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146 results for “Ordination”
Access Network of Henri III of France's apartment according to his 1585 court ordinances
<p>This dataset collects the stipulations of access for courtiers in the royal apartment as described in Henri III of France's 1585 court ordinance (Paris: Archives Nationales, KK 544, fol. 55r-141r).</p> <p>The dataset was created with the aim to establish the degree of accessibility of both the (individual) spaces in the royal apartmant as well as the king himself and analyse in which ways king Henri III of France managed to balance his need for privacy with the courtiers' expectations of access. The results are to be / were published in <em>Current Research in Digital History</em>. </p>
Cherenkov Telescope Data for Ordinal Quantification
<p>This labeled data set is targeted at ordinal quantification. The goal of quantification is not to predict the class label of each individual instance, but the distribution of labels in unlabeled sets of data.</p> <p>With the scripts provided, you can extract the relevant features and labels from the public data set of the FACT Cherenkov telescope. These features are precisely the ones that domain experts from astro-particle physics employ in their analyses. The labels stem from a binning of a continuous energy label, which is common practice in these analyses.</p> <p>We complement this data set with the indices of data items that appear in each sample of our evaluation. Hence, you can precisely replicate our samples by drawing the specified data items. The indices stem from two evaluation protocols that are well suited for ordinal quantification. To this end, each row in the files<em> app_val_indices.csv</em>, <em>app_tst_indices.csv</em>, <em>app-oq_val_indices.csv</em>, <em>app-oq_tst_indices.csv,</em> <em>real_val_indices.csv</em>, and <em>real_tst_indices.csv</em> represents one sample.</p> <p>Our first protocol is the artificial prevalence protocol (APP), where all possible distributions of labels are drawn with an equal probability. The second protocol, APP-OQ(5%), is a variant thereof, where only the smoothest 5% of all APP samples are considered. This variant is targeted at ordinal quantification tasks, where classes are ordered and a similarity of neighboring classes can be assumed. The labels of the FACT data lie on an ordinal scale and, hence, pose such an ordinal quantification task. The third protocol considers "real" distributions of labels. These distributions would be expected by observing the Crab Nebula through the FACT telescope.</p> <p><strong>Usage</strong></p> <p>You can extract the data <em>fact.csv</em> through the provided script <em>extract-fact.jl</em>, which is conveniently wrapped in a <em>Makefile</em>. The <em>Project.toml</em> and <em>Manifest.toml</em> specify the Julia package dependencies, similar to a requirements file in Python.</p> <p><strong>Preliminaries:</strong> You have to have a working Julia installation. We have used Julia v1.6.5 in our experiments.</p> <p><strong>Data Extraction:</strong> In your terminal, you can call either</p> <pre><code>make</code></pre> <p>(recommended) or</p> <pre><code>curl --fail -o fact.hdf5 https://factdata.app.tu-dortmund.de/dl2/FACT-Tools/v1.1.2/gamma_simulations_facttools_dl2.hdf5 julia --project="." --eval "using Pkg; Pkg.instantiate()" julia --project="." extract-fact.jl</code></pre> <p><strong>Outcome: </strong>The first row in the resulting <em>fact.csv</em> file is the header. The first column, named "class_label", is the ordinal class.</p> <p><strong>Further Reading</strong></p> <p>Implementation of our experiments: <a href="https://github.com/mirkobunse/regularized-oq">https://github.com/mirkobunse/regularized-oq</a></p> <p>Original data repository: <a href="https://factdata.app.tu-dortmund.de/">https://factdata.app.tu-dortmund.de/</a></p> <p>Reference analysis by astro-particle physicists: <a href="https://github.com/fact-project/open_crab_sample_analysis">https://github.com/fact-project/open_crab_sample_analysis</a></p>
American Residential Macrosystems - Complete municipal ordinance documents across six U.S. cities, 2017-2019
These data files are the complete city codes, or municipal ordinances (n=156), across the metropolitan regions of Los Angeles, CA; Phoenix, AZ; Miami, FL; Baltimore, MD; Boston, MA; Minneapolis/St. Paul, MN. The documents were gathered for the specific purposes of a content analysis of how cities regulate residential landscapes; however, the documents include regulations on the books for the municipalities sampled for this project.
Artificial Communities Experiment: Species Data for Ordination
This study considers the relative role of competition and facilitation in primary succession of the boreal forest floodplain of Bonanza Creek Experimental Forest. Alder (Alnus tenuifolia Nutt.) is an early colonist of young floodplain sites, where it contributes greatly to soil development through the addition of nitrogen to the soil. White spruce (Picea glauca (Moench.) Voss) typically colonizes such sites later; it becomes established beneath a canopy of alder and willow (Salix spp.) which is sometimes overtopped by young balsam poplar (Populus balsamifera L.). The alder may potentially influence white spruce both positively and negatively. Possible facilitative effects include protection of small seedlings from excess heat or heavy siltation accompanying flooding and addition of soil nitrogen, an effect that may extend for many years. Competition for light (and other resources) before the white spruce has overtopped the alder canopy may prove to be a major negative effects. This study seeks to determine the net balance of these effects on the floodplain successional sequence.
Riparian and upland understory vegetation lifeforms and leaf-litterfall ordination analyses in the Luquillo Forest Dynamics Plot
Riparian areas are proportionally a small component of the forested landscape, they are significant contributors to ecosystem process, terrestrial and aquatic linkages, plant community composition, as well to basal energy resources for aquatic fauna. We describe vegetation and leaf-litterfall composition in relation to past land use in riparian and upland locations in tropical wet forest, Luquillo Forest Dynamics Plot (LFDP), Luquillo Experimental Forest, Puerto Rico. Data collected from 2003 to 2005. Stratified sampling was conducted in riparian and upland areas of LFDP with high and low past land use. Understory vegetation life-form composition were sampled in plots. \<para\> Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.\</para\>
Gene co-ordinates, expression levels; SNP identifiers and functions for Drosophila melanogaster (Sussex LHM population)
<p>Data for SNP context information to add to GWAS results. Specifically, SNP functions, sex-bias in gene expression, official SNP idenfiers from NCBI dbSNP, and gene positions and names (from UCSC Genome Browswer). Most of the input files are on-line and their URLs are stated in the code (make_dmel_accessory_data.sh). Also includes code, logs, and exploratory graphs.</p>
UCI and OpenML Data Sets for Ordinal Quantification
<p>These four labeled data sets are targeted at ordinal quantification. The goal of quantification is not to predict the label of each individual instance, but the distribution of labels in unlabeled sets of data.</p> <p>With the scripts provided, you can extract CSV files from the UCI machine learning repository and from OpenML. The ordinal class labels stem from a binning of a continuous regression label.</p> <p>We complement this data set with the indices of data items that appear in each sample of our evaluation. Hence, you can precisely replicate our samples by drawing the specified data items. The indices stem from two evaluation protocols that are well suited for ordinal quantification. To this end, each row in the files <em>app_val_indices.csv</em>, <em>app_tst_indices.csv</em>, <em>app-oq_val_indices.csv</em>, and <em>app-oq_tst_indices.csv</em> represents one sample.</p> <p>Our first protocol is the artificial prevalence protocol (APP), where all possible distributions of labels are drawn with an equal probability. The second protocol, APP-OQ, is a variant thereof, where only the smoothest 20% of all APP samples are considered. This variant is targeted at ordinal quantification tasks, where classes are ordered and a similarity of neighboring classes can be assumed.</p> <p><strong>Usage</strong></p> <p>You can extract four CSV files through the provided script <em>extract-oq.jl</em>, which is conveniently wrapped in a <em>Makefile</em>. The <em>Project.toml</em> and <em>Manifest.toml</em> specify the Julia package dependencies, similar to a requirements file in Python.</p> <p><strong>Preliminaries:</strong> You have to have a working Julia installation. We have used Julia v1.6.5 in our experiments.</p> <p><strong>Data Extraction:</strong> In your terminal, you can call either</p> <pre><code>make</code></pre> <p>(recommended), or</p> <pre><code>julia --project="." --eval "using Pkg; Pkg.instantiate()" julia --project="." extract-oq.jl</code></pre> <p><strong>Outcome: </strong>The first row in each CSV file is the header. The first column, named "class_label", is the ordinal class.</p> <p><strong>Further Reading</strong></p> <p>Implementation of our experiments: <a href="https://github.com/mirkobunse/regularized-oq">https://github.com/mirkobunse/regularized-oq</a></p>
Figure 5. Ordinations and discriminant analysis. A in Morphometric analysis of Eocene nummulitids in western and central Cuba: taxonomy, biostratigraphy and evolutionary trends
Figure 5. Ordinations and discriminant analysis. A, two-dimensional ordination of studied specimens; genera are separated by different shapes (squares = Operculinoides; polygons = Palaeonummulites; triangles = Heterostegina). B, three-dimensional ordination of the studied specimens emphasizes the variation in the third component, highlighting the differentiation between Heterostegina sp. indet. and Operculinoides. C, discriminant analysis of Heterostegina species and Operculinoides or Palaeonummulites species; parameters are sorted in order of their importance as discriminators.
