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Near-infrared spectra of herbarium accessions of Pistacia leaves
No full textThis dataset contains 7390 near infrared spectra of 160 herbarium specimens of five species of the genus Pistacia.
The herbarium specimens cover a collection period from the 18th century to the present day and come from various countries around the Mediterranean.
The spectra were collected on 109 herbarium specimens and 51 new specimens over species of Pistacia. Herbarium specimens were sampled in the herbaria of the University of Montpellier and the MNHN in Paris. The 51 new specimens are herbarium samples kept at CIRAD Montpellier.
The 5 species included in the study are :
PIALE: Pistacia lentiscus L.
PIATE: Pistacia terebinthus L.
PIARA: Pistacia x saportae Burnat
PIAAT: Pistacia atlantica Desf.
PIAVE: Pistacia vera L.
The spectral data are available in two versions: The first is the raw file with all the raw spectra (30 to 50 spectra per sample) without any treatments. The second is pre-processed spectral data, ready to use without any specific spectral knowledge.
The last file contains all the data associated with the herbarium specimens. We choose to add a set of pictures to illustrate our protocol.
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Dataset for proof-of-concept correlation between protein and textural properties of pounded yam in Côte d'Ivoire
No full textThis file contains Data related a Proof of Concept on the correlation between Protein content of yam and texture attributes of pounded yam. The data was collected in 2024 as part of the RTB Breeding project. These data will help to establish a relationship between protein content and yam textural attributes (extensibility and hardness) evalueted through instrumental analysis. The raw material used in this study is from the experimental plot of research station of CNRA (Centre National de Recherche Agronomique) located in Bouake. Textural analysis was conducted using a TA.XT using a kieffer dough method following a validated Standard Operating Procedures
Germination of Hemileia vastatrix spores in varying combinations of temperature and atmospheric CO2 in controlled conditions (Kenya, 2022)
No full textDataset for germination of Hemileia vastatrix spores, causing coffee leaf rust disease, under varying combinations of temperature and atmospheric CO2 in controlled conditions (in phytotrons), testing thirty combinations of temperatures, ranging from 16°C to 32°C, and atmospheric CO2 concentrations, ranging from 181 to 707 ppm. H. vastatrix spores were collected in a coffee plot (-1.169790, 36.805224) in Kiambu County, Kenya, from October 2022 to December 202
Data on Elaeidobius Population Dynamics and Pollen-Carrying Ability over 15 Months in an Oil Palm Plantation in Cameroon
No full textData collected over a 15-month period from 2010 to 2011 in a 120-hectare oil palm plantation in the southwestern region of Cameroon. The data describe the emergence dynamics of major oil palm pollinators from male inflorescences, visit dynamics for male and female inflorescences, and pollen-carrying ability of insects, as well as the variability of these behaviors according to the stage of anthesis. A complete description of each file is provided below
One Health antibiotic stewardship interventions
No full textThis dataset has been produced in the framework of the WP2 of the DESIGN project. It provides descriptive data about a purposive sampling of existing One Health antibiotic stewardship interventions (general characteristics, development and implementation mode, scope, One Health and equity dimensions, and impacts)
Unlabeled corpora for post-training Language Models on thematic and misinformation classification in a One Health context
No full textThis repository contains four corpora of unlabeled texts used to post-training language models based on selective masking to adapt them to targeted domains within the One Health context. The corpora comprise collections of unannotated texts generally sourced from PubMed and PADI-web, representing two main areas of application: (i) thematic content related to the One Health domain, covering the biomedical, phytosanitary, and syndromic surveillance fields, and (ii) epidemic misinformation. The repository contains 4 files:
PubMed Biomedical_snippets: 10,000 English abstracts of biomedical articles, extracted from the <a href="https://www.kaggle.com/datasets/thedevastator/
pubmed-article-summarization-dataset.">PubMed Article Summarization Dataset
PubMed Plant Health_snippet: 9,388 English abstracts of PubMed articles on plant health, collected by us through web scraping, selecting abstracts with titles and content containing keywords related to plant health (e.g., plant diseases and plant names).
PADI-web Unspecified Diseases_snippet: 8,000 English news articles dedicated to syndromic surveillance (i.e., articles describing unknown diseases and symptoms), collected from the PADI-web tool (https://padi-web.cirad.fr/en).
PADI-web Public Health_snippet: 10,000 English news articles on human epidemics (e.g., Influenza and Ebola), used for the epidemic misinformation domain.
The complete corpora are available under restricted access, while the open-access versions contain only snippets from each corpus
Improved NIRS Calibration for Hardness, Resilience, Cohesiveness, Springiness and Adhesiveness of Gari/Eba (cooked dough), at IITA, Ibadan, Nigeria.
