Rationale

Data and knowledge management (DKM) systems collect, manage, and provide controlled access to data and knowledge resources. These systems may also provide critical analytical and visualization capabilities to support research and decision processes. Data within the DKM may be at any stage of its lifecycle.

The ability to effectively curate, combine, and use scientific and operational data and knowledge resources (e.g., research data sets, databases, knowledge bases, content management systems [CMS]) is integral to the goals of each NIEHS division. The goals vary across the institute, including enabling data-driven scientific discovery, informing health policies and funding decisions, and informing business operations. The ability to use data- and knowledge-driven approaches is increasingly providing competitive advantage for researchers and is necessary for informed and defensible decision-making. Further, data- and knowledge-driven approaches are integral to fulfilling the NIH mandate for scientific rigor and transparency in the biomedical sciences.

The ability to use interoperable terminologies and semantics between systems is a critical requirement to bring together and make effective use of disparate data and knowledge resources. Unfortunately, DKM systems, whether they are commercial off-the-shelf tools or in-house developed tools, can be costly to acquire, develop, and customize, as well as maintain. The rapid pace in data and knowledge generation, and evolution of DKM solutions, is a stressor for planning, budgets, and staff resources. Investments in DKM infrastructure, expertise, and policies will balance costs, meet central needs, support innovation, and enable users in the scientific and business stakeholder communities.

The scope of DKM includes:

• Content comprising structured or unstructured data, and information owned or managed by NIEHS (directly or via contract) or other data that is publicly available.
• Technologies that directly relate to the creation, management, maintenance, and use of NIEHS data and knowledge assets. This includes commercial, externally procured, and internally developed databases and knowledge bases.
• Products that are acquired or created for collecting and using data to inform scientific discovery, or decision support for environmental health policies.
• Tools or systems used in, or necessary for, production, enterprise management, operational-level activities (e.g., CMS, search engine, storage management system, and others).

Although DKM overlaps with all other landscape areas, including laboratory, clinical, security and privacy, commodity business computing, non-commodity software, and website communications, the focus of I&IT is to ensure NIEHS has a unified approach to meeting DKM needs. The overlap is often use-case specific, but a general rule is that systems producing or consuming data or knowledge for other independent systems are part of the DKM infrastructure.

Goals

Ensure NIEHS-Generated Research Data Meets NIH and NIEHS Policies and FAIR+ Principles for Data Quality, Management, Lifecycle, Security, Access, Discovery, and Sharing

• NIEHS research data, whether generated by intramural or extramural, should be prioritized to comply with FAIR+ principles.
• However, FAIR+ principles do not specifically call for data to be made computable — that is, available in a form that fosters semi-automated and automated search, access, and usage by computer programs. This ability is increasingly critical given the rise in data volume and automated and semi-automated data processes, mining, and analysis tools. Therefore, NIEHS will help lead in making research data computable (i.e., FAIR+).
• The implementation of FAIR+ principles, and how they overlap with research data management practices (e.g., capture of data provenance) and federal records retention requirements has not been worked out. The institute will work with NIH in defining and implementing relevant policies and best practices, especially regarding data of priority to NIEHS.

Adopt an Interoperable Set of Metadata, Terminologies, Vocabularies, and Data Exchange Protocols That Facilitate Data Integration Across Identified Data and Knowledge Management Systems

• Integration of data and summarized knowledge is increasingly valuable for research. However, the cost is high, as integration requires ongoing maintenance. In addition, the number of data sets and DKMs to map between can scale proportionally, if standards that facilitate integration are not planned for and adopted.
• NIEHS will limit integration costs using several strategies. First, prioritizing the adoption of data standards for highly valuable data sets and DKMs. Second, educating and training data producers on data curation practices and providing resources to aid data producers in adopting standards. Third, championing and advancing new and existing methods for linking datasets.
• Efforts exist to advance standards and practices to improve integration, both within NIH and externally (e.g., Research Data Alliance, Global Alliance for Genomics and Health). Partnerships and collaborations with these efforts will aid in promoting environmental health data integration standards and practices.

Provide Data and Knowledge Management Systems and Services That Enable and Promote a Growing Collection of Tools and Capabilities for Data- and Knowledge-Driven Research and Decision Support

• The continued growth in data and knowledge sources, coupled with the growth in realistic biomedical and population-level computer models, are improving the way research discoveries and policy decisions are made.
• However, accessing data, knowledge, and models for data exploration, hypothesis testing, advanced analytics, and machine learning purposes is often a challenging and tedious task. These efforts are often duplicated needlessly across separate efforts and lead to increased percentage of time lost due to data cleaning and wrangling.
• NIEHS I&IT will accelerate the development and adoption of data- and knowledge-driven methods among all staff, by coordinating and supporting the development and usage of core data and knowledge systems (e.g., databases, metadata catalogs, software libraries), services (e.g., application programming interfaces [APIs]), data sets (e.g., for training artificial intelligence algorithms), and software libraries.
• I&IT will further ensure DKM capabilities meet policies around lifecycle, data standards, security, cost efficiencies, and federal regulations, while supporting best practices that foster innovation and best-of-breed solutions.

Align NIEHS Data and Knowledge Management Systems With Select External Efforts

• There are multiple efforts to manage, aggregate, and provide DKM systems and tools for accessing and analyzing biomedical and population-level health data, both within and external to NIH (e.g., National Cancer Institute [NCI] Commons, NIH Data Commons, All of Us cohort). Many of these efforts are making use of public clouds as supporting infrastructure. In aligning with and making use of these efforts, NIEHS will advance use of data standards that promote interoperability and reduce duplication of costly DKM capabilities.
• Multiple challenges exist in aligning with external systems, including technical challenges in interfacing with external systems (e.g., moving data securely between sites, authenticating with external DKM systems) and challenges in adopting evolving standards used by external partners, in addition to the challenge of funding and staffing such efforts (especially where cloud technologies are in use).
• NIEHS I&IT will facilitate alignment by engaging with external efforts to prioritize external systems to work with, advocate around NIEHS needs, and build tools and services to minimize technical challenges in using external systems.

Cultivate a Data Science−Oriented Workforce

• Data- and knowledge-driven approaches (e.g., deep learning for training classifiers, use of vocabularies for enrichment) represent a foundational method of conducting research (in addition to empirical, theoretical, and computational approaches). Increasing training of staff and researchers in data science methods will increase the use and effectiveness of such approaches and reduce the burden on centralized I&IT for developing DKM-related tools and services.
• NIEHS I&IT will facilitate training of data science researchers and staff, both intramural and extramural, by helping identify gaps in training, providing training sessions, promoting a community of data science, and providing infrastructure (e.g., servers, installed software, training data sets) for development of skills.

Strategic Capability Priorities

Provide an NIEHS Information Commons for Querying and Computing Across FAIR+ Designated Data Sets and Knowledge Bases

DKM-06

NIEHS will develop and provide APIs to access and query DKM systems, which provide multiple benefits in enabling data- and knowledge-driven research methods. APIs provide access to statistical distributions that may be difficult to obtain due to data security and sharing concerns, facilitate development of new tools and methods by providing easy-to-obtain results for common queries that allow for auditing of data usage patterns, and inform data management practices. Coordinating the terminologies and vocabularies adopted by APIs such that common standards are adopted can further allow for integration of data between systems without direct mapping of the data systems, allowing for separation of implementations. Existing efforts at NIH and elsewhere (e.g., NIH Data Commons, EPA CompTox, Global Alliance for Genomics and Health) are making strides toward global information commons. Thus, providing APIs will increase NIEHS data utility and use. Success will be understood by the percentage of DKM systems with APIs, and usage of APIs by intramural and extramural researchers, tool developers, and partner resources.

Data and Knowledge Management Theme Map

See Appendix A: I&IT Priorities Support NIEHS Strategic Themes