A new quality management perspective for biodiversity conservation and research: InvestigatingBiospecimenReporting forImprovedStudyQuality (BRISQ) and theStandardPRE-analyticalCode (SPREC) using Natural History Museum and culture collections as case studies

Benson, Erica E.; Harding, Keith; Mackenzie‐Dodds, Jacqueline

doi:10.1080/14772000.2016.1201167

Cited by 4 publications

(3 citation statements)

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“…Experts from multiple life sciences domains have called for the improvement and standardization of the documentation of research and scientific service processes. [20][21][22][23][24][25][26] This has led in turn to the progressive development and implementation of data management and other functional tools, such as discovery services, access pipelines, and standardized data models, enabling the sharing of data and specimens. [27][28][29][30][31][32] In practice, however, there remains a gap between the needs and the reality of the requirements specified in accepted standards, including technical, operational, and legal specifications needed to ensure the trustworthiness and traceability of data and specimens.…”

Section: Introductionmentioning

confidence: 99%

“…It is generally accepted that the reliability of data generated in downstream analytical procedures 17‐19 is significantly impacted by the properties and quality attributes of specimens, which are precursors of the data. Experts from multiple life sciences domains have called for the improvement and standardization of the documentation of research and scientific service processes 20‐26 . This has led in turn to the progressive development and implementation of data management and other functional tools, such as discovery services, access pipelines, and standardized data models, enabling the sharing of data and specimens 27‐32 .…”

Section: Introductionmentioning

confidence: 99%

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Toward a common standard for data and specimen provenance in life sciences

Wittner

Holub

Mascia

et al. 2023

Learning Health Systems

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Open and practical exchange, dissemination, and reuse of specimens and data have become a fundamental requirement for life sciences research. The quality of the data obtained and thus the findings and knowledge derived is thus significantly influenced by the quality of the samples, the experimental methods, and the data analysis. Therefore, a comprehensive and precise documentation of the pre‐analytical conditions, the analytical procedures, and the data processing are essential to be able to assess the validity of the research results. With the increasing importance of the exchange, reuse, and sharing of data and samples, procedures are required that enable cross‐organizational documentation, traceability, and non‐repudiation. At present, this information on the provenance of samples and data is mostly either sparse, incomplete, or incoherent. Since there is no uniform framework, this information is usually only provided within the organization and not interoperably. At the same time, the collection and sharing of biological and environmental specimens increasingly require definition and documentation of benefit sharing and compliance to regulatory requirements rather than consideration of pure scientific needs. In this publication, we present an ongoing standardization effort to provide trustworthy machine‐actionable documentation of the data lineage and specimens. We would like to invite experts from the biotechnology and biomedical fields to further contribute to the standard.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Toward a common standard for data and specimen provenance in life sciences

Wittner

Holub

Mascia

et al. 2023

Learning Health Systems

View full text Add to dashboard Cite

show abstract

“…The significant impact of flawed research results on health, economics and political decisions has been frequently stated 8 – 11 . Consequently, professional societies and research initiatives call for improved and standardized documentation of data and specimens used in research studies 12 – 17 .…”

Section: Introductionmentioning

confidence: 99%

Lightweight Distributed Provenance Model for Complex Real–world Environments

et al. 2022

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Provenance is information describing the lineage of an object, such as a dataset or biological material. Since these objects can be passed between organizations, each organization can document only parts of the objects life cycle. As a result, interconnection of distributed provenance parts forms distributed provenance chains. Dependant on the actual provenance content, complete provenance chains can provide traceability and contribute to reproducibility and FAIRness of research objects. In this paper, we define a lightweight provenance model based on W3C PROV that enables generation of distributed provenance chains in complex, multi-organizational environments. The application of the model is demonstrated with a use case spanning several steps of a real-world research pipeline — starting with the acquisition of a specimen, its processing and storage, histological examination, and the generation/collection of associated data (images, annotations, clinical data), ending with training an AI model for the detection of tumor in the images. The proposed model has become an open conceptual foundation of the currently developed ISO 23494 standard on provenance for biotechnology domain.

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