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10,929 results for “Communities”
Data from: Photoinactivation of Yeast and Biofilm Communities of Candida albicans Mediated by ZnTnHex-2-PyP4+ Porphyrin
<p>Data supporting the figures presented in the research article Photoinactivation of Yeast and Biofilm Communities of <em>Candida albicans</em> Mediated by ZnTnHex-2-PyP<sup>4+</sup> Porphyrin.</p> <p> </p> <p><em>Candida albicans</em> is the main cause of superficial candidiasis. While the antifungals available are defied by biofilm formation and resistance emergence, antimicrobial photodynamic inactivation (aPDI) arises as an alternative antifungal therapy. The tetracationic metalloporphyrin Zn(II) <em>meso</em>-tetrakis(<em>N</em>-n-hexylpyridinium-2-yl)porphyrin (ZnTnHex-2-PyP<sup>4+</sup>) has high photoefficiency and improved cellular interactions. We investigated the ZnTnHex-2-PyP<sup>4+</sup> as a photosensitizer (PS) to photoinactivate yeasts and biofilms of <em>C. albicans</em> strains (ATCC 10231 and ATCC 90028) using a blue light-emitting diode. The photoinactivation of yeasts was evaluated by quantifying the colony forming units. The aPDI of ATCC 90028 biofilms was assessed by the MTT assays, propidium iodide (PI) labeling, and scanning electron microscopy. Mammalian cytotoxicity was investigated in Vero cells using MTT assay. The aPDI (4.3 J/cm<sup>2</sup>) promoted eradication of yeasts at 0.8 and 1.5 µM of PS for ATCC 10231 and ATCC 90028, respectively. At 0.8 µM and same light dose, aPDI-treated biofilms showed intense PI labeling, about 89% decrease in the cell viability, and structural alterations with reduced hyphae. No considerable toxicity was observed in mammalian cells. Our results introduce the ZnTnHex-2-PyP<sup>4+</sup> as a promising PS to photoinactivate both yeasts and biofilms of <em>C. albicans</em>, stimulating studies with other <em>Candida </em>species and resistant isolates.</p>
Dataset for greenhouse gas modelling in diesel dependent communities transitioning to bioenergy
<p>The data presented here are from the research article entitled "Greenhouse gas mitigation potential of replacing diesel fuel with wood-based bioenergy in an arctic Indigenous community: A pilot study in Fort McPherson, Canada". Based on a pilot study realized in Northern Canada and life cycle assessment, we provide a set of key parameters and operational data gathered along the biomass supply chain to build a GHG mitigation scenario and compute the quantity and timing of GHG savings in the off-grid community of Fort McPherson, NWT. Given that GHG mitigation scenarios are often assessed against a relative fossil-fuel reference scenario, we are providing two categories of data; 1) data for the reference fossil fuel scenario and; 2) data along the upstream operations of biomass supply chains. Both categories contain data related to the operational processes as well as forest growth or decomposition of unused feedstock. Although the data presented are mostly derived from the boreal forest, they could help guide other communities beyond the boreal to develop a renewable bioenergy system and assess their GHG mitigation options.</p>
Data from: Plant community stability is associated with a decoupling of prokaryote and fungal soil networks
<p>Data from the manuscript Plant community stability is associated with a decoupling of prokaryote and fungal soil networks: https://doi.org/10.1101/2022.06.21.496867</p>
OTU level 16s sequence data for "Algae drive convergent bacterial community assembly when nutrients are scarce"
<p>16s sequence data at the OTU level for the experiments conducted in "Algae drive convergent bacterial community assembly when nutrients are scarce"</p> <p>The file is in fasta format, which can be read by many software packages including biopython, R, and SILVA's alignment, classification and tree service.</p> <p>The sequence ids can be used to find the phylogeny and OTU ID of the sequences from Supplementary dataset 4. </p>
Opposing community assembly patterns for dominant and non-dominant plant species in herbaceous ecosystems globally
<p>Biotic and abiotic factors interact with dominant plants —the locally most frequent or with the largest coverage— and non-dominant plants differently, partially because dominant plants modify the environment where non-dominant plants grow. For instance, if dominant plants compete strongly, they will deplete most resources, forcing non-dominant plants into a narrower niche space. Conversely, if dominant plants are constrained by the environment, they might not exhaust available resources but instead may ameliorate environmental stressors that usually limit non-dominants. Hence, the nature of interactions among non-dominant species could be modified by dominant species. Furthermore, these differences could translate into a disparity in the phylogenetic relatedness among dominants compared to the relatedness among non-dominants. By estimating phylogenetic dispersion in 78 grasslands across five continents, we found that dominant species were clustered (e.g., co-dominant