fireboy21/tenders_aoc

India Government e-Procurement Tenders & Award-of-Contract (AOC)

A large-scale collection of public-procurement records scraped from India's National Informatics Centre (NIC) e-Procurement portals (the Central Public Procurement Portal and affiliated central / state / organisation portals). The corpus covers two linked views of the tendering lifecycle:

1. Tenders — live and archived tender notices (the call for bids).
2. AOC (Award of Contract) — outcome records showing the awarded value, the selected bidder, and the number of bids received.

⚠️ Provenance & licensing notice. This data was programmatically scraped from public Indian government procurement portals. It is redistributed here for research and transparency purposes. Verify the licensing/terms-of-use of the source portals before any commercial use, and treat all fields as as-scraped (see Limitations).

Dataset at a glance

Config / table	Rows	Description
tenders	3,952,191	Tender notice listings (active + archived)
tender_details	3,178,485	Per-tender detail blob (EMD, dates, category, description…)
aoc_tenders	4,921,960	Award-of-Contract listings
aoc_details	4,540,739	Per-AOC detail blob (contract value, selected bidder, #bids)

• Time span: ~2011 – 2026 (by year)
• Geography: India (central, state, and organisation procurement portals)
• Language: English (with some transliterated / mixed-script free text)
• Source format: two SQLite databases (tenders_vps.db, aoc_tenders.db)

Schema

tenders

Column	Type	Notes
internal_id	string	Portal-internal id
tender_id	string	Tender identifier
detail_url	string	Source URL for the tender detail page
status	string	active (72,574) / archived (3,879,617)
organisation_name	string	Procuring organisation
title	string	Tender title
reference_number	string	Tender reference no.
portal_type	string	org (3,910,366) / state (41,825)
serial_number	string
e_published_date	string	e.g. 11-Jun-2026 11:59 AM
bid_submission_closing_date	string
tender_opening_date	string
corrigendum_url	string
scraped_at	string	Scrape timestamp
partition_id	int	Internal partition key

tender_details

Column	Type	Notes
internal_id	string	Join key → tenders.internal_id
tender_id	string
details_json	string (JSON)	Nested key/value detail map
scraped_at	string

details_json keys (observed): EMD, Name, Address, Location, Tender Fee, Tender Type, Tender Title, Tender Category, Tender Document, ePublished Date, Bid Opening Date, Product Category, Work Description, Organisation Name, Organisation Type, Product Sub-Category, Bid Submission End Date, Tender Reference Number, Bid Submission Start Date, Document Download Start/End Date.

aoc_tenders

Column	Type	Notes
internal_id	string
portal_type	string	central (2,005,258) / state (2,916,702)
year	int	2011–2026
sl_no	string
aoc_date	string	Award date
closing_date	string
title	string
ref_no	string
tender_id	string
org_name	string	Procuring organisation / state
detail_url	string
partition_id	int

aoc_details

Column	Type	Notes
internal_id	string	Join key → aoc_tenders.internal_id
tender_id	string
details_json	string (JSON)	Nested key/value detail map
scraped_at	string

details_json keys (observed): Tender Type, Contract Date, Contract Value, Published Date, Tender Document, Tender Ref. No., Organisation Name, Tender Description, Number of bids received, Name of the selected bidder(s), Address of the selected bidder(s), Date of Completion/Completion Period in Days.

How records link

tenders.internal_id      ─┬─►  tender_details.internal_id
aoc_tenders.internal_id  ─┴─►  aoc_details.internal_id

Each listing row (tenders / aoc_tenders) has a corresponding detail row keyed by internal_id (detail counts are lower than listing counts — not every listing has a scraped detail blob).

Usage

from datasets import load_dataset

# Listings only
tenders = load_dataset("<org>/<dataset>", "tenders", split="train")
aoc     = load_dataset("<org>/<dataset>", "aoc_tenders", split="train")

# Parse the nested detail blob
import json
details = load_dataset("<org>/<dataset>", "tender_details", split="train")
rec = json.loads(details[0]["details_json"])
print(rec["Work Description"], rec["EMD"])

Or query the raw SQLite directly:

import sqlite3, pandas as pd
con = sqlite3.connect("tenders_vps.db")
df = pd.read_sql("SELECT * FROM tenders WHERE status='active' LIMIT 10", con)

Sample rows

10-row previews per table are provided under top10_samples/.

Suggested uses

• Procurement transparency, spend & competition analysis (bids received, award values)
• Org / category text classification and entity extraction
• Retrieval / semantic search over tender descriptions
• Time-series of public spending by state, organisation, and year

Limitations & caveats

• As-scraped, unnormalised. Dates are strings (DD-Mon-YYYY hh:mm AM/PM), monetary values are strings (e.g. "1874075", "₹ 20441") and may contain currency symbols, commas, or be empty. Contract Value / EMD need cleaning before numeric use.
• Encoding artefacts. Some free-text fields contain HTML entity / escape residue (e.g. &ampamp#x0d, ₹).
• Missing values. Detail blobs and many fields can be empty strings; detail tables do not fully cover their listing tables.
• No PII guarantees. Selected-bidder names and addresses are present as published by the source portals; bidders are typically firms but may include individuals.
• No dedup / verification. Rows reflect portal state at scrape time and may include duplicates, corrigenda, or test entries (e.g. titles like test1).

Citation

@misc{india_eproc_tenders_aoc,
  title  = {India Government e-Procurement Tenders \& Award-of-Contract (AOC)},
  year   = {2026},
  note   = {Scraped from NIC / Central Public Procurement Portal e-procurement portals},
  howpublished = {Hugging Face Datasets}
}