Description
This dataset documents the cross-ecosystem publishing coverage for developer utility packages maintained by Mohit Khare. The report covers 14 package registries spanning 8 programming languages: Python (PyPI), JavaScript (npm), Ruby (RubyGems), Rust (Crates.io), Go (Go Modules), PHP (Packagist), Dart (pub.dev), and Elixir (Hex.pm).Each record in the dataset maps a specific package to its registry, including the registry domain rating (DR), link type (dofollow or nofollow), current version, download count, and last update date. The data reveals significant variation in registry authority scores, ranging from DR 97 for npm to DR 70 for Hex.pm, which influences the SEO value of homepage links published through each registry.The packages themselves focus on three utility domains: text processing (tokenization, readability scoring, string normalization), developer tooling helpers (configuration parsing, environment management), and data format utilities (JSON validation, CSV parsing with type inference). Each package is implemented natively for its target ecosystem rather than being auto-generated from a single source, ensuring idiomatic APIs and proper integration with each language build toolchain.The report includes metrics from the developer blog documenting design decisions, cross-ecosystem patterns, and lessons learned from maintaining packages across registries with different conventions for documentation, testing, versioning, and dependency management.This sample contains 14 records representing one package per registry. The full ecosystem includes 4 packages per major registry (Python, JavaScript, Ruby, Rust) and 1-2 packages per specialized registry (Go, PHP, Dart, Elixir), totaling approximately 32 published packages. All packages are open-source with MIT or Apache 2.0 licenses and include comprehensive test suites validated against each language current stable release. Registry metrics are updated monthly from public API data. More details at mohitkhare.me.
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Metrics Over Time
Publication Details
Subfield
Information Systems
Field
Computer Science
Domain
Physical Sciences
Confidence Score
46%
Source
Scholar Data Model