Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,764
datasets available to search
ShareScore release 0.7.1
Dataset results
1,764 results for “fungi”
Root fungi isolated from common Louisiana marsh plants 2017-18.
Nearly all plants are colonized by fungal endophytes, and a growing body of work shows that both environment and host species shape plant-associated fungal communities. However, few studies place their work in a phylogenetic context to understand endophyte community assembly through an evolutionary lens. Here we collected data to investigate environmental and host effects on root endophyte assemblages in coastal Louisiana marshes. We isolated and sequenced culturable fungal endophytes from roots of three-four dominant plant species from each of three sites of varying salinity. We provide data on abundance and taxonomy of the isolated fungal taxa as well as phylogenetic diversity (mean phylogenetic distance, MPD) and phylogenetic composition (based on MPD).
Black Jack Battlefield & Nature Park Arbuscular Mycorrhizal Fungi Inoculation Study, Wellsville, KS, 2021-2022
Black Jack Battlefield & Nature Park (Wellsville, Kansas, USA) has a rich history of Indigenous stewardship, civil war conflict, agriculture, and, most recently, a decades-old ecological restoration of the tallgrass prairie ecosystem. To potentially aid this recovery, we tested the impact of inoculation with arbuscular mycorrhizal (AM) fungi cultured from regional remnant prairies on the survival and growth of 12 native prairie plant species. We grew these plants for a few weeks in the greenhouse with either sterile or AM fungi-inoculated soil. We then planted these into 24 experimental blocks with a 1x1 m field plot for each treatment (48 plots total) containing all 12 plant species when possible. We then tracked the survival and growth of the plants two weeks, about five months, and about fifteen months after planting. Growth was measured as height at all timepoints, and leaf number was included for most species in the latest timepoint. We found impacts of inoculation and species identity on both the survival and growth of the plants.
Effects of ectomycorrhizal fungi on pine litter decomposition in temperate pine forests in California, Florida, and Minnesota
This experiment is designed to assess the generality of the effect of ECM fungi on leaf litter decomposition in temperate pine forests. To assess ECM fungal effects on decomposition, we established and ECM fungal knockdown experiment (via trenching) in nine temperate pine forests in California, Florida, and Minnesota. In litter bags incubated (July 2021-July 2022) in paired trenched and untrenched plots at each site we compared leaf litter decomposition (of native pine litter and a common Pinus strobus litter), fungal community composition (via high throughput sequencing), fungal abundance (via qPCR), decomposition enzyme expression, and soil nutrient availability. Contrary to widely cited theory and other results from a subset of our field sites, we found that ECM fungi either increased or did not impact pine litter decomposition in temperate pine forests.
ECOBREED WP3 entomopathogenic fungi-wireworm data related to Razinger et al. (2020)
<p>Raw data related to Figures 1 to 5 and Table 1 plus suplementary raw data of the publication Razinger et al. (2020) Frontiers in Plant Science 11:535005; doi: 10.3389/fpls.2020.535005.</p>
Arctic specimens in the NHMO DNA bank Fungi & Lichens collection 2022
<p>All Arctic specimens in the NHMO DNA bank Fungi & Lichens collection as of August 2022. See Bjorå et al. 2023 "Collections of Arctic plants, lichens and fungi in the Natural History Museum, University of Oslo, Norway" for further details.</p>
Mycorrhizal Fungi of Native Red Pine Stands in the Forests of the Huron Mountains (1996-2015).
This data includes mycorrhizal fungi population data in Michigan’s Huron Mountains from 1996-2015 collected by Dana Ritcher. Seven stands consisting of primarily pine forests were surveyed for two separate sampling periods annually during the study period.
Effects of fallen Spanish moss (Tillandsia usneoides) on understory plant, invertebrate, and fungi communities
For nearly two years, natural deposition of Spanish moss was excluded from 2m x 2m plots positioned in the understory of a single live oak at each of two field sites. After 29 months, we measured the effects of fallen Spanish moss, relative to unmanipulated control plots that intercepted natural levels of fallen Spanish moss, on understory grass, invertebrate, and fungi communities as well as on litter layer depth, and litter decomposition rates using litter bags. This research was conducted in two fields on Sapelo Island, GA: Long Tabby (LT) and King's Field (KF). Experimental exclusions were maintained by manually removing Spanish moss from experimental plots monthly over the duration of the experiment.
