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60 results for “quality of experience”
Water quality, temperature, ash-free dry mass, photosynthetic activate radiation (PAR), and zooplankton data from a warming and DOC subsidy experiment, 2020 - 2021.
This dataset includes chlorophyll-a concentrations, periphyton biomass estimates, water quality measurements, and qualitative observations from a large-scale mesocosm experiment conducted in the Green Lakes Watershed, Colorado. The experiment was designed to test how earlier lake ice-off and increased dissolved organic material (DOM), associated with terrestrial plant encroachment in alpine watersheds, interactively influence aquatic food webs. In fall 2019, twenty 2600L “megacosms” were established at Sandy Corner (3300 m ASL; 40.042289, -105.584006), left to fill with snowmelt, and maintained throughout the 2020 open water season. The experiment followed a 2 × 2 randomized block design manipulating ice-off timing (via black vs. beige tank coloration) and DOM inputs (presence/absence of willow leaf packs), with five replicates per treatment. All tanks were seeded with sediments and zooplankton from both alpine and montane lakes (Green Lake 1 and Green Lake 4), and instrumented with thermistors recording surface and hypolimnion temperature every two hours year-round. Periphyton growth was monitored using clay tiles, sampled across five time points. Chlorophyll-a concentrations were extracted from filtered water samples and analyzed spectrophotometrically. Periphyton biomass was estimated via ash-free dry mass (AFDM) determinations, based on the mass lost on combustion of material scraped from tiles. Water quality was measured 1–2 times weekly using a YSI ProPlus multiprobe and Li-Cor quantum sensor, and snow/ice cover was qualitatively assessed monthly during winter.
Surface water quality from the Seagrass Recovery Experiment, South Bay, VA 2020-2022
To understand intra-meadow stability, the Seagrass Recovery Experiment was designed to ask 1) is recovery faster at sites with less thermal stress owing to greater exchange with cooler oceanic water at the meadow edge? 2) what is the shape of recovery? and 3) what are the recovery mechanisms? To conduct this experiment, aboveground seagrass biomass was removed from 28.3 m2 plots within the interior and along an edge of a restored seagrass meadow in South Bay, VA. Sites 1-3 correspond to the meadow interior while sites 4-6 correspond to the northern edge. Each site was comprised of a control (i.e., C) where no seagrass was disturbed and a treatment (i.e., T) where seagrass was removed (n = 12 sites total, e.g., 1C, 1T, 2C...). To further characterize differences between the meadow interior and edge, surface water quality samples were also collected and include turbidity, total suspended solids (TSS) concentration, TSS ash-free dry weight, TSS percent organic matter, pelagic chlorophyll concentration, dissolved oxygen saturation, dissolved oxygen concentration, salinity, water temperature, and specific conductivity. These discrete samples were collected monthly between June-October 2020, May-October 2021, and April-October 2022 using a 1-L Nalgene bottle and a handheld YSI Pro Plus Multiparameter meter.
Plant recruitment and seed quality in the Black Sand extended growing season experiment for East Knoll, Audubon, Lefty, and Trough sites, 2018 - 2020.
As a result of climate change, the Rocky Mountain Front Range is experiencing warmer summers and earlier snowmelt. Due to the importance of snow for regulating soil temperature, growing season length, and available moisture in alpine ecosystems, even small shifts in the snow-free period could have large impacts. The focus of the Black Sand Extended Growing Season Length Experiment is to examine how terrain-related differences in climate exposure influence the way alpine habitats respond to climate change via earlier snowmelt. To simulate how climate exposure may affect plant communities, NWT LTER researchers established 5 experimental sites, each containing a pair 10 x 40m rectangular plots. These sites include north and south facing aspects, subalpine and alpine tundra meadows and a range of hydrological conditions (e.g. dry meadows, moist meadows, wet meadows). We accelerated snowmelt in one plot at each site by adding chemically inert black sand, while keeping the second plot as an unmanipulated control; black sand was added to control plots after snow had naturally melted. We used open top warming chambers (OTCs) to increase summer temperature in three subplots within each of the 10 x 40 m plots. This dataset includes measurements of plant recruitment and seed quality.
