{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ " ![FREYA Logo](https://github.com/datacite/pidgraph-notebooks-python/blob/master/images/freya_200x121.png?raw=true) | [FREYA](https://www.project-freya.eu/en) WP2 [User Story 8](https://github.com/datacite/freya/issues/38) | As a longitudinal study, I want to be able to deduplicate the metrics/impact for our data, so that I can see the impact of our study’s data as a whole.\n", " :------------- | :------------- | :-------------\n", "\n", "Scientific datasets may be composed of individual components, whereby the parent and each component are identified by a different DOI and hence can be cited, viewed and downloaded individually. In order to assess the reuse such datasets, their authors must be able to aggregate views, downloads and citations metrics across all the dataset components.
\n", "This notebook uses the [DataCite GraphQL API](https://api.datacite.org/graphql) to retrieve all parts of the dataset: [2014 TCCON Data Release dataset](https://doi.org/10.14291/tccon.ggg2014), so that its overall impact can be quantified.\n", "\n", "**Goal**: By the end of this notebook, for a given dataset with constituent parts, you should be able to display:\n", "- Counts of citations, views and downloads metrics, aggregated across the parent dataset and all its parts;\n", "- An interactive stacked bar plot showing how the metric counts of each part contribute to the corresponding aggregated metric counts, e.g.