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30,948 results for “Profile”
Comparative proteomic profiling of non-treated and linalool-treated KPC-KP cells
<p>Proteomic abundance, gene ontology and KEGG pathway analysis.</p>
mOTUs 1.1 taxonomic profiling of the CAMI 2 Mouse Gut Toy data set, samples 0-63
<strong>Software: </strong>mOTUs<br><strong>SoftwareVersion: </strong>1.1<br><strong>DataURL: </strong> https://data.cami-challenge.org/participate<br><strong>SoftwareURL:</strong> http://www.bork.embl.de/software/mOTUs1/<br><strong>DockerImage:</strong> stefanjanssen/docker_profiling_tools:motu<br><strong>IsBiobox:</strong> True<br><strong>BioboxYAMLFile:</strong> https://zenodo.org/record/3629567/files/biobox.yaml?download=1<br><strong>ReferenceDatabase:</strong> mOTU.v1.padded<br><strong>ShortReadsUsed:</strong> True<br><strong>LongReadsUsed:</strong> False<br><strong>CommandsUsed:</strong> docker run \<br>--volume="/path/to/19122017_mousegut_scaffolds_yaml:/bbx/mnt/yaml:ro" \<br>--volume="/path/to/19122017_mousegut_scaffolds:/bbx/mnt/input:ro" \<br>--volume="/path/to/output:/bbx/mnt/output:rw" \<br>--volume="/path/to/output/metadata:/bbx/metadata:rw" \<br>--volume="/path/to/output/cache:/cache:rw" \<br>stefanjanssen/docker_profiling_tools:motu
FOCUS 0.31 taxonomic profiling of the CAMI 2 Mouse Gut Toy data set, samples 0-63
<strong>Software: </strong>FOCUS<br><strong>SoftwareVersion: </strong>0.31<br><strong>DataURL: </strong> https://data.cami-challenge.org/participate<br><strong>SoftwareURL:</strong> https://github.com/metageni/FOCUS<br><strong>DockerImage:</strong> stefanjanssen/docker_profiling_tools:focus<br><strong>IsBiobox:</strong> True<br><strong>BioboxYAMLFile:</strong> https://zenodo.org/record/3629567/files/biobox.yaml?download=1<br><strong>ReferenceDatabase:</strong> k8_bacterial_and_draft<br><strong>ShortReadsUsed:</strong> True<br><strong>LongReadsUsed:</strong> False<br><strong>CommandsUsed:</strong> docker run \<br>--volume="/path/to/19122017_mousegut_scaffolds_yaml:/bbx/mnt/yaml:ro" \<br>--volume="/path/to/19122017_mousegut_scaffolds:/bbx/mnt/input:ro" \<br>--volume="/path/to/output:/bbx/mnt/output:rw" \<br>--volume="/path/to/output/metadata:/bbx/metadata:rw" \<br>--volume="/path/to/output/cache:/cache:rw" \<br>stefanjanssen/docker_profiling_tools:focus
MetaPhlAn 2.9.21 taxonomic profiling of the CAMI 2 Mouse Gut Toy data set, samples 0-63
<strong>Software: </strong>MetaPhlAn<br><strong>SoftwareVersion: </strong>2.9.21<br><strong>DataURL: </strong> https://data.cami-challenge.org/participate<br><strong>SoftwareURL:</strong> https://bitbucket.org/biobakery/metaphlan2<br><strong>DockerImage:</strong> cami/metaphlan:2.9.21<br><strong>IsBiobox:</strong> True<br><strong>BioboxYAMLFile:</strong> https://zenodo.org/record/3629567/files/biobox.yaml?download=1<br><strong>ReferenceDatabase:</strong> mpa_v29_CHOCOPhlAn_201901 <br><strong>ShortReadsUsed:</strong> True<br><strong>LongReadsUsed:</strong> False<br><strong>CommandsUsed:</strong> docker run \<br>--volume="/path/to/19122017_mousegut_scaffolds_yaml:/bbx/mnt/yaml:ro" \<br>--volume="/path/to/19122017_mousegut_scaffolds:/bbx/mnt/input:ro" \<br>--volume="/path/to/output:/bbx/mnt/output:rw" \<br>--volume="/path/to/output/metadata:/bbx/metadata:rw" \<br>--volume="/path/to/output/cache:/cache:rw" \<br>--volume="/path/to/reference_database:/exchange/db:rw" \<br>cami/metaphlan:2.9.21
MetaPhlAn 2.2.0 taxonomic profiling of the CAMI 2 Mouse Gut Toy data set, samples 0-63
<strong>Software: </strong>MetaPhlAn<br><strong>SoftwareVersion: </strong>2.2.0<br><strong>DataURL: </strong> https://data.cami-challenge.org/participate<br><strong>SoftwareURL:</strong> https://bitbucket.org/biobakery/metaphlan2<br><strong>DockerImage:</strong> stefanjanssen/docker_profiling_tools:metaphlan2<br><strong>IsBiobox:</strong> True<br><strong>BioboxYAMLFile:</strong> https://zenodo.org/record/3629567/files/biobox.yaml?download=1<br><strong>ReferenceDatabase:</strong> mpa_v20_m200<br><strong>ShortReadsUsed:</strong> True<br><strong>LongReadsUsed:</strong> False<br><strong>CommandsUsed:</strong> docker run \<br>--volume="/path/to/19122017_mousegut_scaffolds_yaml:/bbx/mnt/yaml:ro" \<br>--volume="/path/to/19122017_mousegut_scaffolds:/bbx/mnt/input:ro" \<br>--volume="/path/to/output:/bbx/mnt/output:rw" \<br>--volume="/path/to/output/metadata:/bbx/metadata:rw" \<br>--volume="/path/to/output/cache:/cache:rw" \<br>stefanjanssen/docker_profiling_tools:metaphlan2
CAMIARKQuikr 1.0.0 taxonomic profiling of the CAMI 2 Mouse Gut Toy data set, samples 0-63
<strong>Software: </strong>CAMIARKQuikr<br><strong>SoftwareVersion: </strong>1.0.0<br><strong>DataURL: </strong> https://data.cami-challenge.org/participate<br><strong>SoftwareURL:</strong> https://doi.org/10.5281/zenodo.1730572<br><strong>DockerImage:</strong> stefanjanssen/docker_profiling_tools:quickr<br><strong>IsBiobox:</strong> True<br><strong>BioboxYAMLFile:</strong> https://zenodo.org/record/3629567/files/biobox.yaml?download=1<br><strong>ReferenceDatabase:</strong> https://doi.org/10.5281/zenodo.1730572<br><strong>ShortReadsUsed:</strong> True<br><strong>LongReadsUsed:</strong> False<br><strong>CommandsUsed:</strong> docker run \<br>--volume="/path/to/19122017_mousegut_scaffolds_yaml:/bbx/mnt/yaml:ro" \<br>--volume="/path/to/19122017_mousegut_scaffolds:/bbx/mnt/input:ro" \<br>--volume="/path/to/output:/bbx/mnt/output:rw" \<br>--volume="/path/to/output/metadata:/bbx/metadata:rw" \<br>--volume="/path/to/output/cache:/cache:rw" \<br>stefanjanssen/docker_profiling_tools:quickr
MetaPhyler 1.25 taxonomic profiling of the CAMI 2 Mouse Gut Toy data set, samples 0-63
<strong>Software: </strong>MetaPhyler<br><strong>SoftwareVersion: </strong>1.25<br><strong>DataURL: </strong> https://data.cami-challenge.org/participate<br><strong>SoftwareURL:</strong> http://metaphyler.cbcb.umd.edu/<br><strong>DockerImage:</strong> stefanjanssen/docker_profiling_tools:metaphyler<br><strong>IsBiobox:</strong> True<br><strong>BioboxYAMLFile:</strong> https://zenodo.org/record/3629567/files/biobox.yaml?download=1<br><strong>ReferenceDatabase:</strong> 2012<br><strong>ShortReadsUsed:</strong> True<br><strong>LongReadsUsed:</strong> False<br><strong>CommandsUsed:</strong> docker run \<br>--volume="/path/to/19122017_mousegut_scaffolds_yaml:/bbx/mnt/yaml:ro" \<br>--volume="/path/to/19122017_mousegut_scaffolds:/bbx/mnt/input:ro" \<br>--volume="/path/to/output:/bbx/mnt/output:rw" \<br>--volume="/path/to/output/metadata:/bbx/metadata:rw" \<br>--volume="/path/to/output/cache:/cache:rw" \<br>stefanjanssen/docker_profiling_tools:metaphyler
mOTUs 2.5.1 taxonomic profiling of the CAMI 2 Mouse Gut Toy data set, samples 0-63
<strong>Software: </strong>mOTUs<br><strong>SoftwareVersion: </strong>2.5.1<br><strong>DataURL: </strong> https://data.cami-challenge.org/participate<br><strong>SoftwareURL:</strong> https://motu-tool.org/<br><strong>DockerImage:</strong> cami/motus:2.5.1<br><strong>IsBiobox:</strong> False<br><strong>ReferenceDatabase:</strong> mOTUs database version 2.5.0<br><strong>ShortReadsUsed:</strong> True<br><strong>LongReadsUsed:</strong> False<br><strong>CommandsUsed:</strong> for i in {0..63}; do motus profile -f sample_$((i))/reads/anonymous_reads_r1.fq -r sample_$((i))/reads/anonymous_reads_r2.fq -n $((i)) -C precision > sample$((i)).profile ; done<br>cat sample*.profile > cami2_mouse_gut_motus2.5.1.profile
Nanopore raw signal data to Benchmarking the MinION: Evaluating long reads for microbial profiling
