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Application Cases of Inverse Modelling with the PROPTI Framework - Data Set
<p><strong>Contents</strong></p> <p>Set of simulation data, supplementary for a paper submitted to (published: 15 June 2019) the Fire Safety Journal, with the title <a href="https://www.sciencedirect.com/science/article/pii/S0379711219300438">"Application Cases of Inverse Modelling with the PROPTI Framework"</a>. See also our project at <a href="https://www.researchgate.net/project/PROPTI-An-Generalised-Inverse-Modelling-Framework">ResearchGate</a>.</p> <p>This repository contains the complete input data for each IMP run of the mass loss calorimeter, shown in this paper. This comprises of the experimental data files, the templates for the simulation models and the input file for PROPTI.</p> <p>The data base files are provided. This includes the original ones created by PROPTI during the run, as well as the cleaned data base files, used to create the plots, and the extracted best parameter sets per generation. Plots, created during the IMP runs as means of monitoring the progress are also included.</p> <p>Furthermore, the repository contains a small collection of Jupyter notebooks which have been used to process the data base files and create the plots presented in this paper.</p> <p>The full factorial simulations were set up from within a Jupyter notebook. This notebook and the conducted simulations are also part of this repository.</p> <p>Data of the various TGA simulations are provided within a very <a href="https://zenodo.org/record/2538851#.XSXfAXtCSUk">similar repository</a>, linked to a <a href="https://www.researchgate.net/publication/328933654_PROPTI_-_A_Generalised_Inverse_Modelling_Framework">conference paper</a> (ESFSS 2018, Nancy, France).</p> <p>Finally, the simulation input files, PROPTI input, as well as the custom script for file handling in concert with OpenFOAM, are provided.</p> <p> </p> <p><strong>Technical Information</strong></p> <p>Each ZIP archive represents a sub-directory of the original directory. For the analysis scripts, the Jupyter notebooks, to work properly out of the box it is necessary to keep this structure. Thus, simply extract all archives into the same directory.</p> <p>Note: Size on disc, after extraction, is about 4.1 GB. Version 2 adds about 5.1 GB.</p> <p> </p> <p><strong>Version 2:</strong></p> <p>Version 2 contains new IMP runs that address an error in determining the normalised residual mass, see Jupyter Notebook "RevisedTargetAssessment.ipynb", as well as input from the reviewers. The IMP runs are denoted by "08" after the optimisation algorithm label, e.g. "MLC_FSCABC_08_new_75kw_Ins".</p>
Source data belonging to "Visualisation of dCas9 target search in vivo using an open-microscopy framework"
<p>Source data corresponding to "Visualisation of dCas9 target search <em>in vivo</em> using an open-microscopy framework". Contains pTarget and pNonTarget raw datasets, as well as all localization data, cell UV intensity data, cell outline data, and analysed diffusion coefficient lists.</p>
API Database of Python frameworks & Labeled Issues
<p>PyLibAPIs.7z : contains public API data (MongoDB dump) for these frameworks:</p> <ul> <li>TensorFlow</li> <li>Keras</li> <li>Scikit-learn</li> <li>Pandas</li> <li>Flask</li> <li>Django</li> </ul> <p>Label.xlsx: contains issues and their labels</p> <p>Breaking Changes for All Frameworks.pdf: contains the breaking change distributions of all six frameworks</p>
Xcompact3D: An open-source framework for solving turbulence problems on a Cartesian mesh
<p>Contains figures used in + the data with scripts to generate figures for the manuscript (in preparation)</p> <p>Xcompact3D: An open-source framework for solving turbulence problems on a Cartesian mesh</p>
Text-fig. 1. A. Location of the sites of Capo di Fiume, Palena and Pollenzo near Alba. B. Capo di Fiume stratigraphic section. Facies of coastal-transitional marine associations – a. Freshwater marsh and tidal creeks interval, b. Swamp interval, c1–c4. Facies of eustarine bay associations, d1–d6. Facies of open shelf marine associations. Symbols: "black star" – fossiliferous horizon with plant material studied here, 1. mottled grey to dark-brown marls and clayey marls, 2. fissile dark-grey marls and shaly marls, 3. limestones, 4. marly limestones and limey marls, 5. bio-lithoclastic calcarenites, 6. lime conglomerate, 7. massive muddy deposit produced by mass-flow mechanism, 8. diatomitic marls, 9. "terra rossa" soil (modified after Carnevale et al. 2011). in Feather Palm Foliage From The Messinian Of Italy (Capo Di Fiume, Palena And Pollenzo Near Alba) Within The Framework Of Northern Mediterranean Late Miocene Flora
Text-fig. 1. A. Location of the sites of Capo di Fiume, Palena and Pollenzo near Alba. B. Capo di Fiume stratigraphic section. Facies of coastal-transitional marine associations – a. Freshwater marsh and tidal creeks interval, b. Swamp interval, c1–c4. Facies of eustarine bay associations, d1–d6. Facies of open shelf marine associations. Symbols: "black star" – fossiliferous horizon with plant material studied here, 1. mottled grey to dark-brown marls and clayey marls, 2. fissile dark-grey marls and shaly marls, 3. limestones, 4. marly limestones and limey marls, 5. bio-lithoclastic calcarenites, 6. lime conglomerate, 7. massive muddy deposit produced by mass-flow mechanism, 8. diatomitic marls, 9. "terra rossa" soil (modified after Carnevale et al. 2011).
