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2,214 results for “strengths”
Dataset of "Molecular Dynamics Simulations Unveil the Aggregation Patterns and Salting out of Polyarginines at Zwitterionic POPC Bilayers in Solutions of Various Ionic Strengths"
<p>Molecular dynamics simulations are performed for a series of model cell-penetrating peptides (in particular nona-arginines) in aqueous solutions, in contact with model phosphocholine (POPC) membranes in conditions of different ionic strengths. The unusual aggregation properties of peptides at model lipid bilayers are analyzed and different sizes and lifetimes of aggregates are presented.<br>This dataset contains molecular dynamics simulation data with trajectories, input files, and topology files for all studied systems. They contain low peptide concentration in water, low NaCl concentration, high NaCl concentration, low CaCl2 concentration, and high CaCl2 concentration.<br>In addition to low peptide concentration, high peptide concentration in water, low NaCl concentration, high NaCl concentration, low CaCl2 concentration, and high CaCl2 concentration are also studied.</p>
Generalised Oscillator Strengths for the simulation of EELS spectra, with a broader coverage of high energy and minor edges
<p>This deposit contains a tabulated set of generalised oscillator strengths, which are required to compute the double differential cross sections for the inelastic scattering of fast electrons by atoms, i.e. for the simulation of EELS spectra.</p> <p>These tabulated values are calculated self-consistently within the local density approximation using the exchange correlation potential after Perdew [1]. For this a modified version of a program by Hamann is used [2]. Using this atomic potential the wave function of the ejected free electron is calculated, which is normalised by matching it to spherical Bessel and Neumann functions at large distances from the core [3]. The remaining integral constitutes a spherical Bessel transform. Using the convolution theorem this integral is solved with the fast Fourier transformation routine as done in [4]. A further discussion is available along with the code (see below), or more in-depth (but in German) in the <a href="https://www.uni-muenster.de/imperia/md/content/physik_pi/kohl/abschlussarbeiten/lsegger-bsc-arbeit.pdf">Thesis of L. Segger</a>.</p> <p><strong>This updated version offered here greatly expands the number of available edges</strong>, but is otherwise identical to the earlier version uploaded at <a href="https://zenodo.org/record/6599071">https://zenodo.org/record/6599071</a>.</p> <p> </p> <p>The data offered here is in the GOSH file format, a file format developed for the distribution of such datasets. A description of the file format as used here is included in the file `gosh.md`, while an up to date version can be found at:</p> <p><a href="https://gitlab.com/gguzzina/gosh">https://gitlab.com/gguzzina/gosh</a></p> <p>The code used to compute the GOS is publicly available, along with a discussion of the approach and methods, at:</p> <p><a href="https://github.com/Br0Fi/goscalc">https://github.com/Br0Fi/goscalc</a></p>
Sensible heat fluxes control cloud trail strength
<p>This dataset contains a minimal set of data used to create figures in Johnston et al. (2023) Sensible Heat Fluxes Control Cloud Trail Strength, Quarterly Journal of the Royal Meteorological Society. Additional liquid water path data from the control experiment (H250E250) is also provided to better visualise the cloud field in this central experiment.</p>
Snail Shell Strength and Total Crush Force of a Northern Michigan Snail as a Function of Predation Risk at the University of Michigan Biological Station Stream Research Facility (5/31/23-8/1/23)
Many prey organisms respond to the non-consumptive effects of predators by altering their physiology, morphology, and behavior. These inducible defenses can create refuges for prey by decreasing the likelihood of consumption by predators. Some prey, as in marine mollusks, have been shown to alter their morphology in response to the presence of size-limited predation. To extend this work into the freshwater realm, we presented pointed campeloma snails (Campeloma decisum) to chemical cues from a natural predator, the rusty crayfish (Faxonius rusticus), to better understand how snail morphology changes under the threat of predation. The total force needed to crush shells, total shell length, aperture width, and total weight, along with changes to these three body measurements were recorded for each individual and used to quantify morphological changes as a function of risk. Snails exposed to crayfish chemical cues needed significantly more force to crush their shells than controls (p = 0.002). Total shell length was greater in crayfish exposed snails than control snails (p = 0.002), and snails in the crayfish treatment also showed significantly more change in shell length than control snails (p = 0.003). Similarly, aperture width was significantly greater in exposed snails (p = 0.002). However, exposed snails exhibited significantly less change in aperture width than controls (p = 0.017). Finally, we found that snails exposed to crayfish weighed significantly more than snails in the control (p = 0.0009). Thus, the results of this study show that morphology of gastropods is altered in the presence of predators, and this may be an antipredator tactic directly related to risk.
