Metadata Research Articles

Metadata stewardship in nanosafety research: learning from the past, preparing for an “on-the-fly” FAIR future

Introduction:Significant progress has been made in terms of best practice in research data management for nanosafety. Some of the underlying approaches to date are, however, overly focussed on the needs of specific research projects or aligned to a single data repository, and this “silo” approach is hampering their general adoption by the broader research community and individual labs.Methods:State-of-the-art data/knowledge collection, curation management FAIrification, and sharing solutions applied in the nanosafety field are reviewed focusing on unique features, which should be generalised and integrated into a functional FAIRification ecosystem that addresses the needs of both data generators and data (re)users.Results:The development of data capture templates has focussed on standardised single-endpoint Test Guidelines, which does not reflect the complexity of real laboratory processes, where multiple assays are interlinked into an overall study, and where non-standardised assays are developed to address novel research questions and probe mechanistic processes to generate the basis for read-across from one nanomaterial to another. By focussing on the needs of data providers and data users, we identify how existing tools and approaches can be re-framed to enable “on-the-fly” (meta) data definition, data capture, curation and FAIRification, that are sufficiently flexible to address the complexity in nanosafety research, yet harmonised enough to facilitate integration of datasets from different sources generated for different research purposes. By mapping the available tools for nanomaterials safety research (including nanomaterials characterisation, nonstandard (mechanistic-focussed) methods, measurement principles and experimental setup, environmental fate and requirements from new research foci such as safe and sustainable by design), a strategy for integration and bridging between silos is presented. The NanoCommons KnowledgeBase has shown how data from different sources can be integrated into a one-stop shop for searching, browsing and accessing data (without copying), and thus how to break the boundaries between data silos.Discussion:The next steps are to generalise the approach by defining a process to build consensus (meta)data standards, develop solutions to make (meta)data more machine actionable (on the fly ontology development) and establish a distributed FAIR data ecosystem maintained by the community beyond specific projects. Since other multidisciplinary domains might also struggle with data silofication, the learnings presented here may be transferrable to facilitate data sharing within other communities and support harmonization of approaches across disciplines to prepare the ground for cross-domain interoperability.

A FAIR catalog of ontology-driven conceptual models

Multi-domain model catalogs serve as empirical sources of knowledge and insights about specific domains, about the use of a modeling language’s constructs, as well as about the patterns and anti-patterns recurrent in the models of that language crosscutting different domains. They may support domain and language learning, model reuse, knowledge discovery for humans, and reliable automated processing and analysis if built following generally accepted quality requirements for scientific data management. More specifically, not unlike scientific (meta)data, models should be shared according to the FAIR principles (Findability, Accessibility, Interoperability, and Reusability). In this paper, we report on the construction of a FAIR model catalog for Ontology-Driven Conceptual Modeling research, a trending paradigm lying at the intersection of conceptual modeling and ontology engineering in which the Unified Foundational Ontology (UFO) and OntoUML emerged among the most adopted technologies. The catalog, publicly available at https://w3id.org/ontouml-models, currently includes over one hundred and forty models, developed in a variety of contexts and domains.

TINJAUAN PENERAPAN REKAM MEDIS ELEKTRONIK DI DALAM REGISTRASI PASIEN RAWAT JALAN UPTD PUSKESMAS CIPAYUNG DEPOK

Based on observations of SIMPUS at the UPTD Puskesmas Cipayung Depok, the new patient registration module already running quite well, but it is not yet in accordance with the variable guidelines and meta data for electronic medical records, in which the variable guidelines and meta data for electric medical records are meant to be used as standard data elements for ease of compatibility and/or interoperability of data in electronic systems developed by health care facilities. This journal is based on research data collected on January 2023 with a descriptive research method and by collecting data by observation and interviews. With the results of the research conducted, it was found that the early development of the electronic medical record system at the Cipayung Health Center UPTD was started at the end of 2014 and continued with the development of online registration in 2018. Obstacles that still occur frequently and have not been resolved to date are bugs that have appeared, and there is yet a standardization in the application of variables and metadata used in accordance with the guidelines for variables and metadata of electronic medical records. The suggestions given is to adjust the standard variables and metadata, improve the system to avoid bugs, and standardizing variables.

Perubahan Budaya dalam Ritus Pasca Panen Padi

The purpose of this study is to explain the social changes that occur in the cultural dimension of post-harvest rites in Tabanan Regency and their impact on economic growth in Tabanan Regency. The reason for this case being chosen is because the agricultural cultural rites in Bali have experienced many shifts, but no one has conducted research regarding the changes that have occurred from the post-harvest rites by comparing their implementation in the past with the present. This research uses qualitative methods, data collection techniques use literature studies and in-depth interviews where the meta data is taken from books, online journals, and other references as well as the results of interviews with several informants consisting of traditional leaders, the people of Tabanan Regency who are still farming, and assistants regent of Tabanan. This research is analyzed using the concept of social change in the cultural dimension put forward by Himes & Moore by looking at the forms of integration that occur, as well as changes in the dimensions of time, process and impact. The results of the study showed that there was a change in the form of the post-rice harvest rite which was originally complex, now it becomes simpler by removing some of the elements in it. Nevertheless, these changes have had a good impact on economic growth in Tabanan Regency.

Lost in data: recognizing type of time series sensor data using signal pattern classification

AbstractWith the increase in number and size of Internet of Things systems, there is an ever-growing risk of (meta)data loss, as well as the maintenance overhead to mitigate such risks. The experts recognize three main challenges in this area that need to be tackled, namely (1) downsizing the manual work required for configuring sensor networks, (2) recovering metadata, such as sensor type, in case of connection issues, malfunctions or malicious actions in sensor networks, (3) rebuilding metadata lost due to unexpected problems within a data storage. Fortunately, all three challenges can be tackled with a uniform solution, namely the signal type classification approach, which is able to match raw signal to an appropriate data type. In this research, we evaluate and compare different approaches for signal type classification that can be used to recognize a signal type being read from an IoT sensor. This is done by using machine learning methods for modelling a signal represented as raw time series data. Three machine learning classification approaches are taken into a consideration, namely one class, two class and multi-class. According to the results of the evaluation, the most accurate multi-class random forest algorithm can correctly classify unknown signals in $$\sim {75}\%$$ ∼ 75 % of the cases based on only 20 consecutive sensor readings. Moreover, multi-class random forest can detect two most probable classes of monitored signal with the accuracy of 95%.

Characterization of olfactomedin 4+ cells in prostate and urethral-tube epithelium during murine postnatal development and in adult mice

Olfactomedin4 (Olfm4) is expressed in normal mouse prostate. However, Olfm4+ cells in the murine prostate have not been well characterized. In this study, we generated an Olfm4eGFP reporter mouse line with C57BL/6 mice and investigated the distribution of Olfm4/eGFP-expressing cells during postnatal development from P1, P7, P14, P20, P42, P56 to adult male mouse prostate and urethral tube. We observed Olfm4/eGFP expression in urogenital and prostatic epithelial cells during early postnatal development, which persisted into adulthood in urethral-tube and anterior-prostate (AP) epithelium. We found Olfm4+ cells are E-cadherin+/CD44+/Foxa1+ and some of subpopulation are Ck8+/Ck5+/Sca-1-/Ck4-/Syn- in the adult mouse AP epithelium. Functional studies of single-cell preparations of Olfm4/eGFP-expressing cells isolated from adult Olfm4eGFP mouse prostate demonstrated that Olfm4+ cells can grow and form colonies, spheres, or organoids in culture. Bioinformatic analysis of Olfm4+ cells using single-cell RNA sequencing meta data in adult mouse urethra (GSE145865) identified upregulation of genes related to cell and tissue migration and development, as well as upregulation of xenobiotic metabolism signaling pathways. In conclusion, Olfm4eGFP mouse is a novel model to further study Olfm4’s biological functions and Olfm4+ cells may contribute importantly to cellular processes supporting development and homeostasis of the epithelium in murine prostate and urethral tube.

Data Catalogs in the Enterprise: Applications and Integration

AbstractDespite investing heavily in data-related technology and human resources, enterprises are still struggling to derive value from data. To foster data value creation and move toward a data-driven enterprise, adequate data management and data governance practices are fundamental. To support these practices, organizations are building (meta)data management landscapes by combining different tools. Data catalogs are a central part of these landscapes as they enable an overview of available data assets and their characteristics. To deliver their highest value, data catalogs need to be integrated with existing data sources and other data management tools. However, enterprises struggle with data catalog integration because (a) not all data catalog application types foster enterprise-wide data management and data governance alike, and (b) several technical characteristics of data catalog integration remain unclear. These include the supported data sources, data catalog federation, and ways to provision data access. To tackle these challenges, this paper first develops a typology of data catalog applications in the enterprise context. Based on a review of the academic literature and an analysis of data catalog offerings, it identifies four enterprise-internal and three cross-enterprise classes of data catalog applications. Second, an in-depth analysis of 51 data catalog offerings that foster enterprise-wide metadata management examines key characteristics of the technical integration of data catalogs.

ScrutinAI: A visual analytics tool supporting semantic assessments of object detection models

We present ScrutinAI, a Visual Analytics tool to leverage semantic understanding for deep neural network (DNN) prediction analysis, focusing on models for object detection and semantic segmentation. Typical fields of application for such models, e.g., autonomous driving or healthcare, have a high demand for uncovering and mitigating data- and model-inherent shortcomings. Our approach helps analysts and/or auditors use their semantic understanding to identify and investigate potential weaknesses in DNN models. ScrutinAI includes interactive visualizations of the model’s inputs and outputs, interactive plots with linked brushing, data filtering with textual queries on descriptive meta data and an interactive similarity based image retrieval feature. The different views and data filtering options enable global and local inspection of DNN predictions. Overall, the tool fosters hypothesis driven knowledge generation which aids in understanding the model’s inner reasoning. Insights gained during the analysis process mitigate the “black-box character” of the DNN and thus support model improvement and the generation of a safety argumentation for AI applications. We present two case studies on the investigation of DNN models for pedestrian detection from the automotive domain.

Disaggregating RocksDB: A Production Experience

As in the general industry, there is a trend in Meta's data centers to migrate data from locally attached SSDs to cloud storage. We extended RocksDB [26], a widely used open-source storage engine designed and built for local SSDs, to leverage disaggregated storage. RocksDB's design, such as its data and log files' access patterns, makes an append-only distributed file system a desirable underlying storage. At Meta, we built disaggregated RocksDB using Tectonic File System [35], which so far had mainly been used for our data warehouse and blob storage stacks. We identified that metadata overhead and tail latencies were Tectonic's major performance gaps and addressed them accordingly. We improved the reliability, performance and other requirements with both general and customized optimizations to the core engine in RocksDB. We also took the time to deeply understand the common challenges presented by applications running on RocksDB and implemented enhancements to address them. This architecture enabled RocksDB to adapt to a more distributed architecture for performance enhancements.

Data rescue of historical wind observations in Sweden since the 1920s

Abstract. Instrumental measurements of wind speed and direction from the 1920s to the 1940s from 13 stations in Sweden have been rescued and digitized, making 165 additional station years of wind data available through the Swedish Meteorological and Hydrological Institute's open data portal. These stations measured wind through different versions of cup-type anemometers and were mainly situated at lighthouses along the coasts and at airports. The work followed the protocol “Guidelines on Best Practices for Climate Data Rescue” of the World Meteorological Organization consisting of (i) designing a template for digitization, (ii) digitizing records in paper journals by a scanner, (iii) typing numbers of wind speed and direction data into the template, and (iv) performing quality control of the raw observation data. Along with the digitization of the wind observations, meta data from the stations were collected and compiled as support to the following quality control and homogenization of the wind data. The meta data mainly consist of changes in observer and a small number of changes in instrument types and positions. The rescue of these early wind observations can help improve our understanding of long-term wind changes and multidecadal variability (e.g. the “stilling” vs. “reversal” phenomena) but also to evaluate and assess climate simulations of the past. Digitized data can be accessed through the Swedish Meteorological and Hydrological Institute and the following Zenodo repository: https://doi.org/10.5281/zenodo.5850264 (Zhou et al., 2022).

Bias Analysis in Healthcare Time Series (BAHT) Decision Support Systems from Meta Data.

One of the hindrances in the widespread acceptance of deep learning-based decision support systems in healthcare is bias. Bias in its many forms occurs in the datasets used to train and test deep learning models and is amplified when deployed in the real world, leading to challenges such as model drift. Recent advancements in the field of deep learning have led to the deployment of deployable automated healthcare diagnosis decision support systems at hospitals as well as tele-medicine through IoT devices. Research has been focused primarily on the development and improvement of these systems leaving a gap in the analysis of the fairness. The domain of FAccT ML (fairness, accountability, and transparency) accounts for the analysis of these deployable machine learning systems. In this work, we present a framework for bias analysis in healthcare time series (BAHT) signals such as electrocardiogram (ECG) and electroencephalogram (EEG). BAHT provides a graphical interpretive analysis of bias in the training, testing datasets in terms of protected variables, and analysis of bias amplification by the trained supervised learning model for time series healthcare decision support systems. We thoroughly investigate three prominent time series ECG and EEG healthcare datasets used for model training and research. We show the extensive presence of bias in the datasets leads to potentially biased or unfair machine-learning models. Our experiments also demonstrate the amplification of identified bias with an observed maximum of 66.66%. We investigate the effect of model drift due to unanalyzed bias in datasets and algorithms. Bias mitigation though prudent is a nascent area of research. We present experiments and analyze the most prevalently accepted bias mitigation strategies of under-sampling, oversampling, and the use of synthetic data for balancing the dataset through augmentation. It is important that healthcare models, datasets, and bias mitigation strategies should be properly analyzed for a fair unbiased delivery of service.

A Case for Accelerating Standards to Achieve the FAIR Principles of Environmental Health Research Experimental Data.

Funding agencies, publishers, and other stakeholders are pushing environmental health science investigators to improve data sharing; to promote the findable, accessible, interoperable, and reusable (FAIR) principles; and to increase the rigor and reproducibility of the data collected. Accomplishing these goals will require significant cultural shifts surrounding data management and strategies to develop robust and reliable resources that bridge the technical challenges and gaps in expertise. In this commentary, we examine the current state of managing data and metadata-referred to collectively as (meta)data-in the experimental environmental health sciences. We introduce new tools and resources based on invivo experiments to serve as examples for the broader field. We discuss previous and ongoing efforts to improve (meta)data collection and curation. These include global efforts by the Functional Genomics Data Society to develop metadata collection tools such as the Investigation, Study, Assay (ISA) framework, and the Center for Expanded Data Annotation and Retrieval. We also conduct a case study of invivo data deposited in the Gene Expression Omnibus that demonstrates the current state of invivo environmental health data and highlights the value of using the tools we propose to support data deposition. The environmental health science community has played a key role in efforts to achieve the goals of the FAIR guiding principles and is well positioned to advance them further. We present a proposed framework to further promote these objectives and minimize the obstacles between data producers and data scientists to maximize the return on research investments. https://doi.org/10.1289/EHP11484.

The promise and limitations of formal privacy

AbstractDifferential privacy (DP) is in our smart phones, web browsers, social media, and the federal statistics used to allocate billions of dollars. Despite the mathematical concept being only 17 years old, differential privacy has amassed a rapidly growing list of real‐world applications, such as Meta and US Census Bureau data. Why is DP so pervasive? DP is currently the only mathematical framework that provides a finite and quantifiable bound on disclosure risk when releasing information from confidential data. Previous concepts of data privacy and confidentiality required various assumptions about how a bad actor might attack sensitive data. DP is often called formally private because statisticians can mathematically prove the worst‐case scenario privacy loss that could result from releasing information based on the confidential data. Although DP ushered in a new era of data privacy and confidentiality methodologies, many researchers and data practitioners criticize differentially private frameworks. In this paper, we provide readers a critical overview of the current state‐of‐the‐art research on formal privacy methodologies and various relevant perspectives, challenges, and opportunities.This article is categorized under: Applications of Computational Statistics > Defense and National Security

Beyond rankings: Learning (more) from algorithm validation.

Challenges have become the state-of-the-art approach to benchmark image analysis algorithms in a comparative manner. While the validation on identical data sets was a great step forward, results analysis is often restricted to pure ranking tables, leaving relevant questions unanswered. Specifically, little effort has been put into the systematic investigation on what characterizes images in which state-of-the-art algorithms fail. To address this gap in the literature, we (1) present a statistical framework for learning from challenges and (2) instantiate it for the specific task of instrument instance segmentation in laparoscopic videos. Our framework relies on the semantic meta data annotation of images, which serves as foundation for a General Linear Mixed Models (GLMM) analysis. Based on 51,542 meta data annotations performed on 2,728 images, we applied our approach to the results of the Robust Medical Instrument Segmentation Challenge (ROBUST-MIS) challenge 2019 and revealed underexposure, motion and occlusion of instruments as well as the presence of smoke or other objects in the background as major sources of algorithm failure. Our subsequent method development, tailored to the specific remaining issues, yielded a deep learning model with state-of-the-art overall performance and specific strengths in the processing of images in which previous methods tended to fail. Due to the objectivity and generic applicability of our approach, it could become a valuable tool for validation in the field of medical image analysis and beyond.

Blue Brain Nexus: An open, secure, scalable system for knowledge graph management and data-driven science

Modern data-driven science often consists of iterative cycles of data discovery, acquisition, preparation, analysis, model building and validation leading to knowledge discovery as well as dissemination at scale. The unique challenges of building and simulating the whole rodent brain in the Swiss EPFL Blue Brain Project (BBP) required a solution to managing large-scale highly heterogeneous data, and tracking their provenance to ensure quality, reproducibility and attribution throughout these iterative cycles. Here, we describe Blue Brain Nexus (BBN), an ecosystem of open source, domain agnostic, scalable, extensible data and knowledge graph management systems built by BBP to address these challenges. BBN builds on open standards and interoperable semantic web technologies to enable the creation and management of secure RDF-based knowledge graphs validated by W3C SHACL. BBN supports a spectrum of (meta)data modeling and representation formats including JSON and JSON-LD as well as more formally specified SHACL-based schemas enabling domain model-driven runtime API. With its streaming event-based architecture, BBN supports asynchronous building and maintenance of multiple extensible indices to ensure high performance search capabilities and enable analytics. We present four use cases and applications of BBN to large-scale data integration and dissemination challenges in computational modeling, neuroscience, psychiatry and open linked data.

XL-BPMN Model-Based Service Similarity Measurement Technique

In service-oriented developments, existing studies do not give lots of efforts on a formalized and systematic method for measuring similarities between services for their reuse in business models. This deteriorates the reusability of the constructed service due to the developers’ intuition and informal service analyses. In this paper, we propose a technique for measuring similarity of services by analyzing syntax and semantics between services in the eXtended Layered business process modeling notation (XL-BPMN) model. First of all, the profiles of the formalized attributes for specifying services are defined, and the criteria for determining service similarities are established. To measure similarity between services, a technique both a syntactic similarity analysis facilitated by the XL-BPMN model-based edge counting method and a semantic similarity analysis based on meta data registry (MDR)-applied service attributes is specified. To automate analysis, a tool that can support the semantic similarity analysis technique is implemented. An online shopping mall system is investigated and evaluated to verify the effectiveness of the proposed technique. The similarity measurement technique, which is further formalized at upper business levels, can improve the accuracy of service analyses and enhance service reusability by distinguishing services with high similarity levels as common services.

Occupational exposure to nanomaterials: A bibliometric study of publications over the last decade.

Owing to the increased use of nanomaterials, the number of employees and professionals who are exposed to these chemicals is on the rise, despite the paucity of organized data on the possible dangers associated with exposure to these compounds. Multiple studies reveal that the lack of nanosafety awareness among employees and businesses is a serious problem that must be addressed. This shortage of information may result from insufficient knowledge generation or transmission. Academic publications play a significant role in producing new knowledge. This study presents a comprehensive bibliometric analysis of published research on workplace nanosafety which this bibliometric analysis aims to establish the permeability and significance of nanosafety themes from an occupational safety and health viewpoint in academia, to gain a better understanding of the knowledge generation phase in the area, and detected advantages and disadvantages of the topic. Scopus was used as the data source in this study. A total of 1170 publications were gathered and analyzed. The results indicated that the United States is a leader in several aspects of nanosafety at the workplace. The synthesis of co-citation and author-keyword phrases provided insight into determining the focal points of the current study. Analysis of meta data indicates that the number of writers is rising. Nanomaterial toxicity, risk assessment, and occupational exposure are the three hottest topics in this field, according to the result. In addition, the findings included worldwide growth rate, collaborative research fields, keywords, journals, and funding agencies. The advantages and disadvantages of the knowledge creation phase of nanosafety at the workplace were examined in the conclusion.

The Attitude of the Jordanian Media Discourse towards Israel 2017-2021

Objectives: This study aimed to identify the direction of Jordanian media discourse towards Israel.
 Methods: The study used the critical discourse analysis (CDA) method. Articles from opinion writers were analyzed using the newspaper's archive in the "Ideas and Positions" section. The analysis was based on the Meta Data archive database, which contained a total of 5022 articles with their linguistic structures.
 Results: The study found that the word "Israel" was mentioned 1588 times, accounting for 31.62% of the total repetitions. The term had the highest frequency of occurrence in 2017 and 2019, with rates of 8.04% and 9.08% respectively. The research sample also showed a higher frequency of total negative vocabulary and linguistic structures in the years 2018, 2019, and 2017, with rates of 11.43%, 11.05%, and 10.06% respectively. The Jordanian media discourse towards Israel was determined to be 49.14% positive, 36.60% negative, and 10.20% neutral.
 Conclusions: It is recommended to exercise more scrutiny in the use of vocabulary and linguistic structures with positive significance when writing opinion articles for Jordanian newspapers. This is in line with the collective trend of rejecting negative normalization in public opinion. Additionally, caution should be exercised when using the term "Israel" in articles, as it carries a strong positive connotation in the local Jordanian media.

Nanopore Sequencing Discloses Compositional Quality of Commercial Probiotic Feed Supplements

The market for the application of probiotics as a livestock health improvement supplement has increased in recent years. However, most of the available products are quality-controlled using low-resolution techniques and un-curated databases, resulting in misidentification and incorrect product labels. In this work, we deployed two workflows and compared results obtained by full-length 16S rRNA genes (16S) and metagenomic (Meta) data to investigate their reliability for the microbial composition of both liquid and solid forms of animal probiotic products using Oxford Nanopore long-read-only (without short-read). Our result revealed that 16S amplicon data permits to detect the bacterial microbiota even with the low abundance in the samples. Moreover, the 16S approach has the potential to provide species-level resolution for prokaryotes but not for assessing yeast communities. Whereas, Meta data has more power to recover of high-quality metagenome-assembled genomes that enables detailed exploration of both bacterial and yeast populations, as well as antimicrobial resistance genes, and functional genes in the population. Our findings clearly demonstrate that implementing these workflows with long-read-only monitoring could be applied to assessing the quality and safety of probiotic products for animals and evaluating the quality of probiotic products on the market. This would benefit the sustained growth of the livestock probiotic industry.

On Possibilities of Learning the Meta Data for Machine Learning Algorithms

On Possibilities of Learning the Meta Data for Machine Learning Algorithms