Bhavesh Patel

Aydan Gasimova, Paapa Mensah-Kane, Gerard F. Blake et al.

Scientific posters are one of the most common forms of scholarly communication and contain early-stage insights with potential to accelerate scientific discovery. We investigated where posters are shared, to what extent their sharing aligns with the FAIR principles, and how commonly they are reused. We identified 86 platforms hosting posters, with many not assigning persistent identifiers. A total of 150k posters are shared as of 2024 on the 43 platforms where we were able to count, which is relatively low. Looking in more detail at posters shared on Zenodo and Figshare, we found that repositories are not always supporting structured metadata critical for poster discovery, like conference information, and that researchers are not providing such metadata even if they are supported. We also observed that while there is some engagement with posters in terms of views and downloads, citing posters is not yet a common practice. Our recommendations are for the scientific community to encourage poster sharing and reuse and establish clear guidelines to make posters FAIR.

Harry Caufield, Satrajit Ghosh, Sek Wong Kong et al.

AI-readiness describes the degree to which data may be optimally and ethically used for subsequent AI and Machine Learning (AI/ML) methods, where those methods may involve some combination of model training, data classification, and ethical, explainable prediction. The Bridge2AI consortium has defined the particular criteria a biomedical dataset may possess to render it AI-ready: in brief, a dataset's readiness is related to its FAIRness, provenance, degree of characterization, explainability, sustainability, and computability, in addition to its accompaniment with documentation about ethical data practices. To ensure AI-readiness and to clarify data structure and relationships within Bridge2AI's Grand Challenges (GCs), particular types of metadata are necessary. The GCs within the Bridge2AI initiative include four data-generating projects focusing on generating AI/ML-ready datasets to tackle complex biomedical and behavioral research problems. These projects develop standardized, multimodal data, tools, and training resources to support AI integration, while addressing ethical data practices. Examples include using voice as a biomarker, building interpretable genomic tools, modeling disease trajectories with diverse multimodal data, and mapping cellular and molecular health indicators across the human body. This report assesses the state of metadata creation and standardization in the Bridge2AI GCs, provides guidelines where required, and identifies gaps and areas for improvement across the program. New projects, including those outside the Bridge2AI consortium, would benefit from what we have learned about creating metadata as part of efforts to promote AI readiness.

David Ojika, Bhavesh Patel, G. Anthony Reina et al.

Using medical imaging as case-study, we demonstrate how Intel-optimized TensorFlow on an x86-based server equipped with 2nd Generation Intel Xeon Scalable Processors with large system memory allows for the training of memory-intensive AI/deep-learning models in a scale-up server configuration. We believe our work represents the first training of a deep neural network having large memory footprint (~ 1 TB) on a single-node server. We recommend this configuration to scientists and researchers who wish to develop large, state-of-the-art AI models but are currently limited by memory.