https://doi.org/10.5061/dryad.7d7wm3822

Give a brief summary of dataset contents, contextualized in experimental procedures and results.

Description of the data and file structure

The dataset file contains five sheets: (1) taxonomic revisions of published acanthomorph specimens from the Doushantuo Formation from 55 previous publications that provide clear, published, microfossil image and clearly designated stratigraphic horizons; and updated occurrence data of Doushantuo acanthomorphic acritarchs and certain sphaeromorphic taxa considered as stratigraphically useful (i.e., Schizofusa zangwenlongii Grey, 2005) based on the revised taxonomy, with each occurrence assigned to a specific fossil collection; (2) acritarch relative abundance data from this and six previous publications for rarefaction analysis in this study; (3) presence/absence data of the Doushantuo acritarchs (based on occurrence data in sheet 1) for NMDS and network analysis with R in this study; (4) species abbreviations in sheets 2 and 3; (5) species loadings generated by the NMDS analysis and plotted in Fig. 41.3 of this study.

In sheet 1 ("updated occurrence data"), each row represents a published specimen with at least one figure, from references labelled from 2 to 56 (1 represent the current study). Each column represents an attribute of that specimen, including its source of publication (column A) and reference number for that publication (column B) used in Table 3 of the paper related to this dataset, figure number (column C), published taxonomy (column D), locality and sedimentary facies (columns E–G), stratigraphic horizon (column H, Member 1/2/3/4 for specimens obtained from the Doushantuo Formation in the Yangtze Gorges, Changyang, and Zhangjiajie areas, n/a for specimens from other localities), collection ID (column I, each collection is defined as an acanthomorphic acritarch assemblage from a stratigraphic unit at a specific area reported in an independent study, and represents a sample unit in the NMDS or network analysis as a vector composed of presence/absence data of all acritarch species), current classification (column J), and source of re-identification (column K, publication/the current study that reassigned the specimen to taxonomy in column J, or n/a if taxonomy of the specimen is not changed/cannot be determined). "n/a" represents not applicable (not provided by the source publication unless otherwise specified above).

Sheet 2 ("abundance for rarefaction") provides abundance (specimen number) of each acritarch species (column A, full names are provided in sheet 4) recovered by one specific study at 11 sections (columns B–L). Numbers in each cell represents specimen number of one species at one section. Data sources can be found in Table 2 and the Result section of the paper related to this dataset. This table need to be transposed to be directly read and applied in rarefaction analysis in R using the rarefy function in the vegan package.

Sheet 3 ("presence for NMDS & network") provides occurrence data (presence, 1/absence, 0) of each species (row 1, full names are provided in sheet 4) from 83 collections (rows 2–84, definition of a collection see above), based on taxonomic identification and update given in sheet 1. This table can be directly read and applied in NMDS/network analysis in R (for packages and functions that performed these analyses see the Materials and Methods section of the paper related to this package.

Sheet 4 ("species abbr") lists species names for abbreviations of species in sheets 2 and 3.

Sheet 5 ("NMDS species loadings") provides loading values of each species on the NMDS 1 and NMDS 2 axis in the adopted NMDS result of this study. Plot of the loading numbers is shown in Figure 41.3 of the paper related to this dataset.

Sharing/Access information

Data sources are provided in caption of Table 3 and References of the paper related to this dataset.

Code/Software

The species loadings data (in sheet 5, NMDS species loadings) are generated with R using the metaMDS function from the vegan package in Rstudio (R Core Team, 2018), with distance using the Raup-Crick similarity and maximum iteration = 100.