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2,960 results for “elements”
IODP Expedition 355 Elemental analysis (CHNS)
Fundamental elemental component (total carbon, hydrogen, nitrogen, and sulfur) fluctuations help define the origin, depositional environment, and diagenetic alteration of source materials. To determine C, H, N, and S, solid samples are reacted with a catalyst, separated by chromatography, and detected by thermal conductivity on a FlashEA 1112 CHNS elemental analyzer. Organic carbon can be directly measured on the elemental analyzer by acidification of the sample to drive off carbonate as carbon dioxide before analyzing. Total organic carbon on this report is measured rather than calculated.
IODP Expedition 355 ICP-AES elemental analysis (solids)
Elemental contents in hard rock and sediment samples was measured by inductively coupled plasma - atomic emission spectrocopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.
IODP Expedition 355 ICP-AES elemental analysis (interstitial water)
Elemental concentration in interstitial water samples was measured by inductively coupled plasma - atomic emission spectroscopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.
Software, Dataset, and Techreport: Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration
<p>This upload contains a techreport titled "Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration" together with the software (with documentation) and dataset generating the results. The software is also available on GitHub at https://github.com/croci/mpfem-paper-experiments-2024/ . The GitHub version may be updated in the future. This upload corresponds to commit number 8506dd368b84655201c8c72b1307239b9b4e43fd . See README.md file for installation instructions. The manuscript is also available on the arXiv: https://arxiv.org/abs/2410.12614.</p>
IODP Expedition 356 ICP-AES elemental analysis (interstitial water)
Elemental concentration in interstitial water samples was measured by inductively coupled plasma - atomic emission spectroscopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.
IODP Expedition 353 Elemental analysis (CHNS)
Fundamental elemental component (total carbon, hydrogen, nitrogen, and sulfur) fluctuations help define the origin, depositional environment, and diagenetic alteration of source materials. To determine C, H, N, and S, solid samples are reacted with a catalyst, separated by chromatography, and detected by thermal conductivity on a FlashEA 1112 CHNS elemental analyzer. Organic carbon can be directly measured on the elemental analyzer by acidification of the sample to drive off carbonate as carbon dioxide before analyzing. Total organic carbon on this report is measured rather than calculated.
IODP Expedition 353 ICP-AES elemental analysis (interstitial water)
Elemental concentration in interstitial water samples was measured by inductively coupled plasma - atomic emission spectroscopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.
IODP Expedition 359 ICP-AES elemental analysis (interstitial water)
Elemental concentration in interstitial water samples was measured by inductively coupled plasma - atomic emission spectroscopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.
IODP Expedition 359 Elemental analysis (CHNS)
Fundamental elemental component (total carbon, hydrogen, nitrogen, and sulfur) fluctuations help define the origin, depositional environment, and diagenetic alteration of source materials. To determine C, H, N, and S, solid samples are reacted with a catalyst, separated by chromatography, and detected by thermal conductivity on a FlashEA 1112 CHNS elemental analyzer. Organic carbon can be directly measured on the elemental analyzer by acidification of the sample to drive off carbonate as carbon dioxide before analyzing. Total organic carbon on this report is measured rather than calculated.
Generation of transcriptional novelty by transposable element insertions in Arabidopsis, Genome Sequencing and eccDNA Data
<p><strong>Raw Illumina sequencing data from the Manuscript entitled "Generation of transcriptional novelty by transposable element insertions in Arabidopsis"</strong></p> <p><strong>A. Illumina genome sequencing reads of Arabidopsis control and hcLines that contain novel transposable element insertions.</strong></p> <p>To identify the genomic position of the new <em>ONSEN</em> insertions, the extracted DNA of the 11 selected lines (nine lines with new insertions and two control lines) was sent to BGI, Hong-Kong for Illumina paired-end 150 bp sequencing, aiming for a minimum of 20X sequencing coverage. Quality control of the raw reads was done using FastQC (Andrews S. (2010). FastQC: a quality control tool for high throughput sequence data. Available online at: <a href="http://www.bioinformatics.babraham.ac.uk/projects/fastqc">http://www.bioinformatics.babraham.ac.uk/projects/fastqc</a>) and trimming/clipping was done using Trimmomatic with parameters ILLUMINACLIP: TruSeq3:2:30:10 LEADING:20 TRAILING:20 SLIDINGWINDOW:4:20 and MINLEN:36. Quality of the reads was deemed excellent and no further actions were taken.</p> <p>Samples identifications: genome_hcLineX with "_1" indicating the forward and "_2" the reverse reads.