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1,812 results for “In situ”
In situ observations of Ganymede's outflowing ionosphere
<p>The file contains values for ionospheric moments from the Juno flyby of Ganymede on 7 June 2021, during the period between 16:50 to 17:00, UTC. A description of the flyby and the data set is found in Valek et al. (2022), <em>In situ ion composition observations of the Ganynmede's outflowing ionosphere</em>, GRL DOI:10.1029/2022GL100281.</p>
Dataset generated to evaluate in situ sampling strategies to reconstruct fine-scale ocean currents in the context of SWOT satellite mission (H2020 EuroSea project)
<p><strong>Dataset generated in Subtask 2.3.1 of the H2020 EuroSea project.</strong></p> <ul> <li> <p><em>H2020 EuroSea project:</em><br> The H2020 EuroSea project aims at improving and integrating the European Ocean Observing and Forecasting System (see official website: <a href="https://eurosea.eu/">https://eurosea.eu/</a>). It has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862626).</p> </li> <li> <p><em>Task 2.3:</em><br> Task 2.3 has the objective to improve the design of multi-platform experiments aimed to validate the Surface Water and Ocean Topography (SWOT) satellite observations with the goal to optimize the utility of these observing platforms. Observing System Simulation Experiments (OSSEs) have been conducted to evaluate different configurations of the in situ observing system, including rosette and underway CTD, gliders, conventional satellite nadir altimetry and velocities from drifters. High-resolution models have been used to simulate the observations and to represent the “ocean truth”. Several methods of reconstruction have been tested: spatio-temporal optimal interpolation, machine-learning techniques, model data assimilation and the MIOST tool. The planned OSSEs are detailed in this public report <a href="https://doi.org/10.3289/eurosea_d2.1">Barceló-Llull et al. (2020)</a> and the complete analysis is available here <a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al. (2022)</a>. Contributors to Task 2.3 are CSIC (Spain), CLS (France), SOCIB (Spain), IMT-Atlantique (France) and Ocean-Next (France).</p> </li> <li> <p><em>Subtask 2.3.1:</em><br> Subtask 2.3.1 aims to evaluate different in situ sampling strategies to reconstruct fine-scale ocean currents (~20 km) in the context of SWOT. An advanced version of the classic optimal interpolation used in field experiments, which considers the spatial and temporal variability of the observations, has been applied to reconstruct different configurations with the objective to evaluate the best sampling strategy to validate SWOT.</p> </li> <li> <p><em>Where?</em><br> The analysis focuses on two regions of interest: (i) the western Mediterranean Sea and (ii) the Subpolar North West Atlantic. In the western Mediterranean Sea, the target area is located within a swath of SWOT, while in the North West Atlantic the region of study includes a crossover of SWOT during the fast-sampling phase.</p> </li> </ul> <p><strong>Report with the full analysis</strong></p> <p>The complete analysis can be found in this report: <a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al. (2022)</a>.</p> <p><strong>Codes for the analysis</strong></p> <p>The codes generated to develop Subtask 2.3.1 can be found on GitHub: <a href="https://github.com/bbarcelollull/EuroSea_subTask_2.3.1">https://github.com/bbarcelollull/EuroSea_subTask_2.3.1</a></p> <p><strong>The dataset</strong></p> <p>The dataset includes:</p> <p>1) Model outputs used to simulate the observations in different configurations in both regions of study. The folder "2D_model_outputs" contains 2D data used to simulate SSH observations for the analysis of the temporal correlation scale (<a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al., 2022</a>, p. 28-42). The folder "3D_model_outputs" contains 3D model outputs used to simulate observations of temperature and salinity. Note that eNATL60 outputs have been interpolated onto a new regular grid. </p> <p>2) Simulated configurations (or sampling strategies) in each region (PKL file format).</p> <p>3) Observations simulated in each configuration in both regions of study. The observations simulated are temperature and salinity. ADCP horizontal velocities are also simulated, however for eNATL60 they will be corrected in the future to account for the rotated original axes. File format: region_configuration_period_model.nc. The folder "SSH" includes the simulated SSH observations for the analysis of the temporal correlation scale (<a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al., 2022</a>, p. 28-42).