Find research datasets worth reusing

opencc-by-4.0Jun 2022View details →

Conformational ensembles used in "Assessment of forward models for the hydrodynamic radius of intrinsically disordered proteins. Pesce et al. 2022"

Ensemble of intrinsically disordered proteins used in: "Assessment of forward models for the hydrodynamic radius of intrinsically disordered proteins. Pesce et al. 2022". Ensembles are produced with Flexible-meccano and Langevin simulations with CALVADOS for: <ul> <li>Hst5</li> <li>RS</li> <li>DSS1</li> <li>Sic1</li> <li>ProTa</li> <li>NHE6cmdd</li> <li>A1</li> <li>aSyn</li> <li>ANAC046</li> <li>GHR-ICD</li> <li>Tau</li> </ul>

opencc-by-4.0Dec 2022View details →

Processing steps to generate a Digital Surface Model based on SPOT-7 tri-stereo images published in the study "An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar inundation areas at volcan Copahue (Argentina & Chile)" in the Journal of South American Earth Sciences https://doi.org/10.1016/j.jsames.2022.104138

The Digital Surface Model (DSM) was created from SPOT-7 tri-stereo images for the Copahue volcano between the border of Argentina and Chile. Two versions of the DSM are provided: an unfiltered product and a final, filtered product. The final product has a spatial resolution of 5-m and was used for lahar inundation modeling for the Copahue volcano (Viotto, Toyos, and Bookhagen 2022, <a href="https://doi.org/10.1016/j.jsames.2022.104138">https://doi.org/10.1016/j.jsames.2022.104138</a> : An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at Volcán Copahue (Argentina & Chile). Journal of South American Earth Sciences ). The dataset provided should be cited together with the article.  DSM processing  The source images were given by a SPOT-7 snow- and cloud-free triplet (Nadir, Backward and Forward) of 1.5 m spatial resolution from 19 April 2018 (SPOT Image, Airbus Defence and Space GmbH, distributed by CONAE; Dataset ID:  SEN_SPOT7_20180419_142955500_000, delivered by CONAE as DS_SPOT7_20180419). The data were processed with the suite of digital photogrammetry tools AMES Stereo Pipeline ASP (Beyer et al., 2018). The procedure for the generation of the DSM is summarized by following steps:  <ol> <li> The orbital parameters (RCP models) were adjusted using the bundle adjustment tool with no ground control points, since they were unavailable. </li> <li> The scenes were map-projected onto the NASADEM (spatial resolution of 30 m)  elevation dataset, assisted by the results of the orbital adjustment in Step 1. </li> <li>The stereo correlation of the map-projected scenes including the results of the adjusted orbital parameters, was performed three times, using as first scene (i.e., primary image) the nadir (N), backward (B), and forward (F) images . In each run, the order of images to perform the stereo correlation was: N-F-B, F-N-B, and B-N-F. Thus, three point clouds were generated. Specific ASP correlator settings (other than defaults parameters; for details see the provided stereo-default file) were set in the following way:  Correlation Kernel: 15 x 15 pixels; Sub-pixel Refinement Kernel: 21 x 21 pixels; Subpixel Refinement Mode: 2 (Weighted Affine Adaptive Window Correlator EM)</li> <li> The three point clouds were merged into one point cloud with a regular grid of 5 m (unfiltered product, known as DSM_Copahue_UTM19S_WGS84_5m_raw.tif). </li> </ol> The quality of the final point cloud was assessed by comparing the unfiltered DSM with a spatial resolution of 12-m against the WorldDEMTM  elevation dataset (Collins et al., 2015). The WorldDEM was provided by Airbus Defence and Space GmbH under license for the scope of the Viotto et al., 2022 study. The comparison of the pixel-to-pixel heights above the ellipsoid (WGS84) between the two datasets resulted in a mean difference of 0.67 m and a standard deviation of +/- 4.82 m.  