Environmental Research Communications (Jan 2023)

Connecting physical and social science datasets: challenges and pathways forward

  • Sameer H Shah,
  • Cassandra R O’Lenick,
  • Jessica S Wan,
  • Alexandra Ramos-Valle,
  • Kevin D Ash,
  • Olga V Wilhelmi,
  • Catrin M Edgeley,
  • Maria J Molina,
  • Jessica Moulite,
  • Carlo Andre Chunga Pizarro,
  • Kelsey Emard,
  • Olivia Z Cameron,
  • James M Done,
  • Cleo Wölfle Hazard,
  • Thomas M Hopson,
  • Mikah Jones,
  • Forrest Lacey,
  • Michée Arnold Lachaud,
  • Danica Lombardozzi,
  • Michael Méndez,
  • Rebecca E Morss,
  • Katharine Ricke,
  • Fernando Tormos-Aponte,
  • William R Wieder,
  • Christopher L Williams

DOI
https://doi.org/10.1088/2515-7620/acf6b4
Journal volume & issue
Vol. 5, no. 9
p. 095007

Abstract

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The integration of physical and social science data can enable novel frameworks, methodologies, and innovative solutions important for addressing complex socio-environmental problems. Unfortunately, many technical, procedural, and institutional challenges hamper effective data integration—detracting from interdisciplinary socio-environmental research and broader public impact. This paper reports on the experiences and challenges of social and physical data integration, as experienced by diverse Early Career Researchers (ECRs), and offers strategies for coping with and addressing these challenges. Through a workshop convened by the National Center for Atmospheric Research (NCAR) Innovator Program, 33 participants from different disciplines, career stages, and institutions across the United States identified four thematic data integration challenges related to complexity and uncertainty, communication, scale, and institutional barriers. They further recommended individual, departmental, and institutional scale responses to cope with and address these integration challenges. These recommendations seek to inform faculty and department support for ECRs, who are often encouraged—and even expected—to engage in integrative, problem-focused, and solutions-oriented research.

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