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6,498 results for “Physicality”
A "short blanket" dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the underlying many-body physics?
<p>Deep neural network (DNN) potentials have recently gained popularity in computer simulations of a wide range of molecular systems, from liquids to materials.<br> In this study, we explore the possibility of combining the computational efficiency of the DeePMD framework and the demonstrated accuracy of the MB-pol data-driven many-body potential to train a DNN potential for large-scale simulations of water across its phase diagram.<br> We find that the DNN potential is able to reliably reproduce the MB-pol results for liquid water but provides a less accurate description of the vapor-liquid equilibrium properties.<br> This shortcoming is traced back to the inability of the DNN potential to correctly represent many-body interactions.<br> An attempt to explicitly include information about many-body effects results in a new DNN potential that exhibits the opposite performance, being able to correctly reproduce the MB-pol vapor-liquid equilibrium properties but losing accuracy in the description of the liquid properties.<br> These results suggest that DeePMD-based DNN potentials are not able to correctly "learn" and, consequently, represent many-body interactions, which implies that DNN potentials may have limited ability to predict properties for state points that are not explicitly included in the training process.<br> The computational efficiency of the DeePMD framework can still be exploited to train DNN potentials on data-driven many-body potentials, which can thus enable large-scale, "chemically accurate" simulations of various molecular systems, with the caveat that the target state points must have been adequately sampled by the reference data-driven many-body potential in order to guarantee a faithful representation of the associated properties.</p>
Dataset for article "From X-rays to physical parameters: a comprehensive analysis of thermal tidal disruption event X-ray spectra"
<p>This repository contains the data used in the modeling of TDE X-ray emission within the article: Mummery et al. 2023, " From X-rays to physical parameters: a comprehensive analysis of thermal<br> tidal disruption event X-ray spectra" published as Mummery et al. 2023, MNRAS, 519, 5828</p>
Heat Transfer Physics - Flat Plate Model
<p>The dataset includes unprocessed and processed temperature evolution plots for wide range of experimental conditions corresponding to ice crystal icing performed at the icing wind tunnel of TU Braunschweig within the scope of MUSIC-haic project. In addition to temperature plots the dataset also includes information on the design and components of the test article as well as the respective test matrix. It covers wide range of parametric variation including heat flux, wet bulb temperature, flow velocity and ice water content and provides a sound basis for calibration and validation of numerical tools.</p>
Dataset for publication "Synthesis and physical characteristics of narrow bandgap chalcogenide SnZrSe3"
<p>This dataset contains underlying data of publication entitled "Synthesis and physical characteristics of narrow bandgap chalcogenide SnZrSe<sub>3</sub>"<span>. </span></p> <p>Data comprises: </p> <p>-Sample description.txt (synthesis conditions of the sample presented in the publication)</p> <p>-F1IS_V_6244.cif (crystallographic information file about SnZrSe<sub>3</sub> structure as determined by single-crystal XRD method).</p> <p>- XRD.zip (raw XRD patterns of powder samples presented in the publication in .ras and .raw formats).</p> <p>-Raman.zip (raw Raman spectra in .txt format and OriginPro project file where data was processed and plotted).</p> <p>-Optics.zip (raw diffuse reflectance data in .txt format and OriginPro project file where data was processed and plotted).</p> <p>-JV-T.zip (J-V curves at specific temperature in .txt format and OriginPro project files where data was processed and plotted).</p> <p>-Images.zip (optical photographs of the sample and untreated SEM images of SnZrSe3 crystals).</p> <p>-Extented data.pdf (additional information supporting claims in the publication with direct link to the main text, such as figures).</p>
Supplemental tables for a study of the seasonal Impacts of the Physical Environment on Biogeochemical Cycles in Arctic Lakes of the Mackenzie River Delta
