2023-04-17 11:52:26 +00:00 · 2023-04-17 08:41:02 +00:00 · 2023-04-17 08:42:06 +00:00 · 2023-04-17 09:24:09 +00:00
37 changed files with 934 additions and 642 deletions
--- a/conda-env.yml
+++ b/conda-env.yml
@ -4,18 +4,22 @@ channels:
  - defaults
 dependencies:
  - python=3.10
-  - click>=8.1.3,<8.2
+  - click=8.1.*
-  - numpy>=1.22,<1.23
+  - numpy>=1.22,<1.24
-  - datalad>=0.15.4,<0.18
+  - pandas>=1.4.0,<1.6
  - pandas>=1.4.0,<1.5
  - nibabel>=3.2.0,<4.1
-  - nilearn>=0.9.0,<1.0
+  - nilearn>=0.9.0,<=0.10.0
  - sqlalchemy>=1.4.27,<= 1.5.0
  - pyyaml>=5.1.2,<7.0
-  - seaborn>=0.11.2,<0.12
+  - h5py=3.8.*
-  - Sphinx>=5.0.2,<5.1
+  - seaborn=0.11.*
-  - sphinx-gallery>=0.10.1,<0.11
+  - Sphinx=5.3.*
-  - numpydoc>=1.4.0,<1.5
+  - sphinx-gallery=0.11.*
  - furo>=2022.9.29,<2023.0.0
  - numpydoc=1.5.*
  - sphinx-copybutton=0.5.*
  - towncrier=22.12.*
  - sphinxcontrib-mermaid=0.8.*
  - tox
  - ipykernel
  - isort
@ -28,5 +32,5 @@ dependencies:
  - codespell
  - pip
  - pip:
-    - sphinx-rtd-theme>=1.0.0,<1.1
+    - datalad>=0.15.4,<0.19
-    - sphinx-multiversion>=0.2.4,<0.3
+    - julearn==0.2.5
--- a/docs/builtin.rst
+++ b/docs/builtin.rst
@ -2,12 +2,12 @@
 .. _builtin:
-Built-in Pipeline steps and data
+Built-in Pipeline Components
-================================
+============================
-Data Grabbers
+Data Grabber
-------------
+------------
 ..
    Provide a list of the DataGrabbers that are implemented or planned.
@ -50,13 +50,15 @@ Available
     - Done
     - 0.0.1
   * - :class:`.JuselessDataladUKBVBM`
-     - UKB VBM dataset preprocessed with CAT. Available for Juseless only.
+     - | UKB VBM dataset preprocessed with CAT.
       | Available for Juseless only.
     - Restricted
     - ``junifer.configs.juseless``
     - Done
     - 0.0.1
   * - :class:`.JuselessDataladCamCANVBM`
-     - CamCAN VBM dataset preprocessed with CAT. Available for Juseless only.
+     - | CamCAN VBM dataset preprocessed with CAT.
       | Available for Juseless only.
     - Restricted
     - ``junifer.configs.juseless``
     - Done
@ -80,19 +82,22 @@ Available
     - Done
     - 0.0.1
   * - :class:`.JuselessDataladAOMICID1000VBM`
-     - AOMIC ID1000 VBM dataset. Available for Juseless only.
+     - | AOMIC ID1000 VBM dataset.
       | Available for Juseless only.
     - Restricted
     - ``junifer.configs.juseless``
     - Done
     - 0.0.1
   * - :class:`.JuselessDataladIXIVBM`
-     - `IXI VBM dataset <https://brain-development.org/ixi-dataset/>`_. Available for Juseless only.
+     - | `IXI VBM dataset <https://brain-development.org/ixi-dataset/>`_.
       | Available for Juseless only.
     - Restricted
     - ``junifer.configs.juseless``
     - Done
     - 0.0.1
   * - :class:`.JuselessUCLA`
-     - UCLA fMRIPrep dataset. Available for Juseless only.
+     - | UCLA fMRIPrep dataset.
       | Available for Juseless only.
     - Restricted
     - ``junifer.configs.juseless``
     - Done
@ -117,8 +122,8 @@ Planned
     - :gh:`47`
-Markers
+Marker
-------
+------
 ..
    Provide a list of the Markers that are implemented or planned.
@ -185,13 +190,15 @@ Available
     - Done
     - 0.0.1
   * - :class:`.EdgeCentricFCParcels`
-     - Calculate edge-centric functional connectivity over parcellation, as found in
+     - | Calculate edge-centric functional connectivity over parcellation, as
-       `Jo et al. (2021) <https://doi.org/10.1016/j.neuroimage.2021.118204>`_
+       | found in
       | `Jo et al. (2021) <https://doi.org/10.1016/j.neuroimage.2021.118204>`_
     - Done
     - 0.0.2
   * - :class:`.EdgeCentricFCSpheres`
-     - Calculate edge-centric functional connectivity over spheres placed on coordinates,
+     - | Calculate edge-centric functional connectivity over spheres placed on
-       as found in `Jo et al. (2021) <https://doi.org/10.1016/j.neuroimage.2021.118204>`_
+       | coordinates, as found in
       | `Jo et al. (2021) <https://doi.org/10.1016/j.neuroimage.2021.118204>`_
     - Done
     - 0.0.2
   * - :class:`.TemporalSNRParcels`
@ -199,7 +206,8 @@ Available
     - Done
     - 0.0.2
   * - :class:`.TemporalSNRSpheres`
-     - Calculate temporal signal-to-noise ratio using spheres placed on coordinates
+     - | Calculate temporal signal-to-noise ratio using spheres placed on
       | coordinates
     - Done
     - 0.0.2
@ -217,11 +225,12 @@ Planned
     - Compute connectedness
     - :gh:`34`
   * - Permutation entropy, Range entropy, Multiscale entropy and Hurst exponent
-     - Calculate Permutation entropy, Range entropy, Multiscale entropy and Hurst exponent
+     - | Calculate Permutation entropy, Range entropy, Multiscale entropy and
       | Hurst exponent
     - :gh:`61`
-Parcellations
+Parcellation
-------------
+------------
 ..
    Provide a list of the Parcellations that are implemented or planned.
@ -266,8 +275,10 @@ Available
       | ``TianxS2x3TxMNI6thgeneration``, ``TianxS2x7TxMNI6thgeneration``,
       | ``TianxS3x3TxMNI6thgeneration``, ``TianxS3x7TxMNI6thgeneration``,
       | ``TianxS4x3TxMNI6thgeneration``, ``TianxS4x7TxMNI6thgeneration``,
-       | ``TianxS1x3TxMNInonlinear2009cAsym``, ``TianxS2x3TxMNInonlinear2009cAsym``,
+       | ``TianxS1x3TxMNInonlinear2009cAsym``,
-       | ``TianxS3x3TxMNInonlinear2009cAsym``, ``TianxS4x3TxMNInonlinear2009cAsym``
+       | ``TianxS2x3TxMNInonlinear2009cAsym``,
       | ``TianxS3x3TxMNInonlinear2009cAsym``,
       | ``TianxS4x3TxMNInonlinear2009cAsym``
     - 0.0.1
     - | Tian, Y., Margulies, D.S., Breakspear, M. et al.
       | Topographic organization of the human subcortex
@ -309,7 +320,8 @@ Planned
       | https://doi.org/10.1016/j.neuroimage.2013.05.081.
   * - Mindboggle 101
     - | Klein, A., & Tourville, J.
-       | 101 labeled brain images and a consistent human cortical labeling protocol.
+       | 101 labeled brain images and a consistent human cortical labeling
       | protocol.
       | Frontiers in Neuroscience (2012).
       | http://doi.org/10.3389/fnins.2012.00171/abstract
   * - Destrieux
@ -326,13 +338,13 @@ Planned
       | https://doi.org/10.1093/cercor/bhw157
   * - Buckner
     - | Buckner, R.L., Krienen, F.M., Castellanos, A., Diaz, J.C., Yeo, B.T.T.
-       | The organization of the human cerebellum estimated by intrinsic functional
+       | The organization of the human cerebellum estimated by intrinsic
-       | connectivity.
+       | functional connectivity.
       | Journal of Neurophysiology, Volume 106(5), Pages 2322–2345 (2011).
       | https://doi.org/10.1152/jn.00339.2011
       | Yeo, B.T.T., Krienen, F.M., Sepulcre, J. et al.
-       | The organization of the human cerebral cortex estimated by intrinsic functional
+       | The organization of the human cerebral cortex estimated by intrinsic
-       | connectivity.
+       | functional connectivity.
       | Journal of Neurophysiology, Volume 106(3), Pages 1125–1165 (2011).
       | https://doi.org/10.1152/jn.00338.2011
@ -359,17 +371,18 @@ Available
   * - Cognitive action control
     - ``CogAC``
     - 0.0.1
-     - | Cieslik, E.C., Mueller, V.I., Eickhoff, C.R., Langner, R., Eickhoff, S.B.
+     - | Cieslik, E.C., Mueller, V.I., Eickhoff, C.R., Langner, R.,
-       | Three key regions for supervisory attentional control: Evidence from neuroimaging
+       | Eickhoff, S.B.
-       | meta-analyses.
+       | Three key regions for supervisory attentional control: Evidence from
       | neuroimaging meta-analyses.
       | Neuroscience & Biobehavioral Reviews, Volume 48, Pages 22-34 (2015).
       | https://doi.org/10.1016/j.neubiorev.2014.11.003.
   * - Cognitive action regulation
     - ``CogAR``
     - 0.0.1
     - | Langner, R., Leiberg, S., Hoffstaedter, F., Eickhoff, S.B.
-       | Towards a human self-regulation system: Common and distinct neural signatures
+       | Towards a human self-regulation system: Common and distinct neural
-       | of emotional and behavioural control.
+       | signatures of emotional and behavioural control.
       | Neuroscience & Biobehavioral Reviews, Volume 90, Pages 400-410 (2018).
       | https://doi.org/10.1016/j.neubiorev.2018.04.022.
   * - Default mode network
@ -381,8 +394,10 @@ Available
       | Journal of neurophysiology, Volume 103(1), Pages 297-321 (2010).
       | https://doi.org/10.1152/jn.00783.2009
       | Buckner, R.L., Andrews‐Hanna, J.R., & Schacter, D.L.
-       | The brain's default network: anatomy, function, and relevance to disease.
+       | The brain's default network: anatomy, function, and relevance to
-       | Annals of the New York Academy of Sciences, Volume 1124(1), Pages 1-38 (2008).
+       | disease.
       | Annals of the New York Academy of Sciences, Volume 1124(1), Pages 1-38
       | (2008).
       | https://doi.org/10.1196/annals.1440.011
   * - Missing formal name
     - ``eMDN``
@ -392,15 +407,16 @@ Available
     - ``Empathy``
     - 0.0.1
     - | Bzdok, D., Schilbach, L., Vogeley, K. et al.
-       | Parsing the neural correlates of moral cognition: ALE meta-analysis on morality,
+       | Parsing the neural correlates of moral cognition: ALE meta-analysis on
-       | theory of mind, and empathy.
+       | morality, theory of mind, and empathy.
       | Brain Structure and Function, Volume 217(4), Pages 783-796 (2012).
       | https://doi.org/10.1007/s00429-012-0380-y
   * - Extended social-affective default
     - ``eSAD``
     - 0.0.1
     - | Amft, M., Bzdok, D., Laird, A.R. et al.
-       | Definition and characterization of an extended social-affective default network.
+       | Definition and characterization of an extended social-affective default
       | network.
       | Brain structure & function, Volume 220, Pages 1031–1049 (2015).
       | https://doi.org/10.1007/s00429-013-0698-0
   * - Extended multiple-demand network
@ -422,25 +438,28 @@ Available
     - ``MultiTask``
     - 0.0.1
     - | Worringer, B., Langner, R., Koch, I. et al.
-       | Common and distinct neural correlates of dual-tasking and task-switching:
+       | Common and distinct neural correlates of dual-tasking and
-       | a meta-analytic review and a neuro-cognitive processing model of human multitasking.
+       | task-switching: a meta-analytic review and a neuro-cognitive processing
       | model of human multitasking.
       | Brain structure & function, Volume 224(5), Pages 1845–1869 (2019).
       | https://doi.org/10.1007/s00429-019-01870-4
   * - Physiological stress
     - ``PhysioStress``
     - 0.0.1
     - | Kogler, L., Müller, V.I., Chang, A. et al.
-       | Psychosocial versus physiological stress — Meta-analyses on deactivations and
+       | Psychosocial versus physiological stress — Meta-analyses on
-       | activations of the neural correlates of stress reactions.
+       | deactivations and activations of the neural correlates of stress
       | reactions.
       | NeuroImage, Volume 119, Pages 235-251 (2015).
       | https://doi.org/10.1016/j.neuroimage.2015.06.059.
   * - Reward-related decision making
     - ``Rew``
     - 0.0.1
     - | Liu, X., Hairston, J., Schrier, M., Fan, J.
-       | Common and distinct networks underlying reward valence and processing stages:
+       | Common and distinct networks underlying reward valence and processing
-       | A meta-analysis of functional neuroimaging studies.
+       | stages: A meta-analysis of functional neuroimaging studies.
-       | Neuroscience & Biobehavioral Reviews, Volume 35(5), Pages 1219-1236 (2011).
+       | Neuroscience & Biobehavioral Reviews, Volume 35(5), Pages 1219-1236
       | (2011).
       | https://doi.org/10.1016/j.neubiorev.2010.12.012.
   * - Missing formal name
     - ``Somatosensory``
@ -450,23 +469,24 @@ Available
     - ``ToM``
     - 0.0.1
     - | Bzdok, D., Schilbach, L., Vogeley, K. et al.
-       | Parsing the neural correlates of moral cognition: ALE meta-analysis on morality,
+       | Parsing the neural correlates of moral cognition: ALE meta-analysis on
-       | theory of mind, and empathy.
+       | morality, theory of mind, and empathy.
       | Brain Structure and Function, Volume 217(4), Pages 783-796 (2012).
       | https://doi.org/10.1007/s00429-012-0380-y
   * - Vigilant attention
     - ``VigAtt``
     - 0.0.1
     - | Langner, R., & Eickhoff, S.B.
-       | Sustaining attention to simple tasks: a meta-analytic review of the neural
+       | Sustaining attention to simple tasks: a meta-analytic review of the
-       | mechanisms of vigilant attention.
+       | neural mechanisms of vigilant attention.
       | Psychological bulletin, Volume 139 4, Pages 870-900 (2013).
       | https://doi.org/10.1037/a0030694
   * - Working memory
     - ``WM``
     - 0.0.1
     - | Rottschy, C., Langner, R., Dogan, I. et al.
-       | Modelling neural correlates of working memory: A coordinate-based meta-analysis.
+       | Modelling neural correlates of working memory: A coordinate-based
       | meta-analysis.
       | NeuroImage, Volume 60, Pages 830-846 (2012).
       | https://doi.org/10.1016/j.neuroimage.2011.11.050.
   * - Areal functional network from Power et al. (2011)
@ -496,19 +516,20 @@ Planned
     - Publication
   * - Emotional scene and face processing (EmoSF)
     - | Sabatinelli, D., Fortune, E.E., Li, Q. et al.
-       | Emotional perception: Meta-analyses of face and natural scene processing.
+       | Emotional perception: Meta-analyses of face and natural scene
       | processing.
       | NeuroImage, Volume 54(3), Pages 2524-2533 (2011).
       | https://doi.org/10.1016/j.neuroimage.2010.10.011.
   * - Perceptuo-motor network
     - | Heckner, M.K., Cieslik, E.C., Eickhoff, S.B. et al.
-       | The Aging Brain and Executive Functions Revisited: Implications from Meta-analytic
+       | The Aging Brain and Executive Functions Revisited: Implications from
-       | and Functional-Connectivity Evidence.
+       | Meta-analytic and Functional-Connectivity Evidence.
       | Journal of Cognitive Neuroscience, Volume 33(9), Pages 1716–1752 (2021).
       | https://doi.org/10.1162/jocn_a_01616
-Masks
+Mask
-----
+----
 ..
