Metadata Generation Research Articles

Enhancing accessibility and discoverability of digital archive images through automated image recognition tool

This research paper presents a comprehensive evaluation of the effectiveness of Imagga and Google cloud vision application programming interface (API) as image recognition tools for generating metadata in digital archive images. The assessment encompasses a diverse range of archive images, including those without text, images with text, and both color and black-and-white images. Through the use of evaluation metrics such as cosine similarity, word overlap similarity, recall, precision, and F1 score, the performance of these tools is quantitatively measured. The findings highlight the strong individual performance of both Imagga and Google cloud vision API, with the combined metadata outputs achieving significantly higher scores across all metrics. This emphasizes the potential benefits of employing a combined approach, leveraging the strengths of multiple tools to enhance the reliability and robustness of the metadata extraction process. The findings contribute to the advancement of metadata management in digital archives and underscore the importance of utilizing multiple tools for improved performance in image metadata generation.

FAIR assessment of nanosafety data reusability with community standards.

Nanomaterials hold great promise for improving our society, and it is crucial to understand their effects on biological systems in order to enhance their properties and ensure their safety. However, the lack of consistency in experimental reporting, the absence of universally accepted machine-readable metadata standards, and the challenge of combining such standards hamper the reusability of previously produced data for risk assessment. Fortunately, the research community has responded to these challenges by developing minimum reporting standards that address several of these issues. By converting twelve published minimum reporting standards into a machine-readable representation using FAIR maturity indicators, we have created a machine-friendly approach to annotate and assess datasets' reusability according to those standards. Furthermore, our NanoSafety Data Reusability Assessment (NSDRA) framework includes a metadata generator web application that can be integrated into experimental data management, and a new web application that can summarize the reusability of nanosafety datasets for one or more subsets of maturity indicators, tailored to specific computational risk assessment use cases. This approach enhances the transparency, communication, and reusability of experimental data and metadata. With this improved FAIR approach, we can facilitate the reuse of nanosafety research for exploration, toxicity prediction, and regulation, thereby advancing the field and benefiting society as a whole.

FAIR+R: Making Clinical Data Reliable Through Qualitative Metadata.

Metadata are often the first access to data repositories for researchers within secondary use. Through automatic metadata generation and metadata harvesting the amount of data about data has been growing ever since. In order to make data not only FAIR but also reliable, the aspect of metadata quality has to be considered. But as earlier assessments of metadata of different repositories showed, metadata quality still lacks behind its capability. Providing an extensive literature review the authors conclude nine measures to assess metadata in relation to clinical care repositories, such as Medical Data Integration Centers (MeDICs). Proceeding from these measures the authors propose an addition of the FAIR Guiding Principles by adding a fifth block for Reliability including three principles, that resulted from the measures presented. The results form the basis for the future work of an assessment of metadata, that is stored in a MeDIC.

Repairing raw metadata for metadata management

With the exponential growth of data production, the generation of metadata has become an integral part of the process. Metadata plays a crucial role in facilitating enhanced data analytics, data integration, and resource management by offering valuable insights. However, inconsistencies arise due to deviations from standards in metadata recording, including missing attribute information, publishing URLs, and provenance. Furthermore, the recorded metadata may exhibit inconsistencies, such as varied value formats, special characters, and inaccurately entered values. Addressing these inconsistencies through metadata preparation can greatly enhance the user experience during data management tasks.This paper introduces MDPrep, a system that explores the usability and applicability of data preparation techniques in improving metadata quality. Our approach involves three steps: (1) detecting and identifying problematic metadata elements and structural issues, (2) employing a keyword-based approach to enhance metadata elements and a syntax-based approach to rectify structural metadata issues, and (3) comparing the outcomes to ensure improved readability and reusability of prepared metadata files.

FAIR-EuMon: a FAIR-enabling resource for biodiversity monitoring schemes.

Within the scope of the Helmholtz Metadata Collaboration (HMC), the ADVANCE project - Advanced metadata standards for biodiversity survey and monitoring data: supporting of research and conservation - aimed at supporting rich metadata generation with interoperable metadata standards and semantic artefacts that facilitate data access, integration and reuse across terrestrial, freshwater and marine realms. HMC's mission is to facilitate the discovery, access, machine-readability, and reuse of research data across and beyond the Helmholtz Association. We revised, adapted and expanded existing metadata schemas, vocabularies and thesauri to build a FAIR metadata schema and a metadata entry form built on it for users to provide their metadata instances focused on biodiversity monitoring data. The schema is FAIR because it is both machine-interpretable and follows domain-relevant community standards. This report provides a general overview of the project results and instructions on how to access, re-use and complete the metadata form.

