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4,276 results for “Estimation”
Estimate of the atmospherically-forced contribution to sea surface height variability based on altimetric observations
<p>This repository contains the estimate of the atmospherically-forced contribution to sea level variability described in <a href="http://doi.org/10.1016/j.pocean.2020.102314">Close et al, 2020</a>, and derived from the Ssalto/Duacs altimeter products produced and distributed by the Copernicus Marine and Environment Monitoring Service (CMEMS) (<a href="http://www.marine.copernicus.eu">http://www.marine.copernicus.eu</a>).</p> <p>The files contain successive 5-day averages of sea level anomaly, with the same global coverage and 0.25° grid as the Ssalto/Duacs altimeter products. The estimate is created using a spatial bandpass filter, with cutoff scales of ~1.5° and 10.5°. Zeros in the mask file indicate regions in which it has not been possible to evaluate the quality of the estimate.</p> <p>The cutoff scales applied to the altimetry data were determined through analysis of output from the OceaniC Chaos – ImPacts, strUcture, predicTability (Penduff et al, 2014) experiment, comprising a 50-member ensemble of ocean-sea ice model hindcasts with 0.25° horizontal resolution (<a href="http://doi.org/10.5194/gmd-10-1091-2017">Bessières et al., 2017</a>). The spatiotemporal coherence between the model-based estimates of the atmospherically-forced (ensemble mean) and total simulated sea surface height signals was analysed, and found to exhibit distinct partitioning between the atmospherically-forced and intrinsic contributions in a spatial (but not temporal) sense, thus suggesting that meaningful estimation of the two components can be achieved based on simple spatial filtering. Verification of the method using the model data indicates good accuracy, with a global mean correlation of 0.9 between the estimate based on spatial filtering and the ensemble mean sea surface height. Full details of the methodology and verification may be found in <a href="http://doi.org/10.1016/j.pocean.2020.102314">Close et al, 2020</a>.</p> <p>----</p> <p><strong>References</strong>:</p> <p>Bessières, L., Leroux, S., Brankart, J.-M., Molines, J.-M., Moine, M.-P., Bouttier, P.-A., Penduff, T., Terray, L., Barnier, B., and Sérazin, G., 2017. Development of a probabilistic ocean modelling system based on NEMO 3.5: application at eddying resolution, Geosci. Model Dev., 10, 1091–1106, <a href="https://doi.org/10.5194/gmd-10-1091-2017">doi: 10.5194/gmd-10-1091-2017</a>.</p> <p>Close, S., Penduff, T., Speich, S. and Molines J.-M., 2020. A means of estimating the intrinsic and atmospherically-forced contributions to sea surface height variability applied to altimetric observations. Progr. Oceanogr. <a href="https://doi.org/10.1016/j.pocean.2020.102314">doi: 10.1016/j.pocean.2020.102314</a></p> <p>Penduff, T., Barnier, B. , Terray, L., Bessières, L., Sérazin, G., Grégorio, S., Brankart, J., Moine, M., Molines, J., Brasseur, P., 2014. Ensembles of eddying ocean simulations for climate, CLIVAR Exchanges, Special Issue on High Resolution Ocean Climate Modelling, 19.</p>
Arctic Ocean state estimates for 2013 using the GECCO model
<p>The dataset contains the 2013 data of a 10-year ocean synthesis (2007-2016) obtained by assimilating available observations of sea ice and ocean parameters into the GECCO model. Data from, among others, several satellite programs such as AMSRE, SSMI, AMSR2, Envisat, Jason, Cryosat., AVHRR, and SMOS, and available moorings in the Davis Strait, the Bering Strait, the Fram Strait, the Barents Sea Opening, and by the Nansen and Amundsen Basins Observational System (NABOS), the North Pole Environmental Observatory (NPEO), and the Beaufort Gyre Exploration Project (BGEP) project. A detailed description can be found in Lyu et al., 2020.</p> <p>Guokun Lyu, Nuna Serra, Armin Koehl and Detlef Stammer, 2020. INTAROS Deliverable 6.4 Ice-ocean statistics and state estimation V1. https://intaros.nersc.no/sites/intaros.nersc.no/files/D6.4_INTAROS_Data_assimilation_v1.3.pdf </p>
Arctic Ocean state estimates for 2012 using the GECCO model
<p>The dataset contains the 2012 data of a 10-year ocean synthesis (2007-2016) obtained by assimilating available observations of sea ice and ocean parameters into the GECCO model. Data from, among others, several satellite programs such as AMSRE, SSMI, AMSR2, Envisat, Jason, Cryosat., AVHRR, and SMOS, and available moorings in the Davis Strait, the Bering Strait, the Fram Strait, the Barents Sea Opening, and by the Nansen and Amundsen Basins Observational System (NABOS), the North Pole Environmental Observatory (NPEO), and the Beaufort Gyre Exploration Project (BGEP) project. A detailed description can be found in Lyu et al., 2020.</p> <p>Guokun Lyu, Nuna Serra, Armin Koehl and Detlef Stammer, 2020. INTAROS Deliverable 6.4 Ice-ocean statistics and state estimation V1. https://intaros.nersc.no/sites/intaros.nersc.no/files/D6.4_INTAROS_Data_assimilation_v1.3.pdf </p>
Supplementary data: The added value of Bayesian inference for estimating biotransformation rates of organic contaminants in aquatic invertebrates.
<p>Supporting information for the article "<strong>The added value of Bayesian inference for estimating biotransformation rates of organic contaminants in aquatic invertebrates.</strong>"</p> <p>This provides all the R script and .csv files for each dataset. </p>
Supplementary data for the manuscript "Technical note: Estimating aqueous solubilities and activity coefficients of mono- and α,ω-dicarboxylic acids using COSMO-RS-DARE"
<p>.cosmo files (BP-TZVPD-FINE) of dicarboxylic acids (C2-C8), dimers and monohydrates of mono- (C1-C6) and dicarboxylic acids, and water dimer.</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>
Global vegetation productivity from 1981 to 2018 estimated from remote sensing data
<p>The MUltiscale Satellite remotE Sensing (MUSES) global vegetation productivity dataset includes gross primary productivity (GPP) and net primary productivity (NPP) data from 1981 to 2018. GPP and NPP were estimated with a light use efficiency (LUE) model and MUSES leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FAPAR) products.</p> <p>The MUSES product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>The detail information of the MUSES 5-km global GPP and NPP products are as below:</p> <p>Name: MUSES 5-km global GPP and NPP products</p> <p>Period: 1981-2018</p> <p>Spatial resolution: 0.05°</p> <p>Temporal resolution: 8 days</p> <p>Projection: geographic latitude/longitude</p> <p>Data format: Tiff</p> <p>Data type: integer (16bit)</p> <p>Upper left coordinates: -180°E, 90°N</p> <p>Scale factor: 100</p> <p>Unit: gCm<sup>-2</sup>d<sup>-1</sup></p> <p> </p> <p><span>Citation (Please cite these papers when these data are used)</span></p> <p><span>1. Wang, J.M., Sun, R., Zhang, H.L., Xiao, Z.Q., Zhu A.R., Wang, M.J., Yu, T., Xiang, K.L.,</span><span> </span><span>New global MuSyQ GPP/NPP remote sensing products from 1981 to 2018. </span><span>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14, 5596-5612.</span></p> <p><span>2. Wang, M.J.; Sun, R.; </span><span>Zhu, A.R.;</span><span> Xiao, Z. Q. Evaluation and Comparison of Light Use Efficiency</span><span> </span><span>and Gross Primary Productivity Using Three</span><span> </span><span>Different Approaches. <span>Remote Sensing</span>. <span>2020</span>, 12, 1003.</span></p> <p><span>3. Yu, T.; Sun, R.; Xiao, Z.Q. ;Zhang , Q.; Liu, G.; Cui, T.X.; Wang, J.M. Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data. <span>Remote sensing. </span><span>2018, </span>10, 327.</span></p>
DeepLabCut: markerless pose estimation of user-defined body parts with deep learning
<p>This data entry contains <strong>annotated mouse data from the <a href="https://www.nature.com/articles/s41593-018-0209-y">DeepLabCut Nature Neuroscience paper</a></strong>.</p> <p>This data entry contains a public release of annotated mouse data from the DeepLabCut paper. The trail-tracking behavior is part of an investigation into odor guided navigation, where one or multiple wildtype (C57BL/6J) mice are running on a paper spool and following odor trails. These experiments were carried out by Alexander Mathis & Mackenzie Mathis in the Murthy lab at Harvard University. </p> <p>Data was recorded by two different cameras (640×480 pixels with Point Grey Firefly (FMVU-03MTM-CS), and at approximately 1,700×1,200 pixels with Grasshopper 3 4.1MP Mono USB3 Vision (CMOSIS CMV4000-3E12)) at 30 Hz. The latter images were cropped around mice to generate images that are approximately 800×800. </p> <p>Here we share 1066, frames from multiple experimental sessions observing 7 different mice. Pranav Mamidanna labeled the snout, the tip of the left and right ear as well as the base of the tail in the example images. The data is organized in <a href="https://www.nature.com/articles/s41596-019-0176-0">DeepLabCut 2.0 project structure</a> with images and annotations in the labeled-data folder. The names are pseudocodes indicating mouse id and session id, e.g. m4s1 = mouse 4 session 1.</p> <p>Code for loading, visualizing & training deep neural networks available at <a href="http://https://github.com/DeepLabCut/DeepLabCut"> https://github.com/DeepLabCut/DeepLabCut</a>.</p>
Datasets For "Estimating Maximum Extent of Auroral Equatorward Boundary using Historical and Simulated Surface Magnetic Field Data", Blake et al. (2020), JGR
<p>Datasets and sample Python codes for the 2020 paper <em>"Estimating Maximum Extent of Auroral Equatorward Boundary using Historical and Simulated Surface Magnetic Field Data"</em>, by Blake et al., submitted to the Journal of Gephysical Research, Space Physics. </p> <p>Up-to-date Python codes can be found at <a href="https://github.com/TerminusEst/Auroral_Boundary_Geomag">https://github.com/TerminusEst/Auroral_Boundary_Geomag</a></p> <p>The complete SWMF simulation folders (including parameter and log files etc.) can be requested from <a href="https://ccmc.gsfc.nasa.gov/index.php">NASA's Community Coordinated Modeling Center</a>.</p> <p>#########</p> <p><strong>Data/ </strong>contains the following:</p> <p><strong>Data/HIST_DATA.txt </strong>contains the minimum Dst values and calculated maximum extents of the auroral equatorward boundaries for 25 years of INTERMAGNET data (1991-2016). The fourth column is the standard deviation of the calculated auroral boundary in degrees. </p> <p><strong>Data/Boundary_Fits.csv </strong>contains the calculated minimum Dst values, and calculated auroral boundaries using Method 1 and Method 2 (see main paper's ttext), for each of the 15 SWMF simulations. Also included are the uncertainties for each calculation.</p> <p><strong>Data/SWMF_outputs/ </strong>contains 15<strong> </strong>.txt files,<strong> </strong>each of which correspond to an SWMF simulation of the same name given in Table 1 in the main text. These data are for the magnetic longitude, magnetic latitude and maximum calculated <em>E<sub>H</sub> </em>(V/km) for each simulation.</p> <p>#########</p> <p><strong>Codes/ </strong>contains two python scripts, and some sample data. These scripts correspond to Section 2 in the main text:</p> <p>1) <strong>Boundary_Calc.py</strong> calculates the extent of the auroral boundary using magnetic latitudes and maximum calculated <em>E<sub>H</sub></em> values from multiple INTERMAGNET sites. </p> <p>2) <strong>Efield_Calc.py </strong>calculates the E-field for a single INTERMAGNET site using the Quebec 1-D resistivity model.</p> <p>A more detailed description of these codes can be found here: <a href="https://github.com/TerminusEst/Auroral_Boundary_Geomag">https://github.com/TerminusEst/Auroral_Boundary_Geomag</a></p> <p> </p>
Estimation and mapping of the material stocks of buildings of Europe
<p>This data repository includes the results of the paper:<strong> Estimation and mapping of the material stocks of buildings of Europe: a novel nighttime lights-based approach</strong></p> <p>Link to the paper: <a href="https://doi.org/10.1016/j.resconrec.2021.105509">https://doi.org/10.1016/j.resconrec.2021.105509</a></p> <p>The data layers are in the high resolution of individual NLCs (Nighttime Light Cells) and aggregated to the standardized spatial units of NUTS2, NUTS3, and 47 countries and territories in Europe.</p> <p>Refer to SI Codebook.xls for details of the different fields within each layer.</p>
Analysing the data-driven approach of dynamically estimating positioning accuracy (data)
<p>The train/validation/test sets used in the study <strong>"<em>Analysing the data-driven approach of dynamically estimating positioning accuracy</em>"</strong>.</p> <p>Preprint: <a href="https://arxiv.org/abs/2011.10478">https://arxiv.org/abs/2011.10478</a></p> <p>Published paper: <a href="https://ieeexplore.ieee.org/document/9500369">https://ieeexplore.ieee.org/document/9500369</a></p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>The full dataset used in this study, is the public dataset <em>lorawan_dataset_antwerp.csv </em> (and its related file <em>lorawan_antwerp_gateway_locations.json)</em> which can be access here:</p> <pre><a href="https://zenodo.org/record/3904158#.X4_h7y8RpQI">https://zenodo.org/record/3904158#.X4_h7y8RpQI</a></pre> <p>The above link is related to the following publication, in which the original dataset was published:</p> <p><a href="http://www.mdpi.com/2306-5729/3/2/13">http://www.mdpi.com/2306-5729/3/2/13</a></p> <p>The credit for the creation of the dataset goes to Aernouts, Michiel; Berkvens, Rafael; Van Vlaenderen, Koen; and Weyn, Maarten.</p>
Estimating TOF from ultrasound signal over bare steel sample of 5 mm thickness
<p>These data have been obtained using the CEIT ultrasound testbed exciting a PZT piezoelectric sensor with a +/-15 volts pulse located over a 5 mm bare steel sample. The testbed receives the ultrasound response and estimates the TOF measuring the distance between two consecutive echoes. The aim is to develop an embedded system to measure the loss of thickness produced by corrosion.</p>
"Groupitizing": a strategy for numerosity estimation
<p>Each file contains a matrix called “matr”. Each row of the matrix “matr” is a trial. </p> <p>The columns contain the following information:</p> <ol> <li>subject response</li> <li>the presented numerosity</li> <li>subject response </li> <li>zeros</li> <li>stimulus duration</li> <li>Response time</li> <li>Grouped (1) or random (2) presentation </li> <li>1</li> <li>1</li> <li>Number of items of the upper-left quadrant</li> <li>Number of items of the lower-left quadrant</li> <li>Number of items of the upper-right quadrant</li> <li>Number of items of the lower - right quadrant</li> </ol>
The Cube++ Illumination Estimation Dataset
<p>Computational color constancy has the important task of reducing the influence of the scene illumination on the object colors. As such, it is an essential part of the image processing pipelines of most digital cameras. One of the important parts of the computational color constancy is illumination estimation, i.e. estimating the illumination color. When an illumination estimation method is proposed, its accuracy is usually reported by providing the values of error metrics obtained on the images of publicly available datasets. However, over time it has been shown that many of these datasets have problems such as too few images, inappropriate image quality, lack of scene diversity, absence of version tracking, violation of various assumptions, GDPR regulation violation, lack of additional shooting procedure info, etc.</p> <p>Dataset description and code is available on <a href="https://github.com/Visillect/CubePlusPlus">https://github.com/Visillect/CubePlusPlus</a></p> <p>Cube++ aims to alleviate many of the mentioned problems and to help the illumination estimation research. It consists of 4890 images with known illumination colors as well as with additional semantic data that can further make the learning process more accurate. Due to the usage of the SpyderCube color target, for every image there are two ground-truth illumination records covering different directions. Because of that, the dataset can be used for training and testing of methods that perform the single or two-illuminant estimation. This makes it superior to many similar existing datasets.</p> <p>Also, <strong>a smaller (2GB) and easy to use version of the dataset named SimpleCube++ is provided in SimpleCube++.zip file.</strong></p> <p>For a fast download please use zenodo-get. To install it use the following commands:</p> <pre><code>pip install zenodo-get zenodo_get https://zenodo.org/record/4153431 --output-dir=Cube++</code></pre> <p>Please note, that PNG0.zip is missed here. To download it type the following commands</p> <pre><code>cd Cube++/ wget https://storage.yandexcloud.net/cubepng0/PNG0.zip</code></pre> <p>or (slower)::</p> <pre><code>wget ftp://vis.iitp.ru/Cube++/PNG0.zip</code></pre> <p> </p>
Map data of historical global estimates of soil respiration
<p>The map data of global soil respiration converted to NetCDF format.</p><p>All open access available estimates were collated.</p><p>Shoji Hashimoto, Akihiko Ito, Kazuya Nishina (2023) "Divergent data-driven estimates of global soil respiration". Communications Earth & Environment, 4 Article number: 460</p><p><a href="https://doi.org/10.1038/s43247-023-01136-2 ">https://doi.org/10.1038/s43247-023-01136-2</a> </p><p>Refer to Table 1 for the study ID and data source or the attributions of the NetCDF file. </p>
BeBOD estimates of incidence, prevalence, and years lived with disability for 57 cancer types, 2004-2021
<p><strong>Belgian National Burden of Disease Study</strong></p><p><strong>Estimates of the morbidity burden of disease for 57 cancer sites</strong></p><p><i>Incidence</i></p><p>Data on new cancer cases in Belgium are collected by the <a href="https://kankerregister.org/Annual%20Tables">Belgian Cancer Registry</a> (BCR). For the current study, we selected 80 ICD-10 (C00.0-96.9 and chronic myeloid neoplasms) codes resulting in 57 cancer sites. Data were extracted by year (from 2004 to 2021), age group (5-years), sex and region (N=3). We excluded "Respiratory system and intrathoracic organs, NOS (not otherwise specified)" from further analyses because of too few cases.</p><p><i>Prevalence</i></p><p>Prevalence estimates were estimated using the above-described incidence estimates and the survival estimates also provided by BCR, derived from linkage with the Belgian Crossroads Bank for Social Security. We used a 10-year prevalence perspective meaning that from the year 2013 onwards, we were able to define the prevalence in a given year as the sum of person-months spent in the different health states. Specifically, we used a microsimulation approach to simulate future health states for each year-, age-, sex-, region- and cancer-specific cohort of incident cases.</p><p>See for more details: <a href="https://doi.org/10.1186/s12885-021-09109-4">https://doi.org/10.1186/s12885-021-09109-4</a></p><p><i>Years Lived with Disability</i></p><p>Years Lived with Disability (YLDs) were calculated using both an incidence and prevalence perspective as a measure of morbidity. YLDs are calculated as the product of the number of prevalent cases with the disability weight (DW), averaged over the different health states of the disease. The DWs reflect the relative reduction in quality of life, on a scale from 0 (perfect health) to 1 (death). We calculate YLDs using the Global Burden of Disease DWs.</p>
BeBOD estimates of mortality, years of life lost, prevalence, years lived with disability, and disability-adjusted life years for 38 causes, 2013-2020
<p><strong>Belgian National Burden of Disease Study</strong></p> <p><strong>Estimates of the burden of disease</strong></p> <p><em>Causes of death</em></p> <p>Our estimates are based on the official causes of death database compiled by <a href="https://statbel.fgov.be/en/themes/population/mortality-life-expectancy-and-causes-death/causes-death">Statbel</a>. We first map the ICD-10 codes of the underlying causes of death to the Global Burden of Disease cause list, consisting of 131 unique causes of deaths. Next, we perform a probabilistic redistribution of ill-defined deaths to specific causes, to obtain a specific cause of death for each deceased person.</p> <p><em>Years of Life Lost</em></p> <p>In addition to counting the number of deaths, we also calculate Years of Life Lost (YLLs) as a measure of premature mortality. YLLs correspond to the life expectancy at the age of death, and therefore give a higher weight to deaths occurring at younger ages. We calculate YLLs using the Global Burden of Disease reference life table, which represents the theoretical maximum number of years that people can expect to live.</p> <p><em>Prevalence</em></p> <p>Our estimates are based on the GBD cause list for morbidity by <a href="https://www.healthdata.org/">IHME</a>. We first select for each of the 38 causes, the most suitable local data source as described in the <a href="https://www.sciensano.be/en/biblio/belgian-national-burden-disease-study-guidelines-calculation-dalys-belgium-2">protocol</a>. Next, we calculate the prevalence by year, region, age, and sex, to obtain a prevalence for each of the included diseases.</p> <p><em>Years Lived with Disability</em></p> <p>In addition to calculating the number of prevalent cases, we also calculate Years Lived with Disability (YLDs) as a measure of morbidity. YLDs are calculated as the product of the number of prevalent cases with the disability weight (DW), averaged over the different health states of the disease. The DWs reflect the relative reduction in quality of life, on a scale from 0 (perfect health) to 1 (death). We calculate YLDs using the Global Burden of Disease DWs.</p> <p><em>Disability-Adjusted Life Years</em></p> <p>Disability-Adjusted Life Years (DALYs) are a measure of overall disease burden, representing the healthy life years lost due to morbidity and mortality. DALYs are calculated as the sum of YLLs and YLDs for each of the considered diseases.</p>
Example rainfall event for blended rainfall estimates to test PYRAMID
<p>Rainfall accumulations (mm) and average rainfall rates (mm/h) obtained from the different merged fields, for two 6 hour rainfall events occurring on 30-09-2022 at 12:00 and 06-09-2022 at 10:30. Data includes Met Office (Met) and Urban Observatory (UO) weather radars and Environment Agency (EA) and additional (Add) rain gauges.</p>
Data for "How do ecologists estimate occupancy in practice?" by Goldstein et al.
<p> Data for review of occupancy estimtaion methods by Goldstein et al.</p> <p> </p> <p>Please see the accompanying manuscript for full methodology. We will link to it when the manuscript is published.</p> <p> </p> <p>This upload contains three datasets: "binary_scores_phase1.csv", "binary_scores_phase2.csv" and "modsel_results.csv". Each is a .csv file containing data from a survey of occupancy estimation practices. Each row represents one peer-reviewed paper, while each column represents a characteristic of the paper. Most columns are TRUE/FALSE, indicating whether or not the paper satisfied the relevant criterion.</p> <p> </p> <p>One additional raw resource is provided. The .zip file "all_papers_2022-04-07.zip" contains 6 .xls files giving the full set of papers returned by the original Web of Science search. These are unmodified from the initial search.</p> <p> </p> <p>All datasets contain the column:</p> <p>ID - A unique ID for each paper; it most cases, a DOI. When Web of Science returned an invalid DOI, the ID is set to the paper title instead.</p> <p> </p> <p>Note that all papers in Phase 2 are also in Phase 1, and all model selection papers are in both Phase 1 and Phase 2. The ID column can be used to join datasets.</p> <p> </p> <p>Across both datasets, if all options in a category are FALSE or if a field is NA, that may mean that the review team was unable to determine what choices the authors made.</p> <p> </p> <p>binary_scores_phase1.csv gives the results of Phase 2 of the review. It contains the following columns:</p> <p> </p> <p>coll_newdata - Did the authors analyze newly collected data?</p> <p>coll_existing - Did the authors analyze existing, previously published data?</p> <p>coll_longterm - Did the authors analyze data produced by a long-term monitoring program?</p> <p>coll_particip - Did the authors analyze participatory science data?</p> <p>eco_frshwtr - Was the study system a freshwater ecosystem?</p> <p>eco_marine - Was the study system a marine ecosystem?</p> <p>eco_terra - Was the study system a terrestrial ecosystem?</p> <p>region_USA - Were the data collected in the USA?</p> <p>region_NoAm - Were the data collected in North America?</p> <p>region_CenAm - Were the data collected in Central America?</p> <p>region_SoAm - Were the data collected in South America?</p> <p>region_Africa - Were the data collected in Africa?</p> <p>region_Eur - Were the data collected in Europe?</p> <p>region_Asia - Were the data collected in Asia?</p> <p>region_Oceania - Were the data collected in Oceania?</p> <p>framework_MLE - Did the authors estimate models in a maximum likelihood framework?</p> <p>framework_ML - Did the authors estimate models in a machine learning framework?</p> <p>framework_Bayes - Did the authors estimate models in a Bayesian framework?</p> <p>gof_AUC - Did the authors use area-under-the-curve to evaluate their models?</p> <p>gof_CV - Did the authors use cross validation to evaluate their models?</p> <p>gof_PPC - Did the authors use poserior predictive checks to evaluate their models?</p> <p>gof_parboot - Did the authors use parametric bootstrapping to evaluate their models?</p> <p>gof_bayespv - Did the authors use Bayesian p-values to evaluate their models?</p> <p>gof_any - Did the authors conduct any model checking?</p> <p>taxon_mammal - Were some or all of the study species mammals?</p> <p>taxon_bird - Were some or all of the study species birds?</p> <p>taxon_herp - Were some or all of the study species herptiles (reptiles and amphibians)?</p> <p>taxon_fish - Were some or all of the study species fish?</p> <p>taxon_arthro - Were some or all of the study species arthropods?</p> <p>taxon_othinv - Were some or all of the study species non-arthropod invertebrates?</p> <p>soft_unmarked - Did the authors estimate models using the software unmarked?</p> <p>soft_PRESENCE - Did the authors estimate models using PRESENCE-family software?</p> <p>soft_MARK - Did the authors estimate models using MARK-family software?</p> <p>soft_JAGS - Did the authors estimate models using JAGS-family software?</p> <p>soft_lme4 - Did the authors estimate models using the software lme4?</p> <p>soft_MaxEnt - Did the authors estimate models using the software MaxEnt?</p> <p>soft_baseR - Did the authors estimate models using custom models written in base-R?</p> <p>soft_NR - Did the authors fail to clearly report what software they used to estimate models?</p> <p>soft_other - Did the authors estimate models using some other software?</p> <p>nspecies - How many species did the authors study?</p> <p>nspec_1 - Did the authors analyze data on exactly 1 species?</p> <p>nspec_2 - Did the authors analyze data on exactly 2 species?</p> <p>nspec_3_5 - Did the authors analyze data on 3-5 species?</p> <p>nspec_6_10 - Did the authors analyze data on 6-10 species?</p> <p>nspec_11_20 - Did the authors analyze data on 11-20 species?</p> <p>nspec_20plus - Did the authors analyze data on more than 20 species?</p> <p>compare_avg - Did the authors conduct model averaging?</p> <p>compare_sel - Did the authors conduct model selection?</p> <p>compare_other - Did the authors compare multiple models without averaging or selecting between them?</p> <p>modsel_AICc - Did the authors use AICc in model selection or averaging?</p> <p>modsel_AIC - Did the authors use AIC in model selection or averaging?</p> <p>modsel_other - Did the authors use another information criterion in model selection or averaging?</p> <p>dattype_DND - Were some or all of the data collected as detection-nondetection data?</p> <p>dattype_count - Were some or all of the data collected as count data?</p> <p>dattype_PO - Were some or all of the data collected as presence-only data?</p> <p>dattype_other - Were some or all of the data collected in some other form?</p> <p>survey_visual - Were some or all of the data collected using in-person visual surveys?</p> <p>survey_audio - Were some or all of the data collected using in-person audio surveys?</p> <p>survey_camtrap - Were some or all of the data collected using camera trap surveys?</p> <p>survey_capture - Were some or all of the data collected using animal capture surveys?</p> <p>survey_sign - Were some or all of the data collected using sign surveys?</p> <p>survey_passaudio - Were some or all of the data collected using passive acoustic surveys?</p> <p>survey_DNA - Were some or all of the data collected using eDNA surveys?</p> <p>survey_other - Were some or all of the data collected using some other survey protocol?</p> <p>modtype_SSOM - Did the authors analyze data with an SSOM?</p> <p>modtype_DynOcc - Did the authors analyze data with a dynamic occupancy model?</p> <p>modtype_GLM - Did the authors analyze data with a GLM?</p> <p>modtype_cooccur - Did the authors analyze data with a multispecies co-occurrence model?</p> <p>modtype_community - Did the authors analyze data with a multispecies community model?</p> <p>modtype_MaxEnt - Did the authors analyze data with a MaxEnt model?</p> <p>modtype_other - Did the authors analyze data with another model?</p> <p>affil_acad - Did any of the authors have an academic affiliation?</p> <p>affil_govt - Did any of the authors have a government affiliation?</p> <p>affil_other - Did any of the authors have a private or NGO affiliation?</p> <p> </p> <p> </p> <p>binary_scores_phase2.csv gives the results of Phase 2 of the review. It contains the following columns:</p> <p> </p> <p>code_avail - Did we determine that the authors published their model fitting code?</p> <p>data_avail - Did we determine that the authors published their data?</p> <p>nsite - At how many sites were data collected?</p> <p>nsite_lt20 - Were data collected at 20 or fewer sites?</p> <p>nsite_21_50 - Were data collected at 20-50 sites?</p> <p>nsite_51_100 - Were data collected at 51-100 sites?</p> <p>nsite_101p - Were data collected at more than 100 sites?</p> <p>time_pds - Over how many primary time periods (e.g. sampling seasons) were data collected?</p> <p>time_pds_1 - Were data collected during a single time period?</p> <p>time_pds_2_3 - Were data collected during 2-3 time periods?</p> <p>time_pds_4p - Were data collected during 4 or more time periods?</p> <p>nrepl - Roughly how many replicate surveys were collected per site?</p> <p>nrepl_1 - Was only one replicate survey conducted per site?</p> <p>nrepl_2_3 - Were 2-3 replicate surveys conducted per site?</p> <p>nrepl_4_5 - Were 4-5 replicate surveys conducted per site?</p> <p>nrepl_6p - Were 6 or more replicate surveys conducted per site?</p> <p>window - What sampling window was used to discretize continuous-time sampling? (continuous-time studies only; otherwise NA)</p> <p>det_window_lt_day - Was a sampling unit of less than one day used to discretize sampling?</p> <p>det_window_1day - Was a sampling unit of one day used to discretize sampling?</p> <p>det_window_2_6day - Was a sampling unit of 2-6 days used to discretize sampling?</p> <p>det_window_7_14day - Was a sampling unit of l7-14 days used to discretize sampling?</p> <p>det_window_15p_day - Was a sampling unit of 15 days or more used to discretize sampling?</p> <p>homerange_is_bigger - Did the authors describe their survey area per site as bigger than the target species' home range?</p> <p>homerange_is_smaller -Did the authors describe their survey area per site as smaller than the target species' home range?</p> <p>informative_priors - Did the authors use informative priors in a Bayesian analysis?</p> <p>time_in_mod_as_covar - Did the authors include primary time periods in the model as a covariate?</p> <p>time_in_mod_separate_model - Did the authors use separate models to analyze data collected during different primary time periods?</p> <p>time_in_mod_other - Did the authors include primary time periods in the model in some other way?</p> <p>ncovar_det - How many covariates were included in the best model's detection submodel?</p> <p>ncovar_det_zero - Did the detection submodel include 0 covariates?</p> <p>ncovar_det_1_3 - Did the detection submodel include 1-3 covariates?</p> <p>ncovar_det_4p - Did the detection submodel include 4 or more covariates?</p> <p>ncovar_occ - How many covariates were included in the best model's occupancy submodel?</p> <p>ncovar_occ_zero - Did the occupancy submodel include 0 covariates?</p> <p>ncovar_occ_1_3 - Did the occupancy submodel include 1-3 covariates?</p> <p>ncovar_occ_4p - Did the occupancy submodel include 4 or more covariates?</p> <p>covars_both_mods - Were any variables considered in both submodels?