All Entries in the Category "Working with Search Results"
Results lists can be sorted in both descending and ascending order.
This option, which is currently undergoing revision, takes into account both the closeness of the query match and the paper’s citation count.
This option sorts based on a paper’s publication date (month and year).
This option sorts based on a paper’s number of citations.
This option is a measure of recent popularity. It sorts on the 90-day read count of a paper: the number of times a paper has been accessed in the ADS in the last 90 days.
Use this button to export your search results in different formats. Currently available options are ADS Classic, BibTeX, AASTeX and Endnote.
The Citation Metrics Report
The Citation Metrics Report is an overview of citations, usage and derived indicators for a set of ADS records. This means that all quantities in this overview are solely based on data from the ADS. For each paper, a “read” is counted if an ADS user runs a search in our system and then requests to either view the paper’s full bibliographic record or download the fulltext. Please note that in computing readership numbers we attempt to remove log entries generated by robots, users coming to an ADS record from an external search engine, and multiple clicks from the same user. The ADS has an automated procedure that attempts to match the references in the bibliography of a record to existing records in the ADS database. If an ADS record has N references associated with it, it means that the corresponding paper has at least N references in its bibliography, but potentially more if there were references that our procedure was unable to match. Keep this in mind when regarding citation and citation-derived information.
How to View Metrics:
- To access the metrics view for a list of results, on the search results page go to the Explore button –> Citation Metrics.
- To access metrics for a single article, go to the article detail view and find the metrics in the left-hand navigation.
(Note that we do not remove self-citations based on author name, because of author disambiguation problems. We apply a list-based removal of self-citations.)
For a list of N papers (i=1,…N), where Nauthi is the number of authors for publication i and Ci the number of citations that this paper received, the normalized citation count for each article is Ci/Nauthi, and the ‘normalized citations’ for this list of N papers is the sum of these N numbers.
Article Usage Metrics
The ADS collects information about the rate that articles have been accessed in our system. This data includes both short-term access data (recent views) and records of article access rates over the years. Article usage rates include both page views of the article detail page in the ADS, and full-text downloads of the article. The value for just the full-text downloads is presented as a seperate field in the metrics page.
This data encompasses the past 90 days of access data for articles in the ADS, including full-text downloads. This value also includes read and download information from arXiv. You can sort a list of ADS search results by this value, or view it in the results graph visualization.
Metrics for Indices
Hirsch’s h-index is the largest number H such that H publications have at least H citations. It attempts to measure the productivity and impact of a researcher in a single number. Wikipedia entry
The m-index is the h-index divided by the time (years) between the first and most recent publication.
iN-index (where N is 10 or 100)
The iN-index is the number of publications with at least N citations.
Given a set of articles ranked in decreasing order of the number of citations that they received, the g-index is the (unique) largest number such that the top g articles received (together) at least g2 citations.
The total research impact of a scholar (tori) is calculated using the reference lists of the citing papers, where self-citations are removed. The contribution of each citing paper is then normalized by the number of remaining references in the citing papers and the number of authors in the cited paper. The tori-index is defined as the amount of work that others have devoted to his/her research, measured in research papers (see Pepe & Kurtz 2012).
The research impact quotient (riq) equals the square root of the tori-index, divided by the time between the first and last publication, multiplied by 1000 (see Pepe & Kurtz 2012).
Read10 is the current readership rate for all an individual’s papers published in the most recent ten years, normalized for number of authors (see Kurtz et al. 2005).
Paper Count Metrics
Normalized paper count
For a list of N papers (i=1,…N), where Nauthi is the number of authors for publication i, the normalized paper count is the sum over 1/Nauthi
The author network detects groups of authors and connections between those groups within a set of results.
An image of the author network for John Huchra with the link overlay activated.
How the network is made
The author network takes the top 200 most frequently appearing authors within your result set, measures the frequency of collaboration between them, and displays color-coded groups of authors organized around a center point.
How to use it
Clicking on an inside edge of a group will show you all papers from that group. Clicking on an outside edge, or an author name, will show you all papers by that particular author.
Quickly drill down to relevant results To quickly narrow down your search results to papers from a certain collaboration group, select the group, click the “add to filter” button, and filter your ADS search.
Visually explore your results set To get an at-a-glance overview of a scientist’s career, search “author:LastName,FirstName” in the ADS, view the network, and quickly see an organized overview of important collaborators.
Answer specific questions about the results set
- To find authors who collaborate not only within their group but outside of it, check the “view link overlay” box and see which authors tend to collaborate with authors in other groups.
- To see the collaborations that have been most fruitful in terms of citations yielded, under “Size wedges based on”, click “Paper Citations”.
The paper network detects groups of papers based on shared references between those papers. In general, papers with many shared references will tend to have similar topics.
Paper network for John Huchra.
How the network is made
The paper network creates groups of papers that share a significant number of references, and names those groups by looking for shared, unique words in their titles.
How to use it
View your results grouped by sub-topic Because papers with similar references tend to be on similar subjects, you can see a rough guide to the main topics within your search results.
Find the most significant papers on different topics Click a group to see information about the most cited papers from that group in the right info pane.
Find relevant papers NOT in your result set Clicking on a group will also show you in the right pane the most commonly referenced papers from a group. Often these papers do not appear in your actual result set, and yet given their influence they might be highly relevant to your area of interest.
