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1,542 results for “Calcium”
iSDAsoil: soil extractable Calcium for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil extractable Calcium log-transformed predicted at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_log.ca_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Calcium mean value,</li> <li>sol_log.ca_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Calcium model (prediction) errors,</li> </ul> <p>Model errors were derived using bootstrapping: md is derived as standard deviation of individual learners from 5-fold cross-validation (using spatial blocking). The model 5-fold cross-validation (<a href="https://mlr.mlr-org.com/reference/makeStackedLearner.html">mlr::makeStackedLearner</a>) for this variable indicates:</p> <pre><code>Variable: log.ca_mehlich3 R-square: 0.84 Fitted values sd: 1.24 RMSE: 0.543 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -6.0376 -0.2577 0.0076 0.2756 5.3825 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 5.737959 3.850998 1.490 0.136 regr.ranger 1.054018 0.003175 331.978 < 2e-16 *** regr.xgboost -0.030930 0.003939 -7.853 4.1e-15 *** regr.cubist 0.061829 0.003561 17.364 < 2e-16 *** regr.nnet -0.855297 0.561006 -1.525 0.127 regr.cvglmnet -0.065040 0.003225 -20.166 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.5428 on 144593 degrees of freedom Multiple R-squared: 0.8403, Adjusted R-squared: 0.8402 F-statistic: 1.521e+05 on 5 and 144593 DF, p-value: < 2.2e-16 </code></pre> <p>To back-transform values (y) to ppm use the following formula:</p> <pre><code>ppm = expm1( y / 10 )</code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>
Animation to visualize the electron beam damage induced in calcium silicate hydrate phases
<p>This dataset visualizes the electron beam damage induced by a scanning electron microscope (SEM) in calcium silicate hydrates (C-S-H). The specimen used is 28 days hydrated alite (water/solid = 0.5). It was scanned using a thermofischer scientific Helios G4 UX microscope at 350 V/25 pA with a stage bias of 200 V.</p> <p>This animation was an afterthought. Therefore, the dataset provides multiple magnifications and resolutions and some of the images are not in focus. Nevertheless, It can be seen, that the C-S-H needle in the right half of the image significantly deformes within a timespan of 124 seconds of constant scanning of that region.</p> <p><strong>File content:</strong></p> <ul> <li>All images ending with "raw" are the raw images provided by the SEM software including all metadata.</li> <li>The file "C3S_CSH_e-beam-damage_aligned stack.tif" contains the aligned image set using the SIFT algorithm. It contains the correct scaling if opened with ImageJ.</li> <li>The file "C3S_CSH_e-beam-damage_animation.gif" provides the final animation including a overlayed scalebar.</li> </ul>
Empirical relationship between calcium triplet equivalent widths and [Fe/H] using Gaia photometry
<p>I present a new empirical relationship for red giant branch stars between the overall metallicity of the star and the sum of equivalent widths of the near-infrared calcium triplet (CaT) spectral lines. This method takes advantage of the all-sky photometry and astrometry of the Gaia mission, and the archival spectra from 2050 red giant branch stars from 18 globular clusters (-0.69>[Fe/H]>-2.44) acquired with the Anglo-Australian Telescope's AAOmega spectrograph.</p> <ul> <li><cluster_name>.tar.gz <ul> <li>Raw and reduced spectra for 18 globular clusters (NGC104, NGC6752, NGC6809, NGC288, NGC7099, NGC362, NGC6218, NGC4590, IC4499, NGC1904, ESO452-SC06, ESO280-SC12, NGC1851, NGC6624, NGC2298, Pal 5, NGC5024, Terzan 8, NGC5053)</li> <li>Lists of likely members</li> <li>Measured equivalent widths and radial velocities</li> </ul> </li> <li>code.tar.gz <ul> <li>running.py – calculates the equivalent widths and radial velocities of the stars from the spectra</li> <li>cat_new_params.py – calculates the best fitting empirical relationship</li> </ul> </li> <li>paper.tar.gz <ul> <li>The LaTeX source for the submitted RNAAS paper.</li> </ul> </li> </ul> <p> </p>
Nano-sized calcium carbonate particles in cement mortars (DS18)
<p>This dataset will provide the selection of the optimal mix-design of cement mortars, optimizing the characteristics of nanoCaCO3 particles (additional percentages, morphology, particle size distribution, crystal phase) according to their use in cement-based composites. These commercial nanoparticles have characteristics comparable with those of the synthesized particles used up to now in the RECODE project.</p>
RECODE_DS19.Toxicological profile of calcium carbonate nanoparticles for industrial applications
<p>The documentation will include: for the <em>in vitro</em> and <em>in vivo </em>studies all the data acquired after the exposure of cells or zebrafish to the nano-sized CaCO3 particles.</p>
Super-resolution analysis of the origins of the elementary events of ER calcium release in dorsal root ganglion neurons
<p>This is the data supplement for the paper entitled, "Super-resolution analysis of the origins of the elementary events of ER calcium release in dorsal root ganglion neurons"<br><br>There are two principal subdirectories within the enclosed zip file:</p><ol><li>10xEExM_data: The directory contains two exemplar datasets each of 10x Enhanced expansion microscopy images of IP3R1 and RyR immunolabelling in DRG soma, at the sub-plasmalemmal regions.<br> </li><li>Correlative Analysis: The directory contains two sub-directories of worked examples of data and correlative analysis of calcium sparks and dSTORM images of RyR and IP3R. The instructions for the code, run in IDL, are included in the Readme.txt enclosed within.</li></ol>
Two datasets to illustrate quantitative analysis methods for fluorescent calcium measurements
<p>Two datasets in HDF5 formats used for illustrating some quantitative data analysis methods.</p> <p><strong>CCD_calibration.hdf5</strong>: Imago/SensiCam CCD camera (Till Photonics) calibration data set. <br> Fluorescence measurments were made using a fluorescent plastic slide. 10 exposure times from 10 to 100 ms (each making an HDF5 group) were used. For each exposure time 100 exposures were performed (with 200 ms between each). The fluorescence measured in each of the 60 x 80 pixels of the camera are stored in the stack data set of each group. The time data set (a vector) of each group contains the time at which each illumination was done. These recordings were done by Andreas Pippow (Kloppenburg Laboratory Cologne University, http://cecad.uni-koeln.de/Prof-Peter-Kloppenburg.82.0.html). <br> They were used in: Sébastien Joucla, Andreas Pippow, Peter Kloppenburg and Christophe Pouzat (2010) Quantitative estimation of calcium dynamics from ratiometric measurements: A direct, non-ratioing, method. Journal of Neurophysiology 103: 1130-1144.</p> <p><strong>Data_POMC.hdf5</strong>: POMC data set recorded by Andreas Pippow (Kloppenburg Laboratory Cologne University, http://cecad.uni-koeln.de/Prof-Peter-Kloppenburg.82.0.html). 168 measurements performed with a CCD camera recording Fura-2 fluorescence (excitation wavelength: 340 nm). The size of the CCD chip is 60 x 80 pixels. A stimulation (depolarization induced calcium entry) comes at time 527. <br>Details about this data set can be found in: Joucla et al (2013) Estimating background-subtracted fluorescence transients in calcium imaging experiments: A quantitative approach. Cell Calcium. 54 (2): 71-85.</p> <p> </p>
Data supporting: "Calcium-driven In Silico Inactivation of a Human Olfactory Receptor"
<p>In this repository we deposited trajectories and input files for the paper "Calcium-driven In Silico Inactivation of a Human Olfactory Receptor".</p> <p>The data is organised as follow:</p> <p> </p> <p>DATA:</p> <p>CA / NA / NO_IONS / NEUTRAL</p> <ul> <li>centroid.pdb # centroid calculated with GMX</li> <li>step5_input.gro # input file from CHARMM GUI</li> <li>topol.top # topol file from CHARMM GUI</li> <li>MDPs # folder containing mdp files from CHARMM GUI</li> <li>toppar # folder containing topology files from CHARMM GUI </li> </ul> <p> </p> <p>TRJs:</p> <p>CA / NA / NO_IONS / NEUTRAL</p> <ul> <li>ref.pdb # reference pdb file</li> <li>trj1.xtc # trajectory from replica 1</li> <li>trj2.xtc # trajectory from replica 2</li> <li>trj3.xtc # trajectory from replica 3</li> <li>trj4.xtc # trajectory from replica 4</li> <li>trj5.xtc # trajectory from replica 5</li> <li>trj6.xtc # trajectory from replica 6 (NEUTRAL only)</li> </ul>
Inhibition of striatal dopamine release by the L-type calcium channel inhibitor isradipine co-varies with risk factors for Parkinson's
