This grant continues support for two years for research conducted by the Reservoirs and Fluxes community of the Deep Carbon Observatory. Led by Marie Edmonds of Cambridge University and Erik Hauri of the Carnegie Institution for Science and comprising some 120 core members across the globe, the Reservoirs and Fluxes community is engaged in a coordinated research program to advance our understanding of the volume, distribution, and movement of Earth’s carbon. Major research goals include improving our knowledge of the global budget of fluxes of gases from volcanoes; learning about carbon in the mantle and its changes through time by studying the diamonds and their inclusions that were formed very deep; improving estimates of the global circulation of carbon in Earth’s interior and fluid dynamics of carbon; and improving knowledge of the chemical forms, mineral hosts, and reactions of carbon moving between reservoirs. The third and fourth activities are key for the DCO’s program-wide initiative to build a system of models simulating the origins and movements of deep carbon through Earth’s history, the paramount synthetic effort of the DCO, which could also be its greatest scientific legacy. The majority of grant funds provide partial support for each of about ten post-docs at six different institutions.

The SDSS data management structure, software, and interface has been on the frontier of astronomy since it was developed in the early 2000s. Many leading astronomical data centers use, integrate, and rely heavily on SDSS data, and these data are routinely accessed by amateur astronomers, students, and the public. This grant provides support to upgrade two back-end components of the SDSS data archive. The first is the Science Archive Server (SAS), housed at the University of Utah. SAS includes SDSS’s raw and calibrated images, and the SDSS spectrum files, all of which are primarily used by professional astronomers. The second is the Catalog Archive Server (CAS), hosted at Johns Hopkins University. CAS contains the primary catalog data and all metadata extracted from the raw images and spectra. CAS helps to facilitate research from astronomers both within and outside of the collaboration, as it serves as the primary link between SDSS data and other data sets in astronomy. In addition to modernizing and expanding the core functioning of these two systems, the upgrades will help improve the integration of SDSS data with broader outreach and public education efforts, including better connections with SDSS Voyages, the newly developed web portal devoted to public engagement.

Written in Python, scikit-learn is an open source machine learning software package used widely across the natural and social sciences (the “software paper” that introduced scikit-learn in 2011 has been cited over 4,700 times). Its maintainers have identified a set of improvements that would make it substantially more efficient for scientific users and enable more reproducible research, but which would require more focused time than any contributor can currently offer. This grant provides funds to Columbia University’s Andreas Mьller, one of the current core maintainers of scikit-learn, to design and implement the identified improvements. These include more flexible data types, better integration with Jupyter notebooks for model exploration, and some technical fixes that will substantially improve platform stability and performance.

A whole lot of science takes place in spreadsheets. Many researchers still bring their data into Excel as a convenient environment for exploration and analysis. Unfortunately, Excel has none of the attributes of a modern platform for reproducible computational research: it is not easily extensible to interoperate with data repositories; does not easily allow for version control; and cannot take advantage of substantial investments in open source scientific software packages. Nokome Bentley, a New Zealand-based fisheries scientist and software developer, has been developing a project called Stencila Sheets, an authoring tool that offers users familiar Google Docs–style interfaces, but is something quite different under the hood. His vision is a spreadsheet where each cell can hold data or code written in R, Python, Julia, or several other computing languages, with the output of a given cell addressable by any other cell in the sheet. The proximate goal is not to develop a direct competitor to Excel, but rather to offer spreadsheet users an easy bridge into the open-source ecosystem of reproducible computational science. Funds from this grant will allow further development of the Stencila platform over the next year, including increased integration with the Jupyter computing ecosystem, the development of a standalone desktop client, and the addition of features like real-time collaboration and import/export from other platforms.