AI-READI · Aydawka · Jun 5, 2025 · Jun 4, 2025 · Jun 4, 2025 · Jun 4, 2025
@@ -109,6 +109,8 @@ The ASCII .hea file includes some of the same elements as the table mentioned ab
 
 ### Additional resources
 
+**Consider starting with our example [Jupyter notebooks](https://github.com/AI-READI/ai-readi-notebooks)** to explore the dataset further.
+
 There is more than one protocol for obtaining and reporting ECG information. This study used the common 12-lead protocol which is described in several resources.
 
 - [General information on the ECG protocols and reading an ECG](https://en.wikipedia.org/wiki/Electrocardiography)

@@ -142,3 +142,7 @@ Resources to learn more about SSSOM:
 ## Race, ethnicity, gender, and date/month of birth
 
 Note that the fields for race, ethnicity, gender, and date/month of birth are all coded as '0' in the person.csv file because this information is omitted from the data. Birthday has to be released with birth year only per the HIPAA law for Safe Harbor. Additionally, we have decided to remove race, ethnicity, and gender information from this public set of the AI-READI dataset to prevent stigmatizing findings. More details are available in the [healthsheet](https://docs.aireadi.org/docs/1/dataset/healthsheet) regarding which variables were removed.
+
+## Additional resources
+
+**Consider starting with our example [Jupyter notebooks](https://github.com/AI-READI/ai-readi-notebooks)** to explore the dataset further.
@@ -163,3 +163,8 @@ print(‘ Data saved to hba1c.csv')
 | value_source_value            | Source value for the measurement value      | 65                          | 5.7                 |
 | measurement_event_id          | Unique identifier for the measurement event | 0                           | 0                   |
 | meas_event_field_concept_id   | Concept ID for the measurement event field  | 0                           | 0                   |
+
+
+## Additional resources
+
+**Consider starting with our example [Jupyter notebooks](https://github.com/AI-READI/ai-readi-notebooks)** to explore the dataset further.
@@ -153,3 +153,7 @@ The MoCA data is organized per subject (person_id) and within each subject block
 | Years of education [#] - Reported   | Subject's Education      | 42528764               |
 | Time to complete survey             | Total Time               | 1988422                |
 | Montreal cognitive assessment score | Total Score              | 37174522               |
+
+## Additional resources
+
+**Consider starting with our example [Jupyter notebooks](https://github.com/AI-READI/ai-readi-notebooks)** to explore the dataset further.
@@ -73,3 +73,8 @@ The .csv files are designed for easy opening in Python and/or Jupyter Notebooks.
 | ---------------- | --------------------------------------- | ------------------------------------------- | ---------------------- |
 | 1                | Right Foot: Number of correct responses | Right Foot - Felt: (10.0)                   | 2005200159             |
 | 2                | Left Foot: Number of correct responses  | Left Foot - Felt: (8.0)                     | 2005200161             |
+
+
+## Additional resources
+
+**Consider starting with our example [Jupyter notebooks](https://github.com/AI-READI/ai-readi-notebooks)** to explore the dataset further.
@@ -165,3 +165,8 @@ print('Data saved to height_cm.csv')
 | value_source_value            | Source value for the measurement value      | (can be empty)      | (can be empty)      |
 | measurement_event_id          | Unique identifier for the measurement event | 0                   | 0                   |
 | meas_event_field_concept_id   | Concept ID for the measurement event field  | 0                   | 0                   |
+
+
+## Additional resources
+
+**Consider starting with our example [Jupyter notebooks](https://github.com/AI-READI/ai-readi-notebooks)** to explore the dataset further.
@@ -330,3 +330,7 @@ The observation.csv data file is organized per subject (person_id) and within ea
 | value_source_value            | Source data value                                            | blank                                                     |
 | observation_event_id          | Observation event identifier                                 | 0                                                         |
 | obs_event_field_concept_id    | Observation event field concept                              | 0                                                         |
+
+## Additional resources
+
+**Consider starting with our example [Jupyter notebooks](https://github.com/AI-READI/ai-readi-notebooks)** to explore the dataset further.
@@ -35,6 +35,7 @@ Humidity indicates the amount of water vapor in the air, reflecting an environme
 
 Volatile Organic Compounds (VOCs) are a group of organic chemicals that easily evaporate into the air at room temperature. They are emitted from common foods such as wine, cheese, vinegar, salad dressings, and bananas. Non-food sources include paints, cleaning products, furniture, and building materials.
 The environmental sensor measures VOC levels and assigns a VOC Index. It assesses the current VOC status relative to recent history in a room, akin to our nose adjusting to air changes. Using a 24-hour moving average as a baseline, the VOC Index detects variations in VOC levels. This index ranges from 0 (low or none noticeable) to a maximum of 500.
+For additional information, please refer to https://sensirion.com/resource/application_note/voc_index.
 