Matters of Police Ordinances. SKOS vocabulary
<p>The file <code>polmat.ttl</code>contains a systematic taxonomy of more than 1.800 matters regulated by police ordinances (last revised: Oct 2021). It is a SKOS vocabulary encoded in ttl/"turtle" format.</p> <p>The taxonomy has originated in the project "<a href="https://www.lhlt.mpg.de/research-project/repertory-of-policeyordnungen" rel="nofollow">Repertory of Policeyordnungen</a>" of the (then) <a href="https://www.lhlt.mpg.de/en" rel="nofollow">Max Planck Institute for Legal History and Legal Theory</a>. Initially this system of categories has been established in German in 1996 by Karl Härter and Michael Stolleis.</p> <p>The taxonomy has been SKOS-ified in November 2020 by Annemieke Romein and Andreas Wagner.</p> <p>The taxonomy is published under CC0, but we will be happy if you give attribution when you use it.</p>
Рис. 2. Распредение биомассы Mytilus trossulus septentrionalis на литорали дальневоcточных морей России. Здесь и далее на гистограммах по оси абцисс после географических пунктов в скобках укаЗана выборка (число иЗученных проб), по оси ординат – максимальные ЗначениЯ биомассы вида. Под Значением биомассы 0.1 г/м² подраЗумеваютсЯ качественные пробы. СокраЩениЯ (бмп) и (топ) оЗначают соответственно беринговоморское и тихоокеанское побережьЯ Восточной Камчатки. Побережье Зал. Петра Великого от устьЯ р. Туманной к северу до м. Поворотного условно отноcитсЯ к южному Приморью; побережье к северу от м. Поворотного (пос. Преображение, б. СоколовскаЯ) до б. Ольга, включительно, условно относитсЯ к среднему Приморью; побережье к северу от б. Ольга до м. Белкина и материковое побережье Татарского пролива относим к северному Приморью. Fig. 2. The distribution of biomass of Mytilus trossulus septentrionalis in the intertidal zone of the Far Eastern seas of Russia. Here and throughout on histograms, on the abcissa is the number of studied samples (numbers in parentheses following the names geographic localities), on the ordinate is the maximum biomass of species. The number 0.1 g wet wt m-2 means the qualitative samples. Abbreviations (bmp) and (top) mean the Bering Sea coast and the Pacific coast of eastern Kamchatka. The coast of Peter the Great Bay from the mouth of the Tumannaya River to Cape Povorotny is conditionally referred to as southern Primorye; the area north of Cape Povorotny (Preobrazhenie Settlement, Sokolovskaya Bay) to Olga Bay inclusive is conditionally referred to as middle Primorye; north of Olga Bay to Cape Belkin and the mainland coast of the Tatar Strait to as northern Primorye. in Bivalve mollusks of the intertidal zone of the Far Eastern seas of Russia
Рис. 2. Распредение биомассы Mytilus trossulus septentrionalis на литорали дальневоcточных морей России. Здесь и далее на гистограммах по оси абцисс после географических пунктов в скобках укаЗана выборка (число иЗученных проб), по оси ординат – максимальные ЗначениЯ биомассы вида. Под Значением биомассы 0.1 г/м² подраЗумеваютсЯ качественные пробы. СокраЩениЯ (бмп) и (топ) оЗначают соответственно беринговоморское и тихоокеанское побережьЯ Восточной Камчатки. Побережье Зал. Петра Великого от устьЯ р. Туманной к северу до м. Поворотного условно отноcитсЯ к южному Приморью; побережье к северу от м. Поворотного (пос. Преображение, б. СоколовскаЯ) до б. Ольга, включительно, условно относитсЯ к среднему Приморью; побережье к северу от б. Ольга до м. Белкина и материковое побережье Татарского пролива относим к северному Приморью. Fig. 2. The distribution of biomass of Mytilus trossulus septentrionalis in the intertidal zone of the Far Eastern seas of Russia. Here and throughout on histograms, on the abcissa is the number of studied samples (numbers in parentheses following the names geographic localities), on the ordinate is the maximum biomass of species. The number 0.1 g wet wt m-2 means the qualitative samples. Abbreviations (bmp) and (top) mean the Bering Sea coast and the Pacific coast of eastern Kamchatka. The coast of Peter the Great Bay from the mouth of the Tumannaya River to Cape Povorotny is conditionally referred to as southern Primorye; the area north of Cape Povorotny (Preobrazhenie Settlement, Sokolovskaya Bay) to Olga Bay inclusive is conditionally referred to as middle Primorye; north of Olga Bay to Cape Belkin and the mainland coast of the Tatar Strait to as northern Primorye.
Random dataset of HTR-ed police ordinances Bern - to test Annif
<p>This dataset consists of 4450 (out of the original 4932) police ordinances that were identified by <span>Claudia Schott-Volm, <em>Repertorium der Policeyordnungen der Frühen Neuzeit (#7). Orte der Schweizer Eidgenossenschaft: Bern und Zürich</em> (Frankfurt am Main, 2006) for the Swiss City-State of Bern (1528-1798). </span></p> <p><span>They have been randomly placed in a test (500), training (3550) and validation set (500) to test the tool Annif. The HTR-recognition was done in 2020 with the - now terminated HTR+-model 'German-Kurrent_XVI-XVIII++French' in the then commonly used Expert Version of Transkribus (READ-COOP SCE).</span></p>
SI Figure 3: Compositional difference among eukaryotic microinvertebrate external and internal microbiomes, using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars centroid location of each microbiome type. Communities do not cluster by animal, microbiome type, mat type, or stream. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 3: Compositional difference among eukaryotic microinvertebrate external and internal microbiomes, using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars centroid location of each microbiome type. Communities do not cluster by animal, microbiome type, mat type, or stream.
