The role of software in science: a knowledge graph-based analysis of software mentions in PubMed Central.

Science across all disciplines has become increasingly data-driven, leading to additional needs with respect to software for collecting, processing and analysing data. Thus, transparency about software used as part of the scientific process is crucial to understand provenance of individual research data and insights, is a prerequisite for reproducibility and can enable macro-analysis of the evolution of scientific methods over time. However, missing rigor in software citation practices renders the automated detection and disambiguation of software mentions a challenging problem. In this work, we provide a large-scale analysis of software usage and citation practices facilitated through an unprecedented knowledge graph of software mentions and affiliated metadata generated through supervised information extraction models trained on a unique gold standard corpus and applied to more than 3 million scientific articles. Our information extraction approach distinguishes different types of software and mentions, disambiguates mentions and outperforms the state-of-the-art significantly, leading to the most comprehensive corpus of 11.8 M software mentions that are described through a knowledge graph consisting of more than 300 M triples. Our analysis provides insights into the evolution of software usage and citation patterns across various fields, ranks of journals, and impact of publications. Whereas, to the best of our knowledge, this is the most comprehensive analysis of software use and citation at the time, all data and models are shared publicly to facilitate further research into scientific use and citation of software.

The Search as Learning Spaceship: Toward a Comprehensive Model of Psychological and Technological Facets of Search as Learning.

Using a Web search engine is one of today's most frequent activities. Exploratory search activities which are carried out in order to gain knowledge are conceptualized and denoted as Search as Learning (SAL). In this paper, we introduce a novel framework model which incorporates the perspective of both psychology and computer science to describe the search as learning process by reviewing recent literature. The main entities of the model are the learner who is surrounded by a specific learning context, the interface that mediates between the learner and the information environment, the information retrieval (IR) backend which manages the processes between the interface and the set of Web resources, that is, the collective Web knowledge represented in resources of different modalities. At first, we provide an overview of the current state of the art with regard to the five main entities of our model, before we outline areas of future research to improve our understanding of search as learning processes.

Microalgae wastewater treatment: Biological and technological approaches.

Current global environmental issues raise unavoidable challenges for our use of natural resources. Supplying the human population with clean water is becoming a global problem. Numerous organic and inorganic impurities in municipal, industrial, and agricultural waters, ranging from microplastics to high nutrient loads and heavy metals, endanger our nutrition and health. The development of efficient wastewater treatment technologies and circular economic approaches is thus becoming increasingly important. The biomass production of microalgae using industrial wastewater offers the possibility of recycling industrial residues to create new sources of raw materials for energy and material use. This review discusses algae-based wastewater treatment technologies with a special focus on industrial wastewater sources, the potential of non-conventional extremophilic (thermophilic, acidophilic, and psychrophilic) microalgae, and industrial algae-wastewater treatment concepts that have already been put into practice.