<p>submitted abstract</p> <p>We conducted two- and six-year-long deployments of continuous water samplers (OsmoSamplers) and sensors (Temperature, pressure, light level, dissolved oxygen (DO) and conductivity) in nine lakes within the mid- to outer-delta region of the Mackenzie River and documented biogeochemical fluctuations (Mn, Fe, sulfate, and DO), defined physical processes that that drive such fluctuations, and constrained the impact of lake solutes on annual riverine fluxes. Five lakes were in the mid-delta region near Inuvik, NT, two lakes were in the outer delta, and two lakes were on the Arctic coastal plain and were not impacted by the Mackenzie River. In general, temperature minima occurred in September/October, indicative of ice formation, and distinct hydrostatic pressure (water level) anomalies occurred in May/June associated with ice breakup, lasting for days to months and impacting lake levels up to 4.2 m higher than “normal”. Such anomalies coincide with a dramatic change in solute concentrations. Systematic changes in solute concentrations indicate redox-driven biogeochemical reactions, salt exclusion during ice formation, and continuous to sporadic exchange of river water. Redox reactions were regulated by DO inputs stemming from atmospheric, photosynthetic, and riverine sources. During ice-covered periods dissolved sulfate may be conservative but was generally removed. Manganese and iron concentrations showed phases of production and removal during ice-covered periods, but both were produced overall. Calculated solute fluxes from lake waters alone to the Arctic Ocean may only impact yearly riverine fluxes for solutes that exceed ten times the river concentration prior to ice breakup (e.g., Mn and Fe).</p>
RNA-Seq data from: Hox genes modulate physical forces to differentially shape small and large intestinal epithelia
<p>Hox genes are highly conserved, master regulators of spatial patterning in the embryo, but how these factors trigger regional morphogenesis has largely remained a mystery. In the developing gut, Hox genes help demarcate identities of the small and large intestines early in embryogenesis, which ultimately leads to their specialization in both form and function. While the midgut forms villi, the hindgut develops flat, brain-like sulci that resolve into heterogeneous outgrowths. Combining mechanical measurements and mathematical modeling, we demonstrate that the posterior Hox gene Hoxd13 regulates biophysical phenomena that shape the hindgut lumen. We further show that Hoxd13 acts through the TGFβ pathway to thicken, stiffen, and promote isotropic growth of the subepithelial mesenchyme; together, these features lead to hindgut surface buckling. TGFβ, in turn, promotes collagen deposition to affect mesenchymal geometry and growth. We thus identify a cascade of events downstream of positional genetic identity that direct posterior intestinal morphogenesis. </p> <p>To identify genes and pathways that are directly or indirectly regulated by Hoxd13 to affect posterior gut morphogenesis in the chick, we compared mesodermal transcriptomes of wild-type midgut and hindgut intestinal samples, as well as mesodermal samples from a Hoxd13-overexpressing midgut at E12 and E14. Tissues were dissected and endoderm layers were removed manually before RNA extraction and downstream processing. Unbiased clustering was used to identify genes commonly differentially expressed in the hindgut and Hoxd13-misexpressing midgut. This submission contains bulk RNA-seq raw data (fastq.bz2 files) and processed .txt files with read counts. Experiment information is provided in .xlsx Metadata file used for NCBI GEO submission.</p>
FESOM-REcoM model data: A regime shift on Weddell Sea continental shelves with local and remote physical-biogeochemical implications is avoidable in a 2°C scenario
<p>This data set includes the minimal data necessary to reproduce the findings of Nissen et al. (2023). Output of model simulations with the global ocean biogeochemical model FESOM1.4-REcoM2 is provided. In particular, besides information on the model grid, the data set includes annual mean water mass properties (temperature, salinity, density, oxygen, pH) and freshwater fluxes from sea ice and ice shelves and decadal averages of air-sea CO2 fluxes and deep-ocean carbon accumulation rates. Model results are provided from 1980-2100 for the four emission scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 (sorted from low emission to high emission).</p> <p>Please see README for more information on the individual files. </p> <p>Data set belongs to: </p> <p>Nissen, C., R. Timmermann, M. Hoppema, and J. Hauck, 2023: A regime shift on Weddell Sea continental shelves with local and remote physical-biogeochemical implications is avoidable in a 2°C scenario. <em>J. Climate</em>, <a href="https://doi.org/10.1175/JCLI-D-22-0926.1">https://doi.org/10.1175/JCLI-D-22-0926.1</a>, in press.</p>
Outputs of the Jupyter Notebook - Learning the Underlying Physics of a Simulation Model of the Ocean's Temperature (CIRC23)
<p>The dataset contains the outputs of the notebook "Learning the Underlying Physics of a Simulation Model of the Ocean's Temperature (CIRC23)" published in The Environmental Data Science Book.</p>
Selected near-bottom and other variables from NW European shelf physics-biogeochemistry downscaled ocean climate projections, 3-member ensemble.
