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FIGURE 11 in Phylogenetically vetted and stratigraphically constrained fossil calibrations within Aves
FIGURE 11. Phylogenetic tree showing position of Pulchrapollia gracilis, illustrating the divergence calibrated by NMH A6207. Placement of Pulchrapollia gracilis as a stem parrot is supported by multiple analyses (see text).
FIGURE 7 in Phylogenetically vetted and stratigraphically constrained fossil calibrations within Aves
FIGURE 7. Phylogenetic tree showing position of Scaniacypselus wardi, illustrating the divergence calibrated by NHMUKA5430.
FIGURE 2 in Phylogenetically vetted and stratigraphically constrained fossil calibrations within Aves
FIGURE 2. Stratigraphic distribution of fossil anatoids in the Paleogene. The minimum possible age of the calibrating specimen of Vegavis iaai is presented, and midpoints of age ranges are shown for the ages of other Paleogene anatoid specimens.
FIGURE 4 in Phylogenetically vetted and stratigraphically constrained fossil calibrations within Aves
FIGURE 4. Stratigraphic distribution of fossil Sphenisciformes in the Paleogene. The minimum possible age of the calibrating specimen of Waimanu manneringi is presented, and midpoints of age ranges are shown for the ages of other Paleogene penguin specimens.
FIGURE 1 in Phylogenetically vetted and stratigraphically constrained fossil calibrations within Aves
FIGURE 1. Phylogenetic tree showing the position of Vegavis iaai, illustrating the divergence calibrated by MLP 93-I-3-1.
FIGURE 8 in Phylogenetically vetted and stratigraphically constrained fossil calibrations within Aves
FIGURE 8. Stratigraphic distribution of fossil Pan-Apodidae in the Paleogene. The minimum possible age of the calibrating specimen NHMUK A5430 is presented, and midpoints of age ranges are shown for the ages of other Paleogene swift specimens.
FIGURE 6 in Phylogenetically vetted and stratigraphically constrained fossil calibrations within Aves
FIGURE 6. Stratigraphic distribution of fossil Coracii in the Paleogene. The minimum possible age of the calibrating specimen of Primobucco mcgrewi is presented, and midpoints of age ranges are shown for the ages of other Paleogene roller in specimens. A precise stratigraphic placement for the Condé-en-Brie specimen is not available (see text).
FIGURE 10 in Phylogenetically vetted and stratigraphically constrained fossil calibrations within Aves
FIGURE 10. Stratigraphic distribution of fossil Coliiformes in the Paleogene. The minimum possible age of the calibrating specimen of Sandcoleus copiosus is presented, and midpoints of age ranges are shown for the ages of other Paleogene mousebird specimens.
FIGURE 13 in Phylogenetically vetted and stratigraphically constrained fossil calibrations within Aves
FIGURE 13. Phylogenetic tree showing position of Messelirrisor halcyrostris, illustrating the divergence calibrated by SMF-ME 1883a+b.
FIGURE 3 in Phylogenetically vetted and stratigraphically constrained fossil calibrations within Aves
FIGURE 3. Phylogenetic tree showing position of Waimanu manneringi, illustrating the divergence calibrated by CM zfa35.
FIGURE 12 in Phylogenetically vetted and stratigraphically constrained fossil calibrations within Aves
FIGURE 12. Stratigraphic distribution of fossil Pan-Psittaciformes in the Paleogene. The minimum possible age of the calibrating specimen NMH A6207 is presented, and midpoints of age ranges are shown for the ages of other Paleogene parrot specimens. Note that the London Clay Formation also contains multiple associated skeletons and isolated bones in addition to the holotype of Pulchrapollia gracilis that serves as a calibration point.
FIGURE 9 in Phylogenetically vetted and stratigraphically constrained fossil calibrations within Aves
FIGURE 9. Phylogenetic tree showing position of Sandcoleus copiosus, illustrating the divergence calibrated by USNM 433912.
FIGURE 5 in Phylogenetically vetted and stratigraphically constrained fossil calibrations within Aves
FIGURE 5. Phylogenetic tree showing position of Primobucco mcgrewi, illustrating the divergence calibrated by USNM 336484.
