SUMMARY:

Vumonic provides its clients email receipt datasets on weekly, monthly, or quarterly subscriptions, for any online consumer vertical. We gain consent-based access to our users' email inboxes through our own proprietary apps, from which we gather and extract all the email receipts and put them into a structured format for consumption of our clients. We currently have over 1M users in our India panel.

If you are not familiar with email receipt data, it provides item and user-level transaction information (all PII-wiped), which allows for deep granular analysis of things like marketshare, growth, competitive intelligence, and more.

VERTICALS:

• Ecommerce (Amazon, Flipkart, Myntra, Nykaa)
• Taxi (Uber, Ola)
• Food Delivery (Swiggy, Zomato)
• OTT (Netflix, Amazon Prime Video, Disney+)
• Appstore (Apple App Store and Google Playstore)
• OTA (Expedia, Booking.com, GoIbibo)
• E-wallets (PhonePe, PayTM)
• Education (Byju's, Unacademy)

PRICING/QUOTE:

Our email receipt data is priced market-rate based on the requirement. To give a quote, all we need to know is:

• what vertical you are interested in
• how often do you wish to receive the data, and
• do you want any backdata (e.g. from 2019 onwards)

Send us over this info and we can answer any questions you have, provide sample, and more.

Access Email Address data for automotive industry professionals globally with Success.ai. Includes verified professional histories, work emails, and phone numbers. Best price guaranteed.

Premium B2C Consumer Database - 269+ Million US Records

Supercharge your B2C marketing campaigns with comprehensive consumer database, featuring over 269 million verified US consumer records. Our 20+ year data expertise delivers higher quality and more extensive coverage than competitors.

Core Database Statistics

Consumer Records: Over 269 million

Email Addresses: Over 160 million (verified and deliverable)

Phone Numbers: Over 76 million (mobile and landline)

Mailing Addresses: Over 116,000,000 (NCOA processed)

Geographic Coverage: Complete US (all 50 states)

Compliance Status: CCPA compliant with consent management

Targeting Categories Available

Demographics: Age ranges, education levels, occupation types, household composition, marital status, presence of children, income brackets, and gender (where legally permitted)

Geographic: Nationwide, state-level, MSA (Metropolitan Service Area), zip code radius, city, county, and SCF range targeting options

Property & Dwelling: Home ownership status, estimated home value, years in residence, property type (single-family, condo, apartment), and dwelling characteristics

Financial Indicators: Income levels, investment activity, mortgage information, credit indicators, and wealth markers for premium audience targeting

Lifestyle & Interests: Purchase history, donation patterns, political preferences, health interests, recreational activities, and hobby-based targeting

Behavioral Data: Shopping preferences, brand affinities, online activity patterns, and purchase timing behaviors

Multi-Channel Campaign Applications

Deploy across all major marketing channels:

Email marketing and automation

Social media advertising

Search and display advertising (Google, YouTube)

Direct mail and print campaigns

Telemarketing and SMS campaigns

Programmatic advertising platforms

Data Quality & Sources

Our consumer data aggregates from multiple verified sources:

Public records and government databases

Opt-in subscription services and registrations

Purchase transaction data from retail partners

Survey participation and research studies

Online behavioral data (privacy compliant)

Technical Delivery Options

File Formats: CSV, Excel, JSON, XML formats available

Delivery Methods: Secure FTP, API integration, direct download

Processing: Real-time NCOA, email validation, phone verification

Custom Selections: 1,000+ selectable demographic and behavioral attributes

Minimum Orders: Flexible based on targeting complexity

Unique Value Propositions

Dual Spouse Targeting: Reach both household decision-makers for maximum impact

Cross-Platform Integration: Seamless deployment to major ad platforms

Real-Time Updates: Monthly data refreshes ensure maximum accuracy

Advanced Segmentation: Combine multiple targeting criteria for precision campaigns

Compliance Management: Built-in opt-out and suppression list management

Ideal Customer Profiles

E-commerce retailers seeking customer acquisition

Financial services companies targeting specific demographics

Healthcare organizations with compliant marketing needs

Automotive dealers and service providers

Home improvement and real estate professionals

Insurance companies and agents

Subscription services and SaaS providers

Performance Optimization Features

Lookalike Modeling: Create audiences similar to your best customers

Predictive Scoring: Identify high-value prospects using AI algorithms

Campaign Attribution: Track performance across multiple touchpoints

A/B Testing Support: Split audiences for campaign optimization

Suppression Management: Automatic opt-out and DNC compliance

Pricing & Volume Options

Flexible pricing structures accommodate businesses of all sizes:

Pay-per-record for small campaigns

Volume discounts for large deployments

Subscription models for ongoing campaigns

Custom enterprise pricing for high-volume users

Data Compliance & Privacy

VIA.tools maintains industry-leading compliance standards:

CCPA (California Consumer Privacy Act) compliant

CAN-SPAM Act adherence for email marketing

TCPA compliance for phone and SMS campaigns

Regular privacy audits and data governance reviews

Transparent opt-out and data deletion processes

Getting Started

Our data specialists work with you to:

1. Define your target audience criteria

2. Recommend optimal data selections

3. Provide sample data for testing

4. Configure delivery methods and formats

5. Implement ongoing campaign optimization

Why We Lead the Industry

With over two decades of data industry experience, we combine extensive database coverage with advanced targeting capabilities. Our commitment to data quality, compliance, and customer success has made us the preferred choice for businesses seeking superior B2C marketing performance.