floodX Preprocessed Data (all experiments, mixed quality)
<p>This package contains a preprocessed subset of data for the floodX flooding experiments, for which the data quality is <strong>not guaranteed</strong>.</p>
Raw results of the ORPHEUS quality of experience tests done by b<>com
<p>This Excel document contains the raw results of the ORPHEUS project Quality of Experience tests done by b<>com. The first sheet contains the results while the second contains additional information about the data presented in the first sheet. More details about the experiments and the corresponding results can be found in the project's Deliverable 5.6.</p>
Figure 1 in Soil quality, leaf litter quality, and microbial biomass interactively drive soil respiration in a microcosm experiment
Figure 1. Principal components analysis of (A) soil quality and (B) leaf litter quality across the experimental treatments (Table S1-2). Soil quality was quantified as a combination of soil pH, C, N, and C:N; leaf litter quality was quantified as a combination of leaf Ca, C, lignin, Mg, N, P, C:N, C:P, and N:P. Soil and leaf litter were collected from Hainich National Park, Germany.
Data from an experiment on the effects of using an experimental framework on the quality of research designs
Open the record for dataset details and reuse information.
Data from: Egg laying rather than host quality or host feeding experience drives habitat estimation in the parasitic wasp Nasonia vitripennis
<p>In variable environments, sampling information on habitat quality is essential for making adaptive foraging decisions. In insect parasitoids, females foraging for hosts have repeatedly been shown to employ behavioral strategies that are in line with predictions from optimal foraging models. Yet, which cues exactly are employed to sample information on habitat quality, has rarely been investigated. Using the gregarious parasitoid <i>Nasonia vitripennis</i> (Walker) (Hymenoptera: Pteromalidae), we provided females with different cues about hosts to elucidate, which of them would change a wasp's posterior behavior suggesting a change in information status. We employed posterior clutch size decisions on a host as proxy for a female's estimation of habitat quality. Taking into account changes in physiological state of the foraging parasitoid, we tested whether different host qualities encountered previously change the subsequent clutch size decision in females. Additionally, we investigated whether other kinds of positive experiences - such as ample time to investigate hosts, host feeding, or egg laying - would increase a wasp's estimated value of habitat quality. Contrary to our expectations, quality differences in previously encountered hosts did not affect clutch size decisions. However, we found that prior egg laying experience changes posterior egg allocation to a host, indicating a change in female information status. Host feeding and the time available for host inspection, though correlated with egg laying experience, did not seem to contribute to this change in information status.</p>
Resources for "BMF CP 91: Socio-demographic factors, illness experience, severity perception, and sensitivity to air quality index"
<p><span>The current study is conducted to examine the following research questions:</span></p> <ul> <li><span>What are the factors associated with the sensitivity towards the air quality rating index to reduce outdoor activities?</span></li> </ul>
An empirical study on the influence of developers' experience on software test code quality
<p>Software developers' engagement in open-source software projects lies in different levels of participation, e.g., core or peripheral developers. Recent studies have investigated the role of developers' contributions and their influence on software quality. However, few studies investigate the relationship between the developers' experience and test code quality in software projects. In this study, we aim to investigate the relationship between the developers' experience and the test code quality from the perspective of test smells. We performed an empirical study to analyze the insertion and removal of test smells in four open-source Java projects. We collected 18 test smells and calculated their authorship through the projects' Tags. The four software projects contain 386 test classes and 5,178 test smells. We found out that the insertion of 67.28\% of test smells occurs during the test class creation, and the removal of 20.88\% of test smells occurs during the evolution of projects. In addition, core developers are responsible for inserting 88.91\% and removing 89.82\% test smells. Core developers insert and remove more test smells than the peripheral developers. Most test smell removal is due to test code deletion, which may indicate that both core and peripheral developers are unaware of test smells in test code.</p>
