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864 results for “numerative”
The dataset used in numerical simulations based on the resistivity structure of Kusatsu-Shirane Volcano, Japan
<p>The dataset used in the numerical simulations based on the resistivity structure of Kusatsu-Shirane Volcano, Japan. </p> <p>Descriptions of included files:</p> <p><strong>TOUGH_model.zip</strong></p> <p>The data used in the numerical simulations by the TOUGH3 code. It includes ten cases with different permeability structures considered.</p> <p><strong>HT_model.zip</strong></p> <p>The data used in the numerical simulations by the HYDROTHERM code. It includes seven cases with different permeability structures considered.</p>
'Reconciling Surface Deflections From Simulations of Global Mantle Convection' : Numerical model dataset
<p><strong>Output data from TERRA simulations included in 'Reconciling Surface Deflections From Simulations of Global Mantle Convection'</strong></p> <p>Dataset includes:</p> <ul> <li>Full density field (`NC_visc_dens_037.tar.gz`)</li> <li>Radial stresses (directory `radial_stresses`)</li> <li>Spherical harmonic coefficients for density field (`density_sph.037`)</li> <li>Radial viscosity factors (`visc.dat`)</li> </ul> <p>Radial stresses are calculated at depths of:</p> <ul> <li>0 km (surface)</li> <li>45 km</li> <li>180 km</li> <li>270 km</li> </ul> <p>and with various amounts of shallow structure removed:</p> <ul> <li>NC_DT_rmir0 - No shallow structure removed</li> <li>NC_DT_rmir1 - 45 km removed</li> <li>NC_DT_rmir2 - 90 km removed</li> <li>NC_DT_rmir3 - 135 km removed</li> <li>NC_DT_rmir5 - 225 km removed</li> <li>NC_DT_rmir7 - 270 km removed</li> </ul>
code for: The cerebellum computes frequency dynamics for motions with numerical precision and cross-individual uniformity
<p>MATLAB codes for data analyses performed in "The cerebellum computes frequency dynamics for motions with numerical precision and cross-individual uniformity".</p>
Data set for paper: "Numerical analysis of plastic deformation evolution in polycrystalline copper during cyclic loading with different frequencies"
<p>The dataset contains information necessary for performing the numerical analysis presented in the related paper. The input data are suited for the finite element code Z-set (http://www.zset-software.com/). The resulting data from the numerical calculations and source data for paper figures can be used for further analysis. These data are provided in ASCII format in text files and can be processed by any relevant software.</p>
Lab data and numerical results bichromatic waves
<p>Lab data and numerical results of bichromatic waves groups propagating over a constant slope.</p>
FIGURE 6 in Vertebrate endemism in south-eastern Africa numerically redefines a biodiversity hotspot
FIGURE 6. Areas and centres of endemism within the south-east Africa dominion (a) areas of endemism as identified from the Parsimony Analysis of Endemicity as identified and coded in Fig 4d; A—Albany-Knysna, B—Maputaland-Natal-Pondoland, C—Eastern Escarpment, (b) broad centres of endemism as identified and coded in the cluster dendrogram in Fig. 4a; a— Maputaland, b—Knysna, c—Drakensberg, d—Albany, e—Natal-Pondoland, f—Mpumalanga Escarpment, and (c) narrow centres of endemism identified and numbered in the cluster dendrogram in Fig. 4a; 1—Drakensberg-KwaZulu-Natal Escarpment, 2—Albany Coastal Belt, 3—Amatola-Winterberg, 4—Natal Midlands, 5—Natal Coastal Belt-Ngoye, 6— Transkei Coastal Belt, 7—Waterberg, 8—Northern Middleveld, 9—Soutpansberg, 10—Wolkberg. The number of endemics restricted to each area/centre of endemism is given in brackets, illustrated by a graduated grey scale and listed in Table 1.
FIGURE 5 in Vertebrate endemism in south-eastern Africa numerically redefines a biodiversity hotspot
FIGURE 5. Patterns of vertebrate endemism within the south-east Africa dominion defined by the dendrogram in Fig. 4a, comprising 28 operational graphical units (acronyms in pane (b) and labelled in Fig. 1. (a) south-east Africa dominion endemic species richness (south-east African endemism sensu stricto); (b) range-restricted species richness (narrow endemism); (c) weighted endemism (normalised); (d) weighted endemism per unit area (normalised). Each endemism measure is illustrated by a graduated grey scale of five classes, determined by natural breaks calculated using Jenk's optimisation.
