A dataset containing the contents of the US National Fungus Collections Fungus-Host-Location database with citations. Searchable version available at https://nt.ars-grin.gov/fungaldatabases. Dataset current as of 2021 Nov. 05. Resources in this dataset:Resource Title: United States National Fungus Collections Fungus-Host Dataset. File Name: Fungus-Host-Data_20211105.csvResource Description: Snapshot of Fungus-Host-Location-Reference dataset as of 2021 Nov. 05.Resource Software Recommended: Microsoft Excel 365,url: https://www.microsoft.com/en-us/microsoft-365/microsoft-office

This website contains a list of five fungal genome databases from The J. Craig Venter Institute. Aspergillus genomes: -Aspergillus fumigatus (strain-Af 293) -Aspergillus clavatus -Neosartorya fischeri Other Fungal Genomes: -Cryptococcus neoformans (strain-JEC21) -Coccidioides posadasii

The USDA-ARS U.S. National Fungus Collections (BPI) currently houses approximately one-million reference specimens. Data associated with over 925,000 specimens have been computerized and are available on-line. In addition reports of fungi on plants provide a comprehensive account of the host range and geographic distribution of fungi on plants throughout the world. Data are continuously added to the databases from herbarium specimens and newly published fungus-host distributions and disease reports. Additional databases contain taxonomic literature references and accurate scientific names of plant pathogenic fungi.

The U.S. National Fungus Collections (BPI) are the “Smithsonian for fungi” and are the repository for over one million fungal specimens worldwide - the largest such collections in the world. The collection includes preserved organisms, their parts and products, and their associated data. Information associated with these specimens constitute an enormous data resource, especially about plant-associated fungi. The collections document fungi through time and space for the past 200 years. Data from the labels of more than 750,000 of the specimens have been entered into a database. These labels have information on the host on which the fungus was found and the locality in which the specimen was collected. Sixty percent of these specimens are from the United States and thus represent a large body of information about the fungi in this country. Data entry has been completed for the Uredinales (rusts), the Ustilaginales (smuts), the Polyporales (polypores), the Deuteromycetes (imperfect fungi), the Ascomycetes, and the C.G. Lloyd collections. Recent progress has been made in the computerization of specimens of the agarics and the "lower" fungi including the Oomycetes and Chytridiomycetes. Resources in this dataset: Resource Title: Fungal databases - Specimens. File Name: Web Page, url: https://nt.ars-grin.gov/fungaldatabases/specimens/specimens.cfm The direct database form link

This mycology, or microbiology, dataset directly compares different fungal databases to each other. Such things like the occurrence rate (overall content), how many have their @ mentioned, how many pictures, how many species are ID'd, how many families, genera, and species. As well as total Taxa.

Some rows and columns will have 0's and it felt counter-productive to remove said databases because of the nature of the data. However, feel free to do whatever you want or need so that this information can accomplish the very simple goal of being useful.

UNITE is a rDNA sequence database designed to provide a stable and reliable platform for sequence-borne identification of all fungal species. UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the International Nucleotide Sequence Databases (INSD: GenBank, ENA, DDBJ) are clustered to approximately the species level by applying a set of dynamic distance values (0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.

Database containing observations of fungi and Mycetozoa mainly from Denmark. New observations are continuously added through the registration portal http://svampe.databasen.org, which was developed as part of the "Danmarks Svampeatlas" project. The project is a collaboration between the Natural History Museum of Denmark and Department of Biology, University of Copenhagen, the Danish Mycological Society and MycoKey. The project received generous financial support from Aage V. Jensen Naturfond. The aim of Svampeatlas is to compile all Basidiomycota from Denmark and to increase the knowledge of fungal distribution and ecology in Denmark, by making this information publicly available. With more than 400 active users contributing to the project, there has been more than 325.000 finds with a total of about 2.500 species of Basidiomycota. In addition a similar number of older finds has been imported from various published sources, persona and project databases.

A relational database with dynamic querying and data integration that can be used by researchers to identify genetic sequences with a high probability of being associated with aflatoxin accumulation resistance, according to multiple lines of evidence. CFRAS-DB integrates genomic, proteomic, and genetic data from multiple studies in maize dealing with aflatoxin accumulation or Aspergillus flavus resistance., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Comprehensive collection of high quality microbial genomics reference data for bacteria, viruses, and fungi in holdings of American Type Culture Collection.

