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The documentation, preservation and rescue of biological diversity increasingly uses living biological samples. Persistent associations between species, biosamples, such as tissues and cell lines, and the accompanying data are indispensable for using, exchanging and benefiting from these valuable materials. Explicit authentication of such biosamples by assigning unique and robust identifiers is therefore required to allow for unambiguous referencing, avoid identification conflicts and maintain reproducibility in research. A predefined nomenclature based on uniform rules would facilitate this process. However, such a nomenclature is currently lacking for animal biological material. We here present a first, standardized, human-readable nomenclature design, which is sufficient to generate unique and stable identifying names for animal cellular material with a focus on wildlife species. A species-specific human- and machine-readable syntax is included in the proposed standard naming scheme, allowing for the traceability of donated material and cultured cells, as well as data FAIRification. Only when it is consistently applied in the public domain, as publications and inter-institutional samples and data are exchanged, distributed and stored centrally, can the risks of misidentification and loss of traceability be mitigated. This innovative globally applicable identification system provides a standard for a sustainable structure for the long-term storage of animal bio-samples in cryobanks and hence facilitates current as well as future species conservation and biomedical research.

In the Netherlands, the Open Government Act (Wet openbare overheid or Woo/Wob in Dutch) is in effect, with the primary objective of ensuring a more transparent government. In line with the legislation, a search engine named Woogle has been designed and developed to centralize documents published under the Open Government Act. The Estonian Public Information Act serves a similar purpose and requires all public institutions to publish information generated during official duties, fostering transparency and public oversight. Currently, Estonia's document repositories are decentralized, and content search is not supported, which hinders people's ability to efficiently locate information. This study aims to assess public information accessibility in Estonia and to apply Woogle's design and techniques to Estonia's document repositories, thereby evaluating its potential for broader European implementation. The methodology involved web scraping data and documents from 57 Estonian public institutions' document repositories. The results indicate that Woogle's design and techniques can be implemented in Estonia. From a technical perspective, the alignment of the fields was successful, while it was found that content-wise, the Estonian data present challenges due to inconsistencies and lack of comprehensive categorization. The findings suggest potential scalability across European countries, pointing to a broader applicability of the Woogle model for creating a corpus of Freedom of Information Act documents in Europe. The collected data are available as a dataset. [ABSTRACT FROM AUTHOR]

Background: The dung beetle genus Scarabaeus (Coleoptera, Scarabaeinae, Scarabaeini), predominantly found in the arid regions of the Old World, includes three endemic species inhabiting the dry ecosystems of western and southern Madagascar. These species are presumed to form a monophyletic clade nested within the African Scarabaeus. Semantic modelling of phenotypes using ontologies represents a transformative approach to species description in biology, making phenotypic data FAIR and computable. The recently developed Phenoscript language enables the creation of semantic, computable species descriptions using a syntax akin to human natural language (NL). However, Phenoscript has not yet been tested as a tool for describing new taxa. New information: In this study, we test the utility of Phenoscript by describing a new species, Scarabaeus (sensu lato) sakalava sp. nov. from Madagascar. The initial description is composed directly in Phenoscript, replacing the traditional natural language format. This Phenoscript description is then translated into a human-readable form using the Phenospy tool for publication purposes. Additionally, the Phenoscript description is converted into an RDF graph, making it understandable by computers using semantic technologies. Scarabaeus sakalava sp. nov. is found in western central Madagascar and is closely related to S. viettei (Paulian, 1953) from north-western Madagascar. We provide an updated identification key and distribution map for all Malagasy Scarabaeus and discuss their systematic placement. [ABSTRACT FROM AUTHOR]

Objective: This paper introduces a privacy-preserving federated machine learning (ML) architecture built upon Findable, Accessible, Interoperable, and Reusable (FAIR) health data. It aims to devise an architecture for executing classification algorithms in a federated manner, enabling collaborative model-building among health data owners without sharing their datasets. Materials and methods: Utilizing an agent-based architecture, a privacy-preserving federated ML algorithm was developed to create a global predictive model from various local models. This involved formally defining the algorithm in two steps: data preparation and federated model training on FAIR health data and constructing the architecture with multiple components facilitating algorithm execution. The solution was validated by five healthcare organizations using their specific health datasets. Results: Five organizations transformed their datasets into Health Level 7 Fast Healthcare Interoperability Resources via a common FAIRification workflow and software set, thereby generating FAIR datasets. Each organization deployed a Federated ML Agent within its secure network, connected to a cloud-based Federated ML Manager. System testing was conducted on a use case aiming to predict 30-day readmission risk for chronic obstructive pulmonary disease patients and the federated model achieved an accuracy rate of 87%. Discussion: The paper demonstrated a practical application of privacy-preserving federated ML among five distinct healthcare entities, highlighting the value of FAIR health data in machine learning when utilized in a federated manner that ensures privacy protection without sharing data. Conclusion: This solution effectively leverages FAIR datasets from multiple healthcare organizations for federated ML while safeguarding sensitive health datasets, meeting legislative privacy and security requirements.

Biodiversity data, particularly species occurrence and abundance, are indispensable for testing empirical hypothesis in natural sciences. However, datasets built for research programmes do not often meet FAIR (findable, accessible, interoperable and reusable) principles, which raises questions about data quality, accuracy and availability. The 21st century has markedly been a new era for data science and analytics and every effort to aggregate, standardise, filter and share biodiversity data from multiple sources have become increasingly necessary. In this study, we propose a framework for refining and conforming secondary biodiversity data to FAIR standards to make them available for use such as macroecological modelling and other studies. We relied on a Darwin Core base model to standardise and further facilitate the curation and validation of data related including the occurrence and abundance of multiple taxa of a region that encompasses estuarine ecosystems in an ecotonal area bordering the easternmost Amazonia. We further discuss the significance of feeding standardised public data repositories to advance scientific progress and highlight their role in contributing to the biodiversity management and conservation.

