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185 results for “Health Monitoring”
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.
SFEM Dataset for Structural Health Monitoring
<p>The data is generated through Spectral Finite Element Methods (SFEM) solver for Ultrasonic Guided Wave based Structural Health Monitoring. The data is used in repository "https://github.com/mahindrautela/DINS-SHM" and the paper "Ultrasonic guided wave based structural damage detection and localization using model assisted convolutional and recurrent neural networks".</p>
South Bay Salt Pond Restoration Project – Phase-1 (2010-2012) Sentinel Species Health Monitoring.
The South Bay Salt Pond Restoration Program (SBSPRP) is the largest wetland restoration project in the western United States, restoring approximately 15,000 acres of former salt evaporation ponds (southbaysaltpond.org) to benefit wildlife and fish populations. Restoration on a large scale comes with many risks and uncertainties. Therefore, restoration was planned in several phases, with an adaptive management approach and applied scientific studies to address the uncertainty of different restoration strategies. These strategies included breaching ponds to create fully tidal habitats, installing tide gates to create muted tidal habitats and active management of existing ponds. This mosaic of restoration designs was intended to benefit many species of salt marsh dependent biota, including birds, fish and mammalian species. The Longjaw Mudusucker (Gillichthys mirabilis) is a resident estuarine fish, ranging from Mexico to Humboldt Bay, California, USA, and is one of the most abundant fishes in high intertidal salt-marsh habitat. The Longjaw Mudsucker depends on high intertidal creeks in marshes dominated by pickleweed (Sarcocornia sp). The fish reside within burrows in soft sediments and is the only fish species that can remain in intertidal creeks during low tide when the creeks completely de-water. Longjaw Mudsucker have a wide tolerance range for salinity, up to 80-ppt and can be the only fish species to occupy industrial salt ponds in the San Francisco Estuary. In this study, UC Davis conducted minnow trap sampling in remnant pickleweed marshes and adjacent salt pond restorations to document the distribution, relative abundance, and condition (length-weight) of fish occupying these extant and restored habitats. During the pilot effort in late summer-fall of 2010 we conducted minnow trap sampling across a number of sites in the Alviso Marsh, Eden Landing Marsh, Ravenswood Marsh and Bair Island Marsh, sampling muted restoration ponds, tidal restoration ponds and remn
Preliminary table of Citizen Science initiatives for monitoring soil health
<p>This matrix is the result of collaborative work for Deliverable 1.1 (WP1; T1.1) of the ECHO project. It facilitated the creation of an overview of the current state of the art in projects, initiatives, or activities that have already involved citizens in monitoring soil health, from both inside and outside the European Union. From this, strategic recommendations for ECHO were derived, ensuring that this project not only makes a valuable contribution to the field of soil health monitoring but also sets a precedent for future citizen science endeavors.</p>
EJPSOIL ARTEMIS on-farm monitoring of soil health and ecosystems services (meta)data
<p>This database includes the data and metadata from the initial on-farm monitoring od soil health and soil related ecosystem services of the EJPSOIL ARTEMIS project. </p>
Raw Data for Evaluation of Measurement Uncertainty in Structural Health Monitoring Systems Under Temperature Influence
<p>The documentation on these laboraty tests is titled "Documentation.pdf"</p> <p> </p> <p>Raw data from distance measurements using laser triangulation sensors acquired under different temperatures are provided. Six sensors were tested per experiment (CSV file), and in each experiment the boundary conditions are varied as follows:<br><br>00RawData_LTS_1m: The entire measurement system is subject to temperature change, with initial distances chosen as LTS1/LTS2=17 mm, LTS3/LTS4=21 mm nd LTS5/LTS6=25 mm.<br><br>01RawData_LTS_1m_SwitchedDistances: The entire measurement system is subject to temperature change, with the selected initial distances of LTS1/LTS2=25 mm, LTS3/LTS4=17 mm nd LTS5/LTS6=21 mm.