Context: Research software is instrumental in producing research results. It plays a special role in advancing scientific discovery through tasks such as data analysis, simulation, and visualisation. However, despite its importance, the organisations that produce research software face challenges in managing research software projects. Existing frameworks for software engineering often overlook the needs of research software, including research software engineering practices and open science principles. Without clear guidance, organisations that produce research software must develop and invent new techniques for research software engineering, which is a slow and costly process. Objective: This work presents RSMM, a maturity model designed to improve organisational practices in research software project management. Methods: The initial version of RSMM was developed through a systematic literature review. Expert interviews were then conducted to evaluate and refine the model. Finally, multiple case studies were carried out to validate RSMM and demonstrate its applicability in real-world settings. Results: The final version of RSMM (v1.0) comprises 79 best practices, 17 capabilities, and 10 maturity levels, organised into 4 focus areas: ‘Software Project Management’ , ‘Research Software Management’ , ‘Community Engagement’ , and ‘Software Adoptability’ . We provide a comprehensive analysis of RSMM v1.0 and demonstrate its practical applicability through two illustrative case studies. Conclusion: The RSMM is designed to help organisations in evaluating and improving their research software project management by assessing a project’s current maturity level and providing best practices across four focus areas to guide its progression.

The growing usage of research software in the research community has highlighted the need to recognize and acknowledge the contributions made not only by researchers but also by Research Software Engineers. However, the existing methods for crediting research software and Research Software Engineers have proven to be insufficient. In response, we have developed FAIRSECO, an extensible open source framework with the objective of assessing the impact of research software in research through the evaluation of various factors. The FAIRSECO framework addresses two critical information needs: firstly, it provides potential users of research software with metrics related to software quality and FAIRness. Secondly, the framework provides information for those who wish to measure the success of a project by offering impact data. By exploring the quality and impact of research software, our aim is to ensure that Research Software Engineers receive the recognition they deserve for their valuable contributions.

Recently, Digital Data Marketplaces (DDMs) are gaining wide attention as a sharing platform among different organizations. That is due to the fact that sharing the information and participating in research collaborations play an important role in addressing multiple scientific challenges. To increase trust among participating organizations multiple contracts and agreements should be established in order to determine regulations and policies about who has access to what. Describing these agreements in a general model to be applicable in different DDMs is of utmost importance. In this paper, we present a semantic model for describing the access policies by means of semantic web technologies. In particular, we use and extend the Open Digital Rights Language (ODRL) to describe the pre-established agreements in a DDM.

One of the most challenging tasks in the field of information visualization is to design tools for high-dimensional scientific data. Existing visual data analytics tools either require expert knowledge (e.g. programming) or they are commercial tools such as Dash, Spotfire and Tableau. Spot provides a user-friendly, interactive data exploration environment for high-dimensional data sets. The main focus of Spot is on scientific use, with the aim to facilitate open science, data sharing and reuse. For example, iDARK project, which aims to collect existing dark matter models, will be using Spot in order to allow researchers to compare the particle physics data sets. This abstract gives an overview of main features of Spot, an open source data analytics platform, and its capabilities.

Abstract We study the complexity of deciding the equality of streams specified by systems of equations. There are several notions of stream models in the literature, each generating a different semantics of stream equality. We pinpoint the complexity of each of these notions in the arithmetical or analytical hierarchy. Their complexity ranges from low levels of the arithmetical hierarchy such as Π 0 2 for the most relaxed stream models, to levels of the analytical hierarchy such as Π 1 1 and up to subsuming the entire analytical hierarchy for more restrictive but natural stream models. Since all these classes properly include both the semi-decidable and co-semi-decidable classes, it follows that regardless of the stream semantics employed, there is no complete proof system or algorithm for determining equality or inequality of streams. We also discuss several related problems, such as the existence and uniqueness of stream solutions for systems of equations, as well as the equality of such solutions.

We consider a large wireless network constituting a radio telescope. Each of the anticipated 3000 nodes is triggered to collect data for further analysis at a rate of more than 200 Hz, mostly caused by noisy environmental sources. However, relevant cosmic rays occur only a few times a day. As every trigger has an associated 12.5KB of data, and considering the size of the telescope in number of nodes and covered area, centralized processing is not an option. We propose a fully decentralized event detection algorithm based on collaborative local data analysis, effectively filtering out only those triggers that need further (centralized) processing. As we show through performance evaluations, the crux in the design is finding the right balance between accuracy and efficient use of resources such as the communication bandwidth in the unreliable communication environment.

We study the complexity of deciding the equality of infinite objects specified by systems of equations, and of infinite objects specified by λ-terms. For equational specifications there are several natural notions of equality: equality in all models, equality of the sets of solutions, and equality of normal forms for productive specifications. For λ-terms we investigate Böhm-tree equality and various notions of observational equality. We pinpoint the complexity of each of these notions in the arithmetical or analytical hierarchy.

We propose a natural generalisation of asynchronous bounded delay (ABD) network models. The commonly used ABD models assume a known bound on message delay. This assumption is often too strict for real-life applications. To this end we introduce a novel probabilistic network model, called asynchronous bounded expected delay (ABE), which requires a known bound on the expected message delay. While the conditions of ABD networks restrict the set of possible executions, in ABE networks all asynchronous executions are possible, but executions with extremely long delays are less probable. The ABE model captures asynchrony that occurs in sensor networks and ad-hoc networks.