Updated README

README.rst

@@ -29,7 +29,7 @@ PynPoint
 
 PynPoint is a generic, end-to-end pipeline for the data reduction and analysis of high-contrast imaging data of planetary and brown dwarf companions. The package is stable, extensively tested, and actively maintained.
 
-For a first impression, please have a look at the `SPHERE/ZIMPOL <https://www.eso.org/sci/facilities/paranal/instruments/sphere.html>`_ H-alpha dataset of `HD 142527B <http://ui.adsabs.harvard.edu/abs/2019A%26A...622A.156C>`_, which can be downloaded `here <https://people.phys.ethz.ch/~stolkert/pynpoint/hd142527_zimpol_h-alpha.tgz>`_.
+For a first impression, `this example <https://people.phys.ethz.ch/~stolkert/pynpoint/hd142527_zimpol_h-alpha.tgz>`_ of `HD 142527B <https://ui.adsabs.harvard.edu/abs/2019A%26A...622A.156C/abstract>`_ shows a typical workflow.
 
 Documentation
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@@ -44,7 +44,7 @@ Please subscribe to the `mailing list <https://pynpoint.readthedocs.io/en/latest
 Attribution
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-If you use PynPoint in your publication then please cite `Stolker et al. (2019) <http://ui.adsabs.harvard.edu/abs/2019A%26A...621A..59S>`_. Please also cite `Amara & Quanz (2012) <http://ui.adsabs.harvard.edu/abs/2012MNRAS.427..948A>`_ as the origin of PynPoint, which focused initially on the use of principal component analysis (PCA) as a PSF subtraction method. In case you use specifically the PCA-based background subtraction module or the wavelet based speckle suppression module, please give credit to `Hunziker et al. (2018) <http://ui.adsabs.harvard.edu/abs/2018A%26A...611A..23H>`_ or `Bonse, Quanz & Amara (2018) <http://ui.adsabs.harvard.edu/abs/2018arXiv180405063B>`_, respectively.
+If you use PynPoint in your publication then please cite `Stolker et al. (2019) <https://ui.adsabs.harvard.edu/abs/2019A%26A...621A..59S/abstract>`_. Please also cite `Amara & Quanz (2012) <https://ui.adsabs.harvard.edu/abs/2012MNRAS.427..948A/abstract>`_ as the origin of PynPoint, which focused initially on the use of principal component analysis (PCA) as a PSF subtraction method. In case you use specifically the PCA-based background subtraction module or the wavelet based speckle suppression module, please give credit to `Hunziker et al. (2018) <https://ui.adsabs.harvard.edu/abs/2018A%26A...611A..23H/abstract>`_ or `Bonse, Quanz & Amara (2018) <https://ui.adsabs.harvard.edu/abs/2018arXiv180405063B/abstract>`_, respectively.
 
 Contributing
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