Metadata Information Research Articles

Benchmarking of automatic quality control checks for ocean temperature profiles and recommendations for optimal sets

Millions of in situ ocean temperature profiles have been collected historically using various instrument types with varying sensor accuracy and then assembled into global databases. These are essential to our current understanding of the changing state of the oceans, sea level, Earth’s climate, marine ecosystems and fisheries, and for constraining model projections of future change that underpin mitigation and adaptation solutions. Profiles distributed shortly after collection are also widely used in operational applications such as real-time monitoring and forecasting of the ocean state and weather prediction. Before use in scientific or societal service applications, quality control (QC) procedures need to be applied to flag and ultimately remove erroneous data. Automatic QC (AQC) checks are vital to the timeliness of operational applications and for reducing the volume of dubious data which later require QC processing by a human for delayed mode applications. Despite the large suite of evolving AQC checks developed by institutions worldwide, the most effective set of AQC checks was not known. We have developed a framework to assess the performance of AQC checks, under the auspices of the International Quality Controlled Ocean Database (IQuOD) project. The IQuOD-AQC framework is an open-source collaborative software infrastructure built in Python (available from https://github.com/IQuOD). Sixty AQC checks have been implemented in this framework. Their performance was benchmarked against three reference datasets which contained a spectrum of instrument types and error modes flagged in their profiles. One of these (a subset of the Quality-controlled Ocean Temperature Archive (QuOTA) dataset that had been manually inspected for quality issues by its creators) was also used to identify optimal sets of AQC checks. Results suggest that the AQC checks are effective for most historical data, but less so in the case of data from Mechanical Bathythermographs (MBTs), and much less effective for Argo data. The optimal AQC sets will be applied to generate quality flags for the next release of the IQuOD dataset. This will further elevate the quality and historical value of millions of temperature profile data which have already been improved by IQuOD intelligent metadata and observational uncertainty information (https://doi.org/10.7289/v51r6nsf).

Digital Twins and Enabling Technologies in Museums and Cultural Heritage: An Overview.

This paper presents an overview of various types of virtual museums (ViM) as native artifacts or as digital twins (DT) of physical museums (PM). Depending on their mission and features, we discuss various enabling technologies and sensor equipment with their specific requirements and complexities, advantages and drawbacks in relation to each other at all stages of a DT's life cycle. A DT is a virtual construct and embodies innovative concepts based on emerging technologies (ET) using adequate sensor configurations for (meta-)data import and exchange. Our keyword-based search for articles, conference papers, (chapters from) books and reviews yielded 43 contributions and 43 further important references from Industry 4.0, Tourism and Heritage 4.0. After closer examination, a reference corpus of 40 contributions was evaluated in detail and classified along with their variants of DT-content-, communication-, and collaboration-centric and risk-informed ViMs. Their system features correlate with different application areas (AA), new or improved technologies-mostly still under development-and sensors used. Our proposal suggests a template-based, generative approach to DTs using standardized metadata formats, expert/curator software and customers'/visitors' engagement. It advocates for stakeholders' collaboration as part of a comprehensive validation and verification assessment (V&VA) throughout the DT's entire life cycle.

The DataHarmonizer: a tool for faster data harmonization, validation, aggregation and analysis of pathogen genomics contextual information.

Pathogen genomics is a critical tool for public health surveillance, infection control, outbreak investigations as well as research. In order to make use of pathogen genomics data, they must be interpreted using contextual data (metadata). Contextual data include sample metadata, laboratory methods, patient demographics, clinical outcomes and epidemiological information. However, the variability in how contextual information is captured by different authorities and how it is encoded in different databases poses challenges for data interpretation, integration and their use/re-use. The DataHarmonizer is a template-driven spreadsheet application for harmonizing, validating and transforming genomics contextual data into submission-ready formats for public or private repositories. The tool's web browser-based JavaScript environment enables validation and its offline functionality and local installation increases data security. The DataHarmonizer was developed to address the data sharing needs that arose during the COVID-19 pandemic, and was used by members of the Canadian COVID Genomics Network (CanCOGeN) to harmonize SARS-CoV-2 contextual data for national surveillance and for public repository submission. In order to support coordination of international surveillance efforts, we have partnered with the Public Health Alliance for Genomic Epidemiology to also provide a template conforming to its SARS-CoV-2 contextual data specification for use worldwide. Templates are also being developed for One Health and foodborne pathogens. Overall, the DataHarmonizer tool improves the effectiveness and fidelity of contextual data capture as well as its subsequent usability. Harmonization of contextual information across authorities, platforms and systems globally improves interoperability and reusability of data for concerted public health and research initiatives to fight the current pandemic and future public health emergencies. While initially developed for the COVID-19 pandemic, its expansion to other data management applications and pathogens is already underway.

