Find research datasets worth reusing

opencc-by-4.0Dec 2020View details →

Compressible Hydrodynamics Simulation Data for "Standing Shock Prevents Propagation of Sparks in Supersonic Explosive Flows"

Background This data is a 2D cross-section from a 3D compressible hydrodynamics simulation (Hyburn / AMRex code) of a rapid decompression / shock tube experiment at Special Technologies Laboratory. The simulated shot is a pure argon gas decompression from 1000Psi to atmosphere.  This data is used in figures 3 and 5 of the paper "Standing Shock Prevents Propagation of Sparks in Supersonic Explosive Flows". Electric sparks and explosive flows have long been associated with each other. Flowing dust particles originate charge through contact and separate based on inertia, resulting in strong electric fields supporting sparks. These sparks can cause explosions in dusty environments, especially those rich in carbon, such as coal mines and grain elevators. Recent observations of explosive events in nature and decompression experiments indicate that supersonic flows of explosions may alter the electrical discharge process. Shocks may suppress parts of the hierarchy of the discharge phenomena, such as leaders. In our decompression experiments, a shock tube ejects a flow of gas and particles into an expansion chamber. We imaged an illuminated plume from the decompression of a mixture of argon and <100 mg of diamond particles and observe sparks occurring below the sharp boundary of a condensation cloud. We also performed hydrodynamics simulations of the decompression event that provide insight into the conditions supporting the observed behavior. Simulation results agree closely with the experimentally observed Mach disk shock shape and height. This represents direct evidence that the sparks are sculpted by the outflow. The spatial and temporal scale of the sparks transmit an impression of the shock tube flow, a connection that could enable novel instrumentation to diagnose currently inaccessible supersonic granular phenomena. Accessing Data The data is saved as python numpy zipped archives numbered by the timestep in the simulation. Files starting with 'tube' contain data from inside the shock tube. Files starting with 'near_vent' contain data from the expansion chamber above the nozzle.  All units are in SI. Each .npz file is an array file generated with python numpy.savez(). It can be opened with: import numpy as np data = np.load('<name>.npz') The data is an python dictionary. The dictionary keys can be displayed with: print(data.files) The numpy arrays can be accessed by keyname: print(data['keyname']) The key names correspond to physical quantities (density, temperature, etc.). All particle quantities are 0 as the simulation did not include particles.

opencc-by-4.0Jul 2022View details →

Stress analysis and Q-factor of free-standing (La,Sr)MnO3 oxide resonators (Dataset)

Datafiles of the article "Stress analysis and Q-factor of free-standing (La,Sr)MnO3 oxide resonators"

