WOS-46985

This TopicNet preparation contains 46,972 retained English scientific abstracts from the 46,985-row WOS-46985 source, with 134 observed global classes and 7 parent codes. It provides raw abstracts, two normalized text representations, labels, the original numeric hierarchy and generated research splits. It is a derived preparation of the Web of Science dataset, version 6, DOI 10.17632/9rw3vkcfy4.6.

Population, labels and splits

Raw duplicate identity is source abstract text after casefold and whitespace collapse. Raw duplicates are checked first, then identical ordered lemma sequences: a same-class group keeps its smallest document ID, while a group with conflicting global classes is quarantined in full. Documents with empty normalized sequences are removed. The removal counts are 0 same-class raw duplicates, 0 rows in raw conflicts, 13 same-class lemma duplicates, 0 rows in lemma conflicts and 0 empty normalized documents; 13 rows are removed in total.

Near duplicates are grouped, not deleted: body five-token shingle Jaccard similarity ≥0.9 identifies 18 pairs among retained documents. Families combine the raw/lemma duplicate relations and these near-duplicate links. Each family stays in one split. Iterative multilabel stratification uses seeds 20260824 and 20260825, targeting approximately 60%/20%/20%. These are newly generated research splits, not official source train/test splits: fit 28,181, calibration 9,366, validation 9,425. They are disjoint and exhaustive. The Hugging Face split corpus contains every retained document; it is not fit.

Y.txt supplies the authoritative global class, stored as a string in data/labels.json. YL1.txt supplies the parent code and YL2.txt the local child code, which must be interpreted together with its parent. The observed source codes have 134 global classes and seven parent codes. The source hierarchy is inconsistent: global classes 40 and 43 each occur under parents 2 and 5, and global class 76 occurs under parents 4 and 5. These source values are preserved. No class names are inferred and numeric classes are not assumed comparable across WOS subsets.

Retained class counts:

Global class	Documents
0	432
1	398
2	445
3	387
4	379
5	412
6	415
7	443
8	377
9	416
10	403
11	43
12	402
13	379
14	425
15	392
16	365
17	447
18	44
19	375
20	54
21	426
22	417
23	392
24	413
25	646
26	378
27	148
28	276
29	274
30	344
31	429
32	419
33	389
34	397
35	391
36	394
37	387
38	350
39	387
40	510
41	376
42	365
43	373
44	368
45	296
46	389
47	404
48	395
49	362
50	416
51	360
52	371
53	402
54	346
55	420
56	386
57	387
58	361
59	289
60	335
61	410
62	423
63	384
64	441
65	447
66	395
67	148
68	418
69	384
70	412
71	373
72	309
73	357
74	368
75	321
76	264
77	317
78	262
79	294
80	309
81	339
82	260
83	335
84	350
85	198
86	237
87	353
88	327
89	97
90	290
91	222
92	372
93	341
94	129
95	291
96	288
97	182
98	358
99	315
100	246
101	336
102	90
103	305
104	267
105	254
106	371
107	178
108	253
109	197
110	359
111	301
112	320
113	339
114	343
115	298
116	148
117	128
118	202
119	188
120	118
121	257
122	365
123	142
124	361
125	746
126	566
127	622
128	652
129	566
130	576
131	749
132	699
133	509

Representations and preprocessing

The aligned source files are WOS46985/X.txt, Y.txt, YL1.txt and YL2.txt in WebOfScience.zip. Stable IDs are wos46985_<six-digit-zero-based-source-row>. raw_text is the complete source X.txt line without its line terminator. There is no title field.

Both UTF-8 CSV files have exactly id,raw_text,vw_text columns and lexicographically ordered IDs. The VW string begins with the document ID and contains only the |@word namespace; labels are separate metadata. In noow, normalized token occurrences retain their order. In bow, the same tokens are represented as token:count with lexicographically sorted tokens. Neither representation truncates the prepared token sequence.

