Search

This technical report is aimed at guiding the reporting of data on analytical test results, and related metadata, to EFSA in the context of the activities for the surveillance and monitoring of African Swine Fever. The objective is to explain in detail the individual data elements that are included in the EFSA Standard Sample Description version 2 (SSD2) data model. The guidance is intended to support the reporting countries in data transmission using eXtensible Markup Language (XML) data file transfer through the Data Collection Framework (DCF) according to the protocol described in the EFSA Guidance on Data Exchange version 2 (GDE2). The data elements are explained, including information about data type, list of allowed terms and associated business rules. Instructions about how to report common sampling schemes are also provided to ensure harmonised reporting among countries. [ABSTRACT FROM AUTHOR]

Systematic reviews are vital instruments for researchers to understand broad trends in a field and synthesize evidence on the effectiveness of interventions in addressing specific issues. The quality of a systematic review depends critically on having comprehensively surveyed all relevant literature on the review topic. In addition to database searching, handsearching is an important supplementary technique that helps increase the likelihood of identifying all relevant studies in a literature search. Traditional handsearching requires reviewers to manually browse through a curated list of field‐specific journals and conference proceedings to find articles relevant to the review topic. This manual process is not only time‐consuming, laborious, costly, and error‐prone due to human fatigue, but it also lacks replicability due to its cumbersome manual nature. To address these issues, this paper presents a free and open‐source Python package and an accompanying web‐app, Paperfetcher, to automate the retrieval of article metadata for handsearching. With Paperfetcher's assistance, researchers can retrieve article metadata from designated journals within a specified time frame in just a few clicks. In addition to handsearching, it also incorporates a beta version of citation searching in both forward and backward directions. Paperfetcher has an easy‐to‐use interface, which allows researchers to download the metadata of retrieved studies as a list of DOIs or as an RIS file to facilitate seamless import into systematic review screening software. To the best of our knowledge, Paperfetcher is the first tool to automate handsearching with high usability and a multi‐disciplinary focus. [ABSTRACT FROM AUTHOR]

Despite large public investments in facilitating the secondary use of data, there is little information about the specific factors that predict data's reuse. Using data download logs from the Inter‐university Consortium for Political and Social Research (ICPSR), this study examines how data properties, curation decisions, and repository funding models relate to data reuse. We find that datasets deposited by institutions, subject to many curatorial tasks, and whose access and preservation is funded externally, are used more often. Our findings confirm that investments in data collection, curation, and preservation are associated with more data reuse. [ABSTRACT FROM AUTHOR]

The amount of scientific papers is growing together with the development of science itself; but, although there is an unprecedented availability of large citation indexes, some daily activities of researchers remain time‐consuming and poorly supported. In this paper, we present Visual Bibliographies (VisualBib), a real‐time visual platform, designed using a zz‐structure‐based model for linking metadata and a narrative, visual approach for showing bibliographies. VisualBib represents a usable, advanced, and visual tool, which simplifies the management of bibliographies, supports a core set of bibliographic tasks, and helps researchers during complex analyses on scientific bibliographies. We present the variety of metadata formats and visualization methods, proposing two use case scenarios. The maturity of the system implementation allowed us two studies, for evaluating both the effectiveness of VisualBib in providing answers to specific data analysis tasks and to support experienced users during real‐life uses. The results of the evaluation are positive and describe an effective and usable platform. [ABSTRACT FROM AUTHOR]

A substantial proportion of trial registrations are not linked to corresponding published articles, limiting analyses and new tools. Our aim was to develop a method for finding articles reporting the results of trials that are registered on ClinicalTrials.gov when they do not include metadata links. We used a set of 27,280 trial registration and article pairs to train and evaluate methods for identifying missing links in both directions—from articles to registrations and from registrations to articles. We trained a classifier with six distance metrics as feature representations to rank the correct article or registration, using recall@K to evaluate performance and compare to baseline methods. When identifying links from registrations to published articles, the classifier ranked the correct article first (recall@1) among 378,048 articles in 80.8% of evaluation cases and 34.9% in the baseline method. Recall@10 was 85.1% compared to 60.7% in the baseline. When predicting links from articles to registrations, recall@1 was 83.4% for the classifier and 39.8% in the baseline. Recall@10 was 89.5% compared to 65.8% in the baseline. The proposed method improves on our baseline document similarity method to be feasible for identifying missing links in practice. Given a ClinicalTrials.gov registration, a user checking 10 ranked articles can expect to identify the matching article in at least 85% of cases, if the trial has been published. The proposed method can be used to improve the coupling of ClinicalTrials.gov and PubMed, with applications related to automating systematic review and evidence synthesis processes. [ABSTRACT FROM AUTHOR]

Repeatability is the cornerstone of science, and it is particularly important for systematic reviews. However, little is known on how researchers' choice of database, and search platform influence the repeatability of systematic reviews. Here, we aim to unveil how the computer environment and the location where the search was initiated from influence hit results.We present a comparative analysis of time‐synchronized searches at different institutional locations in the world and evaluate the consistency of hits obtained within each of the search terms using different search platforms.We revealed a large variation among search platforms and showed that PubMed and Scopus returned consistent results to identical search strings from different locations. Google Scholar and Web of Science's Core Collection varied substantially both in the number of returned hits and in the list of individual articles depending on the search location and computing environment. Inconsistency in Web of Science results has most likely emerged from the different licensing packages at different institutions.To maintain scientific integrity and consistency, especially in systematic reviews, action is needed from both the scientific community and scientific search platforms to increase search consistency. Researchers are encouraged to report the search location and the databases used for systematic reviews, and database providers should make search algorithms transparent and revise access rules to titles behind paywalls. Additional options for increasing the repeatability and transparency of systematic reviews are storing both search metadata and hit results in open repositories and using Application Programming Interfaces (APIs) to retrieve standardized, machine‐readable search metadata. [ABSTRACT FROM AUTHOR]

Genetic data represent a relatively new frontier for our understanding of global biodiversity. Ideally, such data should include both organismal DNA‐based genotypes and the ecological context where the organisms were sampled. Yet most tools and standards for data deposition focus exclusively either on genetic or ecological attributes. The Genomic Observatories Metadatabase (GEOME: geome‐db.org) provides an intuitive solution for maintaining links between genetic data sets stored by the International Nucleotide Sequence Database Collaboration (INSDC) and their associated ecological metadata. GEOME facilitates the deposition of raw genetic data to INSDCs sequence read archive (SRA) while maintaining persistent links to standards‐compliant ecological metadata held in the GEOME database. This approach facilitates findable, accessible, interoperable and reusable data archival practices. Moreover, GEOME enables data management solutions for large collaborative groups and expedites batch retrieval of genetic data from the SRA. The article that follows describes how GEOME can enable genuinely open data workflows for researchers in the field of molecular ecology. [ABSTRACT FROM AUTHOR]

The size and complexity of the biomaterials literature makes systematic data analysis an excruciating manual task. A practical solution is creating databases and information resources. Implant design and biomaterials research can greatly benefit from an open database for systematic data retrieval. Ontologies are pivotal to knowledge base creation, serving to represent and organize domain knowledge. To name but two examples, GO, the gene ontology, and CheBI, Chemical Entities of Biological Interest ontology and their associated databases are central resources to their respective research communities. The creation of the devices, experimental scaffolds, and biomaterials ontology (DEB), an open resource for organizing information about biomaterials, their design, manufacture, and biological testing, is described. It is developed using text analysis for identifying ontology terms from a biomaterials gold standard corpus, systematically curated to represent the domain's lexicon. Topics covered are validated by members of the biomaterials research community. The ontology may be used for searching terms, performing annotations for machine learning applications, standardized meta‐data indexing, and other cross‐disciplinary data exploitation. The input of the biomaterials community to this effort to create data‐driven open‐access research tools is encouraged and welcomed. [ABSTRACT FROM AUTHOR]

One of the main challenges in medical image retrieval is the increasing volume of image data, which render it difficult for domain experts to find relevant information from large data sets. Effective and efficient medical image retrieval systems are required to better manage medical image information. Text‐based image retrieval (TBIR) was very successful in retrieving images with textual descriptions. Several TBIR approaches rely on models based on bag‐of‐words approaches, in which the image retrieval problem turns into one of standard text‐based information retrieval; where the meanings and values of specific medical entities in the text and metadata are ignored in the image representation and retrieval process. However, we believe that TBIR should extract specific medical entities and terms and then exploit these elements to achieve better image retrieval results. Therefore, we propose a novel reranking method based on medical‐image‐dependent features. These features are manually selected by a medical expert from imaging modalities and medical terminology. First, we represent queries and images using only medical‐image‐dependent features such as image modality and image scale. Second, we exploit the defined features in a new reranking method for medical image retrieval. Our motivation is the large influence of image modality in medical image retrieval and its impact on image‐relevance scores. To evaluate our approach, we performed a series of experiments on the medical ImageCLEF data sets from 2009 to 2013. The BM25 model, a language model, and an image‐relevance feedback model are used as baselines to evaluate our approach. The experimental results show that compared to the BM25 model, the proposed model significantly enhances image retrieval performance. We also compared our approach with other state‐of‐the‐art approaches and show that our approach performs comparably to those of the top three runs in the official ImageCLEF competition. [ABSTRACT FROM AUTHOR]

