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1,819 results for “experimental data”
Monthly precipitation data from a network of standard gauges at the Jornada Experimental Range (Jornada Basin LTER) in southern New Mexico, January 1916 - ongoing
This ongoing dataset contains monthly precipitation measurements from a network of standard can rain gauges at the Jornada Experimental Range in Dona Ana County, New Mexico, USA. Precipitation physically collects within gauges during the month and is manually measured with a graduated cylinder at the end of each month. This network is maintained by USDA Agricultural Research Service personnel. This dataset includes 39 different locations but only 29 of them are current. Other precipitation data exist for this area, including event-based tipping bucket data with timestamps, but do not go as far back in time as this dataset.
Jornada Basin and Experimental Range Mesquite Herbicide Project (JERHM) Core Methods Data, 2020-2022
This dataset includes contains line-point intercept, plant height, gap, and species inventory data collected over three years (2020-2022) as part of the Jornada Experimental Range Herbicide Mesquite Project (JERHM). Data were collected to assess plant community composition and structural change to herbicide application across a Black grama (Bouteloua eriopoda) grassland to Honey mesquite (Neltuma glandulosa [=Prosopis glandulosa]) shrubland encroachment gradient. Twenty sets of paired, 5-hectare plots (n=40 plots total) were established across a N. glandulosa encroachment gradient in 2020. One plot within each plot pair received an aerial application of herbicide in 2021, with the second plot left untreated by herbicide as a control. Data were collected annually following the following the Monitoring Manual for Grassland, Shrubland, and Savanna Ecosystems (Herrick et al. 2017) on each of three, 50m permanent transects established on each plot. These data are also available within the Landscape Data Commons (https://landscapedatacommons.org/) under ProjectKey=Jornada_JERHM. There are no immediate plans to continue data collection.
Litterfall in tabonuco (subtropical wet) forest in the Luquillo Experimental Forest, Puerto Rico (MRCE Litterfall data)
Treatments common to both sites are quarterly fertilization (macro- and micronutrients) and unmanipulated. At El Verde, a third set of plots was subject to a one time removal of litter and woody debris generated by Hurricane Hugo (September 1989). Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Experimental Understory Food Web data in the El Verde area of the Luquillo Experimental Forest
These data include date, treatment, block number, number of coquies, number of anoles, number of insects collected on two sticky traps, number of insects counted on 4 Piper glabrescens and 4 Manilkara bidentata seedlings, percent herbivory on the aforementioned plants and number of spiders. All these measurements were taken within exclosures for closed controls, anole exclusions, coqui exclusions and total exclusions. Open controls were sampled from an area of similar dimension not enclosed in an exclosure. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Long-Term Elevation Plots (LTEP) (Altitudinal transects vegetation data along three rivers in the Luquillo Experimental Forest)
The composition of plant communities changes with elevation in the Luquillo Experimental Forest (LEF). The goal of this project is to document the patterns of these changes, and in particular, to determine whether the distributions of individual species are independent of one another, or whether they are related, in either a congruent or a hierarchical manner. Thirty-two permanent vegetation plots, each 50m by 20m are being established in the LEF, with 5 plots along the Icacos river, 11 along the Mamayes river and 16 along the Sonadora stream. The plots were established at every 100m in elevation, starting at 200m above sea level. All woody, free-standing stems greater than 1cm dbh were marked, identified and mapped into 5x5 subquadrats. We anticipated that gradient analysis will show whether the distributions of species are coincident or independent, enabling us to evaluate whether separate, genuine plant communities exist in the LEF. Because the plots are permanent, we also expected that they allow us to better evaluate how different vegetation types, at different elevations, respond to large scale disturbances, especially hurricanes. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Daily Meteorological data from East Peak, Sabana the Luquillo Experimental Forest
This data publication contains daily means from laser ceilometer data from Sabana and the mountain top station of East Peak, additional data for ozone data from Bisley, and meteorological data collected from Bisley are available from the USDA Forest Service Data Archive at https://doi.org/10.2737/RDS-2022-0050 All sites are located in the Luquillo Experimental Forest (El Yunque National Forest) in Puerto Rico. Atmospheric data include: mean cloud observed frequency, mean lowest height cloud (cloud base), mean daytime mixing layer height observed frequency, mean lowest mixing layer height from hours 7am and 7pm only, and mean daytime mixing layer height collected from February 2013 through April 2021. Also included is mean ozone amount collected from April 2008 through early April 2021. The cloud and mixing layer frequency and low values were calculated using the Automated Surface Observing System (ASOS) method employed at airports in the area. Weather data include high elevation East Peak mean northeast wind speed (wind rose quartiles 45° to 90°), mean southeast wind speed (wind rose quartiles 90° to 135°), and mean total wind speed measured from October 2009 through 2020 (and a few months in 2021). Additional weather data collected from January 2002 through mid October 2020 include mid elevation Bisley mean precipitation, mean relative humidity, and mean temperature. A subset of these data are here: https://doi.org/10.2737/RDS-2022-0050 please use this DOI when citing this dataset. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Luquillo Experimental Forest atmospheric and high and mid elevation weather data.
The data archive is here:https://doi.org/10.2737/RDS-2022-0050 please use this DOI when citing this dataset. This data publication contains daily means from laser ceilometer data from Sabana, ozone data from Bisley, and meteorological data collected from Bisley and the mountain top station of East Peak, all located on the Luquillo Experimental Forest (El Yunque National Forest) in Puerto Rico. Atmospheric data include: mean cloud observed frequency, mean lowest height cloud (cloud base), mean daytime mixing layer height observed frequency, mean lowest mixing layer height from hours 7am and 7pm only, and mean daytime mixing layer height collected from February 2013 through April 2021. Also included is mean ozone amount collected from April 2008 through early April 2021. The cloud and mixing layer frequency and low values were calculated using the Automated Surface Observing System (ASOS) method employed at airports in the area. Weather data include high elevation East Peak mean northeast wind speed (wind rose quartiles 45° to 90°), mean southeast wind speed (wind rose quartiles 90° to 135°), and mean total wind speed measured from October 2009 through 2020 (and a few months in 2021). Additional weather data collected from January 2008 through mid October 2020 include mid elevation Bisley mean precipitation, mean relative humidity, and mean temperature. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Frog grid data (Bisley Experimental Watershed)
Population estimates from 1987 to 1995 are reported for the terrestrial anuran, Eleutherodactylus coqui, from four long-term study plots in the Luquillo Experimental Forest of northeastern Puerto Rico. The major factor influencing population size during this time was Hurricane Hugo, which deposited much of the canopy onto the forest floor in 1989. Population densities since Hurricane Hugo have been influenced by succession, with continued high densities associated with thickets of Cecropia and Heliconia. Trefalls, which are similar to hurricanes on a local scale, also were shown to influence population sizes. Years with prolonged dry periods reduced numbers of juvenile frogs, but rainfall patterns alone did not explain most population variation. Population levels of invertebrate predators were related to variation in frog numbers. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Experimental data of the paper "Trial-based Heuristic Tree Search for MDPs with Factored Action Spaces"
<p>This data set contains the code of our planner and of the planner that was used as baseline, the benchmark set that was used to perform experiments as well as the parsed values and basic reports that are reported in the paper. More information can be found in the README that is also included.</p>
Experimental data for PanDDA analysis of the bromodomain of human FALZ
<p>The repository contains processed data from the entire crystallographic fragment screen of the bromodomain of human nucleosome-remodeling factor subunit BPTF (FALZ). Crystals of FALZ were screened against the DSPL and 3D-Fragment Consortium Libraries by X-ray Crystallography at the XChem facility of Diamond Light Source beamline I04-1 (FALZ_XChem_screen.tar.bz2). Additionally, metadata about the experiment can be found in the <em>mainTable</em> of the corresponding SQLite database file (FALZ_XChem_screen.sqlite). All identified ligand-bound structures were deposited in the Protein Data Bank under Group ID <strong><a href="https://www.rcsb.org/search/structure?q=pdbx_deposit_group.group_id:G_1002123">G_1002123</a></strong>. The individual PDB codes are:</p> <ul> <li>FALZA-x0079 5R4G</li> <li>FALZA-x0085 5R4H</li> <li>FALZA-x0172 5R4I</li> <li>FALZA-x0177 5R4J</li> <li>FALZA-x0271 5R4K</li> <li>FALZA-x0309 5R4L</li> <li>FALZA-x0402 5R4M</li> <li>FALZA-x0438 5R4N</li> </ul> <p>All structures necessary to reproduce the deposited PanDDA event maps which were used for ligand identification were deposited in the Protein Data Bank under PDB ID <a href="https://www.rcsb.org/structure/5R4O">5R4O</a> (group ID <strong><a href="https://www.rcsb.org/search/structure?q=pdbx_deposit_group.group_id:G_1002124">G_1002124</a></strong>).</p> <p> </p> <p><strong><em>Usage:</em></strong></p> <p>download <em>FALZ_XChem_screen.tar.bz2</em> and save into the desired project directory, e.g.</p> <pre><strong>/home/me/FALZ</strong></pre> <p>unpack the tar archive:</p> <pre><strong>tar –xvjf FALZ_XChem_screen.tar.bz2</strong></pre> <p>run pandda, e.g.</p> <pre><strong>pandda.analyse data_dirs="/home/me/FALZ/*" out_dir="/home/me/FALZ_pandda" pdb_style=dimple.pdb mtz_style=dimple.mtz</strong></pre> <p>For more information about PanDDA, please check the <a href="http://www.ccp4.ac.uk/html/pandda.html">PanDDA CCP4 program documentation</a>.</p> <p> </p> <p><em><strong>Reference:</strong></em></p> <p>Pearce, N. M. <em>et al.</em> A multi-crystal method for extracting obscured crystallographic states from conventionally uninterpretable electron density. <em>Nature Communications</em> <strong>8</strong>, ncomms15123 (2017).</p> <p> </p>
Experimental data for PanDDA analysis of human JMJD1B
<p>The repository contains processed data from the entire crystallographic fragment screen of human JMJD1B at the XChem facility of Diamond Light Source beamline I04-1 (JMJD1BA_XChem_screen.tar.bz2). Additionally, metadata about the experiment can be found in the <em>mainTable</em> of the corresponding SQLite database file (JMJD1BA_XChem_screen.sqlite). All identified ligand-bound structures were deposited in the Protein Data Bank under Group ID <strong><a href="https://www.rcsb.org/search/structure?q=pdbx_deposit_group.group_id:G_1002146">G_1002146</a>. </strong>All structures necessary to reproduce the deposited PanDDA event maps which were used for ligand identification were deposited in the Protein Data Bank under PDB ID <a href="https://www.rcsb.org/structure/5R7X">5R7X</a> (group ID <strong><a href="https://www.rcsb.org/search/structure?q=pdbx_deposit_group.group_id:G_1002141">G_1002141</a></strong>).</p> <p> </p> <p><strong><em>Usage:</em></strong></p> <p>download <em>JMJD1BA_XChem_screen.tar.bz2</em> and save into the desired project directory, e.g.</p> <p><strong>/home/me/JMJD1B</strong></p> <p>unpack the tar archive:</p> <p><strong>tar –xvjf JMJD1BA_XChem_screen.tar.bz2</strong></p> <p>run pandda, e.g.</p> <p><strong>pandda.analyse data_dirs="/home/me/JMJD1B/*" out_dir="/home/me/JMJD1B_pandda" pdb_style=dimple.pdb mtz_style=dimple.mtz</strong></p> <p>For more information about PanDDA, please check the <a href="http://www.ccp4.ac.uk/html/pandda.html">PanDDA CCP4 program documentation</a>.</p> <p> </p> <p><strong><em>Reference:</em></strong></p> <p>Pearce, N. M. <em>et al.</em> A multi-crystal method for extracting obscured crystallographic states from conventionally uninterpretable electron density. <em>Nature Communications</em> <strong>8</strong>, ncomms15123 (2017).</p> <p> </p>
TERMINUS WP4: Enzyme immobilization, protection, and triggering. TASK 4.2: Experimental data
<p>A. E. Delorme, J.-M. Andanson and V. Verney: Improving Laccase Thermostability with aqueous Natural Deep Eutectic Solvents, <em>Int. J. Biol. Macromol.</em>, (2020), <a href="https://doi.org/10.1016/j.ijbiomac.2020.07.022">doi.org/10.1016/j.ijbiomac.2020.07.022</a></p> <p><strong>Abstract</strong></p> <p>The wide-spread use of laccases in industry is often limited due to the enzyme inactivation over time at conditions which exceeds the operating conditions of the enzymes, which are neutral pH and ambient temperatures (30-40 °C). Today, the most common strategies used to improve enzyme stability are chemical modifications and immobilization of enzymes on solid supports. Although, these techniques have shown promise in improving enzyme stability, they are often synthetically demanding and unsustainable in terms of costs and synthesis route. Deep Eutectic Solvents (DESs) have attracted considerable attention as reaction media in biocatalysis due to their promising compatibility with enzymes and sustainable derivation. In this contribution we demonstrate the possibility of applying DESs as incubation media to inhibit thermal inactivation of laccase T. Versicolor. For example we show that by incubating laccase in 25 wt% of a betaine-xylitol based DES at 70 °C for 15 minutes, the measured residual activity of laccase is a near 10 fold greater than the measured residual activity of laccase when incubated without the DES. The drastic enhancement of the enzyme thermostability by pre-incubation of laccase in DES media showcases a facile, cheap and green method of boosting the stability laccase.</p> <p> </p> <p><strong>Dataset</strong></p> <p>This dataset contains all the UV-kinetic raw data used to calculate the laccase activity in the article “Improving Laccase Thermostability with aqueous Natural Deep Eutectic Solvents”. Data are available in a compressed .zip file with 1 folder (Laccase-thermostability-DES_v1.0_TER_WP4_D4-2) containing 3 files:</p> <p> </p> <ul> <li>one tabular file saved in .xlsx format containing all UV-kinetic raw data used to calculate the relative and residual laccase activities for figure 1-6 in the article (<a href="https://doi.org/10.1016/j.ijbiomac.2020.07.022">doi.org/10.1016/j.ijbiomac.2020.07.022</a>). The Laccase-thermostability-DES_v1.0_TER_WP4_D4-2.xlsx file contains the UV kinetic absorption spectra (at wavelength 417 nm) and each row in represent one spectrum. The spectra are grouped under laccase incubation temperature and length of time of incubation. For each incubation time, three solutions were prepared which signifies the three trials under each incubation times. Each sheet in the .xlsx file represent the data set collected for each laccase incubation medium</li> <li>The Materials_and_experimental_method-D4-2-Laccase-Thermostability-DES.pdf file details the experimental method and conditions for the data acquisition presented in the Laccase-thermostability-DES_v1.0_TER_WP4_D4-2.xlsx. Guidance is also provided on how to use the data to calculate the laccase activity and thermostability.</li> <li>The_metadata_information-D4-2-Laccase-Thermostability-DES.pdf includes more detailed metadata information for the dataset represented in the excel file Laccase-thermostability-DES_v1.0_TER_WP4_D4-2.xlsx.</li> </ul>
Experimental data for bulk valley transport and Berry curvature spreading at the edge of flat bands
<p>This dataset was used in our study of bulk valley transport and Berry curvature spreading at the edge of flat bands in twisted double bilayer graphene.</p>
TERMINUS WP4 Enzyme immobilization, protection, and triggering. TASK 4.1 TASK 4.2 Experimental data – Hydrolytic enzyme immobilization and triggering
<p>The use of polymer-degrading enzymes is an attractive and effective method for the management of plastic waste. Synthetic polyesters such as poly(ethylene terephthalate) (PET) or polyurethane (PUR) have been shown to be susceptible to enzymatic degradation by microbial polyester hydrolases, as well as biopolyesters such as poly(lactic acid) (PLA), poly(butylene succinate) (PBS), and polycaprolactone (PCL). However, raw enzymes are not used in polymer formulations because the high processing temperatures would deteriorate the proteins (whose enzymes are made of), by destroying their macromolecular structure and catalytic center. The possibility of a direct use of enzyme in a polymer formulation thorough an opportune protective system, able to preserve the activity of the enzyme and increase its thermal stability, could open to new materials degradable “on-demand” at the end-of life. Therefore, significant progresses could be possible for example in the field of plastic packaging, which currently represents 40% of the total production of plastic in EU and requires the consumption of more than 19 million tons of oil and gas.</p> <p>This dataset includes some of the experimental raw data presented by UNIBO in deliverable D4.1 and D4.3, namely FT-IR analysis, X-ray diffraction analysis, TGA analysis, UV-Vis spectrophotometer. Data are available in a compressed .zip file with 1 folder (Hydrolytic enzyme immobilization and triggering_v1.0_TER_WP4_D4.1_D4.3) containing 6 files, 4 .xlsx files containing the FT-IR, XRD, TGA, Enzyme release kinetics and Thermostability raw data, 1 .pdf file describing the experimental methods and materials and a second .pdf file outlining the metadata and information.</p> <p>1_Hydrolytic enzyme immobilization and triggering _FTIR.xlsx contains all the FTIR raw data and curves of the Immobilized enzyme systems prepared.</p> <p>2_Hydrolytic enzyme immobilization and triggering _XRD.xlsx contains all the XRD raw data and profiles of the Immobilized enzyme systems prepared.</p> <p>3_Hydrolytic enzyme immobilization and triggering _TGA.xlsx contains all the TGA raw data and curves of the Immobilized enzyme systems prepared.</p> <p>4_Hydrolytic enzyme immobilization and triggering release activity and thermal resistance.xlsx contains all the raw data and graphs related to the protein content, activity of the Immobilized enzyme systems prepared after release and the thermal stress experiment data.</p> <p>Materials and experimental method_D4.1_D4.3_Hydrolytic enzyme immobilization and triggering.pdf contains the details of the experimental method and conditions for the data acquisition presented in the data set.</p> <p>Metadata information for WP4 dataset_D4.1_D4.3_Hydrolytic enzyme immobilization and triggering.pdf includes more detailed metadata information for the dataset presented.</p>
