Find research datasets worth reusing

opencc-by-3.0Aug 2020View details →

International Mailing List | International Email List | Infos B4B

Procure the most important and vitals of the market and gain leads like never before from our data experts made <a href="https://infosb4b.com/international-mailing-list/">International Email List.</a>

openmit-licenseJan 2022View details →

fauci-email: a json digest of Anthony Fauci's released emails

We provide a processed JSON version of the 3234 page PDF document of Anthony Fauci's emails that were released in 2021 to provide a better understanding of the United States government response to the COVID-19 pandemic. The main JSON file contains a collection of 1289 email threads with 2761 emails among the threads, which includes 101 duplicate emails. For each email, we provide information about the sender, recipients, CC-list, subject, email body text, and email time stamp (when available). We also provide a number of derived datasets stored in individual JSON files: 5 different types of derived email networks, 1 email hypergraph, 1 temporal graph, and 3 tensors. Details for the data conversion process, the construction of the derived datasets, and subsequent analyses can all be found in an online technical report at <a href="https://arxiv.org/abs/2108.01239">https://arxiv.org/abs/2108.01239</a>. Updated code for processing and analyzing the data can be found at <a href="https://github.com/nveldt/fauci-email">https://github.com/nveldt/fauci-email</a>.

opencc-by-4.0Aug 2018View details →

Enron Email Time-Series Network

We use the <a href="https://www.kaggle.com/wcukierski/enron-email-dataset">Enron email dataset</a> 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 vi 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 eij 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.

opencc-by-4.0Jun 2012View details →

Data and material for: "Content classification of development emails"

This data and material support the paper "Content classification of development emails" published in the proceedings of the 34th International Conference on Software Engineering (ICSE 2012). Every software system has a history. We find traces of a system's history in software repositories, which are used by developers when building and maintaining their systems. Each repository tells us a part of the history, from its perspective: Issue repositories murmur dark events involving defective and flawed entities; versioning system repositories narrate about restless artifacts and classes that nobody would ever touch; mailing list archives report of unexpected stories on developers’ interactions and opinions. But... can we seriously trust these repositories? Can we just listen to what they tell us and behave accordingly? Many wise researchers warmly warned us about the risks of showing such a naive faith in data repositories: Versioning system repositories might seduce us with enchanting stories of always changing entities, but in reality many of these entities may just modify their make up and maintain their old behaviour; or issue repositories might tell us a partial truth about certain very special entities, or developers. We do agree with these researchers: Especially natural language documents contain information in different languages, surrounded by much noise. We must pay a special attention when using them. We created MUCCA, a classification method to use when dealing with natural language documents. It recognizes source code fragments, patches, stack traces, noise, and natural language with significantly high accuracy. In this way, it allows one to subsequently apply ad hoc analysis techniques to exploit the peculiarities of each category, and extract reliable information. This Zenodo upload supports the paper that describe our work on this topic.   1. Source code & Virtual Image MUCCA is written in <a href="http://www.cincomsmalltalk.com/main/products/visualworks/overview/">Cincom VisualWorks Smalltalk</a> and is composed of several components. You can download the source code of the following MUCCA components from this upload (<code>mucca-source_code</code> folder): <ul> <li>Miler2, the core of MUCCA, including metamodels, importers, classification engine, etc.;</li> <li>MailPeek, our web application for the manual classification of email content;</li> <li>PetitIsland, our grammar to generate island parsers;</li> <li>PetitJava, the grammar of Java, which we implemented for PetitParser;</li> <li>PetitSTrace, our island parser for java stack traces.</li> </ul> Note that, in order to make Miler2 run, you will also need the following external Smalltalk components: <a href="http://www.moosetechnology.org/">Moose</a>, <a href="http://www.glorp.org/">Glorp</a>, <a href="http://www.seaside.st/">Seaside</a>, TwoFlower, MetaDB, <a href="http://www.lukas-renggli.ch/blog/petitparser-1">PetitParser</a>. In addition, we make use of the <a href="http://www.cs.waikato.ac.nz/ml/weka/">Weka</a> workbench, for the machine learning tasks. You can download the two trained classifiers that compose MUCCA (<code>mucca-classifiers</code> folder): Naive Bayes based classifier (<code>classifier1-nb.model</code>), Decision Tree based classifier (<code>classifier2-dt.model</code>). Alternatively, we created a <a href="http://www.virtualbox.org/">VirtualBox</a> image with a pre-configured VisualWorks environment, which includes all the MUCCA components, and pre-requisites (both Smalltalk and Java): <code>MUCCA.ova</code> (Both user and password are <code>muccauser</code>).   2. Benchmark To train machine-learning classifiers and evaluate the effectiveness of the different approaches, we manually create a benchmark, in which emails are classified at character granularity. Given the time and effort needed to create such a benchmark, we humbly think it is a valuable contribution to the community. With the help of this benchmark, other researchers can reproduce our experiments and devise new classification methods, which can be immediately compared to ours. You can download the dataset from the <a href="https://github.com/ilredeitopi/mucca-dataset">GitHub repository</a> (a dump of the GitHub repository is uploaded here (<code>benchmark/githubDump.zip</code>), or download the full database dump in PostgreSQL format (<code>benchmark/benchmarkDump.tar.bz2</code>).

