Metadata Fields Research Articles

Archives and Special Collections at the University of La Verne: Putting the Repository of a Small Liberal Arts College to Work Online

Archives and Special Collections at the Wilson Library of the University of La Verne, located in inland southern California, offers an informative case study of descriptive practices and metadata attached to digital collections at a small liberal arts college. Since recruiting a staff specifically tasked to manage the archives, the Wilson Library has increased the number of collections available to patrons online through the creation of a digital collections Web page. Digitized, hosted collections include the papers of a faculty member from the early 20th century, photographs of early La Verne, historic local newspapers, and manuscript sources regarding Japanese American internment. Metadata fields at Wilson Library have developed to encompass a greater variety of contextual information about digitized records, improving users' ability to put the collections to use for research. Ultimately, this case study demonstrates what a library at a small university can accomplish with a dedicated staff and a clear objective, even with limited resources.

Issues of Visual Search Methods in Digital Repositories

Repositories are important infrastructures which allow the dissemination of large collections of digital resources hosted in museums, libraries, academic institutions or specialized documentation centers. However, there are nowadays several limitations associated with irrelevant search results based on a knowledge area. Some studies have highlighted the major role of information visualization strategies based on Simple Knowledge Organization Systems (SKOS) so as to mitigate such difficulties. The main goal of this article is to present recommendations using information visualization based on SKOS for the development of navigational search interfaces in digital repositories focused on learning process. We use card sorting as methodology in order to obtain qualitative results in our study. As preliminary results we found that taxonomies in visual search engines improve the access to large collections of digital resources based on SKOS, but it depends on the design of taxonomy concepts defined in digital repositories. Finally, it is recommended that the creators of repositories focus their efforts on define levels of relationship and partnership between digital resources using knowledge representation structures like thesauri or ontologies; work with usable visualization interfaces like tree, radial or icicle; and link relevant metadata fields with the navigation structure.

In situ measurements of animal morphological features: A non‐invasive method

Abstract Measurements of morphological features are important for ecological studies, especially on free‐ranging wild animal species. Conventionally, specimens either dead or in captivity are used for morphometric studies, which is difficult in the case of wild species for several reasons. Capturing would be even futile when research questions are relating to issues such as prey size selection or estimation of intake rate under field conditions, where in situ morphometric measurements are inevitable. Remotely estimating morphometric features via, avian bill and head lengths are always laden with wide variances. Hence, we developed this technique for in situ measurements of animal morphology through digital photographs. This non‐invasive novel method for in situ estimation of individuals’ morphometric measurements uses digital photographs’ metadata. We extracted the metadata (field of view, focal length, subject‐distance, etc.,) in exchangeable image file format from digital images using freely available software, and the unknown sizes were estimated from each image. Cardboard (fish) models were, at first, used to estimate the accuracy of measurements comparing actual and estimated values, and the same protocol was repeated on museum specimens (bird). Finally, we checked the repeatability of this method under field conditions using free‐ranging Chickens (Gallus gallus domesticus). The results under different conditions were subjected to rigorous statistical testing. Upon comparing the actual with estimated values, obtained from three distinct types of objects (1. Cardboard model, 2. Museum specimens and 3. Live birds under field conditions), variations were found non‐significant affirming the assumption that actual value (Y) = estimated value (X) with inconsequential variance of the Y from X. Moreover, both the relative and absolute errors between the actual and estimated values were so low, indicating that this method yielded neither overestimation nor underestimation. The advantage of this non‐invasive method is that it could generate reasonably accurate morphometric measurements at a relatively low cost for routine morphometric works in field conditions. We envisage significant utility of this non‐invasive technique in different fields of science such as biology, morphology, ecology, evolution, wildlife, conservation science, agriculture, forensics and engineering design.

Best Practices for Submitting Promotional 2253 Submissions in the New Module 1 Specification.

June 2016 marks 1 full year since the optional implementation of US Module 1 Specification v2.3. Since its release, pharma companies have dealt with expanded submission-level metadata fields, the ability to submit advertising and promotional submissions, and more rigid validation criteria. Prior to transitioning, sponsors must ensure their building and validating tools are up-to-date, that a system is in place to integrate departments working together for the first time, and that all guidance documents are understood and implemented. Sponsors now can group submissions across application types, which will help save rework across submissions. Looking at one submission type now accepted in eCTD, this paper will explore the new functionality that has created new possibilities and challenges for Regulatory Operations.

Leveraging Technical Library Expertise for Big Data Management

Shell Australia’s librarians were key stakeholders in a project to improve the databases and processes used to manage the company’s geophysical data. Geophysical data are challenging to manage: they are complex ‘big’ data and defy traditional cataloguing. During the project, the librarians collaborated with geophysicists, geophysical data analysts, IT support specialists and database architects. Their contributions as library and information science (LIS) professionals included providing advice on required metadata fields, developing controlled vocabularies and naming conventions, defining required search parameters, identifying opportunities for added functionalities, undertaking database testing and providing feedback, populating the database and developing procedures. The librarians brought a long-term perspective to the review of geophysical data management, essential for data preservation beyond the business cycle. Cross-disciplinary engagement led to the implementation of tools and processes that provide improved metadata capture, clearer connections between projects and data, improved search functionality and better data management. Collaboration provided the librarians with an opportunity to learn more about geophysical data and its life cycle, and provided an opportunity to demonstrate the value of LIS skills in big data management.

Secure provision of unconsented radiological imaging for research

ABSTRACT
 Objectives
 
 Design and implement an architecture for managing unconsented DICOM imaging
 Maintain sufficient data to define research cohorts when data quality is unknown
 Perform project-level linkage and extraction into a Safe Haven (SH) environment
 Extract large image volumes for multiple projects with limited storage constraints
 Provide applications for an imaging research workflow within the SH environment
 Serve as a prototype for the Farr/NHS Scotland project to create a research dataset from Scotland’s national PACS
 
 ApproachThe software architecture builds on the Research Data Management Platform (RDMP) developed at Dundee’s Health Informatics Centre (HIC) within Farr@Dundee. The RDMP provides core services common to loading any dataset, with configuration and extensibility points for dataset-specific implementations. This architecture augments the RDMP with scalable micro-services performing peripheral functions.
 Images are sourced from the local PACS server in Ninewells Hospital and cached securely within HIC using an implementation for the RDMP with a custom server to query/retrieve data.
 Data stored in the catalogue should be anonymous, according to the Scottish SH model. The imaging dataset is poorly understood, with several potentially identifiable free-text fields which may contain information required for defining suitable research cohorts. The load process only permits verified metadata fields into the anonymised catalogue; a Mongo database stores other data for later analysis, should a field subsequently be required for cohort definition.
 A DICOM extraction implementation is provided, using DICOM Confidential for anonymisation and a project-specific remapping of DICOM GUIDs.
 Two provisioning methods have been designed. A basic copy when sufficient storage is available, and a more sophisticated method using a custom filesystem to provide separate project-specific views onto shared image files.
 ResultsA full end-to-end solution has been developed, from initial caching through to provisioning anonymised images. Two imaging cohorts have been loaded, one with over 5000 studies. NHS Tayside CT and MR data since 2008 is currently being loaded.
 Two projects have had anonymised extracts released using the ‘copy’ method. The custom filesystem method has been developed and tested with limited amounts of data.
 This work has highlighted anonymisation, cohort creation and SH issues which require further exploration.
 ConclusionA production system for securely providing linked DICOM imaging to researchers has been implemented, serving as a testbed for a national system which will provide a unique population-level resource for researchers.

Redesigning the DOE Data Explorer to Embed Dataset Relationships at the Point of Search and to Reflect Landing Page Organization

Scientific research is producing ever-increasing amounts of data. Organizing and reflecting relationships across data collections, datasets, publications, and other research objects are essential functionalities of the modern science environment, yet challenging to implement. Landing pages are often used for providing ‘big picture’ contextual frameworks for datasets and data collections, and many large-volume data holders are utilizing them in thoughtful, creative ways. The benefits of their organizational efforts, however, are not realized unless the user eventually sees the landing page at the end point of their search. What if that organization and ‘big picture’ context could benefit the user at the beginning of the search? That is a challenging approach, but The Department of Energy’s (DOE) Office of Scientific and Technical Information (OSTI) is redesigning the database functionality of the DOE Data Explorer (DDE) with that goal in mind. Phase I is focused on redesigning the DDE database to leverage relationships between two existing distinct populations in DDE, data Projects and individual Datasets, and then adding a third intermediate population, data Collections. Mapped, structured linkages, designed to show user relationships, will allow users to make informed search choices. These linkages will be sustainable and scalable, created automatically with the use of new metadata fields and existing authorities. Phase II will study selected DOE Data ID Service clients, analyzing how their landing pages are organized, and how that organization might be used to improve DDE search capabilities. At the heart of both phases is the realization that adding more metadata information for cross-referencing may require additional effort for data scientists. OSTI’s approach seeks to leverage existing metadata and landing page intelligence without imposing an additional burden on the data creators.

Practical Application of the Dublin Core Standard for Enterprise Metadata Management

EDITOR'S SUMMARYLarge organizations relying heavily on knowledge work require effective capture and reuse of information, enabled through consistent use of standardized enterprise content metadata. The Jet Propulsion Laboratory (JPL) has undertaken a standardization effort, building an internal content schema based on established metadata field standards that are content‐ and application‐agnostic but locally customizable for application to a broad variety of repositories. The JPL adopted the Dublin Core standard, with its Simple and Qualified properties as well as further refined Custom sub‐properties. The JPL Resource Schema serves as an enterprise‐wide metadata standard, while specific application profiles state the available fields and field labels for each repository or content management system. The schema's terms are drawn from two distinct but semantically related vocabularies and linked by an intermediary registry tying granular listings for specific applications to enterprise‐level terms. The registry mappings permit the use of both local metadata and higher level or external systems. The effort has demonstrated the importance of consistent application of both granular and general metadata for information capture and revealed important lessons about adopting the Dublin Core standard in a large enterprise setting.

Seqenv: linking sequences to environments through text mining.

Understanding the distribution of taxa and associated traits across different environments is one of the central questions in microbial ecology. High-throughput sequencing (HTS) studies are presently generating huge volumes of data to address this biogeographical topic. However, these studies are often focused on specific environment types or processes leading to the production of individual, unconnected datasets. The large amounts of legacy sequence data with associated metadata that exist can be harnessed to better place the genetic information found in these surveys into a wider environmental context. Here we introduce a software program, seqenv, to carry out precisely such a task. It automatically performs similarity searches of short sequences against the “nt” nucleotide database provided by NCBI and, out of every hit, extracts–if it is available–the textual metadata field. After collecting all the isolation sources from all the search results, we run a text mining algorithm to identify and parse words that are associated with the Environmental Ontology (EnvO) controlled vocabulary. This, in turn, enables us to determine both in which environments individual sequences or taxa have previously been observed and, by weighted summation of those results, to summarize complete samples. We present two demonstrative applications of seqenv to a survey of ammonia oxidizing archaea as well as to a plankton paleome dataset from the Black Sea. These demonstrate the ability of the tool to reveal novel patterns in HTS and its utility in the fields of environmental source tracking, paleontology, and studies of microbial biogeography. To install seqenv, go to: https://github.com/xapple/seqenv.

Title, Description, and Subject are the Most Important Metadata Fields for Keyword Discoverability

A Review of:
 Yang, L. (2016). Metadata effectiveness in internet discovery: An analysis of digital collection metadata elements and internet search engine keywords. College & Research Libraries, 77(1), 7-19. http://doi.org/10.5860/crl.77.1.7 
 
 Objective – To determine which metadata elements best facilitate discovery of digital collections.
 
 Design – Case study.
 
 Setting – A public research university serving over 32,000 graduate and undergraduate students in the Southwestern United States of America. 
 
 Subjects – A sample of 22,559 keyword searches leading to the institution’s digital repository between August 1, 2013, and July 31, 2014. 
 
 Methods – The author used Google Analytics to analyze 73,341 visits to the institution’s digital repository. He determined that 22,559 of these visits were due to keyword searches. Using Random Integer Generator, the author identified a random sample of 378 keyword searches. The author then matched the keywords with the Dublin Core and VRA Core metadata elements on the landing page in the digital repository to determine which metadata field had drawn the keyword searcher to that particular page. Many of these keywords matched to more than one metadata field, so the author also analyzed the metadata elements that generated unique keyword hits and those fields that were frequently matched together. 
 
 Main Results – Title was the most matched metadata field with 279 matched keywords from searches. Description and Subject were also significant fields with 208 and 79 matches respectively. Slightly more than half of the results, 195 keywords, matched the institutional repository in one field only. Both Title and Description had significant match rates both independently and in conjunction with other elements, but Subject keywords were the sole match in only three of the sampled cases. 
 
 Conclusion – The Dublin Core elements of Title, Description, and Subject were the most frequently matched fields in keyword searches. Academic librarians should focus on these elements when creating records in digital repositories to optimize traffic to their site from search engines.

Detection of fake opinions using time series

Today's e-commerce is highly depended on increasingly growing online customers’ reviews posted in opinion sharing websites. This fact, unfortunately, has tempted spammers to target opinion sharing websites in order to promote and demote products. To date, different types of opinion spam detection methods have been proposed in order to provide reliable resources for customers, manufacturers and researchers. However, supervised approaches suffer from imbalance data due to scarcity of spam reviews in datasets, rating deviation based filtering systems are easily cheated by smart spammers, and content based methods are very expensive and majority of them have not been tested on real data hitherto.The aim of this paper is to propose a robust review spam detection system wherein the rating deviation, content based factors and activeness of reviewers are employed efficiently. To overcome the aforementioned drawbacks, all these factors are synthetically investigated in suspicious time intervals captured from time series of reviews by a pattern recognition technique. The proposed method could be a great asset in online spam filtering systems and could be used in data mining and knowledge discovery tasks as a standalone system to purify product review datasets. These systems can reap benefit from our method in terms of time efficiency and high accuracy. Empirical analyses on real dataset show that the proposed approach is able to successfully detect spam reviews. Comparison with two of the current common methods, indicates that our method is able to achieve higher detection accuracy (F-Score: 0.86) while removing the need for having specific fields of Meta data and reducing heavy computation required for investigation purposes.

Utilisation of Metadata Fields and Query Expansion in Cross-Lingual Search of User-Generated Internet Video

Recent years have seen significant efforts in the area of Cross Language Information Retrieval (CLIR) for text retrieval. This work initially focused on formally published content, but more recently research has begun to concentrate on CLIR for informal social media content. However, despite the current expansion in online multimedia archives, there has been little work on CLIR for this content. While there has been some limited work on Cross-Language Video Retrieval (CLVR) for professional videos, such as documentaries or TV news broadcasts, there has to date, been no significant investigation of CLVR for the rapidly growing archives of informal user generated (UGC) content. Key differences between such UGC and professionally produced content are the nature and structure of the textual UGC metadata associated with it, as well as the form and quality of the content itself. In this setting, retrieval effectiveness may not only suffer from translation errors common to all CLIR tasks, but also recognition errors associated with the automatic speech recognition (ASR) systems used to transcribe the spoken content of the video and with the informality and inconsistency of the associated user-created metadata for each video. This work proposes and evaluates techniques to improve CLIR effectiveness of such noisy UGC content. Our experimental investigation shows that different sources of evidence, e.g. the content from different fields of the structured metadata, significantly affect CLIR effectiveness. Results from our experiments also show that each metadata field has a varying robustness to query expansion (QE) and hence can have a negative impact on the CLIR effectiveness. Our work proposes a novel adaptive QE technique that predicts the most reliable source for expansion and shows how this technique can be effective for improving the CLIR effectiveness for UGC content.

