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A white paper setting out strategic requirements of the European Network for Earth System Modelling (ENES) Climate Data Infrastructure (CDI).

The European Network for Earth System Modelling (ENES) has operated since 2001 to support advances in climate modelling and understanding. ENES has been supported by a succession of infrastructure projects (IS-ENES, IS-ENES2, and IS-ENES3; 2009-2022) as well as by an HPC Centre of Excellence (ESiWACE, ESiWACE2; 2015-2022). These activities, carried across multiple institutions exploiting mutual support, have been delivering on a community infrastructure strategy first published in 2012 and updated in 2017 [isenes2012, isenes2017], and jointly deliver the ENES Research Infrastructure (ENES-RI). The data service element, also known as the ENES Climate Data Infrastructure (ENES-CDI), provides a range of services which are critical for the efficient exchange and exploitation of coordinatedclimate simulations which underpin major advances in climate science and decisions on social, political and economic adjustments associated with climate change. This document outlines the overall objectives of the ENES climate data infrastructure, with some detail in terms of current activities, and near-term aspirations. It will be updated as the community infrastructure strategy evolves. &nbsp

The IS-ENES3 data management plan is based on standard data management principles applied in the context of an infrastructure project supporting the earth system climate modeling community and downstream users. As the project consists of many different types of datasets, an inventory of datasets has been used to develop a set of data categories, and a separate plan is in place for each of those categories.

Climate research like CMIP (Coupled Model Intercomparison Project) and IPCC (Intergovernmental Panel on Climate Change) Assessment Reports, currently in their 6th cycle, depend heavily on data, which is well-curated and remains available on the long-term (see abstract by Juckes et al., 2018). To improve the traceability of climate research and assessments, the CMIP6 data sources need to be referenced. Data references and thus giving credit to data providers, their funders and contributors have been part of principles for Good Scientific Practice (e.g. DFG, 2013) for several years, but have not yet become a natural and effective part of research workflows. For CMIP6 for the first time in the history of CMIP, it is possible to cite the evolving CMIP6 data on model/MIP and experiment granularities (Stockhause and Lautenschlager, 2017). A good example how to cite data in an article’s reference list is provided in the paper by Durack et al. (2018). The CMIP6 data citation service enables data users to find data references: in the ESGF (Earth System Grid Federation) portals for datasets, using Google’s Dataset Search, or on the FurtherInfoUrl (global attribute of CMIP6 NetCDF files) landing page. Accompanying initiatives and projects such as the “Enabling FAIR Data” project (Stall et al., 2018) are developing best practices for the integration of data and other digital resources into author guidelines and the general research workflow in collaboration with academic publishers and repositories. Thereby data FAIRness (Findable, Accessible, Interoperable, Reusable data) in the Earth sciences is advanced. Reference Deutsche Forschungsgemeinschaft (DFG) (2013). Safeguarding Good Scientific Practice. doi:https://doi.org/10.1002/9783527679188.oth1. Durack, P. J., et al. (2018). Toward standardized data sets for climate model experimentation, Eos, 99, doi:https://doi.org/10.1029/2018EO101751. Published on 02 July 2018. Juckes, M., et al. (2018). The role of the IPCC Data Distribution Centre in supporting assessments of climate change, submitted abstract for “CMIP6 Model Analysis Workshop”, 25-28 March 2019. Stall, S., et al. (2018). Advancing FAIR data in Earth, Space, and Environmental Science, Eos, 99, doi:https://doi.org/10.1029/2018EO109301. Published on 05 November 2018. Stockhause, M., and M. Lautenschlager (2017). CMIP6 Data Citation of Evolving Data. Data Science Journal, 16, p.30. doi:https://doi.org/10.5334/dsj-2017-030., Contribution to CMIP6 Model analysis Workshop, 25-28 March 2019, Barcelona (Spain) with poster and 2 min presentation

This report provides statistics on the data download from the Reference Data Archive for climate model output hosted at the IPCC DDC at WDCC/DKRZ (http://ipcc.wdc-climate.de) for 2017eng.

submitted by Michael Lautenschlager Literaturverzeichnis: Blatt 30-33 Zusammenfassung in englischer und deutscher Sprache Paris-Lodron-University of Salzburg, Masterarbeit, 2019 (VLID)5061240

Project: Literature - Literature e.g. grey literature Summary: The issue 'Jahrbuch 2014-2015'; focuses on DKRZ's new supercomputer system HLRE-3 'Mistral' (only available in German, published in July 2016). The pdf documents may be freely downloaded and used. Re-use of single parts (i.e. pictures) is only permitted for non-commercial purposes - except for those pictures that are copyrighted by commercial stock photo agencies. Details can be found in the corresponding picture credits ('Bildnachweis'). For any commercial use, a written agreement of DKRZ is required. Copyright: DKRZ (Creative Commons: CC BY-NC-ND 4.0). The yearbook is also available under the following link: https://www.dkrz.de/pdfs/downloads/dkrz-jahrbuch-2014-2015?lang=de

This document describes the originally envisioned technical implementation plan for Handle-based PIDs for CMIP6 using ESGF services., Original location was at https://www.earthsystemcog.org/site_media/projects/wip/CMIP6_PID_Implementation_Plan.pdf Relocated to Zenodo in 2021 after notification that the project website is no longer reachable.

The improvements in data citation in climate sciences are presented on the use case CMIP6 (Coupled Model Intercomparison Project Phase 6) together with its challenges and future directions. A special emphasis lies on the barriers for data citation and approaches to lower these barriers. Within CMIP5 (Coupled Model Intercomparison Project Phase 5) the citation of the data was not possible prior its long-term archival in the IPCC Data Distribution Centre (DDC). The Reference Data Archive for AR5 (Assessment Report 5) was built up after the submission deadline for part 1 of the AR5. This was too late for many scientific articles. But even the AR5 data in the IPCC DDC is rarely cited in literature in spite of annual download volumes between one and three PBytes. On the other hand, the request for a citation possibility for the evolving CMIP6 data prior to long-term archival came from the CMIP6 data providers. The additional provision of data citations for the project input4MIPs (input data for CMIP6) could raise the scientists’ awareness of the discrepancy between the readiness to cite data and the desire to be cited and get credit. The CMIP6 Citation Service is a pragmatic approach built on existing services and services under development, such as ESGF (Earth System Grid Federation) as data infrastructure component, DataCite as DOI registration agency, and Scholix services for tracking data usage information. Other principles followed to overcome barriers of data citation are: Collect data and literature references in the data citation metadata to enable data-data and data-literature interlinking. Visibility of data citation information in the ESGF data portals (low barrier to access data citation information) Provide data usage information in literature for the data providers, data node managers and their funders (requested by some ESGF data node managers) The CMIP6 Citation Service is an implementation only of the credit part of the RDA WGDC recommendation for the citation of dynamic data. The second part, the identification of the data subset underlying an article, is planned for CMIP7 as a data cart approach comprising multiple pre-defined CMIP6 DataCite DOIs. Additional policies on the long-term data availability are required., presented at fall meeting AGU 2017 in ESSI session: "IN047: Optimizing Data Sharing in Transdisciplinary Research: Giving Credit Where Credit is Due"

The talk given at the IPCC AR6 Data Workshop (19/20 September 2017) at DKRZ in Hambrug, Germany, between IPCC AR6 Working Group I and II TSUs and IPCC DDC Managers from BADC and WDCC/DKRZ gives an overview over the history and the current status and future directions of the IPCC TGICA (Task Group on Data and Scenario Support for Impact and Climate Analysis) and the IPCC DDC (Data Distribution Centre).

The talk given at the IPCC AR6 Data Workshop (19/20 September 2017) at DKRZ in Hambrug, Germany, between IPCC AR6 Working Group I and II TSUs and IPCC DDC Managers from BADC and WDCC/DKRZ gives an overview over the current data services within the IPCC Data Distribution Centre (DDC) hosted at DKRZ and the future plans to improve the support for the IPCC Working Groups and their authors during the 6th assessment cycle.

This PICO presented at EGU 2017 describes the two services, which DKRZ contributes to the CMIP6/AR6 infrastructure: Data Citation and the IPCC Data Distribution Centre (IPCC DDC) Reference Data Archive of the climate model output / CMIP6 Data Pool.

This report provides statistics on the data download from the Reference Data Archive for climate model output hosted at the IPCC DDC at WDCC/DKRZ (http://ipcc.wdc-climate.de) for 2016.

Not every digital research data archive is fully funded. Charging fees for data services may help archives to survive. Archives which follow this funding stream have to decide who has to pay, data producers or users, and how much. For the calculation of data service prices a multi-linear price function is suggested. Variables are the number of datasets and the data volume. Different user requirements and data complexity are mirrored as service levels in the price function. Its utilization is shown in a case study at the World Data Centre for Climate (WDCC).

This report provides statistics on the data download from the Reference Data Archive for climate model output hosted at the IPCC DDC at WDCC/DKRZ (http://ipcc.wdc-climate.de) for 2015.

This document is a WIP (WGCM Infrastructure Panel: http://www.earthsystemcog.org/projects/wip/) paper on the data citation and long-term archival of CMIP6 data. It will cover the description of the different data citation paths used for early citations after ESGF data publication towards the citation of stable reference data in the IPCC DDC AR6. The requirements for citations will be laid out in respect to Force 11’s Joint Declaration of Data Citation Principles (http://www.force11.org/datacitation) and including technical needs within the ESGF data infrastructure as well as data policies.

This report provides statistics on the data download from the Reference Data Archive for climate model output hosted at the IPCC DDC at WDCC/DKRZ (http://ipcc.wdc-climate.de) for 2014.

