Set Of Metadata Research Articles

An analysis and comparison of keyword recommendation methods for scientific data

To classify and search various kinds of scientific data, it is useful to annotate those data with keywords from a controlled vocabulary. Data providers, such as researchers, annotate their own data with keywords from the provided vocabulary. However, for the selection of suitable keywords, extensive knowledge of both the research domain and the controlled vocabulary is required. Therefore, the annotation of scientific data with keywords from a controlled vocabulary is a time-consuming task for data providers. In this paper, we discuss methods for recommending relevant keywords from a controlled vocabulary for the annotation of scientific data through their metadata. Many previous studies have proposed approaches based on keywords in similar existing metadata; we call this the indirect method. However, when the quality of the existing metadata set is insufficient, the indirect method tends to be ineffective. Because the controlled vocabularies for scientific data usually provide definition sentences for each keyword, it is also possible to recommend keywords based on the target metadata and the keyword definitions; we call this the direct method. The direct method does not utilize the existing metadata set and therefore is independent of its quality. Also, for the evaluation of keyword recommendation methods, we propose evaluation metrics based on a hierarchical vocabulary structure, which is a distinctive feature of most controlled vocabularies. Using our proposed evaluation metrics, we can evaluate keyword recommendation methods with an emphasis on keywords that are more difficult for data providers to select. In experiments using real earth science datasets, we compare the direct and indirect methods to verify their effectiveness, and observe how the indirect method depends on the quality of the existing metadata set. The results show the importance of metadata quality in recommending keywords.

Crossref: The sustainable source of community-owned scholarly metadata

This paper describes the scholarly metadata collected and made available by Crossref, as well as its importance in the scholarly research ecosystem. Containing over 106 million records and expanding at an average rate of 11% a year, Crossref’s metadata has become one of the major sources of scholarly data for publishers, authors, librarians, funders, and researchers. The metadata set consists of 13 content types, including not only traditional types, such as journals and conference papers, but also data sets, reports, preprints, peer reviews, and grants. The metadata is not limited to basic publication metadata, but can also include abstracts and links to full text, funding and license information, citation links, and the information about corrections, updates, retractions, etc. This scale and breadth make Crossref a valuable source for research in scientometrics, including measuring the growth and impact of science and understanding new trends in scholarly communications. The metadata is available through a number of APIs, including REST API and OAI-PMH. In this paper, we describe the kind of metadata that Crossref provides and how it is collected and curated. We also look at Crossref’s role in the research ecosystem and trends in metadata curation over the years, including the evolution of its citation data provision. We summarize the research used in Crossref’s metadata and describe plans that will improve metadata quality and retrieval in the future.

Metabolism-associated molecular classification of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a disease with unique management complexity because it displays high heterogeneity of molecular phenotypes. We herein aimed to characterize the molecular features of HCC by the development of a classification system that was based on the gene expression profile of metabolic genes. Integrative analysis was performed with a metadata set featuring 371 and 231 HCC human samples from the Cancer Genome Atlas and the International Cancer Genome Consortium, respectively. All samples were linked with clinical information. RNA sequencing data of 2752 previously characterized metabolism‐related genes were used for non‐negative matrix factorization clustering, and three subclasses of HCC (C1, C2, and C3) were identified. We then analyzed the metadata set for metabolic signatures, prognostic value, transcriptome features, immune infiltration, clinical characteristics, and drug sensitivity of subclasses, and compared the resulting subclasses with previously published classifications. Subclass C1 displayed high metabolic activity, low α‐fetoprotein (AFP) expression, and good prognosis. Subclass C2 was associated with low metabolic activities and displayed high expression of immune checkpoint genes, demonstrating drug sensitivity toward cytotoxic T‐lymphocyte‐associated protein‐4 inhibitors and the receptor tyrosine kinase inhibitor cabozantinib. Subclass C3 displayed intermediate metabolic activity, high AFP expression level, and bad prognosis. Finally, a 90‐gene classifier was generated to enable HCC classification. This study establishes a new HCC classification based on the gene expression profiles of metabolic genes, thereby furthering the understanding of the genetic diversity of human HCC.

