diff --git a/specparam/algorithms/algorithm.py b/specparam/algorithms/algorithm.py
index e2e42a48..efa5719c 100644
--- a/specparam/algorithms/algorithm.py
+++ b/specparam/algorithms/algorithm.py
@@ -33,12 +33,15 @@ class Algorithm():
         Data object with spectral data and metadata.
     results : Results
         Results object with model fit results and metrics.
+    model : SpectralModel, optional
+        The model object this object is linked to, to provide access to other attributes.
     debug :  bool
         Whether to run in debug state, raising an error if encountered during fitting.
     """
 
     def __init__(self, name, description, public_settings, private_settings=None,
-                 data_format='spectrum', modes=None, data=None, results=None, debug=False):
+                 data_format='spectrum', modes=None, data=None, results=None, model=None,
+                 debug=False):
         """Initialize Algorithm object."""
 
         self.name = name
@@ -66,6 +69,8 @@ def __init__(self, name, description, public_settings, private_settings=None,
 
         self.set_debug(debug)
 
+        self._model = model
+
 
     def _fit_prechecks(self, verbose):
         """Pre-checks to run before the fit function - if are some, overload this function."""
@@ -178,13 +183,14 @@ class AlgorithmCF(Algorithm):
     """
 
     def __init__(self, name, description, public_settings, private_settings=None,
-                 data_format='spectrum', modes=None, data=None, results=None, debug=False):
+                 data_format='spectrum', modes=None, data=None, results=None,
+                 model=None, debug=False):
         """Initialize Algorithm object."""
 
         Algorithm.__init__(self, name=name, description=description,
                            public_settings=public_settings, private_settings=private_settings,
                            data_format=data_format, modes=modes, data=data, results=results,
-                           debug=debug)
+                           model=model, debug=debug)
 
         self._cf_settings_desc = CURVE_FIT_SETTINGS
         self._cf_settings = SettingsValues(self._cf_settings_desc.names)
diff --git a/specparam/algorithms/spectral_fit.py b/specparam/algorithms/spectral_fit.py
index 328f0f33..c2be3fc2 100644
--- a/specparam/algorithms/spectral_fit.py
+++ b/specparam/algorithms/spectral_fit.py
@@ -106,7 +106,7 @@ class SpectralFitAlgorithm(AlgorithmCF):
     def __init__(self, peak_width_limits=(0.5, 12.0), max_n_peaks=np.inf, min_peak_height=0.0,
                  peak_threshold=2.0, ap_percentile_thresh=0.025, ap_guess=None, ap_bounds=None,
                  cf_bound=1.5, bw_std_edge=1.0, gauss_overlap_thresh=0.75, maxfev=5000,
-                 tol=0.00001, modes=None, data=None, results=None, debug=False):
+                 tol=0.00001, modes=None, data=None, results=None, model=None, debug=False):
         """Initialize base model object"""
 
         # Initialize base algorithm object with algorithm metadata
@@ -115,7 +115,7 @@ def __init__(self, peak_width_limits=(0.5, 12.0), max_n_peaks=np.inf, min_peak_h
             description='Original parameterizing neural power spectra algorithm.',
             public_settings=SPECTRAL_FIT_SETTINGS_DEF,
             private_settings=SPECTRAL_FIT_PRIVATE_SETTINGS_DEF,
-            modes=modes, data=data, results=results, debug=debug)
+            modes=modes, data=data, results=results, model=model, debug=debug)
 
         ## Public settings
         self.settings.peak_width_limits = peak_width_limits
diff --git a/specparam/data/data.py b/specparam/data/data.py
index 7f0d94af..d7f43803 100644
--- a/specparam/data/data.py
+++ b/specparam/data/data.py
@@ -7,6 +7,7 @@
 
 from specparam.sim.gen import gen_freqs
 from specparam.data import SpectrumMetaData, ModelChecks
+from specparam.utils.array import unlog
 from specparam.utils.spectral import trim_spectrum
 from specparam.utils.checks import check_input_options
 from specparam.reports.strings import gen_data_str
@@ -36,6 +37,8 @@ class Data():
         Whether to check the spectral data. If so, raises an error for any NaN / Inf values.
     format : {'power'}
         The representation format of the data.
+    model : SpectralModel, optional
+        The model object this object is linked to, to provide access to other attributes.
 
