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30,947 results for “Profile”
Scripts and data for the manuscript "Transcriptomic profiling of gill biopsies to define predictive markers for seawater survival in farmed Atlantic salmon"
<p>This dataset supports the manuscript titled "Transcriptomic profiling of gill biopsies to define predictive markers for seawater survival in farmed Atlantic salmon." It contains comprehensive RNA-seq count data from gill biopsies of approximately 3000 Atlantic salmon smolt, collected during the SynchroSmolt project. The data is supplemented with RNA-seq counts from two prior photoperiod smolt experiments (2013_shortdays and 2017_winterlength) and single-nucleus RNA-seq (snRNA-seq) data from an additional experiment.</p> <p><strong>Key Dataset Elements:</strong><br>- <strong>RNA-seq read counts and metadata</strong> for three experiments, detailing various growth, condition, and survival indicators.<br>- <strong>Scripts for analysis</strong> include differential expression analysis, random forest model preparation and execution, and cell-type-specific gene analysis.<br>- <strong>Intermediate data outputs</strong> such as normalized RNA-seq counts, results from differential expression analyses, and random forest model inputs and outputs.</p> <p><br>This dataset facilitates the exploration of gene expression-based predictive modeling for seawater survival, revealing key insights into the influence of photoperiod history and developmental gene regulation on Atlantic salmon's transition to seawater.</p>
Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics: fastq & bam files - Sample S5_Rec
<p>You can find here the fastq and bam files related to the datasets used in the publication: </p> <p>In this particular upload, you can find the fastq (version1) and bam (version2) files of the two replicates of sample S5_Rec (A121573)</p> <p><strong>Valdeolivas, A., Amberg, B., Giroud, N. <em>et al.</em> Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics. <em>npj Precis. Onc.</em> 8, 10 (2024). https://doi.org/10.1038/s41698-023-00488-4</strong></p> <p> </p> <p> </p>
Y-STR Profiles and Y-Haplogroups of 327 Samples from Tak and Ranong, Thailand
<p>This dataset comprises Y-STR profiles and Y-SNP haplogroups of 327 unrelated Thai male individuals from the borderland provinces of Tak (n = 274) and Ranong (n = 53), Thailand. Genomic DNA was extracted from blood samples stored on Whatman™ FTA™ cards and analyzed using advanced genotyping techniques. Y-SNP haplogroup assignments were performed using yHaplo v1.1.2 and referenced against the ISOGG Y-DNA Haplogroup Tree v15.73. Y-STR profiling was conducted with the ForenSeq™ DNA Signature Prep Kit on the MiSeq FGx platform, ensuring high-resolution allele characterization validated in the Thai population. </p>
Data and scripts supporting "Metabolic profiling of patient-derived organoids reveals nucleotide synthesis as a metabolic vulnerability in malignant rhabdoid tumors"
<p>This submission contains the processed sequencing data and analysis scripts, as well as the raw and processed LC-MS metabolomics files accompanying our manuscript "Metabolic profiling of patient-derived organoids reveals nucleotide synthesis as a metabolic vulnerability in malignant rhabdoid tumors" (Cell Reports Medicine, 2024)</p>
Hydrography profiles from ship CTD captured during PolarFront cruise 2024-01
<p>See the Cruise Report (Daase 2024) for details.</p> <p>Daase, M. (2024). PolarFront January 2024 Cruise Report. Zenodo. <a href="https://doi.org/10.5281/zenodo.10623810" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10623810</a></p>
Deliverable 6.4 Report on microbial CUE across a soil profile gradient
<p>This deliverable describes experimental data obtained from the Annex 2. EnergyLink Proposal - Addendum submitted on 8th of December 2022. The tasks in WP3, WP4 and WP5 were extended to include sub-soil samples. Specifically, annex 2 focused on exploring whether aboveground diversification affected the microbiome and microbial carbon cycling in top- and subsoil differently. Therefore, in this task, the Clever Cover Cropping experiment (the Netherlands) was sampled at 0-30 cm and 30-60 cm.</p>
Morphological profiling data resource enables prediction of chemical compound properties
<p><span><span>Morphological profiling with the Cell Painting assay has </span><span>emerged</span><span> as a promising method in drug discovery research. The assay captures morphological changes across various cellular compartments enabling the rapid prediction of compound </span></span><span><span><span>bioactivity</span></span></span><span><span>. We present a comprehensive morphological profiling resource using the carefully curated and well-annotated EU-OPENSCREEN Bioactive Compounds. </span><span>The data was generated across four imaging sites with high-throughput confocal microscopes using the </span><span>Hep</span><span> G2 as well as the U2 OS cell line.</span><span> We employed an extensive assay optimization process to achieve high data quality across the different sites. An analysis of the extracted profiles </span><span>validates</span><span> the robustness of the generated data. <span>We used this resource to compare the morphological features of the different cell lines. By correlating the profiles with overall activity, cellular toxicity, several specific mechanisms of action (MOAs), and protein targets, we </span><span>demonstrate</span><span> the dataset's potential for </span><span>facilitating</span><span> more extensive exploration of mechanisms of action.</span> </span></span></p>
