Stian Soiland-Reyes; Peter Sefton; Mercè Crosas; Leyla Jael Castro; Frederik Coppens; José M. Fernández; Daniel Garijo; Björn Grüning; Marco La Rosa; Simone Leo; +6 more
Stian Soiland-Reyes; Peter Sefton; Mercè Crosas; Leyla Jael Castro; Frederik Coppens; José M. Fernández; Daniel Garijo; Björn Grüning; Marco La Rosa; Simone Leo; Eoghan Ó Carragáin; Marc Portier; Ana Trisovic; RO-Crate Community; Paul Groth; Carole Goble;
An increasing number of researchers support reproducibility by including pointers to and descriptions of datasets, software and methods in their publications. However, scientific articles may be ambiguous, incomplete and difficult to process by automated systems. In this paper we introduce RO-Crate, an open, community-driven, and lightweight approach to packaging research artefacts along with their metadata in a machine readable manner. RO-Crate is based on Schema$.$org annotations in JSON-LD, aiming to establish best practices to formally describe metadata in an accessible and practical way for their use in a wide variety of situations. An RO-Crate is a structured archive of all the items that contributed to a research outcome, including their identifiers, provenance, relations and annotations. As a general purpose packaging approach for data and their metadata, RO-Crate is used across multiple areas, including bioinformatics, digital humanities and regulatory sciences. By applying "just enough" Linked Data standards, RO-Crate simplifies the process of making research outputs FAIR while also enhancing research reproducibility. An RO-Crate for this article is available at https://www.researchobject.org/2021-packaging-research-artefacts-with-ro-crate/ Comment: 42 pages. Submitted to Data Science
Publisher: Springer Science and Business Media LLC
Project: EC | WIDE (742545), EC | WIDE (742545)
AbstractScientific writings, as one essential part of human culture, have evolved over centuries into their current form. Knowing how scientific writings evolved is particularly helpful in understanding how trends in scientific culture developed. It also allows us to better understand how scientific culture was interwoven with human culture generally. The availability of massive digitized texts and the progress in computational technologies today provide us with a convenient and credible way to discern the evolutionary patterns in scientific writings by examining the diachronic linguistic changes. The linguistic changes in scientific writings reflect the genre shifts that took place with historical changes in science and scientific writings. This study investigates a general evolutionary linguistic pattern in scientific writings. It does so by merging two credible computational methods: relative entropy; word-embedding concreteness and imageability. It thus creates a novel quantitative methodology and applies this to the examination of diachronic changes in the Philosophical Transactions of Royal Society (PTRS, 1665–1869). The data from two computational approaches can be well mapped to support the argument that this journal followed the evolutionary trend of increasing professionalization and specialization. But it also shows that language use in this journal was greatly influenced by historical events and other socio-cultural factors. This study, as a “culturomic” approach, demonstrates that the linguistic evolutionary patterns in scientific discourse have been interrupted by external factors even though this scientific discourse would likely have cumulatively developed into a professional and specialized genre. The approaches proposed by this study can make a great contribution to full-text analysis in scientometrics.
Publisher: International Committee on Computational Linguistics
Country: United Kingdom
Project: EC | M and M (741134)
Recently, domain-general recurrent neural networks, without explicit linguistic inductive biases, have been shown to successfully reproduce a range of human language behaviours, such as accurately predicting number agreement between nouns and verbs. We show that such networks will also learn number agreement within unnatural sentence structures, i.e. structures that are not found within any natural languages and which humans struggle to process. These results suggest that the models are learning from their input in a manner that is substantially different from human language acquisition, and we undertake an analysis of how the learned knowledge is stored in the weights of the network. We find that while the model has an effective understanding of singular versus plural for individual sentences, there is a lack of a unified concept of number agreement connecting these processes across the full range of inputs. Moreover, the weights handling natural and unnatural structures overlap substantially, in a way that underlines the non-human-like nature of the knowledge learned by the network.
Marine phytoplankton are believed to account for more than 45% of photosynthetic net primary production on Earth, and hence are at the base of marine food webs and have an enormous impact on the entire Earth system. Their members are found across many of the major clades of the tree of life, including bacteria (cyanobacteria) and multiple eukaryotic lineages that acquired photosynthesis through the process of endosymbiosis. Our understanding of their distribution in marine ecosystems and their contribution to biogeochemical cycles have increased since they were first described in the 18th century. Here, we review historical milestones in marine phytoplankton research and how their roles were gradually understood, with a particular focus on insights derived from large-scale ocean exploration. We start from the first observations made by explorers and naturalists, review the initial identification of the main phytoplankton groups and the appreciation of their function in the influential Kiel and Plymouth schools that established biological oceanography, to finally outline the contribution of modern large-scale initiatives to understand this fundamental biological component of the ocean. Fil: Pierella Karlusich, Juan José. Centre National de la Recherche Scientifique. Ecole Normale Supérieure; Francia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Ibarbalz, Federico Matias. Centre National de la Recherche Scientifique. Ecole Normale Supérieure; Francia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina Fil: Bowler, Chris. Centre National de la Recherche Scientifique. Ecole Normale Supérieure; Francia. Centre National de la Recherche Scientifique; Francia