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348 results for “Core data”
Data for: Terahertz orbital angular momentum modes with flexible twisted hollow core antiresonant fiber
<p>Supporting data for the published work on "Terahertz orbital angular momentum modes with flexible twisted hollow core antiresonant fiber". The data here reported are all the necessary data to reproduce the figures in the paper both measurements an simulations (except for the analytical results, which are obtained directly from the formulas included in the paper). The Info file describes each file, how they have been obtained and what they have been used for. </p>
Supporting data for manuscript "Geochemical Characterization of Insoluble Particle Clusters in Ice Cores Using Two-dimensional Impurity Imaging"
<p>Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) offers micron-resolution 2D chemical imaging, which has been adapted recently to ice core analysis. The datasets are supporting information for the manuscript "Geochemical Characterization of Insoluble Particle Clusters in Ice Cores Using Two-dimensional Impurity Imaging" accepted for publication at Geochemistry, Geophysics, Geosystems (10.1029/2022GC010595). Measurements were performed at the Ca’Foscari University of Venice, considered as analytes are 23Na, 24Mg, 27Al, 29Si, 43Ca, 56Fe and 88Sr. Background and drift correction as well as image construction were performed using the software HDIP (Teledyne Photon Machines, Bozeman, MT, USA). Impurity maps are acquired as a pattern of lines, without overlap in the direction perpendicular to that of the scan, and without any further spatial interpolation. In a sample of the EGRIP Greenland ice core (from about 1256.95 m depth), maps were obtained over 3 adjacent areas. For each of the maps, for every chemical channel the intensities (in counts, after background and drift correction) are provided as a separate file, named as “ds01_Area1_Na.csv”, etc. These maps were obtained using a 20 µm square spot. This data can be used to obtain the images shown in the manuscript. For the additional map shown as Figure 9 in the manuscript, data were obtained using a LA-ICP-TOFMS for imaging a sample of the last glacial period in the EPICA Dome C (EDC) ice core, bag 1065. The maps were acquired using a 35 µm square spot, with 50% overlap between neighboring pixels to increase the spatial resolution horizontally.</p>
Gross methane production and consumption estimated for intact soil cores from agricultural plots including environmental covariates and example raw isotope pool dilution data
This study was performed to determine how different soil moistures, soil sources, and agricultural practices affected the gross CH4 fluxes (i.e., rates of methanogenesis) of soils. We extracted intact soil cores from two agricultural sites in the USA in row crop plots under conventional, no-till, and organic management. We then took them to the lab, manipulated their moisture levels, incubated them at room temperature for 22 weeks, and measured gas fluxes at weeks 6 and 21. We developed and utilized a new form of CH4 isotope pool dilution (IPD) to estimate gross CH4 production and consumption fluxes. This new method can measure IPD in a bag headspace that loses volume over time due to sampling. We fit the IPD model to the data and extracted gross CH4 production (P) and consumption (K) constants. These along with calculated fluxes and covariates measured (e.g., moisture, inorganic N) are reported in the main data table.
California Harmful Algal Bloom Monitoring and Alert Program Data (Darwin Core Archive format)
The Harmful Algal Bloom Monitoring and Alert Program (HABMAP) was formed in 2008 and provides updates on current algal blooms and facilitates information exchange among scientists, federal and state managers, and the general public in California. A major component of this program is regional HAB monitoring with support from the Southern California Coastal Ocean Observing System (SCCOOS) and the Central and Northern California Ocean Observing System (CeNCOOS). Water samples and net tows are collected once per week at piers to monitor for HAB species, the neurotoxin domoic acid, and water quality data including temperature, chlorophyll-a, and nutrients. The sampling stations represented in this dataset include Santa Cruz Wharf, Monterey Wharf, Cal Poly Pier, Stearns Wharf, Santa Monica Pier, Newport Beach Pier, and Scripps Pier. These data are consolidated and reformatted into Darwin Core Archive (DwC-A) format from the level 1 site-specific datasets hosted on the SCCOOS ERDDAP server: (https://erddap.sccoos.org/erddap/tabledap/index.html?page=1).