SI Figure 2: Compositional differences among bacterial microinvertebrate external and internal microbiomes as well as mats they were isolated from using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars show centroids of microbiome types for each animal host. All host microbiomes (internal and external) are distinct from mat communities (P<0.05), but external microbiomes are more similar to mats than internal microbiomes are to mats. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 2: Compositional differences among bacterial microinvertebrate external and internal microbiomes as well as mats they were isolated from using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars show centroids of microbiome types for each animal host. All host microbiomes (internal and external) are distinct from mat communities (P<0.05), but external microbiomes are more similar to mats than internal microbiomes are to mats.
Figure. CCA ordination of bird species. The eigenvalue of the first axis is 0.164 and of the second axis is 0.127. Species abbreviations are the first 3 letters of their genus followed by the first 3 letters of the species epithet. in Birds and small urban parks: a study in a high plateau city
Figure. CCA ordination of bird species. The eigenvalue of the first axis is 0.164 and of the second axis is 0.127. Species abbreviations are the first 3 letters of their genus followed by the first 3 letters of the species epithet.
Figure. Constrained ordination plot as produced from canonical correspondence analysis (CCA). The variability of environmental variables is summarized on Axis 1 and Axis 2 of the constrained biplot, explaining the variability of the trophic groups included in the red fox's diet. Trophic groups are shown with black line (unfilled) pyramids, whereas environmental variables are shown with black filled pyramids. Proximity and distance of response centroids to predictor centroids indicate positive and negative correlations between them, respectively. in Factors affecting the diet of the red fox (Vulpes vulpes) in a heterogeneous Mediterranean landscape
Figure. Constrained ordination plot as produced from canonical correspondence analysis (CCA). The variability of environmental variables is summarized on Axis 1 and Axis 2 of the constrained biplot, explaining the variability of the trophic groups included in the red fox's diet. Trophic groups are shown with black line (unfilled) pyramids, whereas environmental variables are shown with black filled pyramids. Proximity and distance of response centroids to predictor centroids indicate positive and negative correlations between them, respectively.
Fig. 5. Ordination plot for principal components 1 and 2 in Evaluating sexual dimorphism in the ambrosia beetle Xyleborus affinis (Coleoptera: Curculionidae) using geometric morphometrics
Fig. 5. Ordination plot for principal components 1 and 2 representing elytra shape variation between sexes of Xyleborus Affinis: deformation grids describing variation between sexes on the 2 first principal components are presented.
Fig. 6. Ordination plot for principal components 1 and 2 in Evaluating sexual dimorphism in the ambrosia beetle Xyleborus affinis (Coleoptera: Curculionidae) using geometric morphometrics
Fig. 6. Ordination plot for principal components 1 and 2 representing pronotum shape variation between sexes of Xyleborus Affinis: deformation grids describing variation between sexes on the 2 first principal components are presented.
Fig. 3. Ordination ofElmomorphus brevicornis Sharp, 1888 in Taxonomic revision of the genus Elmomorphus Sharp, 1888 I. Japanese and Korean species (Coleoptera: Dryopidae)
Fig. 3. Ordination ofElmomorphus brevicornis Sharp, 1888 (black circles) andElmomorphus amamiensis Nomura, 1959 (red squares) along PC1 and PC2 axis. A. Males. B. Females.
Figure 37 in Atlas of Myriapod Biogeography. I. Indigenous Ordinal and Supra-Ordinal Distributions in the Diplopoda: Perspectives on Taxon Origins and Ages, and a Hypothesis on the Origin and Early Evolution of the Class
Figure 37. Known (solid lines) and projected (dashed line) distributions of the Order Callipodida. The question mark denotes the record from Budapest, Hungary, whose origin is uncertain.
Figure 29 in Atlas of Myriapod Biogeography. I. Indigenous Ordinal and Supra-Ordinal Distributions in the Diplopoda: Perspectives on Taxon Origins and Ages, and a Hypothesis on the Origin and Early Evolution of the Class
Figure 29. Known (solid lines) and projected (dashed lines) distributions of the Order Spirostreptida s. l. (sensu Hoffman 1980a, Shelley 2003a), parameters as in Fig. 1. Asterisk, location of Protosilvestria sculpta, the Oligocene fossil from Quercy, France (Mauriès 1992).
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