12 datasets found
  1. M

    French Polynesia Literacy Rate

    • macrotrends.net
    csv
    Updated Jun 30, 2025
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    MACROTRENDS (2025). French Polynesia Literacy Rate [Dataset]. https://www.macrotrends.net/global-metrics/countries/pyf/french-polynesia/literacy-rate
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    csvAvailable download formats
    Dataset updated
    Jun 30, 2025
    Dataset authored and provided by
    MACROTRENDS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Polynesia, French Polynesia
    Description

    Historical chart and dataset showing French Polynesia literacy rate by year from N/A to N/A.

  2. M

    St. Martin (French part) Literacy Rate

    • macrotrends.net
    csv
    Updated Jun 30, 2025
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    MACROTRENDS (2025). St. Martin (French part) Literacy Rate [Dataset]. https://www.macrotrends.net/global-metrics/countries/maf/st-martin-french-part/literacy-rate
    Explore at:
    csvAvailable download formats
    Dataset updated
    Jun 30, 2025
    Dataset authored and provided by
    MACROTRENDS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Saint Martin, French
    Description

    Historical chart and dataset showing St. Martin (French part) literacy rate by year from N/A to N/A.

  3. w

    Dataset of books called Reading bande dessinee : critical approaches to...

    • workwithdata.com
    Updated Apr 17, 2025
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    Work With Data (2025). Dataset of books called Reading bande dessinee : critical approaches to French-language comic strip [Dataset]. https://www.workwithdata.com/datasets/books?f=1&fcol0=book&fop0=%3D&fval0=Reading+bande+dessinee+%3A+critical+approaches+to+French-language+comic+strip
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    Dataset updated
    Apr 17, 2025
    Dataset authored and provided by
    Work With Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    French
    Description

    This dataset is about books. It has 1 row and is filtered where the book is Reading bande dessinee : critical approaches to French-language comic strip. It features 7 columns including author, publication date, language, and book publisher.

  4. Data from: iRead4Skills Dataset 2: annotated corpora by level of complexity...

    • zenodo.org
    • chef.afue.org
    • +2more
    Updated Jul 25, 2024
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    Alice Pintard; Alice Pintard; Thomas François; Thomas François; Nagant de Deuxchaisnes Justine; Nagant de Deuxchaisnes Justine; Sílvia Barbosa; Sílvia Barbosa; Maria Leonor Reis; Maria Leonor Reis; Michell Moutinho; Michell Moutinho; Ricardo Monteiro; Ricardo Monteiro; Raquel Amaro; Raquel Amaro; Susana Correia; Susana Correia; Sandra Rodríguez Rey; Sandra Rodríguez Rey; Keran Mu; Keran Mu; Marcos Garcia González; Marcos Garcia González; André Bernárdez Braña; Xavier Blanco Escoda; Xavier Blanco Escoda; André Bernárdez Braña (2024). iRead4Skills Dataset 2: annotated corpora by level of complexity for FR, PT and SP [Dataset]. http://doi.org/10.5281/zenodo.12821882
    Explore at:
    Dataset updated
    Jul 25, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Alice Pintard; Alice Pintard; Thomas François; Thomas François; Nagant de Deuxchaisnes Justine; Nagant de Deuxchaisnes Justine; Sílvia Barbosa; Sílvia Barbosa; Maria Leonor Reis; Maria Leonor Reis; Michell Moutinho; Michell Moutinho; Ricardo Monteiro; Ricardo Monteiro; Raquel Amaro; Raquel Amaro; Susana Correia; Susana Correia; Sandra Rodríguez Rey; Sandra Rodríguez Rey; Keran Mu; Keran Mu; Marcos Garcia González; Marcos Garcia González; André Bernárdez Braña; Xavier Blanco Escoda; Xavier Blanco Escoda; André Bernárdez Braña
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The iRead4Skills Dataset 2: annotated corpora by level of complexity for FR, PT and SP is a collection of texts categorized by complexity level and annotated for complexity features, presented in xlsx format. These corpora were compiled, classified and annotated under the scope of the project iRead4Skills – Intelligent Reading Improvement System for Fundamental and Transversal Skills Development, funded by the European Commission (grant number: 1010094837). The project aims to enhance reading skills within the adult population by creating an intelligent system that assesses text complexity and recommends suitable reading materials to adults with low literacy skills, contributing to reducing skills gaps and facilitating access to information and culture (https://iread4skills.com/).

