Exchange Format Research Articles

What Matters for an occurrenceID and What Is an occurrenceID That Matters?

In the Darwin Core data standard (Darwin Core Maintenance Group 2023), the concept of dwc:Occurrence (Wieczorek et al. 2012) in ecological data presents a data model construct not commonly found in data management practices used by ecological data collectors. We frequently encounter raw data without an Occurrence table. For example, the concept of Occurrence can be represented as a cell, where the rows represent sampling sites, the columns represent species, and the value of each cell indicates the count of the species at a specific site. A value in a cell in such a matrix can be interpreted as x number of individuals of species y occurred at sampling site z. While data providers tend to track data on tangible individual components (e.g., species, location, sample), generating "Occurrence records" typically requires pivoting and/or joining tables associated to these components. Maintaining a stable and persistent occurrenceID for an Occurrence record created through data transformation is not an easy task. This is especially true for long-term monitoring datasets, where the underlying tables used to generate Occurrence records are continuously updated. Additionally, most ecological data collectors are focused on the primary use of the data, not on the long term integration and accessibility of the data. The Occurrence concept is only required in data exchange format but not needed in ecological data management practices. The disconnect between the practical data management needs of data collectors and the abstractions required for data exchange raises challenges, particularly with an increasing expectation for globally unique and persistent occurrenceIDs. This presentation will explore the difficulties of creating and managing occurrenceIDs for data providers and managers, especially those who manage data using basic systems such as spreadsheets and simple relational databases. Maintaining stability and persistence of identifiers for inherently artificial constructs like Occurrences within the original, component-based data structure can pose significant challenges. We will explore why meaningful identifiers for occurrenceIDs are often preferred by data providers. We will unpack different use cases and delve into how and why occurrenceIDs were constructed for each use case. Through this discussion, we hope to spark a conversation that informs future data modeling efforts and addresses the inherent artificiality of Occurrences.

Perspective: on the importance of extensive, high-quality and reliable deposition of biomolecular NMR data in the age of artificial intelligence.

Artificial intelligence (AI) models are revolutionising scientific data analysis but are reliant on large training data sets. While artificial training data can be used in the context of NMR processing and data analysis methods, relating NMR parameters back to protein sequence and structure requires experimental data. In this perspective we examine what the biological NMR community needs to do, in order to store and share its data better so that we can make effective use of AI methods to further our understanding of biological molecules. We argue, first, that the community should be depositing much more of its experimental data. In particular, we should be depositing more spectra and dynamics data. Second, the NMR data deposited needs to capture the full information content required to be able to use and validate it adequately. The NMR Exchange Format (NEF) was designed several years ago to do this. The widespread adoption of NEF combined with a new proposal for dynamics data specifications come at the right time for the community to expand its deposition of data. Third, we highlight the importance of expanding and safeguarding our experimental data repository, the Biological Magnetic Resonance Data Bank (BMRB), not only in the interests of NMR spectroscopists, but biological scientists more widely. With this article we invite others in the biological NMR community to champion increased (possibly mandatory) data deposition, to get involved in designing new NEF specifications, and to advocate on behalf of the BMRB within the wider scientific community.

Modeling Enzyme Kinetics: Current Challenges and Future Perspectives for Biocatalysis.

Biocatalysis is becoming a data science. High-throughput experimentation generates a rapidly increasing stream of biocatalytic data, which is the raw material for mechanistic and novel data-driven modeling approaches for the predictive design of improved biocatalysts and novel bioprocesses. The holistic and molecular understanding of enzymatic reaction systems will enable us to identify and overcome kinetic bottlenecks and shift the thermodynamics of a reaction. The full characterization and modeling of reaction systems is a community effort; therefore, published methods and results should be findable, accessible, interoperable, and reusable (FAIR), which is achieved by developing standardized data exchange formats, by a complete and reproducible documentation of experimentation, by collaborative platforms for developing sustainable software and for analyzing data, and by repositories for publishing results together with raw data. The FAIRification of biocatalysis is a prerequisite to developing highly automated laboratory infrastructures that improve the reproducibility of scientific results and reduce the time and costs required to develop novel synthesis routes.

Challenges in building scholarly knowledge graphs for research assessment in open science

Abstract Open science has revolutionized scholarly communication and research assessment by introducing research data and software as first-class citizens. Scholarly knowledge graphs (SKGs) are expected to play a crucial role in generating research assessment indicators being able to aggregate bibliographic metadata records and semantic relationships describing all research products and their links (e.g., citations, affiliations, funding). However, the rapid advance of open science has led to publication workflows that do not adequately support and guarantee the authenticity of products and metadata quality required for research assessment. Additionally, the heterogeneity of research communities and the multitude of data sources and exchange formats complicate the provision of consistent and stable SKGs. This work builds upon the experience gained from pioneering and addressing these challenges in the OpenAIRE Graph SKG. The aim is twofold and broader. First, we identify obstacles to the creation of SKGs for research assessment caused by the state-of-the-art publishing workflows for publications, software, and data. Second, we describe repurposing SKGs as tools to monitor such workflows to identify and heal their shortcomings, taking advantage of tools, techniques, and practices that support the actors involved, namely research communities, scientists, organizations, data source providers, and SKG providers, to improve the Open Science scholarly publishing ecosystem.

Cybersecurity Certification Requirements for Distributed Energy Resources: A Survey of SunSpec Alliance Standards

This survey paper explores the cybersecurity certification requirements defined by the SunSpec Alliance for Distributed Energy Resource (DER) devices, focusing on aspects such as software updates, device communications, authentication mechanisms, device security, logging, and test procedures. The SunSpec cybersecurity standards mandate support for remote and automated software updates, secure communication protocols, stringent authentication practices, and robust logging mechanisms to ensure operational integrity. Furthermore, the paper discusses the implementation of the SAE J3072 standard using the IEEE 2030.5 protocol, emphasizing the secure interactions between electric vehicle supply equipment (EVSE) and plug-in electric vehicles (PEVs) for functionalities like vehicle-to-grid (V2G) capabilities. This research also examines the SunSpec Modbus standard, which enhances the interoperability among DER system components, facilitating compliance with grid interconnection standards. This paper also analyzes the existing SunSpec Device Information Models, which standardize data exchange formats for DER systems across communication interfaces. Finally, this paper concludes with a detailed discussion of the energy storage cybersecurity specification and the blockchain cybersecurity requirements as proposed by SunSpec Alliance.

Introduction of a free sound system design exchange format (SDE) for data from different sound systems for noise predictions with complex summation

To consider the correlated sources of sound systems from different manufacturers in noise predictions equally with complex summation, a free exchange format for sound system planning software on the one hand and environmental noise software on the other is being defined in a cooperation with d&b audiotechnik, L-Acoustics and SoundPLAN: SDE = Sound/System Design/Data Exchange/Export This paper introduces the SDE format, its motivation and structure. With increasing open-air events in urban areas, potential problems with noise also increase. Proactive noise predictions can help prevent noise from becoming a problem in the first place. Until now it has not been possible to use data from planned sound systems from different manufacturers for equivalent calculations with complex summation. Modern sound systems have high directivity, especially loudspeaker arrays. The directivity of arrays results from superposition of sound waves from all loudspeakers. This effect is used specifically with line arrays and subwoofer arrays to optimize sound and coverage in audience areas. However, the resulting emission of an entire sound system is not constant over distance. Time-of-flight and relative phase at the receiver points must be considered with complex summation.

Fostering primary students’ competences for democratic culture in EFL

Abstract The PEACE project was a teaching unit which was designed, implemented and evaluated in two primary school classes in Japan and Germany respectively. The project was carried out in an asynchronous virtual exchange format between the two classes. The Reference Framework of Competences for Democratic Culture was used as a basis for the teaching unit, with the framework’s descriptors for young learners serving as tools for designing and assessing both the teaching unit and its outcomes in the form of the participating students’ products and utterances. Using the NVivo coding software for content analysis, the study investigated the deployment of the various areas of competence by the students during the teaching unit and compared these across the classes. This was done to gain insights into both the context-dependency of the deployment of competences for democratic culture as well as the practicability and potentials of carrying out a bilateral asynchronous virtual exchange aiming to promote competences for democratic culture in young learners.

