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580 results for “pattern analysis”
Emotion Category and Face Perception Task Optimized for Multivariate Pattern Analysis
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North Temperate Lakes LTER: Patterns of Soil Phosphorus - Y Plot Analysis 2001
In natural soils, patterns of variance are generated by driving forces such as parent materials, climate, hydrology, relief, disturbance and biological activity. These drivers, operating at particular scales and interacting with other drivers across scales, create a complex pattern of soil variability. Human activity may change the natural patterns of variance by changing the scale at which the governing processes are operating or the governing processes that are dominant at a given scale. In the case of soils and phosphorus (P) concentrations, this may involve changing dominant forces from plant-soil interactions and parent material to fertilizer inputs. Here, we examine the hypothesis that human activity changes natural patterns of variance in soil P concentrations across several spatial scales. We measured soil P concentrations and variability at 3 distinct levels of analysis - among sites, within a field, and within a 10-m diameter plot - and across 4 management regimes - remnant prairie, lawns, cash grain farms, and dairies. Variance changed across scale in any one management regime and across management regimes at the same scale. Rescaling the pattern of P accumulation and variability has implications for managing P runoff from uplands. For sample sites on private property, specific site location information, such as GPS coordinates, is not included in these datasets. If you have a need for this information, please get in touch with the contact person listed above Number of sites: 30
Supplementary Materials for "Exploration of User Privacy in 802.11 Probe Requests with MAC Address Randomization Using Temporal Pattern Analysis"
<p>Supplementary Materials for "Exploration of User Privacy in 802.11 Probe Requests with MAC Address Randomization Using Temporal Pattern Analysis"</p> <p>This package contains an anonymized packets of 802.11 probe requests captured in in December 2021 at Universitat Jaume I . The packet capture file is in the standardized *.pcap binary format and can be opened with any packet analysis tool such as Wireshark or scapy (Python packet analysis and manipulation package).</p>
Dataset used for the analysis described in "Spatial patterns and controls on wind erosion in the Great Basin"
<p>This data set contains AERO model outputs and associated Bureau of Land Management Assessment, Inventory, and Monitoring calculated values for functional plant group cover estimates for monitoring plots across the Great Basin. Versrion 2 (V2) includes MLRA number and sampling year column ("sample_yr") that were omitted in previous version.</p>
Spartina alterniflora aboveground biomass patterns from Landsat 5 TM imagery (1984-2011) and external driver data used in multivariate analysis.
We used Landsat 5 TM satellite imagery to derive aboveground biomass estimates for the three height classes (tall, medium, short) of Spartina alterniflora on the Centeral Georgia Coast. We used geospatial techniques to scale up in situ measurements of aboveground S. alterniflora aboveground biomass to landscape level estimates using 294 Landsat images acquired between 1984 to 2011. For each scene we extracted data from the same 63 sampling polygons, containing 1,222 pixels covering 1.1 million m^2. Using univariate and linear multiple regression tests, we compared Landsat derived biomass estimates for three S. alterniflora size classes against a suite of abiotic drivers. Drivers included monthly mean values for Altamaha River Discharge, Palmer Drought Severity Index, Standardized Precipitation Index, Mean Sea Level, Precipitation, and Temperature.
