注册 找回密码
搜索
查看: 779|回复: 3

《自然》:一到十人较小团队倾向于提出新想法和概念

[复制链接]
发表于 2021-11-23 12:07:38 | 显示全部楼层 |阅读模式
本帖最后由 邓文龙 于 2021-11-23 16:25 编辑

作者:James Evans

来源:《自然》 发布时间:2019/2/16 12:52:14

研究显示较小团队倾向于提出新想法和概念

《自然》在线发表的一项研究分析了逾6500万个论文、专利和软件产品,结果显示较小的团队倾向于提出新的想法和概念,而较大的团队倾向于发展现有的想法和概念。

在许多科学和技术领域,大型科研团队越来越多。这种联合的力量加快了科学和技术取得重大进步的速度——这在没有这种集体资源和专业知识的情况下难以实现。但是,小型团队和大型团队的科学产出是否存在系统性差异尚不十分明了。

美国芝加哥大学的James Evans及其同事通过对数百万论文、专利和软件产品进行分析,考察了1954年至2014年间的团队合作情况。

他们采用一种指标来衡量论文或产品如何建立在以前的工作基础上,发现在研究期限内,一到十人的小团队倾向于利用新的想法和概念来颠覆科学和技术。相比之下,较大的团队倾向于发展现有的想法。

研究人员总结表示,小型团队和大型团队对于科学和技术的蓬勃发展都至关重要。(来源:中国科学报 唐一尘)

相关论文信息:DOI: 10.1038/s41586-019-0941-9

https://www.nature.com/articles/s41586-019-0941-9

https://www.nature.com/articles/ ... =news.sciencenet.cn

http://news.sciencenet.cn/htmlpa ... 12521495349185.shtm



小团队的创新性研究“供养”了大团队吗?
2019/01/24
导读
小团队擅长提出问题,大团队擅长回答问题
研究者通过大规模论文数据研究社会中创新现象的例子。研究揭示了小团队与大团队之间的关系:小团队的创新性研究“供养”了大团队;小团队的研究特征在于创新,但小团队的研究更容易不被社会认可,其起步成功过程更加艰难。
总结
小团队的创新性研究供养了大团队,小团队创造新的研究方向,大团队发展这些研究方向,当小团队的研究获得公众认可与赞赏时,该研究类别的大团队比例显著增加;小团队的研究特征在于创新,大团队的研究特征在于投入大量的资源发展,小团队的研究更容易不被社会认可,其起步成功过程更加艰难。
而对于一个团队,创新能力和每一个人都密不可分,个人的时间精力,年龄、与社群的连接将会影响团队的思考能力、创新能力。
作者简介
徐绘敏,南京大学新闻传播学院计算传播学方向,数据科学爱好者
http://www.zhishifenzi.com/depth/depth/5088.html



吴令飞Nature发文:大团队渐进发展,小团队颠覆式创新
2019年02月14日 10:45 分类:未分类 阅读:877 评论:0
导语
2019年2月13日,Nature 杂志官网在线发表了一篇以Large Teams Have Developed Science and Technology; Small Teams Have Disrupted It为题文章(article),介绍了对于团队创新的最新研究成果,发现小团队比大团队更能做出颠覆式的创新成果。
本文第一作者吴令飞是集智科学家,集智-凯风研读营学者,腾讯研究院×集智俱乐部AI&Society第三期讲者。第二作者王大顺是美国西北大学凯洛格商学院、复杂系统研究中心副教授。通讯作者 James A. Evans 是美国芝加哥大学社会学系教授,知识实验室主任,腾讯研究院×集智俱乐部AI&Society第十二期讲者。
http://swarma.blog.caixin.com/archives/198404



小团队更容易做出大发现:Nature论文发现团队规模与颠覆性创新成反比
领研网
「科研圈」官方网站 - 专注科技人才与学术分享
6 人赞同了该文章
对于现代学术界来说,“大科学团队”意味着世界领先的技术设备、学界权威领衔的庞大团队、稳定充裕的资金供给和高影响力的研究成果。不过近期发表在 Nature 上的一篇论文告诉我们,大科学团队更像是一个建立在已有研究基础上的加速器,那些小团队才是贡献颠覆性成果、开拓学术未知领域的先锋。
https://zhuanlan.zhihu.com/p/57808762

小团队研究
https://www.google.com/search?q= ... p;bih=625&dpr=1



华成旅行社  欢迎来电咨询:

电话:03-3833-9823  / 03-5688-1863
FAX :03-3833-9873  / 03-3834-5891

SOFTBANK电话:080-3416-2275   担当:小郭  微信号:08034162275
SOFTBANK电话:090-2172-4325   担当:小于  微信号:TYOSCL4325
SOFTBANK电话:080-3398-4387   担当:小李  微信号:huacheng4387
SOFTBANK电话:080-3523-4388   担当:小何  微信号:huacheng602
SOFTBANK电话:080-3084-4389   担当:小马  微信号:huacheng858

http://www.kaseisyoji.com/forum.php?mod=forumdisplay&fid=10

 楼主| 发表于 2021-11-24 19:33:44 | 显示全部楼层
本帖最后由 邓文龙 于 2021-11-24 20:31 编辑

谷歌译:

团队科学的拥护者声称,向更大的科学和技术团队的转变实现了解决现代社会中复杂且需要跨学科解决方案的问题的基本功能6-8。尽管已经证明了团队规模对团队成员的专业和职业利益 9,但几乎没有证据支持大型团队针对知识发现和技术发明进行优化的观点。对群体的实验和观察研究表明,大群体中的个体思维和行为不同——他们产生的想法较少 10,11,回忆较少的学到的信息 12,更经常地拒绝外部观点 13 并且倾向于中和彼此的观点 14。小型和大型团队在应对与创新相关的风险方面也可能有所不同。大型团队,例如大型商业组织,可能会专注于拥有巨大潜在市场的确定性赌注,而收益更多、损失更少的小团队可能会承担新的、未经考验的机会,具有高增长和失败的潜力15,导致明显不同的结果。这些可能性促使我们探索越来越小的团队对科学和技术进步的影响,以及这些团队如何以不同的方式搜索和收集知识。

先前的研究表明,大型文章和专利团队获得的引用次数略多2,16。然而,单靠引用计数并不能捕捉到不同类型的贡献。这可以从两篇著名文章之间的差异中看出:一篇关于自组织临界性 17(BTW 模型,以作者姓名首字母命名),另一篇关于玻色-爱因斯坦凝聚 18(沃尔夫冈·凯特勒因此获得 2001 年诺贝尔奖物理)(图1,扩展数据图1b)。这两篇文章的引用次数相似,但在 BTW 模型文章之后的大多数研究都只引用了模型本身,而没有提及文章中的参考文献。相比之下,Bose-Einstein 凝聚的文章几乎总是与 Bose19、Einstein20 和其他前因共同引用。两篇论文之间的差异并不体现在引用次数上,而是体现在它们是否提出或解决了科学问题——它们分别是颠覆了还是发展了现有的科学思想21。 BTW 模型启动了新的研究流,而玻色-爱因斯坦凝聚的实验实现则阐述了先前提出的可能性。

为了系统地评估小型和大型团队在推动科技进步中的作用,我们从三个相关但不同的领域收集了大规模数据集(参见方法):(1) Web of Science (WOS) 数据库,其中包含更多1954 年至 2014 年间发表的文章超过 4200 万篇,其中引用 6.11 亿次; (2) 1976-2014年美国专利商标局授权专利500万件,专利申请人新增引用6500万件; (3) GitHub (2011–2014) 上有 1600 万个软件项目和 900 万个分叉,GitHub 是一个流行的网络平台,允许用户在同一代码库上进行协作,并通过复制和构建代码来“引用”其他库。

