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28 results for “depth sensor”
Time series of high-frequency sensor data measuring water temperature, dissolved oxygen, conductivity, specific conductance, total dissolved solids, chlorophyll a, phycocyanin, turbidity, and fluorescent dissolved organic matter at discrete depths in Carvins Cove Reservoir, Virginia, USA in 2020-2025
We monitored water quality in Carvins Cove Reservoir (Roanoke, Virginia, USA; 37.3697 -79.958) with high-frequency (10-minute) sensors in 2020-2025. Carvins Cove Reservoir is owned and managed by the Western Virginia Water Authority as a primary drinking water source. This data package consists of datasets from two separate deployments. First, from July 2020 - August 2021, depth profiles of water temperature were measured on 1-meter intervals using HOBO temperature pendant loggers deployed from 0.1 m below the surface of the reservoir to 10 m depth, and also at 15 and 20 m depth. Additionally, water temperature was measured in the Sawmill Branch inflow at 0.5 m depth using HOBO temperature pendant loggers. Second, from 9 April 2021 - 31 December 2025, depth profiles of water temperature were measured on 1-meter intervals from 0.1 m below the surface of the reservoir to 11 m depth and additionally at 15 and 19 m. A YSI EXO2 sonde measured water temperature, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, dissolved oxygen, and fluorescent dissolved organic matter at ~1.5 m depth. A YSI EXO3 sonde measured water temperature, conductivity, specific conductance, total dissolved solids, dissolved oxygen, and fluorescent dissolved organic matter at ~9 m depth, which corresponds to the depth of a water outtake valve. The thermistors, EXO3 sonde, and pressure sensor were deployed at stationary, fixed elevations (referred to as positions) deployed off of the dam near the water outtake valves. Due to variable water levels in the reservoir, the depths of these sensors varied over time. In contrast, the EXO2 was deployed on a buoy from 2021-2022 and remained at 1.5 m depth as the water level fluctuated. However, in 2023, the buoy disappeared in a storm, and after that the EXO2 was deployed at a stationary elevation as the water level fluctuated around the sensor. The EXO2 was redeployed on the buoy in 2024. The monitoring site's maximum de
Time series of high-frequency sensor data measuring water temperature, dissolved oxygen, pressure, conductivity, specific conductance, total dissolved solids, chlorophyll a, phycocyanin, fluorescent dissolved organic matter, and turbidity at discrete depths in Falling Creek Reservoir, Virginia, USA in 2018-2025
We monitored water quality in Falling Creek Reservoir (Vinton, Virginia, USA; 37.30325 -79.8373) with high-frequency (10-minute) sensors in 2018-2025. All variables were measured at the deepest site of the reservoir adjacent to the dam. Falling Creek Reservoir is owned and managed by the Western Virginia Water Authority as a primary drinking water source for Roanoke, Virginia. This data product consists of one dataset compiled of depth profiles of water temperature on 1-m intervals from 0.1 to 9 m depth; dissolved oxygen at 5 m and 9 m depth; pressure at 9 m depth; and temperature, dissolved oxygen, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, fluorescent dissolved organic matter, turbidity, and pressure at ~1.6 m depth. The dataset is accompanied by a sensor maintenance log and quality assurance/quality control analysis scripts.
