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580 results for “pattern analysis”
Dataset on Spatial Analysis and Clustering of Deforestation in the Amazon Biome: Spatio-Temporal Patterns and Priority Areas
<p>The dataset was developed with the aim of facilitating the development of a methodology to identify and evaluate deforestation patterns and trends in the Amazon. This innovative method combines deforestation alerts from the Real-Time Deforestation Detection System (DETER) with detailed information on various land categories, including environmental protection areas, settlements, rural properties, undesignated public forests, indigenous lands, and conservation units. The integration of this robust data allowed for the precise identification of areas at risk of deforestation, significantly strengthening monitoring and control activities aimed at combating deforestation in the Amazon region.</p> <p> </p> <p><strong>Spatial resolution</strong></p> <p>The data are available with a spatial resolution of 25 x 25 km (625 km²) and cover the Amazon biome.</p> <p> </p> <p><strong>Temporal resolution </strong></p> <p>Period of observed data: 2017 and 2021</p> <p> </p> <p><strong>Coordinate reference system</strong> </p> <p>Geographic Coordinate System with Datum SIRGAS 2000 (EPSG:5880)</p> <p> </p> <p><strong>Data format</strong></p> <p>Data is provided as Shapefile.</p> <p> </p> <p><strong>Dataset usage</strong> </p> <p>It is free to use, but please make sure to cite the repository and our paper properly if you use this dataset.</p> <p> </p> <p><strong>Publication & further information</strong></p> <p>For additional scenario information, please contact Francisco Gilney Silva Bezerra (franciscogilney@gmail.com).</p>
Data from: The effects of human-altered habitat spatial pattern on frugivory and seed dispersal: a global meta-analysis
<p>Seed dispersal by frugivorous animals is important for plant mobility, regeneration, and persistence. Human-caused landscape change is thought to disrupt seed dispersal, but evidence is scarce. We performed a comprehensive meta-analysis on the effects of habitat spatial pattern on frugivory and seed dispersal. We found 233 effects from 71 studies. At a patch or local scale, altered habitat spatial pattern was measured as declining patch size, increasing patch isolation, or habitat edge (vs. interior). At a landscape scale it was measured as declining amount of habitat, increasing mean patch isolation, increasing number of patches, or increasing habitat edge in the landscape.</p> <p>We found overall negative effects of altered habitat spatial pattern on: (i) the quantity of frugivory or seed dispersal, (ii) the number of species involved in a plant-frugivore interaction, and (iii) seed dispersal distance. Moderator variable analysis was only possible for the first of these. It revealed negative responses of the quantity of frugivory or seed dispersal to habitat loss at both the local scale (declining patch size), and the landscape scale (declining habitat amount), but little evidence for a response to habitat edge at either scale. In addition, altered habitat spatial pattern reduced the quantity of frugivory or seed dispersal more strongly in temperate than tropical areas. Finally, the few-recorded effects of landscape-scale fragmentation per se (increasing patch density or edge density) on the quantity of frugivory or seed dispersal were mixed and weak. Our meta-analysis reinforces the notion that habitat loss is a major threat to frugivory and seed dispersal by animals, and reveals an insufficiency of studies of the effects of habitat fragmentation per se. Thus, based on the current literature, we conclude that maintaining and increasing habitat amount is vital for maintaining seed dispersal by frugivorous animals.</p>
Data from: Computed tomographic analysis of dental system of three Jurassic ceratopsians: implications for the evolution of the tooth replacement pattern and diet in early-diverging ceratopsians
<p><span>T</span><span>he </span><span>dental system of ceratops</span><span>ids is among the most specialized structure in Dinosauria</span><span>, and includes high angled wear surfaces, split tooth roots, and multiple teeth in each tooth family. However, the early evolution of this unique dental system is generally poorly understood due to a lack of knowledge of the dental morphology and development in early-diverging ceratopsians.</span><span> Here we study the dental system of </span><span>three</span><span> of the earliest-diverging Chinese ceratopsians</span><span>: </span><em><span>Yinlong</span></em><span> and <em>Hualianceratops</em> from the early Late Jurassic of Xinjiang</span><span>,</span><span> and <em>Chaoyangsaurus</em> from the Late Jurassic of Liaoning. By using micro-computed tomographic analyses, our study has revealed significant new information regarding the dental system of these early ceratopsians, including </span><span>no</span><span> more than five replacement teeth in each jaw quadrant; at most one generation of replacement teeth in each alveolus; nearly full resorption of the functional tooth root during tooth replacement; and occlusion with low-angled, concave wear facets that differs significantly from the shearing occlusal system seen in ceratopsids. <em>Yinlong</em> displays an increase in the number of maxillary tooth alveoli and a decrease in the number of replacement teeth during ontogeny as well as the retention of remnants of functional teeth in the largest individual.</span> <span>Early-diverging ceratopsians thus display a relatively slow tooth replacement rate compared to late-diverging ceratopsians.</span> <span>Combined with paleobotany and palaeoenvironment data, <em>Yinlong</em> likely uses gastroliths to triturate foodstuffs, and t</span><span>he difference in diet strategy might have influenced the pattern of tooth replacement in later-diverging ceratopsians.</span></p>
Supplementary Files - Transcriptomic analysis of CPM-positive hiPSCs-derived liver progenitor cells in a microfluidic device shows zonation-like patterns.
