Are Chemists Sharing Their Homework?

Dec 1, 2025

When it comes to open data in organic chemistry, the answer appears to be: not really - at least, not yet.

Dr Sally Bloodworth

In a recent study published in the Beilstein Journal of Organic Chemistry, Dr Sally Bloodworth, Dr Cerys Willoughby, and Professor Simon J. Coles from the University of Southampton analysed 240 papers from 12 leading journals to examine how well researchers are adhering to FAIR (Findable, Accessible, Interoperable, Reusable) data practices. The findings were clear: while most authors meet the standards required of them by journal guidelines, very few go beyond them to follow recommended best practices.

Only 1% of papers shared original nuclear magnetic resonance (NMR) data – despite NMR being one of the most common techniques used to determine the structure of chemical compounds. In fact, across the entire study, there were no standout examples of truly open data sharing.

So, what’s holding researchers back? According to Sally, it’s not reluctance – it’s time. With chemists juggling grant applications, teaching, and lab supervision, there’s little incentive to go the extra mile when journals don’t require it.

“Researchers aren’t resistant to change,” she explains. “They’re just busy.”

The study was driven by a frustration Sally experienced years ago when trying to build on a published method — only to find crucial details missing. “I had to spend a lot of time re-learning the lessons that had enabled another research group to be successful, in order to make progress myself,” she recalls. “That is probably the point at which I began to think more about the importance of open data in chemistry.”

There is a strong case for researchers sharing their data. Open data doesn’t just make life easier for other chemists – it can accelerate discovery through secondary analysis of the data using machine-learning methods. Sally cites a recent example where a graph neural network – a type of computer model that learns by looking at how different pieces of information are connected – was trained on a relatively small dataset, predicted the outcomes of over 3,000 reactions.

“Now just imagine how long it would take to have tested those 3000 reactions in the laboratory!”she notes.“It would requiremonthsof discovery work for a well-equipped industrial lab and would be impossible to achieve in a smaller academic lab – it justwouldn’tbe done. The new discovery relied entirely on themachine-learning (ML) technique. Nowit’sgreat when a research group has the capacity to apply ML to their own dataset, but otherwise researchers can make their data openly available for re-analysis by others.”

Of course, building this kind of data-enabled future requires more than just vision. There are still major cultural and systemic barriers in place. One of them is the way we perceive other researchers in our area.

“In the past, we have been rather trapped by the idea that researchers are often in competition with each other for funding, rather than in collaboration,”Sally explains. “But the importance of collaboration has grown hugely during my career, and this will naturally lead to a culture that is more open to sharing.”

So, what would help accelerate that change? Sally believes one of the most impactful – and achievable – steps would be requiring raw NMR data in its original form (known as FID files) along with clear chemical identifiers. These identifiers, such as SMILES (Simplified Molecular Input Line Entry System) or InChIs (International Chemical Identifiers), allow data to be easily read and interpreted by both humans and machines. “It would be a modest change for journals, but it would engage a huge portion of the community – everyone uses NMR!”she says.“The resources to support authors already exist; it’s an achievable step.”

Shifting the culture of chemistry towards openness will require more than just good intentions. Sally believes that systems of recognition and reward will be key. “In the same way that publishing high-quality papers in leading journals influences a researcher’s career development and ability to win funding, a system of recognising data sharing could also lead to those opportunities.”

Initiatives like the Physical Sciences Data Infrastructure (PSDI) are working to bridge the gap, developing tools and standards that make FAIR data easier to achieve — and supporting researchers to get there.

“If we want organic chemistry to embrace open data, we need systems that reward it and resources that support it. PSDI will be incredibly important in this objective.”

And to any organic chemists still on the fence about data sharing? Sally offers this encouragement:

“Even if you have no interest in using automated methods yourself, there will almost certainly be someone in your field who is. If you make your data available, you’re creating an opportunity for new discoveries – and you might even gain a new collaborator or citation in the process.”

So, are chemists sharing their homework? Not yet. But the groundwork is being laid — and with collaboration, consensus, and the right support, change is coming.

📖 Read the full paper: https://www.beilstein-journals.org/bjoc/articles/21/70

🔗 Learn more about PSDI and its work to support FAIR data in physical sciences: https://psdi.ac.uk

Modern scientific research workflows use a plethora of diverse software tools and file formats. Unfortunately, the file formats that one software tool can export are often incompatible with the formats required for import by another. Furthermore, the current capabilities for converting data between these different formats are often slow, unclear and error-prone, particularly because data formats vary in their structure and in the amount of information they can represent, making conversion between specific formats complex and sometimes resulting in information loss. PSDI’s Data Conversion Service (DCS) was created to address this challenge, offering researchers a single, trusted place to convert data formats while helping them understand the likely quality and limitations of different conversions.

