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4,669 results for “Environment;”
Efficient and accurate framework for genome-wide gene-environment interaction analysis in large-scale biobanks
<p>Gene-environment interaction (GxE) analysis elucidates the interplay between genetic predispositions and environmental influences, offering significant potential for precision medicine. With the increasing use of electronic health records (EHR) linked to genetic data in large-scale biobanks, genome-wide association studies (GWAS) have expanded to encompass complex traits with intricate structures, such as time-to-event and ordinal categorical traits. Although these complex traits convey more phenotypic information, most existing scalable genome-wide GxE analysis approaches only focus on quantitative or binary traits. In this work, we propose a scalable and accurate analysis framework, SPAGxE<sub>CCT</sub>, that is applicable to a wide variety of trait types. We extend SPAGxE to SPAGxE+, which can account for sample relatedness. In addition, we extend SPAGxE<sub>CCT</sub> to SPAGxEmix<sub>CCT</sub>, which accounts for population stratification and is applicable to include individuals from multiple ancestries or admixed populations. We applied SPAGxE<sub>CCT</sub>, SPAGxE+, and SPAGxEmix<sub>CCT</sub> to analyze time-to-event traits in UK Biobank. For the SPAGxE<sub>CCT</sub> analyses, 281,149 White British individuals were included. For the SPAGxE+ analyses, 337,367 WB individuals with sample relatedness were included. For the SPAGxEmix<sub>CCT</sub> analyses, 338,044 individuals from all ancestries were included. SPAGxE<sub>CCT</sub>, SPAGxE+, and SPAGxEmix<sub>CCT</sub> are computationally efficient to analyze large datasets with hundreds of thousands of individuals, can accurately control type I error rates while remaining powerful to identify novel GxE findings.</p>
Low-Cost Sensors and Multitemporal Remote Sensing for Operational Turbidity Monitoring in an East African Wetland Environment - Measurements and Locations
<p>Many wetlands in East Africa are farmed and wetland reservoirs are used for irrigation, livestock, and fishing. Water quality and agriculture have a mutual influence on each other. Turbidity is a principal indicator of water quality and can be used for, otherwise, unmonitored water sources. Low-cost turbidity sensors improve in situ coverage and enable community engagement. The availability of high spatial resolution satellite images from the Sentinel-2 multispectral instrument and of bio-optical models, such as the Case 2 Regional CoastColor (C2RCC) processor, has fostered turbidity modeling. However, these models need local adjustment, and the quality of low-cost sensor measurements is debated. We tested the combination of both technologies to monitor turbidity in small wetland reservoirs in Kenya. We sampled ten reservoirs with low-cost sensors and a turbidimeter during five Sentinel-2 overpasses. Low-cost sensor calibration resulted in an R² of 0.71. The models using the C2RCC C2X-COMPLEX (C2XC) neural nets with turbidimeter measurements (R² = 0.83) and with low-cost measurements (R² = 0.62) performed better than the turbidimeter-based C2X model. The C2XC models showed similar patterns for a one-year time series, particularly around the turbidity limit set by Kenyan authorities. This shows that both the data from the commercial turbidimeter and the low-cost sensor setup, despite sensor uncertainties, could be used to validate the applicability of C2RCC in the study area, select the better-performing neural nets, and adapt the model to the study site. We conclude that combined monitoring with low-cost sensors and remote sensing can support wetland and water management while strengthening community-centered approaches.</p> <p>The provided dataset includes a point shapefile with the studied reservoirs in central Kenya and a data table with the sampling date (Sentinel-2 overpass plus/minus one day), low-cost sensor setup number, reservoir ID, sampling location within the reservoir, the voltage measurements of the three respective low-cost sensor heads for sensor setups A and B, the averaged voltage, and the turbidimeter measured turbidity value in nephelometric turbidity units (NTU).</p> <p>The study is available in (please cite):</p> <div> <div>Steinbach, S., Rienow, A., Chege, M.W., Dedring, N., Kipkemboi, W., Thiong’o, B.K., Zwart, S.J., Nelson, A., 2024. Low-Cost Sensors and Multitemporal Remote Sensing for Operational Turbidity Monitoring in an East African Wetland Environment. <em>IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing</em> <em>17</em>, 8490–8508. <a href="https://doi.org/10.1109/JSTARS.2024.3381756">https://doi.org/10.1109/JSTARS.2024.3381756</a></div> </div> <p>This research was supported in part by the German Federal Ministry of Education and Research (BMBF) through the Project “Participatory Approach to Environmental Conservation of the Muringato Catchment Area for Sustainable Management and Enhanced Ecosystem Health” (CITGI4Muringato) under Grant Agreement No. 01DG20022.</p>
