GCRI · Labs
Reproduksibilitas dan hasil negatif
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.
Papan protokol dan replikasi.
Layar yang akan digunakan sebuah tim untuk mengikuti subjek ini: ukuran, status, dan keputusannya. Nama adalah contoh dan nilai bersifat ilustratif.
Papan protokol dan replikasi
Reproducibility and negative results · Labs · Quarter 1
Protokol
| PR-594 sensor drift | Eksperimen lapangan | Ya | Disetujui | Berjalan |
| PR-714 AI triage | Diacak | Ya | Kondisi | Penyiapan |
| PR-551 message recall | Eksperimen survei | Ya | Disetujui | Selesai |
| PR-896 heat exposure | Kohort | Tidak | Tertunda | Draf |
Replication R-20
- Efek asli
- 0.3 SD
- Efek replikasi
- 0.3 SD
- Putusan
- Lebih kecil, tanda sama
- Diterbitkan
- dengan data dan kode
Ketersediaan testbed
Negative results matter in every domain.
- KesehatanDalam praktikNull trial results prevent wasted effort.What a reader can checkPublication of null results
- AirDalam praktikFailed interventions inform utilities.What a reader can checkFailure records
- PanganDalam praktikVarieties that did not perform are recorded.What a reader can checkTrial outcomes
Negative results are published in the seri Bukti Labs. 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.
Model, asumsi, data, dan ketidakpastiannya, dalam bentuk yang akan diperiksa ilmuwan atau insinyur. Strukturnya mengikuti metode yang dipublikasikan. Nilai yang digambar bersifat ilustratif, bukan temuan.
Model, persamaan, dan asumsi
The model structure used for reproducibility and negative results, the assumptions it makes and how each is tested.
Ri = f(Hi, Ei, Vi, Ci)bahaya, keterpaparan, kerentanan, dan kapasitas penanggulanganEAL = ∑k pk · Lkatas peristiwa k dengan probabilitas tahunan pkxt+1 = A xt + B ut + εtA carries the couplings, u the shock, ε the error| Asumsi | Mengapa ini dibuat | Bagaimana ini diuji |
|---|---|---|
| Bahaya dan eksposur bersifat independen dalam satu peristiwa | Menjaga integral kerugian tetap dapat dihitung | Bandingkan dengan pencocokan kopula pada catatan gabungan |
| Keterkaitan bersifat linear di dekat titik operasi | Membiarkan guncangan merambat melalui A | Uji di luar rentang linear lalu bandingkan |
| Kurva kerentanan dapat dipindahkan antartempat | Kurva lokal jarang tersedia | Validasi terhadap kerugian teramati jika tersedia |
- Masukan
- dataset berversi dengan asal-usul yang tercatat
- Keluaran
- distribusi, bukan angka tunggal
- Kode
- dirilis bersama kartu model dan pengujiannya
- Reproduksi
- data, kode, lingkungan, dan seed yang sama
Apa yang diukur, dan seberapa baik.
Setiap indikator membawa satuan, frekuensi, jenis sumber di baliknya, dan label bukti yang akan dibawa suatu nilai. Tidak ada nilai yang ditampilkan: sebuah program menyediakannya berdasarkan wewenangnya sendiri.
| Indikator | Unit | Kadensi | Jenis sumber | Label |
|---|---|---|---|---|
| Computational reproducibility | percent of sampled studies regenerated from shared code and data | per audit | Reproducibility audits | TERAMATI |
| Replication outcome | proportion of replications meeting the prespecified criterion | per programme | Coordinated replication projects | TERAMATI |
| Retraction notices | count by field and reason | tahunan | Retraction Watch database via Crossref | Statistik terbitan |
| Time from concern to correction | bulan | per case | Journal and integrity office records | DILAPORKAN |
| Post-retraction citations | citations per retracted article | tahunan | Citation data linked to retraction records | TERAMATI |
| Null and negative results deposited | count of records | tahunan | Repositories and registries | DILAPORKAN |
| Data available on request | percent of articles whose data could be obtained | per audit | Data request audits | TERAMATI |
Asal bukti.
Set data publik bernama dan catatan kelembagaan, masing-masing dengan kelas lisensinya. Sumber berdasarkan perjanjian hanya digunakan sesuai ketentuannya dan tidak pernah diterbitkan ulang.
| Sumber | Apa yang diberikannya | Kelas lisensi |
|---|---|---|
| Retraction Watch database (Crossref) | Retractions, corrections and expressions of concern with reasons | Terbuka, dengan atribusi |
| 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 | Terbuka, CC0 |
| FORRT Replication Database | Replication attempts and outcomes, mostly in the social and behavioural sciences | Terbuka, dengan atribusi |
| 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 |
Siapa yang memutuskan, dan apa yang dilayani bukti.
Laboratorium prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Siapa yang memutuskan | Pertanyaannya | Bukti yang dilayani halaman ini |
|---|---|---|
| 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 |
| Penyandang dana penelitian | 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 |
Keluaran yang dihasilkan topik ini
- 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 pemodelan yang dapat direproduksi. 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.
Cara hal ini dikaji, dan apa yang tidak dapat dinyatakan metodenya.
- 01Probabilitas dan statistikaPerencanaan ukuran sampel dan daya uji · Meta-analisis dengan penilaian biasDiterapkan di sini
- 02Kuantifikasi ketidakpastian dan validasi modelAnggaran ketidakpastian pengukuran · Analisis sensitivitas model simulasiDiterapkan di sini
- 03Inferensi kausal dan evaluasi dampakUji coba teracak dan peluncuran bertahap · Evaluasi program kuasi-eksperimentalDiterapkan di sini
- 04Kecerdasan buatan dan pembelajaran mesinPenyaringan berbantuan untuk tinjauan sistematis · Model pengganti untuk memandu eksperimenDiterapkan di sini
Where this page’s references lead.
Badan dan dokumen yang menjadi acuan halaman ini. Status dan edisi berubah; setiap tautan menuju badan penerbitnya.
Tanya jawab.
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?
Kontribusikan bukti dari halaman ini.
Laporan 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.
- SurelGerbang GW-GCRIKirim dari halaman iniBukti, koreksi, kasus, metode, tinjauan, terjemahan, atau masukan profil. Kiriman tiba dengan label halaman ini, sehingga editor pemiliknya menerimanya.
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- LaporanGCRILihat semua cara berkontribusiJenis kontribusi, cara masing-masing ditinjau, kredit yang Anda terima, lisensinya, dan apa yang tidak pernah diterima.
Jangan pernah mengirim data pribadi, rincian infrastruktur sensitif, atau apa pun yang tidak berhak Anda bagikan. Kontribusi ditinjau sebelum digunakan. Mengirimkannya tidak memberikan hak kepenulisan atau editorial, dan sponsor tidak pernah membeli sebuah temuan.
Bawa metode, dataset, atau pertanyaan.
Informasi dan pertanyaan sudah terbuka hari ini. Sebuah pertanyaan menerima nomor referensi dan menyebutkan langkah berikutnya; lamaran, pembayaran, alokasi komputasi, dan pendaftaran hanya dibuka ketika tim, ketentuan, dan kapasitas tinjauannya tersedia.
Dokumen hukum.
Peta, data, dan gambar di situs ini ditampilkan untuk konteks dan informasi umum. Semua itu bukan nasihat, prakiraan, atau peringatan; batasan informasinya dijelaskan dalam Penafian dan Pemberitahuan Data.