<br><br>02RawData_LTS_1m_SwitchedDistances2: The entire measurement system is subject to temperature change, with initial distances selected as LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>03RawData_LTS_1m_OnlySensor: Only the sensors of the measuring system are subject to temperature change, where the selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>04RawData_LTS_1m_OnlyMeasuringAmplifier: Only the measuring amplifiers of the measuring system are subject to temperature change. The selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>05RawData_LTS_1m_OnlyCable: Only the cables of the measurement system are subject to the temperature change. The selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>Tested temperature range: -10°C to 50°C<br>Measuring frequency: 1 Hz<br>Measuring amplifier: Q.bloxx.XL A107 Gantner Instruments<br>Cable: 4-pole, 1.00 m length<br>Sensor: OM20-P0026.HH.YIN laser triangulation sensor from Baumer</p>
Wind turbine blade structural health monitoring dataset
<p>The dataset is related to a unique experiment conducted at ETH Zurich in collaboration with the Institute of Fluid Flow Machinery, Polish Academy of Sciences. The synchronisation between fatigue loading and guided wave excitation and sensing is unique. The dataset can be used to construct and test damage indexes for structural health monitoring.</p> <p>The tests were carried out on a Sonkyo Windspot 3.5 kW wind turbine blade equipped with strain gauges, a thermocouple, and five piezoelectric transducers. One piezoelectric transducer was used for Hann windowed sine excitation whereas the remaining piezoelectric transducers were used as sensors. The fatigue loading was induced by using a 1 kN capable Tira shaker. The fatigue program is explained in the readme.txt file and involves overloading the blade with a crane up to the blade's failure. The shaker was excited by a sine signal of frequency around the first resonant frequency of the wind turbine blade. The synchronisation with guided wave excitation was realised during three characteristic moments: (1) at maximum amplitude of sine, (2) at zero crossing, and (2) at the minimum amplitude of sine. This stage of the experiment is called 'dynamic' for short, and the data is stored in respective 'raw' folders. After each set of 1000 cycles, the shaker was stopped until the blade stopped vibrating. Then another set of guided wave measurements was taken at the blade's rest position. This stage of the experiment is called 'static' for short, and the data is stored in respective 'average' folders. It contains signals averaged over 10 measurements. During the whole process strain as well as temperature were measured.</p> <p>Three files are included for data visualization: (1) 'plot_strain_temperature.m', (2) 'read_plot_static.m', and (3) 'read_plot_dynamic.m'. These are MATLAB scripts showing how to load data and visualize the dependence of strains and temperatures on fatigue cycle number or time, plot exemplary signals of guided waves, and construct a damage index for structural health monitoring of the wind turbine blade.</p> <p>The details of experimental setup can be found in the paper.</p>
SHM - Structural Health Monitoring Dataset
<p>Synthetic and Real-World Data as described in</p> <p>C. Kralovec, B. Lehner, M. Kirchmayr, M. Schagerl, "Sandwich Face Layer Debonding Detection and Size Estimation<br> by Machine Learning-based evaluation of Electromechanical Impedance Measurements", Sensors, 2023</p> <p> </p> <p>The implementation of the experiments can be found here (Python):</p> <p>https://github.com/berni-lehner/structural_health_monitoring</p>
Björkö Wind Turbine Version 1 (45kW) high frequency Structural Health Monitoring (SHM) data
<p>The Chalmers wind turbine has variable speed operation with a direct driven generator and a frequency converter, it also has a digital control system developed by Chalmers. The wind turbine has a rated power of 45 kW and rated speed of 75 rpm. The wooden tower is 30 m high, the blades of carbon fibres are 7.5 m long, and the turbine diameter is 15.9 m. The individually blade pitch system is electrical. The turbine is situated on the island Björkö at Skarviksvägen, 20 km west of Göteborg city. The coordinates are: 57.71818820625921, 11.683382148764485.</p> <p><br> 69 SCADA and structural vibration and loads Channels timeseries (sampled at 20 and 100 Hz) such as nacelle accelerations, tower and blades bending moments are included.</p> <p><br> Structured metadata about wind turbine characteristics, SCADA, vibration and loads channels are included as JSON files and CSV.</p> <p>This particular dataset consisting of high frequency sampled data, is intended for condition and structural health analysis.