Many frameworks have been developed to support science data visualization. These frameworks can be integrated or modular and very often involve massaging the underlying source science data (via a set of specialized procedural steps) into a generalized derivative form (images, grids, tiles) facilitating the visualization process. Visualization frameworks might also simply be data browsers.

We want to identify examples of frameworks that exist within the Earth sciences community and encourage the developers, maintainers, and users of those systems to participate in this workshop at the summer meeting.

For this workshop we are soliciting 15-20 minute presentations that address the following core questions regarding visualization frameworks:

• What are the challenges/functions that the framework is attempting to address?
• What varieties of data products are represented within these frameworks? (e.g., considering data formats, types, and topics -- HDF, CSV, vector, point, remotely sensed, in-situ, etc.)
• How can these frameworks be expanded/adapted to other science datasets? Have frameworks been developed with extensibility in mind or are there requirements that necessitate solutions that are unique to the source data?
• What are the processing considerations when generating intermediate data products that then feed the frameworks?
• How do the above considerations reflect the intended audience and infrastructure?

Demonstrations of existing frameworks are certainly welcome as long as the core questions are addressed as part of that demonstration.

Presenters

Kevin Ward, NASA Earth Observations
NASA Earth Observations (NEO): Data Imagery for Education and Visualization
NASA Earth Observations (NEO) is a framework that supports the storage and delivery of global imagery of NASA remote sensing data. NEO targets non-traditional data users, primarily in education and outreach: formal and informal educators, museum and science center personnel, professional communicators, and citizen scientists. These user communities have a need for these types of data but do not have the domain knowledge to locate the various source data and do not possess the tools and expertise required to produce imagery from those data. NEO strives to provide a middleware approach to meeting their needs.

NEO currently hosts imagery from more than 50 different datasets with daily, weekly, and/or monthly temporal resolutions. The imagery from these datasets is produced in coordination with several data partners who are affiliated either with the instrument science teams or with the respective data processing center.

NEO is a system of three components -- website, WMS (Web Mapping Service), and ftp archive -- which together are able to meet the wide-ranging needs of our users. Some of these needs include the ability to: view and manipulate imagery using the NEO website -- e.g., applying color palettes, resizing, exporting to a variety of formats including PNG, JPEG, KMZ (Google Earth), GeoTIFF; access the NEO collection via a standards-based API (WMS); and create customized exports for users (delivered via ftp) such as Science on a Sphere, NASA’s Earth Observatory, and others.

Yuan Ho, UCAR
Visualization of Geo-science data with Unidata's Integrated Data Viewer
The Integrated Data Viewer (IDV) from Unidata is a Java(TM)-based software framework that provides new and innovative ways of displaying and analyzing Earth science data, as well as common 2D, 3D, and 4D visualization capabilities. Many features designed in the IDV can be used to view the daily weather as a weather enthusiast,  to analyze the model output in the climate research, to study satellite observations, to navigate the deep ocean environment, or to explore the complex three-dimensional data in geophysics. The IDV software library can be easily be used and extended to create custom geoscience applications beyond the atmospheric science realm. The IDV community has expanded from the traditional synoptic meteorology community to include many new disciplines (e.g., climatology,  hydrology, oceanography, geophysics, etc.).  In this presentation we will present the software architecture and several user examples of these communities for their academic usage and scientific discovery.

David Mintz, EPA
AirData – some design considerations
The challenges in designing a data delivery system can be overwhelming. How do you design a system that is efficient, sustainable, agile, and doesn’t break your budget? Recently, EPA updated its AirData website to meet several initiatives – to consolidate interfaces, leverage existing products, avoid copying and restoring data, and accommodate future needs. This presentation will demonstrate a few visualization tools, focusing on system design and user interface considerations. Topics include how and when to connect directly to source data and the concept of using of cron jobs to generate intermediate products.

