Dataset

This dataset was created by Sanjana Murthy

Released under CC BY-NC-SA 4.0

Contents

This data contains Sort & Filter functions

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

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.

General information This item containst data sets for Schlegel et al, Nano Letters, 2023. DOI: https://doi.org/10.1021/acs.nanolett.2c04884 It contains confocal images, lattice light sheet images, flow cytometry data, compiled data as excle sheet and raw figure files. Abstract Speed is key during infectious disease outbreaks. It is essential, for example, to identify critical host binding factors to pathogens as fast as possible. The complexity of host plasma membrane is often a limiting factor hindering fast and accurate determination of host binding factors as well as high-throughput screening for neutralizing antimicrobial drug targets. Here, we describe a multiparametric and high-throughput platform tackling this bottleneck and enabling fast screens for host binding factors as well as new antiviral drug targets. The sensitivity and robustness of our platform were validated by blocking SARS-CoV-2 particles with nanobodies and IgGs from human serum samples. Data usage Researchers are welcome to use the data contained in the dataset for any projects. Please cite this item upon use or when published. We encourage reuse using the same CC BY 4.0 License. Data Content Excel files for graphs Microscopy Images Flow cytometry data Software to open files: .csv: Fiji (https://imagej.net/software/fiji/downloads) or Microsoft Excel .xlsx: Microsoft Excel .tif, .lsm: Fiji (https://imagej.net/software/fiji/downloads) .pzfx: GraphPad Prism .svg: Inkscape (https://inkscape.org/) .fcs: FCS Express .pdf: AdobeAcrobat or Mozilla Firefox .ijm: Fiji (https://imagej.net/software/fiji/downloads)

This dataset was created to evaluate students’ performance in the most recent school examination. The goal is to help the school administration understand overall academic achievement, examine score distribution across grades, and identify student groups that may need additional academic support to improve learning outcomes.

The dataset provides detailed student result records, including subjects, scores, grades, and performance categories. It serves as a practical resource for educators, analysts, and data learners who wish to explore educational data using Excel or data analytics tools.

Tool Used: Microsoft Excel Spreadsheet

Data Frame Process: This analysis followed the Google Data Analytics data-phase approach, which involves:

Ask: Define the key questions and objectives

Prepare: Organize and clean the student result data

Process: Perform calculations and structure the data in Excel

Analyze: Evaluate performance trends and identify weak areas

Share: Present findings using tables, charts, and summaries

Act: Provide actionable recommendations to improve student outcomes

This is the raw data supporting the findings (both main text and supplementary) for our manuscript "Staphylococcus aureus cell wall structure and dynamics during host-pathogen interaction". Each excel file contains the raw data for each figure. Murine work was carried out according to UK law in the Animals (Scientific Procedures) Act 1986, under Project License P3BFD6DB9 (Staphylococcus aureus and other pathogens, pathogenesis to therapy, University of Sheffield Review Board).

The Excel-based Pizza Sales Analysis Project is designed to analyze and optimize the sales performance of a pizza restaurant. Utilizing Microsoft Excel's powerful tools and functions, this project aims to provide insights into various aspects of the business, including sales trends, customer preferences, and profitability.

Key Features:

Sales Data Management:

Organize and manage sales data efficiently using Excel spreadsheets. Input sales data such as date, time, order details, customer information, and revenue. Data Analysis:

Perform data analysis to identify trends, patterns, and correlations in pizza sales. Utilize Excel functions such as SUM, AVERAGE, COUNT, and PivotTables for data aggregation and analysis. Analyze sales performance over different time periods (daily, weekly, monthly) to spot seasonal trends and peak hours. Customer Segmentation:

Segment customers based on their purchasing behavior, frequency of orders, and preferences. Identify loyal customers and high-value segments to tailor marketing strategies and promotions effectively. Product Analysis:

Analyze the performance of different pizza flavors, sizes, and toppings. Determine the bestselling products and identify opportunities for introducing new offerings or optimizing existing ones. Sales Forecasting:

Use historical sales data to forecast future sales, enabling better inventory management and resource allocation. Implement forecasting models such as moving averages or exponential smoothing to predict demand accurately. Profitability Analysis:

Calculate the profitability of individual pizza orders, considering factors such as ingredients cost, labor, and overhead expenses. Identify low-margin products or inefficient processes to improve overall profitability. Visualization and Reporting:

Create visually appealing charts, graphs, and dashboards to present sales insights and performance metrics. Generate comprehensive reports summarizing key findings, actionable recommendations, and performance metrics. Benefits:

Improved decision-making: Data-driven insights enable informed decisions regarding menu offerings, pricing strategies, and marketing initiatives. Enhanced efficiency: Streamlined sales data management and analysis processes save time and resources. Increased profitability: By identifying and addressing sales inefficiencies and optimizing product offerings, the project aims to boost profitability. Better customer satisfaction: Understanding customer preferences allows for personalized marketing and improved customer service. The Excel-Based Pizza Sales Analysis Project empowers pizza restaurant owners and managers to optimize sales performance, enhance customer satisfaction, and drive business growth through data-driven decision-making.

Purposive sampling was the method we chose to collect the data. We obtained information from two after-school coaching programs that voluntarily provided their online learning data to us in 2020 during the pandemic. Batches of 45 and 75 students each were used to organize the data, which were then combined to create a single dataset with 399 entries. Two phases of collection took place: on January 17, 2023, and on February 12, 2023. The initial data recording was done using Google Learning Management System's Google Classroom. The data was then exported to local storage by the classroom faculties and then passed onto the researchers. Excel was used to organize the data, with rows representing individual students and columns representing different topics. The dataset, which consists of four mock tests and sixteen physics topics, was gathered from grade 10 physics instructors and students. Every pupil was given a unique ID to protect their privacy, resulting in 399 distinct entries overall. The coaching institution standardized the dataset to score it out of 100 for consistency. It is important to note that for students who did not take the majority of the exams, the institutions did not gather or transmit missing data. The dataset displays a spread with a standard deviation of 20.5 and an average score of 69.547.

This provides a link to the Washington Secretary of State's Corporations Search tool. The Corporations Data Extract feature is no longer available. Customers needing a list of multiple businesses can use our advanced search to create a list of businesses under specific parameters. You can export this information to an Excel spreadsheet to sort and search more extensively. Below are the steps to perform this type of search. The more specified parameter searches provide narrower search results. Please visit our Corporations and Charities Filing System by following this link https://ccfs.sos.wa.gov/ Scroll down to the “Corporation Search” section and click the “Advanced Search” button on the right. Under the first section, specify how you would like the business name searched. Only use this for single business lookups unless all the businesses you are searching have a common name (use the “contains” selection). Select the appropriate business type from the dropdown if you are looking for a list of a specific business type. For a list of a particular business type with a specific status, select that status under “Business Status.” You can also search by expiration date in this section. Under the “Date of Incorporation/Formation/Registration,” you can search by start or end date. Under the “Registered Agent/Governor Search” section, you can search all businesses with the same registered agent on record or governor listed. Once you have made all your search selections, click the green “Search” button at the bottom right of the page. A list will populate; scroll to the bottom and select the green Excel document icon with CSV. An Excel document should automatically download. If you have popups blocked, please unblock our site, and try again. Once you have opened the downloaded Excel spreadsheet, you can adjust the width of each column and sort the data using the data tab. You can also search by pressing CTRL+F on a Windows keyboard.

These Excel workbooks organize Arctic and Antarctic sea ice data in spreadsheets for ease of analysis. See the “Documentation” tab in each workbook for more description, and links to more information about the workbooks. See included PDF file for more detailed documentation.

