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36 results for “large scale experiments”
Data from: Spatial and temporal aridity gradients provide poor proxies for plant-plant interactions under climate change: a large-scale experiment
1. Plant-plant interactions may critically modify the impact of climate change on plant communities. However, the magnitude and even direction of potential future interactions remains highly debated, especially for water limited ecosystems. Predictions range from increasing facilitation to increasing competition with future aridification. 2. The different methodologies used for assessing plant-plant interactions under changing environmental conditions may affect the outcome but they are not equally represented in the literature. Mechanistic experimental manipulations are rare compared to correlative approaches that infer future patterns from current observations along spatial climatic gradients. 3. Here, we utilize a unique climatic gradient in combination with a large-scale, long-term experiment to test whether predictions about plant-plant interactions yield similar results when using experimental manipulations, spatial gradients or temporal variation. We assessed shrub-annual interactions in three different sites along a natural rainfall gradient (spatial) during 9 years of varying rainfall (temporal) and 8 years of dry and wet manipulations of ambient rainfall (experimental) that closely mimicked regional climate scenarios. 4. The results were fundamentally different among all three approaches. Experimental water manipulations hardly altered shrub effects on annual plant communities for the assessed fitness parameters biomass and survival. Along the spatial gradient, shrub effects shifted from clearly negative to mildly facilitative towards drier sites, whereas temporal variation showed the opposite trend: more negative shrub effects in drier years. 5. Based on our experimental approach, we conclude that shrub-annual interaction will remain similar under climate change. In contrast, the commonly applied space-for time approach based on spatial gradients would have suggested increasing facilitative effects with climate change. We discuss potential mechanisms governing the differences among the three approaches. 6. Our study highlights the critical importance of long-term experimental manipulations for evaluating climate change impacts. Correlative approaches, e.g. along spatial or temporal gradients, may be misleading and overestimate the response of plant-plant interactions to climate change.
Data related to "Near-bed sediment transport during offshore bar migration in large-scale experiments"
<p>Abstract: </p> <p>This paper presents novel insights into hydrodynamics and sediment fluxes in large-scale laboratory experiments with bi-chromatic wave groups on a relatively steep initial slope (1:15). An Acoustic Concentration and Velocity Profiler provided detailed information of velocity and sand concentration near the bed from shoaling up to the outer breaking zone including suspended sediment and sheet flow transport. The morphological evolution was characterized by offshore migration of the outer breaker bar. Decomposition of the total net transport revealed a balance of onshore-directed, short wave-related and offshore-directed, current-related net transport. The short wave-related transport mainly occurred as bedload over small vertical extents. It was linked to characteristic intrawave sheet flow layer expansions during short wave crests. The current-related transport rate featured lower maximum flux magnitudes but occurred over larger vertical extents. As a result, it was larger than the short wave-related transport rate in all but one cross-shore position, driving the bar's offshore migration. Net flux magnitudes of the infragravity component were comparatively low but played a non-negligible role for total net transport rate in certain cross-shore positions. Net infragravity flux profiles sometimes featured opposing directions over the vertical. The fluxes were linked to a standing infragravity wave pattern and to the correlation of the short wave envelope, controlling suspension, with the infragravity wave velocity.</p> <p>About the data:</p> <p>The data on beach profile (from mechanical profiler), velocity (from ACVP and ADV), sand concentration (from ACVP and OBS) and water surface elevation (from RWG, AWG and PT) measurements is given in .mat (MATLAB) files.