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934 results for “Amino acids”
Primary producer biomarker profiles of bulk carbon and nitrogen stable isotopes (SI), compound specific stable isotopes of carbon in essential amino acids (CSIA-EAA) and their fatty acid (FA) collected from the Beaufort Sea coastal lagoons,2021-2024
Within Stefansson Sound in Prudhoe Bay, AK various organic matter sources were collected to determine multiple biomarker baseline profiles (i.e., bulk carbon and nitrogen stable isotopes (SI), compound specific stable isotopes of carbon in essential amino acids (CSIA-EAA), fatty acids (FA)). Some organic matter sources were collected from Elson lagoon in Utqiaġvik, AK and Kaktovik and Jago lagoons in Kaktovik, AK to supplement low sample sizes in some organic matter source groups. Kelp, red algae, terrestrial plants, phytoplankton, and ice algae were collected in 2024 with some supplement samples collected in 2021 - 2023. Stable isotope values of δ13C and δ15N are reported as “del_13c” and “del_15n”, respectively. Individual fatty acids are reported as the percent relative to total fatty acids for 23 fatty acids: C11:0, C12:0, C14:0, C15:1, C15:0, C16:0, C16:1n7, C17:0, C17:1, C18:0, C18:1n9 trans, C18:2n6 cis, C18:1n7, C18:3n3, C20:0, C18:3n6, C20:4n6, C21:0, C22:0, C22:1n9, C23:0, C24:0, C22:6n3. Stable isotope values of δ13C are reported in the following essential amino acids: Valine (Val), Leucine (Leu), iLeu (isoleucine), Methionine (Met), Phenylalanine (Phe). Additionally, we used ice algal diatoms collected in the Arctic (landfast ice near Utqiaġvik, Alaska) and cultured in a laboratory setting at the University of Alaska Fairbanks to compare the CSIA-EAA fingerprints of field (composites) ice algal samples and isolate diatoms samples.
Darwin: an amino acid sequence collection of complete proteomes from eukaryotes with different phylogenetic affinities (v. 03_2020_137)
<p><strong>Background</strong></p> <p>Every time we find an interesting gene in an organism of interest, the first question is often “how widely is this gene distributed in the eukaryotic kingdom?”. Naturally, one could use NCBI BLAST search against the non-redundant sequence database provided by GenBank to answer this question. However, it can be cumbersome to parse the results and assign them to taxonomic units. It is also not straightforward to get an overview of which eukaryotic groups are represented in the results. Top BLAST hits can be crowded with sequences from closely-related organisms making it difficult gain an overview of the overall distribution across eukaryotes. To streamline this process, we developed an in-house database of complete eukaryotic proteomes. We tagged each sequence with a eukaryotic group handle (two-character symbol) and combined them into a single data set searchable by standalone BLAST on one’s own computer. We named this data set “Darwin” to reflect the diverse nature of the sequences it contains. </p> <p><strong>Methods</strong></p> <p>We downloaded predicted proteomes in FASTA format from different sources such as GenBank, Joint Genome Institute (Depart of Energy, USA), Broad Institute (Massachusetts Institute of Technology, USA), Phytozome and a number of other specialized websites catering for a specific organism such as the Arabidopsis Information Resource (TAIR), or the Saccharomyces Genome Database (SGD). All the organisms we included in Darwin are listed in Table 1. To reduce redundancy, we took care not to include the same species more than once unless subspecies were known to show wide diversity. Each sequence header was tagged with a eukaryotic group handle composed of two-character symbols (based on Keeling <em>et al</em>., 2005). These handles clearly appear in BLAST output and can be parsed easily. We combined sequences from all proteomes into a single data set and named it “Darwin”.