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.
1,448
datasets available to search
ShareScore release 0.7.1
Dataset results
1,448 results for “proteomic”
The Cortical Synaptic Transcriptome is Organized by Clocks, but its Proteome is Driven by Sleep
<p>Images, ROIs and databases with the results of Particle analysis from RNA in situ hybridization performed with the RNA scope technology onto CA1 and cortex from mice collected at 6 different times of day .</p>
Activity-based proteomics reveals nine target proteases for the recombinant protein-stabilizing inhibitor SlCYS8 in Nicotiana benthamiana
<p>Complete dataset (MS Label-free quantification) for the publication 'Activity-based proteomics reveals nine target proteases for the recombinant protein-stabilizing inhibitor <em>Sl</em>CYS8 in <em>Nicotiana benthamiana</em>'</p> <p>DOI: 10.1111/pbi.13092</p> <p> </p>
Proteomic characterization of intra-tumor heterogeneity in human endometrial cancer.
<p>Endometrial cancer (EC) is one of the most frequently diagnosed gynecological cancers worldwide, and its prevalence has increased by more than 50% over the last two decades. Despite the understanding of the major signaling pathways driving the growth and metastasis of EC cells, clinical trials targeting these signaling pathways in human patients have reported poor outcomes. Heterogeneous nature of EC is suspected to be one of the key reasons for the failure of targeted therapies. However, no study so far has explored EC heterogeneity within the same patient at the proteomic level. In this study, we isolated proteins from tumor tissue samples obtained from different sites of EC from individual patients (~2-4 samples/patient). We then performed a SWATH-based comparative proteomic analysis, using liquid chromatography-tandem mass spectrometry (LC-MS/MS), to profile the protein content of different areas within EC tissue. Our results highlighted an average of 1424 unique proteins in 20 patient-derived EC tissues with a confidence corresponding to a false discovery rate below 1%. We have identified protein biomarkers that differentiate between premenopausal vs postmenopausal cancer, macroscopic vs microscopic tumor, more vs less invasive cancer, and DNA mismatch repair defective vs intact tumor. Furthermore, the data revealed a list of unique proteins that, for the same patient, exist only in a single EC location but are not present in other locations within the same tumor. Overall, our proteomic analysis highlighted that tumor tissue samples collected from different sites of EC within the same patient can harbor diverse protein profiles. Importantly, this study sets the foundation for further investigations into the mechanisms of endometrial heterogeneity and some of the proteins identified here may represent potential novel EC drug targets.</p>
Data mining antibody sequences for database searching in bottom-up proteomics
<p>Mass spectrometry (MS)-based proteomics is a powerful method for identifying and quantifying antibodies. Among the various MS approaches, bottom-up proteomics is especially effective for analyzing thousands of antibodies in complex mixtures. In this method, proteins are enzymatically digested into smaller peptides, typically using the protease trypsin, which are then analyzed via mass spectrometry. These peptides are matched to sequences in standard databases like UniProt or NCBI-RefSeq for identification.</p> <p>However, a major limitation of this approach is the absence of comprehensive disease-specific antibody databases. Current databases, such as UniProt, include only a fraction of the antibody sequences present in the human body. For instance, as of January 2024, UniProt contains just 38,800 immunoglobulin sequences, far short of the billions of antibodies the human immune system can produce. As a result, relying on such limited databases can lead to under-detection of antibodies, particularly those associated with specific diseases. Expanding antibody databases with disease-specific sequences is crucial for improving the accuracy of MS-based proteomics in identifying antibodies relevant to human health.