<p>Supplementary Files for the paper intitled Transcriptomic analysis of CPM-positive hiPSCs-derived liver progenitor cells in a microfluidic device shows zonation-like patterns. </p>
Meta-analysis of elevational changes in the intensity of trophic interactions: similarities and dissimilarities with latitudinal patterns
<p>The premise that the intensity of biotic interactions decreases with increasing latitudes and elevations is broadly accepted; however, whether these geographical patterns can be explained within a common theoretical framework remains unclear. Our goal was to identify the general pattern of elevational changes in trophic interactions and to explore the sources of variation among the outcomes of individual studies. Meta-analysis of 226 effect sizes calculated from 136 publications demonstrated a significant but interaction-specific decrease in the intensity of herbivory, carnivory and parasitism with increasing elevation. Nevertheless, this decrease was not significant at high latitudes and for interactions involving endothermic organisms, for herbivore outbreaks or for herbivores living within plant tissues. Herbivory similarly declined with increases in latitude and elevation, whereas carnivory showed a fivefold stronger decrease with elevation than with latitude and parasitism increased with latitude but decreased with elevation. Thus, although these gradients share a general pattern and several sources of variation in trophic interaction intensity, we discovered important dissimilarities, indicating that elevational and latitudinal changes in these interactions are partly driven by different factors. We conclude that the scope of the latitudinal biotic interaction hypothesis cannot be extended to incorporate elevational gradients.</p>
alfa-synuclein spectra for the paper "Linear discriminant analysis reveals hidden patterns in NMR chemical shifts of intrinsically disordered proteins"
<p>The experimental data for the paper "Linear discriminant analysis<br> reveals hidden patterns in NMR chemical shifts of intrinsically<br> disordered proteins" - four spectra of an intrinsically disordered<br> protein alfa-synuclein:</p> <p>3D HNCO</p> <p>4D HabCab(CO)NH</p> <p>4D (H)N(CA)CONH</p> <p>4D HNCACO</p> <p>All spectra were acquired using non-uniform sampling (schedules included<br> as 'schedule.txt' files in a format of 'Kozminski' method in VnmrJ). 3D<br> spectrum (HNCO_nuFT.ucsf) was processed using multidimensional Fourier transform<br> (http://nmr.cent3.uw.edu.pl/software, program 'toastd'). Additionally, we provide a spectrum cleaned of NUS artifacts (HNCO_artifacts_cleaned.ucsf) using program handy (http://nmr.cent3.uw.edu.pl/software, program 'handy').</p> <p>4D spectra were processed using sparse multidimensional Fourier transform<br> (http://nmr.cent3.uw.edu.pl/software, program 'reduced'), based on a<br> HNCO peak list ('peak.list' file). Files with processing parameters are<br> included as 'parameters.txt' files.</p> <p>The resulting spectra are included as Sparky .ucsf files. The files for<br> 4D spectra are the collections of 2D cross-sections with the<br> cross-section number corresponding to the numbering of HNCO peaks in the<br> 'peak.list' file.</p> <p>The folder 'Application2_assignment_transfer' contains Sparky 'ucsf' and 'save' files of the two-dimensional NH projection of the HNCO spectrum with the experimental (HSQC_exp.list) and BMRB (HSQC_bmrb.list) peak lists read into the spectrum. The experimental peaks are marked with arbitrary numbers and BMRB peaks - with names of the preceding aa-residues. The correct assignment is shown in the file 'assignment.txt'.</p>
Analyses, data and figures related to: "Connecting ships: Using dendrochronological network analysis to determine the wood provenance of Roman-period river barges found in the Lower Rhine region and visualise wood use patterns"
<p>Analyses, data and figures related to: "Connecting ships: using dendrochronological network analysis to determine the wood provenance of Roman-period river barges found in the Lower Rhine region and to visualise patterns of wood use" by Ronald M. Visser (Saxion University of Applied Sciences, Deventer, the Netherlands) and Yardeni Vorst (Vorst wood research, Zaandam, the Netherlands) submitted to the International Journal of Wood Culture</p>
Fig.3 in Fenetic Analysis Of Bombina Bombina Ventral Spots Pattern In 8 Localizations In Latvia
Fig.3. Examples of phenomorphs' clusters.
