docs: rewrite data card to follow HF official template

README.md

@@ -1,15 +1,12 @@
 ---
-license:
-  - cc-by-4.0
-  - cc0-1.0
+# For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
 language:
   - en
   - ja
-task_categories:
-  - text-retrieval
-pretty_name: ReCaRe — A Bilingual Legal Benchmark for Revision Candidate Retrieval
-size_categories:
-  - 100K<n<1M
+license:
+  - cc-by-4.0
+  - cc0-1.0
 tags:
   - legal
   - law
@@ -17,11 +14,19 @@ tags:
   - bilingual
   - retrieval
   - benchmark
-  - bm25
-  - dense-retrieval
-  - cross-lingual
   - eu-law
   - japanese-law
+annotations_creators:
+  - expert-generated
+language_creators:
+  - expert-generated
+pretty_name: ReCaRe — A Bilingual Legal Benchmark for Revision Candidate Retrieval
+size_categories:
+  - 100K<n<1M
+source_datasets:
+  - original
+task_categories:
+  - text-retrieval
 configs:
   - config_name: corpus-en
     data_files:
@@ -89,51 +94,103 @@ configs:
         path: metadata-ja/dataset.jsonl
 ---
 
-# ReCaRe — A Bilingual Legal Benchmark for Revision Candidate Retrieval
+# Dataset Card for ReCaRe — A Bilingual Legal Benchmark for Revision Candidate Retrieval
 
-> ReCaRe (pronounced "re-care") supports retrieval research on **document
-> maintenance**: locating which provisions of a legal corpus must change when
-> a new amendment is proposed, and which other provisions must change with
-> them. Built from European Union law (EUR-Lex) and Japanese law (e-Gov)
-> across 703 amendment events and ~181k articles, ReCaRe defines two
-> Revision Candidate Retrieval (RCR) tasks — Rat2Rev and Rev2Rev — over a
-> shared bilingual corpus.
+<!-- Provide a quick summary of the dataset. -->
 
-- **Repository**: <https://huggingface.co/datasets/kasys/ReCaRe>
-- **Owner / Org**: `kasys` (HuggingFace) — corresponds to `kasys-lab` on GitHub
-- **License**: CC BY 4.0 (EU subset) / CC BY 4.0 + CC0 1.0 (Japanese subset)
-- **DOI**: _to be filled in once HF Settings → Generate DOI is run on `v0.1.0`_
-- **Version**: `v0.1.0` (initial public release for CIKM 2026 Resource paper)
-- **Code**: analysis & baseline code at `kasys-lab/ReCaRe` (GitHub, see #15)
+ReCaRe (pronounced "re-care") is a bilingual legal benchmark for **Revision
+Candidate Retrieval (RCR)** — locating the provisions of a legal corpus
+that constitute plausible candidates for an authoritative revision. It
+spans European Union law (EUR-Lex, English) and Japanese law (e-Gov,
+Japanese), with 703 amendment events and ~181k articles, supporting two
+retrieval tasks (Rat2Rev and Rev2Rev) over a single shared corpus.
 
----
+## Dataset Details
+
+### Dataset Description
 
-## Dataset summary
+<!-- Provide a longer summary of what this dataset is. -->
 
-Document corpora in regulated settings are not static. Statutes are amended,
-internal policies are revised, software specifications are updated. Yet most
-information retrieval (IR) research has framed retrieval as a one-shot
-question-answering problem over a frozen corpus, leaving the IR aspects of
-*document maintenance* — finding which documents need to change, and which
-other documents must change with them — comparatively underexplored.
+Document corpora in regulated domains evolve: statutes are amended, internal
+policies revised, software specifications updated. Yet most information
+retrieval research has framed retrieval as a one-shot question-answering
+problem over a frozen corpus, leaving the IR aspects of *document
+maintenance* — finding which documents need to change, and which other
+documents must change with them — comparatively underexplored.
 
