• Dataset for "The genome sequence of the wild tomato Solanum pimpinellifolium provides insights into salinity tolerance"

      Bougouffa, Salim; Morton, Mitchell J. L.; Lightfoot, D. J.; Mohamad Razali, Rozaimi; Alam, Intikhab; Essack, Magbubah; Arold, Stefan T.; Kamau, Allan; Schmöckel, Sandra M.; Pailles, Yveline; Shahid, Mohammed; Michell, Craig T; Al-Babili, Salim; Ho, Shwen; Tester, Mark A.; Bajic, Vladimir B.; Negrão, Sónia (2016)
    • Supplementary Material for: Observations and cloud-resolving modeling of haboob dust storms over the Arabian Peninsula

      Anisimov, Anatolii; Axisa, Duncan; Kucera, Paul A.; Mostamandi, Suleiman; Stenchikov, Georgiy L. (2018-06-28)
    • Data for "Space-time Tomography for Continuously Deforming Objects"

      Zang, Guangming; Idoughi, Ramzi; Tao, Ran; Lubineau, Gilles; Wonka, Peter; Heidrich, Wolfgang (2018-04-26)
    • Data for Figures 6 and 8 for "Time-dependent Pore Filling"

      Jang, Junbong; Santamarina, Carlos; Sun, Zhonghao (2018)
    • Seismic Experiment at North Arizona To Locate Washington Fault - 3D Data Interpolation

      Hanafy, Sherif M.; Lui, Shengdong; Wang, Xin; Dai, Wei; van der Neut, Joost; Ghassal, Bandar Ismail; Wu, Qiong (2008-10)
      The recorded data is interpolated using sinc technique to create the following two data sets 1. Data Set # 1: Here, we interpolated only in the receiver direction to regularize the receiver interval to 1 m, however, the source locations are the same as the original data (2 and 4 m source intervals). Now the data contains 6 lines, each line has 121 receivers and a total of 240 shot gathers. 2. Data Set # 2: Here, we used the result from the previous step, and interpolated only in the shot direction to regularize the shot interval to 1 m. Now, both shot and receivers has 1 m interval. The data contains 6 lines, each line has 121 receivers and a total of 726 shot gathers.
    • Seismic Experiment at North Arizona To Locate Washington Fault - 3D Field Test

      Hanafy, Sherif M; Lui, Shengdong; Wang, Xin; Dai, Wei; van der Neut, Joost; Ghassal, Bandar Ismail; Wu, Qiong (2008-10)
      No. of receivers in the inline direction: 80, Number of lines: 6, Receiver Interval: 1 m near the fault, 2 m away from the fault (Receivers 1 to 12 at 2 m intervals, receivers 12 to 51 at 1 m intervals, and receivers 51 to 80 at 2 m intervals), No. of shots in the inline direction: 40, Shot interval: 2 and 4 m (every other receiver location). Data Recording The data are recorded using two Bison equipment, each is 120 channels. We shot at all 240 shot locations and simultaneously recorded seismic traces at receivers 1 to 240 (using both Bisons), then we shot again at all 240 shot locations and we recorded at receivers 241 to 480. The data is rearranged to match the receiver order shown in Figure 3 where receiver 1 is at left-lower corner, receivers increase to 80 at right lower corner, then receiver 81 is back to left side at Y = 1.5 m, etc.
    • Seismic Experiment at North Arizona To Locate Washington Fault - 2D Field Test

      Hanafy, Sherif M.; Lui, Shengdong; Wang, Xin; Dai, Wei; van der Neut, Joost; Ghassal, Bandar Ismail; Wu, Qiong (2008)
    • Qademah Fault Seismic Data Set - Northern Part

      Hanafy, Sherif M.; Lu, Kai; Hota, Mrinal Kanti; Guo, Bowen; Tarhini, Ahmad (2015-01)
      Objective: Is the Qademah fault that was detected in 2010 the main fault? We collected a long 2D profile, 526 m, where the fault that was detected in 2010 is at around 300 m. Layout: We collected 264 CSGs, each has 264 receivers. The shot and receiver interval is 2 m. We also collected an extra 48 CSGs with offset = 528 to 622 m with shot interval = 2 m. The receivers are the same as the main survey.
    • Qademah Fault Artificial Ambient Noise Test

      Hanafy, Sherif M.; AlTheyab, Abdullah (2014)
      This data set was collected on 7 Dec. 2014 by Sherif and Abdullah. The receiver layout is the same as that of the passive data test at the same location, which is described as follow: 288 receivers are used and arranged as follow - 12 lines, cross-line offset = 10 m - 24 receiver in each line, inline offset = 5 m - Additional 24 receivers are placed at line # 6, where the receiver interval is decreased to 2.5 m. Data Recording: We start recording at 10:10 am and stop recording at 11:25 am. Each record has total of 20 s, with time interval of 0.004 ms and around 2 s overlap between each two successive files. Source: We used a piece of wood attached to a pick-up truck to create the noise; we drove around the array of receivers in a rectangle-shape route during the recording time.
    • Qademah Fault Passive Data

      Hanafy, Sherif M.; Lu, Kai; Hota, Mrinal Kanti; Guo, Bowen; Tarhini, Ahmad (2014)
      OBJECTIVE: In this field trip we collect passive data to 1. Convert passive to surface waves 2. Locate Qademah fault using surface wave migration INTRODUCTION: In this field trip we collected passive data for several days. This data will be used to find the surface waves using interferometry and then compared to active-source seismic data collected at the same location. A total of 288 receivers are used. A 3D layout with 5 m inline intervals and 10 m cross line intervals is used, where we used 12 lines with 24 receivers at each line. You will need to download the file (rec_times.mat), it contains important information about 1. Field record no 2. Record day 3. Record month 4. Record hour 5. Record minute 6. Record second 7. Record length P.S. 1. All files are converted from original format (SEG-2) to matlab format P.S. 2. Overlaps between records (10 to 1.5 sec.) are already removed from these files
    • Qademah Fault 3D Survey