FIGURE 4 in Vertebrate endemism in south-eastern Africa numerically redefines a biodiversity hotspot
FIGURE 4. Proposed zoogeographical regionalisation based on all vertebrate species endemic to south-eastern Africa sensu lato (a) The Phenetic Cluster Analysis (PCA) dendrogram of hierarchical relationships between operational geographic units (OGUs), using Jaccard's dissimilarity index and the UPGMA algorithm. (b) The resulting regionalisation; dominions, provinces and subprovinces are listed with codes in the table c, while all entities labeled on the map are districts, except for Maputaland subprovince (no districts within). (c) the Hierarchy of proposed zoogeographical entities. All "biogeographical taxa" were detected based on phenon lines at arbitrary levels of dissimilarity, in a way to generate maximally contiguous geographical clusters, except in the identification of the Karoo dominion (dash line), where a subjective decision was made considering the separation between relatively mesic and arid areas in almost all published zoogeographical regionalisations. The centres of endemism (BCOEs = broad and NCOEs = narrow) for south-east Africa dominion are also defined (see text for their interpretation). (d) The strict concensus area cladogram of OGUs from Parsimony Analysis of Endemicity (PAE), highlighting similarities to the PCA in the recovery of the South-east Africa dominion and the Greater Maputaland-Pondoland- Albany province (two OGUs shown in red dashed circles detected in the PAE as outliers are slightly enlarging the province compared to the PCA).
FIGURE 3 in Vertebrate endemism in south-eastern Africa numerically redefines a biodiversity hotspot
FIGURE 3. (a) Numerical refinement of the Greater Maputaland-Pondoland-Albany (GMPA) region, based on the cross-taxon consensus on a region maximally congruent to its qualitative delimitation proposed by Perera et al. (2011) recovered from phenetic relationships of OGUs for each taxonomic group, with further consensus from both Phenetic Cluster Analysis and Parsimony Analysis of Endemicity for all taxa combined. See text for calculation of the percentage consensus. C = core of the numerically refined GMPA region; E = an extension to the numerically refined GMPA region; *according to consensus, the Knysna OGU can be regarded as a part of the core region of GMPA, but treated as an extension due to the transitional nature of its biota between the GMPA and the Cape Floristic Region biodiversity hotspot (see text for details); (b) The core region (thick boundary) and extensions (dashed boundary) of the numerically refined GMPA region of vertebrate endemism in relation to the proposed qualitative delimitation of the same, and the Maputaland-Pondoland-Albany biodiversity hotspot; (c) Congruence of the numerically refined boundary of the GMPA to its qualitative delimitation, from Phenetic Cluster Analyses for individual vertebrate groups, for macro-ecologically releted groups (see text for details), and for all vertebrates combined, as well as for Parsimony Analysis of Endemicity for all vertebrates combined.
FIGURE 2 in Vertebrate endemism in south-eastern Africa numerically redefines a biodiversity hotspot
FIGURE 2. Contiguous geographical clusters maximally congruent to the qualitative delimitation of the Greater Maputaland- Pondoland-Albany (GMPA) region of vertebrate endemism proposed by Perera et al. (2011) (indicated in light green), recovered from phenetic cluster analysis for (a) all vertebrates (n = 300) (b) terrestrial vertebrates (n = 270), (c) non-volant vertebrates (n = 250), (d) herpetofauna (n = 189), (e) freshwater fish (n = 31), (f) birds (n = 51), (g) amphibians (n = 40), (h) reptiles (n = 149), (i) mammals (n = 29), and (j) from parsimony analysis of endemicity for all vertebrates (n = 300). Different colours denote congruent geographical clusters in dendrograms and relevant maps for different analyses.
