Datasets:

lordim
/

BenchRep-T

	@@ -1 +1,110 @@
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+  "name": "BenchRep-T",
+  "description": "BenchRep-T is a benchmark for TCRβ (T-cell receptor β-chain) immune-repertoire classification. It bundles the multi-disease Mal-ID cohort together with five external disease cohorts (sequenced on the Adaptive Biotechnologies immunoSEQ platform: Savola_RA, Musvosvi_TB, Rawat_T1D, Mitchell_T1D, Emerson_CMV), providing per-specimen repertoire files, harmonized sample metadata, and auxiliary files used for the sequencing-depth scaling-law and driver-sequence experiments. Each specimen (one sequenced repertoire) is one example; the classification target is the specimen-level disease label. See the full description at https://huggingface.co/datasets/lordim/BenchRep-T.",
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+    "AIRR",
+    "immunoseq",
+    "repertoire-classification"
+  ],
+  "license": "https://choosealicense.com/licenses/mit/",
+  "url": "https://huggingface.co/datasets/lordim/BenchRep-T",
+  "dataCollection": "Bulk TCRβ immune-repertoire sequencing aggregated from previously published, de-identified human cohorts. Mal-ID is multi-disease B/T-cell receptor sequencing (Zaslavsky et al. 2025); the five external cohorts are Adaptive Biotechnologies immunoSEQ TCRβ repertoires from published disease studies: Savola et al. 2017 (rheumatoid arthritis), Musvosvi et al. 2023 (tuberculosis), Rawat et al. 2026 and Mitchell et al. 2022 (type 1 diabetes), and Emerson et al. 2017 (cytomegalovirus).",
+  "dataCollectionType": "Secondary aggregation and harmonization of previously published, de-identified human immune-repertoire sequencing datasets.",
+  "dataCollectionTimeframe": "Source studies published 2017–2026.",
+  "dataPreprocessingProtocol": "Repertoire files were harmonized into per-specimen rearrangement tables (AIRR-style for Mal-ID; Adaptive immunoSEQ exports for the external cohorts) and gzip-compressed. Specimen-level disease labels and cross-validation fold assignments are provided in per-cohort metadata.tsv files. Specimens without a usable label were excluded (e.g., 66 BCR-only specimens dropped from Mal-ID; 25 unknown-CMV-status specimens dropped from Emerson_CMV).",
+  "dataAnnotationProtocol": "Specimen-level disease/status labels are inherited from the clinical annotations of the original source studies; no additional manual annotation was performed for this benchmark.",
+  "dataUseCases": "Intended for benchmarking machine-learning methods that classify disease or exposure status from an individual's TCRβ immune repertoire. Not intended for clinical diagnosis or individual-level medical decision-making.",
+  "dataBiases": "Cohorts differ in demographic composition, recruitment, sequencing platform and depth, and case/control balance. Because labels come from heterogeneous studies, models may learn cohort- or platform-specific batch effects rather than disease biology, and performance may not transfer across cohorts or to underrepresented populations.",
+  "dataLimitations": "Each cohort comprises a modest number of specimens (91–761). Only the TCRβ chain is provided (no paired α/β chains and no BCR). Mal-ID uses a different assay from the immunoSEQ external cohorts, limiting cross-cohort comparability. Demographic/clinical covariates are limited and vary by cohort.",
+  "dataSocialImpact": "Repertoire-based classifiers could support non-invasive diagnostics, but deploying models that underperform on underrepresented demographic groups risks inequitable outcomes; this dataset is intended for research benchmarking only.",
+  "personalSensitiveInformation": "Data consist of de-identified human immune-repertoire sequences plus a limited set of demographic and clinical fields (e.g., age, sex, ancestry/race, and HLA typing or autoantibody status for some cohorts). No direct identifiers are included. Note that immune-repertoire and HLA data can in principle be re-identifying; use in accordance with the data-use terms of the original source studies.",
+  "dataReleaseMaintenancePlan": "Released as a static benchmark snapshot under the MIT License via the Hugging Face Hub; any updates are tracked through the repository commit history."
+}