A Wi-Fi Channel State Information (CSI) and Received Signal Strength (RSS) data-set for human presence and movement detection
<p>This data-set consists of antenna-wise received signal strength (RSS) and channel state information (CSI) data. Both types of data have been captured using the <a href="https://dhalperi.github.io/linux-80211n-csitool/">Intel CSI Tools</a>. The RSS data have been used in our paper "Detecting Human Movement from Ambient Wi-Fi Signal Strength".</p> <p>This release extends the README with a data dictionary for the annotations. We hope to add more information about the data acquisition process (e.g., data acquisition protocols).</p>
Identification of strengths and weaknesses of cooperative efforts within the wider Caribbean using a network approach
<p>Dataset associated to Ramírez-Ramírez RD, Montilla LM, Cavada-Blanco F and Croquer A. Identification of strengths and weaknesses of cooperative efforts within the wider Caribbean using a collaboration network approach [version 1; not peer reviewed]. <em>F1000Research</em> 2016, <strong>5</strong>:799 (poster) (doi: 10.7490/f1000research.1111809.1)</p>
IODP Expedition 391 Vane shear strength (AVS)
Shear strength was measured on section halves using a GEISA automated vane shear (AVS) frame and device controller. The device is suitable for sediment not affected by cementation (i.e., saturated, clay-rich, soft sediment). Shear strength measurements should be considered only approximate, particularly because the influence of pore pressure changes during the undrained experiment cannot be estimated. Report includes vane shear strength at the sample's failure point, maximum torque angle, penetration direction, and rate of vane rotation.
IODP Expedition 378 Vane shear strength (Torvane)
Sediment strength was measured on section halves using a Humbolt Manufacturing Torvane handheld instrument. The Torvane is suitable for sediment not affected by cementation (i.e., saturated, clay-rich, soft sediment).
IODP Expedition 378 Compressional strength (penetrometer)
Compressional strength was measured on section halves using a Humbolt Manufacturing Pocket Penetrometer. The penetrometer is only suitable for sediment not affected by cementation (i.e., saturated, clay-rich, soft sediment). Shear strength can be calculated from the measured compressional strength. Compressional strength measurements should be considered only approximate, particularly because the influence of pore pressure changes during the undrained experiment cannot be estimated. Report includes compressional strength results, penetration direction, and adapter foot information.
IODP Expedition 371 Vane shear strength (AVS)
Shear strength was measured on section halves using a GEISA automated vane shear (AVS) frame and device controller. The device is suitable for sediment not affected by cementation (i.e., saturated, clay-rich, soft sediment). Shear strength measurements should be considered only approximate, particularly because the influence of pore pressure changes during the undrained experiment cannot be estimated. Report includes vane shear strength at the sample's failure point, maximum torque angle, penetration direction, and rate of vane rotation.
