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35 results for “Plot analysis”
North Temperate Lakes LTER: Patterns of Soil Phosphorus - Y Plot Analysis 2001
In natural soils, patterns of variance are generated by driving forces such as parent materials, climate, hydrology, relief, disturbance and biological activity. These drivers, operating at particular scales and interacting with other drivers across scales, create a complex pattern of soil variability. Human activity may change the natural patterns of variance by changing the scale at which the governing processes are operating or the governing processes that are dominant at a given scale. In the case of soils and phosphorus (P) concentrations, this may involve changing dominant forces from plant-soil interactions and parent material to fertilizer inputs. Here, we examine the hypothesis that human activity changes natural patterns of variance in soil P concentrations across several spatial scales. We measured soil P concentrations and variability at 3 distinct levels of analysis - among sites, within a field, and within a 10-m diameter plot - and across 4 management regimes - remnant prairie, lawns, cash grain farms, and dairies. Variance changed across scale in any one management regime and across management regimes at the same scale. Rescaling the pattern of P accumulation and variability has implications for managing P runoff from uplands. For sample sites on private property, specific site location information, such as GPS coordinates, is not included in these datasets. If you have a need for this information, please get in touch with the contact person listed above Number of sites: 30
Constraining the dense matter equation of state with joint analysis of NICERand LIGO/Virgo measurements: Data for generating plots
<p>In this repository you will find a Jupyter notebook with code to generate the plots from the paper <em>Constraining the dense matter equation of state with joint analysis of NICER and LIGO/Virgo measurements</em> by Raaijmakers et al. (2020).</p>
Datasets used for analysis and plotting in the study by Zhou et al. "Antarctic vortex dehydration in 2023 as a substantial removal pathway for Hunga Tonga-Hunga Ha'apai water vapour"
<p>These data are model simulated water vapour (H2O) and ozone (O3) model mixing ratios between 2022 and 2023 that were used to create figures for the study by Zhou et al. "Antarctic vortex dehydration in 2023 as a substantial removal pathway for Hunga Tonga-Hunga Ha'apai water vapour".</p><p>We use the TOMCAT/SLIMCAT 3-D off-line chemical transport model (Chipperfield, 2006) to represent the Hunga Tonga-Hunga Ha'apai (HTHH) H2O plume and quantify its longevity and ozone impacts. The model was run at a horizontal resolution of 2.8 degrees and 32 levels from the surface to about 60 km forced with ECMWF ERA5 meteorology. </p><p>A control simulation (file name with "MPC741") without treatment of HTHH was integrated from 1980 to October 2023. Output from run control for January 1st, 2022 was used to intialise a HTHH H2O perturbed run (run HT, file name with "MPC744") until October 2023 with the injection of 150 Tg of H2O into the low-mid stratosphere at southern subtropical latitudes. To test the possible future evolution of the HTHH H2O three further model runs were performed. These were integrated from January 1st, 2023 until 2030 using repeating ERA-5 meteorology for 2022. Run Con_2022 (file name with "MPC741__2022pd") was an extension of run control; run HT2022 was an extension of run HT (file name with "MPC744_2022pd")<i>, </i>and run HT2022ns (file name with "MPC744_2022pdns") was the same as run HT_2022 but had sedimentation of PSC particles turned off. Please see our paper for more information about the simulations.</p>
Dead Sea Scrolls data collection (images, labels, prediction plots) for dating ancient manuscripts using radiocarbon and AI-based writing style analysis
<p>The dataset is associated with the following article:<br>Title: <strong>Dating ancient manuscripts using radiocarbon and AI-based writing style analysis</strong><br>Authors: Mladen Popović, Maruf A. Dhali, Lambert Schomaker, Johannes van der Plicht, Kaare Lund Rasmussen, Jacopo La Nasa, Ilaria Degano, Maria Perla Colombini, and Eibert Tigchelaar<br><em>(Under review)</em></p> <p>This data set is collected for the ERC project:<br>The Hands that Wrote the Bible: Digital Palaeography and Scribal Culture of the Dead Sea Scrolls<br>PI: Mladen Popović<br>Grant agreement ID: 640497<br>Project website: <a href="https://cordis.europa.eu/project/id/640497">https://cordis.europa.eu/project/id/640497</a></p> <p> </p> <p><strong>Copyright (c) </strong> University of Groningen, 2024. All rights reserved.<br><strong>Disclaimer and copyright notice for all data contained on the *.tar.gz files:</strong></p> <p><strong>1)</strong> permission is hereby granted to use the data for research purposes. It is not allowed to distribute this data for commercial purposes.