Refresh ER schema diagrams (1-71 of 71)

ctu-accidents/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: ctu-accidents
----
-
 # ctu-accidents
 
 Traffic accident database consists of all accidents that happened in Slovenia's capital city Ljubljana between the years 1995 and 2006. Sourced from the CTU Prague Relational Learning Repository (https://relational.fel.cvut.cz/).
@@ -13,10 +6,6 @@ Traffic accident database consists of all accidents that happened in Slovenia's
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `2004-03-01 00:00:00`, test `2005-03-01 00:00:00` (rows up to a split's timestamp are the inputs for that split).
-
 ## Tasks
 
 | task | kind | type | description |
@@ -28,8 +17,6 @@ Splits: validation `2004-03-01 00:00:00`, test `2005-03-01 00:00:00` (rows up to
 
 ```python
 import relbench
-ds = relbench.load_dataset("ctu-accidents")
-task = relbench.load_task("ctu-accidents", "<task>")
+ds = relbench.load_dataset("relbench/redelex/ctu-accidents")
+task = relbench.load_task("relbench/redelex/ctu-accidents", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).

ctu-adventureworks/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: ctu-adventureworks
----
-
 # ctu-adventureworks
 
 Adventure Works 2014 (OLTP version) is a sample database for Microsoft SQL Server, which has replaced Northwind and Pub sample databases that were shipped earlier. The database is about a fictious, multinational bicycle manufacturer called Adventure Works Cycles. Sourced from the CTU Prague Relational Learning Repository (https://relational.fel.cvut.cz/).
@@ -13,10 +6,6 @@ Adventure Works 2014 (OLTP version) is a sample database for Microsoft SQL Serve
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `2014-03-01 00:00:00`, test `2014-05-01 00:00:00` (rows up to a split's timestamp are the inputs for that split).
-
 ## Tasks
 
 | task | kind | type | description |
@@ -28,8 +17,6 @@ Splits: validation `2014-03-01 00:00:00`, test `2014-05-01 00:00:00` (rows up to
 
 ```python
 import relbench
-ds = relbench.load_dataset("ctu-adventureworks")
-task = relbench.load_task("ctu-adventureworks", "<task>")
+ds = relbench.load_dataset("relbench/redelex/ctu-adventureworks")
+task = relbench.load_task("relbench/redelex/ctu-adventureworks", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).

ctu-airline/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: ctu-airline
----
-
 # ctu-airline
 
 Airline on-time data are reported each month to the U.S. Department of Transportation (DOT), Bureau of Transportation Statistics (BTS) by the 16 U.S. air carriers that have at least 1 percent of total domestic scheduled-service passenger revenues, plus two other carriers that report voluntarily. The data cover nonstop scheduled-service flights between points within the United States (including territories) as described in 14 CFR Part 234 of DOT's regulations. Data are available since January 1995. Sourced from the CTU Prague Relational Learning Repository (https://relational.fel.cvut.cz/).
@@ -13,10 +6,6 @@ Airline on-time data are reported each month to the U.S. Department of Transport
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `2016-01-18 00:00:00`, test `2016-01-25 00:00:00` (rows up to a split's timestamp are the inputs for that split).
-
 ## Tasks
 
 | task | kind | type | description |
@@ -28,8 +17,6 @@ Splits: validation `2016-01-18 00:00:00`, test `2016-01-25 00:00:00` (rows up to
 
 ```python
 import relbench
-ds = relbench.load_dataset("ctu-airline")
-task = relbench.load_task("ctu-airline", "<task>")
+ds = relbench.load_dataset("relbench/redelex/ctu-airline")
+task = relbench.load_task("relbench/redelex/ctu-airline", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).

ctu-atherosclerosis/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: ctu-atherosclerosis
----
-
 # ctu-atherosclerosis
 
 The study STULONG is a longitudinal 20 years lasting primary preventive study of middle-aged men. The study aims to identify prevalence of atherosclerosis RFs in a population generally considered to be the most endangered by possible atherosclerosis complications, i.e., middle-aged men. Sourced from the CTU Prague Relational Learning Repository (https://relational.fel.cvut.cz/).
@@ -13,10 +6,6 @@ The study STULONG is a longitudinal 20 years lasting primary preventive study of
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `2262-04-11`, test `2262-04-11` (rows up to a split's timestamp are the inputs for that split).
-
 ## Tasks
 
 | task | kind | type | description |
@@ -27,8 +16,6 @@ Splits: validation `2262-04-11`, test `2262-04-11` (rows up to a split's timesta
 
 ```python
 import relbench
-ds = relbench.load_dataset("ctu-atherosclerosis")
-task = relbench.load_task("ctu-atherosclerosis", "<task>")
+ds = relbench.load_dataset("relbench/redelex/ctu-atherosclerosis")
+task = relbench.load_task("relbench/redelex/ctu-atherosclerosis", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).

ctu-basketballmen/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: ctu-basketballmen
----
-
 # ctu-basketballmen
 
 CTU relational dataset 'basketballmen' from the CTU Prague Relational Learning Repository (https://relational.fel.cvut.cz/).
@@ -13,10 +6,6 @@ CTU relational dataset 'basketballmen' from the CTU Prague Relational Learning R
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `2262-04-11`, test `2262-04-11` (rows up to a split's timestamp are the inputs for that split).
-
 ## Tasks
 
 | task | kind | type | description |
@@ -27,8 +16,6 @@ Splits: validation `2262-04-11`, test `2262-04-11` (rows up to a split's timesta
 
 ```python
 import relbench
-ds = relbench.load_dataset("ctu-basketballmen")
-task = relbench.load_task("ctu-basketballmen", "<task>")
+ds = relbench.load_dataset("relbench/redelex/ctu-basketballmen")
+task = relbench.load_task("relbench/redelex/ctu-basketballmen", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).

ctu-basketballwomen/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: ctu-basketballwomen
----
-
 # ctu-basketballwomen
 
 CTU relational dataset 'basketballwomen' from the CTU Prague Relational Learning Repository (https://relational.fel.cvut.cz/).
@@ -13,10 +6,6 @@ CTU relational dataset 'basketballwomen' from the CTU Prague Relational Learning
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `2262-04-11`, test `2262-04-11` (rows up to a split's timestamp are the inputs for that split).
-
 ## Tasks
 
 | task | kind | type | description |
@@ -27,8 +16,6 @@ Splits: validation `2262-04-11`, test `2262-04-11` (rows up to a split's timesta
 
 ```python
 import relbench
-ds = relbench.load_dataset("ctu-basketballwomen")
-task = relbench.load_task("ctu-basketballwomen", "<task>")
+ds = relbench.load_dataset("relbench/redelex/ctu-basketballwomen")
+task = relbench.load_task("relbench/redelex/ctu-basketballwomen", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).

ctu-biodegradability/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: ctu-biodegradability
----
-
 # ctu-biodegradability
 
 This is an older data set of chemical structures containing 328 compounds labeled by their half-life for aerobic aqueous biodegradation. Sourced from the CTU Prague Relational Learning Repository (https://relational.fel.cvut.cz/).
@@ -13,10 +6,6 @@ This is an older data set of chemical structures containing 328 compounds labele
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `2262-04-11`, test `2262-04-11` (rows up to a split's timestamp are the inputs for that split).
-
 ## Tasks
 
 | task | kind | type | description |
@@ -27,8 +16,6 @@ Splits: validation `2262-04-11`, test `2262-04-11` (rows up to a split's timesta
 
 ```python
 import relbench
-ds = relbench.load_dataset("ctu-biodegradability")
-task = relbench.load_task("ctu-biodegradability", "<task>")
+ds = relbench.load_dataset("relbench/redelex/ctu-biodegradability")
+task = relbench.load_task("relbench/redelex/ctu-biodegradability", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).

ctu-bupa/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: ctu-bupa
----
-
 # ctu-bupa
 
 Evaluation of patients on liver disorder. Sourced from the CTU Prague Relational Learning Repository (https://relational.fel.cvut.cz/).
@@ -13,10 +6,6 @@ Evaluation of patients on liver disorder. Sourced from the CTU Prague Relational
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `2262-04-11`, test `2262-04-11` (rows up to a split's timestamp are the inputs for that split).
-
 ## Tasks
 
 | task | kind | type | description |
@@ -27,8 +16,6 @@ Splits: validation `2262-04-11`, test `2262-04-11` (rows up to a split's timesta
 
 ```python
 import relbench
-ds = relbench.load_dataset("ctu-bupa")
-task = relbench.load_task("ctu-bupa", "<task>")
+ds = relbench.load_dataset("relbench/redelex/ctu-bupa")
+task = relbench.load_task("relbench/redelex/ctu-bupa", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).

