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.
84
datasets available to search
ShareScore release 0.9.0
Dataset results
84 results for “seasonal variability”
Long-term seasonally and annually aggregated climatic variables for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert, derived from single-day NASA Daymet images, 2000 to 2022
This data package consists of multiple decades of bioclimatic raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona, USA, temporally aggregated by year and by four meteorological seasons (winter, spring, summer, fall). We sourced each bioclimatic variable from 1-km resolution gridded estimates of daily climatic data from NASA Daymet V4, including daily mean (ppt) and total precipitation (ppt_sum), daily maximum air temperature (temp_max), daily minimum air temperature (temp_min), incident shortwave radiation flux density (srad), and daily average partial pressure of water vapor (vp). For each of these six variables, we created temporally aggregated raster images by calculating mean pixel-values of each for each season and year, as well as producing a seventh variable of seasonally and annually summed precipitation (ppt_sum). Finally, we exported images as individual GeoTIFF raster files, each with five bands corresponding values summarized annually (band 1) and seasonally (bands 2-5). All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031
Seasonal and annual summary statistics of urbanization, vegetation, land surface temperature, and bioclimatic variables derived from remotely-sensed imagery in areas surrounding long-term bird monitoring locations in the greater Phoenix, Arizona, USA metropolitan area (1997-2023)
This data package consists of 26 years (1998-2023) of environmental data and 22 years (2000-2022) years of bioclimatic data associated with CAP-LTER long-term point-count bird censusing sites (https://doi.org/10.6073/pasta/4777d7f0a899f506d6d4f9b5d535ba09), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). The environmental variables include land surface temperature (LST), three spectral indices of vegetation and water – the normalized difference vegetation index (NDVI), the soil adjusted vegetation index (SAVI), and modified normalized difference water index (MNDWI) – and four spectral indices of impervious surface/urbanization. Impervious surface indices include the normalized difference built-up index (NDBI), the normalized difference impervious surface index (NDISI), the enhanced normalized differences impervious surface index (ENDISI), and the normalized impervious surface index (NISI). LST and all spectral indices were derived from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. The seven bioclimatic variables (e.g., air temperature, precipitation) were sourced from 1-km resolution gridded estimates of daily climatic data from NASA Daymet V4. We created temporally-aggregated Daymet raster images by calculating mean pixel-values for each season and year, as well as seasonally and annually summed precipitation. We summarized the values of each environmental variable by generating variously-sized (100-m, 500-m, 1000-m) buffers around each bird point count location and extracting weighted mean values of each environmental variable, with each pixel's values weighted by the proportion of its area falling within the buffer. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of s
Seasonal to decadal western boundary current variability from sustained ocean observations
<p> </p> <p>Cross-transect velocity time series for HR-XBT transects IX21, PX30, and PX40 in support of: <a href="http://doi.org/10.1029/2022GL097834">Chandler et al. (2022). Seasonal to decadal western boundary current variability from sustained ocean observations.</a> </p> <p> </p> <p>Each netcdf file includes the following variables:</p> <ul> <li>time</li> <li>longitude</li> <li>latitude</li> <li>depth</li> <li>vel</li> <li>gvel_LNM</li> <li>long_for_vel_err</li> <li>lat_for_vel_err</li> <li>vel_err</li> <li>wbc_transport</li> </ul> <p> </p> <p>See also <a href="https://github.com/mlchandler/wbc_sustained_obs">https://github.com/mlchandler/wbc_sustained_obs</a></p>
Model output used in the manuscript "Seasonality in carbon flux attenuation explains spatial variability in transfer efficiency"
<p>This *.zip file contains the model output from seasonal variability experiments using the NPZD-DOP GEOMAR biogeochemical model (<a href="https://doi.org/10.1016/j.pocean.2010.05.002" target="_blank" rel="noopener">Kriest et al., 2010</a>) coupled with the MITgcm 2.8deg ocean circulation via the transport matrix method (<a href="https://doi.org/10.1016/j.ocemod.2004.04.002" target="_blank" rel="noopener">Khatiwala et al., 2005</a>; <a href="https://doi.org/10.1029/2007GB002923" target="_blank" rel="noopener">Khatiwala, 2007</a>; <a href="https://doi.org/10.5281/zenodo.1246300" target="_blank" rel="noopener">Khatiwala, 2018</a>).</p> <p>These model outputs are presented and discussed in the Preprint "<em>Seasonality in carbon flux attenuation explains spatial variability in transfer efficiency</em>", published by Geophysical Research Letters (<a href="https://doi.org/10.1029/2023GL107050" target="_blank" rel="noopener">de Melo Viríssimo et al., 2024</a>). The manuscript describes the experiments performed, the parameter values used and the modifications done to the original model. For this matter, we also refer you to <a href="https://doi.org/10.1029/2021GB007101" target="_blank" rel="noopener">de Melo Viríssimo et al. (2022)</a>.</p> <p>All files uploaded were generated from simulations run by the authors, except: the grid file, the salinity field, and the temperature field, which came with the model; and the density fields, who were computed from the MITgcm 2.8deg transport matrix by Dr Rafaelle Bernadello, using a TEOS-10 Matlab routine (<a href="http://www.teos-10.org/">http://www.teos-10.org/</a>).</p> <p>For specific information about each file uploaded, please refer to the README file. If you have any questions, please feel free to contact me.</p>
Ecophysiological variables of common shrub and grass species during the growing season following simulated sandblasting trials at the Jornada Experimental Range, New Mexico, USA, 2018 and 2019
In this dataset, we report ecophysiological variables of contrasting perennial grass (Bouteloua eriopoda, Sporobolus airoides, and Aristida purpurea) and shrub (Prosopis glandulosa, Atriplex canescens, and Larrea tridentata) functional groups before and after a series of simulated sandblasting events with various intensities and frequencies. We hypothesized that grass species are more susceptible to the resulting "sandblasting" (i.e., abrasive damage by wind-blown particulates) than shrubs, thus contributing to the shift from grass to shrub dominance. To test this, we conducted a wind tunnel experiment at the USDA Jornada Experimental Range in 2018 and 2019 growing seasons. Potted plants were subjected to different levels of sandblasting in a novel portable wind tunnel, and plants’ ecophysiological responses including leaf gas exchange and nighttime leaf stomatal conductance were quantified. All tested plants were then grown in benign greenhouse conditions to investigate plant recovery post sandblasting. This dataset contains data about plant biomass and height, leaf chlorophyll content, leaf gas exchange, stomatal conductance, and water use efficiency (WUE) under the experimental treatments above. This study is complete.
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.
Seasonal Carbonate Chemistry Variability in Marine Surface Waters of the Pacific Northwest. Data Archive.
<p>This archive includes two .nc files (NetCDF format) containing observational data (discrete and mooring) from marine surface waters of the Pacific Northwest that have not yet been submitted to a long-term data repository. These data contributed to the development of seasonal cycle data products described in the manuscript by Fassbender et al. A metadata file is provided for the discrete data subset (upper 10 m of discrete observational data); however, the complete cruise datasets and metadata will be submitted for archival in the National Centers for Environmental Information’s (NCEI) Ocean Carbon and Acidification Data repository (<a href="https://www.nodc.noaa.gov/oceanacidification/">https://www.nodc.noaa.gov/oceanacidification/</a>). Data subsets are provided here for accelerated public access. Data users are encouraged to download the complete datasets from NCEI once they are available (<a href="https://www.nodc.noaa.gov/oceanacidification/stewardship/data_portal.html">https://www.nodc.noaa.gov/oceanacidification/stewardship/data_portal.html</a>). Metadata for the University of Washington Oceanic Remote Chemical/Optical Analyzer (ORCA) mooring observations used by Fassbender et al., including the temperature and salinity data from the Dabob Bay and Twanoh moorings, are not provided here. Quality control protocols applied to the ORCA mooring data are outlined in the Quality Assurance Project Plan (<a href="http://nwem.ocean.washington.edu/ORCA_QAPP.pdf">http://nwem.ocean.washington.edu/ORCA_QAPP.pdf</a>; Newton and Devol, 2012).</p>
Seasonal and longitudinal variability in Io's SO2 atmosphere from 22 years of IRTF/TEXES observations
