Scientific Software Development Is Not an Oxymoron

Baxter, Susan M.; Day, S.; Fetrow, Jacquelyn S.; Reisinger, Stephanie J.

doi:10.1371/journal.pcbi.0020087

Cited by 85 publications

(78 citation statements)

References 16 publications

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“…Certain problems can be solved relatively easily by the adoption of tools and practices that are already routine among software developers (Baxter et al 2006). In particular the use of version control systems, and the extensive support for documentation standards are as necessary for effective model development as for software development and exactly the same tools can be used.…”

Section: Discussion and Open Issuesmentioning

confidence: 99%

Interoperability of Neuroscience Modeling Software: Current Status and Future Directions

et al. 2007

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Neuroscience increasingly uses computational models to assist in the exploration and interpretation of complex phenomena. As a result, considerable effort is invested in the development of software tools and technologies for numerical simulations and for the creation and publication of models. The diversity of related tools leads to the duplication of effort and hinders model reuse. Development practices and technologies that support interoperability between software systems therefore play an important role in making the modeling process more efficient and in ensuring that published models can be reliably and easily reused. Various forms of interoperability are possible including the development of portable model description standards, the adoption of common simulation languages or the use of standardized middleware. Each of these approaches finds applications within the broad range of current modeling activity. However more effort is required in many areas to enable new scientific questions to be addressed. Here we present the conclusions of the "Neuro-IT Interoperability of Simulators" workshop, held at the 11th computational neuroscience meeting in e-mail: erik@tnb.ua.ac.be. 19-20 2006; http://www.cnsorg.org). We assess the current state of interoperability of neural simulation software and explore the future directions that will enable the field to advance. NIH Public Access

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Section: Discussion and Open Issuesmentioning

confidence: 99%

Interoperability of Neuroscience Modeling Software: Current Status and Future Directions

et al. 2007

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“…Since every simulation is in fact a form of software, commercial software development techniques and best practices can be employed (Baxter et al 2006;Joppa et al 2013;Rossiter 2015). A large number of techniques have been Nevertheless, it is unrealistic to assume that, even if extensive testing was undertaken and best practice followed, all simulations are error free.…”

Section: Entities and Rules Of Interaction ("Defining The Ontology")mentioning

confidence: 99%

So You Think You Can Model? A Guide to Building and Evaluating Archaeological Simulation Models of Dispersals

Romanowska¹

2015

Human Biology

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With the current surge of simulation studies in archaeology there is a growing concern for the lack of engagement and feedback between modellers and domain specialists. To facilitate this dialogue I present a compact guide to the simulation modelling process applied to a common research topic and the focus of this special issue of Human Biology-human dispersals. The process of developing a simulation is divided into nine steps grouped in three phases. ThePre-print version. Visit digitalcommons.wayne.edu/humbiol after publication for the final version.conceptual phase consists of identifying research questions (step 1) and finding the most suitable method (step 2), designing the general framework and the resolution of the simulation (step 3) and then by filling in that framework with the modelled entities and the rules of interactions (step 4). This is followed by the technical phase of coding and testing (step 5), parameterising the simulation (step 6) and running it (step 7). In the final phase the results of the simulation are analysed and re-contextualised (step 8) and the findings of the model are disseminated in publications and code repositories (step 9). Each step will be defined and characterised and then illustrated with examples of published human dispersals simulation studies. While not aiming to be a comprehensive textbookstyle guide to simulation, this overview of the process of modelling human dispersals should arm any non-modeller with enough understanding to evaluate the quality, strengths and weaknesses of any particular archaeological simulation and provide a starting point for further exploration of this common scientific tool.All archaeologists start modelling the moment they step out of the excavation trench. We interpret the individual finds (e.g., pots, skeletons, buildings etc.) within certain frameworks (e.g., pottery typologies, bone taxonomies, architectural types etc.) and analyse sets of finds to detect population level patterns (e.g., cultural similarities, age profiles of bone assemblages, urbanPre-print version. Visit digitalcommons.wayne.edu/humbiol after publication for the final version. development, etc.) with strict analytical rigour. However, the interpretations of these patterns in terms of human behaviour and causality are predominantly built using natural language, i.e. they are constructed in the form of conceptual models that hypothesise which causal mechanisms might have led from actions of individual actors (the owners of pots, users of skeletons and inhabitants of buildings) to the detected population-level patterns. These causal relationships are often described as, for example, "culture A influenced culture B," "the dispersal reached area C," or "population D outcompeted population E." Although inferences like these are made on the basis of rigorously collected and analysed data, and are commonly built upon extensive research and a good understanding of multiple strands of evidence, they nevertheless represent a thought experiment and are therefore limited ...

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“…The in-lab software development typically focuses on relatively specialized problems that make it difficult to scale-up the analyses or to transport them to different environments (Baxter et al, 2006). In-lab solutions are rarely shared across the community due to differences in data formats, lack of incentives, high development costs, and an inability to provide for adequate support.…”

Section: Tools and Cyberinfrastructure Utilization: Bioinformatics Usmentioning

confidence: 99%

Opportunities and Challenges for the Life Sciences Community

Kolker

Stewart

Özdemir

2012

OMICS: A Journal of Integrative Biology

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Twenty-first century life sciences have transformed into data-enabled (also called data-intensive, data-driven, or big data) sciences. They principally depend on data-, computation-, and instrumentation-intensive approaches to seek comprehensive understanding of complex biological processes and systems (e.g., ecosystems, complex diseases, environmental, and health challenges). Federal agencies including the National Science Foundation (NSF) have played and continue to play an exceptional leadership role by innovatively addressing the challenges of dataenabled life sciences. Yet even more is required not only to keep up with the current developments, but also to proactively enable future research needs. Straightforward access to data, computing, and analysis resources will enable true democratization of research competitions; thus investigators will compete based on the merits and broader impact of their ideas and approaches rather than on the scale of their institutional resources. This is the

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Scientific Software Development Is Not an Oxymoron

Cited by 85 publications

References 16 publications

Interoperability of Neuroscience Modeling Software: Current Status and Future Directions

Interoperability of Neuroscience Modeling Software: Current Status and Future Directions

So You Think You Can Model? A Guide to Building and Evaluating Archaeological Simulation Models of Dispersals

Opportunities and Challenges for the Life Sciences Community

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