Enhancing Scientific Rigor, Transparency and Replicability

NINDS encourages investigator-initiated proposals that address the above approaches to improving scientific rigor and transparency, especially in preclinical neuroscience. Such projects ensure high-quality research by promoting experimental and analytical rigor, transparent reporting, and measures to reduce systematic experimental biases. Critical gaps include effective and sustainable approaches to enhancing attention to and reinforcing practices that ensure rigor and reduce experimental bias at multiple levels of the scientific enterprise (e.g., laboratory, institutions, publishers, funders). NINDS anticipates that this research will be appropriately supported through research projects (e.g., R21 for areas 1 or 2), education projects (e.g., R25 for areas 2 or 3), conference grants (e.g., R13 for area 3), or similar approaches, and aims to support innovative approaches that will, by improving rigor and transparency, contribute to more reliable and translatable neuroscience research.

The All of Us Researcher Workbench offers a uniquely powerful, NIH-supported platform for advancing rigorous and transparent scientific practices across biomedical research. The Researcher Workbench provides credentialed investigators with secure access to rich, multimodal data from over 700,000 participants (e.g., electronic health records, surveys, physical measurements, wearables data, genomics, etc.). Researchers can share analysis environments through collaborative workspace features, allowing for broad reproducibility and adaptive workflows beyond what traditional supplementary materials allow. Standardization through the OMOP Common Data Model ensures consistent variable definitions across studies, reducing ambiguity and supporting rigorous cross-study comparison and data reuse. Researchers are encouraged to use the All of Us Researcher Workbench to advance rigorous research projects, replication studies, and generalizability research in areas outlined in this topic. 

The Environmental influences on Child Health Outcomes (ECHO) Cohort, part of the NIH ECHO Program, is well-poised to support replicability and reproducibility studies. The ECHO Cohort has data on >170,000 children and family members from the prenatal period through adolescence and seeks to understand the effects of a broad range of early environmental influences on child health and development. ECHO makes data and biospecimens available to the broad scientific community through their ancillary studies process (https://echochildren.org/echo-ancillary-studies/).

NCCIH encourages investigator-initiated proposals to enhance rigor, transparency, and reproducibility in complementary and integrative health research. Of particular interest are studies that strengthen methodological standards, replication, and translational validity in: 

• glymphatic and lymphatic brain clearance mechanisms;
• imaging biomarkers for pain responses to complementary and integrative health interventions;
• precision probiotic interventions and reverse translation approaches; and
• mechanistic effects and clinical efficacy of natural products, including product integrity and analytical validation.

Applications that standardize definitions, validate biomarkers, replicate findings across sites and populations, and establish robust experimental and analytical frameworks to improve reliability and generalizability, including in the context of whole person health, are encouraged.

NCI encourages applications that improve research replicability and assessment of research validity by:

• Developing novel frameworks and approaches to promote appropriate characterization of banked biospecimens, comprehensive metadata documentation, and suitability for intended analytic assays, thereby strengthening the validity of research outputs;
• Leveraging existing, high-quality datasets and their associated metadata for secondary re-use and/or re-analysis;
• Sharing research data, consistent with NIH policies and procedures, and detailed protocols and metadata to enable secondary data re-use and re-analysis.

NEI seeks applications that strengthen the rigor, reproducibility, replicability, and transparency of vision research. Areas of interest include, but are not limited to: 

• Developing and incentivizing transparent reporting approaches in vision research publications and presentations (e.g., emphasizing methodology, protocols, and data sharing) 
• Establishing and promoting FAIR- and TRUST-aligned data standards, including common data elements, interoperable metadata, and standardized data-sharing practices 
• Creating open-source tools and platforms for data integration, independent validation, and assessment of robustness and generalizability 
• Implementing innovative research practices to enhance validity, reproducibility, replicability, and transparency (e.g., forums to incentivize, integrate, and synthesize related studies) 
• Providing accessible training resources and metrics to promote and assess rigorous, transparent research practices across the vision science community

NHGRI is interested in investigator-initiated proposals that advance rigorous and reproducible practices in genomic and multiomic studies. Novel genomic technologies and analytical approaches necessitate careful attention to study design, data generation, integration, analysis, validation, and interpretation to ensure robust and reliable results. 

