Library Data Specialist — Cataloguing, Data Cleaning & Web Research
Messy spreadsheets, catalogs and lists in — clean, verified data out.
Catalogues with subject headings and DDC classification, deduplicated spreadsheets, pivot recaps and dashboards, and B2B lead lists researched from public sources — every row traceable to its source.
9,692records merged & cleaned
1,653duplicate pairs resolved
100%records completed with subject headings & DDC
B.Sc. in Library & Information Science, UIN Maulana Malik Ibrahim Malang (2026, GPA 3.46) · Based in Malang, Indonesia · Working with clients locally and internationally
Several library catalog exports merged into one clean master catalogue. Duplicate pairs were detected and resolved, and every record was completed with subject headings and DDC classification.
9,692records merged & cleaned
1,653duplicate pairs detected & resolved
100%records completed with subject headings & DDC
PLATE 01 — Catalog Consolidation
PLATE 02 · Before — raw merged catalogue dataPLATE 03 · After — cleaned, deduplicated, classified
Demo dataset — example work
Case 2 — Excel Data Cleaning, Before / After
Example of a typical cleaning job on a demonstration dataset: inconsistent spellings, mixed date formats and duplicate rows standardised into one clean table. The dataset is fictional and shown to demonstrate method and output format.
PLATE 04 — Data Cleaning
PLATE 05 · Before (demo data)PLATE 06 · After (demo data)
Demo dataset — example work
Case 3 — Recap, Pivot Table & Dashboard
Example recap workflow on a demonstration dataset: a cleaned table summarised with pivot tables and presented as a simple dashboard. The dataset is fictional and shown to demonstrate method and output format.
Case 4 — B2B Lead List Sample: 25 US Healthcare Businesses
A real sample researched from public sources: 25 US healthcare businesses (dentists, dermatology, chiropractic, physical therapy, veterinary, optometry and more). Every one of the 25 rows includes a public business email and phone number, and every row carries the source URL it was verified from.
Intake & scope check — we agree exactly which columns/fields are in scope before work starts.
Cleaning / cataloguing — the work itself: dedupe, standardise, classify, research.
Mandatory QC pass — format validation, a second deduplication check, and verification that no out-of-scope columns were changed.
Delivery with a QC report — you receive the finished file plus a short report of what was changed and found.
05 — Pricing
Pricing — International Clients
Service
Rate
Data cleaning, cataloguing, research & recap work
From$10/hour
Fixed-scope projects (e.g. a lead list or a catalogue batch)
Project-based quote — ask with your scope and deadline
A small paid or sample-first start is welcome on larger jobs, so you can check quality before committing to the full dataset.
06 — FAQ
Frequently Asked Questions
How do we start?
Send your file or a description of the job by email. You first receive a free sample — 25 rows of your data, or the first page of your document — so you can judge the quality before paying anything.
How do I send my files?
Attach them to an email or share a Drive link. Excel, CSV, PDF and clear photos of documents are all fine.
How long does it take?
Small jobs — up to about 1,000 rows — typically take 2 days. Your fixed quote always states the exact deadline before we start.
What do I receive?
The cleaned file plus a QC report listing every change made. If any row breaks the rules we agreed, tell me and I correct it.
How do I pay?
Through the platform you hired me on (Fastwork, Freelancer or Projects.co.id), or another method we agree on before work starts. You never pay before seeing the free sample.
07 — Indonesia
Untuk Klien Indonesia — Perpustakaan & Bisnis Lokal
Saya membantu perpustakaan sekolah/kampus dan bisnis di Indonesia merapikan data: katalogisasi & klasifikasi DDC, pembersihan dan deduplikasi data Excel, rekap & pivot, serta riset daftar kontak bisnis dari data publik. Setiap pekerjaan melewati QC dan disertai laporan perubahan.
