Description: We offer guidance on preparing messy or raw data for analysis. Our workshops and consultations cover techniques for detecting/removing errors, handling missing values, deduplicating records, transforming variables, reshaping data, and standardizing formats. We are able to support tools/languages like OpenRefine, Python and R.
Tools we support: OpenRefine, Python (pandas), R (tidyverse)
How we help: Imagine you've collected 1000 survey responses, but the data contains inconsistences, errors and requires some cleaning up prior to analysis. We offer:
Description: Imagine you have hours of important audio recordings from meetings, interviews, or lectures that need to be converted into searchable text with speaker identification. We offer:
Tools we support: Whisper and PyAnnote (via Hugging Face)
How we help:
The library will cover the cost of transcription and speaker diarization for up to 4 hours of audio content.
How we help: For users with no prior experience in Python or R programming, the library offers co-designed workshops tailored to the specific research tasks in business, social science, and economics disciplines.
Tools we support:
Case example: These are some of the workshops that we have conducted in the past:
How we help: Analyzing qualitative data such as interview transcripts or free text includes discovering and identifying themes, establishing the relationship between them, and linking themes to theoretical models.
For users with no prior experience in thematic coding or qualitative data analysis, the library offers workshops to understand the underlying principles and how to apply them to your research.
Tools we support:
Case Example: These are some of the workshops that we have conducted in the past:
How we help:
Provide guidance on anonymizing/de-identifying sensitive data to protect individual privacy while preserving data utility. This applies to both tabular data tables as well as qualitative data such as interview transcripts. We can help by conducting a consultation on introductory concepts, pointing you to the tools and resources, or work with you closely to come up with a strategy to anonymize your dataset.
We recommend anonymizing your dataset before analysis or sharing with collaborators. This protects participant information, prepares data for repository deposit (e.g., SMU RDR), ensures compliance with ethical standards and regulations, and facilitates collaboration and data sharing.
Tools we support:
Case Example:
How we help:
Support researchers with various aspects of Research Data Management (RDM), including:
We offer guidance and support throughout the research lifecycle to ensure best practices in RDM.
Tools we support:
How we help:
We provide researchers, undergraduate and postgraduate students with access to a diverse range of research software spanning the entire research lifecycle. This service offers opportunities to explore new tools, gain access to subscription and license-based software, and enhance research capabilities. Software is available through remote access or on-site at the Investment and Data Studio (IDS).
Tools available:
Refer to this page for the full list of available software and tools.
Case Example:
A student used IDS computers to complete a data-intensive assignment that their laptop couldn't handle. The better processing power and memory of IDS machines allowed them to efficiently run Python for analyzing large datasets, overcoming the limitations of their personal devices.
How we can help:
We provide customized workshops or guidance on the following:
Tools we support: