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566 results for “analytics”
Figure 2 in A story of becoming a horticultural threat, cypress jewel beetle Lamprodila festiva (Coleoptera, Buprestidae): analytical approach of its European escalation based on bibliographical sources
Figure 2. The distribution of the content of publications on Lamprodila festiva as a function of time.
Figure 1. A in A story of becoming a horticultural threat, cypress jewel beetle Lamprodila festiva (Coleoptera, Buprestidae): analytical approach of its European escalation based on bibliographical sources
Figure 1. A spherical coordinate system on Earth is used for calculating the propagation vectors (a). The projection method of the observation points around the hot points as the centres of the new reference coordinate system (b).
Nosemosis negatively affects honeybee survival: experimental and meta-analytic evidence
<p><span>Nosemosis, caused by microsporidian parasites of the genus <em>Nosema</em>, is considered a significant health concern for insect pollinators, including the economically important honeybee (<em>Apis mellifera</em>). Despite its acknowledged importance, the impact of this disease on honeybee survivorship remains unclear. Here, a standard laboratory cage trial was used to compare mortality rates between healthy and <em>Nosema</em>-infected honeybees. Additionally, a systematic review and meta-analysis of existing literature were conducted to explore how nosemosis contributes to increased mortality in honeybees tested under standard conditions. The review and meta-analysis included 50 studies that reported relevant experiments involving healthy and <em>Nosema</em>-infected individuals. Studies lacking survivorship curves or information on potential moderators, such as spore inoculation dose, age of inoculated bees, or factors that may impact energy expenditure, were excluded. Both the experimental results and meta-analysis revealed a consistent, robust effect of infection, indicating a threefold increase in mortality among the infected group of honeybee workers (hazard ratio for infected individuals = 3.16 [1.97, 5.07] and 2.99 [2.36, 3.79] in the experiment and meta-analysis, respectively). However, the meta-analysis also indicated high heterogeneity in the effect magnitude, which was not explained by our moderators. Furthermore, there was a serious risk of bias within studies and potential publication bias across studies. The findings underscore knowledge gaps in the literature. It is stressed that laboratory cage trials should be viewed as an initial step in evaluating the impact of <em>Nosema</em> on mortality and that complementary field and apiary studies are essential for identifying effective treatments to preserve honeybee populations.</span></p>
TRANSFORMING CUSTOMER RETENTION IN FINTECH INDUSTRY THROUGH PREDICTIVE ANALYTICS AND MACHINE LEARNING
<p>In recent years, the fintech industry has experienced rapid growth, driven by technological advancements and evolving consumer expectations. Fintech companies offer innovative financial services, such as digital banking, investment platforms, and payment solutions, catering to the needs of a tech-savvy customer base. However, as competition intensifies, customer retention has emerged as a critical challenge for these companies. According to a study by Ransom (2021), acquiring a new customer can cost five times more than retaining an existing one, making it imperative for fintech organizations to focus on strategies that enhance customer loyalty. The financial technology (fintech) sector has experienced unprecedented growth in recent years, fundamentally transforming how individuals and businesses access and manage financial services. Characterized by the integration of technology with financial services, fintech encompasses a wide array of offerings, including digital banking, peer-to-peer lending, robo-advisory services, and payment processing. As of 2023, the global fintech market was valued at approximately $309 billion and is projected to reach around $1.5 trillion by 2030, according to a report by Fortune Business Insights. This remarkable growth is largely attributed to advancements in digital technology, increasing smartphone penetration, and a growing consumer preference for online financial solutions. Moreover, the COVID-19 pandemic accelerated the adoption of digital financial services, as consumers sought contactless transactions and remote banking options.</p>
Detailed analytical results for archaeological textiles from the Uden–Slabroekse Heide elite burial (Early Iron Age, Netherlands)
<p>The dataset contains 3D volume of the mineralised textile, showing four superimposed layers. The textile layer was segmented on the basis of the fibre orientation used for the 3D rendering of the vanished colour pattern.</p>
Evolution of fault reactivation potential in the greater Ruhr region (Germany). Results from analytical and numerical studies.
