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625 results for “Ruling”
3DPC of the Rules Reservoir in Granada (Spain)
<p>The Rules Reservoir is located in the vicinity of the Sierra Nevada mountains in Granada, Spain. In this reservoir, a multidisciplinary team conducted a study of slope movements in natural terrain and anthropogenic embankments using geophysical and remote sensing techniques. The results of this work were published in the "Landslides" journal in 2021, and the full article can be accessed through the following link: </p><p><a href="https://link.springer.com/article/10.1007/s10346-021-01728-z">https://link.springer.com/article/10.1007/s10346-021-01728-z</a></p><p> </p><p>Three sources of data were utilized, obtained from the IGN Data Center through the PNOA project. The point clouds used in the analysis were as follows:</p><ol><li>Year 2000, Quinquennial flight. Reconstruction performed with Structure-from-Motion (SfM) using Metashape.</li><li>Year 2014, LiDAR flight.</li><li>Year 2016, PNOA flight. Reconstruction carried out with Structure-from-Motion (SfM) using Metashape.</li></ol><p>Both raw and rasterized point cloud datasets are available for conducting the Difference of Digital Elevation Models (DEMs) comparisons.</p>
Data from: Comparison of priority rules, machine allocation, and stage allocation strategies for hybrid flow shop instances using combinatorial logic and a standard trace format
<p>datsets.zip contains benchmark data by Ruiz et al. (2008), Naderi et al.(2010), and Wittwock (1988). The according publications are listed in the related works section. The instances are publicly available under unclear license.</p><p>results.zip contains computational results for the aforementioned benchmarks using the algorithms described in "Comparison of priority rules, machine allocation, and stage allocation strategies for hybrid flow shop instances using combinatorial logic and a standard trace format". The data is licensed under Creative Commons Attribution 4.0 International license.</p>
CLDF dataset derived from the Leipzig Glossing Rules
The Leipzig Glossing Rules
Reservoir operating rules to create effective fish migration passages in a reservoir [Dataset]
Open the record for dataset details and reuse information.
Replication Package for Gang Rule: Understanding and countering criminal governance
<p>Blattman, Christopher and Duncan, Gustavo and Lessing, Benjamin and Tobon, Santiago. (Forecoming). "Gang Rule: Understanding and Countering Criminal Governance". The Review of Economic Studies</p> <p>The package contains all the code and data necessary to reproduce the figures and tables in the aforementioned paper. All raw data that can be disclosed was collected by the authors, and are available under a Creative Commons Non-commercial license. </p>
Learning tasks and result data for the 2024 GECCO short paper Length-niching Selection and Spatial Crossover in Variable-length Evolutionary Rule Set Learning
<p>Learning tasks and result data for the 2024 GECCO short paper Length-niching Selection and Spatial Crossover in Variable-length Evolutionary Rule Set Learning by David Pätzel, Richard Nordsieck and Jörg Hähner.</p>
Figure 9 in Multiple convergences in the evolutionary history of the testate amoeba family Arcellidae (Amoebozoa: Arcellinida: Sphaerothecina): when the ecology rules the morphology
Figure 9. Netzelia lobostoma and Cucurbitella mespiliformis: scanning electron micrographs of oral and lateral view of the test. The images on the right represent details of the collar. On the left, a photograph of a typical habitat for these species, and original drawings of Netzelia lobostoma (Leidy, 1874) and of Cucurbitella mespiliformis (Penard, 1902).
Figure 8 in Multiple convergences in the evolutionary history of the testate amoeba family Arcellidae (Amoebozoa: Arcellinida: Sphaerothecina): when the ecology rules the morphology
Figure 8. Arcella guadarramensis: scanning electron micrographs of the aboral and oral sides of the test. The images on the right represent a detail of the test and the structure of the aperture. On the left, a photograph of a typical habitat for this species, and original drawing of the closest resembling species, Galeripora artocrea (Leidy, 1879).
Figure 7 in Multiple convergences in the evolutionary history of the testate amoeba family Arcellidae (Amoebozoa: Arcellinida: Sphaerothecina): when the ecology rules the morphology
Figure 7. Arcella conica: scanning electron micrographs of the aboral, oral and lateral sides of the test. The images on the right represent a detail of the test and the structure of the aperture. On the left, photographs of a typical habitat for the species, and original drawing of Arcella conica (Playfair, 1918).
