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ABSTRACTThis perspective piece focuses on attempts to reshape research in Aotearoa New Zealand in ways that honour Te Tiriti o Waitangi, the country's founding treaty between Māori and the Crown. These obligations establish dimensions of responsible research in this context that go beyond European-devised Responsible Innovation (RI) frameworks. We reflect on these requirements and how they are addressed within two public research organisations. Considering growing tensions between attempts to decolonise the Aotearoa science system and systemic challenges to enacting responsible research, we explore initiatives that seek to translate Te Tiriti obligations into research practices. While not articulated as RI, we discuss commonalities between these approaches and tenets of RI. Our reflections highlight the centrality of meaningful partnerships for enabling responsible research and the institutional structures required to support such relationships within Aotearoa. The paper concludes with questions around if, and how, the maturing RI scholarship can support decolonising research practices.

ABSTRACTThis Perspective considers the potential value and limitations of introducing Responsible Innovation approaches from the Global North into Australia and Aotearoa New Zealand. We reflect on the relationship of predominantly Western European understandings of responsibility and endogenous ‘Antipodean’ forms of responsibility, including Indigenous relationalities, collective stewardship, and care that extends to more-than-human worlds. Commencing a dialogue between these different modes of responsibility, we outline incommensurabilities, complementarities, and tensions within two core dimensions of Responsible Innovation, namely anticipation and inclusion. To realise benefits from introducing Responsible Innovation and avoid neo-colonial enforcements of adventitious understandings of responsibility, ‘Antipodean’ practitioners need to understand their multidimensional cultural contexts with the aim of collectively reconfiguring responsible practices. Pursuing intercultural dialogue with ‘Antipodean’ modes of responsibility can, in turn, help to responsibly innovate Responsible Innovation outside of the Global North and strengthen the concept’s foundational ethos of collective stewardship by making it more inclusive and multifaceted.

Agricultural digitalization is providing growing amounts of real-time digital data. Biophysical simulation models can help interpret these data. However, these models are subject to complex uncertainties, which has prompted calls for interdisciplinary research to better understand and communicate modelling uncertainties and their impact on decision-making. This article develops two corresponding insights from an interdisciplinary project in a New Zealand agricultural research organization. First, we expand on a recent Royal Society Open Science journal article (van der Bles et al. 2019 Royal Society Open Science 6, 181870 (doi:10.1098/rsos.181870)) and suggest a threefold conceptual framework to describe direct, indirect and contextual uncertainties associated with biophysical models. Second, we reflect on the process of developing this framework to highlight challenges to successful collaboration and the importance of a deeper engagement with interdisciplinarity. This includes resolving often unequal disciplinary standings and the need for early collaborative problem framing. We propose that both insights are complementary and informative to researchers and practitioners in the field of modelling uncertainty as well as to those interested in interdisciplinary environmental research generally. The article concludes by outlining limitations of interdisciplinary research and a shift towards transdisciplinarity that also includes non-scientists. Such a shift is crucial to holistically address uncertainties associated with biophysical modelling and to realize the full potential of agricultural digitalization.

Synthetic herbicides have revolutionised agricultural weed control. Herbicide resistance (HR) is a natural process through which weeds evolve to be no longer susceptible to a herbicide. Repeated use of similar herbicides can lead to the proliferation of resistant weed populations, with detrimental on-farm effects. To date, 267 weed species worldwide are resistant to at least one herbicide. Yet, achieving universal uptake of best practice principles to manage HR remains difficult. Historically not a high priority for New Zealand cropping farmers, resistance may be more prevalent than commonly assumed. This article contributes to emerging national management strategies and the international scholarship on the human dimensions of HR. Regarding resistance as a socio-biological challenge, we draw on qualitative social research with agricultural stakeholders in New Zealand's main cropping region to outline important psychosocial preconditions for effective resistance management. Our findings show that these preconditions include: influencing awareness and attitudes, knowledge and skills; approaching HR as a shared responsibility; and supporting long-term and holistic thinking. We conclude that these preconditions form the social foundations for agricultural stakeholders' capacity to enact best practice principles to continuously re-solve HR. This novel framing allows analytical differentiation between the capacity and ability to act, with practical recommendations and future research needing to address both components of effective HR management., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Espig, Henwood. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Agricultural digitalization is providing growing amounts of real-time digital data. Biophysical simulation models can help interpret these data. However, these models are subject to complex uncertainties, which has prompted calls for interdisciplinary research to better understand and communicate modelling uncertainties and their impact on decision-making. This article develops two corresponding insights from an interdisciplinary project in a New Zealand agricultural research organization. First, we expand on a recent Royal Society Open Science journal article (van der Bles et al . 2019 Royal Society Open Science 6 , 181870 (doi:10.1098/rsos.181870)) and suggest a threefold conceptual framework to describe direct, indirect and contextual uncertainties associated with biophysical models. Second, we reflect on the process of developing this framework to highlight challenges to successful collaboration and the importance of a deeper engagement with interdisciplinarity. This includes resolving often unequal disciplinary standings and the need for early collaborative problem framing. We propose that both insights are complementary and informative to researchers and practitioners in the field of modelling uncertainty as well as to those interested in interdisciplinary environmental research generally. The article concludes by outlining limitations of interdisciplinary research and a shift towards transdisciplinarity that also includes non-scientists. Such a shift is crucial to holistically address uncertainties associated with biophysical modelling and to realize the full potential of agricultural digitalization., Competing Interests: We declare we have no competing interests., (© 2020 The Authors.)

