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1,705 results for “vector”
Reference grids (vector) and their centroids for harmonization of analysis
<p>This dataset contains reference grids (vector) and their centroids for harmonized analysis. All the files are <em>geoparquet</em>, they are described below. Production procedure is available at projects GitHub repository (https://github.com/aavotins/HiQBioDiv/blob/main/Templates/TemplateGrids_Vector.R):</p> <ul> <li>"tikls100_sauzeme.parquet" contains terrestrial territory of Latvia divided in 100-by-100 m polygon grid cells. Contains fields: <ul> <li>"id" feature ID;</li> <li>"yes" a fields with values "1";</li> <li>"tks50km" with ID's of topographic map of Latvia pages (TKS-93 M:50000);</li> <li>"rinda300" with ID's matching file "tikls300_sauszeme.parquet";</li> <li>"ID1km" with ID's matching file "tikls1km_sauszeme.parquet";</li> <li>"rinda500" with ID's matching file "tikls500_sauszeme.parquet";</li> <li>"geom" a {sf} geometry definition field.</li> </ul> </li> <li>"tikls300_sauzeme.parquet" contains terrestrial territory of Latvia divided in 300-by-300 m polygon grid cells. Contains fields: <ul> <li>"rinda300" feature ID;</li> <li>"x" a {sf} geometry definition field.</li> </ul> </li> <li>"tikls500_sauzeme.parquet" contains terrestrial territory of Latvia divided in 500-by-500 m polygon grid cells. Contains fields: <ul> <li>"rinda500" feature ID;</li> <li>"tks50km" with ID's of topographic map of Latvia pages (TKS-93 M:50000);</li> <li>"x" a {sf} geometry definition field.</li> </ul> </li> <li>"tikls1km_sauzeme.parquet" contains terrestrial territory of Latvia divided in 1000-by-1000 m polygon grid cells. Contains fields: <ul> <li>"ID1km" feature ID;</li> <li>"yes" a fields with values "1";</li> <li>"tks50km" with ID's of topographic map of Latvia pages (TKS-93 M:50000);</li> <li>"geometry" a {sf} geometry definition field.</li> </ul> </li> <li>"pts100_sauszeme.parquet" contains centroids of file "tikls100_sauszeme.parquet" with their attribute fields;</li> <li>"pts300_sauszeme.parquet" contains centroids of file "tikls300_sauszeme.parquet" with their attribute fields and additionally "tks50km" with ID's of topographic map of Latvia pages (TKS-93 M:50000);</li> <li>"pts500_sauszeme.parquet" contains centroids of file "tikls500_sauszeme.parquet" with their attribute fields;</li> <li>"pts1000_sauszeme.parquet" contains centroids of file "tikls1km_sauszeme.parquet" with their attribute fields;</li> <li>"tks93_50km.parquet" contains topographic map of Latvia pages (TKS-93 M:50000). Contains fields: <ul> <li>"NOSAUKUMS" with a page name;</li> <li>"NUMURS" with a page number;</li> <li>"Shape_Length" an attribute from ESRI File Geodatabase;</li> <li>"Shape_Area" an attribute from ESRI File Geodatabase;</li> <li>"Shape" a {sf} geometry definition field;</li> </ul> </li> <li>All the above mentioned files are stored also as layers in geopakage file "vector_grids.gpkg" having the same names and attributes.</li> </ul>
Vectorized Hydrogeology Map of Rondônia - Brazil
<p>Vectorized Hydrogeology Map for the State of Rondônia - Brazil.</p> <p>Hydrogeology map was vectorized from CPRM [SERVIÇO GEOLÓGICO DO BRASIL]. 1998. State of Rondônia Hydrogeological Map. [Porto Velho]. Map. Scale: 1:1.000.000. Programa de Recursos Hídricos - PRH. Available on https://rigeo.sgb.gov.br/handle/doc/5364.<br><br>Citation: CPRM [SERVIÇO GEOLÓGICO DO BRASIL]. 1998. State of Rondônia Hydrogeological Map. [Porto Velho]. Map. Scale: 1:1.000.000. Programa de Recursos Hídricos - PRH.</p>
Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European Marine Species, developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Native ecological niche models of 1508 European species (894 fish and 614 non fish) developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines, for 2019 and under RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, at 0.5° spatial resolution.</p>
Biodiversity Index, in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European Marine Species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Biodiversity Index in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European marine species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution. The Index counts the number of species (among the 1508) potentially present in each 0.5° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.</p>
Ecological Niche Models of 96 European Marine Species, for 2019, developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines at 0.1° Resolution
<p>Native ecological niche models of 96 European marine species of particular commercial and conservation interest developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines, for 2019 at 0.1° spatial resolution.</p>
Ensemble Ecological Niche Models and Biodiversity Index for 2019 of 96 European Marine Species based on Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, AquaMaps, and Support Vector Machines at 0.1° Resolution
<p>Ensemble Ecological Niche Models for 2019 of 96 European marine species of particular commercial and conservation interest, based on Ecological Niche Models developed with (i) Artificial Neural Networks, (ii) Maximum Entropy, (iii) Support Vector Machines, and (iv) AquaMaps at 0.1° Resolution. The data report, for each 0.1° cell, how many models (from 0 to 4) overcome a model-specific decision threshold to assess species presence in the cell. A Biodiversity Index is also provided as the count of the number of species (among the 96) potentially present in each 0.1° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.</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>
Dataset of normalized 24-hour vectors
<p>Normalized data in 24-hour vectors representing pressure (P) and flow (Q) fluctuations during a day.</p> <p>dd_*.csv files are the pair-wise distance matrix for different measures (euclidean, DTW and GAK).</p> <p>anomalias_manuales.csv saves a boolean vector of manually selected anomalies during research.</p> <p> </p> <p>The proyect can be found at <a href="https://github.com/javialonsaso/TFM2020">github.com/javialonsaso/TFM2020.</a></p>
Data from "Tracking the Vector Acceleration with a Hybrid Quantum Accelerometer Triad"
<p>This upload includes data shown in the figures of the Paper "Tracking the Vector Acceleration with a Hybrid Quantum Accelerometer Triad".</p>
E4warning_Vector_Raster_Extents_AdminUnits
<p><strong>E4Warning Vector and raster Analysis extents and admin units</strong></p> <p>E4warning standard extents and polygons were defined at the start of the project. There sets of standatd geographies are provided.</p> <p> </p> <p><strong>File Names:</strong></p> <div>a) Folder POLYGONS/e4vnpolygons0921.zip: admin unit Polygon shapes – </div> <div>E4VNDATAPOLYGONS = deslivered NUTS 3 and GAUL 2 depending on country good for standardised data entry. Geographic projection</div> <div>E4VNMAPPOLYGONS = deslivered Admin units chosen to be more or less equal size – best for visibility in maps</div> <div>b) Folder POLYGONS/e4grid10ddclp.zip = decimal degree grids – 0.1 degree (about 10km)</div> <div>c) Folder RASTERS: e4vnextentland10km.zip = 0.08333 deg resolution extent, 1 = land, 0 - water</div> <div>d) Folder RASTERS: e4vnextentlandwater1km.zip = 0.008333 deg resolution extent, 1 = land, 0 - water</div> <div>e) Folder RASTERS: e4vnextentlandwater5km.zip =0.0416667 deg resolution extent, 1 = land, 0 - water</div> <div>g) Folder RASTERS: e4vnextentpcland10km.zip = 0.08333 deg resolution extent, value = PC of pixel that is land</div>
Dengue_WNV_vector_Models_modeledsuitability_EU
<p><strong>Abstract:</strong></p> <p>Ensembled spatial models were produced for disease vectors by combining Random Forest and Boosted Regression Trees spatial modelling outputs, implemented using the VECMAP modelling suite, using a standard set of covariates including Fourier Processed Remotely Sensed environmental variables, land use proportions, human population and elevation. The training data offered to the model process include point location and polygon data from the VectorNet project (<a href="https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/vector-net" target="_blank" rel="noopener">https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/vector-net</a>), from the Global Biodiversity Information Facility (<a href="http://www.gbif.org/" target="_blank" rel="noopener">www.gbif.org</a>) and a series of national databases. Absence data assembled from absence records, complemented by points assigned to unsuitable areas defined by environmental and climatic thresholds obtained from experts or from the literature</p> <p> </p> <p>Two sets of vectors have been modelled: </p> <ol> <li>Potential Dengue Vectors (Global= <em>Aedes albopictus</em>, <em>Aedes aegypti</em>; Europe = <em>Aedes koreicus</em> and <em>Aedes japonicus</em>) and a combination of Ae. albopictus and Ae. Aegypti calculated as Mean (aegypti +(albopictus/2)). Filename E4DENGUEVECTORMODELS.zip</li> <li>Potential WNV vectors (<em>Culex pipiens</em>, <em>Culex torrentium</em>, <em> Culex modestus, Aedes vexans) </em>Filename e4WNVVECTORMODELS.zip</li> </ol> <p> </p>
Global River Topology (GRIT) vector datasets
<p>The Global River Topology (GRIT) is a vector-based, global river network that not only represents the tributary components of the global drainage network but also the distributary ones, including multi-thread rivers, canals and delta distributaries. It is also the first global hydrography (excl. Antarctica) produced at 30m raster resolution. It is created by merging Landsat-based river mask (GRWL) with elevation-generated streams to ensure a homogeneous drainage density outside of the river mask (rivers narrower than approx. 30m). Crucially, it uses a new 30m digital terrain model (FABDEM, based on TanDEM-X) that shows greater accuracy over the traditionally used SRTM derivatives. After vectorisation and pruning, directionality is assigned by a combination of elevation, flow angle, heuristic and continuity approaches (based on RivGraph). The network topology (lines and nodes, upstream/downstream IDs) is available as layers and attribute information in the GeoPackage files (readable by QGIS/ArcMap/GDAL).</p> <p>A map of GRIT segments labelled with OSM river names is available here: <a href="https://michelwortmann.com/research/gritv05-segments-river-names/" target="_blank" rel="noopener">Map with names</a></p> <p><strong>Report bugs and feedback</strong></p> <p>Your feedback and bug reports are welcome here: <a href="https://forms.gle/JrT58QStNKBHPJAH6" target="_blank" rel="noopener">GRIT bug report form</a></p> <p>The feedback may be used to improve and validate GRIT in future versions.</p> <p><strong>Regions</strong></p> <p>Vector files are provided in 7 regions with the following codes:</p> <ul> <li>AF - Africa</li> <li>AS - Asia (excl. Siberia)</li> <li>EU - Europe</li> <li>NA - North America</li> <li>SA - South America</li> <li>SI - Siberia</li> <li>SP - South Pacific/Australia</li> </ul> <p>The domain polygons (GRITv06_domain_GLOBAL.gpkg.zip) provide 60 subcontinental catchment groups that are available as vector attributes. They allow for more fine-grained subsetting of data (e.g. with ogr2ogr --where and the domain attribute).</p> <p>Vector files are provided both in the original equal-area Equal Earth Greenwich projection (EPSG:8857) as well as in geographic WGS84 coordinates (EPSG:4326).</p> <p><strong>Change log</strong></p> <ul> <li>v0.6 - 2024-05-30 <ul> <li>Rivers/streams outside of the GRWL mask forced by all OSM water lines (not only those with waterway=river/canal)</li> <li>Some manual directions in the Irrawaddy delta and fixed erronous sink in the Volga delta</li> </ul> </li> <li>v0.5 - 2024-02-14 <ul> <li>Cyclicity and discontinuities resolved through improved algorithms, bug fixes, more sophisticated cycle solving algorithms and some manually forced directions. Only insignificant cycles (non-sinks, less than 50) were removed.</li> <li>Added segment and reach attributes</li> <li>Computational domain fixes</li> <li>Segments include OSM river names</li> <li>Asia domain split into Siberia and rest of Asia</li> <li>Vector files available in EPSG:8857 and EPSG:4326</li> </ul> </li> <li>v0.4 - 2023-03-11<br> <ul> <li>First globally complete dataset published</li> </ul> </li> </ul> <p><strong>Network segments</strong></p> <p>Lines between inlet, outlet, confluence and bifurcation nodes. Files have lines and nodes layers.</p> <p><em><strong>Attribute description of lines layer</strong></em></p> <table> <tbody> <tr> <th>Name</th> <th>Data type</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>cat</td> <td>integer</td> <td>domain internal feature ID</td> </tr> <tr> <td>global_id</td> <td>integer</td> <td>global river segment ID, same as FID</td> </tr> <tr> <td>catchment_id</td> <td>integer</td> <td>global catchment ID</td> </tr> <tr> <td>upstream_node_id</td> <td>integer</td> <td>global segment node ID at upstream end of line</td> </tr> <tr> <td>downstream_node_id</td> <td>integer</td> <td>global segment node ID at downstream end of line</td> </tr> <tr> <td>upstream_line_ids</td> <td>text</td> <td>comma-separated list of global river segment IDs connecting at upstream end of line</td> </tr> <tr> <td>downstream_line_ids</td> <td>text</td> <td>comma-separated list of global river segment IDs connecting at downstream end of line</td> </tr> <tr> <td>direction_algorithm</td> <td>float</td> <td>code of RivGraph method used to set the direction of line</td> </tr> <tr> <td>width_adjusted</td> <td>float</td> <td>median river width in m without accounting for width of segments connecting upstream/downstream</td> </tr> <tr> <td>length_adjusted</td> <td>float</td> <td>segment length in m without accounting for width of segments connecting upstream/downstream in m</td> </tr> <tr> <td>is_mainstem</td> <td>integer</td> <td>1 if widest segment of bifurcated flow or no bifurcation upstream, otherwise 0</td> </tr> <tr> <td>strahler_order</td> <td>integer</td> <td>Strahler order of segment, can be used to route in topological order</td> </tr> <tr> <td>length</td> <td>float</td> <td>segment length in m</td> </tr> <tr> <td>azimuth</td> <td>float</td> <td>direction of line connecting upstream-downstream nodes in degrees from North</td> </tr> <tr> <td>sinuousity</td> <td>float</td> <td>ratio of Euclidean distance between upstream-downstream nodes and line length, i.e. 1 meaning a perfectly straight line</td> </tr> <tr> <td>drainage_area_in</td> <td>float</td> <td>drainage area at beginning of segment, partitioned by width at bifurcations, in km2</td> </tr> <tr> <td>drainage_area_out</td> <td>float</td> <td>drainage area at end of segment, partitioned by width at bifurcations, in km2</td> </tr> <tr> <td>drainage_area_mainstem_in</td> <td>float</td> <td>drainage area at beginning of segment, following the mainstem, in km2</td> </tr> <tr> <td>drainage_area_mainstem_out</td> <td>float</td> <td>drainage area at end of segment, following the mainstem, in km2</td> </tr> <tr> <td>bifurcation_balance_out</td> <td>float</td> <td>(drainage_area_out - drainage_area_mainstem_out) / max(drainage_area_out, drainage_area_mainstem_out), dimensionless ratio</td> </tr> <tr> <td>grwl_overlap</td> <td>float</td> <td>fraction of the segment overlapping with the GRWL river mask</td> </tr> <tr> <td>grwl_value</td> <td>integer</td> <td>dominant GRWL value of segment</td> </tr> <tr> <td>name</td> <td>text</td> <td>river name from Openstreetmap where available, English preferred</td> </tr> <tr> <td>name_local</td> <td>text</td> <td>river name from Openstreetmap where available, local name</td> </tr> <tr> <td>n_bifurcations_upstream</td> <td>integer</td> <td>number of bifurcations upstream of segment</td> </tr> <tr> <td>domain</td> <td>text</td> <td>catchment group ID, see domain index file</td> </tr> </tbody> </table> <p> </p> <p><em><strong>Attribute description of nodes layer</strong></em></p> <table> <tbody> <tr> <th>Name</th> <th>Data type</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>cat</td> <td>integer</td> <td>domain internal feature ID</td> </tr> <tr> <td>global_id</td> <td>integer</td> <td>global river node ID, same as FID</td> </tr> <tr> <td>catchment_id</td> <td>integer</td> <td>global catchment ID</td> </tr> <tr> <td>upstream_line_ids</td> <td>text</td> <td>comma-separated list of global river segment IDs flowing into node</td> </tr> <tr> <td>downstream_line_ids</td> <td>text</td> <td>comma-separated list of global river segment IDs flowing out of node</td> </tr> <tr> <td>node_type</td> <td>text</td> <td>description of node, one of bifurcation, confluence, inlet, coastal_outlet, sink_outlet, grwl_change</td> </tr> <tr> <td>grwl_value</td> <td>integer</td> <td>GRWL code at node</td> </tr> <tr> <td>grwl_transition</td> <td>text</td> <td>GRWL codes of change at grwl_change nodes</td> </tr> <tr> <td>cycle</td> <td>integer</td> <td>>0 if segment is part of an unresolved cycle, 0 otherwise</td> </tr> <tr> <td>continuity_violated</td> <td>integer</td> <td>1 if flow continuity is violated, otherwise 0</td> </tr> <tr> <td>drainage_area</td> <td>float</td> <td>drainage area, partitioned by width at bifurcations, in km2</td> </tr> <tr> <td>drainage_area_mainstem</td> <td>float</td> <td>drainage area, following the mainstem, in km2</td> </tr> <tr> <td>n_bifurcations_upstream</td> <td>integer</td> <td>number of bifurcations upstream of node</td> </tr> <tr> <td>domain</td> <td>text</td> <td>catchment group, see domain index file</td> </tr> </tbody> </table> <p> </p> <p><strong>Network reaches</strong></p> <p>Segment lines split to not exceed 1km in length, i.e. these lines will be shorter than 1km and longer than 500m unless the segment is shorter. A simplified version with no vertices between nodes is also provided. Files have lines and nodes layers.</p> <p><em><strong>Attribute description of lines layer</strong></em></p> <table> <tbody> <tr> <th>Name</th> <th>Data type</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>cat</td> <td>integer</td> <td>domain internal feature ID</td> </tr> <tr> <td>segment_id</td> <td>integer</td> <td>global segment ID of reach</td> </tr> <tr> <td>global_id</td> <td>integer</td> <td>global river reach ID, same as FID</td> </tr> <tr> <td>catchment_id</td> <td>integer</td> <td>global catchment ID</td> </tr> <tr> <td>upstream_node_id</td> <td>integer</td> <td>global reach node ID at upstream end of line</td> </tr> <tr> <td>downstream_node_id</td> <td>integer</td> <td>global reach node ID at downstream end of line</td> </tr> <tr> <td>upstream_line_ids</td> <td>text</td> <td>comma-separated list of global river reach IDs connecting at upstream end of line</td> </tr> <tr> <td>downstream_line_ids</td> <td>text</td> <td>comma-separated list of global river reach IDs connecting at downstream end of line</td> </tr> <tr> <td>grwl_overlap</td> <td>float</td> <td>fraction of the reach overlapping with the GRWL river mask</td> </tr> <tr> <td>grwl_value</td> <td>integer</td> <td>dominant GRWL value of node</td> </tr> <tr> <td>grwl_width_median</td> <td>float</td> <td>median width of the GRWL river mask, meters</td> </tr> <tr> <td>grwl_width_std</td> <td>float</td> <td>standard deviation of width of