Machine-readable Data Research Articles

Urban Digital Twins as socio-technical infrastructures for city regeneration and decarbonization

Abstract The awareness of the huge carbon footprint and the related targets aimed at reducing it, require cities to be supported in their climate neutral transition. Urban Digital Twins (UDTs) have emerged as cutting-edge technologies to address urban complexity and to exploit the massive amount of machine-readable data produced by the city physical and non-physical systems. At the centre of the recent debate and scientific research, UDTs are addressed in a rapidly growing literature, pilot projects, proof-of-concepts and applications to several contexts. Studies and prototypes are leading the maturity development of UDT to gradually improve, contributing to gaining consensus among key stakeholders and urban decision makers, but the state of the art has not proved how exactly its potential benefits will be pursued and sustained. In this evolving scenario, the paper objective is the analysis of a literature review and a case studies selection, to identify the current use of the UDT as enabling tool in the comprehensive and joint framework of urban regeneration and decarbonization, addressing, in particular, the under-researched and poorly conceptualized integration of socio-economic-cultural systems.

A link prediction method for Chinese financial event knowledge graph based on graph attention networks and convolutional neural networks

Finance is a knowledge-intensive domain in nature, with its data containing a significant amount of interconnected information. Constructing a financial knowledge graph is an important application for transforming financial text/web content into machine-readable data. However, the complexity of Chinese financial knowledge and the dynamic and evolving nature of Chinese financial data often lead to incomplete knowledge graphs. To address this challenge, we propose a novel link prediction method for Chinese financial event knowledge graph based on Graph Attention Networks and Convolutional Neural Networks. Our method begins with the construction of the foundational Chinese financial event knowledge graph using a relational triple extraction module integrated with a large language model framework, along with a Prompting with Iterative Verification (PiVe) module for validation. To enhance the completeness of the knowledge graph, we introduce an encoder-decoder framework, where a graph attention network with joint embeddings of financial event entities and relations acts as the encoder, while a Convolutional Knowledge Base embedding model (ConvKB) serves as the decoder. This framework effectively aggregates crucial neighbor information and captures global relationships among entity and relation embeddings. Extensive comparative experiments demonstrate the utility and accuracy of this method, ultimately enabling the effective completion of Chinese financial event knowledge graphs.

Matters of assuring functional dependability compliance of digital manufacturing

Aim. To propose a methodological approach to ensuring the functional dependability of industrial facilities using SMART documents in the context of import-independent digital processes. Methods. The evolving applicability of the dependability theory and information systems design has defined the methodological provisions for algorithmising the application of SMART standards for minimising functional faults and failures of industrial processes. Digital models of production lines were examined as tools for machine-recognisable representation of standards. The proposed form of regulatory documents is intended to enable the transition from machine-readable data to machine-understandable content. Findings. The application of the proposed methods of content systematisation in the context of manufacturing process standardisation shows that the classification of process data based on standardised characteristics of digital information exchange processes is one of the key methodological provisions ensuring unambiguous interpretation of regulatory requirements. Specialised projects often use individual provisions rather than complete regulatory documents. In this case, in order to ensure machine-understandability of the developed content, it is proposed using special identifiers, e.g., «paragraph», «graphic object», «table cell». The deployment of such identification tools will allow creating a class of SMART standards that collect into a single document the provisions regarding the delivery of intended results in situations when the operating equipment is diverse. Publications created using intelligent processing of SMART documents are considered as containers of structured and unstructured data that take into account the conditions of particular projects. The existing dynamics of the demand for advanced industrial products often determines the admissibility of its industrial production by various companies with the parallel use of unique technologies. The conditions of such an organisation impose their limitations on the comparability of various process requirements, therefore, it is proposed to harmonise the presentation of the results of managerial, design, and process engineering solutions using the term «manufacturing system asset». The introduction of relative result estimates as part of the digital format is an efficient mechanism for ensuring management consistency. In this context, the importance of a uniform regulatory framework as the foundation for such assessments is growing. As one of the ways of minimising faults and failures of industrial equipment, it is proposed to create a situation centre with a decision support system based on the regulatory data with harmonised manufacturing system asset requirements. An experience of Industry 4.0 regulation is described in the review of the ISA digital manufacturing series of standards. It was shown that the deployment of SMART standards improves the stability of interaction between information management systems and the functional dependability of production lines. The introduction of provisions regulating the use of algorithms for the development, editing, and examination of draft regulatory documents into the content machine-understandability facilities is an important factor in ensuring the functional dependability of products. Conclusions. The presented approach is focused on the methodology for developing standards that take into account requirements from various subject areas. In the context of ongoing import substitution in manufacturing, the paper examines the feasibility of minimizing digital process faults and failures caused by the use of regulatory frameworks that are not equivalent to the provisions of international documents and take into account the specificity of Russian industrial systems.

