Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
9
datasets available to search
ShareScore release 0.9.0
Dataset results
9 results for “Fluvial systems”
Global map of Martian fluvial systems
<p>This dataset represents an update of previous global maps of Martian fluvial systems. We included all the valleys longer than 20 km and mapped them as vector-based polylines within the QGIS software, using the more recent and, to date, the best resolution THEMIS (Thermal Emission Imaging Spectrometer) daytime IR mosaic (100 m/pixel). In addition, we used, where necessary (for small-scale systems and valleys with high erosion), CTX (Contex Camera) data, with a resolution up to 6 m/pixel. The imagery data were coupled with the MOLA (Mars Orbiter Laser Altimeter Mosaic) mosaic which has a spatial resolution of 463 m/pixel. At low latitudes, we used an equidistant cylindrical projection, while at high latitudes, we used sinusoidal and polar stereographic projections to represent and analyze the data. Topographic information and data of higher image quality (new THEMIS mosaic plus CTX data) than those of previous manual maps, allowed us to identify new structures and more tributaries for a large number of systems. An attribute table is associated to our dataset including useful information such as coordinates, total length and an approximative maximum age indication for each system. The latter has been obtained coupling our map with the global geologic map of Tanaka et al. (2014) which represents, to date, the most accurate dating of the planet surface.</p> <p><strong>Attribution</strong>:</p> <p>If you use this data set in your own work, please cite this DOI:<br> 10.5281/zenodo.1051038<br> <br> Please also cite these works: </p> <p>Alemanno et al.: 2018, Global Map of Martian Fluvial Systems: Age and Total Eroded Volume Estimations, Earth and Space Science Journal, 5, 560-577, doi:<a href="https://doi.org/10.1029/2018EA000362">https://doi.org/10.1029/2018EA000362</a><br> Orofino et al.: 2018, Estimate of the water flow duration in large Martian fluvial systems. Planetary and Space Science Journal, 163, 83-96. doi: <a href="https://doi.org/10.1016/j.pss.2018.06.001">10.1016/j.pss.2018.06.001</a><br> Alemanno G.: 2018, Study of the fluvial activity on Mars through mapping, sediment transport modelling and spectroscopic analyses. PhD dissertation thesis, <a href="https://arxiv.org/abs/1805.02208">arXiv:1805.02208</a> [astro-ph.EP].</p>
Fluvial Response to Differential Activity of the Litang Fault System: Implications for Fault Kinematics and Geodynamics in the Southeastern Tibetan Plateau
<p>Supporting information accompanies the publication "Fluvial Response to Differential Activity of the Litang Fault System: Implications for Fault Kinematics and Geodynamics in Southeastern Tibetan Plateau ". </p> <p>Table S1 presents information related to all the extracted river channels including the drainage area and outlet elevation. The elevation, horizontal and vertical migration distances of knickpoints, as well as<em> ksn</em> values above and below the knickpoints have also been included.</p> <p>All the river longitudinal profiles extracted in this study, including the knickpoint position on the longitudinal profiles and the high-precision DEM data presented in Figures 9a and 10a, are provided .</p>
Geometrical forms and influencing factors of distributive fluvial system
<p>Distributive Fluvial System developed around Sugan Lake Basin is studied by using geographic information software such as Google Earth, Global Mapper and digital elevation model(DEM). The gradient, fan radius,draining basin area and perimeter,the area and perimeter of DFSs around Sugan Lake Basin are analyzed.</p>
Distribution. Amazonian lowlands of E Brazil S of the Amazon River, extending S to the cerrado biome in EC Brazil, primarily in the Rio Tapajos, Rio Xingu, and Rio Tocantins-Araguaia fluvial systems of Para, Maranhao, Tocantins, Minas Gerais, Goias, and Mato Grosso states. in Echimyidae
Distribution. Amazonian lowlands of E Brazil S of the Amazon River, extending S to the cerrado biome in EC Brazil, primarily in the Rio Tapajos, Rio Xingu, and Rio Tocantins-Araguaia fluvial systems of Para, Maranhao, Tocantins, Minas Gerais, Goias, and Mato Grosso states.
Data and code for the publication "Assessing the Behavior of Microplastics in Fluvial Systems: Infiltration and Retention Dynamics in Streambed Sediments" - Part 2(2)
<p><strong>Background</strong></p><p>The dataset contains data on Microplastic transport experiments run in an experimental flume of the University of Bayreuth. It was analysed in the paper by J.P. Boos, F. Dichgans, J.H. Fleckenstein, B.S. Gilfedder and S. Frei, "Assessing the Behavior of Microplastics in Fluvial Systems: Infiltration and Retention Dynamics in Streambed Sediments", currently under review in Water Resources Research</p><p> </p><p><strong>Description of the dataset</strong></p><p>This dataset contains data used for individual particle detection, and is a companion of the main dataset (10.5281/zenodo.10083568). The files need to be downloaded and merged into the given folder structure. Put the folder "1Pix" along with the folder "10Pix" to the folder in "210812/Data-FIS/matlab/2_Experiment/exp/".</p><p> </p><p><strong>Disclaimer</strong></p><p>The data and code are provided as is without any warranty.</p><p> </p><p><strong>Funding</strong></p><p>Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -– Project Number 391977956 –- SFB 1357.</p>
Data and code for the publication "Assessing the Behavior of Microplastics in Fluvial Systems: Infiltration and Retention Dynamics in Streambed Sediments" - Part 1(2)
<p><strong>Background</strong></p><p>The dataset contains data on Microplastic transport experiments run in an experimental flume of the University of Bayreuth. It was analysed in the paper by J.P. Boos, F. Dichgans, J.H. Fleckenstein, B.S. Gilfedder and S. Frei, "Assessing the Behavior of Microplastics in Fluvial Systems: Infiltration and Retention Dynamics in Streambed Sediments", currently under review in Water Resources Research</p><p> </p><p><strong>Description of the dataset</strong></p><p>This dataset is the main dataset used for the analysis. There is a twin archive connected to this one, which contains the dataset which was used for the individual particle detection routines (10.5281/zenodo.10081788). The files have to be downloaded and merged into the folder structure.</p><p>The following data is included</p><ul><li>individual experimental data and results in the folders<ul><li><strong>210812</strong> (10 µm, coarse sand, low-flow)</li><li><strong>220727</strong> (1 µm, coarse sand, low flow)</li><li><strong>220803</strong> (3 µm, coarse sand, low-flow)</li><li><strong>220818</strong> (1 µm, fine sand, low-flow)</li><li><strong>220901</strong> (1 µm, coarse sand, high-flow)</li></ul></li><li><strong>Comparison</strong> (comparing individual results of the experiments)</li><li><strong>Scripts</strong> (contains the individual matlab scripts)</li><li><strong>labbook.xlsx</strong> (contains metadata on the experiments, which are read out in the matlab scripts)</li></ul><p> </p><p><strong>Description of the code</strong></p><p>The matlab scripts *.m contain the code to read and analyse all experimental data. The scripts are divided for the different input file types.</p><ul><li>Main scripts to analyze experimental data<ul><li><strong>Experiment_Main.m </strong>Main routine for individual experiments, reading and analysing Fluorometer, Levelogger, Flowmeter, Ultrasonics PIV</li><li><strong>Experiment_Main_Compare.m </strong>Comparison of individual experiment results</li></ul></li><li>FIS-dataset<ul><li><strong>FIS_Cal_Individual.m: </strong>Realizes individual calibrations of one experiment</li><li><strong>FIS_Cal_Result.m: </strong>Merges individual calibrations of one experiment</li><li><strong>Experiment_FIS.m: </strong>Load data of one experiment, detect interfaces. Followed by<ul><li><strong>Experiment_FIS_1pix</strong>: Individual particle detection (for 10 µm experiment, no binning)</li><li><strong>Experiment_FIS_10pix</strong>: Particle cloud analysis (all experiments, binning 10 Pix * 10 Pix)</li></ul></li><li><strong>Experiment_FIS_10pix_compare.m: </strong>Compare results of particle cloud analysis for all experiments.</li></ul></li><li>Fluo-data<ul><li><strong>Fluo_Cal.m </strong>Realizes calibration for Fluorometer devices</li></ul></li><li>PIV-dataset<ul><li><strong>PIV_individual.m </strong>Individual analysis of Particle Image Velocimetry (in total 9 different subdatasets, from 3 camera positions, and each 3 different illumination positions)</li><li><strong>PIV_merge.m </strong>Merge<strong> </strong>9 individual results of PIV for a result for one experiment</li></ul></li><li>Profiler-dataset<ul><li><strong>Profiler.m </strong>Analyses data from bedform profiling (merging individual measurements after the experiment)</li><li><strong>Profiler_Compare.m </strong>Compares bedform elevations and metrics between the 5 experiments (acquired after the experiment)</li><li><strong>Profiler_Time.m </strong>Analyses temporal change of bedform elevation during the experiment</li></ul></li></ul><p> </p><p><strong>Disclaimer</strong></p><p>The data and code are provided as is without any warranty.</p><p> </p><p><strong>Funding</strong></p><p>Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -– Project Number 391977956 –- SFB 1357.</p>
Data for "Mars' hourglass landforms as local source-to-sink fluvial systems"
Open the record for dataset details and reuse information.
Textural and compositional dataset of fluvial and aeolian sand in the Yarlung Tsangpo dune system
<p>The dataset comprises 5 tables including sample information, grain size distributions, grain roundness, sand petrography and heavy-mineral tables. The sample information table includes River/Dune types, names, locality, collected people, collected year and spatial coordinate, whereas other tables provide detailed sand textural and compositional information. The detailed textural and compositional dataset allow us to evaluate the differences between fluvial and aeolian sand in the river-fed Yarlung Tsangpo system, and The "multiple window" approach provides opportunity to explore intersample and intrasample variability, and help to understand the grain-size control on sand texture and composition. The dataset is a supplement to the manuscript "Fluvial-aeolian interaction and compositional variability in the river-fed Yarlung Tsangpo dune system (southern Tibet)" by Liang et al. (2023) submitted to JGR: Earth Surface. The data should <strong>NOT</strong> be used commercially.</p>
The Canossa-San Romano fault system and the River Secchia fluvial terraces (Northern Apennines, Italy)
<p>Dataset of Canossa-San Romano fault system and and the River Secchia fluvial terraces (Northern Apennines, Italy). The data are the structural geology survey of mesofault and the tree-order of terraces alon Rivers Secchia </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.