Reference formatting cleanup

data_sharing_ms.md

@@ -54,7 +54,7 @@ For the scientific community it allows 1) the results of existing analyses to be
 2) data to be combined in meta-analyses to reach general conclusions [@fienberg1985],
 3) new approaches to be applied to the data and new questions asked using it [@fienberg1985], and
 4) approaches to scientific inquiry that could not be considered without broad scale data sharing [@hampton2013].
-As a result, data sharing is increasingly required by funding agencies (@poisot2013; e.g., [NSF](http://www.nsf.gov/bfa/dias/policy/dmp.jsp), [NIH](http://grants.nih.gov/grants/guide/notice-files/NOT-OD-03-032.html), [NSERC](http://www.nserc-crsng.gc.ca/Professors-Professeurs/FinancialAdminGuide-GuideAdminFinancier/Responsibilities-Responsabilites_eng.asp), [FWF](http://www.fwf.ac.at/en/public_relations/oai/index.html)), journals [@whitlock2010], and potentially by law (e.g. [FASTR](http://doyle.house.gov/sites/doyle.house.gov/files/documents/2013%2002%2014%20DOYLE%20FASTR%20FINAL.pdf), [OSTP Policy](http://www.whitehouse.gov/sites/default/files/microsites/ostp/ostp_public_access_memo_2013.pdf)).
+As a result, data sharing is increasingly required by funding agencies [@poisot2013, e.g., [NSF](http://www.nsf.gov/bfa/dias/policy/dmp.jsp), [NIH](http://grants.nih.gov/grants/guide/notice-files/NOT-OD-03-032.html), [NSERC](http://www.nserc-crsng.gc.ca/Professors-Professeurs/FinancialAdminGuide-GuideAdminFinancier/Responsibilities-Responsabilites_eng.asp), [FWF](http://www.fwf.ac.at/en/public_relations/oai/index.html)], journals [@whitlock2010], and potentially by law (e.g. [FASTR](http://doyle.house.gov/sites/doyle.house.gov/files/documents/2013%2002%2014%20DOYLE%20FASTR%20FINAL.pdf), [OSTP Policy](http://www.whitehouse.gov/sites/default/files/microsites/ostp/ostp_public_access_memo_2013.pdf)).
 For data collectors it provides the potential for
 1) credit for publication of data products  [@poisot2013],
 2) increased citation metrics  [@piwowar2007; @piwowar2013], and
@@ -162,7 +162,7 @@ We provide three simple recommendations to help ensure that tabular data are pro
 ![Examples of how to restructure two common issues with tabular data. (a) Each cell should only contain a single value. If more than one value is present then the data should be split into multiple columns. (b) There should be only one column for each type of information. If there are multiple columns then the column header should be stored in one column and the values from each column should be stored in a single column.](Data_formatting.jpg)
 
 While cross-tab data can be useful for its readability, and may be appropriate for data collection, this format makes it difficult to link the records with additional data (e.g., the location and environmental conditions at a site) and it cannot be properly used by most common database management and analysis tools (e.g., relational databases, dataframes in R and Python, etc.).
-If tabular data are currently in a cross-tab structure, there are tools to help restructure the data including functions in Excel, R (e.g., melt() function in the R package reshape; @wickham2007), and Python (e.g., melt() function in the Pandas Python module [http://pandas.pydata.org/](http://pandas.pydata.org/)).
+If tabular data are currently in a cross-tab structure, there are tools to help restructure the data including functions in Excel, R [e.g., melt() function in the R package reshape; @wickham2007], and Python (e.g., melt() function in the Pandas Python module [http://pandas.pydata.org/](http://pandas.pydata.org/)).
 
 In addition to following these basic rules you should also make sure to use descriptive column names [@borer2009].
 Descriptive column names make the data easier to understand and therefore make data interpretation errors less likely.

data_sharing_refs.bib

@@ -173,7 +173,7 @@ @article{piwowar2013
   journal={PeerJ PrePrints},
   volume={1},
   pages={e1},
-  year={2013}
+  year={2013},
   url={http://dx.doi.org/10.7287/peerj.preprints.1}
 }