German climate research initiative PalMod phase II (www.palmod.de) is presented here as an exclusive example where the project end-product is unique, scientific paleo-climate data. PalMod-II data products include output from three state-of-the-art coupled climate models of varying complexity and spatial resolutions simulating the climate of the past 130,000 years. In addition to the long time series of modeling data, a comprehensive compilation of paleo-observation data is prepared to facilitate model-model and model-proxy intercomparison and evaluation. Being a large multidisciplinary project, a dedicated RDM (Research Data Management) approach is applied within the cross-cutting working group for PalMod-II. The DMP (Data Management Plan), as a living document, is used for documenting the data-workflow framework that defines the details of paleo-climate data life-cycle. The workflow containing the organisation, storage, preservation, sharing and long-term curation of the data is defined and tested.  In order to make the modeling data inter-comparable across the PalMod-II models and easily analyzable by the global paleo-climate community, model data standardization (CMORization) workflows are defined for individual PalMod models and their sub-models. The CMORization workflows contain setup, definition, and quality assurance testing of CMIP6¹ based standardization processes adapted to PalMod-II model simulation output requirements with a final aim of data publication via ESGF². PalMod-II data publication via ESGF makes the paleo-climate data an asset which is (re-)usable beyond the project life-time.

The PalMod-II RDM infrastructure enables common research data management according to the FAIR³ data principles across all the working groups of PalMod-II using common workflows for the exchange of data and information along the process chain. Applying data management planning within PalMod-II made sure that all the data related workflows were defined, continuously updated if needed and made available to the project stakeholders. End products of PalMod-II which consist of unique long term scientific paleo-climate data (model as well as paleo-proxy data) are made available for re-use via the paleo-climate research community as well as other research disciplines (e.g., land-use, socio-economic studies etc.).

1. Coupled Model Intercomparison Project phase 6 (https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6)

2. Earth System Grid Federation (https://esgf.llnl.gov)

3. Findable, Accessible, Interoperable, Reusable

How to cite: Gehlot, S., Peters-von Gehlen, K., Lammert, A., and Thiemann, H.: Data Management for PalMod-II – data workflow and re-use strategy, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15391, https://doi.org/10.5194/egusphere-egu23-15391, 2023.

The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) is a community-driven climate impact modeling initiative that aims to contribute to a quantitative and cross-sectoral synthesis of the various impacts of climate change, including associated uncertainties. ISIMIP is organized into simulation rounds for which a simulation protocol defines a set of common scenarios. Participating modeling groups run their simulations according to these scenarios and with a common set of climatic and socioeconomic input data. The model output data are collected by the ISIMIP team at the Potsdam Institute for Climate Impact Research (PIK) and made publicly available in the ISIMIP repository. Currently the ISIMIP Repository at data.isimip.org includes data from over 150 impact models spanning across 13 different sectors. It comprises of over 100 Tb of data.

As the world's largest data archive of model-based climate impact data, ISIMIP output data is used by a very diverse audience inside and outside of academia, for all kind of research and analyses. Special care is taken to enable persistent identification, provenience, and citablity. A set of workflows and tools ensure the conformity of the model output data with the protocol and the transparent management of caveats and updates to already published data. Datasets are referenced using unique internal IDs and hash values are stored for each file in the database.

In recent years, this process has been significantly improved by introducing a machine-readable protocol, which is version controlled on GitHub and can be accessed over the internet. A set of software tools for quality control and data publication accesses this protocol to enforce a consistent data quality and to extract metadata. Some of the tools can be used independently by the modelling groups even before submitting the data. After the data is published on the ISIMIP Repository, it can be accessed via web or using an API (e.g. for access from Jupyter notebooks) using the same controlled vocabularies from the protocol. In order to make the data citable, DOI for each output sector are registered with DataCite. For each DOI, a precise list of each contained dataset is maintained. If data for a sector is added or replaced, a new, updated DOI is created.

While the specific implementation is highly optimized to the peculiarities of ISIMIP, the general ideas should be transferable to other projects. In our presentation, we will discuss the various tools and how they interact to create an integrated curation and publishing workflow.

How to cite: Klar, J. and Mengel, M.: A machine-actionable workflow for the publication of climate impact data of the ISIMIP project, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6375, https://doi.org/10.5194/egusphere-egu23-6375, 2023.

Some disciplines, e.g. Astrophysics or Earth system sciences, work with large to very large amounts of data. Storing this data, but also processing it, is a challenge for researchers because novel concepts for processing data and workflows have not developed as quickly. This problem will only become more pronounced with the ever increasing performance of High Performance Computing (HPC) – systems.

At the German Climate Computing Center, we analysed the users, their goals and working methods. DKRZ provides the climate science community with resources such as high-performance computing (HPC), data storage and specialised services and hosts the World Data Center for Climate (WDCC). In analysing users, we distinguish between two main groups: those who need the HPC system to run resource-intensive simulations and then analyse them, and those who reuse, build on and analyse existing data. Each group subdivides into subgroups. We have analysed the workflows for each identified user and found identical parts in an abstracted form and derived Canonical Workflow Modules. In the process, we critically examined the possible use of so-called FAIR Digital Objects (FDOs) and checked to what extent the derived workflows and workflow modules are actually future-proof.

We will show the analysis of the different users, the Canonical workflow and the vision of the FDOs. Furthermore, we will present the framework Freva and further developments and implementations at DKRZ with respect to the reproducibility of simulation-based research in the ESS.

How to cite: Anders, I., Thiemann, H., Bergemann, M., Kadow, C., and Lucio-Eceiza, E.: Towards reproducible workflows in simulation based Earth System Science, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17263, https://doi.org/10.5194/egusphere-egu23-17263, 2023.

The number of models, as well as data inputs and outputs, are continuously growing as scientists continue to push the boundaries of spatial, temporal, and sectoral details being captured. This study presents the framework being developed to manage the Global Change Intersectoral Modeling System (GCIMS) eco-system of human-Earth system models. We discuss the challenges of ensuring continuous deployment and integration, reproducibility, interoperability, containerization, and data management for the growing suite of GCIMS models. We investigate the challenges of model version control and interoperability between models using different software, operating on different temporal and spatial scales, and focusing on different sectors. We also discuss managing transparency and accessibility to models and their corresponding data products throughout our integrated modeling lifecycle.

How to cite: Khan, Z., Vernon, C., Thompson, I., and Patel, P.: GCIMS – Integration: Reproducible, robust, and scalable workflows for interoperable human-Earth system modeling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4525, https://doi.org/10.5194/egusphere-egu23-4525, 2023.

The flexible open-source research data management toolkit CaosDB is used in a diversity of fields such as turbulence physics, legal research, maritime research and glaciology. It is used to link research data and make it findable and retrievable and to keep it consistent, even if the data model changes.

CaosDB is used in the glaciology department at the Alfred Wegener Institute in Bremerhaven for the management of ice core samples and related measurements and analyses. Researchers can use the system to query for ice samples linked to, e.g., specific measurements for which they then can request to borrow for further analyses. This facilitates inter-laboratory collaborative research on the same samples. The system helped to solve a number of needs for the researchers, such as: A revision system which intrinsically keeps track of changes to the data and in which state samples were, when certain analyses were performed. Automated gathering of information for the publication in a meta-data repository (Pangaea). Tools for storing, displaying and  querying geospatial information and graphical summaries of all the measurements and analyses performed on an ice core. Automatic data extraction and refinement into data records in CaosDB so that users do not need to enter the data manually. A state machine which guarantees certain workflows, simplifies development and can be extended to trigger additional actions upon transitions.

We demonstrate how CaosDB enables researchers to create and work with semantic data objects. We further show how CaosDB's semantic data structure enables researchers to publish their data as FAIR Digital Objects.

How to cite: Spreckelsen, F., tom Wörden, H., Hornung, D., Fitschen, T., Schlemmer, A., and Freitag, J.: Integrating sample management and semantic research-data management in glaciology, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7427, https://doi.org/10.5194/egusphere-egu23-7427, 2023.

In our project we are employing semantic data management with the Open Source research data management system (RDMS) CaosDB [1] to link empirical data and simulation output from Earth System Models [2]. The combined management of these data structures allows us to perform complex queries and facilitates the integration of data and meta data into data analysis workflows.

One particular challenge for analyses of model output is to keep track of all necessary meta data of each simulation during the whole digital workflow. Especially for open science approaches it is of great importance to properly document - in human- and computer-readable form - all the information necessary to completely reproduce obtained results. Furthermore, we want to be able to feed all relevant data from data analysis back into our data management system, so that we are able to perform complex queries also on data sets and parameters stemming from data analysis workflows.

A specific aim of this project is to re-analyse existing sets of simulations under different research questions. This endeavour can become very time consuming without proper documentation in an RDMS.

We implemented a workflow, combining semantic research data management with CaosDB and Jupyter notebooks, that keeps track of data loaded into an analysis workspace. Procedures are provided that create snapshots of specific states of the analysis. These snapshots can automatically be interpreted by the CaosDB crawler that is able to insert and update records in the system accordingly. The snapshots include links to the input data, parameter information, the source code and results and therefore provide a high-level interface to the full chain of data processing, from empirical and simulated raw data to the results. For example, input parameters of complex Earth System Models can be extracted automatically and related to model performance. In our use case, not only automated analyses are feasible, but also interactive approaches are supported.

• [1] Fitschen, T.; Schlemmer, A.; Hornung, D.; tom Wörden, H.; Parlitz, U.; Luther, S. CaosDB—Research Data Management for Complex, Changing, and Automated Research Workflows. Data 2019, 4, 83. https://doi.org/10.3390/data4020083
• [2] Schlemmer, A., Merder, J., Dittmar, T., Feudel, U., Blasius, B., Luther, S., Parlitz, U., Freund, J., and Lennartz, S. T.: Implementing semantic data management for bridging empirical and simulative approaches in marine biogeochemistry, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11766, https://doi.org/10.5194/egusphere-egu22-11766, 2022.

How to cite: Schlemmer, A. and Lennartz, S.: Transparent and reproducible data analysis workflows in Earth System Modelling combining interactive notebooks and semantic data management, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13347, https://doi.org/10.5194/egusphere-egu23-13347, 2023.

NuoroForestrySchool (a study center of the Department of Agriculture, University of Sassari, Italy) has developed and published a ‘data documentation procedure’ (link to NFS-DDP) enabling the improvement of the dataset FAIRness that any data collector wishes to share as open data. Datasets are frequently shared as spreadsheet files. While this tool is very handy in data preparation and preliminary analysis, its structure and composition are not very effective for storing and sharing consolidated data, unless data structures are extremely simple. NFS-DDP takes in input a spreadsheet in which data are organized as relational tables, one per sheet, while four additional sheets contain metadata standardized according to the Dublin Core specifications. The procedure outputs an SQLite relational database (including data and metadata) and a pdf-file documenting the database structure and contents. A first example application of the proposed procedure was shared by Giadrossich et al. (2022) on the PANGEA repository, concerning experimental data of erosion in forest soil measured during artificial rainfall. The zip-archive that can be downloaded contains the experiment data and metadata processed by NFS-DDP. At the following link is available a test document where basic statistics are computed to show how NFS-DDProcedure facilitates the understanding and correct processing of the shared dataset. 

The NFS-DataDocumentationProcedure provides a simple solution for organizing and archiving data aiming to i) achieve a more FAIR archive, ii) exploit data consistency and comprehensibility of semantic connections in the relational database, ii) produce a report documenting the collection and organization of data, providing an effective and concise overview of the whole with all details at hand.

Giadrossich, F., Murgia, I., Scotti, R. (2022). Experiment of water runoff and soil erosion with and without forest canopy coverage under intense artificial rainfall. PANGAEA. DOI:10.1594/PANGAEA.943451

How to cite: Giadrossich, F., Murgia, I., and Scotti, R.: Proposal of a simple procedure to derive a more FAIR open data archive than a spreadsheet or a set of CSV files, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9852, https://doi.org/10.5194/egusphere-egu23-9852, 2023.

Environmental sensor networks produce ever-growing volumes of time series data with great potential to broaden the understanding of complex spatiotemporal environmental processes. However, this growth also imposes its own set of new challenges. Especially the error-prone nature of sensor data acquisition is likely to introduce disturbances and anomalies into the actual environmental signal. Most applications of such data, whether it is used in data analysis, as input to numerical models or modern data science approaches, usually rely on data that complies with some definition of quality.

To move towards high-standard data products, a thorough assessment of a dataset's quality, i.e., its quality control, is of crucial importance. A common approach when working with time series data is the annotation of single observations with a quality label to transport information like its reliability. Downstream users and applications are hence able to make informed decisions, whether a dataset in its whole or at least parts of it are appropriate
for the intended use.

