Find research datasets worth reusing

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.

openCC (other)Mar 2024View details →

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.

openCC (other)Mar 2024View details →

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.

openCC (other)Jun 2025View details →

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Interagency Ecological Program and US Fish and Wildlife Service: San Francisco Estuary Enhanced Delta Smelt Monitoring Program Data, 2016-2024

The Enhanced Delta Smelt Monitoring Program (EDSM) was initiated by the U.S. Fish and Wildlife Service in 2016. The main purpose of EDSM is to provide information about endemic Delta Smelt (Hypomesus transpacificus) population sizes and distributions within the upper San Francisco Estuary. To track the life cycle of this annual species, larval trawling with a fine-mesh (20 mm) net is conducted during the spring months, and Kodiak trawling for juveniles and adults occurs during the summer, fall, and winter months. Sampling sites are chosen via a stratified random sampling design. A minimum of two tows are conducted at each site, and field staff typically sample between 18 and 41 sites weekly. All fish collected are identified and enumerated, and a subset are measured for body length. Environmental data (water temperature, conductivity, dissolved oxygen, turbidity, depth) are also measured. In addition to Hypomesus spp., this long-term monitoring dataset can also be useful in evaluating the status and trends of other species of interest, especially pelagic fishes. For more information: https://www.fws.gov/office/lodi-fish-and-wildlife

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Sap-flux and associated environmental data from ash tree monitoring at four urban parks in St. Paul, Minnesota, USA, from May to November of 2023.

We measured the sap flux density of eighteen ash trees (Fraxinus spp.) of varying health and canopy conditions across four urban parks in the City of St. Paul, MN, USA in summer 2023 with a low-cost, compact data logger system we designed in-house. Although many ash trees in the city have either been killed or removed to control the spread of Emerald Ash Borer, chemical insecticide treatments are available for trees that are in early stages infestation. The trees selected for the research have all been receiving insecticide treatment for a few years, but their health and canopy conditions vary. We also have collocated temperature, soil moisture, and precipitation measurements at the same site for summer 2023.

openCC (other)Apr 2025View details →

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-4.0Jan 2022View details →

Hydrogeological data of groundwater and precipitation monitored in the Vögelsberg landslide catchment

Data contains hydrogeological data of precipitation and groundwater within the catchment of the Vögelsberg landslide (Tyrol, Austria) monitored between 2017-11-22 and 2021-07-05. The dataset provides time series of discharge, temperature, electrical conductivity and stable isotope ratios in groundwater and precipitation. Dataset is associated to following preprint: “Pfeiffer, J.; Zieher, T.; Schmieder, J.; Bogaard, T.; Rutzinger, M. and Spötl, C. (2021) Spatial assessment of probable recharge areas - Investigating the hydrogeological controls of an active deep-seated gravitational slope deformation, Natural Hazards and Earth System Sciences Discussions, Vol. 2021, p. 1-29, <a href="https://doi.org/10.5194/nhess-2021-388">https://doi.org/10.5194/nhess-2021-388</a>”. Accompanying readme file gives a detailed description of data fields contained in the published data.

opencc-by-4.0Dec 2019View details →

Non-refractory particulate sulfate and chloride data from a time of flight aerosol chemical speciation monitor around the Southern Ocean in the austral summer of 2016/17, during the Antarctic Circumnavigation Expedition (ACE).

Dataset abstract The Antarctic Circumnavigation Expedition (ACE) campaign was conducted between 20th December 2016 and 19th March 2017. The time of flight aerosol chemical speciation monitor (ToF-ACSM, Aerodyne Research Inc.) was deployed. It is capable of providing 10-minute resolution chemical compositions of NR-PM1 (non-refractory particulate matter with aerodynamic diameter smaller than 1 µm), including sulphate, nitrate, ammonium and organics. Chloride is refractory and can only be measured qualitatively, that is relative changes in intensity are trustworthy while absolute concentrations are a clear underestimation, because most of the chloride is in refractory form as part of sea salt in the marine environment. Since this ACSM dataset was collected on the ship, the ship exhaust will occasionally interfere with the natural signal. Therefore data gaps exist. The overall concentrations of particulate organics, nitrate and ammonium remained low, mostly below detection limit, except during the polluted periods. Thus, we do not report these three components. Only sulphate can be retrieved as a quantitative variable from this dataset. This dataset provides limited information on the chemical composition of sub-micron non-refractory aerosol in the Southern Ocean and gives hints on potential sources. Chloride clearly reflects the contribution of sea salt to the aerosol population. This can be checked by relating the particulate chloride to wind speed (Landwehr et al., 2019; 10.5281/zenodo.3379590) and particles with large diameters (Schmale et al., 2019; 10.5281/zenodo.2636709). Particulate sulphate may originate from a variety of sources: sea salt (minor contribution), anthropogenic emissions and natural marine emissions of dimethylsulfide, which is converted to SO2 and sulphuric acid in the atmosphere and can subsequently partition into the particle phase via gas-phase or aqueous phase reactions (Schmale et al., 2019). Dataset contents <ul> <li>raw_chl_SO4_mz_55_57_manual_with_flags.csv, data file, comma-separated values</li> <li>README.txt, metadata, text</li> <li>data_file_header.txt, metadata, text</li> <li>calibration_info.csv, metadata, comma-separated values</li> </ul>

opencc-by-4.0Sep 2024View details →

Labeled Time Series Data of Force/Torque for Monitoring Assembly Processes with a Delta Robot

This dataset comprises 524 recordings of 6-dimensional time series data, capturing forces in three directions and torques in three directions during the assembly of small car model wheels. The data was collected using an equidistant sampling method with a sampling period of 0.004 seconds. Each time series represents the process of assembling one wheel, specifically the placement of a tire onto a rim, and includes a label indicating whether the assembly was successful (OK). The wheels were assembled in batches of four, and the recordings were obtained over six different days. The labels of recordings from two (days 3 and 4) of the six days are invalid as described in [1].  The labels presented in this data set are only binary (they do not describe the reason of the failure). The labels of recordings from days 5 and 6 are created by human while the other labels came from a convolutional neural network based computer vision classifier and can be inaccurate as described in section 5.4 of [1].    <h4>Dataset Structure:</h4> <ul> <li>File: <code>ForceTorqueTimeSeries.csv</code> <ul> <li>Columns: <ul> <li><code>idx (1-524)</code>: Index of the recording corresponding to the assembly of one wheel.</li> <li><code>label (true/false)</code>: Indicates whether the assembly was successful (TRUE = product is OK).</li> <li><code>meas_id (1-6)</code>: Identifier for the day on which the recording was made (refer to Table 2.1 in [1]).</li> <li><code>force_x</code>: X-component of the force measured by the sensor mounted on the delta robot's end effector.</li> <li><code>force_y</code>: Y-component of the force.</li> <li><code>force_z</code>: Z-component of the force.</li> <li><code>torque_x</code>: X-component of the torque.</li> <li><code>torque_y</code>: Y-component of the torque.</li> <li><code>torque_z</code>: Z-component of the torque.</li> </ul> </li> </ul> </li> </ul> <h4>Additional Files:</h4> <ul> <li><code>IMG_3351.MOV</code>: A video demonstrating the assembly process for one batch of four wheels.</li> <li><code>F3-BP-2024-Trna-Ales-Ales Trna - 2024 - Anomaly detection in robotic assembly process using force and torque sensors.pdf</code>: Bachelor thesis [1] detailing the dataset and preliminary experiments on fault detection.</li> <li><code>F3-BP-2024-Hanzlik-Vojtech-Anomaly_Detection_Bachelors_Thesis.pdf</code>: Bachelor thesis [2] describing the data acquisition process.</li> </ul> <h3>References:</h3> <ol> <li>Trna, A. (2024). Anomaly detection in robotic assembly process using force and torque sensors [Bachelor’s thesis, Czech Technical University in Prague].</li> <li>Hanzlik, V. (2024). Edge AI integration for anomaly detection in assembly using Delta robot [Bachelor’s thesis, Czech Technical University in Prague].</li> </ol>

opencc-by-4.0May 2019View details →

SQLite database to accompany the paper, "Statistical learning mitigation of false positives from template-detected data in automated acoustic wildlife monitoring"

This dataset is a SQLite database that accompanies methods and analysis described in the paper, "Statistical learning mitigation of false positives from template-detected data in automated acoustic wildlife monitoring" (Balantic & Donovan 2019, Bioacoustics, https://www.tandfonline.com/doi/full/10.1080/09524622.2019.1605309).  A Github repository containing code for using the SQLite database also accompanies this paper at: <a href="https://github.com/cbalantic/false-positive-mitigation">http://github.com/cbalantic/false-positive-mitigation</a>

opencc-by-4.0Jul 2023View details →

Monipar Database: smartwatch movement data to monitor motor competency in subjects with Parkinson's disease

Movement data was collected through smartwatches to monitor motor competence in subjects with Parkinson's Disease (PD). The data set collected for the Monipar study consists of triaxial acceleration data from 21 subjects with PD and 7 healthy control subjects when performing a set of physical exercises while wearing an off-the-shelf smartwatch. Each participant performed the complete set of eight exercises once a week, commonly on the same day and at a similar time. Three Matlab files are provided that contain the raw data of the experimental subgroups: (1) Supervised, (2) Remote, and (3) Healthy control. Additionally, two Matlab files are provided containing the Tremor Labels for selected subjects in the experimental subgroups: (1) Supervised and (2) Remote.While the implementation of the experimental protocol for collecting movement data followed a consistent approach for all participants, three distinct experimental subgroups were established:Remote group: This subgroup consisted of individuals diagnosed with Parkinson's disease (PD) who completed the experimental protocol at their regular PD association.Supervised group: This subgroup comprised PD patients who underwent the experimental protocol under circumstances similar to the remote group. Additionally, clinical scoring (MDS-UPDRS) is reported for this group in the file "MONIPAR SUBJECTS DATA.xlsx"Healthy control group: This subgroup consisted of healthy participants who performed exercises under the supervision of research project team members.Data was collected using a sample rate of 50Hz and expressed in m/s^2.Check the "Monipar_README.txt" file for details about this dataset. Further details are contained in the following reference -- if you use this dataset, please cite:Sigcha, L., Polvorinos-Fernández, C., Costa, N., Costa, S., Arezes, P., Gago, M., ... & Pavón, I. "Monipar: Movement data collection tool to monitor motor symptoms in Parkinson's disease using smartwatches and smartphones". Frontiers in Neurology, 14, 1326640. <a href="https://doi.org/10.3389/fneur.2023.1326640">https://doi.org/10.3389/fneur.2023.1326640</a>References:Sigcha, L. et al. (2022). Bradykinesia Detection in Parkinson's Disease Using Smartwatches' Inertial Sensors and Deep Learning Methods. Sensors 11, 3879Sigcha, L. et al. (2021). Automatic Resting Tremor Assessment in Parkinson's Disease Using Smartwatches and Multitask Convolutional Neural Networks. Sensors 21, 291.Funding:This research was funded by the following projects:(1) "Tecnologías Capacitadoras para la Asistencia, Seguimiento y Rehabilitación de Pacientes con Enfermedad de Parkinson". Centro Internacional sobre el envejecimiento, CENIE (código 0348_CIE_6_E) Interreg V-A España-Portugal (POCTEP).(2) FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.

openCC0May 2022View details →

Mohonk Preserve Forest Health Monitoring Data 2018-2021

In 2018, the Mohonk Preserve’s Daniel Smiley Research Center implemented a long-term research project aimed at inventorying forest vegetation and monitoring forest health. The protocol was adapted from the National Park Service’s Northeast Temperate Network (https://www.nps.gov/im/netn/forest-health.htm). This project monitors the composition and structure of the Mohonk Preserve forests, and collects data for assessing forest soil condition, impacts of white-tailed deer herbivory, and land cover. In 2018, 24 plots were established in four habitat types: Eastern hemlock forest (n = 6), white ash forest (n = 6), historic prescribed burn forest (n = 6), and randomly selected forest (n = 6). In 2021, an additional 14 plots were established in two historic Breeding Bird Survey research areas: Eastern hemlock forest (n = 8) and pitch pine forest (n = 6). All data collection occurred between the months of June through August. Plots are scheduled to be resampled every four years.

openCC0Mar 2025View details →

High-Frequency and Water Quality Monitoring Data of Long Pond at Grafton Lakes State Park, New York, United States, 2024

This collection of datasets contains high frequency data captured through Hobo, Minidot, and water level sensors, as well as data collected from manual sampling days. Long Pond is located in Grafton New York, USA named for its long shape and shallower depth (max depth is around 8 meters). Using a buoy, sensors were attached to a rope at the deepest point discoverable and deployed. Data covers all information recorded from 2024-04-30 to 2024-10-09. Times are recorded in Eastern Standard Time. Temperature Readings were taken every 10 minutes at 1 meter intervals by both Minidots and hobo sensors (1.52-7.52 m). Dissolved Oxygen was similarly collected every 10 minutes by the Minidots at depths 1.52, 6.52, and 7.52 meters. Manual measurements (YSI and Secchi disk) were recorded on sampling days, as well as water samples that were assessed for water quality parameters from the top and bottom of the lake. Water level data was also collected in 12 hour intervals.

openCC (other)Apr 2025View details →

Biological and Physical Monitoring Data of Restored Oyster Reef in Savannah River, Savannah, GA from May 2023 - February 2025

For the purposes of this study, we constructed two oyster reefs in Savannah, GA, USA using standard spat-on-shell restoration methodology. Reefs were constructed 1-2 meters from the marsh edge to reduce wave energy as it approached the shoreline, similar to a breakwater. We then conducted monitoring on the biological function of the reef, including live juvenile oyster coverage, size, and abundance for approximately 18 months. We also quantified the energy flux of waves offshore and onshore of the reef using water pressure measurements to determine the capability of these reefs at reducing wave energy. The oyster reefs in this study decreased wave energy by up to 40% compared to paired, non-reef control sites. Constructed oyster reefs also experienced healthy oyster population growth throughout the study, with live juvenile coverage of 17-40% almost 18 months post-deployment. This study took place in an erosion-prone area due to recreational and commercial boating traffic at the nearby Port of Savannah. Our results indicate that using restored oyster reefs as living shorelines is a technique with high potential for preventing shoreline loss in coastal areas vulnerable to anthropogenically-caused erosion. Restored Reef Site 1: 32.067957°, -80.985005° Control Site 1: 32.0675194°, -80.986369° Restored Reef Site 2: 32.062663°, -80.965147° Control Site 2: 32.063261°, -80.965889°

openCC (other)May 2025View details →

National Park Service - South Florida/Caribbean Inventory & Monitoring Network - BISC1 SET Surface Water level data from in Biscayne National Park, Florida, USA (2016-2025)

Surface water level data (m) was collected in Biscayne National Park (BISC) by the South Florida/Caribbean Inventory and Monitoring Network (SFCN) as part of the Soil Elevation Table (SET) vital sign monitoring program. Water level data collected from 2016 to 2025 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This dataset belongs to Site 1, known as BISC-SET-1 or BISC1. This data-package is complete.

openCC (other)May 2025View details →

National Park Service - South Florida/Caribbean Inventory & Monitoring Network - BISC2 SET Surface Water level data from in Biscayne National Park, Florida, USA (2017-2025)

Water level data (m) was collected in Biscayne National Park (BISC) by the National Park Service - South Florida/Caribbean Inventory and Monitoring Network (SFCN) as part of the Soil Elevation Table (SET) vital sign monitoring program. Water level data collected from 2017-2025 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This dataset belongs to Site 2, known as BISC-SET-2 or BISC2. This data-package is complete.