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3,206 results for “properties”
Historical GIS Data for Harvard Forest Properties from 1908 to Present
Since 1908, the Harvard Forest has conducted forest surveys approximately every 10-20 years on its three largest tracts (total 1033 ha). These maps have been digitized along with maps of environmental factors (topography, soils), disturbance (1938 hurricane, historical land-use), and silvicultural treatments. These datalayers will allow researchers to understand the influence of environment factors, disturbances, and silviculture on the structure and composition of modern forest stands as well as assisting in locating and describing research sites. The dataset also includes an elevation grid (NED 30 meter cells), and a shapefile of linear features (trails, stonewalls, etc). Original maps were transcribed to standardized basemaps by various researchers. These basemaps were then scanned and digitized as shapefiles in ArcView GIS 3.2. The shapefiles were then transformed to Massachusetts State Plane Meters NAD83 projection in ArcGIS and rubbersheeted to align better with aerial photographs downloaded from MassGIS. Locations of control points will be permanently archived at the Harvard Forest to facilitate transformation of future datalayers.
Chamber level gas fluxes and soil biogeochemical properties from a tidal salt marsh and an impounded brackish wetland in South Carolina, USA
Archived data from a research project assessing the role of plants in driving methane fluxes from coastal wetland systems. Data collection occurred at the inland edge of a salt marsh and a diked, brackish impounded wetland in Georgetown County, South Carolina during 2022 and 2023. The first archived data table includes soil biogeochemical properties for all soil samples (0-10 cm and 10-50 cm mineral soil depth) collected approximately monthly from our two study sites. Specific biogeochemical properties include copy numbers of the mCRA gene, soil C and N concentrations, soil C:N ratios, soil moisture, pH, conductivity, organic matter content and alive and dead root biomass. The second archived data table includes methane and carbon dioxide fluxes from chambers over plants (whole-plant gas fluxes), adjacent to plants (plant-adjacent chambers) and in non-vegetated areas (non-vegetated fluxes) measured approximately monthly at our two study sites. Metadata associated with flux measurements are also included: leaf area, dead stems, oxidation reduction potential at four depths, atmospheric pressure, incoming solar radiation, relative humidity, chamber temperature, windspeed, water salinity, water temperature and water column depth.
Soil nitrogen availability vs. acidification: effects on soil respiration, heterotrophic respiration, and soil physicochemical properties in mixed temperate forests in central New York, USA (2019-2022)
In 2011, an experimental nitrogen x pH manipulation study was initiated in mixed temperate forests in central New York, USA to disentangle the often-confounded roles of nitrogen (N) and soil pH in driving various ecosystem processes. This data package contains soil physicochemical properties (soil pH, resin available nitrogen), soil temperature, in situ soil respiration, and heterotrophic respiration measured from laboratory incubations of soils collected from experimental plots. Soil pH was measured both pre-treatment (2009-2010) and after 8 and 11 years of experimental treatment. All other properties were measured between 9 and 12 years after treatment initiation.
Long-term soil properties after different biochar feedstock treatments in a Southwest Virginia Pasture, 2024
Biochar is an agricultural amendment that can improve soil health and promote carbon (C) sequestration. Effects of biochar can vary and depend on the biochar feedstock, method of production, soil conditions, and amendment method and frequency. These data include soil physicochemical properties from plots amended with hay, softwood, and hardwood biochar types produced under similar conditions (479°C – 522°C for 3.5-10.2 hours) with and without a nitrogen addition (porcine blood meal) in a randomized complete block design after 4.5 years. Plots were first established at the Virginia Tech Catawba Sustainability Center in Catawba, VA in June of 2019 and sampled in March 2024. Soil measurements include total nitrogen, total carbon, carbon:nitrogen ratios, gravimetric moisture, pH, electrical conductivity, dissolved inorganic nitrogen (NO3 and NH4), bulk density, and moisture from bulk density measurements. These data contribute to a long-term understanding of different biochar feedstock effects on Southwest Virginia pasture soils.
Soil Properties in CRUI Land Use Project at Harvard Forest 1995-1998
Soil properties and processes were evaluated on three types of colonial agricultural land-use - plowing, pasturing, and selective tree removal in a woodlot that ceased in the mid to late 1800s. Plowing, the most intensive type of agricultural disturbance, mixes soil to a depth of approximately 15cm, homogenizing the soil resources and likely reducing diversity in microenvironments. Removing trees and replacing them with grasses for pasture decreases the organic matter amount and types of inputs to the system, decreasing resource diversity. Woodlots, altered by selective and chronic tree removal, would have more limited decreases in resources and microenvironments. This study defines forest soil legacies using data from plots located at Harvard Forest in both amounts of soil resources and spatial heterogeneity of those soil resources. We found that for several soil parameters measured on previously cultivated and preciously pastured lands at the Harvard Forest, a legacy exists in the mineral soil, but the forest floor appears to have largely recovered from the agricultural disturbance. Parameters examined included soil mass, bulk density, organic matter content, pH, C, N, nitrogen mineralization and nitrification, Ca, Mg, K, and P.
Barrier Island Plant and Soil Properties on Hog and Metompkin Islands, Virginia, 2021-2022
Dune building has the potential to impact the entire barrier island ecosystem, and these grasses therefore serve as ecosystem engineers. Protection offered by dune ridges directly impacts the adjacent swale habitat, modifying both biotic and abiotic factors. In order to better understand how dune building impacts the island ecosystem as a whole, we quantified sediment accretion, plant percent cover, stem numbers, and soil characteristics (chlorides, bulk density, %OM, %C, %N). These characteristics were assessed on two islands with varied disturbance intensities. Hog island is infrequently disturbed, and resists change driven by storms and overwash. Metompkin island is frequently disturbed and undergoes high rates of overwash and island migration.
Dataset on surface peat stoichiometry and physical properties in boreal undrained peatlands in Finland, Natural Resources Institute Finland (Luke) and Geological Survey of Finland (GTK)
<p><strong>Dataset on surface peat stoichiometry and physical properties in boreal undrained peatlands in Finland </strong></p><p><strong>Creators: </strong>Larmola T, Anttila J, Turunen J, Laine-Petäjäkangas A, Ovaskainen J, Laatikainen M </p><p>The dataset consists of peat properties in a subset of 16 undrained peatland sites (32 peat samples) in Geological Survey of Finland (GTK) national peatland inventory. These sites were sampled between 2002 and 2017 and the subset selected from GTK peat sample archives. These 16 sites represented two pine-<i>Sphagnum-</i> dominated site types (IR, KR) and two treeless sedge fen types (VSN, RhSN) all in 4 replicates and sampled in 2 depths 20-40, 40-60cm). </p><p><strong>Peat analyses</strong> The peat samples were analyzed for C:H:N:S and ash concentration with Leco 628 CHNS analyzer following standard SFS EN13039 with FINAS accredited adjustments JOK3023. The dry matter content was analyzed after drying the sample at 105 ℃ and ash content based on loss on ignition at 550 ℃. The O concentration was determined by difference: %O = 100 - % (ash + total C + N + H + S).</p><p><strong>Stoichiometric calculations</strong>The O concentration was determined by difference: %O = 100 - % (ash + total C + N + H + S). Atomic ratios of C:N, H:C and O:C were calculated based on the individual sample mass values. The C oxidation state (Cox), the oxidative ratio (OR), and the degree of unsaturation (DU) were calculated following equations in the study by Masiello et al. (2008). The analyses are described in more detail in Turunen et al. (manuscript). </p><p>Related datasets used in the same publication are:</p><p>Larmola T, Anttila J, Alm J Dataset on surface peat stoichiometry and physical properties in boreal forestry-drained peatlands in Finland</p><p>Turunen J. (2023). Surface peat data, Geological Survey of Finland (Version 1) [Data set]. Zenodo. <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.8434148&data=05%7C01%7Cluke.tuula.larmola%40valtion.mail.onmicrosoft.com%7Cc48ffad4c0e341d0fa5808dbcaff0289%7C7c14dfa4c0fc47259f0476a443deb095%7C0%7C0%7C638326968887189768%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=AyYOxR7Mas2ef8y3wI7oCrzHWSyqBzt%2FJB0CMN%2BJUiU%3D&reserved=0">https://doi.org/10.5281/zenodo.8434148</a></p><p> </p><p><strong>Data column description </strong></p><p>ID - Site identifier</p><p>site - undrained peatland (UDP) for all rows</p><p>ncoord - North coordinate (latitude), degrees.