grasses), suggesting dominant species are likely organized by environmental filtering, and that non-dominant species were either randomly assembled or overdispersed. Traits showed similar trends for those sites (<50%) with sufficient trait data. Furthermore, several lineages scattered in the phylogeny had more non-dominant species than expected at random, suggesting that traits common in non-dominants are phylogenetically conserved and have evolved multiple times. We also explored environmental drivers of the dominant/non-dominant disparity. We found different assembly patterns for dominants and non-dominants, consistent with asymmetries in assembly mechanisms. Among the different postulated mechanisms, our results suggest two complementary hypotheses seldom explored: (1) Non-dominant species include lineages adapted to thrive in the environment generated by dominant species. (2) Even when dominant species reduce resources to non-dominant ones, dominant species could have a stronger positive effect on some non-dominants by ameliorating environmental stressors affecting them, than by depleting resources and increasing the environmental stress to those non-dominants. These results show that the dominant/non-dominant asymmetry has ecological and evolutionary consequences fundamental to understand plant communities.</p>
Perspectives and experiences of Covid-19: Two Irish studies of families in disadvantaged communities
<p>Data files related to the manuscript <em>Perspectives and experiences of Covid-19: Two Irish studies of families in disadvantaged communities</em>. The manuscript includes two studies. The following materials are shared below.</p> <p>Study 1: </p> <p>- Qualitative data (Microsoft Office Excel file)</p> <p>- Codebook for coding the qualitative data developed through content analysis (pdf file)</p> <p>Study 2:</p> <p>- Qualitative data (Microsoft Office Excel file)</p> <p>Data are named using the following naming convention: Project acronym_Date (YYYYMMDD)_Study_Type of data_Type of participant_Version number of the file.</p> <p>Both studies in the manuscript were developed by the Childhood Development Initiative (CDI), Dublin, Ireland. Study 1 was conducted within the project PEAR_EC, that has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 890925. Study 2 was conducted within the Child Poverty research project, funded by Tusla under the Area Based Childhood funding and the Child and Youth Participation Initiatives grant.</p>
Characterizing Measures for the Assessment of Cluster Analysis and Community Detection
<p><strong>Description. </strong>The dataset is constituted of:</p> <ul> <li>`figs.zip`: an archive containing the plot files;</li> <li>`data&results.zip`: an archive containing the necessary data to perform our analysis, as well as result files.</li> </ul> <p>These are the resources used in the following articles:</p> <ol> <li>N. Arınık, V. Labatut and R. Figueiredo, "Characterizing measures for the assessment of cluster analysis and community detection", Modèles & Analyse des Réseaux : Approches Mathématiques & Informatiques (MARAMI), 2020. ⟨<a href="https://hal.archives-ouvertes.fr/hal-02993542">hal-02993542</a>⟩</li> <li>N. Arınık, R. Figueiredo, and V. Labatut, “Characterizing and comparing external measures for the assessment of cluster analysis and community detection,” <em>IEEE Access </em>9:20255–20276, 2021. DOI: <a href="http://doi.org/10.1109/access.2021.3054621">10.1109/access.2021.3054621</a> ⟨<a href="https://hal.archives-ouvertes.fr/hal-03124118">hal-03124118</a>⟩</li> </ol> <p><strong>Source code. </strong>The associated source code is available on GitHub: <a href="https://github.com/CompNet/ExtMeasEval">https://github.com/CompNet/ExtMeasEval</a></p> <p><strong>Citation. </strong>If you use these data, please cite the paper [2].</p> <p><br><code>@Article{Arinik2021,</code><br><code> author = {Arınık, Nejat and Figueiredo, Rosa and Labatut, Vincent},</code><br><code> title = {Characterizing and Comparing External Measures for the Assessment of Cluster Analysis and Community Detection},</code><br><code> journal = {IEEE Access},</code><br><code> year = {2021},</code><br><code> volume = {9},</code><br><code> pages = {20255-20276},</code><br><code> doi = {10.1109/access.2021.3054621},</code><br><code>}</code></p>
Dataset for Starting at the community: Treatment seeking pathways of children with suspected severe malaria in Uganda
<p>Dataset for the publication <strong>"Starting at the community: Treatment seeking pathways of children with suspected severe malaria in Uganda".</strong></p> <p>Data from the Community Access to Rectal Artesunate for Malaria (CARAMAL) Project, 2018-2021</p> <p>Descriptive analysis of treatment-seeking pathways of and antimalarial treatment provision for children under 5 years with suspected severe malaria in three districts of Northern Uganda. All children first sought treatment from a community health worker before being referred to a higher-level facility.</p>
Data and Software Archive for "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada"
