In this thesis, scientific inquiry is explored as a knowledge-oriented learning objective within lower and upper secondary education. In previous research about scientific inquiry, three key aspects have been emphasised. First, studies have highlighted the importance of students learning to do scientific inquiry (SI), with activities such as formulating researchable questions. Secondly, students must develop an understanding of the nature of scientific inquiry (NOSI), for example, recognising that there is no single ‘scientific method’. Thirdly, students' meaning-making of scientific inquiry is considered important, involving engagement in open inquiries and navigating the contingent pathways they may entail. This thesis focuses on exploring the consequences of treating these three aspects as explicit learning objectives within science classrooms. The theoretical framework of the thesis is grounded in a pragmatic perspective on knowledge development, emphasising knowledge development as holistic, contextual, fallibilistic, contingent, and transactional.

Moreover, characteristics of didactic practice-based research are outlined in the thesis. Didactic practice-based research is characterised by a What – the research object being directed at the relationship between student–content–teacher; a How – a symmetrical and complementary collaboration between researchers and teachers; and a Why – ensuring high ecological validity and thus relevance for educational practice.

The thesis is based on three sub-studies. In the first sub-study knowledge products generated through didactic practice-based research are explored, resulting in a typology of different forms of knowledge products valuable for teaching practice. The second sub-study focuses on scientific inquiry, identifying key factors that influence students' learning progression, including their opportunity to take epistemic agency. Furthermore, aesthetics is included in the description of how students make meaning of scientific inquiry. The third study examines a specific aspect of scientific inquiry, namely how students formulate researchable questions. Findings suggest that teaching should be designed to enable students to understand this aspect in relation to the entire inquiry process, allowing them to connect their questions to other aspects of inquiry.

The thesis highlights the importance of orienting teaching towards authentic scientific practices, which often involve navigating uncertainty and building upon scientific knowledge to explore the unknown. A central conclusion is that students engaging in open-ended investigations require teacher guidance to develop knowledge in relation to scientific inquiry as a knowledge-oriented learning objective.

A successful energy transition hinges on developing new, efficient, and sustainable catalysts. To develop such catalysts, we must improve our understanding of heterogeneous catalytic mechanisms. Real catalytic interfaces are not static systems, but are complex and highly dynamic, and to gain truly applicable information it is imperative to study catalytic interfaces under realistic conditions, while the reaction occurs. In this thesis, a range of methods – from relatively simple product quantification with mass spectrometry to state-of-the-art X-ray spectroscopic measurements of high-pressure and electrocatalytic reactions – are used to gain mechanistic information about a series of heterogeneous catalytic reactions.

First, the mechanism of water-promoted CO oxidation is studied over two supported Au-nanoparticle catalysts, Au-TiO₂ and Au-Fe₂O₃. It is observed that CO₂ production occurs over the Au-Fe₂O₃ catalyst under O₂-free conditions, which is inconsistent with previously proposed mechanisms. Guided by isotope exchange measurements and density functional theory, we propose a new Mars-van Krevelen mechanism over Au-Fe₂O₃.

Next, the Haber-Bosch reaction over Fe and Ru single crystals is investigated using high-pressure X-ray photoelectron spectroscopy (XPS). We find that all surfaces are fully metallic under reaction conditions. Additionally, we show that the rate determining step over Ru is the dissociation of adsorbed N₂. On the Fe surfaces, the rate determining step is also N₂ dissociation at high temperatures, but at lower temperatures the hydrogenation steps become rate limiting.

Using the same instrument, a method is developed for studying electrode-electrolyte interfaces using XPS and is used to probe a Cu(111) electrode surface during the CO reduction reaction. The results suggest that, firstly, the mechanism for methane formation on this surface occurs via an atomic carbon intermediate, and that the mechanism for acetate formation on Cu(111) must proceed, at least in part, via a surface-based carboxylation step.

Finally, a new high-performance catalyst for the alkaline hydrogen evolution reaction (HER) is presented, NiCo-Co₂Mo₃O₈, and the reaction mechanism is investigated using K-edge X-ray absorption spectroscopy and Kβ X-ray emission spectroscopy. The results point towards in-situ formed Mo(III) sites within the oxide phase as being the active sites for hydrogen evolution, while Co(II) sites aid H₂O adsorption.

