Mohammed Ba-Aoum’s Post

If citations could talk, would they reveal the secrets of science? ⬇️ Ever wondered how citations map the intricate web of scientific communication? The Social Systems Citations Theory (SSCT) tackles this question, blending past theories to focus on how publications and citations act as the lifeblood of science networks. 🌐 This new study by Robin Haunschild and Lutz Bornmann takes SSCT further ahead, proposing a procedure to create global overlay maps using #OpenAlex — a free and open resource for publication and citation data. Imagine visualizing how your institution’s research fits into the broader scientific universe! 🗺️ Key takeaways: 📌 6 base maps for a user's customization needs. 📌 A normalization method to compare different datasets. 📌 Overlay maps showcasing individual and institutional research. Check out the full article and explore the visual side of citation networks: https://lnkd.in/ewKv3ark #OpenScience #CitationMapping #OpenRepositories #ScienceCommunication #PolarisOS #OpenAccess

Do you have an opinion on open science? Perhaps you have used publicly available data or open-source code in research. Perhaps you have (successfully or unsuccessfully) made data or code freely available, or tried to make research reproducible. Perhaps you have commercialised an idea, and thought about if and how to share the underlying science. What are the pros and cons of open science? How does it help or hinder scientific progress? Please do share your thoughts by commenting on this post. (With thanks to Scriberia and the Turing Way for the illustration - from https://lnkd.in/e2VCUFYu , CC BY 4.0)

WithdrarXiv: A Large-Scale Dataset for Retraction Study https://buff.ly/3Zsg3KL Abstract: Retractions play a vital role in maintaining scientific integrity, yet systematic studies of retractions in computer science and other STEM fields remain scarce. We present WithdrarXiv, the first large-scale dataset of withdrawn papers from arXiv, containing over 14,000 papers and their associated retraction comments spanning the repository's entire history through September 2024. Through careful analysis of author comments, we develop a comprehensive taxonomy of retraction reasons, identifying 10 distinct categories ranging from critical errors to policy violations. We demonstrate a simple yet highly accurate zero-shot automatic categorization of retraction reasons, achieving a weighted average F1-score of 0.96. Additionally, we release WithdrarXiv-SciFy, an enriched version including scripts for parsed full-text PDFs, specifically designed to enable research in scientific feasibility studies, claim verification, and automated theorem proving. These findings provide valuable insights for improving scientific quality control and automated verification systems. Finally, and most importantly, we discuss ethical issues and take a number of steps to implement responsible data release while fostering open science in this area.

🔔 In this LSE Impact Blog I talk about the three key ingredients that publishers need in order to support, and achieve, full open science practices in their publications. 1️⃣ ◼ Firstly, policies that strongly encourage or require sharing of research objects, during peer review or at publication are the groundwork to enable publishers to collaborate with researchers in following open science behaviours. 2️⃣ 🔷 Secondly, policies must be supported by technical solutions that make it easy for busy researchers to share and link the outputs of research to their publications. Publishers, in collaboration with technological platforms, can create technical solutions to meet these needs as part of the submission process. 3️⃣ 🔶Thirdly, the human element is essential to ensure authors are supported with their specific needs. Utilising their editorial expertise, publishers can make it easier for researchers to follow the best practice guidance when it comes to metadata standards, protocols for data and code deposition or object citation. 💯 Lastly, the above 'tripartite formula' for promoting open science practices, needs to work hand in hand with a scientific community that readily understands, supports and embraces open science. This was the case with computational sciences as I show in the blog through our data collected from the journal Nature Computational Science. ❇ From this early data, we can see that linking key research objects to the submission of manuscripts and setting up supportive policies and editorial practices can enable achieving full open science. Let me know what you think!

Quantitative research in the social and natural sciences is increasingly dependent on new datasets and forms of code. Making these resources open and accessible is a key aspect of open research and underpins efforts to maintain research integrity. Erika Pastrana, PhD explains how Springer Nature Group developed Nature Computational Science to be fully compliant with open research and data principles. #OpenScience #OpenResearch #OpenSource

Sad to see how the Santa Fe Institute is now producing ultra-reductionist nonsense about life being computational. It can't get any more "mistaking the map for the territory."  https://lnkd.in/dehcnBs4 SFI used to be a forerunner in complexity science, but apparently no more. We've published 2 papers already showing that this view is fundamentally, logically inconsistent. Argument #1: https://lnkd.in/dmf_g9kD Argument #2:  https://lnkd.in/db-aEZ-X

I had a wonderful experience presenting my work at the International Conference on Computational Social Science. I'll be writing up more information about the project on my website (larrimiller.com) soon, but in the meantime, feel free to check out the attached file to get an idea of what I presented! #ic2s2 #computationalsocialscience

We are happy to share the acceptance of our work in the Journal of Computational Science (JOCS) Elsevier. Title: Unsupervised Continual Learning by Cross-Level, Instance-Group and Pseudo-Group Discrimination with Hard Attention Tldr: We propose a novel framework for unsupervised continual learning achieving negligible or zero forgetting without relying on replay buffer. Our approach performs comparably or surpasses the supervised baselines in standard benchmarks like CIFAR and mini-Imagenet. Congrt to authors Ankit Malviya Sayak Dhole #AIatIITI #ContinualLearning #UnsupervisedLearning

Another milestone achieved. 🎉 This paper will become SOTA for future unsupervised continual learning approaches without replay buffer. #innovation #buffer_free #continual_learning #AI

⭐ 𝐄𝐱𝐜𝐢𝐭𝐞𝐝 𝐭𝐨 𝐒𝐡𝐚𝐫𝐞 𝐚 𝐒𝐢𝐠𝐧𝐢𝐟𝐢𝐜𝐚𝐧𝐭 𝐀𝐜𝐡𝐢𝐞𝐯𝐞𝐦𝐞𝐧𝐭! ⭐ I'm happy to share that my latest Research paper, titled "𝐋𝐚𝐧𝐝𝐬𝐥𝐢𝐩 𝐓𝐫𝐢𝐠𝐠𝐞𝐫 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐆𝐥𝐨𝐛𝐚𝐥 𝐋𝐚𝐧𝐝𝐬𝐥𝐢𝐝𝐞 𝐂𝐚𝐭𝐚𝐥𝐨𝐠 𝐃𝐚𝐭𝐚𝐬𝐞𝐭 𝐔𝐬𝐢𝐧𝐠 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦," has been published in IEEE Xplore! In this study, we delve into the innovative use of machine learning algorithms to enhance the classification of landslip triggers. This breakthrough aims to significantly improve public safety and operational efficiency in various environments. I sincerely thank my co-author, Dr. F. Sangeetha Francelin Vinnarasi, Professor, Department of Information Technology, St. Joseph's Institute of Technology, who played a major role in this work and supported this journey. 📄 Access the full paper here https://lnkd.in/gFxduyAB #Research #MachineLearning #Landslide #PublicSafety #IEEE #Innovation #Collaboration