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4,669 results for “Environment;”
Data from: Passive sampling of environmental DNA in aquatic environments using 3D-printed hydroxyapatite samplers
<p>The study of environmental DNA released by aquatic organisms in their habitat offers a fast, non-invasive and sensitive approach to monitor their presence. Common eDNA sampling methods such as water filtration and DNA precipitation are time consuming, require difficult-to-handle equipment and partially integrate eDNA signals. To overcome these limitations, we created the first proof of concept of a passive, 3D-printed and easy-to-use eDNA sampler. We designed the samplers from hydroxyapatite (HAp samplers), a natural mineral with a high DNA adsorption capacity. The porous structure and shape of the samplers were designed to optimise DNA adsorption and facilitate their handling in the laboratory and in the field. Here we show that HAp samplers can efficiently collect genomic DNA in controlled set-ups, but can also collect animal eDNA under controlled and natural conditions with yields similar to conventional methods. However, we also observed large variations in the amount of DNA collected even under controlled conditions. A better understanding of the DNA-hydroxyapatite interactions on the surface of the samplers is now necessary to optimise the eDNA adsorption and to allow the development of a reliable, easy-to-use and reusable eDNA sampling tool.</p>
How efficient is symbiont-mediated protection to heat under daily fluctuating thermal environments? A study on an aphid-bacterium mutualism
<p>These are the two datasets for the article "<strong>How efficient is symbiont-mediated protection to heat under daily fluctuating thermal environments? A study on an aphid-bacterium mutualism</strong>".</p> <p>We examined the effects of four thermal regimes defined by their combination of mean temperature (18 or 25°C) and diurnal thermal range (day-night fluctuations: 5 or 10°C) on the interaction between the aphid <em>Acyrthosiphon pisum</em>, its obligate symbiont <em>Buchnera aphidicola </em>and the facultative heat-protective symbiont <em>Serratia symbiotica</em>. We used two clonal lines of <em>A. pisum </em>uninfected (5A) or infected with <em>S. symbiotica </em>(5AR). Thermal treatments were applied form aphid birth to death. We recorded both aphid traits (immature survival rate, longevity, immature development time, total fecundity, body size) and symbiont titres at the onset of reproduction (for both <em>B. aphidicola </em>and <em>S. symbiotica</em>), expressed relatively to aphid gene copy number through quantitative PCRs (qPCRs).</p> <p><strong>Aphid traits</strong></p> <p>. Individual = identification of the individual</p> <p>. Line = aphid clonal line, either deprived of (5A) or infected with <em>S. symbiotica </em>(5AR)</p> <p>. MT = Mean Temperature, either 18°C or 25°C</p> <p>. DTR = Diurnal Temperature Range (day-night fluctuations), either 5°C or 10°C</p> <p>. surv.adult = whether (Y) or not (N) the individual survived until reaching reproduction (immature survival rate)</p> <p>. adulthood = time elapsed between birth and first reproduction (in days)</p> <p>. lifespan = time elapsed between birth and death (in days)</p> <p>. offspring = total number of larvae produced by each aphid throughout lifetime</p> <p>. tibia = hind tibia length as a proxy of body size (in mm)</p> <p>. wing = whether (Y) or not (N) the individual developed wings following imaginal moult</p> <p> </p> <p><strong>Symbiont titres</strong></p> <p>. Individual = identification of the individual</p> <p>. Line = aphid clonal line, either deprived of (A) or infected with <em>S. symbiotica </em>(AR)</p> <p>. MT = Mean Temperature, either 18°C or 25°C</p> <p>. DTR = Diurnal Temperature Range (day-night fluctuations), either 5°C or 10°C</p> <p>. QPCR = identification of the microplate used during qPCR analyses (to handle pseudoreplication)</p> <p>. EfAlph = number of copies of aphid housekeeping gene elongation factor 1-alpha (<em>EF1-α</em>)</p> <p>. SdEfAlph = standard deviation of the aphid gene copy number, calculated over three technical replicates</p> <p>. DnaKBa = number of copies of <em>B. aphidicola </em>gene <em>dnaK</em></p> <p>. SdDnaKBa = standard deviation of <em>B. aphidicola </em>gene copy number, calculated over three technical replicates</p> <p>. DnakSs = number of copies of <em>S. symbiotica </em>gene <em>dnaK</em></p> <p>. SdDnaKSs = standard deviation of <em>S. symbiotica </em>gene copy number, calculated over three technical replicates</p> <p>. RappBa = ratio between DnaKBa and EfAlph to calculate relative titre of <em>B. aphidicola</em></p> <p>. RappSs = ratio between DnaKSs and EfAlph to calculate relative titre of <em>S. symbiotica</em></p>
Preliminary Testing of a System for the Multimodal Analysis of Gait Training in a Virtual Reality Environment
<p>The dataset is related to the paper Piazza, C.; Pirovano, I.; Mastropietro, A.; Genova, C.; Gagliardi, C.; Turconi, A.C.; Malerba, G.; Panzeri, D.; Maghini, C.; Reni, G.; Rizzo, G. and Biffi E. Development and Preliminary Testing of a System for the Multimodal Analysis of Gait Training in a Virtual Reality Environment. Electronics 2021, 10, 2838. https://doi.org/10.3390/electronics10222838. </p> <p>It includes multimodal data (EEG, EMG, kinematics and kinetics) of a healthy child (HC) and a child with hemiparesis (CP01) during walking in a virtual environmment (GRAIL, Motek).</p> <p>Data can be opened by using EEGLab and Matlab </p> <p> </p> <p> </p>
dataset from paper with title: "Direct Phenological Responses but Later Growth Stimulation upon Spring and Summer/Autumn Warming of Prunus spinosa L. in a Common Garden Environment"
<p>abstract of paper: "Future<strong> </strong>predictions of forest ecosystem responses are a challenge, as global temperatures will further rise in the coming decades at an unprecedented rate. The effect of elevated temperature on growth performance and phenology of three <em>Prunus spinosa </em>L. provenances (originating from Belgium, Spain, and Sweden) in a common garden environment was investigated. One-year-old seedlings were grown in greenhouse conditions and exposed to ambient and elevated temperatures in the spring (on average 5.6 °C difference) and in the late summer/autumn of 2018 (on average 1.9 °C difference), while they were kept hydrated, in a factorial design. In the following years, all plants experienced the same growing conditions. Bud burst, leaf senescence, height, and diameter growth were recorded. Height and radial growth were not affected in the year of the treatments (2018) but were enhanced the year after (2019), whereas phenological responses depended on the temperature treatments in the year of the treatments (2018) with little carry-over effects in the succeeding years. Spring warming enhanced more height growth in the succeeding year, whereas summer/autumn warming stimulated more radial growth. Spring warming advanced bud burst and shortened the leaf opening process whereas summer/autumn warming delayed leaf senescence and enlarged the duration of this phenophase. These results can help predict the putative shifts in species composition of future forests and woody landscape elements."</p>
The influence of history, geography and environment on patterns of diversification in the western terrestrial garter snake
<p><b>Aim:</b> A central aim of biogeography is to understand how biodiversity is generated and maintained across landscapes. Here, we establish phylogenetic and population genetic patterns in a widespread reptile to quantify the influence of historical biogeography and current environmental variation on patterns of genetic diversity.</p> <p><b>Location: </b>Western North America.</p> <p><b>Taxon: </b>Western terrestrial garter snake, <i>Thamnophis elegans</i>.</p> <p><b>Methods: </b>We used double-digest RADseq to estimate phylogenetic relationships and characterize population genetic structure across the three widespread subspecies of <em>T. elegans</em>: <em>T. e. vagrans</em> (wandering garter snake), <em>T. e. elegans </em>(mountain garter snake) and <em>T. e. terrestris</em> (coast garter snake). We assessed patterns of dispersal and vicariance across biogeographic regions using ancestral area reconstruction (AAR) and deviations from isolation-by-distance across the landscape using estimated effective migration surfaces (EEMS). We identified environmental variables potentially shaping local adaptation in regional lineages using genetic-environment association (GEA) analyses.</p> <p><b>Results:</b> We recovered three well-differentiated genetic groups that correspond to the three subspecies. AAR analyses inferred the eastern Cascade Range as the ancestral area, with dispersal to both the east and west across western North America. Populations of <em>T. e. elegans</em> displayed a latitudinal gradient in genetic variation across the Sierra Nevada and northern California, while populations of <em>T. e. terrestris</em> show discrete genetic breaks consistent with well-known biogeographic barriers. Lastly, GEA analyses identified allele frequency shifts at loci associated with a common set of environmental variables in both <em>T. e. elegans</em> and <em>T. e. terrestris</em>.</p> <p><b>Main Conclusion: </b><em>T. elegans </em>is composed of distinct evolutionary lineages, each with its own geographic range and history of diversification. <em>T. e. elegans</em> and <em>T. e. terrestris</em> show unique patterns of diversification as populations dispersed from east to west and while adapting to the new environments they colonized. Historical events, landscape features and environmental variation have all contributed to patterns of differentiation in <em>T. elegans</em>.</p> <p> </p>
uDALES 1.0: a large-eddy-simulation model for urban environments
<p>Data accompanying the GMD article 'uDALES 1.0: a large-eddy-simulation model for urban environments'</p> <p>https://doi.org/10.5194/gmd-2021-255</p> <p>1) Figures shown in the manuscript</p> <p>2) Input to rerun the presented cases in uDALES</p> <p>3) Model output and Matlab scripts to recreate the figures</p>
Data from: Environment but not geography explains genetic variation in the invasive and largely panmictic European starling in North America
Populations of invasive species that colonize and spread in novel environments may differentiate both through demographic processes and local selection throughout the genome. European starlings (Sturnus vulgaris) were introduced to New York in 1890 and subsequently spread throughout North America, becoming one of the most widespread and numerous bird species on the continent. Genome-wide comparisons across starling individuals and populations can identify demographic and/or selective factors that facilitated this rapid and successful expansion. We investigated patterns of genomic diversity and differentiation using reduced-representation genome sequencing (ddRADseq) of 17 starling populations. Consistent with this species' high dispersal rates and rapid expansion history, we found low genome-wide differentiation and few FST outliers even at a continental scale. Despite starting from a founding population of approximately 180 individuals, North American starlings do not seem to have undergone a detectable genetic bottleneck: they have maintained an extremely large effective population size since introduction. We find more than 200 variants that correlate with temperature and/or precipitation. Genotype-environment associations (but not outlier scans) identify these SNPs against a background of negligible genome- and range-wide divergence. Such variants fall in the coding regions of genes associated with metabolism, stress, and neurological function. This evidence for incipient local adaptation in North American starlings suggests that it can evolve rapidly even in wide-ranging and evolutionarily young populations. This survey of genomic signatures of expansion in North American starlings is the most comprehensive to date and complements ongoing studies of world-wide local adaptation in these highly dispersive and invasive birds.
