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7,195 results for “SET”
Data sets for modeling double strand break susceptibility and interrogating structural variation in cancer
<p>This is data used and produced for the study of "Modeling double strand break susceptibility to interrogate structural variation in cancer". </p> <p><strong>Background: </strong>Structural variants (SVs) are known to play important roles in a variety of cancers, but their origins and functional consequences are still poorly understood. Many SVs are thought to emerge from errors in the repair processes following DNA double strand breaks (DSBs).</p> <p><strong>Results:</strong> We used experimentally quantified DSB frequencies in cell lines with matched chromatin and sequence features to derive the first quantitative genome-wide models of DSB susceptibility. These models are accurate and provide novel insights into the mutational mechanisms generating DSBs. Models trained in one cell type can be successfully applied to others, but a substantial proportion of DSBs appear to reflect cell type specific processes. Using model predictions as a proxy for susceptibility to DSBs in tumours, many SV-enriched regions appear to be poorly explained by selectively neutral mutational bias alone. A substantial number of these regions show unexpectedly high SV breakpoint frequencies given their predicted susceptibility to mutation and are therefore credible targets of positive selection in tumours. These putatively positively selected SV hotspots are enriched for genes previously shown to be oncogenic. In contrast, several hundred regions across the genome show unexpectedly low levels of SVs, given their relatively high susceptibility to mutation. These novel coldspot regions appear to be subject to purifying selection in tumours and are enriched for active promoters and enhancers.</p> <p><strong>Conclusions:</strong> We conclude that models of DSB susceptibility offer a rigorous approach to the inference of SVs putatively subject to selection in tumours.</p>
Giant vortex clusters in a two-dimensional quantum fluid: Data sets
<p>This data set collates the experimental and simulation data for the paper "Giant vortex clusters in a two-dimensional quantum fluid."</p> <p><strong>Database S1: Data_Excel_Sheet.xlsx </strong>contains the data shown in Figs. 1, 3, and 4 of the paper.</p> <p><strong>Database S2: Exp_Vortex_Location_Data.zip</strong> contains the experimental vortex positions.</p> <p><strong>Database S3: 2DGPE.zip</strong> contains the outputs of the 2D GPE simulations, along with the generating scripts.</p> <p><strong>Database S4: MonteCarlo_raw.zip</strong> contains the Monte Carlo outputs used to generate the shown in Fig. 1. </p> <p>Additional scripts to reproduce the MC data, GPE data, and reproduce the figures may be found at <a href="https://github.com/UQBEC/GiantVortices">https://github.com/UQBEC/GiantVortices</a>.</p> <p> </p>
Dispa-SET Balkans - Dataset
<p><strong>Dispa-SET for the Balkans region</strong></p> <p>This is input data of the Dispa-SET model, applied to the Balkans Region:</p> <ul> <li>Config Files</li> <li>Database Files</li> </ul>
Improving source code change set analysis by using a visual tool. Controlled experiment dataset.
