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Figure 1 in Biogeography of marine tintinnid ciliates (Ciliophora, Tintinnida): a Scale-Dependent Model
Figure 1. Sample sites in the Sevastopol Bay: 1 - "2 miles"; 2 – "Ravelin"; 3 – "Cape of the Pontoon crossing"; 4 – "Sukharnaya Beam". Modified from Gavrilova & Dovgal (2019b).
Figure 18 in Biogeography of marine tintinnid ciliates (Ciliophora, Tintinnida): a Scale-Dependent Model
Figure 18. Results of cluster analysis (Simpson index) of species compositions of tintinnid ciliates in the Southern Ocean; in nodes of dendrogram, the results of bootstrap-analysis are marked.
Fig. 1 in Distant-dependent capture probabilities of Mediterranean and oriental fruit flies (Diptera: Tephritidae) in a food-based trap in a Hawaiian mango orchard
Fig. 1. Number of captures (A) and percentage of captures (B: captures divided by released) for Bactrocera dorsalis released from 5, 10, or 20 m from a centrally located trap baited with torula yeast borax solution. For a given replicate, 200 individuals of each sex were released. Symbols represent means with standard error (N = 7 replicates in all cases). Within each sex, numbers of captures differed significantly between distances marked with different upper case letters. Within a distance, numbers of captures differed significantly between the sexes if marked with different lower case letters.
Unravelling the Dependence of Aptamer–Target Binding Orientation and Strength on Nucleotide Modification Size, Position, and Number: A Computational Case Study of TBA
<p>Data deposition for "Unravelling the Dependence of Aptamer–Target Binding Orientation and Strength on Nucleotide Modification Size, Position, and Number: A Computational Case Study of TBA" published in Nucleic Acids Research. Data includes custom paramaterized nucleobase residues for use in AMBER molecular dyanmics simulations. Initial structures and setup code along with starting structures for generation of simulations in AMBER (pmemd.cuda). A in-house "MD-Generator" script to create nessessary files with our select simulation settings. Lastly, also included are CPPTRAJ analysis commands and scripts with visualizion scripts for use in Pymol 2.5 and above. </p>
Fig. 3 in Diversity and density-dependence relationship between hymenopteran egg parasitoids and the corn leafopper (Hemiptera: Cicadellidae) in maize agroecosystem vs. teosinte wild habitat
Fig. 3. Relationship between the number of exposed Dalbulus maidis eggs and number of Anagrus virlai within the crop maize habitat on (A) maize sentinel plants, and (B) teosinte sentinel plants.
Fig. 1 in Diversity and density-dependence relationship between hymenopteran egg parasitoids and the corn leafopper (Hemiptera: Cicadellidae) in maize agroecosystem vs. teosinte wild habitat
Fig. 1. Relationship between the number of exposed Dalbulus maidis eggs and number of adult parasitoids (of any species) found in the crop maize habitat on (A) maize sentinel plants, and (B) teosinte sentinel plants.
Fig. 2 in Richness of Chrysomelidae (Coleoptera) depends on the area and habitat structure in semideciduous forest remnants
Fig. 2. Rarefaction and extrapolation curve (a) and sample-coverage curve (B) of Chrysomelidae assemblage from remnant forest fragments in Dourados, Mato Grosso do Sul, Brazil. In both figures, solid lines represent observed data, dashed lines represent the extrapolation (900 individuals) and shaded areas the 95% confidence intervals (based on a bootstrap method with 1,000 replications).
Fig. 1 in Richness of Chrysomelidae (Coleoptera) depends on the area and habitat structure in semideciduous forest remnants
Fig. 1. Brazil (light grey), Mato Grosso do Sul State (dark grey) and the regions where the Chrysomelidae assemblage was collected (circles).
Fig. 3 in Richness of Chrysomelidae (Coleoptera) depends on the area and habitat structure in semideciduous forest remnants
Fig. 3. Path diagram showing the direct and indirect effects of the variables on the Chrysomelidae species richness from remnant forest fragments in Dourados, Mato Grosso do Sul, Brazil. The directions of the arrows indicate the direction of effects and the width of arrows is proportional to the effect size. The black arrows indicate significant effects (alpha ≤ 0.05) and the gray arrows indicate insignificant effects.
