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412 results for “sensor data”
Multi-Sensor Ice Analysis Data: Analysis for Belgica Bank, North East Greenland 2019-20
<p>The intention is that this dataset can be used for machine learning and deep neural network training/validation, and it distinguishes sea ice concentration, type and form derived from manual analysis of a combination of different satellite sensors including ALOS-2, Sentinel-1, COSMO-SkyMed, Sentinel-2, and ICESAT-2. The region chosen for the analysis was the Belgica Bank area offshore of North East Greenland, as this is an area which experiences a wide variety of sea ice, and iceberg, conditions throughout the year. The dataset consists of two parts: 11 days of individual sea ice interpretations, one for each month in the period from April 2019 to March 2020, with the exception of October 2019, and iceberg surveys derived from Sentinel-2 for spring in 2019 and 2020. </p> <p>The dataset includes a user guide issued by MET Norway as report 10/2022 (see https://www.met.no/publikasjoner/met-report) in which the first part describes the data sources, nomenclature, file formats and data in the analysis. A second part of the report compares synthetic aperture radar (SAR) data from both L-band ALOS-2 and C-band Sentinel-1 satellites, and identifies the visible synergies and anomalies. The results confirm that there are variations in backscatter signatures between ALOS-2 and Sentinel-1 data when comparing them for different sea ice situations and conditions. ALOS-2 data in many cases is proven to be a reliable and beneficial source of data when it comes to identifying icebergs, ridges, determining sea ice type, and also distinguishing ice and water compared to standalone Sentinel-1 data.</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>
Ion Implantation Sensor and Process Target Data for Predicting Ion Beam Tuning in Semiconductor Manufacturing
<h2><strong>Dataset Description:</strong></h2> <p>This dataset is designed to predict ion beam tuning setup processes in semiconductor manufacturing, in terms of tuning success or failure, and tuning duration. It is split into <strong><code>X</code></strong> and <code><strong>y</strong></code> to allow for supervised learning approaches.</p> <ul> <li><code><strong>X</strong></code> represents the current equipment condition and the process targets of the currently processed and the upcoming lot, as defined within recipes.</li> <li><code><strong>y</strong></code> represents the ion beam tuning setup report, which informs about the tuning success ratio and tuning duration. These setups are necessary, when switching between recipes to prepare the equipment for processing the next lot. <strong><code>y</code></strong> contains three labels, enabling classification of (1) tuning success or fail, and (2) prolonged tuning, as well as (3) estimation of tuning duration as a regression task.</li> </ul> <p>About <strong><code>X</code></strong>:</p> <p>Each lot is processed with a specific recipe to achieve the process target. The tuning takes place before the first wafer of the to-be-tuned recipe is processed. Each row in <strong><code>X</code></strong> includes logistical information such as the equipment used for processing and parsed recipe / process target information for the current and upcoming lot. The majority of data consists out of aggregated metrics of equipment-internally tracked sensor traces, recording physical parameters such as gas flows, temperatures, voltages and currents. When analyzed in conjunction with the processed recipe, these sensors provide insights into the current equipment condition. </p> <p>About <code><strong>y</strong></code>:</p> <p>The <code>setup_result</code> column indicates the success or failure of tuning - with <code>setup_result=0</code> indicating tuning success, while <code>setup_result=1</code> signals tuning failure. If the first tuning attempt fails, there may be follow-up attempts, but these are not included in this dataset. The <code>duration</code> column represents the tuning duration in seconds, as used for regression analysis. The <code>duration_interval</code> column is a binary label for prolonged tunings, i.e. <code>duration_interval=1</code> for instances, which take more than 6 minutes to tune.</p> <p>For reproducibility of the corresponding paper's results:</p> <ol> <li>The dataset contains the same carefully curated subset of features.</li> <li>The train_test_split() has already been performed, thus we provide <code>x_train</code> and <code>x_valid</code> separately.</li> <li>To reduce the effect of outliers in the data, the sensor data has already been scaled, as derived from <code>x_train</code>.</li> </ol> <p>In summary, these datasets (<code><strong>X</strong></code>, <code><strong>y</strong></code>) provide comprehensive information for predicting ion beam tuning in semiconductor manufacturing, making it a valuable resource for researchers and practitioners in the field.</p> <h2><strong>Python Code for Reproducibility:</strong></h2> <p>Furthermore, we share a jupyter notebook <code>ionbeamtuning.ipynb</code> with Python code to train the best performing model on the provided data, as described in the paper. To execute the code, you may need to install any missing packages specified in the <code>requirements.txt</code>, as indicated within the notebook.</p>
