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595 results for “Supplemental Data”
Data supplement for "Alignment of scanning lidars in offshore wind farms" - Wind Energy Science Journal
<p>These data are supplements for the calculations of the methods from the article "Alignment of scanning lidars in offshore wind farms".<br> The data was used to produce the results from the publication and is intended to be used here as sample data for illustrative purposes.</p>
Quantifying the Sensitivity of Sea Level Change in Coastal Localities to the Geometry of Polar Ice Mass Flux -- Supplemental Data Set: Sea Level Sensitivity Kernels
<p><strong>Quantifying the Sensitivity of Sea Level Change in Coastal Localities to the Geometry of Polar Ice Mass Flux<br> SUPPLEMENTAL DATA SET: SEA LEVEL SENSITIVITY KERNELS</strong></p> <p>To accompany</p> <p> Jerry X. Mitrovica, Carling C. Hay, Robert E. Kopp, Christopher Harig, and<br> Konstantin Laytchev (2018). Quantifying the Sensitivity of Sea Level Change<br> in Coastal Localities to the Geometry of Polar Ice Mass Flux. Journal of<br> Climate. doi: 10.1175/JCLI-D-17-0465.1.</p> <p>We provide sea level kernels for ~740 tide gauge sites in the Permanent Service for Mean Sea Level (PSMSL) database (Holgate et al., 2013). Kernels associated with sensitivities to Greenland and Alaskan glacier melt are given on a spatial grid covering the globe, with 512 latitude rows (i=1,512) and 1024 longitude (j=1,1024) columns.</p> <p>Longitude values are evenly spaced moving eastward from Greenwich (the jth grid point has an east longitude value of (j-1)×360°/1024). Latitude values are Gauss-Legendre points beginning close to the North Pole and ending near the South Pole. Kernels associated with sensitivities to Antarctic melt are given on a spatial grid covering the globe, with 256 (Gauss-Legendre) latitude rows (i=1,256) and 512 longitude (j=1,512) columns. Longitude values are evenly spaced moving eastward from Greenwich.</p> <p>The format of the files is: </p> <p> grid_sitenumber_region.txt</p> <p>where “region” is either “green” (Greenland), “ant” (Antarctic) or “Alaska” (Alaska). The list of sites (and site numbers) is provided in the sites.txt file. The first 8 sites in this list were test sites and can be ignored.</p>
Data set supplementing "Benchmarking triage capability of symptom checkers against that of medical laypersons: Survey study"
<p>This is the de-identified data set used to conduct the analyses in the study published as Original Research in the JMIR under the title "Benchmarking triage capability of symptom checkers against that of medical laypersons: Survey study" (https://doi.org/10.2196/24475)</p> <p>The data set contains the assessments of the urgency of symptoms to 45 fictitious clinical case vignettes by 91 US participants, and the participants' age, gender and level of education. Data for the symptom checker apps is needed to fully reproduce our study and can be found in the appendix of the paper "Evaluation of symptom checkers for self diagnosis and triage: audit study" by Semigran et al. (2015) (https://doi.org/10.1136/bmj.h3480).</p>
Supplemental data for characterization of alpha and beta interactions using the HeXe setup [Eur. Phys. J. C 82, 361]
<p>Repository with supplemental data to:<br> <strong>Characterization of alpha and beta interactions in liquid xenon</strong>. Jörg, F., Cichon, D., Eurin, G. <em>et al. Eur. Phys. J. C</em> <strong>82, </strong>361 (2022) <a href="https://doi.org/10.1140/epjc/s10052-022-10259-3">10.1140/epjc/s10052-022-10259-3</a><br> A pre-print of the article is available <em>on arXiv: </em><a href="http://arxiv.org/abs/2109.13735">2109.13735</a></p> <p><strong>Note: </strong>When re-using the data, please make sure to cite the article (and not only the dataset)</p> <p><br> The files contain the measured data points (as well as their statistical and systematic uncertainties) as shown in the publication.