UX | UCSC OSPO

From Friction to Flow: Why I'm Building Widgets for Reproducible Research

Tue, 24 Jun 2025 00:00:00 +0000

This summer, I’m building Jupyter Widgets to reduce friction in reproducible workflows on Chameleon. Along the way, I’m reflecting on what usability teaches us about the real meaning of reproducibility.

Supercomputing Competition: Reproducibility Reality Check

My first reproducibility experience threw me into the deep end—trying to recreate a tsunami simulation with a GitHub repository, a scientific paper, and a lot of assumptions. I was part of a student cluster competition at the Supercomputing Conference, where one of our challenges was to reproduce the results of a prior-year paper. I assumed “reproduce” meant something like “re-run the code and get the same numbers.” But what we actually had to do was rebuild the entire computing environment from scratch—on different hardware, with different software versions, and vague documentation. I remember thinking: If all these conditions are so different, what are we really trying to learn by conducting reproducibility experiments? That experience left me with more questions than answers, and those questions have stayed with me. In fact, they’ve become central to my PhD research.

Summer of Reproducibility: Lessons from 100+ Experiments on Chameleon

I’m currently a PhD student and research software engineer exploring questions around what computational reproducibility really means, and when and why it matters. I also participated in the Summer of Reproducibility 2024, where I helped assess over 100 public experiments on the Chameleon platform. Our analysis revealed key friction points—especially around usability—that don’t necessarily prevent reproducibility in the strictest sense, but introduce barriers in terms of time, effort, and clarity. These issues may not stop an expert from reproducing an experiment, but they can easily deter others from even trying. This summer’s project is about reducing that friction—some of which I experienced firsthand—by improving the interface between researchers and the infrastructure they rely on.

From Psychology Labs to Jupyter Notebooks: Usability is Central to Reproducibility

My thinking shifted further when I was working as a research software engineer at Purdue, supporting a psychology lab that relied on a complex statistical package. For most researchers in the lab, using the tool meant wrestling with cryptic scripts and opaque parameters. So I built a simple Jupyter-based interface to help them visualize input matrices, validate settings, and run analyses without writing code. The difference was immediate: suddenly, people could actually use the tool. It wasn’t just more convenient—it made the research process more transparent and repeatable. That experience was a turning point for me. I realized that usability isn’t a nice-to-have; it’s critical for reproducibility.

Since that first experience, I’ve leaned into building better interfaces for research workflows—especially using Jupyter Widgets. Over the past few years, I’ve developed and taught tutorials on how to turn scientific notebooks into interactive web apps, including at the SciPy conference in 2023 and 2024. These tutorials go beyond the basics: I focus on building real, multi-tab applications that reflect the complexity of actual research tools. Teaching others how to do this has deepened my own knowledge of the widget ecosystem and reinforced my belief that good interfaces can dramatically reduce the effort it takes to reproduce and reuse scientific code. That’s exactly the kind of usability work I’m continuing this summer—this time by improving the interface between researchers and the Chameleon platform itself.

Making Chameleon Even More Reproducible with Widgets

This summer, I’m returning to Chameleon with a more focused goal: reducing some of the friction I encountered during last year’s reproducibility project. One of Chameleon’s standout features is its Jupyter-based interface, which already goes a long way toward making reproducibility more achievable. My work builds on that strong foundation by improving and extending interactive widgets in the Python-chi library — making tasks like provisioning resources, managing leases, and tracking experiment progress on Chameleon even more intuitive. For example, instead of manually digging through IDs to find an existing lease, a widget could present your current leases in a dropdown or table, making it easier to pick up where you left off and avoid unintentionally reserving unnecessary resources. It’s a small feature, but smoothing out this kind of interaction can make the difference between someone giving up or trying again. That’s what this project is about.

Looking Ahead: Building for People, Not Just Platforms

I’m excited to spend the next few weeks digging into these questions—not just about what we can build, but how small improvements in usability can ripple outward to support more reproducible, maintainable, and accessible research. Reproducibility isn’t just about rerunning code; it’s about supporting the people who do the work. I’ll be sharing updates as the project progresses, and I’m looking forward to learning (and building) along the way. I’m incredibly grateful to once again take part in this paid experience, made possible by the 2025 Open Source Research Experience team and my mentors.

Widgets for Python-chi in Jupyter

Tue, 18 Feb 2025 00:00:00 +0000

Overview

Reproducibility challenges in research extend beyond code and environments to the experimental workflow itself. When experiments involve dynamic resource allocation, monitoring, and reconfiguration, researchers often struggle to document these interactive steps in a way that others can precisely follow. The lack of structured workflow documentation and real-time feedback creates barriers for reviewers attempting to reproduce experiments, as they cannot easily verify whether their resource configurations match the original experiment’s state. This project addresses these challenges by developing interactive Jupyter widgets that make experiment resource management more visual, intuitive, and self-documenting—transforming ad-hoc command sequences into reproducible workflows that automatically log interactions and configuration changes while providing immediate visual feedback on experiment topology and resource states.