Developing an Empirically-based Framework of Metadata Change and Exploring Relation between Metadata Change and Metadata Quality in MARC Library Metadata

Managing metadata quality and metadata change contributes to more effective knowledge management in various organizations. This study sought to test and refine the metadata change framework initially developed for characterizing the change in digit al library metadata, in the study of traditional library metadata. As well, the study investigates how the recent shift to the new resource description standard Resource Description and Access (RDA) has impacted the change in library metadata records. Using a content analysis approach, we assessed various quantitative and qualitative aspects of metadata change over time in library metadata records. The study reveals that in the time since the transition to the new standard, library metadata records have received a high level of attention from metadata editors as expressed in overall metadata change frequency and a variety of 3 change categories and 23 subcategories observed. Results suggests the most frequently occurring categories and subcategories of change within the library metadata records, the metadata fields most widely applied in the records and the ones most often undergoing changes, as well as the institutions that most often make changes to the records. Based on the changes observed in our study, we identify relations between specific metadata change categories and subcategories and metadata quality criteria of accuracy, completeness, and consistency. The directions for further research into library metadata change and connections between metadata change categories and metadata quality criteria are suggested.

CLARINO language resource DSpace repository at the University of Bergen Library

CLARINO is a Norwegian infrastructure project jointly funded by the Research Council of Norway and a consortium of Norwegian universities and research institutions. Its goal is to implement the Norwegian part of CLARIN. Every CLARIN Centre type B is required to run a dedicated language resource data repository in accordance with certain criteria for good practice and compatibility in the CLARIN infrastructure. The University of Bergen Library (UBL) which participates in CLARINO was assigned the task of implementing and running a repository to primarily manage the resources at the University of Bergen. The repository is also open to other partners in CLARINO and to the whole CLARIN community.In 2013 the University of Bergen decided to use the open software application DSpace, as modified by the Institute of Formal and Applied Linguistics at the Charles University in Prague for their CLARIN/LINDAT repository. This poster and demo presents the architecture of the CLARINO Bergen repository. We describe the motivation for using the LINDAT repository as a model. We describe the process of setting up in Bergen the required functions by adapting the LINDAT software where needed.UBL had some previous experience with DSpace for the implementation of the Bergen Open Research Archive. This experience showed that DSpace is a functional and stable platform which is open source and well maintained by an active user community. It provides long term storage and linking, suitable authentication mechanisms, handling of licenses for downloading of resources, and metadata can be harvested at an OAI-PMH endpoint.Our poster presents the Bergen CLARINO repository as a good example of sharing of technical solutions. We will show the main features which has been added in LINDAT in order adapt DSpace for use in CLARIN, namely CMDI metadata integration and a method for license handling which adds the possibility of signing licenses.We will point out remaining technical challenges. A major one has to do with the metadata flow. Our system is able to generate CMDI metadata, as required by CLARIN, from DSpace internal metadata fields. However, there are differences between the Norwegian (CLARINO) and Czech (LINDAT) metadata profiles. Furthermore, DSpace cannot accommodate arbitrary new CMDI profiles. A principal limitation is that DSpace local metadata are a flat structure while CMDI has a hierarchical structure with possible embedding of components in components.

Medical Content Searching, Retrieving, and Sharing Over the Internet: Lessons Learned From the mEducator Through a Scenario-Based Evaluation.

