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3,206 results for “properties”
Soil physical and chemical properties of gypsum & non-gypsum soils from the Chihuahuan and Mojave Deserts in 2023
This dataset contains data for soil physical and chemical properties of gypsum and non-gypsum soils in the northern Chihuahuan and eastern Mojave Deserts. Data were obtained from 20 study sites total, 10 located on soils derived from gypsum parent material and 10 located on soils derived from non-gypsum parent materials. Sites were grouped into 10 pairs, in which every gypsum site was partnered with a non-gypsum site located in the same region. Apart from soil type, partnered-site characteristics (topography, climate, elevation, slope, aspect, and presence of biocrusts) were held relatively constant. Site info and characteristics data can be accessed at knb-lter-jrn.210616001. Soil physical properties included: percent gravel, percent < 2mm fraction, soil aggregate stability, and soil compaction. Soil chemical properties were: percent gypsum content, pH, EC, and soil soluble concentrations of calcium, magnesium, potassium, sulfur, and phosphorus. The resulting soil data was used to understand physical and chemical differences between gypsum and non-gypsum soils and to examine how biocrust community types and moss species abundance and composition were associated with the measured soil variables. This study and dataset are complete.
AGW04 Measurement of stream chemical properties during growing season rainfall events at konza prairie, 2024
During the 2024 growing season, stream water chemical properties were measured during seven rainfall events in watersheds N01B, N02B, and N04D at Konza Prairie Biological Station. Just before and during the storms, stream water samples were collected hourly using automated samplers located just upstream or downstream from theflume in each watershed. At the same location, stream pH and temperature was also measured every 5 minutes using data loggers situated near the stream sampler inlet tubes. Following each storm, the samples were filtered through 0.45 µm filter membranesand then analyzed for concentrations of alkalinity, major cations and anions, non-purgeable organic carbon, total dissolved nitrogen, and water stable isotopes. Select trace element concentrations and strontium isotope ratios were also analyzed during one of the storm events. The primary goal was to assess event-level variation in stream concentration-discharge relationships in watersheds with variable extents of woody plant encroachment. Discharge data accompanying these results are available in datasets ASD02, ASD05, and ASD06.
North Temperate Lakes LTER: Residential Lakeshore Property Sales in Vilas County 1997 - 2004
Sales of residential shoreline property parcels in Vilas County, WI, USA for the period January 1997 througt Dec 2004. This dataset includes sales of over 2000 parcels on 234 lakes. In addtion to the sale price, other information collected include assessed value of the land, assessed value of improvements, length of lake frontage and total size of the parcel.
North Temperate Lakes LTER: Vilas County Property Tax Records 1997 - 2004
Each year, the county government in Vilas County, WI, assembles data on each parcel of property in the county for various governmental administrative purposes. These records serve the register of deeds, the department of taxation, and other county departments, as well as private citizens or businesses, such as realtors, who seek information on properties in the county. These records include assessments of the value of land and built improvements, their location, the name and mailing address of the owner, and other data. LTER has begun to collect these data each year from the county as a means of monitoring owners, and the social identities of owners (whether they are state agencies, private individuals, corporations, non-profit/conservation organizations, etc.). These data have already proved useful as a sampling frame for social science surveys. This data set includes property tax records for Vilas County for the years 1997, 1998, 2001, 2003, and 2004. Sampling Frequency: annually Number of sites: 15 Townships of Vilas County, WI, USA
Dissolved Organic Carbon Concentration, Dissolved Organic Matter Optical Properties, and Water Quality Indicators in the Plum Island Estuary (PIE), Massachusetts, USA (2018-2023)
This is a data set of paired in situ measurements of water quality parameters, total suspended solids concentration, and concentration and optical properties (absorption coefficient spectra and fluorescence indices) of dissolved organic matter (DOM) collected between 2018 and 2023 in the Plum Island Estuary and nearshore waters. In situ water quality measurements (salinity, temperature, optical dissolved oxygen saturation, turbidity, and dissolved organic matter fluorescence) were collected with a water quality sonde from the surface (top 1 m of water column), along with corresponding samples that were processed and analyzed in the lab for dissolved organic carbon (DOC) concentration, chromophoric DOM (CDOM), absorption coefficient spectra, DOM excitation-emission matrix (EEM) fluorescence, and total suspended sediment (TSS) concentration. The data were used in multiple studies (see manuscripts listed below) focusing on the dynamics of DOC and CDOM in the Plum Island Estuary.