Coral thermotolerance retained following year-long exposure to a novel environment
<p>Description of datasets:</p> <h4>HTSeqCounts</h4> <p>Raw HTSeq count data used for downstream RNA sequencing analyses and visualisations including Principal Component Analysis and Differential Gene Expression analysis of the four habitat groups (mangrove to reef, reef to reef, wild mangrove and wild reef).</p> <h4>GO enriched gene results:</h4> <p>GO enriched genes summarised by broad categories of GO Slims for <em>Pocillopora acuta</em> corals originating from a mangrove system vs colonies from an adjacent reef site, translocated to a reef environment for one year. Samples for gene expression were collected during an acute heat stress assay to assess differentially enriched genes between mangrove and reef corals under 36 ºC. Enriched GO terms were grouped into broader functional categories using the “goSlim” function in the GSEABase R package with GOslim generic obo as the reference database to allow for a summary of key functions enriched in groups under heat stress. See manuscript methods for further detail on differential gene expression analysis.</p> <h4>2022-2023 mangrove/reef temperature and pH</h4> <p>Temperature and pH data measured in the Low Isles mangrove lagoon and reef habitat using HOBO MX2510 loggers deployed from February 2022 - February 2023. </p> <h4>Methylated DNA data</h4> <p>Percent DNA methylation relative to total DNA of mangrove to reef, reef to reef and wild mangrove groups under acute temperature stress during the February 2023 CBASS experiment.</p> <h4>Analysis code.zip</h4> <p>A copy of all R code used in the data analysis of this project</p> <p> </p>
Data and Videos for Argos: a toolkit for tracking multiple animals in complex visual environments
<p>Original videos used and data generated for the article "Argos: a toolkit for tracking multiple animals in complex visual environments".</p> <p>The data contains original videos used as input to the Argos Tracking tool, the generated raw tracks in Pandas-HDF5 format, and the corrected tracks after processing with Argos Review tool.</p> <p>It also includes a zip archive with ground truth tracks along with tracks detected from two videos by Argos and several other tracking tools for comparison using the HOTA metric organized in a folder structure suitable for the TrackEval tool.</p>
Distributed Predictive Drone Swarms in Cluttered Environments
<p>This folder contains data, videos, and supplementary material for the article titled "Distributed Predictive Drone Swarms in Cluttered Environments".</p> <p>In the article, we present a Distributed Model Predictive Control (DMPC) algorithm for drone swarm navigation in two types of cluttered environments, i.e., a forest and a funnel-like environment. <br> </p> <p>The material in `zenodo_upload` is organized as follows.<br> 1. a data folder, with the logs of simulation and hardware experiments;<br> 2. an analysis folder, with Matlab scripts that analyze the logs in the data folder;<br> 3. a plotting folder, with Matlab functions used by the analysis scripts;<br> 4. an mp4 video file, on simulation and hardware experiments;<br> 5. a pdf, with supplementary materials.<br> <br> The `qp_swarm` folder contains MATLAB code for simulation experiments.</p>
Dataset: Environment effects on X-ray absorption spectra with quantum embedded real-time Time-dependent density functional theory approaches
<p>This dataset collects the outputs from real-time TDDFT simulation of X-ray absorption of halides in model systems, using the frozen density embedding (FDE) and block-orthogonalized Manby-Miller embedding (BOMME), as well as processing tools and scripts used to carry out the calculations.</p>
Environment's Share in Total Government Budget Allocations for R&D
<p>Government Budget Allocations for R&D (GBARD). GBARD data are measuring government support to research and development (R&D) activities, and thereby provide information about the priority Governments give to different public R&D funding activities.</p> <p>GBARD data are compiled using the guidelines laid out in the OECD Guidelines for collecting and reporting data on research and experimental development - Frascati Manual, OECD, 2015 (See related identifiers). </p> <p>GBARD data are broken down by:</p> <p> - Socio-economic objectives (SEOs) in accordance to the Nomenclature for the analysis and comparison of scientific programmes and budget.</p> <p>This dataset calculates the share of the Environment objective compared to the total allocations.</p> <p><br> The source (raw) dataset released by Eurostat: <a href="http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=gba_nabsfin07&lang=en">GBARD by socioeconomic objectives (NABS 2007)[gba_nabsfin07]</a></p>
