Link dataset card to paper and add citation

README.md

@@ -1,11 +1,12 @@
 ---
-pretty_name: LOCUS v1.0
 language:
 - en
-task_categories:
-- text-classification
+license: cc-by-nc-4.0
 size_categories:
 - 1M<n<10M
+task_categories:
+- text-classification
+pretty_name: LOCUS v1.0
 tags:
 - law
 - legal-nlp
@@ -49,74 +50,77 @@ dataset_info:
   splits:
   - name: train
     num_examples: 2211516
-rai:dataLimitations: >
-  - Coverage is not exhaustive. LOCUS v1.0 does not represent every U.S.
-  municipal or county jurisdiction, and jurisdictions vary in how completely
+rai:dataLimitations: '- Coverage is not exhaustive. LOCUS v1.0 does not represent
+  every U.S. municipal or county jurisdiction, and jurisdictions vary in how completely
   their local law is digitized and codified.
 
-  - The `function` and `topic` labels are model-assigned and have not been fully
-  human-validated. Label noise should be expected, particularly at taxonomy
-  boundaries (e.g., Rules vs. Process, Buildings vs. Zoning).
-
-  - The topic taxonomy is intentionally coarse (Buildings, Business, Nuisance,
-  Zoning, Other) and does not capture fine-grained subject matter important for
-  some downstream tasks.
-  
-  - Local law changes over time. The dataset reflects a snapshot and may not
-  match the current law of any given jurisdiction.
-rai:dataBiases: >
-  - Geographic and population skew: larger and more urbanized jurisdictions, and
-  jurisdictions that publish their codes through major codification vendors, are
+  - The `function` and `topic` labels are model-assigned and have not been fully human-validated.
+  Label noise should be expected, particularly at taxonomy boundaries (e.g., Rules
+  vs. Process, Buildings vs. Zoning).
+
+  - The topic taxonomy is intentionally coarse (Buildings, Business, Nuisance, Zoning,
+  Other) and does not capture fine-grained subject matter important for some downstream
+  tasks.
+
+  - Local law changes over time. The dataset reflects a snapshot and may not match
+  the current law of any given jurisdiction.
+
+  '
+rai:dataBiases: '- Geographic and population skew: larger and more urbanized jurisdictions,
+  and jurisdictions that publish their codes through major codification vendors, are
   likely overrepresented relative to small or rural municipalities.
 
-  - State-level legal regimes differ, so the distribution of functions and
-  topics is not uniform across states; aggregate label frequencies should not be
-  assumed to generalize to any individual jurisdiction.
-
-  - The taxonomy itself encodes choices about what counts as "substantive" law
-  (Rules and Enforcement) versus procedural, contextual, or structural
-  provisions. Alternative legal-theoretic framings would produce different
-  labels.
-
-  - Labels were produced by automated classifiers and may reflect biases of
-  those classifiers, including biases inherited from their training data.
-
-  - English-language only. Non-English text occasionally present in U.S. local
-  codes is not handled as a separate class.
-rai:personalSensitiveInformation: >
-  LOCUS v1.0 is built from publicly enacted municipal and county law, which is a
-  matter of public record. However, local ordinances can incidentally contain
-  references to identifiable individuals (e.g., named officials, sponsors, or
-  parties to specific proceedings) and to specific properties or businesses
-  (e.g., addresses in zoning provisions, license holders, named establishments).
-  No effort has been made to redact such incidental references. The dataset does
-  not contain non-public personal data, biometric data, health data, financial
-  account information, or government-issued identifiers.
-rai:dataUseCases: >
-  Intended uses include legal text classification research, analysis of
-  local-law structure and composition, substantive vs. non-substantive filtering
-  for downstream legal NLP pipelines, refinement of legal taxonomies, and weakly
-  supervised or human-in-the-loop annotation workflows. The dataset is not
-  appropriate as legal advice, as a substitute for human legal review, as a
-  complete census of U.S. local law, or as a fully validated benchmark in the
-  absence of additional human auditing.
-rai:dataSocialImpact: >
-  Anticipated positive impacts include lowering the cost of empirical research
-  on local government law, supporting tools that help residents, journalists,
-  and researchers understand municipal regulation, and enabling reproducible
-  study of how local jurisdictions structure their codes. Anticipated risks
-  include over-reliance on automated classifications in settings with legal
-  consequences, misinterpretation of aggregated label frequencies as
-  authoritative statements about the law, and downstream tools surfacing model
-  outputs to end users as if they were vetted legal information. Users deploying
-  systems built on this dataset should clearly disclose the automated and
-  unaudited nature of the labels and should not present outputs as legal advice.
+  - State-level legal regimes differ, so the distribution of functions and topics
+  is not uniform across states; aggregate label frequencies should not be assumed
+  to generalize to any individual jurisdiction.
+
+  - The taxonomy itself encodes choices about what counts as "substantive" law (Rules
+  and Enforcement) versus procedural, contextual, or structural provisions. Alternative
+  legal-theoretic framings would produce different labels.
+
+  - Labels were produced by automated classifiers and may reflect biases of those
+  classifiers, including biases inherited from their training data.
+
+  - English-language only. Non-English text occasionally present in U.S. local codes
+  is not handled as a separate class.
+
+  '
+rai:personalSensitiveInformation: 'LOCUS v1.0 is built from publicly enacted municipal
+  and county law, which is a matter of public record. However, local ordinances can
+  incidentally contain references to identifiable individuals (e.g., named officials,
+  sponsors, or parties to specific proceedings) and to specific properties or businesses
+  (e.g., addresses in zoning provisions, license holders, named establishments). No
+  effort has been made to redact such incidental references. The dataset does not
+  contain non-public personal data, biometric data, health data, financial account
+  information, or government-issued identifiers.
+
+  '
+rai:dataUseCases: 'Intended uses include legal text classification research, analysis
+  of local-law structure and composition, substantive vs. non-substantive filtering
+  for downstream legal NLP pipelines, refinement of legal taxonomies, and weakly supervised
+  or human-in-the-loop annotation workflows. The dataset is not appropriate as legal
+  advice, as a substitute for human legal review, as a complete census of U.S. local
+  law, or as a fully validated benchmark in the absence of additional human auditing.
+
+  '
+rai:dataSocialImpact: 'Anticipated positive impacts include lowering the cost of empirical
+  research on local government law, supporting tools that help residents, journalists,
+  and researchers understand municipal regulation, and enabling reproducible study
+  of how local jurisdictions structure their codes. Anticipated risks include over-reliance
+  on automated classifications in settings with legal consequences, misinterpretation
+  of aggregated label frequencies as authoritative statements about the law, and downstream
+  tools surfacing model outputs to end users as if they were vetted legal information.
+  Users deploying systems built on this dataset should clearly disclose the automated
+  and unaudited nature of the labels and should not present outputs as legal advice.
+
+  '
 rai:hasSyntheticData: false
-license: cc-by-nc-4.0
 ---
 
 # LOCUS v1.0
 
+This repository contains the dataset presented in the paper [Freeing the Law with LOCUS: A Local Ordinance Corpus for the United States](https://huggingface.co/papers/2606.19334).
+
 ## Dataset Summary
 LOCUS v1.0 is a chunk-level dataset of U.S. municipal and county law text labeled by legal function. Each eligible chunk is assigned a `function`, a binary `is_substantive` label, and all substantive provisions are assigned a `topic`.
 The dataset is intended for legal text research, local-law structure analysis, substantive filtering, and downstream taxonomy refinement.
@@ -166,4 +170,15 @@ LOCUS v1.0 should not be treated as:
 - a complete census of all U.S. local law
 - a fully human-validated benchmark without additional auditing
 
-## Citation
+## Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@article{peskoff2026freeing,
+  title={Freeing the Law with LOCUS: A Local Ordinance Corpus for the United States},
+  author={Peskoff, Denis and Barrow, Joe and Vu, Christopher and Davenport, Diag},
+  journal={arXiv preprint arXiv:2606.19334},
+  year={2026}
+}
+```