The summary consists of five values: minimum, first quartile (25th percentile), median (50th percentile), third quartile (75th percentile), and maximum. (XLSX)

Data for Figure SPM.8 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure SPM.8 shows selected indicators of global climate change under the five core scenarios used in this report.

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:

IPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32, doi:10.1017/9781009157896.001.

Figure subpanels

The figure has five panels, with data provided for all panels in subdirectories named panel_a, panel_b, panel_c, panel_d and panel_e.

List of data provided

This dataset contains:

• Historical, SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 Global Surface Air Temperature (GSAT) anomalies relative to 1850-1900 (20 year means)
• Historical, SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 September sea-ice area
• Historical, SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 Global ocean surface pH
• Historical sea level relative to 1900 from gauges (to 1992) and altimeters (1993 on) (offset 0.158 m vs. 1995-2014)
• AR6 sea level projections relative to 1900 (offset 0.158 m vs. 1995-2014)
• AR6 assessed global mean sea level at 2300 relative to 1900 (offset 0.158 m vs. 1995-2014)

The five illustrative SSP (Shared Socio-economic Pathway) scenarios are described in Box SPM.1 of the Summary for Policymakers and Section 1.6.1.1 of Chapter 1.

Data provided in relation to figure

Panel a: Near-Surface Air Temperature

• Data file: panel_a/tas_global_Historical.csv (black line and grey shading)
• Data file: panel_a/tas_global_SSP1_1_9.csv (cyan line)
• Data file: panel_a/tas_global_SSP1_2_6.csv (blue line and blue shading)
• Data file: panel_a/tas_global_SSP2_4_5.csv (orange line)
• Data file: panel_a/tas_global_SSP3_7_0.csv (red line and red shading)
• Data file: panel_a/tas_global_SSP5_8_5.csv (brown line)

Panel b: Sea-Ice Area

• Data file: panel_b/sia_arctic_september_Historical.csv (black line and grey shading)
• Data file: panel_b/sia_arctic_september_SSP1_1_9.csv (cyan line)
• Data file: panel_b/sia_arctic_september_SSP1_2_6.csv (blue line and blue shading)
• Data file: panel_b/sia_arctic_september_SSP2_4_5.csv (orange line)
• Data file: panel_b/sia_arctic_september_SSP3_7_0.csv (red line and red shading)
• Data file: panel_b/sia_arctic_september_SSP5_8_5.csv (brown line)

Panel c: Ocean Surface pH

• Data file: panel_c/phos_global_Historical.csv (black line and grey shading
• Data file: panel_c/phos_global_SSP1_1_9.csv (cyan line
• Data file: panel_b/phos_global_SSP1_2_6.csv (blue line and blue shading)
• Data file: panel_c/phos_global_SSP2_4_5.csv (orange line)
• Data file: panel_c/phos_global_SSP3_7_0.csv (red line and red shading)
• Data file: panel_c/phos_global_SSP5_8_5.csv (brown line)

Panel d: Sea Level

• Data file: panel_d/global_sea_level_observed.csv (black line)
• Data file: panel_d/global_sea_level_projected.csv (cyan, blue, orange, red and brown lines, red and blue shading)

Panel e: Sea Level

• Data file: panel_e: global_sea_level_2300_assessed.csv (columns 2 and 3, SSP1-2.6 scenario; columns 4 to 6 SSP5-8.5 scenario)

Sources of additional information

The following weblinks are provided in the Related Documents section of this catalogue record:

• Link to the report component containing the figure (Summary for Policymakers)

