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62 results for “Experience sampling”
Radial Hydraulic Fracturing Experiment: 1 Cycle of Fracture Propagation, Arrest, and Closure in Molasse de Villarlod Sandstone - Sample M03
<h4><strong>Overview</strong></h4> <p>This dataset encompasses detailed measurements from a lab-scale radial hydraulic fracturing experiment conducted on a cubic sample of Molasse de Villarlod sandstone, designated as Sample M03. The sandstone, sourced from a quarry in Fribourg, Switzerland, is known for its porosity (18.1%) and permeability, making it an ideal material for studying hydraulic fracture processes. The primary focus of the experiment was to observe and analyze the propagation, arrest, and closure of hydraulic fractures under controlled triaxial stress conditions.</p> <h4><strong>Experimental Setup</strong></h4> <p>The experiment was conducted on a cubic sandstone sample with dimensions of 25 × 25 × 25 cm. The sample was placed in a truetriaxial frame that applied confining stresses in all three principal directions:</p> <ul> <li><strong>Vertical Confining Stress:</strong> 7 MPa</li> <li><strong>Horizontal Confining Stress:</strong> 14 MPa</li> </ul> <p>A <strong>viscous glucose fluid containing a UV additive</strong> was used as the fracturing fluid. This fluid was injected through a 1/8'' high-pressure tube cemented with epoxy into a centrally drilled hole within the sample. An axisymmetric notch was created at the injection point to facilitate fracture initiation and promote the planarity of the fracture.</p> <p>The experiment was designed to simulate one cycle of fracture initiation, propagation, arrest, and closure. The closure of the fracture was occured by the leakoff of the fracturing fluid into the surrounding porous medium.</p> <h4><strong>Acoustic Monitoring</strong></h4> <p>To capture the dynamics of fracture propagation and closure, the experiment employed both passive and active acoustic monitoring systems:</p> <ol> <li> <p><strong>Passive Acoustic Monitoring:</strong></p> <ul> <li><strong>Sensors:</strong> 16 Vallen VS150-M passive piezoelectric sensors were used to capture Acoustic Emissions (AEs) within the frequency range of 50 kHz to 600 kHz.</li> <li><strong>Signal Processing:</strong> Continuous signal analysis and denoising were performed on the captured AE data. The STA/LTA algorithm was applied to the denoised signal to identify potential p-wave arrivals, providing insights into the fracture mechanics.</li> </ul> </li> <li> <p><strong>Active Acoustic Monitoring:</strong></p> <ul> <li><strong>Transducers:</strong> A total of 64 piezoelectric transducers were integrated into the loading platens, with 32 acting as sources and 32 as receivers. The array included 10 shear-wave and 54 longitudinal-wave transducers.</li> <li><strong>Signal Generation and Acquisition:</strong> A Ricker excitation signal with a central frequency adjustable between 300 and 750 kHz was generated and amplified using a high-power amplifier. The signal was routed to one of the 32 source transducers via a multiplexer, and the resulting signals were recorded simultaneously by the 32 receiver transducers at a sampling frequency of 50 MHz. Each source was excited 50 times to improve the signal-to-noise ratio, with the complete acquisition sequence taking approximately 2.5 seconds.</li> </ul> </li> </ol> <h4><strong>Additional Measurements</strong></h4> <p>In addition to acoustic monitoring, several other key measurements were recorded during the experiment:</p> <ul> <li><strong>Fluid Injection Parameters:</strong> The pressure and rate of fluid injection were continuously monitored.</li> <li><strong>Flat-Jack and Piston Parameters:</strong> The pressures and volumes exerted by each pair of flat-jacks were recorded at a frequency of 1 Hz.</li> <li><strong>Fracture Opening Measurement:</strong> An eddy current sensor, an electromagnetic inductive device, was placed inside the wellbore at the notch/inlet to directly measure the fracture opening.</li> </ul> <p>All measurements were synchronized using a dedicated LabView application to ensure consistency across the dataset.