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18,658 results for “impacts”
Seismic Reflection Data from the Kentland Impact Structure, Indiana from Robitaille MSc (2024)
<p>This repository contains the correlated and stacked shot gathers and the final unmigrated and migrated files (all in SGY format) collected near the Kentland Crater Impact Structure. These data are associated with the MSc thesis of Brian Robitaille at Purdue University (2024). See citation below.</p>
Radiation Impact of the Halloween GLE Events During the October to November 2003 Period - Data
<p>Computed data that was used within the "Radiation Impact of the Halloween GLE Events During the October to November 2003 Period" paper. The repository has data for GLEs 65-67 and contains the planetary cut-off maps, SEP spectra information, Forbush reconstruction information, interpolated CRAC:DOMO effective dose yield functions, and planetary dose computations. Some animated gifs are included to simply convey the change in dose over each GLE. </p>
ICON-Coast model output for a study on the impact of Arctic coastal erosion on sea water carbonate saturation.
<p>Primary output of the ocean-biogeochemistry model ICON-Coast that has been used to create the figures in a manuscript on the impact of Arctic coastal erosion on sea water carbonate saturation.</p>
The Impact of User Generated Content (UGC) on Impulsive Buying in Live Streaming Marketing
<p><span>The internet is growing, which affects the business industry's ability to keep up with its development by doing live streaming marketing. Live streaming marketing makes consumers make impulsive purchases because consumers can interact with sellers in real time and see people's reviews through comments, making consumers' buying intentions higher. The aim of this study is to explain how user-generated content in live-streaming marketing acts as an impulsive buying factor. It can also be referred to as user-generated content. Other consumers create user-generated content to give their honest review of a product in a video, photo, or other format. This research uses quantitative methods. The data was collected through a questionnaire distributed in June–September 2024 to 154 customers who buy via live streaming. The data collection method used is purposive sampling; the data is then processed using Structural Equation Modeling (SEM) with Smart PLS as tool. There are six variables in this research, there are:<span> </span>user generated content, content authenticity, social interaction, emotional appeal, pleasure, and impulsive buying with seven hypotheses.<span> </span>The results showed that the value of user-generated content, social interaction, and content authenticity in live-streaming marketing has a significant effect on pleasure and emotional appeal so that it can impact impulsive buying for users.</span></p> <p><span><strong><em><span>Keywords: user generated content, live streaming marketing, impulsive buying, UGC, e-commerce</span></em></strong></span></p>
Quantitative assessment of the reservoir-induced and urbanization-induced impact on multivariate flood risk via a nonstationary vine Copula model
<p>Here we show the results of the characteristics of the floods at Huayuankou, Lanzhou and Toudaoguai gauges selected by AMS and POT mentod, respectively. Besides that, the inormation about the reservoirs and the imprevious layer in the control catchment of each station is also uploaded.</p>
Environmental impacts and economic costs of perovskite light-emitting diodes
<p>Environmental impacts and economic costs of perovskite light-emitting diodes (PeLEDs), which are based on the life cycle assessments (LCA) and techno-economic assessments (TEA). They are also the dataset for the manuscript entitled "Towards sustainable perovskite light-emitting diodes".</p>
Impact of Arctic and Antarctic Sudden Stratospheric Warmings on Thermospheric Composition
<p>WACCM-X output files</p> <p>SABER Geostrophic Wind</p> <p>F10.7 cm solar flux and Kp index</p> <p>GOLD <span>Σ</span>O/N2</p> <p>GUVI <span>Σ</span>O/N2</p>
Marinoan Snowball Earth: The Impact of Deglaciation Duration on the Sea-Level History of Continental Margins
<p>Paleogeographies, ice histories, and predicted relative sea level output are available here as Matlab (.mat) files. Complementary text (.txt) files are included for latitude, longitude, and time. For different file formats or any questions to their use, please contact the corresponding author, Freya K Morris at: morrisfreya15@outlook.com</p>
Accountability in Code Review: The Role of Intrinsic Drivers and the Impact of LLMs
