README.md

---
task_categories:
- text-classification
- token-classification
language:
- en
pretty_name: DiMB-RE
size_categories:
- 1K<n<10K
dataset_info:
- config_name: default
  features:
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  download_size: 2048088
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- config_name: re
  features:
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    num_examples: 139
  - name: validation
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    num_examples: 19
  - name: test
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    num_examples: 37
  download_size: 2048088
  dataset_size: 2970875
- config_name: sentence_level
  features:
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  - name: doc_key
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  - name: tokens
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  - name: validation
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    num_examples: 233
  - name: test
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    num_examples: 494
  download_size: 2048088
  dataset_size: 3161889
tags:
- medical
- biology
---

# Dataset Card for "DiMB-RE"

<!-- Provide a quick summary of the dataset. -->

DiMB-RE (Diet-Microbiome Relation Extraction) corpus is a resource for mining diet-microbiome associations from scientific literature.

## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->



- **Curated by:** Gibong Hong, Veronica Hindle, Nadine M. Veasley, Hannah D. Holscher, Halil Kilicoglu
- **Funded by:** University of Illinois Personalized Nutrition Initiative Seed Grant, National Center for Complementary and Integrative Health (NCCIH), Office of Data Science Strategy (ODSS)
- **Shared by:** ScienceNLP Lab, University of Illinois Urbana-Champaign
- **Language(s) (NLP):** English
- **License:** [More Information Needed]

### Dataset Sources

<!-- Provide the basic links for the dataset. -->

- **Repository:** https://github.com/ScienceNLP-Lab/DiMB-RE
- **Paper:** [DiMB-RE: Mining the Scientific Literature for Diet-Microbiome Associations](https://arxiv.org/pdf/2409.19581.pdf)

## Uses

<!-- Address questions around how the dataset is intended to be used. -->

### Direct Use

<!-- This section describes suitable use cases for the dataset. -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->

[More Information Needed]

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

The dataset is provided in a JSON format and has two configurations: a sentence-level view and a document-level view. Each data point in the sentence-level configuration contains the following fields:

- `id`: A unique string identifier for the sentence (e.g., PMC4994979_sent_0).
- `doc_key`: A string identifying the source document (e.g., PMC4994979).
- `tokens`: A list of strings representing the words in the sentence.
- `ner`: A list of dictionaries for each named entity, containing its start and end token indices and its entity type.
- `ner_tags`: A sequence of strings representing the BIO (Beginning, Inside, Outside) tag for each token.
- `triggers`: A list of dictionaries for each relation trigger, containing its start and end token indices and its type (which corresponds to a relation type).
- `relations`: A list of dictionaries, where each dictionary defines a relationship between two entities (a head and a tail), including their token spans, types, the relation type, and the factuality level.
- `triplets`: A list of dictionaries linking a head entity, a tail entity, and a trigger by their token spans.

Example Data Point (sentence_level)
```json
{
    "doc_key": "PMC4994979",
    "id": "PMC4994979_sent_0",
    "ner": [
        {"end": 7, "start": 2, "type": "Nutrient"},
        {"end": 11, "start": 9, "type": "Physiology"}
    ],
    "ner_tags": ["O", "O", "B-Nutrient", "I-Nutrient", "I-Nutrient", "I-Nutrient", "I-Nutrient", "O", "O", "B-Physiology", "I-Physiology", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"],
    "relations": [
        {
            "factuality": "Unknown",
            "head": 0,
            "head_end": 7,
            "head_start": 2,
            "head_type": "Nutrient",
            "tail": 1,
            "tail_end": 11,
            "tail_start": 9,
            "tail_type": "Physiology",
            "type": "AFFECTS"
        }
    ],
    "tokens": ["Effect", "of", "vitamin", "E", "with", "therapeutic", "iron", "supplementation", "on", "iron", "repletion", "and", "gut", "microbiome", "in", "U", ".", "S", ".", "iron", "deficient", "infants", "and", "toddlers", ":", "a", "randomized", "control", "trial"],
    "triggers": [
        {"end": 392, "start": 391, "type": "Nutrient"}
    ],
    "triplets": [
        {
            "head_end": 7,
            "head_start": 2,
            "relation": "0",
            "tail_end": 11,
            "tail_start": 9,
            "trigger_end": 1,
            "trigger_start": 0
        }
    ]
}
```

The document-level view additional contains the following field:

- `sentences`: A list of dictionaries for each sentence, containing its start and end token indices.

