pretty_name: CIMD
license: other
language:
- zh
- en
language_creators:
- found
multilinguality:
- multilingual
source_datasets:
- original
size_categories:
- 100K<n<1M
tags:
- text
- jsonl
- mining
- iron-ore
- metallurgy
- law
- finance
- industry
- rag
- domain-dataset
configs:
- config_name: state_laws
data_files:
- split: train
path: data/text/iron_ore/state_laws/train.jsonl
- config_name: domestic_conference_papers
data_files:
- split: train
path: data/text/iron_ore/domestic_conference_papers/train.jsonl
- config_name: doctoral_dissertations
data_files:
- split: train
path: data/text/iron_ore/doctoral_dissertations/train.jsonl
- config_name: chinese_journals
data_files:
- split: train
path: data/text/iron_ore/chinese_journals/train.jsonl
- config_name: international_journal_of_mining_science_and_technology
data_files:
- split: train
path: >-
data/text/iron_ore/international_journal_of_mining_science_and_technology/train.jsonl
- config_name: international_journal_of_minerals_metallurgy_and_materials
data_files:
- split: train
path: >-
data/text/iron_ore/international_journal_of_minerals_metallurgy_and_materials/train.jsonl
- config_name: industry_research_reports
data_files:
- split: train
path: data/text/iron_ore/industry_research_reports/train.jsonl
- config_name: sintering_and_pelletizing
data_files:
- split: train
path: data/text/iron_ore/sintering_and_pelletizing/train.jsonl
- config_name: ironmaking
data_files:
- split: train
path: data/text/iron_ore/ironmaking/train.jsonl
- config_name: public_opinion
data_files:
- split: train
path: data/text/iron_ore/public_opinion/train.jsonl
- config_name: books
data_files:
- split: train
path: data/text/iron_ore/books/train.jsonl
CIMD
中文说明
数据集概述
CIMD 是面向铁矿石及矿冶产业链的跨来源文本数据集。数据覆盖法律法规、行业规章、政策文件、行业标准、学术论文、会议论文、博士论文、图书、行业研究、企业经营、产能产量、市场交易与舆情观点等核心类型，当前公开版本以统一的 JSONL 记录格式发布，并保留标题、来源类型、作者、时间、语言、关键词、授权类型与来源信息等关键元数据字段。
CIMD 对应的是 OpenCSG 从通用语料向垂直行业、从单一来源向跨来源、从研究数据向数据资产的阶段推进。
行业智能基础设施首先需要稳定、可追溯、可组织的数据底座。
跨来源整合。 CIMD 将制度文本、技术文本、研究文本、经营文本和市场文本放入同一数据体系。单一来源的数据通常只能回答局部问题，例如政策库侧重制度依据，论文库侧重技术原理，市场库侧重价格和交易信号，而行业实际问题往往同时依赖法规边界、工艺机理、企业行为和市场环境。CIMD 将法律法规、政策文件、行业标准、学术论文、博士论文、图书、行业研究、企业经营信息、产能产量、市场交易与舆情观点统一组织，使同一主题可以在多种来源之间形成连续证据链。对于行业检索、RAG、Agent 推理、专题研究、领域微调和评测集构建，这种跨来源结构能够更直接地支撑“制度依据 + 技术材料 + 市场证据”的联合调用，减少跨库拼接带来的语义割裂和上下文缺口。
跨来源结构也为跨文档知识汇聚和证据归因提供了稳定入口。
权威来源支撑。 CIMD 汇集了矿冶行业中具有代表性的权威文本。制度层面，数据覆盖国家法律法规、行业规章、政策文件和标准文本，适合构建具备制度依据和合规边界的行业知识底座。学术层面，数据覆盖中文期刊、国内会议论文、博士学位论文，并纳入 International Journal of Mining Science and Technology、International Journal of Minerals, Metallurgy and Materials 等国际矿冶期刊内容，形成兼具中文产业语境与国际技术视野的专业语料。产业层面，数据进一步延伸至科研院所报告、行业协会报告、券商研究、企业经营信息、产能产量与市场相关材料。来源主体稳定、专业、可追溯，使数据在专业深度、行业可信度和实际应用价值上具备更高上限。
权威来源覆盖资源、采选、烧结球团、炼铁、政策治理与市场研究等关键环节。
完整数据体系。 CIMD 的数据组织不是简单的文件汇总，而是围绕铁矿石及矿冶产业链建立的行业数据体系。完整体系覆盖 9 个一级分类、42 个二级分类、335 个三级/四级来源节点，涵盖法律文件与法规依据、行业规章与管理办法、政策文件与行业指导、行业标准、专利与知识产权、学术与培训资料、互联网舆情与观点分析、企业经营与运营信息、行业研究与市场报告等核心门类。公开快照围绕铁矿石资源、采选加工、烧结球团、炼铁生产、政策治理、企业经营与市场观察形成连续语料结构，覆盖制度依据、技术机理、生产流程、经营信息和市场信号。该体系提升数据覆盖深度，并为专题扩展、增量采集、行业补数、基准任务设计和数据资产编目提供结构框架。
