Dataset

This dataset was created by Sanjana Murthy

Released under CC BY-NC-SA 4.0

Contents

This data contains Sort & Filter functions

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

Advantages 1) Find specific data quickly. 2) Faster data recovery.

Formulae used: 1) INDIRECT 2) FILTER FUNCTION

Search Tab created by using insert shapes and icons and developer tab

Figures for the paper "The Relationship between Commit Message Detail and Defect Proneness in Java Projects on GitHub" submitted to the MSR 2016 Data Mining Challenge. These figures show the number of available Java projects with certain constraints applied. In particular, these constraints are number of contributors to the repository and number of commits to that repository.

A recap of the characteristics of backbone filtering techniques.

HOW TO: - Hierarchy using the category, subcategory & product fields (columns “Product Category” “Product SubCategory”, & “Product Name”). - Group the values ​​of the column "Region" into 2 groups, alphabetically, based on the name of each region.

1. Display a table, which shows, for each value of the product hierarchy you created above, the total amount of sales ("Sales") and profitability ("Profit").
2. The same information as the previous point (2) in a bar chart illustration.
3. Display columns with the total sales amount ("Sales") for each value of the alphabetical grouping of the Region field you created. The color of each column should be derived from the corresponding total shipping cost (“Shipping Cost”). In the Tooltip of the illustration all numeric values ​​should have a currency format.
4. The same diagram as above (3), with the addition of a data filter at visual level filter that will display only the data subset related to sales with positive values ​​for the field "Profit".
5. The same diagram with the above point (3), with the addition of a data filter at visual level filter that will display only the subset of data related to sales with negative values ​​for the field "Profit".
6. Map showing the total amount of sales (size of each point), as well as the total profitability (color of each point). Change the dimensions of the image

BAW Filter Market Outlook

According to our latest research, the global BAW Filter market size stood at USD 5.3 billion in 2024, reflecting robust momentum across telecommunications and consumer electronics. The market is projected to expand at a CAGR of 12.7% from 2025 to 2033, reaching a forecasted value of USD 15.8 billion by 2033. This remarkable growth is primarily driven by the surging adoption of 5G networks and the proliferation of connected devices, which demand advanced filtering solutions for high-frequency signal integrity and performance.

A significant growth factor for the BAW Filter market is the exponential rise in mobile data traffic and the ongoing global rollout of 5G infrastructure. As mobile operators and device manufacturers race to deliver faster, more reliable connectivity, the demand for BAW (Bulk Acoustic Wave) filters, particularly in radios and antennas, has surged. These filters are critical for managing signal interference and ensuring efficient spectrum utilization in high-frequency bands. The increasing complexity of wireless communication standards, combined with the need for miniaturized and high-performance components, is pushing OEMs to integrate BAW filters into a broader array of devices, from flagship smartphones to emerging IoT applications. The relentless pursuit of higher bandwidth and lower latency in communication networks continues to propel investment in advanced filtering technologies, making BAW filters indispensable in the next-generation connectivity landscape.

Another key driver is the rapid evolution of consumer electronics and wearable devices, which are increasingly incorporating advanced wireless communication features. The miniaturization trend in electronics has created a critical need for compact, high-efficiency filtering solutions capable of operating across multiple frequency bands. BAW filters excel in high-frequency applications, offering superior selectivity and low insertion loss compared to traditional SAW (Surface Acoustic Wave) filters. This technological edge is particularly advantageous for manufacturers aiming to deliver multi-band, high-performance devices without compromising on form factor or battery life. The growing adoption of smart home devices, connected health wearables, and portable computing platforms is further expanding the addressable market for BAW filters, as these products require robust RF performance to support seamless connectivity and user experiences.

In addition to telecommunications and consumer electronics, the automotive sector is emerging as a substantial growth avenue for the BAW Filter market. The automotive industry's shift toward connected, autonomous, and electric vehicles is driving demand for sophisticated wireless communication modules, including V2X (vehicle-to-everything) systems, infotainment units, and advanced driver-assistance systems (ADAS). These applications require reliable, high-frequency filtering to manage the increasing complexity of in-vehicle networks and ensure electromagnetic compatibility. As automotive OEMs prioritize safety, connectivity, and user-centric features, BAW filters are becoming integral to the design of next-generation vehicles, supporting a wide range of wireless protocols and frequency bands. The convergence of automotive innovation and wireless technology is set to unlock new opportunities for BAW filter manufacturers over the coming decade.

Regionally, the BAW Filter market exhibits strong growth potential in Asia Pacific, North America, and Europe, with Asia Pacific leading in both production and consumption. The presence of major electronics manufacturing hubs, rapid 5G deployment, and a large base of tech-savvy consumers are fueling demand in the region. North America is witnessing accelerated adoption due to early 5G rollouts and a robust ecosystem of semiconductor and telecommunications companies. Europe, driven by automotive innovation and stringent regulatory standards, is also contributing significantly to market expansion. Meanwhile, emerging markets in Latin America and the Middle East & Africa are gradually embracing advanced wireless technologies, presenting untapped opportunities for future growth.

Example of how I use MS Excel's VLOOKUP() function to filter my data.

Power Filter Market size was valued at USD 268.6 million in 2021 and is poised to grow from USD 279.06 million in 2022 to USD 382.75 million by 2030, growing at a CAGR of 3.8% in the forecast period (2023-2030).

Oxford University Press has developed two lists of offensive words and expressions, specifically developed for filter applications in the contexts of web pages and email. Each list features a grading system describing vocabulary type and offensive strength for each term, plus collocational information to help identify the terms in context.

Coverage: over 2000 words and expressions; 13-category classification system; US and UK usage covered Features: graded by category/subcategory (eg abusive/sexist etc); rated by strength (extreme/moderate/mild); collocational information included; regional usage/source labelling; glosses for obscure senses Format: Excel spreadsheet File Size: 237 kB

An Excel spreadsheet listing the information recorded on each of 18,686 costume designs can be viewed, downloaded, and explored. All the usual Excel sorting possibilities are available, and in addition a useful filter has been installed. For example, to find the number of designs that are Frieze Type #1, go to the top of the frieze type 2 column (column AS), click on the drop-down arrow and unselect every option box except True (i.e. True should be turned on, all other choices turned off). Then in the lower left corner, one reads “1111 of 18686 records found”.

Much more sophisticated exploration can be carried out by downloading the rich and flexible Access Database. The terms used for this database were described in detail in three sections of Deep Blue paper associated with this project. The database can be downloaded and explored.

HOW TO USE THE ACCESS DATABASE 1. Click on the Create Cohort and View Math Trait Data button, and select your cohort by clicking on the features of interest (for example: Apron and Blouse).

Note: Depending on how you exited on your previous visit to the database, there may be items to clear up before creating the cohorts.
a) (Usually unnecessary) Click on the small box near the top left corner to allow connection to Access. b) (Usually unnecessary) If an undesired window blocks part of the screen, click near the top of this window to minimize it. c) Make certain under Further Filtering that all four Exclude boxes are checked to get rid of stripes and circles, and circular buttons, and the D1 that is trivially associated with shoes.

1. Click on Filter Records to Form the Cohort button. Note the # of designs, # of pieces, and # of costumes beside Recalculate.

2. Click on Calculate Average Math Trait Frequency of Cohort button, and select the symmetry types of interest (for example: D1 and D2) .

3. To view the Stage 1 table, click on Create Stage 1 table. To edit and print this table, click on Create Excel (after table has been created). The same process works for Stages 2, 3.and 4 tables.

4. To view the matrix listing the math category impact numbers, move over to a button on the right side and click on View Matrix of Math Category Impact Numbers. To edit and print this matrix, click on Create Excel, use the Excel table as usual.

The open repository consists of two folders; Dataset and Picture. The dataset folder consists file “AWS Dataset Pangandaraan.xlsx”. There are 10 columns with three first columns as time attributes and the other six as atmosphere datasets. Each parameter has 8085 data, and Each parameter has a parameter index at the bottom of the column we added, including mMinimum, mMaximum, and Average values.

For further use, the user can choose one or more parameters for calculating or analyzing. For example, wind data (speed and direction) can be utilized to calculate Waves using the Hindcast method. Furthermore, the user can filter data by using the feature in Excel to extract the exact time range for analyzing various phenomena considered correlated to atmosphere data around Pangandaran, Indonesia.

