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691 results for “Ranking”
Fig. 5 in Upgrading of Three Subspecies of Eudigraphis takakuwai to the Species Rank (Diplopoda: Penicillata: Polyxenida: Polyxenidae)
Fig. 5. Dorsal (A, C, E) and ventral (B, D, F) views of Eudigraphis. A, B: E. takakuwai, female (Waita, Toyoura-cho, Shimonoseki City, 31 August 2017). C, D: E. nigricans, male (Uka, Toyoura-cho, Shimonoseki City, 1 September 2017). E, F: E. kinutensis (campus of Tottori University, Tottori City, 5 January 2018). All the scales = 1 mm.
Fig. 3 in Upgrading of Three Subspecies of Eudigraphis takakuwai to the Species Rank (Diplopoda: Penicillata: Polyxenida: Polyxenidae)
Fig. 3. Unrooted ML phylogenetic tree based on ITS2 sequence data. Bootstrap proportions (BP≥85) of ML and Bayesian posterior probability (BPP≥0.95) are shown at each node (BP/BPP). The names of OTUs show species_locality_sample ID (details on Table 1).
Data set of the article: Language Bias in the Google Scholar Ranking Algorithm
<p>Data of investigation published in the article Cristòfol Rovira; Lluís Codina; Carlos Lopezosa Language Bias in the Google Scholar Ranking Algorithm. Future Internet, 2021, 13.</p> <p><strong>Abstract: </strong>The visibility of academic articles or conference papers depends on their being easily found in academic search engines, above all in Google Scholar. To enhance this visibility, search engine optimization (SEO) has been applied in recent years to academic search engines in order to optimize documents and, thereby, ensure they are better ranked in search pages (i.e., academic search engine optimization or ASEO). To achieve this degree of optimization, we first need to further our understanding of Google Scholar’s relevance ranking algorithm, so that, based on this knowledge, we can highlight or improve those characteristics that academic documents already present and which are taken into account by the algorithm. This study seeks to advance our knowledge in this line of research by determining whether the language in which a document is published is a positioning factor in the Google Scholar relevance ranking algorithm. Here, we employ a reverse engineering research methodology based on a statistical analysis that uses Spearman’s correlation coefficient. The results obtained point to a bias in multilingual searches conducted in Google Scholar with documents published in languages other than in English being systematically relegated to positions that make them virtually invisible. This finding has important repercussions, both for conducting searches and for optimizing positioning in Google Scholar, being especially critical for articles on subjects that are expressed in the same way in English and other languages, the case, for example, of trademarks, chemical compounds, industrial products, acronyms, drugs, diseases, etc.</p>
Toward Estimating the Rank Correlation between the Test Collection Results and the True System Performance
<p>This archive contains the simulated collections and the estimated correlation coefficients. For the full code and description, please refer to https://github.com/julian-urbano/sigir2016-correlation</p>
Training CNNs with Low-Rank Filters for Efficient Image Classification: Trained Models
<p>Models from experiments referenced in the paper "Training CNNs with Low-Rank Filters for Efficient Image Classification", https://arxiv.org/abs/1511.06744</p> <p>Model names differ from those in the paper, but the csv files for each set of experiments relates the paper's name for the model and the real name of the model here:</p> <ul> <li>cifarma.csv: Network-in-Network CIFAR10 Models</li> <li>mitma.csv: MIT Places Models</li> <li>googlenetma.csv: GoogLeNet ILSVRC2012 Models</li> <li>vggma.csv: VGG-11 ILSVRC2012 Models</li> </ul> <p> </p> <p> </p>
Spatial Evolve Algorithm Results for Erdos Renyi Topology Median Normalized Rank - MSc Dissertation
<p>A data set containing the results of the spatial evolve lookup algorithm. The topology used for the spatial tournaments has been the Erdős Rényi random network. The objective function taken into account has been the median normalized rank. Three files are contained here based on the list of strategies,deterministic and non, and on the sample size. </p>
Spatial Evolve Algorithm Results for Random Topology Median Normalized Rank - MSc Dissertation
<p>A data set containing the results of the spatial evolve lookup algorithm. The topologies used for the spatial tournaments has been random between, small world, random and complete. The objective function taken into account has been the median normalized rank. Three files are contained here based on the list of strategies,deterministic and non, and on the sample size. </p>
Spatial Evolve Algorithm Results for Watts Strogatz Topology Median Normalized Rank - MSc Dissertation
<p>A data set containing the results of the spatial evolve lookup algorithm. The topology used for the spatial tournaments has been the Watts Strogatz small world network. The objective function taken into account has been the median normalized rank. Three files are contained here based on the list of strategies,deterministic and non, and on the sample size. </p>
Spatial Evolve Algorithm Results for Erdős Rényi Topology Median Normalized Rank - MSc Dissertation
<p>A data set containing the results of the spatial evolve lookup algorithm. The topology used for the spatial tournaments has been the Erdős Rényi random network. The objective function taken into account has been the median normalized rank. Three files are contained here based on the strategies list, deterministic and non and on the sample size. </p>
Spatial Evolve Algorithm Results for Complete Topology Median Normalized Rank - MSc Dissertation
<p>A data set containing the results of the spatial evolve lookup algorithm. The topology used for the spatial tournaments has been a complete network. The objective function taken into account has been the median normalized rank. Three files are contained here based on the list of strategies,deterministic and non, and on the sample size. </p>
Spatial Evolve Algorithm Results for Random Topology Minimum Normalized Rank - MSc Dissertation
<p>A data set containing the results of the spatial evolve lookup algorithm. The topologies used for the spatial tournaments has been random between, small world, random and complete. The objective function taken into account has been the minimum normalized rank. Two files are contained here based on the sample size. All 132 strategies of the Axelrod have been used. </p>
Supplementary material 9: Integrated legacy literature and prospective publishing dashboard: species-rank treatments from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Dashboard charts summarizing content from 42 articles published either as open access articles published in Zootaxa or in Biodiversity Data Journal, containing treatments on spiders. This page shows data from species-rank treatments. When viewed using a browser (such as Google Chrome) with an internet connection, this page sends a series of queries to Plazi and integrates the results with the Google Charts API to produce 37 interactive dashboard charts.