Social media datasets are not always completely replicable. Having to adhere to requirements of platforms such as Twitter, researchers can only release a list of unique identifiers, which others can then use to recollect the data themselves. This leads to subsets of the data no longer being available, as content can be deleted or user accounts deactivated. To quantify the long‐term impact of this in the replicability of datasets, we perform a longitudinal analysis of the persistence of 30 Twitter datasets, which include more than 147 million tweets. By recollecting Twitter datasets ranging from 0 to 4 years old by using the tweet IDs, we look at four different factors quantifying the extent to which recollected datasets resemble original ones: completeness, representativity, similarity, and changingness. Although the ratio of available tweets keeps decreasing as the dataset gets older, we find that the textual content of the recollected subset is still largely representative of the original dataset. The representativity of the metadata, however, keeps fading over time, both because the dataset shrinks and because certain metadata, such as the users' number of followers, keeps changing. Our study has important implications for researchers sharing and using publicly shared Twitter datasets in their research. [ABSTRACT FROM AUTHOR]

Cybersecurity is one of the main concerns of many organizations today, and accessibility to cybersecurity information in a timely manner is crucial to maintaining cybersecurity. Various repositories of cybersecurity-related information are publicly available on the Internet. However, users are unaware of many of them, and it is impractical for them to keep track of all of them. Cybersecurity information stored in these repositories must be able to be located and accessed by the parties who need it. To address this issue, this paper proposes a mechanism of linking, locating, and discovering various cybersecurity information to improve its accessibility in a timely manner. This mechanism allows us to locate various cybersecurity information having different schemata by generating metadata with which a list of cybersecurity information is managed. The information structure incorporated in this mechanism is unique, and it makes our mechanism flexible and extensible. The structure consists of categories and formats that are linked to each other. The mechanism can propagate information updates to minimize the risk of obsolete information. This paper also introduces a prototype of the mechanism to demonstrate its feasibility, and it analyzes the mechanism's extensibility, scalability, and information credibility. Through this study, we aim to improve the accessibility of cybersecurity information on the Internet and facilitate information sharing beyond organizational borders, with the eventual goal of creating a web of cybersecurity. [ABSTRACT FROM AUTHOR]

We address the challenge of extracting query biased audio summaries from podcasts to support users in making relevance decisions in spoken document search via an audio-only communication channel. We performed a crowdsourced experiment that demonstrates that transcripts of spoken documents created using Automated Speech Recognition (ASR), even with significant errors, are effective sources of document summaries or "snippets" for supporting users in making relevance judgments against a query. In particular, the results show that summaries generated from ASR transcripts are comparable, in utility and user-judged preference, to spoken summaries generated from error-free manual transcripts of the same collection. We also observed that content-based audio summaries are at least as preferred as synthesized summaries obtained from manually curated metadata, such as title and description. We describe a methodology for constructing a new test collection, which we have made publicly available. [ABSTRACT FROM AUTHOR]

In this article, we propose a graph-based interactive bibliographic information retrieval system-GIBIR. GIBIR provides an effective way to retrieve bibliographic information. The system represents bibliographic information as networks and provides a form-based query interface. Users can develop their queries interactively by referencing the system-generated graph queries. Complex queries such as 'papers on information retrieval, which were cited by John's papers that had been presented in SIGIR' can be effectively answered by the system. We evaluate the proposed system by developing another relational database-based bibliographic information retrieval system with the same interface and functions. Experiment results show that the proposed system executes the same queries much faster than the relational database-based system, and on average, our system reduced the execution time by 72% (for 3-node query), 89% (for 4-node query), and 99% (for 5-node query). [ABSTRACT FROM AUTHOR]

Music Information Retrieval ( MIR) evaluation has traditionally focused on system-centered approaches where components of MIR systems are evaluated against predefined data sets and golden answers (i.e., ground truth). There are two major limitations of such system-centered evaluation approaches: (a) The evaluation focuses on subtasks in music information retrieval, but not on entire systems and (b) users and their interactions with MIR systems are largely excluded. This article describes the first implementation of a holistic user-experience evaluation in MIR, the MIREX Grand Challenge, where complete MIR systems are evaluated, with user experience being the single overarching goal. It is the first time that complete MIR systems have been evaluated with end users in a realistic scenario. We present the design of the evaluation task, the evaluation criteria and a novel evaluation interface, and the data-collection platform. This is followed by an analysis of the results, reflection on the experience and lessons learned, and plans for future directions. [ABSTRACT FROM AUTHOR]