<br> All datasets are stored in the .csv format.</p> <ul> <li><strong>20210924_yields_hexe_kr83m.csv</strong><br> This file contains the normalized light and charge yields as a function of the applied field from the measurement with the <sup>83m</sup>Kr source. The data is shown in Figure 16 (dots) of the publication. Furthermore the file contains the LY ratio between the two Isomeric transitions of the <sup>83m</sup>Kr source, shown in Figure 17 of the article.</li> <li><strong>20210924_yields_hexe_rn222.csv</strong><br> This file contains the normalized light and charge yields as a function of the applied field from the measurement with the <sup>222</sup>Rn source. The data is shown in Figure 18 (blue-ish points) of the publication</li> <li><strong>20210924_drift_velocity_hexe_rn222.csv</strong><br> This file contains the measured electron drift velocity in liquid xenon at a temperature of 174.4 K in dependence of the field. The data was acquired using the <sup>222</sup>Rn source. Drift velocity is given in units of mm/µs and the datapoints are shown in Figure 20 (black dots) of the publication </li> <li><strong>20210924_drift_velocity_hexe_kr83m.csv</strong><br> This file contains the measured electron drift velocity in liquid xenon at a temperature of 174.4 K in dependence of the field. The data was acquired using the <sup>83m</sup>Kr source. Drift velocity is given in units of mm/µs and are not displayed in the publications due to visibility reasons.</li> </ul> <p><strong>Minimum working example to plot the drift velocity using the <sup>83m</sup>Kr data:</strong></p> <pre><code class="language-python"> 1 import numpy as np 2 import matplotlib.pyplot as plt 3 4 # load the data set 5 data = np.loadtxt("20220427_drift_velocity_hexe_kr83m.csv", delimiter=",") 6 7 # Plot the systematic uncertainty on the drift field 8 plt.errorbar(data[:,0], data[:,2], xerr=data[:,1], fmt="o", capsize=2, ecolor="darkgray", 9 alpha=0.7, elinewidth=3, color="black") 10 11 # Plot the actual data points 12 plt.errorbar(data[:,0], data[:,2], yerr=data[:,3], fmt="o", color="black") 13 14 # Label the axis and define the range 15 plt.ylabel("Drift Velocity [mm/µs]") 16 plt.xlabel("Drift Field [kV/cm]") 17 plt.xscale("log") 18 plt.xlim(0.006, 2) 19 plt.ylim(0, 2.4) 20 plt.show() </code></pre> <p> </p>
A Surface-Induced Asymmetric Program Promotes Tissue Colonization by a Human Pathogen - Supplemental data Fig 4A
<p>Raw data used for Fig. 4A of the article "A Surface-Induced Asymmetric Program Promotes Tissue Colonization by a Human Pathogen" published in Cell Host & Microbe. This Western Blot dataset is composed of 4 images:</p> <ul> <li>Western Blot 1: whole cell lysate (raw image, and annotated image)</li> <li>Western Blot 2: purified pili (raw image, and annotated image)</li> </ul>
Data supplement for "Land use intensification increasingly drives the spatiotemporal patterns of the global human appropriation of net primary production in the last century"
<p>This data supplements the publication "Land use intensification increasingly drives the spatiotemporal patterns of the global human appropriation of net primary production in the last century" by Thomas Kastner, Sarah Matej, Matthew Forrest, Simone Gingrich, Helmut Haberl, Thomas Hickler, Fridolin Krausmann, Gitta Lasslop, Maria Niedertscheider, Christoph Plutzar, Florian Schwarzmüller, Jörg Steinkamp, Karl-Heinz Erb.</p> <p>For details, please refer to the included readme file and to the publication (<a href="https://doi.org/10.1111/gcb.15932">https://doi.org/10.1111/gcb.15932</a>)</p> <p>In this new Version 1.01, we changed the file structure to make the data more accessible, we added data on means across modulations as used in the paper, and we include csv files with national totals for the different HANPP components.</p>
Multiple-benefit Conservation in Practice: Supplemental Spatial Data for Quantifying Multidimensional Impacts of Landscape Change in California's Sacramento–San Joaquin Delta