No full textThe near-infrared reflectance spectra of Gari_Eba produced from 23 cassava genotypes were collected using the XDS-Rapid Content Analyzer. Gari_Eba dough was homogenized and filled into the equipment's ring cell sample holder. NIR spectra were collected in duplicate for each sample in the 400 to 2485 nm wavelength range. Before NIRS analysis, the instrumental texture profile of each sample was determined using the TA.XT texturometer and the same sample lots were used for the NIRS measurements. Reference data for hardness, cohesiveness, resilience, adhesiveness and springiness were obtained and combined with the NIRS spectra to develop prediction models for the textural attributes. Improved prediction models for the textural qualities of Eba were obtained by pooling the dataset from 2022 and 2023. The coefficient of determination and prediction ( R2cal and R2pred) for hardness, resilience, adhesiveness and cohesiveness were ( 0.83 and 0.65), (0.73 and 0.69), (0.71 and 0.23), (0.79 and 0.43) and (0.77 and 0.50) respectively
Abundance, Biomass, and Functional Traits of Macroinvertebrates and Environmental Parameters in Banana Agroecosystems: A Dataset Comparing Agroforestry and Conventional Systems
No full text[FR] Ce jeu de données contient des données d'abondance, de biomasse et de traits fonctionnels de macroinvertébrés (arthropodes et vers de terre), ainsi que des données sur les propriétés physico-chimiques du sol, les pourcentages d’ouverture de canopée, et les traits de litière. Un total de 144 échantillons a été collecté dans les sols de 6 parcelles de banane (3 conventionnelles et 3 agroforestières) en Martinique. Trois campagnes d'échantillonnage ont été réalisées sur trois années consécutives (2019, 2020 et 2021). Lors de ces trois campagnes, huit points d'échantillonnage indépendant par parcelle ont été prélevés. Deux méthodes distinctes ont été utilisées à chaque point d'échantillonnage : (i) la méthode des quadrats de litière suivie d'extractions Tullgren des macroarthropodes et (ii) la méthode des quadrats de sol suivie d'un tri manuel du sol pour collecter les vers de terre sur 30 cm de profondeur. Les individus ont été identifiés jusqu'au niveau taxonomique le plus précis possible. Les propriétés physicochimiques du sol et la couverture végétale ont été relevées uniquement lors de la dernière campagne d'échantillonnage.
[EN] This dataset includes values for abundance, biomass, and functional traits of macroinvertebrates (arthropods and earthworms), as well as data on soil physicochemical properties, canopy cover and litter traits. A total of 144 samples were collected from the soil of 6 banana fields (3 conventional and 3 agroforestry) in Martinique. Three sampling campaigns were conducted over three consecutive years (2019, 2020, and 2021). Eight independent sampling points were defined per plot for each of which two distinct methods were used: (i) the litter quadrat followed by Tullgren extraction of macroarthropods and (ii) the soil quadrat followed by manual sorting of soil to collect earthworms until 30 cm depth. The individuals were identified to the most precise taxonomic level possible. Soil physico-chemical properties and vegetation cover were measured only during the last campaign
15 year grassland fertilization experiment on pedo-climatic gradient in tropical conditions
No full text15 year of intensive organic and inorganic micro-plot fertilization on different tropical grassland along pedo-climatic gradient on Reunion Island
Arabic and English Spatial Entity Dataset for Animal Disease Surveillance Extracted with PADI-web
No full textAs part of the “Arabic Corpus and Entities Dealing with Animal Disease Surveillance Extracted with PADI-web” dataset (https://doi.org/10.18167/DVN1/2B4WLR), we built a new dataset containing 284 spatial entities in Arabic, their translation into English (manually validated) and their automatic translation by three automatic tools (DeepL, Microsoft Azure, and Reverso). The dataset was updated with two new columns on September 3, 2025: GeoNames ID and GeoNames Feature Class, enabling the matching of spatial entities to the GeoNames gazetteer.
The dataset is organised as a table with twelve columns :
ID: The unique identifier of each article (from PADI-web database)
Arabic Location: The spatial entities in Arabic, manually extracted from 53 articles collected via PADI-web
English Location: The manual translation of spatial entities into English, based on existing field sources such as Google Maps and the GeoNames database
GeoNames ID: The unique ID from the GeoNames database (2022 version of GeoNames: https://www.geonames.org/) corresponding to each spatial entity (empty if no match in GeoNames)
GeoNames Feature Class : The feature class corresponding to the GeoNames ID (empty if no match in GeoNames)
Type: A manually assigned type of spatial entity (country, city, region, village, etc.).
Category: The classification of spatial entities into two categories: absolute spatial entities (ASE) and relative spatial entities (RSE).
Arabic Phrases: The sentence, in Arabic, from which the spatial entity was extracted.
Translation DeepL: The translation of the location by DeepL.
Translation Microsoft Azure: The translation of the location by Microsoft Azure.
Translation Reverso: The translation of the location by Reverso.
English Sentences Translated by DeepL: The translation of the sentence by DeepL.
English Sentences Translated by Microsoft Azure: The translation of the sentence by Microsoft Azure.
English Sentences Translated by Reverso: The translation of the sentence by Reverso.
Absolute spatial entities are direct references to precise, locatable geographic spaces, i.e. entities that can be located on a map or in a geographic database (e.g. cities such as Safi, countries such as Morocco, Egypt, etc.).
Relative spatial entities are entities defined in relation to at least one other spatial entity, using spatial indicators of a topological nature (for example, “الطود شرق” (El-Tod East), “ناحية تلات” (Talat district), etc.)