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Dataset Name: Spam Email Dataset

Description: This dataset contains a collection of email text messages, labeled as either spam or not spam. Each email message is associated with a binary label, where "1" indicates that the email is spam, and "0" indicates that it is not spam. The dataset is intended for use in training and evaluating spam email classification models.

Columns:

text (Text): This column contains the text content of the email messages. It includes the body of the emails along with any associated subject lines or headers.

spam_or_not (Binary): This column contains binary labels to indicate whether an email is spam or not. "1" represents spam, while "0" represents not spam.

Usage: This dataset can be used for various Natural Language Processing (NLP) tasks, such as text classification and spam detection. Researchers and data scientists can train and evaluate machine learning models using this dataset to build effective spam email filters.

Overview

This is a temporal hypergraph dataset, which here means a sequence of timestamped hyperedges where each hyperedge is a set of nodes. In email communication, messages can be sent to multiple recipients. In this dataset, nodes are email addresses at Enron, and a hyperedge is comprised of the sender and all recipients of the email. Only email addresses from a core set of employees are included. Timestamps are in ISO8601 format.

This dataset was collected and prepared by the CALO Project (A Cognitive Assistant that Learns and Organizes). It contains data from about 150 users, mostly senior management of Enron, organized into folders. The corpus contains a total of about 0.5M messages. This data was originally made public and posted to the web by the Federal Energy Regulatory Commission during its investigation.

The email dataset was later purchased by Leslie Kaelbling at MIT and turned out to have a number of integrity problems. A number of folks at SRI, notably Melinda Gervasio, worked hard to correct these problems, and it is thanks to them that the dataset is available. The dataset here does not include attachments, and some messages have been deleted "as part of a redaction effort due to requests from affected employees". Invalid email addresses were converted to something of the form user@enron.com whenever possible (i.e., the recipient is specified in some parseable format like "Doe, John" or "Mary K. Smith") and to no_address@enron.com when no recipient was specified.

Statistics

Some basic statistics of this dataset are:

• number of nodes: 148
• number of timestamped hyperedges: 10,885
• distribution of the connected components:

Component Size, Number

• 143, 1
• 1, 5

Source of original data

Source: email-Enron 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.
• Enron Email Dataset, William Cohen, 2015.

This dataset contains anonymised survey responses from a comprehensive study conducted to explore current email management practices among users. The survey aimed to gain insights into how individuals handle and organize their email communications in various contexts. The survey questionnaire consisted of carefully designed questions related to email usage patterns, organisational strategies, folder structures, and automation utilised for email management. The survey also explored participants' preferences for automated rule-based filtering functionality and any challenges they face in effectively managing their mailbox.

Researchers and professionals interested in email management and information organisation can leverage this dataset for research, analysis, and potential improvements in email client design and functionality.

We kindly request that any publications or research utilising this dataset appropriately acknowledge and cite the original source to ensure proper attribution to the survey and its participants.

Aeslc (Email Subject Generation Task)

A collection of email messages of employees in the Enron Corporation.

By Huggingface Hub [source]

About this dataset

The AESLC (Automatic Extraction of Semantically-Linked Corporate Communications) dataset provides a unique and captivating glimpse into the lives of Enron employees - from the perspective of communications sent via emails during a period between 1999 to 2004. These anonymous emails not only provide fascinating insight into the daily professional activities, interactions, and relationships within Enron employees, but also offer an educational opportunity for those interested in further exploring corporate communication. Containing such features as email body and subject lines, researchers can tap into this invaluable resource to research topics surrounding linguistics, sentiment analysis, and data mining. Unlock their secrets by discovering what messages were shared amongst these before the breach of scandal that caused their company’s downfall!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This comprehensive dataset includes anonymized emails sent by then Enron employees in the period of 1999 and 2004. By delving into this unique dataset, you can gain a deeper insight into the lives of former Enron employees as well as their professional activities and relationships.

In this guide, we'll provide a walkthrough on how to use this dataset and make meaningful discoveries from it. Let's get started!