A high-throughput 3D X-ray histology facility for biomedical research and preclinical applications

Background The University of Southampton, in collaboration with the University Hospital Southampton (UHS) NHS Foundation Trust and industrial partners, has been at the forefront of developing three-dimensional (3D) imaging workflows using X-ray microfocus computed tomography (μCT) -based technology. This article presents the outcomes of these endeavours and highlights the distinctive characteristics of a μCT facility tailored explicitly for 3D X-ray Histology, with a primary focus on applications in biomedical research and preclinical and clinical studies. Methods The UHS houses a unique 3D X-ray Histology (XRH) facility, offering a range of services to national and international clients. The facility employs specialised μCT equipment explicitly designed for histology applications, allowing whole-block XRH imaging of formalin-fixed and paraffin-embedded tissue specimens. It also enables correlative imaging by combining μCT imaging with other microscopy techniques, such as immunohistochemistry (IHC) and serial block-face scanning electron microscopy, as well as data visualisation, image quantification, and bespoke analysis. Results Over the past seven years, the XRH facility has successfully completed over 120 projects in collaboration with researchers from 60 affiliations, resulting in numerous published manuscripts and conference proceedings. The facility has streamlined the μCT imaging process, improving productivity and enabling efficient acquisition of 3D datasets. Discussion & Conclusions The 3D X-ray Histology (XRH) facility at UHS is a pioneering platform in the field of histology and biomedical imaging. To the best of our knowledge, it stands out as the world's first dedicated XRH facility, encompassing every aspect of the imaging process, from user support to data generation, analysis, training, archiving, and metadata generation. This article serves as a comprehensive guide for establishing similar XRH facilities, covering key aspects of facility setup and operation. Researchers and institutions interested in developing state-of-the-art histology and imaging facilities can utilise this resource to explore new frontiers in their research and discoveries.

Machine learning for document classification in an archive of the National Afrikaans Literary Museum and Research Centre


 
 
 
 
 
 
 
 https://dx.doi.org/10.4314/jsasa.v56i1.10
 ISSN: 1012-2796
 ©SASA 2023
 
 
 
 
 
 
 
 
 
 Most archives were established before the digital age, where hardcopies of much smaller volumes were archived. In the information age, archives struggle to accommodate the large volumes of material produced. In addition, many archives, including in South Africa, had to contend with budget cuts that reduced the number of staff available. If digital material is not archived now, it creates the risk of gaps in the historical record in the future. In addition, with digital humanities gaining wider acceptance, large corpuses of digital material are needed, which archives could provide. This study’s aim was to investigate whether document classification using machine learning classifiers is feasible in a South African archive context, with a focus on the National Afrikaans Literary Museum and Research Centre (NALN). The researchers created and trained a document classification model and tested it for accuracy against human classifiers. It followed a basic linguistic approach to prepare specific text documents for text classification, in terms of Galloway and Roux’s (2019) six categories, namely articles, media reports, books, interviews, reviews, and dissertations and theses. The classification was done using two annotators, after which the annotated corpus was employed as training data for machine learning models. Following Rolan et al. (2018), Suominen (2019), and Connelly et al. (2020), Python libraries were used for document classifications. The researchers show that machine learning classifiers can accurately categorise documents into different types. If implemented, this means that archives can improve their collection efforts without spending more on salaries. One way of coping with the information explosion is to develop metadata generation tools, like machine learning and artificial intelligence. If metadata could be automatically generated, it would reduce the pressure on archival personnel by providing a way to handle larger volumes.

A Federated Blockchain Architecture for File Storage with Improved Latency and Reliability in IoT DApp Services.

Blockchain technology can address data falsification, single point of failure (SPOF), and DDoS attacks on centralized services. By utilizing IoT devices as blockchain nodes, it is possible to solve the problem that it is difficult to ensure the integrity of data generated by using current IoT devices. However, as the amount of data generated by IoT devices increases, scalability issues are inevitable. As a result, large amounts of data are managed on external cloud storage or distributed file storage. However, this has the disadvantage of being outside the blockchain network. This makes it difficult to ensure reliability and causes high latency during data download and upload. To address these limitations, we propose a method for managing large amounts of data in the local storage node of a blockchain network with improved latency and reliability. Each blockchain network node stores data, which is synchronized and recovered based on reaching a consensus between smart contracts in a cluster network. The cluster network consists of a service leader node that serves as a gateway for services and a cluster node that stores service data in storage. The blockchain network stores synchronization and recovery metadata created in the cluster network. In addition, we showed that the performance of smart contract execution, network transmission, and metadata generation, which are elements of the proposed consensus process, is not significantly affected. In addition, we built a service leader node and a cluster node by implementing the proposed structure. We compared the performance (latency) of IoT devices when they utilized the proposed architecture and existing external distributed storage. Our results show improvements up to 4 and 10 times reduction in data upload (store) and download latency, respectively.

Sanitizing data for analysis: Designing systems for data understanding

As organizations accumulate vast amounts of data for analysis, a significant challenge remains in fully understanding these datasets to extract accurate information and generate real-world impact. Particularly, the high dimensionality of datasets and the lack of sufficient documentation, specifically the provision of metadata, often limit the potential to exploit the full value of data via analytical methods. To address these issues, this study proposes a hybrid approach to metadata generation, that leverages both the in-depth knowledge of domain experts and the scalability of automated processes. The approach centers on two key design principles—semanticization and contextualization—to facilitate the understanding of high-dimensional datasets. A real-world case study conducted at a leading pharmaceutical company validates the effectiveness of this approach, demonstrating improved collaboration and knowledge sharing among users. By addressing the challenges in metadata generation, this research contributes significantly toward empowering organizations to make more effective, data-driven decisions.

A high-throughput 3D X-ray histology facility for biomedical research and preclinical applications.

The University of Southampton, in collaboration with the University Hospital Southampton (UHS) NHS Foundation Trust and industrial partners, has been at the forefront of developing three-dimensional (3D) imaging workflows using X-ray microfocus computed tomography (μCT) -based technology. This article presents the outcomes of these endeavours and highlights the distinctive characteristics of a μCT facility tailored explicitly for 3D X-ray Histology, with a primary focus on applications in biomedical research and preclinical and clinical studies. The UHS houses a unique 3D X-ray Histology (XRH) facility, offering a range of services to national and international clients. The facility employs specialised μCT equipment explicitly designed for histology applications, allowing whole-block XRH imaging of formalin-fixed and paraffin-embedded tissue specimens. It also enables correlative imaging by combining μCT imaging with other microscopy techniques, such as immunohistochemistry (IHC) and serial block-face scanning electron microscopy, as well as data visualisation, image quantification, and bespoke analysis. Over the past seven years, the XRH facility has successfully completed over 120 projects in collaboration with researchers from 60 affiliations, resulting in numerous published manuscripts and conference proceedings. The facility has streamlined the μCT imaging process, improving productivity and enabling efficient acquisition of 3D datasets. The 3D X-ray Histology (XRH) facility at UHS is a pioneering platform in the field of histology and biomedical imaging. To the best of our knowledge, it stands out as the world's first dedicated XRH facility, encompassing every aspect of the imaging process, from user support to data generation, analysis, training, archiving, and metadata generation. This article serves as a comprehensive guide for establishing similar XRH facilities, covering key aspects of facility setup and operation. Researchers and institutions interested in developing state-of-the-art histology and imaging facilities can utilise this resource to explore new frontiers in their research and discoveries.

A seminatural approach towards restoration, classification, and pattern extraction from heritage Hindi poetry: A probabilistic case study with Dohā chanda

Abstract Indian literary heritage is vast and is of great importance; to explore it, one has to devote them in studying dialects (Khariboli, Haryanavi, Brajbhasha, Awadhi, Bhojpuri, Marwari, etc.) especially when old Hindi is under the lens of observation. Chanda are poetic compositions that have well-defined structures. Dohā is a kind of chanda which, in our work, has been explored using Kabir’s compositions as a case study. Kabir represents the cult of poets who relied on oral means for the propagation and consumption of poetry. The poems were communicated to the later generation through simple acts of recitation and hearing, leading to obvious mutation and multiple versions of the same compositions when documented later and hence needs restoration (at least metrically). Using the knowledge from the state-of-art models metadata generator, Text2Mātrā, and RPaGen and extending beyond them, this article is first of its kind to present Kabir’s dohā within the scope of restoration, metrical computation, and statistics. Starting with the restoration process, the proposed algorithms generate data that are subjected to suitable statistical models. These models highlight the trends in the dataset, which is helpful in realizing abstract patterns inside the textual data.

Deep Neural Networks-based Classification Methodologies of Speech, Audio and Music, and its Integration for Audio Metadata Tagging

Videos contain visual and auditory information. Visual information in a video can include images of people, objects, and the landscape, whereas auditory information includes voices, sound effects, background music, and the soundscape. The audio content can provide detailed information on the story by conducting a voice and atmosphere analysis of the sound effects and soundscape. Metadata tags represent the results of a media analysis as text. The tags can classify video content on social networking services, like YouTube. This paper presents the methodologies of speech, audio, and music processing. Also, we propose integrating these audio tagging methods and applying them in an audio metadata generation system for video storytelling. The proposed system automatically creates metadata tags based on speech, sound effects, and background music information from the audio input. The proposed system comprises five subsystems: (1) automatic speech recognition, which generates text from the linguistic sounds in the audio, (2) audio event classification for the type of sound effect, (3) audio scene classification for the type of place from the soundscape, (4) music detection for the background music, and (5) keyword extraction from the automatic speech recognition results. First, the audio signal is converted into a suitable form, which is subsequently combined from each subsystem to create metadata for the audio content. We evaluated the proposed system using video logs (vlogs) on YouTube. The proposed system exhibits a similar accuracy to handcrafted metadata for the audio content, and for a total of 104 YouTube vlogs, achieves an accuracy of 65.83%.

A Cloud-Native Web Application for Assisted Metadata Generation and Retrieval: THESPIAN-NER

Within the context of the Competence Centre for the Conservation of Cultural Heritage (4CH) project, the design and deployment of a platform-as-a-service cloud infrastructure for the first European competence centre of cultural heritage (CH) has begun, and some web services have been integrated into the platform. The first integrated service is the INFN-CHNet web application for FAIR storage of scientific analysis on CH: THESPIAN-Mask. It is based on CIDOC-CRM-compatible ontology and CRMhs, describing the scientific metadata. To ease the process of metadata generation and data injection, another web service has been developed: THESPIAN-NER. It is a tool based on a deep neural network for named entity recognition (NER), enabling users to upload their Italian-written report files and obtain labelled entities. Those entities are used as keywords either to serve as (semi)automatically custom queries for the database, or to fill (part of) the metadata form as a descriptor for the file to be uploaded. The services have been made freely available in the 4CH PaaS cloud platform.

Computational metadata generation methods for biological specimen image collections

Metadata is a key data source for researchers seeking to apply machine learning (ML) to the vast collections of digitized biological specimens that can be found online. Unfortunately, the associated metadata is often sparse and, at times, erroneous. This paper extends previous research conducted with the Illinois Natural History Survey (INHS) collection (7244 specimen images) that uses computational approaches to analyze image quality, and then automatically generates 22 metadata properties representing the image quality and morphological features of the specimens. In the research reported here, we demonstrate the extension of our initial work to University of the Wisconsin Zoological Museum (UWZM) collection (4155 specimen images). Further, we enhance our computational methods in four ways: (1) augmenting the training set, (2) applying contrast enhancement, (3) upscaling small objects, and (4) refining our processing logic. Together these new methods improved our overall error rates from 4.6 to 1.1%. These enhancements also allowed us to compute an additional set of 17 image-based metadata properties. The new metadata properties provide supplemental features and information that may also be used to analyze and classify the fish specimens. Examples of these new features include convex area, eccentricity, perimeter, skew, etc. The newly refined process further outperforms humans in terms of time and labor cost, as well as accuracy, providing a novel solution for leveraging digitized specimens with ML. This research demonstrates the ability of computational methods to enhance the digital library services associated with the tens of thousands of digitized specimens stored in open-access repositories world-wide by generating accurate and valuable metadata for those repositories.

Automatic Movie Tag Generation System for Improving the Recommendation System

As the content industry develops, the demand for movie content is increasing. Accordingly, the content industry is actively developing super-personalized recommendation systems that match consumers’ tastes. In this paper, we study automatic generation of movie tags to improve the movie recommendation system. We extracted background sounds from movie trailer videos, analyzed the sounds using STFT (Short-Time Fourier transform) and major audio attribute features, and created a genre prediction model. The experimental results show that the pre-collected dataset and the data extracted via the model are similar when compared. In this research, we suggest the methodology of an automatic genre prediction system for movie information from trailer videos. This will help to reduce the time and effort for metadata generation for a recommendation system.

