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30,948 results for “Profile”
IODP Expedition 379 Laser height profile (section half)
Height profile data were measured on the Section Half Multisensor Logger (SHMSL) by a rangefinding laser and recorded in uncorrected height units in millimeters in CSV files.
IODP Expedition 371 Laser height profile (section half)
Height profile data were measured on the Section Half Multisensor Logger (SHMSL) by a rangefinding laser and recorded in uncorrected height units in millimeters in CSV files.
Online Appendix for PhD Thesis Titled "Dissecting Causal Relationships and Molecular Mechanisms in Disease using Genetic Risk Profiles"
<p>This repository contains 23 tables and two figures, which are too big to be included in the Appendix section of my thesis document.</p> <p>The second version includes additional summary statistics of metabolite-PGS associations which can be found at http://mrcieu.mrsoftware.org/metabolites_PRS_atlas/.</p>
Data used for doi.org/10.1029/2012JD018338 : Baars et al, 2012: Aerosol profiling with lidar in the Amazon Basin during the wet and dry season
<p><span>This is the data whoch ahs been used for the publication:</span></p> <p><span><span>Baars, H.</span></span><span>, <span>A. Ansmann</span>, <span>D. Althausen</span>, <span>R. Engelmann</span>, <span>B. Heese</span>, <span>D. Müller</span>, <span>P. Artaxo</span>, <span>M. Paixao</span>, <span>T. Pauliquevis</span>, and <span>R. Souza</span> (<span>2012</span>), <span>Aerosol profiling with lidar in the Amazon Basin during the wet and dry season</span>, <em>J. Geophys. Res.</em>, <span>117</span>, D21201, doi:<a title="Link to external resource: 10.1029/2012JD018338" href="https://doi.org/10.1029/2012JD018338" target="_blank" rel="noopener">10.1029/2012JD018338</a>.</span></p> <p><span>For each of the Figures in the Publication the underlaying data is provided in a respective folder.</span></p> <p><span>The raw data (i.e,. the analyzed lidar data for several case during the one-year campaign in 2008) is provided separately.</span></p> <p><span>As the time of data creation is more than 10 years ago, the data description does not comply to current standards. Thus, in case of any questions, please contact the first author.</span></p> <p> </p> <p> </p> <p> </p>
Development of ferret immune repertoire reference resources and single-cell-based high- throughput profiling assays
<p>We performed long read transcriptome sequencing of ferret splenocyte and lymph node samples full-length, non-chimeric circular consensus sequencing (CCS) reads to obtain over 120,000 high-quality immunoglobin (Ig) and T cell receptor (TCR) transcripts.</p>
IODP Expedition 360 Laser height profile (section half)
Height profile data were measured on the Section Half Multisensor Logger (SHMSL) by a rangefinding laser and recorded in uncorrected height units in millimeters in CSV files.
TCOM-COF2: TOMCAT CTM and Occultation measurement-based Stratospheric COF2 profile data set
<p>TCOM-COF2: TOMCAT CTM and Occultation measurement-based Stratospheric TCOM-COF2 profile data set </p> <p>Sandip S. Dhomse </p> <p>School of Earth and Enviro, University of Leeds, Leeds, UK</p> <p>National Centre for Earth Observations, University of Leeds, Leeds, UK</p> <p> email: s.s.dhomse@leeds.ac.uk</p> <p> </p> <p>Methodology: TOMCAT simulation is performed at T64L32 resolution for the 2000-2023 time period. Collocated COF2 profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Initially, model-measurement differences are calculated for each zonal bins (51 height levels, 10km to 60km). Separate XGBoost regression models are trained for the differences between TOMCAT and measurements at each level for a given latitude bin. XGBoost model is then used to estimate error corrections for all the TOMCAT grids. Estimated corrections for a given model grid that are added to the original TOMCAT simulated daily (at 1.30 local time) COF2 profiles. Height resolved data are then interpolated on 28-pressure levels (300 - 0.1hPa). For overlapping latitude bins, we use averages and then calculate daily zonal mean values. For more details see attached presentation.</p> <p>Dataset also includes two files containing daily mean zonal mean COF2 profiles on height (10-60 km) and pressure (300-0.1 hPa) levels (8766 days/64 latitudes):</p> <p>zmcof2_TCOM_hlev_T2Dz_2000_2023.nc – height level data (10 to 60 km)</p> <p>zmcof2_TCOM_plev_T2Dz_2020_2023.nc – pressure level data (300 to 0.1 hPa)</p> <p>Daily 3D profiles on height and pressure levels would be made available on request. Xarrays “resample” can be used to get monthly means.</p>
