path: root/paper.tex

\documentclass[10.5pt]{article}

%% This is a convenience variable if you are using PGFPlots to build plots
%% within LaTeX. If you want to import PDF files for figures directly, you
%% can use the standard `\includegraphics' command. See the definition of
%% `\includetikz' in `tex/preamble-pgfplots.tex' for where the files are
%% assumed to be if you use `\includetikz' when `\makepdf' is not defined.
\newcommand{\makepdf}{}

%% When defined (value is irrelevant), `\highlightchanges' will cause text
%% in `\tonote' and `\new' to become colored. This is useful in cases that
%% you need to distribute drafts that is undergoing revision and you want
%% to highlight to your colleagues which parts are new and which parts are
%% only for discussion.
\newcommand{\highlightchanges}{}

%% Import the necessary preambles.
\input{tex/src/preamble-style.tex}
\input{tex/build/macros/project.tex}
\input{tex/src/preamble-pgfplots.tex}
\input{tex/src/preamble-biblatex.tex}





\title{Maneage: Customizable Framework for Managing Data Lineage}
\author{\large\mpregular \authoraffil{Mohammad Akhlaghi}{1,2},
        \large\mpregular \authoraffil{Ra\'ul Infante-Sainz}{1,2},
        \large\mpregular \authoraffil{Roberto Baena-Gall\'e}{1,2}\\
  {
    \footnotesize\mplight
    \textsuperscript{1} Instituto de Astrof\'isica de Canarias, C/V\'ia L\'actea, 38200 La Laguna, Tenerife, ES.\\
    \textsuperscript{2} Facultad de F\'isica, Universidad de La Laguna, Avda. Astrof\'isico Fco. S\'anchez s/n, 38200, La Laguna, Tenerife, ES.\\
    Corresponding author: Mohammad Akhlaghi
    (\href{mailto:mohammad@akhlaghi.org}{\textcolor{black}{mohammad@akhlaghi.org}})
  }}
\date{}





\begin{document}%\layout
\thispagestyle{firstpage}
\maketitle

%% Abstract
{\noindent\mpregular
  The era of big data has ushered an era of big responsibility.
  In the absence of reproducibility, as a test on understanding data lineage, the result will be subject to perpetual debate.
  To address this problem, we introduce Maneage (management + lineage) which has already been tested and used in several scientific papers.
  Maneage is founded on the principles of completeness (e.g., no dependency beyond a POSIX-compatible operating system, no administrator privileges, or no network connection), modular and straight-forward design, temporal lineage and free software.
  A project using Maneage is fully stored in machine\--action\-able, and human\--read\-able plain-text format, facilitating version-control, publication, archival, or automatic parsing to extract data provenance.
  The provided lineage is not limited to high-level processing, but also includes building the necessary software from source with fixed versions and build configurations.
  Additionally, the project's final visualizations and narrative report are also included, establishing direct links between the data analysis and the narrative or plots, with the precision of a word in a sentence or a point in a plot.
  Maneage enables incremental projects, where a new project can branch off an existing one, with moderate changes to enable experimentation on published methods.
  Once Maneage is implemented in a sufficiently wide scale, it can aid in automatic and optimized workflow creation through machine learning, or automating data management plans.
  Maneage was a recipient of the research data alliance (RDA) Europe Adoption Grant in 2019 and is also used to write this paper, with snapshot \projectversion.
  \horizontalline

  \noindent
  {\mpbold Keywords:} Data Lineage, Data Provenance, Reproducibility, Scientific Pipelines, Workflows
}

\horizontalline










\section{Introduction}
\label{sec:introduction}

The increasing volume and complexity of data analysis has been highly productive, giving rise to a new branch of ``Big Data'' in many fields of the sciences and industry.
However, given its inherent complexity, the mere results are barely useful alone, questions on its lineage/provenance these commonly follow:
What inputs were used?
How were the configurations or training data chosen?
What operations were done on those inputs, how were the plots made?
See Figure \ref{fig:questions} for a more detailed visual representation of such questions for various stages of the workflow.
\tonote{Johan: add some general references.}

Due to the complexity of modern data analysis, a small deviation in the final result can be due to many different steps, which may be significant.
Publishing the \emph{complete} codes of the analysis is the only way to avoid wasting resources.
For example, \citet{smart18} describes how a 7-year old conflict in theoretical condensed matter physics was only identified after the relative codes were shared.
\citet{miller06} found a mistaken column flipping in a project's workflow, leading to retraction of 5 papers in major journals, including Science.
\citet{baggerly09} highlighted the inadequate narrative description of the analysis and showed the prevalence of simple errors in published results, ultimately calling their work ``forensic bioinformatics''.
\citet{herndon14} and \citet[a self-correction]{horvath15} also reported similar situations and \citet{ziemann16} concluded that one-fifth of papers with supplementary Microsoft Excel gene lists contain erroneous gene name conversions.

\begin{figure}[t]
  \begin{center}
    \includetikz{figure-project-outline}
  \end{center}
  \vspace{-17mm}
  \caption{\label{fig:questions}Graph of a generic project's workflow (connected through arrows), highlighting the various issues/questions on each step.
    The green boxes with sharp edges are inputs and the blue boxes with rounded corners are the intermediate or final outputs.
    The red boxes with dashed edges highlight the main questions on the respective stage.
    The box coverting the software download and build phases shows some common tools software developers use for that phase, but a scientific project is so much more than that.
  }
\end{figure}

The reason such reports are mostly from genomics and bioinformatics is because they have traditionally been more open to publishing workflows: for example \href{https://www.myexperiment.org}{myexperiment.org}, or \href{https://www.genepattern.org}{genepattern.org}, \href{https://galaxyproject.org}{galaxy\-project.org}, and others.
Such integrity checks are a critical component of the scientific method, but are only possible with access to the data \emph{and} its lineage (workflows).
The status in other fields, where workflows are not commonly shared, is probably (much) worse.

The completeness of a paper's published metadata (or ``Methods'' section) can be measured by the ability to reproduce the result without needing to contact the authors.
Several studies have attempted to answer this with different levels of detail, for example, \citet{allen18} found that roughly half of the papers in astrophysics do not even mention the names of any analysis software, while \citet{menke20} found this fraction has greatly improved in medical/biological field and is currently above $80\%$.
\citet{ioannidis2009} attempted to reproduce 18 published results by two independent groups, but only fully succeeded in 2 of them and partially in 6.
\citet{chang15} attempted to reproduce 67 papers in well-regarded economic journals with data and code: only 22 could be reproduced without contacting authors, and more than half could not be replicated at all.
\citet{stodden18} attempted to replicate the results of 204 scientific papers published in the journal Science \emph{after} that journal adopted a policy of publishing the data and code associated with the papers.
Even though the authors were contacted, the success rate was $26\%$.
Generally, this problem is unambiguously felt in the community: \citet{baker16} surveyed 1574 researchers and found that only $3\%$ did not see a ``reproducibility crisis''.

This is not a new problem in the sciences: in 2011, Elsevier conducted an ``Executable Paper Grand Challenge'' \citep{gabriel11}.
The proposed solutions were published in a special edition.
Before that, in an attempt to simulate research projects, \citet{ioannidis05} proved that ``most claimed research findings are false''.
In the 1990s, \citet{schwab2000, buckheit1995, claerbout1992} describe the same problem very eloquently and also provided some solutions they used.
While the situation has improved since the early 1990s, these papers still resonate strongly with the frustrations of today's scientists.
Even earlier, through his famous quartet, \citet{anscombe73} qualitatively showed how distancing of researchers from the intricacies of algorithms/methods can lead to misinterpretation of the results.
One of the earliest such efforts we found was \citet{roberts69}, who discussed conventions in FORTRAN programming and documentation to help in publishing research codes.

