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Collected laboratory data for: Theoretical basis and analyses of temperature responses of water-saturated rocks to rapid changes in confining pressure
<p><span>In this data submission, there are three collected laboratory data sets used in the submitted manuscript: Theoretical basis and analyses of temperature responses of water-saturated rocks to rapid changes in confining pressure. The first data set includes the </span><span>thermophysical properties of the main rock-forming minerals in the crust and water. The second data set includes </span><span>the poroelastic constants for compact and porous rocks. </span><span>The third data set includes <span>the </span>chemical composition (Oxides) and CIPW norm minerals of Karatsu basalt (KBT), and isothermal bulk modulus (<em>K</em><sub>i</sub>) of each mineral.</span></p>
Data from: Geomorphological signatures of known hurricanes and validation of theoretical emplacement formulations: coastal boulder deposits on Cuban low-lying marine terraces
<p>Here we show the models made from SfM photogrammetry for 5 boulders found on the Cuban island emplaced by three hurricanes on the low marine terrace. To generate a 3D-scaled SfM point cloud of each studied boulder, we adapted an easy SfM workflow using Agisoft Metashape software (version 1.7.2). We surveyed each CBD obtaining pictures between 0.5 m - 3 m distance from the boulder and also climbing on the boulder to maximise the diversity of picture angles. The survey was carried over a time lapse of ~30 minutes and under cloudy conditions to avoid shadows created by direct sunlight. For scaling SfM results and assessing the quality of measurements, we used six wooden balls as photogrammetric targets. The diameter of the balls is 30 ± 0.2 x 10-3 m, as verified by repeated caliper measurements. We complemented these targets with two 70 x 10 cm wooden scales. All these targets were used as reference scale bars to correct the spherical distortion inherent to the camera optics and perform an auto-calibration. We further used these targets to check possible distortion after post processing and the generation of the resulting 3D model. In order to create a point cloud and a solid volume, we followed the software workflow: we first aligned the images to identify tiepoints, which were then used in bundle adjustment to compute the 3D dense clouds in a second step.</p>
Understanding Electrochemical Reversibility using Density Functional Theory: Bridging Theoretical Scheme of Squares and Experimental Cyclic Voltammetry
<p>#<strong> Scheme of Squares</strong></p> <p>## <strong>Overview</strong></p> <p>The `SchemeOfSquares.tar` archive contains essential data and examples related to our research on redox reactions. The contents are organized into two primary subfolders: `datasets` and `examples`.</p> <p>##<strong> Getting Started</strong></p> <p>### <em><strong>Extracting the Archive</strong></em></p> <p>To extract the contents of the `SchemeOfSquares.tar` file, use the following command in a Linux environment:</p> <p>```sh<br>tar -xvf SchemeOfSquares.tar<br>```</p> <p>### <strong><em>Directory Structure</em></strong></p> <p>After extracting, you will find the following structure:</p> <p>- **datasets/**<br> - **ET/**: Contains Gaussian input and output files for electron transfer (ET) redox reactions.<br> - **PET/**: Contains Gaussian input and output files for proton-coupled electron transfer (PET) redox reactions.<br> <br>- **examples/**: Includes sample cases discussed in the main paper, along with the corresponding scaling code.</p> <p>## <strong>Details</strong></p> <p>### <em><strong>Datasets</strong></em></p> <p>- **ET Subfolder**: Houses all the data files related to electron transfer reactions. Each file here represents a specific reaction and contains Gaussian input and output data.<br>- **PET Subfolder**: Contains data files for proton-coupled electron transfer reactions, similarly structured with Gaussian input and output data.</p> <p>### <em><strong>Examples</strong></em></p> <p>- The `examples` folder provides illustrative samples that were elaborated upon in the main research paper. This includes the scaling code necessary for replicating the results.</p> <p>## <strong>References</strong></p> <p>For a comprehensive understanding of the data and examples provided, please refer to the main paper associated with this repository.</p> <p>## <strong>Contact</strong></p> <p>For any questions or further information, please contact Amir Mahdian / Arsalan Hahsemi at firstname.lastname@aalto.fi.</p>
Structures for "A Theoretical Investigation on the Aminolysis of Pyromellitic and 1,4,5,8- Naphthalenetetracarboxylic Dianhydrides"
<p>Aniline aminolysis reaction of pyromellitic (PMDA) and 1,4,5,8-naphthalenetetracarboxylic (NTDA) dianhydrides is investigated by means of density functional theory (BP86-D3(BJ)/def2-TZVP). The concerted mechanism is shown to be more favorable for both substrates, in comparison to the stepwise one, with the aminolysis of PMDA presenting lower activation energy than the reaction of NTDA. This result is shown to be related to the lower ring tension in NTDA. Solvation also shows significant influence on the kinetics and thermodynamics of these reactions, as determined by two different methods, implicit with the universal solvation model SMD, and explicit with alchemical FEP molecular dynamics simulations. The comparison between SMD and FEP enables us to explore possible inaccuracies on the SMD model, and examine the suitability of the FEP approach as a physics-based method for the calculation of the solvent contribution in different reactions.</p>
data for the PCI publication "New insights into the population genetics of partially clonal organisms: when seagrass data meet theoretical expectations"
<p><strong>Data analyzed int he article "New insights into the population genetics of partially clonal organisms: when seagrass data meet theoretical expectations", doi </strong> <a href="https://arxiv.org/abs/1902.10240v5">https://arxiv.org/abs/1902.10240v5</a> <strong> doi of the PCI recommandation: </strong>https://doi.org/10.24072/pci.evolbiol.100083</p>
Fig. 12 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 12. The same morphospaces as Fig. 11 showing the distribution of 115 species based on measurements.