    Provide a list of the masks that are implemented or planned.
@ -541,30 +562,33 @@ Available
   * - Nilearn's MNI152 1mm-resolution mask
     - | ``compute_brain_mask``
     - 0.0.2
-     - | Compute the whole-brain mask. This mask is calculated using MNI152 1mm-resolution template mask onto the
+     - | Compute the whole-brain mask. This mask is calculated using
-       | target image. See :func:`nilearn.masking.compute_brain_mask`
+       | MNI152 1mm-resolution template mask onto the target image.
       | See :func:`nilearn.masking.compute_brain_mask`
   * - Nilearn's mask computed from FMRI data
     - | ``compute_epi_mask``
     - 0.0.2
-     - | Compute a brain mask from fMRI data. This is based on an heuristic proposed by T.Nichols: find the least
+     - | Compute a brain mask from fMRI data. This is based on an heuristic
-       | dense point of the histogram, between fractions ``lower_cutoff`` and ``upper_cutoff`` of the total image
+       | proposed by T.Nichols: find the least dense point of the histogram,
-       | histogram. See :func:`nilearn.masking.compute_epi_mask`
+       | between fractions ``lower_cutoff`` and ``upper_cutoff`` of the total
       | image histogram. See :func:`nilearn.masking.compute_epi_mask`
   * - Nilearn's background mask
     - | ``compute_background_mask``
     - 0.0.2
-     - | Compute a brain mask for the images by guessing the value of the background from the border of the image.
+     - | Compute a brain mask for the images by guessing the value of the
       | background from the border of the image.
       | See :func:`nilearn.masking.compute_background_mask`
   * - Nilearn's ICBM152 template gray-matter mask
     - | ``fetch_icbm152_brain_gm_mask``
     - 0.0.2
-     - | Compute a gray-matter mask from the asymmetrical ICBM152 2009 template, release a.
+     - | Compute a gray-matter mask from the asymmetrical ICBM152 2009 template,
       | release a.
       | See :func:`nilearn.datasets.fetch_icbm152_brain_gm_mask`
 Planned
 ~~~~~~~
 ..
  helpful site for creating tables: https://rest-sphinx-memo.readthedocs.io/en/latest/ReST.html#tables
--- a/docs/changes/newsfragments/220.doc
+++ b/docs/changes/newsfragments/220.doc
@ -0,0 +1 @@
 Improve general prose, formatting and code blocks in docs and set line length for ``.rst`` files to 80 by `Synchon Mandal`_
--- a/docs/contribution.rst
+++ b/docs/contribution.rst
@ -86,11 +86,12 @@ Before you submit a pull request, check that it meets these guidelines:
   updated. Consider creating a Python file that demonstrates the usage in
   ``examples/`` directory.
 #. Make sure to create a Draft Pull Request. If you are not sure how to do it,
-   check `here <https://github.blog/2019-02-14-introducing-draft-pull-requests/>`_.
+   check
-#. Note the pull request ID assigned after completing the previous step and create
+   `here <https://github.blog/2019-02-14-introducing-draft-pull-requests/>`_.
-   a short one-liner file of your contribution named as ``<pull-request-ID>.<type>``
+#. Note the pull request ID assigned after completing the previous step and
-   in ``docs/changes/newsfragments/``, ``<type>`` being as per the following
+   create a short one-liner file of your contribution named as
-   convention:
+   ``<pull-request-ID>.<type>`` in ``docs/changes/newsfragments/``, ``<type>``
   being as per the following convention:
   * API change : ``change``
   * Bug fix : ``bugfix``
@ -155,9 +156,10 @@ Writing Examples
 ----------------
 The format used for text is reST. Check the `sphinx reST reference`_ for more
-details. The examples are run and displayed in HTML format using `sphinx gallery`_. To add an
+details. The examples are run and displayed in HTML format using
-example, just create a ``.py`` file that starts either with ``plot_`` or ``run_``,
+`sphinx gallery`_. To add an example, just create a ``.py`` file that starts
-dependending on whether the example generates a figure or not.
+either with ``plot_`` or ``run_``, dependending on whether the example generates
 a figure or not.
 The first lines of the example should be a Python block comment with a title,
 a description of the example, authors and license name.
--- a/docs/extending/coordinates.rst
+++ b/docs/extending/coordinates.rst
@ -6,19 +6,19 @@ Adding Coordinates
 ==================
 Instead of using whole-brain parcellations to aggregate voxel-wise signals from
-MR images (as for example in the :class:`.ParcelAggregation` marker), Junifer
+MR images (as for example in the :class:`.ParcelAggregation` marker), junifer
 allows you to specify a set of coordinates around which to draw spheres to
 aggregate (for example using the :class:`.SphereAggregation` marker) the MR
 signals from individual voxels. Now, before you start specifying your own sets
-of coordinates, check the coordinates that Junifer already has
+of coordinates, check the coordinates that junifer already has
 :ref:`built in <builtin>`. If you simply want to use a well known set of
-coordinates from the literature, there is a reasonable chance, that Junifer
+coordinates from the literature, there is a reasonable chance, that junifer
 provides them already.
 If you checked the in-built coordinates, and they are not there already (for
-example if you came up with your own set of coordinates), then Junifer provides
+example if you came up with your own set of coordinates), then junifer provides
 an easy way for you to register them using the :func:`.register_coordinates`
-function, so you can use your own set of coordinates within a Junifer pipeline.
+function, so you can use your own set of coordinates within a junifer pipeline.
 From the API reference, we can see that it has 3 positional arguments
 (``name``, ``coordinates``, and ``voi_names``) as well as one
@ -26,7 +26,7 @@ optional keyword argument (``overwrite``).
 The ``name`` argument takes a string indicating the name you want to give to
 this set of coordinates. This ``name`` can be used to obtain and operate on a
-set of coordinates in Junifer. For example, you can obtain your coordinates
+set of coordinates in junifer. For example, you can obtain your coordinates
 after registration by providing ``name`` to :func:`.load_coordinates`. We could
 simply call it ``"my_set_of_coordinates"``, but likely you want a more
 descriptive and more informative name most of the time.
@ -36,7 +36,7 @@ The ``coordinates`` argument takes the actual coordinates as a 2-dimensional
 columns (one for each spatial dimension). That is, the first, second, and third
 columns indicate the x-, y-, and z-coordinates in MNI space respectively.
 The number of rows in the array correspond to the number of coordinates that
-belong to this set. Note, that Junifer (as of yet) only works in MNI space, and
+belong to this set. Note, that junifer (as of yet) only works in MNI space, and
 so therefore these coordinates should always be real-world coordinates of the
 MNI space.
@ -60,8 +60,8 @@ packages:
 For the sake of this example, we can create a set of coordinates that belong
 to the default mode network (DMN), and register this set of coordinates with
-Junifer. Note, that Junifer already has a
+junifer. Note, that junifer already has a
-:ref:`set of coordinates built-in<builtin>` ("DMNBuckner") that is associated
+:ref:`set of coordinates built-in <builtin>` ("DMNBuckner") that is associated
 with the DMN. Here, we use the DMN coordinates used in a
 `nilearn example <https://nilearn.github.io/dev/auto_examples/03_connectivity/plot_sphere_based_connectome.html>`_.
@ -93,16 +93,16 @@ simply use this to register our coordinates:
      voi_names=voi_names
  )
-Now, when we run this script, Junifer registers these coordinates and we can
+Now, when we run this script, junifer registers these coordinates and we can
 use them in subsequent analyses. Let's now consider how to use coordinate
 registration in combination with
-:ref:`codeless configuration using a YAML file<codeless>`.
+:ref:`codeless configuration using a YAML file <codeless>`.
 Step 2: Add coordinate registration to the YAML file
 ----------------------------------------------------
 In order to register your coordinates for a pipeline configured by a YAML file,
-you can use the ``with`` keyword provided by Junifer:
+you can use the ``with`` keyword provided by junifer:
 .. code-block:: yaml
--- a/docs/extending/datagrabber.rst
+++ b/docs/extending/datagrabber.rst
@ -8,9 +8,9 @@ Creating Data Grabbers
 Data Grabbers are the first step of the pipeline. Its purpose is to interpret
 the structure of a dataset and provide two specific functionalities:
-1) Given an *element*, provide the path to each kind of data available for this
+#. Given an *element*, provide the path to each kind of data available for this
   element (e.g. the path to the T1 image, the path to the T2 image, etc.)
-2) Provide the list of *elements* available in the dataset.
+#. Provide the list of *elements* available in the dataset.
 In this section, we will see how to create a datagrabber for a dataset. Basic
 aspects of datagrabbers are covered in the
@ -27,11 +27,11 @@ The *element* should be the smallest unit of data that can be processed. That
 is, for each element, there should be a set of data that can be processed, but
 only one of each *data type* (see :ref:`data_types`).
-For example, if we have a dataset from an fMRI study in which:
+For example, if we have a dataset from a fMRI study in which:
-a) both T1w and fMRI was acquired
+a. both T1w and fMRI was acquired
-b) 20 subjects went through an experiment twice
+b. 20 subjects went through an experiment twice
-c) the experiment included resting-stage fMRI and a task named *stroop*
+c. the experiment included resting-stage fMRI and a task named *stroop*
 then the *element* should be composed of 3 items:
@ -40,7 +40,7 @@ then the *element* should be composed of 3 items:
 * ``task``: The task performed, e.g. `rest`, `stroop`
 If any of these items were not part of the element, then we will have more than
-one ``T1w`` and/or ``BOLD`` image for each subject, which is not allowed.
+one ``T1w`` and / or ``BOLD`` image for each subject, which is not allowed.
 Importantly, nothing prevents that one image is part of two different elements.
 For example, it is usually the case that the ``T1w`` image is not acquired for
@ -74,12 +74,11 @@ expressed as a pattern:
 where ``{subject}`` is the replacement for the subject id and ``{session}``
 is the replacement for the session id.
-Since it is a BIDS dataaset, the same happens with the BOLD images. The path to
+Since it is a BIDS dataset, the same happens with the BOLD images. The path to
 the BOLD images can be expressed as a pattern:
 ``{subject}/{session}/func/{subject}_{session}_task-rest_bold.nii.gz``
 This will be the norm in most of the datasets. If your dataset can be expressed
 in terms of patterns, then follow :ref:`extending_datagrabbers_pattern`.
 Otherwise, we recommend that you take time to re-think about your dataset
@ -90,7 +89,6 @@ get your dataset in order.
 If there is no other way, then you can follow :ref:`extending_datagrabbers_base`
 to create a Data Grabber from scratch.
 .. _extending_datagrabbers_pattern:
 Step 3: Create a Data Grabber
@ -99,13 +97,12 @@ Step 3: Create a Data Grabber
 Option A: Extending from PatternDataGrabber
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-The :py:class:`~junifer.datagrabber.PatternDataGrabber` class is an
+The :class:`.PatternDataGrabber` class is an abstract class that has the
-abstract class that has the functionality of understanding patterns embeded
+functionality of understanding patterns embeded in it.
 in it.
 Before creating the datagrabber, we need to define 3 variables:
-* ``types``: A list with the available :ref:`data_types` in our dataset
+* ``types``: A list with the available :ref:`data_types` in our dataset.
 * ``patterns``: A dictionary that specifies the pattern for each data type.
 * ``replacements``: A list indicating which of the elements in the patterns
  should be replaced by the values of the element.
@ -126,19 +123,21 @@ where the dataset is located. For example, if the dataset is located in
 ``/data/project/test/data``, then ``datadir`` should be
 ``/data/project/test/data``. Or, if we want to allow the user to specify the
 location of the dataset, we can expose the variable in the constructor, as in
-this example
+the following example.
-With this defined, we can now create our datagrabber, we will name it
+With the variables defined above, we can create our datagrabber and name it
 ``ExampleBIDSDataGrabber``:
 .. code-block:: python
-    from junifer.datagrabber.pattern import PatternDataGrabber
+    from pathlib import Path
    from junifer.datagrabber import PatternDataGrabber
    class ExampleBIDSDataGrabber(PatternDataGrabber):
-        def __init__(self, datadir):
+        def __init__(self, datadir: str | Path) -> None:
            types = ["T1w", "BOLD"]
            patterns = {
               "T1w": "{subject}/{session}/anat/{subject}_{session}_T1w.nii.gz",
@ -154,19 +153,21 @@ With this defined, we can now create our datagrabber, we will name it
 Our datagrabber is ready to be used by junifer. However, it is still unknown
 to the library. We need to register it in the library. To do so, we need to
-use the :py:func:`~junifer.api.decorators.register_datagrabber` decorator.
+use the :func:`.register_datagrabber` decorator.
 .. code-block:: python
-    from junifer.datagrabber.pattern import PatternDataGrabber
+    from pathlib import Path
    from junifer.api.decorators import register_datagrabber
    from junifer.datagrabber import PatternDataGrabber
    @register_datagrabber
    class ExampleBIDSDataGrabber(PatternDataGrabber):
-        def __init__(self, datadir):
+        def __init__(self, datadir: str | Path) -> None:
            types = ["T1w", "BOLD"]
            patterns = {
               "T1w": "{subject}/{session}/anat/{subject}_{session}_T1w.nii.gz",
@ -193,11 +194,11 @@ set the ``datadir``.
 Optional: Using datalad
-"""""""""""""""""""""""
+~~~~~~~~~~~~~~~~~~~~~~~
 If you are using `datalad`_, you can use the :class:`.PatternDataladDataGrabber`
 instead of the :class:`.PatternDataGrabber`. This class will not only
-interpret patterns, but also use `datalad`_ to `clone` and `get` the data.
+interpret patterns, but also use `datalad`_ to ``clone`` and ``get`` the data.
 The main difference between the two is that the ``datadir`` is not the actual
 location of the dataset, but the location where the dataset will be cloned. It
@ -238,17 +239,16 @@ Now we have our 2 additional variables:
 And we can create our datagrabber:
 .. code-block:: python
    from junifer.datagrabber.pattern import PatternDataladDataGrabber
    from junifer.api.decorators import register_datagrabber
    from junifer.datagrabber import PatternDataladDataGrabber
    @register_datagrabber
    class ExampleBIDSDataGrabber(PatternDataladDataGrabber):
-        def __init__(self):
+        def __init__(self) -> None:
            types = ["T1w", "BOLD"]
            patterns = {
               "T1w": "{subject}/{session}/anat/{subject}_{session}_T1w.nii.gz",
@ -272,29 +272,31 @@ And we can create our datagrabber:
 Option B: Extending from BaseDataGrabber
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-While we could not think of a use case in which the pattern-based data grabber would not be suitable, it is still
+While we could not think of a use case in which the pattern-based datagrabber
-possible to create a datagrabber extending from the :py:class:`~junifer.datagrabber.base.BaseDataGrabber` class.
+would not be suitable, it is still possible to create a datagrabber extending
 from the :class:`.BaseDataGrabber` class.
-In order to create a datagrabber extending from :py:class:`~junifer.datagrabber.base.BaseDataGrabber`, we need to
+In order to create a datagrabber extending from :class:`.BaseDataGrabber`, we
-implement the following methods:
+need to implement the following methods:
 - ``get_item``: to get a single item from the dataset.
 - ``get_elements``: to get the list of all elements present in the dataset
 - ``get_element_keys``: to get the keys of the elements in the dataset.
 .. note::
-   The ``__init__`` method could also be implemented, but it is not mandatory. This is required if the datagrabber
+
-   requires any parameter.
+   The ``__init__`` method could also be implemented, but it is not mandatory.
   This is required if the datagrabber requires any extra parameter.
 We will now implement our BIDS example with this method.
