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3,465 results for “mass”
Mass and Chemistry of Organic Horizons and Surface Mineral Soils on Watershed 1 at the Hubbard Brook Experimental Forest 1996-present
This data set includes chemistry of O-horizons ("forest floor") and the 0-10 cm mineral soil layer in Watershed 1 at Hubbard Book. Calcium in the form of wollastonite (CaSiO3) was added to Watershed 1 in October 1999. The application rate was 1028 kg Ca per ha, and the application was relatively uniform across the watershed. Pre-treatment forest floor surveys were completed in 1996 and 1998. The first post-treatment forest floor survey was completed in 2000. This data set includes mass and thickness data for the sampled layers. Chemical data include concentrations and pools of organic matter, C, N, Ca, Mg, K, P, Mn, Fe, Al, Cu, Pb, and Zn. Soil pH and exchangeable Al, Ca, Mg, K, and H are also included. Sampling is intended to continue at 4 or 5 year intervals. 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.
Mass and Nutrient Loss in Decomposing Hardwood Boles on Watershed 1 at the Hubbard Brook Experimental Forest, 1990 - present
In 1990-1991 segments of boles from felled sugar maple (Acer saccharum), yellow birch (Betula alleghaniensis) and American beech (Fagus grandifolia) trees were placed in the field to study the rate of decomposition and nutrient loss (or gain) over time. The segments incubated in the field, ranging from 0.5-1.3 meters in length, were paired with fresh segments from the same trees. The fresh segments were taken to the lab shortly after felling, dried, weighed and subsampled. Fresh samples of wood and bark were collected separately. Incubated bole segments were collected in 1993 (T1), 1997 (T2), 2001 (T3), 2007 (T4) and 2015/2016 (T5). The whole bole segments were transported to the lab, measured, dried and weighed to determine mass loss. Subsamples of the bole wood and bark were collected for chemical analysis, including C, N, H, Ca, Mg, K, Si, Al, Pb, Zn, Mn and Fe. Chemical analyses were conducted concurrently on the fresh (T0) and incubated samples. This data set includes the masses of the fresh and incubated boles along with the concentrations of the chemical analytes. Element pools in the boles can be calculated by multiplying the concentrations by the mass values. This data set includes chemical data for samples collected in 1993, 2001, and 2007 and their paired fresh samples. Samples from 1997 were measured for mass, but inadvertently discarded prior to chemical analysis. 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.
Oxygen-argon dissolved gas ratios using Equilibrator Inlet Mass Spectrometry (EIMS) and triple oxygen isotopes (TOI) from NES-LTER Transect cruises, ongoing since 2018
In order to calculate net community production (NCP) rates on Northeast U.S. Shelf Long-Term Ecological Research (NES-LTER) transect cruises, gas tracer data were collected with a continuous at-sea mass spectrometer. The ratio of O2/Ar, measured continuously from underway water, yields 8,000-15,000 rates of NCP per cruise. Discrete water samples (50 to 150 per cruise) were collected for triple oxygen isotope (TOI) analysis to estimate gross primary production (GPP) rates and ratios of NCP/GPP. Along-shelf (upstream-downstream) transects were conducted in addition to the main across-shelf transect. This data package provides two types of data tables for NES-LTER transect cruises beginning in 2018: a high-frequency continuous Equilibration Inlet Mass Spectrometer (EIMS) table, provided by year, and a low-frequency discrete triple oxygen isotope (TOI) table with all years combined. Rates calculated from these measurements are provided as separate packages, per year, in the EDI repository.
