CoderBak/anagram-corpus-nc

Anagram Corpus — non-commercial part

Human-written text, real prompts people wrote, and the generation methods of published papers, gathered from public datasets into one format. It is the input of anagram-bench, a benchmark for AI-text detectors: the benchmark samples from here, asks a language model to write or edit text, and labels the results.

Everything in this repository is for non-commercial use only (CC BY-NC, BY-NC-SA, BY-NC-ND, or quoted material). The commercially usable part is CoderBak/anagram-corpus; load both for the full corpus. The two never hold the same rows.

This version holds 4,854,732 rows in 20 subsets (one per source part). It is being built source by source; more subsets will follow.

Loading

from datasets import load_dataset
ds = load_dataset("CoderBak/anagram-corpus-nc", "acl_ocl")   # subset = source; splits = the source's own splits

Each subset keeps its source's own splits (train, test, ...). A source with no official split has one split, full. Every row records its source's licence in license.

Subsets

Subset	Record type	Rows	Splits	Licence	Source
acl_ocl	documents	71,021	full 71,021	CC BY-NC-SA 3.0, CC BY-NC-SA 4.0	ACL OCL: The ACL Anthology Open Corpus
aeslc	documents	18,302	dev 1,960, test 1,906, train 14,436	CC BY-NC-SA 4.0	AESLC: Annotated Enron Subject Line Corpus
corefl	documents	5,638	learners 3,940, native 1,698	CC BY-NC-ND 3.0	COREFL: Corpus of English as a Foreign Language
deceptive_opinion_spam	documents	1,600	full 1,600	CC BY-NC-SA 3.0	Deceptive Opinion Spam Corpus v1.4
echr	documents	11,478	dev 1,380, test 2,998, train 7,100	ECtHR terms: reproduction free of charge with the source acknowledged, for private, information or education use; commercial use needs written permission	ECHR: Neural Legal Judgment Prediction in English (European Court of Human Rights cases)
ellipse	documents	6,482	test 2,571, train 3,911	CC BY-NC-SA 4.0	ELLIPSE: English Language Learner Insight, Proficiency and Skills Evaluation Corpus
ellipse_excluded	documents	2,400	full 2,400	CC BY-NC-SA 4.0	ELLIPSE: English Language Learner Insight, Proficiency and Skills Evaluation Corpus
hupd	documents	4,518,254	full 4,518,254	CC BY-NC-SA 4.0	HUPD: The Harvard USPTO Patent Dataset
ice	documents	902	full 902	CC BY-NC-SA 3.0	ICE-Nigeria: the Nigerian component of the International Corpus of English
icle_locness	documents	500	full 500	CC BY-NC 4.0	ICLE500: a 500-essay open sample of the International Corpus of Learner English (ICLEv3)
idealens	documents	50	human_rewrite 50	CC BY-NC-SA 4.0	TwiceTold (IdeaLens): human stories written from AI outlines
keystroke_learner_writing	documents	1,006	full 1,006	CC BY-NC-SA 4.0	KUPA-KEYS: Logging Keystrokes in Writing by English Learners
keystroke_learner_writing_keystrokes	logs	2,012	full 2,012	CC BY-NC-SA 4.0	KUPA-KEYS: Logging Keystrokes in Writing by English Learners
l1_signals	documents	443	acl_postllm 31, acl_prellm 12, acl_prenn 400	CC BY-NC-SA 3.0, Quoted from the paper (no licence; non-commercial research use with attribution), CC BY-NC-ND 4.0	Can We Still Trace L1 Signals? (academic abstracts labelled with the authors' native language)
methods	methods	676	full 676	Quoted from the paper (no licence; non-commercial research use with attribution), CC BY-NC-SA 4.0, CC BY-NC-ND 4.0, CC BY-NC 4.0	anagram-bench generation methods
moviesum	documents	2,200	test 200, train 1,800, validation 200	CC BY-NC 4.0	MovieSum: An Abstractive Summarization Dataset for Movie Screenplays
no_robots	prompts	10,000	test 500, train 9,500	CC BY-NC 4.0	No Robots
pelic	documents	46,204	full 46,204	CC BY-NC-ND 4.0	PELIC: University of Pittsburgh English Language Institute Corpus
persuade_2	documents	25,996	test 10,402, train 15,594	CC BY-NC-SA 4.0	PERSUADE 2.0
sharechat	prompts	129,568	train 129,568	CC BY-NC 4.0	ShareChat: A Dataset of Chatbot Conversations in the Wild

Columns

Every row has: id (/); source (source name (= subset)); split (the source's own split, or full when it has none); license (licence of this row); source_record_id (the record's id in the source release); source_url (link to the original record, if any); meta (the source's other fields, as released (JSON)).

documents: text (the human-written text (normalised: see below)); title (title, if the source has one); prompt (the assignment or question the writer answered, if the source gives one); genre (register of the text, as labelled for this corpus); date (date written or published (ISO; as precise as the source allows)); pre_chatgpt (date before 2022-11-30 (null if unknown or ambiguous)); author_id (author id as released by the source); parent_id (thread / document the text belongs to); language (fastText lid.176 language code of the text); language_score (its probability); n_words (whitespace words); text_hash (sha1 of the lower-cased, whitespace-collapsed text).

logs: parent_id (id of the row this record belongs to); format (format of the record); data (the record exactly as released).

methods: kind (template | instruction | attack | classifier); task (the anagram-bench task that uses it); name (template, list or attack id); text (the template or instruction, exactly as quoted); system (system message of a template); placeholders (slots a template expects); params (settings of an attack (JSON)); verbatim (true if copied word for word from the source); paper (source paper); paper_url (its URL); location (where in the paper or repository).

prompts: prompt (the first user turn); system (system prompt, if any); messages (the conversation as {role, content}; assistant content is null when its licence differs from the row's); n_turns (number of user turns); human_response (a human-written answer to the prompt, if the source has one); category (task category given by the source); model (chatbot that answered (chat logs)); date (date of the conversation (ISO)); pre_chatgpt (date before 2022-11-30); language (fastText lid.176 language code of the first turn); language_score (its probability); n_words (words in the first turn); text_hash (sha1 of the first turn, lower-cased, whitespace collapsed).

How it was built

• Official releases only, at a pinned version; every record of the release is kept (no sampling, no quality filtering). Rows whose text is empty after normalisation are left out and counted.
• Normalisation (the only change to text) is kept minimal so layout survives: Unicode NFC; Windows/Mac newlines to \n; control characters other than tab and newline removed; spaces at the end of a line removed; blank lines before the first line and blank space after the last removed. Indentation, blank lines, markup, spelling, quotes and dashes are left as released. Method texts are not normalised at all.
• Language is detected with fastText lid.176 on the first 2,000 characters; all languages are kept.
• pre_chatgpt compares the record's date with 2022-11-30 (ChatGPT's release); text written before it is very unlikely to be AI-generated.
• The source's other fields are kept in meta as released.

Source notes

acl_ocl

ACL OCL: The ACL Anthology Open Corpus. Rohatgi, Qin, Aw, Lim, Hu & Kan (EMNLP 2023).

One row per paper of the release's paper table (acl-publication-info.74k.v2.parquet, 73,285 papers, in table order), then one row per GROBID JSON in Full_text_JSON.tar.gz whose ACL id is not in the table (3,880 PDFs, in tar order: mostly proceedings front matter such as prefaces and committee lists, plus some papers missing from the table; the release has no metadata for them; meta.in_publication_table = false). Nothing is filtered; all languages kept. text = the paper's GROBID parse (Full_text_JSON//json//.json, S2ORC 1.0.0 format, generated 2023-01) written as plain text: the pdf_parse body_text paragraphs and then the back_matter paragraphs (acknowledgements, GROBID 'annex' = appendices) in order; whenever a paragraph's section (sec_num + section title) differs from the previous paragraph's, the heading '