opencc-by-4.0Jun 2023View details →

Dataset of Survey on Current Email Management Practices

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.

opencc-by-4.0Nov 2023View details →

email-Enron

<h3>Overview</h3>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 <a href="mailto:user@enron.com">user@enron.com</a> whenever possible (i.e., the recipient is specified in some parseable format like "Doe, John" or "Mary K. Smith") and to <a href="mailto:no_address@enron.com">no_address@enron.com</a> when no recipient was specified.<h4>Statistics</h4>Some basic statistics of this dataset are:<ul><li>number of nodes: 148</li><li>number of timestamped hyperedges: 10,885</li><li>distribution of the connected components:</li></ul>Component Size, Number <ul><li>143, 1</li><li>1, 5</li></ul><h4>Source of original data</h4>Source: <a href="https://www.cs.cornell.edu/~arb/data/email-Enron/">email-Enron dataset</a><h4>References</h4>If you use this dataset, please cite these references:<ul><li><a href="https://doi.org/10.1073/pnas.1800683115">Simplicial closure and higher-order link prediction</a>. Austin R. Benson, Rediet Abebe, Michael T. Schaub, Ali Jadbabaie, and Jon Kleinberg. Proceedings of the National Academy of Sciences (PNAS), 2018.</li><li><a href="https://www.cs.cmu.edu/~enron/">Enron Email Dataset</a>, William Cohen, 2015.</li></ul>

opencc-by-4.0Nov 2023View details →

email-EU

<h3>Overview</h3>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.<h4>Statistics</h4>Some basic statistics of this dataset are:<ul><li>number of nodes: 1,005</li><li>number of timestamped hyperedges: 235,263</li><li>distribution of the connected components:</li></ul>Component Size, Number <ul><li>986, 1</li><li>1, 19</li></ul><h4>Source of original data</h4>Source: <a href="https://www.cs.cornell.edu/~arb/data/email-Eu/">email-Eu dataset</a><h4>References</h4>If you use this dataset, please cite these references:<ul><li><a href="https://doi.org/10.1073/pnas.1800683115">Simplicial closure and higher-order link prediction</a>, Austin R. Benson, Rediet Abebe, Michael T. Schaub, Ali Jadbabaie, and Jon Kleinberg. Proceedings of the National Academy of Sciences (PNAS), 2018.</li><li><a href="https://doi.org/10.1145/3097983.3098069">Local Higher-order Graph Clustering</a>, Hao Yin, Austin R. Benson, Jure Leskovec, and David F. Gleich. Proceedings of KDD, 2017.</li><li><a href="https://doi.org/10.1145/1217299.1217301">Graph Evolution: Densification and Shrinking Diameters</a>, Jure Leskovec, Jon Kleinberg, and Christos Faloutsos. ACM Transactions on Knowledge Discovery from Data, 2007.</li></ul>

opencc-by-4.0Feb 2022View details →

Do Carefully-timed Email Messages Increase Accuracy and Precision in Citizen Scientists' Reports of Events?

Periodic messages are a commonly used tactic for reminding citizen science program participants to take a desired action such as collecting observations. In this study, we evaluate the impact of such messages on the accuracy and precision of observations contributed to Nature’s Notebook, a citizen science phenology observing program. To encourage participants in Nature’s Notebook to log the timing of leaf-out and flowering with maximum accuracy and precision, we email observers three days prior to when the events were expected to occur based on forecast models. Unplanned interruptions to the scripts driving these email prompts allowed us to evaluate whether the messages had the intended impacts.

opencc-by-4.0Jul 2021View details →

Public Emails from Brazilian Smart Cities

18,850 public available emails from 673 Brazilian smart cities featured in the 2020 <a href="https://ranking.connectedsmartcities.com.br/">Connected Smart Cities Ranking</a>. The public pages were scraped using UNINOVE LabCidades' opensource email scraper, available in GitHub as <a href="https://github.com/LabCidades/email-scraper">LabCidades/email-scraper.</a>

opencc-by-4.0Apr 2023View details →

Classification of domain names of scholarly email addresses

This dataset contains the classification of email domain names used in the paper 'Analyzing the use of email addresses in scholarly publications' by Marc Luwel and Nees Jan van Eck. Email domain names are classified as institutional if they are linked to a scholarly organization or as non-institutional if they are linked to an email service provider. The file 'email_domain_name_classification.txt' contains for 11,608 email domain names the classification based on a rule-based approach and after manual validation.

zenodo36/100

BUKTI CORRESPONDING AUTHOR IGNATIA MARTHA HENDRATI Email: ignatia.hendrati.ep@upnjatim.ac.id Journal Cogent Business and Management The Role of Moderation Activities The Influence of The Audit Committee And The Board Of Directors on The Planning of The Sustainability Report

The Role of Moderation Activities The influence of the Audit Committee and the Board of Directors on the Planning of the Sustainability Report Abstract In order to show the consistency of agency theory as a theory to explain the influence of the Audit Committee and the Board of Directors on Sustainability, this study will explore the role of moderating actions of the Audit Committee and the Board of Directors on Sustainability. The firms that make up the demographic and research sample for this study, which uses a quantitative technique, are those that are included in the Jakarta Islamic Index for the years 2017 through 2021. the study's yearly financial report panel data. The data analysis methods employed in this study were robust, fixed effects, random effects, and ordinary least square regression. These methods are one of the regression solution approaches that may be used with a lot of flexibility in research that combines thoughts, ideas, and facts. The first study found that the audit committee had an effect on sustainability, whereas the second found that the board of directors has no effect. Due to the third and fourth conclusions of the role of activities, the audit committee and board of directors are less strong on sustainability. Keywords : Activity; Board of Directors; Sustainability; Audit Committee; Stata JEL Classification: G32, G02, M1, G34, Z1

opencc-by-4.0Jan 2023View details →

zenodo36/100

Phishing Email Curated Datasets

We have curated 11 datasets spanning from 1995 to 2022. If you use this datasets, please cite: 1. A. I. Champa, M. F. Rabbi, and M. F. Zibran, “Why phishing emails escape detection: A closer look at the failure points,” in 12th Interna- tional Symposium on Digital Forensics and Security (ISDFS), 2024, pp. 1–6. 2. A. I. Champa, M. F. Rabbi, and M. F. Zibran, “Curated datasets and feature analysis for phishing email detection with machine learning,” in 3rd IEEE International Conference on Computing and Machine Intelligence (ICMI), 2024, pp. 1–7. Bibtext: 1. @inproceedings{champa2024phishing,   title={Why Phishing Emails Escape Detection: A Closer Look at the Failure Points},   author={Champa, Arifa I and Rabbi, Fazle and Zibran, Minhaz F},   booktitle={2024 12th International Symposium on Digital Forensics and Security (ISDFS)},   pages={1--6},   year={2024},   organization={IEEE} } 2. @inproceedings{champa2024curated,   title={Curated Datasets and Feature Analysis for Phishing Email Detection with Machine Learning},   author={Champa, Arifa I and Rabbi, Md Fazle and Zibran, Minhaz F},   booktitle={3rd IEEE International Conference on Computing and Machine Intelligence (ICMI)},   pages = {1--7},   year={2024} }

opencc-by-4.0Sep 2023View details →

ClinicalTrials.gov36/100

Using Emails and Personalized Phone Calls to Increase Affordable Care Act Marketplace Enrollment Among Households Losing Medicaid

ClinicalTrials.gov study NCT05327712. IPD Sharing: YES. Countries: 1. Publications: 1.

ClinicalTrials.gov36/100

Emails Promoting COVID-19 Vaccination Among Healthcare Workers

ClinicalTrials.gov study NCT04728594. IPD Sharing: YES. Countries: 1. Publications: 1.

ClinicalTrials.gov36/100

Effect of Email Nudges on Plan Switching and Healthcare Utilization Among Unemployment Insurance Recipients

ClinicalTrials.gov study NCT05891418. IPD Sharing: YES. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →

ClinicalTrials.gov32/100

Letters in Addition to Emailed Audit and Feedback in Refining Asthma Treatment in Primary Care

ClinicalTrials.gov study NCT05761873. IPD Sharing: NO. Countries: 1. Publications: 4.

ClinicalTrials.gov32/100

Email Nudges to Improve GDMT (MRA) Adherence in Heart Failure

ClinicalTrials.gov study NCT05806970. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →

ClinicalTrials.gov32/100

Impact of Email Reminders on No-Show Rates for Appointments in an Urology Department

ClinicalTrials.gov study NCT06114602. IPD Sharing: NO. Countries: 1. Publications: 12.

closedIPD-NOFeb 2026View details →

ClinicalTrials.gov28/100

Project ALIVE (A Lifestyle Intervention Via Email)

ClinicalTrials.gov study NCT00607009. IPD Sharing: Not stated. Countries: 0. Publications: 2.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov28/100

Using Personalized Letters and Emails to Increase Health Insurance Take-up and Improve Plan Choice Quality

ClinicalTrials.gov study NCT05216458. IPD Sharing: YES. Countries: 1. Publications: 0.