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.

1,998

datasets available to search

ShareScore release 0.9.0

Dataset results

1,998 results for “Microbial”

Learn how ShareScore rates datasets ↗

zenodo48/100

Soil visible–near infrared (vis–NIR) spectra for the Biomes of Australian Soil Environments (BASE) soil microbial diversity database

Visible–near infrared spectra of 695 soil samples collected in the Biomes of Australian Soil Environments (BASE) soil microbial diversity project (Bissett et al., 2016). The spectra represent reflectance values from 2151 wavelengths that range from 350 nm to 2500 nm with a 1 nm interval. The dataset has unique sample identification numbers and the date of sampling, which can be related to the BASE (Australian Microbiome) database (https://data.bioplatforms.com/organization/australian-microbiome)

opencc-by-4.0Feb 2022View details →

zenodo48/100

Accompanying data for the PhD thesis 'Nanomaterial safety for microbially-colonized hosts'

These files include all data presented in chapter 6 of the dissertation: "Nanomaterial safety for microbially-colonized hosts: microbiota-mediated physisorption interactions and particle-specific toxicity" by Bregje Brinkmann (2022). The data presented in chapters 2-5 have previously been published elsewhere: <ul> <li>Chapter 2: Zenodo (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: Mendeley Data (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: Figshare (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: Mendeley Data (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> 1. Data presented in Figure 6.1: Survival_CFU_(...) 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): <ul> <li>Concentration: Nominal exposure concentration (mg nAg·L-1).</li> <li>Date: The date at which the mortality was scored (Format: DD/MM/YYYY). </li> <li>Family: A code referring to the aquarium of wildtype zebrafish (ABxTL) that were crossed to obtain the larvae for the experiment.  </li> <li>Survival: Percentage of larvae that had survived the treatment.</li> <li>CFU: Number of colony-forming units that was isolated per larva</li> </ul> The methodology for toxicity tests and the procedures to determine CFU counts, have been published in Nanotoxicology: 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>   2. Data presented in Figure 6.2: ABs_DoseResponses_(...) 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: <ul> <li>Concentration: Nominal exposure concentration (mg nAg·L-1). </li> <li>Mortality: Percentage of larvae that had died.</li> <li>Date: The date at which the mortality was scored (Format: DD/MM/YYYY).</li> <li>Family: A code referring to the aquarium of wildtype zebrafish (ABxTL) that were crossed to obtain the larvae for the experiment.  </li> <li>ABs: Duration of the antibiotic/ antifungal pretreatment, either 0, 6 or 72 hours.</li> </ul>   3. Data presented in Figure 6.3: il1beta_eGFP_(...) Three folders comprising fluorescence microscopy images (TIFF format) of il1beta:eGFP reporter zebrafish larvae at 5 dpf: <ul> <li>(...)_replicates1_20200226: images for the first experimental replicate.</li> <li>(...)_replicates2_20200304: images for the second experimental replicate.</li> <li>(...)_replicates3_20200318: images for the third experimental replicate.</li> </ul> For each of the replicates, the following images were acquired: <ul> <li>nZnO_GFP: GFP signal for larvae exposed to nZnO.</li> <li>Znion_GFP: GFP signal for larvae exposed to zinc ions.</li> <li>nZnO_trans: transmitted light images for larvae exposed to nZnO. </li> <li>Znion_trans: transmitted light images for larvae exposed to zinc ions.</li> </ul> Additionally, the following images  have previously been deposited to Mendeley Data (DOI: <a href="http://doi.org/10.1016/j.ecoenv.2022.113522">10.17632/4nfg69v8hy.1</a>): <ul> <li>nAg_GFP: GFP signal for larvae exposed to nAg.</li> <li>nAg_trans: transmitted light images for larvae exposed to nAg.</li> <li>Agion_GFP: GFP signal for larvae exposed to silver ions.</li> <li>Agion_trans: transmitted light images for larvae exposed to silver ions.</li> <li>control_GFP: GFP signal for control larvae that had not been exposed to silver ions or nAg</li> <li>control_trans: transmitted light images for control larvae that had not been exposed to silver ions or nAg.</li> </ul> All image processing steps have been published in Ecotoxicology and Environmental Safety: 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>   Abbreviations: <ul> <li>ABs: antibiotics</li> <li>CFU: colony-forming units</li> <li>dpf: days post-fertilization</li> <li>il1beta: interleukin-1beta</li> <li>nAg: silver nanoparticles (NM-300 K)</li> <li>nZnO: zinc oxide nanoparticles (NM-110)</li> </ul>

