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Tekrarlanabilirlik ve olumsuz sonuçlar

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.

01Sicil

Reproducibility and negative results on the record.

02Soru

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.

Mekanizma

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.

Mekanizma

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.

Mekanizma

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.

Mekanizma

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.

03İzleme

Protokol ve tekrar çalışması panosu.

Bir ekibin bu konuyu izlemek için kullanacağı ekran: ölçütleri, durumları ve kararları. Adlar örnektir ve değerler açıklayıcıdır.

Protokol ve tekrar çalışması panosu

Reproducibility and negative results · Labs · Quarter 1

İki tekrar çalışıyorGCRI · Labs

Protokoller

PR-594 sensor driftSaha deneyiEvetOnaylandıÇalışıyor
PR-714 AI triageRastgeleEvetKoşullarKurulum
PR-551 message recallAnket deneyiEvetOnaylandıTamamlandı
PR-896 heat exposureKohortHayırBeklemedeTaslak

Replication R-20

Özgün etki
0.3 SD
Tekrar etkisi
0.3 SD
Hüküm
Daha küçük, aynı işaret
Yayımlandı
veri ve kodla

Test ortamı kullanılabilirliği

Protocol and replication board: the screen’s structure, terms and states for Reproducibility and negative results.Örnek adlar ve örnekleyici değerler; bir tasarımdır, kayıt veya canlı bir sistem değildir.
04In the five domains

Negative results matter in every domain.

Negative results are published in the Labs Kanıt serisi. The National Academies’ report (2019) distinguishes reproducibility from replicability; DORA (2012) supports assessing research on its merits.

  • Negative results.
  • Replication attempts.
05Yöntemler ve kanıt

How reproducibility and negative results is modelled, measured and tested.

Model, varsayımları, verileri ve belirsizliği, bir bilim insanının veya mühendisin kontrol edeceği biçimde. Yapılar yayımlanmış yöntemleri izler. Çizilen değerler bulgu değil, açıklayıcıdır.

Model, denklemler ve varsayımlar

The model structure used for reproducibility and negative results, the assumptions it makes and how each is tested.

i öğesinin riskiRi = f(Hi, Ei, Vi, Ci)tehlike, maruziyet, kırılganlık ve başa çıkma kapasitesi
Beklenen yıllık kayıpEAL = ∑k pk · Lkyıllık olasılığı pk
Bağlı sistemlerxt+1 = A xt + B ut + εtA carries the couplings, u the shock, ε the error
VarsayımNeden yapıldığıNasıl sınanır
Bir olay içinde tehlike ve maruziyet bağımsızdırKayıp integralini çözülebilir tutarOrtak kayıtlara uydurulmuş bir kopula ile karşılaştırın
Bağlantılar çalışma noktası yakınında doğrusaldırŞokların A üzerinden yayılmasına izin verirDoğrusal aralığın ötesine itin ve karşılaştırın
Kırılganlık eğrileri yerler arasında aktarılabilirYerel eğriler nadiren mevcutturMevcut olduğunda gözlemlenen kayıplarla doğrulayın
Girdiler
kaynağı belgelenmiş, sürümlü veri setleri
Çıktılar
dağılımlar, asla tek sayılar değil
Kod
model kartı ve testlerle birlikte yayımlanır
Yeniden üretim
aynı veri, kod, ortam ve tohum değeri
Standart risk ve durum uzayı formülasyonları; listelenen varsayımlar yöntemin tipik varsayımlarıdır, inceleme için belirtilmiştir, sonuç değildir.
FAIR yol gösterici ilkeleri · GO FAIR ve FORCE11W3C PROV · World Wide Web ConsortiumBelirsizliklerin tutarlı ele alınmasına ilişkin kılavuz · IPCCCEOS Analysis Ready Data · Yer Gözlem Uyduları Komitesi
06Göstergeler

Neyin ve ne kadar iyi ölçüldüğü.

Her gösterge birimini, sıklığını, ardındaki kaynak türünü ve bir değerin taşıyacağı kanıt etiketini taşır. Hiçbir değer gösterilmez: değerleri bir program kendi yetkisi altında sağlar.

GöstergeBirimSıklıkKaynak türüEtiket
Computational reproducibilitypercent of sampled studies regenerated from shared code and dataper auditReproducibility auditsGÖZLENEN
Replication outcomeproportion of replications meeting the prespecified criterionper programmeCoordinated replication projectsGÖZLENEN
Retraction noticescount by field and reasonyıllıkRetraction Watch database via CrossrefYayımlanmış istatistikler
Time from concern to correctionayper caseJournal and integrity office recordsBİLDİRİLMİŞ
Post-retraction citationscitations per retracted articleyıllıkCitation data linked to retraction recordsGÖZLENEN
Null and negative results depositedcount of recordsyıllıkRepositories and registriesBİLDİRİLMİŞ
Data available on requestpercent of articles whose data could be obtainedper auditData request auditsGÖZLENEN
07Kaynaklar

Kanıtın nereden geldiği.