Fig. 10 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 10. Measurements of the whorl expansion rate (Wc) and width of umbilicus (D). Note that c1 and c2 are defined as the distances from the coiling axis to the centers of the whorls. d1 and d2 are distances of umbilical seams from the coiling axis.
Fig. 8 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 8. Sketches of radial cross sections of ammonoids to illustrate various types of shell forms, such as planorbicone (A, B), serpenticone (C, D), oxycone (E), discocone (F), platycone (G, H), spherocone (I, J, K), and cadicone (L). A. Paraceltites elegans. B. Pseudoclymenia dillensis. C. Tropigastrites lahontanus. D. Pterolytoceras sp. E. Beloceras sp. F. Phylloceras consanguineum Gemmellaro. G. Craspedites sp. H. Tetragonites glabrus. I. Damesites sugata. J. Goniatites multiliratus Gordon. K. Latanarcestes sp. L. Cabrieroceras sp.
Fig. 9 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 9. Theoretical morphospace composed of ae and ar displaying several examples of computer−generated ammonoids that represent observed types illustrated in Fig. 8. A. e = 1.5 and r = 0.27. B. e = 1.75 and r = 0.34. C. e = 2.0 and r = 0.17. D. e = 2.3 and r = 0.05. E. e = 2.0 and r = 0.6. F. e = 1.7 and r = 0.27. G. e = 1.8 and r = 0.24. H. e = 2.3 and r = 0.21. I. e = 2.3 and r = 0.32. J. e = 1.95 and r = 0.28. K. e = 1.8 and r = 0.10. L. e = 1.6 and r = 0.8.
Fig. 7 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 7. Relationship between whorl perimeter (L) and square root of cross−sectional area of the whorl (A0.5) which varies with aand avalues. Dashed line i e r in each diagram represents the A0.5/Lcurve in the case of isometry of E and R (a= a= 1.0). A0.5/Lis generally small when ahas a large value. In each i e r i r model, e = 1.9 and r = 0.24.
Fig. 11 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 11. Results of computer simulations. A–D, theoretical morphospaces based on the piggyback whorls model composed of ae and ar showing values of shape parameters (S, A0.5/L, W, D) of a theoretical model corresponding to each combination of aand a. E, negative correlation between Wand S values i c e r c obtained from theoretical models. F, positive correlation between D and S. The values of S, A0.5/L, Wor D are exhibited by the size of the plots, and × indii c cates that a "forbidden" combinations of ae, ar, e, and r was employed in each simulation resulting in failure of defining form (A–D).
Fig. 6 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 6. Allometric growth of ammonoids. Computer models were generated with ae and ar values of 0.97, 1.0, and 1.3. Each diagram shows ontogenetic change in relationship between whorl height (H) and whorl breadth (B) that are standardized by the perimeter of the last whorl (Lmax). Dashed line in each diagram represents the B/H curve in the case of isometry of E and R (ae = ar = 1.0). Note that B/H decreases with growth in the case of ar = 0.97. In each model, e = 1.9 and r = 0.24.
Fig. 5 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 5. Spectrum of the computer−produced ammonoids with various values of E and R when each of them is fixed throughout growth.
Fig. 4 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 4. Schematic diagram of an ammonoid whorl section. The shape of the whorl (S) is represented by the closed curve illustrated here which is given by the parametric equations indicated above the diagram. Figures in the right show examples of hypothetical whorl shapes with systematically varying the S value.
Fig. 3 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 3. Different shapes of succeeding whorl (gray area) piling up on the hemispherical preceding whorl with same Li, l, and A values.
