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.
2,155
datasets available to search
ShareScore release 0.9.0
Dataset results
2,155 results for “Ridging”
Photogrammetric Point Cloud and DSM from UAV campaign at Niwot Ridge, 2017.
Elevation data from 14 August 2017 collected as part of unmanned aerial vehicle (UAV)/drone campaign during Summer 2017. Investigating snow depth varaibility and spatiotemporal variations and controls on vegetation productivity within the Niwot Ridge LTER Saddle Catchment.
Calibrated Red/Near Infrared orthomosaic imagery from UAV campaign at Niwot Ridge, 2017.
Red/Near Infrared data were collected as part of unmanned aerial vehicle (UAV)/drone campaign during Summer 2017. The purpose of the project was to investigate snow depth variability and spatiotemporal variations and controls on vegetation productivity within the Niwot Ridge LTER Saddle Catchment.
Fecal glucocorticoid metabolite levels of American pika (Ochotona princeps) and habitat characteristics of their associated territories found in rock glaciers adjacent to Niwot Ridge and within Rocky Mountain National Park, 2018 - 2019.
To understand whether stress-associated hormones vary with metrics of habitat quality, we measured fecal glucocorticoid metabolite (FGM) levels in the American pika (Ochotona princeps), a small mammal with well-defined habitat (talus), that can vary in quality depending on the presence of rock ice features (RIFs). In 2018, we sampled pika scat from two types of RIFs: “active” rock glaciers thought to harbor subsurface ice recently, and “fossil” rock glaciers considered long devoid of subsurface ice (as classified by Janke 2005, 2007). Specifically, fecal pellets were collected from pika territories located in rock glaciers within eight sites along the Front Range of Colorado: four in Rocky Mountain National Park (2 active, 2 fossil) and four adjacent to Niwot Ridge (2 active, 2 fossil) (pika_fecal_glu_rg.aw.csv). To account for possible seasonal variation in pika FGM, scat samples were collected in the alpine spring and fall. To understand other influences of habitat quality on FGMs, we also measured fine-scale habitat differences between rock glaciers in 2019, including talus depth, clast size, and land cover metrics related to forage (pika_fecal_habitat_rg.aw.csv).
Above and below ground phenology for the Niwot Ridge sensor node array, 2021 - 2022.
The below-ground growing season often extends beyond the above-ground growing season in tundra ecosystems. However, we do not yet know where and when this occurs and whether these phenological asynchronies are driven by variation in local vegetation communities or by spatial variation in microclimate. We deployed root in-growth cores in 4 locations in Niwot Ridge’s sensor node array as part of a multi-site study of above- and below-ground tundra phenology.
Electron shuttling capacity and greenhouse gas production of soils for three high-elevation wetlands at Niwot Ridge, 2024.
High-elevation wetlands are important indicators of how mountain ecosystems may respond to global climate change. These wetlands also act as locations of disproportionate biogeochemical processing on the landscape, but they remain relatively understudied compared to lowland wetlands. This study aimed to characterize redox-active organic matter (RAOM) reduction, a known key control on carbon cycling in high-latitude peatland ecosystems, to better understand biogeochemical cycling in high elevation wetlands and carbon greenhouse gas production at Niwot Ridge LTER. Soils were collected from three different types of wetlands, a subalpine wetland, a periglacial solifluction lobe, and an alpine wet meadow. Samples were incubated at a common temperature in the laboratory to measure RAOM reduction, carbon dioxide production, and methane production over 63-d. This dataset reports the electron shuttling values, a measure of RAOM reduction, and the greenhouse gas production over the incubation period.
0.5-meter elevation lattice grid, Saddle grid, Niwot Ridge LTER, Colorado
This is a 0.5m lattice/DEM derived using the TOPOGRID command. 1:500 scale. This dataset is part of the Saddle grid geographic information system (GIS). Additional information concerning the Niwot Ridge LTER hierarchical GIS can be found in Walker et al. (1993).
Green Lakes Valley land cover classification, Niwot Ridge LTER, Colorado
Land cover data generated by Don Cline (graduate student, CU Boulder Geography), as part of suite of spatial maps made for Green Lakes Valley (see Williams et al. 1999).
10-meter elevation contours, Niwot Ridge LTER Project Area, Colorado
10-meter contour map spanning the Silver Lake Watershed, including Green Lakes Valley, Niwot Ridge LTER, and parts of adjacent Brainard Lake Recreation Area and Indian Peaks Wilderness. Made from a filtered 10-meter lattice, which was made from the Niwot Ridge LTER TIN model (ltertin). This dataset was made to support hierarchical GIS databases at the Niwot Ridge LTER. Additional information concerning the Niwot Ridge LTER hierarchical GIS can be found in Walker et al. (1993).