BackgroundThe mEducator Best Practice Network (BPN) implemented and extended standards and reference models in e-learning to develop innovative frameworks as well as solutions that enable specialized state-of-the-art medical educational content to be discovered, retrieved, shared, and re-purposed across European Institutions, targeting medical students, doctors, educators and health care professionals. Scenario-based evaluation for usability testing, complemented with data from online questionnaires and field notes of users’ performance, was designed and utilized for the evaluation of these solutions.ObjectiveThe objective of this work is twofold: (1) to describe one instantiation of the mEducator BPN solutions (mEducator3.0 - “MEdical Education LINnked Arena” MELINA+) with a focus on the metadata schema used, as well as on other aspects of the system that pertain to usability and acceptance, and (2) to present evaluation results on the suitability of the proposed metadata schema for searching, retrieving, and sharing of medical content and with respect to the overall usability and acceptance of the system from the target users.MethodsA comprehensive evaluation methodology framework was developed and applied to four case studies, which were conducted in four different countries (ie, Greece, Cyprus, Bulgaria and Romania), with a total of 126 participants. In these case studies, scenarios referring to creating, sharing, and retrieving medical educational content using mEducator3.0 were used. The data were collected through two online questionnaires, consisting of 36 closed-ended questions and two open-ended questions that referred to mEducator 3.0 and through the use of field notes during scenario-based evaluations.ResultsThe main findings of the study showed that even though the informational needs of the mEducator target groups were addressed to a satisfactory extent and the metadata schema supported content creation, sharing, and retrieval from an end-user perspective, users faced difficulties in achieving a shared understanding of the meaning of some metadata fields and in correctly managing the intellectual property rights of repurposed content.ConclusionsThe results of this evaluation impact researchers, medical professionals, and designers interested in using similar systems for educational content sharing in medical and other domains. Recommendations on how to improve the search, retrieval, identification, and obtaining of medical resources are provided, by addressing issues of content description metadata, content description procedures, and intellectual property rights for re-purposed content.

HTA Database Canadian Repository

HTA Database Canadian Repository

A library’s information retrieval system (In)effectiveness: case study

Purpose– The purpose of this paper is to evaluate the effectiveness of the information retrieval component of a daily newspaper publisher’s integrated library system (ILS) in comparison with the open source alternatives and observe the impact of the scale of metadata, generated daily by library administrators, on retrieved result sets.Design/methodology/approach– In Experiment 1, the authors compared the result sets of the information retrieval system (IRS) component of the publisher’s current ILS and the result sets of proposed ones with human-assessed relevance judgment set. In Experiment 2, the authors compared the performance of proposed IRS components with the publisher’s current production IRS, using result sets of current IRS classified as relevant. Both experiments were conducted using standard information retrieval (IR) evaluation methods: precision, recall, precision atk,F-measure, mean average precision and 11-point interpolated average precision.Findings– Results showed that: first, in Experiment 1, the publisher’s current production ILS ranked last of all participating IRSs when compared to a relevance document set classified by the senior library administrator; and second, in Experiment 2, the tested IR components’ request handlers that used only automatically generated metadata performed slightly better than request handlers that used all of the metadata fields. Therefore, regarding the effectiveness of IR, the daily human effort of generating the publisher’s current set of metadata attributes is unjustified.Research limitations/implications– The experiments’ collections contained Slovene language with large number of variations of the forms of nouns, verbs and adjectives. The results could be different if the experiments’ collections contained languages with different grammatical properties.Practical implications– The authors have confirmed, using standard IR methods, that the IR component used in the publisher’s current ILS, could be adequately replaced with an open source component. Based on the research, the publisher could incorporate the suggested open source IR components in practice. In the research, the authors have described the methods that can be used by libraries for evaluating the effectiveness of the IR of their ILSs.Originality/value– The paper provides a framework for the evaluation of an ILS’s IR effectiveness for libraries. Based on the evaluation results, the libraries could replace the IR components if their current information system setup allows it.

Correcting Inconsistencies and Errors in Bacterial Genome Metadata Using an Automated Curation Tool in Excel (AutoCurE).

