GCRI · Labs
Reproducibilidad y resultados negativos
Science corrects itself only when failures are recorded: replications that did not reproduce, experiments that found nothing, errors found after publication. Many of these records never appear, so the published literature overstates effects and certainty. This page sets out how irreproducibility arises, how replication and correction work, which records exist, and what editors, integrity offices and funders rely on when they correct or retract.
Reproducibility and negative results on the record.
Reproducibility and negative results: how the risk develops.
Failure records and corrections. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Publication bias filters the record
Significant, novel results are more likely to be written up, accepted and cited than null ones. The literature then shows a biased sample of what was studied, and meta-analyses inherit the bias.
Errors propagate through citation
A flawed or retracted paper keeps being cited, often without mention of its status. Reviews, guidelines and later studies that build on it carry the error forward, sometimes long after the notice.
Reproduction fails for ordinary reasons
Missing code, undocumented software versions, unshared reagents, unclear methods and lost raw data stop others from repeating an analysis or experiment, even when the original work was honest.
Correction is slow and uneven
Investigations take time, journals differ in how they flag problems, and retraction notices can be vague. Readers may not learn that a paper was corrected, and databases may not carry the notice forward.
Tablero de protocolos y replicaciones.
La pantalla que utilizaría un equipo para seguir esta materia: sus mediciones, estados y decisiones. Los nombres son ejemplos y los valores son ilustrativos.
Panel de protocolos y replicación
Reproducibility and negative results · Labs · Quarter 1
Protocolos
| PR-594 sensor drift | Experimento de campo | Sí | Aprobado | En ejecución |
| PR-714 AI triage | Aleatorizado | Sí | Condiciones | Configuración |
| PR-551 message recall | Experimento de encuesta | Sí | Aprobado | Completo |
| PR-896 heat exposure | Cohorte | No | Pendiente | Borrador |
Replication R-20
- Efecto original
- 0.3 SD
- Efecto de réplica
- 0.3 SD
- Veredicto
- Menor, mismo signo
- Publicado
- con datos y código
Disponibilidad de los bancos de pruebas
Negative results matter in every domain.
- SaludEn la prácticaNull trial results prevent wasted effort.What a reader can checkPublication of null results
- AguaEn la prácticaFailed interventions inform utilities.What a reader can checkFailure records
- AlimentaciónEn la prácticaVarieties that did not perform are recorded.What a reader can checkTrial outcomes
Negative results are published in the serie Labs Evidence. The National Academies’ report (2019) distinguishes reproducibility from replicability; DORA (2012) supports assessing research on its merits.
- Negative results.
- Replication attempts.
How reproducibility and negative results is modelled, measured and tested.
El modelo, sus supuestos, sus datos y su incertidumbre, en la forma en que un científico o un ingeniero los comprobaría. Las estructuras siguen métodos publicados. Los valores representados son ilustrativos, no hallazgos.
Modelo, ecuaciones y supuestos
The model structure used for reproducibility and negative results, the assumptions it makes and how each is tested.
Ri = f(Hi, Ei, Vi, Ci)amenaza, exposición, vulnerabilidad y capacidad de afrontamientoEAL = ∑k pk · Lksobre los eventos k con probabilidad anual pkxt+1 = A xt + B ut + εtA carries the couplings, u the shock, ε the error| Supuesto | Por qué se hace | Cómo se prueba |
|---|---|---|
| La amenaza y la exposición son independientes dentro de un evento | Mantiene manejable la integral de pérdidas | Comparar con un ajuste de cópula sobre registros conjuntos |
| Los acoplamientos son lineales cerca del punto de operación | Permite que las perturbaciones se propaguen a través de A | Perturbar más allá del rango lineal y comparar |
| Las curvas de vulnerabilidad se transfieren entre lugares | Las curvas locales rara vez están disponibles | Validar frente a las pérdidas observadas cuando existan |
- Entradas
- conjuntos de datos versionados con procedencia
- Resultados
- distribuciones, nunca cifras únicas
- Código
- publicado con la ficha de modelo y las pruebas
- Reproducción
- mismos datos, código, entorno y semilla
Qué se mide y con qué calidad.
Cada indicador lleva su unidad, su periodicidad, el tipo de fuente que lo respalda y la etiqueta de evidencia que llevaría un valor. No se muestran valores: un programa los aporta bajo su propia autoridad.
| Indicador | Unidad | Cadencia | Tipo de fuente | Etiqueta |
|---|---|---|---|---|
| Computational reproducibility | percent of sampled studies regenerated from shared code and data | per audit | Reproducibility audits | OBSERVADO |
| Replication outcome | proportion of replications meeting the prespecified criterion | per programme | Coordinated replication projects | OBSERVADO |
| Retraction notices | count by field and reason | anual | Retraction Watch database via Crossref | Estadísticas publicadas |
| Time from concern to correction | meses | per case | Journal and integrity office records | NOTIFICADO |
| Post-retraction citations | citations per retracted article | anual | Citation data linked to retraction records | OBSERVADO |
| Null and negative results deposited | count of records | anual | Repositories and registries | NOTIFICADO |
| Data available on request | percent of articles whose data could be obtained | per audit | Data request audits | OBSERVADO |
De dónde procede la evidencia.