Research Ideas

• Analyzing the connections between Enron employees by tracking their email communications over time to uncover trends and correlations.
• Examining the emails for keywords or topics as a way to classify each email in order to gain better understanding of what Enron employees were discussing and what activities they were engaging in.
• Using sentiment analysis techniques on the emails in order to gain insight into the emotional state of Enron employees at different points in time or during particular events or incidents such as when allegations against Enron emerged

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: validation.csv | Column name | Description | |:-----------------|:--------------------------------------------------------------| | email_body | The body of the email sent by Enron employees. (Text) | | subject_line | The subject line of the email sent by Enron employees. (Text) |

File: train.csv | Column name | Description | |:-----------------|:--------------------------------------------------------------| | email_body | The body of the email sent by Enron employees. (Text) | | subject_line | The subject line of the email sent by Enron employees. (Text) |

File: test.csv | Column name | Description | |:-----------------|:--------------------------------------------------------------| | email_body | The body of the email sent by Enron employees. (Text) | | subject_line | The subject line of the email sent by Enron employees. (Text) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Huggingface Hub.

Policies ensuring that research data are available on public archives are increasingly being implemented at the government, funding agency, and journal level. These policies are predicated on the idea that authors are poor stewards of their data, particularly over the long term, and indeed many studies have found that authors are often unable or unwilling to share their data. However, there are no systematic estimates of how the availability of research data changes with time since publication. We therefore requested datasets from a relatively homogenous set of 516 articles published between 2 and 22 years ago, and found that availability of the data was strongly affected by article age. For papers where the authors gave the status of their data, the odds of a dataset being extant fell by 17% per year. In addition, the odds that we could find a working email address for the first, last or corresponding author fell by 7% per year. Our results reinforce the notion that, in the long term, ...

About

From distribution page:

This dataset was collected and prepared by the CALO Project (A Cognitive Assistant that Learns and Organizes). It contains data from about 150 users, mostly senior management of Enron, organized into folders. The corpus contains a total of about 0.5M messages. This data was originally made public, and posted to the web, by the Federal Energy Regulatory Commission during its investigation.

The email dataset was later purchased by Leslie Kaelbling at MIT, and turned out to have a number of integrity problems. A number of folks at SRI, notably Melinda Gervasio, worked hard to correct these problems, and it is thanks to them (not me) that the dataset is available. The dataset here does not include attachments, and some messages have been deleted "as part of a redaction effort due to requests from affected employees". Invalid email addresses were converted to something of the form user@enron.com whenever possible (i.e., recipient is specified in some parse-able format like "Doe, John" or "Mary K. Smith") and to no_address@enron.com when no recipient was specified.

I get a number of questions about this corpus each week, which I am unable to answer, mostly because they deal with preparation issues and such that I just don't know about. If you ask me a question and I don't answer, please don't feel slighted.

I am distributing this dataset as a resource for researchers who are interested in improving current email tools, or understanding how email is currently used. This data is valuable; to my knowledge it is the only substantial collection of "real" email that is public. The reason other datasets are not public is because of privacy concerns. In using this dataset, please be sensitive to the privacy of the people involved (and remember that many of these people were certainly not involved in any of the actions which precipitated the investigation.)

Downloads

Download is "about 400Mb, tarred and gzipped".

Openness

Unknown.

This dataset provides Customer Service Satisfaction results from the Annual Community Survey. The survey questions assess satisfaction with overall customer service for inpiduals who had contacted the city in the past year. For years where there are multiple questions related to overall customer service and treatment, the average of those responses are providing in the summary dataset and the values for each question are provided in the detailed dataset.For years 2010-2014, respondents were first asked "Have you contacted the city in the past year?". If they answered that they had contacted the city, then they were asked additional questions about their experience. The "number of respondents" field represents the number of people who answered yes to the contact question.Responses of "don't know" are not included in this dataset, but can be found in the dataset for the entire Community Survey. A survey was not completed for 2015 (99999 indicates no recorded data).Due to changes in the survey questions, this dataset was last updated in 2017 and may not be updated again. The performance measure dashboard is available at 2.02 Customer Service Satisfaction.Additional InformationSource: Community Attitude SurveyContact: Wydale HolmesContact E-Mail: Wydale_Holmes@tempe.govData Source Type: Excel and PDFPreparation Method: Extracted from Annual Community Survey resultsPublish Frequency: AnnualPublish Method: ManualData Dictionary

Overview This Extended Golf Play Dataset is a rich and detailed collection designed to extend the classic golf dataset. It includes a variety of features to cover many aspects of data science. This dataset is especially useful for teaching because it offers many small datasets within it, each one created for a different learning purpose.