Fig. 2 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 2. Schematic figure of a cross section of the ammonoid shell showing how to define parameters of the piggyback whorls model. The enlarging rate of the whorl periphery (E) is defined as the ratio of the total perimeter of the newly added whorl section (Li) to that of preceding whorl (Li–1). The proportion of the dorsal whorl (R) is given by the ratio of the circumferential length along the dorsal wall (l) with respect to Li. The shape of the whorl section (S) is represented by the ratio of breadth (B) and height (H) of the whorl.
Fig. 1 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 1. Radial cross sections of ammonoids to illustrate allometric growth. A. Beloceras sp., Devonian; Erfoud, Morocco. B. Phylloceras consanguineum Gemmellaro, 1876, Jurassic; Sakaraha, Madagascar. C. Meekoceras gracilitatis White, 1879, Triassic; Crittenden Spring, Nevada. D. Girtyoceras meslerianum (Girty, 1909), Carboniferous; Jackforth Creek, Oklahoma.
Fig. 13 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 13. Results of same computer simulations as shown in Fig. 11 with an ornamented whorl shape. In order to produce an ornamented closed curve, the following parametric equations were used: x = 0.5Bcoso, y = 0.5H(sino + 0.3|sin4o|Q), where Q = H/B/5 if o Ṥ π, while Q = 5B/H if o> π. Note that simulated S values tend to be large as compared with Fig. 11F.
Computational results data for the assoziated publication "Network Interdiction Problems in Urban Transportation: Theoretical Insights and Computational Characteristics"
<p>This repository contains two Excel tables with computational results for our paper "Network Interdiction Problems in Urban Transportation: Theoretical Insights and Computational Characteristics". Each Excel table includes multiple worksheets, each representing different scenarios and models evaluated in our study.</p> <p><strong>Worksheets Overview</strong></p> <p>Each Excel table contains the following worksheets:<br>1. <strong>ML</strong>: Results for the "ML" big M values.<br>2. <strong>MH</strong>: Results for the "MH" big M values.<br>3. <strong>MF</strong>: Results for the "MF" big M values.<br>4. <strong>Path</strong>: Results for the path model.<br>5. <strong>FMInstances</strong>: Results from applying our models on the original Fontaine and Minner (2018) instances.</p> <p><strong>Columns Description</strong></p> <p>Each worksheet contains the following columns:</p> <p>- <strong>Name of Instance</strong>: A complex string with the identifier of the used instance. The relevant part is "_RXXX_", where XXX is the random seed used to generate the instance.<br>- <strong>Number users</strong>: The number of users/commodities in the network.<br>- <strong>B:</strong> The budget (always set to infinity in our instances).<br>- <strong>GUROBI_RUNTIME</strong>: The time limit set for the computations.<br>- <strong>Modelkind</strong>: The type of model used. Possible values are:<br> - INDICATOR: Compact model.<br> - FMbenders: Benders model from Fontaine and Minner (2018).<br> - ICM: Benders-like cuts.<br> - PathModel: Path enumeration model.<br>- <strong>BigM computation</strong>: Time required to compute all the big M values used (not included in the time limit).<br>- <strong>runtime</strong>: Runtime of the selected model.<br>- <strong>BBnodes</strong>: Number of nodes in the Branch & Bound tree.<br>- <strong>gap</strong>: Gap reported by Gurobi after reaching the time limit.<br>- <strong>Cuts BLC</strong>: Number of Benders-like cuts included.<br>- <strong>Time BLC</strong>: Time required for separating Benders-like cuts.<br>- <strong>M improve BLC</strong>: Frequency of improvements to a big M when using the improved big M term in Benders-like cuts.<br>- <strong>Mcutoff_AVE</strong>: Average (non-zero) improvement of a big M when using the improved big M term in Benders-like cuts.<br>- <strong>Cuts FMBenders</strong>: Number of Benders cuts generated in the Fontaine and Minner (2018) model.<br>- <strong>Time FMBenders</strong>: Time required to generate the Benders cuts in the Fontaine and Minner (2018) model.<br>- <strong>Runtime path enum</strong>: Time required to enumerate all paths for the path-based model (not included in the time limit).<br>- <strong>Average Num Path</strong>: Average number of paths generated for a single commodity/user. Multiply this value by the number of users to obtain the absolute number of paths generated.</p> <p><strong>Note on FCP</strong></p> <p>All the results found for the instances already had integer flow solutions. Additionally, we conducted experiments where we explicitly forced the solutions to be integer for the Benders-like cuts model. We observed that the runtimes remained the same, with only some natural insignificant hardware-induced fluctuations. Therefore, we omit reporting these results again.</p> <p><br>For further information or questions, please refer to our paper "Network Interdiction Problems in Urban Transportation: Theoretical Insights and Computational Characteristics" or contact the authors.</p>