Risks for overwintering eggs of the dragonfly Sympetrum vicinum in aquatic and terrestrial environments
<p>Risk-spreading behaviour is often exhibited by animals as a response to unpredictably variable environments. Using field and laboratory studies, we tested the hypothesis that Sympetrum vicinum dragonflies spread the risks of winter environments by laying eggs across a terrestrial–aquatic gradient. Sympetrum vicinum eggs that overwintered in terrestrial and benthic-limnetic habitats had significantly higher hatching success compared with eggs that overwintered in littoral sites. Low success may have been caused by hypoxia due to excess sediment in the littoral samples in the lab. While hypoxia experienced under winter conditions (4°C) had no negative effect on hatching success, hatching in hypoxic and anoxic water significantly decreased hatching success. Opportunistic egg predation by a winter-active caddisfly significantly decreased egg hatching success. Because S. vicinum eggs have a relatively low supercooling point (− 26.25°C), freezing may not be a significant source of mortality in terrestrial or aquatic sites. By ovipositing in both terrestrial and aquatic environments, female dragonflies may be balancing the unpredictable risks of both the failure to inundate the eggs and egg predation. Our research highlights the potential for biotic interactions during winter to shape the behaviour and life-history of aquatic invertebrates.</p>
Data: Survey of Implemented Mitigation Strategies and Further Needs of the United States Food Industry to Control COVID-19 in the Work Environment in Early 2021
<p>Data set related with the COVID-19 needs assessment study</p>
Social environment influences termination of nomadic migration
<p>The final stage of migration, when animals terminate migratory movements and transition to a more sedentary state, remains the least understood phase of migration. Whereas migrants that return to the same locations each year may use mechanisms associated with locating a specific destination, migrants with low site fidelity, such as nomadic migrants, may rely on local environmental cues to determine when to cease migratory movements. Using an experiment with captive birds, we tested whether the presence of a conspecific influences the termination of migration, indicated by changes in behaviour and physiology, in a nomadic migrant (the pine siskin, <em>Spinus pinus</em>). We paired migratory birds with a non-migratory individual or left migratory and non-migratory individuals unpaired. Migratory paired birds had a significant decline in nocturnal activity immediately after pairing and activity levels remained lower two weeks later, with significant declines in energetic reserves and flight muscle size also observed. In contrast, migratory unpaired birds maintained high levels of activity and energetic reserves. These results provide evidence for a role of the social environment in migratory termination decisions. Social cues may be particularly useful in nomadic migrants, such as pine siskins, to facilitate settling in high quality, but unfamiliar, habitats.</p>
Dataset for "Personalised Learning Environments Based on Knowledge Graphs and the Zone of Proximal Development"
<p>The dataset accompanying our paper "Personalised Learning Environments Based on Knowledge Graphs and<br> the Zone of Proximal Development" published in proceedings of CSEDU 2022.</p> <p>In the dataset you will find the raw csv results from both the explorative survey and the evaluation survey.<br> <br> The surveys were made in Google Forms and the results have also been exported as pdf files that are included as well.<br> <br> Finally, screenshots of the application, grouped by module can be found inside the screenshots.zip archive.</p>
Rapid evolution of thermal tolerance and phenotypic plasticity in variable environments
<p>These are the data and code to go with "<strong>Rapid evolution of thermal tolerance and phenotypic plasticity in variable environments</strong>"</p> <p><strong>Figure 01 takes the following data/scripts:</strong></p> <p><a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/20210610_Thally02_Figure01_plot_and_stats.R">20210610_Thally02_Figure01_plot_and_stats.R </a> with <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/track_keeper.csv">track_keeper.csv </a> and <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/corr_Response%20growth%20.csv">corr_Response growth .csv </a>. These files contain growth rates per transfers for all selection environments throughout the experiment and growth rates in correlated environments during reciprocal transplants, respectively. </p> <p><strong>Figure 02 takes the following data/scripts:</strong></p> <p> </p> <p><a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/20210610_Thally02_Figure02_plot_and_stats.R">20210610_Thally02_Figure02_plot_and_stats.R </a> with <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/20161120_res_logis_t000.csv">20161120_res_logis_t000.csv </a>and <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/20161123_resloglint300.csv">20161123_resloglint300.csv .</a> These files contain the output of the shapes of the growth curves (i.e. information on lag time , growth at µmax, K etc) for all samples in all selection environments at t0 and t300, respectively</p> <p><strong>The remaining figures - position not clear at time of submission - take the following data/script. </strong></p> <p>For plasticity in FRRF data, the script 20181204_FRRF_plasticity.R takes the FRRF raw data contained in <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/allfvfmdata_thally_t300_t000.csv">allfvfmdata_thally_t300_t000.csv </a>. Extracted parameters are in files <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/CvaluesThally02.csv">CvaluesThally02.csv</a>, <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/psiPSI_slope_intercept.csv">psiPSI_slope_intercept.csv</a>, and <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/rP_extracted_values.csv">rP_extracted_values.csv </a> and can be analysed using the R script <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/extracted%20parameter%20plots.R">extracted parameter plots.R .</a> R script <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/FRRF%20visualisation%20only%20.R">FRRF visualisation only .R </a> is for visualisation only, as the title suggests. </p> <p>For comparing plasticity/growth , the data are in <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/20170327_giantbigtable.csv">20170327_giantbigtable.csv </a>, and can be visualised/analysed in <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/plast%20vs%20growth.R">plast vs growth.R </a></p> <p>In order to recreate the AMOVAS based on SNVs, use <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/all_variants_fixed-only_using_5x_depth_threshold.csv?versionId=01cbf6bf-fe16-4e7d-aae8-58c527596ebb">all_variants_fixed-only_using_5x_depth_threshold.cvs </a> with <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/amova%20thally02.R?versionId=e9c21378-ee37-4418-9ce1-76db821e645a">amova thally02.R </a></p> <p>For additional information, please contact elisa.schaum@uni-hamburg.de </p>
Eel data (Anguilla anguilla) and associated environment variables for eel in the SUDOE area (SUDOANG project)
<h2><strong>1. DESCRIPTION</strong></h2><h3><strong>1.1. THE SUDOANG PROJECT</strong></h3><p>The <a href="https://sudoang.eu/en/">SUDOANG</a> project aims at providing common tools to managers to support eel conservation in the SUDOE area (Spain, France and Portugal). </p><p>Three main datasets have been used to implement EDA.</p><ul><li>A database of rivers and their attributes along with tools for chaining</li><li>A database of dams.</li><li>A database of electrofishing (current dataset)</li></ul><p>Electrofishing data include site locations, fishing operations which can be done several times at one site, and fish data collected during each operation. The operations are attached to stretches of river or river segments whose characteristics describe the conditions for presence, density, size structure, or silvering rate of the eels. </p><p>The electrofishing operation are classified by type:</p><ul><li>com full two pass fishing</li><li>coa full two pass electrofishing for eel</li><li>iaa eel abundance point sampling</li><li>ber bank sampling</li><li>gm point fishing for large streams</li><li>oth other, or unspecified.</li></ul><h3><strong>1.2. TEMPORAL SCOPE </strong></h3><p>From 1985 to 2018, beware incomplete dataset after 2015 in France.</p><h3><strong>1.3. GEOGRAPHICAL RANGE</strong></h3><p>The SUDOE area including Iberian Peninsula and France.</p><h2><strong>2. DATASETS DESCRIPTION</strong></h2><h3><strong>2.1. DENSITIES AND PRESENCE ABSENCE</strong></h3><h4>Dataset: <a href="https://zenodo.org/api/files/c576c330-0219-4710-a831-717762b117aa/frsppt_12_2020.Rdata?versionId=ad5867d9-1a51-413d-9d27-d269462df9a2">frsppt_12_2020.Rdata</a></h4><p>Most variables are built <a href="https://forgemia.inra.fr/pole-migrateurs/eda/eda_model/-/blob/de433830a1a483fff6404e0f668a38e8e88663a6/eda2.3/report/report2.3/EDA_build.Rnw#L1233">here. </a></p><p>The script to create cumulated values for dams can be found <a href="https://forgemia.inra.fr/pole-migrateurs/eda/eda_model/-/blob/main/S4/BaseEdaRiosRiversegmentsDam.R#L624">here.</a></p><p>For a technical description see the <a href="https://sudoang.eu/wp-content/uploads/2022/02/E411_Briand_et_al_2022_EDA_report_opt-1.pdf">report.</a></p><p>This file contains the following datasets:</p><ul><li>ddd => dataset used to calibrate the presence absence model, 46147 lines</li><li>ddg => dataset used to calibrate the gamma model (only positive values retained), 19993 lines.</li><li>tdd => dataset corresponding to places where transport operation have been identified, presence absence model, 6582 lines.</li><li>tdg => dataset corresponding to places where transport operation have been identified, gamma model (only positive values) 984 lines.</li></ul><p>And the following columns (in alphabetical order):</p><ul><li><i>altitudem</i>: Altitude in meter truncated to 800 m</li><li><i>area_sudo</i>: Area for recruitment (Drouineau et al., 2021)</li><li><i>codesea</i>: Code of the Sea, factor A = Atlantic, M = Mediterranean</li><li><i>country</i>: A factor (FR, SP, PT) for France, Spain and Portugal</li><li><i>country2</i>: Country with grouping for the Iberian Peninsula (SPPT). Other level is France (FR)</li><li><i>cs_height_08_n</i>: Cumulated height from the sea, dam height transformed with power 0.8, no prediction for missing values</li><li><i>cs_height_08_n</i>: Same variable but truncated to 300</li><li><i>cs_height_08_p</i>: Cumulated height from the sea, dam height transformed with power 0.8, with prediction for missing values</li><li><i>cs_height_08_p</i>: Same variable but truncated to 300</li><li><i>cs_height_08_pp</i>: Cumulated height from the sea, dam height transformed with power 0.8, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_08_pps</i>: Cumulated height from the sea, dam height transformed with power 0.8, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li><i>cs_height_10_FR</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams from France are considered when building on a transnational water course</li><li><i>cs_height_10_n</i>: Cumulated height from the sea, no transformation, no prediction for missing values</li><li><i>cs_height_10_n</i>: Same variable but truncated to 200</li><li><i>cs_height_10_p</i>: Cumulated height from the sea, no transformation, missing height are extrapolated from two different models in France and the Iberian Peninsula</li><li><i>cs_height_10_p</i>: Same variable but truncated to 200</li><li><i>cs_height_10_pass0</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams without pass are used to build the cumulated value</li><li><i>cs_height_10_pass1</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams with pass are used to build the cumulated value</li><li><i>cs_height_10_pp</i>: Cumulated height from the sea, no transformation, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_10_ppass0</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams without pass are used to build the cumulated value</li><li><i>cs_height_10_ppass1</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams with pass are used to build the cumulated value</li><li><i>cs_height_10_pps</i>: Cumulated height from the sea, no transformation, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li><i>cs_height_10_pscore0</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams without score are used to build the cumulated value</li><li><i>cs_height_10_pscore1</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams with score (that have been expertised as no or small barrier for eel) are used to build the cumulated value</li><li><i>cs_height_10_PT</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams from Portugal are considered when building on a transnational water course</li><li><i>cs_height_10_score0</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams without score are used to build the cumulated value</li><li><i>cs_height_10_score1</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams with score (that have been expertised as no or small barrier for eel) are used to build the cumulated value</li><li><i>cs_height_10_SP</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams from Spain are considered when building on a transnational water course.</li><li><i>cs_height_12_n</i>: Cumulated height from the sea, dam height transformed with power 1.2, no prediction for missing values.</li><li><i>cs_height_12_n</i>.: Same variable but truncated to 500</li><li><i>cs_height_12_p</i>: Cumulated height from the sea, dam height transformed with power 1.2, with prediction for missing values.