' comes first as its own line; headings and paragraphs separated by a blank line. Paragraph text as released (inline citations and footnote markers kept; GROBID extraction noise not repaired). The abstract is not in text (it is in meta.abstract, the table's GROBID abstract); figure/table captions (ref_entries), the bibliography (bib_entries) and the cite/ref/equation span annotations of the JSON are left out (structured data, not running text; available in the release by ACL id). Papers whose JSON has no body or back-matter text, or that have no JSON (5,552 table papers), use the table's full_text column when it is non-empty (meta.text_from = 'table full_text'); otherwise the row has no text and is counted as dropped_empty by the builder. Result (build of 2026-10-13): 71,021 rows = 67,118 table papers with GROBID JSON text + 370 table papers with the table's full_text (243 of these are only the abstract again) + 3,533 JSON-only PDFs; 6,144 without any text (5,797 table papers, mostly without a parse, and 347 JSON-only PDFs with an empty parse). title = the table title (BibTeX, case-protecting braces kept as released, e.g. '{E}nglish'); for JSON-only rows the GROBID title. date = year and month from the table ('YYYY-MM' when the month field names a month, e.g. 'June', 'May{--}June' -> first month; else 'YYYY'); for JSON-only rows the year in the ACL id (old ids 'P19-1001' -> 2019, new ids '2020.acl-main.1' -> 2020). source_record_id = ACL id; source_url = the table url (else https://aclanthology.org/). meta = every other column of the table as released (abstract, corpus_paper_id, pdf_hash, numcitedby, url, publisher, address, year, month, booktitle, author (BibTeX author string), pages, doi, number, volume, journal, editor, isbn, ENTRYTYPE, ID, language, note) plus in_publication_table, grobid_json (tar member) and text_from; JSON-only rows keep the JSON's own metadata (title, authors, year, venue, identifiers, abstract) under grobid_metadata. Not used: the Base_JSON folder (the same 71,613 parses as single files), the two .pkl section dumps (pickles; 'v3' adds topic labels), the citation-graph parquets, the STop topic csv and the train/val sentence tars of the paper's topic-classification side task.

Licence: The release states its licence inconsistently: README text 'CC BY-NC 4.0' (linked to by-nc/4.0), README badge 'CC BY-NC 4.0' linked to by-nc-sa/4.0, YAML tag 'mit'; the strictest reading, CC BY-NC-SA 4.0, is used. The papers themselves carry the ACL Anthology's licences: CC BY-NC-SA 3.0 for material published before 2016, CC BY 4.0 from 2016 (aclanthology.org). Row licence: cc-by-nc-sa-3.0 for papers before 2016, cc-by-nc-sa-4.0 from 2016 (and when the year is unknown). Non-commercial use only, with attribution and share-alike.

aeslc

AESLC: Annotated Enron Subject Line Corpus. Zhang & Tetreault, This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation (ACL 2019).

Every email of the three official splits: train 14,436, dev 1,960, test 1,906 (18,302 files in enron_subject_line//). One row per file; split = the release's folder name (train | dev | test). text = the email body as released (AESLC's preprocessed body: the authors removed quoted, forwarded and other non-author content and the headers, one sentence per line; kept exactly as in the file). source_record_id = the file name without '.subject' (<inbox|sent>). meta: subject (the original subject line), ann0, ann1, ann2 (dev and test only: subject lines written by three annotators), and from the file name mailbox, folder (inbox = received, sent = written by the mailbox owner) and msg_no. Only the AESLC release is used: no field comes from the upstream CMU Enron corpus (no headers, dates or addresses). The release has no dates or senders: date = '2003', an upper bound (the year the U.S. Federal Energy Regulatory Commission made the Enron emails public), not the date of writing; author_id = None. Nothing is left out.

Licence: CC BY-NC-SA 4.0 (AESLC README and LICENSE.md). Non-commercial use, attribution, share-alike. The underlying Enron emails were made public by the U.S. Federal Energy Regulatory Commission.

corefl

COREFL: Corpus of English as a Foreign Language. Lozano, Díaz-Negrillo & Callies, 'Designing and compiling a learner corpus of written and spoken narratives: COREFL' (in What's in a Narrative?, Peter Lang 2020); COREFL v2.0 (Oct. 2025).

Every text of both subcorpora of the release: 3,940 learner texts and 1,698 native-control texts (5,638, the total on the site's Statistics page), written texts and transcripts of the spoken versions (the same participant retold the same task orally at least 15 days later), in every language (learners write English; native controls write their L1: Spanish, English, Greek, German, ...). Split = subcorpus (learners, native). text = Text exactly as exported (verbatim: CC BY-NC-ND; spoken transcripts keep the transcription conventions). source_record_id = Filename (one Filename, DE_WR_20_14_JW in the native export, names two different texts; the second in file order gets '#2'). meta = every other column of the export as released (L1, Variety of native language, parents' languages, proficiency self-assessments, placement test score raw and %, age, sex, institution, major, years studying English, Medium, Task number, Task title, Writing/audio details, minutes taken, resources used, Original text — the original-script version of a few romanised native texts —, ...; columns differ between the two subcorpora). prompt = the task's wording from the site's Corpus design page (Tasks table): the English wording for texts in English (all learner texts and the L1-English controls), the Spanish wording for the L1-Spanish controls, and the page's bilingual description (English line, then Spanish line) for controls of other L1s, whose instruction language the release does not give; tasks 13 (Frog: retell a picture story, pictures at https://goo.gl/so3S6W) and 14 (Chaplin: retell a 4-minute video clip) have one wording, kept with its link. genre = 'learner narrative' or 'native-speaker narrative', '(written)' or '(spoken, transcribed)' from Medium. date = Year data collection; for the ranges '2014-2016' and '2006-2016' the last year (an upper bound). Nothing is left out; the audio of the spoken texts is not distributed by the site.

