scholarly journals BioNames: linking taxonomy, texts, and trees

Author(s):  
Roderic D M Page

BioNames is a web database of taxonomic names for animals, linked to the primary literature and, wherever possible, to phylogenetic trees. It aims to provide a taxonomic "dashboard" where at a glance we can see a summary of the taxonomic and phylogenetic information we have for a given taxon and hence provide a quick answer to the basic question "what is this taxon?" BioNames combines classifications from the Global Biodiversity Information Facility (GBIF) and GenBank, imagery from the Encyclopedia of Life (EOL), animal names from the Index of Organism Names (ION), and bibliographic data from multiple sources including the Biodiversity Heritage Library (BHL) and CrossRef. The user interface includes display of full text articles, interactive timelines of taxonomic publications, and zoomable phylogenies. It is available at http://bionames.org.

2013 ◽  
Author(s):  
Roderic D M Page

BioNames is a web database of taxonomic names for animals, linked to the primary literature and, wherever possible, to phylogenetic trees. It aims to provide a taxonomic "dashboard" where at a glance we can see a summary of the taxonomic and phylogenetic information we have for a given taxon and hence provide a quick answer to the basic question "what is this taxon?" BioNames combines classifications from the Global Biodiversity Information Facility (GBIF) and GenBank, imagery from the Encyclopedia of Life (EOL), animal names from the Index of Organism Names (ION), and bibliographic data from multiple sources including the Biodiversity Heritage Library (BHL) and CrossRef. The user interface includes display of full text articles, interactive timelines of taxonomic publications, and zoomable phylogenies. It is available at http://bionames.org.


2019 ◽  
Vol 7 ◽  
Author(s):  
Vaughn Shirey ◽  
Sini Seppälä ◽  
Vasco Branco ◽  
Pedro Cardoso

Conservation assessments of hyperdiverse groups of organisms are often challenging and limited by the availability of occurrence data needed to calculate assessment metrics such as extent of occurrence (EOO). Spiders represent one such diverse group and have historically been assessed using primary literature with retrospective georeferencing. Here we demonstrate the differences in estimations of EOO and hypothetical IUCN Red List classifications for two extensive spider datasets comprising 479 species in total. The EOO were estimated and compared using literature-based assessments, Global Biodiversity Information Facility (GBIF)-based assessments and combined data assessments. We found that although few changes to hypothetical IUCN Red List classifications occurred with the addition of GBIF data, some species (3.3%) which could previously not be classified could now be assessed with the addition of GBIF data. In addition, the hypothetical classification changed for others (1.5%). On the other hand, GBIF data alone did not provide enough data for 88.7% of species. These results demonstrate the potential of GBIF data to serve as an additional source of information for conservation assessments, complementing literature data, but not particularly useful on its own as it stands right now for spiders.


Author(s):  
Luis Miguel Pires Ceríaco ◽  
Mariana Pimentel Marques

The herpetological collections of the Instituto de Investigação Científica Tropical (Lisbon, Portugal) are the largest and most diverse collections of amphibians and reptiles in the country. These were collected in the mid-twentieth century in the former Portuguese colonies in Africa and Asia, and were the object of study of several naturalists that used them to describe and catalogue the herpetofauna of those areas. After the independence of these colonies in the mid 1970's, the research on this material nearly halted, and the collections became abandoned, without proper curation and lacking accessibility. In 2015, we started a process to recover these collections (Fig. 1). This encompassed basic curation, e.g. cleaning and substituting jars and fluid preservatives, cataloguing the entire collection, digitizing and georeferencing all the specimens, and making data available through the Global Biodiversity Information Facility at GBIF.org. While doing this, each specimen was also linked to its bibliographic data, its taxonomic identity carefully reviewed, and rare and important specimens (e.g., type specimens) flagged. Currently, the collection is completely accessible, both physically and electronically, and it is being used by researchers and students around the world. Some results have already been published including the description of species new to science (Ceríaco et al. 2016, Ceríaco et al. 2017, Ceríaco 2015, Soares et al. 2018), new country checklists, the publication of an Atlas of Angolan Herpetofauna (Marques et al. 2018), International Union for Conservation of Nature (IUCN) Red List assessments, and student training. This presentation provides an overview of the recovery process of the collection, discusses strategies on how to digitize and make historical collections available to the community, and demonstrates how biological collections amassed during colonial times can be of extreme importance to the study and preservation of present day biodiversity.


Author(s):  
Katharine Barker ◽  
Jonas Astrin ◽  
Gabriele Droege ◽  
Jonathan Coddington ◽  
Ole Seberg

Most successful research programs depend on easily accessible and standardized research infrastructures. Until recently, access to tissue or DNA samples with standardized metadata and of a sufficiently high quality, has been a major bottleneck for genomic research. The Global Geonome Biodiversity Network (GGBN) fills this critical gap by offering standardized, legal access to samples. Presently, GGBN’s core activity is enabling access to searchable DNA and tissue collections across natural history museums and botanic gardens. Activities are gradually being expanded to encompass all kinds of biodiversity biobanks such as culture collections, zoological gardens, aquaria, arboreta, and environmental biobanks. Broadly speaking, these collections all provide long-term storage and standardized public access to samples useful for molecular research. GGBN facilitates sample search and discovery for its distributed member collections through a single entry point. It stores standardized information on mostly geo-referenced, vouchered samples, their physical location, availability, quality, and the necessary legal information on over 50,000 species of Earth’s biodiversity, from unicellular to multicellular organisms. The GGBN Data Portal and the GGBN Data Standard are complementary to existing infrastructures such as the Global Biodiversity Information Facility (GBIF) and International Nucleotide Sequence Database (INSDC). Today, many well-known open-source collection management databases such as Arctos, Specify, and Symbiota, are implementing the GGBN data standard. GGBN continues to increase its collections strategically, based on the needs of the research community, adding over 1.3 million online records in 2018 alone, and today two million sample data are available through GGBN. Together with Consortium of European Taxonomic Facilities (CETAF), Society for the Preservation of Natural History Collections (SPNHC), Biodiversity Information Standards (TDWG), and Synthesis of Systematic Resources (SYNTHESYS+), GGBN provides best practices for biorepositories on meeting the requirements of the Nagoya Protocol on Access and Benefit Sharing (ABS). By collaboration with the Biodiversity Heritage Library (BHL), GGBN is exploring options for tagging publications that reference GGBN collections and associated specimens, made searchable through GGBN’s document library. Through its collaborative efforts, standards, and best practices GGBN aims at facilitating trust and transparency in the use of genetic resources.