Datasets and models for constrained retrosynthesis with multi-objective search and disconnection-aware Chemformer
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The Fate of Nitrogen during Early Silicate Differentiation of Rocky Bodies Constrained by Experimental Mineral-Melt Partitioning
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Dataset for: Constraining the duration and ages of stratigraphic unconformities on Mars using exhumed craters
<h1>Dataset for: Constraining the duration and ages of stratigraphic unconformities on Mars using exhumed craters</h1> <p><strong>Annex & Lewis</strong></p> <h2><strong>Data Product Overview</strong></h2> <p>This repository contains all source data for the publication. Below is a description of each general data product type, software that can load the data, and a list of the file names along with the short description of the data product.</p> <p><strong>Perimeters.gpkg & csv</strong></p> <p>Contains the mapped linear perimeters of the layered deposits discussed in the manuscript. Geopackage files can be read using GIS software like QGIS and ArcGIS as well as the OGR/GDAL suite. CSV is also provided.</p> <p><strong>Craters.gpkg & csv</strong></p> <p>Contains the polygon geometries for the craters counted along the linear perimeters. Geopackage files can be read using GIS software like QGIS and ArcGIS as well as the OGR/GDAL suite. CSV is also provided.</p> <p><strong>environment.yml</strong></p> <p>Anaconda conda environment configuration file that can be used by users to create a functional conda environment.</p> <p><strong>requirements.txt</strong></p> <p>Python dependencies needed to run the included Jupyter Notebook for pip users.</p> <p><strong>linear_crater_counting.ipynb</strong></p> <p>Jupyter notebook file that contains the python code for the manuscript as well as the code to generate all the figures in the paper. </p> <p>The notebook can also be run via Binder at <a href="https://mybinder.org/v2/zenodo/10.5281/zenodo.8298786" target="_blank" rel="noopener">https://mybinder.org/v2/zenodo/10.5281/zenodo.8298786</a></p> <p> </p>
Data for Mercury's crustal porosity as constrained by the planet's bombardment history
<p>This archive contains grids and spherical harmonic files for <em>Mercury's crustal porosity as constrained by the planet's bombardment history Broquet A. & Rolser F. et al, submitted to Geophysical Research Letters (2024). </em></p> <p>The different grid files are provided in netcdf format and are expanded up to spherical harmonic degree 300 for crustal thickness and 80 for porosity. The spherical harmonic model uses the <a href="https://shtools.github.io/SHTOOLS/index.html">SHTOOLs</a> convention. Higher resolution models are available on demand. </p> <p>Porosity_M1.netcdf: Nominal porosity model using the parameters that best-fit gravity-derived porosity estimates</p> <p>Porosity_M2.netcdf: Porosity model using the lunar parameters</p> <p>CrustalThickness_M1.netcdf: Crustal thickness (km) considering the effect of crustal porosity from model M1 and assuming an average grain density of 2950 kg m-3.</p> <p>CrustalThickness_M2.netcdf: Crustal thickness (km) considering the effect of crustal porosity from model M2 and assuming an average grain density of 2950 kg m-3.</p> <p>CrustalThickness_M1_Beu20.netcdf: Crustal thickness (km) considering the effect of crustal porosity from model M1 and lateral grain density variations from Beuthe et al. (2020).</p> <p>CrustalThickness_M2_Beu20.netcdf: Crustal thickness (km) considering the effect of crustal porosity from model M2 and lateral grain density variations from Beuthe et al. (2020).</p> <p>CrustalThickness_2800.netcdf: Crustal thickness (km) considering the an average crustal bulk density of 2800 kg m-3.</p> <p>N20.netcdf: N20 count using a 1000-km moving window.</p> <p>Topo_degstr.sh: Spherical harmonic coefficients for the spectrally truncated Mercury Laser Altimeter model. The truncation uses the degree-strength map of Konopliv et al. (2020). </p>
Data from: Less favorable climates constrain demographic strategies in plants
Correlative species distribution models are based on the observed relationship between species' occurrence and macroclimate or other environmental variables. In climates predicted less favourable populations are expected to decline, and in favourable climates they are expected to persist. However, little comparative empirical support exists for a relationship between predicted climate suitability and population performance. We found that the performance of 93 populations of 34 plant species worldwide – as measured by in situ population growth rate, its temporal variation and extinction risk – was not correlated with climate suitability. However, correlations of demographic processes underpinning population performance with climate suitability indicated both resistance and vulnerability pathways of population responses to climate: in less suitable climates, plants experienced greater retrogression (resistance pathway) and greater variability in some demographic rates (vulnerability pathway). While a range of demographic strategies occur within species' climatic niches, demographic strategies are more constrained in climates predicted to be less suitable.
Data from: The genetic variance but not the genetic covariance of life-history traits changes towards the north in a time-constrained insect
Seasonal time constraints are usually stronger at higher than lower latitudes and can exert strong selection on life history traits and the correlations among these traits. To predict the response of life history traits to environmental change along a latitudinal gradient, information must be obtained about genetic variance in traits and also genetic correlation between traits, i.e., the genetic variance-covariance matrix, G. Here, we estimated G for key life history traits in an obligate univoltine damselfly that faces seasonal time constraints. We exposed populations to simulated native temperatures and photoperiods and common garden environmental conditions in a laboratory setup. Despite differences in genetic variance in these traits between populations (lower variance at northern latitudes), there was no evidence for latitude-specific covariance of the life history traits. At simulated native conditions, all populations showed strong genetic and phenotypic correlations between traits that shaped growth and development. The variance-covariance matrix changed considerably when populations were exposed to common garden conditions compared with the simulated natural conditions, showing the importance of environmentally induced changes in multivariate genetic structure. Our results highlight the importance of estimating variance-covariance matrixes in environments that mimic selection pressures and not only trait variances or mean trait values in common garden conditions for understanding the trait evolution across populations and environments.
Data from: Testing the limits of pheromone stigmergy in spatially constrained robotic swarms
Area coverage and collective exploration are key challenges for swarm robotics. Previous research in this field has drawn inspiration from ant colonies, with real, or more commonly virtual, pheromones deposited into a shared environment to coordinate behaviour through stigmergy. Repellent pheromones can facilitate rapid dispersal of robotic agents, yet this has been demonstrated only for relatively small swarm sizes (N<30). Here, we report findings from swarms of real robots (Kilobots) an order of magnitude larger (N>300), and from realistic simulation experiments up to N=400. We identify limitations to stigmergy in a spatially constrained environment – a free but bounded two-dimensional workspace – using repellent binary pheromone. At larger N a simple, stigmergic avoidance algorithm becomes first no better, then inferior to, the area coverage of non-interacting random walkers. Thus, with ever-increasing swarm sizes, the assumption of robustness and scalability for such approaches may need to be re-examined. Instead, subcellular biology, and diffusive processes, may prove a better source of inspiration at large N in spatially constrained or high agent density environments.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.