Licence: CC BY-NC-ND 3.0 ES (site home page and About > How to cite: 'COREFL is released under a Creative Commons license (CC BY-NC-ND 3.0 ES) (Attribution - NonCommercial - NoDerivatives)'); cite Lozano, Díaz-Negrillo & Callies (2020). Texts kept verbatim. The site's export form asks for the intended usage (we stated research use).

deceptive_opinion_spam

Deceptive Opinion Spam Corpus v1.4. Ott, Choi, Cardie & Hancock (ACL 2011); Ott, Cardie & Hancock (NAACL 2013).

Every review of the release: 1,600 .txt files (4 folders x 400: positive_polarity/deceptive_from_MTurk, positive_polarity/truthful_from_TripAdvisor, negative_polarity/deceptive_from_MTurk, negative_polarity/truthful_from_Web; 20 reviews of each of 20 Chicago hotels per folder). One row per file; text = the file content. The deceptive reviews are not AI text: they were written to order by Mechanical Turk workers (people) who had not stayed at the hotel, so all 1,600 are human-written and all are kept; the class is in meta.label (truthful | deceptive). source_record_id = the file path inside the release folder (e.g. positive_polarity/deceptive_from_MTurk/fold1/d_hilton_1.txt). meta: polarity (positive | negative), label, origin (MTurk, TripAdvisor, or Web = Expedia, Hotels.com, Orbitz, Priceline, TripAdvisor and Yelp), fold (the release's 5 cross-validation folds; not a train/test split), hotel (the file-name code) and hotel_name (from the README). The release has no dates: date = the year of the paper that released the set, an upper bound (2011 for positive, 2013 for negative reviews), not the date of writing. README.md and LICENSE are not rows. Nothing is left out.

Licence: CC BY-NC-SA 3.0 Unported (release LICENSE file and README.md: 'This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License'). Non-commercial use, attribution (cite the papers), share-alike.

echr

ECHR: Neural Legal Judgment Prediction in English (European Court of Human Rights cases). Chalkidis, Androutsopoulos & Aletras (ACL 2019).

Every case of the three official splits, one row per case: EN_train 7,100 cases (DATE 1960-2013), EN_dev 1,380 (1967-2013), EN_test 2,998 (2014-2018) = 11,478 (paper Table 1); the split names are the folder names without 'EN_' (train, dev, test). Each case is one JSON file .json. text = the case's TEXT list (the paragraphs of the facts section of the judgment or decision as extracted by the authors from HUDOC, with the Court's paragraph numbers such as '7. ' kept) joined with one newline, so each released paragraph is one line; 290 paragraphs of the release are empty strings and show as empty lines. Nothing else is changed. title = DOCNAME; date = DATE (the judgment/decision year, the only date released); genre = 'court judgment (statement of facts)' for BRANCH CHAMBER, GRANDCHAMBER and COMMITTEE, 'court admissibility decision (statement of facts)' for ADMISSIBILITY; source_record_id = ITEMID (HUDOC document id, unique across splits); source_url = the HUDOC page of the document. meta = every other field of the case as released: ITEMID, LANGUAGEISOCODE (ENG for all), RESPONDENT, BRANCH, DATE, DOCNAME, IMPORTANCE (HUDOC importance level, 1 = key case .. 4), CONCLUSION, JUDGES, VIOLATED_ARTICLES, VIOLATED_PARAGRAPHS, VIOLATED_BULLETPOINTS, NON_VIOLATED_ARTICLES, NON_VIOLATED_PARAGRAPHS, NON_VIOLATED_BULLETPOINTS (lists as released), plus the file name in the zip (zip_member) and n_paragraphs. Left out: the EN_train_Anon / EN_dev_Anon / EN_test_Anon folders (the same 11,478 cases again with named entities masked by the authors' spaCy run: a derived copy of the same texts) and a __MACOSX resource-fork file. The texts are English versions of the Court's documents (some written in French and translated by the Court's registry); no AI-generated text. The builder's language id marks 11 cases as uk/pl/tr/de: they are English texts dense with local names and original-language terms.

