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90 results for “Laboratory tests”
Dataset for "Machine learning predictions on an extensive geotechnical dataset of laboratory tests in Austria"
<p>This dataset comprises over 20 years of geotechnical laboratory testing data collected primarily from Vienna, Lower Austria, and Burgenland. It includes 24 features documenting critical soil properties derived from particle size distributions, Atterberg limits, Proctor tests, permeability tests, and direct shear tests. Locations for a subset of samples are provided, enabling spatial analysis.</p> <p>The dataset is a valuable resource for geotechnical research and education, allowing users to explore correlations among soil parameters and develop predictive models. Examples of such correlations include liquidity index with undrained shear strength, particle size distribution with friction angle, and liquid limit and plasticity index with residual friction angle.</p> <p>Python-based exploratory data analysis and machine learning applications have demonstrated the dataset's potential for predictive modeling, achieving moderate accuracy for parameters such as cohesion and friction angle. Its temporal and spatial breadth, combined with repeated testing, enhances its reliability and applicability for benchmarking and validating analytical and computational geotechnical methods.</p> <p>This dataset is intended for researchers, educators, and practitioners in geotechnical engineering. Potential use cases include refining empirical correlations, training machine learning models, and advancing soil mechanics understanding. Users should note that preprocessing steps, such as imputation for missing values and outlier detection, may be necessary for specific applications.</p> <p><strong>Key Features</strong>:</p> <ul> <li><strong>Temporal Coverage</strong>: Over 20 years of data.</li> <li><strong>Geographical Coverage</strong>: Vienna, Lower Austria, and Burgenland.</li> <li><strong>Tests Included</strong>: <ul> <li>Particle Size Distribution</li> <li>Atterberg Limits</li> <li>Proctor Tests</li> <li>Permeability Tests</li> <li>Direct Shear Tests</li> </ul> </li> <li><strong>Number of Variables</strong>: 24</li> <li><strong>Potential Applications</strong>: Correlation analysis, predictive modeling, and geotechnical design.</li> </ul> <p><strong>Technical Details</strong>:</p> <ul> <li>Missing values have been addressed using K-Nearest Neighbors (KNN) imputation, and anomalies identified using Local Outlier Factor (LOF) methods in previous studies.</li> <li>Data normalization and standardization steps are recommended for specific analyses.</li> </ul> <p><strong>Acknowledgments</strong>:<br>The dataset was compiled with support from the European Union's MSCA Staff Exchanges project 101182689 Geotechnical Resilience through Intelligent Design (GRID).</p>
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>
Laboratory experiments testing pH, alkalinity and particle impacts on Mn removal
Laboratory experiments were conducted to investigate impacts of pH, alkalinity, and presence of particles on Mn removal in freshwater. The dataset includes monitoring data from: 1) a 14-day experiment in Mn(II) solutions in nanopure water, 2) a 24-hour experiment in Mn(II) solutions in nanopure water, and 3) a 10-day experiment in water from two drinking water reservoirs. The 14-day pH and alkalinity laboratory experiment was conducted starting October 30, 2022 and included sample collection and pH monitoring on day 0, 1, 4, 7, 10, and 14. The 24-hour pH and alkalinity laboratory experiment was conducted starting February 20, 2023 and included sample collection and pH monitoring at 0, 1, 2, 6, 12, and 24 hours. The reservoir water laboratory experiment was conducted starting March 22, 2023 and included sample collection and pH monitoring on day 0, 1, 4, 7, and 10. This experiment tested Mn removal in water collected from the lower water column of Falling Creek Reservoir (FCR) and Carvins Cove Reservoir (CCR), located in Vinton, Virginia, USA and Roanoke, Virginia, USA respectively. Both reservoirs are owned and operated by the Western Virginia Water Authority and are managed as drinking-water sources for the city of Roanoke, VA, USA.
Needs of plant health laboratories and applicability of the horizontal proficiency testing approach based on the questionnaire answers
<p>Data collected in the framework of work package 5 of the Valitest project. They correspond to the needs and expectation concerning proficiency assessment expressed by plant health laboratories during a survey conducted online in 2019.</p>
Periodic Hydraulic Testing Dataset for "Borehole-based fracture unclogging experiment: bridging the gap between laboratory- and field-scale evidence (FRANC)"
<p>This dataset is associated with the SNSF-SPARK project “Borehole-based fracture unclogging experiment: bridging the gap between laboratory- and field-scale evidence (FRANC)”. Please read the ReadMe file for more information.</p>
Evaluation of Materials for Asphalt Mixture Performance, Semi-Circular Bend Laboratory Tests
<p>A study was conducted to evaluate the repeatability of the Flexibility Index of asphalt mixtures obtained according to AASHTO TP-124-16. Three asphalt concrete samples were mixed and compacted using the Superpave Gyratory Compactor in one laboratory. The samples were then cut to specific dimensions for semi-circular bend testing based on the AASHTO Specifications at a single laboratory using a dedicated cutting equipment. The samples were randomized and distributed equally among three different testing labs.</p> <p>The process was repeated three times and in some instances the rate of loading was varied.</p> <p>This experiment allowed to study the repeatability of the the Flexibility Index</p>
Individual datasets investigating combined toxicity of binary mixtures in bees from laboratory tests
<p>This excel file (DOI: https://doi.org/10.5281/zenodo.3383713) provides the individual datasets on binary mixture toxicity (mortality) in bees classified according to route and exposure patterns (i.e. oral, contact, acute and chronic) and mortality endpoints (e.g.LD<sub>50</sub>, LC<sub>50</sub>) for the honeybee (<em>Apis mellifera</em>) and wild bee species (<em>Osmia bicornis</em>, <em>Bombus terrestris</em>). 218 individual binary mixtures were collected and included in the statistical analyses with the majority of toxicological endpoints reported as lethal doses or concentrations (e.g. LD<sub>50</sub>, LC<sub>50</sub>,) for pesticides or pesticides and veterinary drugs combinations with 133, 44 and 41 mixtures reporting acute contact toxicity (i.e. topical application), chronic oral toxicity and acute oral toxicity, respectively. Combined toxicity data for binary mixtures were available as dose response data in honeybees for acute contact toxicity (n=92) and acute oral toxicity.</p> <p>The full data collection and analysis of binary mixtures are described in Carnesecchi et al., 2019 (DOI: 10.1016/j.envint.2019.105256)</p>
Processing, Spectroscopic and Laboratory Testing Data from a Medical Grade Hot-Melt Extrusion Process
<p>This dataset contains a collection of raw processing data, spectroscopic data, and laboratory test results of medical-grade polymer extrusion experiments. The data was collected in several experiments conducted in a hot-melt extrusion process. The process involved extruding PLA through a slit die and drawing the extruded strands onto spools to obtain the desired dimensional and mechanical properties. The strands were later knitted to form the final medical implant. Throughout the experiments, the extrusion process and equipment were upgraded and refined. Various operational scenarios were simulated under different nozzle configurations. The experiments start using a single-screw extruder and later progress to a double-screw extruder. Medical Grade PURASORB PLA (PLDLA 96/4) material was used when the hardware upgrades were complete. This dataset contains many variations in experimental conditions. However, enough overlap exists to derive working datasets from this compiled raw data.</p> <p> </p> <p>Two working datasets have been derived from this compiled raw data. Using a double-screw extruder, both working Datasets investigate polymer degradation in the hot-melt extrusion process. Both derived datasets are included in this collection.</p> <p> </p> <p>Two Jupyter notebooks are included in this data collection. The first notebook gives an example of how an initial dataset can be derived from the raw data using data science techniques. The second notebook gives an example of how a final dataset can be created from the initial dataset.</p>
Simulation and laboratory eddy current testing data - modelling compound defects via perturbation theory
<p>This dataset serves to fit and validate a perturbation approach to the modelling eddy current signals of compound defects. It was obtained during the AIFRI project (Artificial Intelligence for Rail Inspection). The simulation data was generated with the Faraday software by INTEGRATED Engineering Software, using its BEM Solver. The simulation data is supplied as csv. The laboratory data was gathered by Rainer Pohl in the eddy current laboratory of BAM, section 8.4. It is supplied in the DICONDE data format. The first frame of the pixel array in the DICONDE files corresponds to the real part and the second frame corresponds to the imaginary part of the signal. The data set is analyzed in an upcoming article.</p> <p><span> </span></p>
Testing Protocols for Obtaining Reliable PDFs from Laboratory x-ray Sources Using PDFgetX3
