Introduction to Data Curation

The ScholarlyCommons team is excited to announce our new data curation service for datasets that are going to be submitted into ScholarlyCommons, the University of Pennsylvania’s open access institutional repository. This blog post will introduce you to what data curation is, how it improves your research data, and how the process works in ScholarlyCommons. 

Data Curation Introduction

Data curation is the process where a data curator reviews a dataset and its associated documentation for ways to enhance the findability, accessibility, interoperability, and reusability (FAIR). A data curator is a trained professional who inspects the data according to best practices, such as the Data Curation Network’s CURATE(D) workflow. Curation reviews the structure and context of the data and does not evaluate the value of the data’s content. Once curated, feedback is sent to the researcher in order for them to implement the changes. Certain changes, such as removing personally identifiable information, are required before acceptance into a repository while others, like improved documentation, are highly recommended.  

Curation is increasingly offered at high quality data repositories as funders, research organizations, and scholars realize that curating data improves the data’s ability to be used by others in the future. Studies have found that data curation adds value to the data sharing process and increases researcher’s confidence in sharing their data (Marsolek, 2023). It is not an uncommon occurrence to come across a useful dataset only for it to have no documentation, unclear variable meanings, or missing files. Data curation supports both the researcher sharing the data and prospective data users by working to reduce these issues. 

Data Curation for ScholarlyCommons

The ScholarlyCommons data curation service allows you to submit your dataset for review prior to depositing into the repository. Curation requests are made through the Data Curation Request Form found in the How to Request Data Curation instructions. Our expert data curators have been trained by the Data Curation Network and are active participants in the field, so you can be sure it is in capable hands.  You can submit your dataset for curation by either giving us a cloud storage shared access link or uploading the dataset straight into the form. Uploading directly to the form should only be for very small datasets (>100MB). We will confirm the submission and provide you feedback once we have completed curation. You can then implement the recommendations into your dataset. Once completed, the dataset and documentation can be deposited into ScholarlyCommons following the regular submission instructions.  

Want support sharing your data, have a question, or want to collaborate? Contact the ScholarlyCommons team at libraryrepository@upenn.edu.