diff --git a/datasets/dane/README.md b/datasets/dane/README.md
index 4cca9cffb5a..1d8ef5391ab 100644
--- a/datasets/dane/README.md
+++ b/datasets/dane/README.md
@@ -11,16 +11,18 @@ multilinguality:
 - monolingual
 size_categories:
 - 1K<n<10K
-source_datasets: []
+source_datasets:
+- extended|other-Danish-Universal-Dependencies-treebank
 task_categories:
 - structure-prediction
 task_ids:
 - named-entity-recognition
 - part-of-speech-tagging
 paperswithcode_id: dane
+pretty_name: DaNE
 ---
 
-# Dataset Card for [Dataset Name]
+# Dataset Card for DaNE
 
 ## Table of Contents
 - [Dataset Description](#dataset-description)
@@ -48,7 +50,7 @@ paperswithcode_id: dane
 
 ## Dataset Description
 
-- **Homepage:** [Github](https://github.com/alexandrainst/danlp/blob/master/docs/docs/datasets.md#dane)
+- **Homepage:** [DaNE homepage](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#dane)
 - **Repository:** [Github](https://github.com/alexandrainst/danlp)
 - **Paper:** [Aclweb](https://www.aclweb.org/anthology/2020.lrec-1.565)
 - **Leaderboard:**
@@ -56,21 +58,37 @@ paperswithcode_id: dane
 
 ### Dataset Summary
 
-[More Information Needed]
+The Danish Dependency Treebank (DaNE) is a named entity annotation for the Danish Universal Dependencies treebank using the CoNLL-2003 annotation scheme.
+
+The Danish UD treebank (Johannsen et al., 2015, UD-DDT) is a conversion of the Danish Dependency Treebank (Buch-Kromann et al. 2003) based on texts from Parole (Britt, 1998). UD-DDT has annotations for dependency parsing and part-of-speech (POS) tagging. The dataset was annotated with Named Entities for PER, ORG, and LOC by the Alexandra Institute in the DaNE dataset (Hvingelby et al. 2020). 
 
 ### Supported Tasks and Leaderboards
 
-[More Information Needed]
+Parts-of-speech tagging, dependency parsing and named entitity recognition.
 
 ### Languages
 
-[More Information Needed]
+Danish
 
 ## Dataset Structure
 
 ### Data Instances
 
-[More Information Needed]
+This is an example in the "train" split:
+```python
+{
+  'sent_id': 'train-v2-0\n', 
+  'lemmas': ['på', 'fredag', 'have', 'SiD', 'invitere', 'til', 'reception', 'i', 'SID-hus', 'i', 'anledning', 'af', 'at', 'formand', 'Kjeld', 'Christensen', 'gå', 'ind', 'i', 'den', 'glad', 'tresser', '.'],
+  'dep_labels': [35, 16, 28, 33, 19, 35, 16, 35, 18, 35, 18, 1, 1, 33, 22, 12, 32, 11, 35, 10, 30, 16, 34],
+  'ner_tags': [0, 0, 0, 3, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0],
+  'morph_tags': ['AdpType=Prep', 'Definite=Ind|Gender=Com|Number=Sing', 'Mood=Ind|Tense=Pres|VerbForm=Fin|Voice=Act', '_', 'Definite=Ind|Number=Sing|Tense=Past|VerbForm=Part', 'AdpType=Prep', 'Definite=Ind|Gender=Com|Number=Sing', 'AdpType=Prep', 'Definite=Def|Gender=Neut|Number=Sing', 'AdpType=Prep', 'Definite=Ind|Gender=Com|Number=Sing', 'AdpType=Prep', '_', 'Definite=Def|Gender=Com|Number=Sing', '_', '_', 'Mood=Ind|Tense=Pres|VerbForm=Fin|Voice=Act', '_', 'AdpType=Prep', 'Number=Plur|PronType=Dem', 'Degree=Pos|Number=Plur', 'Definite=Ind|Gender=Com|Number=Plur', '_'], 
+  'dep_ids': [2, 5, 5, 5, 0, 7, 5, 9, 7, 11, 7, 17, 17, 17, 14, 15, 11, 17, 22, 22, 22, 18, 5], 
+  'pos_tags': [11, 12, 5, 7, 3, 11, 12, 11, 12, 11, 12, 11, 16, 12, 7, 7, 3, 9, 11, 14, 6, 12, 10], 
+  'text': 'På fredag har SID inviteret til reception i SID-huset i anledning af at formanden Kjeld Christensen går ind i de glade tressere.\n', 
+  'tokens': ['På', 'fredag', 'har', 'SID', 'inviteret', 'til', 'reception', 'i', 'SID-huset', 'i', 'anledning', 'af', 'at', 'formanden', 'Kjeld', 'Christensen', 'går', 'ind', 'i', 'de', 'glade', 'tressere', '.'], 
+  'tok_ids': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]
+}
+```
 
 ### Data Fields
 
@@ -92,7 +110,10 @@ Data Fields:
 
 ### Data Splits
 
-[More Information Needed]
+|             |  Tain  |   Dev  |  Test  |
+|-------------|-------:|-------:|-------:|
+| # sentences |   4383 |    564 |    565 |
+| # tokens    | 80 378 | 10 322 | 10 023 |
 
 ## Dataset Creation
 
@@ -152,6 +173,30 @@ Data Fields:
 
 [More Information Needed]
 
+### Citation Information
+
+```
+@inproceedings{hvingelby-etal-2020-dane,
+    title = "{D}a{NE}: A Named Entity Resource for {D}anish",
+    author = "Hvingelby, Rasmus  and
+      Pauli, Amalie Brogaard  and
+      Barrett, Maria  and
+      Rosted, Christina  and
+      Lidegaard, Lasse Malm  and
+      S{\o}gaard, Anders",
+    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
+    month = may,
+    year = "2020",
+    address = "Marseille, France",
+    publisher = "European Language Resources Association",
+    url = "https://aclanthology.org/2020.lrec-1.565",
+    pages = "4597--4604",
+    abstract = "We present a named entity annotation for the Danish Universal Dependencies treebank using the CoNLL-2003 annotation scheme: DaNE. It is the largest publicly available, Danish named entity gold annotation. We evaluate the quality of our annotations intrinsically by double annotating the entire treebank and extrinsically by comparing our annotations to a recently released named entity annotation of the validation and test sections of the Danish Universal Dependencies treebank. We benchmark the new resource by training and evaluating competitive architectures for supervised named entity recognition (NER), including FLAIR, monolingual (Danish) BERT and multilingual BERT. We explore cross-lingual transfer in multilingual BERT from five related languages in zero-shot and direct transfer setups, and we show that even with our modestly-sized training set, we improve Danish NER over a recent cross-lingual approach, as well as over zero-shot transfer from five related languages. Using multilingual BERT, we achieve higher performance by fine-tuning on both DaNE and a larger Bokm{\aa}l (Norwegian) training set compared to only using DaNE. However, the highest performance isachieved by using a Danish BERT fine-tuned on DaNE. Our dataset enables improvements and applicability for Danish NER beyond cross-lingual methods. We employ a thorough error analysis of the predictions of the best models for seen and unseen entities, as well as their robustness on un-capitalized text. The annotated dataset and all the trained models are made publicly available.",
+    language = "English",
+    ISBN = "979-10-95546-34-4",
+}
+```
+
 ### Contributions
 
-Thanks to [@ophelielacroix](https://github.com/ophelielacroix), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
\ No newline at end of file
+Thanks to [@ophelielacroix](https://github.com/ophelielacroix), [@lhoestq](https://github.com/lhoestq) for adding this dataset.