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74 results for “sales”
North Temperate Lakes LTER: Residential Lakeshore Property Sales in Vilas County 1997 - 2004
Sales of residential shoreline property parcels in Vilas County, WI, USA for the period January 1997 througt Dec 2004. This dataset includes sales of over 2000 parcels on 234 lakes. In addtion to the sale price, other information collected include assessed value of the land, assessed value of improvements, length of lake frontage and total size of the parcel.
Property listings for sale Madrid
<p>This dataset contains the property listings in Madrid as of October 31st, 2021</p> <p>From each listing, we got the following fields:</p> <p><strong>ID</strong>: the listing ID<br> <strong>Listing</strong>: It shows the listing it usually contains the type of property and the street. <br> <strong>Location</strong>: the place of the listing (city, town or neighborhood)<br> <strong>price</strong>: property price<br> <strong>old_price</strong>: previous price before the discount.<br> <strong>discount</strong>: percentage of discount<br> <strong>meters</strong>: size of the property in meters<br> <strong>sq_meter_price</strong>: price of each square meter.<br> <strong>rooms</strong>: number of rooms of the property<br> <strong>floor</strong>: the floor of the property<br> <strong>garage</strong>: It shows if the property has a garage<br> <strong>description</strong>: This field is the description of the property in Spanish.</p> <p>All the data has been scraped from idealista containing the listings as of October 31st, 2021 in Madrid. The process took about 30 hours. </p> <p>Spanish:</p> <p>El dataset contiene los inmuebles listados para su venta en la Comunidad de Madrid a fecha 31 de octubre de 2021.</p> <p>De cada una de las ofertas de venta se han recogido los siguientes campos:</p> <p><strong>ID</strong>: Es el identificador del anuncio<br> <strong>Listing</strong>: Contiene el título del anuncio que normalmente se compone del tipo de vivienda y de la calle en la que se encuentra. <br> <strong>Location</strong>: es la ubicación del inmueble que puede ser la ciudad, el pueblo o el barrio. <br> <strong>price</strong>: precio de venta del inmueble.<br> <strong>old_price</strong>: campo solo disponible en los inmuebles rebajados e indica el precio anterior del inmueble<br> <strong>discount</strong>: porcentaje de descuento del precio.<br> <strong>meters</strong>: metros cuadrados de la vivienda<br> <strong>sq_meter_price</strong>: Precio del metro cuadrado del inmueble<br> <strong>rooms</strong>: Número de habitaciones del inmueble<br> <strong>floor</strong>: Planta del inmueble<br> <strong>garage</strong>: campo que indica si el inmueble dispone de garaje, cuando su valor es nulo indica que no aparece reflejado que disponga de garaje.<br> <strong>description</strong>: Es un campo abierto donde aparece la descripción del inmueble en venta.</p> <p>Los datos han sido extraídos del portal inmobiliario idealista y el dataset contiene los inmuebles listados a fecha 31/10/2021 en la Comunidad de Madrid. Ha sido obtenido mediante un web scraper desarrollado en python en un proceso que ha durado unas 30 horas.</p>
Houses for sale in the Salamanca and Villaverde district of Madrid in April 2022
<p>Dataset that contains data scraped from the websites of <a href="https://www.fotocasa.es/es/">Fotocasa</a> and <a href="https://www.idealista.com/">Idealista</a> between 4<sup>th</sup> and 7<sup>th</sup> April 2022 and it is meant only for academic purposes.</p> <p>Each record describes a house for sale in the Salamanca and Villaverde districts of Madrid by the following fields: id, url, title, location, price, m2, rooms, floor, num-photos, floor-plan, view3d, video, home-staging, description, photo_urls and source.</p> <p>The context of this project is the Data Science Master’s Degree of UOC (Universitat Oberta de Catalunya), specifically the subject Data Typology and Life Cycle’.</p>
BigMart Retail Sales
<p><strong><em>Nothing ever becomes real till it is experienced.</em></strong></p> <p><strong><em>-John Keats</em></strong></p> <p> </p> <p>While we don't know the context in which John Keats mentioned this, we are sure about its implication in data science. While you would have enjoyed and gained exposure to real world problems in this challenge, here is another opportunity to get your hand dirty with this practice problem.</p> <p>_______________________________________</p> <p><strong>Problem Statement :</strong></p> <p>The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and find out the sales of each product at a particular store.</p> <p>Using this model, BigMart will try to understand the properties of products and stores which play a key role in increasing sales.</p> <p>Please note that the data may have missing values as some stores might not report all the data due to technical glitches. Hence, it will be required to treat them accordingly.</p> <p>________________________________________</p> <p><strong>Data :</strong></p> <p>We have 14204 samples in data set.</p> <p> </p> <p><strong>Variable Description</strong></p> <ul> <li><strong>Item Identifier</strong>: A code provided for the item of sale</li> <li><strong>Item Weight</strong>: Weight of item</li> <li><strong>Item Fat Content</strong>: A categorical column of how much fat is present in the item: ‘Low Fat’, ‘Regular’, ‘low fat’, ‘LF’, ‘reg’</li> <li><strong>Item Visibility</strong>: Numeric value for how visible the item is</li> <li><strong>Item Type</strong>: What category does the item belong to: ‘Dairy’, ‘Soft Drinks’, ‘Meat’, ‘Fruits and Vegetables’, ‘Household’, ‘Baking Goods’, ‘Snack Foods’, ‘Frozen Foods’, ‘Breakfast’, ’Health and Hygiene’, ‘Hard Drinks’, ‘Canned’, ‘Breads’, ‘Starchy Foods’, ‘Others’, ‘Seafood’.</li> <li><strong>Item MRP</strong>: The MRP price of item</li> <li><strong>Outlet Identifier</strong>: Which outlet was the item sold. This will be categorical column</li> <li><strong>Outlet Establishment Year</strong>: Which year was the outlet established</li> <li><strong>Outlet Size</strong>: A categorical column to explain size of outlet: ‘Medium’, ‘High’, ‘Small’.</li> <li><strong>Outlet Location Type</strong>: A categorical column to describe the location of the outlet: ‘Tier 1’, ‘Tier 2’, ‘Tier 3’</li> <li><strong>Outlet Type</strong>: Categorical column for type of outlet: ‘Supermarket Type1’, ‘Supermarket Type2’, ‘Supermarket Type3’, ‘Grocery Store’</li> <li><strong>Item Outlet Sales</strong>: The number of sales for an item.</li> </ul> <p>_________________________________________</p> <p><strong>Evaluation Metric:</strong></p> <p>We will use the <strong>Root Mean Square Error </strong>value to judge your response</p>
Underwater images collected by an Autonomous Surface Vehicle in Etang-Sale, Réunion - 2023-12-13
<i>This dataset was collected by an Autonomous Surface Vehicle in Etang-Sale, Réunion - 2023-12-13.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2>Image acquisition</h2> This session has 25.65 GB of MP4 files, which were trimmed into 7487 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 99.39% of these extracted images are useful and 0.61% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. <br> Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 69.84 %, Q2: 30.08 %, Q5: 0.08 % <br> <h2> Bathymetry </h2> The data are collected using a single-beam echosounder <a href="https://ceruleansonar.com/products/sounder-s500" target="_blank">S500</a>. <br> We only keep the values which have a GPS correction in Q1.<br> We keep the points that are the waypoints.<br> We keep the raw data where depth was estimated between 0.2 m and 50.0 m deep. <br> The data are first referenced against the WGS84 ellipsoid. Then we apply the local geoid if available.<br> At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles. <br> The size of the grid cells is 0.173 m. <br> The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
Underwater images collected by an Autonomous Surface Vehicle in Etang-Sale, Réunion - 2023-12-13
<i>This dataset was collected by an Autonomous Surface Vehicle in Etang-Sale, Réunion - 2023-12-13.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2>Image acquisition</h2> This session has 22.32 GB of MP4 files, which were trimmed into 7552 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 99.39% of these extracted images are useful and 0.61% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. <br> Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 85.05 %, Q2: 14.77 %, Q5: 0.18 % <br> <h2> Bathymetry </h2> The data are collected using a single-beam echosounder <a href="https://www.echologger.com/products/single-frequency-echosounder-deep" target="_blank">ETC 400</a>. <br> We only keep the values which have a GPS correction in Q1.<br> We keep the points that are the waypoints.<br> We keep the raw data where depth was estimated between 0.2 m and 50.0 m deep. <br> The data are first referenced against the WGS84 ellipsoid. Then we apply the local geoid if available.<br> At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles. <br> The size of the grid cells is 0.076 m. <br> The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
Underwater images collected by an Autonomous Surface Vehicle in Etang-Sale, Réunion - 2023-12-13
<i>This dataset was collected by an Autonomous Surface Vehicle in Etang-Sale, Réunion - 2023-12-13.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2>Image acquisition</h2> This session has 14.77 GB of MP4 files, which were trimmed into 5014 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 99.9% of these extracted images are useful and 0.1% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. <br> Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 76.08 %, Q2: 23.52 %, Q5: 0.41 % <br> <h2> Bathymetry </h2> The data are collected using a single-beam echosounder <a href="https://www.echologger.com/products/single-frequency-echosounder-deep" target="_blank">ETC 400</a>. <br> We only keep the values which have a GPS correction in Q1.<br> We keep the points that are the waypoints.<br> We keep the raw data where depth was estimated between 0.2 m and 50.0 m deep. <br> The data are first referenced against the WGS84 ellipsoid. Then we apply the local geoid if available.<br> At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles. <br> The size of the grid cells is 0.131 m. <br> The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
Underwater images collected by an Autonomous Surface Vehicle in Etang-Sale, Réunion - 2023-12-13
<i>This dataset was collected by an Autonomous Surface Vehicle in Etang-Sale, Réunion - 2023-12-13.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2>Image acquisition</h2> This session has 26.44 GB of MP4 files, which were trimmed into 8631 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 96.52% of these extracted images are useful and 3.48% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. <br> Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 82.2 %, Q2: 17.4 %, Q5: 0.41 % <br> <h2> Bathymetry </h2> The data are collected using a single-beam echosounder <a href="https://www.echologger.com/products/single-frequency-echosounder-deep" target="_blank">ETC 400</a>. <br> We only keep the values which have a GPS correction in Q1.<br> We keep the points that are the waypoints.<br> We keep the raw data where depth was estimated between 0.2 m and 50.0 m deep. <br> The data are first referenced against the WGS84 ellipsoid. Then we apply the local geoid if available.<br> At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles. <br> The size of the grid cells is 0.21 m. <br> The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
Underwater images collected by an Autonomous Surface Vehicle in Etang-Sale, Réunion - 2023-12-13
<i>This dataset was collected by an Autonomous Surface Vehicle in Etang-Sale, Réunion - 2023-12-13.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2>Image acquisition</h2> This session has 18.61 GB of MP4 files, which were trimmed into 6623 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 99.88% of these extracted images are useful and 0.12% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. <br> Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 94.22 %, Q2: 5.62 %, Q5: 0.16 % <br> <h2> Bathymetry </h2> The data are collected using a single-beam echosounder <a href="https://www.echologger.com/products/single-frequency-echosounder-deep" target="_blank">ETC 400</a>. <br> We only keep the values which have a GPS correction in Q1.<br> We keep the points that are the waypoints.<br> We keep the raw data where depth was estimated between 0.2 m and 50.0 m deep. <br> The data are first referenced against the WGS84 ellipsoid. Then we apply the local geoid if available.<br> At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles. <br> The size of the grid cells is 0.114 m. <br> The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
Franz Sales Ebentheur (e0689)
<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: Franz Sales Ebentheur<br><u>musiXplora-ID</u>: e0689<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/e0689">https://musixplora.de/mxp/e0689</a><br><u>Gender</u>: m<br><u>First Mentioned</u>: 1700<br><u>Sectors</u>: Instrumentenbau<br><u>Professions (Musical)</u>: Geigenbauer<br><u>Other Places of Activity</u>: Augsburg<br><br><br><u>Titel/Medien:</u><br><table><tbody><tr><th>Role</th><th>Sigel</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Related</td><td>De Wit 1902</td><td>Geigenzettel Alter Meister vom 16. bis zur Mitte des 19. Jahrhunderts</td><td><a href="https://musixplora.de/mxp/5001302">5001302</a></td></tr><tr><td>Related</td><td>Lütgendorff 1922</td><td>Die Geigen- und Lautenmacher vom Mittelalter bis zur Gegenwart. 2 Bände. Lüt2</td><td><a href="https://musixplora.de/mxp/5002059">5002059</a></td></tr></tbody></table><br><br><u>Changelog</u>:<br> - v0.0.1: Initial Upload.<br>
Franz Sales Ehrlich (e0685)
<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: Franz Sales Ehrlich<br><u>musiXplora-ID</u>: e0685<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/e0685">https://musixplora.de/mxp/e0685</a><br><u>Gender</u>: m<br><u>Date of Birth</u>: 25 December 1835<br><u>Place of Birth</u>: Bärnau<br><u>Date of Death</u>: 16 December 1883<br><u>Place of Death</u>: Braunau/Inn<br><u>First Mentioned</u>: 1850<br><u>Last Mentioned</u>: 1880<br><u>Sectors</u>: Kirche, Orgelbau<br><u>Professions (Musical)</u>: Orgelbauer<br><u>Main Place of Activity</u>: Braunau/Inn<br><u>Other Places of Activity</u>: Linz, Mauerkirchen<br><br><br><u>Herkunftsfamilie:</u><br><table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>Eltern</td><td>Sohn</td><td>Martin Xaver Ehrlich</td><td><a href="https://musixplora.de/mxp/e0681">e0681</a></td></tr></tbody></table><br><u>Titel/Medien:</u><br><table><tbody><tr><th>Role</th><th>Sigel</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Related</td><td>Bernhard 2003a</td><td>Orgeldatenbank Bayern</td><td><a href="https://musixplora.de/mxp/5001131">5001131</a></td></tr></tbody></table><br><br><u>Changelog</u>:<br> - v0.0.1: Initial Upload.<br>
Franz Sales Bruder (b3594)
<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: Franz Sales Bruder<br><u>musiXplora-ID</u>: b3594<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/b3594">https://musixplora.de/mxp/b3594</a><br><u>Gender</u>: m<br><u>Date of Birth</u>: 1855<br><u>Place of Birth</u>: Undefined<br><u>Date of Death</u>: 1928<br><u>Place of Death</u>: Undefined<br><u>First Mentioned</u>: 1880<br><u>Sectors</u>: Orgelbau<br><u>Professions (Historical)</u>: Drehorgelbauer<br><u>Professions (Musical)</u>: Orgelbauer<br><u>Other Places of Activity</u>: Waldkirch<br><br><br><u>Portfolio:</u><br><table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>Sortimente</td><td>Sortiment</td><td>Drehorgel</td><td><a href="https://musixplora.de/mxp/2001490">2001490</a></td></tr></tbody></table><br><u>Titel/Medien:</u><br><table><tbody><tr><th>Role</th><th>Sigel</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Related</td><td>Matzke 2016</td><td>Die Anfänge des Musikinstrumentenmuseums in Leipzig. Paul de Wits Gästebuch 1893 bis 1905</td><td><a href="https://musixplora.de/mxp/5002009">5002009</a></td></tr><tr><td>Related</td><td>Gästebuch 1893</td><td>Das Gästebuch von Paul de Wit. Mit autographen Eintragungen der Besucher des Musikhistorisches Museums Paul de Wit in Leipzig. von der Eröffnung im Jahr 1893 bis zur Schließung im Jahr 1905. Unveröffentlichtes Manuskript</td><td><a href="https://musixplora.de/mxp/5020488">5020488</a></td></tr></tbody></table><br><br><u>Changelog</u>:<br> - v0.0.1: Initial Upload.<br>
Franz Sales Wörnle (w1944)
<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: Franz Sales Wörnle<br><u>musiXplora-ID</u>: w1944<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/w1944">https://musixplora.de/mxp/w1944</a><br><u>Gender</u>: m<br><u>Confessions</u>: römisch-katholisch<br><u>Date of Birth</u>: 17 August 1758<br><u>Place of Birth</u>: Mittenwald<br><u>Date of Death</u>: 05 November 1826<br><u>Place of Death</u>: Mittenwald<br><u>First Mentioned</u>: 1783<br><u>Sectors</u>: Geigenbau<br><u>Professions (Musical)</u>: Geigenbauer<br><u>Main Place of Activity</u>: Mittenwald<br><br><br><u>Portfolio:</u><br><table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>Sortimente</td><td>Sortiment</td><td>Geige</td><td><a href="https://musixplora.de/mxp/2001463">2001463</a></td></tr></tbody></table><br><u>Titel/Medien:</u><br><table><tbody><tr><th>Role</th><th>Sigel</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Related</td><td>Lütgendorff 1922</td><td>Die Geigen- und Lautenmacher vom Mittelalter bis zur Gegenwart. 2 Bände. Lüt2</td><td><a href="https://musixplora.de/mxp/5002059">5002059</a></td></tr><tr><td>Related</td><td>Lütgendorff 1990</td><td>Die Geigen- und Lautenmacher vom Mittelalter bis zur Gegenwart. Teil 3: Ergänzungsband. Erstellt von Thomas Drescher. Lüt3</td><td><a href="https://musixplora.de/mxp/5002060">5002060</a></td></tr></tbody></table><br><br><u>Changelog</u>:<br> - v0.0.1: Initial Upload.<br>
Franz Sales Jais (j0433)
<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: Franz Sales Jais<br><u>musiXplora-ID</u>: j0433<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/j0433">https://musixplora.de/mxp/j0433</a><br><u>Gender</u>: m<br><u>Confessions</u>: römisch-katholisch<br><u>Date of Birth</u>: 29 January 1720<br><u>Place of Birth</u>: Mittenwald<br><u>Date of Death</u>: 11 April 1804<br><u>Place of Death</u>: Mittenwald<br><u>First Mentioned</u>: 1769<br><u>Sectors</u>: Instrumentenbau<br><u>Professions (Musical)</u>: Geigenbauer<br><u>Main Place of Activity</u>: Mittenwald<br><br><br><u>Herkunftsfamilie:</u><br><table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>Eltern</td><td>Vater</td><td>Anton Jais</td><td><a href="https://musixplora.de/mxp/j0421">j0421</a></td></tr><tr><td>Eltern</td><td>Vater</td><td>Johann Jais</td><td><a href="https://musixplora.de/mxp/j0486">j0486</a></td></tr></tbody></table><br><u>Ausbildung:</u><br><table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>LehrerInnen und AusbilderInnen</td><td>Schüler</td><td>Andreas Jais</td><td><a href="https://musixplora.de/mxp/j0334">j0334</a></td></tr></tbody></table><br><u>Portfolio:</u><br><table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>Sortimente</td><td>Sortiment</td><td>Kontrabass</td><td><a href="https://musixplora.de/mxp/2001461">2001461</a></td></tr></tbody></table><br><u>Titel/Medien:</u><br><table><tbody><tr><th>Role</th><th>Sigel</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Related</td><td>Lütgendorff 1922</td><td>Die Geigen- und Lautenmacher vom Mittelalter bis zur Gegenwart. 2 Bände. Lüt2</td><td><a href="https://musixplora.de/mxp/5002059">5002059</a></td></tr></tbody></table><br><br><u>Changelog</u>:<br> - v0.0.1: Initial Upload.<br>
Venezia e l'approvigionamento del sale (XVI-XVIII secolo)
<p>Il video è stato prodotto durante la ricerca in occasione della mostra Venezia acqua e cibo. Storie della laguna e della città, Venezia 2015.</p>
Historic salvage sale locations (1954 - 1974), Andrews Experimental Forest
Historic salvage sale areas (with buffered roads) are reconstructed. Historic salvage timber sales in the H J Andrews from 1954 - 1974 were outlined on a variety of hard copy maps. These manuscripts were digitized into four non-overlapping coverages. Each coverage was turned into a region and the UNION command was used to combine the data into a single coverage of salvage sale regions. The road layer was buffered by 50 yards (45.72 meters)and these areas were added to the coverage.
Detailed information on cost and sale prices on Polish pig market in the period 2017-mid2022
<p>The database contains detailed information on costs and sale prices of piglets and finishers on Polish martket in the period January 2017 - July 2022. The prices of cereals used for feeding are taken from the average monthly data of the Ministry of Agriculture and the daily stock exchange quotations of Agrolok. The remaining operational costs (veterinary costs, utilities, labor, and transport) were assumed at a constant average level established on the basis of the reference methodological publication of the Danish research and development organization called “Seges Innovation” (2022) and a manual on pig farming (Pawłowski, 2020). The same sources were used to determine the optimal feeding model, which is important for calculating feed costs. Assumptions for calculating feed cost, the cost of falls, labor costs in piglet and finisher production, as well as piglet transportation costs are presented in the excel sheet.</p>
Video Games Sales
<p>This dataset contains a list of video games with sales greater than 100,000 copies. It was generated by a scrape of <a href="http://www.vgchartz.com/">vgchartz.com</a>.</p> <p>Fields include</p> <ul> <li> <p>Rank - Ranking of overall sales</p> </li> <li> <p>Name - The games name</p> </li> <li> <p>Platform - Platform of the games release (i.e. PC,PS4, etc.)</p> </li> <li> <p>Year - Year of the game's release</p> </li> <li> <p>Genre - Genre of the game</p> </li> <li> <p>Publisher - Publisher of the game</p> </li> <li> <p>NA_Sales - Sales in North America (in millions)</p> </li> <li> <p>EU_Sales - Sales in Europe (in millions)</p> </li> <li> <p>JP_Sales - Sales in Japan (in millions)</p> </li> <li> <p>Other_Sales - Sales in the rest of the world (in millions)</p> </li> <li> <p>Global_Sales - Total worldwide sales.</p> </li> </ul> <p>The script to scrape the data is available at <a href="https://github.com/GregorUT/vgchartzScrape">https://github.com/GregorUT/vgchartzScrape</a>.<br> It is based on BeautifulSoup using Python.<br> There are 16,598 records. 2 records were dropped due to incomplete information.</p>
European plant-based foods sales data 2017-2020 (Nielsen Market Track)
<ul> <li>The dataset consists of Excel (.xlsx) files with data on sales of plant-based food products between 2017 and 2020 in a number of European countries (i.e. Austria, Belgium, Denmark, France, Germany, Italy, the Netherlands, Poland, Romania, Spain and the UK.)</li> </ul> <ul> <li>The data are clearly labelled within each file. The key variables (common across datasets) are Value in Euros, Volume in KG/LIT and Volume in Selling Units for a number of meat and dairy substitute food products specific to the retail region.</li> </ul> <ul> <li>The data were originally collected by Nielsen Market Track. They were analysed on the <a href="http://www.smartproteinproject.eu">Smart Protein project</a> in 2021 and used to publish an extensive <a href="https://smartproteinproject.eu/plant-based-food-sector-report/">market data report</a> and to host a <a href="https://www.youtube.com/watch?v=dsIJqvpXXgw">public webinar</a>, both entitled <em>Plant-based foods in Europe: how big is the market?</em></li> </ul> <p> </p>
Availability and trends in sports foods available for sale at New Zealand supermarketsy of sports foods globally and in New Zealand supermarkets
<p>Sports foods are specially formulated to help people achieve specific nutritional or sporting performance goals. Anecdotal evidence suggests increasing availability and marketing of such products to consumers, however, very few studies have looked at in-store product availability. Data for 2013 to 2018 were collected from the Nutritrack database, an online searchable database of all unique packaged foods and beverages sold at four main supermarket chains in New Zealand. Availability of sports foods and on-pack marketing techniques were assessed in 2018 using descriptive analysis, and changes in proportions over time were assessed using Chi-Square analyses. In 2018, the proportion of packaged foods available in major New Zealand supermarkets which were classified as sports foods was 2.1% (n=325), which had increased from 1.8% (n=247) in 2013. Sports foods also appeared in more food groups and subcategories in 2018 compared with 2013 (11 vs. 6 food groups, and 25 vs. 19 subcategories, respectively). The use of on-pack marketing techniques also increased over time, with Nutrient Claims present on 87% of sports foods in 2013 and 98% in 2018. The implications of the increase in product availability and on-pack marketing of sports foods in New Zealand supermarkets warrants consideration from public health, sporting, and consumer sectors.</p> <p>Sports foods are specially formulated to help people achieve specific nutritional or sporting performance goals. Anecdotal evidence suggests increasing availability and marketing of such products to consumers, however, very few studies have looked at in-store product availability. Data for 2013 to 2018 were collected from the Nutritrack database, an online searchable database of all unique packaged foods and beverages sold at four main supermarket chains in New Zealand. Availability of sports foods and on-pack marketing techniques were assessed in 2018 using descriptive analysis, and changes in proportions over time were assessed using Chi-Square analyses. In 2018, the proportion of packaged foods available in major New Zealand supermarkets which were classified as sports foods was 2.1% (n=325), which had increased from 1.8% (n=247) in 2013. Sports foods also appeared in more food groups and subcategories in 2018 compared with 2013 (11 vs. 6 food groups, and 25 vs. 19 subcategories, respectively). Use of on-pack marketing techniques also increased over time, with Nutrient Claims present on 87% of sports foods in 2013 and 98% in 2018. The implications of the increase in product availability and on-pack marketing of sports foods in New Zealand supermarkets warrants consideration from public health, sporting, and consumer sectors.</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.