<p>The replication package for the paper "<span>Accountability in Code Review: The Role of Intrinsic Drivers and the Impact of LLMs</span>".</p> <p>Accountability is an innate part of social systems. It maintains stability and ensures positive pressure on individuals' decision-making. As actors in a social system, software developers are accountable to their team and organization for their decisions. However, the drivers of accountability and how it changes behavior in software development are less understood. In this study, we look at how the social process of code review affects software engineers' sense of accountability for code quality. Since code review is increasingly involving LLM assistance, we also evaluate the impact on accountability when introducing LLM-assisted code reviews. We carried out a two-phased sequential qualitative study ($\textbf{interviews} \rightarrow \textbf{focus groups}$). In Phase I (16 interviews), we sought to investigate the intrinsic drivers of software engineers influencing their sense of accountability for code quality, relying on self-reported claims. In Phase II, we tested these traits in a more natural setting by simulating traditional peer-led reviews with focus groups and then LLM-assisted review sessions. We found that there are four key intrinsic drivers of accountability for code quality: personal standards, professional integrity, pride in code quality, and maintaining one's reputation. In a traditional peer-led review, we observed a transition from \emph{individual} to \emph{collective accountability} when code reviews are initiated. We also found that the introduction of LLM-assisted reviews disrupts this accountability process, challenging the reciprocity of accountability taking place in peer-led evaluations, i.e., one cannot be accountable to an LLM. Our findings imply that the introduction of AI into SE must preserve social integrity and collective accountability mechanisms.</p>
Data and Scripts for "Temperature Extremes and Human Health in Cyprus Investigating the Impact of Heat and Cold Waves"_Version 2
<p>This folder contains the R data and scripts required for reproducing the results of the manuscript "Temperature Extremes and Human Health in Cyprus: Investigating the Impact of Heat and Cold Waves". (Version2)</p>
Enhanced Convective Microphysics Scheme and Its Impacts on Mean Climate Simulation in E3SM
<ol> <li>Simulation data of E3SM with an enhanced convective microphysics scheme.</li> <li>Source code of E3SM with an enhanced convective microphysics scheme.</li> </ol>
Wildlife Diversity and the Impact of Human Disturbance on Activity Patterns: A Case Study of the Labagoumen Nature Reserve, Beijing, China
<p>This version updates the dataset to reflect revised analyses in the resubmitted manuscript to Ecology and Evolution.</p> <p><strong>Title:</strong><br><strong>Data and Code for "Wildlife Diversity and the Impact of Human Disturbance on Activity Patterns: A Case Study of the Labagoumen Nature Reserve, Beijing, China"</strong></p> <p><strong>Description:</strong><br>This dataset contains the raw data and R scripts used to generate specific figures in the manuscript <em>"Wildlife Diversity and the Impact of Human Disturbance on Activity Patterns: A Case Study of the Labagoumen Nature Reserve, Beijing, China,"</em> submitted to <em>Ecology and Evolution</em>. The data were collected from infrared camera traps deployed in the Labagoumen Nature Reserve, Beijing, China, between 2019 and 2022. The dataset includes species detection records, timestamps, and information relevant to assessing the effects of human disturbance on wildlife activity patterns.</p> <p>The accompanying R scripts facilitate data processing, statistical analyses, and figure generation, enabling reproduction of the key results. This dataset may be useful for researchers interested in spatiotemporal wildlife monitoring, human-wildlife interactions, and disturbance ecology.</p> <p><strong>Contents:</strong></p> <ul> <li>raw data (Excel format, <code>.xlsx</code>)</li> <li>R scripts for data analysis and figure generation of <strong>Figures 3-5</strong> (from the raw data file "raw data.xlsx") (Text format, .doc).</li> <li> <p><strong>Figure 1</strong>: Created using <span>ArcMap 10.8 software</span> and Photoshop software. <span>ArcMap 10.8 software </span>was used for processing spatial data and map creation, while Photoshop was used for post-processing and image enhancement.</p> </li> <li> <p><strong>Figure 2 and Appendix Figure. A1</strong>: Designed and modified using Photoshop software.</p> </li> </ul>
Impact of Andreev Bound States within the Leads of a Quantum Dot Josephson Junction