Organic and inorganic data for soil cores from Brazil and Florida Bay seagrasses to support Howard et al 2018, CO2 released by carbonate sediment production in some coastal areas may offset the benefits of seagrass “Blue Carbon” storage, Limnology and Oceanography, DOI: 10.1002/lno.10621
Using piston corers, soils from Florida Bay and Brazilian seagrass meadows were collected to complete organic and inorganic carbon inventories for the top 1 m of soil. Instrumental analyses and loss on ignition at 500C were used to measure C content of downcore slices.
Jornada Basin and Experimental Range Mesquite Herbicide Project (JERHM) Core Methods Data, 2020-2022
This dataset includes contains line-point intercept, plant height, gap, and species inventory data collected over three years (2020-2022) as part of the Jornada Experimental Range Herbicide Mesquite Project (JERHM). Data were collected to assess plant community composition and structural change to herbicide application across a Black grama (Bouteloua eriopoda) grassland to Honey mesquite (Neltuma glandulosa [=Prosopis glandulosa]) shrubland encroachment gradient. Twenty sets of paired, 5-hectare plots (n=40 plots total) were established across a N. glandulosa encroachment gradient in 2020. One plot within each plot pair received an aerial application of herbicide in 2021, with the second plot left untreated by herbicide as a control. Data were collected annually following the following the Monitoring Manual for Grassland, Shrubland, and Savanna Ecosystems (Herrick et al. 2017) on each of three, 50m permanent transects established on each plot. These data are also available within the Landscape Data Commons (https://landscapedatacommons.org/) under ProjectKey=Jornada_JERHM. There are no immediate plans to continue data collection.
Core Site Grid Quadrat Data for the Net Primary Production Study at the Sevilleta National Wildlife Refuge, New Mexico
Begun in spring 2013, this project is part of a long-term study at the Sevilleta LTER measuring net primary production (NPP) across three distinct ecosystems: creosote-dominant shrubland (Site C), black grama-dominant grassland (Site G), and blue grama-dominant grassland (Site B). Net primary production is a fundamental ecological variable that quantifies rates of carbon consumption and fixation. Estimates of NPP are important in understanding energy flow at a community level as well as spatial and temporal responses to a range of ecological processes. Above-ground net primary production is the change in plant biomass, represented by stems, flowers, fruit and foliage, over time and incorporates growth as well as loss to death and decomposition. To measure this change the vegetation variables in this dataset, including species composition and the cover and height of individuals, are sampled twice yearly (spring and fall) at permanent 1m x 1m plots within each site. A third sampling at Site C is performed in the winter. The data from these plots is used to build regressions correlating biomass and volume via weights of select harvested species obtained in SEV999, "Net Primary Productivity (NPP) Weight Data." This biomass data is included in SEV999, "Seasonal Biomass and Seasonal and Annual NPP for Core Grid Research Sites."
