The "0,10pc" in the filename typically denotes a subset of the data containing 10% of the generated time series or specific parameter ranges used for training and testing the model's accuracy in predicting transitions in ecological, climate, or epidemiological systems.
The dataset was developed as part of a study on using deep learning, specifically and Long Short-Term Memory (LSTM) networks, to provide early warning signals for critical transitions (tipping points) in complex systems. Key Details about the Dataset and Paper CriticalVisit0,10pc.zip
Published by researchers such as Bury et al. (often associated with the work on "Deep learning for tipping points" in journals like Nature or PNAS ), the study focuses on training neural networks to outperform classical statistical indicators (like "critical slowing down") in detecting impending regime shifts. The "0,10pc" in the filename typically denotes a
The data is frequently hosted on repositories like Zenodo , GitHub , or as supplementary material for the published paper to allow for reproducibility of the deep learning benchmarks. Legal Issues of Economic Integration Volume 41, Number 2 (often associated with the work on "Deep learning