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Effects of inoculation of restored and remnant prairie soils on growth, nodulation, and root colonization of three prairie legumes
In this dataset, we present legume biomass, nodule number, and % root colonization of three species of prairie legumes (Amorpha canescens, Lespedeza capitata, and Dalea purpurea) when inoculated with soil from one of 6 remnant or 10 restored prairies in southwest Michigan, USA. Plants were grown for 10 weeks in a growth chamber.
Multiple Element Limitation in Northern Hardwood Ecosystems (MELNHE): Root cores and mycorrhizal colonization
Root cores were obtained in 2010 (pre-treatment) from two soil depths, 0-10 cm and 30-50 cm, in two MELNHE stands, C5 and C7, at Bartlett Experimental Forest. Arbuscular mycorrhizal (AM) and ectomycorrhizal (EM) colonization and root length were quantified in each core to determine if AM or EM was more prevalent in shallow or deep soils. Detailed description and analyses of these data can be found in: Nash, J.M., Diggs, F.M. & Yanai, R.D. Length and colonization rates of roots associated with arbuscular or ectomycorrhizal fungi decline differentially with depth in two northern hardwood forests. Mycorrhiza 32, 213–219 (2022). https://doi.org/10.1007/s00572-022-01071-8 These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Accompanying data for the PhD thesis 'Nanomaterial safety for microbially-colonized hosts'
<p><strong>These files include all data presented in chapter 6 of the dissertation:</strong></p> <p><strong>"Nanomaterial safety for microbially-colonized hosts: microbiota-mediated physisorption interactions and particle-specific toxicity" by Bregje Brinkmann (2022).</strong></p> <p>The data presented in chapters 2-5 have previously been published elsewhere:</p> <ul> <li>Chapter 2: <em>Zenodo</em> (DOI: <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.6800734&data=05%7C01%7Cb.w.brinkmann%40cml.leidenuniv.nl%7Cb5837fcc5a1b4cb5e88008da7615fe87%7Cca2a7f76dbd74ec091086b3d524fb7c8%7C0%7C0%7C637952134073527979%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=z4LB7Ziyiy%2BLXe0SllX67AJ%2F9zhfARBXp8QNzZsVg%2B4%3D&reserved=0">10.5281/zenodo.6800734</a>).</li> <li>Chapter 3: <em>Mendeley</em> <em>Data</em> (DOI: <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.17632%2F2d4hcr5cb5.1&data=05%7C01%7Cb.w.brinkmann%40cml.leidenuniv.nl%7Cb5837fcc5a1b4cb5e88008da7615fe87%7Cca2a7f76dbd74ec091086b3d524fb7c8%7C0%7C0%7C637952134073527979%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=x7LFz2OgiJ20%2BD18QlwR9qIwGH%2BCbju4BKqkUaqIoXs%3D&reserved=0">10.17632/2d4hcr5cb5.1</a>)</li> <li>Chapter 4: <em>Figshare</em> (DOI: <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.6084%2Fm9.figshare.c.4923261&data=05%7C01%7Cb.w.brinkmann%40cml.leidenuniv.nl%7Cb5837fcc5a1b4cb5e88008da7615fe87%7Cca2a7f76dbd74ec091086b3d524fb7c8%7C0%7C0%7C637952134073527979%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=n3wegOuHzKihCO%2FKbZhClnYHPyxWI0cALApBgLkUHnQ%3D&reserved=0">10.6084/m9.figshare.c.4923261</a>)</li> <li>Chapter 5: <em>Mendeley Data </em>(DOI: <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.17632%2F4nfg69v8hy.1&data=05%7C01%7Cb.w.brinkmann%40cml.leidenuniv.nl%7Cb5837fcc5a1b4cb5e88008da7615fe87%7Cca2a7f76dbd74ec091086b3d524fb7c8%7C0%7C0%7C637952134073527979%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=0A0ktYDmgfxwc7U%2BgzHzQUj8Q5wixi%2BUzoSzMctlbso%3D&reserved=0">10.17632/4nfg69v8hy.1</a>)</li> </ul> <p><br> <strong>1. Data presented in Figure 6.1:</strong> Survival_CFU_(...)<br> Tab-delimited file with zebrafish larvae survival, and the number of colony-forming units (CFUs) associated with zebrafish larvae, following exposure to silver nanoparticles (nAg) from 3-5 days-post fertilization (dpf):</p> <ul> <li><em>Concentration</em>: Nominal exposure concentration (mg nAg·L<sup>-1</sup>).