What is IPGOD?

The Intellectual Property Government Open Data (IPGOD) includes over 100 years of registry data on all intellectual property (IP) rights administered by IP Australia. It also has derived information about the applicants who filed these IP rights, to allow for research and analysis at the regional, business and individual level. This is the 2019 release of IPGOD.

How do I use IPGOD?

IPGOD is large, with millions of data points across up to 40 tables, making them too large to open with Microsoft Excel. Furthermore, analysis often requires information from separate tables which would need specialised software for merging. We recommend that advanced users interact with the IPGOD data using the right tools with enough memory and compute power. This includes a wide range of programming and statistical software such as Tableau, Power BI, Stata, SAS, R, Python, and Scalar.

IP Data Platform

IP Australia is also providing free trials to a cloud-based analytics platform with the capabilities to enable working with large intellectual property datasets, such as the IPGOD, through the web browser, without any installation of software. IP Data Platform

References

The following pages can help you gain the understanding of the intellectual property administration and processes in Australia to help your analysis on the dataset.

• Patents
• Trade Marks
• Designs
• Plant Breeder’s Rights

Updates

Tables and columns

Due to the changes in our systems, some tables have been affected.

• We have added IPGOD 225 and IPGOD 325 to the dataset!
• The IPGOD 206 table is not available this year.
• Many tables have been re-built, and as a result may have different columns or different possible values. Please check the data dictionary for each table before use.

Data quality improvements

Data quality has been improved across all tables.

• Null values are simply empty rather than '31/12/9999'.
• All date columns are now in ISO format 'yyyy-mm-dd'.
• All indicator columns have been converted to Boolean data type (True/False) rather than Yes/No, Y/N, or 1/0.
• All tables are encoded in UTF-8.
• All tables use the backslash \ as the escape character.
• The applicant name cleaning and matching algorithms have been updated. We believe that this year's method improves the accuracy of the matches. Please note that the "ipa_id" generated in IPGOD 2019 will not match with those in previous releases of IPGOD.

Warning: Large file size (over 1GB). Each monthly data set is large (over 4 million rows), but can be viewed in standard software such as Microsoft WordPad (save by right-clicking on the file name and selecting 'Save Target As', or equivalent on Mac OSX). It is then possible to select the required rows of data and copy and paste the information into another software application, such as a spreadsheet. Alternatively, add-ons to existing software, such as the Microsoft PowerPivot add-on for Excel, to handle larger data sets, can be used. The Microsoft PowerPivot add-on for Excel is available from Microsoft http://office.microsoft.com/en-gb/excel/download-power-pivot-HA101959985.aspx Once PowerPivot has been installed, to load the large files, please follow the instructions below. Note that it may take at least 20 to 30 minutes to load one monthly file. 1. Start Excel as normal 2. Click on the PowerPivot tab 3. Click on the PowerPivot Window icon (top left) 4. In the PowerPivot Window, click on the "From Other Sources" icon 5. In the Table Import Wizard e.g. scroll to the bottom and select Text File 6. Browse to the file you want to open and choose the file extension you require e.g. CSV Once the data has been imported you can view it in a spreadsheet. What does the data cover? General practice prescribing data is a list of all medicines, dressings and appliances that are prescribed and dispensed each month. A record will only be produced when this has occurred and there is no record for a zero total. For each practice in England, the following information is presented at presentation level for each medicine, dressing and appliance, (by presentation name): - the total number of items prescribed and dispensed - the total net ingredient cost - the total actual cost - the total quantity The data covers NHS prescriptions written in England and dispensed in the community in the UK. Prescriptions written in England but dispensed outside England are included. The data includes prescriptions written by GPs and other non-medical prescribers (such as nurses and pharmacists) who are attached to GP practices. GP practices are identified only by their national code, so an additional data file - linked to the first by the practice code - provides further detail in relation to the practice. Presentations are identified only by their BNF code, so an additional data file - linked to the first by the BNF code - provides the chemical name for that presentation.

This step-by-step exercise demonstrates how to use Excel pivot tables to create cross-tabulations from public use microdata files.

What is IPGOD? The Intellectual Property Government Open Data (IPGOD) includes over 100 years of registry data on all intellectual property (IP) rights administered by IP Australia. It also has derived information about the applicants who filed these IP rights, to allow for research and analysis at the regional, business and individual level. This is the 2019 release of IPGOD. # How do I use IPGOD? IPGOD is large, with millions of data points across up to 40 tables, making them too large to open with Microsoft Excel. Furthermore, analysis often requires information from separate tables which would need specialised software for merging. We recommend that advanced users interact with the IPGOD data using the right tools with enough memory and compute power. This includes a wide range of programming and statistical software such as Tableau, Power BI, Stata, SAS, R, Python, and Scalar. # IP Data Platform IP Australia is also providing free trials to a cloud-based analytics platform with the capabilities to enable working with large intellectual property datasets, such as the IPGOD, through the web browser, without any installation of software. IP Data Platform # References The following pages can help you gain the understanding of the intellectual property administration and processes in Australia to help your analysis on the dataset. * Patents * Trade Marks * Designs * Plant Breeder’s Rights # Updates ### Tables and columns Due to the changes in our systems, some tables have been affected. * We have added IPGOD 225 and IPGOD 325 to the dataset! * The IPGOD 206 table is not available this year. * Many tables have been re-built, and as a result may have different columns or different possible values. Please check the data dictionary for each table before use. ### Data quality improvements Data quality has been improved across all tables. * Null values are simply empty rather than '31/12/9999'. * All date columns are now in ISO format 'yyyy-mm-dd'. * All indicator columns have been converted to Boolean data type (True/False) rather than Yes/No, Y/N, or 1/0. * All tables are encoded in UTF-8. * All tables use the backslash \ as the escape character. * The applicant name cleaning and matching algorithms have been updated. We believe that this year's method improves the accuracy of the matches. Please note that the "ipa_id" generated in IPGOD 2019 will not match with those in previous releases of IPGOD.

