This dataset contains a list of sales and movement data by item and department appended monthly. Update Frequency : Monthly

Thomson Data’s B2B leads data offers businesses the right insights to make well-informed decisions. It will help you analyze and understand the industry you target, the businesses operating within that industry, and the competitors, enabling you to make accurate strategic decisions.

For example, you are using cold outreach, Account-Based Marketing (ABM), and other lead strategies. Then, our B2B lead data can play a pivotal role in creating segmented lists of contacts that your team can put to good use to grow your lead pipeline.

What are the Thomson Data’s B2B Leads Data Use Cases? Our goal is to provide high-quality B2B lead data as it will help organizations carry out the following marketing and sales activities.

• ICP development: Build an Ideal Customer Profile (ICP), which is a depiction of your perfect client. Use your ICP as a foundation to find other target audiences who match the ICP parameters and expand your outreach efforts.

• Lead generation: For the lead generation process to be on the right track, the organization must find the ideal customers and the right contact details, which will provided by our B2B Lead data.

• Outbound sales: Our precise B2B leads data heightens the efficiency of the outbound sales, as it primarily depends upon accurate contact records, which are available in our database.

• Demand generation: Depend generation is the umbrella of marketing activities that attract new prospects towards your brand. Once you are aware of the business you are targeting with our B2B lead data, you can power your marketing strategies (content marketing, email marketing, etc.) And More!

Where does Thomson Data’s B2B Lead Data come from? Thomson Data’s B2B lead data is collated from trusted public domains, which are as follows; • B2B Directories • Market Research • Webinars • Online Conferences • Re-Seller Programs • Telemarketing Efforts • Government Records • Publishing Companies • Timeshare Associations • Panel Discussions • And More

Why Choose Thomson Data’s B2B Lead Data?

Thomson Data is the best B2B lead data provider in terms of being updated and quality. It should be your go-to choice if you want global compliant data, and your business is dependent upon calling prospects, as Thomson Data provides a phone number-verified data. Furthermore, by using B2B lead data for email marketing campaigns, businesses can achieve a deliverability rate of 95%. It isn’t that great, but that’s not all; our B2B lead database can be seamlessly downloaded and easily integrated with CRM. It can also be used for global prospecting.

Henceforth, it is a vast database that is regularly updated to ensure your communication with your prospect is hassle-free. So, when you need B2B lead data, make sure you consider Thomson Data as your only option. Send us a request and we will be happy to help you.

Salient Features of Dentists Email Addresses

So make sure that you don’t find excuses for failing at global marketing campaigns and in reaching targeted medical practitioners and healthcare specialists. With our Dentists Email Leads, you will seldom have a reason not to succeed! So make haste and take action today!

1. 1.2 million phone calls per month as a part of a data verification
2. 85% telephone and email verified Dentist Mailing Lists
3. Quarterly SMTP and NCOA verified to keep data fresh and active
4. 15 million verification messages sent every month to validate email addresses
5. Connect with top Dentists across the US, Canada, UK, Europe, EMEA, Australia, APAC and many more countries.
6. egularly updated and cleansed databases to keep it free of duplicate and inaccurate data

How Can Our Dentists Data Help You to Market to Dentists?

We provide a variety of methods for marketing your dental appliances or products to the top-rated dentists in the United States. Take a glance at some of the available channels:

• Email blast • Marketing viability • Test campaigns • Direct mail • Sales leads • Drift campaigns • ABM campaigns • Product launches • B2B marketing

Data Sources

The contact details of your targeted healthcare professionals are compiled from highly credible resources like: • Websites • Medical seminars • Medical records • Trade shows • Medical conferences

What’s in for you? Over choosing us, here are a few advantages we authenticate- • Locate, target, and prospect leads from 170+ countries • Design and execute ABM and multi-channel campaigns • Seamless and smooth pre-and post-sale customer service • Connect with old leads and build a fruitful customer relationship • Analyze the market for product development and sales campaigns • Boost sales and ROI with increased customer acquisition and retention

Our security compliance

We use of globally recognized data laws like –

GDPR, CCPA, ACMA, EDPS, CAN-SPAM and ANTI CAN-SPAM to ensure the privacy and security of our database. We engage certified auditors to validate our security and privacy by providing us with certificates to represent our security compliance.

Our USPs- what makes us your ideal choice?