    This dataset is the result of specifically devised classification and annotation tasks, in which selected texts were organized and distributed to trainers in Adult Learning (AL) and Vocational Education Training (VET) Centres, as well as to adult students in AL and VET centres. This task was conducted via the Qualtrics platform.

    The Dataset 2: annotated corpora by level of complexity for FR, PT and SP is derived from the iRead4Skills Dataset 1: corpora by level of complexity for FR, PT and SP ( https://doi.org/10.5281/zenodo.10055909), which comprises written texts of various genres and complexity levels. From this collection, a subset of texts was selected for classification and annotation. This classification and annotation task aimed to provide additional data and test sets for the complexity analysis systems for the three languages of the project: French, Portuguese, and Spanish. The texts in each of the language corpora were selected taking into account the diversity of topics/domains, genres, and the reading preferences of the target audience of the iRead4Skills project. This percentage amounted to the total of 462 texts per language, which were divided by level of complexity, resulting in the following distribution:

    · 140 Very Easy texts

    · 140 Easy texts

    · 140 Plain texts

    · 42 More Complex texts.

    Trainers were asked to classify the texts according to the complexity levels of the project, here informally defined as:

    • Very Easy (everyone can understand the text or most of the text).
    • Easy (a person with less than the 9th year of schooling can understand the text or most of the text)
    • Plain (a person with the 9th year of schooling can understand the text the first time he/she reads it)
    • More complex (a person with the 9th year of schooling cannot understand the text the first time he/she reads it).

    They were also asked to annotate the parts of the texts considered complex according to various type of features, at word-level and at sentence-level (e.g., word order, sentence composition, etc.), according to following categories:

    Lexical/word-related features

    - unknown word

    - word too technical/specialized or archaic

    - complex derived word

    - points to a previous reference that is not obvious

    - word (other)

    Syntactic/sentence-level features

    - unusual word order

    - too much embedded secondary information

    - too many connectors in the same sentence

    - sentence (other)

    - other (please specify)

    The sets were divided in three parts in Qualtrics and, in each part, the texts are shown randomly to the annotator.

    Students were asked to confirm that they could read without difficulty texts adequate to their literacy level. Each set contained texts from a given level, plus one text of the level immediately above.

    They were also asked to annotate words or sequences of words in the text that they did not understand, according to the following categories:

    - difficult word

    - difficult part of the text

    The complete results and datasets are in TSV/Excel format, in pairs of two files, with one file concerning the results from the classification (trainers)/validation (students) task and one file concerning the results from the annotation task. The complete datasets will be available under creative CC BY-NC-ND 4.0

  5. u

    Data from: iRead4Skills Dataset 1: corpora by complexity level for FR, PT...

    • investigacion.usc.gal
    • explore.openaire.eu
    • +2more
    Updated 2024
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    Pintard, Alice; François, Thomas; Nagant de Deuxchaisnes, Justine; Barbosa, Sílvia; Reis, Maria Leonor; Moutinho, Michell; Monteiro, Ricardo; Amaro, Raquel; Correia, Susana; Rodríguez Rey, Sandra; Garcia González, Marcos; Mu, Keran; Blanco Escoda, Xavier; Pintard, Alice; François, Thomas; Nagant de Deuxchaisnes, Justine; Barbosa, Sílvia; Reis, Maria Leonor; Moutinho, Michell; Monteiro, Ricardo; Amaro, Raquel; Correia, Susana; Rodríguez Rey, Sandra; Garcia González, Marcos; Mu, Keran; Blanco Escoda, Xavier (2024). iRead4Skills Dataset 1: corpora by complexity level for FR, PT and SP [Dataset]. https://investigacion.usc.gal/documentos/668fc40fb9e7c03b01bd388e
    Explore at:
    Dataset updated
    2024
    Authors
    Pintard, Alice; François, Thomas; Nagant de Deuxchaisnes, Justine; Barbosa, Sílvia; Reis, Maria Leonor; Moutinho, Michell; Monteiro, Ricardo; Amaro, Raquel; Correia, Susana; Rodríguez Rey, Sandra; Garcia González, Marcos; Mu, Keran; Blanco Escoda, Xavier; Pintard, Alice; François, Thomas; Nagant de Deuxchaisnes, Justine; Barbosa, Sílvia; Reis, Maria Leonor; Moutinho, Michell; Monteiro, Ricardo; Amaro, Raquel; Correia, Susana; Rodríguez Rey, Sandra; Garcia González, Marcos; Mu, Keran; Blanco Escoda, Xavier
    Description

