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MMS 4 Digital Signal Processor (DSP) Search Coil Magnetometer (SCM), Magnetic Field Power Spectral Density, Level 2 (L2), Fast Mode, 2 s Data
The MMS magnetic field power spectral density (BPSD) is computed onboard by the Digital Signal Processor (DSP). The fast Fourier transform (FFT) calculation is performed on a digitized version of analog signals from the Search Coil Magnetometer (SCM) in the SCM123 coordinate system, see SCM data product guide for details, https://lasp.colorado.edu/mms/sdc/public/datasets/fields/. This data product is computed in space from individual components that are not synchronized to the 1 second pulse. Therefore, the timing between channels can be inaccurate by a fraction of a second. The samples times are interval start times taken from the x component. The spectra are calculated via a 1024-point FFT algorithm on piecewise continuous sets of waveform data. Nine signals can be processed simultaneously. Six of the twelve DC-coupled E, DC-coupled V, or SCM signals (16384 samples/s) are selected for spectral processing at 100% duty cycle. In addition, the three AC-coupled signals (262,144 kS/s) each can be processed at 6.25% duty cycle. Each of the nine signals has 16, 1024-point FFT operations every second; the field-programmable gate array (FPGA) performs 144 FFTs per second. The FFT is performed by an arithmetic logic unit (ALU), which is controlled by a state machine. Both are hard-coded into the FPGA. The operation starts by applying a 1024-point Hanning window onto a waveform. Next, an FFT is implemented. The FFT is broken into a series of "butterfly" operations performed by the ALU. The result has real and imaginary data. Power spectra are calculated by taking the sum of squares of real and imaginary values (the ALU includes a multiplier), which produces a power spectrum with 512 frequency bins. The frequency bins are then combined to give pseudo-logarithmic frequency spacing (del f)/f. The spectra are reduced to 88 frequency bins with (del f)/f between 6% and 12% when possible. Narrow-band emissions can be fit to an accuracy of (del f)/f ~3%, allowing for an accurate determination of plasma density. The spectra can be averaged in time. The fastest reporting rate of any signal is 16 spectra per second. Reporting rates can be as slow a one spectra every 16 s (averaging 256 spectra). The DSP and SCM instrument papers can be found at https://link.springer.com/article/10.1007/s11214-014-0115-x and https://link.springer.com/article/10.1007/s11214-014-0096-9, respectively. The DSP and SCM data product guides can be found at https://lasp.colorado.edu/mms/sdc/public/datasets/fields/.
DAWN GRAND MAP VESTA FAST NEUTRON RESIDUAL V1.0
These data are maps of fast neutron residual count rates from the GRaND plus Z boron loaded sensor. The nominal fast neutron count rates were detrended using the GRaND epithermal neutron map to produced this residual map. The residual map is related to the average atomic mass on Vesta. Details of the map and data reduction are described in LAWRENCEETAL2013.
MMS 4 Digital Signal Processor (DSP) Double Probe (ADP, SDP), Electric Field Power Spectral Density, Level 2 (L2), Fast Mode, 2 s Data
The MMS electric field power spectral density (EPSD) is computed onboard by the Digital Signal Processor (DSP). The fast Fourier transform (FFT) calculation is performed on a digitized version of analog signals from the Axial Double Probe (ADP) and Spin-Plane Double Probe (SDP). This data product is computed in space from individual components that are not synchronized to the 1 second pulse. Therefore, the timing between channels can be inaccurate by a fraction of a second. The samples times are interval start times taken from the x component. The spectra are calculated via a 1024-point FFT algorithm on piecewise continuous sets of waveform data. Nine signals can be processed simultaneously. Six of the twelve DC-coupled E, DC-coupled V, or SCM signals (16384 samples/s) are selected for spectral processing at 100% duty cycle. In addition, the three AC-coupled signals (262,144 kS/s) each can be processed at 6.25% duty cycle. Each of the nine signals has 16, 1024-point FFT operations every second; the field-programmable gate array (FPGA) performs 144 FFTs per second. The FFT is performed by an arithmetic logic unit (ALU), which is controlled by a state machine. Both are hard-coded into the FPGA. The operation starts by applying a 1024-point Hanning window onto a waveform. Next, an FFT is implemented. The FFT is broken into a series of "butterfly" operations performed by the ALU. The result has real and imaginary data. Power spectra are calculated by taking the sum of squares of real and imaginary values (the ALU includes a multiplier), which produces a power spectrum with 512 frequency bins. The frequency bins are then combined to give pseudo-logarithmic frequency spacing (del f)/f. The spectra are reduced to 56 frequency bins with (del f)/f between 6% and 12% when possible. Narrow-band emissions can be fit to an accuracy of (del f)/f ~3%, allowing for an accurate determination of plasma density. The spectra can be averaged in time. The fastest reporting rate of any signal is 16 spectra per second. Reporting rates can be as slow a one spectra every 16 s (averaging 256 spectra). The averaging process has 48-bit accuracy to maximize the dynamic range. The amplitudes undergo a pseudo-logarithmic compression to an 8-bit number representing over 120 dB of dynamic range at ~5% precision.