-ReCaRe formalises two complementary retrieval problems that arise during
-document maintenance:
+ReCaRe formalises two complementary RCR tasks over a shared bilingual
+corpus:
 
-- **Rat2Rev** (Rationale-to-Revision Retrieval): given the *textual rationale*
+- **Rat2Rev (Rationale-to-Revision Retrieval).** Given the textual rationale
   of a proposed amendment (long, abstract), retrieve the concrete articles
   that must be modified to implement the amendment.
-- **Rev2Rev** (Revision-to-Revision Retrieval): given one already-revised
-  article, retrieve the other articles revised in the same legislative event
-  (co-revised articles).
-
-Both tasks share a single bilingual corpus (~91k EU articles in English +
-~90k Japanese articles) and are released under CC BY 4.0 / CC0.
+- **Rev2Rev (Revision-to-Revision Retrieval).** Given one already-revised
+  article, retrieve the other articles revised in the same legislative
+  event (co-revised articles).
 
 The dataset is the resource artifact of the CIKM 2026 paper
 *"ReCaRe: A Bilingual Legal Benchmark for Revision Candidate Retrieval."*
 
-## Configs (12)
+- **Curated by:** Hiroyoshi Itō, Yūma Kurokawa, Makoto P. Kato (University of Tsukuba); Sumio Fujita (LY Corporation).
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** `kasys` (HuggingFace organization); maintained by `mpkato`.
+- **Language(s) (NLP):** English (`en`, EU subset) and Japanese (`ja`, Japanese subset).
+- **License:** CC BY 4.0 (EU subset); CC BY 4.0 / CC0 1.0 (Japanese subset, depending on upstream source).
+
+### Dataset Sources [optional]
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Repository:** <https://huggingface.co/datasets/kasys/ReCaRe>
+- **Paper [optional]:** Itō, Kurokawa, Kato & Fujita. *ReCaRe: A Bilingual Legal Benchmark for Revision Candidate Retrieval.* CIKM 2026 Resource Track (under submission).
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the dataset is intended to be used. -->
+
+### Direct Use
+
+<!-- This section describes suitable use cases for the dataset. -->
+
+ReCaRe is intended as a research benchmark for:
+
+- Training and evaluating multilingual / cross-lingual retrieval models on
+  legal text where queries and target documents differ markedly in length
+  and register.
+- Studying *document maintenance* retrieval: surfacing revision candidates
+  for expert review, distinct from question-answering or paragraph-level
+  retrieval.
+- Comparative benchmarking against general-domain IR resources (e.g. BEIR)
+  to characterise the difficulty of low-overlap, multi-target retrieval
+  with implicit dependency.
+- Ablation of long-context vs. short-context retrieval models, given that
+  Rat2Rev queries are substantially longer than typical IR queries.
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
+
+ReCaRe is **not** designed for, and should not be used as, a basis for:
+
+- Automated legal drafting, automated revision recommendation, or any other
+  production legal workflow without expert review. Retrieved articles are
+  *candidates* for expert review, not authoritative outputs.
+- Provision of legal advice to end-users.
+- Inference of personal or demographic information about individuals — the
+  data contains only public legal text, but personal names appearing in
+  legal records (e.g. drafters, ministers) should not be used to build
+  profiles of those individuals.
+- Generalisation claims about legal systems other than the EU and Japan,
+  or about time periods outside the included windows (EU 2010–2025; JP
+  2019–2025).
+
+## Dataset Structure
+
+<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+
+The dataset is organised as **12 configs** sharing two language-aligned
+corpora:
 
 | Config | Splits | Schema | Records |
 | --- | --- | --- | --- |
@@ -150,26 +207,30 @@ The dataset is the resource artifact of the CIKM 2026 paper
 | `metadata-en` | `metadata` | 16-field amendment metadata (see below) | 91,361 |
 | `metadata-ja` | `metadata` | same | 90,170 |
 
