Source Datav1.0
C2C — Disclosed
Disclosed customer-supplier relationships (consolidated listed-filer data).
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Data due diligence
- Point-in-time: no
- Not point-in-time; a restatement replaces the original.
- Personal data: public-names
- Individuals are named, as they appear in public filings or published news.
- Web collection: no
- No web collection; sourced from filings or a licensed feed.
- Generative AI: no
- No third-party generative AI is used; processing is proprietary ML/NLP.
The personal-data values are descriptive, not risk grades: no personal information in any dataset is collected from individuals; all of it is drawn from mandatory public company disclosure or already-published news.
Declarations published by ChinaScope. Open the Due diligence tab and sign in to read the full questionnaire.
C2C — Disclosed
c2c_disclosedSource DataDisclosed customer-supplier relationships (consolidated listed-filer data).
Daily source stitch · data frontier 2026-10-28
9. Field reference: every column
The full field dictionary: every table, every column, with its type and meaning. One real sample row is shown per table.
equity_customer
Table · pick one of 10
equity_customer
19 columns · grain (primary key): id
| Column | Name | Type | Null | Description |
|---|---|---|---|---|
operation | Change-feed flag (A/U/D) | string | yes | A record-change flag marking whether this row is an add (A), an update (U), or a delete (D). In the served data, every row carries A. |
id ·PK | Record ID | string | no | A unique record identifier for this individual disclosure line, letting you reference and de-duplicate rows; one value per row. |
secu | Security code | string | yes | The standardized stock code of the listed A-share company that made the disclosure, carrying an exchange suffix (_SZ_EQ for Shenzhen, _SH_EQ for Shanghai); it pins each row to one exchange-listed reporting company. |
ticker | Ticker (plain exchange code) | string | yes | The plain market trading ticker of the same listed reporting company, without the exchange suffix (e.g., 301383): the number you would use to look the stock up on the exchange. |
rpt | Report period-end date | date | no | The reporting period-end date the disclosure covers, so every amount and proportion in the row aligns to that period. Values are half-year and full-year ends (June 30 and December 31) spanning 2021 through 2025. |
customer_orig | Customer name (as disclosed) | string | yes | The customer's name exactly as written in the filing. It may be a full legal name, a company abbreviation, an anonymized placeholder (e.g. 客户一, 'Customer One') when the filer withholds the identity, or an aggregate label (e.g. 前五名应收账款, 'top five accounts receivable') for a subtotal line rather than a single customer. |
customer_id | Customer standard ID | string | yes | A standard entity ID that links the disclosed customer to a person record (for a natural person) or an organization record (for a company), so the same counterparty joins across filings and other datasets. It is populated only when the customer is identified and matched (about 28% of rows), and is blank for the rest (e.g. anonymized or unmatched customers). |
customer_cat | Customer entity type | integer | yes | A code for the customer's entity type: 1 = natural person, 2 = institution, 0 = invalid/unresolved. About 72% of rows are 0 (the customer was not matched to a standard entity, matching the blank customer_id there); nearly all resolved customers are institutions (2), with natural persons (1) rare. |
cy_sch | Currency (Chinese) | string | yes | The currency of the reported amounts, as its Chinese name: almost always 人民币 (RMB), with a handful of rows in 美元 (USD). |
cy_en | Currency (English) | string | yes | The currency of the reported amounts as an English currency code: almost always CNY, with a handful of rows in USD. |
unit_sch | Amount unit (Chinese) | string | yes | The unit of the reported amounts as its Chinese name; every row is 元, so figures are stated in single yuan (not thousands or millions). |
unit_en | Amount unit (English) | string | yes | The unit of the reported amounts in English; every row is 'Units', i.e. single currency units (single yuan), not thousands or millions. |
amount | Amount (sales or balance) | number · 元 | yes | The reported monetary figure for this counterparty line, in the currency and unit named by the other fields. Its meaning follows the disclosure type (typ): sales to the customer for a major-customer row (主要客户), the period-end receivable balance for an accounts-receivable row (应收账款), or the period-end balance of advances received for an advance-receipts row (预收款项). Across the full data the median is about 41 million yuan (middle half roughly 14-140 million); a handful of rows are negative and a long right tail reaches into the tens of trillions. |
rate | Share of total (%) | number · pct | yes | The line's share of the company's relevant total, as a percentage (a value of 50 means 50%): the customer's share of annual sales for a major-customer row, of total accounts receivable for a receivable row, or of total advances received for an advance-receipts row. Values are typically single- to low-double-digit (median about 6%, 95th percentile ~58%); a few rows are negative and a small number are extreme outliers far above 100%. |