<p><strong>SUMMARY</strong><br> Spatial data representing climate, proximity to streams, and probability of flooding in the Sacramento-San Joaquin Delta.</p> <p><strong>DESCRIPTION</strong><br> These data were compiled as predictors of the distribution of riparian landbird species and groups of waterbird species, to facilitate projecting the probability of species or group presence across a given landscape. They were used to identify Priority Bird Conservation Areas and in analyses of the impacts of scenarios representing habitat restoration and perennial crop expansion on suitable habitat. These data are required for using the R package "DeltaMultipleBenefits", which provides the code and work flow for repeating the initial analyses or analyzing new scenarios.</p> <p>For additional details about the development and applications of these data, please see: </p> <ul> <li>Dybala KE, et al. (<em>In review</em>) Multiple-benefit Conservation in Practice: A Framework for Quantifying Multi-dimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta</li> <li>Dybala KE, Sesser K, Reiter M, Shuford WD, Golet GH, Hickey C, Gardali T (<em>In review</em>) Priority Bird Conservation Areas in California’s Sacramento–San Joaquin Delta. </li> <li>Dybala KE (2023) <em>DeltaMultipleBenefits: Projecting the Multiple Benefits of Land Cover Change in the Sacramento-San Joaquin River Delta</em>. R package version 1.0.0. doi: 10.5281/zenodo.7718620. Available from: https://pointblue.github.io/DeltaMultipleBenefits.</li> </ul> <p><strong>FUNDING STATEMENT</strong><br> These data were developed as part of the project "Trade-offs and Co-benefits of Landscape Change on Bird Communities and Ecosystem Services in the Sacramento–San Joaquin River Delta", funded by Proposition 1 Delta Water Quality and Ecosystem Restoration Program, Grant Agreement Number – Q1996022, administered by the California Department of Fish and Wildlife.</p> <p><strong>POINT OF CONTACT</strong><br> Kristen Dybala, Point Blue Conservation Science, kdybala@pointblue.org</p> <p><strong>SUGGESTED CITATION</strong><br> Dybala KE. 2023. Multiple-benefit Conservation in Practice: Supplemental Spatial Data for Quantifying Multidimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta. doi:10.5281/zenodo.7672193.</p> <p><strong>DATA DISTRIBUTION</strong><br> Zenodo (https://doi.org/10.5281/zenodo.7672193)</p> <p><strong>PROGRESS</strong><br> Complete</p> <p><strong>UPDATE FREQUENCY</strong><br> None planned</p> <p><strong>DATE</strong><br> These data were compiled in 2022, based on data from WorldClim (representing 1970-2000), National Hydrography Dataset (published 2020), and Point Blue's Water Tracker (representing 2013-2019).</p> <p><strong>FIELD DEFINITIONS</strong></p> <ul> <li><strong>bio_1: </strong>annual mean temperature (C), 1970-2000 (WorldClim; Fick and Hijmans 2017)</li> <li><strong>bio_12:</strong> total annual precipitation (mm), 1970-2000 (WorldClim; Fick and Hijmans 2017)</li> <li><strong>streamdist: </strong>square root of the distance to the nearest stream (m) (National Hydrography Dataset; USGS 2020)</li> <li><strong>pwater_fall:</strong> mean probability of open surface water during the fall, 2013-2019 (Point Blue Water Tracker; Reiter et al. 2018)</li> <li><strong>pwater_win:</strong> mean probability of open surface water during the winter, 2013-2019 (Point Blue Water Tracker; Reiter et al. 2018)</li> </ul> <p><strong>Literature Cited</strong></p> <ul> <li>Fick SE, Hijmans RJ. 2017. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol. 37:4302–4315. <a href="https://doi.org/10.1002/joc.5086">https://doi.org/10.1002/joc.5086</a> </li> <li>Reiter ME, Elliott NK, Barbaree B, Moody D. 2018. An automated open surface water tracking system for California’s Central Valley. Report to the U.S. Fish and Wildlife Service. Petaluma, California: Point Blue Conservation Science. Available from: <a href="https://data.pointblue.org/apps/autowater/ ">https://data.pointblue.org/apps/autowater/ </a></li> <li>[USGS] United States Geological Survey. 