Data Hub Research Articles

NeoVault: empowering neonatal research through a neonate data hub

BackgroundStability during early postnatal life in preterm infants is related to better outcomes. Although vital signs are monitored continuously in Neonatal Intensive Care Unites, this monitoring does not include all physiological parameters nor data such as movement patterns. Although there are scattered sources of data, there is no centralized data hub for neonates information.ResultsWe have created the first neonate data hub for easy and interactive access to upload or download postural, physiological, and medical data of neonates: NeoVault. NeoVault is a platform that provides access to information through two interfaces: 1) via a Web interface (designed for medical personnel, data scientists, researchers); and 2) via a RESTful API (Application Programming Interfaces) -designed for developers-, aiming to integrate access to information into third-party applications. The web access allows searching and filtering according to specific parameters, visualization of data through graphs and images, and generation of datasets in CSV format. Access through the RESTful API is described in OpenAPI, enabling access to information from any device, facilitating it in an interoperable format. Currently, it contains nearly 800,000 postural records and 3.000 physiological data entries. The physiological and postural data stored for each neonate in NeoVault are collected through the NRP (Neonates Recording Platform) tool, which allows for the automatic and reliable collection of data.ConclusionNeoVault is an open platform for simple access to postural, physiological, and medical data of neonates that can be utilized by researchers, data scientists, medical personnel, and programmers. It enables integration into third-party applications and the generation of customized datasets.

Integrating Citizen Experiences in Cultural Heritage Archives with a Linked non-Open Data Hub

Integrating Citizen Experiences in Cultural Heritage Archives with a Linked non-Open Data Hub

Data Hub for Life Cycle Assessment of Climate Change Solutions—Hydrogen Case Study

Life cycle assessment, which evaluates the complete life cycle of a product, is considered the standard methodological framework to evaluate the environmental performance of climate change solutions. However, significant challenges exist related to datasets used to quantify these environmental indicators. Although extensive research and commercial data on climate change technologies, pathways, and facilities exist, they are not readily available to practitioners of life cycle assessment in the right format and structure using an open platform. In this study, we propose a new open data hub platform for life cycle assessment, considering a hierarchical data flow starting with raw data collected on climate change technologies at laboratory, pilot, demonstration, or commercial scales to provide the information required for policy and decision-making. This platform makes data accessible at multiple levels for practitioners of life cycle assessment, while making data interoperable across platforms. The proposed data hub platform and workflow are explained through the polymer electrolyte membrane electrolysis hydrogen production as a case study. The climate change environment impact of 1.17 ± 0.03 kg CO2 eq./kg H2 was calculated for the case study. The current data hub platform is limited to evaluating environmental impacts; however, future additions of economic and social aspects are envisaged.

10.B. Scientific session: The European Health Data Space (EHDS): A sea of change for health data in Europe

Abstract A wealth of data is generated every day throughout our health systems which, if leveraged well, can underpin the digital solutions and policies that will transform health and care. However, the complexity and divergence of rules, structures and processes within and across European Member States make it difficult to fully harness that potential at European scale. The European Health Data Space will address this challenge by empowering Europeans to access and control their health data and by offering a harmonised and simplified framework for the reuse of health data, including for research, innovation, and policymaking purposes. During the workshop, we will present an overview of the European Health Data Space framework and its implementation phases and discuss its benefits for targeted stakeholders, including citizens, researchers and policymakers in public health. Objectives 1. Emphasise the importance of leveraging health data for research, innovation and policymaking; 2. Present the European Health Data Space regulation and what actions are being undertaken to prepare for its implementation; 3. Discuss how access to their health data can enable patients and individuals play an active role in their own health journey; 4. Discuss what will be the main benefits for policymakers and researchers in public health resulting from enhanced access to health data. Introduction by Chair for presenting the European Health Data Space and the setting the scene for the session. Short presentations by speakers: 1. Implementing the EHDS: lessons from the EHDS2 pilot - Marianne Benderra, Health Data Hub, presenting his experience on developing and piloting the EHDS infrastructure for secondary use of health data, highlighting key project developments; 2. How can the EHDS enable participatory healthcare? - Henrique Martins, ISCTE, xShare project on how the xShare “Yellow Button” will empower individuals with access to and control over their health data and may foster the development of knowledge and tools necessary for prevention and management of their health; 3. What will the EHDS enable for research and policymaking? - Nienke Schutte, Scienciano, bringing the perspective of a leading European public health institute how the benefits the EHDS framework can bring for policymaking, research and innovation. 15 min Q&A session, taking questions from the audience, possibly also through Slido. Possible guiding questions: How can stakeholders contribute more broadly to the development of the EHDS? What support is needed to ensure that data users and data holders are prepared when the EHDS framework becomes operational? Conclusions 2 min by Chair. Speakers/Panelists Marianne Benderra French Health Data Hub, Paris, France Henrique Manuel Martins ISCTE, Lisbon, Portugal Nienke Schutte Sciensano, Brussels, Belgium

Barriers, facilitators, and recommendations to increase the use of a clinical decision support tool for managing chronic pain in primary care

Background and ObjectivePrimary care providers (PCPs) use poorly organized patient information in electronic health records (EHR) within a limited time when treating patients with chronic pain. Clinical decision support (CDS) tools assist PCPs by synthesizing patient information and prompting guideline-concordant treatment decisions. A CDS tool- Chronic Pain OneSheet was developed through a user-centered design process to support PCP’s decision-making for patients with chronic noncancer pain. OneSheet aggregates relevant patient information in one place in the EHR. OneSheet also guides PCPs in completing guideline-recommended opioid risk management tasks, tracking patient treatments, and documenting pain-related symptoms. Our objective was to identify barriers, facilitators, and recommendations to increase OneSheet use for chronic noncancer pain management in primary care. MethodsWe conducted 19 qualitative interviews with PCPs from two academic health systems who had access to OneSheet in their EHR. Interview transcripts were coded to identify common themes using a modified thematic approach. ResultsPCPs identified several barriers to using OneSheet, including limited time to address patient needs associated with multiple chronic conditions, resistance to changing established workflows, and complex OneSheet display. PCPs reported several facilitators to using OneSheet, such as OneSheet’s ability to serve as a hub for chronic pain data, easy access to features that facilitate completing mandatory tasks and improved planning for certain patient visits. PCPs recommended prioritizing access to commonly used features, adding display customization capabilities, and expanding access to patients and other team members to increase OneSheet use. ConclusionOur findings highlight the importance of acknowledging the PCP workflow and task load when designing CDS tools. Future CDS tools should balance the extent of information provided with assisting PCPs to fulfill mandatory tasks. Expanding CDS tools to multiple care team members and patients can also lead to higher use by facilitating data entry, leading to more streamlined care delivery.

Round 'Ere: Developing a novel method to engage community on the design of wellbeing data hubs

The promise of community benefit from health data linked at scale is often missing the perspective of communities themselves. This paper outlines a recent project on developing a peer research and public engagement methodology to build community-designed linked and open wellbeing data hubs. Drawing on action research and appreciative inquiry, we aimed to develop a novel method to engage community on the use of linked local data. In Spring 2023 we recruited and trained 15 community researchers in Widnes, UK to co-design and run a project on what feeling well meant for their community. We trained our community researchers in listening skills, methods, and data concepts. Over Summer 2023, they interviewed over 200 of their fellow residents. We then ran three design workshops to analyse their findings and co-design a data and community insights hub with local policymakers and voluntary sector representatives. Through repeated, comparative semi-structured interviews we tracked the community researchers’ progress. We used thematic analysis to explore the benefits and challenges at each stage of the project: recruitment, training, and field work. Benefits ranged from the practical value of capacity building through the research training to the key role of confidence in how community researchers perceived their own success during the project. Challenges ranged from more esoteric concepts like managing expectations of impact to practical concerns around diversity and interview recruitment. Overall, the project evidenced the value of peer research methodologies in involving public in data science both for the participants but also to wider community.

Mapping UK Pain Datasets to the OMOP Common Data Model: Lessons Learned

ObjectiveChronic Pain is defined as pain lasting for three months or longer and a report by the British Pain Society in 2016 found that 28 million adults live with this condition. Chronic pain research is problematic as it is poorly represented in health data and often in silos. Alleviate is the Advanced Pain Discovery Platform (APDP) Data Hub which is removing barriers to access to data. ApproachThe aim for Alleviate is to transform pain data sets within the UK to be Findable, Accessible, Interoperable and Reusable (FAIR) and be more discoverable for researchers via Health Data Research UK’s Cohort Discovery Tool. The tool enables identification of potential cohorts in real-time from datasets Alleviate provides. The onboarding requires datasets to be transformed to the open and widely used Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). ResultsWe collaboratively developed CaRROT tools and used our mapping expertise to transform datasets to OMOP-CDM. The tools have improved efficient mapping of clinical and research data, covering ~10 million individuals’ records. Subsequently we learned valuable lessons on how to accurately and consistently map across data sets to support federated discovery. We also identified a lack of standard vocabulary representations for pain specific data. ConclusionsMapping data to OMOP is not straightforward. Attention to detail is required to ensure that consistent mapping decisions are made to maximise data reusability and interoperability. ImplicationsIn sharing the lessons learned of mapping datasets to OMOP we aim to help improve interoperability of disparate data sources.

Lived Experience Co-Production Within the Alleviate Pain Data Hub

ObjectiveTo build an effective Pain data hub that benefits the public, people with chronic pain, and researchers. Alleviate is the Advanced Pain Discovery Platform (APDP) Data Hub. The aim is to make pain data Findable, Accessible, Interoperable, Reusable (FAIR) while raising awareness of chronic pain to improve the lives of people living with pain. ApproachAlleviate has two lived experience leads plus an Advisory Group (AG) of people with lived experience (PWLE) of chronic pain representing an online reference group of ~400 people. The leads have been involved throughout the project since inception and design, and chair regular project meetings to ensure all voices are heard equally. They lead the development of engagement materials, highlighting the importance of pain research to those with lived experience. This co-production ensures the project to stay focussed on what matters to those living with chronic pain. ResultsAlleviate has developed an inclusive environment for PWLE to be actively involved in decision-making and progression of the project. Together we have co-produced videos, static informational material, social media content and developed a new collaboration with an international pain charity to run a UK-wide pain survey. ConclusionsThis co-production model has allowed us to generate impactful outputs and opportunities, raising awareness of Alleviate and pain research. ImplicationsProviding an inclusive space in the heart of an academic project for PWLE has given additional focus for researchers and provided ownership to the public in activities that directly affect them.

Establishing Australia’s national linked COVID Register

Objective and ApproachThe Australian Institute of Health and Welfare (AIHW) is an independent statutory Australian Government agency tasked with collating health data across federal and state/territory governments, and is accredited to provide complex integration, de-identification and secure access to linked data. To support research into the COVID pandemic, the AIHW has created a national-level COVID Register, which integrates COVID case data from infectious disease registers, and data from hospitals and intensive care, immunisation, deaths, health system usage data, aged care and disability support. ResultsData were probabilistically linked using the AIHW’s linkage spine, matching names, addresses, date of birth and sex. The AIHW data linkage protocols are based on the Five Safes framework which reinforce management of the privacy and confidentiality of data. Linkage rates for COVID cases across participating jurisdictions generally exceeded 95%. Ongoing work aims to increase the coverage of COVID cases, both in time and to include case data from one remaining jurisdiction. ConclusionsLinking disparate datasets from multiple sources has created a critical resource for exploring COVID and patient journeys in Australia. Currently, the Register supports projects to estimate expenditure associated with COVID, describe the impact of COVID for subgroups such as people living rural and remote areas, or those with dementia. ImplicationsUltimately, the Register will be integrated into the National Health Data Hub to enable ongoing health research and analysis through enduring national linked health data. The value of the Register extends beyond COVID and into other infectious diseases and chronic conditions.

Comprehensive scRNA-seq analysis to identify new markers of M2 macrophages for predicting the prognosis of prostate cancer

Background Prostate cancer (PCa) has become the highest incidence of malignant tumor among men in the world. Tumor microenvironment (TME) is necessary for tumor growth. M2 macrophages play an important role in many solid tumors. This research aimed at the role of M2 macrophages’ prognosis value in PCa. Methods Single-cell RNA-seq (scRNA-seq) data and mRNA expression data were obtained from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA). Quality control, normalization, reduction, clustering, and cell annotation of scRNA-seq data were preformed using the Seruat package. The sub-populations of the tumor-associated macrophages (TAMs) were analysis and the marker genes of M2 macrophage were selected. Differentially expressed genes (DEGs) in PCa were identified using limma and the immune infiltration was detected using CIBERSORTx. Then, a weighted correlation network analysis (WGCNA) was constructed to identify the M2 macrophage-related modules and genes. Integration of the marker genes of M2 macrophage from scRNA-seq data analysis and hub genes from WGCNA to select the prognostic gene signature based on Univariate and LASSO regression analysis. The risk score was calculated, and the DEGs, biological function, immune characteristics related to risk score were explored. And a predictive nomogram was constructed. CCK8, Transwell, and wound healing were used to verify cell phenotype changes after co-cultured. Results A total of 2431 marker genes of M2 macrophage and 650 hub M2 macrophage-related genes were selected based on scRNA-seq data and WGCNA. Then, 113 M2 macrophage-related genes were obtained by overlapping the scRNA-seq data and WGCNA results. Nine M2 macrophage-related genes (SMOC2, PLPP1, HES1, STMN1, GPR160, ABCG1, MAZ, MYC, and EPCAM) were screened as prognostic gene signatures. M2 risk score was calculated, the DEGs, Immune score, stromal score, ESTIMATE score, tumor purity, and immune cell infiltration, immune checkpoint expression, and responses of immunotherapy and chemotherapy were identified. And a predictive nomogram was constructed. CCK8, Transwell invasion, and wound healing further verified that M2 macrophages promoted the proliferation, invasion, and migration of PCa (p < 0.05). Conclusions We uncovered that M2 macrophages and relevant genes played key roles in promoting the occurrence, development, and metastases of PCa and played as convincing predictors in PCa.

Gender Recognition from Selfie Images by Merging Convolutional Neural Networks with Genetic Algorithms

Human gender plays an imperative role as social construct and an essential form of an individual’s personality. Gender recognition is a fundamental task for human beings. It is highly reflected in social communication, forensic science, surveillance, and target marketing. Gender recognition previously depended only on standard face images. Using the term “standard” means that the image was taken in a standard light without any background variations and without any cropped parts. But this type of image is not found in real-world. These images are called non-standard images, as they have a lot of variations, like illumination and head pose. The image may also have a lot of faces, where one of them may wear sunglasses or other accessories. Using this type of image will affect the accuracy results of gender recognition approaches. Nowadays, selfie images appear as they are unconstrained images. People take selfie images of themselves. Selfie images are very complex, as some parts of the images are cropped and damaged. This paper proposes a (CNNGA) technique for gender recognition from selfie images by merging a deep learning approach with genetic algorithms. The proposed technique achieves 90.2% accuracy in recognizing gender from the selfie dataset. The experiments use various challenge datasets, which are widely adopted in the scientific community like LFW, Data Hub, FERET, and Caltech-web Faces.

Harmonizing Microneurography Metadata with Local Data Hubs: A Concept.

This work aims to improve FAIR-ness of the microneurography research by integrating the local (meta)data to existing research data infrastructures. In the previous work, we developed an odML based solution for local metadata storage of microneurography data. However, this solution is limited to a narrow community. As a next step, we propose the integration into the Local Data Hubs, data-sharing services within NFDI4Health infrastructure. We outline a first concept, that streams chosen data from the established odMLtables GUI.

Using Health IT Standards to Facilitate Metadata Exchange Between Research Data Management Systems.

The National Research Data Infrastructure for Personal Health Data (NFDI4Health) uses Local Data Hubs (LDHs) to manage locally research studies, documents and sensitive personal data to support controlled data sharing. While research data management (RDM) systems facilitate the storage and preparation of data and metadata as well as organizational access, they often lack support for interoperability standards of the application domain. To support the exchange with external registries of research studies, we chose 17 attributes to characterize the most relevant aspects of clinical trials (in the following named "metadata profile"). We implemented the metadata profile in the RDM system FAIRDOM SEEK using core attributes and SEEK's extended metadata feature and created a mapping conforming to the Health Level 7 Fast Healthcare Interoperability Resources (FHIR) standard version R4. Finally, we implemented a prototype application interface for exports in FHIR-JSON format. We plan to extend the interface to serve central registries and support specific FHIR Implementation Guides from various use cases.

The Ouest Data Hub: An Interregional Health Data Sharing Ecosystem for Research.

The Ouest Data Hub (ODH) a project lead by GCS HUGO which is a cooperation group of University Hospitals in the French Grand Ouest region represents a groundbreaking initiative in this territory, advancing health data sharing and reuse to support research driven by real-world health data. Central to its structure are the Clinical Data Warehouses (CDWs) and Clinical Data Centers (CDCs), essential for analytics and as the linchpin of the ODH's status as an interregional Learning Health System. Aimed at fostering innovation and research, the ODH's collaborative and multi-institutional model effectively utilizes both local and shared resources. Yet, the path is not without challenges, especially in data quality and interoperability, where ongoing harmonization and standard adherence are critical. In 2023, this facilitated access to extensive health data from over 9.3 million patient records, demonstrating the ODH's capacity for both monocentric and multicentric research across various clinical fields, in close collaboration with physicians. The integration of healthcare professionals is crucial, ensuring data's clinical relevance and guiding accurate interpretations. Future expansions of the ODH to new hospitals and data types promise to enhance its model further, already inspiring similar frameworks across France. This scalable model for health data ecosystems showcases the ODH's potential as a foundation for national and supranational data sharing efforts.

A Novel Job Similarity Index for Career Transition in the Mining Industry

In this study, with the primary goal of capturing ongoing digital transformation and automation impacts on the mining industry and its workforce, we conduct several interviews with mining industry experts in the USA and analyze our survey reports qualitatively and quantitatively through exploratory analysis. After the interpretation of the insights of industry experts, we proceed to generate a personalized and customized data analysis through a novel metric based on skills, knowledge, competencies, and occupational requirements, which quantifies the job similarities for occupations in the mining industry based on the publicly available database of the United States Department of Labor. We utilize text analytics to tokenize and classify the interviews to capture a better understanding of major response categories. The temporal analysis shows that the critical competency needs in the data science and autonomy category increases from 28% in current demands to 43%. In defining our metric, we also calculate Kullback–Leibler (KL) divergence for each job profile that enables determining whether and to what extent that job is transitionary in our test set based on the mean, standard deviation, and kurtosis of each job of interest. Our analysis reveals that the in-group job transitions are significantly easier than the between-group transitions, proving our initial assumptions and common sense. The generated heat maps provide the opportunity to present the gap between the current job and desired job profiles that provide feasible career change options, among others, offering individualized career paths for job seekers and promoting potential job transitions. Through the collection of industry-specific individual employee data, the AI system is envisaged to continue to learn as end users engage with the system, thus creating a central data hub specifically for the future workforce in the mining industry. Although the study has limitations on generalizability for qualitative assessments, it presents itself as a valuable application of how qualitative and quantitative approaches could be of value for future worker training in the mining sector.

Desiderata for discoverability and FAIR adoption of health data hubs

BackgroundThe future European Health Research and Innovation Cloud (HRIC), as fundamental part of the European Health Data Space (EHDS), will promote the secondary use of data and the capabilities to push the boundaries of health research within an ethical and legally compliant framework that reinforces the trust of patients and citizens. ObjectiveThis study aimed to analyse health data management mechanisms in Europe to determine their alignment with FAIR principles and data discovery generating best.practices for new data hubs joining the HRIC ecosystem. In this line, the compliance of health data hubs with FAIR principles and data discovery were assessed, and a set of best practices for health data hubs was concluded. MethodsA survey was conducted in January 2022, involving 99 representative health data hubs from multiple countries, and 42 responses were obtained in June 2022. Stratification methods were employed to cover different levels of granularity. The survey data was analysed to assess compliance with FAIR and data discovery principles. The study started with a general analysis of survey responses, followed by the creation of specific profiles based on three categories: organization type, function, and level of data aggregation. ResultsThe study produced specific best practices for data hubs regarding the adoption of FAIR principles and data discoverability. It also provided an overview of the survey study and specific profiles derived from category analysis, considering different types of data hubs. ConclusionsThe study concluded that a significant number of health data hubs in Europe did not fully comply with FAIR and data discovery principles. However, the study identified specific best practices that can guide new data hubs in adhering to these principles. The study highlighted the importance of aligning health data management mechanisms with FAIR principles to enhance interoperability and reusability in the future HRIC.

Kebijakan Penta Helix dalam Pengembangan Parawisata Musik di Kota Ambon

Ambon City is known as the World Music City. As a music city, Ambon has a tourism appeal related to music. This research aims to understand the implementation of the Pentahelix policy by the Ambon City Government for the development of Ambon City as a World Music City, and to formulate and recommend strategies for collaboration among the Pentahelix actors in the implementation of the policy for the development of Ambon City as a World Music City. This research utilizes qualitative descriptive methods. The results of this study indicate that the Pentahelix policy model needs further optimization because the integrity among stakeholders is still low. Furthermore, there are several formulated recommendations and strategies that can be applied, including establishing a collaborative forum for Ambon music tourism, promoting communication among stakeholders, strengthening the AMO as a data hub and coordination center, making AMO an independent institution, stimulating communication and business initiatives, conducting research related to music tourism, developing an appropriate tourism calendar, educating and raising public awareness, conducting promotional campaigns through media, periodic monitoring and evaluation, as well as human resource development.

Health outcomes in children with prenatal opioid exposure with and without neonatal abstinence syndrome in the first seven years of life: An observational cohort study.

Prenatal opioid exposure (POE) is a major public health consequence of the opioid epidemic. Long-term health outcomes associated with POE remain unclear, especially for children with POE without a diagnosis of neonatal abstinence syndrome (NAS). Here, we aimed to describe the health outcomes of children with POE and with POE and NAS compared to unexposed children during the first 7 years of life. In this retrospective observational cohort study, children born between 2015 and 2022 were identified from the Maternal and Infant Data Hub (MIDH), a data repository that continuously integrates maternal, neonatal, and pediatric records from two academic medical centers and one pediatric hospital system in the Midwest, USA. International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10 CM) chapters A00-N99 served as outcomes of interest. Annual incidence and crude incidence rate ratios were calculated to explore descriptive differences between the exposed and unexposed groups. The study included 22,002 children, 20,130 (91.5%) of whom were unexposed and 1872 (8.5%) were exposed. Of the 1872 exposed children, 371 (19.8%) received a diagnosis of NAS (POE + NAS) and 1501 were in the POE-NAS group. Across all 7 years, exposed children had a higher incidence of diagnoses in most ICD-10 CM chapters compared to unexposed children. A consistently higher incidence rate ratio of diagnosis was observed in both POE-NAS and POE + NAS groups (vs. unexposed) related to mental and behavioral disorders, eye diagnoses, and diseases of the musculoskeletal system and gastrointestinal systems. POE is associated with an increased risk of diagnoses in a number of ICD-10 CM chapters throughout childhood. These findings underscore the need for early screening and targeted interventions to support exposed children and improve their well-being. Further research is required to explore underlying mechanisms and develop preventive measures for at-risk populations. Understanding the conditions more often diagnosed in children with prenatal opioid exposure will help to improve care provided to this population. As a result of study findings, nurses who provide care to children with prenatal opioid exposure can prioritize their assessments and allocate time, resources, and education toward areas more likely to be affected.

Improving data participation for the development of artificial intelligence in dermatology

Artificial intelligence (AI) has the potential to significantly impact many aspects of dermatology. The visual nature of dermatology lends itself to innovations in this space. The robustness of AI algorithms depends on the quality, quantity, and variety of data on which it is trained and tested. Image collections can suffer from inconsistencies in image quality, underrepresentation of various anatomic sites and skin tones, and lack of benign counterparts leading to underperformance of algorithms in contexts other than one in which it is developed. Access to care, trust, rights, control, and transparency all play roles in the willingness of patients and health care providers and systems to collect, provide, and share data. Opportunities to improve data participation for the development of AI include the establishment of data hubs and public algorithms, federated learning strategies, development of renumeration ecosystems for patients and systems, and development of criteria and mechanisms for transparency.

EU Customs Data Hub: Data-Driven Approach to Customs in an Ever-Growing Digitalised Custom Landscape

EU Customs Data Hub: Data-Driven Approach to Customs in an Ever-Growing Digitalised Custom Landscape