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942 results for “scenario”
Data From: Conservation planning in an uncertain climate: identifying projects that remain valuable and feasible across future scenarios
<p>Conservation actors face the challenge of allocating limited resources despite uncertainty about future climate. A key goal is to minimize the potential for negative outcomes under future scenarios. Thus, we address a global conservation challenge: how to allocate conservation investments given high uncertainty about future climate conditions. To that end, we present a method for identifying projects that remain valuable and feasible across climate scenarios and apply our framework to freshwater biodiversity conservation in the South-Central USA. We combine data from a recent high-resolution hydrologic planning tool and species distribution models to estimate the conservation feasibility and biodiversity value of river reaches below 38 major reservoirs in the Red River basin.We find that only 13% of sites have high conservation priority across all future climate scenarios and that spatial patterns of conservation priority largely reflect patterns of water availability and fish biodiversity.</p>
Realistic LIGO/Virgo/KAGRA observing scenarios based on O3 public alerts
<p>Efforts to search for electromagnetic counterparts of gravitational-wave sources have intensified dramatically since the 2017 discovery a binary neutron star merger with an associated gamma-ray burst, optical/infrared kilonova, and panchromatic afterglow. Now, one LIGO/Virgo observing run later, there has not yet been a second secure identification of electromagnetic counterpart. This is not unexpected, and can be mostly explained by the localization uncertainty of events from LIGO and Virgo’s most recent, third observing run (“O3”). The official LIGO/Virgo observing scenarios fail to account for improvements in data analysis that allow LIGO/Virgo to detect fainter and hence worse-localized gravitational- wave sources, which increases the number of detections while decreasing the proportion of well-localized “gold-plated” events. Realistic forecasting of gravitational-wave localization performance is paramount because electromagnetic counterpart searches require large commitments of telescope time. We present simulations of the next several LIGO/Virgo/KAGRA observing runs that are based on the statistics of O3 public alerts.</p>
Data set with reference scenarios
<p>Data set with reference scenarios. As it is not possible to include the entire dataset in this report, we only include two Tables on final energy demand. Table 1 shows the Final Energy Demand projections per industrial subsector and EU28 country in the Reference Scenario and Table 2 the Final Energy Demand projections per industrial subsector and EU28 country in the Frozen Efficiency Scenario. The full dataset, including physical production (in ktonnes) and fuel and electricity demand (in TJ) per industrial sub-sector, per fuel type and per EU 28 country is available upon request to the project coordinator.</p>
Data set on Energy Efficiency potentials on top of reference scenarios
<p>As it is not possible to include the entire dataset in this report, we only include the Energy Efficiency potentials for Austria. The full dataset, showing the savings potentials for all EU27 (and UK) countries, is available upon request to the project coordinator.</p>
Real_scenario_ShallowGeothermalSystems_Locarno
<p>Hydro/thermogeological model of Locarno city, southern Switzerland. 10 years simulation of the operation of installed shallow geothermal systems, both closed-loop and open-loop ones with a realistic scenario</p>
Measurement data of the industrial IoT scenario
<p>In these measurements considered in this dataset, 15 measurement points are deployed in the scenario. They are sorted in two groups. Among line 1, all measurement points are LoS scenarios. Among line 2, measurement points 1, 2, 3, 4, 5,and 7 are LoS scenarios, and measurement points 6, 8, 9 are NLoS scenarios. Due to the limitation of the cable length, measurement data of line 2 locations 1-6 is collected at mmwave band. The heights of the Tx antenna and the Rx antenna are 2.05 m and 1.45 m, respectively. The bandwidth of the intermediate frequency filter of the adopted VNA is 2 kHz. Both the Tx and the Rx antennas are omnidirectional antennas. 200 snapshots are collect at each Rx location. In terms of the 3-4 GHz data, the bandwidth is 1 GHz. The number of frequency points swept in each snapshot is 501. In the aspect of 38-39 GHz and 39-40 GHz data, the center frequencies are 38.5 GHz and 39.5 GHz with 1 GHz bandwidth, respectively. The number of frequency points swept in each snapshot is also 501.</p>
Data from: Landscape genetic inferences vary with sampling scenario for a pond breeding amphibian
A critical decision in landscape genetic studies is whether to use individuals or populations as the sampling unit. This decision affects the time and cost of sampling and may affect ecological inference. We analyzed 334 Columbia spotted frogs at 8 microsatellite loci across 40 sites in northern Idaho to determine how inferences from landscape genetic analyses would vary with sampling design. At all sites, we compared a proportion available sampling scheme (PASS), in which all samples were used, to resampled datasets of 2-11 individuals. Additionally, we compared a population sampling scheme (PSS) to an individual sampling scheme (ISS) at 18 sites with sufficient sample size. We applied an information theoretic approach with both restricted maximum likelihood and maximum likelihood estimation to evaluate competing landscape resistance hypotheses. We found that PSS supported a low-density forest model (0.87) and ISS supported this model as well as additional models when testing hypotheses of landcover types that create the greatest resistance to gene flow for Columbia spotted frogs. Increased sampling density and study extent, seen by comparing PSS to PASS, showed a change in model support from a model of only low-density forest to a model of only high-density forest. As number of individuals increased, model support converged at 7 individuals for ISS to PSS. ISS may be useful to increase study extent and sampling density, but may lack power to provide strong support for the correct model with microsatellite datasets. Our results highlight the importance of additional research on sampling design effects on landscape genetics inference.
