Excel spreadsheets by species (4 letter code is abbreviation for genus and species used in study, year 2010 or 2011 is year data collected, SH indicates data for Science Hub, date is date of file preparation). The data in a file are described in a read me file which is the first worksheet in each file. Each row in a species spreadsheet is for one plot (plant). The data themselves are in the data worksheet. One file includes a read me description of the column in the date set for chemical analysis. In this file one row is an herbicide treatment and sample for chemical analysis (if taken). This dataset is associated with the following publication: Olszyk , D., T. Pfleeger, T. Shiroyama, M. Blakely-Smith, E. Lee , and M. Plocher. Plant reproduction is altered by simulated herbicide drift toconstructed plant communities. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 36(10): 2799-2813, (2017).

Dataset

This dataset was created by Aziza Afrin

Contents

Sample data for exercises in Further Adventures in Data Cleaning.

This article describes a free, open-source collection of templates for the popular Excel (2013, and later versions) spreadsheet program. These templates are spreadsheet files that allow easy and intuitive learning and the implementation of practical examples concerning descriptive statistics, random variables, confidence intervals, and hypothesis testing. Although they are designed to be used with Excel, they can also be employed with other free spreadsheet programs (changing some particular formulas). Moreover, we exploit some possibilities of the ActiveX controls of the Excel Developer Menu to perform interactive Gaussian density charts. Finally, it is important to note that they can be often embedded in a web page, so it is not necessary to employ Excel software for their use. These templates have been designed as a useful tool to teach basic statistics and to carry out data analysis even when the students are not familiar with Excel. Additionally, they can be used as a complement to other analytical software packages. They aim to assist students in learning statistics, within an intuitive working environment. Supplementary materials with the Excel templates are available online.

A disorganized toy spreadsheet used for teaching good data organization. Learners are tasked with identifying as many errors as possible before creating a data dictionary and reconstructing the spreadsheet according to best practices.

Dataset

This dataset was created by Pinky Verma

Contents

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.

Separate sheet highlights genes of interest encoding surface markers and transcription factors. Analysis includes means, standard deviation, CoV, and Mac:DC expression ratios. CoV, coefficient of variance; DC, dendritic cell; Mac, macrophage; MPS, mononuclear phagocyte system. (XLSX)

Dataset

This dataset was created by Ayo Apata

Contents

Sample credentialing data.

This dataset contains the valuation template the researcher can use to retrieve real-time Excel stock price and stock price in Google Sheets. The dataset is provided by Finsheet, the leading financial data provider for spreadsheet users. To get more financial data, visit the website and explore their function. For instance, if a researcher would like to get the last 30 years of income statement for Meta Platform Inc, the syntax would be =FS_EquityFullFinancials("FB", "ic", "FY", 30) In addition, this syntax will return the latest stock price for Caterpillar Inc right in your spreadsheet. =FS_Latest("CAT") If you need assistance with any of the function, feel free to reach out to their customer support team. To get starter, install their Excel and Google Sheets add-on.

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

Example of a filtered Microsoft Excel spreadsheet for TaAMY2 single null mutant detection (selected data).

This Excel template is an example taken from the GEO web site (http://www.ncbi.nlm.nih.gov/geo/info/spreadsheet.html#GAtemplates) which has been modified to conform to the SysMO JERM (Just Enough Results Model). Using templates helps with searching and comparing data as well as making it easier to submit data to public repositories for publications.

