Find research datasets worth reusing

opencc-by-4.0Feb 2023View details →

Open synthetic data on travel and charging demand of battery electric cars: An agent-based simulation on three charging behavior archetypes

Background Battery electric vehicles (BEVs) are crucial for a sustainable transportation system. As more people adopt BEVs, it becomes increasingly important to accurately assess the demand for charging infrastructure. However, much of the current research on charging infrastructure relies on outdated assumptions, such as the assumption that all BEV owners have access to home chargers and the "Liquid-fuel" mental model. To address this issue, we simulate the travel and charging demand on three charging behavior archetypes. We use a large synthetic population of Sweden, including detailed individual characteristics, such as dwelling types (detached house vs. apartment) and activity plans (for an average weekday). This data repository aims to provide the BEV simulation's input, assumptions, and output so that other studies can use them to study sizing and location design of charging infrastructure, grid impact, etc. A journal paper published in Transportation Research Part D: Transport and Environment details the method to create the data (particularly Section 2.2 BEV simulation). <a href="https://doi.org/10.1016/j.trd.2023.103645">https://doi.org/10.1016/j.trd.2023.103645</a> Methodology This data product is centered on the 1.7 million inhabitants of the Västra Götaland (VG) region, which includes the second largest city in Sweden, Gothenburg. We specifically simulated 284,000 car agents who live in VG, representing 35% of all car users and 18% of the total population in the region. They spend their simulation day (representing an average weekday) in a variety of locations throughout Sweden. This open data repository contains the core model inputs and outputs. The numbers in parentheses correspond to the data sets. We use individual agents' activity plans (1) and travel trajectories from MATSim simulation for the BEV simulation (2), in which we consider overnight charger access (3), car fleet composition referencing the current private car fleet in Sweden (4), and Swedish road network with slope information (5) with realistic BEV charging & discharging dynamics. For the BEV simulation, we tested ten scenarios of charging behavior archetypes and fast charging powers (6). The output includes the time history of travel trajectories and charging of the simulated BEVs across the different scenarios (7). Data description The current data product covers seven data files. (1) Agents' experienced activity plans File name: 1_activity_plans.csv <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> act_id </td> <td> Activity index of each agent </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> deso </td> <td> Zone code of Demographic statistical areas (DeSO)1 </td> <td> String </td> <td> - </td> </tr> <tr> <td> POINT_X </td> <td> Coordinate X of activity location (SWEREF99TM) </td> <td> Float </td> <td> meter </td> </tr> <tr> <td> POINT_Y </td> <td> Coordinate Y of activity location (SWEREF99TM) </td> <td> Float </td> <td> meter </td> </tr> <tr> <td> act_purpose </td> <td> Activity purpose (work, home, other) </td> <td> String </td> <td> - </td> </tr> <tr> <td> mode </td> <td> Transport mode to reach the activity location (car) </td> <td> String </td> <td> - </td> </tr> <tr> <td> dep_time </td> <td> Departure time in decimal hour (0-23.99) </td> <td> Float </td> <td> hour </td> </tr> <tr> <td> trav_time </td> <td> Travel time to reach the activity location </td> <td> String </td> <td> hour:minute:second </td> </tr> <tr> <td> trav_time_min </td> <td> Travel time in decimal minute </td> <td> Float </td> <td> minute </td> </tr> <tr> <td> speed </td> <td> Travel speed to reach the activity location </td> <td> Float </td> <td> km/h </td> </tr> <tr> <td> distance </td> <td> Travel distance between the origin and the destination </td> <td> Float </td> <td> km </td> </tr> <tr> <td> act_start </td> <td> Start time of activity in minute (0-1439) </td> <td> Integer </td> <td> minute </td> </tr> <tr> <td> act_time </td> <td> Activity duration in decimal minute </td> <td> Float </td> <td> minute </td> </tr> <tr> <td> act_end </td> <td> End time of activity in decimal hour (0-23.99) </td> <td> Float </td> <td> hour </td> </tr> <tr> <td> score </td> <td> Utility score of the simulation day given by MATSim </td> <td> Float </td> <td> - </td> </tr> </tbody> </table> 1 <a href="https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/">https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/</a>   (2) Travel trajectories File name: 2_input_zip Produced by MATSim simulation, the zip folder contains ten files (events_batch_X.csv.gz, X=1, 2, …, 10) of input events for the BEV simulation. They are the moving trajectories of the car agents in their simulation days. <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> time </td> <td> Time in second in a simulation day (0-86399) </td> <td> Integer </td> <td> Second </td> </tr> <tr> <td> type </td> <td> Event type defined by MATSim simulation2 </td> <td> String </td> <td> - </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> link </td> <td> Nearest road link consistent with (5) </td> <td> String </td> <td> - </td> </tr> <tr> <td> vehicle </td> <td> Vehicle ID identical to person </td> <td> Integer </td> <td> - </td> </tr> </tbody> </table> 2 One typical episode of MATSim simulation events: Activity ends (actend) -> Agent’s vehicle enters traffic (vehicle enters traffic) -> Agent’s vehicle moves from previous road segment to its next connected one (left link) -> Agent’s vehicle leaves traffic for activity (vehicle leaves traffic) -> Activity starts (actstart)   (3) Overnight charger access File name: 3_home_charger_access.csv <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> home_charger </td> <td> Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes) </td> <td> Integer </td> <td> - </td> </tr> </tbody> </table>   (4) Car fleet composition File name: 4_car_fleet.csv <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> income_class </td> <td> Income group (0=None, 1=below 180K, 2=180K-300K, 3=300K-420K, 4=above 420K) </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> car </td> <td> Car model class (B=40 kWh, C=60 kWh, D=100 kWh) </td> <td> String </td> <td> - </td> </tr> </tbody> </table>   (5) Road network with slope information File name: 5_road_network_with_slope.shp (5 files in total) <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> length </td> <td> The length of road link </td> <td> Float </td> <td> meter </td> </tr> <tr> <td> freespeed </td> <td> Free speed </td> <td> Float </td> <td> km/h </td> </tr> <tr> <td> capacity </td> <td> Number of vehicles </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> permlanes </td> <td> Number of lanes </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> oneway </td> <td> Whether the segment is one-way (0=no, 1=yes) </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> modes </td> <td> Transport mode (car) </td> <td> String </td> <td> - </td> </tr> <tr> <td> link_id </td> <td> Link ID </td> <td> String </td> <td> - </td> </tr> <tr> <td> from_node </td> <td> Start node of the link </td> <td> String </td> <td> - </td> </tr> <tr> <td> to_node </td> <td> End node of the link </td> <td> String </td> <td> - </td> </tr> <tr> <td> count </td> <td> Aggregated traffic (number of cars travelled per day) </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> slope </td> <td> Slope in percent from -6% to 6% </td> <td> Float </td> <td> - </td> </tr> <tr> <td> geometry </td> <td> LINESTRING (SWEREF99TM) </td> <td> geometry </td> <td> meter </td> </tr> </tbody> </table>   (6) Simulation scenarios specifying the parameter sets File name: 6_scenarios.txt <table> <tbody> <tr> <td> Parameter set (paraset) </td> <td> Strategy 1 </td> <td> Strategy 2 </td> <td> Strategy 3 </td> <td> Fast charging power (kW) </td> <td> Minimum parking time for charging (min) </td> <td> Intermediate charging power (kW) </td> </tr> <tr> <td> 0 </td> <td> 0.2 </td> <td> 0.2 </td> <td> 0.9 </td> <td> 150 </td> <td> 5 </td> <td> 22 </td> </tr> <tr> <td> 1 </td> <td> 0.2 </td> <td> 0.2 </td> <td> 0.9 </td> <td> 50 </td> <td> 5 </td> <td> 22 </td> </tr> <tr> <td> 2 </td> <td> 0.3 </td> <td> 0.3 </td> <td> 0.9 </td> <td> 150 </td> <td> 5 </td> <td> 22 </td> </tr> <tr> <td> 3 </td> <td> 0.3 </td> <td> 0.3 </td> <td> 0.9 </td> <td> 50 </td> <td> 5 </td> <td> 22 </td> </tr> </tbody> </table>   (7) Time history of travel trajectories and charging of the simulated BEVs File name: 7_output.zip Produced by the BEV simulation, the zip folder contains four files (parasetX.csv.gz, X=1, 2, 3, 4) corresponding to the four parameter sets specified in (6). They are the moving trajectories of the car agents with simulated energy and charging time history in their simulation days. <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> home_charger </td> <td> Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes) </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> car </td> <td> Car model class (B=40 kWh, C=60 kWh, D=100 kWh) </td> <td> String </td> <td> - </td> </tr> <tr> <td> seq </td> <td> Sequence ID of time history by agent </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> time </td> <td> Time (0-86399) </td> <td> Integer </td> <td> Second </td> </tr> <tr> <td> purpose </td> <td> Valid for activities (home, work, school, other) </td> <td> String </td> <td> - </td> </tr> <tr> <td> type </td> <td> Event type defined by MATSim simulation </td> <td> String </td> <td> - </td> </tr> <tr> <td> link </td> <td> Link ID (link_id in File 5) </td> <td> String </td> <td> - </td> </tr> <tr> <td> distance_driven </td> <td> Cumulative driven distance in the simulation day </td> <td> Float </td> <td> km </td> </tr> <tr> <td> energy_1 </td> <td> Energy consumed while driving (-) or charging (+) (Strategy 1) </td> <td> Float </td> <td> kWh </td> </tr> <tr> <td> energy_2 </td> <td> Energy consumed while driving (-) or charging (+) (Strategy 2) </td> <td> Float </td> <td> kWh </td> </tr> <tr> <td> energy_3 </td> <td> Energy consumed while driving (-) or charging (+) (Strategy 3) </td> <td> Float </td> <td> kWh </td> </tr> <tr> <td> charger_1 </td> <td> Power rating of the charger (Strategy 1) </td> <td> Float </td> <td> kW </td> </tr> <tr> <td> charger_2 </td> <td> Power rating of the charger (Strategy 2) </td> <td> Float </td> <td> kW </td> </tr> <tr> <td> charger_3 </td> <td> Power rating of the charger (Strategy 3) </td> <td> Float </td> <td> kW </td> </tr> <tr> <td> soc_1 </td> <td> State of charge (0-1, Strategy 1) </td> <td> Float </td> <td> - </td> </tr> <tr> <td> soc_2 </td> <td> State of charge (0-1, Strategy 2) </td> <td> Float </td> <td> - </td> </tr> <tr> <td> soc_3 </td> <td> State of charge (0-1, Strategy 3) </td> <td> Float </td> <td> - </td> </tr> </tbody> </table>

