Flatten directory structure

joss.00012/10.21105.joss.00012.jats

@@ -80,7 +80,7 @@ a Creative Commons Attribution 4.0 International License (CC BY
   scikit-learn API
   (<xref alt="Buitinck et al., 2013" rid="ref-scikit-learn-api" ref-type="bibr">Buitinck
   et al., 2013</xref>).</p>
-  <p>-<inline-graphic mimetype="image" mime-subtype="png" xlink:href="media/mst_example.png" /></p>
+  <p>-<inline-graphic mimetype="image" mime-subtype="png" xlink:href="mst_example.png" /></p>
 </sec>
 </body>
 <back>

joss.00024/10.21105.joss.00024.jats

@@ -82,7 +82,7 @@ a Creative Commons Attribution 4.0 International License (CC BY
   Archive, 2016</xref>)</p>
   <p>The following is a simple demonstration of a visualization made
   with <italic>corner</italic>:</p>
-  <p>-<inline-graphic mimetype="image" mime-subtype="png" xlink:href="media/corner.png" /></p>
+  <p>-<inline-graphic mimetype="image" mime-subtype="png" xlink:href="corner.png" /></p>
 </sec>
 </body>
 <back>

joss.00025/10.21105.joss.00025.jats

@@ -160,7 +160,7 @@ a Creative Commons Attribution 4.0 International License (CC BY
   et al., 2016</xref>) and network analysis
   (<xref alt="Langfelder &amp; Horvath, 2008" rid="ref-WGCNAU003A2008" ref-type="bibr">Langfelder
   &amp; Horvath, 2008</xref>).</p>
-  <p>-<inline-graphic mimetype="image" mime-subtype="png" xlink:href="media/qtl2.png" /></p>
+  <p>-<inline-graphic mimetype="image" mime-subtype="png" xlink:href="qtl2.png" /></p>
   <p>GN is written in python and javascript and contains a rich set of
   tools and libraries that can be written in any computer language. A
   full list of included software can be found in the package named

joss.00031/10.21105.joss.00031.jats

@@ -93,7 +93,7 @@ a Creative Commons Attribution 4.0 International License (CC BY
   Machine Learning Toolbox; (3) Signal Processing Toolbox. Relevance
   vector regression requires the SparseBayes V2 toolbox, which can be
   obtained from http://www.relevancevector.com.</p>
-  <p><inline-graphic mimetype="image" mime-subtype="png" xlink:href="media/fig1_prism.png" />
+  <p><inline-graphic mimetype="image" mime-subtype="png" xlink:href="fig1_prism.png" />
   Figure 1: Illustration of Prism regression procedure, first conducting
   spline regression for each predictor, followed by dimensionality
   reduction and feature selection (panel A). The logo for Prism is shown
@@ -122,7 +122,7 @@ a Creative Commons Attribution 4.0 International License (CC BY
   unpublished study and the pre-processing pipeline is not finalized
   (e.g., correcting for site effects), so further information of the
   dataset is not provided at this time.</p>
-  <p><inline-graphic mimetype="image" mime-subtype="png" xlink:href="media/fig2_output.png" />
+  <p><inline-graphic mimetype="image" mime-subtype="png" xlink:href="fig2_output.png" />
   Figure 2: Output scatter plots for code in
   <monospace>demo.m</monospace>.</p>
   <p>See README.md for further details.</p>

joss.00032/10.21105.joss.00032.jats

@@ -98,7 +98,7 @@ a Creative Commons Attribution 4.0 International License (CC BY
   <p>An example of intervisibility output:</p>
   <fig>
     <caption><p>Intervisibility network</p></caption>
-    <graphic mimetype="image" mime-subtype="jpeg" xlink:href="media/Intervisibility.jpg" xlink:title="" />
+    <graphic mimetype="image" mime-subtype="jpeg" xlink:href="Intervisibility.jpg" xlink:title="" />
   </fig>
 </sec>
 </body>