    The iRead4Skills Dataset 1: corpora by level of complexity for FR, PT and SP is a collection of written texts of several genres and levels of complexity, in txt format, compiled under the scope of the project iReadSkills – Intelligent Reading Improvement System for Fundamental and Transversal Skills Development. The project, funded by the European Commission (grant number: 1010094837) aims to improve reading skills in the adult population by creating an intelligent system that assesses text complexity and suggests appropriate reading materials to adults with low literacy skills, contributing to reducing skills gaps and to provide access to information and culture (https://iread4skills.com/).

    The compilation of this first dataset was based on the complexity levels established as relevant for the project (Very Easy (approx. A1), Easy (approx. A2) and Plain (approx. B1) and on the expected needs of learners and trainers. For some genres, there are also texts of a more complex level. The data will provide the basis for the training and test sets for the complexity analysis systems for the three languages of the project: French, Portuguese, and Spanish. The dataset will be further enhanced, validated, and annotated by end-users, originating forthcoming versions and a second, derived, dataset.

    The resource is composed of three sub corpora: French, Portuguese and Spanish.

    Each of the sub corpora considers different complexity levels and covers texts from the following communication domains:

    01_personal communication

    02_institutional/professional communication

    03_social media

    04_commercial communication/dissemination

    05_non-fiction book

    06_fiction book

    07_didactic book

    08_academic/school

    09_political communication/dissemination

    10_legal documentation

    11_religious texts/dissemination

    French corpus:

    Number of texts: 2 199

    Number of tokens: 530 298

    Spanish corpus:

    Number of texts: 2 533

    Number of tokens: 960 644

    Portuguese corpus:

    Number of texts: 2 933

    Number of tokens: 946 131

    More information about the corpus constitution and text samples is available at https://iread4skills.com/tools-resources/ D3.3 Data set 1: corpora by level of complexity FR, PT and SP.

  6. Let's Learn to Read and Write Haiti

    • catalog.data.gov
    Updated Jun 25, 2024
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    data.usaid.gov (2024). Let's Learn to Read and Write Haiti [Dataset]. https://catalog.data.gov/dataset/lets-learn-to-read-and-write-haiti
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    Dataset updated
    Jun 25, 2024
    Dataset provided by
    United States Agency for International Developmenthttps://usaid.gov/
    Area covered
    Haiti
    Description

    The USAID-funded project, Let’s Learn to Read and Write, rendered in Haitian Creole as “An n aprann li ak ekri (Ann ALE), is a four-year early grade reading and writing (EGR/W) project that is based on evidence generated under the two-year USAID applied research activity Tout Timoun Ap Li (ToTAL) or, in English, “All Children Reading.” Ann ALE supports an expanded effort to achieve a more coordinated, effective and sustainable EGR/W program for an estimated 68,600 children in grades 1-4 in 550 schools in three corridors (Cul-de-Sac, St. Marc and Nord) in Haiti. The project will be co-led by the Haiti’s Ministry of Education, the Ministère de l'Éducation Nationale et de la Formation Professionnelle (MENFP). To accomplish its goals in Haiti’s multi-faceted educational system, Ann ALE will vigorously promote a collaborative approach to build successful partnerships able to overcome longstanding barriers to positive change. For this reason, Ann ALE will help to strengthen the MENFP’s capacity to engage a wide range of public and non-public EGR/W stakeholders to improve reading results in Haitian Creole and French. All interventions will be co-designed and co-implemented with the MENFP to ensure their institutionalization and sustainability.

