add info about reproduction

README.md

@@ -81,6 +81,24 @@ Each UTF-8 CSV has exactly `id,raw_text,vw_text` columns and lexicographically o
 
 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.
 
+## Reproduce from source
+
+The shared standalone code is in the [SciNews preprocessing folder](https://huggingface.co/datasets/TopicNet/SciNews/tree/main/preprocessing). Follow its [setup and code-download instructions](https://huggingface.co/datasets/TopicNet/SciNews/blob/main/preprocessing/README.md) to download only that folder at a full commit revision and create the locked Linux x86-64 environment with Python 3.11.15. Access follows the SciNews repository's permissions. No parent project checkout, BigARTM or TopicNet installation is needed.
+
+From the directory containing the downloaded `preprocessing/` folder, choose writable source storage and fresh output and scratch paths. First obtain the original `ReadMe.txt` from the [official version 6 release](https://data.mendeley.com/datasets/9rw3vkcfy4/6) and place it at `/path/to/sources/wos/ReadMe.txt`, preserving its exact bytes. Its required size and SHA-256 are listed in the shared instructions and checked by the command:
+
+```bash
+python preprocessing/prepare.py download --dataset wos-11967 --sources-dir /path/to/sources
+python preprocessing/prepare.py build --dataset wos-11967 \
+  --sources-dir /path/to/sources \
+  --output-dir /path/to/rebuilt/WOS-11967/data \
+  --scratch-dir /path/to/scratch/wos-11967
+python preprocessing/prepare.py verify --dataset wos-11967 \
+  --data-dir /path/to/rebuilt/WOS-11967/data
+```
+
+The download validates the manually supplied notice and provides pinned `wos/WebOfScience.zip` and NLTK resources under the source directory. All three WOS selectors share these sources; the selector above builds WOS-11967. Build and verify run offline after setup. The output contains only `noow.csv`, `bow.csv`, `labels.json`, `splits.json` and `hierarchy.json`; verification compares them with the recorded SHA-256 values. Builds refuse to overwrite outputs. Preserve failed output and scratch files, and use fresh paths for a retry. The shared code's MIT license does not change the source-text rights below.
+
 ## 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. `data/hierarchy.json` directly maps each ID to {"Y": "...", "YL1": "...", "YL2": "..."}, with all source codes preserved as strings. Join these mappings by ID.

SHA256SUMS

@@ -1,5 +1,5 @@
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+fd964e68343540e675bc520945ef8306eddf6ae38e768bc7446ba1c51321bd7b  README.md
 6da4ab1e624c4ef61c46600d727770f873ff4db3c15541fb481e7af8a715c0ec  SOURCE_README.txt
 4e27d8e46bad08061568222a0931b963f6ad637074b1b227de095af05757d339  data/bow.csv
 5a4ab746cdc75b048143eb8aeac2660535bed1a23cca7d37a6e4775bb9b7d3b6  data/hierarchy.json