Find research datasets worth reusing

Fracture data from Monte Carlo simulation and practical engineering cases

This data is mainly used for inferring each fracture size and spatial pattern from its trace exposed on the rock mass outcrop.

opencc-by-4.0Apr 2022View details →

Mark loss can strongly bias estimates of demographic rates in multi-state models: a case study with simulated and empirical datasets

This archive contains the empirical data analysed in the paper 'Mark loss can strongly bias estimates of demographic rates in multi-state models: a case study with simulated and empirical datasets' by Touzalin et al. (https://doi.org/10.24072/pci.ecology.100416).  The dataset is provided as a .Rdata file ('TLoss_GMdata.Rdata'), and full description of the content is provided in the file 'Readme_TLdata.csv'. All additional details are available in the main text (https://doi.org/10.24072/pci.ecology.100416) or in the supporting information (https://doi.org/10.5281/zenodo.10204538).

opencc-by-4.0Jul 2023View details →

Supplementary material (part 2) for "On the effect of tributary valleys on thermally driven winds in the main valley: a case study in the Inn Valley"

Part 2 of the supplementary material for the master's thesis "On the effect of tributary valleys on thermally driven winds in the main valley: a case study in the Inn Valley." (Deidda 2023, available at <a href="https://resolver.obvsg.at/urn:nbn:at:at-ubi:1-134436">https://resolver.obvsg.at/urn:nbn:at:at-ubi:1-134436</a>). The supplementary material consists in two parts: part 1 includes scripts, datasets, model setup files and figures (available at <a href="https://zenodo.org/records/8089010">https://zenodo.org/records/8089010</a>), while part 2 includes the model output needed to create the figures (see description below).This directory contains part of the model output used for the thesis. The model used is WRF-ARW version 4.4 (Skamarock et al. 2021). Information on the model setup is available in the thesis, Section 2.2. For storage limitations, only the files needed to create the graphs presented in the thesis are available.The file format is type_domain_date.nc where domain is "d01" or "d02" (respectively for the outer or inner domain, see Section 2.2 in the thesis), while type is "wrfout", "mean_out", or "Averaged", where:<ul><li>"wrfout" is the instantaneous WRF output;</li><li>"mean_out" is the time-averaged WRF output, computed using the fork <a href="https://github.com/matzegoebel/WRFlux">WRFlux</a>.</li><li>"Averaged" is a combination of time-averaged and instantaneous outputs. These files were created using the the script Create_intermediate_files.py, available in the <a href="https://zenodo.org/records/8089010">software directory </a>(in Scripts.zip). These files were computed to have a lighter and WRF-like formatted data containing the averaged fields needed for the analysis.</li></ul>

CAIRT/IASI-NG/CAIRT+IASI-NG FL2S results of Case Study Scenario 4 for limb-nadir application

Results of the fast level-2 simulator (FL2S) for Case Study Scenarios 4 (only SO2 files) for limb-nadir application. The files contain altitude-time cross-sections of atmospheric parameters along simulated CAIRT-orbits. The variable extensions denote the original field ('_ori'), the application of the averaging kernel ('_ak') and additional application of noise ('_aknoi') for CAIRT alone, IASI-NG alone, and the combined product CAIRT+IASI-NG.

CAIRT FL2S Results of Case Study Scenario 6 (CSS6) for Auroral NO

Corrected version. Results of the fast level-2 simulator (FL2S) of CAIRT developed within the Earth Explorer 11 Phase 0 Science and Requirements Consolidation Study (SciReC) – CAIRT. The files contain altitude-time cross-sections of atmospheric parameters along simulated CAIRT-orbits. The variable extensions denote the original field ('_ori'), the application of the averaging kernel ('_ak'), additional application of noise ('_aknoi'), application of systematic uncertainties ('_sys'), and application of all effects ('_aknoisys'). Further information is available from the authors.

Supplementary data and code to article "Single-Well Microseismic Focal Mechanism Inversions Using Different Source Models: A Case Study in the Ordos Basin, China"

Supplementary data and code to article "Single-Well Microseismic Focal Mechanism Inversions Using Different Source Models: A Case Study in the Ordos Basin, China".Transformations among the parameters of the moment tensor model refer to the code package from Tape and Tape (https://github.com/carltape/mtbeach/; https://github.com/carltape/surfacevel2strain; Tape and Tape, 2009, 2012, 2013, 2015).Tape, C., P. Muse, M. Simons, D. Dong, and F. Webb (2009). Multiscale estimation of GPS velocity fields, Geophys. J. Int. 179, no.2, 945-971, doi: 10.1111/j.1365-246X.2009.04337.x.Tape, W., and C. Tape (2012). A geometric setting for moment tensors, Geophys. J. Int. 190, no. 1, 476–498, doi: 10.1111/j.1365-246X.2012.05491.x.Tape, W., and C. Tape (2013). The classical model for moment tensors, Geophys. J. Int. 195, no. 3, 1701–1720, doi: 10.1093/gji/ggt302.Tape, W., and C. Tape (2015). A uniform parametrization of moment tensors, Geophys. J. Int. 202, no. 3, 2074–2081, doi: 10.1093/gji/ggv262.

Groundwater level data, aquifer system boundaries, and supplementary tables associated with Jasechko, S. et al. Rapid groundwater decline and some cases of recovery in aquifers globally. Nature, doi.org/10.1038/s41586-023-06879-8 (2024).

Groundwater level data, aquifer system boundaries, and Supplementary Tables associated with Jasechko, S., Seybold, H., Perrone, D., Fan, Y., Shamsudduha, M., Taylor, R.G., Fallatah, O., Kirchner, J.W. Rapid groundwater decline and some cases of recovery in aquifers globally. Nature, https://doi.org/10.1038/s41586-023-06879-8 (2024).

Input geophysical and geological data for "Geologically constrained geometry inversion and null-space navigation to explore alternative geological scenarios: a case study in the Western Pyrenees"

This is a companion dataset to the manuscript:  Geologically constrained geometry inversion and null-space navigation to explore alternative geological scenarios: a case study in the Western Pyrenees,by: Jeremie Giraud , Mary Ford, Guillaume Caumon, Lachlan Grose, Vitaliy Ogarko, Roland Martin, and Paul Cupillard. This dataset contains the input data used in the inversion, in terms of the gravity data and the geological data used in the inversion. The *.txt file contains the gravity data as inverted in the manuscript: X, Y, Z, Value. The *.csv file contains the geological data: location of the contacts and orientation data.

The OPS-SAT case: test dataset

The OPS-SAT case: test dataset is the test split of the official dataset of <a href="https://kelvins.esa.int/opssat/home/">ESA's Kelvins "the OPS-SAT case" challenge</a>, created in collaboration with ESA's Φ-lab and ESA's OPS-SAT spacecraft operations team. It consists of 588 raw patches generated by cropping original acquisitions taken by the OPS-SAT cube-sat using its on-board camera. The patches have a resolution of 200x200 and are provided in .png format. Each patch was assigned through manual labeling to one of the following landcover classes: Agricultural, Cloud, Mountain, Natural, River, Sea_Ice, Snow, and Water.The competition's goal is, given a model of a neural network (in this case the EfficientNet-Lite0), to provide best possible network parameters to perform an on-board classification task. To this goal, competitors were free to use any data, including <a href="https://zenodo.org/records/6524750">The OPS-SAT case dataset</a>, which was released before the start of the competition.The OPS-SAT case: test dataset was hidden from the competitors during the competition and was used to evaluate and rank their submissions. More details about the competition setup and solution evaluation are on the <a href="https://kelvins.esa.int/opssat/home/">Kelvins competition platform</a>.

Fig. 1. Merodon aureus Fabricius, 1805 in An assessment of new character in hoverfly species delimitation using linear and geometric morphometrics - genus Merodon Meigen, 1803 (Diptera: Syrphidae) as a case study

Fig. 1. Merodon aureus Fabricius, 1805, ♂, right wing with the character used in linear morphometric: a = intersection of R4+5 with r-m vein; b = intersection of R4+5 vein with a line drawn in the middle between a and c; c = the intersection of R4+5 with M1 vein; D = the angle formed by the lines that connect a, b and c.

Fig. 4 in An assessment of new character in hoverfly species delimitation using linear and geometric morphometrics - genus Merodon Meigen, 1803 (Diptera: Syrphidae) as a case study

Fig. 4. Results of the geometric morphometric wing shape analysis of species of the Merodon aureus complex. A. Scatter plot of individual scores showing R4+5 vein shape variability. B. Scatter plot of individual scores showing wing shape variability from Vujić et al. (2020c). C. Scatter plot of individual scores showing semilandmark R4+5 vein shape and landmark wing shape variability D. Superimposed outline drawings showing R4+5 vein shape differences among investigated species.

Fig. 5 in An assessment of new character in hoverfly species delimitation using linear and geometric morphometrics - genus Merodon Meigen, 1803 (Diptera: Syrphidae) as a case study

Fig. 5. Results of the geometric morphometric wing shape analysis of males of the Merodon natans group. A. Scatter plot of individual scores showing the R4+5 vein shape variability. B. Scatter plot of individual scores showing the wing shape variability from Vujić et al. (2021c). C. Scatter plot of individual scores showing the semilandmark R4+5 vein shape and landmark wing shape variability D. Superimposed outline drawings showing R4+5 vein shape differences among males of the investigated species.