AbstractObjectiveMethodResultsDemonstrate how patient-level traumatic brain injury (TBI) data from studies in the Federal Interagency Traumatic Brain Injury Research (FITBIR) Informatics System can be harmonized and pooled to examine relationships between TBI and cognitive functioning.We harmonized and pooled data across studies and analyzed rates of probable cognitive functioning deficits by TBI history and severity.Four publicly available FITBIR studies with 3,445 participants included data on cognitive dysfunction, though only one included comparison groups (mild TBI vs. no history of TBI) and could be used in the final comparative analyses. Of the 1,539 participants, 82% had a history of mild TBI and 67% had data suggesting the presence of cognitive dysfunction. Participants with a history of mild TBI were mostly male (87%), 25–39 years old (53%), and Non-Hispanic White (60%). Conclusions: One publicly available FITBIR study reported cognitive dysfunction data as of January 2021, though findings were similar to prior research and supported an association between mild TBI and cognitive dysfunction. This proof-of-concept study shared newly developed methods including harmonization, analysis syntax, and meta-data via the FITBIR website to encourage dissemination of these TBI data resources in line with FAIR data goals. [ABSTRACT FROM AUTHOR]

Background: While sex-based differences in various health scenarios have been thoroughly acknowledged in the literature, we lack sufficient tools and methods that allow for an in-depth analysis of sex as a variable in biomedical research. To fill this knowledge gap, we created MetaFun as an easy-to-use web-based tool to meta-analyze multiple transcriptomic datasets with a sex-based perspective to gain major statistical power and biological soundness. Description: MetaFun is a complete suite that allows the analysis of transcriptomics data and the exploration of the results at all levels, performing single-dataset exploratory analysis, differential gene expression, gene set functional enrichment, and finally, combining results in a functional meta-analysis. Which biological processes, molecular functions or cellular components are altered in a common pattern in different transcriptomic studies when comparing male and female patients? This and other biological questions of interest can be answered with the use of MetaFun. This tool is available at https://bioinfo.cipf.es/metafun while additional help can be found at https://gitlab.com/ubb-cipf/metafunweb/-/wikis/Summary. Conclusions: Overall, Metafun is the first open-access web-based tool to identify consensus biological functions across multiple transcriptomic datasets, helping to elucidate sex differences in numerous diseases. Its use will facilitate the generation of novel biological knowledge that can be used in the research and application of Personalized Medicine considering the sex of patients. [ABSTRACT FROM AUTHOR]

This paper presents the open-source Python software OSADCP developed for the processing of vessel-mounted Acoustic Doppler Current Profiler (VMADCP) data. At this stage, the toolbox is designed for processing VMADCP measurements from open-ocean applications of Teledyne RDI Ocean Surveyor ADCPs and the data acquisition software VMDAS. Based on the VMDAS ENX binary output format, the software contains implementations for cleaning and vector-averaging of single-ping velocity data, verification of the position data, and applying misalignment and amplitude corrections. The procedures of OSADCP are described in detail to encourage the scientific community to use it for their own purposes. The toolbox is an integral part of a workflow implemented on the German marine research vessels in the framework of the Underway Research Data project of the German Marine Research Alliance (DAM). It aims to ensure standardized data acquisition measures, reliable data transfer from the ADCP to shore both near-real-time and in delayed-mode, processing and quality control, and dissemination of the curated data product in the data repository PANGAEA. From PANGAEA, data sets are forwarded to the European marine data hubs Copernicus Marine Service and EMODnet. The workflow that forms the framework for OSADCP is described here as an example of scientific data management that follows the FAIR data guidelines. [ABSTRACT FROM AUTHOR]

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The environmental pressure, CO2 emissions (including embodied energy) and delivery risks of our digital infrastructures are increasing. The exponentially growing digitisation of services that drive the transition from industry 4.0 to industry 5.0 has resulted in a rising materials demand for ICT hardware manufacturing. ICT devices such as laptops and data servers are being used on average for 3 and 4–5 years respectively (van Driel (2020)), while research shows that they should last 7 years before replacement (Journal of Cleaner Production69 (2014), 10–16). A solution is to transition from a linear to a circular economy (CE), through which materials that were previously disposed of as waste are re-entered back into product life-cycles through processes such as reuse, recycling, remanufacturing, repurposing. However, the adoption of the CE in the ICT sector is currently limited due to the lack of tools that support knowledge exchange between sustainability, ICT and technology experts in a standardised manner and the limited data availability, accessibility and interoperability needed to build such tools. Further, the already existing knowledge of the domain is fragmented into silos and the lack of a common terminology restricts its interoperability and usability. These also lead to transparency and responsibility issues along the supply chain. For many years now, the Semantic Web has been known to provide solutions to such issues in the form of ontologies. Several ontologies for the ICT, materials and CE domains have been build and successfully utilised to support processes such as predictive maintenance. However, there is a lack of a systematic analysis of the existing ontologies in these domains. Motivated by this, we present a literature survey and analysis of, but not limited to, existing ontologies for ICT devices such as laptops, materials and the CE. In addition, we discuss the need for findable, accessible, interoperable, reusable (FAIR) data in the CE, different factors such as data privacy and security that affect this and the role of ontologies. [ABSTRACT FROM AUTHOR]

Protein crystallography is an established method to study the atomic structures of macromolecules and their complexes. A prerequisite for successful structure determination is diffraction‐quality crystals, which may require extensive optimization of both the protein and the conditions, and hence projects can stretch over an extended period, with multiple users being involved. The workflow from crystallization and crystal treatment to deposition and publication is well defined, and therefore an electronic laboratory information management system (LIMS) is well suited to management of the data. Completion of the project requires key information on all the steps being available and this information should also be made available according to the FAIR principles. As crystallized samples are typically shipped between facilities, a key feature to be captured in the LIMS is the exchange of metadata between the crystallization facility of the home laboratory and, for example, synchrotron facilities. On completion, structures are deposited in the Protein Data Bank (PDB) and the LIMS can include the PDB code in its database, completing the chain of custody from crystallization to structure deposition and publication. A LIMS designed for macromolecular crystallography, IceBear, is available as a standalone installation and as a hosted service, and the implementation of key features for the capture of metadata in IceBear is discussed as an example. [ABSTRACT FROM AUTHOR]

