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{"title"=>"Computational research on mobile pastoralism using agent-based modeling and satellite imagery", "type"=>"journal", "authors"=>[{"first_name"=>"Takuto", "last_name"=>"Sakamoto", "scopus_author_id"=>"57188732318"}], "year"=>2016, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84962535141", "pui"=>"609059824", "isbn"=>"1932-6203", "scopus"=>"2-s2.0-84962535141", "doi"=>"10.1371/journal.pone.0151157", "issn"=>"19326203"}, "id"=>"0c3eacca-2e16-3b8d-a4dc-e515ea7aa2a9", "abstract"=>"Dryland pastoralism has long attracted considerable attention from researchers in diverse fields. However, rigorous formal study is made difficult by the high level of mobility of pastoralists as well as by the sizable spatio-temporal variability of their environment. This article presents a new computational approach for studying mobile pastoralism that overcomes these issues. Combining multi-temporal satellite images and agent-based modeling allows a comprehensive examination of pastoral resource access over a realistic dryland landscape with unpredictable ecological dynamics. The article demonstrates the analytical potential of this approach through its application to mobile pastoralism in northeast Nigeria. Employing more than 100 satellite images of the area, extensive simulations are conducted under a wide array of circumstances, including different land-use constraints. The simulation results reveal complex dependencies of pastoral resource access on these circumstances along with persistent patterns of seasonal land use observed at the macro level.", "link"=>"http://www.mendeley.com/research/computational-research-mobile-pastoralism-using-agentbased-modeling-satellite-imagery-1", "reader_count"=>9, "reader_count_by_academic_status"=>{"Researcher"=>3, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>2, "Student > Master"=>2}, "reader_count_by_user_role"=>{"Researcher"=>3, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>2, "Student > Master"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Environmental Science"=>2, "Agricultural and Biological Sciences"=>3, "Physics and Astronomy"=>1, "Psychology"=>1, "Social Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>3}, "Unspecified"=>{"Unspecified"=>1}, "Environmental Science"=>{"Environmental Science"=>2}}, "reader_count_by_country"=>{"Netherlands"=>1, "Japan"=>1}, "group_count"=>0}

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• http://doi.org/10.1016/j.agsy.2019.02.004
• http://doi.org/10.1371/journal.pone.0166551
• http://doi.org/10.1016/j.agsy.2018.11.003

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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/4833673"], "description"=>"<p>Each of the four snapshots was taken at the end of a simulation run; these runs were conducted under identical conditions. NOMAD POPULATION was set to 1 for the purpose of illustration.</p>", "links"=>[], "tags"=>["simulation", "resource access", "approach", "circumstance", "Satellite Imagery Dryland pastoralism", "article", "100 satellite images"], "article_id"=>3110533, "categories"=>["Cell Biology", "Biotechnology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified"], "users"=>["Takuto Sakamoto"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0151157.g008", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Evolved_ROUTEs_/3110533", "title"=>"Evolved ROUTEs.", "pos_in_sequence"=>9, "defined_type"=>1, "published_date"=>"2016-03-10 09:40:16"}
• {"files"=>["https://ndownloader.figshare.com/files/4833610"], "description"=>"<p>These maps were generated from the MODIS NDVI dataset (MYD13A3). The greener pixels indicate the sites that have more abundant vegetation, while the browner pixels correspond to those that have sparser vegetation.</p>", "links"=>[], "tags"=>["simulation", "resource access", "approach", "circumstance", "Satellite Imagery Dryland pastoralism", "article", "100 satellite images"], "article_id"=>3110470, "categories"=>["Cell Biology", "Biotechnology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified"], "users"=>["Takuto Sakamoto"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0151157.g003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Monthly_vegetation_changes_in_2014_/3110470", "title"=>"Monthly vegetation changes in 2014.", "pos_in_sequence"=>4, "defined_type"=>1, "published_date"=>"2016-03-10 09:40:16"}
• {"files"=>["https://ndownloader.figshare.com/files/4833874"], "description"=>"<p>The seasonal land-use maps with the corresponding kappa statistics (top row), the means of the ROUTE ranges (bottom left), and the means of the resources available to NOMADs (bottom right) are displayed. These, which are comparable to <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0151157#pone.0151157.g013\" target=\"_blank\">Fig 13</a> above, were derived from the simulations conducted at different levels of temporal control of the agricultural land access; otherwise, the settings were identical to the baseline conditions.</p>", "links"=>[], "tags"=>["simulation", "resource access", "approach", "circumstance", "Satellite Imagery Dryland pastoralism", "article", "100 satellite images"], "article_id"=>3110665, "categories"=>["Cell Biology", "Biotechnology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified"], "users"=>["Takuto Sakamoto"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0151157.g014", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Effects_of_cropland_expansion_on_the_model_/3110665", "title"=>"Effects of cropland expansion on the model.", "pos_in_sequence"=>15, "defined_type"=>1, "published_date"=>"2016-03-10 09:40:16"}
