add hierarchy

README.md

@@ -30,7 +30,7 @@ This TopicNet derived preparation contains 11,965 retained scientific abstracts
 
 ## Population, labels and splits
 
-Raw duplicate identity is body text after casefold and whitespace collapse. There are no raw duplicate groups or cross-label conflicts. Two identical lemma-sequence groups remove two rows; five near-duplicate pairs join families without removing their members. Exported labels use only Y.txt, the authoritative global class. The original source hierarchy has these ambiguities: global label 6 has child values 2 and 3, label 10 has child values 2 and 3, and parent/child pair (2,3) maps to global labels 10 and 11. The original YL1.txt/YL2.txt files and per-document hierarchy metadata are not included in this package. Y values are not repaired and class names are not invented.
+Raw duplicate identity is body text after casefold and whitespace collapse. There are no raw duplicate groups or cross-label conflicts. Two identical lemma-sequence groups remove two rows; five near-duplicate pairs join families without removing their members. Exported labels use only Y.txt, the authoritative global class. The original source hierarchy has these ambiguities: global label 6 has child values 2 and 3, label 10 has child values 2 and 3, and parent/child pair (2,3) maps to global labels 10 and 11. The original YL1.txt/YL2.txt files are not copied as separate text files; their values, together with Y.txt, are included for every retained document in data/hierarchy.json. YL2 is a local child code interpreted with its parent YL1, not a globally unique class. Y values are not repaired and class names are not invented.
 
 Family groups combine raw/lemma duplicate relations, source URL/DOI relations where present, and body five-token shingle Jaccard similarity ≥0.9. Groups are assigned by iterative multilabel stratification with seeds 20260824 and 20260825; all retained members of a group stay together. The resulting disjoint, exhaustive research splits are **fit 7,182, calibration 2,391, validation 2,392**. The Hugging Face split `corpus` contains every retained document; it is not `fit`.
 
@@ -79,11 +79,11 @@ The source adapter reads aligned X/Y/YL1/YL2 lines with zero-based row indices.
 
 Each UTF-8 CSV has exactly `id,raw_text,vw_text` columns and lexicographically ordered document IDs. `raw_text` contains the source abstract. The VW string starts with the document ID followed by only `|@word`. There is no title namespace. Labels are separate from the VW string. `noow` preserves normalized token occurrences in order; `bow` counts the same tokens independently per namespace as `token:count`, with lexicographic token order. The full prepared token sequences are included.
 
-NOOW means occurrence order after preprocessing, not untouched or reversible prose. English preprocessing uses Python 3.11.15, NLTK 3.9.2 and NLTK-data commit `550b6625bcef1f2abff2ff770a5a0d272c9c6b2a`: WordPunctTokenizer, lowercase per original span, minimum token length 2, averaged_perceptron_tagger_eng, WordNet POS mapping J/V/N/R with noun fallback, English stopword removal and `lemma.isalpha()` filtering. Names are not masked.
+NOOW means occurrence order after preprocessing, not untouched or reversible prose. English preprocessing uses Python 3.11.15, NLTK 3.9.2 and NLTK-data commit `550b6625bcef1f2abff2ff770a5a0d272c9c6b2a`: WordPunctTokenizer, lowercase per original span, lowercased input token length at least 2 before POS tagging and lemmatization, averaged_perceptron_tagger_eng, WordNet POS mapping J/V/N/R with noun fallback, English stopword removal and `lemma.isalpha()` filtering. Output lemmas are not filtered by length and can contain one character. Names are not masked.
 
 ## Loading and metadata
 
-The default configuration is `noow`; `bow` contains aggregated counts. Both expose the complete population as the Hugging Face split `corpus`. Labels and the stored fit/calibration/validation memberships are auxiliary files, joined by document ID. `data/labels.json` directly maps each string ID to a list of string labels; `data/splits.json` directly maps each split name to an ordered ID list.
+The default configuration is `noow`; `bow` contains aggregated counts. Both expose the complete population as the Hugging Face split `corpus`. Labels and the stored fit/calibration/validation memberships are auxiliary files, joined by document ID. `data/labels.json` directly maps each string ID to a list of string labels; `data/splits.json` directly maps each split name to an ordered ID list. `data/hierarchy.json` directly maps each ID to {"Y": "...", "YL1": "...", "YL2": "..."}, with all source codes preserved as strings. Join these mappings by ID.
 
 ```python
 from datasets import load_dataset
@@ -109,24 +109,31 @@ with (package / "data/labels.json").open(encoding="utf-8") as handle:
     labels = json.load(handle)
 with (package / "data/splits.json").open(encoding="utf-8") as handle:
     splits = json.load(handle)
+with (package / "data/hierarchy.json").open(encoding="utf-8") as handle:
+    hierarchy = json.load(handle)
 membership = {doc_id: split for split, ids in splits.items() for doc_id in ids}
 annotated = corpus.map(lambda row: {"labels": labels[row["id"]],
+                                   "source_hierarchy": hierarchy[row["id"]],
                                    "research_split": membership[row["id"]]})
 fit = annotated.filter(lambda row: row["research_split"] == "fit")
 ```
 
