Perceptual and technical barriers in sharing and formatting metadata accompanying omics studies

Metadata, often termed "data about data," is crucial for organizing, understanding, and managing vast omics datasets. It aids in efficient data discovery, integration, and interpretation, enabling users to access, comprehend, and utilize data effectively. Its significance spans the domains of scientific research, facilitating data reproducibility, reusability, and secondary analysis. However, numerous perceptual and technical barriers hinder the sharing of metadata among researchers. These barriers compromise the reliability of research results and hinder integrative meta-analyses of omics studies . This study highlights the key barriers to metadata sharing, including the lack of uniform standards, privacy and legal concerns, limitations in study design, limited incentives, inadequate infrastructure, and the dearth of well-trained personnel for metadata management and reuse. Proposed solutions include emphasizing the promotion of standardization, educational efforts, the role of journals and funding agencies, incentives and rewards, and the improvement of infrastructure. More accurate, reliable, and impactful research outcomes are achievable if the scientific community addresses these barriers, facilitating more accurate, reliable, and impactful research outcomes

Research resources: curating the new eagle-i discovery system

Development of biocuration processes and guidelines for new data types or projects is a challenging task. Each project finds its way toward defining annotation standards and ensuring data consistency with varying degrees of planning and different tools to support and/or report on consistency. Further, this process may be data type specific even within the context of a single project. This article describes our experiences with eagle-i, a 2-year pilot project to develop a federated network of data repositories in which unpublished, unshared or otherwise ‘invisible’ scientific resources could be inventoried and made accessible to the scientific community. During the course of eagle-i development, the main challenges we experienced related to the difficulty of collecting and curating data while the system and the data model were simultaneously built, and a deficiency and diversity of data management strategies in the laboratories from which the source data was obtained. We discuss our approach to biocuration and the importance of improving information management strategies to the research process, specifically with regard to the inventorying and usage of research resources. Finally, we highlight the commonalities and differences between eagle-i and similar efforts with the hope that our lessons learned will assist other biocuration endeavors

Metadados para o uso de ferramentas de gestão de dados de investigação com investigadores do I3S

Nas últimas décadas, a produção de dados de investigação tem vindo a crescer muito, principalmente devido ao desenvolvimento tecnológico que transformou todo o fluxo de trabalho dos investigadores. Esta situação cria desafios relativos às atividades de gestão dos dados de investigação, sobretudo ao nível da análise, armazenamento, preservação e partilha desses mesmos dados. A gestão de dados de investigação é essencial para a prática científica e existem bastantes intervenientes nas diferentes etapas deste processo investigadores, agências de financiamento, universidades, curadores que se preocupam com o valor dos dados produzidos. Torna-se também importante apoiar os investigadores com ferramentas que simplifiquem o trabalho necessário na gestão dos seus dados de investigação.As ferramentas eletrónicas de gestão de dados de investigação são ferramentas importantes já que permitem aos investigadores cumprir os requisitos e criar uma ponte entre as diferentes etapas do fluxo da gestão de dados de investigação.A adoção de uma ferramenta de gestão de dados de investigação pode também contribuir para auxiliar a controlar o ciclo de vida dos dados já que é possível armazenar os dados e associar-lhes metadados de modo a torná-los FAIR Findable, Accessible, Interoperable, Reusable. Além disso, a sua integração com repositórios de dados de investigação é também essencial na medida da indexação, preservação e disponibilização dos dados à comunidade científica.Com o objetivo de apoiar os investigadores nas tarefas de gerir os seus dados de investigação, neste trabalho colabora-se com um grupo de investigadores do Instituto de Investigação e Inovação em Saúde (I3S) de modo a testar a plataforma Dendro, ferramenta desenvolvida na FEUP e INESC TEC, assim como para validar um modelo de metadados desenvolvido específico para os domínios dos investigadores.Os resultados obtidos a partir do feedback dos investigadores demonstram que o modelo desenvolvido favorece um ponto de entrada fácil na descrição de dados, mas não impede os investigadores de apresentar limitações e identificar os seus requisitos específicos.In the last decades, the research data production has been growing a lot, mainly due to the technological development which has transformed the entire workflow of the researchers. This situation creates challenges regarding research data management activities, especially at the level of analysis, storage, preservation and sharing of these research data. Research data management is essential to scientific practice and there are many stakeholders involved in the different stages of this process researchers, funding agencies, universities, curators who care about the value of the produced data. In the field of research data management, it is also important to support researchers with tools that simplify their work in managing research data.Electronic research management tools are important as they enable researchers to meet the RDM requirements and create a bridge between the different stages in the flow of research data management.Their adoption can help controlling the data life cycle since it is possible to store the data and associate it with metadata in order to make it FAIR - Findable, Accessible, Interoperable, Reusable. In addition, its integration with research data repositories is also essential as it allows the indexing, preservation and availability of data for the scientific community.In order to support the researchers in their research data management tasks, in this this study we collaborate with a group of researchers from the Institute of Research and Innovation in Health (I3S) in order to test and evaluate Dendro platform, developed in FEUP and INESC TEC, as well as to validate a metadata descriptors model developed specifically for the researchers domain.The results obtained from the researchers feedback show that the developed model favours an easy entry point in the data description tasks but does not prevent researchers from presenting its limitations and identifying their specific requirements

Structuring research methods and data with the research object model:genomics workflows as a case study

Background: One of the main challenges for biomedical research lies in the computer-assisted integrative study of large and increasingly complex combinations of data in order to understand molecular mechanisms. The preservation of the materials and methods of such computational experiments with clear annotations is essential for understanding an experiment, and this is increasingly recognized in the bioinformatics community. Our assumption is that offering means of digital, structured aggregation and annotation of the objects of an experiment will provide necessary meta-data for a scientist to understand and recreate the results of an experiment. To support this we explored a model for the semantic description of a workflow-centric Research Object (RO), where an RO is defined as a resource that aggregates other resources, e. g., datasets, software, spreadsheets, text, etc. We applied this model to a case study where we analysed human metabolite variation by workflows. Results: We present the application of the workflow-centric RO model for our bioinformatics case study. Three workflows were produced following recently defined Best Practices for workflow design. By modelling the experiment as an RO, we were able to automatically query the experiment and answer questions such as "which particular data was input to a particular workflow to test a particular hypothesis?", and "which particular conclusions were drawn from a particular workflow?". Conclusions: Applying a workflow-centric RO model to aggregate and annotate the resources used in a bioinformatics experiment, allowed us to retrieve the conclusions of the experiment in the context of the driving hypothesis, the executed workflows and their input data. The RO model is an extendable reference model that can be used by other systems as well. Availability: The Research Object is available at http://www.myexperiment.org/packs/428 The Wf4Ever Research Object Model is available at http://wf4ever.github.io/r