opencc-by-4.0Dec 2023View details →

EEG data offline and online during motor imagery for standing and sitting

The experiments were conducted in an acoustically isolated room where only the participant and the experimenter were present. Participants voluntarily signed an informed consent form in accordance with the experimental protocol approved by the ethics committee of the Universidad Antonio Nariño. The participant was seated in a chair in a posture that was comfortable for him/her but did not affect data collection. In front of the participant, a 40-inch TV screen was placed at about 3 m. On this screen, a graphical user interface (GUI) displayed images that guided the participant through the experiment. Each experimental session was divided into two phases: an offline phase and an online phase.  The offline experiments consisted of recording participants' EEG signals during motor imagery trials for standing and sitting that were guided by the GUI presented on the TV screen. Six offline runs were conducted in which the participants were standing in three runs and sitting in the other three runs. In each run, the participant had to repeat a block of 30 trials of mental tasks indicated by visual cues continuously presented on the screen in a pseudo-random sequence. The first phase of the experimental session was conducted to construct the offline parts of the dataset: (A) Sit-to-stand and (B) Stand-to-sit. The participant's EEG data were collected from 90 sequences for part A (45 trials of MotorImageryA tasks and 45 trials of IdleStateA tasks) and 90 sequences for part B (45 trials of MotorImageryB tasks and 45 trials of IdleStateB tasks). For each participant, the two machine learning models obtained in the offline phase were used to carry out the online experiment parts of the dataset: (C) Sit-to-stand and (D) Stand-to-sit. Each participant was instructed to select, in no particular order, 30 sequences for part C (15 trials of MotorImageryA tasks and 15 trials of IdleStateA tasks) and 30 other sequences for part D (15 trials of MotorImageryB tasks and 15 trials of IdleStateB tasks). Each trial was unique and was generated pseudo-randomly before the experiment. The database consisted of 32 electroencephalographic files corresponding to the 32 participants. All recordings were collected on channels F3, Fz, F4, FC5, FC1, FC2, FC6, C3, Cz, C4, CP5, CP1, CP2, CP6, P3, Pz, and P4 according to the 10-20 EEG electrode placement standard, grounded to AFz channel and referenced to right mastoid (M2). Each data file contained the data stream in a 2D matrix where rows corresponded to channels and columns corresponded to time samples with a sampling frequency of 250Hz. The following marker numbers encoded information about the execution of the experiment. Marker numbers 200, 201, 202, and 203, indicated the beginning and end of the four steps of the sequence in a trial (resting, fixation, action observation, and imagining). Marker numbers 1, 2, 3, and 4, indicated the figure activated on the screen to the participant perform the task corresponding to 1. actively imagining the sit-to-stand movement (labeled as MotorImageryA), 2. sitting motionless without imagining the sit-to-stand movement (labeled as IdleStateA), 3. standing motionless while actively imagining the stand-to-sit movement (labeled as MotorImageryB), or 4. standing motionless without imagining the stand-to-sit movement (labeled as IdleStateB). Finally, marker numbers 101, 102, 103, and 104, indicated the task detected by the BCI in real time during the online experiment: 101. MotorImageryA, 102. IdleStateA, 103. MotorImageryB, or 104. IdleStateB.

opencc-by-4.0Apr 2018View details →

Sārnāth, Uttar Pradesh. Standing Buddha, drawing of back.

Sārnāth, Uttar Pradesh. Standing Buddha, drawing of back. Original sculpture in the British Museum, London, no. 1880-6 (Transferred from the India Museum).

openCC (other)Aug 2023View details →

Mycorrhizal Fungi of Native Red Pine Stands in the Forests of the Huron Mountains (1996-2015).

This data includes mycorrhizal fungi population data in Michigan’s Huron Mountains from 1996-2015 collected by Dana Ritcher. Seven stands consisting of primarily pine forests were surveyed for two separate sampling periods annually during the study period.

openCC (other)Jul 2023View details →

Data from Sand aggradation alters biofilm standing crop and metabolism in a low-gradient Lake Superior tributary

We conducted a comparative study of biofilm standing crop and metabolism in the Salmon Trout River, a tributary of Lake Superior where watershed disturbances have led to 3-fold increases in streambed fine sediments, predominately sand, in the past decade. We compared biofilm standing crop and metabolism rates using light–dark chambers in reaches where substrate consisted of predominately exposed rock or sand substrates. This data archive includes rates of primary production and respiration, biomass measurements from chambers, and benthic standing crop and water chemistry data collected from the same river sites over the course of a summer. All data were published in Journal of Great Lakes research in 2015, https://doi.org/10.1016/j.jglr.2015.09.004

openCC (other)Jun 2025View details →

UCSB SONGS Mitigation Monitoring: Reef Performance Standard - Fish Standing Stock

These data describe annual estimates of fish standing stock (in tons) supported by the artificial reef, Wheeler North Reef, in Orange County, CA (33.40210N, 117.62420W). Data collection began in 2009 to evaluate the ability of Wheeler North Reef to compensate for losses of kelp forest habitat and associated biota caused by the operation of the San Onofre Nuclear Generating Station (SONGS).

openCustomNov 2016View details →

Fine woody debris inventory data from reference stands and inventory plots in the Pacific Northwest, 1992 to 2000

These data provide an inventory of the mass of downed fine woody debris stored within various forest types. This data is used to determine total organic matter, carbon, and nutrient stores in forests.

openOpenJul 2023View details →

Survey of Aspen leaf miner (Phyllocnistis populiella) Oviposition Per Leaf Sampled in Interior Alaska Aspen Stands near Fairbanks from 2004 to 2022

