Building Sustainable Software Tools for Open Science

Building Sustainable Software Tools for Open Science is sponsored by National Institutes of Health (NIH). This program supports the sustainability and impact of research software tools by enabling the use of best practices and design principles in software development and by leveraging continuing advances in computing.

Sustainable Software Tools For Open Science | Data Science at NIH Sustainable Software Tools For Open Science Sustainable Software Tools For Open Science Software is an integral component of health research due in part to the speed and growth of new technology innovations in the software and computing fields.

An NIH collaboration across 19 institutes, centers, and offices supports the development and enhancement of sustainable software tools for open science by fostering new collaborations between biomedical and clinical scientists and research software engineers.

Building Sustainable Software Tools for Open Science (RFA-OD-24-010) provides a flexible mechanism to support the use of best practices for scientific software development and to promote community engagement for open science.

NIH Research Software Engineer (RSE) Award (RFA-OD-24-011) supports the ability of exceptional Research Software Engineers (RSEs) to contribute their skills in the development and dissemination of NIH-funded research software, tools, and algorithms Administrative Supplements to Support Enhancement of Software Tools for Open Science These supplements have been utilized to: support robustness, sustainability, and scalability of existing biomedical research software tools and workflows.

invest in research software tools with recognized value in a scientific community to enhance their impact by leveraging best practices in software development and advances in cloud computing. support collaborations between researchers and research software engineers to enhance the design, implementation, and “cloud-readiness” of research software.

For more information on software sustainability and researcher engagement, read Dr. Gregurick's blog and conversation with Dr. Daniel S. Katz. The NIH Council of Councils held a meeting January 19 and 20, 2023, where working group leadership presented a concept clearance: “Building Sustainable Foundations for Open Software and Tools in Biomedical and Behavioral Science.

” The concept was approved by the Council of Councils, and you can see the presentation here . 2023: NOT-OD-23-073 expired on March 7, 2023, with 27 awardees. 2022: NOT-OD-22-068 expired on April 13, 2022, with 29 awardees.

2021: NOT-OD-21-091 expired on May 16, 2021, with 41 awardees. 2020: NOT-OD-20-073 expired May 16, 2020, with 28 awardees. Awardee projects and their descriptions are available below.

Click to view FY2023 Award Recipients FY2023 Award Recipients Principal Investigator Institution Project Title NIH IC Christos Davatzikos University of Pennsylvania Machine Learning and Large-scale Imaging analytics for dimensional representations of brain trajectories in aging and preclinical Alzheimers Disease: The brain aging chart and the iSTAGING consortium NIA Leah Hanson Health Partners Institutes A Technology-Driven Intervention to Improve Early Detection and Management of Cognitive Impairment NIA Michelle Bell Yale University Enhancing SPACE, an innovative python package to account for spatial confounding used to estimate climate-sensitive events among older Medicare NIA Mark Vanderlaan University of California Berkeley Efficient Refactoring of Longitudinal Targeted Machine Learning NIAID Joseph Crisco Rhode Island Hospital Multi-modal Tracking of In Vivo Skeletal Structures and Implants NIAMS Cornelia Ulrich University of Utah Enhancing Loon: Increasing Robustness and Generalizing Input Formats for a Visualization Tool for Large-Scale Microscopy Data NCI Oscar Jan Patrick Schuemann Massachusetts General Hospital TOPAS - nBio, a Monte Carlo tool for radiation biology research NCI John Pearson Duke University Real-time mapping and adaptive testing for neural population hypotheses NIDA Cecilia Yeung Fred Hutchinson Cancer Institute Rapid Acute Leukemia Genomic Profiling with CRISPR enrichment and Real-time long-read sequencing NCI Jason Flannick Broad Institute INC The next iteration of the AMP-T2D Knowledge Portal NIDDK Scott Delp Stanford University Mobilize Center: Models for Mobile Sensing and Precision Rehabilitation NIBIB Muriah Wheelock Washington University Implementing best practices in software design for Network Level Analysis NIBIB James Coughlan Smith-Kettle Eye Research Institutes Point and Listen: Augmented Reality Interfaces for the Visually Impaired NEI Leslie Loew University of Connecticut School of Medicine Mechanistic Modeling of Cellular Systems NIGMS Sara Goodwin Science Communication Lab Online Courses for Navigating Research Mentoring Relationships NIGMS Sara Flood University of Minnesota Integrated Current Population Survey Data for Population Dynamics and Health Research NICHD David Beier Seattle Children’s Hospital Open-source Software Development Supplement for 3D quantitative analysis of mouse models of structural birth defects through computational anatomy NICHD Rafael Irizarry Dana- Farber Cancer Enhancing Community Contributions to Bioconductor With Build System Containerization and a GPU for Testing NHGRI Heidi Rehm Broad Institute Inc Enhancing gnomAD Sustainability: Implementing Site Reliability Engineering Principles for Genomic Data Infrastructure NHGRI Lincoln Stein Ontario Institute for Cancer Research Introducing CI/CD Technologies to Optimize Software Development in Reactome NHGRI Yong Chen University of Pennsylvania PheBC: bias correction methods for EHR derived phenotype NLM Qingzhao Yu LSU Health Sciences Trends of disparities in breast cancer progression and health care considering multilevel risk factors NIMHD Sandra Mccoy University of California Berkeley Strengthening the continuity of HIV care in Tanzania with economic support NIMH Mary Disis University of Washington Institute of Translational Health Sciences NCATS Shuzhao Li Jackson Laboratory Enhancement of asari software for metabolomics data processing NIAID Ramy Arnaout Beth Isreal Deaconess Medical Center Cloud Six: Immunology-Inspired Software for Measuring and Modeling Large Datasets NIAID Ankit Parekh Icahn School of Medicine at Mount Sinai Reimagining the diagnosis of obstructive sleep apnea beyond the apnea-hypopnea index NHLBI Click to view FY2022 Award Recipients FY2022 Award Recipients Principal Investigator Institution Project Title NIH IC Cristian T Badea Duke University A multi-channel reconstruction toolkit for computed tomography NIA Helene D Benveniste Yale University Robust workflow software for MRI tracking of glymphatic-lymphatic coupling NCATS Michelle Birkett Northwestern University Enabling cloud deployment of a network data capture tool to improve Partner Services NIDA Helen M Blau Stanford University Improvement and standardization of a bioinformatic software suite for multiplexed imaging NIA Emre H Brookes University of Montana Development of an UltraScan Meta-Scheduler for HPC Job Submission NIGMS Rachel Clipp Kitware Optimizing the Pulse Physiology Engine to Meet Medical Simulation Community Needs NIBIB Lee Cooper Northwestern University Guiding humans to create better labeled datasets for machine learning in biomedical research NLM Salvador Dura-Bernal The State University of New York Development of robust cloud-based software for co-simulation of biophysical circuit and whole-brain network models NIBIB Adam R Ferguson University of California- San Francisco Enhancing the Pan-Neurotrauma Data Commons (PANORAUMA) to a complete open data science tool by FAIR APIs NINDS Johann Eli Gudjonsson University of Michegan Immunogenomics and Systems Biology Core NIAMS Melissa A Haendel University of Colorado Improvements to the LinkML framework to support the Phenomics First open science resource NHGRI Tomas Helikar University of Nebraska- Lincoln Software for collaborative construction, simulation, and analysis of mechanistic computational models of biological systems NIGMS John David Herrington Children's Hospital of Philidelphia Enhancing the Cloud-Readiness of Perceptual Computing Through Data Standardization Software NIMH Brian P Jackson Dartmouth University Laying the Groundwork for Web-based Elemental Imaging Software: The MicroAnalysis Toolkit NIGMS Xia Jing Clemson University Open, interoperable, and configurable clinical decision support modules for OpenMRS, OpenEMR, and beyond NIGMS David Nelson Kennedy University of Massachesetts Enhancing neuroimaging reusability through semantic enrichment NIBIB Yueh Z Lee University of North Carolina- Chapel Hill Arterial input function Independent Measures of Perfusion with Physics Driven Models NINDS X Lucas Lu University of Delaware Prevention of PTOA via regulation of the cytomechanics of chondrocytes NIAMS Gabor T Marth University of Utah Enhancing clinical diagnostic analysis with a robust de novo mutation detection tool NHGRI Michael Churton Neale Virginia Commonwealth University Accelerating Development of OpenMx for Interoperability and Cloud Use NIDA Bence P Olveczky Harvard University An easy-to-use software for 3D behavioral tracking from multi-view cameras NIGMS Liam M Paninski Columbia University Modularization and integration of the International Brain Laboratory spike-sorting pipeline into SpikeInterface NINDS Kathleen Powis Harvard University Immune correlates of tuberculosis and non-tuberculosis infectious morbidity in Southern African HIV-exposed, uninfected infants.

NIAID Emilie Roncali University of California- Davis Improved optical Monte Carlo simulation through standardization, robustness, and training NIBIB Louis J Soslowsky University of Pennsylvania An open-source software for microCT-based longitudinal tracking of musculoskeletal tissues NIAMS Lena H Ting Emory University Biophysical muscle modeling software for enhancing open science NICHD Junichi Tokuda Harvard University Open Software Platform for Data-Driven Image-Guided Robotic Interventions NIBIB Richard W Tsien New York University BrainSTEM - An e-age Experimental Neuroscience Lab Notebook NINDS Linda J Van Eldik University of Kentucky Portable and modular UDS Data Collection software to increase collaboration and engagement of Alzheimer’s Disease Research Center research software engineers NIA Click to view FY2021 Award Recipients FY2021 Award Recipients Principal Investigator Institution Project Title NIH IC Olusola Ajilore University of Illinois, Chicago Unobtrusive Monitoring of Affective Symptoms and Cognition using Keyboard Dynamics Refactor the BiAffect codebase to enable collaborative open science.

The parent grant “Unobtrusive Monitoring of Affective Symptoms and Cognition using Keyboard Dynamics” or UnMASCK, uses a novel digital technology (“BiAffect”) for the study of cognitive dysfunction in the context of mood disorders. BiAffect leverages smartphone keyboard dynamics metadata to unobtrusively and passively monitor cognitive function.

The core technology of BiAffect is a custom-built smartphone virtual keyboard that replaces the native default keyboard, allowing the collection of real-time data of potential clinical relevance while individuals interact with their device as usual within their natural environment.

Working with Sage Bionetworks, the supplement will enable us to refactor the BiAffect codebase to enable more robust multi-developer contribution and version control. We also plan to create standardized data processing pipelines to support collaborations with researchers who may have varying levels of capacity for data science and engineering.

The results will be an innovative, meaningful contribution to collaborative open science, modernizing the biomedical research data ecosystem and making data findable, accessible, interoperable, and reusable (FAIR). With Sage Bionetworks, refactor BiAffect codebase for collaborative open science, so other developers can contribute code and collaborators have pipelines to contribute data.

NIMH Pamela Bjorkman California Institute of Technology (PI) Developing Immunogens to Elicit Broadly Neutralizing anti-HIV-1 Antibodies Enhancing antibody and variant resources by adopting public data streams, standard input formats, and open-source integration The goal of the parent project is to advance our germline-targeting approach to HIV-1 vaccine design by cycles of immunogen design and testing.

Similar strategies guided by antibody discovery are showing exciting promise with a range of other difficult pathogens such as influenza, malaria, hepatitis C, dengue, and Zika virus.

As part of our project's integrated approach to immunogen design, selection, and evaluation, the Bjorkman lab has developed the software package HIV Antibody Database, which was designed to enable frictionless access to, comparisons of, and analyses of broadly neutralizing anti-HIV antibody sequences, structures, and neutralization data.

In recent developments a sister project addressing COVID called Variant Database can quickly search SARS-CoV-2 genome datasets with millions of sequences. This administrative supplement will build on these codebases to improve their robustness and sustainability by integrating publicly available data streams, adopting standard formats for input and output of structural data, and optimizing performance.

We will enhance HIV Antibody Database to automatically download data from the Los Alamos National Laboratory CATNAP database. Antibody Database will also utilize PDBx/mmCIF, a modern, extensible structure file format, and use the RCSB REST-based API for online structure searches. We will ensure that high performance graphics frameworks (e.g., Metal) are fully utilized.

Antibody Database will also be extended for use on other viruses, included access to SARS-CoV-2 data through Variant Database. The build process for Variant Database, an open source tool, will be improved to use a modern package manager. To facilitate wider use, a python API for interacting with Variant Database will be developed.

Refactor antibody and variant and resources.

NIAID John Buse University of North Carolina Chapel Hill CAMP FHIR: Lightweight, Open-Source FHIR Conversion Software to Support EHR Data Harmonization and Research Practical, lightweight data standardization: improving CAMP FHIR software to map clinical common data models to FHIR Clinical common data models (CDMs) such as PCORnet, OMOP, and i2b2, aim to ease data harmonization and interoperability thereby fostering collaboration.

However, when institutions support different CDMs, cross-institutional data sharing can be impeded. While HL7 Fast Healthcare Interoperability Resources (FHIR) is an increasingly key standard for interoperability and data exchange that aims to address these issues, mapping an individual CDM to FHIR is resource intensive.

The parent grant developed the CAMP FHIR software to advance the clinical and translational science mission of the NC TraCS center both in North Carolina and for national CTSA goals. By offering a cloud-based, open-source application that can transform multiple types of input data to FHIR, CAMP FHIR makes it easier and more efficient for organizations with varying data environments to participate in new clinical research partnerships.

The aims of this administrative supplement are to (1) make CAMP FHIR more robust by implementing additional software development best practices, (2) improve interoperability by adding support for bidirectional data transformation and new high-value FHIR resources, and (3) improve usability and accessibility by developing a graphical user interface, adding support for new input types, and ensuring cloud-readiness.

Map to FHIR from different clinical data models.

NCATS Vince Calhoun Georgia State University Multivariate methods for identifying multitask/multimodal brain imaging biomarkers GIFTwrap: a containerized and FAIR cloud-based implementation of the widely used GIFT toolbox This supplement will usher in the next phase of the widely used GIFT software (or Group ICA of fMRI toolbox) which provides data-driven methods for capturing intrinsic functional and structural brain networks.

GIFT has been continuously updated and extended over the past twenty years and has a large amount of functionality that is not available in other tools including 20 different Independent Component Analysis (ICA) algorithms. However, GIFT is primarily based on a standalone software development and analysis model.

We will extend GIFT to interface with and leverage modern software tools, to facilitate comparability across ICA analysis, and to fully engage in the community development movement. The GIFTwrap implementation will be a containerized python compliant tool that will also be accessible via a cloud interface.

The work will open up access to a wide suite of approaches including dozens of different ICA approaches, functional network connectivity, independent vector analysis (a generalization of ICA for multiple datasets), dynamic functional network connectivity, spatial dynamics, connectome visualization, and much more.

We have three main goals: 1) Architecture improvements to facilitate FAIR principles and modernize the tools, 2) to deploy the GIFT tools, especially our robust neuromark and auto-labelling approaches to facilitate comparability across analyses, into a brain imaging data structure app (BIDSapp) for easy use and integration into modern analysis frameworks, and deploy in cloud-based analytic platforms (e.g. brainforge), and 3) to provide a cloud interface for individuals to run fully automated ICA analysis which requires a simple upload of data to the tools.

NIBIB Naomi Caselli Boston University Effects of input quality on ASL vocabulary acquisition in deaf children Use best practices in software development to make the sign language assessments developed under the parent grant openly available, and to make a robust platform for collecting and tagging these data as a step towards large-scale machine-readable sign language datasets.

The majority of deaf children experience a period of limited exposure to language (spoken or signed), which has cascading effects on many aspects of cognition. The parent project aims to understand how children build a vocabulary in sign language, and whether and how this differs for deaf children who have limited exposure to a sign language.

This includes developing American Sign Language (ASL) proficiency tests, and using these tests to examine how children learn ASL. The supplement will use best practices in software development to make these ASL proficiency tests widely available in the community.

We will do so using a code base our lab has already developed-- a single-use application for collecting sign language video data--and convert it to a platform that can be used to collect and tag video data of all kinds. Not only will this platform be used to make the ASL tests accessible, the platform will be made publicly available to other sign language researchers.

This project will remove significant barriers in the field of sign language research, making it much more efficient to develop large-scale machine-readable sign language datasets. Additionally, making the ASL tests widely accessible in the community will help clinicians and researchers track language acquisition among deaf children.

NIDCD Connie Celum University of Washington Viroverse: Bedside through Bioinformatics database retrieval system Transitioning the Viroverse specimen repository database, lab notebook and retrieval and visualization system to a cloud ready application based on the Python Django framework The parent grant supports the Retrovirology and Molecular Data Sciences (RMDS) Core, a component of the University of Washington / Fred Hutchinson Center for AIDS Research.

The largest emphasis of this core is the development and dissemination of new software tools, databases and custom applications for bench support, data management, and visualization across a wide spectrum of bench science-based activities conducted locally, nationally and internationally.

The centerpiece of this effort is the laboratory information management system, Viroverse, as it provides a highly flexible specimen repository database, lab notebook and retrieval and visualization system for data acquired from the bedside through the laboratory bench and bioinformatic analysis.

The Supplement award will enable enhancement of the Viroverse feature set as well as produce a modern, sustainable and cloud ready database platform.

We will migrate Viroverse from a Perl/Catalyst to a Python Django framework, providing a code base that is easy for programmers to contribute to and allow wide community adoption, intense security monitoring, reporting and patching, and that leverages the availability of many publicly available libraries and modules, as well as a broad base of experienced developers.

Importantly, this will allow for multiple authentication backends and supports industry standard encryption and single sign on (SSO) technology. The source code for Viroverse will continue to be made freely available, and we will generate and release a containerized (Docker) version.

Finally, we plan to add significant unit testing and a continuous integration pipeline to ensure overall application stability while maintaining gatekeeper review and merge authority over the codebase as it is developed further.

NIAID Melissa Cline University of California Santa Cruz Eliminating variants of uncertain significance in BRCA1, BRCA2 and beyond Resources for sharing knowledge on the genetic risk of disease An estimated 10% of all cancers arise through inherited genetic risk, through harmful genetic variants in the patient’s germline DNA.

One particularly vivid example is Hereditary Breast and Ovarian Cancer (HBOC) Syndrome arising through harmful variation in the BRCA1 and BRCA2 genes. The lifetime risk of breast or ovarian cancer ranges between 42% and 70% for women who inherit a harmful BRCA variant, versus the average risk of 11% in the U.S. population.

These cancers can often be prevented through detection and clinical management of the genetic risk, provided the risk can be recognized. Most BRCA variants currently have no known clinical impact. The BRCA Exchange was launched in 2016 with the goal of developing new approaches to share data on BRCA variants to catalyze variant interpretation, with BRCA1/2 in HBOC serving as exemplars for additional genes and heritable disorders.

Today, roughly 3,000 users per month visit the site for BRCA variant data aggregated from public variation repositories and additional annotation data curated by the ENIGMA Consortium, the internationally-recognized organization for the expert interpretation of BRCA variants. This has inspired other research consortia to reach out to launch variant data exchanges for the genes and heritable disorders under their purview.

With this supplement, we propose to refactor the BRCA Exchange software to improve its modularity, reusability and cloud readiness. By refactoring the data integration pipeline and database management, we will add flexibility to the data model to allow external consortia to integrate the data that is most informative to their variants.

By integrating the pipeline with cloud APIs, we will enable external consortia to run the pipeline on the NIH secure cloud platforms, alleviating the need for an internal server. Finally we will produce a simplified front end, which collectively will allow external consortia to build and run their own variant data exchanges.

We anticipate that these developments will catalyze research in pediatric and diffuse gastric cancers, as well as contributing valuable new functionality to the parent grant.

NCI Stephania Cormier Louisiana State Baton Rouge LSU Superfund Research Center - Environmentally Persistent Free Radicals “Cloud-based multilevel mediation analysis for investigating adverse effects of particulate matter from hazardous waste remediation and individual risk factors on respiratory health”.

NIEHS Adam Eggebrecht Washington University Illuminating development of infant and toddler brain function with DOT Extending our NeuroDOT tools to develop robust and efficient software for photometric data-anatomy registration and data fidelity assurance for fNIRS and HD-DOT data, and our NLA software tools for general connectome-wide statistical analyses.

The long-term goal of the Parent BRAINS R01 (R01MH122751, ‘Illuminating development of infant and toddler brain function with DOT’) is to advance high-density diffuse optical tomography (HD-DOT) methods for evaluating brain-behavior relationships in infants and toddlers at risk for developing autism spectrum disorder (ASD) while they are awake and engaged within a naturalistic setting.

Funding from this Administrative Supplement NOT-OD-21-091 will promote modernization of our growing components of the data-resources ecosystem by providing crucial support to (1) refactor our Matlab-based NeuroDOT/NLA toolboxes in Python with enhanced development tools and standardization, (2) establish cloud readiness of NeuroDOT/NLA, and (3) expand documentation and support our community of users and developers with tutorials, workshops, and hackathons.

Successful completion of these Aims will both complement and extend the impact of the Parent R01, not only in terms of uncovering longitudinal patterns of covariation of brain function and behavior that may provide novel predictive diagnostic value, but also in terms of harmonizing methods and strategies for high fidelity optical functional brain mapping that will be crucial to ongoing investigations of brain function during engaged behavior in infants and toddlers.

NIMH Evelina Fedorenko MIT The neural architecture of pragmatic processing Establishing a common language in human fMRI: linking the traditional group-averaging fMRI approach and functional localization in individual brains through probabilistic functional atlases for four high-level cognitive networks.

The parent project examines the contributions of three communication-relevant brain networks—the language network, the social cognition network, and the executive-control network—to pragmatic reasoning, the ability to go beyond the literal meaning to understand the intended meaning. The projects adopts the ‘functional localization’ fMRI approach, where networks of interest are defined functionally in each individual brain.

Although this approach is superior to the traditional group-averaging fMRI approach, it is not always feasible, and it is unclear how to relate findings from studies that rely on these disparate approaches.

We will develop and make publicly available probabilistic functional atlases for four brain networks critical for high-level cognition based on the data from extensively validated functional ‘localizer’ paradigms collected under the parent award and in prior work.

Such atlases, based on overlaying large numbers of activation maps, capture not only the areas of most consistent responses but also the inter-individual variability in the locations of functional areas. These probabilistic representations of the network landscapes can therefore help estimate the probability that any given location in the common brain space belongs to a particular functional network.

In this way, probabilistic atlases can provide a critical bridge between two disparate approaches in human fMRI—traditional group-averaging and functional localization in individual brains, as well as link fMRI work with lesion-behavior patient investigations.

The ability to more straightforwardly compare findings across studies is bound to lead to more robust, replicable, and meaningful science in our understanding of human communication and related abilities.

NIDCD Alexander Fleischmann Brown University Odor Memory Traces in the Mouse Olfactory Cortex Open-source software tools to enhance the processing, reproducibility and shareability of integrated multimodal physiology and behavioral data A major challenge for neuroscience research is the complexity and size of multimodal data sets, which often include a combination of calcium imaging, electrophysiology, behavioral tracking through video and other sensors, and electrical or optogenetic stimulation.

Acquisition and analysis of such data typically rely on a mix of vendor built and custom workflows, with diverse file formats and software applications required at various stages of the analysis pipeline. We will improve and extend our calcium imaging and behavior analysis pipeline, built around the Neurodata Without Borders (NWB) standard, into a general purpose, cloud-enabled tool for managing and analyzing systems neuroscience data.

We will generalize the pipeline from our current data format to create a well-documented, user friendly application programming interface (API). We will expand integration checks to include Microsoft Windows and macOS and disseminate the outcome as a Python package through standard repositories like PyPI and Conda-Forge.

We will adapt the pipeline to enable automatic saving to the cloud, generalize the pipeline other open-source software tools, and create a Graphical User Interface to complement the current command line interface.

Together, these enhancements to an already in-use data analysis pipeline will provide a streamlined framework for use by other labs and enhance reproducibility and shareability of integrated neural activity and behavioral data.

NIDCD Julius Fridriksson University of South Carolina at Columbia Center for the Study of Aphasia Recovery (C-STAR) Advanced neuroimaging visualization for cloud computing ecosystems The Center for the Study of Aphasia Recovery (C-STAR, P50-DC014664) explores recovery from language impairments following stroke, bringing together a diverse team of specialists from communication sciences, neurology, psychology, statistics and neuroimaging.

This project acquires a broad range of magnetic resonance imaging (MRI) modalities (structural, diffusion, arterial spin labelling, functional, resting state) from stroke survivors to understand the brain areas critical for language, improve prognosis, and identify the optimal treatment or compensation strategy for each individual.

Our team has developed novel desktop based tools (MRIcroGL and Surfice) to visualize these different modalities. The aim of this supplement is to adapt our methods to a web-based tool (NiiVue) that can work on any device (computer, tablet, phone).