The Ontario government, generates and maintains thousands of datasets. Since 2012, we have shared data with Ontarians via a data catalogue. Open data is data that is shared with the public. Click here to learn more about open data and why Ontario releases it. Ontario’s Open Data Directive states that all data must be open, unless there is good reason for it to remain confidential. Ontario’s Chief Digital and Data Officer also has the authority to make certain datasets available publicly. Datasets listed in the catalogue that are not open will have one of the following labels: If you want to use data you find in the catalogue, that data must have a licence – a set of rules that describes how you can use it. A licence: Most of the data available in the catalogue is released under Ontario’s Open Government Licence. However, each dataset may be shared with the public under other kinds of licences or no licence at all. If a dataset doesn’t have a licence, you don’t have the right to use the data. If you have questions about how you can use a specific dataset, please contact us. The Ontario Data Catalogue endeavors to publish open data in a machine readable format. For machine readable datasets, you can simply retrieve the file you need using the file URL. The Ontario Data Catalogue is built on CKAN, which means the catalogue has the following features you can use when building applications. APIs (Application programming interfaces) let software applications communicate directly with each other. If you are using the catalogue in a software application, you might want to extract data from the catalogue through the catalogue API. Note: All Datastore API requests to the Ontario Data Catalogue must be made server-side. The catalogue's collection of dataset metadata (and dataset files) is searchable through the CKAN API. The Ontario Data Catalogue has more than just CKAN's documented search fields. You can also search these custom fields. You can also use the CKAN API to retrieve metadata about a particular dataset and check for updated files. Read the complete documentation for CKAN's API. Some of the open data in the Ontario Data Catalogue is available through the Datastore API. You can also search and access the machine-readable open data that is available in the catalogue. How to use the API feature: Read the complete documentation for CKAN's Datastore API. The Ontario Data Catalogue contains a record for each dataset that the Government of Ontario possesses. Some of these datasets will be available to you as open data. Others will not be available to you. This is because the Government of Ontario is unable to share data that would break the law or put someone's safety at risk. You can search for a dataset with a word that might describe a dataset or topic. Use words like “taxes” or “hospital locations” to discover what datasets the catalogue contains. You can search for a dataset from 3 spots on the catalogue: the homepage, the dataset search page, or the menu bar available across the catalogue. On the dataset search page, you can also filter your search results. You can select filters on the left hand side of the page to limit your search for datasets with your favourite file format, datasets that are updated weekly, datasets released by a particular organization, or datasets that are released under a specific licence. Go to the dataset search page to see the filters that are available to make your search easier. You can also do a quick search by selecting one of the catalogue’s categories on the homepage. These categories can help you see the types of data we have on key topic areas. When you find the dataset you are looking for, click on it to go to the dataset record. Each dataset record will tell you whether the data is available, and, if so, tell you about the data available. An open dataset might contain several data files. 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You don’t have to provide and personal information – just subscribe to our feeds using any feed reader you like using the corresponding notification web addresses. Copy those addresses and paste them into your reader. Your feed reader will let you know when the catalogue has been updated. The catalogue provides open data in several file formats (e.g., spreadsheets, geospatial data, etc). Learn about each format and how you can access and use the data each file contains. A file that has a list of items and values separated by commas without formatting (e.g. colours, italics, etc.) or extra visual features. This format provides just the data that you would display in a table. XLSX (Excel) files may be converted to CSV so they can be opened in a text editor. How to access the data: Open with any spreadsheet software application (e.g., Open Office Calc, Microsoft Excel) or text editor. Note: This format is considered machine-readable, it can be easily processed and used by a computer. Files that have visual formatting (e.g. bolded headers and colour-coded rows) can be hard for machines to understand, these elements make a file more human-readable and less machine-readable. A file that provides information without formatted text or extra visual features that may not follow a pattern of separated values like a CSV. How to access the data: Open with any word processor or text editor available on your device (e.g., Microsoft Word, Notepad). A spreadsheet file that may also include charts, graphs, and formatting. How to access the data: Open with a spreadsheet software application that supports this format (e.g., Open Office Calc, Microsoft Excel). Data can be converted to a CSV for a non-proprietary format of the same data without formatted text or extra visual features. A shapefile provides geographic information that can be used to create a map or perform geospatial analysis based on location, points/lines and other data about the shape and features of the area. It includes required files (.shp, .shx, .dbt) and might include corresponding files (e.g., .prj). How to access the data: Open with a geographic information system (GIS) software program (e.g., QGIS). A package of files and folders. The package can contain any number of different file types. How to access the data: Open with an unzipping software application (e.g., WinZIP, 7Zip). Note: If a ZIP file contains .shp, .shx, and .dbt file types, it is an ArcGIS ZIP: a package of shapefiles which provide information to create maps or perform geospatial analysis that can be opened with ArcGIS (a geographic information system software program). A file that provides information related to a geographic area (e.g., phone number, address, average rainfall, number of owl sightings in 2011 etc.) and its geospatial location (i.e., points/lines). How to access the data: Open using a GIS software application to create a map or do geospatial analysis. It can also be opened with a text editor to view raw information. Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A text-based format for sharing data in a machine-readable way that can store data with more unconventional structures such as complex lists. How to access the data: Open with any text editor (e.g., Notepad) or access through a browser. Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A text-based format to store and organize data in a machine-readable way that can store data with more unconventional structures (not just data organized in tables). How to access the data: Open with any text editor (e.g., Notepad). Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A file that provides information related to an area (e.g., phone number, address, average rainfall, number of owl sightings in 2011 etc.) and its geospatial location (i.e., points/lines). How to access the data: Open with a geospatial software application that supports the KML format (e.g., Google Earth). Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. This format contains files with data from tables used for statistical analysis and data visualization of Statistics Canada census data. How to access the data: Open with the Beyond 20/20 application. A database which links and combines data from different files or applications (including HTML, XML, Excel, etc.). The database file can be converted to a CSV/TXT to make the data machine-readable, but human-readable formatting will be lost. How to access the data: Open with Microsoft Office Access (a database management system used to develop application software). A file that keeps the original layout and

This repository was created to store, organize, and share data collected for the Eastern Kentucky Project, focusing on hydrological research in the region. It serves as a centralized platform to manage data efficiently and facilitate collaboration among researchers and stakeholders involved in the project.

The repository primarily contains data from level loggers, which are crucial for monitoring and recording water levels, temperature, and other hydrological parameters over time. The collected data has been carefully extracted, processed, and stored in Excel files to ensure compatibility with various analysis tools. This structured format enables easy access and seamless integration into research workflows.

In addition to providing secure storage, the repository is designed to support efficient data sharing, transparency, and interdisciplinary collaboration. By offering a well-organized dataset, it enables researchers to analyze and build upon existing data, promoting high-quality research outputs. The repository ultimately aims to advance understanding and inform decision-making in water resource management for Eastern Kentucky.

Following a request from the European Commission, in 2018 EFSA released a renovated database of host plant species of Xylella spp. (including both species X. fastidiosa and X. taiwanensis) together with a scientific report (EFSA, 2018). EFSA was tasked to maintain and update this database periodically. In May 2021, EFSA released the fourth update of the Xylella spp. host plant database (VERSION 4) with information retrieved from literature search up to December 2020, Europhyt outbreak notifications up to 18 March 2021, and communications of research groups and national authorities (EFSA, 2021). The protocol applied for the extensive literature review, data collection and reporting, as well as results and lists of host plants are described in detail in the related scientific report (EFSA, 2021). The overall number of Xylella spp. host plants determined with at least two different detection methods or positive with one method (between: sequencing, pure culture isolation) reaches now 385 plant species, 179 genera and 67 families (category A – see section 2.4.2 of EFSA (2021)). Such numbers rise to 638 plant species, 289 genera and 87 families if considered regardless of the detection method applied (category E, see section 2.4.2 of EFSA (2021). The Excel files here attached represent the VERSION 4 of the Xylella spp. host plants database. For a detailed description of the information included in the database, please consult the related scientific report (EFSA, 2021). The Excel file “Xylella spp. host plants database – VERSION 4” contains several sheets: the LEGENDA (with extensive description of each table), the full detailed raw data of the Xylella spp. host plant database (sheet “observation”) and several examples of data extraction. Additional Excel files contain the lists of host plant species of X. fastidiosa (subsp. unknown (i.e. not reported), fastidiosa, multiplex, pauca, morus, sandyi, tashke, fastidiosa/sandyi) and X. taiwanensis infected naturally, artificially and in not specified conditions, and according to different categories (A,B,C,D,E – see section 2.4.2 of EFSA (2021)). The Excel file “new_host_plant_species_v4” contain the list of new host plant species added to the database in this fourth update. Question number: EFSA-Q-2017-00221 Correspondence: alpha@efsa.europa.eu Bibliography: EFSA (European Food Safety Authority), 2018. Scientific report on the update of the Xylella spp. host plant database. EFSA Journal 2018;16(9):5408, 87 pp. https://doi.org/10.2903/j.efsa.2018.5408 EFSA (European Food Safety Authority), Delbianco A, Gibin D, Pasinato L and Morelli M, 2021. Scientific report on the update of the Xylella spp. host plant database – systematic literature search up to 31 December 2020. EFSA Journal 2021;19(6):6674, 70 pp. https://doi.org/10.2903/j.efsa.2021.6674

This is a compilation of data collected from the official VNL website (link can be found here).

