Thanks for your patience during our recent outage at scalar.usc.edu. While Scalar content is loading normally now, saving is still slow, and Scalar's 'additional metadata' features have been disabled, which may interfere with features like timelines and maps that depend on metadata. This also means that saving a page or media item will remove its additional metadata. If this occurs, you can use the 'All versions' link at the bottom of the page to restore the earlier version. We are continuing to troubleshoot, and will provide further updates as needed. Note that this only affects Scalar projects at scalar.usc.edu, and not those hosted elsewhere.
12016-06-06T13:37:06-07:00Amanda Harrigan07295098f75f2d31450d0d3cb02418468fc34786819221plain2019-01-16T04:15:56-08:00Kendall Roarkb10f1c4cf8e7b886cc04a9b2a0c4e3a1fd47e68bThe University of Alberta Libraries have followed the following steps to automate the description of studies before depositing to Dataverse.
Created REDCap Project
The first thing we did was create metadata collection instruments using REDCap forms. REDCap is a secure, web-based application designed to support data capture for research studies.
We are using REDCap for a few reasons:
One, because it is licensed and maintained by WCHRI, and many WCHRI researchers already use it to manage their research projects and collect data. We wanted to fit in with already established research data management workflows and systems.
REDCap also gives us a chance to validate the metadata with researchers before creating a public dataverse record, which comes with an automatically generated doi. Which can be a bit of a process to backtrack from once it exists.
REDCap also already has survey functionality, which helps when communicating with researchers.
And finally, the data dictionary function and API allow us to easily manipulate, move and interact with the data in helpful ways.
So far REDCap forms have been created that mimic the structure and metadata elements drawn from Clinicaltrials.gov, the MICYRN Birth Cohort inventory, and Dataverse (version 4.0).
This means that the REDCap project effectively acts as a “metadata repository,” collecting in one place a pool of metadata for each study. Metadata can be pushed in or pulled out from different sources.
We have used r-based scripts to pull relevant metadata from publically available sources using APIs. We identified and mapped existing metadata fields from registries, inventories, and databases to Dataverse fields. So clinicaltrials.gov, the MICYRN Birth Cohort Inventory, and NCBI Pubmed metadata has been mapped to Dataverse’s DDI-based fields to identify common elements.
The mapping has taken into consideration: the name of the metadata field, the definition of the field, and the format of the field. So, for example, we were able to use our mappings to import common elements from the clinicaltrials.gov registry to our dataverse schema in REDCap using APIs. Open Refine was then used to modify the formats of certain values appropriately; for example, names and dates needed to be reformatted.
Relevant metadata was also added from the MICYRN Birth Cohort Inventory and publication information was added through NCBI Pubmed.
12016-09-22T06:58:50-07:00Kendall Roarkb10f1c4cf8e7b886cc04a9b2a0c4e3a1fd47e68bUniversity of AlbertaKendall Roark18University of Alberta Libraries and the Women & Children's Health Research Institute (WCHRI), University of Albertaplain2019-01-16T04:10:50-08:00Kendall Roarkb10f1c4cf8e7b886cc04a9b2a0c4e3a1fd47e68b
1media/rsz_bridge1.jpg2016-02-12T09:00:30-08:00Kendall Roarkb10f1c4cf8e7b886cc04a9b2a0c4e3a1fd47e68bBridging the Research Data DivideKendall Roark47google_maps2019-01-16T04:14:56-08:00Kendall Roarkb10f1c4cf8e7b886cc04a9b2a0c4e3a1fd47e68b