opencc-by-4.0Dec 2022View details →

zenodo48/100

Manipulating a host-native microbial strain compensates for low microbial diversity by increasing weight gain in a wild bird population

<h1>Manipulating a host-native microbial strain compensates for low microbial diversity by increasing weight gain in a wild bird population</h1> <h1> </h1> These files contain data on bacteria present in the guts of wild great tit (Parus major)  obtained from faecal samples and sequenced using Illumina MiSeq. These data resulted from an experiment which provided supplementary mealworms at the nest during the breeding season at number of woodland sites in Cork, Ireland. Approximately half of these nests were given mealworms covered in a freeze dried bacterial powder containing the bacteria Lactobacillus kimchicus, which had been isolated from great tit faeces from the previous season. This treatment aimed to disrupt the gut microbiota of the treatment birds in order to provide evidence for the gut microbiotas role in birds health and fitness. Included here are the 3 elements necessary to create a 'phyloseq object' containing the sample metadata, ASV (Amplicon Sequence Variant) count table and a taxonomy table. The metadata file includes the alpha diversity scores for each individual. The data include all negative control samples taken during sample collection and library preparation, which were removed before the main analyses. All analyses, except for the beta-diversity analyses, were conducted in R. All R code is available on GitHub (https://github.com/shan-e-s\). Raw Sequence data are available in the European Nucleotide Archive under access number PRJEB74941, and ERS18960426-ERS18960697. <h2> </h2> <h2>## Description of the data and file structure </h2> Taxonomy, ASV and metadata files required to create a phyloseq object in R. metadata.csv file contains data on individual birds (i.e. individual samples). The metadata includes descriptions of the bird itself and it's environment, namely: <ul> <li>Rownames: unique sample ID for each sample, corresponds with asvTable.csv. </li> <li>Nest: unique identifier for the nest box associated with the bird being sampled. </li> <li>Sample.ID: unique identifier for the faecal sample or control sample.</li> <li>Bird.ID: Identity of the bird the sample came from, note some individuals sampled twice so some bird.ID's may reoccur in metadata with different Sample.ID.</li> <li>Date: Date the sample was taken dd/mm/yyyy.</li> <li>Day: Date the sample was taken, in days since 1st March.</li> <li>Ring.Mark: British Trust for Ornithology (BTO) metal ring ID where applicable. Birds only ringed at D15 so some young birds do not have IDRings.</li> <li>Site: ID of woodland site  that bird was sampled at.</li> <li>Chick.LetterID: ID letter differentiates between different birds from the same nest. Either 'A'-'F' for nestlings, 'Fe' for females or 'M' for males.</li> <li>Age.code: BTO age code.</li> <li>Age.category: Age category that bird is in. D8 = 8 days post hatching, D15 = 15 days post hatching, adult = 1+ years post hatching.</li> <li>Sex: Bird's sex, only determined for adult birds. Fe = Female, M = Male.</li> <li>Wing_mm: Wing length in mm.</li> <li>Tarsus_mm: minimum tarsus length of bird in mm.</li> <li>Weight_g: bird's weight in grams.</li> <li>Faecal.Sample: bird's age at sampling.</li> <li>newRing: whether bird was fitted with a new BTO ring. Only relevant to adults.</li> <li>Treatment: the experimental treatment group that the bird was in. Either 'Treatment' when nest given L. kimchicus treated mealworms or 'Control' when nest given plain mealworms.</li> <li>Notes: field notes.</li> <li>Main.sample: indicates whether this sample was the main sample to be used for analysis, an alternative sample taken as a backup.</li> <li>Plate: the ID of the PCR plate which the sample was amplified on.</li> <li>Azenta_noPeriod: sample ID given to sequencing facility without special characters. Corresponds to fastq files and ASV table counts.</li> <li>Qubit_prePool: samples qubit score before pooling.</li> <li>Date_extracted: date the sample was extracted on dd/mm/yyyy.</li> <li>SampleType: whehther the sample was a 'main' sample intended for downstream analysis, a 'control' sample for detecting contamination during library preparation, a 'duplicate' for detecting PCR issues, a 'label_error' where sample was suspected of being mislabelled at some point, a 'repeat' sample intended to detect errors or issues, a 'contam' sample which was suspected of being contaminated,  a 'common' sample used across different PCR plates to detect issues. Extraction_notes: notes regarding the DNA extraction of the sample.  </li> <li>LibPrep_notes: notes regarding the library preparation of the sample.</li> <li>Ring.Mark.lab: the ring or sample ID written on the sample tube, recorded to help detect mislabelling.</li> <li>Post_lab_notes: notes regarding issues found post sequencing.</li> <li>NumberOfReads: number of sequence reads associated with the sample.   </li> <li>DistanceToEdge: distance between nest and woodland edge in metres.   </li> <li>BroodSize.D8: number of nestlings in the nest at day-8 post hatching.   </li> <li>BroodSize.D15: number of nestlings in the nest at day-15 post hatching.</li> <li>firstEggLayDate: Date the first egg in the clutch was laid, in days since 1st March.</li> <li>lastEggLayDate: Date the last egg in the clutch was laid, in days since 1st March.</li> <li>Observed: number of unique ASV's (or taxa) detected in the sample.</li> <li>Chao1: Chao1 diversity of the sample.</li> <li>Shannon: Shannon diversity of the sample.</li> </ul> The file 'taxonomy.csv' contains the taxonomic breakdown of each bacterial Amplicon Sequence Variant (ASV) found in the dataset from Phylum to Species. Obtained by using the Naive Bayes Classifier against the Silva (v138) taxonomic database. The file 'asvTable.csv' contains counts of each amplicon sequence variant's occurrence for each individual sample. Samples are rows and taxa are columns.   <h2>Sharing/Access information </h2> All R code is available on GitHub (https://github.com/shan-e-s\).  Raw Sequence data are available in the European Nucleotide Archive under access number PRJEB74941, and ERS18960426-ERS18960697.