Licence: The release (archive.org item and zip) states no licence; the paper (footnote 3) says HUDOC's licensing conditions are compatible with releasing the dataset. The texts are the European Court of Human Rights' own judgments and decisions from HUDOC. The Court's reuse condition (echr.coe.int 'Copyright and Disclaimer', read 2026-10-13): "The information and texts available on the Court's website may be reproduced provided the source is acknowledged (© ECHR-CEDH) and the reproduction is made for private use or for the purposes of information and education in connection with the Court's activities. This authorisation is subject to the condition that the source is indicated and that any such reproduction is free of charge. Persons wishing to use information and texts on the Court's website for any use other than those listed above, and in particular for commercial use, must request prior written permission using the form available on the website." It also notes that the electronic version of judgments may differ from the formally adopted text. Acknowledge the source as © ECHR-CEDH (HUDOC) and cite Chalkidis et al. (ACL 2019).

ellipse

ELLIPSE: English Language Learner Insight, Proficiency and Skills Evaluation Corpus. Crossley, Tian, Baffour, Franklin, Kim, Morris, Benner, Picou & Boser (International Journal of Learner Corpus Research 9(2), 2023).

Every essay of the release, from its three data files. (1) Subset ellipse = the final corpus: split train = every row of ELLIPSE_Final_github_train.csv (3,911 essays), split test = every row of ELLIPSE_Final_github_test.csv inside the password-protected test zip (2,571; password from the README). text = full_text; source_record_id = text_id_kaggle (the release's id, unique); meta = every other column as released (gender, grade, race_ethnicity, SES, task — 'Independent' for all —, prompt, the averaged scores Overall, Cohesion, Syntax, Vocabulary, Phraseology, Grammar, Conventions, the text statistics num_words, num_words2, num_words3, num_sent, num_para, num_word_div_para, MTLD, TTR, Type, Token, and set). The release names each essay's prompt only by a short topic label (prompt column, e.g. 'Benefits of a problem'; 44 labels), not the assignment wording, so the prompt column is left empty and the label is in meta.prompt. (2) The raw rater file ellipsis_raw_rater_scores_anon_all_essay.csv (password-protected zip, password from the README; 8,890 rows: Filename, text_id_kaggle, Text, then Rater/Overall/Cohesion/Syntax/Vocabulary/Phraseology/Grammar/Conventions/Identifying_Info for rater 1 and rater 2) holds every rated essay, final or not. Each of its rows is matched to a final essay: 6,468 by text_id_kaggle; 14 whose id the release mangled into scientific notation (e.g. '6.19E+10') by identical letters-only text (lower-cased, non-letters removed) — this pairs each of the 14 final essays that had no id match with exactly one raw row; and 8 rows without an id whose letters-only text equals a final essay's (a second rating of the same essay; same Filename). These 6,490 raw rows are kept in the final essay's meta.raw_ratings (a list, all columns as released; the raw Text is included only when it differs from full_text after the corpus's text normalisation (Windows line breaks, trailing spaces) — the final texts have small cleanups, mostly a space added after sentence-final full stops — and meta.raw_ratings[i].text_same_as_final says which). (3) Subset ellipse_excluded, split full = the other 2,400 raw rows (no text_id_kaggle; essays the authors did not put in the final corpus): text = Text, meta = the other columns as released, source_record_id = Filename (with '#2' appended to a Filename's second occurrence). Nothing is left out; the rubric (ELL_Rubrics.docx) is not a text record. Anonymisation tokens (Generic_Name, STUDENT_NAME, TEACHER_NAME, ...) are kept as released. The release has no per-essay date: date = 2022-08 for every essay, an upper bound — the training essays were published, and the test essays used as the hidden test set, in Kaggle's Feedback Prize - English Language Learning (2022-08-31 to 2022-11-30), and all raw-file essays were rated before the final corpus was selected from them; essays were written in state tests (in-text years up to 2021). No author ids or URLs are released.

