Metadata Structure Research Articles

Species identification for Indian seafood markets: A machine learning approach with a fish dataset.

The Indian sea fish market is a dynamic and significant sector, contributing to both the domestic economy and the global seafood trade. This fish dataset is specifically curated for machine learning applications in the Indian seafood industry. It includes a comprehensive collection of images representing eight commercially significant fish species native to Indian waters, comprising a total of 8488 images. This dataset serves as a valuable resource for developing and refining machine learning models in the domain. It focuses on the eight most widely consumed fish species in India: Pomfret (scientific name: Pampus argeneus, local name: Paplet), Mackerel (scientific name: Scomber scombrus, local name: Bangda), Black Snapper (scientific name: Apsilus dentatus, local name: Tilapia), Indian Carp (scientific name: Cyprinus carpio, local name: Rohu), Prawn (scientific name: Dendrobranchiata, local name: Kolambi), Pink Perch (scientific name: Zalembius rosaceus, local name: Rani Fish), Indian flathead (scientific name: Lates calcarifer, local name: Bhetki Fish) and Black Pomfret (scientific name: Parastromateus niger, local name: Black Paplet). Article details the data acquisition process, and metadata structure, ensuring accessibility and usability for researchers and industry professionals. This resource facilitates the development of machine learning models for tasks such as species identification within the Indian seafood industry.

Bridging the Gap Between Computational Efficiency and Segmentation Fidelity in Object-Based Image Analysis.

A critical issue in image analysis for analyzing animal behavior is accurate object detection and tracking in dynamic and complex environments. This study introduces a novel preprocessing algorithm to bridge the gap between computational efficiency and segmentation fidelity in object-based image analysis for machine learning applications. The algorithm integrates convolutional operations, quantization strategies, and polynomial transformations to optimize image segmentation in complex visual environments, addressing the limitations of traditional pixel-level and unsupervised methods. This innovative approach enhances object delineation and generates structured metadata, facilitating robust feature extraction and consistent object representation across varied conditions. As empirical validation shows, the proposed preprocessing pipeline reduces computational demands while improving segmentation accuracy, particularly in intricate backgrounds. Key features include adaptive object segmentation, efficient metadata creation, and scalability for real-time applications. The methodology's application in domains such as Precision Livestock Farming and autonomous systems highlights its potential for high-accuracy visual data processing. Future work will explore dynamic parameter optimization and algorithm adaptability across diverse datasets to further refine its capabilities. This study presents a scalable and efficient framework designed to advance machine learning applications in complex image analysis tasks by incorporating methodologies for image quantization and automated segmentation.

Data quality assessment of aggregated LCI datasets: A case study on fossil‐based and bio‐based plastic food packaging

AbstractEnvironmental impacts resulting from plastic food packaging, made from both fossil‐based and bio‐based polymers, are increasingly analyzed in life cycle assessment (LCA) studies. However, the literature reveals significant variations in results for the same polymer within the same scope. To enhance the reliability of these assessments, data quality assessment (DQA) plays a relevant role. However, despite most of the LCA studies employing aggregated life cycle inventory (LCI) datasets, in the literature, DQA methods for aggregated processes are not available. To fill this gap, in this paper, a DQA for aggregated LCI datasets is proposed and demonstrated through its application to 101 aggregated LCI datasets, extracted from Ecoinvent and GaBi databases. The DQA method has been developed by adapting and integrating the pedigree matrix and the data quality ranking proposed by the recently published EC Plastic LCA method. The three data quality indicators (DQIs) used are technological, geographical, and time‐related representativeness. The application of this method exhibits an overall positive evaluation of the selected datasets with differences among the three DQIs. Moreover, it highlights the role of metadata structure in adequately supporting a robust DQA. Indeed, in the absence of a common framework that defines, assesses, and provides access to data quality information, transparency must be assured by the operator in the metadata interpretation and related assumptions along the DQA process. Finally, although the proposed DQA method was developed for the plastic sector, its application can be extended to LCI aggregated datasets relevant to other sectors, materials, and products.

Enriching Earth observation datasets through semantics for climate change applications: The EIFFEL ontology.