A concept to combine DOIs on data collections suitable for citation and analysis of data impact with user-defined PID collections specifying the data subset used in a publication for data traceability is presented at EGU 2013., EGU 2013 poster with abstract_id=EGU2013-4254-1 presented in session ESSI2.4 'Managing data and models: full lifecycle, uncertainty and large scale analytics'

Ladislav Fuks' works are under-recognized in English-speaking academic discourse. He is a valuable contributor not only to the Holocaust Literature genre, but also to film and literature in general. His two English-translated works, Mr. Theodore Mundstock and The Cremator, as well as the film adaptation of The Cremator, examine the role imagination can play in art that addresses atrocity, allowing for a heightened subjective impact on the audience. I critically and comparatively examine Fuks' work to establish his value to literature and Holocaust art. In the first chapter, I frame my argument with questions of art's abilities to represent atrocity and provide relevant background information relating to Fuks' and his experience in wartime Prague. In Chapter Two, I closely read Mr. Theodore Mundstock, concentrating specifically on Fuks' use of metaphor, presentation of incredulity, and commentary on the imagination's capabilities in confronting terror. Chapter Three compares Mr. Theodore Mundstock to the "Momik" section of David Grossman's See Under: Love, focusing on similarities between the title characters. Chapter Four examines Fuks' use of the grotesque in The Cremator and its film adaptation. Chapter Five compares Fuks' works to Aharon Appelfeld's novel Badenheim 1939, emphasizing each author's reliance on the audience's retrospective prescience, which provides a significant psychological impact and avoids contributing to the over-saturation of Holocaust information on the public. I conclude that Fuks should be more highly regarded and widely recognized both academically and in popular culture, as he exhibits similar features as other, more celebrated Holocaust writers, and his innovative contributions defend the value of literature in representing atrocity. Master of Arts

The CERA-2 Data Model

The CERA Metadata Model

Regional climate projections have been performed for Europe with the climate version CLM (version 3.1) of the ’Lokal-Modell’ (LM) of the German Meteorological Service. CLM has been forced with output of the ECHAM5/MPIOM global climate model of the Max Planck Institute for Meteorology, which contributed to the fourth climate assessment report (AR4) of the International Panel on Climate Change (IPCC). The results of the global model for the IPCC A1B and B1 scenarios as well as the reconstruction of the last four decades of the 20th century (20C3M) for the AR4 are dynamically scaled down to the CLM grid cell size of about 18×18 km2. Climatological parameters including all model variables and additional quantities characterising the climate are available from the World Data Centre for Climate database run by the German climate service group Model and Data. They can be accessed by the general public at no costs with the help of M and D. In particular, the ’Service Group Adaptation’ (SGA) has been established at M and D to assist adaptation projects funded by the German Federal Ministry of Education and Research (BMBF) giving advice on the interpretation of the CLM model results.

In the last decade the primary data, research is based on has become a third pillar of scientific work alongside with theoretical reasoning and experiment. Greatly increased computing power and storage, together with web services and other electronic resources have facilitated a quantum leap in new research based on the analysis of great amounts of data. However, traditional scientific communication only slowly changes to new media other than an emulation of paper. This leaves many data inaccessible and, in the long run exposes valuable data to the risk of loss. Most important to the availabilty of data is a valid citation. This means that all fields mandatory for a bibliographic citation are included. In addition a mechanism is needed that ensures that the location of the referenced data on the Internet can be resolved on a long-term. Just using URLs by doing "data management on web servers" does not help at all because it is short-lived, mostly becoming invalid after just a few months. Data publication on the Internet therefore needs a system of reliable pointers to each digital object as integral part of the citation. To achieve this persistence of identifiers for their conventional publications many scientific publishers use Digital Object Identifiers (DOI). The identifier is resolved through the handle system to the valid location (URL) where the dataset can be found. This approach meets one of the prerequisites for citeability of scientific data published online. In addition, the valid bibliographic citation can be included in the catalogues of Libraries. To improve access to data and to create incentives for scientists to make their data accessible, some german data centers initiated a project on publication and citation of scientific data. The project "Publication and Citation of Scientific Data" (STD-DOI) was funded by the German Science Foundation (DFG) between 2003 and 2008. In STD-DOI the German National Library for Science and Technology (TIB Hannover), together with the German Research Centre for Geoscience (GFZ Potsdam), the Alfred Wegener Institute for Polar and Marine Research (AWI) Bremerhaven, the University of Bremen, the Max Planck Institute for Meteorology in Hamburg, and the DLR German Remote Sensing Data Center set up the first system to assign DOIs to data sets and finaly to its publications. The STD-DOI system for data publication is now used by eight data publication agents. Data publication through specific agents addresses specific user communities and cater for their requirements in the data publication process. The registration process between TIB and the publication agents is based on a SOAP web service. This presentation will show the organisational and technical aspects of the data publication process through the STD-DOI project and give examples of a successful workflow towards established data citations in the earth sciences.