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30,947 results for “Profile”
Country/Territory Seismic Risk Profiles
<p>The GEM Foundation has produced a collection of Country/Territory Seismic Risk Profiles that summarize key metrics of seismic risk, to provide stakeholders in risk management an overview of the risk in a region at-a-glance. Each profile presents the following relavant information:</p> <ul> <li>Social indicators, which provide context to the region in question </li> <li>Risk indicators, detailing an occupancy breakdown of exposed value and losses </li> <li>A list of the major earthquakes that have impacted the region </li> <li>Loss per region, providing a breakdown of average annual losses per Administrative level 1 </li> <li>Building classes, depicting the major construction materials used in the region </li> <li>Loss curves, which provide expected losses per different return periods </li> <li>Maps depicting the geographical distribution of hazard, exposure and losses </li> </ul> <p>The risk results are the results of an event-based risk analysis, where 100,000 years of earthquakes are simulated. Three lines of business are considered: residential, commercial, and industrial. Therefore, value or earthquake losses to other building occupancies (e.g., schools, healthcare) and infrastructure are not included.</p>
TINKER_WP3_2D images - profile scans dataset_221123
<p>a subset of the 2d gap images including profile measurements along x and y axes.</p>
SSCS and DCS fastq data for ALR conplastic mice mitochondrial DNA profiling
<p>The SSCS and DCS fq files for ALR conplastic mice associated with https://doi.org/10.5281/zenodo.10127101 repo. </p>
SSCS and DCS fastq data for B6 (wildtype) conplastic mice mitochondrial DNA profiling
<p>The SSCS and DCS fq files for B6 (wildtype) mice associated with https://doi.org/10.5281/zenodo.10127101 repo. </p>
SSCS and DCS fastq data for FVB conplastic mice mitochondrial DNA profiling
<p>The SSCS and DCS fq files for FVB conplastic mice associated with https://doi.org/10.5281/zenodo.10127101 repo. </p>
SSCS and DCS fq data for NZB conplastic mice mitochondrial DNA profiling
<p>The SSCS and DCS fq files for NZB conplastic mice associated with https://doi.org/10.5281/zenodo.10127101 repo. </p>
SSCS and DCS fastq data for AKR conplastic mice mitochondrial DNA profiling
<p>The SSCS and DCS fq files for AKR conplastic mice associated with https://doi.org/10.5281/zenodo.10127101 repo. </p>
Processed data files for conplastic mice mitochondrial DNA profiling
<p>This dataset contains all tables used and generated in analyses for the conplastic mice mitochondrial DNA profiling project. Please refer to serrano2023_directory<em>_</em>of_files.pdf for a description of the files. The serrano2023_directory_of_files.pdf also contains field descriptions for: </p> <ul> <li>somatic_mutations.vcf</li> <li>haplotype_mutations.vcf</li> <li>supertable.txt</li> <li>cleaned_read_depth_per_pos.txt</li> <li>adjusted_mut_freq_for_haplotypes.csv</li> <li>mut_freq_per_type.csv</li> </ul> <p>All scripts that generate the files in this zenodo repo can be found at <a href="https://github.com/sudmantlab/conplastic_mt_profiling">https://github.com/sudmantlab/conplastic_mt_profiling </a></p> <p> </p> <p>Single stranded and duplex consensus fastq files can be found at the following zenodo repositories:</p> <p><a href="https://doi.org/10.5281/zenodo.10403218">(Wildtype) B6 fastq files</a></p> <p><a href="https://doi.org/10.5281/zenodo.10403087">B6-mtAKR fastq files</a></p> <p><a href="https://doi.org/10.5281/zenodo.10397996">B6-mtALR fastq files</a></p> <p><a href="https://doi.org/10.5281/zenodo.10294450">B6-mtFVB fastq files</a></p> <p><a href="https://doi.org/10.5281/zenodo.10211698">B6-mtNZB fastq files</a></p>
FAIR Implementation Profiles for Social Science
<p>The repository consists of several folders.</p> <p><code>/FICs</code> consists of files for FAIR Implementation Communities. <code>/FIPs</code> consists of files for FAIR Implementation Profiles of these communities. <code>/FIP_reports</code> consists of reports for FAIR Implementation Profiles of these communities.</p> <p>In the file <code>Comparison_all_FIPs.xlsx</code> you can find detailed comparison of the FIPs. The file <code>Convergece_matrix.xlsx</code> gives you details of the convergence analysis.</p> <p>Please contact the authors for the latest version of the FIPs since FIPs are living documents.</p> <p>Please report errors to Shuai Wang at <a href="mailto:shuai.wang@vu.nl">shuai.wang@vu.nl</a>. Thank you!</p>
