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Archivierung und Publikation von Forschungsdaten: Die Rolle von digitalen Repositorien am Beispiel des RADAR-Projekts
Disziplinübergreifendes Forschungsdatenmanagement für Hochschulbibliotheken und Projekte zu vereinfachen und zu etablieren – das ist das Ziel von RADAR. Im Sommer 2016 geht mit ‚RADAR – Research Data Repository‘ ein Service an den Start, der Forschenden, Institutionen verschiedener Fachdisziplinen und Verlagen eine generische Infrastruktur für die Archivierung und Publikation von Forschungsdaten anbietet. Zu den Dienstleistungen gehören u. a. die Langzeitverfügbarkeit der Daten mit Handle oder Digital Object Identifier (DOI), ein anpassbares Rollen- und Zugriffsrechtemanagement, eine optionale Peer-Review-Funktion und Zugriffsstatistiken. Das Geschäftsmodell ermutigt Forschende, die anfallenden Nutzungsgebühren des Repositoriums in Drittmittelanträge und Datenmanagementpläne zu integrieren. Publizierte Daten stehen als Open Data zur Nachnutzung wie etwa Data Mining, Metadaten-Harvesting und Verknüpfung mit Suchportalen zur Verfügung. Diese Vernetzung ermöglicht ein nachhaltiges Forschungsdatenmanagement und die Etablierung von Dateninfrastrukturen wie RADAR.
AtMoDat: Improving the reusability of ATmospheric MOdel DATa with DataCite DOIs paving the path towards FAIR data
The 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.
Do researchers need to care about PID systems?
A survey across 1400 scientists in the natural sciences and engineering across Germany conducted in 2016 revealed that although more than 70 % of the researchers are using DOIs for journal publications, less than 10% use DOIs for research data. To the question of why they are not using DOIs more than half (56%) answered that they don’t know about the option to use DOIs for other publications (datasets, conference papers etc.) Therefore it is not surprising that the majority (57 %) stated that they had no need for DOI counselling services. 40% of the questioned researchers need more information and almost 30% cannot see a benefit. Publishers have been using PID systems for articles for years, and the DOI registration and citation are a natural part of the standard publication workflow. With the new digital age, the possibilities to publishing digital research objects beyond articles are bigger than ever – but the respective infrastructure providers are still struggling to provide integrated PID services. Infrastructure providers need to learn from publishers and offer integrated PID services, complementing existing workflows, using researcher’s vocabulary to support usability and promotion. Sell the benefit and enable researchers to focus on what they are best at: Do research (and not worry about the rest)!
RADAR Metadata Kernel with attribute values and controlled vocabularies
A central feature of the RADAR project is a Metadata Kernel, which manages and characterizes all archived and published research data. The kernel aims to enhance the traceability and usability of research data by maintaining a discipline-agnostic character and simultaneously allowing a description of discipline-specific data. For this purpose, a set of generic parameters were chosen, which allow an accurate and consistent identification of a resource for citation and retrieval purposes and also meet the requirements of more discipline-specific datasets. Furthermore, the Kernel provides recommended use instructions along with appropriate examples on how to correctly describe research data. The following metadata profile includes 9 mandatory fields which represent the general core of the scheme. These contain the main requirements for the DOI registration, in accordance with the DataCite Metadata Schema (v 3.1)1 and must be supplied when submitting metadata to RADAR. Additionally, 12 optional metadata parameters serve the purpose of describing discipline-specific data. These were implemented with a combination of controlledvocabularies and free-text entries, thereby covering heterogeneous data produced by a multitude of disciplines. The controlled-vocabulary entries were defined in accordance with established regulations in mind (for example, ISO standards for language and country of origin of the data). RADAR clients who wish to enhance the prospects of their metadata being found, cited and linked to original research are strongly encouraged to submit the optional as along with the mandatory set of properties.