The data on Volleyball World was too separated and unusable, with them categorizing data by Attackers, Blockers, Setters, etc. This makes the data inflexible and hard to use for statistical purposes. I manually copy and pasted the data into an Excel sheet, where I used some functions to clean and organize the data. Some columns found on the official website (like efficiency or success rate) were dropped to keep the dataset simple and generalizable.

Please see column descriptions below: - Name: Name of Player - Team: First three letters of the team they represent - Attack Points: Points scored off spikes and tips - Attack Errors: Points lost on spikes or tips - Attack Attempts: Includes Attack Points, Attack Errors, and spikes/tips that did not lead to points for either team - Block Points: Points scored off of blocks - Block Errors: Points lost from blocks - Rebounds: Blocks that did not lead to points for either team - Serve Points: Services aces directly led to a point - Serve Errors: Points lost directly from serves - Serve Attempts: Serves that did not directly lead to points for either team - Successful Sets: Sets that led to a successful attack - Set Errors: Points lost directly from a set - Set Attempts: Sets that did not directly lead to a point for either team - Spike Digs: Number of tips or spikes that a player dug - Dig Errors: An attempt to dig a tip or spike that lost the defending team a point - Successful Receives: A near-perfect or perfect receive, resulting in an easy-to-set ball for the setter - Receive Errors: An attempt at a serve receive that lost the defending team a point - Receive Attempts: A receive of a serve that got the ball up in a non-ideal spot

We organize all the raw data from our research into a single Excel file titled ‘Source Data’. This file includes raw data of Figs. 1–4, Supplementary Fig. 1, Supplementary Fig. 2, Supplementary Fig. 5, Supplementary Figs. 7–13 and Supplementary Fig. 15–19.

Case Study 1- Bike Sharing Introduction: In 2016, Cyclistic launched a successful bike-share offering. Since then, the program has grown to a fleet of 5,824 bicycles that are geotracked and locked into a network of 692 stations across Chicago. The bikes can be unlocked from one station and returned to any other station in the system anytime. There are two types of members are sharing bike differently! 1.) Annual members- who bought annual membership. 2.) Casual members- who bought or buying single-ride passes, full-day passes.

Phase_1- Ask- 1. Identify the business task- • How do annual members and casual riders use Cyclistic bikes differently? • Why would casual riders buy Cyclistic annual memberships? • How can Cyclistic use digital media to influence casual riders to become members? 2. Consider key stakeholders- Lily Moreno: The director of marketing and manager, Cyclistic marketing analytics team, Cyclistic executive team.

Phase_2- Prepare--
I downloaded and store it in my excel sheet, I am using only one month (April_2020) data, and using excel for solving task, I am also sorting and filtering my data according to requirement. I downloaded data from public source and it’s fully reliable, unbiased. Data is also, complete, consistent and accurate. Phase_3- Process— • I downloaded 202004-divvy-tripdata.cvs data and I unzip the file and converted into .xls file, here I am using only April data because this case study is my first case study and only for my learning, so I want to keep it simple. I am using excel this time because I am more comfortable with excel then other tools. I also want to perform good analysis and don’t want to lost in multiple sheets & large dataset, in initial stage.

• I Checked the data errors, and corrected some errors, I also did some calculation in my sheet, and try to clean data, so I can use sheet appropriately, Phase_4- analyze— I organize my data, performed sorting and filtering multiple time as I needed, did some calculation, add few pivots table and try to analyze data properly, also try to Identify trends and relationships.

Phase_5- Share— • After completing my analysis, I used some charts to present my findings. First, I found Total count of ride is 16383 and annual members took 11552 count of ride what is 71% of total ride, and casual riders took only 29% of ride which is 4831.

