Commit 74695bbb authored by Cord Wiljes's avatar Cord Wiljes

resize

parent e3fc3013
......@@ -150,7 +150,7 @@ The checks are written in \emph{Python 3.6} and use the \emph{lxml} package\foot
The pipeline of the quality check is shown in Figure~\ref{Fig-1: qc_pipeline} below. All steps are described in sections below.
\begin{figure}[!h]
\centering
\includegraphics[width=9cm,height=10.5cm,keepaspectratio]{images/qc_pipeline.pdf}
\includegraphics[width=0.6\linewidth]{images/qc_pipeline.pdf}
\caption{Workflow of Conquaire Quality Check.}
\label{Fig-1: qc_pipeline}
\protect
......@@ -162,7 +162,7 @@ The user is informed via email about the result of applying the test. The mail c
\subsection{Example of pre-configured YAML file}
The pre-configured file has to be stored in the root folder of the repository. For each commit to the repository, it is automatically executed by the CI runner and performs the Conquaire quality checks for the given repository. The user only has to change the value of the \texttt{-d} parameter as it represents the local path to the data inside the repository. In the given example, a folder named \emph{data} inside the repository contains the files which should be tested.
\begin{lstlisting}
\begin{lstlisting}[language=sh]
quality-check:
# Use smallest docker python image.
image: python:3.6-alpine
......@@ -216,7 +216,7 @@ In the CSV file format (\texttt{.csv}), data is organized as a table with comma
The researcher can provide an additional format declaration file (\texttt{.ini}) with his own specifications of the data, e.g., the type of the column and the expected range of the values. The quality check reports a warning if a required entry is missing or a value is out of range or has a wrong type, e.g., a non-numeric value in a numeric column.\\
\begin{figure}[!ht]
\begin{center}
\includegraphics[width=0.67\textwidth]{images/csv.png}
\includegraphics[width=0.85\textwidth]{images/csv.png}
\end{center}
\caption{Example result of the CSV check.}
\label{fig:csv}
......@@ -229,7 +229,7 @@ The researcher can provide an additional doctype definition file (\texttt{.dtd})
\begin{figure}[!ht]
\begin{center}
\includegraphics[width=0.67\textwidth]{images/xml.png}
\includegraphics[width=0.85\textwidth]{images/xml.png}
\end{center}
\caption{Example result of the XML check.}
\label{fig:xml}
......@@ -243,7 +243,7 @@ A red badge indicates not well-formed data or missing FAIR files. The user shoul
The URL of the overall result is provided to the user via email. This mail is sent automatically after every commit.
\begin{figure}[!ht]
\begin{center}
\includegraphics[width=0.67\textwidth]{images/result.png}
\includegraphics[width=0.85\textwidth]{images/result.png}
\end{center}
\caption{Example result of the overall result.html.}
\label{fig:result}
......
......@@ -55,7 +55,7 @@ For an academic institution, it is important to have an infrastructure-based app
%\begin{wrapfigure}{r}{40mm}
\begin{figure}[!ht]
\centering
\includegraphics[width=12cm]{images/scientific_workflow_006.eps}
\includegraphics[width=\linewidth]{images/scientific_workflow_006.eps}
\label{conquaire_workflow}
\caption{Schematic description of Conquaire workflow for research data management}
\end{figure}
......@@ -123,7 +123,7 @@ In each case, the quality testing framework searches for specific file types and
%\begin{wrapfigure}{r}{40mm}
\begin{figure}[!ht]
\centering
\includegraphics[width=6cm]{images/fig3-QualityFeedback.png}
\includegraphics[width=0.4\linewidth]{images/fig3-QualityFeedback.png}
\caption{Example feedback from quality tests.}
\label{example_feedback}
\end{figure}
......
......@@ -46,7 +46,7 @@ Accordingly, the main objective of that study was to relate inter-species differ
\begin{figure}[]
\centering
\includegraphics[width=11cm,keepaspectratio]{images/fig2-Workflow.png}
\includegraphics[width=\textwidth]{images/fig2-Workflow.png}
\caption{\textbf{Research data acquisition and processing pipeline.} For raw data acquisition, whole body motions were recorded with a marker-based motion capture system (Vicon) and an additional digital video camera. Furthermore, the anatomy of the animal, along with the marker positions on different body segments were recorded with a microscope camera. In a first step of manual editing and annotation, marker trajectories of selected episodes were labelled and, potentially, connected in case of recording gaps. This step resulted in a \textit{.c3d}-file, a file format described in section \ref{c3dServerIO}. The body pictures were used to generate a body model containing, for example, segment lengths and information about marker position in a body-centred coordinate system. The model is stored in a MATLAB \textit{.mat}-file. Finally, the kinematic reconstruction was achieved in MATLAB by combining marker trajectories with the body documentation. The resulting processed data, i.e., joint angle time courses, gait pattern, and velocity were saved as another MATLAB file.}
\label{fig:fig2-workflow}
\end{figure}
......@@ -87,7 +87,7 @@ Accordingly, the main objective of that study was to relate inter-species differ
\begin{figure}[]
\centering
\includegraphics{./images/fig3-MotionCaptureBodyKinematics.jpg}
\includegraphics[width=\textwidth]{./images/fig3-MotionCaptureBodyKinematics.jpg}
\caption{\textbf{A marker-based motion capture and whole-body kinematics calculations.} \textbf{A:} Insects were labelled with reflective markers. \textbf{B:} For kinematic analysis, the body was modelled by a branched kinematic chain. The main body chain (left) consists of the three thorax segments (Root, T2, T1) and the head. Six side chains (right) model the legs, with the segments coxa, femur and tibia (cox, fem, tib; only right legs are shown, labelled R1 to R3). All rotation axes (DoF) are indicated (3 for the root segment, 2 for thorax/head segments, and 5 per leg). DoF are denoted according to the subsequent segment and the axis of the local coordinate system around which the rotation is executed. Leg DoF are: cox.x, cox.y, cox.z (labelled for R2 in right panel), fem.y and tib.y (labeled for R1 in right panel). [Fig. 1 A, B of \citep{Theunissen_Duerr_2013}]}
\label{fig:body_kinematics}
\end{figure}
......@@ -167,7 +167,7 @@ Figure \ref{fig:compare_duerr} shows on the left the original panel from the pa
\begin{figure}[]
\centering
\includegraphics[width=12cm]{../ch2-BiologyDuerr/images/fig5-compare.png}
\includegraphics[width=\textwidth]{../ch2-BiologyDuerr/images/fig5-compare.png}
\caption{\textbf{Representative trial of unrestrained walking and climbing behaviour of \textit{C. morosus} as one of the three species investigated in the original paper published by Theunissen et al. \cite{Theunissen_EtAl_2015} (Figure 3).}
The left panel \emph{L} shows the original figure section. The right panel \emph{R} shows the movement as reproduced in the reproduction study conducted in the context of this chapter. The A, B and C subcomponents of the diagram show the following:
\textbf{A}: Movement of the body axis (cyan lines), head (red circles) and front legs (black lines), illustrated by superimposed stick figures every 100 ms.
......
......@@ -74,7 +74,7 @@ The MATLAB file format contains resulting trajectory information in 3D format.
\begin{figure}[]
\centering
%\includegraphics[width=0.4\linewidth]{images/original_workflow.jpg}
\includegraphics[width=0.4\linewidth]{images/Fig4_1_NEU.png}
\includegraphics[width=0.6\linewidth]{images/Fig4_1_NEU.png}
\caption{Procedure to calculate the trajectories of bumblebee flights, original procedure as described in Lobecke et al. \cite{Lobecke2018}}
\label{fig:original_workflow}
\end{figure}
......@@ -140,7 +140,7 @@ However, the differences are clearly small and within an acceptable range.
\begin{figure}[H]
\centering
%\subfloat[CAPTION]{BILDERCODE}\qquad
\includegraphics[width=0.3\linewidth]{images/trajectory_distribution_x.png} \includegraphics[width=0.3\linewidth]{images/trajectory_distribution_y.png} \includegraphics[width=0.3\linewidth]{images/trajectory_distribution_z.png}
\includegraphics[width=0.6\linewidth]{images/trajectory_distribution_x.png} \includegraphics[width=0.3\linewidth]{images/trajectory_distribution_y.png} \includegraphics[width=0.6\linewidth]{images/trajectory_distribution_z.png}
\caption{Distribution of differences between original MATLAB and new Python calculation for the three dimensions, x, y and z respectively}
\label{fig:comparison}
\end{figure}
......@@ -164,7 +164,7 @@ The benefit of the virtualization is that the computational workflow can be exec
\begin{figure}[H]
\centering
\includegraphics[width=0.7\linewidth]{images/Jenkins-Pipeline.jpg}
\includegraphics[width=0.9\linewidth]{images/Jenkins-Pipeline.jpg}
\caption{Flow Chart of Jenkins Continuous Integration pipeline.}
\label{fig:Jenkins-Pipeline}
\end{figure}
......