Figure 5 in A standardised analytical framework for sampling fish communities: relevance to Turkish riverine ecosystems
Figure 5. Trend profiles in total richness (a–e) and nativeness (f) recorded over six years of TLM monitoring in four habitats at the six Icon Sites in the River Murray system (codes in Table 1). See also Table 5.
Figure 1 in A standardised analytical framework for sampling fish communities: relevance to Turkish riverine ecosystems
Figure 1. Map of the River Murray system in south-eastern Australia with indication of the six Icon Sites part of The Living Murray initiative (see Table 1).
Figure 3 in The effects of various lake typologies on the distribution of Chironomus spp. (Diptera), and arguments on optional factors of Water Framework Directive in Türkiye
Figure 3. CCA ordination of the sampled lakes (L1 to L20, except L2), significant environmental variables and species. Environmental variables ranging over (VIF> 10) were omitted from the ordination. A total of 56.62% variance was expressed in the first two axes. Groups (Grp I–IV) spotted by using K-means algorithm were mapped on the biplot. ORP: oxidation and reduction potential.
Figure 4 in The effects of various lake typologies on the distribution of Chironomus spp. (Diptera), and arguments on optional factors of Water Framework Directive in Türkiye
Figure 4. CCA ordination of the sampled lakes (L1 to L20, except L2) based on national altitude class boundaries (R1: <800 m, R2: 800–1600 m, R3:>1600 m). A total of 62.06% variance was expressed in the first two axes. The distinction of the three groups was found to be statistically significant (p <0.05). For the significance levels of the typological factors, see Table 1.
Figure 2 in The effects of various lake typologies on the distribution of Chironomus spp. (Diptera), and arguments on optional factors of Water Framework Directive in Türkiye
Figure 2. Pearson's correlation heatmap of the 20 environmental variables and 2 taxa metrics (H` and J). Color intensity and the size of the circle are proportional to the correlation coefficients: red spectrum indicates a negative correlation, and blue spectrum indicates a positive correlation.
Raw data for "A Composite Bayesian Optimisation Framework for Material and Structural Design under Uncertainty"
<p>This dataset contains the raw data for the paper "A Composite Bayesian Optimisation Framework for Material and Structural Design under Uncertainty" (submitted) by R. P. Cardoso Coelho, A. F. Carvalho Alves, T. M. Nogueira Pires and F. M. Andrade Pires (INEGI and Faculty of Engineering of the University of Porto, Portugal).</p> <p> </p> <p>The data has been generated with the development branch of piglot - an open-source optimisation toolbox (https://github.com/CM2S/piglot). The numerical simulations have been conducted with both an in-house finite element solver (Links) and with the open-source SCA implementation CRATE (https://github.com/bessagroup/CRATE).</p>
FAIRER Data Management Framework: Diagram
<p>A data management framework is a model of the people, policies, standards, and processes needed to manage data. It includes corporate strategy and objectives, data strategy, capabilities, governance, data management, metadata management, data architecture, machine-actionable data management plan (maDMP), and data lifecycle. The result is the production of FAIRER (Findable, Accessible, Interoperable, Reusable, Ethical, and Reproducible) data.</p> <p>DICLAIMER: All views and opinions expressed are those of the co-authors, and do not necessarily reflect the official policy or position of their respective employers, or of any government, agency, or organization.</p>
Fig. 1 in Applying a modified streamlined disease risk analysis framework to a platypus conservation translocation, with special consideration for the conservation of ecto- and endoparasites
Fig. 1. An outline of how parasite conservation can be considered in translocation planning, reproduced from Carlson et al. (2020).
