FuncTree: Functional Analysis and Visualization for Large-Scale Omics Data
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{"title"=>"FuncTree: Functional analysis and visualization for large-scale omics data", "type"=>"journal", "authors"=>[{"first_name"=>"Takeru", "last_name"=>"Uchiyama", "scopus_author_id"=>"56643967300"}, {"first_name"=>"Mitsuru", "last_name"=>"Irie", "scopus_author_id"=>"56644076900"}, {"first_name"=>"Hiroshi", "last_name"=>"Mori", "scopus_author_id"=>"36963737400"}, {"first_name"=>"Ken", "last_name"=>"Kurokawa", "scopus_author_id"=>"7402184954"}, {"first_name"=>"Takuji", "last_name"=>"Yamada", "scopus_author_id"=>"55585001500"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"25974630", "doi"=>"10.1371/journal.pone.0126967", "sgr"=>"84929340219", "isbn"=>"1932-6203", "scopus"=>"2-s2.0-84929340219", "issn"=>"19326203", "pui"=>"604377930"}, "id"=>"690431bc-b717-368b-b5f1-c7d377879127", "abstract"=>"Exponential growth of high-throughput data and the increasing complexity of omics information have been making processing and interpreting biological data an extremely difficult and daunting task. Here we developed FuncTree (http://bioviz.tokyo/functree), a web-based application for analyzing and visualizing large-scale omics data, including but not limited to genomic, metagenomic, and transcriptomic data. FuncTree allows user to map their omics data onto the \"Functional Tree map\", a predefined circular dendrogram, which represents the hierarchical relationship of all known biological functions defined in the KEGG database. This novel visualization method allows user to overview the broad functionality of their data, thus allowing a more accurate and comprehensive understanding of the omics information. FuncTree provides extensive customization and calculation methods to not only allow user to directly map their omics data to identify the functionality of their data, but also to compute statistically enriched functions by comparing it to other predefined omics data. We have validated FuncTree's analysis and visualization capability by mapping pan-genomic data of three different types of bacterial genera, metagenomic data of the human gut, and transcriptomic data of two different types of human cell expression. All three mapping strongly confirms FuncTree's capability to analyze and visually represent key functional feature of the omics data. We believe that FuncTree's capability to conduct various functional calculations and visualizing the result into a holistic overview of biological function, would make it an integral analysis/visualization tool for extensive omics base research.", "link"=>"http://www.mendeley.com/research/functree-functional-analysis-visualization-largescale-omics-data", "reader_count"=>45, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>3, "Researcher"=>21, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>6, "Student > Postgraduate"=>3, "Student > Master"=>6, "Student > Bachelor"=>3, "Professor"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>3, "Researcher"=>21, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>6, "Student > Postgraduate"=>3, "Student > Master"=>6, "Student > Bachelor"=>3, "Professor"=>1}, "reader_count_by_subject_area"=>{"Environmental Science"=>3, "Biochemistry, Genetics and Molecular Biology"=>12, "Nursing and Health Professions"=>1, "Agricultural and Biological Sciences"=>19, "Computer Science"=>8, "Immunology and Microbiology"=>1, "Mathematics"=>1}, "reader_count_by_subdiscipline"=>{"Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>19}, "Computer Science"=>{"Computer Science"=>8}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>12}, "Mathematics"=>{"Mathematics"=>1}, "Environmental Science"=>{"Environmental Science"=>3}}, "reader_count_by_country"=>{"Netherlands"=>1, "Japan"=>1, "Brazil"=>1, "Switzerland"=>1}, "group_count"=>3}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2067943"], "description"=>"<p>Color node represents biological functions that were enriched in the “Epithelial cell of small intestine”(Blue) and “Alternatively activated Macrophage”(Red). Node size corresponds to inverse p-value of enrichment analysis, based on Fisher’s exact test, of the 590 over-expressed genes for “Epithelial cell of small intestine” and 309 over-expressed genes for “Alternatively activated Macrophage”, detected in microarray experiment. Digestive functions including \"Fat digestion and absorption (map04975)\", \"Vitamin digestion and absorption (map04977)\", \"Mineral absorption (map04978)\", \"Bile secretion (map04976)\", and \"Pancreatic secretion (map04972)\" were uniquely enriched, validating our knowledge about intestinal