Findability, Accessibility, Interoperability, And Reusability Research Articles

Real-time genomic surveillance for enhanced control of infectious diseases and antimicrobial resistance

This article advocates for mobilizing pathogen genomic surveillance to contain and mitigate health threats from infectious diseases and antimicrobial resistance (AMR), building upon successes achieved by large-scale genome sequencing analysis of SARS-CoV-2 variants in guiding COVID-19 monitoring and public health responses and adopting a One Health approach. Capabilities of laboratory-based surveillance and epidemic alert systems should be enhanced by fostering (i) universal access to real-time whole genome sequence (WGS) data of pathogens to inform clinical practice, infection control, public health policies, and vaccine and antimicrobial drug research and development; (ii) integration of diagnostic microbiology data, data from testing asymptomatic individuals, pathogen sequence data, clinical data, and epidemiological data into surveillance programs; (iii) stronger cross-sectorial collaborations between healthcare, public health, animal health, and environmental surveillance and research using One Health approaches, toward understanding the ecology and transmission pathways of pathogens and AMR across ecosystems; (iv) international collaboration and interconnection of surveillance networks, harmonization of laboratory methods, and standardization of surveillance methods for global reporting, including on pathogen genomic variant or strain nomenclature; (v) responsible data sharing between surveillance networks, databases, and platforms according to FAIR (findability, accessibility, interoperability, and reusability) principles; and (vi) research on genomic surveillance system implementation and its cost-effectiveness for different pathogens and AMR threats across different settings. Regional and global One Health policies and governance initiatives should foster the concerted development and efficient utilization of pathogen genomic surveillance to protect the health of humans, animals, and the environment.

Making geoscientific lab data FAIR: a conceptual model for a geophysical laboratory database

Abstract. The term of geoscientific laboratory measurements involves a variety of methods in geosciences. Accordingly, the resulting data comprise many different data types, formats, and sizes, respectively. Handling such a diversity of data, e.g. by storing the data in a generally applicable database, is difficult. Some discipline-specific approaches exist, but a geoscientific laboratory database that is generally applicable to different geoscientific disciplines has been lacking up to now. However, making research data available to scientists beyond a particular community has become increasingly important. Global working groups such as the Committee on Data of the International Science Council (CODATA) put effort in the development of tools to improve research data handling. International standards (e.g. ISO 19156) and ontologies (e.g. UCUM) provide a general framework for certain aspects that are elemental for the development of database models. However, these abstract models need to be adapted to meet the requirements of the geoscientific community. Within a pilot project of the NFDI4Earth initiative, we developed a conceptual model for a geoscientific laboratory database. To be able to handle the complex settings of geoscientific laboratory studies, flexibility and extensibility are key attributes of the presented approach. The model is intended to follow the FAIR (findability, accessibility, interoperability, and reusability) data principles to facilitate interdisciplinary applicability. In this study, we consider different procedures from existing database models and include these methods in the conceptual model.

A Connector for Integrating NGSI-LD Data into Open Data Portals.

Nowadays, there are plenty of data sources generating massive amounts of information that, combined with novel data analytics frameworks, are meant to support optimisation in many application domains. Nonetheless, there are still shortcomings in terms of data discoverability, accessibility and interoperability. Open Data portals have emerged as a shift towards openness and discoverability. However, they do not impose any condition to the data itself, just stipulate how datasets have to be described. Alternatively, the NGSI-LD standard pursues harmonisation in terms of data modelling and accessibility. This paper presents a solution that bridges these two domains (i.e., Open Data portals and NGSI-LD-based data) in order to keep benefiting from the structured description of datasets offered by Open Data portals, while ensuring the interoperability provided by the NGSI-LD standard. Our solution aggregates the data into coherent datasets and generate high-quality descriptions, ensuring comprehensiveness, interoperability and accessibility. The proposed solution has been validated through a real-world implementation that exposes IoT data in NGSI-LD format through the European Data Portal (EDP). Moreover, the results from the Metadata Quality Assessment that the EDP implements, show that the datasets' descriptions generated achieve excellent ranking in terms of the Findability, Accessibility, Interoperability and Reusability (FAIR) data principles.