Fig. 4 in New Late Cretaceous mammals from the Intertrappean beds of Rangapur, India and paleobiogeographic framework
Fig. 4. Deccanolestes cf. robustus. Right m1 or m2 (ITV/R/Mm−6) in occlusal (A, B), lingual (C), and labial (D) views. Scale bar 0.5 mm.
Fig. 2 in New Late Cretaceous mammals from the Intertrappean beds of Rangapur, India and paleobiogeographic framework
Fig. 2. Diagramatic sketch of the upper and lower molar measurements. Anteroposterior axes pass through the paracone and metacone for upper molars and through the metaconid and entoconid for lower molars. Abbreviations are as follows: for upper molars, AW, anterior width; PW, posterior width; PW1, posterior width sensu Butler (1990); PRW, protocone shelf width; MCL, postmetacrista length; BL, buccal length; PRL, protocone shelf length; PRH, protocone height; PAH, paracone height; MEH, metacone height; and for lower molars, L, length; TRW, trigonid width; TAW, talonid width; PHL, posthypocristid length; PEL, postentocristid length; PDH, protoconid height; MDH, metaconid height; AML, premetaconid length; TAL, talonid length; TAH, talonid height.
Fig. 7. Theria incertae sedis. A. Left p1 in New Late Cretaceous mammals from the Intertrappean beds of Rangapur, India and paleobiogeographic framework
Fig. 7. Theria incertae sedis. A. Left p1 or p2 (ITV/R/Mm−14) in labial view. B. Right p1 (ITV/R/Mm−17) in labial view. C. Right p3 or p4 (ITV/R/Mm−13) in lingual view. D. Right P3 (ITV/R/Mm−5) in labial (D1) and occlusal (D2, D3) views. Scale bar 0.5 mm.
Fig. 6. Sahnitherium rangapurensis. Right M1 in New Late Cretaceous mammals from the Intertrappean beds of Rangapur, India and paleobiogeographic framework
Fig. 6. Sahnitherium rangapurensis. Right M1 or M2 (ITV/R/Mm−1), holotype in occlusal (A, B), labial (C), anterior (D), and posterior (E) views. Scale bar 0.5 mm.
Fig. 5 in New Late Cretaceous mammals from the Intertrappean beds of Rangapur, India and paleobiogeographic framework
Fig. 5. Deccanolestes? sp. A. Left m1 or m2 (ITV/R/Mm−9) in occlusal (A1, A2), lingual (A3), and labial (A4) views. B. Right m1 or m2 (ITV/R/Mm−8) in occlusal (B1, B2), lingual (B3), and labial (B4) views. Hatched areas indicate breakage. Scale bar 0.5 mm.
FIGURE 18 in A new multi-faceted framework for deciphering diplodocid ontogeny
FIGURE 18. The degrees and variation of neurocentral fusion in diplodocid cervical vertebrae indicate that fusion is not uniformly indicative of maturity. 1, The posteriorly unfused H-MOS Stage 2 Diplodocus sp. MOR 790 8-10-96-204; 2, The completely fused H-MOS Stage 1 diplodocid indeterminate MOR 714 7-22-3-53; 3, The completely unfused HMOS Stage 3 Apatosaurus excelsus (or Brontosaurus excelsus) CM 555; 4, The anteriorly unfused H-MOS Stage 3 Diplodocus sp. MOR 592. Red inset boxes highlight the visible sutures (in blue). Not to scale. Scale bar equals 10 cm.
FIGURE 17 in A new multi-faceted framework for deciphering diplodocid ontogeny
FIGURE 17. Histologic and morphologic commonalities of Diplodocus sp. (MOR 592) and Suuwassea emilieae (ANS 21122) which demonstrate immature maturational states for both animals (H-MOS Stage 3). Histologic section of Suuwassea tibia from Hedrick et al. (2014). The Suuwassea tibia histologic section is from Acta Palaeontologica Polonica, articles are distributed under the terms of the Creative Commons Attribution License (CC BY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For licence details please see http://creativecommons.org/licenses/by/4.0/.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.