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30,947 results for “Profile”
Metabolite profiles from Foxfonna, Svalbard
<p>Raw metabolite data for "Icescape-scale metabolomics reveals cyanobacterial and topographic control of the core metabolism of the cryoconite ecosystem of an Arctic ice cap"</p>
Ulysses Fossae X,Y Topographic Profile Locations
<p>X,Y coordinates for topographic profile cross-sections in Ulysses Fossae, Mars</p>
Velocity and density profiles from thermodynamic modeling
<p>Seismic velocity and density profiles for various mantle temperature and compositions using the stx08 and stx11 data bases. See README in each directory for more detailed descriptions.</p>
Summertime divergence profile over Beijing urban areas as calculated from the radar wind profile MESONET in Beijing
<p>This dataset contains the divergence profiles over Beijing urban areas at 1-h intervals as calculated based on the continuous measurements of horizontal wind from the radar wind profiler MESONET in Beijing. This dataset covered the period from May to September. It is stored in Matlab format. The divergence is organized by an array of 66*24*153 (height * hour * day), and h is its corresponding altitude layer.</p>
Structural characterization of the protein-material interfacial interactions by using lysine reactivity profiling-mass spectrometry
<p>The exploration of interfacial molecular interactions of protein-material integrations and how material modulate the protein structure and activity are essential to the safety evaluation of biomedical micro/nanomaterials, toxicity estimation and design of nano-drugs, and catalytic activity improvement of bio-inorganic functional hybrids. However, characterizing the interfacial molecular details of protein-micro/nanomaterial hybrids remains a great challenge. Herein, we introduce the protocol of lysine reactivity profiling-mass spectrometry (LRP-MS) strategy for probing the interfacial molecular structures between proteins and micro/nanomaterials. LRP-MS utilizes lysine residues as the endogenous probes to characterize the protein localization orientation, interaction sequence regions, binding sites, and modulated protein structures in the protein-material hybrids, which cannot be achieved by traditional spectroscopy methods. We describe the optimized heavy and light two-step isotope dimethyl labeling strategy for protein-material hybrids under their native and denaturing conditions in sequence. The comparative quantification results of lysine reactivity (referred as NLE) are only dependent on the native microenvironments of lysine local structures. We also highlight other critical steps including protein digestion, elution from materials, data processing, and interfacial structure analysis. The two-step isotope labeling steps need about 5 h, and the whole protocol including digestion, liquid chromatography-tandem mass spectrometry, data processing, and structure analysis needs about 3-5 days.</p>
Data products associated with: Tamburo, Withers, Dalba, Moore, and Koskinen (2023) Cassini radio occultation observations of Saturn's ionosphere: Electron density profiles from 2005 to 2013, Journal of Geophysical Research, doi:10.1029/2023JA031310
<p>These data products are associated with Tamburo, Withers, Dalba, Moore, and Koskinen (2023) Cassini radio occultation observations of Saturn’s ionosphere: Electron density profiles from 2005 to 2013, Journal of Geophysical Research, doi:10.1029/2023JA031310. At the time of writing, this manuscript is under review. In the future, these data products will be submitted for archiving at the NASA Planetary Data System (PDS).</p> <p> </p>
Transcriptional profiling of the response to starvation and fattening reveals differential regulation of autophagy genes in mammals
<p>Nutrient deprivation (starvation) induced by fasting and hypercaloric regimens are stress factors that can influence cell and tissue homeostasis in mammals. One of the key cellular responses to changes in nutrient availability is the cell survival pathway, autophagy. While there has been much research into the protein networks regulating autophagy, less is known about the gene expression networks involved in this fundamental process. Here, we applied a network algorithm designed to analyze omics datasets, to identify sub-networks that are enriched for induced genes in response to starvation. This enabled us to identify two prominent active modules composed of key stress-induced transcription factors, including members of the Jun, Fos, and ATF families, and the other comprising autophagosome sub-network genes, including ULK1. The results were validated in the brain, liver, and muscle of fasting mice. Moreover, differential expression analysis of autophagy genes in the brain, liver, and muscle of high-fat diet-exposed mice, showed significant suppression of GABARAPL1 in the liver. Finally, our data provide a resource that may facilitate the future identification of regulators of autophagy.</p>
Excess ground ice profiles from Sentinel 1
<p>Maps of excess ice content profiles were estimated from Sentinel-1 subsidence observations. Results for three study regions in Alaska are included:</p> <ul> <li>Happy Valley, Brooks Foothills</li> <li>Ice Cut, Brooks Foothills</li> <li>Kivalina, Northwestern Alaska</li> </ul> <p>The results for each region are contained in the corresponding .zip file. The subfolders correspond to the different years. The content of each subfolder are as follows:</p> <ul> <li>meta.txt: human-readable metadata: geolocation information (rasterio affine transformation, projection, size of image, vertical grid [m])</li> <li>e_mean.npy: array containing the posterior mean of the excess ground ice profiles e [-]</li> <li>e_quantile.npy: array containing the 10th and 90th posterior percentile of the excess ground ice profiles e [-]</li> </ul> <p> </p>
In-situ hydrographic profiles collected along the Patagonian fjords.
<p>The dataset included the CTDO profilers obtained in Patagonian fjords.</p>
Uninterpreted seismic reflection profile in high resolution
<p>Uninterpreted seismic reflection profile in high resolution</p>
Fig. 3.—Dendrogram obtained after a in Chemical profile of the cutaneous gland secretions from male pampas deer (Ozotoceros bezoarticus)
Fig. 3.—Dendrogram obtained after a hierarchical cluster analysis on the 143 compounds found. Data were obtained from gas chromatographic–mass spectrometry analyses of samples from the preorbital, tarsal, and digital glands, and back hairs of 6 adult and 5 yearling male pampas deer that were collected during the breeding season (March in the Southern Hemisphere). Animals are consecutively numerated as yearling (Y) from 1 to 5 (Y1–Y5) and adults (A) from 6 to 11 (A6– A11). Agglomerative coefficient equals 0.15.
Fig. 2 in Chemical profile of the cutaneous gland secretions from male pampas deer (Ozotoceros bezoarticus)
Fig. 2.—Typical gas chromatography–mass spectrometry traces of gland secretions from a yearling pampas deer. Samples were collected during the breeding season (March in the Southern Hemisphere). The traces show the absence of lateral differences between secretions from digital, tarsal, and preorbital glands: upper traces are left secretions and lower are right.
Fig. 1 in Chemical profile of the cutaneous gland secretions from male pampas deer (Ozotoceros bezoarticus)
Fig. 1.—Detailed gas chromatography–mass spectrometry traces of the 4 kinds of samples analyzed. Samples were collected during the breeding season (March in the Southern Hemisphere). Total ion chromatograms obtained from samples (preorbital, tarsal, and digital glands, and back hairs) from the same adult individual are shown. Magnified regions show the time range where a) short-chained compounds, b) esters, and c) fatty alcohols and sterols concentrate. Peak numbers show the most abundant or ubiquitous compounds and are as in Table 1 (XIII is a sulphurcontaining compound, LXI–LXII and CVI are fatty alcohols, C is an hydrocarbon, CII is squalene, CIX is a sterol, and CXX is cholesterol).
Fig. 4 in Chemical profile of the cutaneous gland secretions from male pampas deer (Ozotoceros bezoarticus)
Fig. 4.—Compound classes that exhibited significant differences related to sample origin. Different letters on columns indicate significant differences (P <0.05, MANOVA). Data were obtained from samples from the preorbital, tarsal, and digital glands, and back hairs of 6 adult and 5 yearling male pampas deer that were collected during the breeding season (March in the Southern Hemisphere).
Benchmarking microbial profilers on the CAMI data
<p>Results in the CAMI format of testing various tools and reference databases.</p> <p>Profilers used:<br> * mOTUs 3.0.3 (https://github.com/motu-tool/mOTUs)<br> * MetaPhlAn 4.0.3 (https://github.com/biobakery/MetaPhlAn)<br> * Bracken 2.8 (https://github.com/jenniferlu717/Bracken)<br> * KMA 1.4.7 (https://bitbucket.org/genomicepidemiology/kma)</p> <p>For KMA, these different reference databases have been evaluated:<br> * Silva (20200116)<br> * Genomic (20220524)<br> * mOTUs db (20221205)</p> <p>See https://github.com/genomicepidemiology/ava-tax-benchmark for details.</p>
Benchmark datasets for "Detecting T-cell expansion and quantifying clone survival from deep profiling of immune repertoires"
<p>T-cell receptor repertoire sequencing datasets describing time courses obtained for vaccination, normal aging and blood transplant cases. Datasets reported here were previously published (except for Tem/Tcm data), this is just a compendium of selected samples that is properly pre-processed and formatted.</p>
Data for 'Evaluating WRF-GC v2.0 predictions of boundary layer and vertical ozone profiles during the 2021 TRACER-AQ campaign in Houston, Texas'
<p>This repository provides observation datasets, model configuration files, model boundary conditions, model input files, and scripts used in the manuscript titled 'Evaluating WRF-GC v2.0 predictions of boundary layer and vertical ozone profiles during the 2021 TRACER-AQ campaign in Houston, Texas'.</p>
Supplementary data as part of the article "Comparing the reaction profiles of single-iron catalytic sites in enzymes and in reticular frameworks for methane-to-methanol oxidation" (https://doi.org/10.1016/j.xcrp.2023.101422)
<p>Cartesian coordinates in the *.XYZ format for all the structures optimized at the M06-L/def2-TZVP in the reactivity study as part of the article "Comparing the reaction profiles of single-iron catalytic sites in enzymes and in reticular frameworks for methane-to-methanol oxidation" (<a href="https://doi.org/10.1016/j.xcrp.2023.101422">https://doi.org/10.1016/j.xcrp.2023.101422</a>)</p>
Serum miRnome Profiling: A Novel Biomarker Platform for the Early Detection of NSCLC
<p>Table S1: DEmiRNAs between cancerous and non-cancerous patients; Table S2: Shared DE miRNAs between subgroups; Table S3: NSCLC vs control_GO_BP_results; Table S4: NSCLC vs control_GO_CC_results; Table S5: NSCLC vs control_GO_MF_results; Table S6: NSCLC vs control_KEGG_results.</p>
data for Multi-tissue H3K27ac profiling of GTEx samples links epigenomic variation to disease
<p>processed data for "Multi-tissue H3K27ac profiling of GTEx samples links epigenomic variation to disease"</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.