Heat Stress Exposure Maps - Urban Planning Scenario 2026 - 2045: Berlin, Germany
<p><strong>Berlin heat stress exposure map: average number of heatwave days per year versus socio economic data - urban planning scenario (2026-2045).</strong></p> <p>Heat stress exposure maps for Berlin representing the average number of heatwave days per year versus socio economic data per statistical unit. The average number of heatwave days per year has been modeled over the reference period 2026-2045 using the present land use / cover situation for the city but combined with urban planning projects information until 2030. Hence, the urban morphology has been updated accordingly.</p> <p><strong>Scenario: </strong>Urban planning scenario (situation LULC today + integrated urban planning projects 2030)</p> <p><strong>Exposure mapping variable: </strong><br /> Total population 2030<br /> Population density inhabitants per hectare 2030</p>
Heat Stress Maps - Urban Planning Scenarios 1986-2005 / 2026 - 2045: Berlin, Germany
<p>Heat stress maps for Berlin representing<br /> * the average number of heatwave days<br /> (1986 – 2005 | 2026 – 2045 | 2081 – 2100)<br /> per statistical unit or per grid</p> <p>The heat stress parameter considered has been modelled over the reference period 2026 - 2045 using the present land use / cover situation for the city but combined urban planning projects information until 2030. Hence, the urban morphology has been updated accordingly.</p> <p>Scenario: Urban Planning</p>
Heat Stress Exposure Maps - Base Scenario 1986 - 2005: Almada, Portugal
<p><strong>Average number of heatwave days per year versus socio economic data - base scenario</strong></p> <p>Heat stress exposure maps for the city of Almada representing the average number of heatwave days per year versus socio economic data per statistical unit. The average number of heatwave days per year has been modelled over the reference period 1986-2005 using the present land use / cover situation for the city.</p> <p><strong>Exposure mapping variable include the following: </strong><br /> Total population 2011<br /> Population density inhabitants per hectare 2011<br /> Number of inhabitants aged 0 to 19 years 2011<br /> Number of inhabitants aged 20 to 65 years 2011<br /> Number of inhabitants aged +65 years 2011<br /> Number of childcare centres 2014<br /> Number of hospitals 2014<br /> Number of schools 2014<br /> Number of schools and universities 2014<br /> Number of universities 2014<br /> Number of resthomes 2014</p>
Heat Stress Maps - Urban Planning Scenarios 1986-2005 / 2026 - 2045: Antwerp, Belgium
<p>Heat stress maps for Antwerp representing<br /> * the average number of heatwave days<br /> (1986 – 2005 | 2026 – 2045 | 2081 – 2100)<br /> per statistical unit or per grid</p> <p>The heat stress parameter considered has been modelled over the reference period 2026 - 2045 using the present land use / cover situation for the city but combined urban planning projects information until 2030. Hence, the urban morphology has been updated accordingly.</p>
Heat Stress Maps - Urban Planning Scenarios 1986-2005 / 2026 - 2045: Almada, Portugal
<p>Heat stress maps for the city of Almada representing<br /> * the average number of heatwave days<br /> (1986 – 2005 | 2026 – 2045 | 2081 – 2100)<br /> per statistical unit or per grid</p> <p>The heat stress parameter considered has been modelled over the reference period 2026 - 2045 using the present land use / cover situation for the city but combined urban planning projects information until 2030. Hence, the urban morphology has been updated accordingly.</p>
Heat Stress Exposure Maps - Urban Planning Scenario 2026 - 2045: Antwerp, Belgium
