This page only provides the drone-view image dataset.

• For the ground-level image dataset, please visit SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part I).
• For the point clouds, please visit SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part III).

The dataset contains drone-view RGB images, depth maps and instance segmentation labels collected from different scenes. Data from each scene is stored in a separate .7z file, along with a color_palette.xlsx file, which contains the RGB_id and corresponding RGB values.

All files follow the naming convention: {central_tree_id}_{timestamp}, where {central_tree_id} represents the ID of the tree centered in the image, which is typically in a prominent position, and timestamp indicates the time when the data was collected.

Specifically, each 7z file includes the following folders:

• rgb: This folder contains the RGB images (PNG) of the scenes and their metadata (TXT). The metadata describes the weather conditions and the world time when the image was captured. An example metadata entry is: Weather:Snow_Blizzard,Hour:10,Minute:56,Second:36.

• depth_pfm: This folder contains absolute depth information of the scenes, which can be used to reconstruct the point cloud of the scene through reprojection.

• instance_segmentation: This folder stores instance segmentation labels (PNG) for each tree in the scene, along with metadata (TXT) that maps tree_id to RGB_id. The tree_id can be used to look up detailed information about each tree in obj_info_final.xlsx, while the RGB_id can be matched to the corresponding RGB values in color_palette.xlsx. This mapping allows for identifying which tree corresponds to a specific color in the segmentation image.

• obj_info_final.xlsx: This file contains detailed information about each tree in the scene, such as position, scale, species, and various parameters, including trunk diameter (in cm), tree height (in cm), and canopy diameter (in cm).

• landscape_info.txt: This file contains the ground location information within the scene, sampled every 0.5 meters.

For birch_forest, broadleaf_forest, redwood_forest and rainforest, we also provided COCO-format annotation files (.json). Two such files can be found in these datasets:

• {name}_coco.json: This file contains the annotation of each tree in the scene.
• {name}_filtered.json: This file is derived from the previous one, but filtering is applied to rule out overlapping instances.

⚠️: 7z files that begin with "!" indicate that the RGB values in the images within the instance_segmentation folder cannot be found in color_palette.xlsx. Consequently, this prevents matching the trees in the segmentation images to their corresponding tree information, which may hinder the application of the dataset to certain tasks. This issue is related to a bug in Colossium/AirSim, which has been reported in link1 and link2.

The bid–ask spread (also bid–offer or bid/ask and buy/sell in the case of a market maker) is the difference between the prices quoted (either by a single market maker or in a limit order book) for an immediate sale (ask) and an immediate purchase (bid) for stocks, futures contracts, options, or currency pairs in some auction scenario. The size of the bid–ask spread in a security is one measure of the liquidity of the market and of the size of the transaction cost.[1] If the spread is 0 then it is a frictionless asset.

dataset for joint flow size and spread measurement and anomaly detection

This data set provides monthly average price values, and the differences among those values, at the farm, wholesale, and retail stages of the production and marketing chain for selected cuts of beef, pork, and broilers. In addition, retail prices are provided for beef and pork cuts, turkey, whole chickens, eggs, and dairy products. Price spreads are reported for last 6 years, 12 quarters, and 24 months. The retail price file provides monthly estimates for the last 6 months. The historical file provides data since 1970.

Concept: Difference between average cost of outstanding loans (ICC) and its average funding cost. Comprises both earmarked and nonearmarked operations. Source: Central Bank of Brazil – Statistics Department 9c2ecd38-11e2-4399-8b1f-d16cc7bb31f6 27443-icc-spread

Dataset Description This synthetic dataset combines climate, environmental, epidemiological, and socio-economic data from 120 countries over 24 years (2000-2023). The aim is to analyze the relationship between climate change, environmental pollution and the spread of infectious diseases (malaria, dengue fever, cholera, Lyme disease). The data is suitable for forecasting tasks, anomaly detection, and risk clustering of regions.

Uniqueness:

• The first dataset that combines long-term climate trends with medical and demographic statistics.

• Rare parameters are included: the UV radiation index, the migration of disease vectors, and the cost of prevention.

• Realistic anomalies (for example, dengue outbreaks in Europe by 2023).

Contain informative data related to COVID-19 pandemic. Specially, figure out about the First Case and First Death information for every single country. The datasets mainly focus on two major fields first one is First Case which consists of information of Date of First Case(s), Number of confirm Case(s) at First Day, Age of the patient(s) of First Case, Last Visited Country and the other one First Death information consist of Date of First Death and Age of the Patient who died first for every Country mentioning corresponding Continent. The datasets also contain the Binary Matrix of spread chain among different country and region.

*This is not a country. This is a ship. The name of the Cruise Ship was not given from the government.
"N+": the age is not specified but greater than N
“No Trace”: some data was not found
“Unspecified”: not available from the authority
“N/A”: for “Last Visited Country(s) of Confirmed Case(s)” column, “N/A” indicates that the confirmed case(s) of those countries do not have any travel history in recent past; in “Age of First Death(s)” column “N/A” indicates that those countries do not have may death case till May 16, 2020.

This dataset contains information useful for studying the spread of the Witty worm. Data were collected on the UCSD Network Telescope between Fri Mar 19 20:01:40 PST 2004 and Wed Mar 25 00:01:40 PST 2004. The dataset is exclusively available through Impact. Up until Feb 2014 the dataset was online in two portions, one public, one restricted. After Feb 2014 the whole Witty dataset was public until mid-2016. The publicly available set of files contains summarized information that does not individually identify infected computers. These are the files in witty/summaries/public. The restricted-access set of files that do contain more sensitive information, including packet traces containing complete IP and UDP headers and partial payload received from hosts spreading the Witty worm March 19-24, 2004. It also includes routing tables and summaries. These are the files in witty/data and witty/summaries/restricted/.

Concept: Difference (spread) between average interest rate on new credit operations in the relevant period in the National Financial System, which are under regulation by the National Monetary Council (CMN) or linked to budget funds, and corresponding average cost of funds. Refers to special financing operations which require proof of proper use of funds, linked to medium and long term production and investments projects. Funds origins are shares of checking accounts and savings accounts and funds from governmental programs. Source: Central Bank of Brazil – Statistics Department 2a7b340d-1ab4-4312-b00e-0969dcd949cf 20837-average-spread-of-earmarked-new-credit-operations---households---total

Concept: Difference (spread) between average interest rate on new credit operations in the reference period in the National Financial System and corresponding average cost of funds. Comprises both earmarked and nonearmarked operations. Source: Central Bank of Brazil – Statistics Department e2dc1628-d570-439b-b7dd-f89ce87d0ca6 20783-average-spread-of-new-credit-operations---total

This dataset provides the spread of the conflict globally in terms of population and country for the years 2000-2016.

This dataset contains the spatiotemporal data used to train the spatiotemporal deep neural networks described in "Modeling the Spread of a Livestock Disease With Semi-Supervised Spatiotemporal Deep Neural Networks". The dataset consists of two sets of NumPy arrays. The first set: X_grid.npy and Y_grid.npy were used to train the convolutional LSTM, while the second set: X_graph.npy, Y_graph.npy, and edge_index.npy were used to train the graph convolutional LSTM. The data consists of spatiotemporally varying environmental and anthropogenic variables along with case reports of vesicular stomatitis. Resources in this dataset:Resource Title: NumPy Arrays of Spatiotemporal Features and VS Cases. File Name: vs_data.zipResource Description: This is a ZIP archive containing five NumPy arrays of spatiotemporal features and geotagged VS cases.Resource Software Recommended: NumPy,url: https://numpy.org/

Dataset

This dataset was created by zhiminV

Released under Apache 2.0

Contents

Explore our meticulously curated Movies dataset and TV shows dataset, designed to cater to diverse analytical and research needs. Whether you're a data scientist, a student, or a business professional, these datasets provide valuable insights into the entertainment industry.

Key Features of the Movies Dataset:

1. Extensive collection of global movies across various genres and languages.

2. Detailed metadata, including titles, release dates, genres, directors, cast, and ratings.

3. Regularly updated to ensure relevance and accuracy.

Why Choose Our TV Shows Dataset?

Our TV shows dataset is your gateway to understanding trends in episodic content. It includes:

• Comprehensive details about popular and niche TV shows.

• Information on episode counts, seasons, ratings, and networks.

• Insights into audience preferences and regional programming.

Applications of These Datasets

These datasets are perfect for:

• Machine learning models for recommendation systems.

• Academic research on media trends and audience behavior.

• Business strategies for entertainment platforms.

Unlock the power of TV show data with our Crawl Feeds TV Shows Dataset. Start analyzing today and gain valuable insights into your favorite shows!

USDA Economic Research Service (ERS) compares prices paid by consumers for food with prices received by farmers for corresponding commodities. This data set reports these comparisons for a variety of foods sold through retail food stores such as supermarkets and super centers. Comparisons are made for individual foods and groupings of individual foods-market baskets-that represent what a typical U.S. household buys at retail in a year. The retail costs of these baskets are compared with the money received by farmers for a corresponding basket of agricultural commodities.

A dataset containing trees in the DLR area in 2019. This dataset is only a partial representation of the actual tree count within DLR and contains fields such as Location, Species, Height, Spread, Trunk and Age. Please note this data is for information purposes only and may not be an exact representation of the infrastructure. Changes and upgrades occurring since then may not be represented.

This dataset is about book subjects. It has 2 rows and is filtered where the books is Spread the joy. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

This dataset is about books. It has 4 rows and is filtered where the book series is The spread of printing, Western hemisphere. It features 9 columns including author, publication date, language, and book publisher.

This dataset contains metadata about all Covid-related YouTube videos which circulated on public social media, but which YouTube eventually removed because they contained false information. It describes 8,122 videos that were shared between November 2019 and June 2020. The dataset contains unique identifiers for the videos and social media accounts that shared the videos, statistics on social media engagement and metadata such as video titles and view counts where they were recoverable. We publish the data alongside the code used to produce on Github. The dataset has reuse potential for research studying narratives related to the coronavirus, the impact of social media on knowledge about health and the politics of social media platforms.

Water Gauge Even Spread

## Overview

Water Gauge Even Spread is a dataset for instance segmentation tasks - it contains Water Level SbeH annotations for 654 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).