Social sensing of urban land use based on analysis of Twitter users' mobility patterns
<p>A companion dataset for the paper "Social sensing of urban land use based on analysis of Twitter users' mobility patterns". This dataset contains five files and one dictionary depicting the preferential return of Twitter users to their key locations and the urban land use types at these locations. More details can be found in the README file. </p>
Identifying patterns and recommendations of and for sustainable open data initiatives: a benchmarking-driven analysis of open government data initiatives among European countries
<p>This dataset contains data collected during a study <a href="https://www.sciencedirect.com/science/article/pii/S0740624X23000989"><em><strong>"Identifying patterns and recommendations of and for sustainable open data initiatives: a benchmarking-driven analysis of open government data initiatives among European countries"</strong></em></a> conducted by <em>Martin Lnenicka (University of Pardubice, Pardubice, Czech Republic), Anastasija Nikiforova (University of Tartu, Tartu, Estonia), Mariusz Luterek (University of Warsaw, Warsaw, Poland), Petar Milic (University of Pristina - Kosovska Mitrovica, Kosovska Mitrovica, Serbia), Daniel Rudmark (University of Gothenburg and RISE Research Institutes of Sweden, Gothenburg, Sweden), Sebastian Neumaier (St. Pölten University of Applied Sciences, Austria), Caterina Santoro (KU Leuven, Leuven, Belgium), Cesar Casiano Flores (University of Twente, Twente, the Netherlands), Marijn Janssen (Delft University of Technology, Delft, the Netherlands), Manuel Pedro Rodríguez Bolívar (University of Granada, Granada, Spain).</em></p> <p>It is being made public both to act as supplementary data for "<em>Identifying patterns and recommendations of and for sustainable open data initiatives: a benchmarking-driven analysis of open government data initiatives among European countries</em>", Government Information Quarterly*, and in order for other researchers to use these data in their own work. </p> <p>***Methodology***</p> <p>The paper focuses on benchmarking of open data initiatives over the years and attempts to identify patterns observed among European countries that could lead to disparities in the development, growth, and sustainability of open data ecosystems. </p> <p>This study examines existing benchmarks, indices, and rankings of open (government) data initiatives to find the contexts by which these initiatives are shaped, both of which then outline a protocol to determine the patterns. The composite benchmarks-driven analytical protocol is used as an instrument to examine the understanding, effects, and expert opinions concerning the development patterns and current state of open data ecosystems implemented in eight European countries - Austria, Belgium, Czech Republic, Italy, Latvia, Poland, Serbia, Sweden. 3-round Delphi method is applied to identify, reach a consensus, and validate the observed development patterns and their effects that could lead to disparities and divides. Specifically, this study conducts a comparative analysis of different patterns of open (government) data initiatives and their effects in the eight selected countries using six open data benchmarks, two e-government reports (57 editions in total), and other relevant resources, covering the period of 2013–2022.</p> <p>***Description of the data in this data set***</p> <p>The file "OpenDataIndex_<em>2013_</em>2022" collects an overview of 27 editions of 6 open data indices - for all countries they cover, providing respective ranks and values for these countries. These indices are:</p> <p>1) Global Open Data Index (GODI) (4 editions)</p> <p>2) Open Data Maturity Report (ODMR) (8 editions)</p> <p>3) Open Data Inventory (ODIN) (6 editions)</p> <p>4) Open Data Barometer (ODB) (5 editions)</p> <p>5) Open, Useful and Re-usable data (OURdata) Index (3 editions)</p> <p>6) Open Government Development Index (OGDI) (2 editions)</p> <p>These data shapes the third context - open data indices and rankings. The second sheet of this file covers countries covered by this study, namely, Austria, Belgium, Czech Republic, Italy, Latvia, Poland, Serbia, Sweden. It serves the basis for Section 4.2 of the paper.</p> <p>Based on the analysis of selected countries, incl. the analysis of their specifics and performance over the years in the indices and benchmarks, covering 57 editions of OGD-oriented reports and indices and e-government-related reports (2013-2022) that shaped a protocol (see paper, Annex 1), 102 patterns that may lead to disparities and divides in the development and benchmarking of ODEs were identified, which after the assessment by expert panel were reduced to a final number of 94 patterns representing four contexts, from which the recommendations defined in the paper were obtained. These patterns are available in the file "OGDdevelopmentPatterns". The first sheet contains the list of patterns, while the second sheet - the list of patterns and their effect as assessed by expert panel.</p> <p>***Format of the file***<br>.xls, .csv (for the first spreadsheet only)</p> <p>***Licenses or restrictions***<br>CC-BY</p> <p> </p> <p>For more info, see README.txt<br> </p>
