Donations List Website

📋Page Status

Page Type:ResponseStyle Guide →Intervention/response page

Quality:52 (Adequate)⚠️

Importance:22.5 (Peripheral)

Last edited:2026-02-03 (3 days ago)

Words:3.6k

Structure:

📊 2📈 0🔗 7📚 6•25%Score: 12/15

LLM Summary:Comprehensive documentation of an open-source database tracking $72.8B in philanthropic donations (1969-2023) across 75+ donors, with particular coverage of EA/AI safety funding. The page thoroughly describes the tool's features, data coverage, and limitations, but is purely descriptive reference material about an infrastructure tool rather than actionable prioritization guidance.

Issues (1):

QualityRated 52 but structure suggests 80 (underrated by 28 points)

Quick Assessment

Aspect	Assessment
Type	Open-source philanthropic data repository and web portal
Primary Function	Tracks and visualizes donation data across donors, donees, cause areas, and time periods
Scale	≈$72.8 billion in donations tracked (1969-2023)
Coverage	75+ donors and recipients; includes major foundations and individual donors
Key Cause Areas	Global health ($23.3B), agriculture ($3.9B), education ($1.8B), AI safety, effective altruism
Creator	Vipul Naik, with significant contributions from Issa Rice
Data Availability	Fully open-source on GitHub; publicly accessible web interface
Primary Users	EA/AI safety researchers, philanthropic analysts, donation transparency advocates

Key Links

Source	Link
Official Website	donations.vipulnaik.com
GitHub Repository	github.com/vipulnaik/donations
EA Forum Tutorial	EA Forum post

Overview

The Donations List Website (DLW) is an open-source database and web portal that aggregates publicly announced philanthropic donations of interest to creator Vipul Naik. The platform tracks approximately $72.8 billion in donations spanning from 1969 to 2023, covering 75+ donors and recipients across multiple cause areas.¹ All underlying data and code are publicly available on GitHub, making it one of the most transparent resources for tracking philanthropic funding flows.²

The project emerged from a need for better transparency in philanthropic giving, particularly within the effective altruism community. By consolidating donation data from public announcements, annual reports, and other disclosed sources, DLW enables researchers to analyze funding trends, identify gaps in charitable giving, and track the influence of major grantmakers. The platform is especially valuable for those researching funding in global health, AI safety, and other EA-relevant cause areas.¹³

DLW distinguishes itself through its comprehensive tracking of not just donation amounts, but also relationships between donors and donees, disclosure timelines, and “money moved” claims by influencers like GiveWell. The site allows users to view donations filtered by cause area, time period, donor type, and geography, providing multiple analytical perspectives on philanthropic activity.¹

History and Development

Origins and Initial Development

Vipul Naik created the Donations List Website to track donations that were publicly announced or shared with permission. The initial data collection was seeded by Issa Rice, who continues to make substantial contributions to the project through contracted work.² According to the project documentation, all financially compensated contributions to the site are tracked on a dedicated contract work page, though ultimate responsibility for errors and inaccuracies remains with Naik.²

The project launched with preliminary data that has undergone continuous vetting and normalization. As of the project’s documentation, developers expected to complete the first round of development by the end of July 2026, indicating ongoing refinement of data quality and platform features.²

Technical Infrastructure

DLW is built as a MySQL database with a PHP-based web interface. The open-source nature of the project means anyone can clone the repository, set up a local instance, and explore or modify the data.² The platform includes several technical features:

Database structure with tables for donors, donees, donations, disclosures, influencers, and cause areas
Similarity calculations that help identify related entities and potential duplicates
Sortable tables using external JavaScript libraries
Multiple view modes including individual-scale donations, full dataset views, and curated top-30 lists²

The repository structure includes SQL schemas, Python scripts for data processing, similarity detection algorithms, and documentation for data updates. This technical openness has made DLW valuable not just as a data source, but as a template for other philanthropic transparency projects.²

Community Reception

Within the effective altruism community, DLW has been recognized as a valuable tool for funding transparency. An EA Forum tutorial post introducing the site praised its utility for “tracing funding origins transparently, especially outside EA circles.”³ Community members have requested additional features, including:

More comprehensive foundation data beyond the currently tracked entities
Aggregation of EA Forum and LessWrong discussions about specific donations
Links between donation records and community commentary³

Users particularly value the site’s ability to track individual donations that aren’t easily found elsewhere, providing visibility into funding patterns that might otherwise remain opaque.³

Major Data Coverage

Tracked Donors and Foundations

DLW tracks over 75 donors, ranging from major private foundations to individual philanthropists. Key tracked entities include:¹³

Bill & Melinda Gates Foundation - One of the largest tracked donors, with extensive global health and agriculture funding
Wellcome Trust - Major biomedical research funder
W.K. Kellogg Foundation - Focus on education and health equity
Arnold Foundation (now Arnold Ventures) - Criminal justice, education, and health policy
Hewlett Foundation - Environment, education, and performing arts
Good Ventures/Open Philanthropy - EA-aligned funding across multiple cause areas
Ford Foundation - Social justice and human rights
Individual donors including Vitalik Buterin and anonymous contributors tracked under pseudonyms like “gwern”³

The platform distinguishes between donations made by private foundations and those made by individuals, allowing analysis of individual-scale philanthropy separately from institutional grantmaking.¹

Cause Area Distribution

The largest cause area tracked by DLW is global health, representing $23.3 billion across the dataset’s timespan. This reflects the substantial funding directed toward infectious disease prevention, maternal and child health, and health systems strengthening by major foundations.¹

Other significant cause areas include:

Agriculture - $3.9 billion, primarily focused on food security and agricultural development
Education - $1.8 billion across various educational levels and geographies
Higher Education and Humanities Scholarship - $2.8 billion, often focused on building institutional capacity
Family Planning - $1.4 billion in reproductive health and contraceptive access
Nutrition - $1.2 billion in programs addressing malnutrition and food fortification
Environmentalism - $1.4 billion in conservation and climate initiatives
Population - $1.7 billion, often overlapping with family planning efforts¹

Notably, the database includes a “FIXME” category representing $20.8 billion in donations where cause area classification is incomplete or ambiguous, indicating areas for future data refinement.¹

EA and AI Safety Funding

While not broken out as a separate top-level category in the main aggregation, DLW tracks donations relevant to effective altruism and AI safety, including:

Open Philanthropy Project grants, which include substantial AI safety funding³
Donations to organizations like the Machine Intelligence Research Institute (MIRI)²
Funding to Lightcone Infrastructure (formerly LessWrong 2.0)²
Grants to the Berkeley Existential Risk Initiative (BERI), tracked both as donor and donee²

This coverage makes DLW particularly valuable for researchers analyzing the growth and evolution of AI safety funding over time, especially when combined with complementary resources like the EA Forum’s historical funding datasets.³

Platform Features and Functionality

DLW organizes information across several specialized page types, each offering different analytical perspectives:²

Main Page - Displays aggregated donation data with three view modes:

Default view showing top 30 donors, donees, and cause areas
Individual-scale view excluding foundation grants
Full view showing all tracked donations

Donor Pages - Dedicated pages for each tracked donor showing their giving history, relationships with donees, and funding patterns over time. For example, the Open Philanthropy Project page includes a comprehensive table of historical disclosures from before the organization stopped announcing them publicly.²

Donee Pages - Pages for each recipient organization showing funding received, donor relationships, and related documentation. Some entities like BERI have both donor and donee pages when they both give and receive grants.²

Donor-Donee Pages - Specialized pages examining specific donor-donee pairs, showing all donations between them, their relationship evolution, and documents mentioning both entities. An example is the page tracking Open Philanthropy donations to MIRI.²

Influencer Pages - Track “money moved” by recommending organizations like GiveWell, comparing claimed money moved against documented donations. This allows verification of money moved claims by cross-referencing actual donor behavior.²

Data Verification and Quality

The platform explicitly notes that data is “preliminary and has not been completely vetted and normalized,” recommending that anyone sharing links include caveats about preliminary status or check with Vipul Naik before sharing without caveats.² This transparency about data limitations reflects a commitment to accuracy over premature claims of comprehensiveness.

