I led a research project to understand the challenges scientists were facing in accessing and utilizing satellite data from NASA's 12 Distributed Active Archive Centers (DAACs). Through in-depth interviews with 91 scientists using 11 different DAAC websites, I identified key user types and pain points in the data access process. Key findings included significant variability in user experience across different DAACs, a lack of awareness of available data resources, and challenges in understanding and utilizing complex data formats. The research findings informed the development of new improved user experiences across the DAACs, leading to a significant increase in scientist satisfaction and data utilization.
Recruiting participants for this research presented significant logistical challenges. To ensure a representative sample, we aimed to recruit 8-10 scientists from each of the 12 DAACs, requiring careful coordination with NASA and each DAAC to identify and contact potential participants. This involved navigating communication across multiple organizations, screening potential participants based on criteria such as recent DAAC usage and research area, and addressing logistical hurdles like scheduling conflicts and time zone differences. Despite these challenges, we successfully recruited a diverse group of 91 scientists, providing a strong foundation for the research.
To observe scientists' interactions with the DAACs, we conducted think-aloud protocols, where participants verbalized their thoughts and actions as they attempted to retrieve sample datasets. Scientists were asked to retrieve data from:
This comparative approach allowed us to identify key differences in user experience across different DAACs.
Our findings revealed that while scientists were generally able to find and download data from their primary DAACs, they encountered significant challenges when navigating unfamiliar DAACs. These challenges included difficulty in locating relevant datasets, understanding data formats and documentation, and navigating the websites. These findings highlighted the need for improved user onboarding, standardized interfaces across DAACs, and more comprehensive and user-friendly documentation to enhance data access and utilization.
RESULTS TABLE: This table displays the successes the participants had for the different DAACs. "Primary" indicates it's a DAAC they used regularly. "Secondary" indicates it's a DAAC they did not use regularly.
To understand the intricacies of scientist workflows, we utilized a combination of methods, including semi-structured interviews, task-based scenarios, and a co-creation exercise. Collaborating closely with each participant, we:
CO-CREATION EXERCISE: This is an example of a participant's process created in the session to find and use data for a research question.
Once we completed the sessions, the analysis presented us with some specific behaviors of the participants. In grouping these behavior patterns, four different user types of emerged.
USER TYPES: These were the four user types discovered from the research.
With an understanding of each of these user types, we were better able to understand how they were performing their research, where they would interact with the DAACs, and where the problems would emerge.
USER JOURNEYS: This was the main deliverable from the study. It captures the different user journeys for the four user types, and the opportunities for each stage of the journey. For a better look, download the PDF version.
In order to give resonance to the data, we described the findings in terms of the user types. We analyzed how each user type approached finding and retrieving data across the DAACs, highlighting the unique challenges and opportunities presented by each user group.
While this was a research project, I leveraged my design skills to translate the findings into actionable recommendations. I created a series of wireframes demonstrating how common design elements, such as standardized search interfaces, consistent data visualizations, and a centralized user onboarding experience, could be implemented across the DAACs to improve data discoverability and overall user experience. My final presentation to NASA and DAAC representatives went through the findings and discussed the feasibility of the proposed design solutions.
WIREFRAME SUGGESTION: This is an example of a high level wireframe created for the final deliverable.
This project was considered a great success. This was determined by:
