Dataset

This dataset was created by Udit Purohit@3

Contents

Dataset

This dataset was created by Jake Laurence Paz

Released under Other (specified in description)

Contents

Our Email Enrichment Service allows you to upload a CSV file with email addresses, and we'll transform that basic data into a rich set of insights. You can include additional fields, like LinkedIn URLs, domains, and company names, to further refine the output. However, even with just an email address, we'll provide detailed information, such as:

First and last name Company name Job title LinkedIn profile Company domain And more depending on availability

The process is simple:

Prepare Your File: If you only have email addresses, that's sufficient. However, including LinkedIn URLs, domains, or names can help improve the accuracy of our enrichment. Provide us your file.

Receive Enriched Data: You'll get a file with enriched details. We'll first verify the email data and if it is a valid email, we'll source data on the person and company in real-time, enabling you to supercharge your outreach or marketing campaigns. Whether you're building prospect lists, personalizing email campaigns, or targeting decision-makers, this data gives you the advantage of deeper insights for better results.

Our service is designed for speed, accuracy, and high-quality data, ensuring your team has what they need to engage effectively.

Ce jeu de données recense les domaines email de contact qui ont été faits par des organisations en contact avec certains services de l'État. C'est un CSV avec 3 colonnes : SIRET domain_email data_source 10000001700010 elysee.fr moncomptepro Les trois colonnes en détails : SIRET : le SIRET de l'organisation domain_email : le domaine email de contact de l'organisation data_source : la source de la donnée La source de donnée provient de l'un des trois services de l'État suivants : moncomptepro : le domaine email a été vérifié manuellement par les équipes de MonComptePro (uniquement organisation publique) trackdechets_postal_mail : le domaine email a été vérifié avec un courrier envoyé par TrackDéchets au siège social de l'organisation alternance_job_contracted : le domaine email a été vérifié avec une signature de contrat d'alternance sur La Bonne Alternance

Success.ai's B2B Email Data for European Professionals offers unprecedented access to a vast dataset of over 700 million verified profiles, meticulously curated to empower your marketing and sales strategies across Europe. This comprehensive database includes work emails, phone numbers, and extensive professional histories, providing the key details you need to connect with decision-makers and influencers in various industries.

Why Choose Success.ai’s B2B Email Data?

Extensive European Coverage: Our dataset spans across the entire European continent, including both EU and non-EU countries, ensuring you can reach professionals in key markets. Verified Contact Details: Each profile is thoroughly verified for accuracy, ensuring you have the most reliable emails and contact numbers at your fingertips. In-depth Professional Histories: Gain insights into the careers of potential leads, including their past roles, industries of expertise, and professional achievements. Data Features:

Work Emails and Phone Numbers: Direct communication channels to engage with prospects effectively. Professional Backgrounds: Detailed histories to help you tailor your outreach and personalize communication. Industry and Role Segmentation: Data segmented by industry and job role to refine your targeting and increase conversion rates. Flexible Delivery and Integration: Our data can be delivered in various formats such as CSV, Excel, or through an API, allowing for easy integration into your existing CRM systems or marketing platforms. This flexibility ensures that you can start leveraging the data quickly, with minimal setup time required.

Competitive Pricing with Best Price Guarantee: We are committed to providing you the best value for your investment. Our Best Price Guarantee ensures you receive the highest quality data at the most competitive rates in the market.

Targeted Applications for B2B Email Data:

Lead Generation: Identify and connect with potential clients by utilizing accurate contact data to support cold emailing and telemarketing efforts. Account-Based Marketing (ABM): Enhance your ABM campaigns by reaching the key stakeholders in target companies directly. Market Research: Use detailed professional backgrounds to analyze market trends and understand the competitive landscape. Event Promotion: Drive attendance to webinars, conferences, and trade shows by reaching out to relevant professionals. Quality Assurance and Compliance:

Data Accuracy: Our stringent verification processes ensure a high level of accuracy, with regular updates to keep the data fresh. Compliance with Data Protection Laws: All data is collected and processed in compliance with GDPR and other relevant legislation, ensuring lawful and ethical use. Support and Consultation:

Customer Support: Our dedicated support team is available to assist with any queries or issues you may encounter. Consultation Services: Benefit from our expertise in data-driven marketing and sales strategies through personalized consultation sessions. Get Started with Success.ai Today: Empower your business with Success.ai’s B2B Email Data for European Professionals and start building meaningful connections that drive growth. Contact us to explore our data solutions and discover how we can help you achieve your business objectives.