### Entity Types
The dataset is annotated with 15 entity types:
- Food, Nutrient, DietPattern, Microorganism, DiversityMetric, Metabolite, Physiology, Disease, Measurement, Enzyme, Gene, Chemical, Methodology, Population, Biospecimen

### Relation Types
Relations capture the interactions between entities and are categorized into 13 types:
- AFFECTS, IMPROVES, WORSENS, ASSOCIATED_WITH, POS_ASSOCIATED_WITH, NEG_ASSOCIATED_WITH, INTERACTS_WITH, INCREASES, DECREASES, CAUSES, PREVENTS, PREDISPOSES, HAS_COMPONENT

Annotations also include relation triggers (the specific word or phrase indicating the relation, e.g., "increased") and factuality levels (Factual, Probable, Possible, Doubtful, Negated, and
Unknown).



## Dataset Creation

### Curation Rationale

<!-- Motivation for the creation of this dataset. -->

The motivation for creating DiMB-RE was the recognition that while the scientific literature contains vast amounts of evidence on diet-microbiome interactions, this knowledge is locked in unstructured text. Manually curated databases are often limited and not scalable. This dataset was created to enable the use of NLP to automatically machine-read the literature, structure this information, and ultimately facilitate knowledge-guided analysis to advance personalized nutrition.

### Source Data

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->

#### 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. -->

The source data consists of titles and abstracts from 165 publications and the full-text Results sections from 30 of those publications. The articles were retrieved from PubMed using a manually crafted search string developed by domain experts in food science and human nutrition. The search terms focused on key concepts in diet-microbiome research.

#### 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. -->

The source data was produced by the authors of the scientific articles included in the corpus. These are researchers from various institutions globally who have published on the topic of diet and the microbiome.

### Annotations

<!-- 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. -->

The annotation was performed by two graduate students in food science and nutrition and a senior investigator with expertise in NLP and biomedical informatics, using the Brat annotation tool. 
The process involved multiple stages of annotation, calculation of inter-annotator agreement (IAA), and adjudication of disagreements to refine the annotation guidelines. 
The final annotations were verified for consistency and accuracy. IAA for entities was reasonable (mean F1-score of 0.69 exact, 0.80 partial), while relation agreement was more modest (mean F1-score of 0.41 exact, 0.54 partial), highlighting the task's difficulty.

#### Who are the annotators?

<!-- This section describes the people or systems who created the annotations. -->

The annotators were Veronica Hindle and Nadine M. Veasley, and Halil Kilicoglu. Gibong Hong also participated in verifying the final annotations.

#### 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. -->

[More Information Needed].

## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```tex
@misc{hong2024dimbreminingscientificliterature,
      title={DiMB-RE: Mining the Scientific Literature for Diet-Microbiome Associations}, 
      author={Gibong Hong and Veronica Hindle and Nadine M. Veasley and Hannah D. Holscher and Halil Kilicoglu},
      year={2024},
      eprint={2409.19581},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2409.19581}, 
}
```

**APA:**

Hong, G., Hindle, V., Veasley, N. M., Holscher, H. D., & Kilicoglu, H. (2024). DiMB-RE: Mining the Scientific Literature for Diet-Microbiome Associations. ArXiv. /abs/2409.19581

## 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 Contributions

Thanks to [@phucdev](https://github.com/phucdev) for adding this dataset.