<h3><strong>ABSTRACT</strong></h3> <p>Ca<sup>2+</sup> entry into nigrostriatal dopamine (DA) neurons and axons via L-type voltage-gated Ca<sup>2+</sup> channels (LTCCs) contributes respectively to pacemaker activity and DA release, and has long been thought to contribute to vulnerability to degeneration in Parkinson’s disease. LTCC function is greater in DA axons and neurons from substantia nigra pars compacta than from ventral tegmental area, but this is not explained by channel expression level. We tested the hypothesis that LTCC-control of DA release is governed rather by local mechanisms, focussing on candidate biological factors known to operate differently between types of DA neurons and/or be associated with their differing vulnerability to parkinsonism, including biological sex, α-synuclein, DA transporters (DATs), and calbindin-D28k (Calb1). We detected evoked DA release <em>ex vivo </em>in mouse striatal slices using fast-scan cyclic voltammetry, and assessed LTCC support of DA release by detecting the inhibition of DA release by the LTCC inhibitors isradipine or CP8. Using genetic knockouts or pharmacological manipulations we identified that striatal LTCC support of DA release depended on multiple intersecting factors, in a regionally and sexually divergent manner. LTCC function was promoted by factors associated with Parkinsonian risk, including male sex, α-synuclein, DAT, and a dorsolateral co-ordinate, but limited by factors associated with protection i.e. female sex, glucocerebrosidase activity, Calb1, and ventromedial co-ordinate. Together, these data show that LTCC function in DA axons, and isradipine effect, are locally governed and suggest they vary in a manner that in turn might impact on, or reflect, the cellular stress that leads to parkinsonian degeneration.</p> <p> </p> <h3><strong>FILE DESCRIPTIONS</strong></h3> <p>This repository contains the following files:</p> <ul> <li>Key Resources Table (.xlsx) - Table containing details on key lab materials (antibodies, mouse lines, and software), and the persistent identifiers for protocols and code used and generated in this study. </li> <li>Source Data (.xlsx) - Excel spreadsheet containing all tabular datasets plotted in Main Figures 1 to 5 (.xlsx).</li> <li>R_Scritps (.R) - Custom written R scripts to perform a classification tree analysis.</li> </ul>
2D LSFM timelapse of cardiomyocyte calcium dynamics
<p>Uploaded zip-folder contains the following files:<br> 1. A representative raw dataset of a 2D LSFM ventricular cardiomyocyte undergoing stimulated calcium transients and calcium sparks (frame_0000.tif -frame_17999.tif)<br> 2. The recorded pacing signal time trace (waveform_test.xslx)<br> 3. Image corresponding to the time-averaged background (AVG_19_35_39_LowNA rolling shutter.tif)<br> 4. Pre-processed nuclear mask matrix (NuclearMask.mat), CMO-channel average (CMO_Average.mat), and CMO channel maximum intensity projection (CMO_MIP). <br> 5. Split and co-registered data for each spectral channel (CMO_frame_00001.tif-CMO_frame_18000.tif, FLUO4_frame_00001.tif -FLUO4_frame_18000.tif).<br> <br> Compressed file size: 14.9 GB<br> Uncompressed file size: 42.8 GB. <br> <br> Related to the following manuscript: <br> Liuba Dvinskikh, Hugh Sparks, Ken MacLeod and Chris Dunsby " <em>High-speed 2D light-sheet fluorescence microscopy enables quantification of spatially varying calcium dynamics in ventricular cardiomyocytes</em>" (2023), <em>In review</em> with Frontiers in Physiology, Cardiac Electrophysiology. </p>
Calcium time series of cortex in a rat model of cortical dysplasia
<p>In vitro Calcium time series of rat (P30) primary motor cortex, were recorder by a CCD camera coupled to stereo-fluoerscence<br> microscope, with a fs = 300ms, following the next sequence: <strong>Basal, <em>Stimulus</em>, Rest.</strong></p> <p>All data is included in a compressed file named <strong>calcium_timeseries.tar.gz.</strong></p> <p>There are two groups of rats: <strong>Control</strong> (control animals), and <strong>BCNU</strong> (experimental animals using the BCNU/carmustine model of cortical dysplasia [1]).</p> <p>Time series are stored in .<strong>csv</strong> files with file names as <strong>R?Pilo-KCl.csv</strong> (where <strong>?</strong> indicates the rat ID). Each of these files holds the two recordings, one for each <em>Stimulus</em>, the first being <em>pilocarpine</em>, followd by <em>KCl</em> used as a control of cellular activity. (pilocarpine, KCl). recording session: The first 150 seconds of these time series correspond to basal activity, followed by 30 s of pilocarpine stimulus, and the rest of spontaneous activity after stimulation, for a total of 15 minutes for each <em>Stimulus</em>. The number of cells recorded varied between animals, as indicated by the number of columns in these .csv files. All of these files have the same number of rows (6000), with each row indicating a frame in the time series. The file <strong>dataEx.png</strong> illustrates this organization.