🎨 Consolidate duplicate bib entries

Author: shnizzedy

docs/_sources/references/references.bib

@@ -98,22 +98,12 @@ @article{Jenk02
   langid = {english}
 }
 
-@techreport{Kiar17,
-  type = {Preprint},
-  title = {A {{High-Throughput Pipeline Identifies Robust Connectomes But Troublesome Variability}}},
-  author = {Kiar, Gregory and Bridgeford, Eric W. and Gray Roncal, William R. and {Consortium for Reliability and Reproducibility (CoRR)} and Chandrashekhar, Vikram and Mhembere, Disa and Ryman, Sephira and Zuo, Xi-Nian and Margulies, Daniel S. and Craddock, R. Cameron and Priebe, Carey E. and Jung, Rex and Calhoun, Vince D. and Caffo, Brian and Burns, Randal and Milham, Michael P. and Vogelstein, Joshua T.},
-  year = {2017},
-  month = sep,
-  doi = {10.1101/188706},
-  langid = {english}
-}
-
-@article{Kiar18,
+@techreport{Kiar18,
+  type= {Preprint},
   title = {A {{High-Throughput Pipeline Identifies Robust Connectomes But Troublesome Variability}}},
   author = {Kiar, Gregory and Bridgeford, Eric W. and Roncal, William R. Gray and {Consortium for Reliability and Reproducibility (CoRR)} and Chandrashekhar, Vikram and Mhembere, Disa and Ryman, Sephira and Zuo, Xi-Nian and Margulies, Daniel S. and Craddock, R. Cameron and Priebe, Carey E. and Jung, Rex and Calhoun, Vince D. and Caffo, Brian and Burns, Randal and Milham, Michael P. and Vogelstein, Joshua T.},
   year = {2018},
   month = apr,
-  pages = {188706},
   publisher = {{bioRxiv}},
   doi = {10.1101/188706},
   copyright = {\textcopyright{} 2018, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/},
@@ -152,7 +142,6 @@ @incollection{nile21
   author = {{The nilearn developers}},
   key={{nilearn developers}},
   year = {2021},
-  abstract = {Page summary: A functional connectome is a set of connections representing brain interactions between regions. Here we show how to extract activation time-series to compute functional connectomes. ...},
   langid = {english},
   url = {https://nilearn.github.io/connectivity/functional_connectomes.html#extracting-signals-on-a-parcellation}
 }
@@ -163,7 +152,6 @@ @incollection{nile21a
   author = {{The nilearn developers}},
   key={{nilearn developers}},
   year = {2021},
-  abstract = {Examples using nilearn.connectome.ConnectivityMeasure: Extracting signals of a probabilistic atlas of functional regions Extracting signals of a probabilistic atlas of functional regions, Extractin...},
   langid = {english},
   url = {https://nilearn.github.io/modules/generated/nilearn.connectome.ConnectivityMeasure.html}
 }

docs/_sources/user/tse.rst

@@ -24,7 +24,7 @@ Configuring ROI Time Series Extraction
 
 #. **Realignment - [ROI to func, func to ROI]:** Choose functional time-series and ROI realignment method. 'ROI to func' will realign the atlas/ROI to functional space (fast). 'func to ROI' will realign the functional time series to the atlas/ROI space. NOTE: in rare cases, realigning the ROI to the functional space may result in small misalignments for very small ROIs - please double check your data if you see issues.
 
-#. **Connectivity Matrix:** A connectivity matrix can be generated via nilearn :cite:`cite-tse-Abra14,cite-tse-nile21,cite-tse-nile21a`, ndmg :cite:`cite-tse-Kiar17,cite-tse-Neur18` or AFNI for the ``Avg`` timeseries output.
+#. **Connectivity Matrix:** A connectivity matrix can be generated via nilearn :cite:`cite-tse-Abra14,cite-tse-nile21,cite-tse-nile21a`, ndmg :cite:`cite-tse-Kiar18,cite-tse-Neur18` or AFNI for the ``Avg`` timeseries output.
 
 .. include:: /user/pipelines/without_gui.rst