</p> <p>The folder “Beach Profiles” contains the measurements from the mechanical profiler before and after each test. To save time, only the morphologically active section of the profiles was measured. Additionally, the folder contains the initial profiles at the start of a sequence (after application of the benchmark waves). Here the full profile was measured.</p> <p>The structure “MobFrame” contains the absolute cross-shore position of the mobile frame (from which detailed measurements were taken) in the considered tests.</p> <p>The folder “ACVP” contains structures with ensemble-averaged velocity and concentration measurements in vertical reference to the undisturbed bed level or a few bins below it (zeta0-coordinate system) sampled at 50.5051Hz. For better interpretation of the measurements, it also features the ensemble-averaged intrawave instantaneous erosion depth (bed elevation) and the upper limit of the sheet flow layer.</p> <p>The folder “ADV” contains structures with the ensemble-averaged ADV data of each test sampled at 100Hz. Apart from the velocity components of each ADV it contains the vertical elevation of each ADV with respect to the ACVP transceiver. The ADV measurements were not subject to the same vertical referencing procedure that was described in the paper for the near-bed ACVP measurements and a more or less constant distance to the bed was assumed.</p> <p>The folder “OBS” contains structures with the ensemble-averaged OBS data of each test sampled at 40Hz. Apart from the concentration measurements it contains the vertical elevation of the OBSs with respect to the ACVP transceiver.</p> <p>The folder “ETA” contains structures with the ensemble-averaged water surface elevation measurements in many different absolute cross-shore locations in the flume sampled at 40Hz.</p> <p>For visualizing the near-bed concentration data, which may not be as trivial as visualizing the rest of the data, an example of MATLAB code is given:</p> <p>%S=ACVP_xx; %to choose which ACVP file you want to look into<br> con=S.c;<br> con(con<1)=1; %to cater for the cells where the logarithm is not defined<br> xphase=linspace(0,1,length(S.solbed)).*ones(size(S.c,2),size(S.c,1));<br> figure; hold on; box on; <br> [C,h]=contourf(xphase,S.z,log10(transpose(con)),[0:0.1:3]); <br> cbh=colorbar; caxis([0 3]); <br> set(h,'edgecolor','none'); <br> tt=get(cbh,'Title'); set(tt,'String','$^{10}log(c)$ $[kg/m^3]$','Interpreter','Latex');<br> plot(xphase(1,:),S.solbed,'k','Linewidth',1.5);<br> plot(xphase(1,:),S.solflo,'r','Linewidth',1.5);<br> xlabel('$t/T_r$','Interpreter','Latex')<br> ylabel('$\zeta_0$ $[m]$','Interpreter','Latex')<br> set(gca,'Fontsize',18)</p> <p> </p> <p> </p>
Data and code from: A large-scale experiment demonstrates line marking reduces power line collision mortality for large terrestrial birds, but not bustards, in the Karoo, South Africa
<p>Line markers are widely used to mitigate bird collisions with power lines, but few studies have robustly tested their efficacy. Power line collisions are an escalating problem for several threatened bird species endemic to southern Africa, so it is critical to know whether or not marking works to adequately manage this problem. Over 8 years, a large-scale experiment was set up on 72 of 117 km of monitored transmission power lines in the eastern Karoo, South Africa, to assess whether line markers reduce bird collision mortality, particularly for Blue Cranes <i>Grus paradisea</i> and Ludwig's Bustards <i>Neotis ludwigii</i>. We tested the two marking devices commonly used in South Africa: bird flappers and static bird flight diverters. Using a before-after-control-impact design, we show that line marking reduced collision rates for Blue Cranes by 92% (95% CI 77-97%) and all large birds by 51% (95% CI 23-68%), but had no effect on bustards. Both marker types appeared similarly effective. Given that monitoring at this site also confirmed high levels of mortality of a range of species of conservation concern, we recommend that marking be widely installed on new power lines. However, other options need to be explored urgently to reduce collision mortality of bustards. Five bustard species were in the top ten list of most frequently found carcasses, and high collision rates of Ludwig's Bustards (0.68 birds·km<sup>-1</sup>·year<sup>-1</sup> uncorrected for survey biases) add to wider concerns about population level effects for this range-restricted and Endangered species. </p> <p>This dataset includes the data and R code for this journal paper.</p>
Data related to "Near-bed sediment transport processes during onshore bar migration in large-scale experiments. Comparison with offshore bar migration."