</li><li><i>cs_height_12_p</i>.: Same variable but truncated to 500</li><li><i>cs_height_12_pp</i>: Cumulated height from the sea, dam height transformed with power 1.2, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_12_pps</i>: Cumulated height from the sea, dam height transformed with power 1.2, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li><i>cs_height_15_n</i>: Cumulated height from the sea, dam height transformed with power 1.5, no prediction for missing values</li><li><i>cs_height_15_n</i>.: Same variable but truncated to 800</li><li><i>cs_height_15_p</i>: Cumulated height from the sea, dam height transformed with power 1.5, with prediction for missing values</li><li><i>cs_height_15_p</i>.: Same variable but truncated to 800</li><li><i>cs_height_15_pp</i>: Cumulated height from the sea, dam height transformed with power 1.5, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_15_pps</i>: Cumulated height from the sea, dam height transformed with power 1.5, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li><i>cumnbdamp</i>: Cumulated number of dam from the sea</li><li><i>cumnbdamso</i>: duplicate of cumnbdamp</li><li><i>cumwettedsurfacebothkm2</i>.: Surface of water downstream from the segment in the river. Corresponds to both riversegment and waterbodies</li><li><i>cumwettedsurfacekm2</i>.: Surface of water downstream from the segment in the river. Corresponds only to rivers</li><li><i>cumwettedsurfaceotherkm2</i>.: Surface of water downstream from the segment in the river. Corresponds only waterbodies (water surfaces, associated with the segment).</li><li><i>densCS</i>: Density from Carle and Strub, number in second pass extrapolated from efficiency if only one pass</li><li><i>dist_from_gibraltar_km</i>: Distance to Gibraltar calculated using an enveloppe along the coastline (e.g. the estuaries ingress inland are not counted for this distance).</li><li><i>distanceseakm</i>: Distance to the sea</li><li><i>distanceseakm</i>.: Distance to the sea, truncated at 500</li><li><i>distancesourcem</i>: distance to the source in meters</li><li><i>downstdrainagewettedsurfaceboth</i>.: Percentage of wetted surface downstream for both rivers and associated waterbodies (cumwettedsurfacebothkm2) divided by land surface for all segments within the basin located at a same or lesser distance to the sea.</li><li><i>downstreamwettedsurface</i>: Percentage of wetted surface downstream (<i>cumwettedsurfacekm2</i>) divided by land surface for all segments within the basin located at a same or lesser distance to the sea</li><li><i>drainage_density_perm</i>.: River length / surface of basin in the basin downstream (m-1) Numeric, multiplied by $10^4$, truncated to 20</li><li><i>ef_fishingmethod</i>: Fishing method. See details in text and Briand et al., (2022)</li><li><i>ef_wetted_area</i>: Surface of the electrofishing station</li><li><i>emu</i>: Eel management unit. See <a href="https://github.com/ices-eg/wg_WGEEL/wiki">Git WGEEL</a></li><li><i>hydraulicdensityperm2</i>.: Number of riversegments per surface of basin (m-2). Numeric Multiplied by 10^6, truncated to 1.5</li><li><i>idsegment</i>: Unique identifier of the segment TEXT</li><li><i>laltitudem</i>.: Log transformed value of altitude (truncated)</li><li><i>lcs_height_10_n</i>.: Log transformed value of cumulated height</li><li><i>lddws</i>: Log transformed value of <i>downstdrainagewettedsurfaceboth</i></li><li><i>ldownstreamwettedsurface</i>: Log of previous column</li><li><i>lriverwidthm</i>.: Log transformed value of river width</li><li><i>month</i>: Month</li><li><i>NCS</i>: Number of eels estimated by Carle and Strub</li><li><i>ob_id</i>: Operation (observation) identifier</li><li><i>op_id</i>: Station (observation place) identifier</li><li><i>riverwidthm</i>: Width of the river in m (comes from various sources: see <a href="https://doi.org/10.5281/zenodo.7546419">Atlas</a> and Briand et al., 2022).</li><li><i>seaidsegment</i>: Identifier of the sea idsegment</li><li><i>temperature</i>: Average temperature from 1960-2000 from the CCM (Vogt, 2007)</li><li><i>temperature.1</i>: duplicate of temperature</li><li><i>transport</i>: code of transport operation</li><li><i>year</i>: year of electrofishing</li></ul><h3><strong>2.2. SIZE STRUCTURE OF EELS</strong> </h3><h4>Dataset <a href="https://zenodo.org/api/files/c576c330-0219-4710-a831-717762b117aa/table_ind.Rdata?versionId=a81c7bb4-0f36-44e3-82ea-86d9a2caac8e">table_ind.Rdata</a> </h4><p>A dataset of 494163 lines. In this dataset, one line correspond to one eel.</p><ul><li><i>altitudem</i>: Altitude in meter</li><li><i>altitudem</i>.: Altitude in meter truncated to 800 m</li><li><i>area_sudo</i>: Area for recruitment (Drouineau et al., 2021)</li><li><i>basin</i>: Name (or code from bd_carthage France) of the basin</li><li><i>codesea</i>: Code of the Sea, factor A = Atlantic, M = Mediterranean</li><li><i>country</i>: A factor (FR, SP, PT) for France, Spain, and Portugal</li><li><i>cs_height_08_n</i>: Cumulated height from the sea, dam height transformed with power 0.8, no prediction for missing values.</li><li><i>cs_height_08_n</i>.: Same variable but truncated to 300</li><li><i>cs_height_08_p</i>: Cumulated height from the sea, dam height transformed with power 0.8, with prediction for missing values.</li><li><i>cs_height_08_p</i>.: Same variable but truncated to 300</li><li><i>cs_height_08_pp</i>: Cumulated height from the sea, dam height transformed with power 0.8, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_08_pps</i>: Cumulated height from the sea, dam height transformed with power 0.8, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li><i>cs_height_10_FR</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams from France are considered when building on a transnational water course.</li><li><i>cs_height_10_n</i>: Cumulated height from the sea, no transformation, no prediction for missing values.