对于每个数据集,我们通过引入一些新事物来评估每项工作破坏其所属科学或技术领域的程度,这些新事物使人们对它所建立的先前工作的注意力黯然失色。我们使用先前设计的衡量标准 22 来识别专利发明中的不稳定和整合;该度量在-1和1之间变化,分别对应于发展或破坏的科学和技术(图1a)。我们以多种方式验证中断措施。首先,我们调查了科学论文中的中断分布(图1b);破坏性 BTW 模型文章位于前 1%,而发展性 Bose-Einstein 凝聚论文位于中断分布的底部 3%。我们还发现,平均而言,获得诺贝尔奖的论文跻身最具破坏性的 2% 文章之列。评论文章是发展性的,具有负的中断平均值(底部 46%),而他们审查的原始研究作品具有正平均值(前 23%)。标题突出的先前工作的文章(例如 Bose-Einstein 凝聚文章)位于底部 25%(补充表1)。我们通过一项调查进一步证实了这些结果,在该调查中,我们要求来自不同领域的学者提出具有颠覆性和发展性的文章;这对称地证实了破坏措施(补充表2)。最后,我们发现在文章的标题中,不同的词与破坏性(“介绍”、“衡量”、“改变”和“推进”)与发展(“认可”、“确认”、“证明”、“理论”)相关联和“模型”)论文(图1c,补充表3)。

我们预测小团队的工作将比大团队的工作更具破坏性。我们的论文、专利和软件数据库有力地证实了这一预测。我们的来源在范围和领域上有所不同,但我们始终观察到,在过去的 60 年中,较大的团队生产的文章、专利和软件的中断分数随着团队成员的增加而显着且单调地下降(图 2a–c,扩展数据图3).具体来说,随着团队成员从 1 人增加到 50 人,他们的论文、专利和产品在测量中断的百分位数上分别下降了 70、30 和 50(扩展数据图3a)。在每种情况下,这都突出了从破坏到发展的转变。这些结果支持这样一种假设,即大型团队可能会被更好地设计或激励来开发当前的科学和技术,而小型团队会以新的问题和机会扰乱科学和技术。

当我们专注于最具破坏性和影响力的工作时,这种现象会被放大(图 2d-f)。我们使用每篇作品收到的引用次数来衡量每篇文章、专利和软件的影响。如图 2d 所示,与五名成员的团队相比,单独作者发表高影响力论文的可能性(在引文的前 5% 中)的可能性相同,但单独撰写的论文的高影响力论文的可能性要高 72%颠覆性(在颠覆性论文的前 5% 中)。相比之下,十人团队获得高影响力论文的可能性要高 50%,但这些贡献更有可能发展系统中已经突出的现有想法,这反映在他们成为最具影响力的想法的可能性非常低破坏性的。通过对专利(图2e)和软件开发(图2f)重复相同的分析,我们发现随着团队规模的扩大,破坏和影响一直在发生变化。

(( 我们预测小团队的工作将比大团队的工作更具破坏性。我们的论文、专利和软件数据库有力地证实了这一预测。我们的来源在范围和领域上有所不同,但我们始终观察到,在过去的 60 年中,较大的团队生产的文章、专利和软件的中断分数随着团队成员的增加而显着且单调地下降(图 2a–c,扩展数据图3).具体来说,随着团队成员从 1 人增加到 50 人,他们的论文、专利和产品在测量中断的百分位数上分别下降了 70、30 和 50(扩展数据图3a)。在每种情况下,这都突出了从破坏到发展的转变。这些结果支持这样一种假设,即大型团队可能会被更好地设计或激励来开发当前的科学和技术,而小型团队会以新的问题和机会扰乱科学和技术。

当我们专注于最具破坏性和影响力的工作时,这种现象会被放大(图 2d-f)。我们使用每篇作品收到的引用次数来衡量每篇文章、专利和软件的影响。如图 2d 所示,与五名成员的团队相比,单独作者发表高影响力论文的可能性(在引文的前 5% 中)的可能性相同,但单独撰写的论文的高影响力论文的可能性要高 72%颠覆性(在颠覆性论文的前 5% 中)。相比之下,十人团队获得高影响力论文的可能性要高 50%,但这些贡献更有可能发展系统中已经突出的现有想法,这反映在他们成为最具影响力的想法的可能性非常低破坏性的。通过对专利(图2e)和软件开发(图2f)重复相同的分析,我们发现随着团队规模的扩大,破坏和影响一直在发生变化。   7,8段 同 上面的5,6段))

随着工作影响的增加,小团队和大团队产生的工作中断的差异被放大(图3a);小团队发表的高影响力论文最具破坏性,而大团队发表的高影响力论文最具发展性。随着文章影响力的增加,作为团队规模函数的中断负斜率急剧增加。即使在具有高影响力的文章和专利池(图 3a,前 5% 的引文)中,统计上更有可能由大团队产生(图 2d),小团队已经破坏了当前的系统。新的想法。我们进一步按时间段(扩展数据图3c)和科学领域(图3b,扩展数据图4)对论文进行了拆分,发现这些连接中断和团队规模的模式在所有时代和 90 % 的学科。唯一一致的例外是工程和计算机科学,其中会议记录而不是期刊文章是出版规范(WOS 数据库只索引期刊文章)。

我们考虑了观察到的小团队和大团队工作之间的差异是否可以简单地归因于他们制作的不同类型文章的破坏潜力的差异;例如,小团队可能会产生更多的理论创新,而大团队可能会产生更多的实证分析。借鉴之前的方法 23,我们将来自 www.arXiv.org 的论文与 WOS 数据库进行匹配,并控制每篇文章中的图数(扩展数据图5a)重复我们的分析,因为实证论文的图往往比理论的。我们的结果表明,较小团队和较大团队之间工作中断的大部分差异并不是由他们贡献理论论文还是经验论文(即有更多或更少的数字)的差异驱动的。当我们考虑其他区别(包括评论与原创研究文章)时,这种关联保持不变。与作者较多的评论文章相比,作者较少的评论文章更具破坏性,就像原始研究文章一样(扩展数据图5b)。

对我们的结果的另一种可能解释是,我们观察到的团队效应的发生是因为参与大型团队的科学家、发明家和软件设计师与组成较小团队的科学家、发明家和软件设计师在性质上不同。但是,当我们将破坏性预测为团队规模的函数时,控制出版年份、主题和作者(图3c,扩展数据图3b,补充表4),我们发现破坏性随着团队规模的增长而减少团队规模继续保持不变,并且对作者的控制大大提高了解释的方差百分比(补充表4)。我们进一步测试了我们的结果相对于中断措施的几种不同定义的稳健性,包括去除自引链接,排除所有但高影响参考和其他变化(扩展数据图5g-i)。在所有变化中,我们的结论保持不变。大团队和小团队之间在中断方面的巨大差异引发了关于这些团队在搜索过去以制定下一篇论文、专利或产品方面有何不同的问题。当我们剖析搜索行为时,我们发现大团队和小团队从事明显不同的实践,这些实践可能与破坏和影响方面的不同贡献有关。具体来说,我们将搜索深度衡量为引用参考文献的平均相对年龄,将搜索流行度衡量为对焦点作品参考文献的中位数引用。我们检查这些搜索策略LETTER RESEARCH跨越科学、技术和软件的领域、时间段和影响水平。我们还使用“睡美人指数”24 将这些搜索策略与这些作品所受到的影响的时间延迟联系起来,该指数捕捉到了随着时间的推移,作品受到的引文注意力的凸性所追踪的注意力延迟爆发。

我们发现独立作者和小团队更多地建立在较旧的、不太流行的想法上(图4,扩展数据图6)。较大的团队更经常将近期、高影响力的工作作为他们的主要灵感来源,并且这种趋势随着团队规模的增加而单调增加。随之而来的是,大型团队会迅速获得更多的引用,因为他们的工作与更多同时代的人直接相关,他们提出了他们的想法,观众也准备好欣赏他们。相反,较小的团队会经历更长的引用延迟;单人和两人研究团队的平均睡美人指数百分位数是十人团队的两倍(扩展数据图7)。因此,即使小团队由于集体注意力的快速衰减而获得的整体认可度较低25-27(如图2a所示),但他们的成功研究产生了连锁反应,成为后来大团队的一个有影响力的来源成功(扩展数据图8)。

