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46 results for “Microbial colonization”
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>
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.
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>
Input data and Supplementary Results for "Turnover in life-strategies recapitulates marine microbial succession colonizing model particles"
<p><strong>README</strong></p> <p>This page contains processed input data used for downstream analysis and some Supplementary Results for the paper:</p> <p>Pascual-García, A., Schwartzman, J., Enke, T.N., Iffland-Stettner, A., Cordero, O.X., Bonhoeffer, S., Turnover in life-strategies recapitulates marine microbial succession colonizing model particles (2022).</p> <p> </p> <p><strong>Input data</strong></p> <p> </p> <ul> <li> <p>File <em>“count_table.ESV.biom”</em>: Table containing the abundance of each Exact Sequence Variant (ESV) in the different samples (biom format).</p> </li> <li> <p>File <em>“count-table_</em><em>metagenomes</em><em>_KEGGs.L3.spf”</em>. Table containing the abundances of genes found in the shotgun metagenomics experiments annotated in KEGG and then aggregated into classes according to the finest classification in KEGG's hierarchy (level 3). This is a tsv-formatted file that can be directly used in STAMP to perform statistical analysis (spf format).</p> </li> <li> <p>File <em>“count-table_</em><em>PICRUST2</em><em>_KEGGs.L3.spf</em>”. Table containing the abundances of genes predicted with PICRUSt v2. These genes were annotated in KEGG and aggregated into classes according to the finest hierarchy in KEGG (level 3). This is a tsv-formatted file that can be directly used in STAMP to perform statistical analysis (spf format).</p> </li> <li> <p>File <em>“count-table_Isolates_KEGGs.L3.spf</em>”. Table containing the abundances of genes found in the isolates genomes that were annotated in KEGG and aggregated into classes according to the finest classification in KEGG's hierarchy (level 3). This is a tsv-formatted file that can be directly used in STAMP to perform statistical analysis (spf format).</p> </li> <li> <p>File <em>“samples_metadata.tsv”</em>. Metadata table describing the samples.</p> </li> <li> <p>File <em>“isolates_metadata.tsv”.</em> Metadata table describing the isolates, it includes shallow phylogenetic levels and a categorical identifier describing the environmental preference estimated for the ESV having a 100% sequence identity with a ZINB-GLM.</p> </li> <li> <p>File <em>“sequences.ESV.</em><em>fasta</em><em>”.</em> File containing the Exact Sequence Variants fasta.</p> </li> </ul> <p><strong>Supplementary Materials</strong></p> <p> </p> <ul> <li> <p>File <em>“qiime2_visualizations.zip”</em>. A file containing visualizations compatible with the qiime2 viewer (simply drag and drop the file in <a href="https://view.qiime2.org/">https://view.qiime2.org/</a>) for each sample or combination of samples, labelled as `$substrate.$medium.$replicate`, where `$medium = {Beads, Seawater}` and `$replicate = {A,B,C}`. If the label is not present for one field, it means that all samples are aggregated for that field e.g.:</p> <ul> <li> <p>“<em>count_table.ESV.Chitosan.Beads.A.bar-plots.</em><em>qzv”</em> Contains the replicate experiment A for communities on the synthetic beads in chitosan.</p> </li> <li> <p>“<em>count_table.ESV.Chitosan.bar-plots.</em><em>qzv”</em> Contains all samples in chitosan (both seawater communities and the three replicates of communities on the beads).</p> </li> </ul> </li> <li> <p>File <em>“README.odt”</em>. This readme in libreoffice format.</p> </li> <li> <p>File <em>“</em><em>Genome_deposition_information.xlsx”. </em> NCBI identifiers for the isolates’ genomes.</p> </li> <li> <p>File "barcodes_to_samples_MGRAST.xlsx". Contains the barcodes of each sample and its metadata as it was deposited in MG-RAST. In a second tab, there is a subset of samples with a low number of reads that MG-RAST analyzed together, generating a single entry (termed "mixed").</p> </li> <li> <p>Access to raw an processed metagenomes and analysis are provided through MG-RAST following [this link](<a href="https://www.mg-rast.org/mgmain.html?mgpage=project&project=mgp85635">https://www.mg-rast.org/mgmain.html?mgpage=project&project=mgp85635</a>).</p> <ul> <li> <p>As of May 30th, 2022, there are two issues with the dataset in MG-RAST which are out of our scope to solve. We will report any update here. The first problem is related to the entry TCCTGAGC-GTAAGGAG-s_2_, which does not load in MG-RAST. These are very low samples and were discarded in most analyses. In addition, you will find in the metadata 17 metagenomes that do not belong to our project.</p> </li> </ul> </li> </ul>
Leaf-shelters facilitate the colonization of arthropods and enhance microbial diversity on plants
Open the record for dataset details and reuse information.
