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203 results for “Impact Test”
Flume Experiment Testing the Impact of Artificial Streambank Roots on Velocity, Reynold's Shear Stress, and Turbulent Kinetic Energy using an Acoustic Doppler Profiler
The data published here is expected to accompany one publicly available dissertation (Chapter 4 of dissertation) and one separate journal publication. Once published and available online, the metadata will be updated with the relevant article information. The journal article/dissertation will have additional information regarding the published datasets and the methods used to collect the data. All data collected from these studies, and the accompanying Acoustic Doppler Profiler MATLAB files, are presented here. Journal Article title: Impact of Flexible and Rigid Artificial Roots on Stream Hydrodynamics
Laboratory experiments testing pH, alkalinity and particle impacts on Mn removal
Laboratory experiments were conducted to investigate impacts of pH, alkalinity, and presence of particles on Mn removal in freshwater. The dataset includes monitoring data from: 1) a 14-day experiment in Mn(II) solutions in nanopure water, 2) a 24-hour experiment in Mn(II) solutions in nanopure water, and 3) a 10-day experiment in water from two drinking water reservoirs. The 14-day pH and alkalinity laboratory experiment was conducted starting October 30, 2022 and included sample collection and pH monitoring on day 0, 1, 4, 7, 10, and 14. The 24-hour pH and alkalinity laboratory experiment was conducted starting February 20, 2023 and included sample collection and pH monitoring at 0, 1, 2, 6, 12, and 24 hours. The reservoir water laboratory experiment was conducted starting March 22, 2023 and included sample collection and pH monitoring on day 0, 1, 4, 7, and 10. This experiment tested Mn removal in water collected from the lower water column of Falling Creek Reservoir (FCR) and Carvins Cove Reservoir (CCR), located in Vinton, Virginia, USA and Roanoke, Virginia, USA respectively. Both reservoirs are owned and operated by the Western Virginia Water Authority and are managed as drinking-water sources for the city of Roanoke, VA, USA.
Dataset annexed to: "Testing ethical impact assessment for nano risk governance"
<p>To test the ethical impact assessment methodology guidelines and tools adapting CEN Workshop Agreement part 2 CWA 17145-2:2017 (E)) to support risk governance of nanomaterials, in the RiskGONE project (https://riskgone.eu/), feedback from stakeholders was requested. This dataset includes the responses of participants in several online meetings. The dataset is linked to the paper "Testing ethical impact assessment for nano risk governance".</p>
Data + Analyses: "Gaze-dependent Coding of Somatosensory Reach Targets after Effector Movement: Testing the Impact of Online Information, Movement Timing, and Target Distance"
<p>This upload contains the experiment scripts (written in Presentation), data, and analyses (performed with MATLAB and SPSS) underlying the publication<strong> </strong>by Mueller & Fiehler (2017). <em>PloS one</em>. doi:<strong>10.1371/journal.pone.0180782</strong></p>
Train and Evaluation Code, Road Classification Models and Test set of the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification"
<p>This repository contains the Python scripts built for training and evaluation of the implementation, together with the test data and the resulting road classification models corresponding to the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification". The scripts make use of the Tensorflow with Keras framework and the additional required dependencies.</p> <p>The training and validation set is based on the binary SROADEX dataset (https://zenodo.org/records/6482346) that was re-split into tiles that feature the image resolutions (256 x 256, 512 x 512, and 1024 x 1024 pixels) and image overlaps (0% and 12.5%) considered in this study. The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography that represents the axes of the different types of roads (urban, interurban and rural). This binary road data contains information from 16 full orthoimages (28.5 km * 18.5 km) with spatial resolution of 0.5 m/pixel from the insular and peninsular Spanish territory. Due to the size on disk of approximately 546 gigabytes, this training and validation data is only available upon request from the corresponding author. The test set has been generated from a novel area of 28.5 km * 18.5 km and features binary road labels. The test sets are provided in the repository for each resolution (with no overlap), so that additional DL models can be evaluated on the same data and compared with the results achieved in this study.</p> <p>The structure of the information shared in this repository is as follows:<br>The scripts have been grouped by tile resolution (256, 512 and 1024). First, the test set and the evaluation script can be found. For each tile resolution, there are two subfolders (corresponding to the "no overlap" and "12.5% overlap"). In each case, the Python scripts for training the models in the three repetitions are shared, and the trained models (H5 format) are shared in compressed form. Finally, for each resolution we also share the testing dataset which consists of two folders.</p> <p>The material is distributed under a CC-BY 4.0 license.</p>
FIGURE A5 in Testing the impact of two key scan parameters on the quality and repeatability of measurements from CT scan data
FIGURE A5. Scatter plot displaying how beam shift, measured by the apparent growth (scale) of the shell in the final x-ray radiograph relative to the first x-ray radiograph, varies with (1) time of day the scan was performed, and (2) the overall scan time. We found that beam shift decreased throughout the day, likely due to the tube warming up as more scans are run. There was no correlation between overall scan time and the amount of beam shift.
FIGURE A1.2 in Testing the impact of two key scan parameters on the quality and repeatability of measurements from CT scan data
FIGURE A1.2. The range of volume measurements for each exposure times when three scans were reconstructed using ten methods. The blue dot represents the simple surface determination used for all shells in this study. The error bars represent the data range for each exposure time.
FIGURE 4. A in Testing the impact of two key scan parameters on the quality and repeatability of measurements from CT scan data
FIGURE 4. A video moving through slices of a pteropods shell scanned at 500 ms exposure time, with five xray radiographs averaged per view and an overall scan time of 75 minutes. There are streak artefacts perpendicular to the shell edge that are likely caused by beam hardening or shell movement during the scan. For video file, see https://palaeo-electronica.org/content/2020/ 2923-investigating-ct-scan-quality.
Dataset: Seeking feedback and taking action: An evidence based evaluation of the impact of using practice tests with inbuilt feedback in a first-year business class
<p><strong>This data set includes both qualitative and quantitative data.</strong></p> <p><strong>Qualitative, survey data sought student opinions on the purpose of feedback and the actions taken following receipt of feedback.</strong></p> <p>1. Survey data collection began with a 13 question survey used in classes in early February 2020. This initial feedback survey was conducted via MS Forms. The data from this is available in the following files: a. 2020-2-17_feedbacksurvey_1_rawdata.csv b. 2020-2-17_feedbacksurvey_1_how-many-times-got-feedback.csv c. 2020-2-17_feedbacksurvey_1_take-action-after-feedback.csv d. 2020-2-17_feedbacksurvey_1_type-feedback-you-prefer.csv e. 2020-2-17_feedbacksurvey_1_purpose-of-feedback.csv f. 2020-2-17_feedbacksurvey_1_define-feedback.csv g. 2020-2-17_feedbacksurvey_1_what-makes-feedback-useful.csv h. 2020-2-17_feedbacksurvey_1_know-how-feedback-effective.csv i. 2020-2-17_feedbacksurvey_1_what-can-institute-do-to-improve-feedback.csv</p> <p><br>2. The first practice test was followed by a short survey of approximately 3 questions (created using MS Forms, a mix of closed and open questions). The data from this is available in the following files: a. 2020-3-2_feedbacksurvey_2_rawdata.csv b. 2020-3-2_action-comments-coded.csv c. 2020-3-2_other-comments-coded.csv d. 2020-3-2_open-question-comments.csv</p> <p>3. The second practice test followed by a 3 question short survey (created using MS Forms, all open questions) The data from this is available in the following files: a. 2020-5-12_feedbacksurvey_3_rawdata.csv b. 2020-5-12_feedbacksurvey_3_what-learn-by-participating.csv c. 2020-5-12_feedbacksurvey_3_what-action-taking.csv d. 2020-5-12_feedbacksurvey_3_has-module-changed-view-of-feedback.csv<br><br><strong>Quantitative data was gathered from Learning Management System, Canvas. This data shows the relationship between number of practice tests taken by students and their resultant grades.</strong></p> <p><br>4. I gathered the number of times students had taken the practice test before the midterm summative exam and compared with the student grades from the 30% midterm summative exam. The data from this is available in the following files: a. 2020-11-26_Canvasdata_midterm.csv</p> <p>5. Following the completion of the second summative exam (worth 70%), Canvas data showing the number of times the students had taken the second practice test and the grade outcomes for the students for the summative 70% test was gathered. The data from this is available in the following files: a. 2020-11-26_Canvasdata_endterm.csv</p> <p><br>6. A regression model was created to examine the impact of number of times that the practice test was taken on final grades, whilst controlling for midterm grades. The data from this is available in the following files: a. 2024-2-12_Anonymised_mid_and_final_grades_with_number_of_practices_before_final_exam.csv</p>
Energy Consumption Due To Test Quality: State of Affairs, Impacts, and Ways Forward
<p>This replication package contains data and scripts used in <strong>"Energy Consumption Due To Test Quality: State of Affairs, Impacts, and Ways Forward"</strong></p> <p>For details, please read README</p>
Impact hammer test of an Aluminium 6082 T6 plate, 260 x 246 x 1.5 mm, free boundary conditions