......@@ -78,7 +78,7 @@ In the study under investigation \cite{Budke2015}, the BINARY technique was used
\begin{figure}[htb]
\centering
\includegraphics[width=10cm]{./images/fig2-BINARY_setup.pdf}
\includegraphics[width=1.0\linewidth]{./images/fig2-BINARY_setup.pdf}
\caption{Schematic picture of the Bielefeld Ice Nucleation ARraY (BINARY) setup. \textbf{(a)} Top view of the 6 x 6 droplet array. The droplets are separated from each other by a polymer lattice creating individual compartments. \textbf{(b)} Side view showing the sealing of the compartments by top and bottom glass slides. \textbf{(c)} Position of the sample array on the Peltier cooling stage inside the cooling chamber. Figure is taken from Budke and Koop,~2015 \cite{Budke2015}.}
\label{binary_setup}
\end{figure}
......@@ -89,7 +89,7 @@ Figure \ref{binary_example}b and c show a representative behavior of the gray va
\begin{figure}[htp]
\centering
\includegraphics[width=8cm]{./images/fig3-BINARY_example.pdf}
\includegraphics[width=0.7\linewidth]{./images/fig3-BINARY_example.pdf}
\caption{Typical experiment with Snomax\textsuperscript{\textregistered}-containing droplets (0.1~$\mu$g~$\mu$L$^{-1}$) showing the automatic detection of ice nucleation events by the change in brightness during freezing. \textbf{(a)} Image series of the 6 x 6 droplet array during cooling. \textbf{(b)} Measured gray value of the droplet compartment indicated by the yellow box in panel (a) during cooling (red) and heating (green). Freezing and melting start at -3.9$^\circ$C and 0.0$^\circ$C, respectively. \textbf{(c)} Plot of the change in gray value between successive images showing peaks at the phase transition temperatures. Threshold values of $\pm1$ for the automatic attribution of freezing and melting are indicated by the dashed lines. Figure is taken from Budke and Koop,~2015 \cite{Budke2015}.}
\label{binary_example}
\end{figure}
......@@ -123,7 +123,7 @@ Figure \ref{binary_plot} presents the main result of the paper in form of a comb
\begin{figure}[htbp]
\centering
\includegraphics[width=10cm]{./images/fig4-BINARY_Plot.pdf}
\includegraphics[width=0.9\linewidth]{./images/fig4-BINARY_Plot.pdf}
\caption{ Experimentally determined active site density per unit mass of Snomax\textsuperscript{\textregistered} $n_m(T)$ versus temperature. Symbol colors indicate data from droplets with different Snomax\textsuperscript{\textregistered} concentrations; symbol size indicates the number of nucleating droplets per temperature interval (0.1$^\circ$C). The temperature range for different classes of IN is also indicated by the colored bars. Figure is taken from Budke and Koop,~2015 \cite{Budke2015}.}
\label{binary_plot}
\end{figure}
......@@ -166,7 +166,7 @@ The resulting workflow implemented in Python reproduces the workflow implemented
\begin{figure}[ht]
\centering
\includegraphics[width=9cm,height=15cm,keepaspectratio]{./images/workflow.pdf}
\includegraphics[width=0.7\linewidth]{./images/workflow.pdf}
\caption{Schematic representation of analytical workflow as implemented in Python program.}
\label{f5-data-workflow}
\protect
......@@ -190,7 +190,7 @@ In addition, Python is open source and is supported by many platforms.
\begin{figure}[!ht]
\centering
\includegraphics[width=15cm,keepaspectratio]{./images/fig6-KoopConquaire-snomaxVsTemp.pdf}
\includegraphics[width=1.0\linewidth]{./images/fig6-KoopConquaire-snomaxVsTemp.pdf}
\caption{Experimentally determined active site density per unit mass of
Snomax\textsuperscript{\textregistered} $nm(T)$ versus temperature. A: Original version of diagram as published by Budke and Koop \cite{Budke2015}; B: diagram resulting from reproducing the computational workflows of Budke and Koop as described in this paper.
Symbol colors indicate data from droplets with different Snomax\textsuperscript{\textregistered} concentrations; symbol size indicates the
......
......@@ -281,7 +281,7 @@ plot1:
\begin{figure}[hbt]
\centering
\includegraphics[keepaspectratio=true, scale=0.8]{images/img3-plot3-Firm-price.png}
\includegraphics[width=0.9\linewidth]{images/img3-plot3-Firm-price.png}
\caption{Plotting the price time series for data covering 4 sets, each consisting of 20 runs of 6.500 iterations, and for 80 Firm agents.}
\label{Fig: price time series}
\end{figure}
......
......@@ -110,7 +110,7 @@ In each file, the various columns such as "Left\_before", "Left\_After", "Right\
The research data workflow lifecycle diagram in Figure \ref{fig2-dataworkflow-2} explains the sequence of the research data processing and tasks for this project.
\begin{figure}
\centering
\includegraphics{images/fig1c4-workflow.pdf}
\includegraphics[width=0.6\linewidth]{images/fig1c4-workflow.pdf}
\caption{Data Workflow}
\label{fig2-dataworkflow-2}
\end{figure}
......@@ -170,11 +170,11 @@ We could thus reproduce the main results of the original paper, showing a positi
\centering
\begin{subfigure}
\centering
\includegraphics[width=.4\linewidth]{images/reproduced/fig_before_after_all.png}
\includegraphics[width=.45\linewidth]{images/reproduced/fig_before_after_all.png}
\end{subfigure}%
\begin{subfigure}
\centering
\includegraphics[width=.4\linewidth]{images/reproduced/fig_diff_all.png}
\includegraphics[width=.45\linewidth]{images/reproduced/fig_diff_all.png}
\end{subfigure}%
\caption{Looking times in percentage at matching image before and after utterance for all eight verbs averaged over all subjects (both 9-month and 10-month olds); Right: Difference in looking times for both 9 and 10-month olds}
\label{looking_times_all}
......@@ -185,11 +185,11 @@ We could thus reproduce the main results of the original paper, showing a positi
\centering
\begin{subfigure}
\centering
\includegraphics[width=.4\linewidth]{images/reproduced/fig_before_after_9month.png}
\includegraphics[width=.45\linewidth]{images/reproduced/fig_before_after_9month.png}
\end{subfigure}%
\begin{subfigure}
\centering
\includegraphics[width=.4\linewidth]{images/reproduced/fig_diff_9month.png}
\includegraphics[width=.45\linewidth]{images/reproduced/fig_diff_9month.png}
\end{subfigure}%
\caption{Left: Looking times in percentage at matching image before and after utterance for all eight verbs averaged over all subjects (9-month olds); Right: Difference in looking times (After-Before) for 9-month olds}
\label{looking_times_9}
......@@ -200,7 +200,7 @@ We could thus reproduce the main results of the original paper, showing a positi
\centering
\begin{subfigure}
\centering
\includegraphics[width=.4\linewidth]{images/reproduced/fig_before_after_10month.png}
\includegraphics[width=.45\linewidth]{images/reproduced/fig_before_after_10month.png}
\end{subfigure}%
\begin{subfigure}
\centering
......
......@@ -307,7 +307,7 @@ The demonstration code, located in the Jupyter \footnote{ \url{https://jupyter.o
\begin{figure}[!ht]
\centering
\includegraphics[width=12cm,height=30cm,keepaspectratio]{images/fig1-deepdisfluency-demo-ipynb-scrshot.png}
\includegraphics[width=0.8\linewidth]{images/fig1-deepdisfluency-demo-ipynb-scrshot.png}
\caption{Tagger output in the Deep Disfluency demo.ipynb file}
\label{fig:disfluencytaggeroutput1}
\end{figure}
......@@ -316,7 +316,7 @@ The demonstration code, located in the Jupyter \footnote{ \url{https://jupyter.o
\begin{figure}[!ht]
\centering
\includegraphics[width=12cm,height=30cm,keepaspectratio]{images/fig2-localjupyter-demo-ipynb-scrshot.png}
\includegraphics[width=0.8\linewidth]{images/fig2-localjupyter-demo-ipynb-scrshot.png}
\caption{Tagger output from the local demo.ipynb file}\label{fig:disfluencytaggeroutput2}
\end{figure}
......
