scholarly journals The NASA Astrophysics Data System: The search engine and its user interface

2000 ◽  
Vol 143 (1) ◽  
pp. 61-83 ◽  
Author(s):  
Guenther Eichhorn ◽  
Michael J. Kurtz ◽  
Alberto Accomazzi ◽  
Carolyn S. Grant ◽  
Stephen S. Murray
2018 ◽  
Vol 186 ◽  
pp. 08001 ◽  
Author(s):  
Alberto Accomazzi ◽  
Michael J. Kurtz ◽  
Edwin A. Henneken ◽  
Carolyn S. Grant ◽  
Donna M. Thompson ◽  
...  

In this paper we provide an update concerning the operations of the NASA Astrophysics Data System (ADS), its services and user interface, and the content currently indexed in its database. As the primary information system used by researchers in Astronomy, the ADS aims to provide a comprehensive index of all scholarly resources appearing in the literature. With the current effort in our community to support data and software citations, we discuss what steps the ADS is taking to provide the needed infrastructurein collaboration with publishers and data providers. A new API provides accessto the ADS search interface, metrics, and libraries allowing users to programmatically automate discovery and curation tasks. The new ADS interface supports a greater integration of content and services with a variety of partners, including ORCID claiming, indexing of SIMBAD objects, and article graphics from a variety of publishers. Finally, we highlight how librarians can facilitate the ingest of gray literature that they curate into our system.


mSystems ◽  
2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Gongchao Jing ◽  
Lu Liu ◽  
Zengbin Wang ◽  
Yufeng Zhang ◽  
Li Qian ◽  
...  

ABSTRACT Metagenomic data sets from diverse environments have been growing rapidly. To ensure accessibility and reusability, tools that quickly and informatively correlate new microbiomes with existing ones are in demand. Here, we introduce Microbiome Search Engine 2 (MSE 2), a microbiome database platform for searching query microbiomes in the global metagenome data space based on the taxonomic or functional similarity of a whole microbiome to those in the database. MSE 2 consists of (i) a well-organized and regularly updated microbiome database that currently contains over 250,000 metagenomic shotgun and 16S rRNA gene amplicon samples associated with unified metadata collected from 798 studies, (ii) an enhanced search engine that enables real-time and fast (<0.5 s per query) searches against the entire database for best-matched microbiomes using overall taxonomic or functional profiles, and (iii) a Web-based graphical user interface for user-friendly searching, data browsing, and tutoring. MSE 2 is freely accessible via http://mse.ac.cn. For standalone searches of customized microbiome databases, the kernel of the MSE 2 search engine is provided at GitHub (https://github.com/qibebt-bioinfo/meta-storms). IMPORTANCE A search-based strategy is useful for large-scale mining of microbiome data sets, such as a bird’s-eye view of the microbiome data space and disease diagnosis via microbiome big data. Here, we introduce Microbiome Search Engine 2 (MSE 2), a microbiome database platform for searching query microbiomes against the existing microbiome data sets on the basis of their similarity in taxonomic structure or functional profile. Key improvements include database extension, data compatibility, a search engine kernel, and a user interface. The new ability to search the microbiome space via functional similarity greatly expands the scope of search-based mining of the microbiome big data.


Web Mining ◽  
2011 ◽  
pp. 307-321 ◽  
Author(s):  
Ricardo Baeza-Yates

Search engine logs not only keep navigation information, but also the queries made by their users. In particular, queries to a search engine follow a power-law distribution, which is far from uniform. Queries and related clicks can be used to improve the search engine itself in different aspects: user interface, index performance, and answer ranking. In this chapter we present some of the main ideas proposed in query mining and we show a few examples based on real data from a search engine focused on the Chilean Web.


2001 ◽  
Vol 10 (4) ◽  
pp. 28-30
Author(s):  
Monique Gómez

1995 ◽  
Vol 39 (1) ◽  
pp. 63-68 ◽  
Author(s):  
A. Accomazzi ◽  
G. Eichhorn ◽  
C.S. Grant ◽  
S.S. Murray ◽  
M.J. Kurtz

Author(s):  
Abdullatif Köksal ◽  
Hilal Dönmez ◽  
Rıza Özçelik ◽  
Elif Ozkirimli ◽  
Arzucan Özgür

AbstractCoronavirus Disease of 2019 (COVID-19) created dire consequences globally and triggered an intense scientific effort from different domains. The resulting publications created a huge text collection in which finding the studies related to a biomolecule of interest is challenging for general purpose search engines because the publications are rich in domain specific terminology. Here, we present Vapur: an online COVID-19 search engine specifically designed to find related protein - chemical pairs. Vapur is empowered with a relation-oriented inverted index that is able to retrieve and group studies for a query biomolecule with respect to its related entities. The inverted index of Vapur is automatically created with a BioNLP pipeline and integrated with an online user interface. The online interface is designed for the smooth traversal of the current literature by domain researchers and is publicly available at https://tabilab.cmpe.boun.edu.tr/vapur/.


2000 ◽  
Vol 143 (1) ◽  
pp. 85-109 ◽  
Author(s):  
Alberto Accomazzi ◽  
Guenther Eichhorn ◽  
Michael J. Kurtz ◽  
Carolyn S. Grant ◽  
Stephen S. Murray

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