Core Features: Outlook: Type of weather (sunny, cloudy, rainy, snowy). Temperature: How hot or cold it is, in Celsius. Humidity: How much moisture is in the air, as a percent. Windy: If it is windy or not (True or False). Play: If golf was played or not (Yes or No). Extra Features: ID: Each player's unique number. Date: The day the data was recorded. Weekday: What day of the week it is. Holiday: If the day is a special holiday (Yes or No). Season: Time of the year (spring, summer, autumn, winter). Crowded-ness: How crowded the golf course is. PlayTime-Hour: How long people played golf, in hours. Text Features: Review: What players said about their day at golf. EmailCampaign: Emails the golf place sent every day. MaintenanceTasks: Work done to take care of the golf course. Mini Datasets Collection This dataset includes a special set of mini datasets:

Each mini dataset focuses on a specific teaching point, like how to clean data or how to combine datasets. They're perfect for beginners to practice with real examples. Along with these datasets, you'll find notebooks with step-by-step guides that show you how to use the data. Learning With This Dataset Students can use this dataset to learn many skills:

Seeing Data: Learn how to make graphs and see patterns. Sorting Data: Find out which data helps to predict if golf will be played. Finding Odd Data: Spot data that doesn't look right. Understanding Data Over Time: Look at how things change day by day or month by month. Grouping Data: Learn how to put similar days together. Learning From Text: Use players' reviews to get more insights. Making Recommendations: Suggest the best time to play golf based on past data. Who Can Use This Dataset This dataset is for everyone:

New Learners: It's easy to understand and has guides to help you learn. Teachers: Great for classes on how to see and understand data. Researchers: Good for testing new ways to analyze data.

Original Data Source: ⛳️ Golf Play Dataset Extended

The total number of user mailboxes in Umeå kommun and how many are active each day of the reporting period. A mailbox is considered active if the user sent or read any email.

We propose various self-exciting point process models for the times when e-mails are sent between individuals in a social network. Using an expectation–maximization (EM)-type approach, we fit these models to an e-mail network dataset from West Point Military Academy and the Enron e-mail dataset. We argue that the self-exciting models adequately capture major temporal clustering features in the data and perform better than traditional stationary Poisson models. We also investigate how accounting for diurnal and weekly trends in e-mail activity improves the overall fit to the observed network data. A motivation and application for fitting these self-exciting models is to use parameter estimates to characterize important e-mail communication behaviors such as the baseline sending rates, average reply rates, and average response times. A primary goal is to use these features, estimated from the self-exciting models, to infer the underlying leadership status of users in the West Point and Enron networks. Supplementary materials for this article are available online.

The dataset consists of a collection of emails categorized into two major classes: spam and not spam. It is designed to facilitate the development and evaluation of spam detection or email filtering systems.

The spam emails in the dataset are typically unsolicited and unwanted messages that aim to promote products or services, spread malware, or deceive recipients for various malicious purposes. These emails often contain misleading subject lines, excessive use of advertisements, unauthorized links, or attempts to collect personal information.

The non-spam emails in the dataset are genuine and legitimate messages sent by individuals or organizations. They may include personal or professional communication, newsletters, transaction receipts, or any other non-malicious content.

The dataset encompasses emails of varying lengths, languages, and writing styles, reflecting the inherent heterogeneity of email communication. This diversity aids in training algorithms that can generalize well to different types of emails, making them robust against different spammer tactics and variations in non-spam email content.