In this paper, we introduce Maneage as a solution to the collective problem of preserving a project's data lineage and its software dependencies.
A project using Maneage will start by branching from the main Git branch of Maneage and starts customizing itself: specifying the necessary software tools for that particular project, adding analysis steps and writing a narrative based on the analysis results.
The temporal provenance of the project is fully preserved in Git, and allows merging of the project with the core branch to update the low-level infra-structure (common to all projects) without changing the high-level steps specific to this project.
In Sections \ref{sec:definitions} \& \ref{sec:principles} the basic concepts are defined and the founding principles of Maneage are discussed.
Section \ref{sec:maneage} describes the internal structure of Maneage and Section \ref{sec:discussion} is a discussion on its benefits, caveats and future prospects.


\section{Definitions}
\label{sec:definitions}

The concepts and terminologies of reproducibility and project/workflow management and design are commonly used differently by different research communities or different solution provides.
As a consequence, before starting with the technical details it is important to clarify the specific terms used.

\begin{enumerate}[label={\bf D\arabic*}]
\item \label{definition:input}\textbf{Input:}
  A project's input is any file that may be usable in other projects.
  The inputs of a project include data or software source code, see \citet{hinsen16} on the fundamental similarity of data and source code.
  Inputs may have initially been created/written (e.g., software source code) or collected (e.g., data) for one specific project, however, they can, and most often will, be used in later projects too.

\item \label{definition:output}\textbf{Output:}
  A project's output is any file that is published at the end.
  The output(s) of a project can be a narrative paper or report with visualizations, datasets (e.g., table(s), image(s), a number, or Boolean: confirming a hypothesis as true or false), automatically generated software source code, or any other computer file.

\item \label{definition:project}\textbf{Project:}
  A project is the series of operations that are done on input(s) to produce outputs.
  This definition is therefore very similar to ``workflow'' \citep{oinn04, reich06, goecks10}, but because the published narrative paper/report is also an output, a project also includes the source of the narrative (e.g., \LaTeX{} or MarkDown) \emph{and} how the visualizations in it were created.

  In a good project, all analysis scripts (e.g., written in Python, packages in R, libraries/programs in C/C++, etc.) are well-defined as an independently managed software with clearly defined inputs, outputs and no side-effects.
  This greatly helps in debugging and experimentation during the project, and their re-usability in later projects.
  As a consequence, such analysis scripts/programs are defined above as ``inputs'' for the project.
  A project hence does not include any analysis source code (to the extent possible), it only manages calls to them.

\item \label{definition:provenance}\textbf{Data Provenance:}
  A dataset's provenance is defined as the set of metadata (in any ontology, standard or structure) that connect it to the components (other datasets or scripts) that produced it.
  Data provenance thus provides a high-level \emph{and structured} view of a project's lineage.
  A good example of this is Research Objects \citep{belhajjame15}.

% This definition of data lineage is inspired from https://stackoverflow.com/questions/43383197/what-are-the-differences-between-data-lineage-and-data-provenance:

% "data provenance includes only high level view of the system for business users, so they can roughly navigate where their data come from.
% It's provided by variety of modeling tools or just simple custom tables and charts.
% Data lineage is a more specific term and includes two sides - business (data) lineage and technical (data) lineage.
% Business lineage pictures data flows on a business-term level and it's provided by solutions like Collibra, Alation and many others.
% Technical data lineage is created from actual technical metadata and tracks data flows on the lowest level - actual tables, scripts and statements.
% Technical data lineage is being provided by solutions such as MANTA or Informatica Metadata Manager. "
\item \label{definition:lineage}\textbf{Data Lineage:}
Data lineage is commonly used interchangeably with Data provenance \citep[for example][]{cheney09}.
For clarity, we define the term ``Data lineage'' as a low-level and fine-grained recording of the data's trajectory in an analysis (not meta-data, but actual commands).
Therefore, data lineage is synonymous with ``project'' as defined above.
\item \label{definition:reproduction}\textbf{Reproducibility \& Replicability:}
  These two terms have been used in the literature with various meanings, sometimes in a contradictory way.
  It is important to highlight that in this paper we are only considering computational analysis: \emph{after} data has been collected and stored as a file.
  Therefore, many of the definitions reviewed in \citet{plesser18}, which are about data collection, do not apply here.
  We adopt the same definition of \citet{leek17,fineberg19}, among others.
  \citet{fineberg19} define reproducibility as \emph{obtaining consistent [not necessarily identical] results using the same input data; computational steps, methods, and code; and conditions of analysis}, or same inputs $\rightarrow$ consistent result.
  They define Replicability as \emph{obtaining consistent results across studies aimed at answering the same scientific question, each of which has obtained its own data}, or different inputs $\rightarrow$ consistent result.
\end{enumerate}










\section{Principles}
\label{sec:principles}

The core principle of Maneage is simple: science is defined by its method, not its result.
\citet{buckheit1995} summarize this nicely by noting that modern scientific papers (narrative combined with plots, tables and figures) are merely advertisements of a scholarship, the actual scholarship is the scripts and software usage that went into doing the analysis.

Maneage is not the first attempted solution to this fundamental problem.
Various solutions have been proposed since the early 1990s, for example RED \citep{claerbout1992,schwab2000}, Apache Taverna \citep{oinn04}, Madagascar \citep{fomel13}, GenePattern \citep{reich06}, Kepler \citep{ludascher05}, VisTrails \citep{bavoil05}, Galaxy \citep{goecks10}, Image Processing On Line journal \citep[IPOL][]{limare11}, WINGS \citep{gil10}, Active papers \citep{hinsen11}, Collage Authoring Environment \citep{nowakowski11}, SHARE \citep{vangorp11}, Verifiable Computational Result \citep{gavish11}, SOLE \citep{pham12}, Sumatra \citep{davison12}, Sciunit \citep{meng15}, Popper \citep{jimenez17}, WholeTale \citep{brinckman19}, and many more.
To highlight the uniqueness of Maneage in this plethora of tools, a more elaborate list of principles are necessary as described below.

\begin{enumerate}[label={\bf P\arabic*}]
\item \label{principle:complete}\textbf{Complete:}
  A project that is complete, or self-contained, does not depend on anything beyond the Portable operating system Interface (POSIX), does not affect the host system, does not require root/administrator privileges, does not need an internet connection (when its inputs are on the file-system), and is stored in a format that does not require any software beyond POSIX tools to open, parse or execute.

  A complete project can automatically access to the inputs (see definition \ref{definition:input}), build its necessary software (instructions on configuring, building and installing those software in a fixed environment), do the analysis (run the software on the data) and create the final narrative report/paper as well as its visualizations, in its final format (usually in PDF or HTML).
  No manual/human interaction is required within a complete project, as \citet{claerbout1992} put it: ``a clerk can do it''.
  Generally, manual intervention in any of the steps above, or an interactive interface, is an incompleteness.
  Finally, the plain-text format is particularly important because any other storage format will require specialized software \emph{before} the project can be opened.

  \emph{Comparison with existing:} Except for IPOL, none of the tools above are complete.
  They all have many dependencies far beyond POSIX, for example, the more recent ones (the project/workflow, not the analysis) are written in Python or rely on Jupyter notebooks.
  Such high-level tools have very short lifespans and evolve very fast, for example Python 3 is not compatible with Python 2.
  They also have a very complex dependency trees, making them extremely vulnerable and hard to maintain, for example, see Figure 1 of \citet{alliez19} on the dependency tree of Matplotlib (one of the smaller Jupyter dependencies).
  The longevity of a workflow (not the analysis itself), is determined by its shortest-lived dependency.