      Hanafy, Sherif M.; Lu, Kai; Hota, Mrinal Kanti; Guo, Bowen; Tarhini, Ahmad (2014)
      Objective: Collect 3D seismic data at Qademah Fault location to 1. 3D traveltime tomography 2. 3D surface wave migration 3. 3D phase velocity 4. Possible reflection processing Acquisition Date: 26 – 28 September 2014 Acquisition Team: Sherif, Kai, Mrinal, Bowen, Ahmed Acquisition Layout: We used 288 receiver arranged in 12 parallel lines, each line has 24 receiver. Inline offset is 5 m and crossline offset is 10 m. One shot is fired at each receiver location. We use the 40 kgm weight drop as seismic source, with 8 to 15 stacks at each shot location.
    • Data: Olduvai Gorge Project Trip # 4

      Hanafy, Sherif M.; Lu, Kai (2016)
    • Data: Olduvai Gorge Project Trip # 3

      Hanafy, Sherif M.; Lu, Kai (2016)
    • Data: Olduvai Gorge Project Trip # 2

      Schuster, Gerard T.; Hanafy, Sherif M.; Lu, Kai (2016)
    • Data: Olduvai Gorge Project Trip # 1

      Schuster, Gerard T.; Hanafy, Sherif M.; Lu, Kai (2016)
    • Dust impact on the Red Sea modelling framework and supporting data

      Osipov, Sergey; Stenchikov, Georgiy L. (2018-01-08)
    • Supplementary Material for: A Geometric Approach to Visualization of Variability in Functional Data

      Xie, Weiyi; Kurtek, Sebastian; Bharath, Karthik; Sun, Ying (Figshare, 2016)
      <p>We propose a new method for the construction and visualization of boxplot-type displays for functional data. We use a recent functional data analysis framework, based on a representation of functions called square-root slope functions, to decompose observed variation in functional data into three main components: amplitude, phase, and vertical translation. We then construct separate displays for each component, using the geometry and metric of each representation space, based on a novel definition of the median, the two quartiles, and extreme observations. The outlyingness of functional data is a very complex concept. Thus, we propose to identify outliers based on any of the three main components after decomposition. We provide a variety of visualization tools for the proposed boxplot-type displays including surface plots. We evaluate the proposed method using extensive simulations and then focus our attention on three real data applications including exploratory data analysis of sea surface temperature functions, electrocardiogram functions and growth curves.</p>
    • Supplementary Material for: Measurements of Positively Charged Ions in Premixed Methane-Oxygen Atmospheric Flames

      Alquaity, Awad B. S.; Han, Jie; Chahine, May; Selim, Hatem; Belhi, Memdouh; Sarathy, Mani; Bisetti, Fabrizio; Farooq, Aamir (Figshare, 2017)
      <p>Cations and anions are formed as a result of chemi-ionization processes in combustion systems. Electric fields can be applied to reduce emissions and improve combustion efficiency by active control of the combustion process. Detailed flame ion chemistry models are needed to understand and predict the effect of external electric fields on combustion plasmas. In this work, a molecular beam mass spectrometer (MBMS) is utilized to measure ion concentration profiles in premixed methane–oxygen argon burner-stabilized atmospheric flames. Lean and stoichiometric flames are considered to assess the dependence of ion chemistry on flame stoichiometry. Relative ion concentration profiles are compared with numerical simulations using various temperature profiles, and good qualitative agreement was observed for the stoichiometric flame. However, for the lean flame, numerical simulations misrepresent the spatial distribution of selected ions greatly. Three modifications are suggested to enhance the ion mechanism and improve the agreement between experiments and simulations. The first two modifications comprise the addition of anion detachment reactions to increase anion recombination at low temperatures. The third modification involves restoring a detachment reaction to its original irreversible form. To our knowledge, this work presents the first detailed measurements of cations and flame temperature in canonical methane–oxygen-argon atmospheric flat flames. The positive ion profiles reported here may be useful to validate and improve ion chemistry models for methane-oxygen flames.</p>
    • Supplementary Material for: Tukey <i>g</i>-and-<i>h</i> Random Fields

      Xu, Ganggang; Genton, Marc G. (Figshare, 2016)
      <p>We propose a new class of transGaussian random fields named Tukey <i>g</i>-and-<i>h</i> (TGH) random fields to model non-Gaussian spatial data. The proposed TGH random fields have extremely flexible marginal distributions, possibly skewed and/or heavy-tailed, and, therefore, have a wide range of applications. The special formulation of the TGH random field enables an automatic search for the most suitable transformation for the dataset of interest while estimating model parameters. Asymptotic properties of the maximum likelihood estimator and the probabilistic properties of the TGH random fields are investigated. An efficient estimation procedure, based on maximum approximated likelihood, is proposed and an extreme spatial outlier detection algorithm is formulated. Kriging and probabilistic prediction with TGH random fields are developed along with prediction confidence intervals. The predictive performance of TGH random fields is demonstrated through extensive simulation studies and an application to a dataset of total precipitation in the south east of the United States. Supplementary materials for this article are available online.</p>