Challenge and Selection :   Visualization 

 I work in an IT department of a pharmaceutical company and part of our projects is to prepare data for   scientists such that they can include it in their hypothesis process . Such projects can also be called   Literature Based Discovery ( LBD ) projects . 

 When I was recently asked if I wanted to work on a new LBD project , I did n't hesitate to join the group .   However , after the first conversation it soon became clear that our established methods would not work   here . We had a huge toolbox , which offered different solutions to various problems , but we had nothing in   our toolbox , which could only contribute in part to the success of this LBD project . I was surprised that we   did n't foresee such a project and therefore did n't extend our toolbox . The question that came up here was   whether this was an “ one off " LBD project request that was only relevant for a very small group of   scientists , or whether other groups of scientists would follow . 

 Application 

 For this reason , I suggested first to find and determine what exactly one would like to achieve with this   LBD project type , and in a further step , one should see how one could achieve it . This knowledge should   then help to make decisions about which new tools to include in our toolbox . We decided to sketch as   much as possible and afterwards to capture the pictures . We did n't want to create functional   specifications , but to visualize the problem in conversations with future users . 

 We mainly worked on electronic whiteboards because we were on different sites . We played through   different scenarios and then photographed them . We thought about the output of future new LBD project   types and decided to produce " synthetic " data as input to such a hypothetical LBD project . This would   very time consuming but the results were very helpful for the next visualization exercise . We created new   " synthetic " reports based on this data and these could be reviewed in virtual whiteboard sessions . 

 Insight and Approach 

 After all we had a good understanding of the new tools we would have to develop in order to implement   this new type of LBD project . The visualization of the processes using " synthetic " data helped everyone to   experience " live " what it means to have the data and what questions could be answered . On the basis of   this knowledge , decisions could be made about which tools to add to the toolbox . Based on the play- through process , one can assume that we have selected the relevant tools . 

 It must be said that the creation of " synthetic " data involves a great deal of effort . We were therefore only   able to create a very small dataset because we were limited in time . However , it turned out afterwards   that the whiteboard discussions with the synthetic data helped a lot to get a " more holistic " overview . All   participants were able to experience live what new possibilities they would have if they had the data in   front of them . 


EMAIL: 
ID_NUM: 
NAME_STUDENT: 
PHONE_NUM: 
STREET_ADDRESS: 
URL_PERSONAL: 
USERNAME: 

### Task: You are a PII (Personal Identity Information) Detection. Given a essays written by students, you have to identify ONLY the following:
NAME_STUDENT - The full or partial name of a student that is not necessarily the author of the essay. This excludes instructors, authors, and other person names.
EMAIL - A student’s email address.
USERNAME - A student's username on any platform.
ID_NUM - A number or sequence of characters that could be used to identify a student, such as a student ID or a social security number.
PHONE_NUM - A phone number associated with a student.
URL_PERSONAL - A URL that might be used to identify a student.
STREET_ADDRESS - A full or partial street address that is associated with the student, such as their home address.
### Input: Challenge and Selection :   Visualization 

 I work in an IT department of a pharmaceutical company and part of our projects is to prepare data for   scientists such that they can include it in their hypothesis process . Such projects can also be called   Literature Based Discovery ( LBD ) projects . 

 When I was recently asked if I wanted to work on a new LBD project , I did n't hesitate to join the group .   However , after the first conversation it soon became clear that our established methods would not work   here . We had a huge toolbox , which offered different solutions to various problems , but we had nothing in   our toolbox , which could only contribute in part to the success of this LBD project . I was surprised that we   did n't foresee such a project and therefore did n't extend our toolbox . The question that came up here was   whether this was an “ one off " LBD project request that was only relevant for a very small group of   scientists , or whether other groups of scientists would follow . 

 Application 

 For this reason , I suggested first to find and determine what exactly one would like to achieve with this   LBD project type , and in a further step , one should see how one could achieve it . This knowledge should   then help to make decisions about which new tools to include in our toolbox . We decided to sketch as   much as possible and afterwards to capture the pictures . We did n't want to create functional   specifications , but to visualize the problem in conversations with future users . 

 We mainly worked on electronic whiteboards because we were on different sites . We played through   different scenarios and then photographed them . We thought about the output of future new LBD project   types and decided to produce " synthetic " data as input to such a hypothetical LBD project . This would   very time consuming but the results were very helpful for the next visualization exercise . We created new   " synthetic " reports based on this data and these could be reviewed in virtual whiteboard sessions . 

 Insight and Approach 

 After all we had a good understanding of the new tools we would have to develop in order to implement   this new type of LBD project . The visualization of the processes using " synthetic " data helped everyone to   experience " live " what it means to have the data and what questions could be answered . On the basis of   this knowledge , decisions could be made about which tools to add to the toolbox . Based on the play- through process , one can assume that we have selected the relevant tools . 

 It must be said that the creation of " synthetic " data involves a great deal of effort . We were therefore only   able to create a very small dataset because we were limited in time . However , it turned out afterwards   that the whiteboard discussions with the synthetic data helped a lot to get a " more holistic " overview . All   participants were able to experience live what new possibilities they would have if they had the data in   front of them .
### Output: 