</li> <li><em>Date</em>: The date at which the mortality was scored (Format: DD/MM/YYYY). </li> <li><em>Family</em>: A code referring to the aquarium of wildtype zebrafish (ABxTL) that were crossed to obtain the larvae for the experiment. </li> <li><em>Survival</em>: Percentage of larvae that had survived the treatment.</li> <li><em>CFU</em>: Number of colony-forming units that was isolated per larva</li> </ul> <p>The methodology for toxicity tests and the procedures to determine CFU counts, have been published in <em>Nanotoxicology</em>:</p> <p>Brinkmann BW, Koch BEV, Spaink HP, Peijnenburg WJGM, Vijver MG. 2020. Colonizing microbiota protect zebrafish larvae against silver nanoparticle toxicity. Nanotoxicology. 14: 725-739. DOI: <a href="http://doi.org/10.1080/17435390.2020.1755469">10.1080/17435390.2020.1755469</a></p> <p> </p> <p><strong>2. Data presented in Figure 6.2:</strong> ABs_DoseResponses_(...)<br> Tab-delimited file with zebrafish larvae mortality following a pretreatment of 0, 6 or 72 hours with an antibiotic and antifungal cocktail, and subsequent exposure to nAg from 3-5 dpf:</p> <ul> <li><em>Concentration</em>: Nominal exposure concentration (mg nAg·L<sup>-1</sup>). </li> <li><em>Mortality</em>: Percentage of larvae that had died.</li> <li><em>Date</em>: The date at which the mortality was scored (Format: DD/MM/YYYY).</li> <li><em>Family</em>: A code referring to the aquarium of wildtype zebrafish (ABxTL) that were crossed to obtain the larvae for the experiment. </li> <li><em>ABs</em>: Duration of the antibiotic/ antifungal pretreatment, either 0, 6 or 72 hours.</li> </ul> <p> </p> <p><strong>3. Data presented in Figure 6.3:</strong> il1beta_eGFP_(...)<br> Three folders comprising fluorescence microscopy images (TIFF format) of il1beta:eGFP reporter zebrafish larvae at 5 dpf:</p> <ul> <li><em>(...)_replicates1_20200226</em>: images for the first experimental replicate.</li> <li><em>(...)_replicates2_20200304</em>: images for the second experimental replicate.</li> <li><em>(...)_replicates3_20200318</em>: images for the third experimental replicate.</li> </ul> <p>For each of the replicates, the following images were acquired:</p> <ul> <li><em>nZnO_GFP</em>: GFP signal for larvae exposed to nZnO.</li> <li><em>Znion_GFP</em>: GFP signal for larvae exposed to zinc ions.</li> <li><em>nZnO_trans</em>: transmitted light images for larvae exposed to nZnO. </li> <li><em>Znion_trans</em>: transmitted light images for larvae exposed to zinc ions.</li> </ul> <p>Additionally, the following images have previously been deposited to <em>Mendeley Data </em>(DOI: <a href="http://doi.org/10.1016/j.ecoenv.2022.113522">10.17632/4nfg69v8hy.1</a>):</p> <ul> <li><em>nAg_GFP</em>: GFP signal for larvae exposed to nAg.</li> <li><em>nAg_trans</em>: transmitted light images for larvae exposed to nAg.</li> <li><em>Agion_GFP</em>: GFP signal for larvae exposed to silver ions.</li> <li><em>Agion_trans</em>: transmitted light images for larvae exposed to silver ions.</li> <li><em>control_GFP</em>: GFP signal for control larvae that had not been exposed to silver ions or nAg</li> <li><em>control_trans</em>: transmitted light images for control larvae that had not been exposed to silver ions or nAg.</li> </ul> <p>All image processing steps have been published in <em>Ecotoxicology and Environmental Safety</em>:</p> <p>Brinkmann BW, Koch BEV, Peijnenburg WJGM, Vijver MG. 2022. Microbiota-dependent TLR2 signaling reduces silver nanoparticle toxicity to zebrafish larvae. Ecotox Environ Saf. 237: 113522. DOI: <a href="http://doi.org/10.1016/j.ecoenv.2022.113522">10.1016/j.ecoenv.2022.113522</a></p> <p> </p> <p><strong>Abbreviations:</strong></p> <ul> <li><em>ABs</em>: antibiotics</li> <li><em>CFU</em>: colony-forming units</li> <li><em>dpf</em>: days post-fertilization</li> <li><em>il1beta</em>: interleukin-1beta</li> <li><em>nAg</em>: silver nanoparticles (NM-300 K)</li> <li><em>nZnO</em>: zinc oxide nanoparticles (NM-110)</li> </ul>
A Surface-Induced Asymmetric Program Promotes Tissue Colonization by a Human Pathogen - Supplemental data Fig 4A
