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942 results for “Scenarios”
Figure 1. Scenarios and experimental design for assessing a in Pilot study: investigating the role of biofouling in transmission of Ostreid herpesvirus 1 (OsHV-1)
Figure 1. Scenarios and experimental design for assessing a range of biofouling organisms for the potential to spread OsHV-1. A. Scenario for the OsHV-1 transmission pathway evaluated by field surveillance. B.1 Scenario for the vessel biofouling OsHV-1 transmission pathway. B.2 Adaptation of the vessel biofouling scenario for a laboratory infection trial with Pacific oysters and biofouling organisms evaluated as intermediates for transmission from injected Pacific oysters to naïve Pacific oysters.
Fig. 2 in Growth Rate Modulation Enables Coexistence in a Competitive Exclusion Scenario Between Microbial Eukaryotes
Fig. 2. Growth curves of Arcella intermedia and Pyxidicula operculata in the monospecific culture experiments (three replicates each). Dots represent the raw sampled data; colored intervals represent the 95% credibility intervals of cell counts from the Bayesian model fitting.
Fig. S2 in Growth Rate Modulation Enables Coexistence in a Competitive Exclusion Scenario Between Microbial Eukaryotes
Fig. S2. Posterior distributions of the logistic model parameters. The values of K are in cells cm–2, r = d–1. P is the detection probability. P has a fixed range between 0.9 and 1. Color lines represents each one of the single-species experiments, color legend is in the right corner of the figure. A.intermedia experiments are Arc 1, 2 and 3. P.operculata experiments are Pyx 1, 2 and 3.
Fig. S1 in Growth Rate Modulation Enables Coexistence in a Competitive Exclusion Scenario Between Microbial Eukaryotes
Fig. S1. Overview of data collection design. Microcosms are assembled and sampled by a sub- sampling strategy where the organisms are counted by eye. Model adjustment considers both the system dynamics and the sampling level.
Fig. S4. Growth curves for A.intermedia when started the experiment with a in Growth Rate Modulation Enables Coexistence in a Competitive Exclusion Scenario Between Microbial Eukaryotes
Fig. S4. Growth curves for A.intermedia when started the experiment with a single cell. Color points represents each one of the single-cell experiments, color legend is in the left corner of the figure. Black line correspond to the average growth between experiments.
Fig. S3 in Growth Rate Modulation Enables Coexistence in a Competitive Exclusion Scenario Between Microbial Eukaryotes
Fig. S3. Posterior distributions of the competition model parameters for the species Arcella intermedia (A) and Pyxidicula operculata (P). Each colored line represent one of the replicates of the competition experiment (color legend shown in the last figure). The values of k are in a logarithmic scale of cells cm-2, r are in days–1. aAP is the competition coefficient of the influence of A species on P (Eq. 3), whereas aPA is the competition coefficient of the influence of P on A (Eq. 4).
Fig. 4 in Growth Rate Modulation Enables Coexistence in a Competitive Exclusion Scenario Between Microbial Eukaryotes
Fig. 4. Posterior estimates of the parameters of models fitted to cell counts in each culture. Each panel shows the medians (dots) and 95% credibility intervals (lines) of posterior distributions of one parameter of the models fitted to data from a replicate (seven for the competition cultures in lower part and three for mono-specific cultures in the upper part). In red, estimates for Arcella intermedia and in blue estimates for Pyxidicula operculata. The values of K are in cm–2, r are in days–1. The competition coefficients are α (red) and β (blue) of Eqs. 3–4.
Fig. 1 in Growth Rate Modulation Enables Coexistence in a Competitive Exclusion Scenario Between Microbial Eukaryotes
Fig. 1. Species used in this study. A – Arcella intermedia LEP isolate 6, magnification 630×. B – Pyxidicula operculata LEP isolate 1, magnification 1000×.
Input data and results of the RECC v2.5 model for the transformation scenarios of the global building stock
<p>This dataset contains the input data and core results of the RECC v2.5 model for the transformation scenarios of the global building stock. For details abou the RECC model, see DOI <a href="https://doi.org/10.1111/jiec.13023" target="_blank" rel="noopener">https://doi.org/10.1111/jiec.13023</a> and the RECC model landing page: <a href="https://www.industrialecology.uni-freiburg.de/odym-recc" target="_blank" rel="noopener">https://www.industrialecology.uni-freiburg.de/odym-recc</a></p> <p>The following data are included in this dataset:</p> <ul> <li>The entire model input database (120 model parameters)</li> <li>The parameters for the sensitivity analysis (8 parameters)</li> <li>The 70 folders with the core results</li> <li>The master classification file RECC_Classifications_Master_V2.0.xlsx</li> <li>The model config file RECC_Config.xlsx</li> <li>The list of scenario configurations RECC_ModelConfig_List.xlsx</li> <li>The result compilation and exporting configuration file RECCv2.5_EXPORT_Combine_Select.xlsx</li> <li>The main result summary file (extracted from the 70 result folders) Results_Extracted_RECCv2.5_10Regs_sep.xlsx</li> <li>The result summary file for comparison with the CRAFT model timber supply RECCv2.5_10Regs_CRAFT_Coupling_SHARE.xlsx</li> <li>The results of the sensitivity analysis: Results_Extracted_RECCv2.5_10Regs_Sensitivity_sep.xlsx</li> </ul> <p>Note that the result folders of the sensitivity analysis are not archived here (too little information in relation to the data volume). They can be requested from the author. The results can also be recreated by running the RECC model with the sensitivity analysis parameters.</p> <p>The model itself is available as Python code from <a href="https://github.com/IndEcol/RECC-ODYM" target="_blank" rel="noopener">https://github.com/IndEcol/RECC-ODYM</a></p>
How much energy can giant reed and Miscanthus produce in marginal lands across Italy? A modelling solution under current and future scenarios
<p>This data were presented in the research paper “How much energy can giant reed and Miscanthus produce in marginal lands across Italy? A modelling solution under current and future scenarios”, currently accepted in the journal Global Change Biology Bioenergy (https://onlinelibrary.wiley.com/journal/17571707).<br>The study delivers a model-based evaluation of how much energy, in the form of biomethane and bioethanol, can be produced by giant reed and Miscanthus across Italy in 2000, 2055 and 2085. Marginal lands were defined as low profitable non-irrigated lands, without mechanization and/or nature conservation limitations. Our findings offer an estimation of achievable energy yields and related stability under current/future climate, identifying critical spots and opportunities at province and regional level across Italy.<br>This work was conducted by the Council for Agricultural Research and Economics and supported by the Italian Ministry of Agricultural, Food and Forestry Policies (MiPAAF) under i) the AGROENER project (D.D. n. 26329, April 1, 2016, http://agroener.crea.gov.it/) and ii) the AgriDigit-Agromodelli project (DM n. 36502 of 20/12/2018, https://www.progettoagridigit.it/il-progetto).</p> <p><br>The database used was split in two main datasets, one for the national case study and one for the provincial case study (Bologna province).<br>The national dataset consists of:<br>1) a gridded shape file (Marginal_Suitable_Areas_National.shp; 500 x 500 m resolution) including marginal lands suitable for Miscanthus and giant reed cultivation across Italy (code_nod field), together with related geographic coordinates;<br>2) a csv file (Results_National.csv) reporting the values of key output variables for each of the marginal lands considered. Output variables are:<br>a. USDA soil texture classification: 1= Loamy, 2=Sandy−loam, 3=Silty−loam, 4= Clay−loam, 5= Sandy−clay−loam, 6=Silty−clay−loam, 7=Loamy−sand, 8=Sandy−clay, 9=Silty−clay, 10=Silty, 11=Clay, 12=Heavy−clay, 13=Sandy.<br>b. soil organic carbon (SOC) classification: SOC≤1.5%=low, 1.5%<SOC≤3%,=medium, otherwise=high;<br>c. maximum soil depth (depth) classification: depth≤50 cm=shallow, otherwise=deep;<br>d. absolute values of aboveground biomass (AGB, Mg ha-1) and energy yields (Giga J ha-1) obtainable from bioethanol (ETA) and biomethane (MET) energy carriers simulated for giant reed (GR) and Miscanthus (MI) in the current scenario;<br>e. minimum (Mn) and maximum (Mx) AGB percentage (%) variations (compared to the baseline) estimated in 2055 (55) and 2085 (85) for RCP 4.5 (4.5) and RCP 8.5 (8.5) scenarios;<br>f. potentially assignable marginal lands to Miscanthus (2) and giant reed (1) crop species in Italy based on attainable energy yields under current (C_Base) and future (2085) time slices, considering the more pessimistic (C_8.5_85_MIN) and optimistic (C_4.5_85_MAX) AGB projection for both crops.