Ithaka 2006 Survey of US Higher Education Faculty Attitudes and Behaviors

Ithaka 2006 Survey of US Higher Education Faculty Attitudes and Behaviors

This study surveys the attitudes and behaviors of US higher education faculty members regarding online resources, the library, and related topics. It covers a wide range of issues, including faculty dependence on electronic scholarly resources, the transition from print to electronic journals, publishing preferences, e-books, and the preservation of scholarly journals.

Ithaka 2006 Survey of US Higher Education Faculty Attitudes and Behaviors

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The Ithaka 2006 Survey provides a detailed overview of the attitudes and behaviors of US higher education faculty members. It explores their relationship with campus libraries, their use of electronic resources, and their preferences for publishing and accessing scholarly materials. This study is a valuable resource for understanding the academic landscape and the transition to digital resources in higher education.

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IC-AnnoMI: In-Context MI Dialogues - GitHub Repository
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IC-AnnoMI: In-Context MI Dialogues - GitHub Repository

The IC-AnnoMI repository contains source code and a synthetic dataset generated through in-context zero-shot LLM prompting for mental health and therapeutic counselling. IC-AnnoMI is a project that generates contextual MI dialogues using large language models (LLMs). The project contains source code and a synthetic dataset generated through zero-shot prompts, aiming to address the data scarcity and inherent bias problems in mental health and therapeutic consultation.

HuggingFaceFW/fineweb-2
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HuggingFaceFW/fineweb-2

FineWeb-2 is a dataset of over 15 trillion tokens of cleaned and deduplicated English web data from CommonCrawl. This is the second iteration of the popular 🍷 FineWeb dataset, bringing high quality pretraining data to over 1000 🗣️ languages.The 🥂 FineWeb2 dataset is fully reproducible, available under the permissive ODC-By 1.0 license and extensively validated through hundreds of ablation experiments.In particular, on the set of 9 diverse languages we used to guide our processing decisions, 🥂 FineWeb2 outperforms other popular pretraining datasets covering multiple languages (such as CC-100, mC4, CulturaX or HPLT, while being substantially larger) and, in some cases, even performs better than some datasets specifically curated for a single one of these languages, in our diverse set of carefully selected evaluation tasks: FineTasks.

electronic media datasets - Mental Health Datasets
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electronic media datasets - Mental Health Datasets

An evolving list of electronic media datasets used to model mental health status. This repository curates a variety of datasets from different sources, including social media platforms, online forums, and academic studies, to support research in mental health modeling and AI applications.

Keywords

IthakaSurveyHigher EducationFaculty AttitudesOnline ResourcesLibraryElectronic JournalsPublishing Preferences

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