20-meter elevation contours, Niwot Ridge LTER Project Area, Colorado
20-meter contour map spanning the Silver Lake Watershed, including Green Lakes Valley, Niwot Ridge LTER, and parts of adjacent Brainard Lake Recreation Area and Indian Peaks Wilderness. Made from a filtered 10-meter lattice, which was made from the Niwot Ridge LTER TIN model (ltertin). This dataset was made to support hierarchical GIS databases at the Niwot Ridge LTER. Additional information concerning the Niwot Ridge LTER hierarchical GIS can be found in Walker et al. (1993).
10-meter elevation contours, Green Lakes Valley, Niwot Ridge LTER, Colorado
10-meter contours clipped with a box made from extents of the Green Lakes Valley 1999 high-resolution orthorectified imagery dataset (glv.tif). This dataset was made to support hierarchical GIS databases at the Niwot Ridge LTER. Additional information concerning the Niwot Ridge LTER hierarchical GIS can be found in Walker et al. (1993).
2-meter elevation contours, Saddle grid, Niwot Ridge LTER, Colorado
Coverage of 2-meter contours at Saddle grid. 1:500 scale. This dataset is part of the Saddle grid geographic information system (GIS). Additional information concerning the Niwot Ridge LTER hierarchical GIS can be found in Walker et al. (1993).
Computed Light Fields Within a Sea Ice Pressure Ridge
<p>Calculated light fields in and around a sea-ice pressure ridge. The dataset contains total scalar irradiance and downwelling planar irradiance calculated in horizontal slices at the given distance form the ice surface. Calculations were performed using Monte-Carlo ray-tracing using Zemax Optic-Studio. In addition horizontal slices through the ridge geometry, as well as total and partial ice thickness in each point of the ridge are given. The fields are provided in python and matlab readable formats.</p> <p>For details please refer to the respective publication "The three-dimensional light field within sea ice ridges" by C. Katlein et al.</p>
Udayagiri, Madhya Pradesh. Plan of the central ridge.
<p>Udayagiri, Madhya Pradesh. Plan of the central ridge, showing location of caves 3, 4, 5, 6, 8 and 13 as well as water features and archaeological features: A) astronomical platform; B) mound marking location of early pillar and lion capital, C) temple mound.</p>
Udayagiri, Madhya Pradesh. Central passage in the ridge.
<p>Udayagiri, Madhya Pradesh. Central passage in the ridge, from the east, showing steps and course of the water cascade on the right.</p>
Supporting Data - Double Ridge Formation over Shallow Water Sills on Jupiter's Moon Europa
<p>This archive contains data produced in support of R. Culberg, D. M. Schroeder, G. Steinbrügge, Double Ridge Formation Over Shallow Water Sills on Jupiter’s Moon Europa, <em>Nature Communications</em>, 2022. This includes the WorldView imagery in Figure 1, the reprocessed radargrams underlying the radar analysis and inversion, and outputs of all inversion and sensitivity test runs. See the README file for a complete description of the files available in this archive.</p>
Supplementary Data - "Analysis of Venusian Wrinkle Ridge Morphometry Using Stereo-Derived Topography: A Case Study from Southern Eistla Regio"
<p>Supplementary data for the manuscript entitled "Analysis of Venusian Wrinkle Ridge Morphometry Using Stereo-Derived Topography: A Case Study from Southern Eistla Regio".</p> <p>Includes data for topographic profiles of wrinkle ridges ("wrinkleridge_profiledata.xlsx"), COULOMB model inputs ("COULOMB_modelinputs.xlsx") and outputs ("COULOMB_modeloutputs.xlsx"), and GIS shapefile data for mapped wrinkle ridges (files labelled "allwrinkleridges" and "studiedwrinkleridges"), topographic profile lines (files labelled "topographicprofilelines"), and the regional profile (files labelled "regionalprofile"). </p>
Identification of Thalweg and Ridge Networks as Landmarks for Terrain Partitioning
<p>Grid digital elevation models having resolution of 1 m or less are increasingly available to scientists and engineers interested in describing current state and evolution of Earth and space topography. Significant information loss is, however, clearly observed when existing terrain analysis methods are used in geophysical modeling, especially when coarse meshes are needed for computational efficiency. The present study shows how thalweg and ridge networks can be extracted automatically from any high-resolution grid digital elevation model without the need to alter the observed topographic data, and how these networks can be used as landmarks for terrain partitioning. The slopeline network extracted