DATABOOK : a standardised framework for dynamic documentation of algorithm design during Data Science projects
Author(s) -
Anesvijevskaia
Publication year - 2021
Publication title -
iassist quarterly
Language(s) - English
Resource type - Journals
eISSN - 2331-4141
pISSN - 0739-1137
DOI - 10.29173/iq989
Subject(s) - documentation , computer science , mediation , field (mathematics) , data sharing , knowledge management , data quality , process management , data science , management science , engineering , operations management , mathematics , sociology , social science , alternative medicine , pure mathematics , medicine , programming language , metric (unit) , pathology
This paper proposes a standard documentation framework for Data Science projects, called Databook. It is a result of five years of action-research on multiple projects in several sectors of activity in France, and of a confrontation of standard theoretical Data Science processes, such as CRISP_DM, with the reality of the field. As a vector for knowledge sharing and capitalisation, the Databook has been identified as one of the main facilitators of Human Data Mediation. Transformed into an operational prototype of simple and minimalist documentation, it has since been tested then on about a hundred Data Science projects, has proven its benefits for the internal and external efficiency of Data Science projects, and can be turned into a more ambitious standard framework for data patrimony valorisation and data quality governance.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom