Markus Konkol

More and more funders, reviewers, and publishers ask researchers to follow Open Science principles and make their research results publicly accessible. In the case of a computational analysis workflow, this means providing access to data and code that produced the figures, tables, and numbers reported in a paper. However, doing so, even in consideration of the FAIR Principles, does not mean others can easily reuse the materials and continue the research. It still requires effort to understand an analysis script (e.g., written in R or python) and extract those parts of a workflow (i.e. the code snippets) that generate, for instance, a particular figure.In this contribution, we demonstrate the concept and realization of the Data-to-Knowledge Package (D2K-Package), a collection of digital assets which facilitate the reuse of computational research results [1]. The heart of a D2K-Package is the reproducible basis composed of the data and code underlying, for instance, a statistical analysis. Instead of simply providing access to the analysis script as a whole, the idea is to structure the code into self-contained and containerized functions making the workflow steps more reusable. Each function follows the input-processing-output-logic and fulfills a certain task such as data processing, analysis, or visualization. Creating such a reproducible basis allows inferring the following components that are also part of the D2K-Package:A virtual lab is a web application, for example, in the form of a JupyterLab environment provided with the help of MyBinder. Users can access it via the browser and obtain a computational environment with all dependencies and the runtime pre-installed. Creating such a virtual lab is possible since all code is containerized and the image is built based on a specification of the used libraries, runtime, and their versions. A virtual lab can help users with programming expertise to engage with the code in a ready-to-use programming environment.A web API service exposes the encapsulated and self-contained functions such that every function has a dedicated URL endpoint. Users can send requests from their analysis script to that endpoint and obtain the results via HTTP. Hence, they can reuse the functions without copying the code snippets or struggling with dependencies. Such a service can be realized using OGC API Processes and pygeoapi.The computational workflow connects the functions to an executable analysis pipeline and acts as an entry point to a complex analysis. Such a workflow can help users obtain a better understanding of the functions and relevant input parameters. By using workflow tools such as the Galaxy platform, also users without programming experience receive the chance to change the parameter configuration and see how the new settings affect the final output.Besides the concepts as outlined above, this contribution will also report on real demonstrators which showcase the idea of a D2K-Package.This project has received funding from the European Commission’s Horizon Europe Research and Innovation programme. Grant agreement No 101094434.1) Paper: Konkol et al. (2025) https://doi.org/10.12688/openreseurope.20221.3

Research data management (RDM) in the Earth system sciences is complex and can be frustrating. Data come in many shapes and formats—observations, model outputs, samples, derived products—and are spread across a wide range of repositories, services, and software ecosystems. These infrastructures differ greatly in metadata quality, interoperability, and FAIR maturity. For researchers, this often means spending too much time figuring out where to publish data, how to describe it properly, or how to reuse existing datasets and software. At the same time, expectations from funders and journals on data outputs continue to rise with respect to openness, curation, and long-term stewardship. Within the German National Research Data Infrastructure (NFDI), the NFDI4Earth consortium tackles these challenges by building practical, community-driven solutions for the Earth system sciences. As developers and designers, our focus is on lowering barriers and making FAIR data and software practices easier to understand and apply in everyday research. In this contribution, we introduce two key building blocks of this effort: the Knowledge Hub and the OneStop4All. The Knowledge Hub (https://knowledgehub.nfdi4earth.de) is a knowledge graph that connects heterogeneous Earth system resources, e.g., datasets, repositories, services, software, and educational materials, using a harmonized metadata model. It harvests metadata from multiple providers—ranging from global aggregators to national and domain-specific services (see, for instance, the Helmholtz DataHub: https://earth-data.de/) - and exposes them through a well-defined SPARQL API. This allows both humans and machines to query, explore, and reuse metadata consistently, and enables developers to build custom applications on top of it. The OneStop4All (https://onestop4all.nfdi4earth.de) builds on the Knowledge Hub to offer a discovery and guidance portal for researchers. It brings together the resources in a single, coherent interface. Cross-domain search and guided navigation help users move along the research data lifecycle without having to know all standards and infrastructures upfront. A central feature is the Repository Wizard, an interactive decision-support tool that helps researchers find suitable repositories for publishing their data based on data type, discipline, and policy constraints. In addition, the NFDI4Earth Label provides a transparent, community-oriented way to communicate repository quality with respect to FAIR principles, sustainability, and relevance for Earth system sciences. Beyond discovery, the OneStop4All puts a strong emphasis on learning and cultural change. It provides integrated access to open educational resources, good-practice guides, and showcases that demonstrate the concrete benefits of FAIR and open data and software. A domain-specific chatbot complements these resources by answering practical questions on metadata, licensing, data publication, and software citation. We will showcase these services, share lessons learned from development, and highlight opportunities for community contributions. From our perspective as a distributed team of designers and software developers, combining harmonized metadata, user-centered services, and hands-on training is key to making FAIR and open research practices work at scale in the Earth system sciences.

