OBJECTIVES/GOALS: Seeking ways to support teams in the preparation for and the implementation of the new National Institutes of Health (NIH) Policy for Data Management and Sharing (DMSP), the integrated Translational Health Research Institute of Virginia (iTHRIV) partnered with the UVA Health Sciences Library to develop training and resources for researchers. METHODS/STUDY POPULATION: Health sciences librarians and iTHRIV (an NIH-NCATS supported Clinical Translational Research Institute) convened a Working Group, inviting representatives from central and unit-specific research support offices (e.g. the Comprehensive Cancer Center), research compliance, regulatory affairs, sponsored programs, institutional review boards, libraries, and data science to review and discuss the DMSP requirements. After an initial orientation to the policy, the group reviewed existing public resources and solicited feedback about steps to best support UVA researchers in compliance. Leveraging the broad expertise of the group, the team provides guidance to researchers on writing the DMS plan and choosing a data repository, and provides tools and templates to support implementation of the policy. RESULTS/ANTICIPATED RESULTS: A library-created website provided policy guidance, including links to NIH-hosted information, resources created by other institutions, and new UVA-specific templates and suggested proposal language. Librarians led a webinar on the new policy and UVA resources which included a speaker from UVA regulatory affairs to describe the new DMSP requirements, and a tour of the new guide. The guide has been viewed over 5000 times to date and librarians have provided consultations and training to individuals and departments. Current plans include developing a user satisfaction survey, reviewing DMSP feedback from submitted proposals, and incorporating lessons learned into the website and future training. DISCUSSION/SIGNIFICANCE: The collaboration between iTHRIV and the Health Sciences Library to support the NIH Data Management and Sharing Policy was a successful partnership that provided leadership at the institutional level to communicate with and engage researchers and utilized the library’s web presence, expertise, and service model to provide direct support.

In the few weight loss studies assessing diet quality, improvements have been minimal and recommended calculation methods have not been used. This secondary analysis of a parallel group randomised trial (regsitered: https://clinicaltrials.gov/ct2/show/NCT03367936) assessed whether self-monitoring with feedback (SM + FB) v. self-monitoring alone (SM) improved diet quality. Adults with overweight/obesity (randomised: SM n 251, SM + FB n 251; analysed SM n 170, SM + FB n 186) self-monitored diet, physical activity and weight. Real-time, personalised feedback, delivered via a study-specific app up to three times daily, was based on reported energy, fat and added sugar intake. Healthy Eating Index 2015 (HEI-2015) scores were calculated from 24-hour recalls. Higher scores represent better diet quality. Data were collected August 2018 to March 2021 and analysed spring 2022. The sample was mostly female (78·9 %) and white (85·4 %). At baseline, HEI-2015 total scores and bootstrapped 95 % CI were similar by treatment group (SM + FB: 63·11 (60·41, 65·24); SM: 61·02 (58·72, 62·81)) with similar minimal improvement observed at 6 months (SM + FB: 65·42 (63·30, 67·20); SM: 63·19 (61·22, 64·97)) and 12 months (SM + FB: 63·94 (61·40, 66·29); SM: 63·56 (60·81, 65·42)). Among those who lost ≥ 5 % of baseline weight, HEI-2015 scores improved (baseline: 62·00 (58·94, 64·12); 6 months: 68·02 (65·41, 71·23); 12 months: 65·93 (63·40, 68·61)). There was no effect of the intervention on diet quality change. Clinically meaningful weight loss was related to diet quality improvement. Feedback may need to incorporate more targeted nutritional content.

The concentration of radiocarbon (14C) differs between ocean and atmosphere. Radiocarbon determinations from samples which obtained their 14C in the marine environment therefore need a marine-specific calibration curve and cannot be calibrated directly against the atmospheric-based IntCal20 curve. This paper presents Marine20, an update to the internationally agreed marine radiocarbon age calibration curve that provides a non-polar global-average marine record of radiocarbon from 0–55 cal kBP and serves as a baseline for regional oceanic variation. Marine20 is intended for calibration of marine radiocarbon samples from non-polar regions; it is not suitable for calibration in polar regions where variability in sea ice extent, ocean upwelling and air-sea gas exchange may have caused larger changes to concentrations of marine radiocarbon. The Marine20 curve is based upon 500 simulations with an ocean/atmosphere/biosphere box-model of the global carbon cycle that has been forced by posterior realizations of our Northern Hemispheric atmospheric IntCal20 14C curve and reconstructed changes in CO2 obtained from ice core data. These forcings enable us to incorporate carbon cycle dynamics and temporal changes in the atmospheric 14C level. The box-model simulations of the global-average marine radiocarbon reservoir age are similar to those of a more complex three-dimensional ocean general circulation model. However, simplicity and speed of the box model allow us to use a Monte Carlo approach to rigorously propagate the uncertainty in both the historic concentration of atmospheric 14C and other key parameters of the carbon cycle through to our final Marine20 calibration curve. This robust propagation of uncertainty is fundamental to providing reliable precision for the radiocarbon age calibration of marine based samples. We make a first step towards deconvolving the contributions of different processes to the total uncertainty; discuss the main differences of Marine20 from the previous age calibration curve Marine13; and identify the limitations of our approach together with key areas for further work. The updated values for ΔR, the regional marine radiocarbon reservoir age corrections required to calibrate against Marine20, can be found at the data base http://calib.org/marine/.

The Greenland ice sheet (GrIS) subglacial hydrological system may undergo a seasonal evolution, with significant geophysical and biogeochemical implications. We present results from a new isotope-mixing model to quantify the relative contributions of surface snow, glacial ice and delayed flow to the bulk meltwater discharge from a small (∼5 km2) land-terminating GrIS outlet glacier during melt onset (May) and at peak melt (July). We use radioactive (222Rn) and stable isotopes (18O, deuterium) to differentiate the water source contributions. Atmospherically derived 7Be further constrains meltwater transit time from the glacier surface to the ice margin. We show that (1) 222Rn is a promising tracer for glacial waters stored at the bed and (2) a quantitative chemical mixing model can be constructed by combining 222Rn and the stable water isotopes. Applying this model to the bulk subglacial outflow from our study area, we find a constant delayed-flow (stored) component from melt onset through peak melt. This component is diluted first by snowmelt and then by increasing glacial ice melt as the season progresses. Results from this pilot study are consistent with the hypothesis that subglacial drainage beneath land-terminating sections of the GrIS undergoes a seasonal evolution from a distributed to a channelized system.