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In the past decade, academic libraries have adjusted their approaches to codify their digital collections metadata standards. At the core of these efforts were conscious editing and reparative metadata initiatives that embraced, at varying levels based on the institution, ethical standards that provided inclusive terminology, addressed historic racist and sexist terms by placing them in context with contemporary language, and improved accessibility by using subject-specific thesauri outside the Library of Congress' controlled vocabularies. Dozens of academic, special, and public libraries have published guides to focused on these issues over the past four years. The approach can be time consuming or feel overwhelming as institutions attempt to boil down what the process of conducting conscious editing or reparative metadata work entails, who is qualified to do the work, and how to evaluate the impacts of such work. At the University of South Florida Libraries, our efforts to improve our overall metadata standards incorporated conscious editing as a guiding framework that, over the course of four years, evolved into a multi-departmental effort aimed at the strategic goal of increasing the accessibility of our collections in the broadest definition of the term. [ABSTRACT FROM AUTHOR]

Understanding the reliability and health of critical business assets is essential for every organization. Digital transformation changes users' behaviours and imposes several challenges to organizations that need to collect and use more diversified data to improve digital services, drive decision-making, and spur innovation. Coupled with data growth and diversification, new approaches for analysing data rely on the schema-on-read approach, prioritizing data collection over data organization. Thus, knowing where data is stored, and how can be used, improves the capability to respond to business changes and thus helps to take better actions. To enable the creation, verification, and upkeep of dashboards in the analytical system, this work proposes a measure data catalog used to support the management and validation of metadata. [ABSTRACT FROM AUTHOR]

• Application of metadata and ontology technology in the information management of ancient Chinese ceramics • Construction of metadata schema and ontology for ancient Chinese ceramics. • Use the collections in the Nankai University Museum as research samples and practical cases. • Visualization of the structure of ontology in terms of the Kang Xi Polychrome Cup with Winter Jasmine in the Nankai University Museum. In the era of digital information, metadata, and ontology technology have increasingly promoted the information management and utilization of museum collections. As one of the most important collection categories in Chinese and even the world's historical museums, the information management of ancient Chinese ceramics has made some progress, but there are still some shortcomings as well. Based on the current need for the information management and utilization of ancient Chinese ceramics, the article used the ceramics in the Nankai University Museum as research samples, constructing the Metadata of Ancient Chinese Ceramics through the comprehensive analysis, integration, and improvement of existing metadata. Moreover, based on that, the study reused and expanded the CIDOC CRM to establish "the Ontology of Ancient Chinese Ceramics" through Protégé, with 20 pieces of ceramics in the Nankai University Museum being added as instances. Last but not least, the study took the Kang Xi Polychrome Cup with Winter Jasmine as an instance and presented its knowledge map through visualization to demonstrate the structure of "the Ontology of Ancient Chinese Ceramics" more clearly. [ABSTRACT FROM AUTHOR]

DNA is a more efficient and long-lasting data storage method. It offers better compression, higher physical density, longer stability, and lower energetic cost than traditional digital methods. Metadata should be embedded in the DNA sequence. Biocybersecurity must be prioritized to prevent attacks with synthetic DNA that could encode malware triggered during DNA sequence analysis. Standardization of the coding and decoding process is necessary for long-term accessibility. Errors in methodology can lead to data loss, emphasizing the need for a reliable protocol for handling samples. Inclusion of identifying features in the DNA is crucial for future recognition of it as data storage. Markers or sequences can indicate that the DNA is synthetic, not biological. This ensures that future generations can recognize and access the stored data even if knowledge of DNA data storage is lost. DNA is an intelligent data storage medium due to its stability and high density. It has been used by nature for over 3.5 billion years. Compared with traditional methods, DNA offers better compression and physical density. DNA can retain information for thousands of years. However, challenges exist in scalability, standardization, metadata gathering, biocybersecurity, and specialized tools. Addressing these challenges is crucial for widespread implementation. Collaboration among experts, as well as keeping the future in mind, is needed to unlock the full potential of DNA data storage, which promises low energy costs, high-density storage, and long-term stability. [ABSTRACT FROM AUTHOR]

Heliophysics and space weather research encompass the effects of solar output on practically the entire Solar System and are fundamentally cross-disciplinary. Cross-domain science investigations, such as in Sun-heliosphere interactions, solar wind-magnetosphere interactions, or magnetosphere-ionosphere coupling, often require the use of data, models, and other digital resources pertaining to different heliophysical domains: the Sun, the solar wind, the magnetosphere, the ionosphere, the thermosphere and the mesosphere. Due to differences in measurement platforms, techniques and instruments, heliophysics data obtained from different domains are diverse and complex, making the resource landscape difficult for untrained users to navigate. Without proper and adequate guidance from domain experts, it is often difficult for early-career scientists and non-domain experts to discover useful datasets and to know from where and how to obtain and understand the data they need to support their research. This paper describes the roles of metadata in providing the identification, location, access protocol, and detailed content description of a digital resource. More specifically, we point out that metadata written according to the Space Physics Archive Search and Extract (SPASE) metadata model are fully compatible with the FAIR principles so that digital resources described using the SPASE model can be uniformly Findable, Accessible, Interoperable, and Reusable. SPASE metadata can thus be the key element, the lingua franca so to speak, that enables unfettered information flow between data systems and services throughout the heliophysics data environment and lowers the understandability barrier of the resources to ensure their independent usability. After describing various components of the heliophysics data environment, their metadata requirements for effective operations, and some essential features of the SPASE metadata model, we then illustrate how metadata in SPASE can enable or facilitate the performance of different science tasks. The current status and future outlook of SPASE are also presented. [ABSTRACT FROM AUTHOR]

Precipitable water vapor (PWV) retrieved from Global Navigation Satellite Systems (GNSS) observations (GNSS-PWV) subjects to non-climatic changepoints (NCCs), mainly due to hardware (e.g., antenna and receiver) changes and geological events, while only the former is documented in station metadata. This study proposed a new strategy to verify the origins of the undocumented NCCs in GNSS-PWV and correct the inhomogeneity based on ERA5 and coordinate time series of GNSS stations. The new strategy initially applies a zero-difference method to detect changepoints in GNSS coordinate time series (coordinate changepoints) and undocumented NCCs in GNSS-PWV with the aid of PWV derived from ERA5 (ERA-PWV) and station metadata. The coordinate changepoints are treated as ancillary to verify the origins of the undocumented NCCs related to geological events in GNSS-PWV since coordinates are also influenced by geological events. After the verification, shifts and directions of all NCCs were robustly estimated and corrected for a final homogenization. The strategy was applied in the GNSS-PWV at 207 stations during 2008–2018 in China. Results showed that among 193 NCCs detected in 110 inhomogeneous GNSS-PWV, only 5 ones (3% of NCCs) were documented in metadata, and 44 ones (23% of NCCs) were related to geological events, indicating the good performance of the new strategy on the identification of the undocumented NCCs. After the homogenization, the RMS and absolute bias between GNSS-PWV and ERA-PWV were decreased by 5% and 22%, and the correlation coefficient of their linear trends was improved to 0.98 from 0.75. [ABSTRACT FROM AUTHOR]

Accurately orientated and matched images and light detection and ranging (LiDAR) point clouds are becoming the new standard for many applications in geomatics. Large data volumes and different observation types make scalability and efficient optimization challenging, particularly for matching based on variants of the iterative closest point algorithm. Here, we present a method to address this by temporally segmenting the pre-processed global navigation satellite system/inertial navigation system solution and representing LiDAR data as voxels with metadata. The efficiency, accuracy, and scalability of different voxel sizes have been assessed in this study. Hybrid adjustment of image tie-points and voxels from LiDAR data optimized the trajectory and sensor corrections for both LiDAR scanners and cameras in the presented experiment. A little over one billion LiDAR points and ∼ 3000 images were matched in < 1. 5 h and showed high accuracy in the range of a few millimeters. This accuracy is comparable to the existing methods for hybrid image and LiDAR adjustment. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

[Display omitted] Machine learning (ML) is widely used to uncover structure–property relationships of materials due to its ability to quickly find potential data patterns and make accurate predictions. However, like alchemists, materials scientists are plagued by time-consuming and labor-intensive experiments to build high-accuracy ML models. Here, we propose an automatic modeling method based on meta-learning for materials property prediction named Auto-MatRegressor, which automates algorithm selection and hyperparameter optimization by learning from previous modeling experience, i.e., meta-data on historical datasets. The meta-data used in this work consists of 27 meta-features that characterize the datasets and the prediction performances of 18 algorithms commonly used in materials science. To recommend optimal algorithms, a collaborative meta-learning method embedded with domain knowledge quantified by a materials categories tree is designed. Experiments on 60 datasets show that compared with the traditional modeling method from scratch, Auto-MatRegressor automatically selects appropriate algorithms at lower computational cost, which accelerates constructing ML models with good prediction accuracy. Auto-MatRegressor supports dynamic expansion of meta-data with the increase of the number of materials datasets and other required algorithms and can be applied to any ML materials discovery and design task. [ABSTRACT FROM AUTHOR]

Recognizing skin cancer in time could greatly increase patients' chances of recovery. For this reason, in recent years, numerous decision support systems have been proposed to help dermatologists in this diagnosis. These systems are generally based on Convolutional Neural Networks and are used for both segmentation and classification of lesions. Although their main goal is to correctly recognize the lesions' type, the preliminary segmentation step has been shown to increase the performance of the classifier. In fact, this is not surprising because physicians also use information on the shape of the lesion to make a diagnosis. Thanks to the ISIC archive, a huge number of skin lesion images, along with the corresponding metadata (type, position, dimension, etc.), are publicly available to train a deep neural network, but, unfortunately, only a small fraction of them are labeled for segmentation. To overcome this limitation, in this paper, a weak supervised approach is proposed to extract the segmentation label maps from the entire ISIC archive. Moreover, to demonstrate the quality of the proposed approach, the generated supervisions were first compared with those available in ISIC and, then, used to train a segmentation network, whose performance was evaluated against that obtained using only the small set of ISIC label maps. To foster reproducibility and to promote future research in lesion segmentation and classification, the generated ISIC Weak Segmentation Map (ISIC_WSM) dataset has been released. As far as we know, this is the first dataset that contains segmentation supervisions for clinical images of skin lesions. [ABSTRACT FROM AUTHOR]

In today's world, 2.5 exabytes of data are generated and processed by the IT industry and different organizations. Processing and managing such a massive volume of Big data is challenging. Hadoop(HDFS) is widely used framework for processing Bigdata. The limitation of HDFS is it can scale only the datanodes and not namenode where the metadata is managed. There is no scaling of name nodes in the present Hadoop architecture, so managing the metadata of the exabyte scale becomes an essential and challenging leading to tightly coupled block storage, namespace scalability, and performance bottleneck issues. The proposed approach dynamic federated metadata management (DFMM) architecture manages the metadata in the name node. DFMM configures multiple name nodes using HDFS federation technique, and metadata is partitioned into numerous name nodes using sharding technique. Sharding manages the metadata using locality-preserving hashing and consistent hashing methods. Locality-preserving hashing stores metadata in the most appropriate name node using the file's parent directory, and metadata is dynamically distributed among Name nodes to maintain load balancing. DFMM also maintains replicas for the high availability and reliability of data. Results showcase the superiority of the proposed DFMM architecture combined with the sharding technique is highly scalable and manages metadata efficiently, and can store up to 1 billion files with a namespace size of 400 GB, enhancing the throughput by 21% and is highly scalable as compared to existing techniques. The comparative study analysis of existing HDFS, and proposed DFMM architecture combined with sharding enhances the performance and manages the metadata more efficiently. [ABSTRACT FROM AUTHOR]

Ensuring the quality of information is a critical ethical issue for any information system. Research Information Management Systems (RIMSs) need to engage researchers in sharing research information and knowledge, and ensuring its quality. This paper introduces a theoretical framework for researcher participation in RIMSs. The framework is grounded in empirical research and can guide the design of RIMSs by defining typologies of researcher activities in RIMSs, related motivations, levels of participation, and metadata profiles. In addition, the framework defines discipline- and seniority-specific priorities for the researcher's activities and motivations. RIMS managers and scholarly communications librarians can use the framework to assemble RIMS service and metadata profiles that are tailored to the researcher's context. Likewise, the framework can guide the construction of communication messages personalized to the researcher's priorities and her or his motivations for engaging in a specific activity, which will enhance the researcher's engagement with the RIMS. [ABSTRACT FROM AUTHOR]

