Data Ecosystem Research Articles

Democratising data within a data-driven research centre: Examining the role of public engagement and involvement

Over the last decade there has been an increasing focus on utilising government-held data for large-scale data linkage research projects, with an emphasis on public benefit. Most, if not all, administrative data research initiatives will recognise public involvement and engagement (PI&E) as a cornerstone of research and emphasise that administrative data is essentially public data, and, therefore, that publics must have a say in how it is used. Much of this focuses on engaging with communities and their representative organisations. What is less explicitly discussed is the role that data-driven research plays within a broader public realm that is increasingly driven by data, or datafied. This paper will examine the ways in which PI&E both challenges and contributes to the datafication of society, through the work of the Administrative Data Research Centre Northern Ireland (ADRC NI), part of ADR UK. It will explore if – and how – PI&E in administrative data research can play a role in democratising this datafied society. The paper will use the Northern Ireland Public Data Panel (NIPDP) and the Voices of Young People in Care (VOYPIC) initiative as case studies to explore the ways in which ADRC NI successfully or unsuccessfully create sites of democratisation within the data ecosystem. Finally, the paper will consider how to amplify the democratic benefits of PI&E in data-driven research while minimising any potential harms, in the form of a potential framework for data justice within PI&E in data-driven research.

An innovative linked Electronic Health Record derived data ecosystem for understanding the complexities of health and ageing.

ObjectiveWe describe the creation of an Electronic Health Record (EHR) derived linked data platform within the Australian National Centre for Healthy Ageing (NCHA). ApproachOur approach involved establishing a core set of EHR data suitable for research from the sole public health provider within a defined geographic region. Relevant items were selected based on published literature and consensus processes and curated within a specialised research data warehouse. Curation involved: data validation, quality assessment, internal linkage between episodes and data harmonisation/merging. An AI supported natural language processing pipeline was implemented to extract text-based data. Approvals were obtained from government agencies to link commonwealth claims data (primary care, medication, aged care, death) and state administrative hospital data, for all residents aged ≥60 years. Publicly available datasets were scoped for inclusion and collaborations established to incorporate local council data. ResultsA core of 113 curated items for >900,000 people over 10 years have been established across 4 hospitals and 10 community/outpatient services. Linked state and commonwealth data were obtained for 179,089 residents aged >60 years (Jan 2010-May 2021, linkage accuracy 98.4%). Environmental (greenery, air pollution, walkability) and Census data have been incorporated for each neighbourhood (Statistical Area 1). Data access, extraction and release has been tested through over 30 use-case projects in: dementia, residential aged care, medication use, homelessness, environmental impacts on health and ageing and health service redesign. ImplicationsThe NCHA Data Platform provides an international exemplar for the use of linked EHR data in advancing population health research.

Co-Creating an Inclusion, Diversity, Equity, and Accessibility Strategy: Defining approach and outcomes in a health data research network

BackgroundHealth Data Research Network Canada (HDRN) has committed to strengthening data use to improve health equity. Key this priority is has been an increased operationalization of Inclusion, Diversity, Equity, and Accessibility (IDEA) across the organization and within the data research processes HDRN Canada supports. Missing, however, was a unifying strategy for embedding IDEA within all HDRN Canada initiatives. ApproachGuided by results from an internal environmental scan and network feedback, HDRN Canada began developing an IDEA strategy in 2023. With the end goal of embedding IDEA across all internal teams and across all HDRN Canada strategic goals, collaborative participation methods were chosen to involve all levels of the organization. A process was designed that included focus groups, interviews, and roundtables. ResultsCo-creating the IDEA strategy required broad buy-in including detailing expectations of the planning process in advance. A flexible management approach that enabled the team to adhere to the defined process allowed the Project Team to manage unanticipated challenges. 4 key strategies emerged focusing on learning, data quality and research, leadership, and community engagement. Developing the strategy with co-creative methods helped to identify practical approaches for HDRN to prioritize IDEA within its complex data research initiatives. ConclusionHDRN Canada has outlined its commitment to IDEA in the health data ecosystem. The co-created strategy supports efforts towards accomplishing its organizational objectives and priorities. It is also a valuable resource for those in the health data space working to ensure data research contributes to equitable health outcomes.

Knowledge-Driven Data Sharing Governance: Developing a data provenance model for constructing context-aware data sharing frameworks for linkage data within the All of Us Research Program.

Health data access and sharing in the U.S. have been hindered by complex privacy and security conditions, limiting research and improvement of health outcomes. This challenge is even greater in minority and sensitive populations - who would benefit most from integrative research. The All of Us Research Program is a National Institutes of Health funded initiative aiming to enhance health research through the analysis of lifestyle, environment, and genetics data from over one million participants, with a focus on communities historically underrepresented in biomedical research. The program is enhancing the utility of data by incorporating passive data streams, such as claims, electronic health records (EHRs), environmental, geocodes, mortality, and residential history. Further, through the use of national health information exchange standards, All of Us also aims to address technical challenges in linking EHRs from health information networks to deliver a more complete longitudinal participant record. However, such an initiative necessitates a thoughtful approach through data awareness, advancing data access in ways that preserve individual privacy while increasing scientific utility. Here, we describe our privacy-preserving record linkage (PPRL) and data privacy risk disclosure control strategy, emphasizing fit-for-purpose data provisioning. Our knowledge-driven data ecosystem architecture leverages a combination of PPRL, data provenance, and geocoding at multiple levels to support context-aware data sharing granular access control.

When we talk about Big Data, What do we really mean? Toward a more precise definition of Big Data.

Despite the lack of consensus on an official definition of Big Data, research and studies have continued to progress based on this "no consensus" stance over the years. However, the lack of a clear definition and scope for Big Data results in scientific research and communication lacking a common ground. Even with the popular "V" characteristics, Big Data remains elusive. The term is broad and is used differently in research, often referring to entirely different concepts, which is rarely stated explicitly in papers. While many studies and reviews attempt to draw a comprehensive understanding of Big Data, there has been little systematic research on the position and practical implications of the term Big Data in research environments. To address this gap, this paper presents a Systematic Literature Review (SLR) on secondary studies to provide a comprehensive overview of how Big Data is used and understood across different scientific domains. Our objective was to monitor the application of the Big Data concept in science, identify which technologies are prevalent in which fields, and investigate the discrepancies between the theoretical understanding and practical usage of the term. Our study found that various Big Data technologies are being used in different scientific fields, including machine learning algorithms, distributed computing frameworks, and other tools. These manifestations of Big Data can be classified into four major categories: abstract concepts, large datasets, machine learning techniques, and the Big Data ecosystem. This study revealed that despite the general agreement on the "V" characteristics, researchers in different scientific fields have varied implicit understandings of Big Data. These implicit understandings significantly influence the content and discussions of studies involving Big Data, although they are often not explicitly stated. We call for a clearer articulation of the meaning of Big Data in research to facilitate smoother scientific communication.

A conceptual framework for the government big data ecosystem (‘datagov.eco’)

The public sector, private firms, and civil society constantly create data of high volume, velocity, and veracity from diverse sources. This kind of data is known as big data. As in other industries, public administrations consider big data as the “new oil" and employ data-centric policies to transform data into knowledge, stimulate good governance, innovative digital services, transparency, and citizens' engagement in public policy. More and more public organizations understand the value created by exploiting internal and external data sources, delivering new capabilities, and fostering collaboration inside and outside of public administrations. Despite the broad interest in this ecosystem, we still lack a detailed and systematic view of it. In this paper, we attempt to describe the emerging Government Big Data Ecosystem as a socio-technical network of people, organizations, processes, technology, infrastructure, standards & policies, procedures, and resources. This ecosystem supports data functions such as data collection, integration, analysis, storage, sharing, use, protection, and archiving. Through these functions, value is created by promoting evidence-based policymaking, modern public services delivery, data-driven administration and open government, and boosting the data economy. Through a Design Science Research methodology, we propose a conceptual framework, which we call ‘datagov.eco’. We believe our ‘datagov.eco’ framework will provide insights and support to different stakeholders’ profiles, including administrators, consultants, data engineers, and data scientists.

Legal approaches in the Artificial Intelligence Act: implications for Ukrain

This article draws attention to the key features of the Laying down harmonised rules on Artificial Intelligence in the EU (Artificial Intelligence Act, AIA). The author analyzes the concept of “Artificial Intelligence Systems” and the approach to determining the risky areas of AI application and other features of the AIA. Promising directions for improving the Ukrainian legislation on artificial intelligence are being formed, in particular, in terms of the data ecosystem and their legal use in artificial intelligence systems.

A quantitative study of vocabulary categorization and reuse in the LIS field linked data

PurposeExploring diverse knowledge organization systems and metadata schemes in linked data, aiming to promote vocabulary usability and high-quality linked data creation within the LIS field.Design/methodology/approachWe used content analysis to select 77 articles from 13 library and information science journals around our research theme. We identified four dimensions: vocabularies participation, reuse, functions, and naming variations in linked data.FindingsThe vocabulary comprises seven main categories and their corresponding 126 vocabularies, which participate in linked data in single, two, and multiple dimensions. These vocabularies are used in the eight LIS subfields. Reusing vocabularies has become integral to linked data publishing, with six categories and their corresponding 66 vocabularies being reused. Ontologies are the most engaged and widely reused category of vocabulary in linked data practice. The mutual support among the three major categories and seven subfunctions of vocabulary promotes the sustainable development of linked data. Under a combination of factors, the phenomenon of terminology name changes and cross-usage between “vocabulary” and “ontology.”Research limitations/implicationsThis study has limitations. Although 77 articles on the topic of vocabularies applied in linked data were analyzed and presented with quantitative statistics and visualizations, the exploration of the topic tends to be a practical activity, with limited presence in scholarly articles. Moreover, this study’s analysis of the practical applications of linked data is relatively limited, and the sample literature focused on articles published in English, which may have affected the diversity and inclusiveness of the research sample.Practical implicationsPractically, this study does not confine the application of content analysis solely to the traditional exploration of knowledge organization topics, development trends, or course content. Instead, it integrates the dual perspectives of linked data and vocabularies, employing content analysis to analyze and objectively reveal the application issues of vocabularies in linked data. The conclusions can provide specific guidelines for future applications of vocabularies in the LIS subfields and contribute to promoting interoperability of vocabularies.Social implicationsThis research explores the relationship between linked data and vocabularies, highlighting the diverse manifestations and challenges of vocabularies in linked data. It provides theoretical references for the construction and further development of vocabularies considering technologies such as linked data, drawing attention to the potential and existing issues associated with linked open data vocabularies.Originality/valueThis study extends the application of content analysis to exploring vocabularies, especially Knowledge Organization Systems and metadata schemes in the LIS field linked data, highlighting the mutually beneficial interactions between linked data and vocabularies. It provides guidance for future vocabularies applications in the LIS field and offers insights into vocabularies construction and the healthy development of linked data ecosystems in the era of information technology.

Federating ‘Aquaculture 4.0’ for data-driven social and environmental sustainability

Aquaculture is undergoing a revolution of data-driven innovation. The rapid uptake of digital technologies in the sector contributes ever larger amounts of data that hold potential for improving decision making for aquatic food system sustainability. Realising the potential of digital tools and new data streams is, however, not inevitable. The current ecosystem of global and production-level digital aquaculture technologies and platforms is highly fragmented with a strong focus on private farm-level data for improving production efficiency, and limited publicly available data that can be used for social and environmental sustainability. Three scenarios for the future development of the aquaculture data ecosystem are possible (1) continued fragmentation; (2) centralisation, and (3) coordination in a federated ecosystem. Recognising a federated ecosystem as having the most potential for contributing to food system sustainability we provide three priority areas for improving data coverage, sharing and integration between and among farm-level and global platforms.

Bridging the gap: Gallup’s role supporting the official statistics ecosystem

The global data ecosystem is changing rapidly. New demands are increasingly being placed on National Statistical Offices (NSOs) worldwide to collect data to track a growing array of indicators. However, many NSOs lack the capacity to collect frequent, representative and high-quality data on even core metrics of national progress, such as food security. In response to this, there has been a growing number of partnerships between NSOs, international organisations such as the United Nations, and private sector organisations to address the data gap, which was only accelerated by the COVID-19 pandemic. Using Gallup, the global research and analytics firm, as an example, this paper highlights a number of areas where the private sector can provide value to the realm of official statistics. By adhering to globally recognised statistical protocols with a firm commitment to the principles of rigour, transparency, and respondent confidentiality, organisations such as Gallup play an important role in supporting the collection of official statistics. They can also bridge key data gaps related to the most pressing challenges of our time, and drive accountability on key issues of national and global development.

Clinical and biobehavioral phenotypic assessments and data harmonization for the RE-JOIN research consortium: Recommendations for common data element selection

BackgroundThe Restoring Joint Health and Function to Reduce Pain (RE-JOIN) Consortium is part of the Helping to End Addiction Long-term® (HEAL) Initiative. HEAL is an ambitious, NIH-wide initiative to speed scientific solutions to stem the national opioid public health crisis. The RE-JOIN consortium’s over-arching goal is to define how chronic joint pain-mediating neurons innervate different articular and peri-articular tissues, with a focus on the knee and temporomandibular joints (TMJ) across species employing the latest neuroscience approaches. The aim of this manuscript is to elucidate the human data gathered by the RE-JOIN consortium, as well as to expound upon its underlying rationale and the methodologies and protocols for harmonization and standardization that have been instituted by the RE-JOIN Consortium. MethodsThe consortium-wide human models working subgroup established the RE-JOIN minimal harmonized data elements that will be collected across all human studies and set the stage to develop parallel pre-clinical data collection standards. Data harmonization considerations included requirements from the HEAL program and recommendations from the consortium’s researchers and experts on informatics, knowledge management, and data curation. ResultsMultidisciplinary experts − including preclinical and clinical researchers, with both clinician-scientists- developed the RE-JOIN’s Minimal Human Data Standard with required domains and outcome measures to be collected across projects and institutions. The RE-JOIN minimal data standard will include HEAL Common Data Elements (CDEs) (e.g., standardized demographics, general pain, psychosocial and functional measures), and RE-JOIN common data elements (R-CDE) (i.e., both general and joint-specific standardized and clinically important self-reported pain and function measures, as well as pressure pain thresholds part of quantitative sensory testing). In addition, discretionary, site-specific measures will be collected by individual institutions (e.g., expanded quantitative sensory testing and gait biomechanical assessments), specific to the knee or TMJ. Research teams will submit datasets of standardized metadata to the RE-JOIN Data Coordinating Center (DCG) via a secure cloud-based central data repository and computing infrastructure for researchers to share and conduct analyses on data collected by or acquired for RE-JOIN. RE-JOIN datasets will have protected health information (PHI) removed and be publicly available on the SPARC portal and accessible through the HEAL Data Ecosystem. ConclusionData Harmonization efforts provide the multidisciplinary consortium with an opportunity to effectively collaborate across decentralized research teams, and data standardization sets the framework for efficient future analyses of RE-JOIN data collected by the consortium. The harmonized phenotypic information obtained will significantly enhance our understanding of the neurobiology of the pain-pathology relationships in humans, providing valuable insights for comparison with pre-clinical models.

Childhood Cancer Survivorship Passport Challenges in the European Health Data Space.

Innovation in cancer therapy has increased childhood cancer survival rates. However, survivors are still at risk of developing late effects. In the digital transformation of the health sector, the Survivorship Passport (SurPass) can support long-term follow-up care plans. Gaps in seamless connectivity among hospital departments, primary care, combined with the time of health professionals required to collect and fill-in health data in SurPass, are barriers to its adoption in daily clinical practice. The PanCareSurPass (PCSP) project was motivated to address these gaps by a new version of SurPass (v2.0) that supports semi-automatic assembly from organizational Electronic Health Record (EHR) systems of the treatment summary data using HL7 FHIR, to create SurPass, and to link it to regional or national digital health infrastructures in six European countries. In this paper we present the methodology used to develop the SurPass technical implementation strategy with special focus on the European Health Data Space (EHDS). The recently provisionally approved EHDS regulation instruments a digital health data ecosystem with opportunities for cost-effective SurPass implementation across Europe. Moving forward, a European HL7 FHIR SurPass Implementation Guide along with synthetic data sets, and validation tools can enrich the European Electronic Health Record Exchange Format (EEHRxF) with use cases on health & wellness of childhood cancer survivors.

Constructing a Clinical Patient Similarity Network of Gastric Cancer.

Clinical molecular genetic testing and molecular imaging dramatically increase the quantity of clinical data. Combined with the extensive application of electronic health records, a medical data ecosystem is forming, which calls for big-data-based medicine models. We tried to use big data analytics to search for similar patients in a cancer cohort, showing how to apply artificial intelligence (AI) algorithms to clinical data processing to obtain clinically significant results, with the ultimate goal of improving healthcare management. In order to overcome the weaknesses of most data processing algorithms that rely on expert labeling and annotation, we uniformly adopted one-hot encoding for all types of clinical data, calculating the Euclidean distance to measure patient similarity and subgrouping via an unsupervised learning model. Overall survival (OS) was investigated to assess the clinical validity and clinical relevance of the model. We took gastric cancers (GCs) as an example to build a high-dimensional clinical patient similarity network (cPSN). When performing the survival analysis, we found that Cluster_2 had the longest survival rates, while Cluster_5 had the worst prognosis among all the subgroups. As patients in the same subgroup share some clinical characteristics, the clinical feature analysis found that Cluster_2 harbored more lower distal GCs than upper proximal GCs, shedding light on the debates. Overall, we constructed a cancer-specific cPSN with excellent interpretability and clinical significance, which would recapitulate patient similarity in the real-world. The constructed cPSN model is scalable, generalizable, and performs well for various data types.

Estimating livestock biomass across diverse populations and data ecosystems.

Estimates of livestock biomass can be used as denominators in disease burden estimates, in addition to informing assessments of resource use and environmental impacts. This article explores the challenges of accurately estimating biomass across different scales and data ecosystems, with a particular focus on the use of biomass in the Global Burden of Animal Diseases programme. The greatest of these challenges is a lack of subgroup (breed, age, sex)-specific data on populations and liveweights at national and subnational level. This can be overcome by using global datasets and generic estimates of liveweight for each species, though this approach fails to account for the diversity of livestock systems.

A scalable and transparent data pipeline for AI-enabled health data ecosystems.

Transparency and traceability are essential for establishing trustworthy artificial intelligence (AI). The lack of transparency in the data preparation process is a significant obstacle in developing reliable AI systems which can lead to issues related to reproducibility, debugging AI models, bias and fairness, and compliance and regulation. We introduce a formal data preparation pipeline specification to improve upon the manual and error-prone data extraction processes used in AI and data analytics applications, with a focus on traceability. We propose a declarative language to define the extraction of AI-ready datasets from health data adhering to a common data model, particularly those conforming to HL7 Fast Healthcare Interoperability Resources (FHIR). We utilize the FHIR profiling to develop a common data model tailored to an AI use case to enable the explicit declaration of the needed information such as phenotype and AI feature definitions. In our pipeline model, we convert complex, high-dimensional electronic health records data represented with irregular time series sampling to a flat structure by defining a target population, feature groups and final datasets. Our design considers the requirements of various AI use cases from different projects which lead to implementation of many feature types exhibiting intricate temporal relations. We implement a scalable and high-performant feature repository to execute the data preparation pipeline definitions. This software not only ensures reliable, fault-tolerant distributed processing to produce AI-ready datasets and their metadata including many statistics alongside, but also serve as a pluggable component of a decision support application based on a trained AI model during online prediction to automatically prepare feature values of individual entities. We deployed and tested the proposed methodology and the implementation in three different research projects. We present the developed FHIR profiles as a common data model, feature group definitions and feature definitions within a data preparation pipeline while training an AI model for "predicting complications after cardiac surgeries". Through the implementation across various pilot use cases, it has been demonstrated that our framework possesses the necessary breadth and flexibility to define a diverse array of features, each tailored to specific temporal and contextual criteria.

