# D3: A Massive Dataset of Scholarly Metadata for Analyzing the State of Computer Science Research

arXiv: https://arxiv.org/abs/2204.13384 · HTML: https://mlsanity.com/paper/2204.13384/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 28 Apr 2022.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Examining Citations of Natural Language Processing Literature](https://mlsanity.com/paper/2005.00912/) (2020)
- [Gender Gap in Natural Language Processing Research: Disparities in Authorship and Citations](https://mlsanity.com/paper/2005.00962/) (2020)
- [NLPExplorer: Exploring the Universe of NLP Papers](https://mlsanity.com/paper/1910.07351/) (2019)
- [DRIFT: A Toolkit for Diachronic Analysis of Scientific Literature](https://mlsanity.com/paper/2107.01198/) (2021)

## Built on by (most cited first, 11 of 11)

- [How Large Language Models are Transforming Machine-Paraphrased Plagiarism](https://mlsanity.com/paper/2210.03568/) (2022)
- [The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Research](https://mlsanity.com/paper/2305.02797/) (2023)
- [We are Who We Cite: Bridges of Influence Between Natural Language Processing and Other Academic Fields](https://mlsanity.com/paper/2310.14870/) (2023)
- [Forgotten Knowledge: Examining the Citational Amnesia in NLP](https://mlsanity.com/paper/2305.18554/) (2023)
- [The Nature of NLP: Analyzing Contributions in NLP Papers](https://mlsanity.com/paper/2409.19505/) (2024)
- [Citation Amnesia: On The Recency Bias of NLP and Other Academic Fields](https://mlsanity.com/paper/2402.12046/) (2024)
- [CS-Insights: A System for Analyzing Computer Science Research](https://mlsanity.com/paper/2210.06878/) (2022)
- [HALvest-Contrastive: Retrieval-Like Authorship Attribution with Patch-Level Late Interaction](https://mlsanity.com/paper/2407.20595/) (2024)
- [Citation Recommendation on Scholarly Legal Articles](https://mlsanity.com/paper/2311.05902/) (2023)
- [Big Tech-Funded AI Papers Have Higher Citation Impact, Greater Insularity, and Larger Recency Bias](https://mlsanity.com/paper/2512.05714/) (2025)
- [Eleven Years of BRACIS: A Meta-Scientific Study of the Brazilian Conference on Intelligent Systems](https://mlsanity.com/paper/2608.09964/) (2026)

## Abstract (arXiv)

DBLP is the largest open-access repository of scientific articles on computer science and provides metadata associated with publications, authors, and venues. We retrieved more than 6 million publications from DBLP and extracted pertinent metadata (e.g., abstracts, author affiliations, citations) from the publication texts to create the DBLP Discovery Dataset (D3). D3 can be used to identify trends in research activity, productivity, focus, bias, accessibility, and impact of computer science research. We present an initial analysis focused on the volume of computer science research (e.g., number of papers, authors, research activity), trends in topics of interest, and citation patterns. Our findings show that computer science is a growing research field (approx. 15% annually), with an active and collaborative researcher community. While papers in recent years present more bibliographical entries in comparison to previous decades, the average number of citations has been declining. Investigating papers' abstracts reveals that recent topic trends are clearly reflected in D3. Finally, we list further applications of D3 and pose supplemental research questions. The D3 dataset, our findings, and source code are publicly available for research purposes.