the GRWL river mask, meters</td> </tr> <tr> <td>length</td> <td>float</td> <td>length of reach in meters</td> </tr> <tr> <td>sinuousity</td> <td>float</td> <td>ratio of eucledian distance betwen upstream-downstream nodes and line length, i.e. 1 meaning a perfectly straight line</td> </tr> <tr> <td>azimuth</td> <td>float</td> <td>direction of line connecting upstream-downstream nodes in degrees from North</td> </tr> <tr> <td>domain</td> <td>text</td> <td>catchment group, see domain index file</td> </tr> </tbody> </table> <p><em><strong>Attribute description of nodes layer</strong></em></p> <table> <tbody> <tr> <th>Name</th> <th>Data type</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>cat</td> <td>integer</td> <td>domain internal feature ID</td> </tr> <tr> <td>segment_node_id</td> <td>integer</td> <td>global ID of segment node at segment intersections, otherwise blank</td> </tr> <tr> <td>n_segments</td> <td>integer</td> <td>number of segments attached to node</td> </tr> <tr> <td>global_id</td> <td>integer</td> <td>global river reach node ID, same as FID</td> </tr> <tr> <td>upstream_line_ids</td> <td>text</td> <td>comma-separated list of global river reach IDs flowing into node</td> </tr> <tr> <td>downstream_line_ids</td> <td>text</td> <td>comma-separated list of global river reach IDs flowing out of node</td> </tr> <tr> <td>domain</td> <td>text</td> <td>catchment group, see domain index file</td> </tr> </tbody> </table> <p> </p> <p><strong>Catchments</strong></p> <p>Catchment outlines for entire river basins (network components, including coastal drainage areas). Catchments for segments (aka. subbasins) and reaches are also available on request.</p> <p><em><strong>Attribute description</strong></em></p> <table> <tbody> <tr> <th>Name</th> <th>Data type</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>cat</td> <td>integer</td> <td>domain internal feature ID</td> </tr> <tr> <td>global_id</td> <td>integer</td> <td>global catchment ID, same as global_id of segment/reach ID if is_coastal == 0 for respective catchments or the catchment_id for component_catchments, same as FID</td> </tr> <tr> <td>area</td> <td>float</td> <td>catchment area in km2</td> </tr> <tr> <td>is_coastal</td> <td>integer</td> <td>1 for coastal drainage areas, 0 otherwise</td> </tr> <tr> <td>domain</td> <td>text</td> <td>catchment group, see domain index file</td> </tr> </tbody> </table> <p> </p> <p><strong>Raster </strong></p> <p>Upstream drainage area and other raster-based products are also available upon request.</p>
Plasmid Maps for a Nuclear Transformation Vector in Chlamydomonas reinhardtii for the Expression and Secretion of the Plastic-Degrading Enzyme (PHL7)
<p><strong>pJP32PHL7 Vector:</strong></p> <ul> <li> <p><strong>Size:</strong> 5692 bp</p> </li> <li> <p><strong>Key Features:</strong></p> <ul> <li><strong>HSP70 Promoter:</strong> A heat shock protein promoter fused with the <em>rbcS2</em> promoter to drive expression of downstream genes.</li> <li><strong>Ble Resistance Gene:</strong> Confers resistance to bleomycin, useful for selection in <em>Chlamydomonas reinhardtii</em>.</li> <li><strong>PHL7 Gene:</strong> Encodes the plastic-degrading enzyme PHL7, inserted downstream of the <em>F2A</em> site for expression in the host.</li> <li><strong>Intron Sequences:</strong> Contains multiple <em>rbcS2</em> introns for enhancing expression in <em>Chlamydomonas</em>.</li> <li><strong>Selectable Marker (AmpR):</strong> Confers ampicillin resistance for selection in <em>E. coli</em>.</li> <li><strong>Replication Origin:</strong> Includes <em>ori</em> and <em>F1 ori</em> for replication in <em>E. coli</em>.</li> </ul> <p> </p> </li> <li> <p><strong>Applications:</strong> This vector is designed for nuclear transformation in <em>Chlamydomonas reinhardtii</em>, enabling the expression and secretion of the plastic-degrading enzyme (PHL7) under the control of a hybrid <em>HSP70</em>rbcS2 promoter.