Contact our team to discuss your specific targeting requirements and receive custom pricing for your marketing objectives.

We present a dataset of open source software developed mainly by enterprises rather than volunteers. This can be used to address known generalizability concerns, and, also, to perform research on open source business software development. Based on the premise that an enterprise's employees are likely to contribute to a project developed by their organization using the email account provided by it, we mine domain names associated with enterprises from open data sources as well as through white- and blacklisting, and use them through three heuristics to identify 17,264 enterprise GitHub projects. We provide these as a dataset detailing their provenance and properties. A manual evaluation of a dataset sample shows an identification accuracy of 89%. Through an exploratory data analysis we found that projects are staffed by a plurality of enterprise insiders, who appear to be pulling more than their weight, and that in a small percentage of relatively large projects development happens exclusively through enterprise insiders.

The main dataset is provided as a 17,264 record tab-separated file named enterprise_projects.txt with the following 29 fields.

url: the project's GitHub URL

project_id: the project's GHTorrent identifier

sdtc: true if selected using the same domain top committers heuristic (9,016 records)

mcpc: true if selected using the multiple committers from a valid enterprise heuristic (8,314 records)

mcve: true if selected using the multiple committers from a probable company heuristic (8,015 records),

star_number: number of GitHub watchers

commit_count: number of commits

files: number of files in current main branch

lines: corresponding number of lines in text files

pull_requests: number of pull requests

github_repo_creation: timestamp of the GitHub repository creation

earliest_commit: timestamp of the earliest commit

most_recent_commit: date of the most recent commit

committer_count: number of different committers

author_count: number of different authors

dominant_domain: the projects dominant email domain

dominant_domain_committer_commits: number of commits made by committers whose email matches the project's dominant domain

dominant_domain_author_commits: corresponding number for commit authors

dominant_domain_committers: number of committers whose email matches the project's dominant domain

dominant_domain_authors: corresponding number for commit authors

cik: SEC's EDGAR "central index key"

fg500: true if this is a Fortune Global 500 company (2,233 records)

sec10k: true if the company files SEC 10-K forms (4,180 records)

sec20f: true if the company files SEC 20-F forms (429 records)

project_name: GitHub project name

owner_login: GitHub project's owner login

company_name: company name as derived from the SEC and Fortune 500 data

owner_company: GitHub project's owner company name

license: SPDX license identifier

The file cohost_project_details.txt provides the full set of 311,223 cohort projects that are not part of the enterprise data set, but have comparable quality attributes.

url: the project's GitHub URL

project_id: the project's GHTorrent identifier

stars: number of GitHub watchers

commit_count: number of commits

This dataset offers a comprehensive collection of Webmotors car listings data, providing an in-depth view of the automotive market. It includes key attributes such as product URLs, brand, model, year, mileage, condition, fuel type, color, transmission type, seller details, information about single ownership, product description, price, location, creation date, image URLs, contact phone numbers, seller's email address, physical address, type of seller, specific locality, number of previous owners, premium listing status, category, and unique posting ID. With subsets available by car brand and model, this dataset enables businesses to access and analyze valuable automotive information tailored to their needs.

Users can leverage this dataset for price comparison, inventory optimization, and customer sentiment analysis. The data can help identify competitive pricing strategies, optimize stock levels based on market demand, and understand consumer preferences and feedback. Whether you are looking to enhance your sales strategy, manage your inventory efficiently, or gain insights into customer behavior, this dataset serves as a crucial resource for driving informed decisions and staying competitive in the dynamic automotive market.

This dataset provides the syntax for email addresses based on the Internet Engineering Task Force (IETF) RC5322 and RC3696.

This dataset contains:

A kmz file for Penmanshiel wind farm in the UK (for opening in e.g. Google Earth)

Static data including turbine coordinates and turbine details (rated power, rotor diameter, hub height, etc.)

10-minute SCADA and events data from the 14 Senvion MM82's at Penmanshiel wind farm, grouped by year from 2016 to mid-2021, which was extracted from our secondary SCADA system (Greenbyte). Note not all signals are available for the entire period, and there is no turbine WT03

Data mappings from primary SCADA to csv signal names

Site substation/PMU meter data where available for the same period

Site fiscal/grid meter data where available for the same period

The dataset has been released by Cubico Sustainable Investments Ltd under a CC-BY-4.0 open data license and is provided as is. However, please provide any feedback you might have on the dataset and format of the data. I'll try and add or link to additional file formats that might be easier to work with (e.g. for use with specific analysis software), and update this dataset periodically (e.g. twice a year), but please prompt me as required.