</p> <h4><strong>Conclusion</strong></h4> <p>This dataset provides a comprehensive view of the hydraulic fracturing behavior of Molasse de Villarlod sandstone under controlled laboratory conditions, with a focus on the processes of fracture propagation, arrest, and closure. The dataset includes raw and processed acoustic data, fluid injection metrics, and direct observations of fracture opening. It is an invaluable resource for researchers and engineers studying hydraulic fracturing, rock mechanics, and related fields. The data is suitable for detailed analysis and modeling of fracture mechanics in porous, permeable sandstones.</p> <h4><strong>Note</strong></h4> <p>Since the fracture did not extend to the boundaries of the sample, the sample was subsequently cut, and a core was extracted. This core was then sent for CT-scan analysis, which was used to reconstruct the residual fracture surfaces and assess their roughness. The dataset from this analysis is available in the Related Work section via the provided URL (Talebkeikhah, M. (2024). CT-Scan Image Dataset of Residual Fluid-Driven Fracture in a Molasse de Villarlod Sandstone Core - Post-Radial Hydraulic Fracture Experiment - M03 Sample [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.13358916" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13358916</a>).</p> <h4><strong>Processing code</strong></h4> <p>Follow the <strong>URL repositories</strong> below to access to the codes for processing these dataset.</p> <p><a href="https://github.com/GeoEnergyLab-EPFL/ActiveAcoustiX">https://github.com/GeoEnergyLab-EPFL/ActiveAcoustiX</a></p> <p><a href="https://github.com/GeoEnergyLab-EPFL/FracLowRate">https://github.com/GeoEnergyLab-EPFL/FracLowRate</a></p> <p><strong>Contact and Support</strong></p> <p>Email:</p> <p>Brice Lecampion: brice.lecampion@epfl.ch</p> <p>Mohsen Talebkeikhah: m.talebkeikhah@gmail.com</p>
Raw data for NMR-POISE: On-the-fly, Sample-tailored Optimisation of NMR Experiments
<p>The NMR-POISE paper can be found at: <em>Anal. Chem.</em> <strong>2021,</strong> <em>93</em> (31), 10735–10739 (DOI: <a href="https://doi.org/10.1021/acs.analchem.1c01767">10.1021/acs.analchem.1c01767</a>).</p> <p>The majority of one- and multi-dimensional NMR experiments, indispensable to chemists in many areas of research, are often run with generic or "compromise" parameter values that are not optimised. This is particularly problematic when robust, automated acquisition on a variety of samples is desired. Here we present a Python package, NMR-POISE (Parameter Optimisation by Iterative Spectral Evaluation), with full integration into Bruker’s TopSpin software, that utilises feedback control for on-the-fly, sample-tailored optimisation of NMR experiments. POISE provides a highly extensible and user-friendly framework which allows its core optimisation algorithms to be implemented in a wide variety of scenarios.</p> <p>The data attached herein provide examples of optimisation procedures where POISE can be used to great effect. The raw NMR data is attached here, together with all of the scripts used for processing and plotting this data (which can be used to directly regenerate the figures in the manuscript).</p> <p>The raw NMR data is in the "datasets" directory, and the processing scripts in the "figures" directory. The scripts can be run as long as this directory structure is maintained, but require v0.4.1 of the "penguins" Python package: this can be installed using the command "pip install penguins=0.4.1" (without quotes). Please refer to the Supporting Information of the POISE paper for more details, including a full description of the individual datasets.</p> <p><strong>Changelog</strong></p> <p>v1.1.0 of this dataset contains extra raw data and figures added during revision of the manuscript.</p>
Delta-15N values for leaf and soil samples from a mesocosm experiment looking at dung beetle presence and the movement of dung-derived nitrogen (DDN)
