cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5285/27a89a5f-ca5d-471a-a0e7-060d0911b2d1
title: Solutions to BAS-PRO model runs for Modelling 1-10MeV Proton Phase Space Density

description: This dataset contains solution data produced by the BAS-PRO proton radiation belt model for the study "Modelling Inner Proton Belt Variability at Energies 1 to 10MeV using BAS-PRO". The solution data is in the form of 3D grids describing phase space density computed during dynamic simulations of Earth's proton belt over the modelling period 2014 to 2018. Three model runs are included: SA19, J81 and S16. Files were produced in May 2021. This work was funded by the Natural Environment Research Council (NERC) via Doctoral Training Programme NE/R009457/1. Richard B. Horne and Sarah A. Glauert were supported by NERC National Capability grants NE/R016038/1 and NE/R016445/1, and by NERC grant NE/V00249X/1 (Sat-Risk), and by Highlight Topic Grant NE/P01738X/1 (Rad-Sat). Giulio Del Zanna acknowledges support from STFC (UK) via the consolidated grants to the atomic astrophysics group (AAG) at DAMTP, University of Cambridge (ST/P000665/1 and ST/T000481/1). Seth G. Claudepierre acknowledges support from NASA Grant no. NNX17AF10G and from RBSP-ECT funding provided by JHU/APL Contract 967399 under NASA's Prime Contract NAS5-01072.
description: Each solution directory contains two sets of grids describing phase space density multiplied by the proton rest mass cubed (units km-6 s3). One set of grids (dynamic output, files ending in 'dyn.txt') describes phase space density at regular epochs (listed in the axis_t_dyn.txt file) throughout the simulation period for energies 1-19MeV. The other set of grids (final output, files not ending in 'dyn.txt') describes phase space density at the final epoch of the simulation across the entire model grid.
description: The outer boundary is driven by fits to equatorial pitch angle distributions derived from the RBSPICE ISRHELT, MagEIS pix2 and REPT instruments, and is subject to some uncertainty. The phase space density of protons with dipole mirror points at 585km altitude or below is assumed to be zero. The phase space density at L &lt; 1.15 is assumed to be zero.
DOI: 10.6084/m9.figshare.16707244.v3
title: Harpy Eagle climate change SDMs

description: Data to support publication DOI: <br>GBIF Occurrence Download. https://doi.org/10.15468/dl.6ikhnj
DOI: 10.6084/m9.figshare.16708225
title: poi data

description: 一种基于加权二次曲面支持向量机的中文兴趣点分类新方法
DOI: 10.6084/m9.figshare.16708225.v2
title: poi data

description: 一种基于加权二次曲面支持向量机的中文兴趣点分类新方法
DOI: 10.6084/m9.figshare.16708225.v1
title: poi data

description: 一种基于加权二次曲面支持向量机的中文兴趣点分类新方法
DOI: 10.5281/zenodo.5541271
title: SeisSol input files of the dynamic rupture scenarios of the 2004 Sumatra-Andaman earthquake published in Ulrich et al. (2021)

description: This dataset contains the input files of the dynamic rupture scenarios of the 2004 Sumatra-Andaman earthquake presented in: Ulrich, T., Gabriel, A. A., Madden, E. H. (2021). Stress, rigidity and sediment strength control megathrust earthquake and tsunami dynamics. doi: 10.31223/osf.io/s9263.<br> <strong>supermucNG_launch_script.sh</strong>: batch script for running a dynamic rupture earthquake scenario on Supermuc NG (LRZ).<br> <br> <strong>parameters_base_slab2.par, parameters_stronger_slab2.par, parameters_weaker_slab2.par</strong>: main parameter file for the base (resp. stronger, resp. weaker sediments) scenario.<br> <br> <strong>Sumatra_material_base_slab2.yaml, Sumatra_material_stronger_slab2.yaml, Sumatra_material_weaker_slab2.yaml</strong>: easi/yaml files describing the rock elastic and visco-plastic properties for each scenario.<br> It calls <strong>Sumatra_rhomulambda.yaml</strong> for the rock elastic properties and <strong>Sumatra_initial_stress_slab2.yaml</strong> for the stress tensor spatial variations.<br> <strong>Sumatra_fault_slab2.yaml</strong>: easi/yaml file describing the spatially variable on-fault parameters for the 3 main scenarios.<br> <strong>lithostaticStress_gamma.yaml</strong>: easi/yaml file describing the variations with depth of the lithostatic pressure, and specifying the pore fluid pressure ratio.<br> <br> <strong>Sumatra_fault_slab2_1d.yaml, Sumatra_initial_stress_slab2_1d.yaml, Sumatra_material_base_slab2_1d.yaml</strong>: easi/yaml files specific to the alternative scenario, which adopts a 1D PREM velocity structure.<br> <strong>Sumatra_fault_slab2_novar.yaml, Sumatra_initial_stress_slab2_novar.yaml, Sumatra_material_base_slab2_novar.yaml</strong>: easi/yaml files specific to the alternative dynamic rupture earthquake scenario in which no regional prestress variations are considered.<br> <br> <strong>Sumatra_slab2_layers_fixed.xdmf, Sumatra_slab2_layers_fixed</strong>: mesh file.
DOI: 10.5281/zenodo.5541270
title: SeisSol input files of the dynamic rupture scenarios of the 2004 Sumatra-Andaman earthquake published in Ulrich et al. (2021)

