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.

Dataset

This dataset was created by Vasu_Thakaew

Released under CC0: Public Domain

Contents

NSF tools and resources-providing information about the impact of NSF's investments in science and engineering research and education-are available for viewing online and downloading. Topics include Fact Sheets, COVID-19 funding and Budget info.

Feature Engineering Platform Market Outlook

According to our latest research, the global 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.

Component Analysis

The feature engi

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!

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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...

To define the relevant publication sample, we used these keywords to perform several queries on the WoS engine. The search also con?siders the year of publication, the title, the abstract, and the author/indexed keywords of the articles. We performed the search on 10th March 2021 in the WoS database with the combination of some keywords.Fourth Industrial Revolution OR Industry 4.0 OR Mechanic* OR Real-Time) AND (Artificial Intelligence OR Machine Learning OR Deep Learning OR Artificial Neutral Network) AND (Predictive maintenance OR Decision making OR Diagnostic OR Prognostic OR Monitoring) AND (Time span: 2000-2021)

Graph and download economic data for Producer Price Index by Commodity: Machinery and Equipment: Engineering and Scientific Instruments (WPU1185) from Dec 1985 to Jun 2026 about instruments, science, engineering, machinery, equipment, commodities, PPI, inflation, price index, indexes, price, and USA.

Data science literacy is vital for undergraduate engineering and science students, yet questions remain about effective integration in curricula. This study investigates the impact of integrating discipline-specific data science modules into existing undergraduate STEM courses at three US universities through a research-practice partnership. Using mixed methods to analyze survey responses from 877 students and instructor grades and interviews across six courses, we examined changes in student data science perception across various demographics, academic levels, and disciplines and compared student and instructor perspective. Results show significant increases in student self-reported perception after completing one or more modules irrespective of course and institution differences. Analysis revealed alignment between student self-assessments and instructor evaluations. Students highlighted benefits including real-world applications and career relevance, while identifying challenges with data analysis tools and varying experience levels. These findings provide insights for educators seeking to integrate data science into curricula.

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.

Background
An understanding of the resources which engineering students use to write their academic papers provides information about student behaviour as well as the effectiveness of information literacy programs designed for engineering students. One of the most informative sources of information which can be used to determine the nature of the material that students use is the bibliography at the end of the students’ papers. While reference list analysis has been utilised in other disciplines, few studies have focussed on engineering students or used the results to improve the effectiveness of information literacy programs. Gadd, Baldwin and Norris (2010) found that civil engineering students undertaking a finalyear research project cited journal articles more than other types of material, followed by books and reports, with web sites ranked fourth. Several studies, however, have shown that in their first year at least, most students prefer to use Internet search engines (Ellis & Salisbury, 2004; Wilkes & Gurney, 2009).

PURPOSE
The aim of this study was to find out exactly what resources undergraduate students studying civil engineering at La Trobe University were using, and in particular, the extent to which students were utilising the scholarly resources paid for by the library. A secondary purpose of the research was to ascertain whether information literacy sessions delivered to those students had any influence on the resources used, and to investigate ways in which the information literacy component of the unit can be improved to encourage students to make better use of the resources purchased by the Library to support their research.

DESIGN/METHOD
The study examined student bibliographies for three civil engineering group projects at the Bendigo Campus of La Trobe University over a two-year period, including two first-year units (CIV1EP – Engineering Practice) and one-second year unit (CIV2GR – Engineering Group Research). All units included a mandatory library session at the start of the project where student groups were required to meet with the relevant faculty librarian for guidance. In each case, the Faculty Librarian highlighted specific resources relevant to the topic, including books, e-books, video recordings, websites and internet documents. The students were also shown tips for searching the Library catalogue, Google Scholar, LibSearch (the LTU Library’s research and discovery tool) and ProQuest Central. Subject-specific databases for civil engineering and science were also referred to. After the final reports for each project had been submitted and assessed, the Faculty Librarian contacted the lecturer responsible for the unit, requesting copies of the student bibliographies for each group. References for each bibliography were then entered into EndNote. The Faculty Librarian grouped them according to various facets, including the name of the unit and the group within the unit; the material type of the item being referenced; and whether the item required a Library subscription to access it. A total of 58 references were collated for the 2010 CIV1EP unit; 237 references for the 2010 CIV2GR unit; and 225 references for the 2011 CIV1EP unit.