Time series of high-frequency sensor data measuring water temperature, dissolved oxygen, conductivity, specific conductance, total dissolved solids, chlorophyll a, phycocyanin, fluorescent dissolved organic matter, and turbidity at discrete depths, and water level in Beaverdam Reservoir, Virginia, USA in 2009-2025
We monitored water level and water quality in Beaverdam Reservoir (Vinton, Virginia, USA; 37.31288, -79.8159) with visual observations and high-frequency (10- to 15-minute resolution) sensors in 2009-2025. All variables were measured at the deepest site of the reservoir adjacent to the dam. Beaverdam Reservoir is owned and managed by the Western Virginia Water Authority as a secondary drinking water source for Roanoke, Virginia. This data package is comprised of three datasets: 1) bvre-waterlevel_2009_2025.csv, 2) bvre-sensorstring_2016_2020.csv, and 3) bvre-waterquality_2020_2025.csv. 1) bvre-waterlevel_2009_2025.csv contains water level observations of the staff gauge at a platform near the reservoir's dam by both the Western Virginia Water Authority and the Virginia Tech Reservoir Group LTREB field crew. This dataset spans 2009 to 2025, with data collection still ongoing. 2) bvre-sensorstring_2016_2020.csv consists of a water temperature profile at ~1-meter intervals from the surface of the reservoir to 10.5 m below the water, complemented by intermittent data collected by a dissolved oxygen logger deployed at 5 m or 10 m. A sonde measuring water temperature, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, dissolved oxygen, fluorescent dissolved organic matter, and turbidity was additionally deployed at ~1.5 m depth. This dataset spans 2016 to 2020, with no additional data collection beyond the last observation. The third dataset is bvre-waterquality_2020_2025.csv, with data collection still ongoing and an accompanying maintenance log. This dataset contains: a) a temperature string with 13 temperature sensors deployed ~1 m apart from the surface to 0.5 m above the sediments of the reservoir; b) two dissolved oxygen sensors, one in the middle of the string and one sensor above the sediments; and c) a pressure sensor just above the sediments. The same sonde from the first 2016-2020 dataset is also included in this 2020-2025 d
Snow depth sensor measurement data for Upper Sub Alpine site, 2010 - 2015.
Effects of infrared heaters on snow accumulation, snowmelt, and snow–atmosphere energy exchange were examined at Niwot Ridge, Colorado (CO). These .zip data files contains hourly snow depth measurements collected using Judd snow depth sensors for water year 2010-2015 (1 October 2009 – 30 September 2015) at the Upper Sub Alpine site, located just southeast of the Tundra Lab, below treeline in the Niwot Ridge Long-Term Ecological Research (NWTLTER) project area. The file contains both level 0 and level 1 (see details in “Process Description” below) hourly snow depth data measured in centimeters, and an accompanying metadata file.
Time series of high-frequency sensors measuring water temperature and dissolved oxygen at discrete depths in Falling Creek Reservoir, Virginia, USA in 2012-2018
We measured water temperature and dissolved oxygen at multiple depths in Falling Creek Reservoir (Vinton, Virginia, USA) with high-frequency (10 to 15-minute) sensors for different durations during 2012 to 2018. Falling Creek Reservoir is owned and managed by the Western Virginia Water Authority as a primary drinking water source for Roanoke, Virginia. All measurements were collected at discrete depths at the deepest site of the reservoir adjacent to the dam. The sensors consisted of: 1) InsiteIG dissolved oxygen and water temperature sensors (Model 20 dissolved oxygen sensor) at both 1 m (November 2015 - December 2018) and 8 m (September 2012 - December 2018) and 2) HOBO (HOBO Pendant Temperature/Light 64K Data Logger) water temperature loggers deployed at 1, 2, 3, 4, 5, 6, 7, 8, and 9.3 m depths (September 2015 - January 2018).
New Hampshire Soil Sensor Network: Snow depth (2012-2022)
The goal of the New Hampshire Soil Sensor Network is to examine spatial and temporal changes in soil properties and processes as the climate changes. Data collected can also calibrate and validate models that examine how ecosystems may respond to changing climate and land use. To determine how soil processes are affected by climate change and land management, this soil sensor network measures snow depth, air temperature, soil temperature, soil volumetric water content, and soil electrical conductivity, as well as soil CO2 fluxes. This data package includes data from snow depth sensors. Data were collected at the following sites: BRT = Bartlett Experimental Forest, Bartlett, NH; BDF = Burley-Demmerit Farm, Lee, NH; DCF = Dowst Cate Forest, Deerfield, NH; HUB = Hubbard Brook Experimental Forest, Woodstock, NH; SBM = Saddleback Mountain, Deerfield, NH; THF = Thompson Farm, Durham, NH; and Trout Pond Brook, Strafford, NH.
