S
Simone Sacchi
Researcher at European University Institute
Publications - 23
Citations - 304
Simone Sacchi is an academic researcher from European University Institute. The author has contributed to research in topics: Digital preservation & Data curation. The author has an hindex of 7, co-authored 22 publications receiving 276 citations. Previous affiliations of Simone Sacchi include Columbia University & University of Illinois at Urbana–Champaign.
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Journal ArticleDOI
Achieving human and machine accessibility of cited data in scholarly publications
Joan Starr,Eleni Castro,Mercè Crosas,Michel Dumontier,Robert R. Downs,Ruth Duerr,Laurel L Haak,Melissa A. Haendel,Ivan Herman,Simon Hodson,Joe Hourclé,John Ernest Kratz,Jennifer Lin,Lars Holm Nielsen,Amy Nurnberger,Stefan Proell,Andreas Rauber,Simone Sacchi,Arthur P. Smith,Mike Taylor,Timothy Clark +20 more
TL;DR: The guidance provided here is intended to help achieve widespread, uniform human and machine accessibility of deposited data, in support of significantly improved verification, validation, reproducibility and re-use of scholarly/scientific data.
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Definitions of dataset in the scientific and technical literature
TL;DR: To prepare for the development of this framework, the definitions of dataset were reviewed and four basic features can be identified as common to most definitions: grouping, content, relatedness, and purpose.
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A conceptual model for video games and interactive media
TL;DR: This model attempts to reflect how users such as game players, collectors, and scholars understand video games and the relationships among them, with future intentions of using this conceptual model as a foundation for developing a union catalog for various libraries and museums.
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Identifying content and levels of representation in scientific data
TL;DR: Two complementary conceptual models for data representation are presented, the Basic Representation Model and the Systematic Assertion Model, and it is shown how these models work together to provide an analytical account of digitally encoded scientific data.
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A Framework for Applying the Concept of Significant Properties to Datasets
TL;DR: This work presents a logic-based formal framework of dataset concepts that provides the levels of abstraction necessary to identify and correctly assign significant properties to their appropriate entities.