When you look at a list of ADS search results, you can sort by date published, by citation count, or by recent popularity of the article in ADS, but you cannot see all of these dimensions at once, and you cannot easily see outliers. The results graph is a customizable scatter chart that allows you to assign values to the x and y axes as well as to the radius of the circle representing a paper.
How to use it
Filter your search results Mouse over a circle to read information about the paper, or drag a square around a number of circles and click on the green “submit filter” button below the graph to limit your search results to the selected papers.
Find newly popular papers The default graph shows the recent views (the number of times a paper has been accessed in the ADS in the last 90 days) as the y-axis value. In general, graphs will show a trend of decreasing reads over time as an article ages. If any paper has a higher read count than predicted by the general trend, it might be worth investigating further.
The word cloud shows you frequently appearing and unique words in your search results.
How the word cloud is made
The word cloud takes words from the titles and abstracts of your search results, counts their frequencies and compares them to the same word’s frequency across the entire ADS corpus.
How to use it
- Move the slider to the left of the word cloud towards “unique” to see those words that appeared relatively frequently in your results but rarely in the rest of ADS.
- Move the slider to the left of the word cloud towards “frequent” to see those words that appear frequently in your results, regardless of how often they appear in the rest of ADS.
Instructions on Downloading Graphics as High-Quality PNGS in Chrome
- Install the SVG Crowbar 2 Bookmarklet
- Open the graph in ADS (currently the paper and author networks have the best support)
- Click the SVG Crowbar icon, you will see something that looks a bit messy, like this:
- Click on the two bottom buttons: #network-viz-main-chart to download the main chart, and #network-viz-time-series to download the accompanying time series graph.
Following each article in a search result there are symbols which indicate the availability of what information is available for the article. These can be used as direct links to the information or as quick indicators of what you can find in the article.
Information is grouped into three categories:
Available full text sources: here you will find links to Publisher Article, Publisher PDF, arXiv e-print, ADS scanned article or ADS PDF. Links that are green mean that this article is “Open Access”, meaning that there is no subscription necessary to access the full text through this link. ADS gives all available links to provide the user with the choice that best suits him/her. Please note that a lot of these links go to resources outside of the ADS and the user may be prompted for a username or password. The external links can be configured to work with your library subscriptions.
- References and Citations: here you will find links to the references in the article and citations to the article. The number of references and citations is indicated by the number in the parentheses. Please note that as with all abstracting and indexing services, the reference and citation lists in the ADS are not complete. There are several sources of incompleteness of citation lists:
- The ADS doesn’t have the cited article in the database. This happens for instance for most papers appearing in mathematics, chemistry, and geophysics journals.
- Our reference resolver program couldn’t interpret the reference. This may be due to errors or incompleteness in the reference, unusual formatting of it, or simply limitations in our program’s abilities.
- We do not have the reference list for the citing paper. This happens for older articles and for articles in journals and conference proceedings that do not supply us with reference lists.
- We are constantly adding to our references by extracting reference lists from scanned articles, trying to improve reference recognition capabilities, and adding new records to our databases, so this work is an ongoing effort which will cause reference and citation lists to change over time.
- Data Links: here you will find links to Data in the article. We provide links to Archival Data (from data centers including MAST, NExSci, Chandra, PDS) and to SIMBAD and to NED.
Clicking on the title of an individual article will bring you to a page showing the abstract view for the chosen article including Publication data, DOI link, keywords and arXiv identifier. On this page you will find links to lists of:
- Citations to the article
- References in the article
- Co-Reads of the article (papers that have been read by people who read this article)
- Graphics in the article
Export functions will allow you to export the article metadata in available formats (currently BibTEX, AASTex and Endnote.)
You can also generate the metrics for the individual article by selecting metrics from the Analyze list.
In addition you will find links to Full Text Sources (Open Access resources are indicated by an open lock) and Data Products. Also shown will be a list of Suggested Articles which are suggested based upon the information for the chosen article.
The ADS uses bibliographic codes (bibcodes) to identify literature in our database. Using a standard bibliographic format, as explained below, we can easily identify different articles and users can efficiently search for them. The bibcode is a 19 digit identifier which describes the journal article. The format was originally adopted by the SIMBAD and NED projects, and follows the syntax: YYYYJJJJJVVVVMPPPPA where:
- YYYY: Year of publication
- JJJJJ: A standard abbreviation for the journal (e.g. ApJ, AJ, MNRAS, Sci, PASP, etc.). A list of abbreviations is available.
- VVVV: The volume number (for a serial) or an abbreviation that specifies what type of publication it is (e.g. conf for conference proceedings, meet for Meeting proceedings, book for a book, coll for colloquium proceedings, proc for any other type of proceedings).
- M: Qualifier for publication:
- E: Electronic Abstract (usually a counter, not a page number)
- L: Letter
- P: Pink page
- Q-Z: Unduplicating character for identical codes
- PPPP: Page number. Note that for page numbers greater than 9999, the page number is continued in the m column.
- A: The first letter of the last name of the first author.
The fields are padded with periods (.) so that the code is always 19 characters long. The journal is left-justified within its 5 characters, and the volume and page are right-justified. New journal abbreviations should be unique, and follow existing naming conventions. As an example, the bibliographic code: 1992ApJ…400L…1W corresponds to the article: Astrophysical Journal Letters volume 400, page L1.