  7. P

    FQuAD Dataset

    • paperswithcode.com
    Updated Feb 18, 2021
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    Martin d'Hoffschmidt; Wacim Belblidia; Tom Brendlé; Quentin Heinrich; Maxime Vidal (2021). FQuAD Dataset [Dataset]. https://paperswithcode.com/dataset/fquad
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    Dataset updated
    Feb 18, 2021
    Authors
    Martin d'Hoffschmidt; Wacim Belblidia; Tom Brendlé; Quentin Heinrich; Maxime Vidal
    Description

    A French Native Reading Comprehension dataset of questions and answers on a set of Wikipedia articles that consists of 25,000+ samples for the 1.0 version and 60,000+ samples for the 1.1 version.

  8. F

    France FR: SPI: Pillar 5 Data Infrastructure Score: Scale 0-100

    • ceicdata.com
    + more versions
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    CEICdata.com, France FR: SPI: Pillar 5 Data Infrastructure Score: Scale 0-100 [Dataset]. https://www.ceicdata.com/en/france/policy-and-institutions/fr-spi-pillar-5-data-infrastructure-score-scale-0100
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    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2016 - Dec 1, 2019
    Area covered
    France
    Variables measured
    Money Market Rate
    Description

    France FR: SPI: Pillar 5 Data Infrastructure Score: Scale 0-100 data was reported at 100.000 NA in 2019. This stayed constant from the previous number of 100.000 NA for 2018. France FR: SPI: Pillar 5 Data Infrastructure Score: Scale 0-100 data is updated yearly, averaging 100.000 NA from Dec 2016 to 2019, with 4 observations. The data reached an all-time high of 100.000 NA in 2019 and a record low of 100.000 NA in 2019. France FR: SPI: Pillar 5 Data Infrastructure Score: Scale 0-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s France – Table FR.World Bank.WDI: Policy and Institutions. The data infrastructure pillar overall score measures the hard and soft infrastructure segments, itemizing essential cross cutting requirements for an effective statistical system. The segments are: (i) legislation and governance covering the existence of laws and a functioning institutional framework for the statistical system; (ii) standards and methods addressing compliance with recognized frameworks and concepts; (iii) skills including level of skills within the statistical system and among users (statistical literacy); (iv) partnerships reflecting the need for the statistical system to be inclusive and coherent; and (v) finance mobilized both domestically and from donors.; ; Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators); Weighted average;

  9. o

    Second language course enrolment

    • data.ontario.ca
    • datasets.ai
    • +2more
    txt, web, xlsx
    Updated Mar 26, 2025
    + more versions
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    Education (2025). Second language course enrolment [Dataset]. https://data.ontario.ca/dataset/second-language-course-enrolment
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    xlsx(13609), txt(4881), txt(3615), xlsx(14195), xlsx(13401), xlsx(13405), xlsx(13652), xlsx(13745), txt(4573), xlsx(13566), txt(3493), web(None), txt(4689), txt(3383), txt(3573), txt(4431), txt(4722), xlsx(15100), txt(4576), txt(3645), xlsx(14083), xlsx(14887), xlsx(13589), xlsx(14910)Available download formats
    Dataset updated
    Mar 26, 2025
    Dataset authored and provided by
    Education
    License

    https://www.ontario.ca/page/open-government-licence-ontariohttps://www.ontario.ca/page/open-government-licence-ontario

    Time period covered
    Mar 7, 2025
    Area covered
    Ontario
    Description

    Data about the number of students enrolled in second language courses. Information is specific to publicly funded secondary schools at the provincial level. Second language courses include:

    • APD (Anglais pour debutant)
    • ALF (Actualisation linguistique en français)
    • PDF (Perfectionnement linguistique du français)
    • ESL (English as a Second Language)
    • ELD (English Literacy Development)
    • FSL (French as a Second Language)
    • NL (Native Languages)

    Data includes:

    • course code
    • course description
    • grade
    • pathway or destination
    • enrolment

    Note: Students enrolled in a course more than once are counted each time they enroll.