Supplementary material 5: Legacy literature dashboard: species-rank treatments from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Dashboard charts summarizing content from 37 open access articles published in Zootaxa containing treatments on spiders. This page shows data from species-rank treatments. When viewed using a browser (such as Google Chrome) with an internet connection, this page sends a series of queries to Plazi and integrates the results with the Google Charts API to produce 37 dashboard charts.
Supplementary material 7: Prospective publishing dashboard: species-rank treatments from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Dashboard charts summarizing content from 5 articles published in Biodiversity Data Journal containing treatments on spiders. This page shows data from species-rank treatments. When viewed using a browser (such as Google Chrome) with an internet connection, this page sends a series of queries to Plazi and integrates the results with the Google Charts API to produce 37 dashboard charts.
Pilot study: Ranking of textual snippets based on the writing style
<p>In this pilot study, we tried to capture humans' behavior when identifying authorship of text snippets. At first, we selected textual snippets from the introduction of scientific articles written by single authors. Later, we presented to the evaluators a source and four target snippets, and then, ask them to rank the target snippets from the most to the least similar from the writing style.</p> <p>The dataset is composed by 66 experiments manually checked for not having any clear hint during the ranking for the evaluators. For each experiment, we have evaluations from three different evaluators.</p> <p>We present each experiment in a single line (in the CSV file), where, at first we present the metadata of the Source-Article (Journal, Title, Authorship, Snippet), and the metadata for the 4 target snippets (Journal, Title, Authorship, Snippet, Written From the same Author, Published in the same Journal) and the ranking given by each evaluator. This task was performed in the open source platform, Crowd Flower. </p> <p>The headers of the CSV are self-explained. In the TXT file, you can find a human-readable version of the experiment. </p> <p>For more information about the extraction of the data, please consider reading our paper: "Extending Scientific Literature Search by Including the Author’s Writing Style" @BIR: http://www.gesis.org/en/services/events/events-archive/conferences/ecir-workshops/ecir-workshop-2017 </p>
A dataset of 8-dimensional Q-factorial Fano toric varieties of Picard rank 2
<p>This is a dataset of randomly generated 8-dimensional Q-factorial Fano toric varieties of Picard rank 2.</p><p>The data is divided into four plain text files:</p><ul><li>bound_7_terminal.txt</li><li>bound_7_non_terminal.txt</li><li>bound_10_terminal.txt</li><li>bound_10_non_terminal.txt</li></ul><p>The numbers 7 and 10 in the file names indicate the bound on the weights used when generating the data. Those varieties with at worst terminal singularities are in the files "bound_N_terminal.txt", and those with non-terminal singularities are in the files "bound_N_non_terminal.txt". The data within each file is de-duplicated, however the data in different files may contain duplicates (for example, it is possible that "bound_7_terminal.txt" and "bound_10_terminal.txt" contain some identical entries).</p><p> </p><p>Each line of a file specifies the entries of a (2 x 10)-matrix. For example, the first line of "bound_7_terminal.txt" is:</p><blockquote><p>[[5,6,7,7,5,2,5,3,2,2],[0,0,0,1,1,2,6,4,3,3]]</p></blockquote><p>and this corresponds to the 8-dimensional Q-factorial Fano toric variety with weight matrix</p><blockquote><p>5 6 7 7 5 2 5 3 2 2</p><p>0 0 0 1 1 2 6 4 3 3</p></blockquote><p>and stability condition given by the sum of the columns, which in this case is</p><blockquote><p>44</p><p>20</p></blockquote><p>It can be checked that, in this case, the corresponding variety has at worst terminal singularities. In this example the largest occurring weight in the matrix is 7.</p><p> </p><p>The number of entries in each file is:</p><ul><li>bound_7_terminal.txt: 5000000</li><li>bound_7_non_terminal.txt: 5000000</li><li>bound_10_terminal.txt: 10000000</li><li>bound_10_non_terminal.txt: 10000000</li></ul><p> </p><p>For details, see the paper:</p><blockquote><p>"Machine learning detects terminal singularities", Tom Coates, Alexander M. Kasprzyk, and Sara Veneziale. Neural Information Processing Systems (NeurIPS), 2023.</p></blockquote><p> </p><p>Magma code capable of generating this dataset is in the file "terminal_dim_8.m". The bound on the weights is set on line 142 by adjusting the value of 'k' (currently set to 10). The target dimension is set on line 143 by adjusting the value of 'dim' (currently set to 8). It is important to note that this code does not attempt to remove duplicates. The code also does not guarantee that the resulting variety has dimension 8. Deduplication and verification of the dimension need to be done separately, after the data has been generated.</p><p> </p><p>If you make use of this data, please cite the above paper and the DOI for this data:</p><p>doi:10.5281/zenodo.10046893</p>
