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189 results for “WISE”
SO-WISE South Atlantic Ocean and Indian Ocean Observational Constraints
<p>This dataset contains an initial set of curated and processed oceanographic observations collected as part of a joint effort between the EU SO-CHIC project and a UKRI Future Leaders Fellowship. It is partly intended to be used as a set of observational constraints for a Weddell Gyre region state estimate, although it can be used for more general analysis purposes as well. It has been used as part of an unsupervised clustering analysis [see Jones (2022) for software and Jones and Zhou (2022) for labelled dataset, see references]. </p> <p><strong>Overall spatial and temporal coverage</strong></p> <ul> <li>Latitude: 85°S-30°S</li> <li>Longitude: 65°W-80°E</li> <li>Time: 1974-2020</li> </ul> <p><strong>Contents</strong></p> <ul> <li>CPOM_SSH: sea-ice corrected sea surface height </li> <li>CTD: temperature and salinity profiles from ship-based CTD casts </li> <li>FLOATS: temperature and salinity profiles from Argo floats </li> <li>SEALS: temperature and salinity profiles from seal-mounted profilers</li> <li>Stress_and_EKE: sea-ice corrected surface stress and EKE </li> <li>XBT: temperature and salinity profiles from expendable bathythermographs (XBTs)</li> </ul> <p><strong>Profile quality control</strong></p> <p>We only consider profiles with good position and time flags, as well as good temperature, salinity, and pressure measurements with good flags. Duplicated profiles are identified when multiple profiles are found within 24 hours over the same 2 km x 2 km grid cell, and only one profile within the spatio-temporal window is used. We then used the MITprof toolbox (Forget, G., 2017) to pre-process the selected profiles, re-gridding them onto 72 standard pressure levels; the vertical interval varies from 20 dbar at the surface to 100 dbar in the deep ocean. </p> <p><strong>SSH processing</strong></p> <p>SSH data is sea-ice corrected version provided by the Centre for Polar Observation and Modelling (CPOM) in the UK. It is composed by two satellite missions, Envisat (2004/05-2012/03) and Cryosat-2 (2010/07-2020/04). The data is available in montly along-track format. A gaussian 300km filter, ±3 std outliner removal and 0.5x0.25 deg interpolation is applied to grid the data. Intersatellite offset is removed using the overlapped period between two missions using the mean difference map. SSH is referenced to EIGEN6C4 geoid to obtain the dynamic ocean topography feild for the computation of geostrophic velocity. See the README in the Stress_and_EKE directory for more information. </p> <p><strong>Sources</strong></p> <ul> <li>Argo floats: <a href="http://argo.ucsd.edu">http://argo.ucsd.edu</a></li> <li>World Ocean Database: <a href="https://www.ncei.noaa.gov/products/world-ocean-database">https://www.ncei.noaa.gov/products/world-ocean-database</a></li> <li>MEOP-CTD Database (seal profilers): <a href="https://www.meop.net/">https://www.meop.net/</a></li> <li>CDRv4 available via NSIDC: <a href="https://nsidc.org/data/G02202">https://nsidc.org/data/G02202</a></li> <li>Polar Pathfinder sea ice drift data via NSIDC: <a href="https://nsidc.org/data/nsidc-0116">https://nsidc.org/data/nsidc-0116</a></li> </ul> <p><strong>Version</strong></p> <p>This is a pre-production version, in that it has not yet been used with a state estimate. </p>
Data to "Point-wise correlations between 10-2 Humphrey visual field and OCT data in open angle glaucoma"
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p>Cirafici, P., Maiello, G., Ancona, C., Masala, A., Traverso, C.E., & Iester M. (in press) Point-wise correlations between Humphrey visual field and OCT data in open angle glaucoma. Eye</p>
Block-wise sparse matrix-vector product dataset and convolutional neural nets for estimating the run time and energy consumption of the sparse matrix-vector product
<p><strong>Introduction</strong></p> <p><strong>SpMV-CNN</strong> is a set of Convolutional Neural Networks (CNNs) that provide accurate estimations of the performance and energy consumption of the SpMV kernel. The proposed CNN-based models use a block-wise approach to make the CNN architecture independent of the matrix size. These models cat be trained to estimate run time as well as total, package and DRAM energy consumption at different processor frequencies.</p> <p><strong>Prerequisites</strong></p> <p><strong>SpMV-CNN</strong> requires Python3 with the following packages:</p> <pre><code>keras==2.1.6 tensorflow==1.8.0 h5py==2.7.1 matplotlib==2.1.1 scikit-learn==0.19.1 </code></pre> <p><strong>Obtaining the dataset</strong></p> <p>The execution time and energy consumption data corresponding to the SpMV operation on a set of sparse matrices from the SuiteSparse Matrix Collection have been obtained on an Intel Xeon E5-2630 core running at frequencies 1.2, 1.6, 2.0, 2.4 GHz. The energy consumption measurements are obtained via the Intel RAPL interface and gathered at three different levels (total, package and DRAM, where total = package + DRAM) for this specific processor.</p> <p>The <code>spmv-cnn-dataset.tgz</code> archive contains the whole dataset, including the following HDF5 files:</p> <pre><code>$ tree . |-- test | |-- f_1200000_b250 | | |-- output_2cubes_sphere_1200000.h5 | | |-- output_apache2_1200000.h5 | | |-- output_bcsstk36_1200000.h5 | | |-- output_cfd1_1200000.h5 | | |-- output_cfd2_1200000.h5 | | |-- output_ct20stif_1200000.h5 | | |-- output_denormal_1200000.h5 | | |-- output_Dubcova2_1200000.h5 | | |-- output_Dubcova3_1200000.h5 | | |-- output_ecology2_1200000.h5 | | |-- output_gyro_1200000.h5 | | |-- output_gyro_k_1200000.h5 | | |-- output_msc10848_1200000.h5 | | |-- output_msc23052_1200000.h5 | | |-- output_nasasrb_1200000.h5 | | |-- output_nd3k_1200000.h5 | | |-- output_offshore_1200000.h5 | | |-- output_oilpan_1200000.h5 | | |-- output_olafu_1200000.h5 | | |-- output_parabolic_fem_1200000.h5 | | |-- output_qa8fm_1200000.h5 | | |-- output_raefsky4_1200000.h5 | | |-- output_s3dkq4m2_1200000.h5 | | |-- output_s3dkt3m2_1200000.h5 | | |-- output_ship_001_1200000.h5 | | |-- output_ship_003_1200000.h5 | | |-- output_shipsec1_1200000.h5 | | |-- output_shipsec5_1200000.h5 | | |-- output_shipsec8_1200000.h5 | | |-- output_smt_1200000.h5 | | |-- output_thermomech_dM_1200000.h5 | | |-- output_thread_1200000.h5 | | `-- output_vanbody_1200000.h5 | |-- f_1600000_b250 | | |-- output_2cubes_sphere_1600000.h5 | | |—- ... | | `-- output_vanbody_1600000.h5 | |-- f_2000000_b250 | | |-- output_2cubes_sphere_2000000.h5 | | |—- ... | | `-- output_vanbody_2000000.h5 | `-- f_2400000_b250 | |-- output_2cubes_sphere_2400000.h5 | |—- ... | `-- output_vanbody_2400000.h5 |-- test_pagerank | |-- f_1200000_b250 | | |-- output_adaptive_1200000.h5 | | |-- output_cit-HepPh_1200000.h5 | | |-- output_delaunay_n22_1200000.h5 | | |-- output_email-Enron_1200000.h5 | | |-- output_email-EuAll_1200000.h5 | | |-- output_europe_osm_1200000.h5 | | |-- output_hugebubbles-00020_1200000.h5 | | |-- output_rgg_n_2_24_s0_1200000.h5 | | |-- output_road_usa_1200000.h5 | | |-- output_Stanford_1200000.h5 | | |-- output_wb-edu_1200000.h5 | | |-- output_web-BerkStan_1200000.h5 | | |-- output_web-Google_1200000.h5 | | |-- output_web-NotreDame_1200000.h5 | | |-- output_wiki-Talk_1200000.h5 | | `-- output_wiki-Vote_1200000.h5 | |-- f_1600000_b250 | | |-- output_adaptive_1600000.h5 | | |—- ... | | `-- output_wiki-Vote_1600000.h5 | |-- f_2000000_b250 | | |-- output_adaptive_2000000.h5 | | |—- ... | | `-- output_wiki-Vote_2000000.h5 | `-- f_2400000_b250 | |-- output_adaptive_2400000.h5 | |—- ... | `-- output_wiki-Vote_2400000.h5 `-- train |-- merged_energy_train_shuffle_f1200000_250.h5 |-- merged_energy_train_shuffle_f1600000_250.h5 |-- merged_energy_train_shuffle_f2000000_250.h5 `-- merged_energy_train_shuffle_f2400000_250.h5 </code></pre> <p>The matrices contained in the merged training files (<code>merged_energy_train_shuffle_fXX00000_250.h5</code>) are the following:</p> <pre><code>$ tree . |-- output_af_0_k101_1200000.h5 |-- output_af_1_k101_1200000.h5 |-- output_af_2_k101_1200000.h5 |-- output_af_3_k101_1200000.h5 |-- output_af_4_k101_1200000.h5 |-- output_af_5_k101_1200000.h5 |-- output_af_shell10_1200000.h5 |-- output_af_shell1_1200000.h5 |-- output_af_shell2_1200000.h5 |-- output_af_shell3_1200000.h5 |-- output_af_shell4_1200000.h5 |-- output_af_shell5_1200000.h5 |-- output_af_shell6_1200000.h5 |-- output_af_shell7_1200000.h5 |-- output_af_shell8_1200000.h5 |-- output_af_shell9_1200000.h5 |-- output_atmosmodd_1200000.h5 |-- output_atmosmodj_1200000.h5 |-- output_atmosmodl_1200000.h5 |-- output_audikw_1_1200000.h5 |-- output_BenElechi1_1200000.h5 |-- output_bmw3_2_1200000.h5 |-- output_bmw7st_1_1200000.h5 |-- output_bmwcra_1_1200000.h5 |-- output_bone010_1200000.h5 |-- output_boneS01_1200000.h5 |-- output_boneS10_1200000.h5 |-- output_bundle_adj_1200000.h5 |-- output_cage14_1200000.h5 |-- output_cage15_1200000.h5 |-- output_circuit5M_1200000.h5 |-- output_circuit5M_dc_1200000.h5 |-- output_CO_1200000.h5 |-- output_consph_1200000.h5 |-- output_CoupCons3D_1200000.h5 |-- output_crankseg_1_1200000.h5 |-- output_crankseg_2_1200000.h5 |-- output_CurlCurl_2_1200000.h5 |-- output_CurlCurl_3_1200000.h5 |-- output_CurlCurl_4_1200000.h5 |-- output_dielFilterV2real_1200000.h5 |-- output_dielFilterV3real_1200000.h5 |-- output_Emilia_923_1200000.h5 |-- output_ESOC_1200000.h5 |-- output_F1_1200000.h5 |-- output_F2_1200000.h5 |-- output_Fault_639_1200000.h5 |-- output_Freescale1_1200000.h5 |-- output_Freescale2_1200000.h5 |-- output_FullChip_1200000.h5 |-- output_G3_circuit_1200000.h5 |-- output_Ga10As10H30_1200000.h5 |-- output_Ga19As19H42_1200000.h5 |-- output_Ga3As3H12_1200000.h5 |-- output_Ga41As41H72_1200000.h5 |-- output_Ge87H76_1200000.h5 |-- output_Ge99H100_1200000.h5 |-- output_Geo_1438_1200000.h5 |-- output_gsm_106857_1200000.h5 |-- output_Hardesty3_1200000.h5 |-- output_hood_1200000.h5 |-- output_Hook_1498_1200000.h5 |-- output_human_gene1_1200000.h5 |-- output_human_gene2_1200000.h5 |-- output_inline_1_1200000.h5 |-- output_JP_1200000.h5 |-- output_kkt_power_1200000.h5 |-- output_ldoor_1200000.h5 |-- output_Long_Coup_dt0_1200000.h5 |-- output_Long_Coup_dt6_1200000.h5 |-- output_mat_104_10000_1200000.h5 |-- output_mat_104_1000_1200000.h5 |-- output_mat_104_5000_1200000.h5 |-- output_mat_112_10000_1200000.h5 |-- output_mat_112_1000_1200000.h5 |-- output_mat_112_5000_1200000.h5 |-- output_mat_120_10000_1200000.h5 |-- output_mat_120_1000_1200000.h5 |-- output_mat_120_5000_1200000.h5 |-- output_mat_128_10000_1200000.h5 |-- output_mat_128_1000_1200000.h5 |-- output_mat_128_5000_1200000.h5 |-- output_mat_16_10000_1200000.h5 |-- output_mat_16_1000_1200000.h5 |-- output_mat_16_5000_1200000.h5 |-- output_mat_24_10000_1200000.h5 |-- output_mat_24_1000_1200000.h5 |-- output_mat_24_5000_1200000.h5 |-- output_mat_32_10000_1200000.h5 |-- output_mat_32_1000_1200000.h5 |-- output_mat_32_5000_1200000.h5 |-- output_mat_40_10000_1200000.h5 |-- output_mat_40_1000_1200000.h5 |-- output_mat_40_5000_1200000.h5 |-- output_mat_48_10000_1200000.h5 |-- output_mat_48_1000_1200000.h5 |-- output_mat_48_5000_1200000.h5 |-- output_mat_56_10000_1200000.h5 |-- output_mat_56_1000_1200000.h5 |-- output_mat_56_5000_1200000.h5 |-- output_mat_64_10000_1200000.h5 |-- output_mat_64_1000_1200000.h5 |-- output_mat_64_5000_1200000.h5 |-- output_mat_72_10000_1200000.h5 |-- output_mat_72_1000_1200000.h5 |-- output_mat_72_5000_1200000.h5 |-- output_mat_80_10000_1200000.h5 |-- output_mat_80_1000_1200000.h5 |-- output_mat_80_5000_1200000.h5 |-- output_mat_8_10000_1200000.h5 |-- output_mat_8_1000_1200000.h5 |-- output_mat_8_5000_1200000.h5 |-- output_mat_88_10000_1200000.h5 |-- output_mat_88_1000_1200000.h5 |-- output_mat_88_5000_1200000.h5 |-- output_mat_96_10000_1200000.h5 |-- output_mat_96_1000_1200000.h5 |-- output_mat_96_5000_1200000.h5 |-- output_memchip_1200000.h5 |-- output_ML_Laplace_1200000.h5 |-- output_mouse_gene_1200000.h5 |-- output_msdoor_1200000.h5 |-- output_m_t1_1200000.h5 |-- output_nd12k_1200000.h5 |-- output_nd24k_1200000.h5 |-- output_nd6k_1200000.h5 |-- output_nlpkkt120_1200000.h5 |-- output_nlpkkt80_1200000.h5 |-- output_PFlow_742_1200000.h5 |-- output_pwtk_1200000.h5 |-- output_rajat31_1200000.h5 |-- output_RM07R_1200000.h5 |-- output_Rucci1_1200000.h5 |-- output_Serena_1200000.h5 |-- output_Si34H36_1200000.h5 |-- output_Si41Ge41H72_1200000.h5 |-- output_Si87H76_1200000.h5 |-- output_SiO2_1200000.h5 |-- output_sls_1200000.h5 |-- output_StocF-1465_1200000.h5 |-- output_TEM152078_1200000.h5 |-- output_TEM181302_1200000.h5 |-- output_thermal2_1200000.h5 |-- output_tmt_sym_1200000.h5 |-- output_torso1_1200000.h5 |-- output_Transport_1200000.h5 |-- output_TSOPF_FS_b300_c2_1200000.h5 |-- output_TSOPF_FS_b300_c3_1200000.h5 |-- output_TSOPF_RS_b2383_1200000.h5 |-- output_TSOPF_RS_b2383_c1_1200000.h5 |-- output_TSOPF_RS_b678_c2_1200000.h5 `-- output_x104_1200000.h5 f_1600000_b250 |-- output_af_0_k101_1600000.h5 |—- ... `-- output_x104_1600000.h5 f_2000000_b250 |-- output_af_0_k101_2000000.h5 |—- ... `-- output_x104_2000000.h5 f_2400000_b250 |-- output_af_0_k101_2400000.h5 |—- ... `-- output_x104_2400000.h5 </code></pre> <p><strong>Creating your own dataset</strong></p> <p>If you wish to create your own training/testing dataset on a different target architecture you need to take the following steps:</p> <ol> <li> <p>Build the SpMV driver:</p> <ol> <li> <p>Go to <code>cd SpMV-driver/src</code></p> </li> <li> <p>Edit makefile and set the PAPI and HDF5 install prefixes.</p> </li> <li> <p>Build the driver via <code>make.</code></p> </li> </ol> </li> <li> <p>Run the SpMV driver: </p> <p><code>./driver <arg0> <arg1> ...</code></p> <p>List of driver arguments:</p> <pre><code>matrix = audikw_1.rb # Input matrix in rb format reps = 10000 # Number of repetitions of the operation to avoid overhead block_size_ini = 250 # Minimum block size block_size_end = 1000 # Maximum block size increment = 250 # Increment between block sizes base = 0 # Starting nnz of the matrix freq = [2400000, 2000000, 1600000, 1200000] # Operating frequency sym = 1 # If 1 the matrix is symmetric. If 0 the matrix is no-symmetric.</code></pre> <p>Example:</p> <p><code>numactl --membind 0 taskset -c 0 ./src/driver audikw_1.rb 10000 250 1000 250 0 2400000</code></p> <p>Note that <code>numactl</code> and <code>taskset</code> utilities are used to guarantee both NUMA and process-to-core affinity.</p> </li> <li> <p>Generating the dataset:</p> <ol> <li> <p>Edit the <code>SpMV-driver/run_all.sh</code> and uncomment the line <code>matrices =</code> in order to launch the driver for Train_symmetric / Train_noSymmetric / Test_symmetric / Test_noSymmetric matrices.</p> </li> <li> <p>Edit the 3rd parameter in the command SpMV-driver/run_driver.sh: 1 for symmetric matrices, 2 for unsymmetric matrices.</p> </li> <li> <p>Edit the command in SpMV-driver/run_driver.sh to select the input parameters of the driver as explained before.