Update README.md hoping to comply with Datasheet standard

Need to add the other datasets, but it would be good if I could generate this instance of writing it manually.

README.md

@@ -23,10 +23,171 @@ task_categories:
 - time-series-forecasting
 language:
 - en
-pretty_name: Appliannces Energy Regression Dataset
+pretty_name: Appliances Energy Regression Dataset
 size_categories:
 - 10K<n<100K
 ---
-# Dataset Card for "tser-appliances-energy"
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+# Dataset Card for Time Series Extrinsic Regression
+
+## Dataset Description
+
+- **Homepage:** [Time Series Extrinsic Regression Repository](http://tseregression.org/)
+- **Repository:** [GitHub code repository](https://github.com/ChangWeiTan/TS-Extrinsic-Regression/tree/master), [Raw data repository](https://zenodo.org/record/3902651)
+- **Paper:** [Monash University, UEA, UCR Time Series Extrinsic Regression Archive](https://arxiv.org/abs/2006.10996)
+- **Leaderboard:** [Baseline results](http://tseregression.org/#results)
+- **Point of Contact:** [Stephen Fox]([email protected])
+
+### Dataset Summary
+
+A collection of datasets from Monash, UEA, and UCR supporting research into Time Series Extrinsic Regression (TSER),
+a regression task of which the aim is to learn the relationship between *a time series and a continuous scalar variable*.
+This task is closely related to time series classification, where a single categorical variable is learned.
+Please read the [paper](https://arxiv.org/abs/2006.10996) for more.
+
+If you use the results or code, please cite the paper
+**"Chang Wei Tan, Christoph Bergmeir, Francois Petitjean, Geoffrey I. Webb, Time Series Extrinsic Regression: Predicting numeric values from time series data"**.
+(Full BibTex citation can be found at the end of this card).
+
+(It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).)
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+## Dataset Structure
+
+### Data Instances
+
+A sample from the training set of Appliances Energy (a multivariate time series dataset) is provided.
+The following is a single record from that dataset:
+
+```python
+{'start': Timestamp('2016-02-28 17:00:00'),
+ 'feat_static_cat': 0,
+ 'to_predict': 19.38,
+ 'timeseries': array([[21.29      , 21.29      , 21.29      , ..., 21.79      ,
+         21.79      , 21.79      ],
+        [31.66666667, 31.92666667, 32.06      , ..., 33.66      ,
+         33.7       , 33.56666667],
+        [19.89      , 19.82333333, 19.79      , ..., 19.79      ,
+         19.79      , 19.79      ],
+        ...,
+        [ 7.        ,  6.83333333,  6.66666667, ...,  5.        ,
+          5.        ,  5.        ],
+        [40.        , 40.        , 40.        , ..., 40.        ,
+         40.        , 40.        ],
+        [-4.2       , -4.16666667, -4.13333333, ..., -4.3       ,
+         -4.16666667, -4.03333333]]),
+ 'item_id': 'item_000'}
+```
+
+### Data Fields
+This format was loosely adapted from [the Gluon format](https://ts.gluon.ai/stable/getting_started/concepts.html)
+and [the HF convention](https://github.com/huggingface/notebooks/blob/main/examples/time_series_datasets.ipynb)
+also seen in the recent [series](https://huggingface.co/blog/time-series-transformers) of [Time Series Transformer notebooks](https://github.com/huggingface/notebooks/blob/main/examples/time-series-transformers.ipynb)
+
+- `start`: a datetime of the first entry of each time series in the data record
+- `feat_static_cat`: the original identifier given to this record
+- `timeseries`: the timeseries itself
+- `to_predict`: continuous variable to predict
+- `item_id`: an identifier given to each record (for e.g. group-by style aggregations)
+
+The `timeseries` field will be a single array in the univariate forecasting scenario, and a 2-D array in the multivariate scenario.
+
+The `to_predict` will be a single number in most cases, or an array in a few instances (noted in the table above **TODO**).
+
+### Data Splits
+
+Train and test are temporally split (i.e. "train" is the past and "test" is the future) 70/30 whenever possible, though some datasets have more particular splits.
+
+For details, see [the paper](https://arxiv.org/abs/2006.10996) and the particular dataset you are interested in. In our porting to HF Hub, we made as few changes as possible.
+
+
+## Dataset Creation
+
+While I (Stephen) did not create the original dataset, I took the initiative to put the data on Hugging Face Hub.
+**Any grievances with the dataset should first and foremost be directed to me**.
+
+### Curation Rationale
+
+To facilitate the evaluation of global forecasting models that are predicting a single-point estimate in the future.
+All datasets in the repository are intended for research purposes and to evaluate the performance of new TSER algorithms.
+This 
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The origins of each dataset are articulated in [the paper](https://link.springer.com/article/10.1007/s10618-021-00745-9).
+
+Minimal preprocess was applied to the dataset, as they are still in their `sktime`-compatible `.ts` format. (As far as Stephen is aware.)
+
+#### Who are the source language producers?
+
+The data comes from the datasets listed in the paper and in the table on [the website](http://tseregression.org/#results)
+
+### Annotations
+
+#### Annotation process
+
+Please see [the paper](https://link.springer.com/article/10.1007/s10618-021-00745-9) for the annotation aggregation propcess
+
+#### Who are the annotators?
+
+The annotation comes from the datasets listed in the paper and in the table on [the website](http://tseregression.org/#results)
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+- [Chang Wei Tan](https://changweitan.com/)
+- [Anthony Bagnall](https://www.uea.ac.uk/computing/people/profile/anthony-bagnall)
+- [Christoph Bergmeir](https://research.monash.edu/en/persons/christoph-bergmeir)
+- [Daniel Schmidt](https://research.monash.edu/en/persons/daniel-schmidt)
+- [Eamonn Keogh](http://www.cs.ucr.edu/~eamonn/)
+- [François Petitjean](https://www.francois-petitjean.com/)
+- [Geoff Webb](http://i.giwebb.com/)
+
+### Licensing Information
+
+[GNU General Public License (GPL) 3](https://www.gnu.org/licenses/gpl-3.0.en.html)
+
+### Citation Information
+
+```tex
+@article{
+  Tan2020TSER,
+  title={Time Series Extrinsic Regression}, 
+  author={Tan, Chang Wei and Bergmeir, Christoph and Petitjean, Francois and Webb, Geoffrey I},
+  journal={Data Mining and Knowledge Discovery},
+  pages={1--29},
+  year={2021},
+  publisher={Springer},
+  doi={https://doi.org/10.1007/s10618-021-00745-9}
+}
+
+```
+### Contributions
+
+[More Information Needed]