Single-cell transcriptomic profiling unveils dysregulation of cardiac progenitor cells and cardiomyocytes in a mouse model of maternal hyperglycemia
<p>Congenital heart disease (CHD) is the most prevalent structural malformations of the heart affecting ∼1% of live births. To date, both damaging genetic variations and adverse environmental exposure such as maternal diabetes have been found to cause CHD. Clinical studies show ∼fivefold higher risk of CHD in the offspring of mothers with pregestational diabetes. Maternal pregestational diabetes affects the gene regulatory networks key to proper cardiac development in the fetus. However, the cell-type specificity of these gene regulatory responses to maternal diabetes and their association with the observed cardiac defects in the fetuses remains unknown. To uncover the transcriptional responses to maternal diabetes in the early embryonic heart, we used an established murine model of pregestational diabetes. In this model, we have previously demonstrated an increased incidence of CHD. Here, we show maternal hyperglycemia (matHG) elicits diverse cellular responses during heart development by single-cell RNA-sequencing in embryonic hearts exposed to control and matHG environment. Through differential gene-expression and pseudotime trajectory analyses of this data, we identified changes in lineage specifying transcription factors, predominantly affecting Isl1+ second heart field progenitors and Tnnt2+cardiomyocytes with matHG. Using in vivo cell-lineage tracing studies, we confirmed that matHG exposure leads to impaired second heart field-derived cardiomyocyte differentiation. Finally, this work identifies matHG-mediated transcriptional determinants in cardiac cell lineages elevate CHD risk and show perturbations in Isl1-dependent gene-regulatory network (Isl1-GRN) affect cardiomyocyte differentiation. Functional analysis of this GRN in cardiac progenitor cells will provide further mechanistic insights into matHG-induced severity of CHD associated with diabetic pregnancies.</p>
IODP Expedition 397 Laser height profile (section half)
Height profile data were measured on the Section Half Multisensor Logger (SHMSL) by a rangefinding laser and recorded in uncorrected height units in millimeters in CSV files.
Temperature Profiles from the Eastern Tropical Pacific (0-300m) from January-February 2023
<p>Vertical temperature profiles taken as part of a research expedition to Clipperton Atoll. Water column temperature profiles were measured down to 300m depth by deploying a <em>RBRduet<sup>3</sup> T.D.</em> sensor<sup> </sup>(Range -5°C to 35°C; Initial accuracy ±0.002°C; Resolution <0.00005°C; time constant <1s). Data (downcast and upcast) is averaged by depth into 1 meter bins, and the standard deviation and number of measurements in each bin are included as columns in the data.</p> <p> </p> <p>This data is supplemented locally for the shallow waters of Clipperton Atoll with 21 water column profiles measured using a Mares Puck Pro dive computer (Range: -10 °C to +50 °C; Resolution: 1°C; Accuracy: ± 2 °C) worn by one of the expedition divers.</p>
Predictive models for off-target binding profiles generation
<p>Models for predicting off-target binding, built with Conformal Prediction, and the <a href="http://cpsign-docs.genettasoft.com">CPSign software</a>. The dataset is part of an upcoming publication (Manuscript in preparation), which will provide more details.</p> <p>The dataset is a GZipped Tar archive, with the models as Java Archive (JAR) files. For every JAR-file, there is also a corresponding audit log, with the extension ".audit.json", produced by the workflow software (<a href="http://scipipe.org">SciPipe</a>) used to train the models. This audit file contains all the shell commands used in the workflow that produced the models.</p>