The global spreadsheets software market is experiencing robust growth, driven by increasing digitalization across industries and the rising adoption of cloud-based solutions. The market, estimated at $20 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033, reaching approximately $35 billion by 2033. This growth is fueled by several factors, including the expanding need for data analysis and visualization across SMEs and large enterprises, the increasing availability of user-friendly and feature-rich spreadsheet software, and the growing preference for collaborative tools that facilitate seamless teamwork. The market is segmented by operating system (Windows, Macintosh, Linux, Others) and user type (SMEs, Large Enterprises). While Microsoft Excel maintains a dominant market share, the rise of cloud-based alternatives like Google Sheets and collaborative platforms is challenging this dominance, fostering competition and innovation. Furthermore, the increasing integration of spreadsheets with other business intelligence tools further enhances their utility and fuels demand. Geographic expansion, particularly in developing economies with rising internet penetration, also contributes significantly to market expansion. However, factors such as the high initial investment in software licenses and the need for specialized training can restrain market growth, particularly for smaller businesses with limited budgets and technical expertise. The increasing complexity of data analysis necessitates enhanced security features and data protection measures, which add cost and require ongoing investment. Moreover, the emergence of advanced analytical tools and specialized data visualization software presents a competitive challenge, demanding continuous innovation and adaptation from existing spreadsheet software providers. Nevertheless, the overall market outlook remains positive, driven by sustained demand from diverse industries and technological advancements within the software landscape.

Analyzing sales data is essential for any business looking to make informed decisions and optimize its operations. In this project, we will utilize Microsoft Excel and Power Query to conduct a comprehensive analysis of Superstore sales data. Our primary objectives will be to establish meaningful connections between various data sheets, ensure data quality, and calculate critical metrics such as the Cost of Goods Sold (COGS) and discount values. Below are the key steps and elements of this analysis:

1- Data Import and Transformation:

• Gather and import relevant sales data from various sources into Excel.
• Utilize Power Query to clean, transform, and structure the data for analysis.
• Merge and link different data sheets to create a cohesive dataset, ensuring that all data fields are connected logically.

2- Data Quality Assessment:

• Perform data quality checks to identify and address issues like missing values, duplicates, outliers, and data inconsistencies.
• Standardize data formats and ensure that all data is in a consistent, usable state.

3- Calculating COGS:

• Determine the Cost of Goods Sold (COGS) for each product sold by considering factors like purchase price, shipping costs, and any additional expenses.
• Apply appropriate formulas and calculations to determine COGS accurately.

4- Discount Analysis:

• Analyze the discount values offered on products to understand their impact on sales and profitability.
• Calculate the average discount percentage, identify trends, and visualize the data using charts or graphs.

5- Sales Metrics:

• Calculate and analyze various sales metrics, such as total revenue, profit margins, and sales growth.
• Utilize Excel functions to compute these metrics and create visuals for better insights.

6- Visualization:

• Create visualizations, such as charts, graphs, and pivot tables, to present the data in an understandable and actionable format.
• Visual representations can help identify trends, outliers, and patterns in the data.

7- Report Generation:

• Compile the findings and insights into a well-structured report or dashboard, making it easy for stakeholders to understand and make informed decisions.

Throughout this analysis, the goal is to provide a clear and comprehensive understanding of the Superstore's sales performance. By using Excel and Power Query, we can efficiently manage and analyze the data, ensuring that the insights gained contribute to the store's growth and success.

Syllabi Information Literacy Miner Hosted on Google Colab https://colab.research.google.com/drive/1N778ot87GI-wJSQHpjRJWkplL4NL5HWb?usp=sharing Purpose: Automatically mine academic syllabi for information literacy (IL) components in order to identify opportunities for liaison librarians to engage with courses. While this Jupyter Notebook can be used as a standalone tool, Baylor University Libraries also maintains a Power BI report that also identifies which courses liaison libraries are already providing instruction. This allows liaison librarians to to identify new IL opportunities with some measure of precision. Overview of Jupyter Notebook Procedures: Load syllabi by uploading files or providing URL to .zip archive Convert syllabi to text format (using textract) IL components are identified by finding verb fragments with the presence of nouns within the specified number of context words The types of IL learning is then identified based on the verb. Types include Library Basics, Research Basics, Research in the Disciplines. Outputs: Pie chart showing proportion of the three IL learning types Column chart showing counts of IL components (verbs with context nouns) Table showing the types of learning identified for each syllabi Table showing each granular IL component for each syllabi. This table is also automatically downloaded as an Excel spreadsheet. Permissions (Copyright 2021 Baylor University Libraries) Use: Licensed under the MIT License - https://opensource.org/licenses/MIT. Citation: Publications and research reports should include the following citation: Joshua Been, Amy James, and Beth Farwell. Syllabi Information Literacy Miner. Waco, TX: Baylor University Libraries. 2021.

ISO 3166-1-alpha-2 English country names and code elements. This list states the country names (official short names in English) in alphabetical order as given in ISO 3166-1 and the corresponding ISO 3166-1-alpha-2 code elements.