Multiple Repositories Research Articles

A Case Study of Allocasuarina robusta Recovery Using History and Biogeography to Identify Future Priorities

Identifying the cause of a threatened species can aid in how best to formulate recovery actions. Recovery can be based on broad concepts and may not reflect a specific community or species requirements. Urban sprawl and intensification of land are known as threatening processes. How a threatening process interacts with a threatened species can aid in the recovery efforts. In South Australia, the species Allocasuarina robusta provides an opportunity to understand how past land usage may direct recovery efforts. Information on past land usage can involve identifying and using data from multiple repositories. The investigation focused on the relationship between changes in land use and herbarium data to understand a relationship between a common and threatened species. As a species evolves and adapts, the conservation practices used, including the methods used for identifying future actions, needs to be reflective of a changing environment. A changing environment can have consequences to biodiversity, creating several issues for a land manager. Traditional species recovery techniques can slow the threatening process down. Sometimes these threats may be visible like grazing from fauna (native and introduced). The threat to Allocasuarina robusta is a change in land use originating from anthropogenic activities. Supplementary planting with tube stock is a well-grounded practice, but the implications from this practice may need further investigation. Natural regeneration is crucial for long term population survival, but in Allocasuarina robusta, this is not occurring. The Allocasuarina robusta investigation aims to explore the relationships between herbarium data and land-use histories to guide future recovery efforts.

Grabseqs: simple downloading of reads and metadata from multiple next-generation sequencing data repositories.

High-throughput sequencing is a powerful technique for addressing biological questions. Grabseqs streamlines access to publicly available metagenomic data by providing a single, easy-to-use interface to download data and metadata from multiple repositories, including the Sequence Read Archive, the Metagenomics Rapid Annotation through Subsystems Technology server and iMicrobe. Users can download data and metadata in a standardized format from any number of samples or projects from a given repository with a single grabseqs command. Grabseqs is an open-source tool implemented in Python and licensed under the MIT license. The source code is freely available at https://github.com/louiejtaylor/grabseqs, the Python Package Index and Anaconda Cloud repository. bushman@pennmedicine.upenn.edu. Supplementary data are available at Bioinformatics online.

Evolution of the LHCb Continuous Integration system

The physics software stack of LHCb is based on Gaudi and is comprised of about 20 interdependent projects, managed across multiple GitLab repositories. At present, the continuous integration (CI) system used for regular building and testing of this software is implemented using Jenkins and runs on a cluster of about 300 cores. LHCb CI pipelines are python-based and relatively modern with some degree of modularity, i.e. the separation of test jobs from build jobs. However, these still suffer from obsoleted design choices that prevent improvements to scalability and reporting. In particular, the resource use and speed have not been thoroughly optimized due to the predominant use of the system for nightly builds, where a feedback time of 8 hours is acceptable. We describe recent work on speeding up pipelines by aggressively splitting and parallelizing checkout, build and test jobs and caching their artifacts. The current state of automatic code quality integration, such as coverage reports, is shown. This paper presents how feedback time from change (merge request) submission to build and test reports is reduced from “next day” to a few hours by dedicated on-demand pipelines. Custom GitLab integration allows easy triggering of pipelines, including linked changes to multiple projects, and provides immediate feedback as soon as ready. Reporting includes a comparison to tests on a unique stable reference build, dynamically chosen for every set of changes under testing. This work enables isolated testing of changes that integrates well into the development workflow, leaving nightly testing primarily for integration tests.

Federated Authorization over Access to Personal Data for Decentralized Identity Management

The digital identity problem is a complex one in large part because it involves personal data, the algorithms which compute insights on the data, and the management of the identifiers that are linked to personal data. The reality of today is that personal data of an individual is distributed throughout the Internet, in both private and public institutions, and increasingly also on the user's devices. In order to empower individuals to have a say in who has access to their personal data and to enable individuals to make use of their data for their own purposes, a coherent and scalable federated authorization architecture is required as a fundamental component of the decentralized identity solution. This federation must allow an individual to easily manage access policies, and to grant and retract consent for access to data distributed across multiple repositories. This article describes the User Managed Access architecture and protocols that provide the foundation for scalable federated authorization.

Pemanfaatan Google Custom Search Untuk Pencarian Satu Pintu Koleksi Perpustakaan BPPT

The Library of Technology Assessment and Application has several collection repositories that are stored on different applications and servers. In searching for collections that are needed, users must search for in each application. There is not yet a single way to search all of this content at once, therefore required a single way searching through all repositories so that users can easily find the information needed. In this study, the Google Custom Search plugin was used to build a one-door search portal, so users can search collections in multiple repositories through single way. The results showed that this plugin can be used to build a one-door search portal. The use of the Google Custom Search plugin is easy and simple. The results of the search showed collections from each repository according to the keywords entered.

Core Metadata Element Recommendations for Institutional Repositories at Texas A&M University Libraries

In 2017, a working group was formed at Texas A&M University Libraries to propose policies that would ensure the quality and consistency of descriptive metadata in the Libraries’ new DAME (Digital Asset Management Ecosystem), which consists of multiple repositories. This article describes the process that it used to identify a “core” set of metadata elements for all resources that would be stored within the DAME, and their recommendations for a three-tiered approach to address different levels of completeness. In addition, this article describes the issues that were encountered when recommending controlled vocabularies and standards for inputting certain metadata elements.

A Novel Meta-Analytic Approach with Applications to Nutrition Research: A Comprehensive Analysis of Sodium Restriction (OR06-05-19)

ObjectivesSince the application of meta-analysis to biomedical research, the breadth of evidence covered in published meta-analyses has diminished. Despite the imperative of literature reviews to present “state of the art” knowledge, current meta-analyses are confined to pooling only select evidence. Imposing a priori criteria on scientific evidence results in biased and myopic conclusions. These current practices, in addition to the vastness of the literature base, ensure that research evidence remains opaque, and unusable to guide research and clinical practice. For example, whether and how much dietary sodium should be restricted for healthy or at-risk populations remains controversial. New epidemiological data suggest a striking increase in mortality with sodium intake <3 g/day, confounding this discussion. Thus, a system enabling investigators to access, review, and interpret all evidence is needed. MethodsMedAware Systems, Inc. is a platform that continuously identifies, extracts and organizes medical research data from multiple repositories. A blinded, cross-validated data extraction process, using artificial intelligence, guides two research scientists to process each study with near 100% accuracy. Intelligent software compares each data field for matches, prompting a senior scientist to review and reconcile any mismatched data fields. Over 200 methodological and clinical parameters are coded and organized, supporting comprehensive meta-analytic reviews of any medical topic.We demonstrate this innovative technology by addressing the effects of dietary sodium intake. We identify and analyze the complete domain of studies pertaining to sodium intake and cardiovascular outcomes. ResultsTo date, 368 studies have been identified. Initial review of patients, interventions, and outcomes data show extreme heterogeneity of this literature domain. This confirms the need for comprehensive evidence review, data extraction, and pooling. ConclusionsSodium intake literature is vast and difficult to interpret. We introduce a comprehensive process to analyze thousands of studies that traditional approaches cannot match. Our database and meta-analytic methodology profile the domain of evidence, summarizing the diversity and efficacy of sodium restriction recommendations. Funding SourcesMedAware Systems, Inc.

A semantic-based discovery service for the Internet of Things

With the Internet of Things (IoT), applications should interact with a huge number of devices and retrieve context data produced by those objects, which have to be discovered and selected a priori. Due to the number, heterogeneity, and dynamicity of resources, discovery services are required to consider many selection criteria, e.g., device capabilities, location, context data type, contextual situations, and quality. In this paper, we describe QoDisco, a semantic-based discovery service that addresses this requirement in IoT. QoDisco is composed of a set of repositories storing resource descriptions according to an ontology-based information model and it provides multi-attribute and range querying capabilities. We have evaluated different approaches to reduce the inherent cost of semantic search, namely parallel interactions with multiple repositories and publish-subscribe interactions. This paper also reports the results of some performance experiments on QoDisco with respect to these approaches to handle resource discovery requests in IoT.