Figs 37-54 in Mg -dependent ATPase activity in triatomine salivary glands (Heteroptera, Triatominae)
Figs 37-54, Salivary glands of male T. infestans (Figs 37-45) and P. megistus (Figs 46-54) submitted to the control method (absence of substrate) of cytochemical for Mg2 +-dependent ATPase. General view and detail of anterior (Figs 37-39; 46-48), median (Figs 40-42; 49-51), and posterior (Figs 43- 45; 52-54) salivary gland. Note the absence of enzyme activity in the nucleus (Nu) and cytoplasm. Magnifications: Figs 37, 40, 43, 46, 49, 52 = 270x; Figs 38, 39, 41, 42, 44, 45, 47, 48, 50, 51, 53, 54 = 1344x.
Figs 19-36 in Mg -dependent ATPase activity in triatomine salivary glands (Heteroptera, Triatominae)
Figs 19-36. Panstrongylus megistus (Burmeister, 1835), salivary glands of males (Figs 19-27) and females (Figs 28-36) submitted to cytochemical reaction for Mg2 +-dependent ATPase. General view and detail of anterior (Figs 19-21, 28-30), median (Figs 22-24, 31-33), and posterior (Figs 25-27, 34-36) salivary gland. Arrows indicate dark nuclear corpuscles with a strong cobalt sulfide precipitation. The arrowheads indicate a positive response in the nuclear envelope. Magnifications: Figs 19, 22, 25, 28, 31, 34 = 270x; Figs 20, 21, 23, 24, 26, 27, 29, 30, 32, 33, 35, 36 = 1344x.
Figs 1-18 in Mg -dependent ATPase activity in triatomine salivary glands (Heteroptera, Triatominae)
Figs 1-18. Triatoma infestans (Klug, 1834), salivary glands of males (Figs 1-9) and females (Figs 10-18) submitted to cytochemical reaction for Mg2 +-dependent ATPase. General view and detail of anterior (Figs 1-3, 10-12), median (Figs 4-6, 13-15), and posterior (Figs 7-9, 16-18) salivary gland. Arrows indicate dark nuclear corpuscles with a strong cobalt sulfide precipitation. The arrowheads indicate a positive response in the nuclear envelope. Magnifications: Figs 1, 4, 7, 10, 13, 16 = 270x; Figs 2, 3, 5, 6, 8, 9, 11, 12, 14, 15, 17, 18 = 1344x.
Kinetic Insights into Glycerol Electrooxidation on Nickel: Current-Dependent Product Distribution and Reaction Mechanism
<p>## FILE DESCRIPTION<br>--------------<br>### Figure 1<br>- Fig1a.txt : Cyclic voltammetry of a Ni-based electrode in 0.1 M LiOH and 50 mM glycerol, measured at 5 mV s^-1. <br>- Fig1b.txt : IS spectra obtained at 1.50 V vs RHE in 0.1 M LiOH and 0.1 M LiOH + 50 mM glycerol.<br>- Fig1c.txt : Chronopotentiometric curves at 1 mA cm^-2, 3 mA cm^-2, 5 mA cm^-2 during 2 hours in 0.1 M LiOH + 50 mM glycerol.</p> <p>### Figure 2<br>- Fig2a.txt : Faradaic Efficiencies for Glycerol oxidation electrolysis at 1 mA cm^-2.<br>- Fig2b.txt : Faradaic Efficiencies for Glycerol oxidation electrolysis at 3 mA cm^-2.<br>- Fig2c.txt : Faradaic Efficiencies for Glycerol oxidation electrolysis at 5 mA cm^-2.<br>- Fig2d.txt : Faradaic Efficiencies for Glycerol oxidation electrolysis at 10 mA cm^-2.<br>- Fig2e.txt : Concentration of reaction products vs. time plots during glycerol oxidation (50 mM) in 0.1 M LiOH at 1 mA cm^-2.<br>- Fig2f.txt : Concentration of reaction products vs. time plots during glycerol oxidation (50 mM) in 0.1 M LiOH at 3 mA cm^-2.