At DataCaptive™, we strive consistently to improve our services and cater to the needs of businesses around the world while keeping up with industry trends.

• Elaborate data mining from credible sources • 7-tier verification, including manual quality check • Strict adherence to global and local data policies • Guaranteed 95% accuracy or cash-back • Free sample database available on request

Guaranteed benefits of our Dentists email database!

85% email deliverability and 95% accuracy on other data fields

We understand the importance of data accuracy and employ every avenue to keep our database fresh and updated. We execute a multi-step QC process backed by our Patented AI and Machine learning tools to prevent anomalies in consistency and data precision. This cycle repeats every 45 days. Although maintaining 100% accuracy is quite impractical, since data such as email, physical addresses, and phone numbers are subjected to change, we guarantee 85% email deliverability and 95% accuracy on other data points.

100% replacement in case of hard bounces

Every data point is meticulously verified and then re-verified to ensure you get the best. Data Accuracy is paramount in successfully penetrating a new market or working within a familiar one. We are committed to precision. However, in an unlikely event where hard bounces or inaccuracies exceed the guaranteed percentage, we offer replacement with immediate effect. If need be, we even offer credits and/or refunds for inaccurate contacts.

Other promised benefits

• Contacts are for the perpetual usage • The database comprises consent-based opt-in contacts only • The list is free of duplicate contacts and generic emails • Round-the-clock customer service assistance • 360-degree database solutions

Dataset Card for "sales-conversations"

This dataset was created for the purpose of training a sales agent chatbot that can convince people. The initial idea came from: textbooks is all you need https://arxiv.org/abs/2306.11644 gpt-3.5-turbo was used for the generation

  Structure

The conversations have a customer and a salesman which appear always in changing order. customer, salesman, customer, salesman, etc. The customer always starts the conversation Who ends the… See the full description on the dataset page: https://huggingface.co/datasets/goendalf666/sales-conversations.

Global B2B Mobile Phone Number Database | 100M+ Verified Contacts | 95% Accuracy Forager.ai provides the world’s most reliable mobile phone number data for businesses that refuse to compromise on quality. With 100 million+ professionally verified mobile numbers refreshed every 3 weeks, our database ensures 95% accuracy – so your teams never waste time on dead-end leads.

Why Our Data Wins ✅ Accuracy You Can Trust 95% of mobile numbers are verified against live carrier records and tied to current job roles. Say goodbye to “disconnected number” voicemails.

✅ Depth Beyond Digits Each contact includes 150+ data points:

Direct mobile numbers

Current job title, company, and department

Full career history + education background

Location data + LinkedIn profiles

Company size, industry, and revenue

✅ Freshness Guaranteed Bi-weekly updates combat job-hopping and role changes – critical for sales teams targeting decision-makers.

✅ Ethically Sourced & Compliant First-party collected data with full GDPR/CCPA compliance.

Who Uses This Data?

Sales Teams: Cold-call C-suite prospects with verified mobile numbers.

Marketers: Run hyper-personalized SMS/WhatsApp campaigns.

Recruiters: Source passive candidates with up-to-date contact intel.

Data Vendors: License premium datasets to enhance your product.

Tech Platforms: Power your SaaS tools via API with enterprise-grade B2B data.

Flexible Delivery, Instant Results

API (REST): Real-time integration for CRMs, dialers, or marketing stacks

CSV/JSON: Campaign-ready files.

PostgreSQL: Custom databases for large-scale enrichment

Compliance: Full audit trails + opt-out management

Why Forager.ai? → Proven ROI: Clients see 62% higher connect rates vs. industry averages (request case studies). → No Guesswork: Test-drive free samples before committing. → Scalable Pricing: Pay per record, license datasets, or get unlimited API access.

B2B Mobile Phone Data | Verified Contact Database | Sales Prospecting Lists | CRM Enrichment | Recruitment Phone Numbers | Marketing Automation | Phone Number Datasets | GDPR-Compliant Leads | Direct Dial Contacts | Decision-Maker Data

Need Proof? Contact us to see why Fortune 500 companies and startups alike trust Forager.ai for mission-critical outreach.

• 500M B2B Contacts • 35M Companies • 20+ Data Points to Filter Your Leads • 100M+ Contact Direct Dial and Mobile Number • Lifetime Support Until You 100% Satisfied

We are the Best b2b database providers for high-performance sales teams. If you get a fake by any chance, you have nothing to do with them. Nothing is more frustrating than receiving useless data for which you have paid money.