GlyComb: A novel glycoconjugate data repository that bridges glycomics and proteomics

The glycosylation of proteins and lipids is known to be closely related to the mechanisms of various diseases such as influenza, cancer, and muscular dystrophy. Therefore, it has become clear that the analysis of post-translational modifications of proteins, including glycosylation, is important to accurately understand the functions of each protein molecule and the interactions among them. In order to conduct large-scale analyses more efficiently, it is essential to promote the accumulation, sharing, and reuse of experimental and analytical data in accordance with the FAIR (Findability, Accessibility, Interoperability, and Re-usability) data principles. However, a FAIR data repository for storing and sharing glycoconjugate information, including glycopeptides and glycoproteins, in a standardized format did not exist. Therefore, we have developed GlyComb (https://glycomb.glycosmos.org) as a new standardized data repository for glycoconjugate data. Currently, GlyComb can assign a unique identifier to a set of glycosylation information associated with a specific peptide sequence or UniProt ID. By standardizing glycoconjugate data via GlyComb identifiers and coordinating with existing web resources such as GlyTouCan and GlycoPOST, a comprehensive system for data submission and data sharing among researchers can be established. Here we introduce how GlyComb is able to integrate the variety of glycoconjugate data already registered in existing data repositories to obtain a better understanding of the available glycopeptides and glycoproteins, and their glycosylation patterns. We also explain how this system can serve as a foundation for a better understanding of glycan function.

Traceable Research Data Sharing in a German Medical Data Integration Center With FAIR (Findability, Accessibility, Interoperability, and Reusability)-Geared Provenance Implementation: Proof-of-Concept Study.

Secondary investigations into digital health records, including electronic patient data from German medical data integration centers (DICs), pave the way for enhanced future patient care. However, only limited information is captured regarding the integrity, traceability, and quality of the (sensitive) data elements. This lack of detail diminishes trust in the validity of the collected data. From a technical standpoint, adhering to the widely accepted FAIR (Findability, Accessibility, Interoperability, and Reusability) principles for data stewardship necessitates enriching data with provenance-related metadata. Provenance offers insights into the readiness for the reuse of a data element and serves as a supplier of data governance. The primary goal of this study is to augment the reusability of clinical routine data within a medical DIC for secondary utilization in clinical research. Our aim is to establish provenance traces that underpin the status of data integrity, reliability, and consequently, trust in electronic health records, thereby enhancing the accountability of the medical DIC. We present the implementation of a proof-of-concept provenance library integrating international standards as an initial step. We adhered to a customized road map for a provenance framework, and examined the data integration steps across the ETL (extract, transform, and load) phases. Following a maturity model, we derived requirements for a provenance library. Using this research approach, we formulated a provenance model with associated metadata and implemented a proof-of-concept provenance class. Furthermore, we seamlessly incorporated the internationally recognized Word Wide Web Consortium (W3C) provenance standard, aligned the resultant provenance records with the interoperable health care standard Fast Healthcare Interoperability Resources, and presented them in various representation formats. Ultimately, we conducted a thorough assessment of provenance trace measurements. This study marks the inaugural implementation of integrated provenance traces at the data element level within a German medical DIC. We devised and executed a practical method that synergizes the robustness of quality- and health standard-guided (meta)data management practices. Our measurements indicate commendable pipeline execution times, attaining notable levels of accuracy and reliability in processing clinical routine data, thereby ensuring accountability in the medical DIC. These findings should inspire the development of additional tools aimed at providing evidence-based and reliable electronic health record services for secondary use. The research method outlined for the proof-of-concept provenance class has been crafted to promote effective and reliable core data management practices. It aims to enhance biomedical data by imbuing it with meaningful provenance, thereby bolstering the benefits for both research and society. Additionally, it facilitates the streamlined reuse of biomedical data. As a result, the system mitigates risks, as data analysis without knowledge of the origin and quality of all data elements is rendered futile. While the approach was initially developed for the medical DIC use case, these principles can be universally applied throughout the scientific domain.

FAIR Data Austria – Paving the Way for Enhanced Research Data Management and Collaboration

In an age dominated by digital information, the importance of accessible and well-managed research data cannot be overstated. The FAIR Data Austria project is at the forefront of pioneering initiatives that cultivate a culture of data sharing and collaboration, adhering to the principles of Findability, Accessibility, Interoperability and Reusability (FAIR). The project addresses the challenges associated with implementing FAIR principles – from technical complexity to cultural barriers – and takes a multi-faceted approach that includes capacity building initiatives, community engagement and the development of tools and infrastructure to facilitate FAIR research practices.

DGIdb 5.0: rebuilding the drug-gene interaction database for precision medicine and drug discovery platforms.

The Drug-Gene Interaction Database (DGIdb, https://dgidb.org) is a publicly accessible resource that aggregates genes or gene products, drugs and drug-gene interaction records to drive hypothesis generation and discovery for clinicians and researchers. DGIdb 5.0 is the latest release and includes substantial architectural and functional updates to support integration into clinical and drug discovery pipelines. The DGIdb service architecture has been split into separate client and server applications, enabling consistent data access for users of both the application programming interface (API) and web interface. The new interface was developed in ReactJS, and includes dynamic visualizations and consistency in the display of user interface elements. A GraphQL API has been added to support customizable queries for all drugs, genes, annotations and associated data. Updated documentation provides users with example queries and detailed usage instructions for these new features. In addition, six sources have been added and many existing sources have been updated. Newly added sources include ChemIDplus, HemOnc, NCIt (National Cancer Institute Thesaurus), Drugs@FDA, HGNC (HUGO Gene Nomenclature Committee)and RxNorm. These new sources have been incorporated into DGIdb to provide additional records and enhance annotations of regulatory approval status for therapeutics. Methods for grouping drugs and genes have been expanded upon and developed as independent modular normalizers during import. The updates to these sources and grouping methods have resulted in an improvement in FAIR (findability, accessibility, interoperabilityand reusability) data representation in DGIdb.

The DO-KB Knowledgebase: a 20-year journey developing the disease open science ecosystem.

In 2003, the Human Disease Ontology (DO, https://disease-ontology.org/) was established at Northwestern University. In the intervening 20 years, the DO has expanded to become a highly-utilized disease knowledge resource. Serving as the nomenclature and classification standard for human diseases, the DO provides a stable, etiology-based structure integrating mechanistic drivers of human disease. Over the past two decades the DO has grown from a collection of clinical vocabularies, into an expertly curated semantic resource of over 11300 common and rare diseases linking disease concepts through more than 37000 vocabulary cross mappings (v2023-08-08). Here, we introduce the recently launched DO Knowledgebase (DO-KB), which expands the DO's representation of the diseaseome and enhances the findability, accessibility, interoperabilityand reusability (FAIR) of disease data through a new SPARQL service and new Faceted Search Interface. The DO-KB is an integrated data system, built upon the DO's semantic disease knowledge backbone, with resources that expose and connectthe DO's semantic knowledge withdisease-related data across Open Linked Data resources. This update includes descriptions of efforts to assess the DO's global impact and improvements to data quality and content, with emphasis on changes in the last two years.

A structured evaluation of genome-scale constraint-based modeling tools for microbial consortia.

Harnessing the power of microbial consortia is integral to a diverse range of sectors, from healthcare to biotechnology to environmental remediation. To fully realize this potential, it is critical to understand the mechanisms behind the interactions that structure microbial consortia and determine their functions. Constraint-based reconstruction and analysis (COBRA) approaches, employing genome-scale metabolic models (GEMs), have emerged as the state-of-the-art tool to simulate the behavior of microbial communities from their constituent genomes. In the last decade, many tools have been developed that use COBRA approaches to simulate multi-species consortia, under either steady-state, dynamic, or spatiotemporally varying scenarios. Yet, these tools have not been systematically evaluated regarding their software quality, most suitable application, and predictive power. Hence, it is uncertain which tools users should apply to their system and what are the most urgent directions that developers should take in the future to improve existing capacities. This study conducted a systematic evaluation of COBRA-based tools for microbial communities using datasets from two-member communities as test cases. First, we performed a qualitative assessment in which we evaluated 24 published tools based on a list of FAIR (Findability, Accessibility, Interoperability, and Reusability) features essential for software quality. Next, we quantitatively tested the predictions in a subset of 14 of these tools against experimental data from three different case studies: a) syngas fermentation by C. autoethanogenum and C. kluyveri for the static tools, b) glucose/xylose fermentation with engineered E. coli and S. cerevisiae for the dynamic tools, and c) a Petri dish of E. coli and S. enterica for tools incorporating spatiotemporal variation. Our results show varying performance levels of the best qualitatively assessed tools when examining the different categories of tools. The differences in the mathematical formulation of the approaches and their relation to the results were also discussed. Ultimately, we provide recommendations for refining future GEM microbial modeling tools.

From principles to reality. FAIR implementation in the nanosafety community

Developing safe and sustainable nanomaterials-based solutions to current global challenges including clean energy, sustainable food production and water security requires access to high quality data and appropriate analysis and modelling approaches. Achieving these challenges requires increased re-use of research data to accelerate progress and support development of new materials that are safe and sustainable for energy capture and storage, nano-agriculture and environmental remediation. The principles of Findability, Accessibility, Interoperability and Reusability (FAIR) provide a roadmap to enhanced data sharing and re-use, but require consensus within the nanosafety community on metadata, ontologies and persistent identifiers (among other things) and guidance to support implementation and achieve machine-readability. Here, we highlight the main focus of the AdvancedNano GO FAIR Implementation Network in supporting the nanosafety community with implementation of FAIR to maximize data-driven safe and sustainable application of nano- and advanced materials.

A global review of ocean ecosystem accounts and their data: Lessons learned and implications for marine policy

Given the ocean ecosystem’s contribution to the environment, economy and human wellbeing, its condition and value need to be accounted for in decision making for a healthy and sustainable society. Ocean accounting aims to incorporate the ocean environment into national accounts by tracking natural assets, the services they provide, and their links to our economies, and social wellbeing. Around the world, adoption of National Ocean Accounts is gathering momentum, taking national accounts “Beyond GDP” to produce more holistic statistics to inform ocean policy. We conducted the first review of publicly available ocean ecosystem accounts, including ten case studies to identify key opportunities and challenges. The spatial scale, scope, methods, and reporting quality of data within the case studies varied greatly and poor data documentation generally hindered the review. Case studies typically focussed on ecosystem assets that were inherently easy to map (e.g., nearshore or intertidal areas) and excluded deeper continental shelf waters, pelagic and offshore deep-sea ecosystems. The focus on easy to map ecosystems suggests that ocean accounts will have difficulty representing national marine jurisdictions with large extents of deeper continental shelf waters, pelagic and offshore deep-sea ecosystems. Ecosystem extent accounts consistently had limited transparency of methods and absent reporting on uncertainty or data variance. We recommend future ocean ecosystem account case studies should consider 1) open science 2) FAIR (Findability, Accessibility, Interoperability and Reusability) data principles 3) reporting error around data. By addressing these points, ocean ecosystem accounts will be more robust, and comparability of case studies through space and time will be improved, making future case studies better tools for decision makers.

Ontology-driven analysis of marine metagenomics: what more can we learn from our data?

The proliferation of metagenomic sequencing technologies has enabled novel insights into the functional genomic potentials and taxonomic structure of microbial communities. However, cyberinfrastructure efforts to manage and enable the reproducible analysis of sequence data have not kept pace. Thus, there is increasing recognition of the need to make metagenomic data discoverable within machine-searchable frameworks compliant with the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles for data stewardship. Although a variety of metagenomic web services exist, none currently leverage the hierarchically structured terminology encoded within common life science ontologies to programmatically discover data. Here, we integrate large-scale marine metagenomic datasets with community-driven life science ontologies into a novel FAIR web service. This approach enables the retrieval of data discovered by intersecting the knowledge represented within ontologies against the functional genomic potential and taxonomic structure computed from marine sequencing data. Our findings highlight various microbial functional and taxonomic patterns relevant to the ecology of prokaryotes in various aquatic environments. In this work, we present and evaluate a novel Semantic Web architecture that can be used to ask novel biological questions of existing marine metagenomic datasets. Finally, the FAIR ontology searchable data products provided by our API can be leveraged by future research efforts.

The role of open research in improving the standards of evidence synthesis: current challenges and potential solutions in systematic reviews

Systematic reviews (SRs) and meta-analyses (MAs) are the cornerstone of evidence-based medicine and are placed at the top of the level-of-evidence pyramid. To date, there are several methodological resources available from international organizations such as the Cochrane Collaboration that aim to aid researchers in conducting high-quality secondary research and promoting reproducibility, transparency and scientific rigour. Nevertheless, researchers still face challenges in most stages of evidence synthesis. Open research and the FAIR (findability, accessibility, interoperability, and reusability) principles are rising initiatives being increasingly implemented in primary research. However, their beneficial role in secondary research is less emphasized. This article addresses how the challenges commonly faced during evidence synthesis research could be overcome using open research practices and currently available open research tools. Despite the phenomenally simple SR workflow, researchers still find tasks such as framing the SR research question, search strategy development, data extraction, and assessing for bias, challenging. The implementation of FAIR practices, including prospective registration at the PROSPERO database, abiding with the PRISMA guidelines, and making all SR data openly available could have significant benefits in avoiding duplication of effort and reducing research waste while improving the reporting standards of SRs. Additionally, this article highlights the need for further education in open research culture to overcome ethical and motivational barriers in implementing open research practices in evidence synthesis. Finally, in the era of technological breakthroughs, artificial intelligence may eventually be incorporated into the process of SRs and should abide by the FAIR standards for open research.

FAIR Aspects of a Health Information Protection and Management System.

Privacy management is a key issue when dealing with storage and distribution of health information. However, FAIR (Findability, Accessibility, Interoperability, and Reusability) principles when sharing information are in increasing demand in several organizations, especially for information generated in public-funded research projects. The two main objectives of the presented work are the definition of a secure and interoperable modular architecture to manage different kinds of medical content (xIPAMS [x, for Any kind of content, Information Protection And Management System] and HIPAMS [Health Information Protection And Management System]), and the application of FAIR principles to that architecture in such a way that privacy and security are compatible with FAIR. We propose the concept of xIPAMS as a modular architecture, following standards for interoperability, which defines mechanisms for privacy, protection, storage, search, and access to health-related information. xIPAMS provides FAIR principles and preserves patient's privacy. For each module, we identify how FAIR principles apply. We have analyzed how xIPAMS, and in particular HIPAMS (Health content), support the FAIR principles focusing on security and privacy. We have identified the FAIR principles supported by the different xIPAMS modules, concluding that the four principles are supported. Our analysis has also considered a possible implementation based on the concept of DACS (Document Access and Communication System), a system storing medical documents in a private and secure way. In addition, we have analyzed security aspects of the FAIRification process and how they are provided by xIPAMS modules.

Interoperable vocabulary for marine microbial flow cytometry

The recent development of biological sensors has extended marine plankton studies from conducting laboratory bench work to in vivo and real-time observations. Flow cytometry (FCM) has shed new light on marine microorganisms since the 1980s through its single-cell approach and robust detection of the smallest cells. FCM records valuable optical properties of light scattering and fluorescence from cells passing in a single file in front of a narrow-collimated light source, recording tens of thousands of cells within a few minutes. Depending on the instrument settings, the sampling strategy, and the automation level, it resolves the spatial and temporal distribution of microbial marine prokaryotes and eukaryotes. Cells are usually classified and grouped on cytograms by experts and are still lacking standards, reducing data sharing capacities. Therefore, the need to make FCM data sets FAIR (Findability, Accessibility, Interoperability, and Reusability of digital assets) is becoming critical. In this paper, we present a consensus vocabulary for the 13 most common marine microbial groups observed with FCM using blue and red-light excitation. The authors designed a common layout on two-dimensional log-transformed cytograms reinforced by a decision tree that facilitates the characterization of groups. The proposed vocabulary aims at standardising data analysis and definitions, to promote harmonisation and comparison of data between users and instruments. This represents a much-needed step towards FAIRification of flow cytometric data collected in various marine environments.

