# Format of Participant-Level Data

There are three options for participant-level data format from PIC-SURE:&#x20;

1. Dataframe or CSV
2. Time Series Data
3. PFB

## 1. Dataframe or CSV Format

Participant-level data brought to an analysis platform using the Dataframe or CSV format will be exported in a single table. In this table, each row represents a participant and each column represents a variable. The variables included in the table are those added as filters to the query and exported with the "Add Variable" action.&#x20;

### Example Table: Dataframe Format

| patient\_id | \Nhanes\demographics\SEX\\ | \Nhanes\demographics\AGE\\ | \Nhanes\laboratory\acrylamide\\ |
| ----------- | -------------------------- | -------------------------- | ------------------------------- |
| 1001        | male                       | 31                         | Yes                             |
| 1003        | male                       | 56                         | No                              |
| 1004        | female                     | 83                         | Yes                             |
| 1005        | female                     | 26                         | Yes                             |

* `patient_id` is a PIC-SURE-generated participant identifier.&#x20;
* Each column is labeled with the concept path of the variable.

## 2. Time Series Data

## 3. Portable Format for Biomedical Data (PFB)

Participant-level data brought to an analysis platform using the PFB format will be handed off in a single file with two tables: the data and data dictionary tables.&#x20;

The **data** will be labeled as `pic_sure_patients_[dataset ID]` and show the participant-level data from PIC-SURE. The columns of this table are the variables, which are labeled as the PIC-SURE concept paths.&#x20;

The **data dictionary** will be labeled as "pic\_sure\_data\_dicitonary\_\[dataset ID]" and will contain information about the variables that have been exported. This includes information about each variable, such as the concept path, description, and display name. The data dictionary also includes DRS URIs, or links to the original data file, which can be used to access the files for further analysis in BDC analysis platforms.

### Example Table: Data Table of PFB

| patient\_id | \example\_study\demographics\sex\\ | \example\_study\demographics\age\\ | \example\_study\exam1\asthma\\ |
| ----------- | ---------------------------------- | ---------------------------------- | ------------------------------ |
| 1001        | Male                               | 31                                 | Never had asthma               |
| 1003        | Male                               | 56                                 | Currently has asthma           |
| 1004        | Female                             | 83                                 |                                |
| 1005        | Female                             | 26                                 | Currently has asthma           |

* `patient_id` is a PIC-SURE-generated participant identifier.&#x20;
* Each column is labeled with the variable's concept path. For many BDC studies, this is formatted as `\phs (study accession number)\pht (dataset accession number)\phv (variable accession number)\variable name\`. For more information, please refer to [Data Organization in BDC-PIC-SURE](https://pic-sure.gitbook.io/pic-sure/explore-data/prepare-for-analysis/broken-reference).&#x20;

{% hint style="info" %}
**A tip with Data "Missing-ness":**

In PIC-SURE output, an empty cell indicates that there is no data available for that variable and participant. This is demonstrated with participant 1004 above; there is an empty cell in the `asthma` column. This means that there is no information available for that participant for asthma status.&#x20;

This is different than cells with `NA` values. If a cell contains `NA`, this was recorded by the study submitters. Depending on the context of the `NA` value, this could be useful information for analysis.&#x20;
{% endhint %}

### Example Table: Data Dictionary Table of PFB

| concept\_path                      | dataset        | description                                 | display\_name | DRS\_URI                       |
| ---------------------------------- | -------------- | ------------------------------------------- | ------------- | ------------------------------ |
| \example\_study\demographics\sex\\ | example\_study | Participant sex recorded by the study       | Sex           | drs\://example.com/unqiueID123 |
| \example\_study\demographics\age\\ | example\_study | Participant age recorded by the study       | Age           | drs\://example.com/uniqueID123 |
| \example\_study\exam1\asthma\\     | example\_study | Exam 1: What is your current asthma status? | Asthma status | drs\://example.com/uniqueID456 |

* Each row of the data dictionary table corresponds to a column in the data table.&#x20;
* DRS URIs link to the study files from which the variable originated.&#x20;