<p>Raw data used for Fig. 4A of the article "A Surface-Induced Asymmetric Program Promotes Tissue Colonization by a Human Pathogen" published in Cell Host & Microbe. This Western Blot dataset is composed of 4 images:</p> <ul> <li>Western Blot 1: whole cell lysate (raw image, and annotated image)</li> <li>Western Blot 2: purified pili (raw image, and annotated image)</li> </ul>
Seq-Scope Mouse Colon Data 1st-seq (GSE266556)
<p>This repository contains the 1st-seq FASTQ files for the mouse colon data deposited in GEO with accession number GSE266556. For Seq-Scope 1st-seq data, it is important to obtain the original readname in order to resolve the spatial coordinates of each HDMI barcode. Although the 1st-seq FASTQ files are already deposited in SRA using the accession numbers associated with GSE266556, it may be difficult to retrieve the original readnames. Therefore, here we are (re-)depositing the FASTQ files containing the original readname for the convenience of users who need access to the original unmodified sequence data. </p>
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).
Soil factors predict initial plant colonization on Puerto Rican landslides
Tropical storms are the principal cause of landslides in montane rainforests, such as the Luquillo Experimental Forest (LEF) of Puerto Rico . A storm in 2003 caused 30 new landslides in the LEF that we used to examine prior hypotheses that slope stability and organically enriched soils are prerequisites for plant colonization. We measured slope stability and litterfall in 1 m2 plots 8-13 months following landslide formation. At 13 months we also measured microtopography, soil characteristics (organic matter, particle size, total nitrogen, and water holding capacity), elevation, distance to forest edge, and canopy cover, as well as plant aboveground biomass, plant cover, and root biomass. 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.
Deschampsia biomass, soil microbes and endophyte root colonization for snowmelt and microbial innoculation transplant experiment in the Green Lakes Valley, 2015-2018
As organisms shift their geographic distributions in response to climate change, biotic interactions have emerged as an important factor driving the rate and success of range expansions. Plant-microbe interactions are an understudied but potentially important factor governing plant range shifts. We studied the distribution and function of microbes present in high-elevation unvegetated soils, areas that plants are colonizing as climate warms, snow melts earlier and the summer growing season lengthens. Using a manipulative snowpack and microbial inoculation transplant experiment, we tested the hypothesis that growing season length and microbial community composition interact to control plant elevational range shifts. We predicted that a lengthening growing season combined with dispersal to patches of soils with more mutualistic microbes and fewer pathogenic microbes would facilitate plant survival and growth in previously unvegetated areas. We identified negative effects on survival of the common alpine bunchgrass Deschampsia cespitosa in both short and long growing seasons, suggesting an optimal growing season length for plant survival in this system that balances time for growth with soil moisture levels. Importantly, growing season length and microbes interacted to affect plant survival and growth, such that microbial community composition increased in importance in suboptimal growing season lengths. Further, plants grown with microbes from unvegetated soils grew as well or better than plants grown with microbes from vegetated soils. These results suggest that the rate and spatial extent of plant colonization of unvegetated soils in mountainous areas experiencing climate change could depend on both growing season length and soil microbial community composition, with microbes potentially playing more important roles as growing seasons lengthen.
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.
Data from: Saltmarsh vegetation and secured woody debris facilitate mangrove re-colonization
<p>Does the presence of saltmarsh vegetation affect the long-term regeneration of the pioneer mangrove species <em>Avicennia germinans</em> in a degraded dwarf forest? Does immobilized coarse woody debris (CWD) affect regeneration similarly? Do larger trees suppress or facilitate intraspecific saplings? The study was conducted in a dwarf mangrove forest in the high intertidal zone on Bragança peninsula in northern Brazil. The spatial patterns of <em>A. germinans</em>, the herbaceous halophyte <em>Sesuvium portulacastrum</em>, and CWD were mapped in three sample plots (each 400 m<sup>2</sup>) during two consecutive vegetation surveys, conducted in 2011 and 2014. Inhomogeneous Poisson and Thomas point-process models were used to assess the distribution of <em>A. germinans</em> life-history stages (seedlings, saplings, and adult dwarf trees), conditioned on the presence of <em>S. portulacastrum</em> and CWD. In addition, intraspecific interactions between trees and regeneration were assessed based on crown projection mapping. Bivariate point pattern analyses were used to assess the dependence of advance regeneration on dwarf <em>A. germinans</em> trees and <em>S. portulacastrum</em>. <em>A. germinans</em> saplings and trees were positively associated with <em>S. portulacastrum</em> and CWD, whereas seedlings were located around tree crowns. The density of fruit-bearing trees was positively associated with sapling density, indicating that regeneration relied on locally dispersed propagules. Herbaceous vegetation and CWD have an important ecological function in degraded mangroves by retaining tidally dispersed propagules. Here, we show that herbaceous vegetation does not suppress the growth of seedlings but facilitates mangrove recolonization. Due to limited tidal dispersal, regeneration relies on local propagule supply. In addition to hydrological restoration, the observed vegetation patterns suggest that, in the absence of propagule-retaining vegetation, restoration of high-intertidal mangroves can be facilitated by establishing nuclei of planted trees and installing secured logs.</p>
Dataset for journal article: Nava et al. (2021) "Microalgae colonization of different microplastic polymers in experimental mesocosms across an environmental gradient"
<p>Dataset and R script for the article "Microalgae colonization of different microplastic polymers in experimental mesocosms across an environmental gradient" by Nava V., Matias M., Castillo-Escrivà A., Messyasz B. and Leoni B. accepted by Global Change Biology.</p>
Formatted TCGA clinical and RNA-Seq data for colon adenocarcinoma (COAD) and rectum adenocarcinoma (READ)
<p>COAD/READ/COADREAD_rnaseq_fpkm.txt files contain TCGA RNA-Seq data in FPKM normalisation for colorectal adenocarcinoma (COAD), rectum adenocarcinoma (READ) or combined (COADREAD).</p> <p>COAD/READ/COADREAD_rnaseq_tpm.txt files contain TCGA RNA-Seq data in TPM normalisation for colorectal adenocarcinoma (COAD), rectum adenocarcinoma (READ) or combined (COADREAD).</p> <p>COAD/READ/COADREAD_clinical_raw.xlsx files contain TCGA clinical data for patients with colorectal adenocarcinoma (COAD), rectum adenocarcinoma (READ) or combined (COADREAD).</p> <p>COAD/READ/COADREAD_rnaseq_clinical_raw.xlsx files contain corresponding information of TCGA clinical data and RNA-Seq data for patients with colorectal adenocarcinoma (COAD), rectum adenocarcinoma (READ) or combined (COADREAD).</p>
COLONOMICS - predictive models for normal colon gene expression and DNA methylation for TWAS and MWAS
<p>We provide significant SNP prediction models derived from the COLONOMICS data (<a href="https://www.colonomics.org">https://www.colonomics.org</a>). Genotypes were obtained by Affymetrix 6.0 array, imputed to TopMed panel. Gene expression was obtained from Affymetrix U219 array, DNA methylation was obtained with Illuminan 450K array and miRNA expression was obtained by NGS. We provide SNP prediction models for 1,758 genes, 30,530 CpG probes and 38 miRNAs obtained from colon normal biopsy samples. These features can be predicted from SNPs located within ±1Mb, which we assumed they act through cis mechanisms. We include the model’s summary statistics and corresponding SNP weights in SQLite objects. Models were trained using the elastic net procedure employed in the PredictDB pipeline (<a href="https://predictdb.org/">https://predictdb.org</a>), according to which only models with a predictive performance p-value < 0.05 and R<sup>2</sup> > 0.1 are considered significant. We adjusted the models by basic covariates, i.e., sex, age, tissue type and colon anatomic location where biopsies were collected (left and right colon). Genome coordinates refer to GRCh37/hg19.</p>