  Many existing tools therefore do not attempt to store the project as plain text, but pre-built binary blobs (containers or virtual machines) that can rarely be recreated\footnote{Using the package manager of the container's OS, or Conda which are both highly dependent on the time they are created.} and also have a short lifespan\footnote{For example Docker only works on Linux kernels that are on long-term support, not older.
    Currently, this is Linux 3.2.x that was initially released 8 years ago in 2012. The current Docker images may not be usable in a similar time frame in the future.}.
  As plain-text, even if it is no longer executable due to much evolved technologies, it is still human read-able and parse-able by any machine.

\item \label{principle:modularity}\textbf{Modularity:}
A project should be compartmentalized or partitioned to independent modules or components with well-defined inputs/outputs having no side-effects.
In a modular project, communication between the independent modules is explicit, providing optimizations on multiple levels:
1) Execution: independent modules can run in parallel, or modules that do not need to be run (because their dependencies have not changed) will not be re-done.
2) Data provenance extraction (recording any dataset's origins).
3) Citation: allowing others to credit specific parts of a project.
4) Usage in other projects.

\emph{Comparison with existing:} Visual workflow tools like Apache Taverna, GenePattern, Kepler or VisTrails do encourage this, but the more recent tools leave such design choices to the experience of project authors.
However, designing a modular project needs to be encouraged and facilitated, otherwise, scientists (that are not usually trained in data management) will not design their projects to be modular, leading to great inefficiencies in terms of project cost or scientific accuracy.

\item \label{principle:complexity}\textbf{Minimal complexity:}
  This principle is essentially Occam's razor: ``Never posit pluralities without necessity'' \citep{schaffer15}, but extrapolated to project management:
  1) avoid complex relations between analysis steps (which is not unrelated to the principle of modularity in \ref{principle:modularity}).
  2) avoid the programming language that is currently in vogue because it is going to fall out of fashion soon and significant resources are required to translate or rewrite it every few years (to stay in vogue).
  The same job can be done with more stable/basic tools, and less effort in the long-run.

  \emph{Comparison with existing:} Most of the existing tools use the popular language/framework of when they were created. For example, a larger fraction of them are written in Python as we come closer to the present time.

\item \label{principle:verify}\textbf{Verifiable inputs and outputs:}
The project should contain automatic verification checks on its inputs (software source code and data) and outputs.
When applied, expert knowledge will not be necessary to confirm the correct reproduction.

\emph{Comparison with existing:} Such verification is usually possible in most systems, but fully maintained by the user.
Automatic verification of inputs is most commonly implemented in some cases, but rarely the outputs.

\item \label{principle:history}\textbf{History and temporal provenance:}
No project is done in a single/first attempt.
Projects evolve as they are being completed.
It is natural that earlier phases of a project are redesigned/optimized only after later phases have been completed.
This is often seen in scientific papers, with statements like ``we [first] tried method [or parameter] XXXX, but YYYY is used here because it showed to have better precision [or less bias, or etc]''.
A project's ``history'' is thus as scientifically relevant as the final, or published version.

\emph{Comparison with existing:} The systems above that are implemented around version control usually support this principle.
However, because they are rarely complete (as discussed in principle \ref{principle:complete}), this history is also not complete.
IPOL, which uniquely stands out in other principles, fails here: only the final snapshot is published.

\item \label{principle:freesoftware}\textbf{Free and open source software:}
  Technically, as defined in Section \ref{definition:reproduction}, reproducibility is also possible with a non-free or non-open-source software (a black box).
  This principle is thus necessary to complement the definition of reproducibility and has many advantages which are critical to the sciences and the industry:
  1) The lineage, and its optimization, can be traced down to the internal algorithm in the software's source.
  2) A free software that may not execute on a future hardware can be modified to work.
  3) A non-free software cannot be distributed by the project, making the whole community reliant only on the proprietary owner's server (even if the proprietary software does not ask for payments).

  \emph{Comparison with existing:} The existing solutions listed above are all free software.
  There are non-free lineage or workflow solutions, but we do not consider them here because of this principle.
\end{enumerate}










\section{Maneage}
\label{sec:maneage}

Maneage is an implementation of the principles of Section \ref{sec:principles}: it is complete (\ref{principle:complete}), modular (\ref{principle:modularity}), has minimal complexity (\ref{principle:complexity}), verifies its inputs \& outputs (\ref{principle:verify}), preserves temporal provenance (\ref{principle:history}) and finally, it is free software (\ref{principle:freesoftware}).
In practice, it is a collection of plain-text files, that are distributed in pre-defined sub-directories by context (a modular source), and are all under version-control, currently with Git.
The main Maneage Branch is a fully working skeleton of a project without much flesh: containing all the low-level infrastructure, but without any actual high-level analysis operations\footnote{In the core Maneage branch, only a simple demo analysis is included to be complete.
  But it can easily be removed: all its files and steps have a \inlinecode{delete-me} prefix.}.
Maneage contains a file called \inlinecode{README-hacking.md}\footnote{Note that the \inlinecode{README.md} file is reserved for the project using Maneage, not maneage itself.} that has a complete checklist of steps to start a new project and remove demonstration parts, there are also hands-on tutorials to help new adopters.

To start a new project, the authors will \emph{clone}\footnote{In Git, the ``clone'' operation is the process of copying all the project's files and history from a repository onto the local system.} Maneage, create their own Git branch over the latest commit, and start their project by customizing that branch.
Customization in their project branch is done by adding the names of the software they need, references to their input data, the analysis commands, visualization commands, and a narrative report which includes the visualizations.
This will usually be done in multiple commits in the project's duration (maybe multiple years), thus preserving the project's history: the causes of all choices, the authors and times of each change, failed tests, and etc.

Figure \ref{fig:files} shows this directory structure containing the modular plain-text files (classified by context in sub-directories) and some representative files in each directory.
The top-level source only has very high-level components: the \inlinecode{project} shell script (POSIX-compliant) that is the main interface to the project, as well as the paper's \LaTeX{} source, documentation and a copyright statement.
Two sub-directories are also present: \inlinecode{tex/} (containing \LaTeX{} files) and \inlinecode{reproduce/} (containing all other parts of the project).

\begin{figure}[t]
  \begin{center}
    \includetikz{figure-file-architecture}
  \end{center}
  \vspace{-5mm}
  \caption{\label{fig:files}
    Directory and file structure in a hypothetical project using this solution.
    Files are shown with small, green boxes that have a suffix in their names (for example \inlinecode{format.mk} or \inlinecode{download.tex}).
    Directories (containing multiple files) are shown as large, brown boxes, where the name ends in a slash (\inlinecode{/}).
    Directories with dashed lines and no files (just a description) are symbolic links that are created after building the project, pointing to commonly needed built directories.
    Symbolic links and their contents are not considered part of the source and are not under version control.
    Files and directories are shown within their parent directory.
    For example the full address of \inlinecode{format.mk} from the top project directory is \inlinecode{reproduce/analysis/make/format.mk}.
  }
\end{figure}

The \inlinecode{project} script is a high-level wrapper to interface with Maneage and in its current implementation has two main phases as shown below: 1) configuration, where the necessary software are built and the environment is setup. 2) analysis, where data are accessed and the software is run on them to create visualizations and the final report.
In practice, these two steps are run with the following commands:

\begin{lstlisting}[language=bash]
  ./project configure      # Build all necessary software from source.
  ./project make           # Do the analysis (download data, run software on data, build PDF).
\end{lstlisting}

Here, we will delve deeper into the implementation and some usage details of Maneage.
Section \ref{sec:usingmake} elaborates why Make (a POSIX tool) was chosen as the main job orchestrator in Maneage.
Sections \ref{sec:projectconfigure} \& \ref{sec:projectanalysis} then discuss the operations done during the configuration and analysis phase.
Afterwards, we describe how Maneage projects benefit from version control in Section \ref{sec:projectgit}.
Section \ref{sec:collaborating} discusses sharing of a built environment and finally, in Section \ref{sec:publishing} the publication/archival of Maneage projects are discussed.

\subsection{Job orchestration with Make}
\label{sec:usingmake}

Scripts (in Shell, Python, or any other high-level language) are usually the first solution that come to mind when non-interactive, or batch, processing is needed (the completeness principle \ref{principle:complete}),
However, the inherent complexity and non-linearity of progress, as a project evolves, make it hard to manage such scripts.
For example, if $90\%$ of a research project is done and only the newly added, final $10\%$ must be executed, a script will always start from the beginning.
It is possible to manually ignore (with conditionals), or manually comment, parts of a script to only do a special part.
However, such conditionals/comments will only add to the complexity and will discourage experimentation on an already completed part of the project.
These problems motivated the creation of Make in the early Unix operating system \citep{feldman79}.

The Make paradigm starts from the end: the final \emph{target}.
In Make's syntax, the process is broken into atomic \emph{rules} where each rule has a single \emph{target} file which can depend on any number of \emph{prerequisite} files.
To build the target from the prerequisites, each rule also has a \emph{recipe} (an atomic script).
The plain-text files containing Make rules and their components are called Makefiles.
Note that Make does not replace scripting languages like the shell, Python or R.
It is a higher-level structure enabling modular/atomic scripts (in any language) to be put in a workflow.
The formal connection of targets with prerequisites that is defined in Make, enables creation of an optimized workflow that is very mature and has withstood the test of time: almost all OSs rely on it.

Besides formalizing data lineage, Make also greatly encourages experimentation in a project because a recipe is executed only when at least one prerequisite is more recent than its target.
Therefore, when only $5\%$ of a project's targets are affected by a change, only they will be recreated, the other $95\%$ remain dormant.
Furthermore, Make first examines the full lineage before starting the execution of recipes.
It can thus execute independent rules in parallel, further improving the speed and encouraging experimentation.

Make is well known by many outside of the software developing communities.
For example, \citet{schwab2000} report how geophysics students have easily adopted it for the RED project management tool.
Because of its simplicity, we have also had very good feedback on using Make from the early adopters of Maneage since the RDA grant, in particular, with graduate students and postdocs.

\subsection{Project configuration}
\label{sec:projectconfigure}

Maneage orchestrates the building of its necessary software in the same language that it orchestrates the analysis: Make (see Section \ref{sec:usingmake}).
Therefore, a researcher already using Maneage for their high-level analysis easily understands, and can customize, the software environment too, without delving into the intricacies of third-party tools.
Most existing tools reviewed in Section \ref{sec:principles}, use package managers like Conda to maintain the software environment, but since conda itself is written in Python, it does not fit in our completeness principle \ref{principle:complete}.
Highly robust solutions like Nix \citep{dolstra04} and GNU Guix \citep{courtes15} do exist, but they require root permissions which is also against that principle.

Project configuration (building the software environment) is managed by the files under \inlinecode{reproduce\-/soft\-ware} of Maneage's source, see Figure \ref{fig:files}.
At the start of project configuration, Maneage needs a top-level directory to build itself on the host filesystem (software and analysis).
We call this the ``build directory'' and it must not be under the source directory (see \ref{principle:modularity}): by default Maneage will not touch any file in its source.
No other location on the running operating system will be affected by the project and the build directory should not affect the result, so its value is not under version control.
Two other local directories can optionally be specified by the project when inputs (\ref{definition:input}) are present locally and do not need to be downloaded: 1) software tarball directory and 2) input data directory.
Sections \ref{sec:buildsoftware} and \ref{sec:softwarecitation} elaborate more on the building of the necessary software and the important problem of software citation.

\subsubsection{Verifying and building necessary software from source}
\label{sec:buildsoftware}

To compile the necessary software from source Maneage currently needs the host to have a C compiler (available on any POSIX-compliant OS).
This C compiler will be used by Maneage to build and install (in the build directory) all necessary software and their dependencies with fixed versions.
The dependency tree goes all the way down to core operating system components like GNU Bash, GNU AWK, GNU Coreutils, and many more on all supported operating systems (including macOS, not just GNU/Linux).
For example, the full list of installed software for this paper is available in Acknowledgments of this paper.
On GNU/Linux OSs, a fixed version of the GNU Binutils and GNU C Compiler (GCC) is also included, and soon Maneage will also install its own fixed version of the GNU C Library to be fully independent of the host on such systems (Task 15390\footnote{\url{https://savannah.nongnu.org/task/?15390}}).
In effect, except for the Kernel, Maneage builds all other components of the GNU OS on the host from source.

The software source code may already be present on the host filesystem, if not, they can be downloaded.
But before being used to build the software, they will be validated by their SHA-512 checksum (which is already stored in the project).
Maneage includes a large collection of scientific software (and their dependencies) that are usually not necessary in all projects.
Therefore, each project has to identify its high-level software in the \inlinecode{TARGETS.conf} file under \inlinecode{re\-produce\-/soft\-ware\-/config} directory, see Figure \ref{fig:files}.
All the high-level software dependencies are codified in Maneage as Make \emph{prerequisites}, so the specified software will be automatically built after their dependencies.

Note that project configuration can be done in a container or virtual machine to facilitate moving the project.
However, the important factor is that such binary blobs are an optional output of Maneage, they are not the its primary storage/archival format.

\subsubsection{Software citation}
\label{sec:softwarecitation}

Maneage contains the full list of built software for each project, their versions and their configuration options.
However, this information is buried deep into each project's source.
Maneage also prints a distilled fraction of this information in the project's final report, blended into the narrative, as seen in the Acknowledgments of this paper.
Furthermore, when the software is associate with a published paper, that paper's Bib\TeX{} entry is also added to the final report and is cited with the software's name and version.
For example\footnote{In this paper we have used very basic tools that are not accompanied by a paper}, see the software acknowledgement sections of \citet{akhlaghi19} and \citet{infante20}.

This is particularly important in the case for research software, where citation is critical to justify continued development.
One notable example that nicely highlights this issue is GNU Parallel \citep{tange18}: every time it is run, it prints the citation information before it starts.
This does not cause any problem in automatic scripts, but can be annoying when reading/debugging the outputs.
Users can disable the notice, with the \inlinecode{--citation} option and accept to cite its paper, or support its development directly by paying $10000$ euros!
This is justified by an uncomfortably true statement\footnote{GNU Parallel's FAQ on the need to cite software: \url{http://git.savannah.gnu.org/cgit/parallel.git/plain/doc/citation-notice-faq.txt}}: ``history has shown that researchers forget to [cite software] if they are not reminded explicitly. ... If you feel the benefit from using GNU Parallel is too small to warrant a citation, then prove that by simply using another tool''.
Most other research software do not resort to such drastic measures, however, citation is important for them.

Given the increasing number of software used in scientific research, the only reliable solution is to automatically cite the used software in the final paper.
For a review of the necessity and basic elements in software citation, see \citet{katz14} and \citet{smith16}.
There are ongoing projects specifically tailored to software citation, including CodeMeta\footnote{\url{https://codemeta.github.io}} and Citation file format\footnote{\url{https://citation-file-format.github.io}} (CFF), a very robust approach is also provided by SoftwareHeritage \citep{dicosmo18}.
We plan to enable these wonderful tools in Maneage.





\subsection{Project's analysis}
\label{sec:projectanalysis}

Once the project is configured (Section \ref{sec:projectconfigure}), a unique and fully controlled environment is available to execute the analysis.
All analysis operations run such that the host OS settings cannot penetrate it, enabling an isolated environment without the extra layer of containers or a virtual machine.
In Maneage, a project's analysis is broken into two phases: 1) preparation and 2) analysis.
The former is mostly necessary to optimize for extremely large datasets and is only useful for advanced users, it also follows an identical internal structure to the later.
Hence, we will not go into it any further and refer the interested reader to the documentation.

A project consists of many steps, including data access (possibly by downloading), running various steps of the analysis on the raw inputs, and creating the necessary plots, figures or tables for a published report, or output datasets for a database.
If all of these steps are organized in a single Makefile, it will become very large, or long, and will be hard to maintain, extend/grow, read, reuse, and cite.
Generally, large files are a bad practice and against the modularity principle (\ref{principle:modularity}).

Maneage is thus designed to encourage and facilitate modularity by distributing the analysis in many Makefiles that contain contextually-similar analysis steps.
In the rest of this paper these modular, or lower-level, Makefiles will be called \emph{subMakefiles}.
The subMakefiles are loaded into the special Makefile \inlinecode{top-make.mk} with a certain order and executed in one instance of Make\footnote{The subMakefiles are loaded into \inlinecode{top-make.mk} using Make's \inlinecode{include} directive.
  Hence no recursion is used (where one instance of Make, calls Make within itself) because recursion is against the minimal complexity principle and can make the code very hard to read \ref{principle:complexity}.}.
When run with the \inlinecode{make} argument, the \inlinecode{project} script (Section \ref{sec:maneage}), calls \inlinecode{top-make.mk}.
All these Makefiles are in \inlinecode{re\-produce\-/anal\-ysis\-/make}, see Figure \ref{fig:files}.
Figure \ref{fig:datalineage} schematically shows these subMakefiles and their relation with each other with the targets they build.

\begin{figure}[t]
  \begin{center}
    \includetikz{figure-data-lineage}
  \end{center}
  \vspace{-7mm}
  \caption{\label{fig:datalineage}Schematic representation of data lineage in a hypothetical project/pipeline using Maneage.
    Each colored box is a file in the project and the arrows show the dependencies between them.
    Green files/boxes are plain text files that are under version control and in the source-directory.
    Blue files/boxes are output files of various steps in the build-directory, located within the Makefile (\inlinecode{*.mk}) that generates them.
    For example, \inlinecode{paper.pdf} depends on \inlinecode{project.tex} (in the build directory and generated automatically) and \inlinecode{paper.tex} (in the source directory and written by hand).
    In turn, \inlinecode{project.tex} depends on all the \inlinecode{*.tex} files at the bottom of the Makefiles above it.
    The solid arrows and built boxes with full opacity are actually described in the context of a demonstration project in this paper.
    The dashed arrows and lower opacity built boxes, just shows how adding more elements to the lineage is also easily possible, making this a scalable tool.
  }
\end{figure}

To avoid getting too abstract in the subsections below, where necessary, we will do a basic analysis on the data of \citet{menke20} and replicate one of their results.
Note that because we are not using the same software\footnote{We cannot use the same software because \citet{menke20} use Microsoft Excel for the analysis which violates several of our principles: \ref{principle:complete}, \ref{principle:complexity} and \ref{principle:freesoftware}.}, this is not a reproduction (see \ref{definition:reproduction}).
In the subsections below, this paper's analysis on that dataset is described using the data lineage graph of Figure \ref{fig:datalineage}.
We will follow Make's paradigm (see Section \ref{sec:usingmake}) of starting the lineage backwards form the ultimate target in Section \ref{sec:paperpdf} (bottom of Figure \ref{fig:datalineage}) to the configuration files \ref{sec:configfiles} (top of Figure \ref{fig:datalineage}).
To better understand this project, we encourage looking into this paper's Maneage source, published as a supplement.

\subsubsection{Ultimate target: the project's paper or report (\inlinecode{paper.pdf})}
\label{sec:paperpdf}

The ultimate purpose of a project is to report the data analysis result, as raw visualizations, or numbers blended in with a narrative.
In Figure \ref{fig:datalineage}, it is shown as \inlinecode{paper.pdf}. Note that it is the only built file (blue box) with no arrows leaving it.
The instructions to build \inlinecode{paper.pdf} are in the \inlinecode{paper.mk} subMakefile.
Its prerequisites include \inlinecode{paper.tex} and \inlinecode{references.tex} (Bib\TeX{} entries for possible citations) in the project source and \inlinecode{project.tex} which is a built product.
\inlinecode{references.tex} formalizes the connections of this project with previous projects on a high-level.

\subsubsection{Values within text (\inlinecode{project.tex})}
\label{sec:valuesintext}

Figures, plots, tables and narrative are not the only analysis products that are included in the paper/report.
In many cases, quantitative values from the analysis are also blended into the sentences of the report's narration.
For example, this sentence in the abstract of \citet[which is written in Maneage]{akhlaghi19}: ``... detect the outer wings of M51 down to S/N of 0.25 ...''.
The value `0.25', for signal-to-noise ratio (S/N), depends on the analysis, and is an output of the analysis just like paper's figures and plots.
Manually typing such numbers in the narrative is prone to very important errors and discourages testing in scientific papers.
Therefore, they must also be automatically generated.

To automatically generate and blend them in the text, Maneage uses \LaTeX{} macros.
In the quote above, the \LaTeX{} source\footnote{\citet{akhlaghi19} is written in Maneage and its \LaTeX{} source is available in multiple ways: 1) direct download from arXiv:\href{https://arxiv.org/abs/1909.11230}{1909.11230}, by clicking on ``other formats'', or 2) the Git or \href{https://doi.org/10.5281/zenodo.3408481}{zenodo.3408481}, links are also available on arXiv's top page.} looks like this: ``\inlinecode{\small detect the outer wings of M51 down to S/N of \$\textbackslash{}demo\-sf\-optimized\-sn\$}''.
T\-he ma\-cro ``\inlinecode{\small\textbackslash{}demosfoptimizedsn}'' is automatically created during in the project and expands to the value ``\inlinecode{0.25}'' when the PDF output is built.
The built \inlinecode{project.tex} file stores all such reported values.

However, managing all the necessary \LaTeX{} macros in one file is against the modularity principle and can be frustrating and buggy.
To address this problem, Maneage has the convention that all subMakefiles \emph{must} contain a fixed target with the same base-name, but with a \inlinecode{.tex} suffix to store reported values generated in that subMakefile.
If it does not need to report any values in text, the file can be empty.
In Figure \ref{fig:datalineage}, these macro files can be seen in every subMakefile, except for \inlinecode{paper.mk} (which does not need it).
These \LaTeX{} macro files thus form the core skeleton of a Maneage project: as shown in Figure \ref{fig:datalineage}, the outward arrows of all built files of any subMakefile ultimately leads to one of these \LaTeX{} macro files, possibly in another subMakefile.

\subsubsection{Verification of outputs (\inlinecode{verify.mk})}
\label{sec:outputverification}

Before the modular \LaTeX{} macro files of Section \ref{sec:valuesintext} are merged into the single \inlinecode{project.tex} file, they need to pass through the verification filter, which is a core principle of Maneage (\ref{principle:verify}).
Note that simply confirming the checksum of the final PDF, or figures and datasets is not generally possible: many tools write the creation date into the produced files.
To avoid such cases the raw data (independent of their metadata like creation date) must be verified, some standards have such features, for example, the \inlinecode{DATASUM} keyword in the FITS format \citep{pence10}.
To facilitate output verification, the project has a \inlinecode{verify.mk} subMakefile (see Figure \ref{fig:datalineage}) and \inlinecode{verify.tex} the only prerequisite of \inlinecode{project.tex} that was described in Section \ref{sec:valuesintext}.
Verification is the boundary between the analytical phase of the paper, and the production of the report.
It has some tests on pre-defined formats, and other formats can easily be added.

