Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,987
datasets available to search
ShareScore release 0.9.0
Dataset results
1,987 results for “mode”
The effects the mode of delivery of microcystin-LR has on rusty crayfish (Faxonius rusticus) behavior and physiology
Microcystin is a deadly toxin produced during algal blooms. The cells release MCLR which can cause significant neurological and behavioral damage to organisms exposed to the toxin. MCLR can exist in the water column and in the sediment and through normal ecological processes move between those two states or location. Given the location of the toxin, it is possible that MCLR in the sediment could have different adverse effects on organisms as opposed to when the toxin is in the water column. We tested that idea using crayfish that were exposed to either nothing, a vehicle to carry the mclr, and mclr. In addition, the toxin and vehicle were dosed either in the water column or in the sediment. Behavioral and physiological measures were taken after 4 days of exposure, The results indicate that at both the behavioral and physiological level, the location of the toxin has different adverse effects.
Dataset for Accuracy of Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and CNN-LSTM Model
<p>This research paper introduces a deep learning hybrid model employing Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) for short-term photovoltaic (PV) solar energy forecasting.The proposed method integrates the Variational Mode Decomposition (VMD) algo-rithm with the CNN-LSTM model to predict PV power generation from a solar farm in Boussada, Algeria, from January 1, 2019, to December 31, 2020. The performance of the developed model is benchmarked against other deep learning models (VMD-CNN, VMD-LSTM, CNN-LSTM) across various time horizons (15, 30, and 60 minutes) to provide a comprehensive evaluation. Our findings exhibit greater performance of the developed model compared to other architectures, showcasing promising results in solar power forecasting. This research contributes to the main goal of enhancing EMS by providing accurate solar energy forecasts.</p>
Zero Modes and Classification of Combinatorial Metamaterials
<p>This dataset contains the simulation data of the combinatorial metamaterial as used for the paper 'Machine Learning of Implicit Combinatorial Rules in Mechanical Metamaterials', as published in Physical Review Letters.</p> <p>In this paper, the data is used to classify each <span class="math-tex">\(k \times k\)</span> unit cell design into one of two classes (C or I) based on the scaling (linear or constant) of the number of zero modes <span class="math-tex">\(M_k(n)\)</span> for metamaterials consisting of an <span class="math-tex">\(n\times n\)</span> tiling of the corresponding unit cell. Additionally, a random walk through the design space starting from class C unit cells was performed to characterize the boundary between class C and I in design space. A more detailed description of the contents of the dataset follows below.</p> <p><strong>Modescaling_raw_data.zip</strong></p> <p>This file contains uniformly sampled unit cell designs for metamaterial M2 and <span class="math-tex">\(M_k(n)\)</span> for <span class="math-tex">\(1\leq n\leq 4\)</span>, which was used to classify the unit cell designs for the data set. There is a small subset of designs for <span class="math-tex">\(k=\{3, 4, 5\}\)</span> that do not neatly fall into the class C and I classification, and instead require additional simulation for <span class="math-tex">\(4 \leq n \leq 6\)</span> before either saturating to a constant number of zero modes (class I) or linearly increasing (class C). This file contains the simulation data of size <span class="math-tex">\(3 \leq k \leq 8\)</span> unit cells. The data is organized as follows.