Quite interesting thoughts. Basically managing data for many needs, motivations, coming from many directions. And please, consider too the metadata, brought us to our knees many times!
Thorny Problems in Data (-Intensive) Science
By Christine L. Borgman, Michael J. Scroggins, Irene V. Pasquetto, R. Stuart Geiger, Bernadette M. Boscoe, Peter T. Darch, Charlotte Cabasse-Mazel, Cheryl Thompson, Milena S. Golshan
Communications of the ACM, August 2020, Vol. 63 No. 8, Pages 30-32 10.1145/3408047
As science comes to depend ever more heavily on computational methods and complex data pipelines, many non-tenure track scientists find themselves precariously employed in positions grouped under the catch-all term "data science." Over the last decade, we have worked in a diverse array of scientific fields, specializations, and sectors, across the physical, life, and social sciences; professional fields such as medicine, business, and engineering; mathematics, statistics, and computer and information science; the digital humanities; and data-intensive citizen science and peer production projects inside and out of the academy.3,7,8,15 We have used ethnographic methods to observe and participate in scientific research, semi-structured interviews to understand the motivations of scientists, and document analysis to illustrate how science is assembled with data and code. Our research subjects range from principal investigators at the top of their fields to first-year graduate students trying to find their footing. Throughout, we have focused on the multiple challenges faced by scientists who, through inclination or circumstance, work as data scientists.
The "thorny problems" we identify are brambly institutional challenges associated with data in data-intensive science. While many of these problems are specific to academe, some may be shared by data scientists outside the university. These problems are not readily curable, hence we conclude with guidance to stakeholders in data-intensive research. ... ' (full article at the link)
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