• I also found that casual riders using ride for some times but members are taking ride anytime no matter if they need bike for long time or short time, they are taking ride without any second thought, because after buying annual pass they no need to pay (any extra money or) every time.

• Clark St & Elm St is a most bike rented point, people took 180 bikes from this station, and 132 are the annual member from that. Also, I found other station where we need more bikes. Likewise, we also can find station name where most people end their ride, so they have plenty space for bikes. Phase_6- Act— Feeling happy to share my finding with you, feeling little confident after completing my first case study.

AI Generated Summary: The Ontario Data Catalogue is a data portal providing access to open datasets generated and maintained by the Ontario government. It allows users to search, access, visualize, and download data in various machine-readable formats, often through APIs, while also indicating licensing terms and data update frequencies. The catalogue also provides tools for data visualization and notifications for dataset updates. About: The Ontario government generates and maintains thousands of datasets. Since 2012, we have shared data with Ontarians via a data catalogue. Open data is data that is shared with the public. Click here to learn more about open data and why Ontario releases it. Ontario’s Digital and Data Directive states that all data must be open, unless there is good reason for it to remain confidential. Ontario’s Chief Digital and Data Officer also has the authority to make certain datasets available publicly. Datasets listed in the catalogue that are not open will have one of the following labels: If you want to use data you find in the catalogue, that data must have a licence – a set of rules that describes how you can use it. A licence: Most of the data available in the catalogue is released under Ontario’s Open Government Licence. However, each dataset may be shared with the public under other kinds of licences or no licence at all. If a dataset doesn’t have a licence, you don’t have the right to use the data. If you have questions about how you can use a specific dataset, please contact us. The Ontario Data Catalogue endeavors to publish open data in a machine readable format. For machine readable datasets, you can simply retrieve the file you need using the file URL. The Ontario Data Catalogue is built on CKAN, which means the catalogue has the following features you can use when building applications. APIs (Application programming interfaces) let software applications communicate directly with each other. If you are using the catalogue in a software application, you might want to extract data from the catalogue through the catalogue API. Note: All Datastore API requests to the Ontario Data Catalogue must be made server-side. The catalogue's collection of dataset metadata (and dataset files) is searchable through the CKAN API. The Ontario Data Catalogue has more than just CKAN's documented search fields. You can also search these custom fields. You can also use the CKAN API to retrieve metadata about a particular dataset and check for updated files. Read the complete documentation for CKAN's API. Some of the open data in the Ontario Data Catalogue is available through the Datastore API. You can also search and access the machine-readable open data that is available in the catalogue. How to use the API feature: Read the complete documentation for CKAN's Datastore API. The Ontario Data Catalogue contains a record for each dataset that the Government of Ontario possesses. Some of these datasets will be available to you as open data. Others will not be available to you. This is because the Government of Ontario is unable to share data that would break the law or put someone's safety at risk. You can search for a dataset with a word that might describe a dataset or topic. Use words like “taxes” or “hospital locations” to discover what datasets the catalogue contains. You can search for a dataset from 3 spots on the catalogue: the homepage, the dataset search page, or the menu bar available across the catalogue. On the dataset search page, you can also filter your search results. You can select filters on the left hand side of the page to limit your search for datasets with your favourite file format, datasets that are updated weekly, datasets released by a particular ministry, or datasets that are released under a specific licence. Go to the dataset search page to see the filters that are available to make your search easier. You can also do a quick search by selecting one of the catalogue’s categories on the homepage. These categories can help you see the types of data we have on key topic areas. When you find the dataset you are looking for, click on it to go to the dataset record. Each dataset record will tell you whether the data is available, and, if so, tell you about the data available. An open dataset might contain several data files. These files might represent different periods of time, different sub-sets of the dataset, different regions, language translations, or other breakdowns. You can select a file and either download it or preview it. Make sure to read the licence agreement to make sure you have permission to use it the way you want. A non-open dataset may be not available for many reasons. Read more about non-open data. Read more about restricted data. Data that is non-open may still be subject to freedom of information requests. The catalogue has tools that enable all users to visualize the data in the catalogue without leaving the catalogue – no additional software needed. Get automatic notifications when datasets are updated. You can choose