• {"files"=>["https://ndownloader.figshare.com/files/4833760"], "description"=>"<p>Each of the line graphs shows the dependence of the mean obtained resources on changes in the following parameters: NOMAD POPULATION (top left), CARRYING CAPACITY (top right), MOVE RANGE (bottom left), and GRAZE RANGE (bottom right).</p>", "links"=>[], "tags"=>["simulation", "resource access", "approach", "circumstance", "Satellite Imagery Dryland pastoralism", "article", "100 satellite images"], "article_id"=>3110608, "categories"=>["Cell Biology", "Biotechnology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified"], "users"=>["Takuto Sakamoto"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0151157.g012", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Effects_of_parameter_changes_on_resource_access_/3110608", "title"=>"Effects of parameter changes on resource access.", "pos_in_sequence"=>13, "defined_type"=>1, "published_date"=>"2016-03-10 09:40:16"}
• {"files"=>["https://ndownloader.figshare.com/files/4833691"], "description"=>"<p>As in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0151157#pone.0151157.g007\" target=\"_blank\">Fig 7</a>, each of the maps shown is a composite image of the dry season (January, February and March) and rainy season (July, August and September) distributions of land-use intensity. These maps were derived from simulation runs that were conducted under conditions in which one of the following parameters was changed from the baseline value: NOMAD POPULATION (top row), CARRYING CAPACITY (second row), MOVE RANGE (third row), and GRAZE RANGE (bottom).</p>", "links"=>[], "tags"=>["simulation", "resource access", "approach", "circumstance", "Satellite Imagery Dryland pastoralism", "article", "100 satellite images"], "article_id"=>3110548, "categories"=>["Cell Biology", "Biotechnology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified"], "users"=>["Takuto Sakamoto"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0151157.g009", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Comparison_of_distributions_of_land_use_intensity_different_parameter_conditions_/3110548", "title"=>"Comparison of distributions of land-use intensity (different parameter conditions).", "pos_in_sequence"=>10, "defined_type"=>1, "published_date"=>"2016-03-10 09:40:16"}
• {"files"=>["https://ndownloader.figshare.com/files/4833550"], "description"=>"<div><p>Dryland pastoralism has long attracted considerable attention from researchers in diverse fields. However, rigorous formal study is made difficult by the high level of mobility of pastoralists as well as by the sizable spatio-temporal variability of their environment. This article presents a new computational approach for studying mobile pastoralism that overcomes these issues. Combining multi-temporal satellite images and agent-based modeling allows a comprehensive examination of pastoral resource access over a realistic dryland landscape with unpredictable ecological dynamics. The article demonstrates the analytical potential of this approach through its application to mobile pastoralism in northeast Nigeria. Employing more than 100 satellite images of the area, extensive simulations are conducted under a wide array of circumstances, including different land-use constraints. The simulation results reveal complex dependencies of pastoral resource access on these circumstances along with persistent patterns of seasonal land use observed at the macro level.</p></div>", "links"=>[], "tags"=>["simulation", "resource access", "approach", "circumstance", "Satellite Imagery Dryland pastoralism", "article", "100 satellite images"], "article_id"=>3110422, "categories"=>["Cell Biology", "Biotechnology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified"], "users"=>["Takuto Sakamoto"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0151157", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Computational_Research_on_Mobile_Pastoralism_Using_Agent_Based_Modeling_and_Satellite_Imagery/3110422", "title"=>"Computational Research on Mobile Pastoralism Using Agent-Based Modeling and Satellite Imagery", "pos_in_sequence"=>1, "defined_type"=>6, "published_date"=>"2016-03-10 09:40:16"}
• {"files"=>["https://ndownloader.figshare.com/files/4833628"], "description"=>"<p>This map was generated from FAO’s GIS dataset on tsetse fly distributions. Darker red pixels correspond to the areas that have a higher probability of tsetse presence.</p>", "links"=>[], "tags"=>["simulation", "resource access", "approach", "circumstance", "Satellite Imagery Dryland pastoralism", "article", "100 satellite images"], "article_id"=>3110491, "categories"=>["Cell Biology", "Biotechnology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified"], "users"=>["Takuto Sakamoto"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0151157.g004", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Predicted_distribution_of_tsetse_flies_i_Morsitans_i_group_/3110491", "title"=>"Predicted distribution of tsetse flies (<i>Morsitans</i> group).", "pos_in_sequence"=>5, "defined_type"=>1, "published_date"=>"2016-03-10 09:40:16"}
