Anti-clustering in the national SARS-CoV-2 daily infection counts - arXiv:2007.1179 - swh:1:dir:fcc9d6b111e319e51af88502fe6b233dc78d5166 - doi:10.5281/zenodo.3951152 https://zenodo.org/record/3951152
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# Maneage: managing data lineage

Copyright (C) 2020-2021 Raul Infante-Sainz infantesainz@gmail.com
See the end of the file for license conditions.

Maneage is a fully working template for doing reproducible research (or writing a reproducible paper) as defined in the link below. If the link below is not accessible at the time of reading, please see the appendix at the end of this file for a portion of its introduction. Some slides are also available to help demonstrate the concept implemented here.

http://akhlaghi.org/reproducible-science.html

Maneage is created with the aim of supporting reproducible research by making it easy to start a project in this framework. As shown below, it is very easy to customize Maneage for any particular (research) project and expand it as it starts and evolves. It can be run with no modification (as described in README.md) as a demonstration and customized for use in any project as fully described below.

A project designed using Maneage will download and build all the necessary libraries and programs for working in a closed environment (highly independent of the host operating system) with fixed versions of the necessary dependencies. The tarballs for building the local environment are also collected in a separate repository. The final output of the project is a paper. Notice the last paragraph of the Acknowledgments where all the necessary software are mentioned with their versions.

Below, we start with a discussion of why Make was chosen as the high-level language/framework for project management and how to learn and master Make easily (and freely). The general architecture and design of the project is then discussed to help you navigate the files and their contents. This is followed by a checklist for the easy/fast customization of Maneage to your exciting research. We continue with some tips and guidelines on how to manage or extend your project as it grows based on our experiences with it so far. There is also a publication checklist, describing the recommended steps to publish your data/code. The main body concludes with a description of possible future improvements that are planned for Maneage (but not yet implemented). As discussed above, we end with a short introduction on the necessity of reproducible science in the appendix.

Please don't forget to share your thoughts, suggestions and criticisms. Maintaining and designing Maneage is itself a separate project, so please join us if you are interested. Once it is mature enough, we will describe it in a paper (written by all contributors) for a formal introduction to the community.

## Why Make?

When batch processing is necessary (no manual intervention, as in a reproducible project), shell scripts are usually the first solution that come to mind. However, the inherent complexity and non-linearity of progress in a scientific project (where experimentation is key) make it hard to manage the script(s) as the project evolves. For example, a script will start from the top/start every time it is run. So if you have already completed 90% of a research project and want to run the remaining 10% that you have newly added, you have to run the whole script from the start again. Only then will you see the effects of the last new steps (to find possible errors, or better solutions and etc).

It is possible to manually ignore/comment parts of a script to only do a special part. However, such checks/comments will only add to the complexity of the script and will discourage you to play-with/change an already completed part of the project when an idea suddenly comes up. It is also prone to very serious bugs in the end (when trying to reproduce from scratch). Such bugs are very hard to notice during the work and frustrating to find in the end.

The Make paradigm, on the other hand, starts from the end: the final target. It builds a dependency tree internally, and finds where it should start each time the project is run. Therefore, in the scenario above, a researcher that has just added the final 10% of steps of her research to her Makefile, will only have to run those extra steps. With Make, it is also trivial to change the processing of any intermediate (already written) rule (or step) in the middle of an already written analysis: the next time Make is run, only rules that are affected by the changes/additions will be re-run, not the whole analysis/project.

This greatly speeds up the processing (enabling creative changes), while keeping all the dependencies clearly documented (as part of the Make language), and most importantly, enabling full reproducibility from scratch with no changes in the project code that was working during the research. This will allow robust results and let the scientists get to what they do best: experiment and be critical to the methods/analysis without having to waste energy and time on technical problems that come up as a result of that experimentation in scripts.

Since the dependencies are clearly demarcated in Make, it can identify independent steps and run them in parallel. This further speeds up the processing. Make was designed for this purpose. It is how huge projects like all Unix-like operating systems (including GNU/Linux or Mac OS operating systems) and their core components are built. Therefore, Make is a highly mature paradigm/system with robust and highly efficient implementations in various operating systems perfectly suited for a complex non-linear research project.

Make is a small language with the aim of defining rules containing targets, prerequisites and recipes. It comes with some nice features like functions or automatic-variables to greatly facilitate the management of text (filenames for example) or any of those constructs. For a more detailed (yet still general) introduction see the article on Wikipedia:

https://en.wikipedia.org/wiki/Make_(software)

Make is a +40 year old software that is still evolving, therefore many implementations of Make exist. The only difference in them is some extra features over the standard definition (which is shared in all of them). Maneage is primarily written in GNU Make (which it installs itself, you don't have to have it on your system). GNU Make is the most common, most actively developed, and most advanced implementation. Just note that Maneage downloads, builds, internally installs, and uses its own dependencies (including GNU Make), so you don't have to have it installed before you try it out.

## How can I learn Make?

The GNU Make book/manual (links below) is arguably the best place to learn Make. It is an excellent and non-technical book to help get started (it is only non-technical in its first few chapters to get you started easily). It is freely available and always up to date with the current GNU Make release. It also clearly explains which features are specific to GNU Make and which are general in all implementations. So the first few chapters regarding the generalities are useful for all implementations.

The first link below points to the GNU Make manual in various formats and in the second, you can download it in PDF (which may be easier for a first time reading).

https://www.gnu.org/software/make/manual/

https://www.gnu.org/software/make/manual/make.pdf

If you use GNU Make, you also have the whole GNU Make manual on the command-line with the following command (you can come out of the "Info" environment by pressing q).

  $info make  If you aren't familiar with the Info documentation format, we strongly recommend running $ info info and reading along. In less than an hour, you will become highly proficient in it (it is very simple and has a great manual for itself). Info greatly simplifies your access (without taking your hands off the keyboard!) to many manuals that are installed on your system, allowing you to be much more efficient as you work. If you use the GNU Emacs text editor (or any of its variants), you also have access to all Info manuals while you are writing your projects (again, without taking your hands off the keyboard!).

## Published works using Maneage

The list below shows some of the works that have already been published with (earlier versions of) Maneage, and some that have been recently submitted for peer review. The previous version of Maneage was called "Reproducible paper template", with a separate git tree. Maneage is evolving rapidly, so some details will differ between the different versions. The more recent papers will tend to be the most useful as good working examples.

• Peper & Roukema (2020, arXiv:2010.03742): The live version of the controlled source is at Codeberg; the main input dataset, a software snapshot, the software tarballs, the project outputs and editing history are available at zenodo.4062461; and the archived git history is available at swh:1:dir:c4770e81288f340083dd8aa9fe017103c4eaf476.

• Roukema (2020, arXiv:2007.11779): The live version of the controlled source is at Codeberg; the main input dataset, a software snapshot, the software tarballs, the project outputs and editing history are available at zenodo.3951152; and the archived git history is available at swh:1:dir:fcc9d6b111e319e51af88502fe6b233dc78d5166.

• Akhlaghi et al. (2020, arXiv:2006.03018): The project's version controlled source is on Gitlab, necessary software, outputs and backup of history is available in zenodo.3872248.

• Infante-Sainz et al. (2020, MNRAS, 491, 5317): The version controlled project source is available on GitLab and is also archived on Zenodo with all the necessary software tarballs: zenodo.3524937.

• Akhlaghi (2019, IAU Symposium 355). The version controlled project source is available on GitLab and is also archived on Zenodo with all the necessary software tarballs: zenodo.3408481.

• Section 7.3 of Bacon et al. (2017, A&A 608, A1): The version controlled project source is available on GitLab and a snapshot of the project along with all the necessary input datasets and outputs is available in zenodo.1164774.

• Section 4 of Bacon et al. (2017, A&A, 608, A1): The version controlled project is available on GitLab and a snapshot of the project along with all the necessary input datasets is available in zenodo.1163746.

