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Digital Science welcomes Gigantum and Ripeta to the family to help increase reproducibility in research

publication date: Apr 15, 2019
author/source: Digital Science


Digital Science, a technology company serving stakeholders across the research ecosystem, welcomes two US-based startups to the Digital Science family of companies: automated reproducibility assessment tool, Ripeta, and data science platform, Gigantum.

Both companies are playing a key role in making scientific research reproducible and more transparent. Ripeta is developing a "credit report" for scientific publications to assess and help improve the transparency needed to effectively communicate research, while Gigantum is an emerging innovator in the area of data science platforms, supporting large-scale, data-oriented scientific research.

Daniel Hook, CEO of Digital Science, said: "Reproducibility of research is one of the critical topics of modern times - if research is to remain trusted by the populations who fund it, then researchers must ensure that their research can be reproduced by others."



Ripeta, a previous Digital Science Catalyst grant winner, aims to make better science easier by identifying and highlighting the important parts of research that should be transparently presented in a manuscript and other materials. The tool detects the key evidence for and assesses reproducibility in the scientific research industry through software and analytics development; improving evidence-based science and fiscal efficiency of research investments. These tools leverage sophisticated machine-learning and natural language processing algorithms to extract key reproducibility elements from research articles.

Ripeta focuses on assessing the quality of the reporting and robustness of the scientific method rather than the quality of the science. The company’s long-term goal includes developing a suite of tools across the broader spectrum of sciences to understand and measure the key standards and limitations for scientific reproducibility across the research lifecycle and enable an automated approach to their assessment and dissemination.

Leslie McIntosh, CEO of Ripeta, said: "While technological innovations have accelerated scientific discoveries, they have complicated scientific reporting. Science is hard and reproducibility is important, so we need to make better science easier.

"We are developing the tools to make research methods transparent, enabling the verifiability, falsifiability and reproducibility of research. We are thrilled to receive this investment with the great team at Digital Science who provide a fantastic partnership. This will propel our work at Ripeta helping make scientific reporting more transparent."



Gigantum is a completely new approach to handling the interaction between code, data and run-time environments in this new data-rich research world. Focused on open-source tools and frameworks, Gigantum helps researchers to better scale the creation, management and dissemination of fully reproducible research and the data science that supports it.

Gigantum seeks to develop a platform that keeps pace with the expanding number of independently created tools and data sources, thereby serving an increasingly diverse but collaborative set of researchers with non-uniform skills, interests, and resources. The team’s technical approach is based on their appreciation that the automated management of data, code, environment, and version history is fundamental to enabling decentralized collaboration.

Dean Kleissas, Gigantum CTO, said: "Despite wide adoption of open-source tools and growing willingness to share research code and data, we still can’t quite escape the evolving difficulties of publishing transparent and easily reproducible research for broad use and consumption.

"When we first spoke with Digital Science, I knew it was a good fit because we immediately clicked on the fact that best practices alone won’t do it. We need technological approaches that enable real changes in what people of all skill levels can do, not just the most skilled."



"Gigantum and Ripeta together address two of the biggest aspects of reproducibility: workflow and communication. Through Gigantum, a researcher can ensure that they have the full provenance of the data objects that appear in their papers, allowing anyone to repeat their data processing and analysis with ease," adds Hook.

"With Ripeta, a researcher is able to check that they have correctly described and referred to the data, methodology and analysis approaches that underpin their arguments."


About Digital Science

Digital Science is a technology company working to make research more efficient. We invest in, nurture and support innovative businesses and technologies that make all parts of the research process more open and effective. Our portfolio includes admired brands including Altmetric, Anywhere Access, Dimensions, Figshare, ReadCube, Symplectic, IFI Claims, GRID, Overleaf, Labguru, BioRAFT, TetraScience, Transcriptic and CC Technology. We believe that together, we can help researchers make a difference. Visit  and follow @digitalsci on Twitter.

About Gigantum

Gigantum is a Washington, DC-based company that is developing a decentralized, scalable and user-friendly data science platform that solves outstanding collaboration and reproducibility problems for both producers and consumers of scientific research. The Gigantum Platform combines a user-friendly open source work environment with a scalable cloud-based publishing platform, allowing researchers to work locally but share globally with a single click. Visit and follow @gigantumscience on Twitter.

About Ripeta

Ripeta detects and predicts reproducibility in the scientific research industry through software and analytics development; improving evidence-based science and fiscal efficiency of research investments. It is effectively a ‘credit report’ for scientific publications. Ripeta provides a suite of tools and services to rapidly screen and assesses manuscripts for the proper reporting of scientific method components. These tools leverage sophisticated machine-learning and natural language processing algorithms to extract key reproducibility elements from research articles.




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