Omkar2118/datasets huggingface dataset:
W3C email corpus

The collection is a crawl of the public W3C (*.w3.org) sites in June 2004.  NIST distributed the collection to TREC participants.  This is a subset of 174,311 emails from the data used at TRECENT 2005.
Parakweet Lab's Email Intent Data Set

This resource contains training and test data for detecting "intent" sentences in email messages. This data comes from the Enron email corpus. Each labeled example is one sentence from an email.  We define "intent" here to correspond primarily to the categories "request" and "propose" in the paper:

Cohen, William W., Vitor R. Carvalho, and Tom M. Mitchell. "Learning to Classify Email into``Speech Acts''." EMNLP. 2004.

In some cases, we also apply the positive label to some sentences from the "commit" category if they contain datetime, which makes them useful.
Detecting the presence of intent in email is useful in many applications, e.g., machine mediation between human and email.

Training data: 4213 cases with 1631 positives
Test data: 991 cases with 277 positives

Also see https://github.com/vseledkin/enron_intent_dataset_verified
Enron People Assignment

The dataset consists of a set of tasks in email and 
the associated people. Within each email, a task is 
indicated by a special <mark> tag (one per HIT); 
a set of email recipients (one or more per email) is
also provided. The subset of recipients (possibly 
empty) who are responsible for the marked task is
also provided. Each recipient is identified by their 
email address. Sometimes recipients are email 
groups(“some_group”@enron.com); they are 
referred to implicitly by the sender of the email.  There are 5998 unique emails.

Introducing paper: Assigning people to tasks identified in email: The EPA dataset for addressee tagging for detected task intent
MoA
Full name: MoA_Long_ModelQA

This is the dataset used by the automatic sparse attention compression method MoA. It enhances the calibration dataset by integrating long-range dependencies and model alignment. MoA utilizes long-contextual datasets, which include question-answer pairs heavily dependent on long-range content.

Introducing paper: MoA: Mixture of Sparse Attention for Automatic Large Language Model Compression
Cognitive load theory and educational multimedia

With two listening files, four multimedia was made. Two multimedia from each listening file. The difference between the two versions prepared from the same audio file was in applying or violating of Mayer’s multimedia learning principles. Thirty-nine participants were randomly divided into two groups. One group watched the with principle of the first audio file and the without principle of the second audio file, and the other group watched the opposite way. Meanwhile, their electroencephalography(EEG) were recorded. Then they participated in a post-test and finally completed a self-reported cognitive load questionnaire.