• Akhlaghi & Ichikawa (2015, ApJS, 220, 1): The version controlled project is available on GitLab. This is the very first (and much less mature!) incarnation of Maneage: the history of Maneage started more than two years after this paper was published. It is a very rudimentary/initial implementation, thus it is only included here for historical reasons. However, the project source is complete, accurate and uploaded to arXiv along with the paper.

## Citation

If you use Maneage in your project please cite Akhlaghi et al. (2020, arXiv:2006.03018). It has been submitted and is under peer review.

Also, when your paper is published, don't forget to add a notice in your own paper (in coordination with the publishing editor) that the paper is fully reproducible and possibly add a sentence or paragraph in the end of the paper shortly describing the concept. This will help spread the word and encourage other scientists to also manage and publish their projects in a reproducible manner.

# Project architecture

In order to customize Maneage to your research, it is important to first understand its architecture so you can navigate your way in the directories and understand how to implement your research project within its framework: where to add new files and which existing files to modify for what purpose. But if this the first time you are using Maneage, before reading this theoretical discussion, please run Maneage once from scratch without any changes (described in README.md). You will see how it works (note that the configure step builds all necessary software, so it can take long, but you can continue reading while its working).

The project has two top-level directories: reproduce and tex. reproduce hosts all the software building and analysis steps. tex contains all the final paper's components to be compiled into a PDF using LaTeX.

The reproduce directory has two sub-directories: software and analysis. As the name says, the former contains all the instructions to download, build and install (independent of the host operating system) the necessary software (these are called by the ./project configure command). The latter contains instructions on how to use those software to do your project's analysis.

After it finishes, ./project configure will create the following symbolic links in the project's top source directory: .build which points to the top build directory and .local for easy access to the custom built software installation directory. With these you can easily access the build directory and project-specific software from your top source directory. For example if you run .local/bin/ls you will be using the ls of Maneage, which is probably different from your system's ls (run them both with --version to check).

Once the project is configured for your system, ./project make will do the basic preparations and run the project's analysis with the custom version of software. The project script is just a wrapper, and with the make argument, it will first call top-prepare.mk and top-make.mk (both are in the reproduce/analysis/make directory).

In terms of organization, top-prepare.mk and top-make.mk have an identical design, only minor differences. So, let's continue Maneage's architecture with top-make.mk. Once you understand that, you'll clearly understand top-prepare.mk also. These very high-level files are relatively short and heavily commented so hopefully the descriptions in each comment will be enough to understand the general details. As you read this section, please also look at the contents of the mentioned files and directories to fully understand what is going on.

Before starting to look into the top top-make.mk, it is important to recall that Make defines dependencies by files. Therefore, the input/prerequisite and output of every step/rule must be a file. Also recall that Make will use the modification date of the prerequisite(s) and target files to see if the target must be re-built or not. Therefore during the processing, many intermediate files will be created (see the tips section below on a good strategy to deal with large/huge files).

To keep the source and (intermediate) built files separate, the user must define a top-level build directory variable (or $(BDIR)) to host all the intermediate files (you defined it during ./project configure). This directory doesn't need to be version controlled or even synchronized, or backed-up in other servers: its contents are all products, and can be easily re-created any time. As you define targets for your new rules, it is thus important to place them all under sub-directories of $(BDIR). As mentioned above, you always have fast access to this "build"-directory with the .build symbolic link. Also, beware to never make any manual change in the files of the build-directory, just delete them (so they are re-built).

In this architecture, we have two types of Makefiles that are loaded into the top Makefile: configuration-Makefiles (only independent variables/configurations) and workhorse-Makefiles (Makefiles that actually contain analysis/processing rules).

The configuration-Makefiles are those that satisfy these two wildcards: reproduce/software/config/*.conf (for building the necessary software when you run ./project configure) and reproduce/analysis/config/*.conf (for the high-level analysis, when you run ./project make). These Makefiles don't actually have any rules, they just have values for various free parameters throughout the configuration or analysis. Open a few of them to see for yourself. These Makefiles must only contain raw Make variables (project configurations). By "raw" we mean that the Make variables in these files must not depend on variables in any other configuration-Makefile. This is because we don't want to assume any order in reading them. It is also very important to not define any rule, or other Make construct, in these configuration-Makefiles.

Following this rule-of-thumb enables you to set these configure-Makefiles as a prerequisite to any target that depends on their variable values. Therefore, if you change any of their values, all targets that depend on those values will be re-built. This is very convenient as your project scales up and gets more complex.

The workhorse-Makefiles are those satisfying this wildcard reproduce/software/make/*.mk and reproduce/analysis/make/*.mk. They contain the details of the processing steps (Makefiles containing rules). Therefore, in this phase order is important, because the prerequisites of most rules will be the targets of other rules that will be defined prior to them (not a fixed name like paper.pdf). The lower-level rules must be imported into Make before the higher-level ones.

All processing steps are assumed to ultimately (usually after many rules) end up in some number, image, figure, or table that will be included in the paper. The writing of these results into the final report/paper is managed through separate LaTeX files that only contain macros (a name given to a number/string to be used in the LaTeX source, which will be replaced when compiling it to the final PDF). So the last target in a workhorse-Makefile is a .tex file (with the same base-name as the Makefile, but in $(BDIR)/tex/macros). As a result, if the targets in a workhorse-Makefile aren't directly a prerequisite of other workhorse-Makefile targets, they can be a prerequisite of that intermediate LaTeX macro file and thus be called when necessary. Otherwise, they will be ignored by Make. Maneage also has a mode to share the build directory between several users of a Unix group (when working on large computer clusters). In this scenario, each user can have their own cloned project source, but share the large built files between each other. To do this, it is necessary for all built files to give full permission to group members while not allowing any other users access to the contents. Therefore the ./project configure and ./project make steps must be called with special conditions which are managed in the --group option. Let's see how this design is implemented. Please open and inspect top-make.mk it as we go along here. The first step (un-commented line) is to import the local configuration (your answers to the questions of ./project configure). They are defined in the configuration-Makefile reproduce/software/config/LOCAL.conf which was also built by ./project configure (based on the LOCAL.conf.in template of the same directory). The next non-commented set of the top Makefile defines the ultimate target of the whole project (paper.pdf). But to avoid mistakes, a sanity check is necessary to see if Make is being run with the same group settings as the configure script (for example when the project is configured for group access using the ./for-group script, but Make isn't). Therefore we use a Make conditional to define the all target based on the group permissions. Having defined the top/ultimate target, our next step is to include all the other necessary Makefiles. However, order matters in the importing of workhorse-Makefiles and each must also have a TeX macro file with the same base name (without a suffix). Therefore, the next step in the top-level Makefile is to define the makesrc variable to keep the base names (without a .mk suffix) of the workhorse-Makefiles that must be imported, in the proper order. Finally, we import all the necessary remaining Makefiles: 1) All the analysis configuration-Makefiles with a wildcard. 2) The software configuration-Makefile that contains their version (just in case its necessary). 3) All workhorse-Makefiles in the proper order using a Make foreach loop. In short, to keep things modular, readable and manageable, follow these recommendations: 1) Set clear-to-understand names for the configuration-Makefiles, and workhorse-Makefiles, 2) Only import other Makefiles from top Makefile. These will let you know/remember generally which step you are taking before or after another. Projects will scale up very fast. Thus if you don't start and continue with a clean and robust convention like this, in the end it will become very dirty and hard to manage/understand (even for yourself). As a general rule of thumb, break your rules into as many logically-similar but independent steps as possible. The reproduce/analysis/make/paper.mk Makefile must be the final Makefile that is included. This workhorse Makefile ends with the rule to build paper.pdf (final target of the whole project). If you look in it, you will notice that this Makefile starts with a rule to create $(mtexdir)/project.tex (mtexdir is just a shorthand name for $(BDIR)/tex/macros mentioned before). As you see, the only dependency of $(mtexdir)/project.tex is $(mtexdir)/verify.tex (which is the last analysis step: it verifies all the generated results). Therefore, $(mtexdir)/project.tex is the connection between the processing/analysis steps of the project, and the steps to build the final PDF.

