Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
488
datasets available to search
ShareScore release 0.9.0
Dataset results
488 results for “laboratory data”
2018-2020 Laboratory measurements of inorganic carbon accompanied by sensor data measurements of in situ inorganic carbon from the Upper Clark Fork River (Montana, USA)
These data were collected to support the Upper Clark Fork River restoration monitoring project supported by the US NSF Long Term Research in Environmental Biology (LTREB) and Consortium for Research in Environmental Water Systems (CREWS) programs. The LTREB monitoring project consists of monthly and bi-weekly water quality monitoring across a 215-km river restoration gradient contaminated by historic mining practices to monitor inorganic phosphorus and nitrogen concentrations, biotic standing stocks, heavy metal contamination, organic and inorganic carbon concentrations, and physicochemical parameters. The original analytical intent for these data was to assess the accuracy of calculating the partial pressure of carbon dioxide (pCO2) from electrochemical and spectrophotometric pH along with total alkalinity (AT). These data correspond to two parts: a tank study and a field application. The tank study was a set of controlled laboratory experiments that took place in a well-mixed temperature-controlled tank of freshwater. Data for the tank study are primarily measurements of electrochemical and spectrophotometric pH, AT, electrical conductivity, temperature, and ionic strength. The field application was used to demonstrate the real-world applicability of the tank study results in the Upper Clark Fork River (USGS HUC 17010201) at the Gold Creek site southeast of Missoula, MT, USA. Data from the field application are primarily high frequency measurements of carbon dioxide, pH, temperature, and electrical conductivity. Additional miscellaneous data were collected for quality control. These field data were collected using field deployments of SAMI sensors from Sunburst Sensors (Missoula, Montana, USA). Electrical conductivity data were collected with a HOBO sensor from Onset Computer Corporation (Bourne, Massachusetts, USA).
Plant and carbon data, snowmelt manipulation experiment, Rocky Mountain Biological Laboratory (RMBL), 2023
These data are from a 2023 snowmelt manipulation experiment in Vera Meadow at the Rocky Mountain Biological Laboratory. We experimentally advanced the snowmelt date in a montane meadow by approximately 12 days using black shade cloths and assessed the effect on plant and carbon dynamics. We measured net ecosystem exchange, gross primary productivity, and soil respiration using a Li-COR 7500 five times biweekly from June to August, plant community composition using the pin-drop method five times biweekly from June to August, and root biomass nine times using bulk soil cores. Using drone imagery, we measured the Normalized Difference Vegetation Index (NDVI). This data package is completed.
Temperature, floral density, and Osmia pollen usage data from seven study sites around the Rocky Mountain Biological Laboratory, Colorado: 2013-2023
Data were collected as part of a study of population dynamics of solitary, cavity-nesting Hymenoptera. Nesting structures ("trap-nests") were established at five study sites along an elevational gradient around the Rocky Mountain Biological Laboratory in 2013. Two additional study sites were added in 2014, and one of the original study sites was dropped at the end of 2015. At each site, a HOBO data-logger placed under a centrally located trap-nest records air temperatures hourly. Floral densities are recorded at each site, typically 1-2 times per week, throughout the growing season, for specific plant taxa known to be used as pollen sources by cavity-nesting bees. In addition, pollen samples are taken from the nests of cavity-nesting bees and the constituent plant taxa identified by microscopic comparison with a reference pollen collection from the study area.