</p> <p>Files named <strong>R?-Coor.csv</strong> (<strong>?</strong> indicates rat ID) show the <em>x</em> and <em>y</em> coordinates of every recorded cell, one for each row, ordered as<br> they appear in the calcium activity recordings. </p> <p><br> Authors:</p> <ul> <li>Ana Aquiles anaaquiles@ciencias.unam.mx</li> <li>Tatiana Fiordelisio tfiorde@ciencias.unam.mx</li> <li>Hiram Luna-Munguía hiram_luna@inb.unam.mx</li> <li>Luis Concha lconcha@unam.mx</li> </ul> <p> </p> <p>1. Benardete, E. A., & Kriegstein, A. R. (2002). Increased excitability and decreased sensitivity to GABA in an animal model of dysplastic cortex. <em>Epilepsia</em>, <em>43</em>(9), 970-982.</p>
Calcium imaging of odor responses in the fruit fly mushroom body
<p><strong>Abstract</strong></p> <p>This dataset contains olfactory responses in the third stage of the olfactory circuit in fruit flies: the mushroom body. The responses are recorded with the GCaMP3 sensor. The methods used to collect the data and the procedures to process them are presented in detail in Campbell et al., 2013, Journal of Neuroscience. The dataset was also used in a recent manuscript by Srinivasan et al., 2023.</p> <p><strong>Methods</strong></p> <p>Please refer to Campbell et al., 2013, Journal of Neuroscience for details. Here, we present a description of how the data was collected, the odors presented, and the analysis, excerpted from Campbell et al., 2013.</p> <p><strong>Animal preparation</strong></p> <p>Flies carrying the genetically encoded calcium sensor UAS-GCaMP3 (Tian et al., 2009) were crossed with OK107-Gal4 flies (Connolly et al., 1996) to drive GCaMP3 expression in essentially all KCs (Lee and Luo, 1999; Aso et al., 2009). All experiments were conducted on female F1 heterozygotes from this cross, aged 2–5 d post-eclosion. Procedures for animal preparation were as described previously (Turner et al., 2008; Murthy and Turner, 2010; Honegger et al., 2011). Flies were anesthetized temporarily on ice and inserted into a small hole cut in the recording platform. The animal’s head was tilted forward, exposing the olfactory organs to the odor delivery nozzle located on the underside of the plat- form. The fly was fixed in place with fast-drying epoxy (Devcon 5 min epoxy). The top of the fly was bathed in oxygenated saline (Wilson et al., 2004) and the cuticle overlying the brain was dissected away. Air sacs overlying the MBs were pushed aside, but we did not attempt to remove the perineural sheath. To minimize movement of the brain inside the head capsule, we removed the pulsatile organ at the neck and the probos- cis retractor muscles that pass over the caudal aspect of the optic lobes.</p> <p> </p> <p><strong>Odor delivery </strong></p> <p>The following chemicals were used as stimuli: 2-heptanone (CAS #110-43- 0), 3-octanol (CAS #589-98-0), 6-methyl-5-hepten-2-one (CAS #110-93-0), ␣-humulene (CAS #6753-98-6), benzaldehyde (CAS #100-52-7), ethyl lactate (CAS #97-64-3), ethyl octanoate (CAS #106-32-1), hexanal (CAS #66-25-1), isoamyl acetate (CAS #123-92-2), 4-methylcyclo- hexanol (CAS #589-91-3), methyl octanoate (CAS #111-11-5), diethyl suc- cinate (CAS #123-25-1), pentanal (CAS #110-62-3), butyl acetate (CAS #123-86-4), 1-octen-3-ol (CAS #3391-86-4), 1-hepten-3-ol (CAS #4938-52- 7), and pentyl acetate (CAS #628-63-7). Odors were presented using a custom-built delivery system that uses serial air dilutions to control odor concentration while maintaining a constant total airflow of 1 L/min at the fly. Experiments were conducted at an odor dilution of 1:100 or, where appropriate, adjusted to match the concentrations used behaviorally. We used a photo-ionization detector (Aurora Scientific) to match concentrations between the imaging rig and the T-maze and to monitor odor delivery throughout each imaging ex- periment. Odor pulses were created by switching between clean and odorized air streams using a synchronous two-way valve (N-Research). This final valve was located 50 cm from the fly, leading to a delay of 300 ms between valve switching and the odor reaching the fly. The flow path was 1/8 inch in diameter throughout, which enabled the system to work near atmospheric pressure at these flow rates. The distance of the valve from the fly and the large tubing diameter virtually eliminated pressure transients caused by valve switching, as measured by the photo-ionization detector and a hot-wire anemometer.