Incomplete Metadata Research Articles

A Bibliometric Analysis of Study of Associations of Certain Genotypes with the Cardiovascular Form of Diabetic Neuropathy.

This bibliometric analysis explores the landscape of research on the associations between specific genotypes and the cardiovascular form of diabetic neuropathy. Diabetes mellitus (DM) is a major contributor to premature mortality, primarily due to increased susceptibility to cardiovascular diseases. The global prevalence of DM is rising, with projections indicating further increases. Diabetic neuropathy, a complication of DM, includes the cardiovascular subtype, posing challenges in diagnosis and management. Understanding the genetic basis of cardiovascular diabetic neuropathy is crucial for targeted therapeutic interventions. The study utilizes bibliometric analysis to synthesize existing literature, identify trends, and guide future research. The Scopus database was searched, applying inclusion criteria for English articles related to genotypes and cardiovascular diabetic neuropathy. The analysis reveals a dynamic field with a notable impact, collaborative efforts, and multidimensional aspects. Publication trends over 1997-2023 demonstrate fluctuating research intensity. Top journals, authors, and affiliations are highlighted, emphasizing global contributions. Keyword analysis reveals thematic trends, and citation analysis identifies influential documents. Limitations include database biases, incomplete metadata, and search query specificity. The urgent need to explore genetic factors in cardiovascular diabetic neuropathy aligns with the increasing global diabetes burden. This analysis provides a comprehensive overview, contributing to the broader discourse on diabetic neuropathy research.

A primer on terrestrial passive acoustic monitoring data management: Best practices and guidelines

Recent advances in recording technology and data storage capabilities have revolutionized how we monitor vocal animals and their habitats. Recently, there has been an explosion of interest in the use of terrestrial passive acoustic monitoring (PAM) which relies on autonomous acoustic recording units (ARU), but terrestrial PAM studies are limited by a lack of standardized protocols for data collection, analysis, and archiving. Here, we draw on collective experiences of recording, analyzing, and archiving, hundreds of years of PAM data to provide guidelines and recommendations for PAM practitioners. First, we highlight some of the major pitfalls in field data collection, including inappropriate recording settings for the research question and incomplete metadata. We then provide recommendations for data management, including suggestions for backing up, compressing, and archiving the data. Our goal is to provide the community with standardized working guidelines that will help facilitate comparative, large-scale terrestrial studies using PAM.

An Australian Model of Cooperative Data Publishing to OBIS and GBIF

The Australian Commonwealth Science and Industrial Research Organisation (CSIRO) hosts both the Australian Ocean Biodiversity Information System (OBIS) and Global Biodiversity Information Facility (GBIF) nodes within the National Collections and Marine Infrastructure (NCMI) business unit. OBIS-AU is led by the NCMI Information and Data Centre and publishes marine biodiversity data in the Darwin Core (DwC) standard via an Integrated Publishing Toolkit (IPT), with over 450 marine datasets at present. The Australian GBIF node is hosted by a separate team at the Atlas of Living Australia (ALA), a national-scale biodiversity analytical and knowledge delivery portal. The ALA aggregates and publishes over 800 terrestrial and marine datasets from a wide variety of research institutes, museums and collections, governments and citizen science agencies, including OBIS-AU. Many OBIS-AU published datasets are harvested and republished by ALA and vice-versa. OBIS-AU identifies, performs Quality Control and formats marine biodiversity and observation data, then publishes directly to the OBIS international data repository and portal, using GBIF IPT technology. The ALA data processing pipeline harvests, aggregates and enhances datasets from many sources with authoritative taxonomic and spatial reference data before passing the data on to GBIF. OBIS-AU and ALA are working together to ensure that the publication pathways for any datasets managed by both (with potential for duplication of records and incomplete metadata harvests) are rationalised and that a single collaborative workflow across both units is followed for publication to GBIF. Recently, the data management groups have established an agreement to cooperatively publish marine data and eDNA data. OBIS-AU have commenced publishing datasets directly to GBIF with ALA endorsement. We present the convergent evolution of OBIS and GBIF data publishing in Australia, adaptive data workflows to maintain data and metadata integrity, challenges encountered, how domain expertise ensures data quality and the benefits of sharing data skills and code, especially in publishing eDNA data types in DwC (using the DNA-derived data extension) and exploring the new CamTrap Data Package using Frictionless data. We also present the work that both data groups are doing toward adopting the GBIF new Unified Data model for publishing data. This Australian case study demonstrates the strengths of collaborative data publishing and offers a model that minimises replication of data in global aggregators through the development of regional integrated data publishing pipelines.