Time Series data from wearable sensors to capture the onset of Fatigue in Runners
<p>The data captured came from mounting a single Shimmer3 IMU on the lumbar of 19 recreational runners. The participants were all regular runners and injury free. The study protocol was reviewed and approved by the human research ethics committee at University College Dublin.<br><br>The data was collected in three segments; in the first, the participant completed a 400m run at a comfortable pace; the second segment consisted of a beep test which acted as the fatiguing protocol for this study; and the last segment where the runner was required to complete the 400m run at their comfortable pace, this time in their fatigued state. The beep test requires the runner to continuously run between two points 20m apart following an audio which produces `beeps' indicating when the person should begin running from one end to the other. The test eventually requires the runner to increase their pace as the interval between the `beeps' reduces as the test progresses. The fatiguing protocol ends when the runner is unable to keep up the increase in pace. The runs were all done on an outdoor running track. The sensor captured acceleration, angular velocity and magnetometer data throughout the three stages of the trials at a sampling rate of 256Hz. The data included here consists of the raw readings from the sensors across the three phases of the run. The data is saved seperately as 'F' for Fatigued, 'NF' for Not Fatigued, and 'BeepTest' for the data collected during the fatiguing process.</p> <p>For the processed and labelled fatigue and non fatigue data, see:</p> <p>https://zenodo.org/records/7997851</p> <p>Kindly cite one of the following papers when using this data:</p> <p>B. Kathirgamanathan, B. Caulfield and P. Cunningham, "Towards Globalised Models for Exercise Classification using Inertial Measurement Units," 2023 IEEE 19th International Conference on Body Sensor Networks (BSN), Boston, MA, USA, 2023, pp. 1–4, doi: 10.1109/BSN58485.2023.10331612</p> <p>B. Kathirgamanathan, T. Nguyen, G. Ifrim, B. Caulfield, P. Cunningham. Explaining Fatigue in Runners using Time Series Analysis on Wearable Sensor Data, XKDD 2023: 5th International Workshop on eXplainable Knowledge Discovery in Data Mining, ECML PKDD, 2023, <a href="http://xkdd2023.isti.cnr.it/papers/223.pdf">http://xkdd2023.isti.cnr.it/papers/223.pdf</a></p>
[Data] Qualify-As-You-Go: Sensor Fusion of Optical and Acoustic Signatures with Contrastive Deep Learning for Multi-Material Composition Monitoring in Laser Powder Bed Fusion Process
<p><br>Growing demand for multi-material Laser Powder Bed Fusion (LPBF) faces process control and quality monitoring challenges, particularly in ensuring precise material composition. This study explores optical and acoustic emission signals during LPBF processes with multiple materials, addressing challenges in process control and ensuring accurate material composition. Experimental data from processing five powder compositions were collected using a custombuilt monitoring system in a commercial LPBF machine. The research categorised signals from LPBF processing various compositions, enhancing prediction accuracy by combining optical with acoustic data and training convolutional neural networks using contrastive learning. Latent spaces of trained models using two contrastive loss functions, clustered acoustic and optical<br>emissions based on similarities, aligning with five compositions. Contrastive learning and sensor fusion were found to be essential for monitoring LPBF processes involving multiple materials. This research advances the understanding of multi-material LPBF, highlighting sensor fusion strategies’ potential for improving quality control in additive manufacturing. Data set for this work is hosted here</p>
Preliminary data collected by 2 prototype Surface Velocity Platform drifters with Barometer and Reference Sensor for Temperature (SVP-BRST)
<p>The SVP-BRST drifter was developed to serve calibration and validation of Sentinel satellite SST retrievals. Two prototypes were deployed in the Mediterranean Sea end of April 2018. Preliminary data collected then until 11 June 2018 are published in this dataset. The drifters were developed and deployed under funding from the European Union's Copernicus Programme. The data are transmitted from the buoy to shore using data format #091 (see References).</p>
A ferrofluid-based sensor to measure bottom shear stresses under currents and waves. Data set: Ferrofluids_Opt_2018_DiDonFranceesco
<p>The experimental calibration of the system for measuring bed shear stresses under currents was carried out at the Hydraulic Laboratory of the University of Catania.</p> <p>In this experimental campaign the magnet S0805 and S0808 were used. The tests were conducted for several bottom configurations (smooth bottom; thin sand d<sub>50</sub>=0.24 mm; coarse sand d<sub>50</sub>=0.56 mm; and mixed sand 70% thin sand and 30% coarse sand). The goals of such tests were: to study the effects of the type of magnets and to carry out a preliminary analysis the ferrofluid behavior over sandy bottom.</p>
A ferrofluid-based sensor to measure bottom shear stresses under currents and waves. Data set: Ferrofluids_Opt_2017_Privitera
<p>The experimental calibration of the system for measuring bed shear stresses under currents was carried out at the Hydraulic Laboratory of the University of Catania.</p> <p>In this experimental campaign magnet type S0805 and a number of magnets equal to 2,3 and 4 were used. The tests were conducted both over a fixed bed (Perspex<sup>©</sup>) and in the presence of mobile beds. The goals of such tests were: to study of the velocity profiles for some fixed and mobile bottoms; to study the effects of the number of magnets on the ferrofluid behavior; preliminary analysis of the bed shear stress over sandy bottom.</p>
Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms"
<p>Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms", submitted for publication. The data consists of: (i) raw data from three nodes with four MICS 2614 metal-oxide ozone sensors deployed in Spain, summer 2017, and (ii) raw data of five alphasense OX-B431 and NO2-B43F electro-chemical sensors, four deployed in Italy and one in Austria, summers 2017 and 2018. Moreover, we have added the calibrated data using four machine learning methods: Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR).</p>
STOP-IT Real-Time Sensor Data Protection (RSDP)
<p>Sensors, and other devices, generate large amounts of data, which can be used for different purposes; for example, controlling the proper functioning of a critical infrastructure, performing predictive maintenance actions or making decisions that improve the productivity of an industrial plant. However, the use or analysis of erroneous or corrupt data can cause catastrophic situations. For this reason, it is very important to be able to guarantee the integrity of the data generated by sensors, or other devices, which will be used to perform relevant actions for a critical infrastructure, industrial plant, etc. The RSDP tool provides exactly that service, it checks the integrity of the data that has been previously stored in the system, and this can be guaranteed thanks to the use of Blockchain, or DLT, technologies.</p>
AirHeritage Datalake: Multi-site, Multi-season, Multi Unit dataset including Fixed and Mobile Citizen science data from networked Air Quality Low-Cost Multi-Sensors devices and reference stations
<p>This datalake comprises several datasets from <strong>37 networked low cost air quality multisensors</strong> (<strong>30</strong> <strong>mobile</strong> ENEA MONICA(tm) + <strong>7</strong> <strong>fixed</strong>) along with <strong>3</strong> (fixed) + <strong>1</strong> (mobile) <strong>reference stations</strong> operated by Campania Regional Envronmental Protection Agency. The datalake is organized in 3 main directories respectively related to fixed nodes, mobile nodes and nearby reference stations including a mobile laboratory used for colocation campaigns; each subdirectory include its own metadata description file.</p> <p>Data, curated by Energy and Data Science Laboratory of ENEA, include multi-weeks colocation periods when low cost devices have been colocated with reference stations as well as operational periods during which sensors are deployed for fixed or mobile monitoring campaigns. Data have been recorded during 2021 and 2022 in a<strong> pervasive, multi-site, multi-seasonal deployment</strong> in Portici, a densely populated small area city (4km2, 55k + inhabitants) located 7km south of Naples, Italy.</p> <p>The datalake consists in actual sensors and reference intrumentations timeseries along with metadata description files with deployment dates and location data. The dataset files include high sampling frequency raw sensor data of quality-controlled sensor network along with co-located reference stations data sets. Sensor data include electrochemical sensors data (intended target pollutants: NO2, O3, CO), Optical sensor data (PM2.5, PM10, PM1) readings along with meteorological parameters. .</p> <p>Further description of sensors and reference instruments are reported in the accompanying paper (see citation request).</p> <p>The dataset can be used for </p> <ul> <li> <strong>advanced (remote/universal/in field) data driven calibration strategies</strong> test or development including <strong>machine learning </strong>models</li> <li><strong>mobile opportunistic data fusion</strong> methods development</li> <li><strong>geomatics and data assimilation</strong> models studies</li> </ul> <p>as well as low cost sensor characterization performance studies. </p>
Data for publication "The ZiCOS-M CO2 sensor network: measurement performance and CO2 variability across Zürich"
<p>Please see README.md for a description of this package. </p> <p>This work was funded by the European Union's Horizon 2020 research and innovation programme, grant agreement number 101037319, named Pilot Applications in Urban Landscapes - towards integrated city observatories for greenhouse gases (PAUL) and is known as ICOS Cities.</p> <p> </p>
Vibration Sensor and Process Data from Ferrosilicon Production
<p><strong>Elkem Facility</strong></p> <p>The facility specializes in producing ferrosilicon (FeSi) and ferrosilicon magnesium (FSM) master alloys. Elkem Bjølvefossen is among the world’s largest producers of FSM. Three reduction furnaces deliver the base metal which is then alloyed and refined to the right quality of FeSi or FSM. These alloys are important additives in the manufacturing of steel products. Silicon in the form of FeSi is used to remove oxygen from the steel and as an alloying element to improve the final quality of the steel. Silicon increases strength and wear resistance, elasticity, i.e., spring steels, scale resistance, and heat resistant steels and lowers electrical conductivity and magnetostriction.</p> <p>After tapping and refining, the ferro-alloys are crushed to grains ranging from 1 mm to 25 mm in size. Consumers of FeSi and FSM have strict requirements for particle size, related mainly to the chemical kinetics of their refining and alloying processes. For this reason, the crushed material is separated in sieves and packaged by particle size before shipment. Two lattice gratings inside the Mogensen shaker separate the material according to required particle size. </p> <p>The subject of the present study is a mechanical shaker platform containing one or more such sieves. The shaker is a <a href="https://www.mogensen.se/">Mogensen S0556</a> that was installed in 1996 in Bjølvefossen and is no longer produced in this type. This device is powered by two counter-rotating 1.2-horsepower AC motors operating at 960 RPM. Together with the spring suspension, these cause an elliptical motion that both transports and scatters the incoming material across the sieve. The shaker is engineered so that the motion transitions from a slanted ellipse at the in-feed to nearly linear at the output.</p> <p><strong>Vibration Data</strong></p> <p>Vibration data from two sensors. Each sensor measures acceleration in three axes with three different ADCs. </p> <p><strong>ERP and MES data</strong></p> <p>Manufacturing Execution System (MES) data as well as process data from the Enterprise Resource Planning (ERP) is given. It is providing information about the material that is currently being produced as well as the data from the scales from the material packing station where the bags with completed production were packed. MES data has a resolution of 5 seconds and the process data from ERP has a resolution of roughly 10 minutes. This operational data is meant to provide insight into the current state and throughput of the facility and will serve as labels for the correlation analysis with the vibration data. </p>
Buoy and Other Sensors Data at the South East Coast of Iceland (Höfn area) on Nov 20 - 24, 2020
<p>This dataset contains the information about sea state and environmental parameters in the area of tidal inlet near Höfn city, South East coast of Iceland. Buoy data is given with original half-hour time spacing. Wind and tidal sensors' data are given with the original 10 min time spacing. All of the given parameters did not undergo any re-interpolation, filtering or any other processing.</p> <p>structure of the "Buoy_data_20201120T010100_to_20201124T125900.csv" file</p> <pre><code>Comma-separated values'(.csv) table (217 rows 3 columns) with the the following fields: 1st column: time in the format dd-Mmm-yyyy HH:MM:SS; 2nd column: Significant wave height (Hs) in meters (m); 3rd column Peak wave period in seconds (s).</code></pre> <p>structure of the "Sea_level_tide_wind_data_20201120T010100_to_20201124T125900.csv" file (originally data was obtained from the <a href="http://vedur.mogt.is/harbor/index.php?action=ReadingsDrillDown&harborid=4&stationid=1011">web page</a> of Weather information system MogT ehf. Hornafjörður, Iceland - the corresponding table fields' names are doubled in Icelandic for convenience) </p> <pre><code>Comma-separated values'(.csv) table (649 rows 5 columns) with the the following fields: 1st column: time in the format dd-mm-yyyy HH:MM:SS; 2nd column: observed sea level (Sjávarhæð) in meters (m); 3rd column astronomical tide (Flóðatafla) in meters (m); 4th column wind speed (Vindur) in meters per second (m/s); 5th column wind direction (Vindátt) in degrees (deg).</code></pre> <p>The corresponding data chunks are published after the kind allowance of the data owners: Vignir Júlíusson and Greipur Sigurðsson.</p> <p> </p>
Supplementary Data for "Identification of Neighborhood Hotspots via the Cumulative Hazard Index: Results from a Community-Partnered Low-cost Sensor Deployment"
<p>These are the underlying data sets needed to build the kriging maps and calculate dissemination block cumulative hazard indices described in the paper. There are three data sets:</p> <ol> <li><strong>"Sampling location names and coordinates.csv"</strong>: locations and IDs of the low-cost sensors and the regulatory monitoring stations used in this work.<strong> [NOTE: </strong>latitudes and longitudes for the sensor deployments have been intentionally rounded to protect the location of volunteer sensor hosts.]</li> <li><strong>"Dissemination Block Populations.csv"</strong>: These are the relevant dissemination blocks in the study domain and their associated populations. This information was originally extracted from: https://censusmapper.ca/#13/49.2430/-123.1252</li> <li><strong>"Daily average concentrations by site and pollutant.csv"</strong>: This contains the PM2.5, NO2 and O3 daily averages for the entire study period across all low-cost sensor sites and regulatory monitoring stations. Refer to "Sampling location names and coordinates.csv" to parse the labels in this data set.</li> </ol> <p>There is also a sample code in Python to construct the kriging maps provided in 2 formats. <strong>[NOTE: </strong>we have intentionally excluded uploading the exact data sets imported by this code; our original data contains exact locations of sensor host volunteers and thus cannot be shared.]</p> <ol> <li><strong>"Jain et al - GeoHealth - Kriging Script.ipynb"</strong>: A Jupyter notebook script to import the data, build kriging maps, calculate CHIs, and export the data.</li> <li><strong>" Jain et al - GeoHealth - Kriging Script.pdf"</strong>: A PDF export of the Jupyter notebook so that you can read the Python scripts even if you are not a Jupyter notebooks user.</li> </ol>
WSD4FEDSRM (Wearable sensor data for fatigue estimation during shoulder rotation movements)
<p>The dataset comprises a collection of many data types during shoulder internal rotation, and external rotation exercises from 34 participants, including demographic information, anthropometric measurements, maximum voluntary isometric contraction force measurements, inertial measuring unit data, surface electromyography recordings, photoplethysmogram data from wearable sensors, as well as measurements from the Borg rating of perceived exertion scale and the Karolinska sleepiness scale.</p>
High-frequency sensor data collected by Stroud Water Research Center in a meadow reach of White Clay Creek from February 2016 through December 2016
High-frequency sensor data from a YSI 600 OMS Optical Monitoring System (every 15 minutes) and Sontek IQ (every 10 minutes) in a meadow reach at White Clay Creek from February through December 2016. Funded by NSF and DEB as part of the LTREB grant to study the recovery of stream ecosystem structure and function during reforestation, Stroud Water Research Center. The parameters in this data package are water temperature, depth, turbidity, conductivity, specific conductance, water pressure, discharge, rivers ection area, and velocity. Data are presented in four tables which likely have significant overlap. The raw data table presents the data exactly as it was downloaded from the Aquarius Database. It is formatted as a "wide" human-readable table. IQ_stream and YSI_stream present only the data from the respective sensors. These tables are gapfilled so that there are no time gaps. Formatted as a "wide" human-readable table. The full_stream table is all of the data, raw and cleaned, from both sensors. It is organized as a long, tidy table and is optimal for machine readability. All of the parameters and table are further explained in the metadata.
High-frequency sensor data collected by Stroud Water Research Center in a meadow reach of White Clay Creek from Janurary 2017 through December 2017
High-frequency sensor data from a YSI 600 OMS Optical Monitoring System (every 15 minutes) and Sontek IQ (every 10 minutes) in a meadow reach at White Clay Creek from January 2017 through December 2017. Funded by NSF and DEB as part of the LTREB grant to study the recovery of stream ecosystem structure and function during reforestation, Stroud Water Research Center. The parameters in this data package are water temperature, depth, turbidity, conductivity, specific conductance, water pressure, discharge, rivers ection area, and velocity. Data are presented in four tables which likely have significant overlap. The raw data table presents the data exactly as it was downloaded from the Aquarius Database. It is formatted as a "wide" human-readable table. IQ_stream and YSI_stream present only the data from the respective sensors. These tables are gapfilled so that there are no time gaps. Formatted as a "wide" human-readable table. The full_stream table is all of the data, raw and cleaned, from both sensors. It is organized as a long, tidy table and is optimal for machine readability. All of the parameters and table are further explained in the metadata.
High-frequency sensor data collected by Stroud Water Research Center in a meadow reach of White Clay Creek from Janurary 2018 through December 2018
High-frequency sensor data from a YSI 600 OMS Optical Monitoring System (every 15 minutes) and Sontek IQ (every 10 minutes) in a meadow reach at White Clay Creek from January 2018 through December 2018. Funded by NSF and DEB as part of the LTREB grant to study the recovery of stream ecosystem structure and function during reforestation, Stroud Water Research Center. The parameters in this data package are water temperature, depth, turbidity, conductivity, specific conductance, water pressure, discharge, rivers ection area, and velocity. Data are presented in four tables which likely have significant overlap. The raw data table presents the data exactly as it was downloaded from the Aquarius Database. It is formatted as a "wide" human-readable table. IQ_stream and YSI_stream present only the data from the respective sensors. These tables are gapfilled so that there are no time gaps. Formatted as a "wide" human-readable table. The full_stream table is all of the data, raw and cleaned, from both sensors. It is organized as a long, tidy table and is optimal for machine readability. All of the parameters and table are further explained in the metadata.
Sensor data associated with Lucius et al. 2020 – Using machine learning to correct for nonphotochemical quenching in high-frequency in vivo fluorometer data.
This document describes a dataset used to produce Using machine learning to correct for nonphotochemical quenching in high-frequency, in vivo fluorometer data, as reported in: Lucius, M.A., Johnston, K.E., Eichler, L.W., Farrell, J.L., Moriarty, V.W. and Relyea, R.A. (2020), Using machine learning to correct for nonphotochemical quenching in high‐frequency, in vivo fluorometer data. Limnol Oceanogr Methods, 18: 477-494. https://doi.org/10.1002/lom3.10378 The dataset consists of high-frequency water quality and meterological sensor data collected from two autonomous vertical profiling platforms deployed on Lake George, NY during the ice-free months of 2017-2019. Water quality data include depth-referenced measurements of chlorophyll fluorescence, water temperature and dissolved oxygen. Meteorological data include surface-incident total radiation as well as two derived values: solar azimuth and 1-hr rolling average of total radiation. Finally, using interpolated data from regularly collected subsurface profiles of photosynthetically active radiation, estimates of subsurface total radiation were estimated and included in this dataset. This dataset does not include raw data. The data used were subjected to quality control procedures of the Jefferson Project, as well as additional outlier removal measures and the creation of derived data (as previously described and described in detail in Lucius et al. 2020).
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.