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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.
- ItemFirst results of a model user survey on a micro-scale model data standard(2020) Voss, Vivien; Schlünzen, K.Heinke; Grawe, David; Heydebreck, Daniel; Ganske, AnetteMicro-scale models are important to assess processes in complex domains, for example cities. The most common data standard for atmospheric model output data are the CF-conventions, a data standard for netCDF files, but this standard is not adapted to the model output of micro-scale models. As a part of the project AtMoDat (Atmospheric Model Data) we want to develop a model data standard for obstacle resolving models (ORM), including the additional variables (i.e. building structures, wall temperatures) used by these models. In order to involve the micro-scale modeller community in this process, a web based survey was developed and distributed in the modeller community via conferences and email. With this survey we want to find out which micro-scale ORMs are currently in use, their model specifics (e.g. used grid, coordinate system), and the handling of the model result data. Furthermore, the survey provides the opportunity to include suggestions and ideas, what we should consider in the development of the standard. Between September 2019 and July 2020, the survey was accessed 29 times, but only 12 surveys were completed. The finished surveys refer to eight different models and their corresponding model information. Results show that these different models use different output formats and processing tools, which results in different model result handling routines. The participants suggested to use the netCDF data format and to provide information on model initialization, model settings and model input along with the model output data. This would enable an easier intercomparison between different models and repetition of model simulations. Standardized model output and variable names would also enhance the development of shared routines for the analysis of micro-scale model data and a better findability of the data with search engines. This survey will remain open with regular assessments of contents (i.e. November 2020, May 2021; https://uhh.de/orm-survey).
- 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.
- ItemMaking the Maturity of Data and Metadata Visible with Datacite DOIs(Washington, DC : ESSOAr, 2020) Kaiser, Amandine; Heydebreck, Daniel; Ganske, Anette; Kraft, AngelinaData maturity describes the degree of the formalisation/standardisation of a data object with respect to FAIRness and quality of the (meta-) data. Therefore, a high (meta-) data maturity increases the reusability of data. Moreover, it is an important topic in data management, which is reflected by a growing number of tools and theories trying to measure it, e.g. the FAIR testing tools assessed by RDA(1) or the NOAA maturity matrix(2). If the results of stewardship tasks cannot be shown directly in the metadata, reusers of data cannot easily recognise which data is easy to reuse. For example, the DataCite Metadata Schema does not provide an explicit property to link/store information on data maturity (e.g. FAIRness or quality of data/metadata). The AtMoDat project (3, Atmospheric Model Data) aims to improve the reusability of published atmospheric model data by scientists, the public sector, companies, and other stakeholders. These data are valuable because they form the basis to understand and predict natural events, including the atmospheric circulation and ultimately the atmospheric and planetary energy budget. As most atmospheric data has been published with DataCite DOIs, it is of high importance that the maturity of the datasets can be easily found in the DOI’s Metadata. Published data from other fields of research would also benefit from easily findable maturity information. Therefore, we developed a Maturity Indicator concept and propose to introduce it as a new property in the DataCite Metadata Schema. This indicator is generic and independent of any scientific discipline and data stewardship tool. Hence, it can be used in a variety of research fields. 1 https://doi.org/10.15497/RDA00034 2 Peng et al., 2015: https://doi.org/10.2481/dsj.14-049 3 www.atmodat.de
- ItemThe ATMODAT Standard enhances FAIRness of Atmospheric Model data(Washington, DC : ESSOAr, 2020) Heydebreck, Daniel; Kaiser, Amandine; Ganske, Anette; Kraft, Angelina; Schluenzen, Heinke; Voss, VivienWithin the AtMoDat project (Atmospheric Model Data, www.atmodat.de), a standard has been developed which is meant for improving the FAIRness of atmospheric model data published in repositories. Atmospheric model data form the basis to understand and predict natural events, including atmospheric circulation, local air quality patterns, and the planetary energy budget. Such data should be made available for evaluation and reuse by scientists, the public sector, and relevant stakeholders. Atmospheric modeling is ahead of other fields in many regards towards FAIR (Findable, Accessible, Interoperable, Reusable, see e.g. Wilkinson et al. (2016, doi:10.1101/418376)) data: many models write their output directly into netCDF or file formats that can be converted into netCDF. NetCDF is a non-proprietary, binary, and self-describing format, ensuring interoperability and facilitating reusability. Nevertheless, consistent human- and machine-readable standards for discipline-specific metadata are also necessary. While standardisation of file structure and metadata (e.g. the Climate and Forecast Conventions) is well established for some subdomains of the earth system modeling community (e.g. the Coupled Model Intercomparison Project, Juckes et al. (2020, https:doi.org/10.5194/gmd-13-201-2020)), other subdomains are still lacking such standardisation. For example, standardisation is not well advanced for obstacle-resolving atmospheric models (e.g. for urban-scale modeling). The ATMODAT standard, which will be presented here, includes concrete recommendations related to the maturity, publication, and enhanced FAIRness of atmospheric model data. The suggestions include requirements for rich metadata with controlled vocabularies, structured landing pages, file formats (netCDF), and the structure within files. Human- and machine-readable landing pages are a core element of this standard and should hold and present discipline-specific metadata on simulation and variable level.
