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Fig. 3 in Protocol Optimization For Genomic Dna Extraction And Rapd-Pcr Of Alien Ponto-Caspian Amphipod Pontogammarus Robustoides
Fig. 3. RAPD fingerprints results from different samples of Pontogammarus robustoides with primers OPA-02 (M- marker, 1-15 runners- different samples of Pontogammarus robustoides; K- control) using RAPD-PCR 10 × Taq buffer with (NH4)2SO4.
Figure 2 in Optimizing in vitro factors for improvement of shoot proliferation of 'Flordaguard' peach rootstock
Figure 2. Shoot proliferation from node explant of 'Flordaguard' peach rootstock. A. Shoot cultured in half strength MS (½N); B. full strength MS medium; C. Induction of multiple shoots in full strength MS supplemented with 0.5 mg L-1 of BAP; D. induction of multiple shoots in half strength MS (½N) supplemented with 0.5 mg L-1 of BAP.
Figure 1 in Optimizing in vitro factors for improvement of shoot proliferation of 'Flordaguard' peach rootstock
Figure 1. Order of the factors tested for improvement in the in vitro multiplication of 'Flordaguard' peach rootstock. I. Salt medium composition in 4 mg L-1 BAP; II. Salt medium composition in 0.5 mg L-1 BAP; III. Culture medium consistency; IV. Explant type and position; and V. Explant growth in half strength MS medium with different BAP doses.
A three-weight surface modeling approach for optimizing small-scale population disaggregation
<p><span>In recent decades, gridded population data at fine scales has become a popular data source for assessing and monitoring the Sustainable Development Goals (SDGs). However, current population disaggregation methods are facing challenges in generating high-precision population grids for small areas with limited data. To fill this gap, we proposed a lightweight population gridding method that combines basic dasymetric mapping and point-based surface modeling, named three-weight surface modeling. In this method, there are three weights designed to describe the population spatial heterogeneity from different perspectives. The first weight is building-volume weight, which is equivalent to the preliminary results of population assignment based on building volume information. The second weight, POI-center weight, incorporates POI categories and aggregation patterns to express the centers with high population density, which is calculated based on the neighborhood accumulation rule of Spearman's correlation coefficients between POIs and population size. The third weight called POI-distance weight, indicates different rates of population decay with distance from high-density centers. The three-weight surface model allows us to dynamically adjust the parameters so as to correct the building-volume weight according to the remaining two POI-related weights for a more accurate population surface. After analyzing the census population and the disaggregation results of 544 villages in three counties (Huishui, Luodian and Pingtang) in southern Guizhou Province, China, we found that the customized three-weight model constructed using the local parameter groups demonstrated better accuracy performance compared to separate dasymetric mapping or point-based surface modeling. Meanwhile, the 10-m population grid generated by the local parameter model (LPTW-POP) exhibited higher resolution and lower errors (RMSE, MAE and MRE) than widely used gridded population datasets like LandScan, WorldPop and GHS-POP.</span></p>
Optimal network sizes for most robust Turing patterns
<p>Dataset for Figures 3 and 4.</p>
An Optimization Framework to Combine Operable Space Maximization with Design of Experiments - Data
<p>Data from figures to accompany paper</p>
Supplementary material 1 from: Bylemans J, Gleeson DM, Lintermans M, Hardy CM, Beitzel M, Gilligan DM, Furlan EM (2018) Monitoring riverine fish communities through eDNA metabarcoding: determining optimal sampling strategies along an altitudinal and biodiversity gradient. Metabarcoding and Metagenomics 2: e30457. https://doi.org/10.3897/mbmg.2.30457
: Data type: Microsoft Word Document (.docx)
Supplementary material 2 from: Bylemans J, Gleeson DM, Lintermans M, Hardy CM, Beitzel M, Gilligan DM, Furlan EM (2018) Monitoring riverine fish communities through eDNA metabarcoding: determining optimal sampling strategies along an altitudinal and biodiversity gradient. Metabarcoding and Metagenomics 2: e30457. https://doi.org/10.3897/mbmg.2.30457
: Data type: statistical data
Figure 3 in Optimal FOraging OF NeOtrOpical Otters (CarnivOra: Mustelidae) in an urban river and predOminance OF generalist and sedentary fish in their diet
Figure 3. Randomized species accumulation curve (Coleman) for fecal samples of Lontra longicaudis collected during the wet and dry seasons in the Santa Lúcia Biological Station (SLBS) between May 2006 and September 2007.
Figure 2 in Optimal FOraging OF NeOtrOpical Otters (CarnivOra: Mustelidae) in an urban river and predOminance OF generalist and sedentary fish in their diet
Figure 2. Stretches of the Timbuí River in the Santa Lúcia Biological Station (SLBS) with environments conducive to the presence of otters: (A) rocky environments; (B) sandbanks covered by vegetation. Images captured on May 27, 2007.
Figure 1 in Optimal FOraging OF NeOtrOpical Otters (CarnivOra: Mustelidae) in an urban river and predOminance OF generalist and sedentary fish in their diet
Figure 1. Santa Lúcia Biological Station (SLBS) in the green area. The highlight (blue line) represents the Timbuí river which crosses the municipal seat of Santa Teresa, Espírito Santo, Brazil.
Fig. 5 in Optimality of phylogenetic nomenclatural procedures
Fig. 5 Polysemy under the traditional procedure; see text for explanations
Quantitative phenotyping of plant roots using spectral electrical impedance tomography: a rhizotron study on maize and bean crops with optimized measurement design
<div> <h1>Data repository</h1> <br> <div>In this repository, the data accompanying the publication "Quantitative phenotyping of plant roots using spectral electrical impedance tomography: a rhizotron study on maize and bean crops with optimized measurement design", published in "Plant Methods", is stored.</div> <div> </div> <div>The publication can be found at https://doi.org/10.1186/s13007-024-01247-7</div> <br> <h2>Dataset structure</h2> <br> <div>The dataset is structured in the following directories:</div> <br> <h3>complex resistivity inversions</h3> <br> <div>The folder "new_filter_approach_v4_allfreqs", contains all inversion results for frequencies from 0.79 Hz to 1 kHz, sorted by plant name. The data are given as tomodirs (see https://github.com/geophysics-ubonn/crtomo_tools for importing tomodir data within a python framework).</div> <br> <div>In the folder "scatterdata_from_invs", electrical parameters from the inversions are given for x-y-locations in the grid.</div> <br> <h3>dd_fit_results_all_cells</h3> <br> <div>In this folder, the results of the Debye-Decomposition for all cells in the grid are given, together with the x-y-locations in the grid.</div> <br> <h3>dd_fit_results_only_plants</h3> <br> <div>In this folder, the results of the Debye-Decomposition for all cells within the root zone in the grid are given, together with the x-y-locations in the grid.</div> <br> <h3>grid</h3> <br> <div>Contains all files to construct the inversion grid using crtomo_tools.</div> <div> </div> <br> <h3>images_plants</h3> </div> <div> <div> </div> <div>Contains photos for all measured plants, labeled according to the datasets in the manuscript.</div> <br> <div> <h3>impedance_data</h3> </div> <br> <div>In the folder "uncorrected", the raw impedance measurements for all plants are saved (only the correction for the resistivity magnitude is applied to account for the mismatch of 2D inversion and 3D measurement). The folder "corrected" containst the imepdance data that was corrected with background water measurements. The file "md_data_impedance.csv" contains the contact resistances and leakage currents for all measurements.</div> <br> <h3>optimization</h3> <br> <div>This folder contains the results for the synthetic modelling using an optimized (OPT_full) and reduced (OPT_middle) scheme, as described in the manuscript. As for the experimental results, inversions are given in tomodir format. The file "exp.csv" contains the xy-coordinates of the outline of the synthetic polarization signature.</div> <br> <h3>plots</h3> <br> <div>Contains all plots that are shown in the manuscript.</div> <br> <h3>root_data</h3> <br> <div>In the folder "root_area_polygons", the xy-coordinates of the outline of each root system in the rhizotron experiments, extracted from images of the front of the rhizotron, are saved. The file "extracted_elec_params_from_area_v4.csv" contains electical parameters from the inversion results for a number of frequencies for all cells that lie within the root area polygons. The file "Root_traits_final.csv" contains the individually measured root traits via weighing and scanning.</div> <br> <h3>scripts</h3> <br> <div>The scripts "0_plot_methods.py", "1_plot_results.py", "2_plot_discussion.py" and "3_plot_supplementary.py" create all plots shown in the manuscript and save them in the "plots"-directory.</div> </div> <p> </p> <p> </p>
Efficiency Optimization of Ge-V Quantum Emitters in Single-Crystal Diamond upon Ion Implantation and HPHT Annealing
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Future forest flux (e.g., GPP, NPP, NEP) simulated by the optimized InTEC model under four SSP-RCP scenarios
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Data for Spangenberg, Simpkins and Wiegand "Species distribution modeling using commonness optimization leads to poor predictions for rare species"
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Figure 4. Optimization of the lyophilized nanoparticles at a N:P 2:1 ratio
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A General Optimization Model for Coupling Topology and Nodal Coordinates in Large-Scale Biorthogonal Tensegrity Structures
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Integrating Novel Stellarator Single-Stage Optimization Algorithms to Design the Columbia Stellarator Experiment - Dataset
<p>Data related to the publication by A. Baillod <em>et.al., </em>Integrating Novel Stellarator Single-Stage Optimization Algorithms to Design the Columbia Stellarator Experiment, https://arxiv.org/abs/2409.05261</p>
Analysis and Optimizing Solar Panels for Offshore Remote Wellhead Platforms as a Sustainable and Renewable Energy Source
<p>Data set</p>
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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.