Abstract Graduate students at the University of Manitoba were surveyed to find out if they used reference management software (RMS), features used, challenges and barriers to using RMS. Interest in different types of PDF management features and training options were also investigated. Both users and non-users of reference management software were invited to participate. Non-users managed their citations and references with a variety of other tools. The principal reasons for non-use were that students were not aware of options that were available, and the amount of time needed to learn the program. RMS users also mentioned the steep learning curve, problems with extracting metadata from PDFs, technical issues, and problems with inaccurate citation styles. Most of the students saved PDF documents to their computer. Students were most interested in full-text searching of PDFs, automatic renaming of PDFs, and automatically extracting citation metadata from a PDF. PDF annotation and reading tools were also of some interest. Mobile features were of the least interest. There were no statistically significant differences in the interest of PDF management features between the user and non-user groups but there were statistically significant differences in the interest of some of the training options between the groups. [ABSTRACT FROM AUTHOR]

The Walter Havighurst Special Collections from University Archives & Preservation at Miami University's King Library has a growing collection of over 600,000 historical postcards, with approximately 30,000 digitized, primarily from the Midwest during 1890–1919. This collection supports various lines of inquiry from users, such as analyzing the evolution of gender portrayal in popular media in the United States. However, manually separating the collection into postcards of males and females would take thousands of hours, which prevents the library from supporting sociological analyses at scale. After assembling an open postcard dataset, we trained deep neural networks (i.e., YOLOv5x object detection models) to automatically detect people and classify them as male or female. Our approach limited biases in favor of one outcome by balancing the number of males and females via multi-label stratified 10-fold cross-validation. We showed that this approach can accurately detect and classify females and confidently detect and label males for the library's collection of historical postcards. Our precision of 94.9 % and recall of 33.0 % from 1890 to 1919 on male gender detection exceed the performances of 94.7 % and 31 % respectively for recognition on World War I postcards in past studies. By employing our trained deep neural networks, the library can enhance its metadata within hours and support new research inquiries at scale. • Users lack experience to navigate the unique materials hosted in online special collections, such as historical postcards. • Special collections are increasingly releasing uncatalogued materials online, which provides even less metadata for users. • Machine learning has emerged as a potential solution to derive additional metadata and support more detailed user queries. • Prior research examined the use of text mining (e.g., for historical novels), but there is little work using computer vision. • We create computer vision models that accurately extract gender from 28,308 postcards in special collections in 4.28 h. [ABSTRACT FROM AUTHOR]

The purpose of this practical case study is to examine the development of Dataverse, a global research data management consortium. This paper is the second in a project focusing on data discoverability and current metadata implementation on the Dataverse portals established by 27 university libraries worldwide. Five research questions were proposed to identify the most popular metadata standards and elements, search interface options, and result display formats by those portals. The data were collected from 27 university libraries worldwide between December 1, 2020 and January 31, 2021. According to the results of the descriptive analyses, the most popular metadata elements for the dataset overview were Subject and Description , while Dataset persistent ID , Publication Date , Title, Author , Contact , Deposit Date , Depositor , Description , and Subject were the most popular elements for the metadata record of each dataset. Publication Year , Author Names , and Subject were found to be the most common search facets used by the portals. English was the most common language used for the search interfaces and metadata descriptions. Based on their findings from this evidence-based study, the authors recommend future research on the development of institutional data portal infrastructure, on stakeholder outreach and training, and on user studies on dataset retrieval. [ABSTRACT FROM AUTHOR]

Otto Neurath's role in the so-called protocol sentence debates is typically framed as primarily an epistemologically radical rejection of empiricist foundationalism. However, less well recognized is that from this debate, Neurath emerges with a conception of protocol statements that functions as a radical reconceptualization of evidence. Whilst recognizably still empiricist, Neurath's conception of evidence breaks with many of the key assumptions that predominate within the empiricist tradition. In rejecting the assumption of an epistemologically privileged relationship between an observer and their own observation reports, Neurath shifts the emphasis onto the importance of contextualizing information that guarantees the stability of observation reports. In so doing, he not only provides a conception of evidence better suited to the actual role of evidence in science, but also anticipates contemporary discussion of the importance of evidential metadata. [ABSTRACT FROM AUTHOR]

• A novel taxonomy of studies that employ Natural Language Processing in tourism research. • A compendium of popular techniques, data sources, and main languages analyzed in the literature. • Analysis of studies and provenance of the first author of manuscripts. • Tables summarizing the most interesting works in the study. • Analysis of the opportunity areas and open challenges that could open new research lines. The social networks and the rapid development of new technologies have led to considerable changes in the tourism industry. Artificial intelligence, in particular natural language processing (NLP), presupposes a significant advantage in obtaining information on the mass content generated by online users concerning tourism services and products. This work presents a systematic review of the use of NLP in the tourism industry and research. We used the well-known PRISMA methodology, and 227 relevant studies over the last decade have been reviewed. Our analysis identified the main methodologies, tools, data sources, and other relevant features in the field. One of the principal contributions of this study is a taxonomy for using NLP in tourism. In addition, metadata were examined using a threefold approach: (i) general statistics, (ii) abstract text analysis, and (iii) keyword networks. Automatic analyses have identified six major topics in applying NLP to tourism issues and have shown that China, the United States, Thailand, and Spain share similar tourism issues or approaches. [ABSTRACT FROM AUTHOR]

The tremendous growth in information over the last decade leads to information overwhelming problems for accessing personalized products. The recommender framework that retrieves user preferences on past interactions is known as collaborative filtering (CF). Although, CF is a prevalent technique amongst the techniques applied in the recommender environment. However, it suffers from many problems like information sparsity, scalability, cold-start, etc. Many investigations have been made to tackle some of these issues with the help of matrix factorization (MF) approaches. However, MF cannot handle the nonlinearity among the data. Deep learning is an advanced learning technique that has shown success in many applications such as image classification, computer vision, natural language processing, etc. Little work has been reported on deep learning techniques in the recommender domain. We propose an efficient deep collaborative recommender system that embeds item metadata to handle the nonlinearity in data and sparsity. The model consists of two stages, wherein the first stage, a neural network, is used to retrieve the data's nonlinear features through embedding vectors. These vectors are concatenated together and fed as input to the second stage of the model. The output of the model yields a partially observed rating. The input and the parameters are simultaneously optimized and updated to minimize errors. The proposed strategy is evaluated against the benchmark techniques on two well-known datasets. The exploratory outcomes signify our approach's exactitude and efficiency. Moreover, the missing values can also be recovered by propagating the embedding vectors from the input to the output layers. [ABSTRACT FROM AUTHOR]

