Find research datasets worth reusing

opencc-by-4.0Feb 2022View details →

CADD-SV - A framework to score the effects of structural variants in health and disease

Required annotation data-set to run the CADD-SV framework; a method to retrieve and integrate a wide set of annotations to predict the effects of SVs. Pre-scored variants as well as additional information on used features. A webserver for online scoring as well as data downloads is available at: https://cadd-sv.bihealth.org/ Source code for CADD-SV is available at GitHub: https://github.com/kircherlab/CADD-SV

opencc-by-4.0Feb 2022View details →

Data for article: A quantitative framework to infer the effect of traits, diversity and environment on dispersal and extinction rates from fossils

Supplementary information for: A quantitative framework to infer the effect of traits, diversity and environment on dispersal and extinction rates from fossils Torsten Hauffe, Mathias M. Pires, Tiago B. Quental, Thomas Wilke, and Daniele Silvestro   <ul> <li> Simulations <ul> <li>Scripts <ul> <li>Scenario1_SamplingHeterogeneity.R: Script to simulate biogeographic histories with sampling heterogeneity</li> <li>Scenario3_SealevelInvasion.R: Script to simulate biogeographic histories where sea level facilitates dispersal and invasion induces extinction</li> <li>Scenario3_DiversityDependence.R: Script to simulate diversity-dependent biogeographic histories</li> <li>Scenario4_TraitDependence.R: Script to simulate trait-dependent biogeographic histories</li> <li>Scenario5_CategoricalTraitDependence.R: Script to simulate trait-dependent biogeographic histories</li> </ul> </li> <li>Results <ul> <li>Scenario1_SamplingHeterogenetiy_alpha05.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 0.5</li> <li>Scenario1_SamplingHeterogenetiy_alpha1.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 1</li> <li>Scenario1_SamplingHeterogenetiy_alpha2.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 2</li> <li>Scenario1_SamplingHeterogenetiy_alpha10.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 10</li> <li>Scenario2_Independent_dispersal_and_extinction.txt: Results of simulation scenario 2 with sea-level independent dispersal and no invasion induced extinction</li> <li>Scenario2_Sealevel_dependent_dispersal_and_independent_extinction.txt: Results of simulation scenario 2 with sea-level dependent dispersal and no invasion induced extinction</li> <li>Scenario2_Sealevel_independent_dispersal_and_invasion_induced_extinction.txt: Results of simulation scenario 2 with sea-level independent dispersal and invasion induced extinction</li> <li>Scenario2_Sealevel_dependent_dispersal_and_invasion_induced_extinction.txt: Results of simulation scenario 2 with sea-level dependent dispersal and invasion induced extinction</li> <li>Scenario3_Independent_dispersal_and_extinction.txt: Results of simulations scenario 3 with diversity-independent dispersal and extinction</li> <li>Scenario3_Diversity_dependent_dispersal_and_independent_extinction.txt: Results of simulations scenario 3 with diversity-dependent dispersal and diversity-independent extinction</li> <li>Scenario3_Independent_dispersal_and_Diversity_dependent_extinction.txt: Results of simulations scenario 3 with diversity-dependent dispersal and diversity-independent extinction</li> <li>Scenario3_Diversity_dependent_dispersal_and_extinction.txt: Results of simulations scenario 3 with diversity-dependent dispersal and extinction</li> <li>Scenario4_Independent_dispersal_and_extinction.txt: Results of scenario 4 with trait-independent dispersal and extinction</li> <li>Scenario4_Trait_dependent_dispersal_and_independent_extinction.txt: Results of scenario 4 with trait-dependent dispersal and independent extinction</li> <li>Scenario4_Independent_dispersal_and_trait_dependent_extinction.txt: Results of scenario 4 with independent dispersal and trait-dependent extinction</li> <li>Scenario4_trait_dependent_dispersal_and_extinction.txt: Results of scenario 4 with trait-dependent dispersal and extinction</li> <li>Scenario5_CatTrait_dependent_dispersal_and_independent_extinction.txt: Results of model 2 with categorical traits (e.g family) influence dispersal but no influence of a category-specific continuous traits</li> </ul> </li> </ul> </li> <li>Carnivora <ul> <li>BinnedOccurrence: Folder with 100 replicates of binned occurrences of max. 330 carnivoran genera throughout the Neogene</li> <li>BodyMass: Folder with 100 replicates of body mass for 330 carnivoran genera</li> <li>Sealevel: Folder with sea level through the Neogene</li> <li>Temperature: Folder with the temperature record of the Neogene</li> <li>Families: Folder with families as taxonomic proxy for phylogeny. FamilyGeneraNumeric.txt is the numeric coding used for the Bayesian analyses of carnivoran biogeography</li> </ul> </li> </ul>

opencc-by-4.0Dec 2021View details →

Coral calcification mechanisms in a warming ocean and the interactive effects of temperature and light

Ross et al 2022 Supplementary data for coral (Acropora nasuta) temperature and light experiments.

Effect of spatial input data quality on SWAT modelling in the Porijõgi catchment

The Porijõgi Catchment near Tartu, Estonia is the study area for this research. Four model setups were created using global/regional level data (HWSD soil, CORINE), and local high-resolution spatial data including the new Estonian high-resolution EstSoil-EH soil dataset and the Estonian Topographic Database (ETAK). The study employed statistical criteria to assess SWAT model performance for monthly simulated stream flows from 2007 to 2019. Data deposit in preparation for article: Effect of spatial input data quality on the uncertainty of the SWAT model, submitted 2022 Alexander Kmoch, Desalew Meseret Moges, Mahdiyeh Sepehrar, Balaji Narasimhan and Evelyn Uuemaa contact: alexander.kmoch@ut.ee

opencc-by-4.0May 2021View details →

Dataset for "Effects of Fracture Connectivity on Rayleigh Wave Dispersion"

The scripts on the main directory reproduce figures 3 to 11 in the paper. The dataset is separated into two folders which should be extracted to the directory of the scripts, frac_dist and parrot_output. frac_dist contains .mat files with the fracture distribution of samples and parrot_output contains the results from the upscaling procedure (Favino et al., 2020). A summary of each code is provided in the readme.

Interactive Visualizations for: "Virgo Filaments II: Catalog and First Results on the Effect of Filaments on galaxy properties"

This deposit includes 13 HTML 3D interactive visualizations of filaments and galaxies investigated in the accepted article, "Virgo Filaments II:  Catalog and First Results on the Effect of Filaments on galaxy properties" by Castignani et al. (accepted, 20-Oct-2021). The specific files correspond to the filaments listed in Table 2 of the accepted manuscript: <table align="left"> <caption>Tabulated HTML files and filaments</caption> <thead> <tr> <th scope="col">HTML File</th> <th scope="col">Filament (Table 2)</th> </tr> </thead> <tbody> <tr> <td> SG_cube_Virgo_Serpens_Filament.html </td> <td> Serpens F. </td> </tr> <tr> <td> SG_cube_Virgo_Coma_Berenices_Filament.html </td> <td> Coma Berenices F. </td> </tr> <tr> <td> SG_cube_VirgoIII_Filament.html </td> <td> VirgoIII F. </td> </tr> <tr> <td> SG_cube_Ursa_Major_Cloud.html </td> <td> Ursa Major Cloud </td> </tr> <tr> <td> SG_cube_NGC5353_4_Filament.html </td> <td> NGC5353/4 F. </td> </tr> <tr> <td> SG_cube_Leo_Minor_Filament.html </td> <td> Leo Minor F. </td> </tr> <tr> <td> SG_cube_LeoII_B_Filament.html </td> <td> LeoII B F. </td> </tr> <tr> <td> SG_cube_Canes_Venatici_Filament.html </td> <td> Canes Venatici F </td> </tr> <tr> <td> SG_cube_W-M_Sheet.html </td> <td> W-M Sheet </td> </tr> <tr> <td> SG_cube_Virgo_Draco_Filament.html </td> <td> Draco F. </td> </tr> <tr> <td> SG_cube_Virgo_Bootes_Filament.html </td> <td> Bootes F. </td> </tr> <tr> <td> SG_cube_Leo_Minor_B_Filament.html </td> <td> Leo Minor B F. </td> </tr> <tr> <td> SG_cube_LeoII_A_Filament.html </td> <td> LeoII A F. </td> </tr> </tbody> </table>                                             For each visualization galaxies within 2 Mpc are color-coded by the 3D local density, and galaxies with separations greater than 2 Mpc are shown with the grey points. The filament spine is shown with the black curve. The files were created with plotly.js v1.58.4.

