The majority of consumers in Japan made use of personal care products to counteract their body odor, as revealed in a survey conducted in May 2022. Body washes were the most commonly named products used to remove odors, with ** percent of respondents. Antiperspirants were also frequently named items, with sprays being preferred over roll-on types.

Table S2. Odor assessment and metagenomic sequencing data for all samples in this study. Table S5. GC-olfactometry of pooled sweat collected from children and teenagers. Table S6. Detected MetaCyc pathways that were found to be associated with malodor based on pathway abudance values from HUMAnN2. No significant association were detected for the head region. Table S7. Key pathways associated wth malodor production and their taxonomic contributors. Table S8. Information on how samples were distributed in library preparation and sequencing batches to avoid batch effects. Table S9. Reads mapped (%) to UniRef90 gene families and MetaCyc pathways. (ZIP 92 kb)

there are ten subfolders corresponding to the training data of ten models.

Dataset Card for Dataset Name

All the images of the dataset come from this kaggle dataset. Some minor modifications have been made to the metadata. All credit goes to the original authors and the contributor on Kaggle.

  Dataset Details





  Dataset Description

Ocular Disease Intelligent Recognition (ODIR) is a structured ophthalmic database of 5,000 patients with age, color fundus photographs from left and right eyes and doctors' diagnostic keywords from doctors.… See the full description on the dataset page: https://huggingface.co/datasets/bumbledeep/odir.

We collected a structured ophthalmic database of 5,000 patients with age, color fundus photographs from left and right eyes and doctors diagnostic keywords from doctors (in short, ODIR-5K). This dataset is ‘‘real-life’’ set of patient information collected by Shanggong Medical Technology Co., Ltd. from different hospitals/medical centers in China. In these institutions, fundus images are captured by various cameras in the market, such as Canon, Zeiss and Kowa, resulting into varied image resolutions. Patient identifying information will be removed. Annotations are labeled by trained human readers with quality control management. They classify patient into eight labels including normal (N), diabetes (D), glaucoma (G), cataract (C), AMD (A), hypertension (H), myopia (M) and other diseases/abnormalities (O) based on both eye images and additionally patient age. The publishing of this dataset follows the ethical and privacy rules of China. Table 1 shows one record from ODIR-5K dat

Odors trigger various emotional responses such as fear of predator odors, aversion to disease or cancer odors, attraction to male/female odors, and appetitive behavior to delicious food odors. Odor information processing for fine odor discrimination, however, has remained difficult to address. The olfaction and color vision share common features that G protein-coupled receptors are the remote sensors. As different orange colors can be discriminated by distinct intensity ratios of elemental colors, such as yellow and red, odors are likely perceived as multiple elemental odors hierarchically that the intensities of elemental odors are in order of dominance. For example, in a mixture of rose and fox-unique predator odors, robust rose odor alleviates the fear of mice to predator odors. Moreover, although occult blood odor is stronger than bladder cancer-characteristic odor in urine samples, sniffer mice can discriminate bladder cancer odor in occult blood-positive urine samples. In forced-choice odor discrimination tasks for pairs of enantiomers or pairs of body odors vs. cancer-induced body odor disorders, sniffer mice discriminated against learned olfactory cues in a wide range of concentrations, where correct choice rates decreased in the Fechner's law, as perceptual ambiguity increased. In this mini-review, we summarize the current knowledge of how the olfactory system encodes and hierarchically decodes multiple elemental odors to control odor-driven behaviors.