<p>Modern software development is performed by developing features in isolated branches by each member of a software development team. When these branches need to be integrated, they have to be manually reviewed by an integrator. Source code reviewing can be a tedious and time consuming task, which is normally performed by hand reviewing a textual diff of the change set.</p> <p>The difficulty of reviewing source code changes can have a negative impact on the accep- tance of these changes. It has been shown that this can imply the rejection of important bug fixes for a software project.</p> <p>In order to facilitate the task of reviewing source code change sets, we designed and implemented a visual tool. Our visual tool helps in assessing source code change sets by providing different views of the change set: an overall overview of the change set with metrics, and a visualization representing the structural changes in the source code. We evaluated our visual tool by performing a controlled experiment with software practitioners.</p> <p>Our experimental results show no significant differences between using our visual tool and a textual diff tool in the following terms: time of analysis, precision of the analysis, and inference of intention of changes. However, we did find a positive user perception, and a reduced mental load when using our visual tool.</p> <p>Anonymized datasets with the answers to the controlled experiments for evaluating the Git Thermite visual tool for assessing source code change sets. Copies of these datasets are provided in both, CSV format, and OpenDocument format.</p> <p>Exact copies of the learning materials that was provided to the participants during the execution of the controlled experiments is also included along the dataset.</p>
Distances for the Gaia RV set with corrected parallaxes
<p>The datasets are described in <a href="https://arxiv.org/abs/1902.02355">https://arxiv.org/abs/1902.02355</a> , which is submitted to MNRAS.</p> <p>Each file contains a distance derivation under different assumptions for the Gaia RV dataset.</p> <p>Please note the following: to allow for freedom in choice of cuts, we have limited ourselves to just minimal cuts: parallax/parallaxerr > 3, detected G,G_BP,G_RP magnitudes > 0 mag, vlos_err < 10 km/s, n_vis > 5, reasonably measured RV (<5550 km/s), distance and 1/parallax < 10 kpc</p> <p>Note that these cuts are NOT sufficient to ensure quality of the sample. We strongly advise to use the quality criteria laid out in Schoenrich, McMillan and Eyer (2019), and test for the effects of a cut in galactic latitude b > 10 degrees for most applications.</p> <p>The file format is .csv (comma separated) with the following columns (a key is in row 1)<br> index: running number in our sample<br> sourceid: Gaia Source ID<br> E_dist: expectation value for the distance, unit is kpc<br> distm2: second moment of distance probability distribution (use the usual formula to get the variance/dispersion)<br> distm3: third moment of distance probability distribution<br> distm4: fourth moment """"<br> parallax: The Gaia parallax _including_ the offset correction chosen, unit is mas<br> parallax/parallaxerr : ratio of parallax to parallax uncertainty. Again, both parallax and parallaxerror contain the corrections. Use this and the last column to obtain the parallax uncertainty for quality cuts<br> x: x coordinate (radial coordinate in local cartesian frame), unit is kpc<br> y: y coordinate in the azimuthal direction, unit is kpc<br> z: z coordinate (perpendicular to the plane), relative to the Sun, unit is kpc. Please add the solar offset from the plane that you favor (0.02 kpc, Joshi et al., or the Gerhard & Bland-Hawthorn review are good values)<br> R: galactocentric radius (in-plane, cylindrical coordinates), unit is kpc<br> U_g: cylindrical radial velocity in a galactocentric frame (positive taken inwards), unit is km/s<br> v_phi: azimuthal velocity (V_g, azimuthal direction), unit is km/s<br> v_z: velocity component upwards perpendicular to the plane, unit is km/s<br> vlos: line-of-sight velocity as provided in the Gaia RV datasets<br> vloserr: line-of-sight velocity error as provided in the Gaia RV datasets<br> gl: Galactic longitude l (degrees)<br> gb: Galactic latitude b (degrees)<br> pml: proper motion in l (mas/yr)<br> pmb: proper motion in b (mas/yr)<br> pml_err: proper motion error/uncertainty in l (mas/yr)<br> pmb_err: proper motion error in b (mas/yr)<br> E_bprp: BP-RP excess flux factor (provided in Gaia catalogue)<br> excessnoise: astrometric excess