Find research datasets worth reusing

opencc-by-4.0Nov 2024View details →

Dataset of the cut-slope failure in the A-7 highway (S Spain)

<div> The dataset contains various datasets and media related to the landslide analysis on the failure occurred in 11 March 2021 in the Km 354.3 of the A-7 Highway (S Spain). Each folder contains specific types of data collected and processed during different stages of the cut-slope assessment, including raw data, processed materials, and media documentation. <h3>Folder Structure</h3> DEMs: Digital Elevation Models (DEMs) representing different stages of the cut-slope. These DEMs serve as raw data for calculating volumes and understanding changes in the slope’s morphology over time. FailureVolumes: Processed datasets used for detailed volume calculations of the landslide. These datasets represent the surfaces used for calculating the volume of the material displaced by the landslide. Orthoimages: Orthoimages of the cut-slope at various stages. These images were acquired by drone, providing high-resolution, georeferenced views that facilitate visual analysis and comparison across different points in time. PointClouds: Point cloud data in LAZ file format, representing different stages of the cut-slope. These point clouds offer a detailed spatial representation of the slope, valuable for further processing and 3D modeling. Videos: Videos captured by drones, documenting the cut-slope’s condition on each data acquisition day during the emergency response and recovery phases. These videos provide a visual context for understanding the progression of the landslide and recovery efforts. ________________ Details on the dataset production and its analysis are provided in the following paper: Galve, J.P., Pérez-García, J.L.,  Ruano, P., Gómez-López, J.M., Reyes-Carmona, C., Moreno-Sánchez, M., Jerez-Longres, P.S., Ghadimi, M., Barra, A., Mateos, R.M., Monserrat, O., Azañón, J.M. (2025) Applications of UAV Digital Photogrammetry in landslide emergency response and recovery activities: the case study of a slope failure in the A-7 highway (S Spain). Landslides. <a href="https://link.springer.com/article/10.1007/s10346-024-02449-9">https://doi.org/10.1007/s10346-024-02449-9</a> The dataset includes materials gathered, acquired and processed during the investigation’s emergency and recovery phases. However, work at the site is ongoing. Please contact us (<a href="mailto:jpgalve@ugr.es">jpgalve@ugr.es</a>) if you require additional information, as new data may have been generated since this dataset was published. <div> </div> </div>

opencc-by-4.0Mar 2022View details →

Effect of live cribwall on slope stability - modelling outputs

These datasets contain outputs from a novel live cribwall model. The model assess the effect of a live cribwall on slope stability over time. The dataset contains Factor of Safety records under different plant cover and climate change scenarios. The model is still unpublished. For more detail, please get in touch aol3@gcu.ac.uk

opencc-by-4.0May 2022View details →

Rapid Landslide Risk Zoning toward Multi-Slope Units of the Neikuihui Tribe for Preliminary Disaster Management repository

Taiwan features steep terrain and a fragile geology environment accompanied by frequent earthquakes and typhoons annually. Meanwhile, with the booming economy and rapid population growth, activities pivot from metropolises to the Taiwan's suburban and mountain areas. However, for example, the Neikuihui tribe in northern Taiwan evolves landslide disasters during extreme rainfall events. To rapidly examine landslide risk in the tribe area for preliminary disaster management, the well-known principle of Risk, which comprises Hazard, Exposure, and Vulnerability, was carefully adapted to scrutinize 14 slope units around the Neikuihui tribe region. The framework of risk zoning is improved based on the previous quantified findings regarding the inventory of the deep-seated landslides in southern Taiwan. Moreover, the proposed procedures comprehensively assess susceptibility, activity, exposure, and vulnerability of each slope unit. The rapid risk zoning analysis of multi-slope units delivers a sloping unit with a high level of landslide risk, and this slope unit did suffer from landslide disasters in the 2016 typhoon event. This study preliminarily proves that the proposed framework and details of rapid risk zoning can help identify a relatively high-risk slope unit around a tribal region and address pre-countermeasures for disaster management.

