AI Training Data | Annotated Checkout Flows for Retail, Restaurant, and Marketplace Websites Overview

Unlock the next generation of agentic commerce and automated shopping experiences with this comprehensive dataset of meticulously annotated checkout flows, sourced directly from leading retail, restaurant, and marketplace websites. Designed for developers, researchers, and AI labs building large language models (LLMs) and agentic systems capable of online purchasing, this dataset captures the real-world complexity of digital transactions—from cart initiation to final payment.

Key Features

Breadth of Coverage: Over 10,000 unique checkout journeys across hundreds of top e-commerce, food delivery, and service platforms, including but not limited to Walmart, Target, Kroger, Whole Foods, Uber Eats, Instacart, Shopify-powered sites, and more.

Actionable Annotation: Every flow is broken down into granular, step-by-step actions, complete with timestamped events, UI context, form field details, validation logic, and response feedback. Each step includes:

Page state (URL, DOM snapshot, and metadata)

User actions (clicks, taps, text input, dropdown selection, checkbox/radio interactions)

System responses (AJAX calls, error/success messages, cart/price updates)

Authentication and account linking steps where applicable

Payment entry (card, wallet, alternative methods)

Order review and confirmation

Multi-Vertical, Real-World Data: Flows sourced from a wide variety of verticals and real consumer environments, not just demo stores or test accounts. Includes complex cases such as multi-item carts, promo codes, loyalty integration, and split payments.

Structured for Machine Learning: Delivered in standard formats (JSONL, CSV, or your preferred schema), with every event mapped to action types, page features, and expected outcomes. Optional HAR files and raw network request logs provide an extra layer of technical fidelity for action modeling and RLHF pipelines.

Rich Context for LLMs and Agents: Every annotation includes both human-readable and model-consumable descriptions:

“What the user did” (natural language)

“What the system did in response”

“What a successful action should look like”

Error/edge case coverage (invalid forms, OOS, address/payment errors)

Privacy-Safe & Compliant: All flows are depersonalized and scrubbed of PII. Sensitive fields (like credit card numbers, user addresses, and login credentials) are replaced with realistic but synthetic data, ensuring compliance with privacy regulations.

Each flow tracks the user journey from cart to payment to confirmation, including:

Adding/removing items

Applying coupons or promo codes

Selecting shipping/delivery options

Account creation, login, or guest checkout

Inputting payment details (card, wallet, Buy Now Pay Later)

Handling validation errors or OOS scenarios

Order review and final placement

Confirmation page capture (including order summary details)

Why This Dataset?

Building LLMs, agentic shopping bots, or e-commerce automation tools demands more than just page screenshots or API logs. You need deeply contextualized, action-oriented data that reflects how real users interact with the complex, ever-changing UIs of digital commerce. Our dataset uniquely captures:

The full intent-action-outcome loop

Dynamic UI changes, modals, validation, and error handling

Nuances of cart modification, bundle pricing, delivery constraints, and multi-vendor checkouts

Mobile vs. desktop variations

Diverse merchant tech stacks (custom, Shopify, Magento, BigCommerce, native apps, etc.)

Use Cases

LLM Fine-Tuning: Teach models to reason through step-by-step transaction flows, infer next-best-actions, and generate robust, context-sensitive prompts for real-world ordering.

Agentic Shopping Bots: Train agents to navigate web/mobile checkouts autonomously, handle edge cases, and complete real purchases on behalf of users.

Action Model & RLHF Training: Provide reinforcement learning pipelines with ground truth “what happens if I do X?” data across hundreds of real merchants.

UI/UX Research & Synthetic User Studies: Identify friction points, bottlenecks, and drop-offs in modern checkout design by replaying flows and testing interventions.

Automated QA & Regression Testing: Use realistic flows as test cases for new features or third-party integrations.

What’s Included

10,000+ annotated checkout flows (retail, restaurant, marketplace)

Step-by-step event logs with metadata, DOM, and network context

Natural language explanations for each step and transition

All flows are depersonalized and privacy-compliant

Example scripts for ingesting, parsing, and analyzing the dataset

Flexible licensing for research or commercial use

Sample Categories Covered

Grocery delivery (Instacart, Walmart, Kroger, Target, etc.)

Restaurant takeout/delivery (Ub...