Using association rule mining and ontologies to generate metadata recommendations from multiple biomedical databases.

Metadata—the machine-readable descriptions of the data—are increasingly seen as crucial for describing the vast array of biomedical datasets that are currently being deposited in public repositories. While most public repositories have firm requirements that metadata must accompany submitted datasets, the quality of those metadata is generally very poor. A key problem is that the typical metadata acquisition process is onerous and time consuming, with little interactive guidance or assistance provided to users. Secondary problems include the lack of validation and sparse use of standardized terms or ontologies when authoring metadata. There is a pressing need for improvements to the metadata acquisition process that will help users to enter metadata quickly and accurately. In this paper, we outline a recommendation system for metadata that aims to address this challenge. Our approach uses association rule mining to uncover hidden associations among metadata values and to represent them in the form of association rules. These rules are then used to present users with real-time recommendations when authoring metadata. The novelties of our method are that it is able to combine analyses of metadata from multiple repositories when generating recommendations and can enhance those recommendations by aligning them with ontology terms. We implemented our approach as a service integrated into the CEDAR Workbench metadata authoring platform, and evaluated it using metadata from two public biomedical repositories: US-based National Center for Biotechnology Information BioSample and European Bioinformatics Institute BioSamples. The results show that our approach is able to use analyses of previously entered metadata coupled with ontology-based mappings to present users with accurate recommendations when authoring metadata.

Tools and workflows for bit-level digital preservation strategy at the University of Cincinnati

Digital preservation is a complex, highly technical activity. Large institutions with many collections in multiple repositories (or repository-like systems) may find it challenging to corral digital collections, preservation masters and metadata into coherent archival information packages for preservation storage. This paper describes the tools and workflows for a bit-level preservation strategy for digital content at the University of Cincinnati Libraries and lessons learned on the way.

Employing Domain Indexes to Efficiently Query Medical Data From Multiple Repositories.

Content-based retrieval still remains one of the main problems with respect to controversies and challenges in digital healthcare over big data. To properly address this problem, there is a need for efficient computational techniques, especially in scenarios involving queries across multiple data repositories. In such scenarios, the common computational approach searches the repositories separately and combines the results into one final response, which slows down the process altogether. In order to improve the performance of queries in that kind of scenario, we present the Domain Index, a new category of index structures intended to efficiently query a data domain across multiple repositories, regardless of the repository to which the data belong. To evaluate our method, we carried out experiments involving content-based queries, namely range and k nearest neighbor (kNN) queries, 1) over real-world data from a public data set of mammograms, as well as 2) over synthetic data to perform scalability evaluations. The results show that images from any repository are seamlessly retrieved, sustaining performance gains of up to 53% in range queries and up to 81% in kNN queries. Regarding scalability, our proposal scaled well as we increased 1) the cardinality of data (up to 59% of gain) and 2) the number of queried repositories (up to 71% of gain). Hence, our method enables significant performance improvements, and should be of most importance for medical data repository maintainers and for physicians' IT support.

ProGeM: a framework for the prioritization of candidate causal genes at molecular quantitative trait loci.

Quantitative trait locus (QTL) mapping of molecular phenotypes such as metabolites, lipids and proteins through genome-wide association studies represents a powerful means of highlighting molecular mechanisms relevant to human diseases. However, a major challenge of this approach is to identify the causal gene(s) at the observed QTLs. Here, we present a framework for the ‘Prioritization of candidate causal Genes at Molecular QTLs’ (ProGeM), which incorporates biological domain-specific annotation data alongside genome annotation data from multiple repositories. We assessed the performance of ProGeM using a reference set of 227 previously reported and extensively curated metabolite QTLs. For 98% of these loci, the expert-curated gene was one of the candidate causal genes prioritized by ProGeM. Benchmarking analyses revealed that 69% of the causal candidates were nearest to the sentinel variant at the investigated molecular QTLs, indicating that genomic proximity is the most reliable indicator of ‘true positive’ causal genes. In contrast, cis-gene expression QTL data led to three false positive candidate causal gene assignments for every one true positive assignment. We provide evidence that these conclusions also apply to other molecular phenotypes, suggesting that ProGeM is a powerful and versatile tool for annotating molecular QTLs. ProGeM is freely available via GitHub.

