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30,948 results for “Profile”
Bug in CCFD stress profile calculation of GROMACS-LS?
<p>In Martini simulations (15 us, last 5 us analyzed) of an asymmetric GM1+POPC lipid bilayer (5+95 upper, 0+100 lower leaflet) strong stress fluctuations arising from the dihedral contributions of CCF decomposition are found. It is possible that these are due to a slight bug in the GROMACS-LS (http://mdstress.org) code, because:</p> <p>1) The fluctuations do not show converge, when more data is added, see the attached plot ‘effect_of_sampling_rate.pdf’.</p> <p>2) The fluctuations (but no other features of the stress profiles) visibly depend on the chosen frame of reference, see ‘effect_of_centering_schemes.pdf’.</p> <p>3) The stress profiles calculated along the coordinates that lay in the membrane plane (x and y), are not constant in CCFD, see ‘profiles_along_x_and_y.pdf’.</p> <p> </p> <p>To assist debugging the code, this repository contains:</p> <p><strong>5to95_0to100_0.trr</strong> --- 1st 33.3% of a 5 us trajectory with snapshots every 100 ps<br> <strong>5to95_0to100_0.trr</strong> --- 2nd 33.3% of a 5 us trajectory with snapshots every 100 ps<br> <strong>5to95_0to100_0.trr</strong> --- 3rd 33.3% of a 5 us trajectory with snapshots every 100 ps</p> <p><strong>5to95_0to100.tpr</strong> --- tpr used for production (Gromacs 5.1.1)<br> <strong>5to95_0to100_rerun.tpr</strong> --- tpr used for stress analysis and centering</p> <p><strong>5to95_0to100.gro</strong> --- gro file after 15 us (10 us relaxation, 5 us production)</p> <p><strong>index.ndx</strong> --- index file used in production<br> <strong>5to95_0to100.ndx</strong> --- index file used in analysis</p> <p><strong>system.top</strong> --- topology file<br> <strong>martini_v2.0_ions.itp</strong> --- martini FF file (system.top expects this to be in folder toppar/)<br> <strong>martini_v2.0_lipids_all_201506.itp</strong> --- martini FF file (system.top expects this to be in folder toppar/)<br> <strong>martini_v2.2.itp</strong> --- martini FF file (system.top expects this to be in folder toppar/)</p> <p><strong>martini_straight_GM1_saveVels.mdp</strong> --- run input file used for production (Gromacs 5.1.1)<br> <strong>martini_straight_GM1_rerunForP.mdp</strong> --- run input file used for analysis and centering</p> <p><strong>effect_of_sampling_rate.pdf</strong> --- plot of results showing that fluctuations in CCFD do not appear to converge when data is added<br> <strong>profiles_along_x_and_y.pdf</strong> --- plot of results showing that in CCFD pressure profiles along the membrane directions are not constant<br> <strong>effect_of_centering_schemes.pdf</strong> --- plot of results showing that choice of center of mass (here three possibilities are shown: CoM of POPC GL1 beads, CoM of POPCs, and CoM of all lipids) visibly affects CCFD fluctuations, but not other features of the stress profile<br> <strong>effect_of_centering_schemes_2.pdf</strong> --- plot of results (similar to 'effect_of_centering_schemes.pdf', but the center of the bilayer is at 3.7 nm instead at 0.0 / 7.4 nm)<br> <strong>individual_components.pdf</strong> --- plot of results showing that the fluctuations in CCFD arise from dihedrals<br> <strong>CCFD_gridsize.pdf</strong> --- plot of results showing that decreasing gridsize has no clear effect on the CCFD fluctuations</p>
Quasar spectra and absorption profile fits of HE 0515-4414 for limiting fine-structure constant variability
<p>This is the first public pre-release upon submission to MNRAS and to obtain a DOI.</p>
Figures 7–12. - Neohemisphaeriusguangxiensis sp. n. 7 Hind wing 8 Male genitalia, in lateral view 9 Aedeagus, in left view 10 Aedeagus, ventral view 11 Genital style, in profile view 12 Anal tube, in dorsal view.
