Funded Projects

Galter Health Sciences Library is a frequent collaborator and contributor to research activities on campus. Some of our current and past projects are listed below, but we invite you to contact us with ideas for collaboration with us anytime! 

Current Projects

InvenioRDM

A born-interoperable research data management repository and data catalog to enable best practices in research data management, sharing and reuse. InvenioRDM makes it easy to collect, preserve and disseminate a wide range of research products, enhancing individual and institutional visibility, promoting people and their expertise, supporting discovery and accessibility by the international scientific community, and promoting open and FAIR science. We’re developing InvenioRDM in collaboration with CERN, other national and international partners and NUCATS.

CTS Personas

The CTS Personas project comprises a portfolio of role-based profiles that represent the translational workforce. Personas can help guide development of resources and services for the translational workforce, such as software, training, communication and more.

  • Gonzales S, Champieux R, Contaxis N, et al. Clinical and Translational Science Personas: Expansion and use cases. Journal of Clinical and Translational Science. 2023;7(1):e147. doi:1017/cts.2023.572

Generalist Repository Ecosystem Initiative (GREI)

The vision of the Generalist Repository Ecosystem Initiative (GREI) is to develop collaborative approaches for data management and sharing through inclusion of established generalist repositories in the NIH data ecosystem and better enable search and discovery of NIH funded data in the generalist repositories. This is one of the steps in the modernization of the data resources ecosystem and aligns with the NIH Strategic Plan for Data Science. Galter Library supports Zenodo’s participation in GREI. (3OT2DB000013-01S4; PI: Holmes)

NNLM National Evaluation Center (NEC)

The Network of the National Library of Medicine National Evaluation Center (NEC) is charged with coordinating the assessment of the impact, efficacy, and value of the Network of the National Library of Medicine (NNLM) activities, services, and resources—with special focus on network member engagement and understanding its impact. The NEC provides NNLM with essential information for developing, maintaining, and evolving effective health information engagement and outreach activities for all Americans. Our cohesive model of process- and outcomes-based evaluation will be supported by best practice use of data and tools to ensure quality, timely evaluation and effective and efficient workflows to better understand the effectiveness of the NNLM and its interconnected components of Regional Medical Libraries (RMLs), Offices, and Centers (ROCs), national initiatives, programs, and members in achieving their objectives. We will strive for a “content-informed” approach, driven by identified needs of NLM, NNLM, and the extensive network of members around the country. This work will build capacity throughout the system, producing a well-trained evaluation workforce for the NNLM, supported by dependable data and tools, robust evaluation services and resources, and workforce and network investments to increase capacity across the NNLM and member organizations. (3U24LM013751-05S1; PI: Holmes)

HF-ETIOLOGY
Heart Failure Endotypes from Ethical, Multi-Modal AI driven and Molecular/Phenotypic Data Integration Enabled Discovery

Precision medicine is rapidly evolving through integrating multi-modal data to tailor treatments and deepen our understanding of diseases like heart failure (HF). Our initiative, HF-ETIOLOGY develops an ethical multi-modal AI framework that harnesses the power of multi-modal data—phenotypic, multi-omic, and socio-behavioral—to identify distinct HF endotypes. Our approach is distinguished by its co-design and iterative development of novel multi-modal AI models for disease endotyping, including advanced Bayesian generative tensor models and temporal tensor models and network medicine approaches. These methods allow us to integrate diverse data modalities and structures, such as longitudinal clinical data and complex multi-omic networks, seamlessly with behavioral and Social Determinants of Health (SDoH) factors. Our project centers around four main objectives: 1) Establish a FAIR-CARE framework to co-design HF- ETIOLOGY data and models; 2) Co-design Generative and Adjustable Prior Bayesian Tensor Factorization (GAP-BTF) to integrate behavioral and SDoH factors with multi-omic network feature learning to identify HF endotypes; 3) Co-design temporal non-negative tensor factorization model (TNTF) to integrate longitudinal phenotypic data while jointly modeling behavioral and SDoH factors for HF endotyping; 4) Identify likely risk genes/targets and pathways and investigate drug repurposing by incorporating SDoH factors for clinically relevant HF endotypes with MAI and network medicine co-design. Central to our methodology is a co-design framework that involves continuous engagement with stakeholders. This collaborative approach ensures that the development of our AI models is informed by clinical insights and aligned with ethical standards, thereby enhancing the practicality and relevance of our research in the clinical setting. (1OT2OD038083-01; PI: Luo)

Past Projects

Machine-actionable Data Management and Sharing Plan: Pilot Project

To enhance research data management infrastructures and services, California Digital Library (CDL)  and the Association of Research Libraries (ARL)  collaborated to address the increasing requirements to share federally funded research data. This project focused on enhancing DMPTool and persistent identifier registries by creating machine-actionable data management plans (maDMSPs). These MaDMSPs enable the communication and sharing of data and information about research across stakeholders and systems, facilitating notifications, verification and automated compliance checks. Galter Library, with support from other Northwestern partners, was a pilot site for this project. For more information, see the maDMSP Pilot Project page.

Clinical Research Data Management (cRDM) Program

Through a grant from the Network of the National Library of Medicine Greater Midwestern Region (NNLM-GMR), the DataLab developed the Clinical Research Data Management (cRDM) Program to support clinical data research workflows. This end-to-end training track was designed to introduce clinical researchers to clinical database architecture and clinical coding standards; teach researchers how to translate research questions into effective queries for proper data extraction; promote transparency and reproducibility in data practices; and ensure compliance with data sharing guidelines.

This grant bolstered resources and support for the Feinberg research community to use clinical research data from the Northwestern Medicine Enterprise Data Warehouse. (PI: Carson)

Medical Education on Film
Preserving the Medical Motion Pictures (1929-1959) of Northwestern University Feinberg School of Medicine

This Recordings at Risk grant from the Council on Library and Information Resources (CLIR) supported the digitization of d82 reels of medical education and archival films shot between 1929 and 1959, making these previously hidden and unavailable films accessible online to researchers and library patrons. Films were cleaned, stabilized, and digitized to produce preservation, production, and access copies with metadata files. Galter Library’s institutional repository host the files and records with robust metadata, and library staff creates linked finding aids with item-level description. Each film was reviewed for content, ethical, and privacy issues. This collection is one of the very few medical motion picture collections available online and offers scholars a rare look at medical filmmaking, educational film, medical imagery, medical education, and more. (PI: Lattal)

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