electricsheepafrica/africa-tunisia-liste-des-theses-doctorantes-de-l-ecole-veterinaire-recues-a5c538b1

liste des thèses doctorantes de l'Ecole vétérinaire reçues de 2017 à 2022 | Africa (Tunisia official open data)

269 rows - 1 Africa country - 2017-2022 - Repackaged by Electric Sheep Africa

TL;DR

This dataset packages one official CSV resource from Tunisia as ML-ready Parquet. The source file is the provenance boundary; all usable indicators or tabular columns from the resource stay together in this repo.

About the source

• Source: liste des thèses doctorantes de l'Ecole vétérinaire reçues de 2017 à 2022
• Publisher: Observatoire National de l'Agriculture
• Resource: liste des thèses doctorantes de l'Ecole vétérinaire reçues de 2017 à 2022
• Format: CSV
• License: Licence Nationale de Données Publiques Ouvertes
• Packaging mode: indicator_long

Geographic coverage

1 Africa country:

Country	Rows	First year	Last year	Name
TUN	269	2017	2022	Tunisia

Indicators or Resource Contents

• liste-des-theses-doctorantes-de-l-ecole-veterinaire-recues-de-2017-a-202-ba231283 - liste des thèses doctorantes de l'Ecole vétérinaire reçues de 2017 à 2022

Schema

Column	Type	Description	Example
indicator_id	string	Stable indicator identifier.	liste-des-theses-doctorantes-de-l-ecole-veterinaire-recues-de-2017-a-202
indicator_name	string	Human-readable indicator name.	liste des thèses doctorantes de l'Ecole vétérinaire reçues de 2017 à 202
country_iso3	string	ISO3 country code.	TUN
country_name	string	Country name.	Tunisia
year	Int64	Observation year.	2018
value	float64	Numeric observation value.	1.0
unit	string	Measurement unit, when available.	source_units_unspecified
dimension_nom	string	Source dimension.	TANAKTI Rahma
dimension_titre	string	Source dimension.	Dynamique saisonnière et distribution spatiale des espèces bactériennes
dimension_support	string	Source dimension.	pdf
source_period_start_year	Int64	First year inferred from source resource metadata.	2017
source_period_end_year	Int64	Last year inferred from source resource metadata.	2022
source_period_label	string	Human-readable period inferred from source resource metadata.	2017-2022
source_provider	string	Publishing organization.	Observatoire National de l'Agriculture
source_dataset	string	Source package title.	liste des thèses doctorantes de l'Ecole vétérinaire reçues de 2017 à 202
source_resource	string	Source resource title.	liste des thèses doctorantes de l'Ecole vétérinaire reçues de 2017 à 202
source_package_id	string	CKAN package UUID.	5f669d82-d99c-415d-adb8-57d1f9280104
source_resource_id	string	CKAN resource UUID.	c80efbb4-35c4-48ff-a040-15a31b98837d
source_url	string	Original source resource URL.	https://catalog.agridata.tn/dataset/94281afb-49fa-4ad4-ae77-0031f59e051e
license_id	string	Source license identifier.	licence-nationale-de-données-publiques-ouvertes
retrieved_at	string	UTC retrieval timestamp.	2026-07-18T23:08:36Z

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-tunisia-liste-des-theses-doctorantes-de-l-ecole-veterinaire-recues-a5c538b1")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

sample_country = df[df["country_iso3"] == "TUN"]

Work with indicators

if "indicator_id" in df.columns:
    print(df["indicator_id"].value_counts().head())
    sample = df.sort_values([c for c in ["indicator_id", "year"] if c in df.columns])

Citation

@misc{electric_sheep_africa_africa_tunisia_liste_des_theses_doctorantes_de_l_ecole_veterinaire_recues_a5c538_2022,
  title        = {liste des thèses doctorantes de l'Ecole vétérinaire reçues de 2017 à 2022 | Africa (Tunisia official open data)},
  author       = {Observatoire National de l'Agriculture},
  year         = {2022},
  url          = {https://catalog.data.gov.tn/dataset/2017-2020},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-tunisia-liste-des-theses-doctorantes-de-l-ecole-veterinaire-recues-a5c538b1}}
}

License

Released under Licence Nationale de Données Publiques Ouvertes.

Original data (c) Observatoire National de l'Agriculture. When using this dataset, please cite both the original source above and the Electric Sheep Africa repackaging.

About Electric Sheep

Electric Sheep Africa is part of the Electric Sheep mission: a unified, ML-ready data layer for Africa on Hugging Face. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use load_dataset() to start working in seconds.

Browse the full collection: huggingface.co/electricsheepafrica

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Provenance: ingested 2026-07-19 via the Electric Sheep pipeline. Source URL: https://catalog.agridata.tn/dataset/94281afb-49fa-4ad4-ae77-0031f59e051e/resource/eafb2c8a-4407-4bb0-a5da-9cb3bad90dc1/download/liste-principale-des-theses-enmv.csv