     Attributes
     ----------
@@ -55,7 +58,7 @@ class Data():
     All power values are stored internally in log10 scale.
     """
 
-    def __init__(self, check_freqs=True, check_data=True, format='power'):
+    def __init__(self, check_freqs=True, check_data=True, format='power', model=None):
         """Initialize Data object."""
 
         self._reset_data(True, True)
@@ -70,6 +73,7 @@ def __init__(self, check_freqs=True, check_data=True, format='power'):
         check_input_options(format, FORMATS, 'format')
         self.format = format
 
+        self._model = model
 
     @property
     def has_data(self):
@@ -154,6 +158,54 @@ def get_meta_data(self):
         return SpectrumMetaData(**{key : getattr(self, key) for key in self._meta_fields})
 
 
+    def get_data(self, component='full', space='log'):
+        """Get a data component.
+
+        Parameters
+        ----------
+        component : {'full', 'aperiodic', 'peak'}
+            Which data component to return.
+                'full' - full power spectrum
+                'aperiodic' - isolated aperiodic data component
+                'peak' - isolated peak data component
+        space : {'log', 'linear'}
+            Which space to return the data component in.
+                'log' - returns in log10 space.
+                'linear' - returns in linear space.
+
+        Returns
+        -------
+        output : 1d array
+            Specified data component, in specified spacing.
+
+        Notes
+        -----
+        The 'space' parameter doesn't just define the spacing of the data component
+        values, but rather defines the space of the additive data definition such that
+        `power_spectrum = aperiodic_component + peak_component`.
+        With space set as 'log', this combination holds in log space.
+        With space set as 'linear', this combination holds in linear space.
+        """
+
+        if not self.has_data:
+            raise NoDataError("No data available to fit, can not proceed.")
+        assert space in ['linear', 'log'], "Input for 'space' invalid."
+
+        if component == 'full':
+            output = self.power_spectrum if space == 'log' \
+                else unlog(self.power_spectrum)
+        elif component == 'aperiodic':
+            output = self._model.results.model._spectrum_peak_rm if space == 'log' else \
+                unlog(self.power_spectrum) / unlog(self._model.results.model._peak_fit)
+        elif component == 'peak':
+            output = self._model.results.model._spectrum_flat if space == 'log' else \
+                unlog(self.power_spectrum) - unlog(self._model.results.model._ap_fit)
+        else:
+            raise ValueError('Input for component invalid.')
+
+        return output
+
+
     def plot(self, plt_log=False, **plt_kwargs):
         """Plot the power spectrum."""
 
@@ -339,10 +391,10 @@ class Data2D(Data):
     All power values are stored internally in log10 scale.
     """
 
-    def __init__(self):
+    def __init__(self, *args, **kwargs):
         """Initialize Data2D object."""
 
-        Data.__init__(self)
+        Data.__init__(self, *args, **kwargs)
 
         self.power_spectra = None
 
@@ -451,10 +503,10 @@ class Data2DT(Data2D):
     All power values are stored internally in log10 scale.
     """
 
-    def __init__(self):
+    def __init__(self, *args, **kwargs):
         """Initialize Data2DT object."""
 
-        Data2D.__init__(self)
+        Data2D.__init__(self, *args, **kwargs)
 
 
     @property
@@ -521,10 +573,10 @@ class Data3D(Data2DT):
     All power values are stored internally in log10 scale.
     """
 
-    def __init__(self):
+    def __init__(self, *args, **kwargs):
         """Initialize Data3D object."""
 
-        Data2DT.__init__(self)
+        Data2DT.__init__(self, *args, **kwargs)
 
         self.spectrograms = None
 
diff --git a/specparam/models/base.py b/specparam/models/base.py
index 0981e2a2..d06c604f 100644
--- a/specparam/models/base.py
+++ b/specparam/models/base.py
@@ -2,7 +2,6 @@
 
 from copy import deepcopy
 
-from specparam.utils.array import unlog
 from specparam.utils.checks import check_array_dim
 from specparam.modes.modes import Modes
 from specparam.modutils.errors import NoDataError
@@ -56,7 +55,7 @@ def add_modes(self, aperiodic_mode, periodic_mode):
             Mode for periodic component, or string specifying which mode to use.
         """
 
-        self.modes = Modes(aperiodic=aperiodic_mode, periodic=periodic_mode)
+        self.modes = Modes(aperiodic=aperiodic_mode, periodic=periodic_mode, model=self)
 
         if getattr(self, 'results', None):
             self.results.modes = self.modes
@@ -66,54 +65,6 @@ def add_modes(self, aperiodic_mode, periodic_mode):
             self.algorithm._reset_subobjects(modes=self.modes, results=self.results)
 
 
-    def get_data(self, component='full', space='log'):
-        """Get a data component.
-
-        Parameters
-        ----------
-        component : {'full', 'aperiodic', 'peak'}
-            Which data component to return.
-                'full' - full power spectrum
-                'aperiodic' - isolated aperiodic data component
-                'peak' - isolated peak data component
-        space : {'log', 'linear'}
-            Which space to return the data component in.
-                'log' - returns in log10 space.
-                'linear' - returns in linear space.
-
-        Returns
-        -------
-        output : 1d array
-            Specified data component, in specified spacing.
-
-        Notes
-        -----
-        The 'space' parameter doesn't just define the spacing of the data component
-        values, but rather defines the space of the additive data definition such that
-        `power_spectrum = aperiodic_component + peak_component`.
-        With space set as 'log', this combination holds in log space.
-        With space set as 'linear', this combination holds in linear space.
-        """
-
-        if not self.data.has_data:
-            raise NoDataError("No data available to fit, can not proceed.")
-        assert space in ['linear', 'log'], "Input for 'space' invalid."
-
-        if component == 'full':
-            output = self.data.power_spectrum if space == 'log' \
-                else unlog(self.data.power_spectrum)
-        elif component == 'aperiodic':
-            output = self.results.model._spectrum_peak_rm if space == 'log' else \
-                unlog(self.data.power_spectrum) / unlog(self.results.model._peak_fit)
-        elif component == 'peak':
-            output = self.results.model._spectrum_flat if space == 'log' else \
-                unlog(self.data.power_spectrum) - unlog(self.results.model._ap_fit)
-        else:
-            raise ValueError('Input for component invalid.')
-
-        return output
-
-
     def print_settings(self, description=False, concise=False):
         """Print out the current settings.
 
diff --git a/specparam/models/event.py b/specparam/models/event.py
index 23a5d745..a8e149ca 100644
--- a/specparam/models/event.py
+++ b/specparam/models/event.py
@@ -57,11 +57,12 @@ def __init__(self, *args, **kwargs):
                                    verbose=kwargs.pop('verbose', True),
                                    **kwargs)
 
-        self.data = Data3D()
+        self.data = Data3D(model=self)
 
         self.results = Results3D(modes=self.modes,
                                  metrics=kwargs.pop('metrics', None),
-                                 bands=kwargs.pop('bands', None))
+                                 bands=kwargs.pop('bands', None),
+                                 model=self)
 
         self.algorithm._reset_subobjects(data=self.data, results=self.results)
 
diff --git a/specparam/models/group.py b/specparam/models/group.py
index ab617622..0d0e78e9 100644
--- a/specparam/models/group.py
+++ b/specparam/models/group.py
@@ -60,11 +60,12 @@ def __init__(self, *args, **kwargs):
                                verbose=kwargs.pop('verbose', True),
                                **kwargs)
 
