The Collaborative Metadata Repository (CoMetaR) Web App: Quantitative and Qualitative Usability Evaluation

Background In the field of medicine and medical informatics, the importance of comprehensive metadata has long been recognized, and the composition of metadata has become its own field of profession and research. To ensure sustainable and meaningful metadata are maintained, standards and guidelines such as the FAIR (Findability, Accessibility, Interoperability, Reusability) principles have been published. The compilation and maintenance of metadata is performed by field experts supported by metadata management apps. The usability of these apps, for example, in terms of ease of use, efficiency, and error tolerance, crucially determines their benefit to those interested in the data. Objective This study aims to provide a metadata management app with high usability that assists scientists in compiling and using rich metadata. We aim to evaluate our recently developed interactive web app for our collaborative metadata repository (CoMetaR). This study reflects how real users perceive the app by assessing usability scores and explicit usability issues. Methods We evaluated the CoMetaR web app by measuring the usability of 3 modules: core module, provenance module, and data integration module. We defined 10 tasks in which users must acquire information specific to their user role. The participants were asked to complete the tasks in a live web meeting. We used the System Usability Scale questionnaire to measure the usability of the app. For qualitative analysis, we applied a modified think aloud method with the following thematic analysis and categorization into the ISO 9241-110 usability categories. Results A total of 12 individuals participated in the study. We found that over 97% (85/88) of all the tasks were completed successfully. We measured usability scores of 81, 81, and 72 for the 3 evaluated modules. The qualitative analysis resulted in 24 issues with the app. Conclusions A usability score of 81 implies very good usability for the 2 modules, whereas a usability score of 72 still indicates acceptable usability for the third module. We identified 24 issues that serve as starting points for further development. Our method proved to be effective and efficient in terms of effort and outcome. It can be adapted to evaluate apps within the medical informatics field and potentially beyond.

Grapevine and Wine Metabolomics-Based Guidelines for FAIR Data and Metadata Management

In the era of big and omics data, good organization, management, and description of experimental data are crucial for achieving high-quality datasets. This, in turn, is essential for the export of robust results, to publish reliable papers, make data more easily available, and unlock the huge potential of data reuse. Lately, more and more journals now require authors to share data and metadata according to the FAIR (Findable, Accessible, Interoperable, Reusable) principles. This work aims to provide a step-by-step guideline for the FAIR data and metadata management specific to grapevine and wine science. In detail, the guidelines include recommendations for the organization of data and metadata regarding (i) meaningful information on experimental design and phenotyping, (ii) sample collection, (iii) sample preparation, (iv) chemotype analysis, (v) data analysis (vi) metabolite annotation, and (vii) basic ontologies. We hope that these guidelines will be helpful for the grapevine and wine metabolomics community and that it will benefit from the true potential of data usage in creating new knowledge being revealed.

Microbench: Automated metadata management for systems biology benchmarking and reproducibility in Python

Motivation: Computational systems biology analyses typically make use of multiple software and their dependencies, which often run across heterogeneous compute environments. This can introduce differences in performance and reproducibility. Capturing metadata (e.g. package versions, GPU model) currently requires repetitious code and is difficult to store centrally for analysis. Even where virtual environments and containers are used, updates over time mean that versioning metadata should still be captured within analysis pipelines to guarantee reproducibility. Results: Microbench is a simple and extensible Python package to automate metadata capture to a file or Redis database. Captured metadata can include execution time, software package versions, environment variables, hardware information, Python version, and more, with plugins. We present three case studies demonstrating Microbench usage to benchmark code execution and examine environment metadata for reproducibility purposes. Availability: Install from the Python Package Index using pip install microbench. Source code is available from https://github.com/alubbock/microbench.

Enterprise-Wide Metadata Management

Metadata management is a crucial success factor for companies today, as for example, it enables exploiting data value fully or enables legal compliance. With the emergence of new concepts, such as the data lake, and new objectives, such as the enterprise-wide sharing of data, metadata management has evolved and now poses a renewed challenge for companies. In this context, we interviewed a globally active manufacturer to reveal how metadata management is implemented in practice today and what challenges companies are faced with and whether these constitute research gaps. As an outcome, we present the company’s metadata management goals and their corresponding solution approaches and challenges. An evaluation of the challenges through a literature and tool review yields three research gaps, which are concerned with the topics: (1) metadata management for data lakes, (2) categorizations and compositions of metadata management tools for comprehensive metadata management, and (3) the use of data marketplaces as metadata-driven exchange platforms within an enterprise. The gaps lay the groundwork for further research activities in the field of metadata management and the industry case represents a starting point for research to realign with real-world industry needs.

Architecting an Enterprise Data Lake, A Covid19 Case Study

Data is increasing at an enormous rate every day. Traditionally data has resided in silosacross any organization,so it’s difficult to have a complete picture for data driven business decision making. Data lake addresses the problem of rate of increase of data by providing “schema on read”, better integration and cheaper storage. It also solves the data silos problemby providing a central platform for a variety of data housing needs. However, implementing a data lake becomes challenging as the implementation needs to address the additional needs like metadata management, data discovery, data governance, data lifecycle management, security and centralized access controls mechanisms. This paper intends to provide a comprehensive architecture of data lake to address these challenges. We have also conducted and documented our experiments with publicly available datasets about COVID19 to validate the design and applicability of the proposed architecture for business analytics purposes.

Collection, curation and the use of humidification to restore nest shape in a research museum bird nest collection

Background Bird nests are an important part of avian ecology. They are a powerful tool for studying not only the birds that built them, but a wide array of topics ranging from parasitology, urbanisation and climate change to evolution. Despite this, bird nests tend to be underrepresented in natural history collections, a problem that should be redressed through renewed focus by collecting institutions. Methods Here we outline the history and current best practice collection and curatorial methods for the nest collection of the Australian National Wildlife Collection (ANWC). We also describe an experiment conducted on nests in the ANWC using ultrasonic humidification to restore the shape of nests damaged by inappropriate storage. Results The experiment showed that damaged nests can be successfully reshaped to close to their original dimensions. Indeed, restored nests were significantly closer to their original shape than they were prior to restoration. Thus, even nests damaged by years of neglect may be fully incorporated into active research collections. Best practice techniques include extensive note taking and photography in the field, subsampling of nests that cannot or should not be collected, appropriate field storage, metadata management, and prompt treatment upon arrival at the collection facility. Conclusions Renewed focus on nest collections should include appropriate care and restoration of current collections, as well as expansion to redress past underrepresentation. This could include collaboration with researchers studying or monitoring avian nesting ecology, and nest collection after use in bird species that rebuild anew each nesting attempt. Modern expansion of museum nest collections will allow researchers and natural history collections to fully realise the scientific potential of these complex and beautiful specimens.

A Research Data Infrastructure Component for the Automated Metadata and Data Quality Extraction to Foster the Provision of FAIR Data in Earth System Sciences

Metadata management is core to support discovery and reuse of data products, and to allow for reproducibility of the research data in Earth System Sciences (ESS). Thus, ensuring acquisition and provision of meaningful and quality assured metadata should become an integral part of data-driven ESS projects.We propose an open-source tool for the automated metadata and data quality extraction to foster the provision of FAIR data (Findable, Accessible, Interoperable Reusable). By enabling researchers to automatically extract and reuse structured and standardized ESS-specific metadata, in particular quality information, in several components of a research data infrastructure, we support researchers along the research data life cycle.