<p><strong>Antwerp heat stress exposure map: average number of heatwave days per year versus socio economic data - urban planning scenario (2026-2045)</strong></p> <p>Heat stress exposure maps for Antwerp representing the average number of heatwave days per year versus socio economic data per statistical unit. The average number of heatwave days per year has been modeled over the reference period 2026-2045 using the present land use / cover situation for the city but combined with urban planning projects information until 2030. Hence, the urban morphology has been updated accordingly.</p> <p><strong>Exposure mapping variable include:</strong><br /> * Total population 2030<br /> * Population density inhabitants per hectare 2030</p>
Heat Stress Exposure Maps - Urban Planning Scenario 2026 - 2045: Almada, Portugal
<p><strong>Almada heat stress exposure map: average number of heatwave days per year versus socio economic data - urban planning scenario (2026-2045)</strong></p> <p>Heat stress exposure maps for the city of Almada representing the average number of heatwave days per year versus socio economic data per statistical unit. The average number of heatwave days per year has been modeled over the reference period 2026-2045 using the present land use / cover situation for the city but combined with urban planning projects information until 2030. Hence, the urban morphology has been updated accordingly.</p> <p>Exposure mapping variable include:<br /> * Total population 2011<br /> * Population density inhabitants per hectare 2011</p>
Heat Stress Maps - Base Scenarios 1986-2005 / 2026 - 2045 / 2081 - 2100: Almada, Portugal
<p>Heat stress maps for the city of Almada representing<br /> * the average number of heatwave days<br /> (1986 – 2005 | 2026 – 2045 | 2081 – 2100)<br /> per statistical unit or per grid</p> <p>* The Urban Heat Island effect at 11pm per year<br /> (1986 - 2005) per statistical unit or per grid</p> <p>The heat stress parameter considered has been modelled over the reference period using the present land use / cover situation for the city.</p> <p>Please note that only the base scenario 1986-2005 has got maps with the 2 heat stress parameters:<br /> * Average number of heat wave days per year<br /> * Urban Heat Island effect at 11pm per year</p>
Heat Stress Maps - Base Scenarios 1986-2005 / 2026 - 2045 / 2081 - 2100: Berlin, Germany
<p>Heat stress maps for Berlin representing<br /> * the average number of heatwave days<br /> (1986 – 2005 | 2026 – 2045 | 2081 – 2100)<br /> per statistical unit or per grid</p> <p>* The Urban Heat Island effect at 11pm per year<br /> (1986 - 2005) per statistical unit or per grid</p> <p>The heat stress parameter considered has been modelled over the reference period using the present land use / cover situation for the city.</p> <p>Please note that only the base scenario 1986-2005 has got maps with the 2 heat stress parameters:<br /> * Average number of heat wave days per year<br /> * Urban Heat Island effect at 11pm per year</p> <p>Scenario: Base scenario (situation LULC today)</p>
Heat Stress Maps - Base Scenarios 1986-2005 / 2026 - 2045 / 2081 - 2100: Antwerp, Belgium
<p>Heat stress maps for Antwerp representing<br /> * the average number of heatwave days<br /> (1986 – 2005 | 2026 – 2045 | 2081 – 2100)<br /> per statistical unit or per grid</p> <p>* The Urban Heat Island effect at 11pm per year<br /> (1986 - 2005) per statistical unit or per grid</p> <p>The heat stress parameter considered has been modelled over the reference period using the present land use / cover situation for the city.</p> <p>Please note that only the base scenario 1986-2005 has got maps with the 2 heat stress parameters:<br /> * Average number of heat wave days per year<br /> * Urban Heat Island effect at 11pm per year</p>
Heat Stress Exposure Maps - Base Scenario 1986 - 2005: Antwerp, Belgium
