Data Science: An Artificial Ecosystem

Xiao-li, Meng

doi:10.1162/99608f92.ba20f892

Cited by 21 publications

(24 citation statements)

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“…Similarly, in the founding editorial of the Harvard Data Science Review , Editor-in-Chief Xiao-Li Meng defines data science as “a collection of disciplines with complementary foundations, perspectives, approaches, and aims, but with a shared grand mission. That is, to use digital technologies and information of any kind to advance human society.” 4 I interpret these two editors’ admirable visions to mean that data science draws from and contributes to many domains while remaining distinct from them. These editors specify that responsible research and social benefit are integral components of data science.…”

Section: Main Textmentioning

confidence: 99%

Who Should Do Data Ethics?

Wylie

2020

Patterns

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Section: Main Textmentioning

confidence: 99%

Who Should Do Data Ethics?

Wylie

2020

Patterns

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“…Data science being an applied science is convincingly illustrated in Jordan (2019). At this stage it is probably healthy to keep an open mind and state what it is not, as was done in Meng (2019). The CityU's composite talent education with DS + X encourages cross fertilization and in-depth collaboration.…”

Section: Data Science Vs First Principlesmentioning

confidence: 99%

Data Science Education With Domain Knowledge and System Principles

Qin

2021

Harvard Data Science Review

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“…This special issue of JRSSA on 'Data Science for Society: Challenges, Developments and Applications' provides us with a great opportunity to reflect on the role and place of statistics within data science, and to contemplate how and what we can do to benefit further the broad data science ecosystem and, ultimately, the human ecosystem (Meng, 2019). The call for the special issue identifies several (but not all) major challenges and opportunities for statisticians and data scientists in general, concerning:…”

Section: What 'S the Difference Between Statistics And Data Science?mentioning

confidence: 99%

Enhancing (Publications on) Data Quality: Deeper Data Minding and Fuller Data Confession

Meng

2021

Journal of the Royal Statistical Society Series A: Statistics in Society

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Statistics typically treats data as inputs for analysis, whereas the broader data science enterprise deals with the entire data life cycle, including the phases that output data. This commentary argues that it would benefit statistics and (data) science if we statisticians were also to treat data as products in and of themselves, and accordingly subject them to data minding, a stringent quality inspection process that scrutinizes data conceptualization, data pre-processing, data curation and data provenance, in addition to data collection, the traditional objective of our emphasis before data analysis. A concrete step in promoting deeper data minding is to encourage fuller data confession in (statistical) publications, that is, to entice-or at least not to disincentivize-the authors into providing more details on the genealogy of a given body of data, including an account of its deliberations, especially with respect to sources of adverse influence on data quality. The collection of articles in this special issue (on data science for societies) provides both the inspiration and aspiration for deeper data minding and fuller data confession.

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Data Science: An Artificial Ecosystem

Cited by 21 publications

References 0 publications

Who Should Do Data Ethics?

Who Should Do Data Ethics?

Data Science Education With Domain Knowledge and System Principles

Enhancing (Publications on) Data Quality: Deeper Data Minding and Fuller Data Confession

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