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1,154 results for “Pooling”
Physical soil characteristics, microbial community composition, extracellular enzymatic activity, biologically based phosphorus (BBP) pools, and available phosphorus from two soil depths, four microhabitats, and four landforms at the Jornada Experimental Range, 2021.
This dataset contains physical soil characteristics, PLFA based microbial community composition, extracellular enzymatic activity, nitrate and ammonium activity, and phosphorus availability in various phosphorus pools (Biologically Based Phosphorus, potassium sulfate, Olsen-P). Soils were collected from two depths (0-2cm, 2-30 cm), four microhabitats (grass, shrub, biocrust, interspace), and four landforms (alluvial flat, alluvial fan remnant, erosional scarplet, fan piedmont – see coordinates) within the Jornada Experimental Range in July 2021 to answer questions about how these variables change across these spatial scales in drylands. This project was a collaboration between researchers at New Mexico State University and The University of Texas at El Paso as part of the Drylands Critical Zone Thematic Cluster within the Critical Zone Network. This dataset is complete.
Inorganic Nitrogen Pools and Tree Composition in Hemlock Removal Experiment at Harvard Forest 2007-2009
This study assessed how the change in leaf litter input and environmental conditions, from that of hemlock stands to earlier successional deciduous stands, will affect inorganic nitrogen availability at the interface of the mineral soil and LFH horizons. In early October 2007 we exchanged 0.5 x 0.5 m "loaves" of LFH horizons from hemlock and deciduous stands between forest types, while keeping control plots in the original forest stands. Inorganic nitrogen outputs from the litter loaves were monitored for 20 months using ion-exchange resins, removing and replacing the resins three times at 5 - 8 month intervals. Results indicated that there were differences between the control hemlock and deciduous loaves - NO3 and NH4 availability were higher in deciduous stands. However, the transfer of litter loaves from one stand type to another did not reveal changes in nutrient availability due to a shift in habitat. Rather, the methodology of moving litter loaves to a new site caused greater changes in nutrient availability, particularly over the summer months.
Reservoir ecosystems support large pools of fish biomass
<p>Supplemental data for "Reservoir ecosystems support large pools of fish biomass".</p> <p>Parisek, C.A., F.A., De Castro, J.D. Colby, G.R. Leidy, S. Sadro, A.L. Rypel. Reservoir ecosystems support large pools of fish biomass. <em>Sci Rep</em> <strong>14</strong>, 9428 (2024). https://doi.org/10.1038/s41598-024-59730-z</p>
Seefeld Cold-Air Pool Experiment (SEECAP): WRF Simulation Output without snow cover January 16 2020 0000 UTC to January 17 2020 1200 UTC
<p>The Seefeld Cold-Air Pool Experiment (SEECAP) focused on the cross-country skiing area Olympiaregion Seefeld and in particular the topographic setting in the Nordic ski arena which favors the formation of cold-air pools and took place between December 2019 and March 2020. The measurement data are described in Rudolph (2022) and Rauchöcker et al. (2024d) and meteorological measurement data associated with SEECAP are published in Rauchöcker et al. (2024c). This upload contains WRF simulation output data for the night between January 16 and January 17 2020 without snow cover and the plotting routines to reproduce the figures in Rauchöcker et al. (2024d). The night between January 16 and January 17 2020 initially featured an ideal cold-air pool formation followed by a interuption by a wind disturbance around midnight. Simulation output for the same night, but with snow cover is also available (Rauchöcker et al., 2024a). The temperature evolution of the measurements agreed much better with the simulation with snow cover and otherwise the same model setting compared to the simulation without snow cover (Rauchöcker et al. 2024d). Also available in a different dataset is output from a simulation with snow cover for the night between January 12 and January 13 2020 (Rauchöcker et al., 2024b), which featured an undisturbed cold-air pool for almost the entire night. This case was considered to feature in Rauchöcker et al. (2024d), but a different case was chosen because some measurement data was not available during this period.</p> <h3><strong>WRF Simulation Output</strong></h3> <p>This Dataset includes data generated with WRFlux v1.4.1 (Göbel et al., 2022), a fork of the Weather Research and Forecasting model WRF (Skamarock et al. 2021). WRFlux allows to calculate the contribution of different processes to the potential temperature tendency at each grid point. The data published here is from the innermost simulation domain with 40m horizontal resolution and 10m vertical resolution close to the surface. The simulations were initialized at 00:00 UTC January 16 2020 and run until 12:00 UTC January 17 2020.</p> <p>Three different simulations were performed: two simulations with modified snow cover as described in Rauchöcker (2022), one each with the MYNN 2.5-order and the SMS-3DTKE PBL parameterizations (a scheme that blends a PBL scheme and a LES subgrid parameteriztion in the greyzone of turbulence), and one without snow cover with the MYNN 2.5-order PBL parameterization. Otherwise the simulations were identical. This dataset includes the simulation without snow cover. A detailed description of the model setup can be found in Rauchöcker et al (2024d) and in the file <em>namelist.input</em> that was used to generate the simulation results.</p> <p>Standard WRF output can be found in <em>wrfout_40m_jan16_nosnow</em>. The mean wind speed components, which were necessary to rotate the tendencies in a coordinate system that is aligned with the valley orientation, are contained in <em>windout_40m_jan16_nosnow</em>. These variables were contained in the unprocessed<em> </em>output files produced by WRFlux; the full files were unfortunately too large to be included here. The postprocessed tendencies are stored in <em>tend_40m_jan16_nosnow.nc</em>.</p>
Minnesota Department of Natural Resources Yellow Perch and Bluegill Diet Study, Lower Pool 4 Mississippi River, 2018-2019
To assess potential dietary overlap and predation between bluegill and yellow perch, we examined stomach content of both species in three backwater contiguous lakes of Lower Pool 4 in the Mississippi River from May 2018 through January 2019. In this area, bluegill have been common for decades, but yellow perch only became abundant following an ecological shift to a clear-water, macrophyte dominated state that occurred from 2007 - 2007. We used daytime electrofishing to collect fish during open-water sampling (spring, summer, and fall) and ice angling during ice-covered sampling (winter). The dataset documents the diet content of one hundred and eighty-nine yellow perch and sixty-one bluegill. Stomach contents were extracted via gastric lavage in the spring, summer, and fall and via stomach removal in the winter. All prey items were categorized to lowest identifiable taxonomic level and quantified via volumetric displacement.
Soil respiration rates, biogeochemical pools, and mineral-associated organic matter from high organic matter and high mineral content coastal wetland soils in Apalachicola, Florida, 2022
This data set was used to observe how the application of dredged sediment would impact soil respirations rates, biogeochemical pools, mineral associated organic matter of coastal wetland soils from Apalachicola, Florida. To achieve this, a combination of intact core and bottle incubations were used, comparing a high organic matter coastal wetland soil to a high mineral content wetland soil which were collected in June, 2022. All laboratory analysis was conducted at the University of Central Florida in Orlando, Florida.
Southern African Power Pool GridPath Model Output Data - Chowdhury et al 2022 Joule
<p>This data repository holds GridPath model output data for the paper Chowdhury, A.K., Deshmukh, R., Wu, G., Uppal, A., Mileva, A., Curry, T., Armstrong, L., Galelli, S., and Kudakwashe, N. (2022) “Enabling a low-carbon electricity system for Southern Africa”, Joule. See Readme for more details. </p>
Variant Data from Pooled Sequencing of Hybrid Kiwifruit
<p>Variant data from pooled sequencing of hybrid <em>Actinidia</em> families segregating for fruit size and Vitamin C Content.</p>
Dietary fibers boost gut microbiome-produced B vitamin pool and alter host immune landscape
<p>This dataset contains fcs files of lymphocytes from the colonic lamina propria, lungs, and spleens of specific-pathogen-free (SPF), gnotobiotic (14-member synthetic microbiota, 14SM) or germ-free (GF) mice fed five distinct rodent diets (Standard chow 1, SC1; Standard chow 2, SC2; Fiber-supplemented diet, FS; Inulin-supplemented diet, IN; or Fiber-free diet, FF), analysed by mass cytometry. Three million cells per organ per animal were transferred into 15 mL conical tubes. For live/dead staining, cells were incubated with 5 μM cisplatin for 5 minutes. Cells were washed, and cell surface staining mix was added containing pre-conjugated antibodies for 30 minutes at room temperature. Samples were washed twice with FACS buffer, then fixed using the FoxP3 Fix/Perm kit (eBiosciences) for 45 minutes at 4°C, followed by permeabilization wash. Samples were then incubated with the intracellular staining mix for 30 minutes at room temperature. Cells were washed with FACS buffer twice, and pellets were resuspended in Cell-ID™ Intercalator-Ir (Fluidigm) in MaxPar fixation solution (Fluidigm, catalogue no. 201192B) and refrigerated overnight, or for up to five days. Prior to acquisition, samples were washed twice with 1X PBS, and then washed twice with deionized water. Cell pellets were further resuspended in deionized water at 0.5 × 10^6 cells/mL and topped up with 10% calibration beads (EQ Four Element Calibration Beads, Fluidigm). All samples were acquired on the Helios Mass Cytometer (Fluidigm). Effector immune populations and activated T cells in the gut accumulate in a microbiota-dependent manner. Shifts in the microbiome according to dietary fiber source and content result in altered concentrations of B vitamins available to the host, which is tied to distinct alterations in innate and adaptive immune populations. </p>
Seefeld Cold-Air Pool Experiment (SEECAP): WRF Simulation Output with snow cover January 12 2020 0000 UTC to January 13 2020 1200 UTC
<p>The Seefeld Cold-Air Pool Experiment (SEECAP) focused on the cross-country skiing area Olympiaregion Seefeld and in particular the topographic setting in the Nordic ski arena which favors the formation of cold-air pools and took place between December 2019 and March 2020. The measurement data are described in Rudolph (2022) and Rauchöcker et al. (2024d) and meteorological measurement data associated with SEECAP are published in Rauchöcker et al. (2024c). This upload contains WRF simulation output data for the night between January 12 and January 13 2020 with snow cover. The night between January 12 and January 13 2020 featured an undisturbed cold-air pool for almost the entire night. This case was considered to feature in Rauchöcker et al. (2024d), but a different case was chosen because some measurement data was not available during this period. Also available in a different dataset are data from simulations of the night between January 16 and January 17 2020, which initially featured ideal condition for cold-air pool formation followed by a disturbance around midnight, with snow cover (Rauchöcker et al., 2024a) and also without snow cover (Rauchöcker et al., 2024b).</p> <h3><strong>WRF Simulation Output</strong></h3> <h3><strong> </strong></h3> <p>This Dataset includes data generated with WRFlux v1.4.1 (Göbel et al., 2022), a fork of the Weather Research and Forecasting model WRF (Skamarock et al. 2021). WRFlux allows to calculate the contribution of different processes to the potential temperature tendency at each grid point. The data published here is from the innermost simulation domain with 40m horizontal resolution and 10m vertical resolution close to the surface. The simulations were initialized at 00:00 UTC January 12 2020 and run until 12:00 UTC January 13 2020 and results for the same night but a coarser grid spacing are described in Rauchöcker (2022). Compared to the simulation with 200m grid spacing presented there, this simulation offers a significantly improved resolution. As input, we used ERA5 reanalysis data, 1-arc second SRTM terrain data and Corine 2018 land cover classification. The simulation was performed with modified snow cover as described in Rauchöcker (2022) and the MYNN 2.5-order PBL parameterization. A detailed description of the model setup can be found in Rauchöcker et al (2024d) and in the file <em>namelist.input</em> that was used to generate the simulation results.</p> <p>Standard WRF output can be found in <em>wrfout_40m_jan12</em>. The mean wind speed components, which were necessary to rotate the tendencies in a coordinate system that is aligned with the valley orientation, are contained in <em>windout_40m_jan12</em>. These variables were contained in the unprocessed output files produced by WRFlux; the full files were unfortunately too large to be included here. The postprocessed tendencies are stored in <em>tend_40m_jan12</em>.</p>
Indicative distribution map for Ecosystem Functional Group F2.9 Geothermal pools and wetlands
<p>This archive contains indicative distribution maps and profiles for <strong>F2.9 Geothermal pools and wetlands</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Indicative distribution map for Ecosystem Functional Group SF1.1 Underground streams and pools
<p>This archive contains indicative distribution maps and profiles for <strong>SF1.1 Underground streams and pools</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Indicative distribution map for Ecosystem Functional Group SM1.2 Anchialine pools
<p>This archive contains indicative distribution maps and profiles for <strong>SM1.2 Anchialine pools</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Gross methane production and consumption estimated for intact soil cores from agricultural plots including environmental covariates and example raw isotope pool dilution data
This study was performed to determine how different soil moistures, soil sources, and agricultural practices affected the gross CH4 fluxes (i.e., rates of methanogenesis) of soils. We extracted intact soil cores from two agricultural sites in the USA in row crop plots under conventional, no-till, and organic management. We then took them to the lab, manipulated their moisture levels, incubated them at room temperature for 22 weeks, and measured gas fluxes at weeks 6 and 21. We developed and utilized a new form of CH4 isotope pool dilution (IPD) to estimate gross CH4 production and consumption fluxes. This new method can measure IPD in a bag headspace that loses volume over time due to sampling. We fit the IPD model to the data and extracted gross CH4 production (P) and consumption (K) constants. These along with calculated fluxes and covariates measured (e.g., moisture, inorganic N) are reported in the main data table.
Baltimore Ecosystem Study: Increased diversity of the regional species pool via seeding augments establishment of native species in experimental vacant lot restorations
The harsh geophysical template characterized by the urban environment combined with people’s choices has led ecologists to invoke environmental filtering as the main ecological phenomena explaining urban biodiversity patterns. Yet, dispersal is often overlooked as a driving factor, especially on expanding vacant land. Does overcoming dispersal limitation by seeding native species in urban environments and increasing the functional or phylogenetic diversity of the seeding pool increase native plant species diversity and abundance in urban vacant land? We took an experimental approach to learn how different dimensions of plant biodiversity within an augmented regional species pool, via seed additions, can explain variation in community structure over a 3-year period. Vacant lots were cleared and manipulated with seeding treatments of high or low phylogenetic and functional diversities from a pool of 28 native species. Establishment success, total native cover and native species richness were followed and compared to cleared, unseeded control lots as well as un-manipulated lots. Seeding increased native plant abundance and richness over uncleared plots, as well as cleared and unseeded control plots. Phylogenetically diverse seed mixtures had greater establishment success than mixtures composed of closely related species. Diversifying seed mixtures increased the likelihood of including species that are better able to establish on vacant land. However, there were no differences in varying levels of either functional or phylogenetic diversity. Augmenting the regional species pool via diverse seed mixtures can enhance native plant cover and richness under the harsh environmental conditions conferred by land abandonment.
Shrimp populations variability in numbers and sizes in response to disturbance and seasons on 20 pools along the reach of Quebrada Prieta, Luquillo Experimental Forest
Shrimp populations were monitored at approximately 3 week intervals to determine the variability in numbers and sizes of each species in response to disturbance and seasons. 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.
StreamFRE: Pool area and depth in Prieta Streams
Measurements of pool depth in Prieta Stream, as part of the Stream Flow Reduction Experiment (StreamFRE). StreamFRE started in October 2016 with the goal of assessing the effects of droughts on stream ecosystems. The project is designed to assess the effect of flow reduction, the main effect of drought on streams. We collected pre-manipulation data from October 2016 to September 2017, when Hurricanes Irma and Maria hit Puerto Rico. Due to hurricane disturbances, the project became a hurricane impact assessment from September 2017 to early 2022, when the flow reduction is scheduled to start. Each month we measured pool depth by taking 5 random measurements through each of the pools. Values are recorded in cm and reported as average and variability in pool depth. Pool depths measures are collected the same day Decapod are sampled and pictures are taken to determine the pool surface area. Pool depth: Each month we measured pool depth by taking 5 random measurements through each of the pools. Values are recorded in cm and reported as average and variability in pool depth. Pool depths measures are collected the same day Decapod are sampled and pictures are taken to determine the pool surface area. 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.
Shrimp populations in Quebrada Prieta (Pools 0, 8, 9, 15) (El Verde)
Freshwater shrimp from the Quebrada Prieta (a tributary to the Sonadora in the Espiritu Santu drainage, have been censused 6 times yearly since 1988. Atya lanipes, Xiphocaris elongata and Macrobrachium spp. are regularly captured and comprise the species in this dataset. 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.
Spatial and temporal differences in shrimp numbers (1 year, 20 pools)
We added woody debris to stream pools in three streams in an experiment designed to increase cover for freshwater shrimp. We trapped four species of freshwater shrimp during 4 months following wood additions. Stream pool morphology was estimated using maximum depth, surface area, and volume. 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.
