diff --git a/CHANGELOG.md b/CHANGELOG.md
index 0eb818e67e..fc0be4e350 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -28,8 +28,10 @@
     - Make Binner accept accelerated=False (#1887)
     - Added checks on memory allocations within `FiniteDifferenceLibrary.cpp` and verified the status of the return in `GradientOperator` (#1929)
     - Build release version of `cilacc.dll` for Windows. Previously was defaulting to the debug build (#1928)
+    - Armijo step size rule now by default initialises the search for a step size from the previously calculated step size (#1934)
   - Changes that break backwards compatibility:
     - CGLS will no longer automatically stop iterations once a default tolerance is reached. The option to pass `tolerance` will be deprecated to be replaced by `optimisation.utilities.callbacks` (#1892)
+     
 
 * 24.1.0
   - New Features:
@@ -54,6 +56,7 @@
     - BlockOperator that would return a BlockDataContainer of shape (1,1) now returns the appropriate DataContainer. BlockDataContainer direct and adjoint methods accept DataContainer as parameter (#1802).
     - BlurringOperator: remove check for geometry class (old SIRF integration bug) (#1807)
     - The `ZeroFunction` and `ConstantFunction` now have a Lipschitz constant of 1. (#1768)
+    - Update dataexample remote data download to work with windows and use zenodo_get for data download (#1774)
   - Changes that break backwards compatibility:
     - Merged the files `BlockGeometry.py` and `BlockDataContainer.py` in `framework` to one file `block.py`. Please use `from cil.framework import BlockGeometry, BlockDataContainer` as before (#1799)
     - Bug fix in `FGP_TV` function to set the default behaviour not to enforce non-negativity (#1826).
diff --git a/Wrappers/Python/cil/optimisation/algorithms/FISTA.py b/Wrappers/Python/cil/optimisation/algorithms/FISTA.py
index b507e42f04..05143e8157 100644
--- a/Wrappers/Python/cil/optimisation/algorithms/FISTA.py
+++ b/Wrappers/Python/cil/optimisation/algorithms/FISTA.py
@@ -213,8 +213,19 @@ def update_objective(self):
         .. math:: f(x) + g(x)
 
         """
-        self.loss.append(self.f(self.x_old) + self.g(self.x_old))
+        self.loss.append(self.calculate_objective_function_at_point(self.x_old))
 
+    def calculate_objective_function_at_point(self, x):
+        """ Calculates the objective at a given point x
+
+        .. math:: f(x) + g(x)
+        
+        Parameters
+        ----------
+        x : DataContainer
+        
+        """
+        return self.f(x) + self.g(x)
 
 class FISTA(ISTA):
 
diff --git a/Wrappers/Python/cil/optimisation/algorithms/GD.py b/Wrappers/Python/cil/optimisation/algorithms/GD.py
index 22e1adcc78..9d3fdbee70 100644
--- a/Wrappers/Python/cil/optimisation/algorithms/GD.py
+++ b/Wrappers/Python/cil/optimisation/algorithms/GD.py
@@ -84,7 +84,7 @@ def set_up(self, initial, objective_function, step_size, preconditioner):
         log.info("%s setting up", self.__class__.__name__)
 
         self.x = initial.copy()
-        self.objective_function = objective_function
+        self._objective_function = objective_function
 
         if step_size is None:
             self.step_size_rule = ArmijoStepSizeRule(
@@ -106,7 +106,7 @@ def set_up(self, initial, objective_function, step_size, preconditioner):
 
     def update(self):
         '''Performs a single iteration of the gradient descent algorithm'''
-        self.objective_function.gradient(self.x, out=self.gradient_update)
+        self._objective_function.gradient(self.x, out=self.gradient_update)
 
         if self.preconditioner is not None:
             self.preconditioner.apply(
@@ -117,7 +117,7 @@ def update(self):
         self.x.sapyb(1.0, self.gradient_update, -step_size, out=self.x)
 
     def update_objective(self):
-        self.loss.append(self.objective_function(self.solution))
+        self.loss.append(self._objective_function(self.solution))
 
     def should_stop(self):
         '''Stopping criterion for the gradient descent algorithm '''
@@ -132,3 +132,20 @@ def step_size(self):
         else:
             raise TypeError(
                 "There is not a constant step size, it is set by a step-size rule")
+
+    def calculate_objective_function_at_point(self, x):
+        """ Calculates the objective at a given point x
+
+        .. math:: f(x) + g(x)
+        
+        Parameters
+        ----------
+        x : DataContainer
+        
+        """
+        return self._objective_function(x)
+    
+    @property
+    def objective_function(self):
+        warn('The attribute `objective_function` will be deprecated in the future. Please use `calculate_objective_function_at_point` instead.', DeprecationWarning, stacklevel=2)  
+        return self._objective_function
\ No newline at end of file
diff --git a/Wrappers/Python/cil/optimisation/utilities/StepSizeMethods.py b/Wrappers/Python/cil/optimisation/utilities/StepSizeMethods.py
index d7a5c47942..a680bd24d7 100644
--- a/Wrappers/Python/cil/optimisation/utilities/StepSizeMethods.py
+++ b/Wrappers/Python/cil/optimisation/utilities/StepSizeMethods.py
@@ -19,6 +19,9 @@
 from abc import ABC, abstractmethod
 import numpy
 from numbers import Number
+import logging
+
+log = logging.getLogger(__name__)
 
 class StepSizeRule(ABC):
     """
@@ -82,6 +85,9 @@ class ArmijoStepSizeRule(StepSizeRule):
         The amount the step_size is reduced if the criterion is not met
     max_iterations: integer, optional, default is numpy.ceil (2 * numpy.log10(alpha) / numpy.log10(2))
         The maximum number of iterations to find a suitable step size 
+    warmstart: Boolean, default is True
+        If `warmstart = True` the initial step size at each Armijo iteration is the calculated step size from the last iteration. If `warmstart = False` at each  Armijo iteration, the initial step size is reset to the original, large `alpha`. 
+        In the case of *well-behaved* convex functions, `warmstart = True` is likely to be computationally less expensive. In the case of non-convex functions, or particularly tricky functions, setting `warmstart = False` may be beneficial. 
 
     Reference
     ------------
@@ -91,14 +97,14 @@ class ArmijoStepSizeRule(StepSizeRule):
 
     """
 
-    def __init__(self, alpha=1e6, beta=0.5, max_iterations=None):
+    def __init__(self, alpha=1e6, beta=0.5, max_iterations=None, warmstart=True):
         '''Initialises the step size rule 
         '''
         
         self.alpha_orig = alpha
         if self.alpha_orig is None: # Can be removed when alpha and beta are deprecated in GD
             self.alpha_orig = 1e6 
-
+        self.alpha = self.alpha_orig
         self.beta = beta 
         if self.beta is None:  # Can be removed when alpha and beta are deprecated in GD
             self.beta = 0.5
@@ -106,6 +112,8 @@ def __init__(self, alpha=1e6, beta=0.5, max_iterations=None):
         self.max_iterations = max_iterations
         if self.max_iterations is None:
             self.max_iterations = numpy.ceil(2 * numpy.log10(self.alpha_orig) / numpy.log10(2))
+            
+        self.warmstart=warmstart
 
     def get_step_size(self, algorithm):
         """
@@ -117,26 +125,33 @@ def get_step_size(self, algorithm):
 
         """
         k = 0
-        self.alpha = self.alpha_orig
-        f_x = algorithm.objective_function(algorithm.solution)
+        if not self.warmstart:  
+            self.alpha = self.alpha_orig
+        
+        f_x = algorithm.calculate_objective_function_at_point(algorithm.solution)
 
         self.x_armijo = algorithm.solution.copy()
-
+        
+        log.debug("Starting Armijo backtracking with initial step size: %f", self.alpha)
+        
         while k < self.max_iterations:
 
             algorithm.gradient_update.multiply(self.alpha, out=self.x_armijo)
             algorithm.solution.subtract(self.x_armijo, out=self.x_armijo)
 
