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Data associated with the 2019 Freshwater Oil Spill Remediation Study (FOReSt) assessing the use of enhanced Monitored Natural Recovery (eMNR) and shoreline washing agent (SWA) of diluted bitumen spills conducted in shoreline enclosures at the IISD Experimental Lakes Area, ON, Canada from 2019 to 2020
The following package includes data from the 2019 Freshwater Oil spill Remediation Study (FOReSt) at the IISD Experimental Lakes Area studying the use of enhanced monitored natural recovery (eMNR) and shoreline washing agent (SWA) as a secondary remediation method for diluted bitumen spills in freshwater shoreline enclosures. This package includes data tables on polycyclic aromatic compound chemistry in water and sediments, basic water quality, nutrient chemistry, and tritium chemistry monitored in the experimental and reference enclosures, and lake reference sites over the duration of the study. Data included in this package was first collected and used in the paper by Palace et al., titled Polycyclic aromatic compounds in freshwater ecosystems following non-invasive remediation of controlled diluted bitumen spills: The Freshwater Oil Spill Remediation Study (FOReSt) at the Experimental Lakes Area, Canada.
Dataset for the fine-tuning of parameters and hyper parameters, and the evaluation of the Scorca agent
<p># Scorca Data<br>This repository contains data collected during multiple tests of the Scorca agent.</p> <p>This data was used to generate the various plots seen in the ICAART paper titled ***Knowledge Modelling, Strategy Designing, and Agent Engineering for Reconnaissance Blind Chess***, and to forge decisions for the agent, i.e. with which strategy to go and which hyperparameters to use. </p> <p>It also contains multiple scripts used to generate the plots in the paper.</p> <p>## Project overview</p> <p>The project is organized into several key directories, each containing specific components of the Scorca agent's testing and data analysis:</p> <p>- /experiments: Contains various experimental data and scripts.<br> - /entropy_comp: Data and scripts related to entropy computation experiments.<br> - /piece_states_removal: Information on experiments involving the removal of piece states.<br> - /sense_comp: Contains sub-directories for adapted entropy, likely senses, and opponent move weight analysis.<br>- /misc_src: Miscellaneous source files and scripts.<br> - /efficiency: Scripts and data related to the efficiency analysis of the agent.<br> - /enemy_bot_movement: Data on enemy bot movement patterns and strategies.<br> - /history: Historical data and analysis scripts.<br> - /naive_entropy: Scripts for naive entropy calculations.<br>- /sense_comp: Sense computation related files.<br> - /all_possible_states: Tracking and analysis of all possible board states.<br> - /likely_senses: Data on the most likely senses used in various game scenarios.<br> - /opp_move_weight: Analysis of opponent move weight in different contexts.</p> <p>For the agent source code, please refer to the Scorca GitHub repository: https://github.com/Robinbux/Scorca.</p> <p>- Contact Robin in case of any inquiry (rb.stoehr@gmail.com)<br>- The licence is CC-BY 4.0.</p>
OpenCitations Meta RDF dataset of agent roles metadata and its provenance information
<p>This dataset is a specialized subset of the OpenCitations Meta RDF data, focusing exclusively on data related to <strong>agent roles</strong> of bibliographic resources<strong> </strong>(<a href="http://purl.org/spar/pro/RoleInTime" target="_blank" rel="noopener">http://purl.org/spar/pro/RoleInTime</a>). These agents can be authors, editors, or publishers. It contains all the metadata and its provenance information, structured specifically around agent roles, in JSON-LD format.</p> <p>The inner folders are named through the <strong>supplier prefix</strong> of the contained entities. It is a prefix that allows you to recognize the entity membership index (e.g., OpenCitations Meta corresponds to <strong>06*0</strong>).</p> <p>After that, the folders have <strong>numeric names</strong>, which refer to the range of contained entities. For example, the 10000 folder contains entities from 1 to 10000. Inside, you can find the <strong>zipped </strong>RDF data.</p> <p>At the same level, additional folders containing the <strong>provenance </strong>are named with the same criteria already seen. Then, the 1000 folder includes the provenance of the entities from 1 to 1000. The provenance is located inside a folder called <strong>prov</strong>, also in zipped JSON-LD format.</p> <p>For example, data related to the entity is located in the folder /ar/06250/10000/1000/1000.zip, while information about provenance in /ar/06250/10000/1000/prov/se.zip</p> <p>Additional information about OpenCitations Meta at the <a href="https://opencitations.net/meta" target="_blank" rel="noopener">official webpage</a>.</p>
