Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
35
datasets available to search
ShareScore release 0.9.0
Dataset results
35 results for “sensory analysis”
Perceptual history propagates down to early levels of sensory analysis
<p>One function of perceptual systems is to construct and maintain a reliable representation of the environment. A useful strategy intrinsic to modern “Bayesian” theories of perception is to take advantage of the relative stability of the input and use perceptual history (priors) to predict current perception. This strategy is efficient but can lead to stimuli being biased toward perceptual history, clearly revealed in a phenomenon known as serial dependence. However, it is still unclear whether serial dependence biases sensory encoding or only perceptual decisions. We leveraged on the “surround tilt illusion”—where tilted flanking stimuli strongly bias perceived orientation—to measure its influence on the pattern of serial dependence, which is typically maximal for similar orientations of past and present stimuli. Maximal serial dependence for a neutral stimulus preceded by an illusory one occurred when the perceived, not the physical, orientations of the two stimuli matched, suggesting that the priors biasing current perception incorporate the effect of the illusion. However, maximal serial dependence of illusory stimuli induced by neutral stimuli occurred when their physical (not perceived) orientations were matched, suggesting that priors interact with incoming sensory signals before they are biased by flanking stimuli. The evidence suggests that priors are high-level constructs incorporating contextual information, which interact directly with early sensory signals, not with highly processed perceptual representations.</p>
Stickleback sensory morphology 2015 & 2017 measurements and analysis files
<p>The peripheral sensory systems, whose morphological attributes help determine the acquisition of distinct types of information, provide a means to quantitatively compare multiple modalities of a species' sensory ecology. We used morphological metrics to characterize multiple sensory modalities—the visual, olfactory, and mechanosensory lateral line sensory systems—for <em>Gasterosteus aculeatus</em>, the three‐spined stickleback, to compare how sensory systems vary in animals that evolve in different ecological conditions. We hypothesized that the dimensions of sensory organs and correlations among sensory systems vary in populations adapted to marine and freshwater environments, and have diverged further among freshwater lake-dwelling populations. Our results showed that among environments, fish differed in which senses are relatively elaborated or reduced. When controlling for body length, littoral fish had larger eyes, more neuromasts, and smaller olfactory tissue area than pelagic or marine populations. We also found differences in the direction and magnitude of correlations among sensory systems for populations even within the same habitat type. Our data suggest that populations take different trajectories in how visual, olfactory, and lateral line systems respond to their environment. For the populations we studied, sensory modalities do not conform in a predictable way to the ecological categories we assigned.</p>
Stickleback sensory morphology 2015 & 2017 measurements and analysis files
Open the record for dataset details and reuse information.
Dataset on the characterization of the flavor of two red wine varieties using sensory descriptive analysis, volatile organic compounds quantitative analysis by GC-MS and odorant composition by GC-MS-O
<p>The dataset contains data that were collected on 2 sets of 8 French red wines from two grape varieties, Pinot Noir (PN) and Cabernet Franc (CF). It provides, for the 16 wines, (i) sensory descriptive data obtained with a trained panel, (ii) volatile organic compounds (VOC) quantification data obtained by Gas Chromatography–Mass Spectrometry (GC-MS) and (iii) odorant composition obtained by Gas Chromatography–Mass Spectrometry–Olfactometry (GC-MS-O).</p> <p> </p> <p>The dataset is a Microsoft Excel Worksheet containing 8 sheets.</p> <p>- Sheet 1: Information</p> <p>Gives information about the sheets contained in this .xlsx file</p> <p>- Sheet 2: Experimental_factors</p> <p>Each row represents a wine</p> <p>Each column corresponds to an experimental factors of the wines (Grape variety, Vintage and Protected Designation of Origin)</p> <p>- Sheet 3: List_sensory_descriptors</p> <p>Lists the 33 sensory descriptors used for the sensory descriptive analysis of the wines</p> <p>- Sheet 4: Sensory_descriptive_analysis</p> <p>Each row represents a wine</p> <p>Each column corresponds to a condition (2640 columns)</p> <p>Senso_(ortho or retro)_(Panelist1 to Panelist 16)_(1 to 33 Sensory descriptors)_(1 to 3 repetitions for ortho and 1 to 2 repetitions for retro)</p> <p>For the ortho (orthonasal) measurements, there is 16 panelists, 33 sensory descriptors and 3 repetitions = 1584 columns</p> <p>For the retro (retronasal) measurements, there is 16 panelists, 33 sensory descriptors and 2 repetitions = 1056 columns</p> <p>Each cell contains a sensory measurement for the corresponding condition in the corresponding wine</p> <p>- Sheet 5: List_VOC</p> <p>Lists the 45 VOC quantified in the wines with their corresponding CAS number</p> <p>VOC: Volatil Organic Compounds</p> <p>- Sheet 6: VOC_quantification</p> <p>Each row represents a wine</p> <p>Each column corresponds to a VOC (45 columns)</p> <p>Each cell contains the quantification of the corresponding VOC in the corresponding wine</p> <p>- Sheet 7: List_GC-MS-O</p> <p>Lists the 49 odor-active compounds identified with their corresponding CAS number and the 34 compounds identified by their apex indice</p> <p>- Sheet 8: GC-MS-O</p> <p>Each row represents a wine</p> <p>Each column corresponds to an odor-active compound identified by its CAS number or by its Apex indice if the compound was not identify (81 odor-active compounds) + the number of judges who smelled the compound and its description (by 8 judges) = 9 columns per odor-active compound for a total of 729 columns</p>
Acids in Coffee - A Review of Sensory Measurements and Meta-Analysis of Chemical Composition
Open the record for dataset details and reuse information.
