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2,704 results for “damage”
Database of measurements for damage detection of steel beam splice connection by Coaxial Correlation Method in 6-D space
<p>This database includes series of measurements of the structure's response taken in six-dimensional space using two 6D sensors, coaxially positioned on either side of the investigated splice connection between two steel beams. The data set consists of two parts. The first part of the data set is measurements for six different specimens with wave type impact – short sweep signal with duration 0.05 s. The second part is the measurements during splice connection degradation of one of the specimens with short impulse. The degradation of a connection is presented by four different states of joints. In the "<strong>Read_me_first.pdf</strong>" is described the experiment, the format of .csv files names and files' structure.</p><p>Used materials, methods and results for the second part of the data set is described in Buka-Vaivade, K.; Kurtenoks, V.; Serdjuks, D. Non-Destructive Damage Detection of Structural Joint by Coaxial Correlation Method in 6D Space. <i>Buildings</i> <strong>2023</strong>, <i>13</i>, 1151. https://doi.org/10.3390/buildings13051151</p>
Damage assessment of a physical beam reinforced with masses - dataset
<p>The dataset beam-signal contains the spectrum vibration signals in the frequency domain measured from a beam reinforced with masses under healthy and faulty conditions. This data is for a commonly used system in various industrial applications. The data can be used for online condition process monitoring to detect and diagnose any anomaly or faulty condition in the system. Hence, the datasets provide the geometric and experimental measurements performed on the beam reinforced with masses for various mass losses considered structural damage. The collected data included the following datasets:</p> <ul> <li>Dataset Mass-position contains 70 sampling positions for the six masses attached to the beam. (<a href="../api/records/8081690/draft/files/Mass%20position.xlsx/content">Mass position</a>)</li> <li>Dataset DI contains 280 damage indexes calculated using the FRAC method. (<a href="../api/records/8081690/draft/files/DI_FRAC_Exp-estimation.xlsx/content">DI_FRAC_Exp-estimation</a>)</li> <li>Dataset beam-signal includes 280 inertances responses magnitudes and respective phases considering 70 samples of healthy and 210 sampled of damaged conditions ( <a href="../api/records/8081690/draft/files/Dataset%20Beam-signal_Healthy.zip/content">Dataset Beam-signal_Healthy, </a><a href="../api/records/8081690/draft/files/Dateset%20Beam-signal_Damaged-2.96.zip/content">Dateset Beam-signal_Damaged-2.96, </a></li> </ul> <p><a href="../api/records/8081690/draft/files/Dataset%20Beam-signal_Damaged-5.92.zip/content"> Dataset Beam-signal_Damaged-5.92, </a><a href="../api/records/8081690/draft/files/Dataset%20Beam-signal_Damaged-8.87.zip/content">Dataset Beam-signal_Damaged-8.87) .</a></p> <p>The dataset beam-signal can be used to develop structural health monitoring techniques for detecting damage and anomalies in the structure. The dataset's Mass-position and DIs can impose parametric uncertainty in the experiment. Stochastic and damage identification metrics can be used for further insights on new monitoring and control techniques. Since the tests include paramedic uncertainty, they can also be employed in uncertainty quantification, stochastic modelling and supervised and unsupervised machine learning techniques. </p> <p>Therefore, the datasets are intended to benefit the scientific community investigating the dynamics of structures and readers interested in experimental practices applied to systems and modelling. These datasets can be used for numerical model validation, identification techniques, uncertainty quantification, machine learning, and structural integrity monitoring algorithms based on experimental measurement samples on the beam reinforced with mass.</p> <p>A detailed description of the experiment can be found in </p> <p>[1] Sousa, A.A.S.R., da Silva Coelho, J., Machado, M.R. et al. Multiclass Supervised Machine Learning Algorithms Applied to Damage and Assessment Using Beam Dynamic Response. J. Vib. Eng. Technol. (2023). https://doi.org/10.1007/s42417-023-01072-7</p> <p>[2] Monitoramento da Integridade Estrutural de Vigas utilizando Técnicas de Aprendizado de Máquina, 2023. Mestrado em Integridade de Materiais da Engenharia - Universidade de Brasília (In Portuguese)</p> <p>[3] Amanda A.S.R. de Sousa, Marcela R. Machado, Experimental vibration dataset collected of a beam reinforced with masses under different health conditions, Data in Brief, 2024, 110043, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2024.110043.</p>
