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Meteorology and soil moisture data collected at multiple frequencies from the Cross-scale Interactions Study (CSIS) Block-4 site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing
This dataset contains summary data collected at the Jornada Basin LTER program's Cross Scale Interactions Study (CSIS) Block-4 site automated weather station and associated soil substation at several temporal scales. Precipitation data are collected at 1-second frequency during rain events, air temperature and wind are summarized every 5-minutes, and all aboveground sensors are summarized at 30-minute, hourly and daily frequencies. Soil moisture is measured at a 30-minute frequency and summarized daily. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; soil moisture, temperature and conductivity. Aboveground sensors are measured and calculated based on 1-second scan rate. Soil moisture is measured every 30-minutes near the weather station and approximately 30-meters distance at a nearby substation. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximately 2.5m. Soil sensors are installed at 10, 20 and 30cm depths.
Meteorology and soil moisture data collected at multiple frequencies from the Cross-scale Interactions Study (CSIS) Block-5 site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing
This dataset contains summary data collected at the Jornada Basin LTER program's Cross Scale Interactions Study (CSIS) Block-5 site automated weather station and associated soil substation at several temporal scales. Precipitation data are collected at 1-second frequency during rain events, air temperature and wind are summarized every 5-minutes, and all aboveground sensors are summarized at 30-minute, hourly and daily frequencies. Soil moisture is measured at a 30-minute frequency and summarized daily. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; soil moisture, temperature and conductivity. Aboveground sensors are measured and calculated based on 1-second scan rate. Soil moisture is measured every 30-minutes near the weather station and approximately 30-meters distance at a nearby substation. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximately 2.5m. Soil sensors are installed at 10, 20 and 30cm depths.
Meteorology and soil moisture data collected at multiple frequencies from the Cross-scale Interactions Study (CSIS) Block-6 site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing
This dataset contains summary data collected at the Jornada Basin LTER program's Cross Scale Interactions Study (CSIS) Block-6 site automated weather station and associated soil substation at several temporal scales. Precipitation data are collected at 1-second frequency during rain events, air temperature and wind are summarized every 5-minutes, and all aboveground sensors are summarized at 30-minute, hourly and daily frequencies. Soil moisture is measured at a 30-minute frequency and summarized daily. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; soil moisture, temperature and conductivity. Aboveground sensors are measured and calculated based on 1-second scan rate. Soil moisture is measured every 30-minutes near the weather station and approximately 30-meters distance at a nearby substation. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximately 2.5m. Soil sensors are installed at 10, 20 and 30cm depths.
Meteorology and soil moisture data collected at multiple frequencies from the Cross-scale Interactions Study (CSIS) Block-7 site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing
This dataset contains summary data collected at the Jornada Basin LTER program's Cross Scale Interactions Study (CSIS) Block-7 site automated weather station and associated soil substation at several temporal scales. Precipitation data are collected at 1-second frequency during rain events, air temperature and wind are summarized every 5-minutes, and all aboveground sensors are summarized at 30-minute, hourly and daily frequencies. Soil moisture is measured at a 30-minute frequency and summarized daily. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; soil moisture, temperature and conductivity. Aboveground sensors are measured and calculated based on 1-second scan rate. Soil moisture is measured every 30-minutes near the weather station and approximately 30-meters distance at a nearby substation. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximately 2.5m. Soil sensors are installed at 10, 20 and 30cm depths.
Meteorology and soil moisture data collected at multiple frequencies from the Cross-scale Interactions Study (CSIS) Block-8 site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing
This dataset contains summary data collected at the Jornada Basin LTER program's Cross Scale Interactions Study (CSIS) Block-8 site automated weather station and associated soil substation at several temporal scales. Precipitation data are collected at 1-second frequency during rain events, air temperature and wind are summarized every 5-minutes, and all aboveground sensors are summarized at 30-minute, hourly and daily frequencies. Soil moisture is measured at a 30-minute frequency and summarized daily. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; soil moisture, temperature and conductivity. Aboveground sensors are measured and calculated based on 1-second scan rate. Soil moisture is measured every 30-minutes near the weather station and approximately 30-meters distance at a nearby substation. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximately 2.5m. Soil sensors are installed at 10, 20 and 30cm depths.
Meteorology and soil moisture data collected at multiple frequencies from the Cross-scale Interactions Study (CSIS) Block-9 site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing
This dataset contains summary data collected at the Jornada Basin LTER program's Cross Scale Interactions Study (CSIS) Block-9 site automated weather station and associated soil substation at several temporal scales. Precipitation data are collected at 1-second frequency during rain events, air temperature and wind are summarized every 5-minutes, and all aboveground sensors are summarized at 30-minute, hourly and daily frequencies. Soil moisture is measured at a 30-minute frequency and summarized daily. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; soil moisture, temperature and conductivity. Aboveground sensors are measured and calculated based on 1-second scan rate. Soil moisture is measured every 30-minutes near the weather station and approximately 30-meters distance at a nearby substation. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximately 2.5m. Soil sensors are installed at 10, 20 and 30cm depths.
Meteorology and soil moisture data collected at multiple frequencies from the Cross-scale Interactions Study (CSIS) Block-10 site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing
This dataset contains summary data collected at the Jornada Basin LTER program's Cross Scale Interactions Study (CSIS) Block-10 site automated weather station and associated soil substation at several temporal scales. Precipitation data are collected at 1-second frequency during rain events, air temperature and wind are summarized every 5-minutes, and all aboveground sensors are summarized at 30-minute, hourly and daily frequencies. Soil moisture is measured at a 30-minute frequency and summarized daily. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; soil moisture, temperature and conductivity. Aboveground sensors are measured and calculated based on 1-second scan rate. Soil moisture is measured every 30-minutes near the weather station and approximately 30-meters distance at a nearby substation. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximately 2.5m. Soil sensors are installed at 10, 20 and 30cm depths.
Meteorology and soil moisture data collected at multiple frequencies from the Cross-scale Interactions Study (CSIS) Block-11 site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing
This dataset contains summary data collected at the Jornada Basin LTER program's Cross Scale Interactions Study (CSIS) Block-11 site automated weather station and associated soil substation at several temporal scales. Precipitation data are collected at 1-second frequency during rain events, air temperature and wind are summarized every 5-minutes, and all aboveground sensors are summarized at 30-minute, hourly and daily frequencies. Soil moisture is measured at a 30-minute frequency and summarized daily. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; soil moisture, temperature and conductivity. Aboveground sensors are measured and calculated based on 1-second scan rate. Soil moisture is measured every 30-minutes near the weather station and approximately 30-meters distance at a nearby substation. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximately 2.5m. Soil sensors are installed at 10, 20 and 30cm depths.
Meteorology and soil moisture data collected at multiple frequencies from the Cross-scale Interactions Study (CSIS) Block-12 site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing
This dataset contains summary data collected at the Jornada Basin LTER program's Cross Scale Interactions Study (CSIS) Block-12 site automated weather station and associated soil substation at several temporal scales. Air temperature and wind are summarized every 5-minutes, and all aboveground sensors are summarized at 30-minute, hourly and daily frequencies. Soil moisture is measured at a 30-minute frequency and summarized daily. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; soil moisture, temperature and conductivity. Aboveground sensors are measured and calculated based on 1-second scan rate. Soil moisture is measured every 30-minutes near the weather station and approximately 30-meters distance at a nearby substation. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximately 2.5m. Soil sensors are installed at 10, 20 and 30cm depths. The nearest precipitation data is available from CSIS Block-13 site automated weather station located 290m distance.