FIGURE 1 in Vertebrate endemism in south-eastern Africa numerically redefines a biodiversity hotspot
FIGURE 1. The study area delimited by 22˚S and 24˚E (dashed lines) and the operational geographic units (OGUs) redrawn from Perera et al. (2011) to fit quarter-degree square borders. Distribution ranges of vertebrate species endemic to south-eastern Africa sensu lato included in the study do not extend beyond the dotted line. Dark grey: the Maputaland-Pondoland-Albany biodiversity hotspot (Mittermeier et al., 2004); light grey: areas added to the above in the proposed qualitative delimitation of the Greater Maputaland-Pondoland-Albany region of vertebrate endemism (Perera et al., 2011); blue line: the Limpopo River; brown line: the southern Great Escarpment, indicating the Nelspoort interval; green lines: country boundaries of South Africa (SA), Lesotho (L) and Swaziland (S). OGUs: ACB—Albany Coastal Belt, AKR—Intrusion of Lower Karoo into Albany, AWB—Amatola-Winterberg, CBV—Central Bushveld, DBP—Drakensberg Plateau, DEE—Drakensberg-Eastern-Cape Escarpment, DKE—Drakensberg-KwaZulu-Natal Escarpment, HUK—Highveld-Upper Karoo, INH—Inhambane, KBV— Kalahari Bushveld, KNY—Knysna, MLV—Mozambique Lowveld, NBV—Northern Bushveld, NCB—Natal Coastal Belt, NDH—Northern Dry Highveld, NGO—Ngoye, NMD—Natal Midlands, NME—Northern Mpumalanga Escarpment, NMH— Northern Mesic Highveld, NMO—Northern Mopane, NMP—Northern Maputaland, NMV—Northern Middleveld, NNT— Northern Natal, PND—Pondoland, SDH—Southern Dry Highveld, SME—Southern Mpumalanga Escarpment, SMH— Southern Mesic Highveld, SMO—Southern Mopane, SMP—Southern Maputaland, SMV—Southern Middleveld, SNB— Sneeuberg, SPB—Soutpansberg, STR—Southern Transkei Coastal Belt, TMD—Transkei Midlands, UKR—Upper Karoo, WLB—Wolkberg, WTB—Waterberg. See text for further details.
OpenFOAM test result data accompanying 'Numerical representation of mountains in atmospheric models'
<p>OpenFOAM test result data accompanying the PhD thesis, 'Numerical representation of mountains in atmospheric models'</p>
Numerical modeling of the concentric gravity wave seeding of low-latitude nighttime medium-scale traveling ionospheric disturbances
<p>The SAMI3/ESF model-generated electron density files (.mat file) associated with each of the cases in the paper.</p>
Spatio-temporal Features of Intra-seasonal Oceanic Variability in the Philippine Sea from Mooring Observations and Numerical Simulations
<p>This dataset contains the NPOCE (http://npoce.org.cn) data used in the following submission for Journal of Geophysical Research: Oceans:</p> <p>Hu, S., J. Sprintall, C. Guan, B. Sun, F. Wang, G. Yang, F. Jia, J. Wang, D. Hu, and F. Chai (2018), Spatio-temporal Features of Intra-seasonal Oceanic Variability in the Philippine Sea from Mooring Observations and Numerical Simulations, Journal of Geophysical Research: Oceans.</p> <p>Variables in this dataset are eddy kinetic energy (EKE) observed by the NPOCE moorings, longitudes, latitudes, depths and dates.</p> <p> </p> <p> </p>
Waveform data for Gravitational waveforms for high spin and high mass-ratio binary black holes: A synergistic use of numerical-relativity codes
<p>This dataset consists of binary black hole waveforms for three different configurations</p> <p>* q=3, chi1=chi2=0.9<br> * q=4, chi1=chi2=0.9<br> * q=5, chi1=chi2=0.9</p> <p>where q = m1/m2 is the ratio of the black hole masses, and chi1 and chi2 are the dimensionless spins of the black holes, oriented parallel to and in the direction of the orbital angular momentum.</p> <p>The waveforms are computed using the 3-dimensional numerical relativity simulation codes</p> <p> SpEC (SPectral Einstein Code -- https://www.black-holes.org/code/SpEC.html)<br> ET (Einstein Toolkit -- https://einsteintoolkit.org)</p> <p>SpEC is computationally efficient and accurate, and produces long waveforms, but in these cases, the simulations crash at the merger. The ET is less computationally efficient and accurate, but the simulations complete successfully. We therefore combine long SpEC inspirals with short ET mergers, blending the waveforms to produce hybrid waveforms.</p> <p>The waveform data directories are named as follows:</p> <p>q=3,chi1=chi2=0.9:<br> SpEC: q3_0.9_0.9_r200<br> ET: ceas_22_e5_2<br> Hybrid: q3_0.9_0.9_r200_hyb_ceas_22_e5_2</p> <p>q=4,chi1=chi2=0.9:<br> SpEC: q4_0.9_0.9_EccNew<br> ET: ceas_20_5_e7<br> Hybrid: q4_0.9_0.9_EccNew_hyb_ceas_20_5_e7</p> <p>q=5,chi1=chi2=0.9:<br> SpEC: q5.0_s0_0_0.9_s0_0_0.9_r200<br> ET: ceas_23_e5_2<br> Hybrid: q5.0_s0_0_0.9_s0_0_0.9_r200_hyb_ceas_23_e5_2</p> <p>The data is in the SXS simulation format (https://data.black-holes.org/waveforms/documentation.html), though only a subset of the SXS-format data is present.</p> <p>Also included is a Mathematica notebook, HybridizeWaveforms.nb, showing how the hybrid can be computed from the separate inspiral and merger waveforms using the open source SimulationTools code (https://simulationtools.org).</p> <p>See the paper</p> <p> I. Hinder, S. Ossokine, H. Pfeiffer and A. Buonanno -- Gravitational waveforms for high spin and high mass-ratio binary black holes: A synergistic use of numerical-relativity codes</p> <p>for more details.</p>
Figure 26 in 'Endless forms most beautiful': taxonomic revision of the planarian Geoplana vaginuloides (Darwin, 1844) and discovery of numerous congeners (Platyhelminthes: Tricladida)
Figure 26. Geoplana boraceia Almeida & Carbayo sp. nov. Sagittal views. A, diagrammatic representation of the copulatory apparatus of specimen MZUSP PL 2070. B, photomicrograph of a sagittal section of the penis bulb of specimen MZUSP PL 2070. C, photomicrograph of dorsal epithelium of penis papilla and cyanophil glands located immediately beneath epithelium of specimen MZUSP PL 2077. D, photomicrograph of posterior portion of female atrium and female genital duct of specimen MZUSP PL 2070.
