TY  - JOUR
A1  - Khider, D.
A1  - Emile-Geay, J.
A1  - McKay, N. P.
A1  - Gil, Y.
A1  - Garijo, D.
A1  - Ratnakar, V
A1  - Alonso-Garcia, M.
A1  - Bertrand, S.
A1  - Bothe, O.
A1  - Brewer, P.
A1  - Bunn, A.
A1  - Chevalier, M.
A1  - Comas-Bru, L.
A1  - Csank, A.
A1  - Dassie, E.
A1  - DeLong, K.
A1  - Felis, T.
A1  - Francus, P.
A1  - Frappier, A.
A1  - Gray, W.
A1  - Goring, S.
A1  - Jonkers, L.
A1  - Kahle, M.
A1  - Kaufman, D.
A1  - Kehrwald, N. M.
A1  - Martrat, B.
A1  - McGregor, H.
A1  - Richey, J.
A1  - Schmittner, A.
A1  - Scroxton, N.
A1  - Sutherland, E.
A1  - Thirumalai, Kaustubh
A1  - Allen, K.
A1  - Arnaud, F.
A1  - Axford, Y.
A1  - Barrows, T.
A1  - Bazin, L.
A1  - Birch, S. E. Pilaar
A1  - Bradley, E.
A1  - Bregy, J.
A1  - Capron, E.
A1  - Cartapanis, O.
A1  - Chiang, H-W
A1  - Cobb, K. M.
A1  - Debret, M.
A1  - Dommain, Réne
A1  - Du, J.
A1  - Dyez, K.
A1  - Emerick, S.
A1  - Erb, M. P.
A1  - Falster, G.
A1  - Finsinger, W.
A1  - Fortier, D.
A1  - Gauthier, Nicolas
A1  - George, S.
A1  - Grimm, E.
A1  - Hertzberg, J.
A1  - Hibbert, F.
A1  - Hillman, A.
A1  - Hobbs, W.
A1  - Huber, M.
A1  - Hughes, A. L. C.
A1  - Jaccard, S.
A1  - Ruan, J.
A1  - Kienast, M.
A1  - Konecky, B.
A1  - Le Roux, G.
A1  - Lyubchich, V
A1  - Novello, V. F.
A1  - Olaka, L.
A1  - Partin, J. W.
A1  - Pearce, C.
A1  - Phipps, S. J.
A1  - Pignol, C.
A1  - Piotrowska, N.
A1  - Poli, M-S
A1  - Prokopenko, A.
A1  - Schwanck, F.
A1  - Stepanek, C.
A1  - Swann, G. E. A.
A1  - Telford, R.
A1  - Thomas, E.
A1  - Thomas, Z.
A1  - Truebe, S.
A1  - von Gunten, L.
A1  - Waite, A.
A1  - Weitzel, N.
A1  - Wilhelm, B.
A1  - Williams, J.
A1  - Winstrup, M.
A1  - Zhao, N.
A1  - Zhou, Y.
T1  - PaCTS 1.0: A Crowdsourced Reporting Standard for Paleoclimate Data
JF  - Paleoceanography and paleoclimatology
N2  - 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.
KW  - standards
KW  - FAIR
KW  - paleoclimate
KW  - paleoceanography
KW  - data
KW  - best practices
Y1  - 2019
U6  - https://doi.org/10.1029/2019PA003632
SN  - 2572-4517
SN  - 2572-4525
VL  - 34
IS  - 10
SP  - 1570
EP  - 1596
PB  - American Geophysical Union
CY  - Washington
ER  - 
TY  - JOUR
A1  - Alcalde, D.
A1  - Mediavilla, E.
A1  - Moreau, O.
A1  - De Rop, Y.
A1  - Barrena, R.
A1  - Gil-Merino, Rodrigo
A1  - McLeod, B. A.
A1  - Motta, V.
A1  - Oscoz, Alejandro
A1  - Serra-Ricart, M.
T1  - QSO 2237+0305 VR light curves from Gravitational Lenses International Time Project optical monitoring
N2  - 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.
Y1  - 2002
ER  -