MeDaX: A Knowledge Graph on FHIR.

In Germany, the standard format for exchange of clinical care data for research is HL7 FHIR. Graph databases (GDBs), well suited for integrating complex and heterogeneous data from diverse sources, are currently gaining traction in the medical field. They provide a versatile framework for data analysis which is generally challenging for raw FHIR-formatted data. For generation of a knowledge graph (KG) for clinical research data, we tested different extract-transform-load (ETL) approaches to convert FHIR into graph format. We designed a generalised ETL process and implemented a prototypic pipeline for automated KG creation and ontological structuring. The MeDaX-KG prototype is built from synthetic patient data and currently serves internal testing purposes. The presented approach is easy to customise to expand to other data types and formats.

Childhood Cancer Survivorship Passport Challenges in the European Health Data Space.

Innovation in cancer therapy has increased childhood cancer survival rates. However, survivors are still at risk of developing late effects. In the digital transformation of the health sector, the Survivorship Passport (SurPass) can support long-term follow-up care plans. Gaps in seamless connectivity among hospital departments, primary care, combined with the time of health professionals required to collect and fill-in health data in SurPass, are barriers to its adoption in daily clinical practice. The PanCareSurPass (PCSP) project was motivated to address these gaps by a new version of SurPass (v2.0) that supports semi-automatic assembly from organizational Electronic Health Record (EHR) systems of the treatment summary data using HL7 FHIR, to create SurPass, and to link it to regional or national digital health infrastructures in six European countries. In this paper we present the methodology used to develop the SurPass technical implementation strategy with special focus on the European Health Data Space (EHDS). The recently provisionally approved EHDS regulation instruments a digital health data ecosystem with opportunities for cost-effective SurPass implementation across Europe. Moving forward, a European HL7 FHIR SurPass Implementation Guide along with synthetic data sets, and validation tools can enrich the European Electronic Health Record Exchange Format (EEHRxF) with use cases on health & wellness of childhood cancer survivors.

Assessing Cross-Border Telemedicine Data Exchange in the European Union: A Call to Action.

Background: Telemedicine offers potential benefits for health care delivery. However, evidence of cross-border telemedicine data exchange within the European Union (EU) remains limited. The objective of this communication provides a brief outline of the regulatory framework, initiatives, and challenges associated with cross-border telemedicine data exchange in the EU, setting the stage for a comprehensive evidence assessment. Methods: We explore the current regulatory landscape (European Health Data Space), existing initiatives (the European Electronic Health Record Exchange Format), and interoperability challenges (e.g., legal, technical, semantic) facing EU cross-border telemedicine data exchange. Results: There is a need for thorough evidence assessment of cross-border telemedicine and related data movements. Conclusion: Understanding the current landscape of cross-border telemedicine is crucial. This article highlights the need for evidence assessment through a formal review to inform future research and policy initiatives in this domain.

Satellite Reference Databases: A literature review of data exchange formats and industrial initiatives

Satellite Reference Databases (SRDBs) aim to manage the definition of telemetry and telecommand objects used by the ground systems to develop satellite operation procedures as well as encode and decode the flow of information between the ground system and the satellite. SRDBs have been identified as a key area of research and industry as they play a major role in the design, operation, and testing of spacecraft. This paper aims to answer research questions about SRDB data exchange formats and SRDB industrial initiatives. To this end, a comprehensive survey of existing research on SRDB has been conducted. To facilitate comprehension of this survey study, a use case is presented to illustrate the various aspects discussed in the literature.

Addressing The Importance of Financed Emissions to The Indonesian Financial Sector

Business trends, including the disclosure of financed emissions, drive more precise sustainability reporting. Financed emissions are greenhouse gas emissions from activities or projects sponsored by financial institutions, such as banks, insurance firms, pension funds, and other financial institutions. This concept emerges from an examination of the function of a financial service institution in distributing capital to companies that emit emissions or have a high carbon footprint. This study aims to examine the phenomenon of financed emissions and formulate policy recommendations to disclose financed emissions in Indonesia. Employing a systematic literature review approach, this study discovered various emission accounting standards, including the GHG Protocol, PCAF, and new standards developed by the ISSB and TCFD. The issuance of green bond instruments, sustainable loans, and the formation of carbon exchanges all contribute to Indonesia’s emission accounting reporting regime. Policy recommendations include the development of environmentally sound KYC policies for financial services institutions, the synchronization of business classification according to the Green Taxonomy or KUBL, and proposals for investment emission intensity taxation in Indonesia. However, hurdles to financed emission disclosure include data quality, complexity of the finance transaction structure's complexity, the technology's equivalency, and incomplete standard setting.

The Diagrammatic Image Schema Language (DISL)

ABSTRACT Image schemas are mental patterns learned from perceptual experiences capturing conceptual constructions in expressions. In linguistic analysis, their visualizations are often context-dependent without a generalizable structure. Addressing this, we introduce The Diagrammatic Image Schema Language: a formal representation language that systematizes a set of visual combination rules for different conceptual primitives from the cognitive science literature. These primitives are distinguished from a formal point of view to allow for more general application. DISL also contains a logical exchange format in which the diagrams may be made machine-readable. Using DISL, the semantic structure of complex scenarios can be represented and computed.

Porównanie komercyjnego i darmowego oprogramowania do przetwarzania chmur punktów

Terrestrial laser scanning technology is becoming increasingly common for automated spatial data acquisition and digitization in the fields of surveying, civil engineering and architecture. The data from measurements made with terrestrial laser scanners are a huge array of points in space, called a point cloud, which describes the captured surface of the object under study. The point cloud processing is performed in specialized software products for handling measurements from laser scanners, which provide different possibilities for manipulating the point cloud and forming different results. The software available on the market differs according to its data processing capabilities and functionalities, application areas, methods used, manufacturer and cost. To be able to perform spatial data processing and analysis correctly and with high quality, it is important to understand the available functionalities of the different software products and their advantages and disadvantages compared to others. A comparison is made for three software packages for point cloud processing - Autodesk ReCap Pro, CloudCompare and Trimble RealWorks. The different functionalities available in the products are described and presented on small building measurements along with their performance accuracy and efficiency. The strengths and weaknesses of the different software products are identified through the comparison performed. The first section describes the basic principles of the terrestrial laser scanning method. In section two, the different point cloud processing software products on the market are presented, together with a description of the different file formats for data exchange and a theoretical section on point cloud registration, filtering, and modelling. The third section contains a presentation of the main functions and processing capabilities in Autodesk ReCap Pro, CloudCompare and Trimble RealWorks software. The fourth section describes the data used for the study, the measurements performed, their processing and results in the three software, together with an assessment of accuracy by control measurements. Section five contains conclusions and implications.

Machine learning workflow for edge computed arrhythmia detection in exploration class missions

Deep-space missions require preventative care methods based on predictive models for identifying in-space pathologies. Deploying such models requires flexible edge computing, which Open Neural Network Exchange (ONNX) formats enable by optimizing inference directly on wearable edge devices. This work demonstrates an innovative approach to point-of-care machine learning model pipelines by combining this capacity with an advanced self-optimizing training scheme to classify periods of Normal Sinus Rhythm (NSR), Atrial Fibrillation (AFIB), and Atrial Flutter (AFL). 742 h of electrocardiogram (ECG) recordings were pre-processed into 30-second normalized samples where variable mode decomposition purged muscle artifacts and instrumentation noise. Seventeen heart rate variability and morphological ECG features were extracted by convoluting peak detection with Gaussian distributions and delineating QRS complexes using discrete wavelet transforms. The decision tree classifier’s features, parameters, and hyperparameters were self-optimized through stratified triple nested cross-validation ranked on F1-scoring against cardiologist labeling. The selected model achieved a macro F1-score of 0.899 with 0.993 for NSR, 0.938 for AFIB, and 0.767 for AFL. The most important features included median P-wave amplitudes, PRR20, and mean heart rates. The ONNX-translated pipeline took 9.2 s/sample. This combination of our self-optimizing scheme and deployment use case of ONNX demonstrated overall accurate operational tachycardia detection.

A multimodal machine learning approach to generate news articles from geo-tagged images

Classical machine learning algorithms typically operate on unimodal data and hence it can analyze and make predictions based on data from a single source (modality). Whereas multimodal machine learning algorithm, learns from information across multiple modalities, such as text, images, audio, and sensor data. The paper leverages the functionalities of multimodal machine learning (ML) application for generating text from images. The proposed work presents an innovative multimodal algorithm that automates the creation of news articles from geo-tagged images by leveraging cutting-edge developments in machine learning, image captioning, and advanced text generation technologies. Employing a multimodal approach that integrates machine learning and transformer algorithms, such as visual geometry group network16 (VGGNet16), convolutional neural network (CNN) and a long short-term memory (LSTM) based system, the algorithm initiates by extracting the location from exchangeable image file format (Exif) data from the image. The features are extracted from the image and corresponding news headline is generated. The headlines are used for generating a comprehensive article with contemporary large language model (LLM). Further, the algorithm generates the news article big-science large open-science open-access multilingual language model (BLOOM). The algorithm was tested on real time photographs as well as images from the internet. In both the cases the news articles generated were validated with ROUGE and BULE score. The proposed work is found to be successful attempt in journalism field.

Food Safety Knowledge Exchange (FSKX) format: Current status and strategic development plans based on a SWOT analysis

The Food Safety Knowledge Exchange (FSKX) format is a community-driven effort initially created to promote the efficient exchange of data and models in the food safety domain. Over the past years this effort was driven by the Risk Assessment Knowledge Integration Platform (RAKIP) Initiative that also provided a number of software tools and FSKX-compliant model files via their website https://foodrisklabs.bfr.bund.de/rakip-initiative/.This paper describes the results of a SWOT analysis that was conducted to identify strategic avenues for enhancing FSKX's usability and adoption. The SWOT analysis identified a number of recommendations for the future evolution of FSKX. First, it is recommended to reduce the complexity of the annotation schema to ease the adoption of the format. Second, a clear distinction between the descriptive part of FSKX and the executable part is proposed. To promote the broad usage of FSKX-compliant models, it is also recommended to develop and provide FSKX-compliant APIs and resources that facilitate cloud-based execution.As part of the research to prioritize future FSKX development options, we also considered the implications of the emerging generative AI technologies, particularly which impact large language models (LLMs) might have in supporting the adoption of FSKX by the research community. Recognizing the format's application potential beyond the food safety domain, we then proposed to re-brand the FSKX acronym as "FAIR Scientific Knowledge Exchange Format" which better reflects its broad applicability in various scientific domains. Our research findings suggest that with the implementation of the improvements identified by the SWOT analysis and the broader availability of generative AI technologies the broad adoption of FSKX as a method to share data and models in a FAIR way comes into reach.

Adaptive optics telemetry standard

Context. The amount of adaptive optics (AO) telemetry generated by visible/near-infrared ground-based observatories is ever greater, leading to a growing need for a standardised data exchange format to support performance analysis, AO research, and development activities that involve large-scale telemetry mining, processing, and curation. Aims. This paper introduces the Adaptive Optics Telemetry (AOT) data exchange format as a standard for sharing AO telemetry from visible/infrared ground-based observatories. AOT is based on the flexible image transport system (FITS) and aims to provide unambiguous and consistent data access across various systems and configurations, including natural and single- or multiple-laser guide-star AO systems. Methods. We designed AOT with a focus on two key use cases: atmospheric turbulence parameter estimation and point-spread function reconstruction. We prototyped and tested the design using existing AO telemetry datasets from multiple systems: single conjugate with natural and laser guide stars, tomographic systems with multi-channel wavefront sensors, and single- and multi-wavefront correctors in systems featuring either a Shack-Hartmann or Pyramid as the main wavefront sensor. Results. The AOT file structure has been thoroughly defined, with specified data fields, descriptions, data types, units, and expected dimensions. To support this format, we have developed a Python package that enables the data conversion, reading, writing, and exploration of AOT files; it has been made publicly available and is compatible with a general-purpose Python package manager. We have demonstrated the flexibility of the AOT format by packaging data from five different instruments, installed on different telescopes.

