The signac framework is designed to simplify the storage, generation and analysis of multidimensional data sets associated with large-scale, file-based computational studies. Any computational work that requires you to manage files and execute workflows may benefit from an integration with signac. Typical examples include hyperparameter optimization for machine learning applications and high-throughput screening of material properties with various simulation methods. The data model assumes that the work can be divided into so called projects, where each project is roughly confined by similarly structured data, e.g., a parameter study.
In signac, the elements of a project's data space are called jobs. Every job is defined by a unique set of well-defined parameters that define the job's context, and it also contains all the data associated with this metadata. This means that all data is uniquely addressable from the associated parameters. With signac, we define the processes generating and manipulating a specific data set as a sequence of operations on a job. Using this abstraction, signac can define workflows on an arbitrary signac data space.