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12
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Dataset results
12 results for “in-situ experiment”
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.
A Dataset for In-situ synchrotron tomography experiments to investigate anisotropic damage of line pipe steel
<p>In this study, anisotropic ductility and associated damage mechanisms of a grade X100 line pipe steel were investigated using in-situ synchrotron-radiation computed tomography (SRCT) of notched round bars. Line pipe materials have anisotropic mechanical properties, such as tensile strength, ductility and toughness. Specimens were tested for loading along both rolling (L) and transverse (T) directions. The <em>in-situ</em> data collected allowed quantifying both specimen deformation (evolution of the cross section) and microscopic damage parameters such as porosity, void shape and void orientation. The data sets provide here are related to the paper <em>"On the origin of the anisotropic damage of X100 line pipe steel, Part I: in-situ synchrotron tomography experiments"</em> being published in <a href="https://www.springer.com/journal/40192">Integrating Materials and Manufacturing Innovation</a>. For each testing direction, dataset are provided using hdf5 and xdmf standarded exchange format. A compressed file is also provided in connection with the analyses explained in the article.</p>
Data-Mining of In-Situ TEM Experiments: Towards Understanding Nanoscale Fracture
<p>Datasets for the publication in the "Computational Materials Science". This is essentially a snapshot of the gitlab repository https://gitlab.com/computational-materials-science/public/publication-data-and-code/2022-data-mining-of-in-situ-tem-experiments that might contain additional updates and scripts. A version of the manuscript can also be found at https://arxiv.org/abs/2206.11355</p>
Litterfall production and litter decomposition experiments: in-situ datasets of nutrient fluxes in two Bornean lowland rain forests associated with Acacia invasion
<p>This dataset contains the original data from which the figures and tables for the article "Differential impacts of <em>Acacia</em> invasion on nutrient fluxes in two distinct Bornean lowland tropical rain forests" were prepared. It documents parameters relevant to nutrient fluxes via litterfall production and leaf litter decomposition rates from 2016 to 2017 in two selected lowland rainforests in Brunei Darussalam that are associated with <em>Acacia</em> invasion. Both litterfall sample collection and litter decomposition bag experiments followed standard protocols. Leaf litterfall fractions from the litterfall production experiment were analysed for nutrient contents of nitrogen (N), phosphorus (P), potassium (K), magnesium (Mg), and calcium (Ca). Nutrient addition and nutrient use efficiency values were calculated based on nutrient concentration and monthly leaf litterfall production in the different habitat types studied. The mean percentage of litter mass remaining, K day<sup>-1</sup>, K year<sup>-1</sup>, half-life t<sub>0.5</sub>, pH values, and nutrient concentrations (N, P, K, Mg, Ca) were calculated for leaf litter samples collected after 336 days in the different habitats.</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>
Videos: The crystallization mechanism of gel-derived SiO2-TiO2 amorphous nanobeads elucidated by high-temperature in-situ experiments
<p>Supplementary materials detailing high-temperature in-situ TEM experiments.</p> <p>Main article available at: <a href="https://doi.org/10.1021/acs.cgd.3c00300">https://doi.org/10.1021/acs.cgd.3c00300</a></p> <p> </p>
[Dataset] enhanced functional connectivity properties of human brains during in-situ nature experience
<p>Raw data</p> <p>Artifact-free EEG raw data recorded by Emotiv Epoc (sample rate 128/s, 14 channels).</p>
Dataset for "Data-Mining of In-Situ TEM Experiments: Towards Understanding Nanoscale Fracture"
<p>Dataset accompanying the publication "Data-Mining of In-Situ TEM Experiments: Towards Understanding Nanoscale Fracture"</p>
Generated datasets for Yue et al. (2020, Earth and Space Science): "Combining In-situ and Satellite Observations to Understand the Vertical Structure of Tropical Anvil Cloud Microphysical Properties During the TC4 Experiment"
