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84 results for “Thrusting”

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zenodo44/100

Earth - Venus Low-Thrust Optimal Transfers / Database A

<p>A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus&#39; orbit&nbsp;starting on the date 7th of May 2005 and arriving at Venus&#39; orbit.&nbsp;</p> <p>This database was generated with a perturbation size of&nbsp;0.2 and contains 429,316 trajectories with 100 samples along each trajectory.</p> <p>The database is in the HDF5 format with 4 dataframes included. These are the &#39;nominal&#39;, &#39;train&#39;, &#39;val&#39; and &#39;test&#39; dataframes each of which contains rows of entries in the following format:</p> <pre>[&#39;t&#39;, &#39;p&#39;, &#39;f&#39;, &#39;g&#39;, &#39;h&#39;, &#39;k&#39;, &#39;L&#39;, &#39;m&#39;, &#39;lp&#39;, &#39;lf&#39;, &#39;lg&#39;, &#39;lh&#39;, &#39;lk&#39;, &#39;lL&#39;, &#39;lm&#39;, &#39;T&#39;, &#39;ux&#39;, &#39;uy&#39;, &#39;uz&#39;, &#39;traj_id&#39;, &#39;sampl_id&#39;, &#39;vf&#39;]</pre> <p>which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function.</p> <p>We are in the process of writing a paper titled &quot;Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks&quot; that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) &quot;Backward Generation of Optimal Samples&quot; method.</p>

opencc-by-4.0Jan 2020View details →
zenodo44/100

Earth - Venus Low-Thrust Optimal Transfers / Database F

<p>A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus&#39; orbit&nbsp;starting on the date 7th of May 2005 and arriving at Venus&#39; orbit.&nbsp;</p> <p>This database was generated with a perturbation size of&nbsp;(5.0, 1.0, 1.0, 0.0, 0.0, 0.01)&nbsp;and contains 557,395 trajectories with 100 samples along each trajectory.</p> <p>The database is in the HDF5 format with 4 dataframes included. These are the &#39;nominal&#39;, &#39;train&#39;, &#39;val&#39; and &#39;test&#39; dataframes each of which contains rows of entries in the following format:</p> <pre>[&#39;t&#39;, &#39;p&#39;, &#39;f&#39;, &#39;g&#39;, &#39;h&#39;, &#39;k&#39;, &#39;L&#39;, &#39;m&#39;, &#39;lp&#39;, &#39;lf&#39;, &#39;lg&#39;, &#39;lh&#39;, &#39;lk&#39;, &#39;lL&#39;, &#39;lm&#39;, &#39;T&#39;, &#39;ux&#39;, &#39;uy&#39;, &#39;uz&#39;, &#39;traj_id&#39;, &#39;sampl_id&#39;, &#39;vf&#39;]</pre> <p>which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function.</p> <p>We are in the process of writing a paper titled &quot;Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks&quot; that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) &quot;Backward Generation of Optimal Samples&quot; method.</p>

opencc-by-4.0Jan 2020View details →
zenodo44/100

Earth - Venus Low-Thrust Optimal Transfers / Database E

<p>A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus&#39; orbit&nbsp;starting on the date 7th of May 2005 and arriving at Venus&#39; orbit.&nbsp;</p> <p>This database was generated with a perturbation size of&nbsp;20.0 and contains 409,076 trajectories with 100 samples along each trajectory.</p> <p>The database is in the HDF5 format with 4 dataframes included. These are the &#39;nominal&#39;, &#39;train&#39;, &#39;val&#39; and &#39;test&#39; dataframes each of which contains rows of entries in the following format:</p> <pre>[&#39;t&#39;, &#39;p&#39;, &#39;f&#39;, &#39;g&#39;, &#39;h&#39;, &#39;k&#39;, &#39;L&#39;, &#39;m&#39;, &#39;lp&#39;, &#39;lf&#39;, &#39;lg&#39;, &#39;lh&#39;, &#39;lk&#39;, &#39;lL&#39;, &#39;lm&#39;, &#39;T&#39;, &#39;ux&#39;, &#39;uy&#39;, &#39;uz&#39;, &#39;traj_id&#39;, &#39;sampl_id&#39;, &#39;vf&#39;]</pre> <p>which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function.</p> <p>We are in the process of writing a paper titled &quot;Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks&quot; that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) &quot;Backward Generation of Optimal Samples&quot; method.</p>