Synthetic Data for Uplift Modeling and Heterogenous Treatment Effect with Known Counterfactuals and ITE

This dataset is designed and simulated for evaluating uplift modeling. The data generation process is based on a logistic regression model - no real data is included or used for generating this dataset. This dataset has several signatures: <ul> <li>It generates features with various patterns associated with the outcome variable and the causal effect (or treatment effect). Thus it is suitable for evaluating feature importance and model interpretation for uplift modeling.</li> <li>The true counterfactual outcomes under control and treatment are known for each user, as well as the true ITE (Individual treatment effect).</li> </ul> This dataset consists of 50 trials (replicates with different random seeds), each trial with 20,000 samples and 36 features. The outcome variable is binary, which makes this dataset for classification problems. The samples are equally split for the control and treatment groups (10,000 samples in each group in each trial). The generated data has three types of features: (1) uplift features influencing the treatment effect on the conversion probability; (2) classification features affecting the conversion probability but independent of the treatment effect; and (3) irrelevant features that are independent of both conversion probability and the treatment effect. To simulate the relationship between uplift features and the treatment effect and classification features and outcome probability, we implement six types of association patterns in the data generation process: linear, quadratic, cubic, ReLU (Rectified Linear Unit), trigonometric function sine, and cosine. In this data set, there are 36 features in total, including 10 classification features, 6 uplift features, and 20 irrelevant features. Column names:     Trial ID: 'trial_id'     Experiment group label: 'treatment_group_key'     Outcome variable (classification label):  'conversion'     Feature names: ['x1_informative',     'x2_informative',     'x3_informative',     'x4_informative',     'x5_informative',     'x6_informative',     'x7_informative',     'x8_informative',     'x9_informative',     'x10_informative',     'x11_irrelevant',     'x12_irrelevant',     'x13_irrelevant',     'x14_irrelevant',     'x15_irrelevant',     'x16_irrelevant',     'x17_irrelevant',     'x18_irrelevant',     'x19_irrelevant',     'x20_irrelevant',     'x21_irrelevant',     'x22_irrelevant',     'x23_irrelevant',     'x24_irrelevant',     'x25_irrelevant',     'x26_irrelevant',     'x27_irrelevant',     'x28_irrelevant',     'x29_irrelevant',     'x30_irrelevant',     'x31_uplift_increase',     'x32_uplift_increase',     'x33_uplift_increase',     'x34_uplift_increase',     'x35_uplift_increase',     'x36_uplift_increase']     True underlying control conversion probability: 'control_conversion_prob'     True underlying treatment conversion probability: 'treatment1_conversion_prob'     True treatment effect:  'treatment1_true_effect'

