Data Import
Load CSV, text, Excel, JSON, SPSS SAV, sample datasets, and supported external sources with a preview and automatic variable detection.
Research data preparation
Prepare trustworthy data. Protect every decision downstream.
DataStatPro provides one guided workspace for importing data, checking its structure, correcting values and metadata, creating variables, defining analysis subsets, converting research formats, and exporting a documented, analysis-ready dataset.
10 focused tools
Metadata aware
Research-file conversion
Analysis-ready workflow
Direct answer
What is research data management?
Research data management is the controlled process of importing, describing, checking, cleaning, transforming, organizing, and exporting data before analysis. It protects the link between recorded values and their meaning. A reliable workflow preserves variable names, labels, measurement roles, category order, value labels, missing-value definitions, filters, and transformation decisions rather than treating data cleaning as an undocumented series of edits.
Complete data workspace
Choose a tool by preparation task
Data Export
Download complete datasets or selected columns in reusable formats such as CSV, JSON, and Excel.
Data Converter
Move between research formats while reviewing variable names, labels, value labels, missing codes, and target-format limitations.
Survey Data Collection
Build structured questionnaires, collect responses, and synchronize the resulting dataset with the analysis workspace.
Data Editor
Edit cells and rows, search, sort, filter, inspect values, and save prepared subsets without leaving the browser.
Data Transform
Recode variables, compute derived measures, define missing-data operations, and apply common transformations.
Dataset Manager
Review dataset dimensions and storage, rename files, organize research datasets, and control the active analysis source.
Variable Editor
Define names, labels, roles, data types, category order, value labels, and recognized missing-value codes.
Data Filter
Create focused analysis samples with explicit AND/OR conditions while keeping the intended population visible.
Sample Data Library
Load curated, documented datasets for teaching, demonstrations, workflow testing, and statistical practice.
Recommended workflow
From source file to analysis-ready dataset
Import
Load a supported file, collect survey responses, or choose a documented sample dataset. Confirm the delimiter, encoding, header row, dimensions, and initial variable detection.
Inspect
Review identifiers, variable types, labels, category coding, missingness, duplicates, implausible observations, date fields, and numerical ranges.
Prepare
Correct values and metadata, recode or compute variables, define missing codes, resolve categories, and create the intended analysis subset.
Validate and export
Recheck the resulting structure, sample size, distributions, and transformation logic before saving an appropriate reusable format.
Good practice: preserve the original source file, document every transformation, and validate the dataset again after any edit or filter.
Reproducible preparation
Four principles for defensible data management
Structure before statistics
Rows, columns, identifiers, measurement scales, categories, and time fields should be correct before choosing a statistical method.
Preserve metadata
Variable names, labels, roles, value labels, category order, and missing-value codes are part of the dataset, not informal notes.
Make transformations explicit
Recoding, computation, filtering, and missing-data decisions should be intentional, reviewable, and reported with the analysis.
Validate after change
Every edit can affect distributions and sample size, so the prepared dataset should be checked again before analysis or export.
Research applications
One preparation layer across research domains
Survey research
Organize categorical coding, define labels, review multi-select structures, and prepare analysis subsets.
Clinical and epidemiological data
Review missing codes, identifiers, dates, measurement types, follow-up fields, and implausible values.
Experimental studies
Recode conditions, compute outcomes, filter protocol deviations, and document comparison groups.
Business operations
Clean transactions, standardize categories, create metrics, and organize reporting datasets.
Cross-software workflows
Convert statistical research formats while retaining readable labels and metadata where the formats permit.
Teaching and demonstrations
Load curated sample datasets and move directly into guided descriptive and inferential analyses.
Frequently asked questions
Prepare data with confidence
Which formats can I import?
Common sources include CSV, delimited text, Excel, JSON, and SPSS SAV where available. The dedicated converter supports additional statistical exchange workflows and explains format-specific limitations.
Should missing codes become blank cells?
Not automatically. Define recognized codes in variable metadata so their original meaning remains documented, then justify any recoding or imputation separately.
What should I verify after a transformation?
Check dimensions, valid sample size, variable types, levels, ranges, missingness, duplicates, formulas, and whether filters represent the intended study population.
When is conversion preferable to export?
Use conversion when moving between research-software formats or when labels, missing codes, and naming rules require explicit review. Use ordinary export for straightforward CSV, JSON, or Excel output.