<p>Selected fields of physical and biogeochemical ocean variables from a 3-member ensemble of coupled physics-biogeochemistry downscaled climate runs on the North Western European Continental Shelf. All ensemble members use the NEMO-ERSEM model suite and cover the 1990-2099 period. Easch member is foced with a different set of atmospheric and oceanic boundary conditions from one of three CMIP5 ESMs that are: HADGEM2-ES, IPSL-CM5A-MR and GFDL-ESM2G. This dataset contains monthly average values saved as 2D fields either near-bottom, at the surface or depth integrated. The variables here saved are near-bottom oxygen, oxygen solubility, oxygen saturation state, temperature and bacterial respiration, surface salinity, depth integrated net primary production, and potential energy anomaly. Additionally the Western Norwegian Trench Current flux is provided (its values come smoothed with a gaussian filter). reference publication: https://doi.org/10.5194/egusphere-2023-1049. The complete set of variables is available from the authors upon request.</p>
Physical, Social, and Biological Attributes for Improved Understanding and Prediction of Wildfires: FPA FOD-Attributes Dataset
<p><strong>Wildfires are increasingly impacting social and environmental systems in the United States. The ability to mitigate the undesirable effects of wildfires increases with the understanding of the social, physical, and biological conditions that co-occurred with or caused the wildfire ignitions and contributed to the wildfire impacts. To this end, we developed the FPA FOD-Attributes dataset, which augments the sixth version of the Fire Program Analysis-Fire Occurrence Database (FPA FOD v6) with nearly 270 attributes that coincide with the date and location of each wildfire ignition in the contiguous United States (CONUS). FPA FOD v6 contains information on the location, jurisdiction, discovery time, cause, and final size of >2.2 million wildfires from 1992-2020 in CONUS. For each wildfire, we added physical (e.g., weather, climate, topography, infrastructure), biological (e.g., land cover, normalized difference vegetation index), social (e.g., population density, social vulnerability index), and administrative (e.g., national and regional preparedness level, jurisdiction) attributes. This publicly available dataset can be used to answer numerous questions about the covariates associated with human- and lightning-caused wildfires. Furthermore, the FPA FOD-Attributes dataset can support descriptive, diagnostic, predictive, and prescriptive wildfire analytics, including the development of machine learning models.</strong></p>
Physical oceanography and meteorological data from the W1M3A observatory, Ligurian Sea (North Western Mediterranean) July 2021 - April 2022
<p>Time series data of physical oceanography (salinity, temperature) and meteorology (atmospheric pressure, wind speed and direction, air temperature and humidity, shortwave radiation, longwave radiation and rain) collected from July 2021 to April 2022 by observatory W1M3A at 1h interval. The file contains tabular data (tab delimited) with the following columns: Day; Month; Year; UTC Hour; Minute ; Longitude [deg]; Latitude [deg]; Atmospheric Pressure [hPa]; Wind speed [m/s]; Wind direction [deg]; Air Temperature [°C]; Relative air humidity [%]; Short wave Radiation [W/m2]; Long wave radiation [W/m2]; Rainfall [mm/h]; Sea temperature @ 6 m [°C]; Sea temperature @ 20 m [°C]; Sea temperature @ 36 m [°C]; Salinity @ 6 m [psu]; Salinity @ 20 m [psu], Salinity @ 36 m [psu].</p>
Physical oceanography and meteorological data from the W1M3A observatory, Ligurian Sea (North Western Mediterranean) October 2020 - July 2021
