Automated research evidence collection gathers measurements, logs, artifacts and decisive results as experiments run. The captured information should identify the procedure and environment so a reviewer can connect a claim to its source.
Collection is not interpretation. Systems should retain failures and anomalous outputs rather than filtering them away, while researchers decide whether the evidence supports a hypothesis or requires another experiment.
Acronyms and aliases
research result capture synonymautomated evidence collection variant
General terms
Related terms
Frequently asked questions
What belongs in automated research evidence?
Relevant measurements, raw outputs, errors, environment details, parameters, artifact hashes and links to the exact procedure should be retained.
Why should failed experiments be included in evidence collection?
Failures can reveal invalid assumptions, technical limits or selection bias and prevent a later report from showing only favorable results.
Videos explaining automated research evidence collection