Whole-genome data are invaluable for large-scale comparative genomic studies. Current sequencing technologies have made it feasible to sequence entire bacterial genomes with relative ease and time with a substantially reduced cost per nucleotide, hence cost per genome. More than 3,000 bacterial genomes have been sequenced and are available at the finished status. Publically available genomes can be readily downloaded; however, there are challenges to verify the specific supporting data contained within the download and to identify errors and inconsistencies that may be present within the organizational data content and metadata. AutoCurE, an automated tool for bacterial genome database curation in Excel, was developed to facilitate local database curation of supporting data that accompany downloaded genomes from the National Center for Biotechnology Information. AutoCurE provides an automated approach to curate local genomic databases by flagging inconsistencies or errors by comparing the downloaded supporting data to the genome reports to verify genome name, RefSeq accession numbers, the presence of archaea, BioProject/UIDs, and sequence file descriptions. Flags are generated for nine metadata fields if there are inconsistencies between the downloaded genomes and genomes reports and if erroneous or missing data are evident. AutoCurE is an easy-to-use tool for local database curation for large-scale genome data prior to downstream analyses.

Extraction of pharmacokinetic evidence of drug-drug interactions from the literature.

Drug-drug interaction (DDI) is a major cause of morbidity and mortality and a subject of intense scientific interest. Biomedical literature mining can aid DDI research by extracting evidence for large numbers of potential interactions from published literature and clinical databases. Though DDI is investigated in domains ranging in scale from intracellular biochemistry to human populations, literature mining has not been used to extract specific types of experimental evidence, which are reported differently for distinct experimental goals. We focus on pharmacokinetic evidence for DDI, essential for identifying causal mechanisms of putative interactions and as input for further pharmacological and pharmacoepidemiology investigations. We used manually curated corpora of PubMed abstracts and annotated sentences to evaluate the efficacy of literature mining on two tasks: first, identifying PubMed abstracts containing pharmacokinetic evidence of DDIs; second, extracting sentences containing such evidence from abstracts. We implemented a text mining pipeline and evaluated it using several linear classifiers and a variety of feature transforms. The most important textual features in the abstract and sentence classification tasks were analyzed. We also investigated the performance benefits of using features derived from PubMed metadata fields, various publicly available named entity recognizers, and pharmacokinetic dictionaries. Several classifiers performed very well in distinguishing relevant and irrelevant abstracts (reaching F1≈0.93, MCC≈0.74, iAUC≈0.99) and sentences (F1≈0.76, MCC≈0.65, iAUC≈0.83). We found that word bigram features were important for achieving optimal classifier performance and that features derived from Medical Subject Headings (MeSH) terms significantly improved abstract classification. We also found that some drug-related named entity recognition tools and dictionaries led to slight but significant improvements, especially in classification of evidence sentences. Based on our thorough analysis of classifiers and feature transforms and the high classification performance achieved, we demonstrate that literature mining can aid DDI discovery by supporting automatic extraction of specific types of experimental evidence.

Mitogenome metadata: current trends and proposed standards

Mitogenome metadata are descriptive terms about the sequence, and its specimen description that allow both to be digitally discoverable and interoperable. Here, we review a sampling of mitogenome metadata published in the journal Mitochondrial DNA between 2005 and 2014. Specifically, we have focused on a subset of metadata fields that are available for GenBank records, and specified by the Genomics Standards Consortium (GSC) and other biodiversity metadata standards; and we assessed their presence across three main categories: collection, biological and taxonomic information. To do this we reviewed 146 mitogenome manuscripts, and their associated GenBank records, and scored them for 13 metadata fields. We also explored the potential for mitogenome misidentification using their sequence diversity, and taxonomic metadata on the Barcode of Life Datasystems (BOLD). For this, we focused on all Lepidoptera and Perciformes mitogenomes included in the review, along with additional mitogenome sequence data mined from Genbank. Overall, we found that none of 146 mitogenome projects provided all the metadata we looked for; and only 17 projects provided at least one category of metadata across the three main categories. Comparisons using mtDNA sequences from BOLD, suggest that some mitogenomes may be misidentified. Lastly, we appreciate the research potential of mitogenomes announced through this journal; and we conclude with a suggestion of 13 metadata fields, available on GenBank, that if provided in a mitogenomes’s GenBank record, would increase their research value.