CONUS and sub-regional monthly cloudiness supporting for the trend analysis: "CONUS Cloud Pattern Change 1980-2020" Vo T.T., Hu L., Xue L., Chen S. (2024)
<p> </p> <p>The dataset supporting for the publication: "CONUS Cloud Pattern Change 1980-2020" Vo T.T., Hu L., Xue L., Chen S. (2024), Journal of Climate. </p> <ul> <li>The data format is in tabular form (.csv, comma delimited). Each row is the <strong>monthly </strong>aggregated for <strong>each </strong>sub-region More specifically, the description of each column with associated with its unit formatted in the table are listed as follows: <ul> <li><strong>datetime</strong>: time of the observation (formatted as month/day/year)</li> <li><strong>original_time_series</strong>: original cloud coverage before processing using the trend analysis mentioned in the paper (unit: percentage, %)</li> <li><strong>enso_neutral</strong>: cloud coverage removing the ENSO effect using linear regression method followed by Gu and Adler 2011 (unit: percentage, %)</li> <li><strong>cloud_coverage</strong>: cloud coverage removing the ENSO effect and seasonality and remainder (refer to the cloud coverage used in the manuscript) using Seasonal Decomposition of Time Series by Loess (STL) method (unit: percentage, %)</li> <li><strong>region_name_list</strong>: name of the sub-region</li> <li><strong>cloud_type</strong>: certain cloud type (all clouds refers to total clouds)</li> </ul> </li> </ul> <p> </p> <p><span>References: </span></p> <p>Gu, G., & Adler, R. F. (2011). Precipitation and temperature variations on the interannual time scale: Assessing the impact of ENSO and volcanic eruptions. <em>Journal of Climate</em>, <em>24</em>(9), 2258–2270. https://doi.org/10.1175/2010JCLI3727.1</p> <p>Cleveland, R. B., Cleveland, W. S., & Terpenning, I. (1990). STL: A Seasonal-Trend Decomposition Procedure Based on Loess. <em>Journal of Official Statistics</em>, <em>6</em>(1), 3. http://ezproxy.montevallo.edu:2048/login?url=https://www.proquest.com/scholarly-journals/stl-seasonal-trend-decomposition-procedure-based/docview/1266805989/se-2?accountid=12538</p> <p> </p>
Data from: Individual Movement - Sequence Analysis Method (IM-SAM): characterising spatio-temporal patterns of animal trajectories across scales and landscapes
<p>Dataset included in Zenodo supports the analyses performed in "<em>Individual Movement - Sequence Analysis Methods (IM-SAM) characterising spatio-temporal patterns of animal trajectories across scales and landscapes.</em>"</p> <p>The dataset includes one RDS file, that can be easily loaded into R using the readRDS function. The RDS file consists out of a list including two objects per animal:</p> <ul> <li>Object 1 contains a data frame with the real and simulated sequences for an animal. e.g., ls[[1]][[1]] </li> <li>Object 2 contains the home range in raster format of an animal. e.g., ls[[1]][[2]]</li> </ul> <p>The data frames in object 1 contain real habitat use sequences and corresponding simulated habitat use sequences generated in the home range of the specific individual (900 simulated sequences: 6 habitat selection rules x 3 selection coefficients x 50 repetitions). Open and closed habitats are respectively encoded by 0 and 1. The first 96 columns of each row in a data frame represent a 16-day habitat use sequence, with a fixed 4-hour relocation interval (0, 4, 8, 12, 16 and 20h). Column names are named as follows: Day_1_0h, Day_1_4h,..., Day_16_20h. In the next columns we provide the selection coefficients (columns 97-99), the habitat selection rules (or pattern, columns 100-102) and the number of missing values (mvs, columns, 103-104) for each of the real and simulated sequences. Note that simulated sequences have no missing values (i.e. values are always 0.00) and for real sequences there is no selection coefficient or habitat selection rule (i.e. values are always xxx).</p> <p>Rownames of simulated sequences are composed out of the habitat selection rule (c, o, a24, a33, a42 and u), the selection coefficient (5, 10, 50) and the replicate (1 to 50), separated by dashes. For example, the first simulated sequence in the first data frame (ls[[1]][[1]][1,]) is described as a24_10_1. The rownames of real sequences instead are composed out of the individuals' identifier, the biweekly period (1 to 23) and the year. For example, the first real sequence in the first data frame (ls[[1]][[1]][901,]) is described as 1_5_2006.</p> <p><br> </p>
EBSD Kikuchi Pattern Analysis, Silicon 15kV
<p>Supplementary Data and Images for Si EBSD pattern analysis as presented in</p> <p>A. Winkelmann, T.B. Britton, G. Nolze "Constraints on the effective electron energy spectrum in backscatter Kikuchi diffraction", Physical Review B (2019)</p>
Data and Analysis Artifacts for Service-Based Evolvability Patterns (Experiment and Metrics)
<p>Two functionally equivalent service-based web-shop systems (one version with selected service-based patterns, one without) were analyzed with a controlled experiment as well as with structural maintainability metrics. This repo contains all analysis artifacts.</p>
Figure. Maps of Turkish provinces and regions. in Checklist of Turkish Raphidioptera on the basis of distribution pattern and biogeographical analysis
Figure. Maps of Turkish provinces and regions.
Figure2 in Analysis of Behavioural Patterns of the Amazonian Manatee, Trichechus manatus manatus, under Human Care (Mammalia:Sirenia)
Figure2. Distribution of respiratory frequencies (diving phases) for each manatee
Figure1 in Analysis of Behavioural Patterns of the Amazonian Manatee, Trichechus manatus manatus, under Human Care (Mammalia:Sirenia)
Figure1. Map of the manatee tank with current and spatial zones (edited © Nuremberg Zoo 2012)
Dataset for "Analysis of complex excitation patterns using Feynman-like diagrams"
<p>Data and scripts to re-create the figures of the paper:</p> <p>Louise Arno, Desmond Kabus, and Hans Dierckx (2024)<br>Analysis of complex excitation patterns using Feynman-like diagrams.<br><a href="https://doi.org/10.48550/arXiv.2307.01508">https://doi.org/10.48550/arXiv.2307.01508</a>.</p>
Data for "Effects of forest dieback on deadwood patterns: large scale trends from a cross-analysis of European databases"
<p><strong><span>Aims</span></strong></p> <p><span>We carried out an opportunistic correlative study between past crown conditions and current deadwood volumes.</span></p> <p><span>Our aim was to mobilise available data on site factors and long-term monitoring of crown vitality indicators in Europe to investigate the influence of current and recent local defoliation levels on plot-level deadwood volume.</span></p> <p><span>For a subset of level I, 16*16-km monitoring plots located throughout Europe, we benefitted from data on both (i) deadwood measurements carried out within the framework of the Forest Focus Biosoil Project </span><span>(Galluzzi et al., 2019)</span><span>, pre-processed into a consistent and harmonized deadwood dataset by </span><span>Puletti et al. (2019)</span><span>, and (ii) defoliation assessments provided yearly since 1989 by the International Co-operative Program on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests), the most comprehensive European monitoring network for the large-scale assessment of forest ecosystem health </span><span>(Vitale et al., 2014)</span><span>. </span></p> <p><span>Biosoil data on deadwood and ICP data on defoliation have never been crossed before.</span></p> <p><span>We used defoliation level as a proxy for the severity of stand dieback. Deadwood patterns can be addressed through deadwood profiles, which subdivide local deadwood stocks into classes based on size, position and decay stage.</span></p> <p><a name="_Toc175840512"></a><a name="_Toc116027761"></a><span><strong><span>ICP database and defoliation protocol</span></strong></span></p> <p><span>The International Cooperative Program to assess and monitor air pollution effects on the forest (ICP Forests) is responsible for an extensive level I monitoring system of forest sites </span><span>(Hauβmann & Fischer, 2004)</span><span>, which has been in operation since 1986. This large-scale level I network is made up of dense, spatially representative sampling points placed throughout European forests on a 16 × 16 km virtual grid, and is dedicated to monitoring forest conditions. The sampling points cover most European forested areas and encompasses ca. 6000 monitoring plots in 42 countries. In each plot, a visual evaluation of defoliation and discoloration of tree crowns is performed annually to survey forest health status (<a href="http://icp-forests.net/page/largescale-forest-condition">http://icp-forests.net/page/largescale-forest-condition</a>). Data management is presently carried out at the Programme Co-ordinating Centre (PCC) of ICP Forests in Eberswalde, Germany, and all data are available upon request. Since 1989, a standardized procedure for “annual surveys of