scholarly journals Biodiversidata: A Collaborative Initiative Towards Open Data Availability in Uruguay

2019 ◽  
Vol 3 ◽  
Author(s):  
Florencia Grattarola ◽  
Daniel Pincheira-Donoso
Keyword(s):  
Open Access ◽  
Data Management ◽  
Data Sharing ◽  
Scientific Research ◽  
Research Data ◽  
Biodiversity Data ◽  
Personal Property ◽  
Incentive Structures ◽  
Management Plans ◽  
The Subject

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.

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

2018 ◽  
Author(s):  
Nicholas Smale ◽  
Kathryn Unsworth ◽  
Gareth Denyer ◽  
Daniel Barr
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Best Practice ◽  
Research Data ◽  
Research Management ◽  
Extensive Literature ◽  
Multiple Use ◽  
Funding Body ◽  
Management Plans ◽  
Recent Developments

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.

scholarly journals A Review of the History, Advocacy and Efficacy of Data Management Plans

1970 ◽  
Vol 15 (1) ◽  
pp. 30
Author(s):  
Nicholas Andrew Smale ◽  
Kathryn Unsworth ◽  
Gareth Denyer ◽  
Elise Magatova ◽  
Daniel Barr
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Best Practice ◽  
Research Data ◽  
Research Management ◽  
Extensive Literature ◽  
Multiple Use ◽  
Funding Body ◽  
Management Plans ◽  
Recent Developments

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.

scholarly journals Data Management Plans A Review

2019 ◽  
Vol 39 (06) ◽  
pp. 322-328
Author(s):  
Cynthia Hudson Vitale ◽  
Heather Moulaison Sandy
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Thematic Analysis ◽  
Text Analysis ◽  
Information Science ◽  
Review Paper ◽  
Scholarly Communication ◽  
Field Research ◽  
Research Data ◽  
Management Plans

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.

Research Data Management Support at Kaunas University of Technology

2021 ◽  
pp. 52-71
Author(s):  
Ieva Cesevičiūtė ◽  
Gintarė Tautkevičienė
Keyword(s):  
Open Access ◽  
Data Management ◽  
National Level ◽  
Open Science ◽  
Research Data ◽  
Management Support ◽  
Research Data Management ◽  
University Of Technology ◽  
Management Plans ◽  
The Baltic

Kaunas University of Technology is one of the largest technical universities in the Baltic region. The university staff has been involved in different Open Access- and Open Science-related activities for more than a decade. Different initiatives have been implemented: stand-alone and series of training and awareness-raising events, promotion of Open Access and Open Science ideas so that institutions develop their Open Access policies and make their repositories compliant with larger research infrastructures. Within the institution, the initiatives of Open Science are implemented as a result of joint effort of the library, the departments of research, studies, and doctoral school. The current tasks involve revising the institutional Open Access guidelines and facilitating the implementation of data management plans in doctoral studies. In this chapter, the aim is to provide an overview of the efforts highlighting the successes and failures on the way to best practice in research data management support both institutionally and on the national level.

scholarly journals Tuuli project: accelerating data management planning in Finnish research organisations

2018 ◽  
Vol 12 (2) ◽  
pp. 107-115 ◽  
Author(s):  
Minna Ahokas ◽  
Mari Elisa Kuusniemi ◽  
Jari Friman
Keyword(s):  
Best Practices ◽  
Data Management ◽  
Data Sharing ◽  
Research Data ◽  
Planning Tool ◽  
Management Planning ◽  
Research Data Management ◽  
Management Plans ◽  
Research Organisations ◽  
And Training

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.

scholarly journals Biodiversity Data Sharing by Public Funded Institutions: Perspectives from India

2019 ◽  
Vol 3 ◽  
Author(s):  
Gautam Talukdar ◽  
Andrew Townsend Peterson ◽  
Vinod Mathur
Keyword(s):  
Open Access ◽  
Data Sharing ◽  
Natural World ◽  
Primary Data ◽  
Biodiversity Data ◽  
Public Funds ◽  
Research Activities ◽  
The Government ◽  
Access To Data