Complete annotations for the tabular data are presented below. Tab Fig 1: (A) The heatmap data of G protein family members in the hippocampal tissue of 6-month-old Wildtype (n = 6) and 5xFAD (n = 6) mice; (B) The heatmap data of G protein family members in the cortical tissue of 6-month-old Wildtype (n = 6) and 5xFAD (n = 6) mice; (C) The data in the overlapping part of the Venn diagram (132 elements); (D) The data information for creating volcano plot; (E) The data information for creating heatmap of GPCR-related DEGs; (F) Expression of Gnb5 in the large sample dataset GSE44772; Control, n = 303; AD, n = 387; (H) Statistical analysis of Gnb5 protein levels from panel G; Wildtype, n = 4; 5xFAD, n = 4; (J) Statistical analysis of Gnb5 protein levels from panel I; Wildtype, n = 4; 5xFAD, n = 4; (L) Quantitative analysis of Gnb5 fluorescence intensity in 5xFAD and Wildtype groups; Wildtype, n = 4; 5xFAD, n = 4. Tab Fig 2: (D) qPCR data of Gnb5 knockout in hippocampal tissue; Gnb5F/F, n = 6; Gnb5-CCKO, n = 6; (E–I, L–N) Animal behavioral tests in mice, Gnb5F/F, n = 22; Gnb5-CCKO, n = 16; (E) Total distance traveled in the open field experiment; (F) Training curve in the Morris water maze (MWM); (F-day6) Data from the sixth day of MWM training; (G) Percentage of time spent by the mouse in the target quadrant in the MWM; (H) Statistical analysis of the number of times the mouse traverses the target quadrant in the MWM; (I) Latency to first reach the target quadrant in the MWM; (L) Baseline freezing percentage of mice in an identical testing context; (M) Percentage of freezing time of mice during the Context phase; (N) Percentage of freezing time of mice during the Cue phase. Tab Fig 3: (D–F, H) MWM tests in mice; Wildtype+AAV-GFP, n = 20; Wildtype+AAV-Gnb5-GFP, n = 23; 5xFAD + AAV-GFP, n = 23; 5xFAD + AAV-Gnb5-GFP, n = 26; (D) Training curve in the MWM; (D-day6) Data from the sixth day of MWM training; (E) Percentage of time spent in the target quadrant in the MWM; (F) Statistical analysis of the number of entries in the target quadrant in the MWM; (H) Movement speed of mice in the MWM; (I–K) The contextual fear conditioning test in mice; 5xFAD + AAV-GFP, n = 23; 5xFAD + AAV-Gnb5-GFP, n = 26; (I) Baseline freezing percentage of mice in an identical testing context; (J) Percentage of freezing time of mice during the Context phase; (K) Percentage of freezing time of mice during the Cue phase; (L) Total distance traveled in the open field test; (M) Percentage of time spent in the center area during the open field test. Tab Fig 4: (B, C) Quantification of Aβ plaques in the hippocampus sections from Wildtype and 5xFAD mice injected with either AAV-Gnb5 or AAV-GFP; Wildtype+AAV-GFP, n = 4; Wildtype+AAV-Gnb5-GFP, n = 4; 5xFAD + AAV-GFP, n = 4; 5xFAD + AAV-Gnb5-GFP, n = 4; (B) Quantification of Aβ plaques number; (C) Quantification of Aβ plaques size; (F, G) Quantification of Aβ pylaques from indicted mice lines; WT&Gnb5F/F&CamKIIa-CreERT+Vehicle, n = 4; 5xFAD&Gnb5F/F&CamKIIa-CreERT+Vehicle, n = 4; 5xFAD&Gnb5F/F&CamKIIa-CreERT+Tamoxifen, n = 4; (F) Quantification of Aβ plaque size; (G) Quantification of Aβ plaque number. Tab Fig 5: (B) Overexpression of Gnb5-AAV in 5xFAD mice affects the expression of proteins related to APP cleavage (BACE1, β-CTF, Nicastrin and APP); Statistical analysis of protein levels; n = 4, respectively; (D) Tamoxifen-induced Gnb5 knockdown in 5xFAD mice affects APP-cleaving proteins; Statistical analysis of protein levels; n = 4, respectively; (F) Gnb5-CCKO mice show altered expression of APP-cleaving proteins; Statistical analysis of protein levels; n = 6, respectively. Tab Fig 7: (C, D) Quantification of Aβ plaques in the overexpressed full-length Gnb5, truncated fragments, and mutant truncated fragment AAV in 5xFAD mice; n = 4, respectively; (C) Quantification of Aβ plaques size; (D) Quantification of Aβ plaques number; (F) Effect of overexpressing full-length Gnb5, truncated fragments, and mutant truncated fragment viruses on the expression of proteins related to APP cleavage process in 5xFAD; Statistical analysis of protein levels; n = 3, respectively. (XLSX)

This dataset was created and deposited onto the University of Sheffield Online Research Data repository (ORDA) on 23-Jun-2023 by Dr. Matthew S. Hanchard, Research Associate at the University of Sheffield iHuman Institute.

The dataset forms part of three outputs from a project titled ‘Fostering cultures of open qualitative research’ which ran from January 2023 to June 2023:

· Fostering cultures of open qualitative research: Dataset 1 – Survey Responses · Fostering cultures of open qualitative research: Dataset 2 – Interview Transcripts · Fostering cultures of open qualitative research: Dataset 3 – Coding Book

The project was funded with £13,913.85 Research England monies held internally by the University of Sheffield - as part of their ‘Enhancing Research Cultures’ scheme 2022-2023.

The dataset aligns with ethical approval granted by the University of Sheffield School of Sociological Studies Research Ethics Committee (ref: 051118) on 23-Jan-2021.This includes due concern for participant anonymity and data management.

ORDA has full permission to store this dataset and to make it open access for public re-use on the basis that no commercial gain will be made form reuse. It has been deposited under a CC-BY-NC license.

This dataset comprises one spreadsheet with N=91 anonymised survey responses .xslx format. It includes all responses to the project survey which used Google Forms between 06-Feb-2023 and 30-May-2023. The spreadsheet can be opened with Microsoft Excel, Google Sheet, or open-source equivalents.

The survey responses include a random sample of researchers worldwide undertaking qualitative, mixed-methods, or multi-modal research.

The recruitment of respondents was initially purposive, aiming to gather responses from qualitative researchers at research-intensive (targetted Russell Group) Universities. This involved speculative emails and a call for participant on the University of Sheffield ‘Qualitative Open Research Network’ mailing list. As result, the responses include a snowball sample of scholars from elsewhere.

The spreadsheet has two tabs/sheets: one labelled ‘SurveyResponses’ contains the anonymised and tidied set of survey responses; the other, labelled ‘VariableMapping’, sets out each field/column in the ‘SurveyResponses’ tab/sheet against the original survey questions and responses it relates to.