joss.00037/10.21105.joss.00037.jats

@@ -127,7 +127,7 @@ a Creative Commons Attribution 4.0 International License (CC BY
   text mining and sentiment analysis tools.</p>
   <fig>
     <caption><p>Sentiment in Jane Austen’s Novels</p></caption>
-    <graphic mimetype="image" mime-subtype="png" xlink:href="media/README-unnamed-chunk-9-1.png" xlink:title="" />
+    <graphic mimetype="image" mime-subtype="png" xlink:href="README-unnamed-chunk-9-1.png" xlink:title="" />
   </fig>
 </sec>
 </body>

joss.00040/10.21105.joss.00040.jats

@@ -148,7 +148,7 @@ a Creative Commons Attribution 4.0 International License (CC BY
   <fig>
     <caption><p>Figure 1: Equipment of the walking study in the research
     project Flow-Machines.</p></caption>
-    <graphic mimetype="image" mime-subtype="png" xlink:href="media/5-11-equipment-setup.png" xlink:title="" />
+    <graphic mimetype="image" mime-subtype="png" xlink:href="5-11-equipment-setup.png" xlink:title="" />
   </fig>
 </sec>
 </body>

joss.00041/10.21105.joss.00041.jats

@@ -159,7 +159,7 @@ a Creative Commons Attribution 4.0 International License (CC BY
   <fig>
     <caption><p>Figure 1: Low cardiolocomotor phase synchronization in a
     run with low flow-experience</p></caption>
-    <graphic mimetype="image" mime-subtype="png" xlink:href="media/cls.png" />
+    <graphic mimetype="image" mime-subtype="png" xlink:href="cls.png" />
   </fig>
 </sec>
 </body>

joss.00045/10.21105.joss.00045.jats

@@ -92,7 +92,7 @@ a Creative Commons Attribution 4.0 International License (CC BY
   <fig>
     <caption><p>Likelihood surfaces and marginalised distributions
     created by ChainConsumer.</p></caption>
-    <graphic mimetype="image" mime-subtype="png" xlink:href="media/example.png" xlink:title="" />
+    <graphic mimetype="image" mime-subtype="png" xlink:href="example.png" xlink:title="" />
   </fig>
 </sec>
 </body>

joss.00046/10.21105.joss.00046.jats

@@ -106,7 +106,7 @@ a Creative Commons Attribution 4.0 International License (CC BY
                     figureSize='APJ_page')</code>
   <fig>
     <caption><p>A GTC produced by <italic>pygtc</italic>.</p></caption>
-    <graphic mimetype="image" mime-subtype="png" xlink:href="media/GTC.png" xlink:title="" />
+    <graphic mimetype="image" mime-subtype="png" xlink:href="GTC.png" xlink:title="" />
   </fig>
   <p><monospace>pygtc</monospace> relies on <monospace>numpy</monospace>
   (<xref alt="Walt et al., 2011" rid="ref-numpy" ref-type="bibr">Walt et

joss.00051/10.21105.joss.00051.jats

@@ -105,7 +105,7 @@ a Creative Commons Attribution 4.0 International License (CC BY
   is available and their location is shown as red circles.</p>
   <fig>
     <caption><p>UK-AIR monitoring stations (August 2016)</p></caption>
-    <graphic mimetype="image" mime-subtype="png" xlink:href="media/MonitoringStations.png" xlink:title="" />
+    <graphic mimetype="image" mime-subtype="png" xlink:href="MonitoringStations.png" xlink:title="" />
   </fig>
 </sec>
 </body>

joss.00054/10.21105.joss.00054.jats

@@ -89,7 +89,7 @@ a Creative Commons Attribution 4.0 International License (CC BY
   representations enabled by the package.</p>
   <fig>
     <caption><p>Cartographic Composition</p></caption>
-    <graphic mimetype="image" mime-subtype="png" xlink:href="media/paper.png" xlink:title="" />
+    <graphic mimetype="image" mime-subtype="png" xlink:href="paper.png" xlink:title="" />
   </fig>
 </sec>
 </body>

