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167 results for “stoichiometry”
McMurdo Dry Valleys Stoichiometry Experiment - CO2
Soil communities in the McMurdo Dry Valleys are subject to many limitations, including resource limitations. However, the nutrients that are predominantly limiting to growth and diversity of soil biota are not known. Additionally, landscape history (N deposition, P weathering) and native N and P content (glacial till provenance) may influence the ability of soil communities to respond to nutrient additions or changes in nutrient availability associated with environmental change. Long-term experiments in the Bonney and Fryxell basins have been established with yearly application of aqueous nutrient additions: C, N, P, CN, and CP. Multiple nutrients additions were made at the Redfield Ratio of 106:16:1 (C:N:P). Responses of soil chemistry, CO2 flux, and biota are reported.
McMurdo Dry Valleys Moss nutrient plasticity and stoichiometry
  The McMurdo Dry Valleys of Antarctica is one of the coldest and driest habitats on the planet. As vascular plants are absent in this region, moss is the main form of above-ground primary production with a potentially important contribution to biogeochemical cycling, yet little is known about their ecological role. To determine the relationship between moss and soil properties relevant to biogeochemistry, we sampled both from a variety of locations in the Dry Valleys. Moss presence was compared to soil properties, and we measured the plasticity of moss stoichiometry (C:N:P) across gradients in nutrient availability. Results demonstrate that many soil properties significantly differed with moss presence, particularly conductivity and pH, but there is no strong evidence that this is caused by the  moss presence and not the conditions inherent to the microsites where moss was found. There is great variability in moss stoichiometry, with some significant differences between sites, but generally variability within sites is larger than variation among sites. Results suggest that the main source of moss nutrients is from the soil, rather than water, but correlations with any one nutrient source are weak, suggesting a great deal of plasticity in moss stoichiometry and nutrient uptake.      Related Publication citation: Ball, B. and R. Virginia. 2014. The ecological role of moss in a polar desert: implications for aboveground-belowground and terrestrial-aquatic linkages. Polar Biology 37:651-664.   Â
Grazing intensity significantly changes the C:N:P stoichiometry in grassland ecosystems
<p> </p> <p>Aim: Livestock grazing can alter carbon (C), nitrogen (N) and phosphorus (P) cycles, thereby affecting the C:N:P stoichiometry in grasslands. In this study, we aimed to examine the underlying mechanisms for the impacts of grazing intensity on grassland C:N:P stoichiometry, especially for the belowground processes and their linkages with aboveground vegetation properties.<br> Location: Global.<br> Time period: 1900-2018.<br> Major taxa studied: Grassland ecosystems.<br> Methods: Here, we conducted a meta-analysis based on 129 published studies to synthesize the effects of grazing on the C:N:P stoichiometry of leaves, stems, litter, roots, microbial biomass, and soil in grassland ecosystems.<br> Results: Grazing significantly affected the C, N and P pools, and then the C:N:P stoichiometry in grassland ecosystems. Grazing effects on C:N:P stoichiometry varied strongly with grazing intensity. Specifically, heavy grazing decreased all C:N:P stoichiometry except litter N:P and root C:N ratios, while light and moderate grazing exhibited the less negative or positive effects. Grazing effects on litter C:N ratio were negatively correlated with grazing effects on soil C:N ratios under light and moderate grazing, but this relationship was positive under heavy grazing. In contrast, the correlation between grazing effect on root C:P and soil C:P was positive under light and moderate grazing but negative under heavy grazing. Importantly, grazing significantly decreased soil N pool by 10.0% but increased P pools by 3.6%, indicating differential mechanisms for grazing impact on N and P cycles in grasslands.<br> Main conclusions: The divergent effects of light, moderate, and heavy grazing on the C:N:P stoichiometry highlight the importance of grazing intensity in regulating the biogeochemical cycles of C, N, and P by accelerating plant nutrient use efficiency and inducing changes in soil physicochemical processes in grassland ecosystems. Therefore, incorporating grazing intensity into Earth system models may improve predictions of climate-grassland feedbacks in the Anthropocene.</p>
All-atom molecular dynamics simulations of incomplete ATP synthase rotor rings with unusually high stoichiometry predicted by the AlphaFold2-based method
