Leaving Community
Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.
News
NIH Announced Two Funding Opportunities for NIDDK Active Grants
Here is the information of the two new funding opportunities from NIH:
Please note that the NIDDK participates in both FOA, and hence most NIDDK active grants with eligible activity code can apply
"Notice of Special Interest (NOSI): Administrative Supplements to Support Collaborations to Improve the AI/ML-Readiness of NIH-Supported Data
Key Dates: First Available Due Date: April 25, 2023
Purpose
This Notice announces the availability of supplements to active grants which are intended to support collaborations that bring together expertise in biomedicine, data management, and artificial intelligence and machine learning (AI/ML) to make NIH-supported data useful and usable for AI/ML analytics. This initiative is aligned with the NIH Strategic Plan for Data Science, which describes actions aimed at modernizing the biomedical research data ecosystem and making data FAIR (Findable, Accessible, Interoperable, and Reusable) with high impact for open science. For the purposes of this Notice, AI/ML is inclusive of machine learning (ML), deep learning (DL), and neural networks (NN).
Background
Artificial intelligence and machine learning (AI/ML) are a collection of data-driven technologies with the potential to significantly advance biomedical research. NIH makes a wealth of biomedical data available and reusable to research communities, however, not all of these data are able to be used efficiently and effectively by AI/ML applications. The goal of this Notice is to make the data generated through NIH-funded research AI/ML-ready and shared through repositories, knowledgebases or other data sharing resources.
For the purposes of this Notice, AI/ML is inclusive of machine learning (ML), deep learning (DL), and neural networks (NN). Making data AI/ML-ready is not simply formulaic. It requires engagement with and feedback from AI/ML applications. Furthermore, feedback from AI/ML applications can improve the understanding of the data to improve future re-use.
Some aspects of AI/ML-readiness are better understood than others. For example, data to be analyzed by AI/ML tools, such as PyTorch and TensorFlow, which are used to build and deploy AI/ML applications, must conform to specific data formats. The FAIR principles, through the use of data and metadata standards (ontologies, taxonomies, terminologies), facilitate combining data from different sources to support biomedical AI/ML applications.
Some other aspects of what is needed to make data AI/ML ready must be discovered through iterative and exploratory testing. These might include how to best represent information for a particular AI/ML use-case, how to correct for noise, and what level of specificity or uncertainty of labels is tolerable for a desired AI/ML application.
For many AI/ML applications, the training dataset must be sufficiently large to be considered AI/ML ready. Thus, readying these data for computation necessitates knowledge of big data management practices, for example how best to prepare data to be partitioned to enable computational feasibility.
Decentralized machine learning (sometimes referred to as Federated or distributed learning) is a paradigm of machine learning where a model is trained iteratively on data in multiple locations. This paradigm can facilitate the use of data that, for privacy or other reasons, cannot be aggregated or moved. Preparing data for decentralized ML requires harmonization and testing as well as capabilities for standardized access and, possibly, enhanced data and model governance to protect privacy.
Furthermore, there are increasing expectations that AI/ML ready data be accompanied by documentation to include information about data provenance and bias to help researchers make more informed and ethical decisions about the selection of data and application of AI/ML-models. For example, imbalanced datasets can result in AI/ML algorithms that lead to biased clinical decisions and, potentially, a misalignment with NIH goals to improve minority health and reduce health disparities for marginalized populations. AI/ML-readiness should be guided by a concern for human and clinical impact and therefore requires attention to ethical, legal, and social implications of AI/ML including but not limited to (1) biases in datasets, algorithms, and applications; (2) issues related to identifiability and privacy; (3) impacts on disadvantaged or marginalized groups; (4) health disparities; and (5) unintended, adverse social, individual, and community consequences of research and development.
It is the NIH vision to establish a modernized and integrated biomedical data ecosystem that adopts the latest data science technologies, including AI and ML, and best practice guidelines arising from community consensus, such as the FAIR principles, and open-source development. This effort is described in the NIH Data Science Strategic Plan and led by the NIH Office of Data Science Strategy (ODSS).
Research Objective
This opportunity is intended to support collaborations that bring together expertise in biomedicine, data management, and AI/ML to improve the AI/ML-readiness of data generated from NIH-funded research and shared through repositories, knowledgebases or other data sharing resources.
Applications submitted in response to this NOSI are strongly encouraged to include the following information:
- Reference(s) to the data under consideration and reasons for this choice.
- Description of the potential impact of scientific advances that could be made from AI/ML applications developed with the data.
- Description of the challenges to be addressed and why the data are not currently AI/ML-ready.
- Description of the proposed method for improving the data AI/ML-readiness.
- Description of how the data will be made available to AI/ML applications and researchers, for example, through NIH repositories, NIH knowledgebases, or other data sharing resources including those appropriate for controlled access data.
- Proposal to demonstrate the use of the transformed data in an AI/ML application.
- Proposed timeline of activities and milestones for the 12-month supplementary funding period.
- Description of the relevant expertise of the supported collaboration.
- Description of how the ethical implications of data will be identified and addressed, including plans to develop and share documentation or datasheets that describes the motivation, composition, collection process and pre-processing, anticipated use cases, and other information relevant for ethical reuse.
NIH is particularly interested in proposals that will advance the ethical development of AI-ready data, and transparent practices that enhance the ethical re-use of data for AI/ML applications.
These supplements may be used to support a variety of activities including, but not limited to, the following:
- Identifying existing shortfalls in AI/ML-readiness and informing the preparation of data for AI/ML through, for example, AI/ML hackathons, mini AI/ML applications, citizen science challenges, or other engagements with the AI/ML community to better understand current gaps in AI/ML-readiness.
- Activities for making data AI/ML-ready that are responsive to the gaps identified. These may include, for example, cleaning or filtering data; imputing missing metadata; data pre-processing; finding data representations to improve the computational efficiency of machine learning; removing spurious artifacts, for example from heterogeneous data sources, that affect learning or inference; data cleaning, wrangling, or filtering to provide a benchmark version of the data; adoption of ontologies or other standards to improve interoperability with other data; removing or characterizing biases and structures that may affect any AI/ML model trained on the data.
- Discovering and identifying imbalances in the data, biases in data labels or metadata, or other attributes of the dataset that would help researchers make better, more ethical decisions when using the data for AI/ML.
- Addressing specific challenges related to harmonizing distributed/federated data for distributed/federated learning.
- Developing and sharing documentation, e.g. datasheets, that document the provenance, motivation, composition, collection process, recommended uses, and other relevant information for AI/ML re-users of the data, including feedback from AI/ML applications already using the data.
- Preparation of social determinants of health (SDOH) information for use in AI/ML applications.
- Preparing data for multi-modal multi-scale AI/ML applications.
These efforts are expected to be informed by best practices in data management and engagement with the AI/ML community.
Significant skills in data management and AI/ML are expected to be needed to identify and address gaps in AI-readiness. Thus, supplements are primarily intended to provide support for data management and AI/ML collaborators, engagement events such as hackathons, and computing and storage costs required to improve the AI-readiness of data.
The scope of each proposed project is defined by and limited to the aims of the funded project for which the supplement is being sought.
Applicants partnering with industry to test novel methods or infrastructures may be considered. The integration of causal models and causal inference in AI/ML is within scope.
A broad range of projects involving the management of data repositories, or other shared data resources are eligible regardless of the scientific area of emphasis. Both open and controlled access data, including clinical data, are within scope.
Awardees should be willing to participate in virtual meetings organized by NIH. Applications that are not appropriate and out of scope for this NOSI include:
- Projects with no engagement with the AI/ML community, or no AI/ML expertise in the proposed collaboration.
- Projects that do not intend to make data generated through NIH-supported research AI/ML-ready.
- Proposals to provide supplemental funding to an award that received supplemental funding under NOT-OD-21-094 or NOT-OD-22-067 (Administrative Supplements to Support Collaborations to Improve the AI/ML-Readiness of NIH-Supported Data).
- Proposals that do not explicitly meet all the requirements stated elsewhere in this NOSI.
- Proposals that are out of scope of the parent award.
- Proposals that do not intend to broadly share AI/ML-ready data by the end of the supplemental award period. Both open and controlled access data should be broadly shared, for example, through an NIH-supported repository, NIH-supported knowledge base, or other data sharing resource.
- Proposals focused on the development and application of AI/ML algorithms that do not intend to make data AI/ML ready.
ICO Specific Considerations
Office of Strategic Coordination
The NIH Office of Strategic Coordination (Common Fund - https://commonfund.nih.gov/) supports multiple transformative research programs that generate new technologies, methods, and data. Many of these programs produced rich public data sets containing multi-dimensional molecular and phenotypic data from humans and model organisms. Established Common Fund data sets listed below are well-poised for increased community use:
- 4D Nucleome (4DN) (https://www.4dnucleome.org/)
- Extracellular RNA Communication (exRNA) (https://exrna.org/)
- Gabriella Miller Kids First (KF) (https://kidsfirstdrc.org/)
- Genotype-Tissue Expression (GTEx) (https://www.gtexportal.org/home/)
- H3Africa (https://www.h3abionet.org/resources/h3africa-archive/)
- Glycoscience (GL) (https://glygen.org/)
- Human BioMolecular Atlas Program (HuBMAP) (https://hubmapconsortium.org/)
- Illuminating the Druggable Genome (IDG) (https://druggablegenome.net/)
- Integrated Human Microbiome Project (iHMP) (https://hmpdacc.org/ihmp/)
- Knockout Mouse Phenotyping Program (KOMP2) (http://www.mousephenotype.org/)
- Library of Integrated Network-based Cellular Signatures (LINCS) (http://lincsproject.org/)
- Metabolomics Workbench (https://www.metabolomicsworkbench.org/)
- Molecular Transducers of Physical Activity in Humans (MoTrPAC) (https://motrpac-data.org/data-access)
- Stimulating Peripheral Activity to Relieve Conditions (SPARC) (https://sparc.science/)
OSC is interested in proposals that substantially leverage at least one of the above datasets. Substantial leverage is defined as use and citation of the dataset(s) in the envisioned research products of the proposed work (manuscripts, presentations, book chapters, portals, etc.).
This Notice announces the availability of supplements to active grants which are intended to support collaborations that bring together expertise in biomedicine, data management, and artificial intelligence and machine learning (AI/ML) to make NIH-supported data useful and usable for AI/ML analytics. This initiative is aligned with the NIH Strategic Plan for Data Science, which describes actions aimed at modernizing the biomedical research data ecosystem and making data FAIR (Findable, Accessible, Interoperable, and Reusable) with high impact for open science. For the purposes of this Notice, AI/ML is inclusive of machine learning (ML), deep learning (DL), and neural networks (NN).
Background
Artificial intelligence and machine learning (AI/ML) are a collection of data-driven technologies with the potential to significantly advance biomedical research. NIH makes a wealth of biomedical data available and reusable to research communities, however, not all of these data are able to be used efficiently and effectively by AI/ML applications. The goal of this Notice is to make the data generated through NIH-funded research AI/ML-ready and shared through repositories, knowledgebases or other data sharing resources.
For the purposes of this Notice, AI/ML is inclusive of machine learning (ML), deep learning (DL), and neural networks (NN). Making data AI/ML-ready is not simply formulaic. It requires engagement with and feedback from AI/ML applications. Furthermore, feedback from AI/ML applications can improve the understanding of the data to improve future re-use.
Some aspects of AI/ML-readiness are better understood than others. For example, data to be analyzed by AI/ML tools, such as PyTorch and TensorFlow, which are used to build and deploy AI/ML applications, must conform to specific data formats. The FAIR principles, through the use of data and metadata standards (ontologies, taxonomies, terminologies), facilitate combining data from different sources to support biomedical AI/ML applications.
Some other aspects of what is needed to make data AI/ML ready must be discovered through iterative and exploratory testing. These might include how to best represent information for a particular AI/ML use-case, how to correct for noise, and what level of specificity or uncertainty of labels is tolerable for a desired AI/ML application.
For many AI/ML applications, the training dataset must be sufficiently large to be considered AI/ML ready. Thus, readying these data for computation necessitates knowledge of big data management practices, for example how best to prepare data to be partitioned to enable computational feasibility.
Decentralized machine learning (sometimes referred to as Federated or distributed learning) is a paradigm of machine learning where a model is trained iteratively on data in multiple locations. This paradigm can facilitate the use of data that, for privacy or other reasons, cannot be aggregated or moved. Preparing data for decentralized ML requires harmonization and testing as well as capabilities for standardized access and, possibly, enhanced data and model governance to protect privacy.
Furthermore, there are increasing expectations that AI/ML ready data be accompanied by documentation to include information about data provenance and bias to help researchers make more informed and ethical decisions about the selection of data and application of AI/ML-models. For example, imbalanced datasets can result in AI/ML algorithms that lead to biased clinical decisions and, potentially, a misalignment with NIH goals to improve minority health and reduce health disparities for marginalized populations. AI/ML-readiness should be guided by a concern for human and clinical impact and therefore requires attention to ethical, legal, and social implications of AI/ML including but not limited to (1) biases in datasets, algorithms, and applications; (2) issues related to identifiability and privacy; (3) impacts on disadvantaged or marginalized groups; (4) health disparities; and (5) unintended, adverse social, individual, and community consequences of research and development.
It is the NIH vision to establish a modernized and integrated biomedical data ecosystem that adopts the latest data science technologies, including AI and ML, and best practice guidelines arising from community consensus, such as the FAIR principles, and open-source development. This effort is described in the NIH Data Science Strategic Plan and led by the NIH Office of Data Science Strategy (ODSS).
Research Objective
This opportunity is intended to support collaborations that bring together expertise in biomedicine, data management, and AI/ML to improve the AI/ML-readiness of data generated from NIH-funded research and shared through repositories, knowledgebases or other data sharing resources.
Applications submitted in response to this NOSI are strongly encouraged to include the following information:
- Reference(s) to the data under consideration and reasons for this choice.
- Description of the potential impact of scientific advances that could be made from AI/ML applications developed with the data.
- Description of the challenges to be addressed and why the data are not currently AI/ML-ready.
- Description of the proposed method for improving the data AI/ML-readiness.
- Description of how the data will be made available to AI/ML applications and researchers, for example, through NIH repositories, NIH knowledgebases, or other data sharing resources including those appropriate for controlled access data.
- Proposal to demonstrate the use of the transformed data in an AI/ML application.
- Proposed timeline of activities and milestones for the 12-month supplementary funding period.
- Description of the relevant expertise of the supported collaboration.
- Description of how the ethical implications of data will be identified and addressed, including plans to develop and share documentation or datasheets that describes the motivation, composition, collection process and pre-processing, anticipated use cases, and other information relevant for ethical reuse.
NIH is particularly interested in proposals that will advance the ethical development of AI-ready data, and transparent practices that enhance the ethical re-use of data for AI/ML applications.
These supplements may be used to support a variety of activities including, but not limited to, the following:
- Identifying existing shortfalls in AI/ML-readiness and informing the preparation of data for AI/ML through, for example, AI/ML hackathons, mini AI/ML applications, citizen science challenges, or other engagements with the AI/ML community to better understand current gaps in AI/ML-readiness.
- Activities for making data AI/ML-ready that are responsive to the gaps identified. These may include, for example, cleaning or filtering data; imputing missing metadata; data pre-processing; finding data representations to improve the computational efficiency of machine learning; removing spurious artifacts, for example from heterogeneous data sources, that affect learning or inference; data cleaning, wrangling, or filtering to provide a benchmark version of the data; adoption of ontologies or other standards to improve interoperability with other data; removing or characterizing biases and structures that may affect any AI/ML model trained on the data.
- Discovering and identifying imbalances in the data, biases in data labels or metadata, or other attributes of the dataset that would help researchers make better, more ethical decisions when using the data for AI/ML.
- Addressing specific challenges related to harmonizing distributed/federated data for distributed/federated learning.
- Developing and sharing documentation, e.g. datasheets, that document the provenance, motivation, composition, collection process, recommended uses, and other relevant information for AI/ML re-users of the data, including feedback from AI/ML applications already using the data.
- Preparation of social determinants of health (SDOH) information for use in AI/ML applications.
- Preparing data for multi-modal multi-scale AI/ML applications.
These efforts are expected to be informed by best practices in data management and engagement with the AI/ML community.
Significant skills in data management and AI/ML are expected to be needed to identify and address gaps in AI-readiness. Thus, supplements are primarily intended to provide support for data management and AI/ML collaborators, engagement events such as hackathons, and computing and storage costs required to improve the AI-readiness of data.
The scope of each proposed project is defined by and limited to the aims of the funded project for which the supplement is being sought.
Applicants partnering with industry to test novel methods or infrastructures may be considered. The integration of causal models and causal inference in AI/ML is within scope.
A broad range of projects involving the management of data repositories, or other shared data resources are eligible regardless of the scientific area of emphasis. Both open and controlled access data, including clinical data, are within scope.
Awardees should be willing to participate in virtual meetings organized by NIH. Applications that are not appropriate and out of scope for this NOSI include:
- Projects with no engagement with the AI/ML community, or no AI/ML expertise in the proposed collaboration.
- Projects that do not intend to make data generated through NIH-supported research AI/ML-ready.
- Proposals to provide supplemental funding to an award that received supplemental funding under NOT-OD-21-094 or NOT-OD-22-067 (Administrative Supplements to Support Collaborations to Improve the AI/ML-Readiness of NIH-Supported Data).
- Proposals that do not explicitly meet all the requirements stated elsewhere in this NOSI.
- Proposals that are out of scope of the parent award.
- Proposals that do not intend to broadly share AI/ML-ready data by the end of the supplemental award period. Both open and controlled access data should be broadly shared, for example, through an NIH-supported repository, NIH-supported knowledge base, or other data sharing resource.
- Proposals focused on the development and application of AI/ML algorithms that do not intend to make data AI/ML ready.
ICO Specific Considerations
Office of Strategic Coordination
The NIH Office of Strategic Coordination (Common Fund - https://commonfund.nih.gov/) supports multiple transformative research programs that generate new technologies, methods, and data. Many of these programs produced rich public data sets containing multi-dimensional molecular and phenotypic data from humans and model organisms. Established Common Fund data sets listed below are well-poised for increased community use:
- 4D Nucleome (4DN) (https://www.4dnucleome.org/)
- Extracellular RNA Communication (exRNA) (https://exrna.org/)
- Gabriella Miller Kids First (KF) (https://kidsfirstdrc.org/)
- Genotype-Tissue Expression (GTEx) (https://www.gtexportal.org/home/)
- H3Africa (https://www.h3abionet.org/resources/h3africa-archive/)
- Glycoscience (GL) (https://glygen.org/)
- Human BioMolecular Atlas Program (HuBMAP) (https://hubmapconsortium.org/)
- Illuminating the Druggable Genome (IDG) (https://druggablegenome.net/)
- Integrated Human Microbiome Project (iHMP) (https://hmpdacc.org/ihmp/)
- Knockout Mouse Phenotyping Program (KOMP2) (http://www.mousephenotype.org/)
- Library of Integrated Network-based Cellular Signatures (LINCS) (http://lincsproject.org/)
- Metabolomics Workbench (https://www.metabolomicsworkbench.org/)
- Molecular Transducers of Physical Activity in Humans (MoTrPAC) (https://motrpac-data.org/data-access)
- Stimulating Peripheral Activity to Relieve Conditions (SPARC) (https://sparc.science/)
OSC is interested in proposals that substantially leverage at least one of the above datasets. Substantial leverage is defined as use and citation of the dataset(s) in the envisioned research products of the proposed work (manuscripts, presentations, book chapters, portals, etc.).
Inquiries
For further information, please consult our Frequently Asked Questions page.
Please direct all inquiries to:
Fenglou Mao, PhD
Office of Data Science Strategy (ODSS)
Division of Program Coordination, Planning, and Strategic Initiatives
Office of the Director
Email: AI-readiness@nih.gov
Notice of Special Interest (NOSI): Administrative Supplements to Support the Exploration of Cloud in NIH-supported ResearchNotice Number: NOT-OD-23-070
For further information, please consult our Frequently Asked Questions page.
Please direct all inquiries to:
Fenglou Mao, PhD
Office of Data Science Strategy (ODSS)
Division of Program Coordination, Planning, and Strategic Initiatives
Office of the Director
Email: AI-readiness@nih.gov
Key Dates: First Available Due Date: April 11, 2023
Purpose
This notice announces the availability of supplemental funds from the Office of Data Science Strategy (ODSS) to NIH-managed or NIH-majority-funded projects that may benefit from using the cloud. The purpose of this announcement is to explore and test potential opportunities for leveraging cloud solutions to enhance existing NIH activities. Projects already using cloud may apply to explore and test cloud capabilities not yet leveraged. This initiative is aligned with the NIH Strategic Plan for Data Science, which describes actions aimed at building a better data infrastructure and a modernized data ecosystem.
Background
The NIH Strategic Plan for Data Science seeks to enable a highly efficient and effective biomedical and behavioral research data ecosystem to meet the increasing data management and analysis needs of NIH researchers in an era when the data volume and complexity from NIH-supported research are increasing rapidly. In particular, NIH seeks to support its researchers to obtain the needed computational capabilities including the access to latest hardware and software. Large scale cloud computing platforms (see NIST SP 800-145 for a definition of Cloud Computing) provide on-demand storage and computing power as well as various software and access to specialized hardware such as GPUs, making big-data research more accessible to the individual biomedical or behavioral researcher.
The potential impact and benefits from cloud computing can be difficult to realize for multiple reasons, many of which are disproportionately felt by under-resourced institutions and communities, and this understanding is consistent with the discussion in recent NIH Virtual Workshop on Broadening Cloud Computing Usage in Biomedical Research and responses from the RFI: NIH Programs to Increase Access to Cloud Computing to Diverse Biomedical Research Institutions. Challenges include a lack of resources for testing cloud-based solutions; uncertainty about costs associated with specific jobs and use cases in a cloud environment; and uncertainty about which workflows would benefit most from the cloud.
Research Objective
The goal of this Notice of Special Interest (NOSI) is to encourage and enable researchers to explore and test opportunities to enhance their research projects by incorporating cloud capabilities. Projects already using cloud may apply to explore and test cloud capabilities not yet leveraged. Projects supported through this NOSI should result in improved understanding of how to best use cloud resources. Specifically, this opportunity is to support proof-of-concept explorations, measurements, or other tests of the suitability and feasibility of using cloud resources to enhance NIH-supported research projects. Proposed projects should result in a better understanding of which use cases are cost effective or enable significant enhancements to strategic data science goals such as facilitating new discoveries through access to modern computing and storage platforms at scale; broadening and diversifying participation in NIH research; facilitating the interoperability of NIH data, for example, by enabling multi-cloud or cross-cloud architectures; enhancing existing projects by utilizing new cloud technologies; or improving the computational and cost efficiencies of research. Applicants should consider the expertise needed for the proposed work and incorporate new partners, as needed.
Applications in response to this NOSI are strongly encouraged to include the following information:
- A description of the research project that will be explored and tested in the cloud environment.
- A description of the cloud solution, which includes but not limited to the cloud services, the cloud service providers (CSPs), and the cloud architecture; and a description of the whole solution if any non-cloud specific software and non-cloud hardware is utilized. Applications may include a diagram(s) for the cloud and/or whole solution architecture.
- How the cloud solution will be leveraged.
- An assessment of the potential impact of cloud solutions on the project if shown to be successful.
- A plan to test and evaluate the impact of cloud solutions with respect to explicitly stated aims, what metrics will be used.
- Personnel and their expertise to carry out the proposed work, including collaborators with relevant cloud skills if needed.
- Timeline and milestones to carry out the proposed work in this supplement funding period.
These supplements may be used to support a variety of activities including, but not limited to, the following:
- Assessing the feasibility and cost-effectiveness of establishing a cloud instance of an existing on-premises project; or of running on-prem jobs or workflows in the cloud.
- Assessing the feasibility of hybrid solutions that make use of both on-prem and cloud resources.
- Testing the feasibility of porting existing research projects, or parts of projects, to new hardware, using cloud services.
- Exploring the benefits of shared computational environments or scaling of environments using cloud.
- Exploring potential efficiencies or computational benefits of running workflows, or parts of workflows, in the cloud.
- Exploring options that avoid or reduce data download/egress costs by carrying out data analysis in the cloud.
Awardees should be willing to participate in virtual meetings organized by NIH.
Applications that are not appropriate and out of scope for this NOSI include:
- Projects with no potential benefit from cloud resources.
- Projects that do not include a way to measure or assess the suitability of cloud resources for particular aims. Projects that are unlikely to result in improved understanding of how to best use cloud resources.
- Projects focused on hardening existing research software. (Please see the FAQ for other funding opportunities to support this type of project.)
- Projects that need additional documents such as IT security certificate and/or data regulatory approval and/or other policy approval in order to conduct the proposed work after the application submission.
- Proposals that require more than one year to conduct the proposed scope of work.
- Proposals that do not explicitly meet all the requirements stated elsewhere in this NOSI.
- Proposals that are out of scope of the parent award.
ICO Specific Considerations
Office of Strategic Coordination
The NIH Office of Strategic Coordination (Common Fund - https://commonfund.nih.gov/) supports multiple transformative research programs that generate new technologies, methods, and data. Many of these programs produced rich public data sets containing multi-dimensional molecular and phenotypic data from humans and model organisms. Established Common Fund data sets listed below are well-poised for increased community use:
- 4D Nucleome (4DN) (https://www.4dnucleome.org/)
- Extracellular RNA Communication (exRNA) (https://exrna.org/)
- Gabriella Miller Kids First (KF) (https://kidsfirstdrc.org/)
- Genotype-Tissue Expression (GTEx) (https://www.gtexportal.org/home/)
- H3Africa (https://www.h3abionet.org/resources/h3africa-archive/)
- Glycoscience (GL) (https://glygen.org/)
- Human BioMolecular Atlas Program (HuBMAP) (https://hubmapconsortium.org/)
- Illuminating the Druggable Genome (IDG) (https://druggablegenome.net/)
- Integrated Human Microbiome Project (iHMP) (https://hmpdacc.org/ihmp/)
- Knockout Mouse Phenotyping Program (KOMP2) (http://www.mousephenotype.org/)
- Library of Integrated Network-based Cellular Signatures (LINCS) (http://lincsproject.org/)
- Metabolomics Workbench (https://www.metabolomicsworkbench.org/)
- Molecular Transducers of Physical Activity in Humans (MoTrPAC) (https://motrpac-data.org/data-access)
- Stimulating Peripheral Activity to Relieve Conditions (SPARC) (https://sparc.science/)
OSC is interested in proposals that substantially leverage at least one of the above datasets. Substantial leverage is defined as use and citation of the dataset(s) in the envisioned research products of the proposed work (manuscripts, presentations, book chapters, portals, etc.).
Inquiries
This notice announces the availability of supplemental funds from the Office of Data Science Strategy (ODSS) to NIH-managed or NIH-majority-funded projects that may benefit from using the cloud. The purpose of this announcement is to explore and test potential opportunities for leveraging cloud solutions to enhance existing NIH activities. Projects already using cloud may apply to explore and test cloud capabilities not yet leveraged. This initiative is aligned with the NIH Strategic Plan for Data Science, which describes actions aimed at building a better data infrastructure and a modernized data ecosystem.
Background
The NIH Strategic Plan for Data Science seeks to enable a highly efficient and effective biomedical and behavioral research data ecosystem to meet the increasing data management and analysis needs of NIH researchers in an era when the data volume and complexity from NIH-supported research are increasing rapidly. In particular, NIH seeks to support its researchers to obtain the needed computational capabilities including the access to latest hardware and software. Large scale cloud computing platforms (see NIST SP 800-145 for a definition of Cloud Computing) provide on-demand storage and computing power as well as various software and access to specialized hardware such as GPUs, making big-data research more accessible to the individual biomedical or behavioral researcher.
The potential impact and benefits from cloud computing can be difficult to realize for multiple reasons, many of which are disproportionately felt by under-resourced institutions and communities, and this understanding is consistent with the discussion in recent NIH Virtual Workshop on Broadening Cloud Computing Usage in Biomedical Research and responses from the RFI: NIH Programs to Increase Access to Cloud Computing to Diverse Biomedical Research Institutions. Challenges include a lack of resources for testing cloud-based solutions; uncertainty about costs associated with specific jobs and use cases in a cloud environment; and uncertainty about which workflows would benefit most from the cloud.
Research Objective
The goal of this Notice of Special Interest (NOSI) is to encourage and enable researchers to explore and test opportunities to enhance their research projects by incorporating cloud capabilities. Projects already using cloud may apply to explore and test cloud capabilities not yet leveraged. Projects supported through this NOSI should result in improved understanding of how to best use cloud resources. Specifically, this opportunity is to support proof-of-concept explorations, measurements, or other tests of the suitability and feasibility of using cloud resources to enhance NIH-supported research projects. Proposed projects should result in a better understanding of which use cases are cost effective or enable significant enhancements to strategic data science goals such as facilitating new discoveries through access to modern computing and storage platforms at scale; broadening and diversifying participation in NIH research; facilitating the interoperability of NIH data, for example, by enabling multi-cloud or cross-cloud architectures; enhancing existing projects by utilizing new cloud technologies; or improving the computational and cost efficiencies of research. Applicants should consider the expertise needed for the proposed work and incorporate new partners, as needed.
Applications in response to this NOSI are strongly encouraged to include the following information:
- A description of the research project that will be explored and tested in the cloud environment.
- A description of the cloud solution, which includes but not limited to the cloud services, the cloud service providers (CSPs), and the cloud architecture; and a description of the whole solution if any non-cloud specific software and non-cloud hardware is utilized. Applications may include a diagram(s) for the cloud and/or whole solution architecture.
- How the cloud solution will be leveraged.
- An assessment of the potential impact of cloud solutions on the project if shown to be successful.
- A plan to test and evaluate the impact of cloud solutions with respect to explicitly stated aims, what metrics will be used.
- Personnel and their expertise to carry out the proposed work, including collaborators with relevant cloud skills if needed.
- Timeline and milestones to carry out the proposed work in this supplement funding period.
These supplements may be used to support a variety of activities including, but not limited to, the following:
- Assessing the feasibility and cost-effectiveness of establishing a cloud instance of an existing on-premises project; or of running on-prem jobs or workflows in the cloud.
- Assessing the feasibility of hybrid solutions that make use of both on-prem and cloud resources.