Enabling openness of valuable information resources: Curbing data subtractability and exclusion

AbstractIn this paper we investigate how data openness can be made possible in communal settings. We adopt a utility perspective that foregrounds the use value of data, conceptualizing them as “goods.” On the basis of this conceptualization we explore 2 key goods' attributes: subtractability and exclusion. Our theoretical basis is built upon concepts from the theory of the commons, power theorizing, and notions related to data and information. Empirically, we investigate openness in the genetics domain through a longitudinal study of the evolving communal infrastructure for data related to 2 genes influencing women's susceptibility to breast and ovarian cancer (BRCA1 and BRCA2). We follow the continuously shifting “topology” of the BRCA information infrastructure and trace the multiple repositories that are put in place and the different arrangements for data collection, curation/quality assurance, access, and control that are tried out. In our analysis, we illustrate the actors' strategies for curbing the subtractability and exclusion attributes of data. We then propose a theoretically informed and empirically grounded framework that can guide understanding and action taking to enable data openness.

A Novel Framework for Data Extraction from Multiple Repositories and Generation of Ontologies using Inverted Indexing Technique

A Novel Framework for Data Extraction from Multiple Repositories and Generation of Ontologies using Inverted Indexing Technique

Maximizing the Storage Capacity of Free Cloud Storage Providers

This study aims to resolve the problem on the free cloud storage services. With innumerable providers, data and files are dispersed across multiple cloud repositories. Users try to have multiple accounts on the same cloud storage provider or to sign up for an account from different providers. Hence, Fuse creates an illusion of a larger capacity of data volume that will be made available for free for public consumption. With Fuse, files are consolidated and managed in a sophisticated manner to ensure data preservation and performance efficiency. This centralized cloud service is made to utilize today’s emerging cloud services such as Google Drive, Dropbox, and Box. With 1 account, users will be able to store files online and access them anywhere with larger storage capacity than what is available today.

A standard-enabled workflow for synthetic biology.

A synthetic biology workflow is composed of data repositories that provide information about genetic parts, sequence-level design tools to compose these parts into circuits, visualization tools to depict these designs, genetic design tools to select parts to create systems, and modeling and simulation tools to evaluate alternative design choices. Data standards enable the ready exchange of information within such a workflow, allowing repositories and tools to be connected from a diversity of sources. The present paper describes one such workflow that utilizes, among others, the Synthetic Biology Open Language (SBOL) to describe genetic designs, the Systems Biology Markup Language to model these designs, and SBOL Visual to visualize these designs. We describe how a standard-enabled workflow can be used to produce types of design information, including multiple repositories and software tools exchanging information using a variety of data standards. Recently, the ACS Synthetic Biology journal has recommended the use of SBOL in their publications.

Finding useful data across multiple biomedical data repositories using DataMed.

The value of broadening searches for data across multiple repositories has been identified by the biomedical research community. As part of the US National Institutes of Health (NIH) Big Data to Knowledge initiative, we work with an international community of researchers, service providers and knowledge experts to develop and test a data index and search engine, which are based on metadata extracted from various data sets in a range of repositories. DataMed is designed to be, for data, what PubMed has been for the scientific literature. DataMed supports the findability and accessibility of data sets. These characteristics—along with interoperability and reusability—compose the four FAIR principles to facilitate knowledge discovery in today's big data–intensive science landscape.

