MIReAD, a minimum information standard for reporting arthropod abundance data

Samuel S.C. Rund, Kyle Braak, Lauren Cator, Kyle Copas, Scott J. Emrich, Gloria I. Giraldo-Calderón, Michael A. Johansson, Naveed Heydari, Donald Hobern, Sarah A. Kelly, Daniel Lawson, Cynthia Lord, Robert M. MacCallum, Dominique G. Roche, Sadie J. Ryan, Dmitry Schigel, Kurt Vandegrift, Matthew Watts, Jennifer M. Zaspel, Samraat Pawar

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


Arthropods play a dominant role in natural and human-modified terrestrial ecosystem dynamics. Spatially-explicit arthropod population time-series data are crucial for statistical or mathematical models of these dynamics and assessment of their veterinary, medical, agricultural, and ecological impacts. Such data have been collected world-wide for over a century, but remain scattered and largely inaccessible. In particular, with the ever-present and growing threat of arthropod pests and vectors of infectious diseases, there are numerous historical and ongoing surveillance efforts, but the data are not reported in consistent formats and typically lack sufficient metadata to make reuse and re-analysis possible. Here, we present the first-ever minimum information standard for arthropod abundance, Minimum Information for Reusable Arthropod Abundance Data (MIReAD). Developed with broad stakeholder collaboration, it balances sufficiency for reuse with the practicality of preparing the data for submission. It is designed to optimize data (re)usability from the “FAIR,” (Findable, Accessible, Interoperable, and Reusable) principles of public data archiving (PDA). This standard will facilitate data unification across research initiatives and communities dedicated to surveillance for detection and control of vector-borne diseases and pests.

Original languageEnglish (US)
Article number40
JournalScientific Data
Issue number1
StatePublished - Dec 1 2019

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Information Systems
  • Education
  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences


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