Find research datasets worth reusing

openCC0Oct 2025View details →

PRP01 Konza prairie long term restoration study of aboveground annual net primary productivity (ANPP)

The experiment is a randomized complete block design with four whole plot hetereogeneity treatments replicated within each of four blocks (n=16 whole plots). The whole plot treatments were created using different combinations of soil depth and nutrient manipulations. The control plots contained no depth or nutrient manipulations. The 'maximum hetereogeneity' plots contained three 2 m x 8 m vertical strips assigned to ambient, enriched and reduced N treatments and four 2 m x 6 m horizontal strips assigned to deep and shallow soil to result in six treatment combinations. The maximum heterogeneity plots are a split-block design. Each plot contained 12 subplots (2 m x 2 m) for sampling. All of the plots had surface soil temporarily removed to a depth of approximately 25 cm and natural limestone slabs were laid in strips assigned to the shallow soil treatment. The soil from all plots was then replaced, leveled, and disked (2-3 cm deep). In February 1998, we incorporated sawdust (49% C; C:N ratio=122) into the strips assigned to the reduced-N treatment. The average C concentration and bulk density in the surface 15 cm following long-term cultivation was 1.5% and 1.2 g cm-3, respectively. Sawdust was tilled into the soil at a rate of 5.5 kg dry wt./m2 to achieve a C concentration representative of native prairie soil (approx.3% C). Surface applications of granular sugar were initiated in 2004 at a rate of 200 g sucrose m-2 (84.22 g C/m2) 3-4 times each growing season. Strips assigned to the enriched-N treatment were fertilized with 5 g N m2/y (applied as ammonium-nitrate) in July of the first growing season and early June of each subsequent year.

openCC (other)Jun 2025View details →

Zooplankton abundance from net tows on Northeast U.S. Shelf Long Term Ecological Research (NES-LTER) Transect cruises, ongoing since 2018

This data package provides abundance data for zooplankton collected during seasonal transect cruises conducted as part of the Northeast U.S. Shelf Long-Term Ecological Research (NES-LTER) program, ongoing since 2018. Zooplankton are collected at standard NES-LTER transect stations (L1–L11) and the Martha’s Vineyard Coastal Observatory (MVCO) via oblique tows, using a 61-cm Bongo net with two mesh sizes (335 µm and 150 µm). The transect extends southward from near Martha’s Vineyard, Massachusetts, reaching approximately 150 km offshore along longitude 70 deg 53 min W, covering the continental shelf from nearshore to the shelf break, with sampling depths between 20 and 200 meters. Only the 335-µm mesh data is included here, as samples from this net are preserved on board and shipped to Morski Instytut Rybacki in Szczecin, Poland, where they are counted and identified to the lowest possible taxonomic level. Counts of taxa identified are provided by the NOAA’s Northeast Fisheries Science Center. Samples from the 150 um are preserved for other purposes and will be published as a separate data package. This second version of the data package includes staged and unstaged abundance data in volumetric (100 m³) and aerial (10 m²) units from the 335-µm net. Supplemental tables provide metadata for the cruises and stations.

openCC (other)Dec 2022View details →

Modeling dataset: Long-term Change in Metabolism Phenology across North-Temperate Lakes, Wisconsin, USA 1979-2019

This dataset includes model configurations, scripts and outputs to process and recreate the outputs from Ladwig et al. (2021): Long-term Change in Metabolism Phenology across North-Temperate Lakes. The provided scripts will process the input data from various sources, as well as recreate the figures from the manuscript. Further, all output data from the metabolism models of Allequash, Big Muskellunge, Crystal, Fish, Mendota, Monona, Sparkling and Trout are included.

openCC (other)Jan 2026View details →

SBC LTER: Reef: Long-term experiment: biomass of kelp forest species

These data represent values of biomass density for more than 200 species of macroalgae, invertebrates and fish measured in fixed plots at five reefs as part of a long-term experiment designed to evaluate the effects of disturbance to giant kelp on the structure and productivity of the benthic community. Taxon-specific relationships between size and mass were applied to field measurements of species abundance to estimate biomass density of each species. The five reefs (Arroyo Quemada 34°28.048’N, 120°07.031’W; Carpinteria 34°23.474’N, 119°32.510’W; Isla Vista 34°23.275’N, 119°32.792’W; Mohawk 34°23.649’N, 119°43.762’W; and Naples 34° 25.342’N, 119° 57.102’W) ranged in depth from 5.8 m to 8.9 m (MLLW) and were chosen to represent a range of physical and biological characteristics known to influence subtidal macroalgal assemblages in the region. A common (but not always persistent) feature on these reefs was the presence of the giant kelp, which forms a dense canopy at the sea surface that suppresses recruitment and growth of understory algae below it. See Methods for more information.