<p>This dataset contains original raw signal data of a blind study sequencing mock community samples. The samples are differing by nucleic acid quantification technique and composition, and consist of twelve prokaryotic species each. The raw signal data permits future basecalling and may assist in development of applications requiring signal level data.</p> <p>Sample 1 (Barcode 1): heterogenous, adjusted by ddPCR</p> <p>Sample 2 (Barcode 2): heterogenous, adjusted by Qubit</p> <p>Sample 3 (Barcode 3): equimolar, adjusted by ddPCR</p> <p>Sample 4 (Barcode 3): equimolar, adjusted by Qubit</p> <p>FLO-MIN106, SQK-LSK108; Flowcell FAH89600</p> <p>Please consider citing our paper</p> <p>Leidenfrost, R.M., Pöther, D., Jäckel, U. <em>et al.</em> Benchmarking the MinION: Evaluating long reads for microbial profiling. <em>Sci Rep</em> <strong>10, </strong>5125 (2020). https://doi.org/10.1038/s41598-020-61989-x</p> <p>https://doi.org/10.1038/s41598-020-61989-x</p>
Dataset related to article "Poly(I:C) stimulation is superior than Imiquimod to induce the antitumoral functional profile of tumor-conditioned macrophages."
<p>Macrophage plasticity is the ability of mononuclear phagocytes to change phenotype, function, and genetic reprogramming upon encounter of specific local stimuli. In the tumor microenvironment, Tumor-Associated Macrophages (TAMs) acquire an immune-suppressive and tumor-promoting phenotype. With the aim to re-educate TAMs to antitumor effectors, in this study, we used two immunestimulatory compounds: the TLR7 agonist Imiquimod (IMQ) and the TLR3 agonist Poly(I:C). To better mimic in vitro the response of TAMs, we used Tumor-Conditioned Macrophages (TC-Mϕ) differentiated in the presence of tumor cell supernatants. Our results show that TC-Mϕ respond differently from conventional M2-polarized macrophages. Upon stimulation with IMQ, TC-Mϕ did not upregulate major histocompatibility complex (MHC II) molecules and unexpectedly expressed increased CD206. With both compounds, TC-Mϕ produced higher levels of inflammatory cytokines than M2 macrophages. IMQ and Poly(I:C) differed in the types of regulated genes and secreted mediators. Reflecting their signaling pathways, only IMQ significantly induced IL-1β and IL-6, while only Poly(I:C) stimulated CXCL10, and both upregulated CCL5. Of note, using a novel cytotoxicity assay, Poly(I:C), but not IMQ, was effective in triggering the cytotoxic activity of TC-Mϕ against cancer cells. Overall, the results demonstrate that Poly(I:C) stimulation of TC-Mϕ is superior than IMQ in terms of macrophage re-education toward antitumor effectors.</p>
Learn2Link: Linking the Social and Academic Profiles of Researchers
<p>Data and code to reproduce work in Learn2Link: Linking the Social and Academic Profiles of Researchers.</p>
Neutron Reflectometry data and code for the manuscript entitled: 'Enrichment of charged monomers explains non-monotonic polymer volume fraction profiles of multi-stimulus responsive copolymer brushes'
<p>Contained in the zip file is the reduced neutron reflectometry data, the code used to analyse the data and jupyter notebooks used implement this code. Data is for proposal number PP4274, experiment number PPR6490 on the Platypus Reflectometer at ANSTO, Australia. The data is name with measurement numbers. The attached excel document describes the condition which corresponds to measurement number. </p>
Data support to Characteristics of persistent heavy fog events retrieved by microwave radiometer profiler during 2010–2013 in northern China
<p>The dataset include the observation data from microwave radiometer, tethered balloon and the surface.</p>
Data from: Profiling extracellular long RNA transcriptome in human plasma and extracellular vesicles for biomarker discovery