in grid digital elevation models is used to determine ridge points, related average rejunction lengths of slopelines extending from ridge points on opposite slopes, exorheic and endorheic basins. Exorheic and endorheic basins are connected through the spilling saddles from endorheic basins to form the thalweg network, and the related ridge network is identified. The obtained thalweg and ridge networks are characterized by using the known concept of drainage area and the new concept of spread area to provide physically meaningful landmarks for terrain partitioning at the desired level of detail. Although the developed methods are inspired by the observation of gravity-driven processes, they support any investigation in Earth and space science where thalweg and ridge networks are relevant topographic features. Potential impacts are exemplified by quantifications of preserved depressions over a mountain area and benefits from physically meaningful unstructured terrain partitioning in surface flow propagation over a complex floodplain.</p>
Tectonic uplift, soil production, soil depth, and rock strength at the Dragon's Back Pressure Ridge, Carrizo Plain, California
<div><em><strong>Tectonic uplift, soil production, soil depth, and rock strength at the Dragon's Back Pressure Ridge, Carrizo Plain, California</strong></em></div> <div> </div> <div>Supporting data for “Landscape transience reveals a bottom-up control on soil production”</div> <div> </div> <div>Emily C. Geyman*, David A. Paige, Michael P. Lamb</div> <div> </div> <div>*Corresponding author: Emily C. Geyman, egeyman@caltech.edu</div> <div> </div> <div>Last updated: July 3, 2024</div> <div> </div> <div> </div> <div>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</div> <div> </div> <div><strong>Dataset overview.</strong></div> <div> </div> <div>This dataset contains:</div> <div> </div> <div>1. Georeferenced TIFF files of the (i) LiDAR-derived surface elevation, (ii) geological map (based on the mapping from Dibblee (1973) and Arrowsmith (1995)), (iii) reconstructed cumulative uplift, and (iv) reconstructed uplift rate at the Dragon’s Back Pressure Ridge, Carrizo Plain, California.</div> <div> </div> <div>2. Raw and processed ground penetrating radar (GPR) observations of soil thickness.</div> <div> </div> <div>3. Geomorphic properties: (i) hilltop erosion rate, (ii) hilltop soil production rate, (iii) hilltop saprolite weakness (based on cone penetrometer observations), and (iv) hilltop soil thickness.</div> <div> </div> <div>4. Raw and processed observations from the cone penetrometer (used to compute the saprolite weakness).</div> <div> </div> <div>5. Soil pit observations.</div> <div> </div> <div>6. Matlab code used to perform the MCMC inversion to generate the uplift reconstructions (item (1) above).</div> <div> </div> <div> </div> <div>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</div> <div> </div> <div> </div> <div><strong>Dataset details.</strong></div> <div> </div> <div>See the publication: “Geyman, E.C., Paige, D.A., and Lamb, M.P. Landscape transience reveals a bottom-up control on soil production. In review. 2024.” for details on the field methodology and data analysis. Details about each data product also are provided below. </div> <div> </div> <div> </div> <div>1. Geotiffs.</div> <div> </div> <div>We provide georeferenced TIFF files of the (i) surface elevation, (ii) geological map, (iii) cumulative uplift, and (iv) uplift rate at Dragon’s Back Pressure Ridge, Carrizo Plain, California. The coordinate system for the geotiffs is WGS84 / UTM Zone 11 N (EPSG:32611). All geotiffs are provided at 0.5 m x 0.5 m spatial resolution. Details about each dataset are provided below.</div> <div> </div> <div>(i) Surface elevation. We use LiDAR data from the 2005 B4 Lidar Project, acquired and processed by the National Center for Airborne Laser Mapping (NCALM). The full LiDAR dataset is available for download from OpenTopography (https://portal.opentopography.org/datasetMetadata?otCollectionID=OT.032018.32611.1). We convert the LiDAR point cloud to a 0.5 m gridded bare earth digital elevation model (DEM). </div> <div> </div> <div>(ii) Geological map. The original geological map of Dibblee (1973, 1999) is available from the United States Geological Survey (USGS) at https://pubs.usgs.gov/of/1999/of99-014/. This mapping was refined by Arrowsmith (1995) and Hilley & Arrowsmith (2008). We modify the geological map using high-resolution satellite imagery (from Google, ESRI, and Bing mosaics), as well as high-resolution imagery from the National Agriculture Imagery Program (NAIP), in