ISEE 2 Fast Plasma Experiment Linearly Interpolated 60 s Resolution data in GSM Coordinates
ISEE-2 Fast Plasma Experiment linearly interpolated to have the measurements on the minute at 60 s resolution data in GSM coordinates. This data set consists of processed solar wind data that has been linearly interpolated to 1 min resolution at the position of the spacecraft using the interp1.m function in MATLAB. This data set was originally constructed by Dr. J.M. Weygand for Prof. R.L. McPherron, who was the principle investigator of two National Science Foundation studies: GEM Grant ATM 02-1798 and a Space Weather Grant ATM 02-08501. These data were primarily used in superposed epoch studies and cross correlation studies on solar wind.
MMS 2 Digital Signal Processor (DSP) Search Coil Magnetometer (SCM), Magnetic Field Power Spectral Density, Level 2 (L2), Fast Mode, 2 s Data
The MMS magnetic field power spectral density (BPSD) is computed onboard by the Digital Signal Processor (DSP). The fast Fourier transform (FFT) calculation is performed on a digitized version of analog signals from the Search Coil Magnetometer (SCM) in the SCM123 coordinate system, see SCM data product guide for details, https://lasp.colorado.edu/mms/sdc/public/datasets/fields/. This data product is computed in space from individual components that are not synchronized to the 1 second pulse. Therefore, the timing between channels can be inaccurate by a fraction of a second. The samples times are interval start times taken from the x component. The spectra are calculated via a 1024-point FFT algorithm on piecewise continuous sets of waveform data. Nine signals can be processed simultaneously. Six of the twelve DC-coupled E, DC-coupled V, or SCM signals (16384 samples/s) are selected for spectral processing at 100% duty cycle. In addition, the three AC-coupled signals (262,144 kS/s) each can be processed at 6.25% duty cycle. Each of the nine signals has 16, 1024-point FFT operations every second; the field-programmable gate array (FPGA) performs 144 FFTs per second. The FFT is performed by an arithmetic logic unit (ALU), which is controlled by a state machine. Both are hard-coded into the FPGA. The operation starts by applying a 1024-point Hanning window onto a waveform. Next, an FFT is implemented. The FFT is broken into a series of "butterfly" operations performed by the ALU. The result has real and imaginary data. Power spectra are calculated by taking the sum of squares of real and imaginary values (the ALU includes a multiplier), which produces a power spectrum with 512 frequency bins. The frequency bins are then combined to give pseudo-logarithmic frequency spacing (del f)/f. The spectra are reduced to 88 frequency bins with (del f)/f between 6% and 12% when possible. Narrow-band emissions can be fit to an accuracy of (del f)/f ~3%, allowing for an accurate determination of plasma density. The spectra can be averaged in time. The fastest reporting rate of any signal is 16 spectra per second. Reporting rates can be as slow a one spectra every 16 s (averaging 256 spectra). The DSP and SCM instrument papers can be found at https://link.springer.com/article/10.1007/s11214-014-0115-x and https://link.springer.com/article/10.1007/s11214-014-0096-9, respectively. The DSP and SCM data product guides can be found at https://lasp.colorado.edu/mms/sdc/public/datasets/fields/.