description: This dataset contains the input files of the dynamic rupture scenarios of the 2004 Sumatra-Andaman earthquake presented in: Ulrich, T., Gabriel, A. A., Madden, E. H. (2021). Stress, rigidity and sediment strength control megathrust earthquake and tsunami dynamics. doi: 10.31223/osf.io/s9263.<br> <strong>supermucNG_launch_script.sh</strong>: batch script for running a dynamic rupture earthquake scenario on Supermuc NG (LRZ).<br> <br> <strong>parameters_base_slab2.par, parameters_stronger_slab2.par, parameters_weaker_slab2.par</strong>: main parameter file for the base (resp. stronger, resp. weaker sediments) scenario.<br> <br> <strong>Sumatra_material_base_slab2.yaml, Sumatra_material_stronger_slab2.yaml, Sumatra_material_weaker_slab2.yaml</strong>: easi/yaml files describing the rock elastic and visco-plastic properties for each scenario.<br> It calls <strong>Sumatra_rhomulambda.yaml</strong> for the rock elastic properties and <strong>Sumatra_initial_stress_slab2.yaml</strong> for the stress tensor spatial variations.<br> <strong>Sumatra_fault_slab2.yaml</strong>: easi/yaml file describing the spatially variable on-fault parameters for the 3 main scenarios.<br> <strong>lithostaticStress_gamma.yaml</strong>: easi/yaml file describing the variations with depth of the lithostatic pressure, and specifying the pore fluid pressure ratio.<br> <br> <strong>Sumatra_fault_slab2_1d.yaml, Sumatra_initial_stress_slab2_1d.yaml, Sumatra_material_base_slab2_1d.yaml</strong>: easi/yaml files specific to the alternative scenario, which adopts a 1D PREM velocity structure.<br> <strong>Sumatra_fault_slab2_novar.yaml, Sumatra_initial_stress_slab2_novar.yaml, Sumatra_material_base_slab2_novar.yaml</strong>: easi/yaml files specific to the alternative dynamic rupture earthquake scenario in which no regional prestress variations are considered.<br> <br> <strong>Sumatra_slab2_layers_fixed.xdmf, Sumatra_slab2_layers_fixed</strong>: mesh file.
DOI: 10.6084/m9.figshare.13143605
title: Saccharomyces_cerevisiae_MP_GS_AUCs

description: Area under curve for functional prediction of GO biological processes for ssNET in comparison to the MP_GS scored network in the format including standard error:<br>GO term -&gt; ssNet AUC -&gt; MP_GS AUC -&gt; SE(W)
DOI: 10.6084/m9.figshare.13143605.v3
title: Saccharomyces_cerevisiae_MP_GS_AUCs

description: Area under curve for functional prediction of GO biological processes for ssNET in comparison to the MP_GS scored network in the format including standard error:<br>GO term -&gt; ssNet AUC -&gt; MP_GS AUC -&gt; SE(W)
DOI: 10.6084/m9.figshare.13143605.v2
title: Saccharomyces_cerevisiae_MP_GS_AUCs

description: Area under curve for functional prediction of GO biological processes for ssNET in comparison to the MP_GS scored network in the format:<br>GO term -&gt; ssNet AUC -&gt; MP_GS AUC
DOI: 10.5285/9c31bc7a-6f16-4d37-8df0-2717f26c718a
title: Evaluation of SaRIF high-energy electron reconstructions and forecasts