Storage enhanced nonlinearities in a cold atomic Rydberg ensemble: experimental data
<p>The data show number of input/output photons under different conditions when coherent pulses of light undergo electromagnetically induced transparency (EIT) in a cold cloud of Rubidium 87 atoms via a ladder system connecting the ground state of 87-Rubidium and different Rydberg levels via (see more details in Distante et al. Phys. Rev. Lett. <strong>117</strong>, 113001 (2016) or in the preprint https://arxiv.org/abs/1605.07478)</p> <p>This is the pre-analysed data from which the results in the paper are derived.</p> <p> </p> <ul> <li>The ODS file contains different sheets which correspond to Rydberg states with different principal quantum numbers</li> <li>The PDF contains useful information regarding the conditions of the experiment under which the data was obtained, such as the optical depth (OD) of the cloud, its dimensions, and the Rabi frequency of the coupling beam.</li> </ul>
Raw experimental data for `Tailoring the Rotational Memory Effect in Multimode Fibers`
<p>Raw data for the article [**Tailoring the Rotational Memory Effect in Multimode Fibers**](https://arxiv.org/abs/2310.19337)</p><p>Measurement of transmission matrices and rotational memory effect for 4 segments of 50 micron core graded index multimode fibers with a numerical aperture of 0.2.</p>
Supplementary Information for "Biocatalytic Ether Lipid Synthesis by an Archaeal Glycerolprenylase" - Experimental Data
<p>This is the external Supplementary Information for our publication "Biocatalytic Ether Lipid Synthesis by an Archaeal Glycerolprenylase", freely available as a preprint from <em>ChemRxiv </em>(<a href="https://doi.org/10.26434/chemrxiv-2024-2lmv8-v2">https://doi.org/10.26434/chemrxiv-2024-2lmv8-v2</a>) and as a peer-reviewed publication from <em>Angewandte Chemie</em> (<a href="https://doi.org/10.1002/anie.202412597">https://doi.org/10.1002/anie.202412597</a>).</p> <p>The .zip files contain the raw data and metadata for all items (supplementary and main text) as well as the calculation results underlying each figure panel. This includes UV, fluorescence and NMR data. The zip files below also contain the ChimeraX session used to prepare all figure panels, the results of crystallization screens and reports on all MPLC runs used for the purification of pyrophosphate substrates. The <strong>NMR data for the synthesized compounds</strong> are described in the un-zipped metadata sheet which is separately stored below.</p> <p>The <strong>computational results</strong> contributed by Sangwar Wadtey Oung are stored in a separate zenodo entry (<a href="../doi/10.5281/zenodo.10635216">https://zenodo.org/doi/10.5281/zenodo.10635216</a>).</p> <p>This work was enabled by our previous studies on continuous reaction monitoring of phosphate-releasing transformations (<a href="https://doi.org/10.1021/acs.analchem.1c05356">https://doi.org/10.1021/acs.analchem.1c05356</a>), which itself builds on principles of spectral unmixing (<a href="https://doi.org/10.1002/cbic.202000204">https://doi.org/10.1002/cbic.202000204</a>), isosbestic normalization (<a href="https://doi.org/10.1002/cbic.202200744">https://doi.org/10.1002/cbic.202200744</a>) and thermodynamic reaction control in enzymatic equilibrium systems (<a href="https://doi.org/10.1021/acscatal.1c02589">https://doi.org/10.1021/acscatal.1c02589</a> & <a href="https://doi.org/10.1002/anie.202218492">https://doi.org/10.1002/anie.202218492</a> & <a href="https://doi.org/10.1002/adsc.201901230">https://doi.org/10.1002/adsc.201901230</a>).</p>
Data for: First experimental time-of-flight-based proton radiography using low gain avalanche diodes
<p><strong>Data for: First experimental time-of-flight-based proton radiography using low gain avalanche diodes</strong><br>The associated publication can be found on https://iopscience.iop.org/article/10.1088/1361-6560/ad3326.<br>All graphs inside the publication can be recreated with this dataset. Similar to the publication, the data for the timewalk and offset correction are only given for one sensor and one channel as they only serve a representative purpose. The raw data for all other channels can be shared upon request. Furthermore, as in the publication, the data for the water-equivalent-thickness (WET) calibration and proton radiography (pRAD) creation are given by the median and the interquartile range of the measured quantities of the individual graphs. Those data are also calibrated. If required, the raw, unprocessed data of each measurement can be shared upon request.