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97 results for “code pattern”
Data and code from: Insect biomass decline scaled to species diversity: General patterns derived from a hoverfly community
<p>To study changes in flying insect communities, and hoverflies in particular, malaise trap samples from a German site were compared between two years (Hallmann et al. 2020). The data files deposited here contain data obtained from six malaise traps in the Wahnbachtal (North Rhine-Westphalia, Germany, 50.851944N, 7.320833E) that were deployed in 1989 and again in 2014, at the exact same locations. Traps were situated in wet meadows as well as tall perennial meadows, in close proximity to shrub corridors, to forest–grassland borders, and to the Wahnbach River and surrounded by agricultural land, essentially a rather heterogeneous habitat. The Wahnbach River and the greater part of the valley are protected for watershed purposes and are subject to nature conservation management by the Wahnbach Talperrenverband. Hence, several restrictions apply to safeguard against water contamination.</p> <p>Total insect biomass collected with these traps was already included in Hallmann et al. (2017), but here we focus on additional information: the abundance and richness of hoverflies (Syrphidae) in each of the collected samples (pots). Methodologies of collection are described in Sorg (1990), Schwan et al. (1993), Sorg et al. (2013), Hallmann et al. (2017), and Ssymank et al. (2018). In brief, malaise traps were deployed throughout the growing season and operated continuously (day and night). Malaise trap construction (e.g., size, material, colouring, and ground sealing) and placing (e.g., positioning, orientation, and slope of the locations) were standardised in all aspects. Insect samples were preserved in 80% ethanol solution. Catches of the six traps investigated in the present study were emptied regularly: On average exposure intervals were 7.0 d (SD = 0.5) in 1989 and 16.7 d (SD = 5.6) in 2014. Across the six traps in 2014 the total exposure time (in number of days) was 42% higher compared to 1989. All collected samples (n = 196) were used in the present analysis with in total 19,604 individual hoverflies counted, distributed over 162 species and 59 genera.</p> <p>To assess how environmental conditions have changed over the 25 year, several additional datasets were assembled. Climatic<br> data were obtained from 169 climatic stations and were used to interpolate daily weather variables to each trap location, using spatiotemporal kriging. These steps are described in detail in Hallmann et al. (2017).</p> <p>Our analysis (see R code) consists of three components. First, we considered total abundance, species richness, and species diversity, at two temporal scales: pooled per year, i.e., across the sampling season, and seasonally (i.e., per day), and we compared these metrics between 1989 and 2014. Second, we examined how total flying biomass (i.e., the weight of all trapped insects, of which hoverflies are only a small proportion) related to total abundance as well as species richness of hoverflies. Third, we derived persistence probabilities and population growth rate trends per species, to examine interspecific variation in these parameters.</p> <p>Descriptions of the deposited files:</p> <p><strong>Groups.csv</strong><br> MF_NR = identifier of each of the six malaise trap locations<br> yrf = year of sampling<br> pot = sample identifier<br> dt = number of sampling days<br> from.dnr = day-of-the-year on which a pot was attached to a malaise trap<br> to.dnr = day-of-the-year on which a pot was collected from a malaise trap<br> mean.daynr = mean day-of-the-year of the sampling period<br> Nspec = number of different hoverfly species found in a pot<br> Nind = number of hoverfly individuals found in a pot</p> <p><strong>Counts.csv</strong><br> A matrix of counts of individual hoverflies per pot per species. The 196 rows represent the pots in the same order as in the file 'Groups.csv'. The columns represent the 162 different hoverfly species found. The scientific species names are indicated in the column headers.</p> <p><strong>PairedData.csv</strong><br> pot = sample identifier<br> JAHR = year of sampling<br> MF_NR = identifier of each of the six malaise trap locations<br> dt = number of sampling days<br> from.dnr = day-of-the-year on which a pot was attached to a malaise trap<br> to.dnr = day-of-the-year on which a pot was collected from a malaise trap<br> NI = number of hoverfly individuals found in a potbiomass.daily<br> NSP = number of different hoverfly species found in a pot<br> biomass.daily = daily fresh weight [gram] of flying insects: total fresh weight in a pot divided by the number of sampling days.