During the research, it often happens that you want to test a step that is not a prerequisite of any higher-level operation. In such cases, you can (temporarily) define that processing as a rule in the most relevant workhorse-Makefile and set its target as a prerequisite of its TeX macro. If your test gives a promising result and you want to include it in your research, set it as prerequisites to other rules and remove it from the list of prerequisites for TeX macro file. In fact, this is how a project is designed to grow in this framework.

## File modification dates (meta data)

While Git does an excellent job at keeping a history of the contents of files, it makes no effort in keeping the file meta data, and in particular the dates of files. Therefore when you checkout to a different branch, files that are re-written by Git will have a newer date than the other project files. However, file dates are important in the current design of Maneage: Make checks the dates of the prerequisite files and target files to see if the target should be re-built.

To fix this problem, for Maneage we use a forked version of Metastore. Metastore use a binary database file (which is called .file-metadata) to keep the modification dates of all the files under version control. This file is also under version control, but is hidden (because it shouldn't be modified by hand). During the project's configuration, Maneage installs to Git hooks to run Metastore 1) before making a commit to update its database with the file dates in a branch, and 2) after doing a checkout, to reset the file-dates after the checkout is complete and re-set the file dates back to what they were.

In practice, Metastore should work almost fully invisibly within your project. The only place you might notice its presence is that you'll see .file-metadata in the list of modified/staged files (commonly after merging your branches). Since its a binary file, Git also won't show you the changed contents. In a merge, you can simply accept any changes with git add -u. But if Git is telling you that it has changed without a merge (for example if you started a commit, but canceled it in the middle), you can just do git checkout .file-metadata and set it back to its original state.

## Summary

Based on the explanation above, some major design points you should have in mind are listed below.

• Define new reproduce/analysis/make/XXXXXX.mk workhorse-Makefile(s) with good and human-friendly name(s) replacing XXXXXX.

• Add XXXXXX, as a new line, to the values in makesrc of the top-level Makefile.

• Do not use any constant numbers (or important names like filter names) in the workhorse-Makefiles or paper's LaTeX source. Define such constants as logically-grouped, separate configuration-Makefiles in reproduce/analysis/config/XXXXX.conf. Then set this configuration-Makefiles file as a prerequisite to any rule that uses the variable defined in it.

• Through any number of intermediate prerequisites, all processing steps should end in (be a prerequisite of) $(mtexdir)/verify.tex (defined in reproduce/analysis/make/verify.mk). $(mtexdir)/verify.tex is the sole dependency of $(mtexdir)/project.tex, which is the bridge between the processing steps and PDF-building steps of the project. # Customization checklist Take the following steps to fully customize Maneage for your research project. After finishing the list, be sure to run ./project configure and project make to see if everything works correctly. If you notice anything missing or any in-correct part (probably a change that has not been explained here), please let us know to correct it. As described above, the concept of reproducibility (during a project) heavily relies on version control. Currently Maneage uses Git as its main version control system. If you are not already familiar with Git, please read the first three chapters of the ProGit book which provides a wonderful practical understanding of the basics. You can read later chapters as you get more advanced in later stages of your work. ## First custom commit 1. Get this repository and its history (if you don't already have it): Arguably the easiest way to start is to clone Maneage and prepare for your customizations as shown below. After the cloning first you rename the default origin remote server to specify that this is Maneage's remote server. This will allow you to use the conventional origin name for your own project as shown in the next steps. Second, you will create and go into the conventional master branch to start committing in your project later. $ git clone https://git.maneage.org/project.git    # Clone/copy the project and its history.
$mv project my-project # Change the name to your project's name.$ cd my-project                                    # Go into the cloned directory.
$git remote rename origin origin-maneage # Rename current/only remote to "origin-maneage".$ git checkout -b master                           # Create and enter your own "master" branch.
$pwd # Just to confirm where you are.  2. Prepare to build project: The ./project configure command of the next step will build the different software packages within the "build" directory (that you will specify). Nothing else on your system will be touched. However, since it takes long, it is useful to see what it is being built at every instant (its almost impossible to tell from the torrent of commands that are produced!). So open another terminal on your desktop and navigate to the same project directory that you cloned (output of last command above). Then run the following command. Once every second, this command will just print the date (possibly followed by a non-existent directory notice). But as soon as the next step starts building software, you'll see the names of software get printed as they are being built. Once any software is installed in the project build directory it will be removed. Again, don't worry, nothing will be installed outside the build directory. # On another terminal (go to top project source directory, last command above)$ ./project --check-config

3. Test Maneage: Before making any changes, it is important to test it and see if everything works properly with the commands below. If there is any problem in the ./project configure or ./project make steps, please contact us to fix the problem before continuing. Since the building of dependencies in configuration can take long, you can take the next few steps (editing the files) while its working (they don't affect the configuration). After ./project make is finished, open paper.pdf. If it looks fine, you are ready to start customizing the Maneage for your project. But before that, clean all the extra Maneage outputs with make clean as shown below.

$./project configure # Build the project's software environment (can take an hour or so).$ ./project make                # Do the processing and build paper (just a simple demo).

# Open 'paper.pdf' and see if everything is ok.

4. Setup the remote: You can use any hosting facility that supports Git to keep an online copy of your project's version controlled history. We recommend GitLab because it is more ethical (although not perfect), and later you can also host GitLab on your own server. Anyway, create an account in your favorite hosting facility (if you don't already have one), and define a new project there. Please make sure the newly created project is empty (some services ask to include a README in a new project which is bad in this scenario, and will not allow you to push to it). It will give you a URL (usually starting with git@ and ending in .git), put this URL in place of XXXXXXXXXX in the first command below. With the second command, "push" your master branch to your origin remote, and (with the --set-upstream option) set them to track/follow each other. However, the maneage branch is currently tracking/following your origin-maneage remote (automatically set when you cloned Maneage). So when pushing the maneage branch to your origin remote, you shouldn't use --set-upstream. With the last command, you can actually check this (which local and remote branches are tracking each other).

git remote add origin XXXXXXXXXX        # Newly created repo is now called 'origin'.
git push --set-upstream origin master   # Push 'master' branch to 'origin' (with tracking).
git push origin maneage                 # Push 'maneage' branch to 'origin' (no tracking).

5. Title, short description and author: You can start adding your name (with your possible coauthors) and tentative abstract in paper.tex. You should see the relevant place in the preamble (prior to \begin{document}. Just note that some core project metadata like the project tile are actually set in reproduce/analysis/config/metadata.conf. So set your project title in there. After you are done, run the ./project make command again to see your changes in the final PDF and make sure that your changes don't cause a crash in LaTeX. Of course, if you use a different LaTeX package/style for managing the title and authors (in particular a specific journal's style), please feel free to use it your own methods after finishing this checklist and doing your first commit.

6. Delete dummy parts: Maneage contains some parts that are only for the initial/test run, mainly as a demonstration of important steps, which you can use as a reference to use in your own project. But they not for any real analysis, so you should remove these parts as described below:

• paper.tex: 1) Delete the text of the abstract (from \includeabstract{ to \vspace{0.25cm}) and write your own (a single sentence can be enough now, you can complete it later). 2) Add some keywords under it in the keywords part. 3) Delete everything between %% Start of main body. and %% End of main body.. 4) Remove the notice in the "Acknowledgments" section (in \new{}) and Acknowledge your funding sources (this can also be done later). Just don't delete the existing acknowledgment statement: Maneage is possible thanks to funding from several grants. Since Maneage is being used in your work, it is necessary to acknowledge them in your work also.

• reproduce/analysis/make/top-make.mk: Delete the delete-me line in the makesrc definition. Just make sure there is no empty line between the download \ and verify \ lines (they should be directly under each other).

