Limits to the pursuit of reproducibility: Emergent data-scarce domains of science

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Recommendations and interventions to promote reproducibility in science have so far largely been formulated in the context of well-established domains characterized by data- And computationally-intensive methods. However, much promising research occurs in little data domains that are emergent and experience data scarcity. This paper presents a longitudinal study of such a domain, deep subseafloor biosphere research. Two important challenges this domain faces in establishing itself are increasing production and circulation of data, and strengthening relationships between domain researchers. Some potential interventions to promote reproducibility may also help the domain to establish itself. However, other potential interventions could profoundly damage the domain’s long-term prospects of maturation by impeding production of new data and undermining critical relationships between researchers. This paper challenges the dominant framing of the pursuit of reproducible science as identifying, and overcoming, barriers to reproducibility. Instead, those interested in pursuing reproducibility in a domain should take into account multiple aspects of that domain’s epistemic culture to avoid negative unintended consequences. Further, pursuing reproducibility is premature for emergent, data-scarce domains: scarce resources should instead be invested to help these domains to mature, for instance by addressing data scarcity.

Original languageEnglish (US)
Title of host publicationTransforming Digital Worlds - 13th International Conference, iConference 2018, Proceedings
EditorsGobinda Chowdhury, Julie McLeod, Val Gillet, Peter Willett
Number of pages11
ISBN (Print)9783319781044
StatePublished - 2018
Event13th International Conference on Transforming Digital Worlds, iConference 2018 - Sheffield, United Kingdom
Duration: Mar 25 2018Mar 28 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10766 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other13th International Conference on Transforming Digital Worlds, iConference 2018
Country/TerritoryUnited Kingdom


  • Data reuse
  • Little data
  • Open code
  • Open data
  • Reproducibility

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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