The dataset consists of 14 directories, one for each earthquake analyzed, including raw data and results. A README.txt file describes the contents of the directories., Data files and modelling results of regional centroid moment tensors obtained for earthquakes in the 2013 CASTOR gas storage seismic crisis, and presented in the article "Fault reactivation by gas injection at an underground gas storage off the east coast of Spain”, by Antonio Villaseñor et al. During September-October of 2013 an intense swarm of earthquakes occurred off the east coast of Spain associated with the injection of the base gas in an offshore underground gas storage. Two weeks after the end of the injection operations, three moderate-sized earthquakes (Mw 4.0-4.1) occurred near the storage. These events were widely felt by the nearby population, leading to the indefinite shut-down of the facility. Here we investigate the source parameters (focal depth and mechanism) of the largest earthquakes in the sequence in order to identify the faults reactivated by the gas injection, and to help understand the processes that caused the earthquakes. Our waveform modeling results indicate that the largest earthquakes occurred at depths of 6-8 km beneath the sea floor, significantly deeper than the injection depth (~ 1800 m). Although we cannot undoubtedly discriminate the fault plane from the two nodal planes of the mechanisms, most evidence seems to favor a NW-SE striking fault plane. We propose that the gas injection reactivated unmapped faults in the Paleozoic basement, with regional orientation possibly inherited from the opening of the Valencia Trough., We thank the seismic networks that provided the waveform data used in this study: IGN
295 (https://doi.org/10.7914/SN/ES), and ICGC (https://doi.org/10.7914/SN/CA). This research was funded by project SEAL
(Ministerio de Ciencia e Innovación, Spain, CGL2017-88864-R), Peer reviewed

Data and figures for the manuscript “Performance of Deep Learning pickers in routine network processing application”. The dataset includes:
- phase arrival times of P and S waves for 3 test datasets obtained using different Deep Learning pickers
- figures showing the results of the comparisons between Deep Learning pickers, Computer resources for this study have been provided by Artemisa, funded by the European Union ERDF and Comunitat Valenciana.
Additional funding has been obtained from Spanish Ministry of Science and Innovation grants CGL2017-88864-R, PID2020-114682RB-C31, and Center of Excellence Severo Ochoa accreditation to ICM-CSIC (Project CEX2019-000928-S), Peer reviewed