Database of measurements for damage detection of steel beam splice connections by Coaxial Correlation Method in 6-D space
<p>This database includes series of measurements of the structure's response taken in six-dimensional space using two 6D sensors, coaxially positioned on either side of the investigated splice connection between two steel beams. The data set consists of measurements for six different specimens with two types of impact – sweep signal with duration 0.5 s and short impulse, during degradation of the splice connections realised by unbolting the bolts in the connections. In the "<strong>Read_me_first.pdf</strong>" is described the experiment, the format of .csv files names and files' structure.</p><p>This database is a continuation of the database Kurtenoks, V., Buka-Vaivade, K., Serdjuks, D., Lapkovskis, V., Mironovs, V., & Podkoritovs, A. (2023). Database of measurements for damage detection of steel beam splice connection by Coaxial Correlation Method in 6-D space (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10077332<br>Suggested by authors data post-processing is described in Buka-Vaivade, K.; Kurtenoks, V.; Serdjuks, D. Non-Destructive Damage Detection of Structural Joint by Coaxial Correlation Method in 6D Space. <i>Buildings</i> <strong>2023</strong>, <i>13</i>, 1151. https://doi.org/10.3390/buildings13051151</p>
Damage Localisation in Fresh Cement Mortar Observed via In Situ (Timelapse) X-ray uCT imaging.
<p>This is dataset to paper: Damage Localisation in Fresh Cement Mortar Observed via In Situ (Timelapse) X-ray uCT imaging.</p>
Examining genome size and nutrient influence on plant damage patterns
Data was collected to examine whether and how plant genome size (GS) interacts with environmental nutrient additions to influence the amount and patterns of damage plants sustain from invertebrate herbivores and fungal pathogens. Plants were selected based on visual abundance in treatment plots in which nitrogen (N), phosphorus (P), or NP combined had been annually added (Cont. is the abbreviation we used for the control plot with no nutrients added). Additionally, plant traits of percent foliar carbon (% C), percent foliar nitrogen (% N), and specific leaf area (SLA) were measured from all the same plants that damage values were observed from. Data was collected from 847 plants (626 forb individuals, 221 grass individuals) in eight grassland sites that are part of the Nutrient Network (https://nutnet.org), a globally distributed experiment in which plots have different nutrient amendment treatments that are administered identically to allow cross-site comparisons of the effects of nutrients on biodiversity patterning. The sites chosen varied along a north-south latitude, longitude, mean annual precipitation (MAP) and mean annual temperature (MAT) gradient in the United States. All field data was collected between May 2022 and August 2022. The sites included in this study are listed below with their respective Nutrient Network site codes. churn.us= Churning Rapids in Hancock, MI spin.us= Spindletop Farm in Lexington, KY temple.us= Temple in Temple, TX kbs.us= Kellogg Biological Station in Hickory Corners, MI konz.us= Konza Prairie Biological Station in Manhattan, KS cgbg.us= Chichaqua Bottoms Greenbelt in Maxwell, IA cdcr.us= Cedar Creek in East Bethel, MN msum.us= Minnesota State University at Moorhead in Moorhead, MN
HURRECON Model for Estimating Hurricane Wind Speed, Direction and Damage
HURRECON is a simple meteorological model that estimates hurricane surface wind speed and direction based on the track, size, and intensity of a hurricane and the surface type (land or water). The model also estimates Fujita-scale wind damage as a function of peak 1/4 mile wind speed and wind gust factor. Estimates can be generated for a single site or a rectangular region. The model is based on published empirical studies of many hurricanes. HURRECON can be used to study the impacts of individual hurricanes or to reconstruct the hurricane disturbance regime for a particular region. For more information on the most recent version of the model please see the published paper (Boose, E. R., K. E. Chamberlin and D. R. Foster. 2001. Landscape and regional impacts of hurricanes in New England. Ecological Monographs 71: 27-48). Additional information is contained in the documentation that accompanies the program. For an updated version of the HURRECON model in R and Python, please see HF446.
Forest Damage Patterns at Harvard Forest in the 1938 Hurricane
This study examined landscape-level patterns of forest damage at the Harvard Forest caused by the 1938 New England Hurricane. For details on methods and results, please see the published paper (Foster, D. R. and E. R. Boose. 1992. Patterns of forest damage resulting from catastrophic wind in central New England, USA. Journal of Ecology 80: 79-98). The Abstract from the paper is reproduced below. "1. The effect of catastrophic winds on a forested landscape in central Massachusetts was examined to investigate the factors controlling the geographic pattern of damage. The study area, Tom Swamp Tract, Harvard Forest, comprises a valley and adjoining hillslopes supporting second growth hardwood and confer stands. Much of the study used records and maps that were analyzed cartographically with a geographic information system (GIS). "2. Areally, forest damage was distributed fairly evenly among different damage classes ranging from no damage to more than 75% of stems broken or uprooted. However, there was a negative exponential size distribution of contiguous areas of the same damage intensity, with a preponderance less than 2 ha; these areas ranged from less than 0.04 ha to more than 35 ha; hurricane damage exhibited a continuum ranging from minor damage of individual trees to extensive blow-down of broad areas of forest. "3. The spatial pattern of wind damage was controlled by vegetation height and composition and by site exposure, which is predominantly determined by slope orientation and angle. Approximately 3% of the stands in the study site occupied protected sites, 31% intermediate sites, and 66% exposed sites. "4. Forest type susceptibility followed the ranking (from highest to lowest): Pinus strobes, conifer plantations, Pinus strobus-hardwood = Tsuga canadensis-hardwood-Pinus strobes, hardwood-Pinus strobes, hardwood. Damage increased with increasing site exposure to wind and increased approximately linearly with stand height. "5. An empirical GIS model of landsca
Database of measurements for damage detection of T-type timber structural joint by Coaxial Correlation Method in 6-D space
<p>This database includes series of measurements of the structure's response taken in six-dimensional space using two 6D sensors, coaxially positioned on either side of the investigated joint between two timber beams connected at an angle of 90⁰. Presented data related to seven different states of joints, five load levels, and two type of input signal (short impulse and sweep signal with duration 0.5 seconds). In the "<strong>Read_me_first.pdf</strong>" is described the experiment, the format of .csv files names and files' structure.</p>
Database of measurements for damage detection of panel-to-panel moment joints in timber structures by Coaxial Correlation Method
<p>This database includes series of measurements of the structure's response taken in six-dimensional space using two 6D sensors, coaxially positioned in two different ways on either side of the investigated panel-to-panel connection. Presented data related to ten different states of joints, two load levels, and two type of input signal (short impulse and sweep signal with duration 0.5 seconds with frequency range from 10 Hz to 2000 Hz). In the "<strong>Read_me_first.pdf</strong>" is described the experiment, the format of .csv files names and files' structure.</p><p>Used materials, methods and results for the case of static load equal to 151.8 kg with sweep-type input signal, and T2 scheme of sensors placement is described in Kurtenoks, V.; Kurajevs, A.; Buka-Vaivade, K.; Serdjuks, D.; Lapkovskis, V.; Mironovs, V.; Podkoritovs, A.; Vilnitis, M. The Quality Assessment of Timber Structural Joints Using the Coaxial Correlation Method. <i>Buildings</i> <strong>2023</strong>, <i>13</i>, 1929. https://doi.org/10.3390/buildings13081929</p>
Crowd4SDG - Crowdsourced image classification and damage assessment
<p>This data set contains crowdsourced classification and damage assessment of images of an earthquake extracted from social media. <br> <br> A data set of 907 images posted on Twitter related to the 2019 Albanian Earthquake, that are filtered and pre-classified using an automated technique is cross-validated for accuracy by two different crowds. One, digital humanitarian volunteers using the crowdsourcing platform <a href="http://www.crowd4ems.org">CROWD4EMS</a> and another, paid micro-taskers of the Amazon Mechanical Turk. In order to compare and evaluate the efficiency and accuracy of the volunteers and the paid micro taskers, ground truth is established with the help of a team of experts, who validated the same set of data. <br> <br> <strong>Parameters considered for volunteer contributions:</strong> The dataset was imported to the Crowd4EMS platform for Crowd contribution. In the forum, each volunteer will see the image to be validated along with the tweet text and the link to the original tweet. The user has to validate whether the given image is <em>relevant or</em> <em>irrelevant</em> to the disaster. In case of doubt, the user can refer to the tutorial explaining the relevance or skip the task. Once the image's relevance is validated, the user will be asked to label the <em>severity</em> of the impact, as seen in the image.</p> <p>The Automated algorithm has pre-classified the images as <em>severe </em>and <em>minimal </em>damage. The Crowd4EMS platform lets the volunteer label them as '<em>severe damage</em>,' <em>moderate damage'</em>,' <em>minimal damage', </em>and' <em>no damage'.</em> Each task has to be answered <em>at least three times</em>, and the final consensus is taken as per the<em> inter-rater agreement. </em><br> <br> <strong>Parameters considered for micro-taskers contribution:</strong> The dataset was imported to the <em>Amazon Mechanical Turk</em> platform for Crowd contribution. In the platform, each worker will see only the image that is to be categorised as follows: The user has to validate whether the given image depicts <em>severe damage, moderate damage, minimal damage, no damage </em>or <em>irrelevant</em> to the disaster. Each task has to be answered <em>at least ten times</em>, and the final consensus is taken as per the<em> inter-rater agreement. </em><br> <br> <strong>Acknowledgements:</strong> We want to thank Muhammad Imran of Qatar Computing Research Institute for sharing their pre-filtered social media imagery dataset on the Albanian earthquake from the Artificial Intelligence for Disaster Response (AIDR) Platform. We would also like to extend our gratitude to the volunteers for their contribution on the Crowd4EMS Platform.<br> </p>
Tree Health Conditions (mortality, damage, disease, bark beetles) in Fuel Reduction Treatments Located Near Communities in Interior Alaska and the Cook Inlet Region of Alaska - Observations from July-August 2023
This dataset contains tree-, transect-, and site-level observations of forest stands at sites that received a fuel reduction treatment. Tree-level observations include species, diameter, living status, damage, disease, and bark beetle presence. Transect-level observations include level of coarse woody debris and bark beetle presence. Sites are categorized by region (recent/ongoing spruce beetle oubreak or endemic spruce beetle population levels) and treatment type (hand-thinned or mechanincally felled and masticated). These observations are from July-August 2023. Sites are located near communities in Interior Alaska and the Cook Inlet Region.
Grasshopper counts and feeding damage at the GCE-LTER Seawater Addition Long-Term Experiment (SALTEx) in 2016
Grasshopper abundance and feeding damage were investigated at the Georgia Coastal Ecosystems (GCE) LTER Seawater Addition Long-Term Experiment (SALTEx) study area approximately monthly in 2016. We conducted visual surveys in each replicate plot and counted 3 species of grasshopper (Romalea, Leptysma, Orchelimum) and scored grasshopper feeding damage on 2 species of plants (Zizaniopsis, Pontederia).