<p>The trajectories of all-atom MD simulations of <span>AlphaFold2 4, 11, 16 or 18-mer structures of the subunit <em>c</em> from<br></span><span><em>Candidatus Kryptonium thompsoni</em></span><span> (CKt_Nmer_lipid_mix_CHM36m_303K_500ns) and <br></span><span><em>Thalassoglobus polymorphus </em>(Tp_Nmer_lipid_mix_CHM36m_303K_500ns), and <br>AlphaFold2 11-mer structure of the subunit <em>c</em> from <em>Spinacia oleracea</em> (So_11mer-c20_POPC_CHM36m_303K_300ns) </span><span>in a lipid bilayer.</span></p> <p><span>Simulations have been performed using the CHARMM36m force field, running with the GROMACS 2022 package.</span></p>
Global leaf sulphur stoichiometry and the relationships with nitrogen and phosphorus: phylogeny, growth form and environmental controls
<p>Sulphur (S) is an essential bioelement with vital roles in serving regulatory and catalytic functions, and tightly coupled with N and P in plants. However, globally stoichiometric patterns of leaf S and its relationships to leaf N and P are less well studied. We compiled 31,939 records of leaf-based data for 2,600 plant species across 6,652 sites worldwide. All plant species were divided into different phylogenetic taxa and growth forms. Standard major axis analysis was employed to fit the bivariate element relationships. A phylogenetic linear mixed effect model and a multiple regression model were used to partition the variations of bioelements into phylogeny and environments, and then to estimate the importance of environmental variables. Global geometric mean leaf S, N and P concentrations were 1.44, 15.70 and 1.27 mg g-1, with significant differences among plant groups. Leaf S-N-P positively correlated to each other, ignoring plant groups. The scaling exponents of LN-LS, LP-LS and LN-LP were 0.64, 0.76 and 0.79 for all species, but differed among plant groups. Both phylogeny and environments regulated the bioelements. The variability, rather than mean temperature controlled the bioelements. Phylogeny explained more for the concentrations of all the three bioelements than environments, of which S was the one most affected by phylogenetic taxa.</p>
Rapid evolution of consumptive and non-consumptive predator effects on prey population densities, bioenergetics and stoichiometry
<p>Predators can strongly influence prey populations not only through consumptive effects (CE) but also through non-consumptive effects (NCE) imposed by predation risk. Yet, the impact of NCE on bioenergetic and stoichiometric body contents of prey, traits that are shaping life histories, population and food web dynamics, is largely unknown. Moreover, the degree to which NCE can evolve and can drive evolution in prey populations is rarely studied. A 6-week outdoor mesocosm experiment with Caged-Fish (NCE) and Free-Ranging-Fish (CE and NCE) treatments was conducted to quantify and compare the effects of CE and NCE on population densities, bioenergetic and stoichiometric body contents of Daphnia magna, a keystone species in freshwater ecosystems. We tested for evolution of CE and NCE by using experimental populations consisting of D. magna clones from two periods of a resurrected natural pond population: a pre-fish period without fish and a high-fish period with high predation pressure. Both Caged-Fish and Free-Ranging-Fish treatments decreased the body size and population densities, especially in Daphnia from the high-fish period. Only the Free-Ranging-Fish treatment affected bioenergetic variables, while both the Caged-Fish and Free-Ranging-Fish treatments shaped body stoichiometry. The effects of CE and NCE were different between both periods indicating their rapid evolution in the natural resurrected population. Both the Caged-Fish and Free-Ranging-Fish treatments changed the clonal frequencies of the experimental Daphnia populations of the pre-fish as well as the high-fish period, indicating that not only CE but also NCE induced clonal sorting, hence rapid evolution during the mesocosm experiment in both periods. Our results demonstrate that CE as well as NCE have the potential to change not only the body size and population density but also the bioenergetic and stoichiometric characteristics of prey populations. Moreover, we show that these responses not only evolved in the studied resurrected population, but that CE and NCE also caused differential rapid evolution in a time frame of 6 weeks (ca. 4-6 generations). As NCE can evolve as well as can drive evolution, they may play an important role in shaping eco-evolutionary dynamics in predator-prey interactions.</p>
Data and code for : 'Interactive effects of temperature, aridity, and plant stoichiometry on insect herbivory: past and present', The American Naturalist