Dataset for "Remapping of Greenland ice sheet surface mass balance anomalies for large ensemble sea-level change projections"
<p>This dataset is used to reproduce the results presented in the following publication:</p> <p>Goelzer, H., Noel, B. P. Y., Edwards, T. L., Fettweis, X., Gregory, J. M., Lipscomb, W. H., van de Wal, R. S. W., and van den Broeke, M. R.: Remapping of Greenland ice sheet surface mass balance anomalies for large ensemble sea-level change projections, The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-188, in review, 2019.</p> <p> </p>
mzrtsim: Raw Data Simulation for Reproducible Gas/Liquid Chromatography–Mass Spectrometry Based Non-targeted Metabolomics Data Analysis
<p>All the data for 'mzrtsim: Raw Data Simulation for Reproducible Gas/Liquid Chromatography–Mass Spectrometry Based Non-targeted Metabolomics Data Analysis'</p> <p>sim.zip is stimulated data for intensity cutoff 0.05. simxcms.csv is peak intensity profiles for their simulated peaks.</p> <p>sim3.zip are simulated data for normal/leading/tailing peaks with tailing factor of 1, 0.8, and 1.5, respectively.</p> <p>All the csv files begin with sim3 are extracted peaks list from the sim3.zip with corresponding data analysis software.</p> <p>csv.zip recorded the m/z, retention time, intensity, and compounds name for simulated compound for each condition (sim.zip and sim3.zip).</p> <p>sep1.mzML: simulation for 8 isomers with similar m/z while different retention times. 7 peaks are non baseline separation peaks. Peaks profile is saved in spe1.csv file.</p> <p>xcms.csv, mzmine.csv, openms.csv: peaks found in sep1.mzML by xcms, mzmine 4.5 and openms, respectively.</p> <p>R code: <a href="https://github.com/yufree/democode/blob/master/meta/simfin.R">https://github.com/yufree/democode/blob/master/meta/simfin.R</a></p> <p>Website of mzrtsim package: https://yufree.github.io/mzrtsim/</p>
BASE-9 binarity and stellar masses from Gaia DR3, 2MASS, and Pan-STARRS data for six open clusters: NGC 2168, NGC 7789, NGC 6819, NGC 2682, NGC 188, NGC 6791
<h2>Data sets as described in "Goodbye to Chi-by-Eye: A Bayesian Analysis of Photometric Binaries in Six Open Clusters", Childs et al. 2023 <a href="https://ui.adsabs.harvard.edu/abs/2023arXiv230816282C/abstract">https://ui.adsabs.harvard.edu/abs/2023arXiv230816282C/abstract</a></h2>
Data from: "Little evidence of inbreeding depression for birth mass, survival and growth in Antarctic fur seal pups"
<p>This data repository contains:</p> <ul> <li><span>"msats_growth_individuals.xlsx" - Microsatellite data (39 loci) of Antarctic fur seals<br></span></li> <li><span>"pup_growth_2017-2020.xlsx" - Birth weight and tagging weight data for pups collected in 2017-2020.<br></span></li> <li><span>"Rebeccas_Samples_Mendel_OriginalPedigree" - SNP array data (75k SNPs) in PLINK format for a subset of individuals<br></span></li> <li><span>"GrowthRM_BI1820_Day60.new.csv" - Repeated weight measures for a subset of individuals</span></li> </ul> <p><strong><br>Manuscript abstract</strong></p> <p><span>Inbreeding depression, the loss of offspring fitness due to consanguineous mating, is generally detrimental for individual performance and population viability.<span> </span>We therefore investigated inbreeding effects in a declining population of Antarctic fur seals (<em>Arctocephalus gazella</em>) at Bird Island, South Georgia.<span> </span>Here, localised warming has reduced the availability of the seal’s staple diet, Antarctic krill, leading to a temporal increase in the strength of selection against inbred offspring, which are increasingly failing to recruit into the adult breeding population.<span> </span>However, it remains unclear whether selection operates before or after nutritional independence at weaning.<span> </span>We therefore used microsatellite data from 885 pups and their mothers, and SNP array data from 98 mother-offspring pairs, to quantify the effects of individual and maternal inbreeding on three important neonatal fitness traits: birth mass, survival and growth.<span> </span>We did not find any clear or consistent effects of offspring or maternal inbreeding on any of these traits.