01
Messy sources
Research data is often scattered across websites, APIs, spreadsheets, PDFs, repositories, and public databases.
Research-ready datasetsfor thesis and academic research
Clean, documented, reproducible datasets for literature reviews, bibliometric studies, public web research, and thesis projects.
Research data problem
Raw data is not research-ready
Research datasets need more than extraction. Sources must be checked, records cleaned, fields documented, and the workflow made reproducible.
02
Unclear feasibility
Before collection begins, you need to know whether a source is accessible, permitted, useful, and stable.
03
Cleaning takes time
Duplicates, missing values, inconsistent fields, encoding issues, and messy exports can stall the project.
04
Methodology matters
For thesis and publication work, reviewers may ask how the dataset was collected, cleaned, and documented.
Research workflow
Scope first. Collect with confidence.
Start with source feasibility, validate a small sample, then move to a full research-ready dataset.
01
Submit your research topic
Tell us your topic, target data, preferred sources, expected fields, estimated size, and deadline.
02
Feasibility check
We review source availability, access options, data quality, limitations, and possible ethics or source risks.
03
Sample dataset
You receive a small sample dataset to validate structure, fields, quality, and usefulness.
04
Final delivery
You receive cleaned data, source logs, documentation, and reproducible scripts when included in scope.
Sample review
A feasibility check before collection begins
Before collecting data, we map available sources, expected fields, access risks, and recommended deliverables.
Feasibility report
Reviewed before collectionOpen / API sourcesAccess risks flagged
Research topic
AI adoption in higher education
Recommended sources
OpenAlex · Crossref · ERIC · Public university pages
Available fields
title · abstract · DOI · authors · year · journal · keywords
Access notes
Prefer open, API-accessible, and client-authorized sources
Risks to avoid
Google Scholar scraping · paywalled full text without authorization · sensitive personal data
Suggested output
cleaned_dataset.csv · data_dictionary.xlsx · methodology_note.pdf
Applications
Built for real research workflows
Common academic data projects we help scope, collect, clean, and document.
Literature review
Literature Review Dataset
Collect paper metadata such as title, authors, DOI, abstract, year, journal, keywords, and source links.
DOIabstractauthorsyearjournal
Best for: SLR, scoping review, thesis background
Bibliometric analysis
Bibliometric Dataset
Prepare publication data for trend analysis, citation mapping, co-author networks, institutions, and topic exploration.
citationsauthorsaffiliationstopics
Best for: trends, networks, publication mapping
Public web research
Public Web Dataset
Collect structured records from public job postings, policy pages, university programs, listings, or news metadata.
URLcategorydaterecord ID
Best for: policy, jobs, education, public listings
Dataset preparation
Dataset Cleaning
Clean and standardize existing CSV, Excel, JSON, or exported research files for analysis and reporting.
deduplicatenormalizevalidatedocument
Best for: messy CSV and Excel exports
Deliverables
Research-ready deliverables
Every project is delivered with structured files, documentation, and source notes so the dataset is easier to inspect, analyze, and explain.
Delivery packageRaw files · Cleaned dataset · Documentation · Reproducible workflow
.json
raw_data.json
Original collected records where applicable.
.csv
cleaned_dataset.csv
Analysis-ready structured dataset.
.xlsx
data_dictionary.xlsx
Explanation of fields, formats, and values.
.csv
source_log.csv
Source URLs, API endpoints, access dates, and collection notes.
.pdf
methodology_note.pdf
Plain-language explanation of the collection and cleaning workflow.
.py
reproducible_script.py
Python script or notebook for a repeatable workflow.
.md
README.md
Project overview, file descriptions, and usage notes.
Access protocol
Source access reviewEthics & source access
Ethics-aware data collection
We work with public, permitted, API-accessible, open, or client-authorized data sources. We do not bypass paywalls, scrape private accounts, or collect sensitive personal data without proper authorization and ethics clearance.
We flag source and access risks early. Researchers remain responsible for any institutional ethics approval required by their project.
Each project can include a short methodology note describing collection scope, source access, cleaning steps, and known limitations.
Collection safeguards
Reviewed before collection- 01No paywall bypassing
- 02No private account scraping
- 03No unauthorized sensitive personal data collection
- 04Respect source limitations and access rules
- 05Source logs and methodology notes included
- 06Client authorization required for restricted sources
Logged
Access basis
Recorded
Access date
Included
Source notes
Service packages
Choose the right starting point
Begin with feasibility, review a sample, then decide whether a full dataset project makes sense.
Project-based pricing after feasibility review. Start with a free feasibility check before any paid work.
Project-based
Best first stepData Feasibility Check
A quick review of your topic, target data, possible sources, risks, and expected fields.
- Source availability review
- Field feasibility
- Risk notes
- Suggested next step
Project-based
Sample Dataset
A small sample dataset to validate structure, quality, and usefulness before full collection.
- 50–200 sample records
- Initial schema
- Source notes
- Quality observations
Project-based
Most completeFull Research Dataset
A complete research-ready dataset with cleaning, documentation, and optional reproducible scripts.
- Raw data where applicable
- Cleaned dataset
- Data dictionary
- Source log
- Methodology note
- Optional Python script/notebook
Project-based
Dataset Cleaning
For researchers who already have messy files and need them cleaned, standardized, and documented.
- CSV/Excel cleaning
- Deduplication
- Field normalization
- Data dictionary
- Clean export
Free feasibility check
Tell us what data you need
Send your research topic, target sources, and expected output. We’ll review feasibility, risks, fields, and the best next step.
or contact us directly