The datasets contains annual counts of aspen leaf miner eggs, incipient mines, and egg scars (indicating egg predation) on aspen leaves over time. There are two files representing pilot data from 2004-2005 and data 2006-2022. The 2004-05 dataset does not include egg scars, indicaitive of egg removal, and therefore totals underestimate total oviposition. The 2006-2022 dataset does include egg scars and therefore can be used to estimate total oviposition per leaf.

openOpenNov 2022View details →

Tree inventory for adjacent stands of Picea mariana and Betula neoalaskana located in the 1958 Murphy Dome fire scar - 2012

This dataset contains the diameter of all trees measured as part of a project examining tree species affects on forest stand characteristics and plant-soil-microbial feedbacks. Study was conducted across 3 spatially explicit blocks containing adjacent stands of Picea mariana and Betula neoalaskana in forest that established following the 1958 Murphy Dome fire. All included tree diameter measurements were collected in Summer 2012.

openCustomJan 2020View details →

Effects of marsh periwinkle (Littoraria irrorata) size and density on cordgrass (Spartina aterniflora) biomass and dead standing material

Marsh periwinkles (Littoraria irrorata) are common in southeastern US salt marshes, including those on Sapelo Island, GA. They are known to affect the productivity of cordgrss (Spartina alterniflora) and also contribute to its decomposition following senescence. The population struture of Littoraria, in terms of the size and densities of individuals, varies widely in space and time. No previous experiments have, however, attempted to manipulate the density and body size of populations of Littoraria and assessd the response of Spartina. We perforned such an experiment in Airport Marsh on Sapelo Island, using small experimental enclosures and measured Spartina biomass and dead standing material after 3 months (mid July - mid Oct, 2012). We assessed response variables by removing all Spartina material from plots before seperating and drying them in the lab.

openCC0Jun 2025View details →

PBG03 Disk pasture meter measurements to estimate plant standing biomass in the Patch-Burn Grazing experiment at Konza Prairie

"PBG" datasets are associated with a long-term, large-scale study that is addressing the effects of fire-grazing interactions in the context of a Patch-Burn Grazing management system designed to promote grassland heterogeneity. Effects of patch-burn grazing management on plant and animal diversity and the nature and variety of wildlife habitat are being assessed in two replicate management units, each consisting of three pastures (watersheds) designated C03A/C03B/C03C and C3SA/C3SB/C3SC. In each patch-burn grazing unit, one watershed is burned and two that are left unburned in a given year. The burning treatments are rotated annually so that each pasture is burned every third year. Each patch-burn grazing unit is paired with an annually-burned pasture for comparison with traditional grazing systems (C01A and C1SB). All grazing units are stocked with cow/calf pairs from approximately 1 May until 1 Oct at a stocking density equal to 3.2 ha per cow/calf. To examine the impact of patch burning and grazing in all 8 units, we monitor changes in plant species composition, residual biomass, grassland bird populations, insect populations, small mammal populations, soil nutrients, and stream water quality1 (1C3SA/C3SB/C3SC unit only). The KSU Department of Animal Science monitors cattle performance, including weight gain and body condition to assess the economic feasibility of using patch-burn management on a widespread basis. This dataset includes both the annual calibration data for the disk pasture meter measurements (PBG031) and the actual disk pasture meter measurements in the PBG experiment (PBG032). Measurements were taken at a total of 64 transects (8 watersheds x 4 sites per watershed x 2 pasture meter transects per site). Each cattle-grazed watershed (designated as of May 2011 C3A, C3B, C3C, and C1A) and the four Shane cattle-grazed watersheds (C1B, C3SA, C3SB, and C3SC) includes 4 plant composition sampling transects (A-D). Pasture meter measurements were taken alo

openCC0Jan 2023View details →

OPD01 Konza Prairie standing dead and litter decomposition (1981-1983)

Standing dead and litter decomposition of big bluestem foliage and flowering stems were measured for two years using litterbag methods. Mass, nitrogen and phosphorus content were measured.

