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Filter Cart

Viewable at the top right of NDA pages, the Filter Cart is a temporary holder for filters and data they select. Filters are added to the Workspace first, before being submitted to The Filter Cart. Data selected by filters in the Filter Cart can be added to a Data Package or an NDA Study from the Data Packaging Page, by clicking the 'Create Data Package / Add Data to Study' button.

The filter cart supports combining multiple filters together, and depending on filter type will use "AND" or "OR"  when combining filters.

Multiple selections from the same filter type will result in those selections being applied with an ‘OR’ condition. For example, if you add an NDA Collection Filter with selections for both collections 2112 and 2563 to an empty Workspace, the subjects from NDA Collection 2112 ‘OR’ NDA Collection 2563 will be added to your Workspace even if a subject is in both NDA Collections. You can then add other NDA Collections to your Workspace which further extends the ‘OR’ condition.

If a different filter type is added to your Workspace, or a filter has already been submitted to the Filter Cart, the operation then performs a logical ‘AND’ operation. This means that given the subjects returned from the first filter, only those subjects that matched the first filter are returned by the second filter (i.e., subjects that satisfied both filters). Note that only the subjects specific to your filter will be added to your Filter Cart and only on data shared with the research community. Other data for those same subjects may exist (i.e., within another NDA Collection, associated with a data structure that was not requested in the query, etc.). So, users should select ‘Find all Subjects Data’ to identify all data for those specific subjects. 

Additional Tips:

  • You may query the data without an account, but to gain access you will need to create an NDA user account and apply for access.  Most data access requires that you or your lab are sponsored by an NIH recognized institution with Federal Wide Assurance (FWA).  Without access, you will not be able to obtain individual-level data. 

    Once you have selected data of interest you can:
  • Create a data package - This allows you to specify format for access/download
  • Assign to Study Cohort - Associate the data to an NDA Study allowing for a DOI to be generated and the data to be linked directly to a finding, publication, or data release. 
  • Find All Subject Data - Depending on filter types being used, not all data associated with a subject will be selected.  Data may be restricted by data structure, NDA Collection, or outcome variables (e.g., NDA Study). ‘Find All Data’ expands the fliter criteria by replacing all filters in your Filter Cart with a single Query by GUID filter for all subjects selected by those filters.

    Please Note:
  • When running a query, it may take a moment to populate the Filter Cart. Queries happen in the background so you can define other queries during this time. 
  • When you add your first filter, all data associated with your query will be added to the Filter Cart (e.g., a Concept, an NDA Collection, a Data Structure/Element, etc.). As you add additional filters, they will also display in the Filter Cart. Only the name of filter will be shown in the Filter Cart, not the underlying structures. 
  • Information about the contents of the Filter Cart can be seen by clicking "Edit”.
  • Once your results appear in the Filter Cart, you can create a data package or assign subjects to a study by selecting the 'Package/Assign to Study' option. You can also 'Edit' or 'Clear' filters.
     

Frequently Asked Questions

  • The Filter Cart currently employs basic AND/OR Boolean logic. A single filter may contain multiple selections for that filter type, e.g., a single NDA Study filter might contain NDA Study 1 and NDA Study 2. A subject that is in EITHER 1 OR 2 will be returned.  Adding multiple filters to the cart, regardless of type, will AND the result of each filter.  If NDA Study 1 and NDA Study 2 are added as individual filters, data for a subject will only be selected if the subject is included in  BOTH 1 AND 2.

  • Viewable at the top right of NDA pages, the Filter Cart is a temporary holder of data identified by the user, through querying or browsing, as being of some potential interest. The Filter Cart is where you send the data from your Workspace after it has been filtered.

  • After filters are added to the Filter Cart, users have options to ‘Create a Package’ for download, ‘Associate to Study Cohort’, or ‘Find All Subject Data’. Selecting ‘Find All Subject Data’ identifies and pulls all data for the subjects into the Filter Cart. Choosing ‘Create a Package’ allows users to package and name their query information for download. Choosing ‘Associate to Study Cohort’ gives users the opportunity to choose the Study Cohort they wish to associate this data.

Glossary

  • Once your filter cart contains the subjects of interest, select Create Data Package/Assign to Data Study which will provide options for accessing item level data and/or assigning to a study.  

  • Once queries have been added to your workspace, the next step is to Submit the Filters in the workspace to the Filter Cart.  This process runs the queries selected, saving the results within a filter cart attached to your account.  