<p>Plate data</p> <p>------------</p> <p>E = 70 GPa</p> <p>ν = 0.33</p> <p>ρ = 2700 kg/m^3</p> <p>h = 1.50 mm (±0.01mm)</p> <p>width = 246 mm</p> <p>length = 260 mm</p> <p> </p> <p>Experiment description</p> <p>-----------------------------</p> <p>- a Cartesian coordinate system (XYZ) is defined in one of the corners of the plate, with XY laying on the mid-plane of the plate;</p> <p>- the X axis is aligned with the length direction;</p> <p>- the Y axis is aligned with the width direction;</p> <p>- the free boundary conditions are approximated experimentally by means of soft cotton wires, and the plate is tested while hanging on the cotton wires that are fixed at two corners, by means of adhesive tapes;</p> <p>- a mesh of 5 by 5 points is defined on the plate, with the following coordinates (in meters):</p> <pre><code class="language-python">1, [0., 0.] 2, [0.065, 0.] 3, [0.13, 0.] 4, [0.195, 0.] 5, [0.26, 0.] 6, [0., 0.0615] 7, [0.065, 0.0615] 8, [0.13, 0.0615] 9, [0.195, 0.0615] 10, [0.26, 0.0615] 11, [0., 0.123] 12, [0.065, 0.123] 13, [0.13, 0.123] 14, [0.195, 0.123] 15, [0.26, 0.123] 16, [0., 0.1845] 17, [0.065, 0.1845] 18, [0.13, 0.1845] 19, [0.195, 0.1845] 20, [0.26, 0.1845] 21, [0., 0.246] 22, [0.065, 0.246] 23, [0.13, 0.246] 24, [0.195, 0.246] 25, [0.26, 0.246]</code></pre> <p>- the accelerometer is fixed at point 5;</p> <p>- the time-response to the hammer impact is obtained for each point by hitting with the impact hammer at each point;</p> <p>- three time-response data points are obtained at each point;</p> <p>- for all measurements a total of 2^15 = 32768 time samples are obtained, corresponding to a time interval of 0 <= t <= 3.840 s, and a minimum sampling rate of 8000 Hz. </p> <p> </p> <p>Details about the hardware</p> <p>----------------------------------</p> <p>1) Data Acquisition Module NI 9234</p> <p>With four input channels simultaneously acquire at rates up to 51.2 kS/s (kilosamples per second). The operating temperature limits are from -40 °C to 70 °C, maximum of 5 g vibration, and maximum of 50 g shock.</p> <p>2) Impact hammer with load cell 086C02</p> <p>The first channel corresponds to the impact hammer load cell measurements, in Newtons [N]. The hardware sensitivity (+-15%) is 50 mV/lbf or 11.2 mV/N.</p> <p>3) Unidirectional Accelerometer 352A24</p> <p>The second channel corresponds to the accelerometer measurements, in gravity units [g], being 1 gravity unit = 9.81 m/s^2. The hardware sensitivity (+-10%) is 100 mV/g or 10.2 mV/(m/s^2).</p> <p> </p> <p> </p> <p>Reading the experimental data</p> <p>--------------------------------------</p> <p>I use the https://nptdms.readthedocs.io/en/stable/quickstart.html module to read the experimental data saved with the "*tdms" extension. The following example illustrates how to read the data at all the points:</p> <pre><code class="language-python">from nptdms import TdmsFile # reading from the experimental results # - the hammer impact force in [N] as a function of time from df.iloc[:, 0] # - the accelerations in [g] as a function of time from df.iloc[:, 1] times = {} hammer_impacts = {} accelerometer = {} for point in usedpoints: tdms = TdmsFile('WaveForms Point%02d.tdms' % point) df = tdms.as_dataframe() df.index = df.index/df.shape[0] * tmax times[point] = df.index hammer_impacts[point] = df.iloc[:, 0] accelerometer[point] = df.iloc[:, 1]</code></pre> <p> </p> <p> </p> <p>Plate model</p> <p>----------------</p> <p>In file "plate_model.py" you can find a validated vibration model of the plate, based on the Python package:</p> <blockquote> <p>Castro, S.G.P. Supporting code for Stability and Analysis of Structures II, Department of Aerospace Structures and Materials, Delft University of Technology, 2023. <a href="https://doi.org/10.5281/zenodo.2583004">DOI: 10.5281/zenodo.2583004</a>.</p> </blockquote>
Data and Code for the paper "GUI Testing of Android Applications: Investigating the Impact of the Number of Testers on Different Exploratory Testing Strategies"
<p>This package contains data and code to replicate the findings presented in our paper titled "<em>GUI Testing of Android Applications: Investigating the Impact of the Number of Testers on Different Exploratory Testing Strategies</em>".