Metadata Record Research Articles

IoT‐based RFID for observing the health's rituals of pilgrims during Hajj

For many faiths and especially for Muslims, Mecca as well as Kaaba, hold a spiritually important place. Although communication takes place in the form of either human to human or human to the device, however, internet's great future is promised by the internet of things (IoT). Machines as well as computers identify the objects along with metadata recording or using radio waves to control individual targets with the help of a Radiofrequency identification system (RFID), which is an automatic technology. This paper aims to propose an RFID based IoT framework for monitoring pilgrims vital along with health signs by engaging them with slice during Hajj. Facilitation regarding detection as well as monitoring of Personal RFID data of pilgrims is the result of this system.

Genomics Observatory Use-Case: The challenge to standardise image and sequence data to Darwin Core format

Genomics Observatory Use-Case: The challenge to standardise image and sequence data to Darwin Core format

A Blockchain-Based OCF Firmware Update for IoT Devices

As the usage growth rate of Internet of Things (IoT) devices is increasing, various issues related to these devices need attention. One of them is the distribution of the IoT firmware update. The IoT devices’ software development does not end when the manufacturer sells the devices to the market. It still needs to be kept updated to prevent cyber-attacks. The commonly used firmware update process, over-the-air (OTA), mostly happens in a centralized way, in which the IoT devices directly download the firmware update from the manufacturer’s server. This central architecture makes the manufacturer’s server vulnerable to single-point-of-failure and latency issues that can delay critical patches from being applied to vulnerable devices. The Open Connectivity Foundation (OCF) is one organization contributing to providing interoperability services for IoT devices. In one of their subject areas, they provide a firmware update protocol for IoT devices. However, their firmware update process does not ensure the integrity and security of the patches. In this paper, we propose a blockchain-based OCF firmware update for IoT devices. Specifically, we introduce two types of firmware update protocol, direct and peer-to-peer updates, integrated into OCF firmware update specifications. In the direct scenario, the device, through the IoT gateway, can download the new firmware update from the manufacturer’s server. Meanwhile, in the peer-to-peer scheme, the device can query the update from the nearby gateways. We implemented our protocol using Raspberry Pi hardware and Ethereum-based blockchain with the smart contracts to record metadata of the manufacturer’s firmware updates. We evaluated the proposed system’s performance by measuring the average throughput, the latency, and the firmware update distribution’s duration. The analysis results indicate that our proposal can deliver firmware updates in a reasonable duration, with the peer-to-peer version having a faster completion time than the direct one.

Managing a Large-Scale Multiomics Project: A Team Science Case Study in Proteogenomics.

Highly collaborative scientists are often called on to extend their expertise to different types of projects and to expand the scope and scale of projects well beyond their previous experience. For a large-scale project involving "big data" to be successful, several different aspects of the research plan need to be developed and tested, which include but are not limited to the experimental design, sample collection, sample preparation, metadata recording, technical capability, data acquisition, approaches for data analysis, methods for integration of different data types, recruitment of additional expertise as needed to guide the project, and strategies for clear communication throughout the project. To capture this process, we describe an example project in proteogenomics that built on our collective expertise and experience. Key steps included definition of hypotheses, identification of an appropriate clinical cohort, pilot projects to assess feasibility, refinement of experimental designs, and extensive discussions involving the research team throughout the process. The goal of this chapter is to provide the reader with a set of guidelines to support development of other large-scale multiomics projects.

Privacy-Preserving Process Mining in Healthcare.

Process mining has been successfully applied in the healthcare domain and has helped to uncover various insights for improving healthcare processes. While the benefits of process mining are widely acknowledged, many people rightfully have concerns about irresponsible uses of personal data. Healthcare information systems contain highly sensitive information and healthcare regulations often require protection of data privacy. The need to comply with strict privacy requirements may result in a decreased data utility for analysis. Until recently, data privacy issues did not get much attention in the process mining community; however, several privacy-preserving data transformation techniques have been proposed in the data mining community. Many similarities between data mining and process mining exist, but there are key differences that make privacy-preserving data mining techniques unsuitable to anonymise process data (without adaptations). In this article, we analyse data privacy and utility requirements for healthcare process data and assess the suitability of privacy-preserving data transformation methods to anonymise healthcare data. We demonstrate how some of these anonymisation methods affect various process mining results using three publicly available healthcare event logs. We describe a framework for privacy-preserving process mining that can support healthcare process mining analyses. We also advocate the recording of privacy metadata to capture information about privacy-preserving transformations performed on an event log.

