skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Zero to a trillion: Advancing surface process studies with open access to high resolution topography
High-resolution topography (HRT) is a powerful observational tool for studying the Earth's surface, vegetation, and urban landscapes, with broad scientific, engineering, and education-based applications. Submeter resolution imaging is possible when collected with laser and photogrammetric techniques using the ground, air, and space-based platforms. Open access to these data and a cyberinfrastructure platform that enables users to discover, manage, share, and process then increases the impact of investments in data collection and catalyzes scientific discovery. Furthermore, open and online access to data enables broad interdisciplinary use of HRT across academia and in communities such as education, public agencies, and the commercial sector. OpenTopography, supported by the US National Science Foundation, aims to democratize access to Earth science-oriented, HRT data and processing tools. We utilize cyberinfrastructure, including large-scale data management, high-performance computing, and service-oriented architectures to provide efficient web-based visualization and access to large, HRT datasets. OT colocates data with processing tools to enable users to quickly access custom data and derived products for their application, with the ultimate goal of making these powerful data easier to use. OT's rapidly growing data holdings currently include 283 lidar and photogrammetric, point cloud datasets (>1.2 trillion points) covering 236,364km2. As a testament to OT's success, more than 86,000 users have processed over 5 trillion lidar points. This use has resulted in more than 290 peer-reviewed publications across numerous academic domains including Earth science, geography, computer science, and ecology.  more » « less
Award ID(s):
1833632
PAR ID:
10387368
Author(s) / Creator(s):
; ;
Editor(s):
Tarolli, P.; Mudd, S.
Date Published:
Journal Name:
Developments in earth surface processes
Volume:
23
ISSN:
0928-2025
Page Range / eLocation ID:
317-338
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; ; ; ; ; ; et al (, ACM)
    Modern science depends on computers, but not all scientists have access to the scale of computation they need. A digital divide separates scientists who accelerate their science using large cyberinfrastructure from those who do not, or who do not have access to the compute resources or learning opportunities to develop the skills needed. The exclusionary nature of the digital divide threatens equity and the future of innovation by leaving people out of the scientific process while over-amplifying the voices of a small group who have resources. However, there are potential solutions: recent advancements in public research cyberinfrastructure and resources developed during the open science revolution are providing tools that can help bridge this divide. These tools can enable access to fast and powerful computation with modest internet connections and personal computers. Here we contribute another resource for narrowing the digital divide: scalable virtual machines running on public cloud infrastructure. We describe the tools, infrastructure, and methods that enabled successful deployment of a reproducible and scalable cyberinfrastructure architecture for a collaborative data synthesis working group in February 2023. This platform enabled 45 scientists with varying data and compute skills to leverage 40,000 hours of compute time over a 4-day workshop. Our approach provides an open framework that can be replicated for educational and collaborative data synthesis experiences in any data- and compute-intensive discipline. 
    more » « less
  2. ; ; ; ; ; (, Zenodo)
    This paper introduces a lidar and radar meteorology science gateway deployed on the NSF Jetstream2 cloud, designed to enhance educational and research activities in atmospheric science. Utilizing the "Zero to JupyterHub with Kubernetes" workflow, we have created a science gateway that integrates lidar and radar meteorology software packages, notably the Lidar Radar Open Software Environment (LROSE). This integration allows users to execute applications directly from the JupyterLab terminal, streamlining the creation of datasets for further anal- ysis and visualization within Jupyter notebooks. By combining traditional command-line operations with modern Python-based tools for data analysis and visualization, this gateway provides a robust end-to-end solution that caters to both educational and research needs. The gateway has already been vital in facilitating LROSE instructional workshops and will see future enhancements such as GPU acceleration to boost performance. Our work demonstrates the significant potential of merging established scientific computing techniques with advanced Python environments, opening new avenues for computational science education and research. 