Plant colonization of moss-dominated soils in the alpine: Microbial and biogeochemical implications
A major impact of global climate change is the decline of mosses and lichens and their replacement by vascular plants. Although we assume this decline will greatly affect ecosystem functioning, particularly in alpine and arctic areas where cryptogams make a substantial amount of biomass, the effects of this change in vegetation on soil microbial communities remains unknown. We asked whether changes in bacterial community composition and enzyme ratios were consistent across two sites in moss versus vascular plant dominated areas. Using data from treeline and subnival ecosystems, we compared bacterial community composition, enzyme activity, and soil chemistry in moss dominated and vascular plant dominated plots of two unique alpine environments. Further, we used a time series to examine plots that actively transitioned from moss dominated to vascular plant dominated over a seven-year time period. Bacterial community composition in the soils under these two vegetation covers was significantly different in both environments and changed over time due to plant colonization. Microbial activity was limited by carbon and phosphorus in all plots and there were no differences in BG:AP enzyme ratios; however, there were significantly higher NAG:AP and BG:AP ratios in vascular plant plots at one site, suggesting the potential for shifts toward microbial N acquisition in vascular plant dominated areas in the alpine. As vascular plants replace mosses under warming conditions, bacterial community composition and nutrient availability shift in ways that may result in changes to biogeochemical cycling and biotic interactions in these vulnerable ecosystems.
High-resolution tracking of microbial colonization in Fecal Microbiota Transplantation experiments via metagenome-assembled genomes
<p>This project contains anvi'o profiles and contigs databases that is used and/or referenced from the Lee STM and Khan SA, <em>et al.</em> study titled "<strong>High-resolution tracking of microbial colonization in Fecal Microbiota Transplantation experiments via metagenome-assembled genomes</strong>". The pre-print of this study is available via http://dx.doi.org/10.1101/090993.</p> <p>To be able to work with the data files you will need anvi'o <strong>v2.1.0</strong> to be installed on your system. For installation instructions, or to have access to a Docker image for anvi'o, please visit this URL: http://merenlab.org/software/anvio</p> <p>Public data:</p> <ul> <li><strong>ANVIO-FMT-D-R01-R02-QUICK-VISUALIZATION.tar.gz</strong>: Data files for a quick visualization of the 97 MAGs and their distribution across the two FMT recipients. A run script in the archive explains how to use this data.<br> </li> <li><strong>ANVIO-FMT-D-R01-R02-MERGED-PROFILE.tar.gz</strong>: The merged anvi'o profile for the entire data, which also contains a collection of 97 MAGs identified in the donor. The profile database contains no hierarchical clustering of contigs, however, individual MAGs can be displayed via the following notation since the collection 'MAGs' describe the organization of contigs in each MAG referenced from the dataset `ANVIO-FMT-D-R01-R02-QUICK-VISUALIZATION`, as well as from the paper: "anvi-refine -c CONTIGS.db -p PROFILE.db -C MAGs -b <em>FMT-Donor_MAG_00054</em>". All MAG names are in the supplementary tables in our paper.<br> </li> <li><strong>ANVIO-FMT-D-R01-R02-MAGs-SUMMARY.tar.gz</strong>: A static HTML website that contains FASTA files for each MAG, and TAB-delimited matrices for coverage and detection values, and others. After unpacking, you can double-click the index.html file. </li> </ul>
Data for: The source of microbial transmission influences niche colonization and microbiome development
<p><span>Early life microbial colonizers shape and support the immature vertebrate immune system. Microbial colonization relies on the vertical route via parental provisioning and the horizontal route via environmental contribution. Vertical transmission is mostly a maternal trait making it hard to determine the source of microbial colonization in order to gain insight in the establishment of the microbial community during crucial development stages. The evolution of unique male pregnancy in pipefishes and seahorses enables the disentanglement of both horizontal and vertical transmission, but also facilitates the differentiation of maternal vs. paternal provisioning ranging from egg development, to male pregnancy and early juvenile development. Using 16s rRNA amplicon sequencing and source-tracker analyses, we revealed how the distinct origins of transmission (maternal, paternal & horizontal) shaped the juvenile internal and external microbiome establishment in the broad-nosed pipefish <em>Syngnathus typhle</em>. Paternal provisioning mainly shaped the juvenile external microbiome, whereas maternal microbes were the main source of the internal juvenile microbiome, later developing into the gut microbiome. This suggests that stability of niche microbiomes may vary </span><span>depending on the route and time point of colonization, the strength of environmental influences (i.e., horizontal transmission), and potentially the homeostatic function of the niche microbiome.</span></p>
Data for: The source of microbial transmission influences niche colonization and microbiome development
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Microbial colonization of microplastics in the Caribbean Sea
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Microbial Colonization of Dairy Free Oral Probiotics
ClinicalTrials.gov study NCT05375396. IPD Sharing: YES. Countries: 1. Publications: 4.
Effect of MVX (Titanium Dioxide) on the Microbial Colonization of Surfaces in an Intensive Care Unit
ClinicalTrials.gov study NCT02348346. IPD Sharing: Not stated. Countries: 1. Publications: 13.
Microbial Diversity of Small Bowel Stoma Effluent and Colonic Faeces
ClinicalTrials.gov study NCT03590418. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Microbial Colonization in Lung Cancer Patients
ClinicalTrials.gov study NCT05748795. IPD Sharing: Not stated. Countries: 1. Publications: 11.
Probiotic Toothpaste for Microbial Colonization
ClinicalTrials.gov study NCT05480020. IPD Sharing: NO. Countries: 1. Publications: 4.
Microbial Colonization of Oral Probiotics
ClinicalTrials.gov study NCT05375383. IPD Sharing: YES. Countries: 1. Publications: 8.
Probiotic Toothpaste to Assess Microbial Colonization
ClinicalTrials.gov study NCT06015958. IPD Sharing: NO. Countries: 1. Publications: 3.
Data from: Microbial community composition interacts with local abiotic conditions to drive colonization resistance in human gut microbiome samples
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Figure 3 from: Horváthová T, Babik W, Bauchinger U (2016) Biofilm feeding: Microbial colonization of food promotes the growth of a detritivorous arthropod. ZooKeys 577: 25-41. https://doi.org/10.3897/zookeys.577.6149
Figure 3 - The effect of diet source (A) and amount of biofilm (B) on the final body mass increase of woodlice (least square means ± SE) after two months of growth (AD, ADF and L represent an artificial diet, an artificial diet inoculated with single faecal pellet, and leaves, respectively). Please note that isopods started at the average body mass of 68.5 mg.
Figure 2 from: Horváthová T, Babik W, Bauchinger U (2016) Biofilm feeding: Microbial colonization of food promotes the growth of a detritivorous arthropod. ZooKeys 577: 25-41. https://doi.org/10.3897/zookeys.577.6149
Figure 2 - The relationship between initial body mass and the final body mass increase of woodlice feeding on the three primary diets (AD, ADF and L represent an artificial diet, an artificial diet inoculated with single faecal pellet, and leaves, respectively) either with small (2 days) or large (8 days) amount of biofilm. Regression lines represent the pooled data either for 2 or 8 days.
ScienceDex guides
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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.