</p> <p>Recently, through next-generation sequencing of antibody gene repertoires, it has become possible to obtain billions of antibody sequences (in amino acid format) by annotating, translating, and numbering antibody gene sequences. These large numbers of sequences are now available in public databases such as the <a href="https://opig.stats.ox.ac.uk/webapps/oas/" rel="nofollow">Observed Antibody Space</a>. We hypothesize that using these theoretical antibody sequences as new databases for bottom-up proteomics could address the current lack of antibody coverage in standard databases.</p> <p>We developed a workflow to create disease-specific antibody peptide databases for bottom-up proteomics. The workflow details are available on <a href="https://github.com/trinhxt/SDU_Immunoinformatics">GitHub</a>. The database and metadata files generated by this workflow are stored in this Zenodo dataset, and they are used in DAT-DB — a web application that allows researchers to obtain FASTA files of disease-specific antibody peptides for direct use in bottom-up proteomics (see <a href="https://trinhxt.shinyapps.io/DAT-DB/">Demo version</a>).</p> <p>Each database file in this dataset is in <em>.duckdb</em> format and contains tables with 10 columns: Sequence, Filename, Patient, BSource, BType, Isotype, N_patient, N_antibody, Length_aa, and CDR3. The "<strong>Sequence</strong>" column contains tryptic peptides. "<strong>Filename</strong>" is the file where the data was collected. "<strong>Patient</strong>" refers to the patient number as listed in <em>metadata2.csv</em>. "<strong>BSource</strong>" refers to the B-cells' source, and "<strong>BType</strong>" refers to the type of B-cells. "<strong>Isotype</strong>" specifies the antibody isotype (IgA, IgD, IgE, IgG, IgM, or Bulk). "<strong>N_patient</strong>" indicates the number of patients having this peptide, and "<strong>N_antibody</strong>" specifies the number of antibodies containing this peptide. "<strong>Length_aa</strong>" indicates the number of amino acids in the peptide, while "<strong>CDR3</strong>" shows whether the peptide is found in the CDR3 region.</p> <p>The file <em>metadata1.csv</em> contains information about each database file, while <em>metadata2.csv</em> provides details about the sources of the collected antibodies.</p>
DATASET - Mass Spectrometry - Snake venom proteomics of Deinagkistrodon acutus
<p><strong>This DATASET collection includes the mass spectrometry files for shotgun proteomics venom investigation the sharp-nosed viper (<em>Deinagkistrodon acutus</em>) from China.</strong></p> <p><strong>Species list:</strong></p> <ol> <li>Deinagkistrodon acutus</li> </ol> <p><strong>Folders 01-02 - BOTTOM-UP SHOTGUN PROTEOMICS</strong>: The venom pool was investigated by the bottom-up shotgun (labled as SG) approach and in short: in-solution processed by DTT, IAC and finally o/n tryptic digested. Samples submitted to HPLC-MS/MS. Folders includes the MS and MS/MS spectra of Deinagkistrodon acutus. Files are included as RAW and MZML format in folder 01 and 02, respectively.</p> <p>Used instrument:</p> <p>Advion TriVersa NanoMate (Advion BioSciences) into an Orbitrap Eclipse Tribrid MS instrument (Thermo Fisher Scientific) with an UltiMate 3000RSLCnano system (Thermo Fisher Scientific) using 50 cm μPAC C18 column (Pharma Fluidics).</p> <p>Modifications: UNIMOD:4 - \"Iodoacetamide derivative.\"</p>
Proteome Data for Amyloid Atlas
<p>Datafile for collected and calculated data for proteins in Amyloid Atlas</p>
Proteomics analysis reveals the mechanism of anemoside B4 in treating clinical mastitis in dairy cows
<p><span>(1) 背景:AB4 对 CM 奶牛的治疗效果已得到证实,但具体机制仍有待探索。本研究基于无标记相对定量蛋白质组学技术,揭示 AB4 治疗 CM 的内在机制;(2) 方法: 根据日龄、体重、泌乳年龄、产奶量和病度等因素,选取 12 头健康的中国荷斯坦奶牛(C 组)和 12 头患有 CM 的中国荷斯坦奶牛(T 组)。将 AB4 注射到 CM 30 mL/次/天的奶牛体内,持续 7 天。收集 C 组任意一天的尾静脉血,第 1 天用药前和 7 天的用药后 T 组。将 C 组 1 个时间点和 T 组 2 个时间点的血浆样品随机分为 3 组 (3 个生物学重复),每个重复取 4 份血浆样品均匀混合。它们被标记为 C1、C2、C3 和 T1-1、T1-2、T1-3、T2-1、T2-2、T2-3。筛选 AB4 治疗 CM 的差异蛋白并进行生物信息学分析;(3) 结果: AB4 可调节 HSPs、RAC1、TGFB1 等蛋白水平,参与趋化因子信号转导、白细胞跨内皮细胞迁移、MAPKs 通路等通路,改善机体炎症反应。此外,与患病和健康奶牛相比,治愈奶牛表现出显著更高的补体水平、β-防御素 12 水平以及调节脂质代谢的能力,表明 AB4 可以直接激活 C2,触发下游反应激活补体系统,同时刺激防御素分泌和调节脂质代谢。</span></p>