The Bureau of the Census has released Census 2000 Summary File 1 (SF1) 100-Percent data. The file includes the following population items: sex, age, race, Hispanic or Latino origin, household relationship, and household and family characteristics. Housing items include occupancy status and tenure (whether the unit is owner or renter occupied). SF1 does not include information on incomes, poverty status, overcrowded housing or age of housing. These topics will be covered in Summary File 3. Data are available for states, counties, county subdivisions, places, census tracts, block groups, and, where applicable, American Indian and Alaskan Native Areas and Hawaiian Home Lands. The SF1 data are available on the Bureau's web site and may be retrieved from American FactFinder as tables, lists, or maps. Users may also download a set of compressed ASCII files for each state via the Bureau's FTP server. There are over 8000 data items available for each geographic area. The full listing of these data items is available here as a downloadable compressed data base file named TABLES.ZIP. The uncompressed is in FoxPro data base file (dbf) format and may be imported to ACCESS, EXCEL, and other software formats. While all of this information is useful, the Office of Community Planning and Development has downloaded selected information for all states and areas and is making this information available on the CPD web pages. The tables and data items selected are those items used in the CDBG and HOME allocation formulas plus topics most pertinent to the Comprehensive Housing Affordability Strategy (CHAS), the Consolidated Plan, and similar overall economic and community development plans. The information is contained in five compressed (zipped) dbf tables for each state. When uncompressed the tables are ready for use with FoxPro and they can be imported into ACCESS, EXCEL, and other spreadsheet, GIS and database software. The data are at the block group summary level. The first two characters of the file name are the state abbreviation. The next two letters are BG for block group. Each record is labeled with the code and name of the city and county in which it is located so that the data can be summarized to higher-level geography. The last part of the file name describes the contents . The GEO file contains standard Census Bureau geographic identifiers for each block group, such as the metropolitan area code and congressional district code. The only data included in this table is total population and total housing units. POP1 and POP2 contain selected population variables and selected housing items are in the HU file. The MA05 table data is only for use by State CDBG grantees for the reporting of the racial composition of beneficiaries of Area Benefit activities. The complete package for a state consists of the dictionary file named TABLES, and the five data files for the state. The logical record number (LOGRECNO) links the records across tables.

Data for Figure SPM.4 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure SPM.4 panel a shows global emissions projections for CO2 and a set of key non-CO2 climate drivers, for the core set of five IPCC AR6 scenarios. Figure SPM.4 panel b shows attributed warming in 2081-2100 relative to 1850-1900 for total anthropogenic, CO2, other greenhouse gases, and other anthropogenic forcings for five Shared Socio-economic Pathway (SSP) scenarios.

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:

IPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32, doi:10.1017/9781009157896.001.

Figure subpanels

The figure has two panels, with data provided for all panels in subdirectories named panel_a and panel_b.

List of data provided

This dataset contains:

• Projected emissions from 2015 to 2100 for the five scenarios of the AR6 WGI core scenario set (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5)
• Projected warming for all anthropogenic forcers, CO2 only, non-CO2 greenhouse gases (GHGs) only, and other anthropogenic components for 2081-2100 relative to 1850-1900, for SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5.

The five illustrative SSP (Shared Socio-economic Pathway) scenarios are described in Box SPM.1 of the Summary for Policymakers and Section 1.6.1.1 of Chapter 1.

Data provided in relation to figure

Panel a:

The first column includes the years, while the next columns include the data per scenario and per climate forcer for the line graphs.

• Data file: Carbon_dioxide_Gt_CO2_yr.csv. relates to Carbon dioxide emissions panel
• Data file: Methane_Mt_CO2_yr.csv. relates to Methane emissions panel
• Data file: Nitrous_oxide_Mt N2O_yr.csv. relates to Nitrous oxide emissions panel

• Data file: Sulfur_dioxide_Mt SO2_yr.csv. relates to Sulfur dioxide emissions panel

  Panel b:

• Data file: ts_warming_ranges_1850-1900_base_panel_b.csv. [Rows 2 to 5 relate to the first bar chart (cyan). Rows 6 to 9 relate to the second bar chart (blue). Rows 10 to 13 relate to the third bar chart (orange). Rows 14 to 17 relate to the fourth bar chart (red). Rows 18 to 21 relate to the fifth bar chart (brown).].

Sources of additional information

The following weblink are provided in the Related Documents section of this catalogue record: - Link to the report webpage, which includes the report component containing the figure (Summary for Policymakers) and the Supplementary Material for Chapter 1, which contains details on the input data used in Table 1.SM.1..(Cross-Chapter Box 1.4, Figure 2). - Link to related publication for input data used in panel a.

Five-number summary of satisfaction scores overall and per device group, country, and cadre.

Data for Figure SPM.7 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure SPM.7 shows the cumulative anthropogenic CO2 emissions taken up by land and ocean sinks by 2100 under the five core scenarios.

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:

IPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32, doi:10.1017/9781009157896.001.

List of data provided

This dataset contains cumulative anthropogenic (human-caused) carbon dioxide (CO2) emissions taken up by the land and ocean sinks under the five core scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5), simulated from 1850 to 2100 by Earth System Models that contributed to the sixth phase of the Coupled Model Intercomparison Project (CMIP6).

The five illustrative SSP (Shared Socio-economic Pathway) scenarios are described in Box SPM.1 of the Summary for Policymakers and Section 1.6.1.1 of Chapter 1.

Data provided in relation to figure

Data file: SPM7_data.csv: each column corresponds to a single scenario, in which rows 2-7 are the bar values, rows 8-10 are the pie chart values and row 11 is the central value in the pie chart.