joss.00062/10.21105.joss.00062.jats

@@ -85,7 +85,7 @@ a Creative Commons Attribution 4.0 International License (CC BY
   calculations in XLSX and CSV formats.</p>
   <fig>
     <caption><p>xpecgen GUI.</p></caption>
-    <graphic mimetype="image" mime-subtype="png" xlink:href="media/DemoPlot.png" xlink:title="" />
+    <graphic mimetype="image" mime-subtype="png" xlink:href="DemoPlot.png" xlink:title="" />
   </fig>
 </sec>
 </body>

joss.00082/10.21105.joss.00082.jats

@@ -134,7 +134,7 @@ a Creative Commons Attribution 4.0 International License (CC BY
   ‘expected utility’ function.</p>
   <fig>
     <caption><p>figure_2</p></caption>
-    <graphic mimetype="image" mime-subtype="png" xlink:href="media/7f3e39fc-7ff9-11e6-8712-aaea188f8bc6.png" xlink:title="" />
+    <graphic mimetype="image" mime-subtype="png" xlink:href="7f3e39fc-7ff9-11e6-8712-aaea188f8bc6.png" xlink:title="" />
   </fig>
 </sec>
 </body>

joss.00087/10.21105.joss.00087.jats

@@ -144,7 +144,7 @@ a Creative Commons Attribution 4.0 International License (CC BY
   et al., 2010</xref>)) in that CTL mapping does not require prior
   information on sets (e.g., pathways) and uses (existing) QTL
   information for inference.</p>
-  <p><inline-graphic mimetype="image" mime-subtype="png" xlink:href="media/Fig1.png" />
+  <p><inline-graphic mimetype="image" mime-subtype="png" xlink:href="Fig1.png" />
   Figure 1: Examples of colocated CTL and QTL profiles, as found in
   <ext-link ext-link-type="uri" xlink:href="http://genenetwork.org/">GeneNetwork</ext-link>
   dataset GN207 (BXD mouse eye mRNA). (a) CTL without a colocalizing QTL
@@ -166,7 +166,7 @@ a Creative Commons Attribution 4.0 International License (CC BY
   abundance measurements. This is especially useful in combined
   datasets, e.g. a combination of: classical phenotypes, protein
   abundance and gene expression (see figure 2).</p>
-  <p><inline-graphic mimetype="image" mime-subtype="png" xlink:href="media/Fig2.png" />
+  <p><inline-graphic mimetype="image" mime-subtype="png" xlink:href="Fig2.png" />
   Figure 2: By network inference CTL discover the genetic wiring of
   classical phenotypes and identify known and new key players in the
   genetic / protein network underlying classical phenotypes using CTL

joss.00092/10.21105.joss.00092.jats

@@ -110,7 +110,7 @@ a Creative Commons Attribution 4.0 International License (CC BY
     the 2012 presidential election, given that they had registered to do
     so. Data is from Gerber et al.
     (<xref alt="2015" rid="ref-gerber2014can" ref-type="bibr">2015</xref>).</p></caption>
-    <graphic mimetype="image" mime-subtype="png" xlink:href="media/partial_dependence.png" xlink:title="" />
+    <graphic mimetype="image" mime-subtype="png" xlink:href="partial_dependence.png" xlink:title="" />
   </fig>
   <p>Partial dependence, as described by Friedman
   (<xref alt="2001" rid="ref-friedman2001greedy" ref-type="bibr">2001</xref>),
@@ -133,7 +133,7 @@ a Creative Commons Attribution 4.0 International License (CC BY
   <fig>
     <caption><p>The permutation importance of the same
     covariates.</p></caption>
-    <graphic mimetype="image" mime-subtype="png" xlink:href="media/importance.png" xlink:title="" />
+    <graphic mimetype="image" mime-subtype="png" xlink:href="importance.png" xlink:title="" />
   </fig>
   <p>Permutation importance estimates the importance of a covariate by
   randomly shuffling its values, breaking any dependence between said
@@ -172,7 +172,7 @@ a Creative Commons Attribution 4.0 International License (CC BY
     with the shape of the points mapped to whether the individual had
     voted in the 2008 election and the size of the point mapped to the
     individual’s age.</p></caption>
-    <graphic mimetype="image" mime-subtype="png" xlink:href="media/proximity.png" xlink:title="" />
+    <graphic mimetype="image" mime-subtype="png" xlink:href="proximity.png" xlink:title="" />
   </fig>
   <p>Due to the tree-structure of a random forest, there is a natural
   way to define a distance between data points: the proportion of times

