Computational science: shifting the focus from tools to models

Computational techniques have revolutionized many aspects of scientific research over the last few decades. Experimentalists use computation for data analysis, processing ever bigger data sets. Theoreticians compute predictions from ever more complex models. However, traditional articles do not permit the publication of big data sets or complex models. As a consequence, these crucial pieces of information no longer enter the scientific record. Moreover, they have become prisoners of scientific software: many models exist only as software implementations, and the data are often stored in proprietary formats defined by the software. In this article, I argue that this emphasis on software tools over models and data is detrimental to science in the long term, and I propose a means by which this can be reversed

Establishing trust in automated reasoning

Since its beginnings in the 1940s, automated reasoning by computers has become a tool of ever growing importance in scientific research. So far, the rules underlying automated reasoning have mainly been formulated by humans, in the form of program source code. Rules derived from large amounts of data, via machine learning techniques, are a complementary approach currently under intense development. The question of why we should trust these systems, and the results obtained with their help, has been discussed by philosophers of science but has so far received little attention by practitioners. The present work focuses on independent reviewing, an important source of trust in science, and identifies the characteristics of automated reasoning systems that affect their reviewability. It also discusses possible steps towards increasing reviewability and trustworthiness via a combination of technical and social measures

Verifiability in computer-aided research: the role of digital scientific notations at the human-computer interface

Most of today’s scientific research relies on computers and software for processing scientific information. Examples of such computer-aided research are the analysis of experimental data or the simulation of phenomena based on theoretical models. With the rapid increase of computational power, scientific software has integrated more and more complex scientific knowledge in a black-box fashion. As a consequence, its users do not know, and do not even have a chance of finding out, which assumptions and approximations their computations are based on. This black-box nature of scientific software has made the verification of much computer-aided research close to impossible. The present work starts with an analysis of this situation from the point of view of human-computer interaction in scientific research. It identifies the key role of digital scientific notations at the human-computer interface, reviews the most popular ones in use today, and describes a proof-of-concept implementation of Leibniz, a language designed as a verifiable digital scientific notation for models formulated as mathematical equations

Essential Tools: Version Control Systems

Did you ever wish you\u27d made a backup copy of a file before changing it? Or before applying a collaborator\u27s modifications? Version control systems make this easier, and do a lot more

Virtualization for computational scientists

International audienc

ReScience C: A Journal for Reproducible Replications in Computational Science

International audienceIndependent replication is one of the most powerful methods to verify published scientific studies. In computational science, it requires the reimplementation of the methods described in the original article by a different team of researchers. Replication is often performed by scientists who wish to gain a better understanding of a published method, but its results are rarely made public. ReScience C is a peer-reviewed journal dedicated to the publication of high-quality computational replications that provide added value to the scientific community. To this end, ReScience C requires replications to be reproducible and implemented using Open Source languages and libraries. In this article, we provide an overview of ReScience C’s goals and quality standards, outline the submission and reviewing processes, and summarize the experience of its first three years of operation, concluding with an outlook towards evolutions envisaged for the near future

ReScience

National audienceLes chercheurs en informatique disposent d’une grande variété d’outils de conception de prototypes, de rédaction, de tests, de validation, mais aussi de partage et de réplication des résultats. Toutefois, les sciences computationnelles (analyse de données ou modélisation) accusent un retard. Dans le meilleur des cas, les auteurs fournissent les sources de leurs travaux sous forme d’une archive compressée et sont convaincus que leur recherche est ainsi reproductible. Mais ce n’est pas tout à fait vrai. Les paramètres à prendre en compte pour une réplication en bonne et due forme sont si nombreux que la probabilité de travailler avec la même configuration que celle de votre collègue est quasi nulle. Dès lors, comment garantir une recherche vraiment reproductible? ReScience est une revue à comité de lecture dédiée aux sciences computationnelles qui encourage la réplication de travaux déjà publiés. ReScience promeut également de nouvelles implémentations en open source afin de s’assurer que les travaux originaux soient reproductibles. De fait, toute la chaîne éditoriale s’en trouve modifiée, comparativement à une revue classique : ReScience est hébergée sur GitHub où chaque nouvelle réplication d’une étude est mise à disposition, accompagnée de commentaires, d’explications et de tests. Chaque soumission est analysée publiquement et testée pour que tout chercheur puisse s’en servir de nouveau. Des réplications dans les domaines des neurosciences, de la biologie, de la physique et de l’écologie sont déjà disponibles