Proteomics and metabolomics data from rodents and humans with PAH.

Proteomics and metabolomics data from rodents and humans with PAH.

Matlab software and data associated with Roth et al. (submitted to Seismica, 2025).

An ongoing challenge in macroevolutionary research is identifying common drivers of diversification amid the complex interplay of many potentially relevant traits, ecological contexts, and intrinsic characteristics of clades. In this study, we used geometric morphometric and phylogenetic comparative methods to evaluate the tempo and mode of morphological evolution in an adaptive radiation of Malagasy birds, the vangas, and their mainland relatives (Aves: Vangidae). To characterize the morphological diversity of the clade we used both landmarks and semilandmark curves from the bill and neurocranium, and linear measurements of pedal bones, all collected from microCT scans of museum specimens. The Malagasy radiation is more diverse in both skull and foot shape. However, rather than following the classic "early burst" of diversification, trait evolution accelerated well after their arrival in Madagascar, likely driven by the evolution of new modes of foraging and especially of a few species with highly divergent morphologies. Anatomical regions showed differing evolutionary patterns, and the presence of morphological outliers impacted the results of some analyses, particularly of trait integration and modularity. Our results demonstrate that the adaptive radiation of Malagasy vangas has evolved exceptional ecomorphological diversity along multiple, independent trait axes, mainly driven by a late expansion in niche space due to key innovations. Our findings highlight the evolution of extreme forms as an overlooked feature of adaptive radiation warranting further study.

Many studies of tropical forest ecology occur in protected areas such as national parks.  We have a limited understanding of the composition and structure of trees in areas outside forests, such as urban areas or along roads. Here we report three datasets that are critical for understanding how tree communities differ in Guanacaste, Costa Rica. Our data allow the user to contrast trees in three different land-use types that represent a rural to urban gradient: forests inside conservation areas (20 plots), fencerows (68 plots), and municipal parks (36 parks).  We measured and identified trees >7cm diameter in addition to collecting ancillary data such as plot area (in hectares) and canopy cover (in percent). We also collated information for each identified species on functional characteristics including: status as native to Guanacaste, Costa Rica, leaf habit (evergreen, deciduous, or other) and whether the tree species produces fruit that are edible for humans. Collectively, these data allow the user to calculate stand-level properties such as basal area and percentage of the tree community that is evergreen.

We present morphological classifications of over 41,000 galaxies out to photometric redshifts of ~2.5 across six square degrees of the Euclid Deep Field North (EDFN) from the Hawaii Twenty Square Degree (H20) survey, a part of the wider Cosmic Dawn survey.This repository contains the data released alongside the research publication "Galaxy Zoo: Cosmic Dawn -- morphological classifications for over 41,000 galaxies in the Euclid Deep Field North from the Hawaii Two-0 Cosmic Dawn survey" (Pearson et al., 2025). Please cite this paper (DOI to follow on publication) when using the data in this repository.The data consists of classifications made by volunteers through the Galaxy Zoo project on the Zooniverse citizen science platform, as well as predicted classifications made by a deep learning model, Zoobot. Also included are the subject images themselves (in PNG/JPG format), a file of metadata for each subject, and a .csv file of all the tags assigned to the subjects by volunteers. Most files are given in .parquet format, which is a fast csv-like format that can be read in with the pandas Python package as DataFrames using pd.read_parquet(loc, columns=[]).A Jupyter notebook gzcd_info_and_example_usage.ipynb is included which provides more details on the data set and examples of how to use it.

We present morphological classifications of over 41,000 galaxies out to photometric redshifts of ~2.5 across six square degrees of the Euclid Deep Field North (EDFN) from the Hawaii Twenty Square Degree (H20) survey, a part of the wider Cosmic Dawn survey.This repository contains the data released alongside the research publication "Galaxy Zoo: Cosmic Dawn -- morphological classifications for over 41,000 galaxies in the Euclid Deep Field North from the Hawaii Two-0 Cosmic Dawn survey" (Pearson et al., 2025). Please cite this paper (DOI to follow on publication) when using the data in this repository.The data consists of classifications made by volunteers through the Galaxy Zoo project on the Zooniverse citizen science platform, as well as predicted classifications made by a deep learning model, Zoobot. Also included are the subject images themselves (in PNG/JPG format), a file of metadata for each subject, and a .csv file of all the tags assigned to the subjects by volunteers. Most files are given in .parquet format, which is a fast csv-like format that can be read in with the pandas Python package as DataFrames using pd.read_parquet(loc, columns=[]).A Jupyter notebook gzcd_info_and_example_usage.ipynb is included which provides more details on the data set and examples of how to use it.