 The first method, ``get_item``, needs to obtain a single
-item from the dataset. Since this dataset requires two variables, ``subject`` and ``session``, we will use them
+item from the dataset. Since this dataset requires two variables, ``subject``
-as parameters of ``get_item``:
+and ``session``, we will use them as parameters of ``get_item``:
 .. code-block:: python
-   def get_item(self, subject, session):
+   def get_item(self, subject: str, session: str) -> dict[str, str]:
      out = {
         "T1w": f"{subject}/{session}/anat/{subject}_{session}_T1w.nii.gz",
         "BOLD": f"{subject}/{session}/func/{subject}_{session}_task-rest_bold.nii.gz",
@ -302,13 +304,17 @@ as parameters of ``get_item``:
      return out
-The second method, ``get_elements``, needs to return a list of all the elements in the dataset. In this case, we
+The second method, ``get_elements``, needs to return a list of all the elements
-know that the dataset contains 3 subjects and 3 sessions, so we can create a list of all the possible combinations.
+in the dataset. In this case, we know that the dataset contains 3 subjects and 3
-However, we need to remember that for session *ses-03* there is no BOLD data.
+sessions, so we can create a list of all the possible combinations. However, we
 need to remember that for session *ses-03* there is no BOLD data.
 .. code-block:: python
-   def get_elements(self):
+   from itertools import product
   def get_elements(self) -> list[str]:
      subjects = ["sub-01", "sub-02", "sub-03"]
      sessions = ["ses-01", "ses-02"]
@ -316,19 +322,19 @@ However, we need to remember that for session *ses-03* there is no BOLD data.
      if "BOLD" not in self.types:
         sessions.append("ses-03")
      elements = []
-      for subject in subjects:
+      for subject, element in product(subjects, sessions):
         for session in sessions:
         elements.append({"subject": subject, "session": session})
      return elements
-And finally, we can implement the ``get_element_keys`` method. This method needs to return a list of the keys that
+And finally, we can implement the ``get_element_keys`` method. This method needs
-represent each of the items in the element tuple. As a rule of thumb, they should be the parameters of the
+to return a list of the keys that represent each of the items in the element
-``get_item`` method, in the same order.
+tuple. As a rule of thumb, they should be the parameters of the ``get_item``
 method, in the same order.
 .. code-block:: python
-   def get_element_keys(self):
+   def get_element_keys(self) -> list[str]:
      return ["subject", "session"]
@ -336,20 +342,21 @@ So, to summarize, our datagrabber will look like this:
 .. code-block:: python
   from junifer.datagrabber.base import BaseDataGrabber
   from junifer.api.decorators import register_datagrabber
   from junifer.datagrabber import BaseDataGrabber
   @register_datagrabber
   class ExampleBIDSDataGrabber(BaseDataGrabber):
-      def get_item(self, subject, session):
+      def get_item(self, subject: str, session: str) -> dict[str, str]:
         out = {
            "T1w": f"{subject}/{session}/anat/{subject}_{session}_T1w.nii.gz",
            "BOLD": f"{subject}/{session}/func/{subject}_{session}_task-rest_bold.nii.gz",
         }
      return out
-      def get_elements(self):
+      def get_elements(self) -> list[str]:
         subjects = ["sub-01", "sub-02", "sub-03"]
         sessions = ["ses-01", "ses-02"]
@ -362,48 +369,59 @@ So, to summarize, our datagrabber will look like this:
               elements.append({"subject": subject, "session": session})
         return elements
-      def get_element_keys(self):
+      def get_element_keys(self) -> list[str]:
         return ["subject", "session"]
 Optional: Using datalad
-"""""""""""""""""""""""
+~~~~~~~~~~~~~~~~~~~~~~~
 If this dataset is in a datalad dataset, we can extend from
 :class:`.DataladDataGrabber` instead of :class:`.BaseDataGrabber`. This will
 allow us to use the datalad API to obtain the data.
 Step 4: Optional: Adding *BOLD confounds*
 -----------------------------------------
-For some analyses, it is useful to have the confounds associated with the BOLD data. This corresponds to the
+For some analyses, it is useful to have the confounds associated with the BOLD
-``BOLD_confounds`` item in the :ref:`Data Object <data_object>` (see :ref:`data_types`). However, the ``BOLD_confounds``
+data. This corresponds to the ``BOLD_confounds`` item in the
-element does not only consists of a ``path``, but it requries more information about the format of the confounds file.
+:ref:`Data Object <data_object>` (see :ref:`data_types`). However, the
-Thus, the ``BOLD_confounds`` element is a dictionary with the following keys:
+``BOLD_confounds`` element does not only consists of a ``path``, but it requries
 more information about the format of the confounds file. Thus, the
 ``BOLD_confounds`` element is a dictionary with the following keys:
 - ``path``: the path to the confounds file.
- ``format``: the format of the confounds file. Currently, this can be either ``fmriprep`` or ``adhoc``.
+- ``format``: the format of the confounds file. Currently, this can be either
  ``fmriprep`` or ``adhoc``.
-The ``fmriprep`` format corresponds to the format of the confounds files generated by `fMRIPrep`_. The
+The ``fmriprep`` format corresponds to the format of the confounds files
-``adhoc`` format corresponds to a format that is not standardized.
+generated by `fMRIPrep`_. The ``adhoc`` format corresponds to a format that is
 not standardised.
 .. note::
   The ``mappings`` key is only required if the ``format`` is ``adhoc``. If the ``format`` is ``fmriprep``, the
   ``mappings`` key is not required.
   The ``mappings`` key is only required if the ``format`` is ``adhoc``. If the
   ``format`` is ``fmriprep``, the ``mappings`` key is not required.
-Currently, Junifer provides only one confound remover step
+Currently, junifer provides only one confound remover step
-(:class:`.fMRIPrepConfoundRemover`), which relies entirely on the ``fmriprep`` confound
+(:class:`.fMRIPrepConfoundRemover`), which relies entirely on the ``fmriprep``
-variable names. Thus, if the confounds are not in ``fmriprep`` format, the user will need to provide the mappings
+confound variable names. Thus, if the confounds are not in ``fmriprep`` format,
-between the *ad-hoc* variable names and the ``fmriprep`` variable names.
+the user will need to provide the mappings between the *ad-hoc* variable names
-This is done by specifying the ``adhoc`` format and providing the mappings as a dictionary in the ``mappings`` key.
+and the ``fmriprep`` variable names. This is done by specifying the ``adhoc``
 format and providing the mappings as a dictionary in the ``mappings`` key.
-In the following example, the confounds file has 3 variables that are not in the ``fmriprep`` format. Thus, we will
+In the following example, the confounds file has 3 variables that are not in the
-provide the mappings for these variables to the ``fmriprep`` format.
+``fmriprep`` format. Thus, we will provide the mappings for these variables to
 the ``fmriprep`` format. For example, the ``get_item`` method could look like
 this:
 .. code-block:: python
-   out["BOLD_confounds"]: {
+   def get_item(
      self, subject: str, session: str
   ) -> dict:
      out = {
         "BOLD": f"{subject}/{session}/func/{subject}_{session}_task-rest_bold.nii.gz",
         "BOLD_confounds": {
            "path": f"{subject}/{session}/func/{subject}_{session}_confounds.tsv",
            "format": "adhoc",
            "mappings": {
@ -415,9 +433,10 @@ provide the mappings for these variables to the ``fmriprep`` format.
         },
      }
 .. note::
-   Not all of the mappings need to be provided. For the moment, this is used only by the
+
-   :class:`.fMRIPrepConfoundRemover` step, which requires variables based on the
+   Not all of the mappings need to be provided. For the moment, this is used
-   strategy selected. However, it is recommended to provide all the mappings, as this will allow the user to
+   only by the :class:`.fMRIPrepConfoundRemover` step, which requires variables
-   choose different strategies with the same dataset.
+   based on the strategy selected. However, it is recommended to provide all the
   mappings, as this will allow the user to choose different strategies with the
   same dataset.
--- a/docs/extending/extension.rst
+++ b/docs/extending/extension.rst
@ -2,17 +2,19 @@
 .. _extending_extension:
-Creating a Junifer extension
+Creating a junifer extension
 ============================
-Junifer is designed to be easily extensible. Through the use of a registry and decorators, we can easily add new
+Junifer is designed to be easily extensible. Through the use of a registry and
-functionality to junifer on runtime. This is done by creating a new python module and importing it before running
+decorators, you can easily add new functionality to junifer during runtime. This
-junifer.
+is done by creating a new Python module and importing it before running junifer.
-A special consideration has to be made when using the :ref:`code-less configuration<codeless>`. In this case, the
+A special consideration has to be made when using the
-``with`` statement can be used to import a module or run a python ``.py`` file.
+:ref:`code-less configuration<codeless>`. In this case, the
 ``with`` statement can be used to import a module or run a Python file.
-In the following example, we instruct junifer to first import ``my_module`` and then run the ``my_file.py`` file.
+In the following example, we instruct junifer to first import ``my_module`` and
 then run the ``my_file.py`` file.
 .. code-block:: yaml
@ -20,9 +22,13 @@ In the following example, we instruct junifer to first import ``my_module`` and
      - my_module
      - my_file.py
-Thus, the code from ``my_file.py`` will be executed before running junifer. This is the ideal place to create junifer
+Thus, the code from ``my_file.py`` will be executed before running junifer. This
-extensions.
+is the ideal place to include junifer extensions.
-.. important:: Some junifer commands will not consider files imported from files included in the ``with`` statement.
+.. important::
-   That is, if ``my_file.py`` imports ``my_other_file.py``, some of the junifer commands will not consider
+
-   ``my_other_file.py``. Either place all the code in one file or add multiple files to the ``with`` statement.
+   Some junifer commands will not consider files imported from files included
   in the ``with`` statement. If ``my_file.py`` imports ``my_other_file.py``,
   some of the junifer commands will not consider ``my_other_file.py``. Either
   place all the code in one file or add multiple files to the ``with``
   statement.
--- a/docs/extending/index.rst
+++ b/docs/extending/index.rst
@ -7,16 +7,15 @@ Extending junifer
 While we aim to provide as many datasets and markers as possible, we are also
 interested in allowing users to extend the functionality with their own
-datagrabbers, preprocessing, markers, etc.
+datagrabbers, preprocessing, markers, etc., .
-This does not mean that the new functionality will have to be included in
+It's not necessary to have the new functionality included in junifer before
-junifer before the user can use them. Instead, the user can simply
+the user can use them. The user can simply create a new Python file, code the
-create a new python file, code the desired functionality and use it with
+desired functionality and use it with junifer. This is the first step towards
-junifer. This is the first step towards including the new functionality in
+including the new functionality in the junifer pipeline.
 the junifer package.
 In this section we will show how to extend junifer, by creating new
-datagrabbers, preprocessing and markers, following the *junifer* way.
+datagrabbers, preprocessing, markers, etc.,  following the *junifer* way.
 .. toctree::
--- a/docs/extending/marker.rst
+++ b/docs/extending/marker.rst
@ -5,111 +5,143 @@
 Creating Markers
 ================
-Computing a marker (a.k.a. *feature*) is the main goal of junifer. While we aim to provide as many markers as possible,
+Computing a marker (a.k.a. *feature*) is the main goal of junifer. While we aim
-it might be the case that the marker you are looking for is not available. In this case, you can create your own marker
+to provide as many markers as possible, it might be the case that the marker you
 are looking for is not available. In this case, you can create your own marker
 by following this tutorial.
-Most of the functionality of a junifer marker has been taken care by the :class:`.BaseMarker` class.
+Most of the functionality of a junifer marker has been taken care by the
-Thus, only a few methods are required:
+:class:`.BaseMarker` class. Thus, only a few methods are required:
-1. ``get_valid_inputs``: a method to obtain the list of valid inputs for the marker. This is used to check that the
+#. ``get_valid_inputs``: The method to obtain the list of valid inputs for the
-   inputs provided by the user are valid. This method should return a list of strings, representing
+   marker. This is used to check that the inputs provided by the user are
-   :ref:`data types <data_types>`
+   valid. This method should return a list of strings, representing
-2. ``get_output_type``: a method to obtain the kind of output of the marker. This is used to check that the output
+   :ref:`data types <data_types>`.
-   of the marker is compatible with the storage. This method should return a string, representing
+#. ``get_output_type``: The method to obtain the kind of output of the marker.
-   :ref:`storage types <storage_types>`
+   This is used to check that the output of the marker is compatible with the
-3. ``compute``: the method that given the data, computes the marker.
+   storage. This method should return a string, representing
-4. ``__init__``: the initialization method, where the marker is configured.
+   :ref:`storage types <storage_types>`.
 #. ``compute``: The method that given the data, computes the marker.
 #. ``__init__``: The initialisation method, where the marker is configured.
-As an example, we will develop a Parcel Mean marker, that is, a marker that first applies a parcellation and
+As an example, we will develop a ``ParcelMean`` marker, a marker that first
-then computes the mean of the data in each parcel. This is a very simple example, but it will show you how to create
+applies a parcellation and then computes the mean of the data in each parcel.
-a new marker.
+This is a very simple example, but it will show you how to create a new marker.
 .. _extending_markers_input_output:
 Step 1: Configure input and output
 ----------------------------------
-This step is quite simple: we need to define the input and output of the marker. Based on the current
+This step is quite simple: we need to define the input and output of the marker.
-:ref:`data types <data_types>`, we can define as valid inputs ``BOLD``, ``VBM_WM`` and ``VBM_GM``.
+Based on the current :ref:`data types <data_types>`, we can have ``BOLD``,
 ``VBM_WM`` and ``VBM_GM`` as valid inputs.
 .. code-block:: python
-    def get_valid_inputs(self):
+    def get_valid_inputs(self) -> list[str]:
-        return ['BOLD', 'VBM_WM', 'VBM_GM']
+        return ["BOLD", "VBM_WM", "VBM_GM"]
-The output of the marker depends on the input. For ``BOLD``, it will be ``timeseries``, while for the rest of the inputs,
+The output of the marker depends on the input. For ``BOLD``, it will be
-it will be ``table``. Thus, we can define the output as:
+``timeseries``, while for the rest of the inputs, it will be ``vector``. Thus,
 we can define the output as:
 .. code-block:: python
-    def get_output_type(self, input_kind):
+    def get_output_type(self, input_kind: str) -> str:
-        if input_kind == 'BOLD':
+        if input_kind == "BOLD":
-            return 'timeseries'
+            return "timeseries"
        else:
-            return 'table'
+            return "vector"
 .. _extending_markers_init:
-Step 2: Initialize the marker
+Step 2: Initialise the marker
 -----------------------------
-In this step we need to define the parameters of the marker. That is, all the parameters that the user can provide
+In this step we need to define the parameters of the marker the user can provide
 to configure how the marker will behave.
-The parameters of the marker are defined in the ``__init__`` method. The :class:`.BaseMarker` class
+The parameters of the marker are defined in the ``__init__`` method. The
-requires two optional parameters:
+:class:`.BaseMarker` class requires two optional parameters:
-1. ``name``: the name of the marker. This is used to identify the marker in the configuration file.
+1. ``name``: the name of the marker. This is used to identify the marker in the
-2. ``on``: a list or string with the data types that the marker will be applied to.
+   configuration file.
 2. ``on``: a list or string with the data types that the marker will be applied
   to.
-.. attention:: Only basic types (*int*, *bool* and *str*) as well as Lists, Tuples and Dictionaries are allowed as
+.. attention::
               parameters. This is because the parameters are stored in a JSON file, and JSON only supports these types.
   Only basic types (*int*, *bool* and *str*), lists, tuples and dictionaries
   are allowed as parameters. This is because the parameters are stored in
   JSON format, and JSON only supports these types.
-In this example, the is only paramater required for the computation is the name of the parcellation to use. Thus, we can
+In this example, only paramater required for the computation is the name of the
-define the ``__init__`` method as follows:
+parcellation to use. Thus, we can define the ``__init__`` method as follows:
 .. code-block:: python
-    def __init__(self, parcellation_name, on=None, name=None):
+    def __init__(
-        self.parcellation_name = parcellation_name
+       self,
       parcellation: str,
       on: str | list[str] | None = None,
       name: str | None = None,
    ) -> None:
        self.parcellation = parcellation
        super().__init__(on=on, name=name)
-.. caution:: Parameters of the marker must be stored as object attributes without using ``_`` as prefix. This is
+.. caution::
             because any attribute that starts with ``_`` will not be considered as a parameter and not stored as
             part of the metadata of the marker.
   Parameters of the marker must be stored as object attributes without using
   ``_`` as prefix. This is because any attribute that starts with ``_`` will
   not be considered as a parameter and not stored as part of the metadata of
   the marker.
 .. _extending_markers_compute:
 Step 3: Compute the marker
 --------------------------
-In this step, we will define the method that computes the marker. This method will be called by junifer when needed,
+In this step, we will define the method that computes the marker. This method
-using the data provided by the datagrabber, as configured by the user. The function ``compute`` has two arguments:
+will be called by junifer when needed, using the data provided by the
 datagrabber, as configured by the user. The method ``compute`` has two
 arguments:
-* ``input``: a dictionary with the data to be used to compute the marker. This will be the corresponding element in the
+* ``input``: a dictionary with the data to be used to compute the marker. This
-  :ref:`Data Object<data_object>` alredy indexing. Thus, the dictionary has at least two keys: ``data`` and ``path``.
+  will be the corresponding element in the :ref:`Data Object<data_object>`
-  The first one contains the data, while the second one contains the path to the data. The dictionary can also contain
+  alredy indexed. Thus, the dictionary has at least two keys: ``data`` and
-  other keys, depending on the data type.
+  ``path``. The first one contains the data, while the second one contains the
-* ``extra_input``: the rest of the :ref:`Data Object<data_object>`. This is useful if you want to use other data to
+  path to the data. The dictionary can also contain other keys, depending on the
-  compute the marker (e.g.: ``BOLD_confounds`` can be used to de-confound the ``BOLD`` data).
+  data type.
 * ``extra_input``: the rest of the :ref:`Data Object<data_object>`. This is
  useful if you want to use other data to compute the marker
  (e.g.: ``BOLD_confounds`` can be used to de-confound the ``BOLD`` data).
 Following the example, we will compute the mean of the data in each parcel using
-:class:`nilearn.maskers.NiftiLabelsMasker`. Importantly, the output of the compute function must be a dictionary.
+:class:`nilearn.maskers.NiftiLabelsMasker`. Importantly, the output of the
-This dictionary will later be passed onto the ``store`` method.
+compute function must be a dictionary. This dictionary will later be passed onto
 the ``store`` method.
-.. hint:: To simplify the ``store`` method, define keys of the dictionary based on the corresponding store functions
+.. hint::
-          in the :ref:`storage types <storage_types>`. For example, if the output is a ``table``, the keys of the
+
-          dictionary should be ``data`` and ``columns``.
+   To simplify the ``store`` method, define keys of the dictionary based on the
   corresponding store functions in the :ref:`storage types <storage_types>`.
   For example, if the output is a ``vector``, the keys of the dictionary should
   be ``data`` and ``col_names``.
 .. code-block:: python
-    from nilearn.maskers import NiftiLabelsMasker
+    from typing import Any
    from junifer.data import load_parcellation
-    def compute(self, input, extra_input):
+    from junifer.data import load_parcellation
    from nilearn.maskers import NiftiLabelsMasker
    def compute(
       self,
       input: dict[str, Any],
       extra_input: dict[str, Any] | None = None,
    ) -> dict[str, Any]:
        # Get the data
        data = input["data"]
@ -126,7 +158,7 @@ This dictionary will later be passed onto the ``store`` method.
        masker = NiftiLabelsMasker(
            labels_img=t_parcellation,
            standardize=True,
-            memory='nilearn_cache',
+            memory="nilearn_cache",
            verbose=5,
        )
@ -134,59 +166,71 @@ This dictionary will later be passed onto the ``store`` method.
        out_values = masker.fit_transform([data])
        # Create the output dictionary
-        out = {"data": out_values, "columns": t_labels}
+        out = {"data": out_values, "col_names": t_labels}
        # If its 3D (BOLD), name each row as "scan"
        if out_values.shape[0] > 1:
            out["row_names"] = "scan"
        return out
 .. _extending_markers_finalize:
-Step 4: Finalize the marker
+Step 4: Finalise the marker
 ---------------------------
-Once all of the above steps are done, we just need to give our marker a name, state its *dependencies* and register it
+Once all of the above steps are done, we just need to give our marker a name,
-using the ``@register_marker`` decorator.
+state its *dependencies* and register it using the ``@register_marker``
 decorator.
-The *dependencies* are the core packages that are required to compute the marker. This will be later used to keep track
+The *dependencies* are the core packages that are required to compute the marker.
-of the versions of the packages used to compute the marker. To inform junifer about the dependencies of a marker,
+This will be later used to keep track of the versions of the packages used to
-we need to define a ``_DEPENDENCIES`` attribute in the class. This attribute must be a set, with the names of the
+compute the marker. To inform junifer about the dependencies of a marker, we need
-packages as strings. For example, the ``ParcelMean`` marker has the following dependencies:
+to define a ``_DEPENDENCIES`` attribute in the class. This attribute must be a
 set, with the names of the packages as strings. For example, the ``ParcelMean``
 marker has the following dependencies:
 .. code-block:: python
-    _DEPENDENCIES = {"nilearn"}
+    _DEPENDENCIES = {"nilearn", "numpy"}
-Finally, we need to register the marker using the ``@register_marker`` decorator. This decorator takes the name of the
+Finally, we need to register the marker using the ``@register_marker`` decorator.
 .. code-block:: python
-    from nilearn.maskers import NiftiLabelsMasker
+    from typing import Any
-    from junifer.data import load_parcellation
+
    from junifer.api.decorators import register_marker
    from junifer.data import load_parcellation
    from junifer.markers.base import BaseMarker
    from nilearn.maskers import NiftiLabelsMasker
    @register_marker
    class ParcelMean(BaseMarker):
        _DEPENDENCIES = {"nilearn", "numpy"}
-        def __init__(self, parcellation_name, on=None, name=None):
+        def __init__(
-            self.parcellation_name = parcellation_name
+           self,
           parcellation: str,
           on: str | list[str] | None = None,
           name: str | None = None,
        ) -> None:
            self.parcellation = parcellation
            super().__init__(on=on, name=name)
-        def get_valid_inputs(self):
+        def get_valid_inputs(self) -> list[str]:
-            return ['BOLD', 'VBM_WM', 'VBM_GM']
+            return ["BOLD", "VBM_WM", "VBM_GM"]
-        def get_output_type(self, input_kind):
+        def get_output_type(self, input_kind: str) -> str:
-            if input_kind == 'BOLD':
+            if input_kind == "BOLD":
-                return 'timeseries'
+                return "timeseries"
            else:
-                return 'table'
+                return "vector"
-        def compute(self, input, extra_input):
+        def compute(
           self,
           input: dict[str, Any],
           extra_input: dict[str, Any] | None = None,
        ) -> dict[str, Any]:
            # Get the data
            data = input["data"]
@ -203,7 +247,7 @@ Finally, we need to register the marker using the ``@register_marker`` decorator
            masker = NiftiLabelsMasker(
                labels_img=t_parcellation,
                standardize=True,
-                memory='nilearn_cache',
+                memory="nilearn_cache",
                verbose=5,
            )
@ -211,11 +255,8 @@ Finally, we need to register the marker using the ``@register_marker`` decorator
            out_values = masker.fit_transform([data])
            # Create the output dictionary
-            out = {"data": out_values, "columns": t_labels}
+            out = {"data": out_values, "col_names": t_labels}
            # If its 3D (BOLD), name each row as "scan"
            if out_values.shape[0] > 1:
                out["row_names"] = "scan"
            return out
@ -227,7 +268,8 @@ Template for a custom Marker
 .. code-block:: python
    from junifer.api.decorators import register_marker
-    from junifer.markers.base import BaseMarker
+    from junifer.markers import BaseMarker
    @register_marker
    class TemplateMarker(BaseMarker):
@ -249,5 +291,5 @@ Template for a custom Marker
            # TODO: compute the marker and create the output dictionary
            # Create the output dictionary
-            out = {"data": None, "columns": None}
+            out = {"data": None, "col_names": None}
            return out
--- a/docs/extending/masks.rst
+++ b/docs/extending/masks.rst
@ -5,28 +5,28 @@
 Adding Masks
 ============
-Many processing steps and markers in Junifer allow you to specify a binary
+Many processing steps and markers in junifer allow you to specify a binary
 mask to select voxels you want to include in the analysis. There are a number
-of masks :ref:`in-built in Junifer already<builtin>`, so check if any of them
+of masks :ref:`in-built in junifer already <builtin>`, so check if any of them
-suit your needs. Check how to use these masks :ref:`here<using_masks>`. Once
+suit your needs. Check how to use these masks :ref:`here <using_masks>`. Once
 you know how to use these masks, and you checked whether the in-built masks
 suit your needs, and you have found that they don't, you can come back here to
 learn how to use your own masks.
 The principle is fairly simple and quite similar to :ref:`adding_parcellations`
-and :ref:`adding_coordinates`. Junifer provides a :func:`.register_mask`
+and :ref:`adding_coordinates`. junifer provides a :func:`.register_mask`
 function that lets you register your own custom masks. It consists of two
 positional arguments (``name`` and ``mask_path``) and one optional keyword
 argument (``overwrite``).
 The ``name`` argument is a string indicating the name of the mask. This name
-is used to refer to that mask in Junifer internally in order to obtain the
+is used to refer to that mask in junifer internally in order to obtain the
 actual mask data and perform operations on it. For example, using the name you
 can load a mask after registration using the
 :func:`.load_mask` function.
 The ``mask_path`` should contain the path to a valid NIfTI image with binary
-voxel values (i.e. 0 or 1). This data can then be used by Junifer to mask other
+voxel values (i.e. 0 or 1). This data can then be used by junifer to mask other
 MR images.
 Step 1: Prepare code to register a mask
@ -37,9 +37,11 @@ look as follows:
 .. code-block:: python
  from junifer.data import register_custom_mask
  from pathlib import Path
  from junifer.data import register_custom_mask
  # this path is only an example, of course use the correct path
  # on your system:
  mask_path = Path("..") / ".." / "my_custom_mask.nii.gz"
@ -47,12 +49,12 @@ look as follows:
  register_mask(name="my_custom_mask", mask_path=mask_path)
 Simple, right? Now we just have to configure a YAML file to register this mask
-so we can use it for :ref:`codeless configuration of junifer<codeless>`.
+so we can use it for :ref:`codeless configuration of junifer <codeless>`.
 Step 2: Configure a YAML file for registration of a mask
 --------------------------------------------------------
-In order to do this, we can use the ``with`` keyword provided by Junifer:
+In order to do this, we can use the ``with`` keyword provided by junifer:
 .. code-block:: yaml
@ -73,10 +75,10 @@ mask as an argument. For example:
 Now, you can simply use this YAML file to run your pipeline. One important
 point to keep in mind is that if the paths given in ``register_custom_mask.py``
-are relative paths, they will be interpreted by Junifer as relative to the
+are relative paths, they will be interpreted by junifer as relative to the
-jobs directory (i.e. where Junifer will create submit files, logs directory and
+jobs directory (i.e. where junifer will create submit files, logs directory and
 so on). For simplicity, you may just want to use absolute paths to avoid
 confusion, yet using relative paths is likely a better way to make your
 pipeline directory/repository more portable and therefore more reproducible for
-others. Really, once you understand how these paths are interpreted by Junifer,
+others. Really, once you understand how these paths are interpreted by junifer,
 it is quite easy.
--- a/docs/extending/parcellations.rst
+++ b/docs/extending/parcellations.rst
@ -5,17 +5,17 @@
 Adding Parcellations
 ====================
-Before you start adding your own parcellations, check whether Junifer has
+Before you start adding your own parcellations, check whether junifer has
-the parcellation :ref:`in-built already<builtin>`. Perhaps, what is available
+the parcellation :ref:`in-built already <builtin>`. Perhaps, what is available
-there will suffice to achieve your goals. However, of course Junifer will not
+there will suffice to achieve your goals. However, of course junifer will not
 have every parcellation available that you may want to use, and if so, it will
-be nice to be able to add it yourself using a format that Junifer understands.
+be nice to be able to add it yourself using a format that junifer understands.
 Similarly, you may even be interested in creating your own custom parcellations
-and then adding them to Junifer, so you can use Junifer to obtain different
+and then adding them to junifer, so you can use junifer to obtain different
 markers to assess and validate your own parcellation. So, how can you do this?
 Since both of these use-cases are quite common, and not being able to use your
-favourite parcellation is of course quite a buzzkill, Junifer actually provides
+favourite parcellation is of course quite a buzzkill, junifer actually provides
 the easy-to-use :func:`.register_parcellation` function to do just that. Let's
 try to understand the API reference and then use this function to register our
 own parcellation.
@ -24,7 +24,7 @@ From the API reference, we can see that it has 3 positional arguments
 (``name``, ``parcellation_path``, and ``parcels_labels``) as well as one
 optional keyword argument (``overwrite``).
-The ``name`` of the parcellation is up to you and will be the name that Junifer
+The ``name`` of the parcellation is up to you and will be the name that junifer
 will use to refer to this particular parcellation. You can think of this as
 being similar to a key in a python dictionary, i.e. a key that is used to
 obtain and operate on the actual parcellation data. This ``name`` must always
@ -46,7 +46,7 @@ features that parcellation-based markers produce in an unambiguous way, such
 that a user can easily identify which ROIs were used to produce a specific
 feature (multiple ROIs, because some features consist of information from two
 or more ROIs, as for example in functional connectivity). Therefore, we provide
-Junifer with a list of strings, that contains the names for each ROI. In this
+junifer with a list of strings, that contains the names for each ROI. In this
 list, the label at the i-th position indicates the i-th integer label (i.e. the
 first label in this list corresponds to the first integer label in the
 parcellation and so on).
@ -54,7 +54,7 @@ parcellation and so on).
 Step 1: Prepare code to register a parcellation
 -----------------------------------------------
-Now we know everything that we need to know to make sure Junifer can use our
+Now we know everything that we need to know to make sure junifer can use our
 own parcellation to compute any parcellation-based marker. For example,
 a simple example could look like this:
@ -82,16 +82,16 @@ a simple example could look like this:
  )
 We can run this code and it seems to work, however, how can we actually
-include the custom parcellation in a Junifer pipeline using a
+include the custom parcellation in a junifer pipeline using a
-:ref:`code-less YAML configuration<codeless>`?
+:ref:`code-less YAML configuration <codeless>`?
 Step 2: Add parcellation registration to the YAML file
 ------------------------------------------------------
-In order to use the parcellation in a Junifer pipeline configured by a YAML
+In order to use the parcellation in a junifer pipeline configured by a YAML
 file, we can save the above code in a python file, say
 ``registering_my_parcellation.py``. We can then simply add this file using the
-``with`` keyword provided by Junifer:
+``with`` keyword provided by junifer:
 .. code-block:: yaml
@ -114,9 +114,9 @@ parcellation when registering it. For example, we can add a
 Now, you can simply use this YAML file to run your pipeline. One important
 point to keep in mind is that if the paths given in
 ``registering_my_parcellation.py`` are relative paths, they will be interpreted
-by Junifer as relative to the jobs directory (i.e. where Junifer will create
+by junifer as relative to the jobs directory (i.e. where junifer will create
 submit files, logs directory and so on). For simplicity, you may just want to
 use absolute paths to avoid confusion, yet using relative paths is likely a
 better way to make your pipeline directory/repository more portable and
 therefore more reproducible for others. Really, once you understand how these
-paths are interpreted by Junifer, it is quite easy.
+paths are interpreted by junifer, it is quite easy.