    more » « less
  3. ; ; ; (, Zenodo)
    To improve accessibility and community knowledge of applications in the Lidar Radar Open Software Environment (LROSE), a team from the National Science Foundation (NSF) National Center for Atmospheric Research, Colorado State University, and NSF Unidata has developed a lidar and radar meteorology science gateway deployed on the NSF Jetstream2 cloud. Utilizing the “Zero to JupyterHub with Kubernetes” workflow, the science gateway integrates LROSE with other lidar and radar meteorology software packages. This integration allows users to execute applications directly from the JupyterLab terminal, streamlining the creation of datasets for further analysis and visualization within Jupyter notebooks. By combining traditional command-line operations with modern Python-based tools for data analysis and visualization, this gateway provides a robust end-to-end solution that caters to both educational and research needs. The gateway has already facilitated LROSE instructional workshops and classroom exercises. Our work demonstrates the significant potential of merging established scientific computing techniques with advanced Python environments, opening new avenues for computational science education and research. The LROSE team has acquired successive allocations on the NSF Jetstream2 cloud at Indiana University through ACCESS. To develop the LROSE Science Gateway, we employed the “Zero to JupyterHub with Kubernetes” workflow ported to the NSF Jetstream2 cloud, enabling rapid and scalable deployment to accommodate a variable number of users. Authentication is managed through either GitHub OAuth or temporary credentials, depending on the situation. Since LROSE is a collection of C/C++ applications, we configured Docker containers based on the Jupyter Docker Stack to integrate the LROSE software, available via the JupyterLab terminal. These containers also include Conda package manager environments equipped with Python packages like Py-ART, CSU RadarTools, and Metpy for further data analysis. A shared drive accessible to all participants contains instructional datasets for lidar and radar data analysis. Tutorials take the form of Jupyter notebooks for use by individuals, in classroom exercises, or at instructional workshops. Some tutorials are complete with pre-loaded examples to quickly visualize workflows and results. Other tutorials guide students how to run the applications independently. All tutorials are hosted on the LROSE Science Gateway GitHub repository, which is open to contributions from colleagues and community members. Future plans include an "intermediate" level workshop on SAMURAI, one of the multi-Doppler wind applications of the LROSE suite. Additionally, work is currently underway to run GUI applications in the same browser-based JupyterLab environment. GUI applications for radar and lidar data visualization utilize the QT framework and present unique technical challenges. The techniques to accomplish GUI access have immediate applications for other GUI programs, such as NSF Unidata's IDV and their version of the AWIPS CAVE data visualization tools. Lastly, as demand for the resources found on the gateway increases, it becomes increasingly important to efficiently manage the Jetstream2 resources allocated by the ACCESS program. LROSE, NSF Unidata, San Diego Supercomputing Center (SDSC), and Indiana University staff are working together to deploy and evaluate Kubernetes cluster auto-scaling. With auto-scaling, resources will no longer sit idle while awaiting new logins and will instead be provisioned on-demand. 
    more » « less
  4. ; ; ; ; (, International Journal of Applied Earth Observation and Geoinformation)
    Scientific workflow management systems (WfMS) provide a systematic way to streamline necessary processes in scientific research. The demand for FAIR (Findable, Accessible, Interoperable, and Reusable) workflows is increasing in the scientific community, particularly in GIScience, where data is not just an output but an integral part of iterative advanced processes. Traditional WfMS often lack the capability to ensure geospatial data and process transparency, leading to challenges in reproducibility and replicability of research findings. This paper proposes the conceptualization and development of FAIR-oriented GIScience WfMS, aiming to incorporate the FAIR principles into the entire lifecycle of geospatial data processing and analysis. To enhance the findability and accessibility of workflows, the WfMS utilizes Harvard Dataverse to share all workflow-related digital resources, organized into workflow datasets, nodes, and case studies. Each resource is assigned a unique DOI (Digital Object Identifier), ensuring easy access and discovery. More importantly, the WfMS complies with the Common Workflow Language (CWL) standard to guarantee interoperability and reproducibility of workflows. It also enables the integration of diverse tools and software, supporting complex analyses that require multiple processing steps. This paper demonstrates the prototype of the GIScience WfMS and illustrates two geospatial science case studies, reflecting its flexibility in selecting appropriate techniques for various datasets and research goals. The user-friendly workflow designer makes it accessible to users with different levels of technical expertise, promoting reusable, reproducible, and replicable GIScience studies. 
    more » « less
  5. ; ; ; ; ; ; ; ; ; ; et al (, Geoscientific Model Development)
    Abstract. Global change research demands a convergence among academic disciplines to understand complex changes in Earth system function. Limitations related to data usability and computing infrastructure, however, present barriers to effective use of the research tools needed for this cross-disciplinary collaboration. To address these barriers, we created a computational platform that pairs meteorological data and site-level ecosystem characterizations from the National Ecological Observatory Network (NEON) with the Community Terrestrial System Model (CTSM) that is developed with university partners at the National Center for Atmospheric Research (NCAR). This NCAR–NEON system features a simplified user interface that facilitates access to and use of NEON observations and NCAR models. We present preliminary results that compare observed NEON fluxes with CTSM simulations and describe how the collaboration between NCAR and NEON that can be used by the global change research community improves both the data and model. Beyond datasets and computing, the NCAR–NEON system includes tutorials and visualization tools that facilitate interaction with observational and model datasets and further enable opportunities for teaching and research. By expanding access to data, models, and computing, cyberinfrastructure tools like the NCAR–NEON system will accelerate integration across ecology and climate science disciplines to advance understanding in Earth system science and global change. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email