NASA has 12 Distributed Active Archive Centers (DAACs) that supply satellite data to scientists. Scientists were struggling with the obtaining the data, so NASA wanted to understand why. The research was designed to understand how and for what purposes DAACs are used. We interviewed 91 scientists that used 11 of the different sites, determining the different user types and where the problems emerged.
- Lead researcher
- Worked closely with clients, one other researcher, and a director
- Performed one-on-one interviews with NASA scientists, capturing their workflow and observing how they used DAACs
- Data analysis
- Created final documentation and presentation
- Coordinating communication between the main NASA client and 12 DAAC clients
- Recruiting scientists that regularly used the DAACs
- Compiling consistent data between researchers for all of the 91 participants
- Analyzing the data for such a large project
- Telling a single narrative for such a broad story
Recruiting and Coordinating
To make sure we were speaking to the right people, we had to be extra vigilant about the recruit. We requested people to contact from our client, which required coordinating communication between the main NASA group and multiple contacts for each of the DAAC groups. Once we received lists of people to contact, we had to ensure they met our requirements, and that there were 8 to 10 scientists that used the main DAAC site that we would be focused on.
Since the main goal of the DAACs is to provide data to scientists, we wanted to see what was happening when they were doing that task. To determine how the DAACs were performing in accomplishing this goal, we asked scientists to retrieve sample data from:
- The primary DAAC they used
- A data-relevant secondary DAAC
The reason for the two DAACs was to see how the DAAC the scientist had experience with performed, then to see what happened when the scientist attempted to do the same task on a DAAC with which they did not have experience. From these explorations, we found that the scientists were able to find and download data from their primary DAAC that they were familiar with, but unable to download data from DAACs with which they were not as familiar.
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.
During the sessions, we:
- Worked with participants to capture their workflows, to understand context
- Built a representation of their workflows on Keynote slides so that we could work with the participant
- Captured high-level steps, then drilled down into details such as tools and pain points
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, we were able to see patterns emerge. In grouping these patterns, we were able to see four different types of user emerge.
USER TYPES: These were the four user types discovered from the research.
We were then able to identify how these users 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.
Findings and Design Suggestions
In order to give resonance to the data, we described the findings in terms of the user types. We discussed how the different user types approached finding and retrieving data in general, and then how that impacted the individual DAACs.
While this was a research project, we took advantage of my design skills when discussing recommendations. I created a series of wireframes with suggestions for how common design elements could be implemented across the DAAC.
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:
- Seeing our suggestions implemented on the DAACs
- Being told by the clients that the work was loved
- Being booked for future work
- Seeing the deliverables like the User Journeys poster on NASA's walls when returning for future work