\subsubsection{The analysis}
\label{sec:analysis}

The basic concepts behind organizing the analysis into modular subMakefiles have already been discussed above, we will thus describe it here with the practical example of replicating Figure 1C of \citet{menke20}, with some enhancements in Figure \ref{fig:toolsperyear}.
As shown in Figure \ref{fig:datalineage}, in this project, we have broken this goal into two subMakefiles: \inlinecode{format.mk} and \inlinecode{demo-plot.mk}.
The former is in charge of converting the Microsoft Excel formatted input into the simple comma-separated value (CSV) format, and the latter is in charge of generating the table to build Figure \ref{fig:toolsperyear}.
In a real project, subMakefiles will be much more complex.
Figure \ref{fig:topmake} shows how the two subMakefiles are thus placed as values to the \inlinecode{makesrc} variable of \inlinecode{top-make.mk}, without their suffix (see Section \ref{sec:valuesintext}).
Note that their location after the standard starting subMakefiles (initialization and download) and before the standard ending subMakefiles (verification and final paper) is important, as well as the order.

\begin{figure}[t]
  \begin{center}
    \includetikz{figure-tools-per-year}
  \end{center}
  \vspace{-5mm}
  \caption{\label{fig:toolsperyear}Fraction of papers mentioning software tools (green line, left vertical axis) to total number of papers studied in that year (light red bars, right vertical axis in log-scale).
    This is an enhanced replica of figure 1C \citet{menke20}, shown here for demonstrating Maneage, see Figure \ref{fig:datalineage} for its lineage and Section \ref{sec:analysis} for how it was organized.
  }
\end{figure}

\begin{figure}[t]
  \input{tex/src/figure-src-topmake.tex}
  \vspace{-3mm}
  \caption{\label{fig:topmake} General view of the High-level \inlinecode{top-make.mk} Makefile which manages the project's analysis that is in various subMakefiles.
    See Figures \ref{fig:files} \& \ref{fig:datalineage} for its location in the project's file structure and its data lineage, as well as the subMakefiles it includes.
  }
\end{figure}

To enhance the original plot, Figure \ref{fig:toolsperyear} also shows the number of papers that were studied each year.
Its horizontal axis also shows the full range of the data (starting from \menkefirstyear) while the original Figure 1C in \citet{menke20} starts from 1997.
Probably, the reason \citet{menke20} decided to avoid earlier years was the small number of papers in earlier years.
For example, in \menkenumpapersdemoyear, they had only studied \menkenumpapersdemocount{} papers.
Note that both the numbers of the previous sentence (\menkenumpapersdemoyear{} and \menkenumpapersdemocount), and the dataset's oldest year (mentioned above: \menkefirstyear) are automatically generated \LaTeX{} macros, see \ref{sec:valuesintext}.
We did not typeset them in this narrative explanation manually.
This step (generating the macros) is shown schematically in Figure \ref{fig:datalineage} with the arrow from \inlinecode{tools-per-year.txt} to \inlinecode{demo-plot.tex}.

To create Figure \ref{fig:toolsperyear}, we used the \LaTeX{} package PGFPlots, therefore, the final analysis output we needed was a simple plain-text table with 3 columns (year, paper per year, tool fraction per year).
This table is shown in the lineage graph of Figure \ref{fig:datalineage} as \inlinecode{tools-per-year.txt} and The PGFPlots source to generate this figure is located in \inlinecode{tex\-/src\-/figure\--tools\--per\--year\-.tex}.
If another plotting tool was desired (for example Python's Matplotlib, or Gnuplot), the built graphic file (for example \inlinecode{tools-per-year.pdf}) could be the target instead of the raw table.

The \inlinecode{tools-per-year.txt} is a value-added table with only \menkenumyears{} rows (counting per year), the original dataset had \menkenumorigrows{} rows (one row for each year of each journal).
We see in Figure \ref{fig:datalineage} that it is defined as a Make \emph{target} in \inlinecode{demo-plot.mk} and that its prerequisite is \inlinecode{menke20-table-3.txt} (schematically shown by the arrow connecting them).
Note that both the row numbers mentioned at the start of this paragraph are also macros.
Again from Figure \ref{fig:datalineage}, we see that \inlinecode{menke20-table-3.txt} is a target in \inlinecode{format.mk} and its prerequisite is the input file \inlinecode{menke20.xlsx}.
The input files (which come from outside the project) are all \emph{targets} in \inlinecode{download.mk} and futher discussed in Section \ref{sec:download}.

Having prepared the full dataset in a simple format, let's report the number of subjects (papers and journals) that were studied in \citet{menke20}.
The necessary file for this measurement is \inlinecode{menke20-table-3.txt}.
Therefore, we do this calculation (with a simple AWK command) and write the results in \inlinecode{format.tex} which is automatically loaded into this paper's source along with the macro files.
In the built PDF paper, the two macros expand to $\menkenumpapers$ (number of papers studied) and $\menkenumjournals$ (number of journals studied) respectively.
This step is shown schematically in Figure \ref{fig:datalineage} with the arrow from \inlinecode{menke20-table-3.txt} to \inlinecode{format.tex}.



\subsubsection{Importing and validating inputs (\inlinecode{download.mk})}
\label{sec:download}

The \inlinecode{download.mk} subMakefile is present in all Maneage projects and contains the common steps for importing the input dataset(s) into the project.
All necessary input datasets for the project are imported through this subMakefile.
This helps in modularity and minimal complexity (\ref{principle:modularity} \& \ref{principle:complexity}): to see what external datasets were used in a project, this is the only necessary file to manage/read.
Also, a simple call to a downloader (for example \inlinecode{wget}) is not usually enough.
Irrespective of where the dataset is \emph{used} in the project's lineage, it helps to maintain relation with the outside world (to the project) in one subMakefile.

Each external dataset has some basic information, including its expected name on the local system (for offline access), the necessary checksum to validate it (either the whole file or just its main ``data'', as discussed in Section \ref{sec:outputverification}), and its URL/PID.
In Maneage, such information regarding a project's input dataset(s) is in the \inlinecode{INPUTS.conf} file.
See Figures \ref{fig:files} \& \ref{fig:datalineage} for the position of \inlinecode{INPUTS.conf} in the project's file structure and data lineage, respectively.
For demonstration, we are using the datasets of \citet{menke20} which are stored in one \inlinecode{.xlsx} file on bioXriv\footnote{\label{footnote:dataurl}Full data URL: \url{\menketwentyurl}}.
Figure \ref{fig:inputconf} shows the corresponding \inlinecode{INPUTS.conf} where the necessary information are stored as Make variables and are automatically loaded into the full project when Make starts (and is most often used in \inlinecode{download.mk}).

\begin{figure}[t]
  \input{tex/src/figure-src-inputconf.tex}
  \vspace{-3mm}
  \caption{\label{fig:inputconf} Contents of the \inlinecode{INPUTS.conf} file for the demonstration dataset of \citet{menke20}.
    This file contains the basic, or minimal, metadata for retrieving the required dataset(s) of a project: it can become arbitrarily long.
    Here, \inlinecode{M20DATA} contains the name of this dataset within this project.
    \inlinecode{MK20MD5} contains the MD5 checksum of the dataset, in order to check the validity and integrity of the dataset before usage.
    \inlinecode{MK20SIZE} contains the size of the dataset in human readable format.
    \inlinecode{MK20URL} is the URL which the dataset is automatically downloaded from (only when its not already present on the host).
    Note that the original URL (footnote \ref{footnote:dataurl}) was too long to display properly here.
  }
\end{figure}


\subsubsection{Configuration files}
\label{sec:configfiles}

The subMakefiles discussed above should only contain the organization of an analysis, they should not contains any fixed numbers, settings or parameters.
Such elements should only be used as variables that are defined in configuration files.
Configuration files enable the logical separation between the low-level implementation and high-level running of a project.
In the data lineage plot of Figure \ref{fig:datalineage}, configuration files are shown as the sharp-edged, green \inlinecode{*.conf} files in the top row.
For example the \inlinecode{INPUTS.conf} file that was shown in Figure \ref{fig:inputconf} and mentioned in Section \ref{sec:download}.
All the configuration files of a project are placed under the \inlinecode{reproduce/analysis/config} (see Figure \ref{fig:files}) subdirectory, and are loaded into \inlinecode{top-make.mk} before any of the subMakefiles, see Figure \ref{fig:topmake}.

The demo analysis of Section \ref{sec:analysis} is a good demonstration of their usage: during that discussion we reported the number of papers studied by \citet{menke20} in \menkenumpapersdemoyear.
However, the year's number is not written by hand in \inlinecode{demo-plot.mk}.
It is referenced through the \inlinecode{menke-year-demo} variable, which is defined in \inlinecode{menke-demo-year.conf}, that is a prerequisite of the \inlinecode{demo-plot.tex} rule.
This is also visible in the data lineage of Figure \ref{fig:datalineage}.
If we later decide to report the number in another year, we simply have to change the value in \inlinecode{menke-demo-year.conf}.
A configuration file is a prerequisite of the target that uses it, hence its date will be newer than \inlinecode{demo-plot.tex}.
Therefore Make will re-execute the recipe to generate the macro file before this paper is re-built and the corresponding year and value will be updated in this paper, always in synchronization with each other and no matter how many times they are used.
Combined with the fact that all source files in Maneage are under version-controlled, this encourages testing of various analysis settings as the project is evolving, leading to more robust scientific results.

\subsubsection{Project initialization (\inlinecode{initialize.mk})}
\label{sec:initialize}

The \inlinecode{initial\-ize\-.mk} subMakefile is present in all projects and is the first subMakefile that is loaded into \inlinecode{top-make.mk} (see Figure \ref{fig:datalineage}).
It does not contain any analysis or major processing steps, it just initializes the system by setting the necessary Make environment as well as other general jobs like defining the Git commit hash of the run as a \LaTeX{} (\inlinecode{\textbackslash{}projectversion}) macro that can be loaded into the narrative.
Papers using Maneage usually put this hash as the last word in their abstract, for example, see \citet{akhlaghi19} and \citet{infante20}.
For the current version of this paper, it expands to \projectversion.

\subsection{Projects as Git branches of Maneage}
\label{sec:projectgit}

Maneage is fully composed of plain-text files, therefore, it can be maintained under version control systems (currently using Git).
Every commit in the version controlled history contains \emph{a complete} snapshot of the data lineage, for more, see the completeness principle (\ref{principle:complete}).
Maneage is maintained by its developers in a central branch, which we will call \inlinecode{man\-eage} hereafter.
The \inlinecode{man\-eage} branch contains all the low-level infrastructure, or skeleton, that is necessary for any project as described in the sections above.
As mentioned in Section \ref{sec:maneage}, to start a new project, users simply clone it from its reference repository and build their own Git branch over the most recent commit.
This is demonstrated in the first phase of Figure \ref{fig:branching} where a project has started by branching-off of commit \inlinecode{0c120cb} in the \inlinecode{maneage} branch.

%% Exact URLs of imported images.
%% Collaboration icon: https://www.flaticon.com/free-icon/collaboration_809522
%% Paper done:         https://www.flaticon.com/free-icon/file_2521838
%% Paper processing:   https://www.flaticon.com/free-icon/file_2521989
\begin{figure}[t]
  \includetikz{figure-branching}
  \vspace{-3mm}
  \caption{\label{fig:branching} Projects start by branching off the main Maneage branch and developing their high-level analysis over the common low-level infrastructure: add flesh to a skeleton.
    The low-level infrastructure can always be updated (keeping the added high-level analysis intact), with a simple merge between branches.
    Two phases of a project's evolution shown here: in phase 1, a co-author has made two commits in parallel to the main project branch, which have later been merged.
    In phase 2, the project has finished: note the identical first project commit and the Maneage commits it branches from.
    The dashed parts of Scenario 2 can be any arbitrary history after those shown in phase 1.
    A second team now wants to build upon that published work in a derivate branch, or project.
    The second team applies two commits and merges their branch with Maneage to improve the skeleton and continue their research.
    The Git commits are shown on their branches as colored ellipses, with their hash printed in them.
    The commits are colored based on the team that is working on that branch.
    The collaboration and paper icons are respectively made by `mynamepong' and `iconixar' and downloaded from \url{www.flaticon.com}.
  }
\end{figure}

After a project starts, Maneage will evolve, for example, new features will be added or low-level bugs will be fixed.
Because all projects branch-off from the same branch that these infrastructure improvements are made, updating the project's low-level skeleton is as easy as merging the \inlinecode{maneage} branch into the project's branch.
For example, in Figure \ref{fig:branching} (phase 1), see how Maneage's \inlinecode{3c05235} commit has been merged into project's branch trough commit \inlinecode{2ed0c82}.

Another useful scenario is reviving a finished/published project at later date, by other researchers as shown in phase 2 of Figure \ref{fig:branching}.
In that figure, a new team of researchers have decided to experiment on the results of the published paper and have merged it with the Maneage branch (commit \inlinecode{a92b25a}) to make it usable for their system (e.g., assuming the original project was completed years ago, and is no longer directly executable).

Other scenarios include a third project that can easily merge various high-level components from different projects into its own branch, thus adding a temporal dimension to their data lineage.
This structure also enables easy propagation of low-level fixes to all projects using Maneage.
Modern version control systems provide many more capabilities that can be leveraged through Maneage in project management, thanks to the shared branch it has with \emph{all} derived projects, and that it is complete (\ref{principle:complete}).

\subsection{Multi-user collaboration on single build directory}
\label{sec:collaborating}

Because the project's source and build directories are separate, it is possible for different users to share a build directory, while working on their own separate project branches during a collaboration.
Similar to the parallel branch that is later merged in phase 1 of Figure \ref{fig:branching}.
To enable this mode, \inlinecode{./project} script has a special \inlinecode{--group} option which takes the name of a (POSIX) user group in the host operating system.
All files built in the build directory are then automatically assigned to this user group, with read and write permissions.
Of course, avoiding conflicts in the build directory, while members are working on different branches is up to the team.

\subsection{Publishing the project}
\label{sec:publishing}

Once the project is complete, it needs to be published.
In a scientific scenario, it is submitted to a journal, while in an industrial world, it is submitted to the customers or employers.
To facilitate the publication of the project's source, Maneage has a special \inlinecode{dist} target during the build process which is activated with the command \inlinecode{./project make dist}.
In this mode, Maneage will not do any analysis, it will simply copy the full project's source (on the given commit) into a temporary directory and compress it into a \inlinecode{.tar.gz} file.
If a Zip compression is necessary, the \inlinecode{dist-zip} target can be called instead \inlinecode{dist}.
Since complete project is in plain-text, this compressed file is usually on the scale of 100 kilobytes.

However, the necessary inputs (\ref{definition:input}) and outputs may be arbitrarily large, from megabytes to petabytes or more.
Therefore, there are various scenarios for the publication of the project: 1) only publishing the source, 2) publishing the source with the data.
In the former case, the output of \inlinecode{dist} (described above) can be submitted to the journal as a supplement, or uploaded to pre-print servers like arXiv that will actually compile the \LaTeX{} source and build their own PDFs.
The Git history can also be archived as a single ``bundle'' file and also submitted as a supplement.
When publishing with datasets, the project's outputs, and inputs (if necessary), can be published on servers like Zenodo.
For example in \citet{akhlaghi19}, along with the source files mentioned above, we also uploaded all the project's necessary software and its final PDF to Zenodo in \href{https://doi.org/10.5281/zenodo.3408481}{zenodo.3408481}\footnote{https://doi.org/10.5281/zenodo.3408481}.










\section{Discussion \& Caveats}
\label{sec:discussion}

Maneage is the final product of various research projects (in astrophysics) over the last 5 years.
The primordial implementation was written for the analysis of \citet{akhlaghi15}.
Since the full analysis pipeline was in plain-text and consumed much less space than a single figure, it was uploaded to arXiv with the paper's \LaTeX{} source, see \href{https://arxiv.org/abs/1505.01664}{arXiv:1505.01664}\footnote{
  To download the \LaTeX{} source of any arXiv paper, click on the ``Other formats'' link, containing necessary instructions and links.}.
The system later evolved in \citet{bacon17}, in particular, the two sections of that paper that were done by M. Akhlaghi: \href{http://doi.org/10.5281/zenodo.1163746}{zenodo.1163746} and \href{http://doi.org/10.5281/zenodo.1164774}{zenodo.1164774}.
With these projects, the skeleton of the system was written as a more abstract ``template'' that could be customized for separate projects.
That template later matured into Maneage by including the installation of all necessary software from source and it was used in \citet[\href{https://doi.org/10.5281/zenodo.3408481}{zenodo.3408481}]{akhlaghi19} and \citet[\href{https://doi.org/10.5281/zenodo.3524937}{zenodo.3524937}]{infante20}.

In the last year and with the Research Data Alliance (RDA) grant that was awarded to Maneage, its user base (and thus its development) grew phenomenally and it has evolved to become much more customizable, well-tested and well-documented.
But it is far from complete: its core architecture will continue evolve after the publication of this paper, therefore a list of the notable changes after the publication of this paper will be kept in the \inlinecode{README-hacking.md} file.

Based on early adopters, we have seen the following caveats for Maneage.
The first caveat is regarding its widespread adoption: by principle, Maneage uses very low-level tools like Git, \LaTeX, Make and command-line tools to run in non-interactive mode.
However, a large fraction of the scientific community are accustomed to interactive graphic user interface (GUI) tools.
But this is not often a final choice: some of our early users simply did not know such tools existed.
After seeing them in action together (as a \emph{complete} Maneage project) they have started using these tools effectively.
Unfortunately by their low-level nature, the documentation of these tools alone discourages scientists, we are thus working on several tutorials and scientist-friendly documentation of such tools, hopefully by collaborating with efforts like \href{http://software.ac.uk}{software.ac.uk} and \href{http://urssi.us}{urssi.us}.
\citet{fineberg19} also note the importance that a project starts by following good practice, not to force it in the end.

A second caveat is the fact that Maneage is effectively an almost complete GNU operating system, tailored to each project.
It is just built ontop of an existing POSIX-compatible operating system, using its kernel.
Maneage has many generic scripts for simplifying the software packaging.
However, maintaining them (updating versions or fixing bugs on some hosts) can take time for a small team.
Because package management (Section \ref{sec:projectconfigure}) is in the same language as the analysis, some users have learnt to package their necessary software or correct some bugs themselves.
They later send those additions as merge-requests to the core Maneage branch, thus propagating the improvement to all projects using Maneage.
With a larger user-base we hope the fraction of such contributors increases and decreases the burden on our core team.

Another caveat that has been raised by some people is that publishing the project's reproducible data lineage immediately after publication may hamper their ability to continue harvesting from all their hard work.
Given the strong integrity checks in Maneage, we believe it has features to address this problem in the following ways:
1) Through the Git history, it is clear how much extra work the other team has added.
In this way, Maneage can contribute to a new concept of authorship in scientific projects and help to quantify Newton's famous ``standing on the shoulders of giants'' quote.
However, this is a long term goal and requires major changes to academic value systems.
2) Authors can be given a grace period where the journal, or some third authority, keeps the source and publishes it a certain interval after publication.

Once Maneage is adopted on a wide scale in a special topic, it is possible to feed them into machine learning algorithms for automatic workflow generation, optimized for certain aspects of the result.
Because Maneage is complete and also includes the project's history, even inputs (software and input data) or failed tests during the projects can enter this optimization process.
Furthermore, writing parsers of Maneage projects to generate Research Objects is trivial, and very useful for meta-research and data provenance studies.

\section{Conclusion \& Summary}
\label{sec:conclusion}

To effectively leaverage the power of big data, we need to have a complete view of its lineage.
However, scientists are rarely trained sufficiently in data management or software development, the plethora of high-level tools, that change every few years also does not help.
Maneage is desigend as a complete template, providing scientists with a built low-level skeleton that scientists can customize for any project and adopt modern, robust and efficient data management in practice on their own projects.

In this paper we introduced Maneage and how it is built upon the principles of completeness, modularity, minimal complexity, verifiable inputs and outputs, temporal provenance, and free software.
We showed how these principles are implemented in an already built structure that users just have to customize for the high-level aspects of their projects and discussed the caveats and advantages of this implementation.
With a larger user-base and wider application in scientific (and hopefully industrial) applications, Maneage will certainly grow and become even more stable user and friendly.

\tonote{One more paragraph will be added here: don't forget to review the caveats}

%% Acknowledgements
\section{Acknowledgments}
The authors wish to thank David Valls-Gabaud, Johan Knapen, Ignacio Trujillo, Roland Bacon, Konrad Hinsen, Yahya Sefidbakht, Simon Portegies Zwart, Pedram Ashofteh Ardakani, Elham Saremi, Zahra Sharbaf and Surena Fatemi for their useful suggestions and feedback on Maneage and this paper.
We also thank Julia Aguilar-Cabello for designing the Maneage logo.
During its development, Maneage has been partially funded (in historical order) by the following institutions:
The Japanese Ministry of Education, Culture, Sports, Science, and Technology ({\small MEXT}) PhD scholarship to M.A and its Grant-in-Aid for Scientific Research (21244012, 24253003).
The European Research Council (ERC) advanced grant 339659-MUSICOS.
The European Union’s Horizon 2020 (H2020) research and innovation programmes No 777388 under RDA EU 4.0 project, and Marie Sk\l{}odowska-Curie grant agreement No 721463 to the SUNDIAL ITN network.
The State Research Agency (AEI) of the Spanish Ministry of Science, Innovation and Universities (MCIU) and the European Regional Development Fund (FEDER) under the grant with reference AYA2016-76219-P.
The IAC project P/300724, financed by the Ministry of Science, Innovation and Universities, through the State Budget.
The Canary Islands Department of Economy, Knowledge and Employment, through the Regional Budget of the Autonomous Community.
The Fundaci\'on BBVA under its 2017 programme of assistance to scientific research groups, for the project "Using machine-learning techniques to drag galaxies from the noise in deep imaging".

\input{tex/build/macros/dependencies.tex}

%% Tell BibLaTeX to put the bibliography list here.
\printbibliography

%% Finish LaTeX
\end{document}

%% This file is part of a paper describing the Maneage workflow system
%%   https://gitlab.com/makhlaghi/maneage-paper
%
%% This file is free software: you can redistribute it and/or modify it
%% under the terms of the GNU General Public License as published by the
%% Free Software Foundation, either version 3 of the License, or (at your
%% option) any later version.
%
%% This file is distributed in the hope that it will be useful, but WITHOUT
%% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
%% FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License
%% for more details.
%
%% You should have received a copy of the GNU General Public License along
%% with this file.  If not, see <https://www.gnu.org/licenses/>.