add info

README.md

@@ -25,15 +25,16 @@ configs:
 
 # SciNews
 
-This TopicNet derived preparation contains the full retained population of 41,854 scientific news bodies with nine source labels, from SciNews by Dongqi Liu, Yifan Wang, Jia Loy and Vera Demberg. It is not the historical 5,000-document screening sample. Original scholarly article text and abstracts are not substituted for news bodies. This is a derived research preparation, not the original release.
+This TopicNet derived preparation contains the full retained population of 41,854 scientific news bodies with nine source labels, from SciNews by Dongqi Liu, Yifan Wang, Jia Loy and Vera Demberg. Original scholarly article text and abstracts are not substituted for news bodies. This is a derived research preparation, not the original release.
 
-## Population, labels and research splits
+
+## Population, labels and splits
 
 Raw duplicate identity is body text after casefold and whitespace collapse. Same-label duplicates retain the lexicographically smallest ID. Two raw duplicate groups remove two documents; eight cross-label conflict groups quarantine 16 documents. There are no identical lemma-sequence duplicate groups. Source URL and normalized DOI families are joined before splitting; one near-duplicate pair joins families without deleting its members. Original source train/test/val counts are 33,497/4,188/4,187 and are provenance, not the research memberships.
 
-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 25,109, calibration 8,370, validation 8,375**. The Hugging Face split `corpus` contains every retained document; it is not `fit`. Original source membership is preserved in accepted audit/documents metadata and does not define these research splits.
+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 25,109, calibration 8,370, validation 8,375**. The Hugging Face split `corpus` contains every retained document; it is not `fit`.
 
-Retained class supports come directly from `audit.json.output_rows`:
+Retained class counts:
 
 | Label | Documents |
 | --- | ---: |
@@ -47,53 +48,28 @@ Retained class supports come directly from `audit.json.output_rows`:
 | Physics | 4851 |
 | Space | 1153 |
 
-## Representations and pinned preparation
-
-The source adapter maps `News_Body` to raw_text, `News_Title` to separate title metadata and `Topic` to the external label. IDs are `scinews_<original-split>_<six-digit-row-index>`; their original split component does not imply research membership.
-
-Both configurations read a single UTF-8 CSV with exactly `id,raw_text,vw_text`, sorted lexicographically by ID. `raw_text` is only the saved `documents.jsonl.raw_text` body. Title text is separate metadata. NOOW VW preserves the saved prepared line exactly, excluding its line terminator: document ID followed by `|@title` followed by `|@word`. It retains every prepared occurrence, including terms outside the fitted vocabulary. BOW counts those same terms separately within each namespace as `token:count`, with lexicographic token ordering and the same namespace ordering. Class labels never appear as VW namespaces.
 
-NOOW means occurrence order after preprocessing, not untouched or reversible prose. The accepted English preprocessing pins 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. The copied audit records the exact stopwords and resource hashes. Names are not masked.
+## Representations and preprocessing
 
-The research model uses **only @word**, with vocabulary fitted on `fit` only, `min_df=5` and `max_df=0.9`. The saved `*-model.vw` and `*.vw` inputs project prepared text to @word; the separate fitted dictionary controls the vocabulary while the sequence retains OOV positions. The CSVs preserve full prepared sequences, including the title namespace where present. Occurrence offsets and chains are separate saved artifacts, not CSV columns.
+The source adapter maps `News_Body` to `raw_text`, normalizes `News_Title` into the VW title namespace and uses `Topic` as the external label. IDs are `scinews_<original-split>_<six-digit-row-index>`; their original split component does not imply research membership.
 
-`data/accepted/documents.jsonl` stores `offsets` and `title_offsets` as `[start, end]` arrays: zero-based Python character positions with an exclusive end (not byte offsets), relative to `raw_text` and `raw_title`, respectively. The corresponding `occurrences` and `title_occurrences` records contain `lemma`, `start`, `end` and `occurrence_index`, aligned in occurrence order with `tokens`/`offsets` and `title_tokens`/`title_offsets`; a lemma need not equal the raw substring. Per-split `data/accepted/scinews/*-occurrences.jsonl` files contain TF-IDF decision records keyed by `document_id` and `occurrence_index`, rather than raw spans. Join `document_id` to the document's `id` and use `occurrence_index` to select its body occurrence and raw span.
+Each UTF-8 CSV has exactly `id,raw_text,vw_text` columns and lexicographically ordered document IDs. `raw_text` contains the news body only; the original headline is not provided separately in this package. The VW string starts with the document ID followed by `|@title` then `|@word`. Title content is available only as normalized tokens in `@title`. 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.
 
-`tfidf-bank.json` and `*-chains.jsonl` preserve the immutable accepted TF-IDF reference bank and extracted chains. These are automatic reference constructions, not gold human annotations. This packaging converts saved representations and does not rerun preprocessing, dictionary creation, reference fitting, scoring or model training. Candidate model results and selected human-audit examples are not 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.
 
-## Loading and metadata joins
+## Loading and metadata
 
-Authenticate with an account that has access if the intended remote repository is private:
+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.
 
 ```python
 from datasets import load_dataset
 
 repo = "TopicNet/SciNews"
-corpus = load_dataset(repo, "noow", split="corpus", token=True)
-bow = load_dataset(repo, "bow", split="corpus", token=True)
+corpus = load_dataset(repo, "noow", split="corpus")
+bow = load_dataset(repo, "bow", split="corpus")
 ```
 
-Labels and research splits are auxiliary JSON files. Their actual schema is a direct ID-to-label-list object and a direct split-name-to-ID-list object, respectively; there are no `labels` or `splits` wrappers:
-
-```python
-import json
-from huggingface_hub import hf_hub_download
-
-def metadata_path(name):
-    return hf_hub_download(repo, "data/accepted/scinews/" + name,
-                           repo_type="dataset", token=True)
-
-with open(metadata_path("labels.json"), encoding="utf-8") as handle:
-    labels = json.load(handle)
-with open(metadata_path("splits.json"), encoding="utf-8") as handle:
-    splits = 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"]],
-                                   "research_split": membership[row["id"]]})
-fit = annotated.filter(lambda row: row["research_split"] == "fit")
-```
-
-For offline access, obtain the complete package including actual LFS contents and set `HF_DATASETS_OFFLINE=1` and `HF_HUB_OFFLINE=1` before starting Python. Use a writable cache:
+For local use, obtain the complete package including actual Git LFS contents. Set `HF_DATASETS_OFFLINE=1` and `HF_HUB_OFFLINE=1` before Python starts and choose a writable cache directory:
 
 ```python
 import json
@@ -105,25 +81,21 @@ corpus = load_dataset(str(package), "noow", split="corpus",
                       cache_dir="/writable/cache")
 bow = load_dataset(str(package), "bow", split="corpus",
                    cache_dir="/writable/cache")
-with (package / "data/accepted/scinews/labels.json").open(encoding="utf-8") as handle:
+with (package / "data/labels.json").open(encoding="utf-8") as handle:
     labels = json.load(handle)
-with (package / "data/accepted/scinews/splits.json").open(encoding="utf-8") as handle:
+with (package / "data/splits.json").open(encoding="utf-8") as handle:
     splits = json.load(handle)
 membership = {doc_id: split for split, ids in splits.items() for doc_id in ids}
-fit = corpus.filter(lambda row: membership[row["id"]] == "fit")
+annotated = corpus.map(lambda row: {"labels": labels[row["id"]],
+                                   "research_split": membership[row["id"]]})
+fit = annotated.filter(lambda row: row["research_split"] == "fit")
 ```
 
-An explicit CSV reader is also available:
-`load_dataset("csv", data_files={"corpus": str(package / "data/noow.csv")}, split="corpus", cache_dir="/writable/cache")`.
-For independent Python CSV inspection, raise `csv.field_size_limit` when needed for long bodies.
-
-## Accepted files and integrity
-
-The byte-preserving accepted tree totals **5,428,036,902 bytes** before adding export CSVs. NOOW CSV is **287,965,748 bytes** and BOW CSV is **259,061,878 bytes**.
+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.
 
