FAIR Re-use: Implications for AI-Readiness

A discussion of how to use the FAIR data principles to ensure quality data is being used in machine learning models. Presented at AI-Ready Data: Navigating the Dynamic Frontier of Metadata and Ontologies April 15-16, 2024Texas Advanced Computing Center (TACC

Online healthy lifestyle support in the perinatal period: What do women want and do they use it?

Unhealthy weight gain and retention during pregnancy and postpartum is detrimental to mother and child. Although various barriers limit the capacity for perinatal health care providers (PHCPs) to offer healthy lifestyle counselling, they could guide women to appropriate online resources. This paper presents a project designed to provide online information to promote healthy lifestyles in the perinatal period. Focus groups or interviews were held with 116 perinatal women and 76 PHCPs to determine what online information perinatal women and PHCPs want, in what form, and how best it should be presented. The results indicated that women wanted smartphone applications (apps) linked to trustworthy websites containing short answers to everyday concerns; information on local support services; and personalised tools to assess their nutrition, fitness and weight. Suggestions for improvement in these lifestyle areas should be practical and tailored to the developmental stage of their child. PHCPs wanted evidence-based, practical information, presented in a simple, engaging, interactive form. The outcome was a clinically endorsed website and app that health professionals could recommend. Preliminary evaluation showed that 10.5% of pregnant women in Western Australia signed up to the app. Use of the app appeared to be equitable across urban and rural areas of low to middle socioeconomic status

Ngala Healthy You, Healthy Baby: a personalized online program to support healthy weight in pregnancy and early life

Perinatal maternal obesity is common and has significant child and maternal health consequences. Online resources have the potential to engage young mothers to adopt healthier lifestyles during pregnancy and postpartum. Intercept interviews with 53 pregnant women at antenatal clinics and focus groups with 67 new mothers at mothers’ groups and playgroups were conducted to determine preferred types and formats of online information and support. The expressed needs of women were matched to behaviour change theory to guide development of the Healthy You Healthy Baby website and Smartphone application. A mix of factual and practical online information, self-assessment, goal-setting and feedback in an interactive format is recommended to motivate and support women to achieve healthy lifestyles in the perinatal period. Referral to online resources by health professionals and quality assurance of content is important to increase the confidence of women to act on it

Improving Traceability Throughout the Data Lifecycle: the DOLCE Approach to Provenance

The Texas Advanced Computing Center (TACC) uses a whole lifecycle approach to data management called the Digital Object LifeCycle Ecosystem (DOLCE). We use DOLCE as a framework to create policies and services that enable TACC to support accessibility to and discovery of digital objects throughout the phases of their lifecycle including generation, processing, description, analysis, storage, and sharing. The ultimate goal of DOLCE is to produce data that aligns with the FAIR Principles of findability, accessibility, interoperability, and reusability. A key aspect of this data curation process is using data provenance to improve reusability of data by tracking the processes used to create, gather, transform, and analyze digital objects. In this poster, we present our goals for improving data provenance using geospatial data as a use case. We will demonstrate how we utilize robust metadata to capture important data processing steps. We will also explain how provenance ties into our development of a data catalog that promotes long-term preservation and reusability.PlanetTexas 2050Texas Advanced Computing Center (TACC

Talkin' 'Bout OER

This is an informational document with talking points about open educational resources for different potential audiences, like faculty, administrators, and students. The document was started as a brainstorming activity at an OER Workshop at UT Austin on July 24, 2018.UT LibrariesCenter for Open Educational Resources and Language Learnin

To Trust or Not to Trust? Developing Trusted Digital Spaces through Timely Reliable and Personalized Provenance

Organizations are increasingly dependent on data stored and processed by distributed, heterogeneous services to make critical, high-value decisions. However, these service-oriented computing environments are dynamic in nature and are becoming ever more complex systems of systems. In such evolving and dynamic eco-system infrastructures, knowing how data was derived is of significant importance in determining its validity and reliability. To address this, a number of advocates and theorists postulate that provenance is critical to building trust in data and the services that generated it as it provides evidence for data consumers to judge the integrity of the results. This paper presents a summary of the STRAPP (trusted digital Spaces through Timely Reliable And Personalised Provenance) project, which is designing and engineering mechanisms to achieve a holistic solution to a number of real-world service-based decision-support systems

Predictive maps in rats and humans for spatial navigation

Much of our understanding of navigation comes from the study of individual species, often with specific tasks tailored to those species. Here, we provide a novel experimental and analytic framework integrating across humans, rats, and simulated reinforcement learning (RL) agents to interrogate the dynamics of behavior during spatial navigation. We developed a novel open-field navigation task ("Tartarus maze") requiring dynamic adaptation (shortcuts and detours) to frequently changing obstructions on the path to a hidden goal. Humans and rats were remarkably similar in their trajectories. Both species showed the greatest similarity to RL agents utilizing a "successor representation," which creates a predictive map. Humans also displayed trajectory features similar to model-based RL agents, which implemented an optimal tree-search planning procedure. Our results help refine models seeking to explain mammalian navigation in dynamic environments and highlight the utility of modeling the behavior of different species to uncover the shared mechanisms that support behavior