同一方法论可以继续向能源、化工、新能源、金融、医疗等行业扩展。
从通用语料到垂直行业知识，数据体系是关键连接层。
元数据完整。 当前版本在每条记录中保留 file_id、data_id、title、source_type、author、original_time、content_time、language、keywords、license_type、source_details 等关键字段。使用者可以按来源、时间、语言、主题和授权边界直接筛选样本，也可以把检索到的文本片段回溯到原始文件和具体记录。对于长文档检索、来源归因、审计留痕、授权控制、质量抽检和数据资产管理，这类记录级元数据比单纯正文更有操作价值，也更适合持续扩展和版本化维护。
面向标准化建设与可信流通。 数据集把标识、来源、分类、时间、授权和来源说明直接放在记录体内，而不是把这些信息留在外部台账里。这样一来，同一份快照可以直接进入数据目录编制、质量抽检、授权审计、责任追踪和可信流通流程，不需要在语料之外再补一套独立的元数据体系。这种组织方式与国家数据局和全国数据标准化技术委员会公开发布的《高质量数据集 建设指南》《高质量数据集 格式要求》《高质量数据集 分类指南》《高质量数据集 质量评测规范》以及《国家数据基础设施建设指引》强调的方向保持同向。
记录内嵌元数据结构，便于对齐高质量数据集建设、格式、分类和质量评测要求。
直接可用于模型与应用。 当前公开版本以统一 JSONL 记录格式发布，不是单纯的原始 PDF 堆积。解析后的记录可直接进入检索、切分、标注、训练、评测和服务流程，便于快速接入 CSGHub / OpenCSG 数据平台，以及 Hugging Face、ModelScope、自建 RAG 系统和行业智能体工作流。对于需要高质量中文专业语料、制度与技术联合语料、跨来源证据检索能力和可管理元数据结构的行业智能场景，CIMD 具备较强的即用性。
统一记录格式可直接进入继续预训练、SFT 与行业评测流程。
同一数据底座适合构建垂直 RAG 系统。
在更复杂的工作流中，也可以作为行业智能体的检索与证据底座。
当前公开版本包含 11 个子集，共 379,648 条有效 JSONL 记录，对应 56,771 个去重 file_id。完整数据体系覆盖 9 个一级分类、42 个二级分类、335 个三级/四级来源节点。
公开仓库地址：
标准与参考文件
- 国家数据局：第一批高质量数据集方向标准/技术文件验证试点典型单位发布
- 全国数据标准化技术委员会：关于发布高质量数据集、全国一体化算力网、数据基础设施、可信数据空间等4个方向19项技术文件的通知
- 技术文件《高质量数据集 建设指南》
- 技术文件《高质量数据集 格式要求》
- 技术文件《高质量数据集 分类指南》
- 技术文件《高质量数据集 质量评测规范》
- 国家数据基础设施建设指引
数据组成
当前公开快照按子集组织在 data/text/iron_ore/ 目录下。
| 子集 | JSONL 记录条数 | 去重 file_id 数 |
存储方式 | 内容 |
|---|---|---|---|---|
state_laws |
99,496 | 7,300 | Git LFS | 法律法规、规章制度、政策文本 |
domestic_conference_papers |
58,116 | 18,787 | Git LFS | 国内会议论文与会议资料 |
doctoral_dissertations |
37,961 | 804 | Git LFS | 博士学位论文 |
public_opinion |
30,705 | 9,427 | Git LFS | 舆情与观点资料 |
chinese_journals |
28,266 | 6,412 | Git LFS | 中文期刊论文 |
international_journal_of_mining_science_and_technology |
16,824 | 2,435 | Git LFS | 英文学术期刊 |
international_journal_of_minerals_metallurgy_and_materials |
15,391 | 2,461 | Git LFS | 英文学术期刊 |
industry_research_reports |
11,647 | 370 | Git LFS | 行业研究、券商、企业与产能相关材料 |
sintering_and_pelletizing |
9,558 | 3,783 | Git LFS | 烧结球团专题资料 |
ironmaking |
8,002 | 3,871 | Git LFS | 炼铁与生产专题资料 |
books |
63,682 | 1,121 | Git LFS | 图书资料 |
快照统计
以下统计基于 2026-03-31 的仓库快照。
| 项目 | 数值 |
|---|---|
| 已声明子集配置 | 11 |
| 当前有效 JSONL 记录条数 | 379,648 |
去重 file_id 数 |
56,771 |
source_type 类别数 |
51 |
| 数据体系层级 | 9 个一级分类，42 个二级分类，335 个三级/四级来源节点 |
统计口径：
JSONL 记录条数指有效 JSONL 记录数，不是原始文件数。- 统计时已排除空行、占位行和无效首行。
- 一份原始文件可以拆分为多条 JSONL 记录。
去重 file_id 数用于表示子集内可识别的源文件数量。Git LFS表示文件内容由 Git Large File Storage 管理；普通git clone之后如需拉取实际内容，需要执行git lfs pull。
语言分布（按 JSONL 记录条数）：
| 语言 | 记录数 |
|---|---|
zh |
229,530 |
en |
114,993 |
other |
35,125 |
format 字段分布（按 JSONL 记录条数）：
| 格式 | 记录数 |
|---|---|
pdf |
346,881 |
jsonl |
30,705 |
docx |
2,054 |
markdown |
1 |
doc |
7 |
主要来源类型（按 JSONL 记录条数）：
source_type |
记录数 |
|---|---|
| 期刊论文 | 116,891 |
| 国家法律法规 | 95,394 |
| 学术出版物 | 57,492 |
| 学位论文 | 38,075 |
| 社会公众与自媒体舆情 | 31,178 |
| 企业基本信息 | 11,226 |
| 产能与产量数据 | 6,278 |
| 行业协会报告 | 4,649 |
| 科研院所报告 | 4,414 |
| 国内产业政策 | 2,393 |
| 期货衍生品交易数据 | 1,405 |
| 会议论文 | 1,317 |