Original Data Source: Spam Mail Prediction Dataset

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Performance Optimization Features

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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

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Data Compliance & Privacy

VIA.tools maintains industry-leading compliance standards:

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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

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Our data specialists work with you to:

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

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GENERAL INFORMATION

Title of Dataset: A dataset from a survey investigating disciplinary differences in data citation

Date of data collection: January to March 2022

Collection instrument: SurveyMonkey

Funding: Alfred P. Sloan Foundation

SHARING/ACCESS INFORMATION

Licenses/restrictions placed on the data: These data are available under a CC BY 4.0 license

Links to publications that cite or use the data:

Gregory, K., Ninkov, A., Ripp, C., Peters, I., & Haustein, S. (2022). Surveying practices of data citation and reuse across disciplines. Proceedings of the 26th International Conference on Science and Technology Indicators. International Conference on Science and Technology Indicators, Granada, Spain. https://doi.org/10.5281/ZENODO.6951437

Gregory, K., Ninkov, A., Ripp, C., Roblin, E., Peters, I., & Haustein, S. (2023). Tracing data: A survey investigating disciplinary differences in data citation. Zenodo. https://doi.org/10.5281/zenodo.7555266

DATA & FILE OVERVIEW

File List

Filename: MDCDatacitationReuse2021Codebookv2.pdf Codebook

Filename: MDCDataCitationReuse2021surveydatav2.csv Dataset format in csv

Filename: MDCDataCitationReuse2021surveydatav2.sav Dataset format in SPSS

Filename: MDCDataCitationReuseSurvey2021QNR.pdf Questionnaire

Additional related data collected that was not included in the current data package: Open ended questions asked to respondents

METHODOLOGICAL INFORMATION

Description of methods used for collection/generation of data:

The development of the questionnaire (Gregory et al., 2022) was centered around the creation of two main branches of questions for the primary groups of interest in our study: researchers that reuse data (33 questions in total) and researchers that do not reuse data (16 questions in total). The population of interest for this survey consists of researchers from all disciplines and countries, sampled from the corresponding authors of papers indexed in the Web of Science (WoS) between 2016 and 2020.

Received 3,632 responses, 2,509 of which were completed, representing a completion rate of 68.6%. Incomplete responses were excluded from the dataset. The final total contains 2,492 complete responses and an uncorrected response rate of 1.57%. Controlling for invalid emails, bounced emails and opt-outs (n=5,201) produced a response rate of 1.62%, similar to surveys using comparable recruitment methods (Gregory et al., 2020).

Methods for processing the data:

Results were downloaded from SurveyMonkey in CSV format and were prepared for analysis using Excel and SPSS by recoding ordinal and multiple choice questions and by removing missing values.

Instrument- or software-specific information needed to interpret the data:

The dataset is provided in SPSS format, which requires IBM SPSS Statistics. The dataset is also available in a coded format in CSV. The Codebook is required to interpret to values.

DATA-SPECIFIC INFORMATION FOR: MDCDataCitationReuse2021surveydata

Number of variables: 95

Number of cases/rows: 2,492

Missing data codes: 999 Not asked

Refer to MDCDatacitationReuse2021Codebook.pdf for detailed variable information.

1. Dataset The data consist of two sources: • A survey sent to former volunteers within one month of the end of their mission to ascertain their satisfaction, the conformity of their mission with the Civic Service framework and their plans for the continuation of their journey; • Data from the Civic Service’s management database to enable the characteristics of volunteers and missions to be taken into account. Two files, with each of the sources, are made available with a unique identifier to match them. 2. Method of data production The data of the Post-Service Civic survey is collected by an online questionnaire, sent to all volunteers whose email address is correctly indicated in the management database of the Civic Service Agency (80 % of the 83,904 volunteers entered in 2018). The e-mail invitation to reply to the questionnaire is sent within one month of the end of the mission. Two reminders are carried out two weeks apart. The overall response rate reached 44 % of the volunteers contacted in 2018. The answers to the open questions in the questionnaire are not included in this dataset for reasons of anonymity of the answers. In questions 13 and 15, open answers are recoded in the “Other” modality. The second dataset contains data from the Civic Service Agency’s management database, matched by a unique identifier with survey responses. These are administrative data, exhaustive, collected and manually filled in by the reception facilities at the time of signing the contract of employment of the young person. Matching difficulties explain the missing data. 3. Metadata This 2018 Post-Service Civic survey is a survey sent by the Civic Service Agency to all volunteers who have started a Civic Service, in France and abroad, at the end of their mission. The data are published under open license. Full year response data are published annually in the first quarter of year n+ 1. 4. Evolution of files In 2018 and 2019, the questionnaire of the Post Service-Civique survey remained the same. The database data matched with the Post Service-Civique survey will be updated for 2019. From 1 January 2020, a new Post-Service Civic questionnaire will be sent to volunteers. This data will be published in 2021. However, the investigation protocol remains the same. 5. Contacts For more information, please contact the Civic Service Agency at asc-opendata@service-civique.gouv.fr.

1. Dataset The data consist of two sources: • A survey sent to former volunteers within one month of the end of their mission to ascertain their satisfaction, the conformity of their mission with the Civic Service framework and their plans for the continuation of their journey; • Data from the Civic Service’s management database to enable the characteristics of volunteers and missions to be taken into account. Two files, with each of the sources, are made available with a unique identifier to match them.

2. Method of data production The data of the Post-Service Civic survey is collected by an online questionnaire, sent to all volunteers whose email address is correctly indicated in the management database of the Civic Service Agency (80 % of the 83,904 volunteers entered in 2018). The e-mail invitation to reply to the questionnaire is sent within one month of the end of the mission. Two reminders are carried out two weeks apart. The overall response rate reached 44 % of the volunteers contacted in 2018. The answers to the open questions in the questionnaire are not included in this dataset for reasons of anonymity of the answers. In questions 13 and 15, open answers are recoded in the “Other” modality. The second dataset contains data from the Civic Service Agency’s management database, matched by a unique identifier with survey responses. These are administrative data, exhaustive, collected and manually filled in by the reception facilities at the time of signing the contract of employment of the young person. Matching difficulties explain the missing data.

3. Metadata This 2018 Post-Service Civic survey is a survey sent by the Civic Service Agency to all volunteers who have started a Civic Service, in France and abroad, at the end of their mission. The data are published under open license. Full year response data are published annually in the first quarter of year n+ 1.

4. Evolution of files In 2018 and 2019, the questionnaire of the Post Service-Civique survey remained the same. The database data matched with the Post Service-Civique survey will be updated for 2019. From 1 January 2020, a new Post-Service Civic questionnaire will be sent to volunteers. This data will be published in 2021. However, the investigation protocol remains the same.

5. Contacts For more information, please contact the Civic Service Agency at asc-opendata@service-civique.gouv.fr.

This dataset relates to research on the connections between archives professionals and research data management. It consists of a single Excel spreadsheet with four sheets, containing an analysis of emails sent to two email discussions lists: Archives-NRA (Archivists, conservators and records managers) and Research-Dataman. The coded dataset and a list of codes used for each mailing list is provided.The two datasets were downloaded from the JiscMail Email Discussion list archives on 27 July 2018. The Archives-NRA dataset was compiled by conducting a free text search for "research data" on the mailing list's archives, and the metadata for every search result was downloaded and coded (144 metadata records in total). The resulting coded dataset demonstrates how frequently archivists and records professionals discuss research data on the Archives-NRA list, the topics which are discussed, and an increase in these discussions over time. The Research-Dataman dataset was compiled by conducting a free text search for "archivist" on the mailing list's archives, and the metadata for every search result was downloaded and coded (197 emails total). The resulting coded dataset demonstrates how frequently data management professionals seek the advice of archivists or advertise vacancies for archivists, and how often archivists email this mailing list. The names and email addresses of the mailing list participants have been redacted for privacy reasons but the original full-text emails can be accessed by members of the respective mailing lists using the URLs provided in the dataset.