Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,413
datasets available to search
ShareScore release 0.9.0
Dataset results
1,413 results for “wetting”
Ecosystem-level Carbon dioxide fluxes in two long-term experimental wet sedge tundra sites near Toolik Lake, AK, ARC LTER 1994.
Ecosystem-level Carbon dioxide fluxes were measured in two long-term experimental wet sedge tundra sites near Toolik Lake, AK. Experimental treatments at each site included factorial nitrogenXphosphorus, greenhouse and shade house and were begun in 1985 (Sag site) or in 1988 (Toolik sites). Fluxes were measured on quadrats that were later sampled for biomass and leaf area.
Plant biomass, leaf area, carbon, nitrogen, and phosphorus in wet sedge tundra, 1994, Arctic LTER, Toolik Lake, Alaska.
Plant biomass, leaf area, carbon, nitrogen, and phosphorus were measured in three wet sedge tundra experimental sites. Treatments at each site included factorial NxP and at the Toolik sites greenhouse and shade house. Treatments started in 1985 (Sag site) and in 1988 (Toolik sites).
Hourly weather data from the Arctic LTER Wet Sedge Inlet Experimental plots from 1994 to present, Toolik Field Station, North Slope, Alaska.
Hourly weather data from the Arctic Tundra LTER wet sedge experimental site at Toolik Lake. The following parameters are measured every minute and averaged every hour: control plot air temperature and relative humidity at 3 meters and greenhouse plot air temperature and relative humidity at 1 meters (inside the greenhouse).
Steady-state operation dataset of an experimental Wet Cooling Tower pilot plant located at Plataforma Solar de Almería
<p>Repository that contains experimental data obtained from a Wet Cooling Tower (WCT) plant located at <a href="https://www.psa.es/es/index.php">Plataforma Solar de Almería</a>.</p> <p>For the article "Wet cooling tower performance prediction in CSP plants: A comparison between artificial neural networks and Poppe’s model", three experimental campaigns were used, quoting from the article:</p> <blockquote> <p>A total of 132 steady-state experimental points have been obtained thanks to the thorough experimentation conducted. These data cover a large variety of ambient conditions (different seasons, days and nights) and thermal loads (from 27 kW to 207 kW). </p> </blockquote> <p> </p> <p>See <code>README.md</code> for a more detailed description and instructions on how to use the data.</p> <h2><br>License</h2> <p><a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0. Attribution 4.0 International</a></p> <p>If the data is used as part of a scientific publication, please cite the source publication:</p> <div> <div>Serrano, Juan Miguel, Pedro Navarro, Javier Ruiz, Patricia Palenzuela, Manuel Lucas, and Lidia Roca. “Wet Cooling Tower Performance Prediction in CSP Plants: A Comparison between Artificial Neural Networks and Poppe’s Model.” <em>Energy</em> 303 (September 15, 2024): 131844. <a href="https://doi.org/10.1016/j.energy.2024.131844">https://doi.org/10.1016/j.energy.2024.131844</a>.</div> </div>
Future hourly wet-bulb globe temperature dataset for 842 cities in Japan
We provide projected hourly wet-bulb globe temperature (WBGT) data for 842 cities in Japan for April to October in the period 2030-2100. The projection is generated by applying prediction models built by learning the relationship between historical hourly WBGT and the daily weather indices using a machine learning method called eXtreme Gradient Boosting, to a future climate scenario data for Japan area called NIES2020. Projection data for the historical period 1980-2014 are also available.
Biomass in wet sedge tundra near the Atigun River crossing of the Dalton Highway, North Slope AK, 1982.
Biomass in wet sedge tundra near the Atigun River crossing of the Dalton Highway, North Slope AK. There were three harvests; Late May-early June; Late July-early August; Late August-early September. See Shaver and Chapin (Ecological Monographs, 61, 1991 pp.1-31).
Soil temperature data collected from the Arctic LTER wet sedge experimental site Toolik Field Station North Slope, Alaska from 1994 to 2020
Soil temperature data collected every 4 hours from a wet sedge site at the Arctic Tundra LTER site at Toolik Lake. Temperatures are measured every 3 minutes and averaged every 4 hours in control, nitrogen alone, phosphorus alone, nitrogen and phosphorus, and greenhouse experimental plots soil temperatures.
Climate data from the former Arctic LTER Toolik Inlet Wet Sedge Flux site, Toolik Field Station, Alaska 2017 to 2025.