Conjuntos de datos públicos y registros institucionales identificados, cada uno con su tipo de licencia. Las fuentes obtenidas por acuerdo solo se utilizan conforme a sus condiciones y nunca se republican.
| Fuente | Lo que aporta | Tipo de licencia |
|---|---|---|
| Retraction Watch database (Crossref) | Retractions, corrections and expressions of concern with reasons | Abierta, con atribución |
| Crossref metadata and Crossmark updates | Links from articles to their corrections and retractions | Open metadata, no restrictions |
| OpenAlex | Works, citations, authors and institutions across disciplines | Abierta, CC0 |
| FORRT Replication Database | Replication attempts and outcomes, mostly in the social and behavioural sciences | Abierta, con atribución |
| OSF projects of coordinated replication programmes | Protocols, data and code of large-scale replications | Open, licence per project |
| PubPeer | Post-publication comments identifying possible problems in papers | Free access, terms of use |
| Zenodo (CERN) | Citable deposits for null results, data and code | Open, licence per record |
Quién decide y a qué sirve la evidencia.
Laboratorios prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Quién decide | La pregunta | Evidencia a la que sirve esta página |
|---|---|---|
| Journal editors and publishers | Whether to correct, retract or issue an expression of concern | Evidence of error or misconduct, author response, COPE guidance, reproduction attempts |
| Research integrity offices | Whether an allegation is substantiated and what follows | Raw data, records, code, interviews and an independent analysis |
| Financiadores de la investigación | Whether to fund replications and require data and code sharing | Reproducibility audit results, replication evidence by field, cost of sharing |
| Guideline developers and systematic reviewers | Whether to rely on a study that has been questioned or corrected | Retraction and correction status, replication evidence, risk-of-bias assessment |
Productos que genera este tema
- Replication protocols registered before data collection, with success criteria fixed
- Reproducibility records listing code, data, environment and the outcome of each rerun
- Deposits of null and negative results with full methods
- Correction logs that link each notice to the works affected
What experts ask about reproducibility.
- Can an independent team re-run the analysis from the published data and code?
- Has the finding been replicated with new data, in a new setting or by a different team?
- Which null or negative results exist, and where are they recorded?
- When an error is found, how is it corrected and how are users of the result told?
- Which parts of a workflow, such as software versions or random seeds, could change the answer?
Why results fail to reproduce.
Many published findings in several disciplines have proved difficult to replicate. The causes are well understood: small samples, flexible analysis, selective reporting, undocumented data processing and incentives that favour novel positive findings. In computational work, results can also depend on software versions, hardware, random seeds and data that change after publication.
Negative results are under-reported because they are harder to publish and seem less interesting. This distorts the evidence base: if only successful trials of an intervention are visible, the intervention looks more effective than it is. Systematic reviews and models built on that evidence inherit the bias.
Corrections matter for the same reason. An error in a widely used dataset or model can propagate into many downstream analyses before it is found.
Practices that support reproducibility.
- 01
Open data and code
Sharing the materials needed to re-run an analysis, within privacy and licensing limits. Some data cannot be shared and must be described instead.
- 02
Computational environments
Recording software versions and environments, a practice developed with modelización reproducible. Long-term preservation remains hard.
- 03
Registered reports
Peer review of the protocol before results exist, so publication does not depend on the outcome.
- 04
Correction records
Dated, visible corrections linked to the original result, rather than silent replacement.
A failed replication does not by itself show the original was wrong; differences in setting, sample or method must be examined before conclusions are drawn.
Cómo se estudia y lo que los métodos no pueden decir.
- 01Probabilidad y estadísticaPlanificación del tamaño muestral y la potencia · Metaanálisis con evaluación de sesgosAplicado aquí
- 02Cuantificación de la incertidumbre y validación de modelosPresupuestos de incertidumbre de medición · Análisis de sensibilidad de modelos de simulaciónAplicado aquí
- 03Inferencia causal y evaluación de impactoPilotos aleatorizados y despliegues escalonados · Evaluación cuasiexperimental de programasAplicado aquí
- 04Inteligencia artificial y aprendizaje automáticoCribado asistido para revisiones sistemáticas · Modelos sustitutos para guiar experimentosAplicado aquí
Where this page’s references lead.
Los organismos y documentos con los que se lee esta página. El estado y las ediciones cambian; cada enlace lleva al organismo emisor.
Preguntas y respuestas.
What is the difference between reproducibility and replicability?
Why publish negative results?
Does retraction always mean misconduct?
Can a retracted paper still be cited?
Will Labs publish a study that found nothing?
How do I report an error in a Labs result?
Are corrected results removed?
Aporte evidencia desde esta página.
serie Reports is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
- Correo electrónicoPasarela GW-GCRIEnvíelo desde esta páginaEvidencia, una corrección, un caso, un método, una revisión, una traducción o un aporte a un perfil. Llega etiquetado con esta página, de modo que lo recibe el editor responsable.
- ZenodoDepósito abiertoDeposítelo y envíelo a la comunidadPublique su propio registro en Zenodo con su ORCID iD y luego envíelo a la comunidad de Reports allí. Los curadores lo revisan; el DOI sigue siendo suyo.
- serie ReportsGCRIVer todas las formas de contribuirLos tipos de contribución, cómo se revisa cada uno, qué crédito recibe, la licencia y qué nunca se acepta.
No envíe nunca datos personales, detalles sensibles de infraestructura ni nada que no tenga derecho a compartir. Un aporte se revisa antes de su uso; enviarlo no confiere autoría ni derechos editoriales, y el patrocinio nunca compra un hallazgo.
Aporte un método, un conjunto de datos o una pregunta.
La información y las consultas están abiertas hoy. Una consulta recibe una referencia e indica el siguiente paso; las solicitudes, pagos, asignaciones de cómputo y registros solo se abren cuando existen su equipo, sus condiciones y la capacidad de revisión.
Documentos jurídicos.
Los mapas, datos y cifras de este sitio se muestran como contexto e información general. No son consejos, pronósticos ni avisos; los límites de la información se exponen en los Descargos de responsabilidad y los Avisos sobre datos.