</p> <p><br>The provincial dataset (case study in the Bologna province) consists of:<br>1) a gridded shape file (Marginal_Suitable_Areas_Provincial.shp, 500 x 500 m resolution) including marginal lands suitable for Miscanthus and giant reed cultivation across the Bologna province (code_nod field), together with related geographic coordinates;<br>2) a csv file (Results_Provincial.csv) reporting the values of key output variables for each of the marginal lands considered. Output variables are:<br>a. absolute values of simulated energy (EN, Giga J ha-1) from bioethanol (ETA) and biomethane (MET) for giant reed (GR) and Miscanthus (MI) in 1995,<br>b. energy percentage variations (compared to the baseline) estimated in 2085 for more optimistic (EN_Mx, i.e., RCP 4.5_max) and pessimistic (EN_Mn, i.e., RCP 8.5_min) projections for giant reed (GR) and Miscanthus (MI) and<br>c. coefficients of variations (CV, %) computed for the whole 30-year period centred on 1995 (B) and 2085 for RCP 4.5_max (CV_Mx) and RCP 8.5_min (CV_Mn) for giant reed (GR) and Miscanthus (MI) in the Bologna province.</p>
Data study "The Impact of Augmented Reality on Biodiversity Learning in a Pedagogical Scenario Based on Analogical Reasoning: An Experimental Study"
<p>This data was collected in 2023 as part of a study on the impact of location-based AR on biodiversity education. </p>
Figure 5. AGLO Scenario Symbols-Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>analyzed AGLO that is displayed to the learner for localization and selection purposes.<br> The second XML element is the scenario element containing a text description of the AGLO<br> and a set of symbols. The description is expressed in natural language and we can notice that it<br> contains four main steps:<br> i) random tree generation;<br> ii) index computation for presentation;<br> iii) parent index computation for answer validation;<br> iv) access to the first two keys for particular feedback generation.<br> In the scenario section depicted in figure 5 several symbols are defined with the following<br> semantics.</p>
Additional radiative forcing (warming) under the different scenarios for ice melt
<p>This data set contains estimates of additional radiative forcing for 3 different sea-ice–albedo feedback (SIAF) scenarios, in addition to the baseline (no additional radiative forcing) scenario. The three scenarios are identical up to 2050, but vary significantly after this date.</p> <p> Full details of methods used to create the dataset are provided within the ReadMe file. </p>
Additional greenhouse gas emissions under different scenarios of permafrost melt'
<p>This dataset contains the underlying data for the following publication Significant implications of permafrost thawing for climate change control, Climatic Change, DOI: 10.1007/s10584-016-1666-5. </p> <p>This data set contains the permafrost emissions used as inputs for the DICE model. These are estimates of the emissions release from permafrost under the RCP 2.6 scenario (GtCO 2 -eq y −1. Three inputs were used: the median, 16th percentile and 84th percentile pathway.</p>
Videos of evolved robot swarms in a simulated collective construction scenario
<p>The videos show robot swarms designed with population coding in an ARGoS simulation.</p> <p>In the videos 1 to 3 the swarm tries to shelter the pivot point in the middle by dragging cylinders in the gray target area.</p> <p>Viedeo 4 and 5 additionally try to collect or respectively avoid as much light as possible.</p>
Dynamic and thermodynamic crossover scenarios in the Kob-Andersen mixture: Insights from multi-CPU and multi-GPU simulations
<p>This dataset is associated with "Dynamic and thermodynamic crossover scenarios in the Kob-Andersen mixture: Insights from multi-CPU and multi-GPU simulations", Daniele Coslovich, Misaki Ozawa, and Walter Kob, Eur. Phys. J. E 62, 41 (2018) [<a href="https://doi.org/10.1140/epje/i2018-11671-2">doi:10.1140/epje/i2018-11671-2</a> <a href="https://arxiv.org/abs/1804.04559">arXiv:1804.04559</a>]</p> <p>It includes scripts and data files to allow for the replication of the figures. EPS figures were generated using gnuplot version 5.0.</p> <p>Notes:</p> <ul> <li>Small differences in the dynamic data for the N=3600 dataset obtained with the MD protocol reflect additional statistics gathered since acceptance of the paper.</li> <li>Figure 6(b) in the published version of the manuscript was obtained using slightly incorrect values of the parameters J, T_0 entering equation 10. This minor issue has been fixed in this dataset.</li> </ul>
Data from: Hydrology induces intraspecific variation in freshwater fish morphology under contemporary and future climate scenarios