</p> <p>4) Reconstructed fields with the spatio-temporal optimal interpolation. File format: region_configuration_period_model_stOI_Lx_Lt_cd_YYYYMMDDhhmm_var.nc (stOI = spatio-temporal optimal interpolation, Lx = spatial correlation scale, Lt = temporal correlation scale, cd = map on the central date of the sampling, YYYYMMDDhhmm = date and time of the map, var = variable interpolated (temperature and salinity) or the derived variables (dynamic height, geostrophic velocities and the Rossby number)).</p> <p>5) Compared fields (ocean truth from model outputs vs. reconstructed fields) for each region and model (PKL file format).</p> <p> </p>
Criteria for prioritizing selection of Mexican maize landrace accessions for conservation in situ or ex situ based on phylogenetic analysis
<p>Data for processed SSR markers in maize accessions. A database in Structured Query Language (SQL) is provided. Please see the text file "READMEmaizeSSR.pdf".</p>
Predicting aboveground and belowground processes in diverse forest ecosystems using remote sensing and in-situ measurements
The Forest and Biodiversity (FAB2) experiment uses native tree species in varying levels of species richness, phylogenetic diversity, and functional diversity planted in 100 m2 and 400 m2 plots at 1 m spacing, appropriate for testing long-term ecosystem consequences. FAB2 was designed and established in conjunction with a prior experiment (FAB1) in which the same set of twelve species was planted in 16 m2 plots at 0.5 m spacing. This data package examines the connections between aboveground and belowground processes in FAB2. This data package includes information on tree diversity and community composition, forest structure, forest understories, soil microbes, net nitrogen mineralization, and canopy nitrogen. A wide variety of data types are included, such as data from hyperspectral and LiDAR remote sensing, percent cover analysis, soil microbial analyses, and soil assays including C:N, pH, and net nitrogen mineralization. This data package is included in the submission of the manuscript entitled “Predicting aboveground and belowground processes in diverse forest ecosystems using remote sensing and in-situ measurements.”
Underwater Photosynthetically Active Radiation (PAR) from in-situ Lake Primary Production Experiments in the McMurdo Dry Valleys of Antarctica (1995-2024, ongoing)
As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, PAR is measured at one depth during primary production experiments in Dry Valley Lakes. This data set quantifies instantaneous underwater PAR at 10-meter depths in Lakes Bonney and Hoare, and at 7-meter depth in Lake Fryxell (depths vary after 2008, check data file for actual depth). Ambient PAR was also measured at the air-ice interface from each of these lakes.
Water column primary production from inorganic carbon uptake for 24h at simulated in situ light levels in deck incubators, collected at Palmer Station Antarctica during Palmer LTER field seasons, 1994-2025.
Primary Production experiments were led by Vernet from the 1994-1995 season through the 2006-2007 season. Schofield is the current lead, beginning in the 2009-2010 season. Methods have been kept consistent as much as possible over the full time series and different Principal Investigators. Primary production is the uptake of inorganic carbon and assimilation of it into organic matter by phytoplankton. Primary production rates, expressed as mgC per m3 per day were measured by the uptake of radioactive (14C) sodium bicarbonate. Water samples are collected throughout the water column at stations within the Palmer LTER region (primarily B and E, to 50m and 65m respectively). Beginning in the 2020-2021 season, Station B is no longer sampled. Water is put into borosilicate bottles, inoculated with 1 uCi of NaH14CO3 per bottle, and incubated in an outdoor deck incubator. The incubator is plumbed to the Palmer Station sea water system to maintain ambient seawater temperature and bottles are screened to in situ light levels. The uptake of 14C-bicarbonate by the phytoplankton was measured in a scintillation counter after a 24-hour incubation period. Primary production experiments were not conducted during the 2020-2021 nor 2023-2024 field seasons. There was no field season in 2021-2022.