  • The Workspace within the General Query Tool is a holding area where you can review your pending filters prior to adding them to Filter Cart. Therefore, the first step in accessing data is to select one or more items and move it into the Workspace. 

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NDA provides a single access to de-identified autism research data. For permission to download data, you will need an NDA account with approved access to NDA or a connected repository (AGRE, IAN, or the ATP). For NDA access, you need to be a research investigator sponsored by an NIH recognized institution with federal wide assurance. See Request Access for more information.

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Acceptability and Engagement Form

52 Shared Subjects

N/A
Clinical Assessments
Satisfaction
05/30/2017
t4rp01
06/26/2017
View Change History
01
Query Element Name Data Type Size Required Description Value Range Notes Aliases
subjectkey GUID Required The NDAR Global Unique Identifier (GUID) for research subject NDAR*
src_subject_id String 20 Required Subject ID how it's defined in lab/project
interview_date Date Required Date on which the interview/genetic test/sampling/imaging/biospecimen was completed. MM/DD/YYYY Required field
interview_age Integer Required Age in months at the time of the interview/test/sampling/imaging. 0 :: 1260 Age is rounded to chronological month. If the research participant is 15-days-old at time of interview, the appropriate value would be 0 months. If the participant is 16-days-old, the value would be 1 month.
sex String 20 Required Sex of the subject M;F; O; NR M = Male; F = Female; O=Other; NR = Not reported gender
version_form String 121 Required Form used/assessment name
t4rp_1 String 100 Recommended How did Texting 4 Relapse Prevention help you understand your illness better?
t4rp_2 String 100 Recommended How do you think Texting 4 Relapse Prevention helped you keep track of your symptoms better?
t4rp_3 String 100 Recommended How did the program affect your daily life?
t4rp_4 String 100 Recommended What parts of the program did you find most interesting or fun?
t4rp_5 String 100 Recommended What things can we change to make Texting 4 Relapse Prevention better in the future?
t4rp_6 String 100 Recommended What parts of Texting 4 Relapse Prevention were confusing or hard to use?
t4rp_7 String 100 Recommended What did you like best about Texting 4 Relapse Prevention?
t4rp_8 String 100 Recommended What did you like the least about Texting 4 Relapse Prevention?
t4rp_9 String 100 Recommended When, if ever, did you find it hard to read the messages?
t4rp_10 String 100 Recommended When, if ever, was hard to reply to the messages?
t4rp_11 String 100 Recommended As a provider, how did Texting 4 Relapse Prevention affect your clinical work?
t4rp_12 String 100 Recommended Did your patients give you any feedback about the program that you think would be helpful for us to know?
t4rp_13 String 100 Recommended What, if any, negative consequences did the intervention have for the patient?
t4rp_14 String 100 Recommended How can we improve Texting 4 Relapse Prevention?
t4rp_15 String 100 Recommended Lastly, if Texting 4 Relapse Prevention were rolled out in clinics in the area, how useful do you think it would be in improving clinical care?
Query t4rp_16 Integer Recommended P. I like the Texting 4 Relapse Prevention program. C. My patients liked the Texting 4 Relapse Prevention program. 1::5 1=strongly disagree; 5=strongly agree pas_a1
Query t4rp_17 Integer Recommended P. The suggestions the program sent me to help with my symptoms were useful for me. 1::5 1=strongly disagree; 5=strongly agree pas_a2
Query t4rp_18 Integer Recommended P. The text messages helped me notice when my symptoms were getting worse faster than I used to before I started the program; C. Texting 4 Relapse Prevention helped my patients better identify when their symptoms got worse earlier than they usually do. 1::5 1=strongly disagree; 5=strongly agree pas_a3
Query t4rp_19 Integer Recommended P. Texting 4 Relapse Prevention made it easier to contact my provider when my symptoms got worse; C. The automated system made it easier for me to quickly follow-up with my patients if needed. 1::5 1=strongly disagree; 5=strongly agree pas_a4
Query t4rp_20 Integer Recommended P. I found the text messages annoying. 1::5 1=strongly disagree; 5=strongly agree pas_a5
Query t4rp_21 Integer Recommended P. The text messages were easy to understand; C. My patients were sometimes confused by the program messages. 1::5 1=strongly disagree; 5=strongly agree pas_a6
Query t4rp_22 Integer Recommended P. The text messages that asked me to reply were easy to answer. 1::5 1=strongly disagree; 5=strongly agree pas_a7
Query t4rp_23 Integer Recommended P. The text messages were positive and helped me feel supported; C. The intervention helped my patients feel supported. 1::5 1=strongly disagree; 5=strongly agree pas_a8