opencc-by-4.0Jun 2020View details →

Heterogeneous Habenular Neuronal Ensembles during Selection of Defensive Behaviors

Optimal selection of threat-driven defensive behaviors is paramount to an animal's survival. The lateral habenula (LHb) is a key neuronal hub coordinating behavioral responses to aversive stimuli. Yet, how individual LHb neurons represent defensive behaviors in response to threats remains unknown. Here, we show that in mice, a visual threat promotes distinct defensive behaviors, namely runaway (escape) and action-locking (immobile-like). Fiber photometry of bulk LHb neuronal activity in behaving animals reveals an increase and a decrease in calcium signal time-locked with runaway and action-locking, respectively. Imaging single-cell calcium dynamics across distinct threat-driven behaviors identify independently active LHb neuronal clusters. These clusters participate during specific time epochs of defensive behaviors. Decoding analysis of this neuronal activity reveals that some LHb clusters either predict the upcoming selection of the defensive action or represent the selected action. Thus, heterogeneous neuronal clusters in LHb predict or reflect the selection of distinct threat-driven defensive behaviors.

opencc-by-4.0Mar 2023View details →

Dataset for visualizing the atomic-scale origin of metallic behavior in Kondo insulators

This dataset contains the raw data files and analysis steps used to produce the figures in the manuscript "Visualizing the atomic-scale origin of metallic behavior in Kondo insulators" Science 379, 1214–1218 (2023)

opencc-by-4.0Apr 2023View details →

Dataset for the article "Development of an integrated socio-hydrological modeling framework for assessing the impacts of shelter location arrangement and human behaviors on flood evacuation processes"

This dataset include the data needed to create the socio-hydrological model to simulate human evacuation processes via a transportation network before a flood hits the residential area. Source code, in JAVA, for generating households in the agent-based model are also provided.