order to follow the contacts of the Pink, Tan, and Gray members of the Paso Robles Formation. The units on the geological map are coded as:</div> <div>1 - Pink Member, Paso Robles Formation</div> <div>2 - Tan Member, Paso Robles Formation</div> <div>3 - Gray Member, Paso Robles Formation</div> <div>4 - Undifferentiated Paso Robles Formation</div> <div>5 - Quaternary alluvium (older)</div> <div>6 - Quaternary alluvium (younger)</div> <div>7-8 - Quaternary landslides and terraces</div> <div> </div> <div>(iii) Cumulative uplift. We follow the general approach of Hilley & Arrowsmith (2008) to reconstruct the cumulative uplift at Dragon’s Back Pressure Ridge based on the observed positions and elevations of the stratigraphic contacts between the Pink, Tan, and Gray members of the Paso Robles Formation. Put simply, since the Pink, Tan, and Gray members of the Paso Robles Formation are initially flat-lying, the progressive increase in elevation of the contacts between these members from the start to the middle of the Dragon’s Back Pressure Ridge records the cumulative tectonic uplift. We perform a Markov Chain Monte Carlo (MCMC) inversion to reconstruct the uplift history that can best explain our geological observations (i.e., the positions and elevations of the Pink, Tan, and Gray members of the Paso Robles Formation). See section 6 for the Matlab code used to perform the MCMC inversion.</div> <div>Dataset A: “cumulative_uplift_mean.tif” -- the mean reconstructed cumulative uplift (units: meters).</div> <div>Dataset B: “cumulative_uplift_uncertainty_IQR.tif” -- the uncertainty of the reconstructed cumulative uplift (units: meters), documented as the inter-quartile range (IQR), the difference between the 75th percentile and the 25th percentile of the MCMC cumulative uplift estimates.</div> <div> </div> <div>(iv) Cumulative uplift rate. The uplift rate dataset is constructed by taking the spatial derivative of the cumulative uplift dataset (iii) in the along-strike direction of the San Andreas Fault, and then converting from space to time using the long-term slip rate on the San Andreas Fault of approximately 33 mm/yr. This is the same approach as used in Hilley & Arrowsmith (2008).</div> <div>Dataset A: “uplift_rate_mean.tif” -- the mean reconstructed uplift rate (units: mm/yr).</div> <div>Dataset B: “uplift_rate_uncertainty_IQR.tif” -- the uncertainty of the reconstructed uplift rate (units: mm/yr), documented as the inter-quartile range (IQR), the difference between the 75th percentile and the 25th percentile of the MCMC estimates.</div> <div> </div> <div> </div> <div>2. Ground penetrating radar (GPR).</div> <div> </div> <div> <p>The GPR data were acquired with a MALA HDR GPR system with a 450 MHz shielded antenna. Data were acquired every 4 cm, tracked by a survey wheel for precise relative positioning. The GPR survey covered approximately 19 km of ridgeline and included 21 short (approximately 10 m) ridgetop profiles with cone penetrometer observations that serve as ground-truth for the depth of the soil-saprolite boundary inferred from the GPR data. The GPR data were processed using the open-source GPRPy software (Plattner, 2020). We constrained sub-surface velocities by fitting 364 diffraction hyperbolas in the GPR transects. The hyperbola fitting supports a spatially-uniform velocity of approximately 0.11 m/ns. The locations and fitted velocities of the individual hyperbolas used to construct this velocity model are included in the file “GPR_velocities.csv.”</p> <p>The folder “Radar450MHz_raw” includes the raw radar data. The folder “Radar450MHz_GPS” includes the GPS data associated with each radar dataset (saved as .cor files). The GPS observations are aggregated in the spreadsheet “GPS_all” in that folder. The shapefile folder includes the final processed GPR-derived soil thickness estimates (soil thickness reported in units of meters) as a .shp file. The coordinate system for the shapefile is WGS84 / UTM Zone 11 N. </p> </div> <div> </div> <div>3. Geomorphic properties.</div> <div> </div> <div>These are the datasets plotted in Figures 3 and 4 of “Geyman, E.C., Paige, D.A., and Lamb, M.P. Landscape transience reveals a bottom-up control on soil production. In review. 2024.” </div> <div> </div> <div> </div> <div>4. Cone penetrometer observations.