Figure 25 in 'Endless forms most beautiful': taxonomic revision of the planarian Geoplana vaginuloides (Darwin, 1844) and discovery of numerous congeners (Platyhelminthes: Tricladida)
Figure 25. Geoplana boraceia Almeida & Carbayo sp. nov. A, dorsal view of the living specimen MZUSP PL 1086 (field number F4358), ~40 mm in length. B, photomicrograph of a sagittal section of the pharynx of specimen MZUSP PL 2077. C, photomicrograph of a sagittal section of penis papilla and female atrium of specimen MZUSP PL 2070. D, micro-computed tomography-based image of a transverse section of the penis papilla of specimen MZUSP PL 2095. Arrows point to muscle detached from the penis bulb running towards the ventral epidermis. Right body side to the right. E, micro-computed tomography-based image of a parasagittal section of the right side of penis bulb of specimen MZUSP PL 2095. Arrows point to muscles detached from the penis bulb running towards the ventral epidermis.
Figure 23 in 'Endless forms most beautiful': taxonomic revision of the planarian Geoplana vaginuloides (Darwin, 1844) and discovery of numerous congeners (Platyhelminthes: Tricladida)
Figure 23. Geoplana pulchella Schultze & Müller, 1857. Photomicrographs of sagittal sections of specimen MZUSP PL 1068. A, fibres seemingly derived from penis bulb running downwards to ventral epidermis behind gonopore region. B, female genital duct and surrounding layer of very thin muscle fibres.
Figure 24. Geoplana chita Froehlich, 1956. A in 'Endless forms most beautiful': taxonomic revision of the planarian Geoplana vaginuloides (Darwin, 1844) and discovery of numerous congeners (Platyhelminthes: Tricladida)
Figure 24. Geoplana chita Froehlich, 1956. A, dorsal view of the live specimen MZUSP PL 716, underdeveloped. B, drawings of dorsum of anterior end (left) and mid-region in dorsal view (top, right) and in three-dimensional section (bottom, right) of the body of a type specimen from Paraná; drawings by E. M. Froehlich; scale bar not available. C, photomicrograph of a sagittal section of the pharynx of specimen MZUSP PL 715. D, photomicrograph of a sagittal section of the copulatory apparatus of specimen MZUSP PL 2086. E, photomicrograph of part of the penis papilla of specimen MZUSP PL 2086 in sagittal section, showing the ejaculatory duct and its surrounding muscle. F, diagrammatic representation of the copulatory apparatus from sagittal sections of specimen MZUSP PL 2086.
Figure 22 in 'Endless forms most beautiful': taxonomic revision of the planarian Geoplana vaginuloides (Darwin, 1844) and discovery of numerous congeners (Platyhelminthes: Tricladida)
Figure 22. Geoplana pulchella Schultze & Müller, 1857. A, dorsal view of the living specimen MZUSP PL 1068, 38 mm in length. B, photomicrograph of a sagittal section of the pharynx of specimen MZUSP PL 1068. C, photomicrograph of a sagittal section of the copulatory apparatus of specimen MZUSP PL 2076. D, diagrammatic representation of the copulatory apparatus from sagittal sections of specimen MZUSP PL 2076.
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.