Ash dieback mortality and damage at the Botanic Garden Meise, Belgium
<p>Four 10m × 10m plots were laid out in the naturally regenerating woodland at the Botanic Garden Meise (WGS84: 50° 55ʹ 37ʺ N, 4° 19ʹ 18ʺ E; 50° 55ʹ 37ʺ N, 4° 19ʹ 17ʺ E; 50° 55' 38.6" N 4° 19ʹ 21ʺ E; 50° 55ʹ 39ʺ N, 4° 19ʹ 29ʺ E). They were selected because the areas contained a large number of ash saplings. Within these plots all ash seedlings greater than 40 cm tall were labelled with a small (2 cm × 4 cm) plastic tag attached with stretchable plant tie. Each tag was engraved with a unique number so that the tree could be identified. These plots were not intended to be replicates but just a convenient method of refinding the tagged trees. In the first year either the height or the girth of the tree was measured with a tape measure, depending upon whether the tree was small enough to measure the height. In the first year and each subsequent year each tree was scored for the apparent damage caused by ash dieback (<em>Hymenoscyphus pseudoalbidus</em>). The same scoring scheme was used as that by Pliūra et al. (2011). This is a 5 point system where 1 is a dead tree; 5 is an undamaged tree and 2–4 are progressively less damaged trees. The plots were laid out on 14 April 2013. In 2014 plots 1 and 2 were scored on 14th April and plots 3 and 4 on 21st April. In 2015 plots 1 and 2 were scored on 6th May and plots 3 and 4 on 30th April.</p>
Data from: "Damage deflection and subsequent damage diffusion in carbon-boron fibre hybrid composites under longitudinal compression"
<p>The datset contains raw data used for the work presented in the journal paper "Damage deflection and subsequent damage diffusion in carbon-boron fibre hybrid composites under longitudinal compression".<br>Specifically, it contains machine recorded data and video recordings (either SEM or with optical microscope) of the compression tests on small scale single edge notched specimens made of IM7/8552 (carbon/epoxy) and HyBor 52 FPI (carbon-boron fibre hybrid composite). It also contains specimens pictures taken during and after the tests (including SEM and optical micrographs).</p> <p>For more details, please refer to the full paper.</p>
RGB orthophoto mosaic, DSM, 3d point cloud and LIDAR LAZ of the flash flood damages in Karavelovo and Bogdan vilages, Bulgaria- September 2, 2022
<p>The present dataset contains geospatial resources aimed at investigating and assessing the consequences of a flash flood of debris flow character, relatively significant in extent and magnitude of damage, in the area of two villages in the Municipality of Karlovo, located in central Bulgaria, which happened on September 2, 2022. For this purpose, an integrated approach based on the combination of digital photogrammetry with high spatial resolution and spatial accuracy, based on a fixed wing unmanned aerial system, and laser altimetry (LIDAR), based on a multirotor unmanned platform, was used. The data collection was carried out 2 days after the occurrence of the disaster, resulting in the generation of valuable information resources that allow not only to spatially and quantitatively determine the damage of the disaster, but also to reveal the mechanism of occurrence of the phenomenon: 1) orthophoto mosaic, Digital surface model-DSM and 3D point cloud (from photogrammetry) 2) Classified 3D point cloud- from LIDAR survey.</p>
Animation to visualize the electron beam damage induced in calcium silicate hydrate phases
<p>This dataset visualizes the electron beam damage induced by a scanning electron microscope (SEM) in calcium silicate hydrates (C-S-H). The specimen used is 28 days hydrated alite (water/solid = 0.5). It was scanned using a thermofischer scientific Helios G4 UX microscope at 350 V/25 pA with a stage bias of 200 V.</p> <p>This animation was an afterthought. Therefore, the dataset provides multiple magnifications and resolutions and some of the images are not in focus. Nevertheless, It can be seen, that the C-S-H needle in the right half of the image significantly deformes within a timespan of 124 seconds of constant scanning of that region.</p> <p><strong>File content:</strong></p> <ul> <li>All images ending with "raw" are the raw images provided by the SEM software including all metadata.</li> <li>The file "C3S_CSH_e-beam-damage_aligned stack.tif" contains the aligned image set using the SIFT algorithm. It contains the correct scaling if opened with ImageJ.</li> <li>The file "C3S_CSH_e-beam-damage_animation.gif" provides the final animation including a overlayed scalebar.</li> </ul>
Radiation damage hot spots formed by two-step electron transfer mediated decay of solvated ions - data
<p>Data set pertaining to the manuscript "Radiation damage hot spots formed by two-step electron transfer mediated decay of solvated ions", accepted for publication in Nature Chemistry.</p> <p>Files with extension .h5 are hdf5-files structured according to the NeXus standard v2022.07, see<br> https://www.nexusformat.org/<br> https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html<br> NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format:<br> * nexpy (distributed with python)<br> * https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)</p> <p>In each NeXus file-entry, two types of spectra are shown:<br> 1. Sweep-averaged spectra, integrated over the non-dispersive coordinate of our detector ('data') if applicable.<br> 2. As-measured data ('raw').</p> <p>Files with extension .csv are comma-separated ascii-files, designed to be opened with a spreadsheet programme.</p> <p><br> The following files are provided:</p> <p>Photoemission data pertaining to ETMD measurements:<br> alcl3-K-etmd.h5 (ETMD after Al K-shell photoionization)<br> alcl3-L23-etmd.h5 (ETMD after Al L-shell photoionization)</p> <p>Calculated energies of the ETMD final states after 1s ionization. The energies were calculated at the CAS-CI/cc-pVDZ level. The states were shifted so that the lowest-energy state corresponds to the LC-ωPBE/aug-cc-pVTZ and aug-cc-pCVTZ value obtained in a polarizable continuum:<br> Dataset_ETMD_after_1s_ionization.csv<br> Dataset_ETMD_after_2p_ionization.csv</p> <p>Geometrical coordinates of the clusters that were used for energy calculation:<br> clusters.dat<br> clusters_small.dat</p> <p>Contact: Uwe Hergenhahn, uhe@fhi.mpg.de .</p> <p> </p> <p>Version history:</p> <p>v3 - Al L2,3 data: Orientation of the analyser hemisphere corrected. Direction of the linear polarization vector added. All other data unchanged.<br> v2 - cluster coordinates added, all other data unchanged.<br> v1 - initial upload.</p>
Effect of the aspen leaf miner feeding damage on aspen leaf gas exchange and water relations from south-facing site on the University of Alaska Fairbanks campus: Fairbanks, Alaska 2018
This dataset addresses the effects of epidermal leaf mining by the aspen leaf miner (Phyllocnistis populiella) on the physiology and water relations of aspen leaves. The dataset contains measurements of gas exchange, water potential, water content, and delta13C of aspen leaves manipulated to bear leaf mining damage on the top (adaxial) leaf surface only, the bottom (abaxial) leaf surface only, or no mining damage.
HURRECON Model for Estimating Hurricane Wind Speed, Direction, and Damage (R and Python)
The HURRECON model estimates wind speed, wind direction, enhanced Fujita scale wind damage, and duration of EF0 to EF5 winds as a function of hurricane location and maximum sustained wind speed. Results may be generated for a single site or an entire region. Hurricane track and intensity data may be imported directly from the US National Hurricane Center's HURDAT2 database. HURRECON is available in R and Python. The R version is available on CRAN as HurreconR. The model is an updated version of the original HURRECON model written in Borland Pascal for use with Idrisi (see HF025). New features include support for: (1) estimating wind damage on the enhanced Fujita scale, (2) importing hurricane track and intensity data directly from HURDAT2, (3) creating a land-water file with user-selected geographic coordinates and spatial resolution, and (4) creating plots of site and regional results. The model equations for estimating wind speed and direction, including parameter values for inflow angle, friction factor, and wind gust factor (over land and water), are unchanged from the original HURRECON model. For more details and sample datasets, see the project website on GitHub (https://github.com/hurrecon-model).
Effect of plant density and light availability on leaf damage in Manilkara bidentata
Variation in herbivory is often associated with plant density and light environment. To determine the effect of these variables on herbivory we studied leaf production and herbivory on saplings, juveniles and adults of Manilkara bidentata (Sapotaceae) in the Luquillo Experimental Forest (LEF), Puerto Rico. The major herbivore of M. bidentata is microlepidoptera leaf miner (Acrocercopssp.; Gracillariidae). To determine the effect of plant density on herbivory, 24 - 20 x 20 m plots were established and the density of saplings, juveniles and adults were determined. Leaf production, herbivory and growth were measured on all saplings in the plots. In addition, plant density was determined in 8-20 x 20 m plots surrounding the 24 focal plots. The effect of light environment was determined by comparing leaf phenology, leaf quality and herbivory in the vertical and horizontal profile. Sapling density in 60 x 60 m plots was associated with increased levels of herbivory. In the vertical profile, leaf production was continuous in the canopy and synchronous for juveniles and saplings and herbivory increased from the canopy (1.3%) towards the understory (35.6%). In the horizontal profile leaf production was related with the light environmen. Saplings in low light environment produced leaves in June, while plants in gaps had a broader peak of leaf production. Differences in leaf phenology did not result in differences in herbivory possibly because there was high variation in herbivory among leaves. Although many saplings lost more than 80% of new leaf area, there was no detectable effect on plant growth. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International
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.