Areas of interest include, but are not limited to:

• Development and benchmarking of rigorous, reproducible, and unbiased methods and workflows for genomic and multiomic data generation and analysis
• Generation of evidence on impact of implementing genomically guided prevention and treatment strategies in clinical care.
• Development, adoption, and dissemination of standards, protocols, and metadata to enhance transparency and reuse of genomic data
• Training and community-based efforts to promote best practices in rigorous study design and analysis in genomics

NHLBI encourages investigator-initiated proposals that address the above approaches to improving scientific rigor and transparency, especially on topics relevant to its Strategic Vision and focus on heart, lung, blood, and sleep disorders, implementation science (HLBSI) and community-engaged research. Use of bio- and data-repositories from the NHLBI (BioLINCC and BioData Catalyst) are encouraged. Also encouraged is the  development and adoption of high-throughput, technology-enabled solutions to address critical reproducibility gaps and enhance rigor across the research lifecycle. Examples include automated laboratory protocols and digital workflow capture; AI-driven tools for data ingestion, harmonization, annotation, and standardized metadata generation; and intelligent systems to guide optimal experimental design, randomization, blinding, and sex as a biological variable (SABV) inclusion. 

NIA encourages proposals that address improving scientific rigor, reproducibility, and transparency in genetic, biological, neuroscientific, translational, clinical, behavioral, social, and economic research on aging and aging-related conditions, including Alzheimer’s disease (AD) and AD-related dementias (ADRD).

Topics of interest to NIA may include, but are not limited to:

• Approaches that strengthen experimental design, transparency, and reporting
• Tools that improve rigor in aging and AD/ADRD models, including in vitro, in silico and in chemico models (new approach methodologies, NAMs) and animals
• Methods that strengthen population-representative research (e.g., causal inference, coordinated analyses, synthetic cohorts, and adoption of common data elements)
• Infrastructure, resources, or core facilities to support rigorous generation and reproduction of data 

Applicants are strongly encouraged to contact NIA program staff to discuss potential projects before submission.

NIAAA seeks studies that strengthen experimental design, analytic methods, and transparent reporting of research on alcohol misuse, Alcohol Use Disorder (AUD), binge, chronic, and developmental alcohol exposure, withdrawal and relapse, co-use with other substances, health consequences, and prevention and treatment interventions. 

• Harmonize protocols, common data elements, and metadata.
• Identify sources of variability that affect replicability and generalizability.
• Validate biomarkers and outcome measures. 
• Establish replicability of the pathological impact of alcohol on comorbid conditions based on time-course, dose-response frameworks.
• Develop training to enhance the rigor and reproducibility of behavioral interventions on AUD.
• Establish reliability of translation of preclinical to clinical research, and clinical RCTs to implementation and sustainability of evidence-based interventions.
• Develop methods for engaging individuals with mild-moderate AUD in treatment.

NIAID encourages applications that strengthen rigor, reproducibility, replicability, and transparency of research on immunologic, allergic, and infectious diseases. Areas of interest include:

• leveraging interoperable data standards to integrate multimodal data (e.g., real-world and experimental data), with provenance and metadata on collection, processing, and variability to enable reproducibility. 
• supporting benchmarking frameworks and metrics to enable reproducible analytical workflows, software, and models aligned with recognized repositories.
• establishing frameworks to characterize biospecimens, including metadata to assess quality, collection conditions, and assay suitability.
• supporting data and assay standardization, including benchmark development to improve measurement consistency.
• conducting studies to validate preclinical and clinical protocols in immunology and infectious diseases, reducing variability and improving cross-study reproducibility and replicability. 

NIAMS encourages investigator-initiated proposals that address the above approaches to improving scientific rigor and transparency in fundamental research that increases our understanding of biological processes and lays the foundation for advances in disease diagnosis, treatment, and prevention. Such projects ensure high-quality research by promoting:

• experimental and analytical rigor,
• transparent reporting, 
• measures to reduce systematic experimental biases. 

Critical gaps include effective and sustainable approaches that ensure rigor and reduce experimental bias at all levels of the scientific enterprise (e.g., laboratory, institutions, publishers, funders). NIAMS anticipates that this research will be appropriately supported through investigator-initiated research projects, education and conference grants.

NIBIB supports rigorous and reproducible technology development, as well as tools to quantify and improve rigor, reproducibility, replicability and transparency in biomedical imaging and bioengineering. Areas of interest include but are not limited to: 

• Development of technological tools and methods that quantify and improve rigor and reproducibility in bioengineered platform development, including quantitative assessment in data and devices. 
• Tools and infrastructure for interoperability of data and metadata for aggregation, AI-development, analysis, and re-analysis. 
• Biomedical imaging methodologies that assess and improve quantitative accuracy, accelerate the development and adoption of hardware and software standards including phantom-based calibration, enhance multi-site and cross-vendor validation, and enable precise measurement and development of imaging biomarkers.
• Training and community dissemination of technology and best practices on improving rigor and reproducibility. 

Research in areas central to NIDA’s mission - such as drug‑induced neuroadaptations, polysubstance interactions, developmental vulnerability, overdose mechanisms, and behavioral and environmental drivers of drug use - often involves complex models that are highly sensitive to methodological variability, making robust experimental design and transparent reporting essential. Priority opportunities for NIDA include developing tools that help researchers implement and document rigor practices in drug addiction‑relevant models; creating platforms that support detailed sharing of protocols, metadata, and common data elements specific to substance use research; and identifying hidden sources of variability that affect replicability in studies involving drug administration, withdrawal paradigms, stress models, or neurocircuitry mapping. Equally important is to harmonize best practices in experimental design and analysis.

NIDCR is interested in applications that strengthen the rigor, reproducibility, replicability, and transparency of dental, oral, and craniofacial (DOC) research. In addition to the topics noted above, areas of interest include the development, comparison, and dissemination of optimal study designs and analytic approaches to minimize bias in clinical and preclinical DOC studies; the evaluation and dissemination of innovative research practices and strategies that enhance scientific rigor, validity, reproducibility, replicability, and transparency; and the use of systematic reviews and meta-analyses to synthesize evidence from DOC research.

NIEHS is interested in:

• The development and implementation of tools, such as common data elements or data and metadata standards, to facilitate data sharing, integration, harmonization, and replication
• Defining frameworks for assessing and publishing the sensitivity of complex data models (e.g., atmospheric dispersion, chemical mixtures) to user-defined analytic choices, including identification of hidden sources of variability that can affect reproducibility
• Re-analysis and systematic review of critical work in the environmental health sciences
• Dissemination and implementation science efforts to enhance the adoption of rigorous research findings
• Training, mentorship, and conferences focused on measuring and/or enhancing the rigor and reproducibility of environmental health sciences research

NIGMS encourages investigator-initiated proposals that address the above approaches to improving scientific rigor and transparency in fundamental research that increases our understanding of biological processes and lays the foundation for advances in disease diagnosis, treatment, and prevention. Such projects ensure high-quality research by promoting:

• experimental and analytical rigor,
• transparent reporting, 
• measures to reduce systematic experimental biases. 

Critical gaps include effective and sustainable approaches that ensure rigor and reduce experimental bias at all levels of the scientific enterprise (e.g., laboratory, institutions, publishers, funders). NIGMS anticipates that this research will be appropriately supported through investigator-initiated research projects, education and conference grants.

NIMH is interested in tools and methods that increase the rigor and reproducibility of mental health research, including but not limited to:

• Improving rigorous methods and approaches in basic neuroscience and behavioral science; translational mental health research across the lifespan and novel treatment development studies; epidemiology; and mental health intervention and services research to optimize treatment response, matching, and sequencing.
• Advancing rigorous experimental and analytic designs for studies using high-dimensional datasets, following recommendations of the NAMHC Workgroup on High Dimensional Data.
• Tools and methods leveraging data from the NIMH Data Archive, especially those that incorporate NIMH Common Data Elements, to inform approaches to harmonize and aggregate datasets and increase rigor and reproducibility.
• Improving rigor by incorporating community engaged approaches into topics such as intervention development, and services and implementation research.

NIMHD is interested in applications that improve the scientific rigor, reproducibility, and transparency of observational, mechanistic, and intervention research that examines novel factors contributing to adverse health outcomes and that tests high-quality interventions to address health disparities and improve population health. Areas of interest ripe for improving rigor, transparency, and replicability include but are not limited to: 

• Adoption of a life-course perspective to evaluate the clinical significance of interventions intended to improve health outcomes, and/or prevent or reduce medical complications.
• Community based participatory research applying robust methodological approaches and implementing community-driven solutions to improve health outcomes.
• Examining biological, environmental, and behavioral mechanisms underlying health problems and testing interventions to reduce risks and improve health outcomes.