opencc-by-4.0Jan 2020View details →
zenodo44/100

Earth - Venus Low-Thrust Optimal Transfers / Database D

<p>A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus&#39; orbit&nbsp;starting on the date 7th of May 2005 and arriving at Venus&#39; orbit.&nbsp;</p> <p>This database was generated with a perturbation size of&nbsp;5.0 and contains 265,603&nbsp;trajectories with 128 samples along each trajectory.</p> <p>The database is in the HDF5 format with 4 dataframes included. These are the &#39;nominal&#39;, &#39;train&#39;, &#39;val&#39; and &#39;test&#39; dataframes each of which contains rows of entries in the following format:</p> <pre>[&#39;t&#39;, &#39;p&#39;, &#39;f&#39;, &#39;g&#39;, &#39;h&#39;, &#39;k&#39;, &#39;L&#39;, &#39;m&#39;, &#39;lp&#39;, &#39;lf&#39;, &#39;lg&#39;, &#39;lh&#39;, &#39;lk&#39;, &#39;lL&#39;, &#39;lm&#39;, &#39;T&#39;, &#39;ux&#39;, &#39;uy&#39;, &#39;uz&#39;, &#39;traj_id&#39;, &#39;sampl_id&#39;, &#39;vf&#39;]</pre> <p>which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function.</p> <p>We are in the process of writing a paper titled &quot;Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks&quot; that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) &quot;Backward Generation of Optimal Samples&quot; method.</p>

opencc-by-4.0Jan 2020View details →
zenodo44/100

Earth - Venus Low-Thrust Optimal Transfers / Database C

<p>A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus&#39; orbit&nbsp;starting on the date 7th of May 2005 and arriving at Venus&#39; orbit.&nbsp;</p> <p>This database was generated with a perturbation size of&nbsp;0.4&nbsp;and contains 764,479 trajectories with 100 samples along each trajectory.</p> <p>The database is in the HDF5 format with 4 dataframes included. These are the &#39;nominal&#39;, &#39;train&#39;, &#39;val&#39; and &#39;test&#39; dataframes each of which contains rows of entries in the following format:</p> <pre>[&#39;t&#39;, &#39;p&#39;, &#39;f&#39;, &#39;g&#39;, &#39;h&#39;, &#39;k&#39;, &#39;L&#39;, &#39;m&#39;, &#39;lp&#39;, &#39;lf&#39;, &#39;lg&#39;, &#39;lh&#39;, &#39;lk&#39;, &#39;lL&#39;, &#39;lm&#39;, &#39;T&#39;, &#39;ux&#39;, &#39;uy&#39;, &#39;uz&#39;, &#39;traj_id&#39;, &#39;sampl_id&#39;, &#39;vf&#39;]</pre> <p>which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function.</p> <p>We are in the process of writing a paper titled &quot;Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks&quot; that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) &quot;Backward Generation of Optimal Samples&quot; method.</p>

opencc-by-4.0Jan 2020View details →
zenodo44/100

Earth - Venus Low-Thrust Optimal Transfers / Database B

<p>A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus&#39; orbit&nbsp;starting on the date 7th of May 2005 and arriving at Venus&#39; orbit.&nbsp;</p> <p>This database was generated with a perturbation size of&nbsp;0.4&nbsp;and contains 382,193&nbsp;trajectories with 100 samples along each trajectory.</p> <p>The database is in the HDF5 format with 4 dataframes included. These are the &#39;nominal&#39;, &#39;train&#39;, &#39;val&#39; and &#39;test&#39; dataframes each of which contains rows of entries in the following format:</p> <pre>[&#39;t&#39;, &#39;p&#39;, &#39;f&#39;, &#39;g&#39;, &#39;h&#39;, &#39;k&#39;, &#39;L&#39;, &#39;m&#39;, &#39;lp&#39;, &#39;lf&#39;, &#39;lg&#39;, &#39;lh&#39;, &#39;lk&#39;, &#39;lL&#39;, &#39;lm&#39;, &#39;T&#39;, &#39;ux&#39;, &#39;uy&#39;, &#39;uz&#39;, &#39;traj_id&#39;, &#39;sampl_id&#39;, &#39;vf&#39;]</pre> <p>which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function.</p> <p>We are in the process of writing a paper titled &quot;Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks&quot; that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) &quot;Backward Generation of Optimal Samples&quot; method.</p>