</p> <p><strong>2) </strong>provider gives no express or implied warranty of any kind, and any implied warranties of merchantability and fitness for purpose are disclaimed.</p> <p><strong>3) </strong>provider shall not be liable for any direct, indirect, special, incidental, or consequential damages arising out of any use of this data.</p> <p><strong>4) </strong>the user should refer to the first public article mentioned above on this data set.</p> <p><strong>5) </strong>the recipient should refrain from proliferating the data set to third parties external to his/her local research group. Please refer interested researchers to this site to obtain their own copy.</p> <p> </p> <p><strong>Organization of the data:<br></strong><em>(Update on 19 April 2024: OxCal data for accepted 2-sigma ranges are updated with the incusion and exclusion of minor peaks. New prediction plots are added after the model is trained with accepted 2-sigma ranges, including minor peaks. The old plots are also kept. <br><br><OLD Updates below; disregard><br>updated on 07-Feb-2024: OxCal data for selected ranges added in a new directory in addition to previously available original OxCal data. Enoch's prediction plots and test images are reorganized for easy access to the users.<br><OLD Updates above; disregard><br><br>Please use the files from this version and disregard the previous two versions: 10.5281/zenodo.10629480 and 10.5281/zenodo.8168210)</em></p> <p>There are four *.tar.gz files:</p> <p><em><strong>C14-Oxcal-data-updated.tar.gz</strong></em> contains one directory with radiocarbon data (OxCal [1] raw data) for all 30 manuscripts. Three additional directories contain name-corrected files for original OxCal data, files with accepted ranges, and files with accepted ranges including minor peaks. Please refer to the original article for details about OxCal data and the manuscripts. 25 out of 30 raw OxCal data are used (accepted ranges only) as the training labels during the training of Enoch, the date prediction model.</p> <p><em><strong>train-images-c14.tar.gz</strong></em> contains the clean and preprocessed (binarized, aligned, and arrangement corrected) training images for the 25 radiocarbon-dated training manuscripts (including 4Q52; 64 images in total). </p> <p><em><strong>test-images-all.tar.gz</strong></em> contains the clean and preprocessed test images for 135 previously undated manuscripts. The images are organized in three different directories: the first one with all 359 images for the 135 manuscripts, the second one with the selected 135 images, and the final one with 25 images to illustrate the poor quality of images. </p> <p><em><strong>Enoch-prediction-new-with-minor-peaks.tar.gz</strong></em> contains the new date prediction plots for each of the 135 test images, where Enoch was trained with the inclusion of minor peaks for the 2-sigma accepted ranges and with a data balancing threshold of 0.05. These plots are used by expert palaeographers' evaluation of Enoch's style-based date predictions of 135 previously undated manuscripts.</p> <p><em><strong>Enoch-predictions.tar.gz</strong></em> contains the date prediction plots for each of the 135 test images. There are two directories inside the *.tar.gz file:<br><br>- <em>prediction-plots-for-selected-135:</em> Prediction plots with data balancing threshold of 0.05. <br>- <em>extra-plots:</em> contains four additional directories:<br> - <em>Enoch-predictions-c14wo4Q52-balanced05:</em> Prediction plots with data balancing threshold of 0.05. <br> - <em>Enoch-predictions-c14wo4Q52-balanced10:</em> Prediction plots with data balancing threshold of 0.1.<br> - <em>Enoch-predictions-c14wo4Q52-unbalanced:</em> Unbalanced raw predictions.<br> - <em>Enoch-predictions-c14wo4Q52-combined:</em> Combined plots with all three prediction plots (unbalanced, 0.05, 0.1).<br>Please refer to the original article for more details.</p> <p>The updated code to run the plot is available here: <a href="https://doi.org/10.5281/zenodo.10998860">https://doi.org/10.5281/zenodo.10998860</a></p> <p><strong>If you have any questions, please get in touch with us:</strong><br>Mladen Popović <m.popovic(at)rug.nl><br>Maruf A. Dhali <m.a.dhali(at)rug.nl><br>Lambert Schomaker <l.r.b.schomaker(at)rug.nl></p> <p> </p> <p><strong>References:</strong><br>1. Bronk Ramsey, C. (2001). Development of the radiocarbon calibration program. <em>Radiocarbon</em>, <em>43</em>(2A), 355-363.</p>
Figure S1. Mediation analysis on the effect of insulin resistance on intraocular pressure. Figure S2. Forest plot showing the OR (95% CI) for EIOP of ALD versus NAFLD and the OR (95% CI) for EIOP of drinkers versus non-drinkers. Abbreviations: OR, odds ratio; CI, confidence interval; ALD, alcoholic liver disease; NAFLD, non-alcoholic fatty liver disease.