Experiment resources for "Quality of Binaural Rendering From Baffled Microphone Arrays Evaluated Without an Explicit Reference"
<div>This data set contains the following resources to reproduce the listening experiment and statistical analysis of the referenced manuscript:</div> <div> <ul> <li>The <em>binaural room impulse responses</em> (<strong>BRIR</strong>s) of all listening conditions presented in the perceptual experiment.</li> <li>The tools to create the listening test infrastructure, including <em>Pure Data</em> (<strong>Pd</strong>) patches and configuration files for the <em>SoundScape Renderer</em> (<strong>SSR</strong>) and <em>graphical user interface</em> (<strong>GUI</strong>).</li> <li>The raw response data as gathered from the experiment subjects.</li> <li>The R and Stan scripts to perform the statistical analysis and generate the resulting plots and tables.</li> </ul> </div> <div> <p> </p> <p>The archive contains the following components described below.</p> <p>Directory "dependencies/":</p> <ul> <li>Matlab, R, Stan, and Pd functions that are utilized in the code and experimental setup</li> <li>Additional dependencies of available open-source projects may be required for certain code functions. If so, the source and setup process for the necessary dependencies are documented in the file header.</li> </ul> </div> <div> <p>Directory "plots/4_Equalization/":</p> <ul> <li>Plots of all available headphone equalization filters as generated by the following Matlab scripts.</li> </ul> <p>Directory "plots/8_User_study/":</p> <ul> <li>Plots of the raw and analyzed experimental results as generated by the following Matlab and R scripts.</li> </ul> </div> <div> <p>Directory "resources/BRIR_auralization/":</p> <ul> <li>Audio files with static binaural auralizations of all listening conditions presented in the perceptual experiment as generated by the following Matlab scripts.</li> <li>The files in "Kemar_HRTF_sofa_N44_adjusted" do not include a headphone equalization.</li> <li>The files in "Kemar_HRTF_sofa_N44_adjusted+Sennheiser_HD650_lin" include the equalization for the <em>Sennheiser HD650</em> headphones employed in the listening experiment. The files are identical to the annotated <a href="http://www.ta.chalmers.se/research/audio-technology-group/audio-examples/jaes-2024a/" target="_blank" rel="noopener">listening examples</a> published for the manuscript.</li> </ul> <p>Directory "resources/BRIR_rendered/":</p> <ul> <li>BRIRs and rendering parameters of all listening conditions presented in the perceptual experiment as generated from the associated <a href="../doi/10.5281/zenodo.8206570" target="_blank" rel="noopener">data set</a> and <a href="https://github.com/HaHeho/baffled-arrays-to-binaural/releases/tag/v2024.JAES" target="_blank" rel="noopener">rendering code</a>.</li> <li>Scene configuration files for the SSR with all listening conditions presented in the perceptual experiment as generated from the following Matlab scripts.</li> </ul> <p>Directory "resources/HPCF_KEMAR/":</p> <ul> <li>Impulse responses of equalization filters for various headphones on the G.R.A.S KEMAR acoustic dummy head as measured for this experiment.</li> </ul> <p>Directory "resources/User_study/":</p> <ul> <li>Various resources for the listening experiment.</li> <li>The files in "Exp1_analysis" include intermediate and final statistical analysis results as generated from the following R scripts.</li> <li>"Exp1_config.json" contains the configuration of the study GUI with conditions presented in the listening experiment.</li> <li>"Exp1_Introduction.pdf" contains the instructions presented to the subjects at the start of the listening experiment.</li> <li>"Exp1_Part2_data_strings.xls" contains all subjects' raw perceptual response data gathered from the listening experiment.</li> <li>"Questionnaire.pdf" contains the questionnaire given to the subjects at the end of the listening experiment.