</p> <p>Simulation data for <span class="math-tex">\(3 \leq k \leq 5\)</span> and <span class="math-tex">\(1 \leq n \leq 4\)</span> is stored in numpy array format (.npy) and can be readily loaded in Python with the Numpy package using the numpy.load command. These files are named "data_new_rrQR_i_n_M_kxk_fixn4.npy", and contain a [Nsim, 1+k*k+4] sized array, where Nsim is the number of simulated unit cells. Each row corresponds to a unit cell. The columns are organized as follows:</p> <ul> <li>col 0: label number to keep track</li> <li>col 1 - k*k+1: flattened unit cell design, numpy.reshape should bring it back to its original <span class="math-tex">\(k \times k\)</span> form. </li> <li>col k*k+1 - k*k+5: number of zero modes <span class="math-tex">\(M_k(n)\)</span> in ascending order of <span class="math-tex">\(n\)</span>, so: <span class="math-tex">\(\{M_k(1), M_k(2), M_k(3), M_k(4)\}\)</span>.</li> </ul> <p><strong>Note:</strong> the unit cell design uses the numbers <span class="math-tex">\(\{0, 1, 2, 3\}\)</span> to refer to each building block orientation. The building block orientations can be characterized through the orientation of the missing diagonal bar (see Fig. 2 in the paper), which can be Left Up (LU), Left Down (LD), Right Up (RU), or Right Down (RD). The numbers correspond to the building block orientation <span class="math-tex">\(\{0, 1, 2, 3\} = \{\mathrm{LU, RU, RD, LD}\}\)</span>.</p> <p>Simulation data for <span class="math-tex">\(3 \leq k \leq 5\)</span> and <span class="math-tex">\(1 \leq n \leq 6\)</span> for unit cells that cannot be classified as class C or I for <span class="math-tex">\(1 \leq n \leq 4\)</span> is stored in numpy array format (.npy) and can be readily loaded in Python with the Numpy package using the numpy.load command. These files are named "data_new_rrQR_i_n_M_kxk_fixn4_classX_extend.npy", and contain a [Nsim, 1+k*k+6] sized array, where Nsim is the number of simulated unit cells. Each row corresponds to a unit cell. The columns are organized as follows:</p> <ul> <li>col 0: label number to keep track</li> <li>col 1 - k*k+1: flattened unit cell design, numpy.reshape should bring it back to its original <span class="math-tex">\(k \times k\)</span> form. </li> <li>col k*k+1 - k*k+5: number of zero modes <span class="math-tex">\(M_k(n)\)</span> in ascending order of <span class="math-tex">\(n\)</span>, so: <span class="math-tex">\(\{M_k(1), M_k(2), M_k(3), M_k(4), M_k(5), M_k(6)\}\)</span>.</li> </ul> <p>Simulation data for <span class="math-tex">\(6 \leq k \leq 8\)</span> unit cells are stored in numpy array format (.npy) and can be readily loaded in Python with the Numpy package using the numpy.load command. Note that the number of modes is now calculated for <span class="math-tex">\(n_x \times n_y\)</span> metamaterials, where we calculate <span class="math-tex">\((n_x, n_y) = \{(1,1), (2, 2), (3, 2), (4,2), (2, 3), (2, 4)\}\)</span> rather than <span class="math-tex">\(n_x=n_y=n\)</span> to save computation time. These files are named "data_new_rrQR_i_n_Mx_My_n4_kxk(_extended).npy", and contain a [Nsim, 1+k*k+8] sized array, where Nsim is the number of simulated unit cells. Each row corresponds to a unit cell. The columns are organized as follows:</p> <ul> <li>col 0: label number to keep track</li> <li>col 1 - k*k+1: flattened unit cell design, numpy.reshape should bring it back to its original <span class="math-tex">\(k \times k\)</span> form. </li> <li>col k*k+1 - k*k+9: number of zero modes <span class="math-tex">\(M_k(n_x, n_y)\)</span> in order: <span class="math-tex">\(\{M_k(1, 1), M_k(2, 2), M_k(3, 2), M_k(4, 2), M_k(1, 1), M_k(2, 2), M_k(2, 3), M_k(2, 4)\}\)</span>.