Effects of Mouthrinsing and Gargling to CT Values of SARS CoV-2 DATASET

Background: Coronavirus disease 2019 can spread rapidly. Surgery in the oral cavity poses a high risk of transmission of severe acute respiratory syndrome coronavirus 2. The American Dental Association and the Centers for Disease Control and Prevention recommend the use of mouthwash containing 1.5% hydrogen peroxide (H2O2) or 0.2% povidone iodine (PI) to reduce the viral load in the upper respiratory tract and decrease the risk of transmission. The aim of the present study was to analyze the effect of mouthrinsing and gargling with mouthwash containing 1% PI, 0.5% PI, 3% H2O2, or 1.5% H2O2 and water on the cycle threshold (CT) value. Methods: In total, 69 subjects recruited from Persahabatan General Hospital who met the inclusion criteria were randomly assigned to one of four treatment groups or the control group. The subjects were instructed to gargle with 15 mL of mouthwash for 30 s in the oral cavity followed by 30 s in the back of the throat three times per day for 5 days. CT values were collected on postprocedural days 1, 3, and 5. Results: The results of the Friedman test significantly differed among the groups. The CT values increased from baseline (day 0) to postprocedural days 1, 3, and 5. Conclusions: Mouthrinsing and Gargling with mouthwash containing 1% PI, 0.5% PI, 3% H2O2, or 1.5% H2O2 and water increased the CT value. Background: Coronavirus disease 2019 can spread rapidly. Surgery in the oral cavity poses a high risk of transmission of severe acute respiratory syndrome coronavirus 2. The American Dental Association and the Centers for Disease Control and Prevention recommend the use of mouthwash containing 1.5% hydrogen peroxide (H2O2) or 0.2% povidone iodine (PI) to reduce the viral load in the upper respiratory tract and decrease the risk of transmission. The aim of the present study was to analyze the effect of mouthrinsing and gargling with mouthwash containing 1% PI, 0.5% PI, 3% H2O2, or 1.5% H2O2 and water on the cycle threshold (CT) value. Methods: In total, 69 subjects recruited from Persahabatan General Hospital who met the inclusion criteria were randomly assigned to one of four treatment groups or the control group. The subjects were instructed to gargle with 15 mL of mouthwash for 30 s in the oral cavity followed by 30 s in the back of the throat three times per day for 5 days. CT values were collected on postprocedural days 1, 3, and 5. Results: The results of the Friedman test significantly differed among the groups. The CT values increased from baseline (day 0) to postprocedural days 1, 3, and 5. Conclusions: Mouthrinsing and Gargling with mouthwash containing 1% PI, 0.5% PI, 3% H2O2, or 1.5% H2O2 and water increased the CT value.

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

Data: Boreal forest soil carbon fluxes one year after a wildfire: Effects of burn severity and management

2018 Boreal forest fires in Sweden: Measurements of soil CO2 and CH4 fluxes, soil microclimate and nutrient content during the first growing season after a wildfire, from forest sites impacted by different fire severity (tree mortality) and post-fire management.   Data used in: Boreal forest soil carbon fluxes one year after a wildfire: Effects of burn severity and management; Julia Kelly, Theresa S. Ibáñez, Cristina Santín, Stefan H. Doerr, Marie-Charlotte Nilsson, Thomas Holst, Anders Lindroth, Natascha Kljun; Global Change Biology, 27, 4181-4195, https://doi.org/10.1111/gcb.15721

opencc-by-4.0Apr 2022View details →

The effects of solar cycle variability on nanodust dynamics in the inner heliosphere: Predictions for future STEREO A/WAVES measurements

This dataset contains results from the associated manuscript in JGR Space Physics. The dataset consists of two-dimensional nanodust grain fluxes in the HEEQ equatorial plane for various specified Carrington Rotations (CRs), as specified in the parent manuscript.

opencc-by-4.0Nov 2021View details →

Cu-doping effects on the ferromagnetic semimetal CeAuGe

Dataset for the Journal article entitled "Cu-doping effects on the ferromagnetic semimetal CeAuGe"

Insider trading regulation and shorting constraints. Evaluating the joint effects of two market interventions.

This dataset contains the raw experimental data and the analysis script for the paper Merl, R., Stöckl, T., Palan, S., 2022. "Insider trading regulation and shorting constraints. Evaluating the joint effects of two market interventions", Journal of Banking and Finance 106490, https://doi.org/10.1016/j.jbankfin.2022.106490. Instructions: 1. Unpack all files into one folder. 2. Open R version 4.1.2 and set the working directory to the folder with all the files. 3. Run Script.R. In case the SPTools package is not available from GitHub anymore, you can also find it included in this dataset so you can install it from here.

opencc-by-4.0May 2022View details →

Artifacts for the ISSTA 2022 Paper: An Empirical Study on the Effectiveness of Static C Code Analyzers for Vulnerability Detection

This repository contains the evaluation script and the corresponding data of the ISSTA'22 paper "An Empirical Study on the Effectiveness of Static C Code Analyzers for Vulnerability Detection".

Underlying Data for Manuscript titled "Hydrothermal Carbonization (HTC) of Dairy Waste: Effect of Temperature and Initial Acidity on the composition and quality of solid and liquid products"

The embodied files include the raw data and initial calculations used to generate the extended data for Manuscript titled "Hydrothermal Carbonization (HTC) of Dairy Waste: Effect of Temperature and Initial Acidity on the composition and quality of solid and liquid products". The files include calculations for Phosphorus Recovery from Hydrochar, as well as Heavy Metals Fractions retrieved by the hydrochar (solid product of Hydrothermal Carbonization).