The second folder, named “Picture,” contains three figures, including the monthly distribution of datasets, temporal data, and wind rose. Furthermore, the user can filter data by using the feature in Excel sheet to extract the exact time range for analyzing various phenomena considered correlated to atmosphere data around Pangandaran, Indonesia

This dataset is about book subjects. It has 3 rows and is filtered where the books is Microsoft Excel 2002. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Nike Sales 2024 – Excel Data Analysis

This project analyzes Nike’s 2024 sales data using Microsoft Excel.

🔍 Project Overview Cleaned and transformed the dataset using Power Query Used PivotTables to summarize: Revenue by region and product line Online sales percentage Units sold by month Built an interactive Excel dashboard with slicers and charts for dynamic filtering

💡 Key Insights - Region X had the highest sales in Q2 - Product Line Y contributed 35% of total revenue - Online sales accounted for 40% of overall sales

📊 Tools Used

• Microsoft Excel
• Power Query
• PivotTables
• Slicers

• Dashboard design

  📝 How to Explore

• Download Nike_Sales_Analysis.xlsx from this dataset

• Open the Excel file → navigate to the Dashboard sheet

• Use slicers to filter data by region, product line, and month

✨ Applications This dashboard can help sales teams track performance and identify growth opportunities across different regions and product categories.

Bird list for Angus K. Gholson, Jr. Nature Park. This is an Excel worksheet. Included are common and scientific names. With Excel, it is easy to sort and filter records. The query was run on eBird 6/17/2023 and includes observations since 1/1/2000.

Automotive BAW Filter Market Outlook

According to our latest research, the global Automotive BAW Filter market size reached USD 1.34 billion in 2024, driven by the rising integration of advanced wireless communication technologies in vehicles. The market is expected to expand at a CAGR of 8.7% from 2025 to 2033, reaching a forecasted value of USD 2.80 billion by 2033. This robust growth is fueled by the increasing adoption of connected vehicles, stringent regulatory standards for in-vehicle connectivity, and the proliferation of advanced driver assistance systems (ADAS) worldwide. As per our latest research, technological advancements and the growing demand for high-frequency filtering solutions in automotive electronics are primary growth drivers for the Automotive BAW Filter market.

The growth trajectory of the Automotive BAW Filter market is notably influenced by the surging demand for in-vehicle infotainment and telematics systems. Modern vehicles are increasingly equipped with sophisticated infotainment platforms that require robust radio frequency (RF) filtering to ensure seamless connectivity and signal integrity. The proliferation of 4G, 5G, and emerging V2X (vehicle-to-everything) communication standards necessitates high-performance BAW filters, which excel in filtering high-frequency signals and minimizing interference. As automakers strive to deliver enhanced user experiences and cater to consumer expectations for always-on connectivity, the integration of BAW filters into infotainment and telematics modules has become indispensable. This trend is further accelerated by the growing popularity of smart dashboards, real-time navigation, and streaming services in passenger cars and commercial vehicles alike.

Another significant growth factor is the rapid advancement and deployment of Advanced Driver Assistance Systems (ADAS) in the automotive sector. ADAS technologies, such as adaptive cruise control, lane departure warning, and collision avoidance, rely heavily on wireless sensors and high-frequency radar systems. The precise operation of these systems depends on the ability to filter out unwanted frequencies and ensure clear signal transmission, a capability where BAW filters demonstrate superior performance compared to other RF filtering technologies. Regulatory mandates for improved vehicle safety and the push towards semi-autonomous and fully autonomous vehicles have led to increased adoption of ADAS, thereby boosting demand for high-quality BAW filters. Furthermore, the miniaturization of automotive components and the need for compact, reliable filtering solutions have positioned BAW filters as a preferred choice among OEMs and tier-1 suppliers.

The electrification of vehicles and the rise of electric vehicles (EVs) represent another pivotal growth driver for the Automotive BAW Filter market. Electric vehicles are characterized by dense electronic architectures, with multiple modules relying on wireless communication for battery management, powertrain control, and remote diagnostics. The susceptibility of these systems to electromagnetic interference necessitates the use of advanced RF filtering solutions, with BAW filters offering the required performance and reliability. As global EV adoption accelerates, driven by environmental concerns and supportive government policies, the demand for BAW filters is expected to witness a substantial uptick. Additionally, the ongoing development of smart mobility solutions and the integration of IoT (Internet of Things) in automotive applications are expected to further fuel market expansion over the forecast period.

Regionally, Asia Pacific continues to dominate the Automotive BAW Filter market, underpinned by the presence of leading automotive manufacturing hubs in China, Japan, and South Korea. The region benefits from robust investments in automotive electronics, rapid urbanization, and a burgeoning middle-class population with increasing purchasing power. North America and Europe follow closely, driven by early adoption of advanced automotive technologies, stringent safety regulations, and a strong focus on vehicle connectivity and electrification. Meanwhile, Latin America and the Middle East & Africa are gradually emerging as growth markets, propelled by improving automotive infrastructure and rising demand for connected vehicles. The regional outlook is further shaped by strategic collaborations between automotive OEMs and technology providers, fostering innovation and accelerating the deployment of next-gene

The global Ceramic Fiber Filter Tube market is poised for significant expansion, projected to reach approximately $3,500 million by 2033, with a robust Compound Annual Growth Rate (CAGR) of around 8.5% from 2025 to 2033. This substantial growth is primarily propelled by the escalating demand for high-efficiency filtration solutions across a spectrum of industries, including chemicals, metallurgy, and machinery. The inherent properties of ceramic fiber filter tubes, such as their exceptional thermal stability, chemical inertness, and superior filtration efficiency at elevated temperatures, make them indispensable in critical industrial processes. The continuous drive for improved product quality, reduced environmental impact through enhanced emission control, and the need for process optimization in energy-intensive sectors are key factors fueling market adoption. Furthermore, ongoing advancements in material science and manufacturing techniques are leading to the development of more durable and cost-effective ceramic fiber filter tubes, further stimulating market penetration. The market's trajectory is characterized by a burgeoning demand for high-temperature catalyst applications, where these filter tubes excel in facilitating chemical reactions and capturing particulate matter under extreme conditions. While the market presents substantial opportunities, certain restraints, such as the initial cost of installation and the availability of alternative filtration technologies, may pose challenges. However, the long-term benefits of operational efficiency, extended equipment lifespan, and compliance with stringent environmental regulations are expected to outweigh these considerations. Geographically, the Asia Pacific region, led by China and India, is anticipated to dominate the market share due to its rapidly industrializing economy and the presence of a large manufacturing base. North America and Europe are also significant contributors, driven by technological innovation and stringent environmental policies. Key players are focusing on research and development to enhance product performance and expand their global reach to capitalize on this burgeoning demand for advanced filtration solutions.

The particle size distribution was measured with a laser particle size analyzer (BT-9300Z, Baite, China);Generated EXCEL file;There are a total of six sets of data, which are the filter residue particle sizes when the molar ratios of K2S2O8/Al2O3 are 0.5:1, 1:1, 2:1, 3:1, and 4:1, respectively;The data unit is μm

Excel Data Analysis & Dashboard Creation: Coffee Sales Insights ☕📊

I recently worked on an Excel data analysis project, where I cleaned, transformed, and visualized sales data to build a dynamic and interactive Coffee Sales Dashboard. Here's a quick rundown of what I did: ✅ Data Cleaning & Preparation: 🔹 Standardized formats and structured data for better readability. 🔹 Used XLOOKUP, INDEX+MATCH for dynamic data consolidation. 🔹 Applied IF functions to categorize and enhance data. ✅ Data Visualization & Analysis: 🔹 Created Pivot Tables to summarize key sales insights. 🔹 Designed an interactive dashboard with Slicers for easy filtering by order date, roast type, coffee size, and loyalty card status. 🔹 Analyzed sales trends over time, top customers, and regional sales performance. 📊 Key Insights: 🔹 The United States had the highest sales, followed by Ireland and the UK. 🔹 Arabica & Robusta were the top-selling coffee types. 🔹 Identified the top 5 customers based on total purchases. 💡 Slicers I implemented Slicers to allow users to dynamically filter the data and explore different trends effortlessly. This made the dashboard more intuitive and insightful! Tools Used: Excel, Pivot Tables, XLOOKUP, INDEX+MATCH, IF Functions, Slicers This project reinforced the power of Excel in business intelligence! Excited to work on more data-driven projects. 🚀

This data set was collected during the VAMOLS Ocean-Cloud Atmosphere-Land Study-Regional Experiment (VOCALS-REx) from the Paposo upper site on the Chilean coast. The data are contained in a single EXCEL file.