Wherever possible (i.e., wherever reputable industry, region or domain-specific databases exist for a given project’s topic or theme), we utilize a combination of self-reported data (e.g., obtained directly from entity websites) and multiple open-source databases. In cases where such databases do not exist, where the methodological rigour and basis of those databases cannot be manually assessed and validated for competency, relevancy, transparency and rigour by our manual analysts, or whether platform-featured entities are absent from such databases (but otherwise qualify for inclusion on the basis of the project's manually-curated analytical framework, which defines the qualifying criteria for entity inclusion and classification), we rely on data self-reported on entity websites.

We use a combination of manual, automated and semi-automated (algorithmic) approaches to data collection, using a hybrid system of our own proprietary algorithms and open-source tools, to aggregate and parse data from a wide variety of publicly-available websites and databases, including both aggregate industry-specific databases, news sources, as well as the websites of the entities featured on the platform. The exact data sources vary depending on the specific nature of the project and the resources available. Wherever possible, we utilize open-source databases with high degrees of reputability, transparency and a clear methodological description of their inclusion criteria, in combination with proprietary algorithms that extract information directly from entity (e.g., company, investor and non-profit) websites.

Yes. Provided that you attribute us as the source of the data, you are able to share or otherwise utilise our data on your website, regardless of commercial vs. non-commercial purpose or intent.

We welcome feedback from industry stakeholders and the public, who are encouraged to write to us at contact@ecosystem-map.info with their comments and inquiries. All feedback will be reviewed manually by our analysts for relevance and validity, and implemented in cases of legitimate error or omission.

While a number of our commercial products and services have high-frequency semi-automated updating of data, typically our open-access ecosystem mapping platforms are prepared for launch, and updated at our discretion. In many cases, on the basis of manual analysis of industry, regional and domain-specific developments and monitoring, we choose to release updated iterations of our ecosystem mapping platforms and projects, sometimes as frequently as every financial quarter for very fast-developing industries or industry-regional domains.

We utilize a wide variety of publicly-available websites and databases, including both aggregate industry-specific databases, news sources, as well as the websites of the entities featured on the platform. The exact data sources vary depending on the specific nature of the project and the resources available. Wherever possible, we utilize open-source databases with high degrees of reputability, transparency and a clear methodological description of their inclusion criteria, in combination with proprietary algorithms that extract information directly from entity (e.g., company, investor and non-profit) websites.

AI is utilized at numerous stages in the process of an ecosystem mapping platform development. We have a variety of internally-developed AI algorithms using machine learning, natural language processing and other techniques for data collection, cleaning and validation, as well as algorithms for automated and semi-automated entity classification (according to type, industry, subsector, etc). We also utilize internally-developed parsing, data manipulation, NLP, and classification algorithms, as well as internally-developed long and short language models (LLMs and SSMs) for entity classification, data-transformation and synthesis (e.g., creating a valid and invariant set of entity-associated data from a number of combined sources), and for more complex sub-analyses like investment trends and industrial-economic assessments.

We have a stringent quality assurance pipeline in place to manually review data obtained by our proprietary algorithms. Measures in place include: Manual preparation of the analytical framework underlying a given ecosystem mapping project, which quantifies and qualifies the criteria for entity classification by type and by industry, sector or, by a qualified analyst with professional experience in the thematic domain of the project Manual review by automated and semi-automated data collection outputs by qualified analysts Deeper manual review of randomised portions of parsed data Manual cross-checking of data between two distinct sources on randomised portions of a given project-specific database for human data validation

Privacy and security are integral to the platform’s functionality. The platform employs advanced security measures for data protection, including encrypted transmission and secure data storage. Adherence to privacy standards ensures the confidential and ethical handling of data uploaded by users, including organisational and stakeholder information.