The sensory basis of schooling by intermittent swimming in the rummy-nose tetra (Hemigrammus rhodostomus) -- [Data analysis]
Open the record for dataset details and reuse information.
Analysis of Sensory Motor Training in Chronic Ankle Instability
ClinicalTrials.gov study NCT03768583. IPD Sharing: NO. Countries: 1. Publications: 3.
Data from: Fine-scale analysis of an assassin bug's behaviour: predatory strategies to bypass the sensory systems of prey
Some predators sidestep environments that render them conspicuous to the sensory systems of prey. However, these challenging environments are unavoidable for certain predators. Stenolemus giraffa is an assassin bug that feeds on web-building spiders; the web is the environment in which this predator finds its prey, but it also forms part of its preys' sophisticated sensory apparatus, blurring the distinction between environment and sensory systems. Stenolemus giraffa needs to break threads in the web that obstruct its path to the spiders, and such vibrations can alert the spiders. Using laser vibrometry, this study demonstrates how S. giraffa avoids alerting the spiders during its approach. When breaking threads, S. giraffa attenuates the vibrations produced by holding on to the loose ends of the broken thread and causing them to sag prior to release. In addition, S. giraffa releases the loose ends of a broken thread one at a time (after several seconds or minutes) and in this way spaces out the production of vibrations in time. Furthermore, S. giraffa was found to maximally reduce the amplitude of vibrations when breaking threads that are prone to produce louder vibrations. Finally, S. giraffa preferred to break threads in the presence of wind, suggesting that this araneophagic insect exploits environmental noise that temporarily impairs the spiders' ability to detect vibrations. The predatory behaviour of S. giraffa seems to be adaptated in intricate manner for bypassing the sophisticated sensory systems of web-building spiders. These findings illustrate how the physical characteristics of the environment, along with the sensory systems of prey can shape the predatory strategies of animals.
Sensory analysis of gluten-freee Foods produced with funcional food: green banana flour.
<p>Completion of course work.</p>
Data from: Fine-scale analysis of an assassin bug's behaviour: predatory strategies to bypass the sensory systems of prey
Open the record for dataset details and reuse information.
Transcriptome analysis by RNA sequencing of primary mouse sensory neurons of the dorsal root ganglia (DRGs) treated with glucosylceramide (GlcCer 24:1) versus vehicle
GEO Series GSE262573. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Single cell RNA-seq analysis of sensory neurons reveal diverse injury responses after sciatic nerve transection
GEO Series GSE71453. Mus musculus. 123 samples. Type: Expression profiling by high throughput sequencing.
Integrated Analysis of Transcriptome and Secretome of Sensory Neurons Reveals Sex Difference in Pathways Relevant to Insulin Sensitivity and Insulin Secretion
GEO Series GSE228189. Mus musculus. 10 samples. Type: Expression profiling by high throughput sequencing.
Single-Cell Analysis of Sensory Experience Regulated Gene Expression in Mouse Visual Cortex
GEO Series GSE102827. Mus musculus. 40 samples. Type: Expression profiling by high throughput sequencing.
Transcriptome analysis of metronidazole-induced sensory neuron ablation in zebrafish
GEO Series GSE72682. Danio rerio. 15 samples. Type: Expression profiling by high throughput sequencing.
scRNA-seq analysis of mouse utricular sensory epithelium at embryonic day 14 and postnatal day 2
GEO Series GSE267093. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
High-resolution single cell transcriptome analysis of zebrafish sensory hair cell regeneration
GEO Series GSE196211. Danio rerio. 7 samples. Type: Expression profiling by high throughput sequencing.
Transcriptome analysis of mouse olfactory sensory neurons expressing 3 different types of olfactory receptor
GEO Series GSE169010. Mus musculus. 9 samples. Type: Expression profiling by high throughput sequencing.
Single nucleus RNA sequencing analysis of vagal sensory neurons
GEO Series GSE267231. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
Comparison of Sensory Analysis After Superficial and Deep Parasternal Intercostal Plane Blocks
ClinicalTrials.gov study NCT06559683. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.