Supplementary data to accompany "phyloFlash: A pipeline for rapid SSU rRNA-targeted profiling of metagenomes"
<p>Usage examples of the phyloFlash pipeline applied to shotgun metagenomic data sets.</p> <p>The phyloFlash software is available from https://github.com/HRGV/phyloFlash. Examples were generated with phyloFlash v3.3b.</p>
Graph topological features extracted from expression profiles of neuroblastoma patients
<p><strong>Introduction</strong></p> <p>This dataset contains the data described in the paper titled "A deep neural network approach to predicting clinical outcomes of neuroblastoma patients." by Tranchevent, Azuaje and Rajapakse. More precisely, this dataset contains the topological features extracted from graphs built from publicly available expression data (see details below). This dataset does not contain the original expression data, which are available elsewhere. We thank the scientists who did generate and share these data (please see below the relevant links and publications).</p> <p> </p> <p><strong>Content</strong></p> <p>File names start with the name of the publicly available dataset they are built on (among "Fischer", "Maris" and "Versteeg"). This name is followed by a tag representing whether they contain raw data ("raw", which means, in this case, the raw topological features) or TF formatted data ("TF", which stands for TensorFlow). This tag is then followed by a unique identifier representing a unique configuration. The configuration file "Global_configuration.tsv" contains details about these configurations such as which topological features are present and which clinical outcome is considered.</p> <p>The code associated to the same manuscript that uses these data is at <a href="https://gitlab.com/biomodlih/SingalunDeep">https://gitlab.com/biomodlih/SingalunDeep</a>. The procedure by which the raw data are transformed into the TensorFlow ready data is described in the paper.</p> <p> </p> <p><strong>File format</strong></p> <p>All files are TSV files that correspond to matrices with samples as rows and features as columns (or clinical data as columns for clinical data files). The data files contain various sets of topological features that were extracted from the sample graphs (or Patient Similarity Networks - PSN). The clinical files contain relevant clinical outcomes.</p> <p>The raw data files only contain the topological data. For instance, the file "Fischer_raw_2d0000_data_tsv" contains 24 values for each sample corresponding to the 12 centralities computed for both the microarray (<em>Fischer-M</em>) and RNA-seq (<em>Fischer-R</em>) datasets. The TensorFlow ready files do not contain the sample identifiers in the first column. However, they contain two extra columns at the end. The first extra column is the sample weights (for the classifiers and because we very often have a dominant class). The second extra column is the class labels (binary), based on the clinical outcome of interest.</p> <p> </p> <p><strong>Dataset details</strong></p> <p>The <em>Fischer</em> dataset is used to train, evaluate and validate the models, so the dataset is split into train / eval / valid files, which contains respectively 249, 125 and 124 rows (samples) of the original 498 samples. In contrast, the other two datasets (<em>Maris</em> and <em>Versteeg</em>) are smaller and are only used for validation (and therefore have no training or evaluation file).</p> <p>The <em>Fischer</em> dataset also has more data files because various configurations were tested (see manuscript). In contrast, the validation, using the <em>Maris</em> and <em>Versteeg</em> datasets is only done for a single configuration and there are therefore less files.