Comprehensive details on the methodologies evaluated  to create the dataset with ASP, can be found in the corresponding master's thesis  “Topografía digital y modelado de lahares en el Volcán Copahue, Argentina-Chile” from S. Viotto (link: https://rdu.unc.edu.ar/handle/11086/15384). Recommended literature about processing DEMs from SPOT imagery is given by Mueting et al., 2021 (<a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330</a>).  Creation of the Final, Filtered DSM product The corrections and improvements applied to the unfiltered product to create the final, filtered DSM (named DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif) are summarized by following steps.    <ol> <li> Water Bodies Delineation </li> </ol> The delineation of the water bodies was based on a mask created from the free access water bodies datasets provided by the Instituto Geográfico Nacional of Argentina (<a href="https://www.ign.gob.ar/NuestrasActividades/InformacionGeoespacial/CapasSIG">https://www.ign.gob.ar/ NuestrasActividades/InformacionGeoespacia l/CapasSIG</a>) and by the Ministerio de Bienes Nacionales in Chile ( <a href="https://www.ide.cl/index.php/aguas-continentales/item/1508-catastro-de-lagos">https://www.ide.cl/index.php /aguas-continentales/item/1508-catastro-de-lagos</a>). A total of 45 lakes within the area of interest were considered. Lakes with areas below or equal to 25 m2 were smoothed with a median filter in the last step. Lakes with areas  above this threshold were filled in with a constant value and their borders  were smoothed with a median filter to provide smooth shorelines. 2 . Void Filling Voids (other than water bodies) were filled with the tool “Close Gaps” from Saga GIS software.   3. Smoothing Finally, the elevation dataset was smoothed with a median filter using a 3 x 3 pixel  window, excluding water bodies filled in the step 1.   Final Remarks and Suggestion The quality assessment of the final version by visual inspection of the hillshades suggested an improvement of the signal to noise ratio. However, the void filling process may be improved.   Dataset Description <table align="center"> <caption> </caption> <tbody> <tr> <td>Digital Surface Models</td> <td> No Data Value = -9999 Format = float 32 bit File Format = GeoTiff Vertical Datum: WGS84 Projection information: EPSG 32719 (UTM19S) Spatial Resolution: 5m (subfix: _5m)  Versions:  <ul> <li> Unfiltered product: without corrections DSM_Copahue_UTM19S_WGS84_5m_raw.tif </li> <li> Final, filtered product: smoothed and void filled DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif </li> </ul> </td> </tr> <tr> <td>Water Bodies Mask</td> <td> No Lake Value = 0 Lakes Values = 1 to 45 File Format= GeoTiff Spatial Resolution: 5m (subfix: _5m) Projection information : EPSG 32719 (UTM19S) WB_mask_5m_UTM19S.tif </td> </tr> </tbody> </table>     Repository structure |__ 01_Scripts     |+ run21_CopahueDSM_AMES_sviotto.sh     |+ stereo.default |__ 02_DSMs     |+ DSM_Copahue_UTM19S_WGS84_5m_raw.tif     |+ DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif     |+  WB_mask_5m_UTM19S.tif References Beyer, R. A., Alexandrov, O., & McMichael, S. (2018). The Ames Stereo Pipeline: NASA's open source software for deriving and processing terrain data. Earth and Space Science, 5, 537– 548. <a href="https://doi.org/10.1029/2018EA000409">https://doi.org/10.1029/2018EA000409</a> Collins, J., Riegler, G., Schrader, H., Tinz, M., 2015. Applying terrain and hydrological editing to TanDEM-X data to create a consumer-ready worlddem product. Int. Arch. Photogram. Rem. Sens. Spatial Inf. Sci. 40 (7), 1149. https://doi.org/10.5194/isprsarchives-XL-7-W3-1149-2015. Mueting, A., Bookhagen, B., & Strecker, M. R. (2021). Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina. Journal of Geophysical Research: Earth Surface, 126, e2021JF006330. <a href="https://doi.org/10.1029/2021JF006330">https://doi.org/10.1029/2021JF006330</a> Viotto, S., Toyos, G., & Bookhagen, B. (2022). An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at volcán copahue (Argentina & Chile). Journal of South American Earth Sciences, 104138.  https://doi.org/10.1016/j.jsames.2022.104138