<b>Description: </b><p>We deployed 18 mesocosms into each of the ecosystem types (logged forest and oil palm) in mid-May 2016, to give the soil one month to recover from the disturbance. We constructed mesocosms from black plastic containers, 40 cm diameter and 25 cm high after removing the base. We dug mesocosms 20 cm into the ground leaving 5cm above the surface. We arranged them in a 6 x 3 grid with a minimum of 3 m between each mesocosm to minimise interaction between the soil nutrient cycling in each mesocosm. As proximity of the seedlings to mature trees may increase competition for nitrogen and other nutrients we recorded the distance of each mesocosm to the nearest mature tree (any species with diameter at breast height > 30 cm) for inclusion in our analyses. <br>We randomly selected 12 of the mesocosms, to receive 15N-labelled dung patties weighing 300 ± SD 2.27 g in logged forest and 410 ± SD 2.04 g in oil palm. We populated six randomly selected mesocoms from within those 12 treated with dung with a standardised dung beetle communities (Fig. 1, Table S2). The remaining six mesocosms were left as soil only controls. We covered each mesocosm with a fine nylon mesh secured with a rubber belt to prevent beetles leaving or colonising the mesocosms, and to standardise any microclimatic effects between treatments. However, after 48 hours we opened the dung beetle treatments for a 24 hours period to allow the beetles to emigrate rather than forcing them to artificially stay in the same pat (cf. Roslin 2000; Slade et al. 2017), and then re-covered the mesocosms with netting.<br>We sampled soil seven times from logged forest over the course of the experiment. Any remaining surface dung was removed prior to soil sampling, and replaced thereafter, in order to reduce the possibility of contamination. If a soil core was unsuccessful (most likely due to beetle channels) a second core was taken directly beside. On each sample day, a core of 10cm depth was taken and split into vertical horizons 0- 2cm, 2-5 cm and 5-10cm. <br>We sampled leaves eight times over the eight-month duration of the experiment, with high frequency during the first month, aimed to capture the initial assimilation of DDN into the plants. We collected one leaf from the Dipterocarpaceae or palm seedlings for each sample event. For dipterocarp seedlings we alternated collection of the terminal leaf from top and bottom (leaving the topmost, newest leaf) between consecutive sample days, and for palm seedlings we sampled the two penultimate leaflets from alternating sides of the mid-stem, from the youngest fully formed frond. As assimilated 15N did not plateau in logged forest during the 8-month timeframe of the experiment, we took a sample after 21 months in order to determine whether all DDN had been turned over in the plant biomass after this time.<br>The leaf and soil samples were dried at 60°C for a minimum of 48 hours. We then ground samples to a fine powder using a ball mill (Retsch UK Ltd., Hope, UK). We weighed ground samples into 6 x 4 mm ultraclean tin capsules (Elemental Microanalysis Ltd., Okehampton, UK) using an ultra-microbalance with readability 1 μg (Mettler-Toledo, Greifensee, Switzerland) to provide sufficient elemental carbon and nitrogen for analysis by continuous flow isotope ratio mass spectrometry (SERCON, Crewe, UK). <br>Isotope ratios are expressed in per mil (‰) relative to international reference standards (Rstandard), which are Atmospheric Nitrogen and Vienna PeeDee Belemnite (VPDB) for nitrogen and carbon, respectively. The delta value describes the isotopic composition of each sample, which signifies a measurement of difference relative to laboratory standards. The calculation of δ values is given by: <br>δHX = [(RSAMPLE /RSTANDARD −1)]*1000</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/11"><b>Using stable isotopes to link biogeochemical processes to biodiversity of conservation concern</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC (Research grant, NE/K016148/1)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Centre (SABC) (Research licence JKM/MBS.1000-2/2 (374) )</li><li>Sabah Biodiversity Centre (SABC) (Research licence JKM/MBS.1000-2/2 JLD.4 (41))</li><li>Sabah Biodiversity Centre (SABC) (Research licence JKM.1000-2/2 JLD.5 (153))</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=5113431">here</a></p><p><b>Files: </b>This consists of 1 file: 3_Kemp_15N_mesocosms_data.xlsx</p><p><b>3_Kemp_15N_mesocosms_data.xlsx</b></p><p>This file contains dataset metadata and 3 data tables:</p><ol><li><p><b>OP leaf</b> (described in worksheet OP_leaf)</p><p>Description: Details the δ15N of leaves sampled in oil palm from palm seedlings across eight sample days up to day 233. </p><p>Number of fields: 13</p><p>Number of data rows: 144</p><p>Fields: </p><ul><li><b>Name</b>: Code for date (ddmm), mesocosms ID and depth (00 = surface; 02 = 2 cm belowground; 05 = 5 cm belowground, and 10 = 10 cm belowground) (Field type: id)</li><li><b>Day</b>: Experimental day as the number of days since day zero (defined by the planting of the seedlings, either dipterocarps or palms) (Field type: id)</li><li><b>Day2</b>: Experimental day as a factor (Field type: id)</li><li><b>Mesocosm</b>: Unique identifier for each of the 18 mesocosms (Field type: id)</li><li><b>Treatment</b>: Treatment assignment (Field type: categorical)</li><li><b>DistMature</b>: Distance from the nearest mature tree (any species with diameter at breast height > 30 cm). (Field type: numeric)</li><li><b>Weight</b>: Sample weight (Field type: numeric)</li><li><b>Beam.Area.N</b>: Measure of the nitrogen peak i.e. calculated as the area under the nitrogen curve by Calisto software. This value is directly related to N_weight. (Field type: numeric)</li><li><b>ugN</b>: Measure of the elemental nitrogen content of the sample (Field type: numeric)</li><li><b>d15N</b>: the delta value of the sample, which describes the ration of 15N: 14N isotopes (Field type: numeric)</li><li><b>Beam.Area.C</b>: Measure of the carbon peak i.e. calculated as the area under the carbon curve by Calisto software. This value is directly related to C_weight (Field type: numeric)</li><li><b>ugC</b>: Measure of the elemental carbon content of the sample (Field type: numeric)</li><li><b>d13C</b>: the delta value of the sample, which describes the ration of 13C: 12C isotopes (Field type: numeric)</li></ul></li><li><p><b>LFE leaf</b> (described in worksheet LFE_leaf)</p><p>Description: Details the δ15N of leaves sampled in logged forest, taken from dipterocarp seedlings across nine sample days, up to day 625.</p><p>Number of fields: 13</p><p>Number of data rows: 147</p><p>Fields: </p><ul><li><b>Name</b>: Code for date (ddmm), mesocosms ID and depth (00 = surface; 02 = 2 cm belowground; 05 = 5 cm belowground, and 10 = 10 cm belowground) (Field type: id)</li><li><b>Day</b>: Experimental day as the number of days since day zero (defined by the planting of the seedlings, either dipterocarps or palms) (Field type: id)</li><li><b>Day2</b>: Experimental day as a factor (Field type: id)</li><li><b>Mesocosm</b>: Unique identifier for each of the 18 mesocosms (Field type: id)</li><li><b>Treatment</b>: Treatment assignment (Field type: categorical)</li><li><b>DistMature</b>: Distance from the nearest mature tree (any species with diameter at breast height > 30 cm). (Field type: numeric)</li><li><b>Weight</b>: Sample weight (Field type: numeric)</li><li><b>Beam.Area.N</b>: Measure of the nitrogen peak i.e. calculated as the area under the nitrogen curve by Calisto software. This value is directly related to N_weight. (Field type: numeric)</li><li><b>ugN</b>: Measure of the elemental nitrogen content of the sample (Field type: numeric)</li><li><b>d15N</b>: the delta value of the sample, which describes the ration of 15N: 14N isotopes (Field type: numeric)</li><li><b>Beam.Area.C</b>: Measure of the carbon peak i.e. calculated as the area under the carbon curve by Calisto software. This value is directly related to C_weight (Field type: numeric)</li><li><b>ugC</b>: Measure of the elemental carbon content of the sample (Field type: numeric)</li><li><b>d13C</b>: the delta value of the sample, which describes the ration of 13C: 12C isotopes (Field type: numeric)</li></ul></li><li><p><b>LFE soil</b> (described in worksheet LFE_soil)</p><p>Description: Details the δ15N of soil sampled in logged forest across seven sample days up to day 64</p><p>Number of fields: 14</p><p>Number of data rows: 375</p><p>Fields: </p><ul><li><b>Name</b>: Code for date (ddmm), mesocosms ID and depth (00 = surface; 02 = 2 cm belowground; 05 = 5 cm belowground, and 10 = 10 cm belowground) (Field type: id)</li><li><b>Day</b>: Experimental day as the number of days since day zero (defined by the planting of the seedlings, either dipterocarps or palms) (Field type: id)</li><li><b>day2</b>: Experimental day as a factor (Field type: id)</li><li><b>Mesocosm</b>: Unique identifier for each of the 18 mesocosms (Field type: id)</li><li><b>Treatment</b>: Treatment assignment (Field type: categorical)</li><li><b>DistMature</b>: Distance from the nearest mature tree (any species with diameter at breast height > 30 cm). (Field type: numeric)</li><li><b>Depth</b>: The depth which the soil sample was taken from, i.e. 0002 is the horizon between the ground surface and 2 cm belowground (Field type: numeric)</li><li><b>Weight</b>: Sample weight (Field type: numeric)</li><li><b>Beam.Area.N</b>: Measure of the nitrogen peak i.e. calculated as the area under the nitrogen curve by Calisto software. This value is directly related to N_weight. (Field type: numeric)</li><li><b>ugN</b>: Measure of the elemental nitrogen content of the sample (Field type: numeric)</li><li><b>d15N</b>: the delta value of the sample, which describes the ration of 15N: 14N isotopes (Field type: numeric)</li><li><b>Beam.Area.C</b>: Measure of the carbon peak i.e. calculated as the area under the carbon curve by Calisto software. This value is directly related to C_weight (Field type: numeric)</li><li><b>ugC</b>: Measure of the elemental carbon content of the sample (Field type: numeric)</li><li><b>d13C</b>: the delta value of the sample, which describes the ration of 13C: 12C isotopes (Field type: numeric)</li></ul></li></ol><p><b>Date range: </b>2016-05-01 to 2017-02-01</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p>
Radial Hydraulic Fracturing Experiment: 3 Cycles of Fracture Propagation, Arrest, and Closure in Molasse de Villarlod Sandstone - Sample M04
<h4><strong>Overview</strong></h4> <p>This dataset encompasses detailed measurements from a lab-scale radial hydraulic fracturing experiment conducted on a cubic sample of Molasse de Villarlod sandstone, designated as Sample M04. The sandstone, sourced from a quarry in Fribourg, Switzerland, is known for its porosity (18.1%) and permeability, making it an ideal material for studying hydraulic fracture processes. The primary focus of the experiment was to observe and analyze the propagation, arrest, and closure of hydraulic fractures under controlled triaxial stress conditions.</p> <h4><strong>Experimental Setup</strong></h4> <p>The experiment was conducted on a cubic sandstone sample with dimensions of 25 × 25 × 25 cm. The sample was placed in a truetriaxial frame that applied confining stresses in all three principal directions:</p> <ul> <li><strong>Vertical Confining Stress:</strong> 5 MPa</li> <li><strong>Horizontal Confining Stress:</strong> 15 MPa</li> </ul> <p>A <strong>viscous glycerol fluid (0.57 Pa.s) containing a UV additive</strong> was used as the fracturing fluid. This fluid was injected through a 1/8'' high-pressure tube cemented with epoxy into a centrally drilled hole within the sample. An axisymmetric notch was created at the injection point to facilitate fracture initiation and promote the planarity of the fracture.</p> <p>The experiment was designed to simulate three cycles of fracture initiation, propagation, arrest, and closure. The closure of the fracture was occured by the leakoff of the fracturing fluid into the surrounding porous medium.</p> <h4><strong>Acoustic Monitoring</strong></h4> <p>To capture the dynamics of fracture propagation and closure, the experiment employed both passive and active acoustic monitoring systems:</p> <ol> <li> <p><strong>Passive Acoustic Monitoring:</strong></p> <ul> <li><strong>Sensors:</strong> 16 Vallen VS150-M passive piezoelectric sensors were used to capture Acoustic Emissions (AEs) within the frequency range of 50 kHz to 600 kHz.</li> <li><strong>Signal Processing:</strong> Continuous signal analysis and denoising were performed on the captured AE data. The STA/LTA algorithm was applied to the denoised signal to identify potential p-wave arrivals, providing insights into the fracture mechanics.</li> </ul> </li> <li> <p><strong>Active Acoustic Monitoring:</strong></p> <ul> <li><strong>Transducers:</strong> A total of 64 piezoelectric transducers were integrated into the loading platens, with 32 acting as sources and 32 as receivers. The array included 10 shear-wave and 54 longitudinal-wave transducers.</li> <li><strong>Signal Generation and Acquisition:</strong> A Ricker excitation signal with a central frequency adjustable between 300 and 750 kHz was generated and amplified using a high-power amplifier. The signal was routed to one of the 32 source transducers via a multiplexer, and the resulting signals were recorded simultaneously by the 32 receiver transducers at a sampling frequency of 50 MHz. Each source was excited 50 times to improve the signal-to-noise ratio, with the complete acquisition sequence taking approximately 2.5 seconds.