Left- and right-handed individuals have different neurological wiring, particularly with regard to language processing. Multiple datasets from the United States and the United Kingdom show that lefties exhibit significant human capital deficits relative to righties. Lefties score 0.1 standard deviations lower on cognitive skill measures, have more behavioral problems, have more learning disabilities such as dyslexia, complete less schooling, and work in occupations requiring less cognitive skill. Most strikingly, lefties have 10-12 percent lower annual earnings than righties, much of which can be explained by observable differences in cognitive skills and behavioral problems. Lefties work in more manually intensive occupations than do righties, further suggesting their primary labor market disadvantage is cognitive rather then physical. I argue here that handedness can be used to explore the long-run impacts of differential brain structure generated in part by genetics and in part by poor infant health.

STUDY DESCRIPTION: To the best of our knowledge there is no study directly investigating the difference between right and left handers MI neural correlates per se and especially in the cerebellum. To achieve a deeper understanding of handedness effects on motor imagery in both right and left-handed individuals, the present study used functional magnetic resonance imaging (fMRI) to compare neural correlates associated with the execution and imagination of a simple task (i.e., squeezing a ball) with the dominant, non-dominant and both hands.

DATASET DESCRIPTION: In the present dataset you can find: - participants.tsv: document with the information reagarding sex, age, handedness, and questionnaires results of all the 51 subjects included. - task-execution_acq-Fs2_bold.json: Information regarding the execution task - task-imagery_acq-Fs2_bold.json: Information regarding the imagery task - sub-108TUSW01100n: folder for each participant, where n is the number associated to the participant (from 1 to 51), including ses 1 folder with the anatomical (anat folder); functional map (fmap folder) and functional data (func folder) for each subject.

Analysis of ‘Young People Survey’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/miroslavsabo/young-people-survey on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Introduction

In 2013, students of the Statistics class at "https://fses.uniba.sk/en/">FSEV UK were asked to invite their friends to participate in this survey.

• The data file (responses.csv) consists of 1010 rows and 150 columns (139 integer and 11 categorical).
• For convenience, the original variable names were shortened in the data file. See the columns.csv file if you want to match the data with the original names.
• The data contain missing values.
• The survey was presented to participants in both electronic and written form.
• The original questionnaire was in Slovak language and was later translated into English.
• All participants were of Slovakian nationality, aged between 15-30.

The variables can be split into the following groups:

• Music preferences (19 items)
• Movie preferences (12 items)
• Hobbies & interests (32 items)
• Phobias (10 items)
• Health habits (3 items)
• Personality traits, views on life, & opinions (57 items)
• Spending habits (7 items)
• Demographics (10 items)

Research questions

Many different techniques can be used to answer many questions, e.g.

• Clustering: Given the music preferences, do people make up any clusters of similar behavior?
• Hypothesis testing: Do women fear certain phenomena significantly more than men? Do the left handed people have different interests than right handed?
• Predictive modeling: Can we predict spending habits of a person from his/her interests and movie or music preferences?
• Dimension reduction: Can we describe a large number of human interests by a smaller number of latent concepts?
• Correlation analysis: Are there any connections between music and movie preferences?
• Visualization: How to effectively visualize a lot of variables in order to gain some meaningful insights from the data?
• (Multivariate) Outlier detection: Small number of participants often cheats and randomly answers the questions. Can you identify them? Hint: [Local outlier factor][1] may help.
• Missing values analysis: Are there any patterns in missing responses? What is the optimal way of imputing the values in surveys?
• Recommendations: If some of user's interests are known, can we predict the other? Or, if we know what a person listen, can we predict which kind of movies he/she might like?

Past research

• (in slovak) Sleziak, P. - Sabo, M.: Gender differences in the prevalence of specific phobias. Forum Statisticum Slovacum. 2014, Vol. 10, No. 6. [Differences (gender + whether people lived in village/town) in the prevalence of phobias.]

• Sabo, Miroslav. Multivariate Statistical Methods with Applications. Diss. Slovak University of Technology in Bratislava, 2014. [Clustering of variables (music preferences, movie preferences, phobias) + Clustering of people w.r.t. their interests.]

Questionnaire

MUSIC PREFERENCES

1. I enjoy listening to music.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
2. I prefer.: Slow paced music 1-2-3-4-5 Fast paced music (integer)
3. Dance, Disco, Funk: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
4. Folk music: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
5. Country: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
6. Classical: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
7. Musicals: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
8. Pop: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
9. Rock: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
10. Metal, Hard rock: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
11. Punk: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
12. Hip hop, Rap: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
13. Reggae, Ska: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
14. Swing, Jazz: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
15. Rock n Roll: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
16. Alternative music: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
17. Latin: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
18. Techno, Trance: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
19. Opera: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)

MOVIE PREFERENCES

1. I really enjoy watching movies.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
2. Horror movies: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
3. Thriller movies: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
4. Comedies: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
5. Romantic movies: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
6. Sci-fi movies: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
7. War movies: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
8. Tales: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
9. Cartoons: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
10. Documentaries: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
11. Western movies: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
12. Action movies: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)

HOBBIES & INTERESTS

1. History: Not interested 1-2-3-4-5 Very interested (integer)
2. Psychology: Not interested 1-2-3-4-5 Very interested (integer)
3. Politics: Not interested 1-2-3-4-5 Very interested (integer)
4. Mathematics: Not interested 1-2-3-4-5 Very interested (integer)
5. Physics: Not interested 1-2-3-4-5 Very interested (integer)
6. Internet: Not interested 1-2-3-4-5 Very interested (integer)
7. PC Software, Hardware: Not interested 1-2-3-4-5 Very interested (integer)
8. Economy, Management: Not interested 1-2-3-4-5 Very interested (integer)
9. Biology: Not interested 1-2-3-4-5 Very interested (integer)
10. Chemistry: Not interested 1-2-3-4-5 Very interested (integer)
11. Poetry reading: Not interested 1-2-3-4-5 Very interested (integer)
12. Geography: Not interested 1-2-3-4-5 Very interested (integer)
13. Foreign languages: Not interested 1-2-3-4-5 Very interested (integer)
14. Medicine: Not interested 1-2-3-4-5 Very interested (integer)
15. Law: Not interested 1-2-3-4-5 Very interested (integer)
16. Cars: Not interested 1-2-3-4-5 Very interested (integer)
17. Art: Not interested 1-2-3-4-5 Very interested (integer)
18. Religion: Not interested 1-2-3-4-5 Very interested (integer)
19. Outdoor activities: Not interested 1-2-3-4-5 Very interested (integer)
20. Dancing: Not interested 1-2-3-4-5 Very interested (integer)
21. Playing musical instruments: Not interested 1-2-3-4-5 Very interested (integer)
22. Poetry writing: Not interested 1-2-3-4-5 Very interested (integer)
23. Sport and leisure activities: Not interested 1-2-3-4-5 Very interested (integer)
24. Sport at competitive level: Not interested 1-2-3-4-5 Very interested (integer)
25. Gardening: Not interested 1-2-3-4-5 Very interested (integer)
26. Celebrity lifestyle: Not interested 1-2-3-4-5 Very interested (integer)
27. Shopping: Not interested 1-2-3-4-5 Very interested (integer)
28. Science and technology: Not interested 1-2-3-4-5 Very interested (integer)
29. Theatre: Not interested 1-2-3-4-5 Very interested (integer)
30. Socializing: Not interested 1-2-3-4-5 Very interested (integer)
31. Adrenaline sports: Not interested 1-2-3-4-5 Very interested (integer)
32. Pets: Not interested 1-2-3-4-5 Very interested (integer)

PHOBIAS

1. Flying: Not afraid at all 1-2-3-4-5 Very afraid of (integer)
2. Thunder, lightning: Not afraid at all 1-2-3-4-5 Very afraid of (integer)
3. Darkness: Not afraid at all 1-2-3-4-5 Very afraid of (integer)
4. Heights: Not afraid at all 1-2-3-4-5 Very afraid of (integer)
5. Spiders: Not afraid at all 1-2-3-4-5 Very afraid of (integer)
6. Snakes: Not afraid at all 1-2-3-4-5 Very afraid of (integer)
7. Rats, mice: Not afraid at all 1-2-3-4-5 Very afraid of (integer)
8. Ageing: Not afraid at all 1-2-3-4-5 Very afraid of (integer)
9. Dangerous dogs: Not afraid at all 1-2-3-4-5 Very afraid of (integer)
10. Public speaking: Not afraid at all 1-2-3-4-5 Very afraid of (integer)

HEALTH HABITS

1. Smoking habits: Never smoked - Tried smoking - Former smoker - Current smoker (categorical)
2. Drinking: Never - Social drinker - Drink a lot (categorical)
3. I live a very healthy lifestyle.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)