    Source: Course enrolment data is reported by schools to the Ontario School Information System. The following secondary schools are included:

    • public
    • Catholic

    To protect privacy, numbers are suppressed in categories with less than 10 students.

    Note:

    • Starting 2018-2019, enrolment numbers have been rounded to the nearest five.
    • Where sum/totals are required, actual totals are calculated and then rounded to the nearest 5. As such, rounded numbers may not add up to the reported rounded totals.

    Related

  10. f

    TEACHING PRACTICES OF READING AND WRITING IN EARLY CHILDHOOD EDUCATION IN...

    • scielo.figshare.com
    • figshare.com
    png
    Updated May 31, 2023
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    ELIANA BORGES C. DE ALBUQUERQUE; ANDREA TEREZA BRITO FERREIRA (2023). TEACHING PRACTICES OF READING AND WRITING IN EARLY CHILDHOOD EDUCATION IN BRAZIL AND FRANCE AND CHILDREN’S KNOWLEDGE OF ALPHABETIC WRITING [Dataset]. http://doi.org/10.6084/m9.figshare.11804229.v1
    Explore at:
    pngAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    SciELO journals
    Authors
    ELIANA BORGES C. DE ALBUQUERQUE; ANDREA TEREZA BRITO FERREIRA
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    France
    Description

    ABSTRACT: This study investigated the reading and writing teaching practices in two pre-school classes (five-year-old children): one belonging to the public schools in Recife/PE (Brazil) and another in Paris (France). It was sought to analyze more specifically the work focused on the appropriation of alphabetic writing from the reading and writing activities developed in the classroom. The methodological procedures were interviews, observations of the daily school routine, and writing words activities with students. The analysis of the collected data showed that the teachers organized their pedagogical work in order to emphasize the use of texts and readings that were already part of the children’s universe, and also developed games and activities that led the students to think about the principles of alphabetic writing. At the end of the school year most students of both classes showed understanding of the relationship between writing and the sound of words.

  11. p

    Trends in Reading and Language Arts Proficiency (2011-2022): French...

    • publicschoolreview.com
    + more versions
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    Public School Review, Trends in Reading and Language Arts Proficiency (2011-2022): French Elementary School vs. Colorado vs. School District No. 3 In The County Of El Paso And State Of [Dataset]. https://www.publicschoolreview.com/french-elementary-school-profile
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    Dataset authored and provided by
    Public School Review
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset tracks annual reading and language arts proficiency from 2011 to 2022 for French Elementary School vs. Colorado and School District No. 3 In The County Of El Paso And State Of

  12. b

    Literacy courses in the City of Brussels

    • opendata.brussels.be
    • opendata.brussel.be
    csv, excel, geojson +1
    Updated Oct 29, 2024
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    (2024). Literacy courses in the City of Brussels [Dataset]. https://opendata.brussels.be/explore/dataset/cours-alphabetisation-vbx/
    Explore at:
    geojson, excel, json, csvAvailable download formats
    Dataset updated
    Oct 29, 2024
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Brussels
    Description

    Literacy courses in the City of BrusselsLearning to speak, write, read and calculate in French/Dutch to adults who have never been to school or who have not obtained any school certificate, in Belgium or abroad, and/or who have not mastered skills equivalent to basic education in any language.More info : https://social.brussels/category/615https://social.brussels/category/616

  13. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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MACROTRENDS (2025). French Polynesia Literacy Rate [Dataset]. https://www.macrotrends.net/global-metrics/countries/pyf/french-polynesia/literacy-rate

French Polynesia Literacy Rate

French Polynesia Literacy Rate

Explore at:
csvAvailable download formats
Dataset updated
Jun 30, 2025
Dataset authored and provided by
MACROTRENDS
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Area covered
Polynesia, French Polynesia
Description

Historical chart and dataset showing French Polynesia literacy rate by year from N/A to N/A.

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