ctu-carcinogenesis/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: ctu-carcinogenesis
----
-
 # ctu-carcinogenesis
 
 For prediction of whether a given molecule is carcinogenic or not. The dataset contains 182 positive carcinogenicity tests and 148 negative tests. Sourced from the CTU Prague Relational Learning Repository (https://relational.fel.cvut.cz/).
@@ -13,10 +6,6 @@ For prediction of whether a given molecule is carcinogenic or not. The dataset c
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `2262-04-11`, test `2262-04-11` (rows up to a split's timestamp are the inputs for that split).
-
 ## Tasks
 
 | task | kind | type | description |
@@ -27,8 +16,6 @@ Splits: validation `2262-04-11`, test `2262-04-11` (rows up to a split's timesta
 
 ```python
 import relbench
-ds = relbench.load_dataset("ctu-carcinogenesis")
-task = relbench.load_task("ctu-carcinogenesis", "<task>")
+ds = relbench.load_dataset("relbench/redelex/ctu-carcinogenesis")
+task = relbench.load_task("relbench/redelex/ctu-carcinogenesis", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).

ctu-cde/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: ctu-cde
----
-
 # ctu-cde
 
 A database containing geospatial information, as well as SAT average scores and Free-or-Reduced-Price Meal eligibility data, for California schools. Sourced from the CTU Prague Relational Learning Repository (https://relational.fel.cvut.cz/).
@@ -13,10 +6,6 @@ A database containing geospatial information, as well as SAT average scores and
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `2262-04-11`, test `2262-04-11` (rows up to a split's timestamp are the inputs for that split).
-
 ## Tasks
 
 | task | kind | type | description |
@@ -27,8 +16,6 @@ Splits: validation `2262-04-11`, test `2262-04-11` (rows up to a split's timesta
 
 ```python
 import relbench
-ds = relbench.load_dataset("ctu-cde")
-task = relbench.load_task("ctu-cde", "<task>")
+ds = relbench.load_dataset("relbench/redelex/ctu-cde")
+task = relbench.load_task("relbench/redelex/ctu-cde", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).

ctu-chess/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: ctu-chess
----
-
 # ctu-chess
 
 CTU relational dataset 'chess' from the CTU Prague Relational Learning Repository (https://relational.fel.cvut.cz/).
@@ -13,10 +6,6 @@ CTU relational dataset 'chess' from the CTU Prague Relational Learning Repositor
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `2262-04-11`, test `2262-04-11` (rows up to a split's timestamp are the inputs for that split).
-
 ## Tasks
 
 | task | kind | type | description |
@@ -27,8 +16,6 @@ Splits: validation `2262-04-11`, test `2262-04-11` (rows up to a split's timesta
 
 ```python
 import relbench
-ds = relbench.load_dataset("ctu-chess")
-task = relbench.load_task("ctu-chess", "<task>")
+ds = relbench.load_dataset("relbench/redelex/ctu-chess")
+task = relbench.load_task("relbench/redelex/ctu-chess", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).

ctu-classicmodels/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: ctu-classicmodels
----
-
 # ctu-classicmodels
 
 The schema is for Classic Models, a retailer of scale models of classic cars. The database contains typical business data such as customers, orders, order line items, products and so on. Sourced from the CTU Prague Relational Learning Repository (https://relational.fel.cvut.cz/).
@@ -13,10 +6,6 @@ The schema is for Classic Models, a retailer of scale models of classic cars. Th
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `2004-11-01 00:00:00`, test `2005-02-01 00:00:00` (rows up to a split's timestamp are the inputs for that split).
-
 ## Tasks
 
 | task | kind | type | description |
@@ -28,8 +17,6 @@ Splits: validation `2004-11-01 00:00:00`, test `2005-02-01 00:00:00` (rows up to
 
 ```python
 import relbench
-ds = relbench.load_dataset("ctu-classicmodels")
-task = relbench.load_task("ctu-classicmodels", "<task>")
+ds = relbench.load_dataset("relbench/redelex/ctu-classicmodels")
+task = relbench.load_task("relbench/redelex/ctu-classicmodels", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).

ctu-cora/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: ctu-cora
----
-
 # ctu-cora
 