Data for publication "Benefits of open access to researchers from lower-income countries: A global analysis of reference patterns in 1980–2020"
<p>Data to reproduce figures for the publication "Benefits of open access to researchers from lower-income countries: A global analysis of reference patterns in 1980–2020" (DOI: 10.1177/01655515241245952). Each file contains the data underlying the figure corresponding to the file name.</p>
Global patterns of soil organic carbon distribution in the 20–100 cm soil profile for different ecosystems: A global meta-analysis
<p><span><span> </span></span><span>The file named <span>“</span>Rawdata.xlsx<span>”</span> contains data sourced from the literature.<span> The file name is “GE_β.tif<span>”</span><span>,</span></span></span><span><span> GE represents</span></span><span> global ecosystems, which including cropland (CL), grassland (GL), and forestland (FL). “FL_β.tif” represents the spatial distribution of β for forestland at 20-100 cm depth. The file name is “GE_d_SOCD.tif”, where SOCD represents soil organic carbon density, d represents soil depth, for example, “FL_20-100_SOCD.tif” represents the spatial distribution of SOCD for forestland at 20-100 cm depth.</span></p>
PALEODEM/ Supplementary materials of the manuscript "Unraveling Early Holocene occupation patterns at El Arenal de la Virgen (Alicante, Spain) open-air site: an integrated palimpsest analysis"
<p>This repository hosts the R code scripts and datasets that allow reproducibility and replicability of the intra-site spatial analyses implemented in the paper:</p> <p>Rabuñal, J.R., Gómez-Puche, M., Polo-Díaz, A., Fernández-López de Pablo, J., 2022. Unraveling Early Holocene occupation histories at open-air sites through integrated chronological, archaeostratigraphical, lithic refitting and spatial analyses: the Arenal de la Virgen (Villena, Alicante) study case. SocArXiv.</p> <p>Contents:</p> <p>AV_Spatial_database.xlsx: main dataset for the intra-site spatial analysis.</p> <p>AV_Lcross_database.xlsx: dataset for the implementation of the cross-type L function.</p> <p>AV_2clusters.rds: dataset for the calculation of the artifact metrics.</p> <p>AV_MovingWindow_Results.csv: dataset with the results of the calculation of the Burnt Microdebris Index and its spatial autocorrelation analysis.</p> <p>AV_DBSCAN_Separation.R: R file containing the code used for the separation of the lithic spatial distribution using the DBSCAN automated density-based clustering algorithm.</p> <p>AV_Spatial_analysis.R: R file containing the code used for the intra-site spatial analysis.</p> <p>AV_MWA_Moran.R: R file containing the code for implementing the calculation and spatial autocorrelation analysis of the Burnt Microdebris Index.</p>
Dataset for statistical analysis of Constrictotermes cyphergaster (Blattodea: Isoptera: Termitidae: Nasutitermitinae) termites behavioural patterns
<p>This statistical analysis corresponds to the behavioral perspective of a large experiment in which <em>Constrictotermes cyphergaster</em> (Blattodea: Isoptera: Termitidae: Nasutitermitinae) termite groups were submitted to four different alarm stimuli. The aim of the work was to known the effect, at the individual scale, of the intensity of alarm stimuli in the emergence of order/disorder in group responses of social groups.</p> <p>The “read-me” file contains a detailed description of each data table used for the analysis. In the data tables, columns correspond to variables and rows to observations. Explanation of variables is on the headers of each data table.</p>
Synthetic Smart Card Data for the Analysis of Temporal and Spatial Patterns
<p>This is a synthetic smart card data set that can be used to test pattern detection methods for the extraction of temporal and spatial data. The data set is tab seperated and based on a stylized travel pattern description for city of Utrecht in The Netherlands and is developed and used in Chapter 6 of the PhD Thesis of Paul Bouman. </p> <p>This dataset contains the following files:</p> <ul> <li>journeys.tsv : the actual data set of synthetic smart card data</li> <li>utrecht.xml : the activity pattern definition that was used to randomly generate the synthethic smart card data</li> <li>validate.ref : a file derived from the activity pattern definition that can be used for validation purposes. It specifies which activity types occur at each location in the smart card data set.</li> </ul>
Dataset from : Detection and Analysis of an Alternate Flow Pattern in a Radial Vaned Diffuser
<p>This dataset pertains to the publication 'Detection and Analysis of an Alternate Flow Pattern in a Radial Vaned Diffuser', V. Moënne-Loccoz, I. Trébinjac, N. Poujol, P. Duquesne. International Journal of Turbomachinery, Propulsion and Power. MDPI (2020).</p><p>Where possible, each figure is provided in CSV format.</p>
Geodetic anomaly detection and analysis in the Campi Flegrei caldera (Italy) deformation pattern of the 2021-2023 escalating unrest phase
<p>Data used within the manuscript: "<strong><span>First evidence of a geodetic anomaly in the Campi Flegrei caldera (Italy) ground deformation pattern revealed by DInSAR and GNSS measurements during the 2021-2023 escalating unrest phase</span>"</strong></p> <p> </p> <p>Archive content:</p> <ul> <li><code>DTSLOS_CNRIREA_20150325_20231021_FB9K</code>: Line of Sight displacement time series retrieved by applying the P-SBAS algorithm to Sentinel-1 data set acquired from ascending orbits (Track 44) over Campi Flegrei caldera in the 20150325 - 20231021 interval. Data format is according to the <a href="https://gitlab.com/epos-tcs-satdata/doc/-/blob/main/TCS_SATD_Product_Description.md#los-displacement-time-series-dtslos" target="_blank" rel="noopener noreferrer">EPOS specification</a>.</li> <li><code>DTSLOS_CNRIREA_20150324_20231020_UJBI</code>: Line of Sight displacement time series retrieved by applying the P-SBAS algorithm to Sentinel-1 data set acquired from descending orbits (Track 22) over Campi Flegrei caldera in the 20150324 - 20231020 interval. Data format is according to the <a href="https://gitlab.com/epos-tcs-satdata/doc/-/blob/main/TCS_SATD_Product_Description.md#los-displacement-time-series-dtslos" target="_blank" rel="noopener noreferrer">EPOS specification</a>.</li> <li><code>Campi_Flegrei_GNSS_Weekly_Timeseries</code>: Weekly displacement time series of Campi Flegrei caldera GNSS network from 2016 to 2023.</li> </ul>
Data from: Using model analysis to unveil hidden patterns in tropical forest structures
<p>Data set of the article entitled: <strong>Using model analysis to unveil hidden patterns in tropical forest structures</strong></p> <p>This data set gives the following structural attributes for 133 forest plots at 9 sites in the tropics:</p> <ul> <li>tree density (ha<sup>-1</sup>)</li> <li>basal area (m<sup>2</sup> ha<sup>-1</sup>)</li> <li>mean diametere (cm)</li> <li>equivalent diameter (cm)</li> <li>density of trees in the dbh class 10-30 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 30-60 cm (ha<sup>-1</sup>)</li> <li>density of trees with dbh ≥ 60 cm (ha<sup>-1</sup>)</li> <li>aboveground dry biomass (Mg ha<sup>-1</sup>)</li> <li>fraction of the biomass of trees with dbh ≥ 60 cm</li> <li>weighted mean wood density (g cm<sup>-3</sup>)</li> <li>density of trees in the dbh class 10-20 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 20-30 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 30-40 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 40-50 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 50-60 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 60-70 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 70-80 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 80-90 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 90-100 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 100-110 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 110-120 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 120-130 cm (ha<sup>-1</sup>)</li> <li>density of trees with dbh ≥ 130 cm (ha<sup>-1</sup>)</li> </ul>
Fig. 5 in Spatial patterns of zooplanktivore Chirostoma species (Atherinopsidae) during water-level fluctuation in the shallow tropical Lake Chapala, Mexico: seasonal and interannual analysis
Fig. 5. (left column) Distribution-based Redundancy Analysis (db-RDA) ordination diagram of Lake Chapala with environmental variables (thick arrows), atherinopsids species (italic letters), sampling sites (numbers), and principal coordinates axes (thin arrows) at dry season (a: May of 1999) and rainy season (b: August of 1999; c: 2000). The fish are: jordani = Chirostoma jordani; consocium = Chirostoma consocium; labarcae = Chirostoma labarcae. The environmental variables are: Temp = temperature, DO = dissolved oxygen, Sal = salinity. In figure 5c shallow sites are in italic and deep sites in regular.
Fig. 3 in Spatial patterns of zooplanktivore Chirostoma species (Atherinopsidae) during water-level fluctuation in the shallow tropical Lake Chapala, Mexico: seasonal and interannual analysis
Fig. 3. GAM results for May and August of site influence on fish density to show differential distribution of species in Lake Chapala. a: Chirostoma jordani; b: Chirostoma consocium; c: Chirostoma labarcae. Circles represent the residuals. Spline fit (solid line) is bound by 95% confidence intervals (dotted lines).
Fig. 2 in Spatial patterns of zooplanktivore Chirostoma species (Atherinopsidae) during water-level fluctuation in the shallow tropical Lake Chapala, Mexico: seasonal and interannual analysis
Fig. 2. GAM results for May of environmental characteristics influence on fish density. a: effect of depth (m) on Chirostoma jordani; b: effect of temperature (°C) on C. jordani; c: effect of salinity on C. consocium. Circles represent the residuals. Spline fit (solid line) is bound by 95% confidence intervals (dotted lines).
Fig. 1 in Spatial patterns of zooplanktivore Chirostoma species (Atherinopsidae) during water-level fluctuation in the shallow tropical Lake Chapala, Mexico: seasonal and interannual analysis
Fig. 1. Map of Lake Chapala, Mexico. Numbers in bold represent sample sites and numbers in italic lake depths.
Fig. 4 in Morphotype And Multivariate Analysis Of The Occlusal Pattern Of The First Lower Molar In European And Asian Arvicoline Species (Rodentia, Microtus, Alexandromys)
Fig. 4. Differentiation on 16 Microtus samples by the morphotypic variation of the occlusal pattern.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.