Wireless communications systems of the future are expected to operate at very high bandwidths of up to multiple Gigahertz. For such systems the power consumption of analog-to-digital converters at the receiver will impose a challenge. Employing 1-bit quantization and temporal oversampling is a promising approach to increase energy efficiency. However, since 1-bit quantization is a highly non-linear operation, channel estimation and synchronization algorithms have to be revised. In this regard, we derive iterative data-aided timing estimators based on the expectation-maximization algorithm and the scoring algorithm assuming white noise at the receiver. Comparing the performance of the expectation-maximization algorithm based estimator, the scoring algorithm, and an existing non-data-aided least-squares estimator by numerical evaluation, we find that the scoring algorithm significantly outperforms the least squares estimator in terms of the mean square error and closely approaches the Cramér-Rao lower bound, different to the expectation-maximization based estimator whose performance stays behind the scoring algorithm. Additionally, we consider colored noise at the receiver and evaluate the influence of the timing estimation error on the system performance in terms of the spectral efficiency using zero-crossing modulation.