Update README.md

README.md

@@ -19,7 +19,7 @@ size_categories:
 ---
 # Dataset Card for Climate Change NER
 
-The Climate Change NER is an English-language dataset containing 534 abstracts of climate-related papers. They have been sourced from the Semantic Scholar Academic Graph "abstracts" dataset. The abstracts have been manually annotated by classifying climate-related tokens in a set of 14 categories.
+The Climate Change NER is an English-language dataset containing 534 abstracts of climate-related papers. They have been sourced from the Semantic Scholar Academic Graph "abstracts" dataset. The abstracts have been manually annotated by classifying climate-related tokens in a set of 13 categories.
 
 ## Dataset Details
 
@@ -36,7 +36,7 @@ by using a seed set of climate-related keywords. The abstracts were annotated by
 
 ### Dataset Sources
 
-- **Source** A subset of Semantic Scholar Academic Graph Dataset (the _abstracts_ dataset)
+- **Source** A subset of Semantic Scholar Academic Graph Dataset (the "abstracts" dataset)
 - **Paper:** TBA
 
 ## Uses
@@ -54,7 +54,7 @@ and containing a unique hash of the document across the entire dataset. Each tok
 - `I` if the token is inside of a named entity instance
 
 If the token is marked with `O` or `I`, the entity type is added. A maximum of one entity type can be assigned to a token. The possible types are:
-`assets`, `climate-named`, `datasets`, `ghg`, `hazards`, `impacts`, `mitigations`, `models`, `nature`, `observations`, `organisms`, `organizations`, `problems`, and `properties`.
+`climate-assets`, `climate-datasets`, `climate-greenhouse-gases`, `climate-hazards`, `climate-impacts`, `climate-mitigations`, `climate-models`, `climate-nature`, `climate-observations`, `climate-organisms`, `climate-organizations`, `climate-problem-origins`, and `climate-properties`.
 
 ```
 -DOCSTART- -X- O O cc560f5c553e1b60e054abe1578227ff
@@ -91,7 +91,7 @@ period O
 The Climate Change NER dataset has 3 splits: _train_, _validation_, and _test_. Below are the statistics for Version 1.0.0 of the dataset.
 
 | Dataset Split | Number of Instances in Split |
-| ------------- | ---------------------------- |
+| :------------ | :--------------------------- |
 | Train         | 382                          |
 | Validation    |  77                          |
 | Test          |  75                          |
@@ -101,27 +101,26 @@ The Climate Change NER dataset has 3 splits: _train_, _validation_, and _test_.
 
 ### Source Data
 
-The source data are abstracts from the Semantic Scholar Academic Graph Dataset (the _abstracts_ dataset).
-This collection is licensed under ODC-BY. (https://opendatacommons.org/licenses/by/1.0/)\n\nBy downloading this data you acknowledge that you have read and agreed to all the terms in this license.\n\nATTRIBUTION\nWhen using this data in a product or service, or including data in a redistribution, please cite the following paper:
+The source data are abstracts from the [Semantic Scholar Academic Graph Dataset](https://www.semanticscholar.org/product/api) (the _abstracts_ dataset).
 
 ### Annotations
 
 The abstracts have been annotated manually. Climate-related tokens are classified according to the following classes:
 
-- `assets`: objects or services of value to humans that can get destroyed or diminished by climate-hazards. Key categories are health, buildings, infrastructure, and crops or livestock.
-- `climate-named`: ?
-- `datasets`: specific collections of climate data with a name. A climate dataset can be the result of observations or of a model, e.g., as a prediction or reanalysis. The data may be lists, tables, databases, inventories or historical records, where the data dominate over attached code.
-- `ghg`: gases that cause heating of the atmosphere (greenhouse gases).
-- `hazards`: hazards with potential negative impact on climate, such as floods, wildfires, droughts, and heatwaves. Where a hazard is named in more detail in a text, the entire term is annotated, e.g., _surface water flood_ or _soil liquefaction_.
-- `impacts`: effects of hazards, primarily negative effects on humans. We also consider impacts on livestock as impacts, as it indirectly affects humans.
-- `mitigations`: activities to reduce climate change or to better deal with the consequences. 
-- `models`: specific physical, mathematical, or artificial intelligence objects, nowadays always computer-executable, used to analyze and usually predict climate parameters.
-- `nature`: aspects of nature that are not alive, such as oceans, rivers, the atmosphere, winds, and snow. 
-- `observations`: climate observation tools with a name. Examples are satellites, radiospectrometers, rain gauges, wildlife cameras, and questionnaires.
-- `organisms`: animals, plants, and other organisms that are considered for their own sakes (in contrast to as food for humans) as climate organisms.
-- `organizations`: real-world organizations with climate-related interests.
-- `problems`: 
-- `properties`: 
+- **climate-assets**: objects or services of value to humans that can get destroyed or diminished by climate-hazards. Key categories are health, buildings, infrastructure, and crops or livestock.
+- **climate-datasets**: specific collections of climate data with a name. A climate dataset can be the result of observations or of a model, e.g., as a prediction or reanalysis. The data may be lists, tables, databases, inventories or historical records, where the data dominate over attached code.
+- **climate-greenhouse-gases**: gases that cause heating of the atmosphere (greenhouse gases).
+- **climate-hazards**: hazards with potential negative impact on climate, such as floods, wildfires, droughts, and heatwaves. Where a hazard is named in more detail in a text, the entire term is annotated, e.g., _surface water flood_ or _soil liquefaction_.
+- **climate-impacts**: effects of hazards, primarily negative effects on humans. We also consider impacts on livestock as impacts, as it indirectly affects humans.
+- **climate-mitigations**: activities to reduce climate change or to better deal with the consequences. 
+- **climate-models**: specific physical, mathematical, or artificial intelligence objects, nowadays always computer-executable, used to analyze and usually predict climate parameters.
+- **climate-nature**: aspects of nature that are not alive, such as oceans, rivers, the atmosphere, winds, and snow. 
+- **climate-observations**: climate observation tools with a name. Examples are satellites, radiospectrometers, rain gauges, wildlife cameras, and questionnaires.
+- **climate-organisms**: animals, plants, and other organisms that are considered for their own sakes (in contrast to as food for humans) as climate organisms.
+- **climate-organizations**: real-world organizations with climate-related interests.
+- **climate-problem-origins**: problems that describe why the climate is changing. Key examples are _fossil fuel_ and _deforestation_. We also mention sectors that can be cited as causes of energy use. For instance, in a text about the energy consumption by the transport sector,
+_transport sector_ is annotated as _problem_.
+- **climate-properties**: properties of the climate itself (not abstract objects like models and datasets) that typically come with values and units.
 
 #### Who are the annotators?
 
@@ -131,6 +130,12 @@ The dataset was annotated by Birgit Pfitzmann
 
 Publication TBA.
 
+
+The source data has been fetched from the Semantic Scholar Academic Graph Datasets ("abstracts" dataset).
+This collection is licensed under [ODC-BY](https://opendatacommons.org/licenses/by/1.0/).
+By downloading this data you acknowledge that you have read and agreed to all the terms in this license.
+When using this data in a product or service, or including data in a redistribution, please cite the following paper:
+
 **BibTeX:**
 
 ```