2020. National Hydrography Dataset Best Resolution (NHD) for Hydrologic Units (HU) 4 - 1802, 1803, 1804, 1805. Reston (VA): U.S. Geological Survey. Available from: <a href="https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products ">https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products </a></li> </ul> <p><strong>ABBREVIATION DEFINITIONS</strong><br> N/A</p> <p><strong>COORDINATE REFERENCE SYSTEM</strong><br> WGS 84 / UTM zone 10N (EPSG:32610)</p> <p><strong>ACCESS & USE CONSTRAINTS</strong><br> CC-by-4.0 (https://creativecommons.org/licenses/by/4.0/)</p> <p><strong>KEYWORDS</strong></p> <ul> <li><strong>Themes:</strong> climate, temperature, precipitation, hydrology, streams, water, flood, remote sensing </li> <li><strong>Place: </strong>Sacramento-San Joaquin River Delta, Central Valley, California</li> </ul>
Supplemental data files: Beyond the reference: gene expression variation and transcriptional response to RNAi in C. elegans
<p>This dataset holds all non-GEO-hosted supplemental data files for manuscript "Beyond the reference: gene expression variation and transcriptional response to RNAi in <em>C. elegans</em>". Please see the linked preprint/publication for full details.</p> <p>The PDF _guide_to_datafiles.pdf gives details on the format and content of each of the included files.</p>
Data supplement to 'Vertical land motion reconstruction unveils non-linear effects on relative sea level changes from 1900-2150'
<p>This is a data supplement to <strong>'Vertical land motion reconstruction unveils non-linear effects on relative sea level changes from 1900-2150</strong>'. It presents a global-scale Vertical Land Motion (VLM) reconstruction that resolves height changes in the period 1995-2020. It is based on the joint probabilistic analysis of an extensive network of more than 11,000 GNSS stations, tide gauges, and satellite altimetry. The approach used to derive this reconstruction is described in the paper. The dataset variables are explained in the .pdf file.</p>
Data publication supplementing "Novel nanoindentation strain rate sweep method for continuously investigating the strain rate sensitivity of materials at the nanoscale"
<p>This data publication contains the results of nanoindentation tests on Fused silica, nanocrystalline nickel, a nanocrystalline FeCr alloy, a bulk metallic glass, the superplastic alloy Zn-22%Al and single crystalline aluminum as well as the method files developed for the G200 nanoindenter. It supplements the publication "Novel nanoindentation strain rate sweep method for continuously investigating the strain rate sensitivity of materials at the nanoscale". The materials are described in more detail in the respective publication. The data publication takes over the sample naming convention from the related publication. </p><p>Nanoindentation measurement were performed by H. Holz at the Max-Planck Institut für Eisenforschung GmbH, Max-Planck-Straße 1, 40237 Düsseldorf, Germany using a G200 nanoindenter (KLA, Milpitas, CA, USA), equipped with a modified Berkovich diamond indenter tip of the type 171-561-500 with a serial number of C-0040446 from Synton MDP (Nidau, Switzerland). Constant strain rate tests, strain rate jump tests, strain rate sweep tests and strain rate sweep reversal tests were performed on each material in Continuous Stiffness Measurement (CSM) mode. The maximum indentation depth was 2200 nm, the CSM amplitude 2 nm and the CSM frequency 45 Hz. Strain rates were varied within the range 0.001 – 0.1 s-1. Further information on the test protocol can be found in the corresponding publication.</p><p>The subfolder "Nanoindentation data" contains the raw data for all valid indents as output by the NanoSuite © software v 7.1.7 and converted to the semicolon-separated format. The naming convention for the folder in which the CSV files are located in gives first the used material, then the method used with additional information to the parameters inputted for the method such as strain rate and indentation depth all separated by an underscore. An example can be "FS_CSR_01s-1" for a constant strain rate tests performed on fused silica with a strain rate target of 0.1 s-1 or "Nc-Ni_SweepReversal_005s-1_0005s-1" for a sweep reversal test performed on the nanocrystalline nickel sample with a targeted initial and ending strain rate of 0.05 s-1 and a strain rate target at which the strain rate direction gets reversed of 0.005 s-1. The CSV files are named either "Results" giving the average results of each test, "Required Inputs" giving information about the parameters used for the experiments, "Inputs Editable Post Test" giving information about the analysis parameters to obtain the results from, and "Test XXX" which include the Raw data of the corresponding test number. In each file the first row gives the data description e.g., "Time", the second row the physical unit e.g., "s" for seconds and from the third row the measured values.</p><p>The Nano Suite method files to perform the experiments on KLA G200 instruments is provided in the folder "G200 methods". The method for the strain rate sweep experiments is called "Strain Rate Sweep.msm" and the method for the strain rate sweep reversal experiments "Strain Rate Sweep Reversal.msm". This method is provided as is and shall be used at your own risk. The authors explicitly decline responsibility for any physical or immaterial damage resulting from the use of this method. Should minor issues occur, some feedback to the authors would be greatly appreciated.</p>
Delta Smelt (Hypomesus transpacificus) biomarker and genetic data from supplemental release into the San Francisco Estuary, 2022
Delta Smelt (Hypomesus transpacificus) is an endangered fish that is endemic to the San Francisco Estuary. As a conservation strategy, hatchery-reared Delta Smelt have been released into the San Francisco Estuary to supplement the wild population. State and federal agencies surveyed the abundance of Delta Smelt, collected fish specimens, and recorded associated environmental data from sampling sites. Delta Smelt specimens were preserved and transported to the University of California, Davis where a variety of biomarkers were assessed on individual fish. This project, including the supplementation of hatchery-reared Delta Smelt and data collection, is ongoing.
Supplemental soil moisture and temperature data from the saddle catchment sensor network, 2019 - 2021.
Hand-held soil moisture measurements were taken at 8 of 16 soil moisture sensors within the sensor network at Niwot Ridge to supplement the continuous measurement system at these locations. The hand-held measurements occur much less frequently than the 10 min sensor data, but they are important in determining the spatial variability of soil moisture in the alpine.
Data supplement for "Bifurcations of front motion in passive and active Allen-Cahn-type equations"
<p>This dataset contains the data and source files for figures 5 and 7-10 in the following publication: </p> <p>F. Stegemerten, S.V. Gurevich, U. Thiele</p> <p><em>'Bifurcations of front motion in passive and active Allen–Cahn-type equations' </em></p> <p>published in 2020 in CHAOS.</p> <p>Please follow the instructions given in 'Readme.txt'.</p>
CLDF dataset with data and supplements for Barlow "Loss of colexification of 'hand' and 'five' in Austronesian languages"
CLDF dataset with data and supplements for Barlow "Loss of colexification of 'hand' and 'five' in Austronesian languages"
Data supplement: Spatial autocorrelation in machine learning for modelling soil organic carbon
