Version 4 of the dataset is available (Sep 19 2019)!

Note this version has significantly more data than Version 2.

Dataset description paper (full version) is available!

https://arxiv.org/pdf/1903.06754.pdf (updated Sep 7 2019)

Tools for visualizing the data is available!

https://github.com/corgiTrax/Gaze-Data-Processor

=========================== Dataset Description ===========================

We provide a large-scale, high-quality dataset of human actions with simultaneously recorded eye movements while humans play Atari video games. The dataset consists of 117 hours of gameplay data from a diverse set of 20 games, with 8 million action demonstrations and 328 million gaze samples. We introduce a novel form of gameplay, in which the human plays in a semi-frame-by-frame manner. This leads to near-optimal game decisions and game scores that are comparable or better than known human records. For every game frame, its corresponding image frame, the human keystroke action, the reaction time to make that action, the gaze positions, and immediate reward returned by the environment were recorded.

Q & A: Why frame-by-frame game mode?

Resolving state-action mismatch: Closed-loop human visuomotor reaction time is around 250-300 milliseconds. Therefore, during gameplay, state (image) and action that are simultaneously recorded at time step t could be mismatched. Action at time t could be intended for a state 250-300ms ago. This effect causes a serious issue for supervised learning algorithms, since label at and input st are no longer matched. Frame-by-frame game play ensures states and actions are matched at every timestep.

Maximizing human performance: Frame-by-frame mode makes gameplay more relaxing and reduces fatigue, which could normally result in blinking and would corrupt eye-tracking data. More importantly, this design reduces sub-optimal decisions caused by inattentive blindness.

Highlighting critical states that require multiple eye movements: Human decision time and all eye movements were recorded at every frame. The states that could lead to a large reward or penalty, or the ones that require sophisticated planning, will take longer and require multiple eye movements for the player to make a decision. Stopping gameplay means that the observer can use eye-movements to resolve complex situations. This is important because if the algorithm is going to learn from eye-movements it must contain all “relevant” eye-movements.

============================ Readme ============================

1. meta_data.csv: meta data for the dataset., including:

GameName: String. Game name. e.g., “alien” indicates the trial is collected for game Alien (15 min time limit). “alien_highscore” is the trajectory collected from the best player’s highest score (2 hour limit). See dataset description paper for details.

trial_id: Integer. One can use this number to locate the associated .tar.bz2 file and label file.

subject_id: Char. Human subject identifiers.

load_trial: Integer. 0 indicates that the game starts from scratch. If this field is non-zero, it means that the current trial continues from a saved trial. The number indicates the trial number to look for.

highest_score: Integer. The highest game score obtained from this trial.

total_frame: Number of image frames in the .tar.bz2 repository.

total_game_play_time: Integer. game time in ms.

total_episode: Integer. number of episodes in the current trial. An episode terminates when all lives are consumed.

avg_error: Float. Average eye-tracking validation error at the end of each trial in visual degree (1 visual degree = 1.44 cm in our experiment). See our paper for the calibration/validation process.

max_error: Float. Max eye-tracking validation error.

low_sample_rate: Percentage. Percentage of frames with less than 10 gaze samples. The most common reason for this is blinking.

frame_averaging: Boolean. The game engine allows one to turn this on or off. When turning on (TRUE), two consecutive frames are averaged, this alleviates screen flickering in some games.

fps: Integer. Frame per second when an action key is held down.

1. [game_name].zip files: these include data for each game, including:

*.tar.bz2 files: contains game image frames. The filename indicates its trial number.

*.txt files: label file for each trial, including:

frame_id: String. The ID of a frame, can be used to locate the corresponding image frame in .tar.bz2 file.

episode_id: Integer (not available for some trials). Episode number, starting from 0 for each trial. A trial could contain a single trial or multiple trials.

score: Integer (not available for some trials). Current game score for that frame.

duration(ms): Integer. Time elapsed until the human player made a decision.

unclipped_reward: Integer. Immediate reward returned by the game engine.

action: Integer. See action_enums.txt for the mapping. This is consistent with the Arcade Learning Environment setup.

gaze_positions: Null/A list of integers: x0,y0,x1,y1,...,xn,yn. Gaze positions for the current frame. Could be null if no gaze. (0,0) is the top-left corner. x: horizontal axis. y: vertical.

1. action_enums.txt: contains integer to action mapping defined by the Arcade Learning Environment.

============================ Citation ============================

If you use the Atari-HEAD in your research, we ask that you please cite the following:

@misc{zhang2019atarihead,

title={Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset},


author={Ruohan Zhang and Calen Walshe and Zhuode Liu and Lin Guan and Karl S. Muller and Jake A. Whritner and Luxin Zhang and Mary M. Hayhoe and Dana H. Ballard},


year={2019},


eprint={1903.06754},


archivePrefix={arXiv},


primaryClass={cs.LG}

}

Zhang, Ruohan, Zhuode Liu, Luxin Zhang, Jake A. Whritner, Karl S. Muller, Mary M. Hayhoe, and Dana H. Ballard. "AGIL: Learning attention from human for visuomotor tasks." In Proceedings of the European Conference on Computer Vision (ECCV), pp. 663-679. 2018.

@inproceedings{zhang2018agil,

title={AGIL: Learning attention from human for visuomotor tasks},

author={Zhang, Ruohan and Liu, Zhuode and Zhang, Luxin and Whritner, Jake A and Muller, Karl S and Hayhoe, Mary M and Ballard, Dana H},

booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},

pages={663--679},

year={2018}

}

US: Secure Internet Servers: per 1 Million People data was reported at 30,282.423 Number in 2017. This records an increase from the previous number of 11,423.281 Number for 2016. US: Secure Internet Servers: per 1 Million People data is updated yearly, averaging 4,713.247 Number from Dec 2010 (Median) to 2017, with 8 observations. The data reached an all-time high of 30,282.423 Number in 2017 and a record low of 2,481.625 Number in 2010. US: Secure Internet Servers: per 1 Million People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Telecommunication. The number of distinct, publicly-trusted TLS/SSL certificates found in the Netcraft Secure Server Survey.; ; Netcraft (http://www.netcraft.com/) and World Bank population estimates.; Weighted Average;

Unemployment Rate in the United States decreased to 4.10 percent in June from 4.20 percent in May of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This deposition includes the full HNOCA dataset for the following pre-print:

He, Z., Dony, L., Fleck, J.S. et al. An integrated transcriptomic cell atlas of human neural organoids. Nature 635, 690–698 (2024). https://doi.org/10.1038/s41586-024-08172-8

This file contains additional data representations and metadata from intermediate processing steps of the HNOCA. For day-to-day use of the HNOCA as a resource, we recommend using the cleaned-up HNOCA object that can be found together with the disease atlas and the extended version of HNOCA in the original Zenodo deposition.

Abstract:

Neural tissues generated from human pluripotent stem cells in vitro (known as neural organoids) are becoming useful tools to study human brain development, evolution and disease. The characterization of neural organoids using single-cell genomic methods has revealed a large diversity of neural cell types with molecular signatures similar to those observed in primary human brain tissue. However, it is unclear which domains of the human nervous system are covered by existing protocols. It is also difficult to quantitatively assess variation between protocols and the specific cell states in organoids as compared to primary counterparts. Single-cell transcriptome data from primary tissue and neural organoids derived with guided or unguided approaches and under diverse conditions combined with large-scale integrative analyses make it now possible to address these challenges. Recent advances in computational methodology enable the generation of integrated atlases across many data sets. Here, we integrated 36 single-cell transcriptomics data sets spanning 26 protocols into one integrated human neural organoid cell atlas (HNOCA) totaling over 1.7 million cells. We harmonize cell type annotations by incorporating reference data sets from the developing human brain. By mapping to the developing human brain reference, we reveal which primary cell states have been generated in vitro, and which are under-represented. We further compare transcriptomic profiles of neuronal populations in organoids to their counterparts in the developing human brain. To support rapid organoid phenotyping and quantitative assessment of new protocols, we provide a programmatic interface to browse the atlas and query new data sets, and showcase the power of the atlas to annotate new query data sets and evaluate new organoid protocols. Taken together, the HNOCA will be useful to assess the fidelity of organoids, characterize perturbed and diseased states and facilitate protocol development in the future.

The number of Twitter users in the United States was forecast to continuously increase between 2024 and 2028 by in total 4.3 million users (+5.32 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 85.08 million users and therefore a new peak in 2028. Notably, the number of Twitter users of was continuously increasing over the past years.User figures, shown here regarding the platform twitter, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Twitter users in countries like Canada and Mexico.

The number of Reddit users in the United States was forecast to continuously increase between 2024 and 2028 by in total 10.3 million users (+5.21 percent). After the ninth consecutive increasing year, the Reddit user base is estimated to reach 208.12 million users and therefore a new peak in 2028. Notably, the number of Reddit users of was continuously increasing over the past years.User figures, shown here with regards to the platform reddit, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once. Reddit users encompass both users that are logged in and those that are not.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Reddit users in countries like Mexico and Canada.

Population: Far East Federal District (FE) data was reported at 7,853,506.000 Person in 2024. This records a decrease from the previous number of 7,866,344.000 Person for 2023. Population: Far East Federal District (FE) data is updated yearly, averaging 7,782,742.500 Person from Dec 1989 (Median) to 2024, with 36 observations. The data reached an all-time high of 8,324,638.000 Person in 2011 and a record low of 6,284,932.000 Person in 2010. Population: Far East Federal District (FE) data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Global Database’s Russian Federation – Table RU.GA002: Population: by Region.