Water column primary production from inorganic carbon uptake for 24h at simulated in situ (SIS) light levels in deck incubators, collected aboard Palmer LTER annual cruises off the coast of the Western Antarctic Peninsula, 1995 – 2023.
Primary Production experiments were led by Vernet from 1995-2008. Schofield is the current lead, beginning in 2009. Methods have been kept consistent as much as possible over the full time series and different Principal Investigators. Primary production is the uptake of inorganic carbon and assimilation of it into organic matter by phytoplankton. Primary production rates, expressed as mgC per m3 per day were measured by the uptake of radioactive (14C) sodium bicarbonate. Water samples are collected throughout the water column at stations along the Western Antarctica Peninsula at regular PAL-LTER grid stations. Water is put into borosilicate bottles, inoculated with 1 uCi of NaH14CO3 per bottle, and incubated in an outdoor deck incubator. The incubator is plumbed to the ship sea water system to maintain ambient seawater temperature and bottles are screened to in situ light levels. The uptake of 14C-bicarbonate by the phytoplankton was measured in a scintillation counter after a 24-hour incubation period. Data is unavailable for the LMG16-01 cruise due to measurement issues. Data is temporarily unavailable for the LMG20-01 cruise. Primary production experiments were not conducted during the 2022 (NBP21-13) nor 2024 (LMG24-01) cruises. There was no cruise in the austral summer of 2021.
ESA-WOC North Atlantic Sea Surface Salinity maps from a multivariate combination of satellite and in situ surface measurements (2010-2018)
<p>We deliver here the daily sea surface salinity level 4 (SSS L4) product developed in the framework of the European Space Agency World Ocean Circulation project (ESA-WOC), covering the period 2010-2018. This product was obtained by adapting to a 1/10° North Atlantic grid the multidimensional optimal interpolation algorithm used within the Copernicus Marine Environment Monitoring Service to retrieve the global SSS multi-year dataset (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: MULTIOBS_GLO_PHY_REP_015_002, dataset_id: dataset-sss-ssd-rep-weekly). This algorithm interpolates SMOS observations and in situ SSS observations considering a space-time-thermal decorrelation function, estimated by including information from high-pass filtered daily SST data (Droghei et al., 2016; Buongiorno Nardelli, 2012). Here, we ingested the L3OS 2Q debiased daily valid ocean salinity values product from SMOS satellite, produced and disseminated by the Centre Aval de Traitement des Données SMOS (CATDS, 2017), OSTIA SST data (CMEMS, <a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id=SST_GLO_SST_L4_REP_OBSERVATIONS_010_011) and CORA5.2 surface salinity values (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, doi: 10.17882/46219TS1, Szekely et al., 2019) as input data, and used CMEMS weekly SSS dataset to build our background field (linearly interpolating it in time between the two closest analysis dates, and upsizing to the 1/10° grid through a cubic spline). All other interpolation parameters were set as in Droghei et al. (2018). </p> <p> </p> <p><em>References:</em></p> <p>Buongiorno Nardelli, B.: A Novel Approach for the High-Resolution Interpolation of In Situ Sea Surface Salinity, J. Atmos. Ocean. Technol., 29(6), 867–879, doi:10.1175/JTECH-D-11-00099.1, 2012.</p> <p>CATDS (2017). CATDS-PDC L3OS 2Q - Debiased daily valid ocean salinity values product from SMOS satellite. CATDS (CNES, IFREMER, LOCEAN, ACRI). http://dx.doi.org/10.12770/12dba510-cd71-4d4f-9fc1-9cc027d128b0</p> <p>Droghei, R., Buongiorno Nardelli, B. and Santoleri, R.: Combining in-situ and satellite observations to retrieve salinity and density at the ocean surface, J. Atmos. Ocean. Technol., 33, 1211–1223, doi:10.1175/JTECH-D-15-0194.1, 2016.</p> <p>Droghei, R., Buongiorno Nardelli, B. and Santoleri, R.: A New Global Sea Surface Salinity and Density Dataset From Multivariate Observations (1993–2016), Front. Mar. Sci., 5(March), 1–13, doi:10.3389/fmars.2018.00084, 2018.</p> <p>Szekely, T., Gourrion, J., Pouliquen, S. and Reverdin, G.: The CORA 5.2 dataset for global in situ temperature and salinity measurements: Data description and validation, Ocean Sci., 15(6), 1601–1614, doi:10.5194/os-15-1601-2019, 2019.</p>
Developing a deep Learning network to retrieve ocean hydrographic profiles in the North Atlantic from combined satellite and in situ measurements: test datasets.