Sources of additional information

The following weblink is provided in the Related Documents section of this catalogue record: - Link to the report webpage, which includes the report component containing the figure (Summary for Policymakers).

The Metropolitan Lagos dataset consists of the files (i) tsetimi_lagos_dataset.sav and (ii) tsetimi_lagos_dataset.xlxs. The two files contain the same number of records (377) and same information. The first file is in IBM SPSS database format while the second is in Microsoft Excel spreadsheet format. The SPSS database format can be accessed in the data view of SPSS. The fieldnames, field descriptions and field types are self-contained in the SPSS database file.

The dataset is part of a nationwide survey on the problems associated with electricity distribution and generation in Nigeria. A pilot survey [1] of this research was conducted in Delta State South-South, Nigeria. The files for the pilot survey are available in [2]. The survey for the Lagos data set was conducted by means of a well-structured questionnaire administered by trained interviewers. The questionnaire for the research collected information on respondents’ bio-data, experience with the services of their distribution companies and observed problems on electricity distribution from the fieldwork. The perception ratings on the services of distributions companies from the electricity customers was on a five-point scale based on the following metrics adapted from [3]: i. Overall satisfaction with services of distribution company; ii. Quality and reliability of power from distribution company; iii. Reasonableness of bills from distribution company; iv. Billing system of distribution company; v. Corporate image of distribution company; vi. Effectiveness of Communication of distribution company with stakeholders; vii. Customers service of the distribution company. The respondents scored the metrics between 0 and 5 inclusive depending on their perception on the above metrics. The scores of the respondents on the observed problems were based on the following items listed below: i. Low voltage; ii. Incessant power outages; iii. Load Shedding; iv. Inadequate number of meters; v. Inadequate distribution lines; vi. Unreasonable price of power; vii. Illegal connections; viii. Inadequate number of transformers; ix. Stealing of Distribution facilities; The respondents assign a score between 0 and 10 inclusive depending on their perception on the level of severity of the observed problems.

References [1] J. Tsetimi, A. O. Atonuje and E. J. Mamadu. An Analysis of a Pilot Survey of the Problems of Electricity Distribution in Delta State, Nigeria. Transactions of Nigerian Institution of Mathematical Physics. 2020; 12(7): 109-116 [2] J. Tsetimi. Customers' Problems with Electricity Distribution in Delta State Nigeria, [dataset], Mendeley Data, V1, doi: 10.17632/msrhyv489k.1. 2020. Accessed 16th February, 2021. Available: http://dx.doi.org/10.17632/msrhyv489k.1 [3] D. Smith, S. Nayak, M. Karig, I. Kosnik, M. Konya, K. Lovett, Z. Liu, and H.Luvai. Assessing Residential Customer Satisfaction for Large Electric Utilities. UMSL, Department of Economics Working Papers. (2011).

This is five years of police use of force data for all 468 New Jersey municipal police departments and the New Jersey State Police compiled by NJ Advance Media for The Force Report.

When police punch, pepper spray or use other force against someone in New Jersey, they are required to fill out a form detailing what happened. NJ Advance Media filed 506 public records requests and received 72,607 forms covering 2012 through 2016. For more data collection details, see our Methodology here. Data cleaning details can be found here.

We then cleaned, analyzed and compiled the data by department to get a better look at what departments were using the most force, what type of force they were using, and who they were using it on. The result, our searchable database, can be found at NJ.com/force. But we wanted to make department-level results — our aggregate data — available in another way to the broader public.

Below you'll find two files:

• UOF_BY_DEPARTMENTS.csv, with every department's summary data, including the State Police. (This is important to note because the State Police patrols multiple towns and may not be comparable to other departments.)
• UOF_STATEWIDE.csv, a statewide summary of the same data.

For more details on individual columns, see the data dictionary for UOF_BY_DEPARTMENTS. We have also created sample SQL queries to make it easy for users to quickly find their town or county.

It's important to note that these forms were self-reported by police officers, sometimes filled out by hand, so even our data cleaning can't totally prevent inaccuracies from cropping up. We've also included comparisons to population data (from the Census) and arrest data (from the FBI Uniform Crime Report), to try to help give context to what you're seeing.

What about the form-level data?

We have included individual incidents on each department page, but we are not publishing the form-level data freely to the public. Not only is that data extremely dirty and difficult to analyze — at least, it took us six months — but it contains private information about subjects of force, including minors and people with mental health issues. However, we are planning to make a version of that file available upon request in the future.

Data analysis FAQ

What are rows? What are incidents?
Every time any police officer uses force against a subject, they must fill out a form detailing what happened and what force they used. But sometimes multiple police officers used force against the same subject in the same incident. "Rows" are individual forms officers filled out, "incidents" are unique incidents based on the incident number and date.

What are the odds ratios, and how did you calculate them?
We wanted a simple way of showing readers the disparity between black and white subjects in a particular town. So we used an odds ratio, a statistical method often used in research to compare the odds of one thing happening to another. For population, the calculation was (Number of black subjects/Total black population of area)/(Number of white subjects/Total white population of area). For arrests, the calculation was (Number of black subjects/Total number of black arrests in area)/(Number of white subjects/Total number of white arrests in area). In addition, when we compared anything to arrests, we took out all incidents where the subject was an EDP (emotionally disturbed person).

What are the NYC/LA/Chicago warning systems?
Those three departments each look at use of force to flag officers if they show concerning patterns, as way to select those that could merit more training or other action by the department. We compared our data to those three systems to see how many officers would trigger the early warning systems for each. Here are the three systems: - In New York City, officers are flagged for review if they use higher levels of force — including a baton, Taser or firearm, but not pepper spray — or if anyone was injured or hospitalized. We calculated this number by identifying every officer who met one or more of the criteria. - In Los Angeles, officers are compared with one another based on 14 variables, including use of force. If an officer ranks significantly higher than peers for any of the variables — technically, 3 standards of deviation from the norm — supervisors are automatically notified. We calculated this number conservatively by using only use of force as a variable over the course of a calendar year. - In Chicago, officers are flagged for review if force results in an injury or hospitalization, or if the officer uses any level of force above punches or kicks. We calculated this number by identifying every officer who met one or more of the criteria.

What are the different levels of force?
Each officer was required to include in the form what type of force they used against a subject. We cleaned and standardized the data to major categories, although officers could write-in a different type of force if they wanted to. Here are the major categories: - Compliance hold: A compliance hold is a painful maneuver using pressure points to gain control over a suspect. It is the lowest level of force and the most commonly used. But it is often used in conjunction with other types of force. - Takedown: This technique is used to bring a suspect to the ground and eventually onto their stomach to cuff them. It can be a leg sweep or a tackle. - Hands/fist: Open hands or closed fist strikes/punches. - Leg strikes: Leg strikes are any kick or knee used on a subject. - Baton: Officers are trained to use a baton when punches or kicks are unsuccessful. - Pepper spray: Police pepper spray, a mist derived from the resin of cayenne pepper, is considered “mechanical force” under state guidelines. - Deadly force: The firing of an officer's service weapon, regardless of whether a subject was hit. “Warning shots” are prohibited, and officers are instructed not to shoot just to maim or subdue a suspect.

E-wallet is an application that enable users to download by using a mobile device. It is a new trend for consumers to use e-wallet application to replace the traditional payment method. With ewallet, a user does not need to bring cash or credit card along with them. It makes users to purchase their needs and wants in a more convenient way. Due to the advancement of technology, there are a lot of ewallet platforms that exist in the market such as Touch n Go, Boost and Grabpay. Consequently, this research aims to study the factors that affect the university students’ intention to use e-wallet. The Technology Acceptance Model (TAM) serves as the theoretical underpinning for this research A total of 140 people from a Malaysian private institution took part in this study. Convenience sampling was used to pick samples, and respondents completed the questionnaire using a Google form and a paper and pencil approach. The questionnaire was created using a nominal scale and a five-point Likert scale. Descriptive analysis, reliability analysis, and multiple regression analysis were utilised to analyse the data in this study. Students, supervisors, academics, researchers, learning institutions, commercial organisations, and the government will all benefit immensely from the data and information gathered in this study because we will be able to examine and understand the factors that influence students' decision to use an E-Wallet for their daily financial operations. This study, however, has certain limitations in that it does not reflect the complete student population in Malaysian tertiary education and only examines four variables: perceived utility, perceived ease of use, perceived danger, and trust. Future study could focus on other impacting elements such as trust, risk, complexity, pervasive technology use, tech-savvy future generations, and so on. Date Submitted: 2021-06-28

Research Hypothesis:

The hypothesis is that service quality and trust significantly influence customer satisfaction with Telkomsel’s Veronika chatbot. Key dimensions include reliability, responsiveness, and empathy in service quality, and trust based on the chatbot's ability, benevolence, and integrity.

Data and Data Collection:

Data for this study were collected from Generation Z users who have experience using Telkomsel’s Veronika chatbot. A structured questionnaire was administered to 240 respondents, 52.9% of whom were female and 47.1% male, with ages ranging from 18 to 22 years. The data collection occurred between May and June 2024, and the questionnaire was distributed via social media platforms such as Instagram, Line, and WhatsApp. Non-probability sampling methods, specifically purposive and quota sampling, were used to ensure that only those familiar with the chatbot were surveyed.

The questionnaire comprised 31 questions designed to assess three key variables: service quality, trust, and customer satisfaction. A five-point Likert scale, ranging from "Strongly Disagree" to "Strongly Agree," was employed for all questions. Service quality was evaluated using the SERVQUAL model, while trust was measured through dimensions of ability, benevolence, and integrity. Customer satisfaction was assessed using items adapted from the Customer Satisfaction Index (CSI).

Key Findings:

1.Service Quality: A significant positive impact on customer satisfaction was found (β = 0.496, p < 0.001), with reliability and responsiveness being key factors. The highest loading (0.837) was on Veronika’s ability to provide alternative solutions.

2.Trust: Trust was also a significant predictor (β = 0.337, p < 0.001), with confidentiality being the most important trust factor (outer loading = 0.835).

3.Customer Satisfaction: Satisfaction was strongly influenced by both service quality and trust, with outer loadings from 0.908 to 0.918, particularly in terms of the chatbot's clarity and communication effectiveness.

Data Interpretation:

Both service quality and trust are essential to customer satisfaction, with service quality being a stronger predictor. Users value reliability and responsiveness more than trust, though both are necessary for high satisfaction. The reliability of the questionnaire was confirmed with high Cronbach’s alpha values, such as 0.938 for service quality.

Conclusion and Implications:

Improving service quality, especially reliability and responsiveness, will enhance user satisfaction. Strengthening trust, particularly in data security, is also crucial. Future research should explore broader demographics and long-term effects, while qualitative studies could offer more insights into user experiences.

Heart Disease Prediction Dataset

This dataset contains 1,888 records merged from five publicly available heart disease datasets. It includes 14 features that are crucial for predicting heart attack and stroke risks, covering both medical and demographic factors. Below is a detailed description of each feature.

Feature Descriptions:

1. age: Age of the patient (Numeric).
2. sex: Gender of the patient. Values: 1 = male, 0 = female.
3. cp: Chest pain type. Values: 0 = Typical angina, 1 = Atypical angina, 2 = Non-anginal pain, 3 = Asymptomatic.
4. trestbps: Resting Blood Pressure (in mm Hg) (Numeric).
5. chol: Serum Cholesterol level (in mg/dl) (Numeric).
6. fbs: Fasting blood sugar > 120 mg/dl. Values: 1 = true, 0 = false.
7. restecg: Resting electrocardiographic results. Values: 0 = Normal, 1 = ST-T wave abnormality, 2 = Left ventricular hypertrophy.
8. thalach: Maximum heart rate achieved (Numeric).
9. exang: Exercise-induced angina. Values: 1 = yes, 0 = no.
10. oldpeak: ST depression induced by exercise relative to rest (Numeric).
11. slope: Slope of the peak exercise ST segment. Values: 0 = Upsloping, 1 = Flat, 2 = Downsloping.
12. ca: Number of major vessels (0-3) colored by fluoroscopy. Values: 0, 1, 2, 3.
13. thal: Thalassemia types. Values: 1 = Normal, 2 = Fixed defect, 3 = Reversible defect.
14. target: Outcome variable (heart attack risk). Values: 1 = more chance of heart attack, 0 = less chance of heart attack.

Dataset Details:

This dataset is a combination of five publicly available heart disease datasets, with a total of 1,888 records. Merging these datasets provides a more robust foundation for training machine learning models aimed at predicting heart attack risk.

Datasets Used:

1. Heart Attack Analysis & Prediction Dataset
   Number of Records: 304
   Reference: Rahman, 2021

2. Heart Disease Dataset
   Number of Records: 1,026
   Reference: Lapp, 2019

3. Heart Attack Prediction (Dataset 3)
   Number of Records: 295
   Reference: Damarla, 2020

4. Heart Attack Prediction (Dataset 4)
   Number of Records: 271
   Reference: Anand, 2018

5. Heart CSV Dataset
   Number of Records: 290
   Reference: Nandal, 2022

Description:

This dataset includes 14 features known to contribute to heart attack risk. It is ideal for training machine learning models aimed at early detection and prevention of heart disease. The records have been cleaned by removing missing data to ensure data integrity. This dataset can be applied to various machine learning algorithms, including classification models such as Decision Trees, Neural Networks, and others.