The Archaeal Proteome Project advances knowledge about archaeal cell biology through comprehensive proteomics
<p>Modern proteomics approaches can explore whole proteomes within a single mass spectrometry (MS) run. However, the enormous amount of MS data generated often remains incompletely analyzed due to a lack of sophisticated bioinformatic tools and expertise needed from a diverse array of fields. In particular, in the field of microbiology, efforts to combine large-scale proteomic datasets have so far largely been missing. Thus, despite their relatively small genomes, the proteomes of most archaea remain incompletely characterized. This in turn undermines our ability to gain a greater understanding of archaeal cell biology.</p> <p>Therefore, we have initiated the Archaeal Proteome Project (ArcPP), a community effort that works towards a comprehensive analysis of archaeal proteomes. Starting with the model archaeon <em>Haloferax volcanii</em>, using state-of-the-art bioinformatic tools, we have:</p> <ul> <li>reanalyzed more than 26 Mio. spectra</li> <li>optimized the analysis using parameter sweeps, multiple search engines implemented in Ursgal, and the combination of results through the combined PEP approach</li> <li>thoroughly controlled false discovery rates for high confidence protein identifications using the picked protein FDR approach and limiting FDR to 0.5%</li> <li>identified more than 45k peptides, corresponding to 3069 proteins (>75% of the proteome) with a median sequence coverage of 55%.</li> <li>analyzed N-terminal protein processing, including N-terminal acetylation and signal peptide cleavage</li> <li>performed a detailed glycoproteomic analysis, identifying >230 glycopeptides corresponding to 45 glycoproteins</li> </ul> <p>Benefiting from the established bioinformatic infrastructure, we will follow up on this analysis focusing on <em>H. volcanii</em> proteogenomics as well as the characterization of additional post-translational modifications. Furthermore, ArcPP will integrate quantitative results obtained from the individual datasets in order to identify common regulatory mechanisms. These studies on the <em>H. volcanii</em> proteome can serve as a blueprint for comprehensive proteomic analyses performed on a diverse range of archaea and bacteria.</p> <p> </p> <p>For further details, please refer to the following publications. Please also cite this work if you use these results for further analyses:</p> <p>Schulze, S., Adams, Z., Cerletti, M. <em>et al.</em> The Archaeal Proteome Project advances knowledge about archaeal cell biology through comprehensive proteomics. <em>Nat Commun</em> <strong>11, </strong>3145 (2020). <a href="https://doi.org/10.1038/s41467-020-16784-7">https://doi.org/10.1038/s41467-020-16784-7</a></p> <p>Schulze, S.; Pfeiffer, F.; Garcia, B.A.; Pohlschroder, M. (2021). Comprehensive glycoproteomics shines new light on the complexity and extent of glycosylation in archaea. <em>PLOS Biol</em>. https://doi.org/10.1371/journal.pbio.3001277</p> <p> </p> <p>An interactive website to explore the combined results can be found at <a href="https://archaealproteomeproject.org/">https://archaealproteomeproject.org/</a></p> <p>Scripts and metadata used for the analysis can be found at <a href="https://github.com/arcpp/ArcPP">https://github.com/arcpp/ArcPP</a></p> <p> </p> <p><strong>Updates version 1.3.0:</strong></p> <p>- includes dataset PXD021827</p> <p><strong>Updates version 1.2.0:</strong></p> <p>- Includes dataset PXD021874<br> - Includes results from a comprehensive glycoproteomic analysis of ArcPP datasets</p> <p><strong>Updates version 1.1.0:</strong><br> - <em>Natrialba magadii</em> results are included in PXD009116.zip</p>
Proteome analysis of seven tissues of Riptortus pedestris
<p>Seven tissues, including guts, fat body, muscles, carcass, testis, salivary glands and ovaries, were collected from Riptortus pedestris. LC-MS/MS was performed by Novogene (Beijing, China)</p>
The analysis of composition and abundance of the raft proteome of microglia using a tandem mass tag (TMT)-based quantitative proteomic analysis.