INTERIM FINDINGS
The initial findings showed that student bibliographies for the three group projects were primarily made up of freely available internet resources which required no library subscription. For the 2010 CIV1EP unit, all 58 resources used were freely available on the Internet. For the 2011 CIV1EP unit, 28 of the 225 resources used (12.44%) required a Library subscription or purchase for access, while the second-year students (CIV2GR) used a greater variety of resources, with 71 of the 237 resources used (29.96%) requiring a Library subscription or purchase for access. The results suggest that the library sessions had little or no influence on the 2010 CIV1EP group, but the sessions may have assisted students in the 2011 CIV1EP and 2010 CIV2GR groups to find books, journal articles and conference papers, which were all represented in their bibliographies

FURTHER RESEARCH
The next step in the research is to investigate ways to increase the representation of scholarly references (found by resources other than Google) in student bibliographies. It is anticipated that such a change would lead to an overall improvement in the quality of the student papers. One way of achieving this would be to make it mandatory for students to include a specified number of journal articles, conference papers, or scholarly books in their bibliographies. It is also anticipated that embedding La Trobe University’s Inquiry/Research Quiz (IRQ) using a constructively aligned approach will further enhance the students’ research skills and increase their ability to find suitable scholarly material which relates to their topic. This has already been done successfully (Salisbury, Yager, & Kirkman, 2012)

CONCLUSIONS & CHALLENGES
The study shows that most students rely heavily on the free Internet for information. Students don’t naturally use Library databases or scholarly resources such as Google Scholar to find information, without encouragement from their teachers, tutors and/or librarians. It is acknowledged that the use of scholarly resources doesn’t automatically lead to a high quality paper. Resources must be used appropriately and students also need to have the skills to identify and synthesise key findings in the existing literature and relate these to their own paper. Ideally, students should be able to see the benefit of using scholarly resources in their papers, and continue to seek these out even when it’s not a specific assessment requirement, though it can’t be assumed that this will be the outcome.

REFERENCES

Ellis, J., & Salisbury, F. (2004). Information literacy milestones: building upon the prior knowledge of first-year students. Australian Library Journal, 53(4), 383-396.

Gadd, E., Baldwin, A., & Norris, M. (2010). The citation behaviour of civil engineering students. Journal of Information Literacy, 4(2), 37-49.

Salisbury, F., Yager, Z., & Kirkman, L. (2012). Embedding Inquiry/Research: Moving from a minimalist model to constructive alignment. Paper presented at the 15th International First Year in Higher Education Conference, Brisbane. Retrieved from http://www.fyhe.com.au/past_papers/papers12/Papers/11A.pdf

Wilkes, J., & Gurney, L. J. (2009). Perceptions and applications of information literacy by first year applied science students. Australian Academic & Research Libraries, 40(3), 159-171.

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

ABSTRACT Grain sampling requires the use of appropriate and accurate equipment. This study aimed to compare grain samplers, manual and mechanical, used in the sampling of soybean loads, during their reception by storage units. The used devices were the manual sampler with 1.80 m length and three opening stages, 2.10 m length and three opening stages; and 2.10 m length and one opening stage, besides the mechanical sampler (pneumatic) and the pelican sampler. The analyzed parameters were the contents of impurity, broken grains, pods, immature grains, and moisture. The significance of effect of treatment was determined by F Test and the means were compared by Tukey test (p < 0.05). The devices used for sampling of soybean grains in vehicles, during their reception by storage units, affect the determination of broken grains, pods and immature grains. However, there was no difference between the types of sampling equipment in the determination of impurity content, and the pelican sampler collected greater percentages of pods and immature grains from the sampled vehicles.

2019 Novel Coronavirus COVID-19 (2019-nCoV) Visual Dashboard and Map:
https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

• Confirmed Cases by Country/Region/Sovereignty
• Confirmed Cases by Province/State/Dependency
• Deaths
• Recovered

Downloadable data:
https://github.com/CSSEGISandData/COVID-19

Additional Information about the Visual Dashboard:
https://systems.jhu.edu/research/public-health/ncov

This dataset consists of two geopackages.

Geopackage 1 is a hand digitised polygon geopackage in EPSG:32747 (WGS 84/UTM47S) format, derived from the six orthomosaic datasets which constitute the DTC time series (spanning 22/12/2022 to 04/02/2025). Orthomosaics were typically visualised and digitised at a scale of 1:200, with more detailed assessments made at 1:50 scale. The geopackage consists of 190 polygons which shows the distribution of temporary items and more permanent infrastructure associated with the Edgeworth David Base and Denman Terrestrial Campaign camps. The geopackage captures the most significant items observed, and does not capture small linear features or minor items (e.g. power cords, thin ropes and tape measures, individual anchor points). The total area encompassed by the polygons is 1,698m2 (0.17 ha).

The second geopackage is hand digitised in EPSG:32747 (WGS 84/UTM47S) format, derived from the six orthomosaic datasets which constitute the DTC time series (spanning 22/12/2022 to 04/02/2025). Typically, these orthomosaics were visualised and digitised at a scale of 1:200, with more detailed assessments made at 1:50 scale. The geopackage consists of lines which show recognisable walking pathways between camp features, as well as drainage ditches associated with the melt lake and/or temporary tent sites. Some pathways were quite distinct and well established, with rocks moved to either side of the path and a well-worn track surface. Other pathways were less distinct and less well established, and may fade with time due to natural processes. Occasionally, reference was also made to orthomosaics from prior and/or post time points to assess features, and/or to the accompanying digital surface models. 85 linear metres of drainage channels and 755 linear metres of walking paths were digitised.

Question Paper Solutions of chapter Tools and Methods Used in Cyber Crime of Cyber Law and Ethics, 8th Semester , Computer Science and Engineering

The SS3 subset of the Montreal Archive of Sleep Studies (MASS) cohort is an open-access database of laboratory-based polysomnography (PSG) recordings defined as : 62 subjects (age 42.5±18.9 years, age range: 20-69 years) 29 males (age 40.4±19.4 years, age range: 20-69 years) 33 females (age 44.2±18.6 years, age range: 20-69 years) 62 PSG recordings (whole night) "* PSG.edf" 20 electrodes in the EEG montage reference is linked-ear reference with a 10 kΩ resistance (LER) 2 EOG channels 3 referential EMG 1 ECG channel 62 Sleep staging files "* Base.edf" Sleep stage scoring rules : AASM Page size (s) : 30

This dataset contains Attenuated Total Reflectance Fourier-Transform Infrared Spectroscopy (ATR-FTIR) spectral data of coffee silverskin obtained from specialty coffees processed under wet and semi-wet postharvest treatments at different temperatures and fermentation times. Raw spectra is provided together with R scripts for exploratory analysis using Principal Component Analysis (PCA). The dataset supports chemometric analysis, spectral fingerprinting, and the development of data-driven and model-based approaches for coffee by-product valorization.

|G1|: the length of the elements in G1; |ID|: the length of ID; |M|: the length of the plaintext M;m: the number of signers (m = 1 in schemes [9]–[11] and our scheme); n: the number of recipients.

The Gas Adsorption Measurement Equipment market grows at 5.21% CAGR, driven by R&D in materials science and pharmaceuticals. Understand key segments and regional dynamics.

The North America Engineering Research And Development (ER&D) Services Market projects 9.16% CAGR growth, driven by software engineering & AI innovation. Evaluate key trends and market shifts. Recent developments include: July 2024: Mitacs and the Natural Sciences and Engineering Research Council (NSERC) have inked a memorandum of understanding, cementing their collaboration in research programs. This partnership not only formalizes their existing relationship but also enhances flexibility for university researchers and industry partners. By aligning efforts, these stakeholders can now craft more robust research and training strategies, ultimately driving innovation in Canada.January 2024: The U.S. National Science Foundation, in collaboration with NVIDIA, has initiated the National Artificial Intelligence Research Resource pilot program. This program, supported by a coalition of 10 federal agencies, private-sector entities, and nonprofits, seeks to democratize access to essential AI tools, fostering responsible innovation and discovery.. Notable trends are: Software Engineering Services to drive the Market.