Dataset on novel Covid-19 in IndiaCoronaviruses are a large family of viruses which may cause illness in animals or humans. In humans, several coronaviruses are known to cause respiratory infections ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). The most recently discovered coronavirus causes coronavirus disease COVID-19 - World Health Organization

ContextPakistan Drone Attacks (2004-2016)The United States has targeted militants in the Federally Administered Tribal Areas [FATA] and the province of Khyber Pakhtunkhwa [KPK] in Pakistan via its Predator and Reaper drone strikes since year 2004. Pakistan Body Count (www.PakistanBodyCount.org) is the oldest and most accurate running tally of drone strikes in Pakistan. The given database (PakistanDroneAttacks.CSV) has been populated by using majority of the data from Pakistan Body Count, and building up on it by canvassing open source newspapers, media reports, think tank analyses, and personal contacts in media and law enforcement agencies. We provide a count of the people killed and injured in drone strikes, including the ones who died later in hospitals or homes due to injuries caused or aggravated by drone strikes, making it the most authentic source for drone related data in this region.We will keep releasing the updates every quarter at this page.ContentGeography: PakistanTime period: 2004-2016Unit of analysis: AttackDataset: The dataset contains detailed information of 397 drone attacks in Pakistan that killed an estimated 3,558 and injured 1,333 people including 2,539 civilians.Variables: The dataset contains Serial No, Incident Day & Date, Approximate Time of the attack, Specific Location, City, Province, Number of people killed who claimed to be from Al-Qaeeda, Number of people killed who claimed to be from Taliban, minimum and maximum count of foreigners killed, minimum and maximum count of civilians killed, minimum and maximum count of civilians injured, special mention (more details) and comments about the attack, longitude and latitude of the location.Sources: Unclassified media articles, hospital reports, think tank analysis and reports, and government official press releases.Acknowledgements & ReferencesPakistan Body Count has been leveraged extensively in scholarly publications, reports, media articles and books. The website and the dataset has been collected and curated by the founder Zeeshan-ul-hassan Usmani.Users are allowed to use, copy, distribute and cite the dataset as follows: “Zeeshan-ul-hassan Usmani, Pakistan Body Count, Drone Attacks Dataset, Kaggle Dataset Repository, Jan 25, 2017.”Past ResearchZeeshan-ul-hassan Usmani and Hira Bashir, “The Impact of Drone Strikes in Pakistan”, Cost of War Project, Brown University, December 16, 2014InspirationSome ideas worth exploring:• How many people got killed and injured per year in last 12 years?• How many attacks involved killing of actual terrorists from Al-Qaeeda and Taliban?• How many attacks involved women and children?• Visualize drone attacks on timeline• Find out any correlation with number of drone attacks with specific date and time, for example, do we have more drone attacks in September?• Find out any correlation with drone attacks and major global events (US funding to Pakistan and/or Afghanistan, Friendly talks with terrorist outfits by local or foreign government?)• The number of drone attacks in Bush Vs Obama tenure?• The number of drone attacks versus the global increase/decrease in terrorism?• Correlation between number of drone strikes and suicide bombings in PakistanQuestions?For detailed visit www.PakistanBodyCount.orgOr contact Pakistan Body Count staff at info@pakistanbodycount.org

This dataset consists of timestamps for coughs contained in files extracted from the ESC-50 and FSDKaggle2018 datasets. Citation This dataset was generated and used in our paper: Mahmoud Abdelkhalek, Jinyi Qiu, Michelle Hernandez, Alper Bozkurt, Edgar Lobaton, “Investigating the Relationship between Cough Detection and Sampling Frequency for Wearable Devices,” in the 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2021. Please cite this paper if you use the timestamps.csv file in your work. Generation The cough timestamps given in the timestamps.csv file were generated using the cough templates given in figures 3 and 4 in the paper: A. H. Morice, G. A. Fontana, M. G. Belvisi, S. S. Birring, K. F. Chung, P. V. Dicpinigaitis, J. A. Kastelik, L. P. McGarvey, J. A. Smith, M. Tatar, J. Widdicombe, "ERS guidelines on the assessment of cough", European Respiratory Journal 2007 29: 1256-1276; DOI: 10.1183/09031936.00101006 More precisely, 40 files labelled as "coughing" in the ESC-50 dataset and 273 files labelled as "Cough" in the FSDKaggle2018 dataset were manually searched using Audacity for segments of audio that closely matched the aforementioned templates, both visually and auditorily. Some files did not contain any coughs at all, while other files contained several coughs. Therefore, only the files that contained at least one cough are included in the coughs directory. In total, the timestamps of 768 cough segments with lengths ranging from 0.2 seconds to 0.9 seconds were extracted. Description The audio files are presented in wav format in the coughs directory. Files named in the general format of "*-*-*-24.wav" were extracted from the ESC-50 dataset, while all other files were extracted from the FSDKaggle2018 dataset. The timestamps.csv file contains the timestamps for the coughs and it consists of four columns:

file_name,cough_number,start_time,end_time

Files in the file_name column can be found in the coughs directory. cough_number refers to the index of the cough in the corresponding file. For example, if the file X.wav contains 5 coughs, then X.wav will be repeated 5 times under the file_name column, and for each row, the cough_number will range from 1 to 5. start_time refers to the starting time of a cough segment measured in seconds, while end_time refers to the end time of a cough segment measured in seconds. Licensing The ESC-50 dataset as a whole is licensed under the Creative Commons Attribution-NonCommercial license. Individual files in the ESC-50 dataset are licensed under different Creative Commons licenses. For a list of these licenses, see LICENSE. The ESC-50 files in the cough directory are given for convenience only, and have not been modified from their original versions. To download the original files, see the ESC-50 dataset. The FSDKaggle2018 dataset as a whole is licensed under the Creative Commons Attribution 4.0 International license. Individual files in the FSDKaggle2018 dataset are licensed under different Creative Commons licenses. For a list of these licenses, see the License section in FSDKaggle2018. The FSDKaggle2018 files in the cough directory are given for convenience only, and have not been modified from their original versions. To download the original files, see the FSDKaggle2018 dataset. The timestamps.csv file is licensed under the Creative Commons Attribution-NonCommercial 4.0 International license.;This work was supported by the National Science Foundation under award IIS-1915599 and EEC-1160483 (ERC for ASSIST).

This is a daily updated dataset of COVID-19 deaths around the world. The dataset contains data of 45 countries. This data was collected from

ContextZeeshan-ul-hassan Usmani’s Genome Phenotype SNPs Raw DataGenomics is a branch of molecular biology that involves structure, function, variation, evolution and mapping of genomes. There are several companies offering next generation sequencing of human genomes from complete 3 billion base-pairs to a few thousand Phenotype SNPs. I’ve used 23andMe (using Illumina HumanOmniExpress-24) for my DNA’s Phenotype SNPs. I am sharing the entire raw dataset here for the international research community for following reasons:I am a firm believer in open dataset, transparency, and the right to learn, research, explores, and educate. I do not want to restrict the knowledge flow for mere privacy concerns. Hence, I am offering my entire DNA raw data for the world to use for research without worrying about privacy. I call it copyleft dataset.Most of available test datasets for research come from western world and we don’t see much from under-developing countries. I thought to share my data to bridge the gap and I expect others to follow the trend.I would be the happiest man on earth, if a life can be saved, knowledge can be learned, an idea can be explore, or a fact can be found using my DNA data. Please use it the way you willContentName: Zeeshan-ul-hassan UsmaniAge: 38 YearsCountry of Birth: PakistanCountry of Ancestors: India (Utter Pradesh - UP)File: GenomeZeeshanUsmani.csvSize: 15 MBSources: 23andMe Personalized Genome ReportThe research community is still progressively working in this domain and it is agreed upon by professionals that genomics is still in its infancy. You now have the chance to explore this novel domain via the dataset and become one of the few genomics early adopters.The data-set is a complete genome extracted from www.23andme.com and is represented as a sequence of SNPs represented by the following symbols: A (adenine), C (cytosine), G (guanine), T (thymine), D (base deletions), I (base insertions), and '_' or '-' if the SNP for particular location is not accessible. It contains Chromosomes 1-22, X, Y, and mitochondrial DNA.A complete list of the exact SNPs (base pairs) available and their data-set index can be found athttps://api.23andme.com/res/txt/snps.b4e00fe1db50.dataFor more information about how the data-set was extracted follow https://api.23andme.com/docs/reference/#genomesMoreover, for a more detailed understanding of the data-set content please acquaint yourself with the description of https://api.23andme.com/docs/reference/#genotypesAcknowledgementsUsers are allowed to use, copy, distribute and cite the dataset as follows: “Zeeshan-ul-hassan Usmani, Genome Phenotype SNPS Raw Data File by 23andMe, Kaggle Dataset Repository, Jan 25, 2017.”Useful LinksYou may use the following human genome database sites for help:GenBank - https://www.ncbi.nlm.nih.gov/genbank/The Human Genome Project - https://www.genome.gov/hgp/Genomes OnLine Database (GOLD) - https://gold.jgi.doe.govComplete Genomics - http://www.completegenomics.com/public-data/InspirationSome ideas worth exploring:Is the individual in question more susceptible to cancer?Does he tend to gain weight?Where is his place of origin?Which gene determines certain biological feature (cancer susceptibility, fat generation rate, hair color etc.How does this phenotype SNPs compare with other similar datasets from the western-world?What would be the likely cause of death for this person?What are the most likely diseases/illnesses this person is going to face in lifetime?What is unique about this dataset?What else you can extract from this dataset when it comes to personal trait, intelligence level, ancestry and body makeup?Sample ReportsPlease check out following reports to understand what can be done with this dataAncestry –https://www.23andme.com/published-report/eeb4f9bbd6b5474f/?share_id=f6c5562848e84586Weight Report -https://you.23andme.com/published/reports/65c9af9f8223456d/?share_id=0126f129e4f3458b