Formation of winter barrier layers in the northern South China Sea: A study based on Argo profiling data from 2008 to 2020
<p>The data was used in the paper “Formation of winter barrier layers in the northern South China Sea: A study based on Argo profiling data from 2008 to 2020”.</p> <p>The data include interpolated Argo temperature/salinity (T/S) profiles for the period 2008-2020 in the 15°N–23°N, 109°E–121°E region, as well as derived mixed layer depth (MLD), isothermal layer depth (ILD), and barrier layer thickness (BLT). The original archival data were obtained from the French Argo Data Center (ftp://ftp.ifremer.fr/ifremer/argo/geo/pacific_ocean).</p>
Robust detection of SARS-CoV-2 exposure in population using T-cell repertoire profiling
<p>The dataset contains processed T-cell receptor repertoire sequencing data from >1200 individuals of different sex and age. Note that only samples with good sequencing coverage are published (>10^5 reads per file). </p> <p>The main aim of our study is to find TCR sequence biomarkers and develop a bioinformatic pipeline that allows building an accurate and robust classifier that distinguishes COVID-19-convalescent donors from unexposed individuals. We performed immunosequencing of the rearranged TCR α and β regions for PBMCs. For the cohort described in this study (Cohort-I) we sequence both chains of the TCR heterodimer as both of these chains are required to properly predict antigen recognition26. We ran conventional T-cell repertoire data analysis and pre-processed data to remove low-coverage samples. </p> <p>Of samples in Cohort-I which passed read count threshold, 383/377 TCR α/β samples were from healthy donors (SARS-CoV-2 PCR test negative or obtained prior to pandemic) and 890/848 were from COVID-19-positive patients. The majority of samples were accompanied by information on HLA class I and II alleles. Samples were prepared and sequenced in nine batches.</p> <p>The metadata for both TCR alpha and beta repertoires contains the following information:</p> <div> <ul> <li>sequencing_date - date when seguencing was performed</li> <li>batch_name - one of the 9 unique batch identifiers</li> <li>sample_id, patient_id - information on sample identifier and donor identifier</li> <li>COVID_status, COVID_IgG, COVID_IgM, COVID_PCR - information on COVID-19 status</li> <li>HLA-A.1, HLA-A.2, HLA-B.1, HLA-B.2, HLA-C.1, HLA-C.2 - MHC class I alleles</li> <li>HLA-DPB1.1, HLA-DPB1.2, HLA-DQB1.1, HLA-DQB1.2, HLA-DRB1.1, HLA-DRB1.2 - MHC class II alleles</li> <li>file_name - name of the corresponding file in <em>fmba_clonotype_usage_tables.zip </em>archive</li> </ul> </div> <p>Each file in <em>fmba_clonotype_usage_tables.zip </em>archive stores the information on either TCR alpha or beta repertoire. Each line in a file corresponds to the unique clonotype and each clonotype is accompanied with the following information:</p> <ul> <li>count - number of reads where the clonotype was detected</li> <li>freq - count of reads with the clonotype divided by thw whole number of reads in a sample</li> <li>cdr3nt, cdr3aa - nucleotide and amino acid sequences of TCR's CDR3 sequence</li> <li>v, d, j - the V/D/J segment name which was used for the clonotype's rearrangement</li> <li>VEnd, DStart, DEnd, JStart - information on VDJ junction positions </li> </ul> <p>We proceed with selecting a set of CDR3 sequences that can serve as biomarkers and form a feature list for COVID-19 status classifier. We also validate the resulting set of clonotypes in several ways. Co-occurence of specific TCR α and β clonotypes can serve as an independent validation for biomarkers and their co-association with some specific pathogen. Additional information on donor HLAs is provided to filter the set of biomarkers based on HLA restriction: association with donor HLA serves as an additional evidence for TCR specificity to a specific set of antigens presented in a given donor and allows detecting the fingerprint of past and present infection. Furthermore, clonotypes with similar sequences can be aggregated into 'metaclonotype' biomarkers based on clonotype graph analysis.</p> <p>Finally, we train various COVID-19 status classifiers on selected batches from Cohort-I data using different algorithms and incorporating different feature sets. Verification of the robustness of our results was performed using independent batches of the Cohort-I and data from Cohort-II published previously.</p>
Multiplexed protein profiling reveals spatial subcellular signaling networks
<ul> <li>Supplementary videos for "<strong>Multiplexed protein profiling reveals spatial subcellular signaling networks</strong>"</li> <li>The videos were made using Volocity 3D visualization software. </li> </ul>
Expression profile of microRNAs in different stages of wound healing in mice
