Open Science & Reproducible Research
A small list of available resources.
- Automated Research Workflows for Accelerated Discovery. National Academy of Sciences.
- Challenges in irreproducible research. (2018) Nature.
- CARE Principles for Indigenous Data Governance
- FAIR Principles. GO FAIR Initiative.
- FAIR Cookbook. FAIRplus project. ELIXIR community.
- FAIR self assessment tool. Australian Research Data Commons.
- FOSTER Open Science Portal.
- Open Science. UNESCO Recommendation on Open Science.
- Open-Source Science Initiative. NASA Science.
- Journal Papers with code.
- ReproducibleScience.org. New York University.
- Reproducible data analysis platform (reana). CERN.
- Reproducibility and Replicability in Science. National Academy of Science.
- Reproducibility in Research. Aya Mitani.
- Software Development Resources for Data Scientists. Isabella Velásquez. RStudio/Blog.
- The Turing Way. A handbook to reproducible, ethical and collaborative data science.
- Why is reproducible open science important, and how can it be accomplished?. Artin Majdi, UA Data Science Institute.
- Fishare.com. UA Figshare
- Directory of Open Access Repositories - OpenDOAR. University of Nottingham, University of Lund. JISC, UK.
- Open Source Framework - OSF
- Registry of Open Access Repositories - ROAR. University of Southampton, UK.
- Zenodo.org. CERN, CH.
Updated: 07/01/2022. (C. Lizarraga)
- Datasets
- Julia Programming Language
- Python Programming Language
- R Programming Language
- UNIX/Linux Command Line Interface (CLI)
- General Data Science
- Machine Learning & Deep Learning
- Probability & Statistics
- Time Series Analysis & Forecasting
- Open Science & Reproducible Research
- AI Tools Landscape
more ...
- UArizona Data Science Workshops
- UArizona Data Lab Workshops (Fall 2023)
- UArizona Data Lab Deep Learning Workshops (Fall 2023)
Carlos Lizárraga, UArizona Data Lab, Data Science Institute, University of Arizona, 2025.
