SCiO Researcher accounts are able to download SCiO spectrum for analysis.

With a Researcher license, you can download your spectrum to use with your own tools. The options for download are .csv (comma separated values) and JSON files.

  1. To download, select a Data Collection.
  2. Within the data collection, select the Samples tab. Click Download All
  3. Select .csv or JSON as the file type and SCiO Lab will prepare the collection for download.
  4. You can continue working and the collection will be downloaded to your computer in the background.
Download your records

Download your spectrum from SCiO Lab


Every SCiO Lab account has an associated user profile.

Making Account Changes

Need to make changes to your user details, password and/or security question?

No problem, we made it painless and fast!

  1. Log in to SCiO Lab at:
  2. Click on your user name in the top hand side right of the screen.
  3. Select my profile.
  4. Click Edit to change your user details and finish your update,
    Select Change Password / Change Security Question from the list of profile values and make your changes.
  5. You’re all done!

Change Profile_account

Don’t forget, SCiO Lab and SCiO Lab Mobile use a unified user profile. Making a change to your password here will change your SCiO password everywhere.

Deleting a Data Collection removes all of the custom attributes and values from the SCiO Universe database.

To Delete a Data Collection:

  1. Select Delete Collection from the Data Collection page menu.
  2. Enter the exact name of the Data Collection you wish to delete (this confirmation step ensures you don’t accidentally delete a collection you wanted to keep).
  3. Select the Remove button. If the name you typed is an exact match to the Data Collection name, the Data Collection will be permanently deleted.

Always double check before confirming a deletion.
Make sure you double check the name of the Data Collection you wish to delete before confirming. Once a Data Collection has been deleted, it cannot be retrieved and must be recreated.

Now that your data collection has been completed, observed, and scrubbed, it is time to actually create your model. Click Create Model and you’ll be presented with a visual guide as to the success (or failure) of your model. You’ll also receive suggestions from us as to what can be improved.

Note: Some iteration may be required before you get a successful model.

Successful Estimation Model

Successful estimation model

R¬2 is >0.8, most of the data points are within the 20% error margin (light gray lines)

Unsuccessful Estimation Model

Unsuccessful estimation model

Low R2, random scatter of data points (no trend)

Good Classification Model

Good Classification Model

Diagonal is bright green, no red blocks meaning no confusion between types. F1 is 1 (perfect score)


Poor Classification Model

Poor Classification Model


There is a mix of red & green blocks showing a  lot of confusion between types


If you need help with your analysis, you can always contact us at Remember that by asking for our help with model or data collection analysis means you grant us permission to access your SCiO Lab account to help you.

Data Scrubbing is the process by which noise, outliers and mistakes are identified and eliminated. Data scrubbing is required for accurate modeling.

Using the records and spectra views, fix any meta-data errors and remove or address the chart outliers. Use the processed and normalized filters to help you find the outliers in the spectra. In the screen below, the outliers are highlighted.
Data Scrubbing_Spotting Outliers

Data scrubbing and Model Creation are an iterative process.
In order to build the best models, data scrubbing should be done until the outliers and anomalies are removed from your collection.

Once you have sufficiently scrubbed your data, it is time to create the model.

While both SCiO Lab Mobile and SCiO Lab can be used to observe data, SCiO Lab is easier to use for this purpose as the view screen is larger, and you can download your data and build models only from SCiO Lab.

Multiple views are available to observe the data:

Scan View
Single Record_Hard Cheese

 Sample View

Observing data_screen2

 Spectrum View

Observing data_screen2

Use the scan view and check for accuracy in each scan. Use the sample view to see multiple scans of the same sample at one time.  The accuracy of your attributes is critical to the success of your model.
Use the spectrum view to look for outliers and see trends.

Once you have observed your data, the next step is to scrub it.

Once you have collected enough samples to consider your Data Collection complete, now it is time to test your model. Your Models will be used as the basis of your future SCiO Applications. If you are a SCiO Developer that only wants to build mobile applications, but you don’t want to create you own models, don’t worry! You’ll be able to build your apps on top of the SCiO Lab Models we release and share.

For more information, see Observing Data and Data Scrubbing.

Data Collections are populated by Samples. Samples are created from the scans + meta-data added to every scan made with your SCiO Sensor. Both automatic and sample attributes are assigned to each sample scan.
Attributes_record screen

Automatic attributes are provided by SCiO and cannot be changed or excluded from any samples. They include things like the date and your location.

Where does the automatic attribute information come from?

The date and location provided are based on your smartphone at the time of the scan. The temperature information is collected by your reading at the time of the scan.

Sample Attributes are the attributes that you create and provide for each sample within a Data Collection to help you distinguish and provide value to your sample field.


To create sample attributes:

  1. Select the +Add Attributes button within the Data Collection.
  2. Select a name for the Sample Attribute.
  3. Select a TYPE for the Attribute from the drop down list.

Depending on the TYPE of Attribute you create, the remaining Attribute fields will automatically adjust.

For example, when you select NUMERIC as the TYPE of Attribute, you’ll need to also select the Numeric Type as well as the relevant UNIT of measurement as shown in the sample below.


Sample Attributes can be built from the following fields:

Attribute TypeDescriptionSub FieldsNotes
String Open TextNoneMake sure there is meaningful information here or you’ll have a hard time making use of this collection. Uses of this attribute might be brand name, manufacture date or other unique attributes that don’t fit well into a structured list.
List Closed set of valuesWhen List is selected, List Type is available. Within List Type, there are three types of lists: Countries Currencies Custom Only when List is selected is the additional field of List Type available. To add a custom List, select custom from the List Type, and enter the values of your list in the Items field.
Number Options: Area Concentration Energy Intensity Length Number of Particles Power Temp Time Period Unitless (arbitrary unit) Volume WeightEach type of quantity contains the related subfields as a Unit when selected. For example, if you select Concentration, you'll need to then select %, ppm or Molar. If you select Energy, you’ll need to select W or mW.
PhotoImage of the sample
DateDate related attributeWhile the date a sample was taken is an automatic attribute, this attribute refers to an additional date you might want to associate such as expiration date or purchase date, etc.
Date and TimeDate and time related attributeWhile the date and time a sample was taken are automatic attributes, this attribute refers to an additional date and time you might want to associate. For example, expiration date and time, or purchase date and time, etc.

Ready to build your new data collection?

Add a new Data Collection:

  1. Decide on object or material you wish to scan and gather your samples.
  2. Navigate and log in to SCiO Lab and select the +Add Collection button.
  3. From the Add New Collection window, enter a NAME for your new collection. In the sample shown, we used Apples.
  4. Give your new data collection a description that is as meaningful and complete as possible including relevant research data so you can easily distinguish it from other collections later on.
  5. Use TAGS to associate your collection with keywords in a useful way. Remember that every scan helps us to build the SCiO Universe Database, so tagging effectively helps everyone.