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4,694 results for “data analysis”
Figure S1: Nutrient concentration of lettuce (Lactuca sativa 'Rex') plants grown at different total incident light levels in deep water culture hydroponics. Lines show multiple regression analysis results, indicating no significant interactions. Each data point represents one plant. N = nitrogen, P = phosphorus, K = potassium, Ca = calcium, Mg = magnesium, S = sulfur, B = boron, Cu = copper, Fe = iron, Mn = manganese, and Zn = zinc.
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Data and analysis code for the paper 'Assessing the hydromechanical control of plant growth'
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public-transport-structure-analysis-data
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Comparative membrane proteomic analysis of Tritrichomonas foetus isolates (non filtered Data Sets)
<p>Tritrichomonas foetus is a flagellated and anaerobic parasite able to infect cattle and felines. Despite its prevalence, there is no effective standardized or legal treatment for T. foetus-infected cattle; the vaccination still has limited success in mitigating infections and reducing abortion risk; and nowadays, the diagnosis of T. foetus presents important limitations in terms of sensitivity and specificity in bovines. Here, we characterize the plasma membrane proteome of T. foetus and identify proteins that are represented in different isolates of this protozoan. Raw proteomics data sets from MALDI-TOF Mass Spectrometry presented here corresponds to six T. foetus isolates (Tf0-Tf5). For Tf2 isolate also five membrane fractions are presented (f1-f5). </p>
Data for A weak coupling mechanism for the early steps of the recovery stroke of myosin VI: a free energy simulation and string method analysis
<p>Representative frames, topology for MD simulation with NAMD and GROMACS, data and notebook to reproduce analyses in the paper "A weak coupling mechanism for the early steps of the recovery stroke of myosin VI: a free energy simulation and string method analysis" (Blanc, Houdusse, Cecchini, PLOS Computational Biology 2024).</p>
Data from: Optimal sampling interval for characterisation of the circadian rhythm of body temperature in homeothermic animals using periodogram and cosinor analysis
<p>Core body temperature (T<sub>c</sub>) is a critical aspect of homeostasis in birds and mammals and is increasingly used as a biomarker of the fitness of an animal to its environment. Periodogram and cosinor analysis can be used to estimate the characteristics of the circadian rhythm of T<sub>c</sub> from data obtained on loggers that have limited memory capacity and battery life. This data set contains five days of core body temperature, measured by loggers implanted into the abdominal cavity, in nine species of birds and mammals.</p>
Model data for: Analysis of the global atmospheric background sulfur budget in a multi-model framework
<p>The present dataset contains all model data used in the model intercomparison in ACP. All data is provided as monthly means. For more data, please contact the first author. V2 addresses inconsistencies in the time axes, vertical coordinates, and variable names between models.</p>
Data and code for Causality analysis and prediction of riverine algal blooms by combining empirical dynamic modeling and machine learning techniques
<p>Hydrological data (including daily water levels, flow velocities, and streamflow discharges) from two hydrological stations, the Hankou Station in the Yangtze River (YR) and the Hanchuan Station in the Han River (HR), were obtained from Hubei Province Hydrology and Water Resources Center.</p> <p>Water quality data (i.e., total nitrogen (TOTN), total phosphorus (TOTP), and water temperature in the Han River) and algae densities at three sections (Baihezui, Qinduankou and Zongguan) were acquired from the Yangtze River Basin Ecological and Environmental Supervision Authority. </p> <p><span>The R script(s) for machine learning models can also be found at <a href="../api/records/10901736/draft/files/Code%20for%20machine%20learning%20classification%20model.R/content" target="_blank" rel="noopener noreferrer">Code for machine learning classification model.R</a>.</span></p> <p> </p>
Data and analysis process for Microbiome processing of organic nitrogen input supports growth and cyanotoxin production of Microcystis aeruginosa cultures
<p>Data and analysis process for manuscript titled "Microbiome processing of organic nitrogen input supports the growth and cyanotoxin production of Microcystis aeruginosa cultures"</p>
Data for Thermo-Mechanical Analysis of Friction Stir Welding
<p><span>This study explores the development and application of machine learning (ML) metamodels for the thermo-mechanical analysis of Friction Stir Welding (FSW). The main objective is to address the challenge of accurately predicting the thermo-mechanical behaviour of materials in FSW processes. Using finite element models, a high-fidelity dataset consisting of 20 Hammersley design datapoints is generated which is then used to develop a low-fidelity dataset of 420 datapoints using KNN imputation. </span></p>
Massive Compression for High Data Rate Macromolecular Crystallography (HDRMX): Impact on Diffraction Data and Subsequent Structural Analysis: Subset with data from 2 deposited PDBs.
<p>Diffraction data from a lysozyme crystal. Data collected at 7.5 keV at the AMX beamline, NSLS-II. 360 degrees were collected, with 0.2 deg per frame. This data set contains 2 folders; 1 from uncompressed data and 1 from data compressed using lossy compression as follow: frames were summed (2x), pixels were binned (2x) and Hcompress with level 24 was applied to uncompressed data. </p>
Data used in the quantitative analysis
<p>These dates are used to Analisys in the Article <span>Enhancing Engineering Education Through Virtual Reality: A Experiment to Generation of Knowledge Retention to Students</span></p>
Factors hampering and facilitating RRI: finds from the analysis of data from four H2020 SwafS CSAs
<p>List of finds regarding factors that facilitate or hamper RRI-oriented change derived from the analysis of data from four relevant H2020 SwafS CSAs, namely GRACE, FIT4RRI, Starbios2, and Resbios</p>
Representative data accompanying the manuscript: Four-dimensional quantitative analysis of cell plate development in Arabidopsis using lattice light sheet microscopy identifies robust transition points between growth phases
<p>Representative data accompanying the manuscript: Sinclair R, Wang M, Jawaid MZ, Longkumer T, Aaron J, Rossetti B, Wait E, McDonald K, Cox D, Heddleston J, Wilkop T, Drakakaki G. (2024). <em>Four-dimensional quantitative analysis of cell plate development in Arabidopsis using lattice light sheet microscopy identifies robust transition points between growth phases.</em> J Exp Bot. 2024 Mar 4: erae091. doi: 10.1093/jxb/erae091.</p> <p>The data show YFP–RABA2a dynamics in dividing cells of Arabidopsis root tips using lattice light sheet microscopy. Treatments with or without Endosidin 7, a cytokinesis-specific callose deposition inhibitor, are shown.</p> <p>Data: </p> <p>22.3 YFP-RABA2A. </p> <p>23.9 YFP-RABA2A ES7 </p>
Static analysis evaluation experiment data
<p><span>This repository contains the experiment artifacts for our paper entitled </span><span>“Comprehensive Evaluation of Static Analysis Tools for Their Performance in Finding Vulnerabilities in Java Code” </span><span>submitted to the IEEE Access Journal.</span></p> <p><strong> </strong></p> <p><span>For each part of our experiment, we mention the related file name in this repository.</span></p> <p><strong> </strong></p> <p><span>Research Methodology:</span></p> <p><span>A. Experiment design: </span><span>no related documents.</span></p> <p><strong> </strong></p> <p><span>B. Preparing the Juliet Test Suite: </span></p> <p><span>In this section, we prepared Juliet for being analyzed by the five tools of the study. The related document for this section is called </span><span>juliet_preparation.pdf.</span></p> <p><strong> </strong></p> <p><span>C. Evaluation metrics: </span><span>no related documents.</span></p> <p><strong> </strong></p> <p><span>D. Experiment execution: </span></p> <p><span>Step 1: </span><span>For each of the five tools, review the documentation to identify and activate the related checker(s).</span></p> <p><span>The related documents of this section are </span><span>pmd_checkers.pdf.</span><span> </span><span>spotbugs_fsb_checkers.pdf,</span><span> </span><span>infer_checkers.pdf</span><span>, and </span><span>sonar_checkers.pdf</span><span>. Those documents include all the checkers that have been used and activated (if they were not active by default) to enable the Juliet analysis using the relevant checkers.</span></p> <p><strong> </strong></p> <p><span>Step 2: </span><span>Run each tool on each CWE and get the output reports.</span></p> <p><span>The related document is called </span><span>running_the_tools.pdf,</span><span> which includes the detailed steps for running each tool.</span></p> <p><strong> </strong></p> <p><span>Step 3: </span><span>For each tool, and each CWE, consider the relevant checker's results.</span></p> <p><span>no related document.</span></p> <p><strong> </strong></p> <p><span>Step 4:</span><span> For each tool, and each CWE, compute TP, FP, TN, and FN.</span></p> <p><span>The related document is </span><span>response_variables.xls</span></p> <p><strong> </strong></p> <p><span>Step 5: </span><span>Compute the response variables for each tool detecting each CWE.</span></p> <p><span>The related document is </span><span>response_variables.xls</span></p> <p><strong> </strong></p> <p><span>Step 6: </span><span>For each tool, compute collective evaluation metrics.</span></p> <p><span>The related document is </span><span>response_variables.xls</span><strong></strong></p>
Lemonade Creek, Yellowstone National Park, USA - Microbial Community Analysis - Genome and Transcriptome Data
<p>Genome and Transcriptome data used for analysis of microbial community function over a diurnal cycle in Lemonade Creek, Yellowstone National Park, USA.</p> <p> </p> <p><code>mags.tar</code> Non-redundant metagenome data (genome assemblies, predicted genes, and gene functional annotations).