<br>- Fig2g.txt : Concentration of reaction products vs. time plots during glycerol oxidation (50 mM) in 0.1 M LiOH at 5 mA cm^-2.<br>- Fig2h.txt : Concentration of reaction products vs. time plots during glycerol oxidation (50 mM) in 0.1 M LiOH at 10 mA cm^-2.</p> <p>### Figure 3<br>- Fig3a.txt : Rate constant comparison at varying current densities applied for the formation of formate, glycolate, glycerate, tartronate and oxalate with its error bar.</p> <p>### Figure 4<br>- Fig4a.txt : Differential optical density (m∆O.D) taken at the maximum absorption peak as a function of potential applied in a solution of 0.1 M LiOH and LiOH 0.1 M + 50 mM glycerol in different regions (capacitive, NiOOH formation and GEOR and OER).<br>- Fig4b.txt : Rate law plot for glycerol and LiOH considering current density as a function of the normalized differential absorption (m∆O.D).</p> <p>### Figure S3<br>- FigS3.txt : X-ray diffraction (XRD) analysis </p> <p><br>### Figure S4<br>- FigS4a.txt : Cyclic Voltammetries in different electrolytes.</p> <p>### Figure S5<br>- FigS5a.txt : Faradaic Efficiencies for Glycerol oxidation electrolysis at 1.53 V vs RHE.<br>- FigS5b.txt : Faradaic Efficiencies for Glycerol oxidation electrolysis at 1.62 V vs RHE.<br>- FigS5c.txt : Faradaic Efficiencies for Glycerol oxidation electrolysis at 1.78 V vs RHE.</p> <p>### Figure S6<br>- FigS6a.txt : pH measurement near to the surface of the electrode and in the bulk of the solution every five minutes at 1 mA cm^-2.<br>- FigS6b.txt : pH measurement near to the surface of the electrode and in the bulk of the solution every five minutes at 3 mA cm^-2.<br>- FigS6c.txt : pH measurement near to the surface of the electrode and in the bulk of the solution every five minutes at 5 mA cm^-2.</p> <p>### Figure S7<br>- FigS7a.txt : UV-Vis absorbance spectra as a function of applied potentials in 0.1 M LiOH.<br>- FigS7b.txt : Chronoamperometry measurement without glycerol.<br>- FigS7c.txt : UV-Vis absorbance spectra as a function of applied potentials in 0.1 M LiOH + 50 mM glycerol.<br>- FigS8d.txt : Chronoamperometry measurement with glycerol.</p> <p>### Figure S8<br>- FigS8a.txt : Differential UV-Vis spectra of pre-catalytic (species formed in capacitive and NiOOH formation region) in 0.1 M LiOH.<br>- FigS8b.txt : Differential UV-Vis spectra of catalytic species (formed in OER and GEOR region) in 0.1 M LiOH.<br>- FigS8c.txt : Differential UV-Vis spectra of pre-catalytic species in 0.1 M LiOH + 50 mM glycerol. <br>- FigS8d.txt : Differential UV-Vis spectra of catalytic species in 0.1 M LiOH + 50 mM glycerol.<br>- FigS8e.txt : Steady state J-V curve with the onset for OER (Oxygen Evolution Reaction) and GEOR (Glycerol Electrooxidation Reaction) indicated.</p> <p>### Figure S9<br>- FigS9.txt : Rate law plot for glycerol and LiOH considering current density (j) as a function of the normalized differential absorption (m∆O.D).</p>
Figure 1 in Domestication level of the most popular aquarium fish species: is the aquarium trade dependent on wild populations?
Figure 1. – Number of aquarium fish species per domestication level (white: freshwater, n = 50 species; black: marine, n = 50 species).
