Human Data Science

D. L. Oberski

doi:10.1016/j.patter.2020.100069

Human Data Science

D. L. Oberski^*

^*Corresponding author for this work

Research output: Contribution to journal › Comment/Letter to the editor › Academic › peer-review

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Abstract

Most data science is about people, and opinions on the value of human data differ. The author offers a synthesis of overly optimistic and overly pessimistic views of human data science: it should become a science, with errors systematically studied and their effects mitigated—a goal that can only be achieved by bringing together expertise from a range of disciplines. Most data science is about people, and opinions on the value of human data differ. The author offers a synthesis of overly optimistic and overly pessimistic views of human data science: it should become a science, with errors systematically studied and their effects mitigated—a goal that can only be achieved by bringing together expertise from a range of disciplines.

Original language	English
Article number	100069
Journal	Patterns
Volume	1
Issue number	4
DOIs	https://doi.org/10.1016/j.patter.2020.100069
Publication status	Published - 10 Jul 2020

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10.1016/j.patter.2020.100069Licence: CC BY

PIIS2666389920300891Final published version, 1.2 MBLicence: CC BY

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title = "Human Data Science",

abstract = "Most data science is about people, and opinions on the value of human data differ. The author offers a synthesis of overly optimistic and overly pessimistic views of human data science: it should become a science, with errors systematically studied and their effects mitigated—a goal that can only be achieved by bringing together expertise from a range of disciplines. Most data science is about people, and opinions on the value of human data differ. The author offers a synthesis of overly optimistic and overly pessimistic views of human data science: it should become a science, with errors systematically studied and their effects mitigated—a goal that can only be achieved by bringing together expertise from a range of disciplines.",

author = "Oberski, \{D. L.\}",

note = "Funding Information: Thanks to Laura Boeschoten, Dong Nguyen, Ayoub Bagheri, Michael Friendly, and Peter van der Heijden for their useful comments. The author was supported by the Dutch Research Council (NWO) [VIDI grant VI. Vidi.195.152 ]. Publisher Copyright: {\textcopyright} 2020 The Author",

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T1 - Human Data Science

AU - Oberski, D. L.

N1 - Funding Information: Thanks to Laura Boeschoten, Dong Nguyen, Ayoub Bagheri, Michael Friendly, and Peter van der Heijden for their useful comments. The author was supported by the Dutch Research Council (NWO) [VIDI grant VI. Vidi.195.152 ]. Publisher Copyright: © 2020 The Author

PY - 2020/7/10

Y1 - 2020/7/10

N2 - Most data science is about people, and opinions on the value of human data differ. The author offers a synthesis of overly optimistic and overly pessimistic views of human data science: it should become a science, with errors systematically studied and their effects mitigated—a goal that can only be achieved by bringing together expertise from a range of disciplines. Most data science is about people, and opinions on the value of human data differ. The author offers a synthesis of overly optimistic and overly pessimistic views of human data science: it should become a science, with errors systematically studied and their effects mitigated—a goal that can only be achieved by bringing together expertise from a range of disciplines.

AB - Most data science is about people, and opinions on the value of human data differ. The author offers a synthesis of overly optimistic and overly pessimistic views of human data science: it should become a science, with errors systematically studied and their effects mitigated—a goal that can only be achieved by bringing together expertise from a range of disciplines. Most data science is about people, and opinions on the value of human data differ. The author offers a synthesis of overly optimistic and overly pessimistic views of human data science: it should become a science, with errors systematically studied and their effects mitigated—a goal that can only be achieved by bringing together expertise from a range of disciplines.

UR - https://www.scopus.com/pages/publications/85102975322

U2 - 10.1016/j.patter.2020.100069

DO - 10.1016/j.patter.2020.100069

M3 - Comment/Letter to the editor

AN - SCOPUS:85102975322

VL - 1

JO - Patterns

JF - Patterns

IS - 4

M1 - 100069

ER -

Human Data Science

Abstract

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