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Academic research runs on data, but getting that data into a form that is actually usable for rigorous analysis is rarely as straightforward as it sounds. A sociologist studying housing patterns might need decades of municipal records that exist in a dozen different formats across a dozen different sources. A finance researcher might need to pull structured data from thousands of corporate filings that were never designed to be machine-readable. A public health team might be sitting on years of survey responses stored in formats that predate the software they are currently using. The data exists, but the gap between where it lives and where it needs to be for serious empirical work can be significant.
That gap is exactly what we work in. Whether the need is sourcing data that does not come pre-packaged, structuring raw material into something an analysis can actually be run on, or building a pipeline that produces results a peer reviewer can follow from start to finish, we build the data infrastructure that lets researchers focus on the research itself rather than the logistics behind it.
What This Includes
Sourcing and extracting data from public records, government databases, regulatory filings, academic repositories, and web-based sources when the dataset needed does not already exist in a usable form
Structuring and cleaning raw datasets so they are organized, consistent, and ready for statistical analysis or modeling
Building reproducible data pipelines where every step from raw source to final dataset is documented, traceable, and verifiable by an outside reviewer
Validating dataset integrity, including identifying missing records, inconsistencies, duplicate entries, and other issues that could affect the reliability of research findings
Merging and reconciling data from multiple sources into a single coherent dataset while preserving the accuracy and traceability of each component
Preparing data documentation and codebooks so that datasets are fully described and usable by collaborators or future researchers working with the same material
Supporting ongoing research projects that require regular data updates, pipeline maintenance, or expansion as the scope of the work evolves
Recent Work
Research data work here has spanned multiple disciplines and a wide range of data challenges, from financial and regulatory data to public records and survey-based research.
At the project level, this has included building infrastructure to extract and process decades of regulatory filings across thousands of organizations, structuring material that arrived in inconsistent formats across many years into a single clean dataset purpose-built for empirical analysis. That kind of work requires not just technical execution but an understanding of what the research actually needs, since a pipeline that produces a dataset nobody can defend in peer review is not a pipeline that does its job.
Work has also included supporting faculty and research teams at the university level with data preparation, sourcing, and pipeline development across projects at various stages, from early-stage data exploration through final dataset preparation ahead of submission.
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Send us a description of what you're working with and what you're trying to accomplish. We'll give you a straight answer about whether this is the right fit, and what it would actually take to solve it.