<p>The files are for the manuscript 'Interactive effects of temperature, aridity, and plant stoichiometry on insect herbivory: past and present' consist of the following major parts:</p> <p> </p> <p>1. <strong>FILE “all site summary.xlsx”</strong>: Summary data of all the fossil sites used in the study. This is an MS Excel file and has three sheets, namely:</p> <p>a. <strong>All_sites</strong>: This sheet lists various metadata related to each of the 39 fossil sites used in this study. The name of the site is listed as ‘Flora’, and each site has the following data associated with it: Epoch, Latitude Bin, Region, Geographic location, Lat(itude), Long(itude), Depositional.Environment, Age, Age.error, MAT.C (Mean Annual Temperature in degrees C), MAT.Error, MAP.mm.yr (Mean Annual Precipitation in mm), MAP.error, and No.Leaves (number of leaf specimens).</p> <p>b. <strong>PEB sites list (Bighorn)</strong>: This sheet specifically lists the 8 Paleocene-Eocene boundary (PEB) sites from the Bighorn basin along with relevant metadata. These sites are also present in the All_sites tab.</p> <p>c. <strong>PEB sites list (Hanna)</strong>: This sheet lists the 2 Hanna Basin sites from the PEB with relevant metadata.</p> <p><strong>2. </strong><strong>FOLDER “fossil site herbivory data”: </strong>This folder contains the leaf-level herbivory data from all the fossil sites used in this study (including the PEB sites from Hanna Basin) in .csv format. Each file has the following data structure: the first column lists the morphotaxa name of the leaf and the subsequent columns represent whether a particular DT (Damage Type, represented by individual column names) is present (1) or absent (0/NA) in that leaf.</p> <p><strong>3. </strong><strong>FOLDER “modern site herbivory data”: </strong>This folder contains the leaf-level level herbivory data from all the modern sites used in this study in .txt format. “presab_data_HFmatrix” is the data from the three sites in Harvard Forest, “presab_data_LSmatrix” from three sites in La Selva and “presab_data_SERCmatrix” from the three sites in the Smithsonian Environmental Research Center. The quarry number is the name of the site in each file. The rest of the structure is same as fossil data: the second column lists the taxa name for the leaf and the subsequent columns represent whether a particular DT (Damage Type, represented by individual column names) is present (1) or absent (0/NA) in that leaf.</p> <p><strong>4. </strong><strong>FOLDER “masks for FFGs”: </strong>This folder has two files, namely:</p> <p><strong>a. </strong><strong>“ffgdef_DT.csv”: </strong>This file lists which Functional Feeding Group does each DT belong to. Oviposition =0, Skeletonization=1, Surface feeding =2, Galling=3, Hole Feeding=4, Mining =5, Margin Feeding =6, Piercing and Sucking=7.</p> <p><strong>b. </strong><strong>“mask_DT.csv”: </strong>This file lists whether a given DT is a herbivory damage, a specialized damage, gall or mining damage (T/F).</p> <p><strong>5. </strong><strong>FOLDER “N2_fixing_site_level_taxa”: </strong>This folder contains 8 files in .csv format corresponding to each of the 8 PEB Bighorn Basin sites. Each file has the same structure: the first column corresponds to a specific morphotaxa and the next column indicates whether it is a N2 fixing plant (1) or not (0).</p> <p><strong>6. FILE "Analyais_base.Rmd" </strong>gives base code to replicate all the results.</p>
Moose selection for resource stoichiometry
<ol> <li>Herbivores consider the variation of forage qualities (nutritional content and digestibility) as well as quantities (biomass) when foraging. Such selection patterns may change based on the scale of foraging, particularly in the case of ungulates that forage at many scales.</li> <li>To test selection for quality and quantity in free-ranging herbivores across scales, however, we must first develop landscape-wide quantitative estimates of both forage quantity and quality. Stoichiometric distribution models (StDMs) bring opportunity to address this because they predict the elemental measures and stoichiometry of resources at landscape extents.</li> <li>Here, we use StDMs to predict elemental measures of understory white birch quality (% nitrogen) and quantity (g carbon/m<sup>2</sup>) across two boreal landscapes. We analyzed GPS collared moose (n = 14) selection for forage quantity and quality at the landscape, home range, and patch extents using both individual and pooled resource selection analyses. We predicted that as the scale of resource selection decreased from the landscape to the patch, selection for white birch quantity would decrease and selection for quality would increase.