-> All configs are JSONL. Use a 4-line snippet (below) to convert qrels to
-> TREC format if your stack expects that.
-
-### `metadata-*` schema (verbatim from construction pipeline)
-
-`amendment_law_id`, `law_id`, `type_of_change`, `egov_compare_url`,
-`law_title_before`, `revision_id_before`, `article_id_before`,
-`article_number_before`, `caption_before`, `text_before`,
-`law_title_after`, `revision_id_after`, `article_id_after`,
-`article_number_after`, `caption_after`, `text_after`.
-
-Records with `amendment_law_id == "None"` are unchanged articles
-(~85–93%); records with non-`None` amendment IDs are revisions traceable to
-a specific amending act. The metadata configs are intended for downstream
-analyses that need full provenance and are **not** required for running the
-retrieval tasks themselves (use the `corpus`, `queries`, `qrels` configs).
-
-## Quick start
-
-### Python (`datasets`)
+All files are JSONL with one JSON object per line. The schemas follow BEIR
+conventions (`_id`, `text`, `query-id`, `corpus-id`, `score`) so that
+existing tooling such as `ir_measures`, `pytrec_eval`, and Pyserini works
+without adapter code.
+
+**`metadata-{en,ja}` schema** (verbatim from the construction pipeline,
+16 fields): `amendment_law_id`, `law_id`, `type_of_change`,
+`egov_compare_url`, `law_title_before`, `revision_id_before`,
+`article_id_before`, `article_number_before`, `caption_before`,
+`text_before`, `law_title_after`, `revision_id_after`, `article_id_after`,
+`article_number_after`, `caption_after`, `text_after`. Records with
+`amendment_law_id == "None"` are unchanged articles (≈85–93%); records
+with non-`None` amendment IDs are revisions traceable to a specific
+amending act. The metadata configs are provided for downstream provenance
+analyses and are not required to run the retrieval tasks themselves.
+
+**Splits.** The `qrels-*` configs use train / validation / test splits
+inherited from the prior DEIM 2026 papers (Itō et al. 2F-01; Kurokawa et
+al. 4F-04) for direct comparability with their reported numbers. The
+`corpus-*`, `queries-*`, and `metadata-*` configs are not split — the full
+corpus is used at retrieval time, and queries split membership is encoded
+through the qrels.
+
+**Quick start.**
 
 ```python
 from datasets import load_dataset
@@ -177,248 +238,176 @@ from datasets import load_dataset
 corpus  = load_dataset("kasys/ReCaRe", "corpus-en", split="corpus")
 queries = load_dataset("kasys/ReCaRe", "queries-rat2rev-en", split="queries")
 qrels   = load_dataset("kasys/ReCaRe", "qrels-rat2rev-en")  # train/validation/test
-
-print(corpus[0])           # {'_id': '...', 'text': '...'}
-print(queries[0])          # {'_id': '...', 'text': '...'}
-print(qrels["test"][0])    # {'query-id': '...', 'corpus-id': '...', 'score': 1}
 ```
 
-### Convert qrels to `trec_eval` format
+## Dataset Creation
 
-```python
-from datasets import load_dataset
-import pandas as pd
-ds = load_dataset("kasys/ReCaRe", "qrels-rat2rev-en", split="test")
-df = pd.DataFrame(ds).assign(iter=0)
-df[["query-id", "iter", "corpus-id", "score"]].to_csv(
-    "qrels.trec", sep=" ", header=False, index=False)
-```
+### Curation Rationale
 
-### BEIR loader (after rename)
+<!-- Motivation for the creation of this dataset. -->
 