description: This data is provided to comply with the AGU's data policy for the publication of Glauert et al., "Evaluation of SaRIF high-energy electron reconstructions and forecasts" in Space Weather, 2021. Each data file corresponds to a figure or table in the paper and covers the period from 1 March 2019 to 1 September 2019. The data sets are as follows: Figure 1 - SaRIF reconstructions of the &gt;800 keV and the &gt;2 MeV flux (in cm-2 s-1 sr-1) measured by the GOES 14 spacecraft. Figure 3 - SaRIF 24-hour forecasts of the &gt;800 keV and the &gt;2 MeV flux (in cm-2 s-1 sr-1) at the location of the GOES 14 spacecraft. Figure 5 - SaRIF reconstructions of the &gt;800 keV and the &gt;2 MeV flux (in cm-2 s-1 sr-1) measured by the GOES 14 spacecraft, with the improvements to the modelling detailed in the paper. Figure 6 - Simulated SaRIF 24-hour forecasts of the &gt;800 keV and the &gt;2 MeV flux (in cm-2 s-1 sr-1) at the location of the GOES 14 spacecraft, with the improvements to the modelling detailed in the paper. Table 6 - Simulated SaRIF 24-hour reconstructions and forecasts of the &gt;800 keV and the &gt;2 MeV flux (in cm-2 s-1 sr-1) at the location of the GOES 14 spacecraft when the outer boundary for the simulation is placed at L* = 6.6. These are the values used to produce the metrics in Table 6. This research was supported by the Natural Environment Research Council (NERC) Highlight Topic Grant NE/P01738X/1 (Rad-Sat), National and Public Good activity grant NE/R016445/1 and ESA contract 4000118861/16/D/MRP (SSA P2-SWE-XIII proto- type)
description: The data was produced by the BAS Radiation Belt Model, as described in Glauert et al., "Evaluation of SaRIF high-energy electron reconstructions and forecasts", Space Weather, 2021.
description: The data is output from the British Antarctic Survey Radiation Belt Model for reconstructions and forecasts made by the SaRIF website. The accuracy of the reconstructions and forecasts, when compared to measurements from the GOES-14 satellite, is discussed in detail in 'Evaluation of SaRIF high-energy electron reconstructions and forecasts', Glauert et al., Journal Geophysical Research, 2021. As an example, the improved reconstructions discussed in the paper show Pearson correlation coefficients of 0.89 and prediction efficiencies of 0.87 for &gt;2MeV electrons.
DOI: 10.5517/ccdc.csd.cc27wppj
title: CCDC 2082198: Experimental Crystal Structure Determination

description: Related Article: Kento Kosugi, Mio Kondo, Shigeyuki Masaoka|2021|Angew.Chem.,Int.Ed.|60|22070|doi:10.1002/anie.202110190
DOI: 10.5517/ccdc.csd.cc27x47k
title: CCDC 2082618: Experimental Crystal Structure Determination

description: Related Article: Ding Chong Han, Yu Hui Tan, Yu Kong Li, Jia Hui Wen, Yun Zhi Tang, Wen Juan Wei, Peng Kang Du, Hao Zhang|2021|Chem.-Eur.J.|27|13575|doi:10.1002/chem.202101707
DOI: 10.5517/ccdc.csd.cc27yl2w
title: CCDC 2084008: Experimental Crystal Structure Determination

description: Related Article: Dante A. Castillo Molina, Taveechai Wititsuwannakul, Frank Hampel, Michael B. Hall, John A. Gladysz|2021|Chem.-Eur.J.|27|13399|doi:10.1002/chem.202101949
DOI: 10.5517/ccdc.csd.cc27yl3x
title: CCDC 2084009: Experimental Crystal Structure Determination

description: Related Article: Dante A. Castillo Molina, Taveechai Wititsuwannakul, Frank Hampel, Michael B. Hall, John A. Gladysz|2021|Chem.-Eur.J.|27|13399|doi:10.1002/chem.202101949
DOI: 10.5517/ccdc.csd.cc27yl4y
title: CCDC 2084010: Experimental Crystal Structure Determination