<p>Abstract:</p> <p>This paper presents novel insights into nearshore sediment transport processes during bar migration on the basis of large-scale laboratory experiments with bichromatic wave groups on a relatively steep initial beach slope (1:15). Insights are based on detailed measurements of velocity and sand concentration near the bed from shoaling up to the outer breaking zone including suspended sediment and sheet flow transport. The analysis focuses on onshore migration under an accretive wave condition but comparison to an erosive condition highlights important differences. Decomposition shows that total net transport mainly results from a balance of short wave-related, bedload net onshore transport and current-related, suspended net offshore transport. When comparing the accretive to the more energetic erosive condition, the balance shifts towards net onshore transport, and onshore migration, because the short wave-related transport does not decrease as much as the current-related transport. This is related to the effects of skewness and asymmetry combined with larger sediment entrainment and undertow magnitude under the erosive condition. Net transports from streaming in the wave boundary layer and from infragravity waves are noticeable but only play a subordinate role. Identified priorities for numerical model development include parametrization of wave nonlinearity effects and better description of wave breaking and its influences on sediment suspension. The present data, unique in their combination of high measurement detail with fully-evolving accretive beach profiles, help to improve numerical modeling of long-term morphological evolution.</p> <p>About the data:</p> <p>The folder “Beach Profiles” contains the measurements from the mechanical profiler before and after each test. To save time, only the morphologically active section of the profiles was measured. Additionally, the folder contains the initial profiles at the start of each sequence (after application of the benchmark waves). Here the full profile was measured.</p> <p>The structure “MobFrame” contains the absolute cross-shore position of the mobile frame (from which detailed measurements were taken) in the considered tests.</p> <p>The folder “ACVP” contains structures with ensemble-averaged velocity and concentration measurements in vertical reference to the undisturbed bed level or a few bins below it (zeta<sub>0</sub>-coordinate system as described in the paper). For better interpretation of the measurements, it also features the ensemble-averaged intrawave instantaneous bed elevation (erosion depth) and the upper limit of the sheet flow layer.</p> <p>The folder “ADV” contains structures with the ensemble-averaged ADV data of each test. Apart from the velocity components of each ADV they contain the vertical elevation of each ADV with respect to the ACVP transceiver. The ADV measurements were not subject to the same vertical referencing procedure that was described in the paper for the near-bed ACVP measurements.</p> <p>The folder “OBS” contains structures with the ensemble-averaged OBS data of each test. Apart from the concentration measurements in each OBS sensor they contain the vertical elevation of each OBS with respect to the ACVP transceiver.</p> <p>The folder “ETA” contains structures with the ensemble-averaged surface elevation data of each test (from different instruments as described in the paper). The location of each instrument is given in absolute cross-shore coordinates x.</p> <p> </p> <p>For visualizing the near-bed concentration data, which may not be as trivial as visualizing the rest of the data, an example of MATLAB code is given:</p> <p>%S=ACVP_xx; %to choose which ACVP file you want to look into</p> <p>con=S.c;</p> <p>con(con<1)=1; %to cater for the cells where the logarithm is not defined</p> <p>xphase=linspace(0,1,length(S.solbed)).*ones(size(S.c,2),size(S.c,1));</p> <p>figure; hold on; box on;</p> <p>[C,h]=contourf(xphase,S.z,log10(transpose(con)),[0:0.1:3]);</p> <p>cbh=colorbar; caxis([0 3]);</p> <p>set(h,'edgecolor','none');</p> <p>tt=get(cbh,'Title'); set(tt,'String','$log_{10}(c)$ $[kg/m^3]$','Interpreter','Latex');</p> <p>plot(xphase(1,:),S.solbed,'k','Linewidth',1.5);</p> <p>plot(xphase(1,:),S.solflo,'r','Linewidth',1.5);</p> <p>xlabel('$t/T_r$','Interpreter','Latex')</p> <p>ylabel('$\zeta_0$ $[m]$','Interpreter','Latex')</p> <p>set(gca,'Fontsize',18)</p>
"Software is the easy part of Software Engineering" - Lessons and Experiences from A Large-Scale, Multi-Team Capstone Course
<p>Our data contains the peer review form that students used to assess each member in a sub-team. Additionally, we included the project description for the capstone project. Please note that our domain experts provided additional in person requirements that students elicited for the project. However, this project description can serve as a good starting point for determining the primary project criteria. Finally, we include a sample software requirements specification that we provided for our students to use for their initial requirements specification. </p>
Code Smells and their Collocations : A Large-scale Experiment on Open-source Systems
<p>This dataset includes classes with code smells, acquired from Qualitas Corpus (QC).<br> Folder 'all' contains data coming from the QC rev.20130901 (92 systems).<br> Folder 'domains' contains data coming from QC rev.20111026 (76 systems updated to their most recent releases from rev.20130901). <br> Folder 'pca' includes results of the PCA analysis, generated with the R prcomp() function for regular PCA, and logisticPCA() function for the binary data.</p> <p>Filenames include information about the base release of the QC, and a number (25, 50 or 75) that specifies the minimum number of detectors that identified a specific smell instance (25%, 50%, and 75%, respectively). For example, if a given code smell in a class X has been identified by 1 out of 4 available detecting tools, then the smell for the class X will be reported in the respective file 25, but not in 50 or 75. Please note, that for smells detected with only one tool, the values would be equal in all datasets (in that case, the smell was detected by 0% or 100% of tools)</p> <p>In all files, "1" denotes that the smell was identified (subject to the limitations with the number of detectors, described above), and “0” that the smell was not found in a given class.</p> <p>The filename also includes the domain abbreviation (app, css, dev, dgdv) or a keyword ALL, which indicates that the dataset includes data from all domains.</p> <p>The smells have been detected by 11 tools. Most of the tools detect more than one smell. <br> Information about the tool used to detect a given smell is given in headers of each file. Additionally, in 'smell detectors.csv' file we present the information about smells detected by a specific tool.</p>
1D-1V Vlasov-Poisson Simulations of Mutual Impedance Experiments in the presence of small-scale and large-scale plasma inhomogeneities - PART 1
<p>=====================================================================================================<br> Author : L. Bucciantini<br> Date : 05/07/2023<br> Laboratory : CNRS-LPC2E, Orléans (France)<br> =====================================================================================================</p> <p>Dear reader, we thank you for your interest in our dataset. In this document, we describe what you will<br> find in it.</p> <p>In case you need help with using the dataset, or if you are interested in mutual impedance experiments,<br> do not hesitate to contact our team in Orléans (pierre.henri@cnrs-orleans.fr, pierre.henri@oca.eu)</p> <p>=====================================================================================================<br> Topic:<br> This dataset contains the outputs of numerical simulations performed to assess<br> the impact of plasma inhomogeneities on the diagnostic performance<br> of mutual impedance experiments.</p> <p><br> Numerical model:<br> The outputs are obtained from a numerical model based on the solution of the 1D-1V Vlasov-Poisson<br> system of equations. The scheme used to solve the model is the one developed<br> by Mangeney et al. (2002), A Numerical Scheme for the Integration of the Vlasov-Maxwell System of Equations. Journal of Computational Physics, (doi: https://doi.org/10.1006/jcph.2002.7071).<br> The 1D-1V Vlasov-Poisson version of this model is described in Henri, et al. (2010), Vlasov-Poisson<br> simulations of electrostatic parametric instability for localized Langmuir wave packets in the solar wind,<br> Journal of Geophysical Research (Space Physics), 115, 6106 (2010)<br> -----------------------------------------------------------------------------------------------------<br> What is inside the dataset:</p> <p>The dataset is composed of 7 different mutual impedance measurements, each corresponding to one<br> directory (see below). The measurements (i.e. directory) correspond to small-scale plasma inhomogeneities at different<br> positions with respect to the mutual impedance antennas, or to a large-scale plasma inhomogeneity.