Public Available Data Set of Process Flows from Internal Physical Inspections in the Failure Analysis Laboratory
<p>This data set was generated in accordance with the semiconductor industry and contains data of certain process flows in Failure Analysis (FA) laboratories focusing on the identification and analysis of anomalies or malfunctions in semiconductor devices. It comprises logistic data about the processing steps for the so-called Internal Physical Inspection (IPI).</p><p>A so-called IPI job is given as a sequence of tasks that must be performed to complete the job they belong to. It has an assigned unique ID and timestamps indicating the submission, the end, and the deadline to be met. A job also has an IPI classification assigned to it, providing general guidelines on the operations to be performed.</p><p>Every task within a job has its own type and working time, as well as the assigned resources. There are two main resources involved:</p><p> - the equipment; the machine used to perform the task,</p><p> - the operator; the person who performed the task.</p><p>In addition, general information about the type of the device to be analyzed is also available, such as the given (anonymized) package and basictype. Data also include the number of stressed samples within a device and the samples a task is performed on.</p><p>The dataset includes data from 4 years, specifically from January 2020 to December 2022.</p><p>Finally, the exact column structure is given as follows (python 3.9.5 datatype):</p><ul><li>JOB_ID [int64]: the unique ID of the job</li><li>JOB_SUBMISSION_DATE [object]: the date of the job submission</li><li>JOB_REQ_END_DATE [object]: the required end date (deadline)</li><li>JOB_FINISH_DATE [object]: the actual end date</li><li>JOB_BASICTYPE_H [object]: the given basictype denotation</li><li>JOB_PACKAGE_H [object]: the package denotation of the device</li><li>JSH_QTY_STRESSED [float64]: number of stressed samples</li><li>TASK_SUBMISSION_DATE [object]: the date of the task submission</li><li>TASK_WORKING_TIME [float64]: the amount of time (hours) the task needs to be completed</li><li>TASK_SAMPLE_NO [object]: the samples the task was performed on </li><li>TASK_CEQ_ID [float64]: the ID of the machine used to perform the task</li><li>TASK_CTKS_ID [int64]: the ID representing the task type</li><li>TASK_USR_ID [int64]: the ID of the operator performing the task</li><li>CIPI_LEVEL_0 [object]: a series of IPI classifications, indicating what is required to execute for a specific job</li></ul>
CENTAUR project laboratory testing data
<p>This dataset contains results from testing carried out at a laboratory facility at the University of Sheffield (UK) as part of the <a href="https://www.sheffield.ac.uk/centaur">CENTAUR project</a>. CENTAUR is an EC funded Horizon 2020 Innovation Action. The project has developed a system to reduce flood risk in urban areas by utilising existing available storage capacity in urban drainage networks through the use of a gate installed in an existing manhole. The gate is controlled by Fuzzy Logic, using data from level sensors.</p> <p>The laboratory facility is described in the 'CENTAUR_Lab_facility.pdf '. Further details of the sensors and logging system are provided in 'Data_File_Column_Descriptions.csv'.</p> <p>The file 'Test_Record.csv' describes all tests carried out. This dataset contains 83 csv data files in for days when good data was collected, these are zipped into 'DataFiles.zip'. Each csv file within the .zip contains the test results for one day, the files are named with the date of testing in the format yymmdd. The csv data files do not include column headers, but a full description of the data in each column is provided in 'Data_File_Column_Descriptions.csv'. The csv files contain data from all sensors, but the time period of the data from each sensor (or sensor set) and timesteps are not the same, hence for each sensor / sensor set there is a separate time column. The sampling interval for the level sensors is given in column 26 of 'Test_Record.csv', this will be correct for the test period, but outside the tests the interval was often increased and this may be seen in the data files. The gate / FCD sampling interval is the same as the Fuzzy Logic interval in column 27 of 'Test_Record.csv', although the position is only reported when the gate / FCD is active - i.e. not fully open. At the end of a test the FCD will return to the fully open position (100%), but this final datapoint is not recorded. The flow rate and downstream valve position sampling interval are given in column 12 of 'Test_Record.csv'.</p> <p>Test numbers and fuzzy logic version ids are simplified for the journal paper 'Demonstrating a Fuzzy Logic algorithm for real-time flow control in a full-scale laboratory environment' which is currently under review with the Urban Water Journal. A correlation between the information in the paper and in 'Test_Record.csv' can be found in 'Paper_Test_Numbers.csv'.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641931.</p>
