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2. Biodiversity genomics research practices require harmonising to meet stakeholder needs in conservationElena Bužan, Christian de Guttry, Chiara Bortoluzzi, Nathaniel R. Street, Kay Lucek, Anna Rosling, Lino Ometto, Alice Mouton, Luísa S. Marins, María José Ruiz-López, José Melo-Ferreira, Elisabet Ottosson, Camila J. Mazzoni, Robert M. Waterhouse, 2025, original scientific article Abstract: Biodiversity resilience relies on genetic diversity, which sustains the evolutionary potential of organisms in dynamic ecosystems. Genomics is a powerful tool for accurately estimating genetic diversity across genomes of species and populations. However, integration of genomic data into conservation efforts faces challenges due to the heterogeneity of approaches employed. Establishing common sets of standards for genomic data production and analysis is essential to consistently interpret results and clearly communicate outcomes to stakeholders. While the European Reference Genome Atlas (ERGA) community has contributed significantly to the standardisation of reference genome methodologies in synergy with other initiatives, there is now an urgent need to extend these principles to downstream analyses. ERGA aims to build on its experience to help establish harmonised approaches in applied biodiversity genomics research, aligned with ongoing efforts to define standardised metrics for measuring and reporting genetic diversity. Establishing consensus on best practices for genome-wide data generation methods and applications will substantially increase accuracy, interpretability, and comparability, together with enhanced stakeholder capacities. By identifying key opportunities and challenges, as well as conducting preliminary stakeholder mapping and examining case studies, the goal is to build an inclusive framework that ensures the relevance and widespread adoption of these best practices: fostering trust and confidence in genomics research practices to meet stakeholder needs in biodiversity conservation. We call upon the broader research community to join efforts in establishing these approaches, recognising the importance of participation of end-users, to foster the integration of genomic data into the toolkit for measuring and reporting genetic diversity.
Keywords: best practices, biodiversity genomics, genome-wide genetic diversity, stakeholder engagement, standardisation, whole genome resequencing data
Published in RUP: 01.07.2025; Views: 130; Downloads: 4
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3. Testing life-cycle assessment data quality with Benford’s law reveals geographic variationBogdan Šinik, Aleksandar Tošić, 2025, original scientific article Abstract: Life-Cycle Assessment (LCA) is a methodology that is used extensively for evaluating the environmental impacts of products and processes throughout their lifetime. The method is highly dependent on the quality and accuracy of the underlying data. Moreover, the data acquisition process can be subjective, raising concerns about potential inconsistencies. In this study, we perform Benford’s law conformity tests (first digit) on all numerical data in ecoinvent, focusing on individual compartments (air, water, soil, and natural resources) and environmental elementary flows (carbon, toxic substances, greenhouse gases, and heavy metals), and discrepancies across continents are examined. Life Cycle Inventory data met the requirements of Benford’s law and generally exhibited high conformity. Substantial differences in conformity were observed between Africa and Europe. Individual processes and measurements were inspected to further isolate potential sources of the non-conformity. The statistical significance of the results was increased using open-source databases available on OpenLCA Nexus, including WorldSteel, OzLCI2019, ELCD, NEEDS, BioenergieDat, and Exiobase. Finally, the Environmental Performance Index (EPI) was used, and a strong correlation between continental Benford conformity results and corresponding EPI scores was observed. The findings suggest that discrepancies in conformity across continents reflect differences in data transparency and reporting practices. European datasets generally show higher conformity, likely owing to the use of more standardized methodologies. In contrast, data from regions with limited infrastructure or less established LCA practices tend to show lower conformity. Benford’s Law offers a simple and computationally efficient alternative to conventional data quality assessments without requiring additional metadata or probabilistic modeling. Its application can support the detection of systemic biases and improve the reliability of LCA-based indicators such as environmental product declarations.
Keywords: anomaly detection, Benford’s law, data integrity
Published in RUP: 12.06.2025; Views: 1334; Downloads: 10
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4. Estimation of task-related dynamic brain connectivity via data inflation and classification model explainabilityPeter Rogelj, 2025, original scientific article Abstract: Study of brain function often involves analyzing task-related switching between intrinsic brain networks, which connect various brain regions. Functional brain connectivity analysis methods aim to estimate these networks but are limited by the statistical constraints of windowing functions, which reduce temporal resolution and hinder explainability of highly dynamic processes. In this work, we propose a novel approach to functional connectivity analysis through the explainability of EEG classification. Unlike conventional methods that condense raw data into extracted features, our approach inflates raw EEG data by decomposition into meaningful components that explain processes in the application domain. To uncover the brain connectivity that affects classification decisions, we introduce a new method of dynamic influence data inflation (DIDI), which extracts signals representing interactions between electrode regions. These inflated data are then classified using an end-to-end neural network classifier architecture designed for raw EEG signals. Saliency map estimation from trained classifiers reveals the connectivity dynamics affecting classification decisions, which can be visualized as dynamic connectivity support maps for improved interpretability. The methodology is demonstrated on two publicly available datasets: one for imagined motor movement classification and the other for emotion classification. The results highlight the dual benefits of our approach: in addition to providing interpretable insights into connectivity dynamics it increases classification accuracy.
Keywords: EEG, functional connectivity, data inflation, classification, explainability, saliency maps
Published in RUP: 04.06.2025; Views: 205; Downloads: 9
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