<p>Spatial autocorrelation in machine learning for modelling soil organic carbon: Data supplement</p> <p><br>Alexander Kmoch, Clay Taylor Harrison, Jeonghwan Choi, Evelyn Uuemaa</p> <p>Spatial autocorrelation, the relationship between nearby samples of a spatial<br>random variable, is often overlooked in machine learning models, leading to<br>biased results. This study investigates various methods to account for spa-<br>tial autocorrelation when predicting soil organic carbon (SOC) using random<br>forest models. Five models incorporating spatial structure were compared<br>against baseline models that did not have any added spatial components.<br>Cross-validation showed slight improvements in accuracy for models consid-<br>ering spatial autocorrelation, while Shapley Additive Explanations confirmed<br>the importance of spatial variables. However, no decrease in spatial autocor-<br>relation of residuals was observed. Raster-based models exhibited enhanced<br>prediction detail, but high-resolution validation data availability limited thor-<br>ough validation. The findings emphasize the value of incorporating spatial<br>autocorrelation for improved SOC prediction in machine learning models.<br>Considerations such as the distribution of predictions and computational<br>complexity should help guide the selection of suitable approaches for specific<br>spatial modelling tasks.</p>
Supplemental data for "Intramolecular feedback regulation of the LRRK2 Roc G domain by a LRRK2 kinase dependent mechanism" (Gilsbach et al., eLife 2024, doi:10.7554/eLife.91083)
<p><strong>Supportive data for the eLife version of record.</strong></p> <p><strong>(1) Data used for the Michaelis Menten Kinetics.</strong></p> <p><strong>HPLC-based assay.</strong> Steady-state kinetic measurements of LRRK2-mediated GTP hydrolysis were performed as previously described (Ahmadian et al., 1997). Briefly, 0.1 µM of full-length LRRK2 was incubated with different amounts of GTP (0, 25, 75, 150, 250, 500, 1000, 2000, 3000 and 5000 µM) and production of GDP was monitored by reversed phase C18 HPLC. To this end, the samples (10 µl) were directly injected on a reversed-phase C18 column (pre-column: Hypersil Gold, 3µm particle size, 4.6x10mm; main column: Hypersil Gold, 5µm particle size, 4.6x250mm, Thermo Scientific) using an Ultimate 3000 HPLC system (Thermo Scientific, Waltham, MA, USA) in HPLC-buffer containing 50 mM KH<sub>2</sub>PO<sub>4</sub>/K<sub>2</sub>HPO<sub>4</sub> pH 6.0, 10 mM tetrabutylammonium bromide and 10-15% acetonitrile. Subsequently, samples were analyzed using the HPLC integrator (Chromeleon 7.2, Thermo Scientific, Waltham, MA, USA). Initial rates of GDP production were plotted against the GTP concentration using GraFit5 (v.5.0.13, Erithacus Software). The number of experiments is indicated in the graph and data point is the average (±s.e.m.) of indicated repetitions. The Michaelis-Menten equation was fitted to determine K<sub>M</sub> (±s.e.) and k<sub>cat</sub> (±s.e.). Excel sheets used for the calculation of means are provided. No values are reported if the HPLC separation failed (e.g. unstable baseline).</p> <p><strong>Charcoal GTP hydrolysis assay. </strong>The [γ-32P]GTP charcoal assay was performed as previously described (Bollag and McCormick, 1995). Briefly, 0.1 µM full-length LRRK2 or 0.5 µM 6xHIS-MBP-RocCOR was incubated with different GTP concentrations, ranging from 75 µM to 8 mM, in the presence of [γ-<sup>32</sup>P] GTP in GTPase assay buffer (30 mM Tris pH 8, 150 mM NaCl, 10 mM MgCl<sub>2</sub>, 5% (v/v) Glycerol and 3 mM DTT). Samples were taken at different time-points and immediately quenched with 5% activated charcoal in 20 mM phosphoric acid. All non-hydrolyzed GTP and proteins were stripped by the activated charcoal and sedimented by centrifugation. The radioactivity of the isolated inorganic phosphates was then measured by scintillation counting. The initial rates of γ-phosphate release and the Michaelis-Menten kinetics were calculated as described above.</p> <p><strong>(2) Profile plots (Raw data) obtained for the Mass photometry analysis for T1343A vs WT LRRK2.</strong></p> <p>MP was performed as described in (Guaitoli et al., 2023).<strong> </strong>Briefly, the dimer ratio of LRRK2 was determined on a Refeyn Two MP instrument (Refeyn). Prior to the experiment, a standard curve relating particle contrasts to molecular weight was established using a Native molecular weight standard (Invitrogen, 1:200 dilution in HEPES-based elution buffer: 50 mM HEPES [pH 8.0], 150 mM NaCl supplemented with 200 µM desthiobiotin). Prior to mass photometry, the proteins, either WT or T1343A LRRK2, were incubated with 0.5 mM ATP or buffer (control) for 30 min at 30 ℃. The LRRK2 protein was diluted to 2x of the final concentration (end concentrations: 75 nM and 100 nM) in elution buffer. The optical setup was focused in 10 μl elution buffer before adding 10 µl of the adjusted protein sample. Depending on the obtained count numbers, acquisition times were chosen between 20 s to 1 min. The dimer ratio in each measurement was normalize according to the equation. The measurement was perfomed in triplicates.</p> <p><strong>(3) AlphaFold3 model of LRRK2-pT1343 either bound to GDP/Mg or GTP/Mg.</strong></p> <p>Using AlphaFold3 (Abramson et al., 2024), we modeled and compared the GDP vs the GTP-state of phospho-T1343 LRRK2. Interestingly, the AlphaFold3 model suggests, that the phosphate group of the pT1343 residue is orientated inwards thereby substituting the gamma phosphate of the GTP in the GDP-bound state of LRRK2. This finding is in well agreement with MD simulations published recently (Stormer et al., 2023).</p> <p><strong>(4) Western blot RAW files for the cell-based phospho Rab asssay (RAW data for Figure 6 supplement 2/ Supplemental Figure 4 in the preprint version, Gilsbach et al, 2024)</strong></p> <p>Cell-based LRRK2 activity assays were performed as previously described (Singh et al., 2022). Briefly,<strong> </strong>HEK293T cells were cultured in DMEM (supplemented with 10% Fetal Bovine Serum and 0.5% Pen/Strep). For the assay, the cells were seeded onto six-well plates and transfected at a confluency of 50-70% with SF-tagged LRRK2 variants using PEI-based lipofection. After 48 hours cells were lysed in lysis buffer [30 mM Tris-HCl (pH7.4), 150 mM NaCl, 1% NonidentP-40 substitute, complete protease inhibitor cocktail, PhosStop phosphatase inhibitors (Roche)]. Lysates were cleared by centrifugation at 10,000 x g and adjusted to a protein concentration of 1 µg/µl in 1x Laemmli Buffer. Samples were subsequently subjected to SDS PAGE and Western Blot analysis to determine LRRK2 pS935 and Rab10 T73 phosphorylation levels, as described below. Total LRRK2 and Rab10 levels were determined as a reference for normalization. For Western blot analysis, protein samples were separated by SDS–PAGE using NuPAGE 10% Bis-Tris gels (Invitrogen) and transferred onto PVDF membranes (Thermo Fisher). To allow simultaneous probing for LRRK2 on the one hand and Rab10 on the other hand, membranes were cut horizontally at the 140 kDa MW marker band. After blocking non-specific binding sites with 5% non-fat dry milk in TBST (1 h, RT) (25 mM Tris, pH 7.4, 150 mM NaCl, 0.1% Tween-20), membranes were incubated overnight at 4°C with primary antibodies at dilutions specified below. Phospho-specific antibodies were diluted in TBST/ 5% BSA (Roth GmbH). Non-phospho-specific antibodies were diluted in TBST/ 5% non-fat dry milk powder (BioRad). Phospho-Rab10 levels were determined by the site-specific rabbit monoclonal antibody anti-pRAB10(pT73) (Abcam, ab230261) and LRRK2 pS935 was determined by the site-specific rabbit monoclonal antibody UDD2 (Abcam, ab133450), both at a dilution of 1:2,000. Total LRRK2 levels were determined by the in-house rat monoclonal antibody anti-pan-LRRK2 (clone 24D8; 1:10,000) (Carrion et al., 2017). Total Rab10 levels were determined by the rabbit monoclonal antibody anti-RAB10/ERP13424 (Abcam, ab181367) at a dilution of 1:5,000. For detection, goat anti-rat IgG or anti-rabbit IgG HRP-coupled secondary antibodies (Jackson ImmunoResearch) were used at a dilution of 1:15,000 in TBST/ 5% non-fat dry milk powder. Antibody–antigen complexes were visualized using the ECL plus chemiluminescence detection system (GE Healthcare) using the Stella imaging system (Raytest) for detection and quantification.</p> <p><strong>Figure 6 Source Data 1:</strong> <span>Images generated by the Stella system are shown which were used for quantification. The annotation file equals Figure6-figure supplement 2 (Gilsbach et al., eLife 2024, doi:10.7554/eLife.91083). The lines corresponding to </span>LRRK2 pS935, total LRRK2, Rab10 pT73 and total Rab10 were <span>used for the quantification shown in Figure 6.</span></p>
Supplemental Data from the article "The SmARTR pipeline: a modular workflow for the cinematic rendering of 3D scientific imaging data"
<h1><strong>Please, refer to <a href="https://github.com/MeVisLab/SmARTR-Networks">this GitHub repository</a> for additional info, updates, issue reports, and discussion<br></strong></h1> <p><strong>A collection of configuration files (SmARTR networks) published in "<a href="https://doi.org/10.1016/j.isci.2024.111475">The SmARTR Pipeline: a modular workflow for the cinematic rendering of 3D scientific imaging data</a>", enabling the creation of cinematic (photorealistic) renderings of 3D data in the FREE software <a href="https://www.mevislab.de/download">MeVisLab</a><br></strong></p> <ul> <li>Each folder in the archive contains one or more SmARTR network files, the scan and mask files required for the practical examples detailed in the <a href="https://www.cell.com/cms/10.1016/j.isci.2024.111475/attachment/8d79036b-acb6-4cda-a5ff-f56317691ebc/mmc1.pdf">Supplemental Data</a> of the article, and an additional folder with LUT presets.</li> </ul>
Supplemental Data for "Eyewall Asymmetries and Their Contributions to the Intensification of an Idealized Tropical Cyclone Translating in Uniform Flow"
<p>The repository contains a set of files required to reproduce the idealized tropical cyclone simulation analyzed in the manuscript entitled "Eyewall asymmetries and their contributions to the intensification of an idealized tropical cyclone translating in uniform flow", submitted to the Journal of the Atmospheric Sciences. See the README file for brief descriptions about the content of each file within this repository.</p> <p>The simulation was produced with the Cloud Model 1 (CM1) version 19.7, and CM1 can be downloaded at https://www2.mmm.ucar.edu/people/bryan/cm1/. </p>
Data supplement for "Gradient flows for coupling order parameters and mechanics"
<p>In this data repository we provide additional information necessary for the generation of the images in the paper "Gradient flows for coupling order parameters and mechanics". The simulation results are generated by a FEniCS code, run on Google Colab and the code is available at <a href="https://github.com/schmellerl/gradient_flows_order_parameters_mechanics">https://github.com/schmellerl/gradient_flows_order_parameters_mechanics</a>. The preprint of the article can be found under the DOI 10.20347/WIAS.PREPRINT.2909.</p> <p> </p> <p> </p>
Data_Supplemental_Tab3_Albendazole reduces endoplasmic reticulum stress induced by Echinococcus multilocularis in mice
<p>Data of supplemental Tab3, “Albendazole reduces endoplasmic reticulum stress induced by Echinococcus multilocularis in mice”</p> <p>The Dataset contains the original supplemental table 3 as PDF-format (PNTD-D-21-00134R2_S_T3.pdf). Related information (meta-data) are provided as one file in TXT format (31003A-179400_PNTD-D-21-00134R2 _FJ_MW_SS_Echinococcus_ST3_M_1.txt) and three files in PDF format (31003A-179400_PNTD-D-21-00134R2 _FJ_MW_SS_Echinococcus_ST3_M_1-3.pdf).</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.