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16, 321–357 (2002).Scientific Workplace 5.5 Full Mediafire amaditat Hate Story Movie Download Telugu Movie darlydal Simply Shredded 12 Week Shred Pdf 134 dansak Paragon NTFS For Mac 15.5.106 - CrackzSoft Q Paragon NTFS For Mac 15.5.106 - Cra !FULL! IDeneb Mac OSX 10.5.8l ((TOP)) Boys At The Pool 42, Rnrde F1-tv-archive nanabbre 64bdbb59a4 13 Download Scientific Workplace 5.5 for Windows XP Latest Version for Windows. SMOTE: synthetic minority over-sampling technique. Research funding: the case for a modified lottery. Tencent and Facebook data validate Metcalfe’s law. Metcalfe’s law after 40 years of ethernet. Unbiased evaluation of ranking metrics reveals consistent performance in science and technology citation data. Long-term propagation of distinct hematopoietic differentiation programs in vivo. G9a histone methyltransferase plays a dominant role in euchromatic histone h3 lysine 9 methylation and is essential for early embryogenesis. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 855–864 (2016).
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node2vec: scalable feature learning for networks. Identification of milestone papers through time-balanced network centrality. The essential role of time in network-based recommendation. Proceedings of the 17th ACM/IEEE Joint Conference on Digital Libraries 49–58 (2017). Learning to predict citation-based impact measures. Using content-based and bibliometric features for machine learning models to predict citation counts in the biomedical literature. Scientific prize network predicts who pushes the boundaries of science. Large teams develop and small teams disrupt science and technology. Early identification of important patents: design and validation of citation network metrics.
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A dynamic network measure of technological change. We Have Met The Enemy… and He Is Us: Lessons from Twenty Years of the Kauffman Foundation’s Investments in Venture Capital Funds and the Triumph of Hope over Experience (Kauffman Foundation, 2012).įunk, R. How Do Venture Capitalists Make Decisions? Working Paper 22587 (National Bureau of Economic Research, 2016). Local bias in venture capital investments. Why the impact factor of journals should not be used for evaluating research. Coercive citation in academic publishing.
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We propose DELPHI as a tool to aid in the construction of diversified, impact-optimized funding portfolios. We demonstrate the framework’s performance by correctly identifying 19/20 seminal biotechnologies from 1980 to 2014 via a blinded retrospective study and provide 50 research papers from 2018 that DELPHI predicts will be in the top 5% of time-rescaled node centrality in the future. We prototype this framework and deduce its performance and scaling properties on time-structured publication graphs from 1980 to 2019 drawn from 42 biotechnology-related journals, including over 7.8 million individual nodes, 201 million relationships and 3.8 billion calculated metrics. Here we describe DELPHI (Dynamic Early-warning by Learning to Predict High Impact), a framework that provides an early-warning signal for ‘impactful’ research by autonomously learning high-dimensional relationships among features calculated across time from the scientific literature. The scientific ecosystem relies on citation-based metrics that provide only imperfect, inconsistent and easily manipulated measures of research quality.