The project includes several data quality mechanisms:

Similarity detection algorithms that identify potential duplicate entities or donations
Manual review processes documented in a data update playbook
Community feedback solicited through EA Forum posts and GitHub issues
Version control through Git, allowing tracking of all data changes over time²

Files like gates-foundation-maps.txt and google-org-discrepancies-to-resolve.txt in the repository indicate ongoing efforts to reconcile inconsistencies and normalize entity names across different data sources.²

Technical Access and Usage

For researchers comfortable with SQL and data analysis, DLW offers multiple access methods:

Web interface for browsing and basic filtering
GitHub repository containing raw SQL files that can be imported into a local MySQL database
API capabilities through the PHP backend (though documentation is limited)
Downloadable datasets implicit in the open-source repository structure²

The technical setup documentation shows that users can clone the repository, initialize a local database, and run the site on localhost for custom analysis or modifications.² This openness enables sophisticated users to perform analyses not supported by the web interface, such as complex multi-table joins or custom cause area classifications.

Research Applications

EA Community Usage

Within the effective altruism community, DLW serves multiple research functions. EA Forum discussions highlight its value for:³

Funding Landscape Analysis - Understanding which cause areas are well-funded versus neglected. By comparing DLW data with EA-specific funding datasets like the EA Forum’s historical funding spreadsheet, researchers can identify gaps where additional resources might have high impact.³

Donor Behavior Tracking - Following how individual donors and foundations evolve their giving over time. This helps identify emerging trends, such as increased attention to AI safety or shifts in global health priorities.³

Transparency and Accountability - Providing public records of funding decisions that might otherwise be difficult to access. Community members specifically praised DLW for making “funding origins transparent” in ways that complement but extend beyond EA-specific resources.³

Money Moved Verification - The influencer pages, particularly for GiveWell, allow independent verification of claimed impact. Researchers can compare what GiveWell reports as money moved with actual documented donations from individuals and foundations to recommended charities.²³

Complementary Resources

EA Forum discussions indicate that DLW is often used alongside other funding information sources:³

EA Forum historical funding datasets - Provide more granular detail on EA-specific organizations
Open Philanthropy’s grants database - Though this may miss approximately 50% of recent Global Catastrophic Risk spending³
Foundation Directory and Candid Learning - For broader foundation research beyond DLW’s tracked entities³
Founders Pledge and Farmkind grant data - Community members have requested these be incorporated into consolidated analyses³

The integration of DLW data with these complementary sources enables more comprehensive funding landscape assessments than any single resource alone.

Limitations for Research

Despite its value, DLW has acknowledged limitations for research applications:

Coverage Boundaries - The dataset tracks donations “of interest to Vipul Naik,” meaning coverage is intentionally selective rather than comprehensive.² Researchers cannot assume absence of data indicates absence of donations.

Preliminary Status - The ongoing data vetting means some information may be incorrect or incomplete. The project’s own documentation recommends including caveats when citing data.²

Time Lag - As with most philanthropic databases, there is typically a delay between when donations occur and when they are publicly disclosed and added to the database.²

Disclosure Dependency - DLW can only track donations that have been publicly announced or shared with permission, missing private grants without disclosure.²

AI Safety Funding Context

For AI safety researchers specifically, DLW provides valuable historical context on funding evolution in the field. The platform tracks donations to key AI safety organizations and can help answer questions about funding centralization and diversification.

Major AI Safety Funders Tracked

While comprehensive AI safety funding data would require integration with specialized resources like Open Philanthropy’s grants database and the Long-Term Future Fund, DLW captures major contributions from:²³

Good Ventures/Open Philanthropy donations to AI safety organizations
Individual philanthropists funding AI safety research
Foundation grants to organizations working on AI governance and policy

The donor-donee pages for AI safety organizations show funding relationships and how they’ve evolved over time, which is valuable for understanding field development and potential funding concentration risks.²

Integration with EA Funding Analysis

A 2025 EA Forum post on historical EA funding data noted that Open Philanthropy’s grants database may miss approximately 50% of 2025 Global Catastrophic Risk (GCR) spending.³ This highlights the value of resources like DLW that aggregate data from multiple disclosure sources rather than relying on a single funder’s reporting.