The survey responses tab/sheet includes a field/column labelled ‘RespondentID’ (using randomly generated 16-digit alphanumeric keys) which can be used to connect survey responses to interview participants in the accompanying ‘Fostering cultures of open qualitative research: Dataset 2 – Interview transcripts’ files.

A set of survey questions gathering eligibility criteria detail and consent are not listed with in this dataset, as below. All responses provide in the dataset gained a ‘Yes’ response to all the below questions (with the exception of one question, marked with an asterisk (*) below):

· I am aged 18 or over · I have read the information and consent statement and above. · I understand how to ask questions and/or raise a query or concern about the survey. · I agree to take part in the research and for my responses to be part of an open access dataset. These will be anonymised unless I specifically ask to be named. · I understand that my participation does not create a legally binding agreement or employment relationship with the University of Sheffield · I understand that I can withdraw from the research at any time. · I assign the copyright I hold in materials generated as part of this project to The University of Sheffield. · * I am happy to be contacted after the survey to take part in an interview.

The project was undertaken by two staff: Co-investigator: Dr. Itzel San Roman Pineda ORCiD ID: 0000-0002-3785-8057 i.sanromanpineda@sheffield.ac.uk

Postdoctoral Research Assistant Principal Investigator (corresponding dataset author): Dr. Matthew Hanchard ORCiD ID: 0000-0003-2460-8638 m.s.hanchard@sheffield.ac.uk Research Associate iHuman Institute, Social Research Institutes, Faculty of Social Science

An Excel spreadsheet containing the full dataset, showing its sub-sampling

This illustrative example demonstrates the applicability of TabbyXL toolset (https://github.com/tabbydoc/tabbyxl) for extracting data items and their relationships from spreadsheet tables.

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.

PROJECT OBJECTIVE

We are a part of XYZ Co Pvt Ltd company who is in the business of organizing the sports events at international level. Countries nominate sportsmen from different departments and our team has been given the responsibility to systematize the membership roster and generate different reports as per business requirements.

Questions (KPIs)

TASK 1: STANDARDIZING THE DATASET

• Populate the FULLNAME consisting of the following fields ONLY, in the prescribed format: PREFIX FIRSTNAME LASTNAME.{Note: All UPPERCASE)
• Get the COUNTRY NAME to which these sportsmen belong to. Make use of LOCATION sheet to get the required data
• Populate the LANGUAGE_!poken by the sportsmen. Make use of LOCTION sheet to get the required data
• Generate the EMAIL ADDRESS for those members, who speak English, in the prescribed format :lastname.firstnamel@xyz .org {Note: All lowercase) and for all other members, format should be lastname.firstname@xyz.com (Note: All lowercase)
• Populate the SPORT LOCATION of the sport played by each player. Make use of SPORT sheet to get the required data

TASK 2: DATA FORMATING

• Display MEMBER IDas always 3 digit number {Note: 001,002 ...,D2D,..etc)
• Format the BIRTHDATE as dd mmm'yyyy (Prescribed format example: 09 May' 1986)
• Display the units for the WEIGHT column (Prescribed format example: 80 kg)
• Format the SALARY to show the data In thousands. If SALARY is less than 100,000 then display data with 2 decimal places else display data with one decimal place. In both cases units should be thousands (k) e.g. 87670 -> 87.67 k and 12 250 -> 123.2 k

TASK 3: SUMMARIZE DATA - PIVOT TABLE (Use SPORTSMEN worksheet after attempting TASK 1) • Create a PIVOT table in the worksheet ANALYSIS, starting at cell B3,with the following details:

• In COLUMNS; Group : GENDER.
• In ROWS; Group : COUNTRY (Note: use COUNTRY NAMES).
• In VALUES; calculate the count of candidates from each COUNTRY and GENDER type, Remove GRAND TOTALs.

TASK 4: SUMMARIZE DATA - EXCEL FUNCTIONS (Use SPORTSMEN worksheet after attempting TASK 1)

• Create a SUMMARY table in the worksheet ANALYSIS,starting at cell G4, with the following details:

• Starting from range RANGE H4; get the distinct GENDER. Use remove duplicates option and transpose the data.
• Starting from range RANGE GS; get the distinct COUNTRY (Note: use COUNTRY NAMES).
• In the cross table,get the count of candidates from each COUNTRY and GENDER type.

TASK 5: GENERATE REPORT - PIVOT TABLE (Use SPORTSMEN worksheet after attempting TASK 1)

• Create a PIVOT table report in the worksheet REPORT, starting at cell A3, with the following information:

• Change the report layout to TABULAR form.
• Remove expand and collapse buttons.
• Remove GRAND TOTALs.
• Allow user to filter the data by SPORT LOCATION.

Process

• Verify data for any missing values and anomalies, and sort out the same.
• Made sure data is consistent and clean with respect to data type, data format and values used.
• Created pivot tables according to the questions asked.

GSA, the nation's largest public real estate organization, provides workspace for over one million federal workers. These employees, along with government property, are housed in space owned by the federal government and in leased properties including buildings, land, antenna sites, etc. across the country.