• reproduce/analysis/make/verify.mk: In the final recipe, under the commented line Verify TeX macros, remove the full line that contains delete-me, and set the value of s in the line for download to XXXXX (any temporary string, you'll fix it in the end of your project, when its complete).

• Delete all delete-me* files in the following directories:

$rm tex/src/delete-me*$ rm reproduce/analysis/make/delete-me*
$rm reproduce/analysis/config/delete-me*  • Disable verification of outputs by removing the yes from reproduce/analysis/config/verify-outputs.conf. Later, when you are ready to submit your paper, or publish the dataset, activate verification and make the proper corrections in this file (described under the "Other basic customizations" section below). This is a critical step and only takes a few minutes when your project is finished. So DON'T FORGET to activate it in the end. • Re-make the project (after a cleaning) to see if you haven't introduced any errors. $ ./project make clean
$./project make  7. Ignore changes in some Maneage files: One of the main advantages of Maneage is that you can later update your infra-structure by merging your master branch with the maneage branch. This is good for many low-level features that you will likely never modify yourself. But it is not desired for some files like paper.tex (you don't want changes in Maneage's default paper.tex to cause conflicts with all the text you have already written for your project). You need to tell Git to ignore changes in such files from the maneage branch during the merge, and just keep your own branch's version. The commands below show how you can avert such future conflicts and frustrations with some known files. Note that only the first echo command has a > (to write over the file), the rest are >> (to append to it). If you want to avoid any other set of files to be imported from Maneage into your project's branch, you can follow a similar strategy (it should happen rarely, if at all!). Generally be very careful about adding files to .gitattributes because it affects the whole file and if a wrong file is ignored, Maneage may break after a merge (some inter-dependent files may not get updated together). We recommend only doing it when you encounter the same conflict in more than one merge, and are sure that it won't affect other files. In such cases please let us know so we can improve the design of Maneage and modularize those components to be easily added here. $ echo "paper.tex merge=ours" > .gitattributes
$echo "tex/src/*.tex merge=ours" >> .gitattributes$ echo "reproduce/analysis/config/*.conf merge=ours" >> .gitattributes
$echo "reproduce/software/config/TARGETS.conf merge=ours" >> .gitattributes$ echo "reproduce/software/config/texlive-packages.conf merge=ours" >> .gitattributes
$git add .gitattributes  8. Copyright and License notice: It is necessary that all the "copyright-able" files in your project (those larger than 10 lines) have a copyright and license notice. Please take a moment to look at several existing files to see a few examples. The copyright notice is usually close to the start of the file, it is the line starting with Copyright (C) and containing a year and the author's name (like the examples below). The License notice is a short description of the copyright license, usually one or two paragraphs with a URL to the full license. Don't forget to add these two notices to any new file you add in your project (you can just copy-and-paste). When you modify an existing Maneage file (which already has the notices), just add a copyright notice in your name under the existing one(s), like the line with capital letters below. To start with, add this line with your name and email address to paper.tex, tex/src/preamble-header.tex, reproduce/analysis/make/top-make.mk, and generally, all the files you modified in the previous step. Copyright (C) 2018-2021 Existing Name <existing@email.address> Copyright (C) 2021 YOUR NAME <YOUR@EMAIL.ADDRESS>  9. Configure Git for fist time: If this is the first time you are running Git on this system, then you have to configure it with some basic information in order to have essential information in the commit messages (ignore this step if you have already done it). Git will include your name and e-mail address information in each commit. You can also specify your favorite text editor for making the commit (emacs, vim, nano, and etc.). $ git config --global user.name "YourName YourSurname"
$git config --global user.email your-email@example.com$ git config --global core.editor nano

10. Your first commit: You have already made some small and basic changes in the steps above and you are in your project's master branch. So, you can officially make your first commit in your project's history and push it. But before that, you need to make sure that there are no problems in the project. This is a good habit to always re-build the system before a commit to be sure it works as expected.

$git status # See which files you have changed.$ git diff                   # Check the lines you have added/changed.
$./project make # Make sure everything builds successfully.$ git add -u                 # Put all tracked changes in staging area.
$git status # Make sure everything is fine.$ git diff --cached          # Confirm all the changes that will be committed.
$git commit # Your first commit: put a good description!$ git push                   # Push your commit to your remote.

11. Read the publication checklist: The publication checklist below is very similar to this one, but for the final phase of your project. For now, you don't have to do any of its steps, but reading it will give you good insight into the later stages of your project. If you already know how you want to publish your project, you can implement many of those steps from the start and during the actual project (in particular how to organize your data files that go into the plots). Making it much easier to complete that checklist when you are ready for submission.

12. Start your exciting research: You are now ready to add flesh and blood to this raw skeleton by further modifying and adding your exciting research steps. You can use the "published works" section in the introduction (above) as some fully working models to learn from. Also, don't hesitate to contact us if you have any questions.

## Other basic customizations

• High-level software: Maneage installs all the software that your project needs. You can specify which software your project needs in reproduce/software/config/TARGETS.conf. The necessary software are classified into two classes: 1) programs or libraries (usually written in C/C++) which are run directly by the operating system. 2) Python modules/libraries that are run within Python. By default TARGETS.conf only has GNU Astronomy Utilities (Gnuastro) as one scientific program and Astropy as one scientific Python module. Both have many dependencies which will be installed into your project during the configuration step. To see a list of software that are currently ready to be built in Maneage, see reproduce/software/config/versions.conf (which has their versions also), the comments in TARGETS.conf describe how to use the software name from versions.conf. Currently the raw pipeline just uses Gnuastro to make the demonstration plots. Therefore if you don't need Gnuastro, go through the analysis steps in reproduce/analysis and remove all its use cases (clearly marked).

• Input dataset: The input datasets are managed through the reproduce/analysis/config/INPUTS.conf file. It is best to gather all the information regarding all the input datasets into this one central file. To ensure that the proper dataset is being downloaded and used by the project, it is also recommended get an MD5 checksum of the file and include that in INPUTS.conf so the project can check it automatically. The preparation/downloading of the input datasets is done in reproduce/analysis/make/download.mk. Have a look there to see how these values are to be used. This information about the input datasets is also used in the initial configure script (to inform the users), so also modify that file. You can find all occurrences of the demo dataset with the command below and replace it with your input's dataset.

$grep -ir wfpc2 ./*  • README.md: Correct all the XXXXX place holders (name of your project, your own name, address of your project's online/remote repository, link to download dependencies and etc). Generally, read over the text and update it where necessary to fit your project. Don't forget that this is the first file that is displayed on your online repository and also your colleagues will first be drawn to read this file. Therefore, make it as easy as possible for them to start with. Also check and update this file one last time when you are ready to publish your project's paper/source. • Verify outputs: During the initial customization checklist, you disabled verification. This is natural because during the project you need to make changes all the time and its a waste of time to enable verification every time. But at significant moments of the project (for example before submission to a journal, or publication) it is necessary. When you activate verification, before building the paper, all the specified datasets will be compared with their respective checksum and if any file's checksum is different from the one recorded in the project, it will stop and print the problematic file and its expected and calculated checksums. First set the value of verify-outputs variable in reproduce/analysis/config/verify-outputs.conf to yes. Then go to reproduce/analysis/make/verify.mk. The verification of all the files is only done in one recipe. First the files that go into the plots/figures are checked, then the LaTeX macros. Validation of the former (inputs to plots/figures) should be done manually. If its the first time you are doing this, you can see two examples of the dummy steps (with delete-me, you can use them if you like). These two examples should be removed before you can run the project. For the latter, you just have to update the checksums. The important thing to consider is that a simple checksum can be problematic because some file generators print their run-time date in the file (for example as commented lines in a text table). When checking text files, this Makefile already has this function: verify-txt-no-comments-leading-space. As the name suggests, it will remove comment lines and empty lines before calculating the MD5 checksum. For FITS formats (common in astronomy, fortunately there is a DATASUM definition which will return the checksum independent of the headers. You can use the provided function(s), or define one for your special formats. • Feedback: As you use Maneage you will notice many things that if implemented from the start would have been very useful for your work. This can be in the actual scripting and architecture of Maneage, or useful implementation and usage tips, like those below. In any case, please share your thoughts and suggestions with us, so we can add them here for everyone's benefit. • Re-preparation: Automatic preparation is only run in the first run of the project on a system, to re-do the preparation you have to use the option below. Here is the reason for this: when its necessary, the preparation process can be slow and will unnecessarily slow down the whole project while the project is under development (focus is on the analysis that is done after preparation). Because of this, preparation will be done automatically for the first time that the project is run (when .build/software/preparation-done.mk doesn't exist). After the preparation process completes once, future runs of ./project make will not do the preparation process anymore (will not call top-prepare.mk). They will only call top-make.mk for the analysis. To manually invoke the preparation process after the first attempt, the ./project make script should be run with the --prepare-redo option, or you can delete the special file above. $ ./project make --prepare-redo

• Pre-publication: add notice on reproducibility**: Add a notice somewhere prominent in the first page within your paper, informing the reader that your research is fully reproducible. For example in the end of the abstract, or under the keywords with a title like "reproducible paper". This will encourage them to publish their own works in this manner also and also will help spread the word.