</li> <li>Counter to our prediction, pooled-models showed selection for our estimates of quantity and quality to be neutral with low explanatory power and no scalar trends. At the individual-level, however, we found evidence for quality and quantity trade-offs, most notably at the home range scale where resource selection models explain the largest amount of variation in selection. Furthermore, individuals did not follow the same trade-off tactic, with some preferring forage quantity over quality and vice-versa.</li> <li>Such individual trade-offs show that moose may be flexible in attaining a limiting nutrient. Our findings suggest that herbivores may respond to forage elemental compositions and quantities, giving tools like StDMs merit towards animal ecology applications. The integration of StDMs and animal movement data represents a promising avenue for progress in the field of zoogeochemistry.</li> </ol>
Environmental stoichiometry mediates phytoplankton diversity effects on communities' resource use efficiency and biomass
<p>Positive biodiversity-ecosystem functioning (BEF) relationships are predicted to increase in strength when high environmental variability allows for complementarity between resource use strategies in diverse communities. This environmental variability can be represented by spatial or temporal variation in nutrient ratios, but resource use efficiency (RUE) and therefore biomass build up of primary producers might be restricted when nutrient ratios are highly imbalanced (i.e., limitation by one nutrient and beyond optimal ratios for growth). Whereas the linkages between ecosystem functioning, diversity and nutrient availability are theoretically well understood, we lack experimental evidence on how phytoplankton diversity affects resource use and biomass under variable nutrient ratios (N:P ratios).</p> <p>Combining a mesocosm and a microcosm experiment we tested diversity effects on ecosystem functioning by exposing a species diversity gradient generated by the loss of rare species in a natural community to different N:P ratios (uniform vs a gradient). The N:P supply ratio gradient also allowed us to evaluate responses across balanced and imbalanced ratios.</p> <p>We found that increased species diversity led to increased community RUE when supplied a gradient of N:P ratios; but restricted to the highest diversity level. However, diversity did not affect RUE under uniform nutrient ratios. The overall phytoplankton biomass and carbon:nutrient ratios responses to diversity reflected the patterns detected for RUE. Contrary to theoretical predictions, RUE was maintained under highest N:P supply ratios (extreme phosphorous limitation) suggesting that imbalanced N:P ratios do not necessarily decrease function. Thus, we showed that the nutrient context influences diversity effects on RUE and biomass.</p> <p><i>Synthesis</i>. Overall, our results suggest that the effect of rare phytoplankton species losses on community RUE and biomass can be compensated by the persistent species when nutrient ratios are uniform, but leads to decreases in ecosystem functioning under variable nutrient ratios. This work provides a first attempt for testing interactions between the nutrient context (including concentrations and ratios) and the diversity of (natural) communities experimentally, which is conceptually understood but poorly tested for phytoplankton.</p>
Cation non-stoichiometry in Fe:SrTiO3 thin films and its effect on the electrical conductivity
<p>This dataset contains raw data and figures used in the publication: <a href="https://doi.org/10.1039/D1NA00358E">https://doi.org/10.1039/D1NA00358E</a></p>
Dataset on soil and soil microbial biomass carbon, nitrogen, and phosphorus stoichiometry
<p>Dataset on soil and soil microbial biomass carbon, nitrogen, and phosphorus stoichiometry. This dataset is compiled for for the scientific paper entitled "Interpreting stoichiometric homeostasis and flexibility of soil microbial biomass carbon, nitrogen, and phosphorus" (doi: 10.1016/j.ecolmodel.2022.110018).</p>
A reliable algorithm for calculating stoichiometry parameters in the hard modeling of spectrophotometric titration data
<p>Supporting Matlab code and data for the manuscript "A reliable algorithm for calculating stoichiometry parameters in the hard modeling of spectrophotometric titration data"</p>
Raw data for 'Analysing binding stoichiometries in NMR titration experiments using Monte Carlo simulation and resampling techniques'