The Domain Name Generator Software market is experiencing robust growth, driven by the increasing demand for online presence among Small and Medium-sized Enterprises (SMEs) and large enterprises alike. The ease of use and cost-effectiveness of these tools are major contributing factors. The market is segmented by operating system (Android and iOS), reflecting the mobile-first approach prevalent in today's digital landscape. While precise market sizing data is unavailable, considering the growth in e-commerce and online businesses, a conservative estimate places the 2025 market value at approximately $500 million. A Compound Annual Growth Rate (CAGR) of 15% is projected for the period 2025-2033, indicating a significant expansion opportunity. Key market drivers include the rising need for unique and memorable domain names, the simplification of the domain registration process, and the integration of these generators with other online business tools. Trends such as AI-powered suggestion algorithms and increased automation are enhancing user experience and efficiency. However, challenges remain, including competition from free domain name suggestion tools and concerns around the security and privacy of user data. The North American market currently holds the largest market share, followed by Europe and Asia-Pacific, driven by high internet penetration and a strong entrepreneurial ecosystem. This trend is expected to continue, with significant growth potential in developing economies. The market is highly competitive, with numerous established players and emerging startups vying for market share. Future growth will depend on the continued innovation in the technology, strategic partnerships, and the adoption of advanced features that meet evolving business needs. The projected growth of the Domain Name Generator Software market is underpinned by the continuous expansion of the internet and the ongoing digital transformation across various industries. The increasing adoption of cloud-based solutions and the integration of domain name generators with website building platforms further fuel market expansion. The market is likely to witness significant consolidation in the coming years, with larger players acquiring smaller companies to enhance their offerings and broaden their reach. Moreover, the growing focus on cybersecurity and data privacy will necessitate robust security measures within domain name generator software, impacting future market developments. The demand for specialized domain name generation tools tailored for specific industry niches is also expected to increase, creating opportunities for niche players. Overall, the outlook for the Domain Name Generator Software market is positive, with continued growth projected over the forecast period, driven by increasing demand, technological advancements, and expanding global internet usage.

The Weather Generator Gridded Data consists of two products:

[1] statistically perturbed gridded 100-year historic daily weather data including precipitation [in mm], and detrended maximum and minimum temperature in degrees Celsius, and

[2] stochastically generated and statistically perturbed gridded 1000-year daily weather data including precipitation [in mm], maximum temperature [in degrees Celsius], and minimum temperature in degrees Celsius.

The base climate of this dataset is a combination of historically observed gridded data including Livneh Unsplit 1915-2018 (Pierce et. al. 2021), Livneh 1915-2015 (Livneh et. al. 2013) and PRISM 2016-2018 (PRISM Climate Group, 2014). Daily precipitation is from Livneh Unsplit 1915-2018, daily temperature is from Livneh 2013 spanning 1915-2015 and was extended to 2018 with daily 4km PRISM that was rescaled to the Livneh grid resolution (1/16 deg). The Livneh temperature was bias corrected by month to the corresponding monthly PRISM climate over the same period. Baseline temperature was then detrended by month over the entire time series based on the average monthly temperature from 1991-2020. Statistical perturbations and stochastic generation of the time series were performed by the Weather Generator (Najibi et al. 2024a and Najibi et al. 2024b).

The repository consists of 30 climate perturbation scenarios that range from -25 to +25 % change in mean precipitation, and from 0 to +5 degrees Celsius change in mean temperature. Changes in thermodynamics represent scaling of precipitation during extreme events by a scaling factor per degree Celsius increase in mean temperature and consists primarily of 7%/degree-Celsius with 14%/degree-Celsius as sensitivity perturbations. Further insight for thermodynamic scaling can be found in full report linked below or in Najibi et al. 2024a and Najibi et al. 2024b.

The data presented here was created by the Weather Generator which was developed by Dr. Scott Steinschneider and Dr. Nasser Najibi (Cornell University). If a separate weather generator product is desired apart from this gridded climate dataset, the weather generator code can be adopted to suit the specific needs of the user. The weather generator code and supporting information can be found here: https://github.com/nassernajibi/WGEN-v2.0/tree/main. The full report for the model and performance can be found here: https://water.ca.gov/-/media/DWR-Website/Web-Pages/Programs/All-Programs/Climate-Change-Program/Resources-for-Water-Managers/Files/WGENCalifornia_Final_Report_final_20230808.pdf

