Data Management and Sharing Plan NIH Example

Table of contents
  1. Components of a Data Management and Sharing Plan
  2. Sample Data Management and Sharing Plan for NIH
  3. Frequently Asked Questions
  4. Conclusion

Welcome to our comprehensive guide on data management and sharing plans, specifically tailored to meet the requirements of the National Institutes of Health (NIH). In this guide, we will walk you through the essential components of a data management and sharing plan, provide examples and best practices, and address common questions related to this crucial aspect of research and funding. Whether you are a seasoned researcher or a graduate student preparing to submit a grant proposal to the NIH, this article is designed to equip you with the knowledge and resources needed to develop a robust data management and sharing plan.

Effective data management and sharing are integral to the research process, contributing to the credibility, transparency, and impact of scientific endeavors. NIH's commitment to promoting data sharing and transparency has led to the establishment of clear guidelines and expectations for grant applicants regarding data management and sharing plans. By understanding and adhering to these requirements, researchers can not only enhance the quality of their proposals but also contribute to the broader scientific community by facilitating access to valuable research data.

Components of a Data Management and Sharing Plan

A comprehensive data management and sharing plan consists of several key components that outline how research data will be handled, maintained, and shared throughout and beyond the duration of the project. These components typically include:

  • Overview of the types and formats of data to be generated
  • Procedures for data collection, organization, and documentation
  • Data storage and security measures
  • Plans for data preservation and archiving
  • Strategies for data sharing, access, and reuse
  • Roles and responsibilities of investigators and collaborators
  • Ethical and legal considerations related to data privacy and intellectual property

Types and Formats of Data

Research projects generate various types of data, including but not limited to experimental results, surveys, interviews, images, and statistical analyses. It is crucial to provide a clear overview of the specific types and formats of data that will be produced during the course of the project. For example, a genomic study may generate DNA sequencing files, gene expression data, and phenotype information, each requiring distinct management and sharing protocols.

Example: In a clinical trial, the data generated may include patient demographics, medical records, treatment outcomes, and adverse events, all of which must be appropriately managed, stored, and shared according to ethical and regulatory standards.

Data Collection and Organization

Describe the procedures and methodologies for data collection, ensuring that the methods are well-documented and reproducible. Additionally, outline the strategies for organizing and structuring the data to facilitate efficient management and future sharing.

Example: In a longitudinal study, data collection may involve repeated measurements of variables over time, necessitating robust protocols for tracking and organizing the evolving dataset.

Data Storage and Security

Address the mechanisms for storing and securing the research data, emphasizing the use of reliable and appropriately protected storage systems to prevent unauthorized access, loss, or corruption of the data.

Example: Utilization of encrypted cloud storage and regular data backups to safeguard against data breaches and ensure data integrity.

Data Preservation and Archiving

Discuss the long-term preservation and archiving procedures for the research data, considering factors such as data retention policies, metadata creation, and compliance with relevant standards and repositories.

Example: The deposition of research data in public repositories such as the NIH Data Archive (NDA) or the Inter-university Consortium for Political and Social Research (ICPSR) to ensure sustained accessibility and usability of the data.

Data Sharing, Access, and Reuse

Present a plan for sharing and providing access to the data, promoting transparency and collaboration while addressing any restrictions or embargoes associated with sensitive or proprietary data.

Example: Establishment of a data access committee to oversee requests for data access, ensuring compliance with data use agreements and ethical considerations.

Roles and Responsibilities

Clearly define the roles and responsibilities of the principal investigator, co-investigators, collaborators, and other involved parties in relation to data management, sharing, and compliance with the proposed plan.

Example: Assignment of a data manager responsible for overseeing the implementation of the data management plan and ensuring that all team members adhere to established protocols.

Ethical and Legal Considerations

Address the ethical and legal aspects of data management and sharing, including considerations related to confidentiality, informed consent, intellectual property rights, and compliance with institutional and regulatory policies.

Example: Adherence to the Health Insurance Portability and Accountability Act (HIPAA) regulations for protecting the privacy and security of identifiable health information in research data.

Sample Data Management and Sharing Plan for NIH

To provide a practical illustration of a data management and sharing plan suitable for submission to the NIH, we have crafted a hypothetical scenario based on a genomic research project focusing on the genetic determinants of rare diseases. This example plan encompasses the essential elements required by the NIH while showcasing a structured and feasible approach to handling and disseminating research data.

Hypothetical Project Title: Unraveling Genetic Variants in Pediatric Neurodegenerative Disorders

Overview of Data Types and Formats

The research project will generate diverse data types, including whole-genome sequencing data, gene expression profiles, clinical phenotypes, and longitudinal follow-up data on pediatric patients diagnosed with neurodegenerative diseases. The genomic data will be stored in standard file formats such as FASTQ, VCF, and BAM, while clinical data will be organized using relational databases and annotated with standardized medical coding systems.

Procedures for Data Collection and Organization

Data collection will follow established protocols for genomic sample processing, sequencing, and quality control. Clinical data will be collected through standardized assessment tools and electronic health record extraction, ensuring comprehensive and standardized data capture. All collected data will be organized using a data management system that integrates genomic and clinical datasets for efficient analysis and retrieval.

Data Storage and Security Measures

Genomic data will be securely stored in a certified data center with restricted access and regular cybersecurity audits. Clinical data will be housed in an institutional HIPAA-compliant server, implementing role-based access controls and encryption to protect patient confidentiality and data integrity.

Plans for Data Preservation and Archiving

The genomic data will be archived in public repositories such as the Database of Genotypes and Phenotypes (dbGaP), accompanied by detailed metadata and sample annotations. Clinical data will be preserved in compliance with institutional data retention policies, ensuring long-term accessibility and traceability for future research.

Strategies for Data Sharing and Reuse

We are committed to sharing de-identified data with qualified researchers through controlled access mechanisms facilitated by established data sharing platforms. A data access committee will review and approve data access requests, ensuring that data usage aligns with the informed consents provided by study participants and regulatory requirements.

Roles and Responsibilities of Investigators

The principal investigator will oversee the overall data management and sharing activities, supported by a dedicated data manager responsible for data curation, quality control, and compliance monitoring. Each co-investigator will assume specific responsibilities for data collection, analysis, and contributing to data sharing initiatives as outlined in the project timeline.

Ethical and Legal Considerations for Data Management

The research team will uphold ethical standards by obtaining informed consent from study participants and ensuring the de-identification of shared data to uphold patient privacy. All data sharing practices will adhere to institutional and regulatory guidelines, respecting intellectual property rights and ethical use of shared data for research purposes.

Frequently Asked Questions

What is the significance of a data management and sharing plan in NIH-funded research?

A data management and sharing plan plays a pivotal role in NIH-funded research by promoting transparency, accountability, and the responsible stewardship of research data. It serves as a roadmap for researchers to outline their strategies for collecting, organizing, safeguarding, and disseminating valuable research data, ultimately enhancing the reproducibility and impact of scientific findings.

Are there specific templates or formats recommended for creating a data management and sharing plan for NIH proposals?

While NIH does not mandate a specific template for data management and sharing plans, they provide detailed guidance on the essential elements that should be addressed within the plan. Applicants are encouraged to adhere to the specific requirements outlined in the funding opportunity announcements (FOAs) and utilize clear and concise language to articulate their data management and sharing strategies effectively.

How should I address data security and privacy concerns in my data management and sharing plan?

When addressing data security and privacy, researchers should outline the technical and administrative measures employed to protect research data from unauthorized access, data breaches, and misuse. Additionally, researchers should address the ethical and legal considerations regarding data privacy, ensuring compliance with institutional and regulatory requirements such as HIPAA for sensitive health data.

What are the best practices for data sharing and access control within a collaborative research project?

Best practices for data sharing involve establishing clear data access policies, utilizing secure data sharing platforms, and implementing controlled access mechanisms to govern data dissemination. Collaborative research projects should designate responsible individuals or committees to oversee data access requests, assess data usage agreements, and ensure that shared data is used ethically and in alignment with the project's goals.


In conclusion, a well-crafted data management and sharing plan is essential for NIH-funded research projects, embodying the principles of transparency, integrity, and responsible data stewardship. By meticulously addressing the components of a robust data management and sharing plan, researchers can advance scientific knowledge, facilitate collaboration, and uphold the NIH's commitment to promoting open science and data sharing. Embracing best practices and exemplary examples of data management and sharing plans will empower researchers to navigate the evolving landscape of research data management while contributing to the collective progress of scientific inquiry.

If you want to know other articles similar to Data Management and Sharing Plan NIH Example you can visit the category Work.

Don\'t miss this other information!

Deja una respuesta

Tu dirección de correo electrónico no será publicada. Los campos obligatorios están marcados con *

Go up
Esta web utiliza cookies propias para su correcto funcionamiento. Contiene enlaces a sitios web de terceros con políticas de privacidad ajenas que podrás aceptar o no cuando accedas a ellos. Al hacer clic en el botón Aceptar, acepta el uso de estas tecnologías y el procesamiento de tus datos para estos propósitos. Más información