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@civic-data-lab.bsky.social

@CivicDataLab@mas.to

LinkedIn // civic-data-lab

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The initial challenge

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• Data base from “förderdatenbank.de”: Website with federal, state and European Union funding programs (~ 2.500)
• Interim result: Open Data
• On behalf of AWO (Workers' Welfare Organization): determining the amount of money spent to fund democracy
• 1. Phase: Text Classification
• Challenges: No labeled data, Democracy is a very broad term
• Codebook, Few-Shot methods and some manual labeling
• 2. Phase: Extraction of Funding Amount (NER)
• 3. Matching with federal/state budget items)

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- Funding programs do not specify maximum amount of money available

- **Funding programs only specify budget per eligible applicant**, so instead of calculating maximum of

- sometimes, even if the formular is extractable, it is based on e.g. employee, but no maximum amount of employee named

- amount is time based but no maximum time/years named

- time based funding or one time funding?

- funds for different kinds of expenses -> e.g personell vs material costs

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Challenges

Complex Search Interface

• The page provides numerous filters and options for refining searches, but this complexity can be overwhelming and may lead to unintended results.

Generic Results

• If search terms or filters aren't specific enough, the system might display a broad range of programs, making it hard to pinpoint a single relevant one.

Restricted Access or Availability

• Certain funding programs may have restricted eligibility or limited timeframes, and their application windows could be closed, as suggested by the phrase "Antragstellung derzeit nicht möglich" in some listings.

Navigation Issues

• Some users may find the layout and structure of the website challenging, which could hinder their ability to complete detailed searches

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New Objective

List all NGOs that received funding in 2023, instead of calculating funding per program. This revised challenge focuses on identifying and listing the names of all funded NGOs, as accurately calculating the amount allocated to each program has proven to be unfeasible.”

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For whom

Citizens & Taxpayers

• They gain a clearer understanding of how public funds are used, helping to build trust in government spending and decision-making.�

Journalists & Researchers

• Access to organized, verifiable data supports investigative reporting, data journalism, and academic research into public finance and policy effectiveness.�

Civil Society & NGOs

• Transparency allows non-profits and advocacy groups to track funding flows, identify trends, and hold institutions accountable.�

Policy Makers & Public Officials

• By highlighting gaps, inefficiencies, or successful programs, the platform provides feedback that can help improve future funding decisions.�

Tech & Data Enthusiasts

• Open access to structured data invites innovation, encouraging developers to build tools, visualizations, or services around public finance information.�

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Benefits

Transparent Public Spending

• Clear view of how BMWK funds are allocated
• Builds public trust through open access to data

Accountability & Anti-Corruption

• Enables public and media oversight
• Deters mismanagement by increasing visibility

Informed Civic Participation

• Empowers citizens to engage in meaningful policy debates
• Makes budget decisions accessible to non-experts

Fuel for Journalism & Research

• Reliable source for investigations and analysis
• Supports data-driven storytelling and academic studies

Smarter Policy Design

• Highlights funding trends and gaps
• Supports evidence-based decision making within government

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Benefits

Empowerment for NGOs

• Benchmark funding and identify opportunities
• Strengthen applications with data-backed insights

Catalyst for Innovation

• Open data for developers and civic tech creators
• Sparks new tools, visualizations, and APIs

Historical & Comparative Insight

• Track changes over time
• Compare funding across programs, years, or administrations

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How much does Germany spend on promoting democracy?

First attempt:

• Scraped data from the Förderdatenbank
• Used NLP to extract funding amounts

Problems:

• No clear spending info.
• Unstructured data.

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• Funding programs do not specify maximum amount of money available
• **Funding programs only specify budget per eligible applicant**, so instead of calculating maximum of
1. sometimes, even if the formular is extractable, it is based on e.g. employee, but no maximum amount of employee named
2. amount is time based but no maximum time/years named
• time based funding or one time funding?
• funds for different kinds of expenses -> e.g personell vs material costs (edite�

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New approach

• Switch to Federal Budget 2023

We focus on:

• Democracy funding (donation register)
• Other NGO funding (Bundestag report)
• Military & full budget (Finance Ministry)

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Our Biggest Challenge: Data Access & Cleaning

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We spent most of our time on:

• Finding, extracting, and cleaning the data
• Making it ready for comparison and visualization

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Treemap Visualisation

• Budget categories shown in one graphic
• Democracy vs. Military vs. Other
• Shows total € and percentage

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Why it matters

• Democracy needs support – also financially
• Our project shows how much is invested
• Helps create transparency and spark discussion

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Limitations

• Our analysis is based on a single document (Bundestag Drucksache 20/1513)
• This report was only published once (for 2023)
• The process is not fully automated� → Some parts required manual extraction from PDFs

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Key factors for your success

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