我们还考虑了这些独特的搜索机制与最近的发现之间的关系,这些发现表明多学科和跨学科团队更经常将来自不同领域的工作联系起来。我们检查了文章参考列表中期刊组合的新颖性,以及与团队规模相关的文章中的关键字组合。这些表明随着团队规模的增加,新颖性的边际增加一致递减,因此对于每个新的团队成员,他们对新颖组合的贡献都会减少(扩展数据图9)。此外,使用先前对非典型组合的衡量 28,我们发现非典型组合缓慢增加到大约 10 人的团队,然后急剧下降到与单独调查员相关的值以下。大型团队促进更广泛的搜索,而小型团队则进行更深入的搜索。

总而言之,我们报告了一种通用且以前未记录的模式,该模式系统地区分了小型和大型团队在创建科学论文、技术专利和软件产品方面的贡献。小团队通过探索和放大来自较旧和不太受欢迎的工作的有前途的想法来破坏科学和技术。大型团队通过解决公认的问题和改进通用设计,取得了近期的成功。部分差异源于小型团队与大型团队所处理的科学和技术实质,但更大的部分似乎是团队规模本身的结果。某些类型的研究需要大型团队的资源,但大型团队需要持续的资金流和成功来“支付账单”29,这使他们对因失败而导致的声誉和支持的损失更加敏感30。我们的发现与对其他领域团队的实地研究一致,这表明获得更多和损失更少的小组更有可能承担新的和未经测试的机会,这些机会有可能实现高速增长和失败15。我们的发现也与对群体的实验和观察研究一致,这些研究表明大群体中的个体如何与小群体中的个体不同地思考和行动 10-14。

小型和大型团队对于蓬勃发展的科技生态都是必不可少的。综上所述,大型团队日益占据主导地位,关于他们感知利益的一系列奖学金2,6-9,28,31 以及我们的调查结果要求对个人和小团体在推进科学和技术方面的重要作用进行新的调查.直接赞助小组研究可能不足以保持其利益。我们分析了 2004 年至 2014 年发表的文章,这些文章承认来自世界各地几个顶级政府机构的财政支持,发现获得这些资金的小团队与大团队在发展而不是破坏其领域的倾向上没有区别(扩展数据图) 10)。与诺贝尔奖论文相比,在所有当代论文中排名前 2% 的论文平均受到干扰,而受资助论文则排在倒数 31% 附近。这可能是由于保守的审查过程、旨在预测此类过程的提案或计划效果,小团队通过对获得资助的提案负责,从而将自己锁定在大团队的惯性中。当我们比较科学的两项主要政策激励措施(资助与奖励)时,我们发现获得诺贝尔奖的文章对小型破坏性团队的抽样显着过度,而那些承认美国国家科学基金会资助的文章对大型开发团队的抽样过度。无论主要驱动因素如何,这些结果都描绘了资金不足的独立调查员和小团队的统一画像,他们通过在更深入和更广泛的信息搜索的基础上产生新的方向来破坏科学和技术。这些结果表明,政府、行业和非盈利的科技资助者需要调查小团队在扩展知识前沿方面的关键作用,即使大团队正在迅速发展它们。

在线内容 任何方法、其他参考资料、自然研究报告摘要、源数据、数据可用性声明和相关登录代码均可在 https://doi.org/10.1038/s41586-019-0941-9 上获得。
收稿日期:2018 年 4 月 20 日;接受:2019 年 1 月 7 日; 2019 年 2 月 13 日在线发布。

https://www.nature.com/articles/ ... =news.sciencenet.cn

 楼主| 发表于 2021-11-24 19:55:11 | 显示全部楼层
本帖最后由 邓文龙 于 2021-11-24 20:02 编辑

Advocates of team science have claimed that a shift to larger teams in science and technology fulfils the essential function of solving problems in modern society that are complex and which require interdisciplinary solutions6–8. Although much has been demonstrated about the professional and career benefits of team size for team members9, there is little evidence that supports the notion that larger teams are optimized for knowledge discovery and technological invention9. Experimental and observational research on groups reveals that individuals in large groups think and act differently—they generate fewer ideas10,11, recall less learned information12, reject external perspectives more often13 and tend to neutralize each other’s viewpoints14. Small and large teams may also differ in their response to the risks associated with innovation. Large teams, such as large business organizations, may focus on sure bets with large potential markets, whereas small teams that have more to gain and less to lose may undertake new, untested opportunities with the potential for high growth and failure15, leading to markedly different outcomes. These possibilities led us to explore the consequences of smaller and larger teams for scientific and technological advance, and how such teams search and assemble knowledge differently.