Every 15 days, our devoted team updates our b2b leads database. In addition, we are always available to assist our clients with whatever data they are working with in order to ensure that our service meets their needs. We keep an eye on our b2b contact database to keep you informed and provide any assistance you require.

With our simple-to-use system and up-to-date B2B contact list, we hope to make your job easier. You’ll be able to filter your data at Lfbbd based on the industry you work in. For example, you can choose from real estate companies or just simply tap into the healthcare business. Our database is updated on a regular basis, and you will receive contact information as soon as possible.

Use our information to quickly locate new business clients, competitors, and suppliers. We’ve got your back, no matter what precise requirements you have.

We have over 500 million business-to-business contacts that you may segment based on your marketing and commercial goals. We don’t stop there; we’re always gathering leads from the right tool so you can reach out to a big database of your clients without worrying about email constraints.

Thanks to our database, you may create your own campaign and send as many email or automated messages as you want. We collect the most viable b2b database to help you go a long way, as we seek to increase your business and enhance your sales.

The majority of our clients choose us since we have competitive costs when compared to others. In this digital era, marketing is more advanced, and customers are less willing to pay more for a service that produces poor results.

That’s why we’ve devised the most effective b2b database strategy for your company. You can also tailor your database and pricing to meet your specific business requirements.

• Connect directly with the right decision-makers, using the most accurate database of emails and direct dials. Build a clean prospecting list that you can plug into your sales tools and generate new leads from, right away • Over 500 million business contacts worldwide. • You could filter your targeted leads by 20+ criteria including job title, industry, location, Revenue, Technology, and more. • Find the email addresses of the professionals you want to contact one by one or in bulk.

UNIDO PUB ON MARKETING RESEARCH AND SALES PROMOTION IN AFRICA - (1) GIVES EXAMPLES OF FACTORIES FAILING ON ACCOUNT OF INADEQUATE SUPPLY OF MARKET INFORMATION (2) COVERS (A) PRAGMATIC APPROACH IN FACING UNCERTAINTIES OF THE AFRICAN MARKETING SITUATION (B) INTER-RELATIONSHIP OF MARKETING VARIABLES (C) RANGE OF FACTORS TO BE CONSIDERED IN A MARKET RESEARCH ASSIGNMENT, CITING CASE OF A FIRM INTRODUCING A NEW BUILDING MATERIALS (ROOFING) PRODUCT.

This dataset was created to simulate a market basket dataset, providing insights into customer purchasing behavior and store operations. The dataset facilitates market basket analysis, customer segmentation, and other retail analytics tasks. Here's more information about the context and inspiration behind this dataset:

Context:

Retail businesses, from supermarkets to convenience stores, are constantly seeking ways to better understand their customers and improve their operations. Market basket analysis, a technique used in retail analytics, explores customer purchase patterns to uncover associations between products, identify trends, and optimize pricing and promotions. Customer segmentation allows businesses to tailor their offerings to specific groups, enhancing the customer experience.

Inspiration:

The inspiration for this dataset comes from the need for accessible and customizable market basket datasets. While real-world retail data is sensitive and often restricted, synthetic datasets offer a safe and versatile alternative. Researchers, data scientists, and analysts can use this dataset to develop and test algorithms, models, and analytical tools.

Dataset Information:

The columns provide information about the transactions, customers, products, and purchasing behavior, making the dataset suitable for various analyses, including market basket analysis and customer segmentation. Here's a brief explanation of each column in the Dataset:

• Transaction_ID: A unique identifier for each transaction, represented as a 10-digit number. This column is used to uniquely identify each purchase.
• Date: The date and time when the transaction occurred. It records the timestamp of each purchase.
• Customer_Name: The name of the customer who made the purchase. It provides information about the customer's identity.
• Product: A list of products purchased in the transaction. It includes the names of the products bought.
• Total_Items: The total number of items purchased in the transaction. It represents the quantity of products bought.
• Total_Cost: The total cost of the purchase, in currency. It represents the financial value of the transaction.
• Payment_Method: The method used for payment in the transaction, such as credit card, debit card, cash, or mobile payment.
• City: The city where the purchase took place. It indicates the location of the transaction.
• Store_Type: The type of store where the purchase was made, such as a supermarket, convenience store, department store, etc.
• Discount_Applied: A binary indicator (True/False) representing whether a discount was applied to the transaction.
• Customer_Category: A category representing the customer's background or age group.
• Season: The season in which the purchase occurred, such as spring, summer, fall, or winter.
• Promotion: The type of promotion applied to the transaction, such as "None," "BOGO (Buy One Get One)," or "Discount on Selected Items."