Feel free to use the data according to the license, however, it would be helpful to me if you could let me know where, how and why you are using the data, so that I can highlight this to the business (and renewables industry) and hopefully promote similar data sharing initiatives. I am particularly interested in performance analysis/improvement opportunities, how the dataset can be augmented with other (open) datasets, and sharing more generally within the renewables industry.

If you would like to get access to other datasets we may hold (e.g. more recent data, data from our other sites, ~30s resolution data, etc.), please let me know, and, if you have any questions or want to discuss open data and this or other initiatives, please contact me and I will endeavour to help.

I would like to thank Cubico's Senior Legal Advisor & Compliance Officer, IT Director, UK Asset Management Team, Executive Committee and my manager for supporting this initiative, as well as our partners GLIL for agreeing to release this data under an open license. I would also like to thank those I have talked to during the process of releasing this data under an open license and the encouragement and advice I have had on the way.

For contact my email address is charlie.plumley@cubicoinvest.com.

You can also access data from Kelmarsh wind farm here.

Access B2B Contact Data for North American Small Business Owners with Success.ai—your go-to provider for verified, high-quality business datasets. This dataset is tailored for businesses, agencies, and professionals seeking direct access to decision-makers within the small business ecosystem across North America. With over 170 million professional profiles, it’s an unparalleled resource for powering your marketing, sales, and lead generation efforts.

Key Features of the Dataset:

Verified Contact Details

Includes accurate and up-to-date email addresses and phone numbers to ensure you reach your targets reliably.

AI-validated for 99% accuracy, eliminating errors and reducing wasted efforts.

Detailed Professional Insights

Comprehensive data points include job titles, skills, work experience, and education to enable precise segmentation and targeting.

Enriched with insights into decision-making roles, helping you connect directly with small business owners, CEOs, and other key stakeholders.

Business-Specific Information

Covers essential details such as industry, company size, location, and more, enabling you to tailor your campaigns effectively. Ideal for profiling and understanding the unique needs of small businesses.

Continuously Updated Data

Our dataset is maintained and updated regularly to ensure relevance and accuracy in fast-changing market conditions. New business contacts are added frequently, helping you stay ahead of the competition.

Why Choose Success.ai?

At Success.ai, we understand the critical importance of high-quality data for your business success. Here’s why our dataset stands out:

Tailored for Small Business Engagement Focused specifically on North American small business owners, this dataset is an invaluable resource for building relationships with SMEs (Small and Medium Enterprises). Whether you’re targeting startups, local businesses, or established small enterprises, our dataset has you covered.

Comprehensive Coverage Across North America Spanning the United States, Canada, and Mexico, our dataset ensures wide-reaching access to verified small business contacts in the region.

Categories Tailored to Your Needs Includes highly relevant categories such as Small Business Contact Data, CEO Contact Data, B2B Contact Data, and Email Address Data to match your marketing and sales strategies.

Customizable and Flexible Choose from a wide range of filtering options to create datasets that meet your exact specifications, including filtering by industry, company size, geographic location, and more.

Best Price Guaranteed We pride ourselves on offering the most competitive rates without compromising on quality. When you partner with Success.ai, you receive superior data at the best value.

Seamless Integration Delivered in formats that integrate effortlessly with your CRM, marketing automation, or sales platforms, so you can start acting on the data immediately.

Use Cases: This dataset empowers you to:

Drive Sales Growth: Build and refine your sales pipeline by connecting directly with decision-makers in small businesses. Optimize Marketing Campaigns: Launch highly targeted email and phone outreach campaigns with verified contact data. Expand Your Network: Leverage the dataset to build relationships with small business owners and other key figures within the B2B landscape. Improve Data Accuracy: Enhance your existing databases with verified, enriched contact information, reducing bounce rates and increasing ROI. Industries Served: Whether you're in B2B SaaS, digital marketing, consulting, or any field requiring accurate and targeted contact data, this dataset serves industries of all kinds. It is especially useful for professionals focused on:

Lead Generation Business Development Market Research Sales Outreach Customer Acquisition What’s Included in the Dataset: Each profile provides:

Full Name Verified Email Address Phone Number (where available) Job Title Company Name Industry Company Size Location Skills and Professional Experience Education Background With over 170 million profiles, you can tap into a wealth of opportunities to expand your reach and grow your business.

Why High-Quality Contact Data Matters: Accurate, verified contact data is the foundation of any successful B2B strategy. Reaching small business owners and decision-makers directly ensures your message lands where it matters most, reducing costs and improving the effectiveness of your campaigns. By choosing Success.ai, you ensure that every contact in your pipeline is a genuine opportunity.

Partner with Success.ai for Better Data, Better Results: Success.ai is committed to delivering premium-quality B2B data solutions at scale. With our small business owner dataset, you can unlock the potential of North America's dynamic small business market.

