Update README.md

README.md

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----
-license: cc-by-nc-sa-4.0
----
+---
+dataset_info:
+  - config_name: processed_events_campaign_finance
+    data_files:
+      - split: train
+        path: data/processed/events_campaign_finance.csv
+  - config_name: processed_events_geographical_industry
+    data_files:
+      - split: train
+        path: data/processed/events_geographical_industry.csv
+  - config_name: processed_events_lobbying
+    data_files:
+      - split: train
+        path: data/processed/events_lobbying.csv
+
+  - config_name: raw_527_committees
+    data_files:
+      - split: train
+        path: data/raw/527_data_open_secrets/cmtes527.csv
+  - config_name: raw_527_expenditures
+    data_files:
+      - split: train
+        path: data/raw/527_data_open_secrets/expends527.csv
+  - config_name: raw_527_receipts
+    data_files:
+      - split: train
+        path: data/raw/527_data_open_secrets/rcpts527.csv
+
+  - config_name: raw_voteview_members
+    data_files:
+      - split: train
+        path: data/raw/HSall_members_VoteView.csv
+  - config_name: raw_voteview_rollcalls
+    data_files:
+      - split: train
+        path: data/raw/HSall_rollcalls.csv
+  - config_name: raw_voteview_votes
+    data_files:
+      - split: train
+        path: data/raw/HSall_votes.csv
+  - config_name: raw_voteview_ideology
+    data_files:
+      - split: train
+        path: data/raw/ideology_scores_quarterly_VoteView.csv
+
+  - config_name: raw_cf_candidates
+    data_files:
+      - split: train
+        path: data/raw/campaign_finance_open_secrets/cands*.csv
+  - config_name: raw_cf_committees
+    data_files:
+      - split: train
+        path: data/raw/campaign_finance_open_secrets/cmtes*.csv
+  - config_name: raw_cf_expenditures
+    data_files:
+      - split: train
+        path: data/raw/campaign_finance_open_secrets/expenditures*.csv
+  - config_name: raw_cf_individuals
+    data_files:
+      - split: train
+        path: data/raw/campaign_finance_open_secrets/indivs*.csv
+  - config_name: raw_cf_pac_other
+    data_files:
+      - split: train
+        path: data/raw/campaign_finance_open_secrets/pac_other*.csv
+  - config_name: raw_cf_pacs
+    data_files:
+      - split: train
+        path: data/raw/campaign_finance_open_secrets/pacs*.csv
+
+  - config_name: raw_committee_assignments
+    data_files:
+      - split: train
+        path: data/raw/committee_assignments.csv
+  - config_name: raw_company_sic
+    data_files:
+      - split: train
+        path: data/raw/company_sic_data.csv
+  - config_name: raw_congress_terms
+    data_files:
+      - split: train
+        path: data/raw/congress_terms_all_github.csv
+  - config_name: raw_sec_financials
+    data_files:
+      - split: train
+        path: data/raw/sec_quarterly_financials.csv
+
+  - config_name: raw_district_industries_estimates
+    data_files:
+      - split: train
+        path: data/raw/district_industries/*_CB_estimates.csv
+  - config_name: raw_district_industries_surveys
+    data_files:
+      - split: train
+        path: data/raw/district_industries/*_CB_survey.csv
+  - config_name: raw_district_industries_dates
+    data_files:
+      - split: train
+        path: data/raw/district_industries/survey_release_dates.csv
+
+  - config_name: raw_naics_2012_crosswalk
+    data_files:
+      - split: train
+        path: data/raw/industry_codes_NAICS/2012-NAICS-to-SIC-Crosswalk.csv
+  - config_name: raw_naics_2013_mappings
+    data_files:
+      - split: train
+        path: data/raw/industry_codes_NAICS/2013-CAT_to_SIC_to_NAICS_mappings.csv
+  - config_name: raw_naics_2017_crosswalk
+    data_files:
+      - split: train
+        path: data/raw/industry_codes_NAICS/2017-NAICS-to-SIC-Crosswalk.csv
+  - config_name: raw_naics_2022_crosswalk
+    data_files:
+      - split: train
+        path: data/raw/industry_codes_NAICS/2022-NAICS-to-SIC-Crosswalk.csv
+  - config_name: raw_naics_effective_dates
+    data_files:
+      - split: train
+        path: data/raw/industry_codes_NAICS/classification_effective_dates.csv
+
+  - config_name: raw_lobbyview_bills
+    data_files:
+      - split: train
+        path: data/raw/lobbying_data_lobbyview/bills.csv
+  - config_name: raw_lobbyview_clients
+    data_files:
+      - split: train
+        path: data/raw/lobbying_data_lobbyview/clients.csv
+  - config_name: raw_lobbyview_issue_text
+    data_files:
+      - split: train
+        path: data/raw/lobbying_data_lobbyview/issue_text.csv
+  - config_name: raw_lobbyview_issues
+    data_files:
+      - split: train
+        path: data/raw/lobbying_data_lobbyview/issues.csv
+  - config_name: raw_lobbyview_network
+    data_files:
+      - split: train
+        path: data/raw/lobbying_data_lobbyview/network.csv
+  - config_name: raw_lobbyview_reports
+    data_files:
+      - split: train
+        path: data/raw/lobbying_data_lobbyview/reports.csv
+license: cc-by-nc-sa-4.0
+task_categories:
+- graph-ml
+- tabular-classification
+tags:
+- finance
+- politics
+- legal
+- gnn
+- temporal-graph
+pretty_name: "HillStreet: Relational Congressional Trading Dataset"
+size_categories:
+- 10M<n<100M
+---
+
+# 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:**
+- `processed_events_lobbying`: Mappings of legislative activity to corporate nodes.
+- `processed_events_campaign_finance`: Itemized PAC/527 donations broadcasted to corporate sectors.
+- `processed_events_geographical_industry`: Industrial-constituency edges linking legislators to companies in their districts.
+
+**Raw Source Tables:**
+The raw, underlying tables are also available as distinct configurations for custom aggregations and feature engineering:
+- **Campaign Finance:** `raw_cf_*` and `raw_527_*` configs.
+- **Legislator Data:** `raw_voteview_*` configs and `raw_committee_assignments`.
+- **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.
+
+> [!WARNING]
+> **Non-Intended Use:** This dataset is for research purposes only. It is not designed for legal determinations of insider trading nor for real-time automated trading.