crown condition’’ has been applied to 24 selected dominant and co-dominant trees with a minimum height of 60 cm and showing no significant mechanical damage. The defoliation and discoloration level of each tree crown is visually assessed on a sliding scale of 5% increments as the percentage of needle/leaf loss in the assessable crown as compared to a reference tree with full foliage. Mean defoliation at the plot scale was defined as the proportion of “damaged” trees i.e., with a defoliation rate of more than 25%, and used as a proxy for plot decline level. In the ICP database, the factors associated with observed defoliation related to natural disturbances or management (i.e., vertebrate or insect herbivory, fungal or fire damage, drought impacts, signs of removal of coarse woody debris, past landscape) were not recorded in a sufficiently standardized way to be used as covariates in our models. Similarly, plot-level living tree density and above-ground biomass for standing living trees (expressed in kg.ha<sup>−1</sup>), presumably surveyed in subplot 2, were not available.</span></p> <p><a name="_Toc175840513"></a><a name="_Toc116027762"></a><span><strong><span>Biosoil database and deadwood protocol</span></strong></span></p> <p><a name="_Toc116027763"></a><span>In the framework of the large collaborative European Forest Focus BioSoil-Biodiversity project</span><span>, a system of circular concentric subplots was built around certain ICP level I plots to collect additional data on stand structure and biodiversity between 2005 and 2008 (Figure 1). </span><span><span>The individual countries were responsible for selecting the ICP level I plots to be included in the BioSoil project </span></span><span><span>(Galluzzi et al., 2019)</span></span><span><span>. Overall, a total of 3243 geocoded Level I plots were considered in 19 European countries </span></span><span><span>(Puletti et al., 2017)</span></span><span><span>: Austria, Belgium (Flanders only), Cyprus, the Czech Republic, Denmark, Finland, France, Germany (eight federal states only), Hungary, Ireland, Italy, Latvia, Lithuania, Poland, Slovakia, Slovenia, Spain, Sweden and the United Kingdom (Figure 1). BioSoil project results are recorded in the multi-dimensional LI-BioDiv geodatabase that contains raw data on forest structure and vegetation records used to calculate simple plot-level structural and compositional forest variables (i.e., biomass, deadwood volume, plant alpha-diversity; </span></span><span><span>Bastrup-Birk et al. 2007; Hiederer & Durant 2010)</span></span><span><span>. At each plot, deadwood was quantified on an area of 400 m<sup>2</sup> (BioSoil subplots 1 and 2, radius of 11.28 m; </span></span><span><span>Puletti et al., 2017)</span></span><span><span>. The deadwood survey included coarse woody debris (including lying dead trees), snags (including standing dead trees) and stumps more than 10 cm in diameter. Only snags and stumps more than 130 cm in height were considered. Diameter, length or height, tree species and decay stage (5 classes) were recorded for each deadwood piece. The raw ICP deadwood data were processed by </span></span><span><span>Puletti et al. (2017, 2019)</span></span><span><span> into a consistent and harmonized pan-European deadwood dataset, which we used in this study. The dataset provides total deadwood volume and the volume of several deadwood types for each plot. Further details can be found in the ICP Forests manual (</span></span><a href="http://icp-forests.net/page/icp-forests-manual"><span><span>http://icp-forests.net/page/icp-forests-manual</span></span></a><span><span>), </span></span><span><span>Puletti et al. (2019)</span></span><span><span> and </span></span><span><span>Augustynczik et al. (2024)</span></span><span><span>.</span></span></p> <p><span><span>In our study, we considered the following response variables</span></span><span>: (i) total deadwood volume, (ii) </span><span>standing deadwood (snags) volume, (iii) volume of ground-lying deadwood, (iv) </span><span>fresh deadwood volume </span><span>(= Vm3_dec1_Biosoil + Vm3_dec2_Biosoil), and (v) decayed deadwood volume = (= Vm3_dec4_Biosoil + Vm3_dec5_Biosoil).