DomainIQ is a comprehensive global Domain Name dataset for organizations that want to build cyber security, data cleaning and email marketing applications. The dataset consists of the DNS records for over 267 million domains, updated daily, representing more than 90% of all public domains in the world.

The data is enriched by over thirty unique data points, including identifying the mailbox provider for each domain and using AI based predictive analytics to identify elevated risk domains from both a cyber security and email sending reputation perspective.

DomainIQ from Datazag offers layered intelligence through a highly flexible API and as a dataset, available for both cloud and on-premises applications. Standard formats include CSV, JSON, Parquet, and DuckDB.

Custom options are available for any other file or database format. With daily updates and constant research from Datazag, organizations can develop their own market leading cyber security, data cleaning and email validation applications supported by comprehensive and accurate data from Datazag. Data updates available on a daily, weekly and monthly basis. API data is updated on a daily basis.

Explore historical ownership and registration records by performing a reverse Whois lookup for the email address ipswichit@csv.org.uk..

Credit report of Csv Via Akcenta Cz A S contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

This package contains Python, shell, awk scripts, and data used to obtain the curated table associated with the above named article. It also contains (in this file) a description of the methods employed to obtain the curated table with details regarding the published articles.

Contents

The following items are included.

• README.md: This file
• article-details.xlsx: Curated table with details of published articles in Microsoft Excel file format
• index.html: HTML document with
  • links to GIJIR materials saved in the Internet Archive
  • a list of all the GIJIR articles’ citation data according to Crossref and links to each article’s locally available landing page, full-text PDF, plus links to Crossref metadata and the article via DOI and original journal URL. (Note that non-local, non-archived links may rot over time.)
• Makefile: Commands that orchestrate the articles’ analysis
• get-metadata.sh: Obtain article metadata pages from the journal’s web site
• apply-to-pdfs.sh: Apply the specified Python script to all article PDFs
• extract-citations-emails.py: Extract number of probable in-text citations and corresponding author email from article PDF
• extract-doi-affiliations.py: Extract article DOI and affiliations from an article’s metadata
• extract-all-doi-affiliations.sh: Extract article DOI and affiliations from all articles’ metadata
• emails-to-csv.awk: Convert emails and article numbers to CSV with URL for sending emails
• ybs-works.json: Results of Crossref query to obtain all the publisher’s works made on 2024-09-22
• ChatGPT: Prompts and responses associated with the generation of a fake article in one of the journal’s topics.
• global-us/metadata/: Article metadata as HTML files collected on 2024-09-10
• global-us/global-us.mellbaou.com/index.php/global/article/download/: A copy of the journal’s article PDFs as crawled on 2024-09-10

This dataset contains the data associated with our research project titled Impact of delayed response on Wearable Cognitive Assistance. A preprint of the associated paper can be found at https://arxiv.org/abs/2011.02555.

GENERAL INFORMATION

1. Title of Dataset: Impact of delayed response on Wearable Cognitive Assistance

2. Author Information

First Author Contact Information Name: Manuel Olguín Muñoz Institution: School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology Address: Malvinas väg 10, Stockholm 11428, Sweden Email: molguin@kth.se Phone Number: +46 73 652 7628

Author Contact Information Name: Roberta L. Klatzky Institution: Department of Psychology, Carnegie Mellon University Address: 5000 Forbes Ave, Pittsburgh, PA 15213 Email: klatzky@cmu.edu Phone Number: +1 412 268 8026

Author Contact Information Name: Mahadev Satyanarayanan Institution: School of Computer Science, Carnegie Mellon University Address: 5000 Forbes Ave, Pittsburgh, PA 15213 Email: satya@cs.cmu.edu Phone Number: +1 412 268 3743

Author Contact Information Name: James R. Gross Institution: School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology Address: Malvinas väg 10, Stockholm 11428, Sweden Email: jamesgr@kth.se Phone Number: +46 8 790 8819

DATA & FILE OVERVIEW

Directory of Files: A. Filename: accelerometer_data.csv Short description: Time-series accelerometer data. Each row corresponds to a sample.