Unfortunately, quality control of time series data is a non-trivial, time-consuming, scientifically undervalued endeavor and is often neglected or executed with insufficient rigor. The presented software, the System for automated Quality Control (SaQC), provides all basic and many advanced building blocks to bridge the gap between data that is usually faulty but expected to be correct in an accessible, consistent, objective and reproducible way. Its user interfaces address different audiences ranging from the scientific practitioner with little access to the possibilities of modern software development to the trained programmer. SaQC delivers a growing set of generic algorithms to detect a multitude of anomalies and to process data using resampling, aggregation, and data modeling techniques. However, one defining component of SaQC is its innovative approach to storing runtime process information. In combination with a flexible quality annotation mechanism, SaQC allows to extend quality labels with fine-grained provenance information appropriate to fully reproduce the system's output.

SaQC is proving its usefulness on a daily basis in a range of fully automated data flows for large environmental observatories. We highlight use cases from the TERENO Network, showcasing how reproducible automated quality control can be implemented into real-world, large-scale data processing workflows to provide environmental sensor data in near real-time to data users, stakeholders and decision-makers.

How to cite: Schäfer, D., Palm, B., Lünenschloß, P., Schmidt, L., and Bumberger, J.: Reproducible quality control of time series data with SaQC, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12971, https://doi.org/10.5194/egusphere-egu23-12971, 2023.

A vast amount of in situ cryospheric data has been collected during publicly funded field campaigns to the polar regions over the past decades. Each individual data set yields important insights into local thermo-physical processes, but they need to be assembled into informative data compilations to unlock their full potential to produce regional or global outcomes for climate change related research. The efficient and sustainable interdisciplinary reuse of such data compilations is of large interest to the scientific community. Yet, the creation of such compilations is often challenging as they have to be composed of often heterogeneous data sets from various data repositories. We will focus on the reuse of data sets in this contribution, while generating extendible data compilations with enhanced reusability.

Data reuse is typically conducted by researchers other than the original data producers, and it is therefore often limited by the metadata and provenance information available. Reuse scenarios include the validation of physics-based process models, the training of data-driven models, or data-integrated predictive simulations. All these use cases heavily rely on a diverse data foundation in form of a data compilation, which depends on high quality information. In addition to metadata, provenance, and licensing conditions, the data set itself must be checked for reusability. Individual data sets containing the same metrics often differ in structure, content, and metadata, which challenges data compilation.

In order to generate data compilations for a specific reuse scenario, we propose to break down the workflow into four steps:
1) Search and selection: Searching, assessing, optimizing search, and selecting data sets.
2) Validation: Understanding and representing data sets in terms of the data collectors including structure, terms used, metadata, and relations between different metrics or data sets.
3) Specification: Defining the format, structure, and content of the data compilation based on the scope of the data sets.
4) Implementation: Integrating the selected data sets into the compilation.

We present a workflow herein to create a data compilation from heterogeneous sea ice core data sets following the previously introduced structure. We report on obstacles encountered in the validation of data sets mainly due to missing or ambiguous metadata. This leaves the (re)user space for subjective interpretation and thus increases uncertainty of the compilation. Examples are challenges in relating different data repositories associated with the same location or the same campaign, the accuracy of measurement methods, and the processing stage of the data. All of which often require a bilateral iteration with the data acquisition team. Our study shows that enriching data reusability with data compilations requires quality-ensured metadata on the individual data set level.

How to cite: Simson, A., Yildiz, A., and Kowalski, J.: Data compilations for enriched reuse of sea ice data sets, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6726, https://doi.org/10.5194/egusphere-egu23-6726, 2023.

Investigating the mechanics of physical processes involved in various geohazards, e.g. gravitational, flow-like mass movements, shallow landslides or flash floods, predicting their temporal or spatial occurrence, and analysing the associated risks clearly benefit from advanced computational process-based or data-driven models. Reproducibility is needed not only for the integrity of the scientific results, but also as a trustbuilding element in practical geohazards engineering. Various complex numerical models or pre-trained machine learning algorithms exist in the literature, for example, to determine landslide susceptibility in a region or to predict the run-out of torrential flows in a catchment. These use FAIR datasets with increasing frequency, for example DEM data to set up the simulation, or open access landslide databases for training and validation purposes. However, we maintain that workflow reproducibility is not ensured simply due to the FAIRness of input or output datasets. Underlying computational or machine learning model needs to be (re)structured to enable the reproducibility and replicability of every step in the workflow so that a model can be (re)built to either reproduce the same results, or can be (re)used to elaborate on new cases or new applications. We propose a data-integrated, platform-independent scientific model publication approach combining self-developed Python packages, Jupyter notebooks, version controlling, FAIR data repositories and high-quality metadata. Model development in the form of a Python package guarantees that model can be run by any end-user, and defining submodules of analysis or visualisation within the package helps the users to build their own models upon the model presented. Publishing the manuscript as a data- and model-integrated Jupyter notebook creates a transparent application of the model, and the user can reproduce any result either presented in the manuscript or in the datasets. We demonstrate our workflow with two applications from geohazards research herein while highlighting the shortcomings of the existing frameworks and suggesting improvements for future applications.