Earth Observation (EO) datasets have become vital for decision support applications, particularly from open satellite portals that provide extensive historical datasets. These datasets can be integrated with in-situ data to power artificial intelligence mechanisms for accurate forecasting and trend analysis. However, researchers and data scientists face challenges in finding appropriate EO datasets due to inconsistent metadata structures and varied keyword descriptions. This misalignment hinders the discoverability and usability of EO data. To address this challenge, the EIFFEL ontology (EIFF-O) is proposed. EIFF-O introduces taxonomies and ontologies to provide (i) global classification of EO data and (ii) linkage between different datasets through common concepts. The taxonomies specified by the European Association of Remote Sensing Companies (EARSC) have been formalized and implemented in EIFF-O. Additionally, EIFF-O incorporates:1.An Essential Climate Variable (ECV) ontology, defined by the Global Climate Observing System (GCOS), is embedded and tailored for Climate Change (CC) applications.2.The Sustainable Development Goals (SDG) ontology is included to facilitate linking datasets to specific targets.3.The ontology extends schema.org vocabularies and promotes the use of JavaScript Object Notation for Linked Data (JSON-LD) formats for semantic web integration. EIFF-O provides a unified framework that enhances the discoverability, usability, and application of EO datasets. The implementation of EIFF-O allows data providers and users to bridge the gap between varied metadata descriptions and structured classification, thereby facilitating better linkage and integration of EO datasets. The EIFFEL ontology represents a significant advancement in the organization and application of EO datasets. By embedding ECV and SDG ontologies and leveraging semantic web technologies, EIFF-O not only streamlines the data discovery process but also supports diverse applications, particularly in Climate Change monitoring and Sustainable Development Goals achievement. The open-source nature of the ontology and its associated tools promotes rapid adoption among developers.

Towards a practical usage for the Sleuth Kit supporting file system add-ons

Most modern digital devices with storage utilize a file system to manage files and directories. Consequently, when digital forensic investigators derive evidence from such devices, they collect and analyze data stored on them through file system analysis. However, there are numerous types of file systems, with new ones continually being developed. Each file system possesses a distinct metadata structure and file management system. Therefore, investigators must possess prior knowledge of the specific file system being examined. Nevertheless, it is challenging for practitioners to be knowledgeable about all existing file systems. To address this issue, forensic software such as The Sleuth Kit (TSK), an open-source forensic tool, is employed for investigations. However, even these tools may not offer complete support for relatively recent file systems.Hence, we propose a structure for integrating a new file system into the open-source forensic tool TSK. Additionally, to validate this proposed structure, we demonstrate that support for five file systems (Ext4, XFS, Btrfs, F2FS, and Hikvision) can be added following this framework. To achieve this, we conducted an analysis of the metadata and file management scheme for these five file systems. Furthermore, we examined the operational procedures of the TSK framework. Based on these analyses, investigation capabilities for the five file systems have been incorporated into TSK. Moreover, reliability verification experiments were conducted on the developed tools; and performance evaluation was carried out in comparison with other commercial digital forensic tools. The findings of this study can serve as a foundation for future forensic studies based on file systems. Additionally, the TSK developed based on the proposed structure can assist investigators in conducting digital forensics effectively.

Enriching Earth observation datasets through semantics for climate change applications: The EIFFEL ontology

Background Earth Observation (EO) datasets have become vital for decision support applications, particularly from open satellite portals that provide extensive historical datasets. These datasets can be integrated with in-situ data to power artificial intelligence mechanisms for accurate forecasting and trend analysis. However, researchers and data scientists face challenges in finding appropriate EO datasets due to inconsistent metadata structures and varied keyword descriptions. This misalignment hinders the discoverability and usability of EO data. Methods To address this challenge, the EIFFEL ontology (EIFF-O) is proposed. EIFF-O introduces taxonomies and ontologies to provide (i) global classification of EO data and (ii) linkage between different datasets through common concepts. The taxonomies specified by the European Association of Remote Sensing Companies (EARSC) have been formalized and implemented in EIFF-O. Additionally, EIFF-O incorporates: 1. An Essential Climate Variable (ECV) ontology, defined by the Global Climate Observing System (GCOS), is embedded and tailored for Climate Change (CC) applications. 2. The Sustainable Development Goals (SDG) ontology is included to facilitate linking datasets to specific targets. 3. The ontology extends schema.org vocabularies and promotes the use of JavaScript Object Notation for Linked Data (JSON-LD) formats for semantic web integration. Results EIFF-O provides a unified framework that enhances the discoverability, usability, and application of EO datasets. The implementation of EIFF-O allows data providers and users to bridge the gap between varied metadata descriptions and structured classification, thereby facilitating better linkage and integration of EO datasets. Conclusions The EIFFEL ontology represents a significant advancement in the organization and application of EO datasets. By embedding ECV and SDG ontologies and leveraging semantic web technologies, EIFF-O not only streamlines the data discovery process but also supports diverse applications, particularly in Climate Change monitoring and Sustainable Development Goals achievement. The open-source nature of the ontology and its associated tools promotes rapid adoption among developers

Europe's Largest Research Infrastructure for Curated Medical Data Models with Semantic Annotations.