opencc-by-4.0Apr 2024View details →

zenodo48/100

Feature selection on microbial profiles of CRC samples with chopin2 (powered by hdlib)

This Zenodo entry contains the result of the feature selection algorithm implemented through a backward variable elimination strategy in <a href="https://github.com/cumbof/chopin2" target="_blank" rel="noopener">chopin2</a> (powered by <a href="https://github.com/cumbof/hdlib" target="_blank" rel="noopener">hdlib</a>) applied on <a href="https://github.com/biobakery/MetaPhlAn" target="_blank" rel="noopener">MetaPhlAn3</a> microbial profiles of a public dataset of metagenomic stool samples collected from patients affected by the colorectal cancer (CRC) as well as from healthy individuals. Microbial profiles have been extracted through the <a href="https://bioconductor.org/packages/release/data/experiment/html/curatedMetagenomicData.html" target="_blank" rel="noopener">curatedMetagenomicData</a> package for R under the IDs ThomasAM_2018a, ThomasAM_2018b, and ThomasAM_2019_a. The feature selection algorithm is implemented as a backward variable elimination method, and it makes use of the vector-symbolic architecture described in <a href="https://doi.org/10.3390/a13090233" target="_blank" rel="noopener">Cumbo F 2020</a>. Deposited data is described below: <ul> <li>datasets.tar.gz: it contains the datasets used as input of chopin2 as the result of merging the three datasets with relative abundances mentioned above, also stratified by age and sex (with prefix RA). The same datasets have been also binarized (with prefix BIN);</li> <li>hd-models.tar.gz: it contains the output of the feature selection performed with chopin2 (powered by hdlib) on the datasets with both relative abundance and binary profiles (RA and BIN);</li> <li>ml-models.tar.gz: it contains the result of the feature selection produced with classical wrapper-based techniques (i.e., Random Forest, Decision Tree, Support Vector Machine, Logistic Regression, and Extreme Gradient Boosting) in addition to a Python 3.8 script to reproduce the results.</li> </ul> Please note that the datasets RA__ThomasAM__species.csv and BIN__ThomasAM__species.csv are also included into the datasets.tar.gz archive.