</p><p>depth - Sampling depth. 20: 0-20 cm, 40: 20-40cm, 60: 40-60cm.</p><p>type - Site type classification according to the Finnish peatland site type system.</p><p>origin - UDP site type. I: treed peatland (peat typically Sphagnum-wood), II: treeless peatland (or sparsely treed, peat typically Sphagnum-sedge)</p><p>type_num - Nutrient level according to site type. 1 is the most nutrient rich and 4 is the least.</p><p>Cmol - Molar carbon concentration in the sample</p><p>Hmol - Molar hydrogen concentration in the sample</p><p>Nmol - Molar nitrogen concentration in the sample</p><p>Omol - Molar oxygen concentration in the sample</p><p>Smol - Molar sulphur concentration in the sample</p><p>bd - Bulk density, kg/m3</p><p>cox - C oxidation state</p><p>or - Oxidative ratio</p><p>du - Degree of unsaturation</p><p>hc - H:C ratio</p><p>cn - C:N ratio</p><p>oc - O:C ratio</p><p><strong>References</strong></p><p>Masiello CA, Gallagher ME, Randerson JT, Deco RM, Chadwick OA (2008) Evaluating two experimental approaches for measuring ecosystem carbon oxidation state and oxidative ratio, Journal of Geophysical Research 113, G03010, <a href="https://doi.org/10.1029/2007JG000534">https://doi.org/10.1029/2007JG000534</a></p><p>Turunen J, Anttila J, Laine-Petäjäkangas A, Ovaskainen J, Laatikainen M, Alm J, Larmola T 2023. Impacts of forestry drainage on surface peat stoichiometry and physical properties in boreal peatlands in Finland. <i>manuscript.</i></p>
Dataset on surface peat stoichiometry and physical properties in boreal forestry-drained peatlands in Finland, Natural Resources Institute Finland
<p><strong>Dataset on surface peat stoichiometry and physical properties in boreal forestry-drained peatlands in Finland</strong></p><p><strong>Creators: Larmola T, Anttila J, Alm J </strong></p><p>The dataset consists of peat properties in a subsample of 30 drained peatland forests in Finland selected from the permanent sample plots of the 8th National Forest Inventory (systematic sample of plots on drained peatland forests, e.g., Hotanen et al. 2006). The subsample included equally different site types of forestry-drained peatlands of those parts of Finland where drainage for forestry is economically viable (Latitude 60-66 ºN, annual temperature sum > 750 dd). </p><p><strong>The site selection criteria</strong> were average peat layer thickness of over 20 cm, no clear-cut areas, site drained before 1995 and ditching had detectably altered hydrology or vegetation. <strong>Peat analyses</strong> Finnish Forest Research Institute (now Natural Resources Institute Finland) sampled peat cores with a box corer in 2002, samples were analysed for bulk density, archived and remaining samples at depths 20-30, 30-40 cm (total of 58) were analysed in 2021. The peat samples were analyzed for C:H:N:S and ash concentration with Leco 628 CHNS analyzer following standard SFS EN13039 with FINAS accredited adjustments JOK3023. The dry matter content was analyzed after drying the sample at 105 ℃ and ash content based on loss on ignition at 550 ℃. </p><p><strong>Stoichiometric calculations</strong>The O concentration was determined by difference: %O = 100 - % (ash + total C + N + H + S). Atomic ratios of C:N, H:C and O:C were calculated based on the individual sample mass values. The C oxidation state (Cox), the oxidative ratio (OR), and the degree of unsaturation (DU) were calculated following equations in the study by Masiello et al. (2008). The analyses are described in more detail in Turunen et al. (manuscript). </p><p>Related datasets used in the same publication are:</p><p>Larmola, T. Anttila J, Turunen J, Laine-Petäjäkangas A, Ovaskainen J, Laatikainen M Dataset on surface peat stoichiometry and physical properties in boreal undrained peatlands in Finland, Natural Resources Institute Finland (Version 1) [Dataset]. Zenodo. doi.org/<strong>10.5281/zenodo.10068486</strong></p><p>Turunen J. (2023). Surface peat data, Geological Survey of Finland (Version 1) [Data set]. Zenodo. <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.8434148&data=05%7C01%7Cluke.tuula.larmola%40valtion.mail.onmicrosoft.com%7Cc48ffad4c0e341d0fa5808dbcaff0289%7C7c14dfa4c0fc47259f0476a443deb095%7C0%7C0%7C638326968887189768%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=AyYOxR7Mas2ef8y3wI7oCrzHWSyqBzt%2FJB0CMN%2BJUiU%3D&reserved=0">https://doi.org/10.5281/zenodo.8434148</a></p><p> </p><p><strong>Data column description</strong></p><p>ID - Site identifier</p><p>site - Forestry-drained peatland (FDP) for all rows</p><p>ncoord - North coordinate (latitude), degrees.