Structural metadata from the majority of clinical studies and routine health care systems is currently not yet available to the scientific community. To provide an overview of available contents in the Portal of Medical Data Models (MDM Portal). The MDM Portal is a registered European information infrastructure for research and health care, and its contents are curated and semantically annotated by medical experts. It enables users to search, view, discuss, and download existing medical data models. The most frequent keyword is "clinical trial" (n = 18,777), and the most frequent disease-specific keyword is "breast neoplasms" (n = 1,943). Most data items are available in English (n = 545,749) and German (n = 109,267). Manually curated semantic annotations are available for 805,308 elements (554,352 items, 58,101 item groups, and 192,855 code list items), which were derived from 25,257 data models. In total, 1,609,225 Unified Medical Language System (UMLS) codes have been assigned, with 66,373 unique UMLS codes. To our knowledge, the MDM Portal constitutes Europe's largest collection of medical data models with semantically annotated elements. As such, it can be used to increase compatibility of medical datasets and can be utilized as a large expert-annotated medical text corpus for natural language processing.

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.

Persistent conference identifiers in CRIS – first implementations with VIVO and URIS

In the context of research information systems (RIS) and research evaluation, academic events are often not recognized as an independent component of the research communication system, but only as an opportunity to publish. In this paper we demonstrate the use of persistent identifiers to enrich RIS with metadata of the events themselves. This allows connecting events to persons (e.g., as speakers or organizers), organizations (e.g. as hosts), or output (talks, keynotes, proceedings etc.). This analysis demonstrates as well the differences and similarities in the metadata structures in order to simplify the integration process and ensure data compatibility. Understanding relations between the key entities involved in managing events is crucial for seamless coordination and effective information flow throughout the event life cycle. The open research information infrastructure ConfIDent is being used as a data source. Target systems are a VIVO-driven discipline-specific RIS on the one hand, and URIS, the national RIS of the Ukraine, on the other hand.

An RFID-Powered Multisensing Fusion Industrial IoT System for Food Quality Assessment and Sensing

The development in Internet of Things (IoT) have empowered revolution in almost all walks of life. Although substantial effort has been made to bring IoT in manufacturing, there is still technical challenges of providing solutions of real-time pervasive multi-sensing, quality evaluation and boundary-less information sharing, which put people at risk from deteriorated food products and food adulteration. This investigation presents an Industrial IoT-based system for food product quality assessment and prediction. This research completes the real-time food quality assessment via radio frequency identification-based multi-sensor fusion for the first time. Also, a novel concept of shelf-life prediction is proposed. The RFID-powered sensors provide a new idea for nondestructive food product and environment sensing. A five-layer architecture considering sensing, controlling, communication, interfaces, and data analysis is highlighted. A novel RFID metadata structure is first proposed to achieve multi-dimensional information traceability and global data sharing. Machine learning-based multi-sensor fusion is proposed to provide accurate quality assessment and prediction. The proposed system is implemented as a smart shelf system to demonstrate its feasibility and advantages. The system is of great significance in improving food safety, reducing food waste and providing powerful information support for food manufacturing line and supply chain management.

A Text Mining Approach to Uncover the Structure of Subject Metadata in the Biodiversity Heritage Library

ABSTRACTWe propose a bottom‐up, data‐driven pipeline to uncover the structure of biodiversity subject metadata using a combination of text mining approaches. In this study, we analyze 721,035 subject terms in the Biodiversity Heritage Library (BHL). We utilize named entity recognition and word‐embedding methods to systematically label and group terms based on their vector‐space distances. The results show that the subject terms from BHL are clustered into several prominent themes relating to environmental regulations, geographic locations, organisms, and subject access points. We hope that our approach can serve as a first step to group similar subject terms together in large‐scale, constant growing digital collections with aggregated metadata from multiple sources. Ultimately, we hope the next phases of this project can become a basis for biodiversity digital libraries to standardize their vocabularies.

Reusing Biomedical Data as Agreed - Towards Structured Metadata for Data Use Agreements.

