Microsoft reported 228K in Employees for its fiscal year ending in June of 2024. Data for Microsoft | MSFT - Employees Total Number including historical, tables and charts were last updated by Trading Economics this last September in 2025.

This dataset is about companies. It has 3 rows and is filtered where the company is Microsoft. It features 5 columns: city, country, employees, and foundation year.

In the second half of 2022, Microsoft received over 24 thousand user data requests from law enforcement agencies worldwide. In the last measured period, the number of user accounts specified in the requests issued by law enforcement agencies also increased in the estimated period.

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.

US Broadband Usage Across Counties

Utilizing Microsoft's Data to Estimate Access

By Amber Thomas [source]

About this dataset

This dataset provides an estimation of broadband usage in the United States, focusing on how many people have access to broadband and how many are actually using it at broadband speeds. Through data collected by Microsoft from our services, including package size and total time of download, we can estimate the throughput speed of devices connecting to the internet across zip codes and counties.

According to Federal Communications Commission (FCC) estimates, 14.5 million people don't have access to any kind of broadband connection. This data set aims to address this contrast between those with estimated availability but no actual use by providing more accurate usage numbers downscaled to county and zip code levels. Who gets counted as having access is vastly important -- it determines who gets included in public funding opportunities dedicated solely toward closing this digital divide gap. The implications can be huge: millions around this country could remain invisible if these number aren't accurately reported or used properly in decision-making processes.

This dataset includes aggregated information about these locations with less than 20 devices for increased accuracy when estimating Broadband Usage in the United States-- allowing others to use it for developing solutions that improve internet access or label problem areas accurately where no real or reliable connectivity exists among citizens within communities large and small throughout the US mainland.. Please review the license terms before using these data so that you may adhere appropriately with stipulations set forth under Microsoft's Open Use Of Data Agreement v1.0 agreement prior to utilizing this dataset for your needs-- both professional and educational endeavors alike!

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How to use the dataset

How to Use the US Broadband Usage Dataset

This dataset provides broadband usage estimates in the United States by county and zip code. It is ideally suited for research into how broadband connects households, towns and cities. Understanding this information is vital for closing existing disparities in access to high-speed internet, and for devising strategies for making sure all Americans can stay connected in a digital world.

The dataset contains six columns: - County – The name of the county for which usage statistics are provided. - Zip Code (5-Digit) – The 5-digit zip code from which usage data was collected from within that county or metropolitan area/micro area/divisions within states as reported by the US Census Bureau in 2018[2].
- Population (Households) – Estimated number of households defined according to [3] based on data from the US Census Bureau American Community Survey's 5 Year Estimates[4].
- Average Throughput (Mbps)- Average Mbps download speed derived from a combination of data collected anonymous devices connected through Microsoft services such as Windows Update, Office 365, Xbox Live Core Services, etc.[5]
- Percent Fast (> 25 Mbps)- Percentage of machines with throughput greater than 25 Mbps calculated using [6]. 6) Percent Slow (< 3 Mbps)- Percentage of machines with throughput less than 3Mbps calculated using [7].

Research Ideas

• Targeting marketing campaigns based on broadband use. Companies can use the geographic and demographic data in this dataset to create targeted advertising campaigns that are tailored to individuals living in areas where broadband access is scarce or lacking.
• Creating an educational platform for those without reliable access to broadband internet. By leveraging existing technologies such as satellite internet, media streaming services like Netflix, and platforms such as Khan Academy or EdX, those with limited access could gain access to new educational options from home.
• Establishing public-private partnerships between local governments and telecom providers need better data about gaps in service coverage and usage levels in order to make decisions about investments into new infrastructure buildouts for better connectivity options for rural communities

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: broadband_data_2020October.csv

Acknowledgements

If you use this dataset in your research,...

Context

Microsoft is an American company that develops and distributes software and services such as: a search engine (Bing), cloud solutions and the computer operating system Windows.

Market cap

Market capitalization of Microsoft (MSFT)

Market cap: $3.085 Trillion USD

As of February 2025 Microsoft has a market cap of $3.085 Trillion USD. This makes Microsoft the world's 2nd most valuable company by market cap according to our data. The market capitalization, commonly called market cap, is the total market value of a publicly traded company's outstanding shares and is commonly used to measure how much a company is worth.

Revenue

Revenue for Microsoft (MSFT)
Revenue in 2024 (TTM): $254.19 Billion USD

According to Microsoft's latest financial reports the company's current revenue (TTM ) is $254.19 Billion USD. In 2023 the company made a revenue of $227.58 Billion USD an increase over the revenue in the year 2022 that were of $204.09 Billion USD. The revenue is the total amount of income that a company generates by the sale of goods or services. Unlike with the earnings no expenses are subtracted.

Earnings

Earnings for Microsoft (MSFT)
Earnings in 2024 (TTM): $110.77 Billion USD

According to Microsoft's latest financial reports the company's current earnings are $254.19 Billion USD. In 2023 the company made an earning of $101.21 Billion USD, an increase over its 2022 earnings that were of $82.58 Billion USD. The earnings displayed on this page are the earnings before interest and taxes or simply EBIT.

End of Day market cap according to different sources On Feb 2nd, 2025 the market cap of Microsoft was reported to be:

• $3.085 Trillion USD by Nasdaq

• $3.085 Trillion USD by CompaniesMarketCap

• $3.085 Trillion USD by Yahoo Finance

Content

Geography: USA

Time period: March 1986- February 2025

Unit of analysis: Microsoft Stock Data 2025

Variables

Acknowledgements

This dataset belongs to me. I’m sharing it here for free. You may do with it as you wish.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F18335022%2F0304ad0416e7e55515daf890288d7f7f%2FScreenshot%202025-02-03%20152019.png?generation=1738662588735376&alt=media" alt="">

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This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Microsoft Caught Red-Handed: $20 Million Fine for Violating Children's Privacy

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

📈 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

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.

Context

This GPS trajectory dataset was collected in (Microsoft Research) Geolife project by 178 users in a period of over four years (from April 2007 to October 2011). A GPS trajectory of this dataset is represented by a sequence of time-stamped points, each of which contains the information of latitude, longitude and altitude. This dataset contains 17,621 trajectories with a total distance of 1,251,654 kilometers and a total duration of 48,203 hours. These trajectories were recorded by different GPS loggers and GPS-phones, and have a variety of sampling rates. 91 percent of the trajectories are logged in a dense representation, e.g. every 1~5 seconds or every 5~10 meters per point.

Content

This dataset recoded a broad range of users’ outdoor movements, including not only life routines like go home and go to work but also some entertainments and sports activities, such as shopping, sightseeing, dining, hiking, and cycling.

Data Format - Trajectory file Every single folder of this dataset stores a user’s GPS log files, which were converted to PLT format. Each PLT file contains a single trajectory and is named by its starting time. To avoid potential confusion of time zone, we use GMT in the date/time property of each point, which is different from our previous release. - PLT format: Line 1…6 are useless in this dataset, and can be ignored. Points are described in following lines, one for each line. Field 1: Latitude in decimal degrees. Field 2: Longitude in decimal degrees. Field 3: All set to 0 for this dataset. Field 4: Altitude in feet (-777 if not valid). Field 5: Date - number of days (with fractional part) that have passed since 12/30/1899. Field 6: Date as a string. Field 7: Time as a string. Note that field 5 and field 6&7 represent the same date/time in this dataset. You may use either of them. Example: 39.906631,116.385564,0,492,40097.5864583333,2009-10-11,14:04:30 39.906554,116.385625,0,492,40097.5865162037,2009-10-11,14:04:35 - Transportation mode labels Possible transportation modes are: walk, bike, bus, car, subway, train, airplane, boat, run and motorcycle. Again, we have converted the date/time of all labels to GMT, even though most of them were created in China. Example: Start Time End TimeTransportation Mode 2008/04/02 11:24:21 2008/04/02 11:50:45 bus 2008/04/03 01:07:03 2008/04/03 11:31:55 train 2008/04/03 11:32:24 2008/04/03 11:46:14 walk 2008/04/03 11:47:14 2008/04/03 11:55:07 car

First, you can regard the label of both taxi and car as driving although we set them with different labels for future usage. Second, a user could label the transportation mode of a light rail as train while others may use subway as the label. Actually, no trajectory can be recorded in an underground subway system since a GPS logger cannot receive any signal there. In Beijing, the light rails and subway systems are seamlessly connected, e.g., line 13 (a light rail) is connected with line 10 and line 2, which are subway systems. Sometimes, a line (like line 5) is comprised of partial subways and partial light rails. So, users may have a variety of understanding in their transportation modes. You can differentiate the real train trajectories (connecting two cities) from the light rail trajectory (generating in a city) according to their distances. Or, just treat them the same.

More: User Guide: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/User20Guide-1.2.pdf

Citation

Please cite the following papers when using this GPS dataset. [1] Yu Zheng, Lizhu Zhang, Xing Xie, Wei-Ying Ma. Mining interesting locations and travel sequences from GPS trajectories. In Proceedings of International conference on World Wild Web (WWW 2009), Madrid Spain. ACM Press: 791-800.

[2] Yu Zheng, Quannan Li, Yukun Chen, Xing Xie, Wei-Ying Ma. Understanding Mobility Based on GPS Data. In Proceedings of ACM conference on Ubiquitous Computing (UbiComp 2008), Seoul, Korea. ACM Press: 312-321. [3] Yu Zheng, Xing Xie, Wei-Ying Ma, GeoLife: A Collaborative Social Networking Service among User, location and trajectory. Invited paper, in IEEE Data Engineering Bulletin. 33, 2, 2010, pp. 32-40.

Inspiration

This trajectory dataset can be used in many research fields, such as mobility pattern mining, user activity recognition, location-based social networks, location privacy, and location recommendation.

Introduction. This document provides an overview of an archive composed of four sections.

[1] An introduction (this document) which describes the scope of the project

[2] Yearly folder, from 2002 until 2010, of the coarse Microsoft Access datasets + the surveys used to collect information for each year. The word coarse does not mean the information in the Microsoft Access dataset was not corrected for mistakes; it was, but some mistakes and inconsistencies remain, such as with data on age or education. Furthermore, the coarse dataset provides disaggregated information for selected topics, which appear in summary statistics in the clean dataset. For example, in the coarse dataset one can find the different illnesses afflicting a person during the past 14 days whereas in the clean dataset only the total number of illnesses appears.

[3] A letter from the Gran Consejo Tsimane’ authorizing the public use of de-identified data collected in our studies among Tsimane’.

[4] A Microsoft Excel document with the unique identification number for each person in the panel study.

Background. During 2002-2010, a team of international researchers, surveyors, and translators gathered longitudinal (panel) data on the demography, economy, social relations, health, nutritional status, local ecological knowledge, and emotions of about 1400 native Amazonians known as Tsimane’ who lived in thirteen villages near and far from towns in the department of Beni in the Bolivian Amazon. A report titled “Too little, too late” summarizes selected findings from the study and is available to the public at the electronic library of Brandeis University:

https://scholarworks.brandeis.edu/permalink/01BRAND_INST/1bo2f6t/alma9923926194001921

A copy of the clean, merged, and appended Stata (V17) dataset is available to the public at the following two web addresses:

[a] Brandeis University:

https://scholarworks.brandeis.edu/permalink/01BRAND_INST/1bo2f6t/alma9923926193901921

[b] Inter-university Consortium for Political and Social Research (ICPSR), University of Michigan (only available to users affiliated with institutions belonging to ICPSR)

http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/37671/utilization

Chapter 4 of the report “Too little, too late” mentioned above describes the motivation and history of the study, the difference between the coarse and clean datasets, and topics which can be examined only with coarse data.

Aims. The aims of this archive are to:

· Make available in Microsoft Access the coarse de-identified dataset [1] for each of the seven yearly surveys (2004-2010) and [2] one Access data based on quarterly surveys done during 2002 and 2003. Together, these two datasets form one longitudinal dataset of individuals, households, and villages.

· Provide guidance on how to link files within and across years, and

· Make available a Microsoft Excel file with a unique identification number to link individuals across years

The datasets in the archive.

· Eight Microsoft Access datasets with data on a wide range of variables. Except for the Access file for 2002-2003, all the other information in each of the other Access files refers to one year. Within any Access dataset, users will find two types of files:

o Thematic files. The name of a thematic file contains the prefix tbl (e.g., 29_tbl_Demography or tbl_29_Demography). The file name (sometimes in Spanish, sometimes in English) indicates the content of the file. For example, in the Access dataset for one year, the micro file tbl_30_Ventas has all the information on sales for that year. Within each micro file, columns contain information on a variable and the name of the column indicates the content of the variable. For instance, the column heading item in the Sales file would indicate the type of good sold. The exac…

The Cloud to Street - Microsoft Flood Dataset (C2S-MS Floods) is a dataset of near-coincident Sentinel-1 and Sentinel-2 data paired with water labels from 18 global flood events. These labels are derived products of MODIS sensor on board NASA's Aqua and Terra satellites produced as a part of the study, "Satellite imaging reveals increased proportion of population exposed to floods," Nature (2021), doi: 10.1038/s41586-021-03695-w. In this collection, we keep the water label which represents the maximum observed flood extent during the time period of the event and the cloud/cloud shadow label for Sentinel-2. For a detailed description of the methods used to generate these labels, please refer to the original paper.

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.

Introduction

We release sharded instructions that are used to simulate single-turn and multi-turn conversations in the paper "LLMs Get Lost in Multi-Turn Conversation". This dataset is released in conjunction with the code repository which can conduct the simulation, located at: https://github.com/microsoft/lost_in_conversation More information in the Github repository is provided on how to use the dataset.

  Dataset Contents

Each sample has the following schema: {… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/lost_in_conversation.

In the second half of 2022, all user data requests issued to Microsoft by law enforcement agencies in the Dominican Republic resulted in the disclosure of only subscriber or transactional data, excluding content data. In the Lithuania, over 90 requests ended up with partial disclosure.

On the behest of the Office of Science and Technology Policy in the White House, six institutions, including ours, have created an open research dataset called COVID-19 Research Dataset (CORD-19) to facilitate the development of question-answering systems that can assist researchers in finding relevant research on COVID-19. As of May 27, 2020, CORD-19 includes more than 100,000 open access publications from major publishers and PubMed as well as preprint articles deposited into medRxiv, bioRxiv, and arXiv. Recent years, however, have also seen question-answering and other machine learning systems exhibit harmful behaviors to humans due to biases in the training data. It is imperative and only ethical for modern scientists to be vigilant in inspecting and be prepared to mitigate the potential biases when working with any datasets. This article describes a framework to examine biases in scientific document collections like CORD-19 by comparing their properties with those derived from the citation behaviors of the entire scientific community. In total, three expanded sets are created for the analyses: 1) the enclosure set CORD-19E composed of CORD-19 articles and their references and citations, mirroring the methodology used in the renowned “A Century of Physics” analysis; 2) the full closure graph CORD-19C that recursively includes references starting with CORD-19; and 3) the inflection closure CORD-19I, that is, a much smaller subset of CORD-19C but already appropriate for statistical analysis based on the theory of the scale-free nature of the citation network. Taken together, all these expanded datasets show much smoother trends when used to analyze global COVID-19 research. The results suggest that while CORD-19 exhibits a strong tilt toward recent and topically focused articles, the knowledge being explored to attack the pandemic encompasses a much longer time span and is very interdisciplinary. A question-answering system with such expanded scope of knowledge may perform better in understanding the literature and answering related questions. However, while CORD-19 appears to have topical coverage biases compared to the expanded sets, the collaboration patterns, especially in terms of team sizes and geographical distributions, are captured very well already in CORD-19 as the raw statistics and trends agree with those from larger datasets.

Context

Parallel corpus made from Parents Teacher Conference Letters (ptcl) templates from Microsoft Translate site: https://www.microsoft.com/en-us/translator/education/parent-teacher-conference-letters/

This kernel describes how the corpus was created: https://www.kaggle.com/alvations/how-was-the-corpus-created

Content

The ms-edu-letters contains the original .docx files.

The ms-ptcl-corpus contains the .txt files with 21 lines each, as much as possible, each language's line numbers should be as parallel to each other. Perhaps Persian, Welsch and Yucatec-Maya maybe not be parallel because the python-docx read didn't read 21 lines for these .docx files.

Acknowledgements

All credits and rights to the corpus goes to Microsoft Translate.

Banner and thumbnail image from NeONBRAND on Unsplash

Using our intelligently designed data dashboard, you can quickly understand how Microsoft Corporation (MSFT) is lobbying the U.S. government, how much they're spending on it, and most importantly - the bills and specific issues on which they lobby.

Gain an informational edge over the market with our Lobbying Data Intelligence Platform. Search for, filter through, and download data from any period of recorded American lobbying history (1999-present). Perform analysis by company, lobbyist, lobbying firm, government agency, or issue.

For lobbying firms: understand your competitors. Understand who is registering with who. Gain insight on quarterly reports and specific issues other firms are lobbying on.

Our lobbying data is collected and aggregated from the U.S. Senate Office of Public Records from 1999-present and is updated on a regular basis. We utilize advanced data science techniques to ensure accurate data points are collected and ingested, match similar entities across time, and tickerize publicly traded companies that lobby.

Our comprehensive and advanced lobbying database is completed with all the information you need, with more than 1.6 million lobbying contracts ready-for-analysis. We include detailed information on all aspects of federal lobbying, including the following fascinating attributes, among much more:

1. Clients: The publicly traded company, privately owned company, interest group, NGO, or state or local government that employs or retains a lobbyist or lobbying firm.

2. Registrants (Lobbying Firms): Either the name of the lobbying firm hired by the client, or the name of the client if the client employs in-house lobbyists.

3. Lobbyists: The names and past government work experience of the individual lobbyists working on a lobbying contract.

4. General Issues: The general issues for which clients lobby on (ex: ENV - Environment, TOB - Tobacco, FAM - Family Issues/Abortion).

5. Specific Issues: A long text description of the exact bills and specific issues for which clients lobby on.

6. Bills Lobbied On: A parsed version of Specific Issues that catches specific HR, PL, and ACTS lobbied on (ex: H.R. 2347, S. 1117, Tax Cuts and Jobs Act).

7. Agencies Lobbied: The names of one or more of 250+ government agencies lobbied on in the contract (ex: White House, FDA, DOD).

8. Foreign Entities: The names and origin countries of entities affiliated with the client (ex: BNP Paribas: France).

Gain access to our highly unique and actionable U.S. lobbying database. Further information on LobbyingData.com and our alternative datasets and database can be found on our website, or by contacting us through Datarade.

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.

Our goals with this dataset were to 1) isolate, culture, and identify two fungal life stages of Aspergillus flavus, 2) characterize the volatile emissions from grain inoculated by each fungal morphotype, and 3) understand how microbially-produced volatile organic compounds (MVOCs) from each fungal morphotype affect foraging, attraction, and preference by S. oryzae. This dataset includes that derived from headspace collection coupled with GC-MS, where we found the sexual life stage of A. flavus had the most unique emissions of MVOCs compared to the other semiochemical treatments. This translated to a higher arrestment with kernels containing grain with the A. flavus sexual life stage, as well as a higher cumulative time spent in those zones by S. oryzae in a video-tracking assay in comparison to the asexual life stage. While fungal cues were important for foraging at close-range, the release-recapture assay indicated that grain volatiles were more important for attraction at longer distances. There was no significant preference between grain and MVOCs in a four-way olfactometer, but methodological limitations in this assay prevent broad interpretation. Overall, this study enhances our understanding of how fungal cues affect the foraging ecology of a primary stored product insect. In the assays described herein, we analyzed the behavioral response of Sitophilus oryzae to five different blends of semiochemicals found and introduced in wheat (Table 1). Briefly, these included no stimuli (negative control), UV-sanitized grain, clean grain from storage (unmanipulated, positive control), as well as grain from storage inoculated with fungal morphotype 1 (M1, identified as the asexual life stage of Aspergillus flavus) and fungal morphotype 2 (M2, identified as the sexual life stage of A. flavus). Fresh samples of semiochemicals were used for each day of testing for each assay. In order to prevent cross-contamination, 300 g of grain (tempered to 15% grain moisture) was initially sanitized using UV for 20 min. This procedure was done before inoculating grain with either morphotype 1 or 2. The 300 g of grain was kept in a sanitized mason jar (8.5 D × 17 cm H). To inoculate grain with the two different morphologies, we scraped an entire isolation from a petri dish into the 300 g of grain. Each isolation was ~1 week old and completely colonized by the given morphotype. After inoculation, each treatment was placed in an environmental chamber (136VL, Percival Instruments, Perry, IA, USA) set at constant conditions (30°C, 65% RH, and 14:10 L:D). This procedure was the same for both morphologies and was done every 2 weeks to ensure fresh treatments for each experimental assay. See file list for descriptions of each data file. Resources in this dataset:Resource Title: Ethovision Movement Assay. File Name: ponce_lizarraga_ethovision_assay_microbial_volatiles_2020.csvResource Software Recommended: Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Olfactometer Round 1 Assay - With Fused Air Permeable Glass. File Name: ponce_lizarraga_first_round_olfactometer_fungal_study_2020.csvResource Software Recommended: Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Olfactometer Round 2 Assay - With Fused Air Permeable Glass Containing Holes. File Name: ponce_lizarraga_second_round_olfactometer_fungal_study_2021.csvResource Software Recommended: Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Small Release-Recapture Assay. File Name: ponce_lizarraga_small_release_recapture_assay.csvResource Software Recommended: Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Large Release-Recapture Assay. File Name: ponce_lizarraga_large_release_recapture_assay.csvResource Software Recommended: Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Headspace Volatile Collection Assay. File Name: sandra_headspace_volatiles_2020.csvResource Software Recommended: Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: README file list. File Name: file_list_stored_grain_Aspergillus_Sitophilus_oryzae.txt