How to cite: Yildiz, A. and Kowalski, J.: Data-integrated executable publications for reproducible geohazards research, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7417, https://doi.org/10.5194/egusphere-egu23-7417, 2023.

Humankind is facing unprecedented global environmental and social challenges in terms of food, water and energy security, resilience to natural hazards, etc. To address these challenges, international organizations have defined a list of policy actions to be achieved in a relatively short and medium-term timespan (e.g., the UN SDGs). The development and use of knowledge platforms is key in helping the decision-making process to take significant decisions and avoid potentially negative impacts on society and the environment.

Scientific models are key tools to transform into information and knowledge the huge amount of data currently available online. Executing a scientific model (implemented as an analytical software) commonly requires the discovery and use of different types of digital resources (i.e. data, services, and infrastructural resources). In the present geoscience technological landscape, these resources are generally provided by different systems (working independently from one another) by utilizing Web technologies (e.g. Internet APIs, Web Services, etc.). In addition, a given scientific model is often designed and developed for execution in a specific computing environment. These are important barriers to enable reproducibility, replicability, and reusability of scientific models –becoming key interoperability requirements for a transparent decision-making process.

This presentation introduces the Virtual Earth Cloud concept, a multi-cloud framework for the generation of information/knowledge from Big Earth Data analytics. The Virtual Earth Cloud allows the execution of computational models to process and extract knowledge from Big Earth Data, in a multi-cloud environment, and thus improving their reproducibility, replicability and reusability.

The development and prototyping of the Virtual Earth Cloud is carried out in the context of the GEOSS Platform Plus (GPP) project, funded by the European Union’s Horizon 2020 Framework Programme, aims to contribute to the implementation of the Global Earth Observation System of Systems (GEOSS) by evolving the European GEOSS Platform components to allow access to tailor-made information and actionable knowledge.

How to cite: Santoro, M., Mazzetti, P., and Nativi, S.: Virtual Earth Cloud: a multi-cloud framework for improving replicability of scientific models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7532, https://doi.org/10.5194/egusphere-egu23-7532, 2023.

Landlab is an open-source Python package designed to facilitate creating, combining, and reusing 2D numerical models. As a core component of the Community Surface Dynamics Modeling System (CSDMS) Workbench, Landlab can be used to build and couple models from a wide range of domains. We present how Landlab provides a platform that fosters a community of model developers and aids them in creating sustainable and FAIR (Findable, Accessible, Interoperable, Reusable) research software.

Landlab’s core functionality can be split into two main categories: infrastructural tools and community-contributed components. Infrastructural tools address the common needs of building new models (e.g. a gridding engine, and numerical utilities for common tasks). Landlab’s library of community-contributed components consists of several dozen components that each model a separate physical process (e.g. routing of shallow water flow across a landscape, calculating groundwater flow, or biologic evolution over a landscape). As these user-contributed components are incorporated into Landlab, they are able to attach to the Landlab infrastructure so that they also become both findable and accessible (through, for example, standardized metadata and versioning) and are maintained by the core Landlab developers.

One key aspect of Landlab’s design is its use of a standard programming interface for all components. This ensures that all Landlab components are interoperable with one another and with other software tools, allowing researchers to incorporate Landlab's components into their own workflows and analyses. By separating processes into individual components, they become reusable and allow researchers to combine components in new ways without having to write new components from scratch.

Overall, Landlab's design and development practices support the principles of FAIR research software, promoting the ability for scientific research to be easily shared and built upon. This design also provides a platform onto which model developers are able to attach their model components and take advantage of Landlab’s development practices and infrastructure and ensure their components also follow FAIR principles.

How to cite: Hutton, E. and Tucker, G.: Landlab: a modeling platform that promotes the building of FAIR research software, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12443, https://doi.org/10.5194/egusphere-egu23-12443, 2023.