Voltage and current data for IEC 62600-30 power quality monitoring from the Mutriku Wave Power Plant and Lir National Ocean Test Facility electrical laboratory
<p>This Technical Note describes the electrical data collected from the Mutriku Wave Power Plant (MWPP) and the Lir National Ocean Test Facility (NOTF) electrical laboratory at the MaREI Centre in the Environmental Research Institute, at University College Cork.</p> <p>In summary, the electrical data collect is for the purpose of analysing the power quality output of a Wave Energy Converter (WEC). The data includes voltage and current signals from the output of a WEC sampled at 15 kHz from the MWPP and a WEC emulator sampled at 20 kHz from the Lir NOTF electrical laboratory. There are 24 datasets from the MWPP taken at various sea state conditions, and there are 56 datasets from the Lir NOTF which are taken with at various sea state conditions, with different control laws, and grid connections.</p> <p>This data is published for purpose of power quality analysis and comparison for future tests. For OPERA, power quality analysis was performed as part of WP5 T5.2 and T5.5, and presented in depth in Deliverables D5.2 and D5.4.</p> <p>See accompanying technical note for more Information.</p>
Research data supporting "Considerations for Implementing Electronic Laboratory Notebooks in an Academic Research Environment"
<p><em>Research data supporting the publication:</em></p> <p><em>Higgins SG, Nogiwa-Valdez AA, Stevens MM, Considerations for Implementing Electronic Laboratory Notebooks in an Academic Research Environment, Nature Protocols, 2021.</em></p> <p>This repository contains the raw survey data of 172 current and historic electronic laboratory notebook (ELN) software packages.</p> <p>Main files:</p> <ul> <li>"ELN_Review_Higgins_2021_Survey.csv" = raw survey data in 'tidy' data format</li> <li>"ELN_Review_Higgins_2021.Rmd" = an R Markdown File (R Notebook) that takes the survey data as input and produces summary statistics and plots. This file was written using R Studio as the IDE.</li> </ul> <p>Derived files, generated from those above:</p> <ul> <li>"ELN_Review_Higgins_2021.nb.html" = a self-contained HTML file that is automatically generated by R Studio, based on the markdown file. This can be opened in any web browser to allow manual inspection of the code and comments without the need for specialist software. Embedded within this file is also the original markdown script (i.e. a copy of the code in "ELN_Review_Higgins_2021.Rmd")</li> <li>"ELN_Review_Higgins_2021_Lifetimes_Interactive_Figure1.html" = an HTML file generated by the script above via the plotly package. It contains an interactive version of the ELN survey data, allowing the user to hover over the timeline and explore the data.</li> <li>"ELN_Review_Higgins_2021_Timeline.pdf" = static version of ELN timeline, used to generate figure in main manuscript.</li> <li>"ELN_Review_Higgins_2021_Releases-Per-Year.pdf" = static version of number of new ELNs per year, used to generate figure in main manuscript.</li> </ul> <p>This survey was generated from a mixture of primary and secondary sources (see references for secondary sources).</p>
JUMP - Data collection - Part II: Zonal jets using three different approaches, laboratory - Global Climate Models - observations.
<p>The formation of large scale structures in three-dimensional (3D) turbulent flows. How small-scale dynamics organize in turbulent flows to grow large scale coherent circulation? is at the heart of fundamental studies in fluid dynamics. It appears to be equally important for our understanding of atmospheric dynamics, oceanography, meteorology and more generally geophysical fluid dynamics. Here, we deliver a data collection that <strong>(1)</strong> gathers measurements of 3D turbulent flows that emulate planetary atmospheres of the gas giants. Turbulent flows are explored using three different approaches, laboratory experiments, numerical simulations and direct planetary observations. All data set are computed in order to easily extract flow properties, i.e. high resolution maps of the different velocity components and flow vorticity (useful for further diagnostic). The data collected are fully discribed in Cabanes et al GRL (2020) "Revealing the intensity of turbulent energy transfer in planetary atmospheres" and can be used to compute <strong>(2)</strong> theoretical diagnostics with the numerical codes that allow to reveal the physical meaning of flow measurements. Numerical codes are available on https://github.com/scabanes</p> <p>We deliver (1) data collection and (2) numerical codes in the following files attached:</p> <p>(1) Data collection:</p> <ul> <li>A PDF file named <strong>JUMP-zonal-jets-data-collection-GRL.pdf</strong> that describes the following data files and nomenclature.