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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.
We present VR observations of QSO 2237+0305 conducted by the Gravitational Lensing International Time Project collaboration from 1999 October 1 to 2000 February 3. The observations were made with the 2.56 m Nordic Optical Telescope at Roque de los Muchachos Observatory, La Palma (Spain). The point-spread function (PSF) fitting method and an adapted version of the ISIS subtraction method have been used to derive the VR light curves of the four components (A-D) of the quasar. The mean errors range in the intervals 0.01-0.04 mag (PSF fitting) and 0.01-0.02 mag (ISIS subtraction), with the faintest component (D) having the largest uncertainties. We address the relatively good agreement between the A and D light curves derived using different filters, photometric techniques, and telescopes. The new VR light curves of component A extend the time coverage of a high-magnification microlensing peak, which was discovered by the OGLE team.