epithelium. Pathways under the category “Transports and catabolism” including \"Lysosome (map04142)\" and \"Phagosome (map04145)\" were uniquely enriched in “Alternatively activated Macrophage”, Pathways categorized under \"Digestive system\", including \"Fat digestion and absorption (map04975)\", \"Vitamin digestion and absorption (map04977)\", \"Mineral absorption (map04978)\", \"Bile secretion (map04976)\", and \"Pancreatic secretion (map04972)\" were significantly enriched in \"Epithelial cell of small intestine\", which validates our knowledge that intestinal epithelium is where many of the digestive process takes place (<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0126967#pone.0126967.s003\" target=\"_blank\">S3 Table</a>). Another characteristic of this cell was the enrichment of pathways categorized under \"Endocrine system\" including, \"PPAR signaling pathway (map03320)\", \"Leukocyte transendothelial migration (map04670)\", \"Renin-angiotensin system (map04614)\", \"Insulin secretion (map04911)\", \"Ovarian steroidogenesis (map04913)\", \"Melanogenesis (map04916)\", and \"Thyroid hormone synthesis (map04918)\". This also is consistent with known knowledge, that the epithelium of the small intestine is also comprised of enteroendocrine cell which produces gastrointestinal hormones to initiate digestive response and protective function against harmful substances [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0126967#pone.0126967.ref021\" target=\"_blank\">21</a>]. Functions that characterize the “Alternatively activated macrophage” were pathways categorized under \"Transports and catabolism\" which includes, \"Lysosome (map04142)\", \"Phagosome (map04145)\", \"Cell adhesion molecules (map04514)\", and \"Cytokine-cytokine receptor interaction (map04060)\". “Lysosome (map04142)” and “Phagosome (map04145)” plays a crucial role during the process where macrophage ingests target pathogens.</p>", "links"=>[], "tags"=>["omics data", "omics information", "FuncTree", "omics base research", "kegg", "transcriptomic data", "predefined omics data", "novel visualization method"], "article_id"=>1414945, "categories"=>["Biological Sciences"], "users"=>["Takeru Uchiyama", "Mitsuru Irie", "Hiroshi Mori", "Ken Kurokawa", "Takuji Yamada"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0126967.g006", "stats"=>{"downloads"=>3, "page_views"=>30, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Transcriptomic_mapping_Epithelial_cell_of_small_intestine_and_alternatively_activated_macrophage_/1414945", "title"=>"Transcriptomic mapping: Epithelial cell of small intestine and alternatively activated macrophage.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-14 03:00:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/2067944"], "description"=>"<p>Gene list of 2,655 prokaryotic organisms were downloaded from the KEGG database (<a href=\"http://www.genome.jp/kegg/\" target=\"_blank\">http://www.genome.jp/kegg/</a>). Using the hierarchical structure of the “Functional tree”, each nodes enrichment were calculated by conducting Wilcoxon signed-rank test between, genes of the tested bacterial genera (<i>Escherichia</i>, <i>Mycobacterium</i>, <i>Streptomyces</i>) that was assigned to that node, and the genes of the remaining prokaryotes assigned to that node. Each node’s value was corrected using node coverage to produce the input data. Final visualization and normalization was conducted using FuncTree.</p>", "links"=>[], "tags"=>["omics data", "omics information", "FuncTree", "omics base research", "kegg", "transcriptomic data", "predefined omics data", "novel visualization method"], "article_id"=>1414946, "categories"=>["Biological Sciences"], "users"=>["Takeru Uchiyama", "Mitsuru Irie", "Hiroshi Mori", "Ken Kurokawa", "Takuji Yamada"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0126967.g007", "stats"=>{"downloads"=>3, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Flowchart_for_pan_genomic_mapping_/1414946", "title"=>"Flowchart for pan-genomic mapping.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-14 03:00:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/2067945"], "description"=>"<p>KO composition for gut environment and non-gut environment were downloaded from the BGI database (<a href=\"http://meta.genomics.cn\" target=\"_blank\">http://meta.genomics.cn</a>) and HMP DACC (<a href=\"http://www.hmpdacc.org/\" target=\"_blank\">http://www.hmpdacc.org/</a>). Calculation of enriched KO was conducted using Wilcoxon signed-rank test between relative abundance of the gut environment and the relative abundance of non-gut environment. Using the hierarchical structure of the “Functional tree”, calculation for the enrichment of the remaining four layers was conducted using Fisher’s exact test. Final visualization and normalization was conducted using FuncTree.