joss.00100/10.21105.joss.00100.jats

@@ -98,7 +98,7 @@ a Creative Commons Attribution 4.0 International License (CC BY
     <caption><p>Figure 1: UCR DTW is approximately 3 orders of magnitude
     faster than a naive sliding-window search using DTW
     distance.</p></caption>
-    <graphic mimetype="image" mime-subtype="png" xlink:href="media/inst/img/dtw-comparison-1.png" xlink:title="" />
+    <graphic mimetype="image" mime-subtype="png" xlink:href="dtw-comparison-1.png" xlink:title="" />
   </fig>
 </sec>
 </body>

joss.00102/10.21105.joss.00102.jats

@@ -91,7 +91,7 @@ a Creative Commons Attribution 4.0 International License (CC BY
   flat-corrected images.</p>
   <p>This M106 image is the product of three stacked acronym-reduced
   images.</p>
-  <p>-<inline-graphic mimetype="image" mime-subtype="png" xlink:href="media/Aligned_m106.png" /></p>
+  <p>-<inline-graphic mimetype="image" mime-subtype="png" xlink:href="Aligned_m106.png" /></p>
 </sec>
 </body>
 <back>

joss.00107/10.21105.joss.00107.jats

@@ -113,7 +113,7 @@ a Creative Commons Attribution 4.0 International License (CC BY
   this at the beginning.</p>
   <fig>
     <caption><p>ReproZip Commands</p></caption>
-    <graphic mimetype="image" mime-subtype="png" xlink:href="media/commands.png" xlink:title="" />
+    <graphic mimetype="image" mime-subtype="png" xlink:href="commands.png" xlink:title="" />
   </fig>
   <p>ReproZip has two steps:</p>
   <list list-type="order">
@@ -130,7 +130,7 @@ a Creative Commons Attribution 4.0 International License (CC BY
   </list>
   <fig>
     <caption><p>Step 1. Packing</p></caption>
-    <graphic mimetype="image" mime-subtype="png" xlink:href="media/packing.png" xlink:title="" />
+    <graphic mimetype="image" mime-subtype="png" xlink:href="packing.png" xlink:title="" />
   </fig>
   <list list-type="order">
     <list-item>
@@ -150,7 +150,7 @@ a Creative Commons Attribution 4.0 International License (CC BY
   </list>
   <fig>
     <caption><p>Step 2. Unpacking</p></caption>
-    <graphic mimetype="image" mime-subtype="png" xlink:href="media/packing.png" xlink:title="" />
+    <graphic mimetype="image" mime-subtype="png" xlink:href="packing.png" xlink:title="" />
   </fig>
   <p>We have extensive documentation
   (<xref alt="Rémi Rampin, n.d." rid="ref-documentation" ref-type="bibr">Rémi