数据体系
完整数据体系按以下九类组织：
- 法律文件与法规依据
- 行业规章与管理办法
- 政策文件与行业指导
- 行业标准
- 专利与知识产权
- 学术与培训资料
- 互联网舆情与观点分析
- 企业经营与运营信息
- 行业研究与市场报告
目录结构
data/
text/
iron_ore/
state_laws/train.jsonl
domestic_conference_papers/train.jsonl
doctoral_dissertations/train.jsonl
chinese_journals/train.jsonl
international_journal_of_mining_science_and_technology/train.jsonl
international_journal_of_minerals_metallurgy_and_materials/train.jsonl
industry_research_reports/train.jsonl
sintering_and_pelletizing/train.jsonl
ironmaking/train.jsonl
public_opinion/train.jsonl
books/train.jsonl
数据结构
当前仓库以 JSONL 为主，每行对应一条解析记录。单个源文件可以对应多条记录，因此文件数与记录数不是同一统计口径。
| 字段 | 类型 | 说明 |
|---|---|---|
format |
string | 来源文件或载体格式 |
file_id |
string | 文件标识 |
raw_chunk |
string | 解析后的文本内容 |
file_name |
string | 原始文件名 |
title |
string | 标题 |
source_type |
string | 来源类型 |
author |
string | 作者、机构或发布主体 |
original_time |
string | 原始发布时间 |
content_time |
string | 内容时间 |
source_details |
string | 公开来源链接或来源说明 |
data_version |
string | 数据版本 |
license_type |
string | 记录级授权类型 |
is_generated |
string | 是否标记为生成内容 |
country |
string | 国家标签 |
language |
string | 语言标签 |
keywords |
array | 关键词 |
data_id |
string | 记录标识 |
示例：
{
"format": "pdf",
"file_id": "241f81dd17a546245faf672cf36fcecd",
"file_name": "iron_ore_report.pdf",
"data_id": "241f81dd17a546245faf672cf36fcecd",
"title": "铁矿石价格（1月3日）",
"source_type": "科研院所报告",
"author": "中国矿产资源研究院",
"original_time": "2024-01-04 00:00:00",
"content_time": "2024-01-03 00:00:00",
"data_version": "1.0.0",
"is_generated": "0",
"country": "中国",
"language": "zh",
"keywords": [
"现货交易",
"金融衍生品"
],
"license_type": "商业授权",
"raw_chunk": "...",
"source_details": "https://example.com/report/iron-ore-2024-01-03"
}
获取与加载
通过 Git 获取：
git lfs install
git clone https://opencsg.com/datasets/OpenCSG/CIMD.git
cd CIMD
git lfs pull
使用 Hugging Face datasets：
from datasets import load_dataset
dataset = load_dataset(
"opencsg/CIMD",
"state_laws",
split="train",
streaming=True,
)
使用 ModelScope：
from modelscope.msdatasets import MsDataset
dataset = MsDataset.load(
dataset_name="CIMD",
namespace="OpenCSG",
subset_name="state_laws",
split="train",
)
适用场景
- 铁矿石与矿冶行业垂直检索与 RAG
- 行业研究助手与证据检索系统
- 政策、法规与合规问答
- 长文档问答与跨来源证据聚合
- 领域继续预训练、SFT 样本构建与数据筛选
- 文档分类、来源识别、主题标注与术语抽取
使用注意
- 当前计数单位为解析记录，不等同于去重后的原始文档数。
- 当前公开子集通过 Git LFS 管理。
- 不同来源之间可能存在重复、近重复或解析噪声。
- 时间字段可能表示发布时间、内容时间或抽取时间。
- 用于训练、分发或商用前，需要结合来源信息核验实际授权范围。
许可说明
使用本数据集需要遵循 OpenCSG 数据集许可协议。仓库 metadata 中的 license: other 表示本数据集采用平台预设列表之外的许可协议；本数据集的实际许可条款以上述协议为准。本数据集支持商业用途。如计划将本数据集、基于本数据集训练或增强的模型、系统、Agent、API 服务或商业产品用于商业场景，请遵循该协议，并发送邮件至 lorraineg@opencsg.com 获取许可。
引用
@dataset{opencsg_cimd_2026,
title = {CIMD: A Cross-Source Industry Corpus for Iron Ore, Mining, Metallurgy, Policy, and Market Intelligence},
author = {OpenCSG},
year = {2026},
url = {https://opencsg.com/datasets/OpenCSG/CIMD},
note = {OpenCSG dataset repository}
}
English
Overview
CIMD is a cross-source text dataset for iron ore and the broader mining-metallurgy value chain. The current public release provides normalized JSONL records that cover legal and policy documents, academic literature, conference materials, doctoral dissertations, books, industry research, enterprise information, production data, market-related content, and public opinion materials. The repository preserves per-record metadata including title, source type, author, timestamps, language, keywords, license tags, and source information.
Within OpenCSG's data roadmap, CIMD moves from general corpora toward vertical, cross-source industry assets. Industry AI infrastructure starts with stable, traceable, and well-structured data, and CIMD serves that layer of the stack.
Cross-source integration. CIMD brings regulatory, technical, research, operational, and market texts into one data system. Single-source collections answer one part of an industry question. Policy repositories provide regulatory boundaries, academic repositories provide technical mechanisms, and market repositories provide transaction and sentiment signals. CIMD organizes laws and regulations, policy documents, standards, academic papers, doctoral dissertations, books, industry research, enterprise information, production data, market materials, and public opinion into one corpus, so users can track the same topic across source types. This structure fits retrieval, evidence aggregation, agent workflows, domain tuning, and benchmark construction.
Authoritative sources. CIMD collects representative and traceable texts from the mining-metallurgy domain. On the institutional side, it covers national laws and regulations, industry rules, policy documents, and standards. On the academic side, it covers zh journal collections, domestic conference papers, doctoral dissertations, and English technical literature from journals such as International Journal of Mining Science and Technology and International Journal of Minerals, Metallurgy and Materials. On the industry side, it extends to research institute reports, industry association reports, brokerage research, enterprise information, production data, and market-facing materials. This source composition gives the dataset strong domain credibility and practical value.
Comprehensive taxonomy. CIMD uses an industry data system rather than a flat document dump. The full structure covers 9 first-level categories, 42 second-level categories, and 335 third/fourth-level source nodes, including legal and regulatory materials, administrative rules, policy guidance, standards, patents and intellectual property, academic and training materials, public opinion and commentary, enterprise operations, and industry research and market reports. The public snapshot forms a continuous corpus around iron ore resources, beneficiation and processing, sintering and pelletizing, ironmaking, policy governance, enterprise operations, and market observation. This structure supports expansion, incremental collection, data cataloging, and benchmark design.