Data to the journal article "The capping agent is the key: Structural alterations of Ag NPs during CO2 electrolysis probed in a zero-gap gas-flow configuration"
<p>This data set corresponds to the journal article "The capping agent is the key: Structural alterations of Ag NPs during CO2 electrolysis probed in a zero-gap gas-flow configuration"</p>
Dataset of the paper entitled methods for high-throughput screening of novel agents against the maize pest, Diabrotica virgifera virgifera (Coleoptera: Chrysomelidae)
<p>Title: Methods for high-throughput screening of novel agents against the maize pest, Diabrotica virgifera virgifera (Coleoptera: Chrysomelidae) </p> <p>Authors: Sri Ita Tarigan, Gyorgy Turoczi, Jozsef Kiss, Stefan Toepfer</p> <p>Abstract: <br>The western corn rootworm, <em>Diabrotica virgifera virgifera</em> (Coleoptera: Chrysomelidae), poses a significant threat to maize crops in North America and Europe, necessitating development of novel, effective, and less disruptive crop protection agents. With recent bans on key insecticides and concerns about overuse of remaining options, there is an urgent need for accessible and comparable screening methods. We propose comparative high-throughput screening methods against the eggs, larvae and adults of this pest, emphasizing the importance of suitable positive controls tailored to the specific bioassay types. We evaluated seven common insecticides (imidacloprid, clothianidin, acetamiprid, novaluron, cypermethrin, chlorpyrifos-methyl, spinosad) against eggs, larvae, and adults as potential positive controls for each of the proposed assay methods. Dipping assays with ready-to-hatch eggs revealed several ingredients to cause mortality; but imidacloprid might be most suitable as a positive control due to its robust dose-response in reducing egg hatching and causing mortality of hatching neonates. Larval bioassays using artificial diet overlay assays revealed mortality caused by all insecticides, with imidacloprid and acetamiprid exhibiting best dose-mortality response curves as well as sublethal effects. Adult bioassays using artificial diet-core overlay assays revealed mortality caused by all insecticides, with cypermethrin or acetamiprid exhibiting best dose-mortality response curves. The provided ED <sub>50</sub>, ED <sub>80</sub> values, and dose-response equations offer valuable insight for researchers in selecting appropriate positive controls for screening new crop protection agents or assessing resistance levels against different life stages of this pest.</p> <p>Data:</p> <p>The data file is related to the screening of commercial insecticides against eggs, first instar larvae (L1) and adults of the maize pest, <em>Diabrotica virgifera virgifera</em> using standard bioassays. We are proposing comparative high-throughput screening methods against the eggs, larvae and adults of this maize pest. This includes the crucial aspect of suitable positive controls tailored to the specific bioassay type. We evaluated seven common insecticides (imidacloprid, clothianidin, acetamiprid, novaluron, cypermethrin, chlorpyrifos-methyl, spinosad) against eggs, larvae, and adults as potential positive controls for each of the proposed assay method. To access effects and dose-responses of commonly used insecticides on eggs, we applied standard screening methods under controlled semi-sterile conditions.</p> <p>For egg bioassays, eggs were transferred to the 200 ml of treatments in the eppendorf tubes and then soaked for 1 hour. Then 20µl with 10 to 20 eggs were pipetted onto a filter paper in a petri dish (150 mm×25 mm). Then 100 µl of sterilized tap water was added for moisture. The pipette tip was replaced between treatments. The eggs been transferred were counted per filter paper and dish (15± 8). The eggs were then incubated in the dishes at 23-25<sup>0</sup>C for 7 days, when the experiment was terminated. Egg hatching, mortality of newly hatching larvae, and days until start of egg hatching were observed under stereo microscope and recorded. Data were collected at 1,3, 5 and 7 days after treatments.