Data from the field experiment on katabatic winds on a steep slope (Grand Colon, French Alps), February 2019
<p>These are the data from the field experiment described in the paper 'Katabatic winds over steep slopes: overview of a field experiment designed to investigate slope-normal velocity and near surface turbulence' by CHARRONDIERE, C., BRUN, C., COHARD, J.M., SICART, J.E., OBLIGADO, M., BIRON, R., COULAUD, C. & GUYARD, H. (2022), Boundary-Layer Meteorol. 187, 29-54.</p> <p> </p>
Supplementary material 3 from: Bongard C, Butler K, Fulthorpe R (2013) Investigation of fungal root colonizers of the invasive plant Vincetoxicum rossicum and co-occurring local native plants in a field and woodland area in Southern Ontario. Nature Conservation 4: 55-76. https://doi.org/10.3897/natureconservation.4.3578
Supplementary material 3 from: Bongard C, Butler K, Fulthorpe R (2013) Investigation of fungal root colonizers of the invasive plant Vincetoxicum rossicum and co-occurring local native plants in a field and woodland area in Southern Ontario. Nature Conservation 4: 55-76. https://doi.org/10.3897/natureconservation.4.3578
Artificial reefs geographical location matters more than shape, age and depth for sessile invertebrate colonization in the Gulf of Lion (NorthWestern Mediterranean Sea)
<p>Artificial reefs (ARs) have been used to support fishing activities. Sessile invertebrates are essential components of trophic networks within ARs, supporting fish productivity. However, colonization by sessile invertebrates is possible only after effective larval dispersal from source populations, usually in natural habitat. While most studies focused on short term colonization by pioneer species, we propose to test the relevance of geographic location, shape, age and depth of immersion on the ARs long term colonization by species found in natural stable communities in the Gulf of Lion. We recorded the presence of five sessile invertebrates species, with contrasting life history traits and regional distribution in the natural rocky habitat, on ARs with different shapes deployed during two immersion time periods (1985 and the 2000s) and in two depth ranges (<20m and >20m). At the local level (~5kms), neither shape, depth nor immersion duration differentiated ARs assemblages. At the regional scale (>30kms), colonization patterns differed between species, resulting in diverse assemblages. This study highlights the primacy of geographical positioning over shape, immersion duration and depth in ARs colonization, suggesting it should be accounted for in maritime spatial planning.</p>
Mycorrhizal colonization, age, and aboveground biomass (g) of Nothofagus pumilio seedlings harvested from Variable Retention treatments at Los Cerros Ranch, Tierra del Fuego, Argentina.
This dataset contains data on Nothofagus pumilio seedlings sampled from a Variable Retention (VR) managed forest in Tierra del Fuego, Argentina seven years after harvesting. We evaluated the effects of a VR timber management system on the EMF community associated with N. pumilio seedlings. We quantified the abundance, composition, and diversity of EMF across aggregate (AR) and dispersed retention (DR) sites within a VR managed area and compared them to primary forest (PF) stands. EMF assemblage and taxonomic identities were determined by ITS-rDNA sequencing of individual root tips sampled from 280 seedlings across three landscape replicates of each VR treatment. To better understand seedling performance, we tested the relationships between fungal colonization, fungal taxonomic composition, seedling biomass, and VR treatment across our study sites. This data was collected as a comparative component to a larger project understanding the effect of mycorrizhae on seedling success after various disturbances such as logging and fire that was ongoing at the Bonanza Creek LTER and other arctic locations.
Root endophyte colonization in the black sand extended growing season experiment for Audubon, Lefty, East Knoll and Trough sites, 2023.