PERSONALITY TRAITS, VIEWS ON LIFE & OPINIONS

1. I take notice of what goes on around me.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
2. I try to do tasks as soon as possible and not leave them until last minute.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
3. I always make a list so I don't forget anything.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
4. I often study or work even in my spare time.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
5. I look at things from all different angles before I go ahead.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
6. I believe that bad people will suffer one day and good people will be rewarded.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
7. I am reliable at work and always complete all tasks given to me.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
8. I always keep my promises.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
9. **I can fall for someone very quickly and then

Pan2023 Dataset Documentation # This is a replication of the "A cross-session motor imagery EEG dataset" dataset, the .mat file version is v7.0. ## Abstract The Pan2023 dataset is a collection of electroencephalography (EEG) signals from 14 subjects performing motor imagery (MI) tasks across two sessions. The dataset aims to facilitate the study of cross-session variability in MI-EEG signals and to support the development of robust brain-computer interface (BCI) systems. ## Dataset Composition The dataset encompasses EEG recordings from 14 subjects, each participating in two sessions. The sessions involve MI tasks with visual cues for left-handed and right-handed movements. Data acquisition was performed using a Neuroscan SynAmps2 amplifier, equipped with 28 scalp electrodes following the international 10-20 system. The EEG signals were sampled at a frequency of 250Hz, with a band-pass filter applied from 0.01 to 200Hz to mitigate power line noise. The collected data is stored in Matlab format, labeled by subject and session number. ## Participants The participant cohort includes 14 individuals (five females), aged 22 to 25, with two reporting left-handedness. All subjects were screened for neurological and movement disorders, ensuring a healthy participant profile for the study. ## Experimental Paradigm Each experimental session comprised 120 trials, segmented into three distinct phases: Rest, Preparation, and Task. During the Rest Period (2 seconds), subjects were instructed to remain relaxed without engaging in mental tasks. The Preparation Period (1 second) involved a 'Ready' cue on the monitor, prompting subjects to focus and prepare for the upcoming MI task. The Task Period (4 seconds) required subjects to perform the MI task, visualizing the movement corresponding to the provided cues, either left or right-handed. This paradigm was designed to occur in a controlled, distraction-free environment. ## Data Acquisition and Preprocessing EEG signals were captured using a Neuroscan SynAmps2 amplifier and 28 scalp electrodes positioned per the 10-20 system. The sampling rate was set at 1000Hz, and a band-pass filter from 0.01 to 200Hz and a notch filter at 50Hz were employed to exclude power line interference. The signals were downsampled to 250Hz and archived in Matlab format, systematically named by subject and session identifiers. ## Data Structure The dataset's structure is encapsulated in a Matlab file, comprising a struct with the following components: - data: A 3D matrix ([n_trials, n_channels, n_samples]) containing the EEG signals. - label: A vector ([n_trials]) denoting each trial's label (1 for left-handed, 2 for right-handed movement). - trial_info: A struct detailing each trial's phase (1 for Rest, 2 for Preparation, 3 for Task), the visual cue (1 for left-handed, 2 for right-handed movement), and the subject's identifier.

This hand recognition dataset comprises a comprehensive collection of hand images from 65 individuals, including both left and right hands, annotated with YOLOv8 formatting.The dataset encompasses 17 distinct classes, denoted as L-L1 to L-L9 for the left hand and R-R1 to R-R8 for the right hand. These classes capture various hand gestures and poses.These images were captured using a standard mobile phone camera, offering a diverse set of images with varying angles and backgrounds. In total, the dataset comprises 405 high-quality images, with 222 representing left hands and 183 representing right hands. The left hand classes are distributed as follows: L-L1 (62 images), L-L2 (56 images), L-L3 (44 images), L-L4 (29 images), L-L5 (14 images), L-L6 (8 images), L-L7 (4 images), L-L8 (2 images), and L-L9 (3 images). Similarly, the right hand classes are distributed as R-R1 (53 images), R-R2 (48 images), R-R3 (38 images), R-R4 (24 images), R-R5 (14 images), R-R6 (4 images), R-R7 (1 image), and R-R8 (1 image).We welcome the machine vision research community to utilise and build upon this dataset to advance the field of hand recognition and its applications.