joss.00124/10.21105.joss.00124.jats

@@ -131,22 +131,22 @@ a Creative Commons Attribution 4.0 International License (CC BY
     <title>Regression example</title>
     <sec id="no-variance-estimated">
       <title>No variance estimated</title>
-      <p>-<inline-graphic mimetype="image" mime-subtype="png" xlink:href="media/plot_mpg_no_variance.png" /></p>
+      <p>-<inline-graphic mimetype="image" mime-subtype="png" xlink:href="plot_mpg_no_variance.png" /></p>
     </sec>
     <sec id="plot-with-variance">
       <title>Plot with variance</title>
-      <p>-<inline-graphic mimetype="image" mime-subtype="png" xlink:href="media/plot_mpg.png" /></p>
+      <p>-<inline-graphic mimetype="image" mime-subtype="png" xlink:href="plot_mpg.png" /></p>
     </sec>
   </sec>
   <sec id="classification-example">
     <title>Classification example</title>
     <sec id="no-variance-estimated-1">
       <title>No variance estimated</title>
-      <p>-<inline-graphic mimetype="image" mime-subtype="png" xlink:href="media/plot_spam_no_variance.png" /></p>
+      <p>-<inline-graphic mimetype="image" mime-subtype="png" xlink:href="plot_spam_no_variance.png" /></p>
     </sec>
     <sec id="plot-with-variance-1">
       <title>Plot with variance</title>
-      <p>-<inline-graphic mimetype="image" mime-subtype="png" xlink:href="media/plot_spam.png" /></p>
+      <p>-<inline-graphic mimetype="image" mime-subtype="png" xlink:href="plot_spam.png" /></p>
     </sec>
   </sec>
   <sec id="community-guidelines">

joss.00142/10.21105.joss.00142.jats

@@ -176,13 +176,13 @@ a Creative Commons Attribution 4.0 International License (CC BY
     vectensis</italic></ext-link>
     (<xref alt="Apweiler et al., 2004" rid="ref-apweiler_protein_2004" ref-type="bibr">Apweiler
     et al., 2004</xref>) as the target proteome.</p></caption>
-    <graphic mimetype="application" mime-subtype="pdf" xlink:href="media/results.pdf" xlink:title="" />
+    <graphic mimetype="application" mime-subtype="pdf" xlink:href="results.pdf" xlink:title="" />
   </fig>
   <fig>
     <caption><p>CRBH model generated from the performance comparison.
     Hits with scores above the blue dotted line will be
     kept.</p></caption>
-    <graphic mimetype="application" mime-subtype="pdf" xlink:href="media/sacPom.cdna.fa.x.Nematostella_vectensis.uniprot.pep.fa.crbl.model.plot.pdf" xlink:title="" />
+    <graphic mimetype="application" mime-subtype="pdf" xlink:href="sacPom.cdna.fa.x.Nematostella_vectensis.uniprot.pep.fa.crbl.model.plot.pdf" xlink:title="" />
   </fig>
 </sec>
 </body>

joss.00161/10.21105.joss.00161.jats

@@ -173,7 +173,7 @@ a Creative Commons Attribution 4.0 International License (CC BY
     data). The top outcomes are displayed after being sorted in
     ascending order.
     <styled-content id="avg_rank"></styled-content></p></caption>
-    <graphic mimetype="application" mime-subtype="pdf" xlink:href="media/figs/mean_rank.pdf" xlink:title="" />
+    <graphic mimetype="application" mime-subtype="pdf" xlink:href="mean_rank.pdf" xlink:title="" />
   </fig>
   <fig>
     <caption><p>Adjusted p-values (using the Benjamini-Hochberg
@@ -183,7 +183,7 @@ a Creative Commons Attribution 4.0 International License (CC BY
     <xref alt="1" rid="avg_rank">1</xref>. The top outcomes are
     displayed after being sorted in ascending order.
     <styled-content id="q_value"></styled-content></p></caption>
-    <graphic mimetype="application" mime-subtype="pdf" xlink:href="media/figs/adj_p_val.pdf" xlink:title="" />
+    <graphic mimetype="application" mime-subtype="pdf" xlink:href="adj_p_val.pdf" xlink:title="" />
   </fig>
   <p>The <monospace>adaptest</monospace> R package provides utilities
   for performing the estimation and hypothesis testing procedures

