diff --git a/README.md b/README.md
index 3d51fca..76fec38 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-***Speech Emotion Recognition (SER) Datasets:*** *A collection of datasets (count=75) for the purpose of emotion recognition/detection in speech.
+***Speech Emotion Recognition (SER) Datasets:*** *A collection of datasets (count=76) for the purpose of emotion recognition/detection in speech.
The table is chronologically ordered and includes a description of the content of each dataset along with the emotions included.
The table can be browsed, sorted and searched under https://superkogito.github.io/SER-datasets/*
| Dataset | Year | Content | Emotions | Format | Size | Language | Paper | Access | License |
@@ -9,6 +9,7 @@ The table can be browsed, sorted and searched under https://superkogito.github.i
| [CAVES](https://rds.westernsydney.edu.au/Institutes/MARCS/2024/Christopher_Davis/) | 2023 | Full hd visual recordings of 10 native cantonese speakers uttering 50 sentences. | Anger, happiness, sadness, surprise, fear, disgust and neutral | Audio | 47 GB | Chinese (cantonese) | [A Cantonese Audio-Visual Emotional Speech (CAVES) dataset](https://link.springer.com/article/10.3758/s13428-023-02270-7) | Open | Available for research purposes only |
| [BANSpEmo](https://data.mendeley.com/datasets/rdwn4bs5ky/2) | 2023 | 792 utterance recordings from 22 unprofessional speakers (11 males and 11 females) of six basic emotional reactions of two sets of sentences. | angry, disgusted, happy, surprised, sad, fear | Audio | 0.555 GB | Bangla | [BANSpEmo: A Bangla Emotional Speech Recognition Dataset](https://arxiv.org/abs/2312.14020) | Open | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) |
| [KBES](https://data.mendeley.com/datasets/vsn37ps3rx/4) | 2023 | 900 audio signals from 35 actors (20 females and 15 males). Each emotion is represented with two intensity levels (low & high) | angry, disgusted, happy, neutral, sad | Audio | 0.337 GB | Bangla | [KBES: A dataset for realistic Bangla speech emotion recognition with intensity level](https://www.sciencedirect.com/science/article/pii/S2352340923008107) | Open | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) |
+| [RESD](https://huggingface.co/datasets/Aniemore/resd_annotated) | 2022 | Russian emotional speech dialogue dataset ~3.5 hours of actor-voiced dialogues, each ~3 minutes long, with speech files (16000 or 44100Hz), with speech-to-text transcripts | anger, disgust, fear, enthusiasm, happiness, neutral, sadness | Audio | 0.48 GB | Russian | [EmoBox: Multilingual Multi-corpus Speech Emotion Recognition Toolkit and Benchmark](https://arxiv.org/abs/2406.07162) | Open | [MIT](https://choosealicense.com/licenses/mit/) |
| [Hi, KIA](https://zenodo.org/records/7091465) | 2022 | A shared short Wakeup Word database focusing on perceived emotion in speech The dataset contains 488 Wakeup Word speech | angry, happy, sad, neutral | Audio | 0.75 GB | Korean | [Hi, KIA: A Speech Emotion Recognition Dataset for Wake-Up Words](https://arxiv.org/abs/2211.03371) | Open | [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) |
| [Emozionalmente](https://zenodo.org/records/6569824) | 2022 | 6902 labeled samples acted out by 431 amateur actors while verbalizing 18 different sentences | anger, disgust, fear, joy, sadness, surprise, neutral | Audio | 0.581 GB | Italian | -- | Open | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) |
| [BanglaSER](https://data.mendeley.com/datasets/t9h6p943xy/5) | 2022 | 1467 Bangla speech-audio recordings by 34 non-professional participating actors (17 male and 17 female) from diverse age groups between 19 and 47 years. | angry, happy, neutral, sad, surprise | Audio | 0.425 GB | Bangla | [BanglaSER: A speech emotion recognition dataset for the Bangla language](https://www.sciencedirect.com/science/article/pii/S235234092200302X) | Open | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) |
diff --git a/src/ser-datasets.csv b/src/ser-datasets.csv
index 28e76b4..dd09676 100644
--- a/src/ser-datasets.csv
+++ b/src/ser-datasets.csv
@@ -5,6 +5,7 @@ Dataset,Year,Content,Emotions,Format,Size,Language,Paper,Access,License
`CAVES `_,2023,Full hd visual recordings of 10 native cantonese speakers uttering 50 sentences.,"Anger, happiness, sadness, surprise, fear, disgust and neutral",Audio,47 GB,Chinese (cantonese),`A Cantonese Audio-Visual Emotional Speech (CAVES) dataset `_,Open,Available for research purposes only
`BANSpEmo `_,2023,792 utterance recordings from 22 unprofessional speakers (11 males and 11 females) of six basic emotional reactions of two sets of sentences.,"angry, disgusted, happy, surprised, sad, fear",Audio,0.555 GB,Bangla,`BANSpEmo: A Bangla Emotional Speech Recognition Dataset `_,Open,`CC BY 4.0 `_
`KBES `_,2023,900 audio signals from 35 actors (20 females and 15 males). Each emotion is represented with two intensity levels (low & high),"angry, disgusted, happy, neutral, sad",Audio,0.337 GB,Bangla,`KBES: A dataset for realistic Bangla speech emotion recognition with intensity level `_,Open,`CC BY 4.0 `_
+`RESD `_,2022,"Russian emotional speech dialogue dataset ~3.5 hours of actor-voiced dialogues, each ~3 minutes long, with speech files (16000 or 44100Hz), with speech-to-text transcripts","anger, disgust, fear, enthusiasm, happiness, neutral, sadness",Audio,0.48 GB,Russian,`EmoBox: Multilingual Multi-corpus Speech Emotion Recognition Toolkit and Benchmark `_,Open,`MIT `_
"`Hi, KIA `_",2022,A shared short Wakeup Word database focusing on perceived emotion in speech The dataset contains 488 Wakeup Word speech,"angry, happy, sad, neutral",Audio,0.75 GB,Korean,"`Hi, KIA: A Speech Emotion Recognition Dataset for Wake-Up Words `_",Open,`CC BY-SA 4.0 `_
`Emozionalmente `_,2022,6902 labeled samples acted out by 431 amateur actors while verbalizing 18 different sentences,"anger, disgust, fear, joy, sadness, surprise, neutral",Audio,0.581 GB,Italian,--,Open,`CC BY 4.0 `_
`BanglaSER `_,2022,1467 Bangla speech-audio recordings by 34 non-professional participating actors (17 male and 17 female) from diverse age groups between 19 and 47 years.,"angry, happy, neutral, sad, surprise",Audio,0.425 GB,Bangla,`BanglaSER: A speech emotion recognition dataset for the Bangla language `_,Open,`CC BY 4.0 `_