The palmprint dataset is captured on left hand. Palmprint dataset is acquired from 15 people with 5 to 8 images of each person. To increase the amount of data in each person, the raw dataset was filtered with Gabor Filter. The characteristics of the Gabor Filter are good applied to palmprint image because the image has many variations of line direction and the thickness. The palmprint dataset has 20 to 32 images each class after applying the Gabor Filter. The author trains the palmprint dataset using the Convolutional Neural Network method.

Attention is needed to perform goal-directed vision-guided movements. We investigated whether the direction of covert attention modulates movement outcomes and dynamics. Right-handed and left-handed volunteers attended to a spatial location while planning a reach toward the same hemifield, the opposite one, or planned a reach without constraining attention. We measured behavioral variables as outcomes of ipsilateral and contralateral reaching and the tangling of behavioral trajectories obtained through principal component analysis as a measure of the dynamics of motor control. We found that the direction of covert attention had significant effects on the dynamics of motor control, specifically during contralateral reaching. Data suggest that motor control was more feedback-driven when attention was directed leftward than when attention was directed rightward or when it was not constrained, irrespectively of handedness. These results may help to better understand the neural bases of asymmetrical neurological diseases like hemispatial neglect.

This dataset includes right and left hand anthropometric measurements such as hand length, hand width, palm length, third finger length, shape index, finger index, and hand surface area. Measurements were collected from 160 professional volleyball players (80 females, 80 males) and 160 sedentary university students (80 females, 80 males), aged between 18 and 25 years. The data were obtained through standardized digital imaging and analyzed using the ImageJ software program. This dataset can be used for sports science research, anthropometric studies, and hand morphology comparisons between volleyball players and non-athletic young adults.

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Methodology

Development on the dataset started with collecting MIDIs of piano compositions which were in Boogie Woogie style. These MIDI compositions were found throughout the internet. Links to the sources of the MIDIs which were used can be found in the documentation of the dataset itself. Ableton was used to separate these MIDI compositions into two parts. Each part is the separate performance of the left hand and the right hand respectively. Both parts were rendered to separate audio signals through a piano preset which was tuned to sound similar to an upright piano.

A set of guidelines to preparing the dataset was also established. Compositions had to fit within the chromatic range of the piano, such to ensure that the synthesized compositions used at least sounded like something which could be reproduced on a real piano. This ultimately meant fitting the composition within the bounds of keys A0 (27.5Hz) and C8 (4186Hz).

Furthermore, the composition had to have a tempo which is true to the style of Boogie Woogie. This meant that synthesized songs had to have a BPM of around 175 beats per minute (BPM). While C4 is technically the middle key and therefore also the border between bass and treble, this separation did not always occur around this point in the compositions that were encountered. Shifting these compositions up or down in octaves was not always desirable either, since this either threw parts of a composition outside of the chromatic range of a piano or simply made the composition itself sound off. Therefore, not all compositions in the set of the Boogie Woogie synthesized piano dataset had its separation point at around C4. Rather, compositions were separated at points where a clear contrast/separation between left hand performance and right hand performance was observed.

The resulting dataset consists of 95 songs. 55 of which are part of the training set, the validation set counts 10 songs and the test set has a total of 30 songs. The split of the dataset over the training, validation and test set is therefore 57.9%, 31.6%, 10.5% respectively. Songs were distributed over the training set, validation set and test set through means of random sampling.

Sources for MIDIs used:

/2. MIDIs/1. RobRio

"Additional Files" at http://www.robrio.com/MIDI.htm

/2. MIDIs/2. MIDIKaraoke

http://www.midi-karaoke.info/21bd2561.html

/2. MIDIs/3. TerryButters

https://www.terrybutters.co.uk/terrybutters3.htm

/2. MIDIs/4. BitMidi

https://bitmidi.com/boogie-woogie-demo-mid https://bitmidi.com/cow-cow-blues-mid https://bitmidi.com/all-boogie-mid https://bitmidi.com/boogie-woogie-mid https://bitmidi.com/boogie-blues-mid https://bitmidi.com/boogie-for-breakfast-mid https://bitmidi.com/boogie-for-jerry-lee-mid https://bitmidi.com/burn-mouth-boogie-mid https://bitmidi.com/cadillac-avenue-boogie-mid https://bitmidi.com/goodnight-boogie-mid https://bitmidi.com/moo-cow-boogie-mid https://bitmidi.com/pinetop-boogie-mid https://bitmidi.com/rolfs-boogie-mid https://bitmidi.com/sour-milk-boogie-mid https://bitmidi.com/the-lively-boogie-nights-mid https://bitmidi.com/the-online-boogie-mid https://bitmidi.com/walkin-on-the-boogie-side-mid https://bitmidi.com/walkin-with-the-boogie-mid https://bitmidi.com/when-the-saints-boogie-mid https://bitmidi.com/bar-code-boogie-mid https://bitmidi.com/boogie-in-gee-mid https://bitmidi.com/c-boogie-mid https://bitmidi.com/d-c-boogie-mid

/2. MIDIs/5. MIDI-Piano

https://sites.google.com/site/midipianos/piano-boogie-woogie

/2. MIDIs/6. MIDIs101

http://www.midis101.com/free_midi/21565/Andr_Boogie_Woogie_Bugle_Boy http://www.midis101.com/free_midi/42799/Countrymusic_Blues_Boogie_Woogie http://www.midis101.com/free_midi/27362/Eddy_Mitchell_Pas_De_Boogie_Woogie http://www.midis101.com/free_midi/36960/Unknown_Boogie_Woogie_1 http://www.midis101.com/free_midi/36961/Unknown_Boogie_Woogie_2

/2. MIDIs/7. AlLevy

https://www.alevy.com/others.htm https://www.alevy.com/ammons.htm https://www.alevy.com/cattlett.htm https://www.alevy.com/woody-bw.htm https://www.alevy.com/pjohnson.htm https://www.alevy.com/kersey.htm https://www.alevy.com/meadelux.htm https://www.alevy.com/lineham.htm https://www.alevy.com/raye.htm https://www.alevy.com/slack.htm https://www.alevy.com/gthomas.htm https://www.alevy.com/cwilliam.htm https://www.alevy.com/marylou.htm https://www.alevy.com/wilson.htm

/2. MIDIs/8. GarysMIDIParadise

http://www.garyrog.50megs.com/midi1.html

/2. MIDIs/9. NostalgicMIDIMusic

http://www.members.tripod.com/~rosemck1/boogie-woogie-bugle-boy.mid http://www.members.tripod.com/~rosemck1/boogie-woogie-bugl...

We provide an additional set of segmentation masks for jewelry in the 11K Hands dataset [1]. We filtered out a total of 3179 hands with jewelry and were manually annotated using CVAT. For ease of use, the maks have the same size and filename as the original images and are exported in png format. The pixel value represents whether jewelry exists, being 0 background and 1 jewelry.

The 11k Hands [1] dataset is a collection of 11,076 hand photos (1600 × 1200 pixels) from 190 people aged 18 to 75 years old. Each hand was shot from both the dorsal and palmar sides, on a uniform white background, at roughly the same distance from the camera. Each image has a metadata record that includes the following information: the subject ID, gender, age, skin color, and a set of information about the captured hand, such as right- or left-hand, hand side (dorsal or palmar), and logical indicators indicating whether the hand image contains accessories, nail polish, or irregularities. You can download here the original 11K Hands dataset and the metadata.

In the future, we will add our paper if accepted. In the meantime, if you use the masks provided on this webpage, please cite our DOI: 10.5281/zenodo.6541286 and the original 11K Hands paper.

[1] Mahmoud Afifi, "11K Hands: Gender recognition and biometric identification using a large dataset of hand images." Multimedia Tools and Applications, 2019.

movements

Introduction

In 2013, students of the Statistics class at "https://fses.uniba.sk/en/">FSEV UK were asked to invite their friends to participate in this survey.

• The data file (responses.csv) consists of 1010 rows and 150 columns (139 integer and 11 categorical).
• For convenience, the original variable names were shortened in the data file. See the columns.csv file if you want to match the data with the original names.
• The data contain missing values.
• The survey was presented to participants in both electronic and written form.
• The original questionnaire was in Slovak language and was later translated into English.
• All participants were of Slovakian nationality, aged between 15-30.

The variables can be split into the following groups:

• Music preferences (19 items)
• Movie preferences (12 items)
• Hobbies & interests (32 items)
• Phobias (10 items)
• Health habits (3 items)
• Personality traits, views on life, & opinions (57 items)
• Spending habits (7 items)
• Demographics (10 items)

Research questions

Many different techniques can be used to answer many questions, e.g.

