The decision to use Jupyter Notebook for results verification turned out to be highly beneficial. Our experience with solutions from Data Driven Construction and Jupyter Notebook has been extremely positive. Overall, the integration of solutions from Data Driven Construction and Jupyter Notebook has greatly improved our workflows. Their combined capabilities and performance have not only optimized data processing, but also produced meaningful results, making them highly recommended tools.

With the help of Python and especially the pandas library, as the DataDrivenConstruction team does, we are now able to perform delivery checks four times faster. With the help of Python and especially the pandas library, as the DataDrivenConstruction team does, we are now able to perform delivery checks four times faster. By turning IFC data into a pandas DataFrame and comparing it with quantification tables or classification databases, we have made our expertise fast and reliable.

Be part of the movement with DataDrivenConstruction! Let’s make true freedom in data formats a reality and catalyze a new era of productivity and innovation in construction. The real and necessary journey from closed to “open” formats has not even started: True freedom in data formats—free from proprietary ties and accessible to all—is what we need to unlock innovation and collaboration across the construction industry, particularly benefiting the 99% of construction, i.e. SMEs and unchaining the sector for accelerated growth