DOME Registry: implementing community-wide recommendations for reporting supervised machine learning in biology.

Supervised machine learning (ML) is used extensively in biology and deserves closer scrutiny. The Data Optimization Model Evaluation (DOME) recommendations aim to enhance the validation and reproducibility of ML research by establishing standards for key aspects such as data handling and processing, optimization, evaluation, and model interpretability. The recommendations help to ensure that key details are reported transparently by providing a structured set of questions.

A roadmap for a middleware as a federation service for integrative data retrieval of agricultural data.

Agriculture is confronted with several challenges such as climate change, the loss of biodiversity and stagnating productivity. The massive increasing amount of data and new digital technologies promise to overcome them, but they necessitate careful data integration and data management to make them usable. The FAIRagro consortium is part of the National Research Data Infrastructure (NFDI) in Germany and will develop FAIR compliant infrastructure services for the agrosystems science community, which will be integrated in the existing research data infrastructure service landscape.

Privacy Risk Assessment for Synthetic Longitudinal Health Data.

Introduction: A modern approach to ensuring privacy when sharing datasets is the use of synthetic data generation methods, which often claim to outperform classic anonymization techniques in the trade-off between data utility and privacy. Recently, it was demonstrated that various deep learning-based approaches are able to generate useful synthesized datasets, often based on domain-specific analyses. However, evaluating the privacy implications of releasing synthetic data remains a challenging problem, especially when the goal is to conform with data protection guidelines.

The German Central Health Study Hub - A Service to Find and Publish Clinical, Public Health and Epidemiolocal Studies and Associated Documents.

Introduction: The German Central Health Study Hub is a service that was initially developed at short notice during the COVID-19 pandemic. Since then, it has been expanded in scope, content, active users and functionality. The service is aimed at two main audiences: data provider and data consumers.

NFDI4Health Workflow and Service for Synthetic Data Generation, Assessment and Risk Management.

Individual health data is crucial for scientific advancements, particularly in developing Artificial Intelligence (AI); however, sharing real patient information is often restricted due to privacy concerns. A promising solution to this challenge is synthetic data generation. This technique creates entirely new datasets that mimic the statistical properties of real data, while preserving confidential patient information.

Dataset of miRNA-disease relations extracted from textual data using transformer-based neural networks.

MicroRNAs (miRNAs) play important roles in post-transcriptional processes and regulate major cellular functions. The abnormal regulation of expression of miRNAs has been linked to numerous human diseases such as respiratory diseases, cancer, and neurodegenerative diseases. Latest miRNA-disease associations are predominantly found in unstructured biomedical literature.

Guidance on how to efficiently find, choose, and use available systematic reviews was developed.

Objectives: The aim of this paper is to provide clinicians and authors of clinical guidelines or patient information with practical guidance on searching and choosing systematic reviews(s) (SR[s]) and, where adequate, on making use of SR(s).

Study Design And Setting: At the German conference of the Evidence-Based Medicine Network (EbM Network) a workshop on the topic was held to identify the most important areas where guidance for practice appears necessary. After the workshop, we established working groups.

Synthetic data generation for a longitudinal cohort study - evaluation, method extension and reproduction of published data analysis results.

Access to individual-level health data is essential for gaining new insights and advancing science. In particular, modern methods based on artificial intelligence rely on the availability of and access to large datasets. In the health sector, access to individual-level data is often challenging due to privacy concerns.

Role of CD4 T-cells for regulating splenic myelopoiesis and monocyte differentiation after experimental myocardial infarction.

Myocardial infarction (MI) induces the generation of proinflammatory Ly6C monocytes in the spleen and the recruitment of these cells to the myocardium. CD4 Foxp3 CD25 T-cells (Tregs) promote the healing process after myocardial infarction by engendering a pro-healing differentiation state in myocardial monocyte-derived macrophages. We aimed to study the effects of CD4 T-cells on splenic myelopoiesis and monocyte differentiation.

Enabling personalised disease diagnosis by combining a patient's time-specific gene expression profile with a biomedical knowledge base.

Background: Recent developments in the domain of biomedical knowledge bases (KBs) open up new ways to exploit biomedical knowledge that is available in the form of KBs. Significant work has been done in the direction of biomedical KB creation and KB completion, specifically, those having gene-disease associations and other related entities. However, the use of such biomedical KBs in combination with patients' temporal clinical data still largely remains unexplored, but has the potential to immensely benefit medical diagnostic decision support systems.

Community-driven governance of FAIRness assessment: an open issue, an open discussion.

Although FAIR Research Data Principles are targeted at and implemented by different communities, research disciplines, and research stakeholders (data stewards, curators, etc.), there is no conclusive way to determine the level of FAIRness intended or required to make research artefacts (including, but not limited to, research data) Findable, Accessible, Interoperable, and Reusable. The FAIR Principles cover all types of digital objects, metadata, and infrastructures.

Sensitivities of a Rapid Test Versus an ELISA Kit for Detecting Anti-SARS-CoV-2 IgM/IgG in Sera from an Egyptian Cohort.

This study aimed to compare diagnostic sensitivities of a rapid test (Rt) and an ELISA kit for detecting anti-SARS-CoV-2 IgM/IgG in virus-RT-PCR-positive (VPP) and virus-RT-PCR-unchecked (VPU) subjects in an Egyptian cohort during the first wave of SARS-CoV-2 infection. The results revealed higher sensitivity of the Rt for detecting IgM/IgG in the VPP subjects. Both the Rt and ELISA showed identical sensitivities for IgM detection in the VPU subjects.

Eleven strategies for making reproducible research and open science training the norm at research institutions.

Reproducible research and open science practices have the potential to accelerate scientific progress by allowing others to reuse research outputs, and by promoting rigorous research that is more likely to yield trustworthy results. However, these practices are uncommon in many fields, so there is a clear need for training that helps and encourages researchers to integrate reproducible research and open science practices into their daily work. Here, we outline eleven strategies for making training in these practices the norm at research institutions.

Bioschemas training profiles: A set of specifications for standardizing training information to facilitate the discovery of training programs and resources.

Stand-alone life science training events and e-learning solutions are among the most sought-after modes of training because they address both point-of-need learning and the limited timeframes available for "upskilling." Yet, finding relevant life sciences training courses and materials is challenging because such resources are not marked up for internet searches in a consistent way. This absence of markup standards to facilitate discovery, re-use, and aggregation of training resources limits their usefulness and knowledge translation potential.

An Annotation Workbench for Semantic Annotation of Data Collection Instruments.

Semantic interoperability, i.e., the ability to automatically interpret the shared information in a meaningful way, is one of the most important requirements for data analysis of different sources.

The Epilepsy Ontology: a community-based ontology tailored for semantic interoperability and text mining.

Motivation: Epilepsy is a multifaceted complex disorder that requires a precise understanding of the classification, diagnosis, treatment and disease mechanism governing it. Although scattered resources are available on epilepsy, comprehensive and structured knowledge is missing. In contemplation to promote multidisciplinary knowledge exchange and facilitate advancement in clinical management, especially in pre-clinical research, a disease-specific ontology is necessary.

CoLoC-seq probes the global topology of organelle transcriptomes.

Proper RNA localisation is essential for physiological gene expression. Various kinds of genome-wide approaches permit to comprehensively profile subcellular transcriptomes. Among them, cell fractionation methods, that couple RNase treatment of isolated organelles to the sequencing of protected transcripts, remain most widely used, mainly because they do not require genetic modification of the studied system and can be easily implemented in any cells or tissues, including in non-model species.

Critical assessment of transformer-based AI models for German clinical notes.

Objective: Healthcare data such as clinical notes are primarily recorded in an unstructured manner. If adequately translated into structured data, they can be utilized for health economics and set the groundwork for better individualized patient care. To structure clinical notes, deep-learning methods, particularly transformer-based models like , have recently received much attention.

We are not ready yet: limitations of state-of-the-art disease named entity recognizers.

Background: Intense research has been done in the area of biomedical natural language processing. Since the breakthrough of transfer learning-based methods, BERT models are used in a variety of biomedical and clinical applications. For the available data sets, these models show excellent results - partly exceeding the inter-annotator agreements.

Introducing the FAIR Principles for research software.

Research software is a fundamental and vital part of research, yet significant challenges to discoverability, productivity, quality, reproducibility, and sustainability exist. Improving the practice of scholarship is a common goal of the open science, open source, and FAIR (Findable, Accessible, Interoperable and Reusable) communities and research software is now being understood as a type of digital object to which FAIR should be applied. This emergence reflects a maturation of the research community to better understand the crucial role of FAIR research software in maximising research value.

DCSO: towards an ontology for machine-actionable data management plans.

The concept of Data Management Plan (DMP) has emerged as a fundamental tool to help researchers through the systematical management of data. The Research Data Alliance DMP Common Standard (DCS) working group developed a set of universal concepts characterising a DMP so it can be represented as a machine-actionable artefact, i.e.

Continuous development of the semantic search engine preVIEW: from COVID-19 to long COVID.

preVIEW is a freely available semantic search engine for Coronavirus disease (COVID-19)-related preprint publications. Currently, it contains >43 800 documents indexed with >4000 semantic concepts, annotated automatically. During the last 2 years, the dynamic situation of the corona crisis has demanded dynamic development.

Pre2Pub-Tracking the Path From Preprint to Journal Article: Algorithm Development and Validation.

Background: The current COVID-19 crisis underscores the importance of preprints, as they allow for rapid communication of research results without delay in review. To fully integrate this type of publication into library information systems, we developed preview: a publicly available, central search engine for COVID-19-related preprints, which clearly distinguishes this source from peer-reviewed publications. The relationship between the preprint version and its corresponding journal version should be stored as metadata in both versions so that duplicates can be easily identified and information overload for researchers is reduced.