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FIGURE 2 in Biases in the study of relationships between biodiversity dynamics and fluctuation of environmental conditions
FIGURE 2. Comparison of the crocodylomorph species diversity used in various studies. 1, comparison of the number of species used for each marine crocodylomorph group; 2, comparison of the per cent of each group and of the genus Steneosaurus in the marine crocodylomorph diversity used (with and without metriorhynchoids).
FIGURE 1 in Biases in the study of relationships between biodiversity dynamics and fluctuation of environmental conditions
FIGURE 1. Comparison between the diversity count of Jurassic-Paleogene marine crocodylomorphs (excluding metriorhynchoids) of Martin et al. (2014) (excluding Pelagosaurus typus), with the data obtained in the present work after several corrections. All the data is phylogenetically corrected. The data used in the present work includes four new species, corrections for the number of Machimosaurus species, and taxonomical corrections (see text).
Supplemental Material to "The Small-Amplitude Dynamics of Spontaneous Tropical Cyclogenesis. Part II: Linear Stability Analysis"
<p>This is the supplemental material to the manuscript "The Small-Amplitude Dynamics of Spontaneous Tropical Cyclogenesis. Part II: Linear Stability Analysis". It deposits:</p> <p>(1) A handwritten math derivation note (math_derivation_note.pdf).</p> <p>(2) A movie version of Figure 1 (Part_II_movie.avi).</p> <p>(3) The Fortran code for amplifying the longwave radiative feedback in CM1 (Radiation_amplification_RAD_parameter_in CM1.txt).</p> <p>(4) The MATLAB postprocessing codes for generating data figures (postprocessing_code.zip).</p> <p>(5) The MATLAB solver of the four-layer QG model (four_layer_QG_solver.m).</p> <p>(6) Supplemental figures (supplement_figures.pdf). </p> <p>Please contact Dr. Hao Fu (haofu@uchicago.edu or haofu736@gmail.com) if you have any questions. </p>
FIGURE 4 in Dynamics of fish larvae recruitment in the hydrographic basin of the Paraguay River in western Brazil
FIGURE 4 | Bipartite plot showing the relationship between the larval densities of the commercially valuable migratory fish species (in orange) and the sampling sites (in brown). The width of the bars is proportional to the number of interactions, that is, the abundance of larvae. The bands (gray) and lines (black) reflect the species-sites connections, with the broader bands representing greater larval densities, while the lines represent the interactions of the species with the sampling sites. Bh = Brycon hilarii; Hp = Hemisorubim platyrhynchos; Pm = Piaractus mesopotamicus; Pl = Prochilodus lineatus; Pspp = Pseudoplatystoma spp.; Sb = Salminus brasiliensis; Sl = Sorubim lima; Zj = Zungaro jahu; Sites: 1 = Cabaçal River site 1; 2 = Cabaçal River site 2; FOR = Formoso River; 4 = Jauru River; 6 = Paraguay River; 7 = Sepotuba River site 1; 8 = Sepotuba River site 2; 9 = Sepotuba River site 3; 10 = Vermelho River.
FIGURE 3 in Dynamics of fish larvae recruitment in the hydrographic basin of the Paraguay River in western Brazil
FIGURE 3 | Results of the Principal Coordinates Analysis (PCoA) based on the Bray-Curtis dissimilarity matrix of the taxonomic composition of the fish larvae in relation to the months sampled in the upper Paraguay River basin in Mato Grosso State, western Brazil. OCT = October (black circle), NOV = November (red circle), DEC = December (green circle), JAN = January (black triangle), FEB = February (red triangle), MAR = March (green triangle).
FIGURE 2 in Dynamics of fish larvae recruitment in the hydrographic basin of the Paraguay River in western Brazil
FIGURE 2 | Results of the Principal Coordinates Analysis (PCoA) based on the Bray-Curtis dissimilarity matrix of the taxonomic composition of the fish larvae in relation to the sites sampled in the upper Paraguay River basin in Mato Grosso State, western Brazil. CAB1 = Cabaçal River site 1 (closed black circle); CAB2 = Cabaçal River site 2 (closed red circle); FOR = Formoso River (black triangle); JAU = Jauru River (red asterisk); JUB = Juba River (blue triangle); PAR = Paraguay River (black cross); SEP1 = Sepotuba River site 1 (open black circle); SEP2 = Sepotuba River site 2 (open red circle); SEP3 = Sepotuba River site 3 (open green circle); VERN = Vermelho River (red square).
