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1,034 results for “Empirical”
Oil Price Uncertainty and Bank Diversification: Empirical Evidence from 447 Banks in China
<p>This is the data set corresponding to the research paper "Oil Price Uncertainty and Bank Diversification: Empirical Evidence from 447 Banks in China".</p>
Replication Package for "A Call for Critically Rethinking and Reforming Data Analysis in Empirical Software Engineer"
<p><strong>Content Overview</strong></p> <p>This replication package contains the following materials:</p> <p>- Data: RAW Focus Group Datasets and Aggregated Results</p> <p>For any issues, questions, or further assistance, please do not hesitate to contact the paper's authors. We are here to help!</p>
Empirical Study and Vision for a Holistic Serious Game Platform for Software Engineering Education
<p>Serious games are becoming increasingly popular in universities to motivate students. Therefore, more and more lecturers are using game-based learning to encourage students to learn the subject matter. However, using serious games in Software Engineering (SE) lectures at universities is rather challenging as the subject is very practice-oriented and requires special kinds of tasks, e.g., practicing programming, software modeling, etc. Thus, building serious games and integrating such SE-specific tasks is a serious technical challenge and requires immense development effort. Therefore, before developing new games, students' preferences should be analyzed to increase the chances that these games will be played. To address this issue, we conducted an empirical study with 105 students to investigate the requirements and chances for using serious games in SE education in universities. Our analysis shows that students' preferences vary significantly and that there is no clear indication of a single game type that motivates all students equally. Therefore, our second contribution is a vision for a microservice-based Holistic Serious Game Platform for Software Engineering Education, which supports efficiently developing different serious games and integrating SE-specific mini-games and tasks. Hence, our empirical results and the presented approach support advancing research and development of serious SE games and also sensitize game developers to the needs of SE education.</p>
Data from: An empirical and mechanistic explanation of abundance-occupancy relationships for a critically endangered nomadic migrant
The positive abundance-occupancy relationship (AOR) is a pervasive pattern in macroecology. Similarly, the association between occupancy (or probability of occurrence) and abundance is also usually assumed to be positive and in most cases constant. Examples of AORs for nomadic species with variable distributions are extremely rare. Here we examined temporal and spatial trends in the AOR over seven years for a critically endangered nomadic migrant which relies on dynamic pulses in food availability to breed. We predicted a negative temporal relationship, where local mean abundances increase when the number of occupied sites decreases, and a positive relationship between local abundances and the probability of occurrence. We also predicted that these patterns are largely attributable to spatiotemporal variation in food abundance. The temporal AOR was significantly negative and annual food availability was significantly positively correlated with the number of occupied sites, but negatively correlated with abundance. Thus, as food availability decreased, local densities of birds increased, and vice-versa. The abundance - probability of occurrence relationship was positive and non-linear, but varied between years due to differing degrees of spatial aggregation caused by changing food availability. Importantly, high abundance (or occupancy) did not necessarily equate to high quality habitat and may be indicative of resource bottlenecks or exposure to other processes affecting vital rates. Our results provide a rare empirical example that highlights the complexity of AORs for species that target aggregated food resources in dynamic environments.
Data from: Dissimilarity of individual microsatellite profiles under different mutation models: empirical approach
Microsatellites (simple sequence repeats, SSRs) still remain popular molecular markers for studying neutral genetic variation. Two alternative models outline how new microsatellite alleles evolve. Infinite alleles model (IAM) assumes that all possible alleles are equally likely to result from a mutation, while stepwise mutation model (SMM) describes microsatellite evolution as stepwise adding or subtracting single repeat units. Genetic relationships between individuals can be analyzed in higher precision when assuming the SMM scenario with allele size differences as a proxy of genetic distance. If population structure is not predetermined in advance, an empirical data analysis usually includes (a) estimating proximity between individual SSR profiles with a selected dissimilarity measure and (b) determining putative genetic structure of a given set of individuals using methods of clustering and/or ordination for the obtained dissimilarity matrix. We developed new dissimilarity indices between SSR profiles of haploid, diploid, or polyploid organisms assuming different mutation models and compared the performance of these indices for determining genetic structure with population data and with simulations. More specifically, we compared SMM with a constant or variable mutation rate at different SSR loci to IAM using data from natural populations of a freshwater bryozoan Cristatella mucedo (diploid), wheat leaf rust Puccinia triticina (dikaryon), and wheat powdery mildew Blumeria graminis (monokaryon). We show that inferences about population genetic structure are sensitive to the assumed mutation model. With simulations, we found that Bruvo's distance performs generally poorly, while the new metrics are capturing the differences in the genetic structure of the populations.
Data from: Species delimitation with ABC and other coalescent-based methods: a test of accuracy with simulations and an empirical example with lizards of the Liolaemus darwinii complex (Squamata: Liolaemidae)
Species delimitation is a major research focus in evolutionary biology because accurate species boundaries are a prerequisite for the study of speciation. New species delimitation methods (SDMs) can accommodate non-monophyletic species and gene tree discordance as a result of incomplete lineage sorting via the coalescent model, but do not explicitly accommodate gene flow after divergence. Approximate Bayesian Computation (ABC) can incorporate gene flow and estimate other relevant parameters of the speciation process while testing alternative species delimitation hypotheses. We evaluated the accuracy of BPP, SpeDeSTEM, and ABC for delimiting species using simulated data and applied these methods to empirical data from lizards of the Liolaemus darwinii complex. Overall, BPP was the most accurate, ABC showed an intermediate accuracy, and SpeDeSTEM was the least accurate under most simulated conditions. All three SDMs showed lower accuracy when speciation occurred despite gene flow, as found in previous studies, but ABC was the method with the smallest decrease in accuracy. All three SDMs consistently supported the distinctness of southern and northern lineages within L. darwinii. These SDMs based on genetic data should be complemented with novel SDMs based on morphological and ecological data to achieve truly integrative and statistically robust approaches to species discovery.
Refining User Stories via Example Mapping: An Empirical Investigation | Online Appendix
<p>This is the online appendix for the paper "Refining User Stories via Example Mapping: An Empirical Investigation" which contains three files:</p> <ul> <li>Refinement Evaluation Tool.pdf - this file includes the RET as presented in the paper (with both the Long Questionnaire and Session Questionnaire)</li> <li>Fizor Results.xlsx - This file includes the data of the Fizor case study. It has three tabs: the Long Questionnaire results, the Session Questionnaire results and the Observation results.</li> <li>Results Pension Management Example Mapping.xlsx - This file includes the data of the pension management firm case study. It has three tabs: the Long Questionnaire results, the Session Questionnaire results and the Observation results.</li> </ul>
Single molecule images for: An empirical energy landscape reveals mechanism of proteasome in polypeptide translocation
<p>The ring-like ATPase complexes in the AAA+ family perform diverse cellular functions that require coordination between the conformational transitions of their individual ATPase subunits. How the energy from ATP hydrolysis is captured to perform mechanical work by these coordinated movements is not known. In this study, we developed a novel approach for delineating the nucleotide-dependent free-energy landscape (FEL) of the proteasome's heterohexameric ATPase complex based on complementary structural and kinetic measurements. We used the FEL to simulate the dynamics of the proteasome and quantitatively evaluated the predicted structural and kinetic properties. The FEL model predictions were widely consistent with experimental observations in this and previous studies and suggested novel features of the mechanism of proteasomal ATPase. We find that the cooperative movements of the ATPase subunits result from the design of the ATPase hexamer entailing a unique free-energy minimum for each nucleotide-binding state. ATP hydrolysis dictates the direction of substrate translocation by triggering an energy-dissipating conformational transition of the ATPase complex.</p>
Data Set: Software-Related Fatal Failures: An Empirical Exploration of RISKS Reports
<p>The data set used in the empirical exploration of RISKS reports. This data set acts as a basis for further research and investigation to categorize and analyze software-related fatal failures spanning more than 30 years.</p>
An Empirical Evaluation of the Effectiveness of Smart Contract Verification Tools - Supplemental Material
<p>An Empirical Evaluation of the Effectiveness of Smart Contract Verification Tools - Supplemental Material</p>
Dataset: Luminosity thresholds of colored surfaces are determined by their upper-limit luminances empirically internalized in the visual system
<p>Repository for the raw data for individual observers reported in a paper titled "Luminosity thresholds of colored surfaces are determined by their upper-limit luminances empirically internalized in the visual system".</p>
Data set for Predicting hospital occupancy for covid-19 patients: a simulation approach based on archetypes of empirical services' trajectories
<p>Data set for the paper: Predicting hospital occupancy for covid-19 patients: a simulation approach based on archetypes of empirical services’ trajectories</p> <p>Based on: Marin-Garcia, J. A., Ruiz, A., Julien, M., & Garcia-Sabater, J. P. (2021). A data generator for covid-19 patients’ care requirements inside hospitals. WPOM-Working Papers on Operations Management, 12(1), 76-115. https://doi.org/10.4995/wpom.15332</p> <p> </p>
Dataset for How open is innovation research?–An empirical analysis of data sharing among innovation scholars