Government Budget Allocations for R&D in Environment
<p>Government Budget Allocations for R&D (GBARD). GBARD data are measuring government support to research and development (R&D) activities, and thereby provide information about the priority Governments give to different public R&D funding activities.</p> <p>GBARD data are compiled using the guidelines laid out in the OECD Guidelines for collecting and reporting data on research and experimental development - Frascati Manual, OECD, 2015 (See related identifiers). </p> <p>GBARD data are broken down by:</p> <p> - Socio-economic objectives (SEOs) in accordance to the Nomenclature for the analysis and comparison of scientific programmes and budget. This dataset uses the Environment objective.<br> <br> The missing data are approximated, forecasted and backcasted by country. In our version of the dataset, 40% more countries, and a 23% larger dataset can be used for supervised and unsupervised learning models, such as machine learning, that require complete datasets compared to the source dataset released by Eurostat: <a href="http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=gba_nabsfin07&lang=en">GBARD by socioeconomic objectives (NABS 2007)[gba_nabsfin07]</a></p>
Accelerated change in the glaciated environments of western Canada revealed through trend analysis of optical satellite imagery (Polygons)
<p>Automatically generated dataset of glacier outlines from the journal article: "Accelerated change in the glaciated environments of western Canada revealed through trend analysis of optical satellite imagery"</p> <p>Research paper: https://www.sciencedirect.com/science/article/pii/S0034425721005824</p> <p>More information can be found here: https://github.com/bevingtona/glacier_change_western_canada</p>
Phenology and production of pollen, nectar and sugar in 1612 plant species from various environments
<p>This dataset is related to the data paper published in Ecology: Filipiak et al, in press, <em>Phenology and production of pollen, nectar and sugar in 1612 plant species from various environments</em>; doi: to be provided when available. This paper must be always properly cited when using the database. Please use any scientific full citation format. <br> <br> <strong>Abstract</strong><br> To predict the quantity and quality of the food available for pollinators in various landscapes over time, it is necessary to collect detailed data on pollen, nectar, and sugar production per unit area and the flowering phenology of plants. Similar data are needed to estimate the contribution of plants to the functioning of food webs via the flow of energy and nutrients through the soil-plant-nectar/pollen-consumer pathway. Current knowledge on this topic is fragmented. This is the first database to compile data on the various food resources produced by 1612 different plant species, belonging to 755 genera and 133 families, including crops and wild plants, annuals and perennials, animal- and wind-pollinated plants, and weeds and trees growing in different ecosystems under various environmental conditions. The dataset consists of 103 parameters related to the traits of plant species, as well as to geographical and environmental factors, allowing for precise calculations of nectar, pollen and energy provided by plants and available to consumers in the considered flora or ecosystem on a daily basis throughout the year. These parameters, gathered by us and extracted from the available literature, describe pollen, nectar and sugar production (where applicable, in mass, volume and concentration units), honey yield, the timing and duration of flowering, flower longevity, numbers of plants and flowers per unit area, related weather conditions (temperature and precipitation), geographical location, landscape, and syntaxonomy. The data were obtained from various, mostly European, pedoclimatic zones, and the majority of the data were available for plant species and communities present in Central Europe, especially in Poland, where research on floral resources has a long tradition. These data are representative of the whole continent and may be used as a reference for plant communities occurring on continents other than Europe since the database allows the consideration of differences in the production of resources by a single plant species growing in different communities. This dataset provides a unique opportunity to test hypotheses related to the functioning of food webs, nutrient cycling, plant ecology, and pollinator ecology and conservation.</p>
Data for article: A quantitative framework to infer the effect of traits, diversity and environment on dispersal and extinction rates from fossils
<p>Supplementary information for:</p> <p><strong>A quantitative framework to infer the effect of traits, diversity and environment on dispersal and extinction rates from fossils</strong></p> <p>Torsten Hauffe, Mathias M. Pires, Tiago B. Quental, Thomas Wilke, and Daniele Silvestro</p> <p> </p><ul> <li> Simulations <ul> <li>Scripts <ul> <li>Scenario1_SamplingHeterogeneity.R: Script to simulate biogeographic histories with sampling heterogeneity</li> <li>Scenario3_SealevelInvasion.R: Script to simulate biogeographic histories where sea level facilitates dispersal and invasion induces extinction</li> <li>Scenario3_DiversityDependence.R: Script to simulate diversity-dependent biogeographic histories</li> <li>Scenario4_TraitDependence.R: Script to simulate trait-dependent biogeographic histories</li> <li>Scenario5_CategoricalTraitDependence.R: Script to simulate trait-dependent biogeographic histories</li> </ul> </li> <li>Results <ul> <li>Scenario1_SamplingHeterogenetiy_alpha05.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 0.5</li> <li>Scenario1_SamplingHeterogenetiy_alpha1.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 1</li> <li>Scenario1_SamplingHeterogenetiy_alpha2.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 2</li> <li>Scenario1_SamplingHeterogenetiy_alpha10.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 10</li> <li>Scenario2_Independent_dispersal_and_extinction.txt: Results of simulation scenario 2 with sea-level independent dispersal and no invasion induced extinction</li> <li>Scenario2_Sealevel_dependent_dispersal_and_independent_extinction.txt: Results of simulation scenario 2 with sea-level dependent dispersal and no invasion induced extinction</li> <li>Scenario2_Sealevel_independent_dispersal_and_invasion_induced_extinction.txt: Results of simulation scenario 2 with sea-level independent dispersal and invasion induced extinction</li> <li>Scenario2_Sealevel_dependent_dispersal_and_invasion_induced_extinction.txt: Results of simulation scenario 2 with sea-level dependent dispersal and invasion induced extinction</li> <li>Scenario3_Independent_dispersal_and_extinction.txt: Results of simulations scenario 3 with diversity-independent dispersal and extinction</li> <li>Scenario3_Diversity_dependent_dispersal_and_independent_extinction.txt: Results of simulations scenario 3 with diversity-dependent dispersal and diversity-independent extinction</li> <li>Scenario3_Independent_dispersal_and_Diversity_dependent_extinction.txt: Results of simulations scenario 3 with diversity-dependent dispersal and diversity-independent extinction</li> <li>Scenario3_Diversity_dependent_dispersal_and_extinction.txt: Results of simulations scenario 3 with diversity-dependent dispersal and extinction</li> <li>Scenario4_Independent_dispersal_and_extinction.txt: Results of scenario 4 with trait-independent dispersal and extinction</li> <li>Scenario4_Trait_dependent_dispersal_and_independent_extinction.txt: Results of scenario 4 with trait-dependent dispersal and independent extinction</li> <li>Scenario4_Independent_dispersal_and_trait_dependent_extinction.txt: Results of scenario 4 with independent dispersal and trait-dependent extinction</li> <li>Scenario4_trait_dependent_dispersal_and_extinction.txt: Results of scenario 4 with trait-dependent dispersal and extinction</li> <li>Scenario5_CatTrait_dependent_dispersal_and_independent_extinction.txt: Results of model 2 with categorical traits (e.g family) influence dispersal but no influence of a category-specific continuous traits</li> </ul> </li> </ul> </li> <li>Carnivora <ul> <li>BinnedOccurrence: Folder with 100 replicates of binned occurrences of max. 330 carnivoran genera throughout the Neogene</li> <li>BodyMass: Folder with 100 replicates of body mass for 330 carnivoran genera</li> <li>Sealevel: Folder with sea level through the Neogene</li> <li>Temperature: Folder with the temperature record of the Neogene</li> <li>Families: Folder with families as taxonomic proxy for phylogeny. FamilyGeneraNumeric.txt is the numeric coding used for the Bayesian analyses of carnivoran biogeography</li> </ul> </li> </ul> <p></p>
Video Examples from: Creating Audio Object-focused Acoustic Environments for Room-Scale Virtual Reality
<p>Video recordings illustrating the issues and possible solutions mentioned in the paper.</p> <p>Please use headphones when watching the videos.</p>
Social Innovations for Circularity in the Built Environment – a Scoping Review and Classification. Supplementary Data to the Bibliometric Review.