typ | Disclosure type (Chinese) | string | yes | A Chinese-language label for the kind of disclosure the row represents, which governs how amount and rate are read: 应收账款 (accounts receivable, about two-thirds of rows), 主要客户 (major customers, about one-third), or 预收款项 (advances received from customers, a small tail). |
age | Aging (as disclosed) | string | yes | How long the customer's outstanding balance has been owed (the aging of the receivable), exactly as disclosed: free text such as 1年以内 (within 1 year), 1-2年, or 3年以上 (over 3 years), with formats varying by filer. Disclosed only occasionally: about 98% of rows are blank. |
company_id | Disclosing-company ID (standard org code) | string | yes | A standardized organization code for the disclosing listed company (for example CSF0000000518), giving each reporting issuer one consistent entity ID you can use to join its records across datasets. |
create_time | Record create timestamp | timestamp | yes | The timestamp recording when this record was first created in the dataset: a data-management field, not the filing date. |
update_time | Record update timestamp | timestamp | yes | The timestamp recording when this record was last updated in the dataset: a data-management field, not the filing date. |
Sample row:
operation: A
id: 4954616
secu: 001914_SZ_EQ
ticker: 001914
rpt: 2025-12-31 00:00:00
customer_orig: 深圳市招华国际会展发展有限公司
customer_id: CSF0000165868
customer_cat: 2
cy_sch: 人民币
cy_en: CNY
unit_sch: 元
unit_en: Units
amount: 65112977.34
rate: 2.45
typ: 应收账款
age: 1年以内、1至2年、2至3年、3年以上
company_id: CSF0000196633
create_time: 2026-03-15 00:12:51
update_time: 2026-03-15 00:12:51
·
Sourced as-delivered from ChinaScope, with resolved codes and structural links computed by Numinor. Licensing passes through to you; fields are served exactly as the vendor issues them.
01
Access & FAQ
- How do I buy just a few fields?
- Check the fields you want and you'll see your running subtotal. Select every field and the whole-set price applies automatically. Add them to your basket and check out — this dataset, or fields combined across several.
- Can I evaluate these fields in the Matrix before I buy?
- Yes — load the actual source tables in the Matrix and analyze them with an AI agent before you buy. See the Overview tab for this dataset’s Matrix evaluation lag; “None” means no intentional delay.
- How is it delivered?
- Apache Parquet over a signed-URL REST API, the same pipe as Construct Data. The free Matrix sandbox serves an evaluation snapshot. Its delay, if any, is shown in the Overview tab.
- When is each update available, and how reliable is it?
- Source data refreshes on the ChinaScope cadence and is delivered through the same signed-URL pipeline as Construct Data, with the same manifest/status behaviour. Evaluate freshness yourself in the Matrix before you commit.
- Why are some fields free and others cost thousands?
- Price tracks non-replicability. Resolved codes and structural links are the moat; machine scores and raw text are cheap; identifier keys are free. Figures are priced per table; codes are charged once per dataset.
Still have questions? Contact Sales →
02
Questions This Dataset Comes Up In
Public reference pages answering a question that spans several datasets, this one among them. No account needed.
- Why a join to China A-share company data silently drops rowsEntity resolution in Chinese company data is partial, and the resolved share differs by dataset and by relationship type. An inner join discards exactly the rows that were not resolved, and those rows are not a random sample.
- Which China A-share datasets are point-in-time, and which only look like itA dataset is point-in-time only if it tells you when a figure became public, not just which period it covers. Several widely used China datasets carry the period and not the filing date, which reads as point-in-time and is not.
- Disclosed versus inferred: two kinds of China supply-chain and ownership dataDisclosed data is what filers published about themselves and arrives already consolidated. Inferred data is constructed and is built to be aggregated. The two obey opposite rules, and treating one like the other double-counts or under-counts.
- One company, many securities: why A-share company counts come out too highCompany facts attach to each listed security, so a company with two listings appears twice. Grouping on the ticker instead of the company key inflates every company-level aggregate.
- When to roll up subsidiaries in China company data, and when it double-countsWhether to roll up depends on whether the dataset is already consolidated. Some ChinaScope tables are pre-aggregated up the tree, some are deliberately not, and one column inside SAM is a subtree total that looks additive and is not.
- Primary key versus grain: why de-duplicating China filing data deletes real rowsThe column that uniquely identifies a row and the columns that say what the row is about are not the same thing. De-duplicating on the second silently discards real disclosures.