--- a/docs/faq.rst
+++ b/docs/faq.rst
@ -23,7 +23,7 @@ The following steps are specific to VSCode and you can choose to go with it:
   .. code-block:: bash
-       conda env create -n <your-environment-name> -f conda-env.yml python=3.9
+       conda env create -n <your-environment-name> -f conda-env.yml python=3.10
       conda activate <your-environment-name>
   The ``conda-env.yml`` can be found at the root of the repository.
--- a/docs/help.rst
+++ b/docs/help.rst
@ -12,14 +12,16 @@ Getting Help
    -- The Beatles, Help!
-This fragment of the song originally appeared in the 1965 film Help! and was written by Lennon and McCartney, just a
+This fragment of the song originally appeared in the 1965 film Help! and was
-few years after the development of Mark 1, the first commercial computer. Maybe just a random coincidence, or maybe
+written by Lennon and McCartney, just a few years after the development of
 Mark 1, the first commercial computer. Maybe just a random coincidence, or maybe
 Lennon was just trying to write an email to McCartney.
-While the song might have been written with another meaning in mind, it is a good way to describe the situation of many
+While the song might have been written with another meaning in mind, it is a good
-researchers who are presented with a new toolbox. Indeed, the situation of many researchers is that the projects they
+way to describe the situation of many researchers who are presented with a new
-are working on are becoming more and more complex in terms of methods and data. Thus, we *open up the doors* to new
+toolbox. Indeed, the situation of many researchers is that the projects they are
-possibilites:
+working on are becoming more and more complex in terms of methods and data. Thus,
 we *open up the doors* to new possibilites:
    | When I was younger, so much younger than today
    | I never needed anybody's help in any way
@ -28,10 +30,12 @@ possibilites:
    -- The Beatles, Help!
-The setback with modern research is that current methods are often more complex and require more computing, which
+The setback with modern research is that current methods are often more complex
-means that we need to learn concepts from computer science, mathematics, statistics, etc. This is a good thing, but it
+and require more computing, which means that we need to learn concepts from
-also means that we need to learn new tools and new ways of thinking, which can be a bit overwhelming, to the point that
+computer science, mathematics, statistics, etc. This is a good thing, but it
-we start relying more and more on other researchers. In the end, we might feel like we lose our independence:
+also means that we need to learn new tools and new ways of thinking, which can be
 a bit overwhelming, to the point that we start relying more and more on other
 researchers. In the end, we might feel like we lose our independence:
    | And now my life has changed in oh so many ways
    | My independence seems to vanish in the haze
@ -40,34 +44,44 @@ we start relying more and more on other researchers. In the end, we might feel l
    -- The Beatles, Help!
-We can continue with the song, but we think you get the point. The point is that we will need help, and we will need
+We can continue with the song, but we think you get the point. The point is that
-to ask for it. We will need to ask for help from our colleagues, from our supervisors, from our friends.
+we will need help, and we will need to ask for it. We will need to ask for help
 from our colleagues, from our supervisors, from our friends.
-We are a small team of researchers and developers, and we are not experts in *everything at once*. Each one of us has
+We are a small team of researchers and developers, and we are not experts in
-a specific expertise, and we are trying to use this expertise to create Junifer. When we conceived Junifer, we thought
+*everything at once*. Each one of us has a specific expertise, and we are trying
-of researchers' problems and tried to come up with the best way to help them, by building a tool that is easy to
+to use this expertise to create Junifer. When we conceived Junifer, we thought
-understand, learn and use. Most importantly, we made it to help. We are here to help you and your research.
+of researchers' problems and tried to come up with the best way to help them, by
 building a tool that is easy to understand, learn and use. Most importantly, we
 made it to help. We are here to help you and your research.
-If you have any questions, problems and / or suggestions, please do not hesitate to contact us. We will be happy to help you
+If you have any questions, problems and / or suggestions, please do not hesitate
-and we will be happy to hear from you.
+to contact us. We will be happy to help you and we will be happy to hear from
 you.
 Seems nice, no? But we have one condition: **help us help you**.
-Communication is the key for you to help us and in turn help you solve your problems. We cannot know what you are
+Communication is the key for you to help us and in turn help you solve your
-trying to do, unless you tell us. **The more detailed explanation you give us, the faster we can help you**.
+problems. We cannot know what you are trying to do, unless you tell us.
-We have opened several communication channels so that you can contact us in the way that is most convenient for you.
+**The more detailed explanation you give us, the faster we can help you**.
 We have opened several communication channels so that you can contact us in
 the way that is most convenient for you.
-Some people prefer to **write, in detail, with code and figures**. If you are one of those, use the
+Some people prefer to **write, in detail, with code and figures**. If you are
-`junifer Discussions`_. site on GitHub. This is a place where you can ask questions, and where you can discuss topics
+one of those, use the `junifer Discussions`_. site on GitHub. This is a place
-such as potential new features, or potential new methods.
+where you can ask questions, and where you can discuss topics such as
 potential new features, or potential new methods.
-Some people do **not have a clear idea of what they want**, but they know that they need help. This is not a
+Some people do **not have a clear idea of what they want**, but they know that
-problem, but it is a bit more involved. Since it will require more frequent interactions to try to understand
+they need help. This is not a problem, but it is a bit more involved. Since it
-what you are trying to do, we have the `junifer matrix channel`_ in which you can chat with us and other junifer users.
+will require more frequent interactions to try to understand what you are trying
 to do, we have the `junifer matrix channel`_ in which you can chat with us and
 other junifer users.
-Finally, some people **prefer to communicate verbally**. If you are one of those, you might want to join our
+Finally, some people **prefer to communicate verbally**. If you are one of those,
-*office hours*. Given that our agenda might vary, office hours will be announced on the
+you might want to join our *office hours*. Given that our agenda might vary,
-`junifer matrix channel`_ chat. Feel free to join and just *listen in* if you are too shy to write.
+office hours will be announced on the `junifer matrix channel`_ chat. Feel free
 to join and just *listen in* if you are too shy to write.
 In short, these are the 3 communication channels to get help:
@ -93,7 +107,8 @@ In short, these are the 3 communication channels to get help:
   Cons:
   * Real-time depends on the availability of the other users.
-   * It might be difficult to follow if several conversation happens at the same time.
+   * It might be difficult to follow if several conversation happens at the same
     time.
 #. Video Calls (*office hours*)
--- a/docs/index.rst
+++ b/docs/index.rst
@ -49,6 +49,8 @@ Funding
 =======
 We thank the `Helmholtz Imaging Platform <https://helmholtz-imaging.de/>`_,
-`SMHB <https://www.fz-juelich.de/en/smhb>`_ and `eBRAIN Health <https://www.ebrain-health.eu/>`_ 
+`SMHB <https://www.fz-juelich.de/en/smhb>`_ and
-(HORIZON-INFRA-2021-TECH-01) for supporting development of Junifer.
+`eBRAIN Health <https://www.ebrain-health.eu/>`_  (HORIZON-INFRA-2021-TECH-01)
-(The funding sources had no role in the design, implementation and evaluation of the pipeline.)
+for supporting development of Junifer.
 (The funding sources had no role in the design, implementation and evaluation of
 the pipeline.)
--- a/docs/installation.rst
+++ b/docs/installation.rst
@ -19,7 +19,8 @@ junifer is compatible with `Python`_ >= 3.8 and requires the following packages:
 * ``pyyaml>=5.1.2,<7.0``
 * ``h5py>=3.8.0,<3.9``
-Depending on the installation method, these packages might be installed automatically.
+Depending on the installation method, these packages might be installed
 automatically.
 Installation
 ------------
@ -32,7 +33,8 @@ Depending on your use-case, junifer can be installed differently:
  for developers.
-Either way, we strongly recommend using `virtual environments <https://realpython.com/python-virtual-environments-a-primer>`_.
+Either way, we strongly recommend using
 `virtual environments <https://realpython.com/python-virtual-environments-a-primer>`_.
 .. _install_latest_release:
@ -60,40 +62,47 @@ Follow the `detailed contribution guidelines <contribution.rst>`_.
 Installing external dependencies
 ================================
-Some markers will require optional external dependencies to be installed. In this section you will
+Some markers will require optional external dependencies to be installed. In
-find a list of all external dependencies that are required for specific markers.
+this section you will find a list of all external dependencies that are required
 for specific markers.
 AFNI
 ----
 To install AFNI, you can always follow the `AFNI official instructions
-<https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/background_install/main_toc.html>`_. Additionally, you can also follow
+<https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/background_install/main_toc.html>`_.
-the following steps to install and configure the AFNI Docker container in your local system.
+Additionally, you can also follow the following steps to install and configure
 the AFNI Docker container in your local system.
 .. important::
-  The AFNI Docker container wrappers add the commands required by junifer. Using these commands have
+   The AFNI Docker container wrappers add the commands required by junifer. Using
-  some limitations, mostly related to handling files and paths. Junifer knows about this and uses these
+   these commands have some limitations, mostly related to handling files and
-  commands in the proper way. Keep this in mind if you try to use the AFNI Docker wrappers outside of junifer.
+   paths. Junifer knows about this and uses these commands in the proper way.
-  These caveats and limitations are not documented.
+   Keep this in mind if you try to use the AFNI Docker wrappers outside of
   junifer. These caveats and limitations are not documented.
-1. Install Docker. You can follow the `Docker official instructions <https://docs.docker.com/get-docker/>`_.
+1. Install Docker. You can follow the
-2. Pull the AFNI Docker image from `Docker Hub <https://hub.docker.com/r/afni/afni>`_:
+   `Docker official instructions <https://docs.docker.com/get-docker/>`_.
 2. Pull the AFNI Docker image from
   `Docker Hub <https://hub.docker.com/r/afni/afni>`_:
-.. code-block:: shell
+.. code-block:: bash
  docker pull afni/afni_make_build
-3. Add the Junifer AFNI scripts to your PATH environmental variable. Run the following command:
+3. Add the Junifer AFNI scripts to your PATH environmental variable. Run the
   following command:
-.. code-block:: shell
+.. code-block:: bash
  junifer setup afni-docker
 Take the last line and copy it to your ``.bashrc`` or ``.zshrc`` file.
-Or, alternatively, you can exceute this command which will update the ``~/.bashrc`` for you:
+Or, alternatively, you can exceute this command which will update the
 ``~/.bashrc`` for you:
-.. code-block:: shell
+.. code-block:: bash
  junifer setup afni-docker | grep "PATH=" | xargs | >> ~/.bashrc
--- a/docs/maintaining.rst
+++ b/docs/maintaining.rst
@ -78,7 +78,8 @@ to generate the proper changelog that should be reflected in
    git push origin --follow-tags
-#. Optional: bump the *MAJOR* or *MINOR* segment of next release (replace ``D.E.0`` with the proper version).
+#. Optional: bump the *MAJOR* or *MINOR* segment of next release (replace
   ``D.E.0`` with the proper version).
 .. code-block:: bash
--- a/docs/starting.rst
+++ b/docs/starting.rst
@ -2,11 +2,11 @@
 .. _starting:
-First steps with Junifer
+First steps with junifer
 ========================
 .. note::
   To scroll the graph left and right, click on the graph and use the arrows on your keyboard.
 .. mermaid::
@ -75,7 +75,6 @@ First steps with Junifer
     contribute_marker(Create a\nMARKER REQUEST\nissue on Github)
     contribute_marker --> final_run
     missing_preprocessing --> contact_help
     missing_mask{"A Mask?"}
--- a/docs/understanding/data.rst
+++ b/docs/understanding/data.rst
@ -10,23 +10,87 @@ Description
 This is the *object* that traverses the steps of the pipeline. It is indeed a
 dictionary of dictionaries. The first level of keys are the :ref:`data types <data_types>`
-and a special key named ``meta`` that contains all the information on the data
+and the values are the corresponding information as dictionaries.
 object including source and previous transformation steps.
-The second level of keys are the actual data. So far, there are two keys used:
+.. code-block:: python
- ``path``: path to the file containing the data.
+   {'BOLD': {...}, 'T1w': {...}}
 - ``data``: the data loaded in memory.
-The :ref:`Data Grabber <datagrabber>` step will only fill the ``path`` value.
+The second level of keys are the actual data. A special second-level key named
-The ``data`` value will be filled by the :ref:`DataReader <datareader>` step, if it is one of the possible file types
+``meta`` is present in each step, that contains all the information on the
-that the datareader can read.
+data type including source and previous transformation steps.
-A point to note is that you never directly interact with the *data object* but it's important to know where and how the object is being manipulated to reason about your pipeline.
+The :ref:`Data Grabber <datagrabber>` step adds the ``path`` second-level key
 which gives the path to the file containing the data. The ``meta`` key in this
 step only contains information about the datagrabber used.
 .. code-block:: python
   {'BOLD': {'meta': {'datagrabber': {'class': 'SPMAuditoryTestingDatagrabber',
                                      'types': ['BOLD', 'T1w']},
                      'dependencies': set(),
                      'element': {'subject': 'sub001'}},
             'path': PosixPath('/var/folders/dv/2lbr8f8j0q12zrx3mz3ll5m40000gp/T/tmpgxcyjfo1/sub001_bold.nii.gz')},
    'T1w': {'meta': {'datagrabber': {'class': 'SPMAuditoryTestingDatagrabber',
                                     'types': ['BOLD', 'T1w']},
                     'dependencies': set(),
                     'element': {'subject': 'sub001'}},
            'path': PosixPath('/var/folders/dv/2lbr8f8j0q12zrx3mz3ll5m40000gp/T/tmpgxcyjfo1/sub001_T1w.nii.gz')}}
 The :ref:`Data Reader <datareader>` step adds the ``data`` second-level key
 which is the actual data loaded into memory. The ``meta`` key in this step
 adds information about the datareader used to read the data.
 .. code-block:: python
   {'BOLD': {'data': <nibabel.nifti1.Nifti1Image object at 0x16b5d8910>,
             'meta': {'datagrabber': {'class': 'SPMAuditoryTestingDatagrabber',
                                      'types': ['BOLD', 'T1w']},
                      'datareader': {'class': 'DefaultDataReader'},
                      'dependencies': {'nilearn'},
                      'element': {'subject': 'sub001'}},
             'path': PosixPath('/var/folders/dv/2lbr8f8j0q12zrx3mz3ll5m40000gp/T/tmpe49321ce/sub001_bold.nii.gz')},
    'T1w': {'data': <nibabel.nifti1.Nifti1Image object at 0x16b5d78d0>,
            'meta': {'datagrabber': {'class': 'SPMAuditoryTestingDatagrabber',
                                     'types': ['BOLD', 'T1w']},
                     'datareader': {'class': 'DefaultDataReader'},
                     'dependencies': set(),
                     'element': {'subject': 'sub001'}},
            'path': PosixPath('/var/folders/dv/2lbr8f8j0q12zrx3mz3ll5m40000gp/T/tmpe49321ce/sub001_T1w.nii.gz')}}
 The :ref:`Preprocess <preprocess>` step, if used, modifies the ``data``
 second-level key's value and appends the ``meta`` key with information about
 the preprocessor.
 The :ref:`Marker <marker>` step removes the ``path`` second-level key,
 replaces the ``data`` second-level key's value with the marker's computed value
 and adds further keys needed for the storage, for example, ``col_names``.
 .. code-block:: python
   {'BOLD': {'col_names': ['root_sum_of_squares_ets'],
             'data': ...,
             'meta': {'datagrabber': {'class': 'SPMAuditoryTestingDatagrabber',
                                      'types': ['BOLD', 'T1w']},
                      'datareader': {'class': 'DefaultDataReader'},
                      'dependencies': {'nilearn'},
                      'element': {'subject': 'sub001'},
                      'marker': {'agg_method': 'mean',
                                 'agg_method_params': None,
                                 'class': 'RSSETSMarker',
                                 'masks': None,
                                 'name': 'RSSETSMarker',
                                 'parcellation': 'Schaefer100x17'},
                      'type': 'BOLD'}}}
 .. note::
   You never directly interact with the *data object* but it's important to know
   where and how the object is being manipulated to reason about your pipeline.
 .. _data_types:
-Data types
+Data Types
 ----------
 .. list-table::
--- a/docs/understanding/datagrabber.rst
+++ b/docs/understanding/datagrabber.rst
@ -8,27 +8,30 @@ Data Grabber
 Description
 -----------
-The *Data Grabber* is an object that can provide an interface to datasets you want to work with in junifer.
+The ``DataGrabber`` is an object that can provide an interface to datasets you
-Every concrete implementation of a datagrabber is aware of a particular dataset's structure and thus allows
+want to work with in junifer. Every concrete implementation of a datagrabber is
-you to fetch specific elements of interest from the dataset. It adds the ``path`` key to each :ref:`data type <data_types>`
+aware of a particular dataset's structure and thus allows you to fetch specific
-in the :ref:`Data object <data_object>`.
+elements of interest from the dataset. It adds the ``path`` key to each
 :ref:`data type <data_types>` in the :ref:`Data object <data_object>`.
-Datagrabbers are intended to be used as context managers. When used within a context, a datagrabber takes care
+Datagrabbers are intended to be used as context managers. When used within a
-of any pre and post steps for interacting with the dataset, for example, downloading and cleaning up. As the interface
+context, a datagrabber takes care of any pre and post steps for interacting with
 the dataset, for example, downloading and cleaning up. As the interface
 is consistent, you always use the same procedure to interact with the datagrabber.
-For example, a concrete implementation of :class:`.DataladDataGrabber` can provide junifer
+For example, a concrete implementation of :class:`.DataladDataGrabber` can
-with data from a Datalad dataset. Of course, datagrabbers are not only meant to work with Datalad datasets but
+provide junifer with data from a Datalad dataset. Of course, datagrabbers are not
-any dataset.
+only meant to work with Datalad datasets but any dataset.
-If you are interested in using already provided datagrabbers, please go to :doc:`../builtin`. And, if you want
+If you are interested in using already provided datagrabbers, please go to
-to implement your own datagrabber, you need to provide concrete implementations of base classes already
+:doc:`../builtin`. And, if you want to implement your own datagrabber, you need
-provided.
+to provide concrete implementations of base classes already provided.
-Base classes
+Base Classes
 ------------
-In this section, we showcase different abstract base classes you might want to use to implement your own datagrabber.
+In this section, we showcase different abstract base classes you might want to
 use to implement your own datagrabber.
 .. list-table::
   :widths: auto
@ -37,18 +40,26 @@ In this section, we showcase different abstract base classes you might want to u
   * - Name
     - Description
   * - :class:`.BaseDataGrabber`
-     - | The abstract base class providing you an interface to implement your own datagrabber.
+     - | The abstract base class providing you an interface to implement your
-       | You should try to avoid using this directly and instead use
+       | own datagrabber. You should try to avoid using this directly and
-       | :class:`.PatternDataGrabber` or :class:`.DataladDataGrabber`.
+       | instead use :class:`.PatternDataGrabber` or
-       | To build your own custom *low-level* datagrabber, you need to at least implement the ``get_elements`` method,
+       | :class:`.DataladDataGrabber`. To build your own custom *low-level*
-       | but most of the time you should also override other existing methods like ``__enter__`` and ``__exit__``.
+       | datagrabber, you need to override the ``get_elements_keys``,
       | ``get_elements`` and ``get_item`` methods, and most of the time you
       | should also override other existing methods like ``__enter__`` and
       | ``__exit__``.
   * - :class:`.PatternDataGrabber`
-     - | It implements functionality to help you define the pattern of the dataset you want to get. For example,
+     - | It implements functionality to help you define the pattern of the
-       | you know that T1 images are found in a directory following this pattern ``{subject}/anat/{subject}_T1w.nii.gz``
+       | dataset you want to get. For example, you know that T1 images are
-       | inside of the dataset. Now you can provide this to the **PatternDataGrabber** and it will be able to get the file.
+       | found in a directory following the pattern:
       | ``{subject}/anat/{subject}_T1w.nii.gz`` inside of the dataset. Now you
       | can provide this to the :class:`.PatternDataGrabber` and it will be
       | able to get the file.
   * - :class:`.DataladDataGrabber`
-     - | It implements functionality to deal with Datalad datasets. Specifically, the ``__enter__`` and ``__exit__`` methods
+     - | It implements functionality to deal with Datalad datasets. Specifically,
-       | take care of cloning and removing the Datalad dataset.
+       | the ``__enter__`` and ``__exit__`` methods take care of cloning and
       | removing the Datalad dataset.
   * - :class:`.PatternDataladDataGrabber`
-     - | It is a combination of :class:`.PatternDataladDataGrabber` and
+     - | It is a combination of :class:`.PatternDataGrabber` and
-       | :class:`.DataladDataGrabber`. This is probably the class you are looking for when using Datalad.
+       | :class:`.DataladDataGrabber`. This is probably the class you are looking
       | for when using Datalad datasets.
--- a/docs/understanding/datareader.rst
+++ b/docs/understanding/datareader.rst
@ -8,22 +8,24 @@ Data Reader
 Description
 -----------
-The *Data Reader* is an object that is responsible for actually reading data files in junifer.
+The ``DataReader`` is an object that is responsible for actually reading data
-It reads the value of the key ``path`` for each :ref:`data type <data_types>` in the :ref:`Data object <data_object>`
+files in junifer. It reads the value of the key ``path`` for each
-and loads them to memory. After reading the data into memory, it adds the key ``data`` to the same level as ``path``
+:ref:`data type <data_types>` in the :ref:`Data object <data_object>` and loads
-and the value is the actual data in the memory.
+them into memory. After reading the data into memory, it adds the key ``data``
 to the same level as ``path`` and the value is the actual data in the memory.
-Datareaders are meant to be used inside the datagrabber context but you can operate on them outside the context as long
+Datareaders are meant to be used inside the datagrabber context but you can
-as the actual data is in the memory and the Python runtime has not garbage-collected it.
+operate on them outside the context as long as the actual data is in the memory
 and the Python runtime has not garbage-collected it.
-For data formats not supported by junifer yet, you can either make your own *Data Reader* or open an issue on
+For data formats not supported by junifer yet, you can either make your own
-`junifer Github`_ and we can help you out.
+*Data Reader* or open an issue on `junifer Github`_ and we can help you out.
-Currently supported file-formats
+File Formats
--------------------------------
+------------
-We already provide a concrete implementation :class:`.DefaultDataReader` which knows how to
+We already provide a concrete implementation :class:`.DefaultDataReader` which
-read the following file formats:
+knows how to read the following file formats:
 .. list-table::
   :widths: auto
--- a/docs/understanding/index.rst
+++ b/docs/understanding/index.rst
@ -10,17 +10,19 @@ tool conceived to extract features from neuroimaging data in an easy-to-use
 manner, with minimal coding and minimal user expertise in the internal aspects.
 Unlike other tools like FSL, SPM, AFNI, etc., junifer is not a toolbox to
-pre-process data, but a toolbox to extract features from previously pre-processed
+pre-process data, but a toolbox to extract features from previously
-data.
+pre-processed data.
 The main idea is that you have a set of images (e.g. a set of functional MRI,
 structural MRI, diffusion MRI, etc.) and you want to extract features to
 later use in statistical analyses or machine learning (for example, using
 julearn_).
-.. important:: Junifer is not a toolbox to create pipelines, but a tool to configure the junifer pipeline, which is
+.. important::
-   intended to be fixed and not to be changed. If you want to create a pipeline, you should use
+
-   other tools like nipype_.
+   Junifer is not a toolbox to create pipelines, but a tool to configure the
   junifer pipeline, which is intended to be fixed and not to be changed. If you
   want to create a pipeline, you should use other tools like nipype_.
 .. toctree::
   :maxdepth: 2
--- a/docs/understanding/marker.rst
+++ b/docs/understanding/marker.rst
@ -8,16 +8,25 @@ Marker
 Description
 -----------
-The ``Marker`` is an object that is responsible for feature extraction. It primarily operates on data loaded
+The ``Marker`` is an object that is responsible for feature extraction. It
-in memory by :ref:`Data Reader <datareader>` and stored in the ``data`` key of each :ref:`data type <data_types>`
+primarily operates on data loaded into memory by :ref:`Data Reader <datareader>`
-in the :ref:`Data object <data_object>`. In some cases, it can also operate on pre-processed data as obtained
+and stored in the ``data`` key of each :ref:`data type <data_types>` in the
-from the :ref:`Preprocess <preprocess>` step of the pipeline. It is important to note that this pre-process is
+:ref:`Data object <data_object>`. In some cases, it can also operate on
-not similar to pre-processing done by tools like FSL, SPM, AFNI, etc. . For example, one can perform confound
+pre-processed data as obtained from the :ref:`Preprocess <preprocess>` step of
-removal on loaded data and then perform feature extraction.
+the pipeline.
-Markers are meant to be used inside the datagrabber context but you can operate on them outside the context as long
+.. important::
 as the actual data is in the memory and the Python runtime has not garbage-collected it.
-If you are interested in using already provided markers, please go to :doc:`../builtin`. And, if you want to implement
+   This pre-process is not similar to pre-processing done by tools like FSL,
-your own marker, you need to provide concrete implementation of :class:`.BaseMarker`. Specifically, you
+   SPM, AFNI, etc., . For example, one can perform confound removal on loaded
-need to override ``get_output_type``, ``store`` and ``compute`` methods.
+   data and then perform feature extraction.
 Markers are meant to be used inside the datagrabber context but you can operate
 on them outside the context as long as the actual data is in the memory and the
 Python runtime has not garbage-collected it.
 If you are interested in using already provided markers, please go to
 :doc:`../builtin`. And, if you want to implement your own marker, you need to
 provide concrete implementation of :class:`.BaseMarker`. Specifically, you
 need to override ``get_valid_inputs``, ``get_output_type`` and ``compute``
 methods.
--- a/docs/understanding/pipeline.rst
+++ b/docs/understanding/pipeline.rst
@ -2,18 +2,22 @@
 .. _pipeline:
-The Junifer Pipeline
+The junifer Pipeline
 ====================
-The junifer pipeline is the main execution path of junifer. It consists of five steps:
+The junifer pipeline is the main execution path of junifer. It consists of five
 steps:
-1. :ref:`Data Grabber <datagrabber>`: Interpret the dataset and provide a list of files.
+1. :ref:`Data Grabber <datagrabber>`: Interpret the dataset and provide a list
   of files.
 2. :ref:`Data Reader <datareader>`: Read the files.
-3. :ref:`Pre-processing <preprocess>`: Prepare the images for marker computation.
+3. :ref:`Pre-processing <preprocess>`: Prepare the images for marker
   computation.
 4. :ref:`Marker Computation <marker>`: Compute the marker.
 5. :ref:`Storage <storage>`: Store the marker values.
-The element that is passed accross the pipeline is called the :ref:`Data Object<data_object>`.
+The element that is passed accross the pipeline is called the
 :ref:`Data Object<data_object>`.
 The following is a graphical representation of the pipeline:
@ -31,8 +35,9 @@ The following is a graphical representation of the pipeline:
     mc --> st
-However, it is usually the case that several markers are computed for the same data. Thus, the *markers* step 
+However, it is usually the case that several markers are computed for the same
-of the pipeline is defined as a list of markers. The following is a graphical representation of the pipeline execution
+data. Thus, the ``Marker Computation`` step of the pipeline is defined as a list
 of markers. The following is a graphical representation of the pipeline execution
 on multiple markers:
 .. mermaid::
@ -64,5 +69,8 @@ on multiple markers:
     mc4 --> st4
     mc5 --> st5
-.. note:: To avoid keeping in memory all of the computed marker, the storage step is called after each marker
+.. note::
-    computation, releasing the memory used to compute each marker.
+
   To avoid keeping in memory all of the computed marker, the storage step is
   called after each marker computation, releasing the memory used to compute
   each marker.
--- a/docs/understanding/preprocess.rst
+++ b/docs/understanding/preprocess.rst
@ -8,32 +8,38 @@ Preprocess
 Description
 -----------
-The ``Preprocess`` step of the pipeline is meant for pre-processing before or after :ref:`Marker <marker>` step
+The ``Preprocess`` is an object meant for pre-processing before or after
-depending on the use-case. For example, you might want to perform confound removal on ``BOLD`` data before
+:ref:`Marker <marker>` step depending on the use-case. For example, you might
-feature extraction.
+want to perform confound removal on ``BOLD`` data before feature extraction.
-This step is still under development and is an optional one for the pipeline to work.
+
 .. note::
   This step is optional for the pipeline to work.
 .. _preprocess_confounds:
 Confound Removal
 ----------------
-The *Confound Removal* step is meant to remove *confounds* from the ``BOLD`` data. The confounds are
+The *Confound Removal* step is meant to remove *confounds* from the ``BOLD``
-extracted from the ``BOLD_confounds`` data (must be provided by the :ref:`Data Grabber <datagrabber>`).
+data. The confounds are extracted from the ``BOLD_confounds`` data (must be
-The confounds are then regressed out from the ``BOLD`` data using :func:`nilearn.image.clean_img`.
+provided by the :ref:`Data Grabber <datagrabber>`). The confounds are then
 regressed out from the ``BOLD`` data using :func:`nilearn.image.clean_img`.
 Currently, junifer supports only one confound removal class:
-:class:`.fMRIPrepConfoundRemover`. This class is meant to remove confounds as described
+:class:`.fMRIPrepConfoundRemover`. This class is meant to remove confounds as
-before, using the output of `fMRIPrep`_ as reference.
+described before, using the output of `fMRIPrep`_ as reference.
 Strategy
 ~~~~~~~~
-This confound remover uses the `nilearn`_ API from :func:`nilearn.interfaces.fmriprep.load_confounds`. That is,
+This confound remover uses the `nilearn`_ API from
-define a *strategy* to extract the confounds from the ``BOLD_confounds`` data. The *strategy* is defined
+:func:`nilearn.interfaces.fmriprep.load_confounds`. That is, define a *strategy*
-by choosing the *noise components* to be used and the *confounds* to be extracted from each noise components.
+to extract the confounds from the ``BOLD_confounds`` data. The *strategy* is
-The *noise components* currently supported are:
+defined by choosing the *noise components* to be used and the *confounds* to be
 extracted from each noise components. The *noise components* currently supported
 are:
 * ``motion``
 * ``wm_csf``
@ -41,20 +47,24 @@ The *noise components* currently supported are:
 The confounds options for each *noise component* are:
-* ``basic``: the basic confounds for each *noise component*. For example, for ``motion``, the basic confounds
+* ``basic``: the basic confounds for each *noise component*. For example, for
-  are the 6 motion parameters (3 translations and 3 rotations). For ``wm_csf``, the basic
+  ``motion``, the basic confounds are the 6 motion parameters (3 translations
-  confounds are the mean signal of the white matter and CSF regions. For ``global_signal``, the basic confound
+  and 3 rotations). For ``wm_csf``, the basic confounds are the mean signal of
  the white matter and CSF regions. For ``global_signal``, the basic confound
  is the mean signal of the whole brain.
 * ``power2``: the basic confounds plus the square of each basic confound.
-* ``derivatives``: the basic confounds plus the derivative of each basic confound.
+* ``derivatives``: the basic confounds plus the derivative of each basic
-* ``full``: the basic confounds, the derivative of each basic confound, the square of each basic confound and the
+  confound.
-  square of each derivative of each basic confound.
+* ``full``: the basic confounds, the derivative of each basic confound, the
  square of each basic confound and the square of each derivative of each basic
  confound.
-The *strategy* is defined as a dictionary, with the *noise components* as keys and the *confounds* as values.
+The *strategy* is defined as a dictionary, with the *noise components* as keys
 and the *confounds* as values.
 Example in python format:
-.. code-block::
+.. code-block:: python
    strategy = {
        "motion": "basic",
@ -64,7 +74,7 @@ Example in python format:
 or in YAML format:
-.. code-block::
+.. code-block:: yaml
    strategy:
        motion: basic
@ -73,7 +83,7 @@ or in YAML format:
 The default value is to use all the *noise components* with the ``full`` *confounds*:
-.. code-block::
+.. code-block:: python
    strategy = {
        "motion": "full",
@ -81,20 +91,22 @@ The default value is to use all the *noise components* with the ``full`` *confou
        "global_signal": "full"
    }
-Other parameters
+Other Parameters
 ~~~~~~~~~~~~~~~~
-Additionaly, the :class:`.fMRIPrepConfoundRemover` supports the following parameters:
+Additionaly, the :class:`.fMRIPrepConfoundRemover` supports the following
 parameters:
 .. list-table::
-   :widths: 10, 30, 5
+   :widths: auto
   :header-rows: 1
   * - Parameter
     - Description
     - Default
   * - ``spike``
-     - Add a spike regressor in the timepoints when the framewise displacement exceeds this threshold.
+     - | Add a spike regressor in the timepoints when the framewise
       | displacement exceeds this threshold.
     - deactivated
   * - ``detrend``
     - Apply detrending on timeseries, before confound removal.
@ -112,5 +124,7 @@ Additionaly, the :class:`.fMRIPrepConfoundRemover` supports the following parame
     - Repetition time, in second (sampling period).
     - from nifti header
   * - ``mask``
-     - If provided, signal is only cleaned from voxels inside the mask. If not, a mask is computed using :func:`nilearn.masking.compute_brain_mask`.
+     - | If provided, signal is only cleaned from voxels inside the mask.
       | If not, a mask is computed using
       | :func:`nilearn.masking.compute_brain_mask`.
     - compute
--- a/docs/understanding/storage.rst
+++ b/docs/understanding/storage.rst
@ -8,27 +8,32 @@ Storage
 Description
 -----------
-The ``Storage`` is an object that is responsible for storing extracted features as computed from :ref:`Marker <marker>`
+The ``Storage`` is an object that is responsible for storing extracted features
-step of the pipeline. If the pipeline is provided with a ``storage-like`` object, the extracted features are stored via
+as computed from :ref:`Marker <marker>` step of the pipeline. If the pipeline is
 provided with a ``storage-like`` object, the extracted features are stored via
 that object else they are kept in memory.
-Storage is meant to be used inside the datagrabber context but you can operate on them outside the context as long
+Storage is meant to be used inside the datagrabber context but you can operate
-as the processed data is in the memory and the Python runtime has not garbage-collected it.
+on them outside the context as long as the processed data is in the memory and
 the Python runtime has not garbage-collected it.
-The :ref:`Markers <marker>` are responsible for defining what *storage kind* (``matrix``, ``vector``, ``timeseries``)
+The :ref:`Markers <marker>` are responsible for defining what *storage kind*
-they support for which :ref:`data type <data_types>` by overriding its ``store`` method. The storage object in turn
+(``matrix``, ``vector``, ``timeseries``) they support for which
-declares and provides implementation for specific *storage kind*. For example, :class:`.SQLiteFeatureStorage`
+:ref:`data type <data_types>` by overriding its ``get_output_type`` method. The
-supports saving ``matrix``, ``vector`` and ``timeseries`` via ``store_matrix``, ``store_vector`` and ``store_timeseries``
+storage object in turn declares and provides implementation for specific
-methods respectively.
+*storage kind*. For example, :class:`.SQLiteFeatureStorage` supports saving
-
+``matrix``, ``vector`` and ``timeseries`` via ``store_matrix``, ``store_vector``
-For storage interfaces not supported by junifer yet, you can either make your own ``Storage`` by providing a concrete
+and ``store_timeseries`` methods respectively.
 implementation of :class:`.BaseFeatureStorage` or open an issue on `junifer Github`_ and we can help you out.
 For storage interfaces not supported by junifer yet, you can either make your
 own ``Storage`` by providing a concrete implementation of
 :class:`.BaseFeatureStorage` or open an issue on `junifer Github`_ and we can
 help you out.
 .. _storage_types:
-Currently supported storage types
+Storage Types
---------------------------------
+-------------
 .. list-table::
   :widths: auto
@ -43,18 +48,18 @@ Currently supported storage types
     -  ``col_names``, ``row_names``, ``matrix_kind``, ``diagonal``
     -  :meth:`.BaseFeatureStorage.store_matrix`
   * - ``vector``
-     - A vector of values with column names
+     - A 1D row vector of values with column names
-     - ``columns``, ``row_names``
+     - ``col_names``
     -  :meth:`.BaseFeatureStorage.store_vector`
   * - ``timeseries``
     - A 2D matrix of values with column names
-     - ``columns``, ``row_names``
+     - ``col_names``
     -  :meth:`.BaseFeatureStorage.store_timeseries`
 .. _storage_interfaces:
-Currently supported storage interfaces
+Storage Interfaces
--------------------------------------
+------------------
 .. list-table::
   :widths: auto
--- a/docs/using/codeless.rst
+++ b/docs/using/codeless.rst
@ -2,11 +2,12 @@
 .. _codeless:
-Code-less configuration
+Code-less Configuration
 =======================
-On of the most important features of junifer is its capacity to run without writing a single line of code. This is
+On of the most important features of junifer is its capacity to run without
-achieved by using a configuration file that is written in YAML_. In this file, we configure the different steps of
+writing a single line of code. This is achieved by using a configuration file
 that is written in YAML_. In this file, we configure the different steps of
 :ref:`pipeline`.
 As a reminder, this is how the pipeline looks like:
@ -25,31 +26,34 @@ As a reminder, this is how the pipeline looks like:
     mc --> st
-Thus, the configuration file must configure each of the sections of the pipeline, as well as some general parameters.
+Thus, the configuration file must configure each of the sections of the pipeline,
 as well as some general parameters.
-As an example, we will generate the configuration file for a pipeline that will extract the mean ``VBM_GM`` values
+As an example, we will generate the configuration file for a pipeline that will
-using two different parcellations and one set of coordinates, from the *Oasis VBM Testing dataset* included in
+extract the mean ``VBM_GM`` values using two different parcellations and one set
-junifer.
+of coordinates, from the ``Oasis VBM Testing dataset`` included in junifer.
 General Parameters
 ------------------
-The general parameters are the ones that are not specific to any of the sections of the pipeline, but configure
+The general parameters are the ones that are not specific to any of the sections
-junifer as a whole. These parameters are:
+of the pipeline, but configure junifer as a whole. These parameters are:
 * ``with``: A section used to specify modules and junifer extensions to use.
 * ``workdir``: The working directory where junifer will store temporary files.
-Since the example uses a specific datagrabber for testing, we need to add ``junifer.testing.registry`` to the
+Since the example uses a specific datagrabber for testing, we need to add
-``with`` section. This will allow junifer to find the datagrabber. We will set the ``workdir`` to ``/tmp``.
+``junifer.testing.registry`` to the ``with`` section. This will allow junifer
 to find the datagrabber. We will set the ``workdir`` to ``/tmp``.
 .. code-block:: yaml
  with: junifer.testing.registry
  workdir: /tmp
-Step-by-step configuration
+Step-by-step Configuration
 --------------------------
 In order to configure the pipeline, we need to configure each step:
@ -60,15 +64,19 @@ In order to configure the pipeline, we need to configure each step:
 * ``markers``
 * ``storage``
-.. important:: The datareader step configuration is optional, as junifer only provides one datareader. Nevertheless,
+.. important::
-    it is possible to extend junifer with custom datareaders, and thus, it is also possible to configure this step.
+
   The datareader step configuration is optional, as junifer only provides one
   datareader. Nevertheless, it is possible to extend junifer with custom
   datareaders, and thus, it is also possible to configure this step.
 Data Grabber
 ^^^^^^^^^^^^
-The ``datagrabber`` section must be configured using the ``kind`` key to specify the datagrabber to use. Additional
+The ``datagrabber`` section must be configured using the ``kind`` key to specify
-keys correspond to the parameters of the datagrabber.
+the datagrabber to use. Additional keys correspond to the parameters of the
 datagrabber.
 For example, to use the :class:`.DataladAOMICPIOP1` datagrabber, we just need to
 specify its name as the ``kind`` key.
@ -78,8 +86,8 @@ specify its name as the ``kind`` key.
  datagrabber:
    kind: DataladAOMICPIOP1
-However, it is also possible to pass parameters to the datagrabber. In this case, we can restrict the datagrabber to
+However, it is also possible to pass parameters to the datagrabber. In this case,
-fetch only the  ``restingstate`` task.
+we can restrict the datagrabber to fetch only the ``restingstate`` task.
 .. code-block:: yaml
@ -87,22 +95,24 @@ fetch only the  ``restingstate`` task.
    kind: DataladAOMICPIOP1
    tasks: restingstate
-In the *Oasis VBM Testing dataset* example, the section will look like this:
+In the ``Oasis VBM Testing dataset`` example, the section will look like this:
 .. code-block:: yaml
  datagrabber:
-    kind: OasisVBMTesting
+    kind: OasisVBMTestingDatagrabber
 Data Reader
 ^^^^^^^^^^^
-As mentioned before, this section is entirely optional, as junifer only provides one data reader
+As mentioned before, this section is entirely optional, as junifer only provides
-(:class:`.DefaultDataReader`), which is the default in case the section is not specified.
+one data reader (:class:`.DefaultDataReader`), which is the default in case the
 section is not specified.
-In any case, the syntax of the section is the same as for the ``datagrabber`` section, using the ``kind`` key to
+In any case, the syntax of the section is the same as for the ``datagrabber``
-specify the data reader to use, and additional keys to pass parameters to the data reader:
+section, using the ``kind`` key to specify the datareader to use, and additional
 keys to pass parameters to the datareader:
 .. code-block:: yaml
@ -110,18 +120,19 @@ specify the data reader to use, and additional keys to pass parameters to the da
      kind: DefaultDataReader
-For the *Oasis VBM Testing dataset* example, we will not specify a ``datareader`` step.
+For the ``Oasis VBM Testing dataset`` example, we will not specify a
 ``datareader`` step.
-Preprocessing
+Preprocess
-^^^^^^^^^^^^^
+^^^^^^^^^^
-Preprocessing is also an optional step, as it might be the case that no pre-processing is needed. In the case that
+Pre-processing is also an optional step, as it might be the case that no
-preprocessing is needed, the section must be configured using the ``kind`` key to specify the preprocessor to use,
+pre-processing is needed. In the case that pre-processing is needed, the section
 must be configured using the ``kind`` key to specify the preprocessor to use,
 and additional keys to pass parameters to the preprocessor.
-For example, to use the :class:`.fMRIPrepConfoundRemover` preprocessor, we just need to specify its
+For example, to use the :class:`.fMRIPrepConfoundRemover` preprocessor, we just
-name as the ``kind`` key, as well as its parameters.
+need to specify its name as the ``kind`` key, as well as its parameters.
 .. code-block:: yaml
@ -136,18 +147,22 @@ name as the ``kind`` key, as well as its parameters.
    standardize: true
-For the *Oasis VBM Testing dataset* example, we will not specify a preprocessing step.
+For the ``Oasis VBM Testing dataset`` example, we will not specify a
 preprocessing step.
-Markers
+Marker
-^^^^^^^
+^^^^^^
-The ``markers`` section diverges from the previous ones, as we need to specify a list of markers. Each marker has a
+The ``markers`` section diverges from the previous ones, as we need to specify
-name that we can use to refer to it later, and a set of parameters that will be passed to the marker.
+a list of markers. Each marker has a name that we can use to refer to it later,
 and a set of parameters that will be passed to the marker.
-For the *Oasis VBM Testing dataset* example, we want to compute the mean ``VBM_GM`` value for each parcel using the
+For the ``Oasis VBM Testing dataset`` example, we want to compute the mean
-Schaefer parcellation (100 parcels, 7 networks), Schaefer parcellation (200 parcels, 7 networks), and the *DMNBuckner*
+``VBM_GM`` value for each parcel using the ``Schaefer parcellation (100 parcels,
-network, using 5mm spheres. Thus, we will configure the ``markers`` section as follows:
+7 networks)``, ``Schaefer parcellation (200 parcels, 7 networks)``, and the
 ``DMNBuckner`` network, using ``5mm`` spheres. Thus, we will configure the
 ``markers`` section as follows:
 .. code-block:: yaml
@ -170,11 +185,12 @@ network, using 5mm spheres. Thus, we will configure the ``markers`` section as f
 Storage
 ^^^^^^^
-Finally, we need to define how and where the results will be stored. This is done using the ``storage`` section,
+Finally, we need to define how and where the results will be stored. This is
-which must be configured using the ``kind`` key to specify the storage to use, and additional keys to pass parameters.
+done using the ``storage`` section, which must be configured using the ``kind``
 key to specify the storage to use, and additional keys to pass parameters.
-For example, to use the :class:`.SQLiteFeatureStorage` storage, we just need to specify where we want
+For example, to use the :class:`.SQLiteFeatureStorage` storage, we just need to
-to store the results:
+specify where we want to store the results:
 .. code-block:: yaml
@ -183,18 +199,20 @@ to store the results:
      uri: /data/junifer/example/oasis_vbm_testing.sqlite
-The full example
+Complete Example
 ----------------
-This is how the full *Oasis VBM Testing dataset* example configuration file looks like:
+This is how the full ``Oasis VBM Testing dataset`` example configuration file
 looks like:
 .. code-block:: yaml
  with: junifer.testing.registry
  workdir: /tmp
  datagrabber:
-    kind: OasisVBMTesting
+    kind: OasisVBMTestingDatagrabber
  markers:
    - name: Schaefer100x7_mean
--- a/docs/using/index.rst
+++ b/docs/using/index.rst
@ -5,9 +5,11 @@
 Using junifer
 =============
-In this section, we will cover the main aspects behind using junifer. We will first explain the basics behind junifer's
+In this section, we will cover the main aspects behind using junifer. We will
-code-less configuration. Then we will show how to use the command line interface to ``run`` junifer and ``collect`` 
+first explain the basics behind junifer's code-less configuration. Then we will
-the results. Finally, we will show how to use the ``queue`` command to interact with HPC and HTC systems.
+show how to use the command line interface to ``run`` junifer and ``collect``
 the results. Finally, we will show how to use the ``queue`` command to interact
 with HPC and HTC systems.
 .. toctree::
   :maxdepth: 2
@ -20,10 +22,11 @@ the results. Finally, we will show how to use the ``queue`` command to interact
 .. _using_components:
-Using junifer common components
+Using Common Components
-------------------------------
+-----------------------
-The following sections explains common components of junifer that can be used across many steps of the pipeline.
+The following sections explains common components of junifer that can be used
 across many steps of the pipeline.
 .. toctree::
   :maxdepth: 2
--- a/docs/using/masks.rst
+++ b/docs/using/masks.rst
@ -5,27 +5,32 @@
 Masks
 =====
-Masks are essentially boolean arrays that are used to constrain the extraction of features to voxels that are
+Masks are essentially boolean arrays that are used to constrain the extraction
-meaningful. For example, in an fMRI imaging study, a mask can be used to constrain the extraction of features to
+of features to voxels that are meaningful. For example, in an fMRI imaging
-voxels that contain a certain ratio of gray matter to white matter / cerebrospinal fluid, ensuring that the features
+study, a mask can be used to constrain the extraction of features to voxels that
-are not extracted from voxels that contain mostly white matter or cerebrospinal fluid, which could add noise to the
+contain a certain ratio of gray matter to white matter / cerebrospinal fluid,
-BOLD signal.
+ensuring that the features are not extracted from voxels that contain mostly
 white matter or cerebrospinal fluid, which could add noise to the BOLD signal.
-Junifer provides a number of built-in masks, which can be listed using the :func:`.list_masks`. Some
+Junifer provides a number of built-in masks, which can be listed using
-masks are images, while other masks can be computed using :ref:`nilearn` functions.
+:func:`.list_masks`. Some masks are images, while other masks can be computed
 using :ref:`nilearn` functions.
-For markers and steps that accept ``masks`` as an argument, the mask can be specified as a string, which will be the
+For markers and steps that accept ``masks`` as an argument, the mask can be
-name of a built-in mask, or as a dictionary in which the **only** key is the built-in mask name and the value is a
+specified as a string, which will be the name of a built-in mask, or as a
-dictionary of keyword arguments to pass to the mask function.
+dictionary in which the **only** key is the built-in mask name and the value is
 a dictionary of keyword arguments to pass to the mask function.
-For example, the following is a valid mask specification that specified the ``GM_prob0.2`` mask.
+For example, the following is a valid mask specification that specified the
 ``GM_prob0.2`` mask.
 .. code-block:: yaml
    masks: GM_prob0.2
-The following is a valid mask specification that specifies the ``compute_brain_mask``  mask (function from nilearn),
+The following is a valid mask specification that specifies the
-with a threshold of 0.5.
+``compute_brain_mask``  mask (function from nilearn), with a threshold of
 ``0.5``.
 .. code-block:: yaml
@ -33,9 +38,11 @@ with a threshold of 0.5.
        compute_brain_mask:
            threshold: 0.5
-Furthermore, junifer allows you to combine several masks using :func:`nilearn.masking.intersect_masks`. This is done by
+Furthermore, junifer allows you to combine several masks using
-specifying a list of masks, where each mask is a string or dictionary as described above. For example, the following
+:func:`nilearn.masking.intersect_masks`. This is done by specifying a list of
-is a valid mask specification that specifies the intersection of the ``GM_prob0.2`` and ``compute_brain_mask`` masks.
+masks, where each mask is a string or dictionary as described above. For example,
 the following is a valid mask specification that specifies the intersection of
 the ``GM_prob0.2`` and ``compute_brain_mask`` masks.
 .. code-block:: yaml
@ -44,9 +51,9 @@ is a valid mask specification that specifies the intersection of the ``GM_prob0.
        - compute_brain_mask:
            threshold: 0.5
-We can also specify the arguments of :func:`nilearn.masking.intersect_masks` (``threshold`` and ``connected``). The
+We can also specify the arguments of :func:`nilearn.masking.intersect_masks`
-following example combines the same masks as the previous one, but computing the full intersection.
+(``threshold`` and ``connected``). The following example combines the same masks
-
+as the previous one, but computing the full intersection.
 .. code-block:: yaml
@ -56,7 +63,8 @@ following example combines the same masks as the previous one, but computing the
            threshold: 0.5
        - threshold: 1  # intersection
-Alternatively, we can also compute the union, even if the voxels do not form a connected component:
+Alternatively, we can also compute the union, even if the voxels do not form a
 connected component:
 .. code-block:: yaml
--- a/docs/using/queueing.rst
+++ b/docs/using/queueing.rst
@ -2,22 +2,27 @@
 .. _queueing:
-Queueing jobs (HPC, HTC)
+Queueing Jobs (HPC, HTC)
 ========================
-Yet another interesting feature of junifer is the ability to queue jobs on computational clusters. This is done by
+Yet another interesting feature of junifer is the ability to queue jobs on
-adding the ``queue`` section in the :ref:`codeless` file and executing the  ``junifer queue`` command.
+computational clusters. This is done by adding the ``queue`` section in the
 :ref:`codeless` file and executing the  ``junifer queue`` command.
-While junifer is meant to support `HTCondor`_, `SLURM`_ and local queueing using `GNU Parallel`_, only HTCondor is
+While junifer is meant to support `HTCondor`_, `SLURM`_ and local queueing
-currently supported. This will be implemented in future relases of junifer. If you are in immediate need of any of these
+using `GNU Parallel`_, only HTCondor is currently supported. This will be
-schedulers, please create an issue on the `junifer github`_ repository.
+implemented in future relases of junifer. If you are in immediate need of any of
 these schedulers, please create an issue on the `junifer github`_ repository.
-The ``queue`` section of the :ref:`codeless` must start by defining the following general parameters:
+The ``queue`` section of the :ref:`codeless` must start by defining the
 following general parameters:
-* ``jobname``: name of the job to be queued. This will be used to name the folder where the job files will be created,
+* ``jobname``: Name of the job to be queued. This will be used to name the
-  as well as any relevant file. Depending on the scheduler, it will also be listed in the queueing system with this
+  folder where the job files will be created, as well as any relevant file.
-  name.
+  Depending on the scheduler, it will also be listed in the queueing system
-* ``kind``: The kind of scheduler to be used. Currently, only ``HTCondor`` is supported.
+  with this name.
 * ``kind``: The kind of scheduler to be used. Currently, only ``HTCondor`` is
  supported.
 Example:
@ -35,34 +40,45 @@ The rest of the parameters depend on the scheduler you are using.
 HTCondor
 --------
-When using HTCondor, junifer will use a DAG to queue one job per element (``junifer run``). As an option, the DAG can
+When using HTCondor, junifer will use a DAG to queue one job per element
-include a final job (``junifer collect``) to collect the results once all of the individual element jobs are finished.
+(``junifer run``). As an option, the DAG can include a final job
 (``junifer collect``) to collect the results once all of the individual element
 jobs are finished.
 The following parameters are avilable for HTCondor:
-* ``env``: Definition of the Python enviroment. It must provide two variables: ``kind`` and ``name``. The ``kind``
+* ``env``: Definition of the Python enviroment. It must provide two variables:
-  corresponds to the kind of virtual environment to use: ``conda``,  ``virtualenv`` (not yet supported) or 
+
-  ``local`` (no virtual enviroment). The ``name`` is the name of the enviroment to use in case a virtual environment
+  * ``kind``: This is the kind of virtual environment to use:
-  is used.
+
-* ``mem``: Memory to be used by the job. It must be provided as a string with the units (e.g. ``2GB``).
+    * ``conda``
    * ``virtualenv`` (not yet supported)
    * ``local`` (no virtual enviroment)
  * ``name``: This is the name of the enviroment to use in case a virtual
    environment is used.
 * ``mem``: Memory to be used by the job. It must be provided as a string with
  the units (e.g. ``2GB``).
 * ``cpus``: Number of CPUs to be used by the job. It must be provided as an int.
-* ``disk``: Disk space to be used by the job. It must be provided as a string with the units (e.g. ``2GB``). Keep in 
+* ``disk``: Disk space to be used by the job. It must be provided as a string
-  mind that junifer uses a local working directory for each job, and datalad datasets might be cloned in this temporary
+  with the units (e.g. ``2GB``). Keep in mind that junifer uses a local working
  directory for each job, and datalad datasets might be cloned in this temporary
  directory.
-* ``extra_preamble``: Extra lines to be added to the HTCondor submit file. This can be used to add
+* ``extra_preamble``: Extra lines to be added to the HTCondor submit file. This
-  extra parameters to the job, such as ``requirements``.
+  can be used to add extra parameters to the job, such as ``requirements``.
-* ``collect``: This parameter allows to include a collect to the DAG to collect the results once all of the individual
+* ``collect``: This parameter allows to include a collect to the DAG to collect
-  element jobs are finished. This is useful if you want to run a ``junifer collect`` job only once all of the
+  the results once all of the individual element jobs are finished. This is
  useful if you want to run a ``junifer collect`` job only once all of the
  individual element jobs are finished. Valid options are:
-  * ``yes``: Include a collect job to the DAG that will be executed even if some of the individual element
+  * ``yes``: Include a collect job in the DAG that will be executed even if some
-    jobs fail.
+    of the individual element jobs fail.
-  * ``on_success_only``: Include a collect job to the DAG, but will only run if all of the individual element jobs are
+  * ``on_success_only``: Include a collect job to the DAG, but will only run if
-    successful.
+    all of the individual element jobs are successful.
  * ``no``: Do not include a collect job to the DAG.
 Example:
 .. code-block:: yaml
@ -74,23 +90,23 @@ Example:
      kind: conda
      name: junifer
    mem: 8G
-    disk: 2GB
+    disk: 2G
-    collect: true
+    collect: "yes"  # wrap it in string to avoid boolean
 Once the :ref:`codeless` file is ready, including the ``queue`` section, you can
 queue the jobs by executing the ``junifer queue`` command.
-Once the :ref:`codeless` file is ready, including the ``queue`` section, you can queue the jobs by executing 
+The ``queue`` command will create a folder with the name of the job (``jobname``)
-the ``junifer queue`` command. 
+under the ``junifer_jobs`` directory in the current working directory.
 The ``queue`` command will create a folder with the name of the job (``jobname``) under the ``junifer_jobs`` directory
 in the current working directory.
 The ``queue`` command accepts the following arguments:
 * ``--help``: Show a help message.
-* ``--verbose`` Set the verbosity level. Options are ``warning``, ``info``, ``debug``.
+* ``--verbose``: Set the verbosity level. Options are ``warning``, ``info``,
-* ``--submit``: Submit the jobs to the queueing system. If not specified, the job submit files will be created but not
+  ``debug``.
-  submitted.
+* ``--submit``: Submit the jobs to the queueing system. If not specified, the
-* ``--overwrite``: Overwrite the job folder if it already exists. If not specified, the command will fail if the job
+  job submit files will be created but not submitted.
-  folder already exists.
+* ``--overwrite``: Overwrite the job folder if it already exists. If not
-* ``--element``: Queue only the specified element(s). If not specified, all elements will be queued.
+  specified, the command will fail if the job folder already exists.
-
+* ``--element``: Queue only the specified element(s). If not specified, all
  elements will be queued.
--- a/docs/using/running.rst
+++ b/docs/using/running.rst
@ -2,17 +2,20 @@
 .. _running:
-Running jobs
+Running Jobs
 ============
-Once we have the :ref:`code-less configuration file <codeless>`, we can use the command line interface to extract the
+Once we have the :ref:`code-less configuration file <codeless>`, we can use the
-features. This is achieved in a two-step process: ``run`` and ``collect``.
+command line interface to extract the features. This is achieved in a two-step
 process: ``run`` and ``collect``.
-The ``run`` command is used to extract the features from each element in the dataset. However, depending on the
+The ``run`` command is used to extract the features from each element in the
-storage interface, this may create one file per subject. The ``collect`` command is then used to collect all of the
+dataset. However, depending on the storage interface, this may create one file
 per subject. The ``collect`` command is then used to collect all of the
 individual results into a single file.
-Assuming that we have a configuration file named ``config.yaml``, the following commands will extract the features:
+Assuming that we have a configuration file named ``config.yaml``, the following
 commands will extract the features:
 .. code-block:: bash
@ -21,19 +24,20 @@ Assuming that we have a configuration file named ``config.yaml``, the following
 The ``run`` command accepts the following additional arguments:
 * ``--help``: Show a help message.
-* ``--verbose`` Set the verbosity level. Options are ``warning``, ``info``, ``debug``.
+* ``--verbose``: Set the verbosity level. Options are ``warning``, ``info``,
-* ``--element``: The *element* to run. If not specified, all elements will be run. This parameter can be specified
+  ``debug``.
-  multiple times to run multiple elements. If the *element* requires several parameters, they can be specified
+* ``--element``: The *element* to run. If not specified, all elements will be
-  by separating them with ``,``.
+  run. This parameter can be specified multiple times to run multiple elements.
  If the *element* requires several parameters, they can be specified by
  separating them with ``,``.
-
+Example of running two elements:
 Example on running two elements:
 .. code-block:: bash
    junifer run config.yaml --element sub-01 --element sub-02
-Example on elements with multiple parameters and verbose output:
+Example of elements with multiple parameters and verbose output:
 .. code-block:: bash
@ -41,14 +45,16 @@ Example on elements with multiple parameters and verbose output:
 .. _collect:
-Collecting results
+Collecting Results
 ==================
-Once the ``run`` command has been executed, the results are stored in the output directory. However, depending on the
+Once the ``run`` command has been executed, the results are stored in the output
-storage interface, this may create one file per subject. The ``collect`` command is then used to collect all of the
+directory. However, depending on the storage interface, this may create one file
 per subject. The ``collect`` command is then used to collect all of the
 individual results into a single file.
-Assuming that we have a configuration file named ``config.yaml``, the following commands will collect the results:
+Assuming that we have a configuration file named ``config.yaml``, the following
 commands will collect the results:
 .. code-block:: bash
@ -57,4 +63,5 @@ Assuming that we have a configuration file named ``config.yaml``, the following
 The ``collect`` command accepts the following additional arguments:
 * ``--help``: Show a help message.
-* ``--verbose`` Set the verbosity level. Options are ``warning``, ``info``, ``debug``.
+* ``--verbose``: Set the verbosity level. Options are ``warning``, ``info``,
  ``debug``.
--- a/junifer/datagrabber/aomic/tests/test_piop1.py
+++ b/junifer/datagrabber/aomic/tests/test_piop1.py
@ -147,5 +147,4 @@ def test_piop1_invalid_tasks():
            "the AOMIC PIOP1 dataset!"
        ),
    ):
        DataladAOMICPIOP1(tasks="thisisnotarealtask")
--- a/junifer/datagrabber/aomic/tests/test_piop2.py
+++ b/junifer/datagrabber/aomic/tests/test_piop2.py
@ -20,7 +20,6 @@ def test_aomic_piop2_datagrabber() -> None:
    task_params = [None, "restingstate"]
    for task_param in task_params:
        dg = DataladAOMICPIOP2(tasks=task_param)
        # change uri here to use fake data instead of real dataset
@ -142,5 +141,4 @@ def test_piop2_invalid_tasks():
            "the AOMIC PIOP2 dataset!"
        ),
    ):
        DataladAOMICPIOP2(tasks="thisisnotarealtask")
--- a/junifer/datagrabber/tests/test_base.py
+++ b/junifer/datagrabber/tests/test_base.py
@ -20,6 +20,7 @@ def test_BaseDataGrabber_abstractness() -> None:
 def test_BaseDataGrabber() -> None:
    """Test BaseDataGrabber."""
    # Create concrete class.
    class MyDataGrabber(BaseDataGrabber):
        def get_item(self, subject):
--- a/junifer/datagrabber/tests/test_hcp.py
+++ b/junifer/datagrabber/tests/test_hcp.py
@ -365,8 +365,7 @@ def test_hcp1200_datagrabber_elements(
    ],
 )
 def test_hcp1200_datagrabber_incorrect_access_icafix(
-    tasks: Optional[str],
+    tasks: Optional[str], ica_fix: bool
    ica_fix: bool
 ) -> None:
    """Test HCP1200 datagrabber incorrect access for icafix.
--- a/junifer/markers/tests/test_markers_base.py
+++ b/junifer/markers/tests/test_markers_base.py
@ -17,6 +17,7 @@ def test_base_marker_abstractness() -> None:
 def test_base_marker_subclassing() -> None:
    """Test proper subclassing of BaseMarker."""
    # Create concrete class
    class MyBaseMarker(BaseMarker):
        def __init__(self, on, name=None) -> None:
--- a/junifer/preprocess/tests/test_preprocess_base.py
+++ b/junifer/preprocess/tests/test_preprocess_base.py
@ -17,6 +17,7 @@ def test_base_preprocessor_abstractness() -> None:
 def test_base_preprocessor_subclassing() -> None:
    """Test proper subclassing of BasePreprocessor."""
    # Create concrete class
    class MyBasePreprocessor(BasePreprocessor):
        def __init__(self, on):
--- a/junifer/storage/tests/test_storage_base.py
+++ b/junifer/storage/tests/test_storage_base.py
@ -19,6 +19,7 @@ def test_BaseFeatureStorage_abstractness() -> None:
 def test_BaseFeatureStorage() -> None:
    """Test proper subclassing of BaseFeatureStorage."""
    # Create concrete class
    class MyFeatureStorage(BaseFeatureStorage):
        """Implement concrete class."""
		`@ -0,0 +1 @@`
							Improve general prose, formatting and code blocks in docs and set line length for ``.rst`` files to 80 by `Synchon Mandal`_