Database used in the study

Atlas of magnetic resonance images and histological sections of a Japanese monkey brain, Rhesus monkey and human. The Brain Explorer allows for display, magnification, and comparison these images. Other formats include a collection of .jpg images, Quicktime VR (allow user to zoom in), and EmonV, a voxel viewer for MacOS X.

Data with images of red monkeys for detection. The label is available in another folder named > labels. This data set was created for a small project with multiple free images of the monkey.

This is a Multi-class classification data set of 10 different classes of 10 different monkey species. These monkey species are :

1. Bald Uakari
2. Emperor Tamarin
3. Golden Monkey
4. Gray Langur
5. Hamadryas Baboon
6. Mandril
7. Proboscis Monkey
8. Red Howler
9. Vervet Monkey
10. White Faced Saki

Each species have their own special characteristics, such as,

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This is the image of Bald Uakari, I've been in suggest they are bald and their face is of red color. You can have a look at the images of all classes in the below data set section.

Challenges

Each monkey species have their own special characteristics. This means that every specie is different from the other. Thus, the model will have to frame its weights in such a manner that it can understand all the differences. This can become a challenge because of the high variation.

Source

This data set is collected from the Internet and collected for educational purpose. I have tried my best to keep all images different. But because some images of Golden Monkey were not available, little bit of data augmentation is applied to balance the class distribution.

The prediction data has highly imbalanced class distribution. So I recommend you to only use it as a prediction or a testing data, not as a validation data.

Owned by : DeepNets

An Open Context "types" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This record is part of the "Database of non-human primate dietary studies" data publication.

These data files contain seed traits from three sources, 1) seed traits from "Seeds of Amazonian Plants", 2) Royal Botanic Gardens Kew Seed Information Database, and 3) seeds dissected from field collections of primate feces and dung balls from dung beetles. The dataset was used in the article published in Biotropica entitled "Seed size and pubescence facilitate secondary dispersal by dung beetles". The data mostly describes seed traits of morphospecies within the feces of brown-headed spider monkeys (Ateles fusciceps) and mantled howler monkeys (Alouatta palliata). Traits included in the data set are size, surface, length, width, shape, color, and dispersal by mammals. Methods 1. Description of methods used for collection/generation of data:

The data file "Seed Traits.csv" was generated from the key in: Cornejo, F., & Janovec, J. (2010). Seeds of Amazonian plants. Princeton University Press. The data file "dispersal syndromes KEW.csv" was generated from Royal Botanic Gardens Kew (2020). Seed Information Database (SID). Version 7.1. https://data.kew.org/sid/. The dispersal data for each non-wind-dispersed genus included in "Seeds of Amazonaian Plants", was matched against the SID, to extract dispersal information, and then a logical variable created "YES/NO", for mammal dispersal. Data from the remaining data files was generated from monkey fecal samples, and dung beetle dung balls collected in the field.

1. Methods for processing the data:

The seed traits from the "Seed Traits.csv" file are all taken from the genus identification key, using the characters defined by the book, size, shape, color, and surface. Some genera have more than one combination of characters. For the data file "dispersal syndromes KEW.csv" dispersal data for each none wind dispersed genus included in "Seeds of Amazonaian Plants", was matched against the SID, to extract dispersal information, and then a logical variable created "YES/NO", for mammal dispersal. Remaining data files are from field-collected data. In the field, the monkey species that produced the feces was identified, and if the sample was a dung ball, the beetle was collected with the ball for identification. Fecal samples and dung balls were dissected to remove the seeds. The seeds were then grouped by morphospecies and identified to genus as well as possible. Seed length and width were measured and the seed surface was characterized.

The cynomolgus monkey (Macaca fascicularis) is a well-known non-human primate species commonly used in non-clinical research. It is important to know basal clinical pathology parameters in order to have a reference for evaluating any potential treatment-induced effects, maintaining health status among animals and, if needed, evaluating correct substantiative therapies. In this study, data from 238 untreated cynomolgus monkeys (119 males and 119 females of juvenile age, 2.5 to 3.5 years) kept under laboratory conditions were used to build up a reference database of clinical pathology parameters. Twenty-two hematology markers, 24 clinical chemistry markers and two blood coagulation parameters were analyzed. Gender-related differences were evaluated using statistical analyses. To assess the possible effects of stress induced by housing or handling involved in treatment procedures, 78 animals (35 males and 35 females out of 238 juvenile monkeys and four adult males and four adult females) were used to evaluate cortisol, corticosterone and behavioral assessment over time. Data were analyzed using a non-parametric statistical test and machine learning approaches. Reference clinical pathology data obtained from untreated animals may be extremely useful for investigators employing cynomolgus monkeys as a test system for non-clinical safety studies.