Previous research demonstrates that large article and patent teams receive slightly more citations2,16. However, citation counts alone cannot capture distinct types of contribution. This can be seen in the difference between two well-known articles: one about self-organized criticality17 (the BTW model, after the authors’ initials) and another about Bose–Einstein condensation18 (for which Wolfgang Ketterle was awarded the 2001 Nobel Prize in Physics) (Fig.1, Extended Data Fig.1b). The two articles have received a similar number of citations, but most research subsequent to the BTW-model article has cited only the model itself without mentioning references from the article. By con-trast, the Bose–Einstein condensation article is almost always co-cited with Bose19, Einstein20 and other antecedents. The difference between the two papers is reflected not in citation counts but in whether they suggested or solved scientific problems—whether they disrupted or developed existing scientific ideas, respectively21. The BTW model launched new streams of research, whereas the experimental realiza-tion of Bose–Einstein condensation elaborated upon possibilities that had previously been posed.

To systematically evaluate the role that small and large teams have in unfolding scientific and technological advances, we collected large-scale datasets from three related but distinct domains (seeMethods): (1) the Web of Science (WOS) database that contains more than 42 million articles published between 1954 and 2014, and 611 million cita-tions among them; (2) 5 million patents granted by the US Patent and Trademark Office from 1976 to 2014, and 65 million citations added by patent applicants; (3) 16 million software projects and 9 million forks to them on GitHub (2011–2014), a popular web platform that allows users to collaborate on the same code repository and ‘cite’ other repositories by copying and building on their code.

For each dataset, we assess the degree to which each work disrupts the field of science or technology to which it belongs by introducing something new that eclipses attention to previous work upon which it has built. We use a measure that was previously designed22 to identify destabilization and consolidation in patented inventions; this measure varies between −1 and 1, which corresponds to science and technology that develops or disrupts, respectively (Fig.1a). We validate the dis-ruption measure in several ways. First, we investigate the distribution of disruption across scientific papers (Fig.1b); the disruptive BTW-model article is located in the top 1%, whereas the developmental Bose–Einstein condensation paper is in the bottom 3% of the disrup-tion distribution. We also find that, on average, Nobel-prize-winning papers register among the 2% most disruptive articles. Review articles are developmental with a negative mean of disruption (bottom 46%), whereas the original research works that they review have a positive mean (top 23%). Articles that headline prominent prior work—such as the Bose–Einstein condensation article—lie in the bottom 25% (Supplementary Table1). We further confirmed these results with a survey in which we asked scholars from diverse fields to propose dis-ruptive and developmental articles; this symmetrically confirmed the disruption measure (Supplementary Table2). Finally, we find that in the titles of articles different words associate with disruptive (‘introduce’, ‘measure’, ‘change’ and ‘advance’) versus developing (‘endorse’, ‘confirm’, ‘demonstrate’, ‘theory’ and ‘model’) papers (Fig.1c, Supplementary Table3).

We predict that work by small teams will be substantially more dis-ruptive than work by large teams. Our databases of papers, patents and software strongly confirm this prediction. Our sources differ in scope and domain, but we consistently observe that over the past 60 years, larger teams produce articles, patents and software with a disruption score that markedly and monotonically declines with each additional team member (Fig.2a–c, Extended Data Fig.3). Specifically, as teams grow from 1 to 50 team members, their papers, patents and products drop in percentiles of measured disruption by 70, 30 and 50, respec-tively (Extended Data Fig.3a). In every case, this highlights a transition from disruption to development. These results support the hypothesis that large teams may be better designed or incentivized to develop cur-rent science and technology, and that small teams disrupt science and technology with new problems and opportunities.