Exploring the utility of metadata record graphs and network analysis for metadata quality evaluation and augmentation

Our study explores the possible uses and effectiveness of network analysis, including Metadata Record Graphs, for evaluating collections of metadata records at scale. We present the results of an experiment applying these methods to records in the University of North Texas (UNT) Digital Library and two sub-collections of different compositions: the UNT Scholarly Works collection, which functions as an institutional repository, and a collection of architectural slide images. The data includes count- and value-based statistics with network metrics for every Dublin Core element in each set. The study finds that network analysis provides useful information that supplements other metrics, for example by identifying records that are completely unconnected to other items through the subject, creator, or other field values. Additionally, network density may help managers identify collections or records that could benefit from enhancement. We also discuss the constraints of these metrics and suggest possible future applications.

Exploring the utility of metadata record graphs and network analysis for metadata quality evaluation and augmentation

Exploring the utility of metadata record graphs and network analysis for metadata quality evaluation and augmentation

Comprehensive aerosol and gas data set from the Sydney Particle Study

Abstract. The Sydney Particle Study involved the comprehensive measurement of meteorology, particles and gases at a location in western Sydney during February–March 2011 and April–May 2012. The aim of this study was to increase scientific understanding of particle formation and transformations in the Sydney airshed. In this paper we describe the methods used to collect and analyse particle and gaseous samples, as well as the methods employed for the continuous measurement of particle concentrations, particle microphysical properties, and gaseous concentrations. This paper also provides a description of the data collected and is a metadata record for the data sets published in Keywood et al. (2016a, https://doi.org/10.4225/08/57903B83D6A5D) and Keywood et al. (2016b, https://doi.org/10.4225/08/5791B5528BD63).

Challenges in the Development and Curation of Species-plot Datasets in South Africa: The National Vegetation Database of phytosociological plots as a case study

The South African National Biodiversity Institute is the custodian of numerous national level botanical and zoological datasets that have been collated over several decades and is mandated to ensure that taxonomic and ecological data are made available to the public through responsible data sharing. This study describes the nature of, and presents/discusses relevant standards for, the case study of the National Vegetation Database; the process adopted in the development of a vegetation-plot database; and current data management practices being undertaken in relation to the various stages of research data management. Phytosociological data is a record of vegetation abundance, richness, density and the associated environmental variables within a specified area or plot which usually includes a record of locality. The study aims to review the diversity of approaches in storing species-plot information in databases and to provide minimum data standards for these datasets. The surveying, classifying, and mapping of vegetation enables monitoring of ecosystems and ultimately can lead to improved conservation planning and land management. A coordinated and integrated approach is therefore needed to record, rectify, and manage these data and capture accurate metadata. Preliminary findings indicate that a lack of version control can impact the authenticity of the data if records are altered or deleted. Data affluence/abundance (currently comprised of 53 500 plots within 384 sample projects, totalling 1 064 770 species occurrence records) is a challenge because these data often differ in formats, varying methodologies, and metadata within these research projects. The curation of plot data requires a standardised approach in the different steps from data acquisition to provision of results. Species names need to coincide with currently accepted taxonomy, and although certain details are specific to a species-plot project depending on their research interest, various other data should be made consistent in terms of field names and formats to improve the quality of the resulting aggregated set of botanical records. All decisions to modify data records to achieve data consistency should be clearly explained in the metadata record for the dataset.