</li> </ul> <p>Matlab script "x4_Gather_Headphone_Compensations.m":</p> <ul> <li>Generate plots of measured headphone compensation filters. Furthermore, the generated minimum phase filters and filters to yield a linear phase response from the headphones are extracted as separate WAV files.</li> </ul> </div> <div> <p>Matlab script "x6_Gather_SSR_Configurations.m":</p> <ul> <li>Collect several specified pre-rendered binaural room impulse response sets into an ASD file. The SSR can load this scene to present all gathered configurations in direct comparison with head tracking.</li> </ul> </div> <div> <p>Readme file "x6a_Normalize_SSR_Loudnesses.txt":</p> </div> <div> <div> <div> <ul> <li>Ideally, the rendering script would implement a measure to provide a reliable estimation of the binaural loudness of the rendered configuration. This could be used to normalize all stimuli levels. However, such a measure is currently not available or implemented.</li> <li>Therefore, tuning the stimuli loudness for the user study was performed beforehand by ear. The adjusted playback levels are set in a modified SSR configuration file for the listening experiment.</li> </ul> </div> <div> <p>Matlab script "x6_Gather_SSR_Configurations.m":</p> <ul> <li>Perform convolution of (rendered) binaural room impulse responses with a source audio signal. This is done for a specified selection of static head orientations and a continuous rotation over all horizontal head orientations.</li> <li>The resulting auralizations are published as <a href="http://www.ta.chalmers.se/research/audio-technology-group/audio-examples/jaes-2024a/">supplementary materials</a> to the manuscript.</li> </ul> <p>Shell script "x7_Start_Study_GUI.sh":</p> <ul> <li>Initialize all required components to perform the perceptual user study, including: <ul> <li>SSR to perform the real-time rendering of the BRIRs with head tracking</li> <li>SSR to extract head-tracking data (in case a Polhemus tracker is used)</li> <li>Pd to extract head-tracking data (in case a Supperware tracker is used)</li> <li>Pd to perform real-time convolution to apply headphone compensation</li> <li>Pd to receive OSC messages from the study GUI</li> <li>Pd to trigger audio file playback from received OSC messages</li> <li>Pd to translate OSC messages into FUDI messages for the SSR</li> <li>The GUI to be used by the participants and implement the study procedure</li> </ul> </li> <li>Some static configuration variables can be adjusted, whereas other parameters are chosen during script execution.</li> </ul> <p>Matlab script "x8_Gather_Study_Data.m":</p> <ul> <li>Transform the raw result data from the questionnaire (*.xls) and the study GUI (*.json) into a compact format (*.xls) that can be loaded to plot the raw data and imported by software for the subsequent statistical analysis.</li> <li>Note that this contains the responses from all subjects, whereas responses from the investigators must be excluded from the statistical analysis (which is implemented in the analysis scripts).</li> </ul> <p>Matlab script "x8a_Plot_Study_Data.m":</p> <ul> <li>Generate a set of violin plots to visualize the initial distribution of the raw perceptual data. The data is split by specified attributes and plotted separately for visual inspection.</li> <li>The data may also be transformed into ranks for a first distribution inspection. Note that implementing the ranking method, notably how ties are resolved, may differ from the technique employed in the statistical analysis.</li> <li>Note that this contains the responses from all subjects, whereas responses from the investigators must be excluded from the statistical analysis (which is implemented in the analysis scripts).</li> </ul> <p>R script "x8b_Analyze_Exp1_Data.R":</p> <ul> <li>Perform the statistical analysis by transforming the observed subject ratings into a predicted distribution of ranks using a hierarchical generalized linear regression model.</li> <li>Executing the statistical model may take some time due to the Bayesian framework employing Markov-chain Monte Carlo simulations.</li> <li>Data is exported at various intermediate steps to be loaded and visualized by the following R script.</li> </ul> <p>R markdown script "x8c_Plot_Exp1_Results.Rmd":</p> <ul> <li>Generate various plots and data tables of the observed data and the predicted results to visualize the distribution and influence of different analysis parameters.</li> <li>Some of the resulting plots were used in the manuscript.</li> <li>"x8c_Plot_Exp1_Results.html" conveniently summarizes all plots and data tables generated by "knitting" the R markdown script.</li> </ul> </div> </div> </div>
