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120 results for “discrete data”
Time series of high-frequency sensor data measuring water temperature, dissolved oxygen, conductivity, specific conductance, total dissolved solids, chlorophyll a, phycocyanin, turbidity, and fluorescent dissolved organic matter at discrete depths in Carvins Cove Reservoir, Virginia, USA in 2020-2025
We monitored water quality in Carvins Cove Reservoir (Roanoke, Virginia, USA; 37.3697 -79.958) with high-frequency (10-minute) sensors in 2020-2025. Carvins Cove Reservoir is owned and managed by the Western Virginia Water Authority as a primary drinking water source. This data package consists of datasets from two separate deployments. First, from July 2020 - August 2021, depth profiles of water temperature were measured on 1-meter intervals using HOBO temperature pendant loggers deployed from 0.1 m below the surface of the reservoir to 10 m depth, and also at 15 and 20 m depth. Additionally, water temperature was measured in the Sawmill Branch inflow at 0.5 m depth using HOBO temperature pendant loggers. Second, from 9 April 2021 - 31 December 2025, depth profiles of water temperature were measured on 1-meter intervals from 0.1 m below the surface of the reservoir to 11 m depth and additionally at 15 and 19 m. A YSI EXO2 sonde measured water temperature, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, dissolved oxygen, and fluorescent dissolved organic matter at ~1.5 m depth. A YSI EXO3 sonde measured water temperature, conductivity, specific conductance, total dissolved solids, dissolved oxygen, and fluorescent dissolved organic matter at ~9 m depth, which corresponds to the depth of a water outtake valve. The thermistors, EXO3 sonde, and pressure sensor were deployed at stationary, fixed elevations (referred to as positions) deployed off of the dam near the water outtake valves. Due to variable water levels in the reservoir, the depths of these sensors varied over time. In contrast, the EXO2 was deployed on a buoy from 2021-2022 and remained at 1.5 m depth as the water level fluctuated. However, in 2023, the buoy disappeared in a storm, and after that the EXO2 was deployed at a stationary elevation as the water level fluctuated around the sensor. The EXO2 was redeployed on the buoy in 2024. The monitoring site's maximum de
Secchi depth data and discrete depth profiles of water temperature, dissolved oxygen, conductivity, specific conductance, photosynthetic active radiation, oxidation-reduction potential, and pH for Beaverdam Reservoir, Carvins Cove Reservoir, Falling Creek Reservoir, Gatewood Reservoir, and Spring Hollow Reservoir in southwestern Virginia, USA 2013-2025
Discrete depth profiles of water temperature, dissolved oxygen, oxidation-reduction potential, conductivity, specific conductance, and pH were collected with multiple handheld water quality probes and discrete depth profiles of photosynthetically active radiation (PAR) were collected with a LI-COR underwater light meter from 2013 to 2025 in five drinking water reservoirs in southwestern Virginia, USA. These reservoirs are: Beaverdam Reservoir (Vinton, Virginia), Carvins Cove Reservoir (Roanoke, Virginia), Falling Creek Reservoir (Vinton, Virginia), Gatewood Reservoir (Pulaski, Virginia), and Spring Hollow Reservoir (Salem, Virginia). Beaverdam, Carvins Cove, Falling Creek, and Spring Hollow Reservoirs are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia, and Gatewood Reservoir is a drinking water source for the Town of Pulaski, Virginia. All discrete depth profiles were collected on approximately 1-meter intervals. The data package consists of two datasets: 1) Secchi depth data; and 2) discrete depth profiles of multiple water quality variables measured by handheld sensors. The Secchi data and discrete depth profiles were measured at the deepest site of each reservoir adjacent to the dam, as well as other in-reservoir sites. Handheld sensor measurements were also collected at a gauged weir on the primary inflow tributary, other inflows, and outflows at Falling Creek Reservoir; inflows and outflows at Beaverdam Reservoir; and inflows at Carvins Cove Reservoir. In 2021, YSI handheld data were also collected from a littoral site in Beaverdam Reservoir. In 2025, YSI handheld data were collected monthly from June to October from nine littoral sites around the perimeter of Falling Creek Reservoir. From 2024 - 2025, additional within-reservoir depth profiles were collected in Carvins Cove Reservoir and multiple sites. Data were collected approximately fortnightly in the spring months (March - Ma
Time series of high-frequency sensor data measuring water temperature, dissolved oxygen, pressure, conductivity, specific conductance, total dissolved solids, chlorophyll a, phycocyanin, fluorescent dissolved organic matter, and turbidity at discrete depths in Falling Creek Reservoir, Virginia, USA in 2018-2025
We monitored water quality in Falling Creek Reservoir (Vinton, Virginia, USA; 37.30325 -79.8373) with high-frequency (10-minute) sensors in 2018-2025. All variables were measured at the deepest site of the reservoir adjacent to the dam. Falling Creek Reservoir is owned and managed by the Western Virginia Water Authority as a primary drinking water source for Roanoke, Virginia. This data product consists of one dataset compiled of depth profiles of water temperature on 1-m intervals from 0.1 to 9 m depth; dissolved oxygen at 5 m and 9 m depth; pressure at 9 m depth; and temperature, dissolved oxygen, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, fluorescent dissolved organic matter, turbidity, and pressure at ~1.6 m depth. The dataset is accompanied by a sensor maintenance log and quality assurance/quality control analysis scripts.
Time series of high-frequency sensor data measuring water temperature, dissolved oxygen, conductivity, specific conductance, total dissolved solids, chlorophyll a, phycocyanin, fluorescent dissolved organic matter, and turbidity at discrete depths, and water level in Beaverdam Reservoir, Virginia, USA in 2009-2025
We monitored water level and water quality in Beaverdam Reservoir (Vinton, Virginia, USA; 37.31288, -79.8159) with visual observations and high-frequency (10- to 15-minute resolution) sensors in 2009-2025. All variables were measured at the deepest site of the reservoir adjacent to the dam. Beaverdam Reservoir is owned and managed by the Western Virginia Water Authority as a secondary drinking water source for Roanoke, Virginia. This data package is comprised of three datasets: 1) bvre-waterlevel_2009_2025.csv, 2) bvre-sensorstring_2016_2020.csv, and 3) bvre-waterquality_2020_2025.csv. 1) bvre-waterlevel_2009_2025.csv contains water level observations of the staff gauge at a platform near the reservoir's dam by both the Western Virginia Water Authority and the Virginia Tech Reservoir Group LTREB field crew. This dataset spans 2009 to 2025, with data collection still ongoing. 2) bvre-sensorstring_2016_2020.csv consists of a water temperature profile at ~1-meter intervals from the surface of the reservoir to 10.5 m below the water, complemented by intermittent data collected by a dissolved oxygen logger deployed at 5 m or 10 m. A sonde measuring water temperature, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, dissolved oxygen, fluorescent dissolved organic matter, and turbidity was additionally deployed at ~1.5 m depth. This dataset spans 2016 to 2020, with no additional data collection beyond the last observation. The third dataset is bvre-waterquality_2020_2025.csv, with data collection still ongoing and an accompanying maintenance log. This dataset contains: a) a temperature string with 13 temperature sensors deployed ~1 m apart from the surface to 0.5 m above the sediments of the reservoir; b) two dissolved oxygen sensors, one in the middle of the string and one sensor above the sediments; and c) a pressure sensor just above the sediments. The same sonde from the first 2016-2020 dataset is also included in this 2020-2025 d
Merged discrete water-column data from PAL LTER research cruises along the Western Antarctic Peninsula, from 1991 to 2024.
Water samples are collected throughout the water column along the Western Antarctic Peninsula at regular LTER grid stations where CTD casts are preformed and in surface waters at underway stations, where CTD casts are not done, using the ship's flow-through seawater system. This dataset is the compilation of water samples collected at these stations, merged from other PAL-LTER datasets. Data includes water column Chlorophyll and Phaeopigment concentrations; phytoplankton accessory pigments -including other chlorophyll's (e.g. chlorophyll b), xanthophylls, and carotenes; primary production rates; bacterial production; dissolved organic carbon; particulate organic carbon and nitrogen; dissolved inorganic nutrients; dissolved oxygen, and dissolved inorganic carbon and alkalinity. Measurements of phytoplankton Fv/Fm measured using a FIRe (Fluorescence Induction and Relaxation) fluorometer are also included, though caution is urged as FIRe data has not been corrected nor QCed. Conductivity, temperature, depth, and associated data from instruments on the sampling rosette (e.g. PAR, beam transmission) for each sampling depth are also included. Phytoplankton accessory pigment data is unavailable for the LMG10-01 cruise due to instrumentation problems and for the LMG12-01 cruise due to a freezer failure which resulted in the loss of samples. Dissolved oxygen measurements were discontinues after 2012, and thus no data is available from 2013 onwards. Dissolved Organic Carbon data is unavailable after 2012 due to instrumentation problems. There is no Particulate Organic Carbon data for cruise PD94-01.
Merged discrete water-column data from annual PAL LTER field seasons at Palmer Station, Antarctica, from 1991 to 2025.
Water samples are collected throughout the water column at regular stations within the Palmer LTER region at Palmer Station, on Anvers Island, Antarctica. CTD casts are preformed and water collected using go-flo bottles or a rosette. Near-surface waters are also sampled occassionally at the Palmer Station seawater intake (pumphouse). This dataset is the compilation of water samples collected at these stations, merged from other PAL-LTER datasets. Data includes water column Chlorophyll and Phaeopigment concentrations; phytoplankton accessory pigments -including other chlorophyll's (e.g. chlorophyll b), xanthophylls, and carotenes; primary production rates; bacterial production; dissolved organic carbon; particulate organic carbon and nitrogen; dissolved inorganic nutrients; dissolved oxygen, and dissolved inorganic carbon and alkalinity. Measurements of phytoplankton Fv/Fm measured using a FIRe (Fluorescence Induction and Relaxation) fluorometer are also included, though caution is urged as FIRe data has not been corrected nor QCed. Conductivity, temperature, depth, and associated data from instruments on the CTD/sampling rosette (e.g. PAR, beam transmission) for each sampling depth are also included. Phytoplankton accessory pigment data is unavailable for the Palmer 2009-2010 season due to instrumentation problems and for the Palmer 2011-2012 season due to a freezer failure which resulted in the loss of samples. There is a temporary data gap for the Palmer 2015-2016, 2016-2017, 2019-2020, 2020-2021, 2023-2024, and 2024-2025 seasons because those samples have not been analyzed yet. Dissolved oxygen measurements and dissolved inorganic carbon/alkalinity measurements are not available for any season. Dissolved Organic Carbon data is unavailable after 2012 due to instrumentation problems. There is no Particulate Organic Carbon after the 2012 season.
Data for the article "Computational micromagnetics based on normal modes: Bridging the gap between macrospin and full spatial discretization"
<p>Data for the article "Computational micromagnetics based on normal modes: Bridging the gap between macrospin and full spatial discretization".</p> <p>Link to publisher: https://www.sciencedirect.com/science/article/abs/pii/S0304885321009197</p> <p>Link to Arxiv preprint: https://arxiv.org/abs/2105.08829</p>
Github commit data for the article "Beyond Zipf's law: Exploring the discrete generalized beta distribution in open-source repositories"
<p><span>This dataframe corresponds to the data used in the Nowak's et al. 2024 article "Beyond Zipf’s law: Exploring the discrete generalized beta distribution in open-source repositories" (see reference below).</span></p> <p><span>It consists of the distirbutions of number of commits per user across a number of GitHub repositories. <br><br>There are three columns:</span></p> <ul> <li><span>repository: the repository name</span></li> <li><span># of commits: the number of commits of a given individual</span></li> <li><span>rank: the user rank in the repository (by decreasing number of commits)<br><br></span></li> </ul> <p><strong><span>Reference:</span></strong></p> <p><span>Nowak, P., Santolini, M., Singh, C., Siudem, G., & Tupikina, L. (2024). Beyond Zipf’s law: Exploring the discrete generalized beta distribution in open-source repositories. <em>Physica A: Statistical Mechanics and Its Applications</em>, <em>649</em>, 129927. <a href="https://doi.org/10.1016/j.physa.2024.129927">https://doi.org/10.1016/j.physa.2024.129927</a></span></p>
Interagency Ecological Program: Discrete water quality and phytoplankton data from the Sacramento River floodplain and Yolo Bypass tidal slough, collected by the Yolo Bypass Fish Monitoring Program, 1998 - 2022
The Yolo Bypass Fish Monitoring Program (YBFMP) operates a rotary screw trap and fyke trap and conducts biweekly beach seine and lower trophic surveys in addition to maintaining water quality instrumentation in the bypass. The YBFMP serves to fill information gaps regarding environmental conditions in the bypass that trigger migrations and enhanced survival and growth of native fishes, as well as provide data for IEP synthesis efforts. YBFMP staff also conduct analyses of YBFMP monitoring data to address pertinent management related questions as identified by IEP. The Yolo Bypass has been identified as a high restoration priority by the National Marine Fisheries Service and US Fish and Wildlife Service Biological Opinions for Delta Smelt, Winter and Spring-run Chinook salmon and by California EcoRestore. The YBFMP informs the restoration actions that are mandated or recommended in these plans and provides critical baseline data on the ecology of the bypass and how it interacts with the broader San Francisco Estuary. Program objectives include: Collecting baseline data on water quality, chlorophyll, lower trophic level biota, and fish in the Yolo Bypass to monitor spatial and temporal changes in trends and abundance; Analyzing and communicating Yolo Bypass data with stakeholders and the scientific and management communities to address pertinent management related questions; Providing technical expertise on Yolo Bypass aquatic ecology and monitoring and sampling methods. We collect discrete water quality data using a YSI ProDSS and sample phytoplankton, chlorophyll and nutrients as discrete water grabs taken biweekly (or weekly during Yolo Bypass inundation) along with lower trophic tows. Water is sampled at three sites along the Yolo Bypass and Sacramento River, then processed and analyzed by an internal DWR laboratory.
Lake Tahoe Nutrients data for discrete water samples
Lake water nutrient data measured on discrete water samples from Lake Tahoe, CA/NV. There are two sampling stations Index (LTP, 39.0972 -120.155) and Mid-lake (MLTP, 39.1417 -120.0153). See methods for details
Lake Tahoe particle size distribution (PSD) data for discrete water samples
Particle size distribution data measured on discrete water samples from Lake Tahoe, CA/NV. There are two sampling stations Index (LTP, 39.0972 -120.155) and Mid-lake (MLTP, 39.1417 -120.0153). See methods for details
Underway discrete chlorophyll and post-calibrated underway fluorometer data during NES-LTER Transect cruises, ongoing since 2019
The continuous underway fluorescence, induced by in vivo chlorophyll-a (Chl-a), of surface waters of the Northeast U.S. shelf is compared to discrete Chl-a samples for post-calibration, collected ship-board as part of the Northeast U.S. Shelf Long-Term Ecological Research (NES LTER). Chl-a values derived from the manufacturer-calibrated sensors (hereafter, “continuous fluorescence”) and collected continuously are often different from the precise Chl-a concentrations obtained from discrete, extracted samples. Moreover, underway fluorometers and manufacturer calibrations differ per cruise. Thus, post-calibration of the continuous fluorescence signals using discrete Chl-a measurements is essential to standardize and compare the high-resolution underway Chl-a data along cruise tracks. For six cruises aboard the R/V Endeavor between summer 2019 and summer 2021, 12 to 22 discrete samples were collected from the underway system to measure Chl-a concentrations. These discrete Chl-a concentrations were then compared, using simple linear regressions (Model I least square fit), to corresponding continuous fluorescence values recorded by the two independent fluorometers installed with the underway system. For each cruise a preferred fluorometer was identified based on the best fit of the linear regression between discrete Chl-a concentrations and continuous fluorescence values. The slope and the intercept of the linear regression were used to post-calibrate continuous fluorescence values into standardized and intercomparable Chl-a concentration. This data package includes a table for the underway discrete Chl-a values and a table for the 1-min post-calibrated continuous fluorescence values for the preferred underway fluorometer per cruise.
A consistent discretization of the single-field two-phase momentum convection term for the unstructured finite volume Level Set / Front Tracking method - data
<p>Research data from the rhoLENT unstructured Level Set / Front Tracking method for simulating two-phase flows with large density ratios. </p>
Data from: Mean landscape-scale incidence of species in discrete habitats is patch size dependent
<p>Contains data and code for the manuscript 'Mean landscape-scale incidence of species in discrete habitats is patch size dependent'.</p> <p>Raw data consist of 202 published datasets collated from primary and secondary (e.g., government technical reports) sources. These sources summarise metacommunity structure for different taxonomic groups (birds, invertebrates, non-avian vertebrates or plants) in different types of discrete metacommunities including 'true' islands (i.e., inland, continental or oceanic archipelagos), habitat islands (e.g., ponds, wetlands, sky islands) and fragments (e.g., forest/woodland or grass/shrubland habitat remnants). </p> <p>The aim of the study was to test whether the size of a habitat patch influences the mean incidences of species within it, relative to the incidence of all species across the landscape. In other words, whether high-incidence (widespread) or low-incidence (narrow-range) species are found more often than expected in smaller or larger patches. To achieve this, a new standardized effect size metric was developed that quantifies the mean observed incidence of all species present in every patch (the geometric mean of the number of patches in which all species were observed) and compares this with an expectation based on re-sampling the incidences of all species in all patches. Meta-regression of the 202 datasets was used to test the relationship between this metric, the 'mean species landscape-scale incidences per patch' (MSLIP), and the size of habitat patches, and for differences in response among metacommunity types and taxonomic groups. </p>
Figure 4. Results from the phylogenetic analysis using discrete data only. A in Exploring phylogenetic relationships of Pteraspidiformes heterostracans (stem-gnathostomes) using continuous and discrete characters
Figure 4. Results from the phylogenetic analysis using discrete data only. A, strict consensus of 275 most parsimonious trees with equal character weights; length 276 steps, consistency index (CI) = 0.35, retention index (RI) = 0.59, and rescaled consistency index (RC) = 0.22. B, strict consensus of four most parsimonious trees with implied character weighting (k = 3) (tree length 23.11). Psammosteidae taxa in bold.