</li> </ul> </li> </ol> <h4><strong>Additional Measurements</strong></h4> <p>In addition to acoustic monitoring, several other key measurements were recorded during the experiment:</p> <ul> <li><strong>Fluid Injection Parameters:</strong> The pressure and rate of fluid injection were continuously monitored.</li> <li><strong>Flat-Jack and Piston Parameters:</strong> The pressures and volumes exerted by each pair of flat-jacks were recorded at a frequency of 1 Hz.</li> <li><strong>Fracture Opening Measurement:</strong> An eddy current sensor, an electromagnetic inductive device, was placed inside the wellbore at the notch/inlet to directly measure the fracture opening.</li> </ul> <p>All measurements were synchronized using a dedicated LabView application to ensure consistency across the dataset.</p> <h4><strong>Conclusion</strong></h4> <p>This dataset provides a comprehensive view of the hydraulic fracturing behavior of Molasse de Villarlod sandstone under controlled laboratory conditions, with a focus on the processes of fracture propagation, arrest, and closure. The dataset includes raw and processed acoustic data, fluid injection metrics, and direct observations of fracture opening. It is an invaluable resource for researchers and engineers studying hydraulic fracturing, rock mechanics, and related fields. The data is suitable for detailed analysis and modeling of fracture mechanics in porous, permeable sandstones.</p> <h4><strong>Note</strong></h4> <p>Since the fracture did not extend to the boundaries of the sample, the sample was subsequently cut, and a core was extracted. This core was then sent for CT-scan analysis, which was used to reconstruct the residual fracture surfaces and assess their roughness. The dataset from this analysis is available in the Related Work section via the provided URL (Talebkeikhah, M. (2024). CT-Scan Image Dataset of Residual Fluid-Driven Fracture in a Molasse de Villarlod Sandstone Core - Post-Radial Hydraulic Fracture Experiment - M04 Sample [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.13357511" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13357511</a>).</p> <h4><strong>Processing code</strong></h4> <p>Follow the <strong>URL repositories</strong> below to access to the codes for processing these dataset.</p> <p><a href="https://github.com/GeoEnergyLab-EPFL/ActiveAcoustiX">https://github.com/GeoEnergyLab-EPFL/ActiveAcoustiX</a></p> <p><a href="https://github.com/GeoEnergyLab-EPFL/FracLowRate">https://github.com/GeoEnergyLab-EPFL/FracLowRate</a></p> <p><strong>Contact and Support</strong></p> <p>Email:</p> <p>Brice Lecampion: brice.lecampion@epfl.ch</p> <p>Mohsen Talebkeikhah: m.talebkeikhah@gmail.com</p>
Vac Sampling arthropod community:The Small Biodiversity Experiment
Biodiversity I (E123), also called the ?small biodiversity experiment,? was designed to determine how the number of species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year that result can be examined. Also, the effects of number of species on carbon and nitrogen in the soil and on the ability of other species to invade can be studied. The experiment contains 147 3 x 3m plots that were randomly allocated 1, 2, 4, 6, 8, 12 or 24 plant species. The particular species in a plot were randomly selected from a set of 24 prairie-grassland species which included seven warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, nine non-legume forbs. Each level of number of species has 20 to 24 replicates. In this experiment not all of the species are in monocultures. The study was established in 1994 by lead investigators David Tilman, David Wedin, Peter Reich, and Johannes Knops. Experiment 123 is similar to Experiment 120, but it uses smaller plots and did not categorize by type of plant species prior to randomizing species to plots. This size of plot in Experiment 123 means that the soils are relatively more homogeneous and the desired number of species can be more easily maintained by frequent hand weeding than with larger plots. However, the small size of plots limits sampling and the nesting of other studies within the plots.