</p>", "links"=>[], "tags"=>["omics data", "omics information", "FuncTree", "omics base research", "kegg", "transcriptomic data", "predefined omics data", "novel visualization method"], "article_id"=>1414947, "categories"=>["Biological Sciences"], "users"=>["Takeru Uchiyama", "Mitsuru Irie", "Hiroshi Mori", "Ken Kurokawa", "Takuji Yamada"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0126967.g008", "stats"=>{"downloads"=>2, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Flowchart_for_human_gut_metagenomic_mapping_/1414947", "title"=>"Flowchart for human gut metagenomic mapping.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-14 03:00:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/2067946", "https://ndownloader.figshare.com/files/2067947", "https://ndownloader.figshare.com/files/2067948", "https://ndownloader.figshare.com/files/2067949"], "description"=>"<div><p>Exponential growth of high-throughput data and the increasing complexity of omics information have been making processing and interpreting biological data an extremely difficult and daunting task. Here we developed FuncTree (<a href=\"http://bioviz.tokyo/functree\" target=\"_blank\">http://bioviz.tokyo/functree</a>), a web-based application for analyzing and visualizing large-scale omics data, including but not limited to genomic, metagenomic, and transcriptomic data. FuncTree allows user to map their omics data onto the “Functional Tree map”, a predefined circular dendrogram, which represents the hierarchical relationship of all known biological functions defined in the KEGG database. This novel visualization method allows user to overview the broad functionality of their data, thus allowing a more accurate and comprehensive understanding of the omics information. FuncTree provides extensive customization and calculation methods to not only allow user to directly map their omics data to identify the functionality of their data, but also to compute statistically enriched functions by comparing it to other predefined omics data. We have validated FuncTree’s analysis and visualization capability by mapping pan-genomic data of three different types of bacterial genera, metagenomic data of the human gut, and transcriptomic data of two different types of human cell expression. All three mapping strongly confirms FuncTree’s capability to analyze and visually represent key functional feature of the omics data. We believe that FuncTree’s capability to conduct various functional calculations and visualizing the result into a holistic overview of biological function, would make it an integral analysis/visualization tool for extensive omics base research.</p></div>", "links"=>[], "tags"=>["omics data", "omics information", "FuncTree", "omics base research", "kegg", "transcriptomic data", "predefined omics data", "novel visualization method"], "article_id"=>1414948, "categories"=>["Biological Sciences"], "users"=>["Takeru Uchiyama", "Mitsuru Irie", "Hiroshi Mori", "Ken Kurokawa", "Takuji Yamada"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0126967.s001", "https://dx.doi.org/10.1371/journal.pone.0126967.s002", "https://dx.doi.org/10.1371/journal.pone.0126967.s003", "https://dx.doi.org/10.1371/journal.pone.0126967.s004"], "stats"=>{"downloads"=>39, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_FuncTree_Functional_Analysis_and_Visualization_for_Large_Scale_Omics_Data_/1414948", "title"=>"FuncTree: Functional Analysis and Visualization for Large-Scale Omics Data", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-05-14 03:00:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/2067921"], "description"=>"<p>The Functional Tree is a circular dendrogram that represents the hierarchical relationship of known biological functions defined in the KEGG database. Each layer of the tree corresponds to each functional category, “Biological Category”, “Biological Process”, “KEGG Pathway”, “KEGG Module”, and the outer most layer corresponds to “KEGG Orthology”. Each layer contains multiple nodes that correspond to the biological functions that are assigned to that functional category.</p>", "links"=>[], "tags"=>["omics data", "omics information", "FuncTree", "omics base research", "kegg", "transcriptomic data", "predefined omics data", "novel visualization method"], "article_id"=>1414929, "categories"=>["Biological Sciences"], "users"=>["Takeru Uchiyama", "Mitsuru Irie", "Hiroshi Mori", "Ken Kurokawa", "Takuji Yamada"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0126967.g001", "stats"=>{"downloads"=>2, "page_views"=>25, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overview_of_the_Functional_Tree_map_/1414929", "title"=>"Overview of the Functional Tree map.