<span> </span>This suggests that selection filters inbred individuals out of the population as juveniles during the time window between weaning and recruitment.<span> </span>Our study brings into focus a poorly understood life-history stage and emphasises the importance of understanding the ecology and threats facing juvenile pinnipeds.</span></p> <p><strong><span>Funding</span></strong></p> <p><span>This research was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) priority programme “Antarctic Research with Comparative Investigations in Arctic Ice Areas” SPP 1158 (project number 424119118) and the SFB TRR 212 (NC³) (Project Numbers 316099922 & 396774617). This work contributes to the Ecosystems project of the British Antarctic Survey, Natural Environmental Research Council, and is part of the Polar Science for Planet Earth Programme.</span></p>
Sensitivity enhancement using chemically reactive gas cluster ion beams in secondary ion mass spectrometry (SIMS)
<p>We report for the first time on significant molecular secondary ion yield increases by modifying the chemistry of a water cluster primary ion beam. This was demonstrated using 70 keV ion beams of 0.15 eV/amu. For the neutral drug Bezafibrate, secondary ion yield enhancements ×5-10 were observed when replacing the Ar carrier gas in a water gas cluster ion beam (GCIB) source with a mixture containing 12% CO2 and 2% O2 in Ar. For the cationic drug Ranitidine the ion yield enhancements using the CO2-containing carrier gas were up to ×20-50 in positive mode and ×2-4 in negative mode. The extent of molecular fragmentation was very similar from both cluster beams. We conclude that additional chemically reactive species are present in the impact zone using the (H2O/CO2)n projectile which promote the formation of secondary ions of both polarity through projectile impact-induced chemical reactions. This methodology can be applied to further extend the capabilities of high-resolution 3-dimensional mass spectral imaging using reactive GCIB-SIMS.</p>
X-rays across the galaxy population: The distribution of AGN accretion rates as a function of stellar mass and redshift
<p>We provide measurements of the probability distribution function of specific black hole accretion rates within a sample of galaxies of a given stellar mass and redshift, <span class="math-tex">\(p(\log \lambda_{sBHAR} | M_*,z)\)</span>. Measurements are provided for all galaxies, star-forming galaxies and quiescent galaxies. We also provide estimates of the AGN duty cycle, <span class="math-tex">\(f(\lambda_{sBHAR} >0.01)\)</span> i.e. the fraction of galaxies with an AGN above a given limit in specific accretion rate, based on the probability distribution functions. Full details are provided in Aird et al. (2018, MNRAS, 474, 1225); please cite this publication if you use these measurements. </p>
Data and Software for: 'The Radius of the High-mass Pulsar PSR J0740+6620 with 3.6 yr of NICER Data'
<p>Posterior sample files associated with the publication "The Radius of the High-mass Pulsar PSR J0740+6620 with 3.6 yr of NICER Data" by Salmi et al. (2024; <a href="https://doi.org/10.48550/arXiv.2406.14466">arXiv.2406.14466</a>; <a href="https://doi.org/10.3847/1538-4357/ad5f1f">https://doi.org/10.3847/1538-4357/ad5f1f</a>).</p> <p>Also included are: the data products; the numeric model files including the telescope calibration products; model modules in the Python language using the X-PSI framework; and Jupyter analysis notebooks.</p> <p>Please refer to the README for detailed information.</p>
Mass spectrometry raw data for "Proteomics reveals substantial differences between in vitro matured abattoir-derived and in vivo matured oocytes in cattle"
