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156 results for “long-term dataset”
Data on the association between garlic mustard and the plant community from a long-term observational dataset in Illinois, U.S.A.
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A Long-Term Micrometeorological and Hydrological Dataset Across an Elevation Gradient in Sagehen Creek, Sierra Nevada, California
We compile and release ~55 years of daily and ~20 years of hourly Micrometeorological and hydrological data from Sagehen Creek a 28 km???^2??? watershed with observation sites spanning 1771 to 2670 m. A USGS gauging station measures streamflow at the catchment outlet. There are three Snow Telemetry (SNOTEL) stations: Independence Camp(2128 m), Independence Creek (1962 m) and Independence Lake (2541 m) that measure hourly precipitation, temperature, soil moisture (at 5, 20, and 50 cm), as well as daily snow water equivalent (SWE) and snow depth. A new method was used to estimate hourly precipitation data using quality controlled daily totals. A NOAA cooperative observer (COOP) station measures daily precipitation, temperature, SWE, and snow depth from 1953-1997 and then measures hourly precipitation, temperature, SWE, and snow depth, relative humidity, and solar radiation from 2001 through 2017 2001-present. There are an additional three towers with data beginning in 2009 measuring snow depth, SWE, solar radiation, barometric pressure, precipitation, relative humidity, and temperature: Tower 1 (1934m), Tower 3 (2114 m), and Tower 4 (2350 m). Wind speed, temperature, and relative humidity measured at 7.6 and 30.5 m at each site. Data from all stations were checked for poor QA/QC and substantial and sophisticated gap-filling techniques were deployed. This dataset holds potential for improving understanding of orographic processes and their implications for streamflow generation in a groundwater-dominated watershed. This data package mirrors the one available at https://doi.org/10.5281/zenodo.2590799. Please check this link for updates and additional information.
Long-Term Wi-Fi fingerprinting dataset and supporting material
<p>WiFi measurements database for UJI's library and supporting material.</p> <p>The measurements were collected by one person using mainly one Android smartphone during 25 months at two floor of the library building from Universitat Jaume I, in Spain. It contains 103,584 WiFi fingerprints, which are organized into datasets. Each dataset is the result of a collection campaign.</p> <p>The supporting material includes Matlab® scripts to load and filter the desired data, and provides examples on possible studies that the database may enable. The supporting material also includes the bookshelves local coordinates.</p> <p>Citation request:</p> <p>Mendoza-Silva, G.M.; Richter, P.; Torres-Sospedra, J.; Lohan, E.S.; Huerta, J. Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning. <em>Data</em> <strong>2018</strong>, <em>3</em>, 3.</p> <p>G.M. Mendoza-Silva, P. Richter, J. Torres-Sospedra, E.S. Lohan, J. Huerta, "Long-Term Wi-Fi fingerprinting dataset and supporting material", Zenodo repository, DOI 10.5281/zenodo.1066041.</p> <p> </p>
AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) anchoring the ERA5-Land under the total volume control by APHRODITE (1999–2015)
<p>AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) is developed by organically combining the ERA5-Land dataset with high spatiotemporal resolutions and continuity and the APHRODITE dataset with high quality.</p> <p><strong>How to cite: Ma, Z., Xu, J., Ma, Y., Zhu, S., He, K., Zhang, S., Ma, W., Xu, X., 2022. AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) anchoring the ERA5-Land under the total volume control by APHRODITE. Bulletin of American Meteorological Society, 103 (4)., DOI: https://doi.org/10.1175/BAMS-D-20-0328.1.</strong></p> <p>Data Format: GeoTIFF</p> <p>Spatial Coverage: 60°E–150°E, 15°S–55°N, land.</p> <p>AERA5-Asia (0.1°/ hourly, 1951–1966, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.4266081">10.5281/zenodo.6367463</a></p> <p>AERA5-Asia (0.1°/ hourly, 1962–1981, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.4266081">10.5281/zenodo.6369796</a></p> <p>AERA5-Asia (0.1°/ hourly, 1982–1998, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.4266081">10.5281/zenodo.4266081</a></p>
A dataset of plant and microbial community structure after long-term grazing and mowing in a semiarid steppe
<p>Grazing and mowing are two dominant management regimes used in grasslands. Although many studies have focused on the effects of grazing intensity on plant community structure, far fewer test how grazing impacts the soil microbial community. Furthermore, the effects of long-term grazing and mowing on plant and microbial community structure are poorly understood. To elucidate how these management regimes affect plant and microbial communities, we collected data from 280 quadrats in a semiarid steppe after 12-year of grazing and mowing treatments. We measured plant species abundance, height, coverage, plant species diversity, microbial biomass, and microbial community composition (G+ and G- bacteria; arbuscular mycorrhizal and saprotrophic fungi; G+/G- and Fungi/Bacteria). In addition, we determined the soil's physical and chemical properties, including soil hardness, moisture, pH, organic carbon, total nitrogen, and total phosphorus. This is a long-term and multifactorial dataset with plant, soil, and microbial attributes which can be used to answer questions regarding the mechanisms of sustainable grassland management in terms of plant and microbial community structure.</p>
Datasets used in "Stomatal conductances influences interannual variability and long-term changes in regional cumulative plant uptake of ozone"
Model archived fields for surface ozone, effective stomatal conductance, and leaf area index corresponding to Clifton, O. E., Lombardozzi, D. L., Fiore, A. M., Paulot, F., & Horowitz, L. W., (2020). Stomatal conductance influences interannual variability and long-term changes in regional cumulative plant uptake, Environmental Research Letters.
Dataset from the paper "Viruses from geothermal springs have ancient origins reflecting long-term interactions with extremophilic red algal mats"
<p>Dataset of viral operational taxonomic units (vOTUs) and viral metagenome-assembled genomes (vMAGs) from the Lemonade Creek, Yellowstone National Park (YNP), USA, from the paper "Viruses from geothermal springs have ancient origins reflecting long-term interactions with extremophilic red algal mats".</p><p>L. Felipe Benites1*, Timothy G. Stephens1, Julia Van Etten1,2, Timeeka James1, William C. Christian4, Kerrie Barry5, Igor V. Grigoriev5,6, Timothy R. McDermott3 and Debashish Bhattacharya1</p><p>1Department of Biochemistry and Microbiology, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, United States of America</p><p>2Graduate Program in Ecology and Evolution, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901 United States of America</p><p>3Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, Montana, United States of America</p><p>4Department of Chemistry and Biochemistry, Montana State University, Bozeman, Montana, United States of America</p><p>5U.S. Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, United States of America</p><p>6Department of Plant and Microbial Biology, University of California Berkeley, Berkeley, CA 94720, United States of America</p>
Dataset: The role of long-term hydrodynamic evolution in the accumulation and preservation of organic carbon-rich deposits in the shelf seas
<p>Output files from harmonic analysis of regional tidal model, glacial isostatic adjustment model input and output (relative sea level and ice sheet extent datasets), and scripts for figure generation.</p> <p>If using these data, please cite: </p> <p><strong>Ward, S.L., Bradley, S.L., Roseby, Z.A., Wilmes, S.B., Vosper, D.F., Roberts, C.M. and Scourse, J.D., 2025. The role of long‐term hydrodynamic evolution in the accumulation and preservation of organic carbon‐rich shelf sea deposits. <em>Journal of Geophysical Research: Oceans</em>, <em>130</em>(4), p.e2024JC022092. <a href="https://doi.org/10.1029/2024JC022092">https://doi.org/10.1029/2024JC022092</a></strong></p> <p> </p>
AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) anchoring the ERA5-Land under the total volume control by APHRODITE (1967–1981)
<p>AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) is developed by organically combining the ERA5-Land dataset with high spatiotemporal resolutions and continuity and the APHRODITE dataset with high quality.</p> <p><strong>How to cite: Ma, Z., Xu, J., Ma, Y., Zhu, S., He, K., Zhang, S., Ma, W., Xu, X., 2022. AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) anchoring the ERA5-Land under the total volume control by APHRODITE. Bulletin of American Meteorological Society, 103 (4)., DOI: https://doi.org/10.1175/BAMS-D-20-0328.1.</strong></p> <p>Data Format: GeoTIFF</p> <p>Spatial Coverage: 60°E–150°E, 15°S–55°N, land.</p> <p>AERA5-Asia (0.1°/ hourly, 1951–1966, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.4266081">10.5281/zenodo.6367463</a></p> <p>AERA5-Asia (0.1°/ hourly, 1982–1998, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.4266081">10.5281/zenodo.4266081</a></p> <p>AERA5-Asia (0.1°/ hourly, 1999–2015, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.4264451">10.5281/zenodo.4264451</a></p>
AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) anchoring the ERA5-Land under the total volume control by APHRODITE (1951–1966)
<p>AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) is developed by organically combining the ERA5-Land dataset with high spatiotemporal resolutions and continuity and the APHRODITE dataset with high quality.</p> <p><strong>How to cite: Ma, Z., Xu, J., Ma, Y., Zhu, S., He, K., Zhang, S., Ma, W., Xu, X., 2022. AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) anchoring the ERA5-Land under the total volume control by APHRODITE. Bulletin of American Meteorological Society, 103 (4)., DOI: https://doi.org/10.1175/BAMS-D-20-0328.1.</strong></p> <p>Data Format: GeoTIFF</p> <p>Spatial Coverage: 60°E–150°E, 15°S–55°N, land.</p> <p>AERA5-Asia (0.1°/ hourly, 1962–1981, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.4266081">10.5281/zenodo.6369796</a></p> <p>AERA5-Asia (0.1°/ hourly, 1982–1998, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.4266081">10.5281/zenodo.4266081</a></p> <p>AERA5-Asia (0.1°/ hourly, 1999–2015, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.4264451">10.5281/zenodo.4264451</a></p>
A Long-Term Land Surface Hydrologic Fluxes and States Dataset for China
<p>** VIC_forcings_4vars: 1/4 Degree Gridded Daily Meteorological VIC Forcing Data Set over China domain </p> <p>1. Data sources</p> <p>This dataset is from 1/1/1952 to 12/31/2012, which were derived by interpolating gauged daily precipitation, maximum temperature, minimum temperature and wind speed of 756 ground mornitoring stations from Chinese Meteorological Administration (CMA). </p> <p>** This section provides only a very brief description of the data source. For a full explanation, the user need refer to the published papers in the references ** </p> <p>2. References to Cite</p> <p>We request that users of this data set cite Zhang et al.(2014) in any reports or publications using it. </p> <p>Zhang, X., Tang, Q., Pan, M., Tang, Y., 2014. A Long-Term Land Surface Hydrologic Fluxes and States Dataset for China. Journal of Hydrometeorology. (Accepted)</p> <p>3. Dataset format</p> <p>This data set is available in netCDF (which cannot be read directly by VIC). </p> <p>Please click the year number in the table below to download the corresponding daily forcings (i.e., precipitation, maximum temperature, minimum temperature and wind speed). </p> <p><br> ** VICoutput_fluxes: VIC Retrospective Land Surface Dadaset over China: 1952-2012</p> <p>1. Background</p> <p>The Variable Infiltration Capacity (VIC) model was driven using the gridded daily observed forcings (including precipitation, maximum temperature, minimum temperature, and wind speed; if necessary, please download the VIC forcings from http://hydro.igsnrr.ac.cn/public/vic_forcings_4vars.html) to simulate the land surface hydrological cycle from 1952-2012 over China. The modeling study was done at a 3-hourly time step and at a spatial resolution of 0.25 degree. Details can be found in the journal article:</p> <p>Zhang, X., Q. Tang, M. Pan, and Y. Tang, 2014: A Long-Term Land Surface Hydrologic Fluxes and States Dataset for China. Journal of Hydrometeorology. doi: 10.1175/JHM-D-13-0170.1</p> <p>2. Archived data information and format</p> <p>This website provides access to parts of the model derived variables(including the water balance variables and states and energy balance varibales) at daily scale. This dataset is available in netCDF format. Please click the varible names below to download the corresponding variable.</p> <p>3. Download the dataset</p> <p>Model Derived Variables, 1952-2012 (Water Balance Variables and States)</p> <p>Evaporation<br> Runoff<br> Baseflow<br> Soil Moisture Layer 1<br> Soil Moisture Layer 2<br> Soil Moisture Layer 3<br> Snow Water Equivalent</p> <p>Contact: tangqh@igsnrr.ac.cn</p>
Long-term (2003-2020) hourly 0.25° global PM2.5 dataset (DeepCAMS) Part-2: 2012-2020
<p>This is part II (2012-2020) of our DeepCAMS.</p> <p>Part I (2003-2011) can be found at: https://doi.org/10.5281/zenodo.6967082</p> <p>Usage: The raw data -- (scaling factor: 0.1) --> the true PM2.5 concentration</p> <p>Paper title: Generating a Long-term (2003-2020) hourly 0.25° global PM2.5 dataset via spatiotemporal downscaling of CAMS with deep learning (DeepCAMS)</p> <p>Paper doi: <a href="https://doi.org/10.1016/j.scitotenv.2022.157747">https://doi.org/10.1016/j.scitotenv.2022.157747</a></p> <pre>If you find our work helpful, please cite it. Thank you very much!</pre>
Dataset for the validation of the Delirium Observation Screening Scale in long-term care facilities in Flanders
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Experimental data from long-term elevated pCO2 exposure in Pacific herring. Datasets include measurements of enviromental treatment conditions, growth and developmental traits, and mortality data from a disease challenge.