Her biri lisans sınıfıyla birlikte, adı belirtilmiş kamuya açık veri kümeleri ve kurumsal kayıtlar. Anlaşmaya dayalı kaynaklar yalnızca koşullarına göre kullanılır ve asla yeniden yayımlanmaz.

KaynakSağladıklarıLisans sınıfı
Retraction Watch database (Crossref)Retractions, corrections and expressions of concern with reasonsAçık, atıf
Crossref metadata and Crossmark updatesLinks from articles to their corrections and retractionsOpen metadata, no restrictions
OpenAlexWorks, citations, authors and institutions across disciplinesAçık, CC0
FORRT Replication DatabaseReplication attempts and outcomes, mostly in the social and behavioural sciencesAçık, atıf
OSF projects of coordinated replication programmesProtocols, data and code of large-scale replicationsOpen, licence per project
PubPeerPost-publication comments identifying possible problems in papersFree access, terms of use
Zenodo (CERN)Citable deposits for null results, data and codeOpen, licence per record
08Kararlar

Kim karar verir ve kanıt neye hizmet eder.

Laboratuvarlar prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.

Kim karar verirSoruBu sayfanın hizmet ettiği kanıt
Journal editors and publishersWhether to correct, retract or issue an expression of concernEvidence of error or misconduct, author response, COPE guidance, reproduction attempts
Research integrity officesWhether an allegation is substantiated and what followsRaw data, records, code, interviews and an independent analysis
Araştırma fon sağlayıcılarıWhether to fund replications and require data and code sharingReproducibility audit results, replication evidence by field, cost of sharing
Guideline developers and systematic reviewersWhether to rely on a study that has been questioned or correctedRetraction and correction status, replication evidence, risk-of-bias assessment

Bu konunun ürettiği çıktılar

  • 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
09Temel sorular

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?
10Mekanizmalar ve etkenler

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.

11Yöntemler ve veriler

Practices that support reproducibility.

  1. 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.

  2. 02

    Computational environments

    Recording software versions and environments, a practice developed with yeniden üretilebilir modelleme. Long-term preservation remains hard.

  3. 03

    Registered reports

    Peer review of the protocol before results exist, so publication does not depend on the outcome.

  4. 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.

12Yöntemler

Nasıl incelendiği ve yöntemlerin söyleyemeyecekleri.

13Where this page’s references

Where this page’s references lead.

Bu sayfanın karşılaştırıldığı kuruluşlar ve belgeler. Durumlar ve sürümler değişir; her bağlantı yayımlayan kuruluşa gider.

14Sorular

Sorular ve yanıtlar.

What is the difference between reproducibility and replicability?
In the usage of the US National Academies, reproducibility means obtaining the same results from the same data and code, and replicability means obtaining consistent results from new data. Usage varies by field, so definitions should be stated.
Why publish negative results?
They show what did not work, prevent wasted repetition and correct the bias that makes effects look larger than they are. A well-designed study with a null result is a finding.
Does retraction always mean misconduct?
No. Papers are also retracted for honest error, irreproducible results or publisher problems. COPE guidance asks that each notice states the reason.
Can a retracted paper still be cited?
It can be cited as retracted, for example in studies of the literature. Citing it as valid evidence is an error that reviewers and editors should catch.
Will Labs publish a study that found nothing?
Yes. A well-conducted study with a null result is designed to be recorded with the same detail as a positive one.
How do I report an error in a Labs result?
Use the correction routes on the Labs trust pageyazın. Alındı bildirimi bir referans numarası verir ve sonraki adımı belirtir.
Are corrected results removed?
No. The original stays visible with a dated correction, so anyone who used it can see what changed.
RaporlarKatkıda bulunun

Bu sayfadan kanıt katkısında bulunun.

Raporlar 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.

GCRILaboratuvarlarLabs KanıtıYöntemler ve Teknik
Laboratuvarlar

Bir yöntem, bir veri seti ya da bir soru getirin.

Bilgi ve başvurular bugün açıktır. Bir başvuru bir referans numarası alır ve bir sonraki adımı belirtir; başvurular, ödemeler, işlem kapasitesi tahsisleri ve kayıtlar yalnızca ekipleri, koşulları ve inceleme kapasiteleri mevcut olduğunda açılır.