NIDCD Andrew Gelman Columbia Improving representativeness in non-probability surveys and causal inference with regularized regression and post-stratification Improving the flow of the Bayesian workflow by enhancing the Stan probabilistic programming platform The parent grant will develop general, flexible, and reliable Bayesian methods for survey sampling adjustment that can be used for a wide range of problems in public health research.

This requires extensive use of the Stan probabilistic programming platform, both to carry out the research itself and to put the resulting methodology into practice. In the supplement we will improve the core Stan platform in three ways: implementing common input and output formats; speeding up the core Stan inference algorithms through more sophisticated parallelization; and improving memory efficiency.

This will improve the overall speed and scalability of inference, allowing for Bayesian methods to be used with increasingly complex models, and in turn allowing more stable and effective inference from non-random samples, a problem that is increasingly relevant when learning about populations in public health.

NIA Guy Genin Washington University Multiscale models of fibrous interface mechanics Strain Analysis Software for Open Science This supplement addresses a critical need in biomechanics and mechanobiology, and eventually in clinical practice: seamlessly analyze large imaging datasets to determine how tissues deform under mechanical loading.

The parent grant (R01AR077793) uses a comprehensive modeling and experimental approach to study how fibrous interfaces transfer load between dissimilar. The supplement will implement previously developed strain tracking algorithms into user friendly software that is cloud-ready and broadly available.

This software will enable quantitative analysis of deformation in biomedical images from a wide range of modalities, including microscopy, ultrasound, and optical. Notably, commercially available software packages for this employ regularization techniques to ensure smooth solutions, and are therefore often unable to accurately identify local tissue deformations or predict soft tissue tears.

We will enable researchers to study strain fields associated with injury patterns and rehabilitation protocols by executing two aims: (1) We will develop open-source software as a plugin to ImageJ for the strain-tracking algorithm in two dimensions (2D), stereo view (2. 5D), and three dimensions (3D).

Best practices will be used for open-source software development, and modules will be created to facilitate the development of a user community. (2) We will develop a working, static code implementation on GitHub that can be run on Amazon AWS using data in the cloud.

This will help overcome the primary obstacle for widespread adoption of strain mapping techniques in musculoskeletal research, namely that the 3D datasets require substantial computational resources to analyze. The work will enable collaboration between the PIs of the parent grant and an expert on open-source software development for clinical and research translation, and enhance the impact of a tool with strong potential.

NIAMS Thomas Gill Yale Claude D.

Pepper Older Americans Independence Center at Yale Yale Study Support Suite (YES3): Dashboard and Web Portal Software Supporting Research Workflow through integrated, customizable REDCap External Modules This data science project will refactor and refine the Yale Study Support Suite (YES3), a suite of REDCap workflow and data management programmatic extensions (external modules) designed to improve the efficiency and quality of research field operations.

The Yale Study Support Suite (YES3): Dashboard and Web Portal Software Supporting Research Workflow through Integrated, Customizable Redcap External Modules The NCATS-funded, Vanderbilt University-developed REDCap platform is used at thousands of institutions worldwide.

A powerful characteristic of REDCap is its support for user-contributed programmatic extensions – external modules – that can add features or UI/UX elements, as well as integration with external informatics resources. Over the past decade, the Operations Core of the NIA-funded Claude D.

Pepper Older Americans Center (OAIC) at Yale (P30AG021342) has built a suite of REDCap external modules that promote efficiencies in workflows and data management. The Yale Study Support Suite (YES3) is in use by studies and data coordinating centers associated with Yale University, including a large PCORI/NIA-funded national pragmatic trial ( D-CARE Study ) led by investigators from three OAICs.

YES3 components include a dashboard for advanced data collection and study operations management that can be tailored to support study-specific workflows; a study portal for disseminating study materials and single or multi-site conduct-of-study monitoring that includes a comparative "site report card" analysis; and an automatable module that exports both code (SAS and R) and data for datamarts.

The REDCap@Yale team will use the NOSI funding to refactor and refine the YES3 codebase into a form suitable for long-term collaborative maintenance by the extensive consortium of REDCap open-source developers. Refactoring will focus on adherence to established coding style and documentation guidelines, design patterns widely in use