Find research datasets worth reusing

opencc-by-4.0Sep 2021View details →

Introducing new metrics for the atmospheric pressure adjustment to thermal structures in the ocean - instantaneous numerical outputs

The data set contains the netcdf files with the instantaneous fields produced by the WRF V3.6.1 numerical model at 2014-10-06_01:00:00 over the Ligurian Sea analyzed in Meroni, A. N., F. Desbiolles and C. Pasquero "Introducing new metrics for the atmospheric pressure adjustment to thermal structures in the ocean".

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Data for: The correlated evolution of foraging mode and reproductive output in lizards

Life-history theory suggests that the optimal reproductive output of an organism is affected by factors such as energy acquisition and predation risk. The observation that some organisms actively search for their prey and others ambush them creates the expectation of different energy needs and predation risk associated with each foraging behavior, the so-called "foraging-mode paradigm". Although this paradigm has been around for decades, the empirical evidence consists of conflicting results derived from competing models based on different mechanisms. For instance, models within the foraging-mode paradigm suggest that widely-foraging females have evolved low reproductive output, because a heavy reproductive load decreases their ability to escape from predators. By contrast, a long-standing prediction of evolutionary theory indicates that organisms subject to high extrinsic mortality, should invest more in reproduction. Here, we present the first partial evidence that widely-foraging species have evolved greater reproductive output than have sit-and-wait species, which we attribute to a larger body size and greater mortality among mobile foragers. According to our findings, we propose a theoretical model that could explain the observed pattern in lizards, suggesting ways for evolutionary ecologists to test mechanistic hypotheses at the intraspecific level.

opencc-zeroMay 2022View details →

opencc-by-4.0May 2022View details →

CNN-based Network Application for Petrophysical Parameter Inversion: Sensitivity Analysis of Input-output Parameters and Network Architecture

The uploaded file includes four groups of data. They have original elastic and reservoir parameters data, the k=5 and k=25 (k means the sampling interval in inline and crossline.), and testing dataset.

opencc-by-4.0May 2022View details →

Postprocessed trajectory output for ice cloud microphysics - ICON and CLaMS-Ice models

Postprocessed output from a trajectory module implemented in ICON v 2.3.0. The trajectories track density, temperature, pressure, specific humidity, cloud ice mass and number mixing ratios, cloud liquid mass and number mixing ratios, graupel mass and number mixing ratios, and ice sedimentation mass and number mixing ratios both into and out of the parcel. They are initiated over the Sichuan basin and allowed to flow for 51 hours westward during which the cross India into the Arabian Sea. This trajectory output is also used to run an offline microphysics box model, CLaMS-Ice. qih-Nih* files contain histograms of ice mass mixing ratio (qi) and ice crystal number concentration (Ni); het-hom-pre* files contain process tendencies from heterogeneous nucleation, homogeneous nucleation, and preexisting ice in CLaMS-Ice; qippmvNi-TRHi* files contain qi and Ni versus a range of cirrus temperatures and a range of supersaturations with respect to ice; qi_ppmv_abs* and Ni_abs* files contain probability distributions of qi and Ni differences over absolute time; and qi_ppmv_norm* and Ni_norm* files contain probability distributions of qi and Ni differences over normalized time. Suffixes in all cases indicate the cloud microphysical setup that the trajectory values were used to run with 1M = one-moment scheme, 2M = two-moment scheme, Tf = temperature fluctuation parameterization in CLaMS-Ice simulations, and noSHflux = no pseudo-mixing tendency included in CLaMS-Ice simulations.

CryoGridLite: Model output of pan-Arctic simulations at 1° resolution from 1700 to 2020

CryoGridLite is a lightweight version of the more complex and process-rich CryoGrid Community model (<a href="https://gmd.copernicus.org/preprints/gmd-2022-127/">Westermann et al. 2022</a>). This archive contains the following output datasets which were produced by the model. <code>./SETUP.zip</code> The parameters varied in the ensemble simulations. <code>./RESULTS.zip</code> The output variables. An overview of the output variables is provided below and in the README.md file of the archived code. The model code can be found here: <a href="https://zenodo.org/record/6619537">10.5281/zenodo.6619537</a> Input data required for pan-Arctic simulations can be found here: <a href="https://zenodo.org/record/6619212">10.5281/zenodo.6619212</a> <table> <thead> <tr> <th>Variable</th> <th>Coordinates</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>H_lat_sum</td> <td>(tile, time, longitude, latitude)</td> <td>Total latent heat content in the entire ground column (550m) [J/m²]</td> </tr> <tr> <td>H_sen_sum</td> <td>(tile, time, longitude, latitude)</td> <td>Total sensible heat content in the entire ground column (550m) [J/m²]</td> </tr> <tr> <td>H_tot_sum</td> <td>(tile, time, longitude, latitude)</td> <td>Total heat content in the entire ground column (550m) [J/m²]</td> </tr> <tr> <td>H_lat_50m</td> <td>(tile, time, longitude, latitude)</td> <td>Total latent heat content in the uppermost 50m of the ground column [J/m²]</td> </tr> <tr> <td>H_sen_50m</td> <td>(tile, time, longitude, latitude)</td> <td>Total sensible heat content in the uppermost 50m of the ground column [J/m²]</td> </tr> <tr> <td>H_tot_50m</td> <td>(tile, time, longitude, latitude)</td> <td>Total heat content in the uppermost 50m of the ground column [J/m²]</td> </tr> <tr> <td>ThawedGround_10m</td> <td>(tile, time, longitude, latitude)</td> <td>Maximum vertically integrated depth of thawed ground (T>0°C) in the uppermost 10m of the ground column [m]</td> </tr> <tr> <td>T_av_all</td> <td>(tile, time, longitude, latitude, depth)</td> <td>Mean annual ground temperature [°C]</td> </tr> <tr> <td>T_min_all</td> <td>(tile, time, longitude, latitude, depth)</td> <td>Minimum annual ground temperature [°C]</td> </tr> <tr> <td>T_max_all</td> <td>(tile, time, longitude, latitude, depth)</td> <td>Maximum annual ground temperature [°C]</td> </tr> <tr> <td>LandArea</td> <td>(latitude, longitude)</td> <td>Total area covered by land within the model grid cell [km²]</td> </tr> </tbody> </table>

Model run output for Warren & Kite 202x

Model run output for oxygen loss modeling and 40Ar degassing modeling for Warren & Kite 202x (in preparation). Full timeseries output for individual runs in zipped file. Summary data in individual csvs. File names with "nomelt" in the title refer to baseline model runs. File names containing "melt" are model runs including runaway greenhouse surface melting. Code for running full model and generating figures from publication available at: https://github.com/aowarren/Venus_O2

Example of input and output datasets of the cost module implemented in the SHERPA modelling tool

Integrated Assessment Model provides a useful framework for evaluating different aspects of air quality policies, spanning from abatement measures to emissions, concentrations, health impacts and costs. These models are then useful to provide a holistic view of the impacts of policies, so that ex-ante one can evaluate how various policies will impact air concentrations, health benefits and implementation costs. Among these Integrated Assessment Models, SHERPA (Screening for High Emission Potentials on Air) has been recently used to evaluate the impact of policies, covering all aspects from measures to health, but without being able to provide the dimension related to abatement measures costs. These three files dataset provide an example of the ability of the new cost module in SHERPA to calculate the cost of emission reductions.

Quantifying research interests in 7,521 mammalian species with h-index: a case study (model output)

Model results for Quantifying research interests in 7,521 mammalian species with h-index: a case study

opencc-by-4.0Jan 2022View details →

Output files of the 3-D thermal modeling in the Alaska subduction zonne

Data products (μ'=0.01275) for ‘Temperature distribution for interplate seismic events in the Alaska subduction zone based on 3-D thermal modeling’ by Kaya Iwamoto, Nobuaki Suenaga and Shoichi Yoshioka.

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Occurrence datasets, model outputs, and R script for 12 termite species used for niche modeling

The advent of citizen-science databases in conjunction with museum specimen locality information has exponentially increased the power and accuracy of ecological niche modeling (ENM). Increased occurrence data has provided colossal potential to understand the distributions of lesser known or endangered species, including arthropods. Although niche modeling of termites has been conducted in the context of invasive and pest species, few studies have been performed to understand the distribution of basal termite genera. Using specimen records from the American Museum of Natural History (AMNH) as well as locality databases, we generated ecological niche models for 12 basal termite species belonging to six genera and three families. We extracted environmental data from the Worldclim 19 bioclimatic dataset v2, along with SoilGrids datasets and generated models using MaxEnt. We chose Optimal models based on partial Receiving Operating characteristic (pROC) and omission rate criterion and determined variable importance using permutation analysis. We also calculated response curves to understand changes in suitability with changes in environmental variables. Optimal models for our 12 termite species ranged in complexity, but no discernible pattern was noted among genera, families, or geographic range. Permutation analysis revealed that habitat suitability is affected predominantly by seasonal or monthly temperature and precipitation variation. Our findings not only highlight the efficacy of largely citizen-science and museum-based datasets, but our models provide a baseline for predictions of future abundance of lesser-known arthropod species in the face of habitat destruction and climate change.

opencc-zeroJul 2022View details →

opencc-by-4.0Jul 2022View details →

Model output dataset for Ardyna et al. (2022) Wildfire aerosol deposition likely amplified a summertime Arctic phytoplankton bloom

Daily July and August 2014 aerosol optical depth (AODVIS) and nitrogen deposition (NDEP) output from simulations using Community Earth System Model. Data owner: Diana Bernstein (diana.bernstein@usm.edu).

