Soil liquefaction and resulting ground failure due to earthquakes presents a significant hazard to distributed infrastructure systems and structures around the world. Currently there is no consensus in liquefaction susceptibility or triggering models. The disagreements between models is a result of incomplete datasets and parameter spaces for model development. The Next Generation Liquefaction (NGL) Project was created to provide a database for advancing liquefaction research and to develop models for the prediction of liquefaction and its effects, derived in part from that database in a transparent and peer-reviewed manner, that provide end users with a consensus approach to assess liquefaction potential within a probabilistic framework. An online relational database was created for organizing and storing case histories which is available at http://nextge nerationliquefaction.org/ (https://www.doi.org/10.21222/C2J040, [1]). The NGL field case history database was recently expanded to include the results of laboratory testing programs because such results can inform aspects of liquefaction models that are poorly constrained by case histories alone. Data are organized by a schema describing tables, fields, and relationships among the tables. The types of information available in the database are test-specific and include processed-data quantities such as stress and strain rather than raw data such as load and displacement. The database is replicated in DesignSafe-CI [2] where users can write queries in Python scripts within Jupyter notebooks to interact with the data.

Soil liquefaction and resulting ground failure due to earthquakes presents a significant hazard to distributed infrastructure systems and structures around the world. Currently there is no consensus in liquefaction susceptibility or triggering models. The disagreements between models is a result of incom-plete datasets and parameter spaces for model development. The Next Gen-eration Liquefaction (NGL) Project was created to provide a database for ad-vancing liquefaction research and to develop models for the prediction of liquefaction and its effects, derived in part from that database in a transpar-ent and peer-reviewed manner, that provide end users with a consensus ap-proach to assess liquefaction potential within a probabilistic framework. An online relational database was created for organizing and storing case histo-ries which is available at http://nextgenerationliquefaction.org/ (https://www.doi.org/10.21222/C2J040, [1]). The NGL field case history da-tabase was recently expanded to include the results of laboratory testing pro-grams because such results can inform aspects of liquefaction models that are poorly constrained by case histories alone. Data are organized by a schema describing tables, fields, and relationships among the tables. The types of information available in the database are test-specific and include processed-data quantities such as stress and strain rather than raw data such as load and displacement. The database is replicated in DesignSafe-CI [2] where users can write queries in Python scripts within Jupyter notebooks to interact with the data.

The Next-Generation Liquefaction database is a resource for the geotechnical hazard community. It is publicly available online under the following digital object identifier (DOI): 10.21222/C2J040. The database organizes objective liquefaction data into tables and fields (columns of information), with the relationships among the tables and fields described by a schema. The data are organized into tables pertaining to (1) sites, including geotechnical and geophysical site investigation data, surface geology information, and laboratory test data; (2) earthquake events, including source and ground motion information; and (3) observations of sites following events. The schema was vetted through community outreach efforts involving multiple workshops and meetings. Users can view the data, download existing data, and upload new data through a geographic information system (GIS)-based graphical user interface. Information uploaded to the database is reviewed by a database working group to verify consistency between uploaded data and source documents. The database is replicated in DesignSafe where users can interact with the data using Python scripts in Jupyter notebooks, view point cloud data using Potree, and interact with geospatial data using QGIS.