-```python
-from datasets import load_dataset
-import pandas as pd, os
-os.makedirs("recare-en/qrels", exist_ok=True)
-load_dataset("kasys/ReCaRe", "corpus-en", split="corpus")\
-    .to_json("recare-en/corpus.jsonl", lines=True, force_ascii=False)
-load_dataset("kasys/ReCaRe", "queries-rat2rev-en", split="queries")\
-    .to_json("recare-en/queries.jsonl", lines=True, force_ascii=False)
-pd.DataFrame(load_dataset("kasys/ReCaRe", "qrels-rat2rev-en", split="test"))\
-    .to_csv("recare-en/qrels/test.tsv", sep="\t", index=False)
-# then:
-from beir.datasets.data_loader import GenericDataLoader
-corpus, queries, qrels = GenericDataLoader(data_folder="recare-en").load(split="test")
-```
+Existing legal-IR benchmarks (e.g. COLIEE, BSARD, LeCaRD, STARD,
+LegalBench-RAG) cover statutory or case-law question answering but not
+*document maintenance* — finding which provisions must change when a
+corpus evolves. ReCaRe was constructed to give the IR community a shared,
+bilingual benchmark for two practically motivated retrieval tasks
+(Rat2Rev, Rev2Rev) over a single legal corpus, in two jurisdictions whose
+legal-revision practices differ (EU consolidation tradition vs. Japanese
+amending-act tradition).
 
----
+### Source Data
 
-## FAIR principles
-
-ReCaRe is published with explicit attention to the FAIR principles
-(Findable, Accessible, Interoperable, Reusable) for research data
-[Wilkinson et al. 2016, *Sci. Data*].
-
-### F — Findable
-
-The dataset has a globally unique persistent identifier — a HuggingFace-issued
-DOI (DataCite) on this page (Settings → Generate DOI on tag `v0.1.0`), and is
-discoverable via HuggingFace Hub search, the `datasets` library, the project
-homepage, and the CIKM 2026 paper. A Zenodo mirror with an additional
-permanent DOI is published in the companion repository. Each record carries
-a stable `_id` (article identifier) traceable back to its source act
-(CELEX number for EU, e-Gov / 法令索引 / 衆議院議案 ID for JP) so that
-individual articles, queries, and amendments can be cited unambiguously.
-
-### A — Accessible
-
-The full dataset is openly accessible without authentication or registration
-via the HuggingFace `datasets` library and via direct HTTPS download from
-this page. The repository is `public` and licenses are
-machine-readable (SPDX) in the YAML front-matter. We commit to keeping the
-artifact accessible for the lifetime of HuggingFace; should the platform
-become unavailable, the Zenodo mirror remains canonical and self-describing
-JSONL means consumers can ingest the data with any standard parser.
-
-### I — Interoperable
-
-The data is stored in **JSONL with BEIR-canonical schemas** so it works out
-of the box with the existing IR ecosystem (BEIR loaders, `ir_measures`,
-`pytrec_eval`, Pyserini, sentence-transformers). Field names follow BEIR
-conventions (`_id`, `text`, `query-id`, `corpus-id`, `score`); a one-paste
-TREC qrels conversion snippet is provided above for `trec_eval`. The
-metadata configs use a flat 16-field schema documented in this README, so
-relational joins back to amendment-level provenance are straightforward.
-
-### R — Reusable
-
-Each record is released under a clearly identified open license (CC BY 4.0
-for EU sources; CC BY 4.0 / CC0 1.0 for Japanese sources, both inherited
-from the upstream public bodies). Provenance is preserved at the record
-level via the `metadata-*` configs, which carry the source amendment ID,
-the canonical comparison URL, and the before/after article text. Construction
-methodology (alignment via Dice/Simpson with minor-edit filtering, query
-typing, train/validation/test split inheritance) is documented in the
-companion paper and summarised in the *Datasheet* section below. The
-versioning scheme is semver-aligned (`v0.1.0` for this release); future
-patches that reissue qrels or correct alignment errors will land as
-`v0.1.x`.
+<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
 
----
+The dataset contains **only public legal text** drawn from official portals:
 