description: Related Article: Dante A. Castillo Molina, Taveechai Wititsuwannakul, Frank Hampel, Michael B. Hall, John A. Gladysz|2021|Chem.-Eur.J.|27|13399|doi:10.1002/chem.202101949
DOI: 10.5517/ccdc.csd.cc27yl5z
title: CCDC 2084011: Experimental Crystal Structure Determination

description: Related Article: Dante A. Castillo Molina, Taveechai Wititsuwannakul, Frank Hampel, Michael B. Hall, John A. Gladysz|2021|Chem.-Eur.J.|27|13399|doi:10.1002/chem.202101949
DOI: 10.5517/ccdc.csd.cc27tm3t
title: CCDC 2080196: Experimental Crystal Structure Determination

description: Related Article: Leonie J. Bole, Marina Uzelac, Alberto Hernán-Gómez, Alan R. Kennedy, Charles T. O’Hara, Eva Hevia|2021|Inorg.Chem.|60|13784|doi:10.1021/acs.inorgchem.1c01276
DOI: 10.5517/ccdc.csd.cc27wdtc
title: CCDC 2081923: Experimental Crystal Structure Determination

description: Related Article: Yanis Lazib, Pascal Retailleau, Tanguy Saget, Benjamin Darses, Philippe Dauban|2021|Angew.Chem.,Int.Ed.|60|21708|doi:10.1002/anie.202107898
DOI: 10.5517/ccdc.csd.cc27sskd
title: CCDC 2079404: Experimental Crystal Structure Determination

description: Related Article: Amit Kumar, Chelsea G. Comadoll, Daniel S. King, Allen G. Oliver, Victor W. Day, James D. Blakemore|2021|Inorg.Chem.|60|14047|doi:10.1021/acs.inorgchem.1c01362
DOI: 10.5517/ccdc.csd.cc283tnw
title: CCDC 2089048: Experimental Crystal Structure Determination

description: Related Article: Mahendra K. Sharma, Christoph Wölper, Gebhard Haberhauer, Stephan Schulz|2021|Angew.Chem.,Int.Ed.|60|21784|doi:10.1002/anie.202108370
DOI: 10.5517/ccdc.csd.cc283tpx
title: CCDC 2089049: Experimental Crystal Structure Determination

description: Related Article: Mahendra K. Sharma, Christoph Wölper, Gebhard Haberhauer, Stephan Schulz|2021|Angew.Chem.,Int.Ed.|60|21784|doi:10.1002/anie.202108370
DOI: 10.5517/ccdc.csd.cc283tqy
title: CCDC 2089050: Experimental Crystal Structure Determination

description: Related Article: Mahendra K. Sharma, Christoph Wölper, Gebhard Haberhauer, Stephan Schulz|2021|Angew.Chem.,Int.Ed.|60|21784|doi:10.1002/anie.202108370
DOI: 10.5517/ccdc.csd.cc283trz
title: CCDC 2089051: Experimental Crystal Structure Determination

description: Related Article: Mahendra K. Sharma, Christoph Wölper, Gebhard Haberhauer, Stephan Schulz|2021|Angew.Chem.,Int.Ed.|60|21784|doi:10.1002/anie.202108370
DOI: 10.5517/ccdc.csd.cc27lnh0
title: CCDC 2073512: Experimental Crystal Structure Determination

description: Related Article: Maxime Tricoire, Luca Münzfeld, Jules Moutet, Nolwenn Mahieu, Léo La Droitte, Eufemio Moreno-Pineda, Frédéric Gendron, Jeremy D. Hilgar, Jeffrey D. Rinehart, Mario Ruben, Boris Le Guennic, Olivier Cador, Peter W. Roesky, Grégory Nocton|2021|Chem.-Eur.J.|27|13558|doi:10.1002/chem.202101599
DOI: 10.5517/ccdc.csd.cc28662s
title: CCDC 2091324: Experimental Crystal Structure Determination

description: Related Article: Michael Wiesinger, Bastian Rösch, Christian Knüpfer, Jonathan Mai, Jens Langer, Sjoerd Harder|2021|Eur.J.Inorg.Chem.|2021|3731|doi:10.1002/ejic.202100529
DOI: 10.5517/ccdc.csd.cc28663t
title: CCDC 2091325: Experimental Crystal Structure Determination