</li> </ul> <p>Simulation data of metamaterial M1 for <span class="math-tex">\(k_x \times k_y\)</span> metamaterials are stored in compressed numpy array format (.npz) and can be loaded in Python with the Numpy package using the numpy.load command. These files are named "smiley_cube_x_y_<span class="math-tex">\(k_x\)</span>x<span class="math-tex">\(k_y\)</span>.npz", which contain all possible metamaterial designs, and "smiley_cube_uniform_sample_x_y_<span class="math-tex">\(k_x\)</span>x<span class="math-tex">\(k_y\)</span>.npz", which contain uniformly sampled metamaterial designs. The configurations are accessed with the keyword argument 'configs'. The classification is accessed with the keyword argument 'compatible'. The configurations array is of shape [Nsim, <span class="math-tex">\(k_x\)</span>, <span class="math-tex">\(k_y\)</span>], the classification array is of shape [Nsim]. The building blocks in the configuration are denoted by 0 or 1, which correspond to the red/green and white/dashed building blocks respectively. Classification is 0 or 1, which corresponds to I and C respectively.</p> <p><strong>Modescaling_classification_results.zip</strong></p> <p>This file contains the classification, slope, and offset of the scaling of the number of zero modes <span class="math-tex">\(M_k(n)\)</span> for the unit cells of metamaterial M2 in Modescaling_raw_data.zip. The data is organized as follows.</p> <p>The results for <span class="math-tex">\(3 \leq k \leq 5\)</span> based on the <span class="math-tex">\(1 \leq n \leq 4\)</span> mode scaling data is stored in "results_analysis_new_rrQR_i_Scen_slope_offset_M1k_kxk_fixn4.txt". The data can be loaded using ',' as delimiter. Every row corresponds to a unit cell design (see the label number to compare to the earlier data). The columns are organized as follows:</p> <p>col 0: label number to keep track</p> <p>col 1: the class, where 0 corresponds to class I, 1 to class C and 2 to class X (neither class I or C for <span class="math-tex">\(1 \leq n \leq 4\)</span>)</p> <p>col 2: slope from <span class="math-tex">\(n \geq 2\)</span> onward (undefined for class X)</p> <p>col 3: the offset is defined as <span class="math-tex">\(M_k(2) - 2 \cdot \mathrm{slope}\)</span></p> <p>col 4: <span class="math-tex">\(M_k(1)\)</span></p> <p>The results for <span class="math-tex">\(3 \leq k \leq 5\)</span> based on the extended <span class="math-tex">\(1 \leq n \leq 6\)</span> mode scaling data is stored in "results_analysis_new_rrQR_i_Scen_slope_offset_M1k_kxk_fixn4_classC_extend.txt". The data can be loaded using ',' as delimiter. Every row corresponds to a unit cell design (see the label number to compare to the earlier data). The columns are organized as follows:</p> <p>col 0: label number to keep track</p> <p>col 1: the class, where 0 corresponds to class I, 1 to class C and 2 to class X (neither class I or C for <span class="math-tex">\(1 \leq n \leq 6\)</span>)</p> <p>col 2: slope from <span class="math-tex">\(n \geq 2\)</span> onward (undefined for class X)</p> <p>col 3: the offset is defined as <span class="math-tex">\(M_k(2) - 2 \cdot \mathrm{slope}\)</span></p> <p>col 4: <span class="math-tex">\(M_k(1)\)</span></p> <p>The results for <span class="math-tex">\(6 \leq k \leq 8\)</span> based on the <span class="math-tex">\(1 \leq n \leq 4\)</span> mode scaling data is stored in "results_analysis_new_rrQR_i_Scenx_Sceny_slopex_slopey_offsetx_offsety_M1k_kxk(_extended).txt". The data can be loaded using ',' as delimiter. Every row corresponds to a unit cell design (see the label number to compare to the earlier data). The columns are organized as follows:</p> <p>col 0: label number to keep track</p> <p>col 1: the class_x based on <span class="math-tex">\(M_k(n_x, 2)\)</span>, where 0 corresponds to class I, 1 to class C and 2 to class X (neither