1452 Global export shipment records of Monkey set with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

This dataset contains the predicted prices of the asset Monkey over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Objectives: Direct comparative work in morphology and growth on widely dispersed wild primate taxa is rarely accomplished, yet critical to understanding ecogeographic variation, plastic local varia- tion in response to human impacts, and variation in patterns of growth and sexual dimorphism. We investigated population variation in morphology and growth in response to geographic variables (i.e., latitude, altitude), climatic variables (i.e., temperature and rainfall), and human impacts in the vervet monkey (Chlorocebus spp.).

Methods: We trapped over 1,600 wild vervets from across Sub-Saharan Africa and the Caribbean, and compared measurements of body mass, body length, and relative thigh, leg, and foot length in four well-represented geographic samples: Ethiopia, Kenya, South Africa, and St. Kitts & Nevis.

Results: We found significant variation in body mass and length consistent with Bergmann’s Rule in adult females, and in adult males when excluding the St. Kitts & Nevis pop...

1. Assessing the diet of wild animals reveals valuable information about their ecology and trophic relationships that may help elucidate dynamic interactions in ecosystems and forecast responses to environmental changes.
2. Advances in molecular biology provide valuable research tools in this field. However, comparative empirical research is still required to highlight strengths and potential biases of different approaches. Therefore, this study compares environmental DNA and observational methods for the same study population and sampling duration.
3. We employed DNA metabarcoding assays targeting plant and arthropod diet items in 823 faecal samples collected over 12 months in a wild population of an omnivorous primate, the vervet monkey (Chlorocebus pygerythrus). DNA metabarcoding data were subsequently compared to direct observations.
4. We observed the same seasonal patterns of plant consumption with both methods, however, DNA metabarcoding showed considerably greater taxonomic coverage and resolution compared to observations, mostly due to the construction of a local plant DNA database. We found a strong effect of season on variation in plant consumption largely shaped by the dry and wet seasons. The seasonal effect on arthropod consumption was weaker but feeding on arthropods was more frequent in spring and summer, showing overall that vervets adapt their diet according to available resources. The DNA metabarcoding assay outperformed also direct observations of arthropod consumption in both taxonomic coverage and resolution.
5. Combining traditional techniques and DNA metabarcoding data can therefore not only provide enhanced assessments of complex diets or reveal trophic interactions to the benefit of wildlife conservationists and managers but also opens new perspectives for behavioural ecologists studying whether diet variation in social species is induced by environmental differences or might reflect selective foraging behaviours.