opencc-by-4.0May 2022View details →

Data from the field experiment on katabatic winds on a steep slope (Grand Colon, French Alps), February 2019

These are the data from the field experiment described in the paper 'Katabatic winds over steep slopes: overview of a field experiment designed to investigate slope-normal velocity and near surface turbulence' by CHARRONDIERE, C., BRUN, C., COHARD, J.M., SICART, J.E., OBLIGADO, M., BIRON, R., COULAUD, C. & GUYARD, H. (2022), Boundary-Layer Meteorol. 187, 29-54.

opencc-by-4.0Jul 2022View details →

3-D view of a slope affected by rockfall

In the compressed folder, there is a 3-D view of the slope affected by rockfall and its defence nearby the town of Lauria (South Italy)  To create a 3-D interactive view of the mitigation works (that can be used with any browser without installing GIS or other software), we used the <a href="https://qgis2threejs.readthedocs.io/en/docs/">Qgis2threejs</a> plugin for QGIS. The LiDAR DTM was used as an elevation layer to create several high-resolution 3-D view models with different layers. <ol> <li>Rockfall barriers </li> <li>Location of 2002 rockfall </li> <li>Area interested by 2017 wildfire</li> <li>building</li> </ol> full Paper    <a href="https://www.mdpi.com/2073-445X/11/11/1951/htm">Merging Historical Archives with Remote Sensing Data: A Methodology to Improve Rockfall Mitigation Strategy for Small Communities</a>

opencc-by-4.0Oct 2022View details →

Estimating surface water availability in high mountain rock slopes using a numerical energy balance model

Model output, forcing data and physical parameters used to estimate water and energy balance. The model was calibrated with field measurements from a study site in the Mont-Blanc massif, at 3842 m a.s.l, at a slope of 55 deegrees and aspect azimut of 150 degrees (south-east). The different ModelOutput files are from simulations at  different elevastions (from 4800 m to 2700 m at steps of 300 m). We used the CryoGrid community model (version 1.0) toolbox (Westermann et al., 2022) to simulate the 1D ground thermal regime and ice/water balance, and estimate the availability of surface water and its potential for infiltration in rock fractures. The S2M-SAFRAN dataset combines output from a numerical weather prediction model and in situ observations, and was originally developed for operational needs to estimate avalanche hazard in mountainous areas (Durand et al., 1993). The S2M-SAFRAN dataset that we used is available for various mountain areas, at elevation steps of 300 m, and with an hourly resolution between the years 1958 to 2021 (Vernay et al., 2022). It includes most parameters that are required for modeling with CryoGrid: Relative humidity, air T, incoming long wavelength radiation, incoming short wavelength solar radiation, and wind speed. To complete the forcing data we used top of the atmosphere incident solar radiation from ERA5 global reanalysis dataset (Hersbach et al., 2020).

opencc-by-4.0May 2018View details →

Identified Charcoal Hearths from "Slope Analysis of 'Digital Elevation Model for Blue Mountain Charcoal Research Project'"

This is a GeoJSON file that lists all of the potential charcoal hearths along the Blue Mountain of eastern Pennsylvania. For a detailed description of how this data was produced, please see: Carter, Benjamin. (2018, May 29). Description of Methods for Identifying Charcoal Hearths along the Blue Mountain of Pennsylvania. (Version 0.1.0). Zenodo. http://doi.org/10.5281/zenodo.1255101 These hearths were identified using this data: Carter, Benjamin. (2018). Slope Analysis of "Digital Elevation Model for Blue Mountain Charcoal Research Project" (Version 0.1.0). Zenodo. http://doi.org/10.5281/zenodo.1252977 The above is derived from: Carter, Benjamin P. (2018). Digital Elevation Model for Blue Mountain Charcoal Research Project (Version 0.1.0). Zenodo. http://doi.org/10.5281/zenodo.1252441