<p>This dataset comprises responses from 241 innovation researchers on their personal data sharing behavior as well as their perceptions of and attitudes towards open research data. This dataset is the supplementary material to Barczak et al. (2021) (<a href="https://doi.org/10.1080/13662716.2021.1967727" target="_blank" rel="noopener">https://doi.org/10.1080/13662716.2021.1967727</a>).</p>
Replication Kit: "Are Unit and Integration Test Definitions Still Valid for Modern Java Projects? An Empirical Study on Open-Source Projects"
<p><strong>Replication Kit for the Paper "Are Unit and Integration Test Definitions Still Valid for Modern Java Projects? An Empirical Study on Open-Source Projects"</strong><br> This additional material shall provide other researchers with the ability to replicate our results. Furthermore, we want to facilitate further insights that might be generated based on our data sets.</p> <p><strong>Structure</strong><br> The structure of the replication kit is as follows:</p> <ul> <li><strong>additional_visualizations</strong>: contains additional visualizations (Venn-Diagrams) for each projects for each of the data sets that we used</li> <li><strong>data_analysis</strong>: contains python scripts that we used to analyze our raw data</li> <li><strong>data_collection_tools</strong>: contains all source code used for the data collection, including the used versions of the <a href="https://github.com/comfort-framework">COMFORT framework</a>, the <a href="https://github.com/ftrautsch/BugFixClassifier">BugFixClassifier</a>, and the used tools of the <a href="https://github.com/smartshark">SmartSHARK environment</a>;</li> <li><strong>mongodb_no_authors</strong>: Archived dump of our MongoDB that we created by executing our data collection tools. The "comfort" database can be restored via the mongorestore command.</li> </ul> <p><br> <strong>Additional Visualizations</strong><br> We provide two additional visualizations for each project:<br> 1) <project_name>\_disj\_ieee\_venn (visualizations for the DISJ data set)<br> 2) <project_name>\_all\_ieee\_venn (visualizations for the ALL data set)</p> <p>For each of these data sets there exist one visualization for each project that shows four Venn-Diagrams for each of the different defect types. These Venn-Diagrams show the number of defects that were detected by either unit, or integration tests (or both).</p> <p>Furthermore, we added boxplots for each of the data sets (i.e., ALL and DISJ) showing the scores of unit and integration tests for each defect type.</p> <p><br> <strong>Analysis scripts</strong><br> Requirements:<br> - python3.5<br> - tabulate<br> - scipy<br> - seaborn<br> - mongoengine<br> - pycoshark<br> - pandas<br> - matplotlib</p> <p>Both python files contain all code for the statistical analysis we performed.</p> <p><strong>Data Collection Tools</strong><br> We provide all data collection tools that we have implemented and used throughout our paper:</p> <ul> <li><strong>BugFixClassifier</strong>: Used to classify our defects.</li> <li><strong>comfort-core</strong>: Core of the comfort framework. Used to classify our tests into unit and integration tests and calculate different metrics for these tests.</li> <li><strong>comfort-jacoco-listner</strong>: Used to intercept the coverage collection process as we were executing the tests of our case study projects.</li> <li><strong>jSHARK</strong>: Library that contains models for the used ORM mapper that is used inside the SmartSHARK environment (for Java).<strong> </strong></li> <li><strong>pycoSHARK</strong>: Library that contains models for the used ORM mapper that is used inside the SmartSHARK environment (for Python).</li> <li><strong>tools-changedistiller</strong>: Version of ChangeDistiller that we used within our comfort-core framework.</li> <li><strong>vcsSHARK</strong>: Used to collect data from the VCSs of the projects.</li> </ul> <p> </p> <p> </p>
Empirical optimal localization / Vertical covariance localization
<p>Data and code / Necker et al. 2022 - "Guidance on how to improve vertical covariance localization based on a 1000-member ensemble"<br> Revised version (R2) of the code and results after reviews</p>
Blocking JavaScript without Breaking the Web: An Empirical Investigation
<p>Anonymous</p>
Data set for EMPIR project MeDDII Paper bmt-2022-0040
<p>Data set for EMPIR project MeDDII Paper</p> <p>https://doi.org/10.1515/bmt-2022-0040</p>
Exploring the configuration space of elemental carbon with empirical and machine learned interatomic potentials
<p>This dataset contains a vertical slice of the data used to generate the results found in the publication "Exploring the configuration space of elemental carbon with empirical and machine learned interatomic potentials"<br> It contains nested sampling input files and trajectory files for each potential studied, as well as the xml files and training data for the new potential, GAP-20U+gr.</p>
Data set for EMPIR project MeDDII Report A2.3.4 D4
<p>Data set for EMPIR project MeDDII Report A2.3.4 D4</p> <p>Report on the use of a calibrated microfluidic multi-parameter chip for the in-line measurement of pressure, viscosity and temperature</p>
Data set for EMPIR project MeDDII Report A2.1.4
<p>Data set for EMPIR project MeDDII Report A2.1.4</p> <p>Validation of the pipe viscometers (primary standards) for the in-line measurement of the dynamic viscosity of Newtonian liquids</p>
ScienceDex guides
Understand access before you commit
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.