Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Data standard = data quality.

ABSTRACT: The relationship between data quality and data standards has not been clearly articulated. While some directly state that data standards increase data quality, others claim the opposite. Depending on the type of data standard and the aspects of data quality considered, both arguments may in fact be correct. We deconstruct a typology of data standards and ap ply a dimensional definition of data quality to clearly articulate the relationship between the two, providing a framework for data quality planning.

SUBMITTER: Nahm M

PROVIDER: S-EPMC4317361 | biostudies-other | 2013

REPOSITORIES: biostudies-other

ACCESS DATA

Json Xml

Publications

Data standard ≠ data quality.

Nahm Meredith M Hammond W Ed WE

Studies in health technology and informatics 20130101

The relationship between data quality and data standards has not been clearly articulated. While some directly state that data standards increase data quality, others claim the opposite. Depending on the type of data standard and the aspects of data quality considered, both arguments may in fact be correct. We deconstruct a typology of data standards and ap ply a dimensional definition of data quality to clearly articulate the relationship between the two, providing a framework for data quality ...[more]

PMID: 23920982

Similar Datasets

Proteomics quality and standard: from a regulatory perspective.

Project description:Proteomics has emerged as a rapidly expanding field dealing with large-scale protein analyses. It is anticipated that proteomics data will be increasingly submitted to the U.S. Food and Drug Administration (FDA) for biomarker qualification or in conjunction with applications for the approval of drugs, medical devices, and other FDA-regulated consumer products. To date, however, no established guideline has been available regarding the generation, submission and assessment of the quality of proteomics data that will be reviewed by regulatory agencies for decision making. Therefore, this commentary is aimed at provoking some thoughts and debates towards developing a framework which can guide future proteomics data submission. The ultimate goal is to establish quality control standards for proteomics data generation and evaluation, and to prepare government agencies such as the FDA to meet future obligations utilizing proteomics data to support regulatory decision.

| S-EPMC8463138 | biostudies-literature

Proteomics quality and standard: from a regulatory perspective

Project description:Cultured mouse lymphoma cells were lysed with a buffer containing 50 mM Tris-HCl (pH 8.0) and 8 M Urea with or without a protease inhibitor cocktail (Sigma). The lysates were desalted and digested with sequencing-grade modified trypsin (Promega). Different amounts of peptides were analyzed using nanoflow RPLC-MS/MS on a linear ion trap mass spectrometer (LTQ XL, Thermo Electron, San Jose, CA). The raw MS/MS data were searched using SEQUEST running under BioWorks (Rev. 3.3.1 SP1) (Thermo Electron, San Jose, CA) against a mouse IPI proteome database (Version 3.62) downloaded from the European Bioinformatics Institute (EBI) (http://www.ebi.ac.uk). For the SEQUEST analysis, the peptide mass tolerance was set as 2.0 Da and the fragment ion tolerance was 1.0 Da. A tryptic enzyme restriction with a maximum of two internal missed cleavage sites was used. The Xcorr versus charge state values were filtered at Xcorr ≥ 1.7 for [M+H]1+ ions, ≥ 2.5 for [M+2H]2+ ions, ≥ 3.2 for [M+3H]3+ ions, and P ≤ 0.01 and ΔCn ≥ 0.08 were applied for identification of all fully tryptic peptides. The peptide ion chromatogram was extracted using a minimum intensity threshold of 100, mass tolerance of 2.0 amu and smoothing point of 5, and the area of the extracted ion chromatographic (XIC) peak was integrated and calculated using the PepQuan module in Bioworks (Thermo Electron, San Jose, CA).

2022-02-15 | PXD000334 | Pride

Consent Codes: Upholding Standard Data Use Conditions.

Project description:A systematic way of recording data use conditions that are based on consent permissions as found in the datasets of the main public genome archives (NCBI dbGaP and EMBL-EBI/CRG EGA).

| S-EPMC4721915 | biostudies-other

PRIDE Inspector Toolsuite: Moving Toward a Universal Visualization Tool for Proteomics Data Standard Formats and Quality Assessment of ProteomeXchange Datasets.

Project description:The original PRIDE Inspector tool was developed as an open source standalone tool to enable the visualization and validation of mass-spectrometry (MS)-based proteomics data before data submission or already publicly available in the Proteomics Identifications (PRIDE) database. The initial implementation of the tool focused on visualizing PRIDE data by supporting the PRIDE XML format and a direct access to private (password protected) and public experiments in PRIDE.The ProteomeXchange (PX) Consortium has been set up to enable a better integration of existing public proteomics repositories, maximizing its benefit to the scientific community through the implementation of standard submission and dissemination pipelines. Within the Consortium, PRIDE is focused on supporting submissions of tandem MS data. The increasing use and popularity of the new Proteomics Standards Initiative (PSI) data standards such as mzIdentML and mzTab, and the diversity of workflows supported by the PX resources, prompted us to design and implement a new suite of algorithms and libraries that would build upon the success of the original PRIDE Inspector and would enable users to visualize and validate PX "complete" submissions. The PRIDE Inspector Toolsuite supports the handling and visualization of different experimental output files, ranging from spectra (mzML, mzXML, and the most popular peak lists formats) and peptide and protein identification results (mzIdentML, PRIDE XML, mzTab) to quantification data (mzTab, PRIDE XML), using a modular and extensible set of open-source, cross-platform libraries. We believe that the PRIDE Inspector Toolsuite represents a milestone in the visualization and quality assessment of proteomics data. It is freely available at http://github.com/PRIDE-Toolsuite/.

| S-EPMC4762524 | biostudies-literature

Quality Control of Biomedicinal Allergen Products - Highly Complex Isoallergen Composition Challenges Standard MS Database Search and Requires Manual Data Analyses.

Project description:Allergy against birch pollen is among the most common causes of spring pollinosis in Europe and is diagnosed and treated using extracts from natural sources. Quality control is crucial for safe and effective diagnosis and treatment. However, current methods are very difficult to standardize and do not address individual allergen or isoallergen composition. MS provides information regarding selected proteins or the entire proteome and could overcome the aforementioned limitations. We studied the proteome of birch pollen, focusing on allergens and isoallergens, to clarify which of the 93 published sequence variants of the major allergen, Bet v 1, are expressed as proteins within one source material in parallel. The unexpectedly complex Bet v 1 isoallergen composition required manual data interpretation and a specific design of databases, as current database search engines fail to unambiguously assign spectra to highly homologous, partially identical proteins. We identified 47 non-allergenic proteins and all 5 known birch pollen allergens, and unambiguously proved the existence of 18 Bet v 1 isoallergens and variants by manual data analysis. This highly complex isoallergen composition raises questions whether isoallergens can be ignored or must be included for the quality control of allergen products, and which data analysis strategies are to be applied.

| S-EPMC4641616 | biostudies-literature

Gold Standard for macromolecular crystallography diffraction data.

Project description:Macromolecular crystallography (MX) is the dominant means of determining the three-dimensional structures of biological macromolecules. Over the last few decades, most MX data have been collected at synchrotron beamlines using a large number of different detectors produced by various manufacturers and taking advantage of various protocols and goniometries. These data came in their own formats: sometimes proprietary, sometimes open. The associated metadata rarely reached the degree of completeness required for data management according to Findability, Accessibility, Interoperability and Reusability (FAIR) principles. Efforts to reuse old data by other investigators or even by the original investigators some time later were often frustrated. In the culmination of an effort dating back more than two decades, a large portion of the research community concerned with high data-rate macromolecular crystallography (HDRMX) has now agreed to an updated specification of data and metadata for diffraction images produced at synchrotron light sources and X-ray free-electron lasers (XFELs). This 'Gold Standard' will facilitate the processing of data sets independent of the facility at which they were collected and enable data archiving according to FAIR principles, with a particular focus on interoperability and reusability. This agreed standard builds on the NeXus/HDF5 NXmx application definition and the International Union of Crystallo-graphy (IUCr) imgCIF/CBF dictionary, and it is compatible with major data-processing programs and pipelines. Just as with the IUCr CBF/imgCIF standard from which it arose and to which it is tied, the NeXus/HDF5 NXmx Gold Standard application definition is intended to be applicable to all detectors used for crystallography, and all hardware and software developers in the field are encouraged to adopt and contribute to the standard.

| S-EPMC7467160 | biostudies-literature

A standard for sharing spatial transcriptomics data.

Project description:Spatial transcriptomic technologies have the potential to reveal critical relationships between the function of genes and cells and their spatial organization. Here, we provide a sharing model for spatial transcriptomics data with the aim of establishing a set of primary data and metadata needed to reproduce analyses and facilitate computational methods development.

| S-EPMC10435375 | biostudies-literature

Strain-specific peptide (SSP) standard: A genetically encoded quality control standard for isobaric tagging strategies

Project description:Isobaric tag-based sample multiplexing strategies are extensively used for global protein abundance profiling. However, such analyses are often confounded by ratio compression resulting from the co-isolation, co-fragmentation, and co-quantification of co-eluting peptides, termed “interference.” Recent analytical strategies incorporating ion mobility and real-time database searching have helped to alleviate interference, yet further assessment is needed. Here, we present the Strain-Specific Peptide (SSP) standard, a TMTpro-tagged reference sample that leverages the genetic variation in the proteomes of eight phylogenetically divergent mouse strains. Typically, a peptide with a missense mutation will have a different mass and retention time than the reference or native peptide. TMT reporter ion signal for the native peptide in strains that encode the mutant peptide suggests interference which can be quantified and assessed using the interference-free index (IFI). We showcase the SSP standard by investigating interference in three common data acquisition methods and by testing the improvements in the IFI when using ion mobility-based gas phase fractionation. In addition, we provide a user-friendly, online viewer to visualize the data and streamline calculation of the IFI. The SSP standard will aid in developing and optimizing isobaric tag-based experiments.

2021-04-03 | PXD023203 | Pride

Standard methods for the management of immunogenetic data.

Project description:In this chapter, we outline some basic principles for the consistent management of immunogenetic data. These include the preparation of a single master data file that can serve as the basis for all subsequent analyses, a focus on the quality and homogeneity of the data to be analyzed, the documentation of the coding systems used to represent the data, and the application of nomenclature standards specific for each immunogenetic system being evaluated. The data management principles discussed here are intended to provide a foundation for the data analysis methods detailed in Chaps. 13 and 14 . The relationship between the data management and analysis methods covered in these three chapters is illustrated in Fig. 3.The application of these data management principles is a first step toward consistent and reproducible data analyses. While it may take extra time and effort to apply them, we feel that it is better to take this approach than to assume that low data quality can be compensated for by large sample sizes.In addition to their relevance for analytical reproducibility, it is important to consider these data management principles from an ethical perspective. The reliability of the data collected and generated as part of a research study should be as important a component of the ethical review of a research application as the security of those data. Finally, in addition to ensuring the integrity of the data from collection to publication, the application of these data management principles will provide a means to foster research integrity and to improve the potential for collaborative data sharing.

| S-EPMC4209945 | biostudies-other

The BioPAX community standard for pathway data sharing.

Project description:Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. The rapid growth of the volume of pathway data has spurred the development of databases and computational tools to aid interpretation; however, use of these data is hampered by the current fragmentation of pathway information across many databases with incompatible formats. BioPAX, which was created through a community process, solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. Using BioPAX, millions of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases. This large amount of pathway data in a computable form will support visualization, analysis and biological discovery.

| S-EPMC3001121 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data