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DATA COLLECTION AND REPOSITORIES: TOPIC SUMMARY


Anyone who has ever done data collection for their research project understands well the chaos of data collection process. One day questionnaires, next day interviews recorded on the laptop, then another two weeks to figure out the meaning behind the strange file name. It is precisely for this reason that knowing how to collect data and work with repositories is so crucial in the MLIS sphere, particularly in regard to the issue of data duration (Borgman, 2015).

I shall begin with data collection process. It is a process of obtaining data for its further analysis. Surveys, interviews, observation and automation are some of the methods of data collection widely used in LIS. All of those techniques have their pros and cons, however, one must remember that whatever way one chooses data should be obtained with full observance of ethical principles. Such important issues like informed consent, privacy, and anonymisation should always be on one's mind. Proper data collection saves one's life in many ways (Digital Curation Centre, 2020).

But what happens after you collect the data? Where do you put it? If you keep the data stored on your own computer or on a USB flash drive, the data will be out of reach from anyone else. Additionally, there is a risk of losing it forever if the device fails. This is when data repositories play their part. Data repositories are digital collections that store and preserve research data. While cloud services, such as Google Drive and Dropbox, are popular choices for storing data, they lack certain features that make data repositories more suitable for researchers.

Which repositories should be considered? Among some great options are Zenodo, an online service hosted by CERN and accepting all types of research outputs, Figshare with its intuitive web interface, ICPSR specialising in social science data management, and Dryad dedicated to life and environmental sciences data curation. In selecting the right repository, it is essential to focus on its governance, metadata quality, access policies, and certifications, such as CoreTrustSeal (Digital Curation Centre, 2020).

Finally, it would be remiss not to consider ethical and legal considerations. Not all data sharing practices are alike. Some data may have sensitive material within it; therefore, anonymization becomes very important. Proper consent is also required from participants before depositing any data into the public domain. Following the FAIR principles will provide guidance on how to manage your data responsibly (Wilkinson et al., 2016).

In conclusion, proper data collecting practices in combination with using repositories will ensure that your data will continue to be discoverable and useful for many years to come. It is critical that MLIS students learn these practices.

 

References

Borgman, C. L. (2015). Big data, little data, no data: Scholarship in the networked world. MIT                     Press.            

Digital Curation Centre. (2020).How to select a data repository. DCC.                                                            https://www.dcc.ac.uk/guidance/how-guides/select-data-repository

Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N.,       Boiten, J. W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T.,                      Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., … Mons, B.                    (2016). The FAIR Guiding Principles for scientific data management and stewardship.                     Scientific Data, 3(1), Article 160018. https://doi.org/10.1038/sdata.2016.18

Zenodo. (2025). Zenodo user guide. CERN. https://about.zenodo.org

 

 Picture: An Institution repository depicting the process of data collection and digital curation


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