Heatwaves characterization derived from observations and climate projections to assess thermal behavior of 7 European city-hubs: Milano, Athens, Logroño, Cork, Gdynia, Lillestrøm and Amsterdam (1981-2100)

This dataset includes the processing results used to create the interactive climate service <a href="https://thermal-assessment.urban.tecnalia.dev/">Thermal Assessment Tool</a>. It provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions and cities in Europe during episodes of extreme heat. A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010. To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the observations the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-gridded-observations-europe?tab=overview">e-OBS</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (RCP4.5) and very high (RCP8.5) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS). The database is organized in three datasets: Regional_eobs_thresholds_Reachout.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are: <ul> <li>region: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li>tmax: daily maximum temperature threshold.</li> <li>tmin: daily minimum temperature threshold.</li> </ul> Historical_eobs_heatwaves_Reachout.csv: heatwaves of the historical period (1981-2021) for each region. The columns are: <ul> <li>region: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li>start: first date of the heatwave.</li> <li>tmax: maximum temperature reached during the heatwave.</li> <li>intensity: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li>duration: duration of the heatwave.</li> </ul> Future_and_baseline_eobs_heatwaves_Reachout.csv: ensemble future projections of heatwaves. The columns are: <ul> <li>hazard_level: it can be a warning, an alert or an alarm.</li> <li>region: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li>experiment: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li>period: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.</li> <li>decade_frequency: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li>decade_frequency_best: only applicable to the future. It determines the best projection among the models.</li> <li>decade_frequency_worst: only applicable to the future. It determines the worst projection among the models.</li> <li>year_days: average annual days.</li> <li>year_tmax_intensity: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li>year_tmin_intensity: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul>

Heatwaves characterization derived from reanalysis and climate projections to assess thermal behavior of regions in Europe (1981-2100)

This dataset provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions in Europe during episodes of extreme heat. A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010. To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the reanalysis the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview">ERA5-Land</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (RCP4.5) and very high (RCP8.5) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS). The database is organized in three datasets: Regional_era5land_thresholds_Europe.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are: <ul> <li>region: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li>tmax: daily maximum temperature threshold.</li> <li>tmin: daily minimum temperature threshold.</li> </ul> Historical_era5land_heatwaves_Europe.csv: heatwaves of the historical period (1981-2021) for each region. The columns are: <ul> <li>region: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li>start: first date of the heatwave.</li> <li>tmax: maximum temperature reached during the heatwave.</li> <li>intensity: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li>duration: duration of the heatwave.</li> </ul> Future_and_baseline_era5land_heatwaves_Europe.csv: ensemble future projections of heatwaves. The columns are: <ul> <li>hazard_level: it can be a warning, an alert or an alarm.</li> <li>region: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li>experiment: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li>period: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.</li> <li>decade_frequency: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li>decade_frequency_best: only applicable to the future. It determines the best projection among the models.</li> <li>decade_frequency_worst: only applicable to the future. It determines the worst projection among the models.</li> <li>year_days: average annual days.</li> <li>year_tmax_intensity: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li>year_tmin_intensity: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul>

Heatwaves characterization derived from observations and climate projections to assess thermal behavior of regions in Europe (1981-2100)

This dataset provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions in Europe during episodes of extreme heat. A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010. To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the observations the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-gridded-observations-europe?tab=overview">e-OBS</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (RCP4.5) and very high (RCP8.5) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS). The database is organized in three datasets: Regional_eobs_thresholds_Europe.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are: <ul> <li>region: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li>tmax: daily maximum temperature threshold.</li> <li>tmin: daily minimum temperature threshold.</li> </ul> Historical_eobs_heatwaves_Europe.csv: heatwaves of the historical period (1981-2021) for each region. The columns are: <ul> <li>region: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li>start: first date of the heatwave.</li> <li>tmax: maximum temperature reached during the heatwave.</li> <li>intensity: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li>duration: duration of the heatwave.</li> </ul> Future_and_baseline_eobs_heatwaves_Europe.csv: ensemble future projections of heatwaves. The columns are: <ul> <li>hazard_level: it can be a warning, an alert or an alarm.</li> <li>region: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li>experiment: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li>period: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.</li> <li>decade_frequency: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li>decade_frequency_best: only applicable to the future. It determines the best projection among the models.</li> <li>decade_frequency_worst: only applicable to the future. It determines the worst projection among the models.</li> <li>year_days: average annual days.</li> <li>year_tmax_intensity: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li>year_tmin_intensity: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul>

Heatwaves characterization derived from reanalysis and climate projections to assess thermal behavior of 7 European city-hubs: Milano, Athens, Logroño, Cork, Gdynia, Lillestrøm and Amsterdam (1981-2100)

This dataset includes the processing results used to create the interactive climate service <a href="https://thermal-assessment.urban.tecnalia.dev/">Thermal Assessment Tool</a>. It provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions and cities in Europe during episodes of extreme heat. A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010. To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the reanalysis the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview">ERA5-Land</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (RCP4.5) and very high (RCP8.5) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS). The database is organized in three datasets: Regional_era5land_thresholds_Reachout.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are: <ul> <li>region: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li>tmax: daily maximum temperature threshold.</li> <li>tmin: daily minimum temperature threshold.</li> </ul> Historical_era5land_heatwaves_Reachout.csv: heatwaves of the historical period (1981-2021) for each region. The columns are: <ul> <li>region: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li>start: first date of the heatwave.</li> <li>tmax: maximum temperature reached during the heatwave.</li> <li>intensity: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li>duration: duration of the heatwave.</li> </ul> Future_and_baseline_era5land_heatwaves_Reachout.csv: ensemble future projections of heatwaves. The columns are: <ul> <li>hazard_level: it can be a warning, an alert or an alarm.</li> <li>region: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li>experiment: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li>period: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.</li> <li>decade_frequency: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li>decade_frequency_best: only applicable to the future. It determines the best projection among the models.</li> <li>decade_frequency_worst: only applicable to the future. It determines the worst projection among the models.</li> <li>year_days: average annual days.</li> <li>year_tmax_intensity: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li>year_tmin_intensity: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul>

opencc-by-4.0Jun 2023View details →

Dataset for the publication: Elucidating the influence of intercalated anions in NiFe LDH on the electrocatalytic behavior of OER: a kinetic study

This contribution comprehends the dataset derived as basis of the publication on alkaline electrochemical oxygen evolution reaction using nickel iron layered double hydroxides (NiFe LDH) with different intercalated anions. In this related study, the dataset was used to analyse the impact of different intercalated anions on the governing reaction mechanism and rate determining step in the electrocatalytic surface mechanism. The dataset comprehends analysis of the three materals: (1) charging current density versus scan rate, (2) X-ray diffraction patterns, (3) polarization curves, (4) steady-state derived Tafel plots, (5) reaction order analysis, (6) impedance spectroscopy, (7) influence of stirring on polarization curves, (8) not iR corrected values for the activity curves, (9) EIS data and (10) Tafel plots obtained through three different methods.

opencc-by-4.0Jun 2023View details →

Data of Chinese treatment group for the research work "Disentangling material, social, and cognitive determinants of human behavior and belief".

This repository contains data files of Chinese treatment group for the research work "Disentangling material, social, and cognitive determinants of human behavior and belief".

opencc-by-4.0Jun 2023View details →

Prenatal diet programs the transgenerational inheritance of brain macro and microstructure defects, coding for anxiety-like behavior in male rats.

T1-w 3DFLASH Preprocessed MRI images used for wistar rat used for the deformation-based morphological (DBM) model are released. Nifti files are duplicated. DBM used only mnc format.

opencc-by-4.0Jul 2023View details →

The effect of normal stress oscillations on fault slip behavior near the stability transition from stable to unstable motion

Tectonic fault zones are subject to normal stress variations with a wide range of spatio-temporal scales. Stress perturbations cover a wide range of frequencies and amplitudes from high frequency seismic waves generated by earthquakes to low frequency transients associated with solid Earth tides. These perturbations can reactivate critically stressed faults and trigger earthquakes. Here, we describe lab experiments to illuminate the physics of such changes in friction and the mechanics of earthquake triggering and fault reactivation. Friction tests were done in a double direct shear configuration for conditions near the stability transition from stable to unstable motion. We studied simulated fault gouge composed of quartz powder and conducted experiments at reference normal stress from 10 to 13.5 MPa. After shearing to steady state sliding, we applied sinusoidal normal stress oscillations of amplitude 0.5 to 2 MPa, and period of 0.5 to 50 s. We performed numerical simulations using measured values of rate/state friction (RSF) parameters to assess our data. Our results show that low frequency stress oscillations cause a Coulomb-like response of shear strength that transitions from stable slip to slow lab earthquakes as frequency increases. At the critical frequency predicted by RSF we observe periodic stick-slip behavior. Perturbations of high amplitude and short period weaken the fault, while lower amplitudes strengthen the fault. We find that a modified RSF formulation is able to accurately match our laboratory data. Our findings highlight the complex effects of stress perturbations for fault strength and the mode of fault slip. The data are uploaded are structured as follow: 1) For each experiment a .txt file of the datafile that is recorded from the machine (raw data) and a binary file containing the elaborated data (data_rp). The experiments information are listed in experiment_info.txt 2) The folder <a href="https://zenodo.org/api/files/89fe30fb-a2cb-4fcb-b9df-db80583fc652/codes_results.zip">codes_results.zip</a> contain the codes of the data analysis and the related results  The data are analyzed using rawPy that can be found at <a href="https://github.com/marcoscuderi/rawPy">https://github.com/marcoscuderi/rawPy</a> For any additional information please do not hesitate to contact the corresponding author Federico Pignalberi at federico.pignalberi@uniroma1.it

openCC (other)Jan 2022View details →

Spatial and Social Behavior of Acanthurus triostegus on Moorea (French Polynesia) and Palmyra Atoll (USA), 2017-2018

These data describe the schooling behavior of coral reef fish on Moorea (French Polynesia) and Palmyra Atoll (USA) in 2017 and 2018. Data are grouped into two sets of observations: 1) surveys measuring the abundance of reef fish and the proportion of those fish occurring in schools, and 2) behavioral observations including time spent grazing and GPS tracks of schooling and solitary Acanthurus triostegus.

openCC (other)Feb 2022View details →

Atlantic sand fiddler differences in antimony, running velocity, and behavior, Sapelo Island, Georgia: 2021

Running velocities and behavior assay data of 42 crabs (21 male and 21 female) from Sapelo Island, Georgia. Each crab had three run trials and four for the behavior assay. Crabs were run on a 1m track and behavior assays timed how long it took for crabs to reemerge from burrows. These data were used to determine major influences on crab behavior.

openCC (other)May 2024View details →

Are you scared yet? Variations to cue components elicits differential prey behavioral responses even when gape limited predators are relatively small.

Anti-predator behavior is often evoked based on measurements of risk calculated from sensory cues emanating from predators independent of physical attack. Yet, the exact sensory indices of cues used in risk assessment remain largely unknown. To examine how different predatory cue indices of information are used in risk assessment, we presented prey with various cues from sublethal gape-limited predators. Rusty crayfish (Faxonius rusticus (Girard, 1852)) were exposed to predatory odors from sublethal-sized largemouth bass (Micropterus salmoides (Lacepède, 1802)) to test effects of changing predator abundance, relative size relationships, and total predator length in flow through mesocosms. Foraging, shelter use, and movement behavior were used to measure cue effects. Foraging time depended jointly upon predator abundance and total predator size (p = 0.030). Specifically, high predator abundance resulted in decreased foraging efforts as gape ratio increased. Similarly, sheltering time depended on the interaction between predator abundance and gape ratio when predator abundance was highest (p = 0.020). Crayfish significantly increased exploration time when gape ratio increased (p = 0.010). Thus, this study shows crayfish can use different indices of predatory cues, namely total predator abundance and relative size ratios, in risk assessment but do so in context-specific ways.

openCC (other)Jul 2024View details →

Detecting small environmental differences: Risk-response curves for predator-induced behavior and morphology. 2008.

Most organisms possess traits that are sensitive to changes in the environment (i.e. plastic traits) which results in the expression of environmentally-induced polymorphisms. While most phenotypically plastic traits have traditionally been treated as threshold switches between induced and uninduced states, there is growing evidence that many traits can respond in a continuous fashion. In this experiment we exposed larval anurans (wood frog tadpoles, Rana sylvatica) to an increasing gradient of predation risk to determine how organisms respond to small environmental changes. We manipulated predation risk in two ways: by altering the amount of prey consumed by a constant number of predators (Dytiscus sp.) and by altering the number of predators that consume a constant amount of prey. We then quantified the expression of predator-induced behavior, morphology, and mass to determine the level of risk that induced each trait, the level of risk that induced the maximal phenotypic response for each trait, whether the different traits exhibited a plateauing response, and whether increasing risk via increasing predator number or via increasing prey consumption induced similar phenotypic changes. We found that all of the traits exhibited fine-tuned, graded responses and most of them exhibited a plateauing response with increased predation risk, suggesting either a limit to plasticity or the reflection of high costs of the defensive phenotype. For many traits, a large proportion of the maximum induction occurred at low levels of risk, suggesting that the chemical cues of predation are effective at extremely low concentrations. In contrast to earlier work, we found that behavioral and morphological responses to increased predator number were simply a response to increased total prey consumption. These results have important implications for models of plasticity evolution, models of optimal phenotypic design, expectations for how organisms respond to fine-grained changes (i.e. wi