opencc-by-4.0Oct 2021View details →

Beach-face slope dataset for Australia

This repository contains a dataset of beach-face slopes for the Australian coastline. It includes more than 13,200 km of sandy coast with estimates of the beach-face slopes provided every 100 m. The methodology and dataset are described in: Vos, K., Deng, W., Harley, M. D., Turner, I. L., and Splinter, K. D. M.: Beach-face slope dataset for Australia, Earth Syst. Sci. Data, 14, 1345–1357, https://doi.org/10.5194/essd-14-1345-2022, 2022. The beach-face slope data is provided in 2 separate GEOJSON files: Australia_slopes_by_transect.geojson and Australia_slopes_by_beach.geojson. The first one presents the data along each transect (total of 132,132 beach transects) and the second one presents the data for each individual beach/embayment (total of 5,207 beaches).  Additionally, there are three layers that contain polygons for the Australian coastal regions, primary compartments and secondary compartments, respectively. The coordinate system for the geospatial layers is WGS84. 1. Australia_slope_by_transect.geojson: contains a geospatial layer with cross-shore transects along the Australian sandy coastline. Each feature in this layer is a transect (2 point linestring) with the following attributes:   - transect_id: Database id for each transect, e.g., aus0001-0000, aus0001-0001, …   - beach_id: Database id for each beach, e.g., aus0001, aus0002, …, aus5255   - beach_slope: estimate of the beach-face slope between Mean Sea Level (MSL) and Mean High Water Springs (MHWS), value between 0.01 and 0.2   - lower_conf_bound: Lower limit of the confidence band for the slope estimate   - upper_conf_bound: Upper limit of the confidence band for the slope estimate   - width_conf_band: Width of confidence band, value between 0 and 0.19)   - sl_points: Number of datapoints in the shoreline time-series used for beach-face slope estimation (minimum set to 100)   - quality_flag: Quality flag indicating the confidence in the slope estimate at this transect (High, Medium or Low)   - coastal_region: Database id corresponding to the 23 coastal regions as identified by Thom et al. (2018)   - primary_comp_id: Database id corresponding to the 100 primary sediment compartments as identified by Thom et al. (2018)   - secondary_comp_id: Database id corresponding  to the 361 secondary sediment Compartments as identified by Thom et al. (2018) 2. Australia_slope_by_beach.geojson: contains a geospatial layer with each individual beach/embayment (as a linestring) along the Australian sandy coastline. Each feature has the following attributes:   - beach_id: Database id for each beach, e.g., aus0001, aus0002, …, aus5255   - beach_slope_average: Average of the beach-face slope at the site, weighted by the width of the confidence bands, value between 0.01 and 0.2   - width_ci_average: Average width of confidence band over the comprised transects, value between 0 and 0.19   - quality_flag: Quality flag indicating the confidence in the slope estimate at this transect (High, Medium or Low)   - mstr: Mean Spring Tide Range at the beach calculated from the closest grid point in the FES2014 global tide model   - hsig_median: Median Significant Wave Height from the closest grid point in the CAWCR re-analysis dataset   - prc_msrt_obs: percentage of the Mean Spring Tide Range observed by the satellite-derived shorelines   - min_tide_obs: Lowest tide level observed by the satellite-derived shorelines   - max_tide_obs: Highest tide level observed by the satellite-derived shorelines   - sl_points_average: Average number of datapoints in the shoreline time-series over the comprised transects   - beach_length: Length of the beach or embayment, very long beaches (>50km) were split to optimise memory usage when downloading the satellite images   - coastal_region: Database id corresponding to the 23 coastal regions as identified by Thom et al. (2018)   - primary_comp_id: Database id corresponding to the 100 primary sediment compartments as identified by Thom et al. (2018)   - secondary_comp_id: Database id corresponding  to the 361 secondary sediment Compartments as identified by Thom et al. (2018) In addition to these two layers, the 3 different levels of the Sediment Compartments framework (Thom et al.. 2018) with their average beach-face slopes are also included here. 3. coastal_regions.geojson: contains a geospatial layer of each coastal region (as polygon) as defined by Thom et al. 2018. Each feature has the following attributes:   - name: name of the coastal region, e.g., Pilbara, Kimberley, etc   - beach_slope_average_by_beach: Beach-face slope in each coastal region averaged across all the individual beaches inside the polygon   - beach_slope_average_by_transect: Beach-face slope in each coastal region, averaged across all the individual 100-m spaced transects inside the polygon 4. primary_compartments.geojson: contains a geospatial layer of each primary sediment compartment (as polygon) as defined by Thom et al. 2018. Each feature has the following attributes:   - primary_comp_id: Database id corresponding to the 100 primary sediment compartments as identified by Thom et al. (2018)   - name: name of each primary sediment compartment   - beach_slope_average_by_beach: Beach-face slope in each coastal region averaged across all the individual beaches inside the polygon   - beach_slope_average_by_transect: Beach-face slope in each coastal region, averaged across all the individual 100-m spaced transects inside the polygon 5. secondary_compartments.geojson: contains a geospatial layer of each secondary sediment compartment (as polygon) as defined by Thom et al. 2018. Each feature has the following attributes:   - secondary_comp_id: Database id corresponding to the 361 secondary sediment compartments as identified by Thom et al. (2018)   - name: name of each secondary sediment compartment   - beach_slope_average_by_beach: Beach-face slope in each coastal region averaged across all the individual beaches inside the polygon   - beach_slope_average_by_transect: Beach-face slope in each coastal region, averaged across all the individual 100-m spaced transects inside the polygon