</p> <p><strong>The data covers:</strong></p> <ul> <li>the measurements sampled at 100 Hz correspond to the period from 05 July 2022 to 9 June 2023</li> <li>the measurements sampled at 20 Hz correspond to the period from 05 July 2022 to 2 August 2023</li> </ul> <p><strong>This repository includes:</strong></p> <p><strong>Time-series data in csv format:</strong></p> <ul> <li>B1_CL4_20.csv (this is the data sampled at 20 Hz)</li> <li>B1_CL4_100.csv (this is the data sampled at 100 Hz)</li> </ul> <p><strong>Metadata:</strong></p> <ul> <li>Bjorko_Sensors_Specs_Metadata.csv (Sensors signals specification in csv format)</li> <li>Bjorko_modes_mapping.csv (numerical integer value representing the wind turbine controller system mode in csv format)</li> <li>Bjorko_modes_mapping.json (numerical integer value representing the wind turbine controller system mode in csv JSON format)</li> <li>Bjorko_digital_io_states_mappings.csv (Description of digital input and output states in the wind turbine controller system in csv format)</li> </ul> <p><strong>Media:</strong></p> <ul> <li>Chalmers-Wind turbine.pdf (description of the wind turbine including pictures)</li> <li>Chalmers wind turbine description 220121-short.pdf (description of the wind turbine including pictures)</li> </ul> <p><strong>Semantic artifacts:</strong></p> <ul> <li>N/A</li> </ul> <p><strong>Other:</strong></p> <ul> <li>N/A</li> </ul> <p>Additional information is available upon request.</p>
Aventa AV-7 ETH Zurich Research Wind Turbine SCADA and high frequency Structural Health Monitoring (SHM) data
<p><strong>General description of wind turbine: </strong>The ETH owned wind turbine is Aventa AV-7, manufactured by Aventa AG in Switzerland and was commissioned in December 2002. The turbine is operated via a belt-driven generator and a frequency converter with a variable speed drive. The rated power of the Aventa AV-7 is 7 kW, beginning production at a wind speed of 2 m/s and having a cut-off speed of 14 m/s. The rotor diameter is 12.8 m with 3 rotor blades, and a hub height is 18m. The maximum rotational speed of the turbine is 63 rpm. The tower is a tubular steel-reinforced concrete structure, supported on concrete foundation, while the blades are made of glassfiber with a tubular steel main-spar. The turbine is regulated via a variable-speed and variable pitch control system.</p> <p><strong>Location of site: </strong>The wind turbine is located in Taggenberg, about 5 km from the city centre of Winterthur, Switzerland. This site is easily accessible by public transport and on foot with direct road access right next to the turbine. This prime location reduces the cost of site visits and allows for frequent personal monitoring of the site when test equipment is installed. The coordinates of the site are: 47°31'12.2"N 8°40'55.7"E.</p> <p><strong>Control and measurement systems and signals: </strong>The turbine is regulated via a variable-speed and collective variable pitch control system.</p> <p><strong>SHM Motivation: </strong>Designed and commissioned in 2002, the Aventa wind turbine in Winterthur is soon reaching its end of design lifetime. In order to assess the various techniques of predicting the remaining useful lifetime, a Structural Health Monitoring (SHM) campaign was implemented by ETH Zurich. The monitoring campaign started in 2020, and is still ongoing. In addition, the setup is used as a research platform on topics such as system identification, operational modal analysis, faults/damage detection and classification. We analyze the influence of operational and environmental conditions on the modal parameters and to further infer Performance Indicators (PIs) for assessing structural behavior in terms of deterioration processes.</p> <p><strong>Data Description: </strong>The tower and nacelle have been instrumented with 11 accelerometers distributed along the length of the tower, nacelle main frame, main bearing and generator. Two full bridge strain gauges are installed on the concrete tower based measuring fore-aft and side-side strain (and can be converted to bending moments) – all acceleration and strain signals sampled at 200Hz. Temperature and humidity are measured at the tower base – 1Hz data. In additional we are collecting operational performance data (SCADA), namely: wind speed, nacelle yaw orientation, rotor RPM, power output and turbine status – SCADA signals are sampled at 10Hz. See appendix for further details of the sensors layout.</p> <p>The measurements/instrumentation setup, type and layout is provided in the pdf files.</p> <p><strong>The data:</strong> the data is provided in zip files corresponding to four use-cases as follows:</p> <ul> <li>Normal operation data for system identification</li> <li>Aerodynamic imbalance on one blade</li> <li>Rotor icing event</li> <li>Failure of the flexible coupling of the linear drive of the collective pitch system</li> </ul> <p>The data for each of the four uses-cases is organized in zip files. The content of each zip file is as follows:</p> <ul> <li>Time-series data in HDF5 format</li> <li>Metadata: <ul> <li>Turbine specification (Aventa-AV-7.json and Aventa-AV-7.yaml)</li> <li>Sensor specification (Aventa_sensors.json )</li> <li>Unstructured description of the Aventa Turbine and the installed sensors (Aventa_Sensors_Specs.xlsx)</li> </ul> </li> <li>Semantic artifacts: <ul> <li>WindIO Wind Turbine YAML schema describing turbine specifications (IEAontology_schema.yaml)</li> <li>Sensor specification JSON schema (sensors_schema.json)</li> </ul> </li> <li>Media: Pictures of leading edge roughness and a clip of wind turbine operation</li> <li>Code: Jupyter notebook containing example code to load metadata from JSON and data from HDF5 files (example.ipynb)</li> </ul> <p>Additional data is available upon request, please contact:</p> <ul> <li>Prof. Dr. Eleni Chatzi (chatzi@ibk.baug.ethz.ch)</li> <li>Dr. Imad Abdallah (ai@rtdt.ai , abdallah@ibk.baug.ethz.ch)</li> </ul> <p>For further details or questions, please contact:</p> <p>Prof. Dr. Eleni Chatzi<br> Chair of Structural Mechanics & Monitoring</p> <p>ETH Zürich<br> <a href="http://www.chatzi.ibk.ethz.ch/">http://www.chatzi.ibk.ethz.ch/</a></p>
Covid-19 Vaccine Monitoring project (CVM)-Electronic Health Record data sources Codelist
<p>This is the code list that was used to identify outcomes and covariates (those tagged as in narrow) in electronic health records of participating data sources in the the CVM study which was addressing the following questions</p> <p> </p> <p>1)<strong> To create and assess readiness of electronic health record data sources for rapid evaluation of safety signals by </strong></p> <ul> <li> <p>Providing an overview of the methods for identification of COVID-19 vaccine exposure in the data sources </p> </li> <li> <p>Monitoring the number of individuals exposed to any COVID-19 vaccine and to compare this to COVID-19 vaccine exposure (benchmark: ECDC vaccine tracker)1 </p> </li> <li> <p>Generation of updated background rates for AESIs </p> </li> </ul> <p><strong>2) To conduct rapid safety assessment studies using electronic healthcare records and support EMA safety assessments. </strong></p> <p>The protocol for this study is publicly available www.encepp.eu/encepp/viewResource.htm?id=42637. The report with results using the code list is publicly available on Zenodo as well. </p> <p> </p> <p> </p> <p> </p>
Network Theme: Can blood sampling become a new data source in the role of self-monitoring and self-management of health? - Dr Mark Elliott (University of Warwick)
<p>This video is the fourth talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021.</p> <p>Network Theme: Can blood sampling become a new data source in the role of self-monitoring and self-management of health? - Dr Mark Elliott (University of Warwick)</p> <p>Bio: <strong><a href="https://warwick.ac.uk/fac/sci/wmg/people/profile/?wmgid=1147">Dr Mark Elliott</a> </strong>Mark is an Associate Professor at the Institute of Digital Healthcare, WMG, University of Warwick (UoW). Mark’s core research focuses on human movement and physiology analytics. His research uses signal processing and data science approaches to monitor, measure and model human movement and physiology to infer health status. He is the PI of the WMG Motion Capture Laboratory. His work further extends into the broader area of using wearable and on-the- body sensing devices to make objective measures of human behaviour and behaviour change. Much of Dr Elliott’s research is highly applied and involves collaborating with commercial and NHS partners. He has received funding from EPSRC, Innovate UK and SBRI Healthcare, as well as direct industrial funding. He is currently Data Analytics Theme Lead for the EPSRC funded OATech+ Network and on the steering committee for the EPSRC funded VSimulators facilities at Bath and Exeter.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/ChdbggScUgo</p>
Dataset: Structural Health Monitoring of a Flexible Wing
<p>This Zenodo entry contains the experimental data underlying the journal article Noise-robust Modal Parameter Identification and Damage Assessment for Aero-structures, in preparation. The document XB-2_SHM_Dataset.pdf serves as the explanatory note for the data contained in SHM_XB2.mat</p>
Monitoring Movement and Health Study
ClinicalTrials.gov study NCT03084302. IPD Sharing: YES. Countries: 1. Publications: 7.
Development of a Self-Powered Structural Health Monitoring System for Transportation Infrastructure
<p>Corresponding data set for Tran-SET Project No. 17PTAM03. Abstract of the final report is stated below for reference:</p> <p>"Roadways and bridges play an important role in the economic and social health of society by connecting commerce and people. Economic growth and population expansion pose considerable burden on the aging infrastructure (i.e., pavements and bridges). There is a pressing need to develop structural health monitoring (SHM) technologies capable of collecting infrastructure utilization data. Doing so inexpensively with self-powered systems will revolutionize infrastructure monitoring technology, and will improve decision making enabling roadway and bridge preservation. In this study, a self-powered battery-less structural health monitoring (SHM) system was developed. It is powered by a thermal energy harvester equipped with thermoelectric generators (TEGs) driven by temperature differentials between the top of asphalt pavements and their lower layers. An innovative 2-tier TEG harvester was designed to limit the downtime of the SHM system when the temperature differentials are insufficient to power a single unit. The 2-tier system requires a minimum of 2.1⁰C in temperature differential to generate the minimum of 40 mV needed to power the SHM system. The SHM system consists of a DC-DC booster to increase the voltage generated by the harvester, a buck controller to bring this voltage down to the 3.3 Volts required for powering the microcontroller, a microcontroller and a wireless transceiver for transmitting the data. Another transceiver carried on-board a pilot vehicle is needed to retrieve the data. Software was developed in this project to allow data communication between the two transceivers. The SHM system developed accepts analogue voltage input from any sensor that generates analogue voltage, (e.g., piezoelectric axle load sensors, strain gauges, temperature gauges and so on). A prototype of this SHM system was constructed and tested in the lab and it is ready for field implementation."</p>
Integrated Health Monitoring and Reinforcement of Transportation Structures with Optimized Low-Cost Multifunctional Braided Cables
<p>Corresponding data set for Tran-SET Project No. 17STTAM04. Abstract of the final report is stated below for reference:</p> <p>"The objective of this research study is to design, fabricate, and characterize multifunctional high strength and self-sensing braided cables and structures using novel Fe-based shape memory alloys (SMAs). The system exploits unique properties of recently developed low-cost super-elastic FeMnAlNi SMAs, which enables excellent super-elastic properties, high strength, and self-sensing in structural health monitoring (SHM) systems. This novel material technology can be coupled with modeling efforts that allow for accurate prediction of both the materials and structural response during sensing. At the conclusion of the project, we have demonstrated that with careful design of processing parameters, it is possible to control the yield strength and superleastic properties of FeMnAlNi SMAs. We have fabricated, for the first time in the world, a large diameter wires and bars from these inexpensive iron based SMAs which are expected to help with scaling up the fabrication of these materials. In addition, we were able to develop a method for fabricating braided cables from the fabricated Fe-SMA wires and a lab scale experimental setup has been designed and built. Prototype design for the braiding weave was created and tested. Finally, an experimental setup has been designed and manufactured to measure changes in the magnetic response of the SMA braided cables under load in order to directly correlate the magnetic response with deflection and strain. Clearly, the successful fabrication of wires and braided cables of the inexpensive iron based SMAs with superelastic strains comparable to nickel-titanium SMAs and with favorable magnetic sensing capabilities have significant implications for transportation infrastructure as these materials can provide structural health monitoring capability while also mitigating large shape changes during natural disasters. Future work needs to focus on revealing the coupling between mechanical properties (in particular damage) and the changes in magnetic properties in order to provide guidance on how these materials can be utilized in structural health monitoring."</p>