</div> <div> </div> <div> <div>This folder contains 3 files:</div> <div>1. ConePenetrometerSummaryTable_Overview.csv: A summary of the 212 cone penetrometer stations. For each station, there is metadata about the location (GPS coordinates), the stratigraphic unit (Pink, Tan, or Gray Member of the Paso Robles Formation), the side of the ridge, (southeast = SW, center = C, or northwest = NW), and the saprolite weakness, calculated as the cone penetrometer ease of penetration [cm/strike] at the position of the soil-saprolite boundary. </div> <div>2. ConePenetrometerSummaryTable_Data.csv: All of the raw observations from the cone penetrometer. The raw observations are the cumulative strike number vs. the cumulative depth of penetration into the ground.</div> <div>3. ConePenetrometerSummaryTableFinal.xlsx: An Excel spreadsheet with the same data from items (1) and (2) above as separate sheets ("Overview") and ("Data").</div> </div> <div> </div> <div> </div> <div>5. Soil pit observations.</div> <div> </div> <div>This folder contains 2 files:</div> <div>1. soil_pit_summary: A summary table containing the soil pit locations and the inferred depth to the soil-saprolite boundary.</div> <div>2. soil_pit_layers: Simplified stratigraphic columns providing grain sizes and classifications (soil vs. saprolite) of the layers identified in each soil pit.</div> <div> </div> <div> </div> <div>6. Matlab code. </div> <div> </div> <div>This folder contains 2 primary Matlab scripts, with supporting data files and helper functions.</div> <div>1. DBPR_uplift: code to reconstruct the tectonic uplift at DBPR based on the positions and elevations of the stratigraphic contacts.</div> <div>1. soil_depth_vs_strength: code to reconstruct Fig. 4 Geyman, E.C., Paige, D.A., and Lamb, M.P. Landscape transience reveals a bottom-up control on soil production. In review. 2024.” </div> <div> </div> <div> </div> <div>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</div> <div> </div> <div> </div> <div><strong>References</strong></div> <div> </div> <div>Arrowsmith, J. R. Coupled tectonic deformation and geomorphic degradation along the San Andreas Fault System. Ph.D. thesis, Stanford University (1995).</div> <div> </div> <div>Dibblee Jr, T. Regional geologic map of San Andreas Fault and related faults and Carrizo Plain, Temblor, Caliente, and La Panza Ranges and vicinity, California. US Geological Survey Miscellaneous Geological Investigations, Map I-757, scale 1:125,000 (1973). </div> <div> </div> <div>Dibblee, T. W. et al. Regional geologic map of San Andreas and related faults in Carrizo Plain, Temblor, Caliente and La Panza Ranges and vicinity, California: A digital database. Tech. Rep., US Geological Survey (1999). </div> <div> </div> <div>Hilley, G. E. & Arrowsmith, J. R. Geomorphic response to uplift along the Dragon’s Back pressure ridge, Carrizo Plain, California. Geology, 36, 367–370 (2008). </div> <div> </div> <div>Plattner, A. M. GPRPy: Open-source ground-penetrating radar processing and visualization software. The Leading Edge 39, 332–337 (2020).</div> <div> </div>
Audiovisual Vignettes of Sea Ice Ridging in the Beaufort Sea in 2007
<p>This presents footage demonstrating the scales of sea ice motion involved in creating ridges under varied degrees of compression and shear. Sound heard in these vignettes is associated with frictional dissipation of kinetic energy during vertical ice displacement. Images shown were recorded during April 2-15, 2007 UTC, as part of the field campaign: Sea Ice Experiment - Dynamic Nature of the Arctic (SEDNA). Footage and photographs presented in this vignette were taken by Andrew Roberts with the assistance of Jennifer Hutchings and Cathleen Geiger. Funding for SEDNA was provided by the National Science Foundation, grant number OPP ARC 0612527. An overview of the SEDNA field campaign is given in: Hutchings, J. K. et al. (2008), Role of Ice Dynamics in the Sea Ice Mass Balance, <em>Eos Trans. AGU</em>, <em>89</em>(50), doi:10.1029/2008EO500003. </p> <p>[Version 2 includes minor corrections and additions to text in Version 1]</p>
From Ridge 2 Reef: An Interdisciplinary Model for Training the Next Generation of Environmental Problem Solvers
<p>This dataset contains the raw data from the evaluation instruments and accompanies the manuscript: "From Ridge 2 Reef: An Interdisciplinary Model for Training the Next Generation of Environmental Problem Solvers". It contains all trainee and advisor interviews from 2018 - 2022, as well as a select few partner interviews. It also contains pre and post-annual trainee survey data and the codebook to decipher the survey data. Rubric criteria and scores are included for trainees enrolled in the R2R Communication Skills course. The R script contains the statistical analyses reported in the manuscript and code used to generate figures.</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.