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1. Big Code

   https://doi.org/10.1111/gean.12330  

   Rey, Sergio J. (June 2022, Geographical Analysis)

   Big data, the “new oil” of the modern data science era, has attracted much attention in the GIScience community. However, we have ignored the role of code in enabling the big data revolution in this modern gold rush. Instead, what attention code has received has focused on computational efficiency and scalability issues. In contrast, we have missed the opportunities that the more transformative aspects of code afford as ways to organize our science. These “big code” practices hold the potential for addressing some ill effects of big data that have been rightly criticized, such as algorithmic bias, lack of representation, gatekeeping, and issues of power imbalances in our communities. In this article, I consider areas where lessons from the open source community can help us evolve a more inclusive, generative, and expansive GIScience. These concern best practices for codes of conduct, data pipelines and reproducibility, refactoring our attribution and reward systems, and a reinvention of our pedagogy. 

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2. The redox code of plants

   https://doi.org/10.1111/pce.14787  

   Mittler, Ron; Jones, Dean P. (December 2023, Plant, Cell & Environment)

   Abstract Central metabolism is organised through high‐flux, Nicotinamide Adenine Dinucleotide (NAD⁺/NADH) and NADP⁺/NADPH systems operating at near equilibrium. As oxygen is indispensable for aerobic organisms, these systems are also linked to the levels of reactive oxygen species, such as H₂O₂, and through H₂O₂to the regulation of macromolecular structures and activities, via kinetically controlled sulphur switches in the redox proteome. Dynamic changes in H₂O₂production, scavenging and transport, associated with development, growth and responses to the environment are, therefore, linked to the redox state of the cell and regulate cellular function. These basic principles form the ‘redox code’ of cells and were first defined by D. P. Jones and H. Sies in 2015. Here, we apply these principles to plants in which recent studies have shown that they can also explain cell‐to‐cell and even plant‐to‐plant signalling processes. The redox code is, therefore, an integral part of biological systems and can be used to explain multiple processes in plants at the subcellular, cellular, tissue, whole organism and perhaps even community and ecosystem levels. As the environmental conditions on our planet are worsening due to global warming, climate change and increased pollution levels, new studies are needed applying the redox code of plants to these changes. 

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3. Community Code Engagements: Summer of Code & Hackathons for Community Building in Scientific Software

   https://doi.org/10.1145/2660398.2660420  

   Trainer, Erik H.; Chaihirunkarn, Chalalai; Kalyanasundaram, Arun; Herbsleb, James D. (January 2014, International Conference on Supporting Group Work)

   Community code engagements -- short-term, intensive software development events -- are used by some scientific communities to create new software features and promote community building. But there is as yet little empirical support for their effectiveness. This paper presents a qualitative study of two types of community code engagements: Google Summer of Code (GSoC) and hackathons. We investigated the range of outcomes these engagements produce and the underlying practices that lead to these outcomes. In GSoC, the vision and experience of core members of the community influence project selection, and the intensive mentoring process facilitates creation of strong ties. Most GSoC projects result in stable features. The agenda setting phase of hackathons reveals high priority issues perceived by the community. Social events among the relatively large numbers of participants over brief engagements tend to create weak ties. Most hackathons result in prototypes rather than finished tools. We discuss themes and tradeoffs that suggest directions for future empirical work around designing community code engagements. 

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4. CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning

   https://doi.org/10.1145/3308558.3313632  

   Yao, Ziyu; Peddamail, Jayavardhan Reddy; Sun, Huan (January 2019, The World Wide Web Conference)

   To accelerate software development, much research has been performed to help people understand and reuse the huge amount of available code resources. Two important tasks have been widely studied: code retrieval, which aims to retrieve code snippets relevant to a given natural language query from a code base, and code annotation, where the goal is to annotate a code snippet with a natural language description. Despite their advancement in recent years, the two tasks are mostly explored separately. In this work, we investigate a novel perspective of Code annotation for Code retrieval (hence called “CoaCor”), where a code annotation model is trained to generate a natural language annotation that can represent the semantic meaning of a given code snippet and can be leveraged by a code retrieval model to better distinguish relevant code snippets from others. To this end, we propose an effective framework based on reinforcement learning, which explicitly encourages the code annotation model to generate annotations that can be used for the retrieval task. Through extensive experiments, we show that code annotations generated by our framework are much more detailed and more useful for code retrieval, and they can further improve the performance of existing code retrieval models significantly. 

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5. LLM-Assisted Code Cleaning For Training Accurate Code Generators.

   Jain, Naman; Zhang, Tianjun; Chiang, Wei-Lin; Gonzalez, Joseph E; Sen, Koushik; Stoica, Ion (October 2023, arXiv)