</p> <p>For <em>Fischer</em>, a few configurations are listed in the global configuration file but there is no corresponding raw data. This is because these items are derived from concatenations of the original raw data (see global configuration file and manuscript for details).</p> <p> </p> <p><strong>References</strong></p> <p>This dataset is associated with Tranchevent L., Azuaje F.. Rajapakse J.C., A deep neural network approach to predicting clinical outcomes of neuroblastoma patients.</p> <p>If you use these data in your research, please do not forget to also cite the researchers who have generated the original expression datasets.</p> <p><em>Fischer</em> dataset:</p> <ul> <li>Zhang W. et al., Comparison of RNA-seq and microarray-based models for clinical endpoint prediction. Genome Biology 16(1) (2015). doi:10.1186/s13059-015-0694-1</li> <li>Wang C. et al., The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance. Nat. Biotechnol. 32(9), 926–932. doi:10.1038/nbt.3001</li> </ul> <p><em>Versteeg</em> dataset:</p> <ul> <li>Molenaar J.J. et al., Sequencing of neuroblastoma identifies chromothripsis and defects in neuritogenesis genes. Nature 483(7391), 589–593. doi:10.1038/nature10910</li> </ul> <p><em>Maris</em> dataset:</p> <ul> <li>Wang Q. et al., Integrative genomics identifies distinct molecular classes of neuroblastoma and shows that multiple genes are targeted by regional alterations in DNA copy number. Cancer Res. 66(12), 6050–6062. doi:10.1158/0008-5472.CAN-05-4618</li> </ul>
Fed4Fire/CDN-X-ALL M3AP, M2AP reference points implementation pcap datatsets for the 3GPP MBMS profile
<p>Following datasets were generated at VICOMTECH (https://www.vicomtech.org) under project/experiment CDN-X-ALL: "CDN edge-cloud computing for efficient cache and reliable streaming aCROSS Aggregated unicast-multicast LinkS".</p> <p>Project funded by Fed4FIRE+ OC5 (<a href="https://www.fed4fire.eu/">https://www.fed4fire.eu</a>) under grant 732638.</p> <p>The following data provide reference pcap captures that include relevant traces with standardized 3GPP interfaces for the 3GPP MBMS (Multimedia Broadcast Multicast Services) profile.</p> <p>Among others, traces include E-UTRAN and Evolved Packet Core pcap captures with standardized 3GPP M2AP and M3AP interfaces and UE MBMS end to end MAC layer PDUs:</p> <p>1- MBMS_OAI_M3AP_M2AP.pcap</p> <p>- OAI eNB LTE; Evolved Universal Terrestrial Radio Access Network (E-UTRAN); M3 Application Protocol (M3AP) in between an MME (Mobility Management Entity) and MCE (Multicast Control Entity) (3GPP TS 36.444 version 14.1.0 Release 14)</p> <p>- OAI eNB LTE; Evolved Universal Terrestrial Radio Access Network (E-UTRAN); M2 Application Protocol (M2AP) in between an MCE (Multicast Control Entity) and eNB (Evolved NodeB) (3GPP TS 36.443 version 14.0.1 Release 14)</p> <p>2- oai_opt_ue_mbms_SIB13_MTCH_MCCH.pcap</p> <p>- OAI UE side LTE MAC and RRC messages</p> <p>Moreover, the above data can be self-re-generated by downloading and building the original open source code pushed (as part of CDN-X-ALL project) to OpenAirInteface5G gitlab open repository. At the time of publishing these data, the original source code falls under branch (https://gitlab.eurecom.fr/oai/openairinterface5g/tree/fed4fire_fec5_cdn-x-all) and is part of ongoing merge request (https://gitlab.eurecom.fr/oai/openairinterface5g/merge_requests/673)</p>
Pressure distribution and schlieren for various locations of transonic compressor profiles
<p>One of the primary objectives of the EU TFAST and TEAMAero projects is to study the shock wave and boundary layer interaction on the suction side of the transonic compressor blade. In this context, the test section is designed and assembled at the IMP PAN laboratory to replicate the flow structure in a transonic compressor cascade.</p> <p>The system's sensitivity to variable parameters in the test section is crucial for achieving the design conditions and ensuring that the flow is influenced solely by the target parameter under study.</p> <p><strong>The provided data</strong> pertains to a study on the system's sensitivity to small changes in the position of the profiles relative to the nozzle (~±2% x/c). The results indicated that the profile position does not significantly alter the overall flow structure or the inflow Mach number. However, it affects the Mach number distribution over the lower profile, particularly upstream of the passage shock within the range of 0.3-0.4x/c.</p> <p><span>Data can be used for CFD validation and sensitivity study. </span>The pressure files and schlieren images are grouped by date.</p> <p> </p>