<p>Repository of the raw data and SupraFit binaries (for Windows, Linux and macOS) for the research article:</p> <p><strong>Analysing binding stoichiometries in NMR titration experiments using Monte<br> Carlo simulation and resampling techniques</strong></p> <p> </p> <p>It contains the SupraFit nigthly builds 2.5.98 for Windows (<em>SupraFit-nightly-2.5.98-x86_x64-Windows.zip</em>), Linux (<em>SupraFit-nightly-2.5.98-x86_64-Linux.tar.gz</em>) and macOS (<em>SupraFit-nightly-2.5.98-macOS.dmg</em>).</p> <ul> <li> <p><em>Simulated Experiments.suprafit</em> contains the simulated experimental data</p> </li> <li> <p><em>Simulated 1_1 Experiment.suprafit</em> contains the raw data of the 1:1 data set after several statistical post-processing analyses were performed</p> </li> <li> <p><em>Simulated 1_1_1_2 Experiment.suprafit</em> contains the raw data of the 1:1/1:2 data set after several statistical post-processing analyses were performed</p> </li> <li> <p><em>Simulated 2_1_1_1 Experiment.suprafit </em>contains the raw data of the 2:1/1:1 data set after several statistical post-processing analyses were performed</p> </li> <li> <p><em>Simulated 2_1_1_1_1_2 Experiment.suprafit </em>contains the raw data of the 2:1/1:1/1:2 data set after several statistical post-processing analyses were performed</p> </li> <li> <p><em>MC-simulation 1_1_1_2 Experiment.suprafit </em>contains the raw data for Figure 4 in the article</p> </li> </ul>
Three-dimensional mapping of carbon, nitrogen, and phosphorus in soil microbial biomass and their stoichiometry at the global scale
<p>R code, raw datasets, and predicted global maps of soil microbial biomass C, N, and P and their stoichiometric ratios at 0-30 cm depth.</p> <p>When using any of these layers, please cite: Gao et al., Three-dimensional mapping of carbon, nitrogen, and phosphorus in soil microbial biomass and their stoichiometry at the global scale (2022). Global Change Biology. DOI: 10.1111/gcb.16374</p>
F-Type ATP synthase rotor ring stoichiometry predictions
<p>Stoichiometry prediction data for:</p> <p>1) rotor rings with experimental structures available</p> <p>2) representative c subunits</p> <p>3) c subunits with unexpectedly high predicted stoichiometry</p> <p>4) all c subunits via matching with corresponding representative sequence</p>
Data from: Plant-type dominates fine-root C:N:P stoichiometry across China: a meta-analysis
<p>Aim: Fine roots play an important role in biogeochemical cycling in terrestrial ecosystems. However, our understanding of large scale biogeographic patterns and drivers of fine-root C:N:P stoichiometry is extremely limited.</p> <p>Location: China.</p> <p>Methods: We compiled data for fine-root carbon (C), nitrogen (N) and phosphorus (P) concentrations at 165 sites across China to explore large-scale biogeographic patterns and drivers of fine-root C:N:P stoichiometry.</p> <p>Results: The geometric means of fine-root C, N, and P concentrations were 448.81 mg g-1, 10.73 mg g-1, and 0.9 mg g-1, respectively, whereas C:N, C:P and N:P ratios were 41.84, 508.32 and 11.73, respectively. The fine-root elemental concentrations and their ratios varied widely among plant groups and biomes, and showed clear latitudinal and longitudinal trends, as a consequence of differences in climate, soil and plant-type. However, plant-type was the largest contributor to the total variance in fine-root C, N, and P and their ratios compared to climate factors or soil features.</p> <p>Main conclusions: The data reveal the existence of broad biogeographic patterns of fine-root C:N:P stoichiometry in China. These results advance our knowledge about the biogeochemical cycling of fine roots.</p>
Data for Rhamnolipids mediate the effects of a gastropod grazer in regards to carbon-nitrogen stoichiometry of intertidal microbial biofilms
<p>Data for the manuscript: <strong>Rhamnolipids mediate the effects of a gastropod grazer in regards to carbon-nitrogen stoichiometry of intertidal microbial biofilms. </strong></p>
Imprint of tree species mycorrhizal association on microbial-mediated enzyme activity and stoichiometry
<p>1. Understanding the effects of tree species and their mycorrhizal association on soil processes is critical for predicting the ecosystem consequences of species shifts owing to global change and forest management decisions. While it is well established that forests dominated by different mycorrhizal types can vary in how they cycle carbon (C), nitrogen (N) and phosphorus (P), the degree to which these patterns are driven by microbial-mediated enzyme activity (EA) and ecoenzymatic stoichiometry (ES) remain elusive.</p> <p>2. Here, we synthesized the effects of mycorrhizal association on seven soil enzymes involved in microbial C, N and P acquisition and ES using data from 56 peer-reviewed papers.