Update README.md

README.md

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 ---
-dataset_info:
-  features:
-  - name: election
-    dtype: string
-  - name: cycle
-    dtype: int64
-  - name: fecyear
-    dtype: float64
-  - name: bonica.rid
-    dtype: string
-  - name: bonica.cid
-    dtype: float64
-  - name: name
-    dtype: string
-  - name: lname
-    dtype: string
-  - name: ffname
-    dtype: string
-  - name: fname
-    dtype: string
-  - name: mname
-    dtype: string
-  - name: title
-    dtype: string
-  - name: suffix
-    dtype: string
-  - name: party
-    dtype: string
-  - name: state
-    dtype: string
-  - name: seat
-    dtype: string
-  - name: district
-    dtype: string
-  - name: distcyc
-    dtype: string
-  - name: ico.status
-    dtype: string
-  - name: cand.gender
-    dtype: string
-  - name: recipient.cfscore
-    dtype: float64
-  - name: recipient.cfscore.dyn
-    dtype: float64
-  - name: contributor.cfscore
-    dtype: float64
-  - name: dwdime
-    dtype: float64
-  - name: dwnom1
-    dtype: float64
-  - name: dwnom2
-    dtype: float64
-  - name: ps.dwnom1
-    dtype: float64
-  - name: ps.dwnom2
-    dtype: float64
-  - name: irt.cfscore
-    dtype: float64
-  - name: composite.score
-    dtype: float64
-  - name: num.givers
-    dtype: int64
-  - name: num.givers.total
-    dtype: int64
-  - name: total.receipts
-    dtype: float64
-  - name: total.disbursements
-    dtype: float64
-  - name: total.indiv.contribs
-    dtype: float64
-  - name: total.unitemized
-    dtype: float64
-  - name: total.pac.contribs
-    dtype: float64
-  - name: total.party.contribs
-    dtype: float64
-  - name: total.contribs.from.candidate
-    dtype: float64
-  - name: ind.exp.support
-    dtype: float64
-  - name: ind.exp.oppose
-    dtype: float64
-  - name: prim.vote.pct
-    dtype: float64
-  - name: pwinner
-    dtype: string
-  - name: gen.vote.pct
-    dtype: float64
-  - name: gwinner
-    dtype: string
-  - name: s.elec.stat
-    dtype: string
-  - name: r.elec.stat
-    dtype: string
-  - name: district.pres.vs
-    dtype: float64
-  - name: fec.cand.status
-    dtype: string
-  - name: recipient.type
-    dtype: string
-  - name: igcat
-    dtype: string
-  - name: comtype
-    dtype: string
-  - name: ICPSR
-    dtype: string
-  - name: ICPSR2
-    dtype: string
-  - name: Cand.ID
-    dtype: string
-  - name: FEC.ID
-    dtype: string
-  - name: NID
-    dtype: string
-  - name: before.switch.ICPSR
-    dtype: float64
-  - name: after.switch.ICPSR
-    dtype: float64
-  - name: party.orig
-    dtype: string
-  - name: nimsp.party
-    dtype: string
-  - name: nimsp.candidate.ICO.code
-    dtype: string
-  - name: nimsp.district
-    dtype: string
-  - name: nimsp.office
-    dtype: string
-  - name: nimsp.candidate.status
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 245924928
-    num_examples: 479502
-  download_size: 53812799
-  dataset_size: 245924928
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+license: other
+task_categories:
+- tabular-classification
+- tabular-regression
+language:
+- en
+tags:
+- political-science
+- campaign-finance
+- ideology-scores
+- elections
+- united-states
+size_categories:
+- 100K<n<1M
 ---
+
+# DIME Recipients Database with Campaign Finance Ideology Scores
+
+## Dataset Description
+
+This dataset contains comprehensive information about political recipients (candidates and committees) in the United States from 1980-2024, including their campaign finance-based ideology scores from the Database on Ideology, Money in Politics, and Elections (DIME).
+
+### Key Features
+
+- **479,502 recipients** across 1980-2024
+- **Campaign Finance (CF) ideology scores** for ideological positioning
+- **Multiple office levels**: Federal (House, Senate), State, Local
+- **Party affiliations** with cleaned coding
+- **Financial data**: Receipts, contributions, expenditures
+
+## Dataset Source
+
+- **Original Source**: Stanford University - Adam Bonica
+- **Website**: https://data.stanford.edu/dime
+- **Primary Citation**: Bonica, Adam. 2014. "Mapping the Ideological Marketplace." American Journal of Political Science 58(2): 367-386.
+
+## Dataset Structure
+
+### Basic Statistics
+
+- **Total Records**: 479,502
+- **Unique Recipients**: 216,371
+- **Time Coverage**: 1980-2024
+- **Total Receipts**: $1,114,933,155,977
+- **Individual Contributions**: $573,053,829,718
+
+### Key Columns
+
+#### Identifiers
+- `bonica.rid`: Unique recipient identifier (primary key)
+- `name`: Recipient name (candidate or committee)
+- `bonica.cid`: Contributor identifier (for matching with contributions)
+
+#### Political Information  
+- `party`: Party code (100=Democrat, 200=Republican, 328=Independent)
+- `recipient.cfscore`: **Campaign Finance ideology score** (-2 to +2, negative=liberal, positive=conservative)
+- `nimsp.office`: Office sought (house, senate, state:lower, local:council, etc.)
