Why do scientists write, and share scientific software? Writing and sharing scientific software is a means to communicate scientific ideas for finding scientific concensus, no more and no less than writing and sharing scientific papers is. Important factors for successful communication are agreeing on an open source license, technological solutions to sharing development, using a language that is understood by many, a system of peer review that fosters evolution and diversity, and sufficient possibility for contributors to be recognized as individual, and awarded. This talk will discuss all these items mostly in the light of the ecosystem of R, a free software environment for statistical computing and graphics, which is largely driven by academics.

Software has become an essential component in basically every part of the research cycle. Still, there are no comprehensive guidelines how core principles of good scientific practice like transparency and reproducibility can and should be applied to research software. Further open questions are how to guarantee high quality of such software, how current and future researchers need to be trained and incentivized to generate sustainable software. To ensure the frictionless reuse and long term availability of software, dedicated framework conditions and infrastructure for research software need to be established. At the same time knowledge about open/libre software licenses needs to be disseminated. These and numerous other issues were so far only partially and inconsistently addressed by German universities and funding/research institutions. The Alliance of Science Organisations which consists of all large German research organisations (DFG, Fraunhofer Society, Helmholtz Association, Leibniz Association, Max Planck Society) and the universities has launched the ad-hoc working group "Research Software" as part of its initiative "Digital Information" to propose solutions for these issues. This working group aims to address the general questions regarding research software and to compile a set of guidelines and recommendations for the German and international research community. In this talk those questions and their potential solutions will be presented and discussed.

The lecture will give an overview on legal requirements of collaboration in science.

Software is the interface between man and the digital machine. Although software is as essential as data, there is much more attention for data management than for sustainable software. In the public policy domain the importance of keeping software alive is largely underestimated. This should change at our earliest possible convenience. The jump forward should be: a systematic approach to software sustainability combined with an international knowledge exchange infrastructure for software sustainability. Other actions regarding software sustainability involve the Software heritage Project and de Software Seal of Approval.

Software is a critical part of modern research and yet there is little support across the scholarly ecosystem for its citation. Inspired by the activities of the FORCE11 working group focused on data citation, the FORCE11 Software Citation Working Group has published a set Software Citation Principles (https://doi.org/10.7717/peerj-cs.86) in September 2016. This has the goal of encouraging broad adoption of a consistent policy for software citation across disciplines and venues. This presentation will discuss the principles (in brief, importance, credit and attribution, unique identification, persistence, accessibility, and specificity), how they will impact the practice of research, and they can be implemented by researchers, publishers, librarians and others who build and maintain repositories, scholars of science, university administrators, and research funders.

The Web contains all kinds of information today. Web archives preserve this data and make it long-term available. However, access is usually only provided by a URL and a timestamp. Hence, there is no deeper meaning attached to archived resources, although collectively they can represent entities, such as software. Moreover, documentation and source code that is available at different points in time, can even represent different versions of a software. Treating them as first-class citizens in web archives enables reliable and permanent references to software, which is normally hard to manage.

The management and analysis of large-scale datasets - described by the term Big Data - involves the three classic dimensions volume, velocity and variety. While the former two are well supported by a variety of software components, the variety dimension is still rather neglected but crucial for the integration and analysis of heterogeneous scientific data. In this talk we discuss the principles of Linked Research Data and practical approaches for their implementation using semantic technologies such as the Data Spaces or Knowledge Graphs. As a concrete example, we discuss the collaborative creation of the BigDataEurope Platform, an open software stack for researchers addressing Europe's societal challenges.

Software is fundamental to all areas of research and science. The move towards Open Science has made it even more important that software is made accessible, reusable and maintainable: all facets of software sustainability. However we still face the challenge of translating the enthusiasm of the Open Access, Open Data and Open Science vanguards to the wider community of researchers who may lack access to infrastructure, skills and effort. This talk will draw on the experiences of the Software Sustainability Institute in working with the long tail of researchers, including the formation of the Journal of Open Research Software, to present a different perspective of software for Open Science.

Mathematical modeling and simulation (MMS) has now been established as an essential part of the scientific work in many disciplines. It is common to categorize the involved numerical data and to some extend the corresponding scientific software as research data. Both have their origin in mathematical models. A holistic approach to research data in MMS should cover all three aspects: models, software, and data. Yet it is unclear, whether a suitable management of the mathematical knowledge related to models is possible and how it would look like. In this talk, we outline an approach to address this problem based on a flexiformal representation of the mathematical knowledge in publications and research software. We will discuss how this can improve the sustainability of research obtained by mathematical modeling and numerical simulations.

Jupyter notebooks provide a document-based interactive environment for performing and recording computation. Notebook documents contain not only the code that is run, but prose and mathematics for describing the analysis, as well as recording the outputs of the computation, from plain text output to rich interactive media, such as HTML and javascript or images and video. Jupyter notebooks are being deployed widely as computational companions for publications, facilitation reproduction of results, and interactive exploration and modification of analyses, including by prominent scientific discoveries such as the LIGO experiment. Being freely available, open source software, Jupyter and IPython aim to improve the accessibility of reproducible practices in computational science.

Blockchain technology has the capacity to make digital goods immutable, transparent, externally provable, decentralized, and distributed. Besides the initial experiment and data acquisition, all remaining parts of the research cycle could take place within a blockchain system. Attribution, data, data postprocessing, publication, research evaluation, incentivisation, and research fund distribution would thereby become comprehensible, open (at will) and provable to the external world. Currently, scientists must be trusted to provide a true and useful representation of their research results in their final publication; blockchain would make much larger parts of the research cycle open to scientific self-correction. Some claim that this bears the potential to be a new approach to the current reproducibility crisis in science, and could reduce waste and make more research results true. Here we will provide an introduction and overview. Please also see our living document on this topic: https://goo.gl/vz6lCn

The talk will start with an introduction to the ideas behind a computationally active living knowledge network secured by a science blockchain. Next a proof of concept demo will be presented highlighting two uses cases. The first will show how a 'traditional' way of conducting science can fit into a living knowledge protocol and the second will show the use of the Dsensor Mapping Protocol to automatically validate the outcomes of science.