<p>The recent discovery of extracellular RNAs in blood, including RNAs in extracellular vesicles (EVs), combined with low-input RNA-sequencing advances have enabled scientists to investigate their role in human disease. To date, most studies have been focusing on small RNAs, and methodologies to optimize long RNAs measurement are lacking. We used plasma RNA to assess the performance of six long RNA sequencing methods, at two different sites, and we report their differences in reads (%) mapped to the genome/transcriptome, number of genes detected, long RNA transcript diversity, and reproducibility. Using the best performing method, we further compare the profile of long RNAs in the EV- and no-EV-enriched RNA plasma compartments. These results provide insights on the performance and reproducibility of commercially available kits in assessing the landscape of long RNAs in human plasma and different extracellular RNA carriers that may be exploited for biomarker discovery.</p>
Load profile data of 50 industrial plants in Germany for one year
<p>This dataset holds the electric load profiles of 50 small and mid-size enterprises in Germany. The load profiles are in 15-minute time resolution for one year. The load is shown in kW as an average over 15 minutes.</p> <p>The dataset is divided into two:</p> <ul> <li>LoadProfile_20IPs_2016 shows load profiles of 20 industrial plants (IP) for the year 2016.</li> <li>LoadProfile_30IPs_2017 shows load profiles of 30 industrial plants (IP) for the year 2017.</li> </ul> <p>The IPs from the dataset for 2016 do not reappear in the dataset for 2017.</p> <p> </p> <p>The dataset LoadProfile_20IPs_2016 is evaluated in the following publication:</p> <ul> <li>Covic, N., Braeuer, F., McKenna, R., Pandzic, H., Optimizing Industrial Facilities’ Active Participation<br> in Electricity Markets under Uncertainty, 2020.</li> </ul> <p>Both datasets together are evaluated in multiple publications:</p> <ul> <li>Braeuer, F., Finck, R., McKenna, R., Comparing empirical and model-based approaches for calculating dynamic grid emission factors: An application to CO2-minimizing storage dispatch in Germany, Journal of Cleaner Production, Volume 266, 2020, 121588, ISSN 0959-6526, https://doi.org/10.1016/j.jclepro.2020.121588.</li> <li>Braeuer, F., Rominger, J., McKenna, R.,Fichtner, W., Battery storage systems: An economic model-based analysis of parallel revenue streams and general implications for industry, Applied Energy, Volume 239, 2019, Pages 1424-1440, ISSN 0306-2619, https://doi.org/10.1016/j.apenergy.2019.01.050.</li> </ul> <p>Enjoy.</p>
Wind velocity vertical profile in a 50 m wind mast at Sisal, Yucatán, Mexico
<p>A meteorological mast, instrumented with sonic anemometers was implemented during the years 2010-2014, in order to study high frequency wind data at five different heights above the ground. The mast is 50 m height, located about 100 m from the shoreline at the Sisal campus of UNAM. An automated acquisition system recorded raw data in a database (server) directly through a RF link.</p> <p>For more information visit: http://ocse.mx/en/experimento/torre-sisal</p> <p> </p>
Neisseria gonorrhoeae clustering to reveal major European WGS-based genogroups in association with antimicrobial resistance (cgMLST and MScgMLST schemas, allelic profile matrices and GrapeTree input file)
<p>This dataset refers to the gene-by-gene analysis of 3791 <em>Neisseria gonorrhoeae</em> genomes from 21 European countries and includes the used cgMLST and MScgMLST loci schemas prepared for the chewBBACA core suite, as well as the associated allelic profile matrices for all genomes. Additionally a <em>.json</em> file is made available for direct input in the GrapTree vizualization software for data/metadata exploration. </p> <p>All novel raw sequence reads used in this study were deposited in the European Nucleotide Archive (ENA) (BioProject PRJEB36482). Additional raw sequence read data used were retrieved from the following ENA BioProjects: PRJEB14933; PRJEB2124; PRJEB23008; PRJEB26560; PRJEB9227; PRJNA275092; PRJNA348107; PRJNA473385; PRJNA315363. </p>
