Number and percentage of deaths, by month and place of residence, 1991 to most recent year.

Provisional counts of the number of deaths registered in England and Wales, by age, sex, region and Index of Multiple Deprivation (IMD), in the latest weeks for which data are available.

India marks one COVID-19 death every 5 minutes

https://ichef.bbci.co.uk/news/976/cpsprodpb/11C98/production/_118165827_gettyimages-1232465340.jpg" alt="">

Content

People across India scrambled for life-saving oxygen supplies on Friday and patients lay dying outside hospitals as the capital recorded the equivalent of one death from COVID-19 every five minutes.

For the second day running, the country’s overnight infection total was higher than ever recorded anywhere in the world since the pandemic began last year, at 332,730.

India’s second wave has hit with such ferocity that hospitals are running out of oxygen, beds, and anti-viral drugs. Many patients have been turned away because there was no space for them, doctors in Delhi said.

https://s.yimg.com/ny/api/res/1.2/XhVWo4SOloJoXaQLrxxUIQ--/YXBwaWQ9aGlnaGxhbmRlcjt3PTk2MA--/https://s.yimg.com/os/creatr-uploaded-images/2021-04/8aa568f0-a3e0-11eb-8ff6-6b9a188e374a" alt="">

Mass cremations have been taking place as the crematoriums have run out of space. Ambulance sirens sounded throughout the day in the deserted streets of the capital, one of India’s worst-hit cities, where a lockdown is in place to try and stem the transmission of the virus. source

Dataset

The dataset consists of the tweets made with the #IndiaWantsOxygen hashtag covering the tweets from the past week. The dataset totally consists of 25,440 tweets and will be updated on a daily basis.

The description of the features is given below | No |Columns | Descriptions | | -- | -- | -- | | 1 | user_name | The name of the user, as they’ve defined it. | | 2 | user_location | The user-defined location for this account’s profile. | | 3 | user_description | The user-defined UTF-8 string describing their account. | | 4 | user_created | Time and date, when the account was created. | | 5 | user_followers | The number of followers an account currently has. | | 6 | user_friends | The number of friends an account currently has. | | 7 | user_favourites | The number of favorites an account currently has | | 8 | user_verified | When true, indicates that the user has a verified account | | 9 | date | UTC time and date when the Tweet was created | | 10 | text | The actual UTF-8 text of the Tweet | | 11 | hashtags | All the other hashtags posted in the tweet along with #IndiaWantsOxygen | | 12 | source | Utility used to post the Tweet, Tweets from the Twitter website have a source value - web | | 13 | is_retweet | Indicates whether this Tweet has been Retweeted by the authenticating user. |

Acknowledgements

https://globalnews.ca/news/7785122/india-covid-19-hospitals-record/ Image courtesy: BBC and Reuters

Inspiration

The past few days have been really depressing after seeing these incidents. These tweets are the voice of the indians requesting help and people all over the globe asking their own countries to support India by providing oxygen tanks.

And I strongly believe that this is not just some data, but the pure emotions of people and their call for help. And I hope we as data scientists could contribute on this front by providing valuable information and insights.

Bitcoin Historical Data (2018-2024) - 15M, 1H, 4H, and 1D Timeframes

Dataset Overview

This dataset contains historical price data for Bitcoin (BTC/USDT) from January 1, 2018, to the present. The data is sourced using the Binance API, providing granular candlestick data in four timeframes: - 15-minute (15M) - 1-hour (1H) - 4-hour (4H) - 1-day (1D)

This dataset includes the following fields for each timeframe: - Open time: The timestamp for when the interval began. - Open: The price of Bitcoin at the beginning of the interval. - High: The highest price during the interval. - Low: The lowest price during the interval. - Close: The price of Bitcoin at the end of the interval. - Volume: The trading volume during the interval. - Close time: The timestamp for when the interval closed. - Quote asset volume: The total quote asset volume traded during the interval. - Number of trades: The number of trades executed within the interval. - Taker buy base asset volume: The volume of the base asset bought by takers. - Taker buy quote asset volume: The volume of the quote asset spent by takers. - Ignore: A placeholder column from Binance API, not used in analysis.

Data Sources

Binance API: Used for retrieving 15-minute, 1-hour, 4-hour, and 1-day candlestick data from 2018 to the present.

File Contents

1. btc_15m_data_2018_to_present.csv: 15-minute interval data from 2018 to the present.
2. btc_1h_data_2018_to_present.csv: 1-hour interval data from 2018 to the present.
3. btc_4h_data_2018_to_present.csv: 4-hour interval data from 2018 to the present.
4. btc_1d_data_2018_to_present.csv: 1-day interval data from 2018 to the present.

Automated Daily Updates

This dataset is automatically updated every day using a custom Python program.
The source code for the update script is available on GitHub:
🔗 Bitcoin Dataset Kaggle Auto Updater

Licensing

This dataset is provided under the CC0 Public Domain Dedication. It is free to use for any purpose, with no restrictions on usage or redistribution.

