This dataset is being used by the repository https://github.com/islamAndAi/QURAN-NLP

Dataset Structure

• data
  • quran
    • corpus (190,655)
      • dictionary (53,924)
      • morphology (128,219)
      • verbs (1,475)
      • lemmas (3,680)
      • lemmas (grouped) (3,357)
    • quran.csv (6,236)
  • hadith (700,000+)
    • arabichadith (62,169)
    • Sanadset (650,615 hadith)
    • thaqalayn (26,975)
    • kaggle_hadith_clean.csv (34,410)
    • kaggle_rawis.csv (24,028)
  • namesofallah (99)
  • surah (114)
  • tafseer (4 * 6,236)
  • translation (9 * 6,236)
  • main_df.csv (6,236)

Motivation

I thought about using my knowledge of ML & NLP on the Quran to make something out of it. I have tried to get a summary of the Verses and Tafasir, getting the sentiment analysis, I have made a Search Engine so that any query can be searched as easily as a person does on Google

This is an open source project and I am trying to host it somewhere so people can use it and make the most out of it.

Collaborations are HIGHLY welcome! If anyone can help with the code or help fact check the search results or summaries that would be a HUGE help!

Looking forward to do something great with Quran & NLP

مَنْ أَنصَارِىٓ إِلَى ٱللَّهِ

Work till now

1. Notebook to scrape data from website: https://www.altafsir.com/
2. Provided English translation and Tafseer of Quran in easy to use CSV format
3. Used NLP to get top 1000 words used in Quran
4. Used sentiment analysis for Quran each surah
5. Text Summarization for Quran & each Surah
6. Search Engine for Quran using Google USE (Universal Sentence Encoder)
7. Similarity Index of Translation & Tafseer
8. Notebook to scrape data from https://thaqalayn.net/ which is a Comprehensive Shia Hadith Library
9. Notebook to scrape https://corpus.quran.com/ which contains corpus of Quran, including dictionary, verbs, lemmas, morphology

Future Goals

1. Add more Data!
2. Add more Tafaseer and translation to better train the NLP model for Search Engine & Analysis
3. Make a end-to-end application so that everyone can benefit from the newly trained models
4. Find insightful things from the Quran
5. Make an Arabic NLP model capable of understanding Quran & Hadees
6. Make a single graph database encompassing Islamic knowledge
7. Making an AI tool to authenticate Hadith

Important Note

If you find any type of error or mistake in the work please correct me. If you find the work interesting feel free to build more on it!

How To Contribute

Feel free to make notebooks on the current data, add more data (authentic and with sources) and have a look at the current data to make sure it is authentic and up-to-date!

This dataset was created by web scraping from the IslamQA website.

Islam Q&A is an academic, educational, da‘wah website which aims to offer advice and academic answers based on evidence from religious texts in an adequate and easy-to-understand manner. These answers are supervised by Shaykh Muhammad Saalih al-Munajjid (may Allah preserve him). The website welcomes questions from everyone, Muslims and otherwise, about Islamic, psychological and social matters.

Religious Populations Worldwide

Religious Populations Worldwide by Year and Category

By Throwback Thursday [source]

About this dataset

The dataset includes data on Christianity, Islam, Judaism, Buddhism, Hinduism, Sikhism, Shintoism, Baha'i Faith, Taoism, Confucianism, Jainism and various other syncretic and animist religions. For each religion or denomination category, it provides both the total population count and the percentage representation in relation to the overall population.

Additionally, - Columns labeled with Population provide numeric values representing the total number of individuals belonging to a particular religion or denomination. - Columns labeled with Percent represent numerical values indicating the percentage of individuals belonging to a specific religion or denomination within a given population. - Columns that begin with ** indicate primary categories (e.g., Christianity), while columns that do not have this prefix refer to subcategories (e.g., Christianity - Roman Catholics).

In addition to providing precise data about specific religions or denominations globally throughout multiple years,this dataset also records information about geographical locations by including state or country names under StateNme.

This comprehensive dataset is valuable for researchers seeking information on global religious trends and can be used for analysis in fields such as sociology, anthropology studies cultural studies among others

How to use the dataset

Introduction:

• Understanding the Columns:

• Year: Represents the year in which the data was recorded.

• StateNme: Represents the name of the state or country for which data is recorded.

• Population: Represents the total population of individuals.

• Total Religious: Represents the total percentage and population of individuals who identify as religious, regardless of specific religion.

• Non Religious: Represents the percentage and population of individuals who identify as non-religious or atheists.

• Identifying Specific Religions: The dataset includes columns for different religions such as Christianity, Judaism, Islam, Buddhism, Hinduism, etc. Each religion is further categorized into specific denominations or types within that religion (e.g., Roman Catholics within Christianity). You can find relevant information about these religions by focusing on specific columns related to each one.

• Analyzing Percentages vs. Population: Some columns provide percentages while others provide actual population numbers for each category. Depending on your analysis requirement, you can choose either column type for your calculations and comparisons.

