How to use
The only input NapEasy needs is a list of comma separated PMCIDs. Note that PMIDs (PubMed identifiers) are different to PMCIDs. Should you only have PMIDs to hand, an easy conversion tool is available online. There are two options to use NapEasy:- 1) If you happen to have PMC IDs of papers you want to highlight, just click the "Use PMC IDs" tab, enter the relevant PMC IDs and submit.
- 2) Otherwise, just simply use the "Search PMC" functionality to look up and select papers based on keyword search; the selected papers can then immediately be submitted for highlighting.
Highlight Result
When the job finishes, the result can be visualised directly by following the job result URI generated at the submission (e.g., sample job result). In addition, from the same webpage, the highlighted sentences can then be downloaded in three different formats (XML, JSON, and plain text) for further analysis. The data structure of the downloaded result is explained as follows.Result Data Structure
Essentially, for a given job, the result contains a list of highlighted sentences for each paper within the job. Here, we elaborate the result data structure using a sample job. Although we use the JSON format (see below) here, the structure applies to XML and text formats as well. At the top level, a result contains two attributes - jobid (a GUID identifying the job) and papers (an array, of whom each element is the highlight result of each paper). In this case, we only have one paper in this job. So, the papers contains just one element. For each paper, the result is an object containing three attributes:- pmcid: the PMCID of the paper
- total_sentences: the total number of sentences in the paper
- highlights: an array of highlighted sentences
Each highlighted sentence is an object having four attributes:
- sid: the sentence ID (an integer sequence number from 1 for the first sentence)
- text: the text content of the sentence
- type: the type of the sentence, which can be one of or a combination of three basic types (goal, findings, method)
- score: a double valued score indicating the importance of the sentence (from 0 to 10). The higher the value; the more important the sentence is.
{
"jobid": "b201e1c9-5dcb-425a-8ddc-a5c4ce03ad0f",
"papers": [
{
"pmcid": "PMC5089825",
"total_sentences": 262,
"highlights": [
{
"sid": "27",
"text": "Anti-NMDAR encephalitis is the most common autoimmune encephalitis described so far, 9 with >900 cases identified worldwide since its first description in 2007.",
"type": "findings-method",
"score": 2.045864771554322
},
...
]
}
]
}