Coherent X-ray Scattering Reveals Nanoscale Fluctuations in Hydrated Proteins

Datasets: "Figure1a.csv": scattering intensity of hydrated proteins in Wide-Angle X-ray Scattering for different fluences (in units of photons/second/area). "Figure1a_inset.csv": scattering intensity of hydrated proteins in Small-Angle X-ray Scattering for different fluences (in units of photons/second/area). "Figure1b.csv": Intensity autocorrelation functions <em>g</em>2 at momentum transfer <em>Q</em> = 0.08 1/nm for different fluences (in units of photons/second/area). "Figure1b_inset.csv": decay rate (in second) as a function of the momentum transfer Q (in 1/nm) for different fluences (in units of photons/second/area). "Figure1c.csv": decay rate (in second) for variable fluence (in photons/second/um^2) at the momentum transfer <em>Q</em> = 0.08 1/nm. "Figure1d.csv": renormalised intensity autocorrelation functions <em>g</em>2 at momentum transfer <em>Q</em> = 0.08 1/nm for variable fluence (in photons/second/um^2), where the time axis is normalised to the corresponding fluence <em>F</em> by calculating t/(1 + <em>a</em> · <em>F</em>·<em>τ</em>0), where <em>τ</em>0 is the equilibrium time constant extracted by extrapolation to <em>F</em>=0 (from data in "Figure1c.csv)" "Figure2a.csv": The Wide-Angle X-ray Scattering scattering intensity at different temperatures T=180-290 K "Figure2b.csv": The Small-Angle X-ray Scattering scattering intensity at different temperatures T=180-290 K "Figure2c.csv": Intensity autocorrelation functions <em>g</em>2 for different temperatures (T=180-290 K) at momentum transfer <em>Q</em> = 0.1 1/nm. "Figure2d-2e.csv": time constants (in second) and the Kohlrausch-Williams-Watts (KWW) exponent extracted from the fits of data in "Figure2c.csv" as a function of temperature (in K) "Figure3b.csv": The normalised variance Chi_<em>T</em> at different temperatures (T=180-290 K) extracted from the two-time correlation functions. "Figure3c.csv": The maximum of the normalised variance Chi_0 as a function of temperature (in K). <br> Additionally, a Jupyter notebook "open-data.ipynb" which shows how to load and plot the data from the csv files in Python.