Examining Herbarium Specimen Citation: Developing a literature-based institutional impact measure

Herbarium specimens are critical components of the research process - providing "what, where, when" evidence for species distributions and through type designation, providing the basis for un-ambiguous, standardised nomenclature facilitating the interpretation of scientific names. Specimen references are embedded within research article texts, by convention usually presented in a relatively formalised fashion. As this is a domain-specific practice, general publishers tend not to provide tools for detecting and tracking specimen references to enable bibliometric-style calculations and navigation to the referenced specimen, as is common practice in literature reference management. This means that it is difficult to measure impact, which affects both the individuals responsible for the collection and determination of herbarium specimens (McDade et al. 2011), and the institutions responsible for their long-term management. Specimen digitisation - creating searchable data repositories of metadata and/or images - has enabled many new and larger scale uses for herbarium specimens and their associated data, and stimulated interest in quantifying usage and measuring institutional impact. To date, these impact measures have been conducted by examining usage statistics for specimen portals, or by text searching for specimen identifier patterns. This research uses text mining and document classification techniques to detect article sections likely to contain specimen references, which are then extracted, classified and counted. A dataset of taxonomic publications categorised into paragraph-level units is used to train a text classifier to predict the presence of specimen references within component units of articles (sections or paragraphs). The input to the classifier is a set of features derived from the text contents of paragraphs, which detect content such as latitude/longitude, dates and bracketed lists of herbarium codes. Article units classified as containing specimen references are processed to extract a minimal representation of the specimen reference, including the abbreviated codes for the institutional holder(s) of the specimen material. This allows total and per-institution counts to be calculated, which can be compared to datasets of Global Biodiversity Information Facility data citations, to institutional-level type citations in nomenclatural acts recorded by the International Plant Names Index and to usage statistics recorded by institutional data repositories. As well as counting specimen references, distinct specimen reference styles are detected and quantified, including the use of numeric and persistent identifiers (Güntsch et al. 2017) which can be used to access a standardised metadata record for the specimen. We will present an assessment of the classification and detection process and initial results, and discuss future work to develop this approach to work with different kinds of literature inputs. These techniques have the potential to allow institutions to make better use of existing information to help assess the use and impact of their specimen and data holdings.

Darwin Core for Agricultural Biodiversity: A metadata extension proposal

Agricultural Biodiversity has been defined by the Convention on Biological Diversity as the set of elements of biodiversity that are relevant to agriculture and food production. These elements are arranged into an agro-ecosystem that compasses "the variability among living organisms from all sources including terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are part: this includes diversity within species, between species and of ecosystems" (UNEP 1992). As with any other field in Biology, Agricultural Biodiversity work produces data. In order to publish data in a way it can be efficiently retrieved on web, one must describe it with proper metadata. A metadata element set is a group of statements made about something. These statements have three elements, named subject (thing represented), predicate (space filled up with data) and object (data itself). This representation is called triples. For example, the title is a metadata element. A book is the subject; title is the predicate; and The Chronicles of Narnia is the object. Some metadata standards have been developed to describe biodiversity data, as ABCD Data Schema, Darwin Core (DwC) and Ecological Metadata Language (EML). The DwC is said to be the most used metadata standard to publish data about species occurrence worldwide (Global Biodiversity Information Facility 2019). "Darwin Core is a standard maintained by the Darwin Core maintenance group. It includes a glossary of terms (in other contexts these might be called properties, elements, fields, columns, attributes, or concepts) intended to facilitate the sharing of information about biological diversity by providing identifiers, labels, and definitions. Darwin Core is primarily based on taxa, their occurrence in nature as documented by observations, specimens, samples, and related information" (Biodiversity Information Standards (TDWG) 2014). Within this thematic context, a master research project is in progress at the Federal University of Minas Gerais in partnership with the Brazilian Agricultural Research Corporation (EMBRAPA). It aims to apply the DwC on Brazil’s Agricultural Biodiversity data. A pragmatic analysis of DwC and DwC Extensions demonstrated that important concepts and relations from Agricultural Biodiversity are not represented in DwC elements. For example, DwC does not have significant metadata to describe biological interactions, to convey important information about relations between organisms in an ecological perspective. Pollination is one of the biological interactions relevant to Agricultural Biodiversity, for which we need enhanced metadata. Given these problems, the principles of metadata construction of DwC will be followed in order to develop a metadata extension able to represent data about Agricultural Biodiversity. These principles are the Dublin Core Abstract Model, which present propositions for creating the triples (subject-predicate-object). The standard format of DwC Extensions (see Darwin Core Archive Validator) will be followed to shape the metadata extension. At the end of the research, we expect to present a model of DwC metadata record to publish data about Agricultural Biodiversity in Brazil, including metadata already existent in Simple DwC and the new metadata of Brazil’s Agricultural Biodiversity Metadata Extension. The resulting extension will be useful to represent Agricultural Diversity worldwide.