noise<br> Gmag: Gaia G magnitude<br> Rmag: Gaia G_RP magnitude<br> B-Rcolour: G_BP - G_RP (colour in mag)<br> nvis: number of visibility periods<br> eclat: ecliptic latitude (degrees)</p> <p>Each file contains a different set of assumptions that result in a near-zero average distance bias, as discussed in the paper:</p> <p>We recommend using either:<br> gaiaRVdelp54delsp43 : Increased parallax uncertainty by 0.043 mas in quadrature, corrected for parallax offset of 0.054 mas</p> <p>gaiaRVdelpeqspdelsp43 : Increased parallax uncertainty by 0.043 mas in quadrature, corrected for a parallax offset = modified parallax error</p> <p>For comparison and applications that need long-range in distance s, it may be interesting to use:<br> gaiaRVdelp48delsp00 : only increased parallaxes by an offset of 0.048 mas. Distance bias on average is stable out to 4kpc or more, but you have to be wary of random errors.</p> <p><br> All distances were derived with the standard prior described in Schoenrich, McMillan and Eyer (2019); the parameter for the additional exponential function in the prior at very large s > 4 kpc is 0.06/kpc</p> <p>If you need specific subsamples of stars, we advice to draw a new prior with the method of Schoenrich & Aumer (2017) and then re-derive the distances. We are also happy to help with that and provide you with the necessary code.</p> <p>Please note that our definition (angular distance per time in the direction of l, b) of pml and pmb is frequently noted with a * in other derivations.</p> <p>Please also note that why we used z_Sun = 0.02 kpc in the underlying analysis and distance prior, the value in the catalogue is given relative to the Sun (i.e. add the Solar offset that you favour).</p>
Processed TCGA pan-cancer data set used in the I-Boost paper (Wong et al. 2019)
<p>This data set contains the clinical and genomics data for 1,420 subjects analyzed in the paper: Wong KY, Fan C, Tanioka M, Parker JS, Nobel AB, Zeng D, Lin DY, Perou CM. I-Boost: an integrative boosting approach for predicting survival time with multiple genomics platforms. <em>Genome Biology.</em> 2019. It contains data on time to death, cancer type, 4 clinical variables, expression of 12,434 genes, somatic mutation of 130 genes, expression of 305 miRNA, expression of 136 proteins or phospho-proteins, copy number of 216 DNA segments, and 497 gene expression modules. Data on time to death, clinical variables, somatic mutation, copy number variation, mRNA expression, and miRNA expression were derived from the pan-cancer data set at Synapse (syn2468297 at <a href="https://www.synapse.org/#!Synapse:syn2468297">https://www.synapse.org/#!Synapse:syn2468297</a>). The protein expression data were obtained from Broad GDAC Firehose (<a href="https://gdac.broadinstitute.org/runs/stddata__2016_01_28/">https://gdac.broadinstitute.org/runs/stddata__2016_01_28/</a>).</p>
Data set for "Measuring synchronization and anticipation between individual investors from their daily performance"
<p>The data stored here is used as a support of the paper "Measuring<br> synchronization and anticipation between individual investors from their<br> daily performance" where a measure based on Mutual Information and<br> Transfer of Entropy is used in order to map investors' behaviour and which<br> ones are following same behavioural patterns.</p> <p>The study linked to this data is published on pre-print Arxiv.org<br> with the following citation:</p> <p><br> Mario Gutiérrez-Roig, Javier Borge-Holthoeffer, Alex Arenas and<br> Josep Perelló. Measuring synchronization and anticipation between<br> individual investors from their daily performance (2018)</p>
Large scale experiments for an alternative erosion control measure using sand-filled geosystems. Data set produced at the CIEM flume, Hydralab+
<p>Sand-filled geosystems have the potential to mimic aspects of natural and nature-based features that can enhance the resilience of coastal areas challenged by climate, with additional (structural) reinforcement.</p> <p>Knowledge gaps can be identified. For instance, (i) the sediment transport mechanisms around the geosystem; (ii) the amount of erosion in the leeside when the system is overtopped; (iii) quantitative contribution the geosystem for the wave overtopping reduction; and (iv) failure mechanisms of the geosystem under extreme conditions. Specific tests are proposed in order to fill the defined knowledge gap and answer the following research questions:</p> <ol> <li>How do nearshore coastal processes (wave transformation, sediment transport) and wave structure interactions during extreme events differ from those during more usual big storm conditions for situations with and without the geosystem?