Thread by @AlexTamkin

Love the "data science maturity levels" in @Patterns_CP

Interesting way to contextualize research at a glance (reminds me a bit of @justsaysinmice)

Full list in thread:

1) Concept

Basic principles of a new data science output observed and reported (e.g., statement of principles, dataset, new algorithm, new theoretical concept, theoretical system infrastructure)

2) Proof-of-concept

Data science output has been formulated, implemented, and tested for one domain/problem (e.g., dataset with rich domain-specific metadata, algorithm coded up as software, principles with expanded guidance on how to implement them)

3) Development/pre-production

Data science output has been rolled out/validated across multiple domains/problems

4) Production

Data science output is validated, understood, and regularly used for multiple domains/problems (e.g., operational data-sharing service across institutes/countries, ML algorithm to tag images, shared data infrastructure to manage access to compute/archive resources)

5) Mainstream

Data science output is well understood and (nearly) universally adopted (e.g., the iInternet, citation of articles using DOIs)

More info about the levels + rationale here!
cell.com/patterns/dsml