Get Started Today Request a sample or customize your dataset to fit your unique...

ESS-DIVE’s (Environmental Systems Science Data Infrastructure for a Virtual Ecosystem) dataset metadata reporting format is intended to compile information about a dataset (e.g., title, description, funding sources) that can enable reuse of data submitted to the ESS-DIVE data repository. The files contained in this dataset include instructions (dataset_metadata_guide.md and README.md) that can be used to understand the types of metadata ESS-DIVE collects. The data dictionary (dd.csv) follows ESS-DIVE’s file-level metadata reporting format and includes brief descriptions about each element of the dataset metadata reporting format. This dataset also includes a terminology crosswalk (dataset_metadata_crosswalk.csv) that shows how ESS-DIVE’s metadata reporting format maps onto other existing metadata standards and reporting formats. Data contributors to ESS-DIVE can provide this metadata by manual entry using a web form or programmatically via ESS-DIVE’s API (Application Programming Interface). A metadata template (dataset_metadata_template.docx or dataset_metadata_template.pdf) can be used to collaboratively compile metadata before providing it to ESS-DIVE. Since being incorporated into ESS-DIVE’s data submission user interface, ESS-DIVE’s dataset metadata reporting format, has enabled features like automated metadata quality checks, and dissemination of ESS-DIVE datasets onto other data platforms including Google Dataset Search and DataCite.

We use the Enron email dataset to build a network of email addresses. It contains 614586 emails sent over the period from 6 January 1998 until 4 February 2004. During the pre-processing, we remove the periods of low activity and keep the emails from 1 January 1999 until 31 July 2002 which is 1448 days of email records in total. Also, we remove email addresses that sent less than three emails over that period. In total, the Enron email network contains 6 600 nodes and 50 897 edges.

To build a graph G = (V, E), we use email addresses as nodes V. Every node v_i has an attribute which is a time-varying signal that corresponds to the number of emails sent from this address during a day. We draw an edge e_ij between two nodes i and j if there is at least one email exchange between the corresponding addresses.

Column 'Count' in 'edges.csv' file is the number of 'From'->'To' email exchanges between the two addresses. This column can be used as an edge weight.

The file 'nodes.csv' contains a dictionary that is a compressed representation of time-series. The format of the dictionary is Day->The Number Of Emails Sent By the Address During That Day. The total number of days is 1448.

'id-email.csv' is a file containing the actual email addresses.

Making a freedom of information request https://myaccount.northyorks.gov.uk/foi/request If the information you are requesting is not available already, and relates to one of our services, you can make a request for information using our online form. Make an freedom of information request We generally reply to requests by email, but if you would like the information in any specific format let us know when you make your request. We will respond as soon as possible and within 20 working days. After submitting a request Your request will be passed on to the relevant service area, who will be responsible for replying to you. We will always provide as much information as possible, but there are rules that allow us to withhold certain types of information. For example, if providing the information would infringe someone else's privacy or, if the information you have asked for is not environmental, would take longer than 18 hours to acquire. If we are unable to provide any information we will explain why. See additional information on the reasons why we might not be able to provide information. We publish our data so citizens can see how we work and where money is spent. The data is published in an accessible format and can be freely reused in accordance with the open data licence.

Important information about the change of format of the transport offer https://eu.ftp.opendatasoft.com/sncf/infogtfsnetex/KHSS_fb_petit.jpg" alt=""> | In order to comply with the regulatory requirements of the LOM law, significant changes have been introduced since July 2021: * Daily feeding of the Transilien transport offer; * The introduction of new standards (ILICO, ICAR); * A new structure of the tender; * In fact, a change in the formats for making the transport offer available on open data via, in addition to the provision of a GTFS format, a NeTEx format, which is the reference format defined at European level. Below you will find some explanations: * Download as pptx. * Download as pdf. The GTFS file is available here: The file in NeTEX format is available here: The transcodification file (V8) can be downloaded as an attachment. If you have any questions, you can contact us at the following email address: LD.OpenData-Transilien@sncf.fr ---|---

This dataset contains a list of email address categories.

This dataset contains:

A kmz file for Kelmarsh wind farm in the UK (for opening in e.g. Google Earth)

Static data including turbine coordinates and turbine details (rated power, rotor diameter, hub height, etc.)

10-minute SCADA and events data from the 6 Senvion MM92's at Kelmarsh wind farm, grouped by year from 2016 to mid-2021, which was extracted from our secondary SCADA system (Greenbyte). Note not all signals are available for the entire period

Data mappings from primary SCADA to csv signal names

Site substation/PMU meter data where available for the same period

Site fiscal/grid meter data where available for the same period

MERRA2 and ERA5 since 2000 up to the same period

The dataset has been released by Cubico Sustainable Investments Ltd under a CC-BY-4.0 open data license and is provided as is. However, please provide any feedback you might have on the dataset and format of the data. I'll try and add or link to additional file formats that might be easier to work with (e.g. for use with specific analysis software), and update this dataset periodically (e.g. twice a year), but please prompt me as required.