LDMD: A database of microbes in human lung disease.

Lungs were initially thought to be sterile. However, with the development of sequencing technologies, various commensal microorganisms, especially bacteria, have been observed in the lungs of healthy humans. Several studies have also linked lung microbes to infectious lung diseases. However, few databases have focused on the metagenomics of lungs to provide microbial compositions and corresponding metadata information. Such a database would be handy for researching and treating lung diseases. To provide researchers with a preliminary understanding of lung microbes and their research methods, the LDMD collated nearly 10,000 studies in the literature covering over 30 diseases, gathered basic information such as the sources of lung microbe samples, sequencing methods, and processing software, as well as analyzed the metagenomic sequencing characteristics of lung microbes. Besides, the LDMD also contained data collected in our laboratory. In this study, we established the Lung Disease Microorganisms Database (LDMD), a comprehensive database of microbes involved in lung disease. The LDMD offered sequence analysis capabilities, allowing users to upload their sequencing results, align them with the data collated in the database, and visually analyze the results. In conclusion, the LDMD possesses various functionalities that provide a convenient and comprehensive resource to study the lung metagenome and treat lung diseases.

Juror interpretations of metadata and content information: implications for the going dark debate

AbstractThe rise of consumer encryption has led to a fierce debate over whether the loss of potential evidence due to encryption will be offset by the increase in evidence available from electronic metadata. One major question raised by this debate is how jurors will interpret and value metadata as opposed to content information. Though there are plausible arguments in favor of the persuasive power of each type of evidence, to date no empirical study has examined how ordinary people, potential jurors, view each of these sorts of evidence.We address this issue through a series of survey experiments that present respondents with hypothetical criminal trials, randomly assigning them to descriptions featuring either metadata or content information. These studies show that the relative power of content and metadata information is highly contextual. Content information and metadata can be equally useful when conveying logically equivalent information. However, content information may be more persuasive where the defendant’s state of mind is critical, while metadata can more convincingly establish a pattern of behavior. This suggests that the rise of encryption will have a heterogeneous effect on criminal cases, with the direction of the effect depending on the facts that the prosecution must prove.

CRNet: a multimodal deep convolutional neural network for customer revisit prediction

Since mobile food delivery services have become one of the essential issues for the restaurant industry, predicting customer revisits is highlighted as one of the significant academic and research topics. Considering that the use of multimodal datasets has gained notable attention from several scholars to address multiple industrial issues in our society, we introduce CRNet, a multimodal deep convolutional neural network for predicting customer revisits. We evaluated our approach using two datasets [a customer repurchase dataset (CRD) and mobile food delivery revisit dataset (MFDRD)] and two state-of-the-art multimodal deep learning models. The results showed that CRNet obtained accuracies and Fi-Scores of 0.9575 (CRD) and 0.9436 (MFDRD) and 0.9730 (CRD) and 0.9509 (MFDRD), respectively, thus achieving higher performance levels than current state-of-the-art multimodal frameworks (accuracy: 0.7417–0.9012; F1-Score: 0.7461–0.9378). Future research should aim to address other resources that can enhance the proposed framework (e.g., metadata information).

Quantivine: A Visualization Approach for Large-Scale Quantum Circuit Representation and Analysis.

Quantum computing is a rapidly evolving field that enables exponential speed-up over classical algorithms. At the heart of this revolutionary technology are quantum circuits, which serve as vital tools for implementing, analyzing, and optimizing quantum algorithms. Recent advancements in quantum computing and the increasing capability of quantum devices have led to the development of more complex quantum circuits. However, traditional quantum circuit diagrams suffer from scalability and readability issues, which limit the efficiency of analysis and optimization processes. In this research, we propose a novel visualization approach for large-scale quantum circuits by adopting semantic analysis to facilitate the comprehension of quantum circuits. We first exploit meta-data and semantic information extracted from the underlying code of quantum circuits to create component segmentations and pattern abstractions, allowing for easier wrangling of massive circuit diagrams. We then develop Quantivine, an interactive system for exploring and understanding quantum circuits. A series of novel circuit visualizations is designed to uncover contextual details such as qubit provenance, parallelism, and entanglement. The effectiveness of Quantivine is demonstrated through two usage scenarios of quantum circuits with up to 100 qubits and a formal user evaluation with quantum experts. A free copy of this paper and all supplemental materials are available at https://osf.io/2m9yh/?view_only=0aa1618c97244f5093cd7ce15f1431f9.

Academic Payment Tokenization: An Online Payment System for Academia Utilizing Non-Fungible Tokens and Permissionless Blockchain

Nowadays, traditional payment methods such as cash, cheques, credit/debit cards, and bank transfers are commonly used for financial transactions across a wide range of industries, including healthcare, real estate, supply chain, gaming, music, finance, retail, agriculture, tourism, pharmaceuticals, and the education sector too. However, these methods often involve transaction fees, potential delays, and security concerns. These methods lack transparency and traceability features, making tracking, authenticating, and verifying payments and their associated records difficult. To overcome these issues, we propose a Non-Fungible Token (NFT) based online payment system that leverages blockchain technology (BT) features and security to revolutionize financial expenses in the education sector. We also present wallet generation, fee tokenization and NFT generation processes. These processes utilize Ethereum and an interplanetary file system (IPFS) to store metadata information associated with fee payment details securely. We use Remix IDE, MetaMask wallet, and Sepolia test network to deploy and analyze the proposed system's results. We have also used the JMeter simulation toolkit to conduct comprehensive experiments to evaluate the performance of the proposed system in terms of throughput, latency, and response time. Our assessment identifies the increase in performance parameters with the number of users. This initiative will positively contribute towards addressing the issues of financial transactions in the education sector.

From simulation to dissemination: automation of data and metadata management

Working with Computational Fluid Dynamics (CFD) can produce numerous datasets, which contain different physical parameters and study settings. Management and dissemination of such data benefits greatly from a comprehensive data description (metadata), ideally a specialized Metadata Information System (MIS), and adequate long-term storage (data warehouse). To avoid the manual creation of rival metadata, an automation method has been developed, which adds metadata automatically by the simulation and post-processing programs. The automation method described here is an example for data curation suitable for a professional work environment. This method begins with the collection and creation of metadata and ends with the dissemination and publication of the data. This procedure reduces the challenging amount of tedious and error-prone workload, avoids redundancy, enhances efficiency, and is thus a means of quality assurance. Basis for the automation process is an open-source metadata information system (MIS) which has been adapted to the techno-scientific demand of simulation metadata. It stores the metadata in an SQL database and provides Open Geospatial Consortium (OGC) compliant services and communication interface. A hierarchical metadata management concept was initiated for efficient management of numerous datasets. Subsequently, the metadata validation and dissemination are automated via a middleware, which compiles metadata in an XML file, imports the metadata into the MIS and transfers the data to a long-term repository via a Representational State Transfer (REST) interface. Finally, the data and metadata are interlinked and published.

Sentiment analysis of halal food trends during COVID-19

Due to the pandemic of Corona Virus Disease (Covid-19), consumer awareness of halal products has increased. In this context, the government is also attempting to raise public knowledge about halal and thoyib products' growth and the halal industry's present trend. This study aims to ascertain public sentiment regarding halal food throughout the last four years, from 2019 to 2022. The method combines descriptive statistical analysis, meta-analysis, and sentiment analysis using secondary data in the form of metadata from 88 Scopus-indexed articles, then processed using Microsoft Excel 2019 and Senti Strength. This study discovered that the trend toward healthy halal food continued to grow throughout the pandemic, and the halal product market maintained a good performance. With the growth of the halal industry in the halal food and beverage sector, it is hoped that it will become a strong advocate for the SDGs, particularly the third goal, which aims to ensure a healthy life and promote prosperity while also assisting economic growth through the strengthening of the halal value chain, which is the primary strategy for accelerating sharia economic growth.

FAIR data station for lightweight metadata management and validation of omics studies.