</li> <li>How do feedbacks between the hydrodynamics and morphology of natural and nature-based features affect flooding, erosion, and recovery of coastal areas when erosion is limited by the 'geosystem'?</li> <li>How to conceive a dynamic coastal protection that can easily adapt to climate change in areas experiencing coastal squeeze (i.e. dense urban environment and human infrastructure with sea encroaching land) and vulnerable to coastal erosion and flooding risks?</li> </ol> <p>The set of experiments, done at the large wave Flume (CIEM) in Barcelona, are here described in order to answer the previous questions. These experiments started on October 2018 and ended at the end of November 2018. These tests include different configurations:<br>an initial benchmark tests in order to test the wave conditions were no geosystem protection is used, a second layout with a geotube used as a geosystem protection and finally a third layout were geobags are used as a protection.</p>
Large scale experiments for an alternative erosion control measure using sand-filled geosystems. Data set produced at the CIEM flume, Hydralab+
<p>Sand-filled geosystems have the potential to mimic aspects of natural and nature-based features that can enhance the resilience of coastal areas challenged by climate, with additional (structural) reinforcement.</p> <p>Knowledge gaps can be identified. For instance, (i) the sediment transport mechanisms around the geosystem; (ii) the amount of erosion in the leeside when the system is overtopped; (iii) quantitative contribution the geosystem for the wave overtopping reduction; and (iv) failure mechanisms of the geosystem under extreme conditions. Specific tests are proposed in order to fill the defined knowledge gap and answer the following research questions:</p> <ol> <li>How do nearshore coastal processes (wave transformation, sediment transport) and wave structure interactions during extreme events differ from those during more usual big storm conditions for situations with and without the geosystem?</li> <li>How do feedbacks between the hydrodynamics and morphology of natural and nature-based features affect flooding, erosion, and recovery of coastal areas when<br> erosion is limited by the 'geosystem'?</li> <li>How to conceive a dynamic coastal protection that can easily adapt to climate change in areas experiencing coastal squeeze (i.e. dense urban environment and human infrastructure with sea encroaching land) and vulnerable to coastal erosion and flooding risks?</li> </ol> <p>The set of experiments, done at the large wave Flume (CIEM) in Barcelona, are here described in order to answer the previous questions. These experiments started on October 2018 and ended at the end of November 2018. These tests include different configurations: an initial benchmark tests in order to test the wave conditions were no geosystem protection is used, a second layout with a geotube used as a geosystem protection and finally a third layout were geobags are used as a protection.</p>
Enrichment of gene sets altered in dram1 mutant zebrafish larvae
<p>Enrichment of gene sets altered in dram1 mutant zebrafish larvae.</p> <p>A. Gene Ontology categories significantly over and underrepresented in the significant genes differentially regulated between <em>dram1</em><sup>∆19n/∆19n </sup>PBS-injected mutants compared to <em>dram1</em><sup>+/+</sup> larvae.</p> <p>B. Gene sets from the MSigDB C2 database significantly positively correlated to the <em>dram1</em><sup>∆19n/∆19n </sup>mutants transcriptome compared to <em>dram1</em><sup>+/+</sup> larvae.</p> <p>C. Gene sets from the MSigDB C2 database significantly negatively correlated to the <em>dram1</em><sup>∆19n/∆19n </sup>mutants transcriptome compared to <em>dram1</em><sup>+/+</sup> larvae.</p>
Data set for Sicoli et al. - Conformational tuning of a DNA-bound transcription factor
<p>The data set contains NMR, EPR and MD data. The NMR folder contains 1H-15N correlation NMR data of DNA-bound MAX with a paramagnetic MTSL spin label at position 5, with a chemically reduced, diamagnetic spin label, respectively. The EPR folder contains DEER data of MAX for three difference labeling positions R5C, G35C and R55C, with and without bound DNA. The MD folder contains MD trajecrories at three different temperatures, 310 K, 320 K and 330 K. Further details can be found in the readme.txt files in the respective folders.</p>
