The NASA-ADS Abstract Service provides a sophisticated search capability for the literature in Astronomy, Planetary Sciences, Physics/Geophysics, and Space Instrumentation. The ADS is funded by NASA and access to the ADS services is free to anybody worldwide without restrictions. It allows the user to search the literature by author, title, and abstract text. The ADS database contains over 3.6 million references, with 965,000 in the Astronomy/Planetary Sciences database, and 1.6 million in the Physics/Geophysics database. 2/3 of the records have full abstracts, the rest are table of contents entries (titles and author lists only). The coverage for the Astronomy literature is better than 95% from 1975. Before that we cover all major journals and many smaller ones. Most of the journal literature is covered back to volume 1. We now get abstracts on a regular basis from most journals. Over the last year we have entered basically all conference proceedings tables of contents that are available at the Harvard Smithsonian Center for Astrophysics library. This has greatly increased the coverage of conference proceedings in the ADS. The ADS also covers the ArXiv Preprints. We download these preprints every night and index all the preprints. They can be searched either together with the other abstracts or separately. There are currently about 260,000 preprints in that database. In January 2004 we have introduced two new services, full text searching and a personal notification service called "myADS". As all other ADS services, these are free to use for anybody.
The purpose of this study was to investigate differences between abstracts of posters presented at the 79(th) (2002) and 80(th) (2003) Annual Session & Exhibition of the American Dental Education Association (ADEA) and the published full-length articles resulting from the same studies. The abstracts for poster presentation sessions were downloaded, and basic characteristics of the abstracts and their authors were determined. A PubMed search was then performed to identify the publication of full-length articles based on those abstracts in a peer-reviewed journal. The differences between the abstract and the article were examined and categorized as major and minor differences. Differences identified included authorship, title, materials and methods, results, conclusions, and funding. Data were analyzed with both descriptive and analytic statistics. Overall, 89 percent of the abstracts had at least one variation from its corresponding article, and 65 percent and 76 percent of the abstracts had at least one major and minor variation, respectively, from its corresponding article. The most prevalent major variation was in study results, and the most prevalent minor variation was change in the number of authors. The discussion speculates on some possible reasons for these differences.
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A graphical abstract (GA) represents a piece of artwork that is intended to summarize the main findings of an article for readers at a single glance. Many publishers currently encourage authors to supplement their articles with GAs, in the hope that such a convenient visual summary will facilitate readers with a clearer outline of papers that are of interest and will result in improved overall visibility of the respective publication. To test this assumption, we statistically compared publications with or without GA published in Molecules between March 2014 and March 2015 with regard to several output parameters reflecting visibility. Contrary to our expectations, manuscripts published without GA performed significantly better in terms of PDF downloads, abstract views, and total citations than manuscripts with GA. To the best of our knowledge, this is the first empirical study on the effectiveness of GA for attracting attention to scientific publications. PMID:27649137
A graphical abstract (GA) represents a piece of artwork that is intended to summarize the main findings of an article for readers at a single glance. Many publishers currently encourage authors to supplement their articles with GAs, in the hope that such a convenient visual summary will facilitate readers with a clearer outline of papers that are of interest and will result in improved overall visibility of the respective publication. To test this assumption, we statistically compared publications with or without GA published in Molecules between March 2014 and March 2015 with regard to several output parameters reflecting visibility. Contrary to our expectations, manuscripts published without GA performed significantly better in terms of PDF downloads, abstract views, and total citations than manuscripts with GA. To the best of our knowledge, this is the first empirical study on the effectiveness of GA for attracting attention to scientific publications.
This study aims to classify abstract content based on the use of the highest number of words in an abstract content of the English language journals. This research uses a system of text mining technology that extracts text data to search information from a set of documents. Abstract content of 120 data downloaded at www.computer.org. Data grouping consists of three categories: DM (Data Mining), ITS (Intelligent Transport System) and MM (Multimedia). Systems built using naive bayes algorithms to classify abstract journals and feature selection processes using term weighting to give weight to each word. Dimensional reduction techniques to reduce the dimensions of word counts rarely appear in each document based on dimensional reduction test parameters of 10% -90% of 5.344 words. The performance of the classification system is tested by using the Confusion Matrix based on comparative test data and test data. The results showed that the best classification results were obtained during the 75% training data test and 25% test data from the total data. Accuracy rates for categories of DM, ITS and MM were 100%, 100%, 86%. respectively with dimension reduction parameters of 30% and the value of learning rate between 0.1-0.5.
The function of a non-protein-coding RNA is often determined by its structure. Since experimental determination of RNA structure is time-consuming and expensive, its computational prediction is of great interest, and efficient solutions based on thermodynamic parameters are known. Frequently, however, the predicted minimum free energy structures are not the native ones, leading to the necessity of generating suboptimal solutions. While this can be accomplished by a number of programs, the user is often confronted with large outputs of similar structures, although he or she is interested in structures with more fundamental differences, or, in other words, with different abstract shapes. Here, we formalize the concept of abstract shapes and introduce their efficient computation. Each shape of an RNA molecule comprises a class of similar structures and has a representative structure of minimal free energy within the class. Shape analysis is implemented in the program RNAshapes. We applied RNAshapes to the prediction of optimal and suboptimal abstract shapes of several RNAs. For a given energy range, the number of shapes is considerably smaller than the number of structures, and in all cases, the native structures were among the top shape representatives. This demonstrates that the researcher can quickly focus on the structures of interest, without processing up to thousands of near-optimal solutions. We complement this study with a large-scale analysis of the growth behaviour of structure and shape spaces. RNAshapes is available for download and as an online version on the Bielefeld Bioinformatics Server.
(Abstract only) The databases with the APASS DR9, Gaia DR1, and the Pan-STARRs 3pi DR1 data releases are publicly available for use. There is a bit of data-mining involved to download and manage these reference stars. This paper discusses the use of these databases to acquire accurate photometric references as well as techniques for improving results. Images are prepared in the usual way: zero, dark, flat-fields, and WCS solutions with Astrometry.net. Images are then processed with Sextractor to produce an ASCII table of identifying photometric features. The database manages photometics catalogs and images converted to ASCII tables. Scripts convert the files into SQL and assimilate them into database tables. Using SQL techniques, each image star is merged with reference data to produce publishable results. The VYSOS has over 13,000 images of the ONC5 field to process with roughly 100 total fields in the campaign. This paper provides the overview for this daunting task.
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