-All 34 files from the accepted source subtree are preserved byte for byte under `data/accepted/`, including the original nested `scinews/` directory. Root accepted files include documents, audit, config, spec, diagnostics, manifest, checksum manifest and TF-IDF bank. The nested directory retains labels, splits, vocabulary, eligible labels, dictionary exports/native dictionary and per-split prepared/model VW, occurrences, summaries and chains. Saved machine paths are provenance, not loading requirements.
+## Files and integrity
 
-[EXPORT_PROVENANCE.json](EXPORT_PROVENANCE.json) records each original file size/hash, input source pins, the pinned source manifest digest, verbatim source README identity, conversion rules and both CSV sizes/hashes. The original inner checksum manifests are unchanged. Root [SHA256SUMS](SHA256SUMS) covers every package file except itself and Git internals; run `sha256sum -c SHA256SUMS` from this directory. Git LFS attributes apply to CSV, JSONL, VW and native dictionaries; small JSON metadata and cards remain ordinary Git files.
+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.
 
 ## Rights, limitations and citation
 
@@ -133,4 +105,4 @@ These local packages are intended for private research storage; remote visibilit
 
 Dongqi Liu, Yifan Wang, Jia Loy and Vera Demberg (2024), [*SciNews: From Scholarly Complexities to Public Narratives – A Dataset for Scientific News Report Generation*](https://aclanthology.org/2024.lrec-main.1258/), LREC-COLING. See the [project page](https://dongqi.me/projects/SciNews).
 
-Report this TopicNet derived population and the source manifest hash in [EXPORT_PROVENANCE.json](EXPORT_PROVENANCE.json). The [research repository](https://github.com/Alvant/Alekseev2026IntraTextCoherence) contains the [source adapters and preprocessing](https://github.com/Alvant/Alekseev2026IntraTextCoherence/blob/master/work/wintra/lib/dataset_adapters.py) and [model input preparation](https://github.com/Alvant/Alekseev2026IntraTextCoherence/blob/master/work/wintra/experiments/prepare_model_inputs.py). It is currently private and access-controlled; viewing those links requires permission. Code is linked rather than bundled, and this package does not promise public standalone reproducibility.
+When citing this preparation, identify its retained population and repository revision as well as the source publication.

SHA256SUMS

@@ -0,0 +1,7 @@
+947f03cf79a99d7f9061220c575021c918ae95f64ab75c4112917be804fb51cd  .gitattributes
+0f3765d3a88eaa3870a228f6c895fa6f35d9fb55f5f27b379b26cb08463a4df0  README.md
+dffd02d522c1cb894d4fd1eebe0e4b6098d045fac77755fc8f49341388b32750  SOURCE_README.md
+a37cd1f1cb47681e9a0d8a2054a56844fb1a9738eddb67668de22d06d78231d0  data/bow.csv
+b9959f976605f8b418f6565fec8228e1bfbb0e8a7c314f0d1cf9b79258e9ac7c  data/labels.json
+40df21605e7e4dd6a8f58a49938b252329cb78781c933645d0dc5ff605078c9c  data/noow.csv
+d2e221cca3c7297d5b84f0d18dc2a7ba448e3775e33ff96a5e4bd30b33d13400  data/splits.json