opencc-by-4.0Sep 2023View details →

The Instabilities of the Antarctic Slope Current in an Idealized Model

This dataset includes the model configuration and long-term mean data used in the paper entitled 'The Instabilities of the Antarctic Slope Current in an Idealized Model'.

openCC0Jul 2018View details →

Water quality measurements, stream order, channel slope and hydraulic equations of conterminous USGS sites: 1919-2009.

Streams and rivers emit petagrams of CO2 yet there is little known about how discharge (Q) variability impacts stream CO2 at broad scales. Herein, we compiled historical water quality (including pH, alkalinity and temperature) measurements for conterminous USGS sites and coupled them with daily Q for this analysis (the water_quality.csv dataset, 10,822 sites). Based on this dataset, NHDplus channel slopes (NHDplus_slopeSO.csv, 24,764 sites) and hydraulic geometry equations (lm_vQ.csv, 12,854 sites), we calculated partial pressure of dissolved CO2 (pCO2), gas transfer velocity (k) and CO2 effluxes (F) for a total of 813 USGS sites across conterminous US. We derived hydrologic responses (log-linear regressions) for pCO2, k and F versus Q at each site and explored how these responses varied across stream order and different regions. Ancillary datasets provided coordinates (coor_sites.xls), hydrologic unit code (HUC.csv), and watershed area of conterminous USGS sites (watersheds_area.csv).

Hourly weather data from the Arctic LTER Moist Acidic Tussock Experimental plots from 2000 to 2010, Toolik Filed Station, North Slope, Alaska.

Hourly weather data from the LTER Moist Acidic Tussock Experimental plots. The station was installed in 1990 in block 2 of the Toolik LTER experimental moist acidic tussock plots. The plots are located on a hillside near Toolik Lake (68 38' N, 149 36'W). Global solar radiation, photosynthetic active radiation, unfrozen precipitation, air temperature, relative humidity, wind speed, and wind direction are measured at 3 meters. Additional sensors in greenhouses and shade houses plots measure air temperature, relative humidity and photosynthetic active radiation during the growing season. The sensors are read every minute and averaged or totaled every hour.

The role of down-slope water and nutrient fluxes in the response of Arctic hill slopes to climate change, output from MBLGEMIII for typical tussock-tundra hill slope near Toolik Field Station, Alaska.

