Knowra Reproducibility Reproducibility Reproducibility is the ability to obtain consistent results when a study is repeated using the same data and methods. It depends on transparent procedures and access to the materials needed to repeat the analysis.
Computational notebook : A document combining executable code, results, and explanatory text in a single computational record. It records the steps and outputs of an analysis in a form that can be rerun.
Replicability : The ability to obtain consistent findings in a new study using newly collected data. It tests whether findings recur beyond the original data and analysis.
Replication crisis : A recognized pattern of difficulty reproducing findings across several research fields. Its investigations exposed how often published results failed to recur under repeated testing.
FAIR data principles : Principles for making data findable, accessible, interoperable, and reusable. They guide data management so others can locate and reuse research data.
Computational reproducibility : The ability to reproduce computational results from the same data, code, and computational environment. It isolates rerunning analyses from repeating data collection or experimental procedures.
Version control : A system for recording and managing changes to files over time. It preserves which versions of code and analysis produced a reported result.
Robustness : The stability of a result under reasonable changes to assumptions, methods, or data. It asks whether conclusions survive analytical choices rather than an exact rerun.
Publication bias : The tendency for published research to overrepresent statistically significant or otherwise positive findings. Selective publication can hide failed repetitions and exaggerate confidence in findings.
Data management plan : A document specifying how research data will be collected, organized, stored, and shared. Planning data handling early prevents missing records from blocking later repetition.
Reproducible builds : Software builds that produce identical outputs from the same source code and build environment. They offer a stringent model for checking whether computational outputs depend on hidden conditions.
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