RSS 1.0Datasets
Recent Submissions
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VCC-LF dataset(2019-09-01) [Dataset]This is readme for VCC-LF dataset. This dataset provides light field mat files that capture by Lytro I. The light field resolusion is [h,w,u,v,d]. If you use these data or our toolkit code, please cite our paper properly @inproceedings{ lirsiggraphasia2019, title={Hierarchical and View-invariant Light Field Segmentation by Maximizing Entropy Rate on 4D Ray Graphs}, author={Li, Rui and Heidrich, Wolfgang}, booktitle={ACM Transactions on Graphics (Proc. SIGGRAPH Asia)}, year={2019}, publisher={ACM} }
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NASA GISS ModelE modelling framework and supporting data for the study of the role of the SO2 radiative effect in sustaining the volcanic winter and soothing the Toba impact on climate(2019-07-10) [Dataset]This dataset is the supporting information for the article "The role of the SO2 radiative effect in sustaining the volcanic winter and soothing the Toba impact on climate." It includes the model, rundecks, auxiliary information and other data necessary to reproduce the simulations described in the paper.
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Polarization Image Dataset(2019-04-18) [Dataset]The polarization image dataset is a collection of 40 high resolution color polarization data, with three polarization components, total intensity, Degree of Linear Polarization (DoLP), Angle of Linear Polarization (AoLP), for each scene. This dataset could be used for research in polarization imaging, including but not limited to the design of imaging polarimeter, polarization image processing, algorithm development, polarization visualization, applications etc. This dataset consists of versatile scenes with natural unpolarized and polarized illuminations. They have been collected in a Division of Time (DoT) manner, with a high extinction ratio polarizer in front of a camera oriented at 0, 45, 90 and 135 degrees respectively. At each angle, 100 images are captured and averaged to suppress noise. A 2 x 2 binning is further adopted to suppress noise. Itot, DoLP and AoLP are then calculated with the four resulting images. The final size of each polarization image is 1024 x 1024 x 3. In addition, various visualization methods are provided along with the data. To run the visualization code, MATLAB 2018b or higher is required.
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Data for "grain-displacive gas migration in fine-grained sediments"(2019-02-03) [Dataset]
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UAV Pathplanning Dataset & Benchmark(2018-12-04) [Dataset]This dataset consists of trajectories, proxy meshes and images generated for pathplanning on real and synthetic scenes. It includes a benchmarking tool allowing new trajectories to output camera images for reconstruction. The tool also allows error and completleness comparison with the groundtruth models and reconstructions
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Data for : Poly(A) Dataset for PAS sequences and pseudo-PAS sequences Classification (fasta format)(2018-11-15) [Dataset]This Dataset contains DNA sequences of the human genome hg38 from GENCODE folder at EBI ftp server (ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_28/GRCh38.primary_assembly.genome.fa.gz) A-Positive set (PAS sequences) Using GENCODE annotation for poly(A) (ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_28/gencode.v28.polyAs.gff3.gz) We selected poly(A) signal annotation. Using bedtools-slop option, we found regions extended 300 bp upstream and 300 bp downstream of the poly(A) hexamer. With the bedtools-getfasta option, we extracted 606 bp fasta sequences from these regions. After eliminating duplicates, we obtained 37’516 presumed true functional poly(A) signal (PAS) sequences. Sequences from this set will be denoted as positive. B- Negative set (pseudo-PAS sequences) For the negative set, we looked for regions extended outside the region covering 1’000 bp upstream and downstream of the positive poly(A) hexamer signal using bedtools-complement. Homer tool was used to find matches for the 12 most frequent human poly(A) variants. Since the number of matches was huge, sampling was used to select 37’516 pseudo-PAS sequences. Sampling was done from each chromosome proportionally to the lengths of the chromosomes and also to the expected frequency of the poly(A) variants. Out of these predictions, for each PAS hexamer, we selected the same number of pseudo-PAS sequences as in the positive set. Training and testing sets We selected randomly from each of the positive and negative datasets 20% of sequences for the independent test data. The testing set thus consisted of 15’020 sequences. The remaining data represented the training set that consisted of 60’012 sequences. Both datasets are balanced relative to the true PAS and pseudo-PAS sequences.