</p> <p><strong>ModelFrame.csv</strong><br> MF_NR = identifier of each of the six malaise trap locations<br> yrf = year of sampling<br> pot = sample identifier<br> dt = number of sampling days<br> from.dnr = day-of-the-year on which a pot was attached to a malaise trap<br> to.dnr = day-of-the-year on which a pot was collected from a malaise trap<br> mean.daynr = mean day-of-the-year of the sampling period<br> plot = identifier of each of the six malaise trap locations<br> date = date for which the weather variables are interpolated<br> daynr = day-of-the-year for which the weather variables are interpolated<br> altitude = altitude [m] of the malaise trap locations<br> year = year of sampling<br> temperature = interpolated temperature [degrees Celsius]<br> precipitation = interpolated precipitation [mm per day]<br> wind.speed = interpolated wind speed [m/s]</p> <p><strong>Data_Rcode.pdf</strong><br> This pdf provides the R-code behind the analysis of the Hoverfly data. Three datasets are provided along with this R-code document, namely "Counts.csv", "Groups.csv", "PairedData.csv" and "ModelFrame.csv". Additionally, the BUGS-code ""syrphidModel.jag" is required for running the daily-activity model in JAGS.</p> <p><strong>syrphidModel.jag</strong><br> This BUGS-code is required for running the daily-activity model in JAGS.</p>
Supplemental data and code for "Global patterns in water flux partitioning: Irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff"
<p>This dataset provides all data compiled and generated for the manuscript entitled "Global patterns in water flux partitioning: Irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff" (https://doi.org/10.1016/j.oneear.2023.08.002). This includes the boundaries for 3614 hydrological catchments, the curated data used for analysis and modelling, the developed machine learning model, shapley values and area of applicability results, and data for global extrapolation</p> <p>It also contains a markdown file ('code.html') which shows how to access and use the data, and generic sample codes used to generate these results.</p> <p> </p> <p> </p> <p> </p>
Data and Code: Decreased spinal inhibition leads to undiversified locomotor patterns
<p>Data and Code belonging to the article 'Increased spinal excitation causes decreased locomotor complexity'.<br>Available as preprint at: <a href="https://www.biorxiv.org/content/10.1101/2022.04.21.489087v2">https://www.biorxiv.org/content/10.1101/2022.04.21.489087v2</a></p> <p><strong>Included files</strong><br>CODE: </p> <p>- runExampleNet.m: this trains and tests an example network and plots the network outputs. The settings for the signal and the network can all be changed in this file.</p> <p>- runMFT.m: this runs the meanfield theory analysis for various levels of Imbalance and g_tot</p> <p>- createSignal.m: function to create the test and train signals, called from runExampleNet.m</p> <p>- ESN_EI.m: function for the <em>echo state network</em> with distinct excitatory and inhibitory populations, called from runExampleNet.m</p> <p>- getNPG.m: function to get parameter values for the desired imbalances and overall parameter levels</p> <p>DATA: <br>Myonardo (a 3D musculoskeletal model) output for walking slowly (XXX=WalkingI), walking fast (XXX=WalkingII) and running (XXX=Running), used in createSignal.</p> <p>Each folder contains:</p> <p>- XXX.qtm: File with the labelled qualisys data </p> <p>- XXX.mat: File with the recorded kinetic ('force') and kinematic ('trajectories') data </p> <p>- modelOutput.mat: the relevant outputs of the myonardo simulation, used to create the signals in createSignal: </p> <p>- includes: time, right heel marker, muscle length, muscle velocity and muscle activation</p> <p>- musclenames.mat: file indicating which column corresponds to what muscle, necessary for signal creation in createSignal.</p> <p> </p> <p>Upon request, the model simulations results can be shared (several GB). Contact: mdegraaf@uni-muenster.de</p> <p> </p>
The dataset for the study of code change patterns in Python
<p>The dataset of Python projects used for the study of code change patterns and their automation. The dataset lists 120 projects, divided into four domains — Web, Media, Data, and ML+DL.</p>
Typology of antipassive coding patterns. Data
<p>MS-DOS CSV files with the data that have been used in the paper <strong>Typology of antipassive coding patterns </strong>by<strong> </strong>Ilja A. Seržant, Katarzyna Maria Janić, Darja Dermaku, Oneg Ben Dror.</p>
Typology of antipassive coding patterns and frequency effects of antipassives
Basic information about antipassives in languages with at least some ergative patterns
Data and code for publication: "Floristic Patterns in the Andes of Northern Patagonia's Forests"
<p>This dataset supports the study "Floristic Patterns in the Andes of <br>Northern Patagonia's Forests" published in Vegetation Classification and Survey, which<br>investigates the relationship between plant communities and environmental drivers in <br>the Andes of northwest Patagonia, Argentina. It also employs both expert-based and <br>numerical classification methods to explore floristic patterns across steep gradients <br>of aridity and temperature. The project provides a detailed dataset of 141 vegetation <br>samples, using advanced statistical methods to define six distinct plant communities <br>and their environmental drivers. It aims to refine existing vegetation classifications <br>for the study area and inform conservation efforts.</p>
Data and R code from: Relics of beavers past: time and population density drive scale-dependent patterns of ecosystem engineering