</p> <p>Each directory contains a number of folders. Each folder corresponds to the emission of a signal at<br> different frequency.<br> -----------------------------------------------------------------------------------------------------<br> List of directories:</p> <p>(Note : each directory corresponds to one mutual impedance measurement)</p> <p>L : These outputs correspond to one mutual impedance measurement in<br> small antenna emission amplitude, which corresponds to a linear<br> plasma response to the emission.</p> <p>s_xx : Each of these outputs corresponds to one mutual impedance measurement in correspondance of<br> a small-scale plasma inhomogeneity at xx Debye lengths of distance from the emitting antenna<br> </p> <p>-----------------------------------------------------------------------------------------------------<br> List of folders inside the directories:</p> <p>Folders begin with the name "000" and have increasing index. Inside the same directory, each folder<br> represents a different simulation used to build the same mutual impedance measurement.</p> <p>------------------------------------------------------------------------------------------------------<br> List of files inside the folders:</p> <p>density_e.npz : electron density inside the box, in function of time (tempo)</p> <p>density_p.npz : ion density inside the box, in function of time (tempo)</p> <p>E.npz : electric field in the box, in function of time (tempo)</p> <p>qrho.npz : electric potential in the box, in function of time (tempo)</p> <p>qrho_imposed.npz : electric charge imposed at the emitting antennas, in function of time (tempo)</p> <p>tempo.npz : time-vector for density, electric field, electric potential and charge vectors</p> <p>TEST_Luca.dat : parameters describing the characteristics of the simulated plasma box</p> <p><br> [<br> Note : the previous files can be opened as follows</p> <p>import numpy as np</p> <p>vector_file_name = np.load('file_name.npz') # Use these for the .npz files<br> characteristics_of_the_box = np.genfromtxt('TEST_Luca.dat',skip_header=1)</p> <p>]</p> <p><br> -------------------------------------------------------------------------------------------------------<br> Characteristics of the simulated plasma box (TEST_Luca.dat)</p> <p>nx : amount of spatial grid points</p> <p>xl : physical size of the spatial box, expressed in Debye length</p> <p>tt_w : time resolution for ion and electron density, electric field, electric potential and charge</p> <p>rap_m : ion-to-electron mass ratio</p> <p>R_p : ion-to-electron temperature ratio</p> <p>dt : time step used to evolve in time the numerical simulation</p> <p>emission : emission frequency</p> <p>power : amplitude of the electric charge imposed at the emitting antennas</p> <p><br> (Note : all parameters not listed here but present inside the TEST_Luca.dat file correspond to additional<br> functionalities of the model. For the use of this dataset, they can be discarded.)</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Dataset for large-scale experiment of block-grain phase transition
<p>Dataset related to the manuscript “Block-grain phase transition in rock avalanches: insights from large-scale experiments”, submitted to the Journal of Geophysical Research: Solid Earth.</p> <p>The dataset provides the raw and processed data for the large-scale experiments of block-grain phase transition due to fragmentation, including parameters reflecting the dynamics, fragmentation, deposit.</p> <p><strong>S1_data_velocity</strong> provides the average velocity at the front, middle and tail of the moving mass determined by the laser ranging sensors under different experimental configurations.</p> <p><strong>S2_data_fragmentation</strong> provides the fragment size distribution and relative breakage ratio.</p> <p><strong>S3_data_deposit </strong>provides the parameters of deposit, including the runout distances, friction coefficients.</p> <p><strong>S4_data_seismic </strong>provides the parameters used to characterize the seismic signal excited by the moving mass, including the envelope curve, marginal spectrum and seismic energy.</p>
Data from: Demographic drivers of a refugee species: large-scale experiments guide strategies for reintroductions of hirola
Open the record for dataset details and reuse information.