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Advances of Laser-based Underwater Wireless Optical Communication(2018-10-28) [Dataset]The dataset summarized recent advances, including data rate and distance, achieved by published literature in underwater optical communication (UWOC).
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Advances of Laser and LED-based Visible Light Communication(2018-10-28) [Dataset]The dataset summarized recent advances, including data rate and distance, achieved by published literature in laser diode (LD) and light-emitting diode (LED)-based visible light communication (VLC).
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Thesis Chapter 3 tables(2018-09-24) [Dataset]The dataset includes the IDs, phenotypic measurements, multivariate analysis and genome-wide association data collected from the Arabidopsis HapMap population that was used in a high-throughput phenotyping experiment under salt stress to identify the underlying genetic components.
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Data and codes used in "Dust emission modeling using a new high-resolution dust source function in WRF-Chem with implications for air quality over the Middle East and North Africa (MENA)"(2018-07-30) [Dataset]A detailed description of the data files is given in a separate 'ReadMe' file.
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Supplementary Material for: Observations and cloud-resolving modeling of haboob dust storms over the Arabian Peninsula(2018-06-28) [Dataset]This dataset is a supplementary material for "Observations and cloud-resolving modeling of haboob dust storms over the Arabian Peninsula" paper published in JGR. The data contains WRF-Chem source code, namelist file, initial conditions, chemical emissions, a sample of input boundary conditions (for the first day of simulation) and two sample output timesteps for both WRF-Chem domains (the run without spectral nudging). This data subset is enough to test the reproducibility of the simulation. The full model output cannot be uploaded due to data size limitations, and it is available from the authors and KAUST supercomputing Laboratory.
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Data for "Space-time Tomography for Continuously Deforming Objects"(2018-04-26) [Dataset]This is the data for the paper "Space-time Tomography for Continuously Deforming Objects". There are six data sets, including the rose, fluid, dough, lentils, sugar and mushrooms. For each data set, two zip files are provided: 1) input data: the input file that stacked all the post processed projection images, and the geometry file for each projection. 2) results: the reconstructed results using the method described in the paper.
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Dust impact on the Red Sea modelling framework and supporting data(2018-01-08) [Dataset]This dataset is the supporting information for the article "Simulating the Regional Impact of Dust on the Middle East Climate and the Red Sea". It includes the model, initial and boundary conditions, dust optical properties and other data necessary to reproduce the simulations described in the paper.
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Data for Figures 6 and 8 for "Time-dependent Pore Filling"(2018) [Dataset]
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CCDC 1477674: Experimental Crystal Structure Determination : (1,3-bis(2,4,6-trimethylphenyl)imidazol-2-ylidene)-trimethyl-gallium(Cambridge Crystallographic Data Centre, 2017) [Dataset]An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
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CCDC 1477676: Experimental Crystal Structure Determination : (1,3-bis(2,6-diisopropylphenyl)imidazol-2-ylidene)-trimethyl-indium(Cambridge Crystallographic Data Centre, 2017) [Dataset]An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
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CCDC 1477681: Experimental Crystal Structure Determination : (1,3-bis(2,6-diisopropylphenyl)imidazolidin-2-ylidene)-trimethyl-gallium(Cambridge Crystallographic Data Centre, 2017) [Dataset]An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
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CCDC 1477682: Experimental Crystal Structure Determination : (1,3-bis(2,6-diisopropylphenyl)imidazolidin-2-ylidene)-trimethyl-indium(Cambridge Crystallographic Data Centre, 2017) [Dataset]An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
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CCDC 1477677: Experimental Crystal Structure Determination : (1,3-bis(2,6-diisopropylphenyl)imidazol-2-ylidene)-trimethyl-gallium(Cambridge Crystallographic Data Centre, 2017) [Dataset]An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.