Ten simple rules for getting started on Twitter as a scientist

Cheplygina, Veronika; Hermans, Felienne; Albers, Casper J.; Bielczyk, Natalia; Smeets, Ionica

doi:10.1371/journal.pcbi.1007513

Cited by 55 publications

(57 citation statements)

References 11 publications

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“…1B. Here it is shown the daily stock closing 80 price (NASDAQ Composite 2020) of Twitter, a popular social media amongst scientists (Cheplygina et al 2020). Due to the SARS-CoV-2 and related COVID-19 outbreak (WHO 2020), after around 60 working days of positive trend, the stock price started to oscillate only to fall drastically by 44% in just 19 working days.…”

Section: Introductionmentioning

confidence: 94%

Fractal analysis of muscle activity patterns during locomotion: pitfalls and how to avoid them

Santuz

Akay

2020

Preprint

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Time-dependent physiological data, such as electromyogram (EMG) recordings from multiple 15 muscles, is often difficult to interpret objectively. Here, we used EMG data gathered during mouse locomotion to investigate the effects of calculation parameters and data quality on two metrics for fractal analysis: the Higuchi's fractal dimension (HFD) and the Hurst exponent (H).A curve is fractal if it repeats itself at every scale or, in other words, if its shape remains unchanged when zooming in the curve at every zoom level. Many linear and nonlinear analysis 20 methods are available, each of them aiming at the explanation of different data features. In recent years, fractal analysis has become a powerful nonlinear tool to extract information from physiological data not visible to the naked eye. It can present, however, some dangerous pitfalls that can lead to misleading interpretations. To calculate the HFD and the H, we have extracted muscle synergies from normal and mechanically perturbed treadmill locomotion from the 25 hindlimb of adult mice. Then, we used one set per condition (normal and perturbed walking) of the obtained time-dependent coefficients to create surrogate data with different fluctuations over the original mean signal. Our analysis shows that HFD and H are exceptionally sensitive to the presence or absence of perturbations to locomotion. However, both metrics suffer from variations in their value depending on the parameters used for calculations and the presence of 30 quasi-periodic elements in the time series. We discuss those issues giving some simple suggestions to reduce the chance of misinterpreting the outcomes. New & NoteworthyDespite the lack of consensus on how to perform fractal analysis of physiological time series, many studies rely on this technique. Here, we shed light on the potential pitfalls of using the 35 Submitted Manuscript 3 Higuchi's fractal dimension and the Hurst exponent. We expose and suggest how to solve the drawbacks of such methods when applied to data from normal and perturbed locomotion by combining in vivo recordings and computational approaches.

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Section: Introductionmentioning

confidence: 94%

Fractal analysis of muscle activity patterns during locomotion: pitfalls and how to avoid them

Santuz

Akay

2020

Preprint

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“…Students, fans, scientists, academics, and institutions hilariously interact during the weeks of the tournament and, to a lesser extent, throughout the year. In this way, March Mammal Madness reaches many “publics” and explicitly dismantles boundaries among scientists, students, and the broader members of society ( Varner, 2014 ; Jarreau et al, 2019 ; Cheplygina et al, 2020 ), an important component in stemming misinformation ( Scheufele and Krause, 2019 ).…”

Section: Emergent Community: Public Scientists and Institutionsmentioning

confidence: 99%

March Mammal Madness and the power of narrative in science outreach

Hinde

Amorim

Brokaw

et al. 2021

eLife

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March Mammal Madness is a science outreach project that, over the course of several weeks in March, reaches hundreds of thousands of people in the United States every year. We combine four approaches to science outreach – gamification, social media platforms, community event(s), and creative products – to run a simulated tournament in which 64 animals compete to become the tournament champion. While the encounters between the animals are hypothetical, the outcomes rely on empirical evidence from the scientific literature. Players select their favored combatants beforehand, and during the tournament scientists translate the academic literature into gripping “play-by-play” narration on social media. To date ~1100 scholarly works, covering almost 400 taxa, have been transformed into science stories. March Mammal Madness is most typically used by high-school educators teaching life sciences, and we estimate that our materials reached ~1% of high-school students in the United States in 2019. Here we document the intentional design, public engagement, and magnitude of reach of the project. We further explain how human psychological and cognitive adaptations for shared experiences, social learning, narrative, and imagery contribute to the widespread use of March Mammal Madness.

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“…Furthermore, should a news outlet release information on social media about your article, ensure that it accurately reflects the content of your research and is only released after the embargo period lifts for your article. 10…”

Section: Tip 4: Know Your Journal's Embargo Policymentioning

confidence: 99%