-            f_x_a = algorithm.objective_function(self.x_armijo)
+            f_x_a = algorithm.calculate_objective_function_at_point(self.x_armijo)
             sqnorm = algorithm.gradient_update.squared_norm()
             if f_x_a - f_x <= - (self.alpha/2.) * sqnorm:
                 break
             k += 1.
             self.alpha *= self.beta
+        
+        log.info("Armijo rule took %d iterations to find step size", k)
 
         if k == self.max_iterations:
             raise ValueError(
                 'Could not find a proper step_size in {} loops. Consider increasing alpha or max_iterations.'.format(self.max_iterations))
+        
         return self.alpha
 
 
diff --git a/Wrappers/Python/cil/utilities/dataexample.py b/Wrappers/Python/cil/utilities/dataexample.py
index cd0fb93de3..1acdf66411 100644
--- a/Wrappers/Python/cil/utilities/dataexample.py
+++ b/Wrappers/Python/cil/utilities/dataexample.py
@@ -25,10 +25,9 @@
 import os.path
 import sys
 from zipfile import ZipFile
-from urllib.request import urlopen
-from io import BytesIO
 from scipy.io import loadmat
 from cil.io import NEXUSDataReader, NikonDataReader, ZEISSDataReader
+from zenodo_get import zenodo_get
 
 class DATA(object):
     @classmethod
@@ -46,21 +45,15 @@ def get(cls, size=None, scale=(0,1), **kwargs):
 class REMOTEDATA(DATA):
 
     FOLDER = ''
-    URL = ''
-    FILE_SIZE = ''
+    ZENODO_RECORD = ''
+    ZIP_FILE = ''
 
     @classmethod
     def get(cls, data_dir):
         return None
 
     @classmethod
-    def _download_and_extract_from_url(cls, data_dir):
-        with urlopen(cls.URL) as response:
-            with BytesIO(response.read()) as bytes, ZipFile(bytes) as zipfile:
-                zipfile.extractall(path = data_dir)
-
-    @classmethod
-    def download_data(cls, data_dir):
+    def download_data(cls, data_dir, prompt=True):
         '''
         Download a dataset from a remote repository
 
@@ -71,14 +64,18 @@ def download_data(cls, data_dir):
 
         '''
         if os.path.isdir(os.path.join(data_dir, cls.FOLDER)):
-            print("Dataset already exists in " + data_dir)
+            print("Dataset folder already exists in " + data_dir)
         else:
-            if input("Are you sure you want to download " + cls.FILE_SIZE + " dataset from " + cls.URL + " ? (y/n)") == "y":
-                print('Downloading dataset from ' + cls.URL)
-                cls._download_and_extract_from_url(os.path.join(data_dir,cls.FOLDER))
-                print('Download complete')
-            else:
+            user_input = input("Are you sure you want to download {cls.ZIP_FILE} dataset from Zenodo record {cls.ZENODO_RECORD}? [Y/n]: ") if prompt else 'y'
+            if user_input.lower() not in ('y', 'yes'):
                 print('Download cancelled')
+                return False
+
+            zenodo_get([cls.ZENODO_RECORD, '-g', cls.ZIP_FILE, '-o', data_dir])
+            with ZipFile(os.path.join(data_dir, cls.ZIP_FILE), 'r') as zip_ref:
+                zip_ref.extractall(os.path.join(data_dir, cls.FOLDER))
+            os.remove(os.path.join(data_dir, cls.ZIP_FILE))
+            return True
 
 class BOAT(CILDATA):
     @classmethod
@@ -195,15 +192,21 @@ def get(cls, **kwargs):
 class WALNUT(REMOTEDATA):
     '''
     A microcomputed tomography dataset of a walnut from https://zenodo.org/records/4822516
+
+    Example
+    --------
+    >>> data_dir = 'my_PC/data_folder'
+    >>> dataexample.WALNUT.download_data(data_dir) # download the data
+    >>> dataexample.WALNUT.get(data_dir) # load the data
     '''
     FOLDER = 'walnut'
-    URL = 'https://zenodo.org/record/4822516/files/walnut.zip'
-    FILE_SIZE = '6.4 GB'
+    ZENODO_RECORD = '4822516'
+    ZIP_FILE = 'walnut.zip'
 
     @classmethod
     def get(cls, data_dir):
         '''
-        A microcomputed tomography dataset of a walnut from https://zenodo.org/records/4822516
+        Get the microcomputed tomography dataset of a walnut from https://zenodo.org/records/4822516
         This function returns the raw projection data from the .txrm file
 
         Parameters
@@ -227,15 +230,21 @@ def get(cls, data_dir):
 class USB(REMOTEDATA):
     '''
     A microcomputed tomography dataset of a usb memory stick from https://zenodo.org/records/4822516
+
+    Example
+    --------
+    >>> data_dir = 'my_PC/data_folder'
+    >>> dataexample.USB.download_data(data_dir) # download the data
+    >>> dataexample.USB.get(data_dir) # load the data
     '''
     FOLDER = 'USB'
-    URL = 'https://zenodo.org/record/4822516/files/usb.zip'
-    FILE_SIZE = '3.2 GB'
+    ZENODO_RECORD = '4822516'
+    ZIP_FILE = 'usb.zip'
 
     @classmethod
     def get(cls, data_dir):
         '''
-        A microcomputed tomography dataset of a usb memory stick from https://zenodo.org/records/4822516
+        Get the microcomputed tomography dataset of a usb memory stick from https://zenodo.org/records/4822516
         This function returns the raw projection data from the .txrm file
 
         Parameters
@@ -259,15 +268,21 @@ def get(cls, data_dir):
 class KORN(REMOTEDATA):
     '''
     A microcomputed tomography dataset of a sunflower seeds in a box from https://zenodo.org/records/6874123
+
+    Example
+    --------
+    >>> data_dir = 'my_PC/data_folder'
+    >>> dataexample.KORN.download_data(data_dir) # download the data
+    >>> dataexample.KORN.get(data_dir) # load the data
     '''
     FOLDER = 'korn'
-    URL = 'https://zenodo.org/record/6874123/files/korn.zip'
-    FILE_SIZE = '2.9 GB'
+    ZENODO_RECORD = '6874123'
+    ZIP_FILE = 'korn.zip'
 
     @classmethod
     def get(cls, data_dir):
         '''
-        A microcomputed tomography dataset of a sunflower seeds in a box from https://zenodo.org/records/6874123
+        Get the microcomputed tomography dataset of a sunflower seeds in a box from https://zenodo.org/records/6874123
         This function returns the raw projection data from the .xtekct file
 
         Parameters
@@ -279,6 +294,7 @@ def get(cls, data_dir):
         -------
         ImageData
             The korn dataset
+
         '''
         filepath = os.path.join(data_dir, cls.FOLDER, 'Korn i kasse','47209 testscan korn01_recon.xtekct')
         try:
@@ -293,10 +309,40 @@ class SANDSTONE(REMOTEDATA):
     '''
     A synchrotron x-ray tomography dataset of sandstone from https://zenodo.org/records/4912435
     A small subset of the data containing selected projections and 4 slices of the reconstruction
+
+    Example
+    --------
+    >>> data_dir = 'my_PC/data_folder'
+    >>> dataexample.SANDSTONE.download_data(data_dir) # download the data
+    >>> dataexample.SANDSTONE.get(data_dir) # load the data
     '''
     FOLDER = 'sandstone'
-    URL = 'https://zenodo.org/records/4912435/files/small.zip'
-    FILE_SIZE = '227 MB'
+    ZENODO_RECORD = '4912435'
+    ZIP_FILE = 'small.zip'
+
+    @classmethod
+    def get(cls, data_dir, filename):
+        '''
+        Get the synchrotron x-ray tomography dataset of sandstone from https://zenodo.org/records/4912435
+        A small subset of the data containing selected projections and 4 slices of the reconstruction
+        Parameters
+        ----------
+        data_dir: str
+           The path to the directory where the dataset is stored. Data can be downloaded with dataexample.SANDSTONE.download_data(data_dir)
+
+        file: str
+            The slices or projections to return, specify the path to the file within the data_dir
+
+        Returns
+        -------
+        ImageData
+            The selected sandstone dataset
+        '''
+        extension = os.path.splitext(filename)[1]
+        if extension == '.mat':
+            return loadmat(os.path.join(data_dir,filename))
+        raise KeyError(f"Unknown extension: {extension}")
+        
 
 class TestData(object):
     '''Class to return test data
diff --git a/Wrappers/Python/test/test_algorithms.py b/Wrappers/Python/test/test_algorithms.py
index e3f6302d78..845b5a44ce 100644
--- a/Wrappers/Python/test/test_algorithms.py
+++ b/Wrappers/Python/test/test_algorithms.py
@@ -105,13 +105,10 @@ def test_GD(self):
 
         alg = GD(initial=initial, objective_function=norm2sq, step_size=step_size,
                  atol=1e-9, rtol=1e-6)
-        alg.max_iteration = 1000
         alg.run(1000,verbose=0)
         self.assertNumpyArrayAlmostEqual(alg.x.as_array(), b.as_array())
         alg = GD(initial=initial, objective_function=norm2sq, step_size=step_size,
-                 atol=1e-9, rtol=1e-6, max_iteration=20, update_objective_interval=2)
-        alg.max_iteration = 20
-        self.assertTrue(alg.max_iteration == 20)
+                 atol=1e-9, rtol=1e-6, update_objective_interval=2)
         self.assertTrue(alg.update_objective_interval == 2)
         alg.run(20, verbose=0)
         self.assertNumpyArrayAlmostEqual(alg.x.as_array(), b.as_array())
@@ -132,7 +129,6 @@ def test_update_interval_0(self):
         norm2sq = LeastSquares(identity, b)
         alg = GD(initial=initial,
                  objective_function=norm2sq,
-                 max_iteration=20,
                  update_objective_interval=0,
                  atol=1e-9, rtol=1e-6)
         self.assertTrue(alg.update_objective_interval == 0)
@@ -176,7 +172,6 @@ def test_gd_constant_step_size_init(self):
     def test_gd_fixed_step_size_rosen(self):
 
         gd = GD(initial=self.initial, objective_function=self.f, step_size=0.002,
-                max_iteration=3000,
                 update_objective_interval=500)
         gd.run(3000, verbose=0)
         np.testing.assert_allclose(
@@ -224,23 +219,18 @@ def test_GDArmijo(self):
         norm2sq = LeastSquares(identity, b)
 
         alg = GD(initial=initial, objective_function=norm2sq)
-        alg.max_iteration = 100
         alg.run(100, verbose=0)
         self.assertNumpyArrayAlmostEqual(alg.x.as_array(), b.as_array())
-        alg = GD(initial=initial, objective_function=norm2sq,
-                 max_iteration=20, update_objective_interval=2)
-        # alg.max_iteration = 20
-        self.assertTrue(alg.max_iteration == 20)
+        alg = GD(initial=initial, objective_function=norm2sq, update_objective_interval=2)
         self.assertTrue(alg.update_objective_interval==2)
         alg.run(20, verbose=0)
         self.assertNumpyArrayAlmostEqual(alg.x.as_array(), b.as_array())
 
     def test_gd_armijo_rosen(self):
-        armj = ArmijoStepSizeRule(alpha=50, max_iterations=150)
+        armj = ArmijoStepSizeRule(alpha=50, max_iterations=50, warmstart=False)
         gd = GD(initial=self.initial, objective_function=self.f, step_size=armj,
-                max_iteration=2500,
                 update_objective_interval=500)
-        gd.run(2500,verbose=0)
+        gd.run(3500,verbose=0)
         np.testing.assert_allclose(
             gd.solution.array[0], self.scipy_opt_high.x[0], atol=1e-2)
         np.testing.assert_allclose(
@@ -262,31 +252,26 @@ def test_FISTA(self):
         log.info("initial objective %s", norm2sq(initial))
 
         alg = FISTA(initial=initial, f=norm2sq, g=ZeroFunction())
-        alg.max_iteration = 2
         alg.run(20, verbose=0)
         self.assertNumpyArrayAlmostEqual(alg.x.as_array(), b.as_array())
 
         alg = FISTA(initial=initial, f=norm2sq, g=ZeroFunction(),
-                    max_iteration=2, update_objective_interval=2)
+                     update_objective_interval=2)
 
-        self.assertTrue(alg.max_iteration == 2)
         self.assertTrue(alg.update_objective_interval == 2)
 
         alg.run(20, verbose=0)
         self.assertNumpyArrayAlmostEqual(alg.x.as_array(), b.as_array())
 
         # Testing g=None
-        alg = FISTA(initial=initial, f=norm2sq, g=None,
-                    max_iteration=2, update_objective_interval=2)
-        self.assertTrue(alg.max_iteration == 2)
+        alg = FISTA(initial=initial, f=norm2sq, g=None, update_objective_interval=2)
         self.assertTrue(alg.update_objective_interval == 2)
         alg.run(20, verbose=0)
         self.assertNumpyArrayAlmostEqual(alg.x.as_array(), b.as_array())
 
         # Testing f=None
         alg = FISTA(initial=initial, f=None, g=L1Norm(b=b),
-                    max_iteration=2, update_objective_interval=2)
-        self.assertTrue(alg.max_iteration == 2)
+                     update_objective_interval=2)
         self.assertTrue(alg.update_objective_interval == 2)
         alg.run(20, verbose=0)
         self.assertNumpyArrayAlmostEqual(alg.x.as_array(), b.as_array())
@@ -294,7 +279,7 @@ def test_FISTA(self):
         # Testing f and g is None
         with self.assertRaises(ValueError):
             alg = FISTA(initial=initial, f=None, g=None,
-                        max_iteration=2, update_objective_interval=2)
+                        update_objective_interval=2)
 
     def test_FISTA_update(self):
 
@@ -319,7 +304,7 @@ def test_FISTA_update(self):
 
         # ista run 10 iteration
         tmp_initial = ig.allocate()
-        fista = FISTA(initial=tmp_initial, f=f, g=g, max_iteration=1)
+        fista = FISTA(initial=tmp_initial, f=f, g=g)
         fista.run(1)
 
         # fista update method
@@ -348,11 +333,11 @@ def test_FISTA_update(self):
         self.assertTrue(res1 == res2)
 
         tmp_initial = ig.allocate()
-        fista1 = FISTA(initial=tmp_initial, f=f, g=g, max_iteration=1)
+        fista1 = FISTA(initial=tmp_initial, f=f, g=g)
         self.assertTrue(fista1.is_provably_convergent())
 
         fista1 = FISTA(initial=tmp_initial, f=f, g=g,
-                       max_iteration=1, step_size=30.0)
+                        step_size=30.0)
         self.assertFalse(fista1.is_provably_convergent())
 
     def test_FISTA_Norm2Sq(self):
@@ -367,13 +352,11 @@ def test_FISTA_Norm2Sq(self):
         opt = {'tol': 1e-4, 'memopt': False}
         log.info("initial objective %s", norm2sq(initial))
         alg = FISTA(initial=initial, f=norm2sq, g=ZeroFunction())
-        alg.max_iteration = 2
         alg.run(20, verbose=0)
         self.assertNumpyArrayAlmostEqual(alg.x.as_array(), b.as_array())
 
         alg = FISTA(initial=initial, f=norm2sq, g=ZeroFunction(),
-                    max_iteration=2, update_objective_interval=3)
-        self.assertTrue(alg.max_iteration == 2)
+                     update_objective_interval=3)
         self.assertTrue(alg.update_objective_interval == 3)
 
         alg.run(20, verbose=0)
@@ -419,7 +402,6 @@ def test_FISTA_Denoising(self):
 
         initial = ig.allocate()
         fista = FISTA(initial=initial, f=reg, g=fid)
-        fista.max_iteration = 3000
         fista.update_objective_interval = 500
         fista.run(3000, verbose=0)
         rmse = (fista.get_output() - data).norm() / data.as_array().size
@@ -483,7 +465,7 @@ def test_update(self):
 