<p><em><span>In vitro</span></em><span> production (IVP) of bovine embryos still has its limitations such as low blastocyst rate and lower embryo quality, resulting in lower pregnancy rates following the transfer of IVP embryos compared to <em>in vivo</em> produced embryos. </span><span>Given these differences in developmental competence, RNA sequencing and microarray technology have been applied to describe the differences in transcriptional activity between <em>in vitro</em> and <em>in vivo</em> produced embryos. All but one of these studies solely utilized oocytes obtained from slaughterhouse material for the <em>in vitro</em> production of embryos, thereby introducing the possibility, that differences between IVP and <em>in vivo</em> embryos are in part attributable to differing sources of oocytes. The aim of the present study was therefore to compare the proteome of oocytes retrieved from slaughterhouse material, with and without a period of <em>in vitro</em> maturation and <em>in vivo</em> matured oocytes obtained from donor cattle following superovulation. <span>For each group the protein pattern of four biological replicates containing ten oocytes each were analyzed via SWATH<sup>TM</sup>-MS.</span></span></p>
S31 | WRTMSD | Wiley Registry of Tandem Mass Spectral Data, MSforID
<p>This is the collection associated with list S31 WRTMSD on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/?q=suspect-list-exchange">https://www.norman-network.com/?q=suspect-list-exchange</a></p> <p>S31</p> <p>WRTMSD</p> <p><strong>Wiley Registry of Tandem Mass Spectral Data, MSforID</strong></p> <p>WRTMSD <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/210119Update/WRTMSD_wDTXSIDs_24012019.csv">CSV</a>, <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/210119Update/WRTMSD_wDTXSIDs_24012019.xlsx">XLSX</a> (24/01/2019)</p> <p>CompTox <a href="https://comptox.epa.gov/dashboard/chemical_lists/wrtmsd">WRTMSD List</a></p> <p>WRTMSD <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/210119Update/WRTMSD_InChIKeys.txt">InChIKeys </a>(24/01/2019)</p> <p>The "Wiley Registry of Tandem Mass Spectral Data, MSforID" contains high-quality tandem MSacquired on a QqTOF instrument, developed by Herbert Oberacher (Medical University of Innsbruck, Austria). More information at <a href="http://www.msforid.com/">www.msforid.com</a> </p>
Variability and bias in measurements of metals mass fractions in automobile shredder residue
<p>Measured mass fractions of various metals in individually digested test samples of automobile shredder light fraction (single_digestions_ppm.csv) and the calculated means and standard deviations of these (mean_sd_ppm.csv). For all metadata see accompanying readme file Loevik2019_metal_mass_fractions_in_automobile_SLF_Readme.txt.</p>
Global and regional glacier mass changes from 1961 to 2016
<p>Supplementary data tables with the results from Zemp et al. (2019) entitled "<em><strong>Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016</strong></em>", Nature:</p> <p><strong>Data Tables 1a-t | Temporal variabilities for glaciological clusters based on variance decomposition model. </strong>Annual results are made available as csv-files for all 20 cluster: Zemp_etal_results_clusters.zip</p> <p><strong>Data Tables 2a-t | Regional and global mass balance and mass change results from 1961-2016. </strong>Annual results are made available as csv-files for all 19 regions (cf. RGI 6.0) as well as for the global sum: Zemp_etal_results_regions_global.zip</p> <p><strong>Data Table 3 | Glacier mass changes for Central Europe from 1961-2016. </strong>Annual results as in Data Table 2 but with examples for multi-year error calculations are made available as Excel-file: Zemp_etal_results_region-CEU_errorcalcs.xlsx</p> <p><strong>Note</strong>: The corresponding full sample of glaciological and geodetic observations for individual glaciers are publicly available from the World Glacier Monitoring Service: http://doi.org/10.5904/wgms-fog-2018-11</p> <p><strong>Version 1.1</strong><br> This version provides the results from the glaciological clusters (Data Tables 1a-t) as used by Zemp et al. (2019). In addition, it contains corrected results for region Iceland (Data Table 2, region_6_ISL) and correspondingly for the global sum (Data Table 2, global); the results for the other regions remain unchanged. For more details on the correction of the regional results for Iceland, see Zemp et al. (2019, Nature), Author Correction.</p> <p><strong>Version 1.0</strong><br> Data tables containing the results for the 20 glaciological clusters (Data Tables 1a-t) as well as for the 19 regions and global sums (Data Tables 2a-t) related to the publication by Zemp et al. (2019, Nature). We note that this version erroneously contains pre-release versions of results for most of the glaciological clusters that were not used in Zemp et al. (2019, Nature).</p> <p> </p>
