MOSAiC Data Policy

Immerz, Antonia; Frickenhaus, Stephan; Gathen, Peter von der; Shupe, Matthew; Morris, Sara; Nicolaus, Marcel; Schneebeli, Martin; Regnery, Julia; Fong, Allison A.; Snoeijs, Pauline; Geibert, Walter; Rabe, Benjamin; Herber, Andreas; Krumpen, Thomas; Singha, Suman; Jaiser, Ralf; Ransby, Daniela; Schumacher, Stefanie; Driemel, Amelie; Gerchow, Peter; Schäfer, Angela; Schewe, Ingo; Ajjan, Mohammad; Glöckner, Frank Oliver; Schäfer-Neth, Christian; Jones, Christopher S.; Goldstein, Jesse A.; Jones, Matt; Prakash, Giri; Rex, Markus

doi:10.5281/zenodo.4537178

Cited by 3 publications

(2 citation statements)

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“…The radiosonde data are available at the PANGAEA Data Publisher at https://doi.org/10.1594/PANGAEA.928656 (Maturilli et al, 2021). These data are subject to the MOSAiC Data Policy (Immerz et al, 2019) and will be openly available after 1 January 2023.…”

Section: Discussionmentioning

confidence: 99%

Testing the efficacy of atmospheric boundary layer height detection algorithms using uncrewed aircraft system data from MOSAiC

et al. 2022

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Abstract. During the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, meteorological conditions over the lowest 1 km of the atmosphere were sampled with the DataHawk2 (DH2) fixed-wing uncrewed aircraft system (UAS). These in situ observations of the central Arctic atmosphere are some of the most extensive to date and provide unique insight into the atmospheric boundary layer (ABL) structure. The ABL is an important component of the Arctic climate, as it can be closely coupled to cloud properties, surface fluxes, and the atmospheric radiation budget. The high temporal resolution of the UAS observations allows us to manually identify the ABL height (ZABL) for 65 out of the total 89 flights conducted over the central Arctic Ocean between 23 March and 26 July 2020 by visually analyzing profiles of virtual potential temperature, humidity, and bulk Richardson number. Comparing this subjective ZABL with ZABL identified by various previously published automated objective methods allows us to determine which objective methods are most successful at accurately identifying ZABL in the central Arctic environment and how the success of the methods differs based on stability regime. The objective methods we use are the Liu–Liang, Heffter, virtual potential temperature gradient maximum, and bulk Richardson number methods. In the process of testing these objective methods on the DH2 data, numerical thresholds were adapted to work best for the UAS-based sampling. To determine if conclusions are robust across different measurement platforms, the subjective and objective ZABL determination processes were repeated using the radiosonde profile closest in time to each DH2 flight. For both the DH2 and radiosonde data, it is determined that the bulk Richardson number method is the most successful at identifying ZABL, while the Liu–Liang method is least successful. The results of this study are expected to be beneficial for upcoming observational and modeling efforts regarding the central Arctic ABL.

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Section: Discussionmentioning

confidence: 99%

Testing the efficacy of atmospheric boundary layer height detection algorithms using uncrewed aircraft system data from MOSAiC

et al. 2022

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“…This was necessary given the complex legal landscape associated with the scope of the international work and the policy was meant to reflect the good faith that IYS participants were expected to act in. The policy was based on the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) (Immerz et al, 2019), a similar international science effort. Critically, the policy established timelines for submitting various data types focussing on rapid data submission for data generated at sea and appropriate delays for data generated from subsequent laboratory analysis.…”

Section: Methodsmentioning

confidence: 99%

Data Mobilization Through the International Year of the Salmon Ocean Observing System

