Datafication is driving organizations to invest in data commons not only to share the costs of data generation, analysis, and curation; but more importantly, to realize synergies in precompetitive research collaborations where private and public motives interact (i.e., semicommons). The fanfare surrounding datafication often hails the sophisticated algorithms used to develop large quantities of data toward greater insight, naïvely assuming that more data equals better data. Yet, for datafication in general and precompetitive research specifically, less attention is awarded to what actually constitutes data and evidence in the first place—that is, to its genesis, construction, and interpretation by heterogeneous scientific and commercial entities. We present the case of Open Targets, a precompetitive collaboration in the life sciences, where publicly funded research, nonprofit foundations, and for-profit pharma collaborate to generate and share data in genomics, proteomics, and bioinformatics. We theorize about the process of data commoning, a political activity in the semicommons where data are created, evidential value is assembled, and scientific meaning converges as data travels, or journeys across creators, validators, and users. Our findings highlight the effects of relational dynamics and political nature of data journeys: why these dynamics form, how they are manifest in a precompetitive semicommons, and what implications this can have for the mobility of data as shared, public good.

中文翻译：

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生命科学中的数据共享


数据化正在推动组织投资数据共享，不仅是为了分担数据生成、分析和管理的成本，而且是为了分担数据生成、分析和管理的成本。但更重要的是，在私人和公共动机相互作用的竞争前研究合作中实现协同效应（即半公共）。围绕数据化的大肆宣传常常赞扬用于开发大量数据以获得更深入洞察的复杂算法，天真地假设更多数据等于更好的数据。然而，对于一般的数据化，特别是竞争前的研究，人们很少关注数据和证据的实际构成，即不同科学和商业实体的起源、构建和解释。我们介绍 Open Targets 的案例，这是生命科学领域的一项竞争前合作，其中公共资助的研究、非营利基金会和营利性制药公司合作生成和共享基因组学、蛋白质组学和生物信息学方面的数据。我们对数据共享的过程进行了理论分析，数据共享是半共享中的一种政治活动，数据在其中创建，证据价值被组装，科学意义随着数据的传播或跨越创建者、验证者和用户的旅程而汇聚。我们的研究结果强调了数据旅程的关系动态和政治性质的影响：为什么这些动态会形成，它们如何在竞争前的半公共领域表现出来，以及这对作为共享公共利益的数据流动性有何影响。