Add authors and NeurIPS 2026 citation; replace review-era anonymized text

Fills the dataset card citation, croissant creator/citeAs, and DATASHEET creator and public code/data locations.

DATASHEET.md

@@ -14,7 +14,7 @@ BenchRep-T was created to provide a fixed, reproducible benchmark for comparing
 
 **Who created the dataset and on behalf of which entity?**
 
-Anonymized during review.
+BenchRep-T was created by Alejandro Buendia\*, Chiho Im\*, Anshul Kundaje, Scott D Boyd, and Liel Cohen-Lavi (\* co-first authors) for the NeurIPS 2026 Evaluations and Datasets Track paper "BenchRep-T: A Systematic Evaluation of T-Cell Repertoire-Based Disease Diagnostics."
 
 **Who funded the creation of the dataset?**
 
@@ -197,7 +197,7 @@ Raw source data remain at their original repositories, including Synapse and Ada
 
 **Is the preprocessing software available?**
 
-Yes. Cleaning scripts, cohort adapters, fixed-fold metadata, and per-method environments are provided in the anonymized code repository linked with the review submission. A permanent, versioned public code URL should replace the anonymous review link in the camera-ready version.
+Yes. Cleaning scripts, cohort adapters, fixed-fold metadata, and per-method environments are provided in the public code repository at https://github.com/boyd-lab/BenchRep-T.
 
 ---
 
@@ -225,7 +225,7 @@ Yes. BenchRep-T must not be used for clinical diagnosis, treatment decisions, in
 
 **How is the dataset distributed?**
 
-During review, code, fold assignments, and release-eligible benchmark artifacts are supplied through the anonymized supplementary repository associated with the submission. Raw source data remain at Synapse or immuneACCESS. The repository README names a future public data mirror, but that mirror was not publicly verifiable during this datasheet audit; the datasheet therefore does not claim that all cleaned repertoires are currently available for anonymous download.
+The cleaned repertoires, per-cohort metadata with fixed fold assignments, and auxiliary task files are distributed through the Hugging Face Hub at https://huggingface.co/datasets/lordim/BenchRep-T. Code is available at https://github.com/boyd-lab/BenchRep-T. Raw source data remain at Synapse or immuneACCESS.
 
 Before public release, the authors should publish a manifest that identifies, for each file, its source, checksum, applicable source terms, redistribution status, and preprocessing version. Croissant metadata should be claimed only after a Croissant record is actually included in the release.
 

README.md

@@ -141,5 +141,19 @@ Source studies for each cohort:
 
 ## Citation
 
-This dataset accompanies a manuscript currently under **double-blind peer review**.
-Author and citation details will be added once the review process is complete.
+BenchRep-T is described in the following paper, to appear in the **NeurIPS 2026 Evaluations and Datasets Track**:
+
+> Alejandro Buendia\*, Chiho Im\*, Anshul Kundaje, Scott D Boyd, and Liel Cohen-Lavi. "BenchRep-T: A Systematic Evaluation of T-Cell Repertoire-Based Disease Diagnostics." *Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track*, 2026.
+>
+> \* Equal contribution (co-first authors).
+
+```bibtex
+@inproceedings{buendia2026benchrep,
+  title     = {BenchRep-T: A Systematic Evaluation of T-Cell Repertoire-Based Disease Diagnostics},
+  author    = {Buendia, Alejandro and Im, Chiho and Kundaje, Anshul and Boyd, Scott D and Cohen-Lavi, Liel},
+  booktitle = {Advances in Neural Information Processing Systems, Evaluations and Datasets Track},
+  year      = {2026}
+}
+```
+
+Code: [github.com/boyd-lab/BenchRep-T](https://github.com/boyd-lab/BenchRep-T). A preprint is available on [bioRxiv](https://www.biorxiv.org/content/10.64898/2026.06.09.727013v1.abstract).

croissant.json

@@ -77,10 +77,29 @@
     "lordim/BenchRep-T",
     "BenchRep-T"
   ],
-  "creator": {
-    "@type": "Person",
-    "name": "Anonymous"
-  },
+  "creator": [
+    {
+      "@type": "Person",
+      "name": "Alejandro Buendia"
+    },
+    {
+      "@type": "Person",
+      "name": "Chiho Im"
+    },
+    {
+      "@type": "Person",
+      "name": "Anshul Kundaje"
+    },
+    {
+      "@type": "Person",
+      "name": "Scott D Boyd"
+    },
+    {
+      "@type": "Person",
+      "name": "Liel Cohen-Lavi"
+    }
+  ],
+  "citeAs": "@inproceedings{buendia2026benchrep,\n  title     = {BenchRep-T: A Systematic Evaluation of T-Cell Repertoire-Based Disease Diagnostics},\n  author    = {Buendia, Alejandro and Im, Chiho and Kundaje, Anshul and Boyd, Scott D and Cohen-Lavi, Liel},\n  booktitle = {Advances in Neural Information Processing Systems, Evaluations and Datasets Track},\n  year      = {2026}\n}",
   "keywords": [
     "mit",
     "1K<n<10K",
@@ -106,4 +125,4 @@
   "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."
-}
+}