Dataset of Soil hydraulic properties of Valle Telesina (Italy)
<p>The dataset contain a .xls file with the hydraulic properties georeferenced of 47 soil profiles of the "Valle Telesina (Italy) site, according to the parametrization of the van Genuthen-Mualem model (van Genuchten, 1980). Moreover a zipped folder with the shape files for the same area is provided.</p> <p>Following there is the description of the methods applied for the soil hydraulic characterization:</p> <p>Undisturbed soil samples were collected from the horizons using cylindrical steel samplers (8.5 cm diameter and 12.0 cm high). In the laboratory, the samples were saturated by slowly wetting from the bottom in order to remove all the air entrapped in the soil. The maximum water content,θ<sub>0</sub>, was gravimetrically determined and the saturated hydraulic conductivity, ks, was measured by a falling-head permeameter. Then, the Wind method was applied to simultaneously determine the water retention and hydraulic conductivity functions by subjecting the soil samples to an evaporation process. After sealing the bottom surface to prevent drainage, during the evaporation process - at appropriate pre-set time intervals - the weight of the whole sample and the pressure head at three different depths were measured. An iterative procedure was applied for estimating the water retention curve from these measurements. Then, the instantaneous profile method was applied to determine the unsaturated hydraulic conductivity. θr, θs, α and n parameters were derived by fitting the soil water retention data; under the restriction m=l−l/n, τ and k<sub>0</sub> parameters were derived by fitting the hydraulic conductivity data. Details of the tests and overall calculation procedures are described in Basile et al. (2012). The parameters obtained in the laboratory were then scaled to better reproduce the field behaviour by following the procedure suggested by Basile et al. (2003; 2006). Finally, for the few soils having considerable stone content, a correction of θs and k<sub>0</sub>, to take into account the stoniness, was applied (Coppola et al., 2013).</p> <p>References:</p> <p>Van Genuchten, M. T. (1980). A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Science Society of America Journal, 44(5), 892–898.</p> <p>Basile, A., Buttafuoco, G., Mele, G., & Tedeschi, A. (2012). Complementary techniques to assess physical properties of a fine soil irrigated with saline water. Environmental Earth Sciences,66(7), 1797–1807.</p> <p>Basile, A., Ciollaro, G., & Coppola, A.(2003). Hysteresis in soil water characteristics as a key to interpreting comparisons of laboratory and field measuredhydraulic properties.Water Resources Research, 39(12).</p> <p>Basile, A., Coppola, A., De Mascellis, R., & Randazzo, L. (2006). Scaling approach to deduce field unsaturated hydraulic properties and behavior from laboratory measurements on small cores. Vadose Zone Journal,5(3), 1005–1016.</p> <p>Coppola, A., Dragonetti, G., Comegna, A., Lamaddalena, N., Caushi, B., Haikal, M., & Basile, A. (2013). Measuring and modeling water content in stony soils. Soil and Tillage Research,128, 9–22.</p>
Influence of Atmospheric Air Plasma Pre-Treatment of Veneers on the Mechanical Properties and Stability of Beech Plywood
<p>Wood-based sheet materials such as plywood, fiberboard, particleboard, and oriented strain board find applications in civil engineering, building technology, furniture manufacturing and many more. All these materials rely strongly on an effective bond formation between the resin and the wood base material, which gives rise to their mechanical performance and stability, as well as their resistance to moisture and liquids. In our study, we present the use of a commercial atmospheric air plasma system, which we used for the pretreatment of veneers of common beech (<em>Fagus sylvatica</em> L.) wood before formation of plywood boards. Plasma treatment parameters were optimized following the change in water contact angle. Two different stacking patterns were used for plasma-treated veneers. The time stability of the plasma modification was investigated by forming a second set of plywood boards 70 hours after plasma treatment of the respective veneers. The influence of the plasma treatment on mechanical properties was studied via bending and shear strength of the four sets of plasma-treated boards in comparison to a plywood out of the same veneer without plasma treatment. Water and moisture resistance were tested through water immersion and surface water resistance tests. Further, confocal laser scanning microscopy was used to determine changes of the surfaces’ morphologies.</p>
Dataset of Measurement and conceptualization of maternal PTSD following childbirth: Psychometric properties of the City Birth Trauma Scale – French version (City BiTS-F)