B. Filename: block_aggregate.csv
  Short description: Contains the block- and slice-level aggregates for each of the metrics and statistics present in this dataset. Each row corresponds to either a full block or a slice of a block, see below for details.


C. Filename: block_metadata.csv
  Short description: Contains the metadata for each block in the task for each participant. Each row corresponds to a block.

D. Filename: bvp_data.csv
  Short description: Time-series blood-volume-pulse data. Each row corresponds to a sample.


E. Filename: eeg_data.csv
  Short description: Time-series electroencephalogram data, represented as power per band. Each row corresponds to a sample; power was calculated in 0.5 second intervals.


F. Filename: frame_metadata.csv
  Short description: Contains the metadata for each video frame processed by the cognitive assistant. Each row corresponds to a processed frame.


G. Filename: gsr_data.csv
  Short description: Time-series galvanic skin response data. Each row corresponds to a sample.


H. Filename: task_step_metadata.csv
  Short description: Contains the metadata for each step in the task for each participant. Each row corresponds to a step in the task.


I. Filename: temperature_data.csv
  Short description: Time-series thermometer data. Each row corresponds to a sample.

Additional Notes on File Relationships, Context, or Content (for example, if a user wants to reuse and/or cite your data, what information would you want them to know?):

• The data contained in these CSVs was obtained from 40 participants in a study performed with approval from the Carnegie Mellon University Institutional Research Board. In this study, participants were asked to interact with a Cognitive Assistant while wearing an array of physiological sensors. The data contained in this dataset corresponds to the actual collected data, after some preliminary preprocessing to convert from sensors readings into meaningful values.

• Participants have been anonymized using random integer identifiers.

• block_aggregate.csv can be replicated by cross-referencing the start and end timestamps of each block in block_metadata.csv and the timestamps for each desired metric.

• The actual video frames mentioned in frame_metadata.csv are not included in the dataset since their contents were not relevant to the research.

File Naming Convention: N/A

DATA DESCRIPTION FOR: accelerometer_data.csv

1. Number of variables: 7

2. Number of cases/rows: 1844688

3. Missing data codes: N/A

4. Variable list:

   A. Name: timestamp Description: Timestamp of the sample.

   B. Name: x Description: Acceleration reading from the x-axis of the accelerometer in g-forces [g].

   C. Name: y Description: Acceleration reading from the y-axis of the accelerometer in g-forces [g].

   D. Name: z Description: Acceleration reading from the z-axis of the accelerometer in g-forces [g].

   E. Name: ts Description: Time difference with respect to first sample.

   F. Name: participant Description: Denotes the numeric ID representing each individual participant.

   G. Name: delay Description: Delay that was being applied on the task when this reading was obtained in time delta format.

DATA DESCRIPTION FOR: block_aggregate.csv

1. Number of variables: 16

2. Number of cases/rows: 2520

3. Missing data codes:

   • Except for the 'slice' columns, empty cells mean that the data is not applicable or was removed from the dataset due to noise or instrument failure.
   • For the 'slice' column, a missing value indicates that the row corresponds to the whole block as opposed to a slice of it.

4. Variable List:

   A. Name: participant Description: Denotes the numeric ID representing each individual participant.

   B. Name: block_seq Description: Denotes the position of the block in the task. Ranges from 1 to 21.

   C. Name: slice Description: Index of the 4-step slice of the block over which the data was aggregated. Ranges from 0 to 2, however higher values are only applicable for blocks of appropriate length (i.e. blocks of length 4 only have a 0-slice, length 8 have 0 and 1, and length 12 have slices from 0 to 2). A missing value indicates that this row instead contains aggregate values for the whole block.

   D. Name: block_length Description: Length of the block. Valid values are 4, 8 and 12.

   C. Name: block_delay Description: Delay applied to the block, in seconds.

   F. Name: start Description: Timestamp marking the start of the block or slice.

   G. Name: end Description: Timestamp marking the end of the block or slice.

   H. Name: duration Description: Duration of the block or slice, in seconds.

   I. Name: exec_time_per_step_mean Description: Mean execution time for each step in the block or slice.

   J. Name: bpm_mean Description: Mean heart rate, in beats-per-minute, for the block or slice.

   K. Name: bpm_std Description: Standard deviation of the heart rate, in beats-per-minute, for the block or slice.

   L. Name: gsr_per_second Description: Galvanic skin response in microsiemens, summed and then normalized by block or slice duration.

   M. Name: movement_score Description: Movement score for the block or slice. The movement score is calculated as the sum of the magnitude of all the acceleration vectors in the block or slice, divided by duration in seconds.

   N. Name: eeg_alpha_log_mean Description: Log of the average EEG power for the alpha band for the, block or slice.

   O. Name: eeg_beta_log_mean Description: Log of the average EEG power for the beta band for the, block or slice.

   P. Name: eeg_total_log_mean Description: Log of the average EEG power for the complete EEG signal, for the block or slice.

DATA DESCRIPTION FOR: block_metadata.csv

1. Number of variables: 8

2. Number of cases/rows: 880

3. Missing data codes: N/A

4. Variable list:

   A. Name: participant Description: Denotes the numeric ID representing each individual participant.

   B. Name: seq Description: Index of the block in the task, ranging from 0 to 21. Note that block 0 is not to be included in aggregate calculations.

   C. Name: length Description: Length of the block in number of steps.

   D. Name: delay Description: Delay applied to the block.

   E. Name: start Description: Timestamp marking the start of the block.

   F. Name: end Description: Timestamp marking the end of the block.

   G. Name: duration Description: Duration of the block as a timedelta.

   H. Name: exec_time Description: Execution time of the block as a timedelta.

DATA DESCRIPTION FOR: bvp_data.csv

1. Number of variables: 8

2. Number of cases/rows: 3683504

3. Missing data codes: Columns bpm and ibi only contain values for rows corresponding to a sample taken at a heartbeat.

4. Variable list:

   A. Name: ts Description: Time difference with respect to first sample.

   B. Name: timestamp Description: Timestamp of the sample.

   C. Name: bvp Description: Blood-volume-pulse reading, in millivolts.

   D. Name: onset Description: Boolean indicating if this sample corresponds to the onset of a pulse.

   E. Name: bpm Description: Instantaneous beat-per-minute value.

   F. Name: ibi Description: Instantaneous inter-beat-interval value.

   G. Name: delay Description: Delay that was being applied on the task when this reading was obtained in time delta format.

   H. Name: participant Description: Denotes the numeric ID representing each individual

This Dataset contains the IDs of 5,427,024 commit authors who have created commits in git version control system, and have more than 1 ID in git. It is a compressed CSV file (separated by ; ) with 14,861,538 author IDs, where the first column is the group ID, which is same as the first (randomly selected) author ID of the group, and the second column is the author ID that is part of the group. If an author was found to have 2 different IDs: I1, I2, then it is recorded in the file in 2 separate lines, with the lines being I1;I1 and I1;I2, i.e. the first column is the group identifier, which is one of the IDs in a group, and the second column contains the different author IDs in separate lines. This data set contains email addresses for various Git author's, but the '@' within the email address has been replaced with a '#'.

This dataset contains all current and future addresses within the municipality of Arnhem. Every customer of the data from the BAG is legally obliged to report possible inaccuracies or incompleteness to the administrator of the registration. This can preferably be done by email to: bagbeheer@arnhem.nl The address details are updated monthly from the BAG.