RDA Training Methods and Application Examples

The first standard rules for cataloging, Panizzi, were introduced. These rules, which have changed and developed in various periods, have been tried to be applied with Machine Readable Cataloging (MARC), which is a machine-readable data chart, with the change in information technologies in 1966. In the ongoing process, the second edition of AACR was published in 1978 due to difficulties in bibliographic identification of the diversified information sources. Between 2004-2005, the JSC (Joint Steering Committee for Revision of Anglo-American Cataloging Rules) group wanted to publish AACR 3 as a result of new needs in cataloging, but RDA (Resource Description and Access) was appropriate as the name for the new rules. seen. Since 2010, the new cataloging standard, RDA, has become widespread worldwide. The transition to RDA brought many changes with it. Providing the necessary training for the implementation of RDA is one of the starting points of this change. In the studies conducted on RDA in different countries, it has been understood that the subject of “RDA Education” is one of the important issues in the transition process. The methods to be followed while giving this training are very important. Similar and different methods for education have been developed between countries. The aim of this article is to help develop a method in RDA education in our country by examining the RDA education in these different countries.

An Online Nanoinformatics Platform Empowering Computational Modeling of Nanomaterials by Nanostructure Annotations and Machine Learning Toolkits.

Modern nanotechnology has generated numerous datasets from in vitro and in vivo studies on nanomaterials, with some available on nanoinformatics portals. However, these existing databases lack the digital data and tools suitable for machine learning studies. Here, we report a nanoinformatics platform that accurately annotates nanostructures into machine-readable data files and provides modeling toolkits. This platform, accessible to the public at https://vinas-toolbox.com/, has annotated nanostructures of 14 material types. The associated nanodescriptor data and assay test results are appropriate for modeling purposes. The modeling toolkits enable data standardization, data visualization, and machine learning model development to predict properties and bioactivities of new nanomaterials. Moreover, a library of virtual nanostructures with their predicted properties and bioactivities is available, directing the synthesis of new nanomaterials. This platform provides a data-driven computational modeling platform for the nanoscience community, significantly aiding in the development of safe and effective nanomaterials.

Investigating Lexical and Syntactic Differences in Written and Spoken English Corpora

This paper presents an analysis of the differences between written text and the transcription of spoken text using current Natural Language Processing (NLP) methods. The purpose of the study is to investigate the long and rich history of attempts to differentiate spoken and written text in fields such as linguistics, communication, and rhetoric, which date back to the early 20th century. Given the availability of large quantities of machine-readable data and machine learning algorithms that can handle them, it is possible to use a large number of derived features. The research focuses on syntactic and lexical differences in written books and transcriptions of speeches by United States presidents. The analysis investigates morphological, lexical, syntactical, and text-level aspects. In this process, multiple features have been considered including lexical diversity, syllable count, frequency of parts of speech, and features relating to the parse tree, like the average length of noun phrases, and the use of interrogative sentences, among others. This study will enhance our understanding of the difference between written text and the transcription of spoken text in various disciplines including computer science, applied linguistics, communication, and similar fields.

Issues and paths forward in the identification and reuse of historic analog records

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.

A One Health Platform for Future Epidemic Preparedness.

Here, we introduce the EpiConnect Intelligence Platform (ECIP), a platform facilitating rapid, transparent data sharing and analysis to support researchers and public health officials in Europe, with a focus on Italy. ECIP provides reliable, concise, machine-readable data to aid in epidemiological understanding, standardize case characteristics, and estimate key parameters. The platform adheres to FAIR (findable, accessible, interoperable, reusable) principles, offering easily accessible and downloadable datasets for researchers' endeavors. Future enhancements include involving national public health authorities, expanding data streams, and fostering collaboration between experts and users for improved epidemic risk monitoring. Shared standards among diverse surveillance systems are advocated to achieve common strategic goals, emphasizing the need for forward-looking policies to empower professionals to analyze disease dynamics in the context of evolving health crises. The recent emergencies underscore the importance of collective efforts towards shared strategic goals, highlighting the necessity for coordinated action to address mutual concerns affecting everyone's lives.

Natural Language Processing for Radiation Oncology: Personalizing Treatment Pathways.

Natural language processing (NLP), a technology that translates human language into machine-readable data, is revolutionizing numerous sectors, including cancer care. This review outlines the evolution of NLP and its potential for crafting personalized treatment pathways for cancer patients. Leveraging NLP's ability to transform unstructured medical data into structured learnable formats, researchers can tap into the potential of big data for clinical and research applications. Significant advancements in NLP have spurred interest in developing tools that automate information extraction from clinical text, potentially transforming medical research and clinical practices in radiation oncology. Applications discussed include symptom and toxicity monitoring, identification of social determinants of health, improving patient-physician communication, patient education, and predictive modeling. However, several challenges impede the full realization of NLP's benefits, such as privacy and security concerns, biases in NLP models, and the interpretability and generalizability of these models. Overcoming these challenges necessitates a collaborative effort between computer scientists and the radiation oncology community. This paper serves as a comprehensive guide to understanding the intricacies of NLP algorithms, their performance assessment, past research contributions, and the future of NLP in radiation oncology research and clinics.

Towards computable taxonomic knowledge: Leveraging nanopublications for sharing new synonyms in the Madagascan genus Helictopleurus (Coleoptera, Scarabaeinae).

Numerous taxonomic studies have focused on the dung beetle genus Helictopleurus d'Orbigny, 1915, endemic to Madagascar. However, this genus stilll needs a thorough revision. Semantic technologies, such as nanopublications, hold the potential to enhance taxonomy by transforming how data are published and analysed. This paper evaluates the effectiveness of nanopublications in establishing synonyms within the genus Helictopleurus. In this study, we identify four new synonyms within Helictopleurus: H.rudicollis (Fairmaire, 1898) = H.hypocrita Balthasar, 1941 syn. nov.; H.vadoni Lebis, 1960 = H.perpunctatus Balthasar, 1963 syn. nov.; H.halffteri Balthasar, 1964 = H.dorbignyi Montreuil, 2005 syn. nov.; H.clouei (Harold, 1869) = H.gibbicollis (Fairmaire, 1895) syn. nov. Helictopleurus may have a significantly larger number of synonyms than currently known, indicating potentially inaccurate estimates about its recent extinction.We also publish the newly-established synonyms as nanopublications, which are machine-readable data snippets accessible online. Additionally, we explore the utility of nanopublications in taxonomy and demonstrate their practical use with an example query for data extraction.

