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Partnership with Pistoia Alliance Opens Up Knowledge Sharing Opportunities

Digital transformation and emerging tech gain prominence in life sciences research

Amsterdam, NL – IOS Press recently became a member of the Pistoia Alliance, which is beneficial not only for the company but also for the wider network of editors and board members. This exciting new partnership is all about working collaboratively, which we hope the researchers in our audience will be able to tap into. The alliance seeks to “overcome common research and development (R&D) obstacles by identifying root causes, developing standards and best practices, sharing pre-competitive data and knowledge, and executing technology pilots.” There are numerous projects with which to get involved, including artificial intelligence, data visualization, and FAIR implementation, and plenty of wide-ranging events.

Pistoia Alliance is a not-for-profit members’ organization – conceived in 2007 and incorporated in 2009 by representatives from four pharmaceutical companies who met at a conference in Pistoia, Italy – working to lower barriers to innovation in life science and healthcare R&D through pre-competitive collaboration. IOS Press membership means that editors and board members in our network can access all Pistoia Alliance projects and those who are interested in cross-industry collaboration and innovation can take part. 

Investing in emerging tech

With more than 100 members – ranging from global organizations, to medium enterprises, to start-ups, to individuals – it is possible to leverage the collective expertise of the entire membership base and there are opportunities to collaborate on projects that generate value for the global life sciences community. Having said that, it is not only life science researchers who are invited to participate. There are numerous ongoing projects focusing on artificial intelligence (AI), blockchain, data analytics, quantum computing, etc., and it is these emerging technologies that are expected to have the highest level of investment in the life sciences in the coming year, according to attendees at the Pistoia Alliance conference in April 2021.

“Now more than ever, research is occurring at the intersection between industries. Companies must embrace this trend and work together to tackle future challenges. We must advance quickly from disease treatment to disease cure, and finally to disease prevention,” commented Dr. Steve Arlington, president of the Pistoia Alliance in the press release following on from the event. “Pooling resources and skills, and investing in emerging tech like AI and blockchain will enable us to better address future public health crises. Recently we have seen the benefits of collaboration during the development of vaccines, therapies, and diagnostic tools to combat the COVID-19 pandemic. We must now apply this mindset to the multitude of other challenges we currently face.”

The reach of the Pistoia Alliance is global and ever far reaching, with an initiative held in June to share the organization’s knowledge-sharing vision engaging with life sciences and healthcare communities in the Asia-Pacific region. A recording of this webinar is available to view online here.

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Data science plays crucial role in global knowledge sharing 

Pistoia Alliance brings together the key constituents to identify the root causes that lead to R&D inefficiencies and develop best practices and technology pilots to overcome common obstacles. There are currently 21 active projects with which experts can get involved. The aim for many ongoing projects is to see the development of best practice protocols and technology pilots to overcome common obstacles. With the vast amount of life science research being undertaken in diverse locations utilizing widely different formats, unsiloing data is a key driver to implementing procedures that can enable researchers to easily retrieve, communicate, and share data.

One such Pistoia Alliance initiative is its DataFAIRy: Bioassay project, which aims to convert bioassay data into machine-readable formats that adhere to the FAIR guiding principles of Findable, Accessible, Interoperable, and Reusable. This project has successfully annotated almost 500 assays using a Natural Language Processing model that has been custom-built to recognize life sciences language. A second phase of the project has just launched that aims to scale the annotation process, with a goal for the data model to become the industry standard. A recent press release stated: “Adhering to the DataFAIRy model will reduce the time scientists spend searching and planning assay experiments. In addition, assay metadata is a popular data type for post-hoc data mining. However, most of these published data and metadata are not in a form suitable for automated mining. They are partially annotated in public data banks, but the volume, depth, and quality of these annotations are inadequate for addressing many current and future business questions.”

Best practice for FAIR data 

Another Pistoia Alliance project on this theme is FAIR Implementation, which is building best practices through the ongoing development of the FAIR ToolKit and through the development of a guide for the implementation of FAIR for clinical trial and healthcare data. This project aligns with initiatives such as a “FAIR Academic Publishing Implementation Network,” which has been proposed by Jan Velterop and Erik Schultes [see the Labs post about this here, with a short extract from the original article in Information Services & Use]. This aims to bring together scientific publishing entities to mutually encourage best practices for semantic enrichment of the material they publish. 

Semantic enrichment in published research articles

Efforts are underway to produce a set of semantically enriched articles according to the FAIR principles, with the methodology and tools used, in order for such a set to serve as a demonstrator of the feasibility of the FAIR implementation by publishers. A key reason IOS Press became a member of Pistoia Alliance is to get involved in the important work being done to establish best practices for semantic enrichment, on the understanding that it is a joint responsibility of research establishments and scientific publishing organizations alike. For more information about the benefits of semantic enrichment, see the endbox below.

In some areas of research, the use of semantically enriched material is already further advanced than in the scientific publishing world – for example in the area of pharmacology and the research-heavy pharmaceutical industry. These have come together in the Pistoia Alliance, which is working closely with the GO FAIR organization to foster cooperation between the members of the alliance and beyond.



Discover more about the Pistoia Alliance at: pistoiaalliance.org.

If you are in the IOS Press audience network (editor, board member, etc.) and interested in participating in any of the Pistoia Alliance projects, please get in touch with Carmel McNamara at: c.mcnamara@iospress.nl.

About IOS Press
IOS Press is headquartered in Amsterdam with satellite offices in the USA, Germany, India and China and serves the information needs of scientific and medical communities worldwide. IOS Press now publishes more than 90 international peer-reviewed journals and about 70 book titles each year on subjects ranging from computer science, artificial intelligence, and engineering to medicine, neuroscience, and cancer research. iospress.com

Making Scientific Communication Better

by Jan Velterop (independent open science advocate)

The FAIR guiding principles – i.e. making published scientific information Findable, Accessible, Interoperable, and Reusable – are gaining traction (in the USA, scientific literature that adheres to these principles is often called “AI-ready”). Publishers and editors have an important role here. The provision of accurate, unambiguous, and complete metadata is an obvious goal, but beyond that there is a world to win, for science communication in its broadest sense, by ensuring that claims made and data presented in the scientific literature are themselves clear and unambiguous. There are significant challenges in that regard, simply because concepts used in the text, even when they are clear and unambiguous, are not necessarily given the same names and descriptions by different researchers. Efforts to deal with this potential lack of comparability have been made through the construction of “controlled vocabularies.” Albeit that there are many controlled vocabularies, and they have to be mapped onto one another for the desired effect. If controlled vocabularies are used, significant concepts in scientific text and data, and the relationship between concepts in claims and assertions, can be “semantically enriched” and so made much more suitable for reliable comparison in machine-aided analyses, particularly where it concerns large amounts of text, as in meta-analyses, for instance.