring talks, a World Café to discuss ideas and solutions related to RDM and an exchange of experts following their affiliation to the different Leibniz sections. The workshop revealed that most institutions, independent of scientific fields, face similar overarching problems with respect to RDM, e.g. missing incentives and no awareness of the benefits that would arise from a proper RDM and data sharing. The event also endorsed that the Research Data Working Group of the Leibniz Association (AK Forschungsdaten) is a place for the exchange of all topics around RDM and enables discussions on how to refine RDM at all institutions and in all scientific fields.


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