The Pilot ENCOMPASS CI is a multi-national, transdisciplinary, and multi-sector initiative to adopt systematic and science-based approaches to societal tradeoffs. The project leverages existing relationships and infrastructure with partners across the Americas. Since 2009 the initiative has brought people into collaborative discussions, building technical knowledge and bridging across sectors that are often at odds over management of earth resources.
Today, participants are co-designing a renewable energy plant with the goal of establishing operation by the indigenous community as a sustainable business. The proposed plant serves as a focal topic for participatory modeling and a sustained dialogue process for the multi-sector stakeholder group. The research builds from existing data to establish the schema for dynamic data collection, real-time modeling of energy-water systems, and design for citizen science education.
This proposed work advances theoretical approaches for sustainability sciences and has practical implications for resource allocation, strategic planning, and science policy. The projects seek to improve levels of understanding and open possibilities for collaborative problem solving by engaging industry, academics, and indigenous communities in a long-term participatory modeling process.
Shortening the cycle from initial field data collection with flexible mobile or handheld devices improves efficiency, productivity, automation, and integrity in data flows, from data collection to sample processing to database management and analysis, and finally publication.
The Data Flow Infrastructure Initiative (DFII) introduces handheld devices with integrated barcode scanners as a mechanism to enhance research productivity and information access. These devices are established technology and provide a flexible but consistent platform for research data collection and data management. They are not in widespread use yet in the research community. Benefits of the DFII to include:
- A reduction of costs related to data entry and shift of human and financial resources toward data collection or analysis.
- Better understanding of time and costs involved at each step of data collection.
- An increase in data recorded digitally allowing for ease of public data access and better long-term data archiving.
- Stronger linkages between experimental design, database organization, and data collection.
- Reduced time between data collection and publication.
- Increased data quality and less entry error.
- Innovations in teaching and data dissemination.
3DDY is a prototype data pipeline and workflow for converting geospatial data into formats that are easy to reuse. Version 0.01 of 3DDY is an early prototype that combines the use of scripts, GDAL commands, and high performance computing resources to enable conversion of topographic datasets.
3DDY makes it possible for a user to select Digital Elevation Model (DEM) terrain data from any place on Earth and convert in a reproducible way. Data and information are frequently linked with geographic locations and displaying or presenting information with the topographic content can help people understand it better.
Outputs from the 3DDY process are intended for use in data visualization, data analysis, web and mapping applications. Future enhancements to the pipeline are intended to add support for additional input and output formats and automated processes for exposing data attributes.