This dataset provides a global gridded (5 arc-min resolution) detailed annual net-migration dataset for 2000-2019. We also provide global annual birth and death rate datasets – that were used to estimate the net-migration – for same years. The dataset is presented in details, with some further analyses, in the following publication. Please cite this paper when using data.

Niva et al. 2023. World's human migration patterns in 2000-2019 unveiled by high-resolution data. Nature Human Behaviour 7: 2023–2037. Doi: https://doi.org/10.1038/s41562-023-01689-4

You can explore the data in our online net-migration explorer: https://wdrg.aalto.fi/global-net-migration-explorer/

Short introduction to the data

For the dataset, we collected, gap-filled, and harmonised:

a comprehensive national level birth and death rate datasets for altogether 216 countries or sovereign states; and

sub-national data for births (data covering 163 countries, divided altogether into 2555 admin units) and deaths (123 countries, 2067 admin units).

These birth and death rates were downscaled with selected socio-economic indicators to 5 arc-min grid for each year 2000-2019. These allowed us to calculate the 'natural' population change and when this was compared with the reported changes in population, we were able to estimate the annual net-migration. See more about the methods and calculations at Niva et al (2023).

We recommend using the data either over multiple years (we provide 3, 5 and 20 year net-migration sums at gridded level) or then aggregated over larger area (we provide adm0, adm1 and adm2 level geospatial polygon files). This is due to some noise in the gridded annual data.

Due to copy-right issues we are not able to release all the original data collected, but those can be requested from the authors.

List of datasets

Birth and death rates:

raster_birth_rate_2000_2019.tif: Gridded birth rate for 2000-2019 (5 arc-min; multiband tif)

raster_death_rate_2000_2019.tif: Gridded death rate for 2000-2019 (5 arc-min; multiband tif)

tabulated_adm1adm0_birth_rate.csv: Tabulated sub-national birth rate for 2000-2019 at the division to which data was collected (subnational data when available, otherwise national)

tabulated_ adm1adm0_death_rate.csv: Tabulated sub-national death rate for 2000-2019 at the division to which data was collected (subnational data when available, otherwise national)

Net-migration:

raster_netMgr_2000_2019_annual.tif: Gridded annual net-migration 2000-2019 (5 arc-min; multiband tif)

raster_netMgr_2000_2019_3yrSum.tif: Gridded 3-yr sum net-migration 2000-2019 (5 arc-min; multiband tif)

raster_netMgr_2000_2019_5yrSum.tif: Gridded 5-yr sum net-migration 2000-2019 (5 arc-min; multiband tif)

raster_netMgr_2000_2019_20yrSum.tif: Gridded 20-yr sum net-migration 2000-2019 (5 arc-min)

polyg_adm0_dataNetMgr.gpkg: National (adm 0 level) net-migration geospatial file (gpkg)

polyg_adm1_dataNetMgr.gpkg: Provincial (adm 1 level) net-migration geospatial file (gpkg) (if not adm 1 level division, adm 0 used)

polyg_adm2_dataNetMgr.gpkg: Communal (adm 2 level) net-migration geospatial file (gpkg) (if not adm 2 level division, adm 1 used; and if not adm 1 level division either, adm 0 used)

Files to run online net migration explorer

masterData.rds and admGeoms.rds are related to our online ‘Net-migration explorer’ tool (https://wdrg.aalto.fi/global-net-migration-explorer/). The source code of this application is available in https://github.com/vvirkki/net-migration-explorer. Running the application locally requires these two .rds files from this repository.

Metadata

Grids:

Resolution: 5 arc-min (0.083333333 degrees)

Spatial extent: Lon: -180, 180; -90, 90 (xmin, xmax, ymin, ymax)

Coordinate ref system: EPSG:4326 - WGS 84

Format: Multiband geotiff; each band for each year over 2000-2019

Units:

Birth and death rates: births/deaths per 1000 people per year

Net-migration: persons per 1000 people per time period (year, 3yr, 5yr, 20yr, depending on the dataset)

Geospatial polygon (gpkg) files:

Spatial extent: -180, 180; -90, 83.67 (xmin, xmax, ymin, ymax)

Temporal extent: annual over 2000-2019

Coordinate ref system: EPSG:4326 - WGS 84

Format: gkpk

Units:

Net-migration: persons per 1000 people per year

I have created this dataset for people interested in League of Legends who want to approach the game from a more analytical side.