<p>In this work, we explored data acquisition protocols and improved data reduction protocols using PDFgetX3 to obtain reliable data for atomic pair distribution function (PDF) analysis from a laboratory-based Mo x-ray source. A variable counting scheme is described that preferentially counts in the high-angle region of the diffraction pattern. The effects on the resulting PDF are studied by varying the overall count time, the use of Soller slits, and limiting the out-of-plane divergence of the incident beam. The protocols are tested using an amorphous silica and a quartz sample. We also present a modification to the current PDFgetX3 data corrections to take care of sample absorption, which was previously neglected in the use of that program for high-energy synchrotron x-ray data. We show that, despite limitations in the Q-range and flux of laboratory instruments, reasonable data for PDF model fits may be obtained using the best protocols in a few hours of counting. </p>
Datasets, codes and video clips for the laboratory flume tests of granular flow
<p>Datasets, video clips, and codes related to the paper 'Insight into granular flow dynamics relying on basal stress measurements: from experimental flume tests', submitted to the Journal of Geophysical Research: Solid Earth.</p> <p>The datasets provides the raw and processed data for the laboratory flume tests of granular flow including parameters reflecting the granular flow behavior, basal normal stresses measured by a force plate, and deposit parameters of the granular flows.</p> <p><strong>S1_data_granular flow_velocity</strong> provides data of the velocity profiles with a 0.1 second time interval, the depth-averaged velocities, the depth-averaged shear rates, and the solid inertial stresses of the granular flows under different experimental conditions. The original data were calculated through particle image velocimetry (PIV) method. The images for PIV analysis were recorded by a high-speed camera.</p> <p><strong>S2_data_granular flow_stress</strong> provides the raw data of the measured basal normal stresses of the granular flows for all tests. The mean and fluctuating stress components extracted by applying a moving window average filter are also listed in the Table.</p> <p><strong>S3_data_granular flow_flow depth</strong> provides the data of the granular flow depth extracted every 0,02 s through a image processing method based on the high-speed photographs. </p> <p><strong>S4_data_granular flow_deposit</strong> provides the parameters of the granular flow deposits for all tests including the apparent friction coefficient and equivalent friction coefficient. The deposit parameters were calculated based on the digital surface model (DSM) of deposit, which were obtained through a oblique photogrammetry method.</p> <p><strong>S5_data_granular flow_density</strong> gives the data of the dynamic bulk flow densities of the granular flows under all experimental conditions. The dynamic bulk densities were calculated according to the measured and calculated normal stresses.</p> <p>The videos of the granular flows under different experimental conditions during their propagation are provided in <strong>'S6_video_granular flow.zip'</strong> to show the granular flow behavior and its evolution. <strong>S6_video_granular flow</strong> includes the side-view of the granular flows under all experimental conditions and front-view of the IMF-223 granular flow . </p> <p><strong>S7_codes_data analysis</strong> provides the computer codes for the extraction of mean and fluctuating components and the calculation of granular flow depth. The former includes one file for conducting moving average filter. The latter contains four files, which are used for median filter, image erosion, threshold segmentation and floodfill, extracting flow depth. </p>
The Data Set for the Publication "Laboratory Testing of Small Scale Solar Facade Module with Phase Change Material and Adjustable Insulation Layer"
<p>In the scope of the ambitious EU goals of carbon neutrality in 2050, building energy efficiency is one of the crucial segments. To ensure a faster energy transition process, innovations are needed at various built environment-related sectors - starting from building components up to urban level energy management advancements.</p> <p>Phase change material (PCM) enriched building components allow to shift the existing paradigm - to make a switch from the static building components to dynamic ones able to take an active part in building energy balance by ensuring energy storage in the building thermal envelope.</p> <p>The data set presented here supports the paper "Laboratory Testing of Small Scale Solar Facade Module with Phase Change Material and Adjustable Insulation Layer". The design of the façade module, experimental setup, used equipment, the plan of the experiment, and obtained results are described in the paper. The provided data set provides heat-flux and average temperature (in PCM) measurements.</p>
Dataset: Heart Test Laboratories, Inc. (HSCSW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Heart Test Laboratories, Inc. (HSCS) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Figure 1 in Compatibility of entomopathogenic nematodes with plant extracts and post-exposure virulence test under laboratory condition
Figure 1. Percentage survival of the EPN Species in aqueous and ethanol extracts of A. amatymbica and E. elephantina after 72 h exposure. Different lower-case characters represent significant differences at p <0.05.