Tectonic uplift, soil production, soil depth, and rock strength at the Dragon's Back Pressure Ridge, Carrizo Plain, California
<div><em><strong>Tectonic uplift, soil production, soil depth, and rock strength at the Dragon's Back Pressure Ridge, Carrizo Plain, California</strong></em></div> <div> </div> <div>Supporting data for “Landscape transience reveals a bottom-up control on soil production”</div> <div> </div> <div>Emily C. Geyman*, David A. Paige, Michael P. Lamb</div> <div> </div> <div>*Corresponding author: Emily C. Geyman, egeyman@caltech.edu</div> <div> </div> <div>Last updated: July 3, 2024</div> <div> </div> <div> </div> <div>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</div> <div> </div> <div><strong>Dataset overview.</strong></div> <div> </div> <div>This dataset contains:</div> <div> </div> <div>1. Georeferenced TIFF files of the (i) LiDAR-derived surface elevation, (ii) geological map (based on the mapping from Dibblee (1973) and Arrowsmith (1995)), (iii) reconstructed cumulative uplift, and (iv) reconstructed uplift rate at the Dragon’s Back Pressure Ridge, Carrizo Plain, California.</div> <div> </div> <div>2. Raw and processed ground penetrating radar (GPR) observations of soil thickness.</div> <div> </div> <div>3. Geomorphic properties: (i) hilltop erosion rate, (ii) hilltop soil production rate, (iii) hilltop saprolite weakness (based on cone penetrometer observations), and (iv) hilltop soil thickness.</div> <div> </div> <div>4. Raw and processed observations from the cone penetrometer (used to compute the saprolite weakness).</div> <div> </div> <div>5. Soil pit observations.</div> <div> </div> <div>6. Matlab code used to perform the MCMC inversion to generate the uplift reconstructions (item (1) above).</div> <div> </div> <div> </div> <div>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</div> <div> </div> <div> </div> <div><strong>Dataset details.</strong></div> <div> </div> <div>See the publication: “Geyman, E.C., Paige, D.A., and Lamb, M.P. Landscape transience reveals a bottom-up control on soil production. In review. 2024.” for details on the field methodology and data analysis. Details about each data product also are provided below. </div> <div> </div> <div> </div> <div>1. Geotiffs.</div> <div> </div> <div>We provide georeferenced TIFF files of the (i) surface elevation, (ii) geological map, (iii) cumulative uplift, and (iv) uplift rate at Dragon’s Back Pressure Ridge, Carrizo Plain, California. The coordinate system for the geotiffs is WGS84 / UTM Zone 11 N (EPSG:32611). All geotiffs are provided at 0.5 m x 0.5 m spatial resolution. Details about each dataset are provided below.</div> <div> </div> <div>(i) Surface elevation. We use LiDAR data from the 2005 B4 Lidar Project, acquired and processed by the National Center for Airborne Laser Mapping (NCALM). The full LiDAR dataset is available for download from OpenTopography (https://portal.opentopography.org/datasetMetadata?otCollectionID=OT.032018.32611.1). We convert the LiDAR point cloud to a 0.5 m gridded bare earth digital elevation model (DEM). </div> <div> </div> <div>(ii) Geological map. The original geological map of Dibblee (1973, 1999) is available from the United States Geological Survey (USGS) at https://pubs.usgs.gov/of/1999/of99-014/. This mapping was refined by Arrowsmith (1995) and Hilley & Arrowsmith (2008). We modify the geological map using high-resolution satellite imagery (from Google, ESRI, and Bing mosaics), as well as high-resolution imagery from the National Agriculture Imagery Program (NAIP), in order to follow the contacts of the Pink, Tan, and Gray members of the Paso Robles Formation. The units on the geological map are coded as:</div> <div>1 - Pink Member, Paso Robles Formation</div> <div>2 - Tan Member, Paso Robles Formation</div> <div>3 - Gray Member, Paso Robles Formation</div> <div>4 - Undifferentiated Paso Robles Formation</div> <div>5 - Quaternary alluvium (older)</div> <div>6 - Quaternary alluvium (younger)</div> <div>7-8 - Quaternary landslides and terraces</div> <div> </div> <div>(iii) Cumulative uplift. We follow the general approach of Hilley & Arrowsmith (2008) to reconstruct