FIGURE 1 in Dynamics of fish larvae recruitment in the hydrographic basin of the Paraguay River in western Brazil
FIGURE 1 | Study area in the Brazilian upper Paraguay River basin in the Brazilian state of Mato Grosso, sampled during the 2017/18 and 2018/2019 breeding seasons. CAB1 = Cabaçal River site 1; CAB2 = Cabaçal River site 2; FOR = Formoso River; JAU = Jauru River; JUB = Juba River; PAR = Paraguay River; SEP1 = Sepotuba River site 1; SEP2 = Sepotuba River site 2; SEP3 = Sepotuba River site 3; VERN = Vermelho River. The overlap of the FOR/SEP1, JUB/SEP2 and SEP3/PAR sites corresponds to the proximity of the collected water bodies.
Large mass hierarchies from strongly-coupled dynamics—Data release
<p>This release contains data associated with the publication <a href="https://arxiv.org/abs/1605.04258">Large mass hierarchies from strongly-coupled dynamics</a> (<a href="https://doi.org/10.1007/JHEP06(2016)114">JHEP 06 (2016) 114</a>)</p> <p>It comprises four files:</p> <ul> <li><code>README.md</code>: Containing this information, and a more detailed description of the dataset.</li> <li><code>Fig4.csv</code>: the data shown in Figure 4 of <a href="https://doi.org/10.1007/JHEP06(2016)114">the paper</a>. The mass $M$ of composite spin-0 and spin-2 states, and their excitations, computed for $c_1 = 0 = A_0$, as a function of $\Delta$, for $r_{UV} = 25$ and $r_{IR} = 10^{-6}$, normalised to the mass $M_0$ of the lightest scalar.</li> <li><code>Fig5.csv</code>: the data shown in Figure 5 of <a href="https://doi.org/10.1007/JHEP06(2016)114">the paper</a>. The mass $M$ of the composite spin-0 and spin-2 states, computed for $c_1 = 0 = A_0$, as a function of $-A(r_{IR}) = \log (\Lambda_0 / \Lambda_{IR})$, for $\Delta = 1.5$, normalised to the mass $M_T$ of the lightest tensor. $A(r_{UV}) - A(r_{IR})$ is fixed to 8.</li> <li><code>DataRelease.nb</code>: A Mathematica notebook that will take the above two files and generate plots similar to those shown in <a href="https://doi.org/10.1007/JHEP06(2016)114">the paper</a>.</li> </ul> <p> </p>
Reproduction package for the paper 'Evidence for a dynamic corona in the short-term time lags of black hole X-ray binary MAXI J1820+070'
<p>This is a basic reproduction package for the paper 'Evidence for a dynamic corona in the short-term time lags of black hole X-ray binary MAXI J1820+070', Bollemeijer et al., 2024, MNRAS, 528, 558-576.</p> <ul> <li>This reproduction package aims for open science, with the internal API designation of 'Gold'.</li> <li>Authors: Niek Bollemeijer, Phil Uttley, Arkadip Basak, Adam Ingram, Jakob van den Eijnden, Kevin Alabarta, Diego Altamirano, Zaven Arzoumanian, Douglas J.K. Buisson, Andrew C. Fabian, Elizabeth Ferrara, Keith Gendreau, Jeroen Homan, Erin Kara, Craig Markwardt, Ronald A. Remillard, Andrea Sanna, James F. Steiner, Francesco Tombesi, Jingyi Wang, Yanan Wang and Abderahmen Zoghbi</li> <li>Paper DOI: https://doi.org/10.1093/mnras/stad3912</li> <li>Arxiv DOI: https://doi.org/10.48550/arXiv.2312.09835</li> <li>Published in the Monthly Notices of the Royal Astronomical Society (date of acceptance: 2023/12/12)</li> </ul> <h2>Raw Data</h2> <ul> <li>Raw event files for the described NICER observations can be obtained from the HEASARC at https://heasarc.gsfc.nasa.gov/cgi-bin/W3Browse/w3browse.pl. Select NICER as the telescope and search for MAXI_J1820+070.</li> <li>We used HEASoft v6.28 with standard reprocessing settings to obtain event lists to make light curves. See paper for details.</li> </ul> <h2>Software</h2> <ul> <li>Linux Ubuntu 22.04.</li> <li>Jupyter Notebook (7.0.7)</li> <li>Programming languages used: Python (3.12.1)</li> <li>Python packages used: numpy (1.26.3), matplotlib (3.8.2), scipy (1.12.0), astropy (6.0.0)</li> </ul> <h2>Figures and Tables</h2> <ul> <li>Figures can be reproduced from the ./figures/ folder.</li> <li>All material and data used are available as intermediate data products.</li> <li>Jupyter notebooks (.ipynb files) can be used to make all figures. Running all cells at once does not work, but you can choose the figure you want to remake and executing the relevant cells will result in those figures.</li> </ul> <h2>Intermediate data products</h2> <ul> <li>The light curve arrays that are made in the first few cells of the main Jupyter Notebook can be found in 'datafiles.zip'. </li> <li>The parameters for the Lorentzian fits of the power spectra and the grouping of different observations can be found in 'qpofitsc.txt' and 'obsidsa.txt', respectively, in the same zipped folder.</li> </ul> <h2>End-to-End analysis scripts</h2> <ul> <li>The three Jupyter notebooks that have been added can be used to make the figures and reproduce the main results of the paper. Evidence_for_a_dynamic_corona_main.ipynb is about the main body of the paper, Evidence_for_a_dynamic_corona_energy_bands.ipynb is used for a part of the Discussion involving multiple narrow energy bands and Evidence_for_a_dynamic_corona_sim.ipynb is about the simulations described in Appendix A.</li> </ul>