NOOW means occurrence order after normalization, not original or reversible prose. English preprocessing uses Python 3.11.15, NLTK 3.9.2 and NLTK-data commit 550b6625bcef1f2abff2ff770a5a0d272c9c6b2a: WordPunctTokenizer, lowercase, 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. Near-duplicate shingles use lowercased WordPunct body tokens of length at least two containing letters only.

Reproduce from source

The shared standalone code is in the SciNews preprocessing folder. Follow its setup and code-download instructions to download only that folder at a full commit revision, create the locked Linux x86-64 environment with Python 3.11.15, and record a full server build and verification. 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 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:

python preprocessing/prepare.py download --dataset wos-46985 --sources-dir /path/to/sources
python preprocessing/prepare.py build --dataset wos-46985 \
  --sources-dir /path/to/sources \
  --output-dir /path/to/rebuilt/WOS-46985/data \
  --scratch-dir /path/to/scratch/wos-46985
python preprocessing/prepare.py verify --dataset wos-46985 \
  --data-dir /path/to/rebuilt/WOS-46985/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-46985. 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; the alternative is bow. Both expose the full retained population as corpus:

from datasets import load_dataset

repo = "TopicNet/WOS-46985"
corpus = load_dataset(repo, "noow", split="corpus")
bow = load_dataset(repo, "bow", split="corpus")

Obtain the complete local package, including actual Git LFS contents. Set HF_DATASETS_OFFLINE=1 and HF_HUB_OFFLINE=1 before Python starts for offline loading, and choose a writable cache directory:

import json
from pathlib import Path
from datasets import load_dataset

package = Path("/path/to/WOS-46985")
corpus = load_dataset(str(package), "noow", split="corpus",
                      cache_dir="/writable/cache")
bow = load_dataset(str(package), "bow", split="corpus",
                   cache_dir="/writable/cache")
labels = json.loads((package / "data/labels.json").read_text(encoding="utf-8"))
splits = json.loads((package / "data/splits.json").read_text(encoding="utf-8"))
hierarchy = json.loads((package / "data/hierarchy.json").read_text(encoding="utf-8"))
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")

labels.json directly maps each ID to [Y], a list containing one string class. splits.json directly maps fit, calibration and validation to ordered ID arrays. hierarchy.json directly maps each ID to {"Y": "...", "YL1": "...", "YL2": "..."}, preserving the three source codes as strings. Join all metadata by ID, not by JSON position.

For remote metadata, download each auxiliary file and use the same ID joins:

import json
from huggingface_hub import hf_hub_download

metadata = {}
for name in ("labels", "splits", "hierarchy"):
    path = hf_hub_download(repo, f"data/{name}.json", repo_type="dataset")
    with open(path, encoding="utf-8") as handle:
        metadata[name] = json.load(handle)

Related datasets and overlap

Related preparations are TopicNet/WOS-5736, TopicNet/WOS-11967 and TopicNet/WOS-46985. They are not independent datasets. Comparing the 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. Research splits are generated independently for each preparation; combining subsets for training and evaluation can therefore leak shared documents across splits.

Files and integrity

The package contains .gitattributes, this card, SHA256SUMS, the verbatim SOURCE_README.txt, data/noow.csv, data/bow.csv, labels, splits and hierarchy. Run sha256sum -c SHA256SUMS from the package root to check the listed files.

Rights, limitations and citation

The official version 6 release declares CC BY 4.0. SOURCE_README.txt is preserved verbatim, including Copyright (c) 2017 Kamran Kowsari, the permission notice and the request to cite HDLTex. Preserve the copyright and permission notices in copies or substantial portions and cite the source. The release license does not establish that all abstracts or other third-party content are free of additional rights.

Source selection, historical coverage and class imbalance limit generalization. Source hierarchy codes are preserved without inferred corrections. Family grouping reduces known duplicate leakage, but some semantic duplicates may remain. The abstracts may contain personal information and outdated claims. Assess the corpus's suitability for your application before relying on it for 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. DOI: 10.1109/ICMLA.2017.0-134.

When citing this preparation, identify its retained population and repository revision as well as the source publication.