<p>This archive contains the data sets generated from the research conducted by Yue et al. (2020) titled "Combining In-situ and Satellite Observations to Understand the Vertical Structure of Tropical Anvil Cloud Microphysical Properties During the TC4 Experiment" published in Earth and Space Science. The method to generated the following data sets is described in Yue et al. (2020) and stored as Matlab .mat files.</p> <p>CombiningTC4_Satellite_eof_cov_mat.mat contains the correlation matrix shown in Figure 1a.</p> <p>TC4_processed.mat contains the correlation matrix shown in Figure 1b.</p> <p>RO_processed.mat contains the correlation matrix shown in Figure 2a.</p> <p>RVOD_processed.mat contains the correlation matrix shown in Figure 2b.</p> <p>ICE_processed.mat contains the correlation matrix shown in Figure 2c.</p> <p> </p> <p> </p>
SASS (Subsonics Assessment) Ozone and NOx Experiment (SONEX) DC-8 In-Situ Meteorological and Navigation Data
SONEX_TraceGas_AircraftInSitu_DC8_Data_1 is the in-situ meteorological and navigation data collected onboard the DC-8 aircraft during the SASS (Subsonics Assessment) Ozone and NOx Experiment (SONEX) suborbital campaign. Data collection for this product is complete.The SASS (Subsonics Assessment) Ozone and NOx Experiment (SONEX) was an international, multi-organizational mission that took place in October-November 1997. NASA was the US sponsor of SONEX that partnered with POLINAT-2 (Pollution from Aircraft Emissions in the North Atlantic Flight Corridor) funded by the German DLR (Deutsches Zentrum für Luft- und Raumfahrt) or German Aerospace Agency. NASA flew the DC-8 aircraft out of NASA/Ames Research Center. DLR operated an instrumented Falcon 20 aircraft. The staging locations for NAFC sampling were primarily Bangor, Maine (US), and Shannon, Ireland. Subsonic aircraft emissions impact several aspects of atmospheric composition: nitrogen oxides (NOx), CO, and hydrocarbons from emissions can perturb upper tropospheric/lower stratospheric (UT/LS) ozone; water vapor, soot, and sulfur oxides (SOx) emitted by aircraft may perturb clouds and aerosols, changing UT/LS radiative forcing and global temperature.In SONEX and POLINAT, flights were conducted in the vicinity of the North Atlantic Flight Coordinator (NAFC) to observe the impact of aircraft emissions on NOx and ozone (O3). The DC-8 aircraft payload (Singh et al., 1999) primarily measured in-situ CO, CO2, hydrocarbons/halocarbons, O3, aerosols (Dibb et al., 2000), OH/HO2, water vapor, nitric acid (Talbot et al., 1999), photolysis rates, temperature, pressure, winds, NOx, and NOy.Three sampling approaches were implemented during SONEX. First, special meteorological (Fuelberg et al., 2000) were developed to allow targeted sampling for air parcels affected by aircraft emissions and various meteorological events, e.g., convection, lightning (Jeker et al., 2000), stratospheric intrusions (Cho et al., 2000). Second, because the NAFC had not been extensively sampled in the past, it was important for SONEX to characterize the climatology of trace species like CN (Wang et al., 2000), NOx and NOy (Koike et al., 2000). Third, tracers (Simpson et al., 2000; Thompson et al., 1999) and model sensitivity studies (Meijer et al., 2000) were employed for Air Mass Identification. This sampling strategy answered the following questions: Where and when are air masses found with the greatest aircraft influence? When and where was stratospheric air sampled? SONEX showed a substantial impact of aircraft emissions on UT/LS NOx and CN in the vicinity of fresh aircraft emissions. However, during October-November 1997 over the NAFC, UT/LS NOx was dominated by surface emissions redistributed by convection and augmented by lightning.
SASS (Subsonics Assessment) Ozone and NOx Experiment (SONEX) DC-8 In-Situ Aerosol Data
SONEX_Aerosol_AircraftInSitu_DC8_Data_1 is the in-situ aerosol data collected onboard the DC-8 aircraft during the SASS (Subsonics Assessment) Ozone and NOx Experiment (SONEX) suborbital campaign. Data was collected via in-situ instrumentation, including multiple condensation nuclei counters (CNCs), ACIR (Aerosol/Cloud Particle Impactor/Replicator), FSSP, PCASP, and SAGA. Data collection for this product is complete.The SASS (Subsonics Assessment) Ozone and NOx Experiment (SONEX) was an international, multi-organizational mission that took place in October-November 1997. NASA was the US sponsor of SONEX that partnered with POLINAT-2 (Pollution from Aircraft Emissions in the North Atlantic Flight Corridor) funded by the German DLR (Deutsches Zentrum für Luft- und Raumfahrt) or German Aerospace Agency. NASA flew the DC-8 aircraft out of NASA/Ames Research Center. DLR operated an instrumented Falcon 20 aircraft. The staging locations for NAFC sampling were primarily Bangor, Maine (US), and Shannon, Ireland. Subsonic aircraft emissions impact several aspects of atmospheric composition: nitrogen oxides (NOx), CO, and hydrocarbons from emissions can perturb upper tropospheric/lower stratospheric (UT/LS) ozone; water vapor, soot, and sulfur oxides (SOx) emitted by aircraft may perturb clouds and aerosols, changing UT/LS radiative forcing and global temperature.In SONEX and POLINAT, flights were conducted in the vicinity of the North Atlantic Flight Coordinator (NAFC) to observe the impact of aircraft emissions on NOx and ozone (O3). The DC-8 aircraft payload (Singh et al., 1999) primarily measured in-situ CO, CO2, hydrocarbons/halocarbons, O3, aerosols (Dibb et al., 2000), OH/HO2, water vapor, nitric acid (Talbot et al., 1999), photolysis rates, temperature, pressure, winds, NOx, and NOy.Three sampling approaches were implemented during SONEX. First, special meteorological (Fuelberg et al., 2000) were developed to allow targeted sampling for air parcels affected by aircraft emissions and various meteorological events, e.g., convection, lightning (Jeker et al., 2000), stratospheric intrusions (Cho et al., 2000). Second, because the NAFC had not been extensively sampled in the past, it was important for SONEX to characterize the climatology of trace species like CN (Wang et al., 2000), NOx and NOy (Koike et al., 2000). Third, tracers (Simpson et al., 2000; Thompson et al., 1999) and model sensitivity studies (Meijer et al., 2000) were employed for Air Mass Identification. This sampling strategy answered the following questions: Where and when are air masses found with the greatest aircraft influence? When and where was stratospheric air sampled? SONEX showed a substantial impact of aircraft emissions on UT/LS NOx and CN in the vicinity of fresh aircraft emissions. However, during October-November 1997 over the NAFC, UT/LS NOx was dominated by surface emissions redistributed by convection and augmented by lightning.
SASS (Subsonics Assessment) Ozone and NOx Experiment (SONEX) DC-8 In-Situ Meteorological and Navigation Data
SONEX_TraceGas_AircraftInSitu_DC8_Data_1 is the in-situ meteorological and navigation data collected onboard the DC-8 aircraft during the SASS (Subsonics Assessment) Ozone and NOx Experiment (SONEX) suborbital campaign. Data collection for this product is complete. The SASS (Subsonics Assessment) Ozone and NOx Experiment (SONEX) was an international, multi-organizational mission that took place in October-November 1997. NASA was the US sponsor of SONEX that partnered with POLINAT-2 (Pollution from Aircraft Emissions in the North Atlantic Flight Corridor) funded by the German DLR (Deutsches Zentrum für Luft- und Raumfahrt) or German Aerospace Agency. NASA flew the DC-8 aircraft out of NASA/Ames Research Center. DLR operated an instrumented Falcon 20 aircraft. The staging locations for NAFC sampling were primarily Bangor, Maine (US), and Shannon, Ireland. Subsonic aircraft emissions impact several aspects of atmospheric composition: nitrogen oxides (NOx), CO, and hydrocarbons from emissions can perturb upper tropospheric/lower stratospheric (UT/LS) ozone; water vapor, soot, and sulfur oxides (SOx) emitted by aircraft may perturb clouds and aerosols, changing UT/LS radiative forcing and global temperature. In SONEX and POLINAT, flights were conducted in the vicinity of the North Atlantic Flight Coordinator (NAFC) to observe the impact of aircraft emissions on NOx and ozone (O3). The DC-8 aircraft payload (Singh et al., 1999) primarily measured in-situ CO, CO2, hydrocarbons/halocarbons, O3, aerosols (Dibb et al., 2000), OH/HO2, water vapor, nitric acid (Talbot et al., 1999), photolysis rates, temperature, pressure, winds, NOx, and NOy. Three sampling approaches were implemented during SONEX. First, special meteorological (Fuelberg et al., 2000) were developed to allow targeted sampling for air parcels affected by aircraft emissions and various meteorological events, e.g., convection, lightning (Jeker et al., 2000), stratospheric intrusions (Cho et al., 2000). Second, because the NAFC had not been extensively sampled in the past, it was important for SONEX to characterize the climatology of trace species like CN (Wang et al., 2000), NOx and NOy (Koike et al., 2000). Third, tracers (Simpson et al., 2000; Thompson et al., 1999) and model sensitivity studies (Meijer et al., 2000) were employed for Air Mass Identification. This sampling strategy answered the following questions: Where and when are air masses found with the greatest aircraft influence? When and where was stratospheric air sampled? SONEX showed a substantial impact of aircraft emissions on UT/LS NOx and CN in the vicinity of fresh aircraft emissions. However, during October-November 1997 over the NAFC, UT/LS NOx was dominated by surface emissions redistributed by convection and augmented by lightning.
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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.