opencc-by-4.0Feb 2023View details →

Quantitative Assessment of the Impact of Future Land Use Changes on Flood Risk Using Remote Sensing, Machine Learning, and a Hydraulic Model

The RF Machine learning code  Topological, geomorphology, geology, metrological information of the Tajan watershed. Land use land cover images of the Tajan watershed River, transportation roads, villages map  Global damage function datasets.

opencc-by-4.0Jan 2023View details →

Code and model outputs for article "Assessing the potential of backscattering as a proxy for phytoplankton carbon biomass"

Scripts from the scientific article "Assessing the potential of backscattering as a proxy for phytoplankton carbon biomass" Submitted to Global Biogeochemical Cycles Camila Serra-Pompei, Anna Hickman, Gregory L. Britten, Stephanie Dutkiewicz This repository contains all the files needed to run the MITgcm Biogeochemical and Optical model (MITgcmBgc, a.k.a. The Darwin model) and generate the figures in the paper. Model outputs are also provided, so there is no need to run the entire model to generate the figures. The main folder "codes_phyto_bbp_paper.zip" contains all the files to process model output and reproduce the figures from the paper. The main files are:     - "Script_main.m" loads and processes model outputs and generates figures 2 to 9.     - "Script_new_optical_params" generates the optical parameters that are used as inputs in the MITgcmBgc model. It also generates figure 1.     - "Script_sensitivity_analysis" Performs offline sensitivity analysis and figure 10. The "mitgcm_darwin_3.zip" folder contain the code of the MITgcm with the Biogeochemical and Optical modules (Darwin package), with the configurations used in this study. Explanations of the model can be found in https://darwin3.readthedocs.io/en/latest/phys_pkgs/darwin.html  These folders also contain the BGC-Argo floats data and climatological bbp and Chl NASA MODIS-Aqua GIOP data used in this study. The BGC-Argo data used in this article (download date: 27/10/2021) is available in "Other_extra_files/Argo_matrix.mat". The BGC-Argo data were collected and made freely available by the International Argo Program and the national programs that contribute to it (https://argo.ucsd.edu,  https://www.ocean-ops.org, https://doi.org/10.17882/42182).  The Argo Program is part of the Global Ocean Observing System. BGC-Argo float data was extracted using the BGC-Argo-Mat Matlab toolbox (Frenzel et al. 2021). The MODIS-Aqua satellite remote sensing data used in this article (download date: 23/12/2022) is available here in the folder "nasa_data". Satellite remote sensing data was extracted from NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group; (2014): MODIS-Aqua Ocean Color Data; NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group. http://dx.doi.org/10.5067/AQUA/MODIS_OC.2014.0

opencc-by-4.0Apr 2023View details →

Assessing Eolian Snow Redistribution in Din-Gad Catchment, Central Himalaya, using Remote Sensing and Modelling

This directory contains files related to the MSc graduation research project of Luc van Dijk of the Department of Physical Geography, Utrecht University. The project is titled "Assessing Eolian Snow Redistribution in Din-Gad Catchment, Central Himalaya, using Remote Sensing and Modelling" and was completed on April 14, 2023. Below is a description of the files in this directory. Satellite_imagery.zip Folder containing all the pre-processed satellite images, that were exported from Google Earth Engine. Additional to the standard image bands, the bands 'NDSI', 'SC' and 'ASI' are present. These describe the Normalized Difference Snow Index, the Snow Cover and the Avalanche Susceptibility Index, respectively. The Google Earth Engine pre-processing script can be found here: https://code.earthengine.google.com/b7fe3ca48de410c9a3fff842f88a1e7f SPHY_output_SnowStorage.zip Folder containing the SPHY output maps (variable: SnowStorage in mm) of the study domain. SRCM.py The python-based Snow Redistribution Classification Model. SRCM_output.zip Folder containing the SRCM output files. Each file has 8 bands: (1) snowmelt, (2) snow removal by avalanching, (3) eolian snow removal, (4) unexplained snow removal, (5) snowfall, (6) snow deposition by avalanching, (7) eolian snow deposition, (8) unexplained snow deposition. SRCM_output_aggregated_wind_heatmaps.zip Folder containing the results of SRCM_output.zip, but only bands 3 and 7 and aggregated per month. WindNinja_output_resampled.zip Folder containing the wind fields that were downscaled from ERA5-Land data using WindNinja. The wind fields were converted from vector (speed, direction), to 2-band raster layers (speed, direction) and resampled from 100 m to 30 m resolution.

dryad32/100

Data for: Combining stable isotopes, trace elements, and distribution models to assess the geographic origins of migratory bats

The expansion of industrial-scale wind-energy facilities has increased the production of low-carbon emission energy but has also resulted in mortality of wildlife, including migratory bats. Management decisions can be limited by a lack of understanding of the geographic impact of bats killed at wind-energy facilities. Several studies have leveraged stable hydrogen isotope ratios (δ2H) of bat fur to illuminate this issue but are limited in the precision of conclusion because δ2H values vary primarily across latitudinal and elevational bands. One approach to increase the precision of geographic assignment is to combine independent inferences about spatial location from additional biomarkers and other related information. To test this possibility, we assigned known-origin individuals of three bat species commonly killed at on-shore wind-energy facilities in North America (Lasiurus borealis, L. cinereus, and Lasionycteris noctivagans) to probable origin using δ2H values, trace element concentrations, and species distribution models. We used cross-validation calibrated combined model tuning to determine the degree to which assignment probabilities improved when combining datasets. We found that combining markers typically performed better than single approaches. For L. borealis and L. cinereus, combining all three data sources outperformed any single or other combined approach. With an accuracy set at 80%, an average of 39.7% and 36.0% of each species' total geographic range was considered a potential origin, respectively; stable hydrogen alone included 51.8% and 50.6% of the total geographic area. In contrast, for L. noctivagans, including trace elements did not increase precision, and adding distribution data to δ2H values only improved precision by 0.6%. Thus, we found that a combination of multiple biomarkers typically, but not always, outperforms single marker approaches, and optimized combinations of different markers outperform equal weighting of each marker. From a practical perspective, δ2H values performed better than trace elements alone; in cases where cost is a limiting factor, the stable hydrogen should be the single biomarker used in conjunction with species distribution models. Overall, these results highlight the importance of validating methods for each species they are applied to and show that combining information from intrinsic biomarker approaches is a useful tool to document bat movements.

