Journal:A roadmap for LIMS at NIST Material Measurement Laboratory

Full article title	A roadmap for LIMS at NIST Material Measurement Laboratory
Author(s)	Greene, Gretchen; Ragland, Jared; Trautt, Zachary; Lau, June; Plante, Raymond; Taillon, Joshua; Creuziger, Adam; Becker, Chandler; Bennett, Joseph; Blonder, Niksa; Borsuk, Lisa; Campbell, Carelyn; Friss, Adam; Hale, Lucas; Halter, Michael; Hanisch, Robert; Hardin, Gary; Levine, Lyle; Maragh, Samantha; Miller, Sierra; Muzny, Christopher; Newrock, Marcus; Perkins, John; Plant, Anne; Ravel, Bruce; Ross, David; Scott, John H.; Szakal, Chris; Tona, Alessandro; Vallone, Peter
Author affiliation(s)	National Institute of Standards and Technology
Year published	2022
Volume and issue	NIST Technical Note 2216
Page(s)	i–iii, 1–17
DOI	10.6028/NIST.TN.2216
Distribution license	Public domain
Website	https://www.nist.gov/publications/roadmap-lims-nist-material-measurement-laboratory
Download	https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=934610 (PDF)

Foreword

Over the past decade, emerging technology in laboratory and computational science has changed the landscape for research by accelerating the production, processing, and exchange of data. The NIST Material Measurement Laboratory community recognizes that to keep pace with the transformation of measurement science to a digital paradigm, it is essential to implement laboratory information management systems (LIMS). Effective introduction of LIMS early in the research life cycle provides direct support for planning and execution of experiments and accelerating research productivity. From this perspective, LIMS are not passive entities with isolated interaction, but rather key resources supporting collaboration, scientific integrity, and transfer of knowledge over time. They serve as a delivery system for organizational contributions to the broader federated data community, supporting both controlled and open access, determined by the sensitivity of the research.

The overall goal of a successful LIMS is to empower a research community by establishing common tools providing access to laboratory data resources. Modern LIMS should therefore provide several core functions and touchpoints:

• Workflow management – A research workflow describes steps to be performed to derive results. These patterns serve as a prescription for LIMS to control the progression of data

and associated services or tools. Automation of a workflow simplifies the transfer of information through defined interfaces from a network of systems.

• Repository of data – Effective storage and retrieval of data (raw and derived)—including associated metadata, data products, calibration, software, logs, etc.—facilitates data discovery, processing, collaboration, and dissemination.

• Creation of data products and tools – A LIMS should support storage and processing of raw data, leading to products which can be shared and consumed. Examples would include sample data, instrument-generated data, and algorithms generating defined outputs. Inclusion of data models provides context and structure, and machine learning integration may generate related data which could be combined into more comprehensive data models. Tools may include visualization, evaluation, and analysis packages offering users advanced capabilities for their research projects.

• Organization of data for search and retrieval – Tools and interfaces give users access to sophisticated searches of data holdings and efficient mechanisms for data transfer in standardized formats. Searching should extend to domain or project-specific semantics, be coupled closely with related data, and go beyond individual research projects to include super-searches (e.g., use-case-driven interoperability between LIMS).

• Long-lived, stable, and agile structures – LIMS require institutional and architectural sustainability for long baseline research and curatorship. Technology tends to change faster than the practical lifetime of research programs, so paths must exist for maintaining IT infrastructure and introducing faster and more complex processes.

• Standards and best practices – LIMS benefit from standardization to support collaborations among research communities and make data workflows efficient and affordable. Community buy-in for standards and best practices is an essential part of LIMS, and organizational shared expertise naturally serves as a means for coordination and adaptation of standards.

• User involvement – In all the core functions listed above, it is critical to involve the subject matter experts from the beginning. LIMS should establish a working team that explicitly includes representatives from the end user community.

Abstract

Instrumentation generates data faster and in greater quantity than ever before, and inter-laboratory 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 the FAIR Data Principles.

Keywords: data, laboratory information management systems, experimental data, research data, research workflows

Introduction

Beginning late 2019, MML initiated as part of its strategic plan the development of "next-generation" data and informatics with a focus on LIMS as a key resource to support research data and science. This effort was complemented by initiatives for enhancing data management planning and data systems infrastructure. The first year’s groundwork established common needs for both individual researchers and teams to engage more readily with LIMS, with a goal of building greater capacity for interaction and use of data. A vision for LIMS was written to convey the purpose of these collective efforts:

“Laboratory information management systems will provide MML scientists a practical means for repeatability, traceability, reproducibility, efficiency, and compliance of research, serving as a beacon to both intramural and extramural community stakeholders.”

Notes

This document falls in the U.S. public domain and is republished courtesy of the National Institute of Standards and Technology. This presentation is faithful to the original, with only a few minor changes to presentation, spelling, and grammar.