• {"files"=>["https://ndownloader.figshare.com/files/4834015"], "description"=>"<p>Seasonal land-use classification in the baseline condition.</p>", "links"=>[], "tags"=>["simulation", "resource access", "approach", "circumstance", "Satellite Imagery Dryland pastoralism", "article", "100 satellite images"], "article_id"=>3110749, "categories"=>["Cell Biology", "Biotechnology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified"], "users"=>["Takuto Sakamoto"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0151157.t002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Seasonal_land_use_classification_in_the_baseline_condition_/3110749", "title"=>"Seasonal land-use classification in the baseline condition.", "pos_in_sequence"=>17, "defined_type"=>3, "published_date"=>"2016-03-10 09:40:16"}
• {"files"=>["https://ndownloader.figshare.com/files/4833568"], "description"=>"<p>The flow of the NOMAD behavior.</p>", "links"=>[], "tags"=>["simulation", "resource access", "approach", "circumstance", "Satellite Imagery Dryland pastoralism", "article", "100 satellite images"], "article_id"=>3110434, "categories"=>["Cell Biology", "Biotechnology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified"], "users"=>["Takuto Sakamoto"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0151157.g001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/The_flow_of_the_NOMAD_behavior_/3110434", "title"=>"The flow of the NOMAD behavior.", "pos_in_sequence"=>2, "defined_type"=>1, "published_date"=>"2016-03-10 09:40:16"}
• {"files"=>["https://ndownloader.figshare.com/files/4833643"], "description"=>"<p>This map was generated from the MODIS land classification dataset (MCD12Q1). The yellow pixels correspond to the sites that are classified as ‘croplands’ according to the UMD classification scheme. The green pixels indicate the other land classes, such as ‘grasslands’ and ‘savannas’.</p>", "links"=>[], "tags"=>["simulation", "resource access", "approach", "circumstance", "Satellite Imagery Dryland pastoralism", "article", "100 satellite images"], "article_id"=>3110500, "categories"=>["Cell Biology", "Biotechnology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified"], "users"=>["Takuto Sakamoto"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0151157.g005", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Distribution_of_croplands_in_2010_/3110500", "title"=>"Distribution of croplands in 2010.", "pos_in_sequence"=>6, "defined_type"=>1, "published_date"=>"2016-03-10 09:40:16"}
• {"files"=>["https://ndownloader.figshare.com/files/4833712"], "description"=>"<p>Each of the line graphs shows the dependence of kappa statistics on changes in the following parameters: NOMAD POPULATION (top left), CARRYING CAPACITY (top right), MOVE RANGE (bottom left), and GRAZE RANGE (bottom right). These statistics quantify the similarity of the land-use pattern observed in a given parameter setting to the baseline pattern. These were computed using three different values, 0.001 (red), 0.01 (blue) and 0.1 (yellow), for the land-use threshold.</p>", "links"=>[], "tags"=>["simulation", "resource access", "approach", "circumstance", "Satellite Imagery Dryland pastoralism", "article", "100 satellite images"], "article_id"=>3110569, "categories"=>["Cell Biology", "Biotechnology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified"], "users"=>["Takuto Sakamoto"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0151157.g010", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Comparison_of_kappa_statistics_/3110569", "title"=>"Comparison of kappa statistics.", "pos_in_sequence"=>11, "defined_type"=>1, "published_date"=>"2016-03-10 09:40:16"}
• {"files"=>["https://ndownloader.figshare.com/files/4833940"], "description"=>"<p>The main parameters of the model.</p>", "links"=>[], "tags"=>["simulation", "resource access", "approach", "circumstance", "Satellite Imagery Dryland pastoralism", "article", "100 satellite images"], "article_id"=>3110698, "categories"=>["Cell Biology", "Biotechnology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified"], "users"=>["Takuto Sakamoto"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0151157.t001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/The_main_parameters_of_the_model_/3110698", "title"=>"The main parameters of the model.", "pos_in_sequence"=>16, "defined_type"=>3, "published_date"=>"2016-03-10 09:40:16"}
• {"files"=>["https://ndownloader.figshare.com/files/4833805"], "description"=>"<p>The seasonal land-use maps with the corresponding kappa statistics (top row), the means of the ROUTE ranges (bottom left), and the means of the resources available to NOMADs (bottom right) are displayed. The kappa statistics were calculated by setting the land-use threshold to 0.01. These graphs, which are similar to Figs <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0151157#pone.0151157.g009\" target=\"_blank\">9</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0151157#pone.0151157.g011\" target=\"_blank\">11</a>, were derived from simulations that were conducted with different values of DISRUPTION EFFECT; otherwise, the settings were identical to the baseline condition.</p>", "links"=>[], "tags"=>["simulation", "resource access", "approach", "circumstance", "Satellite Imagery Dryland pastoralism", "article", "100 satellite images"], "article_id"=>3110635, "categories"=>["Cell Biology", "Biotechnology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified"], "users"=>["Takuto Sakamoto"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0151157.g013", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Effects_of_tsetse_flies_on_the_model_/3110635", "title"=>"Effects of tsetse flies on the model.", "pos_in_sequence"=>14, "defined_type"=>1, "published_date"=>"2016-03-10 09:40:16"}
