Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Taxize: taxonomic search and retrieval in R.

ABSTRACT: All species are hierarchically related to one another, and we use taxonomic names to label the nodes in this hierarchy. Taxonomic data is becoming increasingly available on the web, but scientists need a way to access it in a programmatic fashion that's easy and reproducible. We have developed taxize, an open-source software package (freely available from http://cran.r-project.org/web/packages/taxize/index.html) for the R language. taxize provides simple, programmatic access to taxonomic data for 13 data sources around the web. We discuss the need for a taxonomic toolbelt in R, and outline a suite of use cases for which taxize is ideally suited (including a full workflow as an appendix). The taxize package facilitates open and reproducible science by allowing taxonomic data collection to be done in the open-source R platform.

SUBMITTER: Chamberlain SA

PROVIDER: S-EPMC3901538 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

taxize: taxonomic search and retrieval in R.

Chamberlain Scott A SA Szöcs Eduard E

F1000Research 20130918

All species are hierarchically related to one another, and we use taxonomic names to label the nodes in this hierarchy. Taxonomic data is becoming increasingly available on the web, but scientists need a way to access it in a programmatic fashion that's easy and reproducible. We have developed taxize, an open-source software package (freely available from http://cran.r-project.org/web/packages/taxize/index.html) for the R language. taxize provides simple, programmatic access to taxonomic data fo ...[more]

PMID: 24555091

Similar Datasets

Distributed search and fusion for wine label image retrieval.

Project description:With the popularity of wine culture and the development of artificial intelligence (AI) technology, wine label image retrieval becomes more and more important. Taking an wine label image as an input, the goal of this task is to return the wine information that the user hopes to know, such as the main brand and sub-brand of the wine. The main challenge in wine label image retrieval task is that there are a large number of wine brands with the imbalance of their sample images which strongly affects the training of the retrieval system based on deep learning. To solve this problem, this article adopts a distribted strategy and proposes two distributed retrieval frameworks. It is demonstrated by the experimental results on the large scale wine label dataset and the Oxford flowers dataset that both our proposed distributed retrieval frameworks are effective and even greatly outperform the previous state-of-the-art retrieval models.

| S-EPMC9575874 | biostudies-literature

Ensembl BioMarts: a hub for data retrieval across taxonomic space.

Project description:For a number of years the BioMart data warehousing system has proven to be a valuable resource for scientists seeking a fast and versatile means of accessing the growing volume of genomic data provided by the Ensembl project. The launch of the Ensembl Genomes project in 2009 complemented the Ensembl project by utilizing the same visualization, interactive and programming tools to provide users with a means for accessing genome data from a further five domains: protists, bacteria, metazoa, plants and fungi. The Ensembl and Ensembl Genomes BioMarts provide a point of access to the high-quality gene annotation, variation data, functional and regulatory annotation and evolutionary relationships from genomes spanning the taxonomic space. This article aims to give a comprehensive overview of the Ensembl and Ensembl Genomes BioMarts as well as some useful examples and a description of current data content and future objectives. Database URLs: http://www.ensembl.org/biomart/martview/; http://metazoa.ensembl.org/biomart/martview/; http://plants.ensembl.org/biomart/martview/; http://protists.ensembl.org/biomart/martview/; http://fungi.ensembl.org/biomart/martview/; http://bacteria.ensembl.org/biomart/martview/.

| S-EPMC3170168 | biostudies-literature

Improved data retrieval from TreeBASE via taxonomic and linguistic data enrichment.

Project description:BackgroundTreeBASE, the only data repository for phylogenetic studies, is not being used effectively since it does not meet the taxonomic data retrieval requirements of the systematics community. We show, through an examination of the queries performed on TreeBASE, that data retrieval using taxon names is unsatisfactory.ResultsWe report on a new wrapper supporting taxon queries on TreeBASE by utilising a Taxonomy and Classification Database (TCl-Db) we created. TCl-Db holds merged and consolidated taxonomic names from multiple data sources and can be used to translate hierarchical, vernacular and synonym queries into specific query terms in TreeBASE. The query expansion supported by TCl-Db shows very significant information retrieval quality improvement. The wrapper can be accessed at the URL http://spira.zoology.gla.ac.uk/app/tbasewrapper.phpThe methodology we developed is scalable and can be applied to new data, as those become available in the future.ConclusionSignificantly improved data retrieval quality is shown for all queries, and additional flexibility is achieved via user-driven taxonomy selection.

| S-EPMC2685121 | biostudies-literature

Development of a Search Strategy for an Evidence Based Retrieval Service.

Project description:BackgroundPhysicians are often encouraged to locate answers for their clinical queries via an evidence-based literature search approach. The methods used are often not clearly specified. Inappropriate search strategies, time constraint and contradictory information complicate evidence retrieval.AimsOur study aimed to develop a search strategy to answer clinical queries among physicians in a primary care setting.MethodsSix clinical questions of different medical conditions seen in primary care were formulated. A series of experimental searches to answer each question was conducted on 3 commonly advocated medical databases. We compared search results from a PICO (patients, intervention, comparison, outcome) framework for questions using different combinations of PICO elements. We also compared outcomes from doing searches using text words, Medical Subject Headings (MeSH), or a combination of both. All searches were documented using screenshots and saved search strategies.ResultsAnswers to all 6 questions using the PICO framework were found. A higher number of systematic reviews were obtained using a 2 PICO element search compared to a 4 element search. A more optimal choice of search is a combination of both text words and MeSH terms. Despite searching using the Systematic Review filter, many non-systematic reviews or narrative reviews were found in PubMed. There was poor overlap between outcomes of searches using different databases. The duration of search and screening for the 6 questions ranged from 1 to 4 hours.ConclusionThis strategy has been shown to be feasible and can provide evidence to doctors' clinical questions. It has the potential to be incorporated into an interventional study to determine the impact of an online evidence retrieval system.

| S-EPMC5147858 | biostudies-literature

Expert Search Strategies: The Information Retrieval Practices of Healthcare Information Professionals.

Project description:BackgroundHealthcare information professionals play a key role in closing the knowledge gap between medical research and clinical practice. Their work involves meticulous searching of literature databases using complex search strategies that can consist of hundreds of keywords, operators, and ontology terms. This process is prone to error and can lead to inefficiency and bias if performed incorrectly.ObjectiveThe aim of this study was to investigate the search behavior of healthcare information professionals, uncovering their needs, goals, and requirements for information retrieval systems.MethodsA survey was distributed to healthcare information professionals via professional association email discussion lists. It investigated the search tasks they undertake, their techniques for search strategy formulation, their approaches to evaluating search results, and their preferred functionality for searching library-style databases. The popular literature search system PubMed was then evaluated to determine the extent to which their needs were met.ResultsThe 107 respondents indicated that their information retrieval process relied on the use of complex, repeatable, and transparent search strategies. On average it took 60 minutes to formulate a search strategy, with a search task taking 4 hours and consisting of 15 strategy lines. Respondents reviewed a median of 175 results per search task, far more than they would ideally like (100). The most desired features of a search system were merging search queries and combining search results.ConclusionsHealthcare information professionals routinely address some of the most challenging information retrieval problems of any profession. However, their needs are not fully supported by current literature search systems and there is demand for improved functionality, in particular regarding the development and management of search strategies.

| S-EPMC5643841 | biostudies-literature

Impact of PubMed search filters on the retrieval of evidence by physicians.

Project description:Physicians face challenges when searching PubMed for research evidence, and they may miss relevant articles while retrieving too many nonrelevant articles. We investigated whether the use of search filters in PubMed improves searching by physicians.We asked a random sample of Canadian nephrologists to answer unique clinical questions derived from 100 systematic reviews of renal therapy. Physicians provided the search terms that they would type into PubMed to locate articles to answer these questions. We entered the physician-provided search terms into PubMed and applied two types of search filters alone or in combination: a methods-based filter designed to identify high-quality studies about treatment (clinical queries "therapy") and a topic-based filter designed to identify studies with renal content. We evaluated the comprehensiveness (proportion of relevant articles found) and efficiency (ratio of relevant to nonrelevant articles) of the filtered and nonfiltered searches. Primary studies included in the systematic reviews served as the reference standard for relevant articles.The average physician-provided search terms retrieved 46% of the relevant articles, while 6% of the retrieved articles were relevant (corrected) (the ratio of relevant to nonrelevant articles was 1:16). The use of both filters together produced a marked improvement in efficiency, resulting in a ratio of relevant to nonrelevant articles of 1:5 (16 percentage point improvement; 99% confidence interval 9% to 22%; p < 0.003) with no substantive change in comprehensiveness (44% of relevant articles found; p = 0.55).The use of PubMed search filters improves the efficiency of physician searches. Improved search performance may enhance the transfer of research into practice and improve patient care.

| S-EPMC3281182 | biostudies-literature

MEDLINE search retrieval issues: A longitudinal query analysis of five vendor platforms.