<br><br>In the following, a description of the individual files and corresponding figures in the publication is given. If not specified otherwise, the physical units are given in brackets next to the name of the corresponding physical quantity (usually first line in file):<br><br></p> <ul> <li><em><strong>Figure 6:</strong></em> <ul> <li> RawToTspectrumrescaledLGAD3.txt: <ul> <li>Describes the re-scaled time-over-threshold (ToT) spectrum measured inside the third LGAD of the time-of-flight-based ion computed tomography (TOF-iCT) demonstrator using 800 MeV protons (Figure 6a). The first column gives the channel number on the LGAD (channelnr[#]), the second column, the ToT value measured in this channel (ToT[ps]) and the third channel, the corresponding occurrence (counts[#]).</li> </ul> </li> <li>ToTspectrumrescaledLocMaxLGAD3.txt <ul> <li>Describes the re-scaled ToT spectrum measured inside the third LGAD of the TOF-iCT demonstrator using only the local ToT maxima inside each 4D-cluster. The spectrum was obtained using 800 MeV protons (Figure 6b). The first column gives the channel number on the LGAD (channelnr[#]), the second column, the ToT value measured in this channel (ToT[ps]) and the third channel, the corresponding occurrence (counts[#]).</li> </ul> </li> </ul> </li> <li><em><strong>Figure 7:</strong></em> <ul> <li>offsetpraecalib.txt: <ul> <li>Describes the raw, uncalibrated time difference spectrum in LGAD3 measured between all channels on LGAD3 and a central reference channel on LGAD4 (figure 7a). The first column represents the detector channel nr in LGAD3, the second column the raw, uncalibrated time difference between LGAD3 and LGAD4 (TDiff[ns]) and the third column the number of corresponding counts (counts[#]).</li> </ul> </li> <li>offsetpraecalib.txt: <ul> <li>Describes the time walk and offset-calibrated time difference spectrum in LGAD3 measured between all channels on LGAD3 and a central reference channel on LGAD4 (figure 7b). The first column represents the detector channel nr in LGAD3, the second column the calibrated time difference between LGAD3 and LGAD4 (TDiff[ns]) and the third column the number of corresponding counts (counts[#]).</li> </ul> </li> <li> praetwdata.txt: <ul> <li>Describes the ToT dependence of the measured time difference between LGAD1 and LGAD2 using the raw ToT of channel 31 in LGAD1 (figure 7c). The first column represents the raw, unscaled and uncalibrated ToT in LGAD 1 (ToT[ns]), the second column the measured time difference (TDiff[ns]) and the last column, the number of corresponding counts (counts[#]). A ToT cut on the reference channel on LGAD2 has been applied in advance to guarantee a correlation between only true particle hits in the second sensor.</li> </ul> </li> <li>posttwdata.txt <ul> <li>Describes the time walk-calibrated ToT vs TDiff spectrum using the measured time difference between LGAD1 and LGAD2 and the ToT of channel 31 in LGAD1 (figure 7d). The first column represents the ToT in LGAD 1 (ToT[ns]), the second column the measured time difference (TDiff[ns]) and the last column the number of corresponding counts (counts[#]). A ToT cut on the reference channel on LGAD2 has been applied in advance to guarantee a correlation between only true particle hits in the second sensor.</li> </ul> </li> </ul> </li> <li><em><strong>Figure 8:</strong></em> <ul> <li>tofinaridata.txt: <ul> <li>Describes the measured TOF in air through the scanner w.r.t the TOF measured at 800MeV, i.e. the median TOF value at 800MeV was subtracted from all data points (Figure 8a). The first column describes the beam energy (beamenergy[MeV]), the second column the first quartile of the measured TOF per pixel (TOFperpixelQ1[ps]), the second column the median TOF per pixel (TOFperpixelQ2[ps]) and the last column the third quartile of the measured TOF per pixel (TOFperpixelQ3[ps]).</li> </ul> </li> <li>tofinairtheodata.txt: <ul> <li>Describes the theoretical TOF in air through the scanner w.r.t the theoretical TOF at 800MeV, i.e. the theoretical TOF value at 800MeV was subtracted from all data points (Figure 8a).</li> </ul> </li> <li>intrinsictimeresolution.txt: <ul> <li>Describes the energy dependence of the intrinsic time resolution per channel measured inside LGAD1 (figure 8b). The first column represents the primary beam energy (beamenergy[MeV), the second column the corresponding energy loss in MIPs (relativeenergylossi[MIP]), the third column the first quartile of the intrinsic time resolution per LGAD channel (timeresperpixelQ1[ps]), the fourth column the median of the intrinsic time resolution per LGAD channel and the last column the third quartile of the intrinsic time resolution per LGAD channel (timeresperpixelmedian[ps],timeresperpixelQ3[ps]).</li> </ul> </li> </ul> </li> <li><em><strong>Figure 9:</strong></em> <ul> <li>wetcalib.txt <ul> <li>Describes the measured TOF increase per pixel w.r.t to the TOF in air (i.e. without a phantom) for a given WET and primary beam energy. The first column represents the WET of the irradiated sample (WET[mm]), the second column the used beam energy (beamenergy[MeV]), the third column the first quartile of the measured TOF distribution (TOFperpixelQ1[ps]), the fourth column the median (TOFperpixelQ2[ps]) and the sixth column the third quartile (TOFperpixelQ3[ps]).</li> <li>For each energy, a fifth-order polynomial was used to fit the WET and the TOF increase (Delta TOF(E)~sum_i a_i*(WET_i )^i, with i in [0,5] ). The fit parameters are given in the following for each beam energy:<br> <ul> <li>83 MeV: a_i=[-4.70496227e-02,4.64323118e-01, -2.71391535e-02,4.23655842e-03, -1.13034255e-04,1.23725678e-06]</li> <li>100.4 MeV: a_i=[-3.28976022e-02,-3.68818468e-02,1.96339858e-02,7.31585040e-04, -4.38697681e-05 ,7.52163384e-07]</li> </ul> </li> </ul> </li> </ul> </li> <li><em><strong>Figure 10:</strong></em> <ul> <li>wetsperpixel83MeV.txt <ul> <li>Describes the proton radiography (pCR) for 83 MeV (Figure 10a). The first column represents the x position of the pixel (x[mm]), the second column the y position of the pixel (y[mm]) and the last column the corresponding WET (WET[mm]).</li> </ul> </li> <li>wetsperpixel83MeV.txt <ul> <li>Describes the proton radiography (pCR) for 100.4 MeV (Figure 10b). The first column represents the x position of the pixel (x[mm]), the second column the y position of the pixel (y[mm]) and the last column the corresponding WET (WET[mm]).</li> </ul> </li> </ul> </li> <li><em><strong>Figure 11:</strong></em> <ul> <li>wetdistrdata83MeV.txt <ul> <li>Describes the measured TOF per pixel inside the ROI for 83 MeV protons (Figure 11a). The first column represents the lower boundary of each WET bin (WETlowerbinboundary[mm]), the second column the upper boundary of each WET bin (WETupperbinboundary[mm) and the last column the corresponding counts per bin (counts[#]).</li> </ul> </li> <li>wetdistrdata100MeV.txt <ul> <li>Describes the measured TOF per pixel inside the ROI for 100.4 MeV protons (Figure 11b). The first column represents the lower boundary of each WET bin (WETlowerbinboundary[mm]), the second column the upper boundary of each WET bin (WETupperbinboundary[mm) and the last column the corresponding counts per bin (counts[#]).</li> </ul> </li> </ul> </li> </ul>
A data set from an extensive experimental benchmark study of the Hell Bridge Test Arena subject to imposed damage
<p>A data set from an extensive experimental benchmark study of the Hell Bridge Test Arena (HBTA), a full-scale steel bridge subject to imposed damage, has been established. The data set includes organized dynamic response and load measurement data of the bridge under different structural state conditions, where the structural state conditions range from an undamaged (reference) state to known damage states. Furthermore, the data set includes acceleration and strain data from the response monitoring and acceleration data from the load monitoring, where a modal vibration shaker is used as an excitation source. The data is collected in one h5-file (hierarchical data format version 5) with a sampling rate of 100 Hz. Signal processing and resampling of the data has been performed according to the description provided in the references below. The data set is now published in this open-access data repository and can be accessed and downloaded freely. As such, the data set provides an important benchmark to the scientific community within bridge damage detection and SHM.</p>
A coupled MD-FE methodology to characterize mechanical interphases in polymeric nanocomposites: pseudo-experimental data
