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At least 19 recordsLinked to original sources

PubMed2002

Towards a semantic medical Web: HealthCyberMap's tool for building an RDF metadata base of health information resources based on the Qualified Dublin Core Metadata Set.

BACKGROUND: HealthCyberMap (http://healthcybermap.semanticweb.org/) aims at mapping Internet health information resources in novel ways for enhanced retrieval and navigation. This is achieved by collecting appropriate resource metadata in an unambiguous form that preserves semantics. MATERIAL/METHODS: We modelled a qualified Dublin Core (DC) metadata set ontology with extra elements for resource quality and geographical provenance in Prot g -2000. A metadata collection form helps acquiring resource instance data within Prot g . The DC subject field is populated with UMLS terms directly imported from UMLS Knowledge Source Server using UMLS tab, a Prot g -2000 plug-in. The project is saved in RDFS/RDF. RESULTS: The ontology and associated form serve as a free tool for building and maintaining an RDF medical resource metadata base. The UMLS tab enables browsing and searching for concepts that best describe a resource, and importing them to DC subject fields. The resultant metadata base can be used with a search and inference engine, and have textual and/or visual navigation interface(s) applied to it, to ultimately build a medical Semantic Web portal. Different ways of exploiting Prot g -2000 RDF output are discussed. CONCLUSIONS: By making the context and semantics of resources, not merely their raw text and formatting, amenable to computer 'understanding,' we can build a Semantic Web that is more useful to humans than the current Web. This requires proper use of metadata and ontologies. Clinical codes can reliably describe the subjects of medical resources, establish the semantic relationships (as defined by underlying coding scheme) between related resources, and automate their topical categorisation.

Humans↗

PubMed2003

Metadata element set in a quality-controlled subject gateway: a step to a health semantic Web.

BACKGROUND: Quality-controlled subject gateways are Internet services which apply a selected set of targeted measures to support systematic resource discovery. Considerable manual effort is used to process a selection of resources which meet quality criteria and to display a extensive description and indexing of these resources with standards-based metadata. OBJECTIVE: Several metadata element sets are proposed to describe, index and quality health resources to be included in a French quality-controlled health gateway called CISMeF. The main objectives were to enhance Internet health document retrieval and navigation, and to allow interoperability with other Internet services. RESULTS: The Dublin Core metadata element set is used to describe and index all Internet health resources included in CISMeF. For teaching resources, some elements from IEEE1484 Learning Object Metadata are also used. For evidenced-base medicine resources, specific metadata are employed which assess the health content quality. The HIDDEL metadata set is used to enhance transparency, trust and quality of health information on the Internet. CONCLUSION: Comprehensive metadata element sets can be extremely useful to describe, index and assess health resources on the Internet in a quality-controlled subject gateway. Machine-readable metadata creates an Semantic Web which is more efficient for end-users as compared to the current Web.

Abstracting and Indexing↗

PubMed1986

The POSCH data processing experience. The problem of metadata.

Metadata, the term, may be new but metadata, the concept, is not new. For purposes of this paper, the term metadata is defined as the data used to define, store, retrieve, combine, analyze, and present the data values (the "real" data, so to speak). As clinical research studies get larger, it becomes desirable to use automated managers of the metadata. The Program on the Surgical Control of the Hyperlipidemias (POSCH), a national multiclinic clinical trial, manages most of its metadata manually but has been experimenting with ways to more fully automate them. Its Information Management System (IMS) is described with special emphasis on how it manages the metadata. The case is presented for further automation and standardization of metadata in large clinical research studies so that costs can be contained and smaller increments of "progress" can be measured. The concept of a Metadatabase Management System (MDBMS) is developed and illustrated using POSCH.

Arteriosclerosis↗

PubMed2002

A metadata classification schema for semantic content analysis of videos.

Simple ancillary metadata, such as those encompassed by the 15 elements of the Dublin Core, may be sufficient and entirely appropriate for basic coarse-granularity cross-domain resource discovery. However, they are insufficient and inappropriate for content description of complex data types such as videos, which require more detailed relational models. We propose a metadata classification schema for the characterization of items and events in videos that permits subsequent query by content. Following MPEG-7 nomenclature, metadata intrinsic to the information content of the video are defined as either structural or semantic, where structural metadata are numerical feature primitives produced by analysing the colour, shape, texture, structure and motion within the video frames, whereas semantic metadata describe the locations and timings of individual items and particular actions or events in the video, and are thus of higher information value. In this paper, the semantic metadata required to describe the visual information content of videos are defined and classified into four distinct classes: Media Entities; Content Items; Events; and Supplementary Items, and three types of property tables are defined: Identity Tables; Spatio-Temporal Position Tables; and Event Tables, in which these metadata may be stored in a relational database.

Journal Article↗

PubMedDate not supplied

A model for enhancing Internet medical document retrieval with "medical core metadata".

OBJECTIVE: Finding documents on the World Wide Web relevant to a specific medical information need can be difficult. The goal of this work is to define a set of document content description tags, or metadata encodings, that can be used to promote disciplined search access to Internet medical documents. DESIGN: The authors based their approach on a proposed metadata standard, the Dublin Core Metadata Element Set, which has recently been submitted to the Internet Engineering Task Force. Their model also incorporates the National Library of Medicine's Medical Subject Headings (MeSH) vocabulary and MEDLINE-type content descriptions. RESULTS: The model defines a medical core metadata set that can be used to describe the metadata for a wide variety of Internet documents. CONCLUSIONS: The authors propose that their medical core metadata set be used to assign metadata to medical documents to facilitate document retrieval by Internet search engines.

Abstracting and Indexing↗

PubMed2005

A system of metadata to control the process of query, aggregating, cleaning and analysing large datasets of primary care data.

BACKGROUND: Metadata is data that describes other data or resources. It has a defined number of named elements that convey meaning. Medical data are complex to process. For example, in the Primary Care Data Quality (PCDQ) renal programme, we need to collect over 300 variables because there are so many possible causes of renal disease. These variables are not just single columns of data--all are extracted as code plus date, while others are code-date-value. Metadata has the potential to improve the reliability of processing large datasets. OBJECTIVE: To define unique and unambiguous metadata headings for clinical data and derived variables. METHOD: We defined the look-up tables we would use as a controlled vocabulary to name the core clinical concepts within the metadata. We added six other elements to describe data: (1) the study or audit name; (2) the query used to extract the data; (3) the data collection number; (4) the type of data, including specifying the units; (5) the repeat number (if the variable was extracted more than once); and (6) a processing suffix that defines how the data have been processed. RESULTS: The metadata system has enabled the development of a query library and an analysis syntax library that make data processing and analysis more efficient. Its stability means greater effort can be put into more complex data processing, and some semiautomation of processes. However, the system has had implementation problems. It has been particularly hard to stop clinicians using multiple synonyms for the same variable. CONCLUSIONS: The PCDQ metadata system provides an auditable method of data processing. It is a method that should improve the reliability, validity and efficiency of processing routinely collected clinical data. This paper sets out to demystify our data processing method and makes the PCDQ metadata system available to clinicians and data processors who might wish to adopt it.

Electronic Data Processing↗

PubMed2001

Kaiser Permanente's "metadata-driven" national clinical intranet.

This paper describes the approach taken to build Kaiser Permanente's national clinical intranet. A primary objective for the site is to facilitate resource discovery, which is enabled by the use of "metadata", or data (fields and field values) that describe the various resources available. Users can perform full text queries and/or fielded searching against the metadata. Metadata serves as the organizing principle of the site--it is used to index documents, sort search results, and structure the site's table of contents. The site's use of metadata--what it is, how it is created, how it is applied to documents, how it is indexed, how it is presented to the user in the search and the search results interface, and how it is used to construct the table of contents for the web site--will be discussed in detail. The result is that KP's national clinical intranet has coupled the power of Internet-like full text search engines with the power of MedLine-like fielded searching in order to maximize search precision and recall. Organizing content on the site in accordance with the metadata promotes overall consistency. Issues currently under investigation include how to better exploit the power of the controlled terminology within the metadata; whether the value gained is worth the cost of collecting metadata; and how automatic classification algorithms might obviate the need for manual document indexing.

Computer Communication Networks↗

PubMed2004

Managing complex change in clinical study metadata.

In highly functional metadata-driven software, the interrelationships within the metadata become complex, and maintenance becomes challenging. We describe an approach to metadata management that uses a knowledge-base subschema to store centralized information about metadata dependencies and use cases involving specific types of metadata modification. Our system borrows ideas from production-rule systems in that some of this information is a high-level specification that is interpreted and executed dynamically by a middleware engine. Our approach is implemented in TrialDB, a generic clinical study data management system. We review approaches that have been used for metadata management in other contexts and describe the features, capabilities, and limitations of our system.

Artificial Intelligence↗

PubMed2006

GIS, geostatistics, metadata banking, and tree-based models for data analysis and mapping in environmental monitoring and epidemiology.

By the example of environmental monitoring, some applications of geographic information systems (GIS), geostatistics, metadata banking, and Classification and Regression Trees (CART) are presented. These tools are recommended for mapping statistically estimated hot spots of vectors and pathogens. GIS were introduced as tools for spatially modelling the real world. The modelling can be done by mapping objects according to the spatial information content of data. Additionally, this can be supported by geostatistical and multivariate statistical modelling. This is demonstrated by the example of modelling marine habitats of benthic communities and of terrestrial ecoregions. Such ecoregionalisations may be used to predict phenomena based on the statistical relation between measurements of an interesting phenomenon such as, e.g., the incidence of medically relevant species and correlated characteristics of the ecoregions. The combination of meteorological data and data on plant phenology can enhance the spatial resolution of the information on climate change. To this end, meteorological and phenological data have to be correlated. To enable this, both data sets which are from disparate monitoring networks have to be spatially connected by means of geostatistical estimation. This is demonstrated by the example of transformation of site-specific data on plant phenology into surface data. The analysis allows for spatial comparison of the phenology during the two periods 1961-1990 and 1991-2002 covering whole Germany. The changes in both plant phenology and air temperature were proved to be statistically significant. Thus, they can be combined by GIS overlay technique to enhance the spatial resolution of the information on the climate change and use them for the prediction of vector incidences at the regional scale. The localisation of such risk hot spots can be done by geometrically merging surface data on promoting factors. This is demonstrated by the example of the transfer of heavy metals through soils. The predicted hot spots of heavy metal transfer can be validated empirically by measurement data which can be inquired by a metadata base linked with a geographic information system. A corresponding strategy for the detection of vector hot spots in medical epidemiology is recommended. Data on incidences and habitats of the Anophelinae in the marsh regions of Lower Saxony (Germany) were used to calculate a habitat model by CART, which together with climate data and data on ecoregions can be further used for the prediction of habitats of medically relevant vector species. In the future, this approach should be supported by an internet-based information system consisting of three components: metadata questionnaire, metadata base, and GIS to link metadata, surface data, and measurement data on incidences and habitats of medically relevant species and related data on climate, phenology, and ecoregional characteristic conditions.

Databases, Factual↗

PubMed2006

Extended cooperation in clinical studies through exchange of CDISC metadata between different study software solutions.

OBJECTIVES: Our objectives were to analyze the possibility of an exchange of an entire clinical study between two different and independent study software solutions. The question addressed was whether a software-independent transfer of study metadata can be performed without programming efforts and with software routinely used for clinical research. METHODS: Study metadata was transferred with ODM standard (CDISC). Study software systems employed were MACRO (InferMed) and XTrial (XClinical). For the Proof of Concept, a test study was created with MACRO and exported as ODM. For modification and validation of the ODM export file XML-Spy (Altova) and ODM-Checker (XML4Pharma) were used. RESULTS: Through exchange of a complete clinical study between two different study software solutions, a Proof of Concept of the technical feasibility of a system-independent metadata exchange was conducted successfully. The interchange of study metadata between two different systems at different centers was performed with minimal expenditure. A small number of mistakes had to be corrected in order to generate a syntactically correct ODM file and a "vendor extension" had to be inserted. After these modifications, XTrial exhibited the study, including all data fields, correctly. However, the optical appearance of both CRFs (case report forms) was different. CONCLUSIONS: ODM can be used as an exchange format for clinical studies between different study software. Thus, new forms of cooperation through exchange of metadata seem possible, for example the joint creation of electronic study protocols or CRFs at different research centers. Although the ODM standard represents a clinical study completely, it contains no information about the representation of data fields in CRFs.

Algorithms↗

PubMed2025

HTSinfer: inferring metadata from bulk Illumina RNA-Seq libraries.

SUMMARY: The Sequencing Read Archive is one of the largest and fastest-growing repositories of sequencing data, containing tens of petabytes of sequenced reads. Its data is used by a wide scientific community, often beyond the primary study that generated them. Such analyses rely on accurate metadata concerning the type of experiment and library, as well as the organism from which the sequenced reads were derived. These metadata are typically entered manually by contributors in an error-prone process, and are frequently incomplete. In addition, easy-to-use computational tools that verify the consistency and completeness of metadata describing the libraries to facilitate data reuse, are largely unavailable. Here, we introduce HTSinfer, a Python-based tool to infer metadata directly and solely from bulk RNA-sequencing data generated on Illumina platforms. HTSinfer leverages genome sequence information and diagnostic genes to rapidly and accurately infer the library source and library type, as well as the relative read orientation, 3' adapter sequence and read length statistics. HTSinfer is written in a modular manner, published under a permissible free and open-source license and encourages contributions by the community, enabling easy addition of new functionalities, e.g. for the inference of additional metrics, or the support of different experiment types or sequencing platforms. AVAILABILITY AND IMPLEMENTATION: HTSinfer is released under the Apache License 2.0. Latest code is available via GitHub at https://github.com/zavolanlab/htsinfer, while releases are published on Bioconda. A snapshot of the HTSinfer version described in this article was deposited at Zenodo at 10.5281/zenodo.13985958.

Metadata↗

PubMed2025

Pre-Meta: priors-augmented retrieval for LLM-based metadata generation.

MOTIVATION: While high-throughput sequencing technologies have dramatically accelerated genomic data generation, the manual processes required for dataset annotation and metadata creation impede the efficient discovery and publication of these resources across disparate public repositories. Large language models (LLMs) have the potential to streamline dataset profiling and discovery. However, their current limitations in generalizing across specialized knowledge domains, particularly in fields such as biomedical genomics, prevent them from fully realizing this potential. This article presents Pre-Meta, an LLM-agnostic and domain-independent data annotation pipeline with an enriched retrieval procedure that leverages related priors-such as pre-generated metadata tags and ontologies-as auxiliary information to improve the accuracy of automated metadata generation. RESULTS: Validated using five selected metadata fields sampled across 1500 papers, the Pre-Meta assisted annotation experiment-without finetuning and prompt optimization-demonstrates a systemic improvement in the annotation task: shown through a 23%, 72%, and 75% accuracy gain from conventional RAG adoptions of GPT-4o mini, Llama 8B, and Mistral 7B respectively. AVAILABILITY AND IMPLEMENTATION: The code, data access, and scripts are available at: https://github.com/SINTEF-SE/LLMDap.

Metadata↗

PubMed2025

ClarID: A Human-Readable and Compact Identifier Specification for Biomedical Metadata Integration.

BACKGROUND: In biomedical research, subjects and biospecimens are commonly tracked using simple IDs or UUIDs, which guarantee uniqueness but convey no embedded semantic information. Contextual metadata (such as tissue type, diagnosis, or assay) is often stored separately, making integration, cohort selection, and downstream analysis cumbersome. While structured barcoding systems exist in large consortia (e.g., TCGA, GTEx) or domain-specific contexts (e.g., SPREC, GOLD), no unified, extensible framework currently spans both subjects and biosamples in a human- and machine-readable way. METHODS: We developed ClarID, a domain-agnostic specification that supports two identifier formats: (i) a human-readable form (e.g., 'CNAG_Test-HomSap-00001-LIV-TUM-RNA-C22.0-TRT-P1W' that encodes key metadata such as project, species, subject_id, tissue, assay, disease, timepoint and duration (from that event); and (ii) a compact version named 'stub' (e.g., 'CT01001LTR0N401T1W') optimized for filenames, pipelines, and labeling.ClarID is implemented through an open-source command-line tool, ClarID-Tools, which processes tabular metadata files (CSV/TSV) and uses a YAML-based codebook to generate, decode, and validate identifiers, as well as to create and read QR codes. The tool supports bulk and single-sample processing and allows easy integration with institutional workflows. RESULTS: To demonstrate ClarID's utility, we applied it to datasets from the Genomic Data Commons (GDC), generating interpretable identifiers for more than 113,000 clinical records (subjects) and 4,255 biospecimen records. All materials, including pre-processing scripts, input and encoded data, are publicly available and fully reproducible via the accompanying GitHub repository and Google Colab. CONCLUSIONS: ClarID fills a critical gap between opaque accession numbers and rich metadata schemas by embedding key context directly into structured identifiers. It enhances traceability, facilitates downstream analysis, and remains adaptable to project-specific needs through a configurable codebook. The accompanying ClarID-Tools software is freely available, together with full documentation and reproducible pipelines, at https://github.com/CNAG-Biomedical-Informatics/clarid-tools.

Biosample identifiers↗

PubMed2006

[Description and integration of biomedical information resources by metadata].

With the development and utilization of computer techniques, Internet is playing an important role in information dissemination. There are abundant biomedical resources on the Internet. Accessing biomedical information is more dependent on the Internet than ever. It is important to explore new methods to describe and manage information resources. We have analyzed biomedical databases, search engines, web sites, and the metadata adopted by biomedical databases. The results show that biomedical information resources are characterized by electronic format, networking, dynamia, and dispersion. Describing a resource with metadata allows it to be understood by both humans and machines in ways that promote interoperability. Metadata interoperability has to be the underlying principle for networked information management. It directly impinges on information sharing, interchange, and accessibility across the boundaries of systems, languages, and geographic locations. We can use metadata to describe biomedical information and to integrate resources. It will benefit the people to access, select, and utilize biomedical information resources.

Computer Communication Networks↗

PubMed2026

pmultiqc: An Open-Source, Lightweight, and Metadata-Oriented QC Reporting Library for MS Proteomics.

The increasing scale and complexity of proteomics data demand robust, scalable, and interpretable quality control (QC) frameworks to ensure data reliability and reproducibility. Here, we present pmultiqc, an open-source Python package that standardizes and generates web-based QC reports across multiple proteomics data analysis platforms. Built on top of the widely adopted MultiQC framework, pmultiqc offers specialized modules tailored to mass spectrometry workflows, with full initial support for quantms, DIA-NN, MaxQuant/MaxDIA, FragPipe, and mzIdentML/mzML-based pipelines. The package computes a wide range of QC metrics, including raw intensity distributions, identification rates, retention time consistency, and missing value patterns, and presents them in interactive, publication-ready reports. By leveraging sample metadata in the Sample and Data Relationship Format format, pmultiqc enables metadata-aware QC and introduces, for the first time in proteomics, QC reports and metrics guided by standardized sample metadata. Its modular architecture allows easy extension to new workflows and formats. Alongside comprehensive documentation and examples for running pmultiqc locally or integrated into existing workflows, we offer a cloud-based service that enables users to generate QC reports from their own data or public PRIDE datasets.

Proteomics↗

PubMed2026

Meta2DB: curated shotgun metagenomic feature sets and metadata for health state prediction.

SUMMARY: Meta2DB is a curated metagenomic and metadata database that provides structurally consistent microbiome taxonomy feature count tables for 13 897 samples across 84 studies, 23 disease states, and 34 geographical locations. All samples were uniformly processed using a streamlined metagenomic classification pipeline that employs a unique and comprehensive reference database indexed to contain all sequences across all kingdoms of life that were present in the NCBI Nucleotide (nt) database retrieved on 4 January 2023. This pipeline leverages high-performance computing (HPC) resources at Lawrence Livermore National Laboratory and was used to process 50TB of publicly available raw metagenomic sequence data. Extensive metadata curation was carried out through a combination of manual curation and automated parsing, producing a consistent inter-study metadata table specifically structured to facilitate training of ML models for prediction of human health. AVAILABILITY: Data is available at https://gdo-meta2db.llnl.gov/ and https://zenodo.org/records/17315984.

Metadata↗

PubMed2026

Microbiome Datahub: an open-access platform integrating environmental metadata, taxonomy, and functional annotation for comprehensive metagenome-assembled genome datasets.

BACKGROUND: Metagenome-assembled genomes (MAGs) provide crucial insights into the genomic diversity of uncultured microbes. However, MAG datasets deposited in public repositories such as INSDC are often difficult to reuse due to heterogeneous quality, inconsistent taxonomic and functional annotations, and insufficiently curated environmental metadata. While secondary MAG databases such as MGnify, IMG/M, and SPIRE provide standardized resources, they reconstruct MAGs de novo from public metagenomic reads and therefore do not represent the original MAGs reported in publications. RESULTS: To address this gap, we developed Microbiome Datahub, an open-access platform that systematically aggregates and re-annotates original MAGs from INSDC. We collected 214,427 MAGs, predicted genes by DFAST, performed quality assessment with CheckM, standardized taxonomic assignments with GTDB-Tk, inferred 27 phenotypic traits using Bac2Feature, assigned proteins to MBGD ortholog clusters and KEGG Orthology IDs using PZLAST, and annotated environmental metadata with the Metagenome and Microbes Environmental Ontology. Across these MAGs, the average completeness was 80.5% and contamination 1.8%; notably, the most frequent values were >95% completeness and <1% contamination, indicating that the majority of MAGs are of high quality. Comparative analyses showed that Microbiome Datahub provides phylogenetically and environmentally diverse MAGs: while the majority originated from vertebrate gut environments, a substantial number were also recovered from other habitats such as groundwater, including nearly 10,000 MAGs from the Patescibacteria. Inference of 27 phenotypic traits, including optimum growth temperature, further revealed ecological differentiation across phyla. Protein clustering revealed 56 million identity 40% clusters, with the majority unique compared with MGnify and GlobDB, and ~19% of proteins unassigned to MBGD ortholog clusters, underscoring their novelty. CONCLUSIONS: Microbiome Datahub integrates MAG genome sequences, gene and protein predictions, quality metrics, environmental and taxonomic annotations, ortholog cluster assignments, and phenotype predictions, all accessible via a web interface, API, and bulk downloads. By combining original MAGs with curated metadata and functional annotations, Microbiome Datahub constitutes a comprehensive and reusable resource that will accelerate microbiome and microbial genomics research. Video Abstract.

Metagenome↗

PubMed2006

Identifying and closing gaps in environmental monitoring by means of metadata, ecological regionalization and geostatistics using the UNESCO biosphere reserve Rhoen (Germany) as an example.

In Germany, environmental monitoring is intended to provide a holistic view of the environmental condition. To this end the monitoring operated by the federal states must use harmonized, resp., standardized methods. In addition, the monitoring sites should cover the ecoregions without any geographical gaps, the monitoring design should have no gaps in terms of ecologically relevant measurement parameters, and the sample data should be spatially without any gaps. This article outlines the extent to which the Rhoen Biosphere Reserve, occupying a part of the German federal states of Bavaria, Hesse and Thuringia, fulfills the listed requirements. The investigation considered collection, data banking and analysis of monitoring data and metadata, ecological regionalization and geostatistics. Metadata on the monitoring networks were collected by questionnaires and provided a complete inventory and description of the monitoring activities in the reserve and its surroundings. The analysis of these metadata reveals that most of the monitoring methods are harmonized across the boundaries of the three federal states the Rhoen is part of. The monitoring networks that measure precipitation, surface water levels, and groundwater quality are particularly overrepresented in the central ecoregions of the biosphere reserve. Soil monitoring sites are more equally distributed within the ecoregions of the Rhoen. The number of sites for the monitoring of air pollutants is not sufficient to draw spatially valid conclusions. To fill these spatial gaps, additional data on the annual average values of the concentrations of air pollutants from monitoring sites outside of the biosphere reserve had therefore been subject to geostatistical analysis and estimation. This yields valid information on the spatial patterns and temporal trends of air quality. The approach illustrated is applicable to similar cases, as, for example, the harmonization of international monitoring networks.

Ecology↗