</span></p> <p><span>A few environmental covariates were collected from the Biosoil data: (i) management intensity (grouped into two classes: recently harvested, i.e., with management evidence within the last 10 years; and not recently harvested, i.e., unmanaged (no management evidence) or managed a long time ago (management evidence but more than 10 years previously), (ii) average stand age (separated into 3 classes: mature [>100 yrs], mid-aged [41-100 yrs], young [1-40 yrs]), (iii) elevation (above sea level, a.s.l.), a continuous quantitative variable, (iv) dominant tree genus, and (v) forest type, depending on the dominant tree species: coniferous, deciduous or mixed.</span></p> <p><a name="_Toc175840514"></a><a name="_Toc116027764"></a><span><strong><span>Database joint</span></strong></span><span><strong><span>: <a name="_Toc116027765"></a>plot matching in time series</span></strong></span></p> <p><span>After harmonizing plot names and coordinates in the two datasets (ICP-defoliation and Biosoil-deadwood), only plots with matched data in both datasets were selected. Plots with a maximum of one year’s discontinuity in the data were retained, and the missing values were reconstructed from the average values in contiguous years. Plots with discontinuities in defoliation measurements of more than 2 years were deleted. We matched defoliation measurements for the Biosoil-ICP datasets from 1989 to 2007 and finally obtained 2,070 five-year, 1,804 ten-year and 1,399 fifteen-year time series. This approach made it possible to define three 10-year time series [1995-2005, 1996-2006, 1997-2007] with plots in 17 countries, from five plots in Ireland and nine in the United Kingdom, to 337 plots in Finland and 461 in France.</span></p> <p><a name="_Toc175840515"></a><a name="_Toc116027766"></a><span><strong><span>Calculation of global defoliation metrics</span></strong></span></p> <p><span>We calculated 16 univariate metrics to summarize changes in defoliation throughout the 10-year period prior to the Biosoil deadwood measurements. Some of the selected parameters describe the immediate possible effects of defoliation severity in the recent past on a given year: (i) defoliation level of the previous year (n-1), (ii) defoliation level of the year before the previous year (n-2), (iii) defoliation level of the year two years before the previous year (n-3). Other defoliation metrics relate to the cumulative effects of defoliation levels in the near or the distant past: (i) average defoliation level over the last two years, (ii) average defoliation level over the last three years, (iii) average defoliation level over the last five years, (iv) average defoliation level over the first five years of the 10-year time series, and (v) time elapsed since last peak defoliation. Several other parameters depict general trends in the level of defoliation over the 10-year time series: for cumulative metrics: (i) arithmetic mean of annual defoliation level; (ii) geometric mean of annual defoliation level; (iii) Area Under the defoliation time Curve (AUC), i.e., the cumulative sum of defoliation levels; and for the overall trend: (iv) the estimated slope of the linear regression line for defoliation level over time. Finally, some of the metrics reflect defoliation severity and repetition along the 10-year time series, and their potentially time-lagged effects: (i) maximum defoliation level; (ii) total number of years elapsed after the dieback peak level, whether successive or not; (iii) the number of peaks, consecutive or discontinuous, i.e., the number of severe defoliation events and defoliation frequency; and (iv) duration of the longest peak, i.e., the longest continuous time during which the level of defoliation was greater than the relative threshold.</span></p> <p><span>A peak in defoliation was defined as a year in which the level of defoliation exceeded a relative threshold, i.e., the third quartile value. In our 10-year time series, the peak value was 25% and above. <span><span> </span></span></span></p>
Replication Package for "Guided Pattern Mining for API Misuse Detection by Change-Based Code Analysis"