This dataset is based on the TravisTorrent dataset released 2017-01-11 (https://travistorrent.testroots.org), the Google BigQuery GHTorrent dataset accessed 2017-07-03, and the Git log history of all projects in the dataset, retrieved 2017-07-16 - 2017-07-17.

We selected projects hosted on GitHub that employ the Continuous Integration (CI) system Travis CI. We identified the projects using the TravisTorrent data set and considered projects that:

1. were active for one year before the first build with Travis CI (before_ci),
2. used Travis CI at least for one year (during_ci),
3. had commit or merge activity on the default branch in both of these phases, and
4. used the default branch to trigger builds.

To derive the time frames, we employed the GHTorrent Big Query data set. The resulting sample contains 321 projects. Of these projects, 214 are Ruby projects and 107 are Java projects. The mean time span before_ci was 2.9 years (SD=1.9, Mdn=2.3), the mean time span during_ci was 3.2 years (SD=1.1, Mdn=3.3). For our analysis, we only consider the activity one year before and after the first build.

We cloned the selected project repositories and extracted the version history for all branches (see https://github.com/sbaltes/git-log-parser). For each repo and branch, we created one log file with all regular commits and one log file with all merges. We only considered commits changing non-binary files and applied a file extension filter to only consider changes to Java or Ruby source code files. From the log files, we then extracted metadata about the commits and stored this data in CSV files (see https://github.com/sbaltes/git-log-parser).

The dataset contains the following files:

tr_projects_sample_filtered.csv
A CSV file with information about the 321 selected projects.

tr_sample_commits_default_branch_before_ci.csv
tr_sample_commits_default_branch_during_ci.csv
One CSV file with information about all commits to the default branch before and after the first CI build. Only commits modifying, adding, or deleting Java or Ruby source code files were considered. Those CSV files have the following columns:

project: GitHub project name ("/" replaced by "_").
branch: The branch to which the commit was made.
hash_value: The SHA1 hash value of the commit.
author_name: The author name.
author_email: The author email address.
author_date: The authoring timestamp.
commit_name: The committer name.
commit_email: The committer email address.
commit_date: The commit timestamp.
log_message_length: The length of the git commit messages (in characters).
file_count: Files changed with this commit.
lines_added: Lines added to all files changed with this commit.
lines_deleted: Lines deleted in all files changed with this commit.
file_extensions: Distinct file extensions of files changed with this commit.

tr_sample_merges_default_branch_before_ci.csv
tr_sample_merges_default_branch_during_ci.csv
One CSV file with information about all merges into the default branch before and after the first CI build. Only merges modifying, adding, or deleting Java or Ruby source code files were considered. Those CSV files have the following columns:

project: GitHub project name ("/" replaced by "_").
branch: The destination branch of the merge.
hash_value: The SHA1 hash value of the merge commit.
merged_commits: Unique hash value prefixes of the commits merged with this commit.
author_name: The author name.
author_email: The author email address.
author_date: The authoring timestamp.
commit_name: The committer name.
commit_email: The committer email address.
commit_date: The commit timestamp.
log_message_length: The length of the git commit messages (in characters).
file_count: Files changed with this commit.
lines_added: Lines added to all files changed with this commit.
lines_deleted: Lines deleted in all files changed with this commit.
file_extensions: Distinct file extensions of files changed with this commit.
pull_request_id: ID of the GitHub pull request that has been merged with this commit (extracted from log message).
source_user: GitHub login name of the user who initiated the pull request (extracted from log message).
source_branch : Source branch of the pull request (extracted from log message).

Complete list of all 302 Check `n Go POI locations in the the USA with name, geo-coded address, city, email, phone number etc for download in CSV format or via the API.

Complete list of all 72 Valor Healthcare clinic POI locations in the the USA with name, geo-coded address, city, email, phone number etc for download in CSV format or via the API.