opencc-by-4.0Jan 2020View details →
zenodo44/100

Earth - Venus Low-Thrust Optimal Transfers / Database G

<p>A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus&#39; orbit&nbsp;starting on the date 7th of May 2005 and arriving at Venus&#39; orbit.&nbsp;</p> <p>This database was generated with a perturbation size of&nbsp;(5.0, 1.0, 1.0, 0.0, 0.0, 0.01)&nbsp;and contains 999,985 trajectories with 100 samples along each trajectory.</p> <p>The database is in the HDF5 format with 4 dataframes included. These are the &#39;nominal&#39;, &#39;train&#39;, &#39;val&#39; and &#39;test&#39; dataframes each of which contains rows of entries in the following format:</p> <pre>[&#39;t&#39;, &#39;p&#39;, &#39;f&#39;, &#39;g&#39;, &#39;h&#39;, &#39;k&#39;, &#39;L&#39;, &#39;m&#39;, &#39;lp&#39;, &#39;lf&#39;, &#39;lg&#39;, &#39;lh&#39;, &#39;lk&#39;, &#39;lL&#39;, &#39;lm&#39;, &#39;T&#39;, &#39;ux&#39;, &#39;uy&#39;, &#39;uz&#39;, &#39;traj_id&#39;, &#39;sampl_id&#39;, &#39;vf&#39;]</pre> <p>which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function.</p> <p>We are in the process of writing a paper titled &quot;Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks&quot; that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) &quot;Backward Generation of Optimal Samples&quot; method.</p>

opencc-by-4.0Jan 2020View details →
zenodo44/100

Microearthquake sequence recorded close to the Montello thrust system (Southeastern Alps)

<p>Seismic catalogues (QuakeML format) of a&nbsp;microearthquake sequence occurred on August 2021 in the&nbsp;Montello thrust area&nbsp;and recorded by the Collalto seismic network (<a href="https://doi.org/10.7914/SN/EV">10.7914/SN/EV</a>); the seismic catalogues&nbsp;are provided, separately, for different earthquake location codes (h71=Hypo71; hel=Hypoellipse; hypoDD=HypoDD; migraloc=Migraloc). DATA_tgz_SAC.tar contains earthquake&nbsp;waveforms&nbsp;(SAC format) with P and S arrival times used for the locations. Other details in Peruzza et al. (2022, doi:&nbsp;10.3389/feart.2022.1044296). &nbsp;</p>

opencc-by-4.0Nov 2022View details →
zenodo44/100

Morphotectonic indices and earthquake dataset of Batui Thrust Segment, Banggai, Indonesia

<p>To measure the tectonic activity of a region, morphotectonic, a quantitave measurement of landscapes are carried out. Combination between geological, geomorphology, and earthquake data approach are powerful source to determine process that make up mountainous landscape. The combined data are applied to Pagimana and adjacent area, Banggai, Indonesia by dividing into eight watersheds and aim the Batui Thurst as main objective. The data comprised of 8.5m vertical resolution Digital Elevation Model (DEM) and earthquakes catalog were downloaded from the open-source database and extracted using composited GIS software. We use seven morphometry indices consist of Mountain Front Sinousity (Smf), Drainage Basin: Asymmetry Factor (AF) and Transverse Topographic Symmetry (T), &nbsp;Hypsometric Integral (HI), Channel Sinuosity (S), Ratio of Valley Floor Width to Valley Height (Vf), Stream Length-Gradient Index (SL), and Basin Elongation Ratio (Re) and embed them on eight watersheds which taken by remote sensing. Weighing indices and watersheds matrix also calculated to define correlation between each watershed on each index. Fieldwork also carried out to ensure the presence of Batui Thrust and other related structural geology which have been developed on the Pagimana. This dataset is potentially can be used for geologist, geophysicist and geomorpher to get new insight on the forming of East Arm of Sulawesi landscape and the Banggai-Sula Microcontinent development. Beside, the urban planner and risk assessor can be easily analyze the dataset to avoid and minimize effect of the potential earthquake hazard in the future.</p>

opencc-by-4.0Nov 2022View details →
zenodo40/100

Low-temperature thermochronology database of the Carpathian fold-and-thrust belt (ZFT; ZHe; AFT; AHe) compilation from 1999 to 2023

<p>The following tables contain the low-temperature thermochronology dataset used in the Carpathian belt's exhumation model inversion. This dataset includes information from four thermochronometers: Apatite and Zircon Fission Track (AFT and ZFT), as well as (U-Th)/He on Apatites and Zircons (AHe and ZHe). Compiled from 1999 to 2023, this database encompasses available literature data related to low-temperature thermochronology in the region.&nbsp;</p><p>Excel table as well as a PDF file describe the database.</p>

opencc-by-4.0Nov 2023View details →
zenodo40/100

Text-fig. 1. a: Po Plain and foothills of the Northern Apennine in Northern Italy (inset) with the location of Oriolo (black star) and other Early and Middle Pleistocene plant localities, Enza and Stirone. Red lines indicate the frontal thrust arcs (modified from Martinetto et al. 2015). b: The "La Salita" section, Oriolo and chronology of the two "Sabbie gialle" cycles based on large mammals and palaeomagnetic correlation (modified from Toniato et al. 2017; IMMS 2020* [Italian Mediterranean Marine Stages] updated from Cohen and Gibbars 2020; GTS 2021* [Global Time Scale] updated from Head et al. 2021). c: Quarry "La Salita", Oriolo, in 1987. Main unconformities (U) separating the two "Sabbie gialle" cycles and terrestrial deposits on top are shown. Leaf symbols indicate the positions of some of the layers rich in fossil leaves (photo by G. B. Vai, modified). d: Surroundings of Faenza with the location of Oriolo and adjacent coeval sites yielding plant macrofossils. in The Late Early Pleistocene Flora Of Oriolo, Faenza (Italy): Assembly Of The Modern Forest Biome

Text-fig. 1. a: Po Plain and foothills of the Northern Apennine in Northern Italy (inset) with the location of Oriolo (black star) and other Early and Middle Pleistocene plant localities, Enza and Stirone. Red lines indicate the frontal thrust arcs (modified from Martinetto et al. 2015). b: The "La Salita" section, Oriolo and chronology of the two "Sabbie gialle" cycles based on large mammals and palaeomagnetic correlation (modified from Toniato et al. 2017; IMMS 2020* [Italian Mediterranean Marine Stages] updated from Cohen and Gibbars 2020; GTS 2021* [Global Time Scale] updated from Head et al. 2021). c: Quarry "La Salita", Oriolo, in 1987. Main unconformities (U) separating the two "Sabbie gialle" cycles and terrestrial deposits on top are shown. Leaf symbols indicate the positions of some of the layers rich in fossil leaves (photo by G. B. Vai, modified). d: Surroundings of Faenza with the location of Oriolo and adjacent coeval sites yielding plant macrofossils.

opencc-by-4.0Aug 2022View details →
zenodo40/100

Fig. 7 in Suraqalatia Brasieri Görmuş, Lawa & Nuaimy, 2017 (Larger Benthic Foraminifera; Suraqalatiidae N. Fam.) From The Late Maastrichtian Of The Tarbur Formation (Zagros Fold-Thrust-Belt) And Remarks On Dicyclina Munier-Chalmas, 1887

Fig. 7 Dicyclina schlumbergeri Munier-Chalmas, Late Maastrichtian Tarbur Formation of the Naghan section. a–b Oblique equatorial sections showing embryonic chamber, kidney-shaped in outline. Note the presence of primary partitions only (arrows). c Oblique section of a juvenile specimen. d, f Partial, slightly oblique axial sections. e Fragmentary equatorial section. Scale bars = 1 mm. Thin-sections: 2NG 191 (a), NG 83 (b), NG 13-1 (c), NG 34 (d), NG 49 (e), 2NG 34 big (f).

opencc-by-4.0Jul 2018View details →
zenodo40/100

Fig. 3 in Suraqalatia Brasieri Görmuş, Lawa & Nuaimy, 2017 (Larger Benthic Foraminifera; Suraqalatiidae N. Fam.) From The Late Maastrichtian Of The Tarbur Formation (Zagros Fold-Thrust-Belt) And Remarks On Dicyclina Munier-Chalmas, 1887

Fig. 3 Suraqalatia brasieri Görmüş, Lawa &amp; Al Nuaimy, upper Maastrichtian Tarbur Formation of Mandegan (a, c) and Naghan sections (b, d, f); oblique, partly tangential sections. e development of annular chambers (in grey) in some orbitolinids (modified from Douglass, 1960, fig. 21) and two sections (X–X' and Y–Y') comparable to those illustrated in a–d, and f. Abbreviations: b = beam, ch = chamber, ib = intercalary beam, s = septum, sn = subepidermal network. Thin-sections: Rt 63 (a), 2NG 183 (b), Rt 36 (c), 2NG 146 (d), 2NG 169 (f).

opencc-by-4.0Jul 2018View details →
zenodo40/100

Fig. 6 in Suraqalatia Brasieri Görmuş, Lawa & Nuaimy, 2017 (Larger Benthic Foraminifera; Suraqalatiidae N. Fam.) From The Late Maastrichtian Of The Tarbur Formation (Zagros Fold-Thrust-Belt) And Remarks On Dicyclina Munier-Chalmas, 1887

Fig. 6 Dicyclina sampoi Cherchi &amp; Schroeder, from the Cenomanian of the Sarvak Formation, Zagros Zone, SW Iran, slightly oblique axial section (from Omidvar et al., 2014, fig. 3.10 as Dicyclina schlumbergeri). Megalospheric specimen showing embryonic apparatus and bilateral symmetric pores (foramina) enabling the communication with the first cyclic postembryonic chamber. These are located between the supra- (upper; in yellow) and subembryonic zones (below; in red) (arrows). Both zones are subdivided by primary (b = beams) and secondary partitions (ib = intercalary beams).