Is DIA proteomics data FAIR? Current data sharing practices, available bioinformatics infrastructure and recommendations for the future.

Data independent acquisition (DIA) proteomics techniques have matured enormously in recent years, thanks to multiple technical developments in, for example, instrumentation and data analysis approaches. However, there are many improvements that are still possible for DIA data in the area of the FAIR (Findability, Accessibility, Interoperability and Reusability) data principles. These include more tailored data sharing practices and open data standards since public databases and data standards for proteomics were mostly designed with DDA data in mind. Here we first describe the current state of the art in the context of FAIR data for proteomics in general, and for DIA approaches in particular. For improving the current situation for DIA data, we make the following recommendations for the future: (i) development of an open data standard for spectral libraries; (ii) make mandatory the availability of the spectral libraries used in DIA experiments in ProteomeXchange resources; (iii) improve the support for DIA data in the data standards developed by the Proteomics Standards Initiative; and (iv) improve the support for DIA datasets in ProteomeXchange resources, including more tailored metadata requirements.

European Registry of Materials: global, unique identifiers for (undisclosed) nanomaterials

Management of nanomaterials and nanosafety data needs to operate under the FAIR (findability, accessibility, interoperability, and reusability) principles and this requires a unique, global identifier for each nanomaterial. Existing identifiers may not always be applicable or sufficient to definitively identify the specific nanomaterial used in a particular study, resulting in the use of textual descriptions in research project communications and reporting. To ensure that internal project documentation can later be linked to publicly released data and knowledge for the specific nanomaterials, or even to specific batches and variants of nanomaterials utilised in that project, a new identifier is proposed: the European Registry of Materials Identifier. We here describe the background to this new identifier, including FAIR interoperability as defined by FAIRSharing, identifiers.org, Bioregistry, and the CHEMINF ontology, and show how it complements other identifiers such as CAS numbers and the ongoing efforts to extend the InChI identifier to cover nanomaterials. We provide examples of its use in various H2020-funded nanosafety projects.Graphical

Raman technology application for plastic waste management aligned with FAIR principle to support the forthcoming plastic and environment initiatives

Plastic production and worldwide use of plastic materials have continued to rise due to their convenience and excellent marketing advantages. This is generating an environmental crisis and global scale pollution which is one of the greatest threats to our planet. One of the best responses could be accomplished by improving recycling and waste management strategies. In this paper we conducted Raman analyses of representative stock of plastics aged in terrestrial or aquatic environments spanning in age up to 15 years. We aimed to establish any potential influence of the aging conditions on the Raman signature of specific plastics. This information is further used to build up a Raman logic gate for automatic sorting of plastic waste recovered from environment. Pigments and aging introduced indeed small changes in the Raman signature of the respective plastics. However, we were able to identify unique spectral ranges characteristic for the main plastic types and intensity threshold of fingerprint bands sufficiently strong for building robust Raman barcodes for sorting. Waste plastics Raman data handling and the proposed methodology for sorting complies with the FAIR (Findability, Accessibility, Interoperability and Reusability) principles of scientific data, being useful for researchers, policymakers and stakeholders. Our spectral characterization of solid plastic waste comes in support of improved waste plastic management and could have economic and environmental positive impact.

Moving translational mass spectrometry imaging towards transparent and reproducible data analyses: a case study of an urothelial cancer cohort analyzed in the Galaxy framework

BackgroundMass spectrometry imaging (MSI) derives spatial molecular distribution maps directly from clinical tissue specimens and thus bears great potential for assisting pathologists with diagnostic decisions or personalized treatments. Unfortunately, progress in translational MSI is often hindered by insufficient quality control and lack of reproducible data analysis. Raw data and analysis scripts are rarely publicly shared. Here, we demonstrate the application of the Galaxy MSI tool set for the reproducible analysis of a urothelial carcinoma dataset.MethodsTryptic peptides were imaged in a cohort of 39 formalin-fixed, paraffin-embedded human urothelial cancer tissue cores with a MALDI-TOF/TOF device. The complete data analysis was performed in a fully transparent and reproducible manner on the European Galaxy Server. Annotations of tumor and stroma were performed by a pathologist and transferred to the MSI data to allow for supervised classifications of tumor vs. stroma tissue areas as well as for muscle-infiltrating and non-muscle infiltrating urothelial carcinomas. For putative peptide identifications, m/z features were matched to the MSiMass list.ResultsRigorous quality control in combination with careful pre-processing enabled reduction of m/z shifts and intensity batch effects. High classification accuracy was found for both, tumor vs. stroma and muscle-infiltrating vs. non-muscle infiltrating urothelial tumors. Some of the most discriminative m/z features for each condition could be assigned a putative identity: stromal tissue was characterized by collagen peptides and tumor tissue by histone peptides. Immunohistochemistry confirmed an increased histone H2A abundance in the tumor compared to the stroma tissues. The muscle-infiltration status was distinguished via MSI by peptides from intermediate filaments such as cytokeratin 7 in non-muscle infiltrating carcinomas and vimentin in muscle-infiltrating urothelial carcinomas, which was confirmed by immunohistochemistry. To make the study fully reproducible and to advocate the criteria of FAIR (findability, accessibility, interoperability, and reusability) research data, we share the raw data, spectra annotations as well as all Galaxy histories and workflows. Data are available via ProteomeXchange with identifier PXD026459 and Galaxy results via https://github.com/foellmelanie/Bladder_MSI_Manuscript_Galaxy_links.ConclusionHere, we show that translational MSI data analysis in a fully transparent and reproducible manner is possible and we would like to encourage the community to join our efforts.

A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data

Neural operators can learn nonlinear mappings between function spaces and offer a new simulation paradigm for real-time prediction of complex dynamics for realistic diverse applications as well as for system identification in science and engineering. Herein, we investigate the performance of two neural operators, which have shown promising results so far, and we develop new practical extensions that will make them more accurate and robust and importantly more suitable for industrial-complexity applications. The first neural operator, DeepONet, was published in 2019 (Lu et al., 2019), and its original architecture was based on the universal approximation theorem of Chen & Chen (1995). The second one, named Fourier Neural Operator or FNO, was published in 2020 (Li et al., 2020), and it is based on parameterizing the integral kernel in the Fourier space. DeepONet is represented by a summation of products of neural networks (NNs), corresponding to the branch NN for the input function and the trunk NN for the output function; both NNs are general architectures, e.g., the branch NN can be replaced with a CNN or a ResNet. According to Kovachki et al. (2021), FNO in its continuous form can be viewed conceptually as a DeepONet with a specific architecture of the branch NN and a trunk NN represented by a trigonometric basis. In order to compare FNO with DeepONet computationally for realistic setups, we develop several extensions of FNO that can deal with complex geometric domains as well as mappings where the input and output function spaces are of different dimensions. We also develop an extended DeepONet with special features that provide inductive bias and accelerate training, and we present a faster implementation of DeepONet with cost comparable to the computational cost of FNO, which is based on the Fast Fourier Transform.We consider 16 different benchmarks to demonstrate the relative performance of the two neural operators, including instability wave analysis in hypersonic boundary layers, prediction of the vorticity field of a flapping airfoil, porous media simulations in complex-geometry domains, etc. We follow the guiding principles of FAIR (Findability, Accessibility, Interoperability, and Reusability) for scientific data management and stewardship. The performance of DeepONet and FNO is comparable for relatively simple settings, but for complex geometries the performance of FNO deteriorates greatly. We also compare theoretically the two neural operators and obtain similar error estimates for DeepONet and FNO under the same regularity assumptions.