opencc-zeroMay 2023View details →

opencc-by-4.0Jul 2023View details →

Assessing the improvement of Tropical Cyclone representation in the IPSL model with increasing resolution -- Data

This folder contains the following data, used in the artcle "Assessing the improvement of Tropical Cyclone representation in the IPSL model with increasing resolution": * TC tracks in all the simulations, before and after the STJ filtering * Composite NetCDFs * Large scale variables used in the GPI, and the GPI itself, in ICO-VHR and ERA5

opencc-by-4.0Dec 2020View details →

Assessment of equilibrium climate sensitivity of the Community Earth System Model version 2 through simulation of the Last Glacial Maximum

Simulation data (TS, FSNT, and FLNT) and apap cloud feedback analysis for CESM2 LGM simulation Simulation boundary condition files in 1-degree resolution: boundary_condition_files.zip Please cite:  Zhu, J., Otto-Bliesner, B. L., Brady, E. C., Poulsen, C. J., Tierney, J. E., Lofverstrom, M., & DiNezio, P. (2021). Assessment of equilibrium climate sensitivity of the Community Earth System Model version 2 through simulation of the Last Glacial Maximum. Geophysical Research Letters, n/a(n/a), e2020GL091220. https://doi.org/10.1029/2020GL091220

opencc-by-4.0Aug 2023View details →

Data and model code: Assessing the Implications of Hydrogen Blending on the European Energy System towards 2050

Dataset for Assessing the Implications of Hydrogen Blending on the European Energy System towards 2050

A BCG Challenge Model Study to Assess Anti-mycobacterial Immunity Induced by BCG and a Candidate TB Vaccine, MVA85A

ClinicalTrials.gov study NCT01194180. IPD Sharing: Not stated. Countries: 1. Publications: 2.

controlledIPD-YESFeb 2026View details →

Assessing an Educational Intervention Program on Knowledge, Attitude and Behaviour Towards Pregnancy Prevention Based on Health Belief Model Amongst Adolescent Girls in Northern Ghana

ClinicalTrials.gov study NCT03384251. IPD Sharing: YES. Countries: 1. Publications: 8.

Pelvic Floor Disorder Assessment of Knowledge and Symptoms: an Educational Model in Spanish-Speaking Women

ClinicalTrials.gov study NCT04829721. IPD Sharing: NO. Countries: 1. Publications: 13.

Study Assessing Two Models of Hypofractionated Protontherapy on Large Choroidal Melanomas

ClinicalTrials.gov study NCT02602756. IPD Sharing: Not stated. Countries: 1. Publications: 0.

Participatory Action Research to Evaluate the Delivery of the MISSION ABC Service Model and Assess Health Service and Clinical Outcomes

ClinicalTrials.gov study NCT03096509. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.

A Combined Model Based on Spleen Stiffness, Liver Stiffness and Platelets for Assessing Portal Hypertension in Compensated Cirrhosis (CHESS2202)

ClinicalTrials.gov study NCT05251272. IPD Sharing: UNDECIDED. Countries: 1. Publications: 10.

Composite Health Assessment Risk Model (CHARM) for Older Adults (BMT CTN 1704)

ClinicalTrials.gov study NCT03992352. IPD Sharing: NO. Countries: 1. Publications: 28.

A Clinical Assessment of the Full Range of Functional Vision With Three Tecnis Multifocal IOL 1-Piece Models in Bilateral Cataract Extraction

ClinicalTrials.gov study NCT02863159. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.

Challenge Model for Assessment of Human TB Immunity

ClinicalTrials.gov study NCT01868464. IPD Sharing: Not stated. Countries: 1. Publications: 1.

Comprehensive Assessment of Atherosclerotic Ischemic Stroke Risk and Development of a Prediction Model

ClinicalTrials.gov study NCT06315153. IPD Sharing: NO. Countries: 1. Publications: 4.

The Predictability of the Necessity for Cardiology Consultation in Patients Scheduled for Non-Cardiac Surgery Using Artificial Intelligence Models in Preoperative Anesthesia Assessment

ClinicalTrials.gov study NCT07395713. IPD Sharing: NO. Countries: 1. Publications: 0.