An Analysis of Engineering-as-Marketing Tools

Strategies for Expanding Business Reach

By Ian Greenleigh [source]

About this dataset

The engineering-as-marketing tools available today allow startups to maximize and take advantage of the engineering talents they possess. By creating useful tools such as calculators, widgets and microsites, businesses can get in front of potential customers and lead them to their products or services.

This dataset provides a comprehensive list of companies who are using engineering as a marketing strategy and the respective tools these companies have created for it. For each company you get information about their name, product/service, tool name, what the tool does and a URL for further information about it. Additionally there is an extra notes field providing more details about each company’s market habit or any other additional facts that could be relevant in understanding better the use cases these companies are leading with this new way of doing marketing through engineering driven strategies.

With this data you will be able to take a closer look at how effectively this strategy is working while being able to compare different approaches taken inside each industry vertical in order to maximize conversions among leads generated by all these amazing pieces work made possible by software engineers everywhere devoted every day making our lives easier constantly!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

Analyzing this data allows users to gain insights into how successful companies are using engineering-as-marketing techniques to generate leads and expand their customer base. It also provides a valuable resource for other organizations wanting to learn more about how other organizations have achieved success with such practices.

This dataset can be used in many ways such as:

• Analyzing different trends in which engineering-as-marketing techniques are being used across multiple industries
• Examining whether certain techniques lead to higher lead generation or increased customer base

• Comparing effectiveness between companies using different types of tools etc.

  To get started with this dataset, simply load it up into some kind of data analysis software package that supports csv file processing capabilities such as Tableau or R Studio. Then define each column appropriately by adding appropriate labels onto them so that they can be understood easily when looked at from a first glance perspective by yourself or other members on your team who are looking over your datasets before any analyses start happening on those files within your chosen data analysis software package . Now you should be all set up for analyzing this dataset!

Research Ideas

• Leveraging this data to understand the effectiveness of engineering-as-marketing for various companies.
• Creating a sentiment analysis of customers’ responses to engineering-as-marketing tools in order to determine which tools are most popular and successful.
• Analyzing what types of engineering-as-marketing tools have been most successful with specific customer segments, to inform future product development and marketing tactics

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Engineering as Marketing.csv | Column name | Description | |:-------------------|:-------------------------------------------------------------------| | Company name | The name of the company. (String) | | What co does | A brief description of what the company does. (String) | | Tool name | The name of the engineering-as-marketing tool. (String) | | What tool does | A brief description of what the tool does. (String) | | URL | The URL of the engineering-as-marketing tool. (String) | | Notes | Additional notes about the engineering-as-marketing tool. (String) |

Acknowledgements

If you use this dataset in your research, please credit the ori...

Data science literacy is increasingly vital for undergraduate engineering and science students, yet questions remain about effective integration approaches across established curricula. This study presents a case study investigating the impact of integrating discipline-specific data science modules into existing undergraduate STEM courses at three different universities in the United States (US) through a multi-university research-practice partnership examining both student perspectives and instructor course assessments. Using mixed methods analysis of survey responses from 877 students and instructors' grades and interviews across six courses, we examined changes in students' perceptions of data science across various demographics, academic levels, and disciplines and compared student and instructor perspectives. Results show significant increases in students' self-reported motivation, skills, interest, and confidence after completing one or more modules, with initial perception being the strongest predictor of final perception after controlling for course and institution differences. Analysis revealed general alignment between student self-assessments and instructor evaluations. Students highlighted benefits including real-world applications and career relevance, while identifying challenges with data science tools and varying experience levels. These findings provide insights for engineering educators seeking to integrate data science into their curricula.

Publication speed, impact, uncited share, self-citation, retractions and scale for International Journal on Software Tools for Technology Transfer and The Art Science and Engineering of Programming, each metric with its unit and which direction is better.

This dataset provides a comprehensive listing of software tools developed or utilized by NASA across its various research centers. Each entry includes a unique case number, the associated NASA center, the date of software release or approval (SRA Date), the final software release agreement type (e.g., Open Source, General US, Academic Worldwide), and the official NASA Technology Transfer Report (NTR) title describing the software.

The dataset highlights the wide range of applications NASA supports through software—ranging from engineering analysis tools and machine learning components to visualization systems and simulation platforms. Whether distributed as open source or under more restrictive licenses, these tools represent decades of technological innovation, much of which has been made available for academic, commercial, and government use.

By exploring this dataset, researchers, developers, and educators can gain insight into the types of software NASA relies on, collaborates over, and shares with the broader community, reflecting its ongoing commitment to advancing science and technology through transparency and knowledge transfer.

Dataset

This dataset was created by Vasu_Thakaew

Released under CC0: Public Domain

Contents

Publication speed, impact, uncited share, self-citation, retractions and scale for International Journal of Machine Tools and Manufacture and Journal of Manufacturing Science and Engineering, Transactions of the ASME, each metric with its unit and which direction is better.

Feature Engineering Platform Market Size and Growth Outlook

Across all regions, the feature engineering platform market size in 2024 stands at USD 1.42 billion, with a robust CAGR of 23.8% projected from 2025 to 2033. The market is anticipated to reach USD 11.67 billion by 2033, driven by the increasing adoption of artificial intelligence and machine learning across diverse industries. This growth is fueled by the critical need for advanced data preparation and transformation tools that enable organizations to extract valuable insights and enhance predictive model performance.

One of the primary growth factors for the feature engineering platform market is the exponential increase in data generation from various sources such as IoT devices, enterprise applications, and digital transactions. Organizations are striving to leverage this data to gain a competitive edge, and feature engineering platforms play a pivotal role in converting raw data into meaningful features that improve the accuracy and efficiency of machine learning models. As businesses recognize the importance of high-quality features in driving model success, investment in automated and scalable feature engineering tools has surged. These platforms help streamline the data preparation process, reduce manual intervention, and accelerate the deployment of AI-driven solutions.

Another significant driver is the rapid advancement of artificial intelligence and machine learning technologies, which has heightened the demand for platforms that can automate and optimize feature engineering workflows. The complexity of modern data science projects often necessitates sophisticated feature engineering techniques, including feature selection, extraction, and transformation. Feature engineering platforms are increasingly integrating with popular machine learning frameworks and offering out-of-the-box capabilities for data scientists and analysts. This integration not only enhances productivity but also ensures consistency and repeatability in model development. Additionally, the rise of citizen data scientists and democratization of AI has further underscored the need for user-friendly feature engineering tools that can be used by professionals with varying levels of technical expertise.

Furthermore, the growing emphasis on model transparency, explainability, and regulatory compliance is compelling organizations to adopt feature engineering platforms that offer traceable and auditable data transformation processes. Industries such as BFSI, healthcare, and manufacturing are subject to stringent data governance requirements, making it essential to document and validate every step of the feature engineering pipeline. Modern platforms provide robust auditing, version control, and collaboration features, enabling teams to maintain compliance while fostering innovation. The trend toward cloud-based and hybrid deployment models also contributes to market expansion, as organizations seek scalable, flexible, and cost-effective solutions that can support their evolving data science needs.

Regionally, North America dominates the feature engineering platform market, owing to its mature technology ecosystem, high investment in AI research, and presence of leading platform providers. Europe follows closely, driven by digital transformation initiatives and regulatory mandates around data usage. The Asia Pacific region is experiencing the fastest growth, propelled by rapid industrialization, increased adoption of AI technologies, and government support for digital innovation. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as organizations in these regions increasingly recognize the value of data-driven decision-making. Overall, the global outlook for the feature engineering platform market remains highly optimistic, with sustained investment and technological advancements expected to drive continued expansion through 2033.

Feature Engineering Platform Market by Component

&l

Publication speed, impact, uncited share, self-citation, retractions and scale for International Journal of Materials Science and Engineering and Journal of Intelligent Manufacturing and Special Equipment, each metric with its unit and which direction is better.