The same structure can extend to adjacent sectors with deeper sub-domains, supply-chain coverage, and finer quality control.
Rich metadata. Each record keeps fields such as file_id, data_id, title, source_type, author, original_time, content_time, language, keywords, license_type, and source_details. Users can filter by source, time window, language, topic, or authorization boundary, and they can trace a retrieved chunk back to the underlying record and source file. For long-document retrieval, provenance tracking, audit logs, license control, and ongoing dataset maintenance, this is materially more useful than plain text alone.
Alignment with standardization and trusted circulation. The dataset keeps identifiers, source labels, classification fields, timestamps, authorization tags, and source notes inside each record instead of leaving them in a separate ledger. That means the same snapshot can move into cataloging, quality review, authorization checks, accountability workflows, and trusted data-space services without a second metadata pass outside the corpus. This layout follows the direction reflected in the published High-Quality Dataset Construction Guide, High-Quality Dataset Format Requirements, High-Quality Dataset Classification Guide, High-Quality Dataset Quality Evaluation Specification, and the Guidelines for National Data Infrastructure Development.
Ready for models and applications. The public release uses normalized JSONL records rather than raw source files alone. The parsed records move directly into retrieval, chunking, annotation, training, evaluation, and serving workflows, and teams can plug them into the CSGHub / OpenCSG data platform, Hugging Face, ModelScope, custom RAG systems, and industry agent pipelines. For domain applications that require zh materials, combined regulatory and technical evidence, cross-source retrieval, and manageable metadata, CIMD is directly usable.
The same record format also supports vertical RAG systems, agent workflows, continued pretraining, fine-tuning, and document intelligence pipelines.
The current public release contains 11 subsets, 379,648 effective JSONL records, and 56,771 unique file_id values. The full taxonomy covers 9 first-level categories, 42 second-level categories, and 335 third/fourth-level source nodes.
Public repository:
Standards and References
- National Data Administration: Release of the first batch of pilot units for high-quality dataset standards and technical documents
- National Data Standardization Technical Committee: Notice on releasing 19 technical documents across high-quality datasets, the national integrated computing network, data infrastructure, and trusted data spaces
- High-Quality Dataset Construction Guide
- High-Quality Dataset Format Requirements
- High-Quality Dataset Classification Guide
- High-Quality Dataset Quality Evaluation Specification
- Guidelines for National Data Infrastructure Development
Data Composition
The current public snapshot sits under data/text/iron_ore/.
| Subset | JSONL records | Unique file_id values |
Storage | Content |
|---|---|---|---|---|
state_laws |
99,496 | 7,300 | Git LFS | Laws, regulations, policy, and institutional texts |
domestic_conference_papers |
58,116 | 18,787 | Git LFS | Domestic conference papers and conference materials |
doctoral_dissertations |
37,961 | 804 | Git LFS | Doctoral dissertations |
public_opinion |
30,705 | 9,427 | Git LFS | Public opinion and commentary materials |
chinese_journals |
28,266 | 6,412 | Git LFS | zh-language academic journals |
international_journal_of_mining_science_and_technology |
16,824 | 2,435 | Git LFS | English academic literature |
international_journal_of_minerals_metallurgy_and_materials |
15,391 | 2,461 | Git LFS | English academic literature |
industry_research_reports |
11,647 | 370 | Git LFS | Industry research, brokerage, enterprise, and production-related materials |
sintering_and_pelletizing |
9,558 | 3,783 | Git LFS | Sintering and pelletizing materials |
ironmaking |
8,002 | 3,871 | Git LFS | Ironmaking and production materials |
books |
63,682 | 1,121 | Git LFS | Books |
Snapshot Statistics
The following statistics correspond to the repository snapshot dated 2026-03-31.
| Item | Value |
|---|---|
| Declared subset configs | 11 |
| Effective JSONL records | 379,648 |
Unique file_id values |
56,771 |
Distinct source_type values |
51 |
| Taxonomy depth | 9 first-level categories, 42 second-level categories, 335 third/fourth-level source nodes |
Counting rules:
JSONL recordsrefers to effective JSONL records, not source-file counts.- The counts exclude empty lines, placeholder lines, and non-record first lines.
- One source file can produce more than one JSONL record.
Unique file_id valuesgives the file-level count within a subset.Git LFSmeans Git Large File Storage holds the file content, and users may need to rungit lfs pullafter cloning.