CMY01 Mycorrhizal colonization and plant community responses to long-term suppression of Mycorrhizal Fungi
Twenty replicate permanent 2x2 m plots were established in early 1991 along a randomly located transect, with a 2m space between each plot, on the following watersheds: 1B, 1D, annually burned HQB, 10B, 20D and infrequently burned HQB. Ten of the plots were randomly assigned as long-term mycorrhizal suppression plots. In each of these plots, AM fungi were suppressed by the application of the fungicide benomyl as a soil drench (7.5 liters per plot) at the rate of 1.25 g/m2 (active ingredient). The mycorrhizal suppression plots were treated biweekly throughout each growing season (April through October) beginning in 1991. The control plots each received no fungicide, but an equivalent volume of water (7.5 liters) was applied biweekly. To evaluate the effectiveness of the fungicide, three soil cores (2.5 cm diameter x 14 cm deep) were removed from both fungicide-treated and control plots each October throughout the study. Roots were extracted from the soil, washed free of soil, stained in trypan blue (Phillips and Hayman, 1970), and examined microscopically to assess percentage root colonization by mycorrhizal fungi using a Petri dish scored in 1-cm squares (Daniels et al.1981).
Interactions between plants and fungi and their roles in decay rates and CO2 release in five tropical leaf species
A microcosm experiment was used to test for the effects of interactions between particular plant and fungal decomposer species on rates of leaf decomposition. Each microcosm contained one species of leaf that was sterilized with gamma irradiation and then inoculated with a single fungus. Five plant species and ten fungal species (two dominants from each of the litter types) were used in all possible combinations. Plant species were selected for pair-wise comparisons based on phylogenetic relationships and litter quality characteristics. Decomposition was measured by both mass loss and CO2 release. Differences in weight loss and CO2 evolution were highly significant for plants, fungal species, and their interactions. Mass loss was positively correlated with CO2 evolution. Contrary to our hypotheses, however, microfungal dominants did not decompose their source leaves faster than microfungal dominants from other leaf species, nor were responses to other types of specificity detected. Matching of fungi to leaf substrates by their source, by phylogenetic relationships, or by chemical, physical and structural characteristics was not associated with consistent increases in decomposition. Although previously documented differences in microfungal species composition and dominance among decomposing leaves of different trees were confirmed in this study, such differences apparently do not directly affect the rates of ecosystem processes. The presence in a few of the microcosms of a generalist basidiomycete that had ligninolytic enzymes, Melanotus eccentricus, significantly accelerated the rate of decomposition. Non-specific basidiomycetes may therefore have a stronger effect on early stages of leaf litter decomposition than host-selective microfungi. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Pue
Fungi of the Greater Antilles
Over 20 researchers and cooperators were enlisted to produce a survey of the basidiomycetes of Puerto Rico, the Virgin Islands, Dominican Republic, and Jamaica. This included all basidiomycetes except rusts and smuts. These islands in the Greater Antilles were chosen for this survey for several reasons. There was previously very limited documentation on macrobasidiomycete diversity for these islands, although recent studies had shown that at least 15% of the species were undescribed. This project complemented previous and ongoing surveys in Mexico, Costa Rica, and Venezuela. The information gained from this project will ultimately help us to understand differences in colonization and rates of speciation among different groups of basidiomycetes in island chains. The investigators in this project discovered at least 75 new species and varieties so far, as well as several new genera (Pegler et al. 1998; Samuels & Lodge 1996) and one possible new family or order (see 'Aliens' under GENUS). Various cooperators have graciously provided identification of ascomycetes, myxomycetes, and mitosporic fungi, which are also included in the database. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Arbuscular mycorrhizal fungi and dark septate endophytes root colonization in Upper Green Lakes Valley, 2007-2016
Arbuscular mycorrhizal fungi (AMF) and dark septate endophytes (DSE) are two fungal groups that colonize plant roots and can benefit plant growth, but little is known about their landscape distributions. We performed sequencing and microscopy on a variety of plants across a high-elevation landscape featuring plant density, snowpack, and nutrient gradients. Percent colonization by both AMF and DSE varied significantly among plant species, and DSE colonized forbs and grasses more than sedges. AMF were more abundant in roots at lower elevation areas with lower snowpack and lower phosphorus and nitrogen levels, suggesting increased hyphal recruitment by plants to aid in nutrient uptake. DSE colonization was highest in areas with less snowpack and higher inorganic nitrogen levels, suggesting an important role for these fungi in mineralizing organic nitrogen. Both of these groups of fungi are likely to be important for plant fitness and establishment in areas limited by phosphorus and nitrogen.