• Accessing Historical Data: The dataset includes records from multiple years allowing you to analyze trends in religious populations over time. You can filter data based on specific years using Excel filters or programming languages like Python.

• Filtering Data by State/Country: If you are interested in understanding religious populations in a particular state or country, use filters to focus on that region's data only.

Example - Extracting Information:

Let's say you want to analyze Hinduism's growth globally from 2000 onwards:

• Identify Relevant Columns:
• Year: to filter data from 2000 onwards.

• Hindu - Total (Percent): to analyze the percentage of individuals identifying as Hindus globally.

• Filter Data:

• Set a filter on the Year column and select values greater than or equal to 2000.

• Look for rows where Hindu - Total (Percent) has values.

• Analyze Results: You can now visualize and calculate the growth of Hinduism worldwide after filtering out irrelevant data. Use statistical methods or graphical representations like line charts to understand trends over time.

Conclusion: This guide has provided you with an overview of how to use the Rel

Research Ideas

• Comparing religious populations across different countries: With data available for different states and countries, this dataset allows for comparisons of religious populations across regions. Researchers can analyze how different religions are distributed geographically and compare their percentages or total populations across various locations.
• Studying the impact of historical events on religious demographics: Since the dataset includes records categorized by year, it can be used to study how historical events such as wars, migration, or political changes have influenced religious demographics over time. By comparing population numbers before and after specific events, resea...

Associated with manuscript titled: Fifty Muslim-majority countries have fewer COVID-19 cases and deaths than the 50 richest non-Muslim countriesThe objective of this research was to determine the difference in the total number of COVID-19 cases and deaths between Muslim-majority and non-Muslim countries, and investigate reasons for the disparities. Methods: The 50 Muslim-majority countries had more than 50.0% Muslims with an average of 87.5%. The non-Muslim country sample consisted of 50 countries with the highest GDP while omitting any Muslim-majority countries listed. The non-Muslim countries’ average percentage of Muslims was 4.7%. Data pulled on September 18, 2020 included the percentage of Muslim population per country by World Population Review15 and GDP per country, population count, and total number of COVID-19 cases and deaths by Worldometers.16 The data set was transferred via an Excel spreadsheet on September 23, 2020 and analyzed. To measure COVID-19’s incidence in the countries, three different Average Treatment Methods (ATE) were used to validate the results. Results published as a preprint at https://doi.org/10.31235/osf.io/84zq5(15) Muslim Majority Countries 2020 [Internet]. Walnut (CA): World Population Review. 2020- [Cited 2020 Sept 28]. Available from: http://worldpopulationreview.com/country-rankings/muslim-majority-countries (16) Worldometers.info. Worldometer. Dover (DE): Worldometer; 2020 [cited 2020 Sept 28]. Available from: http://worldometers.info

Global Religious Demographics

Global religious demographics over time

By Throwback Thursday [source]

About this dataset

The dataset contains information on a wide range of religions, including Christianity, Judaism, Islam, Buddhism, Hinduism, Sikhism, Shintoism, Baha'i Faith, Taoism, Confucianism, Jainism, Zoroastrianism, Syncretic Religions (religious practices that blend elements from multiple faiths), Animism (belief in spiritual beings in nature), Non-Religious individuals or those without any religious affiliation.

For each religion and region/country combination recorded in the dataset we have the following information:

• Total population: The total population of the region or country.
• Religious affiliation percentages: The percentages of the population that identify with specific religious affiliations.
• Subgroup populations/percentages: The populations or percentages within specific denominations or sects of each religion.

The dataset also provides additional variables like Year and State Name (for regional data) for further analysis.

How to use the dataset

• Understanding the Columns

  The dataset contains several columns with different categories of information. Here's a brief explanation of some important columns:

  • Year: The year in which the data was recorded.
  • Total Population: The total population of a country or region.
  • State Name (StateNme): The name of the state or region.

  Each religion has specific columns associated with it, such as Christianity, Buddhism, Islam, Hinduism, Judaism, Taoism, Shintoism etc., representing its percentage and population for each category/denomination within that religion.

• Selecting Specific Data

  If you are interested in exploring data related to a particular religion or geographic location:

  • To filter data by Religion: Identify relevant columns associated with that religion such as 'Christianity', 'Buddhism', 'Islam', etc., and extract their respective percentage and population values for analysis.

    Example: If you want to analyze Christianity specifically, extract columns related to Christianity like 'Christianity (Percent)', 'Christianity (Population)', etc.

    Note: There might be multiple columns related to a specific religion indicating different categories or denominations within that religion.

  • To filter data by Geographic Location: Utilize the 'State Name' column ('StateNme') to segregate data corresponding to different states/regions.

    Example: If you want to analyze religious demographics for a particular state/region like California or India:

    i) Filter out rows where State Name is equal to California or India.

    ii) Extract relevant columns associated with your selected religion as mentioned above.

• Finding Trends and Insights

  Once you have selected the specific data you are interested in, examine patterns and trends over time or across different regions.

