Greg Newman

Last name: 

I am a research scientist, ecologist, and informatics specialist at the Natural Resource Ecology Laboratory (NREL) at Colorado State University (CSU). I received my PhD from CSU in citizen science, community-based monitoring, and ecological informatics. My current research focuses on designing and evaluating the effectiveness of cyber-infrastructure support systems for citizen science programs. My research interests include evaluating various citizen science program models, understanding the socio-ecological benefits of engaging the public in scientific research, designing and evaluating data management systems for socio-ecological research, assessing the value of local and traditional ecological knowledge for conservation and education outcomes, and developing spatial-temporal decision support systems.

Research Scientist
Research Interests (Specific): 
Citizen Science, Socio-Ecological Systems (SES), local ecological knowledge, community based monitoring, community based natural resource management, collaborative conservation
Research Projects: 
Project Title:
Project Location Coordinates: 
40.464223, -105.065917
Project Location Details: 
Study Species: 
All terrestrial and aquatic species
Project Citations: 

Crall, A., G. Newman, D. M. Waller, T. J. Stohlgren, K. Holfelder, and J. Graham. 2011. Assessing Citizen Science Data Quality: An Invasive Species Case Study. Conservation Letters 4:433-442.

Crall, A. W., K. Holfelder, D. M. Waller, G. J. Newman, and J. Graham. 2012. The Impacts of an Invasive Species Citizen Science Training Program on Participant Attitudes, Behavior, and Science Literacy. Public Understanding of Science 22(6) 745–764.

Graham, J., G. Newman, C. Jarnevich, R. Shory, and T. J. Stohlgren. 2007. A global organism detection and monitoring system for non-native species. Ecological Informatics 2:177-183.

Newman, G., M. Chandler, M. Clyde, B. McGreavy, M. Haklay, H. Ballard, S. Gray, R. Scarpino, R. Hauptfeld, D. Mellor, and J. Gallo. 2016. Leveraging the power of place in citizen science for effective conservation decision making. Biological Conservation. Biological Conservation Available online 11 August 2016.

Newman, G., A. Crall, M. Laituri, J. Graham, T. J. Stohlgren, J. C. Moore, K. Kodrich, and K. Holfelder. 2010. Teaching citizen science skills online: Implications for invasive species training programs. Applied Environmental Education and Communication 9:276-286.
Newman, G., J. Graham, A. Crall, and M. Laituri. 2011. The art and science of multi-scale citizen science support. Ecological Informatics 6:217-227.

Newman, G., A. Wiggins, A. Crall, E. Graham, S. Newman, and K. Crowston. 2012. The future of citizen science: emerging technologies and shifting paradigms. Frontiers in Ecology and the Environment 10:298-304.
Newman, G., D. E. Zimmerman, A. Crall, M. Laituri, J. Graham, and L. Stapel. 2010. User friendly web mapping: Lessons from a citizen science website. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 24:1851-1869.

Wang, Y., N. Kaplan, G. Newman, and R. Scarpino. 2015. A New Model for Managing, Documenting, and Sharing Citizen Science data. PLOS Biology October 22:1-5.

Project Description: is a global support platform for citizen science projects. The platform currently supports 351+ projects globally – ranging from projects monitoring stream quality to maple syrup productivity to wildlife populations to invasive species – and has generated 636,535+ rigorous scientific observations. It is fully customizable to the extent that projects can define what they wish to measure, document how they measure it, and build customized datasheets for real-time data entry online and via mobile smartphone applications. The platform integrates a full suite of data exploration and visualization tools to empower people to create their own customized visualizations of trends, relationships, and comparisons. All data are automatically visualized on a variety of maps, and tools exist for volunteer management, communications, alerts/notifications, bulk uploading of legacy datasets, and download (export) of data in a variety of formats.