<p>This study evaluated the effects of ocean acidification on the health, survival, fitness, and disease susceptibility of early life stage Pacific herring. Wild larvae were reared from hatching under three pCO2 treatments [low (~650 μatm), intermediate (~1,500 μatm), and high (~3,000 μatm)] to determine the effects of elevated pCO2 on larval growth, yolk consumption, and foraging capabilities and to evaluate how chronic exposure to elevated pCO2 affects long-term growth and maximum swimming speed. After prolonged exposure (98 d), we tested how elevated pCO2 experienced during development altered the susceptibility of juvenile Pacific herring to VHS following water-borne exposure to VHSV. This directory includes datasets for: 1) daily pH and temperature measurements from replicate rearing tanks; 2) carbon chemistry measurements from discrete seawater samples; 3) morphometric and feeding data for larval herring (1 - 16 days post hatch); 4) standard length measurements from larval and juvenile samples (1 - 98 days post hatch); 5) All dry mass measurements from larval and juvenile samples (1 - 98 days post hatch); 6) survival statistics for whole experiment ( 1- 98 days post hatch); 7) maximum swim speed measurements on juvenile herring; 8) disease challenge mortality and viral titer data; 8) a README file containing information on the dataset. </p>
Dataset_Performance of different wheat varieties and their associated microbiome under contrasting tillage and fertilization intensities: Insights from a Swiss Long-Term Field Experiment
<p>Data collected in the frame of the<span> SolACE (</span><span><a href="https://www.solaceeu.net/"><span>https://www.solaceeu.net/</span></a></span><span>) project which has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 727247 and the State Secretariat for Education, Research and Innovation SERI under no. 17.00094. Data table includes all data reported in the manuscript entitled "</span></p> <p><span>Performance of different wheat varieties and their associated microbiome under contrasting tillage and fertilization intensities: Insights from a Swiss Long-Term Field Experiment</span>"</p> <p>and all additional data which had been collected during the field sampling campaign on additional agronomic, soil and microbial data, which were not analysed as part of the above mentioned manuscript. </p>
Data from: A framework for the identification of long-term social avoidance in longitudinal datasets
Animal sociality is of significant interest to evolutionary and behavioural ecologists, with efforts focused on the patterns, causes and fitness outcomes of social preference. However, individual social patterns are the consequence of both attraction to (preference for) and avoidance of conspecifics. Despite this, social avoidance has received far less attention than social preference. Here, we detail the necessary steps to generate a spatially explicit, iterative null model which can be used to identify non-random social avoidance in longitudinal studies of social animals. We specifically identify and detail parameters which will influence the validity of the model. To test the usability of this model, we applied it to two longitudinal studies of social animals (Eastern water dragons (Intellegama leseurii) and bottlenose dolphins (Tursiops aduncus) to identify the presence of social avoidances. Using this model allowed us to identify the presence of social avoidances in both species. We hope that the framework presented here inspires interest in addressing this critical gap in our understanding of animal sociality, in turn allowing for a more holistic understanding of social interactions, relationships and structure.
Victorian Water and Climate dataset: long-term streamflow, climate, and vegetation observation records and catchment attributes
<p>This dataset contains streamflow, climate, and vegetation data for 155 minimally impaired catchments in Victoria, Australia. </p> <p>What's included:</p> <p>- Streamflow long-term observation records in daily, monthly, and hydroannual (based on March to February water year) resolution</p> <p>- Catchment climate characteristics in daily, monthly, and hydroannual resolution</p> <p>- Catchment vegetation characteristics in monthly resolution and actual evapotranspiration estimate in monthly and hydroannual resolution</p> <p>- Catchment boundaries</p> <p>- Catchment attributes (topographic, geological, soil, groundwater, vegetation, and human impacts characteristics)</p> <p>- Hydroclimatic characteristics for 3 different periods.</p> <p>Precipitation (p), streamflow (q), and PET (pet) data and their derivatives are in mm (i.e. normalised by catchment area). </p> <p>A paper containing a detailed description of the data including units, data sources, and processing details is submitted, please contact Margarita Saft for a private copy of the draft.</p>
(11)-Strobl2023A-DS0001--0010 – Ten Tribolium castaneum long-term live imaging datasets of embryonic development acquired with light sheet fluorescence microscopy
<p>(11)-Strobl2023A-DS0001--0010 – Ten <em>Tribolium castaneum</em> long-term live imaging datasets of embryonic development acquired with light sheet fluorescence microscopy</p>
Multiple long-term, landscape-scale datasets reveal intraspecific spatial variation in temporal trends for bird species
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Data from: A framework for the identification of long-term social avoidance in longitudinal datasets
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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.