Biogeography of root fungi in grasslands
Aim: Roots and rhizospheres host diverse microbial communities that can influence the fitness, phenotypes, and environmental tolerances of host plants. Documenting the biogeography of microbiomes can detect the potential for a changing environment to disrupt host-microbe interactions, particularly in cases where microbes, such as root-associated Ascomycota, buffer hosts against abiotic stressors. We evaluated whether root-associated fungi had poleward declines in diversity as occur for many animals and plants, tested whether microbial communities shifted near host plant range edges, and determined the relative importance of latitude, climate, edaphic factors, and host plant traits as predictors of fungal community structure. Location: North American plains grasslands Taxon: Foundation North American grass species ⎯ Andropogon gerardii, Bouteloua eriopoda, B. gracilis, B. dactyloides, and Schizachyrium scoparium and their root-associated fungi Methods: At each of 24 sites representing three replicate latitudinal gradients spanning 17° latitude, we collected roots from 12 individual plants per species along five transects spaced 10 m apart (40 m × 40 m grid). We used next-generation sequencing of the fungal ITS2 region, direct fungal culturing from roots, and microscopy to survey fungi associated with grass roots. Results: Root-associated fungi did not follow the poleward declines in diversity documented for many animals and plants. Instead, host plant identity had the largest influence on fungal community structure. Edaphic factors outranked climate or host plant traits as correlates of fungal community structure; however, the relative importance of these environmental predictors differed among plant species. As sampling approached host species range edges, fungal composition converged among individual plants of each grass species. Main conclusions: Environmental predictors of root-associated fungi depended strongly on host plant species identity. Biogeographic patte
Widespread Polycistronic Transcripts in Fungi Revealed by Single-Molecule mRNA Sequencing
<p>Genes in prokaryotic genomes are often arranged into clusters and co-transcribed into poly- cistronic RNAs. Isolated examples of polycistronic RNAs were also reported in some higher eukaryotes but their presence was generally considered rare. Here we developed a long- read sequencing strategy to identify polycistronic transcripts in several mushroom forming fungal species including Plicaturopsis crispa, Phanerochaete chrysosporium, Trametes ver- sicolor, and Gloeophyllum trabeum. We found genome-wide prevalence of polycistronic transcription in these Agaricomycetes, involving up to 8% of the transcribed genes. Unlike polycistronic mRNAs in prokaryotes, these co-transcribed genes are also independently transcribed. We show that polycistronic transcription may interfere with expression of the downstream tandem gene. Further comparative genomic analysis indicates that polycis- tronic transcription is conserved among a wide range of mushroom forming fungi. In sum- mary, our study revealed, for the first time, the genome prevalence of polycistronic transcription in a phylogenetic range of higher fungi. Furthermore, we systematically show that our long-read sequencing approach and combined bioinformatics pipeline is a generic powerful tool for precise characterization of complex transcriptomes that enables identifica- tion of mRNA isoforms not recovered via short-read assembly.</p>
Data - Ant identity determines the fungi richness and composition of a myrmecochorous seed
<p>Data set and analyse used in the manuscript title "<span>Ant </span><span>identity determines the fungi richness and composition of myrmecochorous seeds". In this manuscript w<span>e explore the effects of seed manipulation on fungi communities promoted by two ants with contrasting effects on seed germination and antimicrobial strategies. We hypothesize that i) seeds manipulated by <em>Atta sexdens</em> (increase seed germination and has broad cleaning strategies) will present lower fungi richness than those manipulated by <em>Acromyrmex subterraneus</em> (impair seed germination and has narrow cleaning strategies); <span>ii) seeds manipulated by </span><em>A. sexdens </em>and<em> Ac. subterraneus </em>will present<em> </em>dissimilar<em> </em><span>fungi composition. </span>We tested the hypotheses by identifying fungi morphotypes present in three groups of seeds: i) manipulated by <em>Atta sexdens</em>; ii) manipulated by <em>Ac. subterraneus</em>; iii) unmanipulated. </span></span><span>From the seeds manipulated by ants, we randomly take a sub-sample of 20 seeds per nest to evaluate the fungi community. We also took 20 unmanipulated seeds (the ones left outside each experimental nest). Therefore, we had three seed treatment groups: <span><span> </span></span>i) manipulated by <em>A. sexdens</em> (20 seeds per nest = 80 seeds)<em>;</em> ii) manipulated by <em>Ac. Subterraneus</em> (20 seeds per nest = 80 seeds)<em> </em>and iii) control - unmanipulated seeds left outside of each experimental nest (20 seeds outside of each nest = 160 seeds).</span><span>To allow the fungi growth on seeds, we placed each seed separately on sterile Petri dishes (90 x15 mm) filled with 15 ml of Potato-Dextrose-Agar (PDA) culture medium. We then transported each Petri dish to a Bio-Oxygen-Demand incubator (BOD) at 25°C for 28 days. After that period, we sampled the fungi and prepared microscope slides for each fungus morphotype found in each Petri dish. We identified the fungi to the lower taxonomic level possible using “The genera of Hyphomycetes” <span><span>(Seifert et al. 2011)</span></span> and the website mycobank.org . We used this method because it is widely used to identify pathogens in seeds, has a low cost and has good specificity to identify fungi<span> </span>. Furthermore, PDA medium is a non-selective fungi growth media suitable for a broad range of fungi species.</span></p> <p><span><span> </span></span></p>
Data and outputs for chapter 'The impacts of the 2019-20 wildfires on Australian fungi ' in 'Australia's Megafires: Biodiversity Impacts and Lessons from 2019-2020
<p><strong>Dataset includes raw data downloaded from the following sources: fungi_data.csv</strong></p> <ul> <li>Atlas of Living Australia occurrence download: https://doi.org/10.26197/ala.9e0ca388-9da2-4096-b1a3-26e2aaa51d8a. Accessed 2021-09-16. GBIF.org (16 September 2021)</li> <li>GBIF Occurrence Download https://doi.org/10.15468/dl.secenk</li> <li>Fungimap (https://fungimap.org.au/ (data obtained directly from Fungimap Inc.)</li> <li>MycoPortal (https://mycoportal.org/portal/index.php)</li> <li>iNaturalist (https://www.inaturalist.org/home)</li> </ul> <p><strong>Output files from point and polygon overlap with fire layer:</strong></p> <ul> <li>Fungi and fire analysis point overlap.xlsx</li> <li>Fungi and fire analysis polygon overlap.xlsx</li> </ul> <p> </p> <p> </p>
Universal metabolic model for Fungi
<p>Universal metabolic model for Fungi. It is a combination of enzymatic reactions collected from literature and reaction databases (Kegg, metacyc, Rhea).</p>
Gene/Protein BridgeDb ID Mapping Database (Ensembl Fungi 49)
<p>Ensembl Fungi 49 derived ID mapping database for use with BridgeDb.<br> The scripts used to create these databases based on Ensembl BioMart can be found at <a href="https://github.com/bridgedb/create-bridgedb-genedb">https://github.com/bridgedb/create-bridgedb-genedb</a>.</p> <p>This work was funded by the <a href="https://fairplus-project.eu/">FAIRplus project</a> (grant agreement no 802750) and <a href="https://www.nwo.nl/en/researchprogrammes/open-science/open-science-fund/open-science-fund-2021-awarded-grants">NWO Open Science Fund</a> (grant no <a href="https://www.nwo.nl/en/projects/203001121">203.001.121</a>).</p>
Machine learning classifiers for species classification of fungi using error-prone long-reads on extended metabarcodes
