Data Management and Stewardship

This data management plan is for the demonstrator study in the EU-funded EATRIS-Plus Project, involving the reuse of genomic data and the generation, analysis and integration of multi-omics data from a cohort of 127 healthy blood donors. Data has been generated by five project partners in five different EU countries. The purpose of the data generation is to establish multi-omics reference values based on high-quality reference protocols of a well-characterised population cohort of healthy individuals.

In this reference study, blood samples of 127 healthy individuals were analyzed with a wide range of -omics technologies, resulting in the most comprehensive -omics profiling data set that is publicly available. The molecular measurements that are available here, can be used as reference values for any future (multi-)omics studyies. Along with phenotypic information (Sex, Age, BMI etc. and measured cell types levels) on the healthy subjects, omics data types are included.

MOLGENIS is a modular web application for scientific data. The modular platform allows to capture data and metadata for multi-omcis studies and is highly customisable. It is developed by the Genomics Coordination Center (GCC) of the University Medical Center Groningen (NL).

Multi-omics refers to a family of complex experimental designs where researchers apply more than one molecular profiling technology – capturing, for example, the genome, proteome and metabolome – across a common set of biological samples. These experiments offer a wealth of opportunities for subsequent analyses, but the size of the resulting datasets and the diversity of the study designs makes data sharing inherently challenging. In this collection, we present a series of multi-omics studies where the authors have used innovative means to maximize the accessibility and reusability of their datasets.

Metadata registry cataloguing health datasets of HDR UK. Datasets can be searched by phenotype, accessibility, data standards, and more.

FAIR Data Cube is a collection of tools and services to facilitate multi-omics data FAIRification, metadata registration, metadata searches, and federated data analysis. It is being developed by the Netherlands X-omics Initiative.

This repository provides the source code of phenotype database application. It was developed for storing biological study data and metadata and provides an interactive and graphical user interface. Collections of previously used contology terms can be cashed to promote reuse of term across data sets.

Searchable data catalogue for health and life science datasets provided by Health-RI, the Dutch national initiative to facilitate and stimulate an integrated health data infrastructure accessible for researchers, citizens, care providers and industry

FAIRsharing is a registry for community-developed standards including data or metadata standards and ontologies, databases, and policies. It provies an extensive search functionality to search by omics type, level of curation or support by communities.

This document describes the FAIR data stewardship and tool development guidelines of the Netherlands X-omics Initiative.

The RDMkit collects best practices and guidelines to help making data FAIR (Findable, Accessible, Interoperable and Reusable). In addition to a comperehensive introduction to research data lifecycle and FAIR management, curated collections offer solutions and tool collection for various domains or data manegment tasks.

This website of the GO FAIR initiative gives an overview of the FAIR (Findable, Accessible, Interoperable, Reusable) data principles including specific guidelines and practical examples.

FAIR Cookbook is an online resource for the Life Sciences with recipes that help you to make and keep data Findable, Accessible, Interoperable and Reusable (FAIR). It provides a comprehensive introduction to FAIR topics and technologies and offers tutorials for implementing FAIR practices.

The 1+MG Framework is a series of components based on the output of the 1+MG projects that provide guidance on ELSI, data quality, data standards, and technical infrastructure standards and APIs. According to the 1+MG Framework Editorial Guidelines, the content is approved and maintained by working group leads or their deputies. Requests for changes or additions to the core content are open to all members of the consortium. National Implementations can be contributed by all member states.

The report describes the EATRIS-Plus FAIRification strategy underpining the development of this resource – the multi-omics toolbox (MOTBX). Developed as part of the EATRIS-Plus project, the aim is to provide researchers with a comprehensive understanding of molecular profiles in personalised medicine. The report focuses on implementing FAIR (Findability, Accessibility, Interoperability, and Reusability) principles to ensure reproducibility and reusability of multi-omics cohort data. The EATRIS-Plus FAIRification process is described, including the use of standards, formats, and tools. The report emphasises the adoption of the ISA metadata framework for capturing experimental metadata along with omics-specific data standards. These files adhere to reporting guidelines for individual omics types and facilitate integration of multi-omics data in personalised health and medicine research projects.

This website is home to Investigation/Study/Assay (ISA) tools, the open-source ISA metadata framework, abstract model and software suite that supports capturing metadata of multi-omics experiments and is supported by several EMBL-EBI data archives.

EATRIS-Plus project template for FAIRification of phenotype data. The GitHub repository contains a Jupyter notebook to capture phenotype information with the GA4GH Phenopackets standard as used for the EATRIS-Plus multi-omics reference cohort. A software container is provided to run the notebook.

EATRIS-Plus project template for FAIRification of multi-omics metadata. This GitHub repository contains a Jupyter notebook to create Investigation/Study/Assay (ISA) metadata in ISA-Tab and ISA-JSON format following the metadata structure used for the EATRIS-Plus multi-omics reference cohort.

Featured article on the MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments.

Currently, a lack of consensus exists on how best to perform and interpret quantitative real-time PCR (qPCR) experiments. The problem is exacerbated by a lack of sufficient experimental detail in many publications, which impedes a reader’s ability to evaluate critically the quality of the results presented or to repeat the experiments. The Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines target the reliability of results to help ensure the integrity of the scientific literature, promote consistency between laboratories, and increase experimental transparency. MIQE is a set of guidelines that describe the minimum information necessary for evaluating qPCR experiments. Included is a checklist to accompany the initial submission of a manuscript to the publisher. By providing all relevant experimental conditions and assay characteristics, reviewers can assess the validity of the protocols used. Full disclosure of all reagents, sequences, and analysis methods is necessary to enable other investigators to reproduce results. MIQE details should be published either in abbreviated form or as an online supplement. Following these guidelines will encourage better experimental practice, allowing more reliable and unequivocal interpretation of qPCR results.