Data Management Infrastructure Research Articles

Research Data Management in Simulation Science: Infrastructure, Tools, and Applications

AbstractResearch Data Management (RDM) has gained significant traction in recent years, being essential to allowing research data to be, e.g., findable, accessible, interoperable, and reproducible (FAIR), thereby fostering collaboration or accelerating scientific findings. We present solutions for RDM developed within the DFG-Funded Cluster of Excellence EXC2075 Data-Integrated Simulation Science (SimTech). After an introduction to the scientific context and challenges faced by simulation scientists, we outline the general data management infrastructure and present tools that address these challenges. Exemplary domain applications demonstrate the use and benefits of the proposed data management software solutions. These are complemented by additional measures for enablement and dissemination to foster the adoption of these techniques.

Seismic Monitoring of a Deep Geothermal Field in Munich (Germany) Using Borehole Distributed Acoustic Sensing.

Geothermal energy exploitation in urban areas necessitates robust real-time seismic monitoring for risk mitigation. While surface-based seismic networks are valuable, they are sensitive to anthropogenic noise. This study investigates the capabilities of borehole Distributed Acoustic Sensing (DAS) for local seismic monitoring of a geothermal field located in Munich, Germany. We leverage the operator's cloud infrastructure for DAS data management and processing. We introduce a comprehensive workflow for the automated processing of DAS data, including seismic event detection, onset time picking, and event characterization. The latter includes the determination of the event hypocenter, origin time, seismic moment, and stress drop. Waveform-based parameters are obtained after the automatic conversion of the DAS strain-rate to acceleration. We present the results of a 6-month monitoring period that demonstrates the capabilities of the proposed monitoring set-up, from the management of DAS data volumes to the establishment of an event catalog. The comparison of the results with seismometer data shows that the phase and amplitude of DAS data can be reliably used for seismic processing. This emphasizes the potential of improving seismic monitoring capabilities with hybrid networks, combining surface and downhole seismometers with borehole DAS. The inherent high-density array configuration of borehole DAS proves particularly advantageous in urban and operational environments. This study stresses that realistic prior knowledge of the seismic velocity model remains essential to prevent a large number of DAS sensing points from biasing results and interpretation. This study suggests the potential for a gradual extension of the network as geothermal exploitation progresses and new wells are equipped, owing to the scalability of the described monitoring system.

Collaboration on Machine-Learned Potentials with IPSuite: A Modular Framework for Learning-on-the-Fly.

The field of machine learning potentials has experienced a rapid surge in progress, thanks to advances in machine learning theory, algorithms, and hardware capabilities. While the underlying methods are continuously evolving, the infrastructure for their deployment has lagged. The community, due to these rapid developments, frequently finds itself split into groups built around different implementations of machine-learned potentials. In this work, we introduce IPSuite, a Python-driven software package designed to connect different methods and algorithms from the comprehensive field of machine-learned potentials into a single platform while also providing a collaborative infrastructure, helping ensure reproducibility. Furthermore, the data management infrastructure of the IPSuite code enables simple model sharing and deployment in simulations. Currently, IPSuite supports six state-of-the-art machine learning approaches for the fitting of interatomic potentials as well as a variety of methods for the selection of training data, running of ab initio calculations, learning-on-the-fly strategies, model evaluation, and simulation deployment.

Effects of Results-Based-Financing on the Availability of Primary Registers in Private Health Facilities in Koulikoro, Mali

Summary: Registries provide an infrastructure for data evaluation and management as well as aknowledge collection system (library). The register in the health field allows you to acquire knowledge relevant and actionable to find solutions to problems current. Objective: The aim is to assess the availability of primary registers in private health centers. Methods and Materials: We carried out a retrospective, prospective and comparative survey on the availability of registers in the third quarter of 2022 and the third quarter of 2023 in private health centers having signed a performance contract with the Koulikoro contracting and verification agency as part of results-based financing. A total of 14 private health centers participated in this study. Results: We note an evolution in the availability rate of registers between the two periods evaluated. The availability of home visit, malnutrition and minor surgery registers was 0% in the third quarter of 2022 and it became respectively 4%, 7% and 80% in the third quarter of 2023. Conclusion: This study revealed that the regular verification of primary data recorded in registers is a factor leading to the increase in the availability of registers in private health centers.