Fiber-optic Distributed Temperature Sensing and Wind Profiler Data during the Shallow Cold Pool Experiment
<p>The <a href="https://www.eol.ucar.edu/field_projects/scp">Shallow Cold Pool (SCP) experiment</a> was an <a href="https://www.eol.ucar.edu/observing_facilities/isfs">Integrated Surface Flux System (ISFS)</a> deployment conducted by the <a href="https://ncar.ucar.edu/">National Center for Atmospheric Research (NCAR)</a>, the <a href="https://ceoas.oregonstate.edu/">College of Earth, Ocean and Atmospheres (CEOAS)</a>, the <a href="https://bee.oregonstate.edu/">Department of Biological & Ecological Engineering (BEE)</a>, and the <a href="https://ctemps.org/">Center for Transformative Environmental Monitoring Programs (CTEMPS)</a> of <a href="https://oregonstate.edu/">Oregon State University</a>, in a shallow gully within the Pawnee Grasslands, Coloradp, USA. The primary goal of SCP was to examine the formation and maintenance of common shallow cold pools. These cold pools had not been previously examined with turbulence measurements and very little was known about their dynamics and interaction with gravity waves and other submesoscale motions.</p> <p>SCP consisted of a dense network of ultrasonic anemometers with 19 units being installed at 1m above ground level (agl) and 8 being mounted at different heights on a 20m high tower. In addition, air temperature, humidity, and carbon dioxide concentrations measurements were taken. This data can be found on <a href="https://data.eol.ucar.edu/project/SCP">https://data.eol.ucar.edu/project/SCP</a>.</p> <p>The unique observational technique featured in SCP was a cross-valley transect of the innovative active and passive fiber-optic distributed sensing technique (FODS) using a Distributed Temperature Sensing (DTS) unit (Model Ultima SR, Silixa, London, UK) as well as a ground-based acoustic wind profiler (SODAR, PCS2000-24, Metek GmbH, Elmshorn, Germany) in addition to the classical sonic anemometer network. The data archived in this submission publishes the FODS data and contains data for nine (9) nights between 16th November until 27th November between the hours of 19:00 and 05:00 MST (Local time). Details of the FODS setup are contained in <a href="https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3508?af=R">Pfister et al. (2019)</a> and <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2015GL066729">Sayde et al. (2015</a>).<br> The fiber-optic cross-valley transect was 240m long and stretched from the North to the South shoulder of the gully and contained FODS observations at three heights (0.5m, 1m, 2m agl). By combining passive and active FODS, air temperatures and wind speeds were measured spatially continuously with a temporal and spatial resolution of 5s and 25cm, respectively. Air temperatures were measured with an unheated white-PVC jacketed optical glass fiber cable with an outer diameter of 0.9mm, while for the wind speed measurements an additional actively heated stainless-steel uncoated fiber-optic cable (1.3mm outer diameter) was deployed. Wind speeds were derived from the difference between the heated and unheated fiber-optic pair similar to a hotwire anemometer (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2015GL066729">Sayde et al. 2015</a>).<br> The acoustic wind profiler (Sound Detection and Ranging, SODAR) was installed at the gully bottom about 200m down the gully from the fiber-optic transect (between station A18 and A19) and measured with a 5-min resolution, a 10-m gate range, and 17000 Hz, see map in <a href="https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3508?af=R">Pfister et al. (2019)</a>. The observational range was between 10m to 320m agl. The data provided is the cluster data output of the wind profiler, which is quality-controlled by the internal data processing software. The published data include horizontal wind speed (speed), wind direction (direction), unrotated along-wind component (u_unrot), unrotated cross-wind component (v_unrot), and unrotated vertical-wind component (w_unrot).</p> <p>By combining the fiber-optic distributed sensing, the sensor network, and the wind profiler, we were able to investigate specific class of submeso-scale motions in detail. The submeso-scale motion occurred frequently during SCP, significantly impacted air temperature, wind speed and direction, as well as the near-surface turbulence within less than a few minutes. These motions are not described or categorized by existing boundary layer regimes or concepts. Consequently, further research on submeso-scale motions using continuous FODS measurements is necessary to better understand the stable boundary layer.</p> <p> </p> <p>Pfister, L., Sayde, C., Selker, J., Mahrt, L., & Thomas, C. K. (2019). Classifying the Nocturnal Atmospheric Boundary Layer into Temperature and Flow Regimes. <em>Quart. J. Roy. Meteorol. Soc.</em>, <em>145</em>(721), 1515–1534. <a href="https://doi.org/10.1002/qj.3508">https://doi.org/10.1002/qj.3508</a></p> <p> </p> <p>Sayde, C., Thomas, C. K., Wagner, J., & Selker, J. S. (2015). High-resolution wind speed measurements using actively heated fiber optics. <em>Geophys. Res. Lett.</em>, <em>42</em>(22), 10,064–10,073. <a href="https://doi.org/10.1002/2015GL066729">https://doi.org/10.1002/2015GL066729</a></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.