<p>Figure S1. Mediation analysis on the effect of insulin resistance on intraocular pressure.</p> <p>Figure S2. Forest plot showing the OR (95% CI) for EIOP of ALD versus NAFLD and the OR (95% CI) for EIOP of drinkers versus non-drinkers. Abbreviations: OR, odds ratio; CI, confidence interval; ALD, alcoholic liver disease; NAFLD, non-alcoholic fatty liver disease.</p>
Рис. 6. Графики Зависимости оценок варианс (S2) от средней плотности (D) популЯций наЗемных моллюсков B. cylindrica (А) и M. cartusiana (В): 1 – участок № 1, 2010 г.; 2 – участок № 2, 2011 г.; 3 – участок № 4, 2012 г.; 4 – участок № 5, 2012 г. Fig. 6. Variance estimation (S2) and average density (D) of the land snail B. cylindrica (А) and M. cartusiana (В) population scatter plots: 1 – site 1, 2010; 2 – site 2, 2011; 3 – site 4, 2012; 4 – site 5, 2012. in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 6. Графики Зависимости оценок варианс (S2) от средней плотности (D) популЯций наЗемных моллюсков B. cylindrica (А) и M. cartusiana (В): 1 – участок № 1, 2010 г.; 2 – участок № 2, 2011 г.; 3 – участок № 4, 2012 г.; 4 – участок № 5, 2012 г. Fig. 6. Variance estimation (S2) and average density (D) of the land snail B. cylindrica (А) and M. cartusiana (В) population scatter plots: 1 – site 1, 2010; 2 – site 2, 2011; 3 – site 4, 2012; 4 – site 5, 2012.
Fig. 10 Damage plots for the 3 in On Roth's "human fossil" from Baradero, Buenos Aires Province, Argentina: morphological and genetic analysis
Fig. 10 Damage plots for the 3' (A) and 5ʹ ends (B) for the four libraries [each line corresponds to a different library and treatment (shotgun or capture)], obtained with the built-in version of DamageProfiler (v0.4.9) for nf-core/eager (v. 2.4.3). The number of reads would be sufficient to clearly distinguish the substitution patterns characteristic for aDNA
Figure. Constrained ordination plot as produced from canonical correspondence analysis (CCA). The variability of environmental variables is summarized on Axis 1 and Axis 2 of the constrained biplot, explaining the variability of the trophic groups included in the red fox's diet. Trophic groups are shown with black line (unfilled) pyramids, whereas environmental variables are shown with black filled pyramids. Proximity and distance of response centroids to predictor centroids indicate positive and negative correlations between them, respectively. in Factors affecting the diet of the red fox (Vulpes vulpes) in a heterogeneous Mediterranean landscape
Figure. Constrained ordination plot as produced from canonical correspondence analysis (CCA). The variability of environmental variables is summarized on Axis 1 and Axis 2 of the constrained biplot, explaining the variability of the trophic groups included in the red fox's diet. Trophic groups are shown with black line (unfilled) pyramids, whereas environmental variables are shown with black filled pyramids. Proximity and distance of response centroids to predictor centroids indicate positive and negative correlations between them, respectively.
Data and analysis and plotting scripts for Swaminathan et al., "Regional Impacts Poorly Constrained by Climate Sensitivity"
<p>The datasets included here are of the plotted data from the figures of the paper entitled "Regional Impacts Poorly Constrained by Climate Sensitivity", by Ranjini Swaminathan, Jacob Schewe, Jeremy Walton, Klaus Zimmermann, Colin Jones, Richard A. Betts, Chantelle Burton, Chris D. Jones, Matthias Mengel, Christopher Reyer, Andrew G. Turner & Katja Weigel, submitted for publication in Earth's Futures. Scripts used for plotting and analysis are also included.</p>
uncropped western blots for analysis of RPN13 ubiquitylation and NRF1 activation by protein aggregates, as well as source data for qPCR plots and flow cytometry gating and FCS files for agDD-GFP in HeLa or HEK cells
<p>This entry contains uncropped blots for Fig 4D and Fig S4C, Fig. 5B, Fig S5 and Fig S6, and the raw FCS files for Flow Cytometry data in doi.org/10.1101/2024.08.30.610524.</p>
Fig. 2. Principal Component Analysis plot showing the 42 in Evidence of genetic connectivity between fragmented pig populations in a tropical urban city-state
Fig. 2. Principal Component Analysis plot showing the 42 individuals from the Central Catchment Nature Reserve (CCNR) and the Northeast differentiated by sex and age class. Individuals exhibiting genetic admixture are labelled. Percentage variation accounted for by each principal component is indicated in brackets.
Fig. S1. Principal Component Analysis plot showing 28 in Evidence of genetic connectivity between fragmented pig populations in a tropical urban city-state
Fig. S1. Principal Component Analysis plot showing 28 out of 42 individuals from the Central Catchment Nature Reserve (CCNR) and the Northeast with kinship values <0.2. Individuals are differentiated by sex and age class. Individuals exhibiting genetic admixture are labelled. Percentage variation accounted for by each principal component is indicated in brackets.
Fig. 4. Dot matrix plots for ITS-1 in Molecular phylogenetics and sequence analysis of two cave-dwelling Dugesia species from Southeast Asia (Platyhelminthes: Tricladida: Dugesiidae)
Fig. 4. Dot matrix plots for ITS-1 sequences of Dugesia species. a, D. deharvengi sequence (708 bp) showing a 25 bp repeat sequence: ATACTTAAAAATGGGCGTAT(A/G)CAAT at positions 224–248, and 277–301; b, D. aethiopica sequence (616 bp) against D. sicula. Note the presence of a 30 bp repeat sequence ATGCATATTTAATAAAAGGTGTATGCATGA at positions 216–245 and 271–300.
Text-fig. 7. Plot of discriminant scores (R1/R2) of individual M1 of Apodemus spp. from particular Pleistocene biozones superimposed onto a plot of variation ranges for the respective variables for the Recent Apodemus sample (based on the discrimination analysis of total set of characters, both metric and non-metric). in Genus Apodemus In The Pleistocene Of Central Europe: When Did The Extant Taxa Appear?
Text-fig. 7. Plot of discriminant scores (R1/R2) of individual M1 of Apodemus spp. from particular Pleistocene biozones superimposed onto a plot of variation ranges for the respective variables for the Recent Apodemus sample (based on the discrimination analysis of total set of characters, both metric and non-metric).
Text-fig. 6. Plot of discriminant scores (R1/R2) of individual m1 and M1 teeth of Apodemus spp. from particular Pleistocene biozones superimposed onto a plot of variation ranges for the respective variables for the Recent Apodemus sample (based on the discrimination analysis of metric variables of M1 and m1). in Genus Apodemus In The Pleistocene Of Central Europe: When Did The Extant Taxa Appear?
Text-fig. 6. Plot of discriminant scores (R1/R2) of individual m1 and M1 teeth of Apodemus spp. from particular Pleistocene biozones superimposed onto a plot of variation ranges for the respective variables for the Recent Apodemus sample (based on the discrimination analysis of metric variables of M1 and m1).