<p>This repository provides the data sets and scripts used in the paper "Guided Pattern Mining for API Misuse Detection by Change-Based Code Analysis" by Sebastian Nielebock, Robert Heumüller, Kevin Michael Schott, and Frank Ortmeier from the Faculty of Computer Science of the Otto-von-Guericke University Magdeburg, Germany. This paper is published in Springer's "Automated Software Engineering - An International Journal" in August 2021. The article is available as open access at <a href="https://dx.doi.org/10.1007/s10515-021-00294-x">https://dx.doi.org/10.1007/s10515-021-00294-x</a>. A preprint is available under <a href="https://arxiv.org/abs/2008.00277">https://arxiv.org/abs/2008.00277</a>.</p> <p>All scripts and data sets are provided by the authors and come without any guarantee. For any issues regarding replication do not hesitate to contact us ({sebastian.nielebock,robert.heumueller, kevin.schott, frank.ortmeier} <at> ovgu.de)</p> <p>If you use or refer to these datasets, please cite our paper using the following BibTex entry.</p> <pre>@article{NielebockAPIFilterSearch2021, title = {Guided Pattern Mining for API Misuse Detection by Change-Based Code Analysis}, author = {Sebastian Nielebock and Robert Heum\"{u}ller and Kevin Michael Schott and Frank Ortmeier}, editor = {Springer}, journal = {Springer Automated Software Engineering - An International Journal}, number = {15}, pages = {1-48}, volume = {28}, url = {https://arxiv.org/abs/2008.00277}, doi = {10.1007/s10515-021-00294-x}, year = {2021}, } </pre>
Data and codes to replicate the analysis in: The spatial ecology of conflicts: Unravelling patterns of wildlife damage at multiple scales
<p><span><span>Human encroachment into natural habitats is typically followed by conflicts derived from wildlife damages to agriculture and livestock. Spatial risk modelling is a useful tool to gain understanding of wildlife damage and mitigate conflicts. Although resource selection is a hierarchical process operating at multiple scales, risk models usually fail to address more than one scale, which can result in the misidentification of the underlying processes. Here, we addressed the multi-scale nature of wildlife damage occurrence by considering ecological and management correlates interacting from household to landscape scales. We studied brown bear (<i>Ursus arctos</i>) damage to apiaries in the North-eastern Carpathians as our model system. Using generalized additive models, we found that brown bear tendency to avoid humans and the habitat preferences of bears and beekeepers determine the risk of bear damage at multiple scales. Damage risk at fine scales increased when the broad landscape context also favoured damages. Furthermore, integrated-scale risk maps resulted in more accurate predictions than single-scale models. Our results suggest that principles of resource selection by animals can be used to understand the occurrence of damages and help mitigate conflicts in a proactive and preventive manner. </span></span></p>
Data from: Genome-wide analysis reveals associations between climate and regional patterns of adaptive divergence and dispersal in American pikas
<p>Understanding the role of adaptation in species responses to climate change is important for evaluating the evolutionary potential of populations and informing conservation efforts. Population genomics provides a useful approach for identifying putative signatures of selection and the underlying environmental factors or biological processes that may be involved. Here, we employed a population genomic approach within a space-for-time study design to investigate the genetic basis of local adaptation and reconstruct patterns of movement across rapidly changing environments in a thermally-sensitive mammal, the American pika (<i>Ochotona princeps</i>). Using genotypic data at 49,074 single nucleotide polymorphisms (SNPs), we analyzed patterns of genome-wide diversity, structure, and migration along three independent elevational transects located at the northern extent (Tweedsmuir South Provincial Park, British Columbia, Canada) and core (North Cascades National Park, Washington, USA) of the Cascades lineage. We identified 899 robust outlier SNPs within- and among-transects. Of those annotated to genes with known function, many were linked with cellular processes related to climate stress including ATP-binding, ATP citrate synthase activity, ATPase activity, hormone activity, metal ion-binding, and protein-binding. Moreover, we detected evidence for contrasting patterns of directional migration along transects across geographic regions that suggest an increased propensity for American pikas to disperse among lower elevation populations at higher latitudes where environments are generally cooler. Ultimately, our data indicate that fine-scale demographic patterns and adaptive processes may vary among populations of American pikas, providing an important context for evaluating biotic responses to climate change in this species and other alpine-adapted mammals.</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.