Datasets:

ibm
/

Climate-Change-NER

@@ -1,5 +1,12 @@
 ---
-license: odc-by
 task_categories:
 - token-classification
 language:
@@ -20,16 +27,17 @@ The Climate Change NER is an English-language dataset containing 534 abstracts o
 We introduce a comprehensive dataset for developing and evaluating NLP models tailored towards understanding and addressing climate-related topics across various domains.
 The **Climate Change NER** is a manually curated dataset of 534 abstracts from climate-related articles. They have been extracted from the [Semantic Scholar Academic Graph](https://www.semanticscholar.org/product/api) (_abstracts_ dataset)
-by using a seed set of climate-related keywords. The abstracts were annotated by tagging relevant tokens with the IOB format (inside, outside, beginning) using 14 differents categories.
 - **Curated by:** Birgit Pfitzmann
-- **Shared by [optional]:** Cesar Berrospi Ramis
 - **Language(s) (NLP):** English
 - **License:** [Open Data Commons Attribution License (ODC-By)](https://opendatacommons.org/licenses/by/1-0/)
-### Dataset Sources [optional]
-- **Paper [optional]:** [More Information Needed]
 ## Uses
@@ -91,65 +99,37 @@ The Climate Change NER dataset has 3 splits: _train_, _validation_, and _test_.
 ## Dataset Creation
-### Curation Rationale
-[More Information Needed]
 ### 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:
-#### Data Collection and Processing
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-[More Information Needed]
-#### Who are the source data producers?
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
-[More Information Needed]
-### Annotations [optional]
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
-#### Annotation process
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
-[More Information Needed]
 #### Who are the annotators?
 The dataset was annotated by Birgit Pfitzmann
-[More Information Needed]
-#### Personal and Sensitive Information
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
 **BibTeX:**
@@ -164,21 +144,6 @@ Users should be made aware of the risks, biases and limitations of the dataset.
 }
 ```
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
 ## Dataset Card Contact
-Cesar Berrospi Ramis [@ceberam](https://github.com/ceberam))

 ---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+multilinguality:
+- monolingual
+license:
+- odc-by
 task_categories:
 - token-classification
 language:
 We introduce a comprehensive dataset for developing and evaluating NLP models tailored towards understanding and addressing climate-related topics across various domains.
 The **Climate Change NER** is a manually curated dataset of 534 abstracts from climate-related articles. They have been extracted from the [Semantic Scholar Academic Graph](https://www.semanticscholar.org/product/api) (_abstracts_ dataset)
+by using a seed set of climate-related keywords. The abstracts were annotated by tagging relevant tokens with the IOB format (inside, outside, beginning) using 14 differents categories:
 - **Curated by:** Birgit Pfitzmann
+- **Shared by:** Cesar Berrospi Ramis
 - **Language(s) (NLP):** English
 - **License:** [Open Data Commons Attribution License (ODC-By)](https://opendatacommons.org/licenses/by/1-0/)
+### Dataset Sources
+- **Source** A subset of Semantic Scholar Academic Graph Dataset (the _abstracts_ dataset)
+- **Paper:** TBA
 ## Uses
 ## Dataset Creation
 ### 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:
+### 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`:
 #### Who are the annotators?
 The dataset was annotated by Birgit Pfitzmann
+## Citation
+Publication TBA.
 **BibTeX:**
 }
 ```
 ## Dataset Card Contact
+Cesar Berrospi Ramis [@ceberam](https://github.com/ceberam)