Semantically Linking Specimens and Images

Many of the world’s natural history collections are creating high resolution digital images of their specimens. They often make these available on the web through some form or zoomable viewer. For historical reasons, a hotchpotch of technologies are used to achieve this. This diversity has lead to two issues. Firstly, maintenance becomes costly as technologies need replacing. Secondly there is little chance to share data between institutions or provide a unified user experience. A researcher visiting four different virtual collections may have four very different experiences. Similar issues exist in the archives and libraries disciplines. They also need to share high resolution, annotated images of the physical objects in their care. In response to this issue many have coalesced around the International Image Interoperability Framework (IIIF). IIIF is a set of shared application programming interface (API) specifications for interoperable functionality in digital image repositories. It separates the notion of a viewer, which may be used as part of a website or other application, and the web services that feed data to that viewer. By using a common API for serving data about images, different viewers can be used to view the same images, thus providing an upgrade path that does not require replacement of viewer and server software at the same time and allows different viewers to be used for the same image data. Potentially more importantly, it facilitates the construction of applications that view data from different collections as if they were in the same place. From the researcher’s point of view, the experience could be the same whether the virtual specimen is hosted locally or in a museum on another continent. There is one important thing that has been deliberately omitted from the IIIF standard. This has both enabled its rapid adoption but also makes it incomplete for building research applications. IIIF transmits no semantic data about the subject of the images, only labels. The IIIF data therefore needs to be bound to semantically rich data about the specimens being viewed, in some uniform way. Consortium of Taxonomic Facilities (CETAF ) specimen identifiers are now widely adopted by natural history collections in Europe. Each individual collection object is designated by a URI chosen and maintained by the institution owning the specimen (Groom et al. 2017, Güntsch et al. 2018, Güntsch et al. 2017, HYAM et al. 2012). Under Linked Data conventions, content negotiation is used at the server so that users accessing an object using a web-browser are redirected to a human-readable representation of the object, typically a web-page, whilst software systems requiring machine-processable representations are redirected to an RDF-encoded metadata record. CETAF specimen identifiers are therefore ideal partners for IIIF representations of specimens. But how should we join the two together in a semantically rich way that will be generally understandable? SYNTHESYS+ is a European Commission funded programme that facilitates collaboration and network building among European natural history collections. It is concerned with both physical and virtual access to the 390 million specimens of plants and animals housed in participating institutions. Under Task 4.3 of this project, we have been working to create a reliable way to link between the RDF metadata about specimens and images of those specimens in IIIF as well as from images of specimens back to metadata of those specimens. By January 2021, we aim to have ten exemplar institutions publishing IIIF manifest files linked to CETAF identifiers for a few million specimens and for this to act as a catalyst for wider adoption in the natural history community. This presentation gives an update on the rollout of these implementations, paying particular attention to the challenges of semantically annotating specimens with images.

MathSciNet: A comparative analysis of American Mathematical Society and EBSCO platforms

This research identifies the similarities and differences between the MathSciNet search features and functions of the American Mathematical Society (AMS) and EBSCOhost platforms. A series of iterative MathSciNet queries were performed on both platforms. For each query, the total number of results and search behavior were documented and analyzed. Observations and comparisons are quantitative. The findings surface the need to deeply understand the AMS and EBSCOhost platform features, functions, record metadata, and indexing for MathSciNet. Furthermore, this research can serve as a foundation for additional quantitative and new qualitative comparisons between these two platforms and also for other platforms.

Associations Between the Principle of Provenance and Metadata: Practical Implementation and Cases