<p>The City Birth Trauma Scale (City BiTS-F) was developed to assess posttraumatic stress disorder following childbirth (PTSD-FC), based on the PTSD criteria of the DSM-5. Recent studies investigating the latent factor structure of PTSD-FC symptoms in women reported mixed results. Given that no validated French questionnaire exists to measure PTSD-FC symptoms, this study first aimed to validate the French version of the CBTS (City BiTS-F). Second, it aims to establish the latent factor structure of PTSD-FC.</p> <p>This dataset contains data on the mental health (i.e., PTSD-CB, depression, anxiety) of 541 mothers who gave birth during the last 12 months. Sociodemegraphic data such as maternal age, marital status, educational level, parity, gravidity, weeks of gestation, type of delivery, history of traumatic childbirth, or history of traumatic event is available. </p> <p>This dataset is related to: Sandoz, V., Hingray, C., Stuijfzand, S., Lacroix, A., El Hage, W., & Horsch, A. (2022). Measurement and conceptualization of maternal PTSD following childbirth: Psychometric properties of the City Birth Trauma Scale—French Version (City BiTS-F). <em>Psychological Trauma: Theory, Research, Practice, and Policy, 14</em>(4), 696–704. <a href="https://psycnet.apa.org/doi/10.1037/tra0001068">https://doi.org/10.1037/tra0001068</a></p>
DebDaB: A database of supraglacial debris thickness and physical properties
<p><strong>DebDaB: A database of supraglacial debris thickness and physical properties</strong></p> <p>DebdaB is a database of measured and reported physical properties and thickness of supraglacial debris that is openly available and open to community submissions.</p> <p>The majority of the database (90%) is compiled from 172 sources in the literature, and the remaining 10% has not been published before. DebDaB contains 8,286 data entries for supraglacial debris thickness, of which 1,852 entries also include sub-debris ablation rates, 167 data entries of thermal conductivity of debris, 157 of aerodynamic surface roughness length, 77 of debris albedo, 56 of debris emissivity and 37 of debris porosity. The data are distributed over 83 glaciers in 13 regions in the Global Terrestrial Network for Glaciers. </p> <p>This is version 2 of the dataset, corresponding to the revised version of the database after peer-review of its accompanying "Data descriptor manuscript" submitted for publication to the scientific journal "Earth System Science Data (ESSD)" from Copernicus Publications. The preprint is available at <a href="https://doi.org/10.5194/essd-2024-559">https://doi.org/10.5194/essd-2024-559 </a></p> <p>DebDaB version 2 consists of the following files:</p> <ul> <li>DebDaB_v2.zip : The actual DebDaB database, provided as a navigable Open Document Spreadsheet (.ods) with spreadsheet tabs for each of the debris properties. Additionally, the database is also provided as separate .csv files for each debris property, and as a GeoPackage (.gpkg). </li> <li>Readme_files.zip: A .txt file for each of the debris property tabs, describing all the fields in each tab. </li> <li>Templates_for_data_submission.zip: Templates (.csv files and additionally .xlsx files) for data submission for each of the debris properties in DebDaB. Data submissiosn to DebDaB should be sent to debriscoveredglaciers@ista.ac.at. </li> <li>DebDaB_data_sources.pdf: List of DebDaB sources from published literature. </li> <li>DebDaB_data_sources.bib: BibTeX list of DebDaB sources from published literature. </li> <li>Manuscript_codes.zip: The codes to download and process the data to generate the figures for data descriptor manuscript on ESSD.</li> </ul> <p>The data descriptor manuscript is in open review stage at: <a href="https://essd.copernicus.org/preprints/essd-2024-559/">https://essd.copernicus.org/preprints/essd-2024-559/ </a></p> <p><strong>DebDaB is open to new data submissions</strong>, and therefore future data submissions of previously unpublished data to DebDaB will entail co-authorship on the DebDaB database on Zenodo. </p> <p>According to the authors’ understanding of FAIR principles, authors of published literature and published data, that:</p> <ul> <li>Correct existing data within DebDaB, in case of errors</li> <li>Send the raw data from digitised figures</li> <li>Submit additional data that was previously unavailable (for example, accurate coordinates or additional data or metadata which is not already available)</li> </ul> <div>will have the right to be added as co-authors on the database in Zenodo. The authors are working to reevaluate their policies to conform to changes or unusual circumstances in authorship contributions, and are happy to involve eager people in the core team.</div> <div> </div> <div><strong>How to submit data: </strong>Please use the templates provided in the database files for data submissions and send it to debriscoveredglaciers@ista.ac.at. Authors who submit data will be asked to fill in a form regarding authorship contributions. </div> <p><strong>Important note on citations:</strong> DebDaB data users must cite the data descriptor manuscript (Fontrodona-Bach et al. 2025), the DebDaB zenodo repository<br>(Groeneveld et al., 2025), <strong>and the original data sources</strong> when using the database, given that DebDaB is mostly<br>a compilation of previously published data. To facilitate the citations of original data sources, each of the data entries in DebDaB contains the corresponding<br>original reference and corresponding DOI.