</p> <p><strong>B. Illumina eccDNA sequencing of Arabidopsis control and hcLines following stress treatments</strong></p> <p>Extrachromosomal circular DNA was prepared and sequenced as follows: twenty plants from each petri dish were pooled separately and DNA was extracted using the CTAB method (<a href="https://dx.doi.org/10.17504/protocols.io.quidwue">dx.doi.org/10.17504/protocols.io.quidwue</a>). Following the mobilome-seq method described in (Lanciano et al., 2017), for all samples, we digested linear DNA from 2 µg of total DNA for 17 hours at 37<sup>o</sup>C using 10 U of PlasmidSafe (<em>LubioScience cat# E3101K</em>), followed by enzyme denaturation (30 mins at 70<sup>o</sup>C). Digested DNA was precipitated with isopropanol supplemented with 1 µg of GlycoBlue coprecipitant (<em>Fisher Scientific cat# 10391565</em>). Circular DNA was then amplified through rolling circle amplification (RCA) with the Illustra TempliPhi kit (<em>GE Healthcare cat# 25-6400-10</em>), following the manufacturer recommendation and leaving the reaction for 16h at 30<sup>o</sup>C. DNA was once again precipitated with isopropanol and sent for Illumina paired end 150 bp sequencing at BGI, Hong Kong. </p> <p>Samples identification: </p> <p>eccDNA_A.thaliana_ctrl: control reads</p> <p>eccDNA_A.thaliana_HS: heat stressed plants reads</p> <p>eccDNA_A.thaliana_AZ_HS: reads of alpha-amanitin, zebularine and heat-stressed plants</p> <p>"R1" indicates forward and "R2" reverse reads.</p> <p> </p>
Partitioned linkage disequilibrium scores for active regulatory elements in ROADMAP datasets
<p>Partitioned linkage disequilibrium scores for active regulatory elements in ROADMAP epigenomics datasets, to accompany paper Lynall et al 2021</p> <p>Accompanying code available at https://github.com/maryellenlynall/psychimmgen2021</p> <p>Active regulatory elements annotations are a union of the following IDEAS annotations, representing enhancers and active promoters (see http://bx.psu.edu/~yuzhang/Roadmap_ideas/trackDb_test.txt for IDEAS track hubs): </p> <p>4_Enh<br> 6_EnhG<br> 8_TssAFlnk<br> 10_TssA<br> 14_TssWk<br> 17_EnhGA</p> <p>tissues.txt provides the list of ROADMAP tissues </p> <p>The partitioned_LD_scores folder contains partitioned LD scores in a format suitable for stratified LDSC analysis for European participants</p>
PQRI Elemental Impurity Interlaboratory Study Raw Data
<p>The pharmaceutical industry recently implemented a new paradign in drug product elemental impurity (EI) analysis in ICH Q3D Guidelines and USP General Chapters <232> and <233>, which allow for EI analysis by inductively coupled plasma-mass spectrometry (ICP-MS) and similar techniques. To date, there have been few systematic evaluations of laboratory performance on EI analysis in drug products due to a lack of standardized samples and ongoing efforts to adopt best practices. We organized an interlaboratory study to provide a data-driven way to address key technical challenges faced by laboratories during the implementation of EI regulations, including laboratory equipment, interference correction strategies, and the method of calculating the final concentrations.</p> <p>The data contained in this file includes the analytical results and characteristics for all participant laboratories in a format readable and analyzable by R. A manuscript describing the analysis of this data is in preparation, with a target submission date of October 2021.</p>
Datasets belonging to the paper "Dual phase patterning during a congruent grain boundary phase transition in elemental copper"
<p>This repository contains the raw data of the experimental STEM imaging and the data corresponding to the simulations and theoretical calculations of the paper "Dual phase patterning during a congruent grain boundary phase transition in elemental copper" available under <a href="https://doi.org/10.1038/s41467-022-30922-3">https://doi.org/10.1038/s41467-022-30922-3</a> .</p> <p>See the file README.md for a detailed description.</p>
Surfalex HF formability study - Workflow 6 - Generate random volume element
<p>This MatFlow workflow is the sixth in a set of eight workflows developed during our formability study of the Surfalex HF (AA6016A) material. In this workflow, we generate a comparison volume element from a random texture and equiaxed microstructure. This RVE is used in a comparison of the simulated Lankford coefficients between the Surfalex model RVE and this "random" RVE.</p> <p>This workflow can be downloaded and explored in a Jupyter notebook, as explained in the <a href="https://github.com/LightForm-group/surfalex_data_explorer">GitHub repository here</a>.</p>
Surfalex HF formability study - Workflow 1 - Generate volume element
<p>This MatFlow workflow is the first in a set of eight workflows developed during our formability study of the Surfalex HF (AA6016A) material. In this first workflow, we generated a representative volume element (RVE) for the Surfalex HF material. To do this, we sampled 2000 orientations from a CTF file generated from EBSD measurements on the sheet RD-TD plane. The MTEX toolbox was used to sample the texture. The grain morphology was approximated using a Voronoi tessellation that was subsequently stretched by a factor of 1.5 in the RD direction, to mimic the slight grain elongation that was observed. The pre-processing tools in the DAMASK package were used to generated the RVE.</p> <p>This workflow can be downloaded and explored in a Jupyter notebook, as explained in the <a href="https://github.com/LightForm-group/surfalex_data_explorer">GitHub repository here</a>.</p>