<p><span><span><span><span><span><span><span><span><span><span><span>Wound healing response is characterized by progression from clot formation to an inflammatory phase, a repair phase, and finally remodeling. MicroRNAs (miRNAs) are 20-24 nucleotide noncoding RNAs that can affect protein expression at the posttranscriptional level by modulating the stability and translation of corresponding messenger RNAs (mRNAs). The roles that miRNAs play in various physiological and pathological processes have received increasingly widespread attention. However, the differentially expressed miRNAs involved in different stages of wound healing and their roles remain to be further explored. In this study, we established wound model on the shaved dorsum using a punch biopsy tool in C57BL/6J mice. Biopsies were harvested on postoperative days 0, 1, 3, and 7. Then, we identified the differentially expressed miRNAs in different stages of wound healing through Illumina sequencing and miRDeep2 analysis. This study help to find out the expression profile of microRNAs during normal wound healing in mice.</span></span></span></span></span></span></span></span></span></span></span></p>
Dataset used to support the performances of six reanalysis profile products
<p>This is a dataset used to support the article "<strong>Performances of six reanalysis profile products in </strong><strong>the </strong><strong>atmospheric correction of passive microwave data for estimating land surface temperature under cloudy-sky conditions</strong>"</p>
Vertical profiles of 3D cluster properties
<p>The two zip files contain the properties of the 3D clusters used in the submission version of the Paper "Size-dependence of surface-rooted three-dimensional convective objects in continental shallow cumulus simulations". The paper was submitted to JAMES in May 2021.</p> <p>When unzipped, the data is stored in python panda dataframes in pkl files. Warning! Unzipping the files increases the data size by a factor of 20.</p> <p>The two python scripts contain the functions used to segment the 3D snapshots into individual objects. The most interesting things are in proc_watershed, cusize_functions is just a collection of mostly abandoned functions. The functions in proc_watershed are reasonably well commented.</p>
latency profiling
<p>latency profiling</p>
Clear-sky profile database for the development of Land Surface Temperature algorithms
<p>This dataset includes clear sky atmospheric profiles from the European Centre for Medium Range Forecast (ECMWF) version-5 reanalysis (ERA5), specially selected to support the development of algorithms of Land Surface Temperature (LST) retrieval from Earth observation (EO) data. The profiles were re-sampled from an ERA5 dataset covering the 2009-2019 period, with a 1x1 degree spatial resolution, hourly sampling and using the full vertical resolution (137 model levels). The re-sampling technique is based on a dissimilarity criterion applied to profiles of temperature and specific humidity, in order to obtain regular distributions of atmospheric variables of relevance for LST retrieval in the Thermal Infrared (TIR) spectral range. The database is limited to clear-sky conditions over land, being therefore suitable for the development of satellite land products relying on optical and thermal infrared imagery in general, despite targeting especially LST.</p> <p><strong>Dataset description:</strong></p> <p>The dataset is divided in multiple netCDF4 files based on the range of skin temperature (Tskin; Kelvin) and the range of total column water vapour (TCWV; mm). Each file includes the following variables:</p> <ul> <li>Time</li> <li>Longitude</li> <li>Latitude</li> <li>2-m temperature (t2m)</li> <li>Surface pressure (sp)</li> <li>Total cloud cover (tcc)</li> <li>Total column water vapour (tcwv)</li> <li>Skin temperature (skt)</li> <li>Surface emissivity (emis)</li> <li>Land cover classification (lcc)</li> <li>Temperature profile (t)</li> <li>Specific humidity profile (q)</li> <li>Ozone profile (o3)</li> <li>Pressure profile (p)</li> </ul> <p>All profiles are provided on model levels. For each profile, 6 values of skin temperature and 25 values of emissivity are provided (see publication for details). Emissivity values correspond to the wavelengths of ~11 and ~12 µm.</p> <p><strong>Credit:</strong></p> <p>To use this data please cite this dataset and the respective journal publication:</p> <p>Ermida, S.L.; Trigo, I.F. (2022) A Comprehensive Clear-Sky Database for the Development of Land Surface Temperature Algorithms. <em>Remote Sens.</em>, <em>14</em>, 2329. <a href="https://doi.org/10.3390/rs14102329">https://doi.org/10.3390/rs14102329</a> </p> <p> </p> <p><strong>Access: </strong></p> <p>Currently, Zenodo does not provide a simple way to download datasets with a large number of files. We recomend trying the <a href="https://zenodo.org/record/3676567#.YnJAxtPMJhE">Zenodo_get</a> to simplify the download.</p>
Natural 15N abundance of bulk soil N, ammonium, and nitrate in soil profiles
