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412 results for “sensor data”
Climate data for saddle catchment sensor network, 2017 - ongoing.
Spatial and temporal variability characterizes virtually all ecosystems, with resource supply changing over the course of growing season and across years due to climate variation. To better understand spatial heterogeneity in ecological response across landscape positions, we established a 16-node sensor array within a 45 hectare catchment landscape that measures temporal variability of important biogeochemical and hydrological controls on ecosystem processes. The array was established at the Niwot Saddle catchment in order to accompany long term water quality and discharge records taken at the top and bottom of this catchment. The region forms an important ecological linkage between the the terrestrial areas of the Niwot Ridge LTER and the aquatic component in the Green Lakes Valley.
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
Continuous stream CO2 and temperature data and sensor calibration grab samples from five NEON sites (CARI, COMO, KING, MART, WALK), August 2021-April 2024.
This package contains: 1) sensor-based measurements of dissolved CO2 concentration and temperature, and 2) dissolved CO2 concentration from grab samples that were used to calibrate the sensor data, collected at five stream sites in the NEON network (CARI- Caribou Creek, AK; COMO- Como Creek, CO; KING- Kings Creek, KS; MART- Martha Creek, WA; and WALK- Walker Branch, TN) between August 2021 - April 2024. The grab sample dataset contains a combination of samples collected by NEON (DP1.20097.001) and additional samples collected by project personnel. All samples were collected using the headspace equilibration method, and dissolved CO2 concentrations were calculated using the 'neonDissGas' R package (https://github.com/NEONScience/NEON-dissolved-gas). The sensor dataset contains CO2 concentrations measured with an eosGP CO2 gas probe, averaged to 15-minute intervals and corrected to align with grab sample concentrations using a site-specific grab versus sensor regression. Due to inaccuracies in the eosGP temperature data, we instead include the temperature data from NEON that was used to convert CO2 between units of ppmv and umol/L (DP1.20053.001 for CARI, KING, MART, and WALK, and data from the multiparameter sonde for COMO). All NEON data used in this data package references the RELEASE-2025 version of each data product (downloaded February 2025).
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
Air Quality Index (AQI) data from PurpleAir sensor at H.J. Andrews Experimental Forest LTER
This dataset contains hourly air quality and meteorological measurements collected from a PurpleAir sensor deployed at the H.J. Andrews Experimental Forest Long Term Ecological Research (LTER) site. The data includes four variables: timestamp (in Pacific Time), relative humidity (%), temperature (°C), and particulate matter concentrations (PM2.5 in µg/m³ using the CF=1 correction factor). The sensor provides continuous monitoring of local air quality conditions, with particular focus on fine particulate matter that can impact ecosystem health and visibility. Data are recorded at hourly intervals and timestamped in ISO 8601 format with UTC offset. PM2.5 values are reported using PurpleAir's CF=1 (Correction Factor 1) algorithm, which is optimized for atmospheric particulate matter. This dataset supports long-term environmental monitoring objectives at the Andrews Forest LTER and provides baseline air quality data for research on atmospheric conditions, wildfire smoke impacts, and climate-ecosystem interactions in Pacific Northwest forest ecosystems.