  • Plotting data using visualizations: Use graphical tools such as line charts, bar charts, or pie charts to visualize how religious demographics have changed over the years or vary across different regions.

  • Analyzing population proportions: By comparing the percentage values of different religions for a given region or over time, you can gather insights into changes in religious diversity.

• Comparing Religions

  If you wish to compare multiple religions:

Research Ideas

• Comparing religious affiliations across different countries or regions: With data on various religions such as Christianity, Islam, Buddhism, Judaism, Hinduism, etc., researchers can compare the religious affiliations of different countries or regions. This can help in understanding the cultural and religious diversity within different parts of the world.
• Exploring the growth or decline of specific religions: By examining population numbers for specific religions such as Jainism, Taoism, Zoroastrianism, etc., this dataset can be used to investigate the growth or decline of these religious groups over time. Researchers can analyze factors contributing to their popularity or decline in particular regions or countries

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: ThrowbackDataThursday 201912 - Religion.csv | Column name...

This dataset contains the Arab-West Report special reports that were published in 2004.This dataset mainly contains the writings of Cornelis Hulsman ,Drs., among other authors on topics related to Muslim- Christian relations and interfaith dialogue between the West and Islamic world. Additionally this dataset contains reports pertaining to certain Muslim –Christian incidents and reports about allegations of forced conversions of Coptic girls. Some of the articles addressed the issue of missionaries.Further reports address monastic life and recommendations of Arab-West Report's work by other social figures.Furthermore, the dataset included commentary on published material from other sources (reviews/critique of articles from other media).Some of the themes that characterized this dataset:-A description of the history of the conflicts around the development of the convent of Patmos on the Cairo-Suez road.-An overview of a book titled “Christians versus Muslims in Modern Egypt: The Century-Long Struggle for Coptic Equality” by S. S. Hasan.- Rumors of forced conversions Of Coptic girls: A report by Hulsman stated that the US Copts Association published a press release on March 25, 2004 with the title “Coptic Pope Denounces Forced Conversion of Coptic Girls.” He criticized that the US Copts Association for not making much of an effort, if any, to check the veracity of the rumors.- A Glimpse into Monastic Life in Egypt: A Visit to St. Maqarius Monastery:- Another report covered the incident in which a priest and two members of the church board of Taha al-ʿAmeda died after an accident with a speeding car driven by a police officer.- A critique of Al-Usbuʿa newspapers: the author accused the newspaper of cherry-picking statements by Coptic extremists, who are much disliked in the US Coptic community and who have no following. He considered that quoting statements from such isolated radicals gives readers the impression that they represent much more than a few individuals. It has all appearance that al-Usbuʿa has highlighted these radicals to create fear and harm the reputation of US Copts in Egypt.- A number of reports highlighted a visit and the speech delivered by the Archbishop of Canterbury, Dr George Carey (Lord Carey) at the Azhar entitled “Muslims/Christian Relationships: A New Age Of Hope?”- A report covered the first visit made by Archbishop Rowan Williams to the Diocese of Egypt since he became the Archbishop of Canterbury. The archbishop met with President Mubarak, Dr. Muhammad Sayyed Tantawi, the Grand Imam of the Azhar, Pope Shenouda and also laid the foundation stone of Harpur Community Health Centre in Sadat City.- Updates on the developments of AWR’s work to create an electronic archive of information pertaining to relations between Muslims and Christians in the Arab-World in general and Egypt in particular.Additionally, this dataset also provides updates of the then-under construction - Center for Arab-West Understanding (CAWU) web-based Electronic Documentation Center (EDC) for contemporary information covering Arab-West and Muslim-Christian relations.- A report discussed the misconceptions of Christians in Islam.- An editorial commenting on the assassination of Theo van Gogh resulted in a debate in Dutch media about the limits of the freedom of expression.- An article calling on the western readers to be careful with Christian persecution stories from Egypt, they may be true but also may be rumours.-The Muslim World And The West; What Can Be Done To Reduce Tensions?-Text of a lecture for students and professors of different faculties at the University of Copenhagen, , about plans to establish the Center for Arab-West Understanding in Cairo, Egypt.- Escalations following the alleged conversion of A priest’s wife to IslamThe list of authors’ featurd in this dataset goes as follows:Cornelis Hulsman, Drs. , Wolfram Reiss, Rev. Dr. , John H. Watson, Kim Kwang-Chan, Dr. , Kamal Abu al-Majd, Fiona McCallum, Mary Picard , Jeff Adams, Dr., Rev., Jennie Marshall , Marcos Emil Mikhael, Usamah W. al-Ahwani, Sawsan Jabrah and Nirmin Fawzi, Hānī Labīb, George Carey (Lord), Rowan Williams, Lambeth Palace Press Office, H.G. Bishop Munir Hanna Anis Armanius, Eildert Mulder, Rīhām Saʿīd, Tharwat al-Kharabāwī, Geir Valle, Janique Blattman, Iqbal Barakah , Munā ʿUmar, Dieter Tewes, ʿAmr Asʿad Khalīl, Dr., Janique Blattmann, Vera Milackova, Tamir Shukri, and Christiane Paulus All reports are written in English, though some reports feature Arabic text or cite Arabic sources.

