ETL Preprocessing with Multiple Data Sources for Academic Data Analysis

Author(s):  
Gant Gaw Wutt Mhon ◽  
Nang Saing Moon Kham
Author(s):  
Ann Harrison

The Benetech Human Rights Data Analysis Group (HRDAG) (http://www.hrdag.org/) analyzes the patterns and magnitude of large-scale human rights violations. Together with local partners, HRDAG collects and preserves human rights data and helps NGOs and other human rights organizations accurately interpret quantitative findings. HRDAG statisticians, programmers, and data analysts develop methodologies to determine how many of those killed and disappeared have never been accounted for - and who is most responsible. This account illustrates how HRDAG pioneered the calculation of scientifically sound statistics about political violence from multiple data sources including the testimony of witnesses who come forward to tell their stories. It describes methodologies that HRDAG analysts have developed to ensure that statistical human rights claims are transparently, demonstrably, and undeniably true.


2015 ◽  
pp. 578-595
Author(s):  
Ann Harrison

The Benetech Human Rights Data Analysis Group (HRDAG) (http://www.hrdag.org/) analyzes the patterns and magnitude of large-scale human rights violations. Together with local partners, HRDAG collects and preserves human rights data and helps NGOs and other human rights organizations accurately interpret quantitative findings. HRDAG statisticians, programmers, and data analysts develop methodologies to determine how many of those killed and disappeared have never been accounted for - and who is most responsible. This account illustrates how HRDAG pioneered the calculation of scientifically sound statistics about political violence from multiple data sources including the testimony of witnesses who come forward to tell their stories. It describes methodologies that HRDAG analysts have developed to ensure that statistical human rights claims are transparently, demonstrably, and undeniably true.


Author(s):  
Lijing Wang ◽  
Aniruddha Adiga ◽  
Srinivasan Venkatramanan ◽  
Jiangzhuo Chen ◽  
Bryan Lewis ◽  
...  

Omega ◽  
2021 ◽  
pp. 102479
Author(s):  
Zhongbao Zhou ◽  
Meng Gao ◽  
Helu Xiao ◽  
Rui Wang ◽  
Wenbin Liu

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Jin Chen ◽  
Tianyuan Chen ◽  
Yifei Song ◽  
Bin Hao ◽  
Ling Ma

AbstractPrior literature emphasizes the distinct roles of differently affiliated venture capitalists (VCs) in nurturing innovation and entrepreneurship. Although China has become the second largest VC market in the world, the unavailability of high-quality datasets on VC affiliation in China’s market hinders such research efforts. To fill up this important gap, we compiled a new panel dataset of VC affiliation in China’s market from multiple data sources. Specifically, we drew on a list of 6,553 VCs that have invested in China between 2000 and 2016 from CVSource database, collected VC’s shareholder information from public sources, and developed a multi-stage procedure to label each VC as the following types: GVC (public agency-affiliated, state-owned enterprise-affiliated), CVC (corporate VC), IVC (independent VC), BVC (bank-affiliated VC), FVC (financial/non-bank-affiliated VC), UVC (university endowment/spin-out unit), and PenVC (pension-affiliated VC). We also denoted whether a VC has foreign background. This dataset helps researchers conduct more nuanced investigations into the investment behaviors of different VCs and their distinct impacts on innovation and entrepreneurship in China’s context.


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