opencc-by-4.0Apr 2020View details →

Govindnagar, Mathurā (Uttar Pradesh). Standing Buddha of the time of Kumāragupta

<a href="https://siddham.network/object/ob00066/">OB0006</a> Govindnagar, Mathura (Uttar Pradesh). Standing Buddha of the time of Kumāragupta, now in the Archaeological Museum, Mathura.

opencc-by-4.0Feb 2020View details →

Standing Balance Experiment with Long Duration Random Pulses Perturbation

Standing balance experiment and the measured data-set are fundamental for identifying postural feedback controllers. As the generalized feedback controllers can only be identified from long duration balance data (under random external perturbations), a standing balance experiment is conducted and the long duration motion data was recorded. The data-set includes the perturbation reaction data from eight subjects. Each subject performed four experiment trials, including two quiet standing and two perturbed trials. Each trial lasted five minutes. A total of 80 minutes quiet standing and 80 minutes perturbed standing data are included in this data-set. Recorded information including three dimensional trajectories of thirty-two markers (27 on subjects' trunk and legs and 5 on the treadmill frame), six dimensional ground reaction forces, and nine Electromyography signals (EMGs, on subjects' right leg). In addition, joint angles and torques were calculated using a human body model and inverse dynamics. Basic statistical analysis of the data is also included. Measured raw data for each subject in each experimental trial includes three files: <ol> <li>Mocapxxxx.txt: contains motion capture marker data, ground reaction force, and 76 analog channels. Data was recorded at 100 Hz sampling rate.</li> <li>Mocapxxxx_Motion Analysis_analog.txt: contains 76 high sampling rate (1000Hz) analog channels' data. Analog data is consisted of the analog singal from the froce sensor on the treadmill, EMG signals in the Delsys EMG sensors, and 3 axises acceeleration signals of the Delsys EMG sensors.</li> <li>Recordxxxx.txt: contains the sway motion data of treadmill and the three-axis acceleration data of two Xsens MTi-10 series sensors.</li> </ol> Measured raw data also includes two files of the unloaded trial, which is used for the inertia compensation. <ol> <li>Mocap0000.txt: contains motion capture marker data (5 markers on the treadmill frame) and ground reaction forces.</li> <li>Record0000.txt: contains the treadmill sway motion data and the acceleration data (three-axis) of two Xsens MTi-10 series sensors.</li> </ol> Processed data of each subject in each experimental trial contains four files: <ol> <li>Mocapxxxx.txt: contains the gap filled motion capture marker data and the inertia compensated ground reaction force data.</li> <li>Motionxxxx.txt: contains the calculated the trajectories of three joints' (hip, knee, and ankle) angles, angular velocities, moments, and joint contact forces.</li> <li>Data_infoxxxx.txt: contains the quality of recorded raw marker data (percentage and biggest duration of missing marker data), and the percentage of removed inertia artifacts in ground reaction forces</li> <li>MotionAnalysis.fig: shows the mean and standard deviation of three joints' trajectories in four experimental trials.</li> </ol> There are two more plots in the processed data folder which shows the joint motion/moment and the raw/compensated ground reaction forces of one example experimental trial (subject 07 trial 03). The processed data was generated using the code in the 'Processing_Code' folder. The code was wrote using Matlab and the main function is "Data_Processing_Main.m" More details of the standing balance experiment can be found in the document 'Standing_Balance_Experiment_with_Long_Duration_Random_Pulses_Perturbation.pdf'

opencc-by-4.0Jul 2020View details →

Data from: Trade-off between standing biomass and productivity in species-rich tropical forest: evidence, explanations and implications

These files are the R code and plot data files used for calculating species population turnover of biomass and abundance in a tropical forest plot. This dataset is a processed subset of the original dataset used in our analysis of biomass turnover across tree populations as demonstrated in <a href="https://doi.org/10.1111/1365-2745.13485">the main paper</a>. Readers interested in using the Pasoh 50-ha plot data for purposes other than reviewing our analysis are advised to contact the <a href="https://www.frim.gov.my/">Forest Research Institute Malaysia (FRIM)</a> and the <a href="https://forestgeo.si.edu/">Center for Tropical Forest Science-Forest Global Earth Observatory (CTFS-Forest GEO)</a>, Smithsonian Tropical Research Institute.

opencc-by-4.0Dec 2020View details →

Aukana අවුකන (near Kekirawa) Sri Lanka. Standing Buddha, detail.