Context:

This dataset showcases 113 leading Generative AI tools in 2025, covering venue for text, image, video, audio, and more. It provides details on companies, release years, open-source status, APIs, and methods. The data highlights the growth, diversity, and innovation of AI technologies shaping the digital future.

Feature Distribution:

tool_name → Name of the AI tool (e.g., ChatGPT, Claude).

company → Organization behind the tool.

category_canonical → Main category (LLMs, Image Gen, etc.).

modality_canonical → Primary modality (text, image, multimodal).

open_source → Whether the tool is open-source (1 = Yes, 0 = No).

api_available → Availability of API (1 = Yes, 0 = No).

api_status → API status (e.g., active, unavailable).

website / source_domain → Official website and domain.

release_year → Year of release.

years_since_release → How many years since launch.

modality_count → Total number of modalities supported by each tool.

Graph and download economic data for Producer Price Index by Commodity: Machinery and Equipment: Engineering and Scientific Instruments (WPS1185) from Jan 1990 to Aug 2026 about instruments, engineering, science, machinery, equipment, commodities, PPI, inflation, price index, indexes, price, and USA.

This dataset includes instruments and the data of the study

Purpose: As increasing amounts and types of speech data become accessible, health care and technology industries increasingly demand quantitative insight into speech content.The potential for speech data to provide insight into cognitive, affective, and psychological health states and behavior crucially depends on the ability to integrate speech data into the scientific process. Current engineering methods for acquiring, analyzing, and modeling speech data present the opportunity to integrate speech data into the scientific process. Additionally, machine learning systems recognize patterns in data that can facilitate hypothesis generation, data analysis, and statistical modeling. The goals of the present article are (a) to review developments across these domains that have allowed real-time magnetic resonance imaging to shed light on aspects of atypical speech articulation; (b) in a parallel vein, to discuss how advancements in signal processing have allowed for an improved understanding of communication markers associated with autism spectrum disorder; and (c) to highlight the clinical significance and implications of the application of these technological advancements to each of these areas.Conclusion: The collaboration of engineers, speech scientists, and clinicians has resulted in (a) the development of biologically inspired technology that has been proven useful for both small- and large-scale analyses, (b) a deepened practical and theoretical understanding of both typical and impaired speech production, and (c) the establishment and enhancement of diagnostic and therapeutic tools, all having far-reaching, interdisciplinary significance.

This project investigated how new online teaching tools can be used to support independent student learning, in undergraduate chemistry units. These new tools provide opportunities to provide adaptive learning, which is personalised to the needs of individual students. The new tools support students in developing good study habits, allowing them to work independently and at their own pace. These are relatively new software packages that have become easily and commercially available to educators in the past decade.

This project implemented an adaptive learning tool within undergraduate chemistry units at QUT. The project investigated how well the students engaged with the tools, whether they enjoy using them and if they find them helpful to their study. It also quantified the effect that use of these tools has on student outcomes, such as assessment performance and unit grades. This was done by comparing quantitative assessment outcomes and grades, and comparing it to data on individual student usage, which can be accessed through the tools themselves.