Note: Please use the following view to be able to see the entire Dataset Description: https://data.ct.gov/Environment-and-Natural-Resources/Hazardous-Waste-Portal-Manifest-Metadata/x2z6-swxe Dataset Description Outline (5 sections) • INTRODUCTION • WHY USE THE CONNECTICUT OPEN DATA PORTAL MANIFEST METADATA DATASET INSTEAD OF THE DEEP DOCUMENT ONLINE SEARCH PORTAL ITSELF? • WHAT MANIFESTS ARE INCLUDED IN DEEP’S MANIFEST PERMANENT RECORDS ARE ALSO AVAILABLE VIA THE DEEP DOCUMENT SEARCH PORTAL AND CT OPEN DATA? • HOW DOES THE PORTAL MANIFEST METADATA DATASET RELATE TO THE OTHER TWO MANIFEST DATASETS PUBLISHED IN CT OPEN DATA? • IMPORTANT NOTES INTRODUCTION • All of DEEP’s paper hazardous waste manifest records were recently scanned and “indexed”. • Indexing consisted of 6 basic pieces of information or “metadata” taken from each manifest about the Generator and stored with the scanned image. The metadata enables searches by: Site Town, Site Address, Generator Name, Generator ID Number, Manifest ID Number and Date of Shipment. • All of the metadata and scanned images are available electronically via DEEP’s Document Online Search Portal at: https://filings.deep.ct.gov/DEEPDocumentSearchPortal/ • Therefore, it is no longer necessary to visit the DEEP Records Center in Hartford for manifest records or information. • This CT Data dataset “Hazardous Waste Portal Manifest Metadata” (or “Portal Manifest Metadata”) was copied from the DEEP Document Online Search Portal, and includes only the metadata – no images. WHY USE THE CONNECTICUT OPEN DATA PORTAL MANIFEST METADATA DATASET INSTEAD OF THE DEEP DOCUMENT ONLINE SEARCH PORTAL ITSELF? The Portal Manifest Metadata is a good search tool to use along with the Portal. Searching the Portal Manifest Metadata can provide the following advantages over searching the Portal: • faster searches, especially for “large searches” - those with a large number of search returns unlimited number of search returns (Portal is limited to 500); • larger display of search returns; • search returns can be sorted and filtered online in CT Data; and • search returns and the entire dataset can be downloaded from CT Data and used offline (e.g. download to Excel format) • metadata from searches can be copied from CT Data and pasted into the Portal search fields to quickly find single scanned images. The main advantages of the Portal are: • it provides access to scanned images of manifest documents (CT Data does not); and • images can be downloaded one or multiple at a time. WHAT MANIFESTS ARE INCLUDED IN DEEP’S MANIFEST PERMANENT RECORDS ARE ALSO AVAILABLE VIA THE DEEP DOCUMENT SEARCH PORTAL AND CT OPEN DATA? All hazardous waste manifest records received and maintained by the DEEP Manifest Program; including: • manifests originating from a Connecticut Generator or sent to a Connecticut Destination Facility including manifests accompanying an exported shipment • manifests with RCRA hazardous waste listed on them (such manifests may also have non-RCRA hazardous waste listed) • manifests from a Generator with a Connecticut Generator ID number (permanent or temporary number) • manifests with sufficient quantities of RCRA hazardous waste listed for DEEP to consider the Generator to be a Small or Large Quantity Generator • manifests with PCBs listed on them from 2016 to 6-29-2018. • Note: manifests sent to a CT Destination Facility were indexed by the Connecticut or Out of State Generator. Searches by CT Designated Facility are not possible unless such facility is the Generator for the purposes of manifesting. All other manifests were considered “non-hazardous” manifests and not scanned. They were discarded after 2 years in accord with DEEP records retention schedule. Non-hazardous manifests include: • Manifests with only non-RCRA hazardous waste listed • Manifests from generators that did not have a permanent or temporary Generator ID number • Sometimes non-hazardous manifests were considered “Hazar

Diesel Generator in Telecom Market Outlook

The global diesel generator in telecom market size was valued at approximately USD 2.5 billion in 2023 and is projected to reach USD 4.3 billion by 2032, growing at a compound annual growth rate (CAGR) of 5.8% from 2024 to 2032. This growth is driven by the increasing need for reliable and uninterrupted power supply in the telecom sector, as well as the expansion of telecom infrastructure, particularly in emerging markets.

One of the significant growth factors for the diesel generator in telecom market is the continuous expansion of telecom networks to cater to the increasing demand for high-speed internet and mobile services. As telecom operators invest heavily in new base transceiver stations (BTS), data centers, and network operations centers (NOCs), the need for reliable backup power solutions becomes critical. Diesel generators are preferred in these settings due to their reliability, efficiency, and ability to provide continuous power during outages. Additionally, the growing trend of remote working and digitalization has further accentuated the need for robust telecom infrastructure, thereby driving the market growth.

Another key growth factor is the increasing frequency of natural disasters and power outages, which disrupt telecom services, especially in regions prone to such events. Diesel generators offer a dependable solution to maintain continuous operations during such disruptions. The telecom sector's reliance on uninterrupted power supply to ensure seamless communication services underscores the importance of diesel generators. In regions with unstable power grids or frequent load shedding, diesel generators play a crucial role in ensuring that telecom services remain unaffected, thereby supporting market growth.

The rising adoption of advanced technologies such as 5G and the Internet of Things (IoT) is also propelling the demand for diesel generators in the telecom sector. The deployment of 5G networks requires the establishment of new telecom infrastructure, including small cell sites and edge data centers, which necessitate reliable power backup solutions. Moreover, the proliferation of IoT devices, which rely heavily on continuous connectivity, further underscores the importance of robust power backup systems. As telecom operators strive to provide consistent and high-quality services, the demand for diesel generators is expected to increase significantly.

From a regional perspective, Asia Pacific is anticipated to witness substantial growth in the diesel generator in telecom market. The region's burgeoning telecom industry, driven by the rapid digital transformation and increasing internet penetration, is a key factor contributing to this growth. Countries such as China and India are investing heavily in expanding their telecom infrastructure, which in turn, fuels the demand for diesel generators. Additionally, North America and Europe are expected to show steady growth due to the ongoing upgrades in telecom infrastructure and the need for reliable power solutions in these regions.

Power Rating Analysis

Diesel generators in the telecom market can be categorized based on their power rating, which includes segments such as Below 100 kVA, 100-350 kVA, 350-1000 kVA, and Above 1000 kVA. Each of these segments serves specific applications within the telecom sector, with varying demand dynamics.

The Below 100 kVA segment primarily caters to small-scale telecom installations such as small cell sites and micro-BTS units. These generators are compact, cost-effective, and can be easily deployed in urban and suburban areas where space is a constraint. The growing deployment of small cell sites for enhancing 5G network coverage is expected to drive the demand for generators in this power rating segment. Telecom operators are increasingly focusing on densification of their networks, which involves setting up numerous small cell sites that require reliable backup power, thereby boosting the market for Below 100 kVA diesel generators.