Licence: CC BY-NC-SA 4.0 (official README: "The data is provided under a CC BY-NC-SA 4.0 DEED Attribution-NonCommercial-ShareAlike 4.0 International license"; the repo has no LICENSE file). Non-commercial use, attribution, share-alike.

ellipse_excluded

ELLIPSE: English Language Learner Insight, Proficiency and Skills Evaluation Corpus. Crossley, Tian, Baffour, Franklin, Kim, Morris, Benner, Picou & Boser (International Journal of Learner Corpus Research 9(2), 2023).

Every essay of the release, from its three data files. (1) Subset ellipse = the final corpus: split train = every row of ELLIPSE_Final_github_train.csv (3,911 essays), split test = every row of ELLIPSE_Final_github_test.csv inside the password-protected test zip (2,571; password from the README). text = full_text; source_record_id = text_id_kaggle (the release's id, unique); meta = every other column as released (gender, grade, race_ethnicity, SES, task — 'Independent' for all —, prompt, the averaged scores Overall, Cohesion, Syntax, Vocabulary, Phraseology, Grammar, Conventions, the text statistics num_words, num_words2, num_words3, num_sent, num_para, num_word_div_para, MTLD, TTR, Type, Token, and set). The release names each essay's prompt only by a short topic label (prompt column, e.g. 'Benefits of a problem'; 44 labels), not the assignment wording, so the prompt column is left empty and the label is in meta.prompt. (2) The raw rater file ellipsis_raw_rater_scores_anon_all_essay.csv (password-protected zip, password from the README; 8,890 rows: Filename, text_id_kaggle, Text, then Rater/Overall/Cohesion/Syntax/Vocabulary/Phraseology/Grammar/Conventions/Identifying_Info for rater 1 and rater 2) holds every rated essay, final or not. Each of its rows is matched to a final essay: 6,468 by text_id_kaggle; 14 whose id the release mangled into scientific notation (e.g. '6.19E+10') by identical letters-only text (lower-cased, non-letters removed) — this pairs each of the 14 final essays that had no id match with exactly one raw row; and 8 rows without an id whose letters-only text equals a final essay's (a second rating of the same essay; same Filename). These 6,490 raw rows are kept in the final essay's meta.raw_ratings (a list, all columns as released; the raw Text is included only when it differs from full_text after the corpus's text normalisation (Windows line breaks, trailing spaces) — the final texts have small cleanups, mostly a space added after sentence-final full stops — and meta.raw_ratings[i].text_same_as_final says which). (3) Subset ellipse_excluded, split full = the other 2,400 raw rows (no text_id_kaggle; essays the authors did not put in the final corpus): text = Text, meta = the other columns as released, source_record_id = Filename (with '#2' appended to a Filename's second occurrence). Nothing is left out; the rubric (ELL_Rubrics.docx) is not a text record. Anonymisation tokens (Generic_Name, STUDENT_NAME, TEACHER_NAME, ...) are kept as released. The release has no per-essay date: date = 2022-08 for every essay, an upper bound — the training essays were published, and the test essays used as the hidden test set, in Kaggle's Feedback Prize - English Language Learning (2022-08-31 to 2022-11-30), and all raw-file essays were rated before the final corpus was selected from them; essays were written in state tests (in-text years up to 2021). No author ids or URLs are released.

Licence: CC BY-NC-SA 4.0 (official README: "The data is provided under a CC BY-NC-SA 4.0 DEED Attribution-NonCommercial-ShareAlike 4.0 International license"; the repo has no LICENSE file). Non-commercial use, attribution, share-alike.

hupd

HUPD: The Harvard USPTO Patent Dataset. Suzgun, Melas-Kyriazi, Sarkar, Kominers & Shieber (NeurIPS 2023 Datasets and Benchmarks; arXiv 2022).