Aukana අවුකන (near Kekirawa) Sri Lanka. Standing Buddha, detail, as documented in 02/2012.

opencc-by-4.0Dec 2020View details →

Ionization rate simulation data for "Standing Shock Prevents Propagation of Sparks in Supersonic Explosive Flows"

Background This is a set of 3d data containing ionization rates computed from Hyburn hydrodynamic simulations contained in a Matlab .mat file, along with a plot in both .png and Matlab .fig format, and a Matlab script for plotting. This data is used in figure 5 of the paper "Standing Shock Prevents Propagation of Sparks in Supersonic Explosive Flows". Electric sparks and explosive flows have long been associated with each other. Flowing dust particles originate charge through contact and separate based on inertia, resulting in strong electric fields supporting sparks. These sparks can cause explosions in dusty environments, especially those rich in carbon, such as coal mines and grain elevators. Recent observations of explosive events in nature and decompression experiments indicate that supersonic flows of explosions may alter the electrical discharge process. Shocks may suppress parts of the hierarchy of the discharge phenomena, such as leaders. In our decompression experiments, a shock tube ejects a flow of gas and particles into an expansion chamber. We imaged an illuminated plume from the decompression of a mixture of argon and <100 mg of diamond particles and observe sparks occurring below the sharp boundary of a condensation cloud. We also performed hydrodynamics simulations of the decompression event that provide insight into the conditions supporting the observed behavior. Simulation results agree closely with the experimentally observed Mach disk shock shape and height. This represents direct evidence that the sparks are sculpted by the outflow. The spatial and temporal scale of the sparks transmit an impression of the shock tube flow, a connection that could enable novel instrumentation to diagnose currently inaccessible supersonic granular phenomena. Accessing Data The .mat file can be opened in Matlab to examine data. The 3d arrays contained therein can be viewed in various ways, including using the enclosed script with syntax like plot_isosurfaces(xg,yg,zg,density,max(density(:)),pressure,max(pressure(:))) to produce the included isosurface plot. The data arrays contained are: e: electric field magnitude alpha: ionization rate lengths: ionization lengths (equal to 1/alpha) eOverN: electric field divided by gas number density alphaOverN: ionization rate divided by gas number density density: gas mass density pressure: gas pressure x,y,z: spatial coordinates xg,yg,zg: spatial coordinates in 3d meshgrid format, for Matlab plotting The electric field e was artificially generated from velocities in Hyburn output; alpha was computed from BOLSIG+ with Hyburn input; density and pressure data were from Hyburn.

opencc-by-4.0Jun 2021View details →

Radiative transfer modeling in structurally-complex stands: what aspects matter most?: Dataset

This repository is linked to the paper "Radiative transfer modeling in structurally-complex stands: what aspects matter most?" submitted to Annals of Forest Science and written by Frédéric ANDRÉ (corresponding author), Louis DE WERGIFOSSE, François DE COLIGNY, Nicolas BEUDEZ, Gauthier LIGOT, Vincent GAUTHRAY-GUYÉNET, Benoit COURBAUD and Mathieu JONARD. The repository contains the three following files : <ul> <li>CalibrationResults.csv: Bayes factors and summary statistics of parameter estimates for each calibration run</li> <li>ParameterPosteriorDistributions.csv: median values and 90% credible intervals for the parameter posterior distributions</li> <li>StatisticalComparison.csv: statistics (Fractional bias, Root mean square error, Paired Student test, Pearson correlation coefficient, Parameters of the Deming regression between observed and predicted values) used to compare the 'Best model configurations'</li> </ul> For more information concerning this repository or the study, please do not hesitate to contact Frédéric ANDRÉ (frederic.andre@uclouvain.be) or Mathieu JONARD (mathieu.jonard@uclouvain.be).