-        self.data = Data2D()
+        self.data = Data2D(model=self)
 
         self.results = Results2D(modes=self.modes,
                                  metrics=kwargs.pop('metrics', None),
-                                 bands=kwargs.pop('bands', None))
+                                 bands=kwargs.pop('bands', None),
+                                 model=self)
 
         self.algorithm._reset_subobjects(data=self.data, results=self.results)
 
diff --git a/specparam/models/model.py b/specparam/models/model.py
index a4666e3c..05423125 100644
--- a/specparam/models/model.py
+++ b/specparam/models/model.py
@@ -95,14 +95,14 @@ def __init__(self, aperiodic_mode='fixed', periodic_mode='gaussian',
             update_converters(DEFAULT_CONVERTERS, converters)
         BaseModel.__init__(self, aperiodic_mode, periodic_mode, converters, verbose)
 
-        self.data = Data()
+        self.data = Data(model=self)
 
-        self.results = Results(modes=self.modes, metrics=metrics, bands=bands)
+        self.results = Results(modes=self.modes, metrics=metrics, bands=bands, model=self)
 
         algorithm_settings = {} if algorithm_settings is None else algorithm_settings
         self.algorithm = check_algorithm_definition(algorithm, ALGORITHMS)(
             **algorithm_settings, modes=self.modes, data=self.data,
-            results=self.results, debug=debug, **model_kwargs)
+            results=self.results, debug=debug, model=self, **model_kwargs)
 
 
     @replace_docstring_sections([docs_get_section(Data.add_data.__doc__, 'Parameters'),
diff --git a/specparam/models/time.py b/specparam/models/time.py
index 3c7e30d6..d50764a0 100644
--- a/specparam/models/time.py
+++ b/specparam/models/time.py
@@ -53,11 +53,12 @@ def __init__(self, *args, **kwargs):
                                     verbose=kwargs.pop('verbose', True),
                                     **kwargs)
 
-        self.data = Data2DT()
+        self.data = Data2DT(model=self)
 
         self.results = Results2DT(modes=self.modes,
                                   metrics=kwargs.pop('metrics', None),
-                                  bands=kwargs.pop('bands', None))
+                                  bands=kwargs.pop('bands', None),
+                                  model=self)
 
         self.algorithm._reset_subobjects(data=self.data, results=self.results)
 
diff --git a/specparam/modes/modes.py b/specparam/modes/modes.py
index 82ef2281..5f4bf707 100644
--- a/specparam/modes/modes.py
+++ b/specparam/modes/modes.py
@@ -17,9 +17,11 @@ class Modes():
         Aperiodic mode.
     periodic : str or Mode
         Periodic mode.
+    model : SpectralModel, optional
+        The model object this object is linked to, to provide access to other attributes.
     """
 
-    def __init__(self, aperiodic, periodic):
+    def __init__(self, aperiodic, periodic, model=None):
         """Initialize modes."""
 
         # Set list of component names
@@ -29,6 +31,8 @@ def __init__(self, aperiodic, periodic):
         self.aperiodic = check_mode_definition(aperiodic, AP_MODES)
         self.periodic = check_mode_definition(periodic, PE_MODES)
 
+        self.model = model
+
 
     def check_params(self):
         """Check the description of the parameters for each mode."""
diff --git a/specparam/results/results.py b/specparam/results/results.py
index 4d1c7c0e..da504229 100644
--- a/specparam/results/results.py
+++ b/specparam/results/results.py
@@ -38,6 +38,8 @@ class Results():
         Metrics object with metric definitions.
     bands : Bands or dict or int or None
         Bands object with band definitions, or definition that can be turned into a Bands object.
+    model : SpectralModel, optional
+        The model object this object is linked to, to provide access to other attributes.
 