Reviews and syntheses: The promise of big diverse soil data, moving current practices towards future potential

Abstract. In the age of big data, soil data are more available and richer than ever, but – outside of a few large soil survey resources – they remain largely unusable for informing soil management and understanding Earth system processes beyond the original study. Data science has promised a fully reusable research pipeline where data from past studies are used to contextualize new findings and reanalyzed for new insight. Yet synthesis projects encounter challenges at all steps of the data reuse pipeline, including unavailable data, labor-intensive transcription of datasets, incomplete metadata, and a lack of communication between collaborators. Here, using insights from a diversity of soil, data, and climate scientists, we summarize current practices in soil data synthesis across all stages of database creation: availability, input, harmonization, curation, and publication. We then suggest new soil-focused semantic tools to improve existing data pipelines, such as ontologies, vocabulary lists, and community practices. Our goal is to provide the soil data community with an overview of current practices in soil data and where we need to go to fully leverage big data to solve soil problems in the next century.

Bibliometric Analysis: Forest Fire Controlling Policy in Indonesia

This study aims to identify and analyze the intensity of research developments with the topic of forest fire controlling policy in Indonesia in the last five years from 2017 to 2021 through bibliometric analysis. The research method uses a literature review with data collection through the publish or perish software from the google scholar database with the keywords “forest fire Indonesia,” “forest fire management, Indonesia,” forest fire control, Indonesia,” and “forest fire policy, Indonesia.” Data analysis using the VOSviewer application. The results showed 154 journal articles discussing forest fire controlling policy in Indonesia between 2017 and 2021 accessed on December 18, 2021. The term "implementation" became the most central and widely discussed term in previous studies. As a comparison, in 2021, the terms "coordination policy" and "annual forest fire” will be the last issues to be discussed. The limitation of this study is research using the Google Scholar database. It is open to all articles, including journals, reports, papers assignments, and articles that have been uploaded to the Google database. In addition, incomplete metadata such as year and source of publication become undetectable when filtered. Following research, more analysis will be conducted using other indexing databases like Scopus or Web of Science.

Interpretative Labor and the Bane of Nonstandardized Metadata in Public Health Surveillance and Food Safety

Abstract Open-source DNA sequence databases have long been touted as beneficial to public health, including the facilitation of earlier detection and response to infectious disease outbreaks. Of critical importance to harnessing these benefits is the metadata that describe general and other domain-specific attributes (eg, collection location, isolate type) of a sample. Unlike the sequence data, metadata are often incomplete and lack adherence to an international standard. Here, we describe the problem posed by such variable and incomplete metadata in terms of interpretative labor costs (the time and energy necessary to make sense of the signal in the genetic data) and the impact such metadata have on foodborne outbreak detection and response. Improving the quality of sequence-associated metadata would allow for earlier detection of emerging food safety hazards and allow faster response to foodborne outbreaks.

Multifaceted CrossRef DOI: Do We Use All Functions?

The article deals with some new functions of DOI CrossRef system. Based on current database of more than 100 million of records containing DOIs and advanced sets of metadata new important functions of DOIs emerged. DOI is now widely used for discovery and accounting of publications’ mentions on the Internet by altmetric services and CrossRef DOI Event Data. DOI CrossRef is also served as a basis for Unpaywall sevice which defines open access status of publications. Analyzed are typical problems of Russian publications preventing correct discovery of open access evidence by Unpaywall: missing DOIs, incomplete metadata registered with CrossRef and on publications web pages on the Internet, mistakes in DOIs.