Data set on linguistic similarity of German dialects
<p>- The data provide information on pairwise dialect similarities for 439 NUTS 3 regions in Germany</p> <p><em>- </em>Data come from the maps and questionnaires of the Sprachatlas des Deutschen Reichs, digitized using ArcGIS software</p> <p>- The data source is a questionnaire with translations of standardized German sentences into local dialects between 1879 and 1888</p> <p>- The measure is defined as the number of co-occurrences for all pairs of sites (z-scaled)</p>
Human SET domain bifurcated 1 (SETDB1), Tudor domain; A Target Enabling Package
<p>SETDB1 is a H3K9 methyltransferase involved in transcriptional silencing with a catalytic SET domain and a triple Tudor domain containing a methyl-lysine binding site. SGC Toronto previously solved the apo structure of the Tudor domain (PDB code 3DLM). Amplification of SETDB1 in over 15% lung adenocarcinoma correlates with high mRNA and protein levels and its depletion in SETDB1-amplified cells reduces cancer growth in cell culture and nude mice models, whereas its overexpression increases tumour invasiveness (Rodriguez-Paredes et al. Oncogene 2014, Shah et al. Epigenetic Chromatin 2014). Several histone methyltransferases are known to have non-catalytic functions that might be alternative targeting strategies. For instance, recognition of H3K9 methylation by the ankyrin repeat of the methyltransferase GLP is required for efficient establishment of H3K9 methylation (Liu et al. Genes Dev. 2015). No catalytic domain inhibitor of SETDB1 has been reported to date. The goal of this TEP is to enable the discovery of potent, selective compounds targeting the Tudor domain of SETDB1.</p>
Serial Rotation Electron Diffraction (automated continuous RED) raw data sets
<p><strong>Serial Rotation Electron Diffraction (automated continuous RED) raw data sets </strong></p> <p>Containing:</p> <p>TIFF images for particle recognition</p> <p>SMV files for XDS processing</p> <p>XDS input files (automatically generated)</p>
Synthetic Data for Neutrophil Analysis: Sets with irregular shapes and Poisson noise
<p><strong>Synthetic Datasets with irregular shapes and Poisson noise.</strong></p> <p><strong>Part of the PhagoSight neutrophil tracking and analysis package (Henry, et al., PLOS ONE, 2013):</strong></p> <p> </p> <p>https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0072636</p> <p>http://www.phagosight.org</p> <p>https://github.com/phagosight/phagosight</p> <p> </p> <p>A series of synthetic data sets that reproduce different behaviour characteristics of migrating neutrophils were generated in MATLAB. The data sets consisted of six artificial neutrophils that travelled along paths that presented different conditions of tortuosity, times to activation and proximity to other neutrophils during 98 time frames.</p> <p>Numerous data sets of neutrophils in zebrafish were carefully observed before setting the characteristics. Six trajectories were manually determined by setting the row, column positions of the centroids at every time point for 98 time frames. Each trajectory was designed so that it would represent different neutrophil behaviours: some trajectories were very oriented and had movements with uniform distance between time frames, whilst others were less uniform and would move at different velocities, some were tortuous whilst others were straight. The trajectories of cells 1 and 2 collided several times in the second half of the time frames whilst cells 3 and 4 collided at the beginning of the movement. Cell 6 migrated without meandering and then stopped at the end (which represents the wound area of an inflammation-based experiment) whilst 5 presented a delayed activation. </p> <p>Each time frame consisted of 11 slices of z-stack each with 275 x 275 pixels, where the neutrophils were formed by <strong>irregular shapes </strong>(sum of Gaussians) and <strong>Poisson Noise</strong> (check the corresponding sets with regular shapes, i.e. Gaussians with Gaussian noise plus another set with a <strong>single large neutrophil</strong> and Poisson noise) distributions of higher intensities than the background. The orientation of the Poisson varied according to the displacement of the artificial neutrophils, <em>i.e.</em>they were round when the cells were static, or elongated when in movement. The tracks with the Shapes were saved as the <em>gold standard</em> and five different data sets were generated by adding varying levels of white Poisson noise resulting in data sets with distributions with increasing similarity between the neutrophils and the background reflected by the decreasing values of the Bhattacharyya Distance (1.61, 1.25, 1, 0.66, 0.45) as defined by Coleman 1979.</p> <p> </p> <p>Files corresponding to the sets with irregular shapes and Poisson noise (noise increases from 1 to 5):</p> <ul> <li><strong> x,y,t trajectories ThreeDTracks</strong></li> <li><strong> Ground Truth syntheticData_P_mat_La </strong></li> <li><strong> First data set syntheticData_P1_mat_Re</strong></li> <li><strong> Second data set syntheticData_P2_mat_Re</strong></li> <li><strong> Third data set syntheticData_P3_mat_Re</strong></li> <li><strong> Fourth data set syntheticData_P4_mat_Re</strong></li> <li><strong> Fifth data set syntheticData_P5_mat_Re</strong></li> </ul> <p>Corresponding GIF files are also included as illustrations of the cells in motion.</p> <p> </p> <p>Main Reference:</p> <p><a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0072636"><strong><em>PhagoSight</em>: An Open-Source MATLAB® Package for the Analysis of Fluorescent Neutrophil and Macrophage Migration in a Zebrafish Model</strong> </a><br> Henry KM, Pase L, Ramos-Lopez CF, Lieschke GJ, Renshaw SA, Reyes-Aldasoro CC. (2013) <em>PhagoSight</em>: An Open-Source MATLAB® Package for the Analysis of Fluorescent Neutrophil and Macrophage Migration in a Zebrafish Model. PLOS ONE 8(8): e72636. <a href="https://doi.org/10.1371/journal.pone.0072636">https://doi.org/10.1371/journal.pone.0072636</a></p>
Synthetic Data for Neutrophil Analysis: Sets with regular shapes and Gaussian noise
<p><strong>Synthetic Datasets with regular shapes and Gaussian noise.</strong></p> <p><strong>Part of the PhagoSight neutrophil tracking and analysis package (Henry, et al., PLOS ONE, 2013):</strong></p> <p> </p> <p>https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0072636</p> <p>http://www.phagosight.org</p> <p>https://github.com/phagosight/phagosight</p> <p> </p> <p>A series of synthetic data sets that reproduce different behaviour characteristics of migrating neutrophils were generated in MATLAB. The data sets consisted of six artificial neutrophils that travelled along paths that presented different conditions of tortuosity, times to activation and proximity to other neutrophils during 98 time frames.</p> <p>Numerous data sets of neutrophils in zebrafish were carefully observed before setting the characteristics. Six trajectories were manually determined by setting the row, column positions of the centroids at every time point for 98 time frames. Each trajectory was designed so that it would represent different neutrophil behaviours: some trajectories were very oriented and had movements with uniform distance between time frames, whilst others were less uniform and would move at different velocities, some were tortuous whilst others were straight. The trajectories of cells 1 and 2 collided several times in the second half of the time frames whilst cells 3 and 4 collided at the beginning of the movement. Cell 6 migrated without meandering and then stopped at the end (which represents the wound area of an inflammation-based experiment) whilst 5 presented a delayed activation. </p> <p>Each time frame consisted of 11 slices of z-stack each with 275 x 275 pixels, where the neutrophils were formed by Gaussian distributions of higher intensities than the background and <strong>Gaussian noise </strong>(check the corresponding irregular shapes with Poisson noise plus another set with a <strong>single large neutrophil</strong> and Poisson noise). The orientation of the Gaussians varied according to the displacement of the artificial neutrophils, <em>i.e.</em>they were round when the cells were static, or elongated when in movement. The tracks with the Gaussians were saved as the <em>gold standard</em> and five different data sets were generated by adding varying levels of white Gaussian noise resulting in data sets with distributions with increasing similarity between the neutrophils and the background reflected by the decreasing values of the Bhattacharyya Distance (1.61, 1.25, 1, 0.66, 0.45) as defined by Coleman 1979.</p> <p> </p> <p>Files corresponding to the sets with irregular shapes and Poisson noise (noise increases from 1 to 6):</p> <ul> <li><strong> x,y,t trajectories ThreeDTracks</strong></li> <li><strong> Ground Truth syntheticData0_mat_Re </strong></li> <li><strong> First data set syntheticData1_mat_Re</strong></li> <li><strong> Second data set syntheticData2_mat_Re</strong></li> <li><strong> Third data set syntheticData3_mat_Re</strong></li> <li><strong> Fourth data set syntheticData4_mat_Re</strong></li> <li><strong> Fifth data set syntheticData5_mat_Re</strong></li> <li><strong> Sixth data set syntheticData6_mat_Re</strong></li> </ul> <p> </p> <p>Corresponding GIF files are also included as illustrations of the cells in motion.