<p>This is the Zenodo archive for the manuscript "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada" (Mucaki EJ, Shirley BC and Rogan PK. <em>F1000Research</em> 2021, <strong>10</strong>:1312, DOI: <a href="http://dx.doi.org/10.12688/f1000research.75891.1">10.12688/f1000research.75891.1</a>). This study aimed to produce community-level geo-spatial mapping of patterns and clusters of symptoms, and of confirmed COVID-19 cases, in near real-time in order to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 positive individuals. This archive will contain data and image files from this study, which were too numerous to be included in the manuscript for this study. It also provides all program files pertaining to the <em>Geostatistical Epidemiology Toolbox </em>(Geostatistical analysis software package to be used in ArcGIS), as well as all other scripts described in this manuscript and other software developed (cluster, outlier, streak identification and pairing)..</p> <p>We also provide a guide which provides a general description of the contents of the four sections in this archive (<em>Documentation_for_Sections_of_Zenodo_Archive.docx</em>). If you have any intent to utilize the data provided in Section 3, we greatly advise you to review this document as it describes the output of all geostatistical analyses performed in this study in detail.</p> <p><strong>Data Files:</strong></p> <p><strong>Section 1. "Section_1.Tables_S1_S7.Figures_S1_S11.zip"</strong></p> <p>This section contains all additional tables and figures described in the manuscript "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada". Additional tables S1 to S7 are presented in an Excel document. These 7 tables provide summary statistics of various geostatistical tests described in the study (“Section 1 – Tables S1-S4”) and lists all identified single and paired high-case cluster streaks (“Section 1 – Tables S5-S7”). This section also contains 11 additional figures referred to in the manuscript (“Section 1 – Figures S1-S11”) both individually and within a Word document which describes them.</p> <p><strong>Section 2. "Section_2.Localized_Hotspot_Lists.zip"</strong></p> <p>All localized hotspots (identified through kriging analysis) were catalogued for each municipality evaluated (Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, Windsor/Essex). These files indicate the FSA in which the hotspot was identified, the date in which it was identified (utilizing 3-day case data at the postal code level), the amount of cases which occurred within the FSA within these 3 dates, the range of cases interpolated by kriging analysis (between 5-10, 10-15, 15-20, 20-25, 25-30, 30-35, 35-40, 40-50, >50), and whether or not the FSA was deemed a hotspot by Gi* relative to the rest of Ontario on any of the three dates evaluated. Please see Section 4 for map images of these localized hotspots.</p> <p><strong>Section 3. "Section_3.All-Data_Files.Kriging_GiStar_Local_and_GlobalMorans.2020_2021"</strong></p> <p>Section 3 – All output files from the geostatistical tests performed in this study are provided in this section. This includes the output from Ontario-wide FSA-level Gi* and Cluster and Outlier analyses, and PC-level Cluster and Outlier, Spatial Autocorrelation, and kriging analysis of 6 municipal regions. It also includes kriging analysis of 7 other municipal regions adjacent to Toronto (Ajax, Brampton, Markham, Mississauga, Pickering, Richmond Hill and Vaughan). This section also provides data files from our analyses of stratified case data (by age, gender, and at-risk condition). All coordinates presented in these data files are given in “PCS_Lambert_Conformal_Conic” format. Case values between 1-5 were masked (appear as “NA”).</p> <p><strong>Section 4. "Section_4.All_Map_Images_of_Geostat_Analyses.zip"</strong></p> <p>Sets of image files which map the results of our geostatistical analyses onto a map of Ontario or within the municipalities evaluated (Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, Windsor/Essex) are provided. This includes: Kriging analysis (PC-level), Local Moran's I cluster and outlier analysis (FSA and PC-level), normal and space-time Gi* analysis, and all images for all analyses performed on stratified data (by age, gender and at-risk condition). Kriging contour maps are also included for 7 other municipal regions adjacent to Toronto (Ajax, Brampton, Markham, Mississauga, Pickering, Richmond Hill and Vaughan). </p> <p><strong>Software:</strong></p> <p>This Zenodo archive also provides all program files pertaining to the <em>Geostatistical Epidemiology Toolbox </em>(Geostatistical analysis software package to be used in ArcGIS), as well as all other scripts described in this manuscript. This geostatistical toolbox was developed by CytoGnomix Inc., London ON, Canada and is distributed freely under the terms of the GNU General Public License v3.0. It can be easily modified to accommodate other Canadian provinces and, with some additional effort, other countries. </p> <p>This distribution of the <em>Geostatistical Epidemiology Toolbox </em>does not include postal code (PC) boundary files (which are required for some of the tools included in the toolbox). The PC boundary shapefiles used to test the toolbox were obtained from <a href="https://www.dmtispatial.com/">DMTI</a> (<a href="https://www.google.com/url?q=https://www.dmtispatial.com/canmap/&sa=D&source=hangouts&ust=1637875735980000&usg=AOvVaw2wG3iVnyGyrkTIkN5FQ4NS">https://www.dmtispatial.com/canmap/</a>) through the Scholar's Geoportal at the University of Western Ontario (<a href="http://geo2.scholarsportal.info/">http://geo2.scholarsportal.info/</a>). The distribution of these files (through sharing, sale, donation, transfer, or exchange) is strictly prohibited. However, any equivalent PC boundary shape file should suffice, provided it contains polygon boundaries representing postal code regions (see guide for more details).</p> <p><strong>Software File 1. "Software.GeostatisticalEpidemiologyToolbox.zip"</strong></p> <p>The Geostatistical Epidemiology Toolbox is a set of custom Python-based geoprocessing tools which function as any built-in tool in the ArcGIS system. This toolbox implements data preprocessing, geostatistical analysis and post-processing software developed to evaluate the distribution and progression of COVID-19 cases in Canada. The purpose of developing this toolbox is to allow external users without programming knowledge to utilize the software scripts which generated our analyses and was intended to be used to evaluate Canadian datasets. While the toolbox was developed for evaluating the distribution of COVID-19, it could be utilized for other purposes. </p> <p>The toolbox was developed to evaluate statistically significant distributions of COVID-19 case data at Canadian Forward Sortation Area (FSA) and Postal Code-level in the province of Ontario utilizing geostatistical tools available through the ArcGIS system. These tools include: 1) Standard Gi* analysis (finds areas where cases are significantly spatially clustered), 2) spacetime based Gi* analysis (finds areas where cases are both spatially and temporally clustered), 3) cluster and outlier analysis (determines if high case regions are an regional outlier or part of a case cluster), 4) spatial autocorrelation (determines the cases in a region are clustered overall) and, 5) Empirical Bayesian Kriging analysis (creates contour maps which define the interpolation of COVID-19 cases in measured and unmeasured areas). Post-processing tools are included that import these all of the preceding results into the ArcGIS system and automatically generate PNG images. </p> <p>This archive also includes a guide ("UserManual_GeostatisticalEpidemiologyToolbox_CytoGnomix.pdf") which describes in detail how to set up the toolbox, how to format input case data, and how to use each tool (describing both the relevant input parameters and the structure of the resultant output files).</p> <p><strong>Software File 2: “Software.Additional_Programs_for_Cluster_Outlier_Streak_Idendification_and_Pairing.zip"</strong></p> <p>In the manuscript associated with this archive, Perl scripts were utilized to evaluate postal code-level Cluster and Outlier analysis to identify significantly, highly clustered postal codes over consecutive periods (i.e., high-case cluster “streaks”). The identified streaks are then paired to those in close proximity, based on the neighbors of each postal code from PC centroid data ("paired streaks"). Multinomial logistic regression models were then derived in the R programming language to measure the correlation between the number of cases reported in each paired streak, the interval of time separating each streak, and the physical distance between the two postal codes. Here, we provide the 3 Perl scripts and the R markdown file which perform these tasks:</p> <p><em>“Ontario_City_Closest_Postal_Code_Identification.pl”</em></p> <p>Using an input file with postal code coordinates (by centroid), this program identifies the nearest neighbors to all postal codes for a given municipal region (the name of this region is entered on the command line). Postal code centroids were calculated in ArcGIS using the “Calculate Geometry” function against DMTI postal code boundary files (not provided). Input from other sources could be used, however, as long as the input includes a list of coordinates with a unique label associated with a particular municipality.</p> <p>The output of this program (for the same municipal region being evaluated) is required for the following two Perl scripts:</p> <p><em>“Local_Morans_Analysis.Recurrent_Clustered_PC_Identifier.pl”</em></p> <p>This program uses the output of postal code-level Cluster and Outlier analysis for a municipality (these files are available in a second Zenodo archive: <a href="http://doi.org/10.5281/zenodo.5585812">doi.org/10.5281/zenodo.5585812</a>) and the output from <em>“Ontario_City_Closest_Postal_Code_Identification.pl” </em>(for the same municipal region) as input to identify high-case clustered postal codes that occur consecutively over a course of several dates (referred to as high-case cluster “streaks”). The script allows for a single day in which the PC was either not clustered or did not meet the minimum case count threshold of ≥ 6 cases within the 3-day sliding window (i.e. if clustered for 3 days, then not significant for one, then clustered for 3 more days, it will considered a 7 day streak). This script also lists any neighbors that are also identified to have streaks during these same dates.