Community members have called for better integration of funding data sources, including incorporating DLW information into consolidated analyses of AI safety funding landscapes.³ Such integration could help identify:

Funding concentration - Whether AI safety funding is overly dependent on a small number of sources
Geographic patterns - Where AI safety funding flows globally
Institutional types - Whether funding favors academic institutions, nonprofits, or other organizational forms
Trend analysis - How AI safety funding has grown (or not) over time relative to other cause areas

Criticisms and Limitations

Methodological Concerns

The preliminary nature of DLW data raises methodological concerns for researchers who might use it for quantitative analysis. According to the project’s own documentation, data has “not been completely vetted and normalized,” and users are advised to include caveats when sharing findings based on the data.²

Specific methodological limitations include:

Selection Bias - Tracking donations “of interest to Vipul Naik” means the dataset reflects one individual’s priorities rather than systematic sampling. This likely results in better coverage of EA-relevant donations than of philanthropic activity generally.²

Classification Challenges - The $20.8 billion in the “FIXME” cause area category demonstrates significant challenges in consistently classifying donations, particularly for foundations with diverse portfolios.¹ Without standardized classification, cross-cause-area comparisons may be misleading.

Incomplete Time Series - Not all tracked donors have complete historical data, making trend analysis difficult for some entities. The dataset’s strength is in recent years (2010s-2020s) with sparser coverage in earlier periods.¹

Attribution Ambiguity - For donations influenced by multiple actors (e.g., a GiveWell recommendation plus independent due diligence), attribution in the influencer tracking may be imperfect.²

Data Quality Issues

The GitHub repository includes files like google-org-discrepancies-to-resolve.txt that document known data quality issues.² While this transparency is commendable, it indicates ongoing challenges in data accuracy. Common data quality concerns in philanthropic databases include:

Entity name variations - The same organization may be listed under multiple names
Missing donation details - Purpose, restrictions, and payment schedules often unavailable
Delayed disclosure - Time lag between donation and public announcement
Incomplete foundation coverage - Community members have noted that more foundations could be added³

Comparison with Professional Databases

DLW is maintained by individuals rather than a professional organization with dedicated data staff. While this enables rapid iteration and community responsiveness, it contrasts with professionally maintained resources like Candid’s Foundation Directory, which has:¹

Dedicated data validation teams
Standardized classification taxonomies
Systematic coverage goals
Regular update schedules

However, professional databases typically have paywalls or access restrictions, whereas DLW’s full openness enables types of analysis and sharing not possible with proprietary data.²

Counterarguments and Strengths

Transparency and Openness

Despite limitations, DLW offers significant advantages through its radical transparency. Every data point is traceable to source documents, every change is tracked in Git history, and the entire database can be downloaded and analyzed independently.² This level of openness is rare in philanthropic data and enables:

Independent verification - Researchers can check DLW’s data against original sources Custom analysis - Technical users can query the database in ways not supported by the web interface Derivative works - Others can build on DLW’s data collection without starting from scratch Educational value - Students and researchers can examine the data structure and methodology²

Community Responsiveness

Unlike static databases, DLW actively solicits community feedback and incorporates suggestions. The EA Forum tutorial post explicitly requested feedback on priorities for expansion, and community members have provided specific suggestions that have informed development priorities.³

This responsiveness makes DLW more aligned with actual user needs than databases designed without community input. The project’s acknowledgment of contributions from Issa Rice and documentation of contracted work demonstrates a collaborative development model.²

Niche Coverage

For certain donors and cause areas, DLW may have better coverage than larger databases. The platform’s EA community origins mean that donations relevant to longtermism, AI safety, and EA-aligned causes likely receive more attention than in general-purpose philanthropic databases.³

Individual donors like Vitalik Buterin who make donations of interest to the EA community are tracked in detail, whereas they might be below the threshold for inclusion in databases focused on major foundations.³

Practical Usage Guidance

For Researchers

Researchers using DLW should:

Understand selection effects - Recognize that coverage reflects curator interests rather than random sampling Cross-reference data - Verify important figures against original sources or other databases Include appropriate caveats - Note preliminary status when citing specific figures Check update recency - Confirm when data was last updated for relevant entities Contact maintainers - Reach out to Vipul Naik for questions about specific data points²

The platform is most appropriate for exploratory analysis, identifying funding patterns, and generating hypotheses rather than as the sole source for definitive quantitative conclusions.