Root litter quality incubation experiment
<p>The dataset contains data of a 2-year incubation experiment of C4 roots litter added to a C3 soil. The soil was sampled after 6, 12 and 24 months and fractionated into particulate organic carbon (POC) and mineral associated organic carbon (MAOC) and each fraction (plus the bulk soil) was measured for total carbon content as well as the abundance of the stable isotope 13C, to distinguish between old (native) and new (added) carbon. There are two files: One contains the raw data with measured C contents, delta 13C values, fraction weights and the subsequently calculated amounts of new and old carbon in all fractions. The second file is related to the first, but contains the final dataset which was used to produce the graphs and tables in the paper.</p>
Data from: Matrix context and patch quality jointly determine diversity in a landscape-scale experiment
Open the record for dataset details and reuse information.
Egg laying rather than host quality or host feeding experience drives habitat estimation in the parasitic wasp Nasonia vitripennis
Open the record for dataset details and reuse information.
The effects of integrated food and bioenergy cropping systems on crop yields, soil health, and biomass quality: the EU and Brazilian experience
<p><strong>Data Set</strong></p> <p>This multiyear and multi-location study provides new insights on the integration of dedicated legume crops within conventional EU (wheat-maize) and Brazilian (sugarcane) farming systems so to meet the forecasted increasing feedstock demands for the production of renewable transport biofuels without negatively affecting food production nor soil fertility. Sunn hemp demonstrated to be a suitable leguminous fiber feedstock to be grown in extended crop rotations. Integrating sunn hemp within the conventional systems in EU did not have negative effects on food yields, while feedstock availability increased up to 2.0 times. In Brazil, sugarcane stalks yield increased up to 15 Mg ha<sup>-1</sup>.</p>
Experiment Data - 952 Assessments of 8 Vision Videos Regarding Overall Video Quality and 15 Individual Quality Characteristics
<p>In 2018, we conducted a within-subjects experiment to investigate how individual quality characteristics of vision videos relate to the overall quality of vision videos from a developer's point of view. 139 undergraduate students who had the role of a developer and actively developed software in projects with real customers at the time of the experiment participated in the experiment. The subjects can be considered as developers due to their experience at the time of the experiment. The subjects were put in the situation that they join an ongoing project in their familiar role as a developer. In this context, we showed the 8 vision videos (one after the other) always with the intent to share the vision of the particular project with the subjects. The undergraduate students subjectively assessed the overall quality and 15 individual quality characteristics of the 8 vision videos by completing an assessment form for each video. After data cleaning, the final data set contains 952 complete assessments of 119 subjects for the 8 vision videos.</p> <p>Each entry of the data set consists of:</p> <ul> <li>Entry ID: The ID of the entry in the dataset.</li> <li>Subject ID: The ID of the subject.</li> <li>Video ID: The ID of the vision video assessed.</li> <li>Overall quality: The subject's assessment of the overall quality of the vision video.</li> <li>Image quality: The subject's assessment of the visual quality of the image of the vision video.</li> <li>Sound quality: The subject's assessment of the auditory quality of the sound of the vision video.</li> <li>Video length [s]: The duration of the vision video in seconds.</li> <li>Focus: The subject's assessment of the compact representation of the vision which is presented in the vision video.</li> <li>Plot: The subject's assessment of the structured presentation of the content of the vision video.</li> <li>Prior knowledge: The subject's assessment of the presupposed prior knowledge to understand the content of the vision video.</li> <li>Clarity: The subject's assessment of the intelligibility of the aspired goals of the vision which is presented in the vision video.