Context

Hadith (an Arabic word) refers to the words and actions of Prophet Mohammed. Those collections of Hadiths have been transmitted through generations of Muslim scholars until they have been collected and written in big collections. The chain of narrators is a main area of study in Islamic scholarship because a single hadith may have multiple chains of narrators (that may or may not overlap). However, it has mainly remained a qualitative field where scholars of Hadith try to determine the authenticity of Hadiths by investigating and validating the chains of narrators who transmitted a given hadith.

Content

This unprecedented dataset contains over 24,000 scholars and narrators along with their teachers/students (and other metadata as well) which will provide a macroscopic overview of how and where hadith have been preserved in the early days of Islam. The dataset can also answer many other questions about whether certain schools of scholarships are more prolific in preserving hadiths than others.

Acknowledgements

This dataset wouldn't have been possible without the great people who have already transcribed this dataset from primary sources and bibliographies to muslimscholars.info database. I only scraped this database with a Python script plus very minimal cleanup.

Inspiration

The idea of collecting the dataset was inspired by this project.

Future Plans

I plan to extend this project by extracting the chain of every hadith and combining it with this dataset.

Recent issues on politics have been dominant in Indonesia that people are divided and become more intolerant of each other. Indonesia has the biggest Muslim population in the world and the role of Islam in Indonesian politics is significant. The current Indonesian government claim that moderate Muslims are loyal to the present political system while the opposing rivals who are often labelled’intolerant and radical Muslims’ by Indonesian mass media often disagree with the central interpretation of democracy in Indonesia. Studies on contributing factors and discourse strategies used in news and articles in secular and Islamic mass media which play a vital role in the construction of Muslim and Islamic identities in Indonesia are, therefore, recommended.

During the present time, COVID-19 situation is the topmost priority in our life. We are introducing a new dataset named Covid Face-Mask Monitoring Dataset which is based on Bangladesh perspective. We have a main concern to detect people who are using masks or not in the street. Furthermore, few people are not wearing masks properly which is harmful for other people and we have the intention to detect them also. Our proposed dataset contains 6,550 images and those images collected from the walking street, bus stop, street tea stall, foot-over bridge and so on. Among the full dataset, we selected 5,750 images for training purposes and 800 images for validation purposes. Our selected dimension is 1080 × 720 pixels for entire dataset. The percentage of validation data from the full dataset is almost 12.20%. We used a personal cell phone camera, DSLR for collecting frames and adding them into our final dataset. We have also planned to collect images from the mentioned place using an action camera or CCTV surveillance camera. But, from Bangladesh perspective it is not easy to collect clear and relevant data for research. To extend, CCTV surveillance cameras are mostly used in the university, shopping complex, hospital, school, college where using a mask is mandatory. But our goal of research is different. In addition, we want to mention that in our proposed dataset there are three classes which are 1. Mask, 2. No_mask, 3. Mask_not_in_position.

EyeFi Dataset

This dataset is collected as a part of the EyeFi project at Bosch Research and Technology Center, Pittsburgh, PA, USA. The dataset contains WiFi CSI values of human motion trajectories along with ground truth location information captured through a camera. This dataset is used in the following paper "EyeFi: Fast Human Identification Through Vision and WiFi-based Trajectory Matching" that is published in the IEEE International Conference on Distributed Computing in Sensor Systems 2020 (DCOSS '20). We also published a dataset paper titled as "Dataset: Person Tracking and Identification using Cameras and Wi-Fi Channel State Information (CSI) from Smartphones" in Data: Acquisition to Analysis 2020 (DATA '20) workshop describing details of data collection. Please check it out for more information on the dataset.

Data Collection Setup

In our experiments, we used Intel 5300 WiFi Network Interface Card (NIC) installed in an Intel NUC and Linux CSI tools [1] to extract the WiFi CSI packets. The (x,y) coordinates of the subjects are collected from Bosch Flexidome IP Panoramic 7000 panoramic camera mounted on the ceiling and Angle of Arrivals (AoAs) are derived from the (x,y) coordinates. Both the WiFi card and camera are located at the same origin coordinates but at different height, the camera is location around 2.85m from the ground and WiFi antennas are around 1.12m above the ground.

The data collection environment consists of two areas, first one is a rectangular space measured 11.8m x 8.74m, and the second space is an irregularly shaped kitchen area with maximum distances of 19.74m and 14.24m between two walls. The kitchen also has numerous obstacles and different materials that pose different RF reflection characteristics including strong reflectors such as metal refrigerators and dishwashers.

To collect the WiFi data, we used a Google Pixel 2 XL smartphone as an access point and connect the Intel 5300 NIC to it for WiFi communication. The transmission rate is about 20-25 packets per second. The same WiFi card and phone are used in both lab and kitchen area.