- ItemA short guide to increase FAIRness of atmospheric model data(Stuttgart : E. Schweizerbart Science Publishers, 2020) Ganske, Anette; Heydebreck, Daniel; Höck, Daniel; Kraft, Angelina; Quaas, Johannes; Kaiser, AmandineThe generation, processing and analysis of atmospheric model data are expensive, as atmospheric model runs are often computationally intensive and the costs of ‘fast’ disk space are rising. Moreover, atmospheric models are mostly developed by groups of scientists over many years and therefore only few appropriate models exist for specific analyses, e.g. for urban climate. Hence, atmospheric model data should be made available for reuse by scientists, the public sector, companies and other stakeholders. Thereby, this leads to an increasing need for swift, user-friendly adaptation of standards.The FAIR data principles (Findable, Accessible, Interoperable, Reusable) were established to foster the reuse of data. Research data become findable and accessible if they are published in public repositories with general metadata and Persistent Identifiers (PIDs), e.g. DataCite DOIs. The use of PIDs should ensure that describing metadata is persistently available. Nevertheless, PIDs and basic metadata do not guarantee that the data are indeed interoperable and reusable without project-specific knowledge. Additionally, the lack of standardised machine-readable metadata reduces the FAIRness of data. Unfortunately, there are no common standards for non-climate models, e.g. for mesoscale models, available. This paper proposes a concept to improve the FAIRness of archived atmospheric model data. This concept was developed within the AtMoDat project (Atmospheric Model Data). The approach consists of several aspects, each of which is easy to implement: requirements for rich metadata with controlled vocabulary, the landing pages, file formats (netCDF) and the structure within the files. The landing pages are a core element of this concept as they should be human- and machine readable, hold discipline-specific metadata and present metadata on simulation and variable level. This guide is meant to help data producers and curators to prepare data for publication. Furthermore, this guide provides information for the choice of keywords, which supports data reusers in their search for data with search engines. © 2020 The authors
- 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.
- ItemAtMoDat: Improving the reusability of ATmospheric MOdel DATa with DataCite DOIs paving the path towards FAIR data(München : European Geosciences Union, 2020) Neumann, Daniel; Ganske, Anette; Voss, Vivien; Kraft, Angelina; Höck, Heinke; Peters, Karsten; Quaas, Johannes; Schluenzen, Heinke; Thiemann, HannesThe generation of high quality research data is expensive. The FAIR principles were established to foster the reuse of such data for the benefit of the scientific community and beyond. Publishing research data with metadata and DataCite DOIs in public repositories makes them findable and accessible (FA of FAIR). However, DOIs and basic metadata do not guarantee the data are actually reusable without discipline-specific knowledge: if data are saved in proprietary or undocumented file formats, if detailed discipline-specific metadata are missing and if quality information on the data and metadata are not provided. In this contribution, we present ongoing work in the AtMoDat project, -a consortium of atmospheric scientists and infrastructure providers, which aims on improving the reusability of atmospheric model data. Consistent standards are necessary to simplify the reuse of research data. Although standardization of file structure and metadata is well established for some subdomains of the earth system modeling community – e.g. CMIP –, several other subdomains are lacking such standardization. Hence, scientists from the Universities of Hamburg and Leipzig and infrastructure operators cooperate in the AtMoDat project in order to advance standardization for model output files in specific subdomains of the atmospheric modeling community. Starting from the demanding CMIP6 standard, the aim is to establish an easy-to-use standard that is at least compliant with the Climate and Forecast (CF) conventions. In parallel, an existing netCDF file convention checker is extended to check for the new standards. This enhanced checker is designed to support the creation of compliant files and thus lower the hurdle for data producers to comply with the new standard. The transfer of this approach to further sub-disciplines of the earth system modeling community will be supported by a best-practice guide and other documentation. A showcase of a standard for the urban atmospheric modeling community will be presented in this session. The standard is based on CF Conventions and adapts several global attributes and controlled vocabularies from the well-established CMIP6 standard. Additionally, the AtMoDat project aims on introducing a generic quality indicator into the DataCite metadata schema to foster further reuse of data. This quality indicator should require a discipline-specific implementation of a quality standard linked to the indicator. We will present the concept of the generic quality indicator in general and in the context of urban atmospheric modeling data.