reproducibility

Project Description Topics: Distributed systems Skills: Java, Go, Python, Bash scripting, Linux, Docker. Difficulty: Hard Size: Large (350 hours) Mentors: Fadhil I. Kurnia Replication is commonly employed to improve system availability and reduce latency.

Data is generated at ever-increasing rates, yet it’s often processed more slowly than it’s collected. Scientific instruments frequently operate below their full capacity or discard valuable data due to network bottlenecks, security domain mismatches, and insufficient real-time processing capabilities.

Topics: genomic processing (e.g., DNA and RNA alignment), workflow scheduling, resource/cluster management, container orchestration Skills: Linux, cloud computing (e.g., OpenStack), cluster manager (e.g., Kubernetes), systems automation (e.g., Bash/Python/Puppet), genomic workflows and applications (e.

StatWrap is a free and open-source assistive, non-invasive discovery and inventory tool to document research projects. It inventories project assets (e.g., code files, data files, manuscripts, documentation) and organizes information without additional input from the user.

Topics: computational pathology, image generation, disentangled representations, latent space manipulation, deep learning Skills: Programming Languages: Proficient in Python, with experience in machine learning libraries such as PyTorch or TensorFlow. Generative Models: Familiarity with Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and contrastive learning methods.

Untyped languages such as JavaScript and Python provide a flexible starting point for software projects, but eventually, the lack of reliable types makes code hard to debug and maintain. Gradually typed languages such as TypeScript, Flow, Mypy, and Pyright address the problem with type checkers that can reason about an ever-growing subset of untyped code.

Introduction Hello! My name is Acheme, and I’m thrilled to have collaborated with my mentors Joaquin Chung and Flavio Castro under the SciStream project. This project aims to develop SciStream-bench, a set of benchmarks and artifacts designed to precisely evaluate the performance of scientific streaming applications across diverse traffic patterns when running over the SciStream framework.

Topics: bioinformatics, spatial transcriptomics, gene expression generation, retrieval-augmented generation, large models Skills: Programming Languages: Proficient in Python, and familiarity with machine learning libraries such as PyTorch. Data Analysis: Experience with spatial transcriptomics datasets and statistical modeling.

Hi, I am Mrigank. As a Summer of Reproducibility 2024 fellow, I have been working on deriving realistic performance benchmarks for Python interpreters with Ben Greenman from the University of Utah.

Namaste🙏🏻! I’m Adi Akhilesh Singh, and I’m excited to share my final updates on the Reproducibility Checklists project by StatWrap, under the mentorship of Luke Rasmussen. Project Overview This project introduces customizable reproducibility checklists in StatWrap, enabling metadata-driven and user-guided generation of checklists.