Interesting article The Data Double Standard from June 2023 by Allison Binley and Joe Bennett, both affiliated with Carleton University, Ottawa, Canada. https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.14110
The discussion broadly applies to iNat, other community science efforts and what the authors describe as “professionally collected data.” Worth reading in full, but abstract and figure-1 copy and pasted below. Related to past forum discussions related to dataset quality, professional data and museum/herbarium records.
- Conservation planning requires extensive amounts of data, yet data collection is expensive, and there is often a trade-off between the quantity and quality of data that can be collected. Researchers are increasingly turning to community science programs to meet their biodiversity data needs, yet the reliability of such data sources is still a common source of debate.
- Here, we argue that professionally collected data are subject to many of the limitations and biases present in community science datasets. We explore four common criticisms of community science data, and comparable issues that exist in data collected by experts: spatial biases, observer variability, taxonomic biases and the misapplication of data. We then outline solutions to these problems that have been developed to make better use of community science data, but can (and should) be equally applied to both kinds of data.
- We highlight four main solutions based on research using community science data that can be applied across all biodiversity data collection and research. Statistical techniques that have been developed for processing community science data can equally help account for spatial biases and observer variation in professional datasets. Benchmarking or vetting one dataset against another can strengthen evidence and uncover unknown sources of biases. Professional and community science datasets can be used together to fill knowledge gaps that are unique to each. Careful study design that accounts for the collection of relevant and important covariate data can help statistically account for sources of bias.
- Currently, a double standard exists in how researchers view data collected by professionals versus those collected by community scientists. Our aim is to ensure that valuable community science data are given the prominent place they deserve, and that data collected by experts are appropriately vetted and biases accounted for using all the tools at our disposal.
Figure 1 Sources of error and bias that are found equally in data collected through both community science and conventional (professional) monitoring.