Non-coding RNAs (ncRNAs) play essential roles in gene regulation, cellular function, and evolution. They act as key regulatory elements in diverse biological processes, such as influencing transcription, splicing, post-transcriptional regulation, and host-pathogen interactions. Despite significant advancements in ncRNA research, fundamental questions remain regarding their regulatory mechanisms and functional impact across different biological contexts. This thesis explores various aspects of ncRNA biology by integrating bulk and single-cell transcriptomics, evolutionary analyses, and infection biology to provide deeper insights into their roles in gene regulation, evolution, and diseases.

In Study I, we investigate the gene regulatory roles of Malat1, a highly expressed long non-coding RNA, in mouse embryonic stem cells. By employing knock-down and bulk RNA sequencing, we identify genes and pathways regulated by Malat1 at both the transcriptional and post-transcriptional levels, shedding light on possible roles in stem cell maintenance and differentiation.

In Study II, we explore the evolutionary landscape of microRNAs by analyzing their structural and functional features across 114 metazoan species. Using MirGeneDB 3.0, we identify conserved and lineage-specific miRNA characteristics, revealing how evolutionary pressures have shaped miRNA expression, processing efficiency, and regulatory function over millions of years.

In Study III, we evaluate the predictive power of single-cell RNA sequencing for gene regulatory network inference. By comparing RNA-based and protein-based regulatory predictions in single cells to gold standard datasets - bulk RNA-seq data for expression analysis and ChIP-seq data for direct transcription factor binding - we assess the reliability of single-cell approaches for reconstructing regulatory interactions and discuss key limitations of single-cell-based inference.

In Study IV, we examine the impact of Toxoplasma gondii infection on neuronal miRNA profiles. Through the analysis of extracellular vesicle-associated miRNAs, we identify infection-induced changes that may contribute to host-pathogen interactions and neuronal cytoskeletal remodeling.

This thesis provides a comprehensive framework for understanding ncRNA functions across multiple biological contexts, integrating perspectives from stem cell biology, gene regulation, evolutionary analysis, and infection biology. The findings contribute to fundamental questions in ncRNA research, offering insights into how non-coding RNAs shape cellular identity and disease mechanisms.

This thesis presents algorithms developed for long-read sequencing techniques, which, since their introduction in the 2010’s become increasingly important approaches in modern bioscientific research. The first two papers cover the development of algorithms for our de novo transcriptome prediction pipeline, the isON pipeline, while the third paper describes an algorithm used for biotechnological analysis of ligated fragments. Paper I introduces isONform, an algorithm capable of predicting different gene products, called isoforms, from a set of long reads sequenced from complementary DNA without the need to rely on a reference or annotation. IsONform is a tool that is part of a larger long-read transcriptome pipeline, isON pipeline, that consists of clustering and error correction steps prior to the isoform prediction. The isONform algorithm is based on the construction of a directed acyclic graph with minimizer-pairs as nodes and connecting neighboring minimizer-pairs on the reads with edges. The algorithm then employs an iterative bubble-popping scheme to merge nodes to ultimately follow all distinct paths through the graph generating the final isoform predictions. The algorithm has been shown to outperform existing state-of-the-art algorithms, while showing comparable results to approaches requiring information of a reference genome and an annotation. Paper II introduces isONclust3, an algorithm used for clustering transcriptomic reads by gene family. The algorithm constitutes the first step employed in pipelines for reference-free prediction of isoforms. The algorithm is based on the minimizer indexing scheme with its novelties being a dynamic clustering approach, assessing and storing minimizers by confidence, and an iterative post-cluster merging step. The algorithm has been shown to scale better, in terms of runtime and memory usage, on large datasets than existing methods while yielding comparable or even better results with respect to clustering quality assessments. We demonstrate that isONclust3 is the only algorithm that can process the clustering of PacBio’s new Revio datasets with tens of millions of reads using typical cluster computing resources (256Gb RAM). These algorithms help to improve the accuracy and efficiency of transcriptomic analysis based on long-read techniques, which is crucial for understanding complex biological systems and diseases. Paper III presents an algorithmic solution, cONcat, to the detection of concatenated fragments in long-read sequencing reads with typical error profiles. The algorithm is based on a greedy heuristic that employs the edit distance measure to find best-fitting fragments and divides the sequence around those points to search for fragment hits on the remaining areas of the read. The algorithm has been shown to be resilient to errors in the data and to be scalable on large numbers of reads.