• {"files"=>["https://ndownloader.figshare.com/files/4833652"], "description"=>"<p>These 12 maps were derived from 20 baseline runs. The parameters were given the values that are underlined in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0151157#pone.0151157.t001\" target=\"_blank\">Table 1</a>. In each of the maps, the whiter pixels indicate the sites that have a larger presence of NOMADs in the corresponding month. In this example, the maximum number of agents per pixel per year is approximately 0.798, which was recorded in June.</p>", "links"=>[], "tags"=>["simulation", "resource access", "approach", "circumstance", "Satellite Imagery Dryland pastoralism", "article", "100 satellite images"], "article_id"=>3110512, "categories"=>["Cell Biology", "Biotechnology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified"], "users"=>["Takuto Sakamoto"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0151157.g006", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Monthly_distribution_of_land_use_intensity_baseline_/3110512", "title"=>"Monthly distribution of land-use intensity (baseline).", "pos_in_sequence"=>7, "defined_type"=>1, "published_date"=>"2016-03-10 09:40:16"}
• {"files"=>["https://ndownloader.figshare.com/files/4833589"], "description"=>"<p>The shaded square indicates the study area. The reference map of Nigeria was taken from the CIA’s World Factbook (<a href=\"https://www.cia.gov/library/publications/the-world-factbook/geos/ni.html\" target=\"_blank\">https://www.cia.gov/library/publications/the-world-factbook/geos/ni.html</a>).</p>", "links"=>[], "tags"=>["simulation", "resource access", "approach", "circumstance", "Satellite Imagery Dryland pastoralism", "article", "100 satellite images"], "article_id"=>3110452, "categories"=>["Cell Biology", "Biotechnology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified"], "users"=>["Takuto Sakamoto"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0151157.g002", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/The_area_of_study_/3110452", "title"=>"The area of study.", "pos_in_sequence"=>3, "defined_type"=>1, "published_date"=>"2016-03-10 09:40:16"}
• {"files"=>["https://ndownloader.figshare.com/files/4833730"], "description"=>"<p>Each of the line graphs shows the dependence of the mean ROUTE range on changes in the following parameters: NOMAD POPULATION (top left), CARRYING CAPACITY (top right), MOVE RANGE (bottom left), and GRAZE RANGE (bottom right).</p>", "links"=>[], "tags"=>["simulation", "resource access", "approach", "circumstance", "Satellite Imagery Dryland pastoralism", "article", "100 satellite images"], "article_id"=>3110584, "categories"=>["Cell Biology", "Biotechnology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified"], "users"=>["Takuto Sakamoto"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0151157.g011", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Effects_of_parameter_changes_on_movement_/3110584", "title"=>"Effects of parameter changes on movement.", "pos_in_sequence"=>12, "defined_type"=>1, "published_date"=>"2016-03-10 09:40:16"}
• {"files"=>["https://ndownloader.figshare.com/files/4833661"], "description"=>"<p>This map is a composite image of the dry season (January, February and March) and rainy season (July, August and September) distributions of land-use intensity. The red pixels indicate the dominance of the dry season land use. The green pixels, in contrast, indicate the rainy season dominance. The yellow pixels denote the land use in which there is no clear seasonal difference in the intensity.</p>", "links"=>[], "tags"=>["simulation", "resource access", "approach", "circumstance", "Satellite Imagery Dryland pastoralism", "article", "100 satellite images"], "article_id"=>3110521, "categories"=>["Cell Biology", "Biotechnology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified"], "users"=>["Takuto Sakamoto"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0151157.g007", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Seasonal_differences_in_land_use_intensity_baseline_/3110521", "title"=>"Seasonal differences in land-use intensity (baseline).", "pos_in_sequence"=>8, "defined_type"=>1, "published_date"=>"2016-03-10 09:40:16"}
• {"files"=>["https://ndownloader.figshare.com/files/4834045"], "description"=>"<p>Example of a confusion matrix.</p>", "links"=>[], "tags"=>["simulation", "resource access", "approach", "circumstance", "Satellite Imagery Dryland pastoralism", "article", "100 satellite images"], "article_id"=>3110782, "categories"=>["Cell Biology", "Biotechnology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified"], "users"=>["Takuto Sakamoto"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0151157.t003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Example_of_a_confusion_matrix_/3110782", "title"=>"Example of a confusion matrix.", "pos_in_sequence"=>18, "defined_type"=>3, "published_date"=>"2016-03-10 09:40:16"}

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Relative Metric

{"start_date"=>"2016-01-01T00:00:00Z", "end_date"=>"2016-12-31T00:00:00Z", "subject_areas"=>[]}

F1000Prime