This dataset contains detailed microscopy-based observations of root colonization by three major fungal guilds—arbuscular mycorrhizal fungi (AMF), dark septate endophytes (DSE), and fine root endophytes (FRE)—across multiple alpine plant species sampled from experimental black sand (early snowmelt) plots in the Rocky Mountains. Root samples were collected from plots subjected to different snowmelt timing treatments ("Early" and "Control") using black sand to cause early snowmelt to investigate the effects of phenological shifts in plants on fungal colonization. Each sample includes metadata on collection date, field location, plant species, and staining/scoring logistics, as well as quantified colonization metrics such as hyphae, arbuscules, vesicles, and other fungal structures. This dataset supports ongoing research into how alpine plant–fungal interactions respond to changing environmental conditions and may help identify shifts in mutualistic and non-mutualistic associations under climate-altered snowmelt regimes.
Supplementary data: Computational analysis of mechanical stress in colonic diverticulosis
<p>The data set contains code, source data, and derivatives data for the results presented in our research paper titled "Computational analysis of mechanical stress in colonic diverticulosis".</p> <p>The "code" contains Abaqus (SIMULIA, Providence, RI) files for the simulations presented in the paper (tested with Abaqus version 6.13) and jupyter notebooks developed to analyze simulation results.</p> <p>The "sourcedata" folder contains Excel files with parameters used for the simulations as well as data directly extracted from the simulation results.</p> <p>The "derivatives" folder contains secondary data calculated based on the files from "sourcedata".</p> <p>The README document included in the dataset contains a more detailed description of the files and folders.</p>
Network analysis reflects the trophic relationship between microbial colonizers and deadwood resources - supporting information
<p>Supporting tables S2 - S5 of "Network analysis reflects the trophic relationship between microbial colonizers and deadwood resources".</p> <p>The file Table_Legends_S2-S5.txt contains all legends, as given below:</p> <p>Table S2: Module-associated trees and OTUs, their relative abundances and identities for the fungal sapwood network; module – name of the module, present – proportion of network version that the OTU is present in (1 = 1000/1000), inBestModule – proportion of the network versions where the OTU is associated with the respective module, percInBestModule – proportion of the network versions where the OTU is associated with the respective module provided that the OTU is part of the network (= inBestModule/present), meanAbundanceModuleSamples – mean relative abundance of the OTU in all samples belonging to the module, meanAbundanceOtherSamples – mean relative abundance of the OTU in all other samples, relAbundanceModuleSamplesVsOthers – meanAbundanceModuleSamples / meanAbundanceOtherSamples .</p> <p>Table S3: Module-associated trees and OTUs, their relative abundances and identities for the fungal heartwood network; module – name of the module, present – proportion of network version that the OTU is present in (1 = 1000/1000), inBestModule – proportion of the network versions where the OTU is associated with the respective module, percInBestModule – proportion of the network versions where the OTU is associated with the respective module provided that the OTU is part of the network (= inBestModule/present), meanAbundanceModuleSamples – mean relative abundance of the OTU in all samples belonging to the module, meanAbundanceOtherSamples – mean relative abundance of the OTU in all other samples, relAbundanceModuleSamplesVsOthers – meanAbundanceModuleSamples / meanAbundanceOtherSamples .</p> <p>Table S4: Module-associated trees and OTUs, their relative abundances and identities for the prokaryotic sapwood network; module – name of the module, present – proportion of network version that the OTU is present in (1 = 1000/1000), inBestModule – proportion of the network versions where the OTU is associated with the respective module, percInBestModule – proportion of the network versions where the OTU is associated with the respective module provided that the OTU is part of the network (= inBestModule/present), meanAbundanceModuleSamples – mean relative abundance of the OTU in all samples belonging to the module, meanAbundanceOtherSamples – mean relative abundance of the OTU in all other samples, relAbundanceModuleSamplesVsOthers – meanAbundanceModuleSamples / meanAbundanceOtherSamples .</p> <p>Table S5: Module-associated trees and OTUs, their relative abundances and identities for the prokaryotic heartwood network; module – name of the module, present – proportion of network version that the OTU is present in (1 = 1000/1000), inBestModule – proportion of the network versions where the OTU is associated with the respective module, percInBestModule – proportion of the network versions where the OTU is associated with the respective module provided that the OTU is part of the network (= inBestModule/present), meanAbundanceModuleSamples – mean relative abundance of the OTU in all samples belonging to the module, meanAbundanceOtherSamples – mean relative abundance of the OTU in all other samples, relAbundanceModuleSamplesVsOthers – meanAbundanceModuleSamples / meanAbundanceOtherSamples .</p>
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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.