         # ista run 10 iteration
         tmp_initial = self.ig.allocate()
-        ista = ISTA(initial=tmp_initial, f=self.f, g=self.g, max_iteration=1)
+        ista = ISTA(initial=tmp_initial, f=self.f, g=self.g)
         ista.run(1)
 
         x = tmp_initial.copy()
@@ -505,7 +487,7 @@ def test_update_g_none(self):
 
         # ista run 10 iteration
         tmp_initial = self.ig.allocate()
-        ista = ISTA(initial=tmp_initial, f=self.f, g=None,  max_iteration=1)
+        ista = ISTA(initial=tmp_initial, f=self.f, g=None)
         ista.run(1)
 
         x = tmp_initial.copy()
@@ -526,7 +508,7 @@ def test_update_f_none(self):
 
         # ista run 1 iteration
         tmp_initial = self.ig.allocate()
-        ista = ISTA(initial=tmp_initial, f=None, g=self.h,  max_iteration=1)
+        ista = ISTA(initial=tmp_initial, f=None, g=self.h)
         ista.run(1)
 
         x = tmp_initial.copy()
@@ -546,23 +528,23 @@ def test_update_f_none(self):
     def test_f_and_g_none(self):
         tmp_initial = self.ig.allocate()
         with self.assertRaises(ValueError):
-            ista = ISTA(initial=tmp_initial, f=None, g=None,  max_iteration=1)
+            ista = ISTA(initial=tmp_initial, f=None, g=None)
 
     def test_provable_condition(self):
 
         tmp_initial = self.ig.allocate()
-        ista1 = ISTA(initial=tmp_initial, f=self.f, g=self.g, max_iteration=10)
+        ista1 = ISTA(initial=tmp_initial, f=self.f, g=self.g)
         self.assertTrue(ista1.is_provably_convergent())
 
         ista1 = ISTA(initial=tmp_initial, f=self.f, g=self.g,
-                     max_iteration=10, step_size=30.0)
+                      step_size=30.0)
         self.assertFalse(ista1.is_provably_convergent())
 
     @unittest.skipUnless(has_cvxpy, "CVXpy not installed")
     def test_with_cvxpy(self):
 
         ista = ISTA(initial=self.initial, f=self.f,
-                    g=self.g, max_iteration=2000)
+                    g=self.g)
         ista.run(2000, verbose=0)
 
         u_cvxpy = cvxpy.Variable(self.ig.shape[0])
@@ -738,7 +720,6 @@ def setup(data, dnoise):
 
         # Setup and run the PDHG algorithm
         pdhg1 = PDHG(f=f1, g=g, operator=operator, tau=tau, sigma=sigma)
-        pdhg1.max_iteration = 2000
         pdhg1.update_objective_interval = 200
         pdhg1.run(1000, verbose=0)
 
@@ -763,7 +744,7 @@ def setup(data, dnoise):
 
         # Setup and run the PDHG algorithm
         pdhg1 = PDHG(f=f1, g=g, operator=operator, tau=tau, sigma=sigma,
-                     max_iteration=2000, update_objective_interval=200)
+                     update_objective_interval=200)
 
         pdhg1.run(1000, verbose=0)
 
@@ -788,7 +769,6 @@ def setup(data, dnoise):
 
         # Setup and run the PDHG algorithm
         pdhg1 = PDHG(f=f1, g=g, operator=operator, tau=tau, sigma=sigma)
-        pdhg1.max_iteration = 2000
         pdhg1.update_objective_interval = 200
         pdhg1.run(1000, verbose=0)
 
@@ -805,28 +785,28 @@ def test_PDHG_step_sizes(self):
         operator = 3*IdentityOperator(ig)
 
         # check if sigma, tau are None
-        pdhg = PDHG(f=f, g=g, operator=operator, max_iteration=10)
+        pdhg = PDHG(f=f, g=g, operator=operator)
         self.assertAlmostEqual(pdhg.sigma, 1./operator.norm())
         self.assertAlmostEqual(pdhg.tau, 1./operator.norm())
 
         # check if sigma is negative
         with self.assertRaises(ValueError):
             pdhg = PDHG(f=f, g=g, operator=operator,
-                        max_iteration=10, sigma=-1)
+                         sigma=-1)
 
         # check if tau is negative
         with self.assertRaises(ValueError):
-            pdhg = PDHG(f=f, g=g, operator=operator, max_iteration=10, tau=-1)
+            pdhg = PDHG(f=f, g=g, operator=operator,tau=-1)
 
         # check if tau is None
         sigma = 3.0
-        pdhg = PDHG(f=f, g=g, operator=operator, sigma=sigma, max_iteration=10)
+        pdhg = PDHG(f=f, g=g, operator=operator, sigma=sigma)
         self.assertAlmostEqual(pdhg.sigma, sigma)
         self.assertAlmostEqual(pdhg.tau, 1./(sigma * operator.norm()**2))
 
         # check if sigma is None
         tau = 3.0
-        pdhg = PDHG(f=f, g=g, operator=operator, tau=tau, max_iteration=10)
+        pdhg = PDHG(f=f, g=g, operator=operator, tau=tau)
         self.assertAlmostEqual(pdhg.tau, tau)
         self.assertAlmostEqual(pdhg.sigma, 1./(tau * operator.norm()**2))
 
@@ -834,7 +814,7 @@ def test_PDHG_step_sizes(self):
         tau = 1.0
         sigma = 1.0
         pdhg = PDHG(f=f, g=g, operator=operator, tau=tau,
-                    sigma=sigma, max_iteration=10)
+                    sigma=sigma)
         self.assertAlmostEqual(pdhg.tau, tau)
         self.assertAlmostEqual(pdhg.sigma, sigma)
 
@@ -843,29 +823,29 @@ def test_PDHG_step_sizes(self):
         sigma = ig1.allocate()
         with self.assertRaises(ValueError):
             pdhg = PDHG(f=f, g=g, operator=operator,
-                        sigma=sigma, max_iteration=10)
+                        sigma=sigma)
 
         # check sigma/tau as arrays, tau wrong shape
         tau = ig1.allocate()
         with self.assertRaises(ValueError):
-            pdhg = PDHG(f=f, g=g, operator=operator, tau=tau, max_iteration=10)
+            pdhg = PDHG(f=f, g=g, operator=operator, tau=tau)
 
         # check sigma not Number or object with correct shape
         with self.assertRaises(AttributeError):
             pdhg = PDHG(f=f, g=g, operator=operator,
-                        sigma="sigma", max_iteration=10)
+                        sigma="sigma")
 
         # check tau not Number or object with correct shape
         with self.assertRaises(AttributeError):
             pdhg = PDHG(f=f, g=g, operator=operator,
-                        tau="tau", max_iteration=10)
+                        tau="tau")
 
