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Supplemental Figure 1- Violin plots illustrating average expression of sox2, her6, isl1, prop1, and fcer1gl
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Figure S1: Forest plot of mean differences in NPB between IR exposed and unexposed workers, removing outlier
<p>Supplementary material for a new publication - revised version</p>
Plot Data: Analysis of thin liquid films driven by SAW
<p>Data of the relevant plots of the thesis</p>
Figs 40–47. Scatter plots generated through PCA using puparium morphometric characters and differentiating sexes. 40, 41 in Determination of sexual dimorphism in the puparia of four whitefly pest species from India (Hemiptera: Aleyrodidae)
Figs 40–47. Scatter plots generated through PCA using puparium morphometric characters and differentiating sexes. 40, 41 – Aleyrodes sp.; 42, 43 – Bemisia tabaci (Gennadius, 1889); 44, 45 – Dialeurodes delhiensis Dialeurodes delhiensis David & Sundararaj, 1992; 46, 47 – Trialeurodes vaporariorum Westwood, 1856.
Plots of metrics from the regridding benchmark with SCRIP, XIOS,ESMF and YAC
<p>The plots of metrics from the regridding benchmark with SCRIP, XIOS,ESMF and YAC</p>
Figure 6. Left mandible EES plots. A, axis 2 in Termite soldier defence strategies: a reassessment of Prestwich's classification and an examination of the evolution of defence morphology using extended eigenshape analyses of head morphology
Figure 6. Left mandible EES plots. A, axis 2 vs. axis 1. B, axis 2 vs. axis 3. C, modelled mode of mandible shape change along axes 1, 2 and 3.
Figure 7. Right mandible EES plots. A, axis 2 in Termite soldier defence strategies: a reassessment of Prestwich's classification and an examination of the evolution of defence morphology using extended eigenshape analyses of head morphology
Figure 7. Right mandible EES plots. A, axis 2 vs. axis 1. B, axis 2 vs. axis 3. C, modelled mode of mandible shape change along axes 1, 2 and 3
Figure 8. Labrum EES plots. A, axis 2 in Termite soldier defence strategies: a reassessment of Prestwich's classification and an examination of the evolution of defence morphology using extended eigenshape analyses of head morphology
Figure 8. Labrum EES plots. A, axis 2 vs. axis 1. B, axis 2 vs. axis 3. C, modelled mode of labrum shape change along axes 1, 2 and 3.
Data from: Comparison of seven simple loss models for runoff prediction at the plot, hillslope and catchment scale in the semiarid southwestern U.S.
<p>Infiltration excess overland flow is the dominant mechanism for runoff generation in many dryland watersheds. Event-based rainfall-runoff models therefore partition precipitation into two components: loss and excess precipitation. The latter is then transformed into a runoff hydrograph. Numerous loss models have been developed over the past century ranging from simple empirical to sophisticated physically based methods. Complex models can lead to equifinality and associated uncertainty at larger spatial scales with varying soil and cover conditions. Simple models are therefore widely used in hydrologic practice. In the absence of measured data in many arid and semiarid regions, model parameters are often estimated based on laboratory or field infiltrometer tests. Given the documented importance of spatial scale on the runoff response in dryland catchments, it is not clear how models parameterized at the point or soil column scale will perform at the hillslope or catchment scale under real-world conditions. In this study, we compared the performance of seven simple loss models with three or less parameters: the Philip, Smith-Parlange, Horton, Kostiakov, curve number (CN), initial and constant (IC) and the linear and constant (LC) models. The latter is a modification of the IC model introduced in this study. We estimated parameters at the plot scale (2.8 m<sup>2</sup><span><span><span><span><span><span><span><span><span>) using rainfall simulation and then tested model performance at the hillslope (1.5–3.7 ha) and catchment scale (2.4–2.8 km</span></span></span></span></span></span></span></span></span><sup>2</sup><span><span><span><span><span><span><span><span><span>) based on measured rainfall-runoff data at two sites in New Mexico and Arizona, U.S. Results show that rainfall simulation can be used successfully to parameterize loss models at the hillslope scale. At the catchment