Errors In Metadata Research Articles

COVER: Alleviating Crash-Consistency Error Amplification in Secure Persistent Memory Systems

Data security (including confidentiality, integrity, and availability) and crash consistency guarantees are essential for building trusted persistent memory (PM) systems. Security and consistency metadata are added to enable the guarantees. Recent studies show that errors in security metadata have the amplified effect, which significantly affects data availability. However, the impact of consistency metadata errors on data availability has rarely been discussed. We identify the crash-consistency error amplification (CCEA) problem, several errors in consistency metadata can make a large portion of data in PM possibly inconsistent. The error sensitivity of consistency metadata is higher than data and security metadata, thus requiring special attention. It is inefficient to address this problem by using the methods that are proposed to alleviate the amplified effect of security metadata errors, because security metadata are generally designed for a single purpose (e.g., integrity verification), while consistency metadata are designed for multiple purposes, including inconsistency locating and recovery. To effectively and efficiently alleviate the CCEA problem, we propose a c rash c o nsistency ver ification approach (COVER) that decouples inconsistency locating and recovery. COVER provides three design options that support different tradeoffs between effectiveness and efficiency. Experimental results show that COVER effectively alleviates the problem with only about 1.0% performance degradation on average compared with the state-of-the-art secure PM design.

Audiovisual Resources Metadata: Analysis of Records Originating From Novice Metadata Creators in Kuwait

Audiovisual resources available online gain more importance in satisfying users’ information needs, yet the quality of metadata that ensures discoverability of these resources is not yet emphasized, mainly due to lack of benchmark data. This article addresses the need by presenting results of the comparative evaluation of accuracy and completeness of Dublin Core metadata records created by metadata beginners to represent audio recordings and video recordings. We present our findings in the context of how the metadata learning is organized at the program which currently prepares most of those entering the information profession in Kuwait. Findings reveal some similarities, as well as some pronounced differences in metadata accuracy and completeness patterns for two kinds of online digital resources: audio recordings and video recordings. Video metadata was found to be of substantially higher quality than audio metadata created by the same beginners. Overall, for audiovisual information resources, we found the Type metadata field to be the least prone to completeness errors, and Format to accuracy errors. Our data suggests that the Source metadata field is the most vulnerable for accuracy errors in both audio metadata and video metadata. However, Dublin Core metadata fields with the highest possibility of completeness errors did not exhibit overlap between the two sets of beginner-created metadata records. We discuss examples of the most common metadata errors and compare results with findings of previous research. Empirical data obtained in this study allows assessing preparedness of information professionals to create metadata that is functional in supporting resource discovery.

Evaluation and impact of descriptive metadata on academic event management in Ukraine: A quantitative study

Objective. The study aims to understand the impact of descriptive metadata in academic events. It focuses on the need for analytical frameworks that consider the events' characteristics and the interests of the participants. Design/Methodology/Approach. The article focuses on academic event management and metadata quality based on user preferences and feedback. It surveyed Ukrainian organizers and scholars between August and October 2022, analyzing the responses of 1,270 participants using descriptive statistics and qualitative analysis in RStudio. Results/Discussion. The survey showed that most (over 84%) of organizers and academics are dissatisfied with the metadata quality, with a third rating it as very bad. Frequent errors in metadata emphasized the need for better management, including a preference for using identifiers like ORCID and DOI and a preference for open access to information about academic events. Conclusions. The results highlight the importance of developing specialized tools for metadata management and standardization of metadata elements in Ukraine to facilitate organization and participation in academic events at national and international levels. Originality/Value. The study makes an important contribution to understanding descriptive metadata management in academic events in Ukraine, suggesting ways to improve efficiency in this area.

Design and quality control of large-scale two-sample Mendelian randomization studies.

Mendelian randomization (MR) studies are susceptible to metadata errors (e.g. incorrect specification of the effect allele column) and other analytical issues that can introduce substantial bias into analyses. We developed a quality control (QC) pipeline for the Fatty Acids in Cancer Mendelian Randomization Collaboration (FAMRC) that can be used to identify and correct for such errors. We collated summary association statistics from fatty acid and cancer genome-wide association studies (GWAS) and subjected the collated data to a comprehensive QC pipeline. We identified metadata errors through comparison of study-specific statistics to external reference data sets (the National Human Genome Research Institute-European Bioinformatics Institute GWAS catalogue and 1000 genome super populations) and other analytical issues through comparison of reported to expected genetic effect sizes. Comparisons were based on three sets of genetic variants: (i) GWAS hits for fatty acids, (ii) GWAS hits for cancer and (iii) a 1000 genomes reference set. We collated summary data from 6 fatty acid and 54 cancer GWAS. Metadata errors and analytical issues with the potential to introduce substantial bias were identified in seven studies (11.6%). After resolving metadata errors and analytical issues, we created a data set of 219 842 genetic associations with 90 cancer types, generated in analyses of 566 665 cancer cases and 1 622 374 controls. In this large MR collaboration, 11.6% of included studies were affected by a substantial metadata error or analytical issue. By increasing the integrity of collated summary data prior to their analysis, our protocol can be used to increase the reliability of downstream MR analyses. Our pipeline is available to other researchers via the CheckSumStats package (https://github.com/MRCIEU/CheckSumStats).

