FAIR digital objects in environmental and life sciences should comprise workflow operation design data and method information for repeatability of study setups and reproducibility of results

ARAapp: filling gaps in the ecological knowledge of spiders using an automated and dynamic approach to analyze systematically collected community data

The ARAMOB data repository compiles meticulously curated spider community datasets from systematical collections, ensuring a high standard of data quality. These datasets are enriched with crucial methodological data that enable the datasets to be aligned in time and space, facilitating data synthesis across studies, respectively, collections. To streamline the analysis of these datasets in a species-specific context, a suite of tailored ecological analysis tools named ARAapp has been developed. By harnessing the capabilities of ARAapp, users can systematically evaluate the spider species data housed within the ARAMOB repository, elucidating intricate relationships with a range of parameters such as vertical stratification, habitat occurrence, ecological niche parameters (moisture and shading) and phenological patterns. Database URL: ARAapp is available at  www.aramob.de/en.

WOMBAT-P: Benchmarking Label-Free Proteomics Data Analysis Workflows

The inherent diversity of approaches in proteomics research has led to a wide range of software solutions for data analysis. These software solutions encompass multiple tools, each employing different algorithms for various tasks such as peptide-spectrum matching, protein inference, quantification, statistical analysis, and visualization. To enable an unbiased comparison of commonly used bottom-up label-free proteomics workflows, we introduce WOMBAT-P, a versatile platform designed for automated benchmarking and comparison. WOMBAT-P simplifies the processing of public data by utilizing the sample and data relationship format for proteomics (SDRF-Proteomics) as input. This feature streamlines the analysis of annotated local or public ProteomeXchange data sets, promoting efficient comparisons among diverse outputs. Through an evaluation using experimental ground truth data and a realistic biological data set, we uncover significant disparities and a limited overlap in the quantified proteins. WOMBAT-P not only enables rapid execution and seamless comparison of workflows but also provides valuable insights into the capabilities of different software solutions. These benchmarking metrics are a valuable resource for researchers in selecting the most suitable workflow for their specific data sets. The modular architecture of WOMBAT-P promotes extensibility and customization. The software is available at https://github.com/wombat-p/WOMBAT-Pipelines.

Software infrastructure and data pipelines established for technical interoperability within a cross-border cooperation for the flora of the Bohemian Forest

Background

The timely and geographical resolutions, as well as the quantity and taxon concepts of records on the occurrence of plants near national borders is often ambiguous. This is due to the regional focus and different approaches of the contributing national and regional databases and networks of the neighbouring countries. Careful data transformation between national data providers is essential for understanding distribution patterns and its dynamics for organisms in areas along the national borders. Sharing occurrence data through the international data aggregator Global Biodiversity Information Facility (GBIF) is also complicated and has to consider that the underlying taxonomic concept and geographic information system of each single GBIF dataset might be different. In addition, some regional data providers have a restrictive (non-cc) licensing policy which does not allow data publication via the GBIF network. Therefore, it is necessary to investigate new ways to make data fit for use for a better and comprehensive understanding of the Flora of the Bohemian Forest.

New information

In this paper, we present a bilateral technical interoperability solution for vascular plant occurrence data for the area between the Czech Republic and Bavaria. We describe the initial state of data providers in both countries and the factual and technical challenges in finding a sustainable concept to establish mutual data sharing. The resulting solution for a functional infrastructure and an agreed data pipeline is described in a step-by-step approach. The new distributed infrastructure allows botanists and other stakeholders from both countries to work within the cross-border context of historical and current plants' distribution.

Reproducible Research Publication Workflow: A Canonical Workflow Framework and FAIR Digital Object Approach to Quality Research Output

In this paper we present the Reproducible Research Publication Workflow (RRPW) as an example of how generic canonical workflows can be applied to a specific context. The RRPW includes essential steps between submission and final publication of the manuscript and the research artefacts (i.e., data, code, etc.) that underlie the scholarly claims in the manuscript. A key aspect of the RRPW is the inclusion of artefact review and metadata creation as part of the publication workflow. The paper discusses a formalized technical structure around a set of canonical steps which helps codify and standardize the process for researchers, curators, and publishers. The proposed application of canonical workflows can help achieve the goals of improved transparency and reproducibility, increase FAIR compliance of all research artefacts at all steps, and facilitate better exchange of annotated and machine-readable metadata.

BEXIS2: A FAIR-aligned data management system for biodiversity, ecology and environmental data

Background

Obtaining fit-to-use data associated with diverse aspects of biodiversity, ecology and environment is challenging since often it is fragmented, sub-optimally managed and available in heterogeneous formats. Recently, with the universal acceptance of the FAIR data principles, the requirements and standards of data publications have changed substantially. Researchers are encouraged to manage the data as per the FAIR data principles and ensure that the raw data, metadata, processed data, software, codes and associated material are securely stored and the data be made available with the completion of the research.

New information

We have developed BEXIS2 as an open-source community-driven web-based research data management system to support research data management needs of mid to large-scale research projects with multiple sub-projects and up to several hundred researchers. BEXIS2 is a modular and extensible system providing a range of functions to realise the complete data lifecycle from data structure design to data collection, data discovery, dissemination, integration, quality assurance and research planning. It is an extensible and customisable system that allows for the development of new functions and customisation of its various components from database schemas to the user interface layout, elements and look and feel.

During the development of BEXIS2, we aimed to incorporate key aspects of what is encoded in FAIR data principles. To investigate the extent to which BEXIS2 conforms to these principles, we conducted the self-assessment using the FAIR indicators, definitions and criteria provided in the FAIR Data Maturity Model. Even though the FAIR data maturity model is developed initially to judge the conformance of datasets, the self-assessment results indicated that BEXIS2 remarkably conforms and supports FAIR indicators. BEXIS2 strongly conforms to the indicators Findability and Accessibility. The indicator Interoperability is moderately supported as of now; however, for many of the lesssupported facets, we have concrete plans for improvement. Reusability (as defined by the FAIR data principles) is partially achieved.

This paper also illustrates community deployment examples of the BEXIS2 instances as success stories to exemplify its capacity to meet the biodiversity and ecological data management needs of differently sized projects and serve as an organisational research data management system.

Microplastics accumulate fungal pathogens in terrestrial ecosystems

Microplastic (MP) is a pervasive pollutant in nature that is colonised by diverse groups of microbes, including potentially pathogenic species. Fungi have been largely neglected in this context, despite their affinity for plastics and their impact as pathogens. To unravel the role of MP as a carrier of fungal pathogens in terrestrial ecosystems and the immediate human environment, epiplastic mycobiomes from municipal plastic waste from Kenya were deciphered using ITS metabarcoding as well as a comprehensive meta-analysis, and visualised via scanning electron as well as confocal laser scanning microscopy. Metagenomic and microscopic findings provided complementary evidence that the terrestrial plastisphere is a suitable ecological niche for a variety of fungal organisms, including important animal and plant pathogens, which formed the plastisphere core mycobiome. We show that MPs serve as selective artificial microhabitats that not only attract distinct fungal communities, but also accumulate certain opportunistic human pathogens, such as cryptococcal and Phoma-like species. Therefore, MP must be regarded a persistent reservoir and potential vector for fungal pathogens in soil environments. Given the increasing amount of plastic waste in terrestrial ecosystems worldwide, this interrelation may have severe consequences for the trans-kingdom and multi-organismal epidemiology of fungal infections on a global scale.

Qiime Artifact eXtractor (qax): A Fast and Versatile Tool to Interact with Qiime2 Archives

Qiime2 is one of the most popular software tools used for analysis of output from metabarcoding experiments (e.g., sequencing of 16S, 18S, or ITS amplicons). Qiime2 introduced a novel and innovative data exchange format: the ‘Qiime2 artifact’. Qiime2 artifacts are structured compressed archives containing a dataset and its associated metadata. Examples of datasets are FASTQ reads, representative sequences in FASTA format, a phylogenetic tree in Newick format, while examples of metadata are the command that generated the artifact, information on the execution environment, citations on the used software, and all the metadata of the artifacts used to produce it. While artifacts can improve the shareability and reproducibility of Qiime2 workflows, they are less easily integrated with general bioinformatics pipelines. Accessing metadata in the artifacts also requires full Qiime2 installation. Qiime Artifact eXtractor (qax) allows users to easily interface with Qiime2 artifacts from the command line, without needing the full Qiime2 environment installed (or activated).