opencc-zeroMay 2024View details →

zenodo48/100

AMnrGC - Amazon river non-reduntant microbial genes catalogue

AMnrGC : Amazon river basin non-redundant microbial gene catalogue                           RELEASE 2018/01                     --------------------------------------   1. INTRODUCTION    AMnrGC is a collection of genes and proteins which were constructed    by use of Amazon river basin openly available metagenomes from    sequencing projects (SRP044326, PRJEB25171 and SRP039390). Briefly,    metagenomes were coassembled by groups made up their geographical    location with Megahit v.1.0 and the contigs were used to gene predictions    by Prodigal v.2.6.3. Genes sequences were length filtered (> 150 bp) and    clustered by CD-HIT-EST (version 4.6) at 95% of nucleotide identity and    90% of overlap of the shorter gene. Theorical protein products were annotated    by the most completes databases up to date and their complete information    is available here.   2. LOCATION    AMnrGC versions will be available on the web only under the current ZENODO    repository: 10.5281/zenodo.1484504   3. FORMAT    Gene entries were named as ">AM_AGSSY_XXX" where XXX represents an unique numerical    identifier. Genes were deposited in their coding phase, because of this, all of them    can be used to generate the protein sequences by transeq function at ORF+1.    The protein entries correspond to genes artifical translation used in the annotations,    and also available, codified in the same way, but containing the indication "_1" in    the end of the header. Example:     Gene:     >AM_AGSSY_151515     Protein:     >AM_AGSSY_151515_1    Annotations were provided as separate tables for each database used to annotate the    sequences. The header of these tables indicates the meaning of each value.   2. FUTURE FORMAT CHANGES    No major changes are expected for the main general format of the database.    New versions should include updated versions of annotations or even additional sequences,    numbered as subsequent entries.   3. ACKNOWLEDGEMENTS        This work is a joint effort of Laboratory of molecular biology from Federal    University of São Carlos, São Paulo, Brazil (LBM/UFSCAR) and Protists group    of Institut del Ciencias del Mar, Barcelone, Spain (ICM). We are grateful to    Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), as well as,                      the spanish funding organ Consejo Superior de Investigaciones Científicas (CSIC).           This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal                      de Nível Superior - Brasil (CAPES) - Finance Code 001.   4. THE AMnrGC TEAM    AMnrGC is maintained by a group of researchers. You can contact    the AMnrGC consortium.        Current curators:    - Célio Dias Santos Júnior (celio.diasjunior@gmail.com)    - Flavio Henrique-Silva (dfhs@ufscar.br)    - Ramiro R. Logares (ramiro.logares@icm.csic.es)   5. COPYRIGHT NOTICE    AMnrGC - Amazon river basin non-redundant microbial gene catalogue    Copyright (C) 2018 The AMnrGC consortium.    This database is provided “as is” and without any warranty of any kind,    of openly available for non-commerical purposes. You can redistribute and/or modify it    as you wish, under the terms of the ODBL 1.0 license:     https://opendatacommons.org/licenses/odbl/1.0/    For commercial purposes, please contact us.   ___________________ The AMnrGC Consortium 2018

opencc-by-4.0Nov 2018View details →

zenodo48/100

Microbial Proteomes with/without experimental optimal growth temperature

This repository contains proteomes of microorganisms with/without experimentally determined optimal growth temperature (OGT), used in the paper 'Li G, Rabe KS, Nielsen J & Engqvist MKM (2019) Machine learning applied to predicting microorganism growth temperatures and enzyme catalytic optima. ACS Synth. Biol. 8: 1411–1420'. There are two .tar.gz files: (1) classified.tar.gz. It contains 5761 proteomes with experimental OGT. The name format of each proteome is '{ogt}_{organism_name}_{organism domain}.fasta'. For example, '36_escherichia_coli_bacteria.fasta' for Escherichia coli. (2) not_classified.tar.gz. It contains 1803 proteomes without experimental OGT. The name format is similar as in classified.tar.gz. The only different is to use  'tt' to represent the unknown OGT value. For example, 'tt_candidatus_azobacteroides_bacteria.fasta'. All proteomes are in fasta format.  If you used the dataset, please kindly cite the paper mentioned above.