</p> <p><strong>Calcium imaging</strong></p> <p>Two-photon imaging was performed using a Prairie Ultima system (Prairie Technologies) and a Ti-Sapphire laser (Chameleon XR; Coher- ent) tuned to 920 nm delivering 8 –10 mW at the sample. All images were acquired with Olympus water-immersion objectives (LUMPlanFl/IR, 60x, numerical aperture 0.9; LUMPlanFl/IR, 40x, numerical aperture 0.8). Imaging planes were selected to maximize the number of visibleKCs. Typically imaging frames were 300 x 300 pixels, acquired with a pixel dwell time of 1.6 s, yielding frame rates near 3.8 Hz. On average, 120 KCs (range: 60 –170) were monitored in one plane. Custom MATLAB (MathWorks) routines were used to control odor presentation and synchronize stimulus delivery with data acquisition. Data were acquired in 20 s sweeps with a 1 s odor pulse triggered 8 s after sweep onset. The interstimulus interval was 25 s. Stimuli were presented in randomly interleaved fashion, adjusted so that the same odor was never presented twice in succession.</p> <p><strong>Imaging analysis</strong></p> <p>Data were analyzed using MATLAB and R (http://www.R-project.org). To correct for motion within the field of view, frames were aligned using 2D image registration approaches. In many cases, a Fourier-based sub-pixel translation correction was sufficient (Guizar-Sicairos et al., 2008). Some animals required an affine transform to cope with global distortions, such as rotational movement of the brain (Thirion, 1998). Where necessary a nonrigid transform was used to correct more localized dis- tortions (Klein et al., 2010). Fluorescent neural tissue was automatically segmented from the surrounding regions. Pixel intensity values from the area outside this boundary were considered to represent background (tissue autofluorescence plus shot noise) and the mean pixel intensity value from the back- ground was then subtracted from the overall image. To quantify the response of the KCs a small, circular region of interest 6 – 8 pixels in diameter was applied to each cell body. This allowed aver- aging of the pixel intensity values from each cell, treating individual KCs as separate units. Care was taken to ensure that each selected cell re- mained within its region of interest over the whole imaging session. Response amplitudes were calculated as the mean change in fluorescence (dF/F) in the 0.5– 4.5 s window after stimulus onset. A statistical test originally described in Honegger et al. (2011) was used to determine whether a KC responded significantly on a given trial. Briefly, the SD of the baseline activity was obtained 8 s before stimulus onset. The response time course was then smoothed using a five-point running average to control for outliers. The peak dF/F in the 0.5– 4.5 s window after stimulus onset was determined. The response was judged to be significant if this peak was 2.33 SDs greater than the baseline, which corresponds to a one-tailed significance test where alpha = 0.01.</p> <p><br> <strong>References</strong></p> <p>Aso Y, Grübel K, Busch S, Friedrich AB, Siwanowicz I, Tanimoto H (2009) The mushroom body of adult Drosophila characterized by GAL4 drivers. J Neurogenet 23:156 –172. </p> <p>Connolly JB, Roberts IJ, Armstrong JD, Kaiser K, Forte M, Tully T, O’Kane CJ (1996) Associative learning disrupted by impaired Gs signaling in Drosophila mushroom bodies. Science 274:2104 –2107.</p> <p>Honegger KS, Campbell RA, Turner GC (2011) Cellular-resolution population imaging reveals robust sparse coding in the Drosophila mushroom body. J Neurosci 31:11772–11785.</p> <p>Lee T, Luo L (1999) Mosaic analysis with a repressible cell marker for studies of gene function in neuronal morphogenesis. Neuron 22:451– 461.</p> <p>Murthy M, Turner GC (2010) In vivo whole-cell recordings in the Drosophila brain. In: Drosophila neurobiology methods: a laboratory manual (Zhang B, Waddell S, Freeman M, eds). Cold Spring Harbor, NY: Cold Spring Harbor Laboratory.</p> <p>Srinivasan, S., Daste, S., Modi, M., Turner, G., Fleischmann, A. & Navlakha, S (2023). Stochastic coding: a conserved feature of odor representations and its implications for odor discrimination. bioRxiv.