Tag: health research

01/08/2016

Robot to find and connect medical scientists working on the same research via Open Data

Sharing research data or Open Science, aims to accelerate scientific discovery, which is of particular importance in the case of new medicines and treatments. A grant proposal by an international research team, led by Dr Chase C. Smith, MCPHS University, and submitted to the Open Science Prize, suggests development of what the authors call The SCience INtroDuction Robot, (SCINDR). The project’s proposal is available in the open access journal Research Ideas and Outcomes (RIO).

Building on an open source electronic lab notebook (ELN) developed by the same team, the robot would discover and alert scientists from around the world who are working on similar molecules in real time. Finding each other and engaging in open and collaborative research could accelerate and enhance medical discoveries.

Already running and being constantly updated, the electronic lab notebook serves to store researchers’ open data in a machine-readable and openly accessible format. The next step before the scientists is to adapt the open source notebook to run SCINDR, as exemplified in their prototype.

“The above mentioned ELN is the perfect platform for the addition of SCINDR since it is already acting as a repository of open drug discovery information that can be mined by the robot,” explain the authors.

Once a researcher has their data stored on the ELN, or on any similar open database, for that matter, SCINDR would be able to detect if similar molecules, chemical reactions, biological assays or other features of importance in health research have been entered by someone else. If the robot identifies another scientist looking into similar features, it will suggest introducing the two to each other, so that they could start working together and combine their efforts and knowledge for the good of both science and the public.

Because of its ability to parse information and interests from around the globe, the authors liken SCINDR to online advertisements and music streaming services, which have long targeted certain content, based on a person’s writing, reading, listening habits or other search history.

“The potential for automatically connecting relevant people and/or matching people with commercial content currently dominates much of software development, yet the analogous idea of automatically connecting people who are working on similar science in real time does not exist,” stress the authors.

“This extraordinary fact arises in part because so few people work openly, meaning almost all the research taking place in laboratories around the world remains behind closed doors until publication (or in a minority of cases deposition to a preprint server), by which time the project may have ended and researchers have moved on or shelved a project.”

“As open science gathers pace, and as thousands of researchers start to use open records of their research, we will need a way to discover the most relevant collaborators, and encourage them to connect. SCINDR will solve this problem,” they conclude.

The system is intended to be tested initially by a community of researchers known as Open Source Malaria (OSM), a consortium funded to carry out drug discovery and development for new medicines for the treatment of malaria.

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Original source:

Smith C, Todd M, Patiny L, Swain C, Southan C, Williamson A, Clark A (2016) SCINDR – The SCience INtroDuction Robot that will Connect Open Scientists. Research Ideas and Outcomes 2: e9995. doi: 10.3897/rio.2.e9995

17/05/2016

Open Science environment Unicorn allows researchers and decision makers to work together

Given that the most important societal needs require multidisciplinary collaboration between researchers and decision makers, a suitable environment has to be provided in the first place. A proposal, prepared by a Finnish consortium and published in the open access journal Research Ideas and Outcomes, suggests a new, open virtual work and modeling platform to support evidence-based decision making in a number of areas, while also abiding by the principles of openness, criticism and reuse.

The Finnish consortium, led by Prof. Pekka Neittaanmäki, University of Jyväskylä, and bringing together Timo Huttula and Janne Ropponen, Finnish Environment Institute, Juha Karvanen and Tero Tuovinen, University of Jyväskylä, Tom Frisk, Pirkanmaa Centre for Economic Development, Transport and the Environment, Jouni Tuomisto, National Institute for Health and Welfare, and Antti Simola, VATT Institute for Economic Research, acknowledge that, “it is not enough that experts push data to politicians.”

“There must be practices for mutual communication: experts must answer policy questions in a defendable and useful way; decision makers must more clearly explain their views using evidence; and there must be ICT tools to support this exchange,” the authors explain. “The focus is on end-users.”

Unicorn is to combine shared practices, tools, data, working environments and concerted actions in order to aggregate open information from multiple databases, and create tools for efficient policy studies.

The consortium have already developed and tested prototypes of such practices and tools in several projects, and insist that they are now ready to apply their experience and knowledge on a larger scale. They are also certain that open data and models are deservedly the “mega trend” nowadays.

“Unicorn directs this trend to paths that are the most beneficial for societal decision making by providing quick, reliable and efficient decision support,” they say.

“Significant saving of resources will be manifested with improved data collection, analyses and modeling. Also, the quality and amount of assessments that can be done to support work.”

“The major challenges related to evidence-based decision making actually are about changing the practices of researchers and decision makers,” according to the authors. Therefore, they see their project as a demonstration of the needed shifts.

Although the approach is applicable in all areas, the researchers are to initially implement them in environment, human health, and regional economy, “as they are complex and challenging enough to offer a good test bed for general development.”

Having already been submitted to the Strategic Funds of Academy of Finland in 2015, the Unicorn environment proposal has been rejected due to overambitiousness and low commercial potential. However, the authors are confident that the Unicorn environment along with its growing community of developers can, in fact, meet a great success. They are currently looking for further funding suggestions and forming new consortiums.

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Original source:

Neittaanmäki P, Huttula T, Karvanen J, Frisk T, Tuomisto J, Simola A, Tuovinen T, Ropponen J (2016) Unicorn-Open science for assessing environmental state, human health and regional economy. Research Ideas and Outcomes 2: e9232. doi: 10.3897/rio.2.e9232

12/05/2016

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.”

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