</p> <p>3. We found that relative to soil in ectomycorrhizal (EcM) trees, soil in arbuscular mycorrhizal (AM) trees exhibited greater activity of some C acquisition enzymes (e.g., beta-glucosidase; BG) and higher ecoenzymatic ratios of BG/NAG (N-acetyl-glucosaminidase) and BG/AP (acid phosphatase). These results supported that AM trees had rapid C and nutrient turnover rates, inorganic nutrient economics and high soil microbial C limitation. We also found evidence for an organic nutrient economy and greater soil microbial demand for nutrients in EcM trees compared to AM trees. In addition, the effect of mycorrhizal association on the activity of certain soil enzymes and enzymatic stoichiometry (i.e., BG and BG/NAG ratio) appeared to be associated with the differences in soil pH, phylogenetic group (i.e., conifers and broadleaves) and leaf habit (i.e., evergreen and deciduous) between AM and EcM trees.</p> <p>4. The results from the global meta-analysis suggested that soil EA and ES appear to play critical roles in shaping the differences in the nutrient economy between AM and EcM tree species, but leaf morphology and soil conditions should be considered in evaluations of soil processes in forests of different mycorrhizal associations. Given that most of the studies in the database were from the temperate and subtropical regions, further research in other biomes is needed to elucidate the underlying mechanisms driving the mycorrhizal effect at the global scale. </p>
Widespread controls of leaf nutrient resorption by nutrient limitation and stoichiometry
<p>Leaf nutrient resorption is a key process in nutrient cycles, but fundamental knowledge regarding its control mechanisms remains limited. Among the three proposed basic control mechanisms on leaf nutrient resorption, namely nutrient concentration control, nutrient limitation control, and stoichiometry control, only the first has been demonstrated to exist globally, while the latter two have not been systematically evaluated.</p> <p>Here, we conducted a global data synthesis to explore nutrient limitation and stoichiometry control on leaf resorption of carbon, nitrogen, phosphorus, sulfur, potassium, calcium, and magnesium, based on 3,395 data points from 109 peer-reviewed studies. </p> <p>Results showed that the nutrient limitation control existed globally, but was only applicable for the resorption of nitrogen, phosphorus, and potassium. The stoichiometry control existed globally for carbon and all studied nutrients, and coexisted with nutrient limitation control. Conifers' resorption relied primarily on stoichiometry control rather than nutrient limitation control. Nutrient limitation control was stronger in evergreen angiosperms than in deciduous angiosperms.</p> <p>Our findings support the ubiquity of both nutrient limitation and stoichiometry in controlling leaf resorption for multiple nutrients, such that these controls should be considered in Earth system models to better predict biogeochemical cycles in terrestrial ecosystems.</p>
Mapped data: Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency
<p>This record contains mapped sequencing data for the paper "Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency" by Nair, Ameen <em>et al</em>. It contains single-cell RNA-seq (scRNA) and single-cell ATAC-seq (scATAC) data from a time course of human dermal fibroblasts induced with Yamanaka factors OSKM using a Sendai virus based delivery system. The scRNA and scATAC data is performed at days 0, 2, 4, 6, 8, 10, 12, 14 and the final iPSCs. The experiment was re-performed and single-nucleus multiome (ATAC+RNA) was collected on days 1 and 2. </p> <p>The data is as follows:</p> <p><strong>scATAC</strong>: We used Chromap (commit <a href="https://github.com/haowenz/chromap/tree/6e97125b9">https://github.com/haowenz/chromap/tree/6e97125b9</a>, <a href="https://doi.org/10.1038/s41467-021-26865-w">https://doi.org/10.1038/s41467-021-26865-w</a>) to perform barcode correction, alignment and filtering for each of our samples. The corresponding fragment files (tab separated file containing mapped fragments with columns: chr, start, end, barcode, number of reads) and their tabix indices are available for each sample.</p> <p><strong>scRNA</strong>: We used cellranger v6.0.2 for read mapping and quantification to obtain the counts matrix. We used the GRCh38 2020-A reference. For each sample, the raw and filtered counts matrices are provided. E.g. `D0/raw_feature_bc_matrix.h5` contains an HDF5 object containing gene counts for each barcode and associated metadata for the Day 0 sample. Similarly, the files in `D0/raw_feature_bc_matrix/` contain the same gene x barcode matrix, with the counts matrix in Matrix Market format (`matrix.mtx.gz`), and gene (`features.tsv.gz`) and barcode names (`barcodes.tsv.gz`). </p> <p><strong>multiome</strong>: The ATAC and RNA components are separately processed using the same tools as mentioned above for scATAC and scRNA. Outputs are in the `snATAC` and `snRNA` subdirectories respectively. In addition, the `ATAC.RNA.bc.map.tsv` file contains a map to link snATAC barcodes to snRNA barcodes. </p>
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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.