<p><span>Like many ecological processes, natural disturbances exhibit scale-dependent dynamics that are largely a function of the magnitude, frequency, and scale at which they are assessed. Ecosystem engineers create patch-scale disturbances that affect ecological processes, yet we know little about how these effects scale across space or vary through time. Here, we investigate how patch disturbances by beavers (<i>Castor canadensis</i>), ecosystem engineers renowned for their pond-creation behavior, affect ecological processes across space and time. We evaluated how beaver population recovery influenced surface water dynamics in relation to population density over 70 years across multiple spatial scales (pond, watershed, and regional) in northern Minnesota. Surface water area was positively related to population density at the watershed scale; however, despite variation in beaver densities (and therefore surface water area) at the watershed scale, regional-scale surface water area was stable through time. This stability appears to have been driven by asynchronous beaver density fluctuations among watersheds, combined with the increasing importance of abandoned ponds. Beavers initially created and occupied larger ponds with greater surface water area, but through time shifted towards occupying smaller ponds. As ponds accumulated on the landscape proportionally more surface water was stored within abandoned ponds, which offset the smaller size of occupied ponds. Beaver engineering—driven by density-dependent mechanisms and the legacy effects from abandoned ponds—not only follows general patterns of patch disturbance dynamics by creating a spatial mosaic of patches, but the organism-created mosaic also appears to generate ecological stability at greater spatial scales. We suggest restoring beavers to landscapes is a viable method for increasing surface water storage and will ultimately help advance numerous conservation and rewilding objectives. Our study demonstrates that ecosystem engineering effects can be scale-dependent, indicating researchers should evaluate the ecological impact of engineers across diverse spatiotemporal scales to fully understand their functional roles in ecosystems.</span></p>
Data and code for Winter conditions structure extratropical patterns of species richness of amphibians, birds, and mammals globally
<p>This repository contains the dataset analyzed in 'Winter conditions structure extratropical patterns of species richness of amphibians, birds, and mammals globally' - published in the journal Global Ecology and Biogeography - and the R code used to generate the correlations, generalized additive models, and related figures presented in the manuscript. Column descriptions for the data can be found in the associated README.txt file. Please refer to the manuscript for further detail on the variables and how they were derived.</p> <p>The Winter Indices (WIs) were derived using satellite remote sensing data from optical (MODIS, snow cover) and microwave (MEaSUREs freeze/thaw, frozen ground) sensors. The species richness maps were derived using IUCN range maps for individual species of amphibians, birds, and mammals (data requests can be made here: <a href="https://www.iucnredlist.org/resources/spatial-data-download">https://www.iucnredlist.org/resources/spatial-data-download</a>). Climatic varibales were derived from WorldClim v2.0 data, elevation from USGS GMTED2010 data, and primary productivity from the cumulative dynamic habitat index available here: <a href="http://silvis.forest.wisc.edu/maps-data/">http://silvis.forest.wisc.edu/maps-data/</a>.</p>
Data and code from: Invasive grass indirectly alters seasonal patterns in seed predation
<p>Invasive species threaten ecosystems globally, but their impacts can be cryptic when they occur indirectly. Invader phenology can also differ from that of native species, potentially causing seasonality in invader impacts. Yet, it is unclear if invader phenology can drive seasonal patterns in indirect effects. We used a field experiment to test if an invasive grass (<em>Imperata cylindrica</em>) caused seasonal indirect effects by altering rodent foraging and seed predation patterns through time. Using seeds from native longleaf pine (<em>Pinus palustris</em>), we found seed predation was 25% greater, on average, in invaded than control plots, but this effect varied by season. Seed predation was 24% - 157% greater in invaded plots during spring and fall months, but invasion had no effect on seed predation in other months. One of the largest effects occurred in October when longleaf pine seeds are dispersed, suggesting potential effects on tree regeneration. Thus, seasonal patterns in indirect effects from invaders may cause underappreciated impacts on ecological communities.</p>
A Google Earth Engine code to analyze e visualize land surface temperature and thermal hot-spot patterns: a Rome (Italy) case study
