Development Of Data Standards Research Articles

Data standards based Mine Side Data Integration Governance Platform Research and Applications

INTRODUCTION: With the advent of the mining digitalization era, the rapid growth and diversity of mine-side data make mining production management more complex. In this context, it becomes crucial to build a set of mine-side data integration and governance platforms based on data standards. The establishment of this platform aims to solve the problems of mining data fragmentation and inconsistent standards and to improve the efficiency of data management and utilization.OBJECTIVES: The purpose of this study is to construct an efficient mine-side data integration and governance platform by studying and applying data standards to address the challenges of data heterogeneity and fragmentation. Through the development of unified data standards, the Author can realize the standardized management of mining data, improve data quality, and provide more reliable support for mining production.METHODS: The study adopts a comprehensive approach. First, a comprehensive understanding of the problems and needs facing mining data management was obtained through a literature review and field research. Second, data standards applicable to mine-side data are developed to ensure that all types of data can be consistently understood and processed. Next, a data integration and governance platform based on these standards is established, and advanced data mining and artificial intelligence technologies are applied to realize automatic integration and cleaning of multi-source data.RESULTS: Through experiments, this study proved that a mine-side data integration governance platform based on data standards was successfully constructed. The platform realizes efficient integration and cleaning of data and improves the consistency and quality of data. In practical application, the platform provides more accurate data support for mining production management and achieves remarkable results.CONCLUSION: This study provides strong theoretical and practical support for the research and application of mine-side data integration and governance platforms based on data standards. Through the development of data standards, an efficient data management platform has been established, which provides practical solutions for solving mining data management challenges. In the future, the performance of the platform can be further optimized, and the updating and maintenance of data standards can be strengthened to adapt to the constant changes in the digital development of the mining industry. This research has a positive impact on promoting the construction of mining industry informatization and improving the level of production management.

Use of machine learning tools to predict health risks from climate-sensitive extreme weather events: A scoping review

Machine learning (ML) algorithms may play a role in predicting the adverse health impacts of climate-sensitive extreme weather events because accurate prediction of such effects can guide proactive clinical and policy decisions. To systematically review the literature that describe ML algorithms that predict health outcomes from climate-sensitive extreme weather events. A comprehensive literature search was performed in the following databases from inception–October 2022: Ovid MEDLINE, Ovid EMBASE, The Cochrane Library, Web of Science, bioRxiv, medRxiv, Institute of Electrical and Electronic Engineers, Google Scholar, and Engineering Village. The retrieved studies were then screened for eligibility against predefined inclusion/exclusion criteria. The studies were then qualitatively synthesized based on the type of extreme weather event. Gaps in the literature were identified based on this synthesis. Of the 6096 records screened, seven studies met the inclusion criteria. Six of the studies predicted health outcomes from heat waves, and one for flooding. Health outcomes described included 1) all-cause non-age standardized mortality rates, 2) heat-related conditions and 3) post-traumatic stress disorder. Prediction models were developed using six validated ML techniques including non-linear exponential regression, logistic regression, spatiotemporal Integrated Laplace Approximation (INLA), random forest and decision tree methods (DT), and support vector machines (SVM). Use of ML algorithms to assess adverse health impacts from climate-sensitive extreme weather events is possible. However, to fully utilize these ML techniques, better quality data suitable for use is desirable. Development of data standards for climate change and health may help ensure model robustness and comparison across space and time. Future research should also consider health equity implications.

Easing the data sharing burden: perspectives and principles to successfully leverage AD data repositories, infrastructures and functionality

AbstractCritical Path Institute (C‐Path) is a nonprofit organization that brings together patient groups, academic institutions, the pharmaceutical industry and regulatory agencies from around the world to improve public health. C‐Path provides the legal, scientific, and regulatory infrastructure to generate a unique neutral environment for medical product development, supported by more than a decade of experience in data standards development, platform development and hosting, patient‐level data privacy stewardship, data platform security, and controlled access methodology. The Critical Path for Alzheimer’s Disease (CPAD) is a pre‐competitive consortium that aims to promote, support, and manage a diverse source and format of data and facilitate knowledge sharing from the Alzheimer disease (AD) drug development community. The CPAD database currently contains 65 clinical trials and observational studies, with over 93,000 individual anonymized patient‐level records, with global access to many datasets through the C‐Path Online Data Repository (CODR). The unparalleled breadth, depth and wealth of data will enable us to discover yet‐to‐be identified unmet needs and unanswered questions. Further, CODR is interoperable with the Global Alzheimer’s Association Interactive Network (GAAIN), Vivli and other data platforms. The secure and neutral data sharing framework of CPAD has resulted in the development of several regulatory‐grade tools that help optimize drug development and accelerate bringing new therapies to patients in need. Sufficient case examples of successful data sharing are now available and should inform and encourage future data sharing initiatives.

Data sharing andontology use among agricultural genetics, genomics, andbreeding databases andresources ofthe Agbiodata Consortium.

Over the last couple of decades, there has been a rapid growth in the number and scope of agricultural genetics, genomics and breeding databases and resources. The AgBioData Consortium (https://www.agbiodata.org/) currently represents 44 databases and resources (https://www.agbiodata.org/databases) covering model or crop plant and animal GGB data, ontologies, pathways, genetic variation and breeding platforms (referred to as 'databases' throughout). One of the goals of the Consortium is to facilitate FAIR (Findable, Accessible, Interoperable, and Reusable) data management and the integration of datasets which requires data sharing, along with structured vocabularies and/or ontologies. Two AgBioData working groups, focused on Data Sharing and Ontologies, respectively, conducted a Consortium-wide survey to assess the current status and future needs of the members in those areas. A total of 33 researchers responded to the survey, representing 37 databases. Results suggest that data-sharing practices by AgBioData databases are in a fairly healthy state, but it is not clear whether this is true for all metadata and data types across all databases; and that, ontology use has not substantially changed since a similar survey was conducted in 2017. Based on our evaluation of the survey results, we recommend (i) providing training for database personnel in a specific data-sharing techniques, as well as in ontology use; (ii) further study on what metadata is shared, and how well it is shared among databases; (iii) promoting an understanding of data sharing and ontologies in the stakeholder community; (iv) improving data sharing and ontologies for specific phenotypic data types and formats; and (v) lowering specific barriers to data sharing and ontology use, by identifying sustainability solutions, and the identification, promotion, or development of data standards. Combined, these improvements are likely to help AgBioData databases increase development efforts towards improved ontology use, and data sharing via programmatic means. Database URL https://www.agbiodata.org/databases.

Digital technologies: Innovations that transform the face of drug development.

Recently, digital health technologies (DHTs) and digital biomarkers have gained a lot of traction in clinical investigations, motivating sponsors, investigators, and regulators to discuss and implement integrated approaches for deploying DHTs. These new tools present new and unique challenges for optimal technology integration in clinical trial processes, including operational, ethical, and regulatory issues. In this paper, we gathered different perspectives to discuss challenges and perspectives from three different stakeholders: industry, US regulators, and a public-private partnership consortium. The complexities of DHT implementation, which include regulatory definitions, defining the scope of validation experiments, and the need for partnerships between BioPharma and the technology sectors, are highlighted. Most of these challenges are related to translation of DHT-derived measures into endpoints that are meaningful to clinicians and patients, participant safety, training, and retention and privacy of data. The example of the Wearable Assessments in the Clinic and Home in PD (WATCH-PD) study is discussed as an example that demonstrated the advantages of pre-competitive collaborations, which include early regulatory feedback, data sharing, and multistakeholder alignment. Future advances in DHTs are expected to spur device-agnostic measured development and incorporate patient reported outcomes in drug development. More efforts are needed to define validation experiments for a defined context of use, incentivize data sharing and development of data standards. Multistakeholder collaborations via precompetitive consortia will help facilitate broad acceptance of DHT-enabled measures in drug development.

Individual differences and diversity in human physiological responses to light.

Exposure to light affects our physiology and behaviour through a pathway connecting the retina to the circadian pacemaker in the hypothalamus – the suprachiasmatic nucleus (SCN). Recent research has identified significant individual differences in the non-visual effects of light,mediated by this pathway. Here, we discuss the fundamentals and individual differences in the non-visual effects of light. We propose a set of actions to improve our evidence database to be more diverse: understanding systematic bias in the evidence base, dedicated efforts to recruit more diverse participants, routine deposition and sharing of data, and development of data standards and reporting guidelines.

A Guide to Using GitHub for Developing and Versioning Data Standards and Reporting Formats

AbstractData standardization combined with descriptive metadata facilitate data reuse, which is the ultimate goal of the Findable, Accessible, Interoperable, and Reusable (FAIR) principles. Community data or metadata standards are increasingly created through an approach that emphasizes collaboration between various stakeholders. Such an approach requires platforms for collaboration on the development process that centers on sharing information and receiving feedback. Our objective in this study was to conduct a systematic review to identify data standards and reporting formats that use version control for developing data standards and to summarize common practices, particularly in earth and environmental sciences. Out of 108 data standards and reporting formats identified in our review, 32 used GitHub as the version control platform, and no other platforms were used. We found no universally accepted methodology for developing and publishing data standards. Many GitHub repositories did not use key features that could help developers to gather user feedback, or to create and revise standards that build on previous work. We provide guidance for community‐driven standard development and associated documentation on GitHub based on a systematic review of existing practices.

Fast-Tracking Health Data Standards Development and Adoption in Real-World Settings: A Pilot Approach.

Pilot-testing is important in standards development because it facilitates agile navigation of the gap between needs for and use of standards in real-world settings and can reveal the practicalities of implementation. As the implementation and use of health data standards are usually more complicated than anticipated, the Office of the National Coordinator for Health Information Technology (ONC) routinely oversees and organizes relevant pilot projects. This article provides an in-depth look into a sample of ONC's standards-focused pilot projects to (1) inform readers of the complexities of developing, implementing, and advancing standards and (2) guide those seeking to evaluate new standards through pilot projects. The ONC's approach to conducting pilot projects begins with identifying a clinical care need, research requirement, or policy outcome that is not well supported by existing standards through a landscape review. ONC then selects a testing approach based on the identified need and maturity of relevant standards. Next, ONC identifies use cases and sites to pilot-test the relevant standard. Once complete, ONC publishes a report that informs subsequent projects and standards development. Pilot projects presented here are organized into three categories related to their demonstrated focus and related approach: (1) improving standards for presenting and sharing clinical genetic data, (2) accelerating the development and implementation of new standards, and (3) facilitating clinical data reuse. Each project illustrates the pilot approach from inception to next steps, capturing the role of collaboration among standards development organizations, stakeholders, and end-users to ensure standards are practical and fit for purpose. The ONC approach identifies implementation difficulties prior to broader adoption and use of standards, and provides insight into the steps needed to scale use of standards. The ONC's organization of pilot projects serves as a natural accelerator for building communities of practice, often providing a well-connected beneficiary of lessons learned.

The Evolution of GCMD Keywords — An instructive tale of data standards development and adoption

The Evolution of GCMD Keywords — An instructive tale of data standards development and adoption

The Development of Library Sector Terminology in Latvia and Problems and Solutions for Translating RDA Standard Terms

    The National Library of Latvia (NLL) is the methodological centre of Latvian libraries in data creation and ensuring the development of data standards as well as the methodical centre co-ordinating the terminology work of library field and related branches. The historical insight shows the development of terminology work and allows to trace the development of terminology work and problems. The terms are adopted and approved by the subcommittee of the Terminology Commission of the Latvian Academy of Science (LAS).    The article highlights the role of the NLL in the implementation and translation of Resource Descriptions and Access (RDA) standard in Latvia. It is the standard for descriptive cataloguing, intended for use by libraries and other cultural organizations such as museums and archives. The RDA standard includes a set of data elements, guidelines and instructions that can be adapted for other information communities around the world, such as museums and archives. The translation problems of RDA Glossary are described, as well as principles and solutions for correct reproduction of RDA terms and their explanations in Latvian. An overview is provided of how the correct, appropriate terminology was followed in the translation process of the terms, so that the chosen term, in the appropriate meaning, could be used throughout the translation and related documents.Finally, the influence of RDA terms on the common data model of Latvia memory institutions for the unified discovery of cultural heritage digital resources is also outlined.

Enabling digitisation to reduce risk in the food system

Enabling digitisation to reduce risk in the food system

We Need Standards for Digital Cooperation to Occur

This article makes the case for a new regional mechanism to coordinate the development of data standards. Data standards are necessary to achieve digital cooperation. We need standards to create data value chains that string together data collection through collaborative platforms in order to generate insights and solve longstanding problems. Organizations of all sizes, whether public, private, or not-for-profit, need a suite of data governance standards to manage issues such as data ownership and use, security, residency, privacy, and the protection of fundamental rights.1

HDMF: Hierarchical Data Modeling Framework for Modern Science Data Standards.

A ubiquitous problem in aggregating data across different experimental and observational data sources is a lack of software infrastructure that enables flexible and extensible standardization of data and metadata. To address this challenge, we developed HDMF, a hierarchical data modeling framework for modern science data standards. With HDMF, we separate the process of data standardization into three main components: (1) data modeling and specification, (2) data I/O and storage, and (3) data interaction and data APIs. To enable standards to support the complex requirements and varying use cases throughout the data life cycle, HDMF provides object mapping infrastructure to insulate and integrate these various components. This approach supports the flexible development of data standards and extensions, optimized storage backends, and data APIs, while allowing the other components of the data standards ecosystem to remain stable. To meet the demands of modern, large-scale science data, HDMF provides advanced data I/O functionality for iterative data write, lazy data load, and parallel I/O. It also supports optimization of data storage via support for chunking, compression, linking, and modular data storage. We demonstrate the application of HDMF in practice to design NWB 2.0 [13], a modern data standard for collaborative science across the neurophysiology community.

692-P: Development and Adoption of CDISC Clinical Data Standards for Type 1 Diabetes (T1D)

Development and adoption of global, non-proprietary and platform-independent CDISC standards a) enables traceability from data collection through data analysis, b) improves data quality, c) reduces costs, d) streamlines processes facilitating data sharing, and e) enables reliable and accurate data reuse. CDISC collaborates with type 1 diabetes subject matter experts to lead a unique effort that brings academia and industry together to create T1D clinical data standards in the areas of pediatrics, devices, exercise, and prevention. The T1D team is tasked with developing standards for collection, tabulation, and analysis of the data by building on existing foundational diabetes standards. They will use a multiphase, consensus-driven process, that includes scoping key data concepts commonly used in T1D clinical studies. The Pediatrics and Devices team has identified several data concepts that would benefit from development of data standards: diabetic ketoacidosis, diabetes history, CGM data, insulin management, laboratory tests related to diabetes, and vital signs percentiles for pediatrics. The public review of the standards developed will be launched in Summer 2019. The Prevention and Exercise team has initiated the scoping process to identify the most urgent areas for standards development, which will be followed by modeling. The public review of these standards will commence beginning of 2020. As the project progresses, the T1D community is encouraged to participate in the informational webinars and public review stages, which will enable all-inclusive development and adoption of the resultant data standards. Factors involved in the adoption of clinical data standards within organizations will be discussed. Disclosure J.M. Owen: None. Funding The Leona M. and Harry B. Helmsley Charitable Trust

The need for integrated public health surveillance to address sexually transmitted and blood-borne syndemics.

A national approach to addressing sexually transmitted and blood-borne infections (STBBIs) was recently articulated in the Public Health Agency of Canada’s new A Pan-Canadian Framework for Action: Reducing the health impact of sexually transmitted and blood-borne infections in Canada by 2030. This Framework promotes an integrated approach, with a focus on the key populations that are affected by overlapping epidemics (i.e., syndemics). We advance the idea that integrating surveillance would be helpful in characterizing and understanding the populations, locations, risk behaviours and other drivers that contribute to STBBI syndemics. The creation of matched or linked data systems that would allow routine reporting of integrated data is challenged by the technical barriers of integrating data silos as well as by the privacy and ethical considerations of merging sensitive individual-level data. Lessons can be learned from jurisdictions where an improved understanding of syndemics, through integrated STBBI surveillance, has led to more efficient and effective operational, program and policy decisions. Emerging enablers include the development of data standards and guidelines, investment in resources to overcome technical challenges and community engagement to support the ethical and non-stigmatizing use of integrated data. The Framework’s call to action offers an opportunity for national discussion on priorities and resources needed to advance STBBI syndemic surveillance for local, regional and national reporting in Canada.

Electronic Health Record-Based Registries: Clinical Research Using Registries in Colon and Rectal Surgery.

Electronic health records (EHRs) or electronic medical records (EMRs) contain a vast amount of clinical data that can be useful for multiple purposes including research. Disease registries are collections of data in predefined formats for population management, research, and other purposes. There are differences between EHRs and registries in the data structure, data standards, and protocols. Proprietary EHR systems use different coding systems and data standards, which are usually kept secret. For EHR data to flow seamlessly into registries, there is the need for interoperability between EHR systems and between EHRs and registries. The levels of interoperability required include functional, structural, and semantic interoperability. EHR data can be manually mapped to registry data, but that is a tedious, resource-intensive endeavor. The development of data standards that can be used as building blocks for both EHRs and registries will help overcome the problem of interoperability.