<p><span><span>Assessment of nitrogen (N) saturation of forests is critical to evaluate how ecosystems will respond to current and future global changes such as N deposition. However, quantifying N saturation remains a challenge. We developed a conceptual model of N saturation stages in forest ecosystems based on i) a hypothetical relative rate of ammonification, nitrification, and denitrification, ii) concentrations of ammonium and nitrate in the soil, and iii) <sup>15</sup>N enrichment pattern of bulk soil N, ammonium, and nitrate in the soil profile. We tested the hypotheses using the data from the five forests located at five sites across eastern Asia, including one boreal forest an dtwo temperate forests in northeastern China, one temperate forest in Japan, and one subtropical forest in south China. The fraction of nitrate in total inorganic N (TIN) indicated that the sites represent an N saturation gradient with one boreal forest being least saturated, three temperate forests moderately saturated, and the tropical forest most saturated. The δ<sup>15</sup>N of bulk soil N increased from topsoil to subsoil more sharply at N-limited sites than at the N-rich sites along the N deposition gradient. We also found distinct <sup>15</sup>N enrichment patterns of bulk soil N, ammonium, and nitrate in the soil profile across the study sites. At the least saturated forest, nitrate was more <sup>15</sup>N-depleted than ammonium only in the organic soil horizon indicating limited nitrification while the <sup>15</sup>N depletion of nitrate than ammonium was observed in the deeper mineral soil in the moderatley and most saturated forests. Furthermore, ammonium was more <sup>15</sup>N-depleted than bulk soil N in the least and moderately saturated forests but more <sup>15</sup>N-enriched than bulk soil N in the most saturated forest. Our study suggests that soil profile patterns of δ<sup>15</sup>N of bulk soil N, ammonium, and nitrate provide information about the relative rates of mineralization, nitrification, and denitrification, thus can be an additional measure of N saturation of forest ecosystem across broad environmental gradients.</span></span></p>
Profiling of mature stage human breastmilk cells identifies host-defense lactocyte sub-populations
<p>Breastmilk is chock-full of nutrients, immunological factors, and cells that aid infant development. Maternal cells are the least studied breastmilk component, and their unique properties are difficult to identify using traditional techniques. Here, we characterized the cells in mature stage breastmilk from healthy donors at the protein, gene, and transcriptome levels. Holistic analysis of flow cytometry, qPCR, and single cell RNA sequencing data identified the predominant cell population as epithelial with smaller populations of macrophages and T cells. Two percent of epithelial cells expressed four stem cell markers: SOX2, TRA-1-60, NANOG, and SSEA4. Furthermore, milk contained six distinct epithelial lactocyte sub-populations, including three previously unidentified sub-populations programmed towards host-defense and intestinal development. Pseudotime analysis delineated the differentiation pathways of epithelial progenitors. Together, these data define healthy human maternal breastmilk cells and provide a basis for their application in maternal and infant medicine.</p>
Mine Haul Truck Rear Dump Profiles
<p>3D CAD files from photogrammetrical surveys of dump and stockpile faces made by mine haul trucks</p>
Kin recognition in Drosophila: Rearing environment and relatedness can modulate gut microbiota and cuticular hydrocarbon odour profiles
<p>From inbreeding avoidance to kin-selected cooperation, social behaviours are frequently reliant on kin recognition. However, kin recognition mechanisms are costly to evolve and currently not very well understood. Recent evidence suggests that, by altering their host's odour, gut and other host-associated microorganisms may provide a promising avenue for understanding kin recognition. In Drosophila melanogaster, kin recognition can mediate mate choice, sexual conflict and larval competition/cooperation, underscoring its important functional role. As is commonly the case, kin recognition in this species depends on both familiarity (i.e. shared rearing environment) and relatedness, and seems to rely mainly on body odours determined by cuticular hydrocarbons. Here, we investigated the degree to which larval rearing environment and relatedness (full-sibs vs. unrelated) determine co-variation between gut microbiota and cuticular hydrocarbons in D. melanogaster. We found that rearing environment strongly determined both microbiota and cuticular hydrocarbon composition, but that these effects were independent from each other. In contrast, relatedness did not influence microbiota composition, but had a strong influence on microbiota diversity, which in turn covaried significantly with cuticular hydrocarbon composition. Our results show that, in D. melanogaster, odours may convey information about both familiarity and relatedness via an interaction between: a) direct effects of the rearing environment on cuticular hydrocarbons and b) indirect effects of relatedness on cuticular hydrocarbons via gut microbiota diversity.</p>
Screening of 67 compounds with predicted biosignature profile similar to remdesivir for anti-SARS-CoV-2 activity
<p>This short report describes the most relevant results of screening compounds with a similar predicted biosignature as remdesivir in a VeroE6 cell-based anti-SARS-CoV-2 assay.</p>
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.