</p> <p><strong>Abstract</strong></p> <p>Graphical User Interface (GUI) testing plays a pivotal role in ensuring the quality and functionality of mobile apps. In this context, Exploratory Testing (ET), a distinctive methodology in which individual testers pursue a creative, and experience-based approach to test design, is often used as an alternative or in addition to traditional scripted testing. Managing the exploratory testing process is a challenging task, that can easily result either in wasteful spending or in inadequate software quality, due to the relative unpredictability of exploratory testing activities, which depend on the skills and abilities of individual testers. A number of works have investigated the<br> diversity of testers’ performance when using ET strategies, often in a crowdtesting setting. These works, however, investigated ET effectiveness in detecting bugs, and not in scenarios in which the goal is to generate a re-executable test suite, as well. Moreover, less work has been conducted on evaluating the impact of adopting different exploratory testing strategies. As a first step towards filling this gap in the literature, in this work we conduct an empirical evaluation involving four open-source Android apps and twenty masters students, that we believe can be representative of practitioners partaking in exploratory testing activities. The students were asked to generate test suites for the apps using a Capture and Replay tool and different exploratory testing strategies. We then compare the effectiveness, in terms of aggregate code coverage, that different-sized groups of students using different exploratory testing strategies may achieve. Results provide deeper insights into code coverage dynamics to project managers interested in using exploratory approaches to test simple Android apps, on which they can make more informed decisions.</p> <p> </p> <p><strong>Contents and Instructions</strong></p> <p>This package contains:</p> <ul> <li><strong>apps-under-test.zip</strong> A zip archive containing the source code of the four Android applications we considered in our study, namely MunchLife, TippyTipper, Trolly, and SimplyDo.</li> <li><strong>apps-under-test-instrumented.zip</strong> A zip archive containing the instrumented source code of the four Android applications we used to compute branch coverage.</li> <li><strong>students-test-suites.zip</strong> A zip archive containing the test suites developed by the students using Uninformed Exploratory Testing (referred to as "Black Box" in the subdirectories) and Informed Exploratory Testing (referred to as "White Box" in the subdirectories). This also includes coverage reports.</li> <li><strong>compute-coverage-unions.zip </strong>A zip archive containing Python scripts we developed to compute the aggregate LOC coverage of all possible subsets of students. The scripts have been tested on MS Windows. To compute the LOC coverage achieved by any possible subsets of testers using IET and UET strategies, run the <em>analysisAndReport.py</em> script. To compute the LOC coverage achieved by mixed crowds in which some testers use a U+IET approach and others use a UET approach, run the <em>analysisAndReport_UET_IET_combinations_emma.py</em> script.</li> <li><strong>branch-coverage-computation.zip </strong>A zip archive containing Python scripts we developed to compute the aggregate branch coverage of all considered subsets of students. The scripts have been tested on MS Windows. To compute the branch coverage achieved by any possible subsets of testers using UET and I+UET strategies, run the <em>branch_coverage_analysis.py</em> script. To compute the code coverage achieved by mixed crowds in which some testers use a U+IET approach and others use a UET approach, run the <em>mixed_branch_coverage_analysis.py</em> script.</li> <li><strong>data-analysis-scripts.zip</strong> A zip archive containing R scripts to merge and manipulate coverage data, to carry out statistical analysis and draw plots. All data concerning RQ1 and RQ2 is available as a ready-to-use R data frame in the <em>./data/all_coverage_data.rds</em> file. All data concerning RQ3 is available in the <em>./data/all_mixed_coverage_data.rds </em>file.</li> </ul>
Data from: Testing for human impacts in the mismatch of living and dead ostracode assemblages at nested spatial scales in subtropical lakes from the Bahamian archipelago
