4.4 Article

PaCTS 1.0: A Crowdsourced Reporting Standard for Paleoclimate Data

期刊

PALEOCEANOGRAPHY AND PALEOCLIMATOLOGY
卷 34, 期 10, 页码 1570-1596

出版社

AMER GEOPHYSICAL UNION
DOI: 10.1029/2019PA003632

关键词

standards; FAIR; paleoclimate; paleoceanography; data; best practices

资金

  1. National Science Foundation through the EarthCube Program [ICER-1541029]
  2. PAGES
  3. NERC [bas0100034] Funding Source: UKRI

向作者/读者索取更多资源

The progress of science is tied to the standardization of measurements, instruments, and data. This is especially true in the Big Data age, where analyzing large data volumes critically hinges on the data being standardized. Accordingly, the lack of community-sanctioned data standards in paleoclimatology has largely precluded the benefits of Big Data advances in the field. Building upon recent efforts to standardize the format and terminology of paleoclimate data, this article describes the Paleoclimate Community reporTing Standard (PaCTS), a crowdsourced reporting standard for such data. PaCTS captures which information should be included when reporting paleoclimate data, with the goal of maximizing the reuse value of paleoclimate data sets, particularly for synthesis work and comparison to climate model simulations. Initiated by the LinkedEarth project, the process to elicit a reporting standard involved an international workshop in 2016, various forms of digital community engagement over the next few years, and grassroots working groups. Participants in this process identified important properties across paleoclimate archives, in addition to the reporting of uncertainties and chronologies; they also identified archive-specific properties and distinguished reporting standards for new versus legacy data sets. This work shows that at least 135 respondents overwhelmingly support a drastic increase in the amount of metadata accompanying paleoclimate data sets. Since such goals are at odds with present practices, we discuss a transparent path toward implementing or revising these recommendations in the near future, using both bottom-up and top-down approaches.

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