</p><p>depth - Sampling depth. 30: 20-30 cm, 40: 30-40cm, avg: average of both depths.</p><p>type - Site type classification according to the Finnish peatland site type system.</p><p>origin – Origin of the FDP site type at undrained state. I: treed peatland (peat typically Sphagnum-wood), II: treeless peatland (or sparsely treed, peat typically Sphagnum-sedge)</p><p>type_num - Nutrient level according to site type. 1 is the most nutrient rich and 4 is the least.</p><p>Cmol - Molar carbon concentration in the sample</p><p>Hmol - Molar hydrogen concentration in the sample</p><p>Nmol - Molar nitrogen concentration in the sample</p><p>Omol - Molar oxygen concentration in the sample</p><p>Smol - Molar sulphur concentration in the sample</p><p>bd - Bulk density, kg/m3</p><p>cox - C oxidation state</p><p>or - Oxidative ratio</p><p>du - Degree of unsaturation</p><p>hc - H:C ratio</p><p>cn - C:N ratio</p><p>oc - O:C ratio</p><p>n - Number of samples. 2 for averages from both depths, 1 for all other rows.</p><p> </p><p><strong>References</strong></p><p>Hotanen JP, Maltamo M, Reinikainen A (2006) Canopy stratification in peatland forests in Finland. Silva Fennica 40:53–82.</p><p>Masiello CA, Gallagher ME, Randerson JT, Deco RM, Chadwick OA (2008) Evaluating two experimental approaches for measuring ecosystem carbon oxidation state and oxidative ratio, Journal of Geophysical Research 113, G03010, <a href="https://doi.org/10.1029/2007JG000534">https://doi.org/10.1029/2007JG000534</a></p><p>Turunen J, Anttila J, Laine-Petäjäkangas A, Ovaskainen J, Laatikainen M, Alm J, Larmola T 2023. Impacts of forestry drainage on surface peat stoichiometry and physical properties in boreal peatlands in Finland. <i>manuscript.</i></p><p> </p>
Properties and formaldehyde removal efficiency of biocarbon-MnO2 particles
<p>The dataset includes information about biocarbon particles doped with different concentrations of MnO2 photocatalyst (BC-MnO2)</p><p>Six different samples: MnO2, biocarbon, BC-MnO2-1, BC-MnO2-2, BC-MnO2-3, BC-MnO2-4</p><p>Characterization of the samples:</p><p>* SEM images collected using scanning electron microscope (Carl Zeiss SUPRA 35 VP)</p><p>* XRD data collected using Bruker D2 Phaser diffractometer</p><p>* Porosity data collected using physisorption analyzer (Autosorb iQ-XR-AG-AG). The dataset contains data about isotherms and pores size distributions from tests under nitrogen gas (meso and macro porosity) and CO2 gas (microporosity).</p><p>Formaldehyde removal potential of the samples: The raw data were generated from an electrochemical formaldehyde sensor (Stox-HCHO) at ambient conditions (temperature of 23 °C, relative humidity between 40% and 46%, and conventional visible light). The sensor was placed in a test chamber equipped with the sensor and 8 µl of formaldehyde (HCHO) solution was injected. Then the test chamber was hermetically closed and changes in formaldehyde levels were measured. The same sensor provided information about temperature and relative humidity in the test chamber. Data was aquired using TVOC-HCHO logger software. The formaldehyde removal efficiency (%) of the samples after 8h of experiment was determined from the raw data.</p>
Dataset for "The influence of the amount of recycled material on the microstructure and properties of the second generation of single-domain YBCO bulks"
<p>The development of a recycling process for various REBCO materials is crucial considering both environmental sustainability and economic efficiency, particularly in light of the upcoming large-scale applications. In this paper, a novel general recycling process based on chemical dissolution was employed to grow REBCO bulks; recycled material obtained by recycling defective YBCO single-domain bulks was added (15 wt. %, 30 wt. % and 45 wt. %) to raw materials to prepare recycled YBCO precursor powder. Subsequently, recycled single-domain YBCO bulks were produced using Top-Seeded Melt Growth. The waste recycling related to of single-domain bulks growth was chosen, as it represents the most challenging form of waste in the context of REBCO superconductor production. The properties and microstructure of recycled bulks were further analyzed to determine the influence of the amount of recycled material used and compared to commercially produced bulks. Single-domain YBCO bulks were grown successfully from the recycled precursor powder. Furthermore, it was found that their properties could be tuned by varying the amount of the added recycled powder, allowing the use of vast amounts of REBCO waste for the preparation of bulks, when achieving the best possible properties is not essential for a given application. Given that the underlying recycling process is designed to work for all REBCO systems and any form of waste, it has significant implications for the sustainability and cost-effectiveness of REBCO superconductor production. </p>