With increasing availability of reusable biomedical data - from cohort studies to clinical routine data, data re-users face the problem to manage transferred data according to the heterogeneous data use agreements. While structured metadata is addressed in many contexts including informed consent, contracts are to date still unstructured text documents. In particular within collaborative and active working groups the actual usage agreement's regulations are highly relevant for the daily practice - can I share the data with colleagues from the same university or the same research network, can they be stored on a PHD student's laptop, can I store the data for further approved data usage requests? In this article, we inspect and review seven different data usage agreements. We focus on digital data that is copied and transferred to the requester's environment. We identified 24 metadata items in the four main categories data usage, storage, and sharing, as well as publication of results. While the topics are largely overlap in the data use agreements, the actual regulations of the topics are diverse. Although we do not explicitly investigate trusted research environments, where data is offered within an analytics platform, we consider them a as subgroup, where most of the practical questions from the data scientist's perspective also arise. With a limited set of structured metadata items, data scientists could have information about the data use agreement at hand along with the transferred data in an easily accessible way.

Catalyst: Optimizing Cache Management for Large In-memory Key-value Systems

In-memory key-value cache systems, such as Memcached and Redis, are essential in today's data centers. A key mission of such cache systems is to identify the most valuable data for caching. To achieve this, the current system design keeps track of each key-value item's access and attempts to make accurate estimation on its temporal locality. All it aims is to achieve the highest cache hit ratio. However, as cache capacity quickly increases, the overhead of managing metadata for a massive amount of small key-value items rises to an unbearable level. Put it simply, the current fine-grained, heavy-cost approach cannot continue to scale. In this paper, we have performed an experimental study on the scalability challenge of the current key-value cache system design and quantitatively analyzed the inherent issues related to the metadata operations for cache management. We further propose a key-value cache management scheme, called Catalyst , based on a highly efficient metadata structure, which allows us to make effective caching decisions in a scalable way. By offloading non-essential metadata operations to GPU, we can further dedicate the limited CPU and memory resources to the main service operations for improved throughput and latency. We have developed a prototype based on Memcached. Our experimental results show that our scheme can significantly enhance the scalability and improve the cache system performance by a factor of up to 4.3.

FSR: A host-storage collaborative mechanism for data path optimization of NDP operations

Emerging computational SSDs are becoming attractive solutions to boost the energy efficiency and performance of data-intensive applications. Such computational SSDs take advantage of the in-storage Near-Data Processing (NDP) architecture that offloads computing tasks to the storage. However, existing NDP-based applications still depend on the host to search the requested file of NDP tasks, which incurs large overhead for moving data between the host and the storage. In this paper, we propose to optimize the data path of NDP-based applications by a host-storage collaborative mechanism called File Semantics Retriever (FSR). The key idea of FSR is to realize the file system layout and the metadata structures in the storage with the collaboration of a library in the host and a handler in the firmware of the computational SSD. FSR can directly locate and fetch the requested file data in the computational SSD. Moreover, we construct a performance model to understand the suitable scenarios of FSR and the appropriate timing for using FSR. We implement and evaluate FSR on a real computational SSD platform. Extensive experimental results show that FSR can reduce the execution time of real-world NDP operations by 54.0% over existing computational SSDs relying on the host-based data path.

EUSEDcollab: a network of data from European catchments to monitor net soil erosion by water

As a network of researchers we release an open-access database (EUSEDcollab) of water discharge and suspended sediment yield time series records collected in small to medium sized catchments in Europe. EUSEDcollab is compiled to overcome the scarcity of open-access data at relevant spatial scales for studies on runoff, soil loss by water erosion and sediment delivery. Multi-source measurement data from numerous researchers and institutions were harmonised into a common time series and metadata structure. Data reuse is facilitated through accompanying metadata descriptors providing background technical information for each monitoring station setup. Across ten European countries, EUSEDcollab covers over 1600 catchment years of data from 245 catchments at event (11 catchments), daily (22 catchments) and monthly (212 catchments) temporal resolution, and is unique in its focus on small to medium catchment drainage areas (median = 43 km2, min = 0.04 km2, max = 817 km2) with applicability for soil erosion research. We release this database with the aim of uniting people, knowledge and data through the European Union Soil Observatory (EUSO).

ViF-GTAD: A new automotive dataset with ground truth for ADAS/AD development, testing, and validation

A new dataset for automated driving, which is the subject matter of this paper, identifies and addresses a gap in existing similar perception datasets. While most state-of-the-art perception datasets primarily focus on the provision of various onboard sensor measurements along with the semantic information under various driving conditions, the provided information is often insufficient since the object list and position data provided include unknown and time-varying errors. The current paper and the associated dataset describe the first publicly available perception measurement data that include not only the onboard sensor information from the camera, Lidar, and radar with semantically classified objects but also the high-precision ground-truth position measurements enabled by the accurate RTK-assisted GPS localization systems available on both the ego vehicle and the dynamic target objects. This paper provides insight on the capturing of the data, explicitly explaining the metadata structure and the content, as well as the potential application examples where it has been, and can potentially be, applied and implemented in relation to automated driving and environmental perception systems development, testing, and validation.

