TL;DR: We can detect individuals with a high probability of being involved in milling papers. The question is: how should we respond? A few years ago, my bike was stolen. It was an organised job. The thieves arrived after dark, cut a hefty lock clean off, and my bike disappeared silently forever. I imagined it dismantled and sold off into some vast black market network. Poor bike. The police admitted that they weren’t going to do anything.

New paper out today with Logan King, Julia McHugh, and Brian Curtice, on pneumatic ribs in Apatosaurus and Brontosaurus (King et al. 2024). This one had an unusual gestation. In the summer of 2002 2022 I did a road trip to Utah and western Colorado with my friend and frequent collaborator Jessie Atterholt.

"Do you have an IRB?" is often the first question anyone asks when a new research idea is proposed. It shouldn't be the last. Despite the personal frustrations of surgeons who deal with them, institutional review boards (IRBs) maintain a revered status within our profession. For instance, the perceived necessity of IRB review is so ubiquitous that most readers will know to ask the above question even if they do not know why.

Last week, The Scholarly Kitchen posted an article by Angela Cochran,Vice President of Publishing at the American Society of Clinical Oncology, about the inability of publishers to deal with research fraud. She writes: Cochran’s argument is that although publishers manage the peer review process, it was never an expectation of peer review that they would perform ‘forensic analysis’ of datasets and associated materials.

What reforms in how we assess and value research are necessary to better equip public science systems for the existential challenges of the 21st century? How can we understand and tackle issues such as inequitable access to scientific literature, increasing strain on peer review systems, and publisher oligopolies?

Recent Advances in Using Machine Learning with Graphs — Part 2 Latest findings in multiple research directions for handling graph construction and network security issues Author · Xuzeng He ( ORCID: 0009–0005–7317–7426) Introduction A graph, in short, is a description of items linked by relations, where the items of a graph are called nodes (or vertices) and their relations are called edges (or links). Examples of

Bridging Human Perception and AI’s Future: The Convergence of Visual Understanding and Semantic Networks Author · Vaibhav Khobragade ( ORCID: 0009–0009–8807–5982) Introduction The fusion of Vision-Language Models ( VLMs ), Generative Models, and Knowledge Graphs ( KGs ) is reshaping how artificial intelligence (AI) understands and interacts with the world.

Understanding the Power and Applications of Natural Language Processing Author Dhruv Gupta ( ORCID: 0009–0004–7109–5403) Introduction We are living in the era of generative AI. In an era where you can ask AI models almost anything, they will most certainly have an answer to the query. With the increased computational power and the amount of textual data, these models are bound to improve their performance.

During the Open Science Retreat I organized a short session where we looking into typing citation intentions using a new nanopublication template. First, let’s describe nanopublications (originally used in doi:10.3233/ISU-2010-0613) a bit. Scholia gives a nice overview of (macro?)publications on the topic.

Incorporating Knowledge Graphs to explain reasoning processes Author Amanda Kau ( ORCID: 0009–0004–4949–9284) Introduction Large language models (LLMs) like GPT-4 possess remarkable language abilities, allowing them to function as chatbots, translators, and much more.