Long-term live imaging and multiscale analysis identify heterogeneity and core principles of epithelial organoid morphogenesis - Image data
<p>The dataset contains raw imaging data from the work:</p> <p>"Long-term live imaging and multiscale analysis identify heterogeneity and core principles of epithelial organoid morphogenesis"</p> <p>The dataset is organized as the following: the "FigureX_" or SupplementaryFigure_X" suffix in the filename refers to the figure in the paper in which the raw data is analyzed and/or visualized. The data is "raw", i.e. not processed. However, in many cases, maximum projections of the original 3D image stacks have been uploaded due to size limitations. The total size of the image stacks approaches 0.5TB. To access the full 3D image stacks please contact the corresponding author (Francesco Pampaloni, fpampalo@bio.uni-frankfurt.de).</p> <p><strong>Authors</strong></p> <p>Lotta Hof<sup>1</sup>*, Till Moreth<sup>1</sup>*, Michael Koch<sup>1</sup>, Tim Liebisch<sup>2</sup>, Marina Kurtz<sup>3</sup>, Julia Tarnick<sup>4</sup>, Susanna M. Lissek<sup>5</sup>, Monique M.A. Verstegen<sup>6</sup>, Luc J.W. van der Laan<sup>6</sup>, Meritxell Huch<sup>7</sup>, Franziska Matthäus<sup>2</sup>, Ernst H.K. Stelzer<sup>1</sup>, Francesco Pampaloni<sup>1§</sup></p> <p><sup>1</sup>Physical Biology Group, Buchmann Institute for Molecular Life Sciences (BMLS), Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany</p> <p><sup>2</sup>Faculty of Biological Sciences, Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany</p> <p><sup>3</sup>Department of Physics, Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany</p> <p><sup>4</sup>Deanery of Biomedical Science, University of Edinburgh, Edinburgh, United Kingdom</p> <p><sup>5</sup>Experimental Medicine and Therapy Research, University of Regensburg, Regensburg, Germany</p> <p><sup>6</sup>Department of Surgery, Erasmus MC – University Medical Center, Rotterdam, The Netherlands</p> <p><sup>7</sup>The Wellcome Trust/CRUK Gurdon Institute, University of Cambridge, Cambridge, United Kingdom. Present address: Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany</p> <p>*contributed equally</p> <p><sup>§</sup>corresponding author: fpampalo@bio.uni-frankfurt.de</p> <p><strong>Abstract</strong></p> <p><em>Background</em></p> <p>Organoids are morphologically heterogeneous three-dimensional cell culture systems and serve as an ideal model for understanding the principles of collective cell behaviour in mammalian organs during development, homeostasis, regeneration and pathogenesis. To investigate the underlying cell organisation principles of organoids, we imaged hundreds of pancreas and cholangio carcinoma organoids in parallel using light sheet and bright field microscopy for up to seven days.</p> <p><em>Results</em></p> <p>We quantified organoid behaviour at single-cell (microscale), individual-organoid (mesoscale), and entire-culture (macroscale) levels. At single-cell resolution, we monitored formation, monolayer polarisation and degeneration, and identified diverse behaviours, including lumen expansion and decline (size oscillation), migration, rotation and multi-organoid fusion. Detailed individual organoid quantifications lead to a mechanical 3D agent-based model. A derived scaling law and simulations support the hypotheses that size oscillations depend on organoid properties and cell division dynamics, which is confirmed by bright field microscopy analysis of entire cultures.</p> <p><em>Conclusion</em></p> <p>Our multiscale analysis provides a systematic picture of the diversity of cell organisation in organoids by identifying and quantifying the core regulatory principles of organoid morphogenesis.</p>
Simulated BCRseq data for the nf-core/airrflow pipeline benchmark
<p>Simulated BCRseq data for the nf-core/airrflow pipeline benchmark.</p> <p>The original repertoires simulated with ImmuneSim (sim_repertoire_orig_repA/repB/repC.tsv) and the clonally expanded repertoires (sim_repertoire_clonally_expandedrepA/repB/repC.tsv), as well as the fasta file formats from the clonally expanded repertoires with and without UMIs are also shared. The fasta files were used to simulate the sequencing reads with various degrees of sequencing errors.</p>
Sulfate Isotope and geochemical ice core data from Dronning Maud Land over the penultimate glacial termination
<p>Sulfur isotope data over the penultimate termination from the EPICA Dronning Maud Land (EDML) ice core as published in Fischer et al., Nature Geosciences, 2024. The file contains:</p> <ul> <li>measured geochemical and sulfur isotope data from the EDML ice core over the time interval 112-153 kyr before present</li> <li>deconvolution of different sulfur sources for the EDML ice core over the time interval 112-153 kyr before present</li> <li>reconstruction of atmospheric sulfate aerosol concentrations from the EDML ice core over the time interval 112-153 kyr before present</li> <li>measured geochemical and sulfur isotope data data from the shallow coastal B38 ice core over the time interval 1964-1968</li> </ul>
Tree diameter growth and increment core δ13C data from a recently thinned forestry-drained site (Lettosuo) in southern Finland.
<p>Dataset includes increment core data from Lettosuo drained peatland forest site. The study site locates in the Tammela municipality in southern Finland (60° 38’ 31’’ N, 23° 57’ 35’’ E). Increment cores were analysed for the ring widths for dominant and suppressed Norway spruce trees, and for the ring δ<sup>13</sup>C values from suppressed Norway spruce trees. Data was collected as a part of BiBiFe (”Biogeochemical and biophysical feedbacks from forest harvesting to climate change”) consortium that is funded by the Academy of Finland. </p> <p> </p> <p>Sampling for increment cores was done during October 2020 for sample trees (10 in total, of which 5 were suppressed trees from thinned area and 5 suppressed trees from control area) and additional sampling was conducted for annual diameter increment for 3 tree groups to increase sample size for diameter growth (suppressed trees in thinned area [n=20], dominant trees in thinned area [n=22] and suppressed trees in control area[n=20]) during March 2021. </p> <p> </p> <p><strong>Tree </strong><strong>ring carbon isotope data</strong></p> <p> </p> <p>Laser ablation IRMS method was applied in the Stable Isotope Laboratory of Luke (SILL) to quantify δ<sup>13</sup>C values in 10 increment cores for the time period 2010–2020, following principles of Schulze et al. (2004) and described in Lehtonen et al (manuscript). Up to 11 evenly spaced “spots” for each annual tree ring were measured to obtain information on the intra-annual variation of δ<sup>13</sup>C of the samples. </p> <p> </p> <p>(1) File: Lettosuo_d13C.xls</p> <p>File includes d13C measurements</p> <p> </p> <p><strong>Data column description below for isotope data: </strong></p> <p> </p> <p><strong>id</strong> stands for tree id [id includes tree identity, year and also spot number]</p> <p><strong>year</strong> is the year of the tree ring</p> <p><strong>nr</strong> is an index for data </p> <p><strong>tree</strong> indicates tree identity "C" for control and "H" for harvest</p> <p><strong>treatment</strong> indicates the treatment of the sampling area (control / harvest)</p> <p><strong>d13C</strong> gives the measured d13C value based on the LA-IRMS measurements</p> <p><strong>season </strong>indicates whether observation originated from the earlywood (EW) or latewood (LW) period, where 1 is EW and 2 is LW</p> <p> </p> <p><strong>Tree ring width measurements</strong></p> <p> </p> <p>In addition to the δ<sup>13</sup>C values, also the ring widths were measured. Here, also additional dominant trees were measured. </p> <p> </p> <p>(3) Files:</p> <p>controlRW.csv</p> <p>dominantRW.csv</p> <p>thinningRW.csv</p> <p> </p> <p>Files include increment core data (in micrometers) from isotope sample trees and additional increment core trees from the control area and harvested area of the site. Dominant trees were measured only from the thinned area. </p> <p> </p> <p>In the .csv files individual columns are for ring widths for individual trees. In the controlRW.csv and thinningRW.csv files first 5 columns include diameter increments from sample trees (those that have also d13C measurements).</p> <p> </p> <p><strong>References:</strong></p> <p> </p> <p>Lehtonen A, Leppä K, Sahlstedt E, Schiestl-Aalto P, Heikkinen J, Young G, Korkiakoski M, Peltoniemi M, Rinne-Garmston K, Sarkkola S, Lohila A, Mäkipää R (manuscript). Fast recovery of Norway spruce trees after thinning from above on a drained peatland forest site.</p> <p> </p> <p>Korkiakoski M, Ojanen P, Penttilä T, Minkkinen K, Sarkkola S, Rainne J, Laurila T, Lohila A (2020) Impact of partial harvest on CH<sub>4</sub> and N<sub>2</sub>O balances of a drained boreal peatland forest. Agric For Meteorol 295:108168.</p> <p> </p> <p>Schulze B, Wirth C, Linke P, Brand WA, Kuhlmann I, Horna V, Schulze E-D (2004) Laser ablation-combustion-GC-IRMS--a new method for online analysis of intra-annual variation of 13C in tree rings. Tree Physiol 24:1193–1201.</p>