<p>Datasets for manuscript "Andres, K. J., Chien, H., and Knouft, J. H. Hydrology induces intraspecific variation in freshwater fish morphology under contemporary and future climate scenarios. Science of the Total Environment. <a href="https://doi.org/10.1016/j.scitotenv.2019.03.292">https://doi.org/10.1016/j.scitotenv.2019.03.292</a>"</p> <p>landmarks.zip: landmarks digitized on images of 1081 specimens using TpsDig2 software.</p> <p>streamflow_estimates.csv: Contemporary (1980-2009) and future (2070-2099) streamflow estimates [avg: average annual streamflow discharge (m3 s-1); cv: coefficient of variation of annual discharge] in sub-basins containing populations of 6 minnow species in IL, USA</p>
Simulations of urban heat island effect in Paris Region during various types of heatwaves, and in different adaptation scenarios
<p><strong>Content</strong><br> - These data present air temperature, in the shade, 2m above grounds in Paris Region (projection: RGF93/Lambert 93, EPSG:2154) at different times of the day, for various heat waves conditions, and in different prospective scenarios for the built-up evolution and adaptation actions implementations.<br> - more information can be found here : https://www.umr-cnrm.fr/ville.climat/spip.php?rubrique45</p> <p><strong>Classification of the data</strong><br> - the first 5 letters (e.g. "CDFFA") present the prospective scenario<br> - the 4 following letters (e.g. "HW34") present the type of heat wave<br> - the following 2 letters (e.g. "D8") present the length of the heat wave (number of days after the beginning of the heat wave)<br> - the final letters (e.g. H15) represent the time (UTC : one hour should be added for French time) of the day</p> <p><strong>Prospective scenarios</strong><br> - the first letter is always C<br> - the second letter represents the expansion scenario. They are presented here : Lemonsu, A., Viguié, V., Daniel, M., Masson, V., 2015. Vulnerability to heat waves: Impact of urban expansion scenarios on urban heat island and heat stress in Paris (France). Urban Climate 14, 586–605.<br> - D stands for "dense development"<br> - F for business as usual scenario ("fil de l'eau" in French)<br> - V for a scenario with 10% more parks<br> - the third letter represents the building evolution scenario<br> - F stands for business as usual scenario<br> - V for a scenario with more insulation and reflective roofs<br> - the third letter represents AC use<br> - F stands for strong AC use<br> - M for moderate AC use<br> - N for no AC use<br> - the fourth letter represents vegetation watering<br> - N stands for no watering<br> - A for watering</p> <p><strong>Heat waves</strong><br> - the figure (e.g. "34" in "HW34") represents the intensity class, in °C of the heat wave. It is more precisely the maximum daily temperature observed without the impact of the urban heat island effect. (Tmax=34, 38, 42, or 46°C).</p> <p><strong>Other information</strong><br> - see the file "aggregated data.xls" for more information and data about energy consumption for AC, and averages of temperatures in the city over the entire day.</p> <p> </p>
Data Storage Report. RODBreak - Wave run-up, overtopping and damage in rubble-mound breakwaters under oblique extreme wave conditions due to climate change scenarios
<p>Wave breaking / run-up / overtopping and their impact on the stability of rubble-mound breakwaters (both at trunk and roundhead) are not adequately characterized yet for climate change scenarios. The same happens with the influence of high-incidence angles on such phenomena.</p> <p>To study these phenomena a stretch of a rubble-mound breakwater (head and part of the adjoining trunk, with a slope of 1(V):2(H)) was built in the wave basin of the LUH, The trunk of the breakwater was 7.5 m long and the head had the same cross section as the exposed part of breakwater. The model was 9.0 m long, 0.82 m high and 3.0 m wide. The angle between the longitudinal axis of the breakwater and the tank wall was 70º. Two types of armour elements (rock and Antifer cubes) were tested.</p> <p>60 tests were carried out in this experiment to assess, under extreme wave conditions (wave steepness of 0.055) with different incidence wave angles (from 40º to 90º), the structure behaviour in what concerns wave run-up, wave overtopping and damage progression of the armour layer.</p> <p>The report describes the data collected in those tests as well as how such data is stored.</p>
Supporting data for "Arctic sea ice response to flooding of the snow layer in future warming scenarios"
<p>Supporting data for "Arctic sea ice response to flooding of the snow layer in future warming scenarios" submitted to Earth's Future in April 2021</p> <p>Contains model output from both the Icepack and CCSM4 experiments from the paper. File descriptions for the Icepack and CCSM4 data are contained in the files README_icepack and README_CCSM respectively.</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.