class I or C for <span class="math-tex">\(1 \leq n_x \leq 4\)</span>)</p> <p>col 2: the class_y based on <span class="math-tex">\(M_k(2, n_y)\)</span>, where 0 corresponds to class I, 1 to class C and 2 to class X (neither class I or C for <span class="math-tex">\(1 \leq n_y \leq 4\)</span>)</p> <p>col 3: slope_x from <span class="math-tex">\(n_x \geq 2\)</span> onward (undefined for class X)</p> <p>col 4: slope_y from <span class="math-tex">\(n_y \geq 2\)</span> onward (undefined for class X)</p> <p>col 5: the offset_x is defined as <span class="math-tex">\(M_k(2, 2) - 2 \cdot \mathrm{slope_x}\)</span></p> <p>col 6: the offset_x is defined as <span class="math-tex">\(M_k(2, 2) - 2 \cdot \mathrm{slope_y}\)</span></p> <p>col 7: <span class="math-tex">\(M_k(1, 1)\)</span></p> <p>Additionally, results including classification for M2.ii can be found in the "results_analysis_unimodal_vs_oligomodal_vs_plurimodal_i_Scen_slope_M_M1k_kxk.txt and "results_analysis_unimodal_vs_oligomodal_vs_plurimodal_i_Scenx_Sceny_slopex_slopey_Mx_My_M1k_kxk.txt" files.</p> <p><strong>Random Walks Data</strong></p> <p>This file contains the random walks for <span class="math-tex">\(3 \leq k \leq 8\)</span> unit cells of metamaterial M2. The random walk starts from a class C unit cell design (classification M2.ii), for each step <span class="math-tex">\(s\)</span> a randomly picked unit cell is changed to a random new orientation for a total of <span class="math-tex">\(s=k^2\)</span> steps. The data is organized as follows.</p> <p>The configurations for each step are stored in the files named "configlist_test_i.npy", where i is a number and corresponds to a different starting unit cell. The stored array has the shape [k*k+1, 2*k+2, 2*k+2]. The first dimension denotes the step <span class="math-tex">\(s\)</span>, where <span class="math-tex">\(s=0\)</span> is the initial configuration. The second and third dimension denote the unit cell configuration in the pixel representation (see paper) padded with a single pixel wide layer using periodic boundary conditions. </p> <p>The class for each configuration are stored in "lmlist_test_i.npy", where i corresponds to the same number as for the configurations in the "configlist_test_i.npy" file. The stored array has the shape [k*k+1], where the index corresponds to the step <span class="math-tex">\(s\)</span> and displays the class for the accompanying unit cell. The stored number corresponds to the class as <span class="math-tex">\(\{0, 1\} = \{\mathrm{I}, \mathrm{C}\}\)</span>. </p>
Influence of long-term changes in solar irradiance forcing on the Southern Annular Mode
<p>This dataset accompanies Wright et al. (2022): Influence of long-term changes in solar irradiance forcing on the Southern Annular Mode, Climate of the Past.</p> <p>This dataset contains:</p> <ul> <li><strong>Solar constant experiments</strong>: monthly files for sea level pressure (psl), surface stress east (tax), surface stress north (tay), screen temperature (tsc), and temperature at X pressure (t[0-18]) for solar constant experiments, specifically <ul> <li>control</li> <li>S+1</li> <li>S+3</li> <li>S+7</li> <li>S+35</li> <li>S-3</li> <li>S-7</li> <li>S-15</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>Transient experiments</strong>: sea level pressure (psl) and screen temperature (tsc) files covering 1-2000 CE using: <ul> <li>Steinhilber_x2 solar forcing (monthly files)</li> <li>Shapiro solar forcing (monthly files)</li> </ul> </li> </ul> <p>These transient experiments are run as an Orbital-Greenhouse gases-Solar forcing experiment, and complement Phipps et al. (2013) (https://zenodo.org/record/3908927)</p> <p> </p>
Coarse mode aerosol particle size distribution collected in the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition.