the cumulative uplift at Dragon’s Back Pressure Ridge based on the observed positions and elevations of the stratigraphic contacts between the Pink, Tan, and Gray members of the Paso Robles Formation. Put simply, since the Pink, Tan, and Gray members of the Paso Robles Formation are initially flat-lying, the progressive increase in elevation of the contacts between these members from the start to the middle of the Dragon’s Back Pressure Ridge records the cumulative tectonic uplift. We perform a Markov Chain Monte Carlo (MCMC) inversion to reconstruct the uplift history that can best explain our geological observations (i.e., the positions and elevations of the Pink, Tan, and Gray members of the Paso Robles Formation). See section 6 for the Matlab code used to perform the MCMC inversion.</div> <div>Dataset A: “cumulative_uplift_mean.tif” -- the mean reconstructed cumulative uplift (units: meters).</div> <div>Dataset B: “cumulative_uplift_uncertainty_IQR.tif” -- the uncertainty of the reconstructed cumulative uplift (units: meters), documented as the inter-quartile range (IQR), the difference between the 75th percentile and the 25th percentile of the MCMC cumulative uplift estimates.</div> <div> </div> <div>(iv) Cumulative uplift rate. The uplift rate dataset is constructed by taking the spatial derivative of the cumulative uplift dataset (iii) in the along-strike direction of the San Andreas Fault, and then converting from space to time using the long-term slip rate on the San Andreas Fault of approximately 33 mm/yr. This is the same approach as used in Hilley & Arrowsmith (2008).</div> <div>Dataset A: “uplift_rate_mean.tif” -- the mean reconstructed uplift rate (units: mm/yr).</div> <div>Dataset B: “uplift_rate_uncertainty_IQR.tif” -- the uncertainty of the reconstructed uplift rate (units: mm/yr), documented as the inter-quartile range (IQR), the difference between the 75th percentile and the 25th percentile of the MCMC estimates.</div> <div> </div> <div> </div> <div>2. Ground penetrating radar (GPR).</div> <div> </div> <div> <p>The GPR data were acquired with a MALA HDR GPR system with a 450 MHz shielded antenna. Data were acquired every 4 cm, tracked by a survey wheel for precise relative positioning. The GPR survey covered approximately 19 km of ridgeline and included 21 short (approximately 10 m) ridgetop profiles with cone penetrometer observations that serve as ground-truth for the depth of the soil-saprolite boundary inferred from the GPR data. The GPR data were processed using the open-source GPRPy software (Plattner, 2020). We constrained sub-surface velocities by fitting 364 diffraction hyperbolas in the GPR transects. The hyperbola fitting supports a spatially-uniform velocity of approximately 0.11 m/ns. The locations and fitted velocities of the individual hyperbolas used to construct this velocity model are included in the file “GPR_velocities.csv.”</p> <p>The folder “Radar450MHz_raw” includes the raw radar data. The folder “Radar450MHz_GPS” includes the GPS data associated with each radar dataset (saved as .cor files). The GPS observations are aggregated in the spreadsheet “GPS_all” in that folder. The shapefile folder includes the final processed GPR-derived soil thickness estimates (soil thickness reported in units of meters) as a .shp file. The coordinate system for the shapefile is WGS84 / UTM Zone 11 N. </p> </div> <div> </div> <div>3. Geomorphic properties.</div> <div> </div> <div>These are the datasets plotted in Figures 3 and 4 of “Geyman, E.C., Paige, D.A., and Lamb, M.P. Landscape transience reveals a bottom-up control on soil production. In review. 2024.” </div> <div> </div> <div> </div> <div>4. Cone penetrometer observations.</div> <div> </div> <div> <div>This folder contains 3 files:</div> <div>1. ConePenetrometerSummaryTable_Overview.csv: A summary of the 212 cone penetrometer stations. For each station, there is metadata about the location (GPS coordinates), the stratigraphic unit (Pink, Tan, or Gray Member of the Paso Robles Formation), the side of the ridge, (southeast = SW, center = C, or northwest = NW), and the saprolite weakness, calculated as the cone penetrometer ease of penetration [cm/strike] at the position of the soil-saprolite boundary. </div> <div>2. ConePenetrometerSummaryTable_Data.csv: All of the raw observations from the cone penetrometer. The raw observations are the cumulative strike number vs. the cumulative depth of penetration into the ground.