On the Moreau–Jean scheme with the Frémond impact law: energy conservation and dissipation properties for elastodynamics with contact, impact and friction — data
<p>This deposit contains the data output of the systems described in <a href="https://hal.science/hal-04230941">On the Moreau–Jean scheme with the Frémond impact law. Energy conservation and dissipation properties for elastodynamics with contact impact and friction.</a> The codes that generated this data are available in another <a href="../records/10953181">deposit</a> archived on Zenodo, as well as in a GitHub repository archived on <a href="https://archive.softwareheritage.org/swh:1:dir:33ff6d960b70505c7939c0ce21c039cabbe1351c;origin=https://github.com/nickcollins-craft/On-the-Moreau-Jean-scheme-with-the-Fremond-impact-law;visit=swh:1:snp:72aede3d3a464732a36ef79c20ef07eebd1f9918;anchor=swh:1:rev:b63b68c25e72d23d7d9ee30225165fa0ebffb3c2">Software Heritage</a>, which is the preferred method of obtaining the codes. Two of the files in this deposit ("deformed_sliding_block_mesh.png" and "sliding_block_mesh.png") are required for one of the codes in the code deposit to run successfully ("block_mesh_plot.py", with the files assumed to be located in the folder specified in the data_folder variable of the file "path_file.py"), but the deposits are otherwise independent.</p>
Mineral spectral refractive index and bulk optical property dataset for aerosol studies
<p>Version 1.3, updated 11/15/2024.</p> <p>Added a file with 27 regional dust sample mineral composition information 'NewRegionalSamples.xlsx',</p> <p>along with the refractive index data.</p> <p>All refractive index files here have 127 rows (wavelengths) and 27 columns (samples)</p> <p>'kall27_coarse.dat' is the imaginary part of the coarse mode. </p> <p>'kall27_fine.dat' is the imaginary part of the fine mode.</p> <p>'nall27_coarse.dat' is the real part of the coarse mode.</p> <p>'nall27_fine.dat' is the real part of the fine mode.</p> <p>Version 1.2, updated 04/23/2024.<br>Major changes: <br>Changed all the data file names to new format: "mix"+{property name}+{number}, rearranged the number of mixing samples</p> <p>Updated all the bulk optical property data. This version use constant values of standard deviation in the lognormal size distribution settings for the coarse mode and the fine mode respectively.</p> <p>The phase matrices are separated from the other bulk properties due to their large file sizes. The readme file is updated correspondingly. The information of scattering angles (498 angles in total) is uploaded as "TAMUdust2020_Angle.dat".</p> <p>Added supplemental file data in 'Supplemental.tar.gz'.</p> <p>Additional refractive indices are zipped in 'AdditionalRefInd.tar.gz'</p> <p>Version 1.1, updated 03/14/2024.<br>Major changes: <br>Added mixed bulk properties for "0 (99%coarse+1%fine)" and "11 (2.0 µm coarse+ 0.4 µm fine)";<br>Added "reff.dat" in the 'BulkProperties.tar.gz'. The data include four columns: fine mode fraction, bulk projected area <A>, bulk volume <V>, effective radius r_eff. The information is for mixed sample number 0 to 11, each corresponds to one row.<br>Added refractive indices for chlorite, mica, smectite, pyroxene, vermiculite and pyroxenes. These groups can be applied in some other models.</p> <p>Version 1.0, uploaded 01/02/2024.</p> <p>This database include supplemental data and files for the publication of this paper:</p> <p>Sensitivities of Spectral Optical Properties of Dust Aerosols to their Mineralogical and Microphysical Properties. Yuheng Zhang, M. Saito, P. Yang, G. L. Schuster, and C. R. Trepte, J. Geophys. Res. Atmos. 2024.</p> <p> </p> <p>*****************************************</p> <p>The supplemental data include:</p> <p>1) 'GroupRefInd.tar.gz' Mineral (group) refractive index files.<br>E. g., 1All_Illite.dat contains the complex refractive index files of illite group. Format (from left to right columns): Wavelength (unit: µm), Real part (n), Imaginary part (k), standard deviation of n, standard deviation of k.</p> <p>The file 'fine_log.dat' includes the mean and standard deviation values of n and k for all the generated fine mode dust samples at 11,044 wavelengths from 0.2 to 50 micron.