MMS 2 Digital Signal Processor (DSP) Double Probe (ADP, SDP), Electric Field Power Spectral Density, Level 2 (L2), Fast Mode, 2 s Data
The MMS electric field power spectral density (EPSD) is computed onboard by the Digital Signal Processor (DSP). The fast Fourier transform (FFT) calculation is performed on a digitized version of analog signals from the Axial Double Probe (ADP) and Spin-Plane Double Probe (SDP). This data product is computed in space from individual components that are not synchronized to the 1 second pulse. Therefore, the timing between channels can be inaccurate by a fraction of a second. The samples times are interval start times taken from the x component. The spectra are calculated via a 1024-point FFT algorithm on piecewise continuous sets of waveform data. Nine signals can be processed simultaneously. Six of the twelve DC-coupled E, DC-coupled V, or SCM signals (16384 samples/s) are selected for spectral processing at 100% duty cycle. In addition, the three AC-coupled signals (262,144 kS/s) each can be processed at 6.25% duty cycle. Each of the nine signals has 16, 1024-point FFT operations every second; the field-programmable gate array (FPGA) performs 144 FFTs per second. The FFT is performed by an arithmetic logic unit (ALU), which is controlled by a state machine. Both are hard-coded into the FPGA. The operation starts by applying a 1024-point Hanning window onto a waveform. Next, an FFT is implemented. The FFT is broken into a series of "butterfly" operations performed by the ALU. The result has real and imaginary data. Power spectra are calculated by taking the sum of squares of real and imaginary values (the ALU includes a multiplier), which produces a power spectrum with 512 frequency bins. The frequency bins are then combined to give pseudo-logarithmic frequency spacing (del f)/f. The spectra are reduced to 56 frequency bins with (del f)/f between 6% and 12% when possible. Narrow-band emissions can be fit to an accuracy of (del f)/f ~3%, allowing for an accurate determination of plasma density. The spectra can be averaged in time. The fastest reporting rate of any signal is 16 spectra per second. Reporting rates can be as slow a one spectra every 16 s (averaging 256 spectra). The averaging process has 48-bit accuracy to maximize the dynamic range. The amplitudes undergo a pseudo-logarithmic compression to an 8-bit number representing over 120 dB of dynamic range at ~5% precision.
MMS 2 Electric Double Probe (EDP) Spacecraft Potential, Level 2 (L2), Fast Mode, 31.25 ms Data
Electric Double Probe, Dual Probe Spacecraft Potential, Level 2, Fast Survey Data. Spacecraft potential is etstimatrd by averaging measurements of the probe-to-spacecraft potential from several probes.
MMS 3 Digital Signal Processor (DSP) Double Probe (ADP, SDP), Electric Field Power Spectral Density, Level 2 (L2), Fast Mode, 2 s Data
The MMS electric field power spectral density (EPSD) is computed onboard by the Digital Signal Processor (DSP). The fast Fourier transform (FFT) calculation is performed on a digitized version of analog signals from the Axial Double Probe (ADP) and Spin-Plane Double Probe (SDP). This data product is computed in space from individual components that are not synchronized to the 1 second pulse. Therefore, the timing between channels can be inaccurate by a fraction of a second. The samples times are interval start times taken from the x component. The spectra are calculated via a 1024-point FFT algorithm on piecewise continuous sets of waveform data. Nine signals can be processed simultaneously. Six of the twelve DC-coupled E, DC-coupled V, or SCM signals (16384 samples/s) are selected for spectral processing at 100% duty cycle. In addition, the three AC-coupled signals (262,144 kS/s) each can be processed at 6.25% duty cycle. Each of the nine signals has 16, 1024-point FFT operations every second; the field-programmable gate array (FPGA) performs 144 FFTs per second. The FFT is performed by an arithmetic logic unit (ALU), which is controlled by a state machine. Both are hard-coded into the FPGA. The operation starts by applying a 1024-point Hanning window onto a waveform. Next, an FFT is implemented. The FFT is broken into a series of "butterfly" operations performed by the ALU. The result has real and imaginary data. Power spectra are calculated by taking the sum of squares of real and imaginary values (the ALU includes a multiplier), which produces a power spectrum with 512 frequency bins. The frequency bins are then combined to give pseudo-logarithmic frequency spacing (del f)/f. The spectra are reduced to 56 frequency bins with (del f)/f between 6% and 12% when possible. Narrow-band emissions can be fit to an accuracy of (del f)/f ~3%, allowing for an accurate determination of plasma density. The spectra can be averaged in time. The fastest reporting rate of any signal is 16 spectra per second. Reporting rates can be as slow a one spectra every 16 s (averaging 256 spectra). The averaging process has 48-bit accuracy to maximize the dynamic range. The amplitudes undergo a pseudo-logarithmic compression to an 8-bit number representing over 120 dB of dynamic range at ~5% precision.
THEMIS-E: On Board Fast Fourier Transform (FFT) power spectra of Electric (EFI) and Magnetic (SCM) field measurements, for particle and wave burst survey modes.
On Board Fast Fourier Transform (FFT) power spectra of Electric (EFI) and Magnetic (SCM) field measurements for particle and wave burst survey modes. Spectra are produced only in Particle Burst and Wave Burst modes; only a preselected four of the signals listed in Table 1 are input at any time. Data fed through the FFT while not in Particle or Wave Burst modes is automatically disgarded. The FFTs (Cooley-Tukey algorithm) are conducted as an integral part of the power spectrum calculation by the Field Programable Gate Arrays (FPGAs). A CORDIC algorithm is used for sine/cosine calculations. The data has raw resolution of 1024 pts for 8,192 sample/sec signals and 2048 pts for 16,384 sample/sec signals (EAC measurements only). Signals at 8,192 samples/sec are handled by 1024-point FFTs, while those at 16,384 samples/sec go through 2048-point FFTs. Past and current signal configurations for specific spacecraft are listed bellow in Table 2. The spectra are arranged into log spaced frequency bins in steps of 16, 32, or 64. Cadence is adjusted to keep packet size constant (i.e. increasing the fequency resolution by a factor of 2 decreases the sampling rate by 1/2). The frequency bins cover a range of 0 Hz to 4 kHz. Table 1: FFT Input Signals. Signal Description SCMX, SCMY, SCMZ: Three axis magnetic fiend from SCM V1 through V6: Probe-spacecraft voltage for all six EFI sensors E12DC, E34DC, E56DC: DC-coupled electric field measured from opposing EFI sensors E12AC, E34AV, E56AC: AC-coupled electric field measured from opposing EFI sensors E12HF: High frequency electric field from EFI Table 2: Spacecraft specific configurations. All probes were initially set to use EDC34, EDC56, SCM2, and SCM3 signals for both particle and wave burst modes. Output was set to 16 frequency bins at 4 Hz. Configuration Changes: 23-27 June 2008: Particle burst spectra on all probes reconfigured to 64 bins at 1 Hz. Table 3: Instrument-Spacecraft Physical Configuration Instrument Alignment in Spacecraft Geometric coordinates (SPG). See THEMIS website for coordinate system details and mechanical drawings. EFI boom 1: Along positive X-axis EFI boom 2: Along negative X-axis EFI boom 3: Along positive Y-axis EFI boom 4: Along negative Y-axis EFI boom 5: Along positive Z-axis EFI boom 6: Along negative Z-axis SCM: *The SCM uses an instrument specific set of axes; an orthogonal system centered instrument with the X-axis 12.1 degrees from the SPG X-axis.