Incorporating structural topic modeling into short text analysis

Abstract The past few decades have seen the rapid development of topic modeling. So far, research has been more concerned with determining the ideal number of topics or meaningful topic clustering words than with applying topic modeling techniques to evaluate linguistic theories. This study proposes the Structural Topic Model (STM)-led framework to facilitate the interpretation of topic modeling results and standardize text analysis. STM encompasses various model training mechanisms, thereby requiring systematic designs to properly combine language studies. “Structural” in STM refers to the inclusion of metadata structure. Unlike the corpus-based keyness approach, STM can capture contextual cues and meta-information for the interpretation of topical results. Besides, STM can make cross-corpora comparisons via topical contrast, a challenging task for corpus-driven related models such as the Biterm Topic Model (BTM). Stylistic variations in song lyrics are taken as an illustration to show how to use the suggested framework to delve into the linguistic theory proposed by Pennebaker (2013). The topical model and iterable model in the proposed paradigm can clarify how pronouns affect style distinction. We believe the proposed STM-led framework can shed light on text analysis by conducting a reproducible cross-corpora comparison on short texts.

The Co-Poem Project: Didactic Resources and Pedagogical Projection for Musical Education in Primary School

Orally transmitted traditional music constitutes an invaluable intangible legacy for understanding the development of local cultures and observing social dynamics over time. The Co-Poem project aims to make a significant corpus of these musical cultures available to any user in the Mediterranean basin by creating the I-Folk database, which allows access and consultation through semantic technologies and artificial intelligence, to promote knowledge of this cultural reality and perpetuate its intergenerational transmission. The conceptualization of both resources is oriented to the practice of music teaching in primary education. From this point of view, the structure of metadata that musically describes each work and the implementation of access resources to the corpus under study are analyzed from the point of view of the pedagogical function. All these issues are materialized in creation of various educational resources based on the possibilities of the I-Folk database to promote meaningful and interactive learning of musical content (melody, rhythm, dance, etc.) based on the defining characteristics of this type of repertoire. Recent interventions and didactic activities in primary schools show that both the I-Folk database and the associated resources and the search and comparative analysis functionalities between repertoires from different territories constitute a tool highly appreciated by teachers and arouses great interest among students.

A case study on migrating large digital collections into a consolidated repository

On July 1, 2020, the University of South Florida underwent a consolidation to merge three independently accredited branch campuses under a single umbrella. The Libraries responded to this change by consolidating their institutional repositories, which included a combination of theses, dissertations, faculty publications, open access journals, faculty publications, and digital collections. To make the most of this shared platform, administration determined that the Tampa campus would also merge their independent Digital Collections into the shared repository. Beginning in spring 2021, a team from the Tampa campus undertook the task of migrating over 65,000 digital objects across platforms into a newly consolidated institutional repository comprised of both digital scholarship and collections from multiple campuses.In addition to the technical improvements, holistic review of metadata structures, and data cleanup, the review opened the door to implement conscious editing initiatives to improve the language and discoverability of collections representing diverse populations. With the scale of the contents, it was not feasible to manually migrate items one at a time, so a batch method was devised. The original repository included item level metadata via METS/MODS XML. A script was created to scrape, concatenate, and transform the metadata into the Digital Commons batch upload XML standard. Accounting for unique metadata schemas throughout the repository, the batch process needed to be updated and revised on a collection-by-collection basis. This case study discusses the project planning, management and implementation of the migration.

An Analysis of Crosswalks from Research Data Schemas to Schema.org

ABSTRACTThe increased number of data repositories has greatly increased the availability of open data. To enable broad discovery and access to research dataset, some data repositories have begun leveraging the web architecture by embedding structured metadata markup in dataset web landing pages using vocabularies from Schema.org and extensions. This paper aims to examine metadata interoperability for supporting global data discovery. Specifically, the paper reports a survey on which metadata schema has been adopted by participating data repositories, and presents an analysis of crosswalks from fourteen research data schemas to Schema.org. The analysis indicates most descriptive metadata are interoperable among the schemas, the most inconsistent mapping is the rights metadata, and a large gap exists in the structural metadata and controlled vocabularies to specify various property values. The analysis and collated crosswalks can serve as a reference for data repositories when they develop crosswalks from their own schemas to Schema.org, and provide the research data community a benchmark of structured metadata implementation.