Machine learning has grown in popularity in the past few years for susceptibility and hazard mapping tasks. Necessary steps for the generation of a susceptibility or hazard map are repeatedly implemented in new studies. We present a Random Forest classifier-based landslide susceptibility and hazard mapping framework to facilitate future mapping studies using machine learning. The framework, as a piece of software, follows the FAIR paradigm, and hence is set up as a transparent, reproducible and modularly extensible workflow. It contains pre-implemented steps from conceptualisation to map generation, such as the generation of input datasets. The framework can be applied to different areas of interest using different environmental features and is also flexible in terms of the desired scale and resolution of the final map. To demonstrate the functionality and validity of the framework, and to explore the challenges and limitations of Random Forest-based susceptibility and hazard mapping, we apply the framework to a test case. This test case conveys the influence of the training dataset on the generated susceptibility maps in terms of feature combination, influence of non-landslide instances and representativeness of the training data with respect to the area of interest. A comparison of the test case results with the literature shows that the framework works reliably. Furthermore, the results obtained in this study complement the findings of previous studies that demonstrate the sensitivity of the training process to the training data, particularly in terms of its representativeness. [ABSTRACT FROM AUTHOR]

The pharmaceutical industry is increasingly embracing laboratory automation to enhance experimental efficiency and operational resilience, particularly through the integration of automated liquid handlers (ALHs). This paper explores the integration of the low-cost Opentrons OT-2 liquid handling robot with F. Hoffmann-La Roche AG's in-house workflow orchestration software, AutoLab, to overcome barriers to lab automation. By leveraging the OT-2′s development-oriented interfaces and AutoLab's modular architecture, we achieved a user-friendly, cost-efficient, and flexible automation solution that aligns with FAIR (findable, accessible, interoperable, reusable) data principles. We demonstrate an advanced workflow development methodology, utilizing the software architecture, that facilitates the creation of two flexible pipetting protocols and medium complexity assays. This deep integration approach diminishes the learning curve for novice users while simultaneously enhancing the overall efficiency and reliability of the experimental workflow. Our findings suggest that such integrations can significantly mitigate the challenges associated with lab automation, including cost, complexity, and adaptability, paving the way for more accessible and robust automated systems in pharmaceutical research.

The heritage science sector is facing a critical need for accessible and comprehensive data resources to facilitate research, preservation efforts, and interdisciplinary collaboration. The concept of FAIR data management involves embracing principles and practices that ensure that data are Findable, Accessible, Interoperable, and Reusable. This work presents an overview of the latest updates on the INFRA-ART Spectral Library, an open access spectral database of cultural-heritage-related materials that was designed as a digital support tool for heritage research specialists that work with (portable) non- or minimally invasive spectroscopic techniques such as X-ray fluorescence (XRF), attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy, or Raman spectroscopy, among others. The database is an ongoing compilation of high-quality curated data that currently incorporates primary ATR-FTIR and XRF spectra and a preliminary dataset of Raman and short-wave infrared (SWIR) reflectance spectra on over 900 different materials typically found in painted works of art. For increased and sustainable accessibility, the database follows the European Commission’s recommendations on access to scientific information, as well as the FAIR guiding principles on research data that result from publicly funded research. The INFRA-ART Spectral Library is registered as a resource within the Open Science Cloud (EOSC) Portal and is among the services offered by the Romanian hub within E-RIHS (European Research Infrastructure for Heritage Science) DIGILAB.

This article introduces the Lake Environmental Data Harvester (LED) System, a robotic platform designed for the development of an innovative solution for monitoring remote alpine lakes. LED is intended as the first step in creating portable robotic tools that are lightweight, cost-effective, and highly reliable for monitoring remote water bodies. The LED system is based on the Shallow-Water Autonomous Multipurpose Platform (SWAMP), a groundbreaking Autonomous Surface Vehicle (ASV) originally designed for monitoring wetlands. The objective of LED is to achieve the comprehensive monitoring of remote lakes by outfitting the SWAMP with a suite of sensors, integrating an IoT infrastructure, and adhering to FAIR principles for structured data management. SWAMP's modular design and open architecture facilitate the easy integration of payloads, while its compact size and construction with a reduced weight ensure portability. Equipped with four azimuth thrusters and a flexible hull structure, SWAMP offers a high degree of maneuverability and position-keeping ability for precise surveys in the shallow waters that are typical of remote lakes. In this project, SWAMP was equipped with a suite of sensors, including a single-beam dual-frequency echosounder, water-quality sensors, a winch for sensor deployment, and AirQino, a low-cost air quality analysis system, along with an RTK-GNSS (Global Navigation Satellite System) receiver for precise positioning. Utilizing commercial off-the-shelf (COTS) components, a Multipurpose Data-Acquisition System forms the basis for an Internet of Things (IoT) infrastructure, enabling data acquisition, storage, and long-range communication. This data-centric system design ensures that acquired variables from both sensors and the robotic platform are structured and managed according to the FAIR principles. [ABSTRACT FROM AUTHOR]

Fungi is a highly diverse group of eukaryotic organisms that live under an extremely wide range of environmental conditions. Nowadays, there is a fundamental focus on observing how biodiversity varies on different spatial scales, in addition to understanding the environmental factors which drive fungal biodiversity. Metabarcoding is a high-throughput DNA sequencing technology that has positively contributed to observing fungal communities in environments. While the DNA sequencing data generated from metabarcoding studies are available in public archives, this valuable data resource is not directly usable for fungal biodiversity investigation. Additionally, due to its fragmented storage and distributed nature, it is not immediately accessible through a single user interface. We developed the MycoDiversity DataBase User Interface (https:// mycodiversity.liacs.nl) to provide direct access and retrieval of fungal data that was previously inaccessible in the public domain. The user interface provides multiple graphical views of the data components used to reveal fungal biodiversity. These components include reliable geo-location terms, the reference taxonomic scientific names associated with fungal species and the standard features describing the environment where they occur. Direct observation of the public DNA sequencing data in association with fungi is accessible through SQL search queries created by interactively manipulating topological maps and dynamic hierarchical tree views. The search results are presented in configurable data table views that can be downloaded for further use. With the MycoDiversity DataBase User Interface, we make fungal biodiversity data accessible, assisting researchers and other stakeholders in using metabarcoding studies for assessing fungal biodiversity. [ABSTRACT FROM AUTHOR]

The development of new functional materials is crucial for addressing global challenges such as clean energy or the discovery of new drugs and antibiotics. Functional material systems are typically composed of functional molecular building blocks, organized across multiple length scales in a hierarchical order. The large design space allows for precise tuning of properties to specific applications, but also makes it time‐consuming and expensive to screen for optimal structures using traditional experimental methods. Machine learning (ML) models can potentially revolutionize the field of materials science by predicting chemical syntheses and materials properties with high accuracy. However, ML models require data to be trained and validated. Methods to automatically extract data from scientific literature make it possible to build large and diverse datasets for ML models. In this article, opportunities and challenges of data extraction and machine learning methods are discussed to accelerate the discovery of high‐performing functional material systems, while ensuring that the predicted materials are stable, synthesizable, scalable, and sustainable. The potential impact of large language models (LLMs) on the data extraction process are discussed. Additionally, the importance of research data management tools is discussed to overcome the intrinsic limitations of data extraction approaches. [ABSTRACT FROM AUTHOR]

The heritage science sector is facing a critical need for accessible and comprehensive data resources to facilitate research, preservation efforts, and interdisciplinary collaboration. The concept of FAIR data management involves embracing principles and practices that ensure that data are Findable, Accessible, Interoperable, and Reusable. This work presents an overview of the latest updates on the INFRA-ART Spectral Library, an open access spectral database of cultural-heritage-related materials that was designed as a digital support tool for heritage research specialists that work with (portable) non- or minimally invasive spectroscopic techniques such as X-ray fluorescence (XRF), attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy, or Raman spectroscopy, among others. The database is an ongoing compilation of high-quality curated data that currently incorporates primary ATR-FTIR and XRF spectra and a preliminary dataset of Raman and short-wave infrared (SWIR) reflectance spectra on over 900 different materials typically found in painted works of art. For increased and sustainable accessibility, the database follows the European Commission's recommendations on access to scientific information, as well as the FAIR guiding principles on research data that result from publicly funded research. The INFRA-ART Spectral Library is registered as a resource within the Open Science Cloud (EOSC) Portal and is among the services offered by the Romanian hub within E-RIHS (European Research Infrastructure for Heritage Science) DIGILAB. [ABSTRACT FROM AUTHOR]

The sharing and documentation of cardiovascular research data are essential for efficient use and reuse of data, thereby aiding scientific transparency, accelerating the progress of cardiovascular research and healthcare, and contributing to the reproducibility of research results. However, challenges remain. This position paper, written on behalf of and approved by the German Cardiac Society and German Centre for Cardiovascular Research, summarizes our current understanding of the challenges in cardiovascular research data management (RDM). These challenges include lack of time, awareness, incentives, and funding for implementing effective RDM; lack of standardization in RDM processes; a need to better identify meaningful and actionable data among the increasing volume and complexity of data being acquired; and a lack of understanding of the legal aspects of data sharing. While several tools exist to increase the degree to which data are findable, accessible, interoperable, and reusable (FAIR), more work is needed to lower the threshold for effective RDM not just in cardiovascular research but in all biomedical research, with data sharing and reuse being factored in at every stage of the scientific process. A culture of open science with FAIR research data should be fostered through education and training of early-career and established research professionals. Ultimately, FAIR RDM requires permanent, long-term effort at all levels. If outcomes can be shown to be superior and to promote better (and better value) science, modern RDM will make a positive difference to cardiovascular science and practice. The full position paper is available in the supplementary materials. [ABSTRACT FROM AUTHOR]

This editorial considers Open Science, what it is, what are its potential benefits, what are the pillars of engagement upon which it rests, and what are some of the main challenges facing its further adoption by research communities. At its core, Open Science involves sharing not only the contents of a traditional research article, but also of any source data and methodologies upon which the reported findings are based. Though some extra work may be required, usually without anyone providing additional resources to do that work, continuous developments in digital technology are making Open Science easier to implement. While not all data is suitable to be shared, Open Science practices are widely supported within the wider research community and funding organizations. [ABSTRACT FROM AUTHOR]

Digitalization with integrated devices, digital and physical assistants, automation, and simulation is setting a new direction for laboratory work. Even with complex research workflows, high staff turnover, and a limited budget some laboratories have already shown that digitalization is indeed possible. However, academic bioprocess laboratories often struggle to follow the trend of digitalization. Due to their diverse research circumstances, high variety of team composition, goals, and limitations the concepts are substantially different. Here, we will provide an overview on different aspects of digitalization and describe how academic laboratories successfully digitalized their working environment. The key aspect is the collaboration and communication between IT‐experts and scientific staff. The developed digital infrastructure is only useful if it supports the laboratory worker and does not complicate their work. Thereby, laboratory researchers have to collaborate closely with IT‐experts in order for a well‐developed and maintainable digitalization concept that fits their individual needs and level of complexity. This review may serve as a starting point or a collection of ideas for the transformation toward a digitalized laboratory. [ABSTRACT FROM AUTHOR]

Fashion is an embodied form of culture. It builds on intimate information about the wearer, concerning e.g. body shape, size, age, weight and style. As fashion is digitalized, intimate body data is shared in online environments, which raises novel concerns about the privacy of fashion consumers. This is especially visible in online shopping, which has revolutionized fashion retail and data collection practices in fashion, particularly as concerns the secure and fair use of consumer data. We argue that the datafication of fashion should be investigated as part of sustainability discourse, and data fairness be established as a responsibility criterion. We analyzed 32 popular Finnish online clothing stores. Data management was studied by analyzing the online stores' network traffic to third parties, investigating the transparency of their privacy policy documents and analyzing possible dark patterns in the website's cookie banners. Our findings raise concerns over data responsibility and privacy in various areas. The analyzed websites had a high number of third-party services, averaging 5.2. Only 17 online stores out of the examined 32 (53%) clearly mentioned that they collected identifying information and product information in their privacy policies, and only five of the stores (16%) had no dark patterns in their cookie banners. Our results also suggest that several brands that are otherwise considered ethical may need to pay more attention to their fair data processing and privacy. This indicates that a wider discussion about data privacy and data handling practices is needed. Fair management of data must be part of the process of evaluating the sustainability of clothing companies. [ABSTRACT FROM AUTHOR]

Excess heat (i.e., Urban Heat Island; UHI) and other urban conditions affect tree physiology with outcomes from enhanced growth to mortality. Resilient urban forests in the face of climate change require species-specific understanding of growth responses. However, previous studies assessing growth dynamics were primarily based on remote sensing of communities rather than individuals, or relied on labor-intensive methods that can limit the spatial coverage necessary to account for highly variable urban growing conditions. Here, we analyze growth dynamics of common urban street tree species over time and across space for Berlin (Germany) combining dendroecological (temporal) and inventory assessments (spatial). First, we show annual increments increased across the 20th century for early (i.e., young) growth. Second, we use an approach relying on open inventory data to identify growth potential in relation to excess heat while accounting for age, potential management effects, and the urban fabric (i.e., planting area; building density, height; available soil nutrients) with generalized additive models for the ten most abundant species. Our analyses showed that younger trees may benefit from increased temperatures, while older individuals feature lower growth at greater UHI magnitudes. Furthermore, planting area as well as building density modulate growth responses to temperature. Lastly, we discuss management implications in the context of climate change mitigation, considering that younger trees are predominantly located at UHI "hot spots" and will undergo the observed age-dependent shift in temperature-growth sensitivity. By relying on increasingly available open data, our approach here is or will be transferable to other urban regions. [ABSTRACT FROM AUTHOR]

Aim: The purpose of this study is to assess the status quo of data sharing in LCA in relation to the FAIR (findability, accessibility, interoperability, and reuse) data principles. Methods: This study investigates how is LCA data from publicly funded research currently shared. Firstly, the focus is on life cycle inventory data shared in journal articles. Given that FAIR data sharing is not only the responsibility of the LCA practitioner, this study further investigates guidelines (e.g., data sharing standards and data management plans) and infrastructure (repositories, data formats, and nomenclature) to identify the tools and services available to LCA community which are essential to enable FAIR data sharing. Results: The study identifies that although there is growing awareness to improve data sharing practices, implementation of FAIR guidelines for data sharing is seldom seen in practice. LCA studies that adhere to FAIR principles are primarily due to use of generic data repositories which provide tools to support data sharing. However, there is no guidance on how LCA specific data should be shared to ensure its findability, accessibility, interoperability, and reusability. This study suggests a workflow to enable FAIRification of LCA data. In addition, the study recommends further efforts within the LCA community on skill and technology development, strategic funding, and recognition of the best practices in relation to data sharing. Conclusion: In conclusion, this study highlights the necessity of data sharing incentives, guidelines, and platforms/repositories specific for the LCA community. [ABSTRACT FROM AUTHOR]

This paper presents the open-source Python software OSADCP developed for the processing of vessel-mounted Acoustic Doppler Current Profiler (VMADCP) data. At this stage, the toolbox is designed for processing VMADCP measurements from open-ocean applications of Teledyne RDI Ocean Surveyor ADCPs and the data acquisition software VMDAS. Based on the VMDAS ENX binary output format, the software contains implementations for cleaning and vector-averaging of single-ping velocity data, verification of the position data, and applying misalignment and amplitude corrections. The procedures of OSADCP are described in detail to encourage the scientific community to use it for their own purposes. The toolbox is an integral part of a workflow implemented on the German marine research vessels in the framework of the Underway Research Data project of the German Marine Research Alliance (DAM). It aims to ensure standardized data acquisition measures, reliable data transfer from the ADCP to shore both near-real-time and in delayed-mode, processing and quality control, and dissemination of the curated data product in the data repository PANGAEA. From PANGAEA, data sets are forwarded to the European marine data hubs Copernicus Marine Service and EMODnet. The workflow that forms the framework for OSADCP is described here as an example of scientific data management that follows the FAIR data guidelines.

This editorial considers Open Science, what it is, what are its potential benefits, what are the pillars of engagement upon which it rests, and what are some of the main challenges facing its further adoption by research communities. At its core, Open Science involves sharing not only the contents of a traditional research article, but also of any source data and methodologies upon which the reported findings are based. Though some extra work may be required, usually without anyone providing additional resources to do that work, continuous developments in digital technology are making Open Science easier to implement. While not all data is suitable to be shared, Open Science practices are widely supported within the wider research community and funding organizations.

Abstract Background The EURO-NMD Registry collects data from all neuromuscular patients seen at EURO-NMD's expert centres. In-kind contributions from three patient organisations have ensured that the registry is patient-centred, meaningful, and impactful. The consenting process covers other uses, such as research, cohort finding and trial readiness. Results The registry has three-layered datasets, with European Commission-mandated data elements (EU-CDEs), a set of cross-neuromuscular data elements (NMD-CDEs) and a dataset of disease-specific data elements that function modularly (DS-DEs). The registry captures clinical, neuromuscular imaging, neuromuscular histopathology, biological and genetic data and patient-reported outcomes in a computer-interpretable format using selected ontologies and classifications. The EURO-NMD registry is connected to the EURO-NMD Registry Hub through an interoperability layer. The Hub provides an entry point to other neuromuscular registries that follow the FAIR data stewardship principles and enable GDPR-compliant information exchange. Four national or disease-specific patient registries are interoperable with the EURO-NMD Registry, allowing for federated analysis across these different resources. Conclusions Collectively, the Registry Hub brings together data that are currently siloed and fragmented to improve healthcare and advance research for neuromuscular diseases.

In the Netherlands, the Open Government Act (Wet openbare overheid or Woo/Wob in Dutch) is in effect, with the primary objective of ensuring a more transparent government. In line with the legislation, a search engine named Woogle has been designed and developed to centralize documents published under the Open Government Act. The Estonian Public Information Act serves a similar purpose and requires all public institutions to publish information generated during official duties, fostering transparency and public oversight. Currently, Estonia’s document repositories are decentralized, and content search is not supported, which hinders people’s ability to efficiently locate information. This study aims to assess public information accessibility in Estonia and to apply Woogle’s design and techniques to Estonia’s document repositories, thereby evaluating its potential for broader European implementation. The methodology involved web scraping data and documents from 57 Estonian public institutions’ document repositories. The results indicate that Woogle’s design and techniques can be implemented in Estonia. From a technical perspective, the alignment of the fields was successful, while it was found that content-wise, the Estonian data present challenges due to inconsistencies and lack of comprehensive categorization. The findings suggest potential scalability across European countries, pointing to a broader applicability of the Woogle model for creating a corpus of Freedom of Information Act documents in Europe. The collected data are available as a dataset.

Background Access to sample-level metadata is important when selecting public metagenomic sequencing datasets for reuse in new biological analyses. The Standards, Precautions, and Advances in Ancient Metagenomics community (SPAAM, https://spaam-community.org) has previously published AncientMetagenomeDir, a collection of curated and standardised sample metadata tables for metagenomic and microbial genome datasets generated from ancient samples. However, while sample-level information is useful for identifying relevant samples for inclusion in new projects, Next Generation Sequencing (NGS) library construction and sequencing metadata are also essential for appropriately reprocessing ancient metagenomic data. Currently, recovering information for downloading and preparing such data is difficult when laboratory and bioinformatic metadata is heterogeneously recorded in prose-based publications. Methods Through a series of community-based hackathon events, AncientMetagenomeDir was updated to provide standardised library-level metadata of existing and new ancient metagenomic samples. In tandem, the companion tool 'AMDirT' was developed to facilitate rapid data filtering and downloading of ancient metagenomic data, as well as improving automated metadata curation and validation for AncientMetagenomeDir. Results AncientMetagenomeDir was extended to include standardised metadata of over 6000 ancient metagenomic libraries. The companion tool 'AMDirT' provides both graphical- and command-line interface based access to such metadata for users from a wide range of computational backgrounds. We also report on errors with metadata reporting that appear to commonly occur during data upload and provide suggestions on how to improve the quality of data sharing by the community. Conclusions Together, both standardised metadata reporting and tooling will help towards easier incorporation and reuse of public ancient metagenomic datasets into future analyses.

Neuroscientists employ a range of methods and generate increasing amounts of data describing brain structure and function. The anatomical locations from which observations or measurements originate represent a common context for data interpretation, and a starting point for identifying data of interest. However, the multimodality and abundance of brain data pose a challenge for efforts to organize, integrate, and analyze data based on anatomical locations. While structured metadata allow faceted data queries, different types of data are not easily represented in a standardized and machine-readable way that allow comparison, analysis, and queries related to anatomical relevance. To this end, three-dimensional (3D) digital brain atlases provide frameworks in which disparate multimodal and multilevel neuroscience data can be spatially represented. We propose to represent the locations of different neuroscience data as geometric objects in 3D brain atlases. Such geometric objects can be specified in a standardized file format and stored as location metadata for use with different computational tools. We here present the Locare workflow developed for defining the anatomical location of data elements from rodent brains as geometric objects. We demonstrate how the workflow can be used to define geometric objects representing multimodal and multilevel experimental neuroscience in rat or mouse brain atlases. We further propose a collection of JSON schemas (LocareJSON) for specifying geometric objects by atlas coordinates, suitable as a starting point for co-visualization of different data in an anatomical context and for enabling spatial data queries. [ABSTRACT FROM AUTHOR]

The Mediterranean Sea, as one of the world's most climate-sensitive regions, faces significant environmental changes due to rising temperatures. Zooplankton communities, particularly copepods, play a vital role in marine ecosystems, yet their distribution dynamics remain poorly understood, especially in the Ligurian Sea. Leveraging open-source software and environmental data, this study adapted a methodology to model copepod distributions from 1985 to 1986 in the Portofino Promontory ecosystem using the Random Forest machine learning algorithm to produce the first abundance and distribution maps of the area. Five copepod genera were studied across different trophic guilds, revealing habitat preferences and ecological fluctuations throughout the seasons. The assessment of model accuracy through symmetric mean absolute percentage error (sMAPE) highlighted the variability in copepod dynamics influenced by environmental factors. While certain genera exhibited higher predictive accuracy during specific seasons, others posed challenges due to ecological complexities. This study underscores the importance of species-specific responses and environmental variability in predictive modeling. Moreover, this study represents the first attempt to model copepod distribution in the Ligurian Sea, shedding light on their ecological niches and historical spatial dynamics. The study adhered to FAIR principles, repurposing historical data to generate three-dimensional predictive maps, enhancing our understanding of copepod biodiversity. Future studies will focus on developing abundance distribution models using machine learning and artificial intelligence to predict copepod standing crop in the Ligurian Sea with greater precision. This integrated approach advances knowledge of copepod ecology in the Mediterranean and sets a precedent for integrating historical data with contemporary methodologies to elucidate marine ecosystem dynamics. [ABSTRACT FROM AUTHOR]

This study highlights the role of Research Data Repositories (RDRs) in the concept of data reuse by examining a use case on migration research, a domain that requires up-to-date and accurate data for research and policy purposes. The survey from which the data for the use case was derived aimed to investigate the alignment of humanitarian assistance and social protection in Greece during the post-2015 refugee crisis. Through our analysis, we try to formulate a new corpus of variables and information that can create a value chain for research and policy purposes related to migration research, as well as to draw useful conclusions from this use case study in relation to the concept of data reuse. We address several issues related to data reuse, such as its definition, the role of research data repositories and research infrastructures in data reuse, as well as the limitations and advantages of reuse. We also present some specific features of the SoDaNet RDR, which hosts the primary data. We argue that comprehensive documentation of data adds value to the data and, through reuse, this value can be recycled to the RDR and, therefore, to potential new reusers. [ABSTRACT FROM AUTHOR]

ABSTRACT Wastewater surveillance has emerged as a crucial public health tool for population-level pathogen surveillance. Supported by funding from the American Rescue Plan Act of 2021, the FDA‘s genomic epidemiology program, GenomeTrakr, was leveraged to sequence SARS-CoV-2 from wastewater sites across the United States. This initiative required the evaluation, optimization, development, and publication of new methods and analytical tools spanning sample collection through variant analyses. Version-controlled protocols for each step of the process were developed and published on protocols.io. A custom data analysis tool and a publicly accessible dashboard were built to facilitate real-time visualization of the collected data, focusing on the relative abundance of SARS-CoV-2 variants and sub-lineages across different samples and sites throughout the project. From September 2021 through June 2023, a total of 3,389 wastewater samples were collected, with 2,517 undergoing sequencing and submission to NCBI under the umbrella BioProject, PRJNA757291. Sequence data were released with explicit quality control (QC) tags on all sequence records, communicating our confidence in the quality of data. Variant analysis revealed wide circulation of Delta in the fall of 2021 and captured the sweep of Omicron and subsequent diversification of this lineage through the end of the sampling period. This project successfully achieved two important goals for the FDA’s GenomeTrakr program: first, contributing timely genomic data for the SARS-CoV-2 pandemic response, and second, establishing both capacity and best practices for culture-independent, population-level environmental surveillance for other pathogens of interest to the FDA.IMPORTANCEThis paper serves two primary objectives. First, it summarizes the genomic and contextual data collected during a Covid-19 pandemic response project, which utilized the FDA’s laboratory network, traditionally employed for sequencing foodborne pathogens, for sequencing SARS-CoV-2 from wastewater samples. Second, it outlines best practices for gathering and organizing population-level next generation sequencing (NGS) data collected for culture-free, surveillance of pathogens sourced from environmental samples.

Irrigation decision-making has evolved from solely depending on farmers’ decisions taken based on the visual analysis of field conditions to making decisions based on crop water need predictions generated using machine learning (ML) techniques. This paper reviews ML related articles to discuss how ML has been used to enhance irrigation decision making. We reviewed 16 studies that used ML approaches for irrigation scheduling prediction and decision-making focusing on the input features, algorithms used and their applicability in real world conditions. ML performances in terms of accuracy, water conservation compared to fixed or threshold-based methods are discussed along with modeling performances. Informed by the 16 research studies, we assessed constraints to the adoption of ML in irrigation decision making at field scale, which include limited data availability coupled with data sharing constraints, and a lack of uncertainty quantification as well as the need for physics informed ML based irrigation scheduling models. To address these limitations, we discussed approaches in future research such as integrating process-based models with ML, incorporating expert knowledge into the modeling procedure, and making data and tools Findable, Accessible, Interoperable, and Reusable (FAIR). These approaches will improve ML modeling outcomes and boost the availability of farm-related data and tools for FAIRer data-driven applications of irrigation modeling.

Introduction: Historic data, often in analog format, is a valuable resource for assessing effects of directional changes in climate and climatic variability. However, historic data can be difficult to locate, interpret, and reformat into a useful state.Methods: Teams of scientists, librarians, archivists, and data managers at four US institutions have undertaken various projects to gather, describe, and in some cases, transform historic data. They have also surveyed researchers who either possess historic data or have used it in their work.Results: Historic data projects involved locating data, writing data descriptions, and connecting with individuals who had knowledge about the data’s collection. The surveys and interviews found that researchers valued historic data and were worried that it was at risk of loss. They noted the lack of best practices.Discussion: Each project attempting to rescue or enhance access to historic data has a unique path but being guided by FAIR principles should be at the core whether or not the end result is machine-readable data. Working with a team incorporating librarians, archivists, and data managers can aid individual researchers’ in producing accessible, and reusable datasets. There is much work to be done in raising awareness about the value of historic data but motivating factors for doing so include its usefulness in environmental research and other disciplines and its risk of loss as researchers retire and are unsure of how to save historic data, both in analog and electronic formats.

Abstract Digitalization with integrated devices, digital and physical assistants, automation, and simulation is setting a new direction for laboratory work. Even with complex research workflows, high staff turnover, and a limited budget some laboratories have already shown that digitalization is indeed possible. However, academic bioprocess laboratories often struggle to follow the trend of digitalization. Due to their diverse research circumstances, high variety of team composition, goals, and limitations the concepts are substantially different. Here, we will provide an overview on different aspects of digitalization and describe how academic laboratories successfully digitalized their working environment. The key aspect is the collaboration and communication between IT‐experts and scientific staff. The developed digital infrastructure is only useful if it supports the laboratory worker and does not complicate their work. Thereby, laboratory researchers have to collaborate closely with IT‐experts in order for a well‐developed and maintainable digitalization concept that fits their individual needs and level of complexity. This review may serve as a starting point or a collection of ideas for the transformation toward a digitalized laboratory.

The Findable, Accessible, Interoperable and Reusable (FAIR) data principles were created to guide the improvement of research data (Wilkinson et al., 2016). As data curators and educators, we often see individual research groups and researchers establish their own unique data collection process, resulting in poor and inconsistent data documentation. At the conclusion of the project, while the data may be accessible and understood by members within the team, it is often not readily usable to anyone outside of those most closely associated with data collection and analysis. The root cause of this is the difficulty to document the pertinent information required to capture the context in which data was captured, processed, and presented. And even when this is attempted it tends to be static and non-machine actionable. As a result, the project data might be FAIR but it is not visible and the cost of re-use is too high as currently few protocols are machine actionable. The availability of context documentation will help other researchers understand and facilitate the re-use the data. Agri-Food Data Canada operates across multiple projects in different fields and run by different institutions. It is a natural environment to recognize the need of decentralized semantic definitions where each research group can influence, modify, or adjust the definition of the data while maintaining integrity of data objects (e.g., schema, data sets, catalogues) across the ecosystem. This practice paper describes the release of the first version of the Semantic Engine leveraging OCA, an architecture to document schemas optimized for decentralized collaboration and reproducibility. OCA leverages new technologies on self-addressing identifiers and enables content-based authority vs. location-based authority. We present here the first results of the Semantic Engine development and the future application.

In this contribution we present a data-driven approach to monitoring and assessing the state of open and FAIR data in an interdisciplinary, federated research ecosystem. The project is part of a multi-method approach by the Helmholtz Metadata Collaboration (HMC) to monitor and assess the state of open and FAIR data practices in the Helmholtz Association of German research centers, Germany’s largest non-university research organization. The approach consists of two parts: a modular data harvesting and assessment pipeline, and an openly accessible dashboard with interactive statistics about the data publications identified with the pipeline. The dashboard provides insight into which data repositories research communities use to publish research data and it allows for assessing systematic gaps of this data with respect to the FAIR data guidelines. We illustrate how the approach can be used to engage communities in FAIR data practices and to counsel data infrastructure towards improving FAIR data across a federated research organization. All software and data discussed here are published under an open license and reusable by data professionals at other research performing organizations.