scale, most models showed positive bias, suggesting that other losses (such as channel or transmission losses) play an important role in determining the catchment runoff response. Rainfall intensity and temporal distribution were found to be crucial for accurate runoff prediction. Models that are sensitive to rainfall intensity during the entire simulation (Philip, Smith-Parlange, Horton, Kostiakov, LC) therefore performed better than those with an initial abstraction term (CN, IC). During intermittent rain, the best results were achieved by methods expressing infiltration capacity as a function of cumulative infiltration (LC, Smith-Parlange). </span></span></span></span></span></span></span></span></span></p>
the diversity of indigenous legume fodder trees and shrubs with plots on the bases of land use and agroecology in Gamo zone, Ethiopia
<p>the data was collected from Gamo zone, SNNPRS, Ethiopia. It was about the diversity of indigenous legume fodder tree and shrub with plots on the bases of land use and agroecology. the data was collected from four land uses namely farm land, homestead, grazing land and area closure in three agroe-ecology zones namely lowland, midland and highland. </p>
Plot folder contents for DETECT model v1.0
<p>These files contain the Matlab script files for plotting time-series of soil CO2 at different depths and soil respiration from the DETECT model. The model is described in the paper:</p> <p>Ryan, E. M., K. Ogle, H. Kropp, Y. Carrillo, K. E. Samuels-Crow, E. Pendall (in review) Modelling soil CO2 production and transport with dynamic source and diffusion terms: Testing the steady-state assumption using DETECT v1.0<em>. </em>In Geoscientific Model Development.</p>
Plots Scripts and Associated Data Sets for: "A Divide-and-Conquer Algorithm for Disordered and Interacting Few-Particle Systems in One Dimension"
<p>The dataset includes data and Python scripts for all figures in the corresponding preprint/article.</p>
Open data for assessing habitats degree of conservation at plot level. An example dataset of forest structural attributes in Val d'Agri (Basilicata, Southern Italy)
<p>We provide a georeferenced dataset of vertical and horizontal structure of forest types belonging to 4 habitat types, sensu Council Directive 92/43/EEC. The dataset includes structural indicators commonly linked to old-growth forests in Europe, in particular the amount of standing and lying deadwood. We collected data on 32 plots (24 of 225 m<sup>2</sup>, and 8 of 100 m<sup>2</sup>, according to different forests type) during spring and summer of 2022, in Val d’Agri (Basilicata, Southern Italy). The dataset we provide follows the common national standard for field data collection in forest habitat types, published by ISPRA in 2016 (https://www.isprambiente.gov.it/public_files/direttiva-habitat/Manuale-142-2016.pdf) with the aim to promote a greater homogeneity in assessment of habitat conservation status at Country and biogeographical level, as requested by the Habitats Directive.</p> <p>Data were acquired by using a free Android application (VegApp, (https://vegapp.de/), and standardized field survey forms.</p> <p>The selection of the location of the sample sites were spatially balanced by using the sofware QGis vers. 3.22 (http://qgis.osgeo.org)</p> <p>plots_Table1 shows the characteristics of the sample sites, and information on type of management, when available.</p> <p>plots_Table2 provides the description and the measuring unit of all the attributes included in the dataset at plot level. It includes parameters used for assessing old-growth forests, linking them to the main type forests management, in order to both select indicators useful for assessing the conservation degree of forest habitat types, and identify good practices for nature conservation in these ecosystems.</p> <p>plots_Table3 contains information on living trees and deadwood at plot level.</p> <p>Trees describes the dataset related to single-tree data, and it contains the following fields for all living trees, exceeding 10 cm DBH, occurring in the plots. The nomenclature follows the Italian checklist available at https://dryades.units.it/floritaly/</p> <p>The shapefile "plots" contains the same information included in plots_Table1.</p>
volcano plot
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ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.