This symposium addressed the interfaces between the laboratory and the new infrastructures currently being set up. Open Science aims to make research and development more effective by better supporting collaboration. The advantages of making data open will be critically reviewed and the development of highly interconnected, collaborative research in data driven laboratories of the future will be discussed. Adoption of the FAIR data principles is an important step to support this.
The What, How and Why of Open science
Beilstein Open Science Symposium 2019
15 – 17 October, 2019
Hotel Jagdschloss Niederwald, Rüdesheim, Germany
This symposium brought together research scientists, data scientists, publishers, funders and other interested parties to review critically current publication practices in chemistry and related sciences.
Scientific Program:
Martin G. Hicks and Carsten Kettner / Beilstein-Institut
See what has been reported on Twitter: #BeilsteinOS2019
→ Download: Abstract Book of the Open Science 2019 Symposium
Aspects covered by this conference
In chemistry, biochemistry and neighbouring areas, funding agencies and national and supranational bodies are strongly advocating the sharing and depositing of data. To make this work the incentive structures for academics need to be realigned, investment in infrastructure and new technologies increased, and the awareness of the advantages of making data available for AI and similar technologies heightened.
The What, How and Why of Open Science
A new science eco-system is growing: Open Science. This is based on the conviction that free access to research publications is not only a moral right of citizens but a necessity to allow the maximum use and impact of research. The current publishing system is no longer fit for purpose; too much emphasis is being placed on using publications for evaluation and not enough on dissemination of new research results. Redressing the balance will not be easy; the publish-or-perish paradigm is detrimental, and could productively be replaced with a more transparent, effective system based on quality and not quantity.
Change is coming, but some scientists are feeling that they will be restricted in their freedom by being mandated to make data available or ensure that their publications are open. Others welcome change, but are hindered by the lack of a framework for structured and standardized data reporting. Open Science aims to make research and development more effective by better supporting collaboration. This can be between research groups, but also between academia and industry. Adoption of the FAIR data principles are an important step to support this, but much needs to be done to ensure that sufficient tools are available so that making data open is not an onerous task for scientists.
In chemistry, biochemistry and neighbouring areas, funding agencies and national and supranational bodies are strongly advocating the sharing and depositing of data. To make this work the incentive structures for academics need to be realigned, investment in infrastructure and new technologies increased, and the awareness of the advantages of making data available for AI and similar technologies heightened. New technologies are diffusing into the lab allowing devices to be interconnected, data automatically recorded, and experiments to be automated.
This symposium addressed the interfaces between the laboratory and the new infrastructures currently being set up. The advantages of making data open will be critically reviewed and the development of highly interconnected, collaborative research in data driven laboratories of the future will be discussed.
The symposium brought together research scientists, data scientists, publishers, funders and other interested parties to review critically their needs and concerns and discuss how they see the future of Open Science developing.
Scientific Program
Tuesday, 15 Oct.
9.00
Opening and Introductory Remarks
Martin G. Hicks
Session Chair: Matthew Todd
9.20
Open Science and the Rebuilding of the Publishing Functions
Jean-Claude Guédon
10.00
Building a National Research Data Commons - Transforming Scholarship Through Transformative Infrastructure
Andrew Teloar
10.40 Poster Presentations
11.00 Coffee Break
11.30
Implementing Open Science in Academic Biomedicine: a Report from the Trenches
Ulrich Dirnagl
12.10
Distributed Drug Discovery and its Application in Neglected Tropical Diseases
Lori Ferrins
12.50 Lunch
Session Chair: Evan Bolton
14.00
Towards Knowledge Graph-based Representation, Augmentation and Exploration of Scholarly Communication
Sören Auer
14.40
Accelerating Biomedical Discovery with an Internet of FAIR Data and Services
Michel Dumontier
15.20 Tea Break and Conference Photo
15.50
Watch out for the Influencers! When Scientific Reasoning Relies on a Single Data Point
Andrej-Nikolai Spiess
16.30
Stories from the "Open Science Revolution": How Scientists Talk about Openness
Rosalind Attenborough
17.10
"The University Cooperative", Learning to Manage Academic Resources as Common Property
Sanli Faez
Wednesday, 16 Oct.
Session Chair: Andrew Hufton
9.00
A Universal Chemical Synthesis Text to Code Translator that Enables the Autonomous Syntehsis of Organic Molecules
Lee Cronin
9.40
Open Science Platform for Materials Science: AiiDA and Materials Cloud
Giovanni Pizzi
10.20 Coffee Break
10.50
Simulation Foundry: Repeatable, Replicable, Reproducible, Open and FAIR Molecular Modelling
Gudrun Gygli
11.30
Towards an Improved Data Ecosystem for Scientists
Evan Bolton
12.10
Wikidata and Scholia as a Hub Linking Chemical Knowledge
Egon Willighagen
12.50 Lunch
Session Chair: Marta Teperek
14.00
Towards a National Research Data Infrastructure for Chemistry in Germany
Christoph Steinbeck
14.40
The GO FAIR Convergence Matrix: Optimizing the Reuse of Existing FAIR-Related Resources
Erik Schultes
15.20
Discussion: The Why, How and What of Open Science
Martin G. Hicks
16.00 Tea Break
Session Chair: Susanna-Assunta Sansone
16.30
Supporting Ireland's Open Research Agenda - HRB's Open Publishing Platform and FAIR Data Stewardship Pilot
Aileen Sheehy
17.10
FAIR Data? Not Without Code! But How to Get there? - Case Study from TU Delft
Marta Teperek
Thursday, 17 OCT.
Session Chair: Lori Ferrins
9.00
Open Science is Accelerating Early Target Discovery and Validation, and will Facilitate the Generation of More Novel Medicines for Patients
Chas Bountra
9.40
Can Openness Pay?
Matthew Todd
10.20
DataSTAGE: Improving Access to FAIR Data to Accelerate Scientific Discovery for Heart, Lung, Blood, and Sleep Research
Rebecca Boyles
11.00 Coffee Break
11.20
Behind the FAIR Brand: Thinkers, Doers and Dreamers
Susanna-Assunta Sansone
12.00
Five Years of Data Sharing at PLOS: Challenges and Opportunities
Iratxe Puebla
12.40
Bridging the Gab Between the Scholarly Literature and Public Data Repositories
Andrew Hufton
13.20
Closing Remarks
Martin G. Hicks