The Object Detection for Olfactory References (ODOR) Dataset Real-world applications of computer vision in the humanities require algorithms to be robust against artistic abstraction, peripheral objects, and subtle differences between fine-grained target classes. Existing datasets provide instance-level annotations on artworks but are generally biased towards the image centre and limited with regard to detailed object classes. The ODOR dataset fills this gap, offering 38,116 object-level annotations across 4,712 images, spanning an extensive set of 139 fine-grained categories. It has challenging dataset properties, such as a detailed set of categories, dense and overlapping objects, and spatial distribution over the whole image canvas. Inspiring further research on artwork object detection and broader visual cultural heritage studies, the dataset challenges researchers to explore the intersection of object recognition and smell perception. How to use The annotations are provided in COCO JSON format. To represent the two-level hierarchy of the object classes, we make use of the supercategory field in the categories array as defined by COCO. In addition to the object-level annotations, we provide an additional CSV file with image-level metadata, which includes content-related fields, such as Iconclass codes or image descriptions, as well as formal annotations, such as artist, license, or creation year. In addition to a zip containing the dataset images, we provide links to their source collections in the metadata file and a Python script to conveniently download the artwork images (download_imgs.py). The mapping between the images array of the annotations.json and the metadata.csv file can be accomplished via the file_name attribute of the elements of the images array and the unique File Name column of the metadata.csv file, respectively.

Ocular Disease Intelligent Recognition (ODIR) is a structured ophthalmic database of 5,000 patients with age, color fundus photographs from left and right eyes, and doctors' diagnostic keywords from doctors.

However, this is the modified version of the original dataset. Extracting each feature to their corresponding images. Here is the list of features: * Normal (N), * Diabetes (D), * Glaucoma (G), * Cataract (C), * Age related Macular Degeneration (A), * Hypertension (H), * Pathological Myopia (M), * Other diseases/abnormalities (O)

ODIR MODEL

## Overview

ODIR MODEL is a dataset for object detection tasks - it contains Eyes Diseases annotations for 684 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Odor masking is a very prominent problem in our daily routines, mainly concerning unpleasant sweat or toilet odors. In the current study we explored the effectiveness of odor masking both on a behavioral and neuronal level. By definition, participants cannot differentiate a fully masked unpleasant odor from the pleasant pure odor used as a masking agent on a behavioral level. We hypothesized, however, that one can still discriminate between a fully masked odor mixture and the pure masking odor on a neuronal level and that, using a reinforcing feedback paradigm, participants could be trained to perceive this difference. A pleasant, lemon-like odor (citral) and a mixture of citral and minor amounts of an unpleasant, goat-like odor (caproic acid) were presented to participants repeatedly using a computer-controlled olfactometer and participants had to decide whether two presented stimuli were the same or different. Accuracy of this task was incentivized with a possible monetary reward. Functional imaging was used throughout the task to investigate central processing of the two stimuli. The participants rated both stimuli as isopleasant and isointense, indicating that the unpleasant odor was fully masked by the pleasant odor. The isolated caproic acid component of the mixture was rated less pleasant than the pleasant odor in a prior experimental session. Although the masked and pure stimuli were not discriminated in the forced-choice task, quality ratings on a dimensional scale differed. Further, we observed an increased activation of the insula and ventral striatum/putamen for the pure in contrast to the fully masked odor, hence revealing a difference in neuronal processing. Our hypothesis that perceptual discrimination and neuronal processing can be enhanced using a reinforcing feedback paradigm is not supported by our data.

To better enforce and regulate nuisance odor concerns, DOEE requires certain facilities known to produce odors to develop an Odor Control Plan (OCP) subject to DOEE approval. This data set is available to show the status of OCPs and provide access to the submitted plans. It includes information on the facilities that have submitted plans, the status of approval or disapproval, and links to the plans.

Taste and odor issues with drinking water stem from the presence of the organic compounds known as methylisoborneol (MIB) and geosmin. These two compounds are not harmful to human health. Rather, the presence of MIB and geosmin negatively affect the drinking water's aesthetics (geosmin causes an earthy smell, while MIB causes a musty taste). Taste and odor thresholds vary from person to person, but normally geosmin can be detected as low as 7-15 parts per trillion, while MIB can be detected as low as 20-30 parts per trillion.

