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424 results for “In-situ”
Data and scripts for "Unraveling secondary ice production in winter orographic clouds through a synergy of in-situ observations, remote sensing and modeling"
<div> <div> <div>This repository contains field observations and processed data from the Weather Research and Forecasting (WRF) model simulations and the Cloud Resolving Model Radar Simulator (CR-SIM), alongside scripts designed to reproduce the figures presented in the paper titled "Unraveling Secondary Ice Production in Winter Orographic Clouds through a Synergy of In-Situ Observations, Remote Sensing, and Modeling." The in-situ and remote sensing measurements were conducted at Mount Helmos in Peloponnese as part of the CALISHTO campaign (https://calishto.panacea-ri.gr/).</div> </div> </div> <div>Preprint accessible at: https://doi.org/10.21203/rs.3.rs-3502790/v1</div>
Data for 'In-Situ EBSD Study of Austenitisation in a Wire-Arc Additively Manufactured High-Strength Steel'
<p>All data supporting the paper 'In-Situ EBSD Study of Austenitisation in a Wire-Arc Additively Manufactured High-Strength Steel'. Includes gifs (movies) of the high temperature in-situ EBSD experiments and the scripts used for analysis.</p>
In-situ parameters, nutrients and dissolved carbon distribution in the water column and pore waters of Arctic Fjords (Western Spitsbergen) during a melting season
<p>A nutrient distribution such as phosphate (PO₄³⁻), ammonium (NH₄⁺), nitrate (NO₃⁻), dissolved silica (Si), total dissolved nitrogen (TN), dissolved organic nitrogen (DON) together with dissolved organic carbon (DOC) and inorganic carbon (DIC), was investigated during a high melting season in 2021 in the western Spitsbergen fjords (Hornsund, Isfjorden, Kongsfjorden, and Krossfjorden). Both the water column and the pore water were investigated for nutrients and dissolved carbon distribution and gradients. The water column concentrations of most measured parameters such as PO₄³⁻, NH₄⁺, NO₃⁻, Si, and DIC showed significant changes among fjords and water masses. In addition, pore water gradients of PO₄³⁻, NH₄⁺, NO₃⁻, Si, DIC and DOC revealed significant variability between fjords and are likely substantial sources of the investigated elements for the water column. The obtained dataset reflects differences in hydrography and biogeochemical ecosystem function of the western Spitsbergen fjords and may form the base for further modelling of physical oceanographic and biogeochemical processes within the investigated fjord systems.</p> <p> </p>
Data: Characterizing the sediment dynamics through in-situ measurements in the abyssal Manila Trench, northeast South China Sea
<p>Along the Manila Trench, a total of four moorings were deployed in <a name="OLE_LINK5"></a>September 2019 and recovered in August 2020. The field measurements in velocity and turbidity were resampled to create hourly dataset.</p>
In-situ observations of surface Chlorophyll-a (HPLC) in the Western Antarctic Peninsula during 2008-2018 (GOAL-FURG)
<p>This dataset contains observations of in-situ chlorophyll-a (mg/m3) along the Western Antarctic Peninsula from 2008 to 2018. All data included in this dataset were collected by the Brazillian High Latitude Oceanography Group (GOAL), based at the Federal University of Rio Grande (FURG), aboard research vessels from the Brazillian Navy.</p> <p>All chlorophyll-a samples were collected at 5 metres depth. Chlorophyll-a concentration was determined through High Pressure Liquid Chromatography. This dataset has been collected thanks to a 10 year effort of sampling by GOAL in the Western Antarctic Peninsula. As such, many researchers, students, and crew members contributed to this valuable dataset. Therefore, we ask that users acknowledge the use of the dataset.</p> <p>Please consult Ferreira et al. 2024 for more information on how the data was collected and analysed.</p>
In-situ observations of nitrate loss factor for "Estimation method is the primary source of uncertainty in cropland nitrate leaching estimate in China"
<p>This database includes In-situ observations of nitrate loss factor. Details can be found in paper named "Estimation method is the primary source of uncertainty in cropland nitrate leaching estimate in China".</p>
Additional data for the "In-situ full field measurement during inter-facial debonding in single fiber composite under transverse load"
<p>The following document is an extension of the <em>In-situ full field measurement during inter-facial debonding in single fiber composite under transverse load</em> publication. It contains guidelines for the experimental results for the single fiber experiment of epoxy matrix and PTFE fiber, epoxy matrix and galvanized steel matrix, modified epoxy matrix and PTFE fiber and modified epoxy matrix and galvanized steel matrix. The detailed data from the experiments is provided with this document as <em>CSV </em>files.</p>
Additional data for "In-situ full field out of plane displacement and strain measurements at the micro-scale in single reinforcement composites under transverse load"
<p>The following document is an extension of the <em>In-situ full field out of plane displacement and strain measurements at the micro-scale in single reinforcement composites under transverse load</em> publication. It contains guidelines for the experimental results for the single fiber experiments and bundle of carbon fiber ones. The detailed data from the experiments is provided with this document as <em>CSV </em>files.</p>
High performance cation exchange membranes synthesized via in-situ emulsion polymerization without organic solvents and corrosive acids
<p>Dataset supporting journal publication:</p> <p><strong>Abstract:</strong> The synthesis of cation exchange membranes (CEMs) usually involves using organic solvents and/or sulfonation process. In this study, green and scalable synthesis of high performance CEMs is achieved without organic solvents and sulfonation. The synthesis is carried out via in-situ polymerization of lithium styrene sulfonate in porous support. Different preparation procedures are developed and optimized. Functional sulfonate groups were successfully loaded onto and into the membrane support, as verified by FTIR. Besides, water plays an important role during membrane synthesis. By reducing the amount of water used, the ratio of functional polymers to membrane support in the synthesized CEMs is increased. Therefore, the synthesized CEMs show increased ion exchange capacity (IEC). This is significant because it means that high IEC can be achieved without introducing cation exchange resins to the membranes. Finally, the synthesized membranes demonstrate high desalination performance. This new methodology may shed new light on preparing CEMs in an efficient and eco-friendly way.</p>
Datasets associated with: Comparing temperature data sources for use in species distribution models: From in-situ logging to remote sensing. Global Ecology and Biogeography
<p>Data associated with the paper 'Comparing temperature data sources for use in species distribution models: From in-situ logging to remote sensing. Global Ecology and Biogeography' by Lembrechts JJ et al., published in Global Ecology and Biogeography.</p> <p>Contains a dataset containing all extracted and measured temperature variables for all 106 measurement plots (climatedata), as well as the climate and species data used in the Species Distribution Models (SDMs). </p> <p>For details on the content of the table, see the readme-file, for details on methodology, see the original paper. </p>
Stress-State Differences between Sedimentary Cover and Basement of the Songliao Basin, NE China: In-situ Stress Measurements at 6–7 km depth of an ICDP Scientific Drilling Borehole (SK-II)
<p>The excel file is the original ASR test data (Nine samples, 6293 - 6846 m vertical depth).</p>
Anthropogenic carbon monoxide emissions during 2014-2020 in China constrained by in-situ observations
<p><strong>The description of the NetCDF files (12×200×350):</strong></p> <ol> <li> <p>The first dimension represents the months, the second represents latitude, and the third represents longitude.</p> </li> <li> <p>The latitude ranges from 15.1°N to 54.9°N, and the longitude ranges from 66.1°E to 135.9°E, with a uniform grid spacing of 0.2° for both.</p> </li> </ol> <p><strong>The units for all files are as follows:</strong></p> <table style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 33.2913%;"><col style="width: 33.2913%;"><col style="width: 33.2913%;"></colgroup> <tbody> <tr> <td> <p>File</p> </td> <td> <p>Format</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>All emission data.zip</p> </td> <td> <p>netcdf</p> </td> <td> <p>kg·m<sup>-2</sup>·s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Emissions in seven regions.csv</p> </td> <td> <p>csv</p> </td> <td> <p>10<sup>3</sup> kt</p> </td> </tr> <tr> <td> <p>Simulated CO concentrations.zip</p> </td> <td> <p>txt</p> </td> <td> <p>μg·m<sup>-3</sup> </p> </td> </tr> </tbody> </table>
Satellite-ground synchronous in-situ dataset of water optical parameters and surface temperature for typical lakes in China
<p>Remote sensing technology has the potential to significantly enhance the lakes large-scale and long-term dynamic monitoring capabilities. High-quality in-situ datasets are essential for improving the accuracy and reliability of remote sensing retrieval of water optical parameters. This dataset provides satellite-ground synchronized in-situ data on water optical parameters for typical lakes in China spanning the period between 2020 and 2023. The dataset includes quality-checked remote sensing reflectance ( ) data and water optical parameter data for chlorophyll-a (Chl-a), total suspended matter (TSM), Secchi disk depth (SDD), andwater surface temperature (WST). It encompasses 586 sampling points across 18 lakes. The dataset exhibits two significant highlights: Firstly, synchronous observations from multiple satellites are coordinated during the data collection process, effectively supporting the retrieval and validation of water remote sensing products. Secondly, it encompasses diverse data types, collecting synchronous measurements of and various water optical parameters. This dataset will be continuously updated, thereby making a substantial contribution to enhancing regional and global lake monitoring capabilities through satellite remote sensing data.</p>
In-situ Electron Paramagnetic Resonance Investigation of Isotope-selective Breathing in MIL-53 during Dihydrogen Adsorption
<p><strong>Description of the dataset:</strong></p> <ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements including CW EPR, Pulsed EPR raw data, N2 isotherm, CO2 isotherm and crystal structure data</li> <li>Files are with filename extensions: <strong>spc, par, dta, dsc, aif, csv, cif, txt and opj</strong></li> <li>Information on <strong>origin of the data</strong>:</li> <li>EPR spectroscopic measurements with filename extensions <strong>spc</strong>, <strong>par dta, dsc</strong>,<strong> txt </strong>and<strong> opj.</strong></li> <li>Data visualisation was conducted using OriginLab version 8 and Microsoft Power Point.</li> <li>X-band CW-EPR spectroscopic measurements data were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li>Pulsed X-band EPR measurements data were generated by Bruker ELEXYS E580 spectrometer.</li> <li><strong>Additional Information </strong>: <ul> <li>specialized abbreviations: <strong>EPR</strong> – Electron Paramagnetic Resonance, <strong>MIL</strong> – Matériaux de l’Institut Lavoisier</li> <li>definitions of variables: <strong>Magnetic field, Pressures, Microwave power.</strong></li> <li>units of measurement: <strong>Gauss (G), milliTesla (mT), millibar(mbar), microwave power (dB)</strong>.</li> <li>abbreviations on the CW-EPR data filename: Dates, Sample name, H2/D2 ads/des (ads= adsorption, des=desorption), microwave power, D2 or H2 pressures, number scans if indicated.</li> <li>abbreviations on the pulsed EPR data filename: Dates, Sample name, H2/D2 ads/des (ads= adsorption, des=desorption), D2 or H2 pressures, pulse sequences, pulse delay, temperature.</li> </ul> </li> </ul>
Data for a publication "Microstructure and mechanical properties of in-situ SiO2-reinforced mechanically alloyed CoCrFeNiMnX (X= 5, 20, 35 at.%) high-entropy alloys"
<p>Dataset contains data that has been used within the manuscript entitled: "Microstructure and mechanical properties of in-situ SiO2-reinforced mechanically alloyed CoCrFeNiMnX (X= 5, 20, 35 at.%) high-entropy alloys". For more information, please read the README.txt file.</p>
Data for the publication "Hydrogen penetration into the NiTi superelastic alloy investigated in-situ by synchrotron diffraction experiments"
<p>This dataset contains the data to the research paper "Hydrogen penetration into the NiTi superelastic alloy investigated in-situ by synchrotron diffraction experiments". The paper describes a<span> microstructural evolution caused by a hydrogen permeation into the NiTi superelastic alloy, which was investigated in-situ using the X-ray synchrotron diffraction. The diffraction data, electrochemical data, TEM pictures, lattice parameters for ab-initio DFT calculations and input parametrs for FEM calculations are included.</span></p>
