Ten simple rules for starting (and sustaining) an academic data science initiative

Parker, Micaela S.; Burgess, Arlyn; Bourne, Philip E.

doi:10.1371/journal.pcbi.1008628

Cited by 13 publications

(20 citation statements)

References 15 publications

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“…Therefore, to ensure utility and longevity of your web app, determine who will be designated as the web app maintainer(s) [32,35]. Additionally, we recommend budgeting time (and funds) to documenting your web app as well as incorporating documentation and project sustaining best practices (e.g., [36][37][38]). If your code relies on established R packages, Python libraries, or other software with particular version numbers, consider using tools like Docker (e.g., [39]) or Conda (e.g., https://docs.conda.io/en/latest/) to manage web app dependencies-the software and code versions that your web app depends on to run-and make it easier for others to replicate your web app.…”

Section: Rule 10: Make It Last: Plan For the Long Haulmentioning

confidence: 99%

Ten Simple Rules for Researchers Who Want to Develop Web Apps

Saia¹,

Nelson²,

Young³

et al. 2022

Preprint

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Growing interest in data-driven, decision-support tools across the life sciences and physical sciences has motivated development of web applications, also known as web apps. Web apps can help disseminate research findings and present research outputs in ways that are more accessible and meaningful to the general public--from individuals, to governments, to companies. Specifically, web apps enable exploration of scenario testing and policy analysis (i.e., to answer “what if?”) as well as co-evolution of scientific and public knowledge. However, the majority of researchers developing web apps receive little formal training or technical guidance on how to develop and evaluate the effectiveness of their web-based decision support tools. Take some of us for example. We (Saia and Nelson) are agricultural and environmental engineers with little experience in web app development, but we are interested in creating web apps to support sustainable aquaculture production in the Southeast. We had user (i.e., shellfish growers) interest, a goal in mind (i.e., develop a new forecast product and decision-support tool for shellfish aquaculturalists), and received funding to support this work. Yet, we experienced several unexpected hurdles from the start of our project that ended up being fairly common hiccups to the seasoned web app developers among us (Young, Parham). As a result, we share the following Ten Simple Rules, which highlight take home messages, including lessons learned and practical tips, of our experience as burgeoning web app developers. We hope researchers interested in developing web apps draw insights from our (in)experience as they set out on their decision support tool development journey.

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Section: Rule 10: Make It Last: Plan For the Long Haulmentioning

confidence: 99%

Ten Simple Rules for Researchers Who Want to Develop Web Apps

Saia¹,

Nelson²,

Young³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Within our School of Data Science, we have given a great deal of thought to this question [3], focusing not so much on the definition itself, but rather on how we embody the meaning and culture of that definition in all aspects of teaching, research, and service to the community. As such, we have arrived at the 4+1 model of data science.…”

Section: Box 1 What Is Data Science? the 4+1 Modelmentioning

confidence: 99%

“…Data science-part computer science, part statistics, part information science, and part applied math-with its unique blend of methodologies and scientific cultures-doesn't acquire meaning until put to practical use. Add to that the influence of the private sector, think DeepMind [3], and you have something new and groundbreaking. As an example, we describe ourselves as a "school without walls"the "school" gives us autonomy in an academic institution, and the "without walls" means we don't own anything but contribute to everything (or at least aim to).…”

mentioning

confidence: 99%

Is “bioinformatics” dead?

Bourne

2021

PLoS Biol

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Why would a computational biologist with 40 years of research experience say bioinformatics is dead? The short answer is, in being the Founding Dean of a new School of Data Science, what we do suddenly looks different. Now that I have your attention, clearly, bioinformatics as a field is very much alive. The name, however, no longer applies to what we actually do in the field. It is not what forward-thinking scientists should be calling themselves in this era of the fourth paradigm of data science [1], where data sharing lies at the core of biology. If you're asking why anyone should care, let me explain. But first, let me acknowledge Florian Markowetz, who started the discussion [2]. As NIH Associate Director for Data Science, I made similar arguments to the Advisory Committee to the NIH Director in 2014. I argued that until the 1980s to 1990s, computation was a complex tool in the hands of a few. The human genome project changed all that. Experiment and computation were synergistic and the promise of bioinformatics-which could not only describe and maintain the massive volume of digital data being generated but also provide the tools needed to assemble and make sense of 3 billion nucleotides-claimed the limelight. The euphoria around what bioinformatics could accomplish was so great that in the early 2000s the private sector snatched up a significant fraction of the computational practitioners to capitalize on the era of human genomics. The initial excitement faded several years later, when bioinformatics could not deliver in the short product cycles that the industry demanded, and bioinformaticians were regarded as mere service providers to experimentally driven research. Still, its promise could not be denied. A new generation of practitioners emerged who were as adept in silico as they were in vivo and/or in vitro. Bioinformatics spread its wings as computational biology and then systems biology, which is roughly where we are today. In 2014, I predicted that by 2020, computation would be steering the biomedical ship, experiment would increasingly be used to confirm predictive models, and causation would increasingly be determined from digital data previously collected by others or by bioinformatically guided laboratory robotics that maximized experimental insights. My predictions about timing were off, but not about outcome. The role of what I'll call digitally based causation and predictive analytics is assured. Thus, how we describe our field, to truly express what we do, is off. What's driven the changes? The same thing that spawned bioinformatics: digital data. But now there is much more of it, and it's not just DNA sequences but data at all scales, from molecules to populations, and it's being collected (high-throughput methods) and generated (via simulations and modeling) at unprecedented rates. Add to that

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“…Regarding documentation, we recommend budgeting time (and funds) for documenting your web app as well as incorporating documentation and project sustaining best practices (e.g., [ 72 – 74 ]). Whenever possible, build your web app using widely supported technologies and include a test suite (see Rule 2) to ensure that the web app code will function properly as an ensemble after you have made changes to the source code and web app dependencies (i.e., the software and code versions that your web app depends on to run).…”

Section: Introductionmentioning

confidence: 99%

“…This tool offers another helpful approach to automate tasks, including running your testing scripts. You can learn more about successful web app documentation, maintenance, and longevity as well as doing open and reproducible science from many resources available online (e.g., [ 76 – 82 ]), several of which are in the ten simple rules collection [ 21 , 37 , 57 , 70 , 72 – 74 , 83 – 87 ].…”

Section: Introductionmentioning

confidence: 99%