<p>We provide here the datasets used for the test and assessment of a deep learning algorithm which is presently candidate for the development of a daily 3D ocean product covering the North Atlantic at 1/10° resolution, over the 2010-2018 period, as part of the European Space Agency World Ocean Circulation project (ESA-WOC). The method is based on a stacked Long Short-Term Memory neural network, coupled to a Monte-Carlo dropout approach, and allows to project satellite-derived sea surface temperature, sea surface salinity and absolute dynamic topography data at depth after training with sparse co-located in situ vertical hydrographic profiles (Buongiorno Nardelli, 2020, doi:<a href="https://www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.3390%2Frs12193151?_sg%5B0%5D=0xE-347r7Hvb80klJcEo811AhUiXq-twG_E6l4yB-BfIKkVtW-lVLGcO02mTFkUczvozYYI0WCPyUBFR3kzWNGGZKg.ftvLheFrzHIJriO4qW2bdxalvR_TWt3MpwUfvto3EemhRgvDRGwJ9Mdy4Xr0IcGCfICivf4j-VqTgKxVvXRogA">10.3390/rs12193151</a>). </p> <p>The test dataset presented here includes different sets of co-located temperature and salinity vertical profiles: </p> <ul> <li>in situ observations extracted from the quality controlled Argo and CTD profiles produced by Copernicus Marine Environment Monitoring Service CORA 5.2 (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, doi: 10.17882/46219TS1, Szekely et al., 2019) and interpolated through a spline on a regularly spaced vertical grid (with 10 m intervals);</li> <li>climatological profiles extracted from World Ocean Atlas 2013 optimally interpolated monthly fields (Locarnini et al., 2013; Zweng et al., 2013), interpolated through a spline on a regularly spaced vertical grid (with 10 m intervals), upsized to a 1/10° horizontal grid through a cubic spline and linearly interpolated in time between the central day of each month;</li> <li>synthetic profiles obtained through three different techniques: multivariate EOF reconstruction, a 2 layer feed-forward network (with 1000 units in each hidden layer) and a stacked LSTM network (with 2 LSTM layers and 35 hidden units)</li> </ul> <p><em>References:</em></p> <p>Buongiorno Nardelli, B.: A Deep Learning network to retrieve ocean hydrographic profiles from combined satellite and in situ measurements, 2020, <em>submitted</em>.</p> <p>Locarnini, R. A., Mishonov, A. V., Antonov, J. I., Boyer, T. P., Garcia, H. E., Baranova, O. K., Zweng, M. M., Paver, C. R., Reagan, J. R., Johnson, D. R., Hamilton, M. and Seidov, D.: World Ocean Atlas 2013. Vol. 1: Temperature., S. Levitus, Ed.; A. Mishonov, Tech. Ed.; NOAA Atlas NESDIS, 73(September), 40, doi:10.1182/blood-2011-06-357442, 2013.</p> <p>Szekely, T., Gourrion, J., Pouliquen, S. and Reverdin, G.: The CORA 5.2 dataset for global in situ temperature and salinity measurements: Data description and validation, Ocean Sci., 15(6), 1601–1614, doi:10.5194/os-15-1601-2019, 2019.</p> <p>Zweng, M. M., Reagan, J. R., Antonov, J. I., Mishonov, A. V., Boyer, T. P., Garcia, H. E., Baranova, O. K., Johnson, D. R., Seidov, D. and Bidlle, M. M.: World Ocean Atlas 2013, Volume 2: Salinity, NOAA Atlas NESDIS, 119(1), 227–237, doi:10.1182/blood-2011-06-357442, 2013.</p> <p> </p>
Raw data for article "In Situ Synchrotron X-Ray Diffraction Characterization of Corrosion Products of a Ti-Based Metallic Glass for Implant Applications" Gostin et al 2018
<p>This repository contains raw data for the article "In Situ Synchrotron X-Ray Diffraction Characterization of Corrosion Products of a Ti-Based Metallic Glass for Implant Applications" by Gostin et al. 2018 in Advanced Healthcare Materials, 7, 1800338 (https://doi.org/10.1002/adhm.201800338).</p> <p>Most data comes from one beamtime at the Diamond synchrotron in the UK in May 2016. It consists of X-ray diffraction images taken in situ in artificial corrosion pits on a Ti-based metallic glass.</p> <p>Please see the README file for more details.</p>