<p>Machine learning models used in the decision tree of linked machine learning models (<a href="https://github.com/teenjes/fungal_ML">https://github.com/teenjes/fungal_ML</a>)</p>
Biogeographic history of a large clade of ectomycorrhizal fungi, the Russulaceae, in the Neotropics and adjacent regions
<p>## Metadata</p> <p>backbone_accessions.tsv - GenBank/INSDC accession numbers for LSU, rpb1 and rpb2 accessions used for the Russulaceae backbone tree including 472 taxa.</p> <p>ITS_sequences_OTUs.tsv - Metadata for all 34,624 ITS sequences used in the study. Columns: "accession": accession ID in analysis – GenBank/INSDC or UNITE accession number for compiled data, lab ID for newly generated data; "specimen": specimen/voucher number, for newly generated sequences; "INSDC_accession": INSDC/GenBank accession for new newly generated data; "taxon": specimen identification; "New": whether ITS sequences was generated in this study (*); "OTU": name of cluster/OTU, if not the sequence accession itself (*); "In_tree": whether sequence is represented in the Russulaceae supertree after filtering steps (*), "lb" long-branch accession removed during tree estimation, "ol" outlier removed during tree estimation; "area": biogeographic area assigned.</p> <p> </p> <p>## Sequences and alignments</p> <p>backbone_concat.fasta - Concatenated LSU-rpb1-rpb2 alignment for 372 backbone taxa.</p> <p>backbone_concat_part.txt - Gene partitions and substitution models applied to the backbone alignment.</p> <p>einsi_clade1_Russula_trimmed.fasta - Alignment of 2,279 representative ITS sequences in the Russula clade; alignment end columns with >90% missing data/gaps were trimmed.</p> <p>einsi_clade2_LactariusMultifurca_trimmed.fasta - Alignment of 621 representative ITS sequences in the Lactarius-Multifurca clade; alignment end columns with >90% missing data/gaps were trimmed.</p> <p>einsi_clade3_Lactifluus_trimmed.fasta - Alignment of 482 representative ITS sequences in the Lactifluus clade; alignment end columns with >90% missing data/gaps were trimmed.</p> <p> </p> <p>## Phylogenetic trees</p> <p>12_make_supertree.R - R script for grafting clade trees onto the backbone tree to produce a supertree.</p> <p>backbone_calibrated.nwk - Time-calibrated Russulaceae backbone phylogeny.</p> <p>backbone_TBE.raxml.support - Russulaceae backbone phylogeny annotated with transfer bootstrap expectation support values.</p> <p>clade1_Russula_TBE.raxml.support - Russula subclade ITS phylogeny (2,279 tips), annotated with transfer bootstrap expectation support values.</p> <p>clade2_LactariusMultifurca_TBE.raxml.support - Lactarius-Multifurca subclade ITS phylogeny (621 tips), annotated with transfer bootstrap expectation support values.</p> <p>clade3_Lactifluus_TBE.raxml.support - Lactifluus subclade ITS phylogeny (482 tips), annotated with transfer bootstrap expectation support values.</p> <p>supertree_calibrated.nwk - Combined Russulaceae supertree, time-calibrated (root age = 1).</p> <p>tree_calibrated_clade1_Russula.nwk - Russula subclade ITS backbone phylogeny, time-calibrated (root age = 1).</p> <p>tree_calibrated_clade2_LactariusMultifurca.nwk - Lactarius-Multifurca subclade ITS backbone phylogeny, time-calibrated (root age = 1).</p> <p>tree_calibrated_clade3_Lactifluus.nwk - Lactifluus subclade ITS backbone phylogeny, time-calibrated (root age = 1).</p> <p> </p> <p>## Biogeographic analysis</p> <p>3_disp_counts.R - R script to count dispersal events between biogeographic areas, based on stochastic mapping output.</p> <p>9_disp_count_time.R - R script to count dispersal events to and from each area through time, based on stochastic mapping output.</p> <p>area_codes.tab - Area letter coding and colours used for biogeographic analysis and plotting.</p> <p>area_shapes.zip - Shapefiles for the nine biogeographic areas defined, based on merged areas from Dinerstein et al. 2017 (https://doi.org/10.1093/biosci/bix014) and Löwenberg-Neto (2014: https://doi.org/10.11646/zootaxa.3802.2.12; 2015: https://doi.org/10.11646/10.11646/zootaxa.3985.4.9).