BioVeL : a virtual laboratory for data analysis and modelling in biodiversity science and ecology

Background: Making forecasts about biodiversity and giving support to policy relies increasingly on large collections of data held electronically, and on substantial computational capability and capacity to analyse, model, simulate and predict using such data. However, the physically distributed nature of data resources and of expertise in advanced analytical tools creates many challenges for the modern scientist. Across the wider biological sciences, presenting such capabilities on the Internet (as "Web services") and using scientific workflow systems to compose them for particular tasks is a practical way to carry out robust "in silico" science. However, use of this approach in biodiversity science and ecology has thus far been quite limited. Results: BioVeL is a virtual laboratory for data analysis and modelling in biodiversity science and ecology, freely accessible via the Internet. BioVeL includes functions for accessing and analysing data through curated Web services; for performing complex in silico analysis through exposure of R programs, workflows, and batch processing functions; for on- line collaboration through sharing of workflows and workflow runs; for experiment documentation through reproducibility and repeatability; and for computational support via seamless connections to supporting computing infrastructures. We developed and improved more than 60 Web services with significant potential in many different kinds of data analysis and modelling tasks. We composed reusable workflows using these Web services, also incorporating R programs. Deploying these tools into an easy-to-use and accessible 'virtual laboratory', free via the Internet, we applied the workflows in several diverse case studies. We opened the virtual laboratory for public use and through a programme of external engagement we actively encouraged scientists and third party application and tool developers to try out the services and contribute to the activity. Conclusions: Our work shows we can deliver an operational, scalable and flexible Internet-based virtual laboratory to meet new demands for data processing and analysis in biodiversity science and ecology. In particular, we have successfully integrated existing and popular tools and practices from different scientific disciplines to be used in biodiversity and ecological research.Peer reviewe

BC-PREDICT Technical Report final.docx

This report describes the software implementation for the BC-PREDICT project. It is designed to be read in conjunction with the source code of the PROCAS2 Collator software published on the University of Manchester’s FigShare site. It is a record of the software and procedures created by Research IT and used in the BC-PREDICT project. It also records how the software interacts with other 3rd party supplied software. The report also highlights areas that were originally required but not used, and any improvements that could be made in future development of this software

XPS and HAXPES of compressed MXene aerogels

X-ray photoelectron spectroscopy and Hard X-Ray Photoelectron Spectroscopy are used to derive a semi-quantitative estimate of Ti3C2Tz MXene terminating group stoichiometry and sample purity.For details of the experiment and uploaded data, please see the attached .pdf

Landmap Datasets

The Joint Information Systems Committee (JISC) funded Landmap service which ran from 2001 to July 2014 collected, modified and hosted a large amount of earth observation data for the majority of the UK, including imagery from ERS satellites, ENVISAT and ALOS, high-resolution Digital Elevation Models (DEMs) and Digital Terrain Models (DTMs) and aerial photography dating back to 1930. After removal of JISC funding in 2013, the Landmap service is no longer operational, with the data now held at the NEODC. Aside from the thermal imagery data which stands alone, the data reside in four collections: optical, elevation, radar and feature. The datasets contained in each are as follows: Optical: Advanced Visible and Near InfraRed Radiometer type 2 (AVNIR-2), Colour InfraRed (CIR), Disaster Monitoring Constellation (DMC), Landsat 4/5, Landsat 7, Near InfraRed (NIR), SPOT (Earth-Observing Satellites) and Topsat data, along with Mediterranean Landsat imagery and Modern and Historical Aerial Photography. Elevation: A 5m resolution DTM for England and Wales (Bluesky) and Scotland (GetMapping), a 2m resolution Digital Surface Model (DSM, equivalent to a DEM) for Wales and Scotland and 1m resolution LiDAR-derived DTMs and DSMs for cities of England and Scotland. Radar: ERS satellites 1&2 radar data, ENVISAT Advanced Synthetic Aperture Radar (ASAR) imagery and ALOS Phased Array type L-band Synthetic Aperture Radar (PALSAR) imagery, of the UK. Features: Building heights and building classes for the main urban conurbations of the UK and Kinematic GPS (KGPS) data for over 6,400km of UK roads