Every application of the release: each JSON file in the 15 yearly archives data/.tar.gz (utility patent applications filed 2004-2018 and published by the USPTO), one row per application, read in archive order, year by year: 4,518,254 applications, exactly the creators' per-year lists (dataset-creation-utilities/lists; the paper says 4,518,263); none has an empty text. The archive year is the filing year by the creators' metadata; for 53 applications the JSON filing_date has another year (kept as released). Nothing is sampled or filtered; the release has no splits (the loader makes train/validation sets from user-chosen filing-date ranges), so split = 'full'. text = the application's abstract, claims and full description, each under a heading line ('Abstract', 'Claims', 'Description'; headings ours), separated by a blank line; an empty field is left out. The fields are as released: HUPD stores each as one line (paragraph breaks were flattened when the creators converted the USPTO XML), with the description's own section headings inline in the text ('CROSS REFERENCE TO RELATED APPLICATIONS This application ...', 'BACKGROUND ...'). HUPD's separate background and summary fields are sections HUPD extracted from the description: in a check of 9,000 applications (3,000 each of 2005, 2010 and 2017), every non-empty background and summary but 2 of 15,941 occurs in full_description once whitespace and the XML residue some of them carry ('detailed-description description="Detailed Description" end="lead"') are disregarded (the 2 differ inside an inline formula). So they are not repeated in text; they are kept unchanged in meta (with their ' HEADING ' tags). title = title; date = filing_date (YYYYMMDD in the release, here YYYY-MM-DD); genre = 'patent application'; source_record_id = application_number; source_url = the application in USPTO Patent Center (https://patentcenter.uspto.gov/applications/; formed by us, the release has no URL); author_id empty (inventors are in meta.inventor_list). meta = every other field of the JSON unchanged (application_number, publication_number, decision (ACCEPTED 2,555,166 / REJECTED 1,220,309 / PENDING 742,779, the creators' outcome label), date_produced, date_published, main_cpc_label, cpc_labels, main_ipcr_label, ipcr_labels, patent_number, filing_date, patent_issue_date, abandon_date, uspc_class, uspc_subclass, examiner_id, examiner_name_last, examiner_name_first, examiner_name_middle, inventor_list, background, summary; strings such as 'None' and 'nan' kept as released) and tar_member (the JSON's path in the archive).

Licence: Stated inconsistently by the creators: HF card metadata 'cc-by-sa-4.0'; HF card text 'HUPD is released under the CreativeCommons Attribution-NonCommercial-ShareAlike 4.0 International'; the paper (arXiv 2207.04043) 'released under the Creative Commons Attribution 4.0 International License'. Rows carry the strictest of these, CC BY-NC-SA 4.0 (non-commercial, attribution, share-alike), and so go to the non-commercial repo. The texts themselves are patent applications published by the USPTO (public record).

ice

ICE-Nigeria: the Nigerian component of the International Corpus of English. Wunder, Voormann & Gut (2010), The ICE Nigeria corpus project: Creating an open, rich and accurate corpus; ICE-Nigeria release of 2015-11-03 (SourceForge).

Every text of the release: all 510 written texts and all 392 spoken transcripts (902 rows, one per text; written first, then spoken, each by category folder and file name). text is the compilers' own plain-text version of the text from the zip, 'txt - without speaker tags/<written|spoken>//.txt'. For written texts it is byte-identical to the 'with speaker tags' copy. For spoken texts the 'with speaker tags' copy prefixes each line with the ELAN tier of its speaker ('Transcription 1 ', ...; 172 multi-speaker transcripts, line for line the same text otherwise, checked on all 392); that speaker sequence is kept in meta.speaker_lines as runs [[tier, number of lines], ...] over the lines of text (null for the 220 single-tier transcripts), so the tagged copy can be rebuilt and turns attributed, and the tiers' speaker metadata is in meta.tiers. Why the txt and not the annotated files: the txt files are the release's plain-text rendering of the same texts and are clean UTF-8, while 87 written XML files have a broken encoding (U+201D saved as e2 80 3f) and one .eaf file is not well-formed. Checked on every written file: the txt equals the XML

Licence: CC BY-NC-SA 3.0 DE (Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Germany, title page of Manual_ICE-Nigeria.pdf in the release; SourceForge lists 'Other License'). Recorded as cc-by-nc-sa-3.0 (the German port has the same terms). Non-commercial use, attribution to the ICE-Nigeria compilers (Gut et al.), share-alike.

icle_locness

ICLE500: a 500-essay open sample of the International Corpus of Learner English (ICLEv3). Granger, Dupont, Meunier, Naets & Paquot (2020), ICLEv3; ICLE500 data release, UCLouvain Dataverse (2024), doi:10.14428/DVN/RIOSSC.

All 500 essays of ICLE500 (the release's Texts/ folder: 500 .txt files, one essay each; 20 per L1 group x 25 groups), one row per essay, in file-name order. Each file starts with a line 'Title: