SIB resource landscape and review

The core mission of ELIXIR is to build a stable and sustainable infrastructure for biological information across Europe. At the heart of this are the data resources, tools and services that ELIXIR offers to the life-sciences community, providing stable and sustainable access to biological data. ELIXIR aims to ensure that these resources are available long-term and that the life-cycles of these resources are managed such that they support the scientific needs of the life-sciences, including biological research. ELIXIR Core Data Resources are defined as a set of European data resources that are of fundamental importance to the wider life-science community and the long-term preservation of biological data. They are complete collections of generic value to life-science, are considered an authority in their field with respect to one or more characteristics, and show high levels of scientific quality and service. Thus, ELIXIR Core Data Resources are of wide applicability and usage. This paper describes the structures, governance and processes that support the identification and evaluation of ELIXIR Core Data Resources. It identifies key indicators which reflect the essence of the definition of an ELIXIR Core Data Resource and support the promotion of excellence in resource development and operation. It describes the specific indicators in more detail and explains their application within ELIXIR's sustainability strategy and science policy actions, and in capacity building, life-cycle management and technical actions. Establishing the portfolio of ELIXIR Core Data Resources and ELIXIR Services is a key priority for ELIXIR and publicly marks the transition towards a cohesive infrastructure.

<ns7:p>The well-being of farmed and companion animals is increasingly recognised as integral to sustainable agroecosystems, companionship, and the One Health approach, which emphasises the interconnected health of people, animals, and the environment. The ELIXIR Domestic Animals Genome and Phenome (DAGP) Community supports genome-to-phenome analyses for farmed and companion animal species. Its aim is to coordinate, discuss, and explore the potential of data technology solutions to address key issues in animal welfare, behaviour, health, infectious diseases, metabolism, nutritional efficiency, and the preservation of genetic diversity and the environment. Through consolidating efforts to develop data standards, coordination, workflows, and visualisation, it will enhance the science underpinning rapidly growing fields in domestic animal genomics, including genome-enabled breeding, population genomics, pangenome analysis, functional genomics, genome editing, paleogenomics, phenotyping, and bio-banking. These standards will adhere to the FAIR data principles and leverage established ontologies to promote best practices in data coordination and archiving. This white paper, prepared by the ELIXIR DAGP Focus Group, summarises the current data infrastructure, resources, and tools available for domestic animal genomics and phenomics, and presents community-led plans and priorities to be implemented to meet the requirements of ELIXIR services and the animal science community. We describe how ELIXIR services can be applied in the domestic animal genomics and phenomics fields, and how we can connect projects and infrastructures that are active in the animal sciences domain. We also discuss three key priority areas: i) expanding the FAANG Data Portal for phenotype data with ELIXIR Data Platforms; ii) supporting submissions of new data types across ELIXIR Core Data Resources , including proprietary data from industry partners; and iii) strengthening connections to existing ELIXIR Communities and international consortia. This article provides a set of priorities for a Domestic Animals Genome and Phenome Community in ELIXIR and outlines the next steps to engage across stakeholders and to consolidate data for domestic animal science in Europe.</ns7:p>

Biological knowledgebases facilitate discovery across the life sciences by structuring experimental findings into human-readable and computable formats. These essential resources are maintained by a small number of professional biocurators worldwide and face combined chronic underfunding and the exponential growth of the literature. In this perspective, we review how artificial intelligence, particularly large language models and agentic systems, can augment literature-curation workflows. Applications include literature recommendation, entity recognition, data extraction, summarization, ontology development, and quality control with emphasis on published use cases at Global Core BioData Resources and ELIXIR Core Data Resources. We identify key challenges, including the scarcity of training data, difficulty in extracting complex relationships, and concerns about error propagation. To address these challenges, we propose a human-in-the-loop framework where generative artificial intelligence approaches accelerate routine tasks while curators provide critical evaluation and domain expertise. We also propose practical recommendations for the community, including the creation of shared benchmark datasets, harmonized evaluation frameworks, and best-practice guidelines for transparent human-in-the-loop AI deployment in biocuration. These synergistic partnerships will be critical to ensure biological rigour, accelerating knowledge integration while maintaining the quality essential for trusted biological resources.

Since its initial release more than a decade ago, the bacterial diversity database Bac Dive ( https://bacdive.dsmz.de ) has grown tremendously in terms of content and functionality. Today, Bac Dive is a comprehensive resource covering the phenotypic diversity of prokaryotes with data on taxonomy, morphology, physiology, cultivation, and more. As the leading database for strain-level information on bacteria and archaea, it has been designated as an ELIXIR Core Data Resource and a Global Core Biodata Resource . These distinctions underscore its importance to the wider microbiological research community. As part of the newly established DSMZ Digital Diversity infrastructure, Bac Dive is now closely linked to several other databases of fundamental importance to the life sciences, including BRENDA, LPSN and SILVA. These databases will be further developed in a coordinated manner and benefit from frequent data exchange. Here we present the current status and recent developments in Bac Dive . The database currently contains 2.7 million data points for 99,392 strains. This includes the world's largest collection of Analytical Profile Index (API) test results, which have now been fully integrated into the database and made searchable. Increasingly, the standardized high-quality data provided by Bac Dive is being used to train artificial intelligence models. The high-confidence genome-based predictions produced by our own models are now also being used to fill content gaps in the database. We further present a novel Bac Dive knowledge graph that provides powerful new search capabilities via a SPARQL endpoint to directly search and analyze the knowledge provided through Bac Dive in a standardized way, supported by a new descriptive ontology.

Data resources are essential for the long-term preservation of scientific data and the reproducibility of science. The SIB Swiss Institute of Bioinformatics provides the life science community with a portfolio of openly accessible, high-quality databases and software platforms, which vary from expert-curated knowledgebases, such as UniProtKB/Swiss-Prot (part of the UniProt consortium) and STRING, to online platforms such as SWISS-MODEL and SwissDrugDesign. SIB’s mission is to ensure that these resources are available in the long term, as long as their return on investment and their scientific impact are high. To this end, SIB provides its resources, in addition to stable financial support, with a range of high-quality, innovative services that are, to our knowledge, unique in the field. Through this first-class management framework with central services, such as user-centric consulting activities, legal support, open-science guidance, knowledge sharing and training efforts, SIB supports the promotion of excellence in resource development and operation. This review presents the ecosystem of data resources at SIB; the process used for the identification, evaluation and development of resources; and the support activities that SIB provides. A set of indicators has been put in place to select the resources and establish quality standards, reflecting their multifaceted nature and complexity. Through this paper, the reader will discover how SIB’s leading tools and databases are fostered by the institute, leading them to be best-in-class resources able to tackle the burning matters that society faces from disease outbreaks and cancer to biodiversity and open science.

Досліджено архітектуру орієнтованої мережі на користувача, яка передбачає використання виділеної абонентської мережі, яка об'єднує всі необхідні функції для надання інформаційних послуг. Враховуючи переваги технології UCN визначено, що орієнтована на користувача архітектура радіомережі природно та ідеально поєднується з децентралізованою технологією, яка є невразливою для DDoS-атак та єдиних точок відмови (SPOF). Розглянуто особливості планування роботи великих мережевих систем за умови гарантованого надання інформаційної послуги із використанням існуючих рішень, що отримані в результаті дослідження роботи центрів обробки даних та застосовуванні їх у масштабі мережі з підтримкою кількох орендарів та паралелізму. Отримані результати дослідження розподілених систем вказують, що головною метою має бути підвищення ефективності, а не оптимальності. Досліджено особливості надання кожному клієнтському пристрою власного «приватного» віртуального екземпляру базової мережі через децентралізовану структуру віртуальних машин чи контейнерів із використанням гнучких програмно-конфігурованих мереж (SDN) та віртуалізованих мережевих функцій (NFV). Розглянуто ідеологію архітектури UCN, яка полягає у формуванні фундаментальної, гнучкої, безпечної та надійною мережевої архітектури із простою структурою та повним набором функцій. Вказані функціональні характеристики підвищують довіру мережі 6G. Досліджено особливості формування цифрового домену користувача спираючись на підхід UCN з чітко вираженою границею, що містить ресурси даних, такі як профіль користувача, профіль служби та профіль ресурсів (включаючи мережні ресурси, так і ресурси кінцевого обладнання). В результаті, визначено правила керування цінними цифровими активами, що належать користувачеві у повній відповідності з нормативними вимогами до безпеки та конфіденційності.

The use of advanced machine learning algorithms in experimental materials science is limited by the lack of sufficiently large and diverse datasets amenable to data mining. If publicly open, such data resources would also enable materials research by scientists without access to expensive experimental equipment. Here, we report on our progress towards a publicly open High Throughput Experimental Materials (HTEM) Database (htem.nrel.gov). This database currently contains 140,000 sample entries, characterized by structural (100,000), synthetic (80,000), chemical (70,000), and optoelectronic (50,000) properties of inorganic thin film materials, grouped in >4,000 sample entries across >100 materials systems; more than a half of these data are publicly available. This article shows how the HTEM database may enable scientists to explore materials by browsing web-based user interface and an application programming interface. This paper also describes a HTE approach to generating materials data, and discusses the laboratory information management system (LIMS), that underpin HTEM database. Finally, this manuscript illustrates how advanced machine learning algorithms can be adopted to materials science problems using this open data resource.

The early design stages of buildings have the highest potential for optimizing energy efficiency. From the aspect of architectural design, the architects can hardly make the best decisions of the complexed variables of geometries and materials. The choice and weighting of different optimization objectives also have no reference while considering the impacts from the whole view of the life cycle. In this paper, a simulation-based parametric approach through the use of multi-objective optimization method is framed. The extraction of geometry variables from the original design and the principles of explaining optimization results obtained from different objective combinations are discussed.This process is carried out in Rhino and Grasshopper. To fully consider the aesthetic and building performance step by step in early design stages, the geometry and material parameters are tested simultaneously. The life cycle inventory data of materials and energy resources are imported by programming, and local data is partly used. Life cycle environmental impacts (PED and GWP), life cycle cost, and operating energy consumption are used as optimization objectives. A case study is verified on a project of a small campus museum building in northern China. The results indicate that the combination of geometry and material values in the optimal solution set is various and needs to be artificially selected according to actual needs. The optimization objectives should be considered comprehensively; an incomplete set may result in poorly behavior of the unselected objectives.

Open data is seen as a promising source of new business, especially in the SME sector, in the form of new products, services and innovative solutions. High importance is seen also in fostering citizens' participation in political and social life and increasing the transparency of public authorities. The forerunners of the open data movement in the public sector are the USA and the UK, which started to open their public data resources in 2009. The first European Union open data related directive was drawn up as early as 2003; however progress in putting the idea into practice has been slow and adoptions by the wider member states are placed in the early 2010s. The beneficial use of open data in real applications has progressed hand in hand with the improvement of other ICT-related technologies. The (raw) data itself has no high value. The economic value comes from a balanced combination of high quality open (data) resources combined with the related value chain. This paper builds up a "big picture" of the role of open data in current society. The approach is analytical and it clarifies the topic from the viewpoints of both opportunities and challenges. The paper covers both general aspects related to open data and results of the research and regional development project conducted by the authors.

An optimization-based maintenance scheduling framework is an essential tool to plan the necessary investment to maintain the required performance of a railway line. In the present study, a methodology is proposed to minimize the present value of the life cycle maintenance costs and maximize the life cycle quality level of the track-bed considering different levels of reliability. Probabilistic degradation models are developed for predicting the evolution of the railway track condition over time. Afterwards, a Genetic Algorithm based optimization procedure is applied for obtaining a set of optimal solutions taking into account several constrains. The proposed methodology is applied to an Italian railway track-line case study. The results show that it is possible to develop a decision support system to help railway managers to schedule railway track maintenance operations based on the optimal trade-off between maintenance costs and railway track geometry condition for different levels of reliability.

Evaluation of the effectiveness of the application of innovative technologies in industry should be considered in the context of general principles and methods used in the implementation of information technology (IT). The main factors considered in these models can be taken as a basis for deciding on the appropriate level of investments channeled into IT systems to ensure a given level of reliability. The goal is to summarize the concepts and principles underlying these models and describe their common characteristics. Systematization of general models of the value estimation created due to investments into information technologies (IT). The main factors considered in these models are the basis for deciding on the appropriate level of investments in IT to ensure a given level of reliability. The characteristics of the principal types of models are given, which are proposed to be divided into four groups: investment-oriented; cost-oriented; models reflecting environmental factors; quantitative assessment models. It is offered to allocate material and non-material factors of information value formation. The resulting value of the technology under consideration for the whole life cycle is calculated as the sum of the material and non-material values of the components. Estimations have been made and a methodology for calculating the economic gains from the use of innovative technologies is demonstrated using the example of JSC Concern “Sozvezdie” (Voronezh).