This is a dataset of an experimental study examining cross-modal associations between odors and temperatures in three cultures: Maniq (N=11) recruited at a forest campsite in the area of Manang district, Satun, Thai (N=24) recruited at the Ubon Ratchathani University and Kasetsart University (Bangkok), and Dutch (N=24) recruited at the Radboud University (Nijmegen). Participants carried out an odor-to-temperature matching task. The task was to sniff an odor and match it to a corresponding temperature, i.e., touch a cup filled with either warm or cold water. The task was administered twice, with an average break of 2 hours in between the two blocks, to check for consistency of odor-temperature matches over time. After the matching tasks, participants smelled the odors again and provided smell descriptions in their native languages. They also rated the odors for familiarity using a 3-point scale (1 = unfamiliar, 2 = somewhat familiar, 3 = familiar). The file "Odor-temperature_matching_task" (xls file) contains responses recorded in the matching task. The file "Naming_task" (xls file) contains the category of the responses (abstract smell term, source-based) provided by the participants in the odor naming task. Files are included as csv files as well. All "other" responses have been excluded from the analysis and are not part of the dataset.

Grooming is a common behavior of some mammals. Previous studies have shown that grooming is a means by which animals clean themselves, remove ectoparasites, and lower their body temperature. It is also involved in olfactory communication. Bats belong to the order Chiroptera and, like most mammals, are the natural host of many ectoparasites. Bat grooming, including licking and scratching, is one of the ways to reduce the adverse effects caused by ectoparasites. Bat grooming may also be induced by exogenous odor. In this study, we used lesser flat-headed bats (Tylonycteris pachypus) to test the hypothesis that exogenous odor affects the self-grooming behavior of bats. Results showed that external odor from distantly related species caused lesser flat-headed bats to spend more time in self-grooming. Lesser flat-headed bats that received odor from humans spent the longest time in self-grooming, followed by those that received odor from a different species of bats (T. robustula). Lesser flat...

Odeuropa Dataset of Olfactory Objects

This dataset is released as part of the Odeuropa project. The annotations are identical to the training set of the ICPR2022-ODOR Challenge.
It contains bounding box annotations for smell-active objects in historical artworks gathered from various digital connections.
The smell-active objects annotated in the dataset either carry smells themselves or hint at the presence of smells.
The dataset provides 15823 bounding boxes on 2192 artworks in 87 object categories.
An additional csv file contains further image-level metadata such as artist, collection, or year of creation.

How to use

• Due to licensing issues, we cannot provide the images directly, but instead provide a collection of links and a download script.
• To get the images, just run the `download_imgs.py` script which loads the images using the links from the `metadata.csv` file. The downloaded images can then be found in the `images` subfolder. The overall size of the downloaded images is c. 200MB.
• The bounding box annotations can be found in the `annotations.json`. The annotations follow the COCO JSON format, the definition is available here.
• The mapping between the `images` array of the `annotations.json` and the `metadata.csv` file can be accomplished via the `file_name` attribute of the elements of the `images` array and the unique `File Name` column of the `metadata.csv` file, respectively.
• Additional image-level metadata is available in the `metadata.csv` file.

Child Odor in Parenting scale (COPs).

OdorDb is a database of odorant molecules, which can be searched in a few different ways. One can see odorant molecules in the OdorDB, and the olfactory receptors in ORDB that they experimentally shown to bind. You can search for odorant molecules based on their attributes or identities: Molecular Formula, Chemical Abstracts Service (CAS) Number and Chemical Class. Functional studies of olfactory receptors involve their interactions with odor molecules. OdorDB contains a list of odors that have been identified as binding to olfactory receptors.

This readme file was generated on 2024-08-13 by Richard Hopper.