Using smart grid infrastructure for authentication and data management in Internet of Medical Things

AbstractThe Internet of Medical Things (IoMT) has recently become the norm in medical operations and emergency situations. A physician or a medical personnel is interested in accessing the patients' data from remote locations using IoMT. The patients' medical management system should allow their doctors and family members to have access to the data, especially in emergency situations. Deploying sensor nodes and managing the large amount of medical data generated by these sensors require a system that provides protection against known security threats and a robust mechanism to recover when subjected to attacks. The proposed scheme that provides mutual authentication between user(s) and power generation nodes, and also mitigate against attacks by validating the identity during the authentication phase. Moreover, the proposed scheme, called Grid‐Based Authentication, uses the existing smart grid infrastructure for communications, which is a model of integrated infrastructure in a smart city. Furthermore, the simulation results show that our proposed scheme yields better performance in terms of average throughput, end to end delay, and computation and communication costs compared with other state‐of‐the‐art approaches.

Roadmap for a European cancer data management and precision medicine infrastructure.

Roadmap for a European cancer data management and precision medicine infrastructure.

Identifying the Barriers to Acceptance of Blockchain-Based Patient-Centric Data Management Systems in Healthcare

A number of recent studies have shown that wastage and inefficiency are a significant problem in all global healthcare systems. One initiative that could radically improve the operational efficiency of health systems is to make a paradigm shift in data ownership—that is, to transition such systems to a patient-centric model of data management by deploying blockchain technology. Such a development would not only make an economic impact, by radically cutting wastage, but would deliver significant social benefits by improving patient outcomes and satisfaction. However, a blockchain-based solution presents considerable challenges. This research seeks to understand the principal factors, which act as barriers to the acceptance of a blockchain-based patient-centric data management infrastructure, in the healthcare systems of the GCC (Gulf Cooperation Council) countries. The study represents an addition to the current literature by examining the perspectives and views of healthcare professionals and users. This approach is rare within this subject area, and is identified in existing systematic reviews as a research gap: a qualitative investigation of motivations and attitudes among these groups is a critical need. The results of the study identified 12 key barriers to the acceptance of blockchain infrastructures, thereby adding to our understanding of the challenges that need to be overcome in order to benefit from this relatively recent technology. The research is expected to be of use to healthcare authorities in planning a way forward for system improvement, particularly in terms of successfully introducing patient-centric systems.

Mapping Hierarchical File Structures to Semantic Data Models for Efficient Data Integration into Research Data Management Systems

Although other methods exist to store and manage data in modern information technology, the standard solution is file systems. Therefore, keeping well-organized file structures and file system layouts can be key to a sustainable research data management infrastructure. However, file structures alone lack several important capabilities for FAIR data management: the two most significant being insufficient visualization of data and inadequate possibilities for searching and obtaining an overview. Research data management systems (RDMSs) can fill this gap, but many do not support the simultaneous use of the file system and RDMS. This simultaneous use can have many benefits, but keeping data in RDMS in synchrony with the file structure is challenging. Here, we present concepts that allow for keeping file structures and semantic data models (in RDMS) synchronous. Furthermore, we propose a specification in yaml format that allows for a structured and extensible declaration and implementation of a mapping between the file system and data models used in semantic research data management. Implementing these concepts will facilitate the re-use of specifications for multiple use cases. Furthermore, the specification can serve as a machine-readable and, at the same time, human-readable documentation of specific file system structures. We demonstrate our work using the Open Source RDMS LinkAhead (previously named “CaosDB”).

DCache integration with CERN Tape Archive

The ever-increasing amount of data that is produced by modern scientific facilities like EuXFEL or LHC puts high pressure on the data management infrastructure at the laboratories. This includes poorly shareable resources of archival storage, typically, tape libraries. To achieve maximal efficiency of the available tape resources, a deep integration between hardware and software components is required. The CERN Tape Archive (CTA) is an open-source storage management system developed by CERN to manage LHC experiment data on tape. Although today CTA’s primary target is CERN Tier-0, the data management group at DESY considers the CTA as a main alternative to commercial HSM systems. dCache has an extensible tape interface that allows connectivity to almost any tape system. Together with the CERN Tape Archive developers, the storage team at DESY is working on seamless integration of CTA into dCache.

Practical use of data lake for better fatigue life estimation

The qualification of components in terms of Durability and Reliability is based on the analysis on a lot of sensors, CAN, IIOT data, which needs a data management infrastructure, to understand the customer usage and variability. Such big data infrastructure is often called « data lake », and may lead to storing huge amount of data. This infrastructure must be generic yet test data-oriented to understand the data structure and its analysis required, and to be optimized for such application. All those data may come from connected equipment, instrumented fleets, test lab or proving ground measurements, digital twins and multi-body dynamics simulations. Further, the data must be managed in terms of quality and traceability. It must be indexed to be able to be retrieved through searches (customer, vehicle, measurement site, engine specification, road condition, usage conditions).Once this step is achieved, the product development team is able to have a better understanding of customer usage and inputs variabilities in different environments and conditions, which have to be taken into account in the product's mission profile or duty cycle. This ad-hoc mission profile enables the creation of realistic, meaningful design and test specifications.Understanding the customer usage and input variabilities enables a probabilistic approach to fatigue life prediction. The uncertainties on inputs (geometry, material and loading) may be propagated through the life process, knowing each input's probability distribution function, using a Monte Carlo analysis. The infrastructure enables the life analysis to be done through multiple runs on cloud-oriented server, which enables automation and streamlining the whole process. A use case will be presented to illustrate the approach and its benefits.

Consolidating Research Data Management Infrastructure: Towards Sustainable Digital Scholarship

The sustainability of digital research outputs, particularly in the humanities where these frequently comprise archives of digital cultural heritage material, has always offered a challenge to the researchers and institutions who have responsibility for them. The amount of upfront care, effort, and funding that goes into developing a research project during the active (and funded) research phase is rarely replicated within the post-project maintenance and curation of the delivered digital assets or archives. What often defines the sustainability of a research project and its archive is a combination of research method and expected life span for the digital collection. Innovation in research data design is often at the expense of its longevity. But this does not need to be so. The tradeoff between longevity and functionality is a false dichotomy. Yet what is clear is that care and consideration in planning the research data storage or archive for a project can make a big difference. A data management plan that meets grant funder requirements is asked for for many research projects, but it is more than simply a funding document. Good research data management ensures that outputs are available online for years to come and available for future research and innovation. This article offers a practical insight to the methods being employed at the University of Oxford to support Digital Humanities scholars (and beyond) safeguard their digital legacy for future generations.

Roadmap for implementation of genomics in healthcare: towards equity in access to genomics

Abstract Issue Implementing genomic medicine in healthcare can bring us closer to making personalised medicine a reality, with notable socioeconomic benefits. Description of the problem The 1+Million Genomes Initiative is promoting a reflection in European countries regarding their national strategies for genomic medicine, and how these contribute to research and improve clinical practice. Implementation of genomic medicine is complex and requires investment and adjustments in the organisation and practices of healthcare services, including technical infrastructure for genomic data generation and management, a trained healthcare workforce, and appropriate ethical and legal frameworks. Methods To promote the implementation of genomic medicine in healthcare systems, and improve its societal impact, the Beyond 1+MG (B1MG) project organised three country exchange visits for capacity building, developed and validated in real world settings a comprehensive Maturity Level Model (B1MG MLM) for genomic medicine, and organized workshops on health economics models for genomics. Results The products resulting from these activities provide policy recommendations for adoption of genomic medicine, an innovative MLM Toolkit for health systems to self-assess their current maturity status regarding genomic medicine implementation, and plan the path for improvement, as well as several brief papers highlighting main challenges and key indicators for health economic evaluation of genomic tests. Overall, this set of B1MG products provide a Playbook to support the development of national or regional roadmaps for the implementation of genomic medicine in healthcare systems. Lessons Country healthcare systems have specific practices, barriers and constraints, so the actions needed for progress in genomic medicine implementation towards optimization are specific to the context of each healthcare system. Key messages • The B1MG Roadmap will support the definition of strategies for adoption of genomics in healthcare systems, towards equity in the access to genomic medicine for all European citizens. • Country healthcare systems actions needed for progress in genomic medicine implementation towards optimization are specific to the context of each healthcare system.

Machine learning and internet of things applications in enterprise architectures: Solutions, challenges, and open issues

SummaryThe rapid growth of the Internet of Things (IoT) has led to its widespread adoption in various industries, enabling enhanced productivity and efficient services. Integrating IoT systems with existing enterprise application systems has become common practice. However, this integration necessitates reevaluating and reworking current Enterprise Architecture (EA) models and Expert Systems (ES) to accommodate IoT and cloud technologies. Enterprises must adopt a multifaceted view and automate various aspects, including operations, data management, and technology infrastructure. Machine Learning (ML) is a powerful IoT and smart automation tool within EA. Despite its potential, a need for dedicated work focuses on ML applications for IoT services and systems. With IoT being a significant field, analyzing IoT‐generated data and IoT‐based networks is crucial. Many studies have explored how ML can solve specific IoT‐related challenges. These mutually reinforcing technologies allow IoT applications to leverage sensor data for ML model improvement, leading to enhanced IoT operations and practices. Furthermore, ML techniques empower IoT systems with knowledge and enable suspicious activity detection in smart systems and objects. This survey paper conducts a comprehensive study on the role of ML in IoT applications, particularly in the domains of automation and security. It provides an in‐depth analysis of the state‐of‐the‐art ML approaches within the context of IoT, highlighting their contributions, challenges, and potential applications.

PLANTdataHUB: a collaborative platform for continuous FAIR data sharing in plant research.

In modern reproducible, hypothesis-driven plant research, scientists are increasingly relying on research data management (RDM) services and infrastructures to streamline the processes of collecting, processing, sharing, and archiving research data. FAIR (i.e., findable, accessible, interoperable, and reusable) research data play a pivotal role in enabling the integration of interdisciplinary knowledge and facilitating the comparison and synthesis of a wide range of analytical findings. The PLANTdataHUB offers a solution that realizes RDM of scientific (meta)data as evolving collections of files in a directory - yielding FAIR digital objects called ARCs - with tools that enable scientists to plan, communicate, collaborate, publish, and reuse data on the same platform while gaining continuous quality control insights. The centralized platform is scalable from personal use to global communities and provides advanced federation capabilities for institutions that prefer to host their own satellite instances. This approach borrows many concepts from software development and adapts them to fit the challenges of the field of modern plant science undergoing digital transformation. The PLANTdataHUB supports researchers in each stage of a scientific project with adaptable continuous quality control insights, from the early planning phase to data publication. The central live instance of PLANTdataHUB is accessible at (https://git.nfdi4plants.org), and it will continue to evolve as a community-driven and dynamic resource that serves the needs of contemporary plant science.

SMART HEALTHCARE EMPOWERMENT: INTEGRATING IOT, CLOUD, AND HEALTHCARE STANDARDS WITH RASPBERRY PI FOR ENHANCED PATIENTDATA COLLECTION

The research paper introduces an innovative framework that harnesses the capabilities of In- ternet of Things (IoT) and cloud computing technologies to revolutionize patient data collection within smart healthcare systems. This framework bears similarities to the Electronic Health Records System (EHRS) in the USA and aligns with the development of a health stack in India. In this context, the Raspberry Pi functions as a central gateway device, facilitating seamless connections between various sensors and medical devices for data acquisition. The collected data is then transmitted to a cloud-based platform for secure storage, comprehensive analysis, and seam- less accessibility by healthcare providers. This system, in line with the development of the health stack in India, aims to create a robust and standardized digital infrastructure for healthcare data management. By doing so, it paves the way for a nationwide health information exchange system, similar to the EHRS [Rahman et al.2019]in the USA. The framework also empowers remote mon- itoring, real-time data analysis, and the provision of personalized healthcare services to patients, thus significantly enhancing the efficiency and effectiveness of healthcare systems in India, while promoting interoperability and data exchange, akin to the EHRS model in the USA.

Establishing the Research Data Management Container in NFDIxCS

NFDIxCS is a consortium within the family of NFDI, which defines and establishes a research data management (RDM) infrastructure for Computer Science (CS). Based on a broad community process, the various types of research data, their metadata and quality criteria are agreed upon in the community. The resulting research data, along with all associated supplementary information and context such as software, metadata, and the corresponding execution environment are provided as an integral part of the overall infrastructure to meet the FAIR principles. This extended abstract addresses the interaction of the presented task area Architectures & Interfaces within the NFDIxCS consortium and the planned activities, challenges, and objectives of the recently launched project.