</p> <p><em>“Local_Morans_Analysis.Clustered_Streak_Pairing_Program.pl”</em></p> <p>This program uses the output from “<em>Local_Morans_Analysis.Recurrent_Clustered_PC_Identifier.pl</em>” to pair streaks that were identified in two closely situated postal codes spatially (requires output from <em>“Ontario_City_Closest_Postal_Code_Identification.pl”)</em>. The output of this script provides the postal codes of the streaks which are paired, describe the interval of each streak (and whether they occur concurrently), the number of cases which occurred during these streaks, and how these streaks are separated (both distance [in meters] and temporally [in days]).</p> <p>"<em>Streak_Analysis_using_Multinomial_Logistic_Regression_Models.Rmd</em>"</p> <p>This R Markdown file contains the code which derived multinomial logistic regression models to describe the relation between the number of COVID-19 case counts, physical distance (in meters), and the time interval between paired streaks (in days). The script then performs a Wald two-tailed z-test to identify which factors are significantly correlated (relative to total case counts between streaks [i.e., the response variable]). The p-values computed from the Wald test are then reported. This script requires the 'multinom' function of the 'nnet' package in R.</p> <p>Two data files in which these models were derived (a list of all consecutive Toronto paired streaks for COVID-19 wave 2 and wave 3) are also included. </p>
Long-term experimental drought alters floral scent and pollinator visits in a Mediterranean plant community despite overall limited impacts on plant phenotype and reproduction
<p>Pollinators are declining globally, with climate change implicated as an important driver. Climate change can induce phenological shifts and reduce floral resources for pollinators, but little is known about its effects on floral attractiveness and how this might cascade to affect pollinators, pollination functions and plant fitness. We used an in situ long-term drought experiment to investigate multiple impacts of reduced precipitation in a natural Mediterranean shrubland, a habitat where climate change is predicted to increase the frequency and intensity of droughts. Focusing on three insect-pollinated plant species that provide abundant rewards and support a diversity of pollinators (<em>Cistus</em> <em>albidus</em>, <em>Salvia</em> <em>rosmarinus</em> and <em>Thymus</em> <em>vulgaris</em>), we investigated the effects of drought on a suite of floral traits including nectar production and floral scent. We also measured the impact of reduced rainfall on pollinator visits, fruit set and germination in <em>S</em>. <em>rosmarinus</em> and <em>C</em>. <em>albidus</em>. Drought altered floral emissions of all three plant species qualitatively, and reduced nectar production in <em>T</em>. <em>vulgaris</em> only. <em>Apis</em> <em>mellifera</em> and <em>Bombus</em> gr. <em>terrestris</em> visited more flowers in control plots than drought plots, while small wild bees visited more flowers in drought plots than control plots. Pollinator species richness did not differ significantly between treatments. Fruit set and seed set in <em>S. rosmarinus</em> and <em>C. albidus </em>did not differ significantly between control and drought plots, but seeds from drought plots had slower germination for <em>S. rosmarinus</em> and marginally lower germination success in <em>C. albidus</em>.</p> <p><em>Synthesis</em>. Overall, we found limited but consistent impacts of a moderate experimental drought on floral phenotype, plant reproduction and pollinator visits. Increased aridity under climate change is predicted to be stronger than the level assessed in the present study. Drought impacts will likely be stronger and this could profoundly affect the structure and functioning of plant-pollinator networks in Mediterranean ecosystems.</p>
Propaganda and fake news on the war in Ukraine: data from Russian-speaking social media communities
<p>The data set contains posts from social media networks popular among Russian-speaking communities. Information was searched based on pre-defined keywords ("war", "special military operation", etc.) and is mainly related to the ongoing war in Ukraine with Russia. After a thorough review and analysis of the data, both propaganda and fake news were identified. The collected data is anonymized. Feature engineering and text preprocessing can be applied to obtain new insights and knowledge from this data set. The data set is useful for the study of information wars and propaganda identification.</p>
Alpine butterflies want to fly high: Species and communities shift upwards faster than their host plants