• Clustering: Given the music preferences, do people make up any clusters of similar behavior?
• Hypothesis testing: Do women fear certain phenomena significantly more than men? Do the left handed people have different interests than right handed?
• Predictive modeling: Can we predict spending habits of a person from his/her interests and movie or music preferences?
• Dimension reduction: Can we describe a large number of human interests by a smaller number of latent concepts?
• Correlation analysis: Are there any connections between music and movie preferences?
• Visualization: How to effectively visualize a lot of variables in order to gain some meaningful insights from the data?
• (Multivariate) Outlier detection: Small number of participants often cheats and randomly answers the questions. Can you identify them? Hint: [Local outlier factor][1] may help.
• Missing values analysis: Are there any patterns in missing responses? What is the optimal way of imputing the values in surveys?
• Recommendations: If some of user's interests are known, can we predict the other? Or, if we know what a person listen, can we predict which kind of movies he/she might like?

Past research

• (in slovak) Sleziak, P. - Sabo, M.: Gender differences in the prevalence of specific phobias. Forum Statisticum Slovacum. 2014, Vol. 10, No. 6. [Differences (gender + whether people lived in village/town) in the prevalence of phobias.]

• Sabo, Miroslav. Multivariate Statistical Methods with Applications. Diss. Slovak University of Technology in Bratislava, 2014. [Clustering of variables (music preferences, movie preferences, phobias) + Clustering of people w.r.t. their interests.]

Questionnaire

MUSIC PREFERENCES

1. I enjoy listening to music.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
2. I prefer.: Slow paced music 1-2-3-4-5 Fast paced music (integer)
3. Dance, Disco, Funk: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
4. Folk music: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
5. Country: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
6. Classical: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
7. Musicals: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
8. Pop: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
9. Rock: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
10. Metal, Hard rock: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
11. Punk: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
12. Hip hop, Rap: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
13. Reggae, Ska: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
14. Swing, Jazz: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
15. Rock n Roll: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
16. Alternative music: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
17. Latin: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
18. Techno, Trance: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
19. Opera: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)

MOVIE PREFERENCES

1. I really enjoy watching movies.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
2. Horror movies: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
3. Thriller movies: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
4. Comedies: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
5. Romantic movies: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
6. Sci-fi movies: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
7. War movies: Don't enjoy at all 1-2-3-4-5 E...

COCO-WholeBody is an extension of COCO dataset with whole-body annotations. There are 4 types of bounding boxes (person box, face box, left-hand box, and right-hand box) and 133 keypoints (17 for body, 6 for feet, 68 for face and 42 for hands) annotations for each person in the image.

Urdu Handwriting Dataset for Demographic Traits Classification was developed in Bahria University, Islamabad, Pakistan as a part of the bachelor's degree final year thesis/project. This is a unique dataset which is the first of its kind. The dataset is composed of 1000 unique handwriting images each taken from unique individuals. It can be seen in the title, the handwriting samples are specifically in Urdu Language. Urdu Handwriting Dataset is made for the Classification of Demographic Traits problem due to which it consists of the demographic information of each individual. Following are the demographic traits that are covered in the dataset:

Gender (Male, Female)

Handedness (Left, Right)

Age-Group (15-20,21-30,31-40,41-50,51-up)

Province (Balochistan, Sindh, Punjab, kpk, gilgit-baltistan, none)

Occupation (Student, Employee, Both, None)

Education (Primary(Below Matriculation), Matriculation, Intermediate, Bachelors, Masters, PHD, None)

The EgoHands dataset contains 48 Google Glass videos of complex, first-person interactions between two people. The main intention of this dataset is to enable better, data-driven approaches to understanding hands in first-person computer vision. The dataset offers

high quality, pixel-level segmentations of hands the possibility to semantically distinguish between the observer’s hands and someone else’s hands, as well as left and right hands virtually unconstrained hand poses as actors freely engage in a set of joint activities lots of data with 15,053 ground-truth labeled hands