Project description:This study compared the results of data collected from a longitudinal query analysis of the MEDLINE database hosted on multiple platforms that include PubMed, EBSCOHost, Ovid, ProQuest, and Web of Science. The goal was to identify variations among the search results on the platforms after controlling for search query syntax. We devised twenty-nine cases of search queries comprised of five semantically equivalent queries per case to search against the five MEDLINE database platforms. We ran our queries monthly for a year and collected search result count data to observe changes. We found that search results varied considerably depending on MEDLINE platform. Reasons for variations were due to trends in scholarly publication such as publishing individual papers online first versus complete issues. Some other reasons were metadata differences in bibliographic records; differences in the levels of specificity of search fields provided by the platforms and large fluctuations in monthly search results based on the same query. Database integrity and currency issues were observed as each platform updated its MEDLINE data throughout the year. Specific biomedical bibliographic databases are used to inform clinical decision-making, create systematic reviews, and construct knowledge bases for clinical decision support systems. They serve as essential information retrieval and discovery tools to help identify and collect research data and are used in a broad range of fields and as the basis of multiple research designs. This study should help clinicians, researchers, librarians, informationists, and others understand how these platforms differ and inform future work in their standardization.

| S-EPMC8101950 | biostudies-literature

An iterative framework for EEG-based image search: robust retrieval with weak classifiers.

Project description:We revisit the framework for brain-coupled image search, where the Electroencephalography (EEG) channel under rapid serial visual presentation protocol is used to detect user preferences. Extending previous works on the synergy between content-based image labeling and EEG-based brain-computer interface (BCI), we propose a different perspective on iterative coupling. Previously, the iterations were used to improve the set of EEG-based image labels before propagating them to the unseen images for the final retrieval. In our approach we accumulate the evidence of the true labels for each image in the database through iterations. This is done by propagating the EEG-based labels of the presented images at each iteration to the rest of images in the database. Our results demonstrate a continuous improvement of the labeling performance across iterations despite the moderate EEG-based labeling (AUC <75%). The overall analysis is done in terms of the single-trial EEG decoding performance and the image database reorganization quality. Furthermore, we discuss the EEG-based labeling performance with respect to a search task given the same image database.

| S-EPMC3748021 | biostudies-literature

Tachyon search speeds up retrieval of similar sequences by several orders of magnitude.

Project description:UnlabelledThe usage of current sequence search tools becomes increasingly slower as databases of protein sequences continue to grow exponentially. Tachyon, a new algorithm that identifies closely related protein sequences ~200 times faster than standard BLAST, circumvents this limitation with a reduced database and oligopeptide matching heuristic.Availability and implementationThe tool is publicly accessible as a webserver at http://tachyon.bii.a-star.edu.sg and can also be accessed programmatically through SOAP.

| S-EPMC3371831 | biostudies-literature

Electronic Medical Record Search Engine (EMERSE): An Information Retrieval Tool for Supporting Cancer Research.

Project description:PurposeThe Electronic Medical Record Search Engine (EMERSE) is a software tool built to aid research spanning cohort discovery, population health, and data abstraction for clinical trials. EMERSE is now live at three academic medical centers, with additional sites currently working on implementation. In this report, we describe how EMERSE has been used to support cancer research based on a variety of metrics.MethodsWe identified peer-reviewed publications that used EMERSE through online searches as well as through direct e-mails to users based on audit logs. These logs were also used to summarize use at each of the three sites. Search terms for two of the sites were characterized using the natural language processing tool MetaMap to determine to which semantic types the terms could be mapped.ResultsWe identified a total of 326 peer-reviewed publications that used EMERSE through August 2019, although this is likely an underestimation of the true total based on the use log analysis. Oncology-related research comprised nearly one third (n = 105; 32.2%) of all research output. The use logs showed that EMERSE had been used by multiple people at each site (nearly 3,500 across all three) who had collectively logged into the system > 100,000 times. Many user-entered search queries could not be mapped to a semantic type, but the most common semantic type for terms that did match was "disease or syndrome," followed by "pharmacologic substance."ConclusionEMERSE has been shown to be a valuable tool for supporting cancer research. It has been successfully deployed at other sites, despite some implementation challenges unique to each deployment environment.

| S-EPMC7265780 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data