</p> <p><strong>Results</strong></p> <p>The current version of Darwin (v. 03_2020_137) contains 2,601,132 amino acid sequences from 137 eukaryotes (Table 1, Data file 1). The sizes of the proteomes were diverse, ranging from ~4000 sequences in some alveolates to 60,000-76,000 in plants. Darwin represents most of the supergroups of eukaryotic kingdom described in Keeling <em>et al.,</em> (2005) except those in Rhizaria whose genomes were not available at the time of data set construction. The data set contains larger numbers of proteomes from fungi and plants reflecting areas of interest in our group. </p> <p><strong>Conclusions</strong></p> <p>Darwin is provided as a text fasta file that can be formatted for BLAST searches on standalone computers. The results from the BLAST searches can be parsed to determine how widely a gene of interest is distributed among different eukaryotes. Simple counting of the eukaryotic group handles would also yield an overview of the distribution across taxa. Darwin is also useful for rapidly finding out whether a gene is missing in particular taxa.</p> <p><strong>Reference</strong></p> <p>Keeling PJ, Burger G, Durnford DG, Lang BF, Lee RW, Pearlman RE, Roger AJ, Gray MW (2005) The tree of eukaryotes. <em>Trends Ecol. Evol.</em> <strong>20:</strong> 670-676</p>
Adsorption free energies and potentials of mean-force for interactions between amino acids, lipid fragments, and nanoparticles
<p>This dataset contains tabulated potentials of mean force (PMFs) and associated adsorption (binding) free energies for interactions of amino acids side chain analogues and lipid fragments (LF) with a range of materials: titanium dioxide, iron oxide, amorphous silica, quartz, and a range of carbon-based materials including amorphous carbon, graphene and carbon nanotubes both in a pristine form and functionalized by certain chemical groups. All data were computed from atomistic molecular dynamics simulations as a part of the SmartNanoTox project 2016-2020. Version 2 of the dataset includes additional materials: zink oxide, zink sulfate in pristine and PMMA-coated forms computed within NanoSolveIt project (2019-2023). The data are intended to be used in coarse-grained models describing interactions of nanomaterials with nanoparticles, for the prediction of the binding affinity of proteins and lipids to nanoparticles, and as biological "fingerprints" of nanomaterials characterizing behavior of the nanomaterials in biological environments. </p>
Rapid root to leaf uptake of inorganic and amino acid nitrogen in three dryland plant species.
Our aim was to quantify inorganic and organic nitrogen (N) uptake and compare short-term nutrient acquisition patterns among three dryland plant species: Bouteloua eriopoda, Achnatherum hymenoides, and Gutierrezia sarothrae collected from a mixed grassland community in the Northern Chihuahuan Desert to better understand how asynchronous resource availability may influence biotic interactions and nutrient retention in these ecosystems. We collected living plants from two locations within the Sevilleta National Wildlife Refuge and transplanted them into pots maintained in the greenhouse with supplemental light and water for two months. We then conducted a greenhouse experiment using these species to compare nutrient uptake of 15N-labeled ammonium (NH4+), nitrate (NO3-), and glutamate (an amino acid) over 12 to 48 hours. Our study examined three main questions: (1) How rapidly do these dryland plants take up available soil N?, (2) Does leaf uptake differ among inorganic and amino acid N forms?, and (3) Do plant species differ in the speed or form of short-term N uptake?. In the greenhouse, we applied one of three isotopic 15N tracers directly to plant roots and quantified N uptake and recovery in leaves after 12, 24, and 48 hours. We found that plants took up inorganic and amino acid N to leaves as rapidly as 12 h following application, and N uptake more than doubled between 24 and 48 h. Inorganic N uptake was 3-4x higher than organic N uptake in all three species, and plants took up ammonium and nitrate at 2-3x faster rates than glutamate. On average, B. eriopoda had higher inorganic N recovery and uptake speeds, while G. sarothrae had the highest organic N uptake over time. A. hymenoides root to leaf uptake was ~50% lower than the other two species after 48 h. Plants showed similar patterns of short-term foliar uptake and recovery indicating a lack of niche partitioning by N form among the three dryland species measured. Our results suggest that soil inorganic N, par
Amino Acids Modulate Liquid-Liquid Phase Separation in vitro and in vivo by Regulating Protein-Protein Interactions
<p>The metadata, plots and microscopy images for the manuscript "Amino Acids Modulate Liquid-Liquid Phase Separation in vitro and in vivo by Regulating Protein-Protein Interactions".</p>
