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1 to 10 of 28 Results

Dataset on pesticidal plants for the control of cabbage pests 8 août 2025 Martin, Pierre; Mondédji, Abla Déla; Silvie, Pierre, 2025, "Dataset on pesticidal plants for the control of cabbage pests", https://doi.org/10.18167/DVN1/AWCNQD, CIRAD Dataverse, V1, UNF:6:Qz83X9pXZIqqGdR2QFCfgA== [fileUNF] The aim of this work was to build a pesticide plant dataset of sufficiently reasonable size to produce numerous rules with variable support. To this end, we have extracted tables 3, 4, and 5 from Mondédji et al. (2021) and completed data in order to have a dataset that respects t...
CabbagePests.tab 8 août 2025 - Dataset on pesticidal plants for the control of cabbage pests Tabular Data - 77,9 KB - 14 Variables, 454 Observations - UNF:6:Qz83X9pXZIqqGdR2QFCfgA== Dataset
ReadMe.pdf 8 août 2025 - Dataset on pesticidal plants for the control of cabbage pests Adobe PDF - 154,5 KB - MD5: ecf9081b78184e7455ec638a2fb308f6 Description of the dataset
Dataset used to generate Variability-Driven User-Story using LLM and Triadic Concept Analysis 7 févr. 2025 Huchard, Marianne; Gutierrez, Alain; Zhang Yulin (Huaxi); Bazin, Alexandre; Martin, Pierre, 2025, "Dataset used to generate Variability-Driven User-Story using LLM and Triadic Concept Analysis", https://doi.org/10.18167/DVN1/GNJMAV, CIRAD Dataverse, V1 The diffusion of knowledge, extracted from the Knomana knowledge base (Silvie et al., 2021) or inferred using Knomana, requires to develop a specific application for each type of final user (e.g. researcher vs. farmer). This involves to develop a family of similar applications, w...
InputDatasetCaseStudy.tar.gz 7 févr. 2025 - Dataset used to generate Variability-Driven User-Story using LLM and Triadic Concept Analysis Gzip Archive - 32,8 KB - MD5: 010b0a8b8d746a934b59a9c4f34a7ecb
InputDatasetSmallExample.tar.gz 7 févr. 2025 - Dataset used to generate Variability-Driven User-Story using LLM and Triadic Concept Analysis Gzip Archive - 1,0 KB - MD5: d68afcc7af4c2292883323b41ea66fcd
Output.tar.gz 7 févr. 2025 - Dataset used to generate Variability-Driven User-Story using LLM and Triadic Concept Analysis Gzip Archive - 49,5 KB - MD5: ab7eeae59357b5d7101cd6c2e011c6ce
README.md 7 févr. 2025 - Dataset used to generate Variability-Driven User-Story using LLM and Triadic Concept Analysis Markdown Text - 226 B - MD5: 5be056f89656dbe809f96c85387f150d
RepositoryDescription.md 7 févr. 2025 - Dataset used to generate Variability-Driven User-Story using LLM and Triadic Concept Analysis Markdown Text - 2,0 KB - MD5: 173f6fa3aa93fcaa5ddc405d1be177ff
Step1Analysis.tar.gz 7 févr. 2025 - Dataset used to generate Variability-Driven User-Story using LLM and Triadic Concept Analysis Gzip Archive - 23,1 KB - MD5: 9fa66dddc260ed514e07dfd515ad1ade

Dataset on pesticidal plants for the control of cabbage pests

8 août 2025

Martin, Pierre; Mondédji, Abla Déla; Silvie, Pierre, 2025, "Dataset on pesticidal plants for the control of cabbage pests", https://doi.org/10.18167/DVN1/AWCNQD, CIRAD Dataverse, V1, UNF:6:Qz83X9pXZIqqGdR2QFCfgA== [fileUNF]

The aim of this work was to build a pesticide plant dataset of sufficiently reasonable size to produce numerous rules with variable support. To this end, we have extracted tables 3, 4, and 5 from Mondédji et al. (2021) and completed data in order to have a dataset that respects t...

CabbagePests.tab

8 août 2025 - Dataset on pesticidal plants for the control of cabbage pests

Tabular Data - 77,9 KB - 14 Variables, 454 Observations -

Dataset

ReadMe.pdf

8 août 2025 - Dataset on pesticidal plants for the control of cabbage pests

Adobe PDF - 154,5 KB -

Description of the dataset

Dataset used to generate Variability-Driven User-Story using LLM and Triadic Concept Analysis

7 févr. 2025

Huchard, Marianne; Gutierrez, Alain; Zhang Yulin (Huaxi); Bazin, Alexandre; Martin, Pierre, 2025, "Dataset used to generate Variability-Driven User-Story using LLM and Triadic Concept Analysis", https://doi.org/10.18167/DVN1/GNJMAV, CIRAD Dataverse, V1

The diffusion of knowledge, extracted from the Knomana knowledge base (Silvie et al., 2021) or inferred using Knomana, requires to develop a specific application for each type of final user (e.g. researcher vs. farmer). This involves to develop a family of similar applications, w...

InputDatasetCaseStudy.tar.gz

7 févr. 2025 - Dataset used to generate Variability-Driven User-Story using LLM and Triadic Concept Analysis

Gzip Archive - 32,8 KB -

InputDatasetSmallExample.tar.gz

7 févr. 2025 - Dataset used to generate Variability-Driven User-Story using LLM and Triadic Concept Analysis

Gzip Archive - 1,0 KB -

Output.tar.gz

7 févr. 2025 - Dataset used to generate Variability-Driven User-Story using LLM and Triadic Concept Analysis

Gzip Archive - 49,5 KB -

README.md

7 févr. 2025 - Dataset used to generate Variability-Driven User-Story using LLM and Triadic Concept Analysis

Markdown Text - 226 B -

RepositoryDescription.md

7 févr. 2025 - Dataset used to generate Variability-Driven User-Story using LLM and Triadic Concept Analysis

Markdown Text - 2,0 KB -

Step1Analysis.tar.gz

7 févr. 2025 - Dataset used to generate Variability-Driven User-Story using LLM and Triadic Concept Analysis

Gzip Archive - 23,1 KB -

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