Charles Thompson, Physical Oceanography Distributed Active Archive Center (PO.DAAC), Jet Propulsion Laboratory
A Generalized Pipeline for Creating and Serving High-Resolution Satellite Imagery
Generating imagery for a wide range of science datasets via a standardized set of processes is challenging.  Inevitably, there is always some number of unique, specialized steps in transforming diverse satellite data products in formats such as HDF or NetCDF into high-quality imagery suitable for outreach, publications, or the general public.  Furthermore, as the spatial resolution of remotely sensed data increases, the corresponding imagery becomes more unwieldy to efficiently serve.  This presentation will discuss the evolving framework behind State of the Oceans (SOTO), a Google Earth based web interface which visualizes near real time oceanographic parameters such as sea surface temperature and ocean winds.  The SOTO backend employs a configurable image generation pipeline soon to be combined with a Tiled WMS server to create a flexible end-to-end imaging system applicable to a wide array of science data products.

Mike McCann, Monterey Bay Aquarium Research Institute
A Framework for in situ oceanographic measurement data access and visualization: The Spatial Temporal Oceanographic Query System (STOQS)
The CF-NetCDF file format and the technologies and conventions surrounding it (THREDDS, Climate Forecast Conventions for Point Observations, etc.) provide a solid foundation for managing archives of diverse collections of data.  This technology stack is used successfully for numerical model output and is beginning to be used more widely for in situ measurement data.

Access efficiency decreases with decreasing dimension of NetCDF data. For example, the Trajectory Common Data Model feature type has only one coordinate dimension, usually Time – positions of the trajectory (Depth, Latitude, Longitude) are stored as non-indexed record variables within the NetCDF file. If client software needs to access data between two depth values or from a bounded area, the whole data set must be read and the selection made by the client software.  This is very inefficient. What is needed is a complement to NetCDF that provides server-side indexing of any variable related to variables of interest.

Geospatial relational database technology provides this capability. The Spatial Temporal Oceanographic Query System (STOQS) has been designed and built to provide efficient access to in situ oceanographic measurement data across any dimension. STOQS is an open source software project built upon a framework of free and open source software for geospatial data. STOQS complements CF-NetCDF and OPeNDAP by providing an ability to index data retrieval across parameter and spatial dimensions in addition to the a priori indexed coordinate dimensions of CF-NetCDF. It also provides a functional bridge to standards-based GIS technologies.

For more information please see http://code.google.com/p/stoqs/

Mahabal Hegde, NASA
GIOVANNI - The Bridge Between Data and Science
Giovanni is a NASA framework for online visualization and analysis of Earth science data. It is intended to abstract the complications of data formats, structures, quality flags, etc. from the user into a Web GUI-driven user interface that is easy to use.  The goal is to transform data exploration from a process that can take days, weeks or months into one that takes only minutes. The current architecture of Giovanni, version 3 (or G3) is being updated to be more flexible and agile with respect to adding datasets and features.

The next generation Giovanni, version 4 (or G4), leverages off-the-shelf components to reduce implementation cost, reduce time-to-market, improve reliability and provide better and wider end-user functionality. Examples of such re-use include selection of Kepler as the workflow engine, OPeNDAP for remote data access/subsetting, YUI for user interfaces, JCS for data caching, NCO tools for data processing and JavaScript based frameworks for client side visualization. The talk will discuss challenges faced along the way and contrast theory and reality in stitching together third party components to provide an optimal solution.

Richard Kim, Physical Oceanography Distributed Active Archive Center (PO.DAAC), Jet Propulsion Laboratory
HITIDE: An Extensible Service-Based Web Interface for the Search, Imaging, and Extraction of Swath-based Geophysical Parameters
PO.DAAC's HITIDE is a web application powered by a trio of web services that facilitate access to and evaluation of swath satellite data products in common scientific file formats such as HDF and NetCDF.  The key component to its capabilities is a tile database which allows for precise search, image, and extract functionality based upon spatial and temporal constraints at a sub-granular resolution.  With this functionality, users in the science community can efficiently find, evaluate, and download subsetted science data products pertaining to their research. As a system, HITIDE represents a framework which can ingest virtually any geolocated data product accessible via Opendap.  This presentation will provide an overview of HITIDE, the considerations when adding a new data product, plus the future direction of development including support of gridded data products, enabling data-specific constraints, and enhanced dynamic imaging capabilities.