<p>This dataset presents results of analytical-probabilistic and numerical studies on the evolution of fault reactivation potential in the greater Ruhr region in western Germany. The dataset includes shapefile of major faults in the greater Ruhr region, with their specific dip angles, strike values, slip tendencies, dilation tendencies, fracture susceptibilities, and reduced-risk dilation tendencies. The results of long-term coupled thermo-hydro-mechanical simulations on two conceptual geothermal systems based on the two most prevailing fault architectures in the greater Ruhr region are also included in the dataset. The model results are saved with the COMSOL Multiphysics software format, where the simulations were carried out.</p> <p>Please check the README files for a detailed explanation of both datasets.</p> <p>_________________________________</p> <p>Update on 06/11/2024</p> <p>New version includes only one updated fully coupled thermo-hydro-mechanical model with a NW-SE-striking fault and a finer mesh.</p>
Repository Analytics and Metrics Portal (RAMP) 2021 data
<p>The Repository Analytics and Metrics Portal (RAMP) is a web service that aggregates use and performance use data of institutional repositories. The data are a subset of data from RAMP, the Repository Analytics and Metrics Portal (<a href="http://ramp.montana.edu/">http://rampanalytics.org</a>), consisting of data from all participating repositories for the calendar year 2021. For a description of the data collection, processing, and output methods, please see the "methods" section below.</p> <p>The record will be revised periodically to make new data available through the remainder of 2021.</p>
Clusters of cause specific neonatal mortality and its association with per capita gross domestic product: a structured spatial analytical approach
<p>Database used for the analysis of the manuscript entitled: Clusters of cause specific neonatal mortality and its association with per capita gross domestic product: a structured spatial analytical approach. The aim of the study was to investigate the cluster areas of asphyxia-associated neonatal mortality and to explore the per capita gross domestic product (GDP) as an associated risk factor in São Paulo State (SP), Brazil.</p>
Human contact network analytics and COVID-19 hospital incidence in France
<p>This data set contains COVID-19 hospital incidence, temperature and human mobility and contact data recorded between 2020-03-24 and 2021-03-30 used in the paper:</p> <p>Selinger et al. 2021: Predicting COVID-19 incidence in French hospitals using human contact network analytics. 10.1016/j.ijid.2021.08.029</p> <p>See methods in the article for detailed descriptions and the data curation process.</p> <p> </p> <p><strong>1) cov_mob_tst_national.csv contains national-level data</strong><br> </p> <p>The columns comprise:</p> <p>incid_hosp: hospital admission incidence </p> <p>incid_rea: ICU admission incidence</p> <p>incid_dc: hospital death incidence </p> <p>incid_rad: incidence of those returned home</p> <p>within_departement_colocation_X%: X%-quantile of colocation probabilities with départements</p> <p>between_departement_colocation_X%: X%-quantile of colocation probabilities between départements</p> <p>fb_population_coverage_X%: X%-quantile of ratio of fb_population over census population in département</p> <p>null_links_X%: X%-quantile of null links across départements</p> <p>clustering_X%: X%-quantile of clustering coefficients across départements</p> <p>ricci_X%: X%-quantile of curvature across départements</p> <p>ricci_min_X%: X%-quantile of minimum curvature across départements</p> <p>ricci_mean_X%: X%-quantile of average curvature across départements</p> <p>ricci_max_X%: X%-quantile of maximum curvature across départements</p> <p>strength_X%: X%-quantile of network strengths across départements</p> <p>betweenness_centrality_X%: X%-quantile of betweenness_centrality scores across départements</p> <p>positive_test_ratio_weekly: ratio of weekly cumulated positive tested over weekly cumulated tests</p> <p>retail_and_recreation_percent_change_from_baseline: Google Mobility Reports</p> <p>grocery_and_pharmacy_percent_change_from_baseline: Google Mobility Reports</p> <p>parks_percent_change_from_baseline: Google Mobility Reports </p> <p>transit_stations_percent_change_from_baseline: Google Mobility Reports</p> <p>workplaces_percent_change_from_baseline: Google Mobility Reports</p> <p>residential_percent_change_from_baseline: Google Mobility Reports</p> <p>mean_temperature_X%: X% quantile of mean daily temperatures averaged over the week across départements</p> <p>min_temperature_X%: X% quantile of minimum daily temperatures averaged over the week across départements</p> <p>max_temperature_X%: X% quantile of maximum daily temperatures averaged over the week across départements</p> <p> </p> <p> </p> <p><strong>2) cov_mob_dep.csv contains département-level data</strong></p> <p>The columns comprise:</p> <p>dep: département code</p> <p>incid_hosp: hospital admission incidence </p> <p>incid_rea: ICU admission incidence</p> <p>incid_dc: hospital death incidence </p> <p>incid_rad: incidence of those returned home</p> <p>week: week (matched to colocation data recording usually on Tuesdays)</p> <p>dep_name: name of the département</p> <p>null_links: number of null