Figures 7–12. - Neohemisphaeriusguangxiensis sp. n. 7 Hind wing 8 Male genitalia, in lateral view 9 Aedeagus, in left view 10 Aedeagus, ventral view 11 Genital style, in profile view 12 Anal tube, in dorsal view.
User-Profiling Dataset
<p>The User-Profiling Dataset provides hand labeled user action videos and frames. The dataset includes over 40k images from 236 video clips containing five different user actions, and five different sequences of actions.</p>
A comparison of human and mouse protein corona profiles of functionalized SiO2 nanocarriers
<p>Proteomic evaluation of the protein corona formed on SiO2 nanoparticles with human and mouse plasma.</p>
A dataset of ground-based vertical profile observations of aerosol, NO2 and HCHO from the hyperspectral vertical remote sensing network in China (2019-2023)
<p>Vertical <span>profile </span>observations of atmospheric composition are crucial for understanding the generation, evolution, and transport of regional air pollution. However, existing technological limitations and costs have resulted in a scarcity of vertical profil<span>e</span> data. This study <span>introduces </span>a high-<span>time-</span>resolution (approximately 15 minutes) dataset of vertical <span>profile </span>observations of atmospheric composition (aerosols, NO2, and HCHO) conducted using passive remote sensing technology across 32 sites in seven major regions of China from 2019 to 2023. The study meticulously documents the vertical distribution, seasonal <span>variations and </span>diurnal <span>pattern</span> of these pollutants, revealing long-term trends in atmospheric composition across various regions of China. This dataset provides essential scientific evidence for regional environmental management and policy-making. Its sharing <span>would </span>facilitate the scientific community <span>in </span>explor<span>ing</span> of source-receptor relationships, investigating the impacts of atmospheric composition on regional and global climate <span>and </span>feedback mechanisms.</p>
A temporally continuous divergence and vorticity dataset in Beijing derived from the radar wind profiler mesonet during 2023
<p>A temporally continuous horizontal divergence and vertical vorticity dataset is produced by horizontal winds derived from the radar wind profilers mesonet in Beijing by applying the triangle method. This dataset covers the period of 2023 with a temporal resolution of 6-minute and a vertical resolution of 120 m. The dataset is of significance for a multitude of scientific research and applications, including air quality, convection initiation and so on. The latest version includes rain flags of triangles.</p>
Comparison of Fixed Single Cell RNA-seq Methods to Enable Transcriptome Profiling of Neutrophils in Clinical Samples
<p>Monitoring neutrophil gene expression is a powerful tool for understanding disease mechanisms, developing new diagnostics, therapies and optimizing clinical trials. Neutrophils are sensitive to the processing, storage and transportation steps that are involved in clinical sample analysis. This study is the first to evaluate the capabilities of technologies from 10X Genomics, PARSE Biosciences, and HIVE (Honeycomb Biotechnologies) to generate high-quality RNA data from human blood-derived neutrophils. Our comparative analysis shows that all methods produced high quality data, importantly capturing the transcriptomes of neutrophils. 10X FLEX cell populations in particular showed a close concordance with the flow cytometry data. Here, we establish a reliable single-cell RNA sequencing workflow for neutrophils in clinical trials: we offer guidelines on sample collection to preserve RNA quality and demonstrate how each method performs in capturing sensitive cell populations in clinical practice.</p> <p><strong>This dataset includes the FACS, 10X 3', Parse, 10X Flex, and Hive data and analysis.</strong></p>
Joint profiling of cell morphology and gene expression during in vitro neurodevelopment
<p>Cell Painting processed data for "Joint profiling of cell morphology and gene expression during in vitro neurodevelopment".</p>
Hourly wind and solar generation profiles for every EIA 2020 plant in the CONUS
<p>Historical hourly time series of wind and solar generation profiles for every plant within the United States (US) that is part of the Energy Information Administration (EIA) 2020 dataset for the years 1980 through 2022. The data uses regional atmospheric climate model simulations and 2020 wind and solar power plant configurations across the entire contiguous US. This data is designed to be be aggregated to the Balancing Authority (BA) scale, or to other scales such as to the nodes of a production cost model which would allow the data to be used to perform reliability assessments and evaluations of technology innovation. There are ongoing efforts to extend this dataset for future climate projections, which additionally require the projection of future infrastructure under a wide range of uncertainties. This historical dataset is a benchmark for those projections and can be used to understand sensitivity to historical inter-annual variability, seasonality, and recent extreme events.</p> <p>For more information please refer to the <a href="https://www.nature.com/articles/s41597-024-03894-w">Scientific Data paper</a> and the <a href="https://github.com/GODEEEP/tgw-gen">code repository</a>.</p> <p>The dataset consists of two components:</p> <ul> <li>Plant configuration files - The plant configuration files (<code>eia_solar_configs.csv</code> and <code>eia_wind_configs.csv</code>) contain all the plant data that is relevant to a generation model, derived from EIA 860 2020 data. Each row corresponds to a single logical plant. In some cases actual plants were split into two logical plants for modeling purposes.</li> <li>Generation data files - The generation data resides in the <code>solar/</code> and <code>wind/</code> directories and consists of one file per year. Each year contains an 8760 (hourly) profile of generation for every plant. The first column in each csv file is the datetime in Coordinated Universal Time (UTC), and the subsequent columns correspond to the <code>plant_code_unique</code> column in the configuration files. Data in these generation files is expressed as a capacity factor, which is generation divided by the plant capacity. To obtain the actual Megawatts (MW) generated, multiply the capacity factor for a plant by the value in the <code>system_capacity</code> column from the respective configuration file.</li> </ul> <p>This research was supported under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL). PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p> <p>Version 1.1.0 adds bias corrected solar and extends the data through 2022</p> <p>Version 1.1.1 adds missing wind years 2021 and 2022</p> <p>Version 1.2.0 Extends the data through 2024</p> <p>Corresponding Author:</p> <ul> <li>Cameron Bracken, cameron.bracken@pnnl.gov</li> </ul>
The Joint Undertaking for Morphological Profiling (JUMP) Consortium Datasets
<h2>What's Changed</h2> <ul> <li>feat: Add SQLite export script and documentation by @shntnu in https://github.com/jump-cellpainting/datasets/pull/166</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/jump-cellpainting/datasets/compare/v0.12...v0.13</p>
NAUTILOS - Temperature profiles 2012-2020 - Fishing Vessels profiles by AdriFOOS platform - CNR IRBIM
<p>Dataset of depth and temperature profiles obtained from 2012 to 2020 using commercial fishing vessels of the AdriFOOS fleet in the Adriatic Sea</p> <p><span lang="EN-GB">Full metadata: </span><a title="https://data-nautilos-h2020.eu/erddap/info/AdriFOOS_profiles_2012-2020/index.html" href="https://data-nautilos-h2020.eu/erddap/info/AdriFOOS_profiles_2012-2020/index.html" target="_blank" rel="noopener"><span lang="EN-GB">https://data-nautilos-h2020.eu/erddap/info/AdriFOOS_profiles_2012-2020/index.html</span></a></p> <p><span lang="EN-GB">This dataset is part of the NAUTILOS project outcomes. NAUTILOS is funded from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 101000825.</span></p>
Data archive for: Exploring the use of machine learning to improve vertical profiles of temperature and moisture
<p>Vertical profiles of temperature and dewpoint are useful in predicting deep convection that leads to severe weather that threatens property and lives. Currently, forecasters rely on observations from radiosonde launches and numerical weather prediction (NWP) models. Radiosonde observations are, however, temporally and spatially sparse, and NWP models contain inherent errors that influence short-term predictions of high-impact events. This work explores using machine learning (ML) to postprocess NWP model forecasts, combining them with satellite data to improve vertical profiles of temperature and dewpoint. We focus on different ML architectures, loss functions, and input features to optimize predictions. Because we are predicting vertical profiles at 256 levels in the atmosphere, this work provides a unique perspective at using ML for 1-D tasks. Compared to baseline profiles from the Rapid Refresh (RAP), ML predictions offer the largest improvement for dewpoint, particularly in the mid- and upper-atmosphere. emperature improvements are modest, but CAPE values are improved by up to 40%. Feature importance analyses indicate that the ML models are primarily improving incoming RAP biases. While additional model and satellite data offer some improvement to the predictions, architecture choice is more important than feature selection in fine-tuning the results. Our proposed deep residual UNet performs the best by leveraging spatial context from the input RAP profiles; however, the results are remarkably robust across model architecture. Further, uncertainty estimates for every level are well-calibrated and can provide useful information to forecasters.</p>
The raw data from the article "KinomeMETA: meta-learning enhanced kinome-wide polypharmacology profiling"
<p>The raw data from the article "KinomeMETA: meta-learning enhanced kinome-wide polypharmacology profiling"</p>
Data for "Modular Bibliographical Profiling of Historic Book Reviews"
<p>This dataset supports the research paper, ""Modular Bibliographical Profiling of Historic Book Reviews." The paper examines different methods of predicting bibliographical details (e.g. author, title, and publisher) of books under review in a corpus of approximately 1,100 historical book reviews. The dataset is comprised of book reviews from ProQuest's American Periodicals Series (APS). This kind of bibliographical profiling is often characterized as a Natural Language Processing (NLP) or Named Entity Recognition (NER) task, but it can be more specifically described as a two-part Named Entity Linking (NEL) task, beginning with a feature extraction stage followed by one of several available matching or classification methods. An attempt has been made to formalize constraints for modular bibliographical profiling (MBP) and shed light on some important choices that are often glossed over or obscured by digital humanities (DH) practitioners. Applying these constraints, the paper evaluates combinations of feature selection (naive bag-of-words [BOW], rule-based feature extraction, and NER using a pre-trained model) with a standardized similarity-based matching strategy (cosine similarity). All tasks are performed on derived text data (term frequency tables), so that data can be shared and all methods can be used on materials available only in non-consumptive formats. These comparisons suggest that naive BOW can perform quite robustly, and that using even a basic pretrained NER model in conjunction with a BOW approach may reduce false positives. </p>
Comparing plasma and skin imprint metabolic profiles in COVID-19 diagnosis and severity assessment_dataset
<p>As SARS-CoV-2 continues to produce new variants, the demand for diagnostics and a better understanding of COVID-19 remain key topics in healthcare. Skin manifestations have been widely reported in cases of COVID-19, but the mechanisms and markers of these symptoms are poorly described. In this cross-sectional study, 101 patients (64 COVID-19 positive patients and 37 controls) were enrolled between April and June 2020, during the first wave of COVID-19, in São Paulo, Brazil. Enrolled patients had skin imprints sampled non-invasively using silica plates; plasma samples were also collected. Samples were used for untargeted lipidomics/metabolomics through high-resolution mass spectrometry. We identified 558 molecular ions, with lipids comprising most of them. We found 245 plasma ions that were significant for COVID-19 diagnosis, compared to 61 from the skin imprints. Plasma samples outperformed skin imprints in distinguishing patients with COVID-19 from controls, with F1-scores of 91.9% and 84.3%, respectively. Skin imprints were excellent for assessing disease severity, exhibiting an F1-score of 93.5% when discriminating between patient hospitalization and home care statuses. Specifically, oleamide and linoleamide were the most discriminative biomarkers for identifying hospitalized patients through skin imprinting, and palmitic amides and N-acylethanolamine 18:0 were also identified as significant biomarkers. These observations underscore the importance of primary fatty acid amides and N-acylethanolamines in immunomodulatory processes and metabolic disorders. These findings confirm the potential utility of skin imprinting as a valuable non-invasive sampling method for COVID-19 screening; a method that may also be applied in the evaluation of other medical conditions.</p><p>Journal of Molecular Medicine https://doi.org/10.1007/s00109-023-02396-3</p>
Supplementary Data for Manuscript 'Observing impacts on luminescence depth profile evolutions from surface altered quartzite using OSL laser scanning and controlled light exposed rock sampling techniques'
<p>The attached file contains supplementary documents offered in the Quaternary Geochronology manuscript 'Observing<strong> </strong>impacts on luminescence depth profile evolutions from surface altered quartzite using OSL laser scanning and controlled light exposed rock sampling techniques' for the LED2023 special issue. </p>
The temperature profile on a silicon-patterned membrane - Fig.3 Dataset
<p>This is the data set used to generate Fig.3 in the paper https://doi.org/10.1103/PRXQuantum.4.040314</p>
Phospho-seq: Integrated, multi-modal profiling of intracellular protein dynamics in single cells
<p> </p><p>Datasets to go along with the publication listed:</p><p>full_object.rds: Brain Organoid Phospho-Seq dataset with ATAC, Protein and imputed RNA data</p><p>rna_object.rds: Reference whole cell scRNA-Seq object on Brain organoids</p><p>multiome_object.rds: Bridge dataset containing RNA and ATAC modalities for Brain organoids</p><p>metacell_allnorm.rds: Metacell object for finding gene-peak-protein linkages in Brain organoid dataset</p><p>fullobject_fragments.tsv.gz: fragment file to go with the full object</p><p>fullobject_fragments.tsv.gz.tbi:index file for the full object fragment file</p><p>multiome_fragments.tsv.gz: fragment file to go with the multiome object</p><p>multiome_fragments.tsv.gz.tbi:index file for the multiome object fragment file</p><p>K562_Stem.rds : object corresponding to the pilot experiment including K562 cells and iPS cells</p><p>K562_stem_fragments.tsv.gz: fragment file to go with the K562_stem object</p><p>K562_stem_fragments.tsv.gz.tbi: index file for the K562_stem object fragment file</p><p>retina.rds : object corresponding to the retinal organoid phospho-seq experiment</p><p>retina_fragments.tsv.gz: fragment file to go with the retina object</p><p>retina_fragments.tsv.gz.tbi: index file for the retina object fragment file</p><p>retina_multi.rds : object corresponding to the retinal organoid phospho-seq-multiome experiment</p><p>retina_multi_fragments.tsv.gz: fragment file to go with the retina_multi object</p><p>retina_multi_fragments.tsv.gz.tbi: index file for the retina_multi object fragment file</p><p>To use the K562, multiome, retina and retina_multiome datasets provided, please use these lines of code to import the object into Signac/Seurat and change the fragment file path to the corresponding downloaded fragment file:</p><p>obj <- readRDS("obj.rds") # remove fragment file information Fragments(obj) <- NULL # Update the path of the fragment file Fragments(obj) <- CreateFragmentObject(path = "download/obj_fragments.tsv.gz", cells = Cells(obj))</p><p>To use the K562 and multiome datasets provided, please use these lines of code to import the object into Signac/Seurat and change the fragment file path to the corresponding downloaded fragment file:</p><p>obj <- readRDS("obj.rds") # remove fragment file information Fragments(obj) <- NULL # Update the path of the fragment file Fragments(obj) <- CreateFragmentObject(path = "download/obj_fragments.tsv.gz", cells = Cells(obj))</p><p>To use the "fullobject" dataset provided, please use these lines of code to import the object into Signac/Seurat and change the fragment file path to the corresponding downloaded fragment file: </p><p>#load the stringr package library(stringr) #load the object obj <- readRDS("obj.rds") # remove fragment file information Fragments(obj) <- NULL #Remove unwanted residual information and rename cells obj@reductions$norm.adt.pca <- NULL obj@reductions$norm.pca <- NULL obj <- RenameCells(obj, new.names = str_remove(Cells(obj), "atac_")) # Update the path of the fragment file Fragments(obj) <- CreateFragmentObject(path = "download/obj_fragments.tsv.gz", cells = Cells(obj))</p><p> </p><p> </p>
TINKER_WP3_2D&3D images - profile scans dataset_221123
<p>2D and 3D images of PCBs showing the gap 2D and 3D information. Moreover, profile measurements are also included in x and y axes.</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.