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26,784 results for “M.”
MInTe2 (M=Ag, Cu) thermal and electronic transport properties
<p>HiPhive, ShengBTE and AMSET files that contain thermal and electronic transport properties of MInTe2 (M=Ag, Cu)</p>
Data in support of "Bounding the localized to ductile transition in porous rocks: what implications for geo-reservoirs?" by GG. Meyer and M. Violay
<p>This repository contains the mechanical data from three triaxial experiments on Volvic trachyandesite in support of the publication "Bounding the localized to ductile transition in porous rocks: what implications for geo-reservoirs?" by GG Meyer and M Violay.</p> <p>The three files correspond to the three different samples as follow:</p> <p>VB21 “dry_slow”. Strain rate of 10-5s-1; no pore fluid<br> Sample length: 0.0770m<br> Sample diameter: 0.0382m</p> <p>VB22 “dry_fast”. Strain rate of 10-4s-1; no pore fluid<br> Sample length: 0.0709m<br> Sample diameter: 0.0372m</p> <p>VB23 “wet_slow”. Strain rate of 10-5s-1; water as pore fluid<br> Sample length: 0.0813m<br> Sample diameter: 0.0381m</p> <p>All files contain 5 columns:<br> Time in s<br> Average strain recorded by LVDT (adimensional)<br> Average axial strain recorded by strain gauges (adimensional)<br> Confining pressure in bar<br> Axial stress in Pa</p>
Spatial soil properties maps for Switzerland at 30 m resolution
<p>The Swiss Soil Property Map (SSPM) was developed using the quantile random forest machine learning algorithm and remotely sensed surrogate information such as terrain, climate, vegetation, and soil covariates. The SSPM dataset provides maps at 30 m resolution for different soil depths (0, 30, 60, and 100 cm) in GeoTIFF format. The mean and respective uncertainty information is provided for each map. Please note that the phosphorus spatial map is only available for the topsoil (0-20 cm) due to the unavailability of the dataset at deeper depths.</p> <table> <caption>Description of soil properties (SP) and their units</caption> <tbody> <tr> <td>SP</td> <td>Description</td> <td>units</td> </tr> <tr> <td>Sand </td> <td>Sand content</td> <td>%</td> </tr> <tr> <td>Clay</td> <td>Clay content</td> <td>%</td> </tr> <tr> <td>OC</td> <td>Organic carbon content </td> <td>%</td> </tr> <tr> <td>N</td> <td>Nitrogen content</td> <td>% </td> </tr> <tr> <td>P</td> <td>Phosphorus content</td> <td>mg/kg</td> </tr> </tbody> </table> <p>For more details / to cite this dataset please use:</p> <ul> <li><strong>Gupta, S. </strong>, Hasler, K. J., Alewell, C.: Mapping soil properties of Switzerland using remote sensing datasets and machine learning approach. Manuscript <strong>submitted</strong>, <strong>Geoderma Regional</strong>, 2023</li> </ul> <p> </p>
Inlist Files for Effects of Full Sphere Convection On M-Dwarf Dynamo Action, Flux Emergence, and Spin-Down
<p>Inlist files for a model of a ZAMS 0.3 M_sun star used as background for global convection simulations in <em>Effects of Full Sphere Convection On M-Dwarf Dynamo Action, Flux Emergence, and Spin-Down</em>. Run using MESA version r12115.</p>
Figure data for "Layered structures in the upper several hundred meters of the Moon along the Chang'E-4 rover's first 1000-m traverse"
<p>These are the raw data and derived data used to generate figures in the paper "Layered structures in the upper several hundred meters of the Moon along the Chang'E-4 rover's first 1000-m traverse." The data files are in the .mat format, and each column in the channel 1 files represents one trace, which contains 8192 rows (samples). Channel 1 has a time window of 20480 ns, with a sampling interval of 2.5 ns. The CE4-channel-2-imaging-result file refers to the subsurface profile of the regolith along the traverse, with a distance resolution of 3.74 cm and a depth resolution of 2.54 cm. </p>
Dataset from 'Klever, L., Beyvers, M., Fiehler, K., Mamassian, P., & Billino, J. (2023). Cross-modal metacognition: Visual and tactile confidence share a common scale. Journal of Vision, 23(5): 3, 1-16. doi: https://doi.org/10.1167/jov.23.5.1
<p>We provide two files:<br> - "data.txt": data file containing the data of our final sample (N=54) on which analyses are based<br> - "column_description.txt": description file providing information on the content of each column in the data file</p> <p>___________________________________________<br> For further questions, please contact:<br> jutta.billino[at]psychol.uni-giessen.de</p>
Figure 2. Dental measurements taken from the left M and the right M1 in A New Species of Extinct False Vampire Bat (Megadermatidae: Macroderma) from the Kimberley Region of Western Australia
Figure 2. Dental measurements taken from the left M and the right M1, based on Hand (1985).