-## Datasheet for Datasets
-
-The following follows the *Datasheets for Datasets* template
-[Gebru et al. 2018, arXiv:1803.09010] and answers each section concisely.
-For details, see the CIKM 2026 paper.
-
-### Motivation
-
-ReCaRe was created because existing legal-IR benchmarks (COLIEE, BSARD,
-LeCaRD, STARD, LegalBench-RAG) cover statutory or case-law question
-answering but not *document maintenance* — finding which provisions must
-change when a corpus evolves. Two retrieval tasks of high practical
-relevance to legal drafting and policy revision (Rat2Rev, Rev2Rev) had no
-shared benchmark. The dataset was constructed by Hiroyoshi Itō, Yūma
-Kurokawa, Makoto P. Kato (University of Tsukuba) and Sumio Fujita
-(LY Corporation) for academic research, with no commissioned funding.
-
-### Composition
-
-Each instance is either an **article** (a single legal provision; ~181k
-total across two languages), a **query** (an amendment rationale text in
-Rat2Rev, or a revised article in Rev2Rev; 703 + 3,162 queries respectively),
-or a **qrel** (a relevance judgment of the form `(query, article)`).
-Coverage spans 340 EU amendment events (2010–2025) and 363 Japanese
-amendment events (2019–2025). Articles contain only public legal text;
-metadata records carry IDs and timestamps but no personal information.
-There are no missing values in the released JSONL — articles or amendments
-without complete provenance were dropped during construction.
-
-### Collection process
-
-Source documents were retrieved from public legal portals: EUR-Lex
-(CC BY 4.0 / CC0; CELEX-numbered consolidated acts) for EU law, and
-e-Gov 法令検索 + 日本法令索引 + 衆議院議案 (CC BY 4.0 / CC0) for Japanese
-law. We downloaded full consolidated text per act, extracted articles by
-the official numbering scheme, and collected explicit
-amendment events (acts that explicitly amend prior acts). Acquisition spanned
-2025-Q3–2026-Q1. No human subjects were involved. The two-language design
-follows from the choice to ground the benchmark in two jurisdictions whose
-legal-revision practices differ (EU consolidation tradition vs. Japanese
-amending-act tradition).
+- **EU subset:** EUR-Lex (CC BY 4.0 / CC0 re-use) — CELEX-numbered
+  consolidated Regulations, Directives, and Decisions (2010–2025).
+- **Japanese subset:** e-Gov 法令検索, 日本法令索引, 衆議院議案 (CC BY 4.0
+  / CC0) — consolidated statutes and amending acts (2019–2025).
 
-### Preprocessing / cleaning / labeling
-
-Articles are paired with revisions via **Dice / Simpson coefficient
-matching** between before/after article text, with **minor-edit removal**
-filtering out punctuation-only and renumbering-only changes. Queries for
-Rat2Rev are the official rationale text of each amending act; queries for
-Rev2Rev are individual revised articles (one query per revised article, up
-to 5 queries per amendment). Qrels are derived deterministically from the
-amendment alignment: a `(rationale, article)` pair is positive iff the
-article is among those revised by that amendment; a `(revised-article,
-article)` pair is positive iff both are revised by the same amendment
-event. Train / validation / test splits are inherited from
-the prior DEIM 2026 papers (Itō et al. 2F-01, Kurokawa et al. 4F-04) for
-direct comparability with their reported numbers. Construction code is
-**not** released (it depends on internal scrapers); the relationship is
-documented in the paper, and label validity is independently verified by
-two-rater blind annotation on a stratified 200-pair sample (see *Limitations*).
-
-### Uses
-
-The dataset is intended for academic IR research on document maintenance,
-revision retrieval, multilingual / cross-jurisdictional retrieval, and as
-a difficulty contrast point for general-domain BEIR benchmarks (low
-query-document lexical overlap, multi-target qrels, implicit dependency).
-It is **not** intended to support automated legal drafting, automated
-revision recommendation in production legal workflows, or legal advice;
-retrieved articles are revision **candidates** for expert review, not
-authoritative outputs. Users should not infer that a model performing well
-on ReCaRe is suitable for unsupervised legal-corpus maintenance. Other
-appropriate uses include: training and evaluation of multilingual dense
-retrievers, ablation of long-context vs. short-context retrieval models,
-cross-task generalisation studies between Rat2Rev and Rev2Rev, and as
-held-out legal benchmark in foundation-model evaluations.
-
-### Distribution
-
-ReCaRe is distributed publicly via HuggingFace Datasets (`kasys/ReCaRe`,
-this page) under CC BY 4.0 / CC0 (per source-language license), with a
-HuggingFace-issued DOI (DataCite) on tag `v0.1.0`. A Zenodo mirror with an
-additional permanent DOI provides redundancy. The dataset is freely
-downloadable without registration. Source code for analysis / baselines is
-distributed separately at `kasys-lab/ReCaRe` (GitHub).
-
-### Maintenance
-
-The dataset is maintained by the authors. Versioning follows semver:
-`v0.1.0` is the initial public release; corrective releases (e.g. label
-fixes, new amendment events) will land as `v0.1.x` (patch) or `v0.x.0`
-(minor). Each release is git-tagged on this HuggingFace repo and mirrored
-to Zenodo. Issues (errata, license clarifications, schema questions) are
-tracked at the project's GitHub repository
-(<https://github.com/mpkato/CIKM2026-ito-kurokawa>). There is no
-guaranteed support timeline, but we intend to keep the dataset accessible
-and answer well-formed issues for at least the duration of the CIKM 2026
-review cycle and the subsequent academic year.
+#### Data Collection and Processing
 
