Find research datasets worth reusing

Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.

3,597

datasets available to search

ShareScore release 0.9.0

Dataset results

3,597 results for “French”

Learn how ShareScore rates datasets ↗

zenodo44/100

French Word Sense Disambiguation with Princeton WordNet Identifiers

This is a dataset for the Word Sense Disambiguation of French using Princeton WordNet identifiers. It contains two training corpora : the SemCor and the WordNet Gloss Corpus, both automatically translated from their original English version, and with sense tags automatically aligned. It contains also a test corpus : the task 12 of SemEval 2013, originally sense annotated with BabelNet identifiers, converted into Princeton WordNet 3.0.

opencc-by-4.0Nov 2019View details →

zenodo44/100

National Checklists 2017: French Polynesia Species List

Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See https://github.com/diatomsRcool/checklists for detailsA list of species from French Polynesia collected using effechecka and geonames polygons

opencc-zeroAug 2024View details →

zenodo44/100

National Checklists 2017: French Guiana Species List

Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See https://github.com/diatomsRcool/checklists for detailsA list of species from French Guiana collected using effechecka and geonames polygons

opencc-zeroAug 2024View details →

zenodo44/100

National Checklists 2017: Territory of the French Southern and Antarctic Lands Species List

Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See https://github.com/diatomsRcool/checklists for detailsA list of species from Territory of the French Southern and Antarctic Lands collected using effechecka and geonames polygons

opencc-zeroAug 2024View details →

zenodo44/100

National Checklists 2019: French Polynesia Species List

Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See https://github.com/diatomsRcool/checklists for details.A list of species from French Polynesia collected using effechecka and geonames polygons

opencc-zeroAug 2024View details →

zenodo44/100

National Checklists 2019: Territory of the French Southern and Antarctic Lands Species List

Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See https://github.com/diatomsRcool/checklists for details.A list of species from Territory of the French Southern and Antarctic Lands collected using effechecka and geonames polygons

opencc-zeroAug 2024View details →

zenodo44/100

National Checklists 2019: French Guiana Species List

Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See https://github.com/diatomsRcool/checklists for details.A list of species from French Guiana collected using effechecka and geonames polygons

opencc-zeroAug 2024View details →

zenodo44/100

GENEActiv accelerometer files collected during the project entitled "Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie" [Eng: "Eating cultures and behaviors of young people in French-speaking Pacific countries in the 21st century: the example of New Caledonia"] (anonymized version - third part)

<a title="GENEActiv" href="https://activinsights.com/technology/geneactiv/" target="_blank" rel="noopener">GENEActiv</a> accelerometer .csv files converted with a 1 second epoch from raw GENEActiv .bin files recorded during the project entitled "Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie" [en: "Eating cultures and behaviors of young people in French-speaking Pacific countries in the 21st century: the example of New Caledonia"]. Devices are 60-Hz triaxial accelerometers. This dataset also contains participantCharacteristics.csv that povides basic information about participants and read_a_binFile_share.R that is a short R code aiming at converting and saving accelerometer data from .bin files in 1 second epoch .csv files (consider the Methods section). Participant characteristics: 10 to 16 years old students and some parents. Number of participants: 231 (206 adolescents + 25 adults). Year of the study: 2018 - 2019. Place of the study: New Caledonia. The accelerometer .csv files with a 1 second epoch and extracted from raw .bin files are available in open datasets: <ul> <li><a title="Open dataset - first part" href="https://doi.org/10.5281/zenodo.12615468" target="_blank" rel="noopener">anonymized version - first part</a></li> <li><a title="Open dataset - second part" href="https://doi.org/10.5281/zenodo.12638746" target="_blank" rel="noopener">anonymized version - second part</a></li> <li><a title="Open dataset - third part" href="https://doi.org/10.5281/zenodo.12682660" target="_blank" rel="noopener">anonymized version - third part</a></li> </ul> The accelerometer raw .bin files are available in restricted datasets: <ul> <li><a title="Restricted dataset - first part" href="https://doi.org/10.5281/zenodo.11594645" target="_blank" rel="noopener">non-anonymized version - first part</a></li> <li><a title="Restricted dataset - second part" href="https://doi.org/10.5281/zenodo.12638965" target="_blank" rel="noopener">non-anonymized version - second part</a></li> <li><a title="Restricted dataset - third part" href="https://doi.org/10.5281/zenodo.12661429" target="_blank" rel="noopener">non-anonymized version - third part</a></li> </ul> Other participant characteristics (age, place of living, cultural community and socio-economic status) are available in a <a title="Information associated with GENEActiv accelerometer files collected during the project entitled "Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie" [en: "Eating cultures and behaviors of young people in French-speaking Pacific countries in the 21st century: the example of New Caledonia"] (non-anonymized information version)" href="https://doi.org/10.5281/zenodo.12195186" target="_blank" rel="noopener">restricted non-anonymized dataset</a>. When using this dataset, please cite the following reference: <a title="Wattelez et al. 2025" href="https://doi.org/10.1016/j.dib.2024.111228" target="_blank" rel="noopener">G. Wattelez, S. Frayon, O. Galy, Assessing physical activity/behavior of adolescents living in the Pacific with accelerometer data: 231 GENEActiv records in New Caledonia, Data in Brief 58 (2025) 111228, doi: 10.1016/j.dib.2024.111228</a>