<p>This dataset contains the reduced Io spectra used in the paper "Seasonal and longitudinal variability in Io's SO2 atmosphere from 22 years of IRTF/TEXES observations" (doi: 10.1016/j.icarus.2024.116151). There are 150 spectra, spanning from 2001 to 2023. These spectra are described in Table 1 of the paper.</p> <p>The spectra in the data file are listed in date order. For each spectrum, we first provide the date (YYMMDD format) and the mean Io central longitude at the time of the observation. This is then followed by the spectrum. Column 1 is the wavelength, in units of microns. Column 2 is the Io spectrum, which has been divided by a Callisto spectrum, flattened in order to correct for any residual continuum slope, and then normalized such that the continuum level is 1. </p>
Dataset for "Topography-based statistical modelling reveals high spatial variability and seasonal emission patches in forest floor methane flux"
<p>This dataset provides measured and upscaled forest floor methane (CH4) fluxes and soil moisture.</p> <p>This dataset is related to the following manuscript:</p> <p>Vainio et al., Topography-based statistical modelling reveals high spatial variability and seasonal emission patches in forest floor methane flux, Biogeosciences, in review. (The discussion preprint is available at https://doi.org/10.5194/bg-2020-263.)</p>
Electron density and altitude of the main ionospheric peak of Mars as observed by Mars Express instruments. Archived data for the paper "Seasonal and geographical variability of the Martian ionosphere from Mars Express observations", submitted to JGR-Planets
<p>This repository contains archived data for the manuscript "Seasonal and geographical variability of the Martian ionosphere from Mars Express observations", published in Journal of Geophysical Research-Planets. Details about the methods to generate the data can be found in the paper.</p> <p>5 data files plus 2 readme text files are included.</p> <p>The file MEx_ionpeak.dat (described in the readme file README_ionpeak.txt) contains the peak electron densities and peak altitudes resulting from 34539 observations. Each record includes 14 columns. The content of each column is:</p> <p>Column 1: Instrument providing the observation (MARSIS or MaRS)<br> Column 2: Mars Year at which the observation was obtained (from MY27 to MY33)<br> Column 3: Solar Longitude (Ls) of the observation (unit: degrees)<br> Column 4: Latitude of the observation (unit: degrees)<br> Column 5: Longitude of the observation (unit: degrees)<br> Column 6: Solar Zenith Angle (SZA) of the observation (unit: degrees)<br> Column 7: F10.7 solar proxy index at 1 Astronomic Unit (unit: solar flux units)<br> Column 8: Peak electron density measured by the instrument (unit: cm-3)<br> Column 9: Peak electron density at the subsolar point, i.e., corrected for the SZA variation (unit: cm-3)<br> Column 10: Peak electron density at the subsolar point and at F10.7 (1AU)=100, i.e., corrected for the SZA and the solar radiation output variations (unit: cm-3)<br> Column 11: Peak electron density at the subsolar point, at F10.7 (1AU)=100 and corrected for the seasonal variation (unit: cm-3)<br> Column 12: Peak altitude measured by the instrument (unit: km)<br> Column 13: Peak altitude at the subsolar point, i.e. corrected for the SZA variation (unit: km)<br> Column 14: Peak altitude at the subsolar point and corrected for the seasonal variation (unit: km)</p> <p> </p> <p>The files eprofiles_MaRS.dat, eprofiles_MARSIS_prof1.dat, eprofiles_MARSIS_prof2.dat and eprofiles_MARSIS_prof3.dat contain 4 electron density profiles. They are described in the file README_eprofiles.txt. Each file includes 2 columns, the first one being the altitude (unit: km) and the second one the electron density (unit: cm-3).</p> <p> </p> <p>Contact: Francisco Gonzalez-Galindo, ggalindo@iaa.es<br> </p>
Supplement A. Wolf et al: 'Western Caucasus regional hydroclimate controlled by cold-season temperature variability since the Last Glacial Maximum'
<p>This repository contains all proxy data presented in A. Wolf et al, "Western Caucasus regional hydroclimate controlled by cold-season temperature variability since the Last Glacial Maximum". The data can be used to replicate figures and analyses presented in the main text. Additionally, data can be accessed in the supplement material and in the data availability statement. </p>
Dataset for "Long-term fluxes of carbonyl sulfide and their seasonality and interannual variability in a boreal forest"
<p>The final dataset used in manuscript "Long-term fluxes of carbonyl sulfide and their seasonality and interannual variability in a boreal forest" by Vesala et al. (2022). The dataset contains carbonyl sulfide (COS) and carbon dioxide (CO2) eddy covariance flux data and in-situ meteorological data measured at Hyytiälä forest in Juupajoki, Southern Finland, as well as meteorological drivers for SiB4 simulations and SiB4 simulated COS flux at the Hyytiälä grid cell from January 2013 to December 2017. Raw data are available upon request from the author.</p>
Data and scripts for 'Sub-seasonal variability of supraglacial ice cliff melt rates and associated processes from time-lapse photogrammetry'
<p>This repository contains three zipped elements used in the study <em>Sub-seasonal variability of supraglacial ice cliff melt rates and associated processes from time-lapse photogrammetry (</em>https://doi.org/10.5194/tc-2022-81):</p> <p>1. The time-lapse DEMs (original and flow-corrected), orthomosaics (not flow-corrected) and cliff outlines (original and flow-corrected) of the 24K and Langtang survey areas used in this study. The spatial resolution is the same as used in the analysis. These zipped files also contain a .csv file (time_selection.csv) indicating for each index (indicated in the file name) the serial date number in days (date origin January 0, 0000).</p> <p>2. The R and Python scripts (Scripts_final.zip) used to process the DEMs and orthomosaics from the time-lapse images as well as the script to calculate the slope-perpendicular melt. These scripts come with .csv and .txt files that serve as template for the required input data format.</p>
Figs 4-8 in Influence of environmental variables on seasonal abundance and relative growth of Macrobrachium amazonicum (Crustacea: Decapoda: Caridea): variations of a continental population
Figs 4-8. Percentage distribution of the independent effect of the abiotic factor on the total abundance (Fig. 4), and on the abundance by demographic category (Figs 5-8) of Macrobrachium amazonicum (Heller, 1862). Grey bars indicate a significant effect (p<0.05), determined by the randomization test. Positive and relative relationships are shown by the bars above and under the horizontal aXis, respectively (EC, conductivity; DO, dissolved oXygen; PI, precipitation; T, water temperature).
Figs 2, 3 in Influence of environmental variables on seasonal abundance and relative growth of Macrobrachium amazonicum (Crustacea: Decapoda: Caridea): variations of a continental population
Figs 2, 3. Percentage of total abundance (Fig. 2) and juveniles, males, non-ovigerous females and ovigerous females (Fig. 3) of Macrobrachium amazonicum (Heller, 1862) along the study period (J, juveniles; M, males; NOF, non-ovigerous female; OF, ovigerous females).
Figure 10 in Seasonal and Interannual Variability of the Barents Sea Temperature
Figure 10. Interannual variability of the North Atlantic Current Index (red) and monthly average temperature anomalies of the Barents Sea (blue) at a depth of 5 meters for the period 1948-2016. after applying the Butterworth bandpass filter from 12 to 16 years (above), and the cross-correlation picture of their material transformations without filtering (below). Preliminary removal of linear trends, centering and normalization of the series to their standard deviations were performed.
Figure 5 in Seasonal and Interannual Variability of the Barents Sea Temperature
Figure 5. Changes in the monthly average temperature anomalies of the Barents Sea at depths of 5 meters (above) and 105 meters (below) for the period 1948-2016, smoothed by 2-year (orange) and 7-year (purple) low-frequency Butterworth filters. Their linear trend is shown by blackline and the accumulated sum of anomalies after the removal of the linear trend by a green line. The circles indicate the average values of the anomalies for the warm (May-October) (red) and cold (November-April) (blue) seasons.
Figure 7 in Seasonal and Interannual Variability of the Barents Sea Temperature
Figure 7. Pictures of the wavelet transform of the monthly average temperature anomalies of the Barents Sea at depths of 5 meters (above) and 105 meters (below) for the period 1948 -2016. The preliminary normalization of the series to their standard deviations was made.
Figure 8 in Seasonal and Interannual Variability of the Barents Sea Temperature
Figure 8. Interannual variability of the Global Atmospheric Oscillation Index (red) and monthly average temperature anomalies of the Barents Sea (blue) at a depth of 5 meters for the period 1948-2016 after applying the Butterworth bandpass filter from 2 to 7 years (above), and the cross-correlation picture of their material transformations without
Figure 4 in Seasonal and Interannual Variability of the Barents Sea Temperature
Figure 4. Changes in the monthly average temperature of the Barents Sea (red) and their linear trend (blue) at depths of 5 meters (above) and 105 meters (below) for the period 1948-2016.
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.