In India, biodiversity data and information are gaining significance for sustainable development and preparing National Biodiversity Strategies and Action Plans (NBSAPs). Civil societies and individuals are seeking open access to data and information generated with public funds, whereas sensitivity requirements often demand restrictions on the availability of sensitive data. In India, the traditional classification of data for sharing was based on the "Open Series Data" model; i.e. data not specifically included remains inaccessible. The National Data Sharing and Accessibility Policy (NDSAP Anonymous 2012Suppl. material 1) published in 2012 produced a new data sharing framework more focused on the declaration of data as closed. NDSAP is a clear statement that data that are produced by the Government of India should be shared openly. Although much of the verbiage is focused on sharing within the Government to meet national goals, the document does include clear statements about sharing with the public. The policy is intended to apply "to all data and information created, generated, collected and archived using public funds provided by the Government of India". The policy is quite clear that it should apply to all such data, and that such data should be categorized into open-access, registered-access, or restricted-access. NDSAP indicates that all Government of India-produced/funded data is to be opened to the broader community, but provides three access categories (open, registered, restricted). Although NDSAP does not offer much guidance about what sorts of data should fall in each of the categories, it clearly focuses on data sensitive in terms of national security (i.e., data that must be restricted), such as high-resolution satellite imagery of disputed border regions. Institutions collecting biodiversity data usually include primary, research-grade data in the restricted-access category and secondary / derived data (e.g., vegetation maps, species distribution maps) in the open or registered-access category. The conservative approach of not making bioidiversity data easily accessible, is not in accordance with the NDSAP policy, which emphasizes the openness of data. It also counters the main currents in science, which are shifting massively in the direction of opening access to data. Though NDSAP was intended for full implementation by 2014, its uptake by the institutions engaged in primary biodiversity data collection has been slow mainly because: providing primary data in some cases can endanger elements of the natural world; and many researchers wish to keep the data that result from their research activities shielded from full, open access out of a desire to retain control of those data for future analysis or publication. providing primary data in some cases can endanger elements of the natural world; and many researchers wish to keep the data that result from their research activities shielded from full, open access out of a desire to retain control of those data for future analysis or publication. Biodiversity data collected as part of institutional activities belong, in some sense, to the institution, and the institution should value such data over the long term. If institutions curate their biodiversity data for posterity, they can reap the benefits. Imagine the returns if biodiversity data from current ongoing projects were to be compared to data collected 50-100 years later. Thus, organizations should emphasize the long-term view of institutionalizing data resources through fair data restrictions and emphasise on public access, rather than on individual rights and control. This approach may be debatable, but we reckon that it will translate into massive science pay-offs.

scholarly journals The Open Data Challenge: An Analysis of 124,000 Data Availability Statements and an Ironic Lesson about Data Management Plans

2020 ◽  
Vol 2 (4) ◽  
pp. 554-568
Author(s):  
Chris Graf ◽  
Dave Flanagan ◽  
Lisa Wylie ◽  
Deirdre Silver
Keyword(s):  
Machine Learning ◽  
Data Management ◽  
Open Data ◽  
Research Data ◽  
Data Availability ◽  
Policy Changes ◽  
Management Plans ◽  
Trends Over Time ◽  
New Research ◽  
Over Time

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.

scholarly journals Práticas e percepções dos pesquisadores brasileiros sobre serviços de acesso aberto a dados de pesquisa | Practices and perceptions of Brazilian researchers on open access to research data

2019 ◽  
Vol 15 (2) ◽  
Author(s):  
Sonia Elisa Caregnato ◽  
Samile Andrea de Souza Vanz ◽  
Caterina Groposo Pavão ◽  
Paula Caroline Jardim Schifino Passos ◽  
Eduardo Borges ◽  
...  
Keyword(s):  
Open Access ◽  
Data Sharing ◽  
Research Data ◽  
Exploratory Analysis ◽  
Data Reuse ◽  
Institutional Repositories ◽  
Open Research

RESUMO O artigo apresenta análise exploratória das práticas e das percepções a respeito do acesso aberto a dados de pesquisa embasada em dados coletados por meio de survey, realizada com pesquisadores brasileiros. As 4.676 respostas obtidas demonstram que, apesar do grande interesse pelo tema, evidenciado pela prevalência de variáveis relacionadas ao compartilhamento e ao uso de dados e aos repositórios institucionais, não há clareza por parte dos sujeitos sobre os principais tópicos relacionados. Conclui-se que, apesar da maioria dos pesquisadores afirmar que compartilha dados de pesquisa, a disponibilização desses dados de forma aberta e irrestrita ainda não é amplamente aceita.Palavras-chave: Dados Abertos de Pesquisa; Compartilhamento de Dados; Reuso de Dados.ABSTRACT This article presents an exploratory analysis of the practices and perceptions regarding open access to research data based on information collected by a survey with Brazilian researchers. The 4,676 responses show that, despite the great interest in the topic, evidenced by the prevalence of variables related to data sharing and use and to institutional repositories, there is no clarity on the part of the subjects on the main related topics. We conclude that, although the majority of the researchers share research data, the availability of this data in an open and unrestricted way is not yet widely accepted.Keywords: Open Research Data; Data Sharing; Data Reuse.

scholarly journals FAIR Data Austria - Aligning the Implementation of FAIR Tools and Services

2021 ◽  
Vol 74 (2) ◽  
Author(s):  
Susanne Blumesberger ◽  
Nikos Gänsdorfer ◽  
Raman Ganguly ◽  
Eva Gergely ◽  
Alexander Gruber ◽  
...  
Keyword(s):  
Data Management ◽  
Support Services ◽  
Research Data ◽  
Research Results ◽  
Management Plans ◽  
Project Objectives ◽  
Training And Support

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.

scholarly journals Research data management and services: Resources for novice data librarians

2017 ◽  
Vol 78 (5) ◽  
pp. 274 ◽  
Author(s):  
Sarah Barbrow ◽  
Denise Brush ◽  
Julie Goldman
Keyword(s):  
Life Cycle ◽  
Data Management ◽  
Academic Libraries ◽  
Research Data ◽  
Research Data Management ◽  
Academic Fields ◽  
Data Life Cycle ◽  
Management Plans ◽  
Share Data

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.

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