Results 261 to 270 of about 4,114,812 (330)

Can social media provide early warning of retraction? Evidence from critical tweets identified by human annotation and large language models

open access: yesJournal of the Association for Information Science and Technology, EarlyView.
Abstract Timely detection of problematic research is essential for safeguarding scientific integrity. To explore whether social media commentary can serve as an early indicator of potentially problematic articles, this study analyzed 3815 tweets referencing 604 retracted articles and 3373 tweets referencing 668 comparable non‐retracted articles. Tweets
Er‐Te Zheng   +3 more
wiley   +1 more source

Open research data integration in universities: How data stewards adapt global policies to local contexts

open access: yesJournal of the Association for Information Science and Technology, EarlyView.
Abstract Global research policies, often driven by political agendas rather than academic expertise, generate pressure on local entities to conform to global standards. This is particularly the case for universities seeking international relevance, which must address Open Research Data (ORD) principles.
Stefan Stepanovic   +2 more
wiley   +1 more source

Iconic Words Are Associated With Iconic Gestures. [PDF]

open access: yesCogn Sci
Wilding E   +3 more
europepmc   +1 more source

Regional Shopping Objectives in British Grocery Retail Transactions Using Segmented Topic Models

open access: yesApplied Stochastic Models in Business and Industry, EarlyView.
ABSTRACT Understanding the customer behaviours behind transactional data has high commercial value in the grocery retail industry. Customers generate millions of transactions every day, choosing and buying products to satisfy specific shopping needs.
Mariflor Vega Carrasco   +4 more
wiley   +1 more source

Risky or rigorous? Developing trustworthiness criteria for AI‐supported qualitative data analysis

open access: yes
Anatomical Sciences Education, EarlyView.
Michelle D. Lazarus   +4 more
wiley   +1 more source

Generative Deep Learning for Advanced Battery Materials

open access: yesBatteries &Supercaps, EarlyView.
This review explores the role of generative deep learning (DL) in battery materials analysis and highlights the fundamental principles of generative DL and its applications in designing battery materials. The importance of using multimodal data is underscored to effectively address the challenges faced during the development of battery materials across
Deepalaxmi Rajagopal   +3 more
wiley   +1 more source

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