Development, Training, Education, and Implementation of Low-Cost Sensing Technologies for Bridge Structural Health Monitoring (SHM)
<p>Corresponding data set for Tran-SET Project No. 17STUNM02. Abstract of the final report is stated below for reference:</p> <p>"Transportation infrastructure needs continuous monitoring. However, traditional inspections cost money and are conducted visually. New technologies for bridge monitoring are expensive and complex. This project involved developing cost-effective sensor technologies that can be applied towards the maintenance of railroad bridges by recording reference-free transverse displacement. More specifically, this project developed new applications of new technologies (Arduino, wireless smart sensors, drones, Hololens) and promoted workforce development with an emphasis on outreach of high-school students. This project was carried out in three main phases: (1) development and validation of technologies, (2) education and outreach to students, and (3) outreach to industry consisting in one professional workshop. The findings from the first phase showed that the data gathered by these new low-cost sensing systems were comparable to the data collected using traditional sensors. Researchers collected the findings of the second phase of the project through surveys conducted from Middle school, High school and college students during and after outreach activities. these surveys showed that many of the participant students got more interested in the use of new technologies after getting familiar with them. Finally, researchers collected the findings of the third phase of the project through a workshop collecting the interest and challenges of the owners of railroad infrastructure. The top interest of railroad owners is to explore the use of new technologies to increase safety in the field. The conclusions of this research include prioritization on developing low-cost technologies that can measure simple parameters in the field of interest to existing inspectors."</p>
Data set for "EghiFit: Smartphone based Behaviour Monitoring and Health Recommendation in a Weight Loss Intervention Study"
<p>Dataset has been created for "EghiFit: Smartphone based Behaviour Monitoring and Health Recommendation in a Weight Loss Intervention Study" paper.</p> <p>We have created a smartphone based behaviour monitoring and recommendation system to aid patients recruited in a weight loss intervention programme.<br>The main interaction element for the patients is <strong>EghiFit</strong> application which was used in context of this dataset for data acquisition and secure transmission to our servers.</p> <p>The data consists of application usage per patient, steps achieved, nutritional information of meals logged, heart rate data, interaction data and more.<br>For more information about the dataset, please take a look at <strong>readme.md</strong> file and our paper.</p>
Data for "Advanced Structural Health Monitoring Method by Integrated Isogeometric Analysis and Distributed Fiber Optic Sensing"
<p>This dataset includes the experiment and simulation data of a new structural health monitoring system using distributed fiber optic sensing (DFOS) and Isogeometric Analysis (IGA).</p> <p>The experiment setup was a 5mm thick PVC pipe with a fiber optic cable wrapped around the outer surface of the pipe. The PVC pipe was subjected to an applied deformation and the distributed strains along the optical fiber was measured with a Neubrescope (NBX7031) instrument using Rayleigh backscattering technology.</p> <p>The simulation was performed using the in-house code JWRIAN-IGA developed in Joining and Welding Research Institute, Osaka University. The simulated data includes deformation, stress and strain distributions of the pipe, and projected one-dimensional fiber strains. The visualization files are post-processed with ParaView software.</p>
Dataset from structural health monitoring of a steel bridge in Sweden
<p>The dataset consists of sensor data from the Vänersborg Bridge in Sweden, comprising 64 bridge opening events registered as accelerations, strains, inclinations, and weather conditions. Data from before, during, and after a verified fracture are provided. The sample of raw data captured the same bridge opening event multiple times over the monitoring duration. Classifying data before and after damage enables the development and verification of routines for novelty detection. </p>
ScienceDex guides
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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.