Control T-cell receptor (TCR) alpha and beta chain nucleotide and amino acid sequences from human and mouse
<p>A dataset of pooled T-cell receptor (TCR) sequences for TCR alpha and beta chains of human and mouse.</p> <p>Sequences are obtained from various samples of healthy individuals/mice using our conventional protocols: see for example [Britanova et al "Dynamics of individual T cell repertoires: from cord blood to centenarians" The Journal of Immunology 2016] and [Izraelson et al. "Comparative analysis of murine T‐cell receptor repertoires." Immunology 2018].</p> <p>The sequences are stored as gzipped clonotype tables in VDJtools format, see [https://vdjtools-doc.readthedocs.io/en/master/input.html#vdjtools-format].</p> <p>This control dataset can be used as a proxy for a generative VDJ rearrangement model to estimate the expected frequency distribution of TCRs and check for enrichment of rare TCR clonotypes and groups of similar TCR sequences. For the implementation of the enrichment analysis, please see CalcDegreeStats routine from VDJtools software, see [https://vdjtools-doc.readthedocs.io/en/master/annotate.html#calcdegreestats].</p> <p>Files named "human.tra.strict.txt.gz", etc are pools of random/naive TCR clonotypes containing unique V/J/CDR3 nucleotide sequence combinations observed in data. The pools.zip file is used for TCR motif inference in VDJdb database [https://github.com/antigenomics/vdjdb-motifs], it contains human.tra.aa.txt, etc files that contain random/naive TCR clonotypes grouped by CDR3 amino acid sequence with the most frequent representative V and J.</p>
Coat protein (CP) and trimmed replication-associated protein (Rep) amino acid alignments, phylogenetic analyses, and associated metadata for ICTV-approved begomovirus RefSeq species exemplars
<p>DATA RETRIEVAL</p> <p>Annotated begomovirus coding sequences corresponding to each begomovirus species exemplar with a RefSeq accession number listed in the ICTV Virus Metadata Resource (VMR #18, 2021-10-19, <a href="https://ictv.global/vmr">https://ictv.global/vmr</a>) were downloaded from GenBank in protein FASTA file format. CP and Rep amino acid sequences were extracted and split into separate data sets for analysis. We confirmed the identity of misannotated ORF products by performing a BLAST search. For exemplar sequences missing ORF annotations (listed in metadata spreadsheet), ORFfinder (<a href="https://www.ncbi.nlm.nih.gov/orffinder/">https://www.ncbi.nlm.nih.gov/orffinder/</a>) was used to identify CP and Rep ORFs that were subsequently translated and added to each corresponding data set after BLAST confirmation.</p> <p>ALIGNMENTS</p> <p>Multiple sequence alignments were constructed using the MUSCLE method (Edgar, 2004) as implemented in MEGA 11 (Tamura et al., 2021) and manually corrected using AliView v1.26<strong> </strong>(Larsson, 2014). After an initial alignment inspection, exemplars with either severely truncated (i.e., length < 50% of the average length of the protein) or very divergent (i.e., causing us to doubt protein homology) CP or Rep sequences were excluded from the data set. Due to the difficulties in aligning the Rep sequences at the N- and C- terminal ends, the Rep alignment was trimmed to eliminate all residues prior to the iteron related domain (i.e., the known Rep functional region closest to the Rep start (Arguello-Astorga & Ruiz-Medrano, 2001)) in the N-terminus and after a conserved geminivirus motif found near the C-terminus, which corresponds to where other circular, Rep-encoding single-stranded DNA viruses possess an arginine finger motif (Kazlauskas et al., 2019; Krupovic et al., 2020). In total, our CP and Rep data sets contained amino acid sequences from 432 begomovirus species exemplars that met our inclusion criteria.