</p> <p>To assess the effect and dose-responses of commonly used insecticides on neonates of <em>D. v. virgifera</em>, we applied artificial diet-overlay bioassays under controlled semi-sterile conditions. Each insecticide was prepared in at least six concentrations. Each bioassay consisted of 3 to 6 polystyrene plates of 96 wells each (07-6096 of Biologix Ltd., USA, or Costar 3917 of Corning Inc., USA). Each well had a volume of 330 µl, with a diameter of 5 mm, a height of 10 mm, and a surface area of 0.34 cm². 190 µl of the diet were pipetted into each 330 µl well, filling each to approximately 2/3<sup>rd </sup>of its capacity. Plates containing the diet were left to dry in a laminar flow cabinet for 45 minutes and then stored overnight at temperatures ranging from 3 to 5°C. The following day, treatments were applied. This is, 17 µl of a treatment was applied to the 0.34 cm<sup>2</sup> diet surface reaching good coverage and therefore forcing the after-placed larvae to feed through (10 to 100 µl pipette Biohit TM Proline). Each treatment was applied to 8 wells per plate. Following application, the plates were allowed to dry for a duration of 1 to 1.5 hours and were subsequently cooled for 1 hour in a refrigerator set at temperatures between 23 to 25°C. Each well received one neonate larva, carefully placed on the diet surface using a fine artist brush. A vigorous and visibly healthy larva was selected, lifted from the end of the abdomen with the brush, maneuvered towards a well surface, and allowed to crawl off the brush onto the diet. To avoid systematic errors, larvae were not arranged in treatment column order but rather in a rectangular pattern. After every 12 individual larvae, the brush was cleaned using 70% ethanol followed by sterile tap water. The filled plate was sealed with an optically clear adhesive qPCR seal sheet (#AB-1170, Termo Scientific, USA, or #BS3017000, Bioleader, USA), enabling data assessments without the need to open the plate. Four to five holes were carefully made with fine 00-insect pins into the seal per well to facilitate aeration. The plates, housing the larvae, were then incubated in a dark, ventilated incubator at a temperature of 23-25 °C and a relative humidity of 50 to 90% for a period of 5 days. We assessed mortality and stunting larvae within 3 and 5 days. </p> <p>To access the effect and dose-responses of common insecticides on <em>D.v.virgifera</em> adults, artificial diet-overlay bioassays with different doses were performed under controlled, semi-sterile conditions. Each insecticide was prepared in at least six concentrations. Active ingredients as specified on the product labels underwent serial dilutions using sterile tap water. Sterilized tap water was used as untreated control. In detail, each bioassay consisted of 6 polystyrene plates of 6 wells each (Eppendorf® 0030720016). Each treatment was applied to 3 wells of each plate per bioassay. The adult diet for a bioassay had been prepared 1-7days before treatment and adult infestation. The diet was prepared under semi-sterile conditions. The diet was poured out to 5-6 sterile 11 mm Petri dishes. The plates with diet were allowed to dry for up to 15 minutes under laminar flow cabinet then stored at 3 to 5°C overnight.The following day, a core of the diet was initially transferred to each well using flamed iron core-cutter (1 cm diameter) under a laminar flow. A core diet was placed each of the 6 wells of the plates. Approximately 40 µl of the treatments were then applied across the surface of diet core (0.34 cm<sup>3</sup>). The following day, a core of the diet was initially transferred to each well using flamed iron core-cutter (1 cm diameter) under a laminar flow. A core diet was placed each of the 6 wells of the plates. Approximately 40 µl of the treatments were then applied across the surface of diet core (0.34 cm<sup>3</sup>). Adult were subsequently transferred from the rearing cage into the wells of the 6-well plates containing the diet and treatments using a tube aspirator. For ease of transfer, the adults were cooled in a fridge for 4 to 7 minutes. Each well plate received 3 to 4 adults. Plates were sealed and incubated at 23-25<sup>0</sup>C, 50–90% r.h, L: D 12:12. Adult mortality were recorded on days 1,3, 5, 7 of experiment. </p> <p>To allow comparisons between experiments, data were standardized to the data of the corresponding negative control, usually sterilized tap water, as follows: standardized data = 100 × (data in negative control - data in treatment)/maximum (data in control or in treatment). The distributions of the data were investigated using histograms and QQ normal and detrended normal probability. Skewness and kurtosis of residuals was also observed for normality of influences of treatments on eggs, neonates, or adults. Equality of variances was assessed using Levene’s test. Multiple comparisons were performed using the Tukey HSD post hoc test for data with equal variances and the Games-Howell post hoc test for data with unequal variances. For each tested insecticide, linear and logarithmic regression models were fit to the dose-response data. In case of significant linear or logartimic relathionships, doses leading to 50% or 80% of relative effects (ED <sub>50,80</sub>) were calculated. </p> <p>The raw data as well as the standardised data are available as a csv file on zenodo. </p> <p> </p> <p> </p>