embargoedcc-by-4.0Jun 2024View details →

zenodo44/100

GENEActiv accelerometer files collected during the project entitled "Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie" [Eng: "Eating cultures and behaviors of young people in French-speaking Pacific countries in the 21st century: the example of New Caledonia"] (anonymized version - second part)

embargoedcc-by-4.0Jun 2024View details →

zenodo44/100

Ressources for End-to-End French Text-to-Speech Blizzard challenge

Here are 289 chapters of 5 audiobooks from Librivox (51:12) read by Nadine Eckert-Boulet (NEB): <ol> <li>Madame Bovary (MB) by Gustave Flaubert (FL) - 3 volumes, 35 chapters (original <a href="https://librivox.org/madame-bovary-french-by-gustave-flaubert">wavs</a>; <a href="https://www.gutenberg.org/cache/epub/14155/pg14155.txt">text</a>)</li> <li>Les mystères de Paris (LMP) by Eugene Sue (ES) - 4 volumes, 83 chapters (original <a href="https://librivox.org/les-mysteres-de-paris-tome-1-by-eugene-sue">wavs1</a>, <a href="https://librivox.org/les-mysteres-de-paris-tome-2-by-eugene-sue/">wavs2</a>,<a href="https://librivox.org/les-mysteres-de-paris-tome-3-by-eugene-sue"> wavs3</a>; <a href="https://www.gutenberg.org/cache/epub/18921/pg18921.txt">text1</a>, <a href="https://www.gutenberg.org/cache/epub/18922/pg18922.txt">text2</a>, <a href="https://www.gutenberg.org/cache/epub/18923/pg18923.txt">text3</a>)</li> <li>Les tribulations d'un chinois en Chine (TCC) by Jules Verne (JV) - 1 volume, 22 chapters (original <a href="https://librivox.org/les-tribulations-dun-chinois-en-chine-by-jules-verne">wavs</a>; <a href="https://www.gutenberg.org/cache/epub/14162/pg14162.txt">text</a>)</li> <li>La fille du pirate (LFDP) by Henri Émile Chevalier (EC) - 7 volumes, 121 chapters (original <a href="https://librivox.org/la-fille-du-pirate-by-henri-emile-chevalier">wavs</a>, <a href="https://www.gutenberg.org/cache/epub/18403/pg18403.txt">text)</a></li> <li>La vampire (VAMP) by Paul Féval (PF) - 1 volume, 28 chapters (original <a href="https://librivox.org/la-vampire-by-feval-paul-henry-corentin">wavs</a>, <a href="https://www.gutenberg.org/cache/epub/10053/pg10053.txt">text</a>)</li> </ol> and 2515 utterances (2:03) read by another female French speaker Aurélie Derbier (AD): <ol> <li>1608 utterances extracted from various books (DIVERS_BOOK_AD*)</li> <li>907 transcripts of the sessions of the French parliament (DIVERS_PARL_01*)</li> </ol> We recently added three speakers from Librivox/Litteratureaudio: <ol> <li>Ezwa (EZWA): L'épouvante by Maurice Level (original <a href="https://librivox.org/lepouvante-by-maurice-level-1010/">wavs</a>; <a href="https://www.gutenberg.org/cache/epub/17794/pg17794.txt">text</a>) - 11 chapters - 4869 utterances> 03:16</li> <li>Pauline Latournerie (PL): Le pédagogue n'aime pas les enfants by Henri Roorda (<a href="https://librivox.org/le-pedagogue-naime-pas-les-enfants-by-henri-roorda/">original wavs</a>; <a href="https://ebooks-bnr.com/ebooks/pdf4/roorda_le_pedagogue_n_aime_pas_les_enfants.pdf">text</a>) - 6 chapters - 1320 utterances> 01:17</li> <li>Jean-Luc Fischer (JLF): L’Affaire Charles Dexter Ward by Howard Phillips Lovecraft (<a href="https://www.litteratureaudio.com/livre-audio-gratuit-mp3/howard-phillips-lovecraft-laffaire-charles-dexter-ward.html">original wavs</a>; <a href="https://www.litteratureaudio.com/textes/H_P_Lovecraft_L_Affaire_CDW.pdf">text</a>) - 16 chapters - 1823 utterances> 02:37</li> </ol> Each .wav file (sampled at 22050Hz) corresponds to one entire chapter. The format of the filenames is: {author's acronym}_{book's acronym}_{reader's acronym}_{volume's number}_{chapter's number} The NEB_train.csv file gives text and phonetic alignments (essentially for MB and LMP) for utterances in 4 fields separated by '|': {filename}|{start_ms}|{end_ms}|{text or phonetic content}. Most utterances are separated by at least a pause of 400ms. The intervals [start_ms:end_ms] comprise leading and trailing silences of 130ms (since wavs are entire chapters, these silences are "true" ambient silences). Same for AD_train.csv. When phonetic alignment has been performed, 2 additional fields have been added: {aligned phones}|{durations in ms}. Each input character or phone has a corresponding aligned phone and a duration. Note that all aligned utterances start and end with an aligned phone of 130ms. The set of aligned phones comprises: <ul> <li>The set of input phones</li> <li>The silence: '__'</li> <li>The symbol '_' for silent characters, e.g. "chat" is aligned with 's^ _ a _'</li> <li>29 combined aligned phones ('a&i', 'a&j', 'b&q', 'd&q','d&z', 'd&z^', 'f&q', 'g&q', 'g&z', 'j&i', 'j&u', 'j&q', 'i&j', 'k&q', 'k&s', 'k&s&q', 'l&q', 'm&q', 'n&q', 'r&w', 'r&q', 's&q', 't&q', 't&s', 't&s^', 'w&a', 'z&q', 'p&q') that align to only one character, e.g. "expatrier" is aligned with 'e^ k&s p a t r i&j e _'</li> </ul> Text is in UTF8. '«»','¬', '~','""','()','[]' are respectively used for speaking quotes, turn switches, three dots, quoted expression, aside quotes, notes. Because of rare occurrences, 'ö' has been transcribed as 'oe'. Paragraphs (two consecutive carriage returns in the original text) are cued by a special character '§'. It usually ends an utterance but could be used within an utterance if its associated pause is too short. When available, phonetic content is given per word in curly brackets '{}'. We use 39 phonetic symbols: <ul> <li>oral vowels: a (fa), e (fée), e^ (fait), x (feu), x^ (coeur), i (riz), y (fut), u (fou), o (faux), o^ (porc)</li> <li>schwa: q (gage)</li> <li>nasal vowels: a~ (rang), e~ (fin), x~ (un), o~ (rond)</li> <li>semi-vowels: h (huit), w (ouate), j (hier)</li> <li>consonants: p (pas), t (tas), k (cas), b (bas), d (dos), g (gars), f (faux), s (sot) , s^ (chat), v (vu), z (zut), z^ (jus), r (riz), l (la), m (ma), n (non), n~ (oignon), ng (camping)</li> </ul>

opencc-by-4.0Mar 2021View details →

zenodo44/100

Wikipedia: wikipedia-fr (French)