ISEE 1 Fast Plasma Experiment Solar Wind Weimer Propagated 60 s Resolution Data in GSE Coordinates
ISEE-1 Fast Plasma Experiment Weimer propagated solar wind data and linearly interpolated to have the measurements on the minute at 60 s resolution data in GSE coordinates. This data set consists of propagated solar wind data that has first been propagated to a position just outside of the nominal bow shock (about 17, 0, 0 Re) and then linearly interpolated to 1 min resolution using the interp1.m function in MATLAB. The input data for this data set is a 1 min resolution processed solar wind data constructed by Dr. J.M. Weygand. The method of propagation is similar to the minimum variance technique and is outlined in Dan Weimer et al. [2003; 2004]. The basic method is to find the minimum variance direction of the magnetic field in the plane orthogonal to the mean magnetic field direction. This minimum variance direction is then dotted with the difference between final position vector minus the original position vector and the quantity is divided by the minimum variance dotted with the solar wind velocity vector, which gives the propagation time. This method does not work well for shocks and minimum variance directions with tilts greater than 70 degrees of the sun-earth line. This data set was originally constructed by Dr. J.M. Weygand for Prof. R.L. McPherron, who was the principle investigator of two National Science Foundation studies: GEM Grant ATM 02-1798 and a Space Weather Grant ATM 02-08501. These data were primarily used in superposed epoch studies References: Weimer, D. R. (2004), Correction to ‘‘Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique,’’ J. Geophys. Res., 109, A12104, doi:10.1029/2004JA010691. Weimer, D.R., D.M. Ober, N.C. Maynard, M.R. Collier, D.J. McComas, N.F. Ness, C. W. Smith, and J. Watermann (2003), Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique, J. Geophys. Res., 108, 1026, doi:10.1029/2002JA009405.
FAST AC Fields, ~5 sec resolution
FAST AC Fields Key Parameter CDF files consists of AC Electric and Magnetic fields measurements spanning a range from approximately 32 Hz to 2 MHz. The time range of each file is roughly 24 hours and consists of several passes over the auroral zone of approximately 20 minute duration, the time resolution is one spin period (approximately 5s). The orbital period of FAST is 133 minutes.
ISEE 2 Fast Plasma Experiment Linearly Interpolated 60 s Resolution data in GSE Coordinates
ISEE-2 Fast Plasma Experiment linearly interpolated to have the measurements on the minute at 60 s resolution data in GSE coordinates. This data set consists of processed solar wind data that has been linearly interpolated to 1 min resolution at the position of the spacecraft using the interp1.m function in MATLAB. This data set was originally constructed by Dr. J.M. Weygand for Prof. R.L. McPherron, who was the principle investigator of two National Science Foundation studies: GEM Grant ATM 02-1798 and a Space Weather Grant ATM 02-08501. These data were primarily used in superposed epoch studies and cross correlation studies on solar wind.
MMS 1 Digital Signal Processor (DSP) Double Probe (ADP, SDP), Electric Field Power Spectral Density, Level 2 (L2), Fast Mode, 2 s Data
The MMS electric field power spectral density (EPSD) is computed onboard by the Digital Signal Processor (DSP). The fast Fourier transform (FFT) calculation is performed on a digitized version of analog signals from the Axial Double Probe (ADP) and Spin-Plane Double Probe (SDP). This data product is computed in space from individual components that are not synchronized to the 1 second pulse. Therefore, the timing between channels can be inaccurate by a fraction of a second. The samples times are interval start times taken from the x component. The spectra are calculated via a 1024-point FFT algorithm on piecewise continuous sets of waveform data. Nine signals can be processed simultaneously. Six of the twelve DC-coupled E, DC-coupled V, or SCM signals (16384 samples/s) are selected for spectral processing at 100% duty cycle. In addition, the three AC-coupled signals (262,144 kS/s) each can be processed at 6.25% duty cycle. Each of the nine signals has 16, 1024-point FFT operations every second; the field-programmable gate array (FPGA) performs 144 FFTs per second. The FFT is performed by an arithmetic logic unit (ALU), which is controlled by a state machine. Both are hard-coded into the FPGA. The operation starts by applying a 1024-point Hanning window onto a waveform. Next, an FFT is implemented. The FFT is broken into a series of "butterfly" operations performed by the ALU. The result has real and imaginary data. Power spectra are calculated by taking the sum of squares of real and imaginary values (the ALU includes a multiplier), which produces a power spectrum with 512 frequency bins. The frequency bins are then combined to give pseudo-logarithmic frequency spacing (del f)/f. The spectra are reduced to 56 frequency bins with (del f)/f between 6% and 12% when possible. Narrow-band emissions can be fit to an accuracy of (del f)/f ~3%, allowing for an accurate determination of plasma density. The spectra can be averaged in time. The fastest reporting rate of any signal is 16 spectra per second. Reporting rates can be as slow a one spectra every 16 s (averaging 256 spectra). The averaging process has 48-bit accuracy to maximize the dynamic range. The amplitudes undergo a pseudo-logarithmic compression to an 8-bit number representing over 120 dB of dynamic range at ~5% precision.