NYU FloodSense street sign mounted flood depth sensor
<p>Water depth level in mm from a sensor mounted on a street sign post at the corner of 5th Street and Hoyt, Brooklyn, NY (40.676640, -73.994595). The sensor is designed to detect flood water that fills the street and blocks vehicle and pedestrian traffic, as well as depositing micro-organisms on the street. Ultrasonic technology is used to detect flood water depth.</p> <p>The sensor transmits its data via LoRaWAN and is equipped with a solar panel for continuous operation.</p> <p>Depth data is collected at ~5min intervals. Time fields are in local time (New York). Date format is: 2020-10-04 20:11:45.742594232-04:00</p> <p>Two flood events have been observed in this dataset between these date ranges:</p> <ol> <li> <p>"2020-11-15 19:37:00.000000000-05:00" to "2020-11-16 00:30:00.000000000-05:00"</p> </li> <li> <p>"2020-11-30 10:20:00.000000000-05:00" to "2020-11-30 13:30:00.000000000-05:00"</p> </li> </ol> <p>Erroneous data has been observed:</p> <ul> <li>There are ~1% decreases in depth measures on days with sun which suggests that the distance sensor is affected by direct sunlight</li> </ul> <p>This data is preliminary and is for prototyping purposes. </p> <p>This dataset will be updated when more data is collected.</p> <p>Please see our github org for sensor information and build instructions: <a href="https://github.com/floodsense">github.com/floodsense</a></p>
NYU FloodSense Gowanus canal mounted sensor depth
<p>Water depth level in mm from a sensor mounted mounted above the Gowanus Canal, Brooklyn, NY (40.674490, -73.994458).</p> <p>The sensor is designed to detect flood water that fills the street and blocks vehicle and pedestrian traffic, as well as depositing micro-organisms on the street. This one is used for data validation.</p> <p>The sensor transmits its data via LoRaWAN and is equipped with a solar panel for continuous operation.</p> <p>Data is collected at ~5min intervals. Time fields are in local time (New York). Time fields are in local time (New York). Date format is: 2020-10-04 20:11:45.742594232-04:00</p> <p>Two flood events have been observed in this dataset between these date ranges:</p> <ol> <li> <p>"2020-11-15 19:37:00.000000000-05:00" to "2020-11-16 00:30:00.000000000-05:00"</p> </li> <li> <p>"2020-11-30 10:20:00.000000000-05:00" to "2020-11-30 13:30:00.000000000-05:00"</p> </li> </ol> <p>One type of erroneous data has been observed:</p> <ul> <li>There are ~1% rises in distance measures on days with sun which suggests that the distance sensor is affected by direct sunlight</li> </ul> <p>This data is prelimary and is for prototyping purposes. Not to be used as a reliable data source as it is.</p> <p>This dataset will be updated when more data is collected.</p> <p>Please see our github org for sensor information and build instructions: <a href="https://github.com/floodsense">github.com/floodsense</a></p> <p> </p>
Applying Sensor Fusion to Augment Hyperspectral Data with Depth Information
<p>The research data for the paper "Applying Sensor Fusion to Augment Hyperspectral Data with Depth Information"<br> <br> Data in the archive "hyperdepth.tar.gz" includes:</p> <p><br> <strong>calibration_images/</strong><br> includes preprocessed images for calibrating both cameras</p> <p><strong>pointclouds/</strong><br> Includes individual hyperspectral point clouds for each view (front, rightmost, right, leftmost, left with postfixes correspondingly: edesta, oikea, oikea2, vasen, vasen2)<br> <br> <strong>raw_images/</strong><br> Two directories "day5" and "day6" which include the raw hyperspectral images and kinect images<br> <br> Some extra images are included which were not used in the research paper.</p> <p> </p> <p><strong>2022-03-11_112336_stereocalibration.json</strong> includes calibration results (mainly the intrinsic camera matrix and extrinsic parameters) for the setup.</p>
FloodSense street sign mounted flood depth sensor
<p><strong>Flood Depth Data (FDD)</strong> collected by a fleet of sensors deployed across 5 boroughs of New York City with a resolution of half an inch or less. The metadata for the sensors is included in the metadata.csv to identify the deployment coordinates of sensors, each with a unique <strong><em>deployment_id</em></strong>. </p> <p>The depth data is collected at least every five minutes and every minute in some locations depending on the ability to harvest solar energy at that deployment location. </p> <p>The final depth data field is <strong><em>depth_proc_mm</em></strong>, and the raw data is <strong><em>dist_mm</em></strong>. </p> <p>The raw measurement values received from the sensor are distance measurements (dist_mm), which are simply distance measurements collected from a ranging ultrasonic-based sensor. These distance measurements are converted to depths using <strong><em>night_median_dist_mm</em></strong> which is a daily calculated median of nighttime sensor readings. Direct sunlight affects ranging measurements due to high variance in the air column between the sensor and the concrete surface that it is mounted over. Additionally, the housing internally heats up when under direct sunlight, which affects the sensor readings and appears as if the surface dips with the daily increase and decrease in temperature during the daytime.</p> <p>After converting to raw depth values, a simple range filter is applied to the data removing any anomalies that lie below 10 millimeters and above unrealistic depth values (for example a person - between 5ft to 6ft), which is named <strong><em>depth_filt_mm</em></strong>.</p> <p>Further, this filtered depth value is processed through data filters eliminating blips, any pulse chains, or a flat line due to garbage or a car parked underneath the sensor. The output of these filters is labeled <strong><em>depth_proc_mm</em></strong>. </p> <p>This data is intended for use by communities, researchers, and New York City government agencies to better understand the frequency, severity, and impacts of flooding in New York City. </p> <p>Here is the live dashboard for these sensors deployed: <a href="https://dataviz.floodnet.nyc/">FloodNet Data Dashboard</a></p> <p>More about this project at <a href="https://www.floodnet.nyc/">FloodNet.NYC</a></p> <p>This is an open-source project and for more information on the sensors and build manuals see the <a href="https://github.com/floodnet-nyc/flood-sensor">FloodNet FloodSensor GitHub page</a></p>
Comparative analysis of ADC values between high-cost (US331-000005-030PA) and low-cost (B07YZLCSRP) depth sensors
<p>The dataset provides a comparison between an expensive depth sensor, the "US331-000005-030PA", and a cheaper sensor, the "B07YZLCSRP". The dataset includes the ADC values from both sensors as well as the offset between them.</p> <p>Data were collected using an autonomous underwater profiler called s-Nautilus at the Real Club de Regatas de Cartagena. During this test, the s-Nautilus profiler was moved to various depths, and the time and 12-bit ADC values from both sensors were recorded.</p> <p>The recorded variables include:</p> <ul> <li><strong>timestamp UNIX (s):</strong> the timestamp indicating the date and time of each measurement.</li> <li><strong>hours (hh:mm:ss):</strong> time of recording of each measurement.</li> <li><strong>incr_time (s): </strong>cumulative time increment for each measurement.</li> <li><strong>ADC cheap sensor (unit of ADC of 12 bits): </strong>12-bit ADC values of depth sensor "B07YZLCSRP" at various depths of the s-Nautilus.</li> <li><strong>ADC expensive sensor (unit of ADC of 12 bits):</strong> 12-bit ADC values of depth sensor "US331-000005-030PA" at various depths of the s-Nautilus.</li> <li><strong>ADC difference (unit of ADC of 12 bits):</strong> difference in ADC values between the two sensors.</li> <li><strong>ADC + offset (unit of ADC of 12 bits):</strong> ADC values of depth sensor "B07YZLCSRP" adjusted by calculated offset.</li> <li><strong>average ADC differences (unit of ADC of 12 bits):</strong> average offset ADC for all measurements.</li> </ul>
Snow depth sensor measurement data for Alpine site, 2010 - 2015
Effects of infrared heaters on snow accumulation, snowmelt, and snow–atmosphere energy exchange were examined at Niwot Ridge, Colorado (CO). These .zip data files contains hourly snow depth measurements collected using Judd snow depth sensors for water year 2010-2015 (1 October 2009 – 30 September 2015) at the Alpine site, located just southwest of the Tundra Lab in the Niwot Ridge Long-Term Ecological Research (NWTLTER) project area. The file contains both level 0 and level 1 (see details in “Process Description” below) hourly snow depth data measured in centimeters, and an accompanying metadata file.
Snow depth sensor measurement data for Lower Sub Alpine site, 2010 - 2015
Effects of infrared heaters on snow accumulation, snowmelt, and snow–atmosphere energy exchange were examined at Niwot Ridge, Colorado (CO). These .zip data files contains hourly snow depth measurements collected using Judd snow depth sensors for water year 2010-2015 (1 October 2009 – 30 September 2015) at the Lower Sub Alpine site, located southeast of the Tundra Lab, below treeline in the Niwot Ridge Long-Term Ecological Research (NWTLTER) project area. The file contains both level 0 and level 1 (see details in “Process Description” below) hourly snow depth data measured in centimeters, and an accompanying metadata file.
NYU FloodSense street sign mounted flood depth sensor
<p>Water depth level in mm from a sensor mounted on a street sign post at the corner of 5th Street and Hoyt, Brooklyn, NY (40.676640, -73.994595). The sensor is designed to detect flood water that fills the street and blocks vehicle and pedestrian traffic, as well as depositing micro-organisms on the street. Ultrasonic technology is used to detect flood water depth.</p> <p>The sensor transmits its data via LoRaWAN and is equipped with a solar panel for continuous operation.</p> <p>Depth data is collected at ~5min intervals. Time fields are in local time (New York). Date format is: 2020-10-04 20:11:45.742594232-04:00</p> <p>Two flood events have been observed in this dataset between these date ranges:</p> <ol> <li> <p>"2020-11-15 19:37:00.000000000-05:00" to "2020-11-16 00:30:00.000000000-05:00"</p> </li> <li> <p>"2020-11-30 10:20:00.000000000-05:00" to "2020-11-30 13:30:00.000000000-05:00"</p> </li> </ol> <p>Erroneous data has been observed:</p> <ul> <li>There are ~1% decreases in depth measures on days with sun which suggests that the distance sensor is affected by direct sunlight</li> </ul> <p>This data is preliminary and is for prototyping purposes. </p> <p>This dataset will be updated when more data is collected.</p> <p>Please see our github org for sensor information and build instructions: <a href="https://github.com/floodsense">github.com/floodsense</a></p>
NYU FloodSense Gowanus canal mounted sensor depth
<p>Water depth level in mm from a sensor mounted mounted above the Gowanus Canal, Brooklyn, NY (40.674490, -73.994458) from October 4th 2020 to January 8th 2021.</p> <p>The sensor is designed to detect flood water that fills the street and blocks vehicle and pedestrian traffic, as well as depositing micro-organisms on the street. This one is used for data validation.</p> <p>The sensor transmits its data via LoRaWAN and is equipped with a solar panel for continuous operation.</p> <p>Data is collected at ~5min intervals. Time fields are in local time (New York). Time fields are in local time (New York). Date format is: 2020-10-04 20:11:45.742594232-04:00</p> <p>Two flood events have been observed in this dataset between these date ranges:</p> <ol> <li> <p>"2020-11-15 19:37:00.000000000-05:00" to "2020-11-16 00:30:00.000000000-05:00"</p> </li> <li> <p>"2020-11-30 10:20:00.000000000-05:00" to "2020-11-30 13:30:00.000000000-05:00"</p> </li> </ol> <p>One type of erroneous data has been observed:</p> <ul> <li>There are ~1% rises in distance measures on days with sun which suggests that the distance sensor is affected by direct sunlight</li> </ul> <p>This data is prelimary and is for prototyping purposes. Not to be used as a reliable data source as it is.</p> <p>This dataset will be updated when more data is collected.</p> <p>Please see our github repo for sensor information and build instructions: <a href="https://github.com/floodsense">github.com/floodsense</a></p>
ODDS: Real-Time Object Detection using Depth Sensors on Embedded GPUs
<p> ODDS Smart Building Depth Dataset</p> <p>#Introduction:</p> <p>The goal of this dataset is to facilitate research focusing on recognizing objects in smart buildings using the depth sensor mounted at the ceiling. This dataset contains annotations of depth images for eight frequently seen object classes. The classes are: person, backpack, laptop, gun, phone, umbrella, cup, and box.</p> <p><br> #Data Collection:</p> <p>We collected data from two settings. We had Kinect mounted at a 9.3 feet ceiling near to a 6 feet wide door. We also used a tripod with a horizontal extender holding the kinect at a similar height looking downwards. We asked about 20 volunteers to enter and exit a number of times each in different directions (3 times walking straight, 3 times walking towards left side, 3 times walking towards right side) holding objects in many different ways and poses underneath the Kinect. Each subject was using his/her own backpack, purse, laptop, etc. As a result, we considered varieties within the same object, e.g., for laptops, we considered Macbooks, HP laptops, Lenovo laptops of different years and models, and for backpacks, we considered backpacks, side bags, and purse of women. We asked the subjects to walk while holding it in many ways, e.g., for laptop, the laptop was fully open, partially closed, and fully closed while carried. Also, people hold laptops in front and side of their bodies, and underneath their elbow. The subjects carried their backpacks in their back, in their side at different levels from foot to shoulder. We wanted to collect data with real guns. However, bringing real guns to the office is prohibited. So, we obtained a few nerf guns and the subjects were carrying these guns pointing it to front, side, up, and down while walking.</p> <p><br> #Annotated Data Description:</p> <p>The Annotated dataset is created following the structure of Pascal VOC devkit, so that the data preparation becomes simple and it can be used quickly with different with object detection libraries that are friendly to Pascal VOC style annotations (e.g. Faster-RCNN, YOLO, SSD). The annotated data consists of a set of images; each image has an annotation file giving a bounding box and object class label for each object in one of the eight classes present in the image. Multiple objects from multiple classes may be present in the same image. The dataset has 3 main directories:</p> <p>1)DepthImages: Contains all the images of training set and validation set. </p> <p>2)Annotations: Contains one xml file per image file, (e.g., 1.xml for image file 1.png). The xml file includes the bounding box annotations for all objects in the corresponding image. </p> <p>3)ImagesSets: Contains two text files training_samples.txt and testing_samples.txt. The training_samples.txt file has the name of images used in training and the testing_samples.txt has the name of images used for testing. (We randomly choose 80%, 20% split)</p> <p><br> #UnAnnotated Data Description:</p> <p>The un-annotated data consists of several set of depth images. No ground-truth annotation is available for these images yet. These un-annotated sets contain several challenging scenarios and no data has been collected from this office during annotated dataset construction. Hence, it will provide a way to test generalization performance of the algorithm.</p> <p><br> #Citation:</p> <p>If you use ODDS Smart Building dataset in your work, please cite the following reference in any publications:<br> @inproceedings{mithun2018odds,<br> title={ODDS: Real-Time Object Detection using Depth Sensors on Embedded GPUs},<br> author={Niluthpol Chowdhury Mithun and Sirajum Munir and Karen Guo and Charles Shelton},<br> booktitle={ ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN)},<br> year={2018},<br> }<br> </p>
Snow depth, air temperature, humidity, soil moisture and temperature, and solar radiation data from the basin-scale wireless-sensor network in American River Hydrologic Observatory (ARHO)
Open the record for dataset details and reuse information.
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): snow depth sensor data, 2012-2017
The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. This data set contains three-hourly measurements of snow depth collected with acoustic sensors for winter warming and control treatment plots.
MCR LTER: Coral Reef: Seawater pH, Temperature and Depth Time Series from Bottom-mounted Sensors on the Fringing Reef, January-March 2012
Bottom-mounted instrumentation (SeaFET, Seabird thermisters, Hobo water level data loggers) sampled for 8 weeks on the fringing reef of Moorea Island, French Polynesia at site LTER Fringe 1. Sampling began in January 2012. The instruments were secured to a cement piling at 3.3 meters depth and 0.7 meters above the sandy bottom. The SeaFET recorded voltages from a thermistor and pH electrodes at a 10-minute sampling interval. Discrete seawater samples were collected using a Niskin bottle during the deployment; pH, salinity, and total alkalinity of this sample were measured to calculate seawater pH (total scale) from raw SeaFET data as well as other carbonate chemistry parameters. Adjacent to the SeaFET were two thermistors and two HOBO® water level data loggers, synchronized with the SeaFET to simultaneously record temperature and depth.
Taiwan Coral Reef: Seawater pH, Temperature and Depth Time Series from Bottom-mounted Sensors on the Fringing Reef in Nanwan Bay, May-July 2012
Bottom-mounted instrumentation (SeaFET, Seabird conductivity/temperature sensor, Hobo water level data loggers) sampled for 7 weeks on the Hobihu fringing reef in Nanwan Bay, Taiwan. Sampling began in May 2012. The instruments were secured to anchored fencing stakes at 4 meters depth and 0.6 meters above the sandy bottom. The SeaFET recorded voltages from a thermistor and pH electrodes at a 10-minute sampling interval. Discrete seawater samples were collected using a Niskin bottle during the deployment; pH, salinity, and total alkalinity of this sample were measured to calculate seawater pH (total scale) from raw SeaFET data as well as other carbonate chemistry parameters. Adjacent to the SeaFET were a Seabird sensor and two HOBO® water level data loggers, synchronized with the SeaFET to simultaneously record conductivity, temperature and depth.
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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.