A selection of analytics metrics for the data.gov.au service. Starting from January 2015 these metrics are aggregated by month and include;

• sessions,
• average session duration,
• bounce rate,
• page views, and
• unique users.

If you have suggestions for additional analytics please send an email to data@pmc.gov.au for consideration.

Success.ai proudly offers our exclusive LinkedIn Data product, targeting C-level executives from around the globe. This premium dataset is meticulously curated to empower your business development, recruitment strategies, and market research efforts with direct access to top-tier professionals.

Global Reach and Detailed Insights: Our LinkedIn Data encompasses profiles of C-level executives worldwide, offering detailed insights that include professional histories, current and past affiliations, as well as direct contact information such as verified work emails and phone numbers. This data spans across industries such as finance, technology, healthcare, manufacturing, and more, ensuring you have comprehensive coverage no matter your sector focus.

Accuracy and Compliance: Accuracy is paramount in executive-level data. Each profile within our dataset undergoes rigorous verification processes, using advanced AI algorithms to ensure data accuracy and reliability. Our datasets are also compliant with global data privacy laws such as GDPR, CCPA, and others, providing you with data you can trust and use with confidence.

Empower Your Business Strategies: Leverage our LinkedIn Data to enhance various business functions:

Sales and Marketing: Directly reach decision-makers, reducing sales cycles and increasing conversion rates. Recruitment and Talent Acquisition: Identify and engage with potential candidates for executive roles within your organization. Market Research and Competitive Analysis: Gain insights into competitor leadership and strategic moves by analyzing executive backgrounds and professional networks. Robust Data Points Include:

Full Names and Titles: Gain access to the full names and current positions of C-level executives. Professional Emails and Phone Numbers: Direct communication channels to ensure your messages reach the intended audience. Company Information: Understand the organizational context with details about the company size, industry, and role within the corporation. Professional History: Detailed career trajectories, highlighting roles, responsibilities, and achievements. Education and Certifications: Educational backgrounds and certifications that enrich the professional profiles of these executives. Flexible Delivery and Integration: Our LinkedIn Data is available in multiple formats, including CSV, Excel, and via API, allowing easy integration into your CRM systems or other sales platforms. We provide continuous updates to our datasets, ensuring you always have access to the most current information available.

Competitive Pricing with Best Price Guarantee: Success.ai offers this valuable data at the most competitive rates in the industry, backed by our best price guarantee. We are committed to providing you with the highest quality data at prices that fit your budget, ensuring excellent return on investment.

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Engage with Success.ai Today: Connect with us to explore how our LinkedIn Data can transform your strategic initiatives. Our data experts are ready to assist you in leveraging the full potential of this dataset to meet your business goals.

Reach out to Success.ai to access the world of C-level executives and propel your business to new heights with strategic data insights that drive success.

This RR interval dataset is derived from 10,000 cases of 24-hour Holter monitoring data sampled at 128 Hz. Among the cases, 9,500 are labeled as non-atrial fibrillation (NAF), and 500 as paroxysmal atrial fibrillation (PAF). These data have been used in the article "Clinician-AI Collaboration: A Win-Win solution for Efficiency and Reliability in Atrial Fibrillation Diagnosis".The dataset formated as CSV file consists of two columns:rr_interval: Represents the interval between consecutive R-peaks, measured in milliseconds.label: Categorical labels for the beats, where:1 indicates AF0 indicates NAF-1 indicates noise or artifactsEach case is named based on its category. NAF cases are labeled as NAF0001.csv through NAF9500.csv, while PAF cases are labeled as PAF0001.csv through PAF0500.csv.For any questions, please contact the email: hustzp@hust.edu.cn

This file contains a list of all edges between the nodes and the number of emails (contained in S5 File) for the Email network. (CSV)

A selection of analytics metrics for the NationalMap service. Starting from September 2015 these metrics are aggregated by month and include;

• sessions,
• average session duration,
• bounce rate,
• page views, and
• unique users.

If you have suggestions for additional analytics please send an email to data@pmc.gov.au for consideration.