Contacts

Details: Category: datasets

For further information about the dataset

Roberto Basili and Mara Monica Tiberti
Istituto Nazionale di Geofisica e Vulcanologia
Via di Vigna Murata, 605 - 00143 Roma, Italy
e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
http://orcid.org/0000-0002-1213-0828
e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
http://orcid.org/0000-0003-2504-853X

For further information about the web services

Roberto Vallone
Istituto Nazionale di Geofisica e Vulcanologia
Via di Vigna Murata, 605 - 00143 Roma, Italy
e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
http://orcid.org/0000-0003-1208-9412

License

Except where otherwise noted, this dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. Contact us to request additional permissions to use the work granted by this license.

FAIR level 3, score 100 (2025-04-10 11:53:22)

Citation

Basili R., Tiberti M.M. (2025). Global Dip (G-DIP 1.0): a collection of earthquake fault dip angles [Data set]. Istituto Nazionale di Geofisica e Vulcanologia (INGV). https://doi.org/10.13127/gdip1.0

DISCLAIMER

The G-DIP datasets distributed in these web pages were designed as a research study to provide innovative, strategic data-service portfolios. For these reasons and their intrinsic nature, they cannot be guaranteed to be complete, accurate, and updated in any part necessary for their use in different contexts or applications. Although we made every possible effort to supply the best available information, no warranty, expressed or implied, is provided regarding the accuracy and reliability of the data supplied here. Users are invited to consider the inherent epistemic uncertainty of such datasets in earthquake-related hazard analyses (e.g., surface displacement, ground failures, ground shaking, tsunami). Users are also cautioned to carefully consider the nature of the dataset contents before using them for decisions concerning personal or public safety or concerning business involving substantial financial or operational consequences.