--- Context: ----------- **Annual Health Survey : Mortality Schedule ** This unit level dataset contains the details of Clinical, Anthropometric & Bio-chemical (CAB) Survey. To supplement the information provided by [Annual Health Survey](https://www.kaggle.com/rajanand/mortality) [(AHS)](https://nrhm-mis.nic.in/hmisreports/AHSReports.aspx),a biomarker component has been introduced in AHS to collect data on nutritional status, life style diseases like **diabetes & hypertension and anemia** in Empowered Action Group (EAG) States & Assam. This component, namely Clinical, Anthropometric and Bio-chemical (CAB) survey, is conducted on a sub-sample of AHS in all EAG States namely Bihar, Chhattisgarh, Jharkhand, Madhya Pradesh, Odisha, Rajasthan, Uttarakhand & Uttar Pradesh and Assam. There are total of 1.89million observations and 53 variables in this dataset. **[Survey:](http://www.who.int/bulletin/volumes/94/4/BLT-15-158493-table-T1.html)** Base line survey - 2010-11 (4.14 million households in the sample) 1st update - 2011-12 (4.28 million households in the sample) 2nd update - 2012-13 (4.32 million households in the sample) These nine states, which account for about 48 percent of the total population, 59 percent of Births, 70 percent of Infant Deaths, 75 percent of Under 5 Deaths and 62 percent of Maternal Deaths in the country, are the high focus States in view of their relatively higher fertility and mortality. Content: ----------- The files contains the below columns. **Variable Names:** 1. state_code 2. district_code 3. rural_urban 4. stratum 5. PSU_ID 6. ahs_house_unit 7. house_hold_no 8. date_survey 9. test_salt_iodine 10. record_code_iodine 11. record_code_iodine_reason 12. sl_no 13. Sex 14. usual_residance 15. usual_residance_Reason 16. identification_code 17. Age_Code 18. Age 19. date_of_birth 20. month_of_birth 21. year_of_birth 22. Weight_measured 23. Weight_in_kg 24. Length_height_measured 25. length_height_code 26. Length_height_cm 27. Haemoglobin_test 28. Haemoglobin 29. Haemoglobin_level 30. BP_systolic 31. BP_systolic_2_reading 32. BP_Diastolic 33. BP_Diastolic_2reading 34. Pulse_rate 35. Pulse_rate_2_reading 36. Diabetes_test 37. fasting_blood_glucose 38. fasting_blood_glucose_mg_dl 39. Marital_status 40. gauna_perfor_not_perfor 41. duration_pregnanacy 42. first_breast_feeding 43. is_cur_breast_feeding 44. day_or_month_for_breast_feeding_code 45. day_or_month_for_breast_feeding 46. water_month 47. ani_milk_month 48. semisolid_month_or_day 49. solid_month 50. vegetables_month_or_day 51. illness_type 52. illness_duration 53. treatment_type **File content:** Mortality_data_dictionary.xlsx : This [**data dictionary**](https://www.kaggle.com/rajanand/cab-survey/downloads/CAB_data_dictionary.xlsx) excel work book has the detailed information about each and every column and codes used in the data. Acknowledgements ---------------- [Department of Health and Family Welfare](https://nrhm-mis.nic.in/hmisreports/AHSReports.aspx), Govt. of India has published this data in [Open Govt Data Platform India portal](https://data.gov.in/catalog/annual-health-survey-clinical-anthropometric-bio-chemical-cab-survey) under [Govt. Open Data License - India](https://data.gov.in/government-open-data-license-india). ---

A dataset listing Washington cities by population for 2024.

A dataset listing Virginia cities by population for 2024.

A dataset listing Georgia cities by population for 2024.

A dataset listing Mississippi cities by population for 2024.

A dataset listing Montana cities by population for 2024.

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As of April 2024, around 16.5 percent of global active Instagram users were men between the ages of 18 and 24 years. More than half of the global Instagram population worldwide was aged 34 years or younger.

              Teens and social media

              As one of the biggest social networks worldwide, Instagram is especially popular with teenagers. As of fall 2020, the photo-sharing app ranked third in terms of preferred social network among teenagers in the United States, second to Snapchat and TikTok. Instagram was one of the most influential advertising channels among female Gen Z users when making purchasing decisions. Teens report feeling more confident, popular, and better about themselves when using social media, and less lonely, depressed and anxious.
              Social media can have negative effects on teens, which is also much more pronounced on those with low emotional well-being. It was found that 35 percent of teenagers with low social-emotional well-being reported to have experienced cyber bullying when using social media, while in comparison only five percent of teenagers with high social-emotional well-being stated the same. As such, social media can have a big impact on already fragile states of mind.