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865 results for “long-term data”
Long-term Plant Biomass Monitoring Data from the Georgia Coastal Ecosystems LTER Project on Sapelo Island, Georgia
The Georgia Coastal Ecosystems LTER program (GCE) monitors plant biomass annually with the goal of testing the hypothesis that end-of-year biomass varies as a function of 1) freshwater discharge from the Altamaha River (especially in low-marsh plots), 2) local rainfall (especially in high-marsh plots), and 3) average sea level. In 2000 we created permanent plots at all 10 GCE marsh monitoring sites. Plots were established at creek-bank and mid-marsh sites (8 plots per zone per site). Most sites are dominated by Spartina alterniflora (smooth cordgrass), but zones at some sites are dominated by Juncus roemerianus, Spartina cynosuroides, or Zizaniopsis miliacea. An additional marsh zone (high marsh Juncus) was established at site 10 in 2005 and site 9 in 2012 to increase replication of sites with Juncus. Plants have been non-destructively monitored in October of every year from 2000 to the present, measuring the stem count, height and flowering status of every plant in each plot. Stem clipping samples were also collected adjacent to plots in 2002, 2007, and 2020, then measured, dried, weighed and statistically analyzed in order to generate allometric regression relationships between height and mass for estimation of plant biomass in corresponding plots. This data set includes cumulative long-term observations of plant stem count, height and biomass per marsh zone, plot and species at 10 GCE LTER sampling sites from 2000 to 2023, and will be updated annually to include the prior year observations.
Long-term Hydrographic Mooring Data from the Georgia Coastal Ecosystems LTER Salinity Monitoring Program - Primary 30 Minute Observational Data
Conductivity, temperature and sub-surface water pressure were measured continuously at fixed hydrographic moorings distributed across the Georgia Coastal Ecosystems LTER study area to document spatial and temporal variability of salinity and its relationship to water level and river discharge. Mooring locations were chosen to span the salinity gradient as well as to take advantage of existing physical infrastructure (e.g. docks or pilings) for mounting instruments and proximity to marsh study sites. Eight moorings were established between 2001 and 2003 to characterize salinity patterns in the three primary sounds in the GCE domain (Sapelo, Doboy and Altamaha), and a ninth mooring was added near a freshwater tidal forest along the Altamaha River in 2014. Observations were logged at 30 minute intervals by Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately quarterly. Salinity, depth and sigma-t (density anomaly) were calculated from the measured parameters using standard UNESCO algorithms, and short-duration gaps (<6 hours) due to instrument swaps, quality control analysis or brief data interruptions were filled by interpolation. Long-duration gaps due to instrument or mooring loss were filled with null values to produce a monotonic time series. This data set includes cumulative 30 minute observations at all 9 moorings through 31-Dec-2022, and will be updated annually to include observations from the prior year.
Long-term Hydrographic Mooring Data from the Georgia Coastal Ecosystems LTER Salinity Monitoring Program - Daily Summarized Data
Conductivity, temperature and sub-surface water pressure were measured continuously at fixed hydrographic moorings distributed across the Georgia Coastal Ecosystems LTER study area to document spatial and temporal variability of salinity and its relationship to water level and river discharge. Mooring locations were chosen to span the salinity gradient as well as to take advantage of existing physical infrastructure (e.g. docks or pilings) for mounting instruments and proximity to marsh study sites. Eight moorings were established between 2001 and 2003 to characterize salinity patterns in the three primary sounds in the GCE domain (Sapelo, Doboy and Altamaha), and a ninth mooring was added near a freshwater tidal forest along the Altamaha River in 2014. Observations were logged at 30 minute intervals by Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately quarterly. Salinity, depth and sigma-t (density anomaly) were calculated from the measured parameters using standard UNESCO algorithms, and short-duration gaps (<6 hours) due to instrument swaps, quality control analysis or brief data interruptions were filled by interpolation. Long-duration gaps due to instrument or mooring loss were filled with null values to produce a monotonic time series. Values flagged as invalid were then removed and interpolated up to 6 hours, and daily-summarized values were calculated by statistical aggregation. This data set includes the daily-summarized data at all 9 moorings through 31-Dec-2022, and will be updated annually to include observations from the prior year.
Long-term Mollusc Population Abundance and Size Data from the Georgia Coastal Ecosystems LTER Fall Marsh Monitoring Program
This data set includes long-term observational data on mollusc species abundance and size distribution at 10 Georgia Coastal Ecosystems marsh sites used for annual plant and invertebrate population monitoring. Infaunal and epifaunal molluscs were hand-collected from within quadrats of known area in mid-marsh and creekbank zones (n = 4 quadrats per zone) at all sites annually in October. Molluscs were also collected from an additional high marsh Juncus zone (n = 4 quadrats) at several sites beginning in 2009. The molluscs were returned to the lab, preserved in ethanol, identified and counted to determine species abundance and density in each plot. The length of each measurable individual was then determined using calipers or an ocular micrometer mounted in a stereomicroscope to determine mollusc size. Population abundance and size measurement data are reported separately by site, zone, plot and species because analyses were performed at different times, specimens were not individually identifiable, and not all individuals were measureable. This data set includes cumulative long-term observations from 2000 to 2022, and will be updated annually to include the prior year observations.
Long-term Burrowing Crab Population Abundance Data from the Georgia Coastal Ecosystems LTER Fall Marsh Monitoring Program
This data set includes long-term observational data on burrowing crab abundance at 10 Georgia Coastal Ecosystems marsh sites used for annual plant and invertebrate population monitoring. Crab abundance was determined by performing surveys of crab hole occurance within replicate 625 square centimeter quadrats and converting the counts to number per square meter. Surveys were performed annually during October within the mid-marsh and creek bank zones at GCE marsh study sites 1 through 10 (i.e. n = 4 per zone at each site). Surveys were also performed in an additional high marsh Juncus zone at several sites beginning in 2009 (i.e. n = 4 quadrats per site). Note that this census method does not differentiate which species made a particular hole and therefore only estimates total burrowing crab abundance, potentially including species Uca pugnax, Uca minax, Uca pugilator, Armases cinereum, Eurytium limosum, Sesarma reticulatum and Panopeus spp. Crab holes that are not actively maintained are quickly covered by tidal activity and other sediment disturbances, therefore plugged holes were assumed to be unoccupied and excluded from the counts. This data set includes cumulative observations from 2000 to 2023, and will be updated annually to include the prior year observations.
Long-term Plant Biomass Monitoring Data from Altamaha River Plant Transition Sites near the Georgia Coastal Ecosystems LTER Project on Sapelo Island, Georgia
The Georgia Coastal Ecosystems LTER program (GCE) monitors plant biomass annually to measure the species and size distribution of plants at 3 sampling sites on the creekbank of the Altamaha River. The sites were chosen to capture the transition from Spartina alterniflora to Spartina cynosuroides (site SCSA) and the transition from Spartina cynosuroides to Zizaniopsis miliacea (sites ZSC1 and ZSC2). The quadrats were established as permanent plots in October 2012 by placing PVC stakes along the creekbank at each site. Plots were evenly spaced, but were not randomly located because the goal was to start with mixtures of vegetation in most of the plots, and vegetation was distributed in patches along the creekbanks. Therefore, these plots provide useful measures of vegetation change, but are not a random sample of the vegetation at the site. Plots will be replaced each year as necessary to replace any lost to disturbance. The plots were visually surveyed and the species, shoot height, and flowering status was recorded individually for each shoot over 10 cm in height present in each plot. Observations from plots exhibiting signs of disturbance were noted in a separate data set (PLT-GCEM-1801c). This data set includes cumulative long-term observations of plant stem count, height and biomass per plot and species at 3 Altamaha River transition sites from 2012 to 2023, and will be updated annually to include the prior year observations.
Long-term fish size data for Wisconsin Lakes Department of Natural Resources and North Temperate Lakes LTER 1944 - 2012 (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/357/2. The abstract below was extracted from the Level 0 data package and is included for context: This dataset describes long-term (1944-2012) variations in individual fish total lengths from Wisconsin lakes. The dataset includes information on 1.9 million individual fish, representing 19 species. Data were collected by Wisconsin Department of Natural Resource fisheries biologists as part of routine lake fisheries assessments. Individual survey methodologies varied over space and time and are described in more detail by Rypel, A. et al., 2016. Seventy-Year Retrospective on Size-Structure Changes in the Recreational Fisheries of Wisconsin. Fisheries, 41, pp.230-243. Available at: http://afs.tandfonline.com/doi/abs/10.1080/03632415.2016.1160894
Long-term fish abundance data for Wisconsin Lakes Department of Natural Resources and North Temperate Lakes LTER 1944 - 2012 (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/356/3. The abstract below was extracted from the Level 0 data package and is included for context: This dataset describes long-term (1944-2012) variations in the relative abundance of fish populations representing nine species in Wisconsin lakes. Data were collected by Wisconsin Department of Natural Resource fisheries biologists as part of routine lake fisheries assessments. Individual survey methodologies varied over space and time and are described in more detail by Rypel, A. et al., 2016. Seventy-Year Retrospective on Size-Structure Changes in the Recreational Fisheries of Wisconsin. Fisheries, 41, pp.230-243. Available at: http://afs.tandfonline.com/doi/abs/10.1080/03632415.2016.1160894
Long-term fish size data for Wisconsin Lakes Department of Natural Resources and North Temperate Lakes LTER 1944 - 2012 (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/345/4, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/357/2. The abstract below was extracted from the Level 0 data package and is included for context: This dataset describes long-term (1944-2012) variations in individual fish total lengths from Wisconsin lakes. The dataset includes information on 1.9 million individual fish, representing 19 species. Data were collected by Wisconsin Department of Natural Resource fisheries biologists as part of routine lake fisheries assessments. Individual survey methodologies varied over space and time and are described in more detail by Rypel, A. et al., 2016. Seventy-Year Retrospective on Size-Structure Changes in the Recreational Fisheries of Wisconsin. Fisheries, 41, pp.230-243. Available at: http://afs.tandfonline.com/doi/abs/10.1080/03632415.2016.1160894
Long-term Agricultural Experiments: Data Management Survey
<p>Results of an online survey used to guage views of researchers within the LTE community on data management issues and knowledge. The survey was broken down in to 4 main questions and can be found at the following link - further responses are still welcome: <a href="https://forms.office.com/e/8DmapwLRr8" target="_blank" rel="noopener">https://forms.office.com/e/8DmapwLRr8</a>.</p> <ul> <li>About your role</li> <li>Data management & sharing</li> <li>Describing LTEs and their data</li> <li>Challenges for data management & sharing </li> </ul> <p>At the time of publication, 55 responses had been recieved.</p> <p>The survey was developed in response to an LTE Conference Workshop held at Rothamsted Research, UK in June 2023.</p>
MacroSheds: a synthesis of long-term biogeochemical, hydroclimatic, and geospatial data from small watershed ecosystem studies
The MacroSheds dataset is an ongoing synthesis of data records from small-watershed ecosystem studies, including those managed by LTER, CZO/CZNet, NEON, and many other networks. While details of instrumentation and sampling methods vary across these studies, the types of data collected and the questions that motivate their analysis are remarkably similar. Nevertheless, little effort toward the compilation of these datasets has previously been made, and comparative watershed analyses have remained limited in scale. The MacroSheds dataset includes daily time series of streamflow (discharge) and stream chemistry, as well as precipitation and precipitation chemistry where available. Each of the 200+ watersheds included in the MacroSheds dataset is described by a comprehensive collection of watershed attributes, summarized from a diverse set of gridded data products. A subset of these watershed attributes conform as closely as possible to the specifications of the CAMELS dataset (https://ral.ucar.edu/solutions/products/camels), allowing the MacroSheds dataset to function as a small-watershed supplement to that corpus, and a resource for hydrologists as well as biogeochemists and watershed ecosystem scientists. Data paper: https://aslopubs.onlinelibrary.wiley.com/doi/full/10.1002/lol2.10325 Data dashboard for visualization: macrosheds.org R package for data access and analysis: https://github.com/MacroSHEDS/macrosheds R package vignettes: https://macrosheds.org/pages/vignettes Live dataset changelog: https://macrosheds.org/pages/changelog.html Questions: mail@macrosheds.org
Long-term demographic dataset for Cladonia perforata, including fine-scale cover, occupancy, and subpopulation area data, 2011-2024
This dataset includes all data pertaining to a long-term demographic study of Cladonia perforata (perforate reindeer lichen), a federally endangered lichen endemic to Florida, including fine-scale cover, occupancy, and population area data, conducted by the Archbold Biological Station Plant Ecology Program. This includes 13 years of data (2011-2024) from nine subpopulation (including seven at Archbold Biological Station, and two at the Lake Wales Ridge Wildlife and Environmental Area, Royce Unit), all located in rosemary scrub habitat within the Lake Wales Ridge metapopulation. This study sought to characterize the fire ecology and long-term population trends for the species, and thus also includes data on prescribed burn severity and time since fire. Data were collected using a stratified random plot design, with occupancy plots (presence/absence within 1.5 meter radius) throughout the subpopulation and a subset of these designated as cover plots only, with this cover data collected as point intercept hits within a 48x48cm area. Cover data also includes microhabitat data – canopy cover in densiometer reading and dominant ground cover. Cover and occupancy data were taken every 3 years for each subpopulation (subpopulations were on different yearly schedules). Subpopulation area was mapped using a submeter GPS unit every 6 years. Subpopulations were resampled for all metrics as soon as possible following a fire, and the sampling schedule was then reset.
Forest tree, woody debris, root ingrowth, soil respiration and characterization data from long-term research plots for LTREB at the University of Michigan Biological Station
The NSF-funded project "LTREB: Drivers of temperate forest carbon storage from canopy closure through successional time" (2014-2024) supports research to meet the following goals: 1) elucidate mechanisms responsible for changes in C storage over decades to centuries; 2) link processes leading to persistence and resilience of forest C storage following disturbance; 3) quantify the effects of potential drivers such as forest structure, N availability, climate change, and atmospheric deposition on decadal and longer-term trajectories of C storage. Field activities for this research are conducted at the University of Michigan Biological Station (UMBS) on a pair of chronosequences and several old reference forests. Synthesis activities utilize data collected from these field sites in support of the LTREB project, as well as data synthesized from other sources (e.g., long-term UMBS plot data, AmeriFlux data, FIA data) all intended to address the core questions of the LTREB project. This dataset has been compiled and expanded over a series of versions, with new data types and observations appended periodically. Presently, the dataset includes observations from tree inventory censuses, woody debris sampling, fine root ingrowth cores, soil respiration measurements, and two sets of soil collections aimed at quantifying a range of physical, chemical, and biological properties of soil.
Particle concentration data from: Long-term measurement of sub-3nm particles and their precursor gases in the boreal forest
<p>The knowledge of the dynamics of sub-3nm particles in the atmosphere is crucial for our understanding of first steps of atmospheric new particle formation. Therefore, accurate and stable long-term measurements of the smallest atmospheric particles are needed. In this study, we analyzed over five years of particle concentrations in size classes 1.1–1.7 nm and 1.7–2.5 nm obtained with the Particle Size Magnifier (PSM) and three years of precursor vapor concentrations measured with the Chemical Ionization Atmospheric Pressure Interface Time-of-Flight mass spectrometer (CI-APi-ToF) at the SMEAR II station in Hyytiälä, Finland. The results show that the 1.1–1.7 nm particle concentrations have a daytime maximum during all seasons, which is due to increased photochemical activity. There are significant seasonal differences in median concentrations of 1.7–2.5 nm particles, underlining the different frequency of new particle formation between seasons. Aerosol precursor vapors have notable diurnal and seasonal differences as well. Sulfuric acid and highly oxygenated organic molecule (HOM) monomer concentrations have clear daytime maxima, while HOM dimers have their maxima during the night. HOM concentrations for both monomers and dimers are the highest during summer and the lowest during winter. Higher median concentrations during summer result from increased biogenic activity in the surrounding forest. Sulfuric acid concentrations are the highest during spring and summer, with autumn and winter concentrations being two to three times lower. A correlation analysis between the sub-3nm concentrations and aerosol precursor vapor concentrations indicates that HOMs, particularly their dimers, and sulfuric acid play a significant role in new particle formation in the boreal forest. Our analysis also suggests that there might be seasonal differences in new particle formation pathways that need to be investigated further. </p> <p> </p>
Belvedere Glacier long-term monitoring Open Data
<p><strong>Introduction </strong></p> <p>This dataset contains extensive, long-term monitoring data on the Belvedere Glacier, a debris-covered glacier located on the east face of Monte Rosa in the Anzasca Valley of the Italian Alps. The data is derived from photogrammetric 3D reconstruction of the full Belvedere Glacier and includes:</p> <ul> <li><strong>dense point clouds</strong> obtained with UAV-based MVS covering the entire glacier body</li> <li>high-resolution<strong> </strong><strong>orthophotos</strong></li> <li>high-resolution<strong> </strong><strong>DEMs</strong></li> </ul> <p>Since 2015, in-situ survey of the glacier have been conducted annually using fixed-wing UAVs until 2020 and quadcopters from 2021 to 2022 to remotely sense the glacier and build high-resolution photogrammetric models. A set of ground control points (GCPs) were materialized all over the glacier area, both inside the glacier and along the moraines, and surveyed (nearly-) yearly with topographic-grade GNSS receivers (Ioli et al., 2022).