- Success Stories
- Safety, Health, & Environmental Compliance
Training the Next Generation of Researchers
CSS staff support the National Institutes of Health (NIH) Division of Occupational Safety and Health by providing Safe Techniques Advance Research Science (STARS) training to summer interns. Following a pause during the pandemic, staff resumed training May 15, 2024. During the in-person STARS training, CSS staff cover key concepts from the pre-requisite NIH Lab Safety online training by working through two case studies: one biological hazard (salmonella typhi handling procedures), and one chemical hazard (acrylamide for gels). CSS trainers provide a show and tell while demonstrating the safety equipment in the mock lab.
We anticipate training hundreds of interns this summer.
We are proud to be training the next generation of researchers!
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Supporting Response Efforts Following California Fires
Following the fires that devastated Los Angeles, California and surrounding areas in January 2025, CSS employee owners were deployed to the area as a subcontractor to Weston Solutions, Inc. to assist with the response. This effort supports U.S. Environmental Protection Agency (EPA) Region 9 Superfund Technical Assessment & Response Team (START) program. Initially, a CSS…
Emergency Response Support for the Republican National Convention
CSS employee owners on contract with the U.S. Environmental Protection Agency’s Portable High-throughput Integrated Laboratory Identification System — a mobile analytical unit known as PHILIS — provided emergency response support for the 2024 Republican National Convention. PHILIS mobile laboratory units are designed to provide onsite analysis of environmental conditions, including air and soil samples, contaminated…
Evaluating the Use of Earth Observations Digital Twin Technologies
Earth Observations Digital Twin technologies are data analytics, artificial intelligence, and advanced modeling technologies that provide an estimate of the true state of the Earth. An Earth Systems Digital Twin is observations-based and grid-flexible with multiple components and high-resolution data over space and time to capture all available observations and feed a variety of direct…