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Dictionary terms
20 resultsNIH Rigor and Reproducibility policy
The set of US National Institutes of Health policies, effective from 2016, requiring applicants and grantees to address scientific premise, scientific rigour, biological variables (including sex as a biological variable), and authentication of key biological and chemical resources in grant applications.
Reproducibility audit
A systematic, post-publication examination of whether a study's published results can be obtained from its deposited data and code, typically performed by an independent analyst.
Reproducibility crisis
The widely reported finding that substantial proportions of published research, particularly in biomedical, psychological, and social sciences, fail to reproduce or replicate when re-tested.
Inferential reproducibility
The degree to which independent analysts reach the same qualitative scientific conclusion from the same data, even where their analytical choices differ.
Results reproducibility
The narrow sense in which a study's reported quantitative results can be recreated from the deposited data using the deposited analysis procedures.
Methods reproducibility
The degree to which a study's methods are reported in sufficient detail that another investigator could re-implement them, independent of whether the same numerical or empirical results would follow.
Empirical reproducibility
The ability to obtain consistent observations when an empirical procedure (laboratory, field, or measurement) is independently repeated under matched conditions.
Computational reproducibility
The narrow technical sense of reproducibility: obtaining the same numerical outputs from the same data and code, on a comparable computational environment.
Reproducibility
The ability to obtain consistent computational or analytical results when the same data and analysis procedures are applied by an independent investigator using the same code and tools.
Reproducibility reviewer
A reviewer whose specific role is to verify that the computational, analytic, or experimental procedures reported in a manuscript can be re-executed by another party using the provided code, data, and instructions, distinct from a content peer reviewer.
Reproducible AI experiment
An AI experiment for which sufficient artefacts and metadata are released (data, code, seed, environment, hyperparameters, training procedure) that an independent investigator can re-run it and obtain numerically equivalent or statistically indistinguishable results.
Model lineage
The chain of provenance for a model recording its base model, the fine-tuning datasets and procedures applied, and any further derivatives, such that any deployed model can be traced back to its constituent training operations.
Scientific rigour
The strict application of the scientific method to ensure unbiased and well-controlled experimental design, methodology, analysis, interpretation, and reporting of results.
Authentication of key resources
The verification, by methods appropriate to the resource type, of the identity and integrity of biological and chemical materials used in research, including cell lines, antibodies, animal models, and specialty chemicals.
Crowdsourced replication
A coordinated effort in which many independent laboratories or teams attempt to replicate the same set of studies under pre-specified protocols, in order to estimate field-wide replicability.
Reproducible Research Practices (RRP)
The set of disciplinary norms, tools, and habits that together raise the probability that published research will be reproducible: literate programming, version control, dependency pinning, data deposit, code release, and reporting standards.
Container image (Docker/Singularity/Apptainer)
A packaged, immutable filesystem and configuration that contains an application together with all its dependencies, runnable identically on any compatible container engine (Docker, Podman, Singularity, Apptainer).
Workflow language (CWL/WDL)
A declarative specification language for describing multi-step computational analyses such that the steps, their inputs and outputs, and their software dependencies are portable across compatible workflow execution engines.
Computational environment
The full software and hardware context in which an analysis runs, including operating system, language runtime, library versions, configuration, environment variables, and hardware-specific dependencies (e.g., GPU drivers).
Open code
The practice of releasing the source code used in a study, under an open-source licence, alongside the publication, such that any reader may inspect, reuse, and re-execute the analysis.
Picklists
1 resultNews & perspectives
10 resultsNews · 2026-06-27
Protocols.io: Enhancing Scientific Reproducibility Through Step-by-Step Method Sharing
1. Introduction to the Role of Protocols.io in Scholarly Infrastructure In the contemporary landscape of global science, open research practices, and institutional data governance, establishing robust standards is crucial. The integration of Protocols.io represents a landmark advancement in addressing long-standing hurdles in scholarly communication, administrative reporting, and metadata curation. This extensive guide provides an expert-level […]
News · 2026-06-20
Reproducibility of Machine Learning Research
ML reproducibility is the ability to obtain consistent results from the same code, data and configuration. This article explains why ML results are hard to reproduce and the practical standards that help: random seeds, data and model versioning, compute reporting, sharing code and weights, and reproducibility checklists.
News · 2026-06-19
Registered Reports: Structural Reforms for Academic Reproducibility
Introduction Academic publishing faces a systemic crisis driven by publication bias—the tendency of journals to favor statistically significant, positive results while rejecting negative or null findings. This bias encourages questionable research practices like p-hacking and HARKing (Hypothesizing After the Results are Known). Registered Reports are a powerful publishing format designed to structurally eliminate these biases. […]
News · 2026-06-18
The Reproducibility Crisis: Key Causes, Solutions, and the Role of Standards
Introduction to Reproducibility Crisis in Scholarly Spaces The reproducibility crisis—revealing that a high percentage of peer-reviewed scientific studies across psychology, medicine, and social sciences cannot be replicated by independent laboratories—is a significant challenge facing modern scholarship. Identifying the Core Causes of Replication Failures The reproducibility crisis is driven by several systemic issues: 1. Publication Bias: […]
News · 2026-06-18
Reproducibility for AI/ML research: model cards, seeds and compute disclosure
Machine-learning results can be strikingly hard to reproduce, often for mundane reasons — an unrecorded random seed, an undocumented dependency, an unstated hardware setup. A practical guide to model cards, seed discipline and compute disclosure.
News · 2026-06-16
Computational Reproducibility in Action: Workflows, Containers, and Notebooks
Introduction to Reproducibility in Scholarly Spaces In computationally-intensive scientific fields, sharing code and data is not enough to guarantee reproducibility. Differences in operating systems, software library versions, and execution environments can lead to conflicting results from identical analysis scripts. The Three Pillars of Computational Reproducibility To achieve complete computational reproducibility, researchers should implement three core […]
News · 2026-06-13
Leveraging the Open Science Framework (OSF) to Enhance Scholarly Transparency and Data Reproducibility
The Crisis of Reproducibility in Contemporary Science Modern scholarship faces a quiet crisis. Across multiple disciplines—from psychology and economics to molecular biology—researchers have struggled to replicate high-profile peer-reviewed studies. This ‘reproducibility crisis’ is driven by several systemic factors, including publication bias, p-hacking, file-drawer effects, and a lack of access to raw datasets and analytical code. […]
News · 2026-06-11
Computational reproducibility: containers, workflows and FAIR4RS
A result you cannot re-run is a claim, not a finding. How containers, workflow languages, Software Heritage and the FAIR4RS principles turn computational work into something others can actually reproduce.
News · 2026-02-19
Reproducibility frameworks in practice: TOP, ARRIVE, CONSORT, PRISMA
A practitioner’s tour of EQUATOR’s reporting guidelines, when each applies, FAIR4RS for software, and the registered-report turn in 2026.
News · 1970-01-01
Sharing protocols and methods: protocols.io, STAR Methods and reproducibility
The compressed methods section is a chronic threat to reproducibility: the steps that actually make an experiment work are often the ones omitted for space. A new generation of platforms treats the protocol as a first-class, versioned and citable research output. This article looks at protocols.io, STAR Methods from Cell Press and Bio-protocol, explains why DOIs for protocols matter, and how detailed, shareable methods support reproducible science.