<p>This repository contains all the raw data and the code used to generate the figures of the article "Impact of Andreev Bound States within the Leads of a Quantum Dot Josephson Junction".</p> <ul> <li>The plotting.ipynb notebook creates the majority of the figures. To run it you need to install proplot. Create a fresh python environment for it, since you might need to downgrade numpy to the 1.19.5 version and matplotlib to the 3.4.3 one. To install the relevant python packages and open the notebook you can download Anaconda and use the following terminal instructions:<br> <pre><code>conda create -n andreev-trimer python=3.10 conda activate andreev-trimer conda install proplot conda install jupyterlab xarray netcdf4 tqdm jupyter lab</code></pre> extraction-3D.ipynb extracts the charge degeneracy points from 3D charge stability diagram measurements. To run it you need to install scikit-image. If you want to export additional .gif images install imageio as well:<br> <pre><code>conda install scikit-image imageio</code></pre> </li> <li>To visualize the 3D charge stability diagrams (Figure 5) you can use plotting-3D.ipynb. To run it you need to install pyvista. A dedicated environment is recommended. To install pyvista and run it with JupyterLab use the following instructions:<br> <pre><code>conda create -n pyvista python=3.9 conda activate pyvista conda install nodejs pip install jupyter pyvista trame jupyter lab</code></pre> </li> <li>Finally, simulations.ipynb computes all the theoretical simulations. To run it you need cython and you can install it in a dedicated environment using the following instructions:<br> <pre><code>conda create -n theory conda activate theory conda install jupyterlab matplotlib cython scipy jupyter lab</code></pre> </li> </ul>
Proteome-wide Prediction of the Functional Impact of Missense Variants with ProteoCast
<p><strong>Proteome-wide Prediction of the Functional Impact of Missense Variants with ProteoCast</strong></p> <p>This dataset contains mutation effect predictions for 22,169 <em>Drosophila melanogaster</em> protein isoforms, classifying over <strong>293 million </strong>amino acid substitutions as neutral, uncertain, or impactful. The predictions were generated using the evolution-based <strong>GEMME </strong>model (E.Laine et <em>al.</em> MBE 2019) with multiple sequence alignments (MSAs) from the highly efficient <strong>ColabFold</strong> protocol (M.Mirdita et<em> al.</em> NatMet 2022, Abakarova et al. <em>GBE</em> 2023). To ensure reliability, we provide<strong> </strong>global (per-protein) and local (per-residue) confidence metrics, since the predictions are sensitive to the input MSA quality.</p> <p>Predictions were validated using natural polymorphisms from the Drosophila Genetic Reference Panel (DGRP) and Drosophila Evolution over Space and Time (DEST2) datasets, as well as FlyBase’s developmentally lethal and hypomorphic mutations. Additionally, the dataset includes sensitivity data for post-translational modifications (PTMs) and short linear motifs (SLiMs), aiding functional site identification.</p> <p>All this data can be visualized at <a href="https://proteocast.ijm.fr/drosophiladb/">proteocast.ijm.fr</a>. </p> <p>Readme:</p> <ol> <li><strong>Drosophila_ProteoCast.tar.gz </strong>- this archive contains ProteoCast predictions and analysis for each unique proteoform, with folder names corresponding to IDs listed in the mapping_database.csv file. A detailed description of the folder structure and contents can be found in ReadMe.txt. </li> <li><strong>data.tar.gz </strong>- this archive contains the data used in this study, sourced from FlyBase, DGRP2, and DEST2.</li> <li><strong>Dmel6.44PredictionsRecap.csv</strong> - this summary file provides detailed information for each proteoform, all FlyBase protein IDs (<em>FBpp_ID</em>) included. It contains the following data: <ul> <li><strong>Identifiers</strong>: FlyBase protein ID (<em>FBpp_ID</em>), protein symbol (Protein_symbol), gene ID (<em>FBgn_ID</em>), transcript ID (<em>FBtr_ID</em>), and UniProt ID if available (<em>UniProt_ID</em>).</li> <li><strong>Protein Characteristics</strong>: Sequence length (<em>Length</em>) and whether the proteoform is representative (<em>Representative_FBpp</em>).</li> <li><strong>MSA and GEMME Predictions</strong>: Fraction of observed mutations (<em>F_obs</em>) and number of sequences (<em>Nb_seq_MSA</em>) in the ColabFold MSA, presence or absence of GEMME predictions (<em>GEMME_prediction</em>), and global confidence score (<em>GlobalConfidence</em>).</li> <li><strong>Mutation Classification</strong>: Thresholds for defining mutations as neutral, uncertain, or impactful (<em>GMM3_uncertain</em>, <em>GMM3_impactful</em>).</li> <li><strong>Genomic Information</strong>: DNA strand (<em>Strand</em>) and exon coordinates (<em>Exons_coordinates</em>).</li> <li><strong>Structural Data</strong>: 3D structure file name if available (<em>Structure_3D_file, Structure_3D</em>).</li> <li><strong>Mutation Counts</strong>: Number of analyzed mutations and affected residues, labelled as lethal, hypomorphic on FlyBase, or from the DEST2 and DGRP datasets (<em>n_Lethal, n_Lethal_res, n_Hypomorphic, n_Hypomorphic_res, n_DEST2, n_DEST2_res, n_DGRP, n_DGRP_res, n_DEST_DGRP_union, n_DEST_DGRP_union_res</em>).</li> </ul> </li> <li><strong>csv.tar.gz </strong>-<strong> </strong>this archive contains the files generated in this study. A detailed description of the folder structure and contents can be found in ReadMe.txt.</li> </ol>
Datasets for 'Multiple impact sources for light plains around the lunar south pole' research paper
