CheckCell

Abstract

Testing and static analysis can help root out bugs in programs, but not in data. This paper introduces data debugging , an approach that combines program analysis and statistical analysis to automatically find potential data errors. Since it is impossible to know a priori whether data are erroneous, data debugging instead locates data that has a disproportionate impact on the computation. Such data is either very important, or wrong. Data debugging is especially useful in the context of data-intensive programming environments that intertwine data with programs in the form of queries or formulas. We present the first data debugging tool, CheckCell, an add-in for Microsoft Excel. CheckCell identifies cells that have an unusually high impact on the spreadsheet's computations. We show that CheckCell is both analytically and empirically fast and effective. We show that it successfully finds injected typographical errors produced by a generative model trained with data entry from 169,112 Mechanical Turk tasks. CheckCell is more precise and efficient than standard outlier detection techniques. CheckCell also automatically identifies a key flaw in the infamous Reinhart and Rogoff spreadsheet.

Waterborne pathogens treatment using biosynthesized silver‐mordenite nanocomposites in aqueous propolis extracts: Characterization and antibacterial activity

“…The changes in the crystal structure of mordenite were confirmed by estimating the unit cell parameters and volume for all samples. The unit cell parameters and volume were calculated using the CheckCell program for all samples [41]. Then, the unit cell volume was plotted in a relation to the variation of AgNO 3 concentrations.…”

Eu Doping in the GdCd7.88 Quasicrystal and Its Approximant Crystal GdCd6

“…17 All the structural refinements were performed in Jana 2006 software, 18 and the crystal structure was visualized using the software VESTA. 19 The unit cell parameter of the AC samples was indexed from the PXRD data in CheckCell software 20 and compared to SCXRD results. The sample composition was asserted using two techniques, scanning…”

Structural Analysis of the Gd–Au–Al 1/1 Quasicrystal Approximant Phase across Its Composition-Driven Magnetic Property Changes

“…Powder X-ray diffraction (PXRD) data were collected on a Bruker D8-ADVANCE diffractometer with θ – 2θ diffraction geometry and Cu–Kα radiation (Kα1 = 1.540598 Å and Kα2 = 1.544390 Å) at room temperature. Powdered samples were applied to a zero-diffraction plate, and the diffraction patterns were measured with an internal silicon standard for the 2θ range of 10–90° with a step size of 0.01°and an exposure time of 1 s. The analysis of PXRD data was performed using HighScore Plus 3.0 software from PANalytical, and the lattice parameters were indexed and refined with the Checkcell program package …”

“…Powdered samples were applied to a zerodiffraction plate, and the diffraction patterns were measured with an internal silicon standard for the 2θ range of 10−90°with a step size of 0.01°and an exposure time of 1 s. The analysis of PXRD data was performed using HighScore Plus 3.0 software from PANalytical, 12 and the lattice parameters were indexed and refined with the Checkcell program package. 13 To collect high-quality single-crystal X-ray diffraction (SCXRD) data, we selected faceted millimeter-sized single grain crystals and removed the residual flux from the surface by cutting them into smaller grains with a size of 50−100 μm to reduce sample absorption effects during SCXRD measurements. All of the SCXRD measurements were performed at room temperature on a D8 venture singlecrystal X-ray diffractometer with a Mo-sealed tube X-ray source (Mo Kα = 0.71073 Å) and a shutterless PHOTON 100 CMOS detector.…”