2020 ◽  
Vol 8 ◽  
Author(s):  
Sonia Ferreira ◽  
Rui Andrade ◽  
Ana Gonçalves ◽  
Pedro Sousa ◽  
Joana Paupério ◽  
...  

The InBIO Barcoding Initiative (IBI) Diptera 01 dataset contains records of 203 specimens of Diptera. All specimens have been morphologically identified to species level, and belong to 154 species in total. The species represented in this dataset correspond to about 10% of continental Portugal dipteran species diversity. All specimens were collected north of the Tagus river in Portugal. Sampling took place from 2014 to 2018, and specimens are deposited in the IBI collection at CIBIO, Research Center in Biodiversity and Genetic Resources. This dataset contributes to the knowledge on the DNA barcodes and distribution of 154 species of Diptera from Portugal and is the first of the planned IBI database public releases, which will make available genetic and distribution data for a series of taxa. All specimens have their DNA barcodes made publicly available in the Barcode of Life Data System (BOLD) online database and the distribution dataset can be freely accessed through the Global Biodiversity Information Facility (GBIF).


Author(s):  
Raul Sierra-Alcocer ◽  
Christopher Stephens ◽  
Juan Barrios ◽  
Constantino González‐Salazar ◽  
Juan Carlos Salazar Carrillo ◽  
...  

SPECIES (Stephens et al. 2019) is a tool to explore spatial correlations in biodiversity occurrence databases. The main idea behind the SPECIES project is that the geographical correlations between the distributions of taxa records have useful information. The problem, however, is that if we have thousands of species (Mexico's National System of Biodiversity Information has records of around 70,000 species) then we have millions of potential associations, and exploring them is far from easy. Our goal with SPECIES is to facilitate the discovery and application of meaningful relations hiding in our data. The main variables in SPECIES are the geographical distributions of species occurrence records. Other types of variables, like the climatic variables from WorldClim (Hijmans et al. 2005), are explanatory data that serve for modeling. The system offers two modes of analysis. In one, the user defines a target species, and a selection of species and abiotic variables; then the system computes the spatial correlations between the target species and each of the other species and abiotic variables. The request from the user can be as small as comparing one species to another, or as large as comparing one species to all the species in the database. A user may wonder, for example, which species are usual neighbors of the jaguar, this mode could help answer this question. The second mode of analysis gives a network perspective, in it, the user defines two groups of taxa (and/or environmental variables), the output in this case is a correlation network where the weight of a link between two nodes represents the spatial correlation between the variables that the nodes represent. For example, one group of taxa could be hummingbirds (Trochilidae family) and the second flowers of the Lamiaceae family. This output would help the user analyze which pairs of hummingbird and flower are highly correlated in the database. SPECIES data architecture is optimized to support fast hypotheses prototyping and testing with the analysis of thousands of biotic and abiotic variables. It has a visualization web interface that presents descriptive results to the user at different levels of detail. The methodology in SPECIES is relatively simple, it partitions the geographical space with a regular grid and treats a species occurrence distribution as a present/not present boolean variable over the cells. Given two species (or one species and one abiotic variable) it measures if the number of co-occurrences between the two is more (or less) than expected. If it is more than expected indicates a signal of a positive relation, whereas if it is less it would be evidence of disjoint distributions. SPECIES provides an open web application programming interface (API) to request the computation of correlations and statistical dependencies between variables in the database. Users can create applications that consume this 'statistical web service' or use it directly to further analyze the results in frameworks like R or Python. The project includes an interactive web application that does exactly that: requests analysis from the web service and lets the user experiment and visually explore the results. We believe this approach can be used on one side to augment the services provided from data repositories; and on the other side, facilitate the creation of specialized applications that are clients of these services. This scheme supports big-data-driven research for a wide range of backgrounds because end users do not need to have the technical know-how nor the infrastructure to handle large databases. Currently, SPECIES hosts: all records from Mexico's National Biodiversity Information System (CONABIO 2018) and a subset of Global Biodiversity Information Facility data that covers the contiguous USA (GBIF.org 2018b) and Colombia (GBIF.org 2018a). It also includes discretizations of environmental variables from WorldClim, from the Environmental Rasters for Ecological Modeling project (Title and Bemmels 2018), from CliMond (Kriticos et al. 2012), and topographic variables (USGS EROS Center 1997b, USGS EROS Center 1997a). The long term plan, however, is to incrementally include more data, specially all data from the Global Biodiversity Information Facility. The code of the project is open source, and the repositories are available online (Front-end, Web Services Application Programming Interface, Database Building scripts). This presentation is a demonstration of SPECIES' functionality and its overall design.


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