 The Cora dataset consists of 2708 scientific publications classified into one of seven classes. The citation network consists of 5429 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words. Sourced from the CTU Prague Relational Learning Repository (https://relational.fel.cvut.cz/).
@@ -13,10 +6,6 @@ The Cora dataset consists of 2708 scientific publications classified into one of
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `2262-04-11`, test `2262-04-11` (rows up to a split's timestamp are the inputs for that split).
-
 ## Tasks
 
 | task | kind | type | description |
@@ -27,8 +16,6 @@ Splits: validation `2262-04-11`, test `2262-04-11` (rows up to a split's timesta
 
 ```python
 import relbench
-ds = relbench.load_dataset("ctu-cora")
-task = relbench.load_task("ctu-cora", "<task>")
+ds = relbench.load_dataset("relbench/redelex/ctu-cora")
+task = relbench.load_task("relbench/redelex/ctu-cora", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).

ctu-countries/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: ctu-countries
----
-
 # ctu-countries
 
 Data of forest area for 247 countries. Sourced from the CTU Prague Relational Learning Repository (https://relational.fel.cvut.cz/).
@@ -13,10 +6,6 @@ Data of forest area for 247 countries. Sourced from the CTU Prague Relational Le
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `2262-04-11`, test `2262-04-11` (rows up to a split's timestamp are the inputs for that split).
-
 ## Tasks
 
 | task | kind | type | description |
@@ -27,8 +16,6 @@ Splits: validation `2262-04-11`, test `2262-04-11` (rows up to a split's timesta
 
 ```python
 import relbench
-ds = relbench.load_dataset("ctu-countries")
-task = relbench.load_task("ctu-countries", "<task>")
+ds = relbench.load_dataset("relbench/redelex/ctu-countries")
+task = relbench.load_task("relbench/redelex/ctu-countries", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).

ctu-craftbeer/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: ctu-craftbeer
----
-
 # ctu-craftbeer
 
 Craft beers labeled by styles and composition. A separate dataset lists breweries by state. Sourced from the CTU Prague Relational Learning Repository (https://relational.fel.cvut.cz/).
@@ -13,10 +6,6 @@ Craft beers labeled by styles and composition. A separate dataset lists brewerie
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `2262-04-11`, test `2262-04-11` (rows up to a split's timestamp are the inputs for that split).
-
 ## Tasks
 
 | task | kind | type | description |
@@ -27,8 +16,6 @@ Splits: validation `2262-04-11`, test `2262-04-11` (rows up to a split's timesta
 
 ```python
 import relbench
-ds = relbench.load_dataset("ctu-craftbeer")
-task = relbench.load_task("ctu-craftbeer", "<task>")
+ds = relbench.load_dataset("relbench/redelex/ctu-craftbeer")
+task = relbench.load_task("relbench/redelex/ctu-craftbeer", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).

ctu-credit/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: ctu-credit
----
-
 # ctu-credit
 
 A bit more complex artificial database with loops. Sourced from the CTU Prague Relational Learning Repository (https://relational.fel.cvut.cz/).
@@ -13,16 +6,10 @@ A bit more complex artificial database with loops. Sourced from the CTU Prague R
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `1999-09-01 00:00:00`, test `1999-10-01 00:00:00` (rows up to a split's timestamp are the inputs for that split).
-
 ## Loading
 
 ```python
 import relbench
-ds = relbench.load_dataset("ctu-credit")
-task = relbench.load_task("ctu-credit", "<task>")
+ds = relbench.load_dataset("relbench/redelex/ctu-credit")
+task = relbench.load_task("relbench/redelex/ctu-credit", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).

ctu-dallas/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: ctu-dallas
----
-
 # ctu-dallas
 
 Officer-involved shootings as disclosed by the Dallas Police Department. Includes separate tables for officer and subject/suspect information. Sourced from the CTU Prague Relational Learning Repository (https://relational.fel.cvut.cz/).
@@ -13,10 +6,6 @@ Officer-involved shootings as disclosed by the Dallas Police Department. Include
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `2014-01-01 00:00:00`, test `2015-01-01 00:00:00` (rows up to a split's timestamp are the inputs for that split).
-
 ## Tasks
 