----
+<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
+
+Acquisition spanned 2025-Q3–2026-Q1. Full consolidated text was downloaded
+per act, articles were extracted by official numbering, and explicit
+amendment events (acts that amend prior acts) were collected. Articles
+were then aligned with their before/after revisions through **Dice /
+Simpson coefficient matching** between paired article texts; **minor-edit
+filtering** (punctuation-only and pure renumbering) removed alignments
+without semantic change.
+
+Queries are derived deterministically from this alignment:
+
+- **Rat2Rev queries** are the official rationale text of each amending act
+  (one query per amendment).
+- **Rev2Rev queries** are individual revised articles (up to five queries
+  per amendment, sampled when more revisions exist).
+
+Qrels are likewise derived deterministically: a `(rationale, article)`
+pair is positive iff the article is among those revised by that amendment;
+a `(revised-article, article)` pair is positive iff both are revised by
+the same amendment event.
+
+The construction pipeline depends on internal scrapers and is not part of
+the public release, but the resulting dataset is fully public so that
+retrieval-side experiments are completely reproducible.
+
+#### Who are the source data producers?
+
+<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
+
+The original legal text was authored by the **European Union legislative
+bodies** (Council, Parliament, Commission, etc.) and by **the Government
+of Japan** (Diet, ministries, etc.), and is published in their respective
+official portals as public law. Producers are state-level institutions
+acting in a public legislative or regulatory capacity; no individual
+authorship metadata is included in the dataset beyond what appears in the
+text of the law itself.
+
+### Annotations [optional]
 
-## Statistics summary
+<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
 
-| Subset | Amendments | Articles | Rat2Rev queries | Rev2Rev queries | Period | License |
-| --- | --- | --- | --- | --- | --- | --- |
-| ReCaRe-EN (EU) | 340 | 91,361 | 340 | 1,509 | 2010–2025 | CC BY 4.0 |
-| ReCaRe-JA (JP) | 363 | 90,170 | 363 | 1,653 | 2019–2025 | CC BY 4.0 / CC0 |
-| **Total** | **703** | **181,531** | **703** | **3,162** | — | — |
+#### Annotation process
 