BAM Data Store

As a partner in several NFDI consortia, the Bundesanstalt für Materialforschung und -prüfung (BAM, German federal institute for materials science and testing) contributes to research data standardization efforts in various domains of materials science and engineering (MSE). To implement a central research data management (RDM) infrastructure that meets the requirements of MSE groups at BAM, we initiated the Data Store pilot project in 2021. The resulting infrastructure should enable researchers to digitally document research processes and store related data in a standardized and interoperable manner. As a software solution, we chose openBIS, an open-source framework that is increasingly being used for RDM in MSE communities. The pilot project was conducted for one year with five research groups across different organizational units and MSE disciplines. The main results are presented for the use case “nanoPlattform”. The group registered experimental steps and linked associated instruments and chemicals in the Data Store to ensure full traceability of data related to the synthesis of ~400 nanomaterials. The system also supported researchers in implementing RDM practices in their workflows, e.g., by automating data import and documentation and by integrating infrastructure for data analysis. Based on the promising results of the pilot phase, we will roll out the Data Store as the central RDM infrastructure of BAM starting in 2023. We further aim to develop openBIS plugins, metadata standards, and RDM workflows to contribute to the openBIS community and to foster RDM in MSE.

Overview of ICARUS-A Curated, Open Access, Online Repository for Atmospheric Simulation Chamber Data.

Atmospheric simulation chambers continue to be indispensable tools for research in the atmospheric sciences. Insights from chamber studies are integrated into atmospheric chemical transport models, which are used for science-informed policy decisions. However, a centralized data management and access infrastructure for their scientific products had not been available in the United States and many parts of the world. ICARUS (Integrated Chamber Atmospheric data Repository for Unified Science) is an open access, searchable, web-based infrastructure for storing, sharing, discovering, and utilizing atmospheric chamber data [https://icarus.ucdavis.edu]. ICARUS has two parts: a data intake portal and a search and discovery portal. Data in ICARUS are curated, uniform, interactive, indexed on popular search engines, mirrored by other repositories, version-tracked, vocabulary-controlled, and citable. ICARUS hosts both legacy data and new data in compliance with open access data mandates. Targeted data discovery is available based on key experimental parameters, including organic reactants and mixtures that are managed using the PubChem chemical database, oxidant information, nitrogen oxide (NOx) content, alkylperoxy radical (RO2) fate, seed particle information, environmental conditions, and reaction categories. A discipline-specific repository such as ICARUS with high amounts of metadata works to support the evaluation and revision of atmospheric model mechanisms, intercomparison of data and models, and the development of new model frameworks that can have more predictive power in the current and future atmosphere. The open accessibility and interactive nature of ICARUS data may also be useful for teaching, data mining, and training machine learning models.

Making China’s water data accessible, usable and shareable

Water data are essential for monitoring, managing, modelling and projecting water resources. Yet despite such data—including water quantity, quality, demand and ecology—being extensively collected in China, it remains difficult to access, use and share them. These challenges have led to poor data quality, duplication of effort and wasting of resources, limiting their utility for supporting decision-making in water resources policy and management. In this Perspective we discuss the current state of China’s water data collection, governance and sharing, the barriers to open-access water data and its impacts, and outline a path to establishing a national water data infrastructure to reform water resource management in China and support global water-data sharing initiatives. This Perspective characterizes the major challenges to open-access water data in China. A potential data management infrastructure to improve the collection, sharing, and use of water data is outlined.

Setting up a data management infrastructure for bioimaging.

While the FAIR (Findable, Accessible, Interoperable, and Re-usable) principles are well accepted in the scientific community, there are still many challenges in implementing them in the day-to-day scientific process. Datamanagement of microscopy images poses special challenges due to the volume, variety, and many proprietary formats. In particular, appropriate metadata collection, abasic requirement for FAIR data, is a real challenge for scientists due to the technical and content-related aspects. Researchers benefit here from interdisciplinary research network with centralized data management. The typically multimodal structure requires generalized data management and the corresponding acquisition of metadata. Here we report on the establishment of an appropriate infrastructure for the research network by a Core Facility and the development and integration of a software tool MDEmicthat allows easy and convenient processing of metadata of microscopy images while providing high flexibility in terms of customization of metadata sets. Since it is also in the interest of the core facility to apply standards regarding the scope and serialization formats to realize successful and sustainable data management for bioimaging, we report on our efforts within the community to define standards in metadata, interfaces, and to reduce thebarriers of daily data management.