The life sciences are one of the biggest suppliers of scientific data. Reusing and connecting these data can uncover hidden insights and lead to new concepts. Efficient reuse of these datasets is strongly promoted when they are interlinked with a sufficient amount of machine-actionable metadata. While the FAIR (Findable, Accessible, Interoperable, Reusable) guiding principles have been accepted by all stakeholders, in practice, there are only a limited number of easy-to-adopt implementations available that fulfill the needs of data producers. We developed the FAIR Data Station, a lightweight application written in Java, that aims to support researchers in managing research metadata according to the FAIR principles. It implements the ISA metadata framework and uses minimal information metadata standards to capture experiment metadata. The FAIR Data Station consists of 3 modules. Based on the minimal information model(s) selected by the user, the "form generation module" creates a metadata template Excel workbook with a header row of machine-actionable attribute names. The Excel workbook is subsequently used by the data producer(s) as a familiar environment for sample metadata registration. At any point during this process, the format of the recorded values can be checked using the "validation module." Finally, the "resource module" can be used to convert the set of metadata recorded in the Excel workbook in RDF format, enabling (cross-project) (meta)data searches and, for publishing of sequence data, in an European Nucleotide Archive-compatible XML metadata file. Turning FAIR into reality requires the availability of easy-to-adopt data FAIRification workflows that are also of direct use for data producers. As such, the FAIR Data Station provides, in addition to the means to correctly FAIRify (omics) data, the means to build searchable metadata databases of similar projects and can assist in ENA metadata submission of sequence data. The FAIR Data Station is available at https://fairbydesign.nl.

Geoportal platform design for agricultural development of small island based on geosmart system

Sustainable management of agricultural resources aims to achieve global food security and the greater interest in achieving several Sustainable Development Goals (SDGs) as proposed by the United Nations, particularly the goals without poverty, without hunger, and terrestrial ecosystems. One method that can be done to facilitate policy-making in achieving these goals is to build a web-based food security information system on the basis of a geosmart system. The purpose of this study is to develop a user-friendly information system that provides thematic databases obtained from various sources, particularly remote sensing data, GIS, GPS, and field surveys, with metadata information that has a coordinate system. The development of this system is expected to provide various thematic services on land and land resource information to facilitate visualization, access, and information services in the study area. This study was conducted on a small island of Kaledupa island, in Wakatobi Regency. In addition to biophysical information, it is expected that this system can also provide information services on socio-economic conditions, agricultural commodities, production, agroecology, soil, and crop suitability. The data presented on the website is currently in the form of a thematic map, a plan map in the form of a map of the island’s spatial pattern, and documentation of sampling data. The long-term target of developing this information system is expected to facilitate users and policy makers in Wakatobi Regency in managing land resources, assessing land degradation, and user-friendly agricultural planning in Wakatobi Regency.

Systematic literature review and bibliometric analysis of archivist competencies in digital curation

Introduction. Digital curation tasks and objectives align with the task of archivists who must digitally plan to ensure safe long-term preservation. As information professionals, archivists must master competencies to become digital curators in the future. This study aims to determine what competencies an archivist must have in digital curation.
 Data Collection Methods. This study uses a systematic literature review (SLR) available in SCOPUS from 2018-2022, considering 11 selected articles. This study also applied bibliometric analysis using VOSViewer to identify the main characteristics, discuss, and provide research opportunities in this field.
 Data Analysis. In the first phase, SLR consists of three steps: planning, conducting, and reporting. In the second phase, the bibliometric using co-occurrence analysis to visualize the keyword network, density, and overlay visualization. 
 Results and Discussion. The results of the bibliometric analysis indicate that there are still several topics regarding digital curation that have not been widely studied, including those related to competence, cultural heritages, data management, career, and learning. Based on the systematic literature review there are 11 selected articles that are relevant to research topics and used for data analysis. 
 Conclusion. From 11 selected articles, it was found that archivist competencies in digital curation can be divided into four categories: cognitive, functional, social, and meta competence. The competencies found have implications for archivists as future digital curators by combining technical skills, including expertise in metadata standards and information organization, as well as personal skills. Based on the findings, it also can be an opportunity for further research to examine digital curation competency seen in the topics that are still rarely studied.

UID: The uranium isotope database

As the parent element in the U-Pb and Pb-Pb radiochronometers, uranium (U) was one of the first heavy elements whose isotopic composition was carefully determined. Thought to be constant until the end of the 20th century, the ratio of the long-lived isotopes of U (238U/235U) has since been shown to be variable at the permil to sub-permil levels in natural materials. Today, the study of U isotopes has found applications in a variety of fields including geo/cosmochronology, oceanic paleoredox reconstruction, magmatic differentiation, environmental remediation, and forensic studies. With thousands of newly reported U isotopic data each year, a real need exists for a comprehensive U isotope database.Here, we introduce a global, updatable, U isotope database (UID), which not only contains the most expansive, internally consistent U isotopic dataset to date (14,591 entries from more than 320 papers), but also includes all other sample data from the original publications, as well as the relevant metadata and sample information to facilitate further analysis. The UID is freely accessible and will be updated regularly. All data are normalized to the widely-used CRM-145 standard, and all assumptions used to convert the published data are explicitly detailed in the paper and the database itself. New data can be easily formatted and submitted for incorporation into the database. Using the UID we provide new recommended δ238U values for certified U standards and geostandards and discuss important applications and future directions for U isotope studies.

Task information types related to data gathering in media studies

PurposeThe purpose of this paper is to examine what types of task information media scholars need while gathering research data to create new knowledge.Design/methodology/approachThe research design is qualitative and user-oriented. A total of 25 media scholars were interviewed about their research processes and interactions with their research data. The interviews were semi-structured, complemented by critical incident interviews. The analysis focused on the activity of gathering research data. A typology of information (task, domain and task-solving information) guided the analysis of information types related to data gathering, with further analysis focusing only on task information types.FindingsMedia scholars needed the following task information types while gathering research data to create new knowledge: (1) information about research data (aboutness of data, characteristics of data, metadata and secondary information about data), (2) information about sources of research data (characteristics of sources, local media landscapes) and (3) information about cases and their contexts (case information, contextual information). All the task information types should be considered when building data services and tools to support media scholars' work.Originality/valueThe paper increases understanding of the concept of task information in the context of gathering research data to create new knowledge and thereby informs the providers of research data services about the task information types that researchers need.

Antimicrobial Resistance Genes Analysis of Publicly Available Staphylococcus aureus Genomes

Staphylococcus aureus is a pathogen that can cause severe illness and express resistance to multiple antimicrobial agents. It is part of the ESKAPE organisms and it has been included by the Centers for Disease Control and Prevention (CDC) of USA in the list of serious threats to humans. Many antimicrobial mechanisms have been identified, and, in particular, antimicrobial resistance genes (ARGs) can be determined by whole genome sequencing. Mobile genetic elements (MGEs) can determine the spread of these ARGs between strains and species and can be identified with bioinformatic analyses. The scope of this work was to analyse publicly available genomes of S. aureus to characterise the occurrence of ARGs present in chromosomes and plasmids in relation to their geographical distribution, isolation sources, clonal complexes, and changes over time. The results showed that from a total of 29,679 S. aureus genomes, 24,765 chromosomes containing 73 different ARGs, and 21,006 plasmidic contigs containing 47 different ARGs were identified. The most abundant ARG in chromosomes was mecA (84%), while blaZ was the most abundant in plasmidic contigs (30%), although it was also abundant in chromosomes (42%). A total of 13 clonal complexes were assigned and differences in ARGs and CC distribution were highlighted among continents. Temporal changes during the past 20 years (from 2001 to 2020) showed that, in plasmids, MRSA and macrolide resistance occurrence decreased, while the occurrence of ARGs associated with aminoglycosides resistance increased. Despite the lack of metadata information in around half of the genomes analysed, the results obtained enable an in-depth analysis of the distribution of ARGs and MGEs throughout different categories to be undertaken through the design and implementation of a relatively simple pipeline, which can be also applied in future works with other pathogens, for surveillance and screening purposes.