openCC (other)Nov 2024View details →

SBC LTER: Reef: Long-term experiment: Taxon-specific seasonal net primary production (NPP) for macroalgae

This dataset provides estimates of seasonal net primary production (NPP) for all taxa of macroalgae sampled in fixed plots of the SBC LTER's long-term kelp removal experiment sites. The experiment was initiated in 2008 at 4 sites; a fifth site as added in 2011. Data collection is ongoing. NPP of understory taxa was calculated using field measurements of irradiance and biomass (derived from abundance) and laboratory estimates of taxon-specific photosynthetic parameters. NPP for the giant kelp, Macrocystis pyrifera, was calculated using linear relationships between frond density in a given season and average NPP for that season.

openCustomAug 2022View details →

Long Term Mammal Data from Powdermill Biological Station 1979-1999

This is a 20-year record of small mammal trapping from the Powdermill Biological Station, Rector, PA 15677 collected by Joseph F. Merritt. It is included here as a comparative source of small mammal data.

opencc-by-4.0Oct 2020View details →

Particle concentration data from: Long-term measurement of sub-3nm particles and their precursor gases in the boreal forest

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.

opencc-by-4.0Nov 2023View details →

Core collapse supernova yield from the post-processing of a long-term 3D simulation

This dataset accompanies the publication "Production of 44Ti and Iron-group Nuclei in the Ejecta of 3D Neutrino-driven Supernovae" published in the Astrophysical Journal Letters Volume 957, Issue 2, id.L25.The dataset consists of an ACII text file that contains the isotopic yields from the post-processing of a 3D long-term supernova simulation for a 18.88 solar mass progenitor model. The yields are given in units of solar masses. Important: The dataset does not include the full stellar yield. It only represents the inner 0.142 solar masses. The total ejecta mass is expected to be larger. The dataset is also available on the websites of the Max-Planck Institute for Astrophysics in Garching, Germany: https://wwwmpa.mpa-garching.mpg.de/ccsnarchive/data/Sieverding2023/The results have been obtained using the open source nuclear reaction network code <a href="https://github.com/starkiller-astro/XNet">XNet.</a>Calculations have been performed on the supercomputing cluster Cobra the Max-Planck Computing and Data Facility (MPCDF) in Garching, Germany.

opencc-by-4.0Apr 2023View details →

Belvedere Glacier long-term monitoring Open Data

Introduction 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: <ul> <li>dense point clouds obtained with UAV-based MVS covering the entire glacier body</li> <li>high-resolution orthophotos</li> <li>high-resolution DEMs</li> </ul> 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). 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). 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.   Belvedere Glacier 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.   Data organization The data are organized by year in compressed zip folders named belvedere_YYYY.zip, 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 (.laz) and they can be inspected e.g., with CloudCompare. Orthophotos and DEMs are georeferenced images (.tif) that can be inspected with any GIS software (e.g., QGIS). 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. All the files are named according to the following naming schema: "belv_YYYY_surveyplatform_datatype[_resolution][vertical_datum][-tile_number].extension" where:  <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> Data Usage 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.   Contributions 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.   Acknowledgements <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> If you use the data, please, cite these our pubblications: Ioli, F., Dematteis, N., Giordan, D., Nex, F., Pinto, L., Deep Learning Low-cost Photogrammetry for 4D Short-term Glacier Dynamics Monitoring. PFG (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> 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> 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>

opencc-by-sa-4.0Apr 2022View details →

Long-term MODIS LST day-time and night-time temperatures, sd and differences at 1 km based on the 2000–2020 time series

Layers include: Land Surface Temperature daytime monthly median value 2000–2017, Land Surface Temperature daytime monthly sd value 2000–2017, Land Surface Temperature daytime monthly day-night difference 2000–2017. Derived using the <a href="https://gitlab.com/openlandmap/global-layers/-/tree/master/input_layers/MOD11A2">data.table package and quantile function in R</a>. We derived four standard statistics: (1) lower 2.5% probability (l.025), median (m), upper 97.5% probability (u.975) and standard deviation (sd). Updated long-term values for 2000–2022+ are pending. Includes also long-term trends (trend.logit.ols) which was produced by fitting regression models to de-seasonalized time-series as explained in this <a href="https://gitlab.com/openlandmap/global-layers/-/blob/master/input_layers/MOD13Q1/03-data-access.ipynb">python tutorial</a>. Basically models are fitted for each pixel and the model parameters are saved as images. For more info about the MODIS LST product see: <a href="https://lpdaac.usgs.gov/products/mod11a2v006/">https://lpdaac.usgs.gov/products/mod11a2v006/</a>. Antarctica is not included. To access and visualize maps use:  <a href="http://www.openlandmap.org/">OpenLandMap.org</a> If you discover a bug, artifact or inconsistency, or if you have a question please use some of the following channels: <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">https://gitlab.com/openlandmap/global-layers/-/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: <ul> <li>clm = theme: climate,</li> <li>lst = variable: land surface temperature,</li> <li>mod11a2.oct.day = determination method: MOD11A2 product, day time values for October,</li> <li>d = median value / sd = standard deviation / u.975 = aggregation/statistics method: 97.5% probability upper quantile,</li> <li>1km = spatial resolution / block support: 1 km,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2000..2017 = time reference: from 2000 to 2017,</li> <li>v1.0 = version number: 1.0,</li> </ul>

opencc-by-4.0May 2022View details →

Influence of long-term changes in solar irradiance forcing on the Southern Annular Mode

This dataset accompanies Wright et al. (2022): Influence of long-term changes in solar irradiance forcing on the Southern Annular Mode, Climate of the Past. This dataset contains: <ul> <li>Solar constant experiments: monthly files for sea level pressure (psl), surface stress east (tax), surface stress north (tay), screen temperature (tsc), and temperature at X pressure (t[0-18]) for solar constant experiments, specifically <ul> <li>control</li> <li>S+1</li> <li>S+3</li> <li>S+7</li> <li>S+35</li> <li>S-3</li> <li>S-7</li> <li>S-15</li> </ul> </li> </ul>      <ul> <li>Transient experiments: sea level pressure (psl) and screen temperature (tsc) files covering 1-2000 CE using: <ul> <li>Steinhilber_x2 solar forcing (monthly files)</li> <li>Shapiro solar forcing (monthly files)</li> </ul> </li> </ul> These transient experiments are run as an Orbital-Greenhouse gases-Solar forcing experiment, and complement Phipps et al. (2013) (https://zenodo.org/record/3908927)

opencc-by-4.0Jul 2024View details →

Fast and long-term super-resolution imaging of ER nano-structural dynamics in living cells using a neural network

Datasets acquired and generated for the manuscript "Fast and long-term super-resolution imaging of ER nano-structural dynamics in living cells using a neural network". The datasets include test, training and time series datasets each containing the raw data and the predicted data where it applies.

opencc-by-sa-4.0Jul 2023View details →

Monthly aggregated Water Vapor MODIS MCD19A2 (1 km): Long-term data (2000-2022)

This data is part of the Monthly aggregated Water Vapor MODIS MCD19A2 (1 km) dataset. Check the related identifiers section on the Zenodo side panel to access other parts of the dataset.General DescriptionThe 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:<ul><li>Monthly time-series: Derived from MCD19A2 v061, 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>Yearly time-series: Derived from monthly time-series, this data provides a yearly time-series aggregated statistics of the monthly time-series data.</li><li>Long-term data (2000-2022): Derived from monthly time-series, this data provides long-term aggregated statistics for the whole series of monthly observations.</li></ul>Data Details<ul><li>Time period: 2000–2022</li><li>Type of data: Water vapor column above the ground (0.001cm)</li><li>How the data was collected or derived: 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>Statistical methods used: Four statistics were derived: standard deviation, percentiles 25, 50, and 75.</li><li>Limitations or exclusions in the data: The dataset does not include data for Antarctica.</li><li>Coordinate reference system: EPSG:4326</li><li>Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.00081, 179.99994, 87.37000)</li><li>Spatial resolution: 1/120 d.d. = 0.008333333 (1km)</li><li>Image size: 43,200 x 17,924</li><li>File format: Cloud Optimized Geotiff (COG) format.</li></ul>SupportIf you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:<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>Name conventionTo 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:<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>

opencc-by-sa-4.0Jul 2023View details →

FAPAR monthly time-series (250 m): Long-term trend (2000-2021)

List of Subdatasets: <ul> <li>Long-term data: <a href="https://doi.org/10.5281/zenodo.8381409">2000-2021</a></li> <li>5th percentile (p05) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408654">2000</a>, <a href="https://doi.org/10.5281/zenodo.8411611">2001</a>, <a href="https://doi.org/10.5281/zenodo.8412712">2002</a>, <a href="https://doi.org/10.5281/zenodo.8413021">2003</a>, <a href="https://doi.org/10.5281/zenodo.8413689">2004</a>, <a href="https://doi.org/10.5281/zenodo.8414639">2005</a>, <a href="https://doi.org/10.5281/zenodo.8411609">2006</a>, <a href="https://doi.org/10.5281/zenodo.8414085">2007</a>, <a href="https://doi.org/10.5281/zenodo.8414960">2008</a>, <a href="https://doi.org/10.5281/zenodo.8415476">2009</a>, <a href="https://doi.org/10.5281/zenodo.8415686">2010</a>, <a href="https://doi.org/10.5281/zenodo.8412154">2011</a>, <a href="https://doi.org/10.5281/zenodo.8414082">2012</a>, <a href="https://doi.org/10.5281/zenodo.8411364">2013</a>, <a href="https://doi.org/10.5281/zenodo.8414933">2014</a>, <a href="https://doi.org/10.5281/zenodo.8415414">2015</a>, <a href="https://doi.org/10.5281/zenodo.8412246">2016</a>, <a href="https://doi.org/10.5281/zenodo.8414083">2017</a>, <a href="https://doi.org/10.5281/zenodo.8411366">2018</a>, <a href="https://doi.org/10.5281/zenodo.8415203">2019</a>, <a href="https://doi.org/10.5281/zenodo.8415549">2020</a>, <a href="https://doi.org/10.5281/zenodo.8387608">2021</a></li> <li>50th percentile (p50) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408710">2000</a>, <a href="https://doi.org/10.5281/zenodo.8408798">2001</a>, <a href="https://doi.org/10.5281/zenodo.8408866">2002</a>, <a href="https://doi.org/10.5281/zenodo.8415319">2003</a>, <a href="https://doi.org/10.5281/zenodo.8415619">2004</a>, <a href="https://doi.org/10.5281/zenodo.8415878">2005</a>, <a href="https://doi.org/10.5281/zenodo.8416080">2006</a>, <a href="https://doi.org/10.5281/zenodo.8416619">2007</a>, <a href="https://doi.org/10.5281/zenodo.8417164">2008</a>, <a href="https://doi.org/10.5281/zenodo.8417513">2009</a>, <a href="https://doi.org/10.5281/zenodo.8417708">2010</a>, <a href="https://doi.org/10.5281/zenodo.8415669">2011</a>, <a href="https://doi.org/10.5281/zenodo.8416000">2012</a>, <a href="https://doi.org/10.5281/zenodo.8416542">2013</a>, <a href="https://doi.org/10.5281/zenodo.8417055">2014</a>, <a href="https://doi.org/10.5281/zenodo.8417467">2015</a>, <a href="https://doi.org/10.5281/zenodo.8415747">2016</a>, <a href="https://doi.org/10.5281/zenodo.8416333">2017</a>, <a href="https://doi.org/10.5281/zenodo.8416835">2018</a>, <a href="https://doi.org/10.5281/zenodo.8417326">2019</a>, <a href="https://doi.org/10.5281/zenodo.8417589">2020</a>, <a href="https://doi.org/10.5281/zenodo.8388078">2021</a></li> <li>95th percentile (p95) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408949">2000</a>, <a href="https://doi.org/10.5281/zenodo.8409059">2001</a>, <a href="https://doi.org/10.5281/zenodo.8409154">2002</a>, <a href="https://doi.org/10.5281/zenodo.8409362">2003</a>, <a href="https://doi.org/10.5281/zenodo.8416487">2004</a>, <a href="https://doi.org/10.5281/zenodo.8417029">2005</a>, <a href="https://doi.org/10.5281/zenodo.8417833">2006</a>, <a href="https://doi.org/10.5281/zenodo.8417996">2007</a>, <a href="https://doi.org/10.5281/zenodo.8418308">2008</a>, <a href="https://doi.org/10.5281/zenodo.8418669">2009</a>, <a href="https://doi.org/10.5281/zenodo.8418986">2010</a>, <a href="https://doi.org/10.5281/zenodo.8417649">2011</a>, <a href="https://doi.org/10.5281/zenodo.8417816">2012</a>, <a href="https://doi.org/10.5281/zenodo.8417959">2013</a>, <a href="https://doi.org/10.5281/zenodo.8418253">2014</a>, <a href="https://doi.org/10.5281/zenodo.8418625">2015</a>, <a href="https://doi.org/10.5281/zenodo.8417759">2016</a>, <a href="https://doi.org/10.5281/zenodo.8417898">2017</a>, <a href="https://doi.org/10.5281/zenodo.8418076">2018</a>, <a href="https://doi.org/10.5281/zenodo.8418442">2019</a>, <a href="https://doi.org/10.5281/zenodo.8418751">2020</a>, <a href="https://doi.org/10.5281/zenodo.8392976">2021</a></li> </ul> General Description The monthly aggregated Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) dataset is derived from <abbr title="glass.umd.edu/FAPAR/MODIS/250m/">250m 8d GLASS V6 FAPAR</abbr>. The data set is derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and LAI data using several other FAPAR products (MODIS Collection 6, GLASS FAPAR V5, and PROBA-V1 FAPAR) to generate a bidirectional long-short-term memory (Bi-LSTM) model to estimate FAPAR. The dataset time spans from March 2000 to December 2021 and provides data that covers the entire globe. The dataset can be used in many applications like land degradation modeling, land productivity mapping, and land potential mapping. The dataset includes: <ul> <li>Long-term:</li> </ul> Derived from monthly time-series. This dataset provides linear trend model for the p95 variable: (1) slope beta mean (p95.beta_m), p-value for beta (p95.beta_pv), intercept alpha mean (p95.alpha_m), p-value for alpha (p95.alpha_pv), and coefficient of determination R2 (p95.r2_m). <ul> <li>Monthly time-series:</li> </ul> Monthly aggregation with three standard statistics: (1) 5th percentile (p05), median (p50), and 95th percentile (p95). For each month, we aggregate all composites within that month plus one composite each before and after, ending up with 5 to 6 composites for a single month depending on the number of images within that month. Data Details <ul> <li>Time period: March 2000–December 2021</li> <li>Type of data: Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</li> <li>How the data was collected or derived: Derived from 250m 8 d GLASS V6 FAPAR using Python running in a local HPC.The time-series analysis were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li> <li>Statistical methods used: for the long-term, Ordinary Least Square (OLS) of p95 monthly variable; for the monthly time-series, percentiles 05, 50, and 95.</li> <li>Limitations or exclusions in the data: The dataset does not include data for Antarctica.</li> <li>Coordinate reference system: EPSG:4326</li> <li>Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.0008094, 179.9999424, 87.37000)</li> <li>Spatial resolution: 1/480 d.d. = 0.00208333 (250m)</li> <li>Image size: 172,800 x 71,698</li> <li>File format: Cloud Optimized Geotiff (COG) format.</li> </ul> Support If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: <a href="https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues">https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues</a> Reference Hackländer, J., Parente, L., Ho, Y.-F., Hengl, T., Simoes, R., Consoli, D., Şahin, M., Tian, X., Herold, M., Jung, M., Duveiller, G., Weynants, M., Wheeler, I., (2023?) "Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution", submitted to PeerJ, preprint available at: <a href="https://doi.org/10.21203/rs.3.rs-3415685/v1">https://doi.org/10.21203/rs.3.rs-3415685/v1</a> Name convention 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: <ol> <li>generic variable name: fapar = Fraction of Absorbed Photosynthetically Active Radiation</li> <li>variable procedure combination: essd.lstm = Earth System Science Data with bidirectional long short-term memory (Bi–LSTM)</li> <li>Position in the probability distribution / variable type: p05/p50/p95 = 5th/50th/95th percentile</li> <li>Spatial support: 250m</li> <li>Depth reference: s = surface</li> <li>Time reference begin time: 20000301 = 2000-03-01</li> <li>Time reference end time: 20211231 = 2022-12-31</li> <li>Bounding box: go = global (without Antarctica)</li> <li>EPSG code: epsg.4326 = EPSG:4326</li> <li>Version code: v20230628 = 2023-06-28 (creation date)</li> </ol>

openCC (other)Jan 2026View details →

Long-term trends in pesticide residues and physical chemical parameters of superficial water samples with accompanying macro-benthic invertebrate community surveys from the Palo Verde National Park, Costa Rica: 1993-1994; 2001; 2004-2005; 2009-2011

During the years 1993-1994, 2001, 2003-2005 and 2009-2011, the Central American Institute for Studies on Toxic Substances (IRET-UNA) executed independent research projects which quantified the presence of pesticide residues on superficial water samples from the Palo Verde National Park (PVNP) and surrounding areas. The PVNP (5460 sq km) is a RAMSAR wetland of international importance, which has been subjected to pesticide pressure from agricultural fields (mainly rice and sugarcane) since the 1960s and 1970s. In 1993, the PVNP wetlands were placed on the RAMSAR Montreux Record, indicating that it was considered an “impaired ecosystem” due to ecotoxicology concerns. Water is the key component of all issues regarding the biodiversity, management, restoration, and economic development of this region. Therefore, water quality is a critical component of many social ecological discussions and research efforts. This data package contains uniform pesticide, biological and water quality data from all PVNP wetland projects (1993- 2011) in order to present long-term trends in the environmental water quality and accompanying biological patterns for this conservation area. Study sites were spatially determined to compare clean upstream waters with a gradient of pesticide-affected waters. Superficial water samples were collected at various sites for chemical (pesticide) analysis and water quality parameters were recorded in situ for environmental monitoring. Corresponding biological sampling was completed to survey benthic macroinvertebrate communities and compare with local eco-toxicological profiles. This data package contains information from four separate projects.

openCC (other)Dec 2023View details →

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.

openCC0Jul 2021View details →

[DEPRECATED] MCR LTER: Coral Reef: Long-term Population Dynamics of Acanthaster planci, ongoing since 2005 (Reformatted to ecocomDP Design Pattern)

This ecocomDP formatted dataset is deprecated due to the fact that the focus of the original L0 dataset is population ecology, not community ecology. This data package is formatted according to the "ecocomDP", a data package design pattern for ecological community surveys, and data from studies of composition and biodiversity. For more information on the ecocomDP project see https://github.com/EDIorg/ecocomDP/tree/master, or contact EDI https://environmentaldatainitiative.org. This Level 1 data package was derived from the Level 0 data package found here: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-mcr&identifier=1039&revision=9 The abstract below was extracted from the Level 0 data package and is included for context: These data describe the abundance of Acanthaster planci, Crown of Thorns Sea stars, surveyed as part of MCR LTER's annual reef fish monitoring program. This study began in 2005 and the dataset is updated annually. The abundances of A. planci observed on a five by fifty meter transect are recorded by a diver using SCUBA. Surveys are conducted between 0900 and 1600 hours (Moorea time) during late July or early August each year. Four replicate transects are surveyed in each of three habitats (forereef, backreef and fringing reef) at six locations, two on each of Moorea's three sides, on the forereef, six locations on the backreef (two on each of Moorea's three sides for a total of 72 individual transects. Transects are permanently marked using a series of small, stainless steel posts affixed to the reef. Transects on the forereef are located at a depth of approximately 12m, those on the backreef are located at a depth of approximately 1.5m and those on the fringing reef are located at a depth of approximately 10m. This monitoring program is consistent with the protocols adopted by the Global Coral Reef Monitoring Network and the Australian Institute of Marine Science for use with the Great Barrier Reef Long-term Monitoring Program. Thes

openCC (other)Oct 2021View details →

SBC LTER: Reef: Long-term experiment: biomass of kelp forest species, ongoing since 2008 (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-sbc/119/7. The abstract below was extracted from the Level 0 data package and is included for context: These data represent values of biomass density for more than 200 species of macroalgae, invertebrates and fish measured in fixed plots at five reefs as part of a long-term experiment designed to evaluate the effects of disturbance to giant kelp on the structure and productivity of the benthic community. Taxon-specific relationships between size and mass were applied to field measurements of species abundance to estimate biomass density of each species. The five reefs (Arroyo Quemado 34°28.048’N, 120°07.031’W; Carpinteria 34°23.474’N, 119°32.510’W; Isla Vista 34°23.275’N, 119°32.792’W; Mohawk 34°23.649’N, 119°43.762’W; and Naples 34° 25.342’N, 119° 57.102’W) ranged in depth from 5.8 m to 8.9 m (MLLW) and were chosen to represent a range of physical and biological characteristics known to influence subtidal macroalgal assemblages in the region. A common (but not always persistent) feature on these reefs was the presence of the giant kelp, which forms a dense canopy at the sea surface that suppresses recruitment and growth of understory algae below it. See Methods for more information. The primary research objective of the Santa Barbara Coastal LTER is to investigate the importance of land and ocean processes in structuring giant kelp (Macrocystis pyrifera ) forest ecosystems. As in many temperate regions, the shallow rocky reefs in the Santa Barbara Channel, California, are dominated by giant kelp forests. Because of their close proximity to shore, kelp forests are influenced by physical and biological processes occurring on land as well as in the open ocean. SBC LTER research foc

openCC (other)May 2024View details →

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.