</p> <p> </p> <p>Main Reference:</p> <p><a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0072636"><strong><em>PhagoSight</em>: An Open-Source MATLAB® Package for the Analysis of Fluorescent Neutrophil and Macrophage Migration in a Zebrafish Model</strong> </a><br> Henry KM, Pase L, Ramos-Lopez CF, Lieschke GJ, Renshaw SA, Reyes-Aldasoro CC. (2013) <em>PhagoSight</em>: An Open-Source MATLAB® Package for the Analysis of Fluorescent Neutrophil and Macrophage Migration in a Zebrafish Model. PLOS ONE 8(8): e72636. <a href="https://doi.org/10.1371/journal.pone.0072636">https://doi.org/10.1371/journal.pone.0072636</a></p>
TROTS - The Radiotherapy Optimisation Test Set
<p>The Radiotherapy Optimisation Test Set (TROTS) is an extensive set of problems originating from radiotherapy (radiation therapy) treatment planning. This dataset is created for 2 purposes: (1) to supply a large-scale dense dataset to measure performance and quality of mathematical solvers, and (2) to supply a dataset to investigate the multi-criteria optimisation and decision-making nature of the radiotherapy problem. The dataset contains 120 problems (patients), divided over 6 different treatment protocols/tumour types. Each problem contains numerical data, a configuration for the optimisation problem, and data required to visualise and interpret the results. The data is stored as HDF5 compatible Matlab files, and includes scripts to work with the dataset.</p> <p> </p> <p>The set as present in this version is of date 13 May 2019. Updated versions of the Scripts and other extensions can be found at the following pages:</p> <p> </p> <p>Persistent page with links: <a href="https://hdl.handle.net/1765/116520">Erasmus University Rotterdam Library</a></p> <p>Mirror project page: <a href="http://www.sebastiaanbreedveld.nl/trots">TROTS Mirror</a></p> <p>Main publication: <a href="https://dx.doi.org/10.1016/j.dib.2017.03.037">S. Breedveld & B. Heijmen, Data for TROTS - The Radiotherapy Optimisation Test Set, Data in Brief 12 (2017) 143-149 </a></p>
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>
EMSL/PNNL Basis Set Exchange Archive
<p>This data reflects the output of the PNNL/EMSL Basis Set Exchange collected in the summer of 2018.</p> <p>The output was collected via script, and contains all basis set and format combinations.</p> <p>In this dataset are archives representing all the available output formats. Within those archives are all the basis sets found on the BSE website.</p> <ul> <li><strong>tar.bz2</strong> files are meant to be use with Linux and have Unix line endings.</li> <li><strong>Zip</strong> files are meant for Windows and have the appropriate line endings for Windows.</li> <li>Archives with the suffix <strong>.min_gen</strong> contain the output of the BSE when the <em>Optimize General Contractions</em> button is checked. </li> </ul> <p>If the output when using <em>Optimize General Contractions</em> was the same as with the button not checked, the output file not kept. Instead, use the file from the archive without the <strong>.min_gen</strong> suffix.</p> <p>For more information about the new basis set exchange, visit the new website: <a href="https://www.basissetexchange.org">https://www.basissetexchange.org</a></p> <p>For questions/comments/issues, email <a href="mailto:bse@molssi.org">bse@molssi.org</a></p>
Prince Edward Island (Canada) Predictive Soil Mapping data set (30 m)
<p>Prince Edward Island (Canada) Predictive Soil Mapping data set. Training points include:</p> <ul> <li>soil organic matter (624 points): unit: percent, g/100g ) samples from the topsoil (0-23 cm depth),</li> <li>soil types (672 points),</li> </ul> <p>Covariate layers include:</p> <ul> <li>DEM derivatives (Channel_Network_Base_Level.tif, MRRTF.tif, Relative_Slope_Position.tif, Slope.tif, TWI.tif, Valley_Depth.tif, Vertical_Distance_To_Channel_Network.tif),</li> <li>landcover_2016_reclassify.tif (categorical values),</li> <li>DSS_soil_polygons.tif (soil polygons),</li> </ul> <p>Some covariates are type numeric, some type factor. To use the pre-processed data download only the RDS file. E.g. "PEI100m.soil.rds" contains all covariates layers resampled to 100 m resolution and all training points.</p>
ScienceDex guides
Understand access before you commit
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.