Terrestrial Lidar Point Cloud Data for: Evaporation and condensation dynamics within saturated epiphyte communities in a Quercus virginiana forest
<p><span>Terrestrial lidar scans were captured using a BLK360 scanner (Leica Geosystems, Norcross, GA, USA) which has a range of 0.5 – 45 m and measurement rate up to 680,000 points s<sup>−1</sup> at the high-resolution setting. A georeferenced, 3-D point cloud of the study site was generated from 12 scans, approximately 50 m apart in both horizontal directions. Scans were performed in orientations intended to maximize branch exposure to the scanner and to scan during optimal weather conditions to minimize occlusion of features due to noise or movement generated by wind. Scan co-registration was done in Leica Geosystem’s Cyclone Register 360 software using its Visual Simultaneous Localization and Mapping algorithm (Visual SLAM) and resulted in relatively low overall co-registration error ranging from 0.005-0.009 m. From this study site point cloud, manual straight-line measurements from the ground to the sensors were made using Leica’s Cyclone Register 360 software.</span></p>
Data Supplement for "Impact of Charged Surfaces on the Structure and Dynamics of Polymer Electrolytes: Insights from Atomistic Simulations"
<p>Data set containing the molecular dynamics simulation data used for the journal article "Impact of Charged Surfaces on the Structure and Dynamics of Polymer Electrolytes: Insights from Atomistic Simulations" (<span>Andreas Thum, </span><span>Diddo Diddens, </span><span>Andreas Heuer, </span><em>J. Phys. Chem. C</em> <strong>2021</strong>, <em>125</em>, 25392−25403, <a href="https://doi.org/10.1021/acs.jpcc.1c07751">https://doi.org/10.1021/acs.jpcc.1c07751</a>).</p>
Data-driven physics-based modeling of pedestrian dynamics
<p>Python package to create physics-based pedestrian models from crowd measurements</p> <p>Github: <a href="https://github.com/c-pouw/physics-based-pedestrian-modeling">https://github.com/c-pouw/physics-based-pedestrian-modeling</a></p>
CESM2.2-8P4Z data supporting Yu et al. (2024): Simulating ecosystem dynamics and marine biogeochemical cycles with multiple plankton functional types
<p><span>This dataset contains the model output from CESM2.2-8P4Z, used in Yu et al. (2024) and</span><span> </span><span>submitted to</span><span> the Journal of Advances in Modeling Earth Systems (JAMES). These are the last 20-year averaged output files from 310 years of the model simulations, which are analyzed in Yu et al., (2024). CESM2.2-8P4Z contains twelve plankton groups, including eight types of phytoplankton:</span><span> </span><span>1</span><span>) picophytoplankton groups: <em>Prochlorococcus</em>, <em>Synechococcus</em>, picoeukaryotes and diazotrophs; 2) nanophytoplankton groups:</span><span> </span><em><span><em>P</em></span></em><em><span><em>haeocystis</em></span></em><span>, <em>coccolithophores</em></span><span> </span><span>and a generic other nanophytoplankton; 3) micro-sized phytoplankton: diatoms</span><span>; and four types of zooplankton:</span><span> </span><span>small microzooplankton (5-20 u</span><span>m, such as ciliates, nanoflagellates), large microzooplankton (20-200 u</span><span>m, such as copepod nauplii, small dinoflagellates etc.), mesozooplankton (200-2000 u</span><span>m, such as smaller copepod, large dinoflagellates) and macrozooplankton (>2000 u</span><span>m, such as larger copepod, krill).</span><span> </span><span>The MARBL-8P4Z model improves seasonal simulation of the spring bloom compared with more simplified MARBL configurations, benefiting from dampened diatom blooms at higher latitudes due to a combination of bottom-up and top-down drivers.</span></p>
Figure data for Dynamical Correlations and Order in Magic-Angle Twisted Bilayer Graphene
<p>This repository contains the data corresponding to all figures in the main text of the 2024 (to appear) Phys. Rev. X article <em>Dynamical Correlations and Order in Magic-Angle Twisted Bilayer Graphene</em>. See README for notes on how to access. </p> <p>The simulation code used to generate this data is available on github.<br>w2dynamics simulations: <a href="https://github.com/lcrippa/w2dynamics-matbg-symmetric">https://github.com/lcrippa/w2dynamics-matbg-symmetric</a><br>TRIQS simulations: <a href="https://github.com/gautamra/TBLG_ordered">https://github.com/gautamra/TBLG_ordered</a></p>
