Bloom is built on the largest structured database of voluntary biodiversity market (VBM) information worldwide. Our data methodology is designed for accuracy, relevance, and timeliness to serve as a trusted data source for market participants. Our system integrates 30,000+ hand-curated data points across five datasets: Schemes, Projects, Transactions, Organizations, and Metrics.
We evaluate sources according to their reliability:
When conflicts arise, higher-ranking sources prevail, unless clear evidence justifies an exception.
Since the market is not yet well-defined, various different data sources can lead to ambiguous data. In such cases, our methodology defaults to conservative interpretation. When uncertainty cannot be resolved, we rely on the minimum plausible values, such as the lowest defensible project size or issuance volume, and apply neutral categorization to avoid overstating claims. If neither of these approaches provides sufficient confidence, we omit the data point entirely until verification is possible.
Every transformation, whether standardization, currency conversion, categorization, or inference, is traceable, reversible, and fully documented within Bloom’s internal systems. This ensures that we maintain a complete and auditable record of how raw information is processed and how each data point reaches its final structured form. While such traceability is maintained internally, not all transformation logic is publicly disclosed, such as, for example, internal decision frameworks or formulas used in Bloom’s dashboards and analytics.
Bloom does not modify information to serve the interests of any market participant. We maintain strict independence and neutrality.
Official scheme registries and publicly released documents from crediting bodies and frameworks. Examples include scheme methodologies, project documentation, verification reports, and credit issuance/retirement records.
Information voluntarily shared by market participants with bloomlabs. Submissions are reviewed, verified, and processed within a week before being incorporated into Bloom.
Structured collaborations with organizations (often under exclusivity agreements) that provide verified datasets, proprietary insights, or non-public market information.
Continuous market research enables bloomlabs to stay up to date and gather as much available information as possible.
Information shared by market coalitions or non-sensitive information shared within market coalitions. We participate in a number of such forums (e.g., Biodiversity Credit Alliance (BCA), International Advisory Panel on Biodiversity Credits (IAPB), EU Commission Expert Group on Nature Credits, Nature Tech Collective (NTC), Climate Collective, etc.).
This dataset covers public and private VBM transactions, including pre-purchases and commitments to purchase.
Sourced from registries, developer submissions, internet research, media, social media, and automated data collection.
This dataset covers VBM projects developed on the ground whose goal is to issue biodiversity credits.
Sourced from registries, developer submissions, internet research, media, social media, and automated data collection. Project data integrates both official registry documentation and publicly shared project developments, as well as developer-provided materials.
This dataset covers VBM standards, frameworks, methodologies and programs that provide the set of rules for project developers to generate credits. Given how extensive it is, the dataset is updated every quarter.
Sourced from internet research, media, LinkedIn, proprietary submissions, and automated data collection.
This dataset covers VBM indicators and metrics used for measurement, reporting and verification (MRV) and credit calculation.
Sourced from each credit scheme and/or directly from the project. Indicators and metrics are extracted and structured from scheme methodologies and associated documentation.
This dataset covers all organizations (companies, public institutions, agencies, NGOs, etc.) linked to VBM by at least a clear intention to contribute/engage with the market.
Sourced from internet research, media, LinkedIn, coalitions, direct submissions, and automated data collection. Organizational profiles combine public information, market involvement, and verified proprietary inputs.
Each item entering Bloom goes through a multi-layer workflow designed to ensure accuracy and consistency. We begin with source validation, confirming the origin, date, authority, and accuracy of the information. Once validated, the data is interpreted and integrated using the bloomlabs’ market taxonomy to ensure it aligns with our standardized structure. Finally, we document each item by attaching metadata that records its source and processing timestamp. Our current internal minimum accuracy threshold is 95%, which we verify through bi-weekly random sampling across datasets.
As of November 2025, we have not yet implemented automatic data collection. The majority of the data collected comes from direct data collection by bloomlabs, and the rest is provided by market participants through direct data submissions or data partnerships. The latter is growing in importance as our network of data partners expands.
The biodiversity credit market spans across diverse methodologies, geographies, ecological outcomes, and validation frameworks.
Bloom standardizes this heterogeneity through a unified categorization system derived from established market taxonomies and our market experience. This enables comparison that is closer to like-for-like across a fragmented and rapidly evolving market.
Examples of standardization include:
Once standardized, data is integrated into Bloom’s database, where it is automatically cross-linked across datasets, deduplicated to remove redundancies, and maintained with full versioning and historical tracking.
Bloom includes a structured protocol for user-reported corrections:
Each correction is logged in Bloom’s internal audit trail.
We update our datasets continuously by monitoring public registries, new market publications, direct submissions, partner data feeds, and information exchanged within market coalitions. In parallel, we maintain ongoing monitoring of media channels and public announcements to ensure that all relevant developments are captured and incorporated into Bloom as soon as they become available.
We conduct regular audits to preserve long-term data accuracy and consistency. Every two weeks, we perform random quality checks across all datasets to validate correctness and identify potential issues. Cross-dataset reconciliation is carried out to detect inconsistencies between related records, and we routinely review historical entries to ensure they remain accurate over time. In parallel, we are developing automated anomaly-detection systems to proactively flag irregularities and strengthen data reliability.
We are in the process of mapping every data point to its original source, the processing steps it has undergone, the standardization logic applied to it, and its full versioning history. This will enable complete traceability and auditable provenance across the entire Bloom database.
Not all registries and schemes publish key information such as transaction data, credit issuances, credit retirements, or verification reports. These gaps limit the level of completeness that any market intelligence platform can achieve.
Information submitted by organizations themselves can introduce optimism bias, selective disclosure, inconsistent levels of precision, or various forms of marketing distortion. While we mitigate these risks through systematic cross-checking and triangulation, self-reporting bias cannot be entirely avoided.
Many transactions in nature markets are confidential, which restricts access to reliable pricing information. As a result, Bloom must rely on disclosed prices, indicative pricing shared by market participants, and inference drawn from structured clues. This introduces a degree of uncertainty into credit pricing.
In most environmental registries, key events such as credit issuance, trading, and retirement are reflected in real time or close to it. However, the broader biodiversity credit market is still in a pre-certification and early development stage, where many activities occur before formal registry inclusion. As a result, Bloom often relies on a wider range of data sources that may not always update in real time. This can create a temporary lag between when an activity occurs and when it is reflected in our datasets.
Some fields rely on categorical inference, historical patterns, or indirect signals when explicit information is not available. These fields are known internally but may not always be directly verifiable, and we treat them with appropriate caution in our analyses.
We respect all copyright, license, and usage restrictions, and only collect data that is legally accessible through public publication, user submissions, or partnerships with explicit permission. All sources are attributed where required, and we honor the terms of every exclusivity agreement.
We maintain strict independence in our research and data operations. We do not receive compensation from any organization in exchange for favorable data interpretation, preferential visibility, or the suppression of negative information.
We retain historical datasets and internal audit logs for at least five years. Raw automatically collected data is purged regularly unless it is required for auditability or compliance purposes.
Lead the development of bioinformatics solutions that integrate biological data with our advanced technologies. Requires expertise in bioinformatics, machine learning, and data analytics.
Assist in lab research and development projects that bridge biology and technology. Ideal for recent graduates passionate about sustainable innovation.
Design intuitive, bio-inspired interfaces and user experiences that reflect our commitment to human-centered technology. Experience in UX/UI and a passion for sustainable design preferred.
Analyze biological and technological data to uncover insights that drive our innovation strategies. Requires a strong background in data science and biotechnology.”
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