         # check warning message if condition is not satisfied
         sigma = 4
         tau = 1/3
         with self.assertWarnsRegex(UserWarning, "Convergence criterion"):
             pdhg = PDHG(f=f, g=g, operator=operator, tau=tau,
-                        sigma=sigma, max_iteration=10)
+                        sigma=sigma)
 
     def test_PDHG_strongly_convex_gamma_g(self):
         ig = ImageGeometry(3, 3)
@@ -880,7 +860,7 @@ def test_PDHG_strongly_convex_gamma_g(self):
         tau = 1.0
 
         pdhg = PDHG(f=f, g=g, operator=operator, sigma=sigma, tau=tau,
-                    max_iteration=5, gamma_g=0.5)
+                     gamma_g=0.5)
         pdhg.run(1, verbose=0)
         self.assertAlmostEqual(
             pdhg.theta, 1.0 / np.sqrt(1 + 2 * pdhg.gamma_g * tau))
@@ -893,12 +873,12 @@ def test_PDHG_strongly_convex_gamma_g(self):
         # check negative strongly convex constant
         with self.assertRaises(ValueError):
             pdhg = PDHG(f=f, g=g, operator=operator, sigma=sigma, tau=tau,
-                        max_iteration=5, gamma_g=-0.5)
+                         gamma_g=-0.5)
 
         # check strongly convex constant not a number
         with self.assertRaises(ValueError):
             pdhg = PDHG(f=f, g=g, operator=operator, sigma=sigma, tau=tau,
-                        max_iteration=5, gamma_g="-0.5")
+                         gamma_g="-0.5")
 
     def test_PDHG_strongly_convex_gamma_fcong(self):
         ig = ImageGeometry(3, 3)
@@ -913,7 +893,7 @@ def test_PDHG_strongly_convex_gamma_fcong(self):
         tau = 1.0
 
         pdhg = PDHG(f=f, g=g, operator=operator, sigma=sigma, tau=tau,
-                    max_iteration=5, gamma_fconj=0.5)
+                     gamma_fconj=0.5)
         pdhg.run(1, verbose=0)
         self.assertEqual(pdhg.theta, 1.0 / np.sqrt(1 +
                          2 * pdhg.gamma_fconj * sigma))
@@ -926,14 +906,14 @@ def test_PDHG_strongly_convex_gamma_fcong(self):
         # check negative strongly convex constant
         try:
             pdhg = PDHG(f=f, g=g, operator=operator, sigma=sigma, tau=tau,
-                        max_iteration=5, gamma_fconj=-0.5)
+                         gamma_fconj=-0.5)
         except ValueError as ve:
             log.info(str(ve))
 
         # check strongly convex constant not a number
         try:
             pdhg = PDHG(f=f, g=g, operator=operator, sigma=sigma, tau=tau,
-                        max_iteration=5, gamma_fconj="-0.5")
+                         gamma_fconj="-0.5")
         except ValueError as ve:
             log.info(str(ve))
 
@@ -947,7 +927,7 @@ def test_PDHG_strongly_convex_both_fconj_and_g(self):
         operator = IdentityOperator(ig)
 
         try:
-            pdhg = PDHG(f=f, g=g, operator=operator, max_iteration=10,
+            pdhg = PDHG(f=f, g=g, operator=operator, 
                         gamma_g=0.5, gamma_fconj=0.5)
             pdhg.run(verbose=0)
         except ValueError as err:
@@ -1031,7 +1011,7 @@ def test_update(self):
         # sirt run 5 iterations
         tmp_initial = self.ig.allocate()
         sirt = SIRT(initial=tmp_initial, operator=self.Aop,
-                    data=self.bop, max_iteration=5)
+                    data=self.bop)
         sirt.run(5)
 
         x = tmp_initial.copy()
@@ -1046,22 +1026,22 @@ def test_update(self):
 
     def test_update_constraints(self):
         alg = SIRT(initial=self.initial2, operator=self.A2,
-                   data=self.b2, max_iteration=20)
+                   data=self.b2)
         alg.run(20,verbose=0)
         np.testing.assert_array_almost_equal(alg.x.array, self.b2.array)
 
         alg = SIRT(initial=self.initial2, operator=self.A2,
-                   data=self.b2, max_iteration=20, upper=0.3)
+                   data=self.b2,  upper=0.3)
         alg.run(20,verbose=0)
         np.testing.assert_almost_equal(alg.solution.max(), 0.3)
 
         alg = SIRT(initial=self.initial2, operator=self.A2,
-                   data=self.b2, max_iteration=20, lower=0.7)
+                   data=self.b2, lower=0.7)
         alg.run(20,verbose=0)
         np.testing.assert_almost_equal(alg.solution.min(), 0.7)
 
         alg = SIRT(initial=self.initial2, operator=self.A2, data=self.b2,
-                   max_iteration=20, constraint=IndicatorBox(lower=0.1, upper=0.3))
+                    constraint=IndicatorBox(lower=0.1, upper=0.3))
         alg.run(20,verbose=0)
         np.testing.assert_almost_equal(alg.solution.max(), 0.3)
         np.testing.assert_almost_equal(alg.solution.min(), 0.1)
@@ -1069,7 +1049,7 @@ def test_update_constraints(self):
     def test_SIRT_relaxation_parameter(self):
         tmp_initial = self.ig.allocate()
         alg = SIRT(initial=tmp_initial, operator=self.Aop,
-                   data=self.bop, max_iteration=5)
+                   data=self.bop)
 
         with self.assertRaises(ValueError):
             alg.set_relaxation_parameter(0)
@@ -1078,7 +1058,7 @@ def test_SIRT_relaxation_parameter(self):
             alg.set_relaxation_parameter(2)
 
         alg = SIRT(initial=self.initial2, operator=self.A2,
-                   data=self.b2, max_iteration=20)
+                   data=self.b2)
         alg.set_relaxation_parameter(0.5)
 
         self.assertEqual(alg.relaxation_parameter, 0.5)
@@ -1095,7 +1075,7 @@ def test_SIRT_nan_inf_values(self):
 
         tmp_initial = self.ig.allocate()
         sirt = SIRT(initial=tmp_initial, operator=Aop_nan_inf,
-                    data=self.bop, max_iteration=5)
+                    data=self.bop)
 
         self.assertFalse(np.any(sirt.M == np.inf))
         self.assertFalse(np.any(sirt.D == np.inf))
@@ -1117,7 +1097,7 @@ def test_SIRT_remove_nan_or_inf_with_BlockDataContainer(self):
         tmp_initial = ig.allocate()
 
         sirt = SIRT(initial=tmp_initial, operator=Aop,
-                    data=bop, max_iteration=5)
+                    data=bop)
         for el in sirt.M.containers:
             self.assertFalse(np.any(el == np.inf))
 
@@ -1127,13 +1107,13 @@ def test_SIRT_with_TV(self):
         data = dataexample.SIMPLE_PHANTOM_2D.get(size=(128, 128))
         ig = data.geometry
         A = IdentityOperator(ig)
-        constraint = TotalVariation(warm_start=False, max_iteration=100)
+        constraint = TotalVariation(warm_start=False)
         initial = ig.allocate('random', seed=5)
         sirt = SIRT(initial=initial, operator=A, data=data,
-                    max_iteration=2, constraint=constraint)
+                     constraint=constraint)
         sirt.run(2, verbose=0)
         f = LeastSquares(A, data, c=0.5)
-        fista = FISTA(initial=initial, f=f, g=constraint, max_iteration=1000)
+        fista = FISTA(initial=initial, f=f, g=constraint)
         fista.run(100, verbose=0)
         self.assertNumpyArrayAlmostEqual(fista.x.as_array(), sirt.x.as_array())
 