As cloud researchers often work with Jupyter Notebooks for interactive data analysis and experimentation, the python-chi library offers a powerful way to automate and control resources on Chameleon Cloud. This project will extend python-chi by adding interactive widgets specifically designed for use in Jupyter, empowering users to launch, monitor, and manage their experiments without leaving the notebook environment. By bringing visual and intuitive controls directly into the user’s workflow, we aim to improve both reproducibility and usability for complex resource management tasks.

Key Outcomes

User-Friendly Jupyter Widgets: Develop a suite of widgets to visualize reserved resources, hardware availability, and experiment topologies in real time.
Integrated Experiment Management: Enable researchers to orchestrate experiments (launch, configure, monitor) within a single, notebook-centric workflow.
Enhanced Feedback & Usability: Provide clear, asynchronous status updates and resource reconfiguration progress, reducing confusion and user error.
Improved Reproducibility: By automating and logging widget interactions, experiments become more traceable and easier to replicate.

Topics: Interactive Data Tools, Cloud Resource Management, DevOps & Automation, User Experience (UX)

Skills:

Python & Jupyter: Experience creating custom Jupyter widgets, using ipywidgets or similar frameworks.
Cloud Automation: Familiarity with how resources are provisioned, monitored, and deprovisioned on Chameleon.
Frontend / GUI Development: Basic understanding of web technologies (HTML/CSS/JavaScript) can be helpful for widget design.
Software Engineering & CI: Ability to version-control, test, and deploy Python packages.

Difficulty: Moderate

Size: Medium

Mentor: Michael Sherman, Mark Powers

Tasks:

Resource Visualization Widgets
- Build custom widgets that show reserved resources (nodes, networks, storage) in Jupyter.
- Provide an interactive topology view for experiments, indicating node statuses and connections.
Experiment Setup & Execution
- Add controls for launching and managing experiments directly from notebooks.
- Show feedback (e.g., progress bars, status messages) as resources are being allocated or reconfigured.
Hardware Availability & Status Tracking
- Implement a widget that provides real-time data on Chameleon’s hardware availability (bare metal, VMs, GPU nodes, etc.).
- Allow users to filter or select specific resources based on current hardware states.
Usability & Feedback Loop
- Gather user feedback on the widget designs and workflows.
- Refine the interface to minimize clicks, improve clarity, and reduce friction for common tasks.

Final Blog: Enhancing User Experience Reproducibility through TROVI Redesign

Wed, 18 Sep 2024 00:00:00 +0000

Hello! My name is Alicia Esquivel Morel, and I’m a graduate research assistant at the University of Missouri – Columbia, pursuing a PhD in Computer Science. This summer, I worked on a project to improve user experience reproducibility through a redesign of TROVI, as part of the Summer of Reproducibility (SoR) program.

Before even starting this project, and me as a rising researcher, I always saw reproducibility as one of the biggest challenges in research. What I wanted to see was always as reproducibility—being able to consistently replicate experiments and share them in a way that others can follow.

TROVI, is a platform designed to help with this. However, as I joined the project, I knew it had room for improvement, not oly in the user interface, but also in the ease of integrating code and data.

This project aimed to address these challenges by redesigning TROVI to streamline experiment replication, making the platform more intuitive and accessible. The goal was simple: create a user-friendly experience that eliminates confusion and frustration, allowing researchers to focus on their work instead of the technical aspects of running a research experiment.

Our goals in the beginning of the summer:

We wanted to simplify TROVI’s interface for intuitive navigation, inspired by platforms like Google Colab.
We wanted to make uploading and sharing code and data easier, with seamless integration with tools like GitHub.
We wanted to create a mechanism for users to provide feedback, allowing TROVI to evolve based on real user needs.

How was the progress and what we have achieved

I started by conducting thorough UX research and a literature review on reproducibility platforms, establishing a solid foundation for the redesign. With user feedback guiding the process, I created wireframes and low-fidelity prototypes, focusing on making the platform more intuitive.

As the project progressed, I built a higher-fidelity prototype that connected various components of the platform, ensuring a seamless user journey. I then tackled the back-end integration, which tied together the front-end flows with TROVI’s API.

Throughout this project, I received valuable support and guidance from my mentors. Mark Powers walked me through TROVI’s architecture and helped me understand exactly what was needed for a successful redesign. Thanks to his mentorship, I not only completed the project but learned a great deal along the way. Thanks Mark Powers!!

Through iterations and feedback from initial user testing, and we the help of Kate Keahey, I refined the design to ensure it met the needs of the research community. By the end of the program, TROVI had evolved into a cohesive, user-friendly platform that leads to enhanced experiment reproducibility.

Accomplishments

A simplified interface that makes navigating, uploading, and collaborating much easier.
GitHub integration that streamlines the process of sharing code and data with collaborators.
A built-in feedback loop that enables TROVI to grow with its users, adapting to their needs as they arise.

The platform is also getting ready to move into production and will soon be available for the research community.

What’s Next?