</li><li><i>cs_height_10_n</i>.: Same variable but truncated to 200</li><li><i>cs_height_10_p</i>: Cumulated height from the sea, no transformation, missing height are extrapolated from two different models in France and the Iberian Peninsula</li><li><i>cs_height_10_p</i>.: Same variable but truncated to 200</li><li><i>cs_height_10_pass0</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams with pass are used to build the cumulated value</li><li><i>cs_height_10_pass1</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams without pass are used to build the cumulated value</li><li><i>cs_height_10_pp</i>: Cumulated height from the sea, no transformation, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_10_ppass0</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams with pass are used to build the cumulated value</li><li><i>cs_height_10_ppass1</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams without pass are used to build the cumulated value</li><li><i>cs_height_10_pps</i>: Cumulated height from the sea, no transformation, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li><i>cs_height_10_pscore0</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams with score (that have been expertised as no or small barrier for eel) are used to build the cumulated value</li><li><i>cs_height_10_pscore1</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams without score are used to build the cumulated value</li><li><i>cs_height_10_PT</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams from Portugal are considered when building on a transnational water course</li><li><i>cs_height_10_score0</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams with score (that have been expertised as no or small barrier for eel) are used to build the cumulated value</li><li><i>cs_height_10_score1</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams without score are used to build the cumulated value</li><li><i>cs_height_10_SP</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams from Spain are considered when building on a transnational water course</li><li><i>cs_height_12_n</i>: Cumulated height from the sea, dam height transformed with power 1.2, no prediction for missing values</li><li><i>cs_height_12_n</i>.: Same variable but truncated to 500</li><li><i>cs_height_12_p</i>: Cumulated height from the sea, dam height transformed with power 1.2, with prediction for missing values</li><li><i>cs_height_12_p</i>.: Same variable but truncated to 500</li><li><i>cs_height_12_pp</i>: Cumulated height from the sea, dam height transformed with power 1.2, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_12_pps</i>: Cumulated height from the sea, dam height transformed with power 1.2, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li><i>cs_height_15_n</i>: Cumulated height from the sea, dam height transformed with power 1.5, no prediction for missing values</li><li><i>cs_height_15_n</i>.: Same variable but truncated to 800</li><li><i>cs_height_15_p</i>: Cumulated height from the sea, dam height transformed with power 1.5, with prediction for missing values</li><li><i>cs_height_15_p</i>.: Same variable but truncated to 800</li><li><i>cs_height_15_pp</i>: Cumulated height from the sea, dam height transformed with power 1.5, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_15_pps</i>: Cumulated height from the sea, dam height transformed with power 1.5, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li><i>cumheightdam</i>: Cumulated height of dam (ignore)</li><li><i>cumnbdamp</i>: Cumulated number of dam from the sea</li><li><i>cumnbdamso</i>: Cumulated number of dam from the sea only for dams whose height is larger than zero</li><li><i>densCS</i>: Density from Carle and Strub, number in second pass extrapolated from efficiency if only one pass.</li><li><i>dist_from_gibraltar_km</i>: Distance to Gibraltar calculated using an enveloppe along the coastline (e.g. the estuaries ingress inland are not counted for this distance).</li><li><i>distanceseakm</i>: Distance to the sea in kilometers</li><li><i>distanceseakm</i>.: Distance to the sea, truncated at 500</li><li><i>distanceseam</i>: Distance to the sea in meters</li><li><i>distancesourcem</i>: Distance to the source in meters</li><li><i>ef_electrofishing_mean</i>: Percentage of wetted surface downstream (<i>cumwettedsurfacekm2</i>) divided by land surface for all segments within the basin located at a same or lesser distance to the sea</li><li><i>ef_fished_length</i>: Length of the fishing operation</li><li><i>ef_fished_width</i>: Width of the fishing operation</li><li><i>ef_fishingmethod</i>: Method of electrofishing</li><li><i>ef_nbpas</i>: Number of pass in the electrofishing operation</li><li><i>ef_wetted_area</i>: Surface of the electrofishing station</li><li><i>emu</i>: Eel Management Unit</li><li><i>id</i>: Comes from uncout R function, which transforms counts into lines</li><li><i>idsegment</i>: Unique identifier of the segment [data type: UUID]. Use the <a href="https://doi.org/10.5281/zenodo.7546419">Atlas</a> to link with spatial table in PostgreSQL</li><li><i>isendoreic</i>: Is the riversegment coming from an endoreic river?</li><li><i>issea</i>: Is the riversegment a sea outlet?</li><li><i>laltitudem</i>.: Log transformed value of altitude (truncated)</li><li><i>lcs_height_10_n</i>.: Log transformed value of cumulated height</li><li><i>lengthm</i>: Length of the electroshing station in meters</li><li><i>lengthriverm</i>: length of the riversegment in meters</li><li><i>lriverwidthm</i>.: Log transformed value of river width</li><li><i>medianflowm3ps</i>: Median flow of the river in cubic meter per second</li><li><i>month</i>: Month</li><li><i>name</i>: Name of the river (Spain and Portugal)</li><li><i>nb_size_measured</i>: Number of size measured in the electrofishing operation</li><li><i>nbp1</i>: Number of eel collected in the first pass</li><li><i>nbp2</i>: Number of eel collected in the second pass</li><li><i>nbp3</i>: Number of eel collected in the third pass</li><li><i>NCS</i>: Number estimated during the electrofishing operation by Carle and Strubb and interpolation of first pass efficiency if only one pass</li><li><i>nextdownidsegment</i>: Code of the next downstream <i>idsegment</i></li><li><i>Npred</i>: Number of eel predicted on the riversegment <i>pdeltagamma </i>* <i>watersurface</i></li><li><i>ob_dp_name</i>: Data provider for the operation</li><li><i>ob_id</i>: Operation (observation) identifier</li><li><i>ob_starting_date</i>: Date of the operation</li><li><i>op_id</i>: Station (observation place) identifier</li><li><i>pdelta</i>: prediction of the delta (presence absence model)</li><li><i>pdeltagamma: pdelta</i>*<i>pgamma</i></li><li><i>pgamma</i>: prediction of the gamma (positive densities model)</li><li><i>rdelta</i>: residuals of the delta (presence absence model)</li><li><i>rdeltagamma</i>: <i>rdelta</i>*<i>rgamma</i></li><li><i>rgamma</i>: residuals of the gamma (positive densities model)</li><li><i>riverwidthm</i>: River width in meters</li><li><i>riverwidthm</i>.: same variable as riverwidthm (no truncation for riverwidth)</li><li><i>rN</i>: residuals in number (difference in number of eels between observed and predicted there are some NA where we had to get the data from "density" as there was no water surface available</li><li><i>seaidsegment</i>: Identifier of the sea idsegment</li><li><i>sector</i>: please ignore</li><li><i>shreeve</i>: Shreve rank of the segment</li><li><i>size</i>: Size class, "1 - <150", "2 - [150-300[", "3 - [300-450[", "4 - [450-600[", "5 - [600-750[", "6 - >=750"</li><li><i>strahler</i>: Strahler rank of the segment</li><li><i>surfacebvkm2</i>: surface of the watershed in m2</li><li><i>surfacebvm2</i>: surface of the watershed in km3</li><li><i>surfaceunitbvm2</i>: surface of the unit basin surrounding the segment in m2</li><li><i>temperature</i>: Average temperature from 1960-2000 from the CCM (Vogt 2007)</li><li><i>temperaturejan</i>: January temperature (France)</li><li><i>temperaturejul</i>: July temperature (France)</li><li><i>totalnumber</i>: Total number of eel caught during the operation</li><li><i>transport</i>: transport area</li><li><i>wettedsurfacem2</i>: Surface of water downstream from the segment in the river. Corresponds only to rivers</li><li><i>wettedsurfaceotherm2</i>: Surface of water downstream from the segment in the river. Corresponds only waterbodies (water surfaces, associated with the segment).