The principle of provenance is the fundamental theory of archives science, which also possesses important reference value in various areas of research and practice. This paper commences with investigating the core value of the principle of provenance, considering the principle to be the unity of theory and method and its core value lies in following historicalism and respecting essential attributes of the archives. Then the authors focus on the realm of government information management and explore the metadata implementation of the core value of the principle of provenance in the era of electronic records. In its essence, the principle of provenance requires full understanding and preservation of the source information of the record. In the age of electronic records this has ascended to the significance of obtaining information, which is often stabilized as metadata in electronic records, reflecting the process of the record’s creation, maintenance and long term preservation. Source information as the contextual information reflecting the record creation process, its typical form of expression in metadata is provenance metadata. Therefore the authors explore the implications of provenance metadata and its essential forms. The authors hold that source information is the indispensable characteristic information in records and archives, and also the essential form for the principle of provenance to exhibit the connotations of provenance. This paper also explains that provenance metadata has further realized the core value of the principle of provenance, playing important roles in ensuring the authenticity, integrity, reliability and usability of electronic records as well as being the basis of their long term preservation and usage. In addition, the authors make concrete exposition on the composition and function of provenance metadata, considering it to be composed of entities and elements, specifically including such metadata as institution, business, environment, relationship and time. Different provenance metadata entities and elements record the source information of the electronic record from its creation until transfer to archives, performing various important functions in government electronic record management. The authors further their research through analyzing and comparing the characteristics of provenance metadata in government electronic record metadata standards at international, national and local jurisdictions levels. Some feasible suggestions for future research are made in the conclusion.

P021 Managing NGS HLA genotyping data in the 17th workshop database

Aim The 17th International HLA and Immunogenetics Workshops (IHIW) focuses on the application of next generation sequencing (NGS) technologies for histocompatibility and immunogenomic clinical and research goals. However, the scope and sheer quantity of NGS data raises challenges for their analysis, collection, exchange and storage. The 17th IHIW has adopted a centralized approach to these issues, with the goals of developing tools, services and systems to capture and manage these NGS data, and leveraging the vast store of sequence and allele data in the IPD-IMGT/HLA Database. Methods We worked with NGS platform and software developers to define NGS typing reports that record NGS data in a uniform fashion. The IHIW database and data-storage facility houses typing reports, FASTQ files, subject and specimen data, and pedigrees, and manages the accounts and data-access privileges for 17th IHIW participants. IHIW database tools and services are scripted in the Perl, R or Python languages. Results We defined three distinct typing report formats [Histoimmunogenetics Markup Language (HML), IHIW eXtensible Markup Language (XML), and GenDx XML formats] that record HLA genotyping data and meta-data in equivalent fashion. Our tools convert HML and GenDx XML to IHIW XML, automate and streamline the submission of IHIW XML reports, validate each typing report upon submission, and automate the standardized reporting of novel polymorphisms. All 17th IHIW HLA data are standardized to IPD-IMGT/HLA Database version 3.25.0, with genotype data generated under other database versions converted to 3.25.0. Conclusions The 17th IHIW database applies our standards, tools and services to consume, validate and store structured NGS HLA data generated by GenDx, Illumina, Immucor, Omixon and One Lambda in an automated fashion. The HLA genotyping data and meta-data in these typing reports are validated and standardized for use in 17th IHIW projects, and the database itself will be available for future workshops.

Investigation into Diligence in Metadata Records of Mysore University Library

Accuracy in cataloguing records is of paramount importance in catalogues. It is not difficult to imagine that errors in the metadata hamper the retrieval of bibliographic records. The present study attempts to investigate the error rate found in the metadata records of the Mysore University Library (MUL). The MUL is one of the oldest university libraries in the country, India. The study selected and analysed the accuracy of 624 metadata records of the Mysore University Library along with 491 records of Library of Congress (LoC). The assessment of the selected records discerned two main types of errors notably: Major and Minor errors. These findings illustrate and prove that the percentage of error rate in MUL is higher than that of LoC. Consequently, the study highlighted that the prevailing error ratio between both institutions reaches almost up to 5:1 (MUL: LoC).

Reengineering storage logistics

ABSTRACTThe University at Albany investigated process improvements for transferring or withdrawing large numbers of books from its stacks. Based on this investigation, this article offers best practices and processes, including finding efficiencies in workflow and implementing ILS batch processing of Excel worksheets containing record metadata, rather than individual processing of titles. Other suggested best practices are a variant of cross-docking, where the processed materials are removed from the shelf to a truck, the barcode scanned into a comma-delimited file for batch processing, and materials directly discarded or transported to storage for immediate shelving, depending on planned final disposition.

A high-precision rule-based extraction system for expanding geospatial metadata in GenBank records.