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Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation: supporting data and outputs

1. Tropical forests are subject to diverse deforestation pressures while their conservation is essential to achieve global climate goals. Predicting the location of deforestation is challenging due to the complexity of the natural and human systems involved but accurate and timely forecasts could enable effective planning and on-the-ground enforcement practices to curb deforestation rates. New computer vision technologies based on deep learning can be applied to the increasing volume of Earth observation data to generate novel insights and make predictions with unprecedented accuracy. 2. Here, we demonstrate the ability of deep convolutional neural networks (CNNs) to learn spatiotemporal patterns of deforestation from a limited set of freely available global data layers, including multispectral satellite imagery, the Hansen maps of annual forest change (2001-2020) and the ALOS PALSAR digital surface model, to forecast deforestation (2021). We designed four model architectures, based on 2D CNNs, 3D CNNs, and Convolutional Long Short-Term Memory (ConvLSTM) Recurrent Neural Networks (RNNs), to produce spatial maps that indicate the risk to each forested pixel (~30 m) in the landscape of becoming deforested within the next year. They were trained and tested on data from two ~80,000 km2 tropical forest regions in the Southern Peruvian Amazon. 3. The networks could predict the location of future forest loss to a high degree of accuracy (F1 = 0.58-0.71). Our best performing model (3D CNN) had the highest pixel-wise accuracy (F1 = 0.71) when validated on 2020 forest loss (2014-2019 training). Visual interpretation of the mapped forecasts indicated that the network could automatically discern the drivers of forest loss from the input data. For example, pixels around new access routes (e.g. roads) were assigned high risk whereas this was not the case for recent, concentrated natural loss events (e.g. remote landslides). 4. CNNs can harness limited time-series data to predict near-future deforestation patterns, an important step in harnessing the growing volume of satellite remote sensing data to curb global deforestation. The modelling framework can be readily applied to any tropical forest location and used by governments and conservation organisations to prevent deforestation and plan protected areas.

opencc-zeroJul 2022View details →

TALON output for WTC11 cell lines

A range of long-read RNA-seq protocols is analyzed with TALON from WTC11 for the LRGASP challenge. We deposit our results here for further analysis.

PlaSim output data from TSI-related numerical experiments

Dear Visitor, Here you can access all the output of the PlaSim simulations in netCDF format. The form of the file names is as follows: TS_History#_m0##.nc , where TS abbreviates surface temperature, # refers to the particular forcing history (C, B, Ta, Tb, N) and ## denotes numbers from 1 to 40.

PlaSim output data from TSI-related numerical experiments

Dear Visitor, Here you can access all the output of the PlaSim simulations in netCDF format. The form of the file names is as follows: TS_History#_m0##.nc , where TS abbreviates surface temperature, # refers to the particular forcing history (C, B, Ta, Tb, N) and ## denotes numbers from 1 to 40.

Seilaplan: Output - Tutorialreihe zur digitalen Planung im Seilgelände

In diesem Video wird der Output, den das Plugin Seilaplan liefert besprochen. Produkte, die ausgegeben werden können sind eine grafische Darstellung (Diagramm) der geplanten Seillinie, ein Kurzbericht, ein ausführlicher technischer Bericht sowie zwei CSV Files, welche die Koordinaten für das Seil, bzw. für die Stützen beinhalten. Außerdem wird die JSON-Datei &bdquo;Projekteinstellungen“ ausgegeben, welche mit Hilfe des Plugins Seilaplan in ein beliebiges QGIS-File geladen werden kann. Kontakt: Christian Kanzian <a href="mailto:christian.kanzian@boku.ac.at">christian.kanzian@boku.ac.at</a> Dieses Video darf gemäß den Vorgaben für CC BY 4.0 Lizenzen unter Verweis auf Böhm, Stephan; Ramstein, Laura; Bont, Leo; Simon, Pierre; Schweier, Janine & Kanzian, Christian (2022): Seilaplan: Output – Tutorialreihe zur digitalen Planung im Seilgelände. Wien: Universität für Bodenkultur Wien. https://doi.org/10.5281/zenodo.6908653 verwendet werden.

FRACTESUS_UC_ANP-5_T0_MCT_output

Fractesus project. Fracture test mini-CT. Raw data ANP-5. UC.