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Data Management Lifecycle and Software Lifecycle Management in the Context of Conducting Science W. Christopher Lenhardt, Stanley Ahalt, Brian Blanton, Laura Christopherson, Ray Idaszak 1 Renaissance Computing Institute (RENCI), University of North Carolina at Chapel Hill Introduction Software, ranging from spreadsheets with formulas to very large and complex numerical models that simulate the earth system, is used throughout most phases of science from experiments to analyses. The utility of this software is based, in part, on whether it fulfills the scientist’s need for which it was created. Science is also defined by the gathering and use of data through observation, measurement, analysis, and simulation, and this data almost invariably is collected, created, and analysed with software. In the digital age, the utility of scientific data is often assessed by its potential for reuse in other contexts particularly for other scientific analyses, applications, and decision support. In this context, data has a lifecycle which moves from inception to creation through use, reuse, and storage. Software too has a lifecycle from inception to creation to testing, deployment, use, evolution, and refactoring. Given the tight connections between scientific data and science software, it is illustrative to compare these two lifecycles to look for commonalities and potentials for synergies between them to create more sustainable software. A comparable effort investigated what data management can learn from software development practices [Schopf, 2012]. This paper will do the opposite by suggesting that the wellaccepted and welldocumented digital science data lifecycle management best practices may inform the development of sustainable scientific software. We describe the data lifecycle and the software lifecycle in more detail to identify commonalities, synergies, and discontinuities between the two approaches. We suggest that there are three initial intersection points that warrant additional investigation: metadata, preservation management, and data flows into and out of science software. Data Management Lifecycle The goal of a data management lifecycle is to ensure that scientific data are collected with enough rigor to support the intended use, to support basic data management, to enable reuse and repurposing of the data, and to allow for the eventual longterm preservation and 1 Corresponding author: [email protected] 1

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Data Management Lifecycle and SoftwareLifecycle Management in the Context of

Conducting Science

W. Christopher Lenhardt,  Stanley Ahalt, Brian Blanton, Laura Christopherson, Ray Idaszak1

Renaissance Computing Institute (RENCI), University of North Carolina at Chapel Hill

IntroductionSoftware, ranging from spreadsheets with formulas to very large and complex numerical modelsthat simulate the earth system, is used throughout most phases of science from experiments toanalyses. The utility of this software is based, in part, on whether it fulfills the scientist’s need forwhich it was created. Science is also defined by the gathering and use of data throughobservation, measurement, analysis, and simulation, and this data almost invariably is collected,created, and analysed with software.  In the digital age, the utility of scientific data is oftenassessed by its potential for reuse in other contexts particularly for other scientific analyses,applications, and decision support. In this context, data has a lifecycle which moves frominception to creation through use, re­use, and storage. Software too has a lifecycle frominception to creation to testing, deployment, use, evolution, and refactoring. Given the tightconnections between scientific data and science software, it is illustrative to compare these twolifecycles to look for commonalities and potentials for synergies between them to create moresustainable software. A comparable effort investigated what data management can learn fromsoftware development practices [Schopf, 2012]. This paper will do the opposite by suggestingthat the well­accepted and well­documented digital science data lifecycle management bestpractices may inform the development of sustainable scientific software. We describe the datalifecycle and the software lifecycle in more detail to identify commonalities, synergies, anddiscontinuities between the two approaches. We suggest that there are three initial intersectionpoints that warrant additional investigation: metadata, preservation management, and data flowsinto and out of science software.

Data Management LifecycleThe goal of a data management lifecycle is to ensure that scientific data are collected withenough rigor to support the intended use, to support basic data management, to enable reuseand repurposing of the data, and to allow for the eventual long­term preservation and

1 Corresponding author: [email protected]


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management of the data. With respect to scientific research, the overarching purpose is tosupport scientific discovery, verifiability, and reproducibility.

Numerous versions of varying complexity exist of an abstracted data management or datalifecycle [Ball, 2012]. However, they all share certain basic steps. These include, but are notlimited to:  Plan, Collect, Quality Control, Document, Preserve, Use. [See Figure 1. See also, and­­manage/life­cycle] The various steps associated with thedata lifecycle are on some level self explanatory. However, the basic process involves thecreation, quality control, storage, and analysis of the data required to support the science. At adeeper level, the lifecycle is concerned with providing the necessary information to effectivelymanage the data in a long­term archival setting and to support reuse beyond the original purposefor which the data were created. Throughout the process, additional metadata anddocumentation are added.

 Figure 1 (Source: DataONE)

The data management lifecycle is well documented and has led to a number of accepted bestpractices and standards. Two such examples are the Metadata Encoding and TransmissionStandard (METS) and the Open Archival Information System Reference Model (OAIS, ISO14721:2012). See also the ESIP Federation Data Management Short Course,

Software LifecycleThe software lifecycle (See Figure 2) focuses on the

development, maturation, and enhancement of aparticular piece or set of software. This lifecyclepertains in its most basic form to the definitionand creation of a piece of software. Similar to datalifecycles, there are various models of differingcomplexity reflecting different underlyingmethodologies for developing software.[Munassar and Govardhan, 2010] Theseapproaches, such as various forms of agile,waterfall, and spiral, are most formally applied to


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the context of commercial or production software. This raises another potential distinction whichis how to describe science software, as opposed to commercial software, which is often notdeveloped according to software development best practices.

Linking the Two Lifecycles and Areas for Additional InvestigationThe effective management of scientific data and science software share certain common goalssuch as supporting reproducible science and supporting potential reuse. The high level stepsrelated to each lifecycle share a certain symmetry. See Figure 3.

We argue that delving into this lifecycle comparison in greater detail may yield interestinginsights to promote sustainable software. For example, consider metadata for data. There aremany different types of metadata, metadata standards, metadata tools for authorship,management, and search and retrieval. The more metadata, the more the value­add for the dataand the data should be easier to manage and reuse. However, questions still remain. Forexample, what level of metadata is enough? Can we leverage science data standards in thecontext of developing science software?

Figure 3: Comparison of High Level Lifecycle Phases for Science Data and Science Software

Based on this preliminary analysis we suggest there are at least three different avenues forexploration that may contribute to the discussion of sustainable scientific software. The first ismetadata, the second is data management principles, and the third is the explicit connections ofdata flows into and out of science software.

MetadataAs data moves through the data management lifecycle, there is increasing value added throughthe creation of additional metadata and documentation. The categories of metadata anddocumentation added may suggest similar types of additional structured content that could orshould be added during the science software development process. A recent article on climatemodel metadata underscores the need to improve the systematic documentation of model


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metadata in order to facilitate model intercomparison and future use.  The implications of thisshould be explored in the context of sustainable science software [Guilyardi, et al, 2013].

Preservation Management PrinciplesExamining in more detail the actual principles behind science data management practices mayalso help to inform the science software development process.  For example, best practicesrelated to putting the data into a repository and also performing archival management may berelevant to how science software is released and managed.  These may be particularlyimportant in the context of validation of science software. Conversely, established open sourcecommunity methods for software may inform how to better achieve open science with opendata.

Data in / Data outFinally, it is a reasonable assertion to state that much of what science software does is workwith data. The software is used to perform data manipulation, data fusion, data transformation,data analyses, visualization, and for modeling. Therefore conducting a more systematicinvestigation of how data standards, and best practices might be used to facilitate the connectionof data to science software might prove fruitful. For example, can the code be used to generatedata that is ‘self­documented’.

Why does this matter?While this paper has addressed the question largely from the data management perspective, it isnot intended to imply that data management has all the answers or that data management can’tlearn from software management. Quite the contrary, we should be doing both. Doing a betterjob of promoting the sustainability of science software is important for a number of reasons,including the support of scientific reproducibility and reuse and longer term viability of sciencesoftware. However, there are two final objectives linked to creating more sustainable sciencesoftware. First, sustainable software should enable new science and second, sustainablesoftware should make the scientist’s job easier.


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Ball, A., 2012. Review of Data Management Lifecycle Models. Other. Bath, UK: University ofBath.


Federation of Earth Science Information Partners (ESIP). Data Management for Scientists ShortCourse. Various authors. 2012­13.

Guilyardi, Eric, and Coauthors, 2013: Documenting climate models and their simulations. Bull.Amer. Meteor. Soc., 94, 623–627.doi:­D­11­00035.1

Metadata Encoding and Transmission Standard (METS)­home.html

Nabil Mohammed Ali Munassar and A. Govardhan. A Comparison Between Five Models OfSoftware Engineering. International Journal of Computer Science Issues, Vol. 7, Issue 5,September 2010. ISSN (Online): 1694­0814­5­94­101.pdf

Open Archival Information System (OAIS) Reference Model

Schopf, Jennifer M.JCDL '12 Proceedings of the 12th ACM/IEEE­CS joint conference on Digital Libraries, Pages153­156. ACM New York, NY, USA

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