joss.00164/10.21105.joss.00164.jats

@@ -107,21 +107,21 @@ a Creative Commons Attribution 4.0 International License (CC BY
   There are four different stages in the data management lifecycle of
   FRIEDA – i) storage planning, ii) provisioning and preparation, iii)
   data placement, and iv) execution.</p>
-  <p>-<inline-graphic mimetype="image" mime-subtype="png" xlink:href="media/figs/lifecycle.png" /></p>
+  <p>-<inline-graphic mimetype="image" mime-subtype="png" xlink:href="lifecycle.png" /></p>
   <p>FRIEDA defines a data control plane and an execution plane. The
   data control plane defines the data partition and distribution
   strategy, whereas the execution plane manages the execution of the
   application using a master-worker paradigm. FRIEDA also provides
   different data management strategies, either to partition the data in
   real-time, or pre-determine the data partitions prior to application
   execution.</p>
-  <p>-<inline-graphic mimetype="image" mime-subtype="png" xlink:href="media/figs/frieda_arch.png" /></p>
+  <p>-<inline-graphic mimetype="image" mime-subtype="png" xlink:href="frieda_arch.png" /></p>
   <p>FRIEDA also provides a module to manage data across multiple
   heterogeneous cloud sites, called the FRIEDA data coordinator. The
   data coordinator is responsible for coordinating data movement to
   different cloud sites based on the properties of data and cloud
   instances.</p>
-  <p>-<inline-graphic mimetype="image" mime-subtype="png" xlink:href="media/figs/datacoordinator.png" /></p>
+  <p>-<inline-graphic mimetype="image" mime-subtype="png" xlink:href="datacoordinator.png" /></p>
   <p>FRIEDA is released on a modified BSD license with an added
   paragraph at the end. FRIEDA supports cloud platforms like Amazon EC2
   and OpenStack. Users need to provide cloud and application

joss.00165/10.21105.joss.00165.jats

@@ -117,7 +117,7 @@ a Creative Commons Attribution 4.0 International License (CC BY
   a Cython function instead of a pure Python function. Comparison
   between different methods for a test problem is given in following
   graph:
-  <inline-graphic mimetype="image" mime-subtype="png" xlink:href="media/PerformanceTests.png" />
+  <inline-graphic mimetype="image" mime-subtype="png" xlink:href="PerformanceTests.png" />
   You can generate above graph via the
   <ext-link ext-link-type="uri" xlink:href="https://github.com/bmcage/odes/blob/master/ipython_examples/Performance%20tests.ipynb">Performance
   notebook</ext-link>.</p>

joss.00170/10.21105.joss.00170.jats

@@ -95,7 +95,7 @@ a Creative Commons Attribution 4.0 International License (CC BY
   analysis.</p>
   <p>The following is an example visualization made using
   <monospace>KraljicMatrix</monospace>’s analysis tools.</p>
-  <p>-<inline-graphic mimetype="image" mime-subtype="png" xlink:href="media/paper.png" /></p>
+  <p>-<inline-graphic mimetype="image" mime-subtype="png" xlink:href="paper.png" /></p>
 </sec>
 </body>
 <back>

joss.00176/10.21105.joss.00176.jats

@@ -104,7 +104,7 @@ a Creative Commons Attribution 4.0 International License (CC BY
   package.</p>
   <fig>
     <caption><p>REM diagram</p></caption>
-    <graphic mimetype="image" mime-subtype="jpeg" xlink:href="media/REM_diagram.jpg" xlink:title="" />
+    <graphic mimetype="image" mime-subtype="jpeg" xlink:href="REM_diagram.jpg" xlink:title="" />
   </fig>
   <p><bold>Figure 1.</bold> Data inputs (<italic>y</italic> and
   <italic>t</italic>) and parameters (<italic>r</italic>,
@@ -118,7 +118,7 @@ a Creative Commons Attribution 4.0 International License (CC BY
   <italic>theta</italic> = zone of detection.</p>
   <fig>
     <caption><p>Density plot</p></caption>
-    <graphic mimetype="image" mime-subtype="png" xlink:href="media/density_plot.png" xlink:title="" />
+    <graphic mimetype="image" mime-subtype="png" xlink:href="density_plot.png" xlink:title="" />
   </fig>
   <p><bold>Figure 2.</bold> Animal densities (individuals.km2) with
   associated variances (SD) calculated via Random Encounter Models,