scholarly journals Practical guidance for using multiple data sources in systematic reviews and meta-analyses (with examples from the MUDS study)

2017 ◽  
Vol 9 (1) ◽  
pp. 2-12 ◽  
Author(s):  
Evan Mayo-Wilson ◽  
Tianjing Li ◽  
Nicole Fusco ◽  
Kay Dickersin ◽  
Keyword(s):  
Systematic Reviews ◽  
Data Sources ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Practical Guidance ◽  
Meta Analyses

scholarly journals P42 Development of an algorithm for determining the stage of innovation of an invasive procedure or device

BJS Open ◽  
2021 ◽  
Vol 5 (Supplement_1) ◽  
Author(s):  
D L Scroggie ◽  
D Elliott ◽  
K Avery ◽  
S Cousins ◽  
N S Blencowe ◽  
...  
Keyword(s):  
Case Studies ◽  
Systematic Reviews ◽  
Video Recording ◽  
Invasive Procedure ◽  
Data Sources ◽  
Surgical Innovation ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Practical Algorithm ◽  
Ideal Framework

Abstract Introduction The IDEAL framework describes a series of stages through which surgical innovations typically pass. For each stage, the framework makes recommendations with the intention of improving the quality of research in surgery. Its adoption has been slow despite publication of practical guidance. It has been recognized that determining the stage of innovation of an invasive procedure or device can be problematic, and this issue may be hindering adoption of the framework. The aim of this study is to develop a practical algorithm for determining the stage of innovation of any invasive procedure or device. Methods Multiple data sources will be used: published literature, intra-operative video recording, interviews with stakeholders, healthcare datasets, and clinical policies and guidelines. Systematic reviews will be conducted to identify guidance relating to surgical innovation, and evaluations or critiques of guidance. Case studies of surgical innovations will be undertaken, to gain an understanding of problems encountered in determining stage of innovation. An algorithm will be developed through an iterative process of testing and refinement, triangulating data from the literature reviews and case studies. Results The findings of the initial systematic reviews will be presented. This will include guidance relating to surgical innovation, and evaluations or critiques of it. Recognized problems in determining stage of innovation will be summarized, including proposed solutions and suggestions for further development. Conclusion This study will use multiple data sources to develop a practical algorithm for determining the stage of innovation of any invasive procedure or device.

scholarly journals Examining Deep Learning Models with Multiple Data Sources for COVID-19 Forecasting

2020 ◽  
Author(s):  
Lijing Wang ◽  
Aniruddha Adiga ◽  
Srinivasan Venkatramanan ◽  
Jiangzhuo Chen ◽  
Bryan Lewis ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Sources ◽  
Learning Models ◽  
Multiple Data Sources ◽  
Multiple Data

Big data and portfolio optimization: A novel approach integrating DEA with multiple data sources

Omega ◽  
2021 ◽  
pp. 102479
Author(s):  
Zhongbao Zhou ◽  
Meng Gao ◽  
Helu Xiao ◽  
Rui Wang ◽  
Wenbin Liu
Keyword(s):  
Big Data ◽  
Portfolio Optimization ◽  
Data Sources ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Novel Approach

scholarly journals A dataset on affiliation of venture capitalists in China between 2000 and 2016

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Jin Chen ◽  
Tianyuan Chen ◽  
Yifei Song ◽  
Bin Hao ◽  
Ling Ma
Keyword(s):  
Data Sources ◽  
Venture Capitalists ◽  
High Quality ◽  
Public Agency ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Multi Stage ◽  
The World ◽  
Prior Literature ◽  
Innovation And Entrepreneurship

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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