Pattern recognition with tracker data: : Improve Your Overall HealthContentThis dataset generated by respondents to a distributed survey via Amazon Mechanical Turk between 03.12.2016-05.12.2016. Thirty eligible Fitbit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring. Individual reports can be parsed by export session ID (column A) or timestamp (column B). Variation between output represents use of different types of Fitbit trackers and individual tracking behaviors / preferences.Starter Kernel(s)- Julen Aranguren: https://www.kaggle.com/julenaranguren/bellabeat-case-study- Anastasiia Chebotina: https://www.kaggle.com/chebotinaa/bellabeat-case-study-with-rInspiration- Human temporal routine behavioral analysis and pattern recognitionAcknowlegementFurberg, Robert; Brinton, Julia; Keating, Michael ; Ortiz, Alexahttps://zenodo.org/record/53894#.YMoUpnVKiP9Some readings- How I analyzed the data from my FitBit to improve my overall health(https://www.freecodecamp.org/news/how-i-analyzed-the-data-from-my-fitbit-to-improve-my-overall-health-a2e36426d8f9/)- How can data from fitness trackers be obtained and analyzed with a forensic approach?(https://conferences.computer.org/eurosp/pdfs/EuroSPW2020-7k9FlVRX4z43j4uE2SeXU0/859700a499/859700a499.pdf)

ContextThis is a historical dataset on the modern Olympic Games, including all the Games from Athens 1896 to Rio 2016. I scraped this data from www.sports-reference.com in May 2018. The R code I used to scrape and wrangle the data is on GitHub. I recommend checking my kernel before starting your own analysis.Note that the Winter and Summer Games were held in the same year up until 1992. After that, they staggered them such that Winter Games occur on a four year cycle starting with 1994, then Summer in 1996, then Winter in 1998, and so on. A common mistake people make when analyzing this data is to assume that the Summer and Winter Games have always been staggered.ContentThe file athlete_events.csv contains 271116 rows and 15 columns. Each row corresponds to an individual athlete competing in an individual Olympic event (athlete-events). The columns are:ID - Unique number for each athleteName - Athlete's nameSex - M or FAge - IntegerHeight - In centimetersWeight - In kilogramsTeam - Team nameNOC - National Olympic Committee 3-letter codeGames - Year and seasonYear - IntegerSeason - Summer or WinterCity - Host citySport - SportEvent - EventMedal - Gold, Silver, Bronze, or NAAcknowledgementsThe Olympic data on www.sports-reference.com is the result of an incredible amount of research by a group of Olympic history enthusiasts and self-proclaimed 'statistorians'. Check out their blog for more information. All I did was consolidated their decades of work into a convenient format for data analysis.InspirationThis dataset provides an opportunity to ask questions about how the Olympics have evolved over time, including questions about the participation and performance of women, different nations, and different sports and events.

ContextThere is a lack of public available datasets on financial services and specially in the emerging mobile money transactions domain. Financial datasets are important to many researchers and in particular to us performing research in the domain of fraud detection. Part of the problem is the intrinsically private nature of financial transactions, that leads to no publicly available datasets.We present a synthetic dataset generated using the simulator called PaySim as an approach to such a problem. PaySim uses aggregated data from the private dataset to generate a synthetic dataset that resembles the normal operation of transactions and injects malicious behaviour to later evaluate the performance of fraud detection methods.ContentPaySim simulates mobile money transactions based on a sample of real transactions extracted from one month of financial logs from a mobile money service implemented in an African country. The original logs were provided by a multinational company, who is the provider of the mobile financial service which is currently running in more than 14 countries all around the world.This synthetic dataset is scaled down 1/4 of the original dataset and it is created just for Kaggle.

Daily level historical dataset of major NIFTY indicesContextThe National Stock Exchange of India Limited (NSE) is the leading stock exchange of India, located in Mumbai. The NIFTY 50 index is National Stock Exchange of India's benchmark broad based stock market index for the Indian equity market.Apart from NIFTY 50 index, there are also other indices like NIFTY Next 50, Nifty Midcap 150 etc. Exploring these indices may help in taking investment decisions.ContentThis dataset has day level information on major NIFTY indices starting from 01 January 2000.Each file represents an index and has the following columnsDate - date of observationOpen - open value of the index on that dayHigh - highest value of the index on that dayLow - lowest value of the index on that dayClose - closing value of the index on that dayVolume - volume of transactionTurnover - turn overP/E - price to earnings ratioP/B - price to book valueDiv Yield - dividend yieldAcknowledgementsThe data is obtained from NSE website(https://www1.nseindia.com/products/content/equities/indices/historical_index_data.htm) with the help of nsepy python package.Photo credits: Photo by M. B. M. on UnsplashInspirationWho wants to predict the future of stock prices? ;)Please reach out to individual owners (most of them are linked in the data description part) and get their consent if you plan to use it commercially.

Explore real industrial data and help manufacturing plants to be more efficientContextIt is not always easy to find databases from real world manufacturing plants, specially mining plants. So, I would like to share this database with the community, which comes from one of the most important parts of a mining process: a flotation plant!PLEASE HELP ME GET MORE DATASETS LIKE THIS FILLING A 30s SURVEY: https://airtable.com/shrJM8TYzNEMNALCvThe main goal is to use this data to predict how much impurity is in the ore concentrate. As this impurity is measured every hour, if we can predict how much silica (impurity) is in the ore concentrate, we can help the engineers, giving them early information to take actions (empowering!). Hence, they will be able to take corrective actions in advance (reduce impurity, if it is the case) and also help the environment (reducing the amount of ore that goes to tailings as you reduce silica in the ore concentrate).ContentThe first column shows time and date range (from march of 2017 until september of 2017). Some columns were sampled every 20 second. Others were sampled on a hourly base.The second and third columns are quality measures of the iron ore pulp right before it is fed into the flotation plant. Column 4 until column 8 are the most important variables that impact in the ore quality in the end of the process. From column 9 until column 22, we can see process data (level and air flow inside the flotation columns, which also impact in ore quality. The last two columns are the final iron ore pulp quality measurement from the lab.Target is to predict the last column, which is the % of silica in the iron ore concentrate.InspirationI have been working in this dataset for at least six months and would like to see if the community can help to answer the following questions:- Is it possible to predict % Silica Concentrate every minute?- How many steps (hours) ahead can we predict % Silica in Concentrate? This would help engineers to act in predictive and optimized way, mitigatin the % of iron that could have gone to tailings.- Is it possible to predict % Silica in Concentrate whitout using % Iron Concentrate column (as they are highly correlated)?