GENERAL INFORMATION

Title of Dataset: Research data supporting "Multi-channel portable odor delivery device for self-administered and rapid smell testing"

Author/Principal Investigator Information Name: Richard Hopper ORCID: https://orcid.org/0000-0003-1863-9008 Institution: University of Cambridge Address: University of Cambridge Department of Engineering Electrical Engineering Division, CAPE Building, 9 JJ Thomson Avenue, Cambridge, CB3 0FA Email: rhh39@cam.ac.uk

Author/Associate or Co-investigator Information Name: Florin Udrea ORCID: https://orcid.org/0000-0002-7288-3370 Institution: University of Cambridge Address: University of Cambridge Department of Engineering Electrical Engineering Division, CAPE Building, 9 JJ Thomson Avenue, Cambridge, CB3 0FA Email: fu@eng.cam.ac.uk

Date of data collection: 2023-02-01 - 2024-07-22

Geographic location of data collection: London, UK & Geneva University Hospitals

Information about funding sources that supported the collection of the data: Funding for the device development was provided by OWidgets Ltd. and funding for the user study was provided by Geneva University Hospital. Funding for device characterization was provided by the Department of Engineering at the University of Cambridge.

SHARING/ACCESS INFORMATION

Licenses/restrictions placed on the data: CC BY-NC-ND (Creative Commons Attribution Non-Commercial No Derivatives 4.0 International licence) or CC BY (Creative Commons Attribution 4.0 International licence) licence.

Links to publications that cite or use the data: "Multi-channel portable odor delivery device for self-administered and rapid smell testing" https://elements.admin.cam.ac.uk/viewobject.html?cid=1&id=1635352

Links to other publicly accessible locations of the data: n/a

Links/relationships to ancillary data sets:

Was data derived from another source? If yes, list source(s): n/a

Recommended citation for this dataset:

DATA & FILE OVERVIEW

File List:

File: fig2a_flow_rates.xlsx Description: Measured airflow rates for the 24 outlet channels of the odor delivery device. Measured using a Flusso FLS-110 flow sensor.

File: fig2b_PID_response.xlsx
Description: Transient response of the photo ionization detector (PID) to an odor exposure generated by the odor delivery device with a 3 s activation time. Response measured using an ion Science MiniPID sensor.

File: fig2c_SMELL-S_odor_pulse_durations.xlsx
Description: Temporal stability of the odor intensity generated by the odor delivery device over a 1 hour time window with 1 s, 2 s, 3 s, 4 s, 5 s and 6 s activation times.

File: fig2d_SMELL-S_continuous_odor_pulse.xlsx
Description: Temporal stability of the odor intensity generated by the odor delivery device over a 600 s (10 minute) time window with continuous activation.

File: fig2e_chan_odor_intensity.xlsx
Description: Channel-to-channel variation in odor intensity generated by the odor delivery device for 3 s activation times.

File: fig2f_intensity_distance_x.xlsx
Description: Spatial distribution of odor intensity, measured over a distance of 40 mm from the outlet of the odor delivery device, in the direction of odor flow.

File: fig2g_intensity_distance_y.xlsx
Description: Spatial distribution of odor intensity across the path of odor flow, measured at a distance of 100 mm from the outlet of the odor delivery device.

File: fig2h_temperature_response.xlsx
Description: Thermal response data giving the variation in measured odor intensity from the odor delivery device with ambient temperature.

File: fig3a_SMELL-S_PID_levels.xlsx Description: Measured odor intensity for the SMELL-S odor stimuli.

Relationship between files, if important: Research data used to produce figures in the paper.

Additional related data collected that was not included in the current data package: Data giving the duration’s for the Sniffin’ Sticks and SMELL-S threshold tests. This data is available for medical research from the corresponding author.

Are there multiple versions of the dataset? If yes, name of file(s) that was updated: n/a Why was the file updated? When was the file updated?

METHODOLOGICAL INFORMATION

Airflow rates from the outlet channels of the device were measured using a Flusso FLS-110 flow sensor. The average flow rate and standard deviation (SD) were derived from a set of 50 measurement cycles.