<p>Average number of heatwave days per year versus socio economic data - base scenario (1986-2005)</p> <p>Heat stress exposure maps for the city of Antwerp representing the average number of heatwave days per year versus socio economic data per statistical unit. The average number of heatwave days per year has been modelled over the reference period 1986-2005 using the present land use / cover situation for the city.</p> <p><strong>Exposure mapping variable include the following: </strong></p> <p>Total population 2014</p> <p>Population density inhabitants per hectare 2014</p> <p>Number of inhabitants aged 0 to 4 years 2014</p> <p>Number of inhabitants aged 0 to 17 years 2014</p> <p>Number of inhabitants aged 18 to 65 years 2014</p> <p>Number of inhabitants aged +65 years 2014</p> <p>Number of schools 2014</p> <p>Number of childcare centers 2014</p> <p>Number of hospitals 2014</p> <p>Number of elderly stay facilities 2014</p>
Heat Stress Exposure Maps - Base Scenario 1986 - 2005: Berlin, Germany (Map-2 & Map-3)
<p>Heat Stress Exposure Maps - Base Scenario 1986 - 2005: Berlin, Germany</p> <p>Map-2 & Map-3 (zip.file) ref. to DOI: 10.5281/zenodo.45015</p>
Lulu - a software simulator for P colonies. Use case scenarios and demonstration videos
<p>The videos show three different examples of using the Lulu P colony simulator.</p> <p>The Lulu P colony simulator is available under an open-source MIT license at https://github.com/andrei91ro/lulu_pcol_sim. All of the secondary applications, including Lulu_Kilobot are available (also under open-source licenses) at https://github.com/andrei91ro.</p> <p>The first two videos present the simulator running addition (+1) and subtraction (-1). In these two examples, the simulator is ran in a step by step mode in order to clearly visualize the results of running each simulation step. For this reason the total simulation time reported at the end of the simulation is in the order of minutes.</p> <p>The average (of five runs) simulation time for a normal (non-interactive) simulation is 0.0021050 seconds for the addition and 0.0047492 seconds for the subtraction examples.</p> <p>The third example (lulu_kilobot_30_steps) presents the simulator running a more complex P colony that controls a Kilobot robot simulated in V-REP. This P colony is based on the subtraction P colony in the sense that each move the robot makes is marked by the removal of an f object from the environment.</p> <p>The input file used in the addition example (lulu_sim_ag_increment):</p> <p>pi = {<br /> A = {l_p};<br /> e = e;<br /> f = f;<br /> n = 2;<br /> env = {f, f, f, l_p};<br /> B = {AG_1};<br /> AG_1 = ({e, e}; < e->f, e<->l_p >, < l_p->e, f<->e >);<br /> }</p> <p>The input file used in the subtraction example (lulu_sim_ag_decrement):</p> <p>pi = {<br /> A = {l_m, l_p, l_z};<br /> e = e;<br /> f = f;<br /> n = 2;<br /> env = {f, f, f, l_m};<br /> B = {AG_1};<br /> AG_1 = ({e, e};<br /> < e->e, e<->l_m >,<br /> < l_m->l_p, e<->f/e<->e >,<br /> < f->e, l_p<->e >,<br /> < l_p->l_z, e<->e >,<br /> < e->e, l_z<->e > );<br /> }</p> <p>The input file used in the Kilobot example (lulu_kilobot_30_steps):</p> <p>pi = {<br /> A = {l_m, m_0, m_S, m_L, m_R, c_R, c_G, c_B};<br /> e = e;<br /> f = f;<br /> n = 2;<br /> env = {f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, l_m};<br /> B = {AG_command, AG_motion};<br /> AG_command = ({e, e};<br /> < e->e, e<->l_m >,<br /> < l_m->m_S, e<->f/e<->e >,<br /> < f->e, m_S<->e >,<br /> < m_S->m_0, e<->e >,<br /> < e->e, m_0<->e > );</p> <p> AG_motion = ({e, e};<br /> < e->l_m, e<->m_S >,<br /> < m_S->e, l_m<->e/e->e ><br /> < e->e, e<->m_0 >,<br /> < m_0->e, e<->m_0/e->e >);<br /> }</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.