Borehole observation, in-situ stress and breakout simulation datasets for BS34 in the Xinchang site, Beishan region
<p>Two datasets are available here as supplementary materials for the study of borehole breakout development.</p> <p>For the dataset 'Borehole Data.zip', it contains the natural fractures, breakouts, and drilling-induced tensile fractues, and mini-frac test results obtained from borehole BS34 in the Xinchang site, Beishan region. For the dataset 'Simulation Data.zip', it includes the simulated stress evolution associated with breakout development in the vicinity of a pre-existing frature, and simulated breakout geometry using a finite element model.</p>
Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth from Dense Satellite and Sparse In-Situ Observations: Preprocessed Satellite and In-situ observation datasets
<p>This record includes all of the prepared data used in the manuscript, "Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth from Dense Satellite and Sparse In-Situ Observations" (citation information forthcoming). As a part of this manuscript, we analyzed the ability for machine learning models to extract sea surface information (from salinity, temperature, sea height anomaly) to predict mixed layer depth. In this manuscript there are two experimental datasets: (1) info derived from CESM POP2 ocean model dataset (1989-1998), and (2) info derived from a combination of satellite sources and MLD from Argo profiles. More details below. </p> <p>All of these data files are preprocessed and organized to be used with the ml-ocean-bl github code found at https://github.com/NCAR/ml-ocean-bl/mloceanbl/.</p> <ul> <li><strong>CESM POP2 Ocean model dataset</strong></li> </ul> <p>Preprocessed sea surface salinity (SSS), temperature (SST), sea surface height anomalies (SSH), and ocean mixed layer depth (MLD, or HMXL) derived from the CESM POP2 Ocean model. Specifically, CESM POP2 model in a hindcast forced by JRA55do atmospheric reanalysis from 1958 to present and initialized with an oceanic climatology as in e.g. <a href="https://journals.ametsoc.org/view/journals/phoc/aop/JPO-D-20-0217.1/JPO-D-20-0217.1.xml">Deppenmeier et al. (2021)</a>. The model outputs include the ocean mixed layer depth (MLD), sea surface salinity (SSS), sea surface temperature (SST), and sea height anomaly (SSH) at a temporal frequency of 5-days and an approximate latitude and longitude resolution of 0.1 degrees.</p> <p>Relevant files:</p> <ol> <li>full_EPO.nc, full_SIO.nc <ul> <li>NetCDF4 containing SSS, SST, SSH, MLD for the equatorial Pacific Ocean (EPO) and southern Indian Ocean (SIO) (see manuscript for details). Data is regridded onto a 1/2 degree lat/lon 5 day grid to correspond with data used for Argo datasets (see below).</li> </ul> </li> <li>clim_EPO.nc, clim_SIO.nc, clim_std_EPO.nc, std_clim_EPO.nc, std_clim_SIO.nc <ul> <li>NetCDF4 containing mean and standard deviation climatologies of SSS, SST, SSH, and MLD for EPO and SIO.</li> </ul> </li> <li>std_anomalies_EPO.nc, std_anomalies_SIO.nc <ul> <li>NetCDF4 containing SSS, SST, SSH, and MLD standardized anomalies for EPO and SIO. This is the dataset directly used for training in aforementioned manuscript. Use with ml-ocean-bl/ml-ocean-test/data. </li> </ul> </li> </ol> <ul> <li><strong>Satellite and Argo datasets</strong></li> </ul> <p>Preprocessed satellite sea surface salinity (SSS), temperature (SST), and sea surface height anomalies (SSH) and Argo-based mixed layer depth (MLD) profiles. Original data can be found at:</p> <p>(SST): Remote Sensing Systems. 2017. MW optimum interpolated SST data set. Ver. 5.0. PO.DAAC, CA, USA. Further information available at at <a href="https://doi.org/10.5067/GHMWO-4FR05">https://doi.org/10.5067/GHMWO-4FR05</a>. Data can be accessed at https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/REMSS/mw_OI/v5.0/.</p> <p>(SSS): Oleg Melnichenko. 2018. Aquarius L4 Optimally Interpolated Sea Surface Salinity. Ver. 5.0. PO.DAAC, CA, USA. Further information at <a href="https://doi.org/10.5067/AQR50-4U7CS">https://doi.org/10.5067/AQR50-4U7CS</a>. Data can be accessed at https://podaac-tools.jpl.nasa.gov/drive/files/SalinityDensity/aquarius/L4/IPRC/v5/7day. </p> <p>(SSH): Zlotnicki, Victor; Qu, Zheng; Willis, Joshua. 2019. SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL1609. Ver. 1812. PO.DAAC, CA, USA. Information available at <a href="https://doi.org/10.5067/SLREF-CDRV2">https://doi.org/10.5067/SLREF-CDRV2</a>. Data can be accessed at https://podaac-tools.jpl.nasa.gov/drive/files/SeaSurfaceTopography/merged_alt/L4/cdr_grid</p> <p>(MLD) Argo-based ocean surface mixed layer depths using the buoyancy gradient definition of Whitt Nicholson and Carranza (2019) processed dataset available at https://doi.org/10.5281/zenodo.4291175.</p> <p>Relevant files:</p> <ol> <li>https://github.com/NCAR/ml-ocean-bl/mloceanbl/preprocess_mld.py and .../preprocess_sss_sst_ssh.py. <ul> <li>Preprocessing code</li> </ul> </li> <li>sss_sst_ssh_anomalies.nc. <ul> <li>Regridded and resampled SSS, SST, SSH onto a 1/2 degree lat/lon 7day grid. Contains preprocessed seasonal data along with anomalies.</li> </ul> </li> <li> mldb_climatology_climatologystd_binned.nc <ul> <li>Smoothed argo-based mixed layer depths are used to calculate climatologies and standardized climatologies. 4 degree lat/lon gridded climatologies.</li> </ul> </li> <li>mldb_full_anomalies_stdanomalies_climatology_stdclimatology.nc <ul> <li>Contains the Argo profile-derived MLD, anomalies, standard anomalies, climatologies, and standardized climatologies with corresponding argo locations, times, and corresponding weeks. </li> </ul> </li> <li>equatorial_pacific_model_oi_re.nc, southern_indian_model_oi_re.nc <ul> <li>Model outputs for the Equatorial Pacific Ocean and Southern Indian Ocean. These gridded files contain the model outputs (vlcnn, vlcnn variance, OI, OI variance, reanalysis, and reanalysis variance - see manuscript for nomenclature details) at each of the 200 weeks available. It should be noted that, in the equatorial Pacific Ocean, the lat/lon location of (-138.75, -9.75) is masked during the training and filled with a NaN in the .nc files. </li> </ul> </li> </ol> <p> </p> <p> </p> <p>Contact D. Foster with any questions.</p> <p> </p>
In-plane, In-situ domain switching in BaTiO3 thin films. BE-PFM data and CV curve.
<p>In-Plane band-exitation piezoresponse force microsopy data and CV curve in BaTiO3<br> The BE-PFM data have been fit to a second harmonic oscillator.<br> The data is stored as as python dictionaries that have been pickled. They contain the fitting paramaters for each pixel in each image.<br> The first image run4_0002_fit.lsqfit with zero bias.<br> The bias for all images are stored in CV.CV, also a python dictionary that has been pickled, and contains the voltage [V] and capacitance [F] for all 129 images.<br> See the jupyter notebook for how to read the data</p>
Data and code for the manuscript Retrieving Water Vapor From an E-band Microwave Link With an Empirical Model Not Requiring In-situ Calibration
<p>Data and code for the manuscript <em>Retrieving Water Vapor From an E-band Microwave Link With an Empirical Model Not Requiring In-situ Calibration</em> accepted for publication to<em> </em> <em>Earth and Space Science</em> in October 2021.</p> <p>The dataset contains 7 month of total losses (transmitted - received power levels) and retrieved water vapor density from a 4.87 km long full-duplex E-band commercial microwave link (CML) operating at 73.5 and 83.5 GHz in Prague, CZ. The CML was operated as a part of a mobile phone backhaul. Furthermore, observations of air temperature, and air relative humidity from sites close to the CML end nodes are provided. Finally, theoretical gaseous attenuation calculated from the air temperature and relative humidity is included as a part of the dataset.</p> <p>Data are stored in semicolon-delimited csv files. Time stamps are in UTC time in the format yyyy-mm-dd HH:MM:SS. All time series are regular and have 5-min temporal resolution. Metadata are stored in text files.</p> <p>The code is in a form of R Markdown files and html notebooks. Results presented in the manuscript Retrieving Water Vapor From an E-band Microwave Link With an Empirical Model Not Requiring In-situ Calibration and in its Supporting information are fully reproducible using this dataset.</p>
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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.