Feel free to use the data according to the license, however, it would be helpful to me if you could let me know where, how and why you are using the data, so that I can highlight this to the business (and renewables industry) and hopefully promote similar data sharing initiatives. I am particularly interested in performance analysis/improvement opportunities, how the dataset can be augmented with other (open) datasets, and sharing more generally within the renewables industry.

If you would like to get access to other datasets we may hold (e.g. more recent data, data from our other sites, ~30s resolution data, etc.), please let me know, and, if you have any questions or want to discuss open data and this or other initiatives, please contact me and I will endeavour to help.

I would like to thank Cubico's Senior Legal Advisor & Compliance Officer, IT Director, UK Asset Management Team, Executive Committee and my manager for supporting this initiative, as well as our partners GLIL for agreeing to release this data under an open license. I would also like to thank those I have talked to during the process of releasing this data under an open license and the encouragement and advice I have had on the way.

For contact my email address is charlie.plumley@cubicoinvest.com.

You can also access data from Penmanshiel wind farm here.

Overview

This hypergraph dataset was generated using email data from a large European research institution for a period from October 2003 to May 2005 (18 months). Information about all incoming and outgoing emails between members of the research institution has been anonymized. The e-mails only represent communication between institution members (the core), and the dataset does not contain incoming messages from or outgoing messages to the rest of the world.

This is a temporal hypergraph dataset, which here means a sequence of timestamped hyperedges where each hyperedge is a set of nodes. Timestamps are in ISO8601 format. In email communication, messages can be sent to multiple recipients. In this dataset, nodes are email addresses at a European research institution. The original data source only contains directed temporal edge tuples (sender, receiver, timestamp), where timestamps are recorded at 1-second resolution. The hyperedges are undirected and consist of a sender and all receivers grouped such that the email between the sender and each receiver has the same timestamp.

Statistics

Some basic statistics of this dataset are:

• number of nodes: 1,005
• number of timestamped hyperedges: 235,263
• distribution of the connected components:

Component Size, Number

• 986, 1
• 1, 19

Source of original data

Source: email-Eu dataset

References

If you use this dataset, please cite these references:

• Simplicial closure and higher-order link prediction, Austin R. Benson, Rediet Abebe, Michael T. Schaub, Ali Jadbabaie, and Jon Kleinberg. Proceedings of the National Academy of Sciences (PNAS), 2018.
• Local Higher-order Graph Clustering, Hao Yin, Austin R. Benson, Jure Leskovec, and David F. Gleich. Proceedings of KDD, 2017.
• Graph Evolution: Densification and Shrinking Diameters, Jure Leskovec, Jon Kleinberg, and Christos Faloutsos. ACM Transactions on Knowledge Discovery from Data, 2007.