Odor intensity was measured using a photo-ionisation detector (MiniPID) from Ion Science. For the measurements of stability, the sensor was positioned at a distance of 25 mm from a 4 mm diameter outlet pipe. Thermal conditions were 25 degC and the odorized airflow rate from the device was 3 L/min. The average odor intensity and standard deviation (SD) for all PID measurements were derived from a set of 10 measurement cycles.

For the short pulse odor intensity repeatability tests, the odor intensity was measured using the PID gas sensor in an indoor environment over a 1 hour time window at a temperature of 25 degC, with an odor activation times repeated every 300 s.

Thermal stability was measured with the device placed in an environmental oven (Thermotron S-1.2 3800). The PID gas sensor was mounted externally to the oven and odorized air fed to it from the olfactometer using 4 mm diameter pipes. Prior to each measurement, the system was left to stabilize for 30 minutes at each temperature point to ensure thermal uniformity.

The spatial distribution of odor intensity was measured by mounting the PID on a motorised stage (Thorlabs, LTS300), having a reach of 300 mm.

We performed a test-retest reliability and accuracy study including healthy subjects (n = 37) and patients with various causes of smell loss (n = 31) at Geneva University Hospital. The study involved subjects aged 18 years of age and over, who came to the hospital for two visits spaced approximately one week apart. During the first visit, participants were tested with the current standard test (Sniffin’ Sticks) and with SMELL-S with the smell delivery device. The order of the tests was randomized. On the second visit, the tests were repeated. We recorded the time needed to complete each test. A t-test was used to uncover differences between groups.

Subjects were tested with the Sniffin’ Sticks test (Burghart, Wedel, Germany), which includes the olfactory threshold, discrimination, and identification sub-tests. The composite score of the three sub-tests was used for the classification of healthy subjects or patients with smell loss \cite{oleszkiewicz2019updated, hummel1997sniffin}. The Sniffin’ Sticks threshold subtest uses phenylethyl alcohol (rose-like odor) in pen-like odor dispensing devices. The stimuli’s have sixteen dilutions in a geometric series. Three pens were presented in a randomized order, with two containing a solvent and the third the target odorant. The subjects must identify the odor-containing pen. An experimental nurse performed a single-staircase test (with ramped odorant concentrations), with three alternative forced choice procedures starting at the most difficult level (level 16 out of 16) according to the user manual. Reversal of the staircase occurs when the odor is correctly identified in two successive trials. The olfactory threshold was defined as the mean of the last four of seven staircase reversals.

Methods for processing the data: The raw data was processed using a Python script.

Instrument- or software-specific information needed to interpret the data: n/a

Standards and calibration information, if appropriate: The flow sensor was calibrated using a calibrated Alicat MFC at Cambridge University.

Environmental/experimental conditions: Measurements were made a room temperature unless indicated.

Describe any quality-assurance procedures performed on the data: The order of the smell study tests was randomized. On the second visit, the tests were repeated.

People involved with sample collection, processing, analysis and/or submission: D.P and R.H. developed the experimental test setup and undertook characterisation tests, with F.U. providing technical guidance. B.N.L. and J.W.H.undertook the clinical trials with the device and analysed the results of the trial data. R.H., D.P. and J.W.H. wrote the paper.

DATA-SPECIFIC INFORMATION FOR: fig2a_flow_rates.xlsx

Number of variables: 2

Number of cases/rows: 24

Variable List: (1) Name: Channel, Description: Odor channel number, Units: Channels 1 - 24. (2) Name: Flow rate, Description: Airflow rates, Units: L/min.

DATA-SPECIFIC INFORMATION FOR: fig2b_PID_response.xlsx

Number of variables: 2

Number of cases/rows: 1

Variable List: (1) Name: Time, Description: Time from start of odor delivery device activation, Units: seconds. (2) Name: PID signal, Description: Measured odor intensity, Units: Normalised sensor response.

