Archive for ‘Thema2 Recherche académique’

16 juillet 2019

Nature is a big library that we should learn to observe and mimic ! Copying nature is being at the edge of high tech. #Biomimicry_design

 

Publicités
16 juillet 2019

Introduction to LCA : Latent Class Analysis #Marketingthema

Latent class analysis (LCA) identifies unobservable subgroups within a population. We work to expand LCA models to allow scientists to better understand the impact of exposure to patterns of multiple risks, as well as the antecedents and consequences of complex behaviors, so that interventions can be tailored to target the subgroups that will benefit most.

Read more here

Some statistical packages able to handle this analysis: 

8 juillet 2019

Watch French research in real-time ! Live consultation of articles from several full-text electronic scientific journals and databases.

You can click on the picture or this link to watch the magic happening in real-time

8 juillet 2019

Visualizing literature review using #VosViewer | Discover new links between authors, topics, keywords, domains, etc.

 

 

Source : https://www.vosviewer.com/getting-started

 

11 juin 2019

JASP 0.10 has been released #JASP

See the list here for all the analyses currently available in JASP. To find out how to perform certain analyses or how to use certain features.

28 mai 2019

Interesting paper on how to identify potential mediators in ‘high-dimensional’ datasets

This paper is very interesting because i believe the future will be more inductive than deductive, datasets, will tell us the theory on how the world works through mining large datasets. The author uses an original method to identify the variables (columns) that have an impact on the dependent variable, without having the limitations related to the methods of structural equations that require a number of observations greater than the number of variables (sometimes the threshold recommended is 10 observations per variable in the file). This method is therefore an original contribution to understand how to look for mediators by making the data talk without any a priori!

Van Kesteren, E. J., & Oberski, D. L. (2019). Exploratory Mediation Analysis with Many Potential Mediators

The paper is available here : read 

14 mai 2019

Death of the author? AI generated books and the production of scientific knowledge

Artificial Intelligence (AI) has been applied to an increasing number of creative tasks from the composition of music, to painting and more recently the creation of academic texts. Reflecting on this development Harry Collins, considers how we might understand AI in the context of academic writing and warns that we should not confuse the work of algorithms with tacit complex socially constructed forms of knowledge.

Apparently there are now academic books generated by artificial intelligence algorithms.  An example just published by Springer Nature, and written by ‘Beta Writer’, is called Lithium-Ion Batteries: A Machine-Generated Summary of Current Research.  I don’t know anything much about Lithium-Ion batteries, nor about how these algorithms work, but I do know something about scientific knowledge and the way it is generated. I have also written three books (without the aid of an algorithm) on artificial intelligence that draw on this knowledge, most recently: Artifictional Intelligence, Against humanity’s surrender to computers.

Full paper here

15 avril 2019

Google offers a new search engine to find #datasets on specific topics

Google offers a new search engine to find datasets on specific topics.  Here is the link:

https://toolbox.google.com/datasetsearch

This could be a great tool to train students on data, or even conduct research on secondary useful datasets.

« In today’s world, scientists in many disciplines and a growing number of journalists live and breathe data. There are many thousands of data repositories on the web, providing access to millions of datasets; and local and national governments around the world publish their data as well. To enable easy access to this data, we launched Dataset Search, so that scientists, data journalists, data geeks, or anyone else can find the data required for their work and their stories, or simply to satisfy their intellectual curiosity.  Similar to how Google Scholar works, Dataset Search lets you find datasets wherever they’re hosted, whether it’s a publisher’s site, a digital library, or an author’s personal web page. To create Dataset search, we developed guidelines for dataset providers to describe their data in a way that Google (and other search engines) can better understand the content of their pages. These guidelines include salient information about datasets: who created the dataset, when it was published, how the data was collected, what the terms are for using the data, etc. We then collect and link this information, analyze where different versions of the same dataset might be, and find publications that may be describing or discussing the dataset. Our approach is based on an open standard for describing this information (schema.org) and anybody who publishes data can describe their dataset this way. We encourage dataset providers, large and small, to adopt this common standard so that all datasets are part of this robust ecosystem. »

Source here

5 avril 2019

Great initiative! criteria used to rank academia is changing in 2019!  

Times Higher Education produced in the 3rd of April a new world ranking for 450 universities based on the United Nations Sustainable Development Goals like (gender equality, sustainable cities and communities, real-world problem solving research, etc.).

31 mars 2019

Choosing A Statistical Test Based On Your Data And Research Question

22 mars 2019

The crisis of scientific research and possible solutions! #ReplicationCrisis #PublishOrPerish #Ethics #OpenScience #PHacking #Publishing_Negative_Results #PhdChat

Part 1 : the diagnostic of the situation 

Part 2 : the possible solutions 

A great document by Corbert report in two parts about the modern science. The first part is a critical diagnostic about the situation, and the second part suggests some solutions to enhance the scientific process and make the findings more trustworthy. If interested in this topic you can also read my post on Hubbart excellent book (Corrupt research) here.

YB

5 mars 2019

Dance of the p Values & reporting intevals by Geoff Cumming

Cumming, Geoff. Understanding the new statistics: Effect sizes, confidence intervals, and meta-analysis. Routledge, 2013.

3 mars 2019

Upcoming marketing conferences deadlines & call for chapters (updated)

By Yassine El Bouchikhi 

Sources used: 

  • ELMAR
  • Conference alerts
  • AFM marketing
  • EMAC
  • Calenda
25 février 2019

The new norm in research will be about QUALITY NOT QUANTITY ! #PublishorPerish culture is harming academia! #Merton_norms to Make Academia Great Again ;)

In 1942, sociologist Robert Merton articulated an ethos of science in “A Note on Science and Technology in a Democratic Order.” He argued that, although no formal scientific code exists, the values and norms of modern science can nevertheless be inferred from scientists’ common practices and widely held attitudes. Merton discussed four idealized norms: Universalism, Communality, Disinterestedness, and Organized Skepticism. In this video, we explore what these norms are and what they mean for the scientific community. « A Note on Science and Technology » can be found at: http://www.collier.sts.vt.edu/5424/pd…

Merton, Robert K. 1973. The Sociology of Science: Theoretical and Empirical Investigations. University of Chicago Press.

Source: Berkeley Initiative for Transparency in the Social Sciences (BITSS)

20 février 2019

Humans Run Experiments, a Robot Writes the Paper

The future of automated scientific writing is upon us—and that’s a good thing.

In 2014, a researcher in France revealed a disturbing fact about the published scientific literature: At least 120 computer-generated manuscripts had made their way into academic conference proceedings, according to his analysis. Those robot-written papers, containing little more than strung-together buzzwords, had been created with a piece of software known as SCIgen, originally written on a lark by a trio of MIT graduate students in 2005. But in the years since, it seemed scientists had repurposed SCIgen to puff up their resumes and boost their professional status. This was understood to be a major scandal.

For Klemen Zupancic, though, the scandal was a source of inspiration. “It got us thinking,” the 32-year-old molecular biologist and tech entrepreneur told me this week from his office in Slovenia. Zupancic is head of sciNote, a tech startup that builds tools for helping scientists to switch from using pen-and-paper laboratory notebooks to more efficient online apps. (The company claims to have about 20,000 users, of which almost half are in the U.S.) When he read about the infiltration of academic journals by robo-generated text, he realized that the same approach might be used for honest ends. If software can publish scientific gobbledygook, then maybe it can write a valid scientific paper, too. So his company set out to create a program that would do just that.

more here