The mycological research in the Northern part of West Siberia stems from isolated studies in the beginning of the 20th century, but regular and systematic research dates back to the 1970-80s. Over the following decades several dozens of researches have worked in the area and more than four hundred scientific works have been published. FUngal Records Database of Northern West Siberia (FuNWS) was developed to accumulate the results of previous studies of species distributions. The FuNWS database includes 28 fields describing species name, publication source, herbarium number, date of sampling, locality information, vegetation, substrate, and others. The occurrences in the database were extracted from previously published works, no herbarium collections or other unpublished records were included in the database. Presently, the dataset includes about 22000 of fungal occurrences recorded in the region, reported from about 130 scientific publications. According to the database summary report, there are about 3358 species identified within the region up-to-date. The richest studied classes are Agaricomycetes (60%) and Lecanoromycetes (30%) with totally 25 classes represented.

UNITE is a fungal rDNA internal transcribed spacer (ITS) sequence database. It focuses on high-quality ITS sequences generated from fruiting bodies collected and identified by experts and deposited in public herbaria. Entries may be supplemented with metadata on describing locality, habitat, soil, climate, and interacting taxa.

Fungal and arthropod consumers constitute the vast majority of global terrestrial biodiversity. Yet, the link from richness and composition of producer (plant) communities to the richness of consumer communities is poorly understood. Fungal and arthropod species richness could be a simple function of producer species richness at a site. Alternatively, it could be a complex function of chemical and structural properties of the producer species making up communities. We used databases on plant-fungus and plant-arthropod trophic links to derive the richness of consumer biota per associated plant species (coined link score). We assessed how well link scores could be predicted by simple attributes of plant species. Next, we used a multi-taxon inventory of 130 sites, representing all major habitat types in a country (Denmark), to investigate whether link scores summed over plant species in communities (coined link sum) could outperform simple plant species richness as predictor of fungal and arthropod richness at the sites. We found plant species’ link scores for both fungi and arthropods to be positively related to plant size, regional occupancy, nativeness and ectomycorrhizal status. Link-based indices generally improved the prediction of richness of fungal and arthropod communities. For fungal communities, both observed link sum (from databases) and predicted link sum (from plant attributes) had high predictive power, while plant richness alone had none. For arthropod communities, predictive performance varied between functional groups. For both fungi and arthropods, richness predictions were further improved by considering abiotic habitat conditions. Our results underline the importance of plants as niche space for the megadiverse groups of arthropods and fungi. The plant-attribute approach holds promise for predicting local and regional consumer richness in areas of the world lacking detailed plant-consumer databases. Methods Data on plant-fungus and plant-arthropod interaction links for the 549 plant species found across the 130 BioWide sites in Denmark. Detailed descriptions of field data collection protocols are found in Brunbjerg AK, Bruun HH, Brøndum L, Classen AT, Dalby L, Fog K, et al. (2019) A systematic survey of regional multi-taxon biodiversity: evaluating strategies and coverage. BMC Ecology 19(1):43. doi: 10.1186/s12898-019-0260-x. Raw data on known interaction links between all relevant plant taxa (1349 taxa on the species or genus levels) and associated arthropod species were retrieved from the BRC database (https://www.brc.ac.uk/dbif/) and similar data regarding associated fungal species from the Danish Fungal Database (https://svampe.databasen.org/). The raw data were processed to obtain an observed arthropod link score and an observed fungal link score per plant species. The calculus of link scores from raw data is detailed in the associated manuscript. Attributes of the 549 plant species used to model their predicted link score (ectomycorrhizal status, native area, occupancy in Denmark, phylogenetic grouping, lifespan, life form and size) were compiled from sources detailed in the associated manuscript.