Early life adversity has sex-dependent effects on survival across the lifespan in rhesus macaques
<p>Data for: Early life adversity has sex-dependent effects on survival across the lifespan in rhesus macaques</p> <p>Patterson, S.K., Andonov, E., Arre, A.M., Martinez, M.I., Negron-Del Valle, J.E., Petersen, R.M, Phillips, D., Rahman, A., Ruiz-Lambides, A., Villanueva, I., Lea, A.J., Snyder-Mackler, N., Brent, L.J., Higham, J.P. "Early life adversity has sex-dependent effects on survival across the lifespan in rhesus macaques." 2024. <em>Philosophical Transactions B. </em></p> <p> </p>
Automatically fixing dependency breaking changes
<h1>Replication Data for Automated Dependency Update Experiments</h1> <h2>Dataset Description</h2> <p>This repository contains a comprehensive collection of data from experiments on automated dependency updates using Large Language Models (LLMs). The dataset consists of two main components:</p> <ol> <li><strong>Execution Traces</strong>: Detailed logs of LLM interactions and repair attempts, captured using OpenTelemetry and OpenInference (<a href="https://github.com/Arize-ai/openinference">https://github.com/Arize-ai/openinference</a>). These traces provide insights into the behavior and performance of various LLMs in addressing breaking changes caused by dependency updates in Java projects.</li> <li><strong>Docker Images</strong>: Pre-built Docker images containing the state of Java projects after successful repair attempts. These images allow for direct inspection and verification of the changes made by our automated repair system.</li> </ol> <p>This dataset enables analysis and replication of our study on automated dependency updates, offering both low-level interaction data and high-level repair outcomes.</p> <h2>Data Components</h2> <h3>1. Execution Traces</h3> <ul> <li><strong>Format</strong>: JSON Lines, persisted via a custom OpenTelemetry exporter</li> <li><strong>Content</strong>: Spans capturing various aspects of LLM interactions, including: <ul> <li>Input processing</li> <li>LLM query generation</li> <li>LLM response parsing</li> <li>Code modification attempts</li> <li>Compilation and test execution results</li> </ul> </li> <li><strong>Purpose</strong>: Enables detailed analysis of LLM decision-making processes and performance metrics</li> </ul> <h3>2. Docker Images</h3> <ul> <li><strong>Format</strong>: Compressed Docker image files (.tar.gz) in a .zip</li> <li><strong>Content</strong>: Maven project states after successful repair attempts with patch(es) applied and seperated in the second layer of each image.</li> <li><strong>Purpose</strong>: Allows direct inspection and verification of code changes made during repairs and the successful maven outcome</li> </ul> <h2>Research Context and Data Usage</h2> <p>This dataset corresponds to experiments described in our study investigating the effectiveness of zero-shot prompting and agentic approaches in automating dependency updates. It provides valuable insights into:</p> <ul> <li>Performance variations across different LLMs</li> <li>Influence of various factors on repair success rates</li> <li>Specific code changes made during successful repairs</li> </ul> <p>This dataset can be used for:</p> <ol> <li>Replicating the experimental results presented in the associated study</li> <li>Conducting further analysis on LLM behavior in software engineering tasks</li> <li>Developing and benchmarking new approaches for automated dependency updates</li> <li>Investigating the decision-making processes of LLMs in code modification tasks</li> <li>Verifying and inspecting successful repair attempts through Docker images</li> </ol> <p> </p> <div> <h2>Repair Replication with Docker</h2> To replicate specific repairs using our pre-built Docker images:<br> <div>1. Ensure you have Docker installed on your system.</div> <br>2. Use the included docker-images.zip, which first has to be unzipped <div> <pre><code>unzip docker-images.zip</code></pre> </div> <div>after which the images can be loaded as follows:</div> <div> <pre><code>docker load < image_name.tar.gz</code></pre> </div> <br> <div>3. Run the Docker container:</div> <pre><code>docker run -it [image-name]</code></pre> <br> <div>These Docker images contain the state of projects after repair attempts, allowing for easy inspection and verification of the changes made by our automated repair system.</div> </div>
Data for "Bulk and fracture process zone contribution to the rate-dependent adhesion amplification in viscoelastic broad-band materials"
<p>This dataset contains all the data generated for the publication</p> <p>[1] A. Maghami, Q. Wang, M. Tricarico et al., Bulk and fracture process zone contribution to the rate-dependent adhesion amplification in viscoelastic broad-band materials. Journal of the Mechanics and Physics of Solids (2024), doi:<br>https://doi.org/10.1016/j.jmps.2024.105844.</p> <p><br>The provided data are those which appear in the figures of Ref. [1]. Data are stored using informative named structures in a ".mat" file. The data are easily accessible through the Commercial Software MATLAB (© 1994-2023 The MathWorks, Inc.) or by using the free software GNU Octave. An exemplary code for loading and plotting the data contained in "data_Sphere_Viscoelastic_BroadBand.mat" is the following:</p> <p>%%%%%%%%%%%%%</p> <div> <div> <div> <div>clc</div> <div>clearvars</div> <div> </div> <div>close all</div> <div> </div> <div> </div> <div>% Create a new figure</div> <div>load('data_Sphere_Viscoelastic_BroadBand.mat');</div> <div> </div> <div> </div> <div>figure</div> <div> </div> <div>% Choose one of the following names:</div> <div>% Fig_3_a, Fig_3_b, Fig_3_c,</div> <div>% Fig_4, Fig_5_a, Fig_5_b, Fig_5_b_inset,</div> <div>% Fig_6, Fig_6_inset, Fig_7, Fig_7_inset,</div> <div>% Fig_8_a, Fig_8_b, Fig_10_a, Fig_10_b</div> <div> </div> <div>name=Fig_3_a;</div> <div>% Note: some plots in the manuscript are in log space.</div> <div> </div> <div>% Plot the data from the structure</div> <div> </div> <div> </div> <div>for i = 1:numel(name.x) hold on</div> <div> </div> <div>x_cell = name.x(i);</div> <div>y_cell = name.y(i);</div> <div>x = cell2mat(x_cell);</div> <div>y = cell2mat(y_cell);</div> <div> </div> <div>plot(x, y);</div> <div>end</div> <div> </div> <div> </div> <div> </div> </div> %%%%%%%%%%%%%</div> </div> <p>The code can be easily adapted for plotting the curves of all the figures contained in Ref. [1]. </p>
Fig. 2 in Reproductive potential and biological characteristics of the parasitoid Cotesia flavipes (Hymenoptera: Braconidae) in Diatraea saccharalis (Lepidoptera: Crambidae) depending on parasitoid-host ratio
Fig. 2. Progeny (A) and sex rato (B) of Cotesia flavipes density in Diatraea saccharalis: 1:1, 3:1, 6:1, 9:1, 12:1 (parasitoids to host) at 25 ± 2 °C, 70 ± 10% RH, and a 12:12 h (L:D) photoperiod.
Fig. 3 in Reproductive potential and biological characteristics of the parasitoid Cotesia flavipes (Hymenoptera: Braconidae) in Diatraea saccharalis (Lepidoptera: Crambidae) depending on parasitoid-host ratio
Fig. 3. Rato of female produced by female (A) and body length (mm) (B) of Cotesia flavipes density in Diatraea saccharalis: 1:1, 3:1, 6:1, 9:1, 12:1 (parasitoids to host) at 25 ± 2 °C, 70 ± 10% RH, and a 12:12 h (L:D) photoperiod.
Fig. 1 in Reproductive potential and biological characteristics of the parasitoid Cotesia flavipes (Hymenoptera: Braconidae) in Diatraea saccharalis (Lepidoptera: Crambidae) depending on parasitoid-host ratio
Fig. 1. Parasitsm percentage of Diatraea saccharalis depending on Cotesia flavipes density: 1:1, 3:1, 6:1, 9:1, 12:1 (parasitoids to host) at 25 ± 2 °C, 70 ± 10% RH, and a 12:12 h (L:D) photoperiod. P = 0.05 (significance level).
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.