</p> <p>Thirion JP (1998) Image matching as a diffusion process: an analogy with Maxwell’s demons. Med Image Anal 2:243–260.</p> <p>Tian L, Hires SA, Mao T, Huber D, Chiappe ME, Chalasani SH, Petreanu L, Akerboom J, McKinney SA, Schreiter ER, Bargmann CI, Jayaraman V, Svoboda K, Looger LL (2009) Imaging neural activity in worms, flies and mice with improved GCaMP calcium indicators. Nat Methods 6:875–881.</p> <p>Turner GC, Bazhenov M, Laurent G (2008) Olfactory representations by Drosophila mushroom body neurons. J Neurophysiol 99:734 –746.</p> <p>Wilson RI, Turner GC, Laurent G (2004) Transformation of olfactory representations in the Drosophila antennal lobe. Science 303:366–370.</p> <p><strong>Usage notes</strong></p> <p>The files are all in csv format, and can be easily opened in R or Python or other programming languages.</p> <p>Please see the README.md file for directions on how to use the data.</p> <p>The dataset included here is broken into two parts. The main dataset was the one that was chiefly used in the Campbell and Srinivasan papers, with the second part containing 7 additional datasets that were used in some figures. A fuller description is available in the README.md file.</p>
Efficiency of the formation of acid-resistant Calcium-oxalate layers on limestone
<p><strong>Scientific background:</strong></p> <p>Carbonate-based stone monuments and buildings are susceptible to weathering in acidic environments. To combat surface corrosion and slow down material deterioration, protective coatings that inhibit calcite dissolution have been proposed. The efficiency and integrity of the coatings was studied by measuring sulfur distribution along the treated surface. Such experiment cannot be efficiently performed with standard SSD PIXE detectors due to high overlap between the strong Ca K x-ray escape peaks and S Kα. For that purpose, a new parallel-beam wavelength dispersive (PB-WDS) X-ray emission spectrometer at JSI have been used which achieves high energy resolution in the eV range and is able to measure S distribution on the surface of treated marble samples.</p> <p><strong>Measurements performed within TNA project:</strong></p> <p>The new PB-WDS X-ray emission spectrometer at J. Stefan Institute (Ljubljana, Slovenia) was used to map the presence of Sulphur on the surface of 13 marble samples treated with different coatings and after exposure to 2% sulfuric acid. Ge(111) crystal analyzer was used in the spectrometer to record the S Ka signal, the overall scan size was 5 × 5 mm<sup>2</sup>.</p> <p><strong>Data files:</strong></p> <p>We are sharing the files produced during measurements. The signal from the detector preamplifier was processed with the XIA DXP-XMAP digital pulse processor. The files are two main formats:</p> <ol> <li>Files containing mapping data. The spectrometer was set to the Bragg angle corresponding to the energy of the S Ka emission line. In a .zip folder, with 4 .mca files for every measured point (extension: _0-Si(Li) detector, _2- PB-WDS spectrometer)</li> <li>High energy resolution spectra recorded at selected points on the sample surface.</li> </ol> <p> </p> <p> </p> <table> <thead> <tr> <th> <p>#</p> </th> <th> <p>Filename</p> </th> <th> <p> </p> </th> </tr> </thead> <tbody> <tr> <td> <p>1</p> </td> <td> <p>VES_A1_5.zip</p> </td> <td> <p>Map of S on VES_A1_5 sample. Ge 111, 100X100 points, 50μm step, 4s/point</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>S_X80_Y85.zip</p> </td> <td> <p>Scan over S Ka and Kb peak on the surface of VES_A1_5 sample. Point position x = 80px, y = 85px</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>VES_A3_5.zip</p> </td> <td> <p>Map of S on VES_A3_5 sample. Ge 111, 100X100 points, 50μm step, 4s/point</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>S_X95_Y33.zip</p> </td> <td> <p>Scan over S Ka and Kb peak on the surface of VES_A3_5 sample. Point position x = 95px, y = 33px</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>VES_A1_12.zip</p> </td> <td> <p>Map of S on VES_A1_12 sample. Ge 111, 40x40 points, 125μm step, 5s/point</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p>S_X3_Y3.zip</p> </td> <td> <p>Scan over S Ka and Kb peak on the surface of VES_A1_12 sample. Point position x = 3px, y = 3px. Z position optimized to maximum at this point</p> </td> </tr> <tr> <td> <p>7</p> </td> <td> <p>S_X20_Y20.zip</p> </td> <td> <p>Scan over S Ka and Kb peak on the surface of VES_A1_12 sample. Point position x = 20px, y = 20px. Z position optimized to maximum at this point</p> </td> </tr> <tr> <td> <p>8</p> </td> <td> <p>S_X35_Y20.zip</p> </td> <td> <p>Scan over S Ka and Kb peak on the surface of VES_A1_12 sample. Point position x = 35px, y = 20px. Z position optimized to maximum at this point</p> </td> </tr> <tr> <td> <p>9</p> </td> <td> <p>CAR_A1_12.zip</p> </td> <td> <p>Map of S on VES_A1_12 sample. Ge 111, 80x80 points, 65μm step, 4s/point</p> </td> </tr> <tr> <td> <p>10</p> </td> <td> <p>CAR_A1_12_back_side.txt</p> </td> <td> <p>Scan over S Ka and Kb peak on the back surface of CAR_A1_12 sample.