Increased time sampling in an evolve-and-resequence experiment with outcrossing Saccharomyces cerevisiae reveals multiple paths of adaptive change
<p>"Evolve and resequence" (E&R) studies combine experimental evolution and whole-genome sequencing to interrogate the genetics underlying adaptation. Due to ease of handling, E&R work with asexual organisms like bacteria can employ optimized experimental design, with large experiments and many generations of selection. By contrast, E&R experiments with sexually reproducing organisms are more difficult to implement, and design parameters vary dramatically among studies. Thus, efforts have been made to assess how these differences, such as number of independent replicates, or size of experimental populations, impact inference. We add to this work by investigating the role of time sampling – the number of discrete timepoints sequence data is collected from evolving populations. Using data from an E&R experiment with outcrossing <i>Saccharomyces cerevisiae </i>in which populations were sequenced 17 times over ~540 generations, we address the following questions: (i) do more timepoints improve the ability to identify candidate regions underlying selection? And (ii) does high-resolution sampling provide unique insight into evolutionary processes driving adaptation? We find that while time sampling does not improve the ability to identify candidate regions, high-resolution sampling does provide valuable opportunities to characterize evolutionary dynamics. Increased time sampling reveals three distinct trajectories for adaptive alleles: one consistent with classic population genetic theory (i.e. models assuming constant selection coefficients), and two where trajectories suggest more context-dependent responses (i.e. models involving dynamic selection coefficients). We conclude that while time sampling has limited impact on candidate region identification, sampling 8 or more timepoints has clear benefits for studying complex evolutionary dynamics.</p>
Data from: Disparity in preemptive end-of-life conversation experience caused by subjective economic status among general Japanese elderly people: a cross-sectional study with stratified random sampling
Objectives: Preemptive conversations (PCs) about end-of-life (EOL) preferences are beneficial for both elderly people and their families to understand and share the preferences. However, the factors which promote/inhibit PCs have yet to be clarified. We therefore aimed to determine the factors related to having PCs with hypothesis that age, subjective economic status and subjective health status are associated with having PC experience. Design: A cross-sectional study administering a questionnaire and using stratified random sampling by gender and region. Setting: Residents aged 65 years or older who were not receiving nursing care as of November 1, 2016, were extracted from the Japanese long-term care insurance system registry in Koriyama City, Fukushima Prefecture, Japan. Participants: 1,575 participants (717 males and 858 females). Outcome: Presence or absence of PC experience with family or friends (yes/no). Results: The mean age of the participants was 74.0 years. A multivariable logistic-regression analysis revealed that having PC experience was significantly associated with gender (OR = 1.907; 95% CI = 1.556, 2.337; p < 0.001), subjective economic status (OR = 0.832; 95% CI = 0.716, 0.966; p=0.016), and subjective happiness (OR = 0.926; 95% CI = 0.880, 0.973; p=0.003). Conclusions: Poor subjective economic status of elderly people may result in absence of EOL conversation experience with their families and friends, hindering the elderly from sharing and understanding the EOL preferences. To promote PCs about EOL, gerontology and public health professionals should give special consideration to the subjective economic status of elderly people.
Experimental dataset: stereo-DIC experiment on uniaxially loaded, S-Shaped, high density polyethylene test sample
<p>Stereo-DIC experiments were performed on uniaxially loaded, s-shaped, high-density polyethylene test sample. 100 stationary images of unloaded test sample were taken for evaluation of DIC noise floor. Stereo calibration image dataset, involving a calibration target with rectangular grid (12 by 9 and pitch of 10 mm), is also made available. Tensile load from the test bench load-cell sampled at each moment an image is captured is available. </p>
Data for collected samples and mating experiment of Acilius Japonicus
<p>Previous studies have predicted that antagonistic intraspecific evolution of sexually dimorphic characters causing rapid speciation can be driven by demographic history and environmental variations. However, researchers have rarely examined this issue in the wild. Here, we examined intraspecific evolution of sexually dimorphic characters and its driving force by using a diving beetle, <em>Acilius japonicus</em>, which has very marked sexually dimorphic characters. Males with wider big suction cups could copulate with females with a higher success rate, whereas the mating durations of females with more hairs on their pronota were shorter. Females in a region with greater interpopulation genetic differentiation had more pronotal hairs. Considering that a previous study showed that less continuity among populations leads to a higher female cost of mating, this result suggests a greater female cost of mating in this region. Females at warmer sites also had more pronotal hairs. In light of the increase in O<sub>2</sub> consumption in warmer water, our result suggests that more pronotal hairs in females at warmer sites have been maintained to prevent prolonged underwater mating at higher O<sub>2</sub> demand. These findings suggest that demographic history and temperature can direct the evolution of sexually dimorphic characters related to sexual conflict in females.</p>
Experiments for 'Sampling-Based Verification of CTMCs with Uncertain Rates'
<p>This artifact accompanies the CAV 2022 paper with the same title. The artifact contains a docker file, which can be unzipped and then loaded with:</p> <pre><code>docker load -i slurf_cav22_docker.tar</code></pre> <p>Please refer to the README for more information.</p>
A 10-day experience sampling dataset on subjective experiences of middle and secondary school students in 2022
<p>This data note presents a dataset from a 10-day experience sampling study conducted among middle and secondary school students in 2022. The dataset includes a brief start-up survey in addition to 40 momentary assessments, enabling analysis of both inter- and intraindividual processes. The start-up survey includes validated measures on students’ perceptions of school-related support from family, peers, and teachers, general self-efficacy, and academic self-efficacy, as well as items on school enjoyment and school absenteeism. The experience sampling data includes items related to positive and negative emotions, lecture characteristics, perceived teacher style, peer relationships as well as sleep quality, breakfast, school readiness, and school day satisfaction. Data were collected using the "RealLife Exp" mobile app. Multiple procedures were undertaken to validate and confirm the reliability of the dataset. The methodological approach utilized for gathering the data featured in this dataset allows for a nuanced analysis of individual and contextual influences along with lagged effects on school well-being, with the potential to discover previously overlooked patterns and trends.</p>
Validation of the Experience Sampling Dietary Assessment Method
ClinicalTrials.gov study NCT06485960. IPD Sharing: NO. Countries: 1. Publications: 1.
