Irish Social Science Data Archive Services for Depositors & Researchers from ISSDA


You can download our ISSDA Services for Depositors & Researchers‌ as a PDF file.





Sharing your data via ISSDA ensures that your data will be professionally curated, will be easily accessible to users now and in the future, and will help to increase the impact and visibility of your own research.

More information on the importance of data sharing is available from UCD’s Research Data Management guide:



We acquire data from academic, research bodies and public sector sources, supporting:

  • Archival preservation
  • Secondary use and analysis for research
  • Teaching and learning use
  • Replication and validation of research

We are happy to discuss any offers of data that come within the thematic scope of the ISSDA collections; broadly, these relate to Irish society and include the societal aspects of environmental and medical data.

Please see ISSDA Collection Development Policy for an outline for the scope of our collections and criteria for evaluating datasets.



1. Preparing data for deposit

Anonymise the data

Please ensure that the data have been anonymised. Anonymisation is the responsibility of the Data Provider, as stated in Clause 3.2.4 of the ISSDA Deposit Licence. Information on anonymisation is available from UCD’s Research Data Management guide: The UK Anonymisation Network (UKAN) also provides an Anonymisation Decision-making Framework, available to download from their website.

Ensure your database is cleaned

A cleaned database is one that only has valid codes for each variable.  This means that each code in the dataset must be described in the data dictionary or questionnaire.  Note that there may be codes used in the dataset that are not mentioned on the questionnaire.   There should be no numerical measures with out of range codes.  Missing data codes must be explicitly stated.  Data should also have been checked, as far as possible for internal consistency (for example a never-smoker should not have a cigarette consumption field completed).

Ideally the database should include long descriptive labels for each variable and labels for each discrete variable value.  In SPSS these would be created using the VALUE LABLES and VARIABLE LABELS commands.

Ideally the dataset format should be based on a commonly used package (e.g. SPSS, STATA, SAS). We also recommend depositing data in multiple formats, for example SPSS plus an open standard, therefore allowing the largest number of users to access the data. Note that ISDDA does not encourage submission of dataset in Excel files. Please see the ISSDA File Format Policy for further information. 

Provide a data dictionary

The data dictionary is a central document that describes the different datasets being deposited, the sample size in each and the storage format. (e.g. SPSS, SAS, Excel).  For each database the data dictionary will list each variable, usually in the order in which it appears in the dataset, giving the variable name, the variable label, and a copy of the exact wording used to elicit the information.  This may be available from the questionnaire but should be repeated in the data dictionary. For derived variables (e.g. Body Mass Index, an SF-36 domain) the formula or algorithm used should be given or referenced.

For each variable the data dictionary should list the valid codes and their meaning.  Missing value codes should be identified and the codes used for ‘irrelevant’ (e.g. date of marriage for someone who was never married).  Often all ‘9’s are used for missing data and all ’8’s for irrelevant data.  The cleaned database should only contain codes that are identified in the data dictionary.  Note that special care should be taken if dates are included in the dataset, and the format should be described.

The data dictionary should also include a description of how the data were anonymised and list the variables (on the questionnaire) not included in the database, or variables which were altered to ensure anonymity (e.g. age groups instead of exact ages).

Include questionnaires

If PAPI (Paper and Pencil Interviews) has been used the questionnaire should be included.  For CAPI (Computer Aided Personal Interviewing) and CATI (Computer Aided Telephone Interviewing) the question wording should be supplied with notes for branched questions (i.e. questions that depend on a positive answer to a previous question).  For CASI (Computer Assisted Self Interviewing) systems such as Survey Monkey, a html file(s) displaying the questions should be provided.

More information on data preparation steps is available from the UK Data Service’s site:

Include blank consent form

Please include a blank copy of the consent form used with your study as part of the documenation. Blank consent form will be archived with the data but will not be made available to the End User unless they request to see it.


2.  When you are ready to deposit

Complete the ISSDA Depositor Form and sign the ISSDA Deposit Licence. Please see the ISSDA Data Acquisition Protocol for details of how data are managed once depsosited with the Archive.


3.  Data checking

CSTAR: Centre for Support and Training in Analysis and Research (

All data and documentation, together with the deposit form, are sent to CSTAR for checking. Specifics that are checked include data quality, anonymisation and the provision of contextual / descriptive information for users.

CSTAR reports back to ISSDA any specific actions that are required and ISSDA then passes on CSTAR’s recommendations to the depositing organisation.


4.  Making data available

ISSDA website:  

Once the work has been completed on preparing the data and documentation, the ISSDA webpage for your data study is created. Recent examples using our new processes and procedures include:

Delivering data

In order to access data, researchers need to first submit an application form which can be accessed from the ISSDA website. Users also agree to and sign an End User Licence.

Datasets are sent out via a secure online download service, called FileSender. The datasets are password protected and encrypted.


At ISSDA, we are in the process of implementing NESSTAR, a software system for publishing data on the web. This will allow users to find, browse, visualise and analyse your data online. To learn more about NESSTAR read our NESSTAR information leaflet for depositors.

Use of data

ISSDA deals with follow-up user enquiries but, on occasion, may need to contact the depositing organisation for assistance with an enquiry.

ISSDA can supply back to the depositing organisation statistical details of data use e.g. type of researcher, location of researcher and details of the research project for which the data is required.



If you have any queries please contact

The Childhood Development Initiative (CDI) have recently published a toolkit for sharing research data: McGrath, B. and Hanan, R., Sharing Social Research Data in Ireland: A Practical Toolkit (2016) Dublin: Childhood Development Initiative (CDI). Available from

Information on all aspects of Research Data Management are available from UCD’s Research Data Management guide: , including:

  • Ethical issues
  • Informed consent
  • Anonymisation
  • Access control
  • Rights and licensing
  • File management
  • Version control
  • File formats
  • Documentation and metadata
  • Funders’ requirements
  • Etc.