A Neighborhood Undersampling Stacked Ensemble (NUS-SE) in imbalanced classification

Stacked ensemble, which formulates an ensemble by using a meta-learner to combine (stack) the predictions of multiple base classifiers, suffers from the problem of suboptimal performance on imbalanced classification. To improve the classification performance of stacked ensemble on imbalanced datasets, we proposed a method named Neighborhood Undersampling Stacked Ensemble (NUS-SE) in this paper. In general, the NUS-SE can be broken down into two proposed components, an undersampling based stacked ensemble framework (US-SE) component and an undersampling technique component. In the metadata generation step of stacked ensemble, a cross-validation-like procedure (CV-prediction) is commonly used. Unfortunately, incomplete metadata with missing prediction values is generated when undersampling is performed within a stacked ensemble which utilized CV-prediction as the metadata generation procedure. Therefore, in the proposed US-SE component, we replaced the standard CV-prediction procedure with our proposed method coined as Subset and Out-of-Subset (S-OOS) prediction procedure as the metadata generation method. S-OOS prediction procedure will generate metadata without missing prediction values and thus enabling the integration of undersampling within stacked ensemble. By integrating undersampling within stacked ensemble, multiple undersampled-data-subsets are used in the training of US-SE’s base learners. While in the undersampling component, we further proposed a novel undersampling technique — Neighborhood Undersampling (NUS) which selects majority instances based on their local neighborhood information. The performance of the NUS-SE is evaluated against those non-resampling based stacked ensemble as baseline methods. The experiment demonstrates that the proposed NUS-SE, which is an undersampling based stacked ensemble, is capable of achieving a better performance when compared to the non-resampling based stacked ensemble.

Differential effects of land use on nutrient concentrations in streams of Pennsylvania

Nutrient pollution of surface waters is a widespread problem, calling for regional assessments of water quality conditions. In this study, we quantified long-term median nutrient concentrations of total nitrogen (TN) and total phosphorus (TP) in streams and rivers of Pennsylvania and explored relationships between stream nutrient concentrations and the land use of their watersheds. Our analysis is based on a synthesis of monitoring data from multiple agencies that included records of nutrient concentrations observed between 2000–2019. Across the state, stream nutrient concentrations observed in predominantly undeveloped areas (e.g., forests, shrubs, and grasslands) have median concentration values of 0.42 mg l−1 for TN and 0.011 mg l−1 for TP, reflecting background concentrations for minimally impacted watersheds. Median stream concentrations of TN in agricultural areas are about eleven times higher than in undeveloped areas; and are about five times higher in developed areas than in undeveloped areas. Median stream concentrations of TP in developed areas have about eight times higher concentrations than undeveloped areas; and are about four times higher in agricultural areas than in undeveloped areas. Concentrations of TN and TP increased substantially as the combined percentage of agricultural and developed land use increased. Fragmented data storage practices (e.g. incomplete metadata, ambiguous site names, and missing coordinates) and inconsistencies in monitoring protocols (e.g., differences in constituents measured, parameter names, and measurement methods) made leveraging the secondary use of multiple sources of data challenging. Nonetheless, our integrated dataset is robust, represents the best data available, and provides a new window into the nutrient status of Pennsylvania’s surface waters. The long-term median nutrient concentrations reveal the magnitude of variability in TN and TP concentrations across the state’s diverse environmental settings of land use, physiography, and geology. This information is useful for interpreting additional monitoring data, informing evaluation of water quality conditions, and guiding watershed management.

Top metadata quality issues including missing metadata, incomplete metadata, inaccessible metadata, incorrect metadata including but not limited to misspellings, incorrect or incomplete dates, big issues like incorrect author names, small issues like too many commas, annoying issues like run on sentences, frustrations like character encoding issues or inconsistently applied vocabularies, metadata that is not kept up to date, metadata created

Top metadata quality issues including missing metadata, incomplete metadata, inaccessible metadata, incorrect metadata including but not limited to misspellings, incorrect or incomplete dates, big issues like incorrect author names, small issues like too many commas, annoying issues like run on sentences, frustrations like character encoding issues or inconsistently applied vocabularies, metadata that is not kept up to date, metadata created

GeoBoost: accelerating research involving the geospatial metadata of virus GenBank records.

GeoBoost is a command-line software package developed to address sparse or incomplete metadata in GenBank sequence records that relate to the location of the infected host (LOIH) of viruses. Given a set of GenBank accession numbers corresponding to virus GenBank records, GeoBoost extracts, integrates and normalizes geographic information reflecting the LOIH of the viruses using integrated information from GenBank metadata and related full-text publications. In addition, to facilitate probabilistic geospatial modeling, GeoBoost assigns probability scores for each possible LOIH. Binaries and resources required for running GeoBoost are packed into a single zipped file and freely available for download at https://tinyurl.com/geoboost. A video tutorial is included to help users quickly and easily install and run the software. The software is implemented in Java 1.8, and supported on MS Windows and Linux platforms. gragon@upenn.edu. Supplementary data are available at Bioinformatics online.