Enabling FAIR data in Earth and environmental science with community-centric (meta)data reporting formats

Research can be more transparent and collaborative by using Findable, Accessible, Interoperable, and Reusable (FAIR) principles to publish Earth and environmental science data. Reporting formats—instructions, templates, and tools for consistently formatting data within a discipline—can help make data more accessible and reusable. However, the immense diversity of data types across Earth science disciplines makes development and adoption challenging. Here, we describe 11 community reporting formats for a diverse set of Earth science (meta)data including cross-domain metadata (dataset metadata, location metadata, sample metadata), file-formatting guidelines (file-level metadata, CSV files, terrestrial model data archiving), and domain-specific reporting formats for some biological, geochemical, and hydrological data (amplicon abundance tables, leaf-level gas exchange, soil respiration, water and sediment chemistry, sensor-based hydrologic measurements). More broadly, we provide guidelines that communities can use to create new (meta)data formats that integrate with their scientific workflows. Such reporting formats have the potential to accelerate scientific discovery and predictions by making it easier for data contributors to provide (meta)data that are more interoperable and reusable.

Sampling from four geographically divergent young female populations demonstrates forensic geolocation potential in microbiomes

Studies of human microbiomes using new sequencing techniques have increasingly demonstrated that their ecologies are partly determined by the lifestyle and habits of individuals. As such, significant forensic information could be obtained from high throughput sequencing of the human microbiome. This approach, combined with multiple analytical techniques demonstrates that bacterial DNA can be used to uniquely identify an individual and to provide information about their life and behavioral patterns. However, the transformation of these findings into actionable forensic information, including the geolocation of the samples, remains limited by incomplete understanding of the effects of confounding factors and the paucity of diverse sequences. We obtained 16S rRNA sequences of stool and oral microbiomes collected from 206 young and healthy females from four globally diverse populations, in addition to supporting metadata, including dietary and medical information. Analysis of these microbiomes revealed detectable geolocation signals between the populations, even for populations living within the same city. Accounting for other lifestyle variables, such as diet and smoking, lessened but does not remove the geolocation signal.

DeepPlayer: An open‐source SignalPlant plugin for deep learning inference

AbstractBackground and ObjectiveMachine learning has become a powerful tool in several computation domains. The most progressive way of machine learning, deep learning, has already surpassed several algorithms designed by human experts. It also applies to the field of biomedical signal processing. However, while many experts produce deep learning models, there is no software platform for signal processing, allowing the convenient use of pre‐trained deep learning models and evaluating them using any inspected signal. This may also hinder understanding, interpretation, and explanation of results. For these reasons, we designed DeepPlayer. It is a plugin for the free signal processing software SignalPlant. The plugin allows loading deep learning models saved in the Open Neural Network Exchange (ONNX) file format and evaluating them on any given signal.MethodsThe DeepPlayer plugin and its graphical user interface were designed in C# programming language and the .NET framework. We used the inference library OnnxRuntime, which supports graphics card acceleration. The inference is executed in asynchronous tasks for a live preview and evaluation of the signals. Model outputs can be exported back to SignalPlant for further processing, such as peak detection or thresholding.ResultsWe developed the DeepPlayer plugin to evaluate deep learning models in SignalPlant. The plugin keeps with SignalPlant's interactive work with signals, such as live preview or easy selection of associated signals. The plugin can load classification or regression models and allows standard pre‐processing and post‐processing methods. We prepared several deep learning models to test the plugin. Additionally, we provide a tutorial training script that outputs an ONNX format model with correctly set metadata information. These, and the source code of the DeepPlayer plugin, are publicly accessible via GitHub and Google Colab service.ConclusionThe DeepPlayer plugin allows running deep learning models easily and interactively. Therefore, experts and non‐AI experts alike can explore and apply deep learning models for (biomedical) signal processing. Its ease of use and interactivity might also contribute to a better understanding and acceptance of AI methods in biomedicine.

Harvesting metadata in clinical care: a crosswalk between FHIR, OMOP, CDISC and openEHR metadata

Metadata describe information about data source, type of creation, structure, status and semantics and are prerequisite for preservation and reuse of medical data. To overcome the hurdle of disparate data sources and repositories with heterogeneous data formats a metadata crosswalk was initiated, based on existing standards. FAIR Principles were included, as well as data format specifications. The metadata crosswalk is the foundation of data provision between a Medical Data Integration Center (MeDIC) and researchers, providing a selection of metadata information for research design and requests. Based on the crosswalk, metadata items were prioritized and categorized to demonstrate that not one single predefined standard meets all requirements of a MeDIC and only a maximum data set of metadata is suitable for use. The development of a convergence format including the maximum data set is the anticipated solution for an automated transformation of metadata in a MeDIC.