Conceptual Model of Data Ecosystem for Non-Tech Small Organizations

Using data effectively is crucial for success in the current business world. Small non-tech organizations struggle with this due to limited resources, different systems, and constantly changing technology. To address these challenges, this study proposes a conceptual framework for designing a flexible and reusable data ecosystem, tailored specifically for small non-tech organizations. It also outlines the feedback loops that drive continuous improvement and innovation within the data ecosystem. Key considerations include data source integration, data quality management, and analytics capabilities. The proposed framework offers small non-tech organizations a structured approach to managing data assets effectively, enabling them to make informed decisions, optimize processes, and drive innovation.

Plugging Public Health Data into the Health IT Ecosystem to Protect National Health

The Covid-19 pandemic exposed many challenges in the United States’ public health systems, and in particular the antiquated data ecosystem that hinders the nation’s ability to use timely data to detect and respond to emerging public health threats. One key challenge of the public health data ecosystem is the limited use of electronic, standardized, and interoperable ways for data to be readily shared between health care delivery and public health systems. At the start of the Covid-19 pandemic, most data exchange between health care and public health, and across public health, was manual, relying on faxes, labor-intensive data entry, and other outdated technology. Although significant progress has been made over the last several years to introduce more standardization and connectivity between public health and health care IT systems, more work is needed. This commentary describes the benefits and potential of robust, modern interoperability between health care and public health systems, defines key policies and technology activities that will drive toward a future state of a highly connected public health and health care data ecosystem, and summarizes potential challenges that must be overcome.

Data-Driven Management: The Impact of Big Data Analytics on Organizational Performance

Inevitably, in such a fast-moving landscape of big data analytics lies the transformative opportunity for any organization to unlock new avenues of growth, operational efficiency, and strategic decision-making. This paper contributes a comprehensive methodology that will help businesses take advantage of big data analytics to secure a continued competitive advantage. At the core of this methodology is to have a robust data governance framework that will establish the security, integrity, and accessibility of the enterprise data assets by specifying relevant policies, processes, and technologies. This, in turn, within such a framework, allows state-of-the-art AI-driven anomaly detection mechanisms for encryption and access control to implement protection measures around sensitive information while enabling secure yet efficient data utilization. The methodology also continues its approach of putting in place an integrated data ecosystem that brings together different pockets or sources of data, such as real-time operation data, customer interaction, and unstructured information. A critical component of this methodology is putting in place the advanced predictive analytics capabilities that could be run based on tapping into the power of machine learning algorithms; by doing so, the organization will be predicting market trends, customer behavior, and risks highly accurately. This will be a very proactive way of making decisions that would let the business efficiently use the available resources, innovate products and services ahead of time, and create a distinctly competitive advantage. The transformative ability of this methodology for big data analytics opened new channels toward growth and innovation and firmly established the organization as an industry leader with long-lasting competitive advantage. The place of data-driven insight, data culture, and responsible data practice has been the key to success in this organization.

Sharing Data from the Human Tumor Atlas Network through Standards, Infrastructure, and Community Engagement.

The Data Coordinating Center (DCC) of the Human Tumor Atlas Network (HTAN) has played a crucial role in enabling the broad sharing and effective utilization of HTAN data within the scientific community. Data from the first phase of HTAN are now available publicly. We describe the diverse datasets and modalities shared, multiple access routes to HTAN assay data and metadata, data standards, technical infrastructure and governance approaches, as well as our approach to sustained community engagement. HTAN data can be accessed via the HTAN Portal, explored in visualization tools-including CellxGene, Minerva, and cBioPortal-and analyzed in the cloud through the NCI Cancer Research Data Commons nodes. We have developed a streamlined infrastructure to ingest and disseminate data by leveraging the Synapse platform. Taken together, the HTAN DCC's approach demonstrates a successful model for coordinating, standardizing, and disseminating complex cancer research data via multiple resources in the cancer data ecosystem, offering valuable insights for similar consortia, and researchers looking to leverage HTAN data.

Gaia-X's CCI Data Ecosystem Strategy and Its Implications for the Domestic CCI Sector

Gaia-X's CCI Data Ecosystem Strategy and Its Implications for the Domestic CCI Sector