Achieving a milestone: AMOR is now archived in Portico and indexed in CAS

&lt;p&gt;The journal of Advances in Modern Oncology Research (AMOR) is proud to announce its partnership with Portico, a leading digital preservation service provider, to ensure the long term accessibility of its published contents. In addition, it is now indexed by the gold standard of chemical information database Chemical Abstracts Services (CAS) after publishing only six bimonthly issues, dating back to October 2015. AMOR’s Editor-in-Chief Dr. Omar Abdel-Rahman proudly shares: “Our vision is to be far more than just a journal, we want to be a platform that publishes high quality cancer research contents from all over the world. I think we are progressing, at an acceptable pace, in that direction."&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;The Portico archive is a “centralized repository of tens of thousands of e-journals, e-books, and other electronic content, replicated to ensure security,” according to the registry organization. “Content comes into the archive under formal preservation agreements with publishers. Content providers submit source files to Portico, and we repackage these source files into an archival format and provide long-term archival management and format migration as needed. Our approach is driven by our commitment to meeting clear preservation goals,” it states. &lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;AMOR’s Managing Editor Dr. TS Jong, when asked to comment about the impact of this development, adds: “AMOR is committed towards meeting the highest international publication standards and as such, has a clear archiving and indexing roadmap to sustain our growth. The journal acknowledges the importance of ensuring the continuous accessibility of our published articles in multiple repositories. Therefore, we are delighted with the recent partnership between AMOR and Portico, a well-known third-party repository service provider, to safeguard the long-term availability of our contents.”&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;Archiving AMOR’s articles within Portico ensures that the materials published are always available for access. In addition, AMOR’s inclusion in Portico – one of the leading digital preservation services in the world – ensures that AMOR is a step-closer towards its goal of being indexed by PubMed or Medline eventually, as Portico is one of the certified repositories according to Medline’s requirement, Dr. Jong explains.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;Meanwhile, Chemical Abstracts Service is an internationally-renowned authority for chemical information that delivers the most complete, cross-linked, and effective digital information for scientific discoveries. CAS, a division of the American Chemical Society, is a source of chemical information and its databases are recognized as the ultimate ‘gold standard’ by the industry. Physically located in Columbus, Ohio, United States, CAS provides updated chemistry content which is maintained by hundreds of PhD scientists from all around the world. The content in CAS covers almost all information and accurate details acquired from thousands of journals, books, patent authorities, web sources, dissertations, conference proceedings, to name a few.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;According to Dr. Jong, AMOR is aware that being indexed in relevant databases would broaden its readership and consequently improve the journal’s standing among other oncology journals. “In this regard, we are excited about our inclusion in the CAS databases. This reflects the quality of our published contents and will certainly pave the way for subsequent recognitions by other academic databases,” he says. Dr. Abdel-Rahman goes on to explain, “Our vision for the development and progress of AMOR is to turn it into a benchmark of good quality cancer research and to disseminate this high-quality research to all those who are interested, without any barriers. That is why the indexing and archiving of AMOR into highly accessed biomedical indices is vital to achieve this goal.”&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;AMOR’s future is bright and hopeful, the EIC says. “We promise our respectable readers and collaborators that AMOR will continue to develop itself and strive to include its contents in other highly accessed biomedical indices,” Dr. Abdel-Rahman concludes. ■&lt;/p&gt;

Multi-objective Middleware for Distributed VMI Repositories in Federated Cloud Environment

Virtualization represents an essential technology in Cloud computing, which allows virtual machines (VM) to be executed within their own environment on top of physical hardware. The modern methods for software delivery are utilizing the concept of Vitual Machine as a efficient tool for software packaging. Typically, VMs are created using specific templates that are stored in proprietary repositories, thus leading to provider lock-in and reduced portability in the cases of simultaneous usage of multiple federated Clouds. Unfortunately, the current state-of-the-art does not provide any efficient means for streamlined management of VM images across multiple repositories, especially within federated Cloud environments. In this paper we present a novel multi-objective middleware for distributed VMI repositories in federated Cloud environment. The middleware has been designed to provide easy to use interface capable of receiving unmodified and functionally complete VM images from its users, and transparently distribute them to a specific Cloud infrastructure in a federation with respect to their size, configuration, and geographical distribution, such that they are loaded, delivered, and executed faster and with improved QoS compared to their current behaviour.

A systematic solution to map processed data in tranSMART to raw data in multiple repositories

A systematic solution to map processed data in tranSMART to raw data in multiple repositories