Mapping platforms into a new open science model for machine learning

Autor:	Thomas Weißgerber, Michael Granitzer
Rok vydání:	2019
Předmět:	Open science General Computer Science Human–computer interaction Computer science 020204 information systems 0103 physical sciences 0202 electrical engineering electronic engineering information engineering 02 engineering and technology 01 natural sciences 010305 fluids & plasmas
Zdroj:	it - Information Technology. 61:197-208
ISSN:	2196-7032 1611-2776
DOI:	10.1515/itit-2018-0022
Popis:	Data-centric disciplines like machine learning and data science have become major research areas within computer science and beyond. However, the development of research processes and tools did not keep pace with the rapid advancement of the disciplines, resulting in several insufficiently tackled challenges to attain reproducibility, replicability, and comparability of achieved results. In this discussion paper, we review existing tools, platforms and standardization efforts for addressing these challenges. As a common ground for our analysis, we develop an open science centred process model for machine learning research, which combines openness and transparency with the core processes of machine learning and data science. Based on the features of over 40 tools, platforms and standards, we list the, in our opinion, 11 most central platforms for the research process in this paper. We conclude that most platforms cover only parts of the requirements for overcoming the identified challenges.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::c258906a301b56f39e542e65dd13933f https://doi.org/10.1515/itit-2018-0022 Zobrazit plný text záznamu