Data Sharing Research Articles

525 A Library-CTSI Collaboration to Support Researcher Compliance with the 2023 NIH Policy for Data Management and Sharing

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.

Generalized overview infographic: a customizable library instructional material on the NIH Data Management and Sharing Policy.

The Generalized Overview of the NIH Data Management and Sharing Policy Effective 2023.01.15 (Generalized Overview) is an instructional material that provides a basic, clear, and linear understanding of the NIH policy and its requirements. While not developing or utilizing new technology, the Generalized Overview is innovative and notable for creatively using a freely available graphic design tool to translate government policy language into an accessible and understandable infographic that can disseminate important information about the NIH DMS Policy needed by researchers and by those who support them. Shared via a Creative Commons license, others may fully adapt the infographic or may simply add their own institutional contact information. The Generalized Overview can be used by any who find themselves responsible for publicizing and/or teaching the NIH Data Management and Sharing Policy at their respective libraries and institutions. It is intended for educational purposes only and should not be used as a substitute for official guidance from the NIH.

The DMPTool NIH DMSP Templates Project.

The DMPTool NIH Data Management and Sharing Plan (DMSP) Templates Project was launched in response to the 2023 NIH Data Management and Sharing (DMS) Policy. This new policy introduced a more structured framework for DMS Plans, featuring six key elements, a departure from the 2003 NIH DMS policy. The project aimed to simplify the process for data librarians, research administrators, and researchers by providing a template with curated guidance, eliminating the need to navigate various policies and guidelines. The template breaks out each Plan section and subsection and provides related guidance and examples at the point of need. This effort has resulted in two NIH DMSP Templates. The first is a generic template (NIH-Default) for all ICs, complying with NOT-OD-21-013 and NOT-OD-22-198. More recently, an NIMH-specific template (NIH-NIMH) was added based on NOT-MH-23-100. As of October 2023, over 5,000 DMS Plans have been written using the main NIH-Default template and the NIH-NIMH alternative template.

Type 2 Diabetes: Tips for Healthy Living

Patient Education| 2021 Type 2 Diabetes: Tips for Healthy Living Connected Content Translation: Diabetes tipo 2: consejos para una vida saludable: (Type 2 Diabetes: Tips for Healthy Living) Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Share Icon Share Facebook Twitter LinkedIn Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Type 2 Diabetes: Tips for Healthy Living. Pediatric Patient Education 2021; 10.1542/peo_document576 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search nav search search input Search input auto suggest search filter All AAP SitesAll PublicationsPediatric Patient Education Search Advanced Search Topics: blood glucose, diabetes mellitus, type 2, glucose, insulin, physical activity Read on for information from the American Academy of Pediatrics (AAP) about managing blood glucose and creating plans for healthy living. Glucose is found in the blood and is the body's main source of energy. The food your child eats is broken down by the body into glucose. Glucose is a type of sugar that gives energy to the cells in the body. The cells need the help of insulin to take the glucose from the blood to the cells. Insulin is made by an organ called the pancreas. In children with type 2 diabetes, the pancreas does not make enough insulin and the cells don't use the insulin very well. Glucose will build up in the blood if it cannot be used by the cells. High blood glucose levels can damage many parts of the body, such as the eyes, kidneys, nerves, and... © 2013 American Academy of Pediatrics You do not currently have access to this content.

Urinary Tract Infections in Children

Patient Education| 2021 Urinary Tract Infections in Young Children Connected Content Translation: Infecciones de las vías urinarias en niños pequeños: (Urinary Tract Infections in Young Children) Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Share Icon Share Facebook Twitter LinkedIn Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Urinary Tract Infections in Young Children. Pediatric Patient Education 2021; 10.1542/peo_document109 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search nav search search input Search input auto suggest search filter All AAP SitesAll PublicationsPediatric Patient Education Search Advanced Search Topics: urinary tract, urinary tract infections, urine, urinary bladder, kidney, fever The urinary tract makes and stores urine. It is made up of the kidneys, ureters, bladder, and urethra (see illustration). The kidneys produce urine. Urine travels from the kidneys down 2 narrow tubes called the ureters to the bladder. The bladder is a thin muscular bag that stores urine until it is time to empty urine out of the body. When it is time to empty the bladder, a muscle at the bottom of the bladder relaxes. Urine then flows out of the body through a tube called the urethra. The opening of the urethra is at the end of the penis in boys and above the vaginal opening in girls. Normal urine has no germs (bacteria). However, bacteria can get into the urinary tract from 2 sources: (1) the skin around the rectum and genitals and (2) the... © 2010 American Academy of Pediatrics, Reaffirmed 01/2017. All rights reserved. You do not currently have access to this content.

Ridesourcing platforms thrive on socio-economic inequality

Limited available market share data seems to suggest that ridesourcing platforms benefit from, even thrive on, socio-economic inequality. We suspect that this is associated with high levels of socio-economic inequality allowing for cheap labour as well as increasing the share of travellers with a considerably above-average willingness to pay for travel time savings and comfort. We test the relation between inequality and system performance by means of an agent-based simulation model representing within-day and day-to-day supply-demand interaction in the ridesourcing market. The model captures travellers’ mode choice with a heterogeneous perception of relevant time components, as well as job seekers’ participation choice with heterogeneous reservation wage. Our experiments cover scenarios for the entire spectrum ranging from perfect equality to extreme inequality. For several of such scenarios, we explore alternative platform pricing strategies. Our analysis shows a strong, positive relationship between socio-economic inequality and ridesourcing market share. This is the outcome of the combination of cheap labour and time-sensitive ridesourcing users, reinforced by network effects inherent to ridesourcing markets. We find that driver earnings are minimal in urban areas with large socio-economic inequality. In such contexts, drivers are likely to face a high platform commission, and yet, fierce competition for passengers.

Temper Tantrums: What Parents Need to Know

Patient Education| 2021 Temper Tantrums Connected Content Translation: Berrinches o rabietas Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Share Icon Share Facebook Twitter LinkedIn Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Temper Tantrums. Pediatric Patient Education 2021; 10.1542/peo_document100 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search nav search search input Search input auto suggest search filter All AAP SitesAll PublicationsPediatric Patient Education Search Advanced Search Topics: temper tantrum It’s hard for young children to hold strong feelings inside. When they feel frustrated or angry, they often cry, scream, or stomp up and down. This is a temper tantrum. Temper tantrums are a normal part of your children’s development. They usually begin around 12 to 18 months of age, get worse between 2 and 3 years, and taper off after that, once children are able to use words to communicate their wants and needs. Here is information from the American Academy of Pediatrics to help parents understand temper tantrums and how best to deal with them. During the toddler years, there is a change in how children process information. They suddenly become more aware that their world can change. They realize they won’t always get what they expect or want. Their young minds are easily overwhelmed, and they don’t know... © 2016 American Academy of Pediatrics. All rights reserved. You do not currently have access to this content.

Enabling privacy-aware interoperable and quality IoT data sharing with context

Sharing Internet of Things (IoT) data across different sectors, such as in smart cities, becomes complex due to heterogeneity. This poses challenges related to a lack of interoperability, data quality issues and lack of context information, and a lack of data veracity (or accuracy). In addition, there are privacy concerns as IoT data may contain personally identifiable information. To address the above challenges, this paper presents a novel semantic technology-based framework that enables data sharing in a GDPR-compliant manner while ensuring that the data shared is interoperable, contains required context information, is of acceptable quality, and is accurate and trustworthy. The proposed framework also accounts for the edge/fog, an upcoming computing paradigm for the IoT to support real-time decisions. We evaluate the performance of the proposed framework with two different edge and fog–edge scenarios using resource-constrained IoT devices, such as the Raspberry Pi. In addition, we also evaluate shared data quality, interoperability and veracity. Our key finding is that the proposed framework can be employed on IoT devices with limited resources due to its low CPU and memory utilization for analytics operations and data transformation and migration operations. The low overhead of the framework supports real-time decision making. In addition, the 100% accuracy of our evaluation of the data quality and veracity based on 180 different observations demonstrates that the proposed framework can guarantee both data quality and veracity.

Secondary use of genomic data: patients’ decisions at point of testing and perspectives to inform international data sharing

International sharing of genomic data files arising from clinical testing of patients is essential to further improve genomic medicine. Whilst the general public are reluctant to donate DNA for research, the choices patients actually make about sharing their clinical genomic data for future re-use (research or clinical) are unknown. We ascertained the data-sharing choices of 1515 patients having genomic testing for inherited conditions or cancer treatment from clinical consent forms. To understand the experiences and preferences of these patients, surveys were administered after test consent (RR 73%). Almost all patients (98%) consented to share their data. Survey respondents’ decision recall was high (90%), but poorer if English was an additional language (p < 0.001). Parents deciding on behalf of children were over-represented amongst data-sharing decliners (p = 0.047) and decliners were more likely to believe that stored data could be easily reidentified (p < 0.001). A quarter of respondents did not know if reidentification would be easy and 44% of them were concerned about this possibility. Of those willing to share data overseas (60%), 23% indicated the recipient researcher’s country would affect their decision. Most respondents (89%) desired some ongoing control over research use of their data. Four preliminary data-sharing profiles emerged; their further development could inform tailored patient resources. Our results highlight considerations for establishment of systems to make clinical genomic data files available for reanalysis locally and across borders. Patients’ willingness to share their data – and value of the resulting research – should encourage clinical laboratories to consider sharing data systematically for secondary uses.

Abstract 920: Insights from the National Cancer Institute’s request for information on existing data sharing processes for NIH-funded research

Abstract Data sharing plays a crucial role in accelerating biomedical research discoveries, promoting rigorous and reproducible research, enabling research data reuse, and fostering public trust. To facilitate data sharing, a coordinated, sustainable, and streamlined infrastructure is required to store, preserve, retrieve, and make data findable, accessible, interoperable, and reusable. Starting January 25, 2023, researchers funded by the National Institutes of Health (NIH) are required to comply with the NIH Data Management and Sharing Policy (https://sharing.nih.gov/data-management-and-sharing-policy/about-data-management-and-sharing-policies/data-management-and-sharing-policy-overview) and indicate how research data funded by the NIH will be managed and broadly shared. As institutions prepare to meet these requirements, the National Cancer Institute (NCI) is looking to understand the existing processes of data storage, preservation, and sharing including what infrastructure and services are available to facilitate data sharing. To carry out this goal, the NCI’s Office of Data Sharing (ODS), dedicated to promoting responsible and efficient sharing of cancer research data, issued a Request for Information (RFI) to understand the existing data sharing processes for NIH-Funded Research. The RFI was designed to collect information on services (e.g. guidance on data standards and file formats), technologies (e.g. storage, compression, retrieval, archival, analysis tools, workbenches), and processes (e.g. governance, communication, education, outreach) that promote data sharing. ODS utilized the RFI to gather input from stakeholders involved in facilitating data sharing. This included personnel from various fields such as research laboratories, scientific instrumentation core facilities, offices of research and/or sponsored programs, provost offices or other research administration, libraries, information technology, and institutional review boards. We also engaged with bioinformaticians and data scientists who are actively contributing to data curation, formatting, and analysis. Here, we present analysis of the responses received, including insights relating to gaps and challenges in data sharing, and proposed next steps and recommendations. Citation Format: Mousumi Ghosh, Nathan Boyd, Subhashini Jagu. Insights from the National Cancer Institute’s request for information on existing data sharing processes for NIH-funded research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 920.

Demystifying the likelihood of reidentification in neuroimaging data: A technical and regulatory analysis

Abstract Sharing research data has been widely promoted in the field of neuroimaging and has enhanced the rigor and reproducibility of neuroimaging studies. Yet the emergence of novel software tools and algorithms, such as face recognition, has raised concerns due to their potential to reidentify defaced neuroimaging data that are thought to have been deidentified. Despite the surge of privacy concerns, however, the risk of reidentification via these tools and algorithms has not yet been examined outside the limited settings for demonstration purposes. There is also a pressing need to carefully analyze regulatory implications of this new reidentification attack because concerns about the anonymity of data are the main reason that researchers think they are legally constrained from sharing their data. This study aims to tackle these gaps through rigorous technical and regulatory analyses. Using a simulation analysis, we first tested the generalizability of the matching accuracies in defaced neuroimaging data reported in a recent face recognition study (Schwarz et al., 2021). The results showed that the real-world likelihood of reidentification in defaced neuroimaging data via face recognition would be substantially lower than that reported in the previous studies. Next, by taking a US jurisdiction as a case study, we analyzed whether the novel reidentification threat posed by face recognition would place defaced neuroimaging data out of compliance under the current regulatory regime. Our analysis suggests that defaced neuroimaging data using existing tools would still meet the regulatory requirements for data deidentification. A brief comparison with the EU’s General Data Protection Regulation (GDPR) was also provided. Then, we examined the implication of NIH’s new Data Management and Sharing Policy on the current practice of neuroimaging data sharing based on the results of our simulation and regulatory analyses. Finally, we discussed future directions of open data sharing in neuroimaging.

Целостность и иерархичность структур данных в жизненном цикле объектов малоэтажного жилищного строительства

The subject area of the article is construction facilities life cycle management, where the object-oriented automated technologies of low-rise housing construction are the subject of consideration. The purpose of this study is to describe the meta-structure of such technologies, consisting of a set of hierarchical structures of the finite elements (premises, structures, engineering equipment, etc.) and for this meta-structure definition the concept of low-rise housing estate construction facilities integrity is introduced. The object of the article's research is hierarchical data structures, on the basis of which object-oriented databases and knowledge bases are formed, serving automated technologies for construction facilities integrity generation within all stages of the life cycle. The article uses the method of decomposition of the low-rise housing estate construction facilities integrity into a variety of automated technologies and their constituent hierarchical data structures, which allows us to identify all subject areas of objects of manor low-rise housing estate construction for computational justifications of the limiting states of finite elements (objects) of architecture, building structures, engineering equipment. Hierarchical structures create a standardized basis for the organization of finite element information models for managing the life cycle of manor low-rise estate housing construction. Decision-makers should prepare hierarchical data structures and algorithms for calculating the justifications for finite elements. The article presents examples of hierarchical data structures schemes for the design stage tasks solving. It is concluded that all information models of finite elements on a variety of subject areas of manor low-rise estate housing construction objects arise during the execution of constructive (algorithmized) processes in the decision-making layers of the corresponding stages of the life cycle of construction objects. It is concluded that all information models of finite elements on a variety of subject areas of manor low-rise estate housing construction objects arise during the execution of constructive (algorithmized) processes in the decision-making layers of the corresponding stages of the life cycle of construction objects. A structuring condition is also proposed: if the main link of the life cycle management technology is the information model of the object, then the fundamental condition for object orientation and meaningful filling of hierarchical data structures is the construction system used in this construction object, as a set of load-bearing and enclosing structures, its components and materials used. The proposed approach will make it possible to create and maintain a shared data environment on a regular and ongoing basis as part of a set of hierarchical data structures of the construction projects under consideration. In the development, it becomes possible to qualitatively improve the technological conditions for decision-making according to various criteria, including criteria for environmental friendliness, energy efficiency, economy, comfort for low-rise residential buildings using neural network data structures and the use of artificial intelligence algorithms. The above together is the basis for the formation of a modern innovative technological order for the objects of estate low-rise housing construction.

Modernizing CDC's Practices and Culture for Better Data Sharing, Impact, and Transparency.

Modernizing CDC's Practices and Culture for Better Data Sharing, Impact, and Transparency.

Investigating the Roles and Responsibilities of Institutional Signing Officials After Data Sharing Policy Reform for Federally Funded Research in the United States: National Survey.

New federal policies along with rapid growth in data generation, storage, and analysis tools are together driving scientific data sharing in the United States. At the same, triangulating human research data from diverse sources can also create situations where data are used for future research in ways that individuals and communities may consider objectionable. Institutional gatekeepers, namely, signing officials (SOs), are therefore at the helm of compliant management and sharing of human data for research. Of those with data governance responsibilities, SOs most often serve as signatories for investigators who deposit, access, and share research data between institutions. Although SOs play important leadership roles in compliant data sharing, we know surprisingly little about their scope of work, roles, and oversight responsibilities. The purpose of this study was to describe existing institutional policies and practices of US SOs who manage human genomic data access, as well as how these may change in the wake of new Data Management and Sharing requirements for National Institutes of Health-funded research in the United States. We administered an anonymous survey to institutional SOs recruited from biomedical research institutions across the United States. Survey items probed where data generated from extramurally funded research are deposited, how researchers outside the institution access these data, and what happens to these data after extramural funding ends. In total, 56 institutional SOs participated in the survey. We found that SOs frequently approve duplicate data deposits and impose stricter access controls when data use limitations are unclear or unspecified. In addition, 21% (n=12) of SOs knew where data from federally funded projects are deposited after project funding sunsets. As a consequence, most investigators deposit their scientific data into "a National Institutes of Health-funded repository" to meet the Data Management and Sharing requirements but also within the "institution's own repository" or a third-party repository. Our findings inform 5 policy recommendations and best practices for US SOs to improve coordination and develop comprehensive and consistent data governance policies that balance the need for scientific progress with effective human data protections.

Дослідження проблем синхронізації та захисту даних при реалізації багатопоточних програм

The issue of shared data usage by threads is especially relevant in modern multi-core and multiprocessor systems. The main problems of implementing multithreaded programs are race conditions, deadlocks, and thread starvation. The aim of the work is to solve the problem of thread racing in multithreaded calculations of resource-intensive tasks with parallel access to shared data using appropriate synchronization mechanisms, such as mutexes. A multithreaded algorithm for implementing a typical task of processing large data arrays with protection of the critical area in concurrent programs running on multiprocessor and multi-core systems has been developed and researched.

Cross-Core Data Sharing for Energy-Efficient GPUs

Graphics Processing Units (GPUs) are the accelerator of choice in a variety of application domains because they can accelerate massively parallel workloads and can be easily programmed using general-purpose programming frameworks such as CUDA and OpenCL. Each Streaming Multiprocessor (SM) contains an L1 data cache (L1D) to exploit the locality in data accesses. L1D misses are costly for GPUs due to two reasons. First, L1D misses consume a lot of energy as they need to access the L2 cache (L2) via an on-chip network and the off-chip DRAM in case of L2 misses. Second, L1D misses impose performance overhead if the GPU does not have enough active warps to hide the long memory access latency. We observe that threads running on different SMs share 55% of the data they read from the memory. Unfortunately, as the L1Ds are in the non-coherent memory domain, each SM independently fetches data from the L2 or the off-chip memory into its L1D, even though the data may be currently available in the L1D of another SM. Our goal is to service L1D read misses via other SMs, as much as possible, to cut down costly accesses to the L2 or the off-chip DRAM. To this end, we propose a new data sharing mechanism, called Cross-Core Data Sharing (CCDS) . CCDS employs a predictor to estimate whether or not the required cache block exists in another SM. If the block is predicted to exist in another SM’s L1D, CCDS fetches the data from the L1D that contains the block. Our experiments on a suite of 26 workloads show that CCDS improves average energy and performance by 1.30 × and 1.20 ×, respectively, compared to the baseline GPU. Compared to the state-of-the-art data-sharing mechanism, CCDS improves average energy and performance by 1.37 × and 1.11 ×, respectively.

Update of the Minimum Information About BIobank Data Sharing (MIABIS) Core Terminology to the 3rd Version.

Introduction: The Minimum Information About BIobank Data Sharing (MIABIS) is a biobank-specific terminology enabling the sharing of biobank-related data for different purposes across a wide range of database implementations. After 4 years in use and with the first version of the individual-level MIABIS component Sample, Sample donor, and Event, it was necessary to revise the terminology, especially to include biobanks that work more in the data domain than with samples. Materials & Methods: Nine use-cases representing different types of biobanks, studies, and networks participated in the development work. They represent types of data, specific sample types, or levels of organization that were not included earlier in MIABIS. To support our revision of the Biobank entity, we conducted a survey of European biobanks to chart the services they provide. An important stakeholder group for biobanks include researchers as the main users of biobanks. To be able to render MIABIS more researcher-friendly, we collected different sample/data requests to analyze the terminology adjustment needs in detail. During the update process, the Core terminology was iteratively reviewed by a large group of experts until a consensus was reached. Results: With this update, MIABIS was adjusted to encompass data-driven biobanks and to include data collections, while also describing the services and capabilities biobanks offer to their users, besides the retrospective samples. The terminology was also extended to accommodate sample and data collections of nonhuman origin. Additionally, a set of organizational attributes was compiled to describe networks. Discussion: The usability of MIABIS Core v3 was increased by extending it to cover more topics of the biobanking domain. Additionally, the focus was on a more general terminology and harmonization of attributes with the individual-level entities Sample, Sample donor, and Event to keep the overall terminology minimal. With this work, the internal semantics of the MIABIS terminology was improved.

NCI Cancer Research Data Commons: Resources to Share Key Cancer Data.

Since 2014, the NCI has launched a series of data commons as part of the Cancer Research Data Commons (CRDC) ecosystem housing genomic, proteomic, imaging, and clinical data to support cancer research and promote data sharing of NCI-funded studies. This review describes each data commons (Genomic Data Commons, Proteomic Data Commons, Integrated Canine Data Commons, Cancer Data Service, Imaging Data Commons, and Clinical and Translational Data Commons), including their unique and shared features, accomplishments, and challenges. Also discussed is how the CRDC data commons implement Findable, Accessible, Interoperable, Reusable (FAIR) principles and promote data sharing in support of the new NIH Data Management and Sharing Policy. See related articles by Brady et al., p. 1384, Pot et al., p. 1396, and Kim et al., p. 1404.

Reidentification of Participants in Shared Clinical Data Sets: Experimental Study

Background Large curated data sets are required to leverage speech-based tools in health care. These are costly to produce, resulting in increased interest in data sharing. As speech can potentially identify speakers (ie, voiceprints), sharing recordings raises privacy concerns. This is especially relevant when working with patient data protected under the Health Insurance Portability and Accountability Act. Objective We aimed to determine the reidentification risk for speech recordings, without reference to demographics or metadata, in clinical data sets considering both the size of the search space (ie, the number of comparisons that must be considered when reidentifying) and the nature of the speech recording (ie, the type of speech task). Methods Using a state-of-the-art speaker identification model, we modeled an adversarial attack scenario in which an adversary uses a large data set of identified speech (hereafter, the known set) to reidentify as many unknown speakers in a shared data set (hereafter, the unknown set) as possible. We first considered the effect of search space size by attempting reidentification with various sizes of known and unknown sets using VoxCeleb, a data set with recordings of natural, connected speech from &gt;7000 healthy speakers. We then repeated these tests with different types of recordings in each set to examine whether the nature of a speech recording influences reidentification risk. For these tests, we used our clinical data set composed of recordings of elicited speech tasks from 941 speakers. Results We found that the risk was inversely related to the number of comparisons an adversary must consider (ie, the search space), with a positive linear correlation between the number of false acceptances (FAs) and the number of comparisons (r=0.69; P&lt;.001). The true acceptances (TAs) stayed relatively stable, and the ratio between FAs and TAs rose from 0.02 at 1 × 105 comparisons to 1.41 at 6 × 106 comparisons, with a near 1:1 ratio at the midpoint of 3 × 106 comparisons. In effect, risk was high for a small search space but dropped as the search space grew. We also found that the nature of a speech recording influenced reidentification risk, with nonconnected speech (eg, vowel prolongation: FA/TA=98.5; alternating motion rate: FA/TA=8) being harder to identify than connected speech (eg, sentence repetition: FA/TA=0.54) in cross-task conditions. The inverse was mostly true in within-task conditions, with the FA/TA ratio for vowel prolongation and alternating motion rate dropping to 0.39 and 1.17, respectively. Conclusions Our findings suggest that speaker identification models can be used to reidentify participants in specific circumstances, but in practice, the reidentification risk appears small. The variation in risk due to search space size and type of speech task provides actionable recommendations to further increase participant privacy and considerations for policy regarding public release of speech recordings.

Parent Perspectives on Documentation and Sharing of Health-Related Social Needs Data.

Parents of pediatric patients are key stakeholders in the design and implementation of health-related social needs (HRSN) screening programs. Yet, there is little research exploring their perspectives on the documentation and sharing of HRSN data. We aimed to examine parents' preferences regarding how HRSN data are documented and shared. We conducted semi-structured interviews with parents of hospitalized children participating in an HRSN screening program at a quaternary care children's hospital. Interviews were coded using an inductive and deductive approach to identify emergent themes. The 20 interviewed parents were uniformly female with 55% identifying as Black or African American and 20% identifying as Hispanic or Latino. Parents expressed comfort with electronic health record documentation of HRSN data and the use of International Classification of Diseases, 10th Revision Z codes as long as this information was used to provide families with meaningful support. Most parents viewed social workers and medical teams as the most appropriate recipients of HRSN data. Few parents felt comfortable with HRSN data being shared with payors. Parents desired transparency around HRSN data sharing. Many expressed concerns that documentation and sharing of HRSN data could lead to unwanted or unsafe disclosures or result in child welfare referrals. Parents expressed comfort with HRSN documentation and sharing with health care providers, but requested that providers be transparent and respect parental preferences regarding data sharing to mitigate potential harms. When implementing HRSN support programs, health systems and payors should prioritize transparency around documentation and data sharing with families.