For Donors and Fundraisers

Prospective donors can use DLW to:

Research funding gaps - Identify cause areas or organizations that may be underfunded Understand donor motivations - See what entities with similar values are funding Track funding trends - Identify emerging areas attracting increased attention Verify money moved claims - Check whether recommenders’ claimed influence matches documented behavior²³

Fundraisers might use the platform to identify foundations whose giving patterns align with their organization’s mission, though they should recognize that absence from the database doesn’t indicate lack of interest in a cause area.

For the Broader Public

DLW serves a broader public transparency function by making philanthropic giving more visible. While most people won’t engage with the technical database, journalists, activists, and informed citizens can use the platform to:

Hold foundations accountable - Track whether giving aligns with stated priorities Understand philanthropic influence - See which causes attract major funding Identify funding sources - Learn who supports particular organizations or movements²

This transparency function is valuable for democratic accountability even if most users never directly access the database.

Future Directions

Planned Improvements

The project documentation indicates ongoing development with completion of a first round expected by July 2026.² Community suggestions for improvements include:³

Expanded foundation coverage - Adding more foundations beyond currently tracked entities Discussion integration - Linking to EA Forum and LessWrong discussions about specific donations Improved cause area classification - Resolving the $20.8 billion in “FIXME” classifications Enhanced verification - Better documentation of data sources and verification processes

Potential Collaborations

DLW could be enhanced through collaboration with:

Open Philanthropy - Direct data sharing could improve accuracy of OP grant records GiveWell - Enhanced integration of money moved tracking with donor verification EA organizations - Coordinated efforts to ensure EA-relevant funding is comprehensively tracked Academic researchers - Partnerships that could improve methodology and expand coverage³

Technical Enhancements

Potential technical improvements include:

API development - Formal API documentation would enable programmatic access Data exports - Standardized export formats (CSV, JSON) for easier analysis Visualization tools - Interactive charts and graphs beyond tabular display Search improvements - Enhanced search functionality across the full database Mobile optimization - Better mobile experience for casual browsing²

Key Uncertainties

Several important questions remain about DLW and its role in the philanthropic data ecosystem:

Sustainability - Will the project continue to receive maintenance and updates long-term, or is it dependent on Vipul Naik’s continued interest?

Comprehensiveness claims - To what extent does the ≈$72.8 billion tracked represent comprehensive coverage of tracked entities versus partial sampling?

Data accuracy - How should researchers weight the “preliminary” data designation when making decisions based on DLW information?

Competitive landscape - Will professional organizations develop similar open databases that supersede DLW, or does its community-driven model offer unique advantages?

Scope evolution - Will DLW expand to cover more donors and cause areas, or maintain its current EA-aligned focus?

Integration potential - Can DLW’s data be effectively integrated with other funding databases to create more comprehensive pictures of philanthropic activity?

Conclusion

The Donations List Website represents a significant contribution to philanthropic transparency, particularly for the effective altruism and AI safety communities. By tracking approximately $72.8 billion in donations across 75+ donors and recipients from 1969 to 2023, the platform provides visibility into funding flows that might otherwise remain opaque.

While the data’s preliminary status and selective coverage require careful interpretation, DLW’s radical openness—with all data and code available on GitHub—enables types of analysis and verification not possible with proprietary databases. For researchers studying AI safety funding, EA-aligned philanthropy, or charitable giving more broadly, DLW serves as a valuable complement to other resources.

The platform’s community-driven development model, documented limitations, and active solicitation of feedback demonstrate a commitment to continuous improvement. As the project continues toward its first major release milestone in mid-2026, it stands as an important example of how transparent data infrastructure can support better-informed decisions in high-stakes domains like AI safety and global health.