<p>To determine the proteins in the membrane raft, we used the TMT-labeling and nano-liquid chromatography mass spectrometry (nano-LC-MS/MS) analysis by Creative Proteomics (NY, USA; https://www.creative-proteomics.com/). Rat primary microglia were treated with IL-6 (25 ng/ml) for 15 min. Membrane rafts were obtained by flotation assay. Samples were prepared from three independent experiments. Proteins in equal volumes of raft fractions were digested with trypsin, desalted, and labeled with a TMT reagent (Thermo Fisher Science). The TMT-labeled peptides were fractionated and analyzed by nano-LC-MS/MS. The resulting MS/MS data were analyzed and searched against the rat protein database using Proteome Discoverer 2.1.</p>
Discovery proteomics by mass spectrometry comparing secreted proteins from young and old mouse lung mesenchymal stromal cells
<p>Lung mesenchymal stromal cells (L-MSCs) were isolated from the young ( 3 months) and old (22-24 months) mice (N = 4 each) following collagenase digestion of the lungs and anchorage-dependent growth. Culture media was collected from young and old L-MSCs (10^6 cells) grown in culture for 24 hrs in serum-free condition. Culture media was then centrifuged to remove any cellular debris. Five (5) ml of supernatants were concentrated using 3kDa Molecular weight cut-off filters, and 10 micrograms of proteins were <span><span><span><span><span>run as multiple MW 1D PAGE fractions using a standard </span><span>GeLC</span><span> approach with a </span><span>nano</span><span>-HPLC in-line with a </span><span>Velos</span><span> Pro Orbitrap MS. </span></span></span></span></span><span><span><span><span><span>Individual data files were searched using </span><span>Sequest</span><span>, the results were then combined for each sample, followed by grouping, filtering, and quantifying by normalized spectral counts. </span></span></span></span></span></p> <p> </p>
Barth Syndrome Cardiac Tissue Proteomics Dataset
<p>Spectral counts from LC/MS proteomics of left ventricular tissue from deidentified Barth Syndrome patients (BTHS), along with tissue from age-matched non-failing controls (NF) and patients with idiopathic dilated cardiomyopathy. </p>
SGLT2-Inhibition reverts urinary peptide changes associated with severe COVID-19: an in-silico proof-of-principle of proteomics-based drug repurposing
<p>Severe COVID-19 is reflected by significant changes in urine peptides. Based on this observation, a clinical test predicting COVID-19 severity, CoV50, was developed and registered as in vitro diagnostic in Germany. We have hypothesized that molecular changes displayed by CoV50, likely reflective of endothelial damage, may be reversed by specific drugs. Such an impact by a drug could indicate potential benefits in the context of COVID-19. To test this hypothesis, urinary peptide data from patients without COVID-19 prior to and after drug treatment were collected from the human urinary proteome database. The drugs chosen were selected based on availability of sufficient number of participants in the dataset (n>20) and potential value of drug therapies in the treatment of COVID-19 based on reports in the literature. In these participants without COVID-19, spironolactone did not demonstrate a significant impact on CoV50 scoring. Empagliflozin treatment resulted in a significant change in CoV50 scoring, indicative of a potential therapeutic benefit. The study serves as a proof-of-principle for a drug repurposing approach based on human urinary peptide signatures. The results support the initiation of a randomised control trial testing a potential positive effect of empagliflozin for severe COVID-19, possibly via endothelial protective mechanisms.</p>
SARS-CoV-2–host proteome interactions for antiviral drug discovery
<p>Images and datasets used in Fig 6 and corresponding supplementary material Image analysis was performed with Harmony 4.9 software (PerkinElmer) with feature extraction and linear classification of N-protein positive cells from the total population as presented in the PlateResults file. Prism files (GraphPad Software) include the calculations for curve fits of drug testing data (4PL logistic regression) as well as area under the curve (AUC) of the fitted curves..</p>
Reassessment of the proteomic composition and function of extracellular vesicles in the seminal plasma
<p>Seminal plasma contains a high concentration of extracellular vesicles (EVs). The heterogeneity of small EVs or the presence of non-vesicular extracellular matter(NV) pose major obstacles in understanding the composition and function of seminal EVs. In this study, we employed high-resolution density gradient fractionation to accurately characterize the composition and function of seminal EVs and NV. We found that the seminal EVs could be divided into three different subtypes, namely high-density EV (EV-H), medium-density EV (EV-M), and low-density EV (EV-L) after purification using iodixanol,while NV was successfully isolated. EVs and NV display different features in size, shape and expression of some classic exosome markers. Both EV-H and NV could markedly promote sperm motility and capacitation compared with EV-M and EV-L, whereas only the NV fraction induced sperm acrosome reaction. Proteomic analysis results showed that EV-H, EV-M, EV-L, and NV had different protein components and were involved in different physiological functions. Further study showed that EV-M might reduce the production of sperm intrinsic reactive oxygen species (ROS) through Glutathione S-transferase Mu 2 (GSTM2).This study provides novel insights into important aspects of seminal EVs constituents and sounder footing to explore their functional properties in male fertility.</p>
UTS Proteomics Subject Raw Data
<p>Raw data for the Postgraduate subject 'Proteomics' Subject # 91572. E coli proteins extracted and trypsin digested before Stage Tip clean up with SDB-RPS. Control and Treatment, three replicate injections of each. Run through Thermo QExactive Plus system with Waters M-class chromatograph with self made and packed 35cm column of 1.7um C18 beads.</p>
Proteomics reveals multiple effects of titanium dioxide and silver nanoparticles in the metabolism of turbot, Scophthalmus maximus
<p>Titanium dioxide (TiO2) and silver (Ag) NPs are among the most used engineered inorganic nanoparticles (NPs);<br> however, their potential effects to marine demersal fish species, are not fully understood. Therefore, this study<br> aimed to assess the proteomic alterations induced by sub-lethal concentrations citrate-coated 25 nm (“P25”) TiO2<br> or polyvinylpyrrolidone (PVP) coated 15 nm Ag NPs to turbot, Scophthalmus maximus. Juvenile fish were exposed<br> to the NPs through daily feeding for 14 days. The tested concentrations were 0, 0.75 or 1.5 mg of each NPs per kg of fish per day. The determination of NPs, Titanium and Ag levels (sp-ICP-MS/ICP-MS) and histological alterations<br> (Transmission Electron Microscopy) supported proteomic analysis performed in the liver and kidney.<br> Proteomic sample preparation procedure (SP3) was followed by LC-MS/MS. Label-free MS quantification<br> methods were employed to assess differences in protein expression. Functional analysis was performed using<br> STRING web-tool. KEGG Gene Ontology suggested terms were discussed and potential biomarkers of exposure<br> were proposed. Overall, data shows that liver accumulated more elements than kidney, presented more histological<br> alterations (lipid droplets counts and size) and proteomic alterations. The Differentially Expressed Proteins<br> (DEPs) were higher in Ag NPs trial. The functional analysis revealed that both NPs caused enrichment of<br> proteins related to generic processes (metabolic pathways). Ag NPs also affected protein synthesis and nucleic<br> acid transcription, among other processes. Proteins related to thyroid hormone transport (Serpina7) and calcium<br> ion binding (FAT2) were suggested as biomarkers of TiO2 NPs in liver. For Ag NPs, in kidney (and at a lower<br> degree in liver) proteins related with metabolic activity, metabolism of exogenous substances and oxidative stress<br> (e.g.: NADH dehydrogenase and Cytochrome P450) were suggested as potential biomarkers. Data suggests<br> adverse effects in turbot after medium/long-term exposures and the need for additional studies to validate<br> specific biological applications of these NPs.</p>
A proteome-wide map of chaperone-assisted protein refolding in a cytosol-like milieu
<p>The journey by which proteins navigate their energy landscapes to their native structures is complex, involving (and sometimes requiring) many cellular factors and processes operating in partnership with a given polypeptide chain's intrinsic energy landscape. The cytosolic environment and its complement of chaperones play critical roles in granting many proteins safe passage to their native states; however, it is challenging to interrogate the folding process for large numbers of proteins in a complex background with most biophysical techniques. Hence, most chaperone-assisted protein refolding studies are conducted in defined buffers on single purified clients. Here, we develop a limited-proteolysis mass spectrometry approach paired within an isotope-labeling strategy to globally monitor the structures of refolding <em>E</em>. <em>coli</em> proteins in the cytosolic medium and with the chaperones, GroEL/ES (Hsp60) and DnaK/DnaJ/GrpE (Hsp70/40). GroEL can refold the majority (85%) of the <em>E</em>. <em>coli</em> proteins for which we have data, and is particularly important for restoring acidic proteins and proteins with high molecular weight, trends that come to light because our assay measures the structural outcome of the refolding process itself, rather than binding or aggregation. For the most part, DnaK and GroEL refold a similar set of proteins, supporting the view that despite their vastly different structures, these two chaperones both unfold misfolded states, as one mechanism in common. Finally, we identify a cohort of proteins that are intransigent to being refolded with either chaperone. The data support a model in which chaperone-nonrefolders may fold most efficiently cotranslationally, and then remain kinetically trapped in their native conformations.</p>
Lung proteome and metabolome endotype in HIV-associated obstructive lung disease
<p><span><strong>Purpose</strong>: Obstructive lung disease is increasingly common among persons with HIV in both smokers and non-smokers. We used aptamer proteomics to identify proteins and associated pathways in HIV-associated obstructive lung disease.</span></p> <p><span><strong>Methods</strong>: Bronchoalveolar lavage fluid (BALF) samples from 26 persons living with HIV with obstructive lung disease were matched to persons living with HIV without obstructive lung disease based on age, smoking status, and antiretroviral treatment. 6,414 proteins were measured using SomaScan aptamer-based assay. We used sparse distance-weighted discrimination (sDWD) to test for a difference in protein expression and permutation tests to identify univariate associations between proteins and forced expiratory volume in 1s precent predicted (FEV1pp). Significant proteins were entered into a pathway overrepresentation analysis (ORA). We also constructed protein-driven endotypes using K-means clustering and performed ORA on the proteins that were significantly different between clusters. We compared protein-associated clusters to those obtained from BALF and plasma metabolomics data on the same patient cohort.</span></p> <p><span><strong>Results</strong>: After filtering, we retained 3872 proteins for further analysis. Based on sDWD, protein expression was able to separate cases and controls. We found </span><span>575 </span><span>proteins that were significantly correlated with FEV1pp </span><span>after multiple comparisons adjustment</span><span>. We identified </span><span>two protein-driven endotypes, one of which was</span><span> associated with poor lung function, and found that insulin and apoptosis pathways were differentially represented. We found similar clusters driven by metabolomics in BALF but not plasma.</span></p> <p><span><strong>Conclusion</strong>: Protein expression differs in persons living with HIV with and without obstructive lung disease. We were not able to identify specific pathways differentially expressed among patients based on FEV1pp; however, we identified a unique protein endotype associated with insulin and apoptotic pathways. </span></p>
Diffusive Dynamics of Bacterial Proteome as a Proxy of Cell Death
<p>This dataset accompanies the article entitled <em>Diffusive Dynamics of Bacterial Proteome as a Proxy of Cell Death</em>, published in ACS Cent. Sci. (<a href="https://doi.org/10.1021/acscentsci.2c01078">https://doi.org/10.1021/acscentsci.2c01078</a>).</p> <p><strong>subboxes_overview.xlsx: </strong>This spreadsheet contains information about the protein composition of each sub-box.</p> <p><strong>BOX_GEOMETRIES.zip: </strong>Starting structures (saved in Gromos87 .gro format) used for the production runs of the all-atom sub-boxes.</p> <p><strong>PROTEIN_COM_MSD.zip: </strong>Text files containing the mean square displacements (MSDs) of the centers of mass (COMs) of individual proteins, as obtained from the production runs of the all-atom sub-boxes. The time is shown in picoseconds while the MSD values are given in nm<sup>2</sup>. For each sub-box, the protein numbering corresponds to the subboxes_overview.xlsx spreadsheet.</p> <p><strong>CHAIN_COM_MSD.zip: </strong>Text files containing the mean square displacements (MSDs) of the centers of mass (COMs) of individual polypeptide chains, as obtained from the production runs of the all-atom sub-boxes. The time is shown in picoseconds while the MSD values are given in nm<sup>2</sup>. For each sub-box, the chain numbering corresponds to the subboxes_overview.xlsx spreadsheet.</p> <p><strong>CHAIN_ROTACF.zip: </strong>Text files containing the rotational autocorrelation functions for individual polypeptide chains, as obtained from the production runs of the all-atom sub-boxes. The time is shown in picoseconds; the autocorrelation functions were calculated using a second-order Legendre polynomial. For each sub-box, the chain numbering corresponds to the subboxes_overview.xlsx spreadsheet.</p>
ScienceDex guides
Understand access before you commit
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.