The REsource for Materials Informatics (REMI) will host a diverse collection of scripting notebooks (Jupyter, Matlab LiveScripts, etc.) for collecting, pre-processing, analyzing, and visualizing materials data. Notebooks are curated using tags aligned to Materials Science and Data Science topics. REMI emerged from the realization that both experts and novices wanted examples of using machine learning for science. Meanwhile, lots of experts are developing digital notebooks (e.g. Jupyter) to demonstrate step-by-step data collection, pre-processing, analysis and visualization. However, before REMI there were no indexed repositories of notebooks with such examples and no community to maintain, build or learn from these resources. REMI aims to fill this gap, enabling scientists to more easily pick up machine learning while also helping to build a community for sharing knowledge, discussing, debating, establish collaborations, and benchmarking methods.