Identifying Coronal Mass Ejection Active Region Sources: An automated approach - Catalogue results
<p>Catalogue of Coronal Mass Ejection (CME) active region sources. Includes a database version and a simplified .csv version. For full details, refer to the source code at <a href="https://github.com/JulioHC00/cmesrc">https://github.com/JulioHC00/cmesrc</a>. We include a README file for each describing each column.</p> <p>We also include the raw data used to generate the catalogue so that results may be reproduced following the steps detailed in <a href="https://github.com/JulioHC00/cmesrc">https://github.com/JulioHC00/cmesrc</a>. This is a collection of data from other works and we provide it only to allow the results to be reproduced</p> <p>Below, we detail the data sources for the raw_data folders</p> <p>==============================<br><strong>RAW DATA SOURCES</strong><br>==============================</p> <p><strong>DIMMINGS FOLDER</strong></p> <p>Data is from Solar Demon, .csv was provided by Emil Kraaikamp through private communication.</p> <blockquote> <p>Solar Demon – an approach to detecting flares, dimmings, and EUV waves on SDO/AIA images<br>Emil Kraaikamp, Cis Verbeeck<br>J. Space Weather Space Clim. 5 A18 (2015)<br>DOI: 10.1051/swsc/2015019</p> </blockquote> <p><strong>HARPNUM_TO_NOAA FOLDER</strong></p> <p>Obtained from http://jsoc.stanford.edu/doc/data/hmi/harpnum_to_noaa/all_harps_with_noaa_ars.txt</p> <p><strong>LASCO FOLDER</strong></p> <p>This CME catalog is generated and maintained at the CDAW Data Center by NASA and The Catholic University of America in cooperation with the Naval Research Laboratory. SOHO is a project of international cooperation between ESA and NASA.</p> <p>Downloaded from https://cdaw.gsfc.nasa.gov/CME_list/</p> <p><strong>MVTS FOLDER</strong></p> <p>Data from</p> <blockquote> <p>Angryk, R.A., Martens, P.C., Aydin, B. et al. Multivariate time series dataset for space weather data analytics. Sci Data 7, 227 (2020). https://doi.org/10.1038/s41597-020-0548-x</p> </blockquote> <p>Available at the Harvard Dataverse</p> <blockquote> <p>Angryk, Rafal; Martens, Petrus; Aydin, Berkay; Kempton, Dustin; Mahajan, Sushant; Basodi, Sunitha; Ahmadzadeh, Azim; Xumin Cai; Filali Boubrahimi, Soukaina; Hamdi, Shah Muhammad; Schuh, Micheal; Georgoulis, Manolis, 2020, "SWAN-SF", https://doi.org/10.7910/DVN/EBCFKM, Harvard Dataverse, V1</p> </blockquote> <p>The DT_SWAN folder contains the same data but with extra columns obtained directly from the Joint Science Operations Center (JSOC) through the python package drms.</p>
Supplementary File: Entertainment interspersed with propaganda: How non-legacy-news accounts deliver explicitly political content to mass audiences on Russia's most popular social network VK
<p>Supplementary file and dataset for the paper "Entertainment interspersed with propaganda: How non-legacy-news accounts deliver explicitly political content to mass audiences on Russia’s most popular social network VK"</p>
The Outer Stellar Mass of Massive Galaxies: A SimpleTracer of Halo Mass with Scatter Comparable to Richness and Reduced Projection Effects