Recent studies highlight conservation management practices as an effective strategy to enhance soil health. However, results vary, particularly regarding which soil health parameters respond most sensitively to these practices. More studies covering a wide range of soil types and climatic conditions are needed to assist farmers in making management decisions on production practices related to soil health. In this study, we collected soil samples (0-15 cm) from 21 (4–50 years) soybean [Glycine max (L.) Merr.]-based cropping systems trials across the United States (US) to assess the impact of management practices on soil health indicators. Soil indicators included wet aggregate stability (WAS), permanganate oxidizable carbon (POXC), organic matter loss-on-ignition (OM-LOI), mineralizable carbon (Min-C), water extractable organic carbon (WEOC), total organic carbon (TOC), soil extractable protein (ACE-N), total nitrogen (TN), pH, soil test phosphorus (STP), and soil test potassium (STK). Our objectives were: (i) to assess the effects of crop rotation, tillage, cover cropping, and artificial drainage on soil health; (ii) to inform soybean farmers about the management practices that are associated with improvements on soil health; and (iii) to develop and share a unique and open soil health dataset with the research community for future global meta-studies. To assess the effects of management practices on soil health indicators, both meta-analysis approach and linear mixed-effect models were used. Two-crop rotations were associated with greater STP values compared to a single-crop. The inclusion of cover crops was associated with greater Min-C and WEOC compared to no cover crops. No-tillage showed more acidic pH than conventional tillage. The remaining soil health indicators tested did not change in response to the management practices assessed. There were no statistically significant differences in observed soil tests between tile-drained and undrained treatments. Overall results suggest that cover crops can play an important role in building soil health in soybean-based cropping systems. Our open-access dataset provides a valuable resource for future research and meta-studies, ultimately contributing to the development of more effective management strategies for promoting more sustainable soybean cropping systems.

Manuscript title:"Population genomics traces zebra mussel invasions along the expanding western North American front in Minnesota, USA to their source waters"Abstract:Zebra mussels (ZMs) continue to transform aquatic ecosystems, threaten native species, and accrue high economic costs in the Northern Hemisphere. In 2024, Minnesota (MN) in the western Great Lakes became the state with the most ZM-infested lakes in the USA. We used population genomics to examine recent ancestry and infer the source water bodies from which ZMs colonized >1/3 of MN lakes infested from 2003-2018 (when spread accelerated), then compared results to traffic between lakes of boaters and anglers, the suspect invasion vectors. Lake Superior, the Upper Mississippi River, Lake Erie, and several inland lakes were our top-ranked inferred sources. We traced ZMs in 51 of 58 infested lakes to source water bodies in-state, but most were not from alleged “superspreaders” (sensu epidemiology). Mille Lacs Lake, a popular angling destination and boater network hub, was a notable exception as the inferred source for three lakes. In three MN lake-rich regions in which spread continues to be concentrated, invasions from nearby were common, and sources were most often not high boat-traffic lakes, suggesting that vectors other than trailered boats need further evaluation. Geographic expansion of our population genomic dataset could provide genomic surveillance and guide prevention of the continuing spread of ZMs.

Manuscript title:"Population genomics traces zebra mussel invasions along the expanding western North American front in Minnesota, USA to their source waters"Abstract:Zebra mussels (ZMs) continue to transform aquatic ecosystems, threaten native species, and accrue high economic costs in the Northern Hemisphere. In 2024, Minnesota (MN) in the western Great Lakes became the state with the most ZM-infested lakes in the USA. We used population genomics to examine recent ancestry and infer the source water bodies from which ZMs colonized >1/3 of MN lakes infested from 2003-2018 (when spread accelerated), then compared results to traffic between lakes of boaters and anglers, the suspect invasion vectors. Lake Superior, the Upper Mississippi River, Lake Erie, and several inland lakes were our top-ranked inferred sources. We traced ZMs in 51 of 58 infested lakes to source water bodies in-state, but most were not from alleged “superspreaders” (sensu epidemiology). Mille Lacs Lake, a popular angling destination and boater network hub, was a notable exception as the inferred source for three lakes. In three MN lake-rich regions in which spread continues to be concentrated, invasions from nearby were common, and sources were most often not high boat-traffic lakes, suggesting that vectors other than trailered boats need further evaluation. Geographic expansion of our population genomic dataset could provide genomic surveillance and guide prevention of the continuing spread of ZMs.