diff --git a/resources/grounding-dataset.md b/resources/grounding-dataset.md
index 4daaa569ab0423f3af862af0044233072b82243f..aa80e244b2b31ef6d1a378b273d09039721f850b 100644
--- a/resources/grounding-dataset.md
+++ b/resources/grounding-dataset.md
@@ -5,8 +5,8 @@ title: Dataset for Grounding of Formulae
 
 ### Basic Information
 
-* Author: Takuto Asakura, André Greiner-Petter, Akiko Aizawa, and Yusuke Miyao
-* Updated: 2021-04-01
+* Author: Takuto Asakura, Yusuke Miyao, and Akiko Aizawa
+* Updated: 2022-01-20
 
 ### Accessibility and License
 
@@ -20,19 +20,12 @@ itself.
 
 ### Description
 
-This is the project to create a dataset for grounding of formulae.
-
-As a trial work, this dataset consists of an annotated long paper (20 pages in
-PDF):
-
-* Simeone, O.: A Very Brief Introduction to Machine Learning with Applications
-to Communication Systems. IEEE Transactions on Cognitive Communications and
-Networking 4(4) (2018)
-
-The original XHTML file of the paper was taken from the [arXMLiv:08.2018
-dataset](/resources/arxmliv-dataset-082018/), and we manually annotated all
-937 identifiers (i.e., `<mi>` tags) in the document to the corresponding
-mathematical objects (meanings).
+This dataset is a ground truth of formula grounding annotation data for 15
+scientific papers. More specifically, a total of 12,352 math identifiers were
+annotated with their referring mathematical concepts, explicitly indicating
+coreference relations within each article. A total of 938 text spans, called
+grounding sources, that were used as the basis for human grounding were
+labeled.
 
 The annotation is performed with our open-source annotation tool
 [MioGatto](https://github.com/wtsnjp/MioGatto). The tool is also suitable for
@@ -40,5 +33,5 @@ viewing the data. Please refer to its documentation for the details.
 
 ### Download
 
-[Download link](https://gl.kwarc.info/SIGMathLing/grounding-dataset-v1)
+[Download link](https://gl.kwarc.info/SIGMathLing/grounding-dataset)
 ([SIGMathLing members](/member/) only)