Cyberinfrastructure integrates advanced computer, information, and communication technologies to empower computation-based and data-driven scientific practice and improve the synthesis and analysis of scientific data in a collaborative and shared fashion. As such, it now represents a paradigm shift in scientific research that has facilitated easy access to computational utilities and streamlined collaboration across distance and disciplines, thereby enabling scientific breakthroughs to be reached more quickly and efficiently. Spatial cyberinfrastructure seeks to resolve longstanding complex problems of handling and analyzing massive and heterogeneous spatial datasets as well as the necessity and benefits of sharing spatial data flexibly and securely. This article provides an overview and potential future directions of spatial cyberinfrastructure. The remaining four articles of the special feature are introduced and situated in the context of providing empirical examples of how spatial cyberinfrastructure is extending and enhancing scientific practice for improved synthesis and analysis of both physical and social science data. The primary focus of the articles is spatial analyses using distributed and high-performance computing, sensor networks, and other advanced information technology capabilities to transform massive spatial datasets into insights and knowledge.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3078415 | PMC |
| http://dx.doi.org/10.1073/pnas.1103051108 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A functional microbiome catalogue crowdsourced from North American rivers.
Nature
January 2025
Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO, USA.
Predicting elemental cycles and maintaining water quality under increasing anthropogenic influence requires knowledge of the spatial drivers of river microbiomes. However, understanding of the core microbial processes governing river biogeochemistry is hindered by a lack of genome-resolved functional insights and sampling across multiple rivers. Here we used a community science effort to accelerate the sampling, sequencing and genome-resolved analyses of river microbiomes to create the Genome Resolved Open Watersheds database (GROWdb).
View Article and Find Full Text PDFForecasting and Predicting Stochastic Agent-Based Model Data with Biologically-Informed Neural Networks.
Bull Math Biol
September 2024
Department of Mathematics and Statistics, The College of New Jersey, Ewing, NJ, 08628, USA.
Collective migration is an important component of many biological processes, including wound healing, tumorigenesis, and embryo development. Spatial agent-based models (ABMs) are often used to model collective migration, but it is challenging to thoroughly predict these models' behavior throughout parameter space due to their random and computationally intensive nature. Modelers often coarse-grain ABM rules into mean-field differential equation (DE) models.
View Article and Find Full Text PDFA review of geospatial exposure models and approaches for health data integration.
J Expo Sci Environ Epidemiol
September 2024
National Institute of Environmental Health Sciences, Division of Translational Toxicology, Predictive Toxicology Branch, Durham, NC, USA.
Background: Geospatial methods are common in environmental exposure assessments and increasingly integrated with health data to generate comprehensive models of environmental impacts on public health.
Objective: Our objective is to review geospatial exposure models and approaches for health data integration in environmental health applications.
Methods: We conduct a literature review and synthesis.
Collaborative Use of Sensor Networks and Cyberinfrastructure to Understand Complex Ecosystem Interactions in a Tropical Rainforest: Challenges and Lessons Learned.
Sensors (Basel)
November 2023
Center for Conservation Biology, Department of Microbiology and Plant Pathology, University of California, Riverside, CA 92507, USA.
Collaborations between ecosystem ecologists and engineers have led to impressive progress in developing complex models of biogeochemical fluxes in response to global climate change. Ecology and engineering iteratively inform and transform each other in these efforts. Nested data streams from local sources, adjacent networks, and remote sensing sources together magnify the capacity of ecosystem ecologists to observe systems in near real-time and address questions at temporal and spatial scales that were previously unobtainable.
View Article and Find Full Text PDFContinental Scale Hydrostratigraphy: Basin-Scale Testing of Alternative Data-Driven Approaches.
Ground Water
January 2024
Integrated GroundWater Modeling Center, Princeton University, Princeton, NJ, USA.
Integrated hydrological modeling is an effective method for understanding interactions between parts of the hydrologic cycle, quantifying water resources, and furthering knowledge of hydrologic processes. However, these models are dependent on robust and accurate datasets that physically represent spatial characteristics as model inputs. This study evaluates multiple data-driven approaches for estimating hydraulic conductivity and subsurface properties at the continental-scale, constructed from existing subsurface dataset components.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!