Instrumentation generates data faster and in greater quantity than ever before, and interlaboratory research is in historic demand domestically and internationally to stimulate economic innovation. Strategic mission needs of the NIST Material Measurement Laboratory (MML) to support a wide array of research disciplines therefore compel our organization to adopt advanced strategies for research data management. Laboratory Information Management Systems (LIMS) provide a framework for managing data from the outset of the research life cycle delivering new capabilities for machine learning (ML), data analysis, collaboration, and dissemination. This roadmap describes our current understanding and strategy for adapting our research workflows for LIMS throughout MML by embracing the use of standards and best practices from data science communities. The NIST research data cyber infrastructure complements these goals for MML by providing a secure environment to host LIMS solutions. Additionally, integration of scientific workflows requires ongoing collaboration to bridge organizational LIMS with external scientific communities. Thus, MML LIMS will evolve over time in synergy with the technology and experimental environments delivering new science. LIMS will broaden our mission impact through adoption of FAIR data principles.