</p> <p>PHYLOGENETIC ANALYSIS</p> <p>Maximum likelihood (ML) trees were inferred with IQ-Tree v2.0.7 (Minh et al., 2020) using the best fitting substitution model identified by the built-in ModelFinder feature (Kalyaanamoorthy et al., 2017). Tree inference was performed with 3000 ultrafast bootstrap (UFBoot) replicates, a perturbation strength of 0.2 and a stopping rule requiring an iteration interval of 500 iterations between unsuccessful improvements to the local optimum. The -bnni flag was enabled to reduce the risk of overestimating branch supports with UFBoot due to severe model violations. The provided phylogenies in NEXUS format are midpoint-rooted and branches are colored based on traditional begomovirus geographic groupings: exemplars sampled in the Americas in orange and exemplars sampled in the 'Africa, Asia, Europe and Oceania' (AAEO) region in blue. </p> <p>METADATA</p> <p>Metadata associated with each ICTV-approved species exemplar (n=445) – including country of isolation, geographic designation (i.e., AAEO/Americas), genome segmentation (i.e., monopartite/bipartite), presence/absence of V2/AV2 gene and length of genome/DNA-A segments – are included. Exemplars not incorporated into the other analyses are highlighted in red on the spreadsheet.</p> <p> </p>
STORM imaging of Bacillus subtilis labeled by fluorescent d-amino acids
<p>Bacillus subtilus cells were labeled by fluorescent d-amino acids, followed by STORM super-resolution imaging.</p> <p>The wide-field image and STORM imaging stack are uploaded.</p>
Nectar chemistry is not only a plant's affair: floral visitors affect nectar sugar and amino acid composition
<p>This dataset contains data used in the analyses performed in the article entitled "Nectar chemistry is not only a plant’s affair: floral visitors affect nectar sugar and amino acid composition". The Excel file contains three sheets. 'Raw data' contains concentration of sugars, amino acids, pollen grains and yeast cells measured in several flowers and plants of <em>Gentiana lutea</em> subsp. <em>symphyandra</em>, belonging to different experimental treatments. 'Amino acid diversity' contains the concentration of specific protein and non-protein amino acids found in a subset of the above mentioned flowers. 'Pollen suspension test' contains the concentration of the same amino acids found in nectar after suspension of pollen of <em>G. lutea</em> at different time intervals (0, 1, 4, and 24 hours).</p>
FIGURE 4 Deduced amino-acid sequences for the Gnrh2 in Differential expression of HPG-axis genes in autotetraploids derived from red crucian carp Carassius auratus red var., × blunt snout bream Megalobrama amblycephala,
FIGURE 4 Deduced amino-acid sequences for the Gnrh2 () and Lhr () genes in Carassius auratus red var. (RCC) and autotetraploid C. auratus red var. ♀ × Megalobrama amblycephala ♂ (4nRR)
The data for: LCR in fungi display functional groups and are depleted in positively charged amino-acids
<p>Abstract</p> <p>The dataset consists of a TAB formatted table (Main_Dataset.tsv), that integrates information about protein domains (pfam_scan, GO terms), low complexity regions (SEG), signal peptides (SignalP), and transmembrane elements (TMHMM) for each of the analysed proteins within 183 fungal proteomes. The Main Dataset has been designed to ease further searches with Linux bash commands, for e.g. sorting and subsetting by the aforementioned traits. This resource can be used by users interested in detailed annotation of particular protein families, sets of organisms, low complexity regions, types of proteins (for instance transmembrane proteins). Thanks to its simple and clear format, it may also be easily enriched with additional data.</p> <p>Data types:</p> <p>Main_Dataset.tsv is a TSV table with protein annotations</p> <p>Estimate of dataset size:</p> <p> 245MB<br><br>Readme file:</p> <p>Main_Dataset.tsv is a TSV table with the following columns:</p> <ol> <li> <p>Assembly ID from NCBI</p> </li> <li> <p>Protein ID (NCBI accession)</p> </li> <li> <p>Protein length</p> </li> <li> <p>Presence of protein domains; Boolean</p> </li> <li> <p>symbolic localization of protein domains; 10 bins scaled to sum up to total protein length</p> </li> <li> <p>number of transmembrane elements predicted with TMHMM</p> </li> <li> <p>total length of transmembrane elements</p> </li> <li> <p>Symbolic localization of transmembrane elements; 10 bins scaled to sum up to total protein length</p> </li> <li> <p>Presence of signal peptide; Boolean</p> </li> <li> <p>Total number of LCR</p> </li> <li> <p>Total length of LCR</p> </li> <li> <p>Symbolic localization of LCR; 3 bins: N-termini (0-0.25 of protein length), middle (0.25-0.75 of protein length), and C-term (0.75-1 protein length)</p> </li> <li> <p>Symbolic localization of LCR; 10 bins scaled to sum up to total protein length</p> </li> <li> <p>LCR sequences in the N-terminal part of protein, separated by a comma</p> </li> <li> <p>LCR sequences in the middle part of protein, separated by a comma</p> </li> <li> <p>LCR sequences in the C-terminal part of the protein, separated by a comma</p> </li> <li> <p>Pfam domains overlapping with LCRs (>80% of LCR length)</p> </li> <li> <p>Pfam domains in protein (ordered by domain start)</p> </li> <li> <p>GO terms based on Pfam domains obtained by mapping on pfam2go, separated with the pipe symbol '|'</p> </li> </ol> <p> </p> <p>Acknowledgements</p> <p>This work was supported by National Science Centre grants (#2021/41/B/NZ2/02426 to AM, #2019/35/D/NZ2/03411 to K.S).</p>