The Correlation between Serum GGT Concentration and Hemoglobin A1c and Fasting Blood Sugar
<p>We cannot confirm a significant association between elevated GGT and HbA1C levels. As GGT is a marker that takes a long time to climb, there was no positive correlation between GGT and FBS, which is related to the length of time patients have diabetes. Although the association of the underlying pathophysiological mechanism between GGT and T2DM has not been precisely elucidated, insulin resistance, oxidative stress, and systemic inflammation appear to be involved. Accordingly, it is proposed that evaluation of additional inflammatory markers such as C-reactive protein by a certified extreme-sensitivity assay is additional in an attempt to confirm this theory. The results of our study on patients were in contrast to studies that showed that GGT levels were significantly higher in all diabetic patients who were not in the healthy control group. In stark contrast, other studies have shown that GGT levels increase only in diabetic patients with chronic diabetic complications such as retinopathy and neuropathy, which may have been due to oxidative stress and the role of GGT in diabetes. Further studies are needed to investigate the Biological underlying mechanism of GGT increase in T2DM patients.</p> <p> </p>
AdvanCT EURAMET project, Southampton XCT scan data, PTB sample, fast scan
<p>AdvanCT EURAMET project, Southampton XCT scan data, PTB sample, fast scan</p> <p>20190312_HUTCH_2220_HT_PTB_fast_002</p> <p>PTB hole plate (6mm) - aluminium</p> <p>Data acquisition system: Custom Nikon metrology custom x-ray tomography system</p> <p>Detector: PerkinElmer XRD 1621 CN3 – HS, 2000x2000 pixels, 200 μm2 pixel size.</p> <p>Nikon 225kVp reflection target microfocus source</p> <p>134ms exposure per frame, 120kVp, 83 μA, 3143 projections, 7 minutes, no shuttling, continious scanning mode </p> <p>S2O: 23.5411643981934</p> <p>S2D: 670.0555</p>
AdvanCT EURAMET project, Southampton XCT scan data, Sphere sample, fast scan
<p>AdvanCT EURAMET project, Southampton XCT scan data, Double ruby sphere sample, fast scan</p> <p>20181107_HUTCH_2074_HD_6mm_ideal_1fpp_2W</p> <p>6mm ruby spheres, touching</p> <p>Data acquisition system: Custom Nikon metrology custom x-ray tomography system</p> <p>Detector: PerkinElmer XRD 1621 CN3 – HS, 2000x2000 pixels, 200 μm2 pixel size.</p> <p>Nikon 225kVp reflection target microfocus source</p> <p>134ms exposure per frame, 90kVp, 22 μA, 3143 projections, 7 minutes, no shuttling, continuous motion scanning mode </p> <p>S2O: 27.8778991699219</p> <p>S2D: 799.6818</p>
AdvanCT EURAMET project, Southampton XCT scan data, EMPA sample, fast scan
<p>AdvanCT EURAMET project, Southampton XCT scan data, EMPA sample, fast scan</p> <p>20190314_HUTCH_2220_HT_EMPA_450fastfilter_003</p> <p>EMPA step cylinder phantom - aluminium</p> <p>Data acquisition system: Custom Nikon metrology custom x-ray tomography system</p> <p>Detector: PerkinElmer XRD 1621 CN3 – HS, 2000x2000 pixels, 200 μm2 pixel size.</p> <p>Nikon 450kVp reflection target microfocus source</p> <p>250ms exposure per frame, 390kVp, 242 μA, 3143 projections, 13 minutes, 8mm copper filter, no shuttling, continious scanning mode </p> <p>S2O: 652.500579833984</p> <p>S2D: 107.011</p>
