zh:news:reports:impact_credit_talent_gender
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zh:news:reports:impact_credit_talent_gender [2018/05/21 15:54] pzczxs [学术报告：科学学的定量分析：影响力、信誉、天才和性别] |
zh:news:reports:impact_credit_talent_gender [2018/05/22 12:39] (当前版本) pzczxs [科学学的定量分析：影响力、信誉、天才和性别] |
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| ====== 科学学的定量分析：影响力、信誉、天才和性别 ====== | ====== 科学学的定量分析：影响力、信誉、天才和性别 ====== | ||
| - | *时间：2018年05月25日 14：00-15：300 | + | *时间：2018年05月25日 14：00-15：30 |
| *地点：北京工业大学经管楼B301 | *地点：北京工业大学经管楼B301 | ||
| ===== 报告人简介 ===== | ===== 报告人简介 ===== | ||
| - | 黄俊铭博士，2007年本科毕业于清华大学物理系，2014年在中科院计算所获得博士学位，导师是李国杰院士。黄俊铭博士2015年赴美国东北大学复杂网络中心艾伯特—拉斯洛 ·巴拉巴西（Albert-László Barabási）课题组做博士后至今。黄博士的研究兴趣是科学学和社会网络分析。Albert-László Barabási是全球复杂网络研究的权威，无标度网络的创立者。 | + | **黄俊铭**博士，2007年本科毕业于清华大学物理系，2014年在中科院计算所获得博士学位，导师是李国杰院士。黄俊铭博士2015年赴美国东北大学复杂网络中心艾伯特—拉斯洛 ·巴拉巴西（Albert-László Barabási）课题组做博士后至今。黄博士的研究兴趣是科学学和社会网络分析。Albert-László Barabási是全球复杂网络研究的权威，无标度网络的创立者。 |
| ===== 报告大纲 ===== | ===== 报告大纲 ===== | ||
| + | **Quantitative Analysis on Science of Science: Impact, Credit, Talent and Gender** | ||
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| Understanding the fundamental mechanism of scientific discoveries is instrumental for policy design that aims at accelerating the scientific progress. The recent availability of large-scale digitized data on scientific publication, impact and collaboration provides opportunities to quantitatively explore hidden patterns governing the intrinsic of science. With a combination of tools from statistics and network science, we explore several questions that lead to quantitative understanding of scientific practice: How do scholar outputs gain impact from the scientific community? How are credits allocated among collaborators? How predictable a scientist would see his/her representative work in career? How do gender differences emerge and evolve in the history of science? With a deeper understanding of those factors behind scientific practice, we provide insights to capture the unfolding of science and suggestions to design policy and interventions | Understanding the fundamental mechanism of scientific discoveries is instrumental for policy design that aims at accelerating the scientific progress. The recent availability of large-scale digitized data on scientific publication, impact and collaboration provides opportunities to quantitatively explore hidden patterns governing the intrinsic of science. With a combination of tools from statistics and network science, we explore several questions that lead to quantitative understanding of scientific practice: How do scholar outputs gain impact from the scientific community? How are credits allocated among collaborators? How predictable a scientist would see his/her representative work in career? How do gender differences emerge and evolve in the history of science? With a deeper understanding of those factors behind scientific practice, we provide insights to capture the unfolding of science and suggestions to design policy and interventions | ||
zh/news/reports/impact_credit_talent_gender.1526889291.txt.gz · 最后更改: 2018/05/21 15:54 由 pzczxs