+- `state`: State abbreviation
+- `district`: Congressional district (for House candidates)
+
+#### Financial Data
+- `total.receipts`: Total money raised
+- `total.indiv.contribs`: Individual contribution amounts  
+- `total.pac.contribs`: PAC contribution amounts
+- `num.givers`: Number of contributors
+
+#### Additional Scores
+- `recipient.cfscore.dyn`: Dynamic CF score (time-varying)
+- `dwnom1`: DW-NOMINATE score (for legislators)
+- `composite.score`: Composite ideology measure
+
+### Data Distribution
+
+#### Party Distribution
+- I: 173,888 (36.3%)
+- D: 154,373 (32.2%)
+- R: 149,643 (31.2%)
+- O: 1,036 (0.2%)
+- L: 226 (0.0%)
+
+#### Office Distribution  
+- : 169,451
+- house: 115,899
+- senate: 42,089
+- local:other: 16,436
+- local:council: 15,877
+
+#### CF Score Distribution
+- **Mean**: 0.171
+- **Std Dev**: 1.020  
+- **Range**: -6.864 to 6.714
+- **Median**: 0.074
+
+## Usage Examples
+
+### Basic Loading
+
+```python
+from datasets import load_dataset
+import pandas as pd
+
+# Load full dataset
+dataset = load_dataset("mliliu/dime-recipients")
+df = dataset['train'].to_pandas()
+
+print(f"Dataset shape: {df.shape}")
+print(f"CF scores available: {df['recipient.cfscore'].notna().sum():,}")
+```
+
+### Filtering Examples
+
+```python
+# Recent federal candidates only
+federal_recent = df[
+    (df['cycle'] >= 2016) & 
+    (df['nimsp.office'].isin(['house', 'senate'])) &
+    (df['recipient.type'] == 'cand')
+]
+
+# Major party candidates with CF scores  
+major_parties = df[
+    df['party'].isin(['100', '200']) &  # Dem/Rep
+    df['recipient.cfscore'].notna()
+]
+
+# Senate candidates by ideology
+senate_liberal = df[
+    (df['nimsp.office'] == 'senate') &
+    (df['recipient.cfscore'] < -0.5)
+]
+```
+
+### Ideology Analysis
+
+```python
+import matplotlib.pyplot as plt
+
+# Plot ideology distribution by party
+dem_scores = df[df['party'] == '100']['recipient.cfscore'].dropna()
+rep_scores = df[df['party'] == '200']['recipient.cfscore'].dropna()
+
+plt.hist(dem_scores, alpha=0.7, label='Democrats', bins=50)
+plt.hist(rep_scores, alpha=0.7, label='Republicans', bins=50)
+plt.xlabel('CF Score (Liberal ← → Conservative)')
+plt.ylabel('Frequency')
+plt.legend()
+plt.show()
+```
+
+## Pre-processed Versions Available
+
+This dataset has been optimized and filtered into several versions:
+
+- **`dime_recent.parquet`**: Records from 2010+ (277,297 rows)
+- **`dime_federal_candidates.parquet`**: House + Senate candidates (157,988 rows)  
+- **`dime_house_candidates.parquet`**: House candidates only (115,899 rows)
+- **`dime_senate_candidates.parquet`**: Senate candidates only (42,089 rows)
+- **`dime_major_parties.parquet`**: Democrat + Republican only (304,016 rows)
+
+## Data Quality Notes
+
+### Missing Data Rates
+- cf_score: 0.0% missing
+- party: 0.0% missing
+- state: 0.0% missing
+- district: 0.0% missing
+
+### Data Cleaning Applied
+- Party codes standardized (100→D, 200→R, 328→I, etc.)
+- CF scores converted to numeric format
+- Office types extracted from NIMSP data
+- Decade groupings added for temporal analysis
+
+## Methodology: Campaign Finance Scores
+
+The CF scores are estimated using a Bradley-Terry model applied to campaign contribution patterns:
+
+1. **Contributors** make donations reflecting ideological preferences
+2. **Recipients** receive donations from ideologically-aligned contributors  
+3. **Scaling algorithm** positions recipients on liberal-conservative dimension
+4. **Scores** range from -2 (very liberal) to +2 (very conservative)
+
+**Key advantages**:
+- Covers candidates, PACs, and committees
+- Available for all time periods  
+- Not dependent on roll-call votes
+- Captures fundraising-based ideology
+
+## Licensing and Citation
+
+### Usage Rights
+- ✅ **Academic Research**: Permitted
+- ❓ **Redistribution**: Contact original authors
+- ❓ **Commercial Use**: Requires permission  
+
+### Required Citation
+
+```bibtex
+@article{bonica2014mapping,
+  title={Mapping the ideological marketplace},
+  author={Bonica, Adam},
+  journal={American Journal of Political Science},
+  volume={58},
+  number={2},
+  pages={367--386},
+  year={2014}
+}
+```
+
+### Additional References
+
+For methodology details:
+- Bonica, Adam. 2016. "Avenues of influence: on the political expenditures of corporations and their directors and executives." *Business and Politics* 18(4): 367-394.
+
+## Technical Details
+
+### File Formats
+- **Parquet**: 37.2 MB (recommended for analysis)
+- **CSV.gz**: 28.8 MB (human-readable)
+- **Sharded**: Available for distributed processing
+
+### Performance Benchmarks
+- **Loading time**: ~3-5 seconds for full dataset
+- **Memory usage**: ~500MB RAM for full dataset in pandas
+- **Query performance**: Optimized with column indices
+
+## Contact
+
+For questions about this dataset preparation:
+- Dataset processing: Created for academic research
+- Original data: Contact Adam Bonica (Stanford)
+- Usage questions: See DIME project documentation
+
+---
+
+*Data card generated on 2025-08-03*
+*Processing pipeline version: 1.0*