Existing intracortical brain computer interfaces (iBCIs) transform neural activity into control signals capable of restoring movement to persons with paralysis. However, the accuracy of the “decoder” at the heart of the iBCI typically degrades over time due to turnover of recorded neurons. To compensate, decoders can be recalibrated, but this requires the user to spend extra time and effort to provide the necessary data, then learn the new dynamics. As the recorded neurons change, one can think of the underlying movement intent signal being expressed in changing coordinates. If a mapping can be computed between the different coordinate systems, it may be possible to stabilize the original decoder’s mapping from brain to behavior without recalibration. We previously proposed a method based on Generalized Adversarial Networks (GANs), called “Adversarial Domain Adaptation Network” (ADAN), which aligns the distributions of latent signals within underlying low-dimensional neural manifolds. However, we tested ADAN on only a very limited dataset. Here we propose a method based on Cycle-Consistent Adversarial Networks (Cycle-GAN), which aligns the distributions of the full-dimensional neural recordings. We tested both Cycle-GAN and ADAN on data from multiple monkeys and behaviors and compared them to a third, quite different method based on Procrustes alignment of axes provided by factor analysis. All three methods are unsupervised and require little data, making them practical in real life. Overall, Cycle-GAN had the best performance and was easier to train and more robust than ADAN, making it ideal for stabilizing iBCI systems over time. Methods Electrophysiology:
Depending on the task, we implanted a 96-channel Utah electrode array (Blackrock Neurotech, Inc.) in either the hand or arm representation area of the primary motor cortex (M1), contralateral to the arm being used for the task. The implant site was pre-planned and finally determined during the surgery with reference to the sulcal patterns and the muscle contractions evoked by intraoperative surface cortical stimulation. For each of monkeys J, S, G, and P, we also implanted intramuscular leads in forearm and hand muscles of the arm used for the task in a separate procedure. Electrode locations were verified during surgery by stimulating each lead. Behavioral task: Monkeys J and S were trained to perform an isometric wrist task, which required them to control the cursor on the screen by exerting forces on a small box placed around one of the hands. The box was padded to comfortably constrain the monkey’s hand and minimize its movement within the box, and the forces were measured by a 6 DOF load cell (JR3 Inc., CA) aligned to the wrist joint. During the task, flexion/extension force moved the cursor right and left respectively, while force along the radial/ulnar deviation axis moved the cursor up and down. Each trial started with the appearance of a center target requiring the monkeys to hold for a random time (0.2 – 1.0 s), after which one of eight possible outer targets selected in a block-randomized fashion appeared, accompanied with an auditory go cue. The monkey was allowed to move the cursor to the target within 2.0 s and hold for 0.8 s to receive a liquid reward. For both decoding and alignment analyses, we only used the data within each single trial (from ‘trial start’ to ‘trial end’). We did not do any temporal alignment with the trials, so the lengths of the trials were different from each other. Monkeys P and G were trained to perform a grasping task, which required them to reach and grasp a gadget placed under the screen with one hand. The gadget was a cylinder for monkey P facilitating a power grasp with the palm and the fingers, while a small rectangular cuboid for monkey G facilitating a key grasp with the thumb and the index finger. A pair of force sensitive resistors (FSRs) were attached on the sides of the gadgets to measure the grasping forces the monkeys applied. The sum and the difference of the FSR outputs were used to determine the position of the cursor on the vertical axis and the horizontal axis respectively. At the beginning of each trial the monkey was required to keep the hand resting on a touch pad for a random time (0.5 – 1.0 s). A successful holding triggered the onset of one of three possible rectangular targets on the screen and an auditory go cue. The monkey was required to place the cursor into the target and hold for 0.6 s by increasing and maintaining the grasping force applied on the gadget. For this task we extracted trials from ‘gocue time’ to ‘trial end’, as the monkeys’ movements were quite random before the gocue.
Monkeys C and M were trained to perform a center-out (CO) reaching task while grasping the upright handle of a planar manipulandum, operated with the upper arm in a parasagittal plane. Monkey C performed the task with the right hand, monkey M with the left. At the beginning of each trial the monkey needed to move the hand to the center of the workspace. One of eight possible outer targets equally spaced in a circle was presented to the monkey after a random waiting period. The monkey needed to keep holding for a variable delay period until receiving an auditory go cue. To receive a liquid reward, the monkey was required to reach the outer target within 1.0 s and hold within the target for 0.5 s. For this task we extracted trials from ‘gocue time’ to ‘trial end’, since the monkeys kept static before the gocue. Monkey M was also trained to perform a random-target (RT) task, reaching a sequence of three targets presented in random locations on the screen to complete a single trial. The RT task used the same apparatus as the CO reach task. At the beginning of each trial the monkey also needed to move the hand to the center of the workspace. Three targets were then presented to the monkey sequentially, and the monkey was required to move the cursor into each of them within 2.0 s after viewing each target. The positions of these targets were randomly selected, thus the cursor trajectory for each trial presented a ‘random-target’ manner. For this task we extracted trials from ‘trial start’ to ‘trial end’. All surgical and experimental procedures were approved by the Institutional Animal Care and Use Committee (IACUC) of Northwestern University under protocol #IS00000367, and are consistent with the Guide for the Care and Use of Laboratory Animals. Data collection and preprocessing:
M1 activity was recorded during task performance using a Cerebus system (Blackrock Neurotech, Inc.). The signals on each channel were digitalized, bandpass filtered (250 ~ 5000 Hz) and converted to spike times based on threshold crossings. The threshold was set with respect to the root-mean square (RMS) activity on each channel and kept consistent across different recording sessions (monkeys J, C and M: -5.5 x RMS; monkey S: -6.25 x RMS; monkey P: -4.75 x RMS; monkey G: -5.25 x RMS). The time stamp and a 1.6 ms snippet of each spike surrounding the time of threshold crossing were recorded. For all analyses in this study, we used multiunit threshold crossings on each channel instead of discriminating well isolated single units. We applied a Gaussian kernel (S.D. = 100 ms) to the spike counts in 50 ms, non-overlapping bins to obtain a smoothed estimate of firing rate as function of time for each channel. The EMG signals were differentially amplified, band-pass filtered (4-pole, 50 ~ 500 Hz) and sampled at 2000 Hz. The EMGs were subsequently digitally rectified and low-pass filtered (4-pole, 10 Hz, Butterworth) and subsampled to 20 Hz. EMG channels with substantial noise were not included in the analyses. For monkeys C and M, we recorded the positions of the endpoint of the reach manipulandum at a sampling frequency of 1000 Hz using encoders in the two joints of the manipulandum.

ej2/seq-monkey-data dataset hosted on Hugging Face and contributed by the HF Datasets community

Though the rhesus monkey is one of the most valuable non-human primate animal models for various human diseases because of its manageable size and genetic and proteomic similarities with humans, proteomic research using rhesus monkeys still remains challenging due to the lack of a complete protein sequence database and effective strategy. To investigate the most effective and high-throughput proteomic strategy, comparative data analysis was performed employing various protein databases and search engines. The UniProt databases of monkey, human, bovine, rat and mouse were used for the comparative analysis and also a universal database with all protein sequences from all available species was tested. At the same time, de novo sequencing was compared to the SEQUEST search algorithm to identify an optimal work flow for monkey proteomics. Employing the most effective strategy, proteomic profiling of monkey organs identified 3,481 proteins at 0.5% FDR from 9 male and 10 female tissues in an automated, high-throughput manner. Data are available via ProteomeXchange with identifier PXD001972. Based on the success of this alternative interpretation of MS data, the list of proteins identified from 12 organs of male and female subjects will benefit future rhesus monkey proteome research.

This dataset contains the predicted prices of the asset Viral Chinese Monkey over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

This data set contains the complete behavioral and neurophysiological data obtained in two behaving monkeys as was analyzed in two publications by Rostami et al. (2024) and Rickert et al. (2009). The experimental task involves a delayed center-out arm-reach in three experimental condition that varied the degree of information (complete or incomplete) about the final movement target as cued at the start of the delay period. The data set contains the trigger events for the experimental cue stimuli and the behavioral events, and it contains acute single-unit recordings from the primary motor cortex (M1) and premotor cortex dorsal (PMd) for both monkeys. All experiments were conducted in the laboratory and under supervision of Dr. Alexa Riehle (Aix-Marseille Université and Research Center Jülich). Please refer to the README file in the data repository for additional information.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Comprehensive dataset containing 1 verified Drunken Monkey locations in Texas, United States with complete contact information, ratings, reviews, and location data.

The investigators conducted experiments with seven macaque monkeys to understand how measures of neural activity in the lateral intraparietal (LIP) cortex corresponded with decision-making as they performed different tasks: a motion discrimination task and two variations on a novel face categorization task where subjects reported the faces' species or expression. They found that neuron firing rates in the LIP cortex varied depending on the task, which suggested that decision-making and action planning were processes that underwent context-dependent interpretation of information. These observations challenge existing models of decision-making in the brain which assume that decision variables are encoded and read out in a consistent manner regardless of the task context.

This dataset contains unprocessed structural, diffusion and rs-functional MRI data acquired in 3 female rhesus macaques. Each monkey underwent multiple sessions. For each session they were lightly anesthetized with Zoletil 100:10 mg/kg and Medetodimine 0.04 mg/kg. The animals were installed in sphinx position with their head restrained by a head-post. Whole-brain images were acquired on a 3 Tesla clinical MR scanner (Philips Achieva) using a custom 8-channel phased array coil (RapidBiomed) specially designed to fit the skull of the animals.