</li> <li>A zip File of the velocity fields in the lab, interpolated on Polar and Cartesian grids <ul> <li><strong>JUMP-JetsInTheLab.zip</strong></li> </ul> </li> <li>A netcdf file of velocity fields of our Saturn reference simulation <ul> <li><strong>uvData-SRS-istep-312000-nstep-50-niz-12.nc</strong></li> </ul> </li> <li>Two netcdf files of velocity fields from Cassini observations of Jupiter<strong> </strong> <ul> <li><strong>uvData-JupObs-istep-0-nstep-4-niz-1.nc</strong></li> <li><strong>StatisticalData-JupObs.nc</strong></li> </ul> </li> <li>A zip file of potential vorticity profiles for Saturn and Jupiter observations <ul> <li><strong>IPV-QGPV-Jupiter-Saturn.zip</strong></li> </ul> </li> </ul> <p>(2) Numerical codes:</p> <ul> <li>Codes for statistical analysis in spherical geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FPOST&sa=D&sntz=1&usg=AFQjCNFuDU0eij4XGxQfReO92CHfJz6PBA">https://github.com/scabanes/POST</a></li> <li>Codes for statistical analysis in cylindrical geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FJUMP&sa=D&sntz=1&usg=AFQjCNGUQ1YIFhSxBAg4Hl_5gOLB_4LxLA">https://github.com/scabanes/JUMP</a></li> <li>Codes for statistical analysis in cartesian geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FJUMP&sa=D&sntz=1&usg=AFQjCNGUQ1YIFhSxBAg4Hl_5gOLB_4LxLA">https://github.com/scabanes/JUMP</a></li> </ul> <p> </p> <p>The purpose of this data collection is to reveal statistical properties of planetary flows. By computing the same analysis on different data sets the researcher allows direct confrontation of planetary observations with idealized laboratory and numerical models. Idealized models are specially designed to sweep on a large array of parameters in order to understand what parameters control planetary global circulation. The data collected and generated by the researcher deliver <strong>(1)</strong> velocity measurements of 3D turbulent flows using the different approaches (observations-laboratory-numerics) and <strong>(2)</strong> guidelines to compute the appropriate statistical analysis through the PTST. Here, the ground-breaking novelty is that the researcher deliver the possibility to compute statistical diagnostics adapted to the different geometries: the spherical geometry of planetary flows, i.e. 2D latitude-longitude maps, the cylindrical geometry of laboratory experiments, i.e. 2D flows in a rotating cylindrical tank, and the Cartesian geometry of idealized numerical simulations. Indeed, the math behind each statistical diagnostics must account for the different geometrical configurations in order to properly confront the different approaches. The PTST is also designed to be easily re-used by different communities such as experimentalists, numericists and atmosphericists that deal with 3D or 2D turbulent flows.</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement N° 797012.</p>
Data to reproduce the results presented in Lake et al. 2021. Journal of Soils and Sediments, https://doi.org/10.1007/s11368-021-03107-6 ("High frequency un-mixing of soil samples using a submerged spectrophotometer in a laboratory setting – implications for sediment fingerprinting")
<p>This repository contains data on (1) the absorbance data and (2) the measured concentrations, to reproduce computational results as presented in:<br> "High frequency un-mixing of soil samples using a submerged spectrophotometer in a laboratory setting – implications for sediment fingerprinting".</p> <p> <br> 1. Absorbance data (200-730 nm wavelengths):</p> <p> * Average absorbance compensated for measured concentrations (average absorbance value per concentration)<br> * Average absorbance compensated for theoretical concentrations (average absorbance value per concentration)<br> * Average raw absorbance measured (average absorbance value per concentration)<br> * Raw absorbance measured (all absorbance values for all concentrations)</p> <p> Data in all 3 files is indicated per soil sample / mixture, with corresponding fraction(s) of soil sample(s) and corresponding (theoretical) input concentration.<br> <br> 2. Measured concentration data:</p> <p> * Measured concentration (average concentrations, tested for all experiments and for all theoretical input concentrations)</p> <p> </p>
Time series data of COVID-19 cases (rT-PCR-confirmed), hospitalisations (laboratory-confirmed), and hospital-associated deaths (laboratory confirmed) in South Africa, by imputed dates of symptom onset, from the start of the pandemic in March 2020 through April 2022.