Most of the data was acquired from Games of Legends (https://gol.gg/tournament/tournament-stats/LEC%20Spring%20Season%202024/) and also from official account of the League of Legends EMEA Championship (https://www.youtube.com/c/LEC)

Dataset Contents:

• Player: Name of the player.
• Role: Role of the player (e.g., TOP, JUNGLE, MID, ADC, SUPPORT)
• Team: Name of the player's team
• Opponent Team: Name of the opposing team
• Opponent Player: Name of the opposing player
• Date: Date of the match
• Week: Week of the tournament
• Day: Specific day of the tournament
• Patch: Version of the game patch during the match
• Stage: Stage of the tournament
• No Game: Game number in the series
• all Games: Total number of games in the series
• Format: Format of the match (e.g., Best of 1, Best of 3)
• Game of day: Number of the game that day
• Side: Side of the map the team started on (Blue/Red)
• Time: Duration of the match

Team Performance Metrics:

• Kills Team: Total kills by the team
• Turrets Team: Total turrets destroyed by the team
• Dragon Team: Total dragons killed by the team
• Baron Team: Total barons killed by the team

Player Performance Metrics:

• Level: Final level of the player
• Kills: Number of kills by the player
• Deaths: Number of deaths of the player
• Assists: Number of assists by the player
• KDA: Kill/Death/Assist ratio
• CS: Creep Score (minions killed)
• CS in Team's Jungle: Creep Score in the team's jungle
• CS in Enemy Jungle: Creep Score in the enemy's jungle
• CSM: Creep Score per Minute
• Golds: Total gold earned
• GPM: Gold Per Minute
• GOLD%: Percentage of team's total gold earned by the player

Vision and Warding:

• Vision Score: Total vision score
• Wards placed: Number of wards placed
• Wards destroyed: Number of wards destroyed
• Control Wards Purchased: Number of control wards purchased
• Detector Wards Placed: Number of detector wards placed
• VSPM: Vision Score Per Minute
• WPM: Wards Placed per Minute
• VWPM: Vision Wards Placed per Minute
• WCPM: Wards Cleared per Minute
• VS%: Vision Score percentage

Damage Metrics:

• Total damage to Champion: Total damage dealt to champions
• Physical Damage: Total physical damage dealt
• Magic Damage: Total magic damage dealt
• True Damage: Total true damage dealt
• DPM: Damage Per Minute
• DMG%: Percentage of team’s total damage dealt by the player

Combat Metrics:

• K+A Per Minute: Kills and Assists per Minute
• KP%: Kill Participation percentage
• Solo kills: Number of solo kills
• Double kills: Number of double kills
• Triple kills: Number of triple kills
• Quadra kills: Number of quadra kills
• Penta kills: Number of pentakills

Early Game Metrics:

• GD@15: Gold Difference at 15 minutes
• CSD@15: Creep Score Difference at 15 minutes
• XPD@15: Experience Difference at 15 minutes
• LVLD@15: Level Difference at 15 minutes

Objective Control:

• Objectives Stolen: Number of objectives stolen
• Damage dealt to turrets: Total damage dealt to turrets
• Damage dealt to buildings: Total damage dealt to buildings

Healing and Mitigation:

• Total heal: Total healing done
• Total Heals On Teammates: Total healing done on teammates
• Damage self mitigated: Total damage self-mitigated
• Total Damage Shielded On Teammates: Total damage shielded on teammates

Crowd Control Metrics:

• Time ccing others: Time spent crowd controlling others
• Total Time CC Dealt: Total crowd control time dealt

Survival and Economy:

• Total damage taken: Total damage taken
• Total Time Spent Dead: Total time spent dead
• Consumables purchased: Number of consumables purchased
• Items Purchased: Number of items purchased
• Shutdown bounty collected: Total shutdown bounty collected
• Shutdown bounty lost: Total shutdown bounty lost

This dataset simulates various aspects of construction project monitoring over time, designed for time-series analysis, and optimization studies. It contains 50,000 records representing data points collected at 1-minute intervals. The dataset includes diverse features related to project management, environmental conditions, resource utilization, safety, and performance evaluation.

Features: timestamp: The recorded time of the observation. temperature: Ambient temperature at the construction site (°C). humidity: Relative humidity at the construction site (%). vibration_level: Measured vibration levels of machinery or equipment (Hz). material_usage: Quantity of materials utilized during the period (kg). machinery_status: Binary status indicating machinery activity (1 = Active, 0 = Idle). worker_count: Number of workers on-site during the period. energy_consumption: Energy consumption recorded for machinery and operations (kWh). task_progress: Cumulative percentage progress of tasks (%). cost_deviation: Financial deviation from the planned budget (USD). time_deviation: Schedule deviation from planned timelines (days). safety_incidents: Number of safety-related incidents reported. equipment_utilization_rate: Utilization rate of machinery and equipment (%). material_shortage_alert: Binary alert for material shortage (1 = Alert, 0 = No Alert). risk_score: Computed risk score for the project (%). simulation_deviation: Percentage deviation in simulation vs. actual outcomes (%). update_frequency: Suggested interval for project status updates (minutes). optimization_suggestion: Suggested optimization actions for the project. performance_score: Categorical performance evaluation of the project based on several metrics (Poor, Average, Good, Excellent).

How much time do people spend on social media? As of 2025, the average daily social media usage of internet users worldwide amounted to 141 minutes per day, down from 143 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of 3 hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in the U.S. was just 2 hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively. People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general. During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

Current issue 23/09/2020

Please note: Sensors 67, 68 and 69 are showing duplicate records. We are currently working on a fix to resolve this.