</div> <div>3. ConePenetrometerSummaryTableFinal.xlsx: An Excel spreadsheet with the same data from items (1) and (2) above as separate sheets ("Overview") and ("Data").</div> </div> <div> </div> <div> </div> <div>5. Soil pit observations.</div> <div> </div> <div>This folder contains 2 files:</div> <div>1. soil_pit_summary: A summary table containing the soil pit locations and the inferred depth to the soil-saprolite boundary.</div> <div>2. soil_pit_layers: Simplified stratigraphic columns providing grain sizes and classifications (soil vs. saprolite) of the layers identified in each soil pit.</div> <div> </div> <div> </div> <div>6. Matlab code. </div> <div> </div> <div>This folder contains 2 primary Matlab scripts, with supporting data files and helper functions.</div> <div>1. DBPR_uplift: code to reconstruct the tectonic uplift at DBPR based on the positions and elevations of the stratigraphic contacts.</div> <div>1. soil_depth_vs_strength: code to reconstruct Fig. 4 Geyman, E.C., Paige, D.A., and Lamb, M.P. Landscape transience reveals a bottom-up control on soil production. In review. 2024.” </div> <div> </div> <div> </div> <div>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</div> <div> </div> <div> </div> <div><strong>References</strong></div> <div> </div> <div>Arrowsmith, J. R. Coupled tectonic deformation and geomorphic degradation along the San Andreas Fault System. Ph.D. thesis, Stanford University (1995).</div> <div> </div> <div>Dibblee Jr, T. Regional geologic map of San Andreas Fault and related faults and Carrizo Plain, Temblor, Caliente, and La Panza Ranges and vicinity, California. US Geological Survey Miscellaneous Geological Investigations, Map I-757, scale 1:125,000 (1973). </div> <div> </div> <div>Dibblee, T. W. et al. Regional geologic map of San Andreas and related faults in Carrizo Plain, Temblor, Caliente and La Panza Ranges and vicinity, California: A digital database. Tech. Rep., US Geological Survey (1999). </div> <div> </div> <div>Hilley, G. E. & Arrowsmith, J. R. Geomorphic response to uplift along the Dragon’s Back pressure ridge, Carrizo Plain, California. Geology, 36, 367–370 (2008). </div> <div> </div> <div>Plattner, A. M. GPRPy: Open-source ground-penetrating radar processing and visualization software. The Leading Edge 39, 332–337 (2020).</div> <div> </div>
Generalised oscillator strength for core-shell electron excitation by fast electrons based on Dirac solutions
<div> <div>The rich information of electron energy-loss spectroscopy (EELS) comes from the complex inelastic scattering process whereby fast electrons transfer energy and momentum to atoms, exciting bound electrons from their ground states to higher unoccupied states. To quantify EELS, the common practice is to compare the cross-sections integrated within an energy window or fit the observed spectrum with theoretical differential cross-sections calculated from a generalized oscillator strength (GOS) database with experimental parameters [1].</div> <div> </div> </div> <div> <div> <div> <div>The previous Hartree-Fock-based [2] or DFT-based [3] GOS was calculated from Schrödinger's solution of atomic orbitals, which does not include the full relativistic effects. Here, we attempt to go beyond the limitations of the Schrödinger solution in the GOS tabulation by including the full relativistic effects using the Dirac equation within the local density approximation using FAC [4], which is particularly important for core-shell electrons of heavy elements with strong spin-orbit coupling. This has been done for all elements in the periodic table (up to Z = 118) for all possible excitation edges using modern computing capabilities and parallelization algorithms. The relativistic effects of fast incoming electrons were included to calculate cross-sections that are specific to the acceleration voltage. We make these tabulated GOS available under an open-source license to the benefit of both academic users as well as allowing integration into commercial solutions.