Sharing is Caring: Working With Other People’s Data

Post provided by Mariana García Criado, Isla Myers-Smith, Lander Baeten, Andrew Cunliffe, Gergana Daskalova, Elise Gallois and Jeffrey Kerby

 

The Team Shrub research group in 2017 on Qikiqtaruk – Herschel Island in the Canadian Arctic. Not only do Team Shrub work with other people’s data, we collect our own to share publicly following open science best practice. (Photo credit: Sandra Angers-Blondin, www.teamshrub.com).

Team Shrub (www.teamshrub.com), are ecologists working to understand how global change alters plant communities and ecosystem processes. In May 2020, Team Shrub held a lab meeting to discuss working with other people’s data. Inspired by the conversation, they decided to put a blog post together to explore the importance of careful data cleaning in open science, provide 10 best practice suggestions for working with other people’s data, and discuss ways forward towards more reproducible science. 

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Stop, think, and beware of default options

Post provided by Paula Pappalardo (with contributions from Elizabeth Hamman, Jim Bence, Bruce Hungate & Craig Osenberg)

Esta publicación también está disponible en español.

You spent months carefully collecting data from articles addressing your favorite scientific question, you have dozens of articles neatly arranged on a spreadsheet, you found software or code to analyze the data, and then daydream about how your publication will be the most cited in your field while making cool plots. If that sounds familiar, you have probably done a meta-analysis. Meta-analysis uses statistical models to combine data from different publications to answer a specific question.

What you may not have realized when going down the meta-analysis rabbit hole, is that small, seemingly inconsequential, choices can greatly affect your results. If you want to know about one of them lurking behind the scenes… read on!

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Para, piensa, y ten cuidado con las configuraciones por defecto

Post escrito por Paula Pappalardo (con aportes de Elizabeth Hamman, Jim Bence, Bruce Hungate & Craig Osenberg)

This post is also available in English.

Pasaste meses laboriosamente colectando datos de artículos científicos acerca de tu pregunta favorita, tienes decenas de artículos perfectamente organizados en una base de datos, ya encontraste el programa o código para analizar los datos, y entonces imaginas como tu publicación va a ser la más citada en tu campo de investigación mientras haces unos gráficos lindísimos. Si esto te suena familiar, seguramente has hecho un meta-análisis. Un meta-análisis usa modelos estadísticos para combinar datos de distintas publicaciones para responder a una pregunta específica.

Lo que quizás no te diste cuenta mientras navegabas los pasos del meta-análisis, es que pequeñas decisiones (a veces pareciendo de muy poca importancia) pueden tener grandes efectos en los resultados. Si quieres saber más acerca de una de estas decisiones en particular… ¡sigue leyendo!

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A Big Database and Big Models Pave the Way for Big Questions in Ecology

This post was provided by Sean McMahon.

Sean is an Associate Editor for Methods in Ecology and Evolution and is a staff scientist at the Smithsonian Institution based at the Smithsonian Environmental Research Center.  His research focuses on the ecological mechanisms that structure forest communities, with interests spanning the fields of demography, physiology, and remote sensing.

The 100th anniversary of the Ecological Society of America was celebrated in Baltimore, Maryland at their Annual Conference in August. This year a record 10,000 ecologists attended the six day event. ESA conferences now boast a staggering number of scientific presentations, ranging from numerous plenary talks, organized oral sessions and regular oral presentation sessions to lightening talks, posters, workshops and mixers. It was both exhilarating and overwhelming, but featured a truly impressive amount of science.

As the sheer magnitude of the event made attending even a fraction of the talks impossible, it feels odd to highlight any particular presentations. Two talks, however – both on the final morning of the conference – did strike me as worth mentioning; not because they featured groundbreaking science, or novel insights, but because they reflect potentially powerful new platforms from which groundbreaking science might develop. Continue reading