NLM encourages investigator-initiated research to advance rigorous and transparent biomedical data science and informatics. NLM is particularly interested in:

• Methods to identify, quantify, and mitigate hidden sources of variability in complex, multimodal datasets (e.g., genomic, imaging, EHR) to improve reproducibility and generalizability.
• Development and validation of robust evaluation frameworks for AI/ML, including benchmarking, bias assessment, and external validation to ensure reliable and replicable results.
• Provenance-rich data standards, common data elements, and interoperable metadata to enable transparent data reuse and interpretation.
• Tools and platforms to share analytic workflows, software, and computational pipelines with full documentation, versioning, and traceability to support reproducibility.

NLM supports research that develops and evaluates these approaches to enable reliable and reusable biomedical research.

The Office of AIDS Research (OAR) is committed to advancing scientific rigor, transparency, and reproducibility across HIV/AIDS research. OAR encourages applications that develop and implement innovative tools, methods, training programs, and community-driven approaches to strengthen experimental design, data reporting, and the adoption of rigorous research practices. OAR is particularly interested in efforts that enhance the validity, reliability, and translatability of HIV/AIDS research findings, including investigator-initiated research that promotes replicability, improves research norms, and disseminates best practices across the scientific community.

OBSSR is interested in scientific practices that ensure rigorous and transparent experimental design, methodology, analysis, and interpretation, which improve validity and reliability of findings in the behavioral and social sciences.

ODP is particularly interested in research to develop and evaluate new methods for design, analysis, and sample size for clinical trials that use clustered designs including Individually Randomized Group-Treatment trials or Group- or Cluster-Randomized Trials (parallel, stepped wedge, crossover). These designs are often appropriate for trials that randomize groups or clusters or that deliver interventions that include group-formatted components (participants receive at least some part of their intervention together with other participants in the same arm, e.g., an in-person or virtual class) or shared intervention agents (e.g., a group leader, therapist, trainer, clinician, or navigator who delivers at least some part of the intervention to more than one participant in the same arm). Please see the NIH Research Methods Resources website for more detail on these designs (https://researchmethodsresources.nih.gov/).

Office of Dietary Supplements (ODS)

ODS seeks to improve the rigor with which dietary supplement research is designed, conducted, and reported. ODS-specific priority areas include, but are not limited to:

• Improving reliable and transparent reporting of the chemical composition of dietary supplement interventions
• Improving research replicability and generalizability through methods that assess dietary intake as a biological variable
• Incentivizing the adoption and/or assessing the impacts of best practices for enhancing the rigor and translational relevance of dietary supplement research

ODSS encourages investigator-initiated proposals such as addressing development and enhancement of standards and data models to promote high-quality research by improving scientific rigor, discovery, reproducibility, accessibility, impact, and efficacy.

As outlined in the ONR Strategic Plan, ONR supports studies to improve the rigor and reproducibility of biomedical research to advance the fundamental understanding of the biology of nutrition and its functional role in critical systems involved in health and disease.

Specifically, ONR supports research focused on standardizing and harmonizing nutrition research methods, measures, and data-capture processes.

ORIP is committed to advancing scientific rigor, transparency, and experimental replicability across biomedical research. ORIP encourages research project grant applications aimed at developing, disseminating and implementing broadly applicable technologies, tools, protocols, training, and resources to aid decision-making processes. ORIP contributes to assessing the value and limitations of research models for establishing confidence in their applications to enhance rigor, replicability, and translatability of research. Of special interest are studies to understand underlying biological and experimental principles which are essential to produce findings that are reliable, replicable, and readily translatable to human health and disease conditions. Proposed models, resources, or technologies must either address research interests of several NIH Institutes and Centers or be applicable to diseases that impact multiple organ systems in order to align with ORIP’s broad mission.

The Office of Research on Women’s Health (ORWH) is committed to advancing sex as a biological variable (SABV) in research. SABV is essential to rigor, validity, generalizability, and translational potential. Congressional language reinforces the value of SABV and directs NIH efforts to support, track, and analyze progress in integrating SABV into biomedical research.

ORWH is interested in: 

• Developing and validating tools, methods, and analytic frameworks, including AI-enabled approaches, to enhance SABV-informed experimental design, quantitative analysis, and reporting
• Common data elements and reporting standards for sex-disaggregated data
• Training programs that promote SABV adoption, at all career stages

The Office of Autoimmune Disease Research in ORWH (OADR-ORWH) is interested in: 

• Developing common data elements (CDE) for autoimmune disease research
• Supporting scientific meetings to build consensus on autoimmune CDE development to support rigor and reproducibility in research