EarthCube RCN: C4P: Collaboration and Cyberinfrastructure for Paleogeosciences

This project establishes and operates the EarthCube Research Coordination Network (RCN)

Collaboration and Cyberinfrastructure for Paleogeosciences (C4P) to advance the role of cyberinfrastructure

(CI) in unraveling the large-scale, long-term evolution of the Earth-Life System through the

study of the geological record.Paleogeosciences research into our large-scale, long-term Earth-Life system is an ambitious

program that informs public debate on human stewardship of the earth and has - since its inception

- caught public imagination on issues such as species survival under environmental changes.

In the last decade, fresh discoveries, plus the application of new technologies of measurement,

data analysis, and modeling have revitalized the science. Bringing to bear recent advances

in computing and mathematics will further quicken the pace of discoveries, synthesis and publication

in the science. This RCN intends to stimulate and grow CI-focused collaborations and

partnerships among paleogeoscientists, paleobiologists, bioinformaticists, stratigraphers,

geochronologists, geographers, data scientists, and computer scientists that will help dissolve

existing intellectual barriers to dramatically improve the application of modern data management

approaches, data mining technologies, and computational methods to better analyze the heterogeneous

and sparse data of the Earth record in sediments, rocks, and ice within the paleogeosciences

and other domains and disciplines.

Activities envisioned include a series of coordinated workshops and webinars, web-enabled networking, outreach events such

as symposia and town-hall meetings, and cross-EarthCube coordination, and creation of an interoperable

C4P resource catalog that will be integrated with other EarthCube resource inventories. High-priority

themes in C4P are the management and representation of age and age uncertainty in data models

and computation to advance interoperability among and analysis of time-referenced data types,

the integration of physical samples into digital data infrastructure, and the need to properly

track and attribute provenance of data to ensure trust in the data and credit to authors.