OPTION: OPTImization Algorithm Benchmarking ONtology

Many optimization algorithm benchmarking platforms allow users to share their experimental data to promote reproducible and reusable research. However, different platforms use different data models and formats, which drastically complicates the identification of relevant datasets, their interpretation, and their interoperability. Therefore, a semantically rich, ontology-based, machine-readable data model that can be used by different platforms is highly desirable. In this paper, we report on the development of such an ontology, which we call OPTION (OPTImization algorithm benchmarking ONtology). Our ontology provides the vocabulary needed for semantic annotation of the core entities involved in the benchmarking process, such as algorithms, problems, and evaluation measures. It also provides means for automatic data integration, improved interoperability, and powerful querying capabilities, thereby increasing the value of the benchmarking data. We demonstrate the utility of OPTION, by annotating and querying a corpus of benchmark performance data from the BBOB collection of the COCO framework and from the Yet Another Black-Box Optimization Benchmark (YABBOB) family of the Nevergrad environment. In addition, we integrate features of the BBOB functional performance landscape into the OPTION knowledge base using publicly available datasets with exploratory landscape analysis. Finally, we integrate the OPTION knowledge base into the IOHprofiler environment and provide users with the ability to perform meta-analysis of performance data.

Application and Challenges of Machine Learning Techniques in Mining Engineering and Material Science

The ultimate objective of modern engineering applications in mining and material science is to develop good quality novel materials with desirable qualities. Machine Learning (ML) is used in the mining industry to provide solutions to complex problems of the mining industry and improve the efficiency of the overall system. ML methods are increasingly being used by materials scientists to uncover hidden trends in data and generate predictions. Furthermore, data centric techniques can provide useful insights into the basic processes that influence material behaviour while simultaneously reducing human labour in large data processing. The ability of persons to find new materials and infer complex relationships is important for the development of new materials. Large amounts of machine-readable data must be available to use statistical methodologies to speed materials research. In mining engineering, ML can be used for analyzing geographical data, assessing the risk of rock fall, predicting equipment failures and impact of mining activities on the environment etc. Material science data may be used in a variety of ways, including property prediction, the search for new materials and discovering synthesis methods. Selecting proper machine learning techniques to provide solutions is very important and that is discussed here. The purposes of this paper are to provide a comprehensive list of different ML techniques which are applied for the mining and material science domain.

THU258 Development Of A Clinical Dataset For Artificial Intelligence: A 30-Year Journey

Abstract Disclosure: S.R. Gumpeny: None. L. Gumpeny: None. Deep learning requires clean and machine-readable data for analysis. Currently there is a mismatch between the availability of accessible clinical data and the development of predictive modeling employing artificial intelligence. The scarcity is particularly acute in India. We describe the construction and evolution of electronic medical records (EMR) in an Endocrine Centre in India. December 1992 marked the end of paper documents at EDC, India. Ever since all records were stored as electronic medical records (EMR). Digitization was preceded by an analysis of work flow to ensure harmonious interaction between patient encounter and capture of data. Over years the EMR evolved across databases and was refined to improve ease of use. Rule based diagnoses, prescriptions, diet advice and auto-calculations were incorporated. Eventually it was set up on a networked platform to allow biographic, lab and anthropometric data to be entered before the patient came to the consultation chamber. Currently, the database has 90,000 individual records, including follow up data of patients with different endocrine disorders. We are at the cusp of linking the EMR with wearable sensors to make it more comprehensive. It is ripe to serve as a platform for deep learning, predictive modelling and artificial intelligence. Account sof its construction, as well as the potential for using AI in clinical care have been documented

Creating a Scholarly API Cookbook: Supporting Library Users with Programmatic Access to Information

Scholarly web-based application programming interfaces (APIs) allow users to interact with information and data programmatically. Interacting with information programmatically allows users to create advanced information query workflows and quickly access machine-readable data for downstream computations. With the growing availability of scholarly APIs from open and commercial library databases, supporting access to information via an API has become a key support area for research data services in libraries. This article describes our efforts with supporting API access through the development of an online Scholarly API Cookbook. The Cookbook contains code recipes (i.e., tutorials) for getting started with 10 different scholarly APIs, including for example, Scopus, World Bank, and PubMed. API tutorials are available in Python, Bash, Matlab, and Mathematica. A tutorial for interacting with library catalog data programmatically via Z39.50 is also included, as traditional library catalog metadata is rarely available via an API. In addition to describing the Scholarly API Cookbook content, we discuss our experiences building a student research data services programming team, challenges we encountered, and ideas to improve the Cookbook. The University of Alabama Libraries Scholarly API Cookbook is freely available and hosted on GitHub. All code within the API Cookbook is licensed with the permissive MIT license, and as a result, users are free to reuse and adapt the code in their teaching and research.

