DOI:10.1038/nature.2015.18703 - Corpus ID: 182440976
Artificial-intelligence institute launches free science search engine
@article{Jones2015ArtificialintelligenceIL, title={Artificial-intelligence institute launches free science search engine}, author={Nicola Jones}, journal={Nature}, year={2015}, url={https://api.semanticscholar.org/CorpusID:182440976}}Semantic Scholar comes from centre backed by Microsoft co-founder Paul Allen and aims to provide real-time knowledge of the natural world to aid in the development of artificial intelligence.
13 Citations
Discovery in an age of artificial intelligence
- J. Luther
- 2016
Computer Science
While there is great interest in data and text mining today, discussions on this topic indicate that progress is incremental and a surprisingly small number of researchers are taking advantage of the opportunities enabled by publishers.
Google Scholar versus Semantic Scholar: a comparative evaluation
- Jeremie Kipeleka
- 2016
Computer Science
This work compared and contrasted Google scholar and semantic scholar and asked whether or not semantic scholar should be adopted as the preferred database and scientific literature search engine.
Report on the 3rd International Workshop on Bibliometric-enhanced Information Retrieval (BIR 2016)
- Philipp MayrIngo FrommholzG. Cabanac
- 2016
Computer Science
This workshop report presents the BIR 2016 workshop, which has been co-located with ECIR for the third time this year, and motivates the workshop and outlines the papers presented at ECIR 2016 in Padua, Italy.
Reproducible Hybrid Time-Travel Retrieval in Evolving Corpora
- Moritz StaudingerFlorina PiroiAndreas Rauber
- 2024
Computer Science
SIGIR-AP
This work presents a hybrid retrieval system combining Lucene for fast retrieval with a column-store-based retrieval system maintaining a versioned and time-stamped index, which ensures retrieval results in evolving document collections are fully reproducible even when document collections and thus term statistics change.
Evaluating Research Impact Based on Semantic Scholar Highly Influential Citations, Total Citations, and Altmetric Attention Scores: The Quest for Refined Measures Remains Illusive
- L. DardasMalik SallamAmanda WoodwardNadia SweisNarjes SweisF. Sawair
- 2023
Computer Science
Publ.
The use of SS HICs in gauging research impact is significantly limited due to the enigmatic method of its calculation and total dependence on artificial intelligence.
Bibliometric analysis of neuroscience publications quantifies the impact of data sharing
- Herve EmissahB. LjungquistG. Ascoli
- 2023
Computer Science, Medicine
It is demonstrated that sharing digital reconstructions of neural morphology via the NeuroMorpho.Org online repository leads to a significant increase of citations to the original article, thus directly benefiting the authors, and can facilitate the recognition of shared data reuse for promotion and tenure considerations, merit evaluations, and funding decisions.
Shalosh B. Ekhad: a computer credit for mathematicians
- Zhiwen HuYiping CuiJian ZhangJacqueline Eviston-Putsch
- 2019
Mathematics, Computer Science
This scientometrical case study is expected to provide a unique opportunity to re-scrutinize the whole story of the Zeilberger–Ekhad theorem, discourage bias in later accounts that may appear, and uncover some unfolded motivations in human–machine cooperation scenarios.
Implicaciones técnicas y prácticas de las Redes Adversarias Generativas a la Ciencia Abierta en Educación
Generative Adversarial Networks (GANs), which are characteristic of Artificial Intelligence, allow the creation of synthetic anonymised data useful for Open Science in educational research. This…
Wasserstein GAN-Based Small-Sample Augmentation for New-Generation Artificial Intelligence: A Case Study of Cancer-Staging Data in Biology
- Yufei LiuYufei LiuZihong Wang
- 2019
Computer Science, Biology
Revisiting Common Assumptions about Arabic Dialects in NLP
- Amr KelegSharon GoldwaterWalid Magdy
- 2025
Linguistics, Computer Science
This analysis indicates that the four assumptions about Arabic dialects can be grouped into distinguishable regional dialects oversimplify reality, and some of them are not always accurate, which might be hindering further progress in different Arabic NLP tasks.
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