Datasets:

benroodman
/

HillStreetSample

@@ -184,29 +184,29 @@ size_categories:
 ---
-# HillStreet: A Relational Dataset for Evaluating Information Channels in Congressional Trading
 **HillStreet** is a large-scale, longitudinal dataset and multimodal dynamic graph formalizing the intersection of Capitol Hill and Wall Street. It spans **13.5 years** of mandatory STOCK Act disclosures (July 2012–December 2025), unifying the congressional trading ecosystem into a single, machine-learning-ready framework.
-## Dataset Summary
 The dataset represents the relationship between **1,137 legislators** and **6,825 companies**. By framing congressional trading as a **dynamic bipartite graph**, HillStreet allows researchers to treat trade signal validation as an edge classification task.
 - **Nodes:** Legislators (session-specific) and Publicly Traded Companies.
 - **Target Edges:** Individual stock trades.
 - **Structural Edges:** Lobbying records, campaign finance (PAC/527) contributions, and geographical/industrial-constituency alignments.
-## Dataset Structure
 HillStreet is divided into pre-built graph objects for deep learning and a relational tabular database accessed via Hugging Face configurations.
-### 1. Dynamic Graph Objects (`.pt` & `.npy`)
 For immediate use in Graph Neural Networks (GNNs) and Temporal Graph Networks (TGNs), the core of HillStreet consists of annual **PyTorch Geometric Temporal** objects.
 - **Graph Files:** `hillstreet_temporal_graph_YEAR.pt`
 - **Temporal Integrity:** Every node feature and edge is instantiated based on its **public disclosure date**, not its reference date, ensuring a look-ahead-bias-free environment for backtesting.
 - **Node Features:** Includes rolling DW-NOMINATE scores, SEC fiscal facts, and Census district employment statistics.
 - **ID Mappings:** `src_id_map.npy` (Legislator Bioguide IDs) and `dst_id_map.npy` (Company Tickers).
-### 2. Relational Tables (Hugging Face Configs)
 For researchers using flat-feature models (XGBoost, LightGBM) or custom graph builders, the structural connective tissue is provided as multiple dataset configurations. You can load these individually using the Hugging Face `datasets` library (e.g., `load_dataset("benroodman/HillStreet", "processed_events_lobbying")`).
 **Processed Edge Tables:**
@@ -221,11 +221,11 @@ The raw, underlying tables are also available as distinct configurations for cus
 - **Corporate & Industry:** `raw_sec_financials`, `raw_naics_*` crosswalks, and `raw_district_industries_*` configs.
 - **Lobbying:** `raw_lobbyview_*` configs.
-## Feature Engineering & Normalization
 To stabilize variance in graph training, continuous features are transformed using signed log-scaling:
 $$x' = \text{sign}(x) \times \log(1 + |x|)$$
-## Intended Use
 - **Trade Signal Validation:** Determining if a trade constitutes a meaningful price signal based on political context.
 - **Graph Representation Learning:** A benchmark for GNNs and TGNs.

 ---
+## HillStreet: A Relational Dataset for Evaluating Information Channels in Congressional Trading
 **HillStreet** is a large-scale, longitudinal dataset and multimodal dynamic graph formalizing the intersection of Capitol Hill and Wall Street. It spans **13.5 years** of mandatory STOCK Act disclosures (July 2012–December 2025), unifying the congressional trading ecosystem into a single, machine-learning-ready framework.
+### Dataset Summary
 The dataset represents the relationship between **1,137 legislators** and **6,825 companies**. By framing congressional trading as a **dynamic bipartite graph**, HillStreet allows researchers to treat trade signal validation as an edge classification task.
 - **Nodes:** Legislators (session-specific) and Publicly Traded Companies.
 - **Target Edges:** Individual stock trades.
 - **Structural Edges:** Lobbying records, campaign finance (PAC/527) contributions, and geographical/industrial-constituency alignments.
+### Dataset Structure
 HillStreet is divided into pre-built graph objects for deep learning and a relational tabular database accessed via Hugging Face configurations.
+#### 1. Dynamic Graph Objects (`.pt` & `.npy`)
 For immediate use in Graph Neural Networks (GNNs) and Temporal Graph Networks (TGNs), the core of HillStreet consists of annual **PyTorch Geometric Temporal** objects.
 - **Graph Files:** `hillstreet_temporal_graph_YEAR.pt`
 - **Temporal Integrity:** Every node feature and edge is instantiated based on its **public disclosure date**, not its reference date, ensuring a look-ahead-bias-free environment for backtesting.
 - **Node Features:** Includes rolling DW-NOMINATE scores, SEC fiscal facts, and Census district employment statistics.
 - **ID Mappings:** `src_id_map.npy` (Legislator Bioguide IDs) and `dst_id_map.npy` (Company Tickers).
+#### 2. Relational Tables (Hugging Face Configs)
 For researchers using flat-feature models (XGBoost, LightGBM) or custom graph builders, the structural connective tissue is provided as multiple dataset configurations. You can load these individually using the Hugging Face `datasets` library (e.g., `load_dataset("benroodman/HillStreet", "processed_events_lobbying")`).
 **Processed Edge Tables:**
 - **Corporate & Industry:** `raw_sec_financials`, `raw_naics_*` crosswalks, and `raw_district_industries_*` configs.
 - **Lobbying:** `raw_lobbyview_*` configs.
+### Feature Engineering & Normalization
 To stabilize variance in graph training, continuous features are transformed using signed log-scaling:
 $$x' = \text{sign}(x) \times \log(1 + |x|)$$
+### Intended Use
 - **Trade Signal Validation:** Determining if a trade constitutes a meaningful price signal based on political context.
 - **Graph Representation Learning:** A benchmark for GNNs and TGNs.