<p>Time series data of physical oceanography (salinity, temperature) and meteorology (atmospheric pressure, wind speed and direction, air temperature and humidity, shortwave radiation, longwave radiation and rain) collected from October 2020 to July 2021 by observatory W1M3A at 1h interval. The file contains tabular data (tab delimited) with the following columns: Day; Month; Year; UTC Hour; Minute ; Longitude [deg]; Latitude [deg]; Atmospheric Pressure [hPa]; Wind speed [m/s]; Wind direction [deg]; Air Temperature [°C]; Relative air humidity [%]; Short wave Radiation [W/m2]; Long wave radiation [W/m2]; Rainfall [mm/h]; Sea temperature @ 6 m [°C]; Sea temperature @ 20 m [°C]; Sea temperature @ 36 m [°C]; Salinity @ 6 m [psu]; Salinity @ 20 m [psu], Salinity @ 36 m [psu].</p>
Physical oceanography and meteorological data from the W1M3A observatory, Ligurian Sea (North Western Mediterranean) May 2017 - June 2018
<p>Time series data of physical oceanography (salinity, temperature) and meteorology (atmospheric pressure, wind speed and direction, air temperature and humidity, shortwave radiation, longwave radiation and rain) collected from May 2017 to June 2018 by observatory W1M3A at 1h interval. The file contains tabular data (tab delimited) with the following columns: Day; Month; Year; UTC Hour; Minute ; Longitude [deg]; Latitude [deg]; Atmospheric Pressure [hPa]; Wind speed [m/s]; Wind direction [deg]; Air Temperature [°C]; Relative air humidity [%]; Short wave Radiation [W/m2]; Long wave radiation [W/m2]; Rainfall [mm/h]; Sea temperature @ 6 m [°C]; Sea temperature @ 20 m [°C]; Sea temperature @ 36 m [°C]; Salinity @ 6 m [psu]; Salinity @ 20 m [psu], Salinity @ 36 m [psu] </p>
Pond data: physical, chemical, and biological characteristics with scientific and United States of America state definitions from literature and legislative surveys
Ponds are often identified by their small size and shallow depths, but the lack of a universal definition hampers science and weakens legal protection. In order to determine a working definition of ‘pond’, we conducted a literature search for scientific definitions, a U.S. state survey for management definitions, and looked at pond ecosystem function using data from the literature search. Our dataset includes physical, chemical, and biological data for 1327 waterbodies ≤ 20 ha in surface area and ≤ 9 m in maximum or mean depth from our literature review. These data have a global distribution, we include a table of latitudes and longitudes, and span many years (1946-2019). We have also included a table of 54 pond definitions from the literature review and a table of U.S. state definitions of ponds, wetlands, and lakes resulting from our survey.
Mountain Lake Chemistry and Physics Profile Data since 2015 at Castle Lake
This data set contains long-term limnology data from Castle Lake (located 5440 ft above sea level in Northern California). The data contained can be broadly grouped into two different types: chemical and physical data. This data set contains dissolved oxygen saturation, dissolved oxygen concentration, chlorophyll-a concentration, CDOM, phycocyanin concentration, conductivity, specific conductivity, pH, salinity, water temperature, pressure, turbidity, sea pressure, density anomaly, and speed of sound at various depths. Sampling Frequency: Continuous measurements were made and recorded down to milliseconds. Sampling dates vary from year to year, starting as early as February and as late as November. This data collection is part of an ongoing project funded by the US National Science Foundation, private donors, US AID, and the University of Nevada's Global Water Center. This package was updated in February 2023 with data from 2021 and 2022. Dissolved O2 concentration data in the 2020 data file was updated to values in units of mg/L instead of umol/L and specific conductivity units in all files was revised to be in microSiemens per centimeter and not milliSiemens per centimeter.