Regiochemical Effects for the Mechanochemical Activation of 9-π-Extended Anthracene-Maleimide Diels-Alder Adducts

Primary data used in the manuscript (NMR, MS, UV-vis, GPC) sorted after compound number according to the manuscript: <ul> <li>CoGEF simulations in the ZIP file "CoGEF simulations"</li> <li>GPC elugrams after sonication in the ZIP file "GPC after sonication"</li> <li>Characterization after synthesis (NMR, MS, GPC) in the ZIP file "synthesis"</li> <li>UV-vis absoprtion spectra over the course of the sonication experiments in the ZIP file "UV-vis after sonication"</li> <li>UV-vis absoprtion spectra of the small control compounds for determination of molar absorptivities in the ZIP file "UV-vis for molar absorptivities"</li> </ul>

Reservoir water quality deterioration due to deforestation emphasizes the indirect effects of global change

This repository contains the dataset linked to the following publication: Article title: Reservoir water quality deterioration due to deforestation emphasizes the indirect effects of global change Journal title: Water Research Article Number: WR_118721 doi: https://doi.org/10.1016/j.watres.2022.118721 Abstract: Deforestation is currently a widespread phenomenon and a growing environmental concern in the era of rapid climate change. In temperate regions, it is challenging to quantify the impacts of deforestation on the catchment dynamics and downstream aquatic ecosystems such as reservoirs and disentangle these from direct climate change impacts, let alone project future changes to inform management. Here, we tackled this issue by investigating a unique catchment-reservoir system with two reservoirs in distinct trophic states (meso‑ and eutrophic), both of which drain into the largest drinking water reservoir in Germany. Due to the prolonged droughts in 2015–2018, the catchment of the mesotrophic reservoir lost an unprecedented area of forest (exponential increase since 2015 and ca. 17.1% loss in 2020 alone). We coupled catchment nutrient exports (HYPE) and reservoir ecosystem dynamics (GOTM-WET) models using a process-based modeling approach. The coupled model was validated with datasets spanning periods of rapid deforestation, which makes our future projections highly robust. Results show that in a short-term time scale (by 2035), increasing nutrient flux from the catchment due to vast deforestation (80% loss) can turn the mesotrophic reservoir into a eutrophic state as its counterpart. Our results emphasize the more prominent impacts of deforestation than the direct impact of climate warming in impairment of water quality and ecological services to downstream aquatic ecosystems. Therefore, we propose to evaluate the impact of climate change on temperate reservoirs by incorporating a time scale-dependent context, highlighting the indirect impact of deforestation in the short-term scale. In the long-term scale (e.g. to 2100), a guiding hypothesis for future research may be that indirect effects (e.g., as mediated by catchment dynamics) are as important as the direct effects of climate warming on aquatic ecosystems.   Data description by Xiangzhen Kong (xiangzhen.kong@ufz.de; xzkong@niglas.ac.cn) 2022-06-20 1. Discharge in the streams from 2010 to 2021 at YRZ site, and from 2010 to 2020 at YHZ_Q site. <ul> <li>File name: dat_discharge_stream_YRZ_YHZ_2010_2021_daily.csv</li> <li>Note: The data is at daily basis but also available at 15-min high frequency basis, which can be requested from the authors.</li> </ul> 2. Nitrate concentration in the streams from 2011 to 2019 at both YRZ and YHZ_Q sites. <ul> <li>File name: dat_nitrate_stream_YRZ_YHZ_2011_2019_daily.csv</li> <li>Note: The data is at daily basis but also available at 15-min high frequency basis, which can be requested from the authors.</li> </ul> 3. Water quality data in the inflows from 2010 to 2021 at biweekly basis, from YRZ and YHZ_WQ sites. <ul> <li>File name: dat_waterquality_stream_YRZ_YHZ_2010_2021_biweekly.csv</li> <li>Note: The data is at biweekly basis, measured at the depth of 0.5m under water surface, from both probe and lab.</li> </ul> 4. Water quality data in the predams from 2010 to 2021 at biweekly basis, from YR1 and YH1 sites. <ul> <li>File name: dat_waterquality_predams_YR1_YH1_2010_2021_biweekly.csv</li> <li>Note: The data is at biweekly basis, measured at the depth of 0.5m under water surface, from both probe and lab.</li> </ul> 5. Water quality data in the predams from 2010 to 2015 at biweekely basis, from YR3 and YH3 sites. <ul> <li>File name: dat_waterquality_predams_YR3_YH3_2010_2015_biweekly.csv</li> <li>Note: The data is at biweekly basis, measured at various water depth, only from lab.</li> </ul> 6. CTD and BBE probe profile data in the predams from 2010 to 2015 at biweekely basis, from YR3 and YH3 sites. <ul> <li>Note: Data stored in the folder "probe_profiles_predam_YR3_YH3_2010_2015_biweekly". The data is at biweekly basis, measured at various water depths. The measurements include water temperature (Celcius), DO (mg/L), Chl-a (mg/m3), bluegreen, green and diatom (all in Chl-a, mg/m3)</li> </ul>