<p>The attached datasets are the shapefiles (areas and crater measurements) for the article ' Multiple impact sources for light plains around the lunar south pole'.</p> <p>The shapefiles can be directly imported in ArcPRO. </p> <p>Also attached is a .png file (image) of the study region with the 22 dated light plains and smooth crater floors color-coded by age, Fig. 4 in the research paper.</p>
Bushmeat yields, species extinction rates and ecosystem-level impacts of bushmeat harvesting as predicted by the Madingley General Ecosystem Model
<p>The datasets contain data generated using the Madingley General Ecosystem Model for experiments decribed in the paper: "T. Barychka, G.M.Mace and D.W.Purves (2021) The Madingley General Ecosystem Model predicts bushmeat yields, species extinction rates and ecosystem-level impacts of bushmeat harvesting. Oikos."</p> <p>The Madingley General Ecosystem Model was used to generate predictions of bushmeat yields, extinction rates and broader ecosystem impacts for a range of harvesting intensities of duiker-sized endothermic herbivores. Duiker antelope (such as <i>Cephalophus callipygus</i> and <i>Cephalophus dorsalis</i>) are the most heavily hunted species in sub-Saharan Africa, contributing 34%-95% of all bushmeat in the Congo Basin. In the Madingley, the harvested group was described as "Heterotroph – Herbivore – Terrestrial – Mobile – Iteroparous– Endotherm", with adult bodymasses of 13-21 kg and juvenile bodymasses of >100 g. Harvesting period was set at 30 years (<em>n </em>=30).</p> <p>In the first experiment, we used the Madingley model to predict bushmeat yields ("Harvested Biomasses") and extinction rates ("Density") from harvesting duiker-sized herbivores using proportional harvesting strategy, with harvest rates ranging from 0 to 0.90. These were compared to the estimates of bushmeat yields and survival probabilities for two duiker antelope species (<i>Cephalophus callipygus</i> and <i>Cephalophus dorsalis) </i>from conventional single-species Beverton-Holt model. </p> <p>In the second experiment, we used the Madingley model to generate data on the state ("State") of the harvested ecosystem. The datasets contain estimates of biomasses, abundances, adult and juvenile bodymasses, etc. of the harvested duiker-sized herbivores as well as unharvested herbivores, omnivores and carnivores present in the simulated ecosystem. In the paper we focused on abundances; however, other estimates e.g., adult bodymasses can be used to conduct further studies on the effects of harvesting in tropical ecosystems.</p> <p>Main results of the experiments are that: 1) the Madingley model gave estimates for optimal harvesting rate, and extinction rate, that were qualitatively and quantitatively similar to the estimates from conventional single-species Beverton-Holt model; 2) the Madingley model predicted a background local extinction probability for the target species of at least 10%; 3) at medium and high levels of harvesting of duiker-sized herbivores, the Madingley model predicted statistically significant, but moderate, reductions in the densities of the targeted functional group; increases in small-bodied herbivores; decreases in large-bodied carnivores; and minimal ecosystem-level impacts overall.</p>
Impact on energy and air quality of connected and autonomous vehicles in an urban context
<p>Despite numerous studies related to autonomous vehicles and connected vehicles (CAVs) and their impact on the economy or on traffic performance (eg, flow management, accidents), there are not many studies that relate these benefits to the environmental component. In this context, the objective of this work consisted in the integrated assessment of the impacts of CAVs on traffic performance, atmospheric emissions CO<sub>2</sub> and NO<sub>x, </sub>and air quality.</p> <p>To this end, a roundabout in the city of Aveiro was selected as a case study, and different scenarios were created: base scenario, considering the current typology of vehicles (conventional); scenario 2, considering defensive behavior CAVs; scenario 3, considering assertive behavior CAVs; and scenario 1, considering all types of vehicles mentioned above. To ensure a comprehensive analysis, all scenarios were evaluated for a period of 24 hours, corresponding to the period of the experimental campaign carried out, and a cascade of models was applied.</p> <p>First, the PTV VISSIM model was applied which allowed, configuring, calibrating and validating the network under study for an evaluation of the traffic performance. Second, the VSP model was applied to estimate atmospheric emissions, Finally, the CFD VADIS model was applied to air quality assessment.</p> <p>The results obtained allowed us to conclude that the introduction of CAVs, promotes longer travel times, especially during times of higher traffic, and an increase in emissions, mainly by the CAVs with defensive behavior. In terms of air quality, there were large differences in terms of NO<sub>2</sub> concentrations, with the CAVs promoting a degradation of air quality, especially during peak traffic hours.</p>