The locality of metadata is a critical parameter of data storage and retrieval performance for an ultra-large distributed storage system. The data of an ultra-large distributed storage system is accessed through its distributed metadata servers (MDSs). The distributed MDSs store and cache partial metadata for scalability and performance. The MDSs are connected through varied latencies multipath network connections of using various metadata distribution techniques. The locality of MDS in the metadata distribution techniques improves the metadata query routing and enhances the performance of the distributed storage system. Motivated by this insight, this work introduces an intelligent locality-aware metadata management technique based on recommendation system that exploits MDS path ratings in a globally distributed ultra-large storage system and proposes a novel locality-aware metadata query routing algorithm. The proposed metadata server locality aware employs collaborative filtering with Stacked AutoEncoder to address the sparsity of the path rating matrix and its overfitting issue. The extensive experiments on real datasets show that the proposed metadata server locality aware technique enhances the locality of metadata operations and improves aggregate operations throughput by 12% to 21% of contemporary distributed metadata management techniques in ultra-large distributed storage systems. [ABSTRACT FROM AUTHOR]

As a foundational technology of the information age, blockchain is an important technology pushing society towards using decentralized systems and peer-to-peer interactions. The emergence of non-fungible tokens (NFTs) is a prominent phenomenon of this era, which aims to revolutionize the ownership and trading of certain assets regarding their characteristics, such as being unique, devoted to special collections, and so on. The development of NFTs as a new asset class is accelerating, and they are expected to become an integral part of the human digital life experience. Throughout the paper, we introduce Dynamic Non-Fungible Tokens (dNFTs) as a new type of NFTs, which could be a paradigm-shifting experience for digital asset ownership. We establish a framework to lighten the future of using dynamic NFTs in different areas and a better understanding of the challenges and prospects in the journey of evolving this technology. [ABSTRACT FROM AUTHOR]

The concept of data lake was proposed more than a decade ago. Although progress has been made in data lake research and applications, there are also numerous issues and challenges need to be addressed. In this paper, we survey some recent developments, provide our observations, as well as our expectations on future research and practice in this area. We start with a discussion on general terminology in data lakes, then review various aspects of data lakes, including an examination on metadata and related issues, information granularities involved in data lakes, unique features such as mixed lazy and eager approach in data lake lifecycle, relationship between data lakes and data mining, interplay between data lakes and data warehousing techniques, as well as some notable developments related to applications of data lakes. We present our observations while we examine these aspects, and wrap up this paper by expressing our hope of developing a unified framework for data lake lifecycle evaluation. [ABSTRACT FROM AUTHOR]

Membrane capacitive deionization (MCDI) is an emerging technology for water purification and desalination. Artificial intelligence (AI) techniques using machine learning (ML) and deep learning (DL) have been widely applied to overcome the drawbacks of conventional numerical models in predicting effluent concentrations, an important parameter to estimate the energy consumption and degree of desalination of the MCDI process. However, despite the remarkable progress of these ML/DL models, relevant studies have only been conducted with limited datasets, leading to a substantial controversy regarding model accuracy. Therefore, in this study, we comprehensively evaluated the performances and applicability of multiple ML/DL models for MCDI using metadata (i.e. , performance indices) and feature importance analyses. One MCDI dataset containing both constant-current and constant-voltage operations was reconfigured uniformly or unevenly depending on operation modes. Subsequently, six ML models and one DL model were used to execute the analyses using performance indices. Collectively, this study suggests that a perfect ML/DL model for the water treatment process may not exist, and understanding data types is as important as selecting appropriate models to build a data-driven AI model when process performance is greatly affected by specific input variables (i.e. , current and voltage). [Display omitted] • Machine/deep learning models predicted membrane capacitive deionization effluent. • Operating datasets were reconfigured as homogenous and heterogeneous types. • Machine/deep models were evaluated by three performance indices (metadata). • Metadata and feature importance were analyzed using statistical tools. • Results reveal that data configurations are as important as prediction model types. [ABSTRACT FROM AUTHOR]

Public access to well-organized migration data repositories is rare. While open migration data are available, most are dispersed, challenging their organization. This work uses a metadata modeling approach to organize migration-related data and research components hosted in an open-source, collaborative research environment. The proposed platform integrates data, computational models, application libraries, and research projects in a unified environment for research. An overview of the metadata modeling process and the XML technology used to integrate the platform components was provided, along with descriptions of the platform's essential features, such as standardized exchange of information for data organization and sharing and collaborative authoring. The design of a prototype collaborative, open-source migration data and modeling research platform using sample data and software components was built and hosted on GitHub for testing and refinement purposes. It allows the authors to collaborate, improve, and assess the platform's functionalities and capabilities. Our platform, COSMOS (Collaborative Open-Source Modeling System), adopted the creation and use of open data formats for alignment with FAIR (Findable, Accessible, Interoperable, and Reusable) data principles to ensure that data at the prototype level is consistent with and can be used by the international migration research community in the future. By providing a standardized framework for data organization, data sharing, and collaboration, the platform can help accelerate scientific discovery and advance our understanding of complex social and cultural phenomena such as immigration. [ABSTRACT FROM AUTHOR]

Energy performance indices are widely used methods for determining and assessing the efficiency of buildings in operation. However, methodologies for indices that compare heat consumption among buildings and at higher temporal granularity than yearly are needed. This makes it possible for consumers to compare themselves to others to encourage them to improve their energy efficiency. For a district heating system, it also provides the district heating company with an overview of consumers' energy efficiency (and consumers' efficiency must be comparable). This is critical due to the interconnectedness of district heating systems, as consumers can indirectly have negative impacts on each other's operations. Having an overview of the efficiency of consumers can provide useful information for the district heating companies to detect unusual consumption and possible faults. This paper therefore has created a framework for the comparison of heat consumption among buildings, by taking into account various contextualities to make comparisons possible. The framework has been tested on a case study, and its capabilities are showcased. • Leveraging building metadata to allow energy consumption comparison among buildings. • Creation of heating consumption performance indexes for use by end-consumers. • Enabling of higher temporal resolution performance indexes increasing usability. • Fault detection based on performance indexes. • Showcasing of methodology on real consumption data from a suburb in Denmark. [ABSTRACT FROM AUTHOR]

Crowdsourcing is an essential data collection method for psychological research. Concerns about the validity and quality of crowdsourced data persist, however. A recent documented increase in the number of invalid responses within crowdsourced data has highlighted the need for quality control measures. Although a number of approaches are recommended, few have been empirically evaluated. The present study evaluated a Cyborg Method that used automated evaluation of participant meta-data and a review of short answer responses. Two samples were recruited – in the first, the Cyborg Method was applied after data collection to gauge the extent to which invalid responses were collected when a priori quality controls were absent. In the second, the Cyborg Method was applied during data collection to determine if the method would proactively screen invalid responses. Results suggested that Cyborg Method identified a substantial portion of invalid responses and both automated and human evaluation components w necessary. Furthermore, the Cyborg Method could be applied proactively to screen invalid responses and substantially reduced the per participant cost of data collection. These results suggest that the Cyborg Method is a promising means by which to collect high quality crowdsourced data. • A substantial portion of crowdsourced data will contain responses that are invalid. • Automated evaluation of a user's IP address can identify a portion of invalid responses. • Reviewing responses to short answer questions can also identify a portion of invalid responses. • IP evaluation and short answer review, a Cyborg Method, are needed to identify valid responses. • The Cyborg Method can be applied proactively to reduce study costs. [ABSTRACT FROM AUTHOR]

Bibliographic references, typically comprising author names, journal titles, paper titles, and publication dates, play a vital role in academic research. Accurately identifying these structured pieces of information from references is a crucial step in developing intelligent bibliographic management systems. However, existing methods often rely on extensive high-quality training data. To mitigate the reliance on extensive training data, we propose a method that integrates prompt learning and contrastive learning for extracting structured information from bibliographic references, named CONT_Prompt_ParseRef. This approach aims to utilize contrastive learning to deepen the understanding of different metadata label types and employ prompt learning to provide specific guidelines for processing and recognition. We constructed a dataset comprising 12,000 samples, available in both Chinese and English versions. The experimental results on this bilingual dataset demonstrate the model's superior performance over existing techniques. Notably, CONT_Prompt_ParseRef shows remarkable robustness in low-resource environments, particularly in scenarios with limited training data, both contrastive and prompt learning play pivotal roles in label extraction from bibliographic references. The ablation study illustrates that omitting either component leads to a decline in performance, with contrastive learning being slightly more influential. [ABSTRACT FROM AUTHOR]

Requirement engineering (RE), a systematic process of eliciting, defining, analyzing, and managing requirements, is a vital phase in systems engineering. In RE, requirement traceability establishes the relationship between the artifacts and supports requirement validation, change management, and impact analysis. Establishing requirement traceability is challenging, especially in the early stages of a complex system design, as requirements constantly evolve and change. Moreover, the involvement of distributed stakeholders in system development introduces collaboration and trust issues. This paper outlines a novel blockchain-based requirement traceability framework that includes a data acquisition template and graph-based visualization. The template enables dual-level traceability (artifact and object) in the RE processes. The traceability information acquired through the templates is stored in the blockchain, where traces are embedded in blocks' metadata and data. Furthermore, the blockchain is represented as a Neo4J property graph where traces can be retrieved using Cypher queries, thus enabling a mechanism to query and examine the history of requirements. The framework's efficacy is showcased by documenting the RE process of an autonomous automotive system. Our results indicated that the framework can record the history of artifacts with constantly changing requirements and can yield secure decentralized ledgers of requirement artifacts. The proposed distributed traceability framework has shown promise to enhance stakeholder collaboration and trust. However, additional user studies should be conducted to bolster our results. [ABSTRACT FROM AUTHOR]

For many years, data quality is among the most commonly discussed issue in Linked Open Data (LOD) due to the huge volume of integrated datasets that are usually heterogeneous. Several ontology-based approaches dealing with quality problems have been proposed. However, when datasets lack a well-defined schema, these approaches become ineffective because of the lack of metadata. Moreover, the detection of quality problems based on an analysis between RDF (Resource Description Framework) triples without requiring ontology statistical and semantical information is not addressed. Keeping in mind that ontologies are not always available and they may be incomplete or misused. In this paper, a novel free-ontology process called LODQuMa is proposed to assess and improve the quality of LOD. It is mainly based on profiling statistics, synonym relationships between predicates, QVCs (Quality Verification Cases), and SPARQL (SPARQL Protocol and RDF Query Language) query templates. Experiments on the DBpedia dataset demonstrate that the proposed process is effective for increasing the intrinsic quality dimensions, resulting in correct and compact datasets. [ABSTRACT FROM AUTHOR]

Harvested metadata on research objects can include links between the primary domain objects such as organizational identifiers associated with dataset, persons identified with ORCIDs linked to publications and publications connected through ISSNs to publishing channels. This kind of linkage is the bread-and-butter of the CRIS systems and usually comprehensively maintained. When it comes to the more subjective description of a domain object, such as keywords, themes, or subject headings, the issues related to data management and modeling become prominent with challenges such as flexibility of free text keywords as opposed to authoritative, but rigid classification systems. Many CRIS objects also already contain an extensive description of the content, just meant for human consumption, in the form of an abstract or similar summary text.With the help of automated data mining and annotation tools, these textual representations can be processed into structured data. This paper presents the processing pipelines implemented as part of the research.fi portal for automatic linking of different research inputs based on automatically extracted ontology concepts and discusses the implications of utilizing them as part of the research.fi platform. But more than simply discussing the annotation of research objects and the creation of word clusters for representation of the semantic content of research objects, we also discuss challenges related to maintaining the automatically produced metadata [ABSTRACT FROM AUTHOR]

Development and implementation of Current Research Information Systems (CRIS) is one of the most transparent and practical approaches to curate research information on a national level. The process of building and implementing such systems is a complex and time consuming where successful results heavily depend on the established research information infrastructure of a country, the interoperability of the systems and the quality of the information which reside in them. The purpose of this paper is to analyse the existing Ukrainian Research Information Infrastructure and identify which databases could be reused and integrated with a national Ukrainian Current Research Information System (URIS). The analysis showed that there are functional databases and registries that collect data on research activities and could be used as a data sources for the URIS. In particular, the Unified State Electronic Database on Education is a potential data source on higher educational institutions, the National Repository of Academic Texts — on metadata on research output, internal database of the National Research Foundation of Ukraine and database on research projects maintained by Ukrainian Institute of Scientific Technical and Economic Information — on projects. Secondly, it was identified that Ukrainian research infrastructure lacks complete, up-to-date registry on researchers. Finally, we discussed the challenges and solutions for further steps in building national CRIS. [ABSTRACT FROM AUTHOR]