<p>To transition the built environment (BE) towards circularity, i.e., maximizing the time resources spend in the BE, thus minimizing negative environmental impacts of resource usage, social innovations (SI) – understood as new ways of doing, organizing, framing, and knowing – are just as important as technological advancements. This article provides an overview of the state of the knowledge regarding SI that contribute to circularity in the BE, and proposes a framework for coherently classifying such SI in terms of their main categories and effects.</p> <p>To identify and understand the current knowledge regarding social innovations (SI) that contribute to circularity in the built environment (BE), a bibliometric review of scientific literature is conducted. It shows that the term social innovation is not frequently used in this contexts although the buzzwords circularity and circular economy are themselves often framed as SI. To assess the characteristics and contribution of SI to circularity in the BE, a scoping review that includes grey literature into the context was conducted and a framework developed to classify predominant SI using concepts from transition studies as well as the systems thinking approach. The framework is designed to help assess the potential of SI and to identify research and/or action gaps. Key findings are (1) There is a broad diversity of SIs that contribute to circularity in the BE already in the focus of research, although they are not always identified as such; (2) Most SI focus on either the design or the demolition phase (i.e., market related phases), whereas user-centered SI are less frequently discussed; and (3) It is crucial to also consider potential sustainability goal conflicts in order to guide policies that address SI as a solution.</p> <p>These datasets are the basis to the bibliometric literature review.</p>
Data on submerged aquatic vegetation and its water environment in Lake Saint-Pierre, Saint Lawrence River, from 2012 to 2016
<p>This dataset is the result of a large collaborative work lead by the GRIL from 2012 to 2015 on a submerged aquatic vegetation meadow located downstream of two agricultural tributaries (Saint-François and Yamaska rivers) in Lake Saint-Pierre, a fluvial lake of the Saint Lawrence River. The data describe plants (as rake biomass and echosounding) and their environment, including water chemistry, current velocity as well as light, temperature and instantaneous meteo. Only echosounding data are available in 2016 and sediments were collected in 2015. Data are organized as a relational database and the GRIL_LSP_database.png provides keys and links between tables as well as data format. Data are in the tables mesure_integree, mesure_spatiale, mesure_verticale, plante_biomass_taxon, plante_recolte, plante_in_situ. The other tables are metadata about spatiotemporal locations and reported measures. Additional data (e.g. zooplankton, sediments) should eventually be made available and associated to this overall GRIL dataset.</p>
The InvL Dataset of Invasive Species from drones in a Land Environment
<p><strong>Summary</strong></p> <p>An original dataset for semantic segmentation, InvL, is introduced, which to the best of the authors' knowledge, is the first dataset of its kind with pixel-level annotations pertaining to invasive species (Himalayan balsam) in a land environment from drones with height 10-30 m (GSD 3-13 mm) using optical imaging and having diverse outdoor blur, noise and contrast.</p> <p><strong>Objectives</strong></p> <p>Automated detection and quantification of invasive species from drones would enable more efficient mapping using simple low-cost technologies. This would help to <em>remove invasive and later protect species. </em>However, it is difficult to distinguish invasive species from common ones from higher altitudes using drone images. Training a machine learning algorithm to accurately detect a given invasive species from images taken in the field requires a massive amount of human-generated training data.</p> <p>Objective is to encourage people to open share images that can be used to develop technology. We are interested expand database and add author for every 1 GB added to the new version InvL or every 200 hours spent to improve annotation quality or annotation type.</p> <p>Objective is to expand establish a reference dataset for automatic extraction using gold standard training sets to gain Intersection over Union (IoU) over 0.8 from drone height over 30 meters with least GSD using low cost drones.</p> <p><strong>Data description</strong></p> <p>This data set contains images of Himalayan balsam (Impatiens glandulifera) taken mostly with the DJI Mavic 2 PRO in Finland from heights 10, 15, 20 and 30 meters, low speed 1- 2 m/s, no filters, 90 degrees angle, still images, GSD varying from 3-13mm, image size ~15 MB, between July (usually no flowers) and August (flowers).</p> <p> </p> <p> </p> <p><strong><em>Table 1. Drone images of invasive species</em></strong></p> <table> <tbody> <tr> <td> <p><strong><em>Label</em></strong></p> </td> <td> <p><strong><em>Name of data file/set</em></strong></p> <p> </p> </td> <td> <p><strong>Dataset size (GB)</strong></p> </td> <td> <p><strong>File type (extension)</strong></p> <p> </p> </td> <td> <p><strong>Data repository and identifier</strong></p> </td> <td> <p><strong>IoU estimate </strong><strong>- model</strong></p> </td> <td> <p><strong>Comment</strong></p> </td> </tr> <tr> <td> <p><em>Himalayan_balsam</em></p> </td> <td> <p><em>InvL/Himalayan_balsam</em></p> <p><em>../ann – contains annotations</em></p> <p><em>…/ann/H10m_Lahti_Peitsikatu_H10m_10072020</em></p> <ul> <li><em>H10 = 10 m UAV heigth</em></li> <li><em>Lahti = city</em></li> <li><em>10072020 (last) = date of fligth</em></li> </ul> <p><em>…/original – orginal UAV images, folder structure same as for “ann”</em></p> </td> <td> <p>2</p> </td> <td> <p>InvL.zip (folder structure with annotations and original images)</p> <p> </p> </td> <td> <p> </p> </td> <td> <p><em>Himalayan balsam </em></p> <p><em>IoU = 0.53, FCN, 15 m with augmentation IoU=0.37, FCN, 30 m</em></p> </td> <td> <p><em>Himalayan balsam, green</em></p> <p><em>Elevation varies in area +- 5m, so actual UAV flight height varies</em></p> <p><em>Each image around 15 MB</em></p> </td> </tr> </tbody> </table> <p><strong>Limitations</strong></p> <p>Annotations do not indicate width or margins.</p> <ul> <li>There is no indication of confidence of annotations.</li> <li>Blur, noise or contrast values are not calculated.</li> <li>Mostly species are easily visible and images with bright sunlight have been removed in selection of images.</li> <li>Annotator needs good and tested instruction to annotate images. Annotator needs to do work for tens of hours with developed and tested instructions to guarantee good quality. After image annotator each image should be checked by an “expert” and reannotated if necessary (silver standard, if this is done twice it is gold standard). If expert is used each image should be annotated once (silver standard) and twice (gold standard).</li> <li>Annotation time for each 15 MB image should be around one hour.</li> <li>Image GSD should be around 5 mm and less for smaller species.</li> <li>In different field areas species color and size varies and mixed vegetation may cause problems.</li> <li>In images there are flowers and non-flowers.</li> </ul> <p><strong>Abbreviations</strong> FCN: a fully convolutional neural network.</p> <p> </p>
Evidence for Early Mesozoic diversification of Hypsimetopidae Nicholls, 1943 (Isopoda), with the description of a new genus from Andhra Pradesh and notes on threats to Indian cave environments
<p>Datafiles and scripts for https://doi.org/10.1093/jcbiol/ruac052</p> <p>Evidence for Early Mesozoic diversification of Hypsimetopidae Nicholls, 1943 (Isopoda), with the description of a new genus from Andhra Pradesh and notes on threats to Indian cave environments</p> <p>George D. F. Wilson1,2 and Shabuddin Shaik 3</p> <p>1 Saugatuck Natural History Laboratory, Saugatuck, MI, USA; gdfw@snhlab.com</p> <p>3 Department of Life Science, Central University of Karnataka, Kadaganchi, 585 367, India; shabu.biologist@gmail.com</p> <p>2 Corresponding author: George D. F. Wilson, P. O. Box 714, Saugatuck, Michigan 49453, USA. e-mail: gdfw@snhlab.com </p> <p>File List:</p> <p>Phreatoi20220525.nex <br> Mesquite data file that contains all data from the DELTA taxonomic database that were used for data presentation, organization, analysis, as well as trees resulting from all analyses. For Mesquite version 3.70; Maddison WP, Maddison DR. 2021. Mesquite: a modular system for evolutionary analysis. Version 3.6 University of British Columbia & Oregon State University., http://www.mesquiteproject.org/ <br> <br> The DELTA database is still being edited and changed so it is not included here<br> For DELTA, see website https://www.delta-intkey.com/<br> publications:<br> Dallwitz MJ. 1980. A general system for coding taxonomic descriptions. TAXON 29: 41-46.<br> Dallwitz MJ, Paine TA, Zurcher EJ. 2000. User's guide to the DELTA system: a general system for processing taxonomic descriptions. CSIRO: Canberra.</p> <p> <br> Phreatoi20220525.tnt - the TNT data file generated by Mesquite<br> Analyses were performed using TNT-64bit, version 1.5, Goloboff PA, Catalano SA. 2016. TNT version 1.5, including a full implementation of phylogenetic morphometrics. Cladistics 32: 221-238.