In August, 2026, GFLI will launch the Data-in Generator Tool for marine ingredients. The cloud-based tool is created to simplify data-in projects to accelerate data generation for the GFLI database, being compliant with the PEFCR for Marine Ingredients as well as GFLI compliant for marine-based feed ingredients.. The tool can be used for PEF studies, GFLI sectoral and PEF and GFLI branded data projects.
The tool:
- Simplifies the overview of required activity data collection
- Reduces the costs for external expertise
- Allows for the by-passing of the GFLI internal review since the tool generates compliant data
- Streamlines the critical external review to be less intensive and therefore reduces the costs
The tool is made available through funding from FHF (The Norwegian Seafood Research Fund), the support of the Norwegian Seafood Federation, & the tool creator and operator LCA.NO.
Costs
GFLI is a member-based non-profit institute, therefore, our prices are reflected for both members and non-members. The costs to get access to the tool is a combined onboarding process and an annual licensing fee for the use of the tool and the background data for the first year. During the onboarding process, additional annual fee costs may apply reflected on the amount of ingredients in scope for your use case.
| Categories | Costs |
| Member license (onboarding & first year annual fee for 1 ingredient) | $ 4000 (USD) |
| Non-member license (onboarding & first year annual fee for 1 ingredient) | $ 7000 (USD) |
| Research license (onboarding & annual fee) | $ 1000 (USD) |
| Annual fee (server maintenance) after first year of operation | $ Depends on # ingredients in scope |
Are you looking into why a membership may be valuable for you? Check out our ‘Become a Member‘ page.
Questions and answers
The tool can be used to collect relevant environmental data for marine ingredients. This includes wild-caught fish, farmed fish, and by-catch; for both human consumption and animal feed production.
While the need for data is growing, reporting standards are created and upheld, but the LCA data projects required to gather and model all relevant data are complex and expensive. GFLI supports the use of primary data while underlining the complexities of gathering high-quality primary data. By simplifying the tasks underneath LCA data projects, which are the use of specific background data, the modelling of the data in the correct way, GFLI believes our members and the feed & food industry as a whole can focus on gathering the data. By focusing on this, we can bridge the gap between conservative averages and the actual market.
The tool is for anyone interested in creating environmental footprint data for marine ingredients. This can be managers, researchers, or any staff within organisations that collects relevant data for a company or representing an ingredient or product. The training module included in the onboarding should prepare you on the major topics within lifecycle assessment principles and the methodological approaches the GFLI upholds.
The DIG-tool, as well as the GFLI database as it was always intended, was to be a (supporting) tool towards meeting regulatory and market-driven standards in environmental footprinting. The DIG-tool is fully compliant with the EU PEF marine fish products (https://www.marinefishpefcr.eu/) and the EU PEF feed for food producing animals (restricted to only fish-based feed ingredients as of June 2026).
The tool is currently designed to process annual activity data. To be compliant with the GFLI methodology, an average of 3 years is required, which is compiled through combining the available years that were filled in.
The tool can also be used for quick environmental footprint results or playing around with activity data to understand the impact it has.
The Data-in Generator Tool is a cloud-based solution through web browser. You will need an internet connection in order to use the tool, and can be used on mobile phone.
The tool provides a comprehensive set of results across 43 impact categories, calculated using two widely recognized methods: EF v3.1 and ReCiPe. This ensures compliance with both the PEFCR for Marine Fish and the standards established by GFLI.
You can assess environmental impacts across a wide range of indicators, including global warming potential (commonly referred to as carbon emissions), acidification potential, water use, fossil and mineral resource use, and human health impacts, among many others.
The tool provides three sets of results per dataset, one for each type of allocation required by GFLI (mass, economic and energy). The calculation is done automatically, based on parameters like yield, calory content and economic value of products. Therefore, the user does not need to stress about doing such calculations on their own, which saves time and prevents mistakes.
The tool also calculates the Data Quality Rating (DQR) by evaluating the overall representativeness of the data collected for each reporting year and applying the weighting factors defined by the standard. To support external verification, users can upload relevant documentation that substantiates the reported data. The DQR is assessed across four dimensions: technological representativeness, temporal representativeness, geographical representativeness, and precision. These four dimensions are combined into a single DQR score, which determines the suitability of the dataset for its intended use in accordance with the PEF and GFLI standards.
The onboarding establishes the knowledge and capability required to use the Data-in Generator Tool effectively, i.e. which ingredients are in scope for your company or data project. It provides the necessary introduction, setup and training so that the company can work independently with its data going forward.
The onboarding is therefore a one-time investment in establishing internal capability, rather than a recurring consultancy cost.
The tool has a dataset subscription to maintain the servers for secure storage, back-ups, and available for use by you anytime you log in. The data subscription also includes an annual update of relevant background data (in case these are updated), while having previous data and calculations remain available in the system.
This provides several advantages:
• Less dependency on external consultants for recurring updates
• Greater internal knowledge and control over the company’s own data
• Continuity between publication cycles, rather than restarting the process every few years
• Less repeated effort, as previous data and calculations can be built upon
• More predictable costs over the dataset’s lifecycle
• Greater flexibility, as companies can update their data when it is available rather than waiting for a new consultancy project
Yes, the DIG-tool is especially helpful for branded data. Through the automatized modelling and results output, using the tool does not require you to pay for a separate branded data internal review.
If you wish to publish the database in the GFLI database, you will be able to do so in the tool through an API that is being developed. The data will integrated into the database on LCIA format for no additional costs, and can take up to a workweek after the approval from the external review(er).
Once integrated, you’ll receive a notification from the DIG-tool once there is a new background dataset update that would affect your dataset, hence by the press of a button it will be updated to always have an up-to-date dataset in the GFLI database. You are also welcome to export your data through the excel format or ‘share link’ with your relevant downstream partners.
By using the tool, you can give GFLI the right to aggregate your company-specific activity data into sectoral datasets. This will only be done when enough data is gathered from organisations for the same types of ingredients to assure confidentiality for each company is reached.