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CassavaBase EMBRAPA

Bem-vindo! Este repositório reúne código, dados e relatórios associados ao processo de automação da inserção de ensaios de mandioca (Manihot esculenta) na plataforma CassavaBase, em colaboração com a EMBRAPA Mandioca e Fruticultura.

O objetivo é padronizar, organizar e gerar arquivos de saída compatíveis com o sistema, garantindo eficiência, consistência e reprodutibilidade no fluxo de dados experimentais.

Pipeline de Análise
Pipeline de Análise

Publicação Associada

Este trabalho faz parte do Projeto de Pesquisa:

  • Processo:
  • Chamada:
  • Período:

Sobre a EMBRAPA Mandioca e Fruticultura

Este projeto foi desenvolvido no âmbito das pesquisas da EMBRAPA Mandioca e Fruticultura, especializada em melhoramento genético, manejo e inovação tecnológica para a cultura da mandioca, com foco em agricultura sustentável e digital.

Recursos Disponíveis

  1. Código de Análise: Scripts em R para pré-processamento, padronização e geração de arquivos de ensaios.
  2. Dados:
    • Ensaios: nomes, locais, estágios de melhoramento e campos experimentais.
    • Acessos: genótipos, sinônimos e controles.
    • Fenotípicos: características agronômicas por acesso e por ano.
  3. Notebooks/Relatórios: Exemplos de execução do pipeline e geração de arquivos de saída.
  4. Saídas: Arquivos .xls prontos para importação na CassavaBase.
Arquitetura do Fluxo
Arquitetura do Fluxo

Como Utilizar

  1. Clone o repositório:

    git clone https://github.com/wevertongomescosta/cassavabaseembrapa.git
  2. Instale as dependências no R:

    install.packages(c("tidyverse", "readxl", "writexl", "stringr"))
  3. Execute o pipeline principal (analysis/index.Rmd) no RStudio.

  4. Os arquivos de saída serão gerados automaticamente na pasta output/.

Contribuição

Contribuições são bem-vindas mediante:
- Abertura de issues para discussão de melhorias
- Submissão de pull requests para correções críticas
- Sugestões de extensões metodológicas

Licença

Este trabalho está licenciado sob CC BY-NC-SA 4.0.
Para uso comercial ou modificações significativas, contate os autores.

Contato

Coordenador
Eder Jorge de Oliveira
Pesquisador – EMBRAPA Mandioca e Fruticultura

Pós-Doutorando
Weverton Gomes da Costa
Pós-Doutorando – EMBRAPA Mandioca e Fruticultura

EMBRAPA Mandioca e Fruticultura:
https://www.embrapa.br/mandioca-e-fruticultura


CassavaBase EMBRAPA

Welcome! This repository contains code, data, and reports related to the automation of cassava (Manihot esculenta) trial insertion into the CassavaBase platform, in collaboration with EMBRAPA Cassava and Fruits.

The goal is to standardize, organize, and generate output files compatible with the system, ensuring efficiency, consistency, and reproducibility in the experimental data workflow.

Analysis Pipeline
Analysis Pipeline

Associated Publication

This work is part of the Research Project:

  • Process:
  • Call:
  • Period:

About EMBRAPA Cassava and Fruits

This project was developed within the research activities of EMBRAPA Cassava and Fruits, a research unit specialized in cassava breeding, management, and technological innovation, with a focus on sustainable and digital agriculture.

Available Resources

  1. Analysis Code: R scripts for preprocessing, standardization, and trial file generation.
  2. Data:
    • Trials: names, locations, breeding stages, and experimental fields.
    • Accessions: genotypes, synonyms, and controls.
    • Phenotypic: agronomic traits by accession and by year.
  3. Notebooks/Reports: Examples of pipeline execution and output generation.
  4. Outputs: .xls files ready for import into CassavaBase.
Workflow Architecture
Workflow Architecture

How to Use

  1. Clone the repository:

    git clone https://github.com/wevertongomescosta/cassavabaseembrapa.git
  2. Install dependencies in R:

    install.packages(c("tidyverse", "readxl", "writexl", "stringr"))
  3. Run the main pipeline (analysis/index.Rmd) in RStudio.

  4. Output files will be automatically generated in the output/ folder.

Contribution

Contributions are welcome via:
- Opening issues for improvement discussions
- Submitting pull requests for critical fixes
- Suggesting methodological extensions

License

This work is licensed under CC BY-NC-SA 4.0.
For commercial use or significant modifications, please contact the authors.

Contact

Project Coordinator
Eder Jorge de Oliveira
Researcher – EMBRAPA Cassava and Fruits

Postdoctoral Researcher
Weverton Gomes da Costa
Postdoctoral Researcher – EMBRAPA Cassava and Fruits

EMBRAPA Cassava and Fruits:
https://www.embrapa.br/mandioca-e-fruticultura


sessionInfo()
R version 4.5.1 (2025-06-13 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default
  LAPACK version 3.12.1

locale:
[1] LC_COLLATE=Portuguese_Brazil.utf8  LC_CTYPE=Portuguese_Brazil.utf8   
[3] LC_MONETARY=Portuguese_Brazil.utf8 LC_NUMERIC=C                      
[5] LC_TIME=Portuguese_Brazil.utf8    

time zone: America/Sao_Paulo
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] vctrs_0.6.5       cli_3.6.5         knitr_1.50        rlang_1.1.6      
 [5] xfun_0.53         stringi_1.8.7     promises_1.3.3    jsonlite_2.0.0   
 [9] workflowr_1.7.2   glue_1.8.0        rprojroot_2.1.1   git2r_0.36.2     
[13] htmltools_0.5.8.1 httpuv_1.6.16     sass_0.4.10       rmarkdown_2.29   
[17] evaluate_1.0.5    jquerylib_0.1.4   tibble_3.3.0      fastmap_1.2.0    
[21] yaml_2.3.10       lifecycle_1.0.4   whisker_0.4.1     stringr_1.5.2    
[25] compiler_4.5.1    fs_1.6.6          Rcpp_1.1.0        pkgconfig_2.0.3  
[29] rstudioapi_0.17.1 later_1.4.4       digest_0.6.37     R6_2.6.1         
[33] pillar_1.11.1     magrittr_2.0.4    bslib_0.9.0       tools_4.5.1      
[37] cachem_1.1.0     

  1. Weverton Gomes da Costa, Pós-Doutorando – EMBRAPA Mandioca e Fruticultura, ↩︎