The 100-350 kVA segment is crucial for medium-sized telecom installations, including BTS units and local data centers. These generators offer a balanced combination of power output and cost-efficiency, making them suitable for a wide range of applications. As telecom operators expand their network coverage and upgrade existing infrastructure, the demand for diesel generators in this power rating segment is expected to grow. Additionally, the increasing number of remote and rural telecom installations, which often face power supply chall

Facebook received 73,390 user data requests from federal agencies and courts in the United States during the second half of 2023. The social network produced some user data in 88.84 percent of requests from U.S. federal authorities. The United States accounts for the largest share of Facebook user data requests worldwide.

Industrial Gensets Maintenance Market Outlook

The global industrial gensets maintenance market size is projected to grow from $5.2 billion in 2023 to $9.5 billion by 2032, exhibiting a CAGR of 6.8% during the forecast period. The market is primarily driven by the increasing reliance on uninterrupted power supply across various industries and the growing adoption of gensets (generator sets) as a reliable source of backup power. As industries become more dependent on continuous power supply for their operations, the demand for regular maintenance and servicing of gensets has surged, fueling market growth.

One of the primary growth factors for the industrial gensets maintenance market is the increasing industrialization and urbanization worldwide. As more industries are established and urban areas expand, the need for reliable and consistent power supply becomes critical. Gensets serve as a dependable backup power source, ensuring that operations remain uninterrupted during power outages. Consequently, the maintenance of these gensets becomes essential to ensure their optimal performance and longevity, thus driving the market demand for maintenance services.

Another significant driver is the aging infrastructure of power grids, particularly in developing countries. As the power grid infrastructure in many regions becomes outdated, the frequency and duration of power outages increase. This has led to a growing reliance on gensets as a backup power solution. Regular maintenance of gensets is crucial to prevent breakdowns and ensure they are ready to provide power when needed. This has resulted in a steady rise in the demand for preventive and corrective maintenance services within the market.

The advancements in technology and the integration of predictive maintenance solutions have also contributed to market growth. Predictive maintenance uses data analytics and IoT (Internet of Things) technologies to monitor the performance and condition of gensets in real-time. This approach allows for the early detection of potential issues, enabling timely maintenance and reducing the risk of unexpected failures. The adoption of predictive maintenance solutions is gaining traction among industries, further propelling the market growth.

From a regional perspective, Asia Pacific is expected to dominate the industrial gensets maintenance market during the forecast period. The rapid industrialization and urbanization in countries like China and India have led to an increased demand for reliable power supply. Additionally, the frequent power outages in these regions have driven the need for gensets and their maintenance services. North America and Europe are also significant markets, with steady demand driven by the aging power infrastructure and stringent regulations regarding power reliability and safety.

Service Type Analysis

Within the industrial gensets maintenance market, the service type segment encompasses preventive maintenance, corrective maintenance, and predictive maintenance. Preventive maintenance involves regular, scheduled servicing of gensets to prevent potential issues and ensure their efficient operation. This type of maintenance is critical as it helps in identifying and addressing minor issues before they escalate into major problems. The demand for preventive maintenance services is high as industries strive to minimize downtime and maintain continuous power supply.

Corrective maintenance, on the other hand, involves repairing gensets after a breakdown or malfunction has occurred. While this type of maintenance is essential for restoring gensets to their operational state, it is often more expensive and time-consuming compared to preventive maintenance. Despite this, corrective maintenance services remain a vital part of the market as they are necessary for addressing unexpected failures and ensuring gensets are back in service as quickly as possible.

Predictive maintenance represents the most advanced form of genset maintenance, leveraging data analytics and IoT technologies to monitor genset performance in real-time. By analyzing data from sensors and other monitoring devices, predictive maintenance can predict potential failures and schedule maintenance activities before breakdowns occur. This proactive approach not only reduces downtime but also extends the lifespan of gensets. The adoption of predictive maintenance is growing, driven by the increasing availability of advanced technologies and the desire for more efficient maintenance practices.

The industrial gensets maintena

The placeholder image generator market is experiencing robust growth, driven by the increasing demand for visually appealing websites and applications without relying on heavy, slow-to-load original images. The market's expansion is fueled by the rise of web design and development, the growing popularity of content creation tools, and the need for efficient prototyping and wireframing. While precise market sizing data is not provided, based on general market trends for similar software-as-a-service (SaaS) offerings and the large number of active players, we can reasonably estimate the 2025 market size to be around $50 million. Considering a conservative Compound Annual Growth Rate (CAGR) of 15%, we project the market to reach approximately $100 million by 2033. Key trends include increasing integration with other design tools, the development of AI-powered image generation features, and a shift towards more customizable and versatile placeholder options. The market faces some restraints, such as the availability of free, basic alternatives and the potential for users to overlook the value proposition of dedicated placeholder generators, especially in smaller-scale projects. The competitive landscape is highly fragmented, with numerous players ranging from established companies to individual developers offering a variety of features and pricing models. The key success factors for vendors include offering a diverse library of placeholder images, seamless integration with popular design tools, and providing a user-friendly interface. Future growth will likely be influenced by the adoption of advanced technologies like AI and machine learning to enhance image quality, personalization, and efficiency. Furthermore, strategic partnerships with other software providers and expanding the available image types and styles will be vital for sustained market leadership. The potential for growth is significant, especially as developers and designers increasingly prioritize efficiency and high-quality visuals in their projects.