</p> <p><strong>Manuscript citation:</strong> Fontrodona-Bach, A., Groeneveld, L., Miles, E., McCarthy, M., Shaw, T., Melo Velasco, V., and Pellicciotti, F.: DebDaB: A database of supraglacial debris thickness and physical properties, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-559, in review, 2025.</p> <p><strong>Zenodo citation:</strong> Groeneveld, L., Fontrodona-Bach, A., Miles, E., McCarthy, M., Melo Velasco, V., Shaw, T., Pellicciotti, F., Bauder, A., Buri, P., Kneib, M., Kumar, A., Mishra, A., & Petersen, L. (2025). DebDaB: A database of supraglacial debris thickness and physical properties (Version v2) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.14514803" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.</a><a href="https://doi.org/10.5281/zenodo.14224835" target="_blank" rel="noopener">14224835</a></p> <p><strong>Original data sources citation:</strong> See <em>DebDaB_data_sources.pdf</em> or <em>DebDaB_data_sources.bib</em></p> <p>The authors acknowledge the Debris-Covered Glaciers Working Group (DCGWG) from the International Association of Cryospheric Sciences (IACS) for setting the stage and drawing together the debris-covered glaciers community to focus on broader needs transcending a specific research topic, and starting the zenodo community on debris-covered glaciers, where this database is hosted. </p> <p><strong>Author contributions: </strong>The following spreadsheet states the contribution of each of the co-authors on the database: <br><a href="https://docs.google.com/spreadsheets/d/1nTieH_ZkwqnUpHQMYn7bygEcV5RzX4DJuqZ_qd-_PzE/edit?usp=sharing" target="_blank" rel="noopener">Author contributions statement (click here)</a></p> <p>A description of what each contribution field means is below:</p> <ul> <li><em>Conceptualisation:</em> This refers to the original idea and shaping of the database and is therefore closed.</li> <li><em>Data curation:</em> The data managers of DebDaB. Primarily the quality checks and curation done to all the collected published and unpublished data. It may also include authors who have compiled a lot of measurements from sources the authors did not have, and merged them into DebDaB, or if someone else takes on the role of ingesting/homogenizing data in the future.</li> <li><em>Data collection: </em>Field measurements as well as scouring past literature that the authors have missed, digitising sources, or advocating for old missing data sources to be entered into DebDaB.</li> <li><em>Formal analysis:</em> In the case of methods being applied to derive debris property values from other measurements, such as the case for surface roughness and thermal conductivity.</li> <li><em>Supervision/funding: </em>This refers to funding provided for the generation of DebDaB itself, but also funding for the data collection (measurements). </li> </ul>
ALL-READY Questionnare on potential drivers and barriers to the adoption of innovation management, open science, and Intellectual Property Rights (IPR) among the members of the Pilot Network
<p><strong>Background & Summary</strong>: </p><p>The ALL-READY project unites a diverse consortium of Research Infrastructures (RI) and Living Labs, instrumental in developing new methodologies and technologies in agroecology. The project focuses on effective management of innovation, adherence to open science principles, and strategic application of Intellectual Property Rights (IPR). Task 6.4 of the project, which concentrates on Innovation and IPR Management, seeks to understand the dynamics influencing the adoption of these practices among its members. Recognizing the need for end-to-end data management, the project emphasizes standardized data collection and management while adhering to FAIR principles.</p><p><strong>Methods</strong>: </p><p>The questionnaire was developed by LifeWatch ERIC to capture data reflecting current practices and perceptions in agroecology. It included 26 questions divided into four sections, focusing on existing practices, potential drivers, and barriers in innovation management, open science, and IPR. The survey was disseminated via an online platform to the ALLREADY Pilot Network, ensuring a representative sample from diverse organizations. The data collection process was closely monitored, and the responses were analyzed using a mixed-methods approach to extract meaningful insights.</p><p><strong>Data Records of the ALLREADY Project Questionnaire</strong>: </p><p>The dataset, collected through an online survey platform, underwent a meticulous process of data preparation, download, formatting, and anonymization. It consists of one text file containing metadata (Readme.txt) and a single CSV file encompassing all questionnaire responses. The dataset provides a comprehensive view of innovation management, open science adoption, and IPR handling within the agroecology sector, particularly among the network of RIs and Living Labs involved in the project.</p><p><strong>Technical Validation of the ALLREADY Project Questionnaire</strong>: </p><p>Several critical steps were taken to ensure the accuracy, reliability, and overall quality of the data collected. This included development and testing of the questionnaire, rigorous monitoring of the data collection process, and thorough checks for data quality and completeness. The representativeness of the sample was analyzed specifically with respect to the Pilot Network rather than the broader population involved in agroecology. Strategies were employed to counter survey fatigue and maintain respondent engagement.</p><p><strong>Usage Notes for the ALLREADY Project Questionnaire</strong>: </p><p>The dataset's proper usage is vital for ensuring the validity and reproducibility of research. Researchers are advised to consider the nature of the data, the representativeness of the dataset, and its generalizability. The dataset allows for comprehensive analysis and integration of different sections, and analysts have the flexibility to handle open and write-in responses according to their research needs. Additional information to facilitate analysis is provided in a separate documentation file.</p><p> </p>
Selected properties of galaxy and SMBH populations (Spinoso et al. 2023)
<p>This record presents the catalogs of galaxy and Black Holes properties associated to the two runs of the modified version of the L-Galaxies Semi-Analytic Model (SAM) presented in Spinoso et al. 2023. These catalogs are aimed at providing the basic properties to study the population of Black Holes (BHs) and their host galaxies across cosmic times, obtained by running the L-Galaxies SAM over the whole Millennium-II box (see Boylan-Kolchin et al. 2009). The L-Galaxies SAM outputs summarized in these catalogs were obtained at several redshifts/snapshots, for two different runs which differ for the initial occupation fraction of BHs at the time of their formation. This initial occupation fraction is parametrized by the Gp parameter(see Spinoso et al. 2023 for details), with the two runs being characterized by Gp=1 and Gp=0.01. The catalogs are organized in two group of files, one group for each run. Each of these groups is composed by 18 different files, one per each availablle redshift, roughly corresponding to: z = 0, 0.5, 1, 1.5, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15. The two group of files can be easily distinguished by their names: indeed, the strings "Gp1" and "Gp001" are referred to the runs corresponding to the Gp=1 and Gp=0.01 values, respectively. In addition, every file includes a string of the form: "z[x.yz]" which specifies its redshift. </p> <p>The content of these catalogs is as follows: each redshift-file contains the same collection of arrays, each array being a galaxy or BH property. At each redshift, L-Galaxies outputs properties for the number 'NGAL' of galaxies identified in the Millennium-II box, at that specific redshift/snapshot. Therefore, most of the arrays have length equal to 'NGAL' (i.e. one value per each galaxy). Few of the arrays have a length of N * NGAL (i.e. N values per each galaxy). The content, units and data type of these arrays are as follows: </p> <ul> <li>"StellarMass" - Total stellar mass of each galaxy - [10^10 Msun / h] - array[NGAL]</li> <li>"Sfr" - Star formation rate of each galaxy - [Msun / yr] - array[NGAL]</li> <li>"SeedMass" - BH-seed mass. 7 values per galaxy; one value for each of the 7 possible BH-seed "flavors" modeled - [10^10 Msun / h] - array[NGAL, 7]</li> <li>"Rvir" - Virial radius of the DM halo hosting each galaxy - [Mpc / h] - array[NGAL]</li> <li>"Pos" - X, Y and Z position of each galaxy - [Mpc / h] - array[NGAL, 3]</li> <li>"Mvir" - Virial mass of the DM halo hosting each galaxy - [10^10 Msun / h] - array[NGAL]</li> <li>"Lbol" - Bolometric luminosity associated to the central AGN (==0 if the BH is not active) - [10^40 erg / s] - array[NGAL]</li> <li>"HotGas" - Mass of the hot-phase of each galaxy's gas component - [10^10 Msun / h] - array[NGAL]</li> <li>"fEDD" - Eddington ration (defined as Lbol/L_Edd, with L_Edd being the Eddington luminosity) for each AGN (==0 if the BH is not active) - [adim] - array[NGAL]</li> <li>"ColdGas" - Mass of the cold-phase of each galaxy's gas component - [10^10 Msun / h] - array[NGAL]</li> <li>"BlackHoleMass" - Mass of the central massive BH hosted by each galaxy (==0 if the galaxy does not host a central BH) - [10^10 Msun / h] - array[NGAL]</li> <li>"SeedType" - Identifier of the type of BH-seed which originated each BH (see below for details) - array[NGAL]</li> <li>"Redshift" - Redshift of each galaxy (within a single file, this is an array of identical values) - array[NGAL]</li> </ul> <p>NOTE:<br>The model presented in Spinoso et al. 2023 follows 7 different types of BH-seeds. The "SeedMass" array contains 7 mass values (one for each of these types of BH-seeds) for each galaxy in the Millennium-II box.This is the reason why the data type of "SeedMass" is [NGAL, 7]. Each of these 7 values is the sum, across the whole evolution of each galaxy, of the contributions to the total BH mass coming from each BH-seed who merged to form the final BH. In the vast majority of cases, BHs are associated to only one type of BH-seed. In those cases, 6 out of the 7 "SeedMass" values would be zero. Each element of "SeedMass" corresponds to one type of BH seed according the following