<p>Despite sometimes strong co-dependencies of insect herbivores and plants, responses of individual taxa to accelerating climate change are typically studied in isolation. Thereby, biotic interactions that potentially limit species in tracking their preferred climatic niches are ignored. Here, we chose butterflies as a prominent representative of herbivorous insects to investigate the impacts of temperature changes and their larval host plant distributions along a 1.4 km elevational gradient in the German Alps. Following a sampling protocol of 2009, we re-visited 33 grassland plots in 2019 over an entire growing season. We quantified changes in butterfly abundance and richness by repeated transect walks on each plot and disentangled the direct and indirect effects of locally assessed temperature, site management, and larval and adult food resource availability on these patterns. Additionally, we determined elevational range shifts of butterflies and host plants at both the community and species level. Comparing the two sampled years (2009, 2019), we found a severe decline in butterfly abundance and a clear upward shift of butterflies along the elevational gradient. We detected shifts in the peak of species richness, community composition and at the species level, whereby mountainous species shifted particularly strongly. In contrast, host plants showed barely any change, neither concerning species richness, nor individual species shifts. Further, temperature and host plant richness were the main drivers of butterfly richness, with change in temperature explaining best the change of richness over time. We conclude that host plants are not yet hindering butterfly species and communities from shifting upwards. However, the mismatch between butterfly and host plant shifts might become a problem for this very close plant-herbivore relationship, especially towards higher elevations, if butterflies fail to adapt to new host plants. Further, our results support the value of conserving traditional extensive pasture use as a promoter of host plants and thereby butterfly richness.</p>
Fig. 1. Study area. C1-5 in Helminth communities from amphibians inhabiting agroecosystems in the Pampean Region (Argentina)
Fig. 1. Study area. C1-5: crop sites, L1- 4: livestock sites (C1: 34°55'13''S; 58°06'33''O; C2: 34°55'54''S; 58°04'29''O; C3: 34°57'36''S; 58°04'57''O; C4: 35°01'42''S; 57°59'44''O; C5: 35°03'06''S; 57°58'35''O; L1: 35°04'27''S; 57°57'23''O; L2: 35°07'46''S; 57°53'11''O; L3: 35°02'22,94''S; 57°48'58,8''O; L4: 35°02'23,2''S; 57°48'58,2''O).
Ecoregion and community structure influences on the foliar elemental niche of balsam fir (Abies balsamea (L.) Mill.) and white birch (Betula papyrifera Marshall)
<p><strong><span>Context</span></strong><span>: Changes in foliar elemental niche properties, defined by axes of carbon (C), nitrogen (N), and phosphorus (P) concentrations, reflect how species allocate resources under different environmental conditions. For instance, elemental niches may differ in response to large-scale latitudinal temperature and precipitation regimes that occur between ecoregions and small-scale differences in nutrient dynamics based on species co-occurrences at a community level.</span></p> <p><strong><span>Methods</span></strong><span>: at a species level, we compared foliar elemental niche hypervolumes for balsam fir (<em>Abies balsamea</em> (L.) Mill.) and white birch (<em>Betula papyrifera</em> Marshall) between a northern and southern ecoregion. At a community level, we grouped our focal species using plot data into conspecific (i.e., only one focal species is present) and heterospecific groups (i.e., both focal species are present) and compared their foliar elemental concentrations under these community conditions across, within, and between these ecoregions. Between ecoregions at the species and community level, we expected niche hypervolumes to be different and driven by regional biophysical effects on foliar N and P concentrations. At the community level, we expected niche hypervolume displacement and expansion patterns for fir and birch, respectively – patterns that reflect their resource strategy.</span></p> <p><strong><span>Results</span></strong><span>: at the species level, foliar elemental niche hypervolumes between ecoregions differed significantly for fir (F = 14.591, p-value = 0.001) and birch (F = 75.998, p-value = 0.001) with higher foliar N and P in the northern ecoregion. At the community level, across ecoregions, the foliar elemental niche hypervolume of birch differed significantly between heterospecific and conspecific groups (F = 4.075, p-value = 0.021) but not for fir. However, both species displayed niche expansion patterns, indicated by niche hypervolume increases of 35.49% for fir and 68.92% for birch. Within the northern ecoregion, heterospecific conditions elicited niche expansion responses, indicated by niche hypervolume increases for fir of 29.04% and birch of 66.48%. In the southern ecoregion we observed a contraction response for birch (niche