Data and code used in "Satellite magnetic data reveal interannual waves in Earth's core"
<p>Eigen mode solutions and code to obtain them for the results presented in <a href="https://doi.org/10.1073/pnas.2115258119">Satellite magnetic data reveal interannual waves in Earth's core</a>. The package uses the freely available code <a href="https://github.com/fgerick/Mire.jl">Mire.jl</a>.</p> <p><strong>Prerequisites</strong></p> <p>Installed python3 with matplotlib ≥v2.1, cmocean and cartopy. A working Julia ≥v1.7.</p> <p><strong>Run</strong></p> <p>In the project folder run</p> <pre><code>julia --project=.</code></pre> <p><br> Then, from within the Julia REPL run</p> <pre><code>]instantiate</code></pre> <p>at first time, to install all dependencies.</p> <p>After that, to compute all plots, run</p> <pre><code>using QGMCSat allfigs()</code></pre> <p>They're automatically saved in the "figs" subfolder of the repository.</p> <p>If loading QGMCSat fails, due to a missing cartopy or cmocean in the python version. Run (within Julia)<br> </p> <pre><code>ENV["PYTHON"] = "python" #this should point to the python version that has cartopy installed ]build PyCall</code></pre> <p><br> To calculate all data, run</p> <pre><code>using QGMCSat calculate_data()</code></pre> <p>This will take several hours/days depending on the machine (needs enough memory).</p> <p>Individual data can be accessed directly through the .jld2 files from Julia. You can check out the individual figure functions to get an idea where which data is stored.</p> <p>If there are any issues or questions, please don't hesitate to get in touch!</p>
Magnetic and stable isotope data from PLG Core
<p>The Poggio le Guaine core was designed to provide a high-resolution age model and a high-resolution relative magnetic paleointensity reference curve for the Aptian-Albian interval of the long normal Cretaceous superchron. The PLG drill hole cored the uppermost Barremian-lowermost Cenomanian succession of the Umbria-Marche Basin deposited in the southern margin of the central-western Tethys Ocean. These pelagic sediments formed following the lithification of the nannofossil-planktonic foraminiferal ooze deposited well above the calcite compensation depth at middle to lower bathyal depths (1000-1500 m) and at ~20°N paleolatitude. The Poggio le Guaine drill site (lat. 43°32'42.72"N; long.12°32'40.92"E) is located on the Monte Nerone ridge at 888 m abovensea level, 6 km west of the town of Cagli (Regione Marche, Italy). Discrete ~8 cm<sup>3</sup> cubic samples were then cut from the center of the split working halve for paleomagnetic analyses (MS and ARM). A total amount of 1227 cubic samples were collected along the studied portion of the PLG core (from 96.02 to 60.00 m; average sampling resolution of ~3 cm). A total of 355 paleomagnetic cubic samples were also used to measure the stable isotopes (δ<sup>18</sup>O and δ<sup>13</sup>C) with a ~10 cm resolution. Our objectives with these data are: (i) to propose a cyclostratigraphic framework for the PLG section using high-resolution magnetic susceptibility (MS), anhysteretic remanent magnetisation (ARM), δ<sup>18</sup>O, and δ<sup>13</sup>C data to provide better constraints for the Aptian climato-chronostratigraphic framework; and (ii) to discuss the impact of the proposed framework on the main events of the Aptian and the Cretaceous time scale.</p>
Sea ice core temperature and salinity data collected during the 2019 SCALE Winter Cruise