Experience Sampling Method in OAB Patients
ClinicalTrials.gov study NCT03435991. IPD Sharing: UNDECIDED. Countries: 1. Publications: 29.
Ant abundance in pitfall traps, subterranean traps, arboreal traps, and Winkler samples at the Territory Wildlife Park experiment
Open the record for dataset details and reuse information.
Data from: Disparity in preemptive end-of-life conversation experience caused by subjective economic status among general Japanese elderly people: a cross-sectional study with stratified random sampling
Open the record for dataset details and reuse information.
Increased time sampling in an evolve-and-resequence experiment with outcrossing Saccharomyces cerevisiae reveals multiple paths of adaptive change
Open the record for dataset details and reuse information.
Data for collected samples and mating experiment of Acilius Japonicus
Open the record for dataset details and reuse information.
Freezing nucleus spectra for hailstone samples in China from droplet freezing experiments—Data
<p>INP concentrations in hailstone samples from droplet freezing experiments.</p>
Tracing low-CO2 fluxes in soil incubation and 13C labeling experiments: a simplified gas sampling system for respiration and photosynthesis measurements
<p>Data set containing data from feature tests (1-3) as well as photosynthesis and respiration measurements.</p>
Spatial Transcriptomic Experiment of Triple-Negative Breast Cancer PDX Model PIM001-P model treatment naive sample
<p>Spatial Transcriptomic Experiment of Triple-Negative Breast Cancer PDX Model PIM001-P model treatment naive sample</p> <p>10X Genomics Visium platform. </p> <p><strong>Library Preparation and Sequencing of PIM001P</strong></p> <p>Tissue sections of 10µm thickness were mounted onto the capture areas of the Visium Spatial Gene Expression slide and stained using hematoxylin and eosin. Tissue sections were permeabilized on a thermocycler for 24 minutes, as determined by the Tissue Optimization step. Poly-adenlyated mRNA is released and captured by surface-bound primers within each capture area. Reverse transcription, template switching, extension, and second strand synthesis are performed on the slide. Full-length, spatially barcoded cDNA transcripts are then denatured from the slide and amplified via PCR prior to library construction. Approximately 110 to 375 ng of amplified cDNA was carried forward into library construction. During library construction, cDNA is enzymatically fragmented to target amplicon size then undergoes end repair, A-tailing, adapter ligation, and then amplified using between 14 and 16 PCR cycles. The resulting libraries were quantitated using the Invitrogen Qubit 2.0 quantitation assay and fragment size assessed with the Agilent Bioanalyzer. A qPCR quantitation was performed on the libraries to determine the concentration of adapter ligated fragments using Applied Biosystems ViiA7 Real-Time PCR System and a KAPA Library Quant Kit (p/n KK4824). All samples were pooled equimolarly and re-quantitated by qPCR, and also re-assessed on the Bioanalyzer.</p> <p><strong>Sequencing: </strong></p> <p>150 pM of equimolarly pooled library was loaded onto the NovaSeq 6000 S4 flowcell and sequenced at the recommended 28-10-10-50 read configuration. PhiX Control v3 adapter-ligated library (Illumina p/n FC-110-3001) was spiked-in at 2% by weight to ensure balanced diversity and to monitor clustering and sequencing performance. A minimum of 300 million read pairs per sample was sequenced. FastQ file generation was executed using 10X Genomics’ Space Ranger mkfastq software.</p> <p> </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.