Scientific Investigation of the Paintings from the Agarbiciu (Cluj County) Wooden Church

The investigation of the painting materials (pigments, binders and varnish) of Agarbiciu wooden church painted walls was done by employing both nondestructive (XRF and reflection FTIR spectroscopy for pigments) and destructive methods (FTIR spectroscopy and GC-MS spectrometry for pigments and binders). The digital restoration of the interior of the wooden church was done using the physical-chemical composition of the painting layer, a metadata set that describes the timeline, detailed photographic documentation and the 3D scanned interior of the church.

The future of interlinked, interoperable and scalable metadata

With the growing diversity of information resources the emphasis on data-centric applications such as big data, metadata, semantics and ontologies has become central. This editorial paper presents a summary of recent developments in metadata, semantics and ontologies - focusing in particular on metadata enriching, linking and interoperability. National libraries and archives are devising new bibliographic models and metadata presentation formats. Bibliographic metadata sets are being made available using these new data formats such as RDF. The new formats are aiming to represent data in granular structures and define unique identification protocols such as URIs. The paper concludes by introducing the five papers included in the special issue. The papers in this special issue present novel approaches to metadata integration, interoperability frameworks, re-use of metadata ontologies and methods of metadata quality analysis.

Towards Findable, Accessible, Interoperable and Reusable (FAIR) Data Repositories: Improving a Data Repository to Behave as a FAIR Data Point | Repositórios para dados localizáveis, acessíveis, interoperáveis e reutilizáveis (FAIR): adaptando um repositório de dados para se comportar como um FAIR Data Point

ABSTRACT Significant effort is required to find, make sense and reuse research data. To tackle this problem, the Findable, Accessible, Reusable and Interoperable (FAIR) data principles describe a minimal set of requirements for data management and stewardship, considered as the technological basis for the European Open Science Cloud. The FAIR data point (FDP) leverages linked data (LD) to expose data and metadata adhering to the FAIR data principles, specifying a set of standardized metadata that a data repository should implement. Data owners can expose datasets, and data users can reuse datasets through RESTful services, enabling interoperability in a web scale. Data repositories and their underlying software only recently started supporting LD, and their metadata are only available as key-value pairs. An open question in this context is how to enable an existing data repository software to be compliant with the FDP specification, i.e., how to add semantic descriptions to data repositories to ensure the semantic interoperability among data from different repositories? This paper describes a semantic proxy solution to enable a data repository software, the EUDAT B2share service to behave as an FDP in a non-invasive and non-intrusive way, enabling the semantic interoperability through semantic translations. Our solution describes a methodology for metadata mapping based on endogenous model-driven transformations from lexicon to semantic models. We show how metadata in key-value pairs from a general-purpose repository can be made compliant with LD technology without changing the repository software. The solution validation includes functional tests of the FDP metadata layers and a performance analysis of the impact of the semantic proxy on data exchange. The results show that B2share can be compliant to FDP specifications with a reduced impact on the data exchange performance. Therefore, the validation shows that the solution is feasible and adequate to transform a general-purpose data repository software in an FDP. Keywords: FAIR Data; Data Reusability; Data Repository Software; FAIR Data Point.

Metadata Concepts for Advancing the Use of Digital Health Technologies in Clinical Research

Digital health technologies (smartphones, smartwatches, and other body-worn sensors) can act as novel tools to aid in the diagnosis and remote objective monitoring of an individual’s disease symptoms, both in clinical care and in research. Nonetheless, such digital health technologies have yet to widely demonstrate value in clinical research due to insufficient data interpretability and lack of regulatory acceptance. Metadata, i.e., data that accompany and describe the primary data, can be utilized to better understand the context of the sensor data and can assist in data management, data sharing, and subsequent data analysis. The need for data and metadata standards for digital health technologies has been raised in academic and industry research communities and has also been noted by regulatory authorities. Therefore, to address this unmet need, we here propose a metadata set that reflects regulatory guidelines and that can serve as a conceptual map to (1) inform researchers on the metadata they should collect in digital health studies, aiming to increase the interpretability and exchangeability of their data, and (2) direct standard development organizations on how to extend their existing standards to incorporate digital health technologies. The proposed metadata set is informed by existing standards pertaining to clinical trials and medical devices, in addition to existing schemas that have supported digital health technology studies. We illustrate this specifically in the context of Parkinson’s disease, as a model for a wide range of other chronic conditions for which remote monitoring would be useful in both care and science. We invite the scientific and clinical research communities to apply the proposed metadata set to ongoing and planned research. Where the proposed metadata fall short, we ask users to contribute to its ongoing revision so that an adequate degree of consensus can be maintained in a rapidly evolving technology landscape.