Use Cases:

• Market Basket Analysis: Discover associations between products and uncover buying patterns.
• Customer Segmentation: Group customers based on purchasing behavior.
• Pricing Optimization: Optimize pricing strategies and identify opportunities for discounts and promotions.
• Retail Analytics: Analyze store performance and customer trends.

Note: This dataset is entirely synthetic and was generated using the Python Faker library, which means it doesn't contain real customer data. It's designed for educational and research purposes.

Unlock the full potential of LinkedIn data with our extensive dataset that combines profiles, company information, and job listings into one powerful resource for business decision-making, strategic hiring, competitive analysis, and market trend insights. This all-encompassing dataset is ideal for professionals, recruiters, analysts, and marketers aiming to enhance their strategies and operations across various business functions. Dataset Features

Profiles: Dive into detailed public profiles featuring names, titles, positions, experience, education, skills, and more. Utilize this data for talent sourcing, lead generation, and investment signaling, with a refresh rate ensuring up to 30 million records per month. Companies: Access comprehensive company data including ID, country, industry, size, number of followers, website details, subsidiaries, and posts. Tailored subsets by industry or region provide invaluable insights for CRM enrichment, competitive intelligence, and understanding the startup ecosystem, updated monthly with up to 40 million records. Job Listings: Explore current job opportunities detailed with job titles, company names, locations, and employment specifics such as seniority levels and employment functions. This dataset includes direct application links and real-time application numbers, serving as a crucial tool for job seekers and analysts looking to understand industry trends and the job market dynamics.

Customizable Subsets for Specific Needs Our LinkedIn dataset offers the flexibility to tailor the dataset according to your specific business requirements. Whether you need comprehensive insights across all data points or are focused on specific segments like job listings, company profiles, or individual professional details, we can customize the dataset to match your needs. This modular approach ensures that you get only the data that is most relevant to your objectives, maximizing efficiency and relevance in your strategic applications. Popular Use Cases

Strategic Hiring and Recruiting: Track talent movement, identify growth opportunities, and enhance your recruiting efforts with targeted data. Market Analysis and Competitive Intelligence: Gain a competitive edge by analyzing company growth, industry trends, and strategic opportunities. Lead Generation and CRM Enrichment: Enrich your database with up-to-date company and professional data for targeted marketing and sales strategies. Job Market Insights and Trends: Leverage detailed job listings for a nuanced understanding of employment trends and opportunities, facilitating effective job matching and market analysis. AI-Driven Predictive Analytics: Utilize AI algorithms to analyze large datasets for predicting industry shifts, optimizing business operations, and enhancing decision-making processes based on actionable data insights.

Whether you are mapping out competitive landscapes, sourcing new talent, or analyzing job market trends, our LinkedIn dataset provides the tools you need to succeed. Customize your access to fit specific needs, ensuring that you have the most relevant and timely data at your fingertips.

Forager.ai's Small Business Contact Data set is a comprehensive collection of over 695M professional profiles. With an unmatched 2x/month refresh rate, we ensure the most current and dynamic data in the industry today. We deliver this data via JSONL flat-files or PostgreSQL database delivery, capturing publicly available information on each profile.

| Volume and Stats |

Every single record refreshed 2x per month, setting industry standards. First-party data curation powering some of the most renowned sales and recruitment platforms. Delivery frequency is hourly (fastest in the industry today). Additional datapoints and linkages available. Delivery formats: JSONL, PostgreSQL, CSV. | Datapoints |

Over 150+ unique datapoints available! Key fields like Current Title, Current Company, Work History, Educational Background, Location, Address, and more. Unique linkage data to other social networks or contact data available. | Use Cases |

Sales Platforms, ABM Vendors, Intent Data Companies, AdTech and more:

Deliver the best end-customer experience with our people feed powering your solution! Be the first to know when someone changes jobs and share that with end-customers. Industry-leading data accuracy. Connect our professional records to your existing database, find new connections to other social networks, and contact data. Hashed records also available for advertising use-cases. Venture Capital and Private Equity:

Track every company and employee with a publicly available profile. Keep track of your portfolio's founders, employees and ex-employees, and be the first to know when they move or start up. Keep an eye on the pulse by following the most influential people in the industries and segments you care about. Provide your portfolio companies with the best data for recruitment and talent sourcing. Review departmental headcount growth of private companies and benchmark their strength against competitors. HR Tech, ATS Platforms, Recruitment Solutions, as well as Executive Search Agencies:

Build products for industry-specific and industry-agnostic candidate recruiting platforms. Track person job changes and immediately refresh profiles to avoid stale data. Identify ideal candidates through work experience and education history. Keep ATS systems and candidate profiles constantly updated. Link data from this dataset into GitHub, LinkedIn, and other social networks. | Delivery Options |

Flat files via S3 or GCP PostgreSQL Shared Database PostgreSQL Managed Database REST API Other options available at request, depending on scale required | Other key features |

Over 120M US Professional Profiles. 150+ Data Fields (available upon request) Free data samples, and evaluation. Tags: Professionals Data, People Data, Work Experience History, Education Data, Employee Data, Workforce Intelligence, Identity Resolution, Talent, Candidate Database, Sales Database, Contact Data, Account Based Marketing, Intent Data.

Metadata The ‘Requests for land values’ database, or DVF, lists all sales of land over the last five years, in mainland France and in the overseas departments and territories — except in Mayotte and Alsace-Moselle. The properties concerned can be built (apartment and house) or unbuilt (plots and farms). The data are produced by Bercy, i.e. by the Directorate-General for Public Finance. They come from the deeds registered with notaries and the information contained in the cadastre. Legal framework: The DVF database does not contain personal data, such as the name of the seller or the buyer of a good. It contains only information on transactions: type of property sold, area, selling price and so on. As these data can be cross-checked with other data already online, the Directorate-General for Public Finance recalls that the use of data from the DVF database cannot have the purpose or effect of allowing the re-identification of data subjects, nor should it be indexed on online search engines. Consult the general conditions of use: https://static.data.gouv.fr/resources/request-de-valeurs-foncieres/20190419-091643/conditions-generales-dutilisation.pdf Fields code_service_ch: not provided reference_document: not entered articles_cgi1: not entered articles_cgi2: not entered articles_cgi3: not entered articles_cgi4: not entered articles_cgi5: No_provision: Each provision of a document has a number. Only the provisions concerning transfers for consideration are returned to the file. The provisions concerning transfers free of charge are removed from the register by the application. The disposition numbers used do not therefore necessarily follow the numerical order date_mutation: Date of signature of nature_mutation document: Sale, sale in the future state of completion, sale of building land, tendering, expropriation or exchange of land value: This is the price or valuation declared in the context of a transfer for consideration. It can correspond to several properties. The details are not traced in the information system no_voie: Number in track b_t_q: Repetition index type_of_way: Track type (example: Street, Avenue,...) code_voie: Track code: Wording of the code_postal route: Common postal code: Wording of the commune code_departement: Common_code department code: Common code prefix_of_section: Prefix of cadastral section section: Cadastral section no_plan: Cadastral plan no_volume: Cadastral volume A condominium lot consists of a private part (apartment, cellar, etc.) and a share of the common part (tenths). Only the first 5 lots are mentioned. If the number of lots exceeds 5, they will not be returned. 1st lot surface_carrez_du_1er_lot: surface area CARREZ of the 1st lot 2nd_lot: 2nd lot surface_carrez_du_2eme_lot: surface area CARREZ of the 2nd lot 3rd_lot: 3rd lot surface_carrez_du_3eme_lot: CARREZ surface area of the third lot, fourth lot: 4th lot surface_carrez_du_4eme_lot: surface area CARREZ of the 4th lot 5th_lot: 5th lot surface_carrez_du_5eme_lot: surface area CARREZ of the 5th lot number_of_lots: Total number of lots per layout code_type_local: Local type code type_local 1: House, 2: apartment, 3: dependency (isolated), 4: Industrial and commercial premises or similar identifier_local: This is the number that identifies each room. The local is a tax concept of built property. The file includes one line per number (per local) with the corresponding real area surface_reelle_bati next to it: The real area is attached to the local identifier. This is the sum of the actual surface area of the premises and the surface areas of the outbuildings (see real estate lexicon) number_pieces_principal: Number of main nature_culture parts: Nature of culture nature_culture_speciale: Nature of special crop surface_terrain: Building land capacity: indicates the presence of racks (non-zero local_type) nb_line: Number of lines of the transaction (number of lines on grouping of a single value of the department code set, common code, date of transfer, nature of transfer, land value, no_disposition) id_parcelle: PCI-type parcel identifier

This dataset details local funding sources for each applicable agency reporting to the National Transit Database in the 2022 and 2023 report years. Examples include Income, Sales, Property and Fuel taxes and Tolls.

NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Revenue Sources database files.

In years 2015-2021, you can find this data in the "Funding Sources" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data.

If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.

This database contains tobacco consumption data from 1970-2015 collected through a systematic search coupled with consultation with country and subject-matter experts. Data quality appraisal was conducted by at least two research team members in duplicate, with greater weight given to official government sources. All data was standardized into units of cigarettes consumed and a detailed accounting of data quality and sourcing was prepared. Data was found for 82 of 214 countries for which searches for national cigarette consumption data were conducted, representing over 95% of global cigarette consumption and 85% of the world’s population. Cigarette consumption fell in most countries over the past three decades but trends in country specific consumption were highly variable. For example, China consumed 2.5 million metric tonnes (MMT) of cigarettes in 2013, more than Russia (0.36 MMT), the United States (0.28 MMT), Indonesia (0.28 MMT), Japan (0.20 MMT), and the next 35 highest consuming countries combined. The US and Japan achieved reductions of more than 0.1 MMT from a decade earlier, whereas Russian consumption plateaued, and Chinese and Indonesian consumption increased by 0.75 MMT and 0.1 MMT, respectively. These data generally concord with modelled country level data from the Institute for Health Metrics and Evaluation and have the additional advantage of not smoothing year-over-year discontinuities that are necessary for robust quasi-experimental impact evaluations. Before this study, publicly available data on cigarette consumption have been limited—either inappropriate for quasi-experimental impact evaluations (modelled data), held privately by companies (proprietary data), or widely dispersed across many national statistical agencies and research organisations (disaggregated data). This new dataset confirms that cigarette consumption has decreased in most countries over the past three decades, but that secular country specific consumption trends are highly variable. The findings underscore the need for more robust processes in data reporting, ideally built into international legal instruments or other mandated processes. To monitor the impact of the WHO Framework Convention on Tobacco Control and other tobacco control interventions, data on national tobacco production, trade, and sales should be routinely collected and openly reported. The first use of this database for a quasi-experimental impact evaluation of the WHO Framework Convention on Tobacco Control is: Hoffman SJ, Poirier MJP, Katwyk SRV, Baral P, Sritharan L. Impact of the WHO Framework Convention on Tobacco Control on global cigarette consumption: quasi-experimental evaluations using interrupted time series analysis and in-sample forecast event modelling. BMJ. 2019 Jun 19;365:l2287. doi: https://doi.org/10.1136/bmj.l2287 Another use of this database was to systematically code and classify longitudinal cigarette consumption trajectories in European countries since 1970 in: Poirier MJ, Lin G, Watson LK, Hoffman SJ. Classifying European cigarette consumption trajectories from 1970 to 2015. Tobacco Control. 2022 Jan. DOI: 10.1136/tobaccocontrol-2021-056627. Statement of Contributions: Conceived the study: GEG, SJH Identified multi-country datasets: GEG, MP Extracted data from multi-country datasets: MP Quality assessment of data: MP, GEG Selection of data for final analysis: MP, GEG Data cleaning and management: MP, GL Internet searches: MP (English, French, Spanish, Portuguese), GEG (English, French), MYS (Chinese), SKA (Persian), SFK (Arabic); AG, EG, BL, MM, YM, NN, EN, HR, KV, CW, and JW (English), GL (English) Identification of key informants: GEG, GP Project Management: LS, JM, MP, SJH, GEG Contacts with Statistical Agencies: MP, GEG, MYS, SKA, SFK, GP, BL, MM, YM, NN, HR, KV, JW, GL Contacts with key informants: GEG, MP, GP, MYS, GP Funding: GEG, SJH SJH: Hoffman, SJ; JM: Mammone J; SRVK: Rogers Van Katwyk, S; LS: Sritharan, L; MT: Tran, M; SAK: Al-Khateeb, S; AG: Grjibovski, A.; EG: Gunn, E; SKA: Kamali-Anaraki, S; BL: Li, B; MM: Mahendren, M; YM: Mansoor, Y; NN: Natt, N; EN: Nwokoro, E; HR: Randhawa, H; MYS: Yunju Song, M; KV: Vercammen, K; CW: Wang, C; JW: Woo, J; MJPP: Poirier, MJP; GEG: Guindon, EG; GP: Paraje, G; GL Gigi Lin Key informants who provided data: Corne van Walbeek (South Africa, Jamaica) Frank Chaloupka (US) Ayda Yurekli (Turkey) Dardo Curti (Uruguay) Bungon Ritthiphakdee (Thailand) Jakub Lobaszewski (Poland) Guillermo Paraje (Chile, Argentina) Key informants who provided useful insights: Carlos Manuel Guerrero López (Mexico) Muhammad Jami Husain (Bangladesh) Nigar Nargis (Bangladesh) Rijo M John (India) Evan Blecher (Nigeria, Indonesia, Philippines, South Africa) Yagya Karki (Nepal) Anne CK Quah (Malaysia) Nery Suarez Lugo (Cuba) Agencies providing assistance: Irani... Visit https://dataone.org/datasets/sha256%3Aaa1b4aae69c3399c96bfbf946da54abd8f7642332d12ccd150c42ad400e9699b for complete metadata about this dataset.

Intelligent Invoice Management System

Project Description:
The Intelligent Invoice Management System is an advanced AI-powered platform designed to revolutionize traditional invoice processing. By automating the extraction, validation, and management of invoice data, this system addresses the inefficiencies, inaccuracies, and high costs associated with manual methods. It enables businesses to streamline operations, reduce human error, and expedite payment cycles.

Problem Statement:
Manual invoice processing involves labor-intensive tasks such as data entry, verification, and reconciliation. These processes are time-consuming, prone to errors, and can result in financial losses and delays. The diversity of invoice formats from various vendors adds complexity, making automation a critical need for efficiency and scalability.

Proposed Solution:
The Intelligent Invoice Management System automates the end-to-end process of invoice handling using AI and machine learning techniques. Core functionalities include:
1. Invoice Generation: Automatically generate PDF invoices in at least four formats, populated with synthetic data.
2. Data Development: Leverage a dataset containing fields such as receipt numbers, company details, sales tax information, and itemized tables to create realistic invoice samples.
3. AI-Powered Labeling: Use Tesseract OCR to extract labeled data from invoice images, and train YOLO for label recognition, ensuring precise identification of fields.
4. Database Integration: Store extracted information in a structured database for seamless retrieval and analysis.
5. Web-Based Information System: Provide a user-friendly platform to upload invoices and retrieve key metrics, such as:
- Total sales within a specified duration.
- Total sales tax paid during a given timeframe.
- Detailed invoice information in tabular form for specific date ranges.

Key Features and Deliverables:
1. Invoice Generation:
- Generate 20,000 invoices using an automated script.
- Include dummy logos, company details, and itemized tables for four items per invoice.

1. Label Definition and Format:

   • Define structured labels (TBLR, CLASS Name, Recognized Text).
   • Provide labels in both XML and JSON formats for seamless integration.

2. OCR and AI Training:

   • Automate labeling using Tesseract OCR for high-accuracy text recognition.
   • Train and test YOLO to detect and classify invoice fields (TBLR and CLASS).

3. Database Management:

   • Store OCR-extracted labels and field data in a database.
   • Enable efficient search and aggregation of invoice data.

4. Web-Based Interface:

   • Build a responsive system for users to upload invoices and retrieve data based on company name or NTN.
   • Display metrics and reports for total sales, tax paid, and invoice details over custom date ranges.

Expected Outcomes: - Reduction in manual effort and operational costs.
- Improved accuracy in invoice processing and financial reporting.
- Enhanced scalability and adaptability for diverse invoice formats.
- Faster turnaround time for invoice-related tasks.

By automating critical aspects of invoice management, this system delivers a robust and intelligent solution to meet the evolving needs of businesses.

This dataset details funding from taxes levied by each applicable agency reporting to the National Transit Database in the 2022 and 2023 report years. Examples include Income, Sales, Property and Fuel taxes and Tolls.

NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Revenue Sources database files.

In years 2015-2021, you can find this data in the "Funding Sources" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data.

If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.

The Shopee Products Dataset is a comprehensive resource that empowers businesses, researchers, and analysts to gain a holistic view of the Shopee e-commerce ecosystem. Whether your goal is to conduct market analysis, optimize pricing strategies, understand customer behavior, or evaluate competitors, this dataset offers the essential information you need to make informed decisions and succeed in the dynamic world of Shopee. At its core, this dataset provides key attributes such as product ID, title, ratings, reviews, pricing details, and seller information, among others. These fundamental data elements offer insights into product performance, customer sentiment, and seller credibility.

Branded food composition databases are an important tool for research, education, healthcare, and policy making, amongst others. Such databases are typically compiled using food labeling data without chemical analyses of specific products. This study aimed to verify whether the labeled sugar content in sugar-sweetened beverages (SSBs) corresponds to the actual sugar content in these products, thus enabling food monitoring studies to be conducted. A secondary objective was to determine the specific types of sugars in these SSBs. A case study was conducted using market share-driven sampling of these beverages from the Slovenian food supply. On the basis of nationwide yearly sales data, 51 best-selling products were sampled in 2020 and analyzed using high-performance liquid chromatography. This sales-driven approach to sampling has been shown to be very useful for conducting food monitoring studies. With the careful selection of a small proportion of available products, we finished with a manageable sample size, reflecting the composition of a majority (69%) of the national market share volume. The analyzed total sugar content was compared with labeled data, within the context of the European Union's regulatory labeling tolerances. In all samples, the sugar content was within the tolerance levels. The most common (N = 41) deviation was within ±10% of the labeled sugar content. In the subcategories, the differences between the analyzed and labeled median sugar contents were not statistically significant. Sucrose was most commonly (N = 36; 71%) used for sweetening, suggesting that the proportion of fructose in most SSBs was around 50%. A higher fructose content was only observed in beverages with fructose–glucose syrup or a higher content of fruit juice. The study results show that the labeled sugar content information in SSBs is reliable and can be used to compile branded food databases and monitor the nutritional quality of foods in the food supply.

The National Marine Sediments Database and Seafloor Characteristics project is a collaborative effort between the National Oceans Office and Geoscience Australia. The aims of the project included identification and collation of existing marine sediment data within the Australian Marine Jurisdiction, development and population of the MARS sediments database and mapping and analysis of sediment data for the Northern Planning Area and the Australian region to provide information for the National Benthic Marine Bioregionalisation. The creation of the MARS database marks the transition to a new era of easy internet access to quantitative seafloor information. This is the first project to critically assess the quality and coverage of Australia's seafloor sediment data on a national scale. The initial phase of the project was the identification and collation of sediment data. At the completion of this phase, the MARS database contained about 25,000 samples for which 138,000 properties had been recorded. Maps of sediment properties were produced using validated quantitative data for two regions: the Northern Planning Area (NPA) and the whole of the Australian Marine Jurisdiction, excluding external territories. These maps show the distribution of measured grain size data (weight percent gravel, sand and mud), calculated mean grain size, as well as sediment classification based on the Folk scheme (Folk, 1954), and carbonate content. Mean grain size data for six of the marine domains were used to model sediment mobility in waters less than 300 m depth, using Geoscience Australia's GEOMAT package. The results of the modelling were produced as maps of tide and wave exceedance, and an energy regime regionalisation. As a direct result of this project, the MARS database is now an important scientific and educational resource for those requiring detailed information on seafloor sediment characteristics within the Australian Marine Domain areas. The maps generated by this project show the level of detail and type of presentation possible when using quantitative data, but significant gaps in measured data coverage were also identified. Some 70% of the total marine domain remains unmapped in terms of measured sediment data. Much of this area is off the continental shelf, although gaps in the data coverage on the shelf are significant, particularly in the South-west and West-central Marine Domains Strategic directions for improving the data coverage include the analysis of existing sediment samples from Australian and overseas repositories. A valuable resource of seabed samples stored in Australia and overseas has been identified. These samples, if analysed, have the potential to double the existing overall measured data coverage for Australia's Marine Domains and would provide a cost-effective way of generating new data. The South-west Marine Domain would provide a useful pilot study to test the utility of analysing existing material.

You can also purchase hard copies of Geoscience Australia data and other products at http://www.ga.gov.au/products-services/how-to-order-products/sales-centre.html

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