Shielding performance of the 98 first chemical elements against trapped particles on GTO
<p>The 1LayerElements.csv file contains the shielding performance results for the first 98 chemical elements against trapped particles in Geostationary Transfer Orbit (GTO).<br> The material properties are sourced from the Geant4 material database, with materials referenced by their Geant4 material database names<br> For elements that are gases under normal conditions, the liquified version from the Geant4 database is used if available, denoted by an "l" prefix (e.g., "G4_lH2").<br> The particle spectra used for this simulation are provided in the files AE9500keV.mac and AP910MeV.mac.<br> The simulated geometry is provided in the file 1LayerElements.gdml<br> <br> The particle spectra were generated with the AE9/AP9 models on SPENVIS, with electron energies starting at 500 keV and proton energies starting at 10 MeV.<br> The simulation was performed with GRAS / Geant4 with the FTFP_BERT physics model using 4.3e+10 electrons and 1e+08 protons directed against slabs of shielding materials of 1.5 g/cm2 depth.<br> The ionizing dose is recorded in 0.5mm thick silicon plates behind the shielding plates.</p> <p>Column A: Z-number of the element.<br> Column B: Name of the material as referenced from the Geant4 material database.<br> Column C: Ionizing dose due to electrons in units of kRad per month.<br> Column D: Absolute statistical error in the electron dose due to the statistical nature of the Monte-Carlo simulation in kRad per month.<br> Column E: ionizing dose due to protons in kRad per month.<br> Column F: Absolute statistical error in the proton dose in kRad per month. <br> Column G: Total ionizing dose from electrons and protons in kRad per month.<br> Column H: Absolute statistical error in the total ionizing dose in kRad per month. <br> Column I: Relative statistical error of the total ionizing dose in per cent.<br> Column J: Rank of the material sorted from lowest total ionizing dose to highest.<br> Column K: Rank of the material sorted from lowest electron dose to highest.<br> Column L: Rank of the material sorted from lowest proton dose to highest.</p> <p>All dose values are rounded according to their uncertainty.</p>
Simulated shielding performance of 2500 two-layer permutations of the first 50 chemical elements against trapped particles on GTO
<p>The 2Layer.csv file contains ionizing dose results of simulating the shielding performance of all 2500 two-layer permutations of the first 50 chemical elements against trapped particles in Geostationary Transfer Orbit (GTO). The material properties are sourced from the Geant4 material database, with materials referenced by their Geant4 material database names. For elements that are gases under normal conditions, the liquified version from the Geant4 database is used if available, denoted by an "l" prefix (e.g., "G4_lH2"). The particle spectra used for this simulation are provided in the files AE9500keV.mac and AP910MeV.mac. The simulated geometry is provided in the file 2Layer.gdml.</p> <p>The particle spectra were generated with the AE9/AP9 models on SPENVIS, with electron energies starting at 500 keV and proton energies starting at 10 MeV. The simulation was performed with GRAS / Geant4 with the FTFP_BERT physics model using 2.1e+11 electrons and 8.4e+09 protons directed against slabs of shielding materials of 1.5 g/cm2 total depth with each of the two layers being 0.75 g/cm2 in depth. The ionizing dose is recorded in 0.5mm thick silicon plates behind the shielding plates.</p> <p>Column A: ID of the material combination<br> Column B: Z-Number of Material 1<br> Column C: Z-Number of Material 2<br> Column D: Name of the first layer material.<br> Column E: Name of the second layer material.<br> Column F: Ionizing dose due to electrons in units of kRad per month.<br> Column G: Statistical error in the electron dose in kRad per month.<br> Column H: Ionizing dose due to protons in kRad per month.<br> Column I: Statistical error in the proton dose in kRad per month.<br> Column J: Total ionizing dose from electrons and protons in kRad per month.<br> Column K: Statistical error in the total ionizing dose in kRad per month.<br> Column L: Rank of the material combination sorted from lowest electron dose to highest.<br> Column M: Rank of the material combination sorted from lowest proton dose to highest.<br> Column N: Rank of the material combination sorted from lowest total ionizing dose to highest.</p> <p>All dose values are rounded according to their statistical uncertainty as reported by GRAS/Geant4.</p>
1000 Genomes Project Transposable Element database
<p>Multi-sample VCF with transposable elements across individuals in the 1KGP dataset. Transposable elements were called using RetroSeq </p>
Extract from the glazed tile heritage database of Covilhã-Portugal (12 elements)
