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36 Research products, page 1 of 4

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  • Publication . 2022
    Open Access
    Authors: 
    Pianzola, Federico; Rebora, Simone;
    Publisher: Open Science Framework
    Project: EC | READIT (792849)

    This is collection of all the stories' titles published on Wattpad at the date: January 2018. It's a corpus of around 30 millions titles in more than 50 different languages. It includes mainly original fiction and a small part of fan fiction (roughly 10%). The R Markdown files regarding the procedures for network analysis and sentiment analysis can be found in the GitHub repository: https://github.com/SimoneRebora/Wattpad_analysis We published an article based on this data https://doi.org/10.1371/journal.pone.0226708

  • Open Access
    Authors: 
    Prakofyeva, Y.; Anegg, M.; Kalle, R.; Simanova, A.; Pruse, B.; Pieroni, A.; Soukand, R.;
    Publisher: F1000 Research Ltd
    Country: Italy
    Project: EC | DiGe (714874), EC | DiGe (714874)

    Background: Historical ethnobotanical data can provide valuable information about past human-nature relationships as well as serve as a basis for diachronic analysis. This data note aims to present a dataset which documented medicinal plant uses, mentioned in a selection of German-language sources from the 19th century covering the historical regions of Estonia, Livonia, Courland, and Galicia. Methods: Data was mainly obtained by systematic manual search in various relevant historical German-language works focused on the medicinal use of plants. Data about plant and non-plant constituents, their usage, the mode of administration, used plant parts, and their German and local names was extracted and collected into a database in the form of Use Reports.

  • Publication . Article . Other literature type . 2022
    Open Access English
    Authors: 
    Elisa Nury; Claire Clivaz; Marta Błaszczyńska; Michael Kaiser; Agata Morka; Valérie Schaefer; Jadranka Stojanovski; Erzsébet Tóth-Czifra;
    Countries: France, Croatia, France
    Project: EC | OPERAS-P (871069)

    International audience; Published in OA on RESSI (http://www.ressi.ch/) at the end of Octobre 2021. We present here highlights from an enquiry on the innovations in scholarly writing in the Humanities and Social Sciences in the H2020 project OPERAS-P. This article explores the theme of Open Research Data and its role in the emergence of new models of scholarly writing. We examine more closely the obstacles and fostering conditions to the publication of research data, both from a social and a technical perspective.

  • Open Access
    Authors: 
    Nikolay Pavlov;
    Publisher: Informa UK Limited
    Project: EC | CDE4Peace (882055)
  • Publication . Article . Other literature type . Preprint . 2021 . Embargo End Date: 01 Jan 2021
    Open Access
    Authors: 
    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
    Publisher: arXiv
    Countries: Netherlands, United Kingdom, Belgium, United Kingdom, Netherlands
    Project: EC | IBISBA 1.0 (730976), SSHRC , EC | PREP-IBISBA (871118), EC | EOSC-Life (824087), EC | RELIANCE (101017501), EC | BioExcel-2 (823830), EC | SYNTHESYS PLUS (823827), EC | IBISBA 1.0 (730976), SSHRC , EC | PREP-IBISBA (871118),...

    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

  • Publication . Article . 2021
    Authors: 
    Perdoncin, Anton;
    Publisher: CAIRN
    Country: France
    Project: EC | LUBARTWORLD (818843)

    International audience

  • Publication . Other literature type . 2021
    Open Access
    Authors: 
    Agren, Quentin; Michaud, Geneviève; Bottoni, Gianmaria;
    Publisher: Zenodo
    Project: EC | SSHOC (823782), EC | SSHOC (823782)

    Presentation given during the 2nd ESS-SUSTAIN-2 (#871063) consortium meeting (21/01/2021) for WP 6.

  • Restricted
    Authors: 
    Yichi Zhang;
    Publisher: Informa UK Limited
    Project: EC | BROKEX (802070)

    A transnational flow of capital exchange during the 19th and early 20th centuries brought planning ideas and modernity into China. Since European countries and America used violence to place China ...

  • Open Access
    Authors: 
    Kun Sun; Haitao Liu; Wenxin Xiong;
    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.

  • Publication . Contribution for newspaper or weekly magazine . Conference object . 2020
    Open Access
    Authors: 
    Jeff Mitchell; Jeffrey S. Bowers;
    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.