Smartphone sensor data (accelerometer, virtual keyboard) collected in-the-wild by Parkinson's Disease patients and Healthy Controls
<p>For detailed description of the dataset see the relevant <a href="https://www.nature.com/articles/s41598-020-78418-8">journal article</a>.</p> <p>Python code for model inference and training is available <a href="https://github.com/alpapado/deep_pd">here</a>.</p> <p> </p> <p><strong>DESCRIPTION</strong></p> <p>The dataset contains accelerometer recodings and keyboard typing data contributed by Parkinson's Disease patients and Healthy Controls. Accelerometer data consists of acceleration values recorded during phone calls and typing data consist of virtual keyboard press and release timestamps. The dataset is divided into two parts: the first part, called SData, contains data from a small, medically evaluated, set of users, while the second part, called GData, contains recordings from a large body of users with self-reported PD labels.</p> <p>The dataset is organized into 5 pickle files:</p> <p>1. <strong>imu_sdata.pickle</strong>: Contains the tri-axial accelerometer recordings for the SData part of the dataset in the form of a list of python dictionaries, one for each participating subject. Accelerometer data have been pre-processed to a sampling frequency of 100Hz and come segmented into non-overlapping 5 second windows. Hence, a segment's dimension will be 500 x 3 samples.</p> <p>Sample Python code for accessing the acceleration data of a subject</p> <pre><code class="language-python">sdata = pickle.load(open('imu_sdata.pickle', 'rb')) subject_list = list(sdata.keys()) ## Data for first subject subject_data = sdata[subject_list[0]] # subject_data is a list of length 4 ## The actual data is in the last element of the list acc_segments = subject_data[-1] num_acc_sessions_for_subject = len(acc_segments) acc_segments_for_first_session = acc_segments[0] acc_segments_for_second_session = acc_segments[1] # ..etc In: print(acc_segments_for_first_session.shape) Out: (3, 500, 3) ## The first accelerometer session for this subject consists of 3 five-second segments. In: print(acc_segments_for_second_session.shape) Out: (8, 500, 3) ## The second accelerometer session for this subject consists of 8 five-second segments.</code></pre> <p>2. <strong>imu_gdata.pickle</strong>: Same layout as imu_sdata.pickle but with data ffrom GData subjects.</p> <p>3. <strong>typing_sdata.pickle</strong>: This files contains the typing data originating from the SData part of the dataset. It is a list of dictionaries with one entry per subject. The typing data are given in the form of concatenated hold time (the time elapsed between press and release of the virtual key) and flight time (the time between releasing a key and press the next) histograms, computed over 10ms bins in the range of [0, 1]s for hold time and [0, 4]s for flight time (an additional bin that contains the values in the (1, +oo) and (4, +oo) intervals is also used). So, the total length of the concatenated histogram is 1000/10 + 1 + 4000/10 + 1 = 502.</p> <p>Sample Python code for accessing the typing data of a subject:</p> <pre><code class="language-python">sdata = pickle.load(open('typing_sdata.pickle', 'rb')) subject_list = list(sdata.keys()) ## Data for first subject subject_data = sdata[subject_list[0]] ## The actual data is in the first element of the list typing_histograms = subject_data[0] num_typing_sessions_for_subject = len(typing_histograms) typing_hist_for_first_session = typing_histograms[0] typing_hist_for_second_session = typing_histograms[1] # ..etc In: print(typing_hist_for_first_session.shape) Out: (502, ) ht_hist = typing_hist_for_first_session[:101] # Hold time histogram of the session ft_hist = typing_hist_for_first_session[101:] # Flight time histogram of the session</code></pre> <p>4. <strong>typing_gdata.pickle</strong>: Same layout as typing_sdata.pickle but with data from GData subjects.</p> <p>5. <strong>subject_metadata.pickle</strong>: A list of dictionaries with one entry per subject containing demographic information. The relevant demographic fields have the following interpretation:<br> 'age': Year of birth,<br> 'gender_id': 0 indicates male, 1 indicates female<br> 'healthstatus_id': 0 indicates PD patient, 1 indicates Healthy with PD family history, 2 indicates Healthy without PD family history</p> <p>In the case of SData subjects, there is also symptom UPDRS scores from one or two medical examinations. These are ncoded in the fields med_eval_1 and med_eval_2.</p> <p> </p> <p><strong>ETHICS & FUNDING</strong></p> <p>The study during which the present dataset was collected is a multi-center study approved in each country available (for more info visit: <a href="http://www.i-prognosis.eu/?page_id=3606">http://www.i-prognosis.eu/?page_id=3606</a>). Informed consent, including permission for third-party access to pseudo-anonymised data, was obtained from all subjects prior to their engagement with the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 690494 - i-PROGNOSIS: Intelligent Parkinson early detection guiding novel supportive interventions (<a href="http://www.i-prognosis.eu/">i-prognosis.eu</a>).</p> <p> </p> <p><strong>CORRESPONDANCE</strong></p> <p>Any inquiries regarding this dataset should be adressed to:</p> <p>Mr. Alexandros Papadopoulos (Electrical & Computer Engineer, PhD candidate)</p> <p>Multimedia Understanding Groupmug<br> Department of Electrical & Computer Engineering<br> Aristotle University of Thessaloniki<br> University Campus, Building C, 3rd floor<br> Thessaloniki, Greece, GR54124</p> <p>Tel: +30 2310 996359, 996365 <br> Fax: +30 2310 996398<br> E-mail: alpapado@mug.ee.auth.gr</p> <p> </p> <p><br> </p> <p> </p>
Extended data for the paper: "SentemQC - A novel and cost-efficient method for quality assurance and quality control of high-resolution frequency sensor data in fresh waters"
<p>Extended data 1 to 4 for the software article:<br>SentemQC - A novel and cost-efficient method for quality assurance and quality control of high-resolution frequency sensor data in fresh waters. </p> <p>The extended data is tables and a Figure output and input from/to SentemQC runs relevant for the SentemQC paper.</p>
2018-2020 Laboratory measurements of inorganic carbon accompanied by sensor data measurements of in situ inorganic carbon from the Upper Clark Fork River (Montana, USA)
These data were collected to support the Upper Clark Fork River restoration monitoring project supported by the US NSF Long Term Research in Environmental Biology (LTREB) and Consortium for Research in Environmental Water Systems (CREWS) programs. The LTREB monitoring project consists of monthly and bi-weekly water quality monitoring across a 215-km river restoration gradient contaminated by historic mining practices to monitor inorganic phosphorus and nitrogen concentrations, biotic standing stocks, heavy metal contamination, organic and inorganic carbon concentrations, and physicochemical parameters. The original analytical intent for these data was to assess the accuracy of calculating the partial pressure of carbon dioxide (pCO2) from electrochemical and spectrophotometric pH along with total alkalinity (AT). These data correspond to two parts: a tank study and a field application. The tank study was a set of controlled laboratory experiments that took place in a well-mixed temperature-controlled tank of freshwater. Data for the tank study are primarily measurements of electrochemical and spectrophotometric pH, AT, electrical conductivity, temperature, and ionic strength. The field application was used to demonstrate the real-world applicability of the tank study results in the Upper Clark Fork River (USGS HUC 17010201) at the Gold Creek site southeast of Missoula, MT, USA. Data from the field application are primarily high frequency measurements of carbon dioxide, pH, temperature, and electrical conductivity. Additional miscellaneous data were collected for quality control. These field data were collected using field deployments of SAMI sensors from Sunburst Sensors (Missoula, Montana, USA). Electrical conductivity data were collected with a HOBO sensor from Onset Computer Corporation (Bourne, Massachusetts, USA).
Hågaån Catchment Continuous Sensor Data, Uppsala, Sweden, 2018-2023
This data package includes continuous sensor data used to model stream metabolism and carbon dioxide emissions from the Hågaån stream catchment in Uppsala, Sweden. Data is in 30 minute increments from 2018 to 2023 and includes discharge, water temperature, turbidity, specific conductivity, temperature, dissolved carbon dioxide, and dissolved oxygen. The data package is complete.
Long-term Atmospheric, Soil and Water Sensor Data from the GCE-LTER Eddy Covariance Flux Tower on Sapelo Island, Georgia
Long-term measurements of various atmospheric, soil and water properties were made using electronic sensors attached to the GCE-LTER eddy covariance flux tower deployed in a Spartina alterniflora salt marsh on Sapelo Island, Georgia. Variables measured include air and water temperature, relative humidity, precipitation, wind speed and direction, soil temperature, water pressure and solar radiation components (i.e. incident and reflected photosynthetically available, total, long-wave and shortwave radiation). Measurements were logged at 5 minute intervals using multiple Campbell Scientific Instruments CR3000 data loggers, and then combined into a single monotonic time series data set. Quality control analyses were performed to remove values deemed invalid due to sensor failure or miscalibration and to assign Q/C qualifiers to values outside expected ranges or failing various sanity and quality checks of the data. Note that some measurements were spatially replicated with multiple sensors deployed in different micro-habitats (e.g. at the tower and in a nearby marsh platform or creek). Sensors were also added to the tower at various times after the initial installation, therefore some variables do not span the entire period of record. Measurements at this site are ongoing, and the data set will be updated annually to include additional observations.
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.
PIE LTER, Wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA, year 2022.
Wind sensor measurements (wind speed and wind direction) for 2022 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.
Continuous multi-sensor wearable data and daily subject-reported fatigue of heathy adults
<p>Fatigue is a broad, multifactorial concept encompassing feelings of reduced physical and mental energy levels. Fatigue strongly impacts health-related quality of life across a huge range of conditions, yet, to date, tools available to understand fatigue are limited. We collected a total of 28 healthy adult subjects and 973 recording days. Recorded data included continuous multimodal wearable sensor time series on physical activity, vital signs, and other physiological parameters at 1-minute temporal resolution, and daily questionnaires (patient-reported outcome scores, PROs) on fatigue. When matching both sensor data and PROs, the datasets contains data from 27 subjects and 405 recording days.</p> <p>Analysis of these multimodal digital data to inform, quantify, and augment subjectively captured non-pathological fatigue measures were published at <em>Luo H., et. al. (2020), Assessment of Fatigue Using Wearable Sensors: A Pilot Study. Digit Biomark</em>.</p> <p>Demographics, sensor parameters and other information on this dataset can be found in the aforementioned manuscript and related supplementary material.</p> <p>Files included are</p> <ul> <li><em>fatiguePROs.csv</em>: daily PROs for all subjects</li> <li>subjectID_*.csv: sensor time series for each subject</li> </ul>
LoRaWAN Dense Indoor Sensor Network (DISN) Transmission Meta Data
<p>We present a large data of indoor Long Range Wide Area Network (LoRaWAN) network metadata to study Dense Indoor Sensor Networks (DISN). We collected 14 million transmissions from 390 sensors between date February 2020 and date September 2020. The transmissions have been received by 3 gateways across 8 floors and distances up to 64 m. The prototype will run in the background throughout the project and the data set will be regularly updated.</p> <p> </p>
AirMLP - SPS30 low-cost sensors and Tecora reference station PM 2.5 data
<p>The information below describes a dataset related to a study conducted in Turin, Italy, involving low-cost laser-scattering SPS30 sensors placed by Wiseair SRL and a Tecora reference station placed by Arpa Piemonte (Italian Air Quality Agency). This dataset spans two different time periods in 2022, specifically from March 1, 2022, to April 29, 2022, and from October 26, 2022, to December 30, 2022. The data in this dataset pertains to the mass concentration of PM2.5 (particulate matter with a diameter of 2.5 micrometres or less).</p><p> </p><p>The reference station's data is divided into two periods and is provided in files named "rf_x.csv." These files contain hourly data and timestamps in GMT+1. Each file has three columns:</p><ul><li>"valid_at" (in Rome local hour, GMT+1)</li><li>"valore_originale" (PM 2.5 raw mass concentration values recorded by the reference station)</li><li>"pm2p5" (PM 2.5 mass concentration validated values by the air quality agency)</li></ul><p>The low-cost sensors, referred to as "ari_xxxx.csv," provide data at approximately 15-minute frequency. These files contain the following columns:</p><ul><li>"valid_at" (in GMT)</li><li>"pm2p5" (PM 2.5 raw mass concentration measured by the SPS30 sensor)</li><li>"relative_humidity" (expressed as a percentage)</li><li>"temperature" (in degrees Celsius)</li><li>"pressure" (in hPA)</li><li>"wind_speed" (in meters per second)</li><li>"cloud_coverage" (expressed as a percentage)</li></ul><p>Notably, the "relative_humidity" and "temperature" values are gathered from sensors placed within a device containing the SPS30 low-cost sensor.</p><p> </p><p>Here's a summary of the specific data files in this dataset:</p><ul><li>"<strong>rf_1.csv</strong>": Hourly data provided by the Air Quality Agency for the first period.</li><li>"<strong>rf_2.csv</strong>": Hourly data provided by the Air Quality Agency for the second period.</li><li>"<strong>arpa_1727.csv</strong>," "<strong>arpa_1952.csv</strong>," and "<strong>arpa_1953.csv</strong>": Three low-cost sensors placed by Wiseair, which refer to the first period.</li><li>"<strong>arpa_1885.csv</strong>" and "<strong>arpa_2049.csv</strong>": Two low-cost sensors placed by Wiseair, that refer to the second period.</li></ul>
COMPAIR traffic and air quality sensor data
<p>Sensor data regarding traffic and air quality was gathered as part of the <a href="https://cordis.europa.eu/project/id/101036563">EU Horizon2020 COMPAIR project</a> in Europe. The pilot cities/regions are Berlin, Athens, Sofia, Plovdiv, and Flanders.<br><br>During the project, the data was published through an <a href="https://sensorthings.wecompair.eu/FROST-Server/v1.1/Things">OGC SensorThings API</a>. To persist after the project, the air quality related are available as CSV exports, with the retention of the API's structure (Location, Thing, Datastream, Sensor, ObservedProperty, and Observation). Observations about air quality contain sensor readings regarding nitrodioxide (NO2), black carbon (BC), particulate matter (PM1.0, PM2.5 and PM10), humidity and temperature. The NO2 observations are calibrated data streams.<br><br>The traffic observations remain available through the <a href="https://app.swaggerhub.com/apis-docs/telraam/Telraam-API/1.2.0">API of the Telraam platform</a>.<br><br><br></p>
The VAROS Synthetic Underwater Data Set: Towards realistic multi-sensor underwater data with ground truth
<p>Underwater visual perception requires being able to deal with bad and rapidly varying illumination and with reduced visibility due to water turbidity. The verification of such algorithms is crucial for safe and efficient underwater exploration and intervention operations. Ground truth data play an important role in evaluating vision algorithms. However, obtaining ground truth from real underwater environments is in general very hard, if possible at all. In a synthetic underwater 3D environment, however, (nearly) all parameters are known and controllable, and ground truth data can be absolutely accurate in terms of geometry. In this paper, we present the VAROS environment, our approach to generating highly realistic underwater video and auxiliary sensor data with precise ground truth, built around the Blender modeling and rendering environment. VAROS allows for physically realistic motion of the simulated underwater (UW) vehicle including moving illumination. Pose sequences are created by first defining way-points for the simulated underwater vehicle which are expanded into a smooth vehicle course sampled at IMU data rate (200Hz). This expansion uses a vehicle dynamics model and a discrete-time controller algorithm that simulates the sequential following of the way-points. The scenes are rendered using the raytracing method, which generates realistic images, integrating direct light, and indirect volumetric scattering. The VAROS dataset version 1 provides images, inertial measurement unit (IMU) and depth gauge data, as well as ground truth poses, depth images and surface normal images.</p>
Rainfall data monitored by acoustic sensors in Zurich and Milan during spring and summer 2022
<p>The database contains rainfall information obtained from acoustic sensors and rain gauges (meteoblue AG) in the cities of Zurich (Switzerland) and Milan (Italy) during field work conducted in spring and summer 2022.</p> <p>Zurich:</p> <p>Continuous rainfall data is provided at 15 min intervals for April 2022; data_acoustic_Zurich.csv - number of rain drops, data_meteoblue.csv - rainfall depth (mm).</p> <p>Milan:</p> <p>Data is provided for 5 rain events in June 2022 at 1 min intervals; data_acoustic_Milan.csv - number of rain drops, data_meteoblue.csv - rainfall depth (mm).</p> <p>The locations of the acoustic sensors and rain gauges can be find in the metadata files: Metadata_acoustic.xlsx and Metadata_meteoblue.xlsx</p> <p>The presented-data passed only a primilinary quality control.</p> <p>Further infromation about the senor networks in Milan and Zurich can be found here: https://doi.org/10.5194/nhess-2022-257</p>
Water quality data from Talkpool sensor
<p>Talkpool installed water quality sensors measuring temperature, conductivity, pH and turbidity in recipients receiving wast water from construction sites to be able to monitor the influence of waste water from construction sites on water quality in those recipients. Sensors are installed both upstream and downstream from the discharge point to be able to measure the effect of the waste water.</p>
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.