Bringing Semantics into Historical Archives with Computer-aided Rich Metadata Generation

This article relies on the idea that a semantically rich metadata layer is required in order to provide an effective, intelligent, and engaging access to historical archives. However, building such a semantic layer represents a well-known bottleneck that can be overcome only by a hybrid strategy, integrating user-generated content and automatic techniques. The PRiSMHA project provides a contribution in this direction with the design and development of the prototype of an ontology-driven platform supporting users in semantic metadata generation. In particular, the main contribution of this article is to show how automatic information extraction techniques (namely, Named Entity and Temporal Expression Recognition) and information retrieved from external datasets in the LOD cloud can support users in the identification and characterization of new entities to annotate documents with.

An Automated Metadata Generation Method for Data Lake of Industrial WoT Applications

Recent trends in the Web of Things (WoT) have led to data explosion. Data lake (DL), as a flexible on-demand heterogeneous data management architecture, has become a feasible solution in data management. Metadata modeling for DLs is the key basis for smart analysis and processing. However, the varieties in structures and semantics of industrial WoT data hinder metadata modeling and maintenance. Moreover, the lack of textual descriptions and the semantics hidden in value streams make it hard to automatically construct semantic metadata. The dynamic nature of WoT requires on-time evolution on metadata. To overcome these challenges, we propose an automated bottom-up metadata generation approach for DL of WoT applications. Applying a data-driven framework, raw data are notated as linked data and self-organizing map-based online clustering is applied to real timely extract data characteristics. To recognize entities, concepts and relations, semantics-based entity discovery approach from short texts is proposed according to the feature of WoT data. The numerical analysis is performed to find the hidden relations from raw values. Full-dimensional metadata with rich semantic knowledge are finally built. Experiments on a real-world dataset are conducted to verify the effectiveness of methods and a case study on an energy WoT system is provided to demonstrate the feasibility of the approach.

IoT-oriented high-efficient anti-malware hardware focusing on time series metadata extractable from inside a processor core

We aim to improve the efficiency of our previously proposed anti-malware hardware; it is a hardware-implemented malware detection mechanism that uses information inside the processor. We previously evaluated a prototype, but, due to its prototypical nature, there remain limitations, such as only detecting certain behaviors, high power consumption, and a tendency to bloat the training model. In this paper, we propose a circuit and a learning method to achieve high efficiency, low power consumption, and light weight for the model. In considering these three issues, we focus on time-series metadata obtained by transforming the processor information. To improve efficiency, we implement predictive detection to predict the behavior of metadata in the malware detection component. This lets the model detect malware within less than 19% of the number of execution cycles of the conventional method. To reduce power consumption, we implement a sampling circuit that interrupts the input to the detection circuit at regular intervals, reducing the system’s uptime by 99% while maintaining judgment accuracy. Finally, for a light weight, we focus on the training process of the metadata generator based on a machine-learning model. By applying sampling learning and feature dimensionality reduction in the training process, a metadata generator approximately 16% smaller than the previous version is created.

Development and implementation of automatic metadata generation framework for SDI using OSS: a case study of Indian NSDI

Development and implementation of automatic metadata generation framework for SDI using OSS: a case study of Indian NSDI

Understanding the Nature of Metadata: Systematic Review.

BackgroundMetadata are created to describe the corresponding data in a detailed and unambiguous way and is used for various applications in different research areas, for example, data identification and classification. However, a clear definition of metadata is crucial for further use. Unfortunately, extensive experience with the processing and management of metadata has shown that the term “metadata” and its use is not always unambiguous.ObjectiveThis study aimed to understand the definition of metadata and the challenges resulting from metadata reuse.MethodsA systematic literature search was performed in this study following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for reporting on systematic reviews. Five research questions were identified to streamline the review process, addressing metadata characteristics, metadata standards, use cases, and problems encountered. This review was preceded by a harmonization process to achieve a general understanding of the terms used.ResultsThe harmonization process resulted in a clear set of definitions for metadata processing focusing on data integration. The following literature review was conducted by 10 reviewers with different backgrounds and using the harmonized definitions. This study included 81 peer-reviewed papers from the last decade after applying various filtering steps to identify the most relevant papers. The 5 research questions could be answered, resulting in a broad overview of the standards, use cases, problems, and corresponding solutions for the application of metadata in different research areas.ConclusionsMetadata can be a powerful tool for identifying, describing, and processing information, but its meaningful creation is costly and challenging. This review process uncovered many standards, use cases, problems, and solutions for dealing with metadata. The presented harmonized definitions and the new schema have the potential to improve the classification and generation of metadata by creating a shared understanding of metadata and its context.