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19,365 results for “watering”
Doodleverse/Segmentation Zoo Res-UNet models for Aerial/NOAA ERI/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images
<p><em><strong>Doodleverse/Segmentation Zoo Res-UNet models for Aerial/NOAA ERI/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images</strong></em></p> <p>Residual-UNet models are trained on 1,179 pairs of human-generated segmentation labels and images from Emergency Response Imagery (ERI) collected by US National Oceanic and Atmospheric Administration (NOAA) after Hurricane Barry, Delta, Dorian, Florence, Ida, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon.</p> <p>The dataset is available here**: https://doi.org/10.5281/zenodo.7268082</p> <p>Models have been created using Segmentation Gym*:</p> <p>Code - https://github.com/Doodleverse/segmentation_gym</p> <p>Paper - https://doi.org/10.1029/2022EA002332</p> <p><br> The model takes input images that are 512 x 512 x 3 pixels, and the output is 512 x 512 x 2, corresponding to 2 classes:</p> <p>1. water <br> 2. other<br> </p> <p>Included here are 6 files with the same root name:</p> <p> '.json' config file: this is the file that was used by Segmentation Gym to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction.<br> '.h5' weights file: this is the file that was created by the Segmentation Gym function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym function `seg_images_in_folder.py`.<br> '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py`<br> '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py`<br> '.zip' of the model in the Tensorflow ‘saved model’ format. It is created by the Segmentation Gym function `utils/gen_saved_model.py`<br> '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</p> <p>Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU</p> <p>References</p> <p><br> *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym</p> <p><br> ** Goldstein, Evan B., Buscombe, Daniel, Budavi, Priyanka, Favela, Jaycee, Fitzpatrick, Sharon, Gabbula, Sai Ram Ajay Krishna, Ku, Venus, Lazarus, Eli D., McCune, Ryan, Shah, Manish, Sigdel, Rajesh, & Tagner, Steven. (2022). Segmentation Labels for Emergency Response Imagery from Hurricane Barry, Delta, Dorian, Florence, Isaias, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7268083</p>
Revisiting Interior Water Mass Responses to Surface Forcing Changes and the Subsequent Effects on Overturning in the Southern Ocean
<p>This dataset contains processed model data used in</p> <p>Tesdal, J.-E., A. MacGilchrist, G., Beadling, R. L., Griffies, S. M., Krasting, J. P., & Durack, P. J. (2023). Revisiting interior water mass responses to surface forcing changes and the subsequent effects on overturning in the Southern Ocean. Journal of Geophysical Research: Oceans, 128, e2022JC019105. <a href="https://doi.org/10.1029/2022JC019105">https://doi.org/10.1029/2022JC019105</a>.</p> <p>The above publication uses two coupled climate models (AOGCMs), GFDL-CM4 and GFDL-ESM4, to assess the impact of perturbations in wind stress and Antarctic ice sheet melting on the Southern Ocean meridional overturning circulation (SO MOC) and associated water mass transformations (WMT).</p> <p>The attached archive includes netCDF files to recreate all figures and tables in <a href="https://doi.org/10.1029/2022JC019105">Tesdal et al. (2023)</a>, including overturning streamfunction (moc), volume storage change (dVdt), surface water mass transformation (swmt), meridional volume transports (mvt) zonal mean potential density referenced to 2000 dbar (sigma2) and mixed layer depth (mld). These variables are derived from preindustrial control (piControl) and idealized perturbation runs of Antarctic melting (Antwater), wind stress (Stress), as well as the combination (Antwater-Stress) using the Flux-Anomaly-Forced Model Intercomparison Project (FAFMIP) protocol.</p> <p>The FAFMIP protocol (<a href="https://doi.org/10.5194/gmd-9-3993-2016">Gregory et al., 2016</a>) involves adding perturbations to the surface fluxes that are computed within the atmosphere-ocean general circulation model (AOGCM) from the state of the system (<a href="https://doi.org/10.1029/2005JC003421">Lowe and Gregory, 2006</a>; <a href="https://doi.org/10.1088/1748-9326/9/3/034004">Bouttes and Gregory, 2014</a>). The perturbations in this dataset were technically added as a flux adjustment similar to that formerly used in AOGCMs (<a href="https://doi.org/10.1007/BF01053472">Sausen et al., 1988</a>).</p> <p>The data files contain processed model output and do not include any raw model output. Model data from the piControl runs of CM4 and ESM4 are available at the Earth System Grid Federation archive (<a href="https://esgf-node.llnl.gov/projects/cmip6">https://esgf-node.llnl.gov/projects/cmip6</a>). The forcing fields (perturbations) used in the perturbation experiments can be found at <a href="https://github.com/becki-beadling/Beadling_et_al_2022_JGROceans">https://github.com/becki-beadling/Beadling_et_al_2022_JGROceans</a>. Python scripts and Jupyter notebooks to reproduce the tables and figures can be accessed at <a href="https://github.com/jetesdal/Tesdal_et_al_2023_JGROceans">https://github.com/jetesdal/Tesdal_et_al_2023_JGROceans</a>.</p> <p><strong>Contents</strong>:</p> <ul> <li>Overturning streamfunction (moc)</li> <li>Volume storage change (dVdt)</li> <li>Surface water mass transformation (swmt)</li> <li>Meridional volume transports (mvt) </li> <li>Zonal-mean potential density referenced to 2000 dbar (sigma2)</li> <li>Mixed layer depth (mld) </li> <li>Antarctic shelf mask</li> <li>Static grid files</li> </ul> <p><strong>Models</strong>:</p> <ul> <li>GFDL-CM4</li> <li>GFDL-ESM4</li> </ul> <p><strong>Simulations</strong>:</p> <ul> <li>Preindustrial control (piControl)</li> <li>Experiment with a 0.1 Sv freshwater perturbation entering at the Antarctic coast (Antwater)</li> <li>Experiment with zonal and meridional wind stress perturbations (Stress)</li> <li>Experiment with combined perturbation of both Antarctic melting and wind stress (Antwater-Stress)</li> </ul> <p><strong>NetCDF file name structure</strong>:<br> <model>_<simulation>_<member_id>_<domain>_<time_period>_<variable>.nc</p> <ul> <li>model: CM4, ESM4</li> <li>simulation: control, antwater, stress, antwaterstress</li> <li>member_id (only for antwater, stress, antwaterstress): 251, 290, 332 (CM4), 101, 151, 201 (ESM4)</li> <li>domain: global, so</li> <li>time_period: yyyy-yyyy (first year to last year)</li> <li>variable: e.g., moc_rho2_online_lores, dVdt_rho2_online_lores, swmt_sigma2_005, sigma2_jmd95_zmean</li> </ul>
Doodleverse/Segmentation Zoo Res-UNet models for Aerial/planecam/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images
<p><em><strong>Doodleverse/Segmentation Zoo Res-UNet models for Aerial/planecam/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images</strong></em></p> <p>These Residual-UNet models have been created using Segmentation Gym*</p> <p>Image size used by model: 1024 x 768 x 3 pixels</p> <p>classes:</p> <ol> <li>water</li> <li>other</li> </ol> <p><br> <strong>File descriptions</strong></p> <p>For each model, there are 5 files with the same root name:</p> <p>1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.</p> <p>2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled.</p> <p>3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</p> <p>4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py`</p> <p>5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py`</p> <p>Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU</p> <p><strong>References</strong></p> <p><br> *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym</p> <p> </p> <p> </p>
Doodleverse/Segmentation Zoo Res-UNet models for Aerial/nadir/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images
<p><em><strong>Doodleverse/Segmentation Zoo Res-UNet models for Aerial/nadir/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images</strong></em></p> <p>These Residual-UNet models have been created using Segmentation Gym* using the following dataset**:</p> <p>Image size used by model: 1024 x 768 x 3 pixels</p> <p>classes:</p> <ol> <li>water</li> <li>other</li> </ol> <p><br> <strong>File descriptions</strong></p> <p>For each model, there are 5 files with the same root name:</p> <p>1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.</p> <p>2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled.</p> <p>3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</p> <p>4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py`</p> <p>5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py`</p> <p>Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU</p> <p><strong>References</strong></p> <p><br> *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym</p> <p>**</p> <p> </p>
Water depth observed in front of lake-terminating glaciers in Patagonia
<p>This is the dataset of water depth observed in front of O'Higgins, Upsala, Viedma, and Tyndall glaciers in southern Patagonia.</p> <p> </p> <p>Data format:</p> <p>Latitude [deg], Longitude [deg], Depth [m]</p>
Smart Meter Water Consumption Measurements
<p><strong>Data</strong><br> Between September 2021 and September 2022, we collected the cumulative water consumption data of 17 households in Germany using the commercially available smart meters <em>Hydrus 1.3, DN 20,00</em> from <em>Diehl Metering</em>.<br> Further information on data collection and description of the dataset can be found in section 3 of the original article, which is available at the following DOI: <a href="https://doi.org/10.3390/jsan12030046">https://doi.org/10.3390/jsan12030046</a></p> <p><strong>Structure</strong><br> The root folder of the <em>Zenodo</em> archive contains the documents <em>readme.md</em>, <em>info.txt</em>, and <em>info.json</em>, as well as the 17 folders for the individual households. The <em>readme.md</em> gives some basic information on the data set and its use. The documents <em>main.txt</em> and <em>main.json</em> contains the metadata for the measurements of all households in both human and machine-readable format.<br> For each test household, there is a separate folder with the documents <em>info.txt </em>and <em>info.json</em>. These contain household-specific metadata. The total water consumption measurements are stored in <em>smartmeter.csv</em>.</p>
Raw data from Cao et al. (2023) "Electron exchange capacity of pyrogenic dissolved organic matter (DOM): Complementarity of square-wave voltammetry in DMSO and mediated chronoamperometry in water"
<p>Measured and fitted data from square-wave voltammetry (SWV) in DMSO for electron exchange capacities (EECs) of pyrogenic natural organic matter (pyDOM) and natural organic matter (NOM) standards. </p> <p>From Cao, H., A. S. Pavitt, J. M. Hudson, P. G. Tratnyek, and W. Xu. 2023. Electron exchange capacity of pyrogenic dissolved organic matter (DOM): Complementarity of square-wave voltammetry in DMSO and mediated chronoamperometry in water. Environ. Sci. Proc. Impacts: ASAP. [10.1039/d3em00009e]</p> <p>The manuscript reports electron accepting capacity (EAC), electron donating capacity (EDC), and electron exchange capacities (EECs) measured with a new method involving square-wave voltammetry in an aprotic solvent (dimethyl sulfoxide, DMSO). The measurement method, fitting of peak areas, and conversion of peak areas to EECs are described in the main text and supporting information of the manuscript.</p> <p>Here we provide the original measured data, baseline corrected data used in the peak fitting, and fitted peak area data that were used to obtain the final EEC values. The data are provided in one .xlsx file that contains multiple tabs: (i) a table of contents, (ii) a summary of the final fitting results, and (iii) tabs numbered R1-R40 containing raw measured data for each pyDOM/NOM sample.</p> <p>The data provided here should be sufficient to replicate and verify all of the analysis described in the manuscript. If you use these data, please cite this Zenodo record (DOI 10.5281/zenodo.7747020) and the original manuscript (DOI: 10.1039/d3em00009e).</p>
Aerial Water Buoys Dataset
<p><strong>Aerial Water Buoys Dataset:</strong></p> <p>Over the past few years, a plethora of advancements in Unmanned Areal Vehicle (UAV) technologies have made possible advanced UAV-based search and rescue operations with transformative impact on the outcome of critical life-saving missions. This dataset aims into helping the challenging task of multi-castaway tracking and following using a single UAV. Due to the difficulty and data protection of capturing footage of people in the sea, we have captured a dataset of buoys in order to conduct experiments on multi-castaway tracking and following. A paper on multi-castaway tracking and following technical details and experiments will be published soon using this dataset.</p> <p>The dataset consists of top-view images of buoys from various altitudes on the coasts of Larnaca and Protaras in Cyprus. Images were captured at different altitudes in order to challenge object detectors to be able to detect smaller objects in case a UAV needs to track multiple targets, which leads to flying at a higher altitude. There is only one class annotated on all images which is labeled as 'buoy'. Additionally, all annotations were converted into VOC and COCO formats for training in numerous frameworks. The dataset consists of the following images and detection objects (buoys):</p> <table> <tbody> <tr> <td>Subset</td> <td>Images</td> <td>Buoys</td> </tr> <tr> <td>Training</td> <td>10814</td> <td>14811</td> </tr> <tr> <td>Validation</td> <td>1350</td> <td>1865</td> </tr> <tr> <td>Testing</td> <td>1352</td> <td>1827</td> </tr> </tbody> </table> <p>It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).</p> <p> </p> <p><strong>**NOTE** If you use this dataset in your research/publication please cite us using the following </strong></p> <blockquote> <p>Antreas Anastasiou, Rafael Makrigiorgis, & Panayiotis Kolios. (2022). Aerial Water Buoys Dataset (1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7288444</p> </blockquote>
Human-to-robot Handovers of Cups with Water
<p><em>If you use this dataset in your work, please cite the following publication (accepted to IEEE iROS 2023):</em></p> <p><em>Lastrico, Linda, Nuno Ferreira Duarte, Alessandro Carfí, Francesco Rea, Alessandra Sciutti, Fulvio Mastrogiovanni, and José Santos-Victor. "Expressing and inferring action carefulness in human-to-robot handovers." In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 9824-9831. IEEE, 2023.</em></p> <p>Setup Description:</p> <p>The experiment is presented as a collaborative task, where the human should help the robot clean the table by handing over the cups, from the rightmost cup to the leftmost, one at a time. The robot receives the cup; in case the cup contains water, it pours the content into the orange bucket, and finally, it places the empty cup in the blue drawer.</p> <p>Participants stand in front of a table with four identical plastic cups placed in a row, equidistant from each other. These cups differ in content, being two empty and two filled with water almost to the brim, constituting two types of objects to be handover: empty or full. Participants faced a Kinova Gen3 robot fixed to a table with two distinct recipients at the robot side. On the left side of the robot, there is an orange bucket meant to contain water, while the blue drawer on the right stores the empty (or emptied) cups. We adopted a within-subject study design where participants are exposed, in a randomized order, to two conditions associated with the controller used by the robot to complete the task: a neutral motion (NEU) and an expressive motion (GAN). The neutral is a simple PID controller and the expressive is Generative Adversarial Network (GAN) which generates robot trajectories that are human-inspired from a previous dataset of humans handing over cups with water and without.</p> <p>Our study involved 15 right-handed participants (8 females, 7 males, average of 26.6 (+-6.2) years old) who provided written informed consent. They were all naive regarding the purpose of the experiments and not directly involved in our research. The self-reported level of knowledge in robotics was: 40.0% professional or advanced, 33.3% average, and 26.7% little or none.<br>360 actions (15 participants x 12 handovers x 2 conditions) were recorded and performed successfully without dropping the cup or spilling the content.</p> <p>Data Description:</p> <p>All the data is synchronize using ROS timestamps.</p> <p>- motion-tracking: Motion Capture data for head, shoulder, and wrist from OptiTrack at 120 Hz + IMU wrist data at 400 Hz. Inside each participant P## folder you will find two other folders P##_neu and P##_gan related to the two interaction conditions where the kinova motion controller changed during the cup pouring and cup placing. Inside each subfolder you will find the following motion tracking data: head.csv, shoulder.csv, wrist.csv, robot.csv (from OptiTrack markers, the robot.csv is the robot's base), imu.csv (from IMU in the wrist), pupil.csv (Pupil ROS node), key.csv (manual labels). All these files have ROS_timestamps that can be used to find the matching frames for each of the sensors. The key.csv are manually picked time flags we marked to define specific moments in the experiment (you can the meaning in the additional notes below).</p> <p>- eye-tracking_#: Pupil-Labs head-mounted eye-trackers at 120Hz for pupil infra-red cameras, and 30 Hz for forward RGB camera. All 16 participants (P##) are present for both robot motion controllers (neutral NEU, and GAN).</p> <p>- go_pro_#: GoPro 1080p video of the size view of the Human-to-robot handovers experiments at 60 Hz. Note that there were 16 participants in this experiment but 3 participants did not give permission to make their image public so we removed the following participants videos: P01, P15, P16.</p> <p> </p>
Mapping of Collective Action Opportunities for Water Stewardship
<p>With this global mapping exercise, we attempted to identify catchments with stronger need and potential of Collective Action for Water Stewardship. On the “need” side we considered a selection of water and biodiversity risk layers from the <a href="https://riskfilter.org/">WWF Risk Filter Suite</a>. On the “potential” side we considered economic factors such as value of crop production, density of business facilities (assets), and potential for cross-industry collaboration. The result is a global map (or a shapefile) of collective action opportunities, highlighting 350 catchments, across 100 river basins and 7 regions of the world, where multiple NGOs and the private sector shall work together to accelerate collective action for water stewardship. See the interactive map <a href="https://panda.maps.arcgis.com/apps/mapviewer/index.html?webmap=8d5c4bf6c6ff46c1a5c00e0b62e0b821">here</a>.</p> <p>This repository hosts all outputs as well as the input data of the mapping exercise. For transparency, reproducibility, as well as for future enhancements, the code is publicly available at <a href="https://github.com/rafaexx/collective_action_opportunities">https://github.com/rafaexx/collective_action_opportunities</a></p> <p>Final outputs are basically the shapefiles in <strong>output/summarise_geo/shp/*</strong> as well as the list of basins in <strong>output/summarise_geo/3_prioritization/hybas6_list_basins_collective_action.xlsx</strong></p> <p>For more details see part III of the report <a href="https://wwfint.awsassets.panda.org/downloads/unpacking-collective-action-in-water-stewardship.pdf">Unpacking Collective Action in Water Stewardship: Shared Solutions for Shared Water Challenges</a>.</p> <p> </p> <p> </p> <p> </p> <p> </p>
MEaSUREs blue band total column water vapor sample data for the Ozone Monitoring Instrument
<p>This dataset contains the MEaSUREs OMI Total Column Water Vapor (TCWV) data and their related data used in the paper titled “Development of the MEaSUREs blue band water vapor algorithm – Towards a long-term data record” by Wang et al. (2023). The unzipped archive contains the following three directories. </p> <ol> <li>OMI-H2O-L2/ contains the MEaSUREs Level 2 data (in molecules/cm2) in netCDF4 format for January and July 2005 and 2006. Selected supporting data are also included in each file.</li> <li>OMI-H2O-L3/ contains MRaSUREs Level 3 data (0.25 degree by 0.25 degree, in molecules/cm2) generated using the standard filtering criteria in netCDF4 format for January and July 2005 and 2006. Selected supporting data are also included.</li> <li>Model3_ncresult/ contains netCDF4 formatted files for the MEaSUREs OMI TCWV data (in mm), the AMSR_E TCWV data sampled onto the corresponding OMI pixel locations, and the LightGBM model 3 predictions for the OMI pixels.</li> </ol> <p>The linux command ‘ncdump -h filename’ can be used to examine the contents of netCDF4 files. Due to the current size limit of Zenodo, only a small subset of the MEaSUREs data is archived here. The full dataset will be released elsewhere, e.g., NASA EARTHDATA GES DISC.</p>
Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 3-band (RGB) images of coasts.
<p><em><strong>Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 3-band (RGB) images of coasts.</strong></em></p> <p><em><strong>Version 3: Updated 2023-04-25</strong></em></p> <p>These Residual-UNet model data are based on RGB (red, green, and blue) images of coasts and associated labels.</p> <p>Models have been created using Segmentation Gym* using the following dataset**: <a href="https://doi.org/10.5281/zenodo.7384242">https://doi.org/10.5281/zenodo.7384242</a></p> <p>Classes: {0=other, 1=water}</p> <p><strong>File descriptions</strong></p> <p>For each model, there are 5 files with the same root name:</p> <p>1. <strong>'.json' </strong>config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.</p> <p>2.<strong> '.h5'</strong> weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled.</p> <p>3.<strong> '_modelcard.json'</strong> model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</p> <p>4. <strong> '_model_history.npz'</strong> model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py`</p> <p>5. <strong> '.png'</strong> model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py`</p> <p>Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU</p> <p> </p> <p><strong>References</strong></p> <p>*Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a></p> <p>** Buscombe, D. (2022). Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, other) (v1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7384242">https://doi.org/10.5281/zenodo.7384242</a></p>
Simulated water models with Apoferritin for use in cryo-EM image simulations with amorphous ice
<p>This dataset contains the atomic coordinates of several water models produced using the NAMD molecular dynamics software. The contents of each file is listed below.</p> <ul> <li><strong><em>water_81_coords.pdb</em></strong> - water only in a cubic box with side length 81A</li> <li><strong><em>water_243_coords.pdb</em></strong> - water only in a cubic box with side length 243A</li> <li><strong><em>water_486_coords.pdb</em></strong> - water only in a cubic box with side length 486A</li> <li><strong>water_567_coords.pdb</strong> - water only in a cubic box with side length 567A</li> <li><strong><em>water_645_coords.pdb</em></strong> - water only in a cubic box with side length 645A</li> <li><strong><em>water_735_coords.pdb</em></strong> - water only in a cubic box with side length 735A</li> <li><strong><em>apo_water_723_coords.pdb</em></strong> - water and apoferritin in a cubic box with side length 723A where apoferritin atoms are constrained</li> </ul>
Carnauba Wax and Beeswax as Structuring Agents for Water-in-Oleogel Emulsions without Added Emulsifiers
<p>This dataset contains the data used in the publication: <br> <strong><em>"Carnauba Wax and Beeswax as Structuring Agents for Water-in-Oleogel Emulsions without Added Emulsifiers</em>" </strong> (https://doi.org/10.3390/foods12091850)</p> <p> </p> <p> </p> <p> </p> <p>Important abbreviations: </p> <ul> <li>#CRW: Indicates the concentration of carnauba wax in the fat phase (e.g., 5CRW - indicates 5%w/w of carnauba wax in the fat phase)</li> <li>#BZW: Indicates the concentration of beeswax in the fat phase (e.g., 5BZW - indicates 5%w/w of beeswax in the fat phase)</li> <li>#W: The number preceding indicates the percentage of water in the sample (e.g., 40W indicates 40%w/w of water in the emulsion)</li> <li>t#: Indicates the number of days in storage (e.g. t1 indicates the sample was measured after 1 day of storage time)</li> <li>LS: Indicates that the sample was prepared on a lab scale</li> <li>PS: Indicates that the sample was prepared on a pilot scale</li> </ul> <p>This dataset contains: </p> <ul> <li>Microscopies <ul> <li>PLM <ul> <li><strong>Figure 6A</strong> - Pilot scale emulsion - 5% carnauba wax - 40% water </li> <li><strong>Figure 6D</strong> - Pilot scale oleogel - 5% carnauba wax<br> </li> <li><strong>Figure 7A</strong> - Pilot scale emulsion - 5% beeswax - 20% water</li> <li><strong>Figure 7D </strong>- Pilot scale oleogel - 5% beeswax<br> </li> <li><strong>Figure 8A </strong>- CRW-S - Lab scale oleogel - 5% carnauba wax</li> <li><strong>Figure 8B - </strong>CRW-D<strong> - </strong>Pilot scale oleogel - 5% carnauba wax</li> <li><strong>Figure 8C - </strong>BZW-S<strong>- </strong>Lab scale oleogel - 5% beeswax</li> <li><strong>Figure 8D</strong> - BZW-D - Pilot scale oleogel - 5% beeswax<br> </li> </ul> </li> <li>Cryo-SEM <ul> <li><strong>Figure 6B </strong>- Pilot scale emulsion - 5% carnauba wax - 40% water </li> <li><strong>Figure 6E</strong> - Pilot scale oleogel - 5% carnauba wax<br> </li> <li><strong>Figure 7B</strong> - Pilot scale emulsion - 5% beeswax - 20% water </li> <li><strong>Figure 7E</strong> - Pilot scale oleogel - 5% beeswax<br> </li> </ul> </li> <li>CLSM <ul> <li><strong>Figure 6C</strong> - Lab scale emulsion - 5% carnauba wax - 40% water </li> <li><strong>Figure 6F</strong> - Lab scale oleogel - 5% carnauba wax<br> </li> <li><strong>Figure 7C</strong> - Lab scale emulsion - 5% beeswax - 40% water</li> <li><strong>Figure 7F </strong>- Lab scale oleogel - 5% beeswax <ul> </ul> </li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li>Rheology data <ul> <li>Strain sweeps <ul> <li><strong>LS_2.5CRW_20W_t1.txt</strong> - Lab scale emulsion - 2.5% carnauba wax - 20% water - day 1</li> <li><strong>LS_5CRW_0W_t1.txt</strong> - Lab scale oleogel - 5% carnauba wax - day 1</li> <li><strong>LS_5CRW_20W_t1.txt</strong> - Lab scale emulsion - 5% carnauba wax - 20% water - day 1</li> <li><strong>LS_5CRW_30W_t1.txt</strong> - Lab scale emulsion - 5% carnauba wax - 30% water - day 1</li> <li><strong>LS_5CRW_40W_t1.txt</strong> - Lab scale emulsion - 5% carnauba wax - 40% water - day 1</li> <li><strong>LS_7.5CRW_20W_t1.txt</strong> - Lab scale emulsion- 7.5% carnauba wax - 20% water - day 1<br> </li> <li><strong>LS_1.5BZW_20W_t1.txt</strong> - Lab scale emulsion - 1.5% beeswax - 20% water - day 1</li> <li><strong>LS_2.25BZW_20W_t1.txt</strong> - Lab scale emulsion - 2.25% beeswax - 20% water - day 1 </li> <li><strong>LS_5BZW_0W_t1.txt</strong> - Lab scale oleogel - 5% beeswax - day 1</li> <li><strong>LS_5BZW_20W_t1.txt</strong> - Lab scale emulsion - 5% beeswax - 20% water - day 1</li> <li><strong>LS_5BZW_30W_t1.txt</strong> - Lab scale emulsion - 5% beeswax - 30% water - day 1</li> <li><strong>LS_5BZW_40W_t1.txt</strong> - Lab scale emulsion - 5% beeswax - 40% water - day 1<br> </li> <li><strong>PS_5BZW_0W_t1.txt</strong> - Pilot scale oleogel - 5% beeswax - day 1</li> <li><strong>PS_5BZW_20W_t1.txt</strong> - Pilot scale emulsion - 5% beeswax - 20% water - day 1</li> <li><strong>PS_5BZW_40W_t1.txt</strong> - Pilot scale emulsion - 5% beeswax - 40% water - day 1</li> <li><strong>PS_5CRW_0W_t1.txt</strong> - Pilot scale oleogel - 5% carnauba wax - day 1</li> <li><strong>PS_5CRW_20W_t1.txt</strong> - Pilot scale emulsion - 5% carnauba wax - 20% water - day 1</li> <li><strong>PS_5CRW_40W_t1.txt</strong> - Pilot scale emulsion - 5% carnauba wax - 40% water - day 1<br> </li> </ul> </li> <li>Data compilation: <ul> <li><strong>Rheology - Average.csv</strong><br> Please consider the following abbreviations in the file <ul> <li>CGC = Critical gelling concentration</li> <li>G' = Storage modulus</li> <li>G'' = Loss modulus</li> <li>G* = Complex modulus</li> <li>Strain LVE = Yield strain </li> <li>COP = Crossover point</li> <li>NaN = indicates that the system was not stable and thus not measured</li> <li>Data is presented as Avgerave ± Standard Deviation (of the three repetitions)</li> </ul> </li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> </ul>
Doodleverse/Segmentation Gym Res-UNet models for 2-class (water, other) segmentation of CoastCam runup timestack imagery
<p><strong>Doodleverse/Segmentation Gym Res-UNet models for 2-class (water, other) segmentation of CoastCam runup timestack imagery</strong></p> <p>This model release is part of the Doodleverse: https://github.com/Doodleverse</p> <p>These Residual-UNet model data are based on RGB (red, green, and blue) images of coasts and associated labels.</p> <p>Models have been created using Segmentation Gym* using an as-yet unpublished dataset of images and associated label images. See https://github.com/Doodleverse for more information about how this model was trained, and how to use it for inference</p> <p>Classes: {0=other, 1=water}</p> <p>File descriptions</p> <p>There are two models; v7 has been trained from scratch, and v8 has been fine-tuned using hyperparameter adjustment. For each model, there are 5 files with the same root name:</p> <p>1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.</p> <p>2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled.</p> <p>3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</p> <p>4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py`</p> <p>5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py`</p> <p>Additionally, </p> <p>1. BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU<br> 2. sample_images.zip contains a few example input files, for model testing</p> <p><br> References</p> <p>*Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym<br> </p>
Models simulating abrupt changes in the Chilika lagoon fishery, the Easter Island community, forest dieback and lake water quality
<p>This deposit is in support of Willcock et al "Earlier collapse of Anthropocene ecosystems driven by multiple faster and noisier drivers". It covers the following items: (i) A list of the files contained within this data deposit; (ii) How to access and download the specialist software required to view and simulate the system dynamics models (STELLA ‘isee Player’); (iii) How to run isee Player to simulate the models; (iv) How to access and download the standard statistical software ‘R’ to run the R scripts; (v) How to load ‘R’ and modify the standard R script to analyse a subset of the model runs. This file will also details the ‘required content’ (e.g. software versions), as specified in the ‘nr-software-policy.pdf’ document.</p> <p>The full descriptions of each of the four system dynamics models used in this manuscript can be read in the following papers:</p> <ol> <li>Lake Chilika – Cooper, G. S. & Dearing, J. A. Modelling future safe and just operating spaces in regional social-ecological systems. <em>Sci. Total Environ.</em> <strong>651</strong>, 2105–2117 (2019), <a href="https://doi.org/10.1016/j.scitotenv.2018.10.118">https://doi.org/10.1016/j.scitotenv.2018.10.118</a></li> <li>Easter Island – Brandt, G. & Merico, A. The slow demise of Easter Island: Insights from a modeling investigation. <em>Front. Ecol. Evol.</em> <strong>3</strong>, 13 (2015), <a href="https://www.frontiersin.org/article/10.3389/fevo.2015.00013">https://www.frontiersin.org/article/10.3389/fevo.2015.00013</a></li> <li>Lake phosphorus – Wang, R. <em>et al.</em> Flickering gives early warning signals of a critical transition to a eutrophic lake state. <em>Nature</em> <strong>492</strong>, 419–22 (2012), <a href="http://dx.doi.org/10.1038/nature11655">http://dx.doi.org/10.1038/nature11655</a></li> <li>TRIFFID - Ritchie, P. D. L., Clarke, J. J., Cox, P. M. & Huntingford, C. Overshooting tipping point thresholds in a changing climate. <em>Nat. 2021 5927855</em> <strong>592</strong>, 517–523 (2021), <a href="http://dx.doi.org/10.1038/nature11655">http://dx.doi.org/10.1038/nature11655</a></li> </ol>
Data and Software for "Gullies on Mars could have formed by melting of water ice during periods of high obliquity"
<p>Code, movies and climate model outputs for "Gullies on Mars could have formed by melting of water ice during periods of high obliquity" by Dickson et al. Science, 2023.</p>
MPIC OMI Total Column Water Vapour (TCWV) Climate Data Record
<p>The upload contains a long-term data set of 1°x 1° monthly mean total column water vapour (TCWV) retrieved in the visible "blue" spectral range from global measurements of the Ozone Monitoring Instrument (OMI). The TCWV data set covers the time range from January 2005 to December 2020.</p>
June 2023 Supplement Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other)
<p><strong>June 2023 Supplement of Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other)</strong></p> <p><strong>Description</strong></p> <p>Supplementary dataset to:</p> <p>Buscombe, Daniel, Goldstein, Evan, Bernier, Julie, Bosse, Stephen, Colacicco, Rosa, Corak, Nick, Fitzpatrick, Sharon, del Jesús González Guillén, Anais, Ku, Venus, Paprocki, Julie, Platt, Lindsay, Steele, Bethel, Wright, Kyle, & Yasin, Brandon. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7335647</p> <p>This supplemental dataset consists of 283 RGB images and 283 associated labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts. Of these, 77 images-label pairs also have a corresponding NIR and SWIR satellite image. The 4 classes are 0=water, 1=whitewater, 2=sediment, 3=other</p> <p>These images and labels have been made using the Doodleverse software package, Doodler*. These images and labels could be used within numerous Machine Learning frameworks for image segmentation, but have specifically been made for use with the Doodleverse software package, Segmentation Gym**.</p> <p>Imagery are a mixture of 10-m Sentinel-2 and 15-m pansharpened Landsat 7, 8, and 9 visible-band imagery of various sizes. NIR, SWIR, Red, Green, and Blue bands only</p> <p><strong>File descriptions</strong></p> <ol> <li>classes.txt, a file containing the class names</li> <li>images.zip, a zipped folder containing the 3-band images of varying sizes and extents</li> <li>labels.zip, a zipped folder containing the 1-band label images</li> <li>overlays.zip, a zipped folder containing a semi-transparent overlay of the color-coded label on the image (blue=0=water, red=1=whitewater, yellow=2=sediment, green=3=other)</li> <li>nir.zip</li> <li>swir.zip</li> </ol> <p><strong>References</strong></p> <p>Buscombe, Daniel, Goldstein, Evan, Bernier, Julie, Bosse, Stephen, Colacicco, Rosa, Corak, Nick, Fitzpatrick, Sharon, del Jesús González Guillén, Anais, Ku, Venus, Paprocki, Julie, Platt, Lindsay, Steele, Bethel, Wright, Kyle, & Yasin, Brandon. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7335647</p> <p>*Doodler: Buscombe, D., Goldstein, E.B., Sherwood, C.R., Bodine, C., Brown, J.A., Favela, J., Fitzpatrick, S., Kranenburg, C.J., Over, J.R., Ritchie, A.C. and Warrick, J.A., 2021. Human‐in‐the‐Loop Segmentation of Earth Surface Imagery. Earth and Space Science, p.e2021EA002085<a href="https://doi.org/10.1029/2021EA002085">https://doi.org/10.1029/2021EA002085</a>. See <a href="https://github.com/Doodleverse/dash_doodler">https://github.com/Doodleverse/dash_doodler.</a></p> <p>**Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a></p> <p> </p>
CALIPR Framework Myelin Water Imaging Supporting Data
<p>Data supporting the publication:</p> <p><em><strong>The CALIPR framework for highly accelerated myelin water imaging with improved precision and sensitivity</strong></em></p> <p> </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.