Language distribution by JSONL records:
| Language | Records |
|---|---|
zh |
229,530 |
en |
114,993 |
other |
35,125 |
format field distribution by JSONL records:
| Format | Records |
|---|---|
pdf |
346,881 |
jsonl |
30,705 |
docx |
2,054 |
markdown |
1 |
doc |
7 |
Dominant source types by JSONL records:
source_type |
Records |
|---|---|
| Journal papers | 116,891 |
| National laws and regulations | 95,394 |
| Academic publications | 57,492 |
| Dissertations | 38,075 |
| Public opinion and self-media content | 31,178 |
| Enterprise basic information | 11,226 |
| Production and capacity data | 6,278 |
| Industry association reports | 4,649 |
| Research institute reports | 4,414 |
| Domestic industrial policy | 2,393 |
| Futures and derivatives trading data | 1,405 |
| Conference papers | 1,317 |
Taxonomy
The full data system covers the following nine first-level categories:
- Legal documents and regulatory basis
- Industry rules and administrative measures
- Policy documents and industry guidance
- Industry standards
- Patents and intellectual property
- Academic and training materials
- Public opinion and commentary
- Enterprise operations and business information
- Industry research and market reports
Repository Layout
data/
text/
iron_ore/
state_laws/train.jsonl
domestic_conference_papers/train.jsonl
doctoral_dissertations/train.jsonl
chinese_journals/train.jsonl
international_journal_of_mining_science_and_technology/train.jsonl
international_journal_of_minerals_metallurgy_and_materials/train.jsonl
industry_research_reports/train.jsonl
sintering_and_pelletizing/train.jsonl
ironmaking/train.jsonl
public_opinion/train.jsonl
books/train.jsonl
Schema
The repository primarily stores JSONL records. Each line corresponds to one parsed record.
| Field | Type | Description |
|---|---|---|
format |
string | Source file or carrier format |
file_id |
string | File identifier |
raw_chunk |
string | Parsed text content |
file_name |
string | Original file name |
title |
string | Title |
source_type |
string | Source type |
author |
string | Author, institution, or publishing body |
original_time |
string | Original publication time |
content_time |
string | Content time |
source_details |
string | Public source URL or source note |
data_version |
string | Record version |
license_type |
string | Record-level license classification |
is_generated |
string | Generated-content flag |
country |
string | Country tag |
language |
string | Language tag |
keywords |
array | Keywords |
data_id |
string | Record identifier |
Access and Loading
Access via Git:
git lfs install
git clone https://opencsg.com/datasets/OpenCSG/CIMD.git
cd CIMD
git lfs pull
Load with Hugging Face datasets:
from datasets import load_dataset
dataset = load_dataset(
"opencsg/CIMD",
"state_laws",
split="train",
streaming=True,
)
Load with ModelScope:
from modelscope.msdatasets import MsDataset
dataset = MsDataset.load(
dataset_name="CIMD",
namespace="OpenCSG",
subset_name="state_laws",
split="train",
)
Intended Uses
- Vertical retrieval and RAG for iron ore, mining, and metallurgy
- Industry research assistants and evidence retrieval systems
- Policy, regulation, and compliance QA
- Long-document QA and cross-source evidence aggregation
- Continued pretraining, SFT data construction, and corpus filtering
- Document classification, source identification, topic tagging, and terminology extraction
Limitations
- Counts in this card use parsed records rather than deduplicated source documents.
- The public subsets rely on Git LFS.
- Duplicate, near-duplicate, and parsing-noise cases may exist.
- Time fields may reflect publication time, content time, or extraction artifacts.
- Verify provenance and the effective authorization scope before redistribution or downstream commercial use.
License
See the OpenCSG Dataset License Agreement. In repository metadata, license: other means the dataset uses a license outside the platform's preset list, and that agreement contains the operative terms. This dataset supports commercial use. For commercial use of the dataset, or any model, system, Agent, API service, or commercial product trained or enhanced with it, follow that agreement and contact lorraineg@opencsg.com for authorization.
Citation
@dataset{opencsg_cimd_2026,
title = {CIMD: A Cross-Source Industry Corpus for Iron Ore, Mining, Metallurgy, Policy, and Market Intelligence},
author = {OpenCSG},
year = {2026},
url = {https://opencsg.com/datasets/OpenCSG/CIMD},
note = {OpenCSG dataset repository}
}