opencc-by-4.0Jul 2018View details →
zenodo36/100

The Late Triassic Longmenshan lateral foreland thrust belt: New insights into the tectonic evolution of the Eastern Tibetian margin

<p>Calcite U-Pb Dating Report.xls:This file&nbsp;displays the calcite U-Pb dating results with errors of thirteen sample.&nbsp;Due to low U concentrations within most samples, only two (CV1 and CV2-1) yielded meaningful U-Pb ages.</p> <p>Occurrence of foliations measured within the Songpan-Ganze terrane.xlsx: This file displays the occurrences of foliations measured within the Songpan-Ganze terrane. Three profiles were measured to study the dip direction of nearly vertical strata (S<sub>0</sub>) and the axial planes of the folds (S<sub>1</sub>).</p>

opencc-by-4.0Oct 2020View details →
zenodo36/100

Ferret mating (active thrust trial)

<p>X-ray trial taken on 23rd May 2019.</p> <p>Trial 5A and Trial 5B: biplanar x-ray motion of two ferrets copulating</p> <p>.csv file: Behaviour coding spreadsheet from the same trial&nbsp;</p>

opencc-by-4.0Nov 2024View details →
zenodo36/100

Time optimal low-thrust rendezvous from an asteroid belt

<p>This dataset allows to train regression models representing the optimal time of flight of a constant acceleration low-thrust trajectory aimed at a randezvous with a target orbiting station (placed at 1.3 Astronomical Units).</p><p>The version 2 of the dataset should be used, correcting a data bias&nbsp;towards simpler transfers present in the previous version.</p><p>The attributes, X, represent the modified equinoctial parameters (p,f,g,h,k,sin(L), cos(L)) of the spacecraft (SI units) at the start of the transfer. L is the true longitude. Different representations of the initial state are likely key to improving any model.</p><p>The time of flight, Y,&nbsp;&nbsp;is given in units of TIME=5022642.890912783s</p><p>The dataset was used for <strong>value function learning</strong> in our paper:</p><p>Izzo, D. and Origer, S.: "Neural representation of a time optimal, constant acceleration rendezvous" -&nbsp;https://arxiv.org/abs/2203.15490</p><p>where we were more interested on the effects/use of the data augmentation technique called "Backward Generation of Optimal Examples" than on the&nbsp;accuracy of the resulting neural model. A MAE of ~25 days is obtained on the test set, when training from the augmented dataset (vs. ~34.08 days from the non augmented one). Both these numbers can likely be improved considerably, constituting a nice challenge for the community.</p><p>The python pickles can be opened as:</p><blockquote><p>with open("filename.pk", "rb") as f:<br>&nbsp; &nbsp; X, Y = pkl.load(f)</p></blockquote><p>else , we also&nbsp;provide the corresponding csv files.</p><p>* <strong>training</strong>: contains 3000 items</p><p>* <strong>test</strong>: contains ~1000 items</p><p>*&nbsp;<strong>training_augmented_32_0.0001</strong>: contains 96000 items (augmented from the 3000 of training)</p>

opencc-by-4.0Mar 2022View details →
zenodo36/100

Analogue Models for comparing and testing the relationship between inverted normal faults and pure thrusting during the positive tectonic inversion

<p>This dataset contains a series of Analogue Models for comparing and testing positive tectonic inversion mechanisms and their newly formed structures . Furthermore, it includes 2-D seismic reflection profiles that can be compared with the models presented here. Finally, examples&nbsp;of natural cases that show tectonic inversion processes are included. Both, seismic lines and photos&nbsp;are located on a segment of Andean forearc, specifically, in the Domeyko Cordillera and the Preandean Basins, northern Chile.</p>

opencc-by-4.0May 2022View details →
zenodo36/100

Coseismic folding during ramp failure at the front of the Sulaiman fold-and-thrust belt

<p>It contains the datasets related to &quot;Coseismic folding during ramp failure at the front of the Sulaiman fold-and-thrust belt&quot;.</p>

opencc-by-4.0Oct 2021View details →
zenodo36/100

An experimental investigation on the water retention behaviour of a silty soil for the computation of the lateral earth thrust on a retaining wall.

<p>The dataset is linked with the conference paper available open access with the same title. In the excel file, each spreadsheet reports the data of the figures available in the manuscript.</p>

opencc-by-4.0May 2019View details →

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record