Data Sharing: Convert Challenges into Opportunities

Figueiredo, Ana Sofia

doi:10.3389/fpubh.2017.00327

Cited by 61 publications

(51 citation statements)

References 40 publications

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“…Data sharing can assist with ongoing research and positively impact derivative 312 research [2,32]. In this regard, we expect that data sharing of the participants arouses a 313 sustainable interest for ongoing or future studies.…”

Section: Data Distribution 311mentioning

confidence: 99%

Reliable data collection in participatory trials to assess digital healthcare apps

Park¹,

Park

Kim

et al. 2020

Preprint

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Digital healthcare mobile apps are experiencing exponential growth due to the development of the mobile network and widespread usage of smartphones. Unlike high growth rates, sufficient validated apps are few. Because mobile health apps are low-risk technology, and it tends to reduce regulatory oversight. With this tendency, the apps have a direct trade characteristic between developers and end-users. The existing platforms are not suitable to collect reliable data for evaluating the effectiveness of the apps. Moreover, these platforms only reflect the perspectives of developers and experts, not end-users. For instance, the methods of data collection in clinical trials are not appropriate for participant-oriented assessment of healthcare apps since its complexity and high cost. Thus, we identified a need for a participant-oriented data collection platform for end-users as an interpretable, systematic, and sustainable tool-as a first step to validate the effectiveness of the apps. To collect reliable data in the participatory trial format, we have defined data preparation, storage, and sharing stage. Interpretable data preparation consists of a protocol database system and semantic feature retrieval method to create a protocol without professional knowledge. Collected data reliability weight calculation belongs to the systematic data storage stage. To sustainable data collection, we integrate the weight method and reward distribution function. We validate the methods with 718 of human participants to conduct statistical tests. The validation results showed that the methods have significant differences in the comparative experiment, and we convinced the methods are essential to reliable data collection. Furthermore, we created a web-based system for our pilot platform to collect reliable data in an integrated pipeline. We validate the platform features with existing clinical and pragmatic trial data collection platforms. In conclusion, we show that the method and platform support reliable data collection, forging a path to effectiveness validation of digital healthcare apps. 1/371 Digital healthcare has become one of the most promising fields in the healthcare 2 industry with the widespread popularity of wireless devices such as smartphones. 3Notably, approximately 200 mobile health apps are listed daily in mobile app stores, 4 and investments in digital health are booming [25,89]. While many of the apps are 5 general wellness applications that help users to manage their activities, some healthcare 6 apps claim to improve a symptom or disease directly. The treatments proposed by the 7 apps take the form of information or visual stimuli, which are effectively delivered to 8 users as digital therapy. reSET-O, an 84-day prescription digital therapeutic app for the 9 treatment of opioid use disorder, has been approved by the U.S. Food and Drug 10 Administration (FDA) [51]. Akili Interactive has clinically demonstrated that interactive 11 digital treatment through a video game, which is under review by the FDA, may 12 i...

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Section: Data Distribution 311mentioning

confidence: 99%

Reliable data collection in participatory trials to assess digital healthcare apps

Park¹,

Park

Kim

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…In economic terms, reusing data allows researchers and institutions to save money, but institutional repositories need a return on investment when they share data; some institutions may be reluctant to invest in sharing data because they may not obtain an immediate return 13 . Based on this point, a mathematical model was created to calculate the break-even point for time spent sharing data in a scientific community versus time gained by reusing data 12 .…”

Section: Benefits Of Reusing Research Datamentioning

confidence: 99%

Reusing Data Technical and Ethical Challenges

Boté¹,

Graells²

2019

DESIDOC Jl. Lib. Info. Technol.

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Research centres, universities and public organisations create datasets that can be reused in research. Reusing data makes it possible to reproduce studies, generate new research questions and new knowledge, but it also gives rise to technical and ethical challenges. Part of these issues are repositories interoperability to accomplish FAIR principles or issues related to data privacy or anonymity. At the same time, funding institutions require that data management plans be submitted for grants, and research tends to be increasingly interdisciplinary. Interdisciplinarity may entail barriers for researchers to reuse data, such as a lack of skills to manipulate data, given that each discipline generates different types of data in different technical formats, often non-standardized. Additionally, the use of standards to validate data reuse and better metadata to find appropriate datasets seem necessary. This paper offers a review of the literature that addresses data reuse in terms of technical, ethical-related issues.