@@ -1144,7 +1124,7 @@ def test_SIRT_with_TV_warm_start(self):
         constraint = 1e6*TotalVariation(warm_start=True, max_iteration=100)
         initial = ig.allocate('random', seed=5)
         sirt = SIRT(initial=initial, operator=A, data=data,
-                    max_iteration=150, constraint=constraint)
+                     constraint=constraint)
         sirt.run(25, verbose=0)
 
         self.assertNumpyArrayAlmostEqual(
@@ -1470,11 +1450,11 @@ def do_test_with_fidelity(self, fidelity):
         F = self.F
 
         admm = LADMM(f=G, g=F, operator=K, tau=self.tau, sigma=self.sigma,
-                     max_iteration=100, update_objective_interval=10)
+                     update_objective_interval=10)
         admm.run(1, verbose=0)
 
         admm_noaxpby = LADMM(f=G, g=F, operator=K, tau=self.tau, sigma=self.sigma,
-                             max_iteration=100, update_objective_interval=10)
+                             update_objective_interval=10)
         admm_noaxpby.run(1, verbose=0)
         np.testing.assert_array_almost_equal(
             admm.solution.as_array(), admm_noaxpby.solution.as_array())
@@ -1506,14 +1486,14 @@ def test_compare_with_PDHG(self):
         tau = 1./normK
 
         pdhg = PDHG(f=F, g=G, operator=K, tau=tau, sigma=sigma,
-                    max_iteration=500, update_objective_interval=10)
+                     update_objective_interval=10)
         pdhg.run(500,verbose=0)
 
         sigma = 1
         tau = sigma/normK**2
 
         admm = LADMM(f=G, g=F, operator=K, tau=tau, sigma=sigma,
-                     max_iteration=500, update_objective_interval=10)
+                      update_objective_interval=10)
         admm.run(500,verbose=0)
         np.testing.assert_almost_equal(
             admm.solution.array, pdhg.solution.array,  decimal=3)
@@ -1564,8 +1544,7 @@ def test_PD3O_PDHG_denoising_1_iteration(self):
         G = 0.5 * L2NormSquared(b=self.data)
         sigma = 1./norm_op
         tau = 1./norm_op
-        pdhg = PDHG(f=F, g=G, operator=operator, tau=tau, sigma=sigma, update_objective_interval = 100, 
-                    max_iteration = 2000)
+        pdhg = PDHG(f=F, g=G, operator=operator, tau=tau, sigma=sigma, update_objective_interval = 100)
         pdhg.run(1)
 
         # setup PD3O denoising  (F=ZeroFunction)   
@@ -1576,8 +1555,7 @@ def test_PD3O_PDHG_denoising_1_iteration(self):
         delta = 1./norm_op
 
         pd3O = PD3O(f=F, g=G, h=H, operator=operator, gamma=gamma, delta=delta,
-                    update_objective_interval = 100, 
-                    max_iteration = 2000)
+                    update_objective_interval = 100)
         pd3O.run(1)      
                
         # PD3O vs pdhg
diff --git a/Wrappers/Python/test/test_dataexample.py b/Wrappers/Python/test/test_dataexample.py
index b1b2acaa52..4ace99a58f 100644
--- a/Wrappers/Python/test/test_dataexample.py
+++ b/Wrappers/Python/test/test_dataexample.py
@@ -25,11 +25,11 @@
 from testclass import CCPiTestClass
 import platform
 import numpy as np
-from unittest.mock import patch, MagicMock 
-from urllib import request
+from unittest.mock import patch 
 from zipfile import ZipFile
 from io import StringIO
-from tempfile import NamedTemporaryFile
+import uuid
+from zenodo_get import zenodo_get
 
 initialise_tests()
 
@@ -157,116 +157,89 @@ def test_load_SIMULATED_CONE_BEAM_DATA(self):
 class TestRemoteData(unittest.TestCase):
 
     def setUp(self):
-        
         self.data_list = ['WALNUT','USB','KORN','SANDSTONE']
-        self.shapes_path = os.path.join(dataexample.CILDATA.data_dir, dataexample.TestData.SHAPES)
 
-    def mock_urlopen(self, mock_urlopen, zipped_bytes):
-        mock_response = MagicMock()
-        mock_response.read.return_value = zipped_bytes
-        mock_response.__enter__.return_value = mock_response
-        mock_urlopen.return_value = mock_response
 
-    @unittest.skipIf(platform.system() == 'Windows', "Skip on Windows")   
-    @patch('cil.utilities.dataexample.urlopen')
-    def test_unzip_remote_data(self, mock_urlopen):
-        '''
-        Test the _download_and_extract_data_from_url function correctly extracts files from a byte string
-        The zipped byte string is mocked using a temporary local zip file
+    def mock_zenodo_get(*args):
+        # mock zenodo_get by making a zip file containing the shapes test data when the function is called
+        shapes_path = os.path.join(dataexample.CILDATA.data_dir, dataexample.TestData.SHAPES)
+        with ZipFile(os.path.join(args[0][4], args[0][2]), mode='w') as zip_file:
+            zip_file.write(shapes_path, arcname=dataexample.TestData.SHAPES)
+
+            
+    @patch('cil.utilities.dataexample.input', return_value='y')
+    @patch('cil.utilities.dataexample.zenodo_get', side_effect=mock_zenodo_get)
+    def test_download_data_input_y(self, mock_zenodo_get, input):
         '''
-        
-        # create a temporary zip file to test the function
-        with NamedTemporaryFile(suffix = '.zip') as tf:
-            tmp_path = os.path.dirname(tf.name)
-            tmp_dir = os.path.splitext(os.path.basename(tf.name))[0]
-            with ZipFile(tf.name, mode='w') as zip_file:
-                zip_file.write(self.shapes_path, arcname=dataexample.TestData.SHAPES)
-                
-            with open(tf.name, 'rb') as zip_file:
-                zipped_bytes = zip_file.read()
-        
-        self.mock_urlopen(mock_urlopen, zipped_bytes)
-        dataexample.REMOTEDATA._download_and_extract_from_url(os.path.join(tmp_path, tmp_dir))
+        Test the download_data function, when the user input is 'y' to 'are you sure you want to download data'
+        The user input to confirm the download is mocked as 'y'
+        The zip file download is mocked by creating a zip file locally
+        Test the download_data function correctly extracts files from the zip file
+        '''        
+        # create a temporary folder in the CIL data directory
+        tmp_dir = os.path.join(dataexample.CILDATA.data_dir, str(uuid.uuid4()))
+        os.makedirs(tmp_dir)
+        # redirect print output
+        capturedOutput = StringIO()                
+        sys.stdout = capturedOutput
+        for data in self.data_list:
+            test_func = getattr(dataexample, data)
+            test_func.download_data(tmp_dir)
+            # Test the data file exists
+            self.assertTrue(os.path.isfile(os.path.join(tmp_dir, getattr(test_func, 'FOLDER'), dataexample.TestData.SHAPES)), 
+                            msg = "Download data test failed with dataset " + data)
+            # Test the zip file is removed
+            self.assertFalse(os.path.isfile(os.path.join(tmp_dir, getattr(test_func, 'ZIP_FILE'))))
+        # return to standard print output
+        sys.stdout = sys.__stdout__
+        shutil.rmtree(tmp_dir)
 
-        self.assertTrue(os.path.isfile(os.path.join(tmp_path, tmp_dir, dataexample.TestData.SHAPES)))
 
-        if os.path.exists(os.path.join(tmp_path,tmp_dir)):
-            shutil.rmtree(os.path.join(tmp_path,tmp_dir)) 
-        
-    @unittest.skipIf(platform.system() == 'Windows', "Skip on Windows")   
-    @patch('cil.utilities.dataexample.input', return_value='n')    
-    @patch('cil.utilities.dataexample.urlopen')
-    def test_download_data_input_n(self, mock_urlopen, input):
+    @patch('cil.utilities.dataexample.input', return_value='n')
+    @patch('cil.utilities.dataexample.zenodo_get', side_effect=mock_zenodo_get)   
+    def test_download_data_input_n(self, mock_zenodo_get, input):
         '''
         Test the download_data function, when the user input is 'n' to 'are you sure you want to download data'
-        The zipped byte string is mocked using a temporary local zip file
         '''
-        
-        # create a temporary zip file to test the function
-        with NamedTemporaryFile(suffix = '.zip') as tf:
-            tmp_path = os.path.dirname(tf.name)
-            tmp_dir = os.path.splitext(os.path.basename(tf.name))[0]
-            with ZipFile(tf.name, mode='w') as zip_file:
-                zip_file.write(self.shapes_path, arcname=dataexample.TestData.SHAPES)
-                
-            with open(tf.name, 'rb') as zip_file:
-                    zipped_bytes = zip_file.read()
-
-        self.mock_urlopen(mock_urlopen, zipped_bytes)
-
+        # create a temporary folder in the CIL data directory
+        tmp_dir = os.path.join(dataexample.CILDATA.data_dir, str(uuid.uuid4()))
+        os.makedirs(tmp_dir)
         for data in self.data_list:
             # redirect print output
-            capturedOutput = StringIO()                 
-            sys.stdout = capturedOutput 
+            capturedOutput = StringIO()
+            sys.stdout = capturedOutput
             test_func = getattr(dataexample, data)
-            test_func.download_data(os.path.join(tmp_path, tmp_dir))
-            self.assertFalse(os.path.isfile(os.path.join(tmp_path, tmp_dir, test_func.FOLDER, dataexample.TestData.SHAPES)), msg = "Failed with dataset " + data)
-            self.assertEqual(capturedOutput.getvalue(),'Download cancelled\n', msg = "Failed with dataset " + data)
+            test_func.download_data(tmp_dir)
+            self.assertFalse(os.path.isfile(os.path.join(tmp_dir, getattr(test_func, 'FOLDER'), dataexample.TestData.SHAPES)), 
+                             msg = "Download dataset test failed with dataset " + data)
+            self.assertEqual(capturedOutput.getvalue(),'Download cancelled\n', 
+                             msg = "Download dataset test failed with dataset " + data)
             # return to standard print output
             sys.stdout = sys.__stdout__ 
 
-        if os.path.exists(os.path.join(tmp_path,tmp_dir)):
-            shutil.rmtree(os.path.join(tmp_path,tmp_dir)) 
-
-    @unittest.skipIf(platform.system() == 'Windows', "Skip on Windows")   
-    @patch('cil.utilities.dataexample.input', return_value='y')    
-    @patch('cil.utilities.dataexample.urlopen')
-    def test_download_data_input_y(self, mock_urlopen, input):
-        '''
-        Test the download_data function, when the user input is 'y' to 'are you sure you want to download data'
-        The zipped byte string is mocked using a temporary local zip file
-        '''
-        
-        with NamedTemporaryFile(suffix = '.zip') as tf:
-            tmp_path = os.path.dirname(tf.name)
-            tmp_dir = os.path.splitext(os.path.basename(tf.name))[0]
-            with ZipFile(tf.name, mode='w') as zip_file:
-                zip_file.write(self.shapes_path, arcname=dataexample.TestData.SHAPES)
-                
-            with open(tf.name, 'rb') as zip_file:
-                    zipped_bytes = zip_file.read()
-
-        self.mock_urlopen(mock_urlopen, zipped_bytes)
-
-        # redirect print output
-        capturedOutput = StringIO()                 
-        sys.stdout = capturedOutput         
+        # Test the zip file IS created with prompt=False i.e. prompt not used
+        dataexample.WALNUT.download_data(tmp_dir, prompt=False)
+        # Test the data file exists
+        self.assertTrue(os.path.isfile(os.path.join(tmp_dir, dataexample.WALNUT.FOLDER, dataexample.TestData.SHAPES)), 
+                        msg = "Download data test failed with dataset " + data)
+        # Test the zip file is removed
+        self.assertFalse(os.path.isfile(os.path.join(tmp_dir, dataexample.WALNUT.ZIP_FILE)))
 
-        for data in self.data_list:
-            test_func = getattr(dataexample, data)
-            test_func.download_data(os.path.join(tmp_path, tmp_dir))
-            self.assertTrue(os.path.isfile(os.path.join(tmp_path, tmp_dir, test_func.FOLDER, dataexample.TestData.SHAPES)), msg = "Failed with dataset " + data)
-        
-        # return to standard print output
-        sys.stdout = sys.__stdout__ 
-        
-        if os.path.exists(os.path.join(tmp_path,tmp_dir)):
-            shutil.rmtree(os.path.join(tmp_path,tmp_dir))
+        shutil.rmtree(tmp_dir)
 
 
-    def test_download_data_bad_URL(self):
+    @patch('cil.utilities.dataexample.input', return_value='y')
+    def test_download_data_empty(self, input):
         '''
-        Test an error is raised when _download_and_extract_from_url has an empty URL
+        Test an error is raised when download_data is used on an empty Zenodo record
         '''
+        remote_data = dataexample.REMOTEDATA
+        remote_data.ZENODO_RECORD = 'empty'
+        remote_data.FOLDER = 'empty'
+        
         with self.assertRaises(ValueError):
-            dataexample.REMOTEDATA._download_and_extract_from_url('.')
+            remote_data.download_data('.')
+
+    def test_a(self):
+        from cil.utilities.dataexample import WALNUT
+            
\ No newline at end of file
diff --git a/Wrappers/Python/test/test_stepsizes.py b/Wrappers/Python/test/test_stepsizes.py
index 860da40896..1ef0fc145c 100644
--- a/Wrappers/Python/test/test_stepsizes.py
+++ b/Wrappers/Python/test/test_stepsizes.py
@@ -21,7 +21,7 @@ def test_step_sizes_called(self):
         step_size_test.get_step_size = MagicMock(return_value=.1)
         f = LeastSquares(A=A, b=data, c=0.5)
         alg = GD(initial=ig.allocate('random', seed=10), objective_function=f, step_size=step_size_test,
-                 max_iteration=100, update_objective_interval=1)
+                  update_objective_interval=1)
 
         alg.run(5)
 
@@ -30,54 +30,115 @@ def test_step_sizes_called(self):
         step_size_test = ConstantStepSize(3)
         step_size_test.get_step_size = MagicMock(return_value=.1)
         alg = ISTA(initial=ig.allocate('random', seed=10), f=f, g=IndicatorBox(lower=0), step_size=step_size_test,
-                   max_iteration=100, update_objective_interval=1)
+                    update_objective_interval=1)
         alg.run(5)
         self.assertEqual(len(step_size_test.get_step_size.mock_calls), 5)
 
         step_size_test = ConstantStepSize(3)
         step_size_test.get_step_size = MagicMock(return_value=.1)
         alg = FISTA(initial=ig.allocate('random', seed=10), f=f, g=IndicatorBox(lower=0), step_size=step_size_test,
-                    max_iteration=100, update_objective_interval=1)
+                     update_objective_interval=1)
         alg.run(5)
         self.assertEqual(len(step_size_test.get_step_size.mock_calls), 5)
 
+class TestStepSizeConstant(CCPiTestClass):
     def test_constant(self):
         test_stepsize = ConstantStepSize(0.3)
         self.assertEqual(test_stepsize.step_size, 0.3)
 