</p> <p>areas_manually_zenodo.csv - Manual assignment of 800 ITS sequences to biogeographic areas based on associated literature records or metadata.</p> <p>corHMM_ER.Rdata - R data archive with input data and results for the corHMM/Mv biogeographic area reconstruction.<br> <br> corHMM_ER_stoch_maps.Rdata - R data archive with results from the corHMM/Mv biogeographic stochastic mapping.</p> <p>disp_counts_focal.tab - Dispersal counts to and from each focal area through time, based on BioGeoBEARS stochastic mapping output.</p> <p>disp_counts_sam_afr.tab - Dispersal counts between Afrotopics and lowland tropical S. America through time, based on BioGeoBEARS stochastic mapping output.</p> <p>disp_matrix_025.txt - Dispersal rates between biogeographic areas (2.5% quantiles), based on stochastic mapping output.</p> <p>disp_matrix_975.txt - Dispersal rates between biogeographic areas (97.5% quantiles), based on stochastic mapping output.</p> <p>disp_matrix_median.txt - Dispersal rates between biogeographic areas (median values), based on stochastic mapping output.</p> <p> </p> <p>## Diversification analysis</p> <p>5_rates_per_area.R - R script to partition diversification rates by biogeographic area, both overall and through time, based on BAMM diversification rates and area stochastic mapping.</p> <p>event_data.txt - Posterior samples of diversification rate regimes estimated with BAMM.</p> <p>div_rates_area_overall.txt - Overall diversification rates per biogeographic area, based on BAMM diversification rates and area stochastic mapping.</p> <p>div_rates_per_area_025.tsv - Diversification rates through time (2.5% quantiles) partitioned by biogeographic area, based on BAMM diversification rates and area stochastic mapping.</p> <p>div_rates_per_area_975.tsv - Diversification rates through time (97.5% quantiles) partitioned by biogeographic area, based on BAMM diversification rates and area stochastic mapping.</p> <p>div_rates_per_area_median.tsv - Diversification rates through time (means) partitioned by biogeographic area, based on BAMM diversification rates and area stochastic mapping.</p> <p>mcmc_out.txt - BAMM posterior sample characteristics.</p>
Global patterns in endemicity and vulnerability of soil fungi
<p>This repository contains the data associated with the paper Tedersoo et al. (2022) <em>Global patterns in endemicity and vulnerability of soil fungi</em> // <strong>Global Change Biology</strong>. DOI:10.1111/gcb.16398</p> <p>Fungi are highly diverse organisms and provide a wealth of ecosystem functions. However, distribution patterns and conservation needs of fungi have been very little explored compared to charismatic animals and plants. Here we assess endemicity patterns, global change vulnerability and conservation priority areas for functional groups of soil fungi based on six global surveys using a high-resolution, long-read metabarcoding approach. Endemicity of all fungi and most functional groups peaks in tropical habitats, including Amazonia, Yucatan, West-Central Africa, Sri Lanka and New Caledonia, with a negligible island effect compared with plants and animals. We also found that fungi are vulnerable mostly to drought, heat and land cover change, particularly in dry tropical regions with high human population density. Fungal conservation areas of highest priority include herbaceous wetlands, tropical forests and woodlands. We suggest that there should be more attention focused on the conservation of fungi, especially tropical root symbiotic arbuscular mycorrhizal and ectomycorrhizal fungi, unicellular early-diverging groups and macrofungi in general. Given the low overlap between endemicity of fungi and macroorganisms, but high matching in conservation needs, detailed analyses on distribution and conservation requirements are warranted for other microorganisms and soil organisms in general.