Spatial behavior and diet data for discrete-choice analyses: data observed and classified from GPS video camera collars worn by female members of the Fortymile Caribou Herd across Alaska, USA, and Yukon, Canada
<p>Competition for resources and space can drive forage selection of large herbivores from the bite through the landscape scale. Animal behavior and foraging patterns are also influenced by abiotic and biotic factors. Fine-scale mechanisms of density-dependent foraging at the bite scale are likely consistent with density-dependent behavioral patterns observed at broader scales, but few studies have directly tested this assertion. Here, we tested if space use intensity, a proxy of spatiotemporal density, affects foraging mechanisms at fine spatial scales similarly to density-dependent effects observed at broader scales in caribou. We specifically assessed how behavioral choices are affected by space use intensity and environmental processes using behavioral state and forage selection data from caribou (<i>Rangifer tarandus granti</i>) observed from GPS video-camera collars using a multivariate discrete-choice modeling framework. We found that the probability of eating shrubs increased with increasing caribou space use intensity and cover of <i>Salix</i> spp. shrubs, whereas the probability of eating lichen decreased. Insects also affected fine-scale foraging behavior by reducing the overall probability of eating. Strong eastward winds mitigated the negative effects of insects and resulted in higher probabilities of eating lichen. Lastly, caribou exhibited foraging functional responses wherein their probability of selecting each food type increased as the availability (% cover) of that food increased. Space use intensity signals of fine-scale foraging were consistent with density-dependent responses observed at larger scales and with recent evidence suggesting declining reproductive rates in the same caribou population. Our results highlight the potential risks of overgrazing on sensitive forage species such as lichen. Remote investigation of the functional responses of foraging behaviors provides exciting future applications where spatial models can identify high-quality habitats for conservation.</p>
Data from: Disruptive selection and the evolution of discrete color morphs in Timema stick insects
<p>A major unresolved issue in biology is why phenotypic and genetic variation is sometimes continuous, yet other times packaged into discrete units of diversity, such as morphs, ecotypes, and species. In theory, ecological discontinuities can impose strong disruptive selection that promotes the evolution of discrete forms, but direct tests of this hypothesis are lacking. Here we show that <span><em>Timema</em> </span>stick insects exhibit genetically-determined color morphs that range from weakly to strongly discontinuous. Color data from nature and a manipulative field experiment demonstrate that greater morph differentiation is associated with shifts from host plants exhibiting more continuous color variation to those exhibiting greater coloration distance between green leaves and brown stems, the latter of which generates strong disruptive selection. Our results show how ecological factors can promote discrete variation, and we further present results on how this can have variable effects on the genetic differentiation that promotes speciation.</p>
Discretized U.S. drought data to support statistical modeling
<p>Drought is a costly and disruptive natural disaster, with widespread implications for agriculture, wildfire, and urban planning. We present a novel data set on US drought built to enable computationally efficient spatio-temporal statistical and probabilistic models of drought. We converted drought data obtained from the widely-used US Drought Monitor (USDM) from continuous shape files to a 0.5-degree regular lattice. These data cover the Continental US from 2000 to mid-2022. Known environmental drivers of drought include those obtained from the North American Land Data Assimilation System (NLDAS-2), US Geological Survey (USGS) streamflow data, and National Oceanic and Atmospheric Administration (NOAA) teleconnections data. The USGS streamflow data is itself a new gridded data product, aggregating point-referenced stream discharges from across the US to a common lattice using watersheds to combine nearby stream data. The resulting data set permits statistical and probabilistic modeling of drought with explicit spatial and/or temporal dependence. Such models could be used to forecast short-range and even season-to-season future droughts with uncertainty, extending the reach and value of the current US Drought Outlook produced by the National Weather Service Climate Prediction Center. </p>
Data from: How to use discrete choice experiments to capture stakeholder preferences in social work research
<p>The primary article (cited below under "Related works") introduces social work researchers to discrete choice experiments (DCEs) for studying stakeholder preferences. The article includes an online supplement with a worked example demonstrating DCE design and analysis with realistic simulated data. The worked example focuses on caregivers' priorities in choosing treatment for children with attention deficit hyperactivity disorder. This dataset includes the scripts (and, in some cases, Excel files) that we used to identify appropriate experimental designs, simulate population and sample data, estimate sample size requirements for the multinomial logit (MNL, also known as conditional logit) and random parameter logit (RPL) models, estimate parameters using the MNL and RPL models, and analyze attribute importance, willingness to pay, and predicted uptake. It also includes the associated data files (experimental designs, data generation parameters, simulated population data and parameters, simulated choice data, MNL and RPL results, RPL sample size simulation results, and willingness-to-pay results) and images. The data could easily be analyzed using other software, and the code could easily be adapted to analyze other data. Because this dataset contains only simulated data, we are not aware of any legal or ethical considerations.</p>
Data from: When discrete characters are wanting: Continuous character integration under the phylospecies concept informs the revision of the Australian land snail <em>Thersites</em> (Eupulmonata, Camaenidae)
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Allen Brain Atlas
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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.