<p>Link to the <strong>Google Earth Engine </strong>(GEE) code: <strong>https://code.earthengine.google.com/cc3ea6593574e321acd7b68c975a9608</strong></p> <p>You can analyze and visualize the following spatial layers by accessing the GEE link: </p> <ol> <li><strong>Daytime summer land surface temperature</strong> (raster data, 30 m horizontal resolution, from Landsat-8 remote sensing data, years 2017-2022)</li> <li><strong>The surface thermal hot-spot pattern </strong>(raster data,30 m horizontal resolution) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS tool. </li> </ol> <p>Here attached the .txt file from the <strong>GEE code</strong>. </p> <p> </p> <p><em>E-mail</em></p> <p>Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it</p> <p>Marco Morabito, CNR-IBE, marco.morabito@cnr.it</p> <p>Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it</p>
A Google Earth Engine code to analyze residential buildings' real estate values, summer surface thermal anomaly patterns and urban features: a Florence (Italy) case study
<ol> </ol> <p>The layers included in the code were from the study conducted by the research group of CNR-IBE (Institute of BioEconomy of the National Research Council of Italy) and ISPRA (Italian National Institute for Environmental Protection and Research), published by the Sustainability journal (<strong>https://doi.org/10.3390/su14148412</strong>).</p> <p>Link to the <strong>Google Earth Engine (GEE) code</strong> <strong>(link: <a href="https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002">https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002</a></strong>)</p> <p>You can analyze and visualize the following spatial layers by accessing the GEE link: </p> <ol> <li><strong>Daytime summer land surface temperature</strong> (raster data, horizontal resolution 30 m, from Landsat-8 remote sensing data, years 2015-2019)</li> <li><strong>Surface thermal hot-spot </strong>(raster data, horizontal resolution 30 m) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS Pro tool.</li> <li><strong>Surface albedo</strong> (raster data, horizontal resolution 10 m, Sentinel-2A remote sensing data, year 2017)</li> <li><strong>Impervious area</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2017)</li> <li><strong>Tree cover</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2018)</li> <li><strong>Grassland area</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2017)</li> <li><strong>Water bodies</strong> (raster data, horizontal resolution 2 m, Geoscopio Platform of Tuscany, year 2016)</li> <li><strong>Sky View Factor</strong> (raster data, horizontal resolution 1 m, lidar data from the OpenData platform of Florence, year 2016)</li> <li><strong>Buildings' units</strong> of Florence (shapefile from the OpenData platform of Florence) include data on the residential real estate value from the Real Estate Market Observatory (OMI) of the National Revenue Agency of Italy (source: https://www1.agenziaentrate.gov.it/servizi/Consultazione/ricerca.htm, accessed on 14 July 2022). Data on the characterization of the buffer area (50 m) surrounding the buildings are included in this shapefile [the names of table attributes are reported in the square brackets]: averaged values of the daytime summer land surface temperature [LST_media], thermal hot-spot pattern [Thermal_cl], mean values of sky view factor [SVF_medio], surface albedo [alb_medio], and average percentage areas of imperviousness [ImperArea%], tree cover [TreeArea%], grassland [GrassArea%] and water bodies [WaterArea%]. </li> </ol> <p>Here attached the .txt file of the <strong>GEE code</strong>. </p> <p> </p> <p><em>E-mail</em></p> <p>Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it</p> <p>Marco Morabito, CNR-IBE, marco.morabito@cnr.it</p> <p>Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it</p>
Data and code from: Functional rarity of plants in German hay meadows - patterns on the species level and mismatches with community species richness
<p>Functional rarity (FR) - a feature combining a species' rarity with the distinctiveness of its traits - represents a promising tool to better understand the ecological importance of rare species and consequently to protect functional diversity more efficiently. Yet, we lack a systematic understanding of FR on both the species level (which species are functionally rare and why) and the community level (how is FR associated with biodiversity and environmental conditions). Here, we quantify FR for 218 plant species from German hay meadows on a local, regional, and national scale by combining data from 6500 vegetation relevés and 15 ecologically relevant traits. We investigate the association between rarity and trait distinctiveness on different spatial scales via correlation measures and show which traits lead to low or high trait distinctiveness via distance-based redundancy analysis. We test how species richness and FR are correlated and use boosted regression trees to determine environmental conditions driving species richness and FR. On the local scale, only rare species showed high trait distinctiveness while on larger spatial scales rare and common species showed high trait distinctiveness. As infrequent trait attributes (e.g., legumes, low clonality) led to higher trait distinctiveness, we argue that functionally rare species are either specialists or transients. While specialists occupy a particular niche in hay meadows leading to lower rarity on larger spatial scales, transients display distinct but maladaptive traits resulting in high rarity across all spatial scales. More functionally rare species than expected by chance occurred in species-poor communities indicating that they prefer environmental conditions differing from characteristic conditions of species-rich hay meadows. Finally, we argue that functionally rare species are not necessarily relevant for nature conservation, since many were transients from surrounding habitats. Yet, FR can facilitate our understanding of why species are rare in a habitat and under which conditions these species occur.</p>