Wikipedia is a multilingual, web-based, free-content encyclopedia project supported by the Wikimedia Foundation and based on a model of openly editable content. EOL harvests articles from wikipedia that are indexed as species or higher taxa.

opencc-by-sa-4.0Aug 2024View details →

zenodo44/100

French indirectly named entities

A set of indirectly entities in French. (indirectly named entities are named entities described by a periphrasis)

opencc-by-4.0Apr 2021View details →

zenodo44/100

DATASET From the Reef to the Ocean: Revealing the Acoustic Range of the Biophony of a Coral Reef (Moorea Island, French Polynesia)

Dataset corresponding to the article "From the Reef to the Ocean: Revealing the Acoustic Range of the Biophony of a Coral Reef (Moorea Island, French Polynesia)". 90 sites were recorded from the reef crest to 10 km in the open ocean off Moorea Island (French Polynesia) in 2016. Recordings were realized with drifting antennas made of a floater and an autonomous recorder EA-SDA14 (RTSys®, Caudan, France) connected to a wideband low-noise hydrophone HTI-92 (High Tech Inc., Long Beach, MS, USA) with a sensitivity of −155 ± 3 dB re 1 V µPa−1 and a flat frequency response from 2 Hz to 50 kHz.

opencc-by-4.0Jan 2023View details →

zenodo44/100

MIGR-TWIT CORPORA. Migration Tweets of French Left-wing Politics.