links</p> <p>betweenness_centrality: betweenness centrality</p> <p>clustering: clustering coefficient</p> <p>strength: network strength</p> <p>ricci_mean: minimum curvature among all edges incident to a département</p> <p>ricci_min: mean curvature across all edges incident to a département </p> <p>ricci_X%: X%-quantile curvature among all edges incident to a département</p> <p>fb_population: number of facebook users </p> <p>facebook_colocation_within_dep: colocation probability within département</p> <p>fb_population_coverage: ratio of fb_population over census population in département</p> <p>facebook_colocation_between_dep_X%: X%-quantile of facebook colocation among all edges incident to the département</p> <p>min_temperature: minimum daily temperature averaged over the week</p> <p>max_temperature: maximum daily temperature averaged over the week</p> <p>mean_temperature: mean daily temperature averaged over the week</p> <p>incid_hosp_Y: incidence of hospital admission from Ynd most colocated département</p> <p>incid_rea_Y: incidence of ICU admission from Ynd most colocated département</p> <p>incid_dc_Y: incidence of hospital deaths from Ynd most colocated département</p> <p>incid_rad_Y: incidence of returned home from Ynd most colocated département</p> <p> </p>
Data for: Analytical transfer function for volcano deformation with T-dependent viscoelasticity
<p>Rocks can modulate the triggering, duration, and style of volcanic eruptions. When heated, the host rocks surrounding a magmatic reservoir is typically considered as a viscoelastic material. The viscoelastic rheology (viscosity especially) is temperature dependent; however, the dynamics and consequence on surface deformation resulting from heterogeneous crustal temperature and viscosity around magmatic reservoirs have not been explored systematically. </p> <p>This dataset incorporates the parameters and numerical codes used for generating results in the manuscript 'History-dependent volcanic ground deformation from broad-spectrum viscoelastic rheology around magma reservoirs' submitted to GRL and authord by Yang Liao, Leif Karlstrom, and Brittany Erickson. The dataset consists of a README file detailing the data structure, a matlab .mat file that contains the parameters assumed in the magma chamber model, and several matlab program .m files that can be applied to the .mat file to generate results presented in the manuscript. </p>
Trap parameters for the fast OSL signal component obtained through analytical separation for various quartz samples
<p>Dataset for article</p> <p>Trap parameters for the fast OSL signal component obtained through analytical separation for various quartz samples</p>
Arbitrary-shape dielectric particles interacting in the linearized Poisson-Boltzmann framework: an analytical treatment
<p>h_kmlsn.mat --</p> <p>the coefficients h<sub>kmls,n</sub> calculated for n_max<=50: one has h<sub>kmls,n</sub> = h<sub>kmlsn</sub>(k+1,m+1,l+1,s+1,n+1), where h<sub>kmlsn</sub> is a 5D array contained in h_kmlsn.mat (note that MATLAB indices must start from 1, while mathematically indices k, m, l, s, and n are >=0).</p> <p>b_nml_approx.m -- it approximates $b_{nml}(\tilde r,\tilde R)$ with a given parameter n_max;</p> <p>derivative_b_nml_approx.m -- it approximates the derivative of $b_{nml}(\tilde r,\tilde R)$ with respect to $\tilde r$ with a given parameter n_max;</p> <p>linear_system_two_bodies_direct_backslash.m -- a simple example of forming the global linear system (14) and solving it directly (without regularization) using the MATLAB "mldivide" (or backslash) operation</p> <p>GSK3beta.zip -- zipped directory with DelPhi files related to Sec. 5.1.3 (see ReadmeGSK3beta.txt);</p> <p>Arginine-Glutamate -- zipped directory with DelPhi files related to Sec. 5.1.4 (see ReadmeARGGlu.txt)</p>
Input data - Wasteaware Cities Benchmark Indicators - WABI 2023 - Global data analytics
<p>This is the input dataset for the research publication "<em>Socio-economic development drives solid waste management performance in cities: A global analysis using machine learning</em>". It features </p> <ul> <li>Metadata info used by R codes</li> <li>Full data set for the WABI, used by the R codes</li> <li>Data required for plotting the map in Figure 1</li> </ul> <p>The independent variables data set refers to specific indicators of the WABI methodology (<a href="https://www.sciencedirect.com/science/article/pii/S0956053X14004905">https://www.sciencedirect.com/science/article/pii/S0956053X14004905</a>) which generates solid waste management and resource recovery profiles for cities. It is applied here for 40 cities around the world. The data set contains also values for a series of explanatory variables, which are measures of the level of socioeconomic development at country level.</p> <p> </p> <p> </p> <p> </p>
Replication package for The Use of Domain-Specific Languages for Visual Analytics: A Systematic Literature Review
<p>In order to provide reproducibility, we have made all the data collected in the study titled: "The Use of Domain-Specific Languages for Visual Analytics: A Systematic Literature Review" as a replication package. This package includes the following files:</p> <ol> <li>A zip file containing the codes used for this Systematic Literature Review from NVIVO software. One separate file for each of the codes in the Zip file. (Code Summary.zip)</li> <li>Data collection form for different rounds of study. (Data_Collection_Form_Final.xlsx)</li> <li>Summary of number of papers retrieved in each round. (Number_of_Retrieved_studies.pdf)</li> </ol>