This phenomenon is amplified when we focus on the most disruptive and impactful work (Fig.2d–f). We measure the impact of each article, patent and software using the number of citations each work received. As shown in Fig.2d, solo authors are just as likely to produce high-im-pact papers (in the top 5% of citations) as teams with five members, but solo-authored papers are 72% more likely to be highly disruptive (in the top 5% of disruptive papers). By contrast, ten-person teams are 50% more likely to score a high-impact paper, yet these contributions are much more likely to develop existing ideas already prominent in the system, which is reflected in the very low likelihood they are among the most disruptive. By repeating the same analyses for patents (Fig.2e) and software development (Fig.2f), we find that disruption and impact consistently diverge as teams grow in size.

Differences in disruption between works produced by small and large teams are magnified as the impact of the work increases (Fig.3a); high-impact papers produced by small teams are the most disruptive, and high-impact papers produced by large teams are the most devel-opmental. As article impact increases, the negative slope of disruption as a function of team size steepens sharply. Even within the pool of high-impact articles and patents (Fig.3a, top 5% of citations), which are statistically more likely produced by large teams (Fig.2d), small teams have disrupted the current system with substantially more new ideas. We further split papers by time period (Extended Data Fig.3c) and scientific field (Fig.3b, Extended Data Fig.4), and found that these patterns linking disruption and team size are stable for all eras and for 90% of disciplines. The only consistent exceptions were observed for engineering and computer science, in which conference proceedings rather than journal articles are the publishing norm (the WOS database indexes only journal articles).

We considered whether observed differences between the work of small and large teams could simply be attributed to differences in disruptive potential for the different types of articles that they produce; for example, small teams may generate more theoreti-cal innovations and large teams more empirical analyses. Drawing on a previous approach23, we matched papers from www.arXiv.org with the WOS database and repeated our analyses controlling for the number of figures in each article (Extended Data Fig.5a), as empirical papers tend to have more figures than theoretical ones. Our results suggest that most of the difference in disruption between work from smaller and larger teams is not driven by differences in whether they contributed theoretical versus empirical papers (that is, had more or less figures). The association remains the same when we consider other distinctions, including review versus original research articles. Review articles with fewer authors are more disruptive than those with more, just as with original research articles (Extended Data Fig.5b).

Another possible explanation for our results is that the team effect thatwe observe occurs because the scientists, inventors and software designers involved in larger teams are qualitatively different from those comprising smaller teams. But when we predict disruptiveness as a function of team size, controlling for publication year, topic and author (Fig.3c, Extended Data Fig.3b, Supplementary Table4), we find that the decrease of disruption with the growth of team size continues to hold, and controlling for authors greatly improves the percentage of variance explained (Supplementary Table4).We further test the robustness of our results against several differ-ent definitions of the disruption measure, including the removal of self-citation links, exclusion of all but high-impact references and other variations (Extended Data Fig.5g–i). Across all variations, our conclu-sions remain the same.The considerable difference in disruption between large and small teams raises questions regarding how these teams differ in searching the past to formulate their next paper, patent or product. When we dissect search behaviour, we find that large and small teams engage in notably different practices that may be related to divergent contributions in disruption and impact. Specifically, we measure search depth as average relative age of references cited and search popularity as median citations to the references of a focal work. We examine these search strategiesLETTER RESEARCHacross fields, time periods and impact levels in science, technology and software. We also relate these search strategies to temporal delay in the impact these works receive using the ‘Sleeping Beauty index’24, which captures a delayed burst of attention traced by convexity in the citation attention that a work receives over time.

We find that solo authors and small teams much more often build on older, less popular ideas (Fig.4, Extended Data Fig.6). Larger teams more often target recent, high-impact work as their primary source of inspiration, and this tendency increases monotonically with team size. It follows that large teams receive more of their citations rapidly, as their work is immediately relevant to more contemporaries whose ideas they develop and audiences primed to appreciate them. Conversely, smaller teams experience a much longer citation delay; the average Sleeping Beauty indexpercentile for solo and two-person research teams is twice that of ten-person teams (Extended Data Fig.7). As a result, even though small teams receive less recognition overall owing to the rapid decay of collective attention25–27 (as shown in Fig.2a), their successful research produces a ripple effect, which becomes an influential source of later large-team success (Extended Data Fig.8).

