{"data":{"id":"10.6084/m9.figshare.1219369","type":"dois","attributes":{"doi":"10.6084/m9.figshare.1219369","prefix":"10.6084","suffix":"m9.figshare.1219369","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Schmidt, Thomas","nameType":"Personal","givenName":"Thomas","familyName":"Schmidt","affiliation":[],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8587-4177","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"2014 Env Microbiol, Supplementary Tables (S1-S9)"}],"publisher":"figshare","container":{},"publicationYear":2014,"subjects":[{"subject":"Computational Biology"},{"subject":"60102 Bioinformatics","subjectScheme":"FOR"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Microbiology"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Biological sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Evolutionary Biology"}],"contributors":[],"dates":[{"date":"2014-10-27","dateType":"Created"},{"date":"2014-10-28","dateType":"Updated"},{"date":"2014","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["4409734 Bytes"],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"# Table_S1.pdf\u003cbr\u003eComposition of an artificial ‘local’ dataset of broad ecological range (BER). A total of 53,999 16S sequences from 18 studies were selected to generate the dataset; additional information, and detailed references are provided in the table. # Table_S2.xlsx\u003cbr\u003eTotal OTU counts per method when clustering a global dataset of 887,870 16S sequences. OTU counts are given per method for different thresholds. For UPARSE, the respective number of clustered sequences that mapped to the UCHIME-filtered dataset is also provided (as UPARSE implements differential on-the-fly chimera filtering, removing different sets of sequences at different clustering thresholds). # Table_S3.xlsx\u003cbr\u003eTable S3. Estimates of α- and β-diversity when clustering the HSM dataset to 97% sequence similarity according to different methods. The table provides raw data of diversity estimates across 21 skin samples for every method. Moreover, trends between methods (Pearson correlation of diversity estimates), absolute shifts (as binary log-ratio) and shift significance (as p-values in one-sided Mann-Whitney-U tests) are also provided for every index. Finally, an overview of the HSM dataset (sequence counts and internal sequence similarities per habitat), as well as pairwise partition similarities between methods at 97% clustering are also provided. # Table_S4.xlsx\u003cbr\u003eAdjusted Mutual Information (AMI) between methods across thresholds when clustering the HSM dataset. Values as shown in Figure S1. # Table_S5.xlsx\u003cbr\u003eNormalized Mutual Information (NMI) between methods across thresholds when clustering the HSM dataset. Values as shown in Figure S2. # Table_S6.xlsx\u003cbr\u003eAdjusted Rand Index (ARI) between methods across thresholds when clustering the HSM dataset. Values as shown in Figure S3. # Table_S7.xlsx\u003cbr\u003eAdjusted Mutual Information (AMI) between methods across thresholds when clustering the global dataset of 887,870 16S sequences. Values as shown in Figure 4 in the main text. Missing values for AL at thresholds \u0026lt;92% and UCLUST / UPARSE \u0026gt;99% as clustering to these thresholds was prohibited by memory requirements (see Text S1). # Table_S8.xlsx\u003cbr\u003eNormalized Mutual Information (NMI) between methods across thresholds when clustering the global dataset of 887,870 16S sequences. Values as shown in Figure S4. Missing values for AL at thresholds \u0026lt;92% and UCLUST / UPARSE \u0026gt;99% as clustering to these thresholds was prohibited by memory requirements (see Text S1). # Table_S9.xlsx\u003cbr\u003eAdjusted Rand Index (ARI) between methods across thresholds when clustering the global dataset of 887,870 16S sequences. Values as shown in Figure S5. Missing values for AL at thresholds \u0026lt;92% and UCLUST / UPARSE \u0026gt;99% as clustering to these thresholds was prohibited by memory requirements (see Text S1).","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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