</li><li><i>year</i>: Year of electrofishing operation</li></ul><h3><strong>2.3. SILVER EEL DATA</strong></h3><h4>Dataset <a href="https://zenodo.org/api/files/c576c330-0219-4710-a831-717762b117aa/silver_eel.Rdata?versionId=9df184ef-241d-46af-989e-2bddc0179df0">silver_eel_2020.Rdata</a></h4><p>A dataset of 20101 lines corresponding to yellow and silver eel along with their measurements for silvering for eel > 150 mm.</p><p>This dataset has been built <a href="https://forgemia.inra.fr/pole-migrateurs/eda/eda_model/-/blob/main/eda2.3/report/report2.3/EDA_build.Rnw#L6454">here</a>, and validated <a href="https://forgemia.inra.fr/pole-migrateurs/eda/eda_model/-/blob/main/eda2.3/report/report2.3/EDA_build.Rnw#L6812">here</a> and <a href="https://forgemia.inra.fr/pole-migrateurs/eda/eda_model/-/blob/main/eda2.3/report/report2.3/EDA_build.Rnw#L8409">here</a>, For more information look at the <a href="https://sudoang.eu/wp-content/uploads/2022/02/E411_Briand_et_al_2022_EDA_report_opt-1.pdf">report</a> especially the annexes.</p><p>The columns are:</p><ul><li><i>BL</i>: Body length</li><li><i>Dv</i>: Vertical eye diameter</li><li><i>FL</i>: Pectoral fin length</li><li><i>MD</i>: Mean eye diameter</li><li><i>W</i>: Eel weight</li><li><i>contrast</i>: Body contrast for the eel</li><li><i>Dh</i>: Horizontal eye diameter</li><li><i>diam_max</i>: Max of horizontal and vertical eye diameter</li><li><i>diam_min</i>: Min of horizontal and vertical eye diameter</li><li><i>distance_foyer_c</i>: sqrt(diam_max^2 - diam_min^2)</li><li><i>excentricite</i>: Eye excentricity = distance_foyer_c / diam_max</li><li><i>IO</i>: Occular index Pankhurst = 100*((Dh+Dv/2)^2*pi/BL</li><li><i>K_ful</i>: Fulton coefficient= 100*W/(BL/10)^3</li><li><i>maturite_durif</i>: Durif (2009) maturity class</li><li><i>oc_surface</i>: Occular surface</li><li><i>sexe_durif</i>: Sex according to Durif (2009)</li><li><i>silver</i>: Is it a silver eel, corresponds to one of "MII", "FIV", "FV" in Durif stage [data type: Boolean]</li><li><i>stade_pankhurst</i>: Stage according to Pankhurst</li><li><i>stage_Durif</i>: I, MII, FII, FIII, FIV, FV see Durif (2009)</li><li><i>pdeltagamma</i>: Density predicted from the delta gamma model in EDA (Briand et al., 2022)</li><li><i>dist_from_gibraltar_km</i>: Distance from Gibraltar along the coastline, negative in the Mediterranean</li><li><i>surfacebvm2</i>: Surface of the watershed in m2</li><li><i>altitudem.</i>: Altitude in m truncated at 400</li><li><i>temperature</i>: Average temperature from 1960-2000 from the CCM (Vogt, 2007)</li><li><i>distanceseakm</i>.: Distance to the sea in km truncated at 500</li><li><i>strahler</i>: Strahler order of the stream</li><li><i>month2</i>: Month of electrofishing with values grouped for <=7 or >=10</li><li><i>month</i>: Month of electrofishing</li><li><i>year</i>: Year of electrofishing</li></ul><h2><strong>3. VERSIONS</strong></h2><ul><li><a href="https://doi.org/10.5281/zenodo.6023561">10.5281/zenodo.6023561 </a>1.0.0 - 2022-02-09 - Initial upload (open access)</li><li><a href="https://doi.org/10.5281/zenodo.6397009">10.5281/zenodo.6397009 </a>1.0.1 - 2023-08-02 - Fixed description (open access)</li></ul><h2><strong>4. READ MORE</strong></h2><ul><li>Atlas of European Eel Distribution (<i>Anguilla anguilla</i>) in Portugal, Spain and France (<a href="https://doi.org/10.5281/zenodo.7546419">10.5281/zenodo.7546419</a>)</li><li>Electrofishing data for eel in the Iberian Peninsula (SUDOANG project) (<a href="https://doi.org/10.5281/zenodo.8348353">10.5281/zenodo.8348353</a>)</li><li>Cumulated dam impact in France and the Iberian Peninsula (SUDOANG project) (<a href="https://doi.org/10.5281/zenodo.8348374">10.5281/zenodo.8348374</a>)</li></ul><h2><strong>5. FUNDING</strong></h2><p>Project co-financed by the INTERREG SUDOE Programme through the European Regional Development Fund (ERDF).</p>
Dataset for: Sensory environment affects Icelandic threespine stickleback's anti-predator escape behaviour
<p>Human-induced changes in climate and habitats push populations to adapt to novel environments, including new sensory conditions, such as reduced visibility. We studied how colonizing newly formed glacial lakes with turbidity-induced low visibility affects anti-predator behaviour in Icelandic threespine sticklebacks. We tested nearly 400 fish from 15 populations and four habitat types varying in visibility and colonization history in their reaction to two predator cues (mechano-visual versus olfactory) in high versus low visibility light treatments. Fish reacted differently to the cues and were affected by lighting environment, confirming that cue modality and light levels are important for predator detection and evasion. Spring-fed fish, especially from the highlands (likely more diverged from marine fish than lowland fish) reacted fastest to mechano-visual cues and were generally most active. Highland glacial fish showed strong responses to olfactory cues and, counter to predictions from the flexible stem hypothesis, the greatest plasticity in response to light levels. This study, leveraging natural, repeated invasions of novel sensory habitats, 1) illustrates rapid changes in antipredator behaviour that follow due to adaptation, early life experience, or both, and 2) suggests an additional role for behavioural plasticity enabling population persistence in the face of frequent changes in environmental conditions.</p>
Data from "Disk Evolution Study Through Imaging of Nearby Young Stars (DESTINYS): A Panchromatic View of DO Tau's Complex Kilo-au Environment'
<p>Reduced data from Huang et al., 2022, "Disk Evolution Study Through Imaging of Nearby Young Stars (DESTINYS): A Panchromatic View of DO Tau's Complex Kilo-astronomical-unit Environment,' ApJ, 930, 171 (arXiv:2204.01758). </p> <p>See Table 1 of the article for the corresponding observing program codes and attributions for archival data (if applicable). </p> <p><strong>Images:</strong></p> <p>DOTau_12CO_automask.image.pbcor.fits: 12CO J=2-1 image cube<br> DOTau_12CO_automask.mom1.fits: 12CO J=2-1 moment 1 map<br> DOTau_12CO_automask.pbcor.2sigcut.mom0.fits: 12CO J=2-1 moment 0 map<br> DOTau_13CO_automask.image.pbcor.fits: 13CO J=2-1 image cube<br> DOTau_13CO_automask.mom1.fits: 13CO J=2-1 moment 1 map<br> DOTau_13CO_automask.pbcor.2sigcut.mom0.fits: 13CO J=2-1 moment 0 map<br> DOTau_C18O_automask.image.pbcor.fits: C18O J=2-1 image cube<br> DOTau_C18O_automask.pbcor.2sigcut.mom0.fits: C18O J=2-1 moment 0 map<br> DOTau_C18O_automask.pbcor.mom1.fits: C18O J=2-1 moment 1 map<br> DOTau_cADI_average.fits: SPHERE H-band cADI image (pixel scale: 0.01225 arcseconds)<br> DOTau_CS_automask.image.pbcor.fits: DO Tau CS J=5-4 image cube<br> DOTau_CS_automask.mom1.fits: DO Tau CS J=5-4 moment 1 map<br> DOTau_CS_automask.pbcor.2sigcut.mom0.fits: DO Tau CS J=5-4 moment 0 map<br> DOTau_DoLP.fits: DO Tau degree of linear polarization map (pixel scale: .0245 arcseconds)<br> DOTau_IDF-RDI.fits: SPHERE total intensity image produced with IDF-RDI (pixel scale: 0.01225 arcseconds)<br> DO-TAU_NICMOS_F110W_MRDILib-18_KL-2_Pixel.fits: HST NICMOS F110W image (pixel scale: 0.075 arcseconds)<br> DO-TAU_NICMOS_F160W_MRDILib-100_KL-1_Pixel.fits: HST NICMOS F160W image (pixel scale: 0.075 arcseconds)<br> DOTau_Qphi_average.fits: SPHERE H-band Qphi image (pixel scale: 0.01225 arcseconds)<br> DO_Tau_STIS_KlipWithin160pixel_counts_s_pixel.fits: HST STIS image (pixel scale: 0.0507 arcseconds)</p> <p><strong>Measurement sets:</strong></p> <p>DOTau_12CO.ms.contsub.tar: Self-calibrated, continuum-subtracted 12CO J=2-1 visibilities<br> DOTau_13CO.ms.contsub.tar: Self-calibrated, continuum-subtracted 13CO J=2-1 visibilities<br> DOTau_C18O.ms.contsub.tar: Self-calibrated, continuum-subtracted C18O J=2-1 visibilities<br> DOTau_CS.ms.contsub.tar: Self-calibrated, continuum-subtracted CS J=5-4 visibilities</p> <p><strong>Scripts:</strong></p> <p>DOTau_1.1mmreduction.py: CASA self-cal and imaging script for CS data <br> DOTau_1.3mmreduction.py: CASA self-cal and imaging script for CO data </p>
A Tungsten Deep Neural-Network Potential for Simulating Mechanical Property Degradation Under Fusion Service Environment
<p>The DP-HYB and DP-SE2potential and the W training database.</p>
Safety of Vulnerable Road Users (VRU's) in Light-Rail Transit (LRT) Environment
<p>Light-rail transit (LRT), which includes modern streetcars, trolleys, and heritage trolleys, is one of the fastest growing modes of public transportation in the United States. To reduce the cost and complexity of construction, most LRT systems have their tracks placed on city streets, in medians, or in separate at-grade rights-of-way with at-grade crossings. Operating light-rail vehicles (LRVs) along these alignments introduces new conflicts and increases the risk of collisions with vulnerable road users (VRUs) including pedestrians, bicyclists, and electric scooter riders.<br> This study has two main objectives: (1) to review and evaluate the existing body of knowledge and the state of practice regarding safety of VRUs in LRT environments; and (2) to synthesize this information and package the results in a “Best Practices Resource Guide” and a companion “PowerPoint Presentation” for use in improving the safety of VRUs in existing LRT systems and advancing the professional capacity of transit workforce. Metropolitan Planning Organizations and State DOTs should also benefit from this resource information in the planning and design of new LRT systems.<br> This report presents a wide range of physical, educational, and enforcement treatments for improving the safety of VRUs in LRT environments. The selection of a particular treatment for use at an LRT grade crossing or station should be based on an engineering study whose scope and complexity depend on local conditions. Factors that should be considered during device selection include 1) pedestrian‐LRV collision experience, 2) pedestrian volumes and peak flow rates, 3) train speeds, frequency of trains, number of tracks, and railroad traffic patterns, 4) sight distances available to pedestrians and LRV operators approaching the crossing, and 5) skew angle, if any, of the crossing relative to the LRT tracks.</p>
Volatile social environments can favour investments in quality over quantity of social relationships
<p>Cooperation does not occur in a vacuum: interactions develop over time in social groups that undergo demographic changes. Intuition suggests that stable social environments favour developing few but strong reciprocal relationships (a 'focused' strategy), while volatile social environments favour the opposite: more but weaker social relationships (a 'diversifying' strategy). We model reciprocal investments under a quality-quantity tradeoff for social relationships. We find that volatility, counterintuitively, can favour a focused strategy. This result becomes explicable through applying the theory of antagonistic pleiotropy, originally developed for senescence, to social life. Diversifying strategies show superior performance later in life, but with costs paid at young ages while the social network is slowly being built. Under volatile environments, many individuals die before reaching sufficiently old ages to reap the benefits. Social strategies that do well early in life are then favoured: a focused strategy leads individuals to form their first few social bonds quickly and to make strong use of existing bonds. Our model highlights the importance of pleiotropy and population age structure for the evolution of cooperative strategies and other social traits, and shows that it is not sufficient to reflect on the fate of survivors only, when evaluating the benefits of social strategies.</p>
Bifurcation points for tropical cyclone genesis in sheared and dry environments - simulation data
<p>Key information to reproduce the idealized WRF ensemble simulations used for tropical cyclone genesis </p>
Data from: Breeding heat tolerant orchardgrass germplasm for summer persistence in high temperature stress environments of the southeastern United States
<p>This is digital research data corresponding to a published manuscript, Breeding heat tolerant orchardgrass germplasm for summer persistence in high temperature stress environments of the southeastern United States, in Crop Science, Volume 61, p. 1915 - 1925. Orchardgrass (Dactylis glomerata L.) could serve as a cool-season perennial in southeastern production systems, but often does not behave as a true perennial under high temperature stress conditions of the region. This work sought to develop heat-tolerant orchardgrass germplasm through recurrent phenotypic selection (RPS) that would both reduce secondary seed dormancy caused by high soil temperatures and improve stand persistence over summer months. Selection was conducted in a growth chamber 40/30 °C (12/12 h, light/darkness), with germinated seedlings subjected to an additional 2–3 weeks of 40/30 °C conditions. The base germplasm (Cycle 0) and selected individuals (Cycles 1–3) were transplanted into the field, then harvested for seed. Forty-degree germination tests compared mean cumulative germination, velocity of germination within 8 days (VOG8), and realized heritability. Stand persistencewas assessed 1 year after transplanting.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.