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Forest Transition Experiment - Vegetation Monitoring on a Coastal Virginia Forest, 2019-2023
This dataset contains data on vegetation (shrubs, trees, non-woody vegetation, seedlings and Phragmites occurrence in permanent plots at the Brownsville Forest near Nassawadox, VA.
Evidence accumulation relates to perceptual consciousness and monitoring
Open the record for dataset details and reuse information.
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>
Gamma dose rate monitoring using a Silicon Photomultiplier-based plastic scintillation detector
<p>Data set in support of the publication "Gamma dose rate monitoring using a Silicon Photomultiplier-based plastic scintillation detector". It contains measurement campaign data, radionuclide sources data, measurement count rate per radionuclide.</p>
Hydrogeological data of groundwater and precipitation monitored in the Vögelsberg landslide catchment
<p>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.</p>
Vibration-based Monitoring of a Small-scale Wind Turbine Blade Under Varying Climate Conditions. Part I: An Experimental Benchmark
<p>This repository contains all publicly available data related to the experimental part of <a href="https://onlinelibrary.wiley.com/doi/epdf/10.1002/stc.2660">Sonkyo-Benchmark</a>. The data of each experimental case (R, A, B, C, D, E, F, G, H, I, J, K, L) and temperature point (-15, -10, -5, 0, 5, 10, 15, 20, 25, 30, 35, 40) are stored in a zip file named "Case_<em>X</em>_(<em>T</em>)", where <em>X</em> denotes the case label and <em>T</em> refers to the temperature value. Each file "Case_<em>X</em>_(<em>T</em>).zip" contains two folders "Case_<em>X</em>_(<em>T</em>)_1" and "Case_<em>X</em>_(<em>T</em>)_2", wherein the test results from the two sensor layouts are stored. </p>
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).
<p><strong>Dataset abstract</strong></p> <p>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.</p> <p>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).</p> <p><strong>Dataset contents</strong></p> <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>
Labeled Time Series Data of Force/Torque for Monitoring Assembly Processes with a Delta Robot
<p>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]. </p> <h4>Dataset Structure:</h4> <ul> <li><strong>File:</strong> <code>ForceTorqueTimeSeries.csv</code> <ul> <li><strong>Columns:</strong> <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><strong><code>IMG_3351.MOV</code>:</strong> A video demonstrating the assembly process for one batch of four wheels.</li> <li><strong><code>F3-BP-2024-Trna-Ales-Ales Trna - 2024 - Anomaly detection in robotic assembly process using force and torque sensors.pdf</code>:</strong> Bachelor thesis [1] detailing the dataset and preliminary experiments on fault detection.</li> <li><strong><code>F3-BP-2024-Hanzlik-Vojtech-Anomaly_Detection_Bachelors_Thesis.pdf</code>:</strong> Bachelor thesis [2] describing the data acquisition process.</li> </ul> <h3>References:</h3> <ol> <li>Trna, A. (2024). <em>Anomaly detection in robotic assembly process using force and torque sensors</em> [Bachelor’s thesis, Czech Technical University in Prague].</li> <li>Hanzlik, V. (2024). <em>Edge AI integration for anomaly detection in assembly using Delta robot</em> [Bachelor’s thesis, Czech Technical University in Prague].</li> </ol>
SQLite database to accompany the paper, "Statistical learning mitigation of false positives from template-detected data in automated acoustic wildlife monitoring"
<p>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). </p> <p>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></p>
Monipar Database: smartwatch movement data to monitor motor competency in subjects with Parkinson's disease
<p>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.</p><p>While the implementation of the experimental protocol for collecting movement data followed a consistent approach for all participants, three distinct experimental subgroups were established:</p><p>Remote group: This subgroup consisted of individuals diagnosed with Parkinson's disease (PD) who completed the experimental protocol at their regular PD association.</p><p>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"</p><p>Healthy control group: This subgroup consisted of healthy participants who performed exercises under the supervision of research project team members.</p><p>Data was collected using a sample rate of 50Hz and expressed in m/s^2.</p><p>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:</p><p>Sigcha, L., Polvorinos-Fernández, C., Costa, N., Costa, S., Arezes, P., Gago, M., ... & Pavón, I. "<strong>Monipar: Movement data collection tool to monitor motor symptoms in Parkinson's disease using smartwatches and smartphones</strong>". <i>Frontiers in Neurology</i>, <i>14</i>, 1326640. <a href="https://doi.org/10.3389/fneur.2023.1326640">https://doi.org/10.3389/fneur.2023.1326640</a></p><p>References:</p><p>Sigcha, L. et al. (2022). Bradykinesia Detection in Parkinson's Disease Using Smartwatches' Inertial Sensors and Deep Learning Methods. Sensors 11, 3879</p><p>Sigcha, L. et al. (2021). Automatic Resting Tremor Assessment in Parkinson's Disease Using Smartwatches and Multitask Convolutional Neural Networks. Sensors 21, 291.</p><p><strong>Funding:</strong></p><p>This research was funded by the following projects:</p><p>(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).</p><p>(2) FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.</p>
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.
Monitoring juvenile Chinook salmon outmigration using rotary screw traps on Deer and Mill creeks
The California Department of Fish and Wildlife (CDFW) conducts juvenile salmonid emigration monitoring on Mill and Deer Creek (Tehama County, CA) annually from October through June using rotary screw traps (RSTs). Data from this monitoring is used to estimate juvenile spring-run Chinook salmon (Oncorhynchus tshawytscha) (spring-run) abundance and passage, identify yearling outmigration timing and alert resource agencies of juvenile spring-run presence in the lower Sacramento-San Joaquin Delta. This data will be included in the development of a juvenile production estimate (JPE) for spring-run Chinook salmon in the Sacramento River as required by Condition of Approval 7.5.2 of Incidental Take Permit No. 2081-2019-006-00 (ITP) issued by CDFW to California Department of Water Resources (DWR) for the long-term operation of the State Water Project. Salmonid data collected from the Mill and Deer RSTs, among other datasets, is also used by the Salmon Monitoring Team (SaMT) to understand the movement of juvenile salmon in the Sacramento River Watershed to estimate the number of winter-run and spring-run Chinook salmon that have entered the Sacramento-San Joaquin Delta (Delta). SaMT is a real-time operations monitoring team required by Condition of Approval 8.1.2 of the ITP which meets weekly from October through June, to provide advice for real-time management of SWP operations to DWR, CDFW, and the Water Operation Management Team (WOMT) to minimize take of winter-run and spring-run Chinook salmon in the Delta.
USFWS Adult White Sturgeon Monitoring, San Joaquin River, 2012-2025
Overview The Central Valley Project Improvement Act (CVPIA) funds habitat improvement work and associated monitoring in the Central Valley of California to increase salmonid populations in furtherance of meeting CVPIA fish doubling goals. This data package contains three datasets for adult White Sturgeon (Acipenser transmontanus) monitoring in the San Joaquin River (SJR) conducted by the US Fish and Wildlife Service, Lodi Fish and Wildlife Office. The primary purpose for this sampling was to capture White Sturgeon and implant acoustic telemetry tags for a tracking project. Therefore, the data are useful for determining when and where White Sturgeon were captured, but they should not be used to determine actual distribution or abundance. SJR_Adult_WST_Set Data This dataset contains data from a sampling program using various methods to catch adult White Sturgeon in the San Joaquin River. Sets were made at targeted locations primarily from March-May in 2012-2024 (other dates were occasionally sampled). SJR_Adult_WST_Catch Data This dataset contains data for individual fish caught via gillnets, trammel nets, setlines, or angling in the San Joaquin River. Species and fork length were recorded for all fish. For White Sturgeon, girth, maturation, tag, and surgery information are provided. SJR_Fish_Taxonomy Data This dataset contains data for fish codes used in the Catch datafile. For each species that was captured, the Species codes are listed with the corresponding Interagency Ecological Program code, common name, taxonomy (Phylum, Class, Order, Family, Genus, and Species), and whether or not the species is native to the region.
Stanislaus River Steelhead Life Cycle Monitoring Program
The Stanislaus River Steelhead Life Cycle Monitoring Program is an ongoing survey starting in 2021 that aims to estimate the amount of steelhead (Onocorhynchus mykiss) present in the Stanislaus River by recording steelhead and redds observed. In addition to estimating the amount of steelhead, the survey aims to collect data relevant to steelhead spawning, documenting environmental conditions such as temperature and flow, and recording other fish activity present.
Monitoring adult Chinook salmon upstream passage on Yuba River
Adult salmonid data on the Yuba River is collected and managed by the Yuba Water Agency and the California Department of Water Resources. Upstream passage data is collected year-round at Daguerre Point Dam, 24 hours a day and 7 days a week. Data from this monitoring is modeled by Brian Poxon and Paul Bratovich to produce estimates of adult escapement (upstream passage) abundance. These data will also be used to inform the development of a juvenile production estimate (JPE) for spring-run Chinook salmon in the Sacramento River Watershed.
Water Monitoring of the Choptank River and Pocomoke River, Maryland, USA
Water monitoring at four locations on the Choptank River and four locations on the Pocomoke River in Maryland, U.S.A., was conducted from 2021 through 2023. Funding and scientific rationale were provided by the National Science Foundation grant 2049073 (“Resolving Sediment Connectivity between Rivers and Estuaries by Tracking Particles with their Microbial Genetic Signature”). The monitoring locations were chosen to measure estuary dynamics from the tidal freshwater zone through the mesohaline estuary. Parameters measured included water temperature, water level, water conductivity (reported as specific conductivity), water turbidity, and water velocity.
Monitoring juvenile Chinook salmon outmigration using rotary screw traps on Deer and Mill creeks 2023 to present
The California Department of Fish and Wildlife (CDFW) conducts juvenile salmonid emigration monitoring on Mill and Deer Creek (Tehama County, CA) annually from October through June using rotary screw traps (RSTs). Data from this monitoring is used to estimate juvenile spring-run Chinook salmon (Oncorhynchus tshawytscha) (spring-run) abundance and passage, identify yearling outmigration timing and alert resource agencies of juvenile spring-run presence in the lower Sacramento-San Joaquin Delta. This data will be included in the development of a juvenile production estimate (JPE) for spring-run Chinook salmon in the Sacramento River as required by Condition of Approval 7.5.2 of Incidental Take Permit No. 2081-2019-006-00 (ITP) issued by CDFW to California Department of Water Resources (DWR) for the long-term operation of the State Water Project. Salmonid data collected from the Mill and Deer RSTs, among other datasets, is also used by the Salmon Monitoring Team (SaMT) to understand the movement of juvenile salmon in the Sacramento River Watershed to estimate the number of winter-run and spring-run Chinook salmon that have entered the Sacramento-San Joaquin Delta (Delta). SaMT is a real-time operations monitoring team required by Condition of Approval 8.1.2 of the ITP which meets weekly from October through June, to provide advice for real-time management of SWP operations to DWR, CDFW, and the Water Operation Management Team (WOMT) to minimize take of winter-run and spring-run Chinook salmon in the Delta. This data package is complimentary to edi.1504 and represents current RST monitoring on Deer and Mill creeks. Data within the current year’s monitoring season are considered provisional.
Juvenile Salmonid Emigration Monitoring in the Stanislaus River at Oakdale, California, 1996-2023
The operation of the rotary screw trap on the lower Stanislaus River at Oakdale Recreation Area is one of the longest-running datasets (1996-2023) in the Central Valley for juvenile salmonids. The primary objectives of the study are to collect data that can be used to estimate the passage of juvenile fall-run Chinook Salmon (Oncorhynchus tshawytscha) and to quantify the raw catch of Oncorhynchus mykiss. Secondary objectives of the trapping operations focus on collecting biological data on juvenile salmonids and gathering environmental data that will be used to develop models that correlate environmental parameters with salmonid size, temporal presence, abundance, and production.
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.
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°
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.