SDSC and NCSA Lead $3.2 Million Project To Make Scientific Data More Discoverable

Published October 26, 2022

By Kimberly Mann Bruch, SDSC and Andrew Helregel, NCSA

The National Science Foundation recently announced funding for a $3.2 million project coined DeCODER, or the Democratized Cyberinfrastructure for Open Discovery to Enable Research, which will standardize how scientific data is described – allowing for search engines for scientific data that not only support discoverability but also facilitate the usage of the data. The DeCODER project is a collaborative research effort between the National Center for Supercomputing Applications (NCSA), San Diego Supercomputer Center (SDSC), Scripps Institution of Oceanography, Virginia Tech, Syracuse University, Texas A&M University and the University of California, Berkeley.

“This effort will assist researchers seeking to reuse data and bridge subdomains, especially in the earth sciences,” said Co-Principal Investigator Christine Kirkpatrick, division director of Research Data Services at SDSC. “We are grateful to be working with many members of EarthCube’s Council of Data Facilities on a framework that will help chart the future for data facilities that host their own specialized interfaces while seeking inclusion in broader domain-centric interfaces like GeoCODES, as well as evolving industry offerings like Google's Data Commons.”

DeCODER, which began Oct. 1, 2022, will expand and extend the successful EarthCube GeoCODES framework and community to unify data and tool description and reuse across geoscience domains.

“The internet works because of defined standards and protocols (e.g. TCP/IP, HTTP, HTML). This allows software, which must be sustained, to change and evolve over time with better software with new features to emerge (e.g. browsers, web servers), while still allowing everything to just work from the user perspective,” said DeCODER PI Kenton McHenry, who is the associate director for software at NCSA. “That’s what we are doing here for research data through the adoption of science-on-schema.”

UC Berkeley's Carl Boettiger will be working closely with the team to focus on the application of the DeCODER platform to improve the discovery, production, comparison and applications of ecological forecasts.

"The DeCODER project will democratize research pipelines such as the production and assessment of ecological forecasts, helping to bridge scientific communities and better inform decision makers,” said Boettiger, an associate professor in the Department of Environmental Science, Policy and Management at UC Berkeley.

Tao Wen, an assistant professor in Earth and Environmental Sciences at Syracuse University, believes the DeCODER project will help make large and diverse datasets more accessible for those working in geochemistry and other fields of research.

“In this big data era, the geoscience subfield of low-temperature geochemistry is falling behind in making research datasets findable, accessible, interoperable and reusable to the geoscience communities,” said Wen. “This is at least partially due to the extremely large variety in the size and scale of datasets being used by low-temperature geochemists to advance their understanding of the geochemical processes in terrestrial Earth’s surface systems.

“For example, such diverse datasets can range from one data point for a mineral specimen to a grid of data points with global coverage. The DeCODER project will help the community adopt the concept of science-on-schema to share data and codes. In addition, DeCODER will deliver search engines that are particularly tailored to low-temperature geochemistry to enable faster finding and compiling of diverse datasets.”

“The past several decades have seen a proliferation in the amount of data documenting Earth’s low-temperature surface processes, such as global carbon cycling through the river-land-atmosphere system and the interplay between anthropogenic footprints and environmental feedbacks,” said Shuang Zhang, assistant professor of oceanography at Texas A&M University. “Coupling data science techniques with these datasets is helping reveal the intrinsic patterns of nature’s low temperature processes that are sometimes extremely difficult to be captured by classical physical process models. However, due to the inherent complexity of Earth’s surface processes, the datasets documenting these processes are usually from a wide range of disciplines and deposition locations, and vary in size and format, which significantly hinders the data-driven discoveries.”

“The DeCODER project will help the community of low-temperature geochemistry to build an online searching framework to retrieve the high-dimensional datasets in a more streamlined and efficient way,” Zhang continued. “Part of the outcome of DeCODER is expected to greatly push forward the fundamental research in using data to delineate Earth’s surface processes and patterns both on the regional and global scale.”

Similar to how internet search engines are used to find information, Virginia Tech will work to advance the discoverability of ecological forecasts through the development of protocols and software to archive and document model predictions of ecological dynamics. For example, if a researcher searches, “find forecasts of algae in lakes across the U.S.,” the search could yield current forecasts to help guide decision making and support environmental management.

Principal Investigator Quinn Thomas, an associate professor in the departments of Forest Resources and Environmental Science and Biological Sciences at Virginia Tech, will then compare these predictions to actual measurements of algae to quantify the strengths and weaknesses of the forecasts. Rather than requiring all ecological forecasters to use a single archive location on the internet, the technology the broader team is developing allows for many archiving locations to be used, thus democratizing the discovery of forecasting expertise.

“The field of ecological forecasting is rapidly growing but needs to be able to look back in time to ask whether we are getting better at predicting the future of ecological systems,” said Thomas. “This project will give us the capacity to answer this question by accelerating the discovery of  previously generated forecasts across multiple domains in ecology without requiring forecasts to be centrally stored.”

The EarthCube program was formed to address the technological challenges surrounding data and software within the geosciences. This led to the adoption of science-on-schema, leveraging schema.org to describe scientific datasets in a consistent manner, and GeoCODES, an open source codebase which allows communities to stand up instances of scientific search engines specific to their domains while building a community of geoscience data users and developers and, ultimately, reducing the time to science. The effort additionally continues the leveraging of notebooks as scholarly objects, introducing a peer review aspect to not only the scientific aspects to the work, but also the software that allowed it to happen and be reproduced by others. Published notebooks are leveraged by GeoCODES and DeCODER to provide analytics with associated datasets.

This project will leverage this effort in the DeCODER platform to enable similar activities and outcomes across additional scientific communities such as ecological forecasting, deep ocean science, and low-temperature geochemical science, and will continue the endeavor to support the scientific community overall in the adoption of schema.org and notebooks, facilitating this by providing DeCODER as an open source resource that can be customized by a given scientific community to create lightweight scientific gateways that bring together relevant distributed resources.

This project is supported by the NSF (award nos. 2209865, 2209863, 2209866 and 2209864).