-For remote metadata, download `data/labels.json` and `data/splits.json` with `huggingface_hub.hf_hub_download(repo, filename, repo_type="dataset")`, then read the same direct mappings. Private repositories require authorized authentication.
+For remote metadata, download `data/labels.json`, `data/splits.json` and `data/hierarchy.json` with `huggingface_hub.hf_hub_download(repo, filename, repo_type="dataset")`, then read the same direct mappings. Private repositories require authorized authentication.
+
+## Related datasets and overlap
+
+Related preparations are [TopicNet/WOS-5736](https://huggingface.co/datasets/TopicNet/WOS-5736) and [TopicNet/WOS-46985](https://huggingface.co/datasets/TopicNet/WOS-46985), alongside [TopicNet/WOS-11967](https://huggingface.co/datasets/TopicNet/WOS-11967). They are not independent datasets. Comparing original source abstracts after casefold and whitespace collapse gives 5,736 shared abstract identities between WOS-5736 and WOS-46985, 11,268 between WOS-11967 and WOS-46985, and 1,708 between WOS-5736 and WOS-11967. These counts describe source abstracts, not final retained populations. WOS-11967 is not entirely contained in WOS-46985 under this comparison. Splits are generated independently for each preparation; combining subsets for training and evaluation can leak shared documents across splits.
 
 ## Files and integrity
 
-The package contains this card, unchanged `.gitattributes`, [SHA256SUMS](SHA256SUMS), two CSV representations and [labels](data/labels.json)/[splits](data/splits.json) metadata, plus the verbatim source README. Run `sha256sum -c SHA256SUMS` from the package root to check every publication file except the checksum list itself. The representation files retain their original generated bytes.
+The package contains this card, unchanged `.gitattributes`, [SHA256SUMS](SHA256SUMS), two CSV representations and [labels](data/labels.json)/[splits](data/splits.json)/[hierarchy](data/hierarchy.json) metadata, plus the verbatim source README. Run `sha256sum -c SHA256SUMS` from the package root to check every publication file except the checksum list itself. The representation files retain their original generated bytes.
 
 ## Rights, limitations and citation
 
-The [Mendeley release, version 6](https://data.mendeley.com/datasets/9rw3vkcfy4/6) declares CC BY 4.0. The verbatim [SOURCE_README.txt](SOURCE_README.txt) retains Copyright (c) 2017 Kamran Kowsari and the permission notice requiring the copyright and permission notices in copies or substantial portions. Preserve those notices and cite the source. The release license is not a blanket guarantee that abstracts or other third-party content have no additional rights.
+The [Mendeley release, version 6](https://data.mendeley.com/datasets/9rw3vkcfy4/6) declares CC BY 4.0; its DOI is [10.17632/9rw3vkcfy4.6](https://doi.org/10.17632/9rw3vkcfy4.6). The verbatim [SOURCE_README.txt](SOURCE_README.txt) retains Copyright (c) 2017 Kamran Kowsari and the permission notice requiring the copyright and permission notices in copies or substantial portions. Preserve those notices and cite the source. The release license is not a blanket guarantee that abstracts or other third-party content have no additional rights.
 
 These local packages are intended for private research storage; remote visibility has not been verified or changed. Private storage does not supply redistribution permission. Labels and topic distributions reflect source selection, historical coverage, class imbalance and automatic preprocessing. Family grouping limits known leakage but does not establish that every semantic duplicate was detected. Normalized representations do not guarantee current facts, absence of personal information or suitability for downstream decisions.
 
-Kamran Kowsari, Donald E. Brown, Mojtaba Heidarysafa, Kiana Jafari Meimandi, Matthew S. Gerber and Laura E. Barnes (2017), *HDLTex: Hierarchical Deep Learning for Text Classification*, IEEE ICMLA.
+Kamran Kowsari, Donald E. Brown, Mojtaba Heidarysafa, Kiana Jafari Meimandi, Matthew S. Gerber and Laura E. Barnes (2017), [*HDLTex: Hierarchical Deep Learning for Text Classification*](https://arxiv.org/abs/1709.08267), IEEE ICMLA. DOI: [10.1109/ICMLA.2017.0-134](https://doi.org/10.1109/ICMLA.2017.0-134).
 
 When citing this preparation, identify its retained population and repository revision as well as the source publication.

SHA256SUMS

@@ -1,7 +1,8 @@
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-303bf68fc20feb4b23046647f098f9d0533c65c7b4710eac8588283ff92bc5cd  README.md
+858403645e465f50b75e9b6b2cfb587994c76ceb9af446891a5250d6865ea4b2  README.md
 6da4ab1e624c4ef61c46600d727770f873ff4db3c15541fb481e7af8a715c0ec  SOURCE_README.txt
 4e27d8e46bad08061568222a0931b963f6ad637074b1b227de095af05757d339  data/bow.csv
+5a4ab746cdc75b048143eb8aeac2660535bed1a23cca7d37a6e4775bb9b7d3b6  data/hierarchy.json
 138c7a5ed0ae4787afdbd306a7fd2732b392ad8db65c22e7ce312c33a178470c  data/labels.json
 101d1c8a690ae0fe1b68491cb144ff42114280e12770f8a332647106bd12580e  data/noow.csv
 608c14b244f02df40b897dfc7de3411352079bed0a507f2fd8ccf70b9c5996d7  data/splits.json