Workplan and Science Traceability Matrix
Discovering Complementary and Overlapping Work
Once a team is formed the building of collaborative structures can begin. One of the greatest potential barriers to cross-team collaboration is that multiple teams may have proposed to do similar tasks. This may set up a tension within the group as each researcher tries to be the first to publish and gain credit for a specific activitity. Therefore it is important that structures be put in place to facilitate both a discovery of overlapping work, as well an opportunity for teams to adjust their workplans so that their work can be collaborative and complementary.
HiMAT Case study
At the start of the HiMAT project, the program manager and science team lead identified overlapping tasks in several proposals. We initiated small group calls to talk through areas of overlap and to discuss possibilities for reducing duplication of effort. These conversations continued during the first in-person team meeting. Shortly after the team meeting, Principal Investigators were asked to submit revised statements of work that helped streamline areas of overlap. In several cases individuals let go of some proposed tasks, recognizing that others had more advanced capabilities. In other cases, dupilcation of effort was seen as a positive way to check each other's work and to come at a problem from multiple directions.
Science Traceability Matrix
As everyone gets more clear on their objectives and proposed activities, it helps to generate a high-level roadmap that succinctly describes what each team plans to do. One way to achieve this is to invite each team to build a Science Traceability Matrix. We borrow this concept from NASA mission planning activities that link science objectives to functional requirements and specifications of satellites.
Here is a sample Science Traceability Matrix from HiMAT:
Project Title:Validating a Glacier Melt Toolbox For High Mountain Asia Using a Remote-Sensing-Driven Data Integration Framework
| Science Objectives | Approach | Data Needed | Expected Outcomes | Datasets Produced | Expected Release Date |
|---|---|---|---|---|---|
| To quantify the mass trends of select basins of the HMA region with a focus on partitioning cryosphere and groundwater variations | resolution operator, optimized for trend analysis, applied to observed/modeled regional mass trends | Best estimates of cryosphere and hydrology trends along with associated uncertainties where available | Information on deficiencies in existing models /datasets; Formal mechanism for using GRACE to validate model output | Residuals of mass not currently captured by existing cryosphere/hydrology observations and models @ greater than 1x1 degree mascon spatial resolution for selected basins | February 2019 |
| To assess HMA glacier mass balance on ~50-year, decadal, annual and seasonal timescales | Generate high-resolution DEMs from DigitalGlobe imagery and integrate with other available DEMs for elevation change analysis | Regional glacier mass balance estimatesCal/val for GMELT models; new capacity for high-resolution geomorphological analyses (lakes, debris cover, landslides, etc) | Published at NSIDC | ||
| To quantify groundwater contributions to terrestrial water storage change in HMA | Collect and analyze available groundwater observations and interpolate across regions with sufficient data coverage | Observation wells supplemented with modeled groundwater estimates | Improved groundwater data to contribute to GRACE forward modeling efforts | Monthly groundwater storage changes (spatial resolution partially dependent on data availability) | February 2019 |