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1,646 results for “plot”
funspace: an R package to build, analyze and plot functional trait spaces
<p>Functional trait space analyses are pivotal to describe and compare organisms' functional diversity across the tree of life. Yet, there is no single application that streamlines the many sometimes-troublesome steps needed to build and analyze functional trait spaces.</p> <p>To fill this gap, we propose funspace, an R package to easily handle bivariate and multivariate (PCA-based) functional trait space analyses. The six functions that constitute the package can be grouped in three modules: 'Building and exploring', 'Mapping', and 'Plotting'. The building and exploring module defines the main features of a functional trait space (e.g., functional diversity metrics) by leveraging kernel density-based methods. The mapping module uses general additive models to map how a target variable distributes within a trait space. The plotting module provides many options for creating flexible and high-quality figures representing the outputs obtained from previous modules. We provide a worked example to demonstrate a complete funspace workflow.</p> <p>funspace will provide researchers working with functional traits across the tree of life with an indispensable asset to easily explore: (i) the main features of any functional trait space, (ii) the relationship between a functional trait space and any other biological or non-biological factor that might contribute to shaping species' functional diversity.</p>
Simulation dataset and plotting scripts used for journal article "Surface modulated dissociation of organic aerosol acids and bases in different atmospheric environments" by Sengupta and Prisle (2024)
<p>Simulation data underlying all figures presented in "Surface modulated dissociation of organic aerosol acids and bases in different atmospheric environments" by Sengupta and Prisle (2024) <a href="http://dx.doi.org/10.1080/02786826.2024.2323641" target="_blank" rel="noopener noreferrer">http://dx.doi.org/10.1080/02786826.2024.2323641</a>. </p> <p>The data for each figure and the plotting scripts are included in a zip file labelled by the figure number as presented in the paper and accompanying supplement.</p>
Electrochemical data plotted in A. Fasano, V. Fourmond and C. Léger, « Outer-sphere effects on the O2 sensitivity, catalytic bias and catalytic reversibility of hydrogenases », Chem. Sc. (2024). doi: 10.1039/D4SC00691G
<p>Text files of the electrochemical data plotted in A. Fasano, V. Fourmond and C. Léger, « Outer-sphere effects on the O2 sensitivity, catalytic bias and catalytic reversibility of hydrogenases », Chem. Sc. (2024) (<a href="http://dx.doi.org/10.1039/D4SC00691G" target="_blank" rel="noopener">doi: 10.1039/D4SC00691G)</a></p>
Supplementary Figures 1 and 2. Showing funnel plots.
<p><strong><span>Supplementary Figure 1. </span></strong><span>Funnel plots for assessment of publication bias regarding </span><span>Barthel Index versus control for all studies (a), studies assessed after three months (b), up to six months (c), the activity of daily living versus control (D), modified Rankin scale versus control (E), and the Caregiver Strain Index versus control (F).</span></p> <p><span> </span></p> <p><strong><span>Supplementary Figure 2. </span></strong><span>Funnel plots for assessment of publication bias regarding mortality rate.</span></p>
Figure 7. Pteraspidiformes phylogeny with genera plotted against their stratigraphical occurrences. A in Exploring phylogenetic relationships of Pteraspidiformes heterostracans (stem-gnathostomes) using continuous and discrete characters
Figure 7. Pteraspidiformes phylogeny with genera plotted against their stratigraphical occurrences. A, discrete and continuous character analysis with implied weighting (k = 3). B, discretized analysis with implied weighting (k = 3). Colours relate to palaeobiogeographical provinces.
F I G U R E 4 Assigned ancestry plot using AIC criteria determining optimal K in Riverscape genetics of the orangethroat darter complex
F I G U R E 4 Assigned ancestry plot using AIC criteria determining optimal K of 5. Each color corresponds to a distinct genetic cluster, and each bar represents an individual's assigned ancestry to each cluster. Names above chart correspond to river basin (HUC6). EB—E. burri, EP— E. pulchellum, ES—E. spectabile, and EU—E. uniporum. Glaciated and unglaciated correspond to areas that were/were not covered in ice during the last glacial maximum.
Fig. 7. Box plots comparing average counts per 10 in Trypanosomiasis: An emerging disease in Alpine swift (Tachymarptis melba) nestlings in Switzerland?
Fig. 7. Box plots comparing average counts per 10 HPF of granulocytes, mononuclear cells, and thrombocytes between positive (n = 20) and negative (n = 20) 45- day-old Alpine swift nestlings sampled in 2022.
Empty plots layer screenshot of the Kunbaja online resource
<p>Secondary information layer "empty plots" of the Kunbaja online resource model built in QGIS as screenhots in the .png file format</p>
Magyar plots layer screenshot of the Kunbaja online resource with Ungar plots highlighted
<p>Secondary information layer "magyar plots" of the Kunbaja online resource model built in QGIS as screenhots in the .png file format with Ungar plots highlighted</p>
Female plots layer screenshot of the Kunbaja online resource
<p>Secondary information layer "female plots" of the Kunbaja online resource model built in QGIS as screenhots in the .png file format</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 4. Structure assignment probability plots for K in Revised taxonomy of eastern North Pacific killer whales ( Orcinus orca ): Bigg's and resident ecotypes deserve species status
Figure 4. Structure assignment probability plots for K = 3 groups from (a) 26 microsatellites: offshore (n = 5), resident (n = 250), Bigg's (n = 116) samples genotyped at ≥ 20 loci) (56; unpublished); (b) 3340 RADseq SNPs (polymorphic in sample set): offshore (n = 7), resident (n = 52) and Bigg's (n = 37) populations [57,62]. Vertical bars represent the individual assignment probability for each group inferred by Structure (groups identified by shading), with samples sorted by a priori ecotype assignment. See electronic supplementary material for methods and data set information.
Data for plots in Restoring Adiabatic State Transfer in Time-Modulated Non-Hermitian Systems
<p><span>Data for plots in Restoring Adiabatic State Transfer in Time-Modulated Non-Hermitian Systems (Physical Review Letters 133, 113802 (2024).</span></p> <p><span>The dataset files contain data and python code for plotting panels (c)-(f) of the Fig.4 in the article.</span></p> <p> </p>
Reproduction code and data for the plot of "Synthesizing survival robot behavior through reinforcement learning for homeostasis"
<pre># Reproduction code and data for the plot of "Synthesizing survival robot behavior through reinforcement learning for homeostasis"<br>Author: Naoto Yoshida<br><br>How to use:<br>1. Clone https://github.com/ugo-nama-kun/journalpaper_robot_2024 from github.<br>2. Extract data_20241119.zip in the cloned repository.<br>3. Run each plot_Fig*.py</pre>
Signal-to-Noise Ratios, DYNAMITE Posteriors, and Combined Detectability Plots for 173 TESS Multi-Planet Systems
<p>Collection of figures for 173 TESS Multi-Planet Systems. Each figure (titled with the system's TOI number) has 2 rows of planets, corresponding to results from the clustered period model (upper) and period ratio model (lower) predictions for an additional planet in each system. The left-most column of the plots show the signal-to-noise ratios (SNR) of simulated transits for planets with a given radius and period in the corresponding TESS light curve. The middle column shows the dot product of the radius and period posteriors generated by DYNAMITE using TESS light curves up to and including Sector 76. The right-most column shows the detectability (SNR) modulated by the DYNAMITE posteriors, showing the location in (P, Rp) space where an additional planet in this system is most likely to be detected under each model.</p>
Scaling plots for 80% and 100% text-pretrained
<p>Scaling plots for 80% and 100% (text80 and text100) in the name<br>All are text-pretrained with 4 epochs of fine-tuning and 28b of image-text training with 10% image caption and 90% text (ratio 0p1 in the name)<br>plots with various ablations at 154m, 411m, 1b scale (appended in the name)</p> <p>The text data is DCLM dataset and the image data is DatacompDR</p>
Gnuplot scripts for plotting selected leaf gas exchange and morphological data of industrial hemp
<p>Gnuplot code (scripts) for reproducing the eight figures of Sunoj et al. 2025 "Foliar gas exchange, morphology, and cannabinoid contents of three hemp varieties in southwest Texas," Agrosystems, Geosciences & Environment, 8, e70101. https://doi.org/10.1002/agg2.70101. Here is a list of the authors of the manuscript: John Sunoj V. S. (1), Xuejun Dong (1), Madhumita Joshi (1), Russell W. Jessup (2), Daniel I. Leskovar (1), and David D. Baltensperger (2). Texas A&M AgriLife Research at Uvalde, Texas, USA (1); Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas, USA (2).</p> <p>The revised upload includes updated gnuplot scripts for reproducing Figures 2, 3, and 8, and Supplemental Figures 1-5 and Supplemental Tables 1-5 of the accepted manuscript by AGE.</p> <p>Code and data to reproduce Figure 2 (pnf_rev.eps): pn_dat_rev.txt, pnf_rev.txt</p> <p>Code and data to reproduce Figure 3 (fmf_rev.eps): fm_dat.txt, fm_rev.txt</p> <p>Code and data to reproduce Figure 8 (allom_1.eps): allom_1.txt, allom_data.csv</p> <p> </p> <p> </p> <p> </p> <p> </p>
Vascular epiphyte census data at the San Lorenzo Crane plot
<p>Long-term community data of vascular epiphytes on a permanent vegetation study at the San Lorenzo Crane plot. The epiphyte community at the San Lorenzo crane plot on 207 tree individuals was first recorded between 1998 to 2000 and again between 2010 to 2012. Two files are available, one per each census.<br> <br> Headers in each file correspond to:</p> <p><strong>- empty header:</strong> row ID<br> <strong>- treenum: </strong>code of each tree, data taken from the CTFS-Tree Censuses and Inventories in Panama (STRI-CTFS) https://stricollections.org/portal/collections/misc/collprofiles.php?collid=27<br> <strong>- spp_code:</strong> epiphyte species code<br> <strong>- tsppc:</strong> host tree species code<br> <strong>- freq:</strong> number of individuals of epiphyte species</p> <p><br> <br> </p>
FIG. 26. Bivariate plots showing A in Fourteen New, Endemic Species Of Shrew (Genus Crocidura) From Sulawesi Reveal A Spectacular Island Radiation
FIG. 26. Bivariate plots showing A, variation in the condyloincisive length and braincase breadth and B, the first two axes from a principal components analysis of 12 cranial measurements among all members of the Small-Bodied Group. Loadings and variance explained are given in table 9.
pY and pSTY phosphoproteomic data for interactive volcano plots - by Glykofridis et al.
<p>These CSV files are generated by Glykofridis et al. (2021) and part of the supplementary data of "<strong>Phosphoproteomic analysis of FLCN inactivation highlights differential kinase pathways and regulatory TFEB phosphoserines</strong>" to be published in Molecular and Cellular Proteomics.</p> <p>The CSV files are used as input to generate (interactive) volcano plots, using the web app VolcanoNoseR. The code is archived here: https://zenodo.org/record/3625858</p> <p>The most up-to-date version of the interactive web app is available here: <a href="https://huygens.science.uva.nl/VolcaNoseR/">https://huygens.science.uva.nl/VolcaNoseR/</a></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.