DATA-SPECIFIC INFORMATION FOR: fig2c_SMELL-S_odor_pulse_durations.xlsx

Number of variables: 2

Number of cases/rows: 6

Variable List: (1) Name: Time, Description: Time from start of odor delivery device activation, Units: minutes. (2) Name: PID signal, Description: Measured odor intensity, Units: Normalised sensor response.

DATA-SPECIFIC INFORMATION FOR: fig2d_SMELL-S_continuous_odor_pulse.xlsx

Number of variables: 2

Number of cases/rows: 1

Variable List: (1) Name: Time, Description: Time from start of odor delivery device activation, Units: seconds. (2) Name: PID signal, Description: Measured odor intensity, Units: Normalised sensor response.

DATA-SPECIFIC INFORMATION FOR: fig2e_chan_odor_intensity.xlsx

Number of variables: 2

Number of cases/rows: 24

Variable List: (1) Name: Channel, Description: Odor channel number, Units: 1 - 24. (2) Name: PID signal, Description: Measured odor intensity, Units: Normalised sensor response.

DATA-SPECIFIC INFORMATION FOR: fig2f_intensity_distance_x.xlsx

Number of

The Object Detection for Olfactory References (ODOR) Dataset Real-world applications of computer vision in the humanities require algorithms to be robust against artistic abstraction, peripheral objects, and subtle differences between fine-grained target classes. Existing datasets provide instance-level annotations on artworks but are generally biased towards the image centre and limited with regard to detailed object classes. The ODOR dataset fills this gap, offering 38,116 object-level annotations across 4,712 images, spanning an extensive set of 139 fine-grained categories. It has challenging dataset properties, such as a detailed set of categories, dense and overlapping objects, and spatial distribution over the whole image canvas. Inspiring further research on artwork object detection and broader visual cultural heritage studies, the dataset challenges researchers to explore the intersection of object recognition and smell perception. How to use To download the dataset images, run the download_imgs.py script in the subfolder. The images will be downloaded to the imgs folder. The annotations are provided in COCO JSON format. To represent the two-level hierarchy of the object classes, we make use of the supercategory field in the categories array as defined by COCO. In addition to the object-level annotations, we provide an additional CSV file with image-level metadata, which includes content-related fields, such as Iconclass codes or image descriptions, as well as formal annotations, such as artist, license, or creation year. For the sake of license compliance, we do not publish the images directly (although most of the images are public domain). Instead, we provide links to their source collections in the metadata file (meta.csv) and a python script to download the artwork images (download_images.py). The mapping between the images array of the annotations.json and the metadata.csv file can be accomplished via the file_name attribute of the elements of the images array and the unique File Name column of the metadata.csv file, respectively.

In some insect species, females may base their choice for a suitable mate on male odor. In the red mason bee, Osmia bicornis, female choice is based on a male’s odor bouquet as well as its thorax vibrations, and its relatedness to the female, a putative form of optimal outbreeding. Interestingly, O. bicornis can be found as two distinct color morphs in Europe, which are thought to represent subspecies and between which we hypothesize that female discrimination may be particularly marked. Here we investigated (i) if these two colors morphs do indeed represent distinct, reproductively differentiated populations, (ii) how odor bouquets of male O. bicornis vary within and between populations, and (iii) whether variation in male odor correlates with genetic distance, which might represent a cue by which females could optimally outbreed. Using GC and GC-MS analysis of male odors and microsatellite analysis of males and females from 9 populations, we show that, in Denmark, an area of subspecies sympatry, the two color morphs at any one site do not differ, either in odor bouquet or in population genetic differentiation. Yet populations across Europe are distinct in their odor profile as well as being genetically differentiated. Odor differences do not, however, mirror genetic differentiation between populations. We hypothesize that populations from Germany, England and Denmark may be under sexual selection through female choice for local odor profiles, which are not related to color morph though which could ultimately lead to population divergence and speciation.