Semantic relatedness algorithm for keyword sets of geographic metadata

ABSTRACTAdvances in linked geospatial data, recommender systems, and geographic information retrieval have led to urgent necessity to assess the overall semantic relatedness between keyword sets of geographic metadata. In this study, a new model is proposed for computing the semantic relatedness between arbitrary two keyword sets of geographic metadata stored in current global spatial data infrastructures. In this model, the overall semantic relatedness is derived by pairing these keywords that are found to be most relevant to each other and averaging their relatedness. To find the most relevant keywords across two keyword sets precisely, the keywords in the keyword set of geographic metadata are divided into three kinds: the thesaurus elements, the WordNet elements, and the statistical elements. The thesaurus-lexical relatedness measure (TLRM), the extended thesaurus-lexical relatedness measure (ETLRM), and the Longest Common Substring method are proposed to compute the semantic relatedness between two thesaurus elements, two WordNet elements, a thesaurus element, and a WordNet element and two statistical elements, respectively. A human data set – the geographic-metadata’s keyword set relatedness dataset, which was used to evaluate the precision of the semantic relatedness measures of keyword sets of geographic metadata, was created. The proposed method was evaluated against the human-generated relatedness judgments and was compared with the Jaccard method and Vector Space Model. The results demonstrated that the proposed method achieved a high correlation with human judgments and outperformed the existing methods. Finally, the proposed method was applied to quantitatively linked geospatial data.

Towards a Federation of Metadata Repositories: Addressing Technical Interoperability.

The utilisation of metadata repositories increasingly promotes secondary use of routinely collected data. However, this has not yet solved the problem of data exchange across organisational boundaries. The local description of a metadata set must also be exchangeable for flawless data exchange. In previous work, a metadata exchange language QL4MDR was developed. This work aimed to examine the applicability of this exchange language. For this purpose, existing MDR implementations were identified and systematically inspected and roughly divided into two categories to distinguish between data integration and query integration. It has been shown that all the implementations can be adapted to QL4MDR. The integration of metadata is an important first step; it enables the exchange of information, which is so urgently needed for the further processing of instance data, from the metadata mappings to the transformation rules.

Every Shred Helps: Assembling Evidence From Orphaned JPEG Fragments

In this paper, we address the problem of forensic photo carving, which serves as one of the key sources of digital evidence in modern law enforcement. We propose efficient algorithms for assembling meaningful photographs from orphaned photofragments, carved without access to file headers, meta-data, or compression settings. The addressed problem raises a novel variant of a jigsaw puzzle with an unknown number of mixed images, missing pieces, and severe brightness and colorization artifacts. We construct an efficient compatibility metric for matching puzzle pieces and a corresponding image stitching procedure which allows us to mitigate these artifacts. To facilitate photo assembly, we perform a forensic analysis of the fragments to provide clues about their location within the frame of the imaging sensor. The proposed algorithm formulates the assembly problem as finding non-overlapping sets in an interval graph spanned over the input fragments. The algorithm exhibits lower computational complexity compared with a popular puzzle-solving approach based on minimal spanning trees.

Unique, Persistent, Resolvable: Identifiers as the Foundation of FAIR

The FAIR principles describe characteristics intended to support access to and reuse of digital artifacts in the scientific research ecosystem. Persistent, globally unique identifiers, resolvable on the Web, and associated with a set of additional descriptive metadata, are foundational to FAIR data. Here we describe some basic principles and exemplars for their design, use and orchestration with other system elements to achieve FAIRness for digital research objects.

Managing Rich Metadata in High-Performance Computing Systems Using a Graph Model

High-performance computing (HPC) systems generate huge amounts of metadata about different entities such as jobs, users, and files. Existing systems can efficiently record and manage part of these metadata, mainly the POSIX metadata of data files (e.g., file size, name, and permissions mode). But another important set of metadata, referred to as “rich” metadata in this study, which record not only wider range of entities (e.g., running processes and jobs) but also more complex relationships between them, are mostly missing in current HPC systems. Yet such rich metadata are critical for supporting many advanced data management functions such as identifying data sources and parameters behind a given result; auditing data usage; or understanding details about how inputs are transformed into outputs. To uniformly and efficiently manage the rich metadata generated in HPC systems, We propose to utilize a graph model in this study. We identify the key challenges of implementing such a graph-based HPC rich metadata management system and present GraphMeta, a graph-based rich metadata management system designed and optimized for HPC platforms, to tackle these challenges. Extensive evaluations on both synthetic and real HPC metadata workloads show its advantages in both performance and scalability compared with existing solutions.

A semi-automatic metadata extraction model and method for video-based e-learning contents

Video-based learning offers a learner a self-paced, lucid, memorizable, and a flexible way of learning. The availability of abundant educational video materials on the web has certainly abetted an individual’s learning means. But the lack of necessary information about the videos makes it difficult for the learner to search and select the exact video as per his/her requirement and suitability in terms of the learner’s learning capability and the material’s relevancy, difficulty level, etc. Educational video recommendation systems also suffer from a similar problem. Extracting the required metadata, by different means, from the learning videos is a plausible solution. Despite the credible research efforts on video metadata extraction, the problem of educational video metadata extraction has been overlooked. This paper proposes a comprehensive approach to extract educational metadata from a learning video. A semiautomatic mechanism that includes manual and computational approaches is introduced for metadata extraction and to evaluate the values of these metadata. Along with identifying a set of specific metadata attributes from IEEE LOM, few additional attributes are suggested which are imperative to assess the suitability of a video-based learning object in terms of the personalized preference and suitability of a learner. The test results are validated by comparing with the manually extracted metadata by experts, on the same videos. The outcome establishes the promising effectiveness of the approach.

Association of Closed-Loop Brain Stimulation Neurophysiological Features With Seizure Control Among Patients With Focal Epilepsy

A bidirectional brain-computer interface that performs neurostimulation has been shown to improve seizure control in patients with refractory epilepsy, but the therapeutic mechanism is unknown. To investigate whether electrographic effects of responsive neurostimulation (RNS), identified in electrocorticographic (ECOG) recordings from the device, are associated with patient outcomes. Retrospective review of ECOG recordings and accompanying clinical meta-data from 11 consecutive patients with focal epilepsy who were implanted with a neurostimulation system between January 28, 2015, and June 6, 2017, with 22 to 112 weeks of follow-up. Recorded ECOG data were obtained from the manufacturer; additional system-generated meta-data, including recording and detection settings, were collected directly from the manufacturer's management system using an in-house, custom-built platform. Electrographic seizure patterns were identified in RNS recordings and evaluated in the time-frequency domain, which was locked to the onset of the seizure pattern. Patterns of electrophysiological modulation were identified and then classified according to their latency of onset in relation to triggered stimulation events. Seizure control after RNS implantation was assessed by 3 main variables: mean frequency of seizure occurrence, estimated mean severity of seizures, and mean duration of seizures. Overall seizure outcomes were evaluated by the extended Personal Impact of Epilepsy Scale questionnaires, a patient-reported outcome measure of 3 domains (seizure characteristics, medication adverse effects, and quality of life), with a range of possible scores from 0 to 300 in which lower scores indicate worse status, and the Engel scale, which comprises 4 classes (I-IV) in which lower numbers indicate greater improvement. Electrocorticographic data from 11 patients (8 female; mean [range] age, 35 [19-65] years; mean [range] duration of epilepsy, 19 [5-37] years) were analyzed. Two main categories of electrophysiological signatures of stimulation-induced modulation of the seizure network were discovered: direct and indirect effects. Direct effects included ictal inhibition and early frequency modulation but were not associated with improved clinical outcomes (odds ratio [OR], 0.67; 95% CI, 0.06-7.35; P > .99). Only indirect effects-those occurring remote from triggered stimulation-were associated with improved clinical outcomes (OR, infinity; 95% CI, -infinity to infinity; P = .02). These indirect effects included spontaneous ictal inhibition, frequency modulation, fragmentation, and ictal duration modulation. These findings suggest that RNS effectiveness may be explained by long-term, stimulation-induced modulation of seizure network activity rather than by direct effects on each detected seizure.