</p> <p>model_ranefs - Did the authors include any random effects?</p> <p>model_expl_spatial - Did the model have an explicit spatial component?</p> <p>motivating_q_range - Was range estimation a main goal motivating the study?</p> <p>motivating_q_drivers - Was identifying drivers of occupancy a main goal motivating the study?</p> <p>motivating_q_trends - Was identifying trends in occupancy a main goal motivating the study?</p> <p>motivating_q_predict - Was predicting occupancy under new conditions a main goal motivating the study?</p> <p>motivating_q_theoretical - Was advancing ecological theory a main goal motivating the study?</p> <p>motivating_q_field_method - Was evaluation of a field method a main goal motivating the study?</p> <p>motivating_q_model_method - Was evaluation of a modeling method a main goal motivating the study?</p> <p>context_conservation - Did the authors contextualize their study as relevant to conservation?</p> <p>context_management - Did the authors contextualize their study as relevant to wildlife management?</p> <p>context_natural_hist - Did the authors contextualize their study as relevant to studying natural history of target species?</p> <p>context_methodology - Did the authors contextualize their study as advancing methodology?</p> <p>detdensity - Did the authors mention the possibility that detection and animal density were confounded?</p> <p>interp_top_mod_as_biol - Did the authors interpret model selection results as evidence for a biological process?</p> <p>nmod_reported_best - Did the authors report only the best model from a model selection workflow?</p> <p>nmod_reported_some - Did the authors report multiple model results from a model selection workflow?</p> <p>nmod_reported_all - Did the authors report all model results from a model selection workflow?</p> <p>priors_reported - Did the authors report their priors in a Bayesian workflow?</p> <p>violation_nonindependence - Do the authors acknowledge violating the assumption of independent data?</p> <p>violation_movement - Do the authors acknowledge violating the assumption of no animal movement?</p> <p>violation_demography - Do the authors acknowledge violating the assumption of no demographic change?</p> <p>violation_det_heterogeneity - Do the authors acknowledge violating the assumption of no unmodeled heterogeneity in detection?</p> <p>violation_yes_other - Do the authors acknowledge violating another assumption?</p> <p>violation_explicit_no - Do the authors state that all assumptions were met?</p> <p>hypotheses_all - Do the authors provide hypotheses for the effect of all covariates?</p> <p>hypotheses_some - Do the authors provide hypotheses for the effect of some covariates?</p> <p>interpret_det_literal - Do the authors interpret detection literally?</p> <p>interpret_det_biol - Do the authors interpret detection as confounded with biology?</p> <p>interpret_vars_significant - Do the authors interpret some variables as significant based on p-values?</p> <p>interpret_vars_credible - Do the authors interpret some variables as meaningful based on Bayesian credible intervals?</p> <p> </p> <p> </p> <p>modsel_results.csv describes the model selection choices made by 64 Phase 2 papers that conducted model selection. In addition to ID, it contains two columns:</p> <p> </p> <p>Submodel approach - Did the authors use a separate-by-submodel approach to model selection, did they only conduct model selection on one submodel, or did they perform variable selection on both submodels simultaneously?</p> <p>Candidate set approach - Did the authors conduct model selection among a set of a priori candidate models, or did they select between arbitrary models based on combinations of all covariates?</p> <p> </p> <p> </p> <p> </p>
Weekly county-level pollution data for China from Zhang, Carleton, Lin, and Zhou (accepted, Nature Sustainability), "Estimating the role of air quality improvements in the decline of suicide rates in China"
<p>This dataset contains weekly, county-level air pollution data for 2,839 counties from 2013 to early 2018. These data are used and described in Zhang, Carleton, Lin, and Zhou (accepted, <em>Nature Sustainability</em>), "Estimating the role of air quality improvements in the decline of suicide rates in China". When the paper is published a link to the manuscript will be added here. </p> <p>The manuscript Methods section details data construction. In summary, these county-level observations are obtained from monitoring stations maintained by the China National Environmental Monitoring Center (CNEMC), which is affiliated with the Ministry of Ecology and Environment of China. CNEMC began publishing hourly air pollution data in 2013, including the Air Quality Index, PM2.5, PM10, ozone, sulfur dioxide, nitrogen dioxide, and carbon monoxide. We average hourly data to the station-day level and use inverse-distance weighting with a radius of 200km to convert data from station to the county level. We average across days to generate county-level weekly values. Any missing station-hour observations in the raw data are omitted in this spatial and temporal aggregation. Our main analysis relies on PM2.5, but all pollutants are released here.</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.