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\title{Data without Software is Just Numbers~~}



\author[1]{James Davenport}%
\author[2]{Catherine Jones}%
\author[1]{James Grant}%
\affil[1]{University of Bath}%
\affil[2]{Affiliation not available}%


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  \date{May 10, 2024}


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%\begin{document}
\title{Data without Software are just numbers}
\author{James Davenport, Hebron and Medlock Professor of Information Technology, University of Bath}
\author{James Grant, Research Software Engineer, University of Bath}
\author{Catherine Jones, Software Engineering Group Leader, Science and technology Facilities Council}

Keywords: Software Management Plans, Reproducible Research

Great strides have been made in encouraging researchers to deposit data sets created by research and in providing the necessary systems to support its storage.  However, data are meaningless unless their provenance is preserved, this may be meta-data about its collection, or creation in simulation, increasingly it will be the analyses  that modify, draw value from raw data and turn numbers into figures.  Without the software trail of these workflows, the output is just numbers and given that 90\% of researchers make use of research software \cite{2017} ensuring that it is recorded is just as vital as the data.  Reproducible research requires a similar effort for software as has happened with data.  In this paper we will argue that data management alone is insufficient, it must be accompanied by good software practice, the training to facilitate it and the will to support this from all stakeholders including appropriate recognition for software output.

Large community software projects generally operate a well-maintained, often open source, repository to manage development. However, the bulk of research software involves smaller, single developer or group written codes and scripts used for day-to-day analysis and plotting. Researchers using programmatic approaches benefit from the efficiency of their approach and are able to demonstrate the reproducibility of their analyses easily, by submitting their workflows as part of their supporting repositories.  The need for reproducible workflows in research is evidenced by the now famous Reinhart and Rogoff Excel slip \cite{Reinhart_2010}\cite{economics}. Providing the right support and guidance to long-tail researchers who create and use software without formal training is an area which the Research Software Engineering community \cite{association} is starting to address.  At the University of Bath we are working with colleagues across the region, % was "in GW4", but this makes no external sense
to introduce formal training for researchers \cite{carpentry}\cite{chryswoodscom} and develop tailored workshops to motivate the use of good practice.
%For instance using version control to research data during analysis can greatly simplify the final submission of data \emph{and} the accompanying software.  

Alongside training, the cultural change that has come with Research Data Management also needs to be mirrored with Research Software Management.  Developing a combined approach will benefit how researchers think about their research output.  That the two are complementary is clear to experts but work is necessary to persuade funders, journals and researchers of the need to change practice.
%(indeed the advert for this conference makes no mention of software!).  
There is movement however with the Software Sustainability Institute \cite{institute} working with the Digital Curation Centre \cite{data} to add a software management plan to their DMPonline service \cite{centre}.  Additionally, Nature Journal require the availability of unpublished codes \emph{on request} \cite{npg} while RCUK and Horizon 2020 guidance is that specialist software should be provided where possible \cite{rcuk}\cite{programme}.  Though we caution that Open Software does not necessarily affect practice or quality itself, it does improve transparency.

Establishing the infrastructure to support both live software development and long-term preservation of released/published software is an active area for research organisations: for example Zenodo allows DOI persistent versioning of Github repositories.  Linked to good management of research software is the developing discipline of software citation \cite{final}\cite{schema}; by changing research culture and practice to routinely cite the software used in the analysis workflow, a better understanding and hence re-use of the research data is enabled alongside appropriate credit given to those whose main focus of research activity is writing the software \cite{project}.  This also involves advocating the recognition of software as research output to ensure that its developers receive credit for these citations.  We will outline the skills training we are exploring to develop good practice in research computing \cite{Wilson_2017} and describe the additional support that RSEs can deliver.  We will also discuss the main blocks and potential solutions for changes to ensuring credit for software.

%\begin{thebibliography}

%\bibitem{SSI2014}
%Hettrick, S. (2014). "SSI RSE Survey 2014". [online] Available at: https://www.software.ac.uk/blog/2016-09-12-its-impossible-conduct-research-without-software-say-7-out-10-uk-researchers [Accessed 21 December 2017].
%\bibitem{Force11}
%Data Citation Synthesis Group: Joint Declaration of Data Citation Principles. Martone M. (ed.) San Diego CA: FORCE11; 2014 https://doi.org/10.25490/a97f-egyk
%\bibitem{DataCite2017}
%DataCite Metadata Working Group. (2017). DataCite Metadata Schema for the Publication and Citation of Research Data. Version 4.1. DataCite e.V. http://doi.org/10.5438/0015
%\bibitem{Gent2015}
%I. Gent, C. Jones, and B. Matthews, “Guidelines for Persistently Identifying Software using
%DataCite - A JISC Research Data Spring Project,” Tech. Rep., 2015. [online]. Available:
%http://purl.org/net/epubs/manifestation/24058292
%[Accessed Dec. 21, 2017].

%\end{thebibliography}



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