opengpl-2.0Jul 2019View details →

zenodo48/100

Microbial biomass and water-extractable carbon on Mt. Kilimanjaro

This dataset presents the value of microbial biomass carbon (MBC) and water-extractable carbon (WOC) at study plots under KiLi project. Microbial biomass carbon (MBC) and water-extractable organic carbon (WOC) – as sensitive and important parameters for soil fertility and C turnover – are strongly affected by land-use changes all over the world. These effects are particularly distinct upon conversion of natural to agricultural ecosystems due to very fast carbon (C) and nutrient cycles and high vulnerability, especially in the tropics. The objective of this study was to use the unique advantage of Mt. Kilimanjaro – altitudinal gradient leading to different tropical ecosystems but developed all on the same soil parent material – to investigate the effects of land-use change and elevation on MBC and WOC contents during a transition phase from dry to wet season. Down to a soil depth of 50 cm, we compared MBC and WOC contents of 2 natural (Ocotea and Podocarpus forest), 3 seminatural (lower montane forest, grassland, savannah), 1 sustainably used (homegarden) and 2 intensively used (maize field, coffee plantation) ecosystems on an elevation gradient from 950 to 2850 m a.s.l. The KiLi project (2010-2018) is a German Science Foundation (DFG) funded research unit (DFG research unit FOR1246) that focuses on biodiversity and ecosystem processes along altitudinal and disturbance gradients on Mt. Kilimanjaro (Tanzania, Africa), capitalizing on its world-wide unique range of climatic and vegetation zones. The research unit comprises 2 central projects and 7 subprojects from various disciplines. On a total of 60 study sites in both natural and human-disturbed ecosystems biodiversity (e.g. plants, soil arthropods, ants, bees, frogs, lizards, bats, birds), related ecosystem processes (decomposition, seed dispersal, pollination, herbivory, predation), and biogeochemical processes and properties of ecosystems (climate, soil properties and nutrient status, regulation of water and carbon fluxes, trace gas emissions, primary productivity, functional diversity) are analyzed.

opencc-by-4.0Aug 2024View details →

zenodo48/100

The impact of beech deadwood on soil properties and microbial diversity

Our research is an attempt to determine the role of decaying wood in shaping the properties of forest soils in mountain ecosystems.

opencc-by-4.0Oct 2024View details →

zenodo48/100

Data and code from "Water availability is a stronger driver of soil microbial processing of organic nitrogen than tree species composition"

#### Data description Data from large scale, long-term tree diversity experiment in southwestern France (<a href="https://sites.google.com/view/orpheeexperiment/home">ORPHEE</a>), additionally manipulating water contraint. Variables presented are soil nitrogen cycling rates measured using isotope pool dilutions. Companion paper is found here: Maxwell TL, Augusto L, Tian Y, Wanek W & Fanin N (2023). Water availability is a stronger driver of soil microbial processing of organic nitrogen than tree species composition. European Journal of Soil Science. <a href="https://doi.org/10.1111/ejss.13350">https://doi.org/10.1111/ejss.13350</a> #### Metadata Soil sampling: July 2020 Maxwell_ShortComm_Data.csv data description ID: unique identifier per sample Block: numbered 1-6. Blocks 1,3,6 are control (unirrigated), Blocks, 2,4,5 are irrigated Plot: numbered plot according to the ORPHEE design. Plot 1 = BP, Plot 5 = PP, Plot 9 = BP_PP Espece: species ID. BP = pure birch (Betula pendula), PP = pure pine (Pinus pinaster), BP_PP (50% mixed birch-pine) Rep: sample replicate, 3 replicates per plot Sample name: long unique identifier per sample. Concatenation of Block, Plot, and Espece PD: gross protein depolymerization rates (micrograms nitrogen per grams dry soil per day = µg N g-1 d-1) AAU: gross free amino acid uptake rates (µg N g-1 d-1) Cmicrobial_ug_g: microbial biomass carbon (µg C g-1) Nmicrobial_ug_g: microbial biomass nitrogen (µg N g-1) MRT_FAA_hrs: mean residence time of free amino acids (hours) FAA_ugN_g: free amino acids (µg N g-1) Moisture_percent: soil moisture percent (%) N_nonfumige_ug_g: nitrogen from non fumigated soils, i.e. extractable N (µg N g-1) C_nonfumige_ug_g: nitrogen from non fumigated soils, i.e. extractableC (µg C g-1)