# Publication checklist

Once your project is complete and you are ready to submit/publish the project, we recommend the following steps to ensure the maximum FAIRness of all your hard work (Findability, Accessibility, Interoperability, and Reusability). This list may seem long, and may take a day or so to complete, but please consider the fact that you have spent months/years on your project, so it is a very small step in your over-all project! Most of it is about organizing things that you can do during your project. So its good to have a look at these from the start of your project.

As you will notice, when you complete this checklist, your projects source will be present in multiple places: Zenodo, SoftwareHeritage, arXiv, your own Git repositories. This is a major advantage of Maneaged(!) projects: because their source is very small (a few hundred kilobytes), there is effectively no cost in keeping multiple redundancies on different servers, just in case one (or more) of them are discontinued in the (near/far) future.

• Reserve a DOI for your datasets: There are multiple data servers that give this functionality, one of the most well known and (currently!) well-funded is Zenodo so we'll focus on it here. Of course, you can use any other service that provides a similar functionality. Once you complete these steps, you can start using/citing your dataset's DOI in the source of your project to finalize the rest of the points. With Zenodo, you can even use the given identifier for things like downloading.

• Reserve DOI: Under the "Basic information" --> "Digital Object Identifier", click on the "Reserve DOI" button.

• Fill basic info: You need to at least fill in the "required fields" (marked with a red star). You will always be able to change any metadata (even after you "Publish"), so don't worry too much about values in the fields, at this phase, its just important that they are not empty.

• Save your project but do not yet publish: Press the "Save" button (at the top or bottom of the page). Do not yet press "Publish" though, since that would make the project public, and freeze the DOI with any possible file you may have uploaded already. We will get to the publication phase in the next steps.

• Record the metadata: Maneage comes with a file to store all the project's metadata: reproduce/analysis/config/metadata.conf. Open this file and store all the information that you currently have: for example the Zenodo DOI, project's Git repository, Copyright owner and license of the data after it becomes public. Keep the empty fields in mind and after obtaining them, don't forget to fill them up.

• Request archival on SoftwareHeritage: Software Heritage is an online project to archive source code and their development histories. It provides wonderful features for archiving source code (not data!) and also for citing special parts of a project's source in any point of its history. So it blends elegantly with the purpose of Maneage. Once you make your project's Git repository publicly accessible (no login required to clone it), you can request that SoftwareHeritage archives it. Its good if you do this as soon as you make your Git repository public. When you are ready, just register your repository's address (the same one you give to git clone) to in SoftwareHeritage's save form.

• Run a spell-check on paper.tex: we all forget ;-)!

• Zenodo/SoftwareHeritage links in paper: put links to the Zenodo-DOI (and SoftwareHeritage source when you make it public) in your paper. Somewhere close to the start, maybe under the keywords/abstract, highlighting that they are supplements for reproducibility. These help readers easily access these resources for supplementary material directly from your PDF paper (sources on SoftwareHeritage and data/software on Zenodo). These links are more trusted/reliable in terms of longevity than Git repositories or private webpages.

• Identify and properly format output data: If you have a plot, figure or table in your paper, you need to verify that data later and publish that data with the paper (see the steps below for both). But before going to those steps, its good if you polish your datasets with the recommendations below:

• Keep published data in a special place: it helps if you keep the to-be-published data files in a special sub-directory under your build directory. In this way, irrespective of which subMakefile builds a published dataset, they won't be lost/scatterred in the middle of all the project's intermediate-built files.

• In plain-text: If the data are in tabular form (for example the X and Y values in your plots), store them as a simple plain-text file (for example with columns separated by white-space characters) or in the more formal Comma-separated values or CSV, format). Generally, its best to set the suffixes to .txt (because most browsers/OSs will automatically know they are plain-text and open them without needing any other software). If you have other types of data (for example images, or very large tables with millions of rows/columns that can be inconvenient in plain-text), feel free to use custom binary formats, but later, in the description of your project on the server, add a note, explaining what software they should use to open them.

• Descriptive names: In some papers there are many files and having cryptic names will only confuse your readers (actually, yourself in two years!). So set the names of the files to be as descriptive as possible, so simply by reading the name of the file, someone who has read the paper will understand what figure it corresponds to. In particular, don't set names like figure-3.txt! In a few months you will forget the order of the figures! Even worse, after the referee report, you may need to re-arrange some figures and you will be forced to rename everything related to each figure (which is very frustrating and prone to errors).

• Good metadata: Raw data are not too useful merely as a series of raw numbers! So don't forget to have good metadata in every file. If its a plain-text file, usually lines starting with a # are ignored. So in the command that generates each dataset, add some extra information (the more the better!) about the dataset as lines starting with #. Based on reproduce/analysis/config/metadata.conf, in initialize.mk, Maneage will produce a default set of basic information for plain-text data and will put it in the $(print-general-metadata) variable. It is thus recommended to print this variable into your plain-text file before printing the actual data (so it shows on top of the file). For a real-world example, see its usage in reproduce/analysis/make/delete-me.mk (in the maneage branch). If you are publishing your data in binary formats, please add all the metadata you see in $(print-general-metadata) into each dataset file (for example keywords in the FITS format). If there are many files, its easy to define a tiny shell-script to do the job on each dataset.

• Link to figure datasets in caption: all the datasets that go into the plots should be uploaded directly to Zenodo so they can be viewed/downloaded with a simple link in the caption. For example see the last sentence of the caption of Figure 1 in arXiv:2006.03018v1, it points to the data that was used to create that figure's top plot. As you see, this will allow your paper's readers (again, most probably your future-self!) to directly access the numbers of each visualization (plot/figure) with a simple click in a trusted server. This also shows the major advantage of having your data as simple plain-text where possible, as described above. To help you keep all your to-be-visualized datasets in a single place, Maneage has the two tex-publish-dir and data-publish-dir directories that are defined in reproduce/analysis/make/initialize.mk, see the comments above their definition for more.

• Verification step: It is very important to automatically verify the outptus of your project. Recall from the customization checklist (above) that you can activate verification by setting the verify-outputs variable to yes in reproduce/analysis/config/verify-outputs.conf. So please activate it and look into the reproduce/analysis/make/verify.mk to add the necessary steps to automatically verify your outputs. Tip: you don't have to generate the checksums manually, just give a wrong value (for example XXXX) so Maneage crashes! In the error message it will then print the actual and expected checksums and you can take the value from there. Outputs that must be verified can be listed as:

• subMakefile LaTeX macro files: these LaTeX macros put numbers into the text. You don't want your readers (actually: yourself in two years!) to have to painfully find and check, by eye, all those tiny numbers buried deep in the ocean of words!