Perfect strangers: Digital asset management and lean Six Sigma

In order to grow, organisations must be nimble at problem-solving, agile at exploiting opportunities and adept at surmounting challenges. Growing a business entails more than just a focus on efficiency and costs, as quality and effectiveness matter too. It is a common misconception that adopting lean processes means doing more with less. In reality, the main focus is on simplifying processes, thus improving output and making work better (for everyone). Lean thinking entails looking at what you do, the kind of problems you have and how you currently solve them. Within the field of digital asset management, problem areas may be anything from mistakes or errors in metadata, to waiting on assets to be uploaded to be used, to moving files from one place to another manually. This paper discusses the benefits of lean continuous improvement for digital asset management. It describes how to utilise lean principles that have been vetted and practised by businesses all over the world, in all kinds of industries, to help them be successful, and discusses how these principles apply to digital asset management.

Modeling Subsurface Explosions Recorded on a Distributed Fiber Optic Sensor

AbstractFiber optic distributed acoustic sensors (DAS) are becoming a widely used tool for seismic sensing. Here we examine recordings of two subsurface chemical explosions, DAG‐1 and DAG‐3, each of which was about one metric ton (TNT equivalent), that were recorded from a helical fiber installed in two boreholes 80 m away from the source location. Several clear phases including the initial P wave, a weak S wave, and a surface reflected P wave are observed on the helical DAS data. We estimate a velocity model using arrival times measured from the fiber. The DAS waveform data were compared with colocated accelerometers at specific depths in both frequency and time domains. The spectra of the DAS data matched spectra estimated from the accelerometer records. Comparisons of observed waveform shape between the accelerometer records and the fiber measurements (strain‐rate) show reasonable agreement except for the data near the event depth. The DAS data and the accelerometer agreed in relative amplitudes but we had difficulties in matching absolute amplitudes, possibly due to errors in metadata. Synthetic strain‐rate waveforms were calculated using a 2D wavenumber algorithm and matched the waveform shape and relative amplitudes. In general, DAS is effective at recording strong ground motions at high spatial density. Comparison of the synthetic seismograms with observed data indicate that the waveforms are not consistent with a pure isotropic explosion source and that the observed S waves originate from very near the source region.

Accessibility evaluation of topographic maps in the National Library of Poland

Abstract. Digital libraries are created and managed mainly by traditional libraries, archives and museums. They collect, process, and make available digitized collections and data about them. These collections often constitute cultural heritage and they include, among others: books (including old prints), magazines, manuscripts, photographs, maps, atlases, postcards and graphics. An example of such a library is the National Library of Poland. It collects and provides digitally available data of about 55,000 maps.The effective use of cultural heritage resources and information from National Library of Poland gives the prerequisites and challenges for multidisciplinary research and cross-sectoral cooperation. These resources are an unlimited source of knowledge, constituting value in themselves but also providing data for many new studies, including interdisciplinary studies of the past. Information necessary for such research is usually distributed across a wide spectrum of fields, formats and languages, reflecting different points of view, and the key task is to find them in digital libraries.The growth of digital library collections requires high-quality metadata to make the materials collected by libraries fully accessible and to enable their integration and sharing between institutions. Consequently, three main metadata quality criteria have been defined to enable metadata management and evaluation. They are: accuracy, consistency, and completeness (Park, 2009, Park and Tosaka, 2010). Different aspects of metadata quality can also be defined as: accessibility, accuracy, availability, compactness, comprehensiveness, content, consistency, cost, data structure, ease of creation, ease of use, cost efficiency, flexibility, fitness for use, informativeness, quantity, reliability, standard, timeliness, transfer, usability (Moen et al., 1998). This list tells us where errors in metadata occur, which can result in hindering or completely disabling access to materials available through a digital library.Archival maps have always been present in the libraries. In the digital age, geographical space has begun to exist in libraries in two aspects: as old maps’ collections, as well as a geographic reference of sources other than cartographic materials. Despite many experiences in this field, the authors emphasize that the main problem is related to the fact that most libraries are not populating the coordinates to the metadata, which is required to enable and support geographical search (Southall and Pridal, 2012).During this stage the concept of research is born and the source materials necessary for the realization of this concept are collected. When using archival maps for such studies, it is important to be aware of detailed literature studies, including cartographic assumptions, the course and accuracy of cartographic works, the way of printing, the scope of updates of subsequent editions, and the period in which the given map was created. The ability to use cartographic materials also depends on the destination map. The awareness of the above issues allows researchers to avoid errors frequently made by non-cartographers, i.e. to prevent comparing maps on different scales and treating them as a basis for formulating very detailed yet unfortunately erroneous conclusions. Thus, one of the key tasks is to find materials that are comparable in terms of scale and that cover the same area and space in the historical period of interest.The research aim is to evaluate the quality of topographic maps metadata provided by the National Library of Poland, which are the basis for effective access to cartographic resources.The first research question is: how should topographic maps be described in metadata to enable finding them in the National Library of Poland? In other words, what kind of map-specific information should be saved in metadata (and in what way) to provide the proper characteristic of the spatially-related object?The second research question is: which topographic maps have the best metadata in such a way as to give the users the best chance of finding the cartographic materials necessary for their research?The paper will present the results of research connected with finding criteria and features to metadata evaluation, it means how archival maps are described. For the maps, it is a set of map features, which are collected in the metadata. This set includes the geographic location, map scale, map orientation, and cartographic presentation methods. The conducted evaluation refers to the quality of metadata, or, in other words, the accessibility of archival cartographic resources.

Achieving and Maintaining Metadata Quality: Toward a Sustainable Workflow for the IDEALS Institutional Repository

ABSTRACTThis article documents the steps taken to assess metadata errors within the IDEALS repository. It describes the workflows established to create accurate and consistent metadata, focusing especially on the batch ingest and retroactive metadata remediation processes. It also seeks to address theoretical issues surrounding the concept of metadata quality.

Google Book Search Project: the Pros and Cons

The article analyzes the Google Book Search project: its objectives, both the declared ones (the radical democratization of knowledge) and real (to create a new market of paid services), stages of the project’s implementation, dynamics of change in its assessments according to the American scientific journalism. It is noted that the project underwent a substantial correction in the course of litigation and developing of settlement agreement with holders of the copyright on the books included in the scope of operation of the Google Book Search. It indicates that the service was gradually modified towards greater comfort and comprehensiveness of the utilities provided. Particular attention is given to the identification of social and cultural implications of the monopolization of knowledge dissemination: the marginalization of large strata of the book heritage as a result of the absolutization of algorithms of the search results automatic ranking; the metadata errors that can mislead users; the preferences that the book search engine gives to the English literature. The article thoroughly analyzes the critical arguments against the cultural expansion of global brand, which were put forward by the professional library community. There is reviewed the position of Jean-Noel Jeanneney, president of the French National Library, who considers the Google book project as continuation of the globalist cultural policy, leading to indirect discrimination of other cultures, which contradicts the Declaration on Cultural Diversity, UNESCO, 2001.

Unsupervised Celebrity Face Naming in Web Videos

This paper investigates the problem of celebrity face naming in unconstrained videos with user-provided metadata . Instead of relying on accurate face labels for supervised learning, a rich set of relationships automatically derived from video content and knowledge from image domain and social cues is leveraged for unsupervised face labeling. The relationships refer to the appearances of faces under different spatio-temporal contexts and their visual similarities. The knowledge includes Web images weakly tagged with celebrity names and the celebrity social networks. The relationships and knowledge are elegantly encoded using conditional random field (CRF) for label inference. Two versions of face annotation are considered: within-video and between-video face labeling. The former addresses the problem of incomplete and noisy labels in metadata, where null assignment of names is allowed—a problem seldom been considered in the literature. The latter further rectifies the errors in metadata, specifically to correct false labels and annotate faces with missing names in the metadata of a video, by considering a group of socially connected videos for joint label inference. Experimental results on a large archive of Web videos show the robustness of the proposed approach in dealing with the problems of missing and false labels, leading to higher accuracy in face labeling than several existing approaches but with minor degradation in speed efficiency.

Investigating the effect of definitions and best practice guidelines on errors in Dublin Core metadata records

Researchers argue that the utility of metadata records depends on the kinds of information used (i.e. definitions and best practice guidelines) when creating metadata. To verify this claim, a mixed factorial design experiment was conducted in which 120 participants were assigned to four groups on the basis of their training level and the kinds of information (definitions only or best practice guidelines) accessible to create metadata records. Participants used Dublin Core to create records for the same resources and errors were analysed and compared across groups. Although participants who used best practice guidelines made significantly fewer errors than those participants who used only definitions, the high error rates observed across all treatment groups suggest the need for additional measures to control errors in metadata records.

Common Errors in Ecological Data Sharing

<strong>Objectives:</strong> (1) to identify common errors in data organization and metadata completeness that would preclude a “reader” from being able to interpret and re-use the data for a new purpose; and (2) to develop a set of best practices derived from these common errors that would guide researchers in creating more usable data products that could be readily shared, interpreted, and used. <strong>Methods:</strong> We used directed qualitative content analysis to assess and categorize data and metadata errors identified by peer reviewers of data papers published in the Ecological Society of America’s (ESA) Ecological Archives. Descriptive statistics provided the relative frequency of the errors identified during the peer review process. <strong>Results:</strong> There were seven overarching error categories: Collection & Organization, Assure, Description, Preserve, Discover, Integrate, and Analyze/Visualize. These categories represent errors researchers regularly make at each stage of the Data Life Cycle. Collection & Organization and Description errors were some of the most common errors, both of which occurred in over 90% of the papers. <strong> Conclusions:</strong> Publishing data for sharing and reuse is error prone, and each stage of the Data Life Cycle presents opportunities for mistakes. The most common errors occurred when the researcher did not provide adequate metadata to enable others to interpret and potentially re-use the data. Fortunately, there are ways to minimize these mistakes through carefully recording all details about study context, data collection, QA/ QC, and analytical procedures from the beginning of a research project and then including this descriptive information in the metadata.

An Experimental Study of Metadata Training Effectiveness on Errors in Metadata Records

Previous studies relate the lack of adequate metadata training with errors often found in metadata records. Due to lack of empirical evidence, such a relationship is not well understood. In this study, metadata records created by individuals who have not received any metadata training are compared to records created by individuals who have received metadata training. The aim is to investigate the relationship that exists between training and errors in metadata records. Using a 2×2×4 mixed experimental group design, 120 participants were asked to use Dublin Core to create records for a web page, an image, a document, and a video. Records were analyzed on the basis of the total number of errors and on the basis of four kinds of errors: incorrect value, incorrect element, inconsistent value, and incomplete information. Results indicate that depending on the kind of error investigated and the resource for which metadata is created, training may or may not have an effect on errors in metadata records. Implications of observations made and limitations of the experimental design are discussed in order to situate the relevance of the present study.

An Assessment of Google Books’ Metadata

This article reports on a study of error rates found in the metadata records of texts scanned by the Google Books digitization project. A review of the author, title, publisher, and publication year metadata elements for 400 randomly selected Google Books records was undertaken. The results show 36% of sampled books in the digitization project contained metadata errors. This error rate is higher than one would expect to find in a typical library online catalog.

Supervised classification of microbiota mitigates mislabeling errors.

The exponential growth of DNA sequencing technologies and concomitant advances in bioinformatics methods are revolutionizing our understanding of diverse microbial communities (Riesenfeld et al., 2004; Tyson et al., 2004; Hugenholtz and Tyson, 2008; Tringe and Hugenholtz, 2008; Caporaso et al., 2010). Large-scale microbial metagenomics studies have particularly exciting applications in the arena of human health, laying the foundation for the Human Microbiome Project (HMP). In the context of the HMP and related efforts, care has been taken to understand the impact of amplification biases or sequencing errors. However, far less attention has been paid to the impact of errors in metadata on biological interpretations and the mitigation of such errors. During processing and pooling of hundreds of samples, some mislabeling is likely. Figure 1 illustrates a real world example: several 16S rRNA amplifications of bacterial community DNA samples collected along a time series were accidentally mislabeled (late switched to early) (Koenig et al., 2010). Automated detection of such errors will be important as datasets become increasingly large and complex.

Experiences in Deploying Metadata Analysis Tools for Institutional Repositories

Current institutional repository software provides few tools to help metadata librarians understand and analyze their collections. In this article, we compare and contrast metadata analysis tools that were developed simultaneously, but independently, at two New Zealand institutions during a period of national investment in research repositories: the Metadata Analysis Tool (MAT) at The University of Waikato, and the Kiwi Research Information Service (KRIS) at the National Library of New Zealand. The tools have many similarities: they are convenient, online, on-demand services that harvest metadata using OAI-PMH; they were developed in response to feedback from repository administrators; and they both help pinpoint specific metadata errors as well as generating summary statistics. They also have significant differences: one is a dedicated tool wheres the other is part of a wider access tool; one gives a holistic view of the metadata whereas the other looks for specific problems; one seeks patterns in the data values whereas the other checks that those values conform to metadata standards. Both tools work in a complementary manner to existing Web-based administration tools. We have observed that discovery and correction of metadata errors can be quickly achieved by switching Web browser views from the analysis tool to the repository interface, and back. We summarize the findings from both tools’ deployment into a checklist of requirements for metadata analysis tools.