     Attributes
     ----------
@@ -54,7 +56,7 @@ class Results():
     """
     # pylint: disable=attribute-defined-outside-init, arguments-differ
 
-    def __init__(self, modes=None, metrics=None, bands=None):
+    def __init__(self, modes=None, metrics=None, bands=None, model=None):
         """Initialize Results object."""
 
         self.modes = modes if modes else Modes(None, None)
@@ -68,6 +70,8 @@ def __init__(self, modes=None, metrics=None, bands=None):
         # Initialize results attributes
         self._reset_results(True)
 
+        self._model = model
+
 
     @property
     def has_model(self):
@@ -258,10 +262,10 @@ class Results2D(Results):
         Results of the model fit for each power spectrum.
     """
 
-    def __init__(self, modes=None, metrics=None, bands=None):
+    def __init__(self, modes=None, metrics=None, bands=None, model=None):
         """Initialize Results2D object."""
 
-        Results.__init__(self, modes=modes, metrics=metrics, bands=bands)
+        Results.__init__(self, modes=modes, metrics=metrics, bands=bands, model=model)
 
         self._reset_group_results()
 
@@ -437,10 +441,10 @@ class Results2DT(Results2D):
         Results of the model fit across each time window.
     """
 
-    def __init__(self, modes=None, metrics=None, bands=None):
+    def __init__(self, modes=None, metrics=None, bands=None, model=None):
         """Initialize Results2DT object."""
 
-        Results2D.__init__(self, modes=modes, metrics=metrics, bands=bands)
+        Results2D.__init__(self, modes=modes, metrics=metrics, bands=bands, model=model)
 
         self._reset_time_results()
 
@@ -506,10 +510,10 @@ class Results3D(Results2DT):
         Each value in the dictionary stores a model fit parameter, as [n_events, n_time_windows].
     """
 
-    def __init__(self, modes=None, metrics=None, bands=None):
+    def __init__(self, modes=None, metrics=None, bands=None, model=None):
         """Initialize Results3D object."""
 
-        Results2DT.__init__(self, modes=modes, metrics=metrics, bands=bands)
+        Results2DT.__init__(self, modes=modes, metrics=metrics, bands=bands, model=model)
 
         self._reset_event_results()
 
diff --git a/specparam/tests/models/test_model.py b/specparam/tests/models/test_model.py
index 9639e9b4..738843de 100644
--- a/specparam/tests/models/test_model.py
+++ b/specparam/tests/models/test_model.py
@@ -145,7 +145,7 @@ def test_fit_null_conversions(tfm):
     null_converters = tfm.modes.get_params('dict')
     ntfm = SpectralModel(converters=null_converters)
 
-    ntfm.fit(tfm.data.freqs, tfm.get_data('full', 'linear'))
+    ntfm.fit(tfm.data.freqs, tfm.data.get_data('full', 'linear'))
     assert np.all(np.isnan(ntfm.results.get_params('aperiodic', version='converted')))
     assert np.all(np.isnan(ntfm.results.get_params('periodic', version='converted')))
 
@@ -154,7 +154,7 @@ def test_fit_custom_conversions(tfm):
     converters = {'periodic' : {'pw' : 'lin_sub'}}
     ntfm = SpectralModel(converters=converters)
 
-    ntfm.fit(tfm.data.freqs, tfm.get_data('full', 'linear'))
+    ntfm.fit(tfm.data.freqs, tfm.data.get_data('full', 'linear'))
     assert not np.array_equal(
         tfm.results.get_params('periodic', 'pw'), ntfm.results.get_params('periodic', 'pw'))
 
@@ -295,7 +295,7 @@ def test_get_data(tfm):
 
     for comp in ['full', 'aperiodic', 'peak']:
         for space in ['log', 'linear']:
-            assert isinstance(tfm.get_data(comp, space), np.ndarray)
+            assert isinstance(tfm.data.get_data(comp, space), np.ndarray)
 
 def test_get_component(tfm):