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Impact of Schistosomiasis, Soil-Transmitted Helminthiasis and Anaemia on preschool and school-age children's health condition: post treatment predictive mapping in Benin Republic.
<p>This dataset provides information about the epidemiology of schistosomiasis, soil transmitted helminthiasis and anemia alongside malnutrition among preschool and school age children in Ouake and Bembereke districts of donga and borgou departments in Benin republic.</p>
MetroPT2: A Benchmark dataset for predictive maintenance
<p><strong>Abstract</strong></p> <p>The MetroPT2 data set is an outcome of a eXplainable Predictive Maintenance (XPM) project with an urban metro public transportation service in Porto, Portugal. The data was collected in 2022 that aimed to evaluate machine learning methods for online anomaly detection and failure prediction. By capturing several analogic sensor signals (pressure, temperature, current consumption), digital signals (control signals, discrete signals), and GPS information (latitude, longitude, and speed), we provide a dataset that can be easily used to evaluate online machine learning methods. This dataset contains some interesting characteristics and can be a good benchmark for predictive maintenance models.</p> <table> <tbody> <tr> <td> <p>Data Set Characteristics:</p> </td> <td> <p>Multivariate Time series</p> </td> <td> <p>Number of Instances:</p> </td> <td> <p>7116940</p> </td> </tr> <tr> <td> <p>Attribute Characteristics:</p> </td> <td> <p>Real</p> </td> <td> <p>Number of Attributes</p> </td> <td> <p>21</p> </td> </tr> <tr> <td> <p>Associated Tracks:</p> </td> <td> <p>Classification, Regression</p> </td> <td> <p>Missing Values</p> </td> <td> <p>N/A</p> </td> </tr> </tbody> </table> <p><strong>Data Set Information:</strong></p> <p>The dataset was collected to support the development of predictive maintenance, anomaly detection, and remaining useful life (RUL) prediction models for compressors using deep learning and machine learning methods.</p> <p>It consists of multivariate time series data obtained from several analogue and digital sensors installed on the compressor of a train. The data span between 2022-04-28 and 2022-07-28 and includes 16 signals, such as pressures, motor current, oil temperature, flowmeter and electrical signals of air intake valves. The monitoring and logging of industrial equipment events, such as temporal behaviour and fault events, were obtained from records generated by the sensors. The data were logged at 1Hz by an onboard embedded device. You can find a schematic diagram of the air production unit of the compressor system in Figure 4 of the accompanying paper [1]. Also, the paper [2] provides a detailed examination of data collection and specifications of various types of potential failures in an air compressor system. </p> <p><strong>Relevant Papers:</strong></p> <p>[1]- Davari, N., Veloso, B., Ribeiro, R.P., Pereira, P.M., Gama, J.: Predictive maintenance based on anomaly detection using deep learning for air production unit in the railway industry. In: 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA). pp. 1–10. IEEE (2021) (DOI: <a href="https://doi.org/10.1109/DSAA53316.2021.9564181">10.1109/DSAA53316.2021.9564181</a>)</p> <p>[2] Veloso, B., Ribeiro, R.P., Pereira, P.M., Gama, J.: The MetroPT dataset for predictive maintenance. Scientific Data 9, no. 1 (2022): 764. (DOI: 10.1038/s41597-022-01877-3)</p> <p>[3]-Barros, M., Veloso, B., Pereira, P.M., Ribeiro, R.P., Gama, J.: Failure detection of an air production unit in the operational context. In: IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning, pp. 61–74. Springer (2020) (DOI: 10.1007/978-3-030-66770-2_5)</p> <p><strong>Failure Information:</strong></p> <p>The dataset is unlabeled, but the failure reports provided by the company are available in the following table. This allows for evaluating the effectiveness of anomaly detection, failure prediction, and RUL estimation algorithms.</p> <p> </p> <table> <tbody> <tr> <td> <p>Nr.</p> </td> <td> <p>Start Time</p> </td> <td> <p>End Time</p> </td> <td>Failure</td> </tr> <tr> <td> <p>1</p> </td> <td> <p>2022-06-04 10:19:24.300</p> </td> <td> <p>2022-06-04 14:22:39.188</p> </td> <td>Air Leak</td> </tr> <tr> <td> <p>2</p> </td> <td> <p>2022-07-11 10:10:18.948</p> </td> <td> <p>2022-07-14 10:22:08.046</p> </td> <td>Oil Leak</td> </tr> </tbody> </table> <p> </p>
Active Region Magnetograms for Solar Flare Prediction: Reduced Resolution Dataset Images
<p>This dataset is the images associated with Dryad dataset https://doi.org/10.5061/dryad.jq2bvq898. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the reduced resolution (224x224 pixels) images in .png format.</p>
Flood water depth prediction with Convolutional Temporal Attention Networks
<p>SwissFlood dataset from Flood water depth prediction with Convolutional Temporal Attention Networks paper.</p>
Dataset and codes of the article "Neural correlates of hierarchical predictive processes in autistic adults"
<p>Data and code related to the article "Neural correlates of hierarchical predictive processes in autistic adults" by Laurie-Anne Sapey-Triomphe, Lauren Pattyn, Veith Weilnhammer, Philipp Sterzer and Johan Wagemans (Nature Communications):</p> <p>- Behavioral dataset of the 26 neurotypical participants (NT_behavioral_data.zip) and of the 26 autistic participants (ASD_behavioral_data.zip)</p> <p>- Source data of the graphics appearing in the article (Source data.xls)</p> <p>- Matlab codes used to run the experiment (Codes_to_run_experiment.zip)</p> <p>- Matlab codes to perform the main behavioral analyses (Codes_behavioral_analyses.zip) and to analyze the behavioral data with the HGF models (Codes_comput_model_analyses.zip)</p> <p>- Matlab codes to preprocess (Codes_fMRI_preprocessing.zip) and run the main fMRI analyses (Codes_fMRI_analyses.zip)</p>
Active region magnetograms for solar flare prediction: Reduced resolution dataset
<p>In this dataset, we provide a comprehensive collection of magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). The dataset incorporates data from three sources and provides SDO Helioseismic and Magnetic Imager (HMI) magnetograms of solar active regions as well as labels of corresponding flaring activity. This dataset will be useful for image analysis or solar physics research related to magnetic structure, its evolution over time, and its relation to solar flares. The dataset will be of interest to those researchers investigating automated solar flare prediction methods, including supervised and unsupervised machine learning (classical and deep), binary and multi-class classification, and regression. This dataset is a minimally processed, user configurable dataset of consistently sized images of solar active regions that can serve as a benchmark dataset for solar flare prediction research. This dataset consists of reduced resolution images (see usage notes below).</p>
Climate change prediction
<p>Surface temperature data from various cities across the world from 1900-2127. The original data is from 1900-2013, and 2014-2127 are predictions based on a random forest model.</p> <p><strong>orig+pred.zip</strong>: Contains all the predictions and orignal data in form of a huge csv file.</p> <p><strong>orig+pred-split.zip</strong>: Contains the same information as above file, but split into multiple csv files for each year. This will help in easy loading on various visualization softwares.</p>
PREDICTING THE PERFORMANCE OF GREEN STORMWATER INFRASTRUCTURE USING MULTIVARIATE LONG SHORT-TERM MEMORY (LSTM) NEURAL NETWORK
<p>The expected performance of Green Stormwater Infrastructure (GSI) is typically quantified through numerical models based on hydrologic parameters and physics-based equations. With numerical models, the choice of a spatio-temporal discretization scheme for the computational domain is a strenuous task that requires extensive calibration and potentially lab-based parameters and experimentation. The performance of GSI has high temporal dynamics due to natural, anthropogenic, and climatic processes that are not well represented by the traditional physics-based hydrologic models, which are calibrated against only a few historical observations and have a user-defined and constrained set of computational outcomes. Deep learning-based predictive models, such as Long Short-Term Memory (LSTM) neural networks, offer an exciting opportunity to quantify GSI performance, accounting for its highly dynamic and constantly evolving nature by leveraging advancements in observational data. A LSTM regression can overcome some of the limitations associated with traditional hydrological models to aid the development of a fully data-informed GSI performance predictor. To demonstrate the LSTM and traditional model outcomes, both methods were applied to a rain garden in Villanova, PA, USA. Specifically, a LSTM model was used to predict the recession of ponded water depth in the rain garden using five years of observed data.</p>
Indoor UWB CIR Data Set for Material Prediction
<p><strong>ABOUT</strong></p> <p>This data set contains spatially distributed CIR of multipath components in indoor environment acquired with ultra wideband (UWB) radio technology in microwave frequency range.<br> The data is labeled with the materials of the surfaces bounding the space (floor, ceiling, walls).<br> The data was collected for training and evaluating machine learning models for CIR-based indoor material prediction, but it may be also used for other studies based on indoor radio propagation data. </p> <p> </p> <p><strong>AUTHORS</strong></p> <p>Teodora Kocevska, Andrej Hrovat, Tomaž Javornik</p> <p>Department of Communication Systems</p> <p>Jožef Stefan Institute, SI-1000 Ljubljana, Slovenia</p> <p> </p> <p><strong>DATA COLLECTION</strong></p> <p>The synthetic data is obtained using <a href="https://www.remcom.com/wireless-insite-em-propagation-software">Remcom Wireless InSite</a> v.3.3.3.<br> The CIR is estimated in 16,875 rooms in total. <br> These rooms belong to 5,625 distinct room types and each room type is considered in three sizes. <br> The number of distinct room types comes from the materials used for the floor, ceiling, and walls, having nine floor-ceiling material combinations and 625 wall-material combinations.</p> <p> </p> <table align="center"> <caption>Room Sizes</caption> <thead> <tr> <th scope="col"> ROOM SIZE</th> <th scope="col">FLOOR/CEILING DIMENSIONS </th> <th scope="col">WALL DIMENSIONS</th> </tr> </thead> <tbody> <tr> <td>S</td> <td>3 m x 3 m</td> <td>3 m x 3 m </td> </tr> <tr> <td>M</td> <td>5 m x 5 m</td> <td>5 m x 3 m </td> </tr> <tr> <td>L</td> <td>7 m x 7 m </td> <td>7 m x 3 m </td> </tr> </tbody> </table> <p> </p> <p><strong>MATERIALS</strong></p> <ul> <li> floor: concrete, wood, floorboard</li> <li> ceiling: concrete, plaster, wood</li> <li> walls: brick, concrete, glass, plaster, wood</li> </ul> <p> </p> <table align="center"> <caption>Electrical properties of materials*</caption> <thead> <tr> <th scope="col">MATERIAL</th> <th scope="col">RELATIVE PERMITTIVITY</th> <th scope="col">CONDUCTIVITY</th> </tr> </thead> <tbody> <tr> <td>brick</td> <td>3.75</td> <td>0.038</td> </tr> <tr> <td>concrete</td> <td>5.31</td> <td>0.120</td> </tr> <tr> <td>glass</td> <td>6.27</td> <td>0.029</td> </tr> <tr> <td>plaster</td> <td>2.94</td> <td>0.036</td> </tr> <tr> <td>wood</td> <td>1.99</td> <td>0.026</td> </tr> <tr> <td>floorboard</td> <td>3.66</td> <td>0.039</td> </tr> </tbody> </table> <p> </p> <p><strong>COMMUNICATION SYSTEM CONFIGURATION</strong></p> <p>Ultra wideband (UWB) radio technology is considered. The parameters of the communication system are set according to 802.15.4-2011** standard.<br> The configuration of system parameters is summarized as follows:</p> <table align="center"> <caption>System configuration</caption> <thead> <tr> <th scope="col"> PARAMETER</th> <th scope="col">CONFIGURATION</th> </tr> </thead> <tbody> <tr> <td>frequency</td> <td>3494.4 MHz</td> </tr> <tr> <td>bandwidth</td> <td>466.2 MHz</td> </tr> <tr> <td>Tx/Rx height</td> <td>1.5 m</td> </tr> <tr> <td>antenna type</td> <td>omni</td> </tr> <tr> <td>polarization</td> <td>vertical</td> </tr> </tbody> </table> <p> </p> <p><strong>RADIO NODE POSITIONS</strong></p> <p>The data is collected using three acquisition layouts as follows:</p> <p>1. Layout 1 </p> <p> Tx in the center of the room and Rx moved over uniform grid covering the room.</p> <p>2. Layout 2</p> <p>Tx in eight positions following circular pattern around the center of the room and Rx moved over uniform grid covering the room. </p> <p>The distance from the center of the room to the circumference of the circle is 0.5 m, and the spacing between the radio nodes is pi/4 rad.</p> <p>3. Layout 3</p> <p>Tx in four positions near the corners of the room (0.375 m from the walls) and Rx moved over uniform grid covering the room.</p> <p>The corners of the grid are 0.25 m apart from the walls and the distance between the nodes is also 0.25 m.</p> <p>Since the grid size is defined relatively to the room size, the total number of grid node positions is different in rooms with different sizes.</p> <p>The total number of grid node positions is 121, 361, and 729 in S, M, and L rooms, respectively.</p> <p> </p> <p><strong>DATA ORGANIZATION</strong></p> <p>Data is saved in .csv files. Each file starts with a header line specifying the column names. <br> The column names included in the .csv files are:</p> <p>- Column 0: layout {center, circle, corners}<br> - Column 1: tx_point_id {1} for Layout 1, {1-8} for Layout 2, and {1-4} for Layout 3<br> - Column 2: rx_point_id {1-121} in S-rooms, {1-361} in M-rooms, and {1-729} in L-rooms<br> - Column 3: 1_phase_deg NUMERIC<br> - Column 4: 1_toa_ns NUMERIC<br> - Column 5: 1_power_dbm NUMERIC<br> - Column 6: 1_power_nw NUMERIC<br> - Column 7: 2_phase_deg NUMERIC<br> - Column 8: 2_toa_ns NUMERIC<br> - Column 9: 2_power_dbm NUMERIC<br> - Column 10: 2_power_nw NUMERIC<br> - Column 11: 3_phase_deg NUMERIC<br> - Column 12: 3_toa_ns NUMERIC<br> - Column 13: 3_power_dbm NUMERIC<br> - Column 14: 3_power_nw NUMERIC<br> - Column 15: 4_phase_deg NUMERIC<br> - Column 16: 4_toa_ns NUMERIC<br> - Column 17: 4_power_dbm NUMERIC<br> - Column 18: 4_power_nw NUMERIC<br> - Column 19: 5_phase_deg NUMERIC<br> - Column 20: 5_toa_ns NUMERIC<br> - Column 21: 5_power_dbm NUMERIC<br> - Column 22: 5_power_nw NUMERIC<br> - Column 23: 6_phase_deg NUMERIC<br> - Column 24: 6_toa_ns NUMERIC<br> - Column 25: 6_power_dbm NUMERIC<br> - Column 26: 6_power_nw NUMERIC<br> - Column 27: 7_phase_deg NUMERIC<br> - Column 28: 7_toa_ns NUMERIC<br> - Column 29: 7_power_dbm NUMERIC<br> - Column 30: 7_power_nw NUMERIC<br> - Column 31: 8_phase_deg NUMERIC<br> - Column 32: 8_toa_ns NUMERIC<br> - Column 33: 8_power_dbm NUMERIC<br> - Column 34: 8_power_nw NUMERIC<br> - Column 35: 9_phase_deg NUMERIC<br> - Column 36: 9_toa_ns NUMERIC<br> - Column 37: 9_power_dbm NUMERIC<br> - Column 38: 9_power_nw NUMERIC<br> - Column 39: 10_phase_deg NUMERIC<br> - Column 40: 10_toa_ns NUMERIC<br> - Column 41: 10_power_dbm NUMERIC<br> - Column 42: 10_power_nw NUMERIC<br> - Column 43: 11_phase_deg NUMERIC<br> - Column 44: 11_toa_ns NUMERIC<br> - Column 45: 11_power_dbm NUMERIC<br> - Column 46: 11_power_nw NUMERIC<br> - Column 47: 12_phase_deg NUMERIC<br> - Column 48: 12_toa_ns NUMERIC<br> - Column 49: 12_power_dbm NUMERIC<br> - Column 50: 12_power_nw NUMERIC<br> - Column 51: 13_phase_deg NUMERIC<br> - Column 52: 13_toa_ns NUMERIC<br> - Column 53: 13_power_dbm NUMERIC<br> - Column 54: 13_power_nw NUMERIC<br> - Column 55: 14_phase_deg NUMERIC<br> - Column 56: 14_toa_ns NUMERIC<br> - Column 57: 14_power_dbm NUMERIC<br> - Column 58: 14_power_nw NUMERIC<br> - Column 59: 15_phase_deg NUMERIC<br> - Column 60: 15_toa_ns NUMERIC<br> - Column 61: 15_power_dbm NUMERIC<br> - Column 62: 15_power_nw NUMERIC<br> - Column 63: room_size_surf_m2 {9} for S-rooms, {25} for M-rooms, and {49} for L-rooms<br> - Column 64: room_size_name {S} for S-rooms, {M} for M-rooms, and {L} for L-rooms<br> - Column 65: room_shape {square}<br> - Column 66: floor_mat {concrete, wood, floorboard}<br> - Column 67: ceiling_mat {concrete, plaster, wood}<br> - Column 68: wall1_mat {brick, concrete, glass, plaster, wood}<br> - Column 69: wall2_mat {brick, concrete, glass, plaster, wood}<br> - Column 70: wall3_mat {brick, concrete, glass, plaster, wood}<br> - Column 71: wall4_mat {brick, concrete, glass, plaster, wood}</p> <p>Each row corresponds to separate radio link defined with the Tx and Rx nodes. <br> It includes information about <br> (i) the CIR-acquisition layout (position of the Txs and Rxs), <br> (ii) the Tx and Rx, <br> (iii) the CIR of 15 strongest multipath components, <br> (iv) the room geometry, and <br> (v) the materials of the surfaces bounding the space. </p> <p>- Column 0 specifies the layout. <br> The following maping is used: <br> Layout 1 -> center, <br> Layout 2 -> circle, and <br> Layout 3 -> corners.<br> - Column 1 specifies the Tx identifier.<br> - Column 2 specifies the Rx identifier.<br> - Columns 3-62 are the input attributes. <br> The input attributest represent the phase (in deg), ToA (in ns), received power (in dBm), and received power (in nW) for 15 strongest multipath components. <br> The column naming is X_Y_Z, where X is the multipath component identifier (1 to 15), Y is the propagation characteristic (phase, toa, or power), and Z is the unit (deg, ns, dbm, or nw).<br> - Column 63 specifies the surface of the room in square meters.<br> - Column 64 specifies the room size category (S, M, or L).<br> - Column 65 specifies the room-base shape.<br> - Columns 66-71 are the target attributes specifying the material of the floor, ceiling, wall 1, wall 2, wall 3, and wall 4, respectively. </p> <p> </p> <p><strong>FOLDER STRUCTURE</strong></p> <p>The folder <em>indoor_CIR_data</em> contains:<br> - one subfolder named <em>CIR_data</em><br> It contains three .csv files with CIR data named by the size of the rooms where the data is acquired.<br> For naming the .csv files the following mapping is considered:<br> - Small.csv -> S-rooms<br> - Medium.csv -> M-rooms<br> - Large.csv -> L-rooms<br> - one subfolder named <em>CIR_acquisition_details</em><br> It contains .png file with schematic representation of the radio node positions considered for obtaining the data.<br> - README.txt file</p> <p>The folder structure is:<br> - CIR_data<br> - Small.csv<br> - Medium.csv<br> - Large.csv<br> - CIR_acquisition_details<br> - radio_node_positions.png<br> - README.txt</p> <p> </p> <p><strong>REFERENCES</strong></p> <p>* R. sector of International Telecommunication Union (ITU-R), “Effects of building materials and structures on radio wave propagation above about 100 MHz,” International Telecommunication Union, ITU-R Recommendation P.2040-2, 2021.</p> <p>** IEEE, “Standard for local and metropolitan area networks–Part 15.4: Low-rate wireless personal area networks (LR-WPANs),” IEEE, Standard IEEE 802.15.4-2011, 2011.</p>
Body size predicts the rate of contemporary morphological change in birds
<p>Variation in evolutionary rates among species is a defining characteristic of the tree of life and may be an important predictor of species' capacities to adapt to rapid environmental change. It is broadly assumed that generation length is an important determinant of microevolutionary rates, and body size is often used as a proxy for generation length. However, body size has myriad biological correlates that could affect evolutionary rates independently from generation length. We leverage two large, independently collected datasets on recent morphological change in birds (52 migratory species breeding in North America and 77 South American resident species) to test how body size and generation length are related to rates of contemporary morphological change. Both datasets show that birds have declined in body size and increased in wing length over the past 40 years. We found, in both systems, a consistent pattern wherein smaller species declined proportionally faster in body size and increased proportionally faster in wing length. By contrast, generation length explained less variation in evolutionary rates than did body size. Although the mechanisms warrant further investigation, our study demonstrates that body size is an important predictor of contemporary variation in morphological rates of change. Given the correlations between body size and a breadth of morphological, physiological, and ecological traits predicted to mediate phenotypic responses to environmental change, the relationship between body size and rates of phenotypic change should be considered when testing hypotheses about variation in adaptive responses to climate change.</p>
Data for: Predicting berry plant habitat under climate change in Bristol Bay, AK
<p>Aim: Climate change is altering suitable habitat distributions of many species in high latitudes. Fleshy fruit-producing plants (hereafter "berry plants"), important in arctic food webs and as subsistence resources for human communities, may be impacted, but their response to a warming and increasingly variable climate at a landscape scale has not yet been examined. Here, we identified influential environmental determinants of berry plant distribution and produced predictions on how climate change might shift these distributions.</p> <p>Location: Bristol Bay and Togiak NRCS Survey Areas, Alaska.</p> <p>Methods: We built species distribution models using the Random Forests algorithm to identify key characteristics and predict the spatial distribution of habitats suitable for five berry plant species: <em>Vaccinium uliginosum</em> L., <em>Empetrum nigrum</em> L., <em>Rubus chamaemorus</em> L., <em>Vaccinium vitis-idaea</em> L., and <em>Viburnum edule</em> (Michx.) Raf. Then, we used future climate projections (2081-2100; representative concentration pathways 4.5, 6.0, & 8.5) to predict shifts in species' suitable habitat distributions based on future climate conditions.</p> <p>Results: The predicted amount and spatial patterns of suitable habitat for the current time period were variable among species, consistent with species' diverse life history attributes and habitat preferences. Future climate models predicted both positive and negative changes to suitable habitat probability for all species; future binary classification maps predicted net declines in suitable habitat area for all species and climate scenarios tested. Models identified elevation, soil characteristics, and January and July temperatures as important drivers of suitable habitat distributions.</p> <p>Main conclusions: Our work contributes to understanding the response of important berry plant species to climate change at a landscape scale. Shifting and retracting distributions may alter where communities have access to harvesting areas, suggesting that access to these resources may become restricted in the future. Our prediction maps may help inform climate adaptation planning as communities anticipate shifting access to harvesting locations.</p>
Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 1307 through 1505
<p>This dataset is the images associated with Dryad dataset https://doi.org/10.5061/dryad.dv41ns23n. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 1307 through 1505 in .fits format.</p>
Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 1064 through 1306
<p>This dataset is the images associated with Dryad dataset https://doi.org/10.5061/dryad.dv41ns23n. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 1064 through 1306 in .fits format.</p>
Predicting Pulsed Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction - sample treated_213nm
<p>RHEED intensity image dataset of sample "<strong>treated_213nm"</strong> in work "Predicting Pulsed Laser Deposition SrTiO<sub>3 </sub>Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction."</p>
Training patches and prediction codes of deep learning (LANA) model for Landsat 8/9 cloud/shadow mask
<p>This dataset includes (i) the image patches dataset and (ii) application/prediction (not training) codes for Landsat 8 cloud and cloud shadow masking used in a paper in review and uploaded here: </p> <p>Hankui Zhang, Dong Luo, David Roy, A learning attention network algorithm (LANA) for accurate Landsat-8 cloud and shadow masking, <em>Remote Sensing of Environment</em> </p> <p>The documentation is in <a href="https://zenodo.org/api/files/5462baa5-2bba-4b0f-92aa-c17681b6464b/l8_training_data_readme_new.pdf?versionId=95253efb-6447-46d8-ae4b-ac0c93b43532">l8_training_data_readme_new.pdf</a>. </p>
Soil organic carbon models need independent time-series validation for reliable prediction
<p>Supplementary Data 1 to the paper: Soil organic carbon models need independent time-series validation for reliable prediction</p> <p>By: Le Noë, J., Manzoni, S., Abramoff, R.Z., Bölscher, T., Bruni, E., Cardinael, R., Ciais, P., Chenu, C., Clivot, H., Derrien, D., Ferchaud, F., Garnier, P., Goll, D., Lashermes, G., Martin, M.P., Rasse, D., Rees, F., Sainte-Marie, J., Salmon, E., Schiedung, M., Schimel, J., Wieder, W.R., Abiven, S., Barré, P., Cécillon, L., Guenet, B.</p>
Data from: Long-term, not short-term, temperatures predict timing of egg-laying in European Starling (Sturnus vulgaris)
<p>Temperature is thought to be an important supplemental cue used by female birds to time breeding. It is widely assumed that late spring temperatures, within ~1 month of egg-laying, are most predictive of laying date, consistent with the idea that temperature is used to fine-tune timing of breeding to year-specific local conditions at the time of laying. Here we show that a relatively broad, long-term, temperature window (Jan 2 – Apr 4, 92 days; r2 = 0.73) best predicted timing of egg-laying in European Starlings (<em>Sturnus vulgaris</em>). A "mid-winter" temperature window was also strongly correlated with laying date (r2 = 0.58) but there was no support for an influence of short-term temperatures immediately before egg-laying. We assessed the relationship between ambient temperature and timing of egg-laying using three complimentary approaches: a) an "unconstrained", exploratory analysis, b) a traditional sliding window approach, and c) specific, biologically-informed temperature windows. Our results contrast markedly with the widely held view, in the avian breeding phenology literature, that immediate, pre-breeding, temperatures best predict short-term variation in laying because they allow birds to fine-tune timing of breeding to local conditions around the time of egg-laying. This means that mechanisms that allow integration of long-term temperature information must exist in birds – perhaps most parsimoniously involving direct effects of temperature on growth of the bird's ectothermic insect prey– even though these are currently poorly characterized.</p>
Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 1506 through 1707
<p>This dataset is the images associated with Dryad dataset https://doi.org/10.5061/dryad.dv41ns23n. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 1506 through 1707 in .fits format.</p>
Fast prediction in marmoset reach-to-grasp movements for dynamic prey - Reach Data and Supplemental Video
<p>Supplemental video and data corresponding to Shaw, L., Wang, K.H., Mitchell, J. (2023) Fast prediction in marmoset reach-to-grasp movements for dynamic prey.</p> <p>1. Video Files</p> <p>MarmoReach 1 is an illustrative example.</p> <p>MarmoReach 2 illustrates the reaching trial shown in Figure 3D.</p> <p>MarmoReach 3-5 are example reach to grasps from grasp clusters found in Figure 2. </p> <p>2. Data</p> <p>marmo_reach_model.mat is a Matlab struct.</p> <p>2D position data of hand and cricket used for analyses related to Figure 3 and Figure 4. </p> <p>x.hand,y.hand = position data of the central hand marker for each trial.</p> <p>x.cricket,y.cricket = position data of the cricket marker for each trial.</p> <p>x.cricketexfull,y.cricketexfull = position data of the cricket marker preceding reach onset for delay analyses. </p> <p>To reconstruct cricket position from beginning to end with the inclusion of the exfull data (prior to reach to reach end) for the second reach, for example, [model.x.cricketexfull{2}' model.x.cricket{2}'].</p> <p> </p>
Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 1708 through 1918
<p>This dataset is the images associated with Dryad dataset https://doi.org/10.5061/dryad.dv41ns23n. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 1708 through 1918 in .fits format.</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.