<p>These are the data for reproducing the results of the publication titled "The Outer Stellar Mass of Massive Galaxies: A Simple Tracer of Halo Mass with Scatter Comparable to Richness and Reduced Projection Effects" by Song Huang et al.</p> <p>Please see the Python scripts and Jupyter notebooks provided in the <a href="https://github.com/dr-guangtou/jianbing">jianbing</a> repo for examples about how to use these data files. And please contact dr.guangtou@gmail.com if you have any questions about these data.</p> <p>-------------------------------------------------------------------------------------------------</p> <p>Here is a brief description of all the files:</p> <p><strong>Data from N-body simulation:</strong></p> <ul> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/mdpl2_halos_0.7333_reduced_logmvir_13.npy?versionId=1648006b-a91a-4300-aadf-c4746d6f3ef2">mdpl2_halos_0.7333_reduced_logmvir_13.npy</a> <ul> <li>Basic information about the dark matter halos from MDPL2 simulation</li> <li>For scale factor = 0.7333 (or z~0.4).</li> <li>Only for halos with logMvir > 13.0.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/mdpl2_particles_0.7333_72m.npy?versionId=ff7d5847-df44-46f5-9bcc-d8a7f3cc040d">mdpl2_particles_0.7333_72m.npy</a> <ul> <li>Particle catalog of the a=0.7333 snapshot from MDPL2</li> <li>This is a down-sampled version with 72 million particles.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/topn_theory_demo.pkl?versionId=7ed87c28-7adc-4987-9e00-b6223c744d42">topn_theory_demo.pkl</a> <ul> <li>These are the data used to create the theoretical demo of the TopN test.</li> <li>It is used for making the figures in <a href="https://github.com/dr-guangtou/jianbing/blob/master/notebooks/figure/fig1.ipynb">this notebook</a>.</li> </ul> </li> </ul> <p><strong>Catalogs of Galaxies or Galaxy Clusters:</strong></p> <ul> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/camira_s16a_cluster_use_bsm.fits?versionId=ca274c83-4025-41c4-b993-3cc9074f08b2">camira_s16a_cluster_use_bsm.fits</a> <ul> <li>The HSC S16A CAMIRA cluster catalog.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/redmapper_hsc_s16a_cluster_bsm.fits?versionId=11608e41-2427-4808-9060-a06139de165c">redmapper_hsc_s16a_cluster_bsm.fits</a> <ul> <li>The HSC S16A redMaPPer cluster catalog.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/redmapper_sdss_cluster_bsm.fits?versionId=b977c4ed-11c9-4751-b32f-60883d2e81b0">redmapper_sdss_cluster_bsm.fits</a> <ul> <li>The SDSS DR8 redMaPPer clusters in the HSC S16A footprint.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/s16a_massive_logm_11.2.fits?versionId=603cb17c-bb64-4aa7-ae05-5ec61c7ee861">s16a_massive_logm_11.2.fits</a> <ul> <li>0.2 <z < 0.5 massive galaxies in the HSC S16A footprint.</li> </ul> </li> </ul> <p><strong>Galaxy-Galaxy Lensing Data:</strong></p> <ul> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/s16a_weak_lensing_medium.hdf5?versionId=593c4ba0-6d7d-4b83-b8d9-01740a351fcd">s16a_weak_lensing_medium.hdf5</a> <ul> <li>A compilation of the weak lensing data to calculate the DeltaSigma profiles.</li> <li>This includes the weak lensing source catalog, photometric redshift calibration file, and the random catalog.</li> <li>"medium" here means we applied the medium criteria for selecting source galaxies. Please refer to <a href="https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.5658S/abstract">Speagle et al. (2019)</a> for the exact meaning of these criteria.</li> <li>We also have a "basic" and "strict" version. Please send your request if you need them.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/topn_public_s16a_medium_precompute.hdf5?versionId=2216ecf9-b836-4dd5-a9dd-7e070e4977bf">topn_public_s16a_medium_precompute.hdf5</a> <ul> <li>A compilation of pre-computed lensing profiles for each individual object in a different galaxy or cluster samples for the TopN test.</li> <li>These are the data used to create the stacked DeltaSigma profiles.</li> <li>We also provide the "strict" and the "basic" versions if you want to test the robustness of the TopN tests against the different selections of source galaxies in weak lensing measurements. You just need these files to generate the stacked DeltaSigma profiles.</li> </ul> </li> </ul>
Oral Squamous Cell Carcinoma - Mass Spectrometry Imaging