<p>readme.txt</p> <p><strong>Abstract:</strong><br> (from [1])</p> <blockquote> <p>This contribution introduces an unconventional procedure to characterize spatial profiles of elastic and inelastic properties inside polymer interphases around nanoparticles. Interphases denote those regions in the polymer matrix whose mechanical properties are influenced by the filler surfaces and thus deviate from the bulk properties. They are of particular relevance in case of nano-sized filler particles with a comparatively large surface-to-volume ratio and hence can explain the frequent observation that the overall properties of polymer nanocomposites cannot be determined by classical mixing rules, which only consider the behavior of the individual constituents.<br> <br> Interphase characterization for nanocomposites poses hardly solvable challengesto the experimenter and is still an unsolved problem in many cases. Instead of real experiments, we perform pseudo experiments using our recently developed Capriccio method, which is an MD-FE domain-decomposition tool specifically designed for amorphous polymers. These pseudo-experimental data then serve as input for a typical inverse parameter identification. With this procedure, spatially varying mechanical properties inside the polymer are, for the first time, translated into intuitively understandable profiles of continuum mechanical parameters.</p> <p><br> As a model material, we employ silica-enforced polystyrene, for which our procedure reveals exponential saturation profiles for Young’s modulus and the yield stress inside the interphase, where the former takes about seven times the bulk value at the particle surface and the latter roughly triples. Interestingly, hardening coefficient and Poisson’s ratio of the polymer remain nearly constant inside the interphase. Besides gaining insight into the constitutive influence of filler particles, these unexpected and intriguing results also offer interesting explanatory options for the failure behavior of polymer nanocomposites.</p> </blockquote> <p> </p> <p><strong>Contact:</strong></p> <p>Maximilian Ries<br> Institute of Applied Mechanics<br> Friedrich-Alexander-Universiät Erlangen-Nürnberg<br> Egerlandstr. 5<br> 91058 Erlangen</p> <p> </p> <p><strong>License:</strong></p> <p>Creative Commons Attribution 4.0 International</p> <p> </p> <p><strong>Context:</strong></p> <p>Data set supplementing journal paper:<br> [1] Ries, M.; Possart, G.; Steinmann, P. & Pfaller, S., "A coupled MD-FE methodology to characterize mechanical interphases in polymeric nanocomposites," <em>International Journal of Mechanical Sciences, </em><em>Elsevier, </em><strong>2021</strong>, 106564.</p> <p>This dataset contains the results of a multiscale study on polystyrene-silica nanocomposites using an atomistic-continuum coupling approach. 120 polystyrene samples, each containing 2 nano-sized silica particles are subjected to uniaxial tension. Here we use coarse-grained molecular dynamics (MD) domain embedded into a larger finite element (FE) region. These two resolutions are coupled in a concurrent multiscale fashion using the so-called Capriccio method. We observe the deformation state of the MD and FE domain, as well as the relative displacement of the two nanoparticles with respect to each other. Based on this pseudo-experimental data, we derive the material properties (Young's modulus, Poisson's ratio, yield stress, hardening) of the interphase forming in the proximity of the nanoparticles in [1].</p> <p>A more detailed description of the used methods can be found in Ries et al. [1].</p> <p> </p> <p><strong>Content:</strong></p> <p>The attached text file contains the following quantities (columns) for all samples (rows):</p> <ul> <li>sample: [initial nanoparticle distance]-ID</li> <li>d0_NP: initial distance of nanoparticles in nm</li> <li>rot_x: rotation of nanoparticles with respect to x-axis in degree</li> <li>d_NP: distance of nanoparticles in nm (after equilibration)</li> <li>Elements: number of finite elements</li> <li>Element_warnings: number of element warnings by Abaqus</li> <li>LS: loadstep 1-6</li> <li>eps_NP(LS): tensile strain of nanoparticles in loadstep LS in %</li> <li>eps_MD(LS): tensile strain of MD domain in loadstep LS in %</li> <li>eps_NP_MD(LS): tensile strain of nanoparticles normalized to eps_MD(LS) in loadstep LS</li> <li>eps_FE(LS): tensile strain of FE domain in loadstep LS in %</li> <li>eps_NP_FE(LS): tensile strain of nanoparticles normalized to eps_FE(LS) in loadstep LS</li> <li>eps_NP_FE(LS): tensile strain of nanoparticles normalized to eps_FE(LS) in loadstep LS</li> <li>u_max(LS): maximum displacement of FE nodes in load step LS in nm</li> <li>F_ext(LS): external force in load step LS in E-11 N</li> </ul> <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.