Functional and structural computed connectivity profiles for pre-defined whole-brain and cortical hull maps
<p>This Repository holds Lead-DBS Lead-Mapper v.2.5.2 computed functional and structural estimated connectivity. The structural dMRI connectivity is computed based on the "HCP1000 6K" connecome and the fMRI connectivity "GSP 1000 Yeo 2011" connectome.</p> <p>Both connectivity metrices were computed using two pre-defined grids:</p> <ol> <li> Cortical hull (1025 points). The grid was estimated based on the <a href="https:/github.com/neuromodulation/py_neuromodulation/blob/main/py_neuromodulation/ConnectivityDecoding/_get_grid_hull.m">SPM cortex_20484 grid</a> and subsampled by every 20th point. The grid is saved as a <a href="(https:/github.com/neuromodulation/py_neuromodulation/blob/main/py_neuromodulation/ConnectivityDecoding/mni_coords_cortical_surface.mat)">.mat file</a> and can be acessed through the main repository .</li> <li>Whole-brain (1236 points). Given the Lead-DBS provided <a href="https://github.com/neuromodulation/py_neuromodulation/blob/main/py_neuromodulation/ConnectivityDecoding/Automated%20Anatomical%20Labeling%203%20(Rolls%202020).nii">AAL3 atlas</a> the whole-brain .nii grid is subsampled by a factor of 150. See here the <a href="https://github.com/neuromodulation/py_neuromodulation/blob/main/py_neuromodulation/ConnectivityDecoding/_get_grid_whole_brain.py">grid computation</a>.</li> </ol> <p>Both grids can be accessed trough the <a href="https://github.com/neuromodulation/py_neuromodulation/tree/main">py_neuromodulation</a> API using the <a href="https://github.com/neuromodulation/py_neuromodulation/blob/main/py_neuromodulation/nm_RMAP.py">nm_RMAP.py</a> module.</p>
The assets of Stress profiling shiny app
Open the record for dataset details and reuse information.
Dataset for "An innovative pure rotational Raman lidar for accurately profiling atmospheric temperature and aerosol/cloud backscatter coefficients"
<p>This is the dataset used in the paper "<span>An innovative pure rotational Raman lidar for accurately profiling atmospheric temperature and aerosol/cloud backscatter coefficients"</span></p>
CEDAR, an online resource for the reporting and exploration of complexome profiling data
<p>Complexome profiling is an emerging ‘omics’ approach that systematically interrogates the composition of protein complexes (the complexome) of a sample, by combining biochemical separation of native protein complexes with mass-spectrometry based quantitation proteomics. The resulting fractionation profiles hold comprehensive information on the abundance and composition of the complexome, and have a high potential for reuse by experimental and computational researchers. However, the lack of a central resource that provides access to these data, reported with adequate descriptions and an analysis tool, has limited their reuse. Therefore, we established the ComplexomE profiling DAta Resource (CEDAR, www3.cmbi.umcn.nl/cedar/), an openly accessible database for depositing and exploring <a title="Learn more about mass spectrometry from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/mass-spectrometry">mass spectrometry</a> data from complexome profiling studies. Compatibility and reusability of the data is ensured by a standardized data and reporting format containing the “minimum information required for a complexome profiling experiment” (MIACE). The data can be accessed through a user-friendly web interface, as well as programmatically using the REST API portal. Additionally, all complexome profiles available on CEDAR can be inspected directly on the website with the profile viewer tool that allows the detection of correlated profiles and inference of potential complexes. In conclusion, CEDAR is a unique, growing and invaluable resource for the study of protein complex composition and dynamics across biological systems.</p>
Fig. 3 in Profiling of volatile and non-volatile metabolites in Polianthes tuberosa L. flowers reveals intraspecific variation among cultivars
Fig. 3. Soluble and wall-bound phenolics profile of tuberose cultivars. Histogram A shows the amount of rutin determined by UHPLC analysis in ng g −1 fr. wt of flower tissue, and B shows the amount of caffeic acid, p-coumaric acid and ferulic acid in ng mg−1 pellet dry wt of. Note: All values are mean ± s.d. of three replicates.