snapshot-tab-pane Overview of the transmission systems market for marine applicationsMarket research analysts at Technavio have predicted that the global transmission systems market for marine applications will grow steadily at a CAGR of more than 3% by 2020. This market research analysis identifies the emergence of the two-speed gearbox to be one of the primary growth factors for this market. Two-speed gearboxes are more fuel-efficient than single mechanical propulsion systems by operating the propellers at a lower speed whenever maximum vessel speed or propeller thrust is not required. However the engine speed is also kept constant, and this enables the electric power generated from the power take-off on the gearbox to remain uninterrupted. Also, these gearboxes reduce noise emissions both inside and outside the ship, and this ensures greater comfort for passengers and crew.The transition from steam to diesel propulsion systems and from paddlewheel to propellers have by-far been the major changes in the inland towing industry. During the predicted period, this industry will witness the adoption of another major trend that will help in lesser fuel consumption, shorter transit times, and improved performance. The adoption of Z-drive propulsion. Z-drive propulsion offers several advantages over conventional propulsion and also enables improved thrust, steering power, and maneuverability. This facilitates efficient and safer operations in most inland towing operations such as navigating bends, currents, and restricted channels, meeting other vessels at close quarters, and transiting constructed bridges and locks.Competitive landscape and key vendorsThough characterized by the presence of a number of regional and international vendors, the market is clearly dominated by large international players. The market is highly capital-intensive which makes it difficult for local players to compete with international vendors in terms of features, qualities, functionalities, and services. As a result of continuous vendor investments in R&D and technological advancements, the coming years will witness the introduction of several new products equipped with new features and technologies. This, in turn, will make the vendor competition in the market more aggressive.The leading vendors in the market are -Rolls-RoyceTwin DiscVelvet DriveWärtsiläZFThe other prominent vendors in the market are Atlantis Marine, Borg Warner, Transocean, and Volvo Penta.Geographical segmentation and analysis of the transmission systems market for marine applicationsAmericasAPACEMEAAccording to this market research and analysis, APAC accounted for almost 69% of the total share of the transmission systems market for marine applications and dominated the industry during 2015. Due to the availability of low-cost labor, the global shipping and offshore energy equipment industry is shifting towards the regions in Asia. This will provide for the exponential increase in demand for the market and as a result, this region will continue to dominate the industry in the next four years as well.Growth drivers, challenges, and upcoming trends: Magnetically geared propulsion motorsInnovate UK is an agency that works with companies, people, and partner organizations to find and drive science and technology innovations that can aid in the growth of UK’s economy. This agency has helped in developing a highly efficient, compact, and low-maintenance motor for marine propulsions. This magnetically geared propulsion motor is extremely compact, provides high-torque-density, and does not require a conventional gearbox. The use of magnetic gearing will lead to a compact, robust, low-maintenance, and flexible propulsion system that can improve efficiencies and also reduce emissions in marine vessels.This report provides a number of factors contributing to the adoption, limitations, and opportunities of the transmission systems market for marine applications. It also offers an analysis of each factor and an estimation of the extent to which the factors are likely to impact the overall market growth.Key questions answered in the report includeWhat will the market size and the growth rate be in 2020?What are the key factors driving the transmission systems market for marine applications?What are the key market trends impacting the growth of the transmission systems market for marine applications?What are the challenges to market growth?Who are the key vendors in the transmission systems market for marine applications?What are the market opportunities and threats faced by the vendors in the transmission systems market for marine applications?Trending factors influencing the market shares of the Americas, APAC, and EMEA.What are the key outcomes of the five forces analysis of the transmission system

Overview

This dataset consists of images of various hand and power tools, specifically designed to aid in training and improving AI-based object recognition systems.

Tool recognition is a crucial component of modern machine learning applications, enabling automated identification in industrial settings, augmented reality, and inventory management. Training machine learning models on diverse tool images can help improve accuracy, especially in real-world environments where lighting conditions, angles, and backgrounds vary significantly.

This dataset contains images of multiple distinct tool types, with each tool category featuring hundreds of labeled samples captured from different perspectives.

Content: 1. Screwdriver AI Dataset - 853 Images 2. Pliers AI Dataset - 918 Images 3. Wrench AI Dataset - 803 Images 4. Hammer AI Dataset - 799 Images 5. Hand Saw AI Dataset - 781 Images 6. Electric Drill AI Dataset - 840 Images 7. Paint Roller AI Dataset - 533 Images 8. Nail AI Dataset - 675 Images 9. Drill Bit AI Dataset - 865 Images 10. Glue Gun AI Dataset - 692 Images 11. Voltage Tester AI Dataset - 890 Images 12. Axe AI Dataset - 643 Images 13. Tape Measure Dataset - 733 Images 14. Nail Gun Dataset - 691 Images 15. Putty Knife Dataset - 888 Images 16. Heat Gun Dataset - 728 Images 17. Level Tool Dataset - 758 Images 18. Paint Brush Dataset - 787 Images 19. Angle Grinder Dataset - 786 Images 20. Utility Knife Dataset - 894 Images 21. Soldering Iron Dataset - 904 Images 22. Extension Cord Dataset - 900 Images 23. Staple Gun Dataset - 370 Images 24. Digital Caliper Dataset - 617 Images 25. Clamp Dataset - 609 Images 26. Concrete Mixer Dataset - 487 Images 27. Ladder Dataset - 754 Images 28. Caulking Gun Dataset - 733 Images 29. Crowbar Dataset - 522 Images 30. Float Tool Dataset - 309 Images 31. Hoe Tool Dataset - 416 Images 32. Generator Dataset - 284 Images 33. Safety Helmet Dataset - 696 Images 34. Safety Glasses Dataset - 520 Images 35. Paint Spray Gun Dataset - 196 Images 36. Construction Earmuffs Dataset - 328 Images 37. Rubber Boots Dataset - 663 Images 38. Toolbelt Dataset - 272 Images 39. Respirator Mask N95 Dataset - 425 Images 40. Paint Respirator Dataset - 373 Images 41. Infrared Digital Thermometer Dataset - 363 Images 42. Corner Trowel Dataset - 309 Images 43. Fire Extinguisher Dataset - 972 Images 44. Plastic Bucket Dataset - 945 Images 45. Paint Tray Dataset - 495 Images 46. Masking Tape Roll Dataset - 449 Images 47. Safety Vest Dataset - 658 Images 48. Digital Torque Wrench Dataset - 270 Images 49. Pry Bar Dataset - 725 Images 50. Chisel Dataset - 963 Images 51. Flashlight Dataset - 956 Images 52. Stainless Steel Wire Brush Dataset - 912 Images 53. Traffic Cone Dataset - 992 Images 54. Jigsaw Dataset - 613 Images 55. Ear Plugs Dataset - 986 Images 56. Wheel Barrow Dataset - 930 Images 57. Rubber Gloves Dataset - 989 Images 58. Trowel Dataset - 952 Images 59. Measuring Wheel Dataset - 978 Images 60. File Tool Dataset - 986 Images 61. Tape Measure Dataset - 988 Images 62. Jack Hammer Dataset - 549 Images 63. LED Light Bulb Dataset - 986 Images 64. Air Compressors Dataset - 981 Images 65. Machete Dataset - 797 Images 66. Kneepads Dataset - 987 Images 67. First AID Kit Dataset - 983 Images 68. Heavy Duty Vacuum Cleaner Dataset - 962 Images 69. Tubing Cutter Dataset - 793 Images 70. Hacksaw Dataset - 968 Images 71. Utility Torch Dataset - 627 Images 72. Welding Gloves Dataset - 993 Images 73. Moisture Meter Dataset - 719 Images 74. Palm Sander Dataset - 802 Images 75. Jack Plane Dataset - 706 Images 76. Mallet Dataset - 970 Images 77. Plunger Dataset - 956 Images 78. Head Flashlight Dataset - 979 Images 79. Stud Crimper Dataset - 746 Images 80. Extension Spring Dataset - 964 Images 81. Bearing Dataset - 969 Images 82. Metal Nut Dataset - 990 Images 83. Tin Snips Dataset - 988 Images 84. Power Socket Dataset - 937 Images 85. Wirecutter Dataset - 878 Images 86. Stud Finder Dataset - 967 Images 87. Socket Wrench Dataset - 996 Images 88. Stainless Steel Washer Dataset - 989 Images 89. Ball Valve Dataset - 973 Images

These images can be utilized to train and evaluate AI-based tool recognition models. The dataset is structured to facilitate easy integration into machine learning pipelines, allowing users to extend it with additional tool images as needed.

If you would like to purchase the entire dataset, which contains 125 different tools and over 100,000 datapoints, please use this link: https://datarade.ai/data-products/diverse-tools-image-dataset-for-machine-learning-oort

SARP_2010_Data is the data collected onboard the NASA DC-8 aircraft during the 2010 deployment of the Student Airborne Research Program (SARP) mission. Data collection for this product is complete.

NASA’s SARP is an eight-week summer internship program for rising senior undergraduate students to acquire hands-on research experience in all aspects of a scientific field campaign. Active since 2009, SARP utilizes one of the major NASA Airborne Science Program flying science laboratories that are used to study Earth system processes, calibration/validation of space-borne observations, and prototyping of instruments for possible satellite missions. Participants assist in the operation of instruments onboard the aircraft to sample atmospheric gases and/or to image land and water surfaces in multiple spectral bands. Students also participate in collecting measurements at field sites. Research areas for SARP include atmospheric chemistry, forest ecology, air quality, and ocean biology.

NSF tools and resources providing information about the impact of NSF's investments in science and engineering research and education.

Question Paper Solutions of chapter Sensor Network Platforms and Tools of Adhoc - Sensor Network, 7th Semester , Computer Science and Engineering

Feature Engineering Automation Market Size and Growth Outlook

The feature engineering automation market size reached USD 1.32 billion in 2024, reflecting robust growth driven by the increasing adoption of advanced analytics and artificial intelligence across industries. The market is poised to expand at a CAGR of 22.4% from 2025 to 2033, with the global market expected to reach USD 9.16 billion by 2033. This remarkable growth is powered by the escalating need for efficient data preprocessing, the proliferation of big data, and the demand for faster, more accurate machine learning models. As per our latest analysis, organizations worldwide are prioritizing automation in their data science pipelines, making feature engineering automation a critical enabler of scalable AI and analytics solutions.

One of the primary growth drivers for the feature engineering automation market is the overwhelming surge in data generation across various sectors. Enterprises are increasingly leveraging IoT devices, social media, and digital transactions, leading to vast, complex datasets that require sophisticated preprocessing before being used in analytics or AI models. Manual feature engineering is time-consuming and often inconsistent, prompting organizations to seek automated solutions that can deliver high-quality features at scale. This trend is particularly pronounced in industries like finance, healthcare, and retail, where real-time insights and predictive accuracy are mission-critical. The ability of feature engineering automation tools to enhance model performance while reducing development time is accelerating their adoption globally.

Another significant factor fueling market expansion is the growing sophistication of machine learning and artificial intelligence applications. As models become more complex and are applied to a broader range of business problems, the demand for robust, scalable, and automated feature engineering solutions intensifies. Automated feature engineering not only streamlines the data preparation process but also enables data scientists to experiment rapidly with different feature sets, thereby improving model accuracy and business outcomes. Additionally, the integration of feature engineering automation into end-to-end MLOps (Machine Learning Operations) pipelines is becoming a best practice, further embedding these solutions into enterprise workflows and driving sustained market growth.

The rapid adoption of cloud computing and the increasing accessibility of advanced analytics platforms are also contributing to the growth of the feature engineering automation market. Cloud-based solutions offer scalability, flexibility, and ease of integration with other data science tools, making them particularly attractive to organizations of all sizes. Small and medium enterprises (SMEs), which may lack extensive in-house data science capabilities, are leveraging cloud-based feature engineering automation to level the playing field against larger competitors. Furthermore, the rise of self-service analytics and the democratization of AI are empowering business users to engage with automated feature engineering tools, expanding the market's user base beyond traditional data scientists.

From a regional perspective, North America continues to dominate the feature engineering automation market, accounting for the largest share in 2024. This leadership is attributed to the region's strong ecosystem of technology vendors, early adoption of AI and analytics, and a highly competitive business environment that prioritizes innovation. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid digital transformation, expanding cloud infrastructure, and increasing investments in AI across countries such as China, India, and Japan. Europe also presents significant growth opportunities, particularly in sectors like healthcare and manufacturing, where regulatory compliance and operational efficiency are key priorities.

The emergence of a Feature Engineering Platform is revolutionizing how organizations approach data preprocessing and model development. By centralizing feature engineering tasks, these platforms provide a unified environment where data scientists can efficiently manage and transform data into meaningful features. This streamlining of processes not only enhances producti

U.S. Department of Labor certified wage filings for Software Engineer roles (FY2026): median $160K, $137K–$187K, from 73,118 filings.