In the last decades, advancements in computational resources have accelerated novel inverse design approaches for structures and materials. In particular data-driven methods leveraging machine learning techniques play a major role in shaping our design processes today. Constructing a large material response database poses practical challenges, such as proper data management, efficient parallel computing and integration with third-party software. Because most applied fields remain conservative when it comes to openly sharing databases and software, a lot of research time is instead being allocated to implement common procedures that would be otherwise readily available. This lack of shared practices also leads to compatibility issues for benchmarking and replication of results by violating the FAIR principles. In this work we introduce a general and user-friendly data-driven package for researchers and practitioners working on design and analysis of materials and structures. The package is called f3dasm (framework for data-driven design & analysis of structures and materials) and it aims to democratize the data-driven process and making it easier to replicate research articles in this field, as well as sharing new work with the community. This work generalizes the original closed-source framework proposed by the Bessa and co-workers [1], making it more flexible and adaptable to different applications, namely by allowing the integration of different choices of software packages needed in the different steps of the data-driven process: (1) design of experiments; (2) data generation; (3) machine learning; and (4) optimization. [1] M. A. Bessa, R. Bostanabad, Z. Liu, A. Hu, D. W. Apley, C. Brinson, W. Chen, & W. K. Liu. A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality. Computer Methods in Applied Mechanics and Engineering, 320, 633–667, 2017.