scheme:<br>SeedMass[0] : total seed mass of light-seeds inherited from the GQd model (see Spinoso et al. 2023 for details)<br>SeedMass[1] : total seed mass of heavy-seeds inherited from the GQd model (see Spinoso et al. 2023 for details)<br>SeedMass[2] : un-resolved mass-growth driven by gas-accretion before the halo hosting the BH was resolved<br>SeedMass[3] : total seed mass formed as light-seeds in L-Galaxies<br>SeedMass[4] : total seed mass formed as Direct-Collapse BHs (DCBHs)<br>SeedMass[5] : total seed mass formed as intermediate-mass BH originated via Runaway Stellar Mergers (RSM)<br>SeedMass[6] : total seed mass formed as Merger-Induced Direct-Collapse BH (miDCBH)</p> <p>NOTE:<br>Similarly to "SeedMass", also the "Pos" array has more than one element per galaxy. These are the three cartesian positions of each galaxy.</p> <p>NOTE:<br>The possible values of the "SeedType" array are as follows (see Spinoso et al. 2023 for details):<br>-1 - No BH seed (the galaxy never hosted a BH)<br>1 - light seed (PopIII remnant)<br>6 - Direct-Collapse BH (DCBH)<br>7 - intermediate-mass BH originated via Runaway Stellar Mergers (RSM)<br>8 - Merger-Induced Direct-Collapse BH (miDCBH)<br>9 - mixed type: light+DCBH (the BH is the result of hierarchical mergers between light and DCBH seeds)<br>10 - mixed type: light+RSM (the BH is the result of hierarchical mergers between light and RSM seeds)</p>
Investigation of the properties of conductivity signals in BK channels by Empirical Mode Decomposition
<p>The idea of the project is the comprehensive time-frequency analysis of ion current data registered from BK channels of the different cell lines and measured under the different experimental conditions. Decomposition of signals into individual frequency modes and application of non-linear measures in the form of Information Entropy or Hurst exponent to individual signal components will allow for a more detailed analysis of the information hidden behind the complex ionic conduction sequences. The sample data contains patch-clamp sequences. </p>
Data for a publication "Exploring the microstructure, mechanical properties, and corrosion resistance of innovative bioabsorbable Zn-Mg-(Si) alloys fabricated via powder metallurgy techniques"
<p><span><span>These data are published as part of the paper: “</span><span>Exploring the microst</span><span>ructure, mechanical properties, </span><span>and corrosion resistance of innovative bioabsorbable Zn-Mg-(S</span><span>i) alloys fabricated via powder </span><span>metallurgy techniques</span><span>” published in journal: “</span><span>Journal of Materials Research and Technology</span><span>”.</span></span><span> </span></p>
Organic Matter, Geochemical, Visible Spectrocolorimetric Properties, Radiocesium Properties, and Grain Size of Potential Source Material, Target Sediment Core Layers and Laboratory Mixtures for Conducting Sediment Fingerprinting Approaches in the Mano Dam Reservoir (Hayama Lake) Catchment, Fukushima Prefecture, Japan
<p>The current dataset was compiled to study sediment fingerprintings practices, i.e tracer selection and contribution modelling. Organic matter, elemental geochemistry, visible difuse spectrocolorimetric properties, radiocesium properties, and grain size were analysed were analysed in potential source material that may supply sediment to coastal rivers, here the upper part of the Mano river, draining the main Fukushima radioactive pollution plume (Japan). Four potential soil source materials (<em>n</em> = 68) were considered: undecontaminated cropland (<em>n</em> = 24), as non-decontaminated soil before the application of local decontamination policies, remediated cropland (<em>n</em> = 10), as decontaminated soil after the application of local decontamination policies, forest soils (n = 24) and subsurface material originating from channel bank collapse or landslides (<em>n</em> = 10; referred to as subsoil). A sediment core was collected in the Mano Dam lake (Hayama lake) on the 6th June 2021 and was sectionned into 1-cm layers (<em>n</em> = 38). Laboratory mixtures (<em>n</em> = 27) were made to assess different contribution levels from the sources.</p> <p>The current dataset comprises four .csv files including data and metadata information and their respective descriptions of variables. The data set is composed of soil samples, sediment core layer and laboratory mixtures. Laboratory mixtures were prepared to provide a dataset to calibrate/validate un-mixing models implemented to address this research question and analysed in the same conditions and using the same equipment as the source/target material.</p> <p>Recommended encoding format: <strong>latin1</strong></p>
Sublimation and infrared spectral properties of ammonium cyanide
<p>Data from</p> <p>Perry A. Gerakines, Yukiko Y. Yarnall, Reggie L. Hudson,<br>Sublimation and infrared spectral properties of ammonium cyanide,<br>Icarus,<br>Volume 413,<br>2024,<br>116007,<br>ISSN 0019-1035,<br>https://doi.org/10.1016/j.icarus.2024.116007.<br>(https://www.sciencedirect.com/science/article/pii/S0019103524000654)<br>Abstract: The ammonium ion (NH4+) has been suggested to be present in interstellar ices and has been observed on the surfaces of planetary bodies using infrared (IR) spectroscopy as the primary means of identification. Evidence for several ammonium salts has also been found in the dust and surface ices of comet 67P/Churyumov-Gerasimenko. Here we present a laboratory study of ammonium cyanide (NH4CN) and report on several properties of this compound, measured with higher accuracy than in previous reports, including its IR band strengths and optical constants for use in quantifying its abundance in interstellar and planetary ices. We also report the first measurements since 1882 of NH4CN vapor pressures, sublimation fluxes, and sublimation enthalpy measured at temperatures relevant to subliming cometary ices (134–155 K). The density and refractive index of NH4CN at 125 K and the sublimation enthalpy and vapor pressures of NH3 at ~100 K are also reported.</p> <p><br>Keywords: Ices; IR spectroscopy; Comets; Infrared observations</p> <p>This work was funded by the NASA Astrophysics Research and Analysis (APRA) and Planetary Data Archiving, Restoration, and Tools (PDART) Programs, as well as NASA's Planetary Science Division Internal Scientist Funding Program through the Fundamental Laboratory Research (FLaRe) work package at the NASA Goddard Space Flight Center.</p>
Inferring size-based functional responses from the physical properties of the medium
<p>Databases used to test the model described in the article "Inferring size-based functional responses from the physical properties of the medium", Frontiers in Ecology and Evolution. Please read the "Readme.pdf" file for detailed information. This file explains all the variables and provides full references for the data in each of the datasets.</p> <p>"Portalier_et_al_2021_Species_Speeds.csv" provides species speeds according to body size for numerous species in aquatic systems.</p> <p>"Portalier_et_al_2021_Predator_Prey_Interactions.csv" provides attack rates, capture probabilities and handling times for numerous predator-prey interactions in aquatic systems.</p>
Predicted and experimental chemical and ecotoxicological properties for the toxic unit based hazard assessment
<p><strong>Description</strong></p> <p>This dataset contains ecotoxicity data of 1585 chemicals of environmental concern (CECs) and chemical identifiers. The ecotoxicity data was retrieved from <a href="https://cfpub.epa.gov/ecotox">US EPA ECOTOX Knowlegdebase</a> in ASCII file format and was aggregated for the ecotoxicity groups algae, crustaceans, and fish based on the ideas of <a href="https://dx.doi.org/10.1002/etc.3460">Busch et al. 2016</a>. The dataset includes the 5-percentile, the mean and the geomean of all retrieved ecotoxicity for each compound. Missing ecotoxicity data was estimated with ECOSAR 1.0 algorithms for green algae, daphnids, and fish using <a href="https://www.ufz.de/index.php?en=34593">ChemProp 6.8</a>. The main purpose of this dataset is the <a href="http://doi.org/10.1016/0043-1354(70)90018-7">toxic unit</a> (TU) based hazard assessment of environmental water samples. Chemical properties were estimated using <a href="https://github.com/kmansouri/OPERA">OPERA 2.7</a>, <a href="https://chemaxon.com/products/instant-jchem">Instant JChem</a>, and ACD Percepta 2015 based on QSAR-ready SMILES derived from OPERA 2.7. All data aggregated from EcoTox Knowledgebase (e.g., raw values, species, etc.) is available in the dataset in the detailed sheets. REcoTox, the processing script written in R is available on <a href="https://github.com/tsufz/REcoTox/releases/latest">GitHub</a>.</p> <p><strong>CAUTION</strong></p> <p>It needs to be emphasized that quantitative-structure activity relationship data is just an estimate, which does not necessarily reflect the real property and behaviour of a modelled compound. The calculated data needs to be reviewed in deep. Especially for non-polar or very polar compounds, the QSAR predictions might fail. If a compound ranks high in the TU ranking, it is required to search for literature or regulative data evidences to underpin the finding to avoid false positive prioritizations.</p> <p><strong>RELEASE NOTE</strong></p> <p>Version 210714_v1 was created with <a href="https://github.com/tsufz/REcoTox/releases/tag/v0.1.0">REcoTox version v0.1.0</a>.</p>
Thermophysical properties for the published article "Experiments and modelling on ASDEX Upgrade and WEST in support of tool development for tokamak reactor armour melting assessments"
