The international Genome sample resource (IGSR): A worldwide collection of genome variation incorporating the 1000 Genomes Project data
Bibliographic record
Abstract
The International Genome Sample Resource (IGSR; http://www.internationalgenome.org) expands in data type and population diversity the resources from the 1000 Genomes Project. IGSR represents the largest open collection of human variation data and provides easy access to these resources. IGSR was established in 2015 to maintain and extend the 1000 Genomes Project data, which has been widely used as a reference set of human variation and by researchers developing analysis methods. IGSR has mapped all of the 1000 Genomes sequence to the newest human reference (GRCh38), and will release updated variant calls to ensure maximal usefulness of the existing data. IGSR is collecting new structural variation data on the 1000 Genomes samples from long read sequencing and other technologies, and will collect relevant functional data into a single comprehensive resource. IGSR is extending coverage with new populations sequenced by collaborating groups. Here, we present the new data and analysis that IGSR has made available. We have also introduced a new data portal that increases discoverability of our data-previously only browseable through our FTP site-by focusing on particular samples, populations or data sets of interest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.022 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.048 | 0.023 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".