Description The FR-L-MIGR-TWIT Corpus is part of the <a href="https://www.ortolang.fr/market/corpora/migr-twit-corpus">MIGR-TWIT CORPORA</a>, diachronic bilingual corpus of Tweets about the topic of migration in Europe. Within the framework of the collaborative research project <a href="https://olindinum.huma-num.fr/recherche/">OLiNDiNUM</a> (Observatoire LINguistique du DIscours NUMérique, [Linguistic Observatory of Online Debate]), the MIGR-TWIT Corpora are created with the aim to study the evolution of the public discourse on migration in Europe during the past dozen years from 2011 to 2022. First two components of the corpus represent migration discourse of right-wing politics in France and in the UK. The FR-L-MIGR-TWIT Corpus represents French left-wing politics' migration discourse on Twitter.   Using the Twitter API v2 Academic Research, the Tweets containing at least one occurrence of lexicon derived from a latin root "migr" of migrare are automatically retrieved from 23 Twitter accounts of French left-wing political figures and parties.   Scientific reference :  Jeon, S. (2025). Le discours numérique sur l'immigration en France entre 2011 et 2022. Une analyse de corpus (Online Discourse on Immigration in France between 2011 and 2022. A Corpus Analysis), PhD Thesis, Université de Lille, France. Contents The downloadable version of FR-L-MIGR-TWIT-2011-2022 Corpus contains 32 CSV files (tabular format). The corpus is presented in simplified and complete versions in terms of metadata. The simplified version corresponds to one single file named FR-L-MIGR-TWIT-2011-2022.csv, containing four basic (meta)data, i.e. identifier, text, posting date and username (that is, data__id, data__text, data__created_at and author__name as the table hearder elements). In addition to these four (meta)data, the elaborate version is provided with all Tweet fields information included as a header element, such as the numbers of Replies, Retweets, Likes and Quotes, etc. This version is also available in one single CSV file named FR-L-MIGR-TWIT-2011-2022_meta.csv. Besides, the elaborate version is provided with three CSV Zip files: 7 CSV files in the zip file named FR-L-MIGR-TWIT-YEAR_meta correspond to grouped years (i.e. FR-L-MIGR-TWIT-2011-2016_meta.csv) or each and every year (e.g. FR-L-MIGR-TWIT-2017_meta.csv, and so on) for the last dozen years. 23 files in the zip file named FR-L-NAME-MIGR-TWIT_meta for each and every component of selected French left-wing political figures and parties (e.g. FR-L-Arthaud-TWIT_meta.csv). The zip file named FR-L-MIGR-TWIT-2011-2022_meta contains yearly Tweets of each and every component of political figures and parties. Detailed information of the FR-L-MIGR-TWIT-2011-2022 CORPUS is illustrated below. <ul> <li>Created at: 2023-04-18</li> <li>Language: FR</li> <li>Coverage: 23 user accounts ; 5,636 Tweets ; 169,818 words</li> <li>Time of data collection: start=2011-01-01 ; end=2022-06-30</li> <li>Keywords: words derived from a latine root “migr” of migrare</li> <li>Corpus composition:</li> </ul> <table> <tbody> <tr> <td>   </td> <td> Political Figure/party </td> <td> Type of representative </td> <td> Username </td> <td> migr-Tweets </td> </tr> <tr> <td> 1 </td> <td> Adrien Quatennens </td> <td> PERSON (M) </td> <td> @AQuatennens </td> <td> 315 </td> </tr> <tr> <td> 2 </td> <td> Alexis Corbière </td> <td> PERSON(M) </td> <td> @Alexiscorbiere </td> <td> 209 </td> </tr> <tr> <td> 3 </td> <td> Anne Hidalgo </td> <td> PERSON (F) </td> <td> @Anne_Hidalgo </td> <td> 801 </td> </tr> <tr> <td> 4 </td> <td> Arnaud Montebourg* </td> <td> PERSON (M) </td> <td> @montebourg </td> <td> 7 </td> </tr> <tr> <td> 5 </td> <td> Benoît Hamon </td> <td> PERSON (M) </td> <td> @benoithamon </td> <td> 172 </td> </tr> <tr> <td> 6 </td> <td> Christiane Taubira </td> <td> PERSON (F) </td> <td> @ChTaubira </td> <td> 11 </td> </tr> <tr> <td> 7 </td> <td> Clémentine Autain </td> <td> PERSON (F) </td> <td> @Clem_Autain </td> <td> 102 </td> </tr> <tr> <td> 8 </td> <td> Danièle Obono </td> <td> PERSON (F) </td> <td> @Deputee_Obono </td> <td> 415 </td> </tr> <tr> <td> 9 </td> <td> Esther Benbassa** </td> <td> PERSON (F) </td> <td> @EstherBenbassa </td> <td> 936 </td> </tr> <tr> <td> 10 </td> <td> François Hollande </td> <td> PERSON (M) </td> <td> @fhollande </td> <td> 28 </td> </tr> <tr> <td> 11 </td> <td> François_Ruffin </td> <td> PERSON (M) </td> <td> @Francois_Ruffin </td> <td> 19 </td> </tr> <tr> <td> 12 </td> <td> Jean-Luc Mélenchon </td> <td> PERSON (M) </td> <td> @JLMelenchon </td> <td> 240 </td> </tr> <tr> <td> 13 </td> <td> Manon Aubry </td> <td> PERSON (F) </td> <td> @ManonAubryFr </td> <td> 182 </td> </tr> <tr> <td> 14 </td> <td> Natalie Arthaud </td> <td> PERSON (F) </td> <td> @n_arthaud </td> <td> 165 </td> </tr> <tr> <td> 15 </td> <td> Philippe Poutou </td> <td> PERSON (M) </td> <td> @PhilippePoutou </td> <td> 83 </td> </tr> <tr> <td> 16 </td> <td> Raphael Glucksmann </td> <td> PERSON (M) </td> <td> @rglucks1 </td> <td> 142 </td> </tr> <tr> <td> 17 </td> <td> Yannick Jadot </td> <td> PERSON (M) </td> <td> @yjadot </td> <td> 374 </td> </tr> <tr> <td> 18 </td> <td> Europe Écologie-Les Verts </td> <td> ORGANIZATION </td> <td> @EELV </td> <td> 484 </td> </tr> <tr> <td> 19 </td> <td> Gauche Républicaine et Socialiste </td> <td> ORGANIZATION </td> <td> @Gauche_RS </td> <td> 73 </td> </tr> <tr> <td> 20 </td> <td> Génération.s </td> <td> ORGANIZATION </td> <td> @GenerationsMvt </td> <td> 165 </td> </tr> <tr> <td> 21 </td> <td> La France Insoumise </td> <td> ORGANIZATION </td> <td> @FranceInsoumise </td> <td> 300 </td> </tr> <tr> <td> 22 </td> <td> Parti Radical Gauche </td> <td> ORGANIZATION </td> <td> @PartiRadicalG </td> <td> 37 </td> </tr> <tr> <td> 23 </td> <td> Parti Socialiste </td> <td> ORGANIZATION </td> <td> @partisocialiste </td> <td> 376 </td> </tr> </tbody> </table> <ul> <li>Political figures and parties, listed in alphabetical order, are selected according to the four criteria: (1) the high number of migr-tweets, (2) the political affiliation, (3) the political careers, that is, the Member of the European Parliament or (4) the presidential candidate during the period between 2011 and 2022. These four criteria are not mutually exclusive.</li> <li>As part of a doctoral thesis (<a href="https://theses.fr/s360032">Jeon, 2025</a>), the FR-L-MIGR-TWIT and FR-R-MIGR-TWIT corpora are compiled, annotated and analyzed through a comparative discourse analysis approach, with the aim to study the semantic construction of migr-lexicon over the period between 2011 and 2022.</li> <li>*One migration Tweet retrieved from the user account @montebourg for the year of 2019 was removed and is not included in his 7 migr-tweets because it refers to the issue of the migration of honey bees.</li> <li>**We later added the user account @EstherBenbassa represented by Esther Benbassa, senator and former member of political party Europe Écologie-Les Verts (representative of the user account @EELV), because of the high number of her migr-tweets that were retweeted by @EELV.</li> </ul> The MIGR-TWIT Corpus consists of three subcorpora for a total amount of 23,869 Tweets and 703,016 words: <ul> <li>FR-R-MIGR-TWIT-2011-2022 Corpus: French Right-wing politics' migr-tweets</li> <li>UK-R-MIGR-RA-TWIT-2011-2022 Corpus: British Right-wing politics' migr-tweets</li> <li>FR-L-MIGR-TWIT-2011-2022 Corpus: French Left-wing politics' migr-tweets </li> </ul>