to get notifications for individual datasets, an organization’s datasets or the full catalogue. You don’t have to provide and personal information – just subscribe to our feeds using any feed reader you like using the corresponding notification web addresses. Copy those addresses and paste them into your reader. Your feed reader will let you know when the catalogue has been updated. The catalogue provides open data in several file formats (e.g., spreadsheets, geospatial data, etc). Learn about each format and how you can access and use the data each file contains. A file that has a list of items and values separated by commas without formatting (e.g. colours, italics, etc.) or extra visual features. This format provides just the data that you would display in a table. XLSX (Excel) files may be converted to CSV so they can be opened in a text editor. How to access the data: Open with any spreadsheet software application (e.g., Open Office Calc, Microsoft Excel) or text editor. Note: This format is considered machine-readable, it can be easily processed and used by a computer. Files that have visual formatting (e.g. bolded headers and colour-coded rows) can be hard for machines to understand, these elements make a file more human-readable and less machine-readable. A file that provides information without formatted text or extra visual features that may not follow a pattern of separated values like a CSV. How to access the data: Open with any word processor or text editor available on your device (e.g., Microsoft Word, Notepad). A spreadsheet file that may also include charts, graphs, and formatting. How to access the data: Open with a spreadsheet software application that supports this format (e.g., Open Office Calc, Microsoft Excel). Data can be converted to a CSV for a non-proprietary format of the same data without formatted text or extra visual features. A shapefile provides geographic information that can be used to create a map or perform geospatial analysis based on location, points/lines and other data about the shape and features of the area. It includes required files (.shp, .shx, .dbt) and might include corresponding files (e.g., .prj). How to access the data: Open with a geographic information system (GIS) software program (e.g., QGIS). A package of files and folders. The package can contain any number of different file types. How to access the data: Open with an unzipping software application (e.g., WinZIP, 7Zip). Note: If a ZIP file contains .shp, .shx, and .dbt file types, it is an ArcGIS ZIP: a package of shapefiles which provide information to create maps or perform geospatial analysis that can be opened with ArcGIS (a geographic information system software program). A file that provides information related to a geographic area (e.g., phone number, address, average rainfall, number of owl sightings in 2011 etc.) and its geospatial location (i.e., points/lines). How to access the data: Open using a GIS software application to create a map or do geospatial analysis. It can also be opened with a text editor to view raw information. Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A text-based format for sharing data in a machine-readable way that can store data with more unconventional structures such as complex lists. How to access the data: Open with any text editor (e.g., Notepad) or access through a browser. Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A text-based format to store and organize data in a machine-readable way that can store data with more unconventional structures (not just data organized in tables). How to access the data: Open with any text editor (e.g., Notepad). Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A file that provides information related to an area (e.g., phone number, address, average rainfall, number of owl sightings in 2011 etc.) and its geospatial location (i.e., points/lines). How to access the data: Open with a geospatial software application that supports the KML format (e.g., Google Earth). Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. This format contains files with data from tables used for statistical analysis and data visualization of Statistics Canada census data. How to access the data: Open with the Beyond 20/20 application. A database which links and combines data from different files or

The data was collected from experiments at the USDA-ARS in Beltsville, MD. The RTqPCR results were collected from processing samples on our BioRad machines. Count data was organized in Excel after being transferred from labnotebooks. Then data was analyzed in R.

Tseng-Myers-dataThis is an excel file of the data used to generate the main findings of the paper Tseng & Myers 2014 PLoS One paper. 5 columns, 156 rows.

Manipulative parasites often alter the phenotype of their hosts along multiple dimensions. ‘Multidimensionality’ in host manipulation could consist in the simultaneous alteration of several physiological pathways independently of one another, or proceed from the disruption of some key physiological parameter, followed by a cascade of effects. We compared multidimensionality in ‘host manipulation’ between two closely related amphipods, Gammarus fossarum and Gammarus pulex, naturally and experimentally infected with Pomphorhynchus laevis (Acanthocephala), respectively. To that end, we calculated in each host–parasite association the effect size of the difference between infected and uninfected individuals for six different traits (activity, phototaxis, geotaxis, attraction to conspecifics, refuge use and metabolic rate). The effects sizes were highly correlated between host–parasite associations, providing evidence for a relatively constant ‘infection syndrome’. Using the same methodology...