The Open Science agenda holds that science advances faster when we can build on existing results. Therefore, research data must be FAIR (Findable, Accessible, Interoperable, and Reusable) in order to advance the findability, reproducibility and reuse of research results. Besides the research data, all the processing steps on these data – as basis of scientific publications – have to be available, too.For good scientific practice, the resulting research software should be both open and adhere to the FAIR principles to allow full repeatability, reproducibility, and reuse. As compared to research data, research software should be both archived for reproducibility and actively maintained for reusability.The FAIR data principles do not require openness, but research software should be open source software. Established open source software licenses provide sufficient licensing options, such that it should be the rare exception to keep research software closed.We review and analyze the current state in this area in order to give recommendations for making research software FAIR and open.

Twitter is a popular tool for publishing potentially interesting information about people's opinions, experiences and news. Mobile devices allow people to publish tweets during real-time events. It is often difficult to identify the subject of a tweet because Twitter users often write using highly unstructured language with many typographical errors. Structured data related to entities can provide additional context to tweets. We propose an approach which associates tweets to a given event using query expansion and relationships defined on the Semantic Web, thus increasing the recall whilst maintaining or improving the precision of event detection. In this work, we investigate the usage of Twitter in discussing the Rock am Ring music festival. We aim to use prior knowledge of the festival's lineup to associate tweets with the bands playing at the festival. In order to evaluate the effectiveness of our approach, we compare the lifetime of the Twitter buzz surrounding an event to the actual programmed event, using Twitter users as social sensors.

PROV-TEMPLATE is a declarative approach that enables designers and programmers to design and generate provenance compatible with the PROV standard of the World Wide Web Consortium. Designers specify the topology of the provenance to be generated by composing templates, which are provenance graphs containing variables, acting as placeholders for values. Programmers write programs that log values and package them up in sets of bindings, a data structure associating variables and values. An expansion algorithm generates instantiated provenance from templates and sets of bindings in any of the serialisation formats supported by PROV. A quantitative evaluation shows that sets of bindings have a size that is typically 40% of that of expanded provenance templates and that the expansion algorithm is suitably tractable, operating in fractions of milliseconds for the type of templates surveyed in the article. Furthermore, the approach shows four significant software engineering benefits: separation of responsibilities, provenance maintenance, potential runtime checks and static analysis, and provenance consumption. The article gathers quantitative data and qualitative benefits descriptions from four different applications making use of PROV-TEMPLATE. The system is implemented and released in the open-source library ProvToolbox for provenance processing. ! 1 INTRODUCTION P ROVENANCE has gained a lot of traction lately in various areas including the Web, legal notices 1 , climate science 2 , scientific workflows [1], [2], [3], computational reproducibility [4], emergency response [5], medical applications 3 , geospatial domain 4 , art and food. The recent standard PROV [6] of the World Wide Web Consortium defines provenance as "as a record that describes the people, institutions, entities, and activities involved in producing, influencing, or delivering a piece of data or a thing." In an increasing number of applications, provenance has become crucial in making systems accountable, by exposing how information flows in systems, and in helping users decide whether information is to be trusted. Provenance is not restricted to computer systems, it can also be used to describe how objects are transformed and people are involved in a physical system [5].Applications and use cases for provenance are well documented in the literature [7], [8], [9], [10]. They include making systems more auditable and accountable [11], reproducing results [12], deriving trust and classification [13], asserting attribution and generating acknowledgments [14], supporting predictive analytics [13], and facilitating traceability [15]. To enable such a powerful functionality, however, one needs to adapt or write applications, so that they generate provenance information, which can then be exploited to offer new benefits to their users.A number of approaches have been proposed to generate provenance: run-time, compile-time, and retrospectively. Runtime generation typically requires applications to be instrumented, and provenance generated accordingly [16],...

ware is a central asset for development activities. For good scientific practice, the resulting research software should be open source. Established open source software licenses provide sufficient options for granting permissions such that it should be the rare exception to keep research software closed. Proper engineering is required for obtaining reusable and sustainable research software. This way, software engineering methods may improve research in other disciplines. However, research in software engineering and computer science itself will also benefit when programs are reused. To study the state of the art in this field, we analyzed research software publishing practices in computer and computational science and observed significant differences: computational science emphasizes reproducibility, while computer science emphasizes reuse. SOFTWARE ENGINEERING FOR SUSTAINABLE RESEARCH SOFTWARE Research software is employed during the scientific discovery process, and it can be an object of study itself. Computational science (also known as scientific computing) involves the development of research software for model simulations and data analytics that facilitate the understanding of natural systems, answering questions that neither theory nor experiment alone is equipped to answer. Computational science is the application of computer science and software engineering principles to solving technical problems, whereas computer science is the study of computer hardware and software design. Despite the increasing importance of research software to the scientific discovery process, well-established software engineering practices are rarely adopted in

Abstract. Despite the increasing popularity of locative interactive stories their poetics are poorly understood, meaning that there is little advice or support for locative authors, and few frameworks for critical analysis. The StoryPlaces project has spent two years working with over sixty authors creating locative stories. Through analyzing the stories themselves, and interviewing readers, we have developed a simple writer's toolkit that highlights the challenges and opportunities offered by locative fiction. In this paper we describe our approach, and outline twelve key pragmatic and aesthetic considerations that we have derived from our experience and analyses. Together these reveal that the main challenge in locative literature lies in aligning the narrative text, the structural logic, and the demands and affordances of the landscape.