Query t4rp_24 Integer Recommended P. Texting 4 Relapse Prevention got in the way of my daily activities. 1::5 1=strongly disagree; 5=strongly agree pas_a9
Query t4rp_25 Integer Recommended P. Texting 4 Relapse Prevention sent me too many text messages every day. 1::5 1=strongly disagree; 5=strongly agree pas_a10
Query t4rp_26 Integer Recommended P. If Texting for Relapse Prevention was offered again, I would sign up for it. 1::5 1=strongly disagree; 5=strongly agree pas_a11
Query t4rp_27 Integer Recommended P. I would recommend the Texting 4 Relapse Prevention program to other patients who have a similar illness as I do; C. I would recommend Texting 4 Relapse Prevention to other clinicians who have patients with schizophrenia or SAD. 1::5 1=strongly disagree; 5=strongly agree pas_a12
Query t4rp_28 Integer Recommended C. As a clinician, I liked the Texting 4 Relapse Prevention program. 1::5 1=strongly disagree; 5=strongly agree
Query t4rp_29 Integer Recommended C. I would be unlikely to use this intervention with my patients if it were made available in my clinic. 1::5 1=strongly disagree; 5=strongly agree
Query t4rp_30 Integer Recommended C. The automated system made it easier for me to monitor my patients. 1::5 1=strongly disagree; 5=strongly agree
Query t4rp_31 Integer Recommended C. The intervention helped my patients better monitor their own symptoms of schizophrenia or schizoaffective disorder. 1::5 1=strongly disagree; 5=strongly agree
Query t4rp_32 Integer Recommended C. The program didn't really help my patient's improve their coping skills related to symptom management. 1::5 1=strongly disagree; 5=strongly agree
Query t4rp_33 Integer Recommended Acceptability score 12::60 pat_acc_scale
Query t4rp_34 Integer Recommended (Patient estimate) How many messages did you receive from the Texting 4 Relapse Prevention program yesterday? pes1
Query t4rp_35 Integer Recommended (Patient estimate) How many messages did you send to the program yesterday? pes2
Query t4rp_36 Integer Recommended (Patient estimate) How many messages did you receive from the program in the last 7 days? Your best guess is fine. pes3
Query t4rp_37 Integer Recommended (Patient estimate) How many messages did you send to the program in the last 7 days? Your best guess is fine. pes4
Query t4rp_38 Integer Recommended (Patient estimate) In general, how often did you read the program messages in the past 7 days? 1::5 1=Never (0 messages in the past week); 2=Rarely (1-5 messages); 3=Sometimes (6-14 messages); 4=Often (15 - 27messages); 5=always (28 messages) pes5
Query t4rp_39 Integer Recommended (Program count) Messages sent previous day:
Query t4rp_40 Integer Recommended (Program count) Messages received previous day:
Query t4rp_41 Integer Recommended (Program count) Messages sent previous week (7 days):
Query t4rp_42 Integer Recommended (Program count) Messages received previous week (7 days):
t4rp_ydx String 10 Recommended years since psychiatric diagnosis
Query psych_hosp_total Integer Recommended Total number of psychiatric hospitalizations
t4rp_txt String 10 Recommended text messaging tenure
t4rp_cell String 10 Recommended length of time with current cell phone number
Query t4rp_sent Float Recommended average number of text messages sent each day
Query t4rp_rec Float Recommended average number of text messages received each day
Data Structure

This page displays the data structure defined for the measure identified in the title and structure short name. The table below displays a list of data elements in this structure (also called variables) and the following information:

  • Element Name: This is the standard element name
  • Data Type: Which type of data this element is, e.g. String, Float, File location.
  • Size: If applicable, the character limit of this element
  • Required: This column displays whether the element is Required for valid submissions, Recommended for valid submissions, Conditional on other elements, or Optional
  • Description: A basic description
  • Value Range: Which values can appear validly in this element (case sensitive for strings)
  • Notes: Expanded description or notes on coding of values
  • Aliases: A list of currently supported Aliases (alternate element names)
  • For valid elements with shared data, on the far left is a Filter button you can use to view a summary of shared data for that element and apply a query filter to your Cart based on selected value ranges

At the top of this page you can also:

  • Use the search bar to filter the elements displayed. This will not filter on the Size of Required columns
  • Download a copy of this definition in CSV format
  • Download a blank CSV submission template prepopulated with the correct structure header rows ready to fill with subject records and upload

Please email the The NDA Help Desk with any questions.