Very minor corrections to the necessity appendix

With this commit, I have corrected some minor typos of this appendix.
They are very minor corrections.

1 files changed, 18 insertions, 13 deletions

diff --git a/tex/src/appendix-necessity.tex b/tex/src/appendix-necessity.tex
index 7db810c..452aa0f 100644
--- a/tex/src/appendix-necessity.tex
+++ b/tex/src/appendix-necessity.tex
@@ -3,6 +3,7 @@
 %% 'tex/src/supplement.tex', it should not be run independently.
 %
 %% Copyright (C) 2020-2021 Mohammad Akhlaghi <mohammad@akhlaghi.org>
+%% Copyright (C) 2021 Raúl Infante-Sainz <infantesainz@gmail.com>
 %
 %% 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
@@ -13,30 +14,34 @@
 %% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
 %% FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License
 %% for more details. See <http://www.gnu.org/licenses/>.
+
+
+
+
+
 \section{Necessity for reproducible research\\(not part of journal article; introductory review for non-specialists)}
 \label{appendix:necessity}
-
 The increasing volume and complexity of data analysis has been highly productive, giving rise to a new branch of ``Big Data'' in many fields of the sciences and industry.
 However, given its inherent complexity, the mere results are barely useful alone.
 Questions such as these commonly follow any such result:
 What inputs were used?
 What operations were done on those inputs? How were the configurations or training data chosen?
-How did the quantitative results get visualized into the final demonstration plots, figures or narrative/qualitative interpretation?
+How did the quantitative results get visualized into the final demonstration plots, figures, or narrative/qualitative interpretation?
 Could there be a bias in the visualization?
 See Figure \ref{fig:questions} for a more detailed visual representation of such questions for various stages of the workflow.
 
 In data science and database management, this type of metadata is commonly known as \emph{data provenance} or \emph{data lineage}.
-Data lineage is being increasingly demanded for integrity checking from both the scientific, industrial and legal domains.
+Data lineage is being increasingly demanded for integrity checking from both the scientific, industrial, and legal domains.
 Notable examples in each domain are respectively the ``Reproducibility crisis'' in the sciences that was reported by \emph{Nature} after a large survey \citeappendix{baker16}, and the General Data Protection Regulation (GDPR) by the European Parliament and the California Consumer Privacy Act (CCPA), implemented in 2018 and 2020, respectively.
 The former argues that reproducibility (as a test on sufficiently conveying the data lineage) is necessary for other scientists to study, check and build-upon each other's work.
-The latter requires the data intensive industry to give individual users control over their data, effectively requiring thorough management and knowledge of the data lineage.
+The latter requires the data-intensive industry to give individual users control over their data, effectively requiring thorough management and knowledge of the data lineage.
 Besides regulation and integrity checks, having robust data governance (management of data lineage) in a project can be very productive: it enables easy debugging, experimentation on alternative methods, or optimization of the workflow.
 
 In the sciences, the results of a project's analysis are published as scientific papers, which have traditionally been the primary conveyor of the lineage of the results: usually in narrative form, especially within the ``Methods'' section of the paper.
 From our own experience, this section is often that which is the most intensively discussed during peer review and conference presentations, showing its importance.
 After all, a result is defined as ``scientific'' based on its \emph{method} (the ``scientific method''), or lineage in data-science terminology.
 In industry, however, data governance is usually kept as a trade secret and is not published openly or widely scrutinized.
-Therefore, the main practical focus here will be in the scientific front, which has traditionally been more open to the publication of methods and anonymous peer scrutiny.
+Therefore, the main practical focus here will be on the scientific front, which has traditionally been more open to the publication of methods and anonymous peer scrutiny.
 
 \begin{figure*}[t]
   \begin{center}
@@ -44,8 +49,8 @@ Therefore, the main practical focus here will be in the scientific front, which
   \end{center}
   \caption{\label{fig:questions}Graph of a generic project's workflow (connected through arrows), highlighting the various issues and questions on each step.
     The green boxes with sharp edges are inputs and the blue boxes with rounded corners are the intermediate or final outputs.
-    The red boxes with dashed edges highlight the main questions at various stages in the workchain.
-    The orange box, surrounding the software download and build phases, lists some commonly recognized solutions to the questions in it; for morediscussion, see Appendix \ref{appendix:independentenvironment}.
+    The red boxes with dashed edges highlight the main questions at various stages in the work chain.
+    The orange box, surrounding the software download and build phases, lists some commonly recognized solutions to the questions in it; for more discussion, see Appendix \ref{appendix:independentenvironment}.
   }
 \end{figure*}
 
