Data

Tools

Indexes

Applications

Experiments

Open Source Science

2022-10-31-oscsa-online

This is a workshop organised by OSCSA for sFDA staff

https://github.com/open-science-community-saudi-arabia/2022-10-31-oscsa-online

Science Score: 18.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ○
    codemeta.json file
  • ○
    .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (12.6%) to scientific vocabulary

Keywords

bioinformatics open-source openscience rstats rstudio training-materials

Scientific Fields

Engineering Computer Science - 40% confidence
Last synced: 6 months ago · JSON representation ·

Repository

This is a workshop organised by OSCSA for sFDA staff

Basic Info
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 1
  • Open Issues: 1
  • Releases: 0
Topics
bioinformatics open-source openscience rstats rstudio training-materials
Created over 3 years ago · Last pushed over 3 years ago
Metadata Files
Readme Contributing License Code of conduct Citation

README.md

Website

Intro to R and Open Science Practices for Biologists Workshop

The workshop is organised by the Open Science Community Saudi Arabia. It’ll be delivered by instructors and facilitators from the Carpentries in MENA.

Welcome to R!

Working with a programming language (especially if it’s your first time) often feels intimidating, but the rewards outweigh any frustrations. An important secret of coding is that even experienced programmers find it difficult and frustrating at times – so if even the best feel that way, why let intimidation stop you? Given time and practice* you will soon find it easier and easier to accomplish what you want.

Why learn to code?

Bioinformatics – like biology – is messy. Different organisms, different systems, different conditions, all behave differently. Experiments at the bench require a variety of approaches – from tested protocols to trial-and-error. Bioinformatics is also an experimental science, otherwise we could use the same software and same parameters for every genome assembly. Learning to code opens up the full possibilities of computing, especially given that most bioinformatics tools exist only at the command line. Think of it this way: if you could only do molecular biology using a kit, you could probably accomplish a fair amount. However, if you don’t understand the biochemistry of the kit, how would you troubleshoot? How would you do experiments for which there are no kits?

R is one of the most widely-used and powerful programming languages in bioinformatics. R especially shines where a variety of statistical tools are required (e.g. RNA-Seq, population genomics, etc.) and in the generation of publication-quality graphs and figures. Rather than get into an R vs. Python debate (both are useful), keep in mind that many of the concepts you will learn apply to Python and other programming languages.

Finally, we won’t lie; R is not the easiest-to-learn programming language ever created. So, don’t get discouraged! The truth is that even with the modest amount of R we will cover today, you can start using some sophisticated R software packages, and have a general sense of how to interpret an R script. Get through these lessons, and you are on your way to being an accomplished R user!

  • We very intentionally used the word practice. One of the other “secrets” of programming is that you can only learn so much by reading about it. Do the exercises in class, re-do them on your own, and then work on your own problems.

Citation and Acknowledgment

Owner

  • Name: Open Science Community Saudi Arabia
  • Login: Open-Science-Community-Saudi-Arabia
  • Kind: organization
  • Email: admin@openscisaudi.com
  • Location: Saudi Arabia

Citation (CITATION) 

Please cite as:

Greg Wilson (ed): "Software Carpentry: Workshop Template."  Version
2016.06, June 2016, https://github.com/carpentries/workshop-template,
10.5281/zenodo.58156.

GitHub Events

Total
Last Year

Committers

Last synced: 8 months ago

All Time
  • Total Commits: 7
  • Total Committers: 1
  • Avg Commits per committer: 7.0
  • Development Distribution Score (DDS): 0.0
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Batool Almarzouq 5****M 7

Issues and Pull Requests

Last synced: 8 months ago

All Time
  • Total issues: 0
  • Total pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Total issue authors: 0
  • Total pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
Pull Request Authors
  • annajiat (1)
Top Labels
Issue Labels
Pull Request Labels

Dependencies

.github/workflows/template.yml actions
  • actions/cache v2 composite
  • actions/checkout master composite
  • actions/setup-python v2 composite
  • r-lib/actions/setup-r v2 composite
  • ruby/setup-ruby v1 composite
.github/workflows/website.yml actions
  • actions/cache v2 composite
  • actions/checkout master composite
  • actions/setup-python v2 composite
  • r-lib/actions/setup-r v2 composite
  • ruby/setup-ruby v1 composite
requirements.txt pypi
  • PyYAML *
  • beautifulsoup4 *
  • git_root *
Gemfile rubygems
  • github-pages >= 0 development
  • webrick >= 1.6.1