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Big Data Jun 2023Big data management is a key enabling factor for enterprises that want to compete in the global market. Data coming from enterprise production processes, if properly...
Big data management is a key enabling factor for enterprises that want to compete in the global market. Data coming from enterprise production processes, if properly analyzed, can provide a boost in the enterprise management and optimization, guaranteeing faster processes, better customer management, and lower overheads/costs. Guaranteeing a proper big data pipeline is the holy grail of big data, often opposed by the difficulty of evaluating the correctness of the big data pipeline results. This problem is even worse when big data pipelines are provided as a service in the cloud, and must comply with both laws and users' requirements. To this aim, assurance techniques can complete big data pipelines, providing the means to guarantee that they behave correctly, toward the deployment of big data pipelines fully compliant with laws and users' requirements. In this article, we define an assurance solution for big data based on service-level agreements, where a semiautomatic approach supports users from the definition of the requirements to the negotiation of the terms regulating the provisioned services, and the continuous refinement thereof.
Topics: Big Data; Data Management
PubMed: 36862683
DOI: 10.1089/big.2021.0369 -
International Journal of Pharmaceutics Jul 2023The pharmaceutical industry continuously looks for ways to improve its development and manufacturing efficiency. In recent years, such efforts have been driven by the...
The pharmaceutical industry continuously looks for ways to improve its development and manufacturing efficiency. In recent years, such efforts have been driven by the transition from batch to continuous manufacturing and digitalization in process development. To facilitate this transition, integrated data management and informatics tools need to be developed and implemented within the framework of Industry 4.0 technology. In this regard, the work aims to guide the data integration development of continuous pharmaceutical manufacturing processes under the Industry 4.0 framework, improving digital maturity and enabling the development of digital twins. This paper demonstrates two instances where a data integration framework has been successfully employed in academic continuous pharmaceutical manufacturing pilot plants. Details of the integration structure and information flows are comprehensively showcased. Approaches to mitigate concerns in incorporating complex data streams, including integrating multiple process analytical technology tools and legacy equipment, connecting cloud data and simulation models, and safeguarding cyber-physical security, are discussed. Critical challenges and opportunities for practical considerations are highlighted.
Topics: Technology, Pharmaceutical; Data Management; Drug Industry; Quality Control; Pharmaceutical Preparations
PubMed: 37257793
DOI: 10.1016/j.ijpharm.2023.123086 -
The Annals of Thoracic Surgery Aug 2020
Topics: Data Management; Heart Valve Diseases; Humans; Mitral Valve; Surgeons
PubMed: 31982439
DOI: 10.1016/j.athoracsur.2019.12.013 -
The Annals of Thoracic Surgery Sep 2019
Topics: Aortic Dissection; Data Management; Databases, Factual; Dissection; Humans
PubMed: 31026429
DOI: 10.1016/j.athoracsur.2019.03.071 -
The Journal of Urology Jan 2020
Topics: Androgen Antagonists; Data Management; Hormone Replacement Therapy; Humans; Lipids; Male; Prostatic Neoplasms, Castration-Resistant
PubMed: 31609669
DOI: 10.1097/01.JU.0000604072.36697.15 -
Journal of Medical Internet Research Nov 2023In the context of the Medical Informatics Initiative, medical data integration centers (DICs) have implemented complex data flows to transfer routine health care data...
BACKGROUND
In the context of the Medical Informatics Initiative, medical data integration centers (DICs) have implemented complex data flows to transfer routine health care data into research data repositories for secondary use. Data management practices are of importance throughout these processes, and special attention should be given to provenance aspects. Insufficient knowledge can lead to validity risks and reduce the confidence and quality of the processed data. The need to implement maintainable data management practices is undisputed, but there is a great lack of clarity on the status.
OBJECTIVE
Our study examines the current data management practices throughout the data life cycle within the Medical Informatics in Research and Care in University Medicine (MIRACUM) consortium. We present a framework for the maturity status of data management practices and present recommendations to enable a trustful dissemination and reuse of routine health care data.
METHODS
In this mixed methods study, we conducted semistructured interviews with stakeholders from 10 DICs between July and September 2021. We used a self-designed questionnaire that we tailored to the MIRACUM DICs, to collect qualitative and quantitative data. Our study method is compliant with the Good Reporting of a Mixed Methods Study (GRAMMS) checklist.
RESULTS
Our study provides insights into the data management practices at the MIRACUM DICs. We identify several traceability issues that can be partially explained with a lack of contextual information within nonharmonized workflow steps, unclear responsibilities, missing or incomplete data elements, and incomplete information about the computational environment information. Based on the identified shortcomings, we suggest a data management maturity framework to reach more clarity and to help define enhanced data management strategies.
CONCLUSIONS
The data management maturity framework supports the production and dissemination of accurate and provenance-enriched data for secondary use. Our work serves as a catalyst for the derivation of an overarching data management strategy, abiding data integrity and provenance characteristics as key factors. We envision that this work will lead to the generation of fairer and maintained health research data of high quality.
Topics: Humans; Data Management; Delivery of Health Care; Medical Informatics; Surveys and Questionnaires
PubMed: 37938878
DOI: 10.2196/48809 -
Dermatologic Surgery : Official... Dec 2019
Topics: Data Management; Pharmacovigilance; Sclerosing Solutions; World Health Organization
PubMed: 31663875
DOI: 10.1097/DSS.0000000000002169 -
Briefings in Bioinformatics Jan 2021Thousands of new experimental datasets are becoming available every day; in many cases, they are produced within the scope of large cooperative efforts, involving a... (Review)
Review
Thousands of new experimental datasets are becoming available every day; in many cases, they are produced within the scope of large cooperative efforts, involving a variety of laboratories spread all over the world, and typically open for public use. Although the potential collective amount of available information is huge, the effective combination of such public sources is hindered by data heterogeneity, as the datasets exhibit a wide variety of notations and formats, concerning both experimental values and metadata. Thus, data integration is becoming a fundamental activity, to be performed prior to data analysis and biological knowledge discovery, consisting of subsequent steps of data extraction, normalization, matching and enrichment; once applied to heterogeneous data sources, it builds multiple perspectives over the genome, leading to the identification of meaningful relationships that could not be perceived by using incompatible data formats. In this paper, we first describe a technological pipeline from data production to data integration; we then propose a taxonomy of genomic data players (based on the distinction between contributors, repository hosts, consortia, integrators and consumers) and apply the taxonomy to describe about 30 important players in genomic data management. We specifically focus on the integrator players and analyse the issues in solving the genomic data integration challenges, as well as evaluate the computational environments that they provide to follow up data integration by means of visualization and analysis tools.
Topics: Data Management; Genome, Human; Genomics; Humans; Metadata
PubMed: 32496509
DOI: 10.1093/bib/bbaa080 -
Drug Discovery Today Apr 2023The FAIR (findable, accessible, interoperable and reusable) principles are data management and stewardship guidelines aimed at increasing the effective use of scientific... (Review)
Review
The FAIR (findable, accessible, interoperable and reusable) principles are data management and stewardship guidelines aimed at increasing the effective use of scientific research data. Adherence to these principles in managing data assets in pharmaceutical research and development (R&D) offers pharmaceutical companies the potential to maximise the value of such assets, but the endeavour is costly and challenging. We describe the 'FAIR-Decide' framework, which aims to guide decision-making on the retrospective FAIRification of existing datasets by using business analysis techniques to estimate costs and expected benefits. This framework supports decision-making on FAIRification in the pharmaceutical R&D industry and can be integrated into a company's data management strategy.
Topics: Retrospective Studies; Research; Drug Industry; Data Management; Pharmaceutical Preparations
PubMed: 36716952
DOI: 10.1016/j.drudis.2023.103510 -
Journal of the American College of... Mar 2020
Topics: Data Management; Early Detection of Cancer; Female; Humans; Mammography
PubMed: 32139022
DOI: 10.1016/j.jacr.2020.01.016