Dataset Information

CRITTERBASE, a science-driven data warehouse for marine biota.

ABSTRACT: Data on marine biota exist in many formats and sources, such as published literature, data repositories, and unpublished material. Due to this heterogeneity, information is difficult to find, access and combine, severely impeding its reuse for further scientific analysis and its long-term availability for future generations. To address this challenge, we present CRITTERBASE, a publicly accessible data warehouse and interactive portal that currently hosts quality-controlled and taxonomically standardized presence/absence, abundance, and biomass data for 18,644 samples and 3,664 benthic taxa (2,824 of which at species level). These samples were collected by grabs, underwater imaging or trawls in Arctic, North Sea and Antarctic regions between the years 1800 and 2014. Data were collated from literature, unpublished data, own research and online repositories. All metadata and links to primary sources are included. We envision CRITTERBASE becoming a valuable and continuously expanding tool for a wide range of usages, such as studies of spatio-temporal biodiversity patterns, impacts and risks of climate change or the evidence-based design of marine protection policies.

SUBMITTER: Teschke K

PROVIDER: S-EPMC9357058 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

CRITTERBASE, a science-driven data warehouse for marine biota.

Teschke Katharina K Kraan Casper C Kloss Paul P Andresen Henrike H Beermann Jan J Fiorentino Dario D Gusky Manuela M Hansen Miriam L S MLS Konijnenberg Rebecca R Koppe Roland R Pehlke Hendrik H Piepenburg Dieter D Sabbagh Tawfik T Wrede Alexa A Brey Thomas T Dannheim Jennifer J

Scientific data 20220806 1

Data on marine biota exist in many formats and sources, such as published literature, data repositories, and unpublished material. Due to this heterogeneity, information is difficult to find, access and combine, severely impeding its reuse for further scientific analysis and its long-term availability for future generations. To address this challenge, we present CRITTERBASE, a publicly accessible data warehouse and interactive portal that currently hosts quality-controlled and taxonomically stan ...[more]

PMID: 35933490

Similar Datasets

Project description:Species distribution data provide the foundation for a wide range of ecological research studies and conservation management decisions. Two major efforts to provide marine species distributions at a global scale are the International Union for Conservation of Nature (IUCN), which provides expert-generated range maps that outline the complete extent of a species' distribution; and AquaMaps, which provides model-generated species distribution maps that predict areas occupied by the species. Together these databases represent 24,586 species (93.1% within AquaMaps, 16.4% within IUCN), with only 2,330 shared species. Differences in intent and methodology can result in very different predictions of species distributions, which bear important implications for scientists and decision makers who rely upon these datasets when conducting research or informing conservation policy and management actions. Comparing distributions for the small subset of species with maps in both datasets, we found that AquaMaps and IUCN range maps show strong agreement for many well-studied species, but our analysis highlights several key examples in which introduced errors drive differences in predicted species ranges. In particular, we find that IUCN maps greatly overpredict coral presence into unsuitably deep waters, and we show that some AquaMaps computer-generated default maps (only 5.7% of which have been reviewed by experts) can produce odd discontinuities at the extremes of a species' predicted range. We illustrate the scientific and management implications of these tradeoffs by repeating a global analysis of gaps in coverage of marine protected areas, and find significantly different results depending on how the two datasets are used. By highlighting tradeoffs between the two datasets, we hope to encourage increased collaboration between taxa experts and large scale species distribution modeling efforts to further improve these foundational datasets, helping to better inform science and policy recommendations around understanding, managing, and protecting marine biodiversity.

Project description:BackgroundWe present a biological data warehouse called Atlas that locally stores and integrates biological sequences, molecular interactions, homology information, functional annotations of genes, and biological ontologies. The goal of the system is to provide data, as well as a software infrastructure for bioinformatics research and development.DescriptionThe Atlas system is based on relational data models that we developed for each of the source data types. Data stored within these relational models are managed through Structured Query Language (SQL) calls that are implemented in a set of Application Programming Interfaces (APIs). The APIs include three languages: C++, Java, and Perl. The methods in these API libraries are used to construct a set of loader applications, which parse and load the source datasets into the Atlas database, and a set of toolbox applications which facilitate data retrieval. Atlas stores and integrates local instances of GenBank, RefSeq, UniProt, Human Protein Reference Database (HPRD), Biomolecular Interaction Network Database (BIND), Database of Interacting Proteins (DIP), Molecular Interactions Database (MINT), IntAct, NCBI Taxonomy, Gene Ontology (GO), Online Mendelian Inheritance in Man (OMIM), LocusLink, Entrez Gene and HomoloGene. The retrieval APIs and toolbox applications are critical components that offer end-users flexible, easy, integrated access to this data. We present use cases that use Atlas to integrate these sources for genome annotation, inference of molecular interactions across species, and gene-disease associations.ConclusionThe Atlas biological data warehouse serves as data infrastructure for bioinformatics research and development. It forms the backbone of the research activities in our laboratory and facilitates the integration of disparate, heterogeneous biological sources of data enabling new scientific inferences. Atlas achieves integration of diverse data sets at two levels. First, Atlas stores data of similar types using common data models, enforcing the relationships between data types. Second, integration is achieved through a combination of APIs, ontology, and tools. The Atlas software is freely available under the GNU General Public License at: http://bioinformatics.ubc.ca/atlas/

Project description:BackgroundResearch addressing value in healthcare requires a measure of cost. While there are many sources and types of cost data, each has strengths and weaknesses. Many researchers appear to create study-specific cost datasets, but the explanations of their costing methodologies are not always clear, causing their results to be difficult to interpret. Our solution, described in this paper, was to use widely accepted costing methodologies to create a service-level, standardized healthcare cost data warehouse from an institutional perspective that includes all professional and hospital-billed services for our patients.MethodsThe warehouse is based on a National Institutes of Research-funded research infrastructure containing the linked health records and medical care administrative data of two healthcare providers and their affiliated hospitals. Since all patients are identified in the data warehouse, their costs can be linked to other systems and databases, such as electronic health records, tumor registries, and disease or treatment registries.ResultsWe describe the two institutions' administrative source data; the reference files, which include Medicare fee schedules and cost reports; the process of creating standardized costs; and the warehouse structure. The costing algorithm can create inflation-adjusted standardized costs at the service line level for defined study cohorts on request.ConclusionThe resulting standardized costs contained in the data warehouse can be used to create detailed, bottom-up analyses of professional and facility costs of procedures, medical conditions, and patient care cycles without revealing business-sensitive information. After its creation, a standardized cost data warehouse is relatively easy to maintain and can be expanded to include data from other providers. Individual investigators who may not have sufficient knowledge about administrative data do not have to try to create their own standardized costs on a project-by-project basis because our data warehouse generates standardized costs for defined cohorts upon request.

Dataset Information

CRITTERBASE, a science-driven data warehouse for marine biota.

Publications

CRITTERBASE, a science-driven data warehouse for marine biota.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets