Is open scholarship an honest signal of researcher integrity? We present preliminary evidence that data and code sharing, preprinting, and other open behaviors are indeed less common in papermill articles.
As AI becomes a major consumer of research, scholarly publishing must evolve: from PDFs for people to structured, high-quality data for machines.
We asked the Program Committee Chairs what they’re looking forward to at this year’s SSP Annual Meeting.
Nicola Davies from IOPP details the publisher’s new data sharing requirements for authors.
Open Café, a new listserv dedicated to the free and open discussion of open scholarship has been met with enthusiasm by the scholarly communication community.
In guest post, Simon Linacre of Digital Science discusses their latest state of open data survey against the backdrop of the recent OSTP memo on expanding public access to research results.
A recent data falsification scandal in Alzheimer’s research raises new questions about perverse incentives in the culture and practice of science.
Twitter does not increase citations, a reanalysis of author data shows. Did the authors p-hack their data?
When a reputable journal refuses to get involved with a questionable paper, science looks less like a self-correcting enterprise and more like a way to amass media attention.
Mark Hahnel looks at the progress that’s been made toward open research data — what’s been achieved, what still needs work, and what happens next?
Revisiting Tim Vines’ 2017 post — Open data continues to gain ground, but is there a revenue stream that would help journals recover the costs of gathering, reviewing and publishing data?
When do new approaches to research communication become an end unto themselves? How much more work can we pile on researchers? Is more information always better than less?
Global initiatives in open are decentralized and disconnected, lacking researcher input and buy-in. An “opens solutions” approach can both embrace and leverage that diversity, ensuring that it all contributes to the greater whole.
Transparency around research methodologies is essential for driving public trust and accurate, reproducible research results.
The FAIR principles answer the ‘How’ question for sharing research data, but we also need consensus on the ‘What’ question.