Every year, the climate software engineering community gathers to determine who has performed exceptional or outstanding work developing community tools for the acceleration of climate science in the Earth System Grid Federation (ESGF) data science domain. This year, the ESGF Executive Committee decided on thirteen winners of the ESGF Achievement Awards. These awards recognize dedicated members of the ESGF community who contribute nationally and internationally to federation efforts. Award recipients exemplify the community’s spirit and determination to succeed.

Alessandra Nuzzo, Maria Mirto, Paola Nassisi, and Sandro Fiore, researchers at the CMCC Foundation – ASC Division won a group award for developing the new ESGF dashboard at the 2017 Earth System Grid Federation (ESGF) Face-to-Face Conference (San Francisco, California, USA).
The dashboard shows up-to-the-moment usage demographics and a statistical overview of data use, such as total number of registered users by continent and country, downloads by continent and country, and total number of data sets and data volume. Capturing federated usage metrics, the dashboard provides a rich set of charts and reports through a web interface, allowing users and system managers to visualize the status of the ESGF infrastructure through smart, user-friendly web gadgets. The CMCC group addressed key challenges such as communicating the most important information in a straightforward way and allowing different users to view specific details simultaneously. Without their critical work in displaying automatic real-time data usage, scientists would have no clear way to determine the importance of their projects’ data contributions to the community.

The Earth System Grid Federation (ESGF) is an open infrastructure for access to distributed geospatial data. ESGF is a multi-agency, international collaboration that aims at developing the software infrastructure needed to facilitate and empower the study of climate change on a global scale. More in detail, ESGF includes services for data discovery, access, analysis and visualization, model output, observations, and reanalysis data while being a successful example of integration of disparate open source technologies into a cohesive, wide functional system.