What is the aim of this project?

FLoRA tracks numerical reproductions, robustness reproductions, and replications, linking each one to the original publication it tests. The goal is to increase the findability of repetition attempts and to enable meta-research on the repeatability of scientific findings, across disciplines, journals, and time.

Unlike the FORRT Replication Database (FReD), FLoRA does not include statistical data. This lets it cover every field of research across the sciences and humanities and allows much faster expansion.

What is a replication?

Replications intentionally repeat prior research to test whether the original findings hold. To be included in FLoRA, a study must:

• Self-identify as a replication before reporting results: replication must be an aim, not just a result
• Identify a specific target study (or studies)
• Replicate a study or experiment: a central claim, not just any empirical pattern

Replications range from close/direct (same methods, same population) to conceptual (same hypothesis, different methods). Most outcomes are tagged Successful, Failed, or Mixed based on how the replication authors characterise their results. A few are recorded as Successful but flawed, and some as other or not yet coded (for example, descriptive only); charts show these separately.

What is a reproduction?

Reproductions verify whether reported results can be obtained from the original study's data and methods. They are coded on two dimensions:

• Computational success: were the original results obtained? (Computationally Successful, Computational Issues, or Technical Failure)
• Robustness: do results hold under reasonable alternative specifications? (Robust or Robustness Challenges)

The two dimensions are assessed independently, and not every reproduction is assessed on both. Each chart counts reference pairs with a verdict on that dimension. ‘Not checked’ and uncoded values are excluded. A pair can appear in both charts.

Structure of FLoRA

FLoRA links each repetition reference to its original, and assigns a standardised outcome based on the repetition report. This simple structure lets us describe recorded replication and reproduction outcomes across time, disciplines and venues, and powers tools like the FLoRA Zotero Plugin, which lists replication and reproduction attempts for any reference you provide.

Is FLoRA representative and comprehensive?

Honestly — neither. We are still working on a systematic literature search, and several structural factors limit coverage:

• Some replications cannot be added because they do not self-identify as such in the metadata we can reach (e.g., the title and abstract).
• In some areas of computational reproduction (for example, CODECHECK certificates), the artefacts are typically only issued for successful numerical reproductions, which makes representative estimates of computational reproducibility difficult.
• Coverage by discipline, language, and publication venue is uneven and reflects where contributors have focused so far.

Treat aggregate statistics as exploratory rather than definitive, and please submit missing entries when you spot them.

FLoRA is the product of years of work by a large community of volunteers. If you'd like to contribute or use the data for your own research, please reach out.