 | task | kind | type | description |
@@ -28,8 +17,6 @@ Splits: validation `2014-01-01 00:00:00`, test `2015-01-01 00:00:00` (rows up to
 
 ```python
 import relbench
-ds = relbench.load_dataset("ctu-dallas")
-task = relbench.load_task("ctu-dallas", "<task>")
+ds = relbench.load_dataset("relbench/redelex/ctu-dallas")
+task = relbench.load_task("relbench/redelex/ctu-dallas", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).

ctu-dcg/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: ctu-dcg
----
-
 # ctu-dcg
 
 The set of positive examples consists of all sentences of up to seven words that can be generated by the DCG in Bratko's book (565 positive examples). The set of negative examples was generated by randomly selecting one word in each positive example and replacing it by a randomly selected word the leads to an incorrect sentence, according to the grammar (565 negative examples). Sourced from the CTU Prague Relational Learning Repository (https://relational.fel.cvut.cz/).
@@ -13,10 +6,6 @@ The set of positive examples consists of all sentences of up to seven words that
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `2262-04-11`, test `2262-04-11` (rows up to a split's timestamp are the inputs for that split).
-
 ## Tasks
 
 | task | kind | type | description |
@@ -27,8 +16,6 @@ Splits: validation `2262-04-11`, test `2262-04-11` (rows up to a split's timesta
 
 ```python
 import relbench
-ds = relbench.load_dataset("ctu-dcg")
-task = relbench.load_task("ctu-dcg", "<task>")
+ds = relbench.load_dataset("relbench/redelex/ctu-dcg")
+task = relbench.load_task("relbench/redelex/ctu-dcg", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).

ctu-diabetes/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: ctu-diabetes
----
-
 # ctu-diabetes
 
 The Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. Sourced from the CTU Prague Relational Learning Repository (https://relational.fel.cvut.cz/).
@@ -13,10 +6,6 @@ The Diabetes dataset consists of 19717 scientific publications from PubMed datab
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `2262-04-11`, test `2262-04-11` (rows up to a split's timestamp are the inputs for that split).
-
 ## Tasks
 
 | task | kind | type | description |
@@ -27,8 +16,6 @@ Splits: validation `2262-04-11`, test `2262-04-11` (rows up to a split's timesta
 
 ```python
 import relbench
-ds = relbench.load_dataset("ctu-diabetes")
-task = relbench.load_task("ctu-diabetes", "<task>")
+ds = relbench.load_dataset("relbench/redelex/ctu-diabetes")
+task = relbench.load_task("relbench/redelex/ctu-diabetes", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).

ctu-dunur/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: ctu-dunur
----
-
 # ctu-dunur
 
 Dunur is a relatedness of two people due to marriage such that A is dunur of B if a child of A is married to a child of B. Sourced from the CTU Prague Relational Learning Repository (https://relational.fel.cvut.cz/).
@@ -13,16 +6,10 @@ Dunur is a relatedness of two people due to marriage such that A is dunur of B i
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `2262-04-11`, test `2262-04-11` (rows up to a split's timestamp are the inputs for that split).
-
 ## Loading
 
 ```python
 import relbench
-ds = relbench.load_dataset("ctu-dunur")
-task = relbench.load_task("ctu-dunur", "<task>")
+ds = relbench.load_dataset("relbench/redelex/ctu-dunur")
+task = relbench.load_task("relbench/redelex/ctu-dunur", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).

ctu-elti/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: ctu-elti
----
-
 # ctu-elti
 
 Elti is a relatedness of two people due to marriage such that A is elti of B if A's husband is a brother of B's husband. Sourced from the CTU Prague Relational Learning Repository (https://relational.fel.cvut.cz/).
@@ -13,16 +6,10 @@ Elti is a relatedness of two people due to marriage such that A is elti of B if
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `2262-04-11`, test `2262-04-11` (rows up to a split's timestamp are the inputs for that split).
-
 ## Loading
 
 ```python
 import relbench
-ds = relbench.load_dataset("ctu-elti")
-task = relbench.load_task("ctu-elti", "<task>")
+ds = relbench.load_dataset("relbench/redelex/ctu-elti")
+task = relbench.load_task("relbench/redelex/ctu-elti", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).

ctu-employee/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: ctu-employee
----
-
 # ctu-employee
 
 CTU relational dataset 'employee' from the CTU Prague Relational Learning Repository (https://relational.fel.cvut.cz/).
@@ -13,10 +6,6 @@ CTU relational dataset 'employee' from the CTU Prague Relational Learning Reposi
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `2000-01-01 00:00:00`, test `2001-01-01 00:00:00` (rows up to a split's timestamp are the inputs for that split).
-
 ## Tasks
 
 | task | kind | type | description |
@@ -28,8 +17,6 @@ Splits: validation `2000-01-01 00:00:00`, test `2001-01-01 00:00:00` (rows up to
 