IntroductionThe lateral habenula (LHb) is an epithalamic nucleus associated with negative valence and affective disorders. It receives input via the stria medullaris (SM) and sends output via the fasciculus retroflexus (FR). Here, we use tractography to reconstruct and characterize this pathway.MethodsMulti-shell human diffusion magnetic resonance imaging (dMRI) data was obtained from the human connectome project (HCP) (n = 20, 10 males) and from healthy controls (n = 10, 6 males) scanned at our institution. We generated LHb afferents and efferents using probabilistic tractography by selecting the pallidum as the seed region and the ventral tegmental area as the output target.ResultsWe were able to reconstruct the intended streamlines in all individuals from the HCP dataset and our dataset. Our technique also aided in identification of the LHb. In right-handed individuals, the streamlines were significantly more numerous in the left hemisphere (mean ratio 1.59 ± 0.09, p = 0.04). In left-handed individuals, there was no hemispheric asymmetry on average (mean ratio 1.00 ± 0.09, p = 1.0). Additionally, these streamlines were significantly more numerous in females than in males (619.9 ± 159.7 vs. 225.9 ± 66.03, p = 0.04).ConclusionWe developed a method to reconstruct the SM and FR without manual identification of the LHb. This technique enables targeting of these fiber tracts as well as the LHb. Furthermore, we have demonstrated that there are sex and hemispheric differences in streamline number. These findings may have therapeutic implications and warrant further investigation.

Analysis of ‘Dementia Prediction Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/shashwatwork/dementia-prediction-dataset on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

Dementia is a syndrome – usually of a chronic or progressive nature – in which there is deterioration in cognitive function (i.e. the ability to process thought) beyond what might be expected from normal aging. It affects memory, thinking, orientation, comprehension, calculation, learning capacity, language, and judgment. Consciousness is not affected. The impairment in cognitive function is commonly accompanied and occasionally preceded, by deterioration in emotional control, social behaviou, or motivation.

Dementia results from a variety of diseases and injuries that primarily or secondarily affect the brain, such as Alzheimer's disease or stroke.

Dementia is one of the major causes of disability and dependency among older people worldwide. It can be overwhelming, not only for the people who have it, but also for their carers and families. There is often a lack of awareness and understanding of dementia, resulting in stigmatization and barriers to diagnosis and care. The impact of dementia on carers, family, and society at large can be physical, psychological, social and e and economic

Content

This set consists of a longitudinal collection of 150 subjects aged 60 to 96. Each subject was scanned on two or more visits, separated by at least one year for a total of 373 imaging sessions. For each subject, 3 or 4 individual T1-weighted MRI scans obtained in single scan sessions are included. The subjects are all right-handed and include both men and women. 72 of the subjects were characterized as nondemented throughout the study. 64 of the included subjects were characterized as demented at the time of their initial visits and remained so for subsequent scans, including 51 individuals with mild to moderate Alzheimer’s disease. Another 14 subjects were characterized as nondemented at the time of their initial visit and were subsequently characterized as demented at a later visit

Acknowledgements

Battineni, Gopi; Amenta, Francesco; Chintalapudi, Nalini (2019), “Data for: MACHINE LEARNING IN MEDICINE: CLASSIFICATION AND PREDICTION OF DEMENTIA BY SUPPORT VECTOR MACHINES (SVM)”, Mendeley Data, V1, doi: 10.17632/tsy6rbc5d4.1 * Dataset is available here.

--- Original source retains full ownership of the source dataset ---

The proximate mechanisms underlying animal personalities, i.e. consistent inter-individual differences in behaviour, are a matter of discussion. Brain lateralization, expressed as the preferred use of the contralateral limb, has been suggested as one of these mechanisms. In this study, we measured a proxy of brain lateralization in captive common marmosets (N = 28) by testing hand preference in a simple food-reaching task and evaluated personality by coding a wide range of behaviours observed in daily situations. We explored the links between personality and both direction and strength of hand preference, as well as age and sex, using linear models. Principal component analysis revealed that the stable behavioural variables were organized in three personality dimensions: Agreeableness, Extraversion and Neuroticism. Regarding hand preference, 14 individuals were left-handed, seven were right-handed and seven were ambilateral. Contrary to our predictions, we did not find any relationship between personality scores and hand preference or sex. Instead, age was a significant predictor of personality scores, with older individuals being more agreeable and less extraverted. The link between brain lateralization and personality seems to be equivocal and dependent on personality and brain lateralization assessment methods. Further examinations of other proximate mechanisms, such as physiology or (epi)genetics, may elucidate what drives personality variation in common marmosets.

The following data set has 10 raw recordings of 3 people each (People were anonymously classified from person 4 to 7) performing Sign Language and the First Ten Numbers of Mexican Sign Language. It is worth mentioning that, for each person, 5 recordings were made with the right hand and 5 with the left hand.

EEG dataset from the triple categorized MI-BCI of left and right hand and biped, with some data from stroke patients (number unknown) and some data from healthy individuals.