FIRE 6.5: Feynman integral reduction with new simplification library

FIRE is a program which performs integration-by-parts (IBP) reductions of Feynman integrals. Originally, the C++ version of FIRE relies on the computer algebra system Fermat by Robert Lewis to simplify rational functions. We present an upgrade of FIRE which incorporates a new library FUEL initially described in a separate publication, which enables a flexible choice of third-party computer algebra systems as simplifiers, as well as efficient communications with some of the simplifiers as C++ libraries rather than through Unix pipes. We achieve significant speedups for IBP reductions of Feynman integrals involving many kinematic variables, when using an open source backend based on FLINT newly added in this work, or the Symbolica backend developed by Ben Ruijl as a potential successor of FORM. Program summaryProgram title: FIRE, version 6.5 (FIRE 6.5)CPC Library link to program files:https://doi.org/10.17632/cy6k69pb3y.2Developer's repository link:https://gitlab.com/feynmanintegrals/fire.gitLicensing provisions: GPLv2Programming language:Wolfram Mathematica 8.0 or higher, C++17Supplementary material: See linked repository for installation instructions.Journal reference of previous version: Comput. Phys. Commun. 247 (2020) 106877Does the new version supersede the previous version?: Yes.Reasons for the new version: The new version no longer relies on a single computer algebra system, Fermat [1], but instead allows a flexible choice of several systems, some of which offer higher performance, especially when the number of variables is large.Summary of revisions: A new library FUEL [2] is used as a core component of the new version of FIRE to access different computer algebra systems as simplifiers of rational function expressions. Since the first release of FUEL described elsewhere, FUEL version 1.0 here has been enhanced with a new backend based on the open source library FLINT [3] that provides highly performant simplification of rational functions.Nature of problem: Feynman integrals of a given family are reduced to a finite set of master integrals, by solving linear equations arising from integration by parts, using Gaussian elimination. The coefficients of the linear equations are generally rational functions in kinematic variables and the spacetime dimension, and the simplification of such rational functions during Gaussian elimination is a key task that is improved in this upgrade of FIRE.Solution method: Computer algebra systems with state-of-the-art capabilities for polynomial GCD computations are used as simplification backends, or simplifiers in short. Due to the design of FIRE, text strings are used as the exchange format for rational functions before and after simplification. A fast C++ parser is written to parse strings into the internal format of an external simplifer, FLINT [3], with state-of-the-art performance for multivariate polynomial calculations. Similarly, the simplifier Symbolica [4] has high performance in both GCD computations and parsing, and has been integrated into FIRE.

TPMS2STEP: Error-Controlled and [formula omitted] Continuity-Preserving Translation of TPMS Models to STEP Files Based on Constrained-PIA

Triply periodic minimal surface (TPMS) is emerging as an important way of designing microstructures. However, there has been limited use of commercial CAD/CAM/CAE software packages for TPMS design and manufacturing. This is mainly because TPMS is consistently described in the functional representation (F-rep) format, while modern CAD/CAM/CAE tools are built upon the boundary representation (B-rep) format. One possible solution to this gap is translating TPMS to STEP, which is the standard data exchange format of CAD/CAM/CAE. Following this direction, this paper proposes a new translation method with error-controlling and C2 continuity-preserving features. It is based on an approximation error-driven TPMS sampling algorithm and a constrained-PIA algorithm. The sampling algorithm controls the deviation between the original and translated models. With it, an error bound of 2ϵ on the deviation can be ensured if two conditions called ϵ-density and ϵ-approximation are satisfied. The constrained-PIA algorithm enforces C2 continuity constraints during TPMS approximation, and meanwhile attaining high efficiency. A theoretical convergence proof of this algorithm is also given. The effectiveness of the translation method has been demonstrated by a series of examples and comparisons. The code will be open-sourced upon publication.