The type of contracts they have been awarded which are currently active e.g GDS General, GDS Orthodontics, PDS Orthodontics etc (if they hold more than one type, please break it down) The value of each contract in pounds sterling. The amount of UOAs allotted in each contract The value per UOA of each contract. The start and end dates of each contract and whether they are permanent (automatically recurring) contracts or have a definite end period (if they hold more than one type, please break it down) Response We can confirm that we do hold the requested information, but some of this information is exempt under section 40(2) of the FOIA (personal information). Question 1 We have redacted Provider’s personal Email Address and Contract Email Address where they are not in the public domain under section 40(2). Section 40(2) is an absolute, prejudice-based exemption and therefore is exempt if disclosure would contravene any of the data protection principles. In order for disclosure to comply with the lawfulness, fairness, and transparency principle, we either need the consent of the data subject(s) or there must be a legitimate interest in disclosure. In addition, the disclosure must be necessary to meet the legitimate interest and finally, the disclosure must not cause unwarranted harm. As we do not have the consent of the data subject(s), the NHSBSA is therefore required to conduct a balancing exercise between legitimate interest of the applicant in disclosure against the rights and freedoms of the data subject(s). The NHSBSA acknowledges that you have a legitimate interest in disclosure of the information in order to provide the full picture of the requested data held by the NHSBSA; however, we have concluded that disclosure of the requested information would cause unwarranted harm and therefore, section 40(2) is engaged. Please see the following link to view the section 40 exemption in full - https://www.legislation.gov.uk/ukpga/2000/36/section/40 Remaining data Please read the below notes to ensure correct understanding of the data. This report contains the details from FP17s processed for the period stated 2024/2025 in the Sheffield, Mansfield & Wakefield Local Authories. The information within the report includes the data items and descriptions listed below. Contracts with a Performance Target/Financial Value greater than one included. Commissioner Code: This is the 3 character code associated with the Commissioning Organisation. This a generic term used to denote health bodies, either Area Teams in England or Health Boards in Wales. Also known as Primary Care Organisations (PCOs) Commissioner Name: This is the name associated with the Commissioning Organisation. This is a generic term used to denote health bodies, either Area Teams in England or Health Boards in Wales. Also known as Primary Care Organisations (PCOs)) Contract Number: This is a unique 10 character number which identifies a contract. Provider Name: The name of the provider Contract Type: Either GDS – General Dental Services, PDS – Personal Dental Services or PDS Plus – Personal Dental Services Plus Financial Year: The financial year associated with the 'Year Month' dimension in the format 'YYYY/YYYY' e.g. 2018/2019 for 201804. Contract Start Date: The start date of the contract, as recorded on CoMPASS. Contract End Date: The end date of the contract, as recorded on CoMPASS (only where an end date has been entered on CoMPASS). Local Authority Name: This is the Local Authority the contract is located in. UOA Performance Target: The contracted units of orthodontic activity to be achieved for the year, as entered on CoMPASS by the PCO. Principal Practice Address - Lines 1-6: This is the main address used for correspondence between the NHSBSA and the dental contract, it is not necessarily the address where treatment takes place. Contract Email Address: The email address of the contract, as entered on CoMPASS. Provider Email Address: The email address of the provider, as entered on CoMPASS Contract Landline Number: The latest entered (on CoMPASS) phone number associated with the contract, as entered on CoMPASS Contract Mobile Number: The latest entered (on CoMPASS) Mobile phone number associated with the contract, as entered on CoMPASS UOA Financial Value: The financial value associated with the UOA performance target for the financial year, as entered on CoMPASS by the PCO. Cost per UOA: This shows the cost per unit of orthodontic activity at the contract. This is calculated by dividing the UOA Financial Value by the UOA Performance Target. Performance Target: The target associated with the service line for the financial year e.g. UDAs, UOAs, CoTs etc., as entered on CoMP. Financial Value: The financial value associated with the service line for the financial year, as entered on CoMPASS by the PCO. Service Line Name: The service name/description as maintained by CoMPASS. Contract Service Recurring Yes/No: Indicates as to whether the service name/description is recurrent or not as maintained by CoMPASS. Publishing this response Please note that this information will be published on our Freedom of Information disclosure log at: https://opendata.nhsbsa.net/dataset/foi-02101

How can the data be used? The data can be used in every possible way. It can be used simply to see the trends of an area policy or to compare the work of different sections of the government. Useful applications can even be created with the data, which can be used by everyone. Who is involved in the project? datosabiertos.malaga.eu is part of the transparency policy that is being carried out in the Málaga City Council. Dataset Request Through the open data email, you can suggest what data to include in this portal. The requests will be analyzed and an attempt will be made to respond by opening new data in open formats. Technical Support If you have detected a technical problem during the normal operation of the open data portal, you can submit the problem in the following link, you can also consult the issues related to this portal by filling out this form. Technical Support Technical Details The following link lists the tools used to implement this portal as well as other technical data of interest. Technical details Dataset Request Through the open data email, you can suggest what data to include in this portal. The requests will be analyzed and an attempt will be made to respond by opening new data in open formats. Technical Support If you have detected a technical problem during the normal operation of the open data portal, you can submit the problem in the following link, you can also consult the issues related to this portal by filling out this form. Technical Support Technical Details The following link lists the tools used to implement this portal as well as other technical data of interest. Technical details Dataset Request Through the open data email, you can suggest what data to include in this portal. The requests will be analyzed and an attempt will be made to respond by opening new data in open formats. Technical Support If you have detected a technical problem during the normal operation of the open data portal, you can submit the problem in the following link, you can also consult the issues related to this portal by filling out this form. Technical Support Technical Details The following link lists the tools used to implement this portal as well as other technical data of interest. Technical details Dataset Request Through the open data email, you can suggest what data to include in this portal. The requests will be analyzed and an attempt will be made to respond by opening new data in open formats. Technical Support If you have detected a technical problem during the normal operation of the open data portal, you can submit the problem in the following link, you can also consult the issues related to this portal by filling out this form. Technical Support Technical Details The following link lists the tools used to implement this portal as well as other technical data of interest. Technical details Dataset Request Through the open data email, you can suggest what data to include in this portal. The requests will be analyzed and an attempt will be made to respond by opening new data in open formats. Technical Support If you have detected a technical problem during the normal operation of the open data portal, you can submit the problem in the following link, you can also consult the issues related to this portal by filling out this form. Technical Support Technical Details The following link lists the tools used to implement this portal as well as other technical data of interest. Technical details Technical Support If you have detected a technical problem during the normal operation of the open data portal, you can submit the problem in the following link, you can also consult the issues related to this portal by filling out this form. Technical Support Technical Details The following link lists the tools used to implement this portal as well as other technical data of interest. Technical details Technical Support If you have detected a technical problem during the normal operation of the open data portal, you can submit the problem in the following link, you can also consult the issues related to this portal by filling out this form. Technical Support Technical Details The following link lists the tools used to implement this portal as well as other technical data of interest. Technical details Technical Support If you have detected a technical problem during the normal operation of the open data portal, you can submit the problem in the following link, you can also consult the issues related to this portal by filling out this form. Technical Support Technical Details The following link lists the tools used to implement this portal as well as other technical data of interest. Technical details Technical Support If you have detected a technical problem during the normal operation of the open data portal, you can submit the problem in the following link, you can also consult the issues related to this portal by filling out this form. Technical Support Technical Details The following link lists the tools used to implement this portal as well as other technical data of interest. Technical details Technical Details The following link lists the tools used to implement this portal as well as other technical data of interest. Technical details Technical Details The following link lists the tools used to implement this portal as well as other technical data of interest. Technical details Technical Details The following link lists the tools used to implement this portal as well as other technical data of interest. Technical details Technical Details The following link lists the tools used to implement this portal as well as other technical data of interest. Technical details Translated from Spanish Original Text: ¿ Cómo se pueden usar los datos ? Los datos se pueden usar de todos los modos posibles. Se puede usar simplemente para ver las tendencias de una política de área o para comparar el trabajo de distintas secciones del gobierno. Incluso pueden crearse aplicaciones útiles con los datos, que pueden ser usados por todos. ¿ Quiénes están involucrados en el proyecto? datosabiertos.malaga.eu es parte de la política de transparencia que se está llevando a cabo en el Ayuntamiento de Málaga. Solicitud de conjunto de datos A través del correo de datos abiertos puede sugerir qué datos incluir en este portal. Las peticiones será analizadas y se intentará dar respuesta mediante la apertura de nuevos datos en formatos abiertos. Soporte técnico Si ha detectado un problema técnico durante el normal funcionamiento del portal de datos abiertos, puede remitir el problema en el siguiente enlace, también podrá consultar los asuntos relacionados con este portal rellenando este formulario. Soporte técnico Detalles Técnicos En el siguiente enlace se enumeran las herramientas usadas para implantar este portal así como otros datos técnicos de interés. Detalles técnicos Solicitud de conjunto de datos A través del correo de datos abiertos puede sugerir qué datos incluir en este portal. Las peticiones será analizadas y se intentará dar respuesta mediante la apertura de nuevos datos en formatos abiertos. Soporte técnico Si ha detectado un problema técnico durante el normal funcionamiento del portal de datos abiertos, puede remitir el problema en el siguiente enlace, también podrá consultar los asuntos relacionados con este portal rellenando este formulario. Soporte técnico Detalles Técnicos En el siguiente enlace se enumeran las herramientas usadas para implantar este portal así como otros datos técnicos de interés. Detalles técnicos Solicitud de conjunto de datos A través del correo de datos abiertos puede sugerir qué datos incluir en este portal. Las peticiones será analizadas y se intentará dar respuesta mediante la apertura de nuevos datos en formatos abiertos. Soporte técnico Si ha detectado un problema técnico durante el normal funcionamiento del portal de datos abiertos, puede remitir el problema en el siguiente enlace, también podrá consultar los asuntos relacionados con este portal rellenando este formulario. Soporte técnico Detalles Técnicos En el siguiente enlace se enumeran las herramientas usadas para implantar este portal así como otros datos técnicos de interés. Detalles técnicos Solicitud de conjunto de datos A través del correo de datos abiertos puede sugerir qué datos incluir en este portal. Las peticiones será analizadas y se intentará dar respuesta mediante la apertura de nuevos datos en formatos abiertos. Soporte técnico Si ha detectado un problema técnico durante el normal funcionamiento del portal de datos abiertos, puede remitir el problema en el siguiente enlace, también podrá consultar los asuntos relacionados con este portal rellenando este formulario. Soporte técnico Detalles Técnicos En el siguiente enlace se enumeran las herramientas usadas para implantar este portal así como otros datos técnicos de interés. Detalles técnicos Solicitud de conjunto de datos A través del correo de datos abiertos puede sugerir qué datos incluir en este portal. Las peticiones será analizadas y se intentará dar respuesta mediante la apertura de nuevos datos en formatos abiertos. Soporte técnico Si ha detectado un problema técnico durante el normal funcionamiento del portal de datos abiertos, puede remitir el problema en el siguiente enlace, también podrá consultar los asuntos relacionados con este portal rellenando este formulario. Soporte técnico Detalles Técnicos En el siguiente enlace se enumeran las herramientas usadas para implantar este portal así como otros datos técnicos de interés. Detalles técnicos Soporte técnico Si ha detectado un problema técnico durante el normal funcionamiento del portal de datos abiertos, puede remitir el problema en el siguiente enlace, también podrá consultar los asuntos relacionados con este portal rellenando este formulario. Soporte técnico Detalles Técnicos En el siguiente enlace se enumeran

About this Dataset

You will get an active email list for real and active buyers who make regular purchases through Amazon and other e-commerce sites. This email list contains 100% original email address. You can also use these emails to increase visits to your website, blog, or YouTube channel. I offer you now, a great treasure to use whenever you want.