hypervolume decreased by 3.66%), and no changes for fir niche hypervolume. Conspecific niche hypervolume comparisons between ecoregions yielded significant differences for fir and birch (F = 7.581, p-value = 0.005 and F = 8.038, p-value = 0.001) as did heterospecific comparisons (F = 6.943, p-value = 0.004, and F = 68.702, p-value = 0.001, respectively). </span></p> <p><strong><span>Conclusions</span></strong><span>: our results suggest species may exhibit biogeographical specific elemental niches – driven by biophysical differences such as those used to describe ecoregion characteristics. We also demonstrate how a species resource strategy may inform niche shift patterns in response to different community settings. Our study highlights how biogeographical differences may influence foliar elemental traits and how this may link to concepts of ecosystem and landscape functionality.</span></p>
Changes in trait covariance along an orographic moisture gradient reveal the relative importance of light- and moisture-driven trade-offs in subtropical rainforest communities
<p>•<span> </span>A range of functional trait-based approaches have been developed to investigate community assembly processes, but most ignore how traits covary within communities. </p> <p>•<span> </span>We combined existing approaches (community-weighted means [CWMs] and functional dispersion [FDis]) with a metric of trait covariance to examine assembly processes in five angiosperm assemblages along a moisture gradient in Australia's subtropics. In addition to testing hypotheses about habitat filtering along the gradient, we hypothesised that trait covariance would be strongest at both ends of the moisture gradient and weakest in the middle, reflecting trade-offs associated with light capture in productive sites and moisture stress in dry sites.</p> <p>•<span> </span>CWMs revealed evidence of climatic filtering, but FDis patterns were less clear. As hypothesised, trait covariance was weakest in the middle of the gradient, but unexpectedly peaked at the second driest site due to the emergence of a clear drought tolerance – drought avoidance spectrum. At the driest site, the same spectrum was truncated at the 'avoider' end, revealing important information about habitat filtering in this system.</p> <p>•<span> </span>Our focus on trait covariance revealed the nature and strength of trade-offs imposed by light and moisture availability, and complemented insights gained about community assembly from existing trait-based approaches. </p>
Dataset of observations of waterbird community wintering in Crimea Reservoirs in 2009-2021
<p>The result of waterbird counting carried out in winter 2009-2021 in Crimean Reservoirs is presented. The dataset contains the number of individual bird species, as well as data on volume and water surface area. In the script file, there is a code applied to analyse the data.</p>
BeeDNA: microfluidic environmental DNA metabarcoding as a tool for connecting plant and pollinator communities
<p><strong>Data repository accompanying the paper 'BeeDNA: microfluidic environmental DNA metabarcoding as a tool for connecting plant and pollinator communities' by Harper et al. (2021).</strong></p> <p><br> <strong>1_Raw_Data.zip</strong><br> This zipped folder contains the raw sequence data (sorted by primer set and demultiplexed) for both sequencing runs (2019-10-24 and 2019-11-11). To decompress each file, run: </p> <pre><code>tar -xvf filename.bz2</code></pre> <p>This will create a folder for each primer set containing the raw reads for each sample/control.</p> <p><br> <strong>2_Anacapa_Bioinformatic_Processing.zip</strong></p> <p>This zipped folder contains all files needed to perform bioinformatic processing with Anacapa. Please process sequence data belonging to each primer set individually (i.e. do not process sequence data belonging to different primer sets together).</p> <p><br> <strong>3_metaBEAT_Bioinformatic_Processing.zip </strong></p> <p>This zipped folder contains the scripts and files needed to perform bioinformatic processing with metaBEAT. Before running the scripts, move the raw reads for each sample belonging to each primer set into the dedicated folder within metaBEAT_Bioinformatic_Processing, e.g. all .fastq files in Raw_Data > BF1_BR1 should be moved to metaBEAT_Bioinformatic_Processing > BF1-BR1 > raw_reads.</p> <p>To run metaBEAT, you will have to install Docker on your computer. Docker is compatible with all major operating systems, but see the Docker documentation for details. On Ubuntu, installing Docker should be as easy as:</p> <pre><code>sudo apt-get install docker.io</code></pre> <p>Once Docker is installed, you can enter the environment by typing:</p> <pre><code>sudo docker run -i -t --net=host --name metaBEAT -v $(pwd):/home/working chrishah/metabeat /bin/bash</code></pre> <p>This will download the metaBEAT image (if not yet present on your computer) and enter the 'container', i.e. the self contained environment (NB: sudo may be necessary in some cases). With the above command, the container's directory /home/working will be mounted to your current working directory (as instructed by $(pwd)). In other words, anything you do in the container's /home/working directory will be synced with your current working directory on your local machine.