Glycerol dialkyl glycerol tetraethers (GDGTs), including both the archaeal isoprenoid GDGTs (isoGDGTs) and the bacterial branched GDGTs (brGDGTs), have been used in paleoclimate studies to reconstruct temperature in marine and terrestrial archives. However, GDGTs are present in many different types of environments, with relative abundances that strongly depend on the depositional setting. This suggests that GDGT distributions can be used more broadly to infer paleoenvironments in the geological past. In this study, we analyzed 1153 samples from a variety of modern sedimentary settings for both isoGDGT and brGDGTs. We used machine learning on the GDGT relative abundances from this dataset to relate the lipid distributions to the physical and chemical characteristics of the depositional settings. We observe a robust relationship between the depositional environment and the lipid distribution profiles of our samples. This dataset was used to train and test the Branched and Isoprenoid GDGT Machine learning Classification algorithm (BIGMaC), which identifies the environment a sample comes from based on the distribution of GDGTs with high accuracy. We tested the model on the sedimentary record from the Giraffe kimberlite pipe, an Eocene maar in subantarctic Canada, and found that the BIGMaC reconstruction agrees with independent stratigraphic information, provides new information about the paleoenvironment of this site, and helps improve paleotemperature reconstruction. In cases where paleoenvironments are unknown or are changing, BIGMaC can be applied in concert with other proxies to generate more refined paleoclimatic records.

The sciences struggle to integrate across disciplines, coordinate across data generation and modeling activities, produce connected open data, and build strong networks to engage stakeholders within and beyond the scientific community. The American Geophysical Union (AGU) is divided into 25 sections intended to encompass the breadth of the geosciences. Here, we introduce a special collection of commentary articles spanning 19 AGU sections on challenges and opportunities associated with the use of ICON science principles. These principles focus on research intentionally designed to be Integrated, Coordinated, Open, and Networked (ICON) with the goal of maximizing mutual benefit (among stakeholders) and cross-system transferability of science outcomes. This article 1) summarizes the ICON principles; 2) discusses the crowdsourced approach to creating the collection; 3) explores insights from across the articles; and 4) proposes steps forward. There were common themes among the commentary articles, including broad agreement that the benefits of using ICON principles outweigh the costs, but that using ICON principles has important risks that need to be understood and mitigated. It was also clear that the ICON principles are not monolithic or static, but should instead be considered a heuristic tool that can and should be modified to meet changing needs. As a whole, the collection is intended as a resource for scientists pursuing ICON science and represents an important inflection point in which the geosciences community has come together to offer insights into ICON principles as a unified approach for improving how science is done across the geosciences and beyond.