In situ FTIR, EXAFS and HR-STEM data for Pd/TiO2 samples under red-ox conditions
<p>Files Pd_photo-oxidation.xmu.dat and Pd_dep-oxidation.xmu.dat contain the sequence of X-ray absorption spectra during starting from the pre-reduced state (after reduction in H2) during heating in O2 from 50 to 400 for Pd_photo and Pd_dep samples, respectively (synthesized using photodeposition and deposition-precipitation methods). The last two columns in each file correspond to the as-synthesized state of the corresponding sample (before reduction in hydrogen) and reference palladium foil. </p> <p>Pd_photo.ftir.dat and Pd_dep.ftir.dat contain the sequence of the FTIR spectra for the same samples taken at room temperature after sending 35 mbar of CO on pre-oxidized samples.</p> <p>Video files show the evolution of the structure of Pd_dep and Pd_photo samples sample under different atmospheres and temperatures, visualized by in situ HR-STEM microscope.</p>
Lemming Mesocosm (Denmark): in-situ fluorescence chlorophyll-a calibration, underlying data
<p>These data files include 2 years (2018-2020) of high-frequency in-situ chlorophyll-a and phycocyanin fluorescence sensor (Turner Designs, Cyclops 7F) data, together with data from various tests conducted with these sensors. Morever, in-vitro chlorophyll-a data for 2018-2020 period is also included.</p> <p>These data are collected in Lemming mesocosm site, Denmark, where there are 24 tanks with 2 nutrient and 3 temperature treatments (2x3 factorial design). There is data from 24 in-situ chlorophyll-a and 12 in-situ phycocyanin fluorescence sensors.</p> <p>In this Dataset folder, there are:</p> <ul> <li>one <strong>Metadata </strong>(*.xlsx)<strong> </strong>file with 3 sheets; <ul> <li>"<em>Descriptive</em>": comprises information on location, authors, study period and the main instrument used, </li> <li>"<em>Structural</em>": includes detailed information on each dataset.</li> <li>"<em>Relational</em>": includes a figure showing the relations between the datasets</li> </ul> </li> <li>thirteen files (*.csv) in LemCP_DataORE that were used to; <ul> <li>conduct in-situ fluorescence sensor tests (i.e. blank variation, linearity check, DOC effect check), </li> <li>calibrate 24 in-situ fluorescence chlorophyll-a sensors</li> <li>plot various figures</li> </ul> </li> </ul>
Immunoglobulin G: SEC-SAXS experiment using in house SAXS instrument with in situ UV-Vis
<p>SEC-SAXS experiment using in house SAXS instrument</p> <p>Sample: Immunoglobuline G<br>Buffer: 50mM Tris, 50 mM NaCl, pH 7.6</p> <table> <tbody> <tr> <td><strong>File Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>SAXS_raw.zip</td> <td>Raw, unreduced data (2D images) in HDF5 format</td> </tr> <tr> <td>SAXS_reduced.zip</td> <td>SAXS data, azimutaly reduced (1D curves). Q-vaules are in 1/nm</td> </tr> <tr> <td>SAXS_UV_raw.zip</td> <td>Raw UV-Vis absorption data collected using in-situ spectrometer</td> </tr> <tr> <td>SAXS_UV_processed.zip</td> <td>UV-Vis data collected using in-situ spectrometer. Absorbance at 280nm extracted as a time series</td> </tr> <tr> <td>HPLC_raw.zip</td> <td>Data from HPLC system natively exported from control software (Unicorn)</td> </tr> <tr> <td>HPLC_extracted.zip</td> <td>Data from HPLC system exported to CSV</td> </tr> <tr> <td>DS11_fractions.zip</td> <td>UV-Vis absorption data collected usig micro volume spectrometer (DeNovix DS-11) on fractions collected using fraction collector.</td> </tr> </tbody> </table> <p> </p>
Probiotics reshape the coral microbiome in situ without detectable off-targeted effects in the surrounding environment.
<p>The R code scripts and Supplementary data files from the paper: "Probiotics reshape the coral microbiome in situ without detectable off-targeted effects in the surrounding environment," accepted in Communications Biology. All R code and data necessary to reproduce the published results are available. </p>
Development of Multiomics in situ Pairwise Sequencing (MiP-Seq) for Single-cell Resolution Multidimensional Spatial Omics
<p>The original data used in the article: Development of Multiomics in situ Pairwise Sequencing (MiP-Seq) for Single-cell Resolution Multidimensional Spatial Omics</p> <p>Delineating the spatial multiomics landscape will pave the way to understanding the molecular basis of physiology and pathology. However, current spatial omics technology development is still in its infancy. Here, we developed a high-throughput targeted in situ sequencing strategy, multiomics in situ pairwise sequencing (MiP-Seq), to efficiently decipher multiplexed DNAs, RNAs, proteins, and small biomolecules at subcellular resolution. MiP-Seq simultaneously sequenced the dual barcode base of padlock probes, dramatically increasing the detection capacity to 10N by N rounds of sequencing. We delineated spatial gene profiles in the hypothalamus using MiP-Seq. Moreover, MiP-Seq was unitized to detect tumor gene mutations and allele-specific expression of parental genes and to differentiate sites with and without the m6A RNA modification at specific sites. MiP-Seq was combined with in vivo Ca2+ imaging and Raman imaging to obtain a spatial multiomics atlas correlated to neuronal activity and cellular biochemical fingerprints. Importantly, we proposed a “signal dilution strategy” to resolve the crowded signals that challenge the applicability of in situ sequencing. Together, our method improves spatial multiomics and precision diagnostics, and facilitates analyzing cell function in connection with gene profiles.</p>
Lemming Mesocosm (Denmark): in-situ fluorescence chlorophyll-a calibration, extended data
<p>These files include a scheme showing the sensor and tank (mesocosm) system, plots from high frequency in-situ chlorophyll-a and phycocyanin fluorescence data and in-vitro chlorophyll-a data collected in Lemming mesocosm site, Denmark, where there are 24 tanks with 2 nutrient and 3 temperature treatments (2x3 factorial design). These plots are based on 24 in-situ chlorophyll-a and 12 in-situ phycocyanin fluorescence sensors from 24 tanks/mesocosms. There is also a table showing the steps for cleaning the high-frequency data.</p>
Effects of Periodic Normal Stress Oscillations on Frictional Properties of Simulated Natural Fault Gouges under In Situ P-T Conditions
<p>Files named by in a format of "Uxxx_xx_xxMPa_xxC" refer to the original mechanical data recorded during experiment.</p> <p>The compressed package includes the files to perform numerical modeling, modeling results and the experimental data for comparison. To replicate the numerical modeling, readers can open the COMSOL project file (".mph" file) using COMSOL software (version >5.4) then input the parameters for the boundary conditions, such as the temperature, load-point velocity, oscillation amplitude and frequency. </p>
Pushing the Study of Point Defects in Thin Film Ferrites to Low Temperatures Using In Situ Ellipsometry