SOURCE_README.md

@@ -0,0 +1,106 @@
+---
+task_categories:
+- summarization
+- text-generation
+- text-classification
+- text2text-generation
+language:
+- en
+size_categories:
+- 10K<n<100K
+license: cc-by-4.0
+extra_gated_heading: "Request Access to the Dataset"
+extra_gated_description: "This dataset is restricted. Please complete the form below to request access. Incomplete requests may be rejected."
+extra_gated_prompt: |
+  By requesting access, you acknowledge that:
+  - You will only use the dataset for non-commercial academic research or educational purposes.
+  - You agree to comply with all applicable data privacy and ethical standards.
+  - Any use involving human subjects will require separate IRB/ethics approval from your institution, if applicable.
+
+extra_gated_fields:
+  Full Name: text
+  Official Email Address: text
+  Institution / Organization: text
+  Department / Research Group: text
+  Position / Title:
+    type: select
+    options:
+      - Professor / PI
+      - Postdoc
+      - PhD Student
+      - Master Student
+      - Undergraduate
+      - Research Scientist / Engineer
+      - label: Other
+        value: other
+  Country: country
+  Intended Use Case (brief description): text
+  Supervisor or Advisor (if student): text
+  Will you process or combine this dataset with other sensitive data? (If yes, please describe): text
+  I confirm I will not use the dataset for any commercial or for-profit purposes: checkbox
+  I agree to follow all relevant data protection, privacy, and ethical guidelines: checkbox
+  I understand that data access may be revoked at any time if terms are violated: checkbox
+---
+# SciNews
+
+The SciNews dataset is designed to facilitate the development and evaluation of models that generate scientific news reports from scholarly articles. This dataset aims to bridge the gap between complex scientific research and the general public by simplifying and summarizing academic content into accessible narratives. It supports tasks like text summarization, simplification, and the automated generation of scientific news, providing a valuable resource for enhancing public engagement with science and technology.
+
+## Dataset Details
+
+### Dataset Description
+
+- **Curated by:** Dongqi Liu, Yifan Wang, Jia Loy, Vera Demberg from the (1). Department of Computer Science and (2). Department of Language Science and Technology at Saarland Informatics Campus, Saarland University, Germany.
+- **Funded by:** This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (Grant Agreement No. 948878).
+- **Language(s) (NLP):** English
+
+### Dataset Sources
+
+
+- **Repository:** The dataset and code related to this work are available at [SciNews Project Page](https://dongqi.me/projects/SciNews).
+- **Paper:** The details about the dataset can be found in the paper "[SciNews: From Scholarly Complexities to Public Narratives – A Dataset for Scientific News Report Generation](https://aclanthology.org/2024.lrec-main.1258/)" by Dongqi Liu, Yifan Wang, Jia Loy, Vera Demberg.
+
+
+## Dataset Creation
+
+### Data Collection and Processing
+
+Data was collected from the Science X platform, an open-access hub for science, technology, and medical research news. Data extraction was performed using web scraping tools like Selenium and BeautifulSoup.
+
+### Annotations
+
+The dataset does not include additional annotations as it is a compilation of existing scientific papers and their corresponding news reports. The quality control included automated and human assessments to ensure the relevance and quality of the news narratives in relation to the original scientific papers.
+
+### Recommendations
+
+Users of the SciNews dataset should be aware of its limitations and biases, particularly when developing models for scientific news generation. Efforts should be made to address potential biases and ensure that generated narratives accurately and fairly represent the original scientific content.
+
+## Citation
+
+**BibTeX:**
+
+```bibtex
+@inproceedings{pu-etal-2024-scinews-scholarly,
+    title = "{S}ci{N}ews: From Scholarly Complexities to Public Narratives {--} a Dataset for Scientific News Report Generation",
+    author = "Liu, Dongqi  and
+      Wang, Yifan  and
+      Loy, Jia E.  and
+      Demberg, Vera",
+    editor = "Calzolari, Nicoletta  and
+      Kan, Min-Yen  and
+      Hoste, Veronique  and
+      Lenci, Alessandro  and
+      Sakti, Sakriani  and
+      Xue, Nianwen",
+    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
+    month = may,
+    year = "2024",
+    address = "Torino, Italy",
+    publisher = "ELRA and ICCL",
+    url = "https://aclanthology.org/2024.lrec-main.1258",
+    pages = "14429--14444",
+}
+```
+
+## Contact
+
+For further inquiries or questions regarding the SciNews dataset, please contact the email address: dongqi.me@gmail.com