The global number of Facebook users was forecast to continuously increase between 2023 and 2027 by in total 391 million users (+14.36 percent). After the fourth consecutive increasing year, the Facebook user base is estimated to reach 3.1 billion users and therefore a new peak in 2027. Notably, the number of Facebook users was continuously increasing over the past years. User figures, shown here regarding the platform Facebook, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

Static Site Generation Platform Market Outlook

According to our latest research, the global static site generation platform market size reached USD 1.24 billion in 2024, exhibiting robust expansion driven by the increasing demand for fast, secure, and scalable web solutions. The market is projected to grow at a remarkable CAGR of 14.8% during the forecast period, reaching an estimated USD 4.12 billion by 2033. Key growth factors include the rapid digital transformation across industries, the proliferation of content-driven websites, and the rising emphasis on website performance and security. The static site generation platform market is witnessing significant traction as enterprises and developers seek modern approaches to web development that deliver superior user experiences, cost efficiency, and enhanced security.

One of the primary growth drivers for the static site generation platform market is the escalating need for high-performance websites that can handle increasing web traffic without compromising on speed or reliability. As businesses expand their digital presence, the limitations of traditional dynamic websites—such as slower load times and higher vulnerability to cyber threats—have become more apparent. Static site generators offer a compelling alternative by pre-rendering web pages, which significantly improves site loading speed and reduces server load. Organizations are increasingly adopting these platforms to enhance search engine optimization (SEO), improve user engagement, and lower infrastructure costs, all of which contribute to the market's robust growth trajectory.

Another significant factor fueling market expansion is the growing adoption of static site generation platforms among developers and enterprises seeking agility, scalability, and streamlined workflows. The integration of headless content management systems (CMS) and the rise of JAMstack architecture have further accelerated the shift towards static site generation. These platforms empower developers to decouple front-end and back-end operations, enabling more flexible and secure development processes. Additionally, the proliferation of open-source tools and frameworks has democratized access to static site generation technologies, fostering innovation and reducing development time. This democratization is particularly beneficial for small and medium enterprises (SMEs), which can now leverage enterprise-grade web solutions without substantial upfront investments.

The increasing focus on cybersecurity and data privacy is also propelling the static site generation platform market forward. Static websites inherently reduce the attack surface for malicious actors, as they do not rely on databases or server-side processing, making them less susceptible to common vulnerabilities such as SQL injection or cross-site scripting (XSS). This security advantage is particularly appealing to sectors such as healthcare, finance, and education, where data protection is paramount. As regulatory requirements around data privacy become more stringent worldwide, organizations are turning to static site generation platforms to ensure compliance while maintaining a seamless digital experience for users.

From a regional perspective, North America currently dominates the static site generation platform market, accounting for the largest share in 2024. This leadership position is attributed to the region’s advanced IT infrastructure, high adoption of cloud technologies, and a strong ecosystem of web development companies and tech startups. Europe follows closely, driven by digital transformation initiatives and a growing emphasis on website security and performance. Meanwhile, the Asia Pacific region is poised for the highest growth rate over the forecast period, fueled by rapid digitalization, increasing internet penetration, and a burgeoning developer community. The market landscape in Latin America and the Middle East & Africa is also evolving, with enterprises in these regions gradually recognizing the benefits of static site generation for modern web development.

Component Analysis

The static site generation platform market is segmented by component into software and services, each playing a pivotal role in the ecosystem. The software segment, which includes static site generators, frameworks, and related developer tools, commands the largest market share. This dominance is driven by the continuous evolution of innovative frameworks such as Gatsby

North America Diesel Gensets Market size was valued at USD 4,802.49 Million in 2024 and is projected to reach USD 7,499.98 Million by 2032, growing at a CAGR of 5.89% from 2025 to 2032.Technological innovation in the diesel genset market is influencing market conditions in North America. Next-generation gensets are being engineered to meet tough emissions standards, including those imposed by the U.S. Environmental Protection Agency (EPA) and Canada's counterpart agencies. Companies are emphasizing the creation of cleaner-burning engines, hybrid gensets with battery storage or solar power and gensets with remote monitoring and predictive maintenance technology. Digitalization is another major trend. Internet of Things (IoT) technology and smart controllers merged allow real-time monitoring, remote troubleshooting and performance optimization, thus makes gensets more efficient and more user-friendly.

Cristiano Ronaldo has one of the most popular Instagram accounts as of April 2024.

              The Portuguese footballer is the most-followed person on the photo sharing app platform with 628 million followers. Instagram's own account was ranked first with roughly 672 million followers.

              How popular is Instagram?

              Instagram is a photo-sharing social networking service that enables users to take pictures and edit them with filters. The platform allows users to post and share their images online and directly with their friends and followers on the social network. The cross-platform app reached one billion monthly active users in mid-2018. In 2020, there were over 114 million Instagram users in the United States and experts project this figure to surpass 127 million users in 2023.

              Who uses Instagram?

              Instagram audiences are predominantly young – recent data states that almost 60 percent of U.S. Instagram users are aged 34 years or younger. Fall 2020 data reveals that Instagram is also one of the most popular social media for teens and one of the social networks with the biggest reach among teens in the United States.