</p> <p>For the period from 1977 to 2001, historical analog images, digitalized with photogrammetric scanners and acquired from aerial platforms, were used in combination with GCPs obtained from recent photogrammetric models (De Gaetani et al., 2021).</p> <p>Before downloading them, you can explore the photogrammetric point clouds of the Belvedere Glacier within web app based on Potree from <a href="https://thebelvedereglacier.it/" target="_blank" rel="noopener">https://thebelvedereglacier.it/</a> (use a web browser from a desktop/laptop for the best experience). Additionally, from here you can also visualize and download the coordinates of the GCPs measured by GNSS every year since 2015.</p> <p> </p> <p><strong>Belvedere Glacier </strong></p> <p>The Belvedere Glacier is an important temperate alpine glacier located on the east face of Monte Rosa in the Anzasca Valley of Italy. The Belvedere Glacier is of particular importance among alpine glaciers because it is a debris-covered glacier and it reaches its lowest elevation at about 1800 m a.s.l. Over the last century, the Belvedere Glacier has experienced extraordinary dynamics, such as a surge-like movement or the formation of a supraglacial lake, which seriously threatened the nearby community of Macugnaga.</p> <p> </p> <p><strong>Data organization</strong></p> <p>The data are organized by year in compressed zip folders named <em>belvedere_YYYY.zip</em>, which can be downloaded independently. Each folder contains all data available for that year (i.e. photogrammetric point clouds, orthophotos, and DEMs) and the corresponding metadata. Metadata is provided as a .json file which contains all the main information for data usage. Point clouds are saved in compressed las format (<em>.laz</em>)<em> </em>and they can be inspected e.g., with CloudCompare. Orthophotos and DEMs are georeferenced images (<em>.tif</em>) that can be inspected with any GIS software (e.g., <em>QGIS</em>).</p> <p>Large point clouds are subdivided into regular tiles, which are numbered in a progressive row-wise order from the bottom-left corner of the point cloud bounding box.</p> <p>All the files are named according to the following naming schema:</p> <p>"belv_YYYY_surveyplatform_datatype[_resolution][vertical_datum][-tile_number].extension"</p> <p>where: </p> <ul> <li>YYYY: is the year of the survey</li> <li>surveyplatform: can be either "uav" for the UAV-based photogrammetry survey or "histo" for the historical aerial datasets.</li> <li>datatype: can be either "pcd" for point clouds, "orthophoto" for orthophotos and "dsm" for DSMs. </li> <li>resolution: on-ground resolution of each pixel in meters. This applies only to raster data (orthophoto and DSMs)</li> <li>vertical_datum: if the DSM is given in orthometric coordinates, the label "ortho" is present in the filename, otherwise the height of the dataset is supposed to be ellipsoidal.</li> <li>tile: tile number, if the data is tiled to avoid large files.</li> </ul> <p><strong>Data Usage</strong></p> <p>This dataset can be used to estimate glacier velocities, volume variations, study geomorphological processes such as the process of moraine collapse, or derive other information on glacier dynamics. If you have any requests on the data provided, data acquisition, or the raw data themselves, you are encouraged to contact us.</p> <p> </p> <p><strong>Contributions</strong></p> <p>The monitoring activity carried out on the Belvedere Glacier was designed and conducted jointly by the Department of Civil and Environmental Engineering (DICA) of Politecnico di Milano and the Department of Environment, Land and Infrastructure Engineering (DIATI) of Politecnico di Torino. The DREAM projects (DRone tEchnnology for wAter resources and hydrologic hazard Monitoring), involving teachers and students from Alta Scuola Politecnica (ASP) of Politecnico di Torino and Milano, contributed to the campaign from 2015 to 2017.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <div>The authors thank CGR SpA for digitizing the historical images (1977, 1991, 2001, 2009) and making them available to the authors for the photogrammetric processing.</div> <div>The authors thank all students and collaborators contributing to the Alta Scuola Politecnica projects DREAM 1, DREAM 2, and DREAM 3 (DRone tEchnnology for wAter resources and hydrologic hazard Monitoring). </div> <div> </div> <div> </div> <p><strong>If you use the data, please, cite these our pubblications:</strong></p> <p>Ioli, F., Dematteis, N., Giordan, D., Nex, F., Pinto, L., Deep Learning Low-cost Photogrammetry for 4D Short-term Glacier Dynamics Monitoring. <em>PFG</em> (2024). <a href="https://doi.org/10.1007/s41064-023-00272-w" target="_blank" rel="noopener">https://doi.org/10.1007/s41064-023-00272-w</a></p> <p>Ioli, F.; Bianchi, A.; Cina, A.; De Michele, C.; Maschio, P.; Passoni, D.; Pinto, L. Mid-Term Monitoring of Glacier’s Variations with UAVs: The Example of the Belvedere Glacier. Remote Sensing, 14, 28 (2022). <a href="https://doi.org/10.3390/rs14010028" target="_blank" rel="noopener">https://doi.org/10.3390/rs14010028</a></p> <p>De Gaetani, C.I.; Ioli, F.; Pinto, L. Aerial and UAV Images for Photogrammetric Analysis of Belvedere Glacier Evolution in the Period 1977–2019. Remote Sensing, 13, 3787 (2021). <a href="https://doi.org/10.3390/rs13183787" target="_blank" rel="noopener">https://doi.org/10.3390/rs13183787</a></p>
Monthly aggregated Water Vapor MODIS MCD19A2 (1 km): Long-term data (2000-2022)
<p>This data is part of the <em>Monthly aggregated Water Vapor MODIS MCD19A2 (1 km)</em> dataset. Check the related identifiers section on the Zenodo side panel to access other parts of the dataset.</p><p><strong>General Description</strong></p><p>The monthly aggregated water vapor dataset is derived from <a href="https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD19A2"><abbr title="MCD19A2 MODIS/Terra+Aqua daily product">MCD19A2 v061</abbr></a>. The Water Vapor data measures the column above ground retrieved from MODIS near-IR bands at 0.94μm. The dataset time spans from 2000 to 2022 and provides data that covers the entire globe. The dataset can be used in many applications like water cycle modeling, vegetation mapping, and soil mapping. This dataset includes:</p><ul><li><strong>Monthly time-series:</strong><br>Derived from <em>MCD19A2 v061</em>, this data provides a monthly aggregated mean and standard deviation of daily water vapor time-series data from 2000 to 2022. Only positive non-cloudy pixels were considered valid observations to derive the mean and the standard deviation. The remaining no-data values were filled using the <abbr title="Moving Window Median">TMWM</abbr> algorithm. This dataset also includes smoothed mean and standard deviation values using the Whittaker method. The quality assessment layers and the number of valid observations for each month can provide an indication of the reliability of the monthly mean and standard deviation values.</li><li><strong>Yearly time-series:</strong><br>Derived from <em>monthly time-series</em>, this data provides a yearly time-series aggregated statistics of the monthly time-series data.</li><li><strong>Long-term data (2000-2022):</strong><br>Derived from <em>monthly time-series</em>, this data provides long-term aggregated statistics for the whole series of monthly observations.</li></ul><p><strong>Data Details</strong></p><ul><li><strong>Time period:</strong> 2000–2022</li><li><strong>Type of data:</strong> Water vapor column above the ground (0.001cm)</li><li><strong>How the data was collected or derived:</strong> Derived from MCD19A2 v061 using <a href="https://earthengine.google.com">Google Earth Engine</a>. Cloudy pixels were removed and only positive values of water vapor were considered to compute the statistics. The time-series gap-filling and time-series smoothing were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li><li><strong>Statistical methods used:</strong> Four statistics were derived: standard deviation, percentiles 25, 50, and 75.</li><li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Antarctica.</li><li><strong>Coordinate reference system:</strong> EPSG:4326</li><li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (-180.00000, -62.00081, 179.99994, 87.37000)</li><li><strong>Spatial resolution:</strong> 1/120 d.d. = 0.008333333 (1km)</li><li><strong>Image size:</strong> 43,200 x 17,924</li><li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li></ul><p><strong>Support</strong></p><p>If you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:</p><ul><li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">GitLab Issues</a></li><li>General questions and comments: <a href="https://disqus.com/home/forums/landgis">LandGIS Forum</a></li></ul><p><strong>Name convention</strong></p><p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p><ol><li>generic variable name: wv = Water vapor</li><li>variable procedure combination: mcd19a2v061.seasconv = MCD19A2 v061 with gap-filling algorithm</li><li>Position in the probability distribution / variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment</li><li>Spatial support: 1km</li><li>Depth reference: s = surface</li><li>Time reference begin time: 20000101 = 2000-01-01</li><li>Time reference end time: 20221231 = 2022-12-31</li><li>Bounding box: go = global (without Antarctica)</li><li>EPSG code: epsg.4326 = EPSG:4326</li><li>Version code: v20230619 = 2023-06-19 (creation date)</li></ol>
