Trinity Tutorial for Transcriptome Assembly
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Trinity is one of the most popular tool to assemble transcripts from RNA-Seq short reads. In this tutorial, we will cover the basic usage of Trinity, best practice and common problems.
How the Little Jupyter Notebook Became a Web App: Managing Increasing Complexity with nbdev
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A tutorial entitled "How the Little Jupyter Notebook Became a Web App: Managing Increasing Complexity with nbdev" presented at SciPy 2023 in Austin, TX. This tutorial is hosted in a series of Jupyter Notebooks which can be accessed in the click of a button using Binder. See the README for more information.
Research Software Engineering Training Materials
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An ongoing collection of RSE training material, workshops, and resources. We are compiling this list as a starting point for future activities. We are especially seeking material that goes beyond basic research computing competency (e.g. what The Carpentries does so well) and is general enough to span multiple domains. Specific tools and technologies used only in one domain, or applicable to only one subset of computing (i.e. HPC) are typically too narrowly focused. When in doubt, submit it to be included or reach out and we’d be happy to discuss.
Recommended Libraries for Cyberinfrastructure Users Developing Jupyter Notebooks
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This repository contains information about Jupyter Widgets and how they can be used to develop interactive workflows, data dashboards, and web applications that can be run on HPC systems and science gateways. Easy to build web applications are not only useful for scientists. They can also be used by software engineers and system admins who want to quickly create tools tools for file management and more!
Awesome Jupyter Widgets (for building interactive scientific workflows or science gateway tools)
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A curated list of awesome Jupyter widget packages and projects for building interactive visualizations for Python code
Data Analysis with R for Educators
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This webinar series is an orientation to R. We start with an overview of R’s history and place in the larger data science ecosystem. Next, we introduce the R Studio user interface and how to access R’s excellent documentation. Finally, we present the fundamental concepts you need to use the R environment and language for data analysis. Along the way, we compare R script files (.R) to R Notebook (.Rmd) files and show how the features of R Notebook support better communication and encourage more dynamic engagement with statistical analysis and code. It is helpful to be familiar with tabular data analysis using statistical software, database tools, or spreadsheet programs.
Workshop materials, including setup directions and slides are available at https://github.com/CornellCAC/r_for_edu/ The Rstudio Cloud project used in the workshop is https://rstudio.cloud/project/4044219.
Science Gateway Tool/Web App Template (Jupyter Notebook + ipywidgets)
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Use this template to turn any science gateway workflow into a web application!
phenoACCESS-24 workshop program materials
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phenoACCESS-24: Workshop on Research Computing and Plant Phenotyping
High-throughput plant phenotyping is computationally intensive, requiring data storage, data processing and analysis, research computing expertise, and mechanisms for data sharing. This workshop is aimed at research computing workforce development by addressing questions such as what is plant phenotyping; what types of data are collected; what are the preprocessing and analytical needs; what tools and platforms exist for data capture, management, analysis, and storage; and how best to collaborate and engage with phenotyping researchers. The full-day agenda will include speakers (scientists and research compute staff); panel discussions (how to work with research computing staff and facilities; how to engage with phenotyping scientists), and networking opportunities (meet-and-greet, ice breakers, small group discussions). The videos and slide decks for the talks are included on the linked page.
Research Software Development in JupyterLab: A Platform for Collaboration Between Scientists and RSEs
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Iterative Programming takes place when you can explore your code and play with your objects and functions without needing to save, recompile, or leave your development environment. This has traditionally been achieved with a REPL or an interactive shell. The magic of Jupyter Notebooks is that the interactive shell is saved as a persistant document, so you don't have to flip back and forth between your code files and the shell in order to program iteratively.
There are several editors and IDE's that are intended for notebook development, but JupyterLab is a natural choice because it is free and open source and most closely related to the Jupyter Notebooks/iPython projects. The chief motivation of this repository is to enable an IDE-like development environment through the use of extensions. There are also expositional notebooks to show off the usefulness of these features.