<p>Dataset for article "Pushing the Study of Point Defects in Thin Film Ferrites to Low Temperatures Using In Situ Ellipsometry" published in <em>Adv. Mater. Interfaces</em> 2021, <strong>8</strong>, 2001881. </p> <p>The data includes:</p> <ul> <li>XRD data of La<sub>1-x</sub>Sr<sub>x</sub>FeO<sub>3</sub> thin films</li> <li>Optical conductivity of LSF films as a function of oxygen partial pressure and Sr content</li> <li>Concentration of electronic holes in LSF thin films as a function of oxygen partial pressure and temperature</li> <li>Defect chemistry models employed for describing the concentration of point defects in LSF thin films.</li> <li>Equilibrium constants for oxygen incorporation reactions in LSF thin films at different temperatures</li> <li>Ellipsometry raw data of LSF50 thin film as a function of equivalent oxygen pressure </li> <li>Electrochemical impedance spectra of the LSF50 thin film at 400 ºC</li> </ul>
Online Resources for Strullu-Derrien et al - The 330–320 Million-Year-Old Tranchée des Malécots (Chaudefonds-sur-Layon, South of the Armorican Massif, France): a Rare Geoheritage Site Containing In Situ Palaeobotanical Remains
<p>This repository contains the following files associate with "The 330–320 Million-Year-Old Tranchée des Malécots (Chaudefonds-sur-Layon, South of the Armorican Massif, France): a Rare Geoheritage Site Containing In Situ Palaeobotanical Remains" by Christine Strullu-Derrien, Alan RT Spencer, Christopher J Cleal and Victor O. Leshyk.</p> <p><strong>Online Resource 1</strong> Model data as a .zip archive (301.5MB) containing .obj/.mtl and texture files for each 3D reconstruction (Models #1-4, whole site reconstruction, detailed reconstruction of the trench, and model of the mine site).</p> <p><strong>Online Resource 2</strong> Video animation showing whole site 3D model (.mp4 | 37.7MB), with quick fly-through of the Tranchée des Malécots showing exposed rock and bedding of the SW wall.</p> <p><strong>Online Resource 3</strong> Video animation showing 3D Model #1 (.mp4 | 35.1MB).</p> <p><strong>Online Resource 4</strong> Video animation showing 3D Model #2 (.mp4 | 83.5MB).</p> <p><strong>Online Resource 5</strong> Video animation showing 3D Model #3 (.mp4 | 45.3MB).</p> <p><strong>Online Resource 6</strong> Video animation showing 3D Model #4 (.mp4 | 65.8MB).</p> <p><strong>Online Resource 7</strong> Video animation showing 3D model of the Malécots mine headframe (.mp4 | 14.9.0MB).</p>
[Dataset] In situ laser-ultrasonic monitoring of Poisson's ratio and bulk sound velocities of steel plates during thermal processes
<p>Data generated and analyzed in the work titled "In situ laser-ultrasonic monitoring of Poisson’s ratio and bulk sound velocities of steel plates during thermal processes". See the associated publication for more context.</p> <p>All files are stored in Matlab's binary MAT-file format.</p> <ul> <li>cutOffs_ZGVs_nu_S1S2_A2A3_S3S6_A4A7.mat <ul> <li>Dispersion relation data of plates obtained from numerical calculation with a range of Poisson's ratios and otherwise arbitrary but fixed material properties.</li> <li>S1S2-, A2A3-, S3S6- and A4A7-ZGV resonance frequencies and k-values</li> <li>L1 and T1 thickness resonance frequencies</li> </ul> </li> <li>lusResults_jmat_dilatometry_data.mat <ul> <li>LUS measurement data and resulting material properties (raw displacement data recorded on the oscilloscope is stored separately to keep the file size reasonable.)</li> <li>Dilatometer measurements</li> <li>JMatPro simulation</li> </ul> </li> <li>lusOscilloscope_data.mat <ul> <li>Normal surface displacement measurement data obtained in situ with LUS and recorded with an oscilloscope</li> </ul> </li> </ul>
ScienceDex guides
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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.