openCC (other)Jun 2024View details →

Relyea, R. A. 2001. Morphological and behavioral plasticity of larval anurans in response to different predators. Ecology 82:523-540.

Many organisms can adjust to a changing environment by developing alternative phenotypes that improve their fitness. Our understanding of phenotypic plasticity is largely based upon observations from single species responding to two different environments and measuring a single plastic trait. In this study, I examine predator-induced phenotypic plasticity in tadpoles by observing how six species of larval anurans respond to five different predator environments in 11 different traits (seven morphological traits, two behavioral traits, growth, and development). The results demonstrate that behavioral and morphological plasticity may be ubiquitous in larval anurans. The six prey species exhibited different responses to the same predator species, and each prey exhibited different responses to different predator species. This suggests that responses to a particular predator may not serve as general defense against all predators; rather, prey express predator-specific suites of responses. I also compared relative differences in plasticity among species and among traits. In contrast to earlier findings using only two predator environments, I found that different anurans possess similar degrees of plasticity for most of their traits when reared in a large number of environments. In addition, behavioral traits were always more plastic than morphological traits. Finally, I examined trait integration to address whether there were apparent trade-offs among traits and limits imposed by the abiotic environment. Trait integration, or the degree of correlated responses among traits across predator environments within a prey species, was very low. This further suggests that the suites of responses are predator specific and may be under independent directions of selection in different predator environments. Trait correlations across prey species indicated that there is an apparent trade-off between tail fin depth and body size. This relationship is supported by selection studies with

openCC (other)Jul 2024View details →

Twice weekly monitoring of a Microtus ochrogaster population and social behavior in alfalfa in eastern Illinois, 1982-1987.

These data were obtained as part of a field study on social behavior of the prairie vole, Microtus ochrogaster in alfalfa. The study was conducted in two adjacent 1-ha alfalfa (Medicago sativa) fields within the University of Illinois Biological Research Area (Phillips Tract). Social groups were monitored over a 63-month period, from March 1982 through July 1984 in field 1 and from October 1983 to May 1987 in field 2, by locating underground and surface nests and subsequent trapping, twice weekly. 4-5 live traps were set around the entrances to underground nests and runways leading to a surface nest of social groups. Each month the study sites were also trapped at a 10-m grid interval as a part of an ongoing 25-year trapping study (Getz, L.L. 2024. Environmental Data Initiative. https://doi.org/10.6073/pasta/2dae8f08578ce31e7817c92a0f6acc87).

openCC (other)Aug 2024View details →

Individual capture history affects site use and defensive behavior of foraging eastern copperheads at a recreational site in eastern Kentucky, 2022

This package contains behavioral, demographic, and environmental data from a study investigating the role individual capture history plays in shaping foraging and defensive behaviors of eastern copperheads (Agkistrodon contortrix) at a ~0.1 hectare recreational site in the Daniel Boone National Forest, Wolfe county, Kentucky. Behavioral data was collected using a four-stage trial simulating in-situ encounters between humans and vipers, where each stage is scored on a 0-3 scale according to the most extreme behavior exhibited. Each individual's total score was the sum of scores in Stages 1-4. Snakes were located via nightly visual surveys of the site during copperheads' active season. Each copperhead was caught after the conclusion of its' behavioral trial, and demographic information including sex, mass, snout-vent length, and total length were recorded. For snakes that had been detected and tagged at this site previous, PIT tag ID and number of years the individual was previously recaptured were also recorded. Air temperature, relative humidity, and soil temperature at a depth of 3 cm were recorded. Within our study system, result suggest that copperheads' defensive response to human approach is best explained by individual capture history, as opposed to temperature or body size.