Selected facets for DataCite Repositories

What is a facet? A facet is a metadata element, usually from a controlled list, that provides counts of records in a query result with particular values for the metadata element. The DataCite JSON Response includes data on a variety of facets for each query done using the DataCite API. Repository Facets DataCite Commons uses facets on repository pages to provide an overview of repositories. For example, the Metadata Game Changers Commons page shows publication year, work types, licenses, creators and contributors, and some other facets as graphics and lists. The facets provided by DataCite can be used to 1) understand characteristics of DataCite metadata, 2) understand some aspects of repository completeness, and 3) provide overviews of repositories. Using Facets to Understand DataCite Metadata DataCite includes facets and facet values in all query results, so they are a useful tool for answering some "big picture" questions about DataCite metadata. Some of these questions were explored during 2022 in DataCite Facets: Understanding DataCite Usage using a tool called DataCite Facets. DataCite Facets and Repository Overviews DataCite facets can be used to provide overviews of any DataCite Repository and understand some characteristics of the repositories. They can also be used, in some cases, to provide insights into some aspects of repository completeness. Repository Facets and Metadata Completeness Many useful repository measures focus on completeness of the metadata, i.e., the portion of records in the repository that include some metadata element. The DataCite facet data can provide some insight into completeness, but we must keep in mind that the facet data are limited to top ten values for most facets (except for published and resourceTypes, which can be > 10). The blog DataCite Facets and Metadata Completeness describes how some facets can be used to provide insights into metadata completeness. This dataset provides selected facets downloaded using the DataCite API and associated statistics as a comma-separated-value (CSV) file. Column definitions: The dataset includes a number of columns for the selected facets: Statistic Description number The number of facet values max The number of occurrences of the most common facet value common The most common facet value total The total number of records in the top 10, i.e. the total listed in the facets homogeneity (HI) An indicator of homogeneity of the facet: maximum count / total count (0.1 = uniform, 1.0 = single item) coverage The % of all records covered by the top 10 (numbers close to 100% are good)何为分面（facet）？ 分面是一类元数据元素，通常取自受控列表，用于统计查询结果中该元数据元素取特定值的记录数量。DataCite JSON 响应（DataCite JSON Response）会为使用DataCite API发起的每一次查询返回各类分面的相关数据。 ### 仓储分面 DataCite公共平台会在仓储页面中使用分面，以直观展示仓储概况。例如，“元数据变革者（Metadata Game Changers）”公共平台页面会以图表与列表形式展示出版年份、作品类型、授权协议、创作者与贡献者等多项分面信息。 DataCite提供的分面可用于以下三类场景：1）解析DataCite元数据的特征；2）研判仓储完整性的部分维度；3）为仓储提供概况展示。 ### 利用分面解析DataCite元数据 DataCite会在所有查询结果中包含分面与分面值信息，因此是解答有关DataCite元数据“宏观层面”问题的实用工具。2022年，研究人员曾借助一款名为“DataCite 分面工具（DataCite Facets）”的程序，在《DataCite 分面：解析DataCite使用情况》（DataCite Facets: Understanding DataCite Usage）一文中探索了部分此类问题。 ### DataCite分面与仓储概况 DataCite分面可用于生成任意DataCite仓储的概况，并解析该仓储的部分特征。在部分场景下，还可用于深入研判仓储完整性的相关维度。 ### 仓储分面与元数据完整性 诸多实用的仓储评估指标均聚焦于元数据完整性，即仓储中包含特定元数据元素的记录占比。DataCite分面数据可为此类完整性研判提供参考，但需注意：大多数分面的分面值仅展示前10项（出版时间与资源类型除外，其展示项可超过10项）。博客文章《DataCite 分面与元数据完整性》（DataCite Facets and Metadata Completeness）阐述了如何利用部分分面深入解析元数据完整性。 ### 数据集概览 本数据集包含通过DataCite API下载的精选分面数据与相关统计值，存储格式为逗号分隔值（CSV，Comma-Separated Values）文件。 #### 字段说明 本数据集包含以下与精选分面相关的字段： | 统计项 | 说明 | | ---- | ---- | | number | 分面值的总数量 | | max | 出现频次最高的分面值的出现次数 | | common | 出现频次最高的分面值 | | total | 前10项分面值对应的记录总数，即分面列表中列出的总记录数 | | homogeneity (HI) | 表征分面分布均匀性的指标：最高频次计数 / 总记录计数（0.1代表分布均匀，1.0代表仅存在单一取值） | | coverage | 前10项分面值覆盖的记录占总记录的百分比（数值接近100%为优）