</p> </td> </tr> <tr> <td> <p>11</p> </td> <td> <p>VES_A1_12_Washed.zip</p> </td> <td> <p>Sample washed under running water. 2250 eV - 2350 eV; stepsize = 1.00eV; 6s/point or 10s/point for back</p> </td> </tr> <tr> <td> <p>12</p> </td> <td> <p>VES_A2_12.zip</p> </td> <td> <p>Line map of S on VES_A2_12. 10x1 points, 1mm stepsize, 10s/point</p> </td> </tr> <tr> <td> <p>13</p> </td> <td> <p>CAR_A3_5.zip</p> </td> <td> <p>Line map of S on CAR_A3_5. 10x1 points, 1mm stepsize, 10s/point</p> </td> </tr> <tr> <td> <p>14</p> </td> <td> <p>CAR_A1_5.zip</p> </td> <td> <p>Line map of S on CAR_A1_5. 10x1 points, 1mm stepsize, 10s/point1</p> </td> </tr> <tr> <td> <p>15</p> </td> <td> <p>VES_A3_12.zip</p> </td> <td> <p>Line map of S on VES_A3_12. 10x1 points, 1mm stepsize, 10s/point + Map of S on VES_A3_12 sample. Ge 111, 50x25 points, 200μm step, 3s/point</p> </td> </tr> <tr> <td> <p>16</p> </td> <td> <p>VES_A2_5.zip</p> </td> <td> <p>Line map of S on VES_A2_5. 5x1 points, 1mm stepsize, 10s/point</p> </td> </tr> <tr> <td> <p>17</p> </td> <td> <p>CAR_A2_5.zip</p> </td> <td> <p>Line map of S on CAR_A2_5. 5x1 points, 1mm stepsize, 10s/point</p> </td> </tr> <tr> <td> <p>18</p> </td> <td> <p>CAR_A2_12.zip</p> </td> <td> <p>Line map of S on CAR_A2_12. 5x1 points, 1mm stepsize, 10s/point</p> </td> </tr> <tr> <td> <p>19</p> </td> <td> <p>CAR_A3_12.zip</p> </td> <td> <p>Line map of S on CAR_A3_12. 5x1 points, 1mm stepsize, 10s/point</p> </td> </tr> </tbody> </table> <p> </p>
Concentration of calcium in stream-dwelling Plethodontid salamanders across six streams located with the Coweeta Hydrologic Laboratory, Otto, NC
We examined the concentration of calcium in several species of stream-dwelling plethodontid salamanders captured across six streams located within the Coweeta LTER site. %Ca was measured for 15 Eurycea wilderae, 15 Desmognathus ocoee, and 20 Desmognathus quadramaculatus. Because D. quadramaculatus larvae represent animals across a 3-4 year larval lifespan, for %Ca analysis we randomly sampled 6-8 D. quadramaculatus from each of three size classes: 18-25 mm SVL, 26-33 mm SVL, and 33-40 mm SVL.
Forest Inventory of a Calcium Amended Northern Hardwood Forest: Watershed 1, 2016, Hubbard Brook Experimental Forest
In order to evaluate the role of Ca supply in regulating the structure and function of base-poor forest and aquatic ecosystems, the Ca content of soil was increased through the application of wollastonite (CaSiO3) in October 1999. Forest inventory surveys were initiated in 1996 and repeated at 5 year intervals. This data set includes 2016 inventory measurements. The data consists of a total inventory of all trees >=10 cm diameter-at-breast-height (dbh) on the whole of the watershed (11.8 ha), as measured in each of the 200 25 m x 25 m plots. Trees >=2 to <=10 cm dbh were subsampled using a 3 meter wide strip along one edge of each 25 m x 25 m plot. With the addition of tree tags in 2006 on all trees >=10 cm dbh, tracking of individual trees is now possible. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
The gamaproteobacterium Achromatium forms intracellular amorphous calcium carbonate and not (crystalline) calcite- dataset
<p>This is the dataset accompanying the paper published in geobiology and titled <em>The gamaproteobacterium Achromatium forms intracellular amorphous calcium carbonate and not (crystalline) calcite</em></p> <p>It comprises SEM and light microscopy images, Raman spectra (txt files) and one excel files containing the results of Raman spectra fits.</p>
Example Datasets for Iliski - Neuronal calcium, RBC velocities and fUS responses to odorant stimuli in the mouse olfactory bulb