Theoretical Models of Ultra-Cool Objects (Brown Dwarfs and Free-Floating Planets) Including Water Latent Heat Effect: Thermal Structure, Spectra, and Photometry
<p><strong>OVERVIEW</strong><br> Data presented here are from the work of Tang et al. (2021), which present a one-dimensional (vertical) atmospheric structure model for ultra-cool objects<br> that includes moist adiabatic convection (water latent heat release effect).<br> Model grids across a range of effective temperatures (350, 300, 250, 200 K), metallicities ([M/H] of 0.0, 0.5, 0.7, 1.5),<br> and gravities (logg of 4.0, 4.5, 4.7, 5.0) that mimic the atmosphere condition of Y-class brown dwarf and Free-Floating Planets.</p> <p><strong>ATMOSPHERIC THERMAL STRUCTURE</strong><br> The pressure-temperature profile are saved under the ./pT_profiles/, which have two folders:</p> <ol> <li>pT_profile_dry_adiabat: for dry adiabatic treatment.</li> <li>pT_profile_moist_adiabat: for moist adiabatic treatment.</li> </ol> <p>File name of each compressed files gives the effective temperature (Teff, in Kelvin), gravity (in MKS), and metallicity (in [M/H]) information as for example:<br> t200g100nc_m0.0.cmp.gz --> Teff of 200K, gravity as 100 m/s2, and [M/H]=0.0.</p> <p>The table formate of the pressure-temperature profile, i.e., the .cmp files, is made to work with the [<a href="https://natashabatalha.github.io/picaso/">PICASO software</a>](https://natashabatalha.github.io/picaso/).<br> Each .cmp file contains</p> <ul> <li>Column 01 (x) : atmosphere pressure level</li> <li>Column 02 (pressure) : atmosphere pressure in bar</li> <li>Column 03 (DEN) : atmosphere density in cm^{-3}</li> <li>Column 04 (temperature) : atmosphere temperature in kelvin</li> <li>Column 05-14 : molecule mixing ratios for H2, He, CH4, H2O, NH3, CO, PH3, H2S, N2, CO2</li> <li>Column 15 (MU) : mean molecular weight in grams/mole</li> </ul> <p><strong>SPECTRA</strong><br> Model spectra are saved under the ./spectra/ with wavelength ranging from 1 to 30 micron.<br> File name of each compressed file gives the effective temperature (Teff, in Kelvin), gravity (in MKS), metallicity (in [M/H]), adiabatic treatment, and<br> the resolving power information as for example:<br> sp_t200_g100_m0.0_dry_R3000.csv.gz --> Teff of 200K, gravity as 100 m/s2, [M/H]=0.0, with dry adiabatic treatment, and R=3000.<br> Each spetrum .cv file contains two columns:</p> <ul> <li>Column 01 wavelength [micron]</li> <li>Column 02 Flux [W/m2/micron], the spectral fluxes are given at the top of the atmosphere, taken to be 1 Jupiter radius.</li> </ul> <p><strong>PHOTOMETRY</strong><br> Photometry data derived from the spectra at the top of the atmosphere (assuming one Jupiter radius) are given in the syn_photometry.csv.<br> Photometry filters are from the Mauna Kea Observatory (MKO) photometry system, the Wide-field Infrared Survey Explorer (WISE), and the<br> Spitzer Space Telescope. Filter profiles and Vega magnitude zero points are from the [<a href="http://svo2.cab.inta-csic.es/theory/fps/">SVO Filter Profile Service</a>](http://svo2.cab.inta-csic.es/theory/fps/)</p> <ul> <li>Column 01 (gravity) , gravity in MKS</li> <li>Column 02 (teff) , effective temperature in Kelvin</li> <li>Column 03 ([M/H]) , metallicity</li> <li>Column 04-12 , photometry magnitude for IRAC36, IRAC45, W1, W2, GiminiM, MKO_J, MKO_H, MKO_K, MKO_Lp all in Vega system.</li> <li>Column 13 (adiabatic), adiabatic treatment; D: dry adiabatic, M: moist adiabatic</li> </ul> <p><strong>CREDITS</strong><br> Please cite Tang et al. (2021, Astrophysical Journal, in press.)<br> [<a href="https://ui.adsabs.harvard.edu/abs/2021arXiv210507000T/abstract">ADS link</a>](https://ui.adsabs.harvard.edu/abs/2021arXiv210507000T/abstract) if you used data here in your research.</p>
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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.
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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.