description: Related Article: Michael Wiesinger, Bastian Rösch, Christian Knüpfer, Jonathan Mai, Jens Langer, Sjoerd Harder|2021|Eur.J.Inorg.Chem.|2021|3731|doi:10.1002/ejic.202100529
DOI: 10.5517/ccdc.csd.cc28664v
title: CCDC 2091326: Experimental Crystal Structure Determination

description: Related Article: Michael Wiesinger, Bastian Rösch, Christian Knüpfer, Jonathan Mai, Jens Langer, Sjoerd Harder|2021|Eur.J.Inorg.Chem.|2021|3731|doi:10.1002/ejic.202100529
DOI: 10.5517/ccdc.csd.cc28665w
title: CCDC 2091327: Experimental Crystal Structure Determination

description: Related Article: Michael Wiesinger, Bastian Rösch, Christian Knüpfer, Jonathan Mai, Jens Langer, Sjoerd Harder|2021|Eur.J.Inorg.Chem.|2021|3731|doi:10.1002/ejic.202100529
DOI: 10.17028/rd.lboro.16708267.v1
title: Supplementary information files for: Landscape controls on nutrient stoichiometry regulate lake primary production at the margin of the Greenland ice sheet

description: Supplementary files for article: Landscape controls on nutrient stoichiometry regulate lake primary production at the margin of the Greenland ice sheetGlobal change is reshaping the physical environment and altering nutrient dynamics across the Arctic. These changes can affect the structure and function of biological communities and influence important climate-related feedbacks (for example, carbon (C) sequestration) in biogeochemical processing hot spots such as lakes. To understand how these ecosystems will respond in the future, this study examined recent (
DOI: 10.17028/rd.lboro.16708267
title: Supplementary information files for: Landscape controls on nutrient stoichiometry regulate lake primary production at the margin of the Greenland ice sheet

description: Supplementary files for article: Landscape controls on nutrient stoichiometry regulate lake primary production at the margin of the Greenland ice sheetGlobal change is reshaping the physical environment and altering nutrient dynamics across the Arctic. These changes can affect the structure and function of biological communities and influence important climate-related feedbacks (for example, carbon (C) sequestration) in biogeochemical processing hot spots such as lakes. To understand how these ecosystems will respond in the future, this study examined recent (
DOI: 10.6084/m9.figshare.16708285
title: Saccharomyces_cerevisiae_BP10_GS_AUCs

description: Area under curve for functional prediction of GO biological processes for ssNET in comparison to the BP10_GS scored network in the format including standard error: <br> GO term -&gt; ssNet AUC -&gt; BP10_GS AUC -&gt; SE(W)
DOI: 10.6084/m9.figshare.16707244.v4
title: Harpy Eagle climate change SDMs

description: Data to support publication DOI: <br>GBIF Occurrence Download. https://doi.org/10.15468/dl.6ikhnj
DOI: 10.6084/m9.figshare.16708285.v2
title: Saccharomyces_cerevisiae_BP10_GS_AUCs

description: Area under curve for functional prediction of GO biological processes for ssNET in comparison to the BP10_GS scored network in the format including standard error: <br> GO term -&gt; ssNet AUC -&gt; BP10_GS AUC -&gt; SE(W)
DOI: 10.6084/m9.figshare.16708285.v1
title: Saccharomyces_cerevisiae_BP10_GS_AUC

description: Area under curve for functional prediction of GO biological processes for ssNET in comparison to the BP10_GS scored network in the format including standard error: <br> GO term -&gt; ssNet AUC -&gt; BP10_GS AUC -&gt; SE(W)
DOI: 10.6084/m9.figshare.16708300
title: Saccharomyces_cerevisiae_BP100_GS_AUCs

description: Area under curve for functional prediction of GO biological processes for ssNET in comparison to the BP100_GS scored network in the format including standard error: <br> GO term -&gt; ssNet AUC -&gt; BP100_GS AUC -&gt; SE(W)
DOI: 10.6084/m9.figshare.16708300.v1
title: Saccharomyces_cerevisiae_BP100_GS_AUCs

description: Area under curve for functional prediction of GO biological processes for ssNET in comparison to the BP100_GS scored network in the format including standard error: <br> GO term -&gt; ssNet AUC -&gt; BP100_GS AUC -&gt; SE(W)
DOI: 10.6084/m9.figshare.16708309
title: V186_D1.0_Saccharomyces_cerevisiae_BP10_GSNet.txt