Where the idea came from

The need for a Data Conversion Service was first identified during research carried out for the PSDI pilot phase at the University of Southampton, which was published in Digital Discovery. This research identified a recurring issue across the physical sciences: researchers were working with data that existed in many different formats, making collaboration and reuse difficult due to a lack of interoperability. Therefore, highlighting that there was a clear need for “data format conversion between different data types in order to facilitate data exchange between different services, and to allow users to collaborate using common formats.”

A key conclusion of this work was that this issue, alongside many other interoperability challenges could best be addressed by identifying existing software that already offers relevant functionality, and creating the infrastructure needed to allow these tools to work together.

Several converters had already been created by the scientific community to address some of these issues, such as Open Babel, although in their current form they were fragmented and offered little insight into conversion quality or potential information loss. Therefore, rather than creating another converter, PSDI’s focus shifted towards making better use of these existing software tools by bringing them together and exposing their capabilities more transparently.

As Dr. Samantha Pearman-Kanza, who was closely involved in shaping the early direction of the service, explains:

Rather than simply creating another conversion tool, the focus was on making the best use of existing software and elevating their offerings. The aim was to help researchers understand what conversions were possible across different scientific data formats , which existing tools could be used, and where the use of these tools for certain conversions might involve compromises in data quality.

From concept to working service

Early ideas explored a search interface that identified possible conversions and directed users to existing conversion software. This quickly evolved into a more researcher-friendly approach: integrating established converters directly into a single service and exposing their options in a consistent way.

Development was carried out by Research Software Engineers Dr. Ray Whorley, Dr. Bryan Gillis and Dr. Don Cruickshank, who initially prototyped the service as a small Python application before expanding it into a fully-fledged web service and suite of downloadable tools.

Reflecting on this evolution, Dr. Whorley says:

The service now incorporates widely used converters such as Open Babel, Atomsk and c2x. Users can upload files, choose input and output formats, apply available conversion options, and download both the converted file and a detailed log. Accessibility has been built in throughout, with users able to customise fonts, sizes and colour schemes.

The Data Conversion Service interface showing format selection, available converters and indicative conversion quality.

Supporting real research workflows

Alongside the web application, the team developed three downloadable tools: a local browser-based version, a command-line tool and a Python library. These are proving particularly valuable for researchers working with sensitive data or automated workflows.

As Dr. Whorley explains:

“The downloadable tools give researchers confidence that their data remains local, and they can be dropped straight into automated workflows.”

This flexibility allows the Data Conversion Service to support everything from quick, one-off conversions to large-scale, repeatable processing pipelines.

Supporting FAIR data and PSDI’s wider ecosystem

Interoperability is a core part of FAIR data practice, and the Data Conversion Service plays a key role in enabling it. Researchers often need to convert the output of one tool into a format that can be used by the next, or to revive legacy data stored in outdated formats. Our service helps reduce the technical barriers to doing both.

Looking ahead

Now that the Data Conversion Service is established, its future direction will be strongly shaped by user feedback. Researchers can report missing formats and conversions directly through the service, and suggestions are already influencing planned enhancements.

Alongside this, there is clear scope for closer integration between the Data Conversion Service and other PSDI tools and services, for example by enabling data transformed through the Data Revival Service (a service which takes scanned handwritten paper lab notebooks and converts them into machine-readable data) to be converted into a wider range of usable formats, or by generating chemical identifiers such as InChI or SMILES from a broader set of input formats for use in discovery services like Cross Data Search.

As Dr. Pearman-Kanza notes:

“The capacity to convert data between different formats is what really unlock reuse across tools, across projects and across disciplines.”

Potential future developments also include support for conversions that require more than one input file, additional conversion tools, chained conversions where no direct route exists, data visualisation, and an API to enable integration with other platforms and services.

A service built with researchers in mind

For the team, seeing the Data Conversion Service grow from an identified need into a live, widely usable tool has been deeply rewarding. The aim is to make data conversion clearer, more transparent and more inclusive, so researchers can spend less time wrestling with formats and software, and more time doing research.

As Dr. Pearman-Kanza puts it:

“If researchers can trust the conversion process and understand its limitations, they are better placed to make informed decisions about how their data can be used. This includes understanding when conversion is appropriate, what can be gained, and what might be lost, which is an important step towards better research practice overall.”

Try the Data Conversion Service

The Data Conversion Service is freely available to use and designed to fit a wide range of research needs, from quick, one-off conversions to integration within automated workflows. Researchers can explore the web-based service, download local tools, and provide feedback directly to help shape future development.

To get started, visit the live service, watch the short introduction video, explore the documentation, or download the tools to use locally within your own workflows.