One-minute averages of air temperature and relative humidity, soil temperature and moisture, and solar radiation from 2017 to 2025 for the former Arctic LTER Toolik wet sedge eddy covariance flux site. This site was originally set up for seasonal eddy covariance flux measurements during the summers of 2010–2015 and partially during winters starting in 2014, until 15 June 2016. Flux data are reported in Eugster et al.(2020)(doi:10.5194/amt-2019-402). IMPORTANT NOTE: The data has only been checked for obvious outliers. Use with caution since most of the data has not been carefully checked. It was decided to release this version instead of waiting until there were resources for a complete check.
Consumer Stocks: Wet weights from Everglades National Park (FCE), South Florida from March 2003 to April 2008
We hypothesize that standing crops of consumers reflect patterns of allochthonous nutrient transport along the estuarine interface at the Florida Coastal Everglades (FCE) LTER. Our goal is to investigate how variation in hydrology, water quality, and disturbance influence secondary production. This data set represents the numeric count data of fish, plants, and other fauna.
Examination of protein-like fluorophores in chromophoric dissolved organic matter (CDOM) in a wetland and coastal environment for the wet and dry seasons of the years 2002 and 2003 (FCE)
Water samples are collected at the end of the dry and the wet season from all LTER sites and stored on ice until return to the lab. They are pre-filtered through pre-combusted GF/F filters and ultrafiltered and concentrated with a Pellicon 2 Mini tangential flow ultrafiltration system.Concentrated samples were then analyzed using fluorescence and SEC-HPLC. This CDOM optical study revealed the presence of two classes of compounds associated with the protein-like peak (peak T; excitation/emission (Ex/Em) maxima at around 280 nm/325 nm), which have very different chemical structures and ecological roles. In addition to proteins, we propose phenolic compounds as possible origins of peak T in coastal and wetland environments. In this study, natural water samples were obtained from subtropical rivers and estuarine environments within the Florida Coastal Everglades (FCE) ecosystem. The samples were ultra-filtered and excitation-emission fluorescence matrices (EEMs) were obtained. The EEMs showed the presence of four peaks with Ex/Em maxima at around 280 nm/325 nm (T), less than 260 nm/460 nm (A), 300 nm/412nm (M), and 350 nm/470 nm (C). To better understand the nature of peak T, the components originating this peak were separated using size exclusion chromatography (SEC) and detected by fluorescence emission at Ex/Em = 280 nm/325 nm. The elution curves revealed the presence of two elution peaks at a molecular weight of greater than 50K (void volume; T1) and around 7.6K (T2). This result suggested the need of cautious interpretation in the use of peak T as a proxy for the detection of proteinaceous materials in wetland and estuarine environments, since significant amounts of potentially interfering phenolic compounds are leached from senescent biomass in wetland and coastal ecosystems. As such EEM spectra of gallic acid an important component of hydrolysable tannins, and condensed tannins extracted from red mangroves (Rhizophora mangle) showed the presence of a peak maxima
Compound hot and dry and wet and windy events in CMIP6 models
<p>NetCDF files containing maps of return periods (in years) for the joint occurrence of</p> <ol> <li>strong surface winds (sfcWind) and heavy rain (pr) and</li> <li>heatwaves (EHF) and drought (SPI) </li> </ol> <p>as realised by models participating in the Coupled Model Intercomparison Project Round 6 (CMIP6). Included is output from models that provided daily data for sfcWind, pr, tmax and tmin (to calculate EHF) and experiments historical, SSP126, SSP245, and SSP585 for ensemble member r1i1p1f1. The base period for the determination of hazard thresholds was 1980 – 2014 for all experiments. Time periods over which return periods were calculated were 1980 – 2014 for the 'historical' experiment and 2066 – 2100 for experiments 'SSP126', 'SSP245', and 'SSP585'.</p> <p>Values for return periods are determined following the method in Ridder et al. (2020a) doi: 10.1038/s41467-020-19639-3; Ridder et al. (2020b) doi: 10.1029/2020GL091152 and Ridder et al. (2021) doi: 10.1038/s41612-021-00224-4. </p> <p>Name convention:</p> <ul> <li> historical experiments: <br> <em>map_RP_${hazardX}_${hazardY}_${CMIP6model}_historical_r1i1p1f1_${model_grid}_19800101_20141231.nc</em></li> <li>ScenarioMIPs:<br> map_RP_<em>${hazardX}_${hazardY}_${CMIP6model}</em>_historic_threshold_${experiment}_r1i1p1f1_2066-2100.nc</li> </ul>