</li> <li>Essence: The subject's assessment of the amount of important core elements, e.g., persons, locations, and entities, which are to be presented in the vision video.</li> <li>Clutter: The subject's assessment of the amount of disrupting and distracting elements, e.g., background actions or noises, that can be inadvertently recorded in the vision video.</li> <li>Completeness: The subject's assessment of the coverage of the three contents of a vision which is presented in the vision video, i.e., the considered problem, the proposed solution, and the improvement of the problem due to the solution.</li> <li>Pleasure: The subject's assessment of the enjoyment of watching the vision video.</li> <li>Intention: The subject's assessment of how well the vision video is suitable for the intended purpose of the given scenario.</li> <li>Sense of responsibility: The subject's assessment of the compliance of the vision video with legal regulations.</li> <li>Support: The subject's assessment of his or her level of acceptance of the vision which is presented in the vision video.</li> <li>Stability: The subject's assessment of the consistency of the vision which is presented in the vision video.</li> </ul> <p>This dataset includes the following files:</p> <ul> <li>"Dataset_Assessments.xlsx" contains the anonymized 952 assessments of the 119 subjects for the 8 vision videos</li> <li>"Assessment_form.docx" contains the assessment form which was used to assess each of the 8 vision videos</li> <li>"Assessment_form.pdf" contains the assessment form which was used to assess each of the 8 vision videos</li> </ul> <p>The 8 vision videos are not included in this dataset since we do not have the explicit consent of the actors to distribute the vision videos.</p> <p>This experiment was designed, conducted, and analyzed by Oliver Karras (<a href="https://twitter.com/KarrasOliver">@KarrasOliver</a>), Kurt Schneider, and Samuel A. Fricker (<a href="https://twitter.com/samuelfricker">@samuelfricker</a>).</p>
Does sexual experience affect the strength of male mate choice for high-quality females in Drosophila melanogaster?
<p><span><span><span><span>Although females are traditionally thought of as the choosy sex, there is increasing evidence in many species that males will preferentially court or mate with certain females over others when given a choice. In the fruit fly, <em>Drosophila melanogaster</em>, males discriminate between potential mating partners based on a number of female traits, including species, mating history, age, and condition. Interestingly, many of these male preferences are affected by the male's previous sexual experiences, such that males increase courtship toward types of females that they have previously mated with and decrease courtship toward types of females that have previously rejected them. <em>D. melanogaster </em>males also show courtship and mating preferences for larger females over smaller females, likely because larger females have higher fecundity. It is unknown, however, whether this preference shows behavioral plasticity based on the male's sexual history as we see for other male preferences. Here, we manipulate the sexual experience of <em>D. melanogaster </em>males and test whether this manipulation has any effect on the strength of male mate choice for large females. We find that sexually inexperienced males have a robust courtship preference for large females that is unaffected by previous experience mating with, or being rejected by, females of differing sizes. Given that female body size is one of the most common targets of male mate choice across insect species, our experiments with <em>D. melanogaster </em>may provide insight into how these preferences develop and evolve.</span></span></span></span></p>
Male Sexual Experience and Its Impact on Quality of Life Before and After Their Sexual Partners Undergo Polypropylene Mesh Augmented Pelvic Floor Reconstruction
ClinicalTrials.gov study NCT01320631. IPD Sharing: Not stated. Countries: 1. Publications: 1.
QUEST: QUality of Life and Experiences of Sarcoma Trajectories
ClinicalTrials.gov study NCT03441906. IPD Sharing: NO. Countries: 2. Publications: 1.
Assessing Environmental Factors in Healthcare Facilities in Order to Improve the Experience of Patients, Staff, and the Quality of Imaging Procedures
ClinicalTrials.gov study NCT03456895. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.