Data from: Spatial and temporal aridity gradients provide poor proxies for plant-plant interactions under climate change: a large-scale experiment
Open the record for dataset details and reuse information.
Data and code from: A large-scale experiment demonstrates line marking reduces power line collision mortality for large terrestrial birds, but not bustards, in the Karoo, South Africa
Open the record for dataset details and reuse information.
Data from: Impacts of species richness on productivity in a large-scale subtropical forest experiment
Open the record for dataset details and reuse information.
1D-1V Vlasov-Poisson Simulations of Mutual Impedance Experiments in the presence of small-scale and large-scale plasma inhomogeneities - PART 2
<p>PART 1 IS FOUND AT <strong>https://doi.org/10.5281/zenodo.8118023</strong></p> <p> </p> <p> </p> <p>=====================================================================================================<br> Author : L. Bucciantini<br> Date : 05/07/2023<br> Laboratory : CNRS-LPC2E, Orléans (France)<br> =====================================================================================================</p> <p>Dear reader, we thank you for your interest in our dataset. In this document, we describe what you will<br> find in it.</p> <p>In case you need help with using the dataset, or if you are interested in mutual impedance experiments,<br> do not hesitate to contact our team in Orléans (pierre.henri@cnrs-orleans.fr, pierre.henri@oca.eu)</p> <p>=====================================================================================================<br> Topic:<br> This dataset contains the outputs of numerical simulations performed to assess<br> the impact of plasma inhomogeneities on the diagnostic performance<br> of mutual impedance experiments.</p> <p><br> Numerical model:<br> The outputs are obtained from a numerical model based on the solution of the 1D-1V Vlasov-Poisson<br> system of equations. The scheme used to solve the model is the one developed<br> by Mangeney et al. (2002), A Numerical Scheme for the Integration of the Vlasov-Maxwell System of Equations. Journal of Computational Physics, (doi: https://doi.org/10.1006/jcph.2002.7071).<br> The 1D-1V Vlasov-Poisson version of this model is described in Henri, et al. (2010), Vlasov-Poisson<br> simulations of electrostatic parametric instability for localized Langmuir wave packets in the solar wind,<br> Journal of Geophysical Research (Space Physics), 115, 6106 (2010)<br> -----------------------------------------------------------------------------------------------------<br> What is inside the dataset:</p> <p>The dataset is composed of 7 different mutual impedance measurements, each corresponding to one<br> directory (see below). The measurements (i.e. directory) correspond to small-scale plasma inhomogeneities at different<br> positions with respect to the mutual impedance antennas, or to a large-scale plasma inhomogeneity.</p> <p>Each directory contains a number of folders. Each folder corresponds to the emission of a signal at<br> different frequency.<br> -----------------------------------------------------------------------------------------------------<br> List of directories:</p> <p>(Note : each directory corresponds to one mutual impedance measurement)</p> <p>L : These outputs correspond to one mutual impedance measurement in<br> small antenna emission amplitude, which corresponds to a linear<br> plasma response to the emission.</p> <p>s_xx : Each of these outputs corresponds to one mutual impedance measurement in correspondance of<br> a small-scale plasma inhomogeneity at xx Debye lengths of distance from the emitting antenna<br> </p> <p>-----------------------------------------------------------------------------------------------------<br> List of folders inside the directories:</p> <p>Folders begin with the name "000" and have increasing index. Inside the same directory, each folder<br> represents a different simulation used to build the same mutual impedance measurement.