Naturally time-averaged accumulations of skeletal remains – death assemblages – provide reliable, albeit temporally coarse, information on the species composition and structure of communities in diverse settings, and their mismatch with local living communities usually signals recent human-driven ecological change. Here, we present the first test of live-dead mismatch as an indicator of human stress using ostracodes. On three islands along a gradient of human population density in the Bahamas, we compared the similarity of living and death assemblages in ten lakes with relatively low levels of human stress to live-dead similarity in eleven physically comparable lakes subject to industrial, agricultural, or other human activities currently or in the past. We find that live-dead agreement in pristine lakes is consistently excellent, boding well for using death assemblages in modern-day and paleolimnological biodiversity assessments. In most comparison of physically-similar paired lakes, sample-level live-dead mismatch in both taxonomic composition and species' rank-abundance is on average significantly greater in the stressed lake; live-dead agreement is not lower in all samples from stressed lakes, but is more variable. When samples are pooled for lake-level and island-level comparisons, stressed lakes still yield lower live-dead agreement but the significance of the difference with pristine lakes decreases – species that occur dead-only (or alive-only) in one sample are likely to occur alive (or dead) in other samples. Inter-island differences in live-dead agreement are congruent with, but not significantly correlated with, differences in human population density. This situation arises from heterogeneity in the timing and magnitudes of stresses and in the extent of post-stress recovery. Live-dead mismatch in ostracode assemblages thus may be a reliable indicator of human impact at the sample-level with the potential to be a widely-applicable tool for identifying impacted habitats, and perhaps, monitor the progress of their recovery.
Gaze-dependent Coding of Somatosensory Reach Targets after Effector Movement: Testing the Impact of Online Information, Movement Timing, and Target Distance
<p>The uploaded files contain data as SPSS data files (.sav) and tab delimited (.csv) textfiles as well as the respective variable desriptions as images (.png). In particular, I uploaded 1) the raw data, 2) the means of reach errors which we analyzed by repeated-measures ANOVA, and 3) the means of ellipse sizes on which repeated-measures ANOVA of precision were based.</p> <p>After publication of the article, I also uploaded the reported analyses and corresponding datafiles (plus some more material) here: </p> <p>https://doi.org/10.5281/zenodo.821307</p>
Test results from quasi-static impact tests over SMC materials reinforced with glass fibre (ramdom and textile)
<p>Test results from quasi-static impact tests over SMC materials with structural properties conceived for automotive applications, reinforced with glass fibre (samples with ramdom distribution of fibres and samples reinforced with GF textile)</p>
Impact of host demography and evolutionary history on endosymbiont molecular evolution: a test in carpenter ants (Genus Camponotus) and their Blochmannia endosymbionts
Obligate endosymbioses are tight associations between symbionts and the hosts they live inside. Hosts and their associated obligate endosymbionts generally exhibit codiversification, which has been documented in taxonomically diverse insect lineages. Host demography (e.g., effective population sizes) may impact the demography of endosymbionts, which may lead to an association between host demography and the patterns and processes of endosymbiont molecular evolution. Here, we used whole-genome sequencing data for carpenter ants (Genus Camponotus; subgenera Camponotus and Tanaemyrmex) and their Blochmannia endosymbionts as our study system to address whether Camponotus demography shapes Blochmannia molecular evolution. Using whole-genome phylogenomics, we confirmed previous work identifying codiversification between carpenter ants and their Blochmannia endosymbionts. We found that Blochmannia genes have evolved at a pace ~30× faster than that of their hosts' molecular evolution and that these rates are positively associated with host rates of molecular evolution. Using multiple tests for selection in Blochmannia genes, we found signatures of positive selection and shifts in selection strength across the phylogeny. Host demography was associated with Blochmannia shifts toward increased selection strengths, but not associated with Blochmannia selection relaxation, positive selection, genetic drift rates, or genome size evolution. Mixed support for relationships between host effective population sizes and Blochmannia molecular evolution suggests weak or uncoupled relationships between host demography and Blochmannia population genomic processes. Finally, we found that Blochmannia genome size evolution was associated with genome-wide estimates of genetic drift and number of genes with relaxed selection pressures. --
Experimental data: Low-velocity out-of-plane impact tests on double-wythe unreinforced brick masonry walls instrumented with optical measurements
<p>This dataset includes the results of laboratory impact tests conducted on natural-scale double-wythe unreinforced brick masonry walls. The walls were spanning vertically between two reinforced concrete slabs and were subjected to low-velocity drop-weight pendulum tests in which they were repeatedly hit until the opening of a breach in the center of the wall. The tests were instrumented with both hard-wired and optical measurements, the latter consisting of high-speed cameras and digital image correlation techniques. Investigated in these tests were the out-of-plane response of the walls and their capacity to resist the impacts. The axial load applied on the top of the walls was varied for two wall configurations and monitored throughout the tests to study the effect of arching on the failure mechanism produced and number of repeated hits needed to open the breach. Of interest was also the evidence of cracking, more specifically the way it initiated on the undamaged walls and next propagated upon consecutive hits. </p> <p>The data generated from the tests is made here available and documented to support further investigations on masonry structures subjected to extreme actions. The dataset includes four ZIP files, ordered from 01 to 05, along with an auxiliary PDF document describing the content and organization of the data. </p> <p>Test implementation and test results that are built upon this data are presented and discussed in the following research article:</p> <blockquote> <p><a href="https://www.sciencedirect.com/science/article/pii/S0734743X23001082?via%3Dihub">Godio M, Flansbjer M, Williams Portal N (2023) Low-velocity out-of-plane impact tests on double-wythe unreinforced brick masonry walls instrumented with optical measurements, International Journal of Impact Engineering</a></p> </blockquote> <p>To cite this data in your work please refer to the article.</p> <p>The Authors</p>
Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.
<p>Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.</p>
The impact of weekly fever-screening and treatment and monthly RDT testing and treatment on the infectious reservoir of malaria in Burkina Faso: results from a cluster-randomised trial
<p>The majority of malaria infections in endemic countries are asymptomatic and a source of onward transmission to mosquitoes. Malaria transmission and disease burden could be reduced by improving early detection and treatment of these infections with active screening approaches. In an 18-month cluster-randomized study in Sapone, Burkina Faso, households were enrolled and randomised to 1 of 3 arms: arm 1 - control; arm 2 - active weekly screening for febrile individuals and treatment if rapid diagnostic test (RDT) positive; or arm 3 – active weekly fever-screening (as arm 2) plus monthly RDT-testing regardless of symptoms. The primary outcome was parasite prevalence by qPCR in the end-of-study cross-sectional survey. Secondary outcomes included parasite and gametocyte prevalence and density in all three end-of-season cross-sectional surveys, incidence of infection, and the transmissibility of infections to mosquitoes. A total of<strong> </strong>906 individuals were enrolled during 2 phases. In Phase 1, 412 individuals were enrolled between August 9 and 17, 2018, and in Phase 2, 494 individuals were enrolled between January 10 and 31, 2019. In the end-of-study cross-sectional survey, malaria parasite prevalence by qPCR was statistically significantly lower in arm 3 (29·26% 79/270), but not in arm 2 (45·66% 121/265), when compared to arm 1 (48·72% 133/273) (RR = 0·65, 95%CI = 0·52 to 0·81, P=0·0001). Total parasite and gametocyte prevalence and density were also significantly lower in arm 3 in all surveys. The largest differences were seen at the end of the dry season, with gametocyte prevalence 78·38 % and transmission potential 98·20% lower in arm 3 vs arm 1. Active monthly RDT testing and treatment can reduce parasite carriage and the infectious reservoir of malaria to <2% when used during the dry season. This insight may inform approaches for malaria control and elimination.</p>
Are rice fields less diverse and more invaded by non-native species than less impacted habitats? A test with wetland microcrustaceans
<p>Raw data of environmental variables and microcrustacean abundance community matrix</p> <p>R scripts used in the study</p>
ScienceDex guides
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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.