              Celebrity influencers on Instagram
              Many celebrities and athletes are brand spokespeople and generate additional income with social media advertising and sponsored content. Unsurprisingly, Ronaldo ranked first again, as the average media value of one of his Instagram posts was 985,441 U.S. dollars.

The Weather Generator Gridded Data consists of two products: [1] statistically perturbed gridded 100-year historic daily weather data including precipitation [in mm], and detrended maximum and minimum temperature in degrees Celsius, and [2] stochastically generated and statistically perturbed gridded 1000-year daily weather data including precipitation [in mm], maximum temperature [in degrees Celsius], and minimum temperature in degrees Celsius. The base climate of this dataset is a combination of historically observed gridded data including Livneh Unsplit 1915-2018 (Pierce et. al. 2021), Livneh 1915-2015 (Livneh et. al. 2013) and PRISM 2016-2018 (PRISM Climate Group, 2014). Daily precipitation is from Livneh Unsplit 1915-2018, daily temperature is from Livneh 2013 spanning 1915-2015 and was extended to 2018 with daily 4km PRISM that was rescaled to the Livneh grid resolution (1/16 deg). The Livneh temperature was bias corrected by month to the corresponding monthly PRISM climate over the same period. Baseline temperature was then detrended by month over the entire time series based on the average monthly temperature from 1991-2020. Statistical perturbations and stochastic generation of the time series were performed by the Weather Generator (Najibi et al. 2024a and Najibi et al. 2024b). The repository consists of 30 climate perturbation scenarios that range from -25 to +25 % change in mean precipitation, and from 0 to +5 degrees Celsius change in mean temperature. Changes in thermodynamics represent scaling of precipitation during extreme events by a scaling factor per degree Celsius increase in mean temperature and consists primarily of 7%/degree-Celsius with 14%/degree-Celsius as sensitivity perturbations. Further insight for thermodynamic scaling can be found in full report linked below or in Najibi et al. 2024a and Najibi et al. 2024b. The data presented here was created by the Weather Generator which was developed by Dr. Scott Steinschneider and Dr. Nasser Najibi (Cornell University). If a separate weather generator product is desired apart from this gridded climate dataset, the weather generator code can be adopted to suit the specific needs of the user. The weather generator code and supporting information can be found here: https://github.com/nassernajibi/WGEN-v2.0/tree/main. The full report for the model and performance can be found here: https://water.ca.gov/-/media/DWR-Website/Web-Pages/Programs/All-Programs/Climate-Change-Program/Resources-for-Water-Managers/Files/WGENCalifornia_Final_Report_final_20230808.pdf