Fig. 3. Box plot showing a in An assessment of new character in hoverfly species delimitation using linear and geometric morphometrics - genus Merodon Meigen, 1803 (Diptera: Syrphidae) as a case study

Fig. 3. Box plot showing a comparison of the angle at the intersection of the R4+5 vein and the middle

Fig. 2. Merodon aureus Fabricius, 1805 in An assessment of new character in hoverfly species delimitation using linear and geometric morphometrics - genus Merodon Meigen, 1803 (Diptera: Syrphidae) as a case study

Fig. 2. Merodon aureus Fabricius, 1805, ♂, right wing with the location of 20 semilandmarks selected for geometric morphometric analysis.

opencc-by-4.0Dec 2022View details →

Wflow_sbm v0.7.3 model cases

This dataset contains six wflow_sbm hydrological model cases that are presented as part of a revised manuscript (after review) submitted to Geosci. Model Dev. (van Verseveld et al., 2024b). This paper describes the wflow_sbm model, as part of the Wflow.jl (v0.7.3) modelling framework (van Verseveld et al, 2024a), and presents some applications and envisaged future developments. van Verseveld, W., Visser, M., Buitink, J., Bouaziz, L., Boisgontier, H., Bootsma, H., Pronk, M., Hartgring, S., Eilander, D., Weerts, A., Dalmijn, B., Hofer, J., & Hegnauer, M. Wflow.jl (v0.7.3). Zenodo. https://doi.org/10.5281/zenodo.10495638, 2024a van Verseveld, W.J., Weerts, A.H., Visser, M., Buitink, J., Imhoff, R.O., Boisgontier. H., Bouaziz, L., Eilander, D., Hegnauer, M., ten Velden, C., and Russell, B., Geosci. Model Dev. (under review), Wflow_sbm v0.7.3, a spatially distributed hydrological model: from global data to local applications, 2024b.

Fig. 3. Box plot showing a in An assessment of new character in hoverfly species delimitation using linear and geometric morphometrics - genus Merodon Meigen, 1803 (Diptera: Syrphidae) as a case study

Fig. 3. Box plot showing a comparison of the angle at the intersection of the R4+5 vein and the middle line for all species used in the analysis.

Fig. 7 in An assessment of new character in hoverfly species delimitation using linear and geometric morphometrics - genus Merodon Meigen, 1803 (Diptera: Syrphidae) as a case study

Fig. 7. Results of the geometric morphometric wing shape analysis of males of the Merodon clavipes and pruni groups. A–B. Scatter plot of individual scores showing the R4+5 vein shape variability. C–D. Scatter plot of individual scores showing the wing shape variability from Vujić et al. (in prep.). E–F. Scatter plot of individual scores showing the semilandmark R4+5 vein shape and landmark wing shape variability. G–H. Superimposed outline drawings showing the R4+5 vein shape differences between the males of the investigated species.

Fig. 6 in An assessment of new character in hoverfly species delimitation using linear and geometric morphometrics - genus Merodon Meigen, 1803 (Diptera: Syrphidae) as a case study

Fig. 6. Results of the geometric morphometric wing shape analysis of females of the Merodon natans group. A. Scatter plot of individual scores showing the R4+5 vein shape variability. B. Scatter plot of individual scores showing wing the shape variability from Vujić et al. (2021c). C. Scatter plot of individual scores showing the semilandmark R4+5 vein shape and landmark wing shape variability D. Superimposed outline drawings showing the R4+5 vein shape differences among females of the investigated species.

opencc-by-4.0Aug 2023View details →

What can radar-based measures of subglacial hydrology tell us about basal shear stress? A case study at Thwaites Glacier, West Antarctica (Interpolated Data)

This dataset accompanies the paper 'What can radar-based measures of subglacial hydrology tell us about basal shear stress? A case study at Thwaites Glacier, West Antarctica' in Journal of Glaciology, and can be used alongside the code found on Github (https://github.com/rohaizharis/inversion_radar2022) to reproduce the figures. The dataset consists of ice-penetrating radar data (specularity and relative reflectivity) and basal shear stress inversions that have been linearly interpolated onto radar flight tracks.

MCIT storm tracks with updraft radar data for large hail cases in the United States (2013 - 2023)

Storm tracking data from 114 hail days in the United States between 2013 and 2023. Cases were selected based on the availability of abundant Storm Prediction Center (SPC) hail reports (either at least 20 reports of hail > 1 cm or five reports > 10 cm) in a range between 20 and 90 km from a NEXRAD radar site. Only cases in a geographic area from 28-48°N and 105-90°E, roughly corresponding to the U.S. Great Plains, were considered. Cells are quality filtered based on these criteria: (1) Cell center within 120 km of the radar range, and (2) continuous tracking for at least 6 timesteps (approx. 30 minutes, depending on radar scan rate). SPC hail data is matched to tracking timesteps. We used this data to train a Random Forest model for hail size nowcasting. Further metadata can be requested from the authors.