List of Files
Here is a list of files included in the dataset:

|- 1_person
  |- 1_person_1.h5
  |- 1_person_2.h5
|- 2_people
  |- 2_people_1.h5
  |- 2_people_2.h5
  |- 2_people_3.h5
|- 3_people
  |- 3_people_1.h5
  |- 3_people_2.h5
  |- 3_people_3.h5
|- 5_people
  |- 5_people_1.h5
  |- 5_people_2.h5
  |- 5_people_3.h5
  |- 5_people_4.h5
|- 10_people
  |- 10_people_1.h5
  |- 10_people_2.h5
  |- 10_people_3.h5
|- Kitchen
  |- 1_person
    |- kitchen_1_person_1.h5
    |- kitchen_1_person_2.h5
    |- kitchen_1_person_3.h5
  |- 3_people
    |- kitchen_3_people_1.h5
|- training
  |- shuffuled_train.h5
  |- shuffuled_valid.h5
  |- shuffuled_test.h5
View-Dataset-Example.ipynb
README.md

In this dataset, folder `1_person/` , `2_people/` , `3_people/` , `5_people/`, and `10_people/` contains data collected from the lab area whereas `Kitchen/` folder contains data collected from the kitchen area. To see how the each file is structured, please see below in section Access the data.

The training folder contains the training dataset we used to train the neural network discussed in our paper. They are generated by shuffling all the data from `1_person/` folder collected in the lab area (`1_person_1.h5` and `1_person_2.h5`).

Why multiple files in one folder?

Each folder contains multiple files. For example, `1_person` folder has two files: `1_person_1.h5` and `1_person_2.h5`. Files in the same folder always have the same number of human subjects present simultaneously in the scene. However, the person who is holding the phone can be different. Also, the data could be collected through different days and/or the data collection system needs to be rebooted due to stability issue. As result, we provided different files (like `1_person_1.h5`, `1_person_2.h5`) to distinguish different person who is holding the phone and possible system reboot that introduces different phase offsets (see below) in the system.

Special note:

For `1_person_1.h5`, this file is generated by the same person who is holding the phone, and `1_person_2.h5` contains different people holding the phone but only one person is present in the area at a time. Boths files are collected in different days as well.

Access the data
To access the data, hdf5 library is needed to open the dataset. There are free HDF5 viewer available on the official website: https://www.hdfgroup.org/downloads/hdfview/. We also provide an example Python code View-Dataset-Example.ipynb to demonstrate how to access the data.

Each file is structured as (except the files under *"training/"* folder):

|- csi_imag
|- csi_real
|- nPaths_1
  |- offset_00
    |- spotfi_aoa
  |- offset_11
    |- spotfi_aoa
  |- offset_12
    |- spotfi_aoa
  |- offset_21
    |- spotfi_aoa
  |- offset_22
    |- spotfi_aoa
|- nPaths_2
  |- offset_00
    |- spotfi_aoa
  |- offset_11
    |- spotfi_aoa
  |- offset_12
    |- spotfi_aoa
  |- offset_21
    |- spotfi_aoa
  |- offset_22
    |- spotfi_aoa
|- nPaths_3
  |- offset_00
    |- spotfi_aoa
  |- offset_11
    |- spotfi_aoa
  |- offset_12
    |- spotfi_aoa
  |- offset_21
    |- spotfi_aoa
  |- offset_22
    |- spotfi_aoa
|- nPaths_4
  |- offset_00
    |- spotfi_aoa
  |- offset_11
    |- spotfi_aoa
  |- offset_12
    |- spotfi_aoa
  |- offset_21
    |- spotfi_aoa
  |- offset_22
    |- spotfi_aoa
|- num_obj
|- obj_0
  |- cam_aoa
  |- coordinates
|- obj_1
  |- cam_aoa
  |- coordinates
...
|- timestamp

The `csi_real` and `csi_imag` are the real and imagenary part of the CSI measurements. The order of antennas and subcarriers are as follows for the 90 `csi_real` and `csi_imag` values : [subcarrier1-antenna1, subcarrier1-antenna2, subcarrier1-antenna3, subcarrier2-antenna1, subcarrier2-antenna2, subcarrier2-antenna3,… subcarrier30-antenna1, subcarrier30-antenna2, subcarrier30-antenna3]. `nPaths_x` group are SpotFi [2] calculated WiFi Angle of Arrival (AoA) with `x` number of multiple paths specified during calculation. Under the `nPath_x` group are `offset_xx` subgroup where `xx` stands for the offset combination used to correct the phase offset during the SpotFi calculation. We measured the offsets as:

|Antennas | Offset 1 (rad) | Offset 2 (rad) |
|:-------:|:---------------:|:-------------:|
| 1 & 2 |   1.1899   |   -2.0071
| 1 & 3 |   1.3883   |   -1.8129

The measurement is based on the work [3], where the authors state there are two possible offsets between two antennas which we measured by booting the device multiple times. The combination of the offset are used for the `offset_xx` naming. For example, `offset_12` is offset 1 between antenna 1 & 2 and offset 2 between antenna 1 & 3 are used in the SpotFi calculation.

The `num_obj` field is used to store the number of human subjects present in the scene. The `obj_0` is always the subject who is holding the phone. In each file, there are `num_obj` of `obj_x`. For each `obj_x1`, we have the `coordinates` reported from the camera and `cam_aoa`, which is estimated AoA from the camera reported coordinates. The (x,y) coordinates and AoA listed here are chronologically ordered (except the files in the `training` folder) . It reflects the way the person carried the phone moved in the space (for `obj_0`) and everyone else walked (for other `obj_y`, where `y` > 0).

The `timestamp` is provided here for time reference for each WiFi packets.

To access the data (Python):

import h5py

data = h5py.File('3_people_3.h5','r')

csi_real = data['csi_real'][()]
csi_imag = data['csi_imag'][()]

cam_aoa = data['obj_0/cam_aoa'][()] 
cam_loc = data['obj_0/coordinates'][()] 

For file inside `training/` folder:

Files inside training folder has a different data structure:

|- nPath-1
  |- aoa
  |- csi_imag
  |- csi_real
  |- spotfi
|- nPath-2
  |- aoa
  |- csi_imag
  |- csi_real
  |- spotfi
|- nPath-3
  |- aoa
  |- csi_imag
  |- csi_real
  |- spotfi
|- nPath-4
  |- aoa
  |- csi_imag
  |- csi_real
  |- spotfi

The group `nPath-x` is the number of multiple path specified during the SpotFi calculation. `aoa` is the camera generated angle of arrival (AoA) (can be considered as ground truth), `csi_image` and `csi_real` is the imaginary and real component of the CSI value. `spotfi` is the SpotFi calculated AoA values. The SpotFi values are chosen based on the lowest median and mean error from across `1_person_1.h5` and `1_person_2.h5`. All the rows under the same `nPath-x` group are aligned (i.e., first row of `aoa` corresponds to the first row of `csi_imag`, `csi_real`, and `spotfi`. There is no timestamp recorded and the sequence of the data is not chronological as they are randomly shuffled from the `1_person_1.h5` and `1_person_2.h5` files.

Citation
If you use the dataset, please cite our paper:

@inproceedings{eyefi2020,
 title={EyeFi: Fast Human Identification Through Vision and WiFi-based Trajectory Matching},
 author={Fang, Shiwei and Islam, Tamzeed and Munir, Sirajum and Nirjon, Shahriar},
 booktitle={2020 IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS)},
 year={2020},

“Religion is a critical factor these days in the public's thinking about contentious policy issues and political matters. An increasing number of Americans have come to view Islam as a religion that encourages violence while a declining number say Islam has a lot in common with their own religion. The public remains divided over whether churches should stay out of politics, even as large numbers say they are comfortable with expressions of faith by political leaders. There also is evidence that next year's presidential vote may again provoke deep religious divisions over social issues, especially homosexual marriage” (Pew Forum). This survey was conducted by The Pew Forum on Religion and Public Life. (ARDA 3/4/2015).

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at the Roper Center for Public Opinion Research at https://doi.org/10.25940/ROPER-31095811. We highly recommend using the Roper Center version as they may make this dataset available in multiple data formats in the future.

Purpose: In this article, we discussed whether Imam Mahdi is an Antichrist or a Promised Saviour Besides Jesus Christ and other topics. This article attempts to deal with the concepts "Occultation", "Imamat", "Mahdaviat", "Mahdism" and Who is Antichrist that prophesied both in Islam and Christianity. Also, this research was conducted to answer and clarify three questions that stated in the Introduction section. Methods: We performed our methods in 4 stages: Identifying studies, Selection of Studies, Collating Studies, Reporting results. Results: One of the statements of those who say that Imam Mahdi is the Antichrist is that he is not included in other religious books at all and his name is not mentioned in the Quran and the Muslims forcibly accepted him as their Messiah, but in response to these people, it is not the Muslims who are forcibly introducing the Imam Mahdi as a savior, but vice versa. The Quran contains many verses that mention Imam Mahdi. Not only the Quran, but also other religious books mention him, even with the titles and names mentioned in the Qur'an. Conclusion: The coming of Imam Mahdi and Jesus Christ is one of the sure promises of God, and these two saviors will appear with God's help and the will of the people. They fill the earth with justice and establish the government of God. We hope this article will take an important step in acquainting people with Imam Mahdi and Jesus Christ and paving the ground for their reappearance.

In Bangladesh, people are sadly not very much concerned about traffic rules. This study focuses on traffic flow patterns at two junctions in Dhaka, Shapla Chattar and Notre Dame College. Footover bridges at both junctions were used to collect video data, which captured single-lane and double-lane traffic situations involving different types of vehicles and also pedestrians crossing. The dataset comprises 11,678 images extracted from the videos, taken at five different time periods on a weekday. These photos were later annotated using roboflow. This dataset provides a unique view on traffic situations in Dhaka, Bangladesh, by presenting unstructured traffic environments at two busy consecutive junctions. Monitoring vehicle fitness, examining pedestrian behavior, and measuring vehicle flow are all possible applications. Researchers can use different machine learning techniques in these areas.

Dataset from Majlis Ugama Islam Singapura. For more information, visit https://data.gov.sg/datasets/d_7100046f24d7bec79118dc027adb9f5e/view

The economic landscape of the United Kingdom has been significantly shaped by the intertwined issues of Brexit, COVID-19, and their interconnected impacts. Despite the country’s robust and diverse economy, the disruptions caused by Brexit and the COVID-19 pandemic have created uncertainty and upheaval for both businesses and individuals. Recognizing the magnitude of these challenges, academic literature has directed its attention toward conducting immediate research in this crucial area. This study sets out to investigate key economic factors that have influenced various sectors of the UK economy and have broader economic implications within the context of Brexit and COVID-19. The factors under scrutiny include the unemployment rate, GDP index, earnings, and trade. To accomplish this, a range of data analysis tools and techniques were employed, including the Box-Jenkins method, neural network modeling, Google Trend analysis, and Twitter-sentiment analysis. The analysis encompassed different periods: pre-Brexit (2011-2016), Brexit (2016-2020), the COVID-19 period, and post-Brexit (2020-2021). The findings of the analysis offer intriguing insights spanning the past decade. For instance, the unemployment rate displayed a downward trend until 2020 but experienced a spike in 2021, persisting for a six-month period. Meanwhile, total earnings per week exhibited a gradual increase over time, and the GDP index demonstrated an upward trajectory until 2020 but declined during the COVID-19 period. Notably, trade experienced the most significant decline following both Brexit and the COVID-19 pandemic. Furthermore, the impact of these events exhibited variations across the UK’s four regions and twelve industries. Wales and Northern Ireland emerged as the regions most affected by Brexit and COVID-19, with industries such as accommodation, construction, and wholesale trade particularly impacted in terms of earnings and employment levels. Conversely, industries such as finance, science, and health demonstrated an increased contribution to the UK’s total GDP in the post-Brexit period, indicating some positive outcomes. It is worth highlighting that the impact of these economic factors was more pronounced on men than on women. Among all the variables analyzed, trade suffered the most severe consequences in the UK. By early 2021, the macroeconomic situation in the country was characterized by a simple dynamic: economic demand rebounded at a faster pace than supply, leading to shortages, bottlenecks, and inflation. The findings of this research carry significant value for the UK government and businesses, empowering them to adapt and innovate based on forecasts to navigate the challenges posed by Brexit and COVID-19. By doing so, they can promote long-term economic growth and effectively address the disruptions caused by these interrelated issues.

Context

This is believed to be an unbiased fact-based dataset to get a better understanding of how much damage that the Islamic extremists are doing to the world.

Content

These are not incidents of ordinary crime involving nominal Muslims killing for money or vendetta. Incidents of deadly violence that are reasonably determined to have been committed out of religious duty - as interpreted by the perpetrator - are only included. Islam needs to be a motive, but it need not be the only factor.

For example, the Munich mall shooting in July, 2016 was by a Muslim, but it is not on the list, because it was not inspired by a sense of religious duty.

The incidents were collected each day from public news sources. There is no rumor or word-of-mouth involved. Although every attempt is made to be accurate and consistent, we are not making the claim that this is a scientific product.

Acknowledgements

This dataset is available here on Kaggle, thanks to TheReligionofPeace.com

Inspiration

The point of this dataset is not to convince anyone that they are in mortal danger or that Muslims are innately dangerous people (they are not, of course). Rather it is to point out the sort of terrorism that some of "Religion of Peace" believers produce. It should be acceptable to question and critique the teachings and phrases interpretation particularly those that are supremacist in nature.

The questionnaire for the fifth study in the series of annual SOM-surveys was divided into seven subject fields: mass media; politics and society; energy, nuclear power and environment; Sweden´s relation to other countries; Islam; leisure; and background. The SOM surveys contains a core of questions asked annually. New questions in SOM90 dealt with good qualities in daily papers; priority of news from different areas; confidence in Swedish mass media; whether different activities should be handled by the public sector or by private enterprises, associations, or by individuals; government intervention; opinions on different suggestions in the Swedish environmental debate, and own environmental activities; attitudes toward Islam. Purpose: The main purpose is to establish time series that enable researchers to analyse how various changes in society affect people's attitudes and behaviour.

Comprehensive ranking dataset of the top 100 YouTube channels in the Religion category. This dataset features 100 channels with detailed statistics including subscriber counts, total video views, video count, and global rankings. The leading channel has 80,900,000 subscribers and 44,224,167,508 total views. Each entry includes comprehensive metrics to analyze channel performance, growth trends, and competitive positioning. This dataset is regularly updated to reflect the latest YouTube channel statistics and ranking changes, providing valuable insights for content creators, marketers, and researchers analyzing YouTube ecosystem trends and channel performance benchmarks.

Hadith QA Pair Dataset

This dataset contains Hadith-based question-answer pairs extracted from four renowned Hadith collections: Musnad Ahmad, Sahih Muslim, Sahih Bukhari, and Jami` at-Tirmidhi.

The dataset is structured as question-answer pairs, where each question is answered using a relevant Hadith along with its reference. It can be utilized to train Large Language Models (LLMs) for text generation and question-answering tasks in Islamic studies.

Features

The dataset consists of the following features:

Example 1

• Question: What is the responsibility of believers when they witness evil in their society?
• Hadith (English Translation):
  "Abu Bakr stood up and praised and glorified Allah, then he said: O people, you recite this verse:
  O you who believe! Take care of your own selves. If you follow the (right) guidance (and enjoin what is right (Islamic Monotheism and all that Islam orders one to do) and forbid what is wrong (polytheism, disbelief and all that Islam has forbidden)], no harm can come to you from those who are in error. (Al-Ma'idah 5:105)
  We heard the Messenger of Allah (ﷺ) say:
  If the people see evil and do not change it, soon Allah will send His punishment upon them all."
• Reference: Musnad Ahmad 1, Book 1, Hadith 1
• Hadith Grade: Sahih (Darussalam)
• Question Number: #1

Example 2

• Question: What are the consequences mentioned for a community that fails to address wrongdoing?
• Hadith (English Translation):
  "Abu Bakr stood up and praised and glorified Allah, then he said: O people, you recite this verse:
  O you who believe! Take care of your own selves. If you follow the (right) guidance (and enjoin what is right (Islamic Monotheism and all that Islam orders one to do) and forbid what is wrong (polytheism, disbelief and all that Islam has forbidden)], no harm can come to you from those who are in error. (Al-Ma'idah 5:105)
  We heard the Messenger of Allah (ﷺ) say:
  If the people see evil and do not change it, soon Allah will send His punishment upon them all."
• Reference: Musnad Ahmad 1, Book 1, Hadith 1
• Hadith Grade: Sahih (Darussalam)
• Question Number: #2

Both questions are related to the same Hadith. This allows generating multiple variations of questions that can be answered using the same Hadith.

Final Split Sizes

Both Sahih Muslim and Sahih Bukhari contain the highest number of Hadiths. In this dataset, we have created a varying number of repeated questions for each Hadith. Depending on the dataset, the number of questions per Hadith varies between 3 to 6. The table below shows the total number of rows in each dataset:

Why This Dataset is Useful

This dataset allows multiple questions to be mapped to the same Hadith.
For example, in the cases above, different questions were generated from the same Hadith. This structure:

• Enables diverse question formats while maintaining Hadith accuracy.
• Enhances LLM training by creating multiple variations of question-answer pairs.
• Improves contextual understanding of Hadith-based answers.

Potential Applications

• Islamic Question-Answering Systems → Helps build AI-powered Hadith assistants.
• Text Generation Models → Trains LLMs on structured Islamic knowledge.
• Religious Studies & Research → Provides an organized dataset for scholars.

Citation

If you use this dataset in your research or project, please cite it as follows:

📌 Dr. Wasif Masood (2024). Hadith QA Pair Dataset. Version 1.0.
Available at: https://huggingface.co/datasets/rwmasood/hadith-qa-pair

BibTeX:

@dataset{rwmasood2024,
 author  = {Dr. Wasif Masood and Empirisch Tech GmbH},
 title   = {Hadith QA Pair Dataset},
 year   = {2024},
 publisher = {Hugging Face},
 url    = {https://huggingface.co/datasets/rwmasood/hadith-qa-pair},
 version  = {1.0},
 license  = {CC BY 4.0},
 institution = {Empirisch Tech GmbH}
}

The Holy Quran is the sacred text of Islam, which Muslims believe is the literal word of God as revealed to the Prophet Muhammad. It is considered the ultimate source of guidance and a comprehensive guide for Muslims worldwide.

A Qur'an dataset, in the context of natural language processing (NLP) projects, would refer to a collection of textual data containing the full text of the Qur'an, arranged in a structured format. This dataset can be used for various NLP tasks such as text classification, sentiment analysis, topic modeling and language generation.

Creating a Qur'an dataset for NLP projects involves extracting text from the Qur'an and annotating it with relevant metadata. Metadata can include information such as chapter titles, verse numbers, translations, and any other linguistic analysis or interpretation.

Such a dataset can be used for a wide variety of NLP applications. For example, sentiment analysis can be done to determine the emotional tone or sentiment expressed in specific chapters or verses of the Qur'an. Text classification algorithms could be trained to categorize verses based on themes or topics. Language generation models could be developed to generate new text that adheres to the linguistic style and content of the Qur'an.

NLP projects using the Qur'an dataset can provide valuable insights into the text, help understand the teachings of the Qur'an, and facilitate the development of applications that assist individuals in studying and understanding the Qur'an. It is important to note that any NLP project involving religious texts should be approached with sensitivity, respect and a deep understanding of the religious and cultural significance of the text.