opencc-by-4.0Feb 2023View details →

zenodo48/100

Identification of microbial exopolymer producers in sandy and muddy intertidal sediments by compound-specific isotope analysis.

This dataset supports the version 2 of the paper entitled Identification of microbial exopolymer producers in sandy and muddy intertidal sediments by compound-specific isotope analysis : Hubas, Cédric; Gaubert-Boussarie, Julie; D’Hondt, An-Sofie; Jesus, Bruno; Lamy, Dominique; Meleder, Vona; Prins, Antoine; Rosa, Philippe; Stock, Willem; Sabbe, Koen. Identification of microbial exopolymer producers in sandy and muddy intertidal sediments by compound-specific isotope analysis. Peer Community Journal, Volume 3 (2023), article no. e104. doi : <a href="https://doi.org/10.24072/pcjournal.336">10.24072/pcjournal.336</a>. <a href="https://peercommunityjournal.org/articles/10.24072/pcjournal.336/">https://peercommunityjournal.org/articles/10.24072/pcjournal.336/</a>

opencc-by-4.0Nov 2022View details →

edi48/100

WARM experiment Soil Microbial Function, RMBL Colorado, 2021

We examined how abiotic (warming), and biotic (presence of dominant plant species) factors interact to affect soil microbial processes in montane meadow ecosystems at high and low elevations at the WaRM experimental sites near the Rocky Mountain Biological Laboratory in Colorado in the West Elk range of the southern Rocky Mountains in Colorado, USA, during the summer 2021 growing season. The low elevation site (low site) is at 2740 m elevation (38.715, -106.823) in an open meadow without tree cover, and the dominant plant species is a flowering forb, Wyethia amplexicaulis. The high elevation site (high site) (3460 m, 38.992, -107.067) is also described as open meadow with no tree cover and is dominated by Juncus drummondii, a monocot, grass-like herb. The low and high elevation sites have a mean summertime temperature of 14.9 and 10.9°C respectively, and a mean summertime precipitation of 143 and 151 mm The WaRM experimental design is a 2 × 2 factorial warming × dominant plant species removal experiment deployed at the high elevation site and the low elevation site. Each of the four treatments are replicated 8 times, for total of 32 plots (each of which is 2 × 2 m) at each elevation with warming imposed via transparent hexagonal open-top chambers (OTCs), 1.5 m in diameter, in the center of each warming plot and the dominant plant species (listed above) removed via clipping at soil level within removal plots. Treatments at this site have been deployed each summer (June-August) since 2013. We analyzed multiple soil microbial responses at three times throughout the growing season: pre-growing season [low site; approx. May 26, high site; approx. July 6], peak-growing season [low site; approx. June 23, high site; approx. July 21], and post-growing season [low site; approx. August 18, high site; approx. Sept 14]. We measured edaphic characteristics including volumetric soil water content. We measured soil microbial functions including soil respiration, microbial metabolic

openCC (other)Feb 2024View details →

edi48/100

Microbial Sulfate Reduction Rates in Soils in the Napa River Watershed, California, USA, 2021-2023

