07/01/2026
🚨 CALL FOR PAPERS 🚨
👉 Thematic Volume: "Machine Learning in Paleontology: from data to insights"
👉 Publication Year: 2026
🔹 Submission Window: from January 1 to April 30, 2026.
🔹 Inquiries: [email protected]
🔹 Guest Editors:
Néstor Toledo (Museo de La Plata)
Guillermo Cassini (Museo Argentino de Ciencias Naturales "Bernardino Rivadavia")
Jens Lallensack (Liverpool John Moores University)
👉 More information: https://buff.ly/PoMUf1j
✔️ PE-APA invites submissions for a special thematic volume dedicated to Machine Learning in Paleontology, focusing on rigorous, transparent, and reproducible applications of data science across the paleontological sciences. As machine learning (ML) and deep learning (DL) become increasingly prominent tools in the analysis of fossil data, there is a growing need for methodological clarity, robust evaluation, and domain-aware implementations that truly advance paleobiological understanding.
✔️ We welcome contributions that apply ML or DL methods to any paleontological domain-including morphology, biomechanics, ichnology, taxonomy, macroevolution, paleoecology, taphonomy, and phylogenetics-provided they demostrate strong methodological foundations. Submission should emphasize clear research questions, careful data curation, reproducible workflows, transparent reporting of algorithms and hyperparameters, and thoughtful integration of paleontological expertise. Studies relying solely on automated black-box predictions without mechanistic interpretation are discouraged.
✔️ All manuscripts will undergo full peer review and must meet PE-APA's open, diamond-access standards. Accepted papers will be published collectively in 2026.
✔️ Researchers from paleontology, data science, computer science, and related fields are invited to contribute to this landmark volume aimed at setting methodological standards for the next generation of quatitative paleobiology.