Fig. 2 in Profiling of volatile and non-volatile metabolites in Polianthes tuberosa L. flowers reveals intraspecific variation among cultivars
Fig. 2. Heat map representing the diversity of nonvolatile compounds detected in P. tuberosa flowers. Floral extracts after derivatization were analysed by GC-MS. Classes 0, 1, 2, 3, 4, 5, and 6 indicates group average abundances of Calcutta double, Calcutta single, Jyothi, Ujwal, Shnigdha, Shringhar, and Phule rajani cultivars respectively. Red and green color denotes highest and lowest relative abundances of non-volatile compounds, respectively. Abbreviations: Met a-D-gal - Methyl α-D-galactofuranoside, Met b-D-gal - Methyl β-D-galactopyranoside, Met b-D-glu - Methyl β-D-glucofuranoside, 4- HBALD - 4-hydroxybenzaldehyde, 4-HBA - 4-hydroxybenzoic acid, 2, 5-DHBA - 2, 5-Dihydroxybenzoic acid. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 5 in Profiling of volatile and non-volatile metabolites in Polianthes tuberosa L. flowers reveals intraspecific variation among cultivars
Fig. 5. Metabolite map representing the normalized relative abundances of few important primary and specialized metabolites during the blooming stage of P. tuberosa flowers. The amount of metabolites from four different P. tuberosa cultivar clusters were colour coded for low to high contents as per the scale shown. Cluster A includes CD, cluster B includes CS, cluster C includes JY and PR and cluster D includes SN, UJ, and SR. Solid arrows represent single step reactions, dotted arrows represent multi step reactions and block arrows represent volatile emission or exchange within the cell. Emitted levels of volatiles are shown outside the cell, while endogenous levels are represented inside the cytosol. Glycosyl-bound volatiles are shown inside in a representative vacuolar structure. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1 in Profiling of volatile and non-volatile metabolites in Polianthes tuberosa L. flowers reveals intraspecific variation among cultivars
Fig. 1. Profile of floral scent volatiles in studied cultivars of P. tuberosa. Stack plots represent (A) volatiles emitted by flowers in μg g−1 fr. wt h−1, (B) free endogenous volatiles of whole flower in μg μg g−1 fr. wt and (C) glycosyl-bound volatiles of whole flower in μg μg g−1 fr. wt. Abbreviations: CD - Calcutta double, CS - Calcutta single, JY - Jyothi, UJ - Ujwal, SN - Shnigdha, SR - Shringhar, and PR - Phule rajani.
Fig. 4 in Profiling of volatile and non-volatile metabolites in Polianthes tuberosa L. flowers reveals intraspecific variation among cultivars
Fig. 4. Multivariate analysis of P. tuberosa cultivars (classes 0–6). The scores plot obtained after PLS-DA analysis has been shown in A, where components 1 and 2 together can explain the maximum variance between the cultivars. Metabolites having higher VIP scores are shown in B where their relative abundances are color coded from high to low as per the scale shown. The dendrogram as shown in C was obtained after hierarchical clustering analysis of studied P. tuberosa cultivars. Classes 0, 1, 2, 3, 4, 5, and 6 represent Calcutta double, Calcutta single, Jyothi, Ujwal, Shnigdha, Shringhar and Phule rajani cultivars. Metabolites enlisted as 1–25 in B are sequentially represented as follows: quinic acid, V_D-limonene (V: detected as emitted volatile), myo-inositol, D-mannose, V_3-carene, EV_cis-methyl eugenol (EV: detected as endogenous volatile), V_Z- β-caryophyllene, V_benzyl salicylate, EV_benzyl benzoate, EV_eugenol, methyl α-D-galactofuranoside, EV_(Z, E)-farnesol, GV_2-nitrocumene (GV: detected as glycosyl-bound volatile), EV_1,8-cineole, V_1,R-α-pinene, Methyl β-D-galactopyranoside, GV_4-propyl benzaldehyde, Methyl α-Dglucofuranoside, V_β-terpineol, D-fructofuranose, L-threonine, EV_2-hydroxy cineole, EV_3-exo-hydroxy-1,8-cineole, EV_5-hydroxy-7(Z)-decenoic acid-δ-lactone and GV_5-hydroxy-7Z-decenoic acid- δ –lactone.
Fig. 3 in Nocturnal floral scent profiles of Myrtaceae fruit crops
Fig. 3. Relative amounts of monoterpenoids, and aliphatic and aromatic compounds in the scents of the studied species of Myrtaceae. N-containing and unknown compounds were not listed as they only occurred in trace amounts. * Data of C. phaea are from Cordeiro et al. (2017).
Fig. 2 in Nocturnal floral scent profiles of Myrtaceae fruit crops
Fig. 2. Number of compounds trapped for the studied species of Myrtaceae. Different letters indicate significant differences among species (Kruskall-Wallis ANOVA followed by post hoc tests). * Data of C. phaea are from Cordeiro et al. (2017).
ScienceDex guides
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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.