</p> <p>The file 'fine_log127.dat' only includes the values at 127 wavelengths from 0.2 to 50 micron (defined in 'swav.txt' and 'lwav.txt'), and is used for the bulk property computations.</p> <p>The files 'coarse_log.dat' and 'coarse_log127.dat' are for the coarse mode dust samples.</p> <p>2) 'CompositionFraction.xlsx': Mineral composition data sources/references and composition data (mean and standard deviation values of each group).<br>'Vlog_coarse.dat': Randomly generated VOLUME FRACTION of 9 mineral groups for the coarse mode dust. Left to right: Illite, Kaolinite, Montmorillonite (Other clays), Quartz, Feldspar, Carbonate, Gypsum (Sulphate), Hematite, Goethite.</p> <p>'Vlog_fine.dat': For the fine mode dust.</p> <p>3) 'RefSources.xlsx': The data source references of mineral refractive indices. We didn't include the olivine, other silicates, soot and titanium-rich minerals in the paper, but the refractive indices are available for those who are interested. Chlorite, Mica and Vermiculite group are mentioned in some studies, and we included the refractive indices for these minerals as well.</p> <p>4) 'DustSamples.tar.gz' Dust sample refractive index files.<br>The files are enclosed in four folders: fine_sw/ fine_lw/ coarse_sw/ coarse_lw/.</p> <p>fine: fine mode. coarse: coarse mode.</p> <p>'sw' means shortwave (< 4 µm, in total 76 wavelengths defined in 'swav.txt') while 'lw' means longwave (>= 4 µm, in total 51 wavelengths defined in 'lwav.txt').</p> <p>All files start with 'rdn', which means that they are computed based on randomly generated composition (data given in sheet 2 of 'CompositionFraction.xlsx').</p> <p>The four digit number after 'rdn' is the index of each dust sample. In total, there are 5,000 samples. The sample composition is the same for the same sample index in the same size mode (fine/coarse). Data file format (from left to right columns): real part, imaginary part.</p> <p>5) 'BulkProperties.tar.gz' Bulk property files (excluding phase matrices)<br>'mixqx.dat' files format (from left to right columns): Extinction efficiency (Qext), Scattering efficiency (Qsca), Backscattering efficiency (Qbck), and Asymmetry coefficient (Qasy). To obtain asymmetry factor, use Qasy/Qsca.</p> <p>'mixbkx.dat' files format (from left to right columns): P11(pi) P12(pi) P22(pi) P33(pi) P34(pi) P44(pi).</p> <p>'x' refers to the number at the end of the file name. It can be 100 ~ 112, each represents a setting of coarse and fine mode effective radius and volume fraction (see details in "reff.dat")</p> <p>'reff.dat' contains the effective radius information of the mixture. It has 7 columns: File number "x", Fine mode volume fraction, Fine mode effective radius (µm), Coarse mode effective radius (µm), Bulk projected area (µm^2), Bulk volume (µm^3), Bulk effective radius (µm).</p> <p>6) 'PhaseMatrices.tar.gz' Phase matrices data<br>'mixphswx.dat' files contain phase matrix results at 532 nm (shortwave). From left to right: P11, P12, P22, P33, P34, P44.</p> <p>'mixphlwx.dat' files contain phase matrix results at 10.5 µm (longwave).</p> <p>There are 635,000 rows in each data file. 635,000 rows = 127 wavelengths * 5,000 samples. Row 1~127 is sample 1, row 128~254 is sample 2, etc.. Suggest to use matlab function 'reshape(property, 127, 5000)' for each column when processing the data.</p> <p>7) 'Supplemental.tar.gz'</p> <p>We also include data files mentioned in the supplemental file of the paper. The adjusted source data files of the nine mineral groups are included.</p> <p>The supplemental bulk property files are named based on the figure number.</p> <p>8) 'AdditionalRefInd.tar.gz'</p> <p>We also include additional refractive indices for chlorite, smectite, vermiculite, mica, dolomite, titanium-rich minerals, pyroxenes and soot. These data can be useful in other models.</p> <p>For more detailed information and datasets, please contact: Yuheng Zhang, yuheng98@tamu.edu or yuhengz98@qq.com.</p>
Data for The UNCOVER Survey: A First-Look HST+JWST Catalog of Galaxy Redshifts and Stellar Populations Properties Spanning 0.2 ≲ z ≲ 15
