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81 results for “In situ observations”
FIGURE 5. A in In situ Observations of the Meso-Bathypelagic Scyphozoan, Deepstaria enigmatica (Semaeostomeae: Ulmaridae)
FIGURE 5. A, Deepstaria enigmatica in a closed/pursed position. B, Close-up image of anastomosing canals in of D. enigmatica.
FIGURE 2 in In situ Observations of the Meso-Bathypelagic Scyphozoan, Deepstaria enigmatica (Semaeostomeae: Ulmaridae)
FIGURE 2. Screenshot taken from video of Deepstaria enigmatica recorded at 974 m, showing the 10 cm spacing of lasers.
FIGURE 1 in In situ Observations of the Meso-Bathypelagic Scyphozoan, Deepstaria enigmatica (Semaeostomeae: Ulmaridae)
FIGURE 1. Canon ME20F-SH configuration showing A, backside, B, frontside, and C, mounted to the front of ROV Hercules.
Fig. 2 in Ex-situ conservation of the critically endangered swamp forest crab Parathelphusa reticulata Ng, 1990 (Decapoda: Brachyura: Gecarcinucidae): observations on its reproduction and biology in captivity
Fig. 2. Top view of captive conditions of a, adult crab (CW: 30 mm); b, crablet (CW: 4 mm); c, juvenile crab (CW: 15 mm); d, Setup for pairing individuals. Photographs: Dian Alisha Binte Misba.
Fig. 4 in Ex-situ conservation of the critically endangered swamp forest crab Parathelphusa reticulata Ng, 1990 (Decapoda: Brachyura: Gecarcinucidae): observations on its reproduction and biology in captivity
Fig. 4. Growth of captive Parathelphusa reticulata (F1 generation) over 52 weeks (N = 20). Vertical bars indicate standard deviations.
An in situ observation dataset of soil hydraulic properties and soil moisture in a high and cold mountainous area on the northeastern Qinghai-Tibet Plateau
<p>Based on soil profile data at depths of 5 cm and 25 cm from 238 sampling sites, and on soil data from 32 soil moisture monitoring stations at depths of 5 cm, 15 cm, 25 cm, 40 cm, and 60 cm, we have compiled a soil hydraulic properties and soil moisture dataset for a high and cold mountainous area, Northeastern Qinghai-Tibet Plateau. Specifically, the soil hydraulic properties include clay, silt, sand, soil organic carbon, soil saturated hydraulic conductivity, soil water retention curve parameters (Van Genuchten model) and soil dry bulk density.</p>
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>
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>
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>
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>
FIGURE 6 in In situ Observations of the Meso-Bathypelagic Scyphozoan, Deepstaria enigmatica (Semaeostomeae: Ulmaridae)
FIGURE 6. Various ventral views of Deepstaria enigmatica.
FIGURE 3 in In situ Observations of the Meso-Bathypelagic Scyphozoan, Deepstaria enigmatica (Semaeostomeae: Ulmaridae)
FIGURE 3. Deepstaria enigmatica (below) with a blue-emitting bioluminescent Tomopteris sp. (above).
FIGURE 4 in In situ Observations of the Meso-Bathypelagic Scyphozoan, Deepstaria enigmatica (Semaeostomeae: Ulmaridae)
FIGURE 4. Time-lapse series of Deepstaria enigmatica closing, illustrating peristaltic motion.
In situ observation dataset under the complete seasonal cycle of lake ice
<p>This dataset contains in situ observations of lake ice under a complete seasonal cycle. The study area is Lake Hanzhang in northeast China and the observation period is from winter 2020 to spring 2021. The observations include meteorological, hydrological and optical data.</p> <p>The files contain the following data fields:</p> <p><strong>Readme</strong> – a detailed description of data</p> <p><strong>Equipment list</strong> – instrument type and accuracy</p> <p><strong>Meteorological data</strong> – air temperature, relative humidity, wind speed, pressure, surface temperature</p> <p><strong>Solar radiation data</strong> – incident solar radiation and reflected solar radiation</p> <p><strong>Ice thickness and snow depth</strong> – ice thickness (ice bottom) and snow depth (ice surface)</p> <p><strong>Ice-water temperature</strong> – ice temperature and water ice temperature</p> <p><strong>Spectral irradiance - incident (air)</strong> – incident spectral irradiance from 320 - 950 nm (air)</p> <p><strong>Spectral irradiance - reflectance (air)</strong> – reflectance spectral irradiance from 320 - 950 nm (air)</p> <p><strong>Spectral irradiance - incident (water - 70 cm to ice surface)</strong> – incident spectral irradiance from 320 - 950 nm (water - 70 cm to ice surface)</p> <p><strong>Spectral irradiance - incident (water - 110 cm to ice surface)</strong> – incident spectral irradiance from 320 - 950 nm (water - 110 cm to ice surface)</p>
Fig. 1 in Ex-situ conservation of the critically endangered swamp forest crab Parathelphusa reticulata Ng, 1990 (Decapoda: Brachyura: Gecarcinucidae): observations on its reproduction and biology in captivity
Fig. 1. Captive breeding facility in the Singapore Botanic Gardens. Photograph: Daniel J. J. Ng.
Data for the publication "In Situ Observation of Room-Temperature Magnesium Metal Deposition on a NASICON/IL Hybrid Solid Electrolyte"
<p>Datasets are obtained from X-ray diffraction (XRD), Rietveld analysis, Scanning electron microscopy (SEM), Electrochemicla impedance spectroscopy (EIS), Nudged Elastic Band (NEB), Chronopotentiometry (CP), Linear sweep voltammetry (LSV), X-ray photoelectron spectroscopy (XPS), Energy-dispersive X-ray spectroscopy (EDS).</p> <p><strong>Abstract:</strong> Secondary batteries using multivalent cations as ionic charge carriers have attracted increasing attention in recent years due to the high theoretical energy density provided by multi-electron redox reactions. However, the high charge density of these cations inevitably leads to sluggish kinetics of ion migration at room temperature, which poses a challenge for the development of solid-state batteries using multivalent ions. Here, we prepared a magnesium ion conducting hybrid solid electrolyte (HSE) consisting of a new NASICON-structured material, Mg0.5Sn2(PO4)3, and a small amount of Mg ionic liquid. The HSE shows superior room-temperature ionic conductivity of 1.11 · 10−4 S cm−1 and an activation energy of 0.36 eV. Due to good compatibility of the HSE with the magnesium metal anode, symmetric MgǀHSE|Mg cells show stable Mg plating and stripping behavior at room temperature. Using in situ electrochemical scanning electron microscopy measurements the room temperature growth-induced fracture of the HSE was observed, giving unequivocal evidence for magnesium deposition. These results may serve as starting point for understanding the magnesium deposition mechanism on solid electrolyte in solid-state batteries.</p>
FIGURE 3 in In situ observations lead to the discovery of the large ctenophore Kiyohimea usagi (Lobata: Eurhamphaeidae) in the eastern tropical Atlantic
FIGURE 3. Schematic illustration of the gastrovascular system of Kiyohimea usagi showing in black the auricle (AC), circumlobal canal (CLC), substomodaeal canal (SSMC), and in dark grey the subtentacular meridional canal (STMC), in light grey the translobal meridional canal (TLMC), and in white the oral canal (OC).
FIGURE 4 in In situ observations lead to the discovery of the large ctenophore Kiyohimea usagi (Lobata: Eurhamphaeidae) in the eastern tropical Atlantic
FIGURE 4. Fly-by observations of Kiyohimea usagi made by the pelagic in situ observation system PELAGIOS.
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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)
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DANDI Archive for NWB datasets
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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.