Single-cell metabolic profiling (scMEP) of human cytotoxic T cells
<p>Datasets for Hartmann FJ et al. (2020) Single-cell metabolic profiling of human cytotoxic T cells. Nature Biotechnology</p> <p>Contains single-cell mass cytometry (CyTOF) datasets for metabolic analysis of human whole blood populations, <em>in vitro</em> T cell activation and analysis of metabolic states in human tissues as well as MIBI-TOF multiplexed images and segmented single-cell data of colorectal carcinoma and healthy colon.</p> <ul> <li>All CyTOF datasets have been manually gated on single, live cells to enable direct import into data analysis software (e.g. R environment)</li> <li>MIBI-TOF images have undergone noise removal as described in Keren et al. (2018) Cell</li> <li>Segmentation masks for MIBI-TOF data contain large non-cellular regions that need to be removed during downstream processing</li> <li>MIBI-TOF derived single-cell data is cell size normalized, arcsinh transformed and percentile normalized and contains manually annotated FlowSOM clustering results</li> </ul>
Data for paper "Effects of inflow conditions on plunge points and vertical profiles of turbidity currents on sloping flume based on 3D numerical simulation"
<p>This set of xlsx files, metadata of model results and Matlab code accompanies the manuscript "Effects of inflow conditions on plunge points and vertical profiles of turbidity currents on sloping flume based on 3D numerical simulation" by Ruoyin Zhang, Baosheng Wu, and Bangwen Zhang. This is the first release of the data.</p>
Data from: Nasopharyngeal microbiome profiles in rural Venezuelan children are associated with respiratory and gastrointestinal infections
<p>Background: Recent research suggests that the microbiota affects susceptibility to both respiratory tract infections (RTIs) and gastrointestinal infections (GIIs). In order to optimize global treatment options, it is important to characterize microbiota profiles across different niches and geographic/socioeconomic areas where RTI and GII prevalences are high.</p> <p>Methods: We performed 16S sequencing of nasopharyngeal swabs from 209 Venezuelan Amerindian children aged 6 weeks–59 months who were participating in a 13-valent pneumococcal conjugate vaccine (PCV13) study. Using random forest models, differential abundance testing, and regression analysis, we determined whether specific bacteria were associated with RTIs or GIIs and variation in PCV13 response.</p> <p>Results: Microbiota compositions differed between children with or without RTIs (P = .018) or GIIs (P = .001). Several species were associated with the absence of infections. Some of these health-associated bacteria are also observed in developed regions, such as Corynebacterium (log2(fold change [FC]) = 3.30 for RTIs and log2(FC) = 1.71 for GIIs), while others are not commonly observed in developed regions, such as Acinetobacter (log2(FC) = 2.82 and log2(FC) = 5.06, respectively). Klebsiella spp. presence was associ- ated with both RTIs (log2(FC) = 5.48) and GIIs (log2(FC) = 7.20).</p> <p>Conclusions: The nasopharyngeal microbiota of rural Venezuelan children included several bacteria that thrive in tropical humid climates. Interestingly, nasopharyngeal microbiota composition not only differed in children with an RTI but also in those with a GII, which suggests a reciprocal interplay between the 2 environments. Knowledge of region-specific microbiota patterns enables tailoring of preventive and therapeutic approaches.</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.