Agricultural sulfur (S) inputs are a major source of anthropogenic S to the environment, often stimulating microbial sulfate reduction (MSR) in downstream environments, which can drive a cascade of unintended ecosystem consequences. This dataset includes samples collected throughout the Napa River Watershed, California, USA, where routine, high S applications to vineyards are common. Sampling was focused confirming the presence of microbial sulfate reduction (MSR) within upland soils, challenging the conventional view that this process is restricted to fully saturated environments. We collected soil samples for sulfate concentrations, sulfate reduction rates (SRRs), and organic C content (Loss On Ignition, LOI) under three different hydrological conditions: while soils were moist (November 2021), dry (January/February 2022), and saturated (January/February 2023). We collected samples from a variety of different land cover types, including vineyard, vineyard stream, grassland, forest, forest stream, and wetland.

openCC (other)Mar 2025View details →

edi48/100

Caribou-Poker Creeks Research Watershed: Dissolved organic carbon and microbial respiration measurements from biodegradable dissolved organic carbon incubations, summer 2021

This dataset contains dissolved organic carbon (DOC) and microbial respiration measurements taken during lab incubations of water collected from the Caribou-Poker Creeks Research Watershed (CPCRW), with the goal of quantifying the proportion of biodegradable dissolved organic carbon (BDOC) and microbial utilization of DOC. Incubations were conducted in June, July, and August 2021 using water from six stream sites throughout the CPCRW. Incubation experiments were designed to measure responses to carbon and nutrient additions as well as different temperatures.

openOpenJan 2024View details →

edi48/100

Effects of Nitrogen Fertilization on Litter and Soil Decomposition: Cumulative Microbial Respiration

The influence of inorganic nitrogen (N) inputs on decomposition is poorly understood. Some prior studies suggest that N may reduce the decomposition of substrates with high concentrations of lignin via inhibitory effects on the activity of lignin-degrading enzymes, although such inhibition has not always been demonstrated. The purpose of E145 was to study the effects of nitrogen (N) addition on decomposition of seven substrates ranging in initial lignin concentrations (from 7.4 - 25.6%) over five years in eight different grassland and forest sites in central Minnesota.

openCC0Feb 2025View details →

edi48/100

Effects of Nitrogen Fertilization on Litter and Soil Decomposition: Daily Microbial Respiration

openCC0Feb 2025View details →

edi48/100

Effects of Nitrogen Fertilization on Litter and Soil Decomposition: Microbial Biomass

openCC0Feb 2025View details →

edi48/100

Inventory of High-resolution phylogenetic profiles of the planktonic microbial communities (via 16S and 18S rRNA gene amplicons) from Shark River Slough and Taylor Slough, Everglades National Park (FCE LTER), Florida, USA, 2017 - ongoing

Planktonic microbial communities mediate many vital biogeochemical processes in wetland ecosystems, yet compared to other aquatic ecosystems, like oceans, lakes, rivers, or estuaries, they remain relatively underexplored. Our study site, the Florida Everglades (USA)—a vast iconic wetland consisting of a slow-moving system of shallow rivers connecting freshwater marshes with coastal mangrove forests and seagrass meadows—is a highly threatened model ecosystem for studying salinity and nutrient gradients, as well as the effects of sea level rise and saltwater intrusion. This dataset provides the first high-resolution phylogenetic profiles of planktonic bacterial and eukaryotic microbial communities (using 16S and 18S rRNA gene amplicons) from these environments. The dataset contains 16S and 18S rRNA data from 2017, and contains 16S rRNA data for monthly (2019) and quarterly water samples (2020-ongoing). The 2017 data are published in Laas et al. 2022. A detailed list of sequence data and their accession numbers in GenBank is provided and will be updated as more data are published. This data package is an inventory of sequence read archive (SRA) entries available through GenBank BioProject PRJNA525456 (at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA525456) and BioProject PRJNA1018945 (at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1018945). This data package is associated with the following publication: Laas, P., Ugarelli, K., Travieso, R., Stumpf, S., Gaiser, E. E., Kominoski, J. S., & Stingl, U. (2022). Water column microbial communities vary along salinity gradients in the Florida Coastal Everglades wetlands. Microorganisms, 10(2), 215. https://doi.org/10.3390/microorganisms10020215 Instead of citing this package, which is an inventory, please cite the original GenBank data or journal article, as appropriate. Citation guidance for the journal article is available on the respective publisher's website.

openCC (other)Feb 2024View details →

edi48/100

Long-term measurements of microbial biomass and activity at the Hubbard Brook Experimental Forest 1994 – ongoing

Long-term monitoring of soil nitrate (NO3-) and ammonium (NH4+) concentrations, microbial biomass carbon (C) and nitrogen (N) content, microbial respiration, potential nitrification and N mineralization rates, pH, and denitrification potential has been ongoing at the Hubbard Brook Experimental Forest since 1994. Samples have been collected in the Bear Brook Watershed (west of Watershed 6) beginning in 1994. In 1998, our sampling regime was extended to Watershed 1 in an effort to monitor and quantify microbial response to a whole-watershed calcium addition. 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.

openCC (other)Jan 2026View details →

edi48/100

Microbial soil data from KBS pilot rainout shelter experiment 2019

Growing season drought can be devastating to crop yields. Soil microbial communities have the potential to buffer yield loss under drought through increasing plant drought tolerance and soil water retention. Microbial inoculation on agricultural fields has been shown to increase plant growth, but few studies have examined the impact of microbial inoculation on plant and soil microbial drought tolerance. We conducted a rainout shelter experiment and subsequent greenhouse experiment to explore three objectives. First, we evaluated the performance of a large rainout shelter design for studying drought in agricultural fields. Second, we tested how crop (corn vs. soybean) and microbial inoculation alter the response of soil microbial composition, diversity, and biomass to drought. Third, we tested whether field inoculation treatments and drought exposure altered microbial communities in ways that promote plant drought tolerance in future generations. In our field experiment, the effects of drought on soil bacterial composition depended on crop type, while drought decreased bacterial diversity in corn plots and drought decreased microbial biomass carbon in soybean plots. Microbial inoculation did not alter overall microbial community composition, plant growth or drought tolerance, despite our efforts to address common barriers to inoculation success. Still, a history of inoculation affected growth of future plant generations in the greenhouse. Our study demonstrates the importance of plant species in shaping microbial community responses to drought and the importance of legacy effects of microbial inoculation.

openCC0Sep 2022View details →

edi48/100

RCS01 Recovery and relative influence of root, microbial, and structural properties of soil on physically sequestered carbon stocks in restored grassland at Konza Prairie

Managing soil to sequester C can help mitigate increasing CO2 in the atmosphere. To maximize this ecosystem service, more knowledge of factors influencing C sequestration is needed. The objectives of this study were to (i) quantify recovery of the roots, microbial biomass and composition, and soil structure across a chronosequence of grassland restorations and (ii) use a structural equation model to develop a data-based hypothesis on the relative influence of physical and biological soil properties on the soil C aggregate fraction diagnostic of sequestered C. We hypothesized measured variables would recover with restoration age. Belowground plant biomass and tissue quality (C/N ratio), soil microbial biomass C, phospholipid fatty acid (PLFA) concentrations, soil structure, and soil C stocks in the bulk soil and each aggregate fraction were quantified from a cultivated field, prairies restored for 1 to 35-yr (n = 6), and a never-cultivated (native) prairie. Root biomass, microbial biomass C, arbuscular mycorrhizal fungi (AMF) PLFA biomass across the chronosequence increase to resemble native prairie following 35 yr of restoration. Many aspects of soil structure (i.e., bulk density, proportional mass of aggre- gate fractions, and aggregate mean weighted diameter) and the distribution C among soil fractions, including C in the micro-within-macro aggregate fraction (sequestered C), also became representative of native prairie within 35 yr of restoration. Total soil C stock and physically protected C increased at a similar rate (23 and 27 g C m-2 yr-1) respectively, across the chronosequence. After 35 yr of restoration, 50% of the total C pool was physically protected. The structural equation modeling developed by these data hypothesizes that microbial biomass C and AMF biomass (microbial composition) have the strongest causal influence on physically protected C. This model needs to be tested using independent sites to achieve greater inference.

openCC0Jan 2023View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record