Results of inter-laboratory testing on COSC-1 cores and reference samples (density, porosity, thermal properties)
<p>The data files contain the results of comparative measurements of thermal properties of four widely used rock references and nine core samples originating from the ICDP COSC-1 borehole, using a steady-state and a transient divided-bar device, a transient plane source device, a modified Angstrom device, as well as two optical thermal conductivity scanners. In addition, the Dewar method provided benchmark values for specific heat capacity. </p> <p>participating institutions and methods (acronyms used in file headers)</p> <p>Chalmers University (CU) transient plane source (TPS)<br>Geological Survey of Sweden (SGU) optical scanning (TCS) <br>Ruhr-Universit ̈at Bochum (RUB) Dewar method, modified Angstrom (mAng), optical scanning (TCS)<br>Aarhus University (AU) transient divided bar (TDB)<br>University College Dublin (UCD) divided bar (DB)</p>
Dataset for triaxial monotonic and cyclic laboratory tests on sand HN31 with fines performed at Université Gustave Eiffel/GERS/SRO
<p>Data from monotonic and triaxial tests obtained during the thesis :</p> <p>Gobbi, S. Caractérisation de paramètres mécaniques d'un sol saturé à partir d'essais de laboratoire et calibration de lois de comportement sous charge dynamique par modélisation numérique, PhD thesis, Université Gustave Eiffel, 2020</p> <p>https://theses.hal.science/tel-03268600</p> <p> </p>
Raw data and supplementary material of the fifth inter-laboratory testing program (RRT5, self-healing concrete with macrocapsules) of the EU COST action SARCOS
<p>This data is the result of a collaboration of scientists working on the development of self-healing concrete within the framework of the European Cooperation in Science and Technology (COST) Action “Self-healing as preventive repair of concrete structures” SARCOS CA15202.</p> <p>In the framework of SARCOS 6 inter-laboratory testing programs are being executed to investigate possible standard test methods for self-healing concrete, each of the testing programs focusing on a different self-healing technique:<br> (1) Concrete with mineral additions,<br> (2) Concrete with the addition of magnesium oxide,<br> (3) Concrete enhanced with crystalline admixtures,<br> (4) High performance fibre reinforced concrete enhanced with crystalline admixtures,<br> (5) Concrete with preplaced macrocapsules containing polymeric healing agent, and<br> (6) Concrete with encapsulated bacteria.</p> <p>The data which can be found here have been obtained in the inter-laboratory testing program 5 "Concrete with preplaced macrocapsules containing polymeric healing agent". In total 6 labs participated in this testing program: Ghent University, Politecnico di Torino, Riga Technical University, Cracow University of Technology, Cambridge University, and KU Leuven (Ghent Technology Campus). All specimens were cast at Ghent University and were then distributed to the different labs, where they were tested.</p> <p>The testing program consisted of tests on both concrete and mortar specimens. The reinforced concrete specimens were cracked in a displacement-controlled three-point bending setup. Subsequently, they were subjected to two capillary water absorption tests, each with a different waterproofing technique. The mortar specimens were not reinforced, instead they were provided with a Carbon Fibre Reinforced Polymer (CFRP) laminate at the top. They were cracked in a force-controlled three-point bending setup, and immediately an active crack width control technique was applied to restrain the crack width of the specimens to a desired crack width range. After measuring of the crack width, the water permeability of the mortar specimens was accessed in a water flow test. In the end, the specimens were cracked open to assess the spread of the polyurethane healing agent.</p>
Dataset: Survey on the actual situation of antibiotic resistant bacteria detection by nucleic acid amplification test in clinical microbiology laboratories at hospitals in Japan: Online survey of participants in workshops organized by the Nara Association of Medical Technologists
<p>The coronavirus disease 2019 pandemic has led to the widespread use of the nucleic acid amplification test (NAAT), along with an increase in demand for SARS-CoV-2 tests. NAAT has been used to detect antimicrobial resistance (AMR) genes since before the pandemic, but the test has been performed in a limited number of facilities. We investigated the current status and background of Japanese clinical laboratories by surveying the implementation of genotypic AST in NAAT, which has become widespread owing to the pandemic. This means that 59% of the respondents possessed NAAT and were using it for genotypic AST. GeneXpert and FilmArray were introduced in the majority of cases (62.5% and 82.6%, respectively), with the pandemic as the trigger. More than half of the respondents cited “rapid detection” (56.0%) and “ICT requests” (52.4%) as the reasons for introducing the system. Regarding usefulness, “contribution to infectious disease treatment” (74.1%) showed the highest percentage. Among the respondents who cited “not implemented”, the most frequent responses were “I have no plans, but I want to do it.” (38.1%) and “would do so if requested by a physician” (33.3%). The most common reason for not implementing the system was concern about increased workload (52.9%). We believe that this is due to changes in the working environment caused by the pandemic and the characteristics of Japanese society. In the future, to promote the adoption of genotypic AST, it will be necessary to approach it through reports on its usefulness from domestic facilities, and simultaneously, improving and enhancing efficiency in work processes will also be essential.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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OpenNeuro
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