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authorRaul Infante-Sainz <infantesainz@gmail.com>2020-04-09 13:07:52 +0100
committerRaul Infante-Sainz <infantesainz@gmail.com>2020-04-09 13:07:52 +0100
commit1e07a59513b1774ca8b0561cc30f6fd2515afef9 (patch)
tree308d1c39c86af1a0be81cbb78efe747bb785a827
parentff6b179bfb8172e50c80ae99506255353400f65b (diff)
Trying to make the Abstract shorter, keywords sorted alphabetically
With this commit, I have tried to make the Abstract a bit shorter. I think it was too long considering that there are plenty of space in the paper to describe some of the points that were noticed in the abstract. The main point is just to try to be atractive to the reader being focused to the main points. In any case I think there are room for improving it. The keywords have also been sorted alphabetically.
-rw-r--r--paper.tex20
1 files changed, 9 insertions, 11 deletions
diff --git a/paper.tex b/paper.tex
index c3c6c25..bc371a1 100644
--- a/paper.tex
+++ b/paper.tex
@@ -47,21 +47,19 @@
%% Abstract
{\noindent\mpregular
The era of big data has also ushered an era of big responsability.
- Without it, the integrity of the result will be a subject of perpetual debate.
- In this paper Maneage is introduced as a low-level solution to this problem.
- Maneage (management + lineage) is an executable workflow for project authors and readers in the sciences or the industry.
- It is designed following principles: complete (e.g., not requiring anything beyond a POSIX-compatible system, administrator previlages or a network connection), modular, fully in plain-text, minimal complexity in design, verifiable inputs and outputs, temporal lineage/provenance, and free software (in scientific applications).
- A project that uses Maneage will have full control over the data lineage, making it exactly reproducible.
- This control goes as far back as the automatic downloading of input data, and automatic building of necessary software that are used to analyze the data, with fixed versions and build configurations.
- It also contains the narrative description of the final project's report (built into a PDF), while providing automatic and direct links between the analysis and the part of the narrative description that it was used.
- Also, starting new projects, or editing previously published papers is trivial because of its version control system.
- If adopted on a wide scale, Maneage can greatly improve scientific collaborations and building upon the work of other researchers instead of the current technical frustrations many researchers experience and can affect their scientific result and interpretations.
+ Without it, the integrity of the results will be a subject of perpetual debate.
+ In this paper, Maneage (management + lineage) is introduced as a low-level solution to this problem.
+ It is designed considering the following principles: complete (e.g., not requiring anything beyond a POSIX-compatible system), modular, fully in plain-text, minimal complexity in design, verifiable inputs and outputs, temporal lineage/provenance, and free software (in scientific applications).
+ A project using Maneage will have full control over the data lineage, making it exactly reproducible.
+ This control goes as far back as the automatic downloading of input data, and automatic building of necessary software that are used in the analysis.
+ It also contains the narrative description of the final project's report (built into a PDF).
+ Adopting Maneage on a wide scale will greatly improve scientific collaborations and building upon the work of other researchers, instead of the current technical frustrations that many researchers experience and can affect their scientific result and interpretations.
It can also be used on more ambitious projects like automatic workflow creation through machine learning tools, or automating data management plans.
- This paper has itself been written in Maneage (snapshot \projectversion).
+ As a demostration, this paper has itself been generated with Maneage (snapshot \projectversion).
\horizontalline
\noindent
- {\mpbold Keywords:} Data Lineage, Data Provenance, Reproducibility, Workflows, scientific pipelines
+ {\mpbold Keywords:} Data Lineage, Data Provenance, Reproducibility, Scientific Pipelines, Workflows
}
\horizontalline