<p>Extract from the tile heritage database of Covilhã - Portugal: geographic and alphanumeric data of 12 buildings with facades fully or partially covered with tiles, of heritage interest (registered in the SIPA), located in the municipality of Covilhã.</p> <p>The information is related to the buildings and tile compositions on its facades.</p> <p>Building data</p> <p>Building location: country, region, district, municipality, parish, street and house number, Lon WGS84, Lat WGS84, X ETRS89, Y ETRS89, Z elevation. SIPA: designation, identification number, link to SIPA data. Building identification: if it does not exist, an ID must be assigned. Protection and conditioning: world heritage, built heritage, benefits from a protection zone, in the process of classification, in study, non-existent. Public interest, national monument, municipal interest. Property type: public, private. Ownership: ownership information. Case number at municipal services: number ID. Typology: residence, industrial, commercial, religious, panel or other. Occupation: property occupation. Architectural style: dominant, secondary. Other decorative elements than tiles: if existing. General building condition (exterior): good, reasonable, bad, doom. Interventions chronology: if existing. Main moments of design, application, conservation, remodeling, restoration, and management. Bibliography, drawings and documents: source, link. Pictures: images.</p> <p>Glazed tile data</p> <p>Tile location: facade, inside building. Tile identification: ID number. Construction period: main construction periods. Local context: brief description of the glazed tile object surroundings, in its geophysical, historical, and socio-cultural aspects. Classification 1: coating, ceramic panel, toponymical plate or other. Classification 2: facade, patio, external staircase, balcony, wall, wall panel, small religious panel, caption. Classification 3: pattern, repeat composition, figurative, ornamental, or loose figure. Iconography: religion, biblical, magic, mysticism, nature, human being – humanity, society - civilization – culture, history, ancient history, literature, classical mythology, abstract ideas and concepts, abstract - non-figurative art. Monochrome or polychromatic: monochrome, polychromatic. Objective and succinct tile description: From general to particular: building, space, glazed tile, and section. Material (ceramic product): faience, stone dust, stoneware, porcelain, terracotta. Tile decoration technique: information on tile manufacture, divided into conformation, decoration and application. Composition dimensions: height x width (meters). Tile dimensions: height x width (centimetres). Tile condition: good, reasonable, bad. Authorship: company, artist, painter or other. Authorship nationality: country. Interventions chronology: if existing. Main moments of design, application, conservation, remodelling, restoration, and management. Bibliography, drawings and documents: source, link. Pictures: images.</p>
Core-loss EELS dataset and neural networks for element identification
<p>We present a large dataset containing simulated core-loss electron energy loss spectroscopy (EELS) spectra with the elemental content as ground-truth labels. Additionally we present some neural networks trained on this data for element identification. </p> <p>The simulated dataset contains zero padded core-loss spectra from 0 to 3072 eV, which represents 107 core-loss edges through all 80 elements from Be up to Bi. The core-loss edges are calculated from the generalised oscillator strength (GOS) database presented by Zhang et al.[1] Generic fine structures using lifetime broadened peaks are used to imitate fine structure due to solid-state effects in experimental spectra. Generic low-loss regions are used to imitate the effect of multiple scattering. Each spectrum contains at least one edge of a given query element and possibly additional edges depending on samples drawn from The Materials Project [2]. The dataset contains for each of the 80 elements: 7000 training spectra, 1500 test spectra, 600 validation spectra and 100 spectra representing only the query element. This results in a total 736 000 labeled spectra.</p> <p>Code on how to <br> - read the simulated data<br> - transform HDF5 format to TFRecord format<br> - train and evaluate neural networks using the simulated data<br> - use the trained networks for automated element identification<br> is available on GitHub at arnoannys/EELS_ID</p> <p>A full report on the simulation of the dataset and the training and evaluation of the neural networks can be found at: Annys, A., Jannis, D. & Verbeeck, J. Deep learning for automated materials characterisation in core-loss electron energy loss spectroscopy. <em>Sci Rep</em> 13, 13724 (2023). https://doi.org/10.1038/s41598-023-40943-7</p> <p>[1] Zezhong Zhang, Ivan Lobato, Daen Jannis, Johan Verbeeck, Sandra Van Aert, & Peter Nellist. (2023). Generalised oscillator strength for core-shell electron excitation by fast electrons based on Dirac solutions (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7729585<br> [2] Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand Ceder, Kristin A. Persson; Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. __APL Mater__ 1 July 2013; 1 (1): 011002. [https://doi.org/10.1063/1.4812323](https://doi.org/10.1063/1.4812323)</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.