Find research datasets worth reusing

opencc-by-4.0Aug 2024View details →

Modelled temperature, mortality impact, and external benefits of cool roofs and rooftop photovoltaics in London - supporting data

Supporting data for "Modelled temperature, mortality impact, and external benefits of cool roofs and rooftop photovoltaics in London" Included are outputs from the Weather Research and Forecast (WRF) model. All simulations cover London, United Kingdom over summer 2018. Scenarios include a "baseline" which represents the real urban climate of the region, and scenarios which model 100% coverage of rooftops with either high albedo materials or solar panels. Data are provided in netCDF format. <ul> <li>The baseline simulation which models the current urban climate of the region WRF_Urb_BouLac_T2-V10-U10_20180525-20180831.nc</li> <li>The 100% rooftop-solar simulation WRF_Urb_BouLac_PV_T2-V10-U10-PSFC-RAINNC-TH2-Q2_20180525-20180831.nc</li> <li>The 100% high-albedo roof simulation WRF_Urb_BouLac_ClRf_T2-V10-U10-PSFC-RAINNC-TH2-Q2_20180525-20180831.nc</li> <li>The non-urban scenario is in WRF_NoUrb_BouLac_T2-V10-U10-PSFC-RAINNC-TH2-Q2_20180525-20180831.nc</li> <li>The power production estimates solarpv_prod_2018.nc</li> <li>wrf_popweighting-main.zip contains the analysis code. It is provided as-is with no guarantee of usability.</li> </ul> T2 means air temperature at 2m height. V10 and U10 are windspeeds at 10m height. PSFC is surface level pressure. TH2 is potential temperature. Q2 is specific humidity at 2m height. More description of the simulations is given in the citing article.

Fig. 5 in Approaches for assessing the impact of Zea mays (Poaceae) on the behavior of Spodoptera frugiperda (Lepidoptera: Noctuidae) and its parasitoid Cotesia marginiventris (Hymenoptera: Braconidae)

Fig. 5. Spodoptera frugiperda moths prefer to oviposit on uninfested Zea mays plants. To test the effect of prior infestation with S. frugiperda (FAW) compared to a non-treated (NT) control plant on S. frugiperda oviposition preference, a pair-wise oviposition assay was performed using 3 independent experiments (Experiments 1–3). In each experiment, 6 uninfested plants and 6 infested plant treatments were used, and egg masses on each plant counted (Table). The total number of egg masses on each treatment was determined and from these data the percent total oviposition (%NT and %infested plant) calculated. The graph shows the mean (± SE) percent parasitism for each treatment,and the treatments were statistically significantly different using a pair-wise t-test: P ≤ 0.05; n = 3.

Fig. 6. Cotesia marginiventris wasps have a in Approaches for assessing the impact of Zea mays (Poaceae) on the behavior of Spodoptera frugiperda (Lepidoptera: Noctuidae) and its parasitoid Cotesia marginiventris (Hymenoptera: Braconidae)

Fig. 6. Cotesia marginiventris wasps have a marginal preference to oviposit on Spodoptera frugiperda on W22 compared to B104 Zea mays inbred plants. To test the effect of different Z. mays varieties on oviposition preference of C. marginiventris, a pair-wise oviposition assay was performed using 13 independent experiments (Experiments 1–13). In each experiment, the number of S. frugiperda larvae recovered from B104 or W22 genotypes that were parasitized by C. marginiventris (P), not-parasitized (NP), or had died shortly afer collection (D), and the percentage of larvae parasitized was calculated by (P/[P + NP]) × 100 for each plant variety (Table). Experiments that had less than 5 parasitized larvae or more than 15 dead (bold) were discarded. The graph shows mean (± SE) percentage parasitism for each treatment. The treatments were not significantly different using a pair-wise t-test with P ≤ 0.05 and n = 8.

Fig. 3 in Approaches for assessing the impact of Zea mays (Poaceae) on the behavior of Spodoptera frugiperda (Lepidoptera: Noctuidae) and its parasitoid Cotesia marginiventris (Hymenoptera: Braconidae)

Fig. 3. Stages of Cotesia marginiventris development. Representative images of C. marginiventris adult (A), larva emerging from S. frugiperda host (B) and pupae (C) are shown. Size bars are 20 µm.

Fig. 4 in Approaches for assessing the impact of Zea mays (Poaceae) on the behavior of Spodoptera frugiperda (Lepidoptera: Noctuidae) and its parasitoid Cotesia marginiventris (Hymenoptera: Braconidae)

Fig. 4. Larval growth assays of Spodoptera frugiperda. Two independent larval growth assays for S. frugiperda on the Zea mays inbred line B104. Graph shows mean larval weights (± SE) from 3 to 7 d afer infestation, n = (35–100). Because these growth assays were done at different times, they were not statistically compared.

Fig. 2 in Approaches for assessing the impact of Zea mays (Poaceae) on the behavior of Spodoptera frugiperda (Lepidoptera: Noctuidae) and its parasitoid Cotesia marginiventris (Hymenoptera: Braconidae)

Fig. 2. Spodoptera frugiperda developmental stages. Line drawing of differences between male and female S. frugiperda pupae (A). Representative images of S. frugiperda larva (B), pupae (C), adults (D), and egg masses (E).

Data for device simulation in the article "Analysing the impact of the hole transport layer on the space charge distribution and hysteresis in perovskite solar cells using capacitance-voltage profiling"

This repository contains the data used to perform device simulation in the article "Analysing the impact of the hole transport layer on the space charge distribution and hysteresis in perovskite solar cells using capacitance-voltage profiling", submitted in September 2024 to the journal Sustainable Energy and Fuels. The authors of this data and the article are E. Regalado-Pérez, Evelyn B. Díaz-Cruz, and J. Villanueva-Cab  The scripts (.m) and input files (.csv) hosted here are based on the files created by the authors of the Driftfusion code, which can be found in the GitHub repository  "barnesgroupICL/Driftfusion" at https://github.com/barnesgroupICL/Driftfusion.

Rapid Impact Crater Relaxation Caused by An Insulating Methane Clathrate Crust on Titan: Data and Marc Files

Data files for several figures in the manuscript "Rapid Impact Crater Relaxation Caused by An Insulating Methane Clathrate Crust on Titan" Published in The Planetary Science Journal. This includes data for the following figures: 4, 6, 7, 8 and 10. Two example Hexagon Marc-Mentat mud files for the axisymmetric thermal simulation and mechanical simulation of a 10 km thick clathrate, 85 km diameter crater are also included. Each column is self-explanatory except for the two relative depth data files. "Relative_Depth_Deep_Fig8" includes the results for simulations that use the initially deeper crater depth, and "Relative_Depth_Shallow_Fig8" includes the results for simulations that use the initially shallower crater depth. The columns are labeled with a shorthand notation for pairs of columns that represent the relative crater depth at specified times in the simulation. An example of time is “t(yr)_v21_120_5” and the corresponding relative depth column is “v21_Rd_120_5.” Time is given in years and relative depth is unitless. These specific examples provide results for a simulation that has a viscosity cutoff of 10^21 Pa s and a 120 km diameter crater with a 5 km thick methane clathrate crust overlying water ice.

opencc-by-4.0Jun 2024View details →

Model output from "Potential impacts of marine fuel regulations on an Arctic stratocumulus case and its radiative response"

Model output from "Potential impacts of marine fuel regulations on an Arctic stratocumulus case and its radiative response", in review at Atmospheric Chemistry and Physics, 2024, same authors. The simulations were done using MIMICA version 4 (Savre at el., 2014) and the data includes all model output presented in the publication.

Supplementary materials for paper "Untangling the drivers of change and policy impact in coastal wetland area in the Yangtze Estuary using causal inference"

<h1>Annual coastal wetland vegetation maps of the Yangtze Estuary from 1986 to 2021</h1>   <h2>Basic information</h2> Using remotely sensed data from Google Earth Engine, we generated a 30 m resolution annual dataset of Yangtze Estuary wetland vegetation for the period 1986-2021. This dataset includes three dominant vegetation types (Spartina alterniflora, Phragmites australis, and Scirpus mariqueter) and tidal flat areas. We combined fieldwork data and high-resolution images for accuracy assessment, achieving an overall accuracy exceeding 80% in different years. Detailed information about the mapping methods can be found in our paper and accompanying supplementary materials. <h2>Notes:</h2> In the image classification scheme: 0-Tidal flats, 1-Spartina alterniflora, 2-Phragmites australis, 3-Scirpus mariqueter. <h2>Usage Policy:</h2> This dataset is a collaborative effort between East China Normal University and Deakin University. If you plan to use our data in a scientific analysis paper or other research work, we strongly recommend contacting us in advance to seek our opinions, citing the unique DOI of this dataset, and considering acknowledging our contributions or including us as co-authors.

opencc-by-4.0Aug 2024View details →

Unlocking Efficiency: Assessing the Impact of Prompt Pay on Government Operations and Financial Transactions in Thailand

1. Promtpay users 2. Monetary Aggregates and Components   Data collected and processed as part of the ODDEA (Overcoming Digital Divide Between Europe and Southeast Asia) EU research project (Project ID: HORIZON MSCA-SE 101086381)

Fig. 2 in Mitochondrial DNA reveals the impact of Pleistocene glaciations on a widespread palearctic bat species

Fig. 2 Chronograms, resulting from BI analysis of the four selected Hypsugo marker sequences ND1 (a), CytB (b), COI (c), and 16 S RNA (d). Only lineage A and B were detected in all four marker datasets. The dots denote nodes with posterior probability value equal or above 0.95. Other nodes were below the value we designated as reliable to successfully infer phylogenetic relations between detected lineages (0.95). The scale bar indicates approximate age in millions of years

Fig. 3 in Mitochondrial DNA reveals the impact of Pleistocene glaciations on a widespread palearctic bat species

Fig. 3 Median-joining haplotype networks for the four marker sequences ND1 (a), CytB (b), COI (c), and 16 S RNA (d). Smaller coloured circles represent a single sequence, while the larger represent two or three. Hala refers to H. alaschanicus and Hstu to H. stubbei

Fig. 1 in Mitochondrial DNA reveals the impact of Pleistocene glaciations on a widespread palearctic bat species

Fig. 1 (a) Geographic origin of assigned Hyspugo savii sequences. For some sequences, only vague localities were available, and the placement therefore might be unprecise (see Online Resource 1). + marks the type locality of H. savii (Pisa, Italy). Projected lineage occurrence areas (coloured polygons) were created using buffer zones of approximately 200 km around each data point following anecdotal citations of migration distance (Juste and Paunović 2016; Dietz and Kiefer 2016) in QGIS v. 3.4.14. Question marks denote areas, where lineage distribution is at present unknown. (b) A H. savii individual photographed near Dragonja River, SW Slovenia, in 2018 by Jan Gojznikar. (c) MCC tree obtained by using the concatenated dataset. Posterior probabilities are shown next to their respective nodes. Value of the scale bar refers to million years before present. Squared tips indicate a concatenated sequence of a single individual, whilst triangles denote multiple concatenated sequences

Towards a cashless society - Examining the Impact of Digital Infrastructure on mPayment Transactions (A Cross-Region Analysis).xlsx

Data collected and processed as part of the ODDEA (Overcoming Digital Divide Between Europe and Southeast Asia) EU research project (Project ID: HORIZON MSCA-SE 101086381).

opencc-by-4.0Jul 2024View details →

Molecular Dynamics Trajectories Exploring the Impact of Phosphorylation on the Physiological Form of Human alpha-Synuclein in Aqueous Solution

<h3>Primary data for the publication "Impact of Phosphorylation on the Physiological Form of Human alpha-Synuclein in Aqueous Solution" by de Bruyn, Dorn, Rossetti, Fernandez, Outeiro, Schulz and Carloni. Submitted to the Journal of Chemical Information and Modeling.</h3> Included are all GROMACS input files, parameterised topologies, starting and final configurations, and trajectories for the lowest temperature replica (at 300 K, lowest of 32 replicas between 300-500 K exchanging according to the REST2 algorithm (Wang et al. 2011)). The data is split into three archives: <ol> <li>all_atom_trajectories.zip <ul> <li>contains all input files and all atom trajectories including solvent</li> <li>trajectories written at 100 ps intervals</li> </ul> </li> <li>protein+ion_trajectories.zip <ul> <li>contains configuration/non-parameterised topologies and trajectories excluding solvent, but including ions</li> <li>trajectories written at 10 ps intervals</li> </ul> </li> <li>additional_simulations.zip <ul> <li>contains the all atom trajectories and input files, and</li> <li>solvent-free trajectories above,</li> <li>for the additional simulations in the Supplemental Information of the article: <ul> <li>includes the DES-Amber-based simulations with 64 replicas between 300-600 K, and</li> <li>a99SB-disp-based simulations</li> </ul> </li> </ul> </li> </ol>   Folders are named according to the following top level scheme: <ul> <li>DES-Amber simulations/ Simulations created using the DES-Amber force field (Tucker et al. 2022)</li> <li>a99SB-disp simulations/ SImulations created using the a99SB-disp force field for Intrinsically Disordered Proteins (IDPs) (Robustelli et al. 2018)</li> </ul> Sub-folders follow the following scheme: <ul> <li>AS/ Simulations of the physiological form of wild-type Human α-Synuclein <ul> <li>unphosphorylated</li> </ul> </li> <li>pAS/ Simulations of the physiological form of wild-type Human α-Synuclein <ul> <li>phosphorylated at S129</li> <li>with double negative charge</li> </ul> </li> <li>pASH/ Simulations of the physiological form of wild-type Human α-Synuclein (a99SB-disp simulations only) <ul> <li>phosphorylated at S129</li> <li>with a single negative charge (monoprotonated)</li> </ul> </li> </ul>

opencc-by-4.0Oct 2024View details →

MACHINE LEARNING APPROACHES FOR DEMAND FORECASTING: THE IMPACT OF CUSTOMER SATISFACTION ON PREDICTION ACCURACY

This study investigates the effectiveness of various machine learning models in predicting product demand based on customer satisfaction data. Four models—Linear Regression, Random Forest, Gradient Boosting, and Support Vector Machine (SVM)—were evaluated using performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R² score. The results indicate that Gradient Boosting achieved the highest accuracy, with an MAE of 2.56, MSE of 12.75, RMSE of 3.57, and R² score of 0.82, effectively capturing the complex, non-linear relationships inherent in customer satisfaction factors. Random Forest also demonstrated strong performance, while Linear Regression and SVM showed limitations in handling intricate datasets. These findings underscore the importance of utilizing advanced machine learning techniques for accurate demand forecasting, highlighting the critical role of customer satisfaction data in enhancing predictive capabilities. The insights gained from this research can guide organizations in optimizing inventory management and improving customer satisfaction in a rapidly evolving market.

opencc-by-4.0Jan 2024View details →

Data and analysis and plotting scripts for Swaminathan et al., "Regional Impacts Poorly Constrained by Climate Sensitivity"

The datasets included here are of the plotted data from the figures of the paper entitled "Regional Impacts Poorly Constrained by Climate Sensitivity", by Ranjini Swaminathan, Jacob Schewe, Jeremy Walton, Klaus Zimmermann, Colin Jones, Richard A. Betts, Chantelle Burton, Chris D. Jones, Matthias Mengel, Christopher Reyer, Andrew G. Turner & Katja Weigel, submitted for publication in Earth's Futures.  Scripts used for plotting and analysis are also included.

opencc-by-4.0Oct 2024View details →

Data and analysis code for Repo et al., "Contrasting forest management strategies: impacts on biodiversity and ecosystem services under changing climate and disturbance regimes"

This repository contains analysis code and pre-processed data for the study "Contrasting forest management strategies: impacts on biodiversity and ecosystem services under changing climate and disturbance regimes" by Repo et al. Data processing and analysis mainly done by Aapo Jantunen, Katharina Albrich Due to respository space limitations, the original model outputs are archived in the Finnish "Allas" data storage service. For access, contact katharina.albrich@luke.fi The code used to process the raw data is included here for reproducibility. If you are interested in using iLand, visit https://iland-model.org/ and https://iland-model.org/iland-book/ for information on using the model and a guide to setting up a landscape. This work was supported by the Ministry of Agriculture and Forestry by funding project Future multifunctional forests and their disturbance risk in the changing climate (Foster) through the “Catch the Carbon” initiative (project number VN/28654/2020). A.R. has been supported by the grant [TRACY Trade-offs and synergies in land-based climate change mitigation and biodiversity conservation decision 322066 by the Academy of Finland.], J. H by the grant [CASCADE - Changing Disturbance Regimes and Forest Landscapes of Fennoscandia 342569 by the Academy of Finland].