Integrated Agent-based Modelling and Simulation of Transportation Demand and Mobility Patterns in Sweden
<h2>About</h2> <p><span>The Synthetic Sweden Mobility (SySMo) model provides a simplified yet statistically realistic microscopic representation of the real population of Sweden. The agents in this synthetic population contain socioeconomic attributes, household characteristics, and corresponding activity plans for an average weekday. This agent-based modelling approach derives the transportation demand from the agents’ planned activities using various transport modes (e.g., car, public transport, bike, and walking).</span></p> <div> <p>This open data repository contains four datasets: </p> <p>(1) Synthetic Agents, </p> </div> <div> <p>(2) Activity Plans of the Agents, </p> </div> <div> <p>(3) Travel Trajectories of the Agents, and </p> </div> <div> <p>(4) Road Network (EPSG: 3006)</p> <p><span>(OpenStreetMap data were retrieved on August 28, 2023, from https://download.geofabrik.de/europe.html, and GTFS data were retrieved on September 6, 2023 from https://samtrafiken.se/)</span></p> <p><span>The database can serve as input to assess the potential impacts of new transportation technologies, infrastructure changes, and policy interventions on the mobility patterns of the Swedish population.</span></p> </div> <h2>Methodology</h2> <p>This dataset contains statistically simulated 10.2 million agents representing the population of Sweden, their socio-economic characteristics and the activity plan for an average weekday. For preparing data for the MATSim simulation, we randomly divided all the agents into 10 batches. Each batch's agents are then simulated in MATSim using the multi-modal network combining road networks and public transit data in Sweden using the package pt2matsim (https://github.com/matsim-org/pt2matsim). </p> <p>The agents' daily activity plans along with the road network serve as the primary inputs in the MATSim environment which ensures iterative replanning while aiming for a convergence on optimal activity plans for all the agents. Subsequently, the individual mobility trajectories of the agents from the MATSim simulation are retrieved.</p> <p>The activity plans of the individual agents extracted from the MATSim simulation output data are then further processed. All agents with negative utility score and negative activity time corresponding to at least one activity are filtered out as the ‘infeasible’ agents. The dataset ‘<strong>Synthetic Agents</strong>’ contains all synthetic agents regardless of their <span>‘<em>feasibility</em>’ (0=excluded & 1=included in plans and trajectories). In the other datasets, only agents with feasible activity plans are included. </span></p> <p>The simulation setup adheres to the MATSim 13.0 benchmark scenario, with slight adjustments. The strategy for replanning integrates BestScore (60%), TimeAllocationMutator (30%), and ReRoute (10%)— the percentages denote the proportion of agents utilizing these strategies. In each iteration of the simulation, the agents adopt these strategies to adjust their activity plans. The "BestScore" strategy retains the plan with the highest score from the previous iteration, selecting the most successful strategy an agent has employed up until that point. The "TimeAllocationMutator" modifies the end times of activities by introducing random shifts within a specified range, allowing for the exploration of different schedules. The "ReRoute" strategy enables agents to alter their current routes, potentially optimizing travel based on updated information or preferences. These strategies are detailed further in W. Axhausen et al. (2016) work, which provides comprehensive insights into their implementation and impact within the context of transport simulation modeling. </p> <h2>Data Description</h2> <h3>(1) Synthetic Agents</h3> <p>This dataset contains all agents in Sweden and their socioeconomic characteristics. </p> <p>The attribute ‘<span><em>feasibility</em></span>’ has two categories: <em>feasible</em><em> agents </em>(73%), and <em>infeasible agents</em> (27%). <span>Infeasible agents are agents with negative utility score and negative activity time corresponding to at least one activity.