Evidence map›Paper›PMID 24926252›Full record

ArticleFrontiers in neuroinformatics2014

Automated collection of imaging and phenotypic data to centralized and distributed data repositories.

Margaret D King, Dylan Wood, Brittny Miller, Ross Kelly, Drew Landis, William Courtney, Runtang Wang, Jessica A Turner, Vince D Calhoun

Abstract read
In one paragraph

Article in Frontiers in neuroinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. SchizConnect: Virtual Data Integration in Neuroimaging.Data integration in the life sciences : ... International Workshop, DILS ... : proceedings. DILS (Conference) · 2015
    Article
  6. Terminology development towards harmonizing multiple clinical neuroimaging research repositories.Data integration in the life sciences : ... International Workshop, DILS ... : proceedings. DILS (Conference) · 2015
    Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Future Directions for Examination of Brain Networks in Neurodevelopmental Disorders.Journal of clinical child and adolescent psychology : the official journal for the Society of Clinical Child and Adolescent Psychology, American Psychological Association, Division 53
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Margaret D KingThe Mind Research Network Albuquerque, NM, USA.
Dylan WoodThe Mind Research Network Albuquerque, NM, USA.
Brittny MillerThe Mind Research Network Albuquerque, NM, USA.
Ross KellyThe Mind Research Network Albuquerque, NM, USA.
Drew LandisThe Mind Research Network Albuquerque, NM, USA.
William CourtneyThe Mind Research Network Albuquerque, NM, USA.
Runtang WangThe Mind Research Network Albuquerque, NM, USA.
Jessica A TurnerThe Mind Research Network Albuquerque, NM, USA ; Department of Psychology, Georgia State University Atlanta, GA, USA.
Vince D CalhounThe Mind Research Network Albuquerque, NM, USA ; Departments of Electrical and Computer Engineering, Neurosciences, Computer Science, and Psychiatry, University of New Mexico Albuquerque, NM, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate data collection at the ground level is vital to the integrity of neuroimaging research. Similarly important is the ability to connect and curate data in order to make it meaningful and sharable with other investigators. Collecting data, especially with several different modalities, can be time consuming and expensive. These issues have driven the development of automated collection of neuroimaging and clinical assessment data within COINS (Collaborative Informatics and Neuroimaging Suite). COINS is an end-to-end data management system. It provides a comprehensive platform for data collection, management, secure storage, and flexible data retrieval (Bockholt et al., 2010; Scott et al., 2011). It was initially developed for the investigators at the Mind Research Network (MRN), but is now available to neuroimaging institutions worldwide. Self Assessment (SA) is an application embedded in the Assessment Manager (ASMT) tool in COINS. It is an innovative tool that allows participants to fill out assessments via the web-based Participant Portal. It eliminates the need for paper collection and data entry by allowing participants to submit their assessments directly to COINS. Instruments (surveys) are created through ASMT and include many unique question types and associated SA features that can be implemented to help the flow of assessment administration. SA provides an instrument queuing system with an easy-to-use drag and drop interface for research staff to set up participants' queues. After a queue has been created for the participant, they can access the Participant Portal via the internet to fill out their assessments. This allows them the flexibility to participate from home, a library, on site, etc. The collected data is stored in a PostgresSQL database at MRN. This data is only accessible by users that have explicit permission to access the data through their COINS user accounts and access to MRN network. This allows for high volume data collection and with minimal user access to PHI (protected health information). An added benefit to using COINS is the ability to collect, store and share imaging data and assessment data with no interaction with outside tools or programs. All study data collected (imaging and assessment) is stored and exported with a participant's unique subject identifier so there is no need to keep extra spreadsheets or databases to link and keep track of the data. Data is easily exported from COINS via the Query Builder and study portal tools, which allow fine grained selection of data to be exported into comma separated value file format for easy import into statistical programs. There is a great need for data collection tools that limit human intervention and error while at the same time providing users with intuitive design. COINS aims to be a leader in database solutions for research studies collecting data from several different modalities.

Indexed as

assessment data collectionCOINSdatabaseintuitiveneuroinformaticstool suite

Identifiers

PMID24926252
PMCPMC4046572

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.