AFM of dropcast MXene nanoparticles - JPK Nanowizard

The JPK Nanowizard AFM is used to obtain average Ti3C2Tz nanoparticle height to deduce the layer thickness of the synthesis products. For a full description of the uploaded data and experimental methods, please see the attached .pdf

An Interactive, Virtual Wind Tunnel using Virtual Reality and Unreal Engine 4

This article presents an integrated, interactive modelling and simulation tool for aerodynamic design and analysis. The development and coupling of a GPU-accelerated Computational Fluid Dynamics (CFD) library, a 3D scanning library and a virtual-reality-powered video game are presented and the resulting virtual wind tunnel described. Users of the tunnel may interact with both geometry and solver from within the virtual environment providing a truly unique tool for performing high-level aerodynamic analysis around imported and scanned objects. Here we introduce the relevant technologies and approaches used to build the components and discuss the technical challenges of integrating them. The flow solver is validated against experimental data for a representative turbulent flow and demonstrates excellent agreement with available data. We also present the peak performance of the solver on current hardware. Limitations and potential expansions for this proof of concept are discussed as well as applications in a range of other scientific fields

Motion, fixation probability and the choice of an evolutionary process

One folder contains the code with which all simulations can be reproduced. The second folder contains all the simulation results necessary for the reproduction of the figures in the research article of the same title

BC-PREDICT Collator source code

This is the source code for the BC-PREDICT (PROCAS-2) Collator project, as described in the Technical Repor

“Not everyone can use Git”: Research Software Engineers’ recommendations for scientist-centred software support (and what researchers really think of them)

Research Software Engineers (RSEs) are at the coalface of ensuring that computational science is accurate, reliable and reproducible, and their views on making progress in this domain are therefore particularly valuable. This is not always recognized by the scientific community, however: work examining the challenge of developing research software tends to focus on the views of academics, and RSEs’ voices are rarely found in the evidence base. We report the results of a project that brings together researchers and RSEs, to determine how to help scientists publish their code. We interview scientists to identify challenges, and we interview RSEs to determine how to overcome them. We formalize the results into a set of recommendations, and evaluate them in a survey completed by 65 computational scientists. The survey shows that improving tool GUIs, linking internal repositories to external ones, and training in version control would all aid scientists in publishing reliable code. It also demonstrates that RSEs' views are valued by researchers: every recommendation received strong support. We conclude that RSEs can play a crucial role in scientific software policy, and their expertise should be officially recognized. Paper available at: https://www.research.manchester.ac.uk/portal/en/publications/not-everyone-can-use-git-research-software-engineers-recommendations-for-scientistcentred-software-support-and-what-researchers-really-think-of-them(669fd8be-f87e-479f-8f8d-da9e1af4f26c).htm

Structural plasticity on the SpiNNaker many-core neuromorphic system

Directories: > input_shape_generation: [code] a Jupyter notebook exemplifying the use of functions which generate a few different input shapes (Gaussian, Pointy, Square). The actual functions used when simulating are in the "simulation_run" folder. >results:[data + code] numpy .npz archives with results for various experiments and presented in the eponymous paper. Folder names containing "vs." show results from performing sensitivity analysis. Additionally, each one contains a folder with a long string of alphanumerical characters as a name; within are contained the raw results of the simulations (connectivity, all of the spiking activity, parameters used). Moreover, a processed data set is available. Files with the prefix "batch" contains information about the entire respective batch run, while "batch_analysis" contains all of the results for each respective batch run. Finally, each folder contains a Jupyter notebook revealing the structure of each result archive, how the figures were plotted etc. >simulation_analysis:[code] Python scripts used to analyse raw data, given in individual files or as a batch. For more details type: ``python simulation_statistics.py --help `` >simulation_run:[code] Python scripts used to simulate the network with a variety of parameters. For more details type: ``python topographic_map_formation.py --help `