The scientific community is growing increasingly aware of the importance of the FAIR data management principles for publishing scientific data. For data providers, this includes creating machine-understandable file structures such as CF-NetCDF files and Darwin Core Archives. To support scientists in creating these, we present significant upgrades to the Nansen Legacy template generator, an open-source tool for generating standardised spreadsheet templates. The new version of the template generator includes an intuitive graphical interface that encourages users to select data and metadata terms from well-established controlled vocabularies like the CF standard names, Darwin Core terms and the Attribute Convention for Data Discovery. The spreadsheet templates created include descriptions for each term and cell restrictions to prevent users from entering invalid values. While the templates generated are not inherently compliant with the FAIR principles, this paper provides suggestions for how users can transform them into FAIR-compliant data structures. The Nansen Legacy template generator helps scientists follow the FAIR data principles, facilitating transparent and reproducible sharing of scientific data. The template generator is hosted at https://www.nordatanet.no/aen/template-generator.

The Stimulating Peripheral Activity to Relieve Conditions (SPARC) data portal is a platform that allows users to access large sets of curated data and tools. Data on the portal is sourced from researchers working in a range of disciplines applied to peripheral nerve activity, including imaging, computational biology, electrophysiology and clinical research. However, many existing tutorials and examples for SPARC do not document examples of interfaces between tools within the SPARC ecosystem. Thus, we see an opportunity to lower the barrier for entry into the SPARC ecosystem, by creating accessible guides to create workflows using multiple SPARC tools. To do this, we have developed an online hub for hosting tutorials that can guide new users through the entire workflow of acquiring data, processing it, and visualizing it, using common, open-source scientific computing tools and crucially, spanning the repertoire of currently available SPARC tools. Our approach encapsulates the workflow within a Jupyter Notebook, which is designed to be readily accessible to new users of SPARC. Having a hub of tutorials that can be easily understood will not only accelerate the growth of the SPARC community but also increase the uptake and usage of tools by members of the community.

This paper is concerned with academic research data repositories, and their curators, in different locations across five global regions. Using an ad-hoc sample, we explore the roles of curators in research data repositories and their perceptions of skills and training, research data management (RDM) practices, and the limitations, strengths, and opportunities they encounter in relation to their work. In addition, the study examines the functions of academic research repositories and the extent to which FAIR principles are integrated into repository strategies. Our results show that while some repositories are operated by consortia of higher education institutions and research centers, in some countries research data repositories are still in their infancy, often with only a limited representation led by one institution. Moreover, curators encounter problems with how researchers submit datasets with unclear information for the reuse of the data. We argue that although data repositories are built to comply with FAIR principles, there is still a lot of work to be done in relation to training researchers on how to use data repositories.

Multiplexed imaging approaches are getting increasingly adopted for imaging of large tissue areas, yielding big imaging datasets both in terms of the number of samples and the size of image data per sample. The processing and analysis of these datasets is complex owing to frequent technical artifacts and heterogeneous profiles from a high number of stained targets To streamline the analysis of multiplexed images, automated pipelines making use of state-of-the-art algorithms have been developed. In these pipelines, the output quality of one processing step is typically dependent on the output of the previous step and errors from each step, even when they appear minor, can propagate and confound the results. Thus, rigorous quality control (QC) at each of these different steps of the image processing pipeline is of paramount importance both for the proper analysis and interpretation of the analysis results and for ensuring the reusability of the data. Ideally, QC should become an integral and easily retrievable part of the imaging datasets and the analysis process. Yet, limitations of the currently available frameworks make integration of interactive QC difficult for large multiplexed imaging data. Given the increasing size and complexity of multiplexed imaging datasets, we present the different challenges for integrating QC in image analysis pipelines as well as suggest possible solutions that build on top of recent advances in bioimage analysis.

Neuroscientists employ a range of methods and generate increasing amounts of data describing brain structure and function. The anatomical locations from which observations or measurements originate represent a common context for data interpretation, and a starting point for identifying data of interest. However, the multimodality and abundance of brain data pose a challenge for efforts to organize, integrate, and analyze data based on anatomical locations. While structured metadata allow faceted data queries, different types of data are not easily represented in a standardized and machine-readable way that allow comparison, analysis, and queries related to anatomical relevance. To this end, three-dimensional (3D) digital brain atlases provide frameworks in which disparate multimodal and multilevel neuroscience data can be spatially represented. We propose to represent the locations of different neuroscience data as geometric objects in 3D brain atlases. Such geometric objects can be specified in a standardized file format and stored as location metadata for use with different computational tools. We here present the Locare workflow developed for defining the anatomical location of data elements from rodent brains as geometric objects. We demonstrate how the workflow can be used to define geometric objects representing multimodal and multilevel experimental neuroscience in rat or mouse brain atlases. We further propose a collection of JSON schemas (LocareJSON) for specifying geometric objects by atlas coordinates, suitable as a starting point for co-visualization of different data in an anatomical context and for enabling spatial data queries.

This study highlights the role of Research Data Repositories (RDRs) in the concept of data reuse by examining a use case on migration research, a domain that requires up-to-date and accurate data for research and policy purposes. The survey from which the data for the use case was derived aimed to investigate the alignment of humanitarian assistance and social protection in Greece during the post-2015 refugee crisis. Through our analysis, we try to formulate a new corpus of variables and information that can create a value chain for research and policy purposes related to migration research, as well as to draw useful conclusions from this use case study in relation to the concept of data reuse. We address several issues related to data reuse, such as its definition, the role of research data repositories and research infrastructures in data reuse, as well as the limitations and advantages of reuse. We also present some specific features of the SoDaNet RDR, which hosts the primary data. We argue that comprehensive documentation of data adds value to the data and, through reuse, this value can be recycled to the RDR and, therefore, to potential new reusers.