Indiana University, University of Rome, Yale University, Leiden University, International Center for Theoretical Physics, University of Paris-Sud
Informatics, Complex Network Science and System Research, Physics, Statistics, Epidemics
188.8.131.52 Burst Detection
A scholarly dataset can be understood as a discrete time series: in other words, a sequence of events/ observations which are ordered in one dimension – time. Observations exist for regularly spaced intervals, e.g., each month or year.
After you add manually this information, save this .csv file somewhere in your computer. Load back this .csv file into Sci2 using 'File > Load'. Select 'Standart csv format' int the pop-up window. A new table will appear in the Data Manager. To visualize these table that contains the results of the Burst Detection algorithm, select the table you just loaded in the Data Manager and run 'Visualization > Temporal > Temporal Bar Graph' with the following parameters:
Horizontal Temporal bar graphs are used to visualize numeric data over time, generating labeled horizontal bars. A PostScript file containing the horizontal bar graph will appear in the Data Manager.
As expected, a larger number of bursts appear, and the new bursts have a smaller weight that those depicted in the first graph. These smaller, more numerous bursting terms permit a more detailed view of the dataset and allow the identification of trends. The "protein" burst starting in 2003, for example, indicates the year in which Alessandro Vespignani started to work with "protein-protein interaction networks," while the burst "epidem" - also from 2001 - is related to the application of complex networks to the analysis of epidemic phenomena in biological networks.
184.108.40.206 Updating the Vespignani Dataset
The original dataset for Alessandro Vespignani was created in 2006. If you wish to update the dataset to gain an understanding for how his research has changed and evolved since 2006 you can obtain a new dataset from the from Web of Science, see 220.127.116.11 ISI Web of Science. However, another way to obtain an individual researcher's publication information is to use their Google Scholar profile, if they have one. One of the biggest benefits to using a Google Scholar profile is that you will get publications not indexed in Web of Science, such as some book chapters. In this example, we will obtain the publication information for Alessandro Vespignani using Google Scholar:
Open Google Scholar in a web browser and search for "Alessandro Vespignani":
If the author or investigator you have searched has a Google Scholar profile, you will see a link to their profile at the top of the results page:
Keep in mind that not every author you search will necessarily have a Google Scholar profile, but for those that do this is a very useful way to get their publication information. Click on the link to view Alessandro Vespignani's profile, and then select all publications and click the export button at the top of his publication list to export the citation information:
The easiest way to import the citation data into Sci2 is to export the data as a CSV file:
After you have specified the export format you can save the CSV file to your desired location by clicking the "Export all articles by Alessandro Vespignani" button. Save the file do your desktop and then load it into Sci2 in the standard CSV format:
Once the data is in Sci2, you will need to normalize the text for the titles before you can run Burst Detection. Run 'Preprocessing > Topical > Lowercase, Tokenize, Stem, and Stopword Text' and select the title parameter:
After you normalize the text for the title field you will notice a "with normalized Title" file in the data manager. You will likely need to edit this file before you can run Burst Detection. Right click on the file in the data manager and select view:
This will open the dataset in Excel (or you preferred spreadsheet editor). You will notice that the Lowercase, Tokenize, Stem, and Stopword Text algorithm has place brackets around the years. You will need to remove these before you can run the Burst Detection algorithm. In Excel, hit 'Ctrl-F' on the keyboard. This will bring up the Find and Replace tool. Highlight the column of years and then perform a find and replace:
You will have to repeat this for the other bracket symbol. This will essentially allow you remove the brackets around the years. Next you will need to remove those publications for which there is no year information. Burst Detection will not run if there are empty values in the date column. You can search for the publications and find the proper date, but the year value could be empty because these are forthcoming publications. In this example, we will just remove all publications without a value in the year column:
You will need to save this file to your desktop and re-load it into Sci2. Then, select the file you have just loaded and run 'Analysis > Topical > Burst Detection' and enter the following parameters:
This will result in a "Burst detection analysis (Year, Title): maximum burst level 1" file in the data manager, right click on this file to view the data:
You will need to edit the data before you can run the Temporal Bar Graph algorithm to visualize the results of the burst detection. First, you should make sure every record has an "End" date or else the Temporal Bar Graph will not run properly. We know that this dataset contains records that are labeled with year of 2013, so that will be our end date for those bursts that are still continuing:
Before you can visualize the results with the Temporal Bar Graph it is important to know that if you want to size bars based on weight, the weight value will be distributed across the length of the burst. In other words, the total area of the bar corresponds to the weight value. So you can have a bar with a high weight value that appears thinner when compared to bar with a lower weight value if the former burst occurs over a longer period than the latter. Finally, before you visualize this dataset, you can add some categories to allow you to color your bars. For example you can sort the records from largest to smallest based on the "totalweight" column and assign strong, medium, and weak categories to these records based on the "totalweight" values:
Now, save the file to your desktop and reload it into Sci2 in the standard CSV format and run 'Visualization > Temporal > Temporal Bar Graph', entering the following parameters:
Note, selecting the "Simplified Layout" option no legend will be created for the map, allowing you to create your own legend that will be accurate based creating new weight values. To learn how to create a legend for your visualization see 2.4 Saving Visualizations for Publication.
To view the visualization, save the file from the data manager by right-clicking and selecting save:
Make sure to save the visualization as a PostScript file:
Save the PostScript file to your desktop, and if you have a version of the Adobe Creative Suite on your machine you can simply double-click the PostScript file to launch Adobe Distiller and automatically convert the PostScript file into a PDF for viewing. However, if you do not have a copy of the Adobe Creative Suite installed on your machine, you can use an online version of GhostScript to convert PostScript files to PDF files: http://ps2pdf.com/. The resulting visualization should look similar to the following:
18.104.22.168 Visualizing Burst Detection in Excel
Its possible to generate a visualization for burst analysis in MS Excel. For this, open the results of the first burst analysis conducted ('Burst detection analysis (Publication Year, Title): maximum burst level 1') in MS Excel, by right clicking on this table in the Data Manager and selecting View.