R for Research Scientists
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A book for researchers who contribute code to R projects: This booklet is the result of my work with the Social Cognition for Social Justice lab. It was developed in response to questions I was getting from students; both grad students that were making software design decisions, and undergraduates who were using things like version control for the first time. Although many tutorials and resources exist for these topics, there was not a single source that I thought covered just enough material to build up to the workflow used by the lab without extraneous detail.
Biopython Tutorial
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The Biopython Tutorial and Cookbook website is a dedicated online resource for users in the field of computational biology and bioinformatics. It provides a collection of tutorials and practical examples focused on using the Biopython library.
The website offers a series of tutorials that cover various aspects of Biopython, catering to users with different levels of expertise. It also includes code snippets and examples, and common solutions to common challenges in computational biology.
NCSA HPC-Moodle
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Self-paced tutorials on high-end computing topics such as parallel computing, multi-core performance, and performance tools. Some of the tutorials also offer digital badges.
Official Documentation of VisIt
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VisIt is a prominent open-source, interactive parallel visualization and graphical analysis tool predominantly used for viewing scientific data. Its GitHub repository offers a detailed insight into the software's source code, documentation, and contribution guidelines. In particular, it offers useful examples on how it
What is fairness in ML?
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This article discusses the importance of fairness in machine learning and provides insights into how Google approaches fairness in their ML models.
The article covers several key topics:
Introduction to fairness in ML: It provides an overview of why fairness is essential in machine learning systems, the potential biases that can arise, and the impact of biased models on different communities.
Defining fairness: The article discusses various definitions of fairness, including individual fairness, group fairness, and disparate impact. It explains the challenges in achieving fairness due to trade-offs and the need for thoughtful considerations.
Addressing bias in training data: It explores how biases can be present in training data and offers strategies to identify and mitigate these biases. Techniques like data preprocessing, data augmentation, and synthetic data generation are discussed.
Fairness in ML algorithms: The article examines the potential biases that can arise from different machine learning algorithms, such as classification and recommendation systems. It highlights the importance of evaluating and monitoring models for fairness throughout their lifecycle.
Fairness tools and resources: It showcases various tools and resources available to practitioners and developers to help measure, understand, and mitigate bias in machine learning models. Google's TensorFlow Extended (TFX) and What-If Tool are mentioned as examples.
Google's approach to fairness: The article highlights Google's commitment to fairness and the steps they take to address fairness challenges in their ML models. It mentions the use of fairness indicators, ongoing research, and partnerships to advance fairness in AI.
Overall, the article provides a comprehensive overview of fairness in machine learning and offers insights into Google's approach to building fair ML models.
Texas A&M HPRC Training Site
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Training Resources and Courses offered by Texas A&M's Research Computing Group
Using Dask on HPC Systems
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A tutorial on the effective use of Dask on HPC resources. The four-hour tutorial will be split into two sections, with early topics focused on novice Dask users and later topics focused on intermediate usage on HPC and associated best practices. The knowledge areas covered include (but are not limited to):
Beginner section
High-level collections including dask.array and dask.dataframe
Distributed Dask clusters using HPC job schedulers
Earth Science data analysis using Dask with Xarray
Using the Dask dashboard to understand your computation
Intermediate section
Optimizing the number of workers and memory allocation
Choosing appropriate chunk shapes and sizes for Dask collections
Querying resource usage and debugging errors
Creating a Mobile Application
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Goes through in detail on how to build an application that can run on Android and IOS devices, using Qt Creator to develop Qt Quick applications. Goes through the setting up, creation, configuration, optimization, and overall deployment. This provides the fundamental basis, need to click around on the site for more specifics.
Data Visualization Tools for Julia
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Plots.jl is the most widely used plotting library for the Julia programming language. It's known for being especially powerful in its versatility and intuitiveness. It's limited set of dependencies and wide applicability across different graphics packages make it especially helpful in visualizing the results of your latest Julia implementation.
However, there are still multiple options available for Julia programmers to visualize their datasets. The second link details a comparison against a variety of Julia packages.
Rockfish at Johns Hopkins University
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Resources and User Guide available at Rockfish
High Performance Computing (HPC) 101 - Cluster
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High Performance Computing (HPC) Cluster
Globus Documentation
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Globus is a data transfer, sharing, automation, and discovery service used by hundreds of thousands of researchers to manage "big data" at universities, research labs, and national systems such as ACCESS. The Globus documentation website provides how-to guides, reference documentation, and examples for Globus's web application, command-line interface, Python software development kit (SDK), and APIs.
MDAnalysis - Python library for the analysis of molecular dynamics simulations
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MDAnalysis is a python based library of tools for the analysis of molecular dynamics simulations. It is able to read and write many popular simulation formats including CHARMM, LAMMPS, GROMACS, and AMBER and more. This link contains the documentation pages of all MDAnalysis functions and has links to tutorials using Jupyter Notebooks.