The metadata reflecting the location of the infected host (LOIH) of virus sequences in GenBank often lacks specificity. This work seeks to enhance this metadata by extracting more specific geographic information from related full-text articles and mapping them to their latitude/longitudes using knowledge derived from external geographical databases. We developed a rule-based information extraction framework for linking GenBank records to the latitude/longitudes of the LOIH. Our system first extracts existing geospatial metadata from GenBank records and attempts to improve it by seeking additional, relevant geographic information from text and tables in related full-text PubMed Central articles. The final extracted locations of the records, based on data assimilated from these sources, are then disambiguated and mapped to their respective geo-coordinates. We evaluated our approach on a manually annotated dataset comprising of 5728 GenBank records for the influenza A virus. We found the precision, recall, and f-measure of our system for linking GenBank records to the latitude/longitudes of their LOIH to be 0.832, 0.967, and 0.894, respectively. Our system had a high level of accuracy for linking GenBank records to the geo-coordinates of the LOIH. However, it can be further improved by expanding our database of geospatial data, incorporating spell correction, and enhancing the rules used for extraction. Our system performs reasonably well for linking GenBank records for the influenza A virus to the geo-coordinates of their LOIH based on record metadata and information extracted from related full-text articles.

Modelling capture probability of Atlantic salmon (Salmo salar) from a diverse national electrofishing dataset: Implications for the estimation of abundance

Estimates of juvenile fish abundance are commonly used to assess fish population health and to underpin assessment tools used in environmental and fisheries management such as the EU Water Framework Directive and EU Habitats Directive. Accurate and precise estimates of capture probability are important for estimating fish abundance from electrofishing data. This paper presents the findings of a maximum likelihood approach for modelling spatio-temporal variability in capture probability of Atlantic salmon in a large (2,837 samples) and diverse national electrofishing dataset. Capture probability was related to explanatory variables indicative of (1) equipment, personnel and protocols, (2) fish size, (3) fish behaviour, (4) habitat, and (5) time. A linear modelling approach allowed for flexible model specification, including smoothers and spatial effects, and rapid model specification, fitting and selection. The use of GIS variables as proxies for directly observed habitat characteristics allowed capture probability to be modelled using aggregated data where there were no common meta-data or habitat recording protocols. Modelled capture probabilities provided more precise estimates of abundance than sample-wise estimates and more accurate estimates than those obtained when assuming constant capture probability. Fisheries and environmental managers should routinely model capture probability when estimating fish abundance from electrofishing data, especially where datasets are geographically widespread, cover a large environmental range or have been collated across multiple organisations. Capture probability models can also facilitate the legitimate use of single pass electrofishing data in assessments of fish abundance providing that there is sufficient spatio-temporal coverage of multi-pass data.

Increasing the quality, comparability and accessibility of phytoplankton species composition time-series data

Phytoplankton diversity and its variation over an extended time scale can provide answers to a wide range of questions relevant to societal needs. These include human health, the safe and sustained use of marine resources and the ecological status of the marine environment, including long-term changes under the impact of multiple stressors. The analysis of phytoplankton data collected at the same place over time, as well as the comparison among different sampling sites, provide key information for assessing environmental change, and evaluating new actions that must be made to reduce human induced pressures on the environment. To achieve these aims, phytoplankton data may be used several decades later by users that have not participated in their production, including automatic data retrieval and analysis. The methods used in phytoplankton species analysis vary widely among research and monitoring groups, while quality control procedures have not been implemented in most cases. Here we highlight some of the main differences in the sampling and analytical procedures applied to phytoplankton analysis and identify critical steps that are required to improve the quality and inter-comparability of data obtained at different sites and/or times. Harmonization of methods may not be a realistic goal, considering the wide range of purposes of phytoplankton time-series data collection. However, we propose that more consistent and detailed metadata and complementary information be recorded and made available along with phytoplankton time-series datasets, including description of the procedures and elements allowing for a quality control of the data. To keep up with the progress in taxonomic research, there is a need for continued training of taxonomists, and for supporting and complementing existing web resources, in order to allow a constant upgrade of knowledge in phytoplankton classification and identification. Efforts towards the improvement of metadata recording, data annotation and quality control procedures will ensure the internal consistency of phytoplankton time series and facilitate their comparability and accessibility, thus strongly increasing the value of the precious information they provide. Ultimately, the sharing of quality controlled data will allow one to recoup the high cost of obtaining the data through the multiple use of the time-series data in various projects over many decades.