Nature Feature: Data sharing and how it can benefit your scientific career

Open science can lead to greater collaboration, increased confidence in findings and goodwill between researchers.

https://www.nature.com/articles/d41586-019-01506-x

"The key ... is to practise “open science by design” ... For example, many researchers now keep data, computer code and other materials in web-based, interactive tools such as the popular Jupyter electronic notebook, which makes online archiving much easier."

"For early-career scientists who prefer producing data to managing it, Mons has this advice: “Go to a university that takes data stewardship seriously.”"

BBC News report on AAAS meeting: Machine learning 'causing science crisis'

Interesting report on the BBC News web site, covering a presentation given at last month's meeting of the AAAS about how the (mis)use of machine learning is contributing to the “reproducibility crisis” in science:

AAAS: Machine learning 'causing science crisis'
By Pallab Ghosh, Science correspondent, BBC News, Washington

Techniques used by thousands of scientists to analyse data are producing results that are misleading and often completely wrong.
Dr Genevera Allen from Rice University in Houston said that the increased use of such systems was contributing to a “crisis in science”.
She warned scientists that if they didn’t improve their techniques they would be wasting both time and money. Her research was presented at the American Association for the Advancement of Science in Washington.

Read the full story:
https://www.bbc.com/news/science-environment-47267081

Elsevier releases report "Research Futures: drivers and scenarios for the next decade"

Elsevier has released a report explaining its part in improving “the information system supporting research” over the next decade...

https://www.elsevier.com/connect/elsevier-research-futures-report

Life sciences data steward function matrix - article

Life sciences data steward function matrix

 Salome Scholtens; Petronella Anbeek;  Jasmin Böhmer; Mirjam Brullemans-Spansier; Marije van der Geest;  Mijke Jetten;  Christine Staiger;  Inge Slouwerhof;  Celia W G van Gelder

Sufficient, high quality data steward expertise and capacity in projects and institutes is one of the necessities for FAIR data management in life- sciences and personalised medicine research. In a ZonMw funded project of UMCG, UMCU, Radboudumc, Radboud University and DTL, supported by the relevant national stakeholders, we are working to make the data steward function concrete, to create consensus on the function and required competencies and to develop tailored education. The overall project aim is to professionalise the data steward function within the life-sciences domain, with a special focus on the implementation of the FAIR data principles. All documents related to this project can be found on the Zenodo Collection “Towards a community-endorsed data steward profession description for life-science research”.

This publication contains the first project deliverable: a matrix, that may function as the basis for a common job description of a data steward that is broadly supported within the Dutch life-sciences community. In the next phase of the project, this matrix will be complemented by knowledge, skills and competencies of a data steward, which will be translated into concrete learning objectives. These in turn will be used to develop an education line and training material for data stewards (including a design for an eLearning module). Sustainable implementation and alignment with existing education will be ensured.

from: https://zenodo.org/record/2561723#.XGNSD88zauM