</div> <div> </div> <div>If you wish to be notfied by the database updates, please register <a href="https://forms.gle/ddpJSPrCbPZNL1oH7" target="_blank" rel="noopener">here</a>.</div> <div> </div> <div>For details, you can find the paper on <a href="https://arxiv.org/abs/2405.10151">arxiv</a>.</div> </div> </div> </div> <p>Database Details:</p> <ul> <li>Covers all elements (Z: 1-108) and all edges</li> <li>Large energy range: 0.01 - 4000 eV</li> <li>Large momentum range: from minimum momentum transfer to double Bethe ridge for each edge. Adaptive momentum sampling is developed in such a manner to maximize the physical information for a given finite number of sampling points. For example, for C edge this range is 0.14 -67 Å-1 </li> <li>Fine log sampling: 128 points for energy and 256 points for momentum</li> <li>Data format: GOSH [3]</li> </ul> <p>Calculation Details:</p> <ul> <li>Single atoms only; solid-state effects are not considered</li> <li>Unoccupied states before continuum states of ionization are not considered; no fine structure</li> <li>Plane Wave Born Approximation</li> <li>Frozen Core Approximation is employed; electrostatic potential remains unchanged for orthogonal states when a core-shell</li> <li>electron is excited</li> <li>Self-consistent Dirac–Fock–Slater iteration is used for Dirac calculations; A modified local density approximation is used for the correct asymptotic behavior of the exchange energy; continuum states are normalized against asymptotic form at large distances</li> <li>Both large and small component contributions of Dirac solutions are included in GOS</li> <li>Final state contributions are included until the contribution of the last states falls below 0.1%. A convergence log is provided for reference.</li> </ul> <p>Version 1.6.5 release note:</p> <ul> <li>Add a compact version of the database which uses (a) single precesion, (b) 80x80 sampling in the energy and momentum space (c) 'gzip' to compress the gos data array. This helps for user with limited bandwidth for downloading.</li> </ul> <p>Version 1.6.1 release note:</p> <ul> <li>Add missing metadata</li> </ul> <p>Version 1.6 release note:</p> <ul> <li>Improved convergence for M and N edges for some elements</li> </ul> <p>Version 1.5 release note:</p> <ul> <li>Adaptive sampling for momentum space (previously it is fixed at 0.05 -50 Å-1, now adaptive for each edge)</li> <li>Improved convergence</li> </ul> <p>Version 1.2 release note:</p> <ul> <li>Add “File Type / File version” information</li> </ul> <p>Version 1.1 release note:</p> <ul> <li>Update to be consistent with GOSH data format [3]</li> <li>All the edges are now within a single hdf5 file.</li> <li>A notable change in particular, the sampling in momentum is in 1/m, instead of previously in 1/Å.</li> <li>Great thanks to Gulio Guzzinati for his suggestions and sending conversion script for GOSH format. </li> </ul> <p> </p> <p>[1] Verbeeck, J., and S. Van Aert. Ultramicroscopy 101.2-4 (2004): 207-224.</p> <p>[2] Leapman, R. D., P. Rez, and D. F. Mayers. The Journal of Chemical Physics 72.2 (1980): 1232-1243.</p> <p>[3] Segger, L, Guzzinati, G, & Kohl, H. Zenodo (2023). doi:10.5281/zenodo.7645765</p> <p>[4] Gu, M. F. Canadian Journal of Physics 86(5) (2008): 675-689.</p>
Exergame for Continuous and Transparent Monitoring of Handgrip Strength and Endurance
<p>Grip Strength reduction is a marker of the age-related functional decline. An anti-stress ball and an exergame to continuously and transparently monitor handgrip strength and endurance in older persons have been developed within the context of the European Project MOVECARE. The description of the exergame can be found in the paper associated to the dataset here uploaded. </p> <p>The game was tested on ten young healthy subjects and the results of maximal handgrip force provided by the game were compared to the handgrip measured with the Jamar dynamometer. High correlation was found, suggesting that the game can represent a promising and non-intrusive tool to help detecting early signs of frailty in community-dwelling older persons. </p> <p>The data here uploaded consist of an excel file with two sheets. </p> <p>- sheet 1: Values of handgrip measured by the Jamar and the anti-stress ball on 10 subjects. Each subject performed three repetitions of each measurement. </p> <p> - sheet 2: the System Usability Scale scores collected on the 10 subjects. Scores of individual questions, as well as total scores, are reported. </p>