+class TestStepSizeArmijo(CCPiTestClass):
+    
+    def setUp(self):
+        self.ig = VectorGeometry(2)
+        self.data = self.ig.allocate('random')
+        self.data.fill(np.array([3.5, 3.5]))
+        self.A = MatrixOperator(np.diag([1., 1.]))
+        self.f = LeastSquares(self.A, self.data)
+
+        
     def test_armijo_init(self):
-        test_stepsize = ArmijoStepSizeRule(alpha=1e3, beta=0.4, max_iterations=40)
+        test_stepsize = ArmijoStepSizeRule(alpha=1e3, beta=0.4, max_iterations=40, warmstart=False)
+        self.assertFalse(test_stepsize.warmstart)        
         self.assertEqual(test_stepsize.alpha_orig, 1e3)
         self.assertEqual(test_stepsize.beta, 0.4)
         self.assertEqual(test_stepsize.max_iterations, 40)
 
         test_stepsize = ArmijoStepSizeRule()
+        self.assertTrue(test_stepsize.warmstart)
         self.assertEqual(test_stepsize.alpha_orig, 1e6)
         self.assertEqual(test_stepsize.beta, 0.5)
         self.assertEqual(test_stepsize.max_iterations, np.ceil(
             2 * np.log10(1e6) / np.log10(2)))
 
     def test_armijo_calculation(self):
-        test_stepsize = ArmijoStepSizeRule(alpha=8, beta=0.5, max_iterations=100)
-        ig = VectorGeometry(2)
-        data = ig.allocate('random')
-        data.fill(np.array([3.5, 3.5]))
-        A = MatrixOperator(np.diag([1., 1.]))
-        f = LeastSquares(A, data)
-        alg = GD(initial=ig.allocate(0), objective_function=f,
-                 max_iteration=100, update_objective_interval=1, step_size=test_stepsize)
-        alg.gradient_update = ig.allocate(-1)
+        test_stepsize = ArmijoStepSizeRule(alpha=8, beta=0.5, max_iterations=100, warmstart=False)
+
+        alg = GD(initial=self.ig.allocate(0), objective_function=self.f,
+                  update_objective_interval=1, step_size=test_stepsize)
+        alg.gradient_update = self.ig.allocate(-1)
+        step_size = test_stepsize.get_step_size(alg)
+        self.assertAlmostEqual(step_size, 4)
+
+        alg.gradient_update = self.ig.allocate(-.5)
+        step_size = test_stepsize.get_step_size(alg)
+        self.assertAlmostEqual(step_size, 8)
+
+        alg.gradient_update = self.ig.allocate(-2)
+        step_size = test_stepsize.get_step_size(alg)
+        self.assertAlmostEqual(step_size, 2)
+
+    def test_armijo_ISTA_and_FISTA(self):
+        test_stepsize = ArmijoStepSizeRule(alpha=8, beta=0.5, max_iterations=100, warmstart=False)
+
+        alg = ISTA(initial=self.ig.allocate(0), f=self.f, g=IndicatorBox(lower=0),
+                     update_objective_interval=1, step_size=test_stepsize)
+        alg.gradient_update = self.ig.allocate(-1)
         step_size = test_stepsize.get_step_size(alg)
         self.assertAlmostEqual(step_size, 4)
 
-        alg.gradient_update = ig.allocate(-.5)
+        alg.gradient_update = self.ig.allocate(-.5)
         step_size = test_stepsize.get_step_size(alg)
         self.assertAlmostEqual(step_size, 8)
 
-        alg.gradient_update = ig.allocate(-2)
+        alg.gradient_update = self.ig.allocate(-2)
         step_size = test_stepsize.get_step_size(alg)
         self.assertAlmostEqual(step_size, 2)
 
+        alg = FISTA(initial=self.ig.allocate(0), f=self.f, g=IndicatorBox(lower=0),
+                     update_objective_interval=1, step_size=test_stepsize)
+        alg.gradient_update = self.ig.allocate(-1)
+        step_size = test_stepsize.get_step_size(alg)
+        self.assertAlmostEqual(step_size, 4)
+
+        alg.gradient_update = self.ig.allocate(-.5)
+        step_size = test_stepsize.get_step_size(alg)
+        self.assertAlmostEqual(step_size, 8)
+
+        alg.gradient_update = self.ig.allocate(-2)
+        step_size = test_stepsize.get_step_size(alg)
+        self.assertAlmostEqual(step_size, 2)
+
+    def test_warmstart_true(self):
+        
+        rule = ArmijoStepSizeRule(warmstart=True, alpha=5000)
+        self.assertTrue(rule.warmstart)
+        self.assertTrue(rule.alpha_orig == 5000)
+        alg = GD(initial=self.ig.allocate(0), objective_function=self.f,
+                  update_objective_interval=1, step_size=rule)
+        alg.update()
+        self.assertFalse(rule.alpha == 5000)
+
+    def test_warmstart_false(self):
+        rule = ArmijoStepSizeRule(warmstart=False,  alpha=5000)
+        self.assertFalse(rule.warmstart)
+        self.assertTrue(rule.alpha_orig == 5000)
+        alg = GD(initial=self.ig.allocate(0), objective_function=self.f,
+                  update_objective_interval=1, step_size=rule)
+        alg.update()
+        self.assertTrue(rule.alpha_orig == 5000)
+        self.assertFalse(rule.alpha_orig == rule.alpha)  
+
+class TestStepSizeBB(CCPiTestClass):
     def test_bb(self):
         n = 10
         m = 5
@@ -218,7 +279,7 @@ def test_bb_converge(self):
         initial = ig.allocate()
         f = LeastSquares(Aop, b=bop, c=2)
         
-        ss_rule=ArmijoStepSizeRule(max_iterations=40)
+        ss_rule=ArmijoStepSizeRule(max_iterations=40, warmstart=False)
         alg_true = GD(initial=initial, objective_function=f, step_size=ss_rule)
         alg_true .run(300, verbose=0)
         
@@ -243,3 +304,4 @@ def test_bb_converge(self):
         self.assertNumpyArrayAlmostEqual(alg.x.as_array(), alg_true.x.as_array(), decimal=3)
         
         
+
diff --git a/docs/source/utilities.rst b/docs/source/utilities.rst
index dad9d4966e..816d491b4b 100644
--- a/docs/source/utilities.rst
+++ b/docs/source/utilities.rst
@@ -53,6 +53,40 @@ Simulated image data
    :members:
    :inherited-members:
 
+Remote data
+-----------
+Remote data classes can be used to access specific datasets from zenodo. These
+datasets are not packaged as part of CIL, instead the `download_data(data_dir)`
+method can be used to download the dataset to a chosen data directory then loaded
+from that data directory using `get(data_dir)`.
+
+Walnut
+------
+
+.. autoclass:: cil.utilities.dataexample.WALNUT
+      :members:
+      :inherited-members:
+
+USB
+------
+
+.. autoclass:: cil.utilities.dataexample.USB
+      :members:
+      :inherited-members:
+
+KORN
+------
+
+.. autoclass:: cil.utilities.dataexample.KORN
+      :members:
+      :inherited-members:
+
+SANDSTONE
+------
+.. autoclass:: cil.utilities.dataexample.SANDSTONE
+      :members:
+      :inherited-members:
+
 
 
 Image Quality metrics
diff --git a/recipe/meta.yaml b/recipe/meta.yaml
index 3355fee710..26b339bfad 100644
--- a/recipe/meta.yaml
+++ b/recipe/meta.yaml
@@ -70,6 +70,7 @@ requirements:
     - ipp >=2021.10
     - tqdm
     - numba
+    - zenodo_get >=1.6
 
   #optional packages with version dependancies
   run_constrained:
diff --git a/scripts/create_local_env_for_cil_development.sh b/scripts/create_local_env_for_cil_development.sh
index 8e65ca665f..d7564dc7dc 100755
--- a/scripts/create_local_env_for_cil_development.sh
+++ b/scripts/create_local_env_for_cil_development.sh
@@ -64,6 +64,7 @@ conda_args=(create --name="$name"
   scikit-image
   scipy
   tqdm
+  zenodo_get'>=1.6'
 )
 if test -n "$cil_ver"; then
   echo "CIL version $cil_ver"
diff --git a/scripts/requirements-test.yml b/scripts/requirements-test.yml
index 73e121d81d..17668b73d8 100644
--- a/scripts/requirements-test.yml
+++ b/scripts/requirements-test.yml
@@ -48,3 +48,4 @@ dependencies:
   - pywavelets
   - numba
   - tqdm
+  - zenodo_get >=1.6