• 2020 해외 공개 English
  Kaggle Wikipedia Web Traffic Weekly Dataset
  • 데이터 제공처 OpenAIRE
  • 데이터 리포지터리
  • 생성자 Godahewa, Rakshitha; Bergmeir, Christoph; Webb, Geoff;
  • 국가연구자번호
  • ntis과제번호
  • 과제명
  • 과제책임자
  • 과제수행기관
  • 부처
  • 라이센스유형 CC-BY-4.0;
  • 주제분류
  • 인용횟수 0
  • doi 10.5281/zenodo.3898338
  • 버전 1.0
  
  

  This is the aggregated version of the daily dataset used in the Kaggle Wikipedia Web Traffic forecasting competition. It contains 145063 time series representing the number of hits or web traffic for a set of Wikipedia pages from 2015-07-01 to 2017-09-05, after aggregating them into weekly.

  The original dataset contains missing values. They have been simply replaced by zeros before aggregation.

• 2020 해외 공개 English
  Kaggle Wikipedia Web Traffic Weekly Dataset
  • 데이터 제공처 OpenAIRE
  • 데이터 리포지터리
  • 생성자 Godahewa, Rakshitha; Bergmeir, Christoph; Webb, Geoff; Hyndman, Rob; Montero-Manso, Pablo;
  • 국가연구자번호
  • ntis과제번호
  • 과제명
  • 과제책임자
  • 과제수행기관
  • 부처
  • 라이센스유형 CC-BY-4.0;
  • 주제분류
  • 인용횟수 0
  • doi 10.5281/zenodo.4656664
  • 버전 1.0
  
  

  This is the aggregated version of the daily dataset used in the Kaggle Wikipedia Web Traffic forecasting competition. It contains 145063 time series representing the number of hits or web traffic for a set of Wikipedia pages from 2015-07-01 to 2017-09-05, after aggregating them into weekly. The original dataset contains missing values. They have been simply replaced by zeros before aggregation.;{"references": ["Google, 2017. Web traffic time series forecasting. URL https://www.kaggle.com/c/web-traffic-time-series-forecasting}

• 2020 해외 공개 Korean
  COVID-19 in India
  • 데이터 제공처 코비드-19
  • 데이터 리포지터리
  • 생성자
  • 국가연구자번호
  • ntis과제번호
  • 과제명
  • 과제책임자
  • 과제수행기관
  • 부처
  • 라이센스유형 CC-BY;
  • 주제분류 보건의료;
  • 인용횟수 0
  • doi 10.22711/0101ZZ013883525511.0
  • 버전 1.0
  
  

  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

• 2021 해외 공개 English
  Pakistan Drone Attacks
  • 데이터 제공처 국가연구데이터플랫폼
  • 데이터 리포지터리
  • 생성자 Zeeshan-ul-hassan Usmani;
  • 국가연구자번호
  • ntis과제번호
  • 과제명
  • 과제책임자
  • 과제수행기관
  • 부처
  • 라이센스유형 CC-BY;
  • 주제분류 정치/행정; 지리/지역/관광; 사회/인류/복지/여성;
  • 인용횟수 0
  • doi 10.34740/kaggle/dsv/8660
  • 버전 1.0
  
  

  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

• 2021 해외 공개 English
  Quality Prediction in a Mining Process
  • 데이터 제공처 국가연구데이터플랫폼
  • 데이터 리포지터리
  • 생성자 EduardoMagalhãesOliveira;
  • 국가연구자번호
  • ntis과제번호
  • 과제명
  • 과제책임자
  • 과제수행기관
  • 부처
  • 라이센스유형 CC-BY;
  • 주제분류 에너지/자원;
  • 인용횟수 0
  • doi 10.22711/0101EF014600901511.0
  • 버전 1.0
  
  

  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)?

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