text stringlengths 2.24k 306k | id int64 170 273M |
|---|---|
Dating individual quasars with the HeII proximity effect
Constraints on the time-scales of quasar activity are key to understanding the formation and growth of supermassive black holes (SMBHs), quasar triggering mechanisms, and possible feedback effects on their host galaxies. However, observational estimates of this ... | 230,523,795 |
Spin, Accretion and the Cosmological Growth of Supermassive Black Holes
If supermassive black holes (SMBHs) are the energy sources that power quasars and active galactic nuclei, then QSO SDSS 1148+5251, the quasar with the highest redshift (z_QSO=6.43), hosts a supermassive black hole formed within 0.9 Gyr after the B... | 16,704,521 |
"Systematic Mutation-based Evaluation of the Soundness of Security-focused Android Static Analysis T(...TRUNCATED) | 231,924,698 |
"Discovering Flaws in Security-Focused Static Analysis Tools for Android using Systematic Mutation\n(...TRUNCATED) | 49,433,957 |
"CrashScope: A Practical Tool for Automated Testing of Android Applications\n\nUnique challenges ari(...TRUNCATED) | 4,762,445 |
"HornDroid: Practical and Sound Static Analysis of Android Applications by SMT Solving\n\nWe present(...TRUNCATED) | 13,271,439 |
"Mass Flow Analysis of SARS-CoV-2 for quantified COVID-19 Risk Analysis\n\nHow may exposure risks to(...TRUNCATED) | 222,377,708 |
"Airborne SARS-CoV-2 Is Rapidly Inactivated by Simulated Sunlight\n\nAbstract Aerosols represent a p(...TRUNCATED) | 219,606,625 |
"Optimal Actor-Critic Policy with Optimized Training Datasets\n\nActor-critic (AC) algorithms are kn(...TRUNCATED) | 237,091,711 |
"GADAM: Genetic-Evolutionary ADAM for Deep Neural Network Optimization\n\nDeep neural network learni(...TRUNCATED) | 29,161,851 |
megapapers-pretrain
This repo holds different preprocessed conditions of the same underlying
paper set as separate configs — load a specific one with
load_dataset("<repo_id>", "<config_name>").
independent
152,520 rows, one row per paper — no concatenation of citing/cited pairs.
These are all of the unique papers that show up in the megapapers
project's (citing, cited) pairs: a Postgres DB scores every citation edge
between two Semantic Scholar papers with an LLM-generated impact category
(High/Medium/Low) — how much the citing paper's ideas, methods, or data
depended on the cited paper. This config is the pool of unique papers
across every edge where that score is High, restricted to the ones
that also have full text available locally in
peS2o v3
(peS2o only has full text for a subset of Semantic Scholar papers, and
each High-impact edge needs both the citing and the cited paper to have
it). Citing and cited papers are pooled together with no distinction kept,
and a paper appearing on both sides of different edges is included once.
Columns:
text(string): the paper's full text.id(int64): the paper's document id.
Usage
from datasets import load_dataset
ds = load_dataset("hannahglz25/megapapers-pretrain", "independent")
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