Over the last few years, Living Labs (LL) have emerged as resilient research and innovation infrastructures to facilitate and promote activities, which can deal with the complexity of health and wellbeing research. Such systems integrate multiple types of data, including personal info or generated by ICT tools and devices (i.e., wearable sensors). Healthcare data models have the ability to integrate such data and focus on relevant content that will follow a specific data representation template that can be extended based on the needs of each LL. In this study, we propose a reference metamodel based on existing schemas to improve the efficiency of the LL research by pursuing a labor-intensive workflow with a hierarchical structure that simplifies the transformation process and minimizes data variation. OmH library has been used as the basis of the metamodel along with other standards in healthcare to design a complete and straightforward data reference model for LL. The presented data model has been designed within the Vitalise (H2020-INFRAIA 2021-2024) project and visualizes representations of the collected data elements and their relations. The main purpose is to provide an extensible data model that: 1) harmonizes representation formats of the information exchanged that will be used from the LL, 2) empowers other LL to develop solutions that adhere to this common definition and 3) provides a shared, common schema. The designed model will precisely represent and handle data from different devices or platforms (i.e., heart rate, BMI) and efficiently describe the collected dataset to produce meaningful metadata. [ABSTRACT FROM AUTHOR]

In view of the current demand of heterogeneous, cross domain, many service platforms, wide distribution and low-cost resource sharing platform construction of agricultural public welfare information service resources, this paper puts forward a set of standardized and orderly data acquisition and interpretation sharing methods. Methods combined with the characteristics of multi domain resource sharing, metadata semantic modeling based on cognition is proposed to establish the mapping between metadata cognition and metadata semantics to resource description model, build a basic framework of resource sharing, extract the minimum core metadata required for resource sharing, define the basic rules for the existence of resource sharing data, and give the resource sharing description model. At the same time, in order to clearly explain the semantic annotation information in the model, the construction method of shared resource semantics is introduced. In order to reduce the difficulty of resource description, sharing and publishing on heterogeneous platforms, a shared resource description tool based on "resource sharing semantic description model" is introduced. This method combines the advantages of existing methods. At the same time, through the production of description tools and the introduction of semantics, the development difficulty and development volume of each service platform are reduced, the development cost is saved, the data sharing efficiency is improved, and the requirements of low-cost, cross domain, light dependence, resource readability and machine solvability for the construction of agricultural public welfare information service resource sharing platform are better solved. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Current reconstruction of 3D images from DICOM (Digital Imaging and Communications in Medicine) files requires strict supervision for the reconstructed images to have the same metadata including slice thickness, spacing between slices, and image resolution. We propose an algorithm for reconstructing 3D images based on medical images in the DICOM format with varied metadata and resizing the 3D images while preserving the annotations. The 3D image resizing may facilitate processing because most current systems cannot handle the huge 3D image data sizes. After resizing the 3D images, the original annotations that can be used as ground truths to train and evaluate machine learning method are preserved by projection. Experimental results show that the proposed method can handle various DICOM files and correctly project annotations onto the resized image. [ABSTRACT FROM AUTHOR]

Recent advancements in data engineering, data science, and secure cloud storage can transform the current state of global Chemistry, Manufacturing, and Controls (CMC) regulatory activities to automated online digital processes. Modernizing regulatory activities will facilitate simultaneous global submissions and concurrent collaborative reviews, significantly reducing global licensing timelines and variability in globally registered product details. This article describes advancements made within the pharmaceutical industry from theoretical concepts to utilization of structured content and data in CMC submissions. The term Structured Content and Data Management (SCDM) outlines the end-to-end scientific data lifecycle from capture in source systems, aggregation into a consolidated repository, and transformation into semantically structured blocks with metadata defining relationships between scientific data and business contexts. Automation of regulatory authoring (termed Structured Content Authoring) is feasible because SCDM makes data both human and machine readable. It will offer health authorities access to the digital data beyond the current standard of PDF documents and, for a review process, SCDM would "enrich the effectiveness, efficiency, and consistency of regulatory quality oversight" (Yu et al., 2019). SCDM is a novel solution for content and data management in regulatory submissions and can enable faster access to critical therapies worldwide. [ABSTRACT FROM AUTHOR]

The paper describes a research on collecting user requirements from international users in order to develop a document repository based on metadata, for collecting, storing, searching and analysing relevant cultural and socioeconomic documents. This research is a part of wider research within the project called MESOC that aims to propose, test and validate an innovative and original approach to measure the societal value and impact of culture, cultural policies and cultural practices. The main contributions of this paper are: a) a set of user requirements in the cultural domain collected from international users, b) a data model and database for the document repository, and c) a metadata model used to satisfy the user requirements. [ABSTRACT FROM AUTHOR]

Conventional approaches to developing biomaterials and implants require intuitive tailoring of manufacturing protocols and biocompatibility assessment. This leads to longer development cycles, and high costs. To meet existing and unmet clinical needs, it is critical to accelerate the production of implantable biomaterials, implants and biomedical devices. Building on the Materials Genome Initiative, we define the concept 'biomaterialomics' as the integration of multi-omics data and high-dimensional analysis with artificial intelligence (AI) tools throughout the entire pipeline of biomaterials development. The Data Science-driven approach is envisioned to bring together on a single platform, the computational tools, databases, experimental methods, machine learning, and advanced manufacturing (e.g., 3D printing) to develop the fourth-generation biomaterials and implants, whose clinical performance will be predicted using 'digital twins'. While analysing the key elements of the concept of 'biomaterialomics', significant emphasis has been put forward to effectively utilize high-throughput biocompatibility data together with multiscale physics-based models, E-platform/online databases of clinical studies, data science approaches, including metadata management, AI/ Machine Learning (ML) algorithms and uncertainty predictions. Such integrated formulation will allow one to adopt cross-disciplinary approaches to establish processing-structure-property (PSP) linkages. A few published studies from the lead author's research group serve as representative examples to illustrate the formulation and relevance of the 'Biomaterialomics' approaches for three emerging research themes, i.e. patient-specific implants, additive manufacturing, and bioelectronic medicine. The increased adaptability of AI/ML tools in biomaterials science along with the training of the next generation researchers in data science are strongly recommended. This leading opinion review paper emphasizes the need to integrate the concepts and algorithms of the data science with biomaterials science. Also, this paper emphasizes the need to establish a mathematically rigorous cross-disciplinary framework that will allow a systematic quantitative exploration and curation of critical biomaterials knowledge needed to drive objectively the innovation efforts within a suitable uncertainty quantification framework, as embodied in 'biomaterialomics' concept, which integrates multi-omics data and high-dimensional analysis with artificial intelligence (AI) tools, like machine learning. The formulation of this approach has been demonstrated for patient-specific implants, additive manufacturing, and bioelectronic medicine. [Display omitted] [ABSTRACT FROM AUTHOR]

The rapid development of nutrition science is embracing digital transformation to generate large amounts of data. Precision nutrition and "Big Data" place increasing demand for data repositories and visualization, which enhances the digital transformation. We defined the need for an integrated nutrition data platform as a web-based platform that can collect, store, track, analyze, monitor, and visually display key metrics in nutrition and health while allowing users to interact with visuals and download data provided in the platform. Interactive dashboards create new opportunities for scholars and practitioners to generate and test hypotheses. We present the development and implementation of the Global Nutrition and Health Atlas (GNHA; https://sites.tufts.edu/gnha/), an open-access online platform covering nutrition and health data with 26 themes and 500+ indicators from 190+ countries up to 30 y. We view GNHA as an interactive tool aiming to share information and perspectives and foster collaborations and innovations. [ABSTRACT FROM AUTHOR]

The recent development of deep learning based fire detection techniques and the availability of smart cameras able to execute these algorithms on the edge paved the way for sophisticated and efficient video-based firefighting systems. However, the limited available data to train these algorithms cast shadows on their robustness and generalization capability. In this survey, we review 153 papers published in the literature and 17 publicly available fire detection datasets with the aim of identifying application scenarios that better describe real-world fire detection challenges. In the proposed taxonomy, these are characterized by two features: i) the fire size in the framed scene that depends on several parameters, foremost the distance from the fire but also the camera optic; ii) the background activity, due to the presence of moving objects that may mislead the detector. On this basis, we analyzed the existing methods under a common scheme according to this new taxonomy and matched the solutions with the needs of specific application scenarios. Similarly, for 9 interesting video datasets acquired from cameras, we labeled 536 videos according to the proposed taxonomy and shared these annotations with the community. The aim of this fire detection review is two-fold: on one hand, we classify the existing scientific works according to the real application scenarios, determining the features that are promising in specific operative conditions; on the other hand, we provide a detailed analysis and annotation of available datasets to promote the development of more reliable validation protocols and the collection of data from missing scenarios. • We propose an application-oriented taxonomy of video-based fire detection approaches. • We analyzed more than 130 works proposing techniques for fire or smoke detection. • We highlight the key features that make a method suitable for a specific scenario. • We share metadata for public datasets to classify each video in a specific scenario. [ABSTRACT FROM AUTHOR]

Recent advances in generative models have paved the way for enhanced generation of natural and medical images, including synthetic brain MRIs. However, the mainstay of current AI research focuses on optimizing synthetic MRIs with respect to visual quality (such as signal-to-noise ratio) while lacking insights into their relevance to neuroscience. To generate high-quality T1-weighted MRIs relevant for neuroscience discovery, we present a two-stage Diffusion Probabilistic Model (called BrainSynth) to synthesize high-resolution MRIs conditionally-dependent on metadata (such as age and sex). We then propose a novel procedure to assess the quality of BrainSynth according to how well its synthetic MRIs capture macrostructural properties of brain regions and how accurately they encode the effects of age and sex. Results indicate that more than half of the brain regions in our synthetic MRIs are anatomically plausible, i.e., the effect size between real and synthetic MRIs is small relative to biological factors such as age and sex. Moreover, the anatomical plausibility varies across cortical regions according to their geometric complexity. As is, the MRIs generated by BrainSynth significantly improve the training of a predictive model to identify accelerated aging effects in an independent study. These results indicate that our model accurately capture the brain's anatomical information and thus could enrich the data of underrepresented samples in a study. The code of BrainSynth will be released as part of the MONAI project at https://github.com/Project-MONAI/GenerativeModels. • A generative model to synthesize high-quality MRIs conditioned on metadata. • A method to quantify anatomical plausibility of synthetic MRIs and aging/sex effects. • Anatomical accuracy of synthetic MRIs varies with regional geometric complexity. • Synthetic MRIs can greatly enhance the training of brain-age predictive models. [ABSTRACT FROM AUTHOR]

The EU-ToxRisk project (2016–2021) was a large European project working towards shifting toxicological testing away from animal tests, towards a toxicological assessment based on comprehensive mechanistic understanding of cause-consequence relationships of chemical adverse effects. More than 40 partners from scientific institutions, industry and regulators coordinated their work towards this goal in a six-year long programme. The breadth and variety of data and knowledge generated, presented a challenging data management landscape. Here, we describe our approach to data management as developed under EU-ToxRisk. The main building blocks of the data infrastructure are: 1) An easy-to-use, extensible data and metadata format; 2) A flexible system with protocols for data capture and sharing from the entire consortium; 3) A methods database for describing and reviewing data generation and processing protocols; 4) Data archiving using a sustainable resource; 5) Data transformation from the archive to the system that provides granular access; 6) Application Programming Interface (API) for access to individual data points; 7) Data exploration and analysis modules, based on a «web notebook» approach to executable data processing documentation; and 8) Knowledge portal that ties together all of the above and provides a collaboration space for information exchange across the consortium. This knowledge infrastructure is being extended and refined for the support of follow-up projects (RISK-HUNT3R, ASPIS cluster, European Open Science Cloud (2021–2026)). [Display omitted] • Flexible, sustainable approach to data management and publishing in toxicology. • Modular system for data and knowledge capture and distribution. • FAIR data supporting New Approach Methods in predictive toxicology and risk assessment. [ABSTRACT FROM AUTHOR]

Many convolutional neural network (CNN) based approaches for skin cancer classification primarily rely on dermatological images, yielding commendable results in classification accuracy. However, leveraging patient metadata, a crucial source of clinical information for dermatologists, can further enhance accuracy. Current methodologies predominantly employ basic joint fusion structures (FS) and fusion modules (FMs) for multi-modal classification, leaving room for advancement in enhancing accuracy through exploration of more sophisticated FS and FM architectures. Thus, this paper introduces a novel fusion method that integrates dermatological images (dermoscopy images or clinical images) with patient metadata for skin cancer classification, focusing on enhancing FS and FM components. Initially, we propose a joint-individual fusion (JIF) structure that simultaneously learns shared features across multi-modality data while preserving specific characteristics. Subsequently, we introduce a multi-modal fusion attention (MMFA) module designed to amplify the most relevant image and metadata features through a combination of self and mutual attention mechanisms, thereby bolstering the decision-making pipeline. Our study compares the efficacy of the proposed JIF-MMFA method with other state-of-the-art fusion techniques across three distinct public datasets. Results demonstrate that the JIF-MMFA method consistently enhances classification outcomes across various CNN backbones, outperforming alternative fusion methodologies on all three datasets. These findings underscore the effectiveness and robustness of our proposed approach in skin cancer classification. • A novel multi-modal fusion network of dermatological images and patient's metadata is proposed. • Joint-individual fusion (JIF) structure is proposed to learn shared and specific features. • Multi-modal fusion attention (MMFA) module is proposed to integrate image and metadata features. • The proposed JIF-MMFA method outperforms other state-of-the-art fusion methods on public datasets. [ABSTRACT FROM AUTHOR]

The goal of this article is to understand the reasons why known-item search queries entered in a discovery system return zero hits. We analyze a sample of 708 known-item queries and classify them into four categories of zero hits with regard to whether the item is held by the library and whether the query is formulated correctly: (1) item in stock, but query incorrect, (2) item not in stock, (3) item in stock, but incomplete or erroneous metadata, (4) query is ambiguous or not understandable. The main reasons for zero hits are caused by acquisition and erroneous search queries. We discuss possible solutions for known-item queries resulting in zero hits from the side of the system and show that 30% of zero hits could easily be avoided by applying automatic spelling correction. We argue that libraries can improve their discovery systems or online catalogs by applying strategies to avoid or cope with zero hits inspired by web search engines and commercial search web sites. [ABSTRACT FROM AUTHOR]

The purpose of this study is to assess the current state of required and desirable qualifications and knowledge described in the job advertisements of data management professionals and related positions. Seventy unique job advertisements were collected from five academic and professional online job lists from January 1, 2015 to April 30, 2015. WordStat, a content-analysis software program, was used to analyze the word/phrase frequency of the job descriptions based on three key factors: the requirement of an MIS/MLS/MLIS degree; prior work experience (the number of years), and the status of job position (limited or permanent appointment). The preliminary results indicate that most job positions require that the successful job candidate must be able to serve faculty and students to collect, manage, and analyze research data with essential qualifications to carry out those tasks. The top phrases regarding expertise, working environments, and qualifications are social sciences , information science , higher education , research library , institutional repositories , and metadata . [ABSTRACT FROM AUTHOR]

Institutional repositories are established tools for archiving and increasing the visibility and availability of academic outputs. Although the potential benefits of institutional repositories are well researched and many funders and institutions already mandate open access publishing via gold or green open access routes, institutional repositories often struggle with lack of growth and sustained workflows for content recruitment. Institutions have come up with various (and often creative) workflows for populating their repositories, including institutional open access mandates, library-mediated self-archiving, fully or partially automated content harvesting and integrations between repositories and Current Research Information Systems (CRIS). Zayed University launched the ZU Scholars 1 1 https://zuscholars.zu.ac.ae institutional repository in fall 2021. Since the beginning, a semi-automated workflow was introduced to populate the repository with publication data from Scopus, Web of Science, Dimensions and Unpaywall using a custom R script. Full text files are added automatically for all Creative Commons licensed articles. This article describes the data harvesting and conversion process, its current limitations and plans for future development. The article also reviews similar content harvesting projects in the context of institutional repositories. • Adopting automated workflows can increase the number of records and Open Access content in institutional repositories • Unpaywall can be used to identify and import Creative Commons licensed full text files into institutional repositories • R programming language is well suitable for harvesting and processing publication data from different sources [ABSTRACT FROM AUTHOR]

Metabolomics plays a critical role in deciphering metabolic alterations within individuals, demanding the use of sophisticated analytical methodologies to navigate its intricate complexity. While many studies focus on single biofluid types, simultaneous analysis of multiple matrices enhances understanding of complex biological mechanisms. Consequently, the development of data fusion methods enabling multiblock analysis becomes essential for comprehensive insights into metabolic dynamics. This study introduces a novel guideline for jointly analyzing diverse metabolomic datasets (serum, urine, metadata) with a focus on metabolic differences between groups within a healthy cohort. The guideline presents two fusion strategies, 'Low-Level data fusion' (LLDF) and 'Mid-Level data fusion' (MLDF), employing a sequential application of Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS), linking the outcomes of successive analyses. MCR-ALS is a versatile method for analyzing mixed data, adaptable at various stages of data processing—encompassing resonance integration, data compression, and exploratory analysis. The LLDF and MLDF strategies were applied to 1H NMR spectral data extracted from urine and serum samples, coupled with biochemical metadata sourced from 145 healthy volunteers. Both methodologies effectively integrated and analysed multiblock datasets, unveiling the inherent data structure and variables associated with discernible factors among healthy cohorts. While both approaches successfully detected sex-related differences, the MLDF strategy uniquely revealed components linked to age. By applying this analysis, we aim to enhance the interpretation of intricate biological mechanisms and uncover variations that may not be easily discernible through individual data analysis. [Display omitted] • MCR-ALS was applied for resonance integration, data compression and exploratory data analysis. • A new guideline is proposed for performing the joint analysis of different metabolomic datasets (serum, urine, metadata). • Two data fusion strategies are proposed which use MCR-ALS in a consecutive way. • Both methodologies were able to successfully merge and process the multiblock datasets. [ABSTRACT FROM AUTHOR]

Primary storage deduplication systems are performance sensitive. Their performance depends upon two factors — metadata access for duplicate detection and strategy for elimination of duplicate data. Various approaches for duplicate detection through suitable caching mechanisms have been proposed in the literature. Most of the approaches assumed that the primary workloads exhibit strong temporal locality. Whereas, this cannot be assumed in the context of Cloud as the workloads locality does not exist with interferences among different workloads on the same system. Duplicate content among the data blocks with different addresses lead to an inefficient utilization of the data cache. In this context, applying deduplication causes sharing of the data blocks among the clients with different access patterns and frequencies. In this situation, LRU cache, which considers only the recency of the references, is not appropriate. In this paper, Hybrid Deduplication System (HDS) containing the content-based cache with a new replacement policy — Modified Adaptive Replacement Cache (ARC), is proposed. The proposed system is simulated in the Linux environment using three different types of FIU traces. Effectiveness of the system is compared with a full deduplication system. Experimental results show that the system has performed consistently better than the full deduplication system in reducing the metadata overhead for all of the three data sets. [ABSTRACT FROM AUTHOR]