Arc fault detection and appliances classification in AC home electrical networks using Recurrence Quantification Plots and Image Analysis
<p>The data provided can be used for the development of methods for the detection of arcing faults in a domestic low-voltage electrical networks (230V - 50 Hz). The data files are current and voltage signatures experimentally measured.</p> <p>The ReadMe file describes :</p> <p>- the test set up and the the procedure followed to make the measurements</p> <p>- the list of household appliances and their main characteristics.</p> <p>- the name of the data files</p> <p>- the type of arcing faults</p> <p> </p>
PLO(SC)²: Plots and Scripts for scRNA-seq analysis
<p><strong>Availability</strong></p> <p>The PLOSC-project is available from <a href="https://github.com/mjoppich/PLOSC">https://github.com/mjoppich/PLOSC</a> .</p> <p>The sequencing data (h5-files) were taken from:</p> <p>Pekayvaz K, Leunig A, Kaiser R, Joppich M, Brambs S, Janjic A, et al. Protective immune trajectories in early<br> viral containment of non-pneumonic SARS-CoV-2 infection. Nature communications. 2022 Feb;13(1):1018.<br> Available from: <a href="https://www.nature.com/articles/s41467-022-28508-0">https://www.nature.com/articles/s41467-022-28508-0</a>.</p> <p><strong>Background</strong><br> scRNA-seq analysis has become a standard technique to study biological systems.<br> With decreasing costs for scRNA-seq experiments, these also become increasingly complex.<br> While the typical scRNA-seq analysis frameworks provide functionalities for the analysis of even such data sets, the required steps to follow for such experiments become complicated.<br> Moreover, default plots are not suitable to provide specific insight into such complex data sets, and should be enhanced, such that camera-ready fully-interpretable plots are provided.</p> <p><strong>Results</strong><br> We thus describe here a collection of plotting and analysis scripts for use in Seurat-based scRNA-seq data analyses.<br> We first provide a collection of script blocks which allows for an easy basic analysis of scRNA-seq from Seurat object creation, filtering, and over data set integration in less than 10 steps.<br> Subsequently, we provide code blocks for the easy differential analysis of the obtained data sets, including visualizations.<br> Finally, several visualizations enhancing the functionalities of scRNA-seq analysis frameworks are presented, such as the enhanced Heatmap and DotPlot.<br> These, particularly, allow the user to specify how the shown values should be scaled, allowing the creation of condition-wise plots.</p> <p><strong>Conclusions</strong><br> With the PLO(SC)² framework the data analysis of scRNA-seq experiments becomes more stream-lined, and visualizations for interpreting complex datasets are provided.<br> The PLO(SC)² scripts are available from GitHub, including a notebook showing how PLO(SC)² is applied on the use-case data presented here. This way, fellow researchers can directly apply the methods on their data.</p>
Data on soil variables (with plot IDs) and grassland species traits used for the analysis of grassland vegetation data by Pillar, V.D. (2024) Trait divergence in plant community assembly is generated by environmental factor interactions. Journal of Vegetation Science, 35, e13259. Available from: https://doi.org/10.1111/jvs.13259
<p>File <a href="../api/records/10983049/draft/files/Plot_IDs_990ua.txt/content" target="_blank" rel="noopener noreferrer">Plot_IDs_990ua.txt</a> contains the IDs of the 1-m2 plots used for the analysis of grassland vegetation data by Pillar, V.D. (2024) Trait divergence in plant community assembly is generated by environmental factor interactions. The plot data are stored in the sPlot database (PPBio South Brazilian Grassland Database).</p> <p>File <a href="../api/records/10983049/draft/files/E_990ua_21SoilVar.txt/content" target="_blank" rel="noopener noreferrer">E_990ua_21SoilVar.txt</a> contains data on soil variables evaluated in the 250 m transects, but here expanded to the 990 1-m2 plots (each transect was sampled using 10 1-m2 pots).</p> <p>File <a href="../api/records/10983049/draft/files/B_769spp_4t.txt/content" target="_blank" rel="noopener noreferrer">B_769spp_4t.txt</a> is the species trait database collected in the framework of several research projects in the Quantitative Ecology Lab (EcoQua) and Grassland Vegetation Studies Lab (LevCamp) of Universidade Federal do Rio Grande do Sul (UFRGS). Data gaps were filled by compiled from the TRY database and data imputation.</p> <p> </p> <p> </p>
Mode Splitting Spectrogram and Analysis Plots, 11 May 2022
<p>Spectrogram collected in Spectrum Lab by Steve WA5FRF. Digitized manually using MATLAB. Digitization and computation code at <a href="https://github.com/KCollins/wa5frf-plots">https://github.com/KCollins/wa5frf-plots</a>.</p>
Data+Analysis+Plotting scripts for "Constraint on the dissipative tidal deformability of neutron stars"
<p>The .zip file contains three directories. </p> <p>1. GW170817-Strain: the raw data (glitch free). Downloaded from https://gwosc.org/events/GW170817/.<br>2. Bilby-Output: the output from running our Bilby sampling scripts. These can be found at https://github.com/JLRipley314/NRTidal-D/tree/main<br>3. Plotting-Scripts: the plotting scripts we used in our paper https://arxiv.org/abs/2312.11659.</p> <p>NOTE: If you want to make sure the plotting scripts work properly, you should download bilby and related dependencies as described in https://github.com/JLRipley314/NRTidal-D/tree/main (or at https://doi.org/10.5281/zenodo.11589416)</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.