<p>Time series data of COVID-19 cases (rT-PCR-confirmed), hospitalisations (laboratory-confirmed), and hospital-associated deaths (laboratory confirmed) in South Africa, by imputed dates of symptom onset, from the start of the pandemic in March 2020 through April 2022. These data were used to estimate the time-varying reproduction number (R) in South Africa, as described in https://www.medrxiv.org/content/10.1101/2022.07.22.22277932v1.full.</p>
ENDGAME - Laboratory Experiment 2023-02-20 Exp. 001 - High Speed Camera data
<p>Shock-tube experiments in combination with high speed Schlieren shadow photography. </p> <p>The shocktube setup consists of a high-pressure reservoir connected with a cylindrical tube through a diaphragm pulse valve which allows a fast release of pressurized gas into the ambient pressure tube. The high-pressure reservoir is filled with compressed air at a given overpressure with respect to ambient pressure (up to 8 bar). The pipe was either empty (i.e. with air at ambient conditions) or filled with a given amount of fluids (water or viscous fluid) or small particles. We adopted pipes with different inner diameters (3 and 4 cm) and different lengths (30 and 80 cm). Images were collected at a frame rate of 30000 fps.</p> <p>When the valve is open, a jet flow is produced, with shock and acoustic waves propagating in the atmosphere, which become visible due to the high speed Schlieren shadow photography.</p>
ENDGAME - Laboratory Experiment 2023-02-20 Exp. 002 - High Speed Camera data
<p>Shock-tube experiments in combination with high speed Schlieren shadow photography. </p> <p>The shocktube setup consists of a high-pressure reservoir connected with a cylindrical tube through a diaphragm pulse valve which allows a fast release of pressurized gas into the ambient pressure tube. The high-pressure reservoir is filled with compressed air at a given overpressure with respect to ambient pressure (up to 8 bar). The pipe was either empty (i.e. with air at ambient conditions) or filled with a given amount of fluids (water or viscous fluid) or small particles. We adopted pipes with different inner diameters (3 and 4 cm) and different lengths (30 and 80 cm). Images were collected at a frame rate of 30000 fps.</p> <p>When the valve is open, a jet flow is produced, with shock and acoustic waves propagating in the atmosphere, which become visible due to the high speed Schlieren shadow photography.</p> <p> </p>
ENDGAME - Laboratory Experiment 2023-02-20 Exp. 005 - High Speed Camera data
<p>Shock-tube experiments in combination with high speed Schlieren shadow photography. </p> <p>The shocktube setup consists of a high-pressure reservoir connected with a cylindrical tube through a diaphragm pulse valve which allows a fast release of pressurized gas into the ambient pressure tube. The high-pressure reservoir is filled with compressed air at a given overpressure with respect to ambient pressure (up to 8 bar). The pipe was either empty (i.e. with air at ambient conditions) or filled with a given amount of fluids (water or viscous fluid) or small particles. We adopted pipes with different inner diameters (3 and 4 cm) and different lengths (30 and 80 cm). Images were collected at a frame rate of 30000 fps.</p> <p>When the valve is open, a jet flow is produced, with shock and acoustic waves propagating in the atmosphere, which become visible due to the high speed Schlieren shadow photography.</p> <p> </p>
ENDGAME - Laboratory Experiment 2023-02-20 Exp. 003 - High Speed Camera data
<p>Shock-tube experiments in combination with high speed Schlieren shadow photography. </p> <p>The shocktube setup consists of a high-pressure reservoir connected with a cylindrical tube through a diaphragm pulse valve which allows a fast release of pressurized gas into the ambient pressure tube. The high-pressure reservoir is filled with compressed air at a given overpressure with respect to ambient pressure (up to 8 bar). The pipe was either empty (i.e. with air at ambient conditions) or filled with a given amount of fluids (water or viscous fluid) or small particles. We adopted pipes with different inner diameters (3 and 4 cm) and different lengths (30 and 80 cm). Images were collected at a frame rate of 30000 fps.</p> <p>When the valve is open, a jet flow is produced, with shock and acoustic waves propagating in the atmosphere, which become visible due to the high speed Schlieren shadow photography.</p>
ENDGAME - Laboratory Experiment 2023-02-20 Exp. 004 - High Speed Camera data
<p>Shock-tube experiments in combination with high speed Schlieren shadow photography. </p> <p>The shocktube setup consists of a high-pressure reservoir connected with a cylindrical tube through a diaphragm pulse valve which allows a fast release of pressurized gas into the ambient pressure tube. The high-pressure reservoir is filled with compressed air at a given overpressure with respect to ambient pressure (up to 8 bar). The pipe was either empty (i.e. with air at ambient conditions) or filled with a given amount of fluids (water or viscous fluid) or small particles. We adopted pipes with different inner diameters (3 and 4 cm) and different lengths (30 and 80 cm). Images were collected at a frame rate of 30000 fps.</p> <p>When the valve is open, a jet flow is produced, with shock and acoustic waves propagating in the atmosphere, which become visible due to the high speed Schlieren shadow photography.</p>
