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@@ -101,17 +101,14 @@ Data Lineage, Provenance, Reproducibility, Scientific Pipelines, Workflows %\IEEEPARstart{F}{irst} word Reproducible research has been discussed in the sciences for at least 30 years \cite{claerbout1992, fineberg19}. -Many reproducible workflow solutions (hereafter, ``solution(s)'') have been proposed, mostly relying on the common technology of the day: starting with Make and Matlab libraries in the 1990s, Java in the 2000s and mostly shifting to Python during the last decade. +Many reproducible workflow solutions (hereafter, ``solutions'') have been proposed, mostly relying on the common technology of the day: starting with Make and Matlab libraries in the 1990s, to Java in the 2000s and mostly shifting to Python during the last decade. Recently, controlling the environment has been facilitated through generic package managers (PMs) and containers. However, because of their high-level nature, such third-party tools for the workflow (not the analysis) develop very fast, e.g., Python 2 code often cannot run with Python 3, interrupting many projects. -Containers (in custom binary formats) are also being heavily used, but are large (Gigabytes) and expensive to archive. -Moreover, once the binary format is obsolete, reading or parsing the project becomes impossible. - The cost of staying up to date within this rapidly evolving landscape is high. Scientific projects, in particular, suffer the most: scientists have to focus on their own research domain, but to some degree they need to understand the technology of their tools, because it determines their results and interpretations. Decades later, scientists are still held accountable for their results. -Hence, the evolving technology landscape creates generational gaps in the scientific community, preventing previous generations from sharing valuable lessons which are too low-level to be published in a traditional scientific paper. +Hence, the evolving technology landscape creates generational gaps in the scientific community, preventing previous generations from sharing valuable lessons which are too hands-on to be published in a traditional scientific paper. As a solution to this problem, here we introduce a set of criteria that can guarantee the longevity of a project based on our experience with existing solutions. @@ -518,7 +515,7 @@ The Pozna\'n Supercomputing and Networking Center (PSNC) computational grant 314 Contact him at david.valls-gabaud@obspm.fr. \end{IEEEbiographynophoto} -\begin{IEEEbiographynophoto}{Roberto Baena-Gall\'e} + \begin{IEEEbiographynophoto}{Roberto Baena-Gall\'e} is a postdoctoral researcher at the Instituto de Astrof\'isica de Canarias, Tenerife, Spain. Before enrolling IAC, he worked at University of Barcelona, Reial Acad\`emia de Ci\`encias i Arts de Barcelona, l'Universit\'e Pierre et Marie Curie and ONERA-The French Aerospace Lab. His research interests are image processing and resolution of inverse problems, with applications to AO corrected FOVs, satellite identification under atmospheric turbulence and retina images. |