Johnson¹,

Stap²

2023

Preprint

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Data mobilization—the process of making data available for appropriate re-use—remains a key barrier in effective salmon management. Data mobilization facilitates data sharing, discovery and reuse, and leads to increased citations, synthesis data products, meta-analyses and robust management-decision support-tools. The International Year of the Salmon High Seas (IYS) Expeditions in 2019, 2020, and 2022 presented a challenge and opportunity for data mobilization efforts due to the scale, volume, diversity of data, and number of nations involved. Here we demonstrate how we mobilized this salmon ocean ecology data—given the international scope of the project—through a natural alignment to the United Nations’ Global Ocean Observing System and Decade of Ocean Science for Sustainable Development. Under this framework datasets were catalogued using metadata records, assigned digital object identifiers, and published to a web accessible data catalogue (https://iys.hakai.org). Where possible, pre-existing community-developed and international data and metadata standards were adopted and data were also published in global open-access repositories. Both the sociocultural and technical solutions implemented deepen the impact of the IYS and lay a foundation and clear path forward for salmon and oceanographic data producing institutions, commissions, foundations, or projects interested in contributing to international data-intensive salmon and oceanographic sciences.

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Data from the MOSAiC Arctic Ocean drift experiment

et al. 2022

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The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) is a multinational interdisciplinary endeavor of a large earth system sciences community. MOSAiC 1 resulted in an unprecedented amount and diversity of observation data over the central Arctic sea ice (Fig. 1): over 660 unique sensors or measurement devices with >8200 registered events, resulting in more than 90000 managed parameters in 73 endorsed sub-projects have been recorded (see https://mosaic-expedition.org/). 122 PIs are registered to work on the data. The year-round experiment started in September 2019 -and ended in October 2020 after a long-lasting preparation phase. The Implementation planning meeting in November 2017 in St. Petersburg was the starting point of its data logistics and data management concepts, with its core organizational component being the MOSAiC Data Policy 2 , described further below. As technical core component the MOSAiC Central Storage (MCS) was drafted, to be operated on board of the research icebreaker Polarstern 3 and mirrored on land, leg by leg, allowing for data sharing and collaboration amongst researchers of the MOSAiC consortium.Another milestone was the Science Meeting in May 2018 in Potsdam, where the term "MOSAiC Data Legacy" has been coined, to pronounce the importance of unique data management principles, technologies, and joint publication strategies in this multi-national consortium. In this aspect, the SHEBA experiment from the late 90ies was seen as a reference 4 . SHEBA Phase II was a year-long experiment taking place from October 1997 to October 1998 in the Arctic Ocean. It generated published data that is the basis of a series of scientific papers (see https://www.eol.ucar.edu/node/644/publications). The SHEBA data legacy has been taken up by the Arctic Data Centre, a long-term data repository, in the form of 184 individually citable datasets (https://arcticdata.io/catalog/data).The MOSAiC Data Policy 2 is the central document defining membership of scientists and data users in the MOSAiC Consortium, and collaboration principles in terms of data storage, data provisioning, data sharing and data publication. Furthermore, it defines the latest date for MOSAiC consortium members to make their data publicly available as 31 January 2023. The overall aim is a rapid dissemination of the scientific data to a diverse community of stakeholders to enable scientific studies of the rapidly changing Arctic system. The consortium includes major data centers and repositories as Pangaea and the Arctic Data Centre. The MOSAiC project stands out for its enormous heterogeneity of data sets, including in-situ observations and samples, sensor data, remote sensing and airborne data, and numerical model results (see process picture Fig. 1). All data are published under the FAIR principles.A first series of three overview papers from the MOSAIC science teams focused on Sea Ice/Snow, Physical Oceanography, and Atmosphere were published in the journal Elementa [5][6][7] . Additional scientific overviews...

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MOSAiC Data Policy

Cited by 3 publications

References 0 publications

Testing the efficacy of atmospheric boundary layer height detection algorithms using uncrewed aircraft system data from MOSAiC

Testing the efficacy of atmospheric boundary layer height detection algorithms using uncrewed aircraft system data from MOSAiC

Data Mobilization Through the International Year of the Salmon Ocean Observing System

Data from the MOSAiC Arctic Ocean drift experiment

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