</p> <p>------------------------------------------------------------------------------------------------------<br> List of files inside the folders:</p> <p>density_e.npz : electron density inside the box, in function of time (tempo)</p> <p>density_p.npz : ion density inside the box, in function of time (tempo)</p> <p>E.npz : electric field in the box, in function of time (tempo)</p> <p>qrho.npz : electric potential in the box, in function of time (tempo)</p> <p>qrho_imposed.npz : electric charge imposed at the emitting antennas, in function of time (tempo)</p> <p>tempo.npz : time-vector for density, electric field, electric potential and charge vectors</p> <p>TEST_Luca.dat : parameters describing the characteristics of the simulated plasma box</p> <p><br> [<br> Note : the previous files can be opened as follows</p> <p>import numpy as np</p> <p>vector_file_name = np.load('file_name.npz') # Use these for the .npz files<br> characteristics_of_the_box = np.genfromtxt('TEST_Luca.dat',skip_header=1)</p> <p>]</p> <p><br> -------------------------------------------------------------------------------------------------------<br> Characteristics of the simulated plasma box (TEST_Luca.dat)</p> <p>nx : amount of spatial grid points</p> <p>xl : physical size of the spatial box, expressed in Debye length</p> <p>tt_w : time resolution for ion and electron density, electric field, electric potential and charge</p> <p>rap_m : ion-to-electron mass ratio</p> <p>R_p : ion-to-electron temperature ratio</p> <p>dt : time step used to evolve in time the numerical simulation</p> <p>emission : emission frequency</p> <p>power : amplitude of the electric charge imposed at the emitting antennas</p> <p><br> (Note : all parameters not listed here but present inside the TEST_Luca.dat file correspond to additional<br> functionalities of the model. For the use of this dataset, they can be discarded.)</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Preparatory Process in Advance of Runaway Fault Rupture through Fluid Injection observed in Laboratory Experiments using a Large Specimen of Sub-meter scale
<p>Fault slip is initiated by locally applied fluid pressure, and it can expand unstably over a wide area causing elastic waves having magnitudes that induce felt or destructive earthquakes. Thus, it is important to examine the unstable expansion of initial slips. However, it is hard to reproduce the process by general setup of laboratory experiment such as triaxial loading tests on cylindrical specimens with inclined faults. In this study, we prepared a cubic specimen of sub-meter scale, which was separated into two triangular prisms by a model fault. The specimen was subjected to bi-axial compressions with different magnitudes. A 2D array of strain gauges was embedded beneath the fault plane to measure the changes in shear strain with the fault slip driven by fluid injection. Based on the experimental results, we discussed the features of fault slips that lead to injection-induced earthquake. The dataset obtained by the experiments were presented in the current data repository.</p>
TRMM LBA (LARGE SCALE BIOSPHERE-ATMOSPHERE) EXPERIMENT (AMPR) V1
The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Tropical Rainfall Measuring Mission - Large Scale Biosphere-Atmosphere Experiment (TRMM-LBA); the second of three TRMM ground validation missions. AMPR data were collected at four distinct microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz) for the time period of January 23 through February 26, 1999. The geographic domain of the TRMM-LBA region was wholly within Brazilian Amazon Basin between 16 S to 6N latitude and 76W to 49 W longitude. The TRMM-LBA mission was to study convection over humid tropical land regions within the range of research-quality radar, lightning, radiosonde and raingage sites located in the Amazon Basin (Rondonia, Brazil).
WALOWA (WAVE LOADS ON WALLS) - LARGE SCALE EXPERIMENTS IN THE DELTA FLUME
<p>WaLoWa stands for Wave Loads on Walls and is a Hydralab+ project funded by the European Union. Ghent University (Belgium), TU Delft (The Netherlands), RWTH Aachen (Germany), Politechnico Bari and University of Florence (Italy) and Flanders Hydraulics Research (Belgium) are jointly working on the WaLoWa project. The user team leader is Ghent University. The WaLoWa project is hosted by Deltares and the Delta Flume facility.</p> <p>When storm walls and buildings are located on top of a dike or promenade, overtopping waves can induce large forces on these structures as has e.g. been observed at the Belgian coast which has a specifically shallow foreshore. Especially during storm season and in times of sea level rise these loads can be highly destructive. It is therefore the key objective of WaLoWa to study overtopped wave loads on structures situated on top of a dike and in shallow foreshore conditions.</p>
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.