<p>The dataset was first featured in <a href="https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/abs/10.1002/pmic.201500458">Widlak, Piotr, et al. "Detection of molecular signatures of oral squamous cell carcinoma and normal epithelium–application of a novel methodology for unsupervised segmentation of imaging mass spectrometry data." <em>Proteomics</em> 16.11-12 (2016): 1613-1621</a>. For the tissue sample's biochemical preparation details, please refer to the original publication.</p> <p>The biological material was collected from five patients who underwent surgery due to Oral Squamous Cell Carcinoma (OSCC). Tissue samples contained both tumor and surrounding healthy tissue.</p> <p>Each specimen was cut into 10 µm sections in a cryostat. During the sample preparation for the MS imaging, a high-resolution optical scan of each section was captured.</p> <p>Tissue sections were subjected to peptide imaging with the use of a MALDI ToF mass spectrometer. Spectra were recorded within <em>m/z</em> range of 800-4,000. A raster width of 100 µm was applied, and 400 shots were collected from each ablation point. The obtained dataset consisted of 45,738 raw spectra with 109,568 mass channels.</p> <p>An experienced pathologist analyzed the optical scan obtained during the data acquisition process, and tissue regions were annotated. For the highest confidence of the results obtained in this work, we will focus on the two tissue samples out of the entire dataset (8,005 and 11,869 spectra), which have the highest confidence labels, as explained by the pathologist.</p> <p>The preprocessing of the spectra was conducted in MATLAB. Standard preprocessing steps were applied to the spectra. Spectra were resampled to unify the <em>m/z</em> axis across the dataset. The baseline was removed with MATLAB procedure <em>msbackadj()</em> from the Bioinformatics Toolbox. Peaks were aligned using Fast Fourier Transform-based spectral alignment. The TIC normalization ensured a similar intensity level for all spectra. Finally, a GMM approach was used to model the spectra. GMM locates the peak but also estimates the peak area instead of a raw magnitude provided by most methods. Note that the peaks in MSI spectra are right-skewed, so the neighboring GMM components resulting from that phenomenon were identified and merged to better correspond to actual chemical compounds. The resulting dataset is characterized by 3,714 GMM components corresponding to MSI spectrum peaks.</p>
Data release for "Things that might Go bump in the night: Assessing structure in the binary black hole mass spectrum"
<p>Data release accompanying "Things that might go bump in the night: Assessing structure in the binary black hole mass spectrum"</p> <p>Included are:</p> <ul> <li>500 mock catalogs containing 69 events each, in netCDF4 format (can be found in `with_z_evo_lalprior_69_evs_prod_mock_PE.tar.gz`)</li> <li>A corresponding injection set using O3 sensitivity (`with_z_evo_lalprior_69_evs_prod_injections.h5`)</li> <li>Files containing hyperposterior samples resulting from a Power Law + Spline fit to 100 of the 69-event mock catalogs (`PowerLawSpline_69evs_20knots_2t100_*_result.json`)</li> <li>Files containing hyperposterior samples resulting from a smoothed power law fit to 100 of the 69-event mock catalogs (`Truncated_69evs_*_result.json`)</li> </ul> <p>Code using these files to create all plots in the paper can be found at https://git.ligo.org/amanda.farah/bump-significance</p> <p>Code used to create the mock catalogs can be found at https://git.ligo.org/amanda.farah/mock-PE</p>
Mass of wastes discharged directly from vessels to the water column
<p>Data set of mass discharge of selected pollutants from shipping in the European region. Created in the framework of the project Evaluation, control and Mitigation of the EnviRonmental impacts of shippinG Emissions (EMERGE).</p> <p>To determine the mass of discharged pollutants, AIS-based ship emission modelling of discharge volumes is combined with results of water effluent analysis. For the discharge volumes, the Ship Traffic Emission Assessment Model (STEAM) is used. The total discharge volume of wastes is comprised of five waste streams: open/close scrubber, grey, black and ballast water. For the content of pollutants in the five waste streams, a bibliographic database of waste stream pollutant concentrations compiled during the project is used.</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.