→ Fig. 10. FESEM images of the test structure in lagenid foraminifers from Recent, Admiralty Bay, King George Island, West Antarctica (A) and from the Jurassic of Gnaszyn, Poland (B, C). A. Unilocular Procerolagena gracilis Williamson, 1848, MWGUW ZI/67/44/02. B. Unilocular Lagena globosa Montagu, 1803, MWGUW ZI/67/61/09. C. Uniserial Nodosaria pulchra Franke, 1936, MWGUW ZI/67/61/26. Oblique cross-sectional views (A1, A2, A4, B1, B2, C); transverse cross-sectional views, showing single-crystal interlocked bundle structures, inner pores which extend along the entire length of the bundles as well as prominent calcite cleavage (A3, B3). Abbreviations: c, prominent calcite cleavage; ip, inner pore. in Chamber arrangement versus wall structure in the high-rank phylogenetic classification of Foraminifera
→ Fig. 10. FESEM images of the test structure in lagenid foraminifers from Recent, Admiralty Bay, King George Island, West Antarctica (A) and from the Jurassic of Gnaszyn, Poland (B, C). A. Unilocular Procerolagena gracilis Williamson, 1848, MWGUW ZI/67/44/02. B. Unilocular Lagena globosa Montagu, 1803, MWGUW ZI/67/61/09. C. Uniserial Nodosaria pulchra Franke, 1936, MWGUW ZI/67/61/26. Oblique cross-sectional views (A1, A2, A4, B1, B2, C); transverse cross-sectional views, showing single-crystal interlocked bundle structures, inner pores which extend along the entire length of the bundles as well as prominent calcite cleavage (A3, B3). Abbreviations: c, prominent calcite cleavage; ip, inner pore.
Fig. 9 in Chamber arrangement versus wall structure in the high-rank phylogenetic classification of Foraminifera
Fig. 9. FESEM images of "monocrystalline" test structure in Spirillinata → foraminifers from the Jurassic of Gnaszyn, Poland (A) and Recent from Ronsard Bay, Western Australia (B). A. Paalzowella pazdroe Bielecka and Styk, 1969, MWGUW ZI/67/61/27; view of the test cross-section (A1); significantly magnified view of the test cross-section (A2, A4, A5); oblique cross-sectional view of the test showing "monocrystalline" test structure (A3); oblique cross sections of the test showing test composed of a few layers (A6, A7). B. Patellina sp., MWGUW ZI/67/61/22; oblique cross sections of the test showing prominent calcite cleavage (B1, B2).
Fig. 8 in Chamber arrangement versus wall structure in the high-rank phylogenetic classification of Foraminifera
Fig. 8. FESEM images of the test structure in Tubothalamea from the Jurassic of Gnaszyn, Poland. A. Ophthalmidium carinatum Pazdro, 1958, MWGUW → ZI/67/08/5.03; front views of the abraded test surface, showing the extrados and porcelain (A1, A2). B.?Cornuspira radiata (Terquem, 1886), MWGUW ZI/67/55/11; front view of the test surface (B1, B3); oblique view of the test cross section, showing the test as being entirely composed of needle-shaped crystallites (B2, B4). C. Planiinvoluta sp., MWGUW ZI/67/57/13; view of the inner test surface (C1); side view of the test cross section, showing irregular meshwork of needle-shaped crystallites (C2). Abbreviations: e, extrados; p, porcelain.
Fig. 7 in Chamber arrangement versus wall structure in the high-rank phylogenetic classification of Foraminifera
Fig. 7. FESEM images of the test structure in Recent calcareous cemented agglutinated textulariid (Globothalamea; A, B) and miliolid (Tubothalamea; C) → foraminifers from Ronsard Bay, Western Australia. A. Textularia sp., MWGUW ZI/67/55/24, front view of the test, showing agglutinated grains and the calcareous nanogranular matrix (A1); details of test wall (A2, A3). B. Gaudryina sp., MWGUW ZI/67/61/16, front view of the test, showing agglutinated grains and the matrix (B1); details of nanogranular matrix (B2, B3). C. Quinqueloculina arenata Said, 1949, MWGUW ZI/67/57/02, front view of the test (C1); oblique cross-sectional view of test showing foreign particle partially embedded in the irregular meshwork of needle-shaped crystallites (C2). Abbreviations: g, foreign particle; m, calcareous matrix. Arrows indicate pores.
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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.