Little guidance has been available to organizations interested in addressing the necessary dimensions of data resources management to ensure data quality in increasingly encountered situations when data usage crosses system boundaries. The basic concept of metadata quality as a foundation for data quality engineering is proposed, as well as an extended data life cycle model consisting of eight phases: metadata creation, metadata structuring, metadata refinement, data creation, data utilization, data assessment, data refinement, and data manipulation. This extended model will enable further development of life cycle phase-specific data quality engineering methods. The paper also expands the concept of applicable data quality dimensions, presenting data quality as a function of four distinct components: data value quality; data representation quality; data model quality; and data architecture quality. Each of these, in turn, is described in terms of specific data quality attributes.

With massive amounts of data being generated in electronic format, there is a need in basic science laboratories to adopt new methods for tracking and analyzing data. An electronic laboratory notebook (ELN) is not just a replacement for a paper lab notebook, it is a new method of storing and organizing data while maintaining the data entry flexibility and legal recording functions of paper notebooks. Paper notebooks are regarded as highly flexible since the user can configure it to store almost anything that can be written or physically pasted onto the pages. However, data retrieval and data sharing from paper notebooks are labor intensive processes and notebooks can be misplaced, a single point of failure that loses all entries in the volume. Additional features provided by electronic notebooks include searchable indices, data sharing, automatic archiving for security against loss and ease of data duplication. Furthermore, ELNs can be tasked with additional functions not commonly found in paper notebooks such as inventory control. While ELNs have been on the market for some time now, adoption of an ELN in academic basic science laboratories has been lagging. Issues that have restrained development and adoption of ELN in research laboratories are the sheer variety and frequency of changes in protocols with a need for the user to control notebook configuration outside the framework of professional IT staff support. In this commentary, we will look at some of the issues and experiences in academic laboratories that have proved challenging in implementing an electronic lab notebook.

The issue of sustainable community development is very relevant in Ukrainian realities. There is found in the article that the main factors that determine the sustainable development of the community are the availability and productivity of extractive resources, human capital, greening of economic development, the quality of local development management. There is analyzed in the article that communities which have significant mineral reserves and actively develop them, the issue of sustainable development and active community participation in the formation and distribution of local budgets is critical. After all, in modern socio-economic conditions due to the violation of the production processes, in many territorial communities of Ukraine, where hydrocarbon production is carried out, the threat of environmentally dangerous man-made accidents and catastrophes has increased significantly. This highlights the need to strengthen the targeted use of funds received by local budgets of territorial communities from the usage of subsoil, in particular by directing them to support socio-economic development, repair of roads and engineering and social infrastructure, strengthening environmental safety. Residents of almost all territorial communities of Ukraine, as a rule, do not even have a general idea of the amount of revenues to the local budget and the structure of its revenue and expenditure parts. There is proposed in the article strengthening community participation through democratic practices (public hearings, participation budgets, discussions, competitions for local initiatives, e-petitions, etc.). Also a separate item of the proposals is the introduction of open data resources, which will reduce the corruption component and minimize the shadow component. In addition, open data resources provide a significant increase in the efficiency of using the financial and budgetary potential of the respective territorial communities.

As society has progressed through periods of evolution and revolution, technology has played a key role as an enabler. In the same manner, mechanical machines of the 1800s drove the industrial revolution, now digitalized machines are driving another industrial revolution. Manufacturers are increasing the digital footprint on the factory floor. It is challenging to harness the vast amounts of data generated, stored, analyzed, archived, and returned. Data centralization has several well-known challenges, such as collection bottlenecks, secure retrieval, single point of failure, and data scheme fragility as data heterogeneity increases. This paper proposes a method of information distribution based on the principle of data at its source . It proposes that contextual data be used at runtime through the creation of dynamic queries that build compositions of different systems. Such system of systems (SoS) compositions handle the flow of data across its life cycle and present it as information to the initiating system. The proposal starts by creating a graph model of the Arrowhead framework. Then, building on the graph model, the query-based approach for specifying, validating, and forming the SoS is proposed. The proposed graph model allows for unambiguous description of systems and their interrelations, including security relations. The proposed composer operates on the edge computing hardware and gives the production floor the ability to extract information without impacting the overall operation of the factory.