We also consider the relationship between these distinctive search mechanisms and recent findings28 that suggest multi- and inter- disciplinary teams more often link work from divergent fields. We examined the novelty of journal combinations within article reference lists and also keyword combinations within articles in relation to team size. These show consistent diminishing marginal increases to novelty with team size, such that with each new team member, their contribution to novel combinations decreases (Extended Data Fig.9). Moreover, using a previous measure of atypical combinations28, we find that atypical combinations increase slowly up to teams of approximately ten and then decrease sharply below the value associated with a solo investigator. Whereas larger teams facilitate broader search, small teams search deeper.

In summary, we report a universal and previously undocumented pattern that systematically differentiates the contributions of small and large teams in the creation of scientific papers, technology patents and software products. Small teams disrupt science and technology by exploring and amplifying promising ideas from older and less-popular work. Large teams develop recent successes, by solving acknowledged problems and refining common designs. Some of this difference results from the substance of science and technology that small versus large teams tackle, but the larger part appears to emerge as a consequence of team size itself. Certain types of research require the resources of large teams, but large teams demand an ongoing stream of funding and success to ‘pay the bills’29, which makes them more sensitive to the loss of reputation and support that comes from failure30. Our findings are consistent with field research on teams in other domains, which demonstrate that small groups with more to gain and less to lose are more likely to undertake new and untested opportunities that have the potential for high growth and failure15. Our findings are also in accordance with experimental and observational research on groups that demonstrates how individuals in large groups think and act differently from those in small groups10–14.

Both small and large teams are essential to a flourishing ecology of science and technology. Taken together, the increasing dominance of large teams, a flurry of scholarship on their perceived benefits2,6–9,28,31 and our findings call for new investigations into the vital role that indi-viduals and small groups have in advancing science and technology. Direct sponsorship of small-group research may not be enough to pre-serve itsbenefits. We analysed articles published from 2004 to 2014 that acknowledged financial support from several top government agencies around the world, and found that small teams with this funding are indistinguishable from large teams in their tendency to develop rather than disrupt their fields (Extended Data Fig.10). In contrast to Nobel Prize papers, which have an average disruption among the top 2% of all contemporary papers, funded papers rank near the bottom 31%. Thiscould result from a conservative review process, proposals designed to anticipate such a process or a planning effect whereby small teams lock themselves into large-team inertia by remaining accountable to a funded proposal. When we compare two major policy incentives for science (funding versus awards), we find that Nobel-prize-winning articles significantly oversample small disruptive teams, whereas those that acknowledge US National Science Foundation funding oversample large developmental teams. Regardless of the dominant driver, these results paint a unified portrait of underfunded solo investigators and small teams who disrupt science and technology by generating new directions on the basis of deeper and wider information search. These results suggest the need for government, industry and non-profit funders of science and technology to investigate the critical role that small teams appear to have in expanding the frontiers of knowledge, even as large teams rapidly develop them.

Online content Any methods, additional references, Nature Research reporting summaries, source data, statements of data availability and associated accession codes are available at    https://doi.org/10.1038/s41586-019-0941-9
Received: 20 April 2018; Accepted: 7 January 2019; Published online 13 February 2019.

https://www.nature.com/articles/ ... =news.sciencenet.cn

 楼主| 发表于 2022-10-13 18:09:50 | 显示全部楼层
诺奖得主迈克尔·莱维特:小团队更能出大成果 2~5人

https://finance.sina.com.cn/tech ... tzscyx7922377.shtml

https://news.sciencenet.cn/htmlnews/2021/10/466462.shtm

http://www.kaseisyoji.com/forum. ... F%E5%9B%A2%E9%98%9F

(小部分日美等网页、中国大陆看不了,一小部分大陆网页、节目,日美等看不了。)

您需要登录后才可以回帖 登录 | 注册

本版积分规则

关于都市网 | 服务条款 | 开放平台 | 广告服务 | 商务洽谈 | 都市网招聘 | 都市网公益 | 客服中心 | 网站导航 | 版权所有

手机版|小黑屋|Comsenz Inc.  

© 2001-2013 源码论坛 Inc.    Powered by Weekend Design Discuz! X3.2

GMT+8, 2022-12-3 06:59 , Processed in 0.085025 second(s), 18 queries .

快速回复 返回顶部 返回列表