Data Management

NTP is committed to making data from its research available to the public. Many of our data and resources are accessible via the web and at no cost, including:

We also host educational activities devoted to data and informatics, such as the Systematic Evaluation of the Application of Zebrafish in Toxicology (SEAZIT) webinar series, among others.

Data management falls under the purview of data science, a field that is expanding rapidly. At NTP and NIEHS, data science seeks to achieve the following goals:

Circular diagram with Data-Driven Discovery and Decisions in the center, surrounded by multi-color wedges labelled with the goals of NTP data science: governance, methods, translation, infrastructure, engagement, and community

Governance. Managing data entails a balance between the need of scientists to advance their research programs, the need of the broader community to share data, the need of institutions to meet regulatory requirements, and the rights of research participants.

Methods. We engage with groups in academia and industry that are on the forefront of developing new data solutions. Clearly articulating our needs helps ensure that new methods will be adaptable to our purposes.

Translation. Novel methods have the potential for accelerating data-driven environmental health research. We seek to identify new methods and technologies and translate them into best practices for NTP researchers.

Infrastructure. We aspire to increase the research community's access to high quality, well-described data, and to ensure that NTP-generated data meets FAIR+ principles and is managed in accordance with our governance policies.

Engagement. A well-defined process is essential for communicating existing policies and best practices in data and knowledge management, as well as receiving feedback to ensure that the needs of NTP researchers are being met.

Community. Our researchers need specialized knowledge and skills to make the most of the data they generate. This goal can best be realized by nurturing a data-oriented workforce within the environmental health community.