Dynamic World is a 10m near-real-time (NRT) Land Use/Land Cover (LULC) dataset that includes class probabilities and label information for nine classes. Dynamic World predictions are available for the Sentinel-2 L1C collection from 2015-06-27 to present. The revisit frequency of Sentinel-2 is between 2-5 days depending on latitude. Dynamic World predictions are generated for Sentinel-2 L1C images with CLOUDY_PIXEL_PERCENTAGE <= 35%. Predictions are masked to remove clouds and cloud shadows using a combination of S2 Cloud Probability, Cloud Displacement Index, and Directional Distance Transform. Images in the Dynamic World collection have names matching the individual Sentinel-2 L1C asset names from which they were derived, e.g: ee.Image('COPERNICUS/S2/20160711T084022_20160711T084751_T35PKT') has a matching Dynamic World image named: ee.Image('GOOGLE/DYNAMICWORLD/V1/20160711T084022_20160711T084751_T35PKT'). All probability bands except the "label" band collectively sum to 1. To learn more about the Dynamic World dataset and see examples for generating composites, calculating regional statistics, and working with the time series, see the Introduction to Dynamic World tutorial series. Given Dynamic World class estimations are derived from single images using a spatial context from a small moving window, top-1 "probabilities" for predicted land covers that are in-part defined by cover over time, like crops, can be comparatively low in the absence of obvious distinguishing features. High-return surfaces in arid climates, sand, sunglint, etc may also exhibit this phenomenon. To select only pixels that confidently belong to a Dynamic World class, it is recommended to mask Dynamic World outputs by thresholding the estimated "probability" of the top-1 prediction.

Content

The primary World Bank collection of development indicators, compiled from officially-recognized international sources. It presents the most current and accurate global development data available, and includes national, regional and global estimates.

Context

This is a dataset hosted by the World Bank. The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore the World Bank using Kaggle and all of the data sources available through the World Bank organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using the World Bank's APIs and Kaggle's API.

Cover photo by Alex Block on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Monthly average radiance composite images using nighttime data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB). As these data are composited monthly, there are many areas of the globe where it is impossible to get good quality data coverage for that month. This can be due to …

As of June 2024, the most popular database management system (DBMS) worldwide was Oracle, with a ranking score of *******; MySQL and Microsoft SQL server rounded out the top three. Although the database management industry contains some of the largest companies in the tech industry, such as Microsoft, Oracle and IBM, a number of free and open-source DBMSs such as PostgreSQL and MariaDB remain competitive. Database Management Systems As the name implies, DBMSs provide a platform through which developers can organize, update, and control large databases. Given the business world’s growing focus on big data and data analytics, knowledge of SQL programming languages has become an important asset for software developers around the world, and database management skills are seen as highly desirable. In addition to providing developers with the tools needed to operate databases, DBMS are also integral to the way that consumers access information through applications, which further illustrates the importance of the software.

Description:

The enclosed data set is the complete, cleaned results of the 2022 Stack Overflow Developer Survey. Free response submissions and personally-identifying information have been removed from the results to protect the privacy of respondents. There are three files besides this README:

1. survey_results_public.csv - CSV file with main survey results, one respondent per row and one column per answer
2. survey_results_schema.csv - CSV file with survey schema, i.e., the questions that correspond to each column name
3. so_survey_2022.pdf - PDF file of the survey instrument

The survey was fielded from May 11, 2022 to June 1, 2022. The median time spent on the survey for qualified responses was 15.08 minutes.

Respondents were recruited primarily through channels owned by Stack Overflow. The top 5 sources of respondents were onsite messaging, blog posts, email lists, Meta posts, banner ads, and social media posts. Since respondents were recruited in this way, highly engaged users on Stack Overflow were more likely to notice the links for the survey and click to begin it.

As an incentive, respondents who finished the survey could opt into a "Census" badge if they completed the survey.

You can find the official published results online.

Find previous survey results online, as well.

Legal:

This database - The Public 2022 Stack Overflow Developer Survey Results - is made available under the Open Database License (ODbL): http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/

TLDR: You are free to share, adapt, and create derivative works from The Public 2022 Stack Overflow Developer Survey Results as long as you attribute Stack Overflow, keep the database open (if you redistribute it), and continue to share-alike any adapted database under the ODbl.

Acknowledgment:

Massive, heartfelt thanks to all Stack Overflow contributors and lurking developers of the world who took part in the survey this year. We value your generous participation more than you know.

Success.ai’s Commercial Real Estate Data for Commercial Real Estate Professionals in Europe provides a highly detailed dataset tailored for businesses looking to engage with key decision-makers in the European commercial real estate market. Covering developers, property managers, brokers, and investors, this dataset includes verified contact data, decision-maker insights, and firmographic details to empower your outreach and strategic initiatives.

With access to over 700 million verified global profiles and data from 70 million businesses, Success.ai ensures your marketing, sales, and partnership efforts are powered by accurate, continuously updated, and AI-validated data. Supported by our Best Price Guarantee, this solution is indispensable for navigating Europe’s thriving commercial real estate sector.

Why Choose Success.ai’s Commercial Real Estate Data?

1. Verified Contact Data for Targeted Outreach

   • Access verified work emails, phone numbers, and LinkedIn profiles of property developers, brokers, asset managers, and investment leads.
   • AI-driven validation ensures 99% accuracy, reducing communication errors and improving outreach effectiveness.

2. Comprehensive Coverage Across Europe’s Real Estate Sector

   • Includes profiles from major European real estate markets such as the UK, Germany, France, Italy, and the Netherlands.
   • Gain insights into regional market dynamics, investment opportunities, and commercial real estate trends.

3. Continuously Updated Datasets

   • Real-time updates capture leadership changes, market expansions, and emerging property developments.
   • Stay aligned with the fast-evolving commercial real estate market and seize opportunities effectively.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible use of data and compliance with legal standards.

Data Highlights:

• 700M+ Verified Global Profiles: Connect with decision-makers, property managers, and brokers in Europe’s commercial real estate sector.
• 70M Business Profiles: Access detailed firmographic data, including company sizes, revenue ranges, and geographic footprints.
• Leadership Insights: Engage with CEOs, asset managers, and real estate directors driving strategic decisions.
• Market Intelligence: Gain visibility into property development projects, investment trends, and regional opportunities.

Key Features of the Dataset:

1. Decision-Maker Profiles in Real Estate

   • Identify and connect with executives, brokers, and property managers overseeing transactions, asset management, and investment strategies.
   • Target professionals responsible for property acquisitions, leasing, and development.

2. Firmographic and Geographic Insights

   • Access detailed business information, including company structures, geographic locations, and market specializations.
   • Pinpoint key players in specific regions and align outreach with localized market needs.

3. Advanced Filters for Precision Campaigns

   • Filter companies by industry focus (commercial properties, retail, industrial), revenue size, or project scope.
   • Tailor your campaigns to address specific challenges, such as tenant retention, sustainability initiatives, or market expansion.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes with real estate professionals.

Strategic Use Cases:

1. Sales and Lead Generation

   • Present property management tools, software solutions, or investment opportunities to real estate firms and property managers.
   • Build relationships with brokers and developers seeking innovative solutions to streamline operations or enhance profitability.

2. Market Research and Competitive Analysis

   • Analyze trends in Europe’s commercial real estate market to guide product development and marketing strategies.
   • Benchmark against competitors to identify growth opportunities, underserved segments, and high-value properties.

3. Partnership Development and Investment Insights

   • Engage with property developers, asset managers, and brokers exploring strategic partnerships or new investment opportunities.
   • Foster alliances that expand market reach, improve property performance, or drive higher returns.

4. Recruitment and Workforce Solutions

   • Target HR professionals and hiring managers recruiting for roles in property management, real estate finance, or asset development.
   • Provide workforce optimization tools, training platforms, or recruitment services tailored to the commercial real estate sector.

Why Choose Success.ai?

1. Best Price Guarantee
   • Access premium-quality commercial real estate data at competitive prices, ensuring strong ROI for your marketing, sales, and business development efforts. ...

This large-scale open dataset consists of outlines of buildings derived from high-resolution 50 cm satellite imagery. It contains 1.8B building detections in Africa, Latin America, Caribbean, South Asia and Southeast Asia. The inference spanned an area of 58M km². For each building in this dataset we include the polygon describing its footprint on the ground, a confidence score indicating how sure we are that this is a building, and a Plus Code corresponding to the center of the building. There is no information about the type of building, its street address, or any details other than its geometry. Building footprints are useful for a range of important applications: from population estimation, urban planning and humanitarian response to environmental and climate science. The project is based in Ghana, with an initial focus on the continent of Africa and new updates on South Asia, South-East Asia, Latin America and the Caribbean. Inference was carried out during May 2023. For more details see the official website of the Open Buildings dataset.

Success.ai’s Commercial Real Estate Data and B2B Contact Data for Global Real Estate Professionals is a comprehensive dataset designed to connect businesses with industry leaders in real estate worldwide. With over 170M verified profiles, including work emails and direct phone numbers, this solution ensures precise outreach to agents, brokers, property developers, and key decision-makers in the real estate sector.

Utilizing advanced AI-driven validation, our data is continuously updated to maintain 99% accuracy, offering actionable insights that empower targeted marketing, streamlined sales strategies, and efficient recruitment efforts. Whether you’re engaging with top real estate executives or sourcing local property experts, Success.ai provides reliable and compliant data tailored to your needs.

Key Features of Success.ai’s Real Estate Professional Contact Data

• Comprehensive Industry Coverage Gain direct access to verified profiles of real estate professionals across the globe, including:

1. Real Estate Agents: Professionals facilitating property sales and purchases.
2. Brokers: Key intermediaries managing transactions between buyers and sellers.
3. Property Developers: Decision-makers shaping residential, commercial, and industrial projects.
4. Real Estate Executives: Leaders overseeing multi-regional operations and business strategies.
5. Architects & Consultants: Experts driving design and project feasibility.

• Verified and Continuously Updated Data

AI-Powered Validation: All profiles are verified using cutting-edge AI to ensure up-to-date accuracy. Real-Time Updates: Our database is refreshed continuously to reflect the most current information. Global Compliance: Fully aligned with GDPR, CCPA, and other regional regulations for ethical data use.

• Customizable Data Delivery Tailor your data access to align with your operational goals:

API Integration: Directly integrate data into your CRM or project management systems for seamless workflows. Custom Flat Files: Receive detailed datasets customized to your specifications, ready for immediate application.

Why Choose Success.ai for Real Estate Contact Data?

• Best Price Guarantee Enjoy competitive pricing that delivers exceptional value for verified, comprehensive contact data.

• Precision Targeting for Real Estate Professionals Our dataset equips you to connect directly with real estate decision-makers, minimizing misdirected efforts and improving ROI.

• Strategic Use Cases

  Lead Generation: Target qualified real estate agents and brokers to expand your network. Sales Outreach: Engage with property developers and executives to close high-value deals. Marketing Campaigns: Drive targeted campaigns tailored to real estate markets and demographics. Recruitment: Identify and attract top talent in real estate for your growing team. Market Research: Access firmographic and demographic data for in-depth industry analysis.

• Data Highlights 170M+ Verified Professional Profiles 50M Work Emails 30M Company Profiles 700M Global Professional Profiles

• Powerful APIs for Enhanced Functionality

  Enrichment API Ensure your contact database remains relevant and up-to-date with real-time enrichment. Ideal for businesses seeking to maintain competitive agility in dynamic markets.

Lead Generation API Boost your lead generation with verified contact details for real estate professionals, supporting up to 860,000 API calls per day for robust scalability.

• Use Cases for Real Estate Contact Data

1. Targeted Outreach for New Projects Connect with property developers and brokers to pitch your services or collaborate on upcoming projects.

2. Real Estate Marketing Campaigns Execute personalized marketing campaigns targeting agents and clients in residential, commercial, or industrial sectors.

3. Enhanced Sales Strategies Shorten sales cycles by directly engaging with decision-makers and key stakeholders.

4. Recruitment and Talent Acquisition Access profiles of highly skilled professionals to strengthen your real estate team.

5. Market Analysis and Intelligence Leverage firmographic and demographic insights to identify trends and optimize business strategies.

• What Makes Us Stand Out? >> Unmatched Data Accuracy: Our AI-driven validation ensures 99% accuracy for all contact details. >> Comprehensive Global Reach: Covering professionals across diverse real estate markets worldwide. >> Flexible Delivery Options: Access data in formats that seamlessly fit your existing systems. >> Ethical and Compliant Data Practices: Adherence to global standards for secure and responsible data use.

Success.ai’s B2B Contact Data for Global Real Estate Professionals delivers the tools you need to connect with the right people at the right time, driving efficiency and success in your business operations. From agents and brokers to property developers and executiv...

Meet Earth EngineGoogle Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities and makes it available for scientists, researchers, and developers to detect changes, map trends, and quantify differences on the Earth's surface.SATELLITE IMAGERY+YOUR ALGORITHMS+REAL WORLD APPLICATIONSLEARN MOREGLOBAL-SCALE INSIGHTExplore our interactive timelapse viewer to travel back in time and see how the world has changed over the past twenty-nine years. Timelapse is one example of how Earth Engine can help gain insight into petabyte-scale datasets.EXPLORE TIMELAPSEREADY-TO-USE DATASETSThe public data archive includes more than thirty years of historical imagery and scientific datasets, updated and expanded daily. It contains over twenty petabytes of geospatial data instantly available for analysis.EXPLORE DATASETSSIMPLE, YET POWERFUL APIThe Earth Engine API is available in Python and JavaScript, making it easy to harness the power of Google’s cloud for your own geospatial analysis.EXPLORE THE APIGoogle Earth Engine has made it possible for the first time in history to rapidly and accurately process vast amounts of satellite imagery, identifying where and when tree cover change has occurred at high resolution. Global Forest Watch would not exist without it. For those who care about the future of the planet Google Earth Engine is a great blessing!-Dr. Andrew Steer, President and CEO of the World Resources Institute.CONVENIENT TOOLSUse our web-based code editor for fast, interactive algorithm development with instant access to petabytes of data.LEARN ABOUT THE CODE EDITORSCIENTIFIC AND HUMANITARIAN IMPACTScientists and non-profits use Earth Engine for remote sensing research, predicting disease outbreaks, natural resource management, and more.SEE CASE STUDIESREADY TO BE PART OF THE SOLUTION?SIGN UP NOWTERMS OF SERVICE PRIVACY ABOUT GOOGLE

Dataset corresponding to the journal article "Mitigating the effect of errors in source parameters on seismic (waveform) inversion" by Blom, Hardalupas and Rawlinson, accepted for publication in Geophysical Journal International. In this paper, we demonstrate the effect or errors in source parameters on seismic tomography, with a particular focus on (full) waveform tomography. We study effect both on forward modelling (i.e. comparing waveforms and measurements resulting from a perturbed vs. unperturbed source) and on seismic inversion (i.e. using a source which contains an (erroneous) perturbation to invert for Earth structure. These data were obtained using Salvus, a state-of-the-art (though proprietary) 3-D solver that can be used for wave propagation simulations (Afanasiev et al., GJI 2018).

This dataset contains:

The entire Salvus project. This project was prepared using Salvus version 0.11.x and 0.12.2 and should be fully compatible with the latter.

A number of Jupyter notebooks used to create all the figures, set up the project and do the data processing.

A number of Python scripts that are used in above notebooks.

two conda environment .yml files: one with the complete environment as used to produce this dataset, and one with the environment as supplied by Mondaic (the Salvus developers), on top of which I installed basemap and cartopy.

An overview of the inversion configurations used for each inversion experiment and the name of hte corresponding figures: inversion_runs_overview.ods / .csv .

Datasets corresponding to the different figures.

One dataset for Figure 1, showing the effect of a source perturbation in a real-world setting, as previously used by Blom et al., Solid Earth 2020

One dataset for Figure 2, showing how different methodologies and assumptions can lead to significantly different source parameters, notably including systematic shifts. This dataset was kindly supplied by Tim Craig (Craig, 2019).

A number of datasets (stored as pickled Pandas dataframes) derived from the Salvus project. We have computed:

travel-time arrival predictions from every source to all stations (df_stations...pkl)

misfits for different metrics for both P-wave centered and S-wave centered windows for all components on all stations, comparing every time waveforms from a reference source against waveforms from a perturbed source (df_misfits_cc.28s.pkl)

addition of synthetic waveforms for different (perturbed) moment tenors. All waveforms are stored in HDF5 (.h5) files of the ASDF (adaptable seismic data format) type

How to use this dataset:

To set up the conda environment:

make sure you have anaconda/miniconda

make sure you have access to Salvus functionality. This is not absolutely necessary, but most of the functionality within this dataset relies on salvus. You can do the analyses and create the figures without, but you'll have to hack around in the scripts to build workarounds.

Set up Salvus / create a conda environment. This is best done following the instructions on the Mondaic website. Check the changelog for breaking changes, in that case download an older salvus version.

Additionally in your conda env, install basemap and cartopy:

conda-env create -n salvus_0_12 -f environment.yml conda install -c conda-forge basemap conda install -c conda-forge cartopy

Install LASIF (https://github.com/dirkphilip/LASIF_2.0) and test. The project uses some lasif functionality.

To recreate the figures: This is extremely straightforward. Every figure has a corresponding Jupyter Notebook. Suffices to run the notebook in its entirety.

Figure 1: separate notebook, Fig1_event_98.py

Figure 2: separate notebook, Fig2_TimCraig_Andes_analysis.py

Figures 3-7: Figures_perturbation_study.py

Figures 8-10: Figures_toy_inversions.py

To recreate the dataframes in DATA: This can be done using the example notebook Create_perturbed_thrust_data_by_MT_addition.py and Misfits_moment_tensor_components.M66_M12.py . The same can easily be extended to the position shift and other perturbations you might want to investigate.

To recreate the complete Salvus project: This can be done using:

the notebook Prepare_project_Phil_28s_absb_M66.py (setting up project and running simulations)

the notebooks Moment_tensor_perturbations.py and Moment_tensor_perturbation_for_NS_thrust.py

For the inversions: using the notebook Inversion_SS_dip.M66.28s.py as an example. See the overview table inversion_runs_overview.ods (or .csv) as to naming conventions.

References:

Michael Afanasiev, Christian Boehm, Martin van Driel, Lion Krischer, Max Rietmann, Dave A May, Matthew G Knepley, Andreas Fichtner, Modular and flexible spectral-element waveform modelling in two and three dimensions, Geophysical Journal International, Volume 216, Issue 3, March 2019, Pages 1675–1692, https://doi.org/10.1093/gji/ggy469

Nienke Blom, Alexey Gokhberg, and Andreas Fichtner, Seismic waveform tomography of the central and eastern Mediterranean upper mantle, Solid Earth, Volume 11, Issue 2, 2020, Pages 669–690, 2020, https://doi.org/10.5194/se-11-669-2020

Tim J. Craig, Accurate depth determination for moderate-magnitude earthquakes using global teleseismic data. Journal of Geophysical Research: Solid Earth, 124, 2019, Pages 1759– 1780. https://doi.org/10.1029/2018JB016902

This dataset provides monthly summaries of evapotranspiration (ET) data from OpenET v2.0 image collections for the period 2008-2023 for all National Watershed Boundary Dataset subwatersheds (12-digit hydrologic unit codes [HUC12s]) in the US that overlap the spatial extent of OpenET datasets. For each HUC12, this dataset contains spatial aggregation statistics (minimum, mean, median, and maximum) for each of the ET variables from each of the publicly available image collections from OpenET for the six available models (DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, SSEBop) and the Ensemble image collection, which is a pixel-wise ensemble of all 6 individual models after filtering and removal of outliers according to the median absolute deviation approach (Melton and others, 2022). Data are available in this data release in two different formats: comma-separated values (CSV) and parquet, a high-performance format that is optimized for storage and processing of columnar data. CSV files containing data for each 4-digit HUC are grouped by 2-digit HUCs for easier access of regional data, and the single parquet file provides convenient access to the entire dataset. For each of the ET models (DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, SSEBop), variables in the model-specific CSV data files include: -huc12: The 12-digit hydrologic unit code -ET: Actual evapotranspiration (in millimeters) over the HUC12 area in the month calculated as the sum of daily ET interpolated between Landsat overpasses -statistic: Max, mean, median, or min. Statistic used in the spatial aggregation within each HUC12. For example, maximum ET is the maximum monthly pixel ET value occurring within the HUC12 boundary after summing daily ET in the month -year: 4-digit year -month: 2-digit month -count: Number of Landsat overpasses included in the ET calculation in the month -et_coverage_pct: Integer percentage of the HUC12 with ET data, which can be used to determine how representative the ET statistic is of the entire HUC12 -count_coverage_pct: Integer percentage of the HUC12 with count data, which can be different than the et_coverage_pct value because the “count” band in the source image collection extends beyond the “et” band in the eastern portion of the image collection extent For the Ensemble data, these additional variables are included in the CSV files: -et_mad: Ensemble ET value, computed as the mean of the ensemble after filtering outliers using the median absolute deviation (MAD) -et_mad_count: The number of models used to compute the ensemble ET value after filtering for outliers using the MAD -et_mad_max: The maximum value in the ensemble range, after filtering for outliers using the MAD -et_mad_min: The minimum value in the ensemble range, after filtering for outliers using the MAD -et_sam: A simple arithmetic mean (across the 6 models) of actual ET average without outlier removal Below are the locations of each OpenET image collection used in this summary: DisALEXI: https://developers.google.com/earth-engine/datasets/catalog/OpenET_DISALEXI_CONUS_GRIDMET_MONTHLY_v2_0 eeMETRIC: https://developers.google.com/earth-engine/datasets/catalog/OpenET_EEMETRIC_CONUS_GRIDMET_MONTHLY_v2_0 geeSEBAL: https://developers.google.com/earth-engine/datasets/catalog/OpenET_GEESEBAL_CONUS_GRIDMET_MONTHLY_v2_0 PT-JPL: https://developers.google.com/earth-engine/datasets/catalog/OpenET_PTJPL_CONUS_GRIDMET_MONTHLY_v2_0 SIMS: https://developers.google.com/earth-engine/datasets/catalog/OpenET_SIMS_CONUS_GRIDMET_MONTHLY_v2_0 SSEBop: https://developers.google.com/earth-engine/datasets/catalog/OpenET_SSEBOP_CONUS_GRIDMET_MONTHLY_v2_0 Ensemble: https://developers.google.com/earth-engine/datasets/catalog/OpenET_ENSEMBLE_CONUS_GRIDMET_MONTHLY_v2_0

Web-based GIS for spatiotemporal crop climate niche mapping Interactive Google Earth Engine Application—Version 2, July 2020 https://cropniche.cartoscience.com https://cartoscience.users.earthengine.app/view/crop-niche Google Earth Engine Code /* ---------------------------------------------------------------------------------------------------------------------- # CropSuit-GEE Authors: Brad G. Peter (bpeter@ua.edu), Joseph P. Messina, and Zihan Lin Organizations: BGP, JPM - University of Alabama; ZL - Michigan State University Last Modified: 06/28/2020 To cite this code use: Peter, B. G.; Messina, J. P.; Lin, Z., 2019, "Web-based GIS for spatiotemporal crop climate niche mapping", https://doi.org/10.7910/DVN/UFC6B5, Harvard Dataverse, V1 ------------------------------------------------------------------------------------------------------------------------- This is a Google Earth Engine crop climate suitability geocommunication and map export tool designed to support agronomic development and deployment of improved crop system technologies. This content is made possible by the support of the American People provided to the Feed the Future Innovation Lab for Sustainable Intensification through the United States Agency for International Development (USAID). The contents are the sole responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government. Program activities are funded by USAID under Cooperative Agreement No. AID-OAA-L-14-00006. ------------------------------------------------------------------------------------------------------------------------- Summarization of input options: There are 14 user options available. The first is a country of interest selection using a 2-digit FIPS code (link available below). This selection is used to produce a rectangular bounding box for export; however, other geometries can be selected with minimal modification to the code. Options 2 and 3 specify the complete temporal range for aggregation (averaged across seasons; single seasons may also be selected). Options 4–7 specify the growing season for calculating total seasonal rainfall and average season temperatures and NDVI (NDVI is for export only and is not used in suitability determination). Options 8–11 specify the climate parameters for the crop of interest (rainfall and temperature max/min). Option 12 enables masking to agriculture, 13 enables exporting of all data layers, and 14 is a text string for naming export files. ------------------------------------------------------------------------------------------------------------------------- ••••••••••••••••••••••••••••••••••••••••••• USER OPTIONS ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• */ // CHIRPS data availability: https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_PENTAD // MOD11A2 data availability: https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD11A2 var country = 'MI' // [1] https://en.wikipedia.org/wiki/List_of_FIPS_country_codes var startRange = 2001 // [2] var endRange = 2017 // [3] var startSeasonMonth = 11 // [4] var startSeasonDay = 1 // [5] var endSeasonMonth = 4 // [6] var endSeasonDay = 30 // [7] var precipMin = 750 // [8] var precipMax = 1200 // [9] var tempMin = 22 // [10] var tempMax = 32 // [11] var maskToAg = 'TRUE' // [12] 'TRUE' (default) or 'FALSE' var exportLayers = 'TRUE' // [13] 'TRUE' (default) or 'FALSE' var exportNameHeader = 'crop_suit_maize' // [14] text string for naming export file // ••••••••••••••••••••••••••••••••• NO USER INPUT BEYOND THIS POINT •••••••••••••••••••••••••••••••••••••••••••••••••••• // Access precipitation and temperature ImageCollections and a global countries FeatureCollection var region = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017') .filterMetadata('country_co','equals',country) var precip = ee.ImageCollection('UCSB-CHG/CHIRPS/PENTAD').select('precipitation') var temp = ee.ImageCollection('MODIS/006/MOD11A2').select(['LST_Day_1km','LST_Night_1km']) var ndvi = ee.ImageCollection('MODIS/006/MOD13Q1').select(['NDVI']) // Create layers for masking to agriculture and masking out water bodies var waterMask = ee.Image('UMD/hansen/global_forest_change_2015').select('datamask').eq(1) var agModis = ee.ImageCollection('MODIS/006/MCD12Q1').select('LC_Type1').mode() .remap([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17], [0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0]) var agGC = ee.Image('ESA/GLOBCOVER_L4_200901_200912_V2_3').select('landcover') .remap([11,14,20,30,40,50,60,70,90,100,110,120,130,140,150,160,170,180,190,200,210,220,230], [1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]) var cropland = ee.Image('USGS/GFSAD1000_V1').neq(0) var agMask = agModis.add(agGC).add(cropland).gt(0).eq(1) // Modify user input options for processing with raw data var years = ee.List.sequence(startRange,endRange) var bounds = region.geometry().bounds() var tMinMod = (tempMin+273.15)/0.02 var tMaxMod = (tempMax+273.15)/0.02 //...

Luxury Cosmetics Pop‑Up Events Dataset

A comprehensive, real-world–anchored synthetic dataset capturing 2,133 luxury beauty pop-up events across global retail hotspots. It focuses on limited-edition product drops, experiential formats, and performance KPIs—especially footfall and sell‑through. The data is designed for analytics use cases such as demand forecasting, footfall modeling, merchandising optimization, pricing analysis, and market expansion studies across regions and venue types.

What this dataset contains

• 2,133 events from global hubs across North America, Europe, Middle East, Asia‑Pacific, and Latin America
• Luxury/premium cosmetics brands and their limited‑release SKUs
• Event formats and retail venue archetypes typical of pop‑up retail
• Time windows and lease lengths aligned with short‑term pop‑up activations
• Core commercial KPIs: price, units sold, sell‑through percentage
• Footfall KPI: average daily footfall modeled by location/format/marketing intensity

Ideal use cases

• Pop‑up ROI and performance benchmarking by brand, city, venue type, and format
• Footfall prediction and location strategy (high‑street vs mall vs airport vs districts)
• Limited-edition launch analytics: pricing vs sell‑through dynamics
• Event planning: lease length and timing windows vs outcomes
• Territory planning: region and city segmentation performance
• Portfolio dashboards: cross‑brand comparisons and trend reporting

File formats

• CSV, JSON, XLSX, and SQLite (table: popups)

Target users

• Retail strategy and analytics teams
• Growth, trade marketing, and brand managers
• Data scientists building forecasting and optimization models
• BI developers building dashboards for pop‑up performance

Column Dictionary

Data quality notes

• City and end_date contain a small proportion of nulls to reflect real‑world reporting gaps (e.g., ongoing events).
• avg_daily_footfall varies by locati...

Content

World Bank Projects & Operations provides access to basic information on all of the World Bank's lending projects from 1947 to the present. The dataset includes basic information such as the project title, task manager, country, project id, sector, themes, commitment amount, product line, procurement notices, contract awards, and financing. It also provides links to publicly disclosed online documents.

For older projects, there is a link to the Archives catalog, which contains records of older documents. Where available, there are also links to contract awards since July 2000.

Context

This is a dataset hosted by the World Bank. The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore the World Bank using Kaggle and all of the data sources available through the World Bank organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using the World Bank's APIs and Kaggle's API.

Cover photo by rawpixel on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Content

GovData360 is a compendium of the most important governance indicators, from 26 datasets with worldwide coverage and more than 10 years of info, designed to provide guidance on the design of reforms and the monitoring of impacts. We have an Unbalanced Panel Data by Dataset - Country for around 3260 governance focused indicators.

Context

This is a dataset hosted by the World Bank. The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore the World Bank using Kaggle and all of the data sources available through the World Bank organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using the World Bank's APIs and Kaggle's API.

Cover photo by John Jason on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Data for replicating The Global Spatial Distribution of Economic Activity: Nature, History, and the Role of Trade (forthcoming 2018; with Vernon Henderson, Tim Squires and David N. Weil) Quarterly Journal of Economics We explore the role of natural characteristics in determining the worldwide spatial distribution of economic activity, as proxied by lights at night, observed across 240,000 grid cells. A parsimonious set of 24 physical geography attributes explains 47% of worldwide variation and 35% of within-country variation in lights. We divide geographic characteristics into two groups, those primarily important for agriculture and those primarily important for trade, and confront a puzzle. In examining within-country variation in lights, among countries that developed early, agricultural variables incrementally explain over 6 times as much variation in lights as do trade variables, while among late developing countries the ratio is only about 1.5, even though the latter group is far more dependent on agriculture. Correspondingly, the marginal effects of agricultural variables as a group on lights are larger in absolute value, and those for trade smaller, for early developers than for late developers. We show that this apparent puzzle is explained by persistence and the differential timing of technological shocks in the two sets of countries. For early developers, structural transformation due to rising agricultural productivity began when transport costs were still high, so cities were localized in agricultural regions. When transport costs fell, these agglomerations persisted. In late-developing countries, transport costs fell before structural transformation. To exploit urban scale economies, manufacturing agglomerated in relatively few, often coastal, locations. Consistent with this explanation, countries that developed earlier are more spatially equal in their distribution of education and economic activity than late developers. This dataset is part of the Global Research Program on Spatial Development of Cities funded by the Multi-Donor Trust Fund on Sustainable Urbanization of the World Bank and supported by the U.K. Department for International Development.

eBird is a collective enterprise that takes a novel approach to citizen science by developing cooperative partnerships among experts in a wide range of fields: population ecologists, conservation biologists, quantitative ecologists, statisticians, computer scientists, GIS and informatics specialists, application developers, and data administrators. Managed by the Cornell Lab of Ornithology eBird’s goal is to increase data quantity through participant recruitment and engagement globally, but also to quantify and control for data quality issues such as observer variability, imperfect detection of species, and both spatial and temporal bias in data collection. eBird data are openly available and used by a broad spectrum of students, teachers, scientists, NGOs, government agencies, land managers, and policy makers. The result is that eBird has become a major source of biodiversity data, increasing our knowledge of the dynamics of species distributions, and having a direct impact on the conservation of birds and their habitats.

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset hosted by the World Bank. The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore the World Bank using Kaggle and all of the data sources available through the World Bank organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using the World Bank's APIs and Kaggle's API.

Cover photo by Markus Spiske on Unsplash
Unsplash Images are distributed under a unique Unsplash License.