Sharing biodiversity data: Best tools and practices via the EU-funded project EU BON

Due to the exponential growth of biodiversity information in recent years, the questions of how to mobilize such vast amounts of data has become more tangible than ever. Best practices for data sharing, data publishing, and involvement of scientific and citizen communities in data generation are the main topic of a recent report by the EU FP7 project Building the European Biodiversity Observation Network (EU BON), published in the innovative Research Ideas & Outcomes (RIO) journal.

The report “Data sharing tools for Biodiversity Observation Networks” provides conceptual and practical advice for implementation of the available data sharing and data publishing tools. A detailed description of tools, their pros and cons, is followed by recommendations on their deployment and enhancement to guide biodiversity data managers in their choices.

“We believe publishing this report in RIO makes a lot of sense given the journal’s innovative concept of publishing unconventional research outcomes such as project reports. This feature provides projects like EU BON with the chance to showcase their results effectively and timely. The report provides a useful practical guide for biodiversity data managers and RIO gives the project an opportunity to share findings with anyone who will make use of such information”, explains Prof. Lyubomir Penev, Managing Director of Pensoft and partner in EU BON.

The new report is the second EU BON contribution featured in a dedicated project outcomes collection in RIO. Together with the data policy recommendations it provides a comprehensive set of resources for the use of biodiversity data managers and users.

“We did our biodiversity data sharing tools comparison from the perspective of the needs of the biodiversity observation community with an eye on the development of a unified user interface to this data – the European Biodiversity Portal (EBP)”, add the authors.

The scientists have identified two main challenges standing in front of the biodiversity data community. On the one hand, there is a variety of tools but none can as stand alone, satisfy all the requirements of the wide variety of data providers. On the other hand, gaps in data coverage and quality demand more effort in data mobilization.

Envisaged information flows between EU BON and LTER Europe, showing the complexity of sharing biodiversity data (from the 3rd EU BON Stakeholder Roundtable, Granada on 9-11 December 2015).
Envisaged information flows between EU BON and LTER Europe, showing the complexity of sharing biodiversity data (from the 3rd EU BON Stakeholder Roundtable, Granada on 9-11 December 2015).

“For the time being a combination of tools combined in a new work-flow, makes the most sense for EU BON to mobilize biodiversity data,” comment the report authors on their findings. “There is more research to be done and tools to be developed, but for the future there is one firm conclusion and it is that the choice of tools should be defined by the needs of those observing biodiversity – the end user community in the broadest sense – from volunteer scientists to decision makers.”

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Original Source:

Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe’er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. doi: 10.3897/rio.2.e9390

 

About EU BON:

EU BON stands for “Building the European Biodiversity Observation Network” and is a European research project, financed by the 7th EU framework programme for research and development (FP7). EU BON seeks ways to better integrate biodiversity information and implement into policy and decision-making of biodiversity monitoring and management in the EU.

 

 

Open neuroscience: Collaborative Neuroimaging Lab finalist for the Open Science Prize

Despite the abundance of digital neuroimaging data, shared thanks to all funding, data collection, and processing efforts, but also the goodwill of thousands of participants, its analysis is still falling behind. As a result, the insight into both mental disorders and cognition is compromised.

The Open Neuroimaging Laboratory framework, promises a collaborative and transparent platform to optimise both the quantity and quality of this invaluable brain data, ultimately gaining a greater insight into both mental disorders and cognition.

The project was submitted for the Open Science Prize competition by Katja Heuer, Max Planck Institute for Human Cognitive and Brain Sciences, Germany, Dr Satrajit S. Ghosh, Massachusetts Institute of Technology (MIT), USA, Amy Robinson Sterling, EyeWire, USA, and Dr Roberto Toro, Institut Pasteur, France. Amongst 96 submissions from all around the globe, it was chosen as one of six teams to compete in the second and final phase of the Prize.

Simply having access and being able to download brain magnetic resonance imaging (MRI) data is not enough to reap all potential benefits. In order for it to be turned into insight and knowledge, it needs to also be queried, pre-processed and analysed, which requires a substantial amount of human curation, visual quality assessment and manual editing. With research being rather patchy, a lot of efforts are currently redundant and unreliable.

On the other hand, the Open Neuroimaging Laboratory aims to aggregate annotated brain imaging data from across various resources, thus improving its searchability and potential for reuse. It is to also develop a tool that will facilitate and encourage the creation of distributed teams of researchers to collaborate together in the analysis of this open data in real time.

“Our project will help transform the massive amount of static brain MRI data readily available online into living matter for collaborative analysis,” explain the researchers.

“We will allow a larger number of researchers to have access to this data by lowering the barriers that prevent their analysis: no data will have to be downloaded or stored, no software will have to be installed, and it will be possible to recruit a large, distributed, group of collaborators online.”

“By working together in a distributed and collaborative way, sharing our work and our analyses, we should improve transparency, statistical power and reproducibility,” they elaborate. “Our aim is to provide to everyone the means to share effort, learn from each other, and improve quality of and trust in scientific output.”Untitled

Having already developed a functional prototype of the BrainBox web application, which provides an interactive online space for collaborative data analyses and discussions, the team will now turn it into a first version with an improved user experience, stability and documentation. Planned for the Open Science Prize Phase 2 are furthering the type of analyses and exploring the development of interfaces for database-wise statistical analyses.

In the spirit of the competition, the scientists have decided to release their code open source on GitHub to facilitate bug fixes, extension and maintainability.

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Original source:

Heuer K, Ghosh S, Robinson Sterling A, Toro R (2016) Open Neuroimaging Laboratory.Research Ideas and Outcomes 2: e9113. doi: 10.3897/rio.2.e9113

Data sharing pilot to report and reflect on data policy challenges via 8 case studies

This week, FORCE2016 is taking place in Portland, USA. The FORCE11 yearly conference is devoted to the utilisation of technological and open science advancements towards a new-age scholarship founded on easily accessible, organised and reproducible research data.

As a practical contribution to the scholarly discourse on new modes of communicating knowledge, Prof. Cameron Neylon, Centre for Culture and Technology, Curtin University, Australia, and collaborators are to publish a series of outputs and outcomes resulting from their ongoing data sharing pilot project in the open access journal Research Ideas and Outcomes (RIO).

Starting with their Grant Proposal, submitted and accepted for funding by the Canadian International Development Research Centre (IDRC), over the course of sixteen months, ending in December 2016, they are to openly publish the project outputs starting with the grant proposal.

The project will collaborate with 8 volunteering IDRC grantees to develop Data Management Plans, and then support and track their development. The project expects to submit literature reviews, Data Management Plans, case studies and a final research article with RIO. These will report and reflect on the lessons they will have learnt concerning open data policies in the specific context of development research. Thus, the project is to provide advice on refining the open research data policy guidelines.

“The general objective of this project is to develop a model open research data policy and implementation guidelines for development research funders to enable greater access to development research data,” sum up the authors.

“Very little work has been done examining open data policies in the context of development research specifically,” they elaborate. “This project will serve to inform open access to research data policies of development research funders through pilot testing open data management plan guidelines with a set of IDRC grantees.”

The researchers agree that data constitutes a primary form of research output and that it is necessary for research funders to address the issue of open research data in their open access policies. They note that not only should data be publicly accessible and free for re-use, but they need to be “technically open”, which means “available for no more than the cost of reproduction, and in machine-readable and bulk form.” At the same time, research in a development context raises complex issues of what data can be shared, how, and by whom.

“The significance of primary data gathered in research projects across domains is its high potential for not only academic re-use, but its value beyond academic purposes, particularly for governments, SME, and civil society,” they add. “More importantly, the availability of these data provides an ideal opportunity to test the key premise underlying open research data — that when it is made publicly accessible in easily reusable formats, it can foster new knowledge and discovery, and encourage collaboration among researchers and organizations.”

However, such openness is also calling for extra diligence and responsibility while sharing, handling and re-using the research data. This is particularly the case in development research, where challenging ethical issues come to the fore. The authors point out the issues, raised by such practice, to be, among others, realistic and cost-effective strategies for funded researchers to collect, manage, and store the various types of data resulting from their research, as well as ethical issues such as privacy and rights over the collected data.

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Original source:

Neylon C, Chan L (2016) Exploring the opportunities and challenges of implementing open research strategies within development institutions. Research Ideas and Outcomes 2: e8880. doi: 10.3897/rio.2.e8880