<p>Temperature and salinity profiles of sea ice cores extracted from in situ sea ice floes and lifted pancakes were measured in the Atlantic sector of the Antarctic Marginal Ice Zone during the Southern oCean seAsonal Experiment (SCALE) winter cruise in 2019 (<a href="http://www.scale.org.za">www.scale.org.za</a>) aboard the SA Agulhas II.</p>
Sea ice core temperature and salinity data collected during the 2019 SCALE Spring Cruise
<p>Temperature and salinity profiles of sea ice cores extracted from in situ sea ice floes and lifted pancakes were measured in the Atlantic sector of the Antarctic Marginal Ice Zone during the Southern oCean seAsonal Experiment (SCALE) spring cruise in 2019 (<a href="http://www.scale.org.za">www.scale.org.za</a>) aboard the SA Agulhas II.</p>
Segmentations of the core of the acoustic radiation in HCP data
<p><strong>Description of the repository</strong>:</p> <p>The goal of our paper (<a href="https://doi.org/10.3389/fneur.2022.934650">https://doi.org/10.3389/fneur.2022.934650</a>) was to segment the acoustic radiation (AR), one of the most important white matter fiber bundles of the hearing system. This repository contains the segmentations masks of the AR we created from 105 subjects of the Human Connectome Project (HCP) young adult dataset (<a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a>). These subjects are exactly the same used by Wasserthal et al. (2018) <a href="https://doi.org/10.1016/j.neuroimage.2018.07.070">https://doi.org/10.1016/j.neuroimage.2018.07.070</a>.</p> <p>In the file "data_training.tar.gz", one directory was created per HCP subject. Every directory contains the file "bundle_masks_AR.nii.gz" that contains the binary masks for the left and right AR.</p> <p>In our paper, we used these masks to train TractSeg. The file best_weights_ep110.npz contains the weights after training TractSeg that can be used in inference for targeting the AR. For using these weights on new data, one can use TractSeg with the option "--exp_name best_weights_ep110.npz". Please read the documentation of TractSeg and our paper for more information.</p> <p> </p> <p>If you use the training data or the pre-trained network, please cite our publication:</p> <p>Malin Siegbahn, Cecilia Engmér Berglin, Rodrigo Moreno. Automatic segmentation of the core of the acoustic radiation in humans. Frontiers in Neurology (2022) 13:934650. doi: 10.3389/fneur.2022.934650</p>
Global ice drilling and archive location data for select ice cores
<p>This document includes ice drill site information and ice core repository information for select ice cores retrieved between 1958 and 2022. Included data are not representative of all ice cores drilled during this time period, nor are they representative of all ice core samples collected and maintained by all of the contributing programs and facilities. Data are presented as they were provided by contributing facilities in 2022, when they were used to generate a figure for an article in Past Global Changes Magazine (doi.org/10.22498/pages.30.2.98).</p> <p>The data describe ice core drilling sites (latitude, longitude, elevation, site name), ice core samples (bottom depth, bottom age, core diameter, core completion date, corresponding publications), and ice core storage facilities (latitude, longitude, name).