The field of system identification and dynamical systems modeling is moving towards developing more efficient and robust algorithms for identifying linear systems from noisy data. Researchers are exploring new methods to balance the trade-off between accurately inferring key system parameters and minimizing approximation errors. Additionally, there is a growing interest in understanding the identifiability of sparse linear ordinary differential equations, which is a crucial aspect of dynamical systems modeling. Recent studies have shown that sparse systems can be unidentifiable with a positive probability, highlighting the need for rethinking the expectations from data-driven modeling. Noteworthy papers include: Minimal Order Recovery through Rank-adaptive Identification, which introduces an algorithm that optimally balances the trade-off between accurately inferring key singular values and minimizing approximation errors. Improved error bounds for Koopman operator and reconstructed trajectories approximations with kernel-based methods, which proposes a new error bound for Koopman operator approximation using Kernel Extended Dynamic Mode Decomposition. Identifiability Challenges in Sparse Linear Ordinary Differential Equations, which characterizes the identifiability of sparse linear ODEs and shows that sparse systems are unidentifiable with a positive probability.