Into the Single Cell Multiverse: an End-to-End Dataset for Procedural Knowledge Extraction in Biomedical Texts

This data repository accompanies the GitHub repository (https://github.com/ylaboratory/flambe) for the paper, Into the Single Cell Multiverse: an End-to-End Dataset for Procedural Knowledge Extraction in Biomedical Texts.
The high level file structure is as follows:

data: contains processed datasets for BioNLP tasks
models: fine-tuned PubmedBERT PyTorch models for tissue and cell type tagging
The data section is further divided into sections depending on downstream use cases:

corpus: the text for 55 full papers from PubMed and PMC
disambiguation: all files used for downstream disambiguation of tissue, cell type, and software terms
sentiment: files for tool context prediction (similar to sentiment classification)
tags: contains IOB and CoNLL tag files for fine-tuning BERT-based models for tissue and cell type tagging, as well as software tagging.
workflow: 3 files of curated tuples for various tool and workflow extraction tasks
Annotation file formats
In this section we describe in detail the various file formats of the accessory files and main annotation files: IOB, CoNLL, disambiguation, and workflow files.
IOB files
Files ending in .iob follow the Inside-outside-beginning tagging format. These files are tab-delimited text files made with the SpaCy English tokenizer having one token per line followed by a tag signifying a named entity. Unlike traditional IOB files, we include additional lines that mark the start and end of papers or abstracts. These lines contain the PMID or PMC identifier in the token column and the words begin or end in the tag column.
CoNLL files
CoNLL files, like the IOB files have tokenized text for both full text and abstracts, but are augmented with additional information such as disambiguated terms and identifiers. Unlike the IOB files, which cover the entire abstract and full text corpus, we release one CoNLL per paper.
Licensing files
Each paper has its own license and usage agreements. We keep track of these licenses for our collection of full text and abstract papers. Each file is indexed either by PubMed Central (pmc) identifiers (in the case of full text), or PubMed ids (pmid). These files can be found in the `data` directory ending in `_licenses.txt`.
Disambiguation files
Tissues and cell types are disambiguated to the NCI Thesaurus. In the `tissue_ned_table.txt` file we take tokens that were present in the full text and abstract files and map them to NCIT identifiers. An additional file `NCI_thesaurus_info.txt` contains the relevant identifiers, names, aliases, and descriptions for the `tissue`, `organ`, `body part`, `fluid`, and `cell type` branches of the ontology.
Tools are manually disambiguated to a standardized name or acronym taken from their initial paper. In `tool_ned_table.txt` we map tokens present in the full text and abstract files to these standardized names. The file `tools_info.txt` maps these standardized names to project websites (personal or GitHub links) and to the original publication when available. The `uns_method_ned.txt` is a tab delimited file that maps generic (unspecified) method tokens present in the full text and abstract files to standardized method names. Where applicable we link the method to a wikipedia or library page (e.g., scikit-learn).
Workflow files
Workflow files are presented as three tab delimited files of tuples.

`sample` file links any experimental assay (e.g., RNA-seq, single cell RNA-seq, ChIP-seq) with tissue and cell type annotations
`tools_applied` file joins samples, tools, and with a standardized description of how the tool is applied (context / mode)
`sequence` file captures the pairwise ordering of applied tools and their contexts
Each of the three files start each new line with PMC identifiers linking defined annotations with relevant papers. Furthermore, the `sample` and `tools_applied` files have sequential id numbers within each PMC for the extraction of unambiguous sample workflows. When one sample in the `sample` file can be described with multiple tissue and cell type annotations we tie it back to the same sequential sample identifier.
We constrain the set of tool contexts / modes to the following list of 23 actions:

Alignment, Alternative Splicing, Batch Correction, Classification, CNV calling, Clustering, Deconvolution, Differential Expression, Dimensionality Reduction, Gene Enrichment / Gene set analysis, Integration, Imputation, Marker Genes / Feature Selection, Networks, Normalization, Quality Control, Quantification, Rare Cell Identification, Simulation, TCR, Tree Inference, Visualization, Variable Genes