Working with Research Papers

Data sources

In the realm of research papers, data can be obtained from two main types of sources: Application Programming Interfaces (APIs) and bulk downloads. These sources often offer both free and paid variations, allowing researchers to access different levels of data and services based on their needs and budget.

Furthermore, these sources may provide the research data in two primary formats: bibliography-only or full-text content. The bibliography-only option offers essential metadata such as title, authors, abstracts, and publication details. On the other hand, the full-text option includes the complete contents of the research papers, enabling more in- depth analysis and insights.

Developers must consider their specific requirements and constraints when choosing between these data sources, based on factors such as data access limitations, cost considerations, and the level of information needed for their research projects.

Full text data

Even when the data source provides full-text content, it may not be readily available in a processable form, such as raw text data. Instead, the data might be available in the form of PDF files.

PDF files, while widely used for document sharing and publication, pose some challenges when it comes to automatic processing and data extraction for developers. Unlike raw text, which can be easily parsed and analyzed by machines, PDF files are essentially image-based representations of text, making it more difficult to extract the textual content programmatically.

To work with data from PDF files, developers often need to employ Optical Character Recognition (OCR) techniques to convert the scanned or image-based text into machine- readable text. This OCR process helps transform the content into a processable format, enabling developers to extract valuable information for analysis and further processing.

It’s crucial for developers to be aware of the format of the available data and be prepared to deal with the complexities introduced by PDF files if they intend to leverage the full- text content for their development projects.

Obtaining text from PDF files can be quite challenging, particularly when dealing with research papers, due to their substantial inclusion of equations, chemical formulas, tables, and diagrams. These complex elements hinder straightforward text extraction processes.

Sources for bulk scholarly data
● CORE
● Semantic Scholar
● ArXiv
● CiteSeer

Scholarly data APIs

● Semantic Scholar
● Lens
● ArXiv

Data attributes

Most research papers have the following fields of interest:

1. Title: usually a short string, but it can often contain special characters (e.g. mathematical symbols such as lambda, alpha, etc.). It is best to encode it as a unicode-formatted or LaTeX-formatted string. This field is often used for searching papers.
2. Abstract: mostly these are single paragraphs. Like the title, they can also have special characters.
3. Authors: a list of names, can be modeled as an array of strings. This field is also frequently used for searching, so it should be indexed.
4. Affiliations: the institutions to which the authors are affiliated, the length of this array should be same as the length of the corresponding authors
5. Publication information: this would constitute the journal, volume, and page
   numbers on which the research article was published, this information is rarely used in this digital age but is important when citing the article. This field may be unavailable for preprints.
6. Year: the publication year. Exact date of publication is not always known.
   Sometimes, only the month and the year are known. So it is best to keep the date fields flexible.
7. Full text: this should ideally be LaTeX encoded to preserve tables, equations,
   chemical formulas, etc.
8. Reference: these are the equivalent of backward citations in patents. References are mostly other research papers but can also be web or newspaper articles, patents, etc. Usually they are stored as arrays of strings, unless, when they can be disambiguated to other documents in the database, in which case the references can point to the unique ID of the other document.
9. DOI number: A unique identifier, equivalent of a patent number.

Contd…

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