 ```python
 import relbench
-ds = relbench.load_dataset("ctu-employee")
-task = relbench.load_task("ctu-employee", "<task>")
+ds = relbench.load_dataset("relbench/redelex/ctu-employee")
+task = relbench.load_task("relbench/redelex/ctu-employee", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).

ctu-ergastf1/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: ctu-ergastf1
----
-
 # ctu-ergastf1
 
 Ergast.com is a webservice that provides a database of Formula 1 races, starting from the 1950 season until today. The dataset includes information such as the time taken in each lap, the time taken for pit stops, the performance in the qualifying rounds etc. of all Formula 1 races from 1950 to 2017. Sourced from the CTU Prague Relational Learning Repository (https://relational.fel.cvut.cz/).
@@ -13,10 +6,6 @@ Ergast.com is a webservice that provides a database of Formula 1 races, starting
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `2014-01-01 00:00:00`, test `2016-01-01 00:00:00` (rows up to a split's timestamp are the inputs for that split).
-
 ## Tasks
 
 | task | kind | type | description |
@@ -27,8 +16,6 @@ Splits: validation `2014-01-01 00:00:00`, test `2016-01-01 00:00:00` (rows up to
 
 ```python
 import relbench
-ds = relbench.load_dataset("ctu-ergastf1")
-task = relbench.load_task("ctu-ergastf1", "<task>")
+ds = relbench.load_dataset("relbench/redelex/ctu-ergastf1")
+task = relbench.load_task("relbench/redelex/ctu-ergastf1", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).

ctu-expenditures/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: ctu-expenditures
----
-
 # ctu-expenditures
 
 The Consumer Expenditure Survey (CE) collects data on expenditures, income, and demographics in the United States. The public-use microdata (PUMD) files provide this information for individual respondents without any information that could identify respondents. PUMD files include adjustments for information that is missing because respondents were unwilling or unable to provide it. The files also have been adjusted to reduce the likelihood of identifying respondents, either directly or through inference. The task is to predict, whether the expenditure is a gift or not. Household ids change from year to year - this is a property of the data source. Original source: www.bls.gov Sourced from the CTU Prague Relational Learning Repository (https://relational.fel.cvut.cz/).
@@ -13,10 +6,6 @@ The Consumer Expenditure Survey (CE) collects data on expenditures, income, and
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `2262-04-11`, test `2262-04-11` (rows up to a split's timestamp are the inputs for that split).
-
 ## Tasks
 
 | task | kind | type | description |
@@ -27,8 +16,6 @@ Splits: validation `2262-04-11`, test `2262-04-11` (rows up to a split's timesta
 
 ```python
 import relbench
-ds = relbench.load_dataset("ctu-expenditures")
-task = relbench.load_task("ctu-expenditures", "<task>")
+ds = relbench.load_dataset("relbench/redelex/ctu-expenditures")
+task = relbench.load_task("relbench/redelex/ctu-expenditures", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).

ctu-financial/README.md

@@ -1,10 +1,3 @@
----
-tags:
-- relbench
-- relational-deep-learning
-pretty_name: ctu-financial
----
-
 # ctu-financial
 
 PKDD'99 Financial dataset contains 606 successful and 76 not successful loans along with their information and transactions. Sourced from the CTU Prague Relational Learning Repository (https://relational.fel.cvut.cz/).
@@ -13,10 +6,6 @@ PKDD'99 Financial dataset contains 606 successful and 76 not successful loans al
 
 ![schema diagram](schema.svg)
 
-Open [`schema.svg`](schema.svg) for a zoomable view: each table shows its columns and types and its row count, with primary keys, foreign keys, time columns, and the foreign-key relationships (crow's-foot notation) between tables.
-
-Splits: validation `1998-01-01 00:00:00`, test `1998-07-01 00:00:00` (rows up to a split's timestamp are the inputs for that split).
-
 ## Tasks
 
 | task | kind | type | description |
@@ -28,8 +17,6 @@ Splits: validation `1998-01-01 00:00:00`, test `1998-07-01 00:00:00` (rows up to
 
 ```python
 import relbench
-ds = relbench.load_dataset("ctu-financial")
-task = relbench.load_task("ctu-financial", "<task>")
+ds = relbench.load_dataset("relbench/redelex/ctu-financial")
+task = relbench.load_task("relbench/redelex/ctu-financial", "<task>")
 ```
-
-Manifest layout (`manifest.yaml` + plain parquet); see the RelBench [CONTRIBUTING guide](https://github.com/snap-stanford/relbench/blob/main/CONTRIBUTING.md).