BackgroundMicrosoft Excel automatically converts certain gene symbols, database accessions, and other alphanumeric text into dates, scientific notation, and other numerical representations. These conversions lead to subsequent, irreversible, corruption of the imported text. A recent survey of popular genomic literature estimates that one-fifth of all papers with supplementary gene lists suffer from this issue.ResultsHere, we present an open-source tool, Escape Excel, which prevents these erroneous conversions by generating an escaped text file that can be safely imported into Excel. Escape Excel is implemented in a variety of formats (http://www.github.com/pstew/escape_excel), including a command line based Perl script, a Windows-only Excel Add-In, an OS X drag-and-drop application, a simple web-server, and as a Galaxy web environment interface. Test server implementations are accessible as a Galaxy interface (http://apostl.moffitt.org) and simple non-Galaxy web server (http://apostl.moffitt.org:8000/).ConclusionsEscape Excel detects and escapes a wide variety of problematic text strings so that they are not erroneously converted into other representations upon importation into Excel. Examples of problematic strings include date-like strings, time-like strings, leading zeroes in front of numbers, and long numeric and alphanumeric identifiers that should not be automatically converted into scientific notation. It is hoped that greater awareness of these potential data corruption issues, together with diligent escaping of text files prior to importation into Excel, will help to reduce the amount of Excel-corrupted data in scientific analyses and publications.

The USDA Agricultural Research Service (ARS) recently established SCINet , which consists of a shared high performance computing resource, Ceres, and the dedicated high-speed Internet2 network used to access Ceres. Current and potential SCINet users are using and generating very large datasets so SCINet needs to be provisioned with adequate data storage for their active computing. It is not designed to hold data beyond active research phases. At the same time, the National Agricultural Library has been developing the Ag Data Commons, a research data catalog and repository designed for public data release and professional data curation. Ag Data Commons needs to anticipate the size and nature of data it will be tasked with handling. The ARS Web-enabled Databases Working Group, organized under the SCINet initiative, conducted a study to establish baseline data storage needs and practices, and to make projections that could inform future infrastructure design, purchases, and policies. The SCINet Web-enabled Databases Working Group helped develop the survey which is the basis for an internal report. While the report was for internal use, the survey and resulting data may be generally useful and are being released publicly. From October 24 to November 8, 2016 we administered a 17-question survey (Appendix A) by emailing a Survey Monkey link to all ARS Research Leaders, intending to cover data storage needs of all 1,675 SY (Category 1 and Category 4) scientists. We designed the survey to accommodate either individual researcher responses or group responses. Research Leaders could decide, based on their unit's practices or their management preferences, whether to delegate response to a data management expert in their unit, to all members of their unit, or to themselves collate responses from their unit before reporting in the survey. Larger storage ranges cover vastly different amounts of data so the implications here could be significant depending on whether the true amount is at the lower or higher end of the range. Therefore, we requested more detail from "Big Data users," those 47 respondents who indicated they had more than 10 to 100 TB or over 100 TB total current data (Q5). All other respondents are called "Small Data users." Because not all of these follow-up requests were successful, we used actual follow-up responses to estimate likely responses for those who did not respond. We defined active data as data that would be used within the next six months. All other data would be considered inactive, or archival. To calculate per person storage needs we used the high end of the reported range divided by 1 for an individual response, or by G, the number of individuals in a group response. For Big Data users we used the actual reported values or estimated likely values. Resources in this dataset: Resource Title: Appendix A: ARS data storage survey questions. File Name: Appendix A.pdfResource Description: The full list of questions asked with the possible responses. The survey was not administered using this PDF but the PDF was generated directly from the administered survey using the Print option under Design Survey. Asterisked questions were required. A list of Research Units and their associated codes was provided in a drop down not shown here. Resource Software Recommended: Adobe Acrobat,url: https://get.adobe.com/reader/ Resource Title: CSV of Responses from ARS Researcher Data Storage Survey. File Name: Machine-readable survey response data.csvResource Description: CSV file includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed. This information is that same data as in the Excel spreadsheet (also provided). Resource Title: Responses from ARS Researcher Data Storage Survey. File Name: Data Storage Survey Data for public release.xlsxResource Description: MS Excel worksheet that Includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

Excel spreadsheet listing university hospitals in Japan, including their names and website URLs.

This repository contains a collection of data about 454 value chains from 23 rural European areas of 16 countries. This data is obtained through a semi-automatic workflow that transforms raw textual data from an unstructured MS Excel sheet into semantic knowledge graphs.In particular, the repository contains:MS Excel sheet containing different value chains details provided by MOuntain Valorisation through INterconnectedness and Green growth (MOVING) European project;454 CSV files containing events, titles, entities and coordinates of narratives of each value chain, obtained by pre-processing the MS Excel sheet454 Web Ontology Language (OWL) files. This collection of files is the result of the semi-automatic workflow, and is organized as a semantic knowledge graph of narratives, where each narrative is a sub-graph explaining one among the 454 value chains and its territory aspects. The knowledge graph is based on the Narrative Ontology, an ontology developed by Institute of Information Science and Technologies (ISTI-CNR) as an extension of CIDOC CRM, FRBRoo, and OWL Time.Two CSV files that compile all the possible available information extracted from 454 Web Ontology Language (OWL) files.GeoPackage files with the geographic coordinates related to the narratives.The HTML files that show all the different SPARQL and GeoSPARQL queries.The HTML files that show the story maps about the 454 value chains.An image showing how the various components of the dataset interact with each other.

In this project, I embarked on a journey to refine my Python skills, particularly focusing on web scraping. Initially inspired by a GitHub project PythonYouTubeSeries/Scraping a Table from a Website.ipynb at main · AlexTheAnalyst/PythonYouTubeSeries · GitHub involving scraping data from a Wikipedia page listing the largest companies in the United States by revenue, I sought to go beyond basic scraping. However, I realized that to stand out among other job applicants, I needed to take this project a step further. So, I decided to not only scrape data but then take my csv file to a excel workbook demonstrating my ability to use a range of data analysis tools and apps to collect, clean, and provide comprehensive analysis based on a suitable use case that would display my ability to deliver actionable insights.

I opted the following case study that fit with the data i had obtained: identifying high-growth sectors and leading companies within those sectors for an investment portfolio. Leveraging Python libraries BeautifulSoup for HTML parsing and requests for web page retrieval, I meticulously extracted data from the Wikipedia page. Once retrieved, I organized this data into a structured format and proceeded to pinpoint the most promising sectors, by performing a cross-industry data analysis. I started by examining the average revenue growth across various sectors. The Petroleum Industry emerged prominently with a high growth rate of 48.89%. The Retail sector, despite a slower growth rate of 7.28%, demonstrated its vast scale, while the Healthcare sector’s growth rate of 10.82% highlighted its impressive performance. This can translate to more robust market stability and resilience during economic fluctuations. While Infotech and airlines also showed significant revenue growth, they were not the primary focus of this study. My ILM Business Analytics certification project on Ryanair will provide detailed insights into the airline industry, available soon on my Kaggle.

The results of my analysis are detailed below:

Petroleum Industry Analysis:

Total Revenue Analysis: ExxonMobil: $827,360 million Chevron Corporation: $492,504 million Marathon Petroleum: $360,024 million Phillips 66: $351,404 million Revenue Growth Analysis: PBF Energy: 0.718 ConocoPhillips: 0.699 Valero Energy: 0.58 Recommendations:

Market Leaders: Investing in ExxonMobil and Chevron Corporation for stability and reliable returns. High-Growth Opportunities: PBF Energy and ConocoPhillips for higher growth potential. Retail Sector Analysis:

Total Revenue Analysis: Walmart: $1,222,578 million Costco: $453,908 million The Home Depot: $314,806 million Best Buy and Publix: Under $200,000 million Revenue Growth Analysis: Costco: 0.158 Publix: 0.135 Best Buy: 0.106 Lowe's: 0.008 Recommendations:

Market Leaders: Walmart for stability and consistent performance. High-Growth Opportunities: Costco and Publix for higher growth potential. Healthcare and Pharmaceutical Industry Analysis:

Total Revenue Analysis: UnitedHealth Group: $648,324 million CVS Health: $644,934 million Cardinal Health: $362,728 million Elevance Health: $313,190 million Revenue Growth Analysis: Pfizer: 0.234 Humana: 0.118 Merck & Co.: 0.158 Bristol-Myers Squibb: 0.005 Recommendations:

Market Leaders: UnitedHealth Group and CVS Health for stability and robust returns. High-Growth Opportunities: Pfizer for substantial growth, with Humana and Merck & Co. also showing strong growth rates.

Conclusion: The Petroleum Industry exhibits substantial growth, making it attractive for future investment opportunities, with ExxonMobil, Chevron, and ConocoPhillips demonstrating strong revenue and impressive growth rates. Similarly, the Retail and Healthcare sectors also present significant investment opportunities, with market leaders providing stability and high-growth companies offering potential for substantial returns.

About This Dataset

This dataset was created as part of a web scraping project for an assignment.
The objective was to extract information from two different websites and save the results into separate Excel files.

Files Included

• Quotes.xlsx → Data scraped from a website containing famous quotes.
• Books.xlsx → Data scraped from a website containing book information.
• Quotes.ipynb / Quotes.py → The scraping code used for the Quotes dataset.
• Books.ipynb / Books.py → The scraping code used for the Books dataset.

Notes

• The data was collected only for educational purposes.
• Only publicly available information was used.

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The Delta Neighborhood Physical Activity Study was an observational study designed to assess characteristics of neighborhood built environments associated with physical activity. It was an ancillary study to the Delta Healthy Sprouts Project and therefore included towns and neighborhoods in which Delta Healthy Sprouts participants resided. The 12 towns were located in the Lower Mississippi Delta region of Mississippi. Data were collected via electronic surveys between August 2016 and September 2017 using the Rural Active Living Assessment (RALA) tools and the Community Park Audit Tool (CPAT). Scale scores for the RALA Programs and Policies Assessment and the Town-Wide Assessment were computed using the scoring algorithms provided for these tools via SAS software programming. The Street Segment Assessment and CPAT do not have associated scoring algorithms and therefore no scores are provided for them. Because the towns were not randomly selected and the sample size is small, the data may not be generalizable to all rural towns in the Lower Mississippi Delta region of Mississippi. Dataset one contains data collected with the RALA Programs and Policies Assessment (PPA) tool. Dataset two contains data collected with the RALA Town-Wide Assessment (TWA) tool. Dataset three contains data collected with the RALA Street Segment Assessment (SSA) tool. Dataset four contains data collected with the Community Park Audit Tool (CPAT). [Note : title changed 9/4/2020 to reflect study name] Resources in this dataset: Resource Title: Dataset One RALA PPA Data Dictionary. File Name: RALA PPA Data Dictionary.csvResource Description: Data dictionary for dataset one collected using the RALA PPA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Two RALA TWA Data Dictionary. File Name: RALA TWA Data Dictionary.csvResource Description: Data dictionary for dataset two collected using the RALA TWA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Three RALA SSA Data Dictionary. File Name: RALA SSA Data Dictionary.csvResource Description: Data dictionary for dataset three collected using the RALA SSA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Four CPAT Data Dictionary. File Name: CPAT Data Dictionary.csvResource Description: Data dictionary for dataset four collected using the CPAT.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset One RALA PPA. File Name: RALA PPA Data.csvResource Description: Data collected using the RALA PPA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Two RALA TWA. File Name: RALA TWA Data.csvResource Description: Data collected using the RALA TWA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Three RALA SSA. File Name: RALA SSA Data.csvResource Description: Data collected using the RALA SSA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Four CPAT. File Name: CPAT Data.csvResource Description: Data collected using the CPAT.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Data Dictionary. File Name: DataDictionary_RALA_PPA_SSA_TWA_CPAT.csvResource Description: This is a combined data dictionary from each of the 4 dataset files in this set.

This polygon files contains 2015-2016 school-year data delineating school attendance boundaries. These data were collected and processed as part of the School Attendance Boundary Survey (SABS) project which was funded by NCES to create geography delineating school attendance boundaries. Original source information that was used to create these boundary files were collected were collected over a web-based self-reporting system, through e-mail, and mailed paper maps. The web application provided instructions and assistance to users via a user guide, a frequently asked questions document, and instructional videos. Boundaries supplied outside of the online reporting system typically fell into one of six categories: a digital geographic file, such as a shapefile or KML file; digital image files, such as jpegs and pdfs; narrative descriptions; an interactive web map; Excel or pdf address lists; and paper maps. 2015 TIGER/line features (that consist of streets, hydrography, railways, etc.) were used to digitize school attendance boundaries and was the primary source of information used to digitize analog information. This practice works well as most school attendance boundaries align with streets, railways, water bodies and similar line features included in the 2015 TIGER/line "edges" files. In those few cases in which a portion of a school attendance boundary serves both sides of a street contractor staff used Esri’s Imagery base map to estimate the property lines of parcels. The data digitized from analog maps and verbal descriptions do not conform to cadastral data (and many of the original GIS files created by school districts do not conform with cadastral or parcel data).The SABS 2015-2016 file uses the WGS 1984 Web Mercator Auxiliary Sphere coordinate system.Additional information about SABS can be found on the EDGE website.The SABS dataset is intended for research purposes only and reflects a single snapshot in time. School boundaries frequently change from year to year. To verify legal descriptions of boundaries, users must contact the school district directly.All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

The intention is to collect data for the calendar year 2009 (or the nearest year for which each business keeps its accounts. The survey is considered a one-off survey, although for accurate NAs, such a survey should be conducted at least every five years to enable regular updating of the ratios, etc., needed to adjust the ongoing indicator data (mainly VAGST) to NA concepts. The questionnaire will be drafted by FSD, largely following the previous BAS, updated to current accounting terminology where necessary. The questionnaire will be pilot tested, using some accountants who are likely to complete a number of the forms on behalf of their business clients, and a small sample of businesses. Consultations will also include Ministry of Finance, Ministry of Commerce, Industry and Labour, Central Bank of Samoa (CBS), Samoa Tourism Authority, Chamber of Commerce, and other business associations (hotels, retail, etc.).

The questionnaire will collect a number of items of information about the business ownership, locations at which it operates and each establishment for which detailed data can be provided (in the case of complex businesses), contact information, and other general information needed to clearly identify each unique business. The main body of the questionnaire will collect data on income and expenses, to enable value added to be derived accurately. The questionnaire will also collect data on capital formation, and will contain supplementary pages for relevant industries to collect volume of production data for selected commodities and to collect information to enable an estimate of value added generated by key tourism activities.

The principal user of the data will be FSD which will incorporate the survey data into benchmarks for the NA, mainly on the current published production measure of GDP. The information on capital formation and other relevant data will also be incorporated into the experimental estimates of expenditure on GDP. The supplementary data on volumes of production will be used by FSD to redevelop the industrial production index which has recently been transferred under the SBS from the CBS. The general information about the business ownership, etc., will be used to update the Business Register.

Outputs will be produced in a number of formats, including a printed report containing descriptive information of the survey design, data tables, and analysis of the results. The report will also be made available on the SBS website in “.pdf” format, and the tables will be available on the SBS website in excel tables. Data by region may also be produced, although at a higher level of aggregation than the national data. All data will be fully confidentialised, to protect the anonymity of all respondents. Consideration may also be made to provide, for selected analytical users, confidentialised unit record files (CURFs).

A high level of accuracy is needed because the principal purpose of the survey is to develop revised benchmarks for the NA. The initial plan was that the survey will be conducted as a stratified sample survey, with full enumeration of large establishments and a sample of the remainder.

v01: This is the first version of the documentation. Basic raw data, obtained from data entry.

The scope of the 2009 BAS is all employing businesses in the private sector other than those involved in agricultural activities.

Included are:
· Non-governmental organizations (NGOs, not-for profit organizations, etc.);
· Government Public Bodies

Excluded are:
· Non-employing units (e.g., market sellers);
· Government ministries, constitutional offices and those public bodies involved in public administration and included in the Central Government Budget Sector;
· Agricultural units (unless large scale/commercial - if the Agriculture census only covers household activities);
· “Non-resident” bodies such as international agencies, diplomatic missions (e.g., high commissions and embassies, UNDP, FAO, WHO);

The survey coverage is of all businesses in scope as defined above. Statistical units relevant to the survey are the enterprise and the establishment. The enterprise is an institutional unit and generally corresponds to legal entities such as a company, cooperative, partnership or sole proprietorship. The establishment is an institutional unit or part of an institutional unit, which engages in one, or predominantly one, type of economic activity. Sufficient data must be available to derive or meaningfully estimate value added in order to recognize an establishment. The main statistical unit from which data will be collected in the survey is the establishment. For most businesses there will be a one-to-one relationship between the enterprise and the establishment, i.e., simple enterprises will comprise only one establishment. The purpose of collecting data from establishments (rather than from enterprises) is to enable the most accurate industry estimates of value added possible.

• Collection start: 2009
• Collection end: 2009

Title NM Double Up Food Bucks Locations 2022 - NMDOUBLEUP2022

Summary NM Double Up Food Bucks Locations as of the New Mexico Farmers Market Association website in April 2022

Notes Geocoded using Geocodio

Source New Mexico Farmers Market Association https://www.doubleupnm.org/locations/

Prepared by EMcRae_NMCDC

Feature Service https://nmcdc.maps.arcgis.com/home/item.html?id=7f2adaba961f4bafaeec4ac6d7912a14

Alias Definition

UID Unique ID

Name Name

Address Address

Hours Hours

Email Email

wicsen Flag - WIC/Senior FRMP

credit Flag - Credit/Debit

snapebt Flag - EBT/SNAP

winter Flag - Winter Hours

Lat Lat

Long Long

Score Accuracy Score

Type Accuracy Type

Number Number

Street Street

UnitTypeUnitNum Unit Type

Unit Number Unit Number

City City

State State

County County

Zip Zip

Country Country

Source Source

Status Status - Open or Closed, for tracking. Pending the addition of previous Double Up locations for identification of closed or new sites. The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updatesTitle: Double Up Food Bucks 2022 - Microsoft Excel VersionItem Type: ExcelSummary: NM Double Up Food Bucks Locations as of the New Mexico Farmers Market Association website in April 2022Notes: Prepared by: Uploaded by EMcRae_NMCDCSource: New Mexico Farmers Market Association https://www.doubleupnm.org/locations/Feature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=24531a2ff02344009e6507aea818e836UID: 75Data Requested: Double Up Food BucksMethod of Acquisition: Data posted on NMFMA websiteDate Acquired: April 2022Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 3Tags: PENDING

Contains a set of data recorded in Microsoft Excel from the evaluation of 45 websites of higher education institutes in Ecuador.

The dataset contains the results of automatic digital accessibility assessment of the official websites of universities that are the best in the field of mathematics. The study was carried out in September 2022. Universities were selected according to the QS World University Rankings 2022 by Subject "Mathematics". Automatic web accessibility assessment was performed using the WAVE web accessibility evaluation tool (WebAIM, USA). The dataset is presented as an Excel file (Auto_testing_top_math_universities.xlsx) in English. The home pages of the official websites of the top 10 universities in the world ("WorldUnivSitesAccessChecking" sheet) and the top 10 universities of the Russian Federation ("RussianUnivSitesAccessChecking" sheet) were analyzed.

About NTPSThe National Teacher and Principal Survey (NTPS) is a system of related questionnaires that provide descriptive data on the context of elementary and secondary education while also giving policymakers a variety of statistics on the condition of education in the United States.The NTPS is a redesign of the Schools and Staffing Survey (SASS), which the National Center for Education Statistics (NCES) conducted from 1987 to 2011. The design of the NTPS is a product of three key goals coming out of the SASS program: flexibility, timeliness, and integration with other Department of Education collections. The NTPS collects data on core topics including teacher and principal preparation, classes taught, school characteristics, and demographics of the teacher and principal labor force every two to three years. In addition, each administration of NTPS contains rotating modules on important education topics such as: professional development, working conditions, and evaluation. This approach allows policy makers and researchers to assess trends on both stable and dynamic topics.Data OrganizationEach table has an associated excel and excel SE file, which are grouped together in a folder in the dataset (one folder per table). The folders are named based on the excel file names, as they were when downloaded from the National Center for Education Statistics (NCES) website.In the NTPS folder, there is a catalog csv that provides a crosswalk between the folder names and the table titles.The documentation folder contains (1) codebooks for NTPS generated in NCES datalabs, (2) questionnaires for NTPS downloaded from the study website and (3) reports related to NTPS found in the NCES resource library

Escaped vs. unescaped text import into excel.

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These are results of a series of laboratory experiments to determine if topical application of methoprene and 20-ecdysone can terminate reproductive diapause of the weevil, Ceratapion basicorne, which is a recently permitted biological control agent of yellow starthistle (Centaurea solstitialis). Adult weevils feed on leaves, creating pin holes, and lay eggs inside leaves. Diapausing weevils were treated with various doses of methoprene (0, 0.01, 0.1, 1.0 micrograms) dissolved in acetone in experiments 1 and 2. They were treated sequentially first with acetone or 20-ecdysone (1.0 microgram) and then with methoprene (1.0 microgram) in experiment 3 and were treated with 20-ecdysone followed by methoprene in experiment 4. Resources in this dataset:Resource Title: data dictionary. File Name: JH Data Dictionary.csvResource Description: description of data fieldsResource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/microsoft-365/excel Resource Title: experiment 1. File Name: JH expt1 data.csvResource Description: Methoprene dissolved in acetone was applied topically at doses of 0.0, 0.01 and 0.1 and 1.0 μg per female weevil, and the number of feeding holes and eggs were recorded daily on cut leaves of yellow starthistle at room temperature (12 h photoperiod, temperature range 17 to 21°C).Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/microsoft-365/excel Resource Title: experiment 2. File Name: JH expt2 data.csvResource Description: Methoprene dissolved in acetone was applied topically at doses of 0.0 and 1.0 μg to female weevils that did not produce eggs in experiment 1. The number of feeding holes and eggs were recorded daily on cut leaves of yellow starthistle at room temperature (12 h photoperiod, temperature range 17 to 21°C).Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/microsoft-365/excel Resource Title: experiment 3. File Name: JH expt3 data.csvResource Description: Three types of treatments were applied with sequential applications 2 days apart: 1) acetone + acetone [AA: control], 2) acetone + methoprene [AM], and 20-ecdysone + methoprene 174 [2M]. All doses were 1.0 μg. The number of feeding holes and eggs were recorded every 2 days on cut leaves of yellow starthistle at room temperature (12 h photoperiod, temperature range 17 to 21°C).Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/microsoft-365/excel Resource Title: experiment 4. File Name: JH expt4 data.csvResource Description: Females from experiment 3 that did not oviposit consistently were treated with 1.0 μg of 20-ecdysone followed 2 days later by 1.0 μg of methoprene. The treatments AA, AM, 2M refer to experiment 3. The number of feeding holes and eggs were recorded every 2 days on cut leaves of yellow starthistle at room temperature (12 h photoperiod, temperature range 17 to 21°C).Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/microsoft-365/excel

We used the University of Illinois’ CCR (http://www.carboncapturereport.org/ ) data collected from the Twitter search application program interface (API) which selected tweets mentioning “climate change” or “global warming.” To gather the data, we used the simple web scraping option in Microsoft Excel to pull the data into an Excel spreadsheet from the website pages on the Carbon Capture Report from the selected sample days of 10/15/2012 to 11/15/2012. Directions for automated data collection into Excel are found at the “Get external data from a Web page” site operated by Microsoft. Note that a systematic inflation of the data was found with cases tagged with “#global warming” but were about listening to music. These cases are marked by the terms “bandung” and “pitbull” and were removed from consideration in the initial publication but are included here to ensure the full and complete data.