• Final data files (for tables, figures, or plots, or as data release). These are the same files described above. If you have followed the guidelines above and stored them as plain-text with comments on top, you can use the provided function verify-txt-no-comments-leading-space which takes the filename and checksum as arguments to avoid the commented lines (which may change) and only verify the data. If your data are in other formats, be sure to verify them without metadata that may change (like date and etc).

• Fill README.md: The README.md is the first place your readers are going to look into. It already has a default text with place-holders in the form of XXXXXX. Please go through its first few paragraphs and replace the place-holders with the relevant information/links or feel free to add/remove anything else. The rest is just basic information that is useful for any Maneage'd project. Just don't forget to tell your readers in README.md that they can learn about this system in the README-hacking.md file (ideally close to the top).

• Confirm if your project builds from scratch: Before publishing anything, you should see if your project can indeed reproduce itself! You may be mistakenly using temporarily created files that aren't built when teh project is built from scratch (this happens a lot and is very dangerous for the integrity of your project!). So, go to a temporary directory, clone your project from its repository and try configuring and building it from scratch in a new-temporary build-directory. It is important to ignore the original directory you developed your project on (source and build): you may have files there that you forgot to import into Git or depended on in the build (it happens!). Ideally, it would be good to try it on a different computer.

• Confirm if ./project make dist works: The special target dist tells the project to build a tarball that is ready to compile the LaTeX PDF without having to do the analysis and build software. This is very useful for servers like arXiv, or some journals. This tarball is also one of the deliverables you want to publish on Zenodo. Once the tarball is created, copy it to a temporary directory outside of Maneage, unpack it and run make (completely ignoring Maneage's ./project script). If you plan to submit your paper to arXiv, the best test is to actually start a test submission on arXiv to upload the tarball there to see if it can build your PDF. Once it works, you can delete that temporary submission for now. Afterwards, try configuring and building it with the tarball by running its ./project (from scratch and without the Git history!). If there is a problem in any of these tests, you can modify what goes into this tarball in reproduce/analysis/make/initialize.mk: go through the steps and add the necessary components until the checks pass.

• Upload all deliverables to Zenodo: With the datasets ready, you can now upload the following deliverables to Zenodo. Except for the data files, put the Git hash of your Maneaged project at the moment of publication in the filename of other uploaded files. The output files shouldn't have a hash in their names because their URL (that goes in the caption of the figures/tables) should be known prior to a commit, creating a cyclic dependency! Ideally the hash should be placed just before the final suffix, for example paper-XXXXXXX.pdf (where XXXXXXX is the Git hash). This will clearly identify the point in history that your file was created.

• paper-XXXXXXX.pdf: you shouldn't just download data to the data server, also upload your paper's PDF so its there with the other raw formats. It will greatly help yourself and others. Most datacenters (like Zenodo) actually also have a PDF viewer that will load automatically before the list of data files. For example see zenodo.3408481.

• project-XXXXXXX.tar.gz: Or the output of make dist as described above.

• project-git.bundle This is the full Git history of the project in one file (which you can actually clone from later!). Its necessary to publish this with your dataset too because Git repositories make no promise on longevity. The way to "bundle" a Git history is described below, in summary, its this command:

$git bundle create my-project-git.bundle --all  • software-XXXXXXX.tar.gz: This is effectively a copy of all the software source code tarballs in your project's .build/software/tarballs. It is necessary to upload these with your project to avoid relying on third party servers. In the future any one of those servers may go down and if so, your project won't be buildable. You can generate this tarball easily with make dist-software. • All the figure (and other) output datasets of the project. Don't rename these files, let them have the same descriptive name mentioned above. Also recall that a link to all these files is also in the caption of the respective figure. • Upload to arXiv: or to any other pre-print server (if you want to). Of course, you can also do this after the initial/final submission to your desired journal. But we'll just add the necessary points for arXiv submission here: • Necessary links in comments: put a link to your project's Git repository, Zenodo-DOI (this is not your paper's DOI, its the data/resources DOI), and/or SoftwareHeritage link in the comments. • Update metadata.conf: Once you have your final arXiv ID (formated as: 1234.56789) put it in reproduce/analysis/config/metadata.conf. • Submission to a journal: different journals accept submissions in different formats, some accept LaTeX, some only want a PDF, or etc. It would be good if you highlight in the cover-letter that your work is reproducible and provide the Zenodo and Software Heritage links (if they are public). If not, you can mention that everything is ready for such a submission after acceptance. • Future versions: Both Zenodo and arXiv allow uploading new versions after your first publication. So it is recommended to put more recent versions of your published projects later (for example after applying the changes suggested by the referee). In Zenodo (unlike arXiv), you only need to publish a new version if the uploaded files have changed. You can always update the project's metadata with no effect on the DOI (so you don't need to upload a new version if you just want to update the metadata). • After acceptance (before publication): Congratulations on the acceptance! The main science content of your paper can't be changed any more, but the paper will now go to the publication editor (for language and style). Your approval of the final proof is necessary before the paper is finally published. Use this period to finalize the final metadata of your project: the journal's DOI. Some journals associate your paper's DOI during this process. So before approving the final proof do these steps: • Add the Journal DOI in reproduce/analysis/config/metadata.conf, and re-build your final data products, so this important metadata is added. • Once you get the final proof, and if everything is OK for you, implement all the good language corrections/edits they have made inside your own copy here and commit it into your project. This will be the final commit of your project before publication. • Submit your final project as a new version to Zenodo (and arXiv). The Zenodo one is most important because your plots will link to it and you want the commit hash in the data files that readers will get from Zenodo to be the same hash as the paper. • Tell the journal's publication editor to correct the hash and Zenodo ID in your final proof confirmation (so the links point to the correct place). Recall that on every new version upload in Zenodo, you get a new DOI (or Zenodo ID). # Tips for designing your project The following is a list of design points, tips, or recommendations that have been learned after some experience with this type of project management. Please don't hesitate to share any experience you gain after using it with us. In this way, we can add it here (with full giving credit) for the benefit of others. • Modularity: Modularity is the key to easy and clean growth of a project. So it is always best to break up a job into as many sub-components as reasonable. Here are some tips to stay modular. • Short recipes: if you see the recipe of a rule becoming more than a handful of lines which involve significant processing, it is probably a good sign that you should break up the rule into its main components. Try to only have one major processing step per rule. • Context-based (many) Makefiles: For maximum modularity, this design allows easy inclusion of many Makefiles: in reproduce/analysis/make/*.mk for analysis steps, and reproduce/software/make/*.mk for building software. So keep the rules for closely related parts of the processing in separate Makefiles. • Descriptive names: Be very clear and descriptive with the naming of the files and the variables because a few months after the processing, it will be very hard to remember what each one was for. Also this helps others (your collaborators or other people reading the project source after it is published) to more easily understand your work and find their way around. • Naming convention: As the project grows, following a single standard or convention in naming the files is very useful. Try best to use multiple word filenames for anything that is non-trivial (separating the words with a -). For example if you have a Makefile for creating a catalog and another two for processing it under models A and B, you can name them like this: catalog-create.mk, catalog-model-a.mk and catalog-model-b.mk. In this way, when listing the contents of reproduce/analysis/make to see all the Makefiles, those related to the catalog will all be close to each other and thus easily found. This also helps in auto-completions by the shell or text editors like Emacs. • Source directories: If you need to add files in other languages for example in shell, Python, AWK or C, keep the files in the same language in a separate directory under reproduce/analysis, with the appropriate name. • Configuration files: If your research uses special programs as part of the processing, put all their configuration files in a devoted directory (with the program's name) within reproduce/software/config. It is much cleaner and readable (thus less buggy) to avoid mixing the configuration files, even if there is no technical necessity. • Contents: It is good practice to follow the following recommendations on the contents of your files, whether they are source code for a program, Makefiles, scripts or configuration files (copyrights aren't necessary for the latter). • Copyright: Always start a file containing programming constructs with a copyright statement like the ones that Maneage starts with (for example in the top level Makefile). • Comments: Comments are vital for readability (by yourself in two months, or others). Describe everything you can about why you are doing something, how you are doing it, and what you expect the result to be. Write the comments as if it was what you would say to describe the variable, recipe or rule to a friend sitting beside you. When writing the project it is very tempting to just steam ahead with commands and codes, but be patient and write comments before the rules or recipes. This will also allow you to think more about what you should be doing. Also, in several months when you come back to the code, you will appreciate the effort of writing them. Just don't forget to also read and update the comment first if you later want to make changes to the code (variable, recipe or rule). As a general rule of thumb: first the comments, then the code. • File title: In general, it is good practice to start all files with a single line description of what that particular file does. If further information about the totality of the file is necessary, add it after a blank line. This will help a fast inspection where you don't care about the details, but just want to remember/see what that file is (generally) for. This information must of course be commented (its for a human), but this is kept separate from the general recommendation on comments, because this is a comment for the whole file, not each step within it. • Make programming: Here are some experiences that we have come to learn over the years in using Make and are useful/handy in research contexts. • Environment of each recipe: If you need to define a special environment (or aliases, or scripts to run) for all the recipes in your Makefiles, you can use a Bash startup file reproduce/software/shell/bashrc.sh. This file is loaded before every Make recipe is run, just like the .bashrc in your home directory is loaded every time you start a new interactive, non-login terminal. See the comments in that file for more. • Automatic variables: These are wonderful and very useful Make constructs that greatly shrink the text, while helping in read-ability, robustness (less bugs in typos for example) and generalization. For example even when a rule only has one target or one prerequisite, always use $@ instead of the target's name, $< instead of the first prerequisite, $^ instead of the full list of prerequisites and etc. You can see the full list of automatic variables here. If you use GNU Make, you can also see this page on your command-line:

$info make "automatic variables"  • Debug: Since Make doesn't follow the common top-down paradigm, it can be a little hard to get accustomed to why you get an error or un-expected behavior. In such cases, run Make with the -d option. With this option, Make prints a full list of exactly which prerequisites are being checked for which targets. Looking (patiently) through this output and searching for the faulty file/step will clearly show you any mistake you might have made in defining the targets or prerequisites. • Large files: If you are dealing with very large files (thus having multiple copies of them for intermediate steps is not possible), one solution is the following strategy (Also see the next item on "Fast access to temporary files"). Set a small plain text file as the actual target and delete the large file when it is no longer needed by the project (in the last rule that needs it). Below is a simple demonstration of doing this. In it, we use Gnuastro's Arithmetic program to add all pixels of the input image with 2 and create large1.fits. We then subtract 2 from large1.fits to create large2.fits and delete large1.fits in the same rule (when its no longer needed). We can later do the same with large2.fits when it is no longer needed and so on. large1.fits.txt: input.fits astarithmetic$< 2 + --output=$(subst .txt,,$@)
echo "done" > $@ large2.fits.txt: large1.fits.txt astarithmetic$(subst .txt,,$<) 2 - --output=$(subst .txt,,$@) rm$(subst .txt,,$<) echo "done" >$@


A more advanced Make programmer will use Make's call function to define a wrapper in reproduce/analysis/make/initialize.mk. This wrapper will replace $(subst .txt,,XXXXX). Therefore, it will be possible to greatly simplify this repetitive statement and make the code even more readable throughout the whole project. • Fast access to temporary files: Most Unix-like operating systems will give you a special shared-memory device (directory): on systems using the GNU C Library (all GNU/Linux system), it is /dev/shm. The contents of this directory are actually in your RAM, not in your persistence storage like the HDD or SSD. Reading and writing from/to the RAM is much faster than persistent storage, so if you have enough RAM available, it can be very beneficial for large temporary files to be put there. You can use the mktemp program to give the temporary files a randomly-set name, and use text files as targets to keep that name (as described in the item above under "Large files") for later deletion. For example, see the minimal working example Makefile below (which you can actually put in a Makefile and run if you have an input.fits in the same directory, and Gnuastro is installed). .ONESHELL: .SHELLFLAGS = -ec all: mean-std.txt shm-maneage := /dev/shm/$(shell whoami)-maneage-XXXXXXXXXX
large1.txt: input.fits
out=$$(mktemp (shm-maneage)) astarithmetic < 2 + --output=$$out.fits
echo "$$out" > @ large2.txt: large1.txt input=$$(cat $<) out=$$(mktemp (shm-maneage)) astarithmetic$$input.fits 2 - --output=$$out.fits rm$$input.fits $$input echo "$$out" >$@
mean-std.txt: large2.txt
input=$$(cat <) aststatistics$$input.fits --mean --std > $@ rm $$input.fits$$input  The important point here is that the temporary name template (shm-maneage) has no suffix. So you can add the suffix corresponding to your desired format afterwards (for example $$out.fits, or $$out.txt). But more importantly, when mktemp sets the random name, it also checks if no file exists with that name and creates a file with that exact name at that moment. So at the end of each recipe above, you'll have two files in your /dev/shm, one empty file with no suffix one with a suffix. The role of the file without a suffix is just to ensure that the randomly set name will not be used by other calls to mktemp (when running in parallel) and it should be deleted with the file containing a suffix. This is the reason behind the rm $$input.fits$$input command above: to make sure that first the file with a suffix is deleted, then the core random file (note that when working in parallel on powerful systems, in the time between deleting two files of a single rm command, many things can happen!). When using Maneage, you can put the definition of shm-maneage in reproduce/analysis/make/initialize.mk to be usable in all the different Makefiles of your analysis, and you won't need the three lines above it. Finally, BE RESPONSIBLE: after you are finished, be sure to clean up any possibly remaining files (due to crashes in the processing while you are working), otherwise your RAM may fill up very fast. You can do it easily with a command like this on your command-line: rm -f /dev/shm/$(whoami)-*.

• Software tarballs and raw inputs: It is critically important to document the raw inputs to your project (software tarballs and raw input data):

• Keep the source tarball of dependencies: After configuration finishes, the .build/software/tarballs directory will contain all the software tarballs that were necessary for your project. You can mirror the contents of this directory to keep a backup of all the software tarballs used in your project (possibly as another version controlled repository) that is also published with your project. Note that software web-pages are not written in stone and can suddenly go offline or not be accessible in some conditions. This backup is thus very important. If you intend to release your project in a place like Zenodo, you can upload/keep all the necessary tarballs (and data) there with your project. zenodo.1163746 is one example of how the data, Gnuastro (main software used) and all major Gnuastro's dependencies have been uploaded with the project's source. Just note that this is only possible for free software.

• Keep your input data: The input data is also critical to the project's reproducibility, so like the above for software, make sure you have a backup of them, or their persistent identifiers (PIDs).

• Version control: Version control is a critical component of Maneage. Here are some tips to help in effectively using it.

• Regular commits: It is important (and extremely useful) to have the history of your project under version control. So try to make commits regularly (after any meaningful change/step/result).

• Keep Maneage up-to-date: In time, Maneage is going to become more and more mature and robust (thanks to your feedback and the feedback of other users). Bugs will be fixed and new/improved features will be added. So every once and a while, you can run the commands below to pull new work that is done in Maneage. If the changes are useful for your work, you can merge them with your project to benefit from them. Just pay very close attention to resolving possible conflicts which might happen in the merge. In particular the "semantic conflicts" that don't show up in Git, but can potentially break your project, for example updates to software versions, or to internal Maneage structure. Hence read the commit messages of git log carefully to see what has changed. The best way to check is to first complete the steps below, then build your project from scratch (from ./project configure in a new build-directory).

# Go to the 'maneage' branch and import updates.
$git checkout maneage$ git pull                            # Get recent work in Maneage

# Read all the commit messages of the newly imported
# features/changes. In particular pay close attention to the ones
# starting with 'IMPORTANT': these may cause a crash in your
# project (changing something fundamental in Maneage).
#
# Replace the XXXXXXX..YYYYYYY with hashs mentioned close to start
# of the 'git pull' command outputs.
$git log XXXXXXX..YYYYYYY --reverse # Have a look at the commits in the 'maneage' branch in relation # with your project.$ git log --oneline --graph --decorate --all # General view of branches.

# Go to your 'master' branch and import all the updates into
# 'master', don't worry about the printed outputs (in particular
# the 'CONFLICT's), we'll clean them up in the next step.
$git checkout master$ git merge maneage

# Ignore conflicting Maneage files that you had previously deleted
# in the customization checklist (mostly demonstration files).
$git status # Just for a check$ git status --porcelain | awk '/^DU/{system("git rm "$NF)}'$ git status             # Just for a check

# If any files have conflicts, open a text editor and correct the
# conflict (placed in between '<<<<<<<', '=======' and '>>>>>>>'.
# Once all conflicts in a file are remoted, the file will be
# automatically removed from the "Unmerged paths", so run this
# command after correcting the conflicts of each file just to make
# sure things are clean.
git status

# TIP: If you want the changes in one file to be only from a
# special branch ('maneage' or 'master', completely ignoring
# changes in the other), use this command:
# $git checkout <BRANCH-NAME> -- <FILENAME> # When there are no more "Unmerged paths", you can commit the # merge. In the commit message, Explain any conflicts that you # fixed. git commit # Do a clean build of your project (to check for "Semanic # conflicts" (not detected as a conflict by Git, but may cause a # crash in your project). You can backup your build directory # before running the 'distclean' target. # # Any error in the build will be due to low-level changes in # Maneage, so look closely at the commit messages in the Maneage # branch and especially those where the title starts with # 'IMPORTANT'. ./project make distclean # will DELETE ALL your build-directory!! ./project configure -e ./project make # When everything is OK, before continuing with your project's # work, don't forget to push both your 'master' branch and your # updated 'maneage' branch to your remote server. git push git push origin maneage  • Adding Maneage to a fork of your project: As you and your colleagues continue your project, it will be necessary to have separate forks/clones of it. But when you clone your own project on a different system, or a colleague clones it to collaborate with you, the clone won't have the origin-maneage remote that you started the project with. As shown in the previous item above, you need this remote to be able to pull recent updates from Maneage. The steps below will setup the origin-maneage remote, and a local maneage branch to track it, on the new clone. $ git remote add origin-maneage https://git.maneage.org/project.git
$git fetch origin-maneage$ git checkout -b maneage --track origin-maneage/maneage

• Commit message: The commit message is a very important and useful aspect of version control. To make the commit message useful for others (or yourself, one year later), it is good to follow a consistent style. Maneage already has a consistent formatting (described below), which you can also follow in your project if you like. You can see many examples by running git log in the maneage branch. If you intend to push commits to Maneage, for the consistency of Maneage, it is necessary to follow these guidelines. 1) No line should be more than 75 characters (to enable easy reading of the message when you run git log on the standard 80-character terminal). 2) The first line is the title of the commit and should summarize it (so git log --oneline can be useful). The title should also not end with a point (., because its a short single sentence, so a point is not necessary and only wastes space). 3) After the title, leave an empty line and start the body of your message (possibly containing many paragraphs). 4) Describe the context of your commit (the problem it is trying to solve) as much as possible, then go onto how you solved it. One suggestion is to start the main body of your commit with "Until now ...", and continue describing the problem in the first paragraph(s). Afterwards, start the next paragraph with "With this commit ...".

• Project outputs: During your research, it is possible to checkout a specific commit and reproduce its results. However, the processing can be time consuming. Therefore, it is useful to also keep track of the final outputs of your project (at minimum, the paper's PDF) in important points of history. However, keeping a snapshot of these (most probably large volume) outputs in the main history of the project can unreasonably bloat it. It is thus recommended to make a separate Git repo to keep those files and keep your project's source as small as possible. For example if your project is called my-exciting-project, the name of the outputs repository can be my-exciting-project-output. This enables easy sharing of the output files with your co-authors (with necessary permissions) and not having to bloat your email archive with extra attachments also (you can just share the link to the online repo in your communications). After the research is published, you can also release the outputs repository, or you can just delete it if it is too large or un-necessary (it was just for convenience, and fully reproducible after all). For example Maneage's output is available for demonstration in a separate repository.

• Full Git history in one file: When you are publishing your project (for example to Zenodo for long term preservation), it is more convenient to have the whole project's Git history into one file to save with your datasets. After all, you can't be sure that your current Git server (for example GitLab, Github, or Bitbucket) will be active forever. While they are good for the immediate future, you can't rely on them for archival purposes. Fortunately keeping your whole history in one file is easy with Git using the following commands. To learn more about it, run git help bundle.

• "bundle" your project's history into one file (just don't forget to change my-project-git.bundle to a descriptive name of your project):

$git bundle create my-project-git.bundle --all  • You can easily upload my-project-git.bundle anywhere. Later, if you need to un-bundle it, you can use the following command. $ git clone my-project-git.bundle


# Future improvements

This is an evolving project and as time goes on, it will evolve and become more robust. Some of the most prominent issues we plan to implement in the future are listed below, please join us if you are interested.

## Package management

It is important to have control of the environment of the project. Maneage currently builds the higher-level programs (for example GNU Bash, GNU Make, GNU AWK and domain-specific software) it needs, then sets PATH so the analysis is done only with the project's built software. But currently the configuration of each program is in the Makefile rules that build it. This is not good because a change in the build configuration does not automatically cause a re-build. Also, each separate project on a system needs to have its own built tools (that can waste a lot of space).

A good solution is based on the Nix package manager: a separate file is present for each software, containing all the necessary info to build it (including its URL, its tarball MD5 hash, dependencies, configuration parameters, build steps and etc). Using this file, a script can automatically generate the Make rules to download, build and install program and its dependencies (along with the dependencies of those dependencies and etc).

All the software are installed in a "store". Each installed file (library or executable) is prefixed by a hash of this configuration (and the OS architecture) and the standard program name. For example (from the Nix webpage):

/nix/store/b6gvzjyb2pg0kjfwrjmg1vfhh54ad73z-firefox-33.1/


The important thing is that the "store" is not in the project's search path. After the complete installation of the software, symbolic links are made to populate each project's program and library search paths without a hash. This hash will be unique to that particular software and its particular configuration. So simply by searching for this hash in the installed directory, we can find the installed files of that software to generate the links.

This scenario has several advantages: 1) a change in a software's build configuration triggers a rebuild. 2) a single "store" can be used in many projects, thus saving space and configuration time for new projects (that commonly have large overlaps in lower-level programs).

# Appendix: Necessity of exact reproduction in scientific research

In case the link above is not accessible at the time of reading, here is a copy of the introduction of that link, describing the necessity for a reproducible project like this (copied on February 7th, 2018):

The most important element of a "scientific" statement/result is the fact that others should be able to falsify it. The Tsunami of data that has engulfed astronomers in the last two decades, combined with faster processors and faster internet connections has made it much more easier to obtain a result. However, these factors have also increased the complexity of a scientific analysis, such that it is no longer possible to describe all the steps of an analysis in the published paper. Citing this difficulty, many authors suffice to describing the generalities of their analysis in their papers.

However, It is impossible to falsify (or even study) a result if you can't exactly reproduce it. The complexity of modern science makes it vitally important to exactly reproduce the final result. Because even a small deviation can be due to many different parts of an analysis. Nature is already a black box which we are trying so hard to comprehend. Not letting other scientists see the exact steps taken to reach a result, or not allowing them to modify it (do experiments on it) is a self-imposed black box, which only exacerbates our ignorance.

Other scientists should be able to reproduce, check and experiment on the results of anything that is to carry the "scientific" label. Any result that is not reproducible (due to incomplete information by the author) is not scientific: the readers have to have faith in the subjective experience of the authors in the very important choice of configuration values and order of operations: this is contrary to the scientific spirit.

This file is part of Maneage (https://maneage.org).

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 http://www.gnu.org/licenses/.