 ### Nitrogen Oxide (nox)
 
@@ -140,3 +141,7 @@ Before issuing the device, the 3 digit unit number from the back of the sensor i
 | screen        | State of the measurement screen                       | boolean [0 to 1]        | 0-screen is off; 1-screen is on | 0                   |
 | ff            | Flicker detection                                     | integer [0 to 2000]     | Hz                              | 1                   |
 | inttemp       | Internal case temperature measured on the RTC board   | float [0.00 to FF.FF    | degrees C                       | 23.50               |
+
+## Additional resources
+
+**Consider starting with our example [Jupyter notebooks](https://github.com/AI-READI/ai-readi-notebooks)** to explore the dataset further.
@@ -178,3 +178,7 @@ DICOM Tags and values **(this represents a subset of the available tags)**
     </tr>
   </tbody>
 </table>
+
+## Additional resources
+
+**Consider starting with our example [Jupyter notebooks](https://github.com/AI-READI/ai-readi-notebooks)** to explore the dataset further.
@@ -412,3 +412,7 @@ In addition to ensuring files are NEMA compliant, further processing in the foll
 | (0028,0011)   | Columns                      | US  | 1   | 2      | 512                          |
 | (0028,0100)   | Bits Allocated               | US  | 1   | 2      | 6                            |
 | (0022,0039)   | Ophthalmic Image Orientation | CS  | 1   | 6      | LINEAR                       |
+
+## Additional resources
+
+**Consider starting with our example [Jupyter notebooks](https://github.com/AI-READI/ai-readi-notebooks)** to explore the dataset further.
@@ -763,3 +763,7 @@ FLOW CUBE: **triton macula 12x12 octa**
 | (0028,0008)   | Bits Allocated          | US  | 1   | 2      | 8                            |
 | (0022,0035)   | Columns                 | US  | 1   | 2      | 512                          |
 | (0028,0010)   | Rows                    | US  | 1   | 2      | 992                          |
+
+## Additional resources
+
+**Consider starting with our example [Jupyter notebooks](https://github.com/AI-READI/ai-readi-notebooks)** to explore the dataset further.
@@ -424,3 +424,7 @@ In addition to ensuring files are NEMA compliant, further processing in the foll
 | Number of Frames          | (0028, 0008) | Number of frames in a Multi-frame Image.                                                 |
 | Rows                      | (0028, 0010) | Number of rows in the image.                                                             |
 | Columns                   | (0028, 0011) | Number of columns in the image.                                                          |
+
+## Additional resources
+
+**Consider starting with our example [Jupyter notebooks](https://github.com/AI-READI/ai-readi-notebooks)** to explore the dataset further.
@@ -1086,3 +1086,8 @@ There may be cases in which certain participants do not have certain files, e.g
 <!-- | average_active_calories_kcal   | Average number of calories burned during the recording period  | 194.11                                                                                         | -->
 <!-- | sensor_sampling_duration_days  | How many days the participant wore the fitness tracker         | 12                                                                                             | -->
 <!-- | fitness_manufacturer_model     | Model of the fitness tracker                                   | Garmin Vivosmart 5                                                                             | -->
+
+
+## Additional resources
+
+**Consider starting with our example [Jupyter notebooks](https://github.com/AI-READI/ai-readi-notebooks)** to explore the dataset further.
@@ -330,3 +330,8 @@ More information can be found here:
 | Glucose_sensor_sampling_duration_days | Number of days of glucose sensor sampling            | 10            |
 | Glucose_sensor_id                     | The number printed on the back of each Dexcom sensor | AB12345678    |
 | Cgm_manufacturer_model                | Manufacturer's model name of the device              | Dexcom G6     |
+
+
+## Additional resources
+
+**Consider starting with our example [Jupyter notebooks](https://github.com/AI-READI/ai-readi-notebooks)** to explore the dataset further.