Datasets are created by the study, so CLOSER doesn't handle any of the data. The study can choose how to organise their datasets to be consistent with their systems and user access.
Therefore there is a many-many relationship between the dataset and the questionnaire. There can be several datasets per questionnaire, or there could be several questionnaires per dataset.
If the study is funded by the ESRC, is is a requirement that the data are deposited in the UKDS, and so the dataset metadata provided by these studies usually matches what is available at the UKDS, and the DOI for this is provided.
For studies that manage their own data access or through other platforms like DPUK, the study should provide dataset metadata which users can request and a stable URL is provided.
Since CLOSER Discovery only provides metadata, it is usually acceptable for the datasets to contain variables which are under controlled access.
Datasets are contained within the sweep/wave in CLOSER Discovery see sweep page for more information.
DDI is a flexible standard and different users are at will to implement the standard in slightly different ways. This is a strength, but when moving between different implementations some adjustments need to be made. Data files should meet certain criteria, this generates a standard SledgeHammer output, which is then lightly edited to provide a consistent structure for ingest into Colectica Repository.
The following provides a detailed overview of the processes involved. Please see the step by step guide for the simple steps.
Variable metadata workflow
...
sledgehammer-cl.bat" ^ -ag uk.cls.bcs70 ^ -rename bcs_75_mcs ^ -ddi 3.2-RP ^ -ddipd proprietary ^ -har ^ -ddilang en-GB ^ -ddiref urn ^ -ddiurn canonical ^ -pretty ^ -opt full ^ -scan ^ -stats max,min,mean,mode,valid,invalid,freq,stdev ^ ../bcs70/bcs_1975/bcs_1975_masc.sav
Metadata Edits (DDI flavour)
For display purposes and for ease of navigation and ingest, a consistent set of names should be applied to the output from SledgeHammer prior to ingest through a series of edit scripts. These are written in python, and if they cannot be run at the study, can be run at CLOSER prior to ingest.
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| Short name | Long Name | DOI | Public |
|---|---|---|---|
| mcs_03_na | MCS2 Neighbourhood Assessment | http://dx.doi.org/10.5255/UKDA-SN-5350-3 | 1 |
| mcs4_teacher | MCS4 Teacher Survey | http://dx.doi.org/10.5255/UKDA-SN-6848-1 | 1 |
| mcs5_sc | MCS5 Child Paper Self-Completion | http://dx.doi.org/10.5255/UKDA-SN-7464-2 | 1 |
| mcs5_teacher | MCS5 Teacher Survey | http://dx.doi.org/10.5255/UKDA-SN-7464-2 | 1 |
| ncds8_sc | NCDS8 Paper Self-Completion | http://dx.doi.org/10.5255/UKDA-SN-6137-2 | 1 |
| pms | Perinatal Mortality Study | http://dx.doi.org/10.5255/UKDA-SN-5565-2 | 1 |
Control File
A control file can be used to batch up the batch files and then run the edits across all the files:
...
If the edits are run, the file can be imported into Colectica Designer to check that it is well formed. Alternatively you can check that DDI-Flavour has been run by checking the XML has the new title at the beginning of the file. Please see the step by step guide to what the setup requirements are and for examples.