Why 'N How: Martinos Center for Biomedical Imaging:
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The Why & How seminar series is designed to introduce research assistants, graduate students, and postdoctoral and clinical fellows – really, anyone who is interested – to the many tools used in medical imaging. These include software tools and most of the major imaging modalities wielded by investigators (MRI, PET, EEG, MEG, optical, TMS and others). As the name of the series suggests, the talks cover both the reasons researchers might need a particular tool and the nuts and bolts of how to apply it. You can watch videos of the overviews below.
Bridges-2 Home Page
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Landing Page for Bridges-2 information
Research Security Operations Center at IU
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The NSF-funded ResearchSOC helps make scientific computing resilient to cyberattacks and capable of supporting trustworthy, productive research through operational cybersecurity services, training, and information sharing necessary to a community as unique and variable as research and education (R&E).
ResearchSOC is a service offering from Indiana University's OmniSOC.
CMake Tutorials
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CMake is an open-source tool used to manage the build process in operating systems. This tutorial takes you through how to use CMake from the very basics with example projects.
Raftlib: Open Source library for concurrent data processing pipelines
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Raftlib is an open-source C++ Library that provides a framework for implementing parallel and concurrent data processing pipelines. It is designed to simplify the development of high-performance data processing applications by abstracting away the complexities of parallelism, concurrency, and data flow management.
It enables stream/data-flow parallel computation by linking parallel compute kernels together using simple right shift operators, similar to C++ streams for string manipulation. RaftLib eliminates the need for explicit usage of traditional threading libraries such as pthreads, std::thread, or OpenMP, which can lead to non-deterministic behavior when misused.
Running Particle-in-Cell Simulations on HPC
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WarpX is an advanced particle-in-cell code used to model particle accelerators, which needs to be run on HPC. This website contains the tutorial on how to build WarpX on various HPC systems such as NERSC along with examples on how to set up post-processing/visualization tools for different physics cases.
CUDA Toolkit Documentation
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NVIDIA CUDA Toolkit Documentation: If you are working with GPUs in HPC, the NVIDIA CUDA Toolkit is essential. You can access the CUDA Toolkit documentation, including programming guides and API references, at this provided website
Thrust resources
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Thrust is a CUDA library that optimizes parallelization on the GPU for you. The Thrust tutorial is great for beginners. The documentation is helpful for anyone using Thrust.
Oakridge Leadership Computing Facility (OLCF) Training Events and Archive
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Upcoming training events and archives of training materials detailing general HPC best practices as well as how to use OLCF resources and services.
Termius - Modern ssh platform
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**Termius: The Modern SSH Client for 2023**
Termius is the future-facing SSH client that's redefining remote server access in 2023. Designed for ease and efficiency, Termius offers a seamless connection experience across all devices, be it mobile or desktop. Gone are the days of re-inputting IP addresses, ports, and passwords; with Termius, one-click connectivity is the new norm.
**How Termius Elevates Remote Server Access:**
1. **One-Click Connectivity:** Save the hassle of remembering and re-entering connection details. Termius provides an immediate connection to your infrastructure with a single click.
2. **Synchronized Across Devices:** Termius ensures that your data, connection settings, and preferences are consistent across all your devices, from mobile to desktop.
3. **Unparalleled Security:** With the Cloud Vault feature, users can securely store their data in an encrypted environment, accessible only from their specific devices. Shared vaults allow for safe connection sharing within teams.
4. **AI-Powered Terminal Experience:** Advanced AI-driven autocomplete means users can input command descriptions, and Termius will swiftly convert them into accurate bash commands, simplifying and enhancing the terminal interaction.
5. **Collaborative Troubleshooting:** Share terminal sessions with teammates, facilitating cooperative problem-solving or knowledge sharing. No additional server-side installations needed.
6. **Automation and Snippets:** Streamline routine processes with the ability to save and run frequently used shell scripts. Sharing these Snippets with your team can lead to increased productivity and fewer manual errors.
7. **All-Device Compatibility:** Whether on iPad, iPhone, Android, macOS, Windows, or Linux, Termius ensures a consistent and fluid experience. The platform's synchronization capability means you're always ready to respond swiftly, irrespective of the device in use.
For professionals and businesses aiming for top-notch server access efficiency, Termius is the gold standard in 2023. Experience the revolution in SSH connectivity and optimize your workflow with Termius.
Optimizing Research Workflows - A Documentation of Snakemake
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Snakemake is a powerful and versatile workflow management system that simplifies the creation, execution, and management of data analysis pipelines. It uses a user-friendly, Python-based language to define workflows, making it particularly valuable for automating and reproducibly managing complex computational tasks in research and data analysis.
AI/ML TechLab - Accelerating AI/ML Workflows on a Composable Cyberinfrastructure
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This technology lab contains a set of sessions to help a new user start an AI project on the ACES cluster, a composable accelerator testbed at Texas A&M University. You will learn how to create and activate a virtual environment, manipulate and visualize data with Pandas and Matplotlib, use Scikit-learn for linear regression and classification applications, and use Pytorch to create and train a simple image classification model with deep neural networks (DNN).