GEO-Bench: m-brick-kiln
<p><strong>GEO-Bench: m-brick-kiln dataset</strong></p> <p>This dataset has been modified to be included in the <a href="https://zenodo.org/communities/geo-bench/">GEO-Bench dataset</a>. All changes with respect to the original version are documented at <a href="https://github.com/ServiceNow/geo-bench">https://github.com/ServiceNow/geo-bench</a>.</p> <p>The original version of this dataset is due to Lee et al. (2021) and is available at: <a href="https://sustainlab-group.github.io/sustainbench/docs/datasets/sdg13/brick_kiln.html">https://sustainlab-group.github.io/sustainbench/docs/datasets/sdg13/brick_kiln.html</a>.</p> <p>See the LICENSE file provided alongside this dataset for applicable licensing information.</p>
GEO-Bench: m-NeonTree
<p><strong>GEO-Bench: m-NeonTree dataset</strong></p> <p>This dataset has been modified to be included in the <a href="https://zenodo.org/communities/geo-bench/">GEO-Bench dataset</a>. All changes with respect to the original version are documented at <a href="https://github.com/ServiceNow/geo-bench">https://github.com/ServiceNow/geo-bench</a>.</p> <p>The original version of this dataset is due to Weinstein et al. (2019) and is available at: <a href="https://github.com/weecology/NeonTreeEvaluation">https://github.com/weecology/NeonTreeEvaluation</a>.</p> <p>See the LICENSE file provided alongside this dataset for applicable licensing information.</p>
GEO-Bench: m-nz-cattle
<p><strong>GEO-Bench: m-nz-cattle dataset</strong></p> <p> This dataset has been modified to be included in the <a href="https://zenodo.org/communities/geo-bench/">GEO-Bench dataset</a>. All changes with respect to the original version are documented at <a href="https://github.com/ServiceNow/geo-bench">https://github.com/ServiceNow/geo-bench</a>. </p> <p> The original version of this dataset is due to Abuaiadah et al. (2022) and is available at: <a href="https://zenodo.org/record/5908869">https://zenodo.org/record/5908869</a>. </p> <p> See the LICENSE file provided alongside this dataset for applicable licensing information. </p>
GEO-Bench: m-pv4ger-seg
<p><strong>GEO-Bench: m-pv4ger-seg dataset</strong></p> <p> This dataset has been modified to be included in the <a href="https://zenodo.org/communities/geo-bench/">GEO-Bench dataset</a>. All changes with respect to the original version are documented at <a href="https://github.com/ServiceNow/geo-bench">https://github.com/ServiceNow/geo-bench</a>. </p> <p> The original version of this dataset is due to Mayer et al. (2022) and is available at: <a href="https://github.com/kdmayer/3D-PV-Locator">https://github.com/kdmayer/3D-PV-Locator</a>. </p> <p> See the LICENSE file provided alongside this dataset for applicable licensing information. </p>
GEO-Bench: m-SA-crop-type
<p><strong>GEO-Bench: m-SA-crop-type dataset</strong></p> <p> This dataset has been modified to be included in the <a href="https://zenodo.org/communities/geo-bench/">GEO-Bench dataset</a>. All changes with respect to the original version are documented at <a href="https://github.com/ServiceNow/geo-bench">https://github.com/ServiceNow/geo-bench</a>. </p> <p> The original version of this dataset is due to the Western Cape Department of Agriculture and the Radiant Earth Foundation (2021) and is available at: <a href="https://mlhub.earth/data/ref_south_africa_crops_competition_v1">https://mlhub.earth/data/ref_south_africa_crops_competition_v1</a>. </p> <p> See the LICENSE file provided alongside this dataset for applicable licensing information. </p>
GEO-Bench: m-cashew-plantation
<p><strong>GEO-Bench: m-cashew-plantation dataset</strong></p> <p> This dataset has been modified to be included in the <a href="https://zenodo.org/communities/geo-bench/">GEO-Bench dataset</a>. All changes with respect to the original version are documented at <a href="https://github.com/ServiceNow/geo-bench">https://github.com/ServiceNow/geo-bench</a>. </p> <p> The original version of this dataset is due to Jin et al. (2021) and is available at: <a href="https://mlhub.earth/data/ts_cashew_benin">https://mlhub.earth/data/ts_cashew_benin</a>. </p> <p> See the LICENSE file provided alongside this dataset for applicable licensing information. </p>