While the core objectives have been successfully met, future improvements could further enhance the platform’s capabilities, such as additional integrations and more advanced collaboration features. User testing will continue to provide insights for ongoing development.

I’m grateful for this opportunity! Thank you for following along!

Trovi redesign process and low fidelity prototype in Figma

Mon, 22 Jul 2024 00:00:00 +0000

Hello! My name is Alicia Esquivel Morel, and I’m a graduate research assistant at the University of Missouri – Columbia, pursuing a PhD in Computer Science. This summer, I’m working on a project to improve user experience reproducibility through a redesign of TROVI, as part of the Summer of Reproducibility (SoR) program. I’m excited to be working with two fabulous mentors, Kate Keahey, and Mark Powers. .

Research Reproducibility with a TROVI Redesign

As researchers, we constantly face challenges replicating experiments due to limitations in current tools. TROVI, a platform designed to facilitate experiment replication, can be hindered by hard-to-follow interfaces and difficulties integrating code and data. This leads to confusion and frustration.

My SoR project tackles these issues by redesigning TROVI to enhance user experience reproducibility. Imagine a user-friendly platform where uploading code, sharing data, and collaborating with colleagues becomes easy and straighforward.

The Redesign’s Goals

Enhanced User Experience: Inspired by user-friendly platforms like Google Colab, we’ll simplify TROVI’s interface for intuitive navigation and ease of use.
Uploads and Sharing: Uploading code and data, as well as collaborating with researchers, are key goals. Integration with platforms like GitHub will further streamline collaboration.
Continuous Improvement: A built-in feedback loop will allow users to provide input and suggestions, ensuring TROVI constantly evolves based on user needs.

Progress I have made so far

The first stage of my project began with conducting User Experience (UX) research and identifying user requirements for TROVI. I then conducted a literature review on reproducibility platforms to learn about efficient methodologies and platforms for reproducibility. This helped establish a clearer project scope. Additionally, I analyzed TROVI end-user feedback to understand redesign needs.

In summary, during the first weeks of the project, I focused on research and requirements gathering, including the literature review on state-of-the-art reproducibility platforms. Before midterm assessment, my work also involved the redesign process, prioritizing improved usability and user experience. I designed wireframes following requierements and user feedback and later translated them into a low-fidelity prototypes. Front-end and back-end considerations were made, such as selecting a front-end language (Vue.js) and a collaborative design tool (Figma).

What do I plan to do over the next weeks?

During the next two weeks, I will address challenges encountered in the design process and make the necessary adjustments to ensure the success of the next steps of the project. A higher-fidelity prototype will be completed, including connections between the different objects and frames. This will facilitate the creation of a front-end with multiple flows in the prototype. Additionally, this will provide a preview of the end-user experience through the design process, without requiring the back-end to be functional or connected yet. I’m also investigating design tool API integrations to access TROVI’s APIs. This will give us the ability to access and isolate any TROVI artifact properties associated with it.

I’m halfway in the redesign process. Next steps will include the integration of both the backend and frontend components to create a cohesive and functional system. We will also facilitate initial user interactions and testing to gather valuable feedback and ensure that the system meets the needs and expectations of end users.
In addition, as I progress, my focus will shift towards enhancing the user experience and refining the final product based on the feedback received. The final two weeks of the program will be dedicated to this critical phase, where I will implement user experience techniques and conduct thorough testing to polish the product. This period will involve close analysis and iteration to address any issues, and an optimize functionality.
By the end of the program, I aim to deliver a functional and user-friendly product that not only meets the initial project goals but also exceeds user expectations.

Stay tuned to see how TROVI is built for reproducible research!!

First Steps in Enhancing User Experience Reproducibility through TROVI Redesign

Wed, 12 Jun 2024 00:00:00 +0000

Hello! My name is Alicia Esquivel Morel, and I’m a graduate research assistant at the University of Missouri – Columbia, pursuing a PhD in Computer Science. This summer, I’m working on a project to improve user experience reproducibility through a redesign of TROVI, as part of the Summer of Reproducibility (SoR) program. Excited to be working with two fabulous mentors; Kate Keahey, and Mark Powers.

Research Reproducibility with a TROVI Redesign

Researchers constantly face challenges replicating experiments due to limitations in current tools. TROVI, a platform designed to facilitate experiment replication, can be hindered by hard to follow interfaces and difficulties integrating code and data. This leads to confusion and frustration.

The Redesign’s Goals

Enhanced User Experience: Inspired by user-friendly platforms like Google Colab, we’ll simplify TROVI’s interface for intuitive navigation and ease of use.
Uploads and Sharing: Uploading code and data, as well as collaborating with researchers are key goals. Integration with platforms like GitHub will further streamline collaboration.
Continuous Improvement: A built-in feedback loop will allow users to provide input and suggestions, ensuring TROVI constantly evolves based on user needs.

The Road Ahead

We’re at the beginning of the redesign process. In the next blog post, I’ll describe the project’s specific goals and the deliverables you can expect.