</p> <p>Contributing facilities include the following: Alfred Wegener Institute (Germany), Australian Antarctic Division (Australia), Australian Antarctic Program Partnership (Australia), Byrd Polar Center - University of Ohio (United States of America), Canadian Ice Core Lab (Canada), Chiba University (Japan), Commonwealth Scientific and Industrial Research Organization (Australia), Institute of Environmental Geosciences - University of Grenoble (France), Institute of Low Temperature Science - University of Hokkaido (Japan), Institute of Polar Science and Engineering - Jilin University (China), Karakoram International University (Pakistan), Lanzhou Institute of Glaciology and Geocryology (China), Nagoya University (Japan), National Institute of Polar Research (Japan), National Science Foundation Ice Core Facility (United States of America), New Zealand National Ice Core Facility (New Zealand, Physics of Ice Climate and Earth - University of Copenhagen (Denmark), Polar Research Institute of China (China), Research Institute for Humanity and Nature (Japan), and Tibet University. </p> <p>We are grateful to each of these facilities for contributing details of their ice core collections for this work. </p> <p> </p> <p> </p> <p>Electronic data accessibility and sample request procedures for a few of these facilities of which the authors are aware are listed below.</p> <p>Australia: data can be obtained from the Australian Antarctic Data Centre (<a href="https://urldefense.com/v3/__https://data.aad.gov.au/__;!!K-Hz7m0Vt54!k4oxTmHZ_w1LKmpFwH8LzlfLDG73TEDLZwozl9Q6dL-wfS_EQG7S75R9T3faMQA7BHyK5mv3Br0-kyWRnumedvhR$">https://data.aad.gov.au</a>); access to ice from the Australian Antarctic Program is via application (see <a href="https://urldefense.com/v3/__https://www.antarctica.gov.au/science/information-for-scientists/__;!!K-Hz7m0Vt54!k4oxTmHZ_w1LKmpFwH8LzlfLDG73TEDLZwozl9Q6dL-wfS_EQG7S75R9T3faMQA7BHyK5mv3Br0-kyWRnosG8VPm$">https://www.antarctica.gov.au/science/information-for-scientists/)</a></p> <p>Denmark: data can be obtained from <a href="https://www.iceandclimate.nbi.ku.dk/data/">www.iceandclimate.nbi.ku.dk/data</a>; the ice sampling request procedure is listed here: <a href="https://www.iceandclimate.nbi.ku.dk/data/samplingprocedure/">https://www.iceandclimate.nbi.ku.dk/data/samplingprocedure/</a> </p> <p>United States: many ice core datasets can be found at the NOAA World Data Center (<a href="https://www.ncei.noaa.gov/products/paleoclimatology/ice-core">https://www.ncei.noaa.gov/products/paleoclimatology/ice-core</a>); the allocation policy for ice core samples can be found here: <a href="https://icecores.org/policy">https://icecores.org/policy</a>.</p>
A data set on "Utilizing Constant Energy Difference between sp-Peak and C 1s Core Level in Photoelectron Spectra for Unambiguous Identification and Quantification of Diamond Phase in Nanodiamonds"
<p>The data set to paper: </p> <p>Utilizing Constant Energy Difference between sp-Peak and C 1s Core Level in Photoelectron Spectra for Unambiguous Identification and Quantification of Diamond Phase in Nanodiamonds</p> <p>Oleksandr Romanyuk1,*, Štěpán Stehlík1,2, Josef Zemek1, Kateřina Aubrechtová Dragounová1,3 and Alexander Kromka1</p> <p>1 Institute of Physics of the Czech Academy of Sciences, Cukrovarnická 10, 162 00 Prague, Czech Republic<br>2 New Technologies—Research Centre, University of West Bohemia, Univerzitní 8, 306 14 Pilsen, Czech Republic<br>3 Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Břehová 7, 115 19 Prague, Czech Republic</p> <p>* corresponding author: romanyuk@fzu.cz</p> <p>Data manager: Kristýna Dostálová: dostalovak@fzu.cz</p> <p>Date of data collection: 1. 1. 2024 - 15. 03. 2024</p> <p>All the data showed in the pictures are provided in X-Y format with described sample. Always, the respective Figure to which the data belong is provided in high resolution. <br>The data are in the following formats: <br>Figure 1: tiff, csv<br>Figure 2: tiff, csv<br>Figure 3: tiff, csv<br>Figure 4: tiff, csv<br>Figure 5: tiff, csv</p> <p>Data acquistion and processing is provided in the Experimental part in the publication: DOI:10.3390/nano14070590</p>
Data for Dodds et al., The direction of core solidification in asteroids: implications for dynamo generation