SeisSol model setup input files and supplement videos for the 3D dynamic rupture models of Wirp et al. 2024
<p>Data required to run the dynamic rupture models presented in Wirp, S. A., Gabriel, A.-A., Ulrich, T., Lorito, S. (2024). The README.txt file contains detailed information about the data and data format.</p>
Supplemental data for "Widespread and rapid dynamics of subglacial water in the Canadian Arctic"
<h2>What's inside</h2> <p>This data set contains:</p> <ol> <li><strong>Inventory:</strong> The inventory of the active subglacial water bodies in the Canadian Arctic.</li> <li><strong>Workflows:</strong> Data analysis code that produces the study results. </li> <li><strong>Results:</strong> The derived ArcticDEM strip data and visualizations generated by this study.</li> </ol> <p>See "README.md" for more information.</p>
Economic Adversity and dynamic neural changes during fear conditioning
<p> </p> <p>The "depression_anxiety_symptoms.RData" file contains data on depression and anxiety across four time points, which can be used for trajectory analysis.</p> <p>The "fp_use_dataframes.RData" file contains data from four blocks of fear learning, specifically including functional activation analysis data of five key regions of interest (ROIs) under conditions such as CS+, CS-, and CS+>CS-, learning slope data, and data for moderation analysis.</p> <p>The "fp_use_dataframes-fc-analysis.RData" file contains data from four blocks of fear learning, specifically including generalized PPI connectivity analysis data under conditions such as CS+, CS-, and CS+>CS-.</p> <p>The "BLA_slope_trajectory_analysis.csv" file contains data that can be used to examine the relationships between brain activity data, inflammation composite scores, and symptom trajectory development.</p> <p>The "code_in_study.R" file contains code for linear mixed model analysis (including three-way and two-way interactions), between-group difference analysis of learning slopes, and related plotting code.</p> <p>The "ANOVA-plot_code.R" file contains code for interaction analysis between economic adversity and background factors (such as childhood maltreatment, subjective SES), post-hoc between-group difference tests, and plotting.</p> <p>The "LCGMs-model fit-comparison.R" file contains code for comparing the differences between unconditional models, linear models, and quadratic change models in trajectory analysis.</p> <p>The "trajectory_analysis.R" file contains code for examining the relationship between the basolateral amygdala's learning slope and depression/anxiety trajectories, as well as code for testing the mediating effect of anxiety trajectories between the basolateral amygdala's learning slope and inflammation.</p>
Fig. 2 in Population dynamics of pests and natural enemies on sugar cane grown in a subtropical region of Brazil
Fig. 2. Population dynamics of the natural enemies Harmonia axyridis, Doru lineare, and Billaea claripalpis in sugar cane from Feb 2013 to Jan 2015 in the municipality of Salto do Jacuí, Rio Grande do Sul State, Brazil. Arrows indicate the harvesting and budding periods.
Fig. 1 in Population dynamics of pests and natural enemies on sugar cane grown in a subtropical region of Brazil
Fig. 1. Population dynamics of sugar cane pests from Feb 2013 to Jan 2015 in the municipality of Salto do Jacuí, Rio Grande do Sul State, Brazil: (A) Means of internodes, attacked internodes, and number of Diatraea saccharalis larvae per culm; (B) Insects of Mahanarva fimbriolata per square m, and infested culms (%) by Saccharicoccus sacchari, and Melanaphis sacchari. Arrows indicate the harvesting and budding periods.
Neural-network-based molecular dynamics simulations reveal that proton transport in water is doubly gated by sequential hydrogen-bond exchange: Neural network potentials training data
<h1>Neural network potentials of an excess proton in bulk water, training data</h1> <p>This dataset contains 2188 configurations labeled at two hybrid DFT levels (revPBE0-D3 and B3LYP-D3).</p> <p>The configurations are given as a single XYZ file: configurations.xyz</p> <p>The box dimensions are written in box.txt</p> <p>The energies for all configurations at a given level of theory are written in energies_LEVEL.txt (one configuration per line)</p> <p>The atomic forces for each configuration at a given level of theory are gathered in a XYZ file: forces_LEVEL.xyz</p> <p>The relative displacements of the Wannier centroids, with respect to the closest oxygen atom, for each configuration at a given level of theory, are in the following XYZ file: wannier-centroids-displacements_LEVEL.xyz</p>
ScienceDex guides
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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.