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264 results for “Odorants”
Odorant Receptor Expression Frequency in Mature Olfactory Sensory Neurons Depends on Lhx2 [123Cre:Emx2]
GEO Series GSE74525. Mus musculus. 8 samples. Type: Expression profiling by array.
Leffingwell Odor Dataset
<p><strong>NOTE: It's easier to download this dataset from <a href="https://github.com/pyrfume/pyrfume">pyrfume</a>. Here's how:</strong></p> <pre><code># First install pyrfume in your Python environment. This can be done easily with pip. # pip install pyrfume import pyrfume molecules = pyrfume.load_data('leffingwell/molecules.csv', remote=True) behavior = pyrfume.load_data('leffingwell/behavior.csv', remote=True) # e.g. to count the number of molecules with each descriptor behavior.sum().sort_values(ascending=False).astype(int) </code></pre> <p>Predicting properties of molecules is an area of growing research in machine learning, particularly as models for learning from graph-valued inputs improve in sophistication and robustness. A molecular property prediction problem that has received comparatively little attention during this surge in research activity is building Structure-Odor Relationships (SOR) models (as opposed to Quantitative Structure-Activity Relationships, a term from medicinal chemistry). This is a 70+ year-old problem straddling chemistry, physics, neuroscience, and machine learning.</p> <p>To spur development on the SOR problem, we curated and cleaned a dataset of 3523 molecules associated with expert-labeled odor descriptors from the <em>Leffingwell PMP 2001</em> database. We provide featurizations of all molecules in the dataset using bit-based and count-based fingerprints, Mordred molecular descriptors, and the embeddings from our trained GNN model (Sanchez-Lengeling et al., 2019). This dataset is comprised of two files: </p> <ol> <li><strong>leffingwell_data.csv</strong>: this contains molecular structures, and what they smell like, along with train, test, and cross-validation splits. More detail on the file structure is found in leffingwell_readme.pdf.</li> <li><strong>leffingwell_embeddings.npz</strong>: this contains several featurizations of the molecules in the dataset.</li> <li><strong>leffingwell_readme.pdf</strong>: a more detailed description of the data and its provenance, including expected performance metrics.</li> <li><strong>LICENSE</strong>: a copy of the CC-BY-NC license language.</li> </ol> <p>The dataset, and all associated features, is freely available for research use under the <a href="https://creativecommons.org/licenses/by-nc/4.0/">CC-BY-NC</a> license.</p> <p>If you use the data in a publication, please cite:</p> <pre>@article{sanchez2019machine, title={Machine learning for scent: Learning generalizable perceptual representations of small molecules}, author={Sanchez-Lengeling, Benjamin and Wei, Jennifer N and Lee, Brian K and Gerkin, Richard C and Aspuru-Guzik, Al{\'a}n and Wiltschko, Alexander B}, journal={arXiv preprint arXiv:1910.10685}, year={2019} }</pre>
Spatial transcriptomic reconstruction of the mouse olfactory glomerular map suggests principles of odor processing
<p>This deposit contains the MERFISH data used in this study.</p>
Beyond distracted eating: Cognitive distraction downregulates odor pleasantness and interacts with weight status
Open the record for dataset details and reuse information.
The Effect of Breast Milk Odor on Pain Response and Salivary Cortisol Level in Preterm Infants
ClinicalTrials.gov study NCT05557435. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Identification of Olfactory Biomarkers of Skin Cancer From Skin Odor Samples: a Pilot Study
ClinicalTrials.gov study NCT06493786. IPD Sharing: NO. Countries: 0. Publications: 0.
Assessment of the Ability to Distinguish Odors in Glaucoma Patients
ClinicalTrials.gov study NCT00304824. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Olfactory stimulation regulates the birth of neurons that express specific odorant receptors
GEO Series GSE157120. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Odorant Receptor Expression Frequency in Mature Olfactory Sensory Neurons Depends on Lhx2 [123cre:double knockout]
GEO Series GSE74526. Mus musculus. 7 samples. Type: Expression profiling by array.
Comparative analysis of Olfactory bulb RNA obtained from control and odor stimulated mice
GEO Series GSE221772. Mus musculus. 2 samples. Type: Expression profiling by array.
Molecular Control of Circuit Plasticity and the Permanence of Imprinted Odor Memory
GEO Series GSE147463. Mus musculus. 129 samples. Type: Expression profiling by high throughput sequencing.
Comparison and functional analysis of odorant-binding proteins and chemosensory proteins in two closely related thrips species, Frankliniella occidentalis and Frankliniella intonsa (Thysanoptera: Thri
GEO Series GSE213075. Frankliniella intonsa; Frankliniella occidentalis. 9 samples. Type: Expression profiling by high throughput sequencing.
Commonality of Odorant Receptor Choice Mechanism Revealed by Analysis of a Highly Represented Odorant Receptor Transgene
GEO Series GSE207626. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Encoding of odors by mammalian olfactory receptors
GEO Series GSE185415. Mus musculus. 332 samples. Type: Other.
An alternative sensing strategy of microglia for pathogen infection: Odorant receptor-pathogen-derived metabolite axis
GEO Series GSE115271. Mus musculus. 10 samples. Type: Expression profiling by high throughput sequencing.
Aviation Safety Reporting System: Cabin Smoke, Fire, Fumes, or Odor Incidents
A sampling of air carrier reports concerning cabin smoke, fire, fumes or odor related events.
Transcriptomic analysis of class I odorant receptor enhancer deletion [eA]
GEO Series GSE133817. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Transcriptomic analysis of class I odorant receptor enhancer deletion [eC]
GEO Series GSE133819. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Transcriptomic analysis of class I odorant receptor enhancer deletion [LD]
GEO Series GSE133820. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Transcriptomic analysis of class I odorant receptor enhancer deletion
GEO Series GSE133821. Mus musculus. 42 samples. Type: Expression profiling by high throughput sequencing.
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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.