“In 2018, the repository received 140,616 new submissions, a 14% increase from 2017. The subject distribution is evolving as Computer Science represented about 26% of overall submissions, and Math 24%. There were about 228 million downloads from all over the world. arXiv is truly a global resource, with almost 90% of supporting funds coming from sources other than Cornell and 70% of institutional use coming from countries other than the U.S….”
“One of the Allen Institute’s priorities is an academically oriented search engine, established in 2015, called Semantic Scholar (slogan: “Cut through the clutter”). The need is great, with more than 34,000 peer-reviewed journals publishing 2.5 million articles a year. “What if a cure for an intractable cancer is hidden within the tedious reports on thousands of clinical studies?,” Etzioni once said.
Although Semantic Scholar has focused so far on computer and biomedical sciences, Etzioni says that the engine will soon push into the social sciences and the humanities as well. The Chronicle spoke with him about information overload, impact factors’ imperfect inevitability, and the promise and perils of AI….”
“Michael Nielsen recognizes that Open Access is often argued about in the abstract. To help the discussion move from the conceptual to the concrete, he recently decided to openly share his experience of writing an open-access book, “Neural Networks and Deep Learning” http://neuralnetworksanddeeplearning.com/chap1.html to illustrate the positive impact and far reach of online publishing….”
Abstract: The main purpose of this chapter is to report how researchers investigating in the area of e-Infrastructures organize their activities of “data and publication management” and themselves rely on research infrastructures to do so. Due to the early age of this field and its rather multidisciplinary computer science character, no well-established research infrastructure is available and researchers tend to follow “infrastructure-flavoured” solutions local to their organizations. As a consequence, the authors of this chapter (from the DLib research group at CNR, Italy and the MADGIK research group at the University of Athens, Greece) opted to approach this study by collecting a number of experiences from relevant stakeholders in the field in order to identify “local infrastructure” commonalities and “research infrastructure” desiderata.
“Welcome to CogPrints, an electronic archive for self-archive papers in any area of Psychology, Neuroscience, and Linguistics, and many areas of Computer Science (e.g., artificial intelligence, robotics, vison, learning, speech, neural networks), Philosophy (e.g., mind, language, knowledge, science, logic), Biology (e.g., ethology, behavioral ecology, sociobiology, behaviour genetics, evolutionary theory), Medicine (e.g., Psychiatry, Neurology, human genetics, Imaging), Anthropology (e.g., primatology, cognitive ethnology, archeology, paleontology), as well as any other portions of the physical, social and mathematical sciences that are pertinent to the study of cognition….”
“Goal 1: Develop an evidence based understanding of current best practices in publishing across computing science.
Recent examples of reflection on peer review, which demonstrated significant variation in accept/reject decisions made by program committees (NIPS), and initiatives such as ACM Artefact Review and SIGCHI RepliCHI Award, show a desire from the research community to improve research and publication practice. This working group will collate an evidence base from the computing science community, bringing together currently disparate efforts in this area. Our on-going survey of practice will be publicised through a blog aimed at computing science researchers and practitioners.
Goal 2: Re-imagine a publishing and dissemination culture that exemplifies the values of open access, open data, and rigour.
Values in publication are changing, with more support than ever for open access, open data, transparency, and accessibility. Often, these values are also mandated by funding bodies that spend public money. We will develop concepts for a modern approach to knowledge sharing that could support new reviewing processes, enable multimedia archives and resources, incentivise reproducibility and open practices based on empirical evidence.
Goal 3: Advocate for change in publishing practice based on empirical evidence and ethical values.
This working group will develop channels to put these concepts into practice. We will disseminate our results to SIG leaders and through the Publications Board to enact change in how publishing practice occurs throughout ACM….”
“Stuart M. Shieber, the James O. Welch, Jr. and Virginia B. Welch Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), has been named a fellow of the Association for Computational Linguistics….As Faculty Director of Harvard’s Office for Scholarly Communication, Shieber has also led Harvard’s efforts to institute open-access policies that are now emulated elsewhere….”
“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. Given the raising complexity of research challenges and the constant increase in data volume, the conditions for achieving reproducible research in the domain are also increasingly difficult to meet. 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.”