Loss of reticulocalbin 2 lowers blood pressure and restrains ANG II-induced hypertension in vivo.

Hypertension affects over 1 billion people worldwide and increases the risk for heart failure, stroke, and chronic kidney disease. Despite high prevalence and devastating impact, its etiology still remains poorly understood for most hypertensive cases. Rcn2, which encodes reticulocalbin 2, is a candidate gene for atherosclerosis that we have previously reported in mice. Here, we identified Rcn2 as a novel regulator of blood pressure in mice. Rcn2 was abundantly expressed in the endothelium and adventitia of normal arteries and was dramatically upregulated in the medial layer of the artery undergoing structural remodeling. Deletion of Rcn2 lowered basal blood pressure and attenuated ANG II-induced hypertension in C57BL/6 mice. siRNA knockdown of Rcn2 dramatically increased production of the nitric oxide (NO) breakdown products nitrite and nitrate by endothelial cells but not by smooth muscle cells. Isolated carotid arteries from Rcn2-/- mice showed an increased sensitivity to the ACh-induced NO-mediated relaxant response compared with arteries of Rcn2+/+ mice. Analysis of a recent meta-data set showed associations of genetic variants near RCN2 with blood pressure in humans. These data suggest that Rcn2 regulates blood pressure and contributes to hypertension through actions on endothelial NO synthase.

Early treatment modifications improve chemotherapy adherence in ovarian cancer patients ≥70 years

ObjectiveElderly ovarian cancer patients are underrepresented in clinical trials and disadvantaged with regard to therapeutic standards compared to other age groups. We explored the specific performance of a subset of patients aged ≥70 years in a large meta-data set of 3 phase III trials. Methods3333 patients with advanced ovarian cancer recruited into 3 clinical phase III trials of the AGO & GINECO study groups were retrospectively analysed for age-specific prognostic and toxicity parameters. ResultsOnly 10% (359/3333) of the patients were aged ≥70 years. This subgroup presented with impaired performance statuses (ECOG 2 14.8 vs 10.1%) and higher FIGO-stages (FIGO IIIC-IV 78.5 vs 73.6%) compared to younger patients. Complete operative tumor resection was achieved less frequently (postoperative tumor burden >10 mm 46.7 vs 33.9%) and elderly received less cycles of platinum/taxane-based chemotherapies (>4 cycles 81.9 vs 90.7%). FIGO-stage, histology, postoperative tumor burden and number of chemotherapy cycles were independent prognostic factors in elderly patients. Elderly patients with ≤4 cycles of chemotherapy showed a median OS of 18.4 months compared to 30.9 months in elderly with 5–6 cycles (p < 0.001). This effect was accentuated in elderly patients after complete tumor resection (cumulative survival benefit of 33.8 months). Analyses of chemotherapeutic delivery revealed that elderly patients with at least one cycle delay had higher chances to complete >4 cycles of chemotherapy. ConclusionsProtocol defined treatment modifications might support completion of >4 cycles of standard chemotherapy in fit elderly OC patients.

Sustainability in Data and Food

As the world population continues to increase, world food production is not keeping up. This means that to continue to feed the world, we will need to optimize the production and utilization of food around the globe. Optimization of a process on a global scale requires massive data. Agriculture is no exception, but also brings its own unique issues, based on how wide spread agricultural data are, and the wide variety of data that is relevant to optimization of food production and supply. This suggests that we need a global data ecosystem for agriculture and nutrition. Such an ecosystem already exists to some extent, made up of data sets, metadata sets and even search engines that help to locate and utilize data sets. A key concept behind this is sustainability—how do we sustain our data sets, so that we can sustain our production and distribution of food? In order to make this vision a reality, we need to navigate the challenges for sustainable data management on a global scale. Starting from the current state of practice, how do we move forward to a practice in which we make use of global data to have an impact on world hunger? In particular, how do we find, collect and manage the data? How can this be effectively deployed to improve practice in the field? And how can we make sure that these practices are leading to the global goals of improving production, distribution and sustainability of the global food supply? These questions cannot be answered yet, but they are the focus of ongoing and future research to be published in this journal and elsewhere.

Cost and schedule overruns in large hydropower dams: an assessment of projects completed since 2000

ABSTRACT This paper presents novel data on cost and schedule overruns in recent dam projects started and completed since 2000 and compares them with pre-existing data sets on projects started before 2000. Combining these data, a meta-data set was created of 184 cost overrun and 191 time overrun data points. For post-2000 projects, the average cost overrun was 33% and schedule overrun was 18% as compared with 46% and 37% respectively for pre-2000 projects. While a decrease in the averages was observed, the change in cost overruns is not statistically significant, whereas the change in time overruns is significant.

Synthetic lethality guiding selection of drug combinations in ovarian cancer.

BackgroundSynthetic lethality describes a relationship between two genes where single loss of either gene does not trigger significant impact on cell viability, but simultaneous loss of both gene functions results in lethality. Targeting synthetic lethal interactions with drug combinations promises increased efficacy in tumor therapy.Materials and methodsWe established a set of synthetic lethal interactions using publicly available data from yeast screens which were mapped to their respective human orthologs using information from orthology databases. This set of experimental synthetic lethal interactions was complemented by a set of predicted synthetic lethal interactions based on a set of protein meta-data like e.g. molecular pathway assignment. Based on the combined set, we evaluated drug combinations used in late stage clinical development (clinical phase III and IV trials) or already in clinical use for ovarian cancer with respect to their effect on synthetic lethal interactions. We furthermore identified a set of drug combinations currently not being tested in late stage ovarian cancer clinical trials that however have impact on synthetic lethal interactions thus being worth of further investigations regarding their therapeutic potential in ovarian cancer.ResultsTwelve of the tested drug combinations addressed a synthetic lethal interaction with the anti-VEGF inhibitor bevacizumab in combination with paclitaxel being the most studied drug combination addressing the synthetic lethal pair between VEGFA and BCL2. The set of 84 predicted drug combinations for example holds the combination of the PARP inhibitor olaparib and paclitaxel, which showed efficacy in phase II clinical studies.ConclusionA set of drug combinations currently not tested in late stage ovarian cancer clinical trials was identified having impact on synthetic lethal interactions thus being worth of further investigations regarding their therapeutic potential in ovarian cancer.

Prediction of Hypoglycemia During Aerobic Exercise in Adults With Type 1 Diabetes.

Fear of exercise related hypoglycemia is a major reason why people with type 1 diabetes (T1D) do not exercise. There is no validated prediction algorithm that can predict hypoglycemia at the start of aerobic exercise. We have developed and evaluated two separate algorithms to predict hypoglycemia at the start of exercise. Model 1 is a decision tree and model 2 is a random forest model. Both models were trained using a meta-data set based on 154 observations of in-clinic aerobic exercise in 43 adults with T1D from 3 different studies that included participants using sensor augmented pump therapy, automated insulin delivery therapy, and automated insulin and glucagon therapy. Both models were validated using an entirely new validation data set with 90 exercise observations collected from 12 new adults with T1D. Model 1 identified two critical features predictive of hypoglycemia during exercise: heart rate and glucose at the start of exercise. If heart rate was greater than 121 bpm during the first 5 min of exercise and glucose at the start of exercise was less than 182 mg/dL, it predicted hypoglycemia with 79.55% accuracy. Model 2 achieved a higher accuracy of 86.7% using additional features and higher complexity. Models presented here can assist people with T1D to avoid exercise related hypoglycemia. The simple model 1 heuristic can be easily remembered (the 180/120 rule) and model 2 is more complex requiring computational resources, making it suitable for automated artificial pancreas or decision support systems.