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- ItemCall to action for global access to and harmonization of quality information of individual earth science datasets(Paris : CODATA, 2021) Peng, Ge; Downs, Robert R.; Lacagnina, Carlo; Ramapriyan, Hampapuram; Ivánová, Ivana; Moroni, David; Wei, Yaxing; Larnicol, Gilles; Wyborn, Lesley; Goldberg, Mitch; Schulz, Jörg; Bastrakova, Irina; Ganske, Anette; Bastin, Lucy; Khalsa, Siri Jodha S.; Wu, Mingfang; Shie, Chung-Lin; Ritchey, Nancy; Jones, Dave; Habermann, Ted; Lief, Christina; Maggio, Iolanda; Albani, Mirko; Stall, Shelley; Zhou, Lihang; Drévillon, Marie; Champion, Sarah; Hou, C. Sophie; Doblas-Reyes, Francisco; Lehnert, Kerstin; Robinson, Erin; Bugbee, KaylinKnowledge about the quality of data and metadata is important to support informed decisions on the (re)use of individual datasets and is an essential part of the ecosystem that supports open science. Quality assessments reflect the reliability and usability of data. They need to be consistently curated, fully traceable, and adequately documented, as these are crucial for sound decision- and policy-making efforts that rely on data. Quality assessments also need to be consistently represented and readily integrated across systems and tools to allow for improved sharing of information on quality at the dataset level for individual quality attribute or dimension. Although the need for assessing the quality of data and associated information is well recognized, methodologies for an evaluation framework and presentation of resultant quality information to end users may not have been comprehensively addressed within and across disciplines. Global interdisciplinary domain experts have come together to systematically explore needs, challenges and impacts of consistently curating and representing quality information through the entire lifecycle of a dataset. This paper describes the findings of that effort, argues the importance of sharing dataset quality information, calls for community action to develop practical guidelines, and outlines community recommendations for developing such guidelines. Practical guidelines will allow for global access to and harmonization of quality information at the level of individual Earth science datasets, which in turn will support open science.
- ItemGlobal Community Guidelines for Documenting, Sharing, and Reusing Quality Information of Individual Digital Datasets(Paris : CODATA, 2022) Peng, Ge; Lacagnina, Carlo; Downs, Robert R.; Ganske, Anette; Ramapriyan, Hampapuram K.; Ivánová, Ivana; Wyborn, Lesley; Jones, Dave; Bastin, Lucy; Shie, Chung-lin; Moroni, David F.Open-source science builds on open and free resources that include data, metadata, software, and workflows. Informed decisions on whether and how to (re)use digital datasets are dependent on an understanding about the quality of the underpinning data and relevant information. However, quality information, being difficult to curate and often context specific, is currently not readily available for sharing within and across disciplines. To help address this challenge and promote the creation and (re)use of freely and openly shared information about the quality of individual datasets, members of several groups around the world have undertaken an effort to develop international community guidelines with practical recommendations for the Earth science community, collaborating with international domain experts. The guidelines were inspired by the guiding principles of being findable, accessible, interoperable, and reusable (FAIR). Use of the FAIR dataset quality information guidelines is intended to help stakeholders, such as scientific data centers, digital data repositories, and producers, publishers, stewards and managers of data, to: i) capture, describe, and represent quality information of their datasets in a manner that is consistent with the FAIR Guiding Principles; ii) allow for the maximum discovery, trust, sharing, and reuse of their datasets; and iii) enable international access to and integration of dataset quality information. This article describes the processes that developed the guidelines that are aligned with the FAIR principles, presents a generic quality assessment workflow, describes the guidelines for preparing and disseminating dataset quality information, and outlines a path forward to improve their disciplinary diversity.
- ItemPublication of Atmospheric Model Data using the ATMODAT Standard(Stuttgart : E. Schweizerbart Science Publishers, 2022) Ganske, Anette; Heil, Angelika; Lammert, Andrea; Kretzschmar, Jan; Quaas, JohannesScientific data should be published in a way so that other scientists can benefit from these data, enabling further research. The FAIR Data Principles are defining the basic prerequisite for a good data publication: data should be Findable, Accessible, Interoperable, and Reusable. Increasingly, research communities are developing discipline-specific data publication standards under consideration of the FAIR Data Principles. A very comprehensive yet strict data standard has been developed for the climate model output within the Climate Model Intercomparison Project (CMIP), which largely builds upon the Climate and Forecast Metadata Conventions (CF conventions). There are, however, many areas of atmospheric modelling where data cannot be standardised according to the CMIP data standard because, e.g., the data contain specific variables which are not covered by the CMIP standard. Furthermore, fulfilling the strict CMIP data standard for smaller Model Intercomparison Projects (MIPs) requires much effort (in time and manpower) and hence the outcome of these MIPs often remains non-standardised. For innovative model diagnostics, preexisting standards are also not flexible enough. For that reason, the ATMODAT standard, a quality guideline for atmospheric model data, was created. The ATMODAT standard defines a set of requirements that aim at ensuring the high reusability of atmospheric model data publications. The requirements include the use of the netCDF file format, the application of the CF conventions, rich and standardised file metadata, and the publication of the data with a DataCite DOI. Additionally, a tool for checking the conformity of data and metadata to this standard, the atmodat data checker, was developed and is available on GitHub under an open licence. By using the more flexible ATMODAT standard, the publication of standardised datasets is simplified for smaller MIPs. This standardisation process is presented as an example using the data of an aerosol-climate model from the AeroCOM MIP. Furthermore, the landing pages of ATMODAT-compliant data publications can be highlighted with the EASYDAB logo. EASYDAB (Earth System Data Branding) is a newly developed quality label for carefully curated and highly standardised data publications. The ATMODAT data standardisation can easily be transferred to data from other disciplines and contribute to their improved reusability.
- ItemNFDI4Chem - Deliverable D3.3.1: Gap analysis report for selected repositories(Genève : CERN, 2023) Bach, Felix; Binder, Kunigunde; Christian Bonatto, Minella; Lutz, Benjamin; Razum, MatthiasThe deliverable 3.3.1 “Gap analysis report for selected repositories” aims both to identify gaps in the coverage regarding data types or disciplines and to close them through adjustments or, if necessary, new developments. In order to accomplish that, the TA3-team performed a gap analysis of the existing relevant repositories by means of individual interviews with the repository leaders. The interview consisted of a series of questions ranging from general information up to metadata standards and ontology, data contents, technical information about Authorisation and Authentication Infrastructure (AAI), API, services and functionality, operating environment as well as software architecture and workflows. The interviews will serve to establish the current degree of maturity as well as the operational fitness of the selected repositories and to derive suitable recommendations aiming to fulfil the yet missing requirements.