Effective societal responses to rapid climate change in the Arctic rely on an accurate representation of region-specific ecosystem properties and processes. However, this is limited by the scarcity and patchy distribution of field measurements. Here, we use a comprehensive, geo-referenced database of primary field measurements in 1,840 published studies across the Arctic to identify statistically significant spatial biases in field sampling and study citation across this globally important region. We find that 31% of all study citations are derived from sites located within 50 km of just two research sites: Toolik Lake in the USA and Abisko in Sweden. Furthermore, relatively colder, more rapidly warming and sparsely vegetated sites are under-sampled and under-recognized in terms of citations, particularly among microbiology-related studies. The poorly sampled and cited areas, mainly in the Canadian high-Arctic archipelago and the Arctic coastline of Russia, constitute a large fraction of the Arctic ice-free land area. Our results suggest that the current pattern of sampling and citation may bias the scientific consensuses that underpin attempts to accurately predict and effectively mitigate climate change in the region. Further work is required to increase both the quality and quantity of sampling, and incorporate existing literature from poorly cited areas to generate a more representative picture of Arctic climate change and its environmental impacts.

An optimal network design was carried out to prioritise the installation or refurbishment of greenhouse gas (GHG) monitoring stations around Africa. The network was optimised to reduce the uncertainty in emissions across three of the most important GHGs: CO 2 , CH 4 , and N 2 O. Optimal networks were derived using incremental optimisation of the percentage uncertainty reduction achieved by a Gaussian Bayesian atmospheric inversion. The solution for CO 2 was driven by seasonality in net primary productivity. The solution for N 2 O was driven by activity in a small number of soil flux hotspots. The optimal solution for CH 4 was consistent over different seasons. All solutions for CO 2 and N 2 O placed sites in central Africa at places such as Kisangani, Kinshasa and Bunia (Democratic Republic of Congo), Dundo and Lubango (Angola), Zo et el e (Cameroon), Am Timan (Chad), and En Nahud (Sudan). Many of these sites appeared in the CH 4 solutions, but with a few sites in southern Africa as well, such as Amersfoort (South Africa). The multi-species optimal network design solutions tended to have sites more evenly spread-out, but concentrated the placement of new tall-tower stations in Africa between 10 N and 25 S. The uncertainty reduction achieved by the multi-species network of twelve stations reached 47.8% for CO 2 , 34.3% for CH 4 , and 32.5% for N 2 O. The gains in uncertainty reduction diminished as stations were added to the solution, with an expected maximum of less than 60%. A reduction in the absolute uncertainty in African GHG emissions requires these additional measurement stations, as well as additional constraint from an integrated GHG observatory and a reduction in uncertainty in the prior biogenic fluxes in tropical Africa.

Geocoding longitudinal and individual-level historical demographic databases enables novel analyses of how micro-level geographic factors affected demographic outcomes over long periods. However, such detailed geocoding involves high costs. Additionally, the high spatial resolution cannot be properly utilized if inappropriate methods are used to quantify the geographic factors. We assess how different geocoding levels and methods used to define geographic variables affects the outcome of detailed spatial and historical demographic analyses. Using a longitudinal and individual-level demographic database geocoded at the property unit level, we analyse the effects of population density and proximity to wetlands on all-cause mortality for individuals who lived in five Swedish parishes, 1850-1914. We compare the results from analyses on three detailed geocoding levels using two common quantification methods for each geographic variable. Together with the method selected for quantifying the geographic factors, even small differences in positional accuracy (20-50 m) between the property units and slightly coarser geographic levels heavily affected the results of the demographic analyses. The results also show the importance of accounting for geographic changes over time. Finally, proximity to wetlands and population density affected the mortality of women and children, respectively. However, all possible determinants of mortality were not evaluated in the analyses. In conclusion, for rural historical areas, geocoding to property units is likely necessary for fine-scale analyses at distances within a few hundred metres. We must also carefully consider

Abstract. In order to meet the increasing needs of housing and services in urban areas, cities are densified. When densifying a city, it is important to provide good living conditions while maintaining a low environmental impact. To ensure this, the urban planning process should include simulations of e.g. noise and daylight conditions. In this paper we describe a newly started projected directed towards the need for quality-assured and harmonised input data to the simulations, in the form of 3D city models. The first part of the paper includes the background and research questions of the project and in the second part a tool for daylight simulations on neighbourhood level is introduced, a tool that will be utilized for evaluating the 3D city model design.

<p>Computational notebooks (e.g. Jupyter notebook) are a popular choice for interactive scientific computing to convey descriptive information together with executable source code. The user can annotate the scientific development of the work, the methods applied, describe ancillary data or the analysis of results, with text, illustrations, figures, and equations. Such ‘executable’ documents provide a paradigm shift in scientific writing, where not only the science is described, but the actual computation and source code are openly available and can be reproduced and validated.</p> <p>Therefore, it is of paramount importance to preserve these documents. A unique and persistent identification (PID) is essential together with providing enough information to execute the source code. Generating a PID for a Jupyter notebook is not technically challenging. We can automatically collect system and run-time information and, with a guided workflow for the user, assemble a rich set of metadata. The collected information allows us to recreate the computational environment and run the source code, which in return (theoretically) should produce the same results as published.</p> <p>The importance of providing a rich set of metadata for all digital objects in a human readable and machine actionable form is well understood and widely accepted as necessity for reproducibility, traceability, and provenance. This is reflected in the FAIR principles (Wilkinson, https://doi.org/10.1038/sdata.2016.18) which are regarded as gold standard by many scientific communities.</p> <p>Pimentel et al. (https://doi.org/10.1109/MSR.2019.00077) analysed over 800’000 Jupyter notebooks from GitHub. 24 % executed without errors and only 4 % produced the same results. The likelihood to successfully compile and run a decade old source code is slim. Long term support for well established operating systems varies between 5 to 10 years, user software support is usually shorter and looking at free and open-source repositories there is often no support (or best effort) offered.</p> <p>We present an approach to safely reproduce the computational environment in the future with a focus on long-term availability. Instead of trying to reinstall the computational environment based on the stored metadata, we propose to archive the docker image, the user space (user installed packages) and finally the source code. Recreating the system in this way is more like restoring a backup, where backup is the equivalent of an entire computer system. It does not solve all the problems but removes a great deal of complexity and uncertainty.<br /><br />Though there are shortcomings in our approach, we believe our solution will lower the threshold for scientists to provide rich meta data, code and results attached to a publication that can be reproduced in the far future.</p>