Microsoft 365 is used by over * million companies worldwide, with over *** million customers in the United States alone using the office suite software. Office 365 is the brand name previously used by Microsoft for a group of software applications providing productivity related services to its subscribers. Office 365 applications include Outlook, OneDrive, Word, Excel, PowerPoint, OneNote, SharePoint and Microsoft Teams. The consumer and small business plans of Office 365 were renamed as Microsoft 365 on 21 April, 2020. Global office suite market share  An office suite is a collection of software applications (word processing, spreadsheets, database etc.) designed to be used for tasks within an organization. Worldwide market share of office suite technologies is split between Google’s G Suite and Microsoft’s Office 365, with G Suite controlling around ** percent of the global market and Office 365 holding around ** percent. This trend is similar across most worldwide regions.

The number of daily active users of Microsoft Teams has stayed the same in the past year, around *** million. Due to the impact of the coronavirus (COVID-19) outbreak and the growing practices of social distancing and working from home, Microsoft has seen dramatic increases in the daily use of their communication and collaboration platform within a short period of time. Microsoft Teams is part of Microsoft 365, a set of collaboration apps and services launched in *********. Increased data consumption from “staying at home”    The average daily in-home data usage in the United States has increased significantly during the coronavirus (COVID-19) outbreak in **********. Compared to the same amount of days in **********, the daily average in-home data usage increased by a total of *** gigabytes in **********, a roughly ** percent increase. Data consumption from the usage of gaming consoles and smartphones increased the most, although the increases can be observed across nearly all device categories. Social media platforms and video and conference all platforms are the technology services that are used the most during the outbreak in the U.S.

Object recognition predominately still relies on many high-quality training examples per object category. In contrast, learning new objects from only a few examples could enable many impactful applications from robotics to user personalization. Most few-shot learning research, however, has been driven by benchmark datasets that lack the high variation that these applications will face when deployed in the real-world. To close this gap, we present the ORBIT dataset, grounded in a real-world application of teachable object recognizers for people who are blind/low vision. We provide a full, unfiltered dataset of 4,733 videos of 588 objects recorded by 97 people who are blind/low-vision on their mobile phones, and a benchmark dataset of 3,822 videos of 486 objects collected by 77 collectors. The code for loading the dataset, computing all benchmark metrics, and running the baseline models is available at https://github.com/microsoft/ORBIT-DatasetThis version comprises several zip files:- train, validation, test: benchmark dataset, organised by collector, with raw videos split into static individual frames in jpg format at 30FPS- other: data not in the benchmark set, organised by collector, with raw videos split into static individual frames in jpg format at 30FPS (please note that the train, validation, test, and other files make up the unfiltered dataset)- *_224: as for the benchmark, but static individual frames are scaled down to 224 pixels.- *_unfiltered_videos: full unfiltered dataset, organised by collector, in mp4 format.

This readme file was generated on 2024-01-15 by Salvatore Romano

GENERAL INFORMATION

Title of Dataset:
A dataset to assess Microsoft Copilot Answers in the Context of Swiss, Bavarian and Hesse Elections.

Author/Principal Investigator Information
Name: Salvatore Romano
ORCID: 0000-0003-0856-4989
Institution: Universitat Oberta de Catalunya, AID4So.
Address: Rambla del Poblenou, 154. 08018 Barcelona.
Email: salvatore@aiforensics.org

Author/Associate or Co-investigator Information
Name: Riccardo Angius
ORCID: 0000-0003-0291-3332
Institution: Ai Forensics
Address: Paris, France.
Email: riccardo@aiforensics.org

Date of data collection:
from 2023-09-21 to 2023-10-02.

Geographic location of data collection:
Switzerland and Germany.

Information about funding sources that supported the collection of the data:
The data collection and analysis was supported by AlgorithmWatch's DataSkop project, funded by Germany’s Federal Ministry of Education and Research (BMBF) as part of the program “Mensch-Technik-Interaktion” (human-technology interaction). dataskop.net
In Switzerland, the investigation was realized with the support of Stiftung Mercator Schweiz.
AI Forensics contribution was supported in part by the Open Society Foundations.
AI Forensics data collection infrastructure is supported by the Bright Initiative.

SHARING/ACCESS INFORMATION

Licenses/restrictions placed on the data:
This publication is licensed under a Creative Commons Attribution 4.0 International License.
https://creativecommons.org/licenses/by/4.0/deed.en

Links to publications that cite or use the data:
https://aiforensics.org//uploads/AIF_AW_Bing_Chat_Elections_Report_ca7200fe8d.pdf

Links to other publicly accessible locations of the data:
NA

Links/relationships to ancillary data sets:
NA

Was data derived from another source?
NA
If yes, list source(s):

Recommended citation for this dataset:
S. Romano, R. Angius, N. Kerby, P. Bouchaud, J. Amidei, A. Kaltenbrunner. 2024. A dataset to assess Microsoft Copilot Answers in the Context of Swiss, Bavarian and Hesse Elections. https://aiforensics.org//uploads/AIF_AW_Bing_Chat_Elections_Report_ca7200fe8d.pdf

DATA & FILE OVERVIEW

File List:
Microsof-Copilot-Answers_in-Swiss-Bavarian-Hess-Elections.csv
The only dataset for this research. It includes rows with prompts and responses from Microsoft Copilot, along with associated metadata for each entry.

Relationship between files, if important:
NA

Additional related data collected that was not included in the current data package:
NA

Are there multiple versions of the dataset?
NA
If yes, name of file(s) that was updated:
Why was the file updated?
When was the file updated?

METHODOLOGICAL INFORMATION

Description of methods used for collection/generation of data:
In our algorithmic auditing research, we adopted for a sock-puppet audit methodology (Sandvig at Al., 2014). This method aligns with the growing interdisciplinary focus on algorithm audits, which prioritize fairness, accountability, and transparency to uncover biases in algorithmic systems (Bandy, 2021). Sock-puppet auditing offers a fully controlled environment to understand the behavior of the system.

Every sample was collected by running a new browser instance connected to the internet via a network of VPNs and residential IPs based in Switzerland and Germany, then accessing Microsoft Copilot through its official URL. Every time, the settings for Language and Country/Region were set to match those of potential voters from the respective regions (English, German, French, or Italian, and Switzerland or Germany). We did not simulate any form of user history or additional personalization. Importantly, Microsoft Copilot's default settings remained unchanged, ensuring that all interactions occurred in the ``Conversation Style" set as ``Balanced".

Sandvig, C.; Hamilton, K.; Karahalios, K.; and Langbort, C. 2014. Auditing algorithms: Research methods for detecting discrimination on internet platforms. Data and discrimination: converting critical concerns into productive inquiry,
22(2014): 4349–4357.

Bandy, J. 2021. Problematic machine behavior: A systematic literature review of algorithm audits. Proceedings of the acm on human-computer interaction, 5(CSCW1): 1–34

Methods for processing the data:
The process involved analyzing the HTML code of the web pages that were accessed. During this examination, key metadata were identified and extracted from the HTML structure. Once this information was successfully extracted, the rest of the HTML page, which primarily consisted of code and elements not pertinent to the needed information, was discarded. This approach ensured that only the most relevant and useful data was retained, while all unnecessary and extraneous HTML components were efficiently removed, streamlining the data collection and analysis process.

Instrument- or software-specific information needed to interpret the data:
NA

Standards and calibration information, if appropriate:
NA

Environmental/experimental conditions:
NA

Describe any quality-assurance procedures performed on the data:
NA

People involved with sample collection, processing, analysis and/or submission:
Salvatore Romano, Riccardo Angius, Natalie Kerby, Paul Bouchaud, Jacopo Amidei, Andreas Kaltenbrunner.

DATA-SPECIFIC INFORMATION FOR:
Microsof-Copilot-Answers_in-Swiss-Bavarian-Hess-Elections.csv

Number of variables: Number of Variables:
33

Number of cases/rows:
5562

Variable List:
prompt - (object) Text of the prompt.
answer - (object) Text of the answer.
country - (object) Country information.
language - (object) Language of the text.
input_conversation_id - (object) Identifier for the conversation.
conversation_group_ids - (object) Group IDs for the conversation.
conversation_group_names - (object) Group names for the conversation.
experiment_id - (object) Identifier for the experiment group.
experiment_name - (object) Name of the experiment group.
begin - (object) Start time.
end - (object) End time.
datetime - (int64) Datetime stamp.
week - (int64) Week number.
attributions - (object) Link quoted in the text.
attribution_links - (object) Links for attributions.
search_query - (object) Search query used by the chatbot.
unlabelled - (int64) Unlabelled flag.
exploratory_sample - (int64) Exploratory sample flag.
very_relevant - (int64) Very relevant flag.
needs_review - (int64) Needs review flag.
misleading_factual_error - (int64) Misleading factual error flag.
nonsense_factual_error - (int64) Nonsense factual error flag.
rejects_question_framing - (int64) Rejects question framing flag.
deflection - (int64) Deflection flag.
shield - (int64) Shield flag.
wrong_answer_language - (int64) Wrong answer language flag.
political_imbalance - (int64) Political imbalance flag.
refusal - (int64) Refusal flag.
factual_error - (int64) Factual error flag.
evasion - (int64) Evasion flag.
absolutely_accurate - (int64) Absolutely accurate flag.
macrocategory - (object) Macro-category of the content.

Missing data codes:
NA

Specialized formats or other abbreviations used:
NA

News dissemination plays a vital role in supporting people to incorporate beneficial actions during public health emergencies, thereby significantly reducing the adverse influences of events. Based on big data from YouTube, this research study takes the declaration of COVID-19 National Public Health Emergency (PHE) as the event impact and employs a DiD model to investigate the effect of PHE on the news dissemination strength of relevant videos. The study findings indicate that the views, comments, and likes on relevant videos significantly increased during the COVID-19 public health emergency. Moreover, the public’s response to PHE has been rapid, with the highest growth in comments and views on videos observed within the first week of the public health emergency, followed by a gradual decline and returning to normal levels within four weeks. In addition, during the COVID-19 public health emergency, in the context of different types of media, lifestyle bloggers, local media, and institutional media demonstrated higher growth in the news dissemination strength of relevant videos as compared to news & political bloggers, foreign media, and personal media, respectively. Further, the audience attracted by related news tends to display a certain level of stickiness, therefore this audience may subscribe to these channels during public health emergencies, which confirms the incentive mechanisms of social media platforms to foster relevant news dissemination during public health emergencies. The proposed findings provide essential insights into effective news dissemination in potential future public health events.

Dataset Description

Overview:

This dataset contains the historical stock prices and related financial information for five major technology companies: Apple (AAPL), Microsoft (MSFT), Amazon (AMZN), Google (GOOGL), and Tesla (TSLA). The dataset spans a five-year period from January 1, 2019, to January 1, 2024. It includes key stock metrics such as Open, High, Low, Close, Adjusted Close, and Volume for each trading day.

Data Collection:

The data was sourced using the yfinance library in Python, which provides convenient access to historical market data from Yahoo Finance.

Contents:

The dataset contains the following columns:

Date: The trading date. Open: The opening price of the stock on that date. High: The highest price of the stock on that date. Low: The lowest price of the stock on that date. Close: The closing price of the stock on that date. Adj Close: The adjusted closing price, accounting for dividends and splits. Volume: The number of shares traded on that date. Ticker: The stock ticker symbol representing each company.

This folder contains one dataset (Microsoft Word), six participant information sheets (PIS, relating to two age groups within UK secondary schools and to parents/guardians), and six assent/consent forms (assent forms for Children and Young People aged under 16, consent forms for parents/guardians consenting for child participation, consent forms for parents/guardians consenting for own participation) used in "The development of a capability wellbeing measure in economic evaluation for children and young people aged 11-15" submitted to Social Science & Medicine. Each of the Microsoft Word files contains a single interview transcript or contain multiple parent and child interviews within one file. Transcripts for CYP participants are labelled beginning PC, those for adults are labelled beginning PA. Data were collected between September 2019 and November 2021. Users will require Microsoft Word to access these data.

This thesis-mpc-dataset-public-readme.txt file was generated on 2020-10-20 by Masud Petronia

GENERAL INFORMATION
1. Title of Dataset: Data underlying the thesis: Multiparty Computation: The effect of multiparty computation on firms' willingness to contribute protected data
2. Author Information A. Principal Investigator Contact Information Name: Masud Petronia Institution: TU Delft, Faculty of Technology, Policy and Management Address: Mekelweg 5, 2628 CD Delft, Netherlands Email: masud.petronia@gmail.com ORCID: https://orcid.org/0000-0003-2798-046X
3: Description of dataset: This dataset contains perceptual data of firms' willingness to contribute protected data through multi party computation (MPC). Petronia (2020, ch. 6) draws several conclusions from this dataset and provides recommendations for future research Petronia (2020, ch. 7.4).
4. Date of data collection: July-August 2020
5. Geographic location of data collection: Netherlands
6. Information about funding sources that supported the collection of the data: Horizon 2020 Research and Innovation Programme, Grant Agreement no 825225 – Safe Data Enabled Economic Development (SAFE-DEED), from the H2020-ICT-2018-2

SHARING/ACCESS INFORMATION
1. Licenses/restrictions placed on the data: CC 0
2. Links to publications that cite or use the data: Petronia, M. N. (2020). Multiparty Computation: The effect of multiparty computation on firms' willingness to contribute protected data (Master's thesis). Retrieved from http://resolver.tudelft.nl/uuid:b0de4a4b-f5a3-44b8-baa4-a6416cebe26f
3. Was data derived from another source? No
4. Citation for this dataset: Petronia, M. N. (2020). Multiparty Computation: The effect of multiparty computation on firms' willingness to contribute protected data (Master's thesis). Retrieved from https://data.4tu.nl/. doi:10.4121/13102430

DATA & FILE OVERVIEW
1. File List: thesis-mpc-dataset-public.xlsxthesis-mpc-dataset-public-readme.txt (this document)
2. Relationship between files: Dataset metadata and instructions
3. Additional related data collected that was not included in the current data package: Occupation and role of respondents (traceable to unique reference), removed for privacy reasons.
4. Are there multiple versions of the dataset? No

METHODOLOGICAL INFORMATION
1. Description of methods used for collection/generation of data: A pre- and post test experimental design. For more information; see Petronia (2020, ch. 5)
2. Methods for processing the data: Full instructions are provided by Petronia (2020, ch. 6)
3. Instrument- or software-specific information needed to interpret the data: Microsoft Excel can be used to convert the dataset to other formats.
4. Environmental/experimental conditions: This dataset comprises three datasets collected through three channels. These channels are Prolific (incentive), LinkedIn/Twitter (voluntarily), and respondents in a lab setting (voluntarily). For more information; see Petronia (2020, ch. 6.1)
5. Describe any quality-assurance procedures performed on the data: A thorough examination of consistency and reliability is performed. For more information; see Petronia (2020, ch. 6).
6. People involved with sample collection, processing, analysis and/or submission: See Petronia (2020, ch. 6)

DATA-SPECIFIC INFORMATION
1. Number of variables: see worksheet experiment_matrix of thesis-mpc-dataset-public.xlsx
2. Number of cases/rows: see worksheet experiment_matrix of thesis-mpc-dataset-public.xlsx
3. Variable List: see worksheet labels of thesis-mpc-dataset-public.xlsx
4. Missing data codes: see worksheet comments of thesis-mpc-dataset-public.xlsx
5. Specialized formats or other abbreviations used: Multiparty computation (MPC) and Trusted Third Party (TTP).

INSTRUCTIONS
1. Petronia (2020, ch. 6) describes associated tests and respective syntax.

The latest closing stock price for Microsoft as of June 18, 2025 is 480.24. An investor who bought $1,000 worth of Microsoft stock at the IPO in 1986 would have $8,056,718 today, roughly 8,057 times their original investment - a 25.94% compound annual growth rate over 39 years. The all-time high Microsoft stock closing price was 480.24 on June 18, 2025. The Microsoft 52-week high stock price is 481.00, which is 0.2% above the current share price. The Microsoft 52-week low stock price is 344.79, which is 28.2% below the current share price. The average Microsoft stock price for the last 52 weeks is 422.77. For more information on how our historical price data is adjusted see the Stock Price Adjustment Guide.

The Highway-Runoff Database (HRDB) was developed by the U.S. Geological Survey, in cooperation with the Federal Highway Administration (FHWA) to provide planning-level information for decision makers, planners, and highway engineers to assess and mitigate possible adverse effects of highway runoff on the Nation’s receiving waters. The HRDB was assembled by using a Microsoft Access database application to facilitate use of the data and to calculate runoff-quality statistics with methods that properly handle censored-concentration data. This data release provides highway-runoff data, including information about monitoring sites, precipitation, runoff, and event-mean concentrations of water-quality constituents. The dataset was compiled from 37 studies as documented in 113 scientific or technical reports. The dataset includes data from 242 highway sites across the country. It includes data from 6,837 storm events with dates ranging from April 1975 to November 2017. Therefore, these data span more than 40 years; vehicle emissions and background sources of highway-runoff constituents have changed markedly during this time. For example, some of the early data is affected by use of leaded gasoline, phosphorus-based detergents, and industrial atmospheric deposition. The dataset includes 106,441 concentration values with data for 414 different water-quality constituents. This dataset was assembled from various sources and the original data was collected and analyzed by using various protocols. Where possible the USGS worked with State departments of transportation and the original researchers to obtain, document, and verify the data that was included in the HRDB. This new version (1.1.0) of the database contains software updates to provide data-quality information within the Graphical User Interface (GUI), calculate statistics for multiple sites in batch mode, and output additional statistics. However, inclusion in this dataset does not constitute endorsement by the USGS or the FHWA. People who use this data are responsible for ensuring that the data are complete and correct and that it is suitable for their intended purposes.

According to Cognitive Market Research, the global Hyperscale Data Center market size will be USD XX million in 2025. It will expand at a compound annual growth rate (CAGR) of XX% from 2025 to 2031.

North America held the major market share for more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Europe accounted for a market share of over XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Asia Pacific held a market share of around XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Latin America had a market share of more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Middle East and Africa had a market share of around XX% of the global revenue and was estimated at a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. KEY DRIVERS

Increased Adoption of Cloud Computing is boosting the market growth

The growing adoption of cloud computing is one of the primary drivers fueling the expansion of the hyperscale data center market. As businesses increasingly move to cloud-based solutions for computing, storage, and application services, there is a heightened demand for data centers capable of handling massive data volumes and complex computational tasks. Hyperscale data centers provide the necessary infrastructure to support this shift, offering high scalability, robust performance, and cost efficiency. For instance, in 2023, a report by Eurostat revealed that 45.2% of enterprises in the EU utilized cloud computing services, a 4.2% increase from 2021. (Source: https://ec.europa.eu/eurostat/web/products-eurostat-news/w/ddn-20231208-1#:~:text=In%202023%2C%2045.2%25%20of%20EU%20enterprises%20purchased%20cloud,4.2%20percentage%20point%20%28pp%29%20increase%20compared%20with%202021.) Leading cloud service providers like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud have significantly invested in hyperscale data centers to support this trend, delivering on-demand computing resources to millions of businesses worldwide. As cloud computing continues to grow in importance, its demand for more processing power and storage will only increase. In fact, by 2026, the global cloud market is expected to reach over USD1 trillion, This surge is being driven by businesses' digital transformation, the rise of remote work, and the need for scalable IT solutions. AWS, for instance, operates some of the world’s largest hyperscale data centers, supporting everything from e-commerce to artificial intelligence applications. Similarly, Microsoft Azure has expanded its data center presence globally, allowing companies to leverage flexible cloud services while meeting security and compliance requirements. Thus, the increasing reliance on cloud services remains a critical driver of the hyperscale data center industry’s rapid growth.

Surge in Internet Users is further driving the Hyperscale Data Centre Market

The surge in internet users has created a significant demand for hyperscale data centers, as increased online activity generates a massive volume of data that needs to be processed, stored, and managed. According to Statistics Canada’s 2022 Canadian Internet Use Survey, internet usage among Canadians aged 15 and older rose from 92% in 2020 to 95% in 2022, highlighting the continued digital adoption across all age groups. (Source: https://www150.statcan.gc.ca/n1/daily-quotidien/230720/dq230720b-eng.htm) This trend is also reflected globally, with the total number of internet users surpassing 5 billion in 2023, according to the International Telecommunication Union (ITU). As more people engage in digital activities—such as streaming, social media, and e-commerce—the volume of data generated increases exponentially, creating a greater need for expansive data center infrastructure to handle the load. Companies like Netflix and YouTube, which rely on large-scale content delivery networks, depend on hyperscale data centers to provide seamless streaming services to billions of users globally. For instance, YouTube serves over 2 billion logged-in users per month, with massive data storage and processing needs. Similarly, Facebook and Instagram, both...

Abstract— The present study deals with Transparent Data Encryption which is a technology used to solve the problems of security of data. Transparent Data Encryption means encryptingdatabases on hard disk and on any backup media. Present day global business environment presents numerous security threats and compliance challenges. To protect against data thefts andfrauds we require security solutions that are transparent by design. Transparent Data Encryption provides transparent, standards-based security that protects data on the network, on disk and on backup media. It is easy and effective protection ofstored data by transparently encrypting data. Transparent Data Encryption can be used to provide high levels of security to columns, table and tablespace that is database files stored onhard drives or floppy disks or CD’s, and other information that requires protection. It is the technology used by Microsoft SQL Server 2008 to encrypt database contents. The term encryptionmeans the piece of information encoded in such a way that it can only be decoded read and understood by people for whom the information is intended. The study deals with ways to createMaster Key, creation of certificate protected by the master key, creation of database master key and protection by the certificate and ways to set the database to use encryption in Microsoft SQLServer 2008.

TIMo (Time-of-Flight Indoor Monitoring) is a dataset of infrared and depth videos intended for the use in Anomaly Detection and Person Detection/People Counting. It features more than 1,500 sequences for anomaly detection, which sum up to more than 500,000 individual frames. For person detection the dataset contains more than than 240 sequences. The data was captured using a Microsoft Azure Kinect RGB-D camera. In addition, we provide annotations of anomalous frame ranges for use with anomaly detection and bounding boxes and segmentation masks for use with person detection. The data was captured in parts from a tilted view and a top-down perspective.

📈 Microsoft Stock Price History (1986–2025)

This dataset provides daily historical stock price data for Microsoft Corporation (MSFT) from March 13, 1986 to April 6, 2025. It includes essential trading information such as open, high, low, close, adjusted close prices, and daily trading volume.

Whether you're a data scientist, financial analyst, or machine learning enthusiast, this dataset is perfect for building models, visualizing trends, or exploring the evolution of one of the world’s largest tech companies.

📂 Dataset Overview

📊 Summary

• Date Range: 1986-03-13 to 2025-04-06
• Total Entries: 9,843
• Average Close Price: ~$64.63
• Max Price (Close): $467.56
• Max Volume: Over 1 billion shares
• Missing Values: None ✅

🔍 Potential Use Cases

• Time-series forecasting using LSTM, ARIMA, or Prophet
• Backtesting trading strategies
• Analyzing long-term financial trends and volatility
• Visualizing market behavior around major events (e.g., dot-com bubble, COVID-19)
• Comparing real vs adjusted stock prices

💡 Project Ideas

• 📉 Predict next-day prices using deep learning
• 📈 Create interactive visualizations with Plotly
• 🧠 Train an ML model to detect bullish/bearish patterns
• 📊 Calculate technical indicators like RSI, MACD, Bollinger Bands

📎 License

This data is publicly available and intended for educational and research purposes only. For actual trading, always refer to a licensed financial data provider.

📬 Stay Connected

If you use this dataset in your project or research, feel free to share your work — I’d love to see it!

1-Kaggle: https://www.kaggle.com/muhammadatiflatif

2-Github: https://github.com/M-Atif-Latif

3-Linkdin: https://www.linkedin.com/in/muhammad-atif-latif-13a171318?utm_source=share&utm_campaign=share_via&utm_content=profile&utm_medium=android_app

4:X:

https://x.com/mianatif5867?s=09

Dataset Card for Cats Vs. Dogs

  Dataset Summary

A large set of images of cats and dogs. There are 1738 corrupted images that are dropped. This dataset is part of a now-closed Kaggle competition and represents a subset of the so-called Asirra dataset. From the competition page:

The Asirra data set Web services are often protected with a challenge that's supposed to be easy for people to solve, but difficult for computers. Such a challenge is often called a CAPTCHA… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/cats_vs_dogs.

The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place. Related datasets representing components of risk across the entire landscape are available in a separate data publication (Scott et al. 2020, https://doi.org/10.2737/RDS-2020-0016). Likewise, transmitted risk to housing units from the source locations where damaging fires originate will be also be delivered in a separate publication.

Vegetation and wildland fuels data from LANDFIRE 2014 (version 1.4.0) form the foundation for wildfire hazard and risk data included in the Wildfire Risk to Communities datasets. As such, the data presented here reflect wildfire hazard from landscape conditions as of the end of 2014. National wildfire hazard datasets of annual burn probability and fire intensity were generated from the LANDFIRE 2014 data by the USDA Forest Service, Rocky Mountain Research Station (Short et al. 2020) using the large fire simulation system (FSim). These national datasets produced with FSim have a relatively coarse cell size of 270 meters (m). To bring these datasets down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30-m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability and intensity into developed areas represented in LANDFIRE fuels data as non-burnable. Additional methodology documentation is provided with the data publication download.

The data products in this publication that represent where people live reflect 2018 estimates of housing unit and population counts from the U.S. Census Bureau, combined with building footprint data from Microsoft (version 1.1), LandScan 2018 where building footprint data were unavailable, USGS building coverage data, and land cover data from LANDFIRE.

The specific raster datasets included in this publication include:

Housing Unit Density (HUDen): HUDen is a nationwide raster of housing-unit density measured in housing units per square kilometer. The HUDen raster was generated using population and housing-unit count and data from the U.S. Census Bureau, building footprint data from Microsoft, and land cover data from LANDFIRE. In Alaska, LandScan 2018 data were used to identify approximate housing unit locations because Microsoft data were not available across the whole state.

Population Density (PopDen): PopDen is a nationwide raster of residential population density measured in persons per square kilometer. The PopDen raster was generated using population count data from the U.S. Census Bureau, building footprint data from Microsoft, and land cover data from LANDFIRE. In Alaska, LandScan 2018 data were used to identify approximate population locations because Microsoft data were not available across the whole state.

Building Coverage (BuildingCover): BuildingCover is a raster of building density measured as the percent cover of buildings within an approximately 5 acre area around each pixel. It includes all buildings and can be used to complement the HUDen raster, which just reflects residential buildings. Building coverage was generated using building footprint data from Microsoft (v1.1), building coverage data from USGS, and land cover data from LANDFIRE. Building Coverage is not available in Alaska because source data were not available across the whole state.

Building Exposure Type (BuildingExposure): Exposure is the spatial coincidence of wildfire likelihood and intensity with communities. The BuildingExposure layer delineates whether buildings at each pixel are directly exposed to wildfire from adjacent wildland vegetation (pixel value of 1), indirectly exposed to wildfire from indirect sources such as embers and home-to-home ignition (pixel values between 0 and 1), or not exposed to wildfire due to distance from direct and indirect ignition sources (pixel value of 0). It is similar to Exposure Type in the companion data publication, RDS-2020-0016, but just where HUDen > 0 or BuildingCover > 0. Pixels where both HUDen and BuildingCover rasters are zero are NoData in the BuildingExposure raster.

Housing Unit Exposure (HUExposure): HUExposure is the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year. It is calculated as the product of wildfire likelihood and housing unit count. Pixels where the HUDen raster is zero are NoData in the HUExposure raster.

Housing Unit Impact (HUImpact): HUImpact is an index that represents the relative potential impact of fire to housing units at any pixel, if a fire occurs there. It incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire. HUImpact does not include the likelihood of fire occurring, and it does not reflect mitigations done to individual structures that would influence susceptibility. It is conceptually similar to Conditional Risk to Potential Structures in the companion data publication, RDS-2020-0016, but also incorporates housing unit count and exposure type. Pixels where the HUDen raster is zero are NoData in the HUImpact raster.

Housing Unit Risk (HURisk): HURisk is an index that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density > 0. It is conceptually similar to Risk to Potential Structures (i.e., Risk to Homes) in the companion data publication, RDS-2020-0016, but also incorporates housing unit count. Pixels where the HUDen raster is zero are NoData in the HURisk raster.The geospatial data products described and distributed here are part of the Wildfire Risk to Communities project. This project was directed by Congress in the 2018 Consolidated Appropriations Act (i.e., 2018 Omnibus Act, H.R. 1625, Section 210: Wildfire Hazard Severity Mapping) to help U.S. communities understand components of their relative wildfire risk profile, the nature and effects of wildfire risk, and actions communities can take to mitigate risk. These data represent the first time wildfire risk to communities has been mapped nationally with consistent methodology. They provide foundational information for comparing the relative wildfire risk among populated communities in the United States.See the Wildfire Risk to Communities website at https://www.wildfirerisk.org for complete project information. The suite of seven raster layers included in this publication are downloadable as zip files by U.S. state. Population Density, Building Coverage, Housing Unit Density, Housing Unit Impact, and Housing Unit Risk are also downloadable as national datasets. National datasets of Housing Unit Exposure and Building Exposure Type are too large for download, but users can request them through the point of contact listed in this metadata document.

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Abstract

Four documents describe the specifications, methods and scripts of the Impact and Risk Analysis Databases developed for the Bioregional Assessments Programme. They are:

1. Bioregional Assessment Impact and Risk Databases Installation Advice (IMIA Database Installation Advice v1.docx).

2. Naming Convention of the Bioregional Assessment Impact and Risk Databases (IMIA Project Naming Convention v39.docx).

3. Data treatments for the Bioregional Assessment Impact and Risk Databases (IMIA Project Data Treatments v02.docx).

4. Quality Assurance of the Bioregional Assessment Impact and Risk Databases (IMIA Project Quality Assurance Protocol v17.docx).

This dataset also includes the Materialised View Information Manager (MatInfoManager.zip). This Microsoft Access database is used to manage the overlay definitions of materialized views of the Impact and Risk Analysis Databases. For more information about this tool, refer to the Data Treatments document.

The documentation supports all five Impact and Risk Analysis Databases developed for the assessment areas:

• Maranoa-Balonne-Condamine: http://data.bioregionalassessments.gov.au/dataset/69075f3e-67ba-405b-8640-96e6cb2a189a

• Gloucester: http://data.bioregionalassessments.gov.au/dataset/d78c474c-5177-42c2-873c-64c7fe2b178c

• Hunter: http://data.bioregionalassessments.gov.au/dataset/7c170d60-ff09-4982-bd89-dd3998a88a47

• Namoi: http://data.bioregionalassessments.gov.au/dataset/1549c88d-927b-4cb5-b531-1d584d59be58

• Galilee: http://data.bioregionalassessments.gov.au/dataset/3dbb5380-2956-4f40-a535-cbdcda129045​

Purpose

These documents describe end-to-end treatments of scientific data for the Impact and Risk Analysis Databases, developed and published by the Bioregional Assessment Programme. The applied approach to data quality assurance is also described. These documents are intended for people with an advanced knowledge in geospatial analysis and database administration, who seek to understand, restore or utilise the Analysis Databases and their underlying methods of analysis.

Dataset History

The Impact and Risk Analysis Database Documentation was created for and by the Information Modelling and Impact Assessment Project (IMIA Project).

Dataset Citation

Bioregional Assessment Programme (2018) Impact and Risk Analysis Database Documentation. Bioregional Assessment Source Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/05e851cf-57a5-4127-948a-1b41732d538c.