......@@ -59,7 +59,7 @@ In order to investigate how previously learned expectations and sensorimotor seq
%\newpage
\begin{figure}[!ht]
\centering
\includegraphics[width=10cm,height=20cm,keepaspectratio]{images/fig1-60Trials.png}
\includegraphics[width=0.8\linewidth]{images/fig1-60Trials.png}
\caption{Computer display in the clicking task experiment}
\label{clicking_task_experiment}
\protect
......@@ -113,7 +113,7 @@ To reveal which mode of attentional selection was predominantly applied before a
%\newpage
\begin{figure}[!ht]
\centering
\includegraphics[width=9cm,height=15cm,keepaspectratio]{images/fig2-FixationPerTrial.png}
\includegraphics[width=0.6\linewidth]{images/fig2-FixationPerTrial.png}
\caption{\textbf{Top panel}: Number of fixations per trial of the three fixation types searching, guiding, and checking. This is Fig.4 (top) from the original paper. \textbf{Middle panel}: Number of searching fixations per action (1-7). No searching fixations can be made during action 8 as there are no future targets. This panel is Fig. 5c from the original paper. \textbf{Bottom panel}: Number of searching fixations per location (2-8). No searching fixations can be made on location 1, as this location is never a future target. This figure is not in the original paper!}
\label{fig-2:Image display of the objects in the clicking task experiment. In all panels, the values of the pre-change baseline (averages of trials 51-60) are presented by grey broken lines and the values of the first change trial (trial 61) are presented in red solid lines. The error bars represent the standard errors of the mean differences between the pre-change and the change values.}
\label{panels_fixations}
......@@ -132,7 +132,7 @@ To reveal how long it takes to incorporate the new clicking sequence, the number
%\newpage
\begin{figure}[!ht]
\centering
\includegraphics[width=9cm,height=15cm,keepaspectratio]{images/fig3-SearchingFixations.png}
\includegraphics[width=0.7\linewidth]{images/fig3-SearchingFixations.png}
\caption{Searching fixations per change trial}
\label{searching_fixations_per_trial}
\protect
......@@ -202,7 +202,7 @@ through SPSS commands. The output of Matlab functions were stored in the third a
\begin{figure}
\centering
\includegraphics[scale=0.5]{images/fig4c6-WorkflowRD.pdf}
\includegraphics[scale=0.7]{images/fig4c6-WorkflowRD.pdf}
\caption{Schematic representation of the analytical workflow used in the paper by Foerster and Schneider\cite{foerster_schneider_2015b}}
\label{workflow_foerster_schneider}
\end{figure}
......
......@@ -139,11 +139,11 @@ python scripts. Data analysis was conducted with SPSS\footnote{\url{https://www
\begin{figure}[ht]
\begin{center}
\includegraphics[width=0.4\textwidth]{images/pepper_setup_table_small.jpg} \hspace{1cm}
\includegraphics[width=0.4\textwidth]{images/pepper_press_small.jpg}
\includegraphics[width=0.45\textwidth]{images/pepper_setup_table_small.jpg} \hspace{1cm}
\includegraphics[width=0.45\textwidth]{images/pepper_press_small.jpg}
\end{center} \\
\begin{center}
\includegraphics[width=0.8\textwidth]{images/calib_small.jpg}
\includegraphics[width=0.9\textwidth]{images/calib_small.jpg}
\end{center}
\caption{Top left: The NAO JSE setup used in one of our Bielefeld setups; Top right: NAO keypress pose; Bottom: Screenshot of the robot calibration GUI}
\label{fig:setup}
......@@ -168,7 +168,7 @@ designed to foster reproducibility of software intensive experiments in robotics
The requirement to support disciplinary tools to design and run experiments will be additionally covered by jsPsych~\cite{de2015jspsych}.
\begin{figure}[h]
\begin{minipage}[c]{0.4\textwidth}
\begin{minipage}[c]{0.45\textwidth}
\includegraphics[width=\textwidth]{images/ICRA_CITK_4_1.png}}
\end{minipage} \hspace{0.5cm}
\begin{minipage}[c]{1\textwidth}
......
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