- Testing the feasibility of porting existing research projects, or parts of projects, to new hardware, using cloud services.
- Exploring the benefits of shared computational environments or scaling of environments using cloud.
- Exploring potential efficiencies or computational benefits of running workflows, or parts of workflows, in the cloud.
- Exploring options that avoid or reduce data download/egress costs by carrying out data analysis in the cloud.
Awardees should be willing to participate in virtual meetings organized by NIH.
Applications that are not appropriate and out of scope for this NOSI include:
- Projects with no potential benefit from cloud resources.
- Projects that do not include a way to measure or assess the suitability of cloud resources for particular aims. Projects that are unlikely to result in improved understanding of how to best use cloud resources.
- Projects focused on hardening existing research software. (Please see the FAQ for other funding opportunities to support this type of project.)
- Projects that need additional documents such as IT security certificate and/or data regulatory approval and/or other policy approval in order to conduct the proposed work after the application submission.
- Proposals that require more than one year to conduct the proposed scope of work.
- Proposals that do not explicitly meet all the requirements stated elsewhere in this NOSI.
- Proposals that are out of scope of the parent award.
ICO Specific Considerations
Office of Strategic Coordination
The NIH Office of Strategic Coordination (Common Fund - https://commonfund.nih.gov/) supports multiple transformative research programs that generate new technologies, methods, and data. Many of these programs produced rich public data sets containing multi-dimensional molecular and phenotypic data from humans and model organisms. Established Common Fund data sets listed below are well-poised for increased community use:
- 4D Nucleome (4DN) (https://www.4dnucleome.org/)
- Extracellular RNA Communication (exRNA) (https://exrna.org/)
- Gabriella Miller Kids First (KF) (https://kidsfirstdrc.org/)
- Genotype-Tissue Expression (GTEx) (https://www.gtexportal.org/home/)
- H3Africa (https://www.h3abionet.org/resources/h3africa-archive/)
- Glycoscience (GL) (https://glygen.org/)
- Human BioMolecular Atlas Program (HuBMAP) (https://hubmapconsortium.org/)
- Illuminating the Druggable Genome (IDG) (https://druggablegenome.net/)
- Integrated Human Microbiome Project (iHMP) (https://hmpdacc.org/ihmp/)
- Knockout Mouse Phenotyping Program (KOMP2) (http://www.mousephenotype.org/)
- Library of Integrated Network-based Cellular Signatures (LINCS) (http://lincsproject.org/)
- Metabolomics Workbench (https://www.metabolomicsworkbench.org/)
- Molecular Transducers of Physical Activity in Humans (MoTrPAC) (https://motrpac-data.org/data-access)
- Stimulating Peripheral Activity to Relieve Conditions (SPARC) (https://sparc.science/)
OSC is interested in proposals that substantially leverage at least one of the above datasets. Substantial leverage is defined as use and citation of the dataset(s) in the envisioned research products of the proposed work (manuscripts, presentations, book chapters, portals, etc.).
Inquiries
Please direct all inquiries to:
Scientific/Research Contact(s)
Fenglou Mao
Office of Data Science Strategy (ODSS)
Division of Program Coordination, Planning, and Strategic Initiatives
Office of the Director
Telephone: 301-451-9389
cloudsupport@nih.gov"
Source and more information:
(1) NOT-OD-23-082 https://grants.nih.gov/grants/guide/notice-files/not-od-23-082.html
(2) NOT-OD-23-070 https://grants.nih.gov/grants/guide/notice-files/not-od-23-070.html
Please direct all inquiries to:
Scientific/Research Contact(s)
Fenglou Mao
Office of Data Science Strategy (ODSS)
Division of Program Coordination, Planning, and Strategic Initiatives
Office of the Director
Telephone: 301-451-9389
cloudsupport@nih.gov"
Source and more information:
(1) NOT-OD-23-082 https://grants.nih.gov/grants/guide/notice-files/not-od-23-082.html
(2) NOT-OD-23-070 https://grants.nih.gov/grants/guide/notice-files/not-od-23-070.html
Search
Recent dkNET News
dkNET News Tags
About
The NIDDK Information Network (dkNET) serves the needs of basic and clinical investigators by providing seamless access to large pools of data and research resources relevant to the mission of The National Institute of Diabetes Digestive and Kidney Diseases (NIDDK). dkNET is hosted at University of California San Diego, and is supported by NIH NIDDK grant 2U24DK097771-09.
Contact Us
Email: info_at_ dknet.org
