This post is intended to continue a conversation started in a separate, now-solved thread regarding the Data Quality Assessment (DQA) question: “Can the Community Taxon be improved?” and titled users are (understandably) saying they “disagree”.
This was solved by this comment; I recommend reading it before continuing so you know what has already been discussed.
Because this is a complex, long-standing system challenge with many moving parts, moving the discussion here allows us to freely brainstorm and examine potential improvements without cluttering specific observation inquiries.
During our initial discussion, a few distinct but overlapping challenges and ideas were brought up regarding how the site processes high-level Research Grade (RG) observations and subsequent identifications:
- Conflation of Observation and Community Taxon: There is an inherent structural challenge in how iNat displays and handles the relationship between individual observation taxa and the broader community taxon consensus.
- The Imbalance of “Resetting” DQA Votes: When a refining ID is added to a high-level RG observation, it can strip away the previous “As good as it can be” DQA votes. This effectively erases a specialist’s intentional assessment and drops the observation to Casual status, which is rarely the user’s intent.
- Decoupling RG Status from Search/Needs ID Filters: One idea discussed was whether decoupling the “Needs ID” search logic from RG status could help. For example, allowing an observation to retain its RG status (to protect its data quality tier) while still triggering a flag to appear in “Needs ID” queries when a new, refining ID is introduced. (This was the imperfect form of the idea, see linked comment earlier for details)
- Automating DQA Interactions: Alternatively, could adding a refining ID to a high-level RG observation automatically toggle a system flag (like an implicit “Yes, it can be improved” state) to alert the community without throwing away previous history or forcing the observation out of Research Grade improperly?
This is a draft framework for discussion, and I don’t claim to have an overarching solution. Given that tweaks to this system carry a burden over millions of observations, I’m eager to hear more perspectives from those who similarly deal with high-volume notifications or those whose specific taxonomic groups result in deadlocks most often.
Hoping to hear your thoughts!