User needs analysis and usability assessment of DataMed - a biomedical data discovery index.

ObjectiveTo present user needs and usability evaluations of DataMed, a Data Discovery Index (DDI) that allows searching for biomedical data from multiple sources.Materials and MethodsWe conducted 2 phases of user studies. Phase 1 was a user needs analysis conducted before the development of DataMed, consisting of interviews with researchers. Phase 2 involved iterative usability evaluations of DataMed prototypes. We analyzed data qualitatively to document researchers’ information and user interface needs.ResultsBiomedical researchers’ information needs in data discovery are complex, multidimensional, and shaped by their context, domain knowledge, and technical experience. User needs analyses validate the need for a DDI, while usability evaluations of DataMed show that even though aggregating metadata into a common search engine and applying traditional information retrieval tools are promising first steps, there remain challenges for DataMed due to incomplete metadata and the complexity of data discovery.DiscussionBiomedical data poses distinct problems for search when compared to websites or publications. Making data available is not enough to facilitate biomedical data discovery: new retrieval techniques and user interfaces are necessary for dataset exploration. Consistent, complete, and high-quality metadata are vital to enable this process.ConclusionWhile available data and researchers’ information needs are complex and heterogeneous, a successful DDI must meet those needs and fit into the processes of biomedical researchers. Research directions include formalizing researchers’ information needs, standardizing overviews of data to facilitate relevance judgments, implementing user interfaces for concept-based searching, and developing evaluation methods for open-ended discovery systems such as DDIs.

An ontology-based search engine for digital reconstructions of neuronal morphology

Neuronal morphology is extremely diverse across and within animal species, developmental stages, brain regions, and cell types. This diversity is functionally important because neuronal structure strongly affects synaptic integration, spiking dynamics, and network connectivity. Digital reconstructions of axonal and dendritic arbors are thus essential to quantify and model information processing in the nervous system. NeuroMorpho.Org is an established repository containing tens of thousands of digitally reconstructed neurons shared by several hundred laboratories worldwide. Each neuron is annotated with specific metadata based on the published references and additional details provided by data owners. The number of represented metadata concepts has grown over the years in parallel with the increase of available data. Until now, however, the lack of standardized terminologies and of an adequately structured metadata schema limited the effectiveness of user searches. Here we present a new organization of NeuroMorpho.Org metadata grounded on a set of interconnected hierarchies focusing on the main dimensions of animal species, anatomical regions, and cell types. We have comprehensively mapped each metadata term in NeuroMorpho.Org to this formal ontology, explicitly resolving all ambiguities caused by synonymy and homonymy. Leveraging this consistent framework, we introduce OntoSearch, a powerful functionality that seamlessly enables retrieval of morphological data based on expert knowledge and logical inferences through an intuitive string-based user interface with auto-complete capability. In addition to returning the data directly matching the search criteria, OntoSearch also identifies a pool of possible hits by taking into consideration incomplete metadata annotation.

Deskripsi Pola Kegagalan Pengajuan Verifikasi Karya Tulis Akhir Mahasiswa UGM dalam Unggah Mandiri

Final paper self-submission system is an application that serves as a means of self-service delivery of the final written work of students in the form of final project (TA), thesis and dissertation online. Not all papers uploaded in the system are approved; some are rejected for some reasons. This study aims at determining: (1) the proportion of papers submitted late in the process of self-upload directly approved / rejected based on the level of study program, i.e. diploma, undergraduate, master, doctoral; (2) the proportion of document in the process of submission directly approved /rejected by the faculty; (3) distribution on the quantity of the documents not approved /rejected in the submission based on the level of study (4) distribution on the number of the documents not approved /rejected in self-submission based on faculty; (5) patterns of errors in the process of self-submission; and (6). problems experienced by students in the submission of documents. The study used observational descriptive approach. The population of the study comprised files of documents uploaded. Samples were document files verified by 3 officers for one month. The results of the study showed: (1) average quantity of documents rejected was 49.28%; (2). errors occured in a pattern submission were incomplete manuscript (43.5%), incomplete metadata (22.1% , and incomplete manuscript and metadata ( 33.5%); (3) causes of errors in self-submission were inaccuracy (35.5%), haste (13.4%), drawbacks in application (11.6%), and haste as well as inaccuracy in understanding the existing guidance (27.7%).

