BEAT: An Open-Science Web Platform

“With the increased interest in computational sciences, machine learning (ML), pattern recognition (PR) and big data, governmental agencies, academia and manufacturers are overwhelmed by the constant influx of new algorithms and techniques promising improved performance, generalization and robustness. Sadly, result reproducibility is often an overlooked feature accompanying original research publications, competitions and benchmark evaluations. The main reasons behind such a gap arise from natural complications in research and development in this area: the distribution of data may be a sensitive issue; software frameworks are difficult to install and maintain; Test protocols may involve a potentially large set of intricate steps which are difficult to handle. To bridge this gap, we built an open platform for research in computational sciences related to pattern recognition and machine learning, to help on the development, reproducibility and certification of results obtained in the field. By making use of such a system, academic, governmental or industrial organizations enable users to easily and socially develop processing toolchains, re-use data, algorithms, workflows and compare results from distinct algorithms and/or parameterizations with minimal effort. This article presents such a platform and discusses some of its key features, uses and limitations. We overview a currently operational prototype and provide design insights.”

Academia.edu | Academia’s Partnership with Britann…

“Academia has teamed up with Encyclopedia Britannica to offer access to all of Britannica’s content to Academia Premium users.

 Academia is also inviting its members to contribute as authors on Britannica’s Publisher Partner Program. We’ve joined dozens of institutions including UC Berkeley, Northwestern University, the University of Melbourne and others in support of the initiative, which aims to expand Britannica’s free, open access content.”

New tool could standardize the process of sharing research materials – Tech Transfer e-News – Tech Transfer Central

“Developed by the UK OpenPlant Synthetic Biology Research Centre and the BioBricks Foundation, OpenMTA honors the rights of researchers and promotes safe, responsible laboratory practices. In addition, the tool is designed to work within the practical realm of tech transfer and to be adaptable to the needs of multiple groups globally.

Goals for OpenMTA include:

  • Free access to the tool, with no royalties or other fees except for appropriate and nominal fees for preparation and distribution;
  • The ability for researchers to modify or repurpose materials available through OpenMTA;
  • Unrestricted selling and sharing of materials, whether it’s part of a collaboration or derivative work;
  • Availability to all kinds of institutions including academic, industrial, federal and community research centers

In its approach to tech transfer, Open MTA is designed to reduce transaction costs, support research collaboration across institutions and even nations, and provide a way for researchers and their labs to be credited for the materials they share.”

Open Material Transfer Agreement (OpenMTA)

“The Open Material Transfer Agreement (OpenMTA) is a simple, standardized legal tool that enables individuals and organizations to share their materials on an open basis….Developed as a collaborative effort led by the BioBricks Foundation and the OpenPlant Initiative, with input from researchers, technology transfer professionals, social scientists, lawyers, and other stakeholders from across the globe, the OpenMTA reflects the values of open communities and the practical realities of technology transfer….”

Open access: Half the time Unpaywall users search for academic journal articles that are legally free to access — Quartz

“Now a new study has found that nearly half of all academic articles that users want to read are already freely available. These studies may or may not have been published in an open-access journal, but there is a legally free version available for a reader to download.

To arrive at this conclusion, researcher Heather Piwowar and her colleagues used data from a web-browser extension they had developed called Unpaywall. When users of the extension land on an academic article, it trawls the web to find if there are free versions to download from places such as pre-print services or those uploaded on university websites.

In an analysis of 100,000 papers queried by Unpaywall, Piwowar and her colleagues found that as many as 47% searched for studies that had a free-to-read version available. The study is yet to be peer-reviewed, but Ludo Waltman of Leiden University told Nature that it is ‘careful and extensive.'”

Science Beam – using computer vision to extract PDF data | Labs | eLife

“There’s a vast trove of science out there locked inside the PDF format. From preprints to peer-reviewed literature and historical research, millions of scientific manuscripts today can only be found in a print-era format that is effectively inaccessible to the web of interconnected online services and APIs that are increasingly becoming the digital scaffold of today’s research infrastructure….Extracting key information from PDF files isn’t trivial. …It would therefore certainly be useful to be able to extract all key data from manuscript PDFs and store it in a more accessible, more reusable format such as XML (of the publishing industry standard JATS variety or otherwise). This would allow for the flexible conversion of the original manuscript into different forms, from mobile-friendly layouts to enhanced views like eLife’s side-by-side view (through eLife Lens). It will also make the research mineable and API-accessible to any number of tools, services and applications. From advanced search tools to the contextual presentation of semantic tags based on users’ interests, and from cross-domain mash-ups showing correlations between different papers to novel applications like ScienceFair, a move away from PDF and toward a more open and flexible format like XML would unlock a multitude of use cases for the discovery and reuse of existing research….We are embarking on a project to build on these existing open-source tools, and to improve the accuracy of the XML output. One aim of the project is to combine some of the existing tools in a modular PDF-to-XML conversion pipeline that achieves a better overall conversion result compared to using individual tools on their own. In addition, we are experimenting with a different approach to the problem: using computer vision to identify key components of the scientific manuscript in PDF format….To this end, we will be collaborating with other publishers to collate a broad corpus of valid PDF/XML pairs to help train and test our neural networks….”