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-14 03:00:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/2067925"], "description"=>"<p>FuncTree is a web-based application that is capable of analyzing and visualizing the functional characteristic of large-scale omics data. As an input, FuncTree expects either a list of node-value association, which associates each node with a designated value, or a list of genes. Value for each node would have to be calculated locally by user, and may represent a number of different variables such as relative abundance. Gene list may represent a list of over-expressed genes in a microarray experiment. Depending on the Input, FuncTree is able to perform various visualizations such as, (i) Direct mapping: directly map the value onto the designated node, (ii) Internal node calculation: Calculate the functional potential of the upper four layer from KO composition, (iii) Statistical analysis: Conduct enrichment analysis for each layer from KO composition against a predefined background data of several human metagenomic dataset, and (iv) Gene set enrichment analysis: Conduct enrichment analysis for each layer from gene list.</p>", "links"=>[], "tags"=>["omics data", "omics information", "FuncTree", "omics base research", "kegg", "transcriptomic data", "predefined omics data", "novel visualization method"], "article_id"=>1414933, "categories"=>["Biological Sciences"], "users"=>["Takeru Uchiyama", "Mitsuru Irie", "Hiroshi Mori", "Ken Kurokawa", "Takuji Yamada"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0126967.g002", "stats"=>{"downloads"=>2, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Flowchart_for_data_mapping_using_FuncTree_/1414933", "title"=>"Flowchart for data mapping using FuncTree.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-14 03:00:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/2067929"], "description"=>"<p>Color node represents biological functions that were uniquely enriched in the pan-genomic data of <i>Escherichia</i> (green), <i>Mycobacterium</i> (red), and <i>Streptomyces</i> (blue). Node size corresponds with the inverse p-value calculated using Wilcoxon signed-rank test, representing how “uniquely” enriched that function was. “Lipopolysaccharide biosynthesis (map00540)”, “Flagellar assembly (map02040)”, and “D-Allose transport system (M00217)” were uniquely enriched for <i>Escherichia</i>, \"Steroid degradation (map00984)\", \"DevS-DevR (redox response) two-component regulatory system (M00482)\", and \"TrcS-TrcR two-component regulatory system (M00463)\" for <i>Mycobacterium</i>, \"Clavulanic acid biosynthesis (map00331)\", \"Xylobiose transport system (M00619)\", and \"N-Acetylglucosamine transport system (M00205)\" for <i>Streptomyces</i>.</p>", "links"=>[], "tags"=>["omics data", "omics information", "FuncTree", "omics base research", "kegg", "transcriptomic data", "predefined omics data", "novel visualization method"], "article_id"=>1414937, "categories"=>["Biological Sciences"], "users"=>["Takeru Uchiyama", "Mitsuru Irie", "Hiroshi Mori", "Ken Kurokawa", "Takuji Yamada"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0126967.g003", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pan_genomic_mapping_Functional_enrichment_of_Escherichia_Mycobacterium_and_Streptomyces_/1414937", "title"=>"Pan-genomic mapping: Functional enrichment of <i>Escherichia</i>, <i>Mycobacterium</i>, and <i>Streptomyces</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-14 03:00:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/2067932"], "description"=>"<p>Color node represents biological functions that were highly variant among fecal samples from different individuals. Node size corresponds to the value of standard deviation of the KO’s relative abundance assigned to that function. “Other glycan degradation (map00511)”, “Glycosaminoglycan degradation (map00531)”, and “Bacterial chemotaxis (map02030)” showed high variability among the KEGG Pathway layer, while “Pectin degradation (M00081)”, \"Peptides/nickel transport system (M00239)\", \"Ribose transport system (M00212)\", and \"ABC-2 type transport system (M00254)\" showed high variability among the KEGG Module layer. The difference in variability pattern between the KEGG Pathway layer and KEGG Module layer in this mapping illustrate the potential problem of focusing on a particular functional category, and shows the necessity for overviewing the broad functional potential across different functional layers.