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“…However, reliable methods for data integration are needed and require a broad range of expertise such as in mathematical and statistical models 9 , computational methods 10 , visualization strategies 11 , and deep understanding of complex phenotypes. Therefore, data sharing should not be limited to the dataset per se but also to analytics in the form of analysis workflows, code, interpretation of results, and meta-data 12 . The concept of a Digital Research Object (DRO) was proposed to group dataset and analytics into one united package 13 .…”

Section: Background and Summarymentioning

confidence: 99%

A multi-omics digital research object for the genetics of sleep regulation

Gobet

Diessler

et al. 2019

Preprint

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13More and more researchers make use of multi-omics approaches to tackle complex cellular 14 and organismal systems. It has become apparent that the potential for re-use and integrate data 15 generated by different labs can enhance knowledge. However, a meaningful and efficient re-16 use of data generated by others is difficult to achieve without in depth understanding of how 17 these datasets were assembled. We therefore designed and describe in detail a digital research 18 object embedding data, documentation and analytics on mouse sleep regulation. The aim of 19 this study was to bring together electrophysiological recordings, sleep-wake behavior, 20 metabolomics, genetics, and gene regulatory data in a systems genetics model to investigate 21 sleep regulation in the BXD panel of recombinant inbred lines. We here showcase both the 22 Experiment 1 and Experiment 2 ( Figure 1) were approved by the veterinary authorities of the 95 state of Vaud, Switzerland (SCAV authorization #2534). 96 97 Animal, breeding, and housing conditions 98 34 BXD lines originating from the University of Tennessee Health Science Center (Memphis, 99 TN, United States of America) were selected for Experiment 1 and Experiment 2. These lines 100 were randomly chosen from the newly generated advanced recombinant inbred line (ARIL) 101 RwwJ panel 4 , although lines with documented poor breeding performance were not 102 considered. 4 additional BXD RI strains were chosen from the older TyJ panel for 103 reproducibility purposes and were obtained directly from the Jackson Laboratory (JAX, Bar 104Harbor, Maine). The names used for some of the BXD lines have been modified over time to 105 reflect genetic proximity. Table 1 lists the BXD line names we used in our files alongside the 106 corresponding current JAX names and IDs. In our analyses, we discarded the BXD63/RwwJ 107 line for quality reasons (see Technical Validation) as well as the 4 older BXD strains that were 108 derived from a different DBA/2 sub-strains, i.e. DBA/2Rj instead of DBA/2J for RwwJ lines 109 21 . The methods below describe the remaining 33 BXD lines, F1 and parental strains. 110 Two breeding trios per BXD strain were purchased from a local facility (EPFL-SV, Lausanne, 111 Switzerland) and bred in-house until sufficient offspring was obtained. The parental strains 112 DBA/2J (D2), C57BL6/J (B6) and their reciprocal F1 offspring (B6D2F1 [BD-F1] and 113 D2B6F1 [DB-F1]) were bred and phenotyped alongside. Suitable (age and sex) offspring was 114 transferred to our sleep-recording facility, where they were singly housed, with food and 115 water available ad libitum, at a constant temperature of 25°C and under a 12 h light/12 h dark 116 cycle (LD12:12, fluorescent lights, intensity 6.6 cds/m 2 , with ZT0 and ZT12 designating light 117 and dark onset, respectively). Male mice aged 11-14 week at the time of experiment were 118 used for phenotyping, with a mean of 12 animals per BXD line among all experiments. Note 119 that 3 BXD lines had a lower replicate number (n), with respect...

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Data Sharing: Convert Challenges into Opportunities

Cited by 61 publications

References 40 publications

Reliable data collection in participatory trials to assess digital healthcare apps

Reliable data collection in participatory trials to assess digital healthcare apps

Reusing Data Technical and Ethical Challenges

A multi-omics digital research object for the genetics of sleep regulation

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