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329 results for “Taste”
Integration of sweet taste and metabolism determines carbohydrate reward-study 3
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Data and Code Accompanying "Retrieving and Analyzing Taste Colexifications from Lexibank"
<p>Data and Code accompanying the study "Retrieving and analyzing taste colexifications from Lexibank" by Olena Shcherbakova and Johann-Mattis List (see <a href="https://calc.hypotheses.org/6398">https://calc.hypotheses.org/6398</a>).</p><p>Information on how to run the code can be found in the study itself.</p>
Taste Quality Representation in the Human Brain
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Microsensor Beverage Tasting (MicroBeTa)
<p>MicroBeTa is a dataset for automatic, "electronic tongue" beverage classification. It includes temporal multivariate readings simultaneously acquired from a temperature sensor and solid-state electrochemical microsensors developed and manufactured by the Chemical Transducers Group at the Institute of Microelectronics of Barcelona (IMB-CNM), CSIC:</p> <p>http://gtq.imb-cnm.csic.es/en</p> <p><strong>Citing the MicroBeTA dataset</strong></p> <p>The MicroBeTA is released under a Creative Commons Attribution license, so please cite it if it is used in your work in any form. Published academic papers should use the citation for our Frontiers in Neuroscience paper. Personal works, such as machine learning projects or blog posts, should provide a URL to this Zenodo page, though referencing our research paper would also be appreciated.</p> <p><em>Academic paper citation</em></p> <p>LeBow N, Rueckauer B, Sun P, Rovira M, Jiménez-Jorquera C, Liu S-C and Margarit-Taulé JM (2021)<br> Real-Time Edge Neuromorphic Tasting From Chemical Microsensor Arrays. Front. Neurosci. 15:771480.<br> http://doi.org/10.3389/fnins.2021.771480</p> <p><em>Personal use citation</em></p> <p>Include a link to this Zenodo page: http://doi.org/10.5281/zenodo.5457501</p> <p><strong>Description</strong></p> <p>The dataset includes seven hours of readings from a sensor array acquired every second during three sessions performed over the course of three days at the IBM-CNM. The array comprises one Pt-100 temperature sensor, one microelectrode each for electrical conductivity and oxidation-reduction potential (ORP), and six ISFET sensors sensitive to specific ions (H<sup>+</sup>, Na<sup>+</sup>, K<sup>+</sup>,Ca<sup>2+</sup>, Cl<sup>-</sup>, and NO<sub>3</sub><sup>-</sup>).</p> <p>The beverage types selected for MicroBeTa are five commercial beverage varieties of white wine, red wine, still water, sparkling water and cava. This beverage selection covers a wide range of characteristics within a limited set of classes, with several semi-overlapping sets of attributes that could be expected to provide insight into how the data from various sensors could be used by the classifier, e.g. still and sparkling water, red wine and cava covering four general cases arising from the presence or absence of carbonation and fermentation byproducts, respectively.</p> <p>All sensors were read out continuously and concurrently during each session, while the sensor array was moved from one beverage sample to another at fixed intervals of five minutes. The sequence of transitions between beverage samples was chosen to cover all combinations from one beverage to another. During each transfer, the sensor array was washed with deionized water before being placed in the next sample to avoid unnecessary cross-contamination of subsequent beverages in the series.</p> <p><strong>Data Files</strong></p> <p><em>clean_dataset.h5: </em>Contains a Python Pandas dataframe including the reading signals from all sensor channels and the labels ('Time', 'H<sup>+</sup>', 'K<sup>+</sup>', 'Na<sup>+</sup>', 'Cl<sup>-</sup>', 'NO<sub>3</sub><sup>-</sup>', 'Ca<sup>2+</sup>', 'Conductivity', 'ORP', 'Temperature', and 'Label', respectively), with the washing and transfer periods as well as transient instabilities of individual sensors discarded.</p> <p><em>preprocessed_dataset_9cols.h5:</em> Contains a Python Pandas dataframe (['n_output_classes', 'samples_train', 'labels_train', 'samples_test', 'labels_test'] columns) of sensor samples for training and testing a classifier model. The data samples are fixed-length, overlapped time windows containing the signal values from all nine sensors ('Temperature','H<sup>+</sup>', 'K<sup>+</sup>', 'Na<sup>+</sup>', 'Cl<sup>-</sup>', 'NO<sub>3</sub><sup>-</sup>', 'Ca<sup>2+</sup>', 'Conductivity', and 'ORP', respectively) over a contiguous range of 16 timestamps. The samples are preprocessed as follows:</p> <ul> <li>Incomplete measurement cycles in which not all beverages are recorded, or measurements of specific beverage samples much shorter than others, are removed entirely. Any measurements lasting significantly longer than five minutes are truncated to that length.</li> <li>A high-pass filter with a cut-off frequency of 0.5 mHz is used to attenuate level offsets in the input signals while emphasizing their dynamic components.</li> <li>Outliers in which at least one sensor channel contains a value further than four standard deviations from the mean are deleted.</li> <li>Each sensor channel is normalized independently using quantile normalization.</li> </ul> <p><em>preprocessed_dataset_7cols.h5:</em> Contains the same Pandas dataframe of sensor samples for training and testing a classifier model as <em>preprocessed_dataset_9cols.h5</em>, but in this case excluding the two least informative sensors ('Temperature' and 'NO<sub>3</sub><sup>-</sup>'). </p> <p><strong>Contact</strong></p> <p>Further details on the creation and validation of MicroBeTa will be disclosed in our Frontiers paper. If you have any questions or comments about the dataset, please feel free to write to:</p> <p>josepmaria.margarit@imb-cnm.csic.es</p> <p> </p>
Taste responsiveness and liking of model food samples
<p>This data sheet consisted of children's taste intensity perception and liking to model food samples of grapefruit juice and vegetable broth. In addition, children's responses regarding PROP responsiveness, food familiarity, stated liking, food choice, and neophobia were also reported in this data set. </p>
Taste sensitivity and eating behaviour preadolescent children-Extended data
<p>This data set contained children's detection threshold responses, their eating behaviour score based on CEBQ (Child Eating Behaviour Questionnaire), and food propensity based on FPQ (Food Propensity Questionnaire). In addition, this extended data also provides the raw questionnaires of CEBQ and FPQ used in the study.</p>
X-mas beer tasting 2018
<p>Result of the yearly tasting of x-mas beer tasting.</p> <p>The event took place at Underåsgatan in Gothenburg, Sweden 2018-12-08.</p> <p>Following variable in the dataset:</p> <ul> <li>Name - name of the beer</li> <li>Brewery - producer of the beer</li> <li>Origin - area/country of origin</li> <li>Alc - percentage of alcohol (by volume)</li> <li>Volume (ml) - Volume in the bottle in milliliter</li> <li>Price - price in SEK (Swedish enkrona)</li> <li>Opening time - the time of the opening of the bottle</li> <li>Grade - average grade in the tasting (0-10)</li> </ul>
ESSENTIA analysis of audio snippets from the Million Song Dataset Taste Profile subset
<p>This upload includes the ESSENTIA analysis output of (a subset of) song snippets from the Million Song Dataset, namely those included in the Taste Profile subset. The audio snippets were collected from 7digital.com and were subsequently analyzed with ESSENTIA 2.1-beta3. Pre-trained SVM models provided by the ESSENTIA authors on their website were applied.</p> <p>The file <strong>msd_song_jsons.rar </strong>contains the ESSENTIA analysis output after applying the SVM models for highlevel feature extraction. Please note that these are 204317 files.</p> <p>The file <strong>msd_played_songs_essentia.csv.gz </strong>contains all one-dimensional real-valued fields of the jsons merged into one csv file with 204317 rows.</p> <p>The full procedure and subsequent analysis is described in</p> <p>Fricke, K. R., Greenberg, D. M., Rentfrow, P. J., & Herzberg, P. Y. (2019). Measuring musical preferences from listening behavior: Data from one million people and 200,000 songs. <em>Psychology of Music</em>, 0305735619868280.</p>
Algae-Bacteria Community Analysis for Drinking Water Taste and Odour Risk Management
<p>The datasets and accompanying R script included in this upload are provided to complement the manuscript titled <em>"Algae-Bacteria Community Analysis for Drinking Water Taste and Odour Risk Management."</em> These resources are intended to facilitate the replication and verification of the analyses presented in the paper.</p> <p> </p> <p> </p>
A temporary broadband seismic array in the largest desert of China: TASTE
<p>This includes the dataset of our manuscript submitted to Seismological Research Letters entitled with <em>A temporary broadband seismic array in the largest desert of China: TASTE</em></p>
Autonomy Is An Acquired Taste: Exploring Developer Preferences for GitHub Bots
<p>Software bots are an important part of software development, and the rise of AI-based code tools will make them even more important. Software bots can do many tasks, and can exhibit a range of interaction behaviors in a project. But in order for the bot to be effective, it must be accepted by the developers and project community with which the bot interacts. The main purpose of this study is to enumerate some of these factors, and to explain what leads to perceived bad behavior in the context of a bot's autonomous actions and persona. We find developers prefer bots which are personable but show little autonomy. This dataset contains the replication package of the study. For Phase I, we shared the full and detailed interview guide, anonymized interview transcripts, and the result of the data analysis, i.e., codebook. For Phase II, the survey design and data. <br> </p>
Fig. 1 in Options for managing Antestiopsis thunbergii (Hemiptera: Pentatomidae) and the relationship of bug density to the occurrence of potato taste defect in coffee
Fig. 1. Effects of pest management tactics on the occurrence of potato taste defect in coffee. No Prun(P) & No Pest (No Pruning and No Pesticide), P & No Pest (Pruning and No Pesticide), P & Fastac (Pruning and Fastac), P & Pyr 5EW (Pruning and Pyrethrum 5EW), P & Pyr EWC (Pruning & Pyrethrum EWC), P & Agroblast (Pruning and Agroblast), and P & Imida (Pruning and Imidacloprid). Fastac sprayed in pruned plots had the lowest levels of potato taste defect whereas the control had the highest. Bars represent the standard error of means. Means followed with the same letter are not statistically different (P ≤ 0.05, ANOVA and Tukey's test).
Taste Peptides and their derivatives
<p>Food Ai Researcher</p>
Strong association between the 12q24 locus and sweet taste preference in the Japanese population revealed by genome-wide meta-analysis: Summary stats
<p>Summary stats of the genome-wide meta-analysis with METAL software in the article "Strong association between the 12q24 locus and sweet taste preference in the Japanese population revealed by genome-wide meta-analysis."</p>
Taste aversion training can educate free-ranging crocodiles against toxic invaders
<p>Apex predators play critical ecological roles, making their conservation a high priority. In tropical Australia, some populations of freshwater crocodiles (<em>Crocodylus johnstoni</em>) have plummeted by >70% due to lethal ingestion of toxic invasive cane toads (<em>Rhinella marina</em>). Laboratory-based research has identified conditioned taste aversion (CTA) as a way to discourage consumption of toads. To translate those ideas into landscape-scale management, we deployed 2,395 baits (toad carcasses with toxin removed and containing a nausea-inducing chemical) across four gorge systems in north-western Australia and monitored bait uptake with remote cameras. Crocodile abundance was quantified with surveys. Free-ranging crocodiles rapidly learned to avoid toad baits but continued to consume control (chicken) baits. Toad invasion at our sites was followed by high rates of crocodile mortality (especially for small individuals) at a control site but not at nearby treatment sites. In areas with high connectivity to other waterbodies, repeated baiting over successive years had continuing positive impacts on crocodile survival. In summary, we succeeded in buffering the often-catastrophic impact of invasive cane toads on apex predators.</p>
Perception of edible insects and insect-based foods among children in Denmark: educational and tasting interventions in online and in-person classrooms
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Comparison of Dexamethasone Oral Preparations to Assess Taste and Acceptance in Children With Asthma and Croup
ClinicalTrials.gov study NCT03705273. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Investigation of the Effects of Bariatric Surgery on Taste Reward in Humans
ClinicalTrials.gov study NCT01531738. IPD Sharing: NO. Countries: 1. Publications: 1.
Snacks, Smiles and Taste Preferences
ClinicalTrials.gov study NCT03631992. IPD Sharing: YES. Countries: 1. Publications: 10.
Gustin Gene Polymorphism and 6-n-propylthiouracil (PROP) Taste
ClinicalTrials.gov study NCT01097915. IPD Sharing: UNDECIDED. 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
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DANDI Archive for NWB datasets
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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.