</p> <p> </p> <p>In each directory are the the following files:</p> <p>- <code>*.mRNA.faa</code> protein sequences of protein-coding genes</p> <p>- <code>*.mRNA.fna</code> nucleotide sequences of protein-coding genes</p> <p>- <code>*.mRNA.gff3</code> genomic location of protein-coding genes</p> <p>- <code>*.mRNA.emapper.tsv</code> eggNOG-mapper annotations for the protein-coding genes</p> <p>- <code>*.mRNA.interproscan.gff3</code> InterProScan annotations for the protein-coding genes</p> <p> </p> <p>In the <code>prokaryote</code> directory there are the following files:</p> <p>- <code>*.rRNA.fna</code> nucleotide sequences of rRNA genes</p> <p>- <code>*.rRNA.gff3</code> genomic location of rRNA genes</p> <p>- <code>*.tRNA.fna</code> nucleotide sequences of tRNA genes</p> <p>- <code>*.tRNA.gff3</code> genomic location of tRNA genes</p> <p>- <code>*.other.fna</code> nucleotide sequences of other genes (i.e., CRISPR, ncRNA, oriC, regulatory_region, repeat_region, tmRNA - if any were predicted)</p> <p>- <code>*.other.gff3</code> genomic location of other genes</p> <p> </p> <p><strong>Eukaryotes</strong></p> <p>Five MAGs from other eukaryotes that were assembled from a coassembly of the Soil samples.</p> <p> </p> <p><strong>Prokaryotes</strong></p> <p>The final dereplicated prokaryote MAGs (at 95% ID). The two <code>*stats*</code> files list the taxonomic information (from <code>GTDB-Tk</code>), completeness (from <code>CheckM</code>), and assembly stats (from the <code>stats.sh</code> script from the <code>bbmap</code> package) for each of the prokaryotic MAGs + the number of predicted protein-coding and non-protein-coding genes predicted in each MAG.</p> <p> </p> <p><strong>Viruses</strong></p> <p>The final dereplicated viral MAGs and vOTUs.</p> <p> </p> <p> </p> <p> </p> <p><code>read_mapping.tar</code> Abundance results from metagenome and metatranscriptome read mapping analysis against the non-redundant metagenome data and predicted genes (respectively). This analysis includes the cyanidiophyceae reference nuclear and organelle genomes.</p> <p> </p> <p><strong>mags</strong></p> <p>Results from <code>bbmaps</code> alignment of metagenome reads against a database of non-redudant metagenome MAGs + cyanidiophyceae reference nuclear and organelle genomes. <code>CoverM</code> was used to calculate MAG abundances.</p> <p> </p> <p><strong>genes</strong></p> <p><code>Salmon</code> abundance quantification of PolyA and RiboMinus metatranscriptome reads mapped against the predicted genes in the non-redudant metagenome MAGs + cyanidiophyceae reference nuclear and organelle genomes.</p>
Raw data for TrackMate tracking output and R script for data analysis
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A Big Data Analysis Algorithm for Massive Sensor Medical Images
<p><span>The smart sensor based big data analysis recommendation system has significant privacy and security concerns when it comes to using sensor medical images for suggestions and monitoring. The danger of security breaches and unauthorized access which might lead to identity theft and privacy violations increases when sending and storing sensitive medical data on the cloud. Insufficient or erroneous patient data can lead to poor treatment decisions, misdiagnoses, and unreliable recommendations. By creating an anomaly detection system based on machine learning specifically for medical image and providing timely treatments and notifications, our effort will improve patient care and well-being. We infer the feature extraction, feature selection, attack detection, and data collection data processing procedures in order to anticipate the anomaly in patient data. We transfer the data, take care of any missing values, and sanitize it using the data pre-processing mechanism. We employed the RFE and DPCA algorithms for feature selection and extraction, respectively. In addition, we applied the AGRNN approach to identify abnormalities. Data arrival rate, resource consumption, propagation delay, transaction epoch, true positive rate, false alarm rate, and RMSE are some of the metrics used to evaluate the proposed task.</span></p>
PixelPop: Nonparametric analysis of correlations in the binary black hole population with LIGO–Virgo–KAGRA data
<p>Data release accompanying the PixelPop papers, analyzing gravitational wave populations.</p> <p>The first dataset (in gwtc3_result_files) is the posterior samples for the runs presented in analysis of LIGO--Virgo--KAGRA data, following the third gravitational wave catalog, see https://arxiv.org/abs/2406.16844. We include a python notebook (example_plot.ipynb) showing how to create the plots presented in this paper.</p> <p>In v2, we also include samples from the predictive distributions. Due to the large uncertainties, marginalizing over the hyperposterior may be a poor representation of the inferred distribution, and so instead we provide samples from the <em>median</em> predictive distribution. That is, samples from the distribution shown in the central panels of the figures. </p> <p>The second dataset (in o4inj_result_files) is the posterior samples accompanying the runs presented in the technical background paper, see https://arxiv.org/abs/2406.16813. </p>
DMSO-TMP-ACN-H2O Co-solvent Bayesian Optimization with Reproducibility and Gas Analysis via OEMS Data
<p>The zipped files contain the data collected and used for the Bayesian optimization (BO) of Coulombic efficiency (and discharge capacity) from the exploration of 4 co-solvents (dimethyl sulfoxide, trimethyl phosphate, acetonitrile, and water) and 2 salts (lithium perchlorate and LiTFSI).</p> <p>The cycling data and the BO clients are contained in BayesianOptimization.zip.</p> <p>The gas analysis data via online electrochemical mass spectrometry (OEMS) are contained in OEMS_data.zip.</p> <p>The cycling data of select repeats from the BO are contained in Reproducibility_data.zip.</p> <p>These are the raw datafiles. Preprocessing and analysis is not included.</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.