This checklist database is a collection of reports from 16 sources referenced in the "data_set" parameter. They consist of: bishop museum: Data from the Bishop museum fungal database board report: From National Agricultural Pest Information System (NAPIS) board reports bugwood: Widely prevalent Fungi, Bacteria, and Virues in Hawaii from bugwood database CAPS survey data: Cooperative Agriculture Pest Survey databases (negative data) crop knowledge master: crop knowledge master website CTAHR New Pest reports: Collected by Dr. Michael Melzer Don Gardner: Data from Don Gardner’s legacy database First report: First reports found from literature search of scientific journals (Google Scholar, Web of Science, PubMed) GBIF: Global Biodiversity Information Facility hawaii checklist: The “2009 Checklist of Plant Diseases in Hawaii” Hawaii NPDN data: National Plant Diagnostic Data from Hawaii (detections from Hawaii and dignosed at labs in AZ, FL, HI, NC, NY, OR, SC, and WI). HDOA new pest advisory: Hawaii Department of Agriculture new pest advisory miscellaneous: personal literature search NAPIS data: National Agricultural Pest Information System database UH extension articles: University of Hawaii extension articles USDA_confirmations: USDA National Agricultural Statistics Service This is a compile of detections and not negative data. The exception would be the CAPS data, in which negative data would represent the entire state. Negative CAPS data are represented by zero values in the island columns. The database consist of 23 columns. Below is a discription of each column. Number of unique values are in parentheses. host_family (186): Scientific family name of plant host host_genus (675): Scientific genus name of plant host host_species (839): Scientific species name of plant host host_scientific_name (1361): Scientfic binomial noenclature of plant host host_common_name (921): Plant host common name from Forest & Kim Starr and USDA PLANTS Database pathogen_genus (3378): Scientific genus name of plant pathogen genus name of plant pathogen pathogen_species (3397): Scientific species name of plant pathogen pathogen_scientific_name (7007): Scientfic binomial noenclature of pathogen host pathogen_group (16): Seperates organisms in pathogen name columns into nematode, entomopathogenic fungi, saprophytic fungi, bacteria, amoeba, unknown, virus, fungi, and parasitic plant pathogen_status (2): Consit of sources that determine organism in pathogen name columns as a plant-pathogen or not (manual research, hawaii checklist pathogen, fungus-host database pathogen, based on name - for viruses only, and NPDN database pathogen) data_set (16): Source of report (details above) status: The status of pathogen present in Hawaii (absent or present) year: Date of report (117 years) Hawaii: Presence of pathogen in the island of Hawaii (1=present, 0=absent, blank=unknown) Maui: Presence of pathogen in the island of Maui (1=present, 0=absent, blank=unknown) Lanai: Presence of pathogen in the island of Lanai (1=present, 0=absent, blank=unknown) Molokai: Presence of pathogen in the island of Molokai (1=present, 0=absent, blank=unknown) Oahu: Presence of pathogen in the island of Oahu (1=present, 0=absent, blank=unknown) Kauai: Presence of pathogen in the island of Kauai (1=present, 0=absent, blank=unknown) Kahoolawe: Presence of pathogen in the island of Kahoolawe (1=present, 0=absent, blank=unknown) Niihau: Presence of pathogen in the island of Niihau (1=present, 0=absent, blank=unknown) city (112): Origin city of reported pathogen complete_ref (8027): Source of report

The MIPS Ustilago maydis Genome Database aims to present information on the molecular structure and functional network of the entirely sequenced, filamentous fungus Ustilago maydis. The underlying sequence is the initial release of the high quality draft sequence of the Broad Institute. The goal of the MIPS database is to provide a comprehensive genome database in the Genome Research Environment in parallel with other fungal genomes to enable in depth fungal comparative analysis. The specific aims are to: 1. Generate and assemble Whole Genome Shotgun sequence reads yielding 10X coverage of the U. maydis genome 2. Integrate the genomic sequence assembly with physical maps generated by Bayer CropScience 3. Perform automated annotation of the sequence assembly 4. Align the strain 521 assembly with the FB1 assembly provided by Exelixis 5. Release the sequence assembly and results of our annotation and analysis to public Ustilago maydis is a basidiomycete fungal pathogen of maize and teosinte. The genome size is approximately 20 Mb. The fungus induces tumors on host plants and forms masses of diploid teliospores. These spores germinate and form haploid meiotic products that can be propagated in culture as yeast-like cells. Haploid strains of opposite mating type fuse and form a filamentous, dikaryotic cell type that invades plant tissue to reinitiate infection. Ustilago maydis is an important model system for studying pathogen-host interactions and has been studied for more than 100 years by plant pathologists. Molecular genetic research with U. maydis focuses on recombination, the role of mating in pathogenesis, and signaling pathways that influence virulence. Recently, the fungus has emerged as an excellent experimental model for the molecular genetic analysis of phytopathogenesis, particularly in the characterization of infection-specific morphogenesis in response to signals from host plants. Ustilago maydis also serves as an important model for other basidiomycete plant pathogens that are more difficult to work with in the laboratory, such as the rust and bunt fungi. Genomic sequence of U. maydis will also be valuable for comparative analysis of other fungal genomes, especially with respect to understanding the host range of fungal phytopathogens. The analysis of U. maydis would provide a framework for studying the hundreds of other Ustilago species that attack important crops, such as barley, wheat, sorghum, and sugarcane. Comparisons would also be possible with other basidiomycete fungi, such as the important human pathogen C. neoformans. Commercially, U. maydis is an excellent model for the discovery of antifungal drugs. In addition, maize tumors caused by U. maydis are prized in Hispanic cuisine and there is interest in improving commercial production. The complete putative gene set of the Broad Institute''s second release is loaded into the database and in addition all deviating putative genes from a putative gene set produced by MIPS with different gene prediction parameters are also loaded. The complete dataset will then be analysed, gene predictions will be manually corrected due to combined information derived from different gene prediction algorithms and, more important, protein and EST comparisons. Gene prediction will be restricted to ORFs larger than 50 codons; smaller ORFs will be included only if similarities to other proteins or EST matches confirm their existence or if a coding region was postulated by all prediction programs used. The resulting proteins will be annotated. They will be classified according to the MIPS classification catalogue receiving appropriate descriptions. All proteins with a known, characterized homolog will be automatically assigned to functional categories using the MIPS functional catalog. All extracted proteins are in addition automatically analysed and annotated by the PEDANT suite.

This dataset provides distribution data of fungi in Switzerland of the National Data and Information Centre, called SwissFungi. SwissFungi is a partner of InfoSpecies, the network of Swiss data and information centers for fauna, flora and fungi. One of its main objectives is to document the spatial and temporal distribution of fungal species in Switzerland. The SwissFungi database currently contains more than 800'000 georeferenced fungi observations, distributed throughout Switzerland. The oldest observations date back to 1770. A large portion of the records date from 1990 until the present day. The database is continuously updated with new fungi records. The data have been validated and originate from national inventories, from research projects, from floristic observations by volunteers as well as from private and public herbaria and from literature. Please note that SwissFungi is not able to verify all incoming fungal records completely concerning correct identification or coordinate errors and therefore cannot guarantee the validity of the information.

This database and mapping tool was produced to allow the identification of sites important to this incredibly diverse range of grassland fungal species for which Scotland is important on a global scale. Promotion of this project could lead to a great amount of these vulnerable sites being managed for their waxcaps, leading directly to the conservation of biodiversity, including several species on the Scottish Biodiversity List. Included layers: Heatmap of grassland fungi to the 10km level. The fewest species per square is represented by the lightest colour and the highest species per square represented by darkest colour. ADVICE: This layer is ideal for giving an overview of records in the area, but it doesn’t mean the fungi are throughout the area, or that the whole area is unimproved grassland. Heatmap of grassland fungi to the 1km level. The fewest species per square is represented by the lightest colour and the highest species per square represented by darkest colour. ADVICE: 1km grid square layer may provide a false picture and a blank square does not necessarily mean that no grassland fungi are there. Accurate georeferencing of biological records before the age of GPS and specialist phone apps was rare with many blocks of records being given the same centroid grid reference. Also, it would be common for many recorders to record the first find of a species on a site and none thereafter. THE 10KM SQUARE SHOULD BE LOOKED AT ALONGSIDE THIS LAYER. Point layer showing the Waxcap Sites. Sites are based centroid grid references for a spread of records in the area. The sites do not have clear boundaries, and so some form of local habitat knowledge is needed to set actual site boundaries within the real-world boundaries of unimproved grassland. • RED: Any site passing any of the SSSI thresholds • AMBER: Any site not passing any of the SSSI thresholds but with more than 11 species of Hygrocybe s.l. or with more than 4 IUCN species or with more than 4 indicator species. • GREEN: Any other site that has records of grassland fungi

Ensembl Genomes consists of five sub-portals (for bacteria, protists, fungi, plants and invertebrate metazoa) designed to complement the availability of vertebrate genomes in Ensembl. This collection is concerned with fungal genomes.

Database contains manually curated natural carbohydrate structures, taxonomy, bibliography, NMR data. Bacterial and Plant and Fungal databases were merged to improve quality of content-dependent services, such as taxon clustering or NMR simulation. These separate databases will be supported in parallel until 2020.

This dataset is a extract from FongiBase (1708–2021), the French National Fungal Database.

It corresponds to the dataset analysed in:

Gautier, M., Moreau, P.-A., Boury, B. & Richard, F. (2022). Unravelling the French National Fungal Database: Geography, Temporality, Taxonomy and Ecology of the Recorded Diversity. Journal of Fungi, 8(9), 926. https://doi.org/10.3390/jof8090926

The dataset includes 1,043,262 fungal records from metropolitan France, covering more than three centuries of observations. It provides information on taxonomy, geography, temporality and ecology of fungal diversity.

Recommended citation:
If you use this dataset, please cite both this deposition and the article above (https://doi.org/10.3390/jof8090926)

Contributors and original data providers (e.g. SMF, SMNF, ADONIF, MNHN, FMBDS, CBNFC-ORI, RNF, and many others) are gratefully acknowledged.

Please note that this is a static version of the database used in the 2022 article.

Identifiers represent experimental growth profiles documenting fungal species' ability to utilize specific carbon sources (plant biomass components, polysaccharides, and monosaccharides) in the FUNG-GROWTH database, which provides phenotypic data to support functional genome annotation and comparative analysis of carbohydrate metabolism across fungal species.