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Recursos.Rmd
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---
title: "Recursos sobre ciencia reproducible"
output: github_document
---
# Recursos generales sobre reproducibilidad
- [Reproducible research in computational science](http://dx.doi.org/10.1126/science.1213847)
- [Nature special: Challenges in irreproducible research](http://www.nature.com/news/reproducibility-1.17552)
- [Ten simple rules for reproducible computational research](http://dx.doi.org/10.1371/journal.pcbi.1003285)
- [Best practices for scientific computing](http://dx.doi.org/10.1371/journal.pbio.1001745)
- [Good enough practices for scientific computing](http://swcarpentry.github.io/good-enough-practices-in-scientific-computing/)
- [Initial steps toward reproducible research](http://kbroman.org/steps2rr/)
- [Tools for reproducible research](http://kbroman.org/Tools4RR)
- [The tao of open science for ecology](http://dx.doi.org/10.1890/ES14-00402.1)
- [Towards standard practices for sharing computer code and programs in neuroscience](http://biorxiv.org/content/early/2016/04/04/045104)
- [Reproducible research is still a challenge](https://ropensci.org/blog/2014/06/09/reproducibility/)
- [rOpenSci reproducibility guide](http://ropensci.github.io/reproducibility-guide/)
- [Reproducible research course](https://www.coursera.org/learn/reproducible-research)
- [Report writing for data science in R](https://leanpub.com/reportwriting)
- [Implementing reproducible research](https://osf.io/s9tya/wiki/home/)
- [Reproducible Research with R and Rstudio](http://christophergandrud.github.io/RepResR-RStudio/)
- [Digital History Methods in R](http://lincolnmullen.com/projects/dh-r/)
- [Doing reproducible science: from your hard-won data to a publishable manuscript without going mad](https://github.com/Pakillo/ReproducibleScience/raw/master/ReproducibleScience.pdf)
# Manejo de datos
- [A guide to data management in ecology and evolution (British Ecological Society)](http://www.britishecologicalsociety.org/wp-content/uploads/Publ_Data-Management-Booklet.pdf)
- [Ecoinformatics: supporting ecology as a data-intensive science](http://dx.doi.org/10.1016/j.tree.2011.11.016)
- [Ten simple rules for the care and feeding of scientific data](http://dx.doi.org/10.1371/journal.pcbi.1003542)
- [Ten simple rules for digital data storage](https://doi.org/10.7287/peerj.preprints.1448v2)
- [Nine simple ways to make it easier to (re)use your data](http://dx.doi.org/10.4033/iee.2013.6b.6.f)
- [Ten simple rules for creating a good data management plan](http://doi.org/10.1371/journal.pcbi.1004525)
- [Data Management Planning Tool](https://dmptool.org/)
- [Ejemplo de data management plan](https://www.dataone.org/sites/all/documents/DMP_Copepod_Formatted.pdf)
- [Bad data guide](https://github.com/Quartz/bad-data-guide)
- [Data Carpentry Spreadsheets for Ecology](http://www.datacarpentry.org/spreadsheet-ecology-lesson/)
- [DataONE Best Practices](https://www.dataone.org/best-practices)
- [Tidy data](http://dx.doi.org/10.18637/jss.v059.i10)
- [Spreadsheet help](http://cdluc3.github.io/spreadsheet-help/)
- [Data organization](http://kbroman.org/dataorg/)
- [Repositorios de datos](http://www.re3data.org/)
- [Open Science Framework](http://osf.io)
- [Paquetes de rOpenSci para publicación de datos](http://ropensci.org/packages/#data_publication)
- [Paquete de R para interaccionar con Open Science Framework](https://github.com/chartgerink/osfr)
- [Ecological Metadata Language](http://knb.ecoinformatics.org/software/eml/)
- [The what, why, and how of born-open data](http://link.springer.com/article/10.3758%2Fs13428-015-0630-z)
# Análisis de datos y documentos dinámicos
- [knitr](http://yihui.name/knitr/)
- [rmarkdown](http://rmarkdown.rstudio.com)
- [IPython](http://ipython.org/)
- [Jupyter](https://jupyter.org/)
- [Interactive notebooks: sharing the code](http://www.nature.com/news/interactive-notebooks-sharing-the-code-1.16261)
- [Dynamic documents with R and knitr](https://www.crcpress.com/Dynamic-Documents-with-R-and-knitr-Second-Edition/Xie/9781498716963)
- [Report writing for data science in R](https://leanpub.com/reportwriting)
- [Implementing reproducible research](https://osf.io/s9tya/wiki/home/)
- [Reproducible Research with R and Rstudio](http://christophergandrud.github.io/RepResR-RStudio/)
# Control de versiones (Git & GitHub)
- [Why you need version control](http://ellisp.github.io/blog/2016/09/16/version-control)
- [A Quick Introduction to Version Control with Git and GitHub](http://dx.doi.org/10.1371/journal.pcbi.1004668)
- [Git can facilitate greater reproducibility and increased transparency in science](http://dx.doi.org/10.1186/1751-0473-8-7)
- [Git and GitHub (Hadley Wickham)](http://r-pkgs.had.co.nz/git.html)
- [R development using GitHub](https://github.com/MangoTheCat/github-workshop#readme)
- [Happy Git and GitHub for the useR](http://happygitwithr.com/)
- [Working with RStudio, Git, GitHub (STAT 545)](http://stat545-ubc.github.io/git00_index.html)
- [Version control with git (R. Fitzjohn)](http://nicercode.github.io/2014-02-13-UNSW/lessons/70-version-control/)
- [Version control with Git (Software Carpentry)](http://software-carpentry.org/v5/novice/git/index.html)
- [A basic tutorial to version control using git (Jon Lefcheck)](http://jonlefcheck.net/2013/11/04/a-basic-tutorial-to-version-control-using-git/)
- [Push, Pull, Fork - GitHub for academics](http://www.hybridpedagogy.com/Journal/push-pull-fork-github-for-academics/)
- [Git for beginners - the definitive practical guide (Stackoverflow)](http://stackoverflow.com/questions/315911/git-for-beginners-the-definitive-practical-guide)
- [Git - the simple guide](http://rogerdudler.github.io/git-guide/)
- [Code School Git intro](https://try.github.io/levels/1/challenges/1)
- [GitHub tutorial (Karl Broman)](http://kbroman.org/github_tutorial/)
- [Getting git right (Atlassian)](https://www.atlassian.com/git/)
- [Hello World (GitHub first steps)](https://guides.github.com/activities/hello-world/)
- [GitHub guides](https://guides.github.com/)
# Organización de proyectos y creación de paquetes
- [A quick guide to organizing computational biology projects](http://dx.doi.org/10.1371/journal.pcbi.1000424)
- [Reproducible Research Project Initialization](https://github.com/Reproducible-Science-Curriculum/rr-init)
- [Designing projects](http://nicercode.github.io/blog/2013-04-05-projects/)
- [Rstudio projects](https://support.rstudio.com/hc/en-us/articles/200526207-Using-Projects)
- [Using R packages as research compendiums](https://github.com/ropensci/rrrpkg)
- [R packages (H. Wickham)](http://r-pkgs.had.co.nz/)
- [R packages (K. Broman)](http://kbroman.org/pkg_primer/)
- [Choose a license](http://choosealicense.com/)
- [A minimal tutorial on make (K. Broman)](http://kbroman.org/minimal_make/)
- [remake](https://github.com/richfitz/remake)
# Manejo de dependencias
- [rctrack: An R package that automatically collects and archives details for reproducible computing](http://dx.doi.org/10.1186/1471-2105-15-138)
- [checkpoint package](https://cran.r-project.org/web/packages/checkpoint/vignettes/checkpoint.html)
- [packrat: reproducible package management for R](https://rstudio.github.io/packrat/)
- [Enhancing reproducibility and collaboration via management of R package cohorts](http://arxiv.org/abs/1501.02284)
- [An introduction to Docker for reproducible research](http://dx.doi.org/10.1145/2723872.2723882)
- [R Docker tutorial](http://ropenscilabs.github.io/r-docker-tutorial/)
- [drat: R repositories made easy](http://eddelbuettel.github.io/drat/)
Para un listado actualizado, consultar https://github.com/ecoinfAEET/Reproducibilidad