opencc-by-4.0Apr 2023View details →

zenodo44/100

French vowels

Dataset of audio recordings. 20 adult French speakers are recorded saying 8 vowels, under 7 conditions, 10 times each.   There are 15 women and 5 men. Each individual prononces 8 vowels <pre><code class="language-html">[&#97;&#771;&#601;&#105;&#111;&#596;&#117;&#121]</code></pre> in 7 conditions: natural, low voice, high voice, short, long, on an ascending and descending scale. There are 10 utterances for each condition.   The records have been made with a Zoom H6 recorder, using the included stereo mic XYH-6. The sampling rate of the recordings is 44100 Hz, 16 bits.

opencc-by-4.0May 2023View details →

zenodo44/100

A Dataset of French Trade Directories from the 19th Century for Nested NER task

This dataset is composed of pages and entries extracted from French directories published between 1798 and 1861. The purpose of this dataset is to evaluate the performance of Nested Named Entity Recognition approaches on 19th century French documents, regarding both clean and noisy texts (due to the OCR engine). Source dataset This dataset has been built from this source dataset : <pre><code class="language-markdown">N. Abadie, S. Baciocchi, E. Carlinet, J. Chazalon, P. Cristofoli, B. Duménieu and J. Perret, A Dataset of French Trade Directories from the 19th Century (FTD), version 1.0.0, May 2022, online at https://doi.org/10.5281/zenodo.6394464.</code></pre> Our experiments // Paper Details about our experiments on nested NER approaches are given in our paper (<a href="https://hal.science/hal-03994759v2">the pre-print version is available here</a>). <pre><code class="language-markdown">Tual, S., Abadie, N., Chazalon, J., Duménieu, B., & Carlinet, E. (2023). A Benchmark of Nested NER Approaches in Historical Structured Documents. Proceedings of the 17th International Conference on Document Analysis and Recognition, San José, California, USA. 2023. Springer. https://hal.science/hal-03994759v2</code></pre> Our code is available on <a href="https://github.com/soduco/paper-nestedner-icdar23-code">Git-Hub</a>. Dataset overview The following list describes the keys of the .JSON file which contain the complete materials of our experiments. - id : Entry unique ID in a given page - box : Bounding box of the entry in the scanned directory page - book : Source directory of the entry (*see more information bellow*) - page : Page ID in a given directory - valid_box : Is the bbox of the entry valid ? (*all bbox are valid here*) - text_ocr_ref` : OCR extracted and manually corrected text of the entry - nested_ner_xml_ref :  text_ocr_ref with nested ner entities - text_ocr_pero : OCR extracted text of the entry with PERO-OCR engine (best engine according to Abadie et al. experiment) - has_valid_ner_xml_pero : Is entities mapping between nested-ner entities annotated by hand on the ref text and pero ocr text correct ? (in our experiments, we only use entries with True value) - nested_ner_xml_pero : Annotated noisy entries produced with PERO OCR - text_ocr_tess : OCR extracted text of the entry with Tesseract engine (*not used in our expriments*) - nested_ner_xml_tess : Is entities mapping between nested-ner entities annotated by hand on the ref text and tesseract text correct? (not used in our experiments) - has_valid_ner_xml_tess : Annotated noisy entries produced with Tesseract. (not used in our experiments) Nested entities are annotated using XML tags. Our hierachy of entities is a *Part Of* a two-levels hierarchy. It means that bottom entities are contained in a top level entity.   Source documents // Copyright and licence This section has been copied from the <a href="https://zenodo.org/record/6394464">original dataset description</a>. The images were extracted from the original source https://gallica.bnf.fr, owned by the *Bibliothèque nationale de France* (French national library). Original contents from the Bibliothèque nationale de France can be reused non-commercially, provided the mention "Source gallica.bnf.fr / Bibliothèque nationale de France" is kept.   => Researchers do not have to pay any fee for reusing the original contents in research publications or academic works. Original copyright mentions extracted from <a href="https://gallica.bnf.fr/edit/und/conditions-dutilisation-des-contenus-de-gallica">https://gallica.bnf.fr/edit/und/conditions-dutilisation-des-contenus-de-gallica</a> on March 29, 2022. The original contents were significantly transformed before being included in this dataset. All derived content is licensed under the permissive *Creative Commons Attribution 4.0 International* license. Links to original contents are given in the window bellow :

opencc-by-4.0Apr 2023View details →

zenodo44/100

Drone raw images of cattle in french grazing areas

The updated data are part of the Horizon Europe project ICAERUS regarding the livestock monitoring use case in a task where the objective is to test, optimize and scale up models regarding animal counting (cattle and sheep). More information here : <a href="https://icaerus.eu/">https://icaerus.eu/</a> The dataset encompasses around 900 raw .jpeg drone images of grazing areas where cattle graze collected between June and August 2023. Drone used were Mavic 3 Enterprise and Thermal. Data collection is underway, with the aim of collecting images throughout the year and on several farms, to capture variability in animal and background colors and brightness conditions. Image tags (“cattle” vs “no cattle”) are not available for the moment but will be in the next months in next versions of this dataset. There is a strong imbalance between images with “cattle” and image with “no cattle” representative of areas to monitor.  The nadir images were collected during flight planned with DJI Pilot 2 at a constant altitude regarding the take-off position (30 m, 60 m, 100 m).  The data are organized by a first directory by farm where the images were collected and then with one directory by flight planned.  A summary is available in the Table_summary.xls. Name of the directory of each planned flight is defined such as DJI_YYYYMMDDHHMM_XX with the date (YYYYMMDD), the hour in UTC+2 (HHMM), and XX representing a mission number.  .exif data of each images provide many information regarding the drone (GPS position, absolute and relative altitude, gimble information, speed etc.). Further details will be added in the next versions of the dataset. The authors of the dataset are opened to any collaboration regarding animal counting models. For more information, please contact: adrien.lebreton@idele.fr