Figure 5 in Multiple convergences in the evolutionary history of the testate amoeba family Arcellidae (Amoebozoa: Arcellinida: Sphaerothecina): when the ecology rules the morphology
Figure 5. Galeripora naiadis, Galeripora bathystoma and Galeripora polypora: scanning electron micrographs of the aboral and oral sides of the test, for G. naiadis the images correspond with pictures of Arcella discoides in Todorov & Bankov (2019). The images on the right represent a detail of the test and the structure of the aperture. On the left, a photograph of a typical habitat for each species, original drawing of the closest resembling species Galeripora discoides (Ehrenberg, 1843), and original drawing of Galeripora bathystoma (Deflandre, 1928) and Galeripora polypora (Penard, 1890).
Figure 4 in Multiple convergences in the evolutionary history of the testate amoeba family Arcellidae (Amoebozoa: Arcellinida: Sphaerothecina): when the ecology rules the morphology
Figure 4. Galeripora galeriformis, Galeripora bufonipellita, Galeripora sitiens and Galeripora balari: scanning electron micrographs of the aboral and oral sides of the test. The images on the right represent detail of the test and the structure of the aperture. On the left, a photograph of a typical habitat for each species, original drawing of the closest resembling species Galeripora arenaria (Greef, 1866), and original drawings of the synonymized species Arcella microstoma Penard, 1890 and Arcella aureola Maggi, 1888.
Figure 2. A in Multiple convergences in the evolutionary history of the testate amoeba family Arcellidae (Amoebozoa: Arcellinida: Sphaerothecina): when the ecology rules the morphology
Figure 2. A, scatterplot of the scores of linear discriminants with x-axis representing discriminant function 1 (LD1) and y-axis representing discriminant function 2 (LD2). Colours represent the different mitochondrial clades and symbols refer to the different sections after Deflandre (1928): squares are for Section 1 'Vulgares', circles for Section 2 'Carinatae' and triangles for Section 3 'Aplanatae'. The drawings represent the different morphotypes. B, the table represents the results of a linear discriminant analysis which determines the relationship between predicted and observed specimens cells for each mitochondrial clade.
Figure 6 in Multiple convergences in the evolutionary history of the testate amoeba family Arcellidae (Amoebozoa: Arcellinida: Sphaerothecina): when the ecology rules the morphology
Figure 6. Galeripora catinus: scanning electron micrographs of the aboral and oral sides of the test. The images on the right represent a detail of the test and the structure of the aperture. On the left, a photograph of a typical habitat for the species, a peat bog and original drawing of Galeripora catinus (Penard, 1890).
Figure 1 in Multiple convergences in the evolutionary history of the testate amoeba family Arcellidae (Amoebozoa: Arcellinida: Sphaerothecina): when the ecology rules the morphology
Figure 1. Bayesian phylogenetic tree based on 52 partial sequences COI mtDNA data, 618-nucleotide position alignment. The posterior probability values (Bayesian analysis) and bootstrap values (maximum-likelihood) are represented at each node, with a letter representing the different mitochondrial clades along the branches. The colours represent the mitochondrial clades that compose the different figures. Next to each species name is the original habitat (freshwater/Sphagnum/terrestrial mosses) and the section according to Deflandre (1928). The drawings show the tests of illustrative species in lateral and oral side views. Drawings by CSZ.