AdvanCT EURAMET project, Southampton XCT scan data, NPL sample, fast scan
<p>AdvanCT EURAMET project, Southampton XCT scan data, NPL sample, slow scan</p> <p>20190312_HUTCH_2220_NPL_Fast225_002</p> <p>NPL phantom - aluminium</p> <p>Data acquisition system: Custom Nikon metrology custom x-ray tomography system</p> <p>Detector: PerkinElmer XRD 1621 CN3 – HS, 2000x2000 pixels, 200 μm2 pixel size.</p> <p>Nikon 225kVp reflection target microfocus source</p> <p>134ms exposure per frame, 90kVp, 113 μA, 3143 projections, 7 minutes, no shuttling, continuous scanning mode </p> <p>S2O: 255.781982421875</p> <p>S2D: 895.7496</p>
A Fast Hop-Biased Approximation Algorithm for the Quadratic Group Steiner Tree Problem
<p>The dataset for our paper 'A Fast Hop-Biased Approximation Algorithm for the Quadratic Group Steiner Tree Problem'. It consists of 5 real KGs (<code>Mondial</code>, <code>OpenCyc</code>, <code>LinkedMDB</code>, <code>YAGO</code>, <code>DBpedia</code>) and 5 synthetic KGs (<code>LUBM-10U</code>, <code>LUBM-50U</code>, <code>LUBM-250U</code>, <code>LUBM-2U</code>, <code>DBP-50K</code>). Each KG is compressed in one file, which including (for example, in <code>LUBM-2U</code>):</p> <ul> <li> <p><code>lubm_2u_nodes.sql</code>: the id, the name and the weight of a node,</p> </li> <li> <p><code>lubm_2u_edges.sql</code>: the ids of two nodes an edge connects,</p> </li> <li> <p><code>lubm_2u_queries.sql</code>: a query consists of some keywords,</p> </li> <li> <p><code>lubm_2u_keymap.sql</code>: a keyword maps to a set of nodes,</p> </li> <li> <p><code>lubm_2u_nodevec.sql</code>: the vector of a node, used to compute quadratic function qw,</p> </li> <li> <p><code>lubm_2u_hub_hop.sql</code>: the hub labeling index to compute in Section 4.1,</p> </li> <li> <p><code>lubm_2u_hub_mix_1.sql</code>: the hub labeling index to compute in Section 4.1 where α=0.1,</p> </li> <li> <p><code>lubm_2u_hub_mix_5.sql</code>: the hub labeling index to compute in Section 4.1 where α=0.5,</p> </li> <li> <p><code>lubm_2u_hub_mix_9.sql</code>: the hub labeling index to compute in Section 4.1 where α=0.9.</p> </li> </ul> <p>You can dump the data into MySQL database. For example,</p> <pre><code>create database lubm_2u; use lubm_2u; source lubm_2u_nodes.sql; …</code></pre> <p>Unfortunately, due to the limit of space, for large KGs (<code>DBpedia</code> and <code>LUBM-250U</code>), we don't directly provide the data of hub labeling, i.e., these two compressed files only contains the first 5 sql files. You should generate hub labeling by yourself where the process is detailed in <a href="https://github.com/nju-websoft/QGSTP-HB/blob/main/README.md">README</a> of our project.</p> <p> </p>
Datasets for the paper "You are where you eat: Effect of mobile food environments on fast food visit" by Bernardo García-Bulle et al.
<p>Datasets for the paper "You are where you eat: "You are where you eat: Effect of mobile food environments on fast food visit" by Bernardo García-Bulle et al. Because of our license restriction to share this dataset, you must request access to the files (below).</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.