Atomistic trajectories from ab-initio molecular dynamics simulations of wetted TiO2 nanoparticle
<p>This repository contains atomistic trajectories from ab-initio molecular dynamics simulations of water and TiO2 nanoparticle described in the paper:</p> <p>E. G. Brandt, L. Agosta and A.P.Lyubartsev, " Reactive wetting properties of TiO2 nanoparticles predicted by ab initio molecular dynamics simulations", Nanoscale, 8, 13385-13398 (2016) DOI: 10.1039/c6nr02791a</p> <p>The trajectories are saved in the .xtc format, and initial structures with specification of atom types are given in the .pdb format.</p> <p>The name of each file contains brief information about the simulated system:</p> <p>TiO2 : composition of the nanoparticle<br> n24 : number of TiO2 units in the nanoparticle<br> anatase/brookite/rutile : type of crystall structure<br> - a number 0 - 30 : number of water molecules in the simulation<br> 2fs - the time step</p> <p>For more details, see the referred paper</p>
Atmospheric Wet Deposition in Urban and Suburban Sites Across the United States
These data are for the publication: Conrad-Rooney, E., J. Gewirtzman, Y. Pappas, V.J. Pasquarella, L.R. Hutyra, and P.H. Templer. 2023. Atmospheric Wet Deposition in Urban and Suburban Sites Across the United States. Atmospheric Environment, https://doi.org/10.1016/j.atmosenv.2023.119783 This study investigated long-term trends in atmospheric wet deposition of nitrogen (ammonium and nitrate), sulfate, cations, and chloride for urban and suburban sites in the U.S. using data from the National Atmospheric Deposition Program and assessed whether urban NADP sites are hotspots for atmospheric wet deposition. To examine potential impacts of urbanization on atmospheric wet deposition, percent impervious surface area and population density data were extracted from Google Earth Engine. The results of this study highlight that urban areas have greater rates of many forms of atmospheric deposition and that there should be more long-term monitoring of atmospheric deposition in cities and suburban sites throughout the U.S. Data sources: - Dewitz, J., and U.S. Geological Survey, 2021, National Land Cover Database (NLCD) 2019 Products (ver. 2.0, June 2021): U.S. Geological Survey data release, doi:10.5066/P9KZCM54 - National Atmospheric Deposition Program (NRSP-3). 2022. NADP Program Office, Wisconsin State Laboratory of Hygiene, 465 Henry Mall, Madison, WI 53706. https://nadp.slh.wisc.edu/networks/national-trends-network/ - United States Census Bureau, TIGER: US Census Blocks, 2010 United States Census. https://developers.google.com/earth-engine/datasets/catalog/TIGER_2010_Blocks#description
Soil biogeochemical variables collected on the Arctic LTER experimental plots in moist acidic, moist non-acidic, wet shrub and shrub tundra, Arctic LTER Toolik Field Station, Alaska 2015
We investigated the effect of long-term warming on multiple soil and microbial carbon, nitrogen, and phosphorus pools, and microbial extracellular enzyme activities, with a particular focus on phosphorus, in Alaskan tundra plots underlain by permafrost
Long-term monitoring of wet, dry, and bulk atmospheric deposition in central Arizona-Phoenix, ongoing since 1999
The aims of this study are to examine (1) the magnitude and spatial variability in the concentration and flux of wet deposited major ions (NO3-N, NH4-N, DOC, PO4-P, Cl, SO4, H+, Ca, Mg, Na, K) across the greater Phoenix metropolitan area, including the developed urban core and outlying desert, and (2) patterns of coarse dry particulate deposition across stated area and provide some minimum estimates on levels of dry deposition of these ions. This study was designed particularly to answer the question: 'To what extent are concentrations and fluxes of these ions enhanced at sites within the urban core relative to undeveloped desert sites upwind and downwind of the city?'. At the outset, the project featured eight wet-dry collectors positioned spatially so as to form a transect running approximately west-to-east across the central Arizona region from outlying desert to the west, upwind of the prevailing synoptic wind direction, through agriculture to urban core sites, and, finally, to two downwind sites in the desert to the east and northeast. As much as possible, these collectors were co-located with Maricopa County or Arizona Department of Environmental Quality monitoring stations. Monitoring at most sampling locations ran from 1999 through the mid-2000s when sampling was discontinued at several sites. Sampling continued at the Lost Dutchman State Park, also a Desert Fertilization experiment site with a focus on atmospheric deposition, through 2016. Sampling continues at a site on the Arizona State University Tempe campus that was added to the program in 2009.
Litterfall in tabonuco (subtropical wet) forest in the Luquillo Experimental Forest, Puerto Rico (MRCE Litterfall data)
Treatments common to both sites are quarterly fertilization (macro- and micronutrients) and unmanipulated. At El Verde, a third set of plots was subject to a one time removal of litter and woody debris generated by Hurricane Hugo (September 1989). Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
PYU16 Socle of Kan Wet Khaung Mound Stone Buddha Image
<p><strong>PYU 16 Socle of Kan Wet Khaung Mound Stone Buddha Image</strong></p> <p>Support: stone Buddha image, socle</p> <p>Lines: A: 6, B: 5, C: 5, D: 5</p> <p>Dimensions (cm): h: 59, w: 57, d: 20</p> <p>Language: Sanskrit, Pyu</p> <p>Original locality: Kan Wet Khaung mound, Sriksetra</p> <p>Present locality: Sriksetra Museum, no. 2013/1/48</p> <p>References: ASI 1927–1928, 128, 145; Ray 1936: 19–20; Luce 1937: 243–244; <em>PR</em>, 41–43; <em>PPPB</em> I, 51, 57 n. 24, 65, 74 n. 22, 131–132; Guy 1997: 91; 2014: 91–92 (cat. 41); Tun Aung Chain 2003: 5–6; Sein Win 2016: 45–60.</p> <p> </p> <p>Data from: Arlo Griffiths, Marc Miyake, Bob Hudson, Julian Wheatley, "Studies in Pyu Epigraphy, I : State of the Field, Edition and Analysis of the Kan Wet Khaung Mound Inscription, and Inventory of the Corpus," <em>BEFEO</em> 103 (2017): 43-205. <a href="http://doi.org/10.5281/zenodo.1478504">http://doi.org/10.5281/zenodo.1478504</a></p>
A 30m Topographic Wetness Index Dataset for the Continental United States
<p>The topographic wetness index was computed at a 30m resolution for the continental United States. This dataset was computed using the triangular multiple flow direction algorithm for computing upslope areas (see https://doi.org/10.1029/2006WR005128). We used the Shuttle Radar Topography Mission (SRTM; https://doi.org/10.1029/2005RG000183) digital elevation model (DEM) for all computations. This analysis was conducted in the R programming language using the System for Automated Geoscientific Analyses (SAGA; https://doi.org/10.5194/gmd-8-1991-2015) for back end computations. This dataset is in the NAD83(NSRS2007) / Conus Albers projection system, EPSG 5072.</p>
Dataset for publication "Importance of Substrate Pore Size and Wetting Behavior in Gas Diffusion Electrodes for CO2 Reduction"
<p>Dataset for the publication "Importance of Substrate Pore Size and Wetting Behavior in Gas Diffusion Electrodes for CO2 Reduction" containing war and processed data used to compose the various figures. </p> <p>DOI Publication: <a href="https://doi.org/10.1021/acsaem.2c03054">https://doi.org/10.1021/acsaem.2c03054</a> </p>
Wet season standardised maximum land surface temperature of the Greater Paramaribo Region 2016-2018
<p>This map shows the maximum land surface temperature for the wet season of the Greater Paramaribo Region, Suriname, 2016-2018. The satellite images stem from the Landsat 8 OLI/TIRS (Operational Landsat Imager/Thermal Infrared Sensor) satellite and were obtained from the United States Geological Survey (USGS). A detailed description is provided in the metadata document.</p><p><i>Tom Remijn, Lisa Best, Rudi van Kanten, Nina Schwarz , Louise Willemen, 2020, Wet season standardised maximum land surface temperature of the Greater Paramaribo Region 2016-2018, product of 'Naar een groen en leefbaarder Paramaribo' by Tropenbos Suriname and University of Twente-ITC.</i> DOI: 10.5281/zenodo.7696837,<i> licensed under the </i><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/"><i>Creative Commons License CC BY-NC-SA 4.0</i></a><i>.</i></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.