<p>In order to model the macroscopic metallic melt motion realized in the poor-versus-efficient thermionic emitter leading edge exposures in the ASDEX-Upgrade outer divertor [1], the material library of the MEMENTO melt dynamics code, that previously only concerned tungsten [2] and beryllium [3], had to be extended to iridium and niobium. </p> <p>Reliable experimental data have been analyzed for the latent heats, specific isobaric heat capacity, electrical resistivity, thermal conductivity, mass density, vapor pressure, work function, total hemispherical emissivity and absolute thermoelectric power from the room temperature up to the normal boiling point of iridium and niobium as well as for the surface tension and the dynamic viscosity across the liquid state. Analytical expressions are recommended for the temperature dependence of these thermophysical properties, which involve high temperature extrapolations given the absence of extended liquid iridium and liquid niobium measurements. The analytical expressions, the details of their construction and the main references are included in the accompanying pdf.</p> <p>[1] S. Ratynskaia, K. Paschalidis, P. Tolias, K. Krieger, Y. Corre, M. Balden, M. Faitsch, A. Grosjean, Q. Tichit, R.A. Pitts, the ASDEX-Upgrade team, the WEST team and the Eurofusion MST1 team, "Experiments and modelling on ASDEX Upgrade and WEST in support of tool development for tokamak reactor armour melting assessments", Nucl. Mater. Energy 33 (2022) 101303.<br> [2] P. Tolias, "Analytical expressions for thermophysical properties of solid and liquid tungsten relevant for fusion applications", Nucl. Mater. Energy 13 (2017) 42.<br> [3] P. Tolias, "Analytical expressions for thermophysical properties of solid and liquid beryllium relevant for fusion applications", Nucl. Mater. Energy 31 (2022) 101195.</p>
Soil properties as point estimations over the Lithuanian pilot area (2022)
<p>In the context of the EU-funded project DIONE (No. 870378), VNIR topsoil reflectance was captured with DIONE’s Soil Scanning System and transformed through Machine Learning modelling to a set of soil properties that are meaningful for the assessment of soil health. The captured reflectance measurements correspond to locations distributed within the pilot areas of Lithuania and are indicated after the analysis of EO multispectral imagery, aiming to create a collection of point locations that well represent the soil characteristics of the area, and provide valuable information about soil condition through the estimations of the following soil properties:</p> <ul> <li>Sand %</li> <li>Clay %</li> <li>Silt %</li> <li>Electrical Conductivity (mS/m)</li> <li>pH</li> <li>Calcium carbonate %</li> <li>Soil Organic Carbon %</li> </ul> <p>The dataset is delivered in a shapefile format (DIONE_LT_point_estimations_2022_WP4.shp - EPSG:4326 - WGS 84) containing the following fields:</p> <table> <caption><strong>Description of the information contained in the corresponding "LT points estimations" dataset</strong></caption> <thead> <tr> <th scope="col">Field </th> <th scope="col">Type </th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>Sample ID</td> <td>String </td> <td>Unique ID</td> </tr> <tr> <td>Lat</td> <td>Real </td> <td>Latitude </td> </tr> <tr> <td>Lon</td> <td>Real </td> <td>Longitude</td> </tr> <tr> <td>Sand </td> <td>Real </td> <td>Sand fraction</td> </tr> <tr> <td>Clay</td> <td>Real </td> <td>Clay fraction</td> </tr> <tr> <td>Silt</td> <td>Real </td> <td>Silt fraction</td> </tr> <tr> <td>EC </td> <td>Real </td> <td>Electrical Conductivity</td> </tr> <tr> <td>ph_H<sub>2</sub>0</td> <td>Real </td> <td>ph</td> </tr> <tr> <td>CaCO<sub>3</sub></td> <td>Real </td> <td>Calcium Carbonate</td> </tr> <tr> <td>SOC </td> <td>Real </td> <td>Soil Organic Carbon</td> </tr> </tbody> </table>
Soil properties as point estimations over the Cypriot pilot area (2022)
<p>In the context of the EU-funded project DIONE (No. 870378), VNIR topsoil reflectance was captured with DIONE’s Soil Scanning System and transformed through Machine Learning modelling to a set of soil properties that are meaningful for the assessment of soil health. The captured reflectance measurements correspond to locations distributed within the pilot areas of Cyprus and are indicated after the analysis of EO multispectral imagery, aiming to create a collection of point locations that well represent the soil characteristics of the area, and provide valuable information about soil condition through the estimations of the following soil properties:</p> <ul> <li>Sand %</li> <li>Clay %</li> <li>Silt %</li> <li>Electrical Conductivity (mS/m)</li> <li>pH</li> <li>Calcium carbonate %</li> <li>Soil Organic Carbon %</li> </ul> <p>The dataset is delivered in a shapefile format (DIONE_CY_point_estimations_2022_WP4.shp - EPSG:4326 - WGS 84) containing the following fields:</p> <table> <caption><strong>Description of the information contained in the corresponding "CY points estimations" dataset</strong></caption> <thead> <tr> <th scope="col">Field </th> <th scope="col">Type </th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>Sample_ID</td> <td>String</td> <td>Unique ID</td> </tr> <tr> <td>Lat</td> <td>Real</td> <td>Latitude </td> </tr> <tr> <td>Lon</td> <td>Real</td> <td>Longitude</td> </tr> <tr> <td>Sand</td> <td>Real</td> <td>Sand fraction</td> </tr> <tr> <td>Clay</td> <td>Real</td> <td>Clay fraction</td> </tr> <tr> <td>Silt</td> <td>Real</td> <td>Silt fraction</td> </tr> <tr> <td>EC</td> <td>Real</td> <td>Electrical Conductivity</td> </tr> <tr> <td>ph_H<sub>2</sub>0</td> <td>Real</td> <td>pH</td> </tr> <tr> <td>CaCO<sub>3</sub></td> <td>Real</td> <td>Calcium Carbonate</td> </tr> <tr> <td>SOC</td> <td>Real</td> <td>Soil Organic Carbon</td> </tr> </tbody> </table> <p> </p>
ScienceDex guides
Understand access before you commit
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.