<p><strong>Dataset abstract</strong></p> <p>The authors would highly appreciate to be contacted if the data is used for any purpose.</p> <p>We measured coarse mode aerosol particle size distributions with an aerodynamic particle sizer (APS, model TSI 3321) at a time resolution of five minutes during the Antarctic Circumnavigation Expedition (ACE). The diameter range is 0.7 to 19 µm. Particles in this size range are indicative of primary sea spray aerosol, biological particles and potentially long-range transported mineral dust. These particles are also important for cloud formation as they act as cloud condensation nuclei or ice nucleating particles, the latter especially in the case of biological particles and mineral dust.</p> <p>Typically the instrument reports data starting from particles with a diameter greater than 500 nm, however, particle number concentrations in the channels below 723 nm were overestimated, which is a common artefact with this instrument.</p> <p>The data have been cleaned from the influence of the exhaust of the research vessel. Temporal coverage is from December 20, 2016 to April 10, 2017. We give five-minute averaged data as dN/dlog(dp), where dN is the particle number concentration per measured size bin normalized over the logarithm of the bin width. The bin width is defined as the distance between two diameters. They are spaced equally in log-space with dlog(dp) = log(d_n+1/d_n) = 1/32. To derive the total particle number concentration one has to integrate over the diameter range taking into account the normalization by dlog(dp).</p> <p><strong>Dataset contents</strong></p> <ul> <li>ACESPACE_coarse_mode_aerosol_particle_size_distribution.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.md, metadata, text</li> </ul> <p>NaN values in a complete row denote missing values because of e.g., ship exhaust contamination, maintenance, instrument failure. NaN values which appear individually or only in small groups reflect that data were below detection limit. For latitude and longitude, NaN values are noted in cases where position data was not available for the given time period.</p> <p><strong>Dataset license</strong></p> <p>This coarse mode aerosol particle size distribution dataset collected during ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Curated mode-of-action data and effect concentrations for chemicals relevant for the aquatic environment
<p>Chemicals in the aquatic environment can be harmful to organisms and ecosystems. Knowledge on effect concentrations as well as on mechanisms and modes of interaction with biological molecules and signaling pathways is necessary to perform chemical risk assessment and identify toxic compounds. To this end, we developed criteria and a pipeline for harvesting and summarizing effect concentrations from the US ECOTOX database for the three aquatic species groups algae, crustaceans, and fish and researched the modes of action of more than 3,300 environmentally relevant chemicals in literature and databases. We provide a curated dataset ready to be used for risk assessment based on monitoring data and the first comprehensive collection and categorization of modes of action of environmental chemicals. Authorities, regulators, and scientists can use this data for the grouping of chemicals, the establishment of meaningful assessment groups, and the development of <em>in vitro</em> and <em>in silico</em> approaches for chemical testing and assessment.</p> <p> </p> <p> </p>
Centre frequencies and uncertainties for "Evidence for a kilometre-scale seismically slow layer atop the core-mantle boundary from normal modes"
<p>A table containing the centre frequencies and uncertainties used for the study presented in "Evidence for a kilometre-scale seismically slow layer atop the core-mantle boundary from normal modes". This table is the same as is contained in the supplementary materials of that paper.</p> <p>Russell, S., Irving, J. C. E., Jagt, L., & Cottaar, S. (2023). Evidence for a kilometer-scale seismically slow layer atop the core-mantle boundary from normal modes. Geophysical Research Letters, 50, e2023GL105684. <a href="https://doi.org/10.1029/2023GL105684">https://doi.org/10.1029/2023GL105684</a></p>
Investigation of the properties of conductivity signals in BK channels by Empirical Mode Decomposition
<p>The idea of the project is the comprehensive time-frequency analysis of ion current data registered from BK channels of the different cell lines and measured under the different experimental conditions. Decomposition of signals into individual frequency modes and application of non-linear measures in the form of Information Entropy or Hurst exponent to individual signal components will allow for a more detailed analysis of the information hidden behind the complex ionic conduction sequences. The sample data contains patch-clamp sequences. </p>
Programmable multi-photon quantum interference in a single spatial mode -- Data and code for simulations
<h2>Description of the data and file structure</h2> <p>This Dataset contains data files with experimental results for the manuscript "<strong>Programmable multi-photon quantum interference in a single spatial mode</strong>" (pre-print version at <a href="https://arxiv.org/abs/2305.11157">https://arxiv.org/abs/2305.11157</a>).</p> <p>The CSV files contain the measured output distributions of our time-bin interferometer, for the various experiments we run. In the first column is the number of counts detected and in the following columns the corresponding output modes. The counts were detected by post-processing the time-tags of the recorded single photon events (a detailed explanation can be found in the Supplementary Informations of the paper).The number of counts is reported for all possible combinations of output modes in order to reconstruct the entire output distribution of collisionless events.</p> <p>The text file contains the data points of the time-bin HOM histogram shown in the paper.</p> <p> </p> <h2>Code/Software</h2> <p>We also provide the Jupyter Notebook (LoopExperiment.ipynb) we used to simulate the experiments, developed by Dr. Tobias Guggemos.</p> <p>The Loop-based architecture is a photonic experiment, that allows scalable implementation of Boson Sampling and arbitrary unitaries on a photonic platform. It can be implemented as a single, sequenced or nested architecture.</p> <p>We use the python framework Perceval to simulate our experiments. We simulate the conversion of the time-bin encoded setup as path encoded photonic qubits.</p> <p>More details can be found in the Notebook.</p>
Short Fatigue Crack Behavior under various Level of Mixed-Mode
<p>This is dataset to paper: Short Fatigue Crack Behavior under various Level of Mixed-Mode</p>
Data for the article "Computational micromagnetics based on normal modes: Bridging the gap between macrospin and full spatial discretization"
<p>Data for the article "Computational micromagnetics based on normal modes: Bridging the gap between macrospin and full spatial discretization".</p> <p>Link to publisher: https://www.sciencedirect.com/science/article/abs/pii/S0304885321009197</p> <p>Link to Arxiv preprint: https://arxiv.org/abs/2105.08829</p>
Near-field images and cross-section of guided modes in a laser-inscribed double-tracks waveguide in TZN:Ag glass sample
<p><strong>Raw images were captured</strong> with a Thorlabs beam monitoring camera, while the waveguides were injected at 633 nm.<br> The fours cross-sections were computed from these raw images.<br> These files are new data from the co-authors among those presented in the review publication "Materials 2020, 13, 3846" (DOI: 10.3390/ma13173846.<br> <strong>Extracted, centered and scaled horizontal cross-sections are given in "Fig12-b-c_final.xlsx"</strong></p> <p>Sample name : TZN:Ag.</p> <p> </p>
Driving a low critical current Josephson junction array with a mode-locked laser
<p>Data for article "Driving a low critical current Josephson junction array with a mode-locked laser".</p>
The 2001 Hawaiian Ocean Mixing Experiment (HOME): Internal-tide Mode-1 Amplitude Data from the Southern Tomographic Array
<p>Ocean acoustic tomography was used to measure tides in the farfield of the Hawaiian Ridge in 2001 during the Hawaiian Ocean Mixing Experiment (HOME). The measurements were components of a suite of large- and small-scale measurements obtained during HOME with the aim of illuminating the pathways of tidal energy that may be driving deep-ocean mixing. Using reciprocal transmissions, the tomographic arrays were designed to measure the radiation of mode-1 internal tides from the Ridge, together with barotropic tidal currents. This publication makes available the tomographic estimates for mode-1 internal-waves derived<br>from the six paths of the southern HOME tomography array.</p>
The 2001 Hawaiian Ocean Mixing Experiment (HOME): Internal-tide Mode-1 Amplitude Data from the Northern Tomographic Array
