RESEARCH DATA

Read a dataset before choosing an analysis

Learn how rows, columns, missing values, duplicates, text fields, and labels affect whether a dataset is ready for a research task.

What you will learn

  • Identify observational units and variable roles.
  • Recognize basic quality signals before analysis.
  • Separate data inspection from actual modeling or interpretation.

LinguaLab Smart Tutor

Ask about the concept, worked example, or exercise you are working on now. The tutor supports understanding without replacing the lesson or doing the task for you.

Current concept: Rows and observational units
The tutor explains the current learning context; it does not replace the learner’s attempt or execute analysis inside the Learning Center.

Concept

Rows and observational units

Each row should represent a stated unit, such as one document, sentence, or response. Mixing units makes counts and evaluation difficult to interpret.

Interactive mini-lab

Inspect a tiny research table

Select every issue that is actually supported by the rows. Repeated labels alone are not duplicate records.

idtextlabeldate
1الخدمة واضحةإيجابي2026-01-02
2—سلبي2026-01-03
3التطبيق مفيدإيجابي2026-01-04
3التطبيق مفيدإيجابي2026-01-04
5الدعم بطيءnegative2026-01-05
Select every issue that is actually supported by the rows. Repeated labels alone are not duplicate records.

Columns and roles

A text column contains the material to process. A label column may contain a human category. Identifiers and metadata should not silently become model features.

Interactive mini-lab

Identify text and label columns

Column names suggest roles, but the researcher must still inspect their contents.

Which column contains the material to analyze?
Which column can hold the human target for classification?

Quality checks

Inspect missing values, exact duplicates, inconsistent labels, encoding, language coverage, and class balance before selecting a workflow.

Interactive mini-lab

Classify data-quality cases

Distinguish absence, duplication, legitimate repetition, and inconsistent representation.

A text cell is empty.
Two rows match in every relevant displayed field.
Different texts both have the label `إيجابي`.
The same class appears as `سلبي` and `negative`.

Inspection is not execution

Describing columns and quality does not train a model or establish a finding. The appropriate production tool performs the selected analysis after the researcher reviews the data.

Interactive mini-lab

Make a readiness decision

Readiness is a reasoned state, not just yes or no.

A dataset has one empty text, an exact duplicate, and inconsistent label forms. What is the best current status?

Worked example

Worked dataset example

Three columns: id · text · label; one row has empty text; two rows share the same id.

  1. Unit: one labeled text per row.

Guided practice

Guided learner exercise

Imagine a table with text, label, date, and source columns.

  1. Name the observational unit.
  2. Choose which column is the text input and which is the human target.
  3. List two quality checks before classification.
  4. State which metadata must be reviewed before using it as a feature.

Understanding check

What is the safest first step after receiving a new research spreadsheet?

What is the safest first step after receiving a new research spreadsheet?

Choose one answer before checking.

Application step

Spreadsheet Explorer

Inspect an actual structured file in the production tool. The lesson does not upload or process data.

Complete the understanding check to open the optional production-tool application.