</p> </li> <li> <p>Run <code>SpMV-driver/run_all.sh</code> to obtain <code>hdf5</code> files that will create the dataset.</p> </li> </ol> </li> <li> <p>Merging the dataset:</p> <p>Run the script</p> <p><code>python3 SpMV-driver/merge_train_matrices.py /path/to/hdf5/matrix/files /output/path</code></p> <p>to obtain a single <code>hdf5</code> file containing all data from individual <code>hdf5</code> files obtained in the previous step. This merged file is the training dataset.</p> </li> </ol> <p><strong>Hyperparameter search</strong></p> <p>The script <code>spmv_cnn_hyperas.py</code> performs the hyperparameter search via the Hyperas tool. This script requires the hdf5 file dataset in the directory <code>dataset/train/</code> and produces both a <code>best_model_*.json</code> and <code>best_run_*.json </code>files in the <code>results/models/</code> directory containing the model structure and hyperparameters of the best performing configuration.</p> <p>This script can be invoked in the following way:</p> <p><code>python3 spmv_cnn_hyper.py 2400000 Time</code></p> <p>where <code>2400000</code> is the operating processor frequency (2.4 GHz) at which the dataset was generated and <code>Time</code> the modeled metric. According to the labels in the dataset, the hyperparameter search can also be performed with the <code>Energy</code>, <code>EPKG</code> and <code>EDRAM</code> metrics, corresponding to the energy measured by the Intel RAPL counters from our Intel Xeon Haswell core. In our case, however, we only search hyperparameters for the <code>Time</code> and <code>Energy</code> metrics at 2.4 GHz. Other metrics and frequencies inherit the best performing model and settings from the previous configuration.</p> <p><strong>Training</strong></p> <p>The script <code>spmv_cnn_train.py</code> performs the training on the best performing models obtained on the previous step. For that, it uses both the <code>best_model_*.json</code> and <code>best_run_*.json</code> files obtained in the hyperparameter search.</p> <p>This script can be invoked in the following way:</p> <p><code>python3 spmv_cnn_train.py 2400000 Time</code></p> <p>where <code>2400000</code> is the operating processor frequency (2.4 GHz) and <code>Time</code> the modeled metric. The training should be performed per metric and frequency. The training produces a file that contains the trained weights, so the model is ready for performing inference (testing).</p> <p><strong>Testing</strong></p> <p>The script <code>spmv_cnn_test.py</code> performs the test on the set of testing matrices involved in the SpMV operation.</p> <p>This script can be invoked in the following way:</p> <p><code>python3 spmv_cnn_test.py 2400000 Time</code></p> <p>where <code>2400000</code> is the operating processor frequency (2.4 GHz) and <code>Time</code> the modeled metric. The test should be performed per metric and frequency. The training produces two files in the <code>results/tests/</code> directory:</p> <ul> <li><code>Pred_*.txt</code>: This file contains the real measurements and the predictions obtained by the CNN for the individual vpos blocks of the testing matrices.</li> <li><code>Test_*.txt</code>: This file summarizes the information of <code>Pred_*.txt</code> file, showing the average relative error among the blocks of each test matrix and the total relative error, which is computed by summing up the real measurements and the predictions for all the blocks of a same matrix and computing the relative error upon those values.</li> </ul> <p><em>Note that this testing step and the two previous steps (hyperparameter search and training) can be performed at once using the <code>run.sh</code> script.</em></p> <p><strong>References</strong></p> <p>Publications describing <strong>SpMV-CNN-Model</strong>:</p> <ul> <li>Barreda, M., Dolz, M.F., Castaño, M.A. et al. Performance modeling of the sparse matrix–vector product via convolutional neural networks. J Supercomputing (2020). <a href="https://doi.org/10.1007/s11227-020-03186-1">https://doi.org/10.1007/s11227-020-03186-1</a></li> </ul> <p><strong>Acknowledgments</strong></p> <p>The <strong>SpMV-CNN-Model</strong> research has been partially supported by:</p> <ul> <li> <p>Project TIN2017-82972-R <strong>“Agorithmic Techniques for Energy-Aware and Error-Resilient High Performance Computing”</strong> funded by the Spanish Ministry of Economy and Competitiveness (2018-2020).</p> </li> <li> <p>Project CDEIGENT/2017/04 <strong>“High Performance Computing for Neural Networks” </strong>funded by the Valencian Government.</p> </li> <li> <p>Project UJI-A2019-11 <strong>“Energy-Aware High Performance Computing for Deep Neural Networks”</strong> funded by the Universitat Jaume I.</p> </li> </ul>
A framework for step-wise explaining how to solve constraint satisfaction problems
<p>We explore the problem of step-wise explaining how to solve constraint satisfaction problems, with a use case on logic grid puzzles. More specifically, we study the problem of explaining the inference steps that one can take during propagation, in a way that is easy to interpret for a person. Thereby, we aim to give the constraint solver explainable agency, which can help in building trust in the solver by being able to understand and even learn from the explanations. The main challenge is that of finding a sequence of simple explanations, where each explanation should aim to be as cognitively easy as possible for a human to verify and understand. This contrasts with the arbitrary combination of facts and constraints that the solver may use when propagating. We propose the use of a cost function to quantify how simple an individual explanation of an inference step is, and identify the explanation-production problem of finding the best sequence of explanations of a CSP. Our approach is agnostic of the underlying constraint propagation mechanisms, and can provide explanations even for inference steps resulting from combinations of constraints. In case multiple constraints are involved, we also develop a mechanism that allows to break the most difficult steps up and thus gives the user the ability to zoom in on specific parts of the explanation. Our proposed algorithm iteratively constructs the explanation sequence by using an optimistic estimate of the cost function to guide the search for the best explanation at each step. Our experiments on logic grid puzzles show the feasibility of the approach in terms of the quality of the individual explanations and the resulting explanation sequences obtained.</p>
Spectral irradiance at Lammi Biological Station Research Forest 2015: for assessing scale-wise similarity of curves with a thick pen
<p>This dataset contains records of the solar spectral energy irradiance (W m<sup>-2</sup> nm<sup>-1</sup>) in the understorey of forest stands at Lammi Biological Station, southern Finland (61◦ 3.24’ N, 25◦ 118 2.23’ E) during the spring of 2015. These spectra allow the change in spectral energy irradiance to be followed through the period of canopy leaf flush. Records are the average of recorded spectra from four points recorded at 40-cm above the forest floor using a Maya 2000 Pro array spectrometer. Spectra were recorded from exactly the same location on three dates, 2015-04-25, 2015-05-22, and 2015-06-05, before, during and after leaf flush. Data were recorded from the understorey of a young Betula stand, an old Betula stand, an old mixed Betula stand, a Quercus stand, and a Picea stand, in three positions: shade, semi-shade from leaves, and full sun in a sunfleck. On each occasion control measurements of spectral energy irradiance in full sun of an open field were also recorded at the beginning, middle and end of each measurement period. All measurements were made during the 2 hours either side of solar noon, on clear-sky days. Details of the sampling method and interpretation are given in the paper, Hartikainen et al., (2018) in Ecology and Evolution, which showcases the use of Thick Pen Transform to compare spectra.</p>
Cumberland River Dam Break Inundation Areas obtained with DSS-WISE
<p>This dataset corresponds to the inundation areas obtained with DSS-WISE for dam failure simulations in the Cumberland River Basin in the United States (in shapefie format). The dam names, National Inventory of Dams (NID) IDs and characteristics are included in the file TestDams.csv. This file also includes the relation between the shapefile file names and the dams.The folder includes the reports generated by DSS-WISE, which include the simulation parameters, and the Human Consequence (HCOM) reports.</p>
Dataset for the manuscript: Pixel-wise programmability enables dynamic high-SNR cameras for high-speed microscopy
<p>These are the data files used to generate the figures in the paper: Pixel-wise programmability enables dynamic high-SNR cameras for high-speed microscopy. DOI: 10.1101/2023.06.27.546748</p>
A tempοral Deep Convolutional Neural Network model on Sentinel-1 Image Time Series for pixel-wise Flood Classification (dataset)
<p>This is a dataset which has been designed to be used for flood time series classification. Each time series is annotated as flood or no-flood and represents a pixel-wise time series derived from stack of Sentinel-1 IW GRD images that have been pre-processed according to <a href="http://doi.org/10.5281/zenodo.6510223">https://doi.org/10.5281/zenodo.6510223</a>.</p>
Fig. 8 in Assessing the quality of biogeochemical coastal data: a step-wise procedure Abstract
Fig. 8: Percentage of assigned quality flags for a) temperature and salinity, b) nutrients and c) chlorophyll a in the LTER-MC dataset grouped by period, following the simplified QF scheme.
Fig. 4 in Assessing the quality of biogeochemical coastal data: a step-wise procedure Abstract
Fig. 4: A) Schematic representation of a possible spike (V2) evaluated based on its previous (V1) and subsequent (V3) values of the profile. B) Examples of identified spikes in nitrate profiles at low (pMC 535, -5 m), medium (●M276, -5m) and high (○MC48, -2 m) concentrations. Spikes are in orange.
Fig. 1 in Assessing the quality of biogeochemical coastal data: a step-wise procedure Abstract
Fig. 1: Geographical position of sampling stations for LTER-MC (●), Si.Di.Mar (●) and TYR (●) datasets.
Fig. 2 in Assessing the quality of biogeochemical coastal data: a step-wise procedure Abstract
Fig. 2: Frequency distribution of individual parameters in the LTER-MC dataset before applying QC (TEMP: temperature, PSAL: salinity, NTRI: nitrites, PHOS: phosphates, AMON: ammonia, NTRA: nitrates, SLCA: silicates, CHLT: chlorophyll a, S: skewness and K: kurtosis).
Fig. 9 in Assessing the quality of biogeochemical coastal data: a step-wise procedure Abstract
Fig. 9: Flags (¡ QF0, pQF2, and ÚQF6) for surface nitrates (NO3) concentrations in the Si.Di.Mar. dataset.
Fig. 6 in Assessing the quality of biogeochemical coastal data: a step-wise procedure Abstract
Fig. 6: Colour-coded flags for temperature data in monthly vertical temperature profiles in the LTER-MC dataset.
Fig. 5 in Assessing the quality of biogeochemical coastal data: a step-wise procedure Abstract
Fig. 5: Chlorophyll a data for April at 10 m depth: comparison of a standard (on the left) and an adjusted box plot (Hubert & Vandervieren, 2008) at LTER-MC.
Fig. 7 in Assessing the quality of biogeochemical coastal data: a step-wise procedure Abstract
Fig. 7: Percentages of quality flags attributed through the QC procedure to the whole LTER-MC dataset (~84,000 data-points).
Dataset: Themes Generative Artificial Intelligence ETF (WISE) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Water Interface Sediment Experiment (WISE) data set produced at the CIEM flume, Hydralab IV
<p>The present work was developed in the framework of the HYDRALAB IV as part of the WISE Joint Research Activity. The experiments were carried out in the large scale wave flume CIEM at Universitat Politècnica de Catalunya (UPC), Barcelona.</p> <p>The data set here presented aims to observe the simultaneous and collocated profiles, of water and sediment flow and the associated bed-dynamics and particle features. The experiments considered have a flume bed configuration which starts with a concrete flat part while the study area is a 1/15 constant sandy slope. The granular beach consisted of commercial well-sorted sand with a medium sediment size d50=0.25 mm. The water depth at the toe of the wave maker is 2.5 m for all tested conditions.</p> <p>Different waves conditions were tested Erosive (Hs=0.47 m and Tp=3.7s) and Accretive (Hs=0.32 m and Tp=4.7s; Hs=0.27 m and Tp=5.3s) while collecting data of velocity, suspended sediment concentration and profile evolution.</p> <p>Due to its size, the data set can not be placed on this repository and will be provided on demand. Please contact with the authors or with the data manager of the CIEM installation.</p> <p>More information can be found on the published papers:</p> <p>Cáceres, I. and Sánchez-Arcilla, A., 2015. Erosive and Accretive mobile bed experiments in large scale tests, Coastal Sediments 2015, San Diego, USA.</p> <p>Eichentopf, S., Cáceres, I. and Alsina, J.M., 2018. Breaker bar morphodynamics under erosive and accretive wave conditions in large-scale experiments. Coastal Engineering, Vol. 138, 36-48.</p> <p>Sánchez-Arcilla, A. and Cáceres, I., 2018. An analysis of nearshore profile and bar development under large scale erosive and accretive waves. Journal of Hydraulic Research, Vol. 56(2), 231-244.</p> <p> </p>
Catalog of X-ray & WISE AGN in the SDSS-IV eBOSS Stripe 82X Survey
<p>This catalog contains 4847 spectroscopically identified X-ray sources and <em>WISE</em> AGN candidates within the 36.8 deg<sup>2</sup> SDSS-IV eBOSS Stripe 82X survey area. This survey overlaps the largest contiguous portion of the Stripe 82 X-ray survey (15.6 deg<sup>2</sup>). Based on X-ray luminosities or <em>WISE</em> <em>W1</em>-<em>W2</em> colors (based on Assef et al. 2018), there are 4730 AGN in this catalog: 1790 X-ray AGN and 3638 <em>WISE</em> AGN, of which 698 are X-ray and <em>WISE</em> AGN. The sample is 82% complete to <em>r</em> ~ 22, where the X-ray and <em>WISE</em> AGN samples are 88% and 82% complete, respectively, at this magnitude limit.</p> <p>The redshifts and spectroscopic classifications include spectra from the SDSS-IV eBOSS Stripe 82X survey, previous data releases of SDSS (Abazajian et al. 2009; Aihara et al. 201; Alam et al. 2015; Albareti et al. 2017; Abolfathi et al. 2019; Pâris et al. 2017, 2018), 2SLAQ (Croom et al. 2009), 6dF (Jones et al. 2004, 2009), and dedicated follow-up programs of Stripe 82 X-ray sources (LaMassa et al. 2016, 2017, and published here for the first time). Description of the catalog columns are given in LaMassa et al. (2019). </p>
Ensemble Learning of Catchment-Wise Optimized LSTMs Enhances Regional Rainfall-Runoff modelling - Case Study: Basque Country, Spain - Data
<div> <div>This data and results are for paper: "Ensemble Learning of Catchment-Wise Optimized LSTMs Enhances Regional Rainfall-Runoff modelling - Case Study: Basque Country, Spain" by Hosseini et al. 2024 (Preprint - Under review J.Hydro 2024) Available at SSRN: <a href="https://ssrn.com/abstract=4918782" target="_blank" rel="noopener">https://ssrn.com/abstract=4918782</a></div> <div> </div> </div>
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.