A universal client for depositing and accessing research data anywhere
A universal client for depositing and accessing research data anywhere. Currently supported services are zenodo and figshare.
View DocumentationGenerate Citation File Format (cff) Metadata for R Packages
The Citation File Format version 1.2.0 doi:10.5281/zenodo.5171937 is a human and machine readable file format which provides citation metadata for software. This package provides core utilities to generate and validate this metadata.
View DocumentationSemantically Rich I/O for the NeXML Format
Provides access to phyloinformatic data in NeXML format. The package should add new functionality to R such as the possibility to manipulate NeXML objects in more various and refined way and compatibility with ape objects.
View DocumentationRead and Write Frictionless Data Packages
Read and write Frictionless Data Packages. A Data Package (https://specs.frictionlessdata.io/data-package/) is a simple container format and standard to describe and package a collection of (tabular) data. It is typically used to publish FAIR (https://www.go-fair.org/fair-principles/) and open datasets.
View DocumentationGenerate CodeMeta Metadata for R Packages
The Codemeta Project defines a JSON-LD format for describing software metadata, as detailed at https://codemeta.github.io. This package provides utilities to generate, parse, and modify codemeta.json files automatically for R packages, as well as tools and examples for working with codemeta.json JSON-LD more generally.
View DocumentationManaging Larger Data on a GitHub Repository
Because larger (> 50 MB) data files cannot easily be committed to git, a different approach is required to manage data associated with an analysis in a GitHub repository. This package provides a simple work-around by allowing larger (up to 2 GB) data files to piggyback on a repository as assets attached to individual GitHub releases. These files are not handled by git in any way, but instead are uploaded, downloaded, or edited directly by calls through the GitHub API. These data files can be versioned manually by creating different releases. This approach works equally well with public or private repositories. Data can be uploaded and downloaded programmatically from scripts. No authentication is required to download data from public repositories.
View DocumentationRead and Write Ecological Metadata Language Files
Work with Ecological Metadata Language (EML) files. EML is a widely used metadata standard in the ecological and environmental sciences, described in Jones et al. (2006), doi:10.1146/annurev.ecolsys.37.091305.110031.
View DocumentationCreate Lightweight Schema.org Descriptions of Data
The goal of dataspice is to make it easier for researchers to create basic, lightweight, and concise metadata files for their datasets. These basic files can then be used to make useful information available during analysis, create a helpful dataset “README” webpage, and produce more complex metadata formats to aid dataset discovery. Metadata fields are based on the Schema.org and Ecological Metadata Language standards.
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