Gruppenbild Teilnehmer EVB Ausflug (Foto M. Gilgen) in Vereinsausflug des EVB ins Engelbergertal
Gruppenbild Teilnehmer EVB Ausflug (Foto M. Gilgen)
Supplementary Material: Numerical Simulations of Seismoacoustic Nuisance Patterns from an Induced M 1.8 Earthquake in the Helsinki, Southern Finland, Metropolitan Area
<p>Seismic waves can couple with the atmosphere and generate sound waves. The influence of faulting mechanisms on earthquake sound patterns provides opportunities for earthquake source characterization. Sound radiated from earthquakes can be perceived as disturbing, even at low ground-shaking levels, which can negatively impact the social acceptance of geoengineering applications. Motivated by consistent reports of felt and heard disturbances associated with the weeks-long stimulation of a 6-km-deep geothermal system in 2018 below the Otaniemi district of Espoo, Helsinki, we conduct fully coupled 3D numerical simulations of wave propagation in the solid Earth and the atmosphere. We assess the sensitivity of the ground shaking and audible noise distributions to the source geometry of the induced earthquakes based on the properties of the largest local magnitude ML 1.8 event. Utilizing recent computational advances and the open-source software SeisSol, we model seismoacoustic frequencies up to 25 Hz, thereby reaching the lower limit of the audible sound frequency range. We present synthetic distributions of shaking and audible sounds at the 50–100 m scale across a 12 km × 12 km area and discuss implications for better under- standing seismic nuisances in metropolitan regions. In five 3D coupled elastic–acoustic scenario simulations that include data on topography and subsurface structure, we analyze the ground velocity and pressure levels of earthquake-generated seismic and acoustic waves. We show that S waves generate the strongest sound disturbance with sound pressure levels ≤ 0.04 Pa. We use statistical analysis to compare our noise distributions with commonly used empirical relationships. We find that our 3D synthetic amplitudes are generally smaller than the empirical predictions and that the interaction of the source mechanism-specific radiation pattern and topography can lead to significant nonlinear effects. Our study highlights the complexity and information content of spatially variable audible effects associated with small induced earthquakes on local scales.</p>
Abb. 2 in Coleophora thymi M. Hering, 1942, neu für die Schweiz (Lepidoptera: Coleophoridae)
Abb. 2. Coleophora thymi, weibliches Tier. (Foto Jürg Schmid)
Abb. 3 in Coleophora thymi M. Hering, 1942, neu für die Schweiz (Lepidoptera: Coleophoridae)
Abb. 3. Coleophora thymi, Skizze des weiblichen Genitals (partim). (Zeichnung Jürg Schmid)
Validation of the satellite-estimated sedimentation rates in reservoirs using 10-m Sentinel-2 satellites and water level data
<p><strong>Overview</strong>: The database contains data used for the validation of satellite-based sedimentation rates in eight reservoirs across the central and western United States using 10-m Sentinel-2 imagery and in-situ level data. Additional validation of the results from combining Sentinel-2 imagery with simulated 27-day Sentinel-3 altimetry levels is also included.</p> <p> </p> <p><strong>This dataset includes</strong>:</p> <ol> <li>Satellite-derived area-level duplets</li> <li>Bathymetry curves from satellite-based estimates</li> <li>Bathymetry curves from survey data</li> <li>Validation</li> </ol>
Abb. 3. Eiablagestelle von M in Eiablage des Ölkäfers Mylabris variabilis (Pallas 1781) (Coleoptera: Meloidae), einem Parasitoiden von Heuschrecken im Wallis
Abb. 3. Eiablagestelle von M. variabilis am 15.7.2018. (Foto R. Büchi)
Abb. 2. Paarung von M in Eiablage des Ölkäfers Mylabris variabilis (Pallas 1781) (Coleoptera: Meloidae), einem Parasitoiden von Heuschrecken im Wallis
Abb. 2. Paarung von M. variabilis auf einem Korbblütler. (Foto R. Büchi)
ScienceDex guides
Understand access before you commit
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.