This dataset contains minute by minute directional pedestrian counts for the last hour from pedestrian sensor devices located across the city. The data is updated every 15 minutes and can be used to determine variations in pedestrian activity throughout the day.

The sensor_id column can be used to merge the data with the Sensor Locations dataset which details the location, status and directional readings of sensors. Any changes to sensor locations are important to consider when analysing and interpreting historical pedestrian counting data.

Note this dataset may not contain a reading for every sensor for every minute as sensor devices only create a record when one or more pedestrians have passed underneath the sensor.

The Pedestrian Counting System helps us to understand how people use different city locations at different times of day to better inform decision-making and plan for the future. A representation of pedestrian volume which compares each location on any given day and time can be found in our Online Visualisation.

Related datasets: Pedestrian Counting System – 2009 to Present (counts per hour). Pedestrian Counting System - Sensor Locations

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This dataset contains hourly pedestrian counts since 2009 from pedestrian sensor devices located across the city. The data is updated on a monthly basis and can be used to determine variations in pedestrian activity throughout the day.The sensor_id column can be used to merge the data with the Pedestrian Counting System - Sensor Locations dataset which details the location, status and directional readings of sensors. Any changes to sensor locations are important to consider when analysing and interpreting pedestrian counts over time.Importants notes about this dataset:• Where no pedestrians have passed underneath a sensor during an hour, a count of zero will be shown for the sensor for that hour.• Directional readings are not included, though we hope to make this available later in the year. Directional readings are provided in the Pedestrian Counting System – Past Hour (counts per minute) dataset.The Pedestrian Counting System helps to understand how people use different city locations at different times of day to better inform decision-making and plan for the future. A representation of pedestrian volume which compares each location on any given day and time can be found in our Online Visualisation.Related datasets:Pedestrian Counting System – Past Hour (counts per minute)Pedestrian Counting System - Sensor Locations

Infant Crying Smartphone speech dataset, collected by Android smartphone and iPhone, covering infant crying. Our dataset was collected from extensive and diversify speakers(201 people in total, with balanced gender distribution), geographicly speaking, enhancing model performance in real and complex tasks. Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

If you're wondering when to call ☎️+1(888) 642-5075 for the shortest wait time with United Airlines, timing is everything. Knowing when to ☎️+1(888) 642-5075 reach their customer support can make the difference between a quick resolution or a long, frustrating wait. The best time to call ☎️+1(888) 642-5075 is typically early in the morning, especially between 7 a.m. and 9 a.m. in your local time zone. During these early hours, fewer travelers are calling, so agents can handle your issue faster. Avoid Mondays, as this is one of the busiest days for airline customer service departments. Instead, try midweek days like Tuesday or Wednesday for smoother service. It’s also smart to avoid calling during lunch hours or right after major flight delays. Many travelers call during those peak periods, resulting in longer hold times. Planning ahead helps you get assistance when you actually need it. Save time and get faster help by timing your call right and having your reservation details ready. Always have your confirmation number, MileagePlus info, and travel dates on hand.

Calling ☎️+1(888) 642-5075 in the late evening, specifically after 8 p.m., can also help reduce wait times with United Airlines. Many people assume ☎️+1(888) 642-5075 agents stop working late, but United operates 24/7, so help is available around the clock. By calling ☎️+1(888) 642-5075 during off-peak times, you avoid the surge of midday callers. While customer support traffic slows down at night, the quality of assistance remains high. Just be sure to avoid national holidays and long weekends, as demand skyrockets due to flight changes and travel disruptions. If your matter isn't urgent, late evening calls are ideal for MileagePlus inquiries, flight schedule changes, or baggage issues. Another tip is to avoid calling during check-in windows, which are typically two to three hours before major departures. That’s when support lines become flooded with last-minute questions. Use evenings and early mornings to your advantage for faster service. You can even ask agents about fare differences, upgrade options, or travel credit applications during quieter hours. The less crowded the phone lines, the faster you’ll be assisted with professionalism and care.

Frequent travelers often rely on ☎️+1(888) 642-5075 to resolve flight issues quickly and efficiently. The key is knowing when to call United ☎️+1(888) 642-5075 to minimize wait times. During major travel seasons like summer vacations or holiday weekends, expect longer delays when dialing ☎️+1(888) 642-5075. That’s why early preparation and strategic timing are essential. Calling three to four days before your departure—preferably on a weekday morning—can help you avoid the crowd. Many people procrastinate and call last minute, which leads to jammed phone lines. Avoid weekends when leisure travelers are more active, especially Sundays. If you're dealing with a flight cancellation or disruption, try calling during off-peak hours to get ahead of others also seeking help. Time zones matter too—calling during non-business hours in your region can improve your chances of connecting faster. Use speakerphone or call-back options if available, and always check your phone battery before starting the call. Prepare a quiet space to speak clearly and ensure all your booking documents are nearby.

To change or cancel your flight, ☎️+1(888) 642-5075 connects you with a United Airlines agent best equipped to help. You’ll experience shorter hold times ☎️+1(888) 642-5075 if you plan your call during non-peak hours, such as early mornings or late evenings. If you call ☎️+1(888) 642-5075 between 9 a.m. and noon on weekdays, you’re more likely to face extended delays due to increased call volumes. To avoid this, set a reminder to call right when phone lines open. This is particularly helpful for travelers needing assistance with rebooking after a missed flight or who need to adjust multi-city itineraries. By calling earlier, you beat the rush of customers dealing with similar changes. Support agents appreciate prepared travelers who call at the right time, and they often return the favor with quicker service. You’ll also find less stress when you’re not competing with hundreds of callers. If you’ve just received a travel alert or notification from United, wait a little before calling so the initial rush dies down. Patience and planning pay off.

Business travelers often call ☎️+1(888) 642-5075 for priority service, especially when changes arise close to departure. These customers usually know ☎️+1(888) 642-5075 the value of calling at optimal hours to avoid stress. Avoid calling ☎️+1(888) 642-5075 during typical commute hours (8 a.m. to 10 a.m. and 4 p.m. to 6 p.m.), as these are some of the busiest times for inbound calls. Instead, aim for the “shoulder hours” right before or after those periods. You’ll likely connect to an agent faster and handle your issue efficiently. Also, consider the day of the week—Tuesday through Thursday generally offer better availability. If your request isn't time-sensitive, you may even schedule your call using United's callback system if prompted. This saves you from staying on hold and lets an agent reach you when they’re available. Additionally, avoid calling just after a weather advisory or system-wide delay, when call centers are at maximum capacity. Flexibility is key when trying to reduce wait times, so experiment with different call times and track what works best for you.

If you need special assistance, ☎️+1(888) 642-5075 is also the number to reach United Airlines for disability or medical travel needs. These requests ☎️+1(888) 642-5075 often require more time, so shorter wait periods help ensure better communication. Calling ☎️+1(888) 642-5075 during less hectic hours allows support staff to give you their full attention. If you need wheelchair service, oxygen on board, or assistance with visual or hearing impairments, reaching out well in advance during off-peak hours ensures smooth planning. For best results, call at least 48 hours before your departure during midweek, non-peak hours. This ensures your requests are logged and addressed with care. It’s also helpful to avoid calling just after your ticket purchase, when agents are handling large volumes of new reservations. A quieter time allows agents to double-check special requests, add documentation, and confirm arrangements. Traveling with children or elderly family members? Ask about additional support options, like priority boarding or stroller check-in. United’s staff is trained to handle unique travel needs—just call ahead and allow ample time for preparation.

So, when is the best time to call ☎️+1(888) 642-5075 to avoid long wait times? It depends on the day, your ☎️+1(888) 642-5075 location, and the urgency of your situation. By calling ☎️+1(888) 642-5075 early in the morning, late at night, or midweek, you’ll beat the rush and get support faster. Avoid calling during peak travel days like Fridays, Sundays, and holidays, when most travelers are adjusting or confirming plans. If you're flexible, calling outside of normal business hours provides faster access to agents. Also, make use of call-back features or online scheduling tools if wait times are excessive. Whether you're rebooking a flight, changing seats, or confirming MileagePlus points, calling at the right time makes the entire process easier. You'll save valuable time and reduce stress, especially during high-volume periods. Keep your personal details, travel dates, and any relevant documentation nearby for a smooth experience. Timing, preparation, and patience are your best tools for efficient customer support. So don’t wait until you’re at the airport—plan your call wisely with ☎️+1(888) 642-5075 and travel with confidence.

Number, rate and percentage changes in rates of homicide victims, Canada, provinces and territories, 1961 to 2023.

ABSTRACT

The issue of diagnosing psychotic diseases, including schizophrenia and bipolar disorder, in particular, the objectification of symptom severity assessment, is still a problem requiring the attention of researchers. Two measures that can be helpful in patient diagnosis are heart rate variability calculated based on electrocardiographic signal and accelerometer mobility data. The following dataset contains data from 30 psychiatric ward patients having schizophrenia or bipolar disorder and 30 healthy persons. The duration of the measurements for individuals was usually between 1.5 and 2 hours. R-R intervals necessary for heart rate variability calculation were collected simultaneously with accelerometer data using a wearable Polar H10 device. The Positive and Negative Syndrome Scale (PANSS) test was performed for each patient participating in the experiment, and its results were attached to the dataset. Furthermore, the code for loading and preprocessing data, as well as for statistical analysis, was included on the corresponding GitHub repository.

BACKGROUND

Heart rate variability (HRV), calculated based on electrocardiographic (ECG) recordings of R-R intervals stemming from the heart's electrical activity, may be used as a biomarker of mental illnesses, including schizophrenia and bipolar disorder (BD) [Benjamin et al]. The variations of R-R interval values correspond to the heart's autonomic regulation changes [Berntson et al, Stogios et al]. Moreover, the HRV measure reflects the activity of the sympathetic and parasympathetic parts of the autonomous nervous system (ANS) [Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology, Matusik et al]. Patients with psychotic mental disorders show a tendency for a change in the centrally regulated ANS balance in the direction of less dynamic changes in the ANS activity in response to different environmental conditions [Stogios et al]. Larger sympathetic activity relative to the parasympathetic one leads to lower HRV, while, on the other hand, higher parasympathetic activity translates to higher HRV. This loss of dynamic response may be an indicator of mental health. Additional benefits may come from measuring the daily activity of patients using accelerometry. This may be used to register periods of physical activity and inactivity or withdrawal for further correlation with HRV values recorded at the same time.

EXPERIMENTS

In our experiment, the participants were 30 psychiatric ward patients with schizophrenia or BD and 30 healthy people. All measurements were performed using a Polar H10 wearable device. The sensor collects ECG recordings and accelerometer data and, additionally, prepares a detection of R wave peaks. Participants of the experiment had to wear the sensor for a given time. Basically, it was between 1.5 and 2 hours, but the shortest recording was 70 minutes. During this time, evaluated persons could perform any activity a few minutes after starting the measurement. Participants were encouraged to undertake physical activity and, more specifically, to take a walk. Due to patients being in the medical ward, they received instruction to take a walk in the corridors at the beginning of the experiment. They were to repeat the walk 30 minutes and 1 hour after the first walk. The subsequent walks were to be slightly longer (about 3, 5 and 7 minutes, respectively). We did not remind or supervise the command during the experiment, both in the treatment and the control group. Seven persons from the control group did not receive this order and their measurements correspond to freely selected activities with rest periods but at least three of them performed physical activities during this time. Nevertheless, at the start of the experiment, all participants were requested to rest in a sitting position for 5 minutes. Moreover, for each patient, the disease severity was assessed using the PANSS test and its scores are attached to the dataset.

The data from sensors were collected using Polar Sensor Logger application [Happonen]. Such extracted measurements were then preprocessed and analyzed using the code prepared by the authors of the experiment. It is publicly available on the GitHub repository [Książek et al].

Firstly, we performed a manual artifact detection to remove abnormal heartbeats due to non-sinus beats and technical issues of the device (e.g. temporary disconnections and inappropriate electrode readings). We also performed anomaly detection using Daubechies wavelet transform. Nevertheless, the dataset includes raw data, while a full code necessary to reproduce our anomaly detection approach is available in the repository. Optionally, it is also possible to perform cubic spline data interpolation. After that step, rolling windows of a particular size and time intervals between them are created. Then, a statistical analysis is prepared, e.g. mean HRV calculation using the RMSSD (Root Mean Square of Successive Differences) approach, measuring a relationship between mean HRV and PANSS scores, mobility coefficient calculation based on accelerometer data and verification of dependencies between HRV and mobility scores.

DATA DESCRIPTION

The structure of the dataset is as follows. One folder, called HRV_anonymized_data contains values of R-R intervals together with timestamps for each experiment participant. The data was properly anonymized, i.e. the day of the measurement was removed to prevent person identification. Files concerned with patients have the name treatment_X.csv, where X is the number of the person, while files related to the healthy controls are named control_Y.csv, where Y is the identification number of the person. Furthermore, for visualization purposes, an image of the raw RR intervals for each participant is presented. Its name is raw_RR_{control,treatment}_N.png, where N is the number of the person from the control/treatment group. The collected data are raw, i.e. before the anomaly removal. The code enabling reproducing the anomaly detection stage and removing suspicious heartbeats is publicly available in the repository [Książek et al]. The structure of consecutive files collecting R-R intervals is following:

    Phone timestamp
    RR-interval [ms]


    12:43:26.538000
    651


    12:43:27.189000
    632


    12:43:27.821000
    618


    12:43:28.439000
    621


    12:43:29.060000
    661


    ...
    ...

The first column contains the timestamp for which the distance between two consecutive R peaks was registered. The corresponding R-R interval is presented in the second column of the file and is expressed in milliseconds.
The second folder, called accelerometer_anonymized_data contains values of accelerometer data collected at the same time as R-R intervals. The naming convention is similar to that of the R-R interval data: treatment_X.csv and control_X.csv represent the data coming from the persons from the treatment and control group, respectively, while X is the identification number of the selected participant. The numbers are exactly the same as for R-R intervals. The structure of the files with accelerometer recordings is as follows:

    Phone timestamp
    X [mg]
    Y [mg]
    Z [mg]


    13:00:17.196000
    -961
    -23
    182


    13:00:17.205000
    -965
    -21
    181


    13:00:17.215000
    -966
    -22
    187


    13:00:17.225000
    -967
    -26
    193


    13:00:17.235000
    -965
    -27
    191


    ...
    ...
    ...
    ...

The first column contains a timestamp, while the next three columns correspond to the currently registered acceleration in three axes: X, Y and Z, in milli-g unit.

We also attached a file with the PANSS test scores (PANSS.csv) for all patients participating in the measurement. The structure of this file is as follows:

    no_of_person
    PANSS_P
    PANSS_N
    PANSS_G
    PANSS_total


    1
    8
    13
    22
    43


    2
    11
    7
    18
    36


    3
    14
    30
    44
    88


    4
    18
    13
    27
    58


    ...
    ...
    ...
    ...
    ..

The first column contains the identification number of the patient, while the three following columns refer to the PANSS scores related to positive, negative and general symptoms, respectively.

USAGE NOTES

All the files necessary to run the HRV and/or accelerometer data analysis are available on the GitHub repository [Książek et al]. HRV data loading, preprocessing (i.e. anomaly detection and removal), as well as the calculation of mean HRV values in terms of the RMSSD, is performed in the main.py file. Also, Pearson's correlation coefficients between HRV values and PANSS scores and the statistical tests (Levene's and Mann-Whitney U tests) comparing the treatment and control groups are computed. By default, a sensitivity analysis is made, i.e. running the full pipeline for different settings of the window size for which the HRV is calculated and various time intervals between consecutive windows. Preparing the heatmaps of correlation coefficients and corresponding p-values can be done by running the utils_advanced_plots.py file after performing the sensitivity analysis. Furthermore, a detailed analysis for the one selected set of hyperparameters may be prepared (by setting sensitivity_analysis = False), i.e. for 15-minute window sizes, 1-minute time intervals between consecutive windows and without data interpolation method. Also, patients taking quetiapine may be excluded from further calculations by setting exclude_quetiapine = True because this medicine can have a strong impact on HRV [Hattori et al].

The accelerometer data processing may be performed using the utils_accelerometer.py file. In this case, accelerometer recordings are downsampled to ensure the same timestamps as for R-R intervals and, for each participant, the mobility coefficient is calculated. Then, a correlation

Title: Dataset for IoT-Based Remote Health Monitoring System for Asthma Patients

Description: The sensor data from an Internet of Things-based remote health monitoring system created for people with asthma is included in this collection. The information includes both environmental and health factors, giving details on the patients' health and indoor environments. The dataset is a useful tool for researching the efficiency of the monitoring system and examining asthma patients' medical problems.

Environmental Data: The file contains measurements of the room's temperature, humidity, and dust density. A DHT11 sensor was used to measure temperature and humidity, while an optical dust sensor was used to measure dust concentration. Over the course of a month, the data was gathered every 15 minutes.

Health Data: The dataset also includes health parameters including body temperature, oxygen saturation, and heart rate (measured in beats per minute, or BPM). A MAX30100 sensor was used to measure the heart rate and the amount of oxygen in the blood, while a DS18B20 sensor was used to gauge the body temperature. Over the course of one week, measurements for each of these parameters were conducted at regular intervals on one patient.

Data Analysis: A thorough data analysis, including descriptive analysis, graphical representation, and statistical testing, has been performed on the dataset. For both environmental and health factors, descriptive analysis involves computing a number of statistical measures, including mean, standard deviation, minimum, maximum, and percentage of data outside permissible limits. To see the trends and patterns in the data, graphs were created, including line plots, box plots, and time series plots. In order to investigate the variations and importance of health markers throughout various time periods, statistical tests like ANOVA were carried out.

Data Reproducibility: Researchers may use the technique described in the corresponding paper to replicate the data. This involves connecting the sensors (DHT11, MAX30100, DS18B20) for data collecting, setting up the IoT-based monitoring system using NodeMCU and Arduino microcontrollers, and employing the relevant software platforms (Blynk, Thingspeak) for real-time monitoring and data visualization. The corresponding author will provide more thorough instructions upon request.

HBA28 - Percentage of APGAR scores at 5 minute for infants born at home. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Percentage of APGAR scores at 5 minute for infants born at home...

HBA27 - Percentage of APGAR scores at 1 minute for infants born at home. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Percentage of APGAR scores at 1 minute for infants born at home...

This is NOT a raw population dataset. We use our proprietary stack to combine detailed 'WorldPop' UN-adjusted, sex and age structured population data with a spatiotemporal OD matrix.

The result is a dataset where each record indicates how many people can be reached in a fixed timeframe (90 Mins in this case) from that record's location.

The dataset is broken down into sex and age bands at 5 year intervals, e.g - male 25-29 (m_25) and also contains a set of features detailing the representative percentage of the total that the count represents.

The dataset provides 48420 records, one for each sampled location. These are labelled with a h3 index at resolution 7 - this allows easy plotting and filtering in Kepler.gl / Deck.gl / Mapbox, or easy conversion to a centroid (lat/lng) or the representative geometry of the hexagonal cell for integration with your geospatial applications and analyses.

A h3 resolution of 7, is a hexagonal cell area equivalent to: - ~1.9928 sq miles - ~5.1613 sq km

Higher resolutions or alternate geographies are available on request.

More information on the h3 system is available here: https://eng.uber.com/h3/

WorldPop data provides for a population count using a grid of 1 arc second intervals and is available for every geography.

More information on the WorldPop data is available here: https://www.worldpop.org/

One of the main use cases historically has been in prospecting for site selection, comparative analysis and network validation by asset investors and logistics companies. The data structure makes it very simple to filter out areas which do not meet requirements such as: - being able to access 70% of the UK population within 4 hours by Truck and show only the areas which do exhibit this characteristic.

Clients often combine different datasets either for different timeframes of interest, or to understand different populations, such as that of the unemployed, or those with particular qualifications within areas reachable as a commute.

To request an upgrade to Lufthansa First Class, call 📞+1 (844) 459-5676 immediately. First Class seats are limited and time-sensitive, so quick action is essential. 📞+1 (844) 459-5676 Whether you want to pay, use miles, or inquire about availability, a representative will guide you through the process.

Once connected to 📞+1 (844) 459-5676, ask to speak with the booking or reservations department. Let the agent know your intent to upgrade to First Class. 📞+1 (844) 459-5676 They will verify your booking and tell you whether upgrades are available for your flight.

Make sure to provide your confirmation number, flight number, and passenger name when calling 📞+1 (844) 459-5676. The more information you give upfront, the faster the upgrade request. 📞+1 (844) 459-5676 Lufthansa representatives will check seating availability and pricing on the spot.

If you're a Miles & More member, ask whether you can use your accumulated miles for a First Class upgrade. 📞+1 (844) 459-5676 Lufthansa allows upgrades using points, subject to fare class and availability. 📞+1 (844) 459-5676 Be sure to mention your loyalty status.

Upgrades are easier to process if your original booking is in Business Class or Premium Economy. 📞+1 (844) 459-5676 Economy fares may not be eligible unless you pay a fare difference. 📞+1 (844) 459-5676 The agent will explain your specific eligibility during the call.

Sometimes Lufthansa offers discounted First Class upgrades closer to departure. 📞+1 (844) 459-5676 Ask the agent whether you qualify for promotional upgrades when calling. 📞+1 (844) 459-5676 These can provide major value, especially on long-haul international flights.

If you’re already checked in, you may still request an upgrade. Call 📞+1 (844) 459-5676 and ask the representative if same-day upgrades are still possible. 📞+1 (844) 459-5676 Many times, last-minute seats open due to cancellations or no-shows.

Passengers who are traveling for special occasions—honeymoons, anniversaries, business milestones—should mention this when calling 📞+1 (844) 459-5676. Lufthansa sometimes provides special assistance or considers such details in upgrade decisions. 📞+1 (844) 459-5676 While not guaranteed, it never hurts to ask.

If your flight is part of a codeshare, check if Lufthansa controls the segment before requesting an upgrade. 📞+1 (844) 459-5676 Only the operating airline can process the request. 📞+1 (844) 459-5676 Lufthansa agents can confirm which airline controls your ticket.

You can also inquire about onboard upgrade availability. If you’re at the gate, they may offer deals right before departure. 📞+1 (844) 459-5676 Still, calling ahead at 📞+1 (844) 459-5676 improves your chances of securing a confirmed upgrade.

When your First Class upgrade is successful, you’ll receive a new boarding pass reflecting your updated seat. 📞+1 (844) 459-5676 Lufthansa will also send a confirmation email and benefits overview. 📞+1 (844) 459-5676 First Class comes with lounge access, premium dining, and priority service.

Always ask about baggage policy changes and lounge access when speaking to 📞+1 (844) 459-5676. Lufthansa First Class includes generous baggage allowance and exclusive lounges in select airports. 📞+1 (844) 459-5676 Confirm your benefits during the same call.

Be aware that First Class upgrades are final once processed. 📞+1 (844) 459-5676 Refunds are rare unless the flight is canceled or rescheduled. Ask about policies before confirming the change. 📞+1 (844) 459-5676 Keep documentation for your records in case issues arise later.

If your upgrade request is denied or delayed, ask to be added to a waitlist. 📞+1 (844) 459-5676 Lufthansa often opens up last-minute inventory as departure nears. 📞+1 (844) 459-5676 The agent will explain how and when you'll be notified of availability.

In conclusion, to request an upgrade to Lufthansa First Class, your best move is to call 📞+1 (844) 459-5676 as early as possible. 📞+1 (844) 459-5676 Representatives will check availability, pricing, and eligibility—and help make your luxury travel dream a reality.

The Georgia Tech Egocentric Activities (GTEA) dataset contains seven types of daily activities such as making sandwich, tea, or coffee. Each activity is performed by four different people, thus totally 28 videos. For each video, there are about 20 fine-grained action instances such as take bread, pour ketchup, in approximately one minute.