</span> </p> <p>File name: 1_syn_pop_all.parquet</p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>PId</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td>Deso</td> <td>Zone code of Demographic statistical areas (DeSO)<sup>1</sup></td> <td>String</td> <td>-</td> </tr> <tr> <td> <pre>kommun</pre> </td> <td>Municipality code</td> <td>Integer</td> <td>-</td> </tr> <tr> <td> <pre>marital </pre> </td> <td>Marital Status (single/ couple/ child)</td> <td>String</td> <td>-</td> </tr> <tr> <td> <pre>sex </pre> </td> <td>Gender (0 = Male, 1 = Female)</td> <td>Integer</td> <td>-</td> </tr> <tr> <td> <pre>age</pre> </td> <td>Age</td> <td>Integer</td> <td>-</td> </tr> <tr> <td> <pre>HId</pre> </td> <td>A unique identifier for households</td> <td>Integer</td> <td>-</td> </tr> <tr> <td> <pre>HHtype </pre> </td> <td>Type of households (single/ couple/ other)</td> <td>String</td> <td>-</td> </tr> <tr> <td> <pre>HHsize </pre> </td> <td>Number of people living in the households</td> <td>Integer</td> <td>-</td> </tr> <tr> <td> <pre>num_babies</pre> </td> <td>Number of children less than six years old in the household</td> <td>Integer</td> <td>-</td> </tr> <tr> <td>employment</td> <td>Employment Status (0 = Not Employed, 1 = Employed)</td> <td>Integer</td> <td>-</td> </tr> <tr> <td>studenthood</td> <td>Studenthood Status (0 = Not Student, 1 = Student)</td> <td>Integer</td> <td>-</td> </tr> <tr> <td>income_class</td> <td>Income Class (0 = No Income, 1 = Low Income, 2 = Lower-middle Income, 3 = Upper-middle Income, 4 = High Income)</td> <td>Integer</td> <td>-</td> </tr> <tr> <td>num_cars</td> <td>Number of cars owned by an individual </td> <td>Integer</td> <td>-</td> </tr> <tr> <td>HHcars</td> <td>Number of cars in the household</td> <td>Integer</td> <td>-</td> </tr> <tr> <td> <pre>feasibility</pre> </td> <td>Status of the individual (1=feasible, 0=infeasible)</td> <td>Integer</td> <td>-</td> </tr> </tbody> </table> <p>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></p> <h3>(2) Activity Plans of the Agents</h3> <p>The dataset contains the car agents’ (agents that use cars on the simulated day) activity plans for a simulated average weekday. </p> <p>File name: <span>2_plans_i.parquet, i = 0, 1, 2, ..., 8, 9. (10 files in total)</span></p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>act_purpose</p> </td> <td> <p>Activity purpose (work/ home/ school/ other)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>PId</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>act_end </p> </td> <td> <p>End time of activity (0:00:00 – 23:59:59)</p> </td> <td> <p>String</p> </td> <td> <p>hour:minute:seco</p> <p>nd</p> </td> </tr> <tr> <td> <p>act_id</p> </td> <td> <p>Activity index of each agent</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>mode</p> </td> <td> <p>Transport mode to reach the activity location</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>POINT_X </p> </td> <td> <p>Coordinate X of activity location (SWEREF99TM)</p> </td> <td> <p>Float</p> </td> <td> <p>metre</p> </td> </tr> <tr> <td> <p>POINT_Y</p> </td> <td> <p>Coordinate Y of activity location (SWEREF99TM)</p> </td> <td> <p>Float</p> </td> <td> <p>metre</p> </td> </tr> <tr> <td> <p>dep_time </p> </td> <td> <p>Departure time (0:00:00 – 23:59:59)</p> </td> <td> <p>String</p> </td> <td> <p>hour:minute:seco</p> <p>nd</p> </td> </tr> <tr> <td> <p>score</p> </td> <td> <p>Utility score of the simulation day as obtained from MATSim</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>trav_time </p> </td> <td> <p>Travel time to reach the activity location</p> </td> <td> <p>String</p> </td> <td> <p>hour:minute:seco</p> <p>nd</p> </td> </tr> <tr> <td> <p>trav_time_min </p> </td> <td> <p>Travel time in decimal minute</p> </td> <td> <p>Float</p> </td> <td> <p>minute</p> </td> </tr> <tr> <td> <p>act_time </p> </td> <td> <p>Activity duration in decimal minute</p> </td> <td> <p>Float</p> </td> <td> <p>minute</p> </td> </tr> <tr> <td> <p>distance</p> </td> <td> <p>Travel distance between the origin and the destination</p> </td> <td> <p>Float</p> </td> <td> <p>km</p> </td> </tr> <tr> <td> <p>speed</p> </td> <td> <p>Travel speed to reach the activity location</p> </td> <td> <p>Float</p> </td> <td> <p>km/h</p> </td> </tr> </tbody> </table> <h3>(3) Travel Trajectories of the Agents</h3> <p>This dataset contains the driving trajectories of all the agents on the road network, <span>and the public transit vehicles used by these agents, including buses, ferries, trams etc. The files are produced by MATSim simulations and organised into 