Data and code related to publication "Migration pulsedness alters patterns of allele fixation and local adaptation in a mainland-island model" - Aubree et al. 2021
<p>Those data sets and codes are related to the manuscript "Migration pulsedness alters patterns of allele fixation and local adaptation in a mainland-island model" available on BioRXiv.</p> <p>All the information that are necessary to use those data sets and codes are contained in the file "readme.txt".</p>
Code and data related to semi-synchronous growth changes and the similarity of tree-ring patterns
<p>This dataset contains the data and code related to Visser, RM. 2021 On the similarity of tree-ring patterns: Assessing the influence of semi-synchronous growth changes on the Gleichläufigkeitskoeffizient for big tree-ring data sets. <em>Archaeometry 63(1)</em>: 204–215. DOI: <a href="https://doi.org/10.1111/arcm.12600">https://doi.org/10.1111/arcm.12600</a>.</p>
Turbulence pattern files used for star cluster formation in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code
<p>** these files are automatically downloaded by Phantom on running the code **</p> <p>The files here are sample cubes containing turbulent driving patterns for the velocity field (vx, vy and vz) used to initiate star cluster formation simulations in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code</p> <p>These can be used to set up initial conditions for a set of simulations similar to those shown in <a href="http://adsabs.harvard.edu/abs/2003MNRAS.339..577B">Bate, Bonnell & Bromm (2003)</a>. The files here are not the original driving patterns used in the BBB03 simulations, but have the same structure, and give a default driving pattern that can be used without having to re-generate the files. A similar set of files was used for the simulations published in <a href="https://ui.adsabs.harvard.edu/abs/2017MNRAS.465..105L">Liptai et al. (2017)</a>.</p> <p>The files were generated with a piece of code written by Volker Bromm, which was originally part of Matthew Bate's sphNG simulation code.</p> <p>For details of how to read these files, see the Phantom source code (<a href="https://github.com/danieljprice/phantom/blob/master/src/setup/velfield_fromcubes.f90">src/setup/velfield_fromcubes.f90</a>)</p>
Data and code relating to Becher, Jackson & Charlesworth. Patterns of genetic variability in genomic regions with low rates of recombination.
<p>Data and code relating to Becher, Jackson & Charlesworth. Patterns of genetic variability in genomic regions with low rates of recombination.</p> <p>Contains genotype data, R code for analysis and visualisation, a SLiM simulation script, README, etc.</p>
Data and code for 'Food insecurity and patterns of dietary intake in a sample of UK adults'
<p>Data and code for ' <strong>Food insecurity and patterns of dietary intake in a sample of UK adults</strong>' by Shinwell et al.</p> <p>For the UK data, the script 'analysis UK dataset.r' is required along with the csv data file.</p> <p>For the NHANES data analyses, the user needs to:</p> <p>a) Download the required 2013-4 NHANES data files as described at https://zenodo.org/record/3361283</p> <p>b) Run the script 'merging.script.r' from https://zenodo.org/record/3361283</p> <p>c) Using the resulting .csv file in conjunction with the script 'analysis NHANES dataset.r' to reproduce the analyses in the paper.</p> <p>The reason for doing it this indirect way is that the raw NHANES data are not ours to share.</p>
Self-organized pattern formation increases local diversity in metacommunities: code and data
<p>Code and data to reproduce the results published in the article "Self-organized pattern formation increases local diversity in metacommunities" (Ecology Letters, https://doi.org/10.1111/ele.13880).</p>
Populations of local direction-selective cells encode global motion patterns generated by self-motion. Data, Code and Model.
<p>Directional tuning of the population of local motion detectors T4/T5 in the visual system of the fruit fly <em>Drosophila melanogaster</em>. Direction tuning and receptive field location was measured by recording responses to visual stimuli containing dark or bright edges/stripes moving into 8 directions. All provided MATLAB scripts were used to analyze and illustrate data show in the manuscript 'Populations of local direction-selective cells encode global motion patterns generated by self-motion.'</p> <p>All data were obtained using <em>in vivo </em>two photon microscopy. Image time series were preprocessed using SIMA python software for motion alignment and further processed using custom written matlab or python code.</p> <p>Please find all relevant information to use the code in the README file.</p>
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.