Long-term (1935-2019) tree population data from remeasurements of a large network of permanent study plots in old-growth forest, Dukes Research Natural Area, Marquette Co., MI, USA
The Dukes Research Natural Area (Hiawatha National Forest, Marquette Co., MI) amounts to ca. 100 ha of minimally disturbed original forests, including a mix of mesic 'hemlock-northern hardwood' types and peaty wetlands dominated by several species of swamp conifers and black ash (Fraxinus nigra). The RNA hosts a regular grid of 250 0.2-acre (~0.08 ha) permanent monitoring (CFI) plots. This package includes tree censuses for subsets of CFI plots conducted in 1935, 1948, and 1974-1980, and repeated censuses with mapped stems from 1989 to 2019. This 84-year record constitutes one of the longest repeated-measurement, permanent-plot data-sets for old-growth temperate forest.
Long-term lake dissolved oxygen and temperature data, 1941-2018
The concentration of oxygen dissolved in water regulates many aspects of aquatic ecosystems, including habitat suitability for biota, greenhouse gas emissions, nutrient cycling, and metal toxicity. However, little is known about how dissolved oxygen (DO) concentrations in lakes are changing through time. The solubility of oxygen in water declines with increasing water temperatures, but other temperature-sensitive processes may suppress or amplify trends through time, making long-term observations essential to understanding DO dynamics and trends. This dataset includes measurements of dissolved oxygen and temperature from greater than 400 widely geographically-distributed lakes, as well as a suite of associated measurements in or around many of the lakes, including watershed land use, water clarity, limiting nutrient concentrations, and chlorophyll concentrations. Analysis of these data reveal widespread losses in dissolved oxygen through time in both surface and deep waters.
Long-term Climate data from the SINERR/GCE/UGAMI weather station at Marsh Landing on Sapelo Island, Georgia, from 03-Jan-2003 to 31-Dec-2019
Air temperature, relative humidity, barometric pressure, precipitation, photosynthetically-available and total solar radiation, and wind speed and direction were measured using an automated Campbell Scientific Instruments climate station installed at Marsh Landing on Sapelo Island, Georgia. Observations were logged at 15 minute intervals throughout the study period. The sensors were mounted on a 10m aluminum tower, with wind sensors mounted at the top, light sensors at approximately 5m, and other sensors at 2-3m to minimize interference from the surrounding landscape. Annual data sets from 2003 to 2019 were then synthesized and standardized to create a long-term, monotonic time series data set that will be updated annually. This climate station was jointly operated by the Sapelo Island National Estuarine Research Reserve, the Georgia Coastal Ecosystems LTER Project, and University of Georgia Marine Institute.
Long-term Atmospheric, Soil and Water Sensor Data from the GCE-LTER Eddy Covariance Flux Tower on Sapelo Island, Georgia
Long-term measurements of various atmospheric, soil and water properties were made using electronic sensors attached to the GCE-LTER eddy covariance flux tower deployed in a Spartina alterniflora salt marsh on Sapelo Island, Georgia. Variables measured include air and water temperature, relative humidity, precipitation, wind speed and direction, soil temperature, water pressure and solar radiation components (i.e. incident and reflected photosynthetically available, total, long-wave and shortwave radiation). Measurements were logged at 5 minute intervals using multiple Campbell Scientific Instruments CR3000 data loggers, and then combined into a single monotonic time series data set. Quality control analyses were performed to remove values deemed invalid due to sensor failure or miscalibration and to assign Q/C qualifiers to values outside expected ranges or failing various sanity and quality checks of the data. Note that some measurements were spatially replicated with multiple sensors deployed in different micro-habitats (e.g. at the tower and in a nearby marsh platform or creek). Sensors were also added to the tower at various times after the initial installation, therefore some variables do not span the entire period of record. Measurements at this site are ongoing, and the data set will be updated annually to include additional observations.
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.