<p>Ocean acoustic tomography was used to measure tides in the farfield of the Hawaiian Ridge in 2001 during the Hawaiian Ocean Mixing Experiment (HOME). The measurements were components of a suite of large- and small-scale measurements obtained during HOME with the aim of illuminating the pathways of tidal energy that may be driving deep-ocean mixing. Using reciprocal transmissions, the tomographic arrays were designed to measure the radiation of mode-1 internal tides from the Ridge, together with barotropic tidal currents. This publication makes available the tomographic estimates for mode-1 internal-waves derived<br>from the six paths of the northern HOME tomography array.</p>
Data for: Terahertz orbital angular momentum modes with flexible twisted hollow core antiresonant fiber
<p>Supporting data for the published work on "Terahertz orbital angular momentum modes with flexible twisted hollow core antiresonant fiber". The data here reported are all the necessary data to reproduce the figures in the paper both measurements an simulations (except for the analytical results, which are obtained directly from the formulas included in the paper). The Info file describes each file, how they have been obtained and what they have been used for. </p>
PMMA Mixed mode fracture
<p>This is a complete dataset of a mixed mode crack propagation experiment on PMMA. In addition to global load and displacement measurements, full displacement fields obtained by DIC are provided. DIC was also used to extract SIF and crack tip position during loading, allowing for validation of computational failure models.</p> <p>The file PMMA_Mixed_mode_fracture.pdf provides for a detailed description of the experiment as well as of the data available on this platform.</p>
HYCOM mode 1 steric SSH M2 amplitude and phase
<p>This data set contains data from a forward global HYCOM simulation (22.1) with realistic tide and atmospheric forcing as discussed in <a href="https://doi.org/10.1016/j.ocemod.2020.101656">https://doi.org/10.1016/j.ocemod.2020.101656</a> (On the interplay between horizontal resolution and wave drag and their effect on tidal baroclinic mode waves in realistic global ocean simulations, 2020, MC Buijsman, GR Stephenson, JK Ansong, BK Arbic, JAM Green, ... Ocean Modelling 152, 101656). <strong>Please cite this article when using these data. </strong></p> <p>This is a 4-km simulation with 41 layers. All data is on the native tripole grid. Data is stored as netcdf4 classic. The 2D data sets are 7055 x 9000 (lat x lon).</p> <p>The data set contains</p> <ol> <li>the M2 mode 1 complex amplitude (RE+i*IM) of steric SSH computed for a two-week time series; the phase is relative to GMT 01-Sep-2016 01:00:00; the mode-1 amplitude is computed from the mode-1 surface pressure value (see <a href="https://doi.org/10.1016/j.ocemod.2020.101656">https://doi.org/10.1016/j.ocemod.2020.101656</a>). </li> <li>Mode-1 eigenspeed</li> <li>Positive seafloor depth, and latitude and longitude coordinates</li> </ol> <p>To convert eigenspeed to wavelength, phase speed and group speed, see <a href="https://doi.org/10.1016/j.ocemod.2020.101656">https://doi.org/10.1016/j.ocemod.2020.101656</a></p> <p><a href="https://sites.google.com/site/maartenbuijsman/">https://sites.google.com/site/maartenbuijsman/</a></p>
Dataset for Broadband three-mode converter and multiplexer based on cascaded symmetric Y-junctions and subwavelength engineered MMI and phase shifters
<p>This dataset contains the raw data for the figures (Fig. 5, Fig. 6 and Fig. 7) in the publication entitled "Broadband three-mode converter and multiplexer based on cascaded symmetric Y-junctions and subwavelength engineered MMI and phase shifters" published by Optics and Laser Technology (DOI: 10.1016/j.optlastec.2023.109513). Datafiles are in .txt format.</p> <p>All relevant information regarding the dataset, how it was obtained and its context is contained in the manuscript. </p>
Dataset for manuscript "Thermal infrared dust optical depth and coarse-mode effective diameter over oceans retrieved from collocated MODIS and CALIOP observations"
<p>This is the long-term satellite retrieval dataset of dust aerosol optical depth at 10 μm (DAOD<sub>10μm</sub>) and dust coarse mode effective diameter (D<sub>eff</sub>) based on collocated MODIS and CALIOP observations from July 2006 to August 2018. The full description is in the manuscript "<strong>Thermal infrared dust optical depth and coarse-mode effective diameter over oceans retrieved from collocated MODIS and CALIOP observations" </strong>by Zheng, Jianyu, et al. The readme file for the data is in "readme_dust_aod_size_product.txt". The variable list of Level-2 data is in "variable_list_L2.txt". The variable list of Level-3 data is in "variable_list_L3.txt".</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.