#SciData19 Writing Competition: Winning Entry #3

We are proud to publish the third of this year's four winning entries for this years Better Science through Better Data writing competition - congratulations to Oriana Genolet
#SciData19 Writing Competition: Winning Entry #3
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Better Science through Better Data 2019

In ‘Better Science through Better Data’ (#scidata19) Springer Nature and The Wellcome Trust partner to bring together researchers to discuss innovative approaches to data sharing, open science, and reproducible research, together with demonstrations of exemplary projects and tools. If you are a researcher, this event will give you the chance to learn more about how research data skills can aid career progression, including how good practice in data sharing can enable you to publish stronger peer-reviewed publications. Tickets for the event have now sold out - but you can register for the live stream to watch our keynote talks as they happen from wherever you are in the world. Keynote speakers Shelley Stall Senior Director, Data Leadership American Geophysical Union (AGU) Shelley Stall is the Senior Director for the American Geophysical Union’s Data Leadership Program. She works with AGU’s members, their organizations, and the broader research community to improve data and digital object practices with the ultimate goal of elevating how research data is managed and valued. Better data management results in better science. Shelley’s diverse experience working as a program and project manager, software architect, database architect, performance and optimization analyst, data product provider, and data integration architect for international communities, both non-profit and commercial, provides her with a core capability to guide development of practical and sustainable data policies and practices ready for adoption and adapting by the broad research community. Shelley’s recent work includes the Enabling FAIR Data project, engaging over 300 stakeholders in the Earth, space, and environmental sciences to make data open and FAIR, targeting the publishing and repository communities to change practices by no longer archiving data in the supplemental information of a paper but instead depositing the data supporting the research into a trusted repository where it can be discovered, managed, and preserved. Her talk is entitled: Your Digital Presence Mikko Tolonen Assistant Professor Faculty of Arts at the University of Helsinki Mikko Tolonen is an assistant professor of Digital Humanities at the University of Helsinki. He is the PI of Helsinki Computational History Group (COMHIS). In 2015-17 he also worked in the National Library of Finland on digitized newspapers as professor of research on digital resources. He is the chair of Digital Humanities in the Nordic Countries (DHN). His current main research focus is on an integrated study of early modern public discourse and knowledge production that combines bibliographic metadata and full-text sources. In 2016, he was awarded an Open Science and Research Award by the Finnish Ministry of Education and Culture. His talk is entitled: Integrating Open Science in the Humanities: the Case of Computational History David Stillwell Lecturer in Big Data Analytics and Quantitative Social Science Judge Business School, University of Cambridge David is Lecturer in Big Data Analytics and Quantitative Social Science at Cambridge University’s Judge Business School. David’s research uses big data to understand psychology. He published papers using social media data from millions of consenting individuals to show that the computer can predict a user’s personality as accurately as their spouse can. This research has important public policy implications. How should consumers’ data be used to target them? Should regulators step in, and if so how? David has spoken at workshops at the EU Parliament and to UK government regulators. David has also published research using various big data sources such as from credit card data and textual data to show that spending money on products that match one’s personality leads to greater life satisfaction, that people tend to date others whose personality is similar, and that people who swear seem to be more honest. His talk is entitled: Getting Big Data: Social scientists must strive to be autonomous from corporate charity. Tomas Knapen Assistant Professor Vrije Universiteit Amsterdam - Cognitive Psychology Tomas is a cognitive neuroscientist whose research focuses on the role sensory topographies (visual retinotopy, auditory tonotopy and bodily somatotopy) play in the detailed organization of the human brain and cognition. For this work, Tomas uses state of the art 7-Tesla MRI techniques. Early-career experiences where he ‘failed to replicate’ previous findings have impressed upon him the need to make research reproducible from top to bottom. Because of this, his lab uses only open methods and puts all their data and methods online. Having invested in these methods, Tomas is convinced that, in the end, it is not a burden to perform open science, rather it provides researchers with great opportunities for ground-breaking science. His talk is entitled: How I learned to stop worrying and love Open Science See the event programme. Meet the Programme Committee. Register for the live stream.

Question: How should researchers be rewarded for data sharing and reproducible research?


Answer: 

Oriana Genolet - Max Planck Institute

The current reward system in science is based on publishing a good amount of papers in high impact factor journals, which will ultimately lead to acquisition of further research funding. Here, speed and novelty are greatly rewarded. The public availability and thorough documentation of generated data should be the basis of high-quality and reproducible research. According to recent polls, 70% of conducted studies cannot be reproduced by scientists, a term coined as the “reproducibility crisis”. It is becoming increasingly clear that the scientific community needs to come up with solutions to address these arising problems. Researchers that invest time and effort in generating reproducible high-quality science should be met with certain rewards and incentives. As a starting point, generating publicly available datasets that meet the TOP guidelines (1) should be a requirement for the publication of research findings in scientific journals. In order to decrease the amount of labor invested by scientists, some publishing houses like Springer Nature have launched a Research Data Support Service, where data and metadata is organized as to meet data sharing requirements and standards. This would ultimately lead to a significant increase in the reusability of published data and in the number of citations of a particular paper. This time-saving incentive could be easily taken over by other journals, making it a standard publishing practice. Furthermore, funding agencies, research institutes or universities could designate one part of their research funds for these specific purposes. In fields such as machine learning, datasets are published as research papers with some of them achieving as many as 3300 citations. Bearing this is mind, creating a journal dedicated to the publication of high-quality datasets would surely draw the interest of many scientists, who could eventually end up publishing several extensively cited papers from one research project. The journal would also benefit from the numerous citations by earning a high-impact factor. Another measure to increase data sharing could be granting financial rewards to researchers that consistently produce high-quality publicly available datasets or that re-utilize them to answer further research questions. This is being carried out by institutions such as the Berlin Institute of Health (BIH) or the whimsically termed “Symbiont” and “Parasite” awards. Regarding the reproducibility crisis, very much like the implementation of a female quota in academia, a “Research Replicability Quota” could be adopted by many scientific research journals, where 5-10% of the journal would be dedicated to the publication of research validations. This way, scientists that invest time and funds in the reproduction of published studies would be rewarded by high impact publications. In conclusion, the scientific community needs to find solutions that improve research transparency, data sharing and reproducibility. In the past years, awareness of the current problematic has been raised and some action has been taken by research institutions and scientific journals. We can only hope that these efforts continue and that, eventually, investigators will make the generation of high-quality research a top priority. 

(1) https://cos.io/top/


Don't forget to register for Better Science through Better Data on November 6th at the Wellcome Collection in London to learn about data sharing and open science.

Meet the other writing competition winners here.

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