ENDGAME - Laboratory Experiment 2023-02-21 Exp. 014 - High Speed Camera data
<div> <p>Shock-tube experiments in combination with high speed Schlieren shadow photography. </p> <p>The shocktube setup consists of a high-pressure reservoir connected with a cylindrical tube through a diaphragm pulse valve which allows a fast release of pressurized gas into the ambient pressure tube. The high-pressure reservoir is filled with compressed air at a given overpressure with respect to ambient pressure (up to 8 bar). The pipe was either empty (i.e. with air at ambient conditions) or filled with a given amount of fluids (water or viscous fluid) or small particles. We adopted pipes with different inner diameters (3 and 4 cm) and different lengths (30 and 80 cm). Images were collected at a frame rate of 30000 fps.</p> <p>When the valve is open, a jet flow is produced, with shock and acoustic waves propagating in the atmosphere, which become visible due to the high speed Schlieren shadow photography.</p> <p> </p> </div>
ENDGAME - Laboratory Experiment 2023-02-21 Exp. 018 - High Speed Camera data
<p>Shock-tube experiments in combination with high speed Schlieren shadow photography. </p> <p>The shocktube setup consists of a high-pressure reservoir connected with a cylindrical tube through a diaphragm pulse valve which allows a fast release of pressurized gas into the ambient pressure tube. The high-pressure reservoir is filled with compressed air at a given overpressure with respect to ambient pressure (up to 8 bar). The pipe was either empty (i.e. with air at ambient conditions) or filled with a given amount of fluids (water or viscous fluid) or small particles. We adopted pipes with different inner diameters (3 and 4 cm) and different lengths (30 and 80 cm). Images were collected at a frame rate of 30000 fps.</p> <p>When the valve is open, a jet flow is produced, with shock and acoustic waves propagating in the atmosphere, which become visible due to the high speed Schlieren shadow photography.</p>
ENDGAME - Laboratory Experiment 2023-02-21 Exp. 013 - High Speed Camera data
<p>Shock-tube experiments in combination with high speed Schlieren shadow photography. </p> <p>The shocktube setup consists of a high-pressure reservoir connected with a cylindrical tube through a diaphragm pulse valve which allows a fast release of pressurized gas into the ambient pressure tube. The high-pressure reservoir is filled with compressed air at a given overpressure with respect to ambient pressure (up to 8 bar). The pipe was either empty (i.e. with air at ambient conditions) or filled with a given amount of fluids (water or viscous fluid) or small particles. We adopted pipes with different inner diameters (3 and 4 cm) and different lengths (30 and 80 cm). Images were collected at a frame rate of 30000 fps.</p> <p>When the valve is open, a jet flow is produced, with shock and acoustic waves propagating in the atmosphere, which become visible due to the high speed Schlieren shadow photography.</p>
ENDGAME - Laboratory Experiment 2023-02-21 Exp. 010 - High Speed Camera data
<p>Shock-tube experiments in combination with high speed Schlieren shadow photography. </p> <p>The shocktube setup consists of a high-pressure reservoir connected with a cylindrical tube through a diaphragm pulse valve which allows a fast release of pressurized gas into the ambient pressure tube. The high-pressure reservoir is filled with compressed air at a given overpressure with respect to ambient pressure (up to 8 bar). The pipe was either empty (i.e. with air at ambient conditions) or filled with a given amount of fluids (water or viscous fluid) or small particles. We adopted pipes with different inner diameters (3 and 4 cm) and different lengths (30 and 80 cm). Images were collected at a frame rate of 30000 fps.</p> <p>When the valve is open, a jet flow is produced, with shock and acoustic waves propagating in the atmosphere, which become visible due to the high speed Schlieren shadow photography.</p>
ENDGAME - Laboratory Experiment 2023-02-21 Exp. 016 - High Speed Camera data
<p>Shock-tube experiments in combination with high speed Schlieren shadow photography. </p> <p>The shocktube setup consists of a high-pressure reservoir connected with a cylindrical tube through a diaphragm pulse valve which allows a fast release of pressurized gas into the ambient pressure tube. The high-pressure reservoir is filled with compressed air at a given overpressure with respect to ambient pressure (up to 8 bar). The pipe was either empty (i.e. with air at ambient conditions) or filled with a given amount of fluids (water or viscous fluid) or small particles. We adopted pipes with different inner diameters (3 and 4 cm) and different lengths (30 and 80 cm). Images were collected at a frame rate of 30000 fps.</p> <p>When the valve is open, a jet flow is produced, with shock and acoustic waves propagating in the atmosphere, which become visible due to the high speed Schlieren shadow photography.</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.