The data is sourced from CSIRO Parkes ATNF.eg http://www.atnf.csiro.au/research/pulsar/psrcat/Feel the pulse of the universeWe're taking signal data from astronomical "pulsar" sources and creating a way to listen to their signals audibly.Pulsar data is available from ATNF at CSIRO.au. Our team at #SciHackMelb has been working on a #datavis to give researchers and others a novel way to explore the Pulsar corpus, especially through the sound of the frequencies at which the Pulsars emit pulses.Link to project page at #SciHackMelb - http://www.the-hackfest.com/events/melbourne-science-hackfest/projects/pulsar-voices/The files attached here include: source data, project presentation, data as used in website final_pulsar.sql, and other methodology documentation. Importantly, see the Github link which contains data manipulation code, html code to present the data, and render audibly, iPython Notebook to process single pulsar data into an audible waveform file. Together all these resources are the Pulsar Voices activity and resulting data.Source Data;* RA - east/west coordinates (0 - 24 hrs, roughly equates to longitude) [theta; transforms RA to 0 - 360*]* Dec - north/south coordinates (-90, +90 roughly equates to latitude i.e. 90 is above north pole, and -90 south pole)* P0 - the time in seconds that a pulsar repeats its signal* f - 1/P0 which ranges from 700 cycles per sec, to some which pulses which occur every few seconds* kps - distance from Earth in kilo-parsecs. 1 kps = 3,000 light years. The furthest data is 30 kps. The galactic centre is about 25,000 light years away i.e. about 8kps.psrcatShort.csv = 2,295 Pulsars all known pulsars with above fields; RA, Dec, ThetapsrcatMedium.csv - add P0 and kps, only 1428 lines - i.e. not available for all 2,295 datapointpsrcatSparse.csv - add P0 and kps, banks if n/a, 2,295 linesshort.txt - important pulsars with high levels of observation (** even more closely examined)pulsar.R - code contributed by Ben Raymond to visualise Pulsar frequency, period in histogrampulsarVoices_authors.JPG - added photo of authors from SciHackMelbAdded to the raw data:- Coordinates to map RA, Dec to screen width(y)/height(x)y = RA[Theta]*width/360; x = (Dec + 90)*height/180- audible frequency converted from Pulsar frequency (1/P0)Formula for 1/P0(x) -> Hz(y) => y = 10 ^ (0.5 log(x) + 2.8)Explanation in text file; Convert1/P0toHz.txtTone generator from: http://www.softsynth.com/webaudio/tone.php- detailed waveform file audible converted from Pulsar signal data, and waveform image (and python notebook to generate; available):The project source is hosted on github at:https://github.com/gazzar/pulsarvoicesAn IPython/Jupyter notebook contains code and a rough description of the method used to process a psrfits .sf filedownloaded via the CSIRO Data Access Portal at http://doi.org/10.4225/08/55940087706E1The notebook contains experimental code to read one of these .sf files and access the contained spectrogram data, processing it to generate an audible signal.It also reads the .txt files containing columnar pulse phase data (which is also contained in the .sf files) and processes these by frequency modulating the signal with an audible carrier.This is the method used to generate the .wav and .png files used in the web interface.https://github.com/gazzar/pulsarvoices/blob/master/ipynb/hackfest1.ipynb A standalone python script that does the .txt to .png and .wav signal processing was used to process 15 more pulsar data examples. These can be reproduced by running the script.https://github.com/gazzar/pulsarvoices/blob/master/data/pulsarvoices.pyProcessed file at: https://github.com/gazzar/pulsarvoices/tree/master/webhttps://github.com/gazzar/pulsarvoices/blob/master/web/J0437-4715.pngJ0437-4715.wav | J0437-4715.png)#Datavis online at: http://checkonline.com.au/tooltip.php. Code at Github linked above. See especially:https://github.com/gazzar/pulsarvoices/blob/master/web/index.phpparticularly, lines 314 - 328 (or search: "SELECT * FROM final_pulsar";) which loads pulsar data from DB and push to screen with Hz on mouseover.Pulsar Voices webpage Functions:1.There is sound when you run the mouse across the Pulsars. We plot all known pulsars (N=2,295), and play a tone for pulsars we had data on frequency i.e. about 75%.2. In the bottom left corner a more detailed Pulsar sound, and wave image pops up when you click the star icon. Two of the team worked exclusively on turning a single pulsars waveform into an audible wav file. They created 16 of these files, and a workflow, but the team only had time to load one waveform. With more time, it would be great to load these files.3. If you leave the mouse over a Pulsar, a little data description pops up, with location (RA, Dec), distance (kilo parsecs; 1 = 3,000 light years), and frequency of rotation (and Hz converted to human hearing).4.If you click on a Pulsar, other pulsars with similar frequency are highlighted in white. With more time I was interested to see if there are harmonics between pulsars. i.e. related frequencies.The TeamMichael Walker is: orcid.org/0000-0003-3086-6094 ; Biosciences PhD student, Unimelb, Melbourne.Richard Ferrers is: orcid.org/0000-0002-2923-9889 ; ANDS Research Data Analyst, Innovation/Value Researcher, Melbourne.Sarath Tomy is: http://orcid.org/0000-0003-4301-0690 ; La Trobe PhD Comp Sci, Melbourne.Gary Ruben is: http://orcid.org/0000-0002-6591-1820 ; CSIRO Postdoc at Australian Synchrotron, Melbourne.Christopher Russell is: Data Manager, CSIRO, Sydney.https://wiki.csiro.au/display/ASC/Chris+RussellAnderson Murray is: orcid.org/0000-0001-6986-9140; Physics Honours, Monash, Melbourne.Contact: richard.ferrers@ands.org.au for more information.What is still left to do?* load data, description, images fileset to figshare :: DOI ; DONE except DOI* add overview images as option eg frequency bi-modal histogram* colour code pulsars by distance; DONE* add pulsar detail sound to Top three Observants; 16 pulsars processed but not loaded* add tones to pulsars to indicate f; DONE* add tooltips to show location, distance, frequency, name; DONE* add title and description; DONE* project data onto a planetarium dome with interaction to play pulsar frequencies.DONE see youtube video at https://youtu.be/F119gqOKJ1U* zoom into parts of sky to get separation between close data points - see youtube; function in Google Earth #datavis of dataset. Link at youtube.* set upper and lower tone boundaries, so tones aren't annoying* colour code pulsars by frequency bins e.g. >100 Hz, 10 - 100, 1 - 10,

Context

Instuctables DIY is a website where people share their DIY projects with the world. I've scraped every project up to 2020 (except the teachers category because it flucked up my scraper by being infinite scroll) Who needs a bloody "teachers" category anyway the projects are way dull.

Content

Each main category has been sorted into its own CSV, each of these consist of subcategories within their respective domain. There are tens of thousands of projects in this dataset.

• CIRCUITS
• COOKING
• CRAFT
• LIVING
• OUTSIDE
• WORKSHOP

NOTE: People occasionally upload their projects twice! You will see duplicate entries with different favorite/view counts

Inspiration

I BUILT AN IDEA GENERATOR BASED ON THE "CIRCUITS" DATA: THE VIDEO

I've always had so many ideas but it always feels like none. So I thought, "Why not build a generator for ideas?" and what better place to get data than instructables. I'm using this data to build an "idea generator" and will post the website link when it is finished for everyone to try out.

Future

When I have the time I will scrape the project "Steps" and directions for each. It is a lot of data already, I can imagine that being near 500MB or so...

Cligen is a stochastic weather generator which produces daily estimates of precipitation, temperature, dewpoint, wind, and solar radiation for a single geographic point, using monthly parameters (means, SD's, skewness, etc.) derived from the historic measurements. Unlike other climate generators, it produces individual storm parameter estimates, including time to peak, peak intensity, and storm duration, which are required to run the WEPP and the WEPS soil erosion models. Station parameter files to run Cligen for several thousand U. S. sites are available for download from this website: also data and software to build station files for international sites. With the exception of Tmin, Tmax, and Tdew temperatures (changed in January 2004), daily estimates for each parameter are generated independently of the others. With the current random number generator, subsequent runs on the same machine made with identical inputs will produce identical results. Users of daily simulation models should consider the impacts of Cligen's characteristics on their application. Individual parameter distributions may be expected to reproduce monthly historic distributions quite well. However, if the model in question is sensitive to the daily interactions of two or more of the parameters Cligen produces, Cligen may not be the most appropriate weather generator to use. This is because for a given day, it generates solar radiation, and maximum and minimum temperatures completely independently from precipitation. Experience and common sense tell us that these parameters are NOT independent. In practice this may not be a huge issue, since it is not uncommon for models to be sensitive to one weather parameter on a daily basis, and relatively insensitive to the others, as long as their monthly trends are preserved. Resources in this dataset:Resource Title: Cligen. File Name: Web Page, url: https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/ Overview, source code downloads, data files, and publications.

Smart Gensets Market Outlook

The global smart gensets market size was valued at approximately USD 6.5 billion in 2023 and is expected to reach USD 11.3 billion by 2032, growing at a compound annual growth rate (CAGR) of 6.3% during the forecast period. This robust growth is driven by the increasing need for uninterrupted power supply in various sectors, coupled with the advancements in smart grid technologies. As industries and residential sectors continue to prioritize energy efficiency and reduced emissions, smart gensets are becoming integral to modern power solutions. The demand for these gensets is further bolstered by their ability to offer real-time monitoring and control, which significantly enhances operational efficiency and reduces downtime.

One of the major growth factors for the smart gensets market is the rising demand for reliable and efficient power solutions in both developed and developing regions. As urbanization continues to increase, the pressure on existing infrastructure to provide stable power has intensified, leading to an uptick in demand for backup power solutions such as smart gensets. These gensets are especially critical in areas prone to power outages and in regions where grid infrastructure is still developing. The integration of smart technologies in gensets allows for better fuel management, predictive maintenance, and integration with renewable energy sources, thus catering to the growing energy needs while maintaining sustainability.

The proliferation of the Internet of Things (IoT) and advancements in communication technologies have further augmented the growth of the smart gensets market. The integration of IoT facilitates remote monitoring and diagnostics, enabling operators to manage gensets more effectively and efficiently. This capability is particularly valuable for industries that require continuous power supply and cannot afford operational downtimes, such as data centers and healthcare facilities. The ability of smart gensets to integrate with cloud-based platforms also allows for data analytics, which can be used to optimize performance and reduce operational costs, thereby driving market expansion.

Environmental concerns and regulatory mandates aimed at reducing carbon footprints are also propelling the demand for smart gensets. Governments across the globe are implementing stringent emission norms, which are fostering the adoption of cleaner fuel alternatives like gas and hybrid gensets. Additionally, smart gensets offer significant improvements in terms of fuel efficiency and emission reduction compared to traditional gensets. This not only helps in complying with regulatory requirements but also appeals to environmentally-conscious consumers and businesses, thereby contributing to market growth.

Regionally, Asia Pacific is expected to dominate the smart gensets market, driven by rapid industrialization and urbanization in countries like China and India. The need for reliable power solutions to support burgeoning industrial activities and rising infrastructure development projects is a key factor driving the market in this region. Meanwhile, North America and Europe are witnessing substantial growth due to the modernization of aging power infrastructure and the increasing adoption of renewable energy sources. In contrast, the Middle East & Africa and Latin America markets are experiencing moderate growth, primarily fueled by economic development and investments in infrastructure improvements.

Fuel Type Analysis

In the smart gensets market, the fuel type segment is crucial as it directly impacts the environmental footprint and operational costs of gensets. Diesel gensets have traditionally held a significant share of the market due to their reliability and efficiency in high-power applications. However, the growing focus on sustainability and regulatory mandates on emissions are driving a shift towards cleaner fuel alternatives. Gas gensets, for instance, are gaining popularity due to their lower emission levels and cost-effectiveness over long-term usage. They are particularly favored in regions with abundant natural gas resources and are becoming a preferred choice for both industrial and commercial applications.

The hybrid gensets segment is emerging as a promising category within the smart gensets market. These gensets combine the benefits of multiple fuel sources, typically integrating renewable energy options with traditional fuels. This approach not only enhances fuel efficiency and reduces emissions but also provides a reliable power backup solution that can operate in diverse conditions. The adoption of

Social media companies are starting to offer users the option to subscribe to their platforms in exchange for monthly fees. Until recently, social media has been predominantly free to use, with tech companies relying on advertising as their main revenue generator. However, advertising revenues have been dropping following the COVID-induced boom. As of July 2023, Meta Verified is the most costly of the subscription services, setting users back almost 15 U.S. dollars per month on iOS or Android. Twitter Blue costs between eight and 11 U.S. dollars per month and ensures users will receive the blue check mark, and have the ability to edit tweets and have NFT profile pictures. Snapchat+, drawing in four million users as of the second quarter of 2023, boasts a Story re-watch function, custom app icons, and a Snapchat+ badge.