Making the Maturity of Data and Metadata Visible with Datacite DOIs
Data 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
ATMODAT Standard v3.0
Within the AtMoDat project (Atmospheric Model Data), a standard has been developed which is meant for improving the FAIRness of atmospheric model data published in repositories. The ATMODAT standard 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. This standard is an updated and translated version of "Bericht über initialen Kernstandard und Kurationskriterien des AtMoDat Projektes (v2.4)
Moving towards FAIRness in Research Data and Software Management
Presentation during the Thüringer FDM Tage 2020 within the workshop "FAIR Research Software and Beyond: How to make the most of your code".
The ATMODAT Standard enhances FAIRness of Atmospheric Model data
Within 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.
- Entwurf - Datenpublikation – Workflows für die Archivierung und Publikation wissenschaftlicher Forschungsdaten in RADAR
Um die Schritte zu einer nachhaltigen, zitierfähigen Datenpublikation in RADAR darzulegen wurden drei exemplarische Workflows entwickelt: • Workflow (A) - Wahl zwischen Angeboten: Archivierung oder Publikation • Workflow (B) - Varianten der Datenpublikation (direkt, mit Embargo, Verlagsanbindung mit Artikel-Review) • Workflow (C) - Übergang Archivierung - Datenpublikation (optionale Ausbaustufe für 2015/16) Workflows A und B stellen in kompakter, graphischer Form die Grundfunktionen von RADAR dem zweistufigen Dienstleistungsmodell dar und sollen die Kunden bei der Wahl der passenden Angebotsstufe, Archivierung oder Archivierung mit integrierter Datenpublikation, unterstützen. Workflow C stellt den Übergang zwischen beiden Angebotsstufen dar, bei denen der Kunde bereits archivierte Daten in wenigen Arbeitsschritten unverändert auf die Ebene der Publikation überführen kann. Die Implementierung dieses Übergangs ist im Anschluss an den Aufbau des RADAR-Grundfunktionen im dritten Projektjahr vorgesehen.
Advancing Research Data Management in Universities of Science and Technology
The white paper ‘Advancing Research Data Management in Universities of Science and Technology’ shares insights on the state-of-the-art in research data management, and recommendations for advancement. A core part of the paper are the results of a survey, which was distributed to our member institutions in 2019 and addressed the following aspects of research data management (RDM): (i) the establishment of a RDM policy at the university; (ii) the provision of suitable RDM infrastructure and tools; and (iii) the establishment of RDM support services and trainings tailored to the requirements of science and technology disciplines. The paper reveals that while substantial progress has been made, there is still a long way to go when it comes to establishing “advanced-degree programmes at our major universities for the emerging field of data scientist”, as recommended in the seminal 2010 report ‘Riding the Wave’, and our white paper offers concrete recommendations and best practices for university leaders, researchers, operational staff, and policy makers. The topic of RDM has become a focal point in many scientific disciplines, in Europe and globally. The management and full utilisation of research data are now also at the top of the European agenda, as exemplified by Ursula von der Leyen addressat this year’s World Economic Forum.However, the implementation of RDM remains divergent across Europe. The white paper was written by a diverse team of RDM specialists, including data scientists and data stewards, with the work led by the RDM subgroup of our Task Force Open Science. The writing team included Angelina Kraft (Head of Lab Research Data Services at TIB, Leibniz University Hannover) who said: “The launch of RDM courses and teaching materials at universities of science and technology is a first important step to motivate people to manage their data. Furthermore, professors and PIs of all disciplines should actively support data management and motivate PhD students to publish their data in recognised digital repositories.” Another part of the writing team was Barbara Sanchez (Head of Centre for Research Data Management, TU Wien) and Malgorzata Goraczek (International Research Support / Data Management Support, TU Wien) who added:“A reliable research data infrastructure is a central component of any RDM service. In addition to the infrastructure, proper RDM is all about communication and cooperation. This includes bringing tools, infrastructures, staff and units together.” Alastair Dunning (Head of 4TU.ResearchData, Delft University of Technology), also one of the writers, added: “There is a popular misconception that better research data management only means faster and more efficient computers. In this white paper, we emphasise the role that training and a culture of good research data management must play.”