</p> </li> </ul> <p><strong>pJP32PHL7dg Vector:</strong></p> <ul> <li> <p><strong>Size:</strong> 5692 bp</p> </li> <li> <p><strong>Key Features:</strong></p> <ul> <li><strong>HSP70 Promoter:</strong> Retains the HSP70 and <em>rbcS2</em> fusion promoter for gene expression.</li> <li><strong>LacZ Alpha Fragment:</strong> Includes a LacZ alpha fragment for blue/white screening.</li> <li><strong>PHL7 Gene:</strong> Encodes the plastic-degrading enzyme PHL7, linked downstream of the <em>F2A</em> site, allowing for expression in the host.</li> <li><strong>Ble Resistance Gene:</strong> Also confers bleomycin resistance for selection in <em>Chlamydomonas</em>.</li> <li><strong>Selectable Marker (AmpR):</strong> Confers ampicillin resistance for selection in <em>E. coli</em>.</li> <li><strong>Intron Sequences:</strong> Contains <em>rbcS2</em> introns for optimizing gene expression in the host organism.</li> </ul> <p> </p> </li> <li> <p><strong>Applications:</strong> The pJP32PHL7dg vector is similarly designed for nuclear transformation in <em>Chlamydomonas reinhardtii.</em> It also facilitates the expression and secretion of the plastic-degrading enzyme PHL7, driven by the hybrid <em>HSP70</em>rbcS2 promoter, but without glycosilation sites.</p> </li> </ul>
Reproduction package for the paper "High-contrast observations of brown dwarf companion HR 2562 B with the vector Apodizing Phase Plate coronagraph"
<p>This is a basic reproduction package for the paper <a href="https://doi.org/10.1093/mnras/stab1893">"High-contrast observations of brown dwarf companion HR 2562 B with the vector Apodizing Phase Plate coronagraph" by Sutlieff et al. (2021)</a>. It aims to provide the most important data products to check and reproduce the main results of the paper.</p>
New upper bounds for some instances from benchmark for vector packing problem
<p>This dataset is a result of the research: Đorđe Stakić, Miodrag Živković, Ana Anokić, "A Reduced Variable Neighborhood Search Approach to the Heterogeneous Vector Bin Packing Problem", Information Technology and Control, 2021, 50(4), 808-826, <a href="https://doi.org/10.5755/j01.itc.50.4.29009">https://doi.org/10.5755/j01.itc.50.4.29009</a> Files are given by algorithm described in it. </p> <p>This dataset consists of 14 solutions with better bounds for instances described in paper: Heßler, K., Gschwind, T., Irnich, S. Stabilized branch-and-price algorithms for vector packing problems. European Journal of Operational Research, 2018, 271(2), 401-419. <a href="https://doi.org/10.1016/j.ejor.2018.04.047">https://doi.org/10.1016/j.ejor.2018.04.047</a> </p> <p>File structure: </p> <p>Instance name: UB: solution (bins with indices of items)</p> <ul> <li>CL_04_100_06: 627</li> <li>CL_04_100_08: 642</li> <li>CL_04_200_01: 1293</li> <li>CL_05_100_06: 314</li> <li>CL_05_100_08: 321</li> <li>CL_05_100_10: 327</li> <li>CL_05_200_02: 627</li> <li>CL_05_200_03: 633</li> <li>CL_05_200_04: 630</li> <li>CL_05_200_05: 632</li> <li>CL_05_200_06: 627</li> <li>CL_05_200_07: 634</li> <li>CL_05_200_08: 635</li> <li>CL_05_200_10: 632</li> </ul>
Vectors for Goode's Homolosine projection
<p>This dataset contains useful vector maps to work with with Goode's Homolosine projection. The list of files included are:</p> <ul> <li> <strong>CounterDomain.geojson</strong> - a polygonal approximation of the Homolosine projection counter-domain. This can be used to fix vectors wrongly projected by programmes that consider the counter-domain to be infinite. It can also be used to represent the seas in global mapping.</li> <li> <strong>ParallelsMeridians.geojson</strong> - a set of meridians and parallels to be used in the creation of global maps.</li> <li><strong>Homolosine.crs</strong> - the PROJ string defining the Homolosine projection (referenced by the GeoJSON slides)</li> <li><strong>LICENCE</strong> - full text of the licence (EUPL-1.2)</li> </ul> <p>These datasets were generated with the open souce programme homolosine-vectors, available at: <a href="https://gitlab.com/ldesousa/homolosine-vectors">https://gitlab.com/ldesousa/homolosine-vectors</a></p>
Reproduction package for the paper "Measuring the variability of directly imaged exoplanets using vector Apodizing Phase Plates combined with ground-based differential spectrophotometry"
<p>This is a basic reproduction package for the paper <a href="https://doi.org/10.1093/mnras/stad249">"Measuring the variability of directly imaged exoplanets using vector Apodizing Phase Plates combined with ground-based differential spectrophotometry" by Sutlieff et al. (2023)</a>. It aims to provide the most important data products to check and reproduce the main results of the paper.</p>
Supplementary Data: Global fits if simplified models for dark matter with GAMBIT II. Vector dark matter with an s-channel vector mediator
<p>This record contains the YAML files, data files, and some of the plotting scripts for: "Global fits of simplified models for dark matter with GAMBIT II. Vector dark matter with an s-channel vector mediator".</p> <p>Samples have been created using GAMBIT and figures can be reproduced with pippi. Plotting scripts (*.pip) are designed to work with either the original version of pippi 2.1 or the forked unreleased version. The provided scripts do not reproduce all the figures in the paper exactly.</p> <p>To save storage space, all samples have been compressed using <code>tar</code>. To inflate each dataset after downloading run <code>tar -zxvf <samples>.hdf5.gz</code>.</p> <p>To facilitate uploading to Zenodo, several of the data files have been thinned to only include enough points to reproduce plots.</p>
Thinking about Vector Symbolic Architectures (video recording)
<p>Video recording of the keynote presentation "Thinking about Vector Symbolic Architectures" given on 2023-06-15 at the <a href="https://sites.google.com/ltu.se/midnightvsa/home?authuser=0">Midnight Sun Workshop on Vector Symbolic Architectures</a> in Luleå, Sweden.</p> <p><strong>Abstract</strong></p> <p>Vector Symbolic Architectures are defined in terms of a very small set of operators acting on a vector space. The task of the VSA researcher is to discover the implications that follow from the definition in terms of the systems that can be implemented with VSAs. The VSA definitions are the researcher’s raw materials, but they also need tools to transform those raw materials into useful hypotheses and system designs. One important tool for a researcher is a conceptual framework, which specifies how the researcher thinks about VSAs and relates them to the other things they know. It is the researcher’s mental model of how VSAs work. The primary requirement for a conceptual framework is that it is productive; it should make it easy for the researcher to generate interesting hypotheses and designs. These hypotheses and designs don’t have to be correct, just plausible. Beating them into shape is a different part of the research process. Most VSA research papers contain a statement of the VSA definition. Very few mention the researcher’s conceptual framework. In this talk I will sketch out my conceptual framework - how I think about Vector Symbolic Architectures - in the hope that it might be interesting and useful to other researchers.</p>
Individual Towns that are Fully or Partially in the Ipswich Watershed - Idrisi Vector File.
This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This layer includes the area within each town in the Ipswich River Watershed in vector form. This map contains complete information and was derived from the ip30_noinfo_towns layer.
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.