The growing demand for reproducible research is based on the expectation that publishing research in this form will enable its reuse and the generation of new knowledge. However, reproducibility alone does not guarantee these benefits. Users still need to make considerable efforts to understand the data and analysis code before they can reuse these components in other contexts. To address this challenge, we introduce the Data-to-Knowledge Package (D2K-Package), a collection of research materials including source code and open FAIR data, virtual labs, web API services, and computational workflows. The D2K-Package’s core is the reproducible basis composed of the data and source code on which an analysis is based. This core is designed such that the other components can be derived from it. The main goal of the package is to help researchers generate new knowledge by facilitating the understanding and encouraging the reuse of reproducible research. We demonstrate the applicability of the D2K-Package with a hydrological use case which can be also used for testing, and discuss its seamless integration into the research cycle.

The growing demand for reproducible research is based on the expectation that publishing research in this form will enable its reuse and the generation of new knowledge. However, reproducibility alone does not guarantee these benefits. Users still need to make considerable efforts to understand the data and analysis code before they can reuse these components in other contexts. To address this challenge, we introduce the Data-to-Knowledge Package (D2K-Package), a collection of research materials including source code and open FAIR data, virtual labs, web API services, and computational workflows. The D2K-Package’s core is the reproducible basis composed of the data and source code on which an analysis is based. This core is designed such that the other components can be derived from it. The main goal of the package is to help researchers generate new knowledge by facilitating the understanding and encouraging the reuse of reproducible research. We demonstrate the applicability of the D2K-Package with a hydrological use case which can be also used for testing, and discuss its seamless integration into the research cycle.

The growing demand for reproducible research is based on the expectation that publishing research in this form will enable its reuse and the generation of new knowledge. However, reproducibility alone does not guarantee these benefits. Users still need to make considerable efforts to understand the data and analysis code before they can reuse these components in other contexts. To address this challenge, we introduce the Data-to-Knowledge Package (D2K-Package), a collection of research materials including source code and open FAIR data, virtual labs, web API services, and computational workflows. The D2K-Package’s core is the reproducible basis composed of the data and source code on which an analysis is based. This core is designed such that the other components can be derived from it. The main goal of the package is to help researchers generate new knowledge by facilitating the understanding and encouraging the reuse of reproducible research. We demonstrate the applicability of the D2K-Package with a hydrological use case which can be also used for testing, and discuss its seamless integration into the research cycle.

GIScience conference authors and researchers face the same computational reproducibility challenges as authors and researchers from other disciplines who use computers to analyse data. Here, to assess the reproducibility of GIScience research, we apply a rubric for assessing the reproducibility of 75 conference papers published at the GIScience conference series in the years 2012-2018. The rubric and process were previously applied to the publications of the AGILE conference series. The results of the GIScience paper assessment are in line with previous findings: descriptions of workflows and the inclusion of the data and software suffice to explain the presented work, but they do not enable the findings to be reproduced by a third party with reasonable effort. We summarise and adapt previous recommendations for improving this dire situation and invite the GIScience community to start a broad discussion on the reusability, quality, and openness of its research. The code and data for this article are published at https://doi.org/10.5281/zenodo.4032875.

Background: The trend toward open science increases the pressure on authors to provide access to the source code and data they used to compute the results reported in their scientific papers. Since sharing materials reproducibly is challenging, several projects have developed solutions to support the release of executable analyses alongside articles. Methods: We reviewed 11 applications that can assist researchers in adhering to reproducibility principles. The applications were found through a literature search and interactions with the reproducible research community. An application was included in our analysis if it (i) was actively maintained at the time the data for this paper was collected, (ii) supports the publication of executable code and data, (iii) is connected to the scholarly publication process. By investigating the software documentation and published articles, we compared the applications across 19 criteria, such as deployment options and features that support authors in creating and readers in studying executable papers. Results: From the 11 applications, eight allow publishers to self-host the system for free, whereas three provide paid services. Authors can submit an executable analysis using Jupyter Notebooks or R Markdown documents (10 applications support these formats). All approaches provide features to assist readers in studying the materials, e.g., one-click reproducible results or tools for manipulating the analysis parameters. Six applications allow for modifying materials after publication. Conclusions: The applications support authors to publish reproducible research predominantly with literate programming. Concerning readers, most applications provide user interfaces to inspect and manipulate the computational analysis. The next step is to investigate the gaps identified in this review, such as the costs publishers have to expect when hosting an application, the consideration of sensitive data, and impacts on the review process.

Many scientific publications report on computational results based on code and data, but even when code and data are published, the main text is usually provided in a separate, traditional format such as PDF. Since code, data, and text are not linked on a deep level, it is difficult for readers and reviewers to understand and retrace how the authors achieved a specific result that is reported in the main text, e.g. a figure, table, or number. In addition, to make use of new the opportunities afforded by data and code availability, such as re-running analyses with changed parameters, considerable effort is required. In order to overcome this issue and to enable more interactive publications that support scientists in more deeply exploring the reported results, we present the concept, implementation, and initial evaluation of bindings. A binding describes which data subsets, code lines, and parameters produce a specific result that is reported in the main text (e.g. a figure or number). Based on a prototypical implementation of these bindings, we propose a toolkit for authors to easily create interactive figures by connecting specific UI widgets (e.g. a slider) to parameters. In addition to inspecting code and data, readers can then manipulate the parameter and see how the results change. We evaluated the approach by applying it to a set of existing articles. The results provide initial evidence that the concept is feasible and applicable to many papers with moderate effort.