README.md

---
license: apache-2.0
task_categories:
- question-answering
- text-classification
language:
- en
- fr
- es
size_categories:
- 10K<n<100K
tags:
- climate
- policy
---

This dataset is curated by [GIZ Data Service Center](https://www.giz.de/expertise/html/63018.html). The source dataset for this
comes from Internal GIZ team (IKI_Tracs) and [Climatewatchdata](https://www.climatewatchdata.org/data-explorer/historical-emissions?historical-emissions-data-sources=climate-watch&historical-emissions-gases=all-ghg&historical-emissions-regions=All%20Selected&historical-emissions-sectors=total-including-lucf%2Ctotal-including-lucf&page=1),
where Climatewatch has analysed Intended nationally determined contribution (INDC), NDC and Revised/Updated NDC of the countries to answer some important questions related to Climate change.

Specifications
- Dataset size: ~85k
- Language: English, French, Spanish

# Columns 
- **index (type:int)**: Unique Response ID
- **ResponseText (type:str)**: Annotated answer/response to query
- **Alpha3 (type:str)**:country alpha-3 code (ISO 3166)
- **Country (type:str)**: country name
- **Document (type:str)**:Name of type of Policy document from which response is provided
- **IkiInfo (type: list[dict])**: Responsetext can appear/occur as answer/response for different kind of query, therefore in that case we preserve all raw information for each occurences.
  Each dictionary object represents one such occurrence for response and provides all raw metadata for an occurrence.In case of None, it means
  the entry belongs to Climate data and not IKI Tracs data)
- **CWInfo (type: list[dict])**:Responsetext can appear/occur as answer/response for different kind of query, therefore in that case we preserve all raw information for each occurences.
  Each dictionary object represents one such occurrence for response and provides all raw metadata for an occurrence. In case of None, it means
  the entry belongs to Iki tracs data and not CW)
- **Source (type:list[str])**: Contains the name of source
- **Target (type:list)**: Value at index 0, represents number of times ResponseText appears as 'Target', and not-Target (value at index 1 )
- **Action (type:list)**: Value at index 0, represents number of times ResponseText appears as 'Action', and not-Action (value at index 1 )
- **Policies_Plans (type:list)**: Value at index 0, represents number of times ResponseText appears as 'Policy/Plan', and not-Policy/Plan (value at index 1 )
- **Mitigation (type:list)**: Value at index 0, represents number of times ResponseText appears in reference to Mititgation and not-Mitigation (value at index 1 )
- **Adaptation (type:list)**: Value at index 0, represents number of times ResponseText appears in reference to Adaptation and not-Adaptation (value at index 1 )
- **language (type:str)**: ISO code of language of ResponseText.
- **context (type:list[str])**: List of paragraphs/textchunk from the document of country which contains the ResponseText. These results are based on Okapi bm25 retriever,
 and hence dont represent ground truth. 
- **context_lang (type:str)**:  ISO code of language of ResponseText. In some cases context and ResponseText are different as annotator have provided the translated response, rather than original text from document.
- **matching_words(type:list[list[[words]])**:For each context, finds the matching words from ResponseText (stopwords not considered).
- **response_words(type:list[words])**:Tokens/Words from ResponseText (stopwords not considered)
- **context_wordcount (type:list[int])**: Number of tokens/words in each context (remember context itself is list of multiple strings, and stopwords not considered)
- **strategy (type:str)**: Can take either of *small,medium,large* value. Represents the length of paragraphs/textchunk considered for finding the right context for ResponseText
- 
        "match_onresponse": [
            0.86,
            0.86,
            0.71
        ],
        "candidate": [
The list of Sectors covered include: Agriculture', 'Coastal Zone', 'Cross-Cutting Area', 'Education', 'Energy', 'Environment', 'Water', 'Buildings', 'Economy-wide', 'Industries', 'Transport', 'Waste', 'Health', 'LULUCF/Forestry', 'Social Development', 'Disaster Risk Management (DRM)', 'Urban','Tourism'.
Some of the important question categories pertaining to climate change(adapted from climatewatchdata) include
- Sectoral Policies
- Sectoral Unconditional Actions
- Building on existing downstream actions
- Sectoral plans
- Sectoral targets
- Action and priority
- Adapt Now sector
- Emission reduction potential
- Capacity Building Needs for Sectoral Implementation
- Sectoral Conditional Actions
- Technology Transfer Needs for Sectoral Implementation
- Conditional part of mitigation target
- Capacity building needs
- Technology needs
- Unconditional part of mitigation target
- Time frame
- Emission reduction potential
No answer category like 'Squad2' is not part of dataset but can be easily curated from existing examples.