<p>The recent UNCOVER survey with the James Webb Space Telescope (JWST) exploits the nearby cluster Abell 2744 to create the deepest view of our universe to date by leveraging strong gravitational lensing. In this work, we perform photometric fitting of more than 50,000 robustly detected sources out to z ~ 15. We show the redshift evolution of stellar ages, star formation rates, and rest-frame colors across the full range of 0.2 < z < 15. The galaxy properties are inferred using the Prospector Bayesian inference framework using informative Prospector-beta priors on masses and star formation histories to produce joint redshift and stellar populations posteriors, and additionally lensing magnification is performed on-the-fly to ensure consistency with the scale-dependent priors. We show that this approach produces excellent photometric redshifts with NMAD ~ 0.03, of a similar quality to the established photometric redshift code EAzY. In line with the open-source scientific objective of the Treasury survey, we publicly release the stellar populations catalog with this paper, derived from the photometric catalog adapting aperture sizes based on source profiles. This release includes posterior moments, maximum-likelihood spectra, star-formation histories, and full posterior distributions, offering a rich data set to explore the processes governing galaxy formation and evolution over a parameter space now accessible by JWST.</p>
Dataset for "Modeling Dipolar Nonprotogenic Solvents with PC-SAFT-Type Equations of State: Pure Substance Properties"
<p>Dipolar nonprotogenic solvents (DNS) are important chemical substances used across a wide range of applications, including renewable green solvent media, sustainable energy sources, and as efficient solvents for fabricating and processing semiconductive materials used in organic photovoltaics. Therefore, for efficient solvent screening or process design, a description and prediction of the thermodynamic properties of DNS using thermodynamic models is essential. This dataset contains calculation results of four different modeling strategies within the PC-SAFT equation of state for pure-substance properties of six DNSs: gamma-valerolactone, propylene carbonate, acetonitrile, dihydrolevoglucosenone, 1-methyl-2-pyrrolidone, and sulfolane. The modeling strategies differ in the treatment of the strong dipolar interactions of DNSs. The pure-substance properties include liquid density, vapor pressure, enthalpy of vaporization, and residual isobaric liquid heat capacity. The PC-SAFT performance was analyzed and evaluated based on the calculated data. Additionally, the dataset includes input files for quantum mechanical calculations of optimal molecular geometries and dipole moments of the considered DNSs using Gaussian 16 software.</p>
AIMEl-DB: Atomic Properties for 44K small organic molecules
<h3>AIMEl-DB: Atomic Properties for 44K small organic molecules</h3> <p>This dataset comprises atomic properties of 44K (44 470) molecules selected from the QM9 database. The file names are based on the same indexing system used for QM9. </p> <p>This dataset includes four types of files:</p> <ul> <li><strong>.com Files<br></strong>Input files for Gaussian 16. Simple-point energy calculations were carried out using the keywords<br><code># B3LYP/6-31G(2df,p) scf=(maxcycle=9999) nosymm output=wfx</code><br><br></li> <li><strong>.log Files<br></strong>Output files from Gaussian 16 calculation with the aformentioned parameters.<br><br></li> <li><strong>.wfx Files<br></strong>Wave function files from Gaussian 16 calculation. These files were used as inputs for QTAIM calculations. <br><br></li> <li><strong>.sumviz Files<br></strong>Output file from AIMAll software. The keywords used for the calculations were<br><code>aimqb -nogui -scp=false -nproc=8 -naat=4 input.wfx</code><br>Each .sumviz file contains more than 30 properties based on the Quantum Theory of Atoms in Molecules (QTAIM).<br><br></li> <li><strong>.csv Files<br></strong>These files contain the results of a in-house treament of .sumviz data. They cointain two calculated atomic properties:<br><br> <ol> <li>Total magnitude of the dipole moment, |mu|</li> <li>Total magnitude of the quadrupole moment, |Q|</li> </ol> </li> </ul> <p> and two extracted atomic properties:<br><br> 3. Electronic Population, N<br> 4. Atomic Energy, E</p> <p> </p> <p>The <code>aimel_merged_44k.csv</code> presents the concatenation of the 44 470 <strong>csv Files. </strong></p> <p>Additionaly, the <code>aimel_merged_38k.csv</code> presents the concatenation of the 38 876 <strong>csv Files. </strong>This file corresponds to the version 1.0 of the dataset. </p> <p><br>If you find this dataset useful, please cite the original paper:</p> <p>Meza-González, B., Ramírez-Palma, D.I., Carpio-Martínez, P. <em>et al.</em> Quantum Topological Atomic Properties of 44K molecules. <em>Sci Data</em> <strong>11</strong>, 945 (2024). https://doi.org/10.1038/s41597-024-03723-0</p> <p> </p> <p> </p>
Soil moisture sensor network, design, location attributes and soil properties, Hainich, Germany, project AquaDiva