-## Limitations
+<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
 
-- **Annotator-construction overlap.** The relevance-label validity check
-  (sampled 200 q-d pairs, 2 raters, blind) is performed by authors of the
-  construction pipeline. Future work: third-party annotation.
+Relevance labels (qrels) are produced **automatically** from the official
+amendment alignment described above; there is no per-pair human labelling
+in the released qrels.
+
+A separate **label-validity audit** is conducted on a stratified sample of
+200 query–document pairs (50 per `(task, language)` slice; per-slice
+strata: 25 positive, 12 hard-negative drawn from BM25 top-100 minus
+positives, 13 random-negative drawn from the corpus minus positives), to
+verify that the construction pipeline's labels agree with human judgement.
+Two annotators independently judge each pair as `relevant` /
+`not relevant` under blind conditions (provenance and stratum hidden in a
+custom web tool). Reported metrics include positive precision (P_pos),
+hard-negative precision (P_neg-hard), random-negative precision
+(P_neg-rand), and Cohen's κ for inter-annotator agreement. Audit results
+appear in the paper; the audit sample is **not** part of the released qrels.
+
+#### Who are the annotators?
+
+<!-- This section describes the people or systems who created the annotations. -->
+
+The label-validity audit annotators are the paper's first authors
+(H. Itō and Y. Kurokawa). Both have research experience in legal
+information retrieval (DEIM 2026 papers 2F-01 and 4F-04). They are also
+authors of the construction pipeline, which is acknowledged as an
+annotator-construction overlap in the paper's Limitations.
+
+#### Personal and Sensitive Information
+
+<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
+
+The dataset contains **only public legal text** released by the European
+Union and the Japanese government. References to officials, ministers, or
+other named parties appear because they are part of the public legislative
+record; they are not subject to research-purpose privacy obligations and
+have not been anonymised. The dataset does not contain personal contact
+information, financial information, health information, or any data that
+would qualify as personal or sensitive under GDPR / Japanese personal
+information protection law.
+
+The label-validity audit (above) is performed by the paper's co-authors on
+already-public legal articles, with no external participants. The work is
+**not subject to IRB review** under the standard "human subjects research"
+definition.
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+- **Annotator-construction overlap.** The label-validity audit is performed
+  by authors of the construction pipeline; despite blind judgement, this
+  raises the possibility of subtle bias toward the pipeline's decisions.
+  Third-party annotation is left as future work.
 - **Coverage windows differ.** EU period is 2010–2025; Japanese period is
-  2019–2025. This is a function of upstream availability and is documented
-  in the paper.
-- **Construction code not released.** The alignment pipeline depends on
+  2019–2025. The longer EU window is a function of upstream availability
+  and means temporal generalisation conclusions across the two subsets
+  must be drawn with care.
+- **Construction code not released.** The alignment pipeline relies on
   internal scrapers (EUR-Lex / e-Gov) that are not part of the release.
-  The dataset itself, however, is fully public so that retrieval-side
+  Independent verification of the relevance signal therefore depends on
+  the documentation in the paper rather than re-runnable construction
+  code; the dataset itself, however, is fully public so retrieval
   experiments are completely reproducible.
 - **Implicit dependency in Rev2Rev.** Some co-revised article pairs share
-  no surface vocabulary; this is the intended difficulty of the task but
-  means lexical retrievers underperform.
+  little surface vocabulary; this is the intended difficulty of the task,
+  but lexical retrievers (e.g. BM25) systematically underperform.
+- **Legal scope.** The dataset is grounded in EU and Japanese law and
+  inherits the conventions, drafting traditions, and any latent biases of
+  those legal systems. Conclusions should not be transferred to other
+  jurisdictions or to non-statutory legal text without further validation.
 - **Not a substitute for legal advice.** The dataset is a research
   resource. Retrieved articles are revision *candidates* for expert review.
 
-## Ethical considerations
+### Recommendations
 
-ReCaRe contains only **public legal text** (statutes and amending acts
-published by the European Union and the Japanese government). No personal
-data, no human-subjects data, and no proprietary content is included.
-References to officials or named parties in legal text appear because they
-are part of the public record and are not subject to research-purpose
-privacy obligations. The annotation step (label validity verification) is
-performed by the paper's co-authors on already-public legal articles, with
-no external participants, so the work is **not subject to IRB review**.
-See the CIKM 2026 paper §3.3 (Ethical Considerations) for the full
-treatment.
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
-## License & attribution
+Users should:
 