<br> Note: TNT counts zero as a number so the first tree, taxon or k paramter is 0, the second is 1, the third is 2 and so on </p> <p>TNT Scripts were written or modified for this project by George D. F. Wilson. They were run using the console in Ubuntu 20.04 but should work using the console version of TNT in other operating systems available from http://www.lillo.org.ar/phylogeny/tnt/. I recommend using the console because it allows you do to multiple analysis with one script. </p> <p>Each script has a banner that explains what is being done. If this fails to appear the first time, enter n and restart the script</p> <p>These are easily modified in a text editor to change the analysis<br> -- tnt.run : standard run of tnt. <br> -- tnt-jacK.run : symmetric jackknife analysis with concavity parameter, select file, concavity and prob parameter <br> -- piwe_rangeK.run : A range of concavity parameters are selected at the beginning and run sequentially<br> -- setk_trans.run : Modified from setk.run by Salvador Arias, Instituto Miguel Lillo, San Miguel de Tucuman, Argentina<br> -- aquickie_bt1000.run : Modified from the standard script distributed with TNT with more iterations of jackknifing<br> <br> If you are new to using TNT, see the information available on http://www.lillo.org.ar/phylogeny/tnt/ as well as these articles:<br> Goloboff PA. 1993. Estimating character weights during tree search. Cladistics 9: 83-91.<br> Goloboff PA. 1997. Self-Weighted Optimization: Tree Searches and Character State Reconstructions under Implied Transformation Costs. Cladistics 13: 225-245.<br> Goloboff PA, Carpenter JM, Arias JS, Esquivel DRM. 2008. Weighting against homoplasy improves phylogenetic analysis of morphological data sets. Cladistics 24: 758-773.<br> Goloboff PA, Catalano SA. 2016. TNT version 1.5, including a full implementation of phylogenetic morphometrics. Cladistics 32: 221-238.<br> Goloboff PA, Farris JS. 2001. Methods for Quick Consensus Estimation. Cladistics 17: S26-S34.<br> Goloboff PA, Farris JS, Källersjö M, Oxelman B, Ramírez MJ, Szumik CA. 2003. Improvements to resampling measures of group support. Cladistics 19: 324-332.<br> Goloboff PA, Farris JS, Nixon KC. 2008. TNT, a free program for phylogenetic analysis. Cladistics 24: 774-786.</p>
Improving students' privacy awareness – Analysis of a pilot survey to design a VR environment for self-paced learning
<p>In this research, we measured the knowledge of students at the University of Debrecen in the field of data privacy awareness, online and password security.</p> <p><strong>Description</strong></p> <ul> <li>In the questionnaire, green-highlighted answer signs the correct answer to each question.</li> <li>Total data set contains the answers to each question and the respondent's age.</li> <li>Correct/incorrect data set contains information if the answer is correct to each question, and it also contains the respondent's age. One means the answer was correct, and zero means the answer was incorrect.</li> </ul>
Auditory Selective Attention Switch in a Virtual Reality Classroom Environment
<p><strong>General</strong></p> <p>The audio-visual Auditory Selective Attention VR Proof of Concept (asaVRpoc) project serves to investigate the auditory selective attention switch in a close-to-real-life classroom setting. This dataset consists of a Unity project and Matlab code used to collect data on the voluntary switching of auditory selective attention in a virtual reality classroom scenario.</p> <p>The dataset contains:</p> <ul> <li>Unity project for visual display and the experiment structure</li> <li>Matlab code for experiment preparation and HpFT measurement</li> <li>Data collected in the experiment (experiment performance, head tracking, questionnaires)</li> </ul> <p><strong>Experiment preparation using Matlab</strong></p> <p>The code and software used to prepare the experiment is provided in the folder<em> "matlab_asaVRpoc"</em>.</p> <p>The Matlab code used to prepare the trials for each participant as well as to measure the HpTFs. For the HpTF measurements, the ITA Toolbox for Matlab was used and is provided (https://git.rwth-aachen.de/ita/toolbox commit hash: 598675ef704c178365f53d41e03ff4b11dea390f). A developmental version of Virtual acoustics (VA) 2020b (https://www.virtualacoustics.org/VA/overview/) is provided.</p> <p>Software requirements:</p> <ul> <li>Matlab 2019a or higher</li> <li>ITA Toolbox for Matlab installed</li> </ul> <p> </p> <p><strong>Experiment conduction in Unity</strong></p> <p>The Unity project is provided in the folder<em> "unity_pc_asaVRpoc"</em>.</p> <p>Therefore, a virtual classroom with some basic furniture is provided. The used models, prefabs and plugins can be found in the Assets folder.</p> <p>Note that the <em>acoustic stimuli are NOT provided</em> with this Unity project. The stimuli are available on request from the Institute for Hearing Technology and Acoustics, RWTH Aachen University.</p> <p>This Unity project was intended for the use in virtual reality using an HMD and respective controllers for input. However, it can also be used on a desktop pc. The mode can be changed using the "VRMode" toggle as described below.<br> The audio reproduction is realized using the Unity plugin for Virtual Acoustics (VA, http://www.virtualacoustics.org/).</p> <p>Software requirements:</p> <ul> <li>Unity 2019.4.21.f1.</li> <li>SteamVR 1.19.7</li> <li>Virtual Acoustics v2021a, VAUnity: https://git.rwth-aachen.de/ita/VAUnity</li> </ul> <p> </p> <p><strong>Data evaluation</strong></p> <p>The collected data is provided in the folder<em> "dataEvaluation_asaVRpoc"</em>. This folder contains three types of data: the raw data collected in the experiment (reaction times and error rates), the head tracking data and responses from the simulator sickness questionnaire (before and after the experiment) and the igroup presence questionnaire (after the experiment). Matlab code for the evaluation of the head tracking data and the questionnaires is provided.</p> <p> </p>
Challenges of high-fidelity air quality modeling in urban environments - PALM sensitivity study during stable conditions (TURBAN)
<h3>Introduction</h3> <p>This dataset contains the PALM model inputs and the source code used to create the simulations for Prague-Legerova scenarios performed in the scope of the <strong>TURBAN</strong> project (<a href="https://www.project-turban.eu/">https://www.project-turban.eu/</a>). Detailed description of the simulations is provided in the referencing scientific paper.</p> <h3>List of simulations</h3> <table> <tbody> <tr> <td><strong>Scenario name</strong></td> <td><strong>Days simulated</strong></td> <td><strong>IBC</strong></td> <td><strong>Configuration changes</strong></td> </tr> <tr> <td>legerovas_s6_sens_base</td> <td>13–15 February 2023</td> <td>ICON</td> <td>-</td> </tr> <tr> <td>legerovas_s6_sens_dtmax</td> <td>13 February 2023</td> <td>ICON</td> <td>dt_max=0.2</td> </tr> <tr> <td>legerovas_s6_sens_heat</td> <td>13 February 2023</td> <td>ICON</td> <td>car anthropogenic heat (custom code)</td> </tr> <tr> <td>legerovas_s6_sens_sgs</td> <td>13 February 2023</td> <td>ICON</td> <td>e_min=0.02</td> </tr> <tr> <td>legerovas_s6_sens_stg</td> <td>13 February 2023</td> <td>ICON</td> <td>STG_PROFILES added</td> </tr> <tr> <td>legerovas_s6_sens_alad</td> <td>13–15 February 2023</td> <td>ALADIN</td> <td>-</td> </tr> <tr> <td>legerovas_s6_sens_alad_heat</td> <td>13 February 2023</td> <td>ALADIN</td> <td>car anthropogenic heat (custom code)</td> </tr> <tr> <td>legerovas_s6_sens_alad_sgs</td> <td>13 February 2023</td> <td>ALADIN</td> <td>e_min=0.02</td> </tr> <tr> <td>legerovas_s6_sens_alad_stg</td> <td>13 February 2023</td> <td>ALADIN</td> <td>STG_PROFILES added</td> </tr> <tr> <td>legerovas_s6_sens_wrf</td> <td>13–15 February 2023</td> <td>WRF</td> <td>-</td> </tr> <tr> <td>legerovas_s6_sens_wrf_heat</td> <td>13 February 2023</td> <td>WRF</td> <td>car anthropogenic heat (custom code)</td> </tr> <tr> <td>legerovas_s6_sens_wrf_sgs</td> <td>13 February 2023</td> <td>WRF</td> <td>e_min=0.02</td> </tr> <tr> <td>legerovas_s6_sens_wrf_stg</td> <td>13 February 2023</td> <td>WRF</td> <td>STG_PROFILES added</td> </tr> </tbody> </table> <h3>Directory structure</h3> <p>The directory inputs contains the model inputs and it is further divided into these subdirectories:</p> <p>- inputs/common: The PALM static driver and the emission drivers for the parent and child domains. These files are common to all simulations</p> <p>- inputs/dynamic/*: These directories contain the dynamic drivers for the parent and child domanis, which contain the initial and boundary conditions (IBC) as well as external radiation data. The three subdirectories aladin, icon and wrf contain IBCs created from the respective mesoscale model outputs. </p> <p>- inputs/legerovas_s6_sens_*: These directories contain the PALM model configuration (p3d) for both domains for each simulation.</p> <p>- inputs/build_config: The included .palm.iofiles configuration file ensures that the files STG_PROFILES are correctly copied from the input directory.</p> <p>The directory palm_sources contains the exact model source used for the simulations. It is derived from the PALM model release 23.04 with additional bugfixes. There are two source archives:</p> <p>- heat.tar.gz: PALM source further modified to include anthropogenic heat from cars, used for the simulations legerovas_s6_sens_*_heat</p> <p>- standard.tar.gz: PALM source used for all other included simulations.</p> <h3>Reproducing the simulations</h3> <p>In order to reproduce the simulations, unpack the respective source code archive and follow the standard installation, configuration and build procedures described in the README.md file within the archive and on the PALM model website http://www.palm-model.org/. Then copy the input files for the respective simulation in the JOBS directory. The common files and the dynamic driver files need to be renamed so that they match the prefix given by the name of the simulation, as is described in the PALM model documentation.</p>
S98 | TIRECHEM | Tire-Related Chemicals in Environment from Literature
<p>This is the collection associated with list S98 TIRECHEM | Tire-Related Chemicals in Environment from Literature on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>This list includes tire-related chemicals found in the environment plus analytical data from literature studies and will be expanded as new data is published.</p> <p>v 0.2.0 : New chemicals added on 13 May 2024</p> <p> </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.