Meteorology and soil moisture data collected at multiple frequencies from the Cross-scale Interactions Study (CSIS) Block-13 site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing
This dataset contains summary data collected at the Jornada Basin LTER program's Cross Scale Interactions Study (CSIS) Block-13 site automated weather station and associated soil substation at several temporal scales. Precipitation data are collected at 1-second frequency during rain events, air temperature and wind are summarized every 5-minutes, and all aboveground sensors are summarized at 30-minute, hourly and daily frequencies. Soil moisture is measured at a 30-minute frequency and summarized daily. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; soil moisture, temperature and conductivity. Aboveground sensors are measured and calculated based on 1-second scan rate. Soil moisture is measured every 30-minutes near the weather station and approximately 30-meters distance at a nearby substation. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximately 2.5m. Soil sensors are installed at 10, 20 and 30cm depths.
Meteorology and soil moisture data collected at multiple frequencies from the Cross-scale Interactions Study (CSIS) Block-14 site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing
This dataset contains summary data collected at the Jornada Basin LTER program's Cross Scale Interactions Study (CSIS) Block-14 site automated weather station and associated soil substation at several temporal scales. Precipitation data are collected at 1-second frequency during rain events, air temperature and wind are summarized every 5-minutes, and all aboveground sensors are summarized at 30-minute, hourly and daily frequencies. Soil moisture is measured at a 30-minute frequency and summarized daily. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; soil moisture, temperature and conductivity. Aboveground sensors are measured and calculated based on 1-second scan rate. Soil moisture is measured every 30-minutes near the weather station and approximately 30-meters distance at a nearby substation. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximately 2.5m. Soil sensors are installed at 10, 20 and 30cm depths.
Meteorology and soil moisture data collected at multiple frequencies from the Cross-scale Interactions Study (CSIS) Block-15 site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing
This dataset contains summary data collected at the Jornada Basin LTER program's Cross Scale Interactions Study (CSIS) Block-15 site automated weather station and associated soil substation at several temporal scales. Precipitation data are collected at 1-second frequency during rain events, air temperature and wind are summarized every 5-minutes, and all aboveground sensors are summarized at 30-minute, hourly and daily frequencies. Soil moisture is measured at a 30-minute frequency and summarized daily. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; soil moisture, temperature and conductivity. Aboveground sensors are measured and calculated based on 1-second scan rate. Soil moisture is measured every 30-minutes near the weather station and approximately 30-meters distance at a nearby substation. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximately 2.5m. Soil sensors are installed at 10, 20 and 30cm depths.
RFP01 Properties of large hillslope blocks and cliff faces along the cottonwood limestone near the konza prairie nature trail
This data is a collection of point observations and measurments of large rock fragments on grassland hillslopes. Data was collected from 30 hillslope transects that extend downslope perpidicular from the bedrock cliff formed from the Cottonwodd limestone. Transects are 30 meters long and 1 meter wide. Observations of blocks include properites such as size, shape, and surface weathering. This data set also includes observtions of cliff properties associated with each transpect location. Measurments we made in field by hand for rock fragments larger than pebble (>64mm).
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>
Lithospheric architecture of the Paranapanema Block and adjacent nuclei using multiple-frequency P-wave seismic tomography
<p>We provide: the tomographic model for dephts 68 to 768 km as text files, where the first column is the longitude, the second is the latitude and the third if the velocity perturbation; the proposed limits for the Paranapanema Block, Luiz Alves Craton and Rio Apa Craton as a csv file (Figure 12 of the paper), where the first column is the name of the feature, the second is the longitude and the third is the latitude; and the abstract for the paper "Lithospheric architecture of the Paranapanema Block and adjacent nuclei using multiple-frequency P-wave seismic tomography".</p>
Implications of Socio-Economic Conditions on Common Mental Disorders: A Case-study of Salt Pan Workers in Marakkanam Block of Tamil Nadu
<p>Socio-economic indicators of Saltpan workers in Marakaanam, Tamil Nadu, India and their SRQ-20 scoring.</p><p>Data regarding; Consumption, Wages, Debt, Social Group, Gender, Age, Ration Card, Education, Distance from work(saltpan), ownership of house and the SRQ-20 Questionanaire used for Screening of CMDs and Distress levels </p>
Europa Chaos Block Shapefiles and Geojson Files in Lower RegMap Images
<p>The included dataset includes the raw polygon shapefiles of the outlines for chaos blocks on Europa in the lower half of the RegMap images. The outlines were generated using the standard definitions of blocks and further subdivision in morphology of plates and knobs based on those previously published by Leonard et al. (2022) in which the author of this dataset is the same that created the majority of the Leonard et al. (2022) dataset. Images used to generate the outlines were the RegMap images within the Photogrammetrically Controlled Galileo Image Mosaics of Europa, produced by the USGS Astrogeology (Bland et al., 2021). The shapefiles do not include the entire metadata and will be uploaded at a later date, but information about chaos block morphology, area in sq km, lon/lat location of center, and chaos terrain location are included. Also included within this dataset are the Geojson files (produced by Marina Dunn) that complement the ArcMap shapefiles, so they could be implemented into other programs more easily. Both datasets have yet to be peer reviewed. </p><p> </p><p> </p><p><strong>References</strong></p><p>Leonard, E.J., Howel, S.M., Mills, A., Senske, D.A., Patthoff, D.A., Hay, H.C.F.C., and Pappalardo, R.T. (2022). Finding Order in Chaos: Quantitive Predictors of Chaos Terrain Morphology on Europa, Volume 49, Issue 8, doi: <a href="https://doi.org/10.1029/2021GL097309">10.1029/2021GL097309.</a></p><p>Bland, Michael T., Weller, Lynn A., Archinal, Brent A., Smith, Ethan, Wheeler, Benjamin H. (2021). Improving the Usability of Galileo and Voyager Images of Jupiter's Moon Europa, Earth and Space Science, Volume 8, Issue 12, doi: <a href="https://doi.org/10.1029/2021EA001935">10.1029/2021EA001935</a>.</p>
Artificial fingerprints engraved through block-copolymers as nanoscale physical unclonable functions for authentication and identification - Dataset
<p>This is the dataset of "Artificial fingerprints engraved through block-copolymers as nanoscale physical unclonable functions for authentication and identification" by Irdi Murataj, Chiara Magosso, Stefano Carignano, Matteo Fretto, Federico Ferrarese Lupi, and Gianluca Milano, Nature Communications (2024), DOI: 10.1038/s41467-024-54492-8</p> <p>Part of this was funded by the project MEMQuD, code 20FUN06. The project has received funding from the EMPIR program co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation program.</p> <p>Part of this work was supported by the European project OpMetBat, code 21GRD01. The project has received funding from the European Partnership on Metrology, cofinanced from the the European Union's Horizon Europe Research and Innovation Programme, and by Participating States.</p> <p>Part of this work was supported by the European Union - Next Generation EU under the National Recovery and Resilience Plan (NRRP), Mission 04 Component 2 Investment 3.1 | Project Code: IR0000027 - CUP: B33C22000710006 - iENTRANCE@ENL: Infrastructure for Energy TRAnsition aNd Circular Economy @EuroNanoLab.</p>
An updated map of GRCh38 linkage disequilibrium blocks based on European ancestry data
<p>A map of approximately independent linkage disequilibrium (LD) blocks has many uses in statistical genetics. Current publicly available LD block maps are based on sparse recombination maps and are only available for GRCh37 (hg19) and prior genome assemblies. We generated LD blocks in GRCh38 for European (EUR) ancestry populations using a recent recombination map based on more than 115,000 individuals. This new map consists of 1,361 independent LD blocks across the 22 autosomal chromosomes and can be accessed at https://github.com/jmacdon/LDblocks_GRCh38</p>
Xanthene[n]arenes: Exceptionally Large, Bowl-Shaped Macrocyclic Building Blocks Suitable for Self-Assembly
<p>Data underlying the figures in the publication “Xanthene[<em>n</em>]arenes: Exceptionally Large, Bowl-Shaped Macrocyclic Building Blocks Suitable for Self-Assembly”, published in <em>J</em><em>ACS Au</em> 2021, 1, 11, 1885–1891. <a href="https://doi.org/10.1021/jacsau.1c00343">https://doi.org/10.1021/jacsau.1c00343</a></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.