<p>Numerical dataset for the data presented in Dodds et al., The direction of core solidification in asteroids: implications for dynamo generation, manuscript submitted to Icarus journal.</p>
Sedimentary charcoal data set from the Villena Paleolake (VL3 core)
<p><strong>1. Data set, and sampling methods</strong></p> <p>This data set contains the quantification of sedimentary charcoal from the VL3 record of the Villena Paleolake (Villena, Alicante, Spain). The analyzed section covers the Early and Middle Holocene section of this deposit according (Jones et al., 2018), depths between -120 a -400 cm, with centimetric sampling interval which has produced a total number of 281 samples.</p> <p><strong>2. Treatment</strong></p> <p>The samples have been treated as follows, adapting the methods published by Rhodes (1998) and Talon <em>et al</em>.(1998):</p> <ul> <li>Samples were soaked in 10% H2O2 for 12h for sediment deflocculation and to bleach non-charcoal organic material.</li> <li>After this first step, it was noted if the samples contained shell fragments.</li> <li>A 10% HCl solution was used in samples with high carbonate content.</li> <li>Then samples were sieved (150µ) under a soft-water jet.</li> <li>The samples were stored in distilled water for later counting.</li> </ul> <p><strong>3. Quantification procedures</strong></p> <ul> <li>Each sample was washed with distilled water, employing a 150 micron sieve.</li> <li>The wet sample was examined under binocular microscope <em>Stereomicroscopy CETI STEDDY-T</em> at 40 magnification, using a reference grid with squares of different size categories (cat. 0,5; cat. 1, cat. 2, cat. 3, cat. 4 and cat. 5). The total number of examined charcoal was also calculated.</li> <li>Each size category corresponds to one of the following area categories Cat. 0,5: 0,015625mm<sup>2</sup>; Cat. 1: 0,0625 mm<sup>2 </sup>(0,25 mm*0,25 mm); Cat. 2: 0,0125 mm<sup>2</sup>; Cat. 3: 0,25 mm<sup>2</sup>; Cat. 4: 0,5625 mm<sup>2</sup>; Cat. 5: 1 mm<sup>2</sup>. In the Excel spreadsheet, the area is multiplied by the number of fragments of each category. The sum of the areas is also calculated.</li> <li>In addition, oocytes and insects were also counted.</li> </ul> <p><strong>References</strong></p> <ul> <li>Carcaillet, C., Bouvier, M., Fréchette, B., Larouche, A. C., Richard, P. J. H. (2001). Comparison of pollen-slide and sieving methods in lacustrine charcoal analyses for local and regional fire history. <em>The Holocene</em> 11 (4): 467- 476.</li> <li>Clark, J. S. (1988). Particle motion and the theory of charcoal analysis: Source area, transport, deposition, and sampling. <em>Quaternary Research</em> 30 (1), 67-80.</li> <li> <p>Jones S.E., Burjachs, F. Ferrer-García C., Giralt, S., Schulte, L., Fernández-López de Pablo, J. (2018). A multi-proxy approach to understanding complex responses of salt-lake catchments to climate variability and human pressure: A Late Quaternary case study from south-eastern, Spain. Quaternary Science Reviews https://doi.org/10.1016/j.quascirev.2017.12.015.</p> </li> <li>Ohlson, M. and Tryterud, E. 2000. Interpretation of the charcoal record in forest soils: forest fires and their production and deposition of macroscopic charcoal. The Holocene, 10(4), 529-525.</li> <li>Rhodes, A. N. (1998). A method for the preparation and quantification of microscopic charcoal from terrestrial and lacustrine sediment cores. <em>The Holocene </em>8 (1), 113-117.</li> <li>Talon, B., Carcaillet, C., Thinon, M. (1998). Etudes pedoanthracologiques des variations de la limite superieure des arbres au cours de l'Holocene dans les Alpes françaises. <em>Geographie physique et Quaternaire</em> 52 (2), 195-208.</li> </ul>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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