2016 Collaborative Research: Leveraging domain repositories in Flyover Country, a mobile app for geoscience outreach, data discovery, and visualization.
Flyover Country is an app available for iOS and Android uses your device's GPS to link web resources such as the Paleobiology Database, Macrostrat and the Neotoma Paleoecological Database to provide geoscientific outreach and research opportunities, both online and offline, while you're flying from one location to another.
2016 Collaborative Research: Neotoma Paleoecology Database, Community-led Cyberinfrastructure for Global Change Research
The Neotoma Paleoecological Database has become a critical part of the geoscience infrastructure. Neotoma is a community supported data repository, a stand-alone database, a data explorer, a research resource, and a tool for processing paleoecological data in R. Neotoma has expanded to include sixteen paleoecological proxies, from mammal fossils to X-Ray Fluoresence data from sedimentary archives. This grant provides ongoing support to the project and expands the utility of the database by increasing proxy holdings, updating bibliographic support & DOI provision and working to build a community around this shared resource. Supported through: NSF Award 1550707
2015 Building Interoperable Cyberinfrastructure (CI) at the Interface between Paleogeoinformatics and Bioinformatics
Interoperability is critical for developing interdisciplinary tools. The Neotoma Paleoecological Database and the Paleobiology Database represent common data holdings that differ largely by timescale. This grant proposes to harmonize database APIs in an effort to improve data usability for the component databases. This grant has resulted in the formation of the EarthLife Consortium and the development of a harmonized API that allows researchers to study biodiversity from the present to the origins of life on earth. Supported through: NSF Award 1541002
2014 Climate Prediction in No-Analogue Space – PI
An Educational Grant to support work reported at Climate Informatics 2015. Climate prediction using pollen-proxy data is predicated on correlative pollen-climate relationships under modern conditions, however pollen assemblages from late-glacial and early Holocene environments often have no modern analogue. Using machine learning methods, along with traditional transfer function approaches we test the ability of pollen-based models to recover climate from pollen assemblages with no modern analogue. Results of this grant can be seen here.