We consider the problem of searching scientific data from vast heterogeneous scientific data repositories. This problem is challenging because scientific data contain relatively little text information compared to other search targets such as web pages. On the other hand, the metadata in scientific data contain other characteristic information such as spatio-temporal information. Although using this information make it possible to improve the search performance, many widely adopted scientific data search engines use this information exclusively for narrowing down search results. In this paper, we propose a novel query generation method using spatial, temporal, and text information based on pseudo relevance feedback. The proposed method generates new spatio-temporal queries from the initial search results. By using these queries, the search results are reranked such that more related results obtain higher rank. The experimental results show that the proposed method outperforms a baseline method when search targets do not have rich text information. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Users of research databases, such as CiteSeerX, Google Scholar, and Microsoft Academic, often search for papers using a set of keywords. Unfortunately, many authors avoid listing sufficient keywords for their papers. As such, these applications may need to automatically associate good descriptive keywords with papers. When the full text of the paper is available this problem has been thoroughly studied. In many cases, however, due to copyright limitations, research databases do not have access to the full text. On the other hand, such databases typically maintain metadata, such as the title and abstract and the citation network of each paper. In this paper we study the problem of predicting which keywords are appropriate for a research paper, using different methods based on the citation network and available metadata. Our main goal is in providing search engines with the ability to extract keywords from the available metadata. However, our system can also be used for other applications, such as for recommending keywords for the authors of new papers. We create a data set of research papers, and their citation network, keywords, and other metadata, containing over 470 K papers with and more than 2 million keywords. We compare our methods with predicting keywords using the title and abstract, in offline experiments and in a user study, concluding that the citation network provides much better predictions. [ABSTRACT FROM AUTHOR]

We introduce a new problem, identifying the type of relation that holds between a pair of similar items in a digital library. Being able to provide a reason why items are similar has applications in recommendation, personalization, and search. We investigate the problem within the context of Europeana, a large digital library containing items related to cultural heritage. A range of types of similarity in this collection were identified. A set of 1,500 pairs of items from the collection were annotated using crowdsourcing. A high intertagger agreement (average 71.5 Pearson correlation) was obtained and demonstrates that the task is well defined. We also present several approaches to automatically identifying the type of similarity. The best system applies linear regression and achieves a mean Pearson correlation of 71.3, close to human performance. The problem formulation and data set described here were used in a public evaluation exercise, the * SEM shared task on Semantic Textual Similarity. The task attracted the participation of 6 teams, who submitted 14 system runs. All annotations, evaluation scripts, and system runs are freely available. [ABSTRACT FROM AUTHOR]

The purpose of this study was to examine user tags that describe digitized archival collections in the field of humanities. A collection of 8,310 tags from a digital portal (Nineteenth-Century Electronic Scholarship, NINES) was analyzed to find out what attributes of primary historical resources users described with tags. Tags were categorized to identify which tags describe the content of the resource, the resource itself, and subjective aspects (e.g., usage or emotion). The study's findings revealed that over half were content-related; tags representing opinion, usage context, or self-reference, however, reflected only a small percentage. The study further found that terms related to genre or physical format of a resource were frequently used in describing primary archival resources. It was also learned that nontextual resources had lower numbers of content-related tags and higher numbers of document-related tags than textual resources and bibliographic materials; moreover, textual resources tended to have more user-context-related tags than other resources. These findings help explain users' tagging behavior and resource interpretation in primary resources in the humanities. Such information provided through tags helps information professionals decide to what extent indexing archival and cultural resources should be done for resource description and discovery, and understand users' terminology. [ABSTRACT FROM AUTHOR]

Research data curation initiatives must support heterogeneous kinds of projects, data, and metadata. This article examines variability in data and metadata practices using 'institutions' as the key theoretical concept. Institutions, in the sense used here, are stable patterns of human behavior that structure, legitimize, or delegitimize actions, relationships, and understandings within particular situations. Based on prior conceptualizations of institutions, a theoretical framework is presented that outlines 5 categories of 'institutional carriers' for data practices: (a) norms and symbols, (b) intermediaries, (c) routines, (d) standards, and (e) material objects. These institutional carriers are central to understanding how scientific data and metadata practices originate, stabilize, evolve, and transfer. This institutional framework is applied to 3 case studies: the Center for Embedded Networked Sensing ( CENS), the Long Term Ecological Research ( LTER) network, and the University Corporation for Atmospheric Research ( UCAR). These cases are used to illustrate how institutional support for data and metadata management are not uniform within a single organization or academic discipline. Instead, broad spectra of institutional configurations for managing data and metadata exist within and across disciplines and organizations. [ABSTRACT FROM AUTHOR]