opencc-by-4.0Aug 2023View details →

zenodo44/100

BLM-AgrF: A New French Benchmark to Investigate Generalization of Agreement in Neural Networks

BLM-AgrF is a French dataset for learning the underlying rules of subject-verb agreement in sentences, developed in the BLM framework, a new task inspired by visual IQ tests known as Raven's Progressive Matrices.  In this task, an instance consists of sequences of sentences with specific attributes. To predict the correct answer as the next element of the sequence, a model must correctly detect the generative model used to produce the dataset.

opencc-by-4.0Oct 2023View details →

edi44/100

Spatial and Social Behavior of Acanthurus triostegus on Moorea (French Polynesia) and Palmyra Atoll (USA), 2017-2018

These data describe the schooling behavior of coral reef fish on Moorea (French Polynesia) and Palmyra Atoll (USA) in 2017 and 2018. Data are grouped into two sets of observations: 1) surveys measuring the abundance of reef fish and the proportion of those fish occurring in schools, and 2) behavioral observations including time spent grazing and GPS tracks of schooling and solitary Acanthurus triostegus.

openCC (other)Jan 2022View details →

edi44/100

MCR LTER: Coral Reef: Water Column nutrient data in lagoons of Moorea, French Polynesia

Nutrients are important for ecosystem structure and community dynamics. To quantify fine-scale patterns of variation in water column nutrients in tropical lagoon ecosystems, nitrite + nitrate, phosphate, and silicate were quantified from water column samples collected in lagoons around Moorea, French Polynesia during April-May starting in 2021 using flow injection with an automated ion analyzer. Over the first three years, water column nitrite + nitrate and silicate were higher in bays and fringing reef habitats, and phosphate was higher in bays.

openCC (other)Sep 2025View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated datasets

Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record

Find research datasets worth reusing

Dataset results

French Word Sense Disambiguation with Princeton WordNet Identifiers

National Checklists 2017: French Polynesia Species List

National Checklists 2017: French Guiana Species List

National Checklists 2017: Territory of the French Southern and Antarctic Lands Species List

National Checklists 2019: French Polynesia Species List

National Checklists 2019: Territory of the French Southern and Antarctic Lands Species List

National Checklists 2019: French Guiana Species List

Ressources for End-to-End French Text-to-Speech Blizzard challenge

Wikipedia: wikipedia-fr (French)

French indirectly named entities

DATASET From the Reef to the Ocean: Revealing the Acoustic Range of the Biophony of a Coral Reef (Moorea Island, French Polynesia)

MIGR-TWIT CORPORA. Migration Tweets of French Left-wing Politics.

French vowels

A Dataset of French Trade Directories from the 19th Century for Nested NER task

Drone raw images of cattle in french grazing areas

BLM-AgrF: A New French Benchmark to Investigate Generalization of Agreement in Neural Networks

Spatial and Social Behavior of Acanthurus triostegus on Moorea (French Polynesia) and Palmyra Atoll (USA), 2017-2018

MCR LTER: Coral Reef: Water Column nutrient data in lagoons of Moorea, French Polynesia

Understand access before you commit

Allen Brain Atlas

Annotated Behaviour and Observability Dataset (ABODe)

DANDI Archive for NWB datasets

International Brain Laboratory public data

OpenNeuro

Your academic CV and NIH biosketch