@@ -62,11 +67,11 @@ Not being able to experiment on the methods of other researchers is an artificia
 A dramatic example showing the importance of sharing code is \citeappendix{miller06}, in which a mistaken flipping of a column was discovered, leading to the retraction of five papers in major journals, including \emph{Science}.
 Ref.\/ \citeappendix{baggerly09} highlighted the inadequate narrative description of analysis in several papers and showed the prevalence of simple errors in published results, ultimately calling their work ``forensic bioinformatics''.
 References \citeappendix{herndon14} and \citeappendix{horvath15} also reported similar situations and \citeappendix{ziemann16} concluded that one-fifth of papers with supplementary Microsoft Excel gene lists contain erroneous gene name conversions.
-Such integrity checks are a critical component of the scientific method, but are only possible with access to the data and codes and \emph{cannot be resolved from analysing the published paper alone}.
+Such integrity checks are a critical component of the scientific method but are only possible with access to the data and codes and \emph{cannot be resolved from analyzing the published paper alone}.
 
-The completeness of a paper's published metadata (or ``Methods'' section) can be measured by a simple question: given the same input datasets (supposedly on a third-party database like \href{http://zenodo.org}{zenodo.org}), can another researcher reproduce the exact same result automatically, without needing to contact the authors?
+The completeness of a paper's published metadata (or ``Methods'' section) can be measured by a simple question: given the same input datasets (supposedly on a third-party database like \href{http://zenodo.org}{zenodo.org}), can another researcher reproduce the same result automatically, without needing to contact the authors?
 Several studies have attempted to answer this with different levels of detail.
-For example \citeappendix{allen18} found that roughly half of the papers in astrophysics do not even mention the names of any analysis software they have used, while \cite{menke20} found that the fraction of papers explicitly mentioning their software tools has greatly improved in medical journals over the last two decades.
+For example, \citeappendix{allen18} found that roughly half of the papers in astrophysics do not even mention the names of any analysis software they have used, while \cite{menke20} found that the fraction of papers explicitly mentioning their software tools has greatly improved in medical journals over the last two decades.
 
 Ref.\/ \citeappendix{ioannidis2009} attempted to reproduce 18 published results by two independent groups, but only fully succeeded in two of them and partially in six.
 Ref.\/ \citeappendix{chang15} attempted to reproduce 67 papers in well-regarded economic journals with data and code: only 22 could be reproduced without contacting authors, and more than half could not be replicated at all.
@@ -83,10 +88,10 @@ While the situation has improved since the early 1990s, the problems mentioned i
 Even earlier yet, through his famous quartet, Anscombe \citeappendix{anscombe73} qualitatively showed how the distancing of researchers from the intricacies of algorithms and methods can lead to misinterpretation of the results.
 One of the earliest such efforts we found was \citeappendix{roberts69}, who discussed conventions in FORTRAN programming and documentation to help in publishing research codes.
 
-From a practical point of view, for those who publish the data lineage, a major problem is the fast evolving and diverse software technologies and methodologies that are used by different teams in different epochs.
+From a practical point of view, for those who publish the data lineage, a major problem is the fast-evolving and diverse software technologies and methodologies that are used by different teams in different epochs.
 Ref.\/ \citeappendix{zhao12} describes it as ``workflow decay'' and recommends preserving these auxiliary resources.
-But in the case of software, this is not as straightforward as for data: if preserved in binary form, software can only be run on a certain operating system on particular hardware, and if kept as source code, its build dependencies and build configuration must also be preserved.
+But in the case of software, this is not as straightforward as for data: if preserved in binary form, the software can only be run on a certain operating system on particular hardware, and if kept as source code, its build dependencies and build configuration must also be preserved.
 Ref.\/ \citeappendix{gronenschild12} specifically studies the effect of software version and environment and encourages researchers to not update their software environment.
 However, this is not a practical solution because software updates are necessary, at least to fix bugs in the same research software.
-Generally, software is not a secular component of projects, where one software package can easily be swapped with another.
+Generally, the software is not a secular component of projects, where one software package can easily be swapped with another.
 Projects are built around specific software technologies, and research in software methods and implementations is itself a vibrant research topic in many domains \citeappendix{dicosmo19}.