Planning applications for temporary development in England's core cities 2000-15 (5,890 cases)

Planning applications data, derived from each core city local authority, provided a record of all applied for development activity over a prolonged period of time. In England, all local authorities have a legal duty to make available certain details relating to planning applications (as a public register) on the internet (PARSOL, 2006). A wide range of information and documentation are made available across a range of data fields. Eight data fields were extracted for our analysis, these included, ‘application number’, ‘status’, ‘application address’, ‘postcode’, ‘development description’, ‘date received’, ‘decision’ as well as ‘appeal decision’ (see PARSOL, 2006: 21). Habitually some data fields were empty requiring a more thorough analysis of the supplied documentation to obtain missing information. Similar to applications for traditional development, applications for almost all forms of temporary use are subject to an application for planning permission. Seven key terms/concepts associated with temporary urbanism were employed to search for and extract applications for temporary development within each core city, these included, ‘temporary’, ‘temporary use’, ‘period of’, ‘use of land’, ‘short term/short-term’, ‘interim’ and ‘meanwhile’. The systematic collection and collation of planning applications data resulted in an end dataset of 5,890 applications for temporary use across the eight core cities over the fifteen-year period of 2000-15. The 5,890 cases were then coded across a range of structural variables associated with the discourse on temporary use to amass city datasets capable of looking in depth at the characteristics of temporary development in the core cities

Spatial distribution of temporary development in Bristol and Liverpool, 2000-15 (1,261 cases)

Reviews addressing how temporary uses have been mobilised over time within specific conurbations or sets of conurbations have remained a rarity (bar extended research on Berlin). A number of studies have highlighted the limited use of spatial data to inform decision-making about contemporary urban issues. With temporary use increasingly visible as a regeneration technique in England, there is a need to study its related spatial properties, as with any other form of land-use. Maps, being graphic representations of various aspects of reality, are indispensable to the effort of understanding and visualising the existing as well as the future urban environment. The purpose of this dataset was to create a spatial account of temporary development in British cities. Two cities were selected for this analysis, Bristol and Liverpool

Open4Citizens Hack1 Evaluation Template

PDF'ed version of the PowerPoint template used by all 5 Open4Citizens project pilots to gather evaluation material from their project cycle 1 hackathon in 2016

Assessing daily GRACE Data Assimilation during flood events of the Brahmaputra River Basin: OL and DA outputs

This dataset contains the result of 36 daily and monthly GRACE Terrestrial Water Storage (TWS) Data Assimilation (DA) experiments performed in the Brahmaputra basin, with the purpose of assessing the potential of daily GRACE DA into water balance models for detecting fast evolving water storage anomalies. In these experiments, monthly and daily GRACE(-FO) TWS data derived from the GRACE level 2 (L2) ITSG-2018 product [1,2] were assimilated into the World Wide Water Resources Assessment model [3,4]. The sequential Data Assimilation (DA) was performed through a classical EnKF as well as an EnKS [5] that allows to smoothly disaggregate monthly TWS increments into the daily model timesteps. Different covariance localization degrees were also considered, ranging from no-localization to a 70 km half-radius localization. The experiments were performed for the years 2004, 2007 and 2008, for which the Brahmaputra River Basin was affected by major flooding events. The considered daily and monthly DA approaches were assessed in terms of their impact on the TWS signal as well as their performance on the vertical and horizontal TWS disaggregation. More details about the processing and the main resulting findings can be found in [6]. Citation Please, when using this dataset, remember to cite: L. Retegui-Schiettekatte, M. Schumacher, H. Madsen, and E. Forootan, “Assessing daily GRACE Data Assimilation during flood events of the Brahmaputra River Basin,” Science of The Total Environment, vol. 975, p. 179181, May 2025, doi: 10.1016/j.scitotenv.2025.179181. References [1] T. Mayer-Gürr et al., “ITSG-Grace2018 - Monthly, Daily and Static Gravity Field Solutions from GRACE.” GFZ Data Services, 2018. doi: 10.5880/ICGEM.2018.003. [2] A. Kvas et al., “ITSG-Grace2018: Overview and Evaluation of a New GRACE-Only Gravity Field Time Series,” Journal of Geophysical Research: Solid Earth, vol. 124, no. 8, pp. 9332–9344, 2019, doi: 10.1029/2019JB017415. [3] A. van Dijk, “The Australian Water Resources Assessment System. Technical Report 3. Landscape Model (version 0.5) Technical Description. CSIRO: Water for a Healthy Country National Research Flagship.,” Jan. 2010. [4] A. I. J. M. van Dijk, J. L. Peña-Arancibia, E. F. Wood, J. Sheffield, and H. E. Beck, “Global analysis of seasonal streamflow predictability using an ensemble prediction system and observations from 6192 small catchments worldwide,” Water Resources Research, vol. 49, no. 5, pp. 2729–2746, 2013, doi: 10.1002/wrcr.20251. [5] B. F. Zaitchik, M. Rodell, and R. H. Reichle, “Assimilation of GRACE Terrestrial Water Storage Data into a Land Surface Model: Results for the Mississippi River Basin,” Journal of Hydrometeorology, vol. 9, no. 3, pp. 535–548, Jun. 2008, doi: 10.1175/2007JHM951.1. [6] L. Retegui-Schiettekatte, M. Schumacher, H. Madsen, and E. Forootan, “Assessing daily GRACE Data Assimilation during flood events of the Brahmaputra River Basin,” Science of The Total Environment, vol. 975, p. 179181, May 2025, doi: 10.1016/j.scitotenv.2025.179181

tcherry: Learning the structure of tcherry trees

Learning the structure of the type of graphical models called t-cherry trees from data. Determines the structure either directly from data or by increasing the order of a t-cherry tree with lower order

How Scrum Adds Value to Achieving Software Quality?

39 interviews with Scrum practitioners on the topic of how Scrum as a method contribute to achieving software quality. Data analysis documentation also available with the dataset. Two validation focus groups transcript also available with the data

On Loss Functions for Supervised Monaural Time-Domain Speech Enhancement

Audio samples related to the experiments conducted in the paper M. Kolbæk et al. “On Loss Functions for Supervised Monaural Time-Domain Speech Enhancement” 2019 (https://arxiv.org/abs/1909.01019

Temporary use morphologies in England's core cities 2000-20 (655 cases)

This dataset is used to add to our understanding of intra-city patterning of temporary development as a complement to intensive historical and narrative-based approaches to temporary use (Martin et al., 2020). As emphasised by Martin et al. (2020: 17) "further research is needed to identify locations where temporary uses are more or less likely to occur based on certain underlying characteristics". The novel data set of 422 temporary use interventions recorded between 2000-15 examines the morphology of temporary development in eight British cities: Birmingham, Bristol, Leeds, Liverpool, Newcastle, Nottingham, Manchester and Sheffield. Reference: Martin, M., Hincks, S. and Deas, I. (2020) Temporary use in England's core cities: Looking beyond the exceptional, Urban Studies, Online First, https://journals.sagepub.com/doi/10.1177/0042098019898076

Algorithms for Reconstruction of Undersampled Atomic Force Microscopy Images Supplementary Material

Two Jupyter Notebooks showcasing reconstructions of undersampled atomic force microscopy images. The reconstructions were obtained using a variety of interpolation and reconstruction methods.Two Jupyter Notebooks showcasing reconstructions of undersampled atomic force microscopy images. The reconstructions were obtained using a variety of interpolation and reconstruction methods

MABRAVO: Multicast Algorithm for Broadcast and Routing over AoIs in Voronoi Overlays

Simulator for the MABRAVO routing algorithms for overlay