</p>", "links"=>[], "tags"=>["omics data", "omics information", "FuncTree", "omics base research", "kegg", "transcriptomic data", "predefined omics data", "novel visualization method"], "article_id"=>1414940, "categories"=>["Biological Sciences"], "users"=>["Takeru Uchiyama", "Mitsuru Irie", "Hiroshi Mori", "Ken Kurokawa", "Takuji Yamada"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0126967.g004", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Functional_variability_of_human_gut_microbiota_/1414940", "title"=>"Functional variability of human gut microbiota.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-14 03:00:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/2067941"], "description"=>"<p>Color node represents biological functions of the human gut microbiota that were uniquely enriched compared to microbiota of other human body sites. For the KO functional layer, node size corresponds to the inverse p-value of Wilcoxon signed-rank test, and for the remaining functional layers, node size corresponds to the inverse p-value of Fisher’s exact test. Pathways that were uniquely enriched in the human gut microbiota were, “Flagellar assembly (map02040)”, “Bacterial chemotaxis (map02030)”, “Methane metabolism (map00680)”, and “Glycosaminoglycan degradation (map00531)”. Modules including “Type III secretion system (map03070)” and “Cobalamin biosynthesis, cobinamide = > cobalamin (M00122)” were shown to be highly enriched on the KEGG Module layer, but not so on the KEGG Pathway layer, which exemplifies a situation where the enrichment pattern differs based on different functional layer, thus supporting the importance for a broad visual overview of the data.</p>", "links"=>[], "tags"=>["omics data", "omics information", "FuncTree", "omics base research", "kegg", "transcriptomic data", "predefined omics data", "novel visualization method"], "article_id"=>1414943, "categories"=>["Biological Sciences"], "users"=>["Takeru Uchiyama", "Mitsuru Irie", "Hiroshi Mori", "Ken Kurokawa", "Takuji Yamada"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0126967.g005", "stats"=>{"downloads"=>5, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Functional_enrichment_of_human_gut_microbiota_/1414943", "title"=>"Functional enrichment of human gut microbiota.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-14 03:00:00"}

PMC Usage Stats | Further Information

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  • {"unique-ip"=>"22", "full-text"=>"21", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"9", "supp-data"=>"4", "cited-by"=>"2", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"19", "full-text"=>"18", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"14", "full-text"=>"19", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}
  • {"unique-ip"=>"12", "full-text"=>"15", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}
  • {"unique-ip"=>"13", "full-text"=>"16", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"4", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}
  • {"unique-ip"=>"16", "full-text"=>"16", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"10"}
  • {"unique-ip"=>"10", "full-text"=>"4", "pdf"=>"7", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"4", "cited-by"=>"0", "year"=>"2019", "month"=>"12"}
  • {"unique-ip"=>"6", "full-text"=>"4", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"2"}
  • {"unique-ip"=>"6", "full-text"=>"6", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"3"}
  • {"unique-ip"=>"5", "full-text"=>"6", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"4"}
  • {"unique-ip"=>"13", "full-text"=>"14", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"5"}
  • {"unique-ip"=>"6", "full-text"=>"6", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"6"}
  • {"unique-ip"=>"9", "full-text"=>"11", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"7"}
  • {"unique-ip"=>"10", "full-text"=>"6", "pdf"=>"6", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"4", "cited-by"=>"0", "year"=>"2020", "month"=>"8"}
  • {"unique-ip"=>"12", "full-text"=>"12", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"9"}
  • {"unique-ip"=>"14", "full-text"=>"14", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2020", "month"=>"10"}
  • {"unique-ip"=>"6", "full-text"=>"5", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"11"}
  • {"unique-ip"=>"10", "full-text"=>"8", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"12"}
  • {"unique-ip"=>"7", "full-text"=>"12", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2021", "month"=>"1"}

Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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