Line-by-line coefficients for an analytical solution of spectrally resolved outgoing longwave radiation
<p>Following <strong>Feng et al., 2023</strong> , the change in spectrally-resolved outgoing longwave radiance <span class="math-tex">\(R\)</span> is:</p> <p><span class="math-tex">\(\pi \frac{R_{\text{trop}}}{R} [B(rT_e) - B(T_e)]\)</span></p> <p>In this equation, B(T_e) refers to Planck Function at temperature T_e, <span class="math-tex">\(R_{trop}\)</span> is the radiance contributed by troposphere, and <span class="math-tex">\(r\)</span> is an emission temperature shift ratio, computed using <strong>Eq. 10</strong> of <strong>Feng et al., 2023 </strong>with line-by-line regression coefficients <span class="math-tex">\(k\)</span> contained in the netCDF file 'lbl_regression_coeff.nc' for each major greenhouse gas. This set of coefficients is derived using an open-source line-by-line radiation code PyLBL (<a href="https://pylbl-1.readthedocs.io/en/latest/">https://pylbl-1.readthedocs.io</a>) following the Appendix of Feng et al., 2023 via Eq. B2, B4, and B6.</p> <p>An example matlab script is included to compute the emission temperature shift ratio based on the line-by-line coefficients and to further predict the spectrally-resolved feedback parameter based on Feng et al., 2023.</p>
Source Code Snippets and Quality Analytics Dataset
<p>This dataset contains the Java code snippets of <a href="https://github.com/github/CodeSearchNet">CodeSearchNet</a>, processed along with their abstract syntax trees and clustered according to their similarity. It also includes static analysis metrics, PMD violations and readability metrics for each snippet.</p> <p>You can use the dataset simply with the following steps:</p> <p> 1. Download the data.</p> <p> 2. Navigate to the download folder and use the mongorestore (<a href="https://docs.mongodb.com/manual/reference/program/mongorestore/">https://docs.mongodb.com/manual/reference/program/mongorestore/</a>) command. (Have in mind to use the --gzip flag)</p>
Analytic results on the massive three-loop form factors: quarkonic contributions
<p>This repository contains the ancillary files to the publication</p> <p>"Analytic results on the massive three-loop form factors: quarkonic contributions"</p> <p>J. Blümlein , A. De Freitas, P. Marquard , N. Rana, and C. Schneider</p>
Supplementary Datasets for: 'A processing and analytics system for microscopy data workflows: the Pycroscopy ecosystem of packages'
<p>The repository contains four independent datasets that are a part of the publication (<a href="https://arxiv.org/abs/2302.14629">arXiv:2302.14629</a>), which delineates the capabilities of the Pycroscopy ecosystem of packages. The details of the individual datasets can be found below. </p> <p>1) bfo_iv_final.hf5: Dataset of I-V curves captured by conductive atomic force microscopy on a BiFeO3 sample. The data has been transformed so that we plot not the log of the current density (J) as a function of the square root of the electric field. The dataset was originally presented in the paper 10.1038/s41467-017-01334-5 </p> <p>2) bto_atomic.dm3: Atomically resolved data BaTiO3 thin film acquired with scanning transmission electron microscopy. These were originally captured in the dm3 file format. This dataset was a part of the publication: doi.org/10.1002/adma.202106426</p> <p>3) EELS_STO.dm3: Scanning transmission electron microscope (STEM)-Electron energy loss spectroscopy (EELS) dataset of SrTiO3.</p> <p>4) STO-stack.h5: High-angle annular dark-field imaging (HAADF) scanning transmission electron microscope (STEM) image stack of SrTiO3. This image stack contains 25 images.</p> <p> </p>
An analytical study of capillary rise dynamics: Critical conditions and hidden oscillations - Research Data
<p>This dataset provides the research data and software for the publication:</p> <p>M. Fricke, E. Ouro-Koura, S. Raju, R. von Klitzing, J. De Coninck, D. Bothe: An analytical study of capillary rise dynamics:<br> Critical conditions and hidden oscillations</p> <p>which was published in the Journal Physica D: Nonlinear Phenomena. The experimental data for the capillary rise of silicon oil, ethanol and ether has been extracted from the publication</p> <p>D. Quéré: Inertial capillarity, EPL 39 533, DOI:10.1209/epl/i1997-00389-2 (1997)</p> <p>using image analysis methods. It is stored in the csv files Silicon_Oil_Quere1997, Ethanol_Quere1997.csv and Ether_Quere1997. Please use the Python scripts silicon-oil.py, ethanol.py and ether.py to solve the ordinary differential equation model and to generate the plots presented in our publication. The Python library matplotlib was used for plotting. A simple explicit Euler scheme proved sufficient to solve the ordinary differential equation in its dimensionless form. It is implemented in the file capRiseOdeSolver.py.</p>
H2020 ENODISE Analytical Data Set configuration C ECL
<p>Preliminary wake-interaction noise and steady-loading noise estimates for a contrarotating propeller system, using analytical modeling.</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.