GCRI · Labs
بازتولیدپذیری و نتایج منفی
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.
تابلوی پروتکل و تکرار.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
تابلوی پروتکل و تکرار
Reproducibility and negative results · Labs · Quarter 1
پروتکل‌ها
| PR-594 sensor drift | آزمایش میدانی | بله | تأییدشده | در حال اجرا |
| PR-714 AI triage | تصادفی‌سازی‌شده | بله | شرایط | راه‌اندازی |
| PR-551 message recall | آزمایش پیمایشی | بله | تأییدشده | کامل |
| PR-896 heat exposure | همگروه | خیر | در انتظار | پیش‌نویس |
Replication R-20
- اثر اولیه
- 0.3 SD
- اثر تکرار
- 0.3 SD
- حکم
- کوچک‌تر، با همان علامت
- منتشرشده
- با داده و کد
در دسترس بودن بستر آزمون
Negative results matter in every domain.
Negative results are published in the مجموعهٔ 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.
مدل، مفروضات، داده‌ها و عدم قطعیت آن، به شکلی که دانشمند یا مهندس بررسی می‌کند. ساختارها از روش‌های منتشرشده پیروی می‌کنند. مقادیر ترسیم‌شده تبیینی‌اند، نه یافته.
مدل، معادلات و مفروضات
The model structure used for reproducibility and negative results, the assumptions it makes and how each is tested.
Ri = f(Hi, Ei, Vi, Ci)مخاطره، مواجهه، آسیب‌پذیری و ظرفیت مقابلهEAL = ∑k pk · Lkبر رویدادهای k با احتمال سالانهٔ pkxt+1 = A xt + B ut + εtA carries the couplings, u the shock, ε the error| فرض | چرا ساخته می‌شود | چگونه آزموده می‌شود |
|---|---|---|
| مخاطره و مواجهه در یک رویداد مستقل‌اند | انتگرال زیان را تحت‌کنترل نگه می‌دارد | مقایسه با برازش مفصل بر سوابق مشترک |
| جفت‌شدگی‌ها نزدیک نقطهٔ کار خطی‌اند | اجازه می‌دهد شوک‌ها از طریق A منتشر شوند | فراتر از دامنهٔ خطی آشفته کنید و مقایسه کنید |
| منحنی‌های آسیب‌پذیری میان مکان‌ها منتقل می‌شوند | منحنی‌های محلی به‌ندرت در دسترس‌اند | اعتبارسنجی در برابر زیان‌های مشاهده‌شده در جاهایی که وجود دارند |
- ورودی‌ها
- مجموعه‌داده‌های نسخه‌دار با خاستگاه
- خروجی‌ها
- توزیع‌ها، هرگز تک‌رقم‌ها
- کد
- منتشرشده با کارت مدل و آزمون‌ها
- بازتولید
- همان داده، کد، محیط و بذر
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | دورهٔ تکرار | Source type | برچسب |
|---|---|---|---|---|
| Computational reproducibility | percent of sampled studies regenerated from shared code and data | per audit | Reproducibility audits | مشاهده‌شده |
| Replication outcome | proportion of replications meeting the prespecified criterion | per programme | Coordinated replication projects | مشاهده‌شده |
| Retraction notices | count by field and reason | annual | Retraction Watch database via Crossref | آمارهای منتشرشده |
| Time from concern to correction | months | per case | Journal and integrity office records | گزارش‌شده |
| Post-retraction citations | citations per retracted article | annual | Citation data linked to retraction records | مشاهده‌شده |
| Null and negative results deposited | count of records | annual | Repositories and registries | گزارش‌شده |
| Data available on request | percent of articles whose data could be obtained | per audit | Data request audits | مشاهده‌شده |
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| منبع | What it gives | Licence class |
|---|---|---|
| Retraction Watch database (Crossref) | Retractions, corrections and expressions of concern with reasons | Open, attribution |
| 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 | Open, CC0 |
| FORRT Replication Database | Replication attempts and outcomes, mostly in the social and behavioural sciences | Open, attribution |
| 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 |
Who decides, and what the evidence serves.
آزمایشگاه‌ها prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| چه کسی تصمیم می‌گیرد | The question | Evidence this page serves |
|---|---|---|
| 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 |
| Research funders | 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 |
Outputs this topic produces
- 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 reproducible modelling. 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.
How it is studied, and what the methods cannot say.
- 01احتمال و آمارSample size and power planning · Meta-analysis with bias assessmentApplied here
- 02کمّی‌سازی عدم قطعیت و اعتبارسنجی مدلMeasurement uncertainty budgets · Sensitivity analysis of simulation modelsApplied here
- 03استنباط علّی و ارزیابی اثرRandomised pilots and stepped rollouts · Quasi-experimental programme evaluationApplied here
- 04هوش مصنوعی و یادگیری ماشینAssisted screening for systematic reviews · Surrogate models to guide experimentsApplied here
Where this page’s references lead.
نهادها و اسنادی که این صفحه بر پایهٔ آن‌ها خوانده می‌شود. وضعیت و ویراست‌ها تغییر می‌کنند؛ هر پیوند به نهاد صادرکننده می‌رود.
پرسش و پاسخ.
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?
شواهد را از همین صفحه ارسال کنید.
گزارش‌ها 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.
- ایمیلدرگاه GW-GCRIاز همین صفحه ارسال کنیدشواهد، یک اصلاحیه، یک مورد، یک روش، یک داوری، یک ترجمه یا ورودی نمایه. با برچسب این صفحه میرسد تا ویراستار مسئول آن را دریافت کند.
- Zenodoسپردهٔ بازآن را واگذار کنید و به جامعه ارائه دهیدسابقهٔ خود را با شناسهٔ ORCID خود در Zenodo منتشر کنید و سپس آن را در همانجا به جامعهٔ Reports ارسال کنید. گزینشگران آن را داوری می‌کنند؛ DOI از آنِ شما میماند.
- گزارش‌هاGCRIهمهٔ راههای مشارکت را ببینیدانواع مشارکت، نحوهٔ بررسی هر یک، اعتباری که دریافت میکنید، مجوز و آنچه هرگز پذیرفته نمیشود.
هرگز داده شخصی، جزئیات حساس زیرساخت یا چیزی را که حق اشتراکش را ندارید نفرستید. هر مشارکت پیش از استفاده داوری می‌شود؛ ارسال آن حق مؤلفی یا ویرایشی نمی‌دهد و حمایت مالی هرگز یافته‌ای را نمی‌خرد.
یک روش، یک مجموعه‌داده یا یک پرسش بیاورید.
اطلاعات و درخواست‌ها امروز باز است. هر درخواست یک شمارهٔ پیگیری می‌گیرد و گام بعدی را مشخص می‌کند؛ درخواست‌های عضویت، پرداخت‌ها، تخصیص رایانش و ثبت‌نام‌ها تنها زمانی باز می‌شوند که تیم، شرایط و ظرفیت داوری آن‌ها فراهم باشد.
اسناد حقوقی.
نقشه‌ها، داده‌ها و ارقام این سایت برای زمینه و اطلاعات عمومی نشان داده می‌شوند. توصیه، پیش‌بینی یا هشدار نیستند؛ حدود این اطلاعات در سلب مسئولیت‌ها و اعلامیه‌های داده بیان شده است.