Association Rules and Semantic Relatedness (ARSR) - Evaluation Data
<p><em><strong>ARSR</strong></em> stands for "<strong><em>Association Rules and Semantic Relatedness</em></strong>",<br> a recommender system that combines association rule mining and semantic relatedness to create recommendations for form fields.</p> <p>ARSR was evaluated with focus on recommending values for fields of metadata forms.<br> The evaluation was performed with two sets of association rules (R1 and R2).<br> <strong>R1</strong> was primarily used to assess the recommendation performance.<br> <strong>R2</strong> served as an alternative and led to similar results.</p> <p>This dataset contains the raw data ("<strong>collected</strong>", JSON format), generated during the evaluation.<br> It includes i.a. input (populated fields and target field), expected output, and the top 40 generated recommendations for each test combination.</p> <p>In addition, the processed data ("<strong>analysed</strong>", CSV format) is provided.<br> It is based on the raw data and is used to calculate metrics and plot the results.</p> <p>The <strong>source code</strong> for ARSR and the evaluation is available on <a href="https://gitlab.com/dlr-dw/arsr">GitLab (gitlab.com)</a>.</p> <p> </p> <p><em>Note:</em><br> To perform the analysis on the raw data (collected) yourself, make sure to follow the setup instruction in the evaluation repository first.<br> More specifically, install dependencies and unzip "data/cedar/test-instances.zip" so that URI mappings can be accessed.<br> Then follow the instructions provided by the README file in the raw data archives (e.g. collected-R1.zip).</p>
Figure 2. Majority-rule consensus tree from a in Shell features and anatomy of the springsnail genus Radomaniola (Caenogastropoda: Hydrobiidae) show a different pace and mode of evolution over five million years
Figure 2. Majority-rule consensus tree from a Bayesian analysis based on the multilocus dataset (COI, 16S and 28S) of Radomaniola. Branch support values (BPP, Bayesian posterior probability; BS, bootstrap support) are provided on nodes. Tip labels correspond to the sequence codes given in the Supporting Information (Table S1). Vertical bars on the right represent entities identified by the molecular species delimitation methods automatic barcode gap discovery (ABGD), Bayesian generalized mixed Yule-coalescent method (bGMYC) and Poisson tree processes species delimitation method in a Bayesian framework (bPTP) and the multi-rate version (mPTP) with Bayesian support values. Scale bar below topology: substitutions per site.
FIGURE 1. Majority-rule consensus tree from a in Integrative revision of the Lygodactylus madagascariensis group reveals an unexpected diversity of little brown geckos in Madagascar's rainforest
FIGURE 1. Majority-rule consensus tree from a Maximum likelihood analysis based on 537 bp of the mitochondrial 16S rRNA gene, for all available samples of the Lygodactylus madagascariensis group. Outer circles of different color mark categories of bootstrap branch support as indicated, inner circles of different color mark categories of Posterior Probability support from a separate Bayesian analysis. Missing circles on the backbone of clade A5 indicate different topology in the ML and BI analysis. Colors correspond to species-level lineages delimited by the ASAP partition with lowest ASAP score, with two exceptions: (i) L. madagascariensis samples from Montagne d'Ambre are shown as different clusters to better illustrate its co-occurrence with other lineages at this site, as well as patterns of allele sharing in the nuclear encoded genes (Fig. 2); (ii) the three uppermost samples of L. guibei were defined by ASAP as separate lineage but are here considered as conspecific with L. guibei in a preliminary way.
FIGURE 3. Majority-rule consensus tree from a in Integrative revision of the Lygodactylus madagascariensis group reveals an unexpected diversity of little brown geckos in Madagascar's rainforest
FIGURE 3. Majority-rule consensus tree from a partitioned maximum likelihood analysis of a multigene dataset of 10,141 bp of fragments of five mitochondrial and eight nuclear markers for all species and candidate species in the Lygodactylus madagascariensis group. Lineages are colored to match the 16S tree and haplotype networks (Figs. 1‒2). A1 to A5 are ad-hoc defined major subclades as discussed in the text. Outer circles of different color mark categories of bootstrap branch support as indicated, inner circles of different color mark categories of Posterior Probability support from a separate partitioned Bayesian analysis.
FIGURE 3. Majority-rule consensus tree for 20002 in Recognition of a new species of Carmenta from New Mexico supported by morphology and mitochondrial cytochrome oxidase I data (Lepidoptera: Sesiidae: Sesiinae: Synanthedonini)
FIGURE 3. Majority-rule consensus tree for 20002 trees kept from the Bayesian analysis of 1 million generations using the morphological and DNA data. Posterior probabilities greater then 75% are shown.
Learning tasks and result data for the 2024 IWERL@GECCO paper A Closer Look at Length-niching Selection and Spatial Crossover in Variable-length Evolutionary Rule Set Learning
<p>Learning tasks and result data for the paper Length-niching Selection and Spatial Crossover in Variable-length Evolutionary Rule Set Learning by David Pätzel, Richard Nordsieck and Jörg Hähner, presented at the International Workshop on Evolutionary Rule-based Machine Learning taking place as part of GECCO 2024.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.