<p>Dataset containing 2 HDF5 files, one per mouse. It is intended to be used to test Iliski, a Transfer Function computation software. Iliski is available on GitLab (<a href="https://gitlab.com/AliK_A/iliski">https://gitlab.com/AliK_A/iliski</a>) along with the User Manual. Refer to the User Manual and to the ReadMe file for more details on Iliski. Data were already published on Zenodo (<a href="https://doi.org/10.5281/zenodo.3773863">https://doi.org/10.5281/zenodo.3773863</a>), but along an old version of the software. This upload is made for clarity purposes.</p> <p>Each file contains acquisitions of responses to odorant stimuli in the olfactory bulb made with :</p> <ul> <li>two-photon linescan microscopy (for Ca2+ and RBC velocity);</li> <li>functional ultrafast ultrasound, acquired from a coronal plane.</li> </ul> <p>HDF5 files tree is as follows :</p> <ul> <li>Data type <ul> <li>Raw : straight out of our extraction software, no specific treatment applied;</li> <li>Aligned : every acquisition has been aligned so that the odor delivery matches the 10 s mark. Acquisitions have also been interpolated to be meaned;</li> <li>Delta : aligned acquisitions are subtracted with the baseline value (between 5 and 10 s);</li> <li>DetaOverBSL : aligned acquisitions are subtracted and then divided with the baseline value.</li> </ul> </li> <li>Data source <ul> <li>Ca : calcium data from GCamP6f expressed in the mitral cells dendritic tufts;</li> <li>RBC : RBC velocities in a capillary near the calcium recording site, simultaneously acquired;</li> <li>FUS : fUS data, coronal plane. Only in FUS folder is two different folders then : High and Lowspeed, corresponding to different filter for fUS treatment, > 80Hz and 10-30Hz respectively.</li> </ul> </li> <li>Stimulation type : Odorant_Quantity_Duration <ul> <li>Odorant type, either Iso Amyl Acetate (AA) or Ethyl tiglate (ET);</li> <li>Odor quantity : measured and calibrated in volt with a photo-ionizator;</li> <li>Odor duration : from 5 s down to 120 ms, a single sniff for a mouse.</li> </ul> </li> </ul> <p>Ca2+ : Calcium</p> <p>fUS : functional ultrafast ultrasound</p> <p>RBC : Red Blood Cell</p>
Supplementary data for calcium-vesicles perform active diffusion in the sea urchin embryo during larval biomineralization
<p><strong>Supplementary datasets for the paper Calcium-vesicles perform active diffusion in the sea urchin embryo during larval biomineralization.</strong></p> <p>Two deskewed and deconvolved lattice light-sheet datasets (100 frames each) from the live-cell experiments are available, a control embryo dataset (01-07-2016_TimeLapse4_DMSO_21hrs_Calcein_FM464) and a VEGFR inhibited dataset (24-06-2016_Timelapse1_Axtinib_150_19hrs_Calcein_FM464). These datasets were used for collecting size and motion statistics. The control embryo dataset is available in raw microscope output without deskew or deconvolution applied (Raw_01-07-2016_TimeLapse4_DMSO_21hrs_Calcein_FM464).</p> <p>Four confocal datasets from the cytoskeletal remodeling experiments are also included, phalloidin stained images, control (Phalloidin PMC DMSO 5 zoom4s) and VEGFR inhibited (Phalloidin PMC Axt 18 zoom4); and myosinIIP stained images, control (Myosin PMC 30h DMSO new slid 4a zoom4) and VEGFR inhibited (Phalloidin PMC Axt 18 zoom4).</p> <p>Source code and instructions for the analysis tools used for both the lattice light-sheet and confocal data is available at: <a href="https://git-bioimage.coe.drexel.edu/opensource/llsm-calcium-vesicles-lever">https://git-bioimage.coe.drexel.edu/opensource/llsm-calcium-vesicles-lever</a></p> <p>Code for the deconvolution and deskew algorithms is available from the Janelia research center at: <a href="https://www.janelia.org/open-science/lattice-light-deconvolution-software-cudadeconv">https://www.janelia.org/open-science/lattice-light-deconvolution-software-cudadeconv</a></p> <p> </p> <p> </p>
Molecular dynamic trajectory of calcium binding T163S mutant for the article "Ca 2+ binding to F-ATP synthase β subunit triggers the mitochondrial permeability transition"
<p>ATP synthase molecular dynamics simulations files for T163S mutants of the beta subunit binding calcium:</p> <p>50ns trajectory (ATPsynth_woh2o_Ca_mut.dcd) and corresponding psf file (ATPsynth_ca_mut.psf)</p>
Molecular dynamic trajectory of calcium binding wild type for the article "Ca 2+ binding to F-ATP synthase β subunit triggers the mitochondrial permeability transition"
<p>ATP synthase molecular dynamics simulations files for wild type of the beta subunit binding calcium:</p> <p>50ns trajectory (ATPsynth_woh2o_Ca_wt.dcd) and corresponding psf file (ATPsynth_ca_wt.psf)</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.