Maximizing Scholarly Access, Impact, and Visibility at the UW Law Library
<p>A video created by UW Law Librarians for the 2021 AALL Innovation Showcase highlighting how the UW Law Library helps UW Law faculty members increase the access, impact, and visibility of their scholarship.</p>
Impact of declining renewable energy costs on electrification in low emission scenarios - Scenario Data
<p>This data archive contains model runs and data analysis files to the research article</p> <p><strong>Impact of declining renewable energy costs on electrification in low emission scenarios</strong></p> <p>by<br> <em>Gunnar Luderer, Silvia Madeddu, Leon Merfort, Falko Ueckerdt, Michaja Pehl, Robert Pietzcker, Marianna Rottoli, Felix Schreyer, Nico Bauer, Lavinia Baumstark, Christoph Bertram, Alois Dirnaichner, Florian Humpenöder, Antoine Levesque, Alexander Popp, Renato Rodrigues, Jessica Strefler, Elmar Kriegler</em></p> <p>forthcoming in <em>Nature Energy (2021).</em></p> <p> </p> <p><em><strong>ModelRuns </strong></em>(directory) contains all model runs of the scenarios underlying the paper.</p> <p><em><strong>DataAnalysis </strong></em>(directory) contains all RMarkDown-Notebooks that were used for the data analysis and the generation of the figures of the paper.</p> <p><em><strong>ScenarioNames.pdf</strong></em> contains the mapping from scenario names used in the paper to the model experiment names (in the directory ModelRuns).<br> <br> <em><strong>ScenarioData_IAMC_Format.xlsx </strong></em>contains the scenario output date in the generic IAMC-format (https://data.ene.iiasa.ac.at/database/) as submitted to the IPCC-AR6-database (https://iiasa.ac.at/web/home/research/researchPrograms/Energy/200513_IPCCwebinar.html)</p>
Outcome of the 3 Impact Workshops held by the ReMO COST Action between 21st and 28th January 2021
<p>These documents are the result of a series of three online workshops held by the Researcher Mental Health (ReMO) COST Action that focussed on the specific impact priorities of each of ReMO's three Working Groups:</p> <ul> <li> <p>Working Group 1: System level - the ERA working environment</p> </li> <li> <p>Working Group 2: Institutional level - Wellbeing Practices in Research Institutions</p> </li> <li> <p>Working Group 3: Local actors - Promoting researcher well-being on a practical level </p> </li> </ul> <p>These three-hours long impact workshops were based on the outcomes of a previous process to perform <a href="https://zenodo.org/record/5643588">SWOT analyses</a> and followed the guidelines and methodology that the ReMO community adapted from the "Impact+ Exercise” that was originally developed for European Commissions’ Erasmus plus projects (<a href="https://www.erasmusplus.org.uk/impact-and-evaluation">https://www.erasmusplus.org.uk/impact-and-evaluation</a>). In these workshops the following procedure was executed:</p> <ul> <li> <p>First, participants established their impact targets for the ReMO network: the <strong>network </strong>(itself), the <strong>researcher</strong>, <strong>stakeholders</strong>, and the research <strong>system</strong>. These targets were derived from the methodological guidelines of the “Impact+ Exercise”, but participants tailored them to the context of ReMO. </p> </li> <li> <p>Participants, randomly assigned to small discussion rounds for brainstorming, identified the most important issues and topics when it comes to researcher mental health and well-being. They also associated those issues and topics with the impact targets.</p> </li> <li> <p>Outcomes were discussed in a plenary session with duplications and similar items (issues and topics) being merged.</p> </li> <li> <p>Participants went through a voting procedure. Every participant could distribute three votes across all topics and issues identified by all participants. Votes were counted and topics were ranked based on the number of votes they received.</p> </li> <li> <p>Participants discussed the ranking and generated an action plan for the ReMO Action, which also served as a guide for research and intervention development to improve researcher mental health.</p> </li> <li> <p>As a result, the three Impact matrices for the three working groups were finalized and synthesized to a project level Impact matrix. </p> </li> </ul> <p>The process aimed to distill a number of key themes and actions, which are critical for developing a model of doctoral education and more general research culture embracing researcher mental health as a key priority. This process led to the publication of the <a href="https://zenodo.org/record/5559806">Researcher Mental Health and Well-being Manifesto</a>.</p> <p>The audience’s responses were anonymous, and they were recorded in a padlet, whose contents are available here.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.