description: Probabilistic functional integrated network for Saccharomyces cerevisiae scored against the biosystems database (BP10_GS).
DOI: 10.6084/m9.figshare.16708309.v1
title: V186_D1.0_Saccharomyces_cerevisiae_BP10_GSNet.txt

description: Probabilistic functional integrated network for Saccharomyces cerevisiae scored against the biosystems database (BP10_GS).
DOI: 10.6084/m9.figshare.16708339
title: V186_D1.0_Saccharomyces_cerevisiae_BP100_GSNet.txt

description: Probabilistic functional integrated network for Saccharomyces cerevisiae scored against the biosystems database (BP100_GS).
DOI: 10.6084/m9.figshare.16708339.v1
title: V186_D1.0_Saccharomyces_cerevisiae_BP100_GSNet.txt

description: Probabilistic functional integrated network for Saccharomyces cerevisiae scored against the biosystems database (BP100_GS).
DOI: 10.5281/zenodo.5541327
title: Supply Chain Shocks due to extreme weather events

description: Projected supply chain shocks due to extreme weather events measured in annual percentage change in a country-sector's export activity compared to the baseline period
DOI: 10.5281/zenodo.5541326
title: Supply Chain Shocks due to extreme weather events

description: Projected supply chain shocks due to extreme weather events measured in annual percentage change in a country-sector's export activity compared to the baseline period
DOI: 10.5281/zenodo.5541146
title: crowdsourced body parameters of workshop attendants at the Helmholtz MT ARD ST3 meeting

description: This data set was crowdsourced at the 2021 Helmholtz MT ARD ST3 meeting from attendants of the Machine Learning Tutorial on Sep 30, 2021. For more details on the event, see<br> https://indico.desy.de/event/28823/ The CSV contains 4 columns: - is_female : fill with 1 if participant is female, filled with 0 if not female - shoesize_europe : your european shoesize - weight_kg : your weight in kilograms - height_cm : your height in centimeters Participants were encouraged to +1 or -1 to individual body properties in case they do not feel confident providing their true numbers.
DOI: 10.5281/zenodo.5541145
title: crowdsourced body parameters of workshop attendants at the Helmholtz MT ARD ST3 meeting

description: This data set was crowdsourced at the 2021 Helmholtz MT ARD ST3 meeting from attendants of the Machine Learning Tutorial on Sep 30, 2021. For more details on the event, see<br> https://indico.desy.de/event/28823/ The CSV contains 4 columns: - is_female : fill with 1 if participant is female, filled with 0 if not female - shoesize_europe : your european shoesize - weight_kg : your weight in kilograms - height_cm : your height in centimeters Participants were encouraged to +1 or -1 to individual body properties in case they do not feel confident providing their true numbers.
DOI: 10.17028/rd.lboro.16708345
title: Supplementary Information files for: In-silico design and experimental validation of TiNbTaZrMoSn to assess accuracy of mechanical and biocompatibility predictive models

description: Supplementary Information files for: In-silico design and experimental validation of TiNbTaZrMoSn to assess accuracy of mechanical and biocompatibility predictive models<br>Numerical design of TiNbTaZrMoSn alloy preceded its manufacture and mechanical, physico-chemical and in vitro characterisation. The specifications of the alloy required a multi-objective optimisation including lower modulus of elasticity than c.p.Ti, high strength, stabilised  crystal structure with a low martensitic start temperature, a narrow solidification range and high biocompatibility. The results reveal that there was a good match between the bulk mechanical properties exhibited by the alloy experimentally and those predicted. Regarding surface properties, independent of roughness effects, the oxide thickness and surface zeta-potential, measured in biologically relevant electrolytes and at physiological pH, appear as important factors in osteoblastic activity (i.e., cell proliferation, measured via DNA, protein and metabolite content, and differentiation, via ALP levels), but not in cell adhesion and viability. The thinner oxide layer and lower absolute value of surface zeta-potentialon the TiNbTaZrMoSn alloy explain its lesser osteogenic properties (i.e., inhibition of ALP activity) compared to the c.p. Ti. This study demonstrates that the numerical models to predict microstructure and bulk mechanical properties of -Ti alloys are robust, but that the prediction of cellular bioactivity lags behind and still requires parameterisation to account for features such as oxide layer composition and thickness, electro-chemical properties and surface charge, and topography to optimise cell response in silico before committing to the costly manufacture and deployment of these alloys in regenerative medicine.<br>