Toward a digital materials mechanical testing lab

To accelerate the growth of Industry 4.0 technologies, the digitalization of mechanical testing laboratories as one of the main data-driven units of materials processing industries is introduced in this paper. The digital lab infrastructure consists of highly detailed and standard-compliant materials testing knowledge graphs for a wide range of mechanical testing processes, as well as some tools that enable the efficient ontology development and conversion of heterogeneous materials’ mechanical testing data to the machine-readable data of uniform and standardized structures. As a basis for designing such a digital lab, the mechanical testing ontology (MTO) was developed based on the ISO 23718 and ISO/IEC 21838-2 standards for the semantic representation of the mechanical testing experiments, quantities, artifacts, and report data. The trial digitalization of materials mechanical testing lab was successfully performed by utilizing the developed tools and knowledge graph of processes for converting the various experimental test data of heterogeneous structures, languages, and formats to standardized Resource Description Framework (RDF) data formats. The concepts of data storage and data sharing in data spaces were also introduced and SPARQL queries were utilized to evaluate how the introduced approach can result in the data retrieval and response to the competency questions. The proposed digital materials mechanical testing lab approach allows the industries to access lots of trustworthy and traceable mechanical testing data of other academic and industrial organizations, and subsequently organize various data-driven research for their faster and cheaper product development leading to a higher performance of products in engineering and ecological aspects.

Machine-readable documents in building information modeling standards

Introduction. Since 2021, adoption of building information modeling technologies has become mandatory in constructing objects using information modeling technologies. Processes of constructing buildings and structures have remained unchanged over the past two years. The time-saving effect of digitalization in construction has not been achieved, and the need to develop information models of capital construction objects resulted in additional costs for manual identification of data in the array of regulatory documents and in ignoring most of the existing requirements. Standard technical documents, even if they are written for automation purposes, do not provide for possible automated data isolation when working with them.Aim: to develop methods for translating standard technical documents into a machine-readable format based on marking up documents with codes of a building information classifier.Materials and methods. The methodology involved analyzing the schemes of documents posted on the website of the Ministry of Construction, which have been issued after the requirements on mandatory use of the classification systems in the development of building information models were included into the Town Planning Code of the Russian Federation. The analysis provided for possible unambiguous identification of data between various XML schemes of documents and the availability of a building information classifier for interfacing with the graphic part of the design documentation made in the format of the information model of the capital construction object.Results. The author proposes rules for constructing text and tabular documents with the inclusion of codes of the building information classifier for the development of XML-schemes. Allocation and markup of the tabular part serve for separating the structured and unstructured parts of the document. The marked tabular part can be exported to CAD systems and can serve a source of software settings. Tables, information about the structured coded tables, and a section of terms and definitions that are used to form the machine-readable data are subject to coding.Conclusion. Machine-readable documents can be generated both at the level of national standard technical documents and at the level of company standards reflecting the current configuration of the software adopted for capital construction projects. A set of structured tables exported from the company standards and national standard technical documents can be adopted to replace the established approach to the formation of requirements for information exchange and project implementation plans using information modeling technologies.

Taking count: A computational analysis of data resources on academic LibGuides

The LibGuides platform is a ubiquitous tool in academic libraries and is commonly used by librarians to compile and share lists of recommended social science numerical data resources with users. This study leverages the machine-accessible nature of the LibGuides platform to collect links to data and statistical resources from over 10,000 LibGuide pages at 123 R1 research institutions. After substantial data cleaning and normalization, an analysis of the most common resources on those guides provides a unique window into the data repositories, libraries, archives, statistical data platforms, and other machine-readable data sources that are most popular on academic library guides. Results show that freely available resources from U.S. government agencies are among the most common to be included on data and statistical resources guides across institutions. Resources requiring paid licenses or memberships for full access, such as Statistical Insight (ProQuest), Social Explorer, and ICPSR are linked to most frequently overall, regardless of the percentage of institutions that include them. Findings also suggest that libraries are more likely to share traditional licensed statistical resources (e.g., Cambridge’s Historical Statistics of the United States) and collections of simple charts and graphs (e.g., Statista) than more robust and complex microdata resources (e.g., IPUMS).

QR code: The global making of an infrastructural gateway

This article traces the history of machine-readable data encoding standards and argues that the QR code has become an infrastructural gateway. Through the analysis of patents, corporate documents and advertising, ethnographic observations, and interviews with professionals, I describe the global making of the QR code and argue that the convergence of data encoding standards, mobile computing, machine vision algorithms, and platform ecosystems has led to the emergence of a new component of computational infrastructures which functions as a gateway between different actors, systems, and practices. The central section of the article covers seven decades of machine-readable data encoding history across different national and regional contexts: from the invention and popularization of the barcode in the United States, through the QR code’s invention in Japan and its success in East Asia, to its platformization in China. By revisiting this history through concepts drawn from the field of infrastructure studies, I argue that QR codes have become infrastructural gateways and conclude that this concept is useful not only to understand the current role of QR codes but also to identify and follow the emergence and change of other gateways in infrastructures to come.

Does Financial Statement Line-Item Comparability Affect Analysts’ Forecasts?

This study investigates comparability of firms’ financial statement presentation, that is, the display of line items on financial statements. The line items that are displayed on financial statements are the result of disclosure choices such as placement, formatting, and aggregation. Given the emergence of machine-readable data (for example, XBRL-tags used in this paper in creating our test variable), a question arises about the relevance of items’ presentation on the face of the human-readable financial statements. This paper examines the relation between line-item comparability (based on the face of financial statements) and financial statement users’ judgments as proxied by attributes of analysts’ forecasts. We posit that greater line-item comparability likely lowers analysts’ processing costs and enhances analysts’ ability to evaluate firms’ economic performance. Our results indicate that greater line-item comparability is associated with more analyst coverage, increased forecast accuracy, reduced forecast dispersion, and greater timeliness of forecasts. Overall, these results suggest that comparability of line items on the face of financial statement enhances informativeness.

Connecting Libraries, Archives, and Museums

Over the last two decades, libraries and archives of natural history museums and botanical gardens in the US have spent major efforts to digitize their holdings. However, transporting these digitized resources from individual repositories to a wider community of researchers is challenging. Many of the primary resources are handwritten which limits their use and reuse because cursive writing and personal shorthand are difficult to decipher and the documents mostly lack machine-readable data. This article presents three case studies from the Harvard University Herbaria (HUH) Botany Libraries and the Harvard University Ernst Mayr Library and Archives (EMLA) of the Museum of Comparative Zoology (MCZ) that utilize crowd-sourcing, detailed access and discovery tools, and open access platforms to make handwritten materials more accessible to researchers by bridging content across collections held within and outside of Harvard University. The case studies show that different approaches can yield opportunities for mining data because transcription of handwritten documents and enhanced metadata allow searching previously unavailable words and phrases such as taxonomic names. Content contributed to the Biodiversity Heritage Library (BHL) and the tools and services available in the BHL were integral to the work. The end result shows how information held in natural history libraries and archives contributes to the expansion of scientific and cultural-historical knowledge by increasing access to previously unavailable historical scientific information through digitization, metadata enhancement, and transcription.