Valentin Danchev
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View article: Towards deployment-centric multimodal AI beyond vision and language
Towards deployment-centric multimodal AI beyond vision and language Open
Multimodal artificial intelligence (AI) integrates diverse types of data via machine learning to improve understanding, prediction, and decision-making across disciplines such as healthcare, science, and engineering. However, most multimod…
View article: Ten simple rules for building and maintaining a responsible data science workflow
Ten simple rules for building and maintaining a responsible data science workflow Open
Contributors and beneficiaries of data-intensive research have become increasingly concerned about social and ethical risks from data science and machine learning applications [1][2][3][4][5][6].Instances of unethical use of technology and…
Reproducible Data Science with Python: An Open Learning Resource Open
This paper describes a computational learning resource on Reproducible Data Science with Python.The resource provides an accessible, hands-on introduction to data science techniques, skills, and workflows necessary to perform open, reprodu…
Data Governance in the Age of Large-Scale Data-Driven Language Technology Open
The recent emergence and adoption of Machine Learning technology, and specifically of Large Language Models, has drawn attention to the need for systematic and transparent management of language data. This work proposes an approach to glob…
Medical journal requirements for clinical trial data sharing: Ripe for improvement Open
Florian Naudet and co-authors discuss strengthening requirements for sharing clinical trial data.
View article: Evaluation of Data Sharing After Implementation of the International Committee of Medical Journal Editors Data Sharing Statement Requirement
Evaluation of Data Sharing After Implementation of the International Committee of Medical Journal Editors Data Sharing Statement Requirement Open
Most trials published in JAMA, Lancet, and NEJM after the implementation of the ICMJE policy declared their intent to make clinical data available. However, a wide gap between declared and actual data sharing exists. To improve transparenc…
View article: The worldwide clinical trial research response to the COVID-19 pandemic - the first 100 days
The worldwide clinical trial research response to the COVID-19 pandemic - the first 100 days Open
Background : Never before have clinical trials drawn as much public attention as those testing interventions for COVID-19. We aimed to describe the worldwide COVID-19 clinical research response and its evolution over the first 100 days of…
View article: The worldwide clinical trial research response to the COVID-19 pandemic - the first 100 days
The worldwide clinical trial research response to the COVID-19 pandemic - the first 100 days Open
Background: Never before have clinical trials drawn as much public attention as those testing interventions for COVID-19. We aimed to describe the worldwide COVID-19 clinical research response and its evolution over the first 100 days of t…
View article: Evaluation of Clinical Trial Data Sharing Policy in Leading Medical Journals
Evaluation of Clinical Trial Data Sharing Policy in Leading Medical Journals Open
Background The benefits from responsible sharing of individual-participant data (IPD) from clinical studies are well recognized, but stakeholders often disagree on how to align those benefits with privacy risks, costs, and incentives for c…
Migration Networks: Applications of Network Analysis to Macroscale Migration Patterns Open
An emerging area of research is the study of macroscale migration patterns as a network of nodes that represent places (e.g., countries, cities, and rural areas) and edges that encode migration ties that connect those places. In this chapt…
Calibrating the Scientific Ecosystem Through Meta-Research Open
While some scientists study insects, molecules, brains, or clouds, other scientists study science itself. Meta-research, or research-on-research, is a burgeoning discipline that investigates efficiency, quality, and bias in the scientific …
Centralized scientific communities are less likely to generate replicable results Open
Concerns have been expressed about the robustness of experimental findings in several areas of science, but these matters have not been evaluated at scale. Here we identify a large sample of published drug-gene interaction claims curated i…
Calibrating the scientific ecosystem through meta-research Open
Whilst some scientists study insects, molecules, brains, or clouds, other scientists study science itself. Meta-research, or “research-on-research”, is a burgeoning discipline that investigates efficiency, quality, and bias in the scientif…
Centralized "big science" communities more likely generate non-replicable results Open
Growing concern that most published results, including those widely agreed upon, may be false are rarely examined against rapidly expanding research production. Replications have only occurred on small scales due to prohibitive expense and…