Large cortical bone pores in the tibia are associated with proximal femur strength - data for reproduction
<p>Results tables for the reproduction of:</p> <p>Iori G, Schneider J, Reisinger A, Heyer F, Peralta L, Wyers C, et al. Large cortical bone pores in the tibia are associated with proximal femur strength. PLOS ONE. doi:10.1371/journal.pone.0215405</p>
Supporting data for "Quantifying the Strength of a Salt Bridge by Neutron Scattering and Molecular Dynamics"
<p>Supporting data for the following published paper: Mason, Jungwirth, Duboué-Dijon, 2019, JPhysChemLett, 10, 3254-3259</p> <p>Contains both data from neutron scattering measurements and input simulation files necessary for reproduction of the work.</p>
IODP Expedition 398 Vane shear strength (AVS)
Shear strength was measured on section halves using a GEISA automated vane shear (AVS) frame and device controller. The device is suitable for sediment not affected by cementation (i.e., saturated, clay-rich, soft sediment). Shear strength measurements should be considered only approximate, particularly because the influence of pore pressure changes during the undrained experiment cannot be estimated. Report includes vane shear strength at the sample's failure point, maximum torque angle, penetration direction, and rate of vane rotation.
IODP Expedition 398 Compressional strength (penetrometer)
Compressional strength was measured on section halves using a Humbolt Manufacturing Pocket Penetrometer. The penetrometer is only suitable for sediment not affected by cementation (i.e., saturated, clay-rich, soft sediment). Shear strength can be calculated from the measured compressional strength. Compressional strength measurements should be considered only approximate, particularly because the influence of pore pressure changes during the undrained experiment cannot be estimated. Report includes compressional strength results, penetration direction, and adapter foot information.
Dataset for Rate and Pressure Dependence of Dilatancy and Fault Strength in Partially-Drained Laboratory Fault Zones
<p>The datasets for Affinito et al., 2024 manuscript. Each experiment is was collected on a 24-bit recorder and 16 channels for hydromechanical data. The purpose of these experiments was to document the transition in fault drainage state as a fuction of shearing rate. Samples were prepared from cores collected at the US-DOE Utah FORGE well 16A.</p>
IODP Expedition 355 Compressional strength (penetrometer)
Compressional strength was measured on section halves using a Humbolt Manufacturing Pocket Penetrometer. The penetrometer is only suitable for sediment not affected by cementation (i.e., saturated, clay-rich, soft sediment). Shear strength can be calculated from the measured compressional strength. Compressional strength measurements should be considered only approximate, particularly because the influence of pore pressure changes during the undrained experiment cannot be estimated. Report includes compressional strength results, penetration direction, and adapter foot information.
IODP Expedition 355 Vane shear strength (AVS)
Shear strength was measured on section halves using a GEISA automated vane shear (AVS) frame and device controller. The device is suitable for sediment not affected by cementation (i.e., saturated, clay-rich, soft sediment). Shear strength measurements should be considered only approximate, particularly because the influence of pore pressure changes during the undrained experiment cannot be estimated. Report includes vane shear strength at the sample's failure point, maximum torque angle, penetration direction, and rate of vane rotation.
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.