-- **EU subset**: data sourced from EUR-Lex under CC BY 4.0 (EUR-Lex
-  re-use notice). Attribution: European Union, EUR-Lex.
-- **Japanese subset**: data sourced from e-Gov 法令検索, 日本法令索引,
-  衆議院議案 — under CC BY 4.0 / CC0 1.0 as published. Attribution: 日本国
-  政府.
-- **ReCaRe construction & curation**: © the authors (Itō, Kurokawa, Kato,
-  Fujita), released under CC BY 4.0 to match the upstream EU subset.
+- Treat the dataset as an evaluation benchmark for retrieval research, not
+  as a training signal for production legal-decision systems.
+- Report results separately by language and task; cross-language averaging
+  can hide systematic differences.
+- Cite both the dataset (this HuggingFace artifact) and the CIKM 2026
+  paper, and acknowledge the upstream public-source licenses (EUR-Lex,
+  e-Gov / 法令索引 / 衆議院議案).
+- Expect that strong off-the-shelf retrievers will show substantial
+  headroom on both tasks; the paper documents this as a feature, not a
+  defect, of the resource.
 
-When using ReCaRe, please cite both the dataset DOI and the CIKM 2026
-paper.
+## Citation [optional]
 
-## Citation
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
 
 ```bibtex
 @inproceedings{ito2026recare,
@@ -428,17 +417,45 @@ paper.
   series    = {CIKM '26},
   year      = {2026},
   publisher = {Association for Computing Machinery},
-  note      = {Resource Track. Dataset DOI: \url{https://doi.org/<HF DOI>} ; \url{https://huggingface.co/datasets/kasys/ReCaRe}},
+  note      = {Resource Track. Dataset: \url{https://huggingface.co/datasets/kasys/ReCaRe}},
 }
 ```
 
-> The DOI placeholder will be replaced once HF Settings → Generate DOI is
-> run on tag `v0.1.0`. Camera-ready citation will list both the HF DOI and
-> the Zenodo mirror DOI.
+**APA:**
+
+> Itō, H., Kurokawa, Y., Kato, M. P., & Fujita, S. (2026). *ReCaRe: A Bilingual Legal Benchmark for Revision Candidate Retrieval.* In Proceedings of the 35th ACM International Conference on Information and Knowledge Management (CIKM '26). [https://huggingface.co/datasets/kasys/ReCaRe](https://huggingface.co/datasets/kasys/ReCaRe)
+
+(DOI to be added once HF Settings → Generate DOI is run on tag `v0.1.0`.)
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
+
+- **RCR (Revision Candidate Retrieval).** The umbrella retrieval task of
+  locating documents that constitute plausible candidates for an
+  authoritative revision.
+- **Rat2Rev (Rationale-to-Revision Retrieval).** Given an amendment's
+  textual rationale, retrieve the concrete articles to be modified.
+- **Rev2Rev (Revision-to-Revision Retrieval).** Given one revised article,
+  retrieve the other articles revised in the same amendment event.
+- **Amendment event.** One amending act (a single Regulation, Directive,
+  or domestic amending law) that modifies one or more prior provisions.
+- **CELEX number.** The unique identifier used by EUR-Lex for EU legal
+  documents.
+
+## More Information [optional]
+
+- **Versioning.** This release is `v0.1.0`; corrective releases will use
+  `v0.1.x` (patch) or `v0.x.0` (minor) and be tagged on this HuggingFace
+  repository. A Zenodo mirror is planned for additional persistence.
+- **Codebase.** Analysis and baseline reproduction code lives at the
+  `kasys-lab/ReCaRe` GitHub repository. The dataset construction pipeline
+  is intentionally not released (relies on internal scrapers).
+
+## Dataset Card Authors [optional]
+
+Hiroyoshi Itō, Yūma Kurokawa, Makoto P. Kato (University of Tsukuba); Sumio Fujita (LY Corporation).
 
-## Contact
+## Dataset Card Contact
 
-- Hiroyoshi Itō (first author, Rev2Rev) — University of Tsukuba
-- Yūma Kurokawa (first author, Rat2Rev) — University of Tsukuba
-- Makoto P. Kato (corresponding author) — `mpkato@slis.tsukuba.ac.jp`
-- Sumio Fujita — LY Corporation
+Makoto P. Kato — `mpkato@slis.tsukuba.ac.jp`