In this proof-of-principle study, we apply tensor decomposition techniques to the Full Configuration Interaction (FCI) wavefunction in order to approximate the wavefunction parameters efficiently and to reduce the overall computational effort. For this purpose, the wavefunction ansatz is formulated in an occupation number vector representation that ensures antisymmetry. If the canonical product format tensor decomposition is then applied, the Hamiltonian and the wavefunction can be cast into a multilinear product form. As a consequence, the number of wavefunction parameters does not scale to the power of the number of particles (or orbitals) but depends on the rank of the approximation and linearly on the number of particles. The degree of approximation can be controlled by a single threshold for the rank reduction procedure required in the algorithm. We demonstrate that using this approximation, the FCI Hamiltonian matrix can be stored with N(5) scaling. The error of the approximation that is introduced is below Millihartree for a threshold of ϵ = 10(-4) and no convergence problems are observed solving the FCI equations iteratively in the new format. While promising conceptually, all effort of the algorithm is shifted to the required rank reduction procedure after the contraction of the Hamiltonian with the coefficient tensor. At the current state, this crucial step is the bottleneck of our approach and even for an optimistic estimate, the algorithm scales beyond N(10) and future work has to be directed towards reduction-free algorithms.

A new approximation for post-Hartree-Fock (HF) methods is presented applying tensor decomposition techniques in the canonical product tensor format. In this ansatz, multidimensional tensors like integrals or wavefunction parameters are processed as an expansion in one-dimensional representing vectors. This approach has the potential to decrease the computational effort and the storage requirements of conventional algorithms drastically while allowing for rigorous truncation and error estimation. For post-HF ab initio methods, for example, storage is reduced to O(d·R·n) with d being the number of dimensions of the full tensor, R being the expansion length (rank) of the tensor decomposition, and n being the number of entries in each dimension (i.e., the orbital index). If all tensors are expressed in the canonical format, the computational effort for any subsequent tensor contraction can be reduced to O(R(2)·n). We discuss details of the implementation, especially the decomposition of the two-electron integrals, the AO-MO transformation, the Møller-Plesset perturbation theory (MP2) energy expression and the perspective for coupled cluster methods. An algorithm for rank reduction is presented that parallelizes trivially. For a set of representative examples, the scaling of the decomposition rank with system and basis set size is found to be O(N(1.8)) for the AO integrals, O(N(1.4)) for the MO integrals, and O(N(1.2)) for the MP2 t(2)-amplitudes (N denotes a measure of system size) if the upper bound of the error in the l(2)-norm is chosen as ε = 10(-2). This leads to an error in the MP2 energy in the order of mHartree.

Biomolecule attachment and lateral micropatterning of biomolecular layers are essential techniques to provide in advanced biochemical and cell culture assays. For that purpose, we introduced a versatile, simple and robust method to functionalise standard polystyrene well plates. Free amino groups were generated on the polystyrene surface by low pressure ammonia plasma treatment. Subsequently, thin films of different maleic anhydride copolymers were covalently attached to the surfaces. The distinct physicochemical properties of the coupled maleic anhydride copolymers provided a broad range of possible attachment schemes of proteins and polysaccharides. Micrometer-sized lateral patterns of these functional coatings were created by plasma etching through silicon masks and subsequent chemical conversion of the etched areas using poly(ethylene glycol). The approach facilitates a wide variety of cell culture experiments allowing a combination of biomolecule coupling and micropatterning within the multi-well plate technology., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

Tensor product decompositions with optimal separation rank provide an interesting alternative to traditional Gaussian-type basis functions in electronic structure calculations. We discuss various applications for a new compression algorithm, based on the Newton method, which provides for a given tensor the optimal tensor product or so-called best separable approximation for fixed Kronecker rank. In combination with a stable quadrature scheme for the Coulomb interaction, tensor product formats enable an efficient evaluation of Coulomb integrals. This is demonstrated by means of best separable approximations for the electron density and Hartree potential of small molecules, where individual components of the tensor product can be efficiently represented in a wavelet basis. We present a fairly detailed numerical analysis, which provides the basis for further improvements of this novel approach. Our results suggest a broad range of applications within density fitting schemes, which have been recently successfully applied in quantum chemistry.