Known-item Searches Resulting in Zero Hits: Considerations for Discovery Systems

ABSTRACT 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.

‘Natural Images’ Project: Re-discovering the photographic collections at Amgueddfa Cymru – National Museum Wales

In 2011, Amgueddfa Cymru – National Museum Wales embarked on a project to digitize a selection of its historic photography holdings. It was the first time that the collections were looked at holistically. The project brought together curators from all collecting areas, encouraging cross departmental engagement, and brought to light collections that spanned numerous departments. How were collections chosen for digitization? What lessons were learned? The issues of incomplete metadata, the use of multiple numerical references and the varied way in which departments recorded data were just some of the issues faced by the project team. After three years, the rich historic photography collections are not only better understood, but there is now a legacy for further research and public dissemination.

El derecho al olvido y otros condicionantes para la re-catalogación de archivos audiovisuales

Without metadata, the value of the stock shots archived as audiovisual heritage decreases due to the limitations of content retrieval. A number of archives are not catalogued yet, and others have a metadata structure but need to be re-catalogued and re-indexed. Incomplete metadata, legal issues related to the right to be forgotten, new relevance of events or people, or new journalistic approaches to the information are some of the triggers of this requirement, which is extremely complex and sometimes unviable. This leads us to assume that there are certain unachievable limitations on audiovisual archives management.

Quantifying and measuring metadata completeness

AbstractCompleteness of metadata is one of the most essential characteristics of their quality. An incomplete metadata record is a record of degraded quality. Existing approaches to measure metadata completeness limit their scope in counting the existence of values in fields, regardless of the metadata hierarchy as defined in international standards. Such a traditional approach overlooks several issues that need to be taken into account. This paper presents a fine‐grained metrics system for measuring metadata completeness, based on field completeness. A metadata field is considered to be a container of multiple pieces of information. In this regard, the proposed system is capable of following the hierarchy of metadata as it is set by the metadata schema and admeasuring the effect of multiple values of multivalued fields. An application of the proposed metrics system, after being configured according to specific user requirements, to measure completeness of a real‐world set of metadata is demonstrated. The results prove its ability to assess the sufficiency of metadata to describe a resource and provide targeted measures of completeness throughout the metadata hierarchy.

New possibilities for metadata creation in an institutional repository context

PurposeThe purpose of this paper is to develop automated methods for creating metadata for documents in an institutional repository.Design/methodology/approachTwo methods are examined for automatically building metadata in an institutional repository context. Text mining techniques are employed to discover relationships among documents with similar content, from which are inferred possible values for missing or incomplete metadata elements. Machine learning techniques are used to identify and extract specific metadata element values from document content.FindingsText mining techniques can be used to cluster documents with similar content. This allows values for metadata elements, like keyword, to be projected from documents with established metadata to related documents. Machine learning techniques are found to be reasonably accurate for extracting from documents values for metadata elements, such as, title, author, and abstract. Results show sufficient promise to support the next phase of the project: the development of assistive tools for use by metadata specialists to create or edit document metadata.Originality/valueThis paper focuses on the use of automated metadata extraction techniques to assist metadata creation, lessening the time and effort required to add documents to institutional repositories.

Researchers converge to improve metadata standards

Metadata are descriptions of data sets that include information such as data quality, keywords, or the spatial or temporal extent of the data set. They provide sufficient information to enable data search, retrieval, transfer, and evaluation. Standardized metadata that can be automatically used by computers without human intervention or interpretation are key components of effective data management, interoperability between distributed data systems, and, most importantly, integrative, interdisciplinary science. Such standardized metadata will reduce the semantic heterogeneity and syntactic incompatibility that currently hinder data management and inter‐operability. Unfortunately, many ocean scientists provide either incomplete metadata to allow for automatic data search, retrieval, transfer, or evaluation, or they provide none at all.