ArticleData in brief2026
A dataset of publications and patents on quantum error correction (1995-2025).
Article in Data in brief, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- A dataset of publications and patents on quantum error correction (1995-2025).Data in brief · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This article presents a harmonized corpus of scholarly publications and patents concerning quantum error correction, the encoding techniques that protect quantum information against noise and that underpin the pursuit of fault-tolerant quantum computation. The corpus spans 1 January 1995 to 31 December 2025 and contains 16,274 records: 14,385 scholarly publications harvested from OpenAlex and enriched from Crossref, and 1889 United States patent publications retrieved from Google Patents Public Data. Publications were collected by matching twenty scope phrases against titles and abstracts, intersected with a quantum-computing topic to suppress unrelated matches; patents were collected under the Cooperative Patent Classification class for quantum error correction. Both record types were mapped onto a common eighteen-field schema, deduplicated within type, and assigned to one of three retrieval tiers according to the strength of the topical match. Each record carries subfield tags and flags marking machine-learning decoder records and adjacent-topic mentions. The precision of the core tier was assessed by manual annotation of a stratified sample, and inter-annotator agreement was measured. The corpus, its variable dictionary, provenance metadata, validation samples, and the complete construction notebook are openly available, supporting reuse in scientometrics, patent analysis, and text mining of the field.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.