</p> <p>Please process sequence data belonging to each primer set individually (i.e. do not process sequence data belonging to different primer sets together). An example of expected outputs can be seen in the Jupyter Notebook for the BF1/BR1 primer set from the 2019-11-11 sequencing run.</p> <p><br> <strong>4_Illinois_Invert_Reference_Database.zip</strong></p> <p>This zipped folder contains all files that were used to generate the custom COI and 16S reference databases for invertebrates that occur in Illinois, U.S. You will need to have metaBEAT installed (see above) before you try to run any Jupyter Notebooks (.ipynb files).</p> <p><br> <strong>5_ecoPCR.zip</strong></p> <p>This zipped folder contains all files used to perform ecoPCR for each primer set evaluated for microfluidic eDNA metabarcoding. You will need to <a href="https://git.metabarcoding.org/obitools/ecopcr/wikis/home">install ecoPCR</a> before running any shell scripts.</p> <p><br> <strong>6_Tidied_Data.zip</strong></p> <p>This zipped folder contains the taxonomically assigned data for both sequencing runs produced by metaBEAT and Anacapa. These were copied over from the folders 2_Anacapa_Bioinformatic_Processing and 3_metaBEAT_Bioinformatic_Processing and rearranged into a more logical order. These files are used as the input for data analysis using R.</p> <p><br> <strong>7_Data_Analysis.zip</strong></p> <p>This zipped folder contains all scripts and metadata required to summarise and statistically analyse data in R.</p> <p> </p> <p><strong>Please contact Dr Lynsey Harper (lynsey.harper2@gmail.com) or Dr Mark Davis (davis63@illinois.edu) if you encounter any issues!</strong></p>
Use of health care services in community-dwelling older adults in two regions of Spain
<p>This dataset contains data on use of health care resources of community-dwelling older adults aged 70 or over, who were functionally independent. Data of health resources use included contacts along two consecutive years with: the general practitioner, primary care nurse, the specialists, visits to emergency rooms, and hospital admissions and length of stay. The data included also information about sex, region, polipharmacy, age-adjusted Charlson Comorbidity Index and funcionality, measured by Timed Up and Go test. The data collection was performed in two Spanish regions. Baseline assessment was done between 2015 and 2016, and patients were followed for 2 years. There were in total 1488 registries considering both years.</p>
Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey - Extended Data 4 - Analysis Data Sheet
<p>This dataset is extended data to the manuscript "Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey" by Schmidt C., Hanne J, Moore J, Meesters C, Ferrando-May E, Weidtkamp-Peters S, and members of the NFDI4BIOIMAGE initiative. [version 1; peer review: awaiting peer review] F1000Research 2022, 11:638, https://doi.org/10.12688/f1000research.121714.1</p> <p>This extended data includes:</p> <p>- Data Analysis Sheet and results table</p> <p>Note: The data is anonymized (i.e., all IP addresses as well as personal comments were deleted)</p> <p>The revised version was published after the peer-review process of the original article on zenodo.org</p>
Data from: Different taxonomic and functional indices complement the understanding of herb-layer community assembly patterns in a southern-limit temperate forest
<p><span>The efficient conservation of vulnerable ecosystems in the face of global change requires a complete understanding of how plant communities respond to various environmental factors. We aim to demonstrate that a combined use of different approaches, traits, and indices representing each of the taxonomic and functional characteristics of plant communities will give complementary information on the factors driving vegetation assembly patterns. We analyzed variation across an environmental gradient in taxonomic and functional composition, richness, and diversity of the herb-layer of a temperate beech-oak forest that was located in northern Spain. We measured species cover and four functional traits: leaf dry matter content (LDMC), specific leaf area (SLA), leaf size, and plant height. We found that light is the most limiting resource influencing herb-layer vegetation. Taxonomic changes in richness are followed by equivalent functional changes in the diversity of leaf size but by opposite responses in the richness of SLA. Each functional index is related to different environmental factors even within a single trait (particularly for LDMC and leaf size). To conclude, each characteristic of a plant community is influenced by different and even contrasting factors or processes. Combining different approaches, traits, and indices simultaneously will help us understand how plant communities work.</span></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.