Soil physical, chemical, and root data from forest stands at Thompson Farm in Durham, NH
In July 2019, quantitative soil pits were excavated alongside replicate power cores from three mature oak-pine forest stands at Thompson Farm (Durham, NH) to characterize soil physical and chemistry properties as well as root depth profiles in control and treatment plots associated with the Thompson Farm DroughtNet project, as well as in the footprint of the Ameriflux tower. Mean total sampling depth across all pits and cores was 84cm, though half of samples reached 100 cm or deeper. Chemical and isotopic analyses for each sample layer include pH, total N, total C, and stable isotope ratios of N and C. Physical data include soil mass, rock volume, bulk density, and soil texture. Oven-dried root mass in each sample layer was recorded in multiple size categories (from pits) or sorted to species (from power cores).
Physical & Chemical Parameters from Lakes in Northern Lower Michigan for RANN Campaign based at the University of Michigan Biological Station, Pellston, MI (1972-1975)
In the early 1970s the University of Michigan Biological Station initiated a series of research projects concerning the quality of the lakes in Northern Michigan under the support of the National Science Foundation through the Research Applied to National Needs (RANN) program. Dr. John Gannon joined the Biological Station's staff in 1972 and directed this program for six years. This research had significant impacts on water quality management of lakes throughout Northern Michigan. The Biological Station continued to receive grants for water quality research for several years after the RANN program had terminated.
Physical & Chemical Parameters from Lakes in Northern Lower Michigan for RANN Campaign based at the University of Michigan Biological Station, Pellston, MI (1972-1975)
In the early 1970s the University of Michigan Biological Station initiated a series of research projects concerning the quality of the lakes in Northern Michigan under the support of the National Science Foundation through the Research Applied to National Needs (RANN) program. Dr. John Gannon joined the Biological Station's staff in 1972 and directed this program for six years. This research had significant impacts on water quality management of lakes throughout Northern Michigan. The Biological Station continued to receive grants for water quality research for several years after the RANN program had terminated.
Mechanisms influencing physically sequestered soil carbon in temperate restored grasslands in South Africa and North America
This dataset contains a measurement of physically protected carbon (microaggregate-within-macroaggregate C) and potential drivers of physically protected C accumulation during grassland restoration. The data were collected from three independent grassland restorations from agriculture in North America and South Africa. Northeast Kansas, USA data were collected in May 2013. Northeast Free State, RSA data were collected in September–November 2005. Southeast Nebraska, USA data were collected in October 2008–May 2008. Aggregate fractionations were performed by hierarchical wet sieving (Six et al 2000). Carbon and N quantification were done by flash combustion-gas chromatography. Microbial biomass C quantification was performed with chloroform fumigation-incubation (chloroform fumigation-extraction in the case of northeast Kansas). Phospholipid fatty acid biomass analysis was conducted using the methods of Bligh and Dyer (1959).
Chemical and physical characteristics of soil samples collected from a chronosequence of reforested urban sites in Lexington, KY USA.
This dataset contains information on soil physical and chemical characteristics across twenty urban reforestation sites planted as part of the Reforest the Bluegrass program in Lexington, Kentucky, USA. At least three plots (and up to nine plots) were established in each site, with additional plots added if forested patches were sufficiently large. Soil samples were collected from each plot in summer 2020. Samples were composited from five subsamples collected from plot center and approximately 1 m from plot center in the four cardinal directions. Subsamples were collected to a depth of approximately 10 cm using a sampling spade. Composite samples were air-dried and passed through a 2cm sieve, and then sent to the University of Kentucky Regulatory Services soils lab for analysis for pH, P, K, Ca, Mg, Zn, particle size distribution, total C, and total N.
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.