Output data sets of the MBL-GEM III model for a typical tussock-tundra hill slope. The model is described in two papers: Le DizÃ¨s, S., Kwiatkowski B.L., Rastetter E.B., Hope A., Hobbie J.E., Stow D., Daeschner S., 2003 Modelling biogeochemical responses of tundra ecosystems to temporal and spatial variations in climate in the Kuparuk River Basin (Alaska), Journal of Geophysical Research Vol. 108 No. D2 10.1029/2001JD000960. Rastetter, E.B., B. L. Kwiatkowski, S. Le DizÃ¨s, and J.E. Hobbie. 2004. The Role of Down-Slope Water and Nutrient Fluxes in the Response of Arctic Hill Slopes to Climate Change. Biogeochemistry 69:37-62.

Total numbers and species of insects taken from rock scrubbings during the summer of 1984-1988, 1993-1994, 1996-1998, in the Kuparuk River experimental reach near Toolik Field Station, North Slope Alaska..

A rock-scrubbing technique was used to collect bottom samples at several different stations with three replicates at each station in the Kuparuk River. The stations are measured relative to the 1984 phosphorus dripper. Only July sampling dates are included in this file (ACG). The samples were preserved in ethanol then picked, sorted, counted, and measured in Duluth using a NIKON MICRO-PLAN II digitizing pad.

openOpenDec 2015View details →

Precipitation cations and anions for June, July and August from a wet/dry precipitation, University of Alaska Fairbanks Toolik Field Station, North Slope of Alaska (68 degrees 37' 42"N, 149 degrees 35' 46"W), Arctic LTER 1989 to 2003

Precipitation, collected from a wet/dry precipitation collector located near University of Alaska Fairbanks Toolik Field Station, North Slope of Alaska (68 degrees 37' 42"N, 149 degrees 35' 46"W) was sent out for standardized EPA rain water analysis. Nutrient chemistry was also run on a sub sample at the field station.

Daily weather and soil temperature data from the Arctic LTER Moist Acidic Tussock Experimental plots for 1990 to 2003, Toolik Filed Station, North Slope, Alaska.

Daily weather and soil temperature data from the Toolik Tussock Experimental plots. In 1990 a Campbell CR21X data logger was installed in block 2 of the Toolik LTER experimental tussock plots. The plots are located on a hillside near Toolik Lake (68 38' N, 149 36'W). Sensors were placed in a control, fertilized, greenhouse, greenhouse fertilized, shade house and shade house fertilized plots.

openOpenDec 2015View details →

Hourly weather data from the Arctic LTER Moist Acidic Tussock Experimental plots from 1990 to 1999, Toolik Field Station, North Slope, Alaska.

Daily weather and soil temperature data from the Arctic LTER Moist Acidic Tussock Experimental plots for 2006 to 2008, Toolik Filed Station, North Slope, Alaska.

openCC (other)Apr 2021View details →

Soil temperature data from the control Arctic LTER Moist Acidic Tussock (MAT89) Experimental plots from 2008 to 2020, Toolik Field Station, North Slope, Alaska.

Soil temperature data from the 1989 LTER Moist Acidic Tussock (MAT89) Experimental plots. The logging station was installed in 1990 in block 2 of a four block experimental block design. The plots are located on a hillside near Toolik Lake (68 38' N, 149 36'W). Two replicates depth profiles (10, 20 ,40 centimeters) were installed in each block 2 experimental plots. Frost heaving has caused uncertain depths of measurements for many of the profiles. This data set contains only the control profiles from 2008 to 2020.

openCC (other)Jan 2020View details →

Photo-oxidation and photomineralization apparent quantum yield dataset for dissolved organic carbon leached from permafrost soils collected from the North Slope of Alaska, July 2018.

Dissolved organic carbon (DOC) was leached from permafrost soils near the Toolik Field Station in the Alaskan Arctic and then characterized for its photochemical properties. Oxygen (O2) consumed from photo-oxidation of permafrost DOC was measured as a function of sunlight wavelength, defined as the apparent quantum yield spectrum of photo-oxidation (O2 consumed per mol photon absorbed by DOC). Carbon dioxide (CO2) produced from photomineralization of permafrost DOC was measured as a function of sunlight wavelength, defined as the apparent quantum yield spectrum of photomineralization (CO2 produced per mol photon absorbed by DOC).