A “datathon” model to support cross-disciplinary collaboration

Aboab, Jérôme; Celi, Leo Anthony; Charlton, Peter; Feng, Mengling; Ghassemi, Mohammad M.; Marshall, Dominic C; Mayaud, Louis; Naumann, Tristan; McCague, Ned; Paik, Kenneth E.; Pollard, Tom; Resche-Rigon, Matthieu; Salciccioli, Justin D.; Stone, David J.

doi:10.1126/scitranslmed.aad9072

Cited by 61 publications

(43 citation statements)

References 14 publications

(13 reference statements)

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“…For example, Johnson et al published all code necessary to reproduce the data description of the MIMIC-III [ 89 ] database in the form of Jupyter notebooks on a GitHub repository [ 90 ]. The use of Jupyter notebooks was encouraged, and a specially developed software platform that integrated with notebooks was provided for a datathon using the MIMIC-III database, rendering all resulting analysis fully reproducible [ 91 ]. Most EHR research analytical tools support literate programming (Table 6 ) and its use can greatly facilitate closing the gap between code and narrative.…”

Section: Methods and Resultsmentioning

confidence: 99%

Methods for enhancing the reproducibility of biomedical research findings using electronic health records

et al. 2017

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BackgroundThe ability of external investigators to reproduce published scientific findings is critical for the evaluation and validation of biomedical research by the wider community. However, a substantial proportion of health research using electronic health records (EHR), data collected and generated during clinical care, is potentially not reproducible mainly due to the fact that the implementation details of most data preprocessing, cleaning, phenotyping and analysis approaches are not systematically made available or shared. With the complexity, volume and variety of electronic health record data sources made available for research steadily increasing, it is critical to ensure that scientific findings from EHR data are reproducible and replicable by researchers. Reporting guidelines, such as RECORD and STROBE, have set a solid foundation by recommending a series of items for researchers to include in their research outputs. Researchers however often lack the technical tools and methodological approaches to actuate such recommendations in an efficient and sustainable manner.ResultsIn this paper, we review and propose a series of methods and tools utilized in adjunct scientific disciplines that can be used to enhance the reproducibility of research using electronic health records and enable researchers to report analytical approaches in a transparent manner. Specifically, we discuss the adoption of scientific software engineering principles and best-practices such as test-driven development, source code revision control systems, literate programming and the standardization and re-use of common data management and analytical approaches.ConclusionThe adoption of such approaches will enable scientists to systematically document and share EHR analytical workflows and increase the reproducibility of biomedical research using such complex data sources.

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Section: Methods and Resultsmentioning

confidence: 99%

Methods for enhancing the reproducibility of biomedical research findings using electronic health records

et al. 2017

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“…One sign of the wider perception of the term hackathon is the prefixes attached to the term and modifications of the term. There exists civic hackathons [22], [19], datathons [23], [24], semesterathons and summerathons , designathons [25]. These examples show that the term hackathon and the approach it represents (i.e.…”

Section: Defining Code Camps and Hackathonsmentioning

confidence: 99%

Code camps and hackathons in education - literature review and lessons learned

Porras¹,

Knutas²,

Ikonen³

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Motivation: Code camps and hackathons been used in education for almost two decades. These approaches are usually intensive and for most times quite practical events for solving some real-world problems with various educational objectives. The objectives and structures of these events differ depending on the role of the event in curricula. Problem statement: Both code camps and hackathons been implemented in various ways, with varying success levels. As expected the implementation of the event varies considerably depending on the objectives set for the event, but that then leads to the difficulty and problem setting to understand what organizing of these events actually mean. For educational context, curricula have also its role in defining the targeted skills and competencies the events has to consider too. Approach: We applied a systematic literature review (SLR) to look at the various definitions and modes of these events. Whether it is called "code camp", or "hackathon", or anything else with the same basic meaning, we want to find out what skills and competencies these events emphasize, how they are used in Computer Science (CS) and Software Engineering (SE) education and what are the general structures of the actual arranged events. Contribution: It is aim of this SLR to i) identify various possible ways of implementing these intensive events, and ii) reflect the results to the lessons we have learned of almost two decades of various intensive code camps and hackathons we have been organizing building and participating into. Based on the results, we claim that there is tremendous potential of using these events in education and in the curriculum than how it has been applied so far.

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“…Publically funded companies are required to do so through legislation, while some companies open data as a form of marketing, increasing the visibility of data. Other forms of data sharing in a more controlled fashion were also identified from the interviews, such as directly between businesses [29], between businesses and consumers [17], [24], or through hackathons [1] and competitions, as is the suggestion for stimulating use of open data [35]. Hackathons and competitions were not identified as a method to share data before when creating a scope of different sharing…”

Section: Discussionmentioning

confidence: 99%

Views on Open Data Business from Software Development Companies

Herala

Kasurinen

Vanhala

2018

J. theor. appl. electron. commer. res.

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The interest towards the concept of open data has increased during the last ten years, as governments and municipalities have decided to open their data repositories. This has led to a new generation of mobile apps, which utilize this data to improve the feature richness and the overall user experience for the customers. In this study, we interviewed representatives of five software organization and discussed their views towards opening data-private and public-and also using the open data in practice. Based on our observations, the companies see very limited scope for the use of open data as a business asset: the main applications seem to gravitate towards function as an additional feature for an existing product, not a source of new innovations or business ventures. The results also illustrate on how little benefit the organizations consider to gain from opening their private data, and what alternatives there are for sharing data in a profitable manner. Additionally, as based on the observations, a strategy classification on the different data sharing methods is formulated and presented.

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A “datathon” model to support cross-disciplinary collaboration

Cited by 61 publications

References 14 publications

Methods for enhancing the reproducibility of biomedical research findings using electronic health records

Methods for enhancing the reproducibility of biomedical research findings using electronic health records

Code camps and hackathons in education - literature review and lessons learned

Views on Open Data Business from Software Development Companies

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