Streamlining clinical research: The National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH) and Department of Defense (DoD) sport-related concussion common data element (CDE) recommendations

ObjectiveThe purpose of the NINDS CDE project is to educate new clinical investigators, increase efficiency and effectiveness of clinical research studies and treatment, increase data quality, facilitate data sharing, significantly reduce study start-up time, and more effectively aggregate information into significant metadata results. In 2016, as part of the NINDS CDE project to develop data standards for all clinical neuroscience research, NINDS, NIH and the DoD initiated the development of Sport-Related Concussion (SRC) CDEs.BackgroundTBI CDE recommendations were published on the NINDS CDE website in 2010, but lacked a thorough inclusion of SRC. In August 2016, a new SRC-specific working group (WG) began developing and identifying CDEs, template case report forms (CRFs), and guidelines to assist investigators conducting SRC-specific clinical research studies.Design/methodsThe CDE WG, which consisted of 34 worldwide SRC research experts, met regularly via teleconference over several months. The WG was divided into 3 subgroups to examine SRC during defined periods relative to time of injury: Acute (72 hours post-concussion), Sub-Acute (3 days-3 months post-concussion) and Persistent/Chronic (3 months and greater post-concussion).ResultsVersion 1.0 of the SRC CDEs were available on the NINDS CDE website in June 2017. These include Core and Supplemental, Highly Recommended CDEs or instruments for cognitive measures and symptom checklists, as well as, other outcomes and endpoints, and sample CRFs for domains typically included in clinical research studies (e.g., vestibular, oculomotor, balance, anxiety, depression).ConclusionThe NINDS CDEs are reviewed and updated regularly as research advancements or changes to specific recommendations are deemed appropriate. Because the CDEs are an evolving resource, continued feedback is important for improved use and utility. The use of SRC CDE recommendations is highly encouraged for SRC related researchers as they serve as a valuable starting point and facilitate streamlining and sharing data.

Sharing Data to Accelerate Medicine Development and Improve Neonatal Care: Data Standards and Harmonized Definitions

Sharing Data to Accelerate Medicine Development and Improve Neonatal Care: Data Standards and Harmonized Definitions

Editorial and News

Editorial and News

Taming Extinct Beasts: Strategies, Projects, and Metrics for Managing the World’s Largest Fossil Collection, the United States National Fossil Collection at the Smithsonian National Museum of Natural History

The Smithsonian National Museum of Natural History (NMNH) Department of Paleobiology (Paleo) is the steward of the world’s largest fossil collection. The collection is made of 40 million objects housed in 11,000 cases plus 2,600 square meters in oversized housing, all spread over four separate facilities. The collection contains fossil representatives of the entire history of life. The Smithsonian has been accessioning fossils since the late 1880s, and the collection is actively growing through field research of museum scientists as well as through the acquisition of sizeable orphaned collections. The collection database (Axiell EMu) contains about 660,000 specimen catalog records of the estimated 14,000,000 records required to digitally database the entire collection. NMNH Paleo strives to care for and manage the collection in a way that meets the highest standards for collections accessibility and accountability. Our collections management priorities are: ensuring the collections are physically preserved, housed, and arranged to ensure long-term preservation and accessibility; making the collections holdings and associated data digitally discoverable and accessible through Smithsonian data-management systems and global data-sharing utilities; establishing and implementing best-practice systems for managing research-quality specimen data and data-lifecycle management; implementing sustainable workflows for mass-digitization specimen databasing, 2D and 3D imaging, georeferencing, and transcription of relevant collections labels and analog records; requiring all registration activities including acquisitions, loans, borrows, disposals, shipments, permits, and repository agreements meet the highest ethical and legal standards for documentation; leveraging our professional expertise in collections management to train students and avocational collectors in fossil collections care; and to actively collaborate with paleobiologists and other museum researchers and stakeholders to advance the discipline of paleobiology and collections-based research. Addressing these priorities at the scale of the NMNH Paleo collection requires a deliberate strategy and disciplined project management, especially given that staff and resources are limited. The NMNH and the overarching Smithsonian organizational structure use several museum- and institution-wide metrics and reporting systems for evaluating the collections against its strategic goals. Within NMNH Paleo, these metrics are applied to and enhanced by projects that address the priorities listed above. The projects presented here were developed as part of a strategy to meet departmental, museum, and institutional goals. They are integrated across the department and include surveys, assessments, and the development of data standards and workflows. The success of the projects is most dependent on strong communication and teamwork among the department staff.