<p>This dataset contains information of the small scale highly resolved soil moisture measurement network that is part of the of the AquaDiva Critical Zone exploratory, Hainich National Park, Germany. The dataset contains information on soil measurement locations, as well as attributes to the location, the design type (random locations vs transects), as well as locations attributes like distance to the next tree and soil properties. Measurement design was first introduced by Metzger et al., (2017), and used in Fischer et al., 2023. See there for more information.</p> <p><strong>References</strong></p> <p>Fischer-Bedtke, C., Metzger, J. C., Demir, G., Wutzler, T., and Hildebrandt, A.: Throughfall spatial patterns translate into spatial patterns of soil moisture dynamics – empirical evidence, Hydrology and Earth System Sciences, https://doi.org/10.5194/hess-2022-418, 2023.</p> <p>Metzger, J. C., Wutzler, T., Dalla Valle, N., Filipzik, J., Grauer, C., Lehmann, R., Roggenbuck, M., Schelhorn, D., Weckmüller, J., Küsel, K., Totsche, K. U., Trumbore, S., and Hildebrandt, A.: Vegetation impacts soil water content patterns by shaping canopy water fluxes and soil properties, Hydrological Processes, 31, 3783–3795, https://doi.org/10.1002/hyp.11274, 2017.</p>
Water properties of Arco Lake, Budd Lake, Deming Lake, and Josephine Lake in Itasca State Park from 2006-2009 and 2019-et seq.
Depth profiles of water column chemical and physical properties were assessed with seasonal-scale frequency from four lakes in the Itasca State Park from 2006-2009 and from 2019-et seq. The data was used to assess the mixing status and major geochemical constituents within the lakes. Several parameters were routinely measured with deployable probes at meter or sub-meter resolution at the deepest location in each lake. Water samples were also collected for laboratory analysis. Bathymetry data collected in 2022 is supplied as rasters.
Water properties of Brownie Lake, MN and Canyon Lake, MI from 2015-2022
Depth profiles of water column chemical and physical properties were assessed with seasonal-scale frequency from two meromictic lakes in the upper Midwest, U.S.A. from 2015 to 2022. Brownie Lake in Minneapolis, MN and Canyon Lake in the Huron Mountains of MI both contain elevated hypolimnetic dissolved iron (i.e. “ferruginous”). Several parameters were routinely measured with deployable probes at meter or sub-meter resolution at the deepest location in each lake. Water samples were also collected for laboratory analysis.
Geochemical characterization and material properties of coastal permafrost near Drew Point, Alaska
Permafrost cores (4.5-7.5 m long) were collected April 10th-19th, 2018, along a geomorphic gradient near Drew Point, Alaska to characterize active layer and permafrost geochemistry and material properties. Cores were collected from a young drained lake basin, an ancient drained lake basin, and primary surface that has not been reworked by thaw lake cycles. Measurements of total organic carbon (TOC) and total nitrogen (TN) content, stable carbon isotope ratios (δ13C) and radiocarbon (14C) analyses of bulk soils/sediments were conducted on 45 samples from 3 permafrost cores. Porewaters were extracted from these same core sections and used to measure salinity, dissolved organic carbon (DOC), total dissolved nitrogen (TDN), anion (Cl-, Br-, SO4 2-, NO3 -), and trace metal (Ca, Mn, Al, Ba, Sr, Si, and Fe) concentrations. Radiogenic strontium (87Sr/86Sr) was measured on a subset of porewater samples. Cores were also sampled for material property measurements such as dry bulk density, water content, and grain size fractions.
Physical and chemical properties of soils on Watershed 5 of Hubbard Brook Experimental Forest, before and after whole-tree harvest
We sampled soils on watershed 5 at the Hubbard Brook Experimental Forest in 1983, prior to a whole-tree harvest conducted in the winter of 1983-84. We resampled in 1986, 1991, and 1998. All sampling was performed using a quantitative soil pit method. Samples of the combined Oi and Oe horizons; the Oa horizon; 0-10 cm, 10-20 cm, and >20 cm layers of mineral soil; and the C horizon were collected. Grab samples of pedogenic mineral horizons were also taken from the sides of a subset of pits in each year. Here we report soil chemistry, mass of soil, percent rock, bulk density, and organic matter. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
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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)
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.