</p> <p>This repository contains the following data associated with the publication:</p> <ul> <li>Supplementary tables S1 - S6 (`<strong>Tables_S1-S6.xlsx</strong>`):</li> </ul> <p>- Table S1. Definition of ecoregions and assignment of samples to ecoregions<br> - Table S2. GSMc dataset used for endemicity analyses<br> - Table S3. Dataset used for modeling endemicity values<br> - Table S4. Dataset used for calculating and mapping vulnerability scores<br> - Table S5. Dataset used for calculating and mapping conservation value<br> - Table S6. Additional funding sources by authors</p> <ul> <li>OTU distribution by samples and ecoregions (`<strong>Data_taxon_assignment_to ecoregions.xlsx</strong>`)</li> </ul> <p>Gridded maps:</p> <ul> <li>Conservation priorities for all fungi and fungal groups</li> </ul> <p>- ConservationPriority_AllFungi.tif<br> - ConservationPriority_AM.tif<br> - ConservationPriority_EcM.tif<br> - ConservationPriority_Moulds.tif<br> - ConservationPriority_NonEcMAgaricomycetes.tif<br> - ConservationPriority_OHPs.tif<br> - ConservationPriority_Pathogens.tif<br> - ConservationPriority_Unicellular.tif<br> - ConservationPriority_Yeasts.tif</p> <ul> <li>The average vulnerability of all fungi and fungal groups and the model uncertainty estimates</li> </ul> <p>- AverageVulnerability_AllFungi.tif<br> - AverageVulnerability_AM.tif<br> - AverageVulnerability_EcM.tif<br> - AverageVulnerability_Moulds.tif<br> - AverageVulnerability_NonEcMAgaricomycetes.tif<br> - AverageVulnerability_OHPs.tif<br> - AverageVulnerability_Pathogens.tif<br> - AverageVulnerabilityUncertainty_AllFungi.tif<br> - AverageVulnerabilityUncertainty_AM.tif<br> - AverageVulnerabilityUncertainty_EcM.tif<br> - AverageVulnerabilityUncertainty_Moulds.tif<br> - AverageVulnerabilityUncertainty_NonEcMAgaricomycetes.tif<br> - AverageVulnerabilityUncertainty_OHPs.tif<br> - AverageVulnerabilityUncertainty_Pathogens.tif<br> - AverageVulnerabilityUncertainty_Unicellular.tif<br> - AverageVulnerabilityUncertainty_Yeasts.tif<br> - AverageVulnerability_Unicellular.tif<br> - AverageVulnerability_Yeasts.tif</p> <ul> <li>The relative importance of predicted vulnerability of all fungi</li> </ul> <p>- RelativeImportanceOfVulnerability_AllFungi.tif</p> <ul> <li>Vulnerability to drought, heat, and land cover change for all fungi</li> </ul> <p>- Vulnerability_AllFungi_Heat-Drought-LandCoverChange.tif<br> - VulnerabilityUncertainty_AllFungi_Heat-Drought-LandCoverChange.tif</p> <ul> <li> Human footprint index based on the Land-Use Harmonisation (LUH2; Hurtt et al., 2020, doi:10.5194/gmd-13-5425-2020) - `<strong>LandCoverChange_1960-2015.tif</strong>`</li> <li> MD5 checksums for all files (`<strong>MD5.md5</strong>`)</li> </ul> <p>Fungal groups:<br> - <strong>AM</strong>, arbuscular mycorrhizal fungi (including all Glomeromycota but excluding all Endogonomycetes)<br> - <strong>EcM</strong>, ectomycorrhizal fungi (excluding dubious lineages)<br> - <strong>NonEcMAgaricomycetes</strong>, non-EcM Agaricomycetes (mostly saprotrophic fungi with usually macroscopic fruiting bodies)<br> - <strong>Moulds</strong> (including Mortierellales, Mucorales, Umbelopsidales and Aspergillaceae and Trichocomaceae of Eurotiales and Trichoderma of Hypocreales)<br> - Putative <strong>pathogens</strong> (including plant, animal and fungal pathogens as primary or secondary lifestyles)<br> - <strong>OHPs</strong>, opportunistic human parasites (excluding Mortierellales)<br> - <strong>Yeasts</strong> (excluding dimorphic yeasts)<br> - <strong>Unicellular</strong>, other unicellular (non-yeast) fungi (including chytrids, aphids, rozellids and other early-diverging fungal lineages)</p> <p>Detailed processing steps can be found here:<br> <a href="https://github.com/Mycology-Microbiology-Center/Fungal_Endemicity_and_Vulnerability">https://github.com/Mycology-Microbiology-Center/Fungal_Endemicity_and_Vulnerability</a></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.