AA-Score: a New Scoring Function Based on Amino Acid Specific Interaction for Molecular Docking
<p>The protein-ligand scoring function plays an important role in computer-aided drug discovery, which is heavily used in virtual screening and lead optimization. In this study, we developed a new empirical protein-ligand scoring function, which is a linear combination of empirical energy components, including hydrogen bond, van der Waals, electrostatic, hydrophobic, π-stacking, π-cation, and metal-ligand interaction. Different from previous empirical scoring functions, AA-Score uses several amino acid-specific empirical interaction components. We tested AA-Score on several test sets. The resulting performance shows AA-Score performs well on scoring, docking, and ranking compared with other widely used traditional scoring functions. Our results suggest that AA-Score gains substantial improvements from using detailed protein-ligand interaction components. Besides, we developed an easy-to-use tool to analyze protein-ligand interaction fingerprint and predict binding affinity using AA-Score.</p>
Supporting dataset for: "Plasma essential amino acid concentration and profile are associated with performance of lactating dairy cows as revealed through meta-analysis and hierarchical clustering"
<p>This dataset was used in the meta-analysis and hierarchical clustering published in "Plasma essential amino acid concentration and profile are associated with performance of lactating dairy cows as revealed through meta-analysis and hierarchical clustering" in the Journal of Dairy Science. We searched Web of Science and Google Scholar databases through March 2020 with the terms “plasma EAA,” “milk urea” or “blood urea,” and “dairy” or lactating dairy”. To be included in our study, the papers must have met the following selection criteria: (1) been published in English in a peer-reviewed journal; (2) reported dietary ingredients on a DM basis and at minimum dietary CP concentration; (3) used treatments based on diet changes (e.g., no infusion trials were included); (4) reported DMI, lactation performance, and milk components yield; (5) reported all individual [EAA]p (excluding Trp); and (6) reported blood urea-N or plasma urea-N. Infusion studies were excluded to avoid possible effects of method of EAA supply (e.g., infusion vs. feeding) and to narrow the scope of application. The final dataset included 22 studies and 96 dietary treatments. For a more complete description of the methods, please refer to the published paper. </p>
Phylogenetic analysis of policistronic amino-acid sequences encoded by 116 flavivirus genomes
<p><span>Recently, Genome Biology and Evolution (11:3341-3352) published three statistical tests for testing whether alignments of sequence data violate the phylogenetic assumption of evolution under homogeneous conditions. The tests extend the matched-pairs tests of symmetry, marginal symmetry, and internal symmetry for pairs of aligned homologous sequences to the case where a whole alignment is considered. Here we reveal that the new tests are misleading. We explain why this is so, reveal how the tests of whole alignments may be done, and release new bioinformatics tools that implement statistically sound methods of dealing with multiple comparisons (i.e., by controlling the family-wise error rate or the false discovery rate). Using the new software to analyse an alignment of amino acids encoded by 116 flavivirus genomes, we reveal, for the first time, that these genomes are unlikely to have evolved under stationary, reversible, and homogeneous Markovian conditions.</span></p>
Data file: Amino acid sequence Cm28; a scorpion toxin
<p>The Data set contains the amino acid sequence of Cm28, a peptide toxin found in the venom of <em>Centruroides margaritatus</em>. The peptide sequence and functional data are reported in an article in the Journal of General Physiology (JGP) under this DOI 10.1085/jgp.202213146 and this data will appear in the UniProt Knowledgebase under the accession<br> number C0HM22.</p>
Depletion of cap-binding protein eIF4E dysregulates amino acid metabolic gene expression
<p><span>Protein synthesis is </span><span>metabolically costly </span><span>and</span><span> must be tightly coordinated with </span><span>changing </span><span>cellular needs and nutrient availability. T</span><span>he cap-binding protein eIF4E</span><span> makes </span><span>the earliest contact between mRNAs and the translation machinery</span><span>, offering a key regulatory nexus</span><span>. </span><span>W</span><span>e acute</span><span>ly</span><span> deplet</span><span>ed</span> <span>this essential protein </span><span>and </span><span>found </span><span>s</span><span>urprisingly modest effects on cell growth and </span><span>recovery of </span><span>protein synthesis.</span><span> Paradoxically, </span><span>impaired protein biosynthesis upregulated </span><span>genes involved in catabolism of aromatic amino acids</span><span>simultaneously with the </span><span>induction of the </span><span>amino acid</span><span> biosynthetic regulon</span> <span>driven</span> <span>by </span><span>the integrated stress response factor</span> <span>GCN4</span><span>. </span><span>W</span><span>e</span><span> further</span><span> identified translation</span><span>al </span><span>control</span><span> of </span><span>PCL5</span><span>, </span><span>a negative regulator of Gcn4, that provides a consistent protein-to-mRNA ratio under varied translation environments. </span><span>This</span> <span>regulation </span><span>depende</span><span>d in part</span><span> on a uniquely long poly-(A) tract in the </span><span>PCL5</span><span> 5´ UTR and poly-(A) binding protein. Collectively, these results highlight</span> <span>how eIF4E connects</span> <span>protein synthesis </span><span>to</span> <span>metabolic gene regulation</span><span>,</span><span>uncover</span><span>ing</span><span> new mechanisms control</span><span>ling</span> <span>translation</span><span> during environmental challenges.</span></p>
Figure 6 in Oral glutamine dipeptide or oral glutamine free amino acid reduces burned injury progression in rats
Figure 6. Graphical representation of Glutathione (ΜM) in the seven days after injury in G1-Control, G2-Dip, and G3-Free AA. G2-Dip presented a larger amount concerning the G1-Control *(P<0.05).
Figure 4 in Oral glutamine dipeptide or oral glutamine free amino acid reduces burned injury progression in rats
Figure 4. Graphical representation of interspace (stasis) regions length (mm) in the seven days after injury in G1-Control,G2-Dip, and G3-FreeAA.G1-Control presented smaller interspaces in 666 relation to the treated groups G2-Dip (P<0.01) and G3-FreeAA *(P<0.01).
Figure 5 in Oral glutamine dipeptide or oral glutamine free amino acid reduces burned injury progression in rats
Figure 5. Graphical representation of fibroblast counts in three fields of the interspace dermis among in the seven days after injury in G1-Control, G2-Dip, and G3-FreeAA. G1-Control presented less amount of fibroblast concerning the treated groups G2-Dip (P<0.01) and G3-Free AA *(P<0.01).
Figure 3 in Oral glutamine dipeptide or oral glutamine free amino acid reduces burned injury progression in rats
Figure 3. Histopathology study of the necrotic areas: (A) Photomicrograph of G2-Dip animal, with necrosis presence in the dermis (superior arrows) just below the epidermis (E) and in the hypodermis (arrows below in the right). Hair follicle (HF). Masson's trichrome; 100x; (B) Photomicrograph of G1-Control animal, hemorrhagic foci are observed in both dermis and hypodermis (arrows). Central blood vessel (BV). Masson's trichrome; 400x; (C) Photomicrograph of G3-FreeAA animal, with a large area of edema in both dermis and hypodermis (arrows). Hair follicle (HF). Masson's trichrome; 100x; (D) Photomicrograph of G1-Control animal: hemorrhagic focus can be observed in the hypodermis and many neutrophils (minor arrows) in a blood vessel (BV) lumen, some in diapedesis through its wall (larger arrow). The thinner arrows show a small intercellular inflammatory infiltrate. Giemsa; 400x.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.