272 Resources

Open Dataset

Showing 137-144 of 272 results
Google Earth Engine

Google Earth Engine

jona.frroku
Open Dataset
Creators: Google LLC
Publication Date: NA
Creators: Google LLC

Google Earth Engine (GEE) is a cloud-based platform that provides access to a vast archive of satellite imagery and geospatial data, along with powerful tools for analysis and visualization. It supports a wide range of datasets from various satellite missions, including Landsat, MODIS, and Sentinel, among others. The platform integrates extensive data archives with powerful computational capabilities, enabling users to analyze and visualize geospatial information effectively. GEE hosts one of the largest publicly available data catalogs, encompassing over 90 PB of analysis-ready satellite imagery and more than 1,000 curated datasets. This includes data from various satellite missions, climate models, and geospatial datasets, providing comprehensive coverage for diverse applications.​ The total data repository of GEE exceeds 90 PB, making it one of the most extensive geospatial data archives available. Given the vast array of datasets and the high temporal resolution of satellite imagery, GEE contains billions of individual observations. For example, daily global coverage by satellites like Sentinel-2 results in a continuous influx of new data, contributing to the platform’s extensive repository. GEE’s datasets span over four decades, with historical imagery dating back to the 1970s. GEE organizes its datasets into a hierarchical structure within the Earth Engine Data Catalog:​

  • Image Collections: These are groups of related images, such as satellite imagery from specific missions (e.g., Landsat, Sentinel). Each image within a collection is associated with metadata, including acquisition date, sensor information, and processing level.​

  • Feature Collections: These datasets consist of vector data representing geographic features like administrative boundaries, roads, or land cover classifications. Each feature includes geometry (points, lines, polygons) and associated attribute information.​

  • Tables: Tabular datasets containing geospatial information, such as climate data, socioeconomic indicators, or environmental statistics, often linked to specific geographic locations.

Copernicus Open Access Hub

Copernicus Open Access Hub

jona.frroku
Open Dataset
Creators: European Space Agency (ESA)
Publication Date: 2014
Creators: European Space Agency (ESA)

Copernicus Open Access Hub provides free access to Sentinel satellite data for Earth observation, including imagery from Sentinel-1, Sentinel-2, Sentinel-3, and Sentinel-5P, supporting environmental and climate research. The dataset serves as an important platform for accessing Earth observation data from the Sentinel satellite constellation. This initiative is integral to the European Union’s Copernicus Programme, designed to monitor and understand our planet’s environment for the benefit of all citizens. The Hub provides unrestricted access to high-resolution satellite imagery and data, ensuring that users worldwide can utilize this information without licensing fees or usage restrictions. The temporal coverage varies by mission, starting in 2014. This provides a continuous record of Earth’s environmental parameters over the past decade. In total, the dataset has a size of more than 45 TB and each sentinel captures thousands of scenes daily:​

  • Sentinel-1: Acquires approximately 2,500 scenes per day.​
  • Sentinel-2: Captures around 1,500 scenes daily.​
  • Sentinel-3: Provides continuous data streams for various parameters.

The datasets is organized based on the specific Sentinel missions and their respective data products:​

  • Sentinel-1 Products:

    • Level-1 GRD (Ground Range Detected): Focused on land and ocean monitoring, these products are radiometrically calibrated and geo-referenced.​
    • Level-1 SLC (Single Look Complex): Suitable for interferometric applications, preserving phase information.​

  • Sentinel-2 Products:

    • Level-1C: Top-of-atmosphere reflectance data, ortho-rectified.​
    • Level-2A: Bottom-of-atmosphere reflectance data, corrected for atmospheric effects.​

  • Sentinel-3 Products:

    • OLCI (Ocean and Land Colour Instrument): Provides data on ocean color and land surface reflectance.​
    • SLSTR (Sea and Land Surface Temperature Radiometer): Measures sea and land surface temperatures.​

  • Sentinel-5P Products:

    • L2 Products: Includes data on atmospheric trace gases like nitrogen dioxide, ozone, and methane.
Earth Observation Data Centre (EODC)

Earth Observation Data Centre (EODC)

jona.frroku
Open Dataset
Creators: European Space Agency (ESA)
Publication Date: 1970
Creators: European Space Agency (ESA)

Earth Observation Data Centre (EODC) provides access to Earth observation data, including Sentinel data and other satellite sources. By integrating extensive data repositories with high-performance computing resources, EODC supports a wide array of applications, including environmental monitoring, climate research, and disaster management. EODC hosts petabytes of EO data, notably serving as a global Copernicus Sentinel Long Term Archive. With support from entities like the Vienna Business Agency, EODC has developed a high-tech infrastructure capable of managing and processing large datasets, making it accessible to industry, science, education, and startups. EODC’s data repository is substantial, encompassing petabytes of EO data. For instance, the initial storage capacity was expanded to hold two petabytes, enabling the storage of Sentinel-1A satellite data covering the entire Earth’s surface. Plans have been in place to further extend this capacity to 20 petabytes over subsequent years, accommodating the continuous influx of satellite data. The exact number of observations within EODC’s repository is vast and continually growing, as it includes comprehensive datasets from missions like Sentinel-1A, which provides global radar imaging. EODC’s datasets primarily cover the period from the launch of the Sentinel satellites in 2014 to the present. Structurally, EODC’s datasets are organized based on satellite missions and thematic applications:​

  • Satellite Mission Archives: Data is categorized by specific missions, such as Sentinel-1A, with each archive containing raw and processed imagery, metadata, and calibration information.​

  • Thematic Datasets: These include processed products tailored for applications like soil moisture monitoring, flood detection, and land cover classification. For example, the Global Flood Monitoring (GFM) service utilizes Sentinel-1 data to produce near-real-time flood monitoring products, integrated within the Copernicus Emergency Management Service. ​

  • Data Formats: The datasets are available in standard geospatial formats such as GeoTIFF for raster data and shapefiles for vector data, ensuring compatibility with common GIS and remote sensing software.

JAXA Earth Observation Data

JAXA Earth Observation Data

jona.frroku
Open Dataset
Creators: Japan Aerospace Exploration Agency (JAXA)
Publication Date: 05/2006
Creators: Japan Aerospace Exploration Agency (JAXA)

​The Japan Aerospace Exploration Agency (JAXA) provides access to satellite data from JAXA’s Earth observation missions, including high-resolution imagery and various environmental parameters. The dataset encompasses over 1,000 TB  of archived satellite data, making it one of the most comprehensive collections of Earth observation data. JAXA operates several Earth observation satellites that provide global environmental and geospatial data. It includes data from multiple JAXA missions, such as ALOS-2 (land observation), GOSAT (greenhouse gases), GCOM-W (water cycle), GCOM-C (climate monitoring), and Himawari (weather observations). The dataset covers several decades of observations, with some datasets extending back to the 1980s.

Key features include:

  • Atmospheric Monitoring: Tracks greenhouse gas levels (CO₂, CH₄), aerosols, water vapor, and cloud properties.
  • Land Observations: Provides land surface temperature, vegetation indices (NDVI, EVI), urban growth mapping, and disaster response data.
  • Oceanic Measurements: Covers sea surface temperature, ocean color, chlorophyll concentration, and sea ice extent.
  • Climate Change Indicators: Tracks long-term changes in Earth’s environment.
  • Disaster Response & Early Warning: Used for real-time monitoring of typhoons, wildfires, and earthquakes.

The total number of observations varies by mission and parameter. Given that JAXA satellites capture data daily, weekly, or monthly, the dataset consists of millions of individual observations spanning decades. For instance:

  • GOSAT greenhouse gas monitoring: Over 56,000 global observation points.
  • ALOS-2 terrain and land cover changes: Millions of SAR images worldwide.
  • GCOM-W & GCOM-C climate data: Continuous Earth monitoring at global scale.
NASA Earth Observation (NEO)

NASA Earth Observation (NEO)

jona.frroku
Open Dataset
Creators: NASA (National Aeronautics and Space Administration)
Publication Date: NA
Creators: NASA (National Aeronautics and Space Administration)

NASA Earth Observation (NEO) is a treasure trove of satellite imagery and remote sensing data. NEO offers daily, weekly, and monthly images captured by various NASA satellites, such as Terra and Aqua. Users can access a wealth of information on climate change, natural disasters, and global ecosystems. This resource is designed to support research, education, and policy-making by delivering accessible and up-to-date Earth observation information. NEO offers data across multiple categories, including:​

  • Atmosphere: Parameters such as aerosol optical thickness, carbon monoxide levels, cloud properties, nitrogen dioxide concentrations, ozone levels, rainfall, and water vapor content.
  • ​Energy: Data on albedo, land surface temperature (day and night), net radiation, outgoing longwave radiation, reflected shortwave radiation, sea surface temperature, and solar insolation.
  • ​Land: Information on active fires, land cover classification, leaf area index, snow cover, and vegetation index (NDVI).
  • ​Life: Metrics such as net primary productivity.
  • ​Ocean: Sea surface temperature data.
  • ​User-Friendly Access: The NEO platform provides an intuitive interface, allowing users to visualize, download, and analyze data without requiring specialized software or expertise.

 

Instagram Posts from Football Players

Instagram Posts from Football Players

Stefan
Open Dataset
Creators: Klostermann, Jan
Publication Date: 2023
Creators: Klostermann, Jan

This dataset includes information on 334,071 Instagram posts from 1,435 male professional football players that were under contract at any of the 56 clubs in the English Premier League, the Spanish La Liga, and the German Bundesliga. The data was colleced December 31th, 2019 and includes the whole history of Instagram posts up to that point in time.

The information provided in the dataset are the following:

  • Player information: Information on each of the football player in the dataset is collected from http://www.transfermarkt.de and includes club, position, market value (at the time of collecting the data), highest market value, and the year in which highest market value was observed. Further, the Instagram account name is provided.
  • Instagram post information: Information on the Instagram posts including the shortcode (which can be used to open the post on instagram.com), date, caption text, number of likes, number of comments, post type (image, sidecar, video).
  • Instagram post images: For each post, we analyzed the content of the image (first image for sidecar posts, first frame for video posts) using Google Vision and extract the number of persons, their age, and their gender. Further, we extract all tags that are included in the image, such as “soccer” or “car”.
  • Additional information: Additional information such as the images of the posts can be requested from the authors.

The dataset has been used in the following paper:

Klostermann, J., Meißner, M., Max, A., & Decker, R. (2023). Presentation of celebrities’ private life through visual social media. Journal of Business Research, 156, 113524.

Please cite the paper when using the dataset for your own research. It is recommended to read the paper for further information on the dataset.

The Economist Historical Advertisements - Master Dataset

The Economist Historical Advertisements - Master Dataset

Stefan
Open Dataset
Creators: Kluge, Stefan; Gehrmann, Leonie; Stahl, Florian
Publication Date: 2023
Creators: Kluge, Stefan; Gehrmann, Leonie; Stahl, Florian

This dataset contains metadata of 512.599 historical advertisements from all 8,840 issues of The Economist magazine, years 1843 to 2014. It is part of a series of datasets related to The Economist Historical Archive (https://www.gale.com/intl/c/the-economist-historical-archive). You will need this Master Dataset, if you want to work with any of the related datasets. Each advertisement entry includes various metadata fields such as publication date, issue number, page number, and advertisement dimensions. This structured information enables detailed analyses of trends and patterns within the advertising practices over time. In total, the dataset has a size of 195,4 MB.

MLW Zettelmaterial

MLW Zettelmaterial

Stefan
Open Dataset
Creators: Bayerische Akademie der Wissenschaften (BAdW)
Publication Date: 2023
Creators: Bayerische Akademie der Wissenschaften (BAdW)

General information:

The data set comprises a total of 114,653 images (18,9 GB), corresponding to 3,507 distinct lemmas.
All images are in RGB, but not uniform in size, i.e. height, and width differ from image to image.
Additionally, the information on the corresponding lemma is available for each image in a separate json file.

Structure:

Most record cards follow the same structure being composed of three main parts.

  • The first one (1), and the one deemed most challenging, is the lemma, which is always located in the upper left corner of the record card.
  • The second part (2) is the index of the text where the lemma is found.
  • The third part (3) contains a text extract in which the word (corresponding to the lemma) occurs in context.

Character inventory:

There is a total of 17 different first letters, eight of which are each upper- and lowercase, as well as one special character.
The capitalization of a word plays a crucial role since a word’s meaning changes depending on capitalization.
Since the majority of our data stems from the S-series of the dictionary, most lemmas start with the letter “s”.
Likewise, a larger number of lemmas also starts with “m”, “v”, “t”, “u”, “l”, and “n”.

Occurrence frequencies:

  • A total of 2,420 lemmas (69%) were found to appear on ten record cards or less
  • 854 lemmas (24.4%) are present on between 10 and 100 record cards
  • 233 lemmas (6.6%)can be found on more than 100 record cards
  • 1,123 lemmas (approximately 36.7%) had only one record card

Lengths:

  • Lemma lengths range from one character up to a maximum of 19 characters.
  • The average length of the lemmas lies between five and six characters.

Availability:

Research activity:

  • Koch, P., Nuñez, G. V., Arias, E. G., Heumann, C., Schöffel, M., Häberlin, A., & Aßenmacher, M. (2023). A tailored Handwritten-Text-Recognition System for Medieval Latin. arXiv preprint arXiv:2308.09368.

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