This excel file will do a statistical tests of whether two ROC curves are different from each other based on the Area Under the Curve. You'll need the coefficient from the presented table in the following article to enter the correct AUC value for the comparison: Hanley JA, McNeil BJ (1983) A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 148:839-843.

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.

The Excel file contains the model input-out data sets that where used to evaluate the two-layer soil moisture and flux dynamics model. The model is original and was developed by Dr. Hantush by integrating the well-known Richards equation over the root layer and the lower vadose zone. The input-output data are used for: 1) the numerical scheme verification by comparison against HYDRUS model as a benchmark; 2) model validation by comparison against real site data; and 3) for the estimation of model predictive uncertainty and sources of modeling errors. This dataset is associated with the following publication: He, J., M.M. Hantush, L. Kalin, and S. Isik. Two-Layer numerical model of soil moisture dynamics: Model assessment and Bayesian uncertainty estimation. JOURNAL OF HYDROLOGY. Elsevier Science Ltd, New York, NY, USA, 613 part A: 128327, (2022).

This is a dataset downloaded off excelbianalytics.com created off of random VBA logic. I recently performed an extensive exploratory data analysis on it and I included new columns to it, namely: Unit margin, Order year, Order month, Order weekday and Order_Ship_Days which I think can help with analysis on the data. I shared it because I thought it was a great dataset to practice analytical processes on for newbies like myself.

In this project, I analysed the employees of an organization located in two distinct countries using Excel. This project covers:

1) How to approach a data analysis project 2) How to systematically clean data 3) Doing EDA with Excel formulas & tables 4) How to use Power Query to combine two datasets 5) Statistical Analysis of data 6) Using formulas like COUNTIFS, SUMIFS, XLOOKUP 7) Making an information finder with your data 8) Male vs. Female Analysis with Pivot tables 9) Calculating Bonuses based on business rules 10) Visual analytics of data with 4 topics 11) Analysing the salary spread (Histograms & Box plots) 12) Relationship between Salary & Rating 13) Staff growth over time - trend analysis 14) Regional Scorecard to compare NZ with India

Including various Excel features such as: 1) Using Tables 2) Working with Power Query 3) Formulas 4) Pivot Tables 5) Conditional formatting 6) Charts 7) Data Validation 8) Keyboard Shortcuts & tricks 9) Dashboard Design

About this dataset

Context

The dataset tabulates the Excel population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Excel. The dataset can be utilized to understand the population distribution of Excel by age. For example, using this dataset, we can identify the largest age group in Excel.

Key observations

The largest age group in Excel, AL was for the group of age 5 to 9 years years with a population of 77 (15.28%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Excel, AL was the 85 years and over years with a population of 2 (0.40%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Excel is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Excel total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Excel Population by Age. You can refer the same here

The polygon dataset represents predicted indoor radon screening levels in counties across the United States. These data were provided by EPA’s Office of Radiation and Indoor Air as an Excel spreadsheet. In order to produce the Web mapping application, the Excel file was joined with a shapefile of U.S. county boundaries downloaded from the U.S. Census Bureau. Those two sets of data were then converted into a single polygon feature class inside a file geodatabase.

The objective of this project was to develop system designs for programs to monitor travel time reliability and to prepare a guidebook that practitioners and others can use to design, build, operate, and maintain such systems. Generally, such travel time reliability monitoring systems will be built on top of existing traffic monitoring systems. The focus of this project was on travel time reliability. The data from the monitoring systems developed in this project – from both public and private sources –included, wherever cost-effective, information on the seven sources of non-recurring congestion. This data was used to construct performance measures or to perform various types of analyses useful for operations management as well as performance measurement, planning, and programming. The datasets in the attached ZIP file support SHRP 2 reliability project L38B, "Pilot testing of SHRP 2 reliability data and analytical products: Minnesota." This report can be accessed via the following URL: https://rosap.ntl.bts.gov/view/dot/3608 This ZIP file package, which is 22.1 MB in size, contains 6 Microsoft Excel spreadsheet files (XLSX). This file package also contains 3 Comma Separated Value files (CSV). The XLSX and CSV files can be opened using Microsoft Excel 2010 and 2016. The CSV files can be opened using most available text editing programs.

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

The dataset includes customer id,Martial Status,Gender,Income,Children,Education,Occupation,Home Owner,Cars,Commute Distance,Region,Age,Purchased Bike. Blog

"SHRP 2 initiated the L38 project to pilot test products from five of the program’s completed projects. The products support reliability estimation and use based on data analyses, analytical techniques, and decision-making framework. The L38 project has two main objectives: (1) to assist agencies in using travel time reliability as a measure in their business practices and (2) to receive feedback from the project research teams on the applicability and usefulness of the products tested, along with their suggested possible refinements. SHRP 2 selected four teams from California, Minnesota, Florida, and Washington. Project L38C tested elements from Projects L02, L05, L07, and L08. Project L02 identified methods to collect, archive, and integrate required data for reliability estimation and methods for analyzing and visualizing the causes of unreliability based on the collected data. Projects L07 and L08 produced analytical techniques and tools for estimating reliability based on developed models and allowing the estimation of reliability and the impacts on reliability of alternative mitigating strategies. Project L05 provided guidance regarding how to use reliability assessments to support the business processes of transportation agencies. The datasets in this zip file, which is 7.83 MB in size, support of SHRP 2 reliability project L38C, "Pilot testing of SHRP 2 reliability data and analytical products: Florida." The accompanying report can be accessed at the following URL: https://rosap.ntl.bts.gov/view/dot/3609 There are 12 datasets in this zip file, including 2 Microsoft Excel worksheets (XLSX) and 10 Comma Separated Values (CSV) files. The Microsoft Excel worksheets can be opened using the 2010 and 2016 versions of Microsoft Word, the CSV files can be opened using most text editors.

Context

This data is random generated in Excel to practice forecasting and visualizations.

Content

The two branches utilize data of thousands of generated product data with nearly 200 different employees. Product ID numbers are randomly generated for each file

Acknowledgements

This project was for my practice

Inspiration

It is important to identify any barriers in recruitment, hiring, and employee retention practices that might discourage any segment of our population from applying for positions or continuing employment at the City of Tempe. This information will provide better awareness for outreach efforts and other strategies to attract, hire, and retain a diverse workforce.This page provides data for the Employee Vertical Diversity performance measure. The performance measure dashboard is available at 2.20 Employee Vertical Diversity. Additional InformationSource:PeopleSoft HCM, Maricopa County Labor Market Census DataContact: Lawrence LaVictoireContact E-Mail: lawrence_lavicotoire@tempe.govData Source Type: Excel, PDFPreparation Method: PeopleSoft query and PDF are moved to a pre-formatted Excel spreadsheet.Publish Frequency: Every six monthsPublish Method: ManualData Dictionary

Poseidon 2.0 is a user-oriented, simple and fast Excel-Tool which aims to compare different wastewater treatment techniques based on their pollutant removal efficiencies, their costs and additional assessment criteria. Poseidon can be applied for pre-feasibility studies in order to assess possible water reuse options and can show decision makers and other stakeholders that implementable solutions are available to comply with local requirements. This upload consists in:

Poseidon 2.0 Excel File that can be used with Microsoft Excel - XLSM

Handbook presenting main features of the decision support tool - PDF

This dataset is linked to following additional open access resources:

Oertlé E, Hugi C, Wintgens T, Karavitis C, Oertlé E, Hugi C, Wintgens T, Karavitis CA. 2019. Poseidon—Decision Support Tool for Water Reuse. Water. 11(1):153. doi:10.3390/w11010153. [accessed 2019 Jan 22]. http://www.mdpi.com/2073-4441/11/1/153 .

Externally hosted supplementary file 1, Oertlé, Emmanuel. (2018, December 5). Poseidon - Decision Support Tool for Water Reuse (Microsoft Excel) and Handbook (Version 1.1.1). Zenodo. http://doi.org/10.5281/zenodo.3341573

Externally hosted supplementary file 2, Oertlé, Emmanuel. (2018). Wastewater Treatment Unit Processes Datasets: Pollutant removal efficiencies, evaluation criteria and cost estimations (Version 1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1247434

Externally hosted supplementary file 3, Oertlé, Emmanuel. (2018). Treatment Trains for Water Reclamation (Dataset) (Version 1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1972627

Externally hosted supplementary file 4, Oertlé, Emmanuel. (2018). Water Quality Classes - Recommended Water Quality Based on Guideline and Typical Wastewater Qualities (Version 1.0.2) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3341570

The Florida Flood Hub for Applied Research and Innovation and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical extreme-precipitation depths fitted to extreme-precipitation data from downscaled climate datasets using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors correspond to the period 2020-59 (centered in 2040) or to the period 2050-89 (centered in the year 2070) as compared to the 1966-2005 historical period. A Microsoft Excel workbook is provided that tabulates best models for each downscaled climate dataset and for all downscaled climate datasets considered together. Best models were identified based on how well the models capture the climatology and interannual variability of four climate extreme indices using the Model Climatology Index (MCI) and the Model Variability Index (MVI) of Srivastava and others (2020). The four indices consist of annual maxima consecutive precipitation for durations of 1, 3, 5, and 7 days compared against the same indices computed based on the PRISM and SFWMD gridded precipitation datasets for five climate regions: climate region 1 in Northwest Florida, 2 in North Florida, 3 in North Central Florida, 4 in South Central Florida, and climate region 5 in South Florida. The PRISM dataset is based on the Parameter-elevation Relationships on Independent Slopes Model interpolation method of Daly and others (2008). The South Florida Water Management District’s (SFWMD) precipitation super-grid is a gridded precipitation dataset developed by modelers at the agency for use in hydrologic modeling (SFWMD, 2005). This dataset is considered by the SFWMD as the best available gridded rainfall dataset for south Florida and was used in addition to PRISM to identify best models in the South Central and South Florida climate regions. Best models were selected based on MCI and MVI evaluated within each individual downscaled dataset. In addition, best models were selected by comparison across datasets and referred to as "ALL DATASETS" hereafter. Due to the small sample size, all models in the using the Weather Research and Forecasting Model (JupiterWRF) dataset were considered as best models.

Abstract

The documentation covers Enterprise Survey panel datasets that were collected in Slovenia in 2009, 2013 and 2019.

The Slovenia ES 2009 was conducted between 2008 and 2009. The Slovenia ES 2013 was conducted between March 2013 and September 2013. Finally, the Slovenia ES 2019 was conducted between December 2018 and November 2019. The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector.

As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms' experiences and enterprises' perception of the environment in which they operate.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must take its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

As it is standard for the ES, the Slovenia ES was based on the following size stratification: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Slovenia ES 2009, 2013, 2019 were selected using stratified random sampling, following the methodology explained in the Sampling Manual for Slovenia 2009 ES and for Slovenia 2013 ES, and in the Sampling Note for 2019 Slovenia ES.

Three levels of stratification were used in this country: industry, establishment size, and oblast (region). The original sample designs with specific information of the industries and regions chosen are included in the attached Excel file (Sampling Report.xls.) for Slovenia 2009 ES. For Slovenia 2013 and 2019 ES, specific information of the industries and regions chosen is described in the "The Slovenia 2013 Enterprise Surveys Data Set" and "The Slovenia 2019 Enterprise Surveys Data Set" reports respectively, Appendix E.

For the Slovenia 2009 ES, industry stratification was designed in the way that follows: the universe was stratified into manufacturing industries, services industries, and one residual (core) sector as defined in the sampling manual. Each industry had a target of 90 interviews. For the manufacturing industries sample sizes were inflated by about 17% to account for potential non-response cases when requesting sensitive financial data and also because of likely attrition in future surveys that would affect the construction of a panel. For the other industries (residuals) sample sizes were inflated by about 12% to account for under sampling in firms in service industries.

For Slovenia 2013 ES, industry stratification was designed in the way that follows: the universe was stratified into one manufacturing industry, and two service industries (retail, and other services).

Finally, for Slovenia 2019 ES, three levels of stratification were used in this country: industry, establishment size, and region. The original sample design with specific information of the industries and regions chosen is described in "The Slovenia 2019 Enterprise Surveys Data Set" report, Appendix C. Industry stratification was done as follows: Manufacturing – combining all the relevant activities (ISIC Rev. 4.0 codes 10-33), Retail (ISIC 47), and Other Services (ISIC 41-43, 45, 46, 49-53, 55, 56, 58, 61, 62, 79, 95).

For Slovenia 2009 and 2013 ES, size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

For Slovenia 2009 ES, regional stratification was defined in 2 regions. These regions are Vzhodna Slovenija and Zahodna Slovenija. The Slovenia sample contains panel data. The wave 1 panel “Investment Climate Private Enterprise Survey implemented in Slovenia” consisted of 223 establishments interviewed in 2005. A total of 57 establishments have been re-interviewed in the 2008 Business Environment and Enterprise Performance Survey.

For Slovenia 2013 ES, regional stratification was defined in 2 regions (city and the surrounding business area) throughout Slovenia.

Finally, for Slovenia 2019 ES, regional stratification was done across two regions: Eastern Slovenia (NUTS code SI03) and Western Slovenia (SI04).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module). Each variation of the questionnaire is identified by the index variable, a0.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond as (-8). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response.

For 2009 and 2013 Slovenia ES, the survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Up to 4 attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals. Further research is needed on survey non-response in the Enterprise Surveys regarding potential introduction of bias.

For 2009, the number of contacted establishments per realized interview was 6.18. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The relatively low ratio of contacted establishments per realized interview (6.18) suggests that the main source of error in estimates in the Slovenia may be selection bias and not frame inaccuracy.

For 2013, the number of realized interviews per contacted establishment was 25%. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 44%.

Finally, for 2019, the number of interviews per contacted establishments was 9.7%. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The share of rejections per contact was 75.2%.

Dataset

This dataset was created by Pinky Verma

Contents

This repository contains the data supporting the manuscript "A Generic Scenario Analysis of End-of-Life Plastic Management: Chemical Additives" (to be) submitted to the Energy and Environmental Science Journal https://pubs.rsc.org/en/journals/journalissues/ee#!recentarticles&adv This repository contains Excel spreadsheets used to calculate material flow throughout the plastics life cycle, with a strong emphasis on chemical additives in the end-of-life stages. Three major scenarios were presented in the manuscript: 1) mechanical recycling (existing recycling infrastructure), 2) implementing chemical recycling to the existing plastics recycling, and 3) extracting chemical additives before the manufacturing stage. Users would primarily modify values on the yellow tab "US 2018 Facts - Sensitivity". Values highlighted in yellow may be changed for sensitivity analysis purposes. Please note that the values shown for MSW generated, recycled, incinerated, landfilled, composted, imported, exported, re-exported, and other categories in this tab were based on 2018 data. Analysis for other years can be made possible with a replicate version of this spreadsheet and the necessary data to replace those of 2018. Most of the tabs, especially those that contain "Stream # - Description", do not require user interaction. They are intermediate calculations that change according to the user inputs. It is available for the user to see so that the calculation/method is transparent. The major results of these individual stream tabs are ultimately compiled into one summary tab. All streams throughout the plastics life cycle, for each respective scenario (1, 2, and 3), are shown in the "US Mat Flow Analysis 2018" tab. For each stream, we accounted the approximate mass of plastics found in MSW, additives that may be present, and non-plastics. Each spreadsheet contains a representative diagram that matches the stream label. This illustration is placed to aid the user with understanding the connection between each stage in the plastics' life cycle. For example, the Scenario 1 spreadsheet uniquely contains Material Flow Analysis Summary, in addition to the LCI. In the "Material Flow Analysis Summary" tab, we represented the input, output, releases, exposures, and greenhouse gas emissions based on the amount of materials inputted into a specific stage in the plastics life cycle. The "Life Cycle Inventory" tab contributes additional calculations to estimate land, air, and water releases. Figures and Data - A gs analysis on eol plastic management This word document contains the raw data used to create all the figures in the main manuscript. The major references used to obtain the data are also included where appropriate.

The spreadsheet includes for each day and for each subject the sleep-related variables measured by sleep logs and wrist actigraphy, as well as compliance data. Sheet 1 has definitions for variable headings in the table and relevant descriptions. For reference, between-subjects variables are reported in Table 2. (XLSX)