Research data from the survey on Smart Cities professional profiles for the Article "Modelling and analyzing the availability of technical professional profiles for the success of Smart Cities projects in Europe"
<p>The file includes the complete version of data collected through the surrvey on recommended profile for two professional roles in the context of Smart Cities (SC) projects: SC engineer and SC technician. It complements the previous version focused on IoT implementation stired at <a href="../doi/10.5281/zenodo.7492254">https://zenodo.org/doi/10.5281/zenodo.7492254</a></p>
Profiling results for electron-positron plus jets production with Pepper
<h2>Profiling results for electron positron plus jets production with Pepper v1.1.1</h2> <h3>Contents</h3> <p>This includes data/output for the following GPU accelerated unweighted event generation runs:</p> <ul> <li>A run over a few seconds to produce pepper-internal timing results (`*timers.csv` files)</li> <li>The same run, but with `nvprof` (`*.nvprof` files)</li> <li>A run with a single event batch, with `ncu --import-source on --set full` (`*.ncu-rep` files)</li> </ul> <p>The simulated process is electron-positron pair production with n jets, with n = 0...4, at 13 TeV. The configuration files (`pepper.ini`) are included. Also the `pepper_cache` directories are included.</p> <p>All results are for the "main" pepper variant (which uses Kokkos to utilise the GPU) and for the "native" pepper variant (which uses CUDA directly).</p> <h3>Software stack and CPU/GPU hardware details</h3> <p>The software/hardware used for the profiling are as follows:</p> <ul> <li>GPU Driver: NVIDIA-SMI: 550.100, Driver Version: 550.100, CUDA Version: 12.4</li> <li>GPU: Tesla V100S-PCIE-32GB</li> <li>Cuda compilation tools: release 11.6, V11.6.124</li> <li>Kokkos 4.3.01</li> <li>gcc (GCC) 11.4.1 20231218 (Red Hat 11.4.1-3)</li> <li>Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz</li> </ul> <p>Note that the event generation includes write-out of event files. The LHEH5 files are written to local SSD storage. The event files are not included in this dataset.</p> <h3>Additional run parameters</h3> <table> <tbody> <tr> <td>e^+ e^-</td> <td>number of batches</td> <td>batch size</td> <td>rate (main variant)</td> <td>rate (native variant)</td> </tr> <tr> <td>+0j</td> <td>20</td> <td>1,179,648</td> <td>5.7e9</td> <td>7.4e9</td> </tr> <tr> <td>+1j</td> <td>20</td> <td>1,179,648 * 2</td> <td>1.4e10</td> <td>3.3e10</td> </tr> <tr> <td>+2j</td> <td>80</td> <td>1,179,648 / 2</td> <td>1.3e10</td> <td>5.4e10</td> </tr> <tr> <td>+3j</td> <td>20</td> <td>1,179,648</td> <td>7.7e9</td> <td>1.6e10</td> </tr> <tr> <td>+4j</td> <td>20</td> <td>1,179,648</td> <td>2.2e9</td> <td>3.1e9</td> </tr> <tr> <td>+5j</td> <td>20</td> <td>1,179,648</td> <td>3.4e8</td> <td>4.2e8</td> </tr> </tbody> </table> <p>The number of batches has been chosen such that the most important sub processes are likely to be sampled and such that the overall runtime is at least a few seconds. The batch size has been chosen by scanning over factors of two and picking the best-performing batch size with the native variant. The event rate is the number given in the Pepper output. It does not include the closing time of the generated HDF5 file, which can be significant for the two lowest multiplicities. This can be checked by inspecting the corresponding relative and absolute timing results in the `*timing.csv` files included in the dataset.</p>
Profiling results for top pair plus jets production with Pepper
<h2>Profiling results for top pair plus jets production with Pepper v1.1.1</h2> <h3>Contents</h3> <p>This includes data/output for the following GPU accelerated unweighted event generation runs:</p> <ul> <li>A run over a few seconds to produce pepper-internal timing results (`*timers.csv` files)</li> <li>The same run, but with `nvprof` (`*.nvprof` files)</li> <li>A run with a single event batch, with `ncu --import-source on --set full` (`*.ncu-rep` files)</li> </ul> <p>The simulated process is top pair production with n jets, with n = 0...4, at 13 TeV. The configuration files (`pepper.ini`) are included. Also the `pepper_cache` directories are included.</p> <p>All results are for the "main" pepper variant (which uses Kokkos to utilise the GPU) and for the "native" pepper variant (which uses CUDA directly).</p> <h3>Software stack and CPU/GPU hardware details</h3> <p>The software/hardware used for the profiling are as follows:</p> <ul> <li>GPU Driver: NVIDIA-SMI: 550.100, Driver Version: 550.100, CUDA Version: 12.4</li> <li>GPU: Tesla V100S-PCIE-32GB</li> <li>Cuda compilation tools: release 11.6, V11.6.124</li> <li>Kokkos 4.3.01</li> <li>gcc (GCC) 11.4.1 20231218 (Red Hat 11.4.1-3)</li> <li>Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz</li> </ul> <p>Note that the event generation includes write-out of event files. The LHEH5 files are written to local SSD storage. The event files are not included in this dataset.</p> <h3>Additional run parameters</h3> <table> <tbody> <tr> <td>TTBar</td> <td>number of batches</td> <td>batch size</td> <td>rate (main variant)</td> <td>rate (native variant)</td> </tr> <tr> <td>+0j</td> <td>20</td> <td>1,179,648</td> <td>1.4e10</td> <td>2.0e10</td> </tr> <tr> <td>+1j</td> <td>20</td> <td>1,179,648</td> <td>1.5e10</td> <td>2.4e10</td> </tr> <tr> <td>+2j</td> <td>20</td> <td>1,179,648 * 2</td> <td>1.3e10</td> <td>2.7e10</td> </tr> <tr> <td>+3j</td> <td>20</td> <td>1,179,648 / 2</td> <td>6.4e9</td> <td>1.4e10</td> </tr> <tr> <td>+4j</td> <td>20</td> <td>1,179,648</td> <td>2.2e9</td> <td>2.2e9</td> </tr> </tbody> </table> <p>The number of batches has been chosen such that the most important sub processes are samples and such that the overall runtime is at least a few seconds. The batch size has been chosen by scanning over factors of two and picking the best-performing batch size with the native variant. The event rate is the number given in the Pepper output. It does not include the closing time of the generated HDF5 file, which can be significant for the two lowest multiplicities. This can be checked by inspecting the corresponding relative and absolute timing results in the `*timing.csv` files included in the dataset.</p>
Geochemistry of a weathering profile and natural waters in the upland Mediterranean catchment of Sapine, Mt-Lozère, southern France
<p>This publication reports geochemical data from an instrumented catchment of the Service National d’Observation “Observatoire Hydro-Météorologique Cévennes-Vivarais” (OHMCV) and of the National Research Infrastructure “Observatoires de la Zone Critique: Applications et Recherche” (OZCAR) of the French “Centre National de la Recherche Scientifique” (CNRS).</p> <p>The presented data are originally contained in Kuessner, M.L. (2018). The interplay between chemical weathering and hydrology in the Critical Zone - insight from trace elements and lithium isotopes [Doctoral dissertation, Institut de Physique du Globe de Paris]</p> <p>Contents</p> <p>Table SC1. Analyses of soil samples in Sapine Creek</p> <p>Table SC2. Analyses of rock samples in Sapine Creek</p> <p>Table SC3. Analyses of rain samples in Sapine Creek and Cloutasse catchments</p> <p>Table SC4. Analyses of stream samples in Sapine Creek</p> <p>Table SC5. Analytical limits for quadrupole ICP-MS measurements</p> <p>Table SC6. Analyses of reference materials for isotope measurements</p>
Literature Curated PPIs from "Accurate and Sensitive Interactome Profiling Using a Quantitative Protein-Fragment Complementation Assay"
<p>This dataset includes the literature curated protein-protein interactions supporting the manuscript titled 'Accurate and Sensitive Interactome Profiling Using a Quantitative Protein-Fragment Complementation Assay'.</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.