An Effective Activation Method for Industrially Produced TiFeMn Powder for Hydrogen Storage [Dataset related to publication]

Data type: XRD patterns; SEM micrographs and EDX maps; particle size distributions; atomic concentrations; hydrogen loading profiles; kinetic models; volume expansions.   Data format: *.opj; *.tif. Origin of the data: laboratory equipment from Hereon (XRD, SEM, PSD Analyzer, BET, XPS, Sievert apparatus) and UniPV (SEM). Software needed to plot the data: folders need to be unzipped, Origin.

opencc-by-4.0Apr 2022View details →

Scripts for quantifying the effect of diamond nano-pillars on the fluorescence of NV centers

Summary Scripts and data can be used to reproduce and build on the numerical results published under the title: "<a href="http://doi.org/10.3390/nano12091516">Optical and Spin Properties of NV Center Ensembles in Diamond Nano-Pillars</a>" by Kseniia Volkova, Julia Heupel, Sergei Trofimov, Fridtjof Betz, Rémi Colom, Rowan W. MacQueen, Sapida Akhundzada, Meike Reginka, Arno Ehresmann, Johann P. Reithmaier, Sven Burger, Cyril Popov, and Boris Naydenov (Nanomaterials 12(9), 1516, 2022). Method The dipole emitters are assumed to be distributed uniformly 30 nm below the top surface of the nano-pillars. They are first integrated with a trapezoidal rule along the azimuth (because of the periodicity this results in a geometrical convergence) and with a 15 point Gauss-Kronrod quadrature rule in radial direction. The main source of error results from the dipole positions being integrated only from 0 to R - min_dist, as it is challenging to model a dipole emitter located only few nanometers from the curved material interface. Further numerical parameters can be adjusted in the input files for JCMsuite. Both, a 3D setup and a 2D setup are provided. The letter exploits the rotational symmetry which results in a smaller memory footprint. Yet, as the distance of the dipole from the symmetry axis increases, many Fourier components are required which leads to long computation times. For further quantitative studies we propose the 3D setup that is the default in the script 'integration.m', which allows to integrate closer to the side walls without increasing the costs. Furthermore in the second data set, shipped together with the data published in the paper, the height has been kept constant. In the paper the height has been chosen according to the fabricated samples. The 90° angle has been assigned to the [111] samples and the correspondingt height of 1400 nm and the 35.3° angle was assigned to the [100] samples and a height of 2200 nm. Structure The directories scattering2D, scattering3D and scatteringFlat contain input files for JCMsuite. The script 'integration.m' can be used to produce new data. With 'plotresults.m' you can either plot the results produced with 'integration.m' or those which were published in the related paper. Please note that the provided example produced with the script 'integration.m' differs from the published data, which has been computed with slightly different parameters. Requirements <ul> <li>JCMsuite 5.2.0</li> <li>Matlab R2019b</li> </ul> In order to produce new data, you must replace the corresponding place holders in the files by a path to your installation of JCMsuite. Free trial licenses are available, please refer to the homepage of <a href="https://jcmwave.com/">JCMwave</a>.