So don't waste your time and start boosting your ecommerce business online.

The buyers will be from:

United States of America Canada Europe Union

$ There are no duplicate emails $ No fake IDs $ Audiences ready to buy

Category

Markets

Keywords

market,emails,email ma,list,buyer

Row Count

70150

Price

$90.00

CMAQ predicted results containing ozone, oxides of nitrogen and other chemical species. This dataset is not publicly accessible because: EPA does not have the dataset as it was created by the Universidad Politécnica de Madrid, Spain. It can be accessed through the following means: The Universidad Politécnica de Madrid created the dataset. Please contact Rafael Borge for the dataset. Email - rafael.borge@upm.es. Format: Dataset includes CMAQ output files in netcdf format. This dataset is associated with the following publication: Paz, D.d.l., R. Borge, J.M.d. Andrés, L. Tovar, G. Sarwar, and S. Napelenok. Summertime tropospheric ozone source apportionment study in Madrid (Spain). Atmospheric Chemistry and Physics. Copernicus Publications, Katlenburg-Lindau, GERMANY, N/A, (2023).

Project Summary This dataset contains all qualitative and quantitative data collected in the first phase of the Pandemic Journaling Project (PJP). PJP is a combined journaling platform and interdisciplinary, mixed-methods research study developed by two anthropologists, with support from a team of colleagues and students across the social sciences, humanities, and health fields. PJP launched in Spring 2020 as the COVID-19 pandemic was emerging in the United States. PJP was created in order to “pre-design an archive” of COVID-19 narratives and experiences open to anyone around the world. The project is rooted in a commitment to democratizing knowledge production, in the spirit of “archival activism” and using methods of “grassroots collaborative ethnography” (Willen et al. 2022; Wurtz et al. 2022; Zhang et al 2020; see also Carney 2021). The motto on the PJP website encapsulates these commitments: “Usually, history is written only by the powerful. When the history of COVID-19 is written, let’s make sure that doesn’t happen.” (A version of this Project Summary with links to the PJP website and other relevant sites is included in the public documentation of the project at QDR.) In PJP’s first phase (PJP-1), the project provided a digital space where participants could create weekly journals of their COVID-19 experiences using a smartphone or computer. The platform was designed to be accessible to as wide a range of potential participants as possible. Anyone aged 15 or older, living anywhere in the world, could create journal entries using their choice of text, images, and/or audio recordings. The interface was accessible in English and Spanish, but participants could submit text and audio in any language. PJP-1 ran on a weekly basis from May 2020 to May 2022. Data Overview This Qualitative Data Repository (QDR) project contains all journal entries and closed-ended survey responses submitted during PJP-1, along with accompanying descriptive and explanatory materials. The dataset includes individual journal entries and accompanying quantitative survey responses from more than 1,800 participants in 55 countries. Of nearly 27,000 journal entries in total, over 2,700 included images and over 300 are audio files. All data were collected via the Qualtrics survey platform. PJP-1 was approved as a research study by the Institutional Review Board (IRB) at the University of Connecticut. Participants were introduced to the project in a variety of ways, including through the PJP website as well as professional networks, PJP’s social media accounts (on Facebook, Instagram, and Twitter) , and media coverage of the project. Participants provided a single piece of contact information — an email address or mobile phone number — which was used to distribute weekly invitations to participate. This contact information has been stripped from the dataset and will not be accessible to researchers. PJP uses a mixed-methods research approach and a dynamic cohort design. After enrolling in PJP-1 via the project’s website, participants received weekly invitations to contribute to their journals via their choice of email or SMS (text message). Each weekly invitation included a link to that week’s journaling prompts and accompanying survey questions. Participants could join at any point, and they could stop participating at any point as well. They also could stop participating and later restart. Retention was encouraged with a monthly raffle of three $100 gift cards. All individuals who had contributed that month were eligible. Regardless of when they joined, all participants received the project’s narrative prompts and accompanying survey questions in the same order. In Week 1, before contributing their first journal entries, participants were presented with a baseline survey that collected demographic information, including political leanings, as well as self-reported data about COVID-19 exposure and physical and mental health status. Some of these survey questions were repeated at periodic intervals in subsequent weeks, providing quantitative measures of change over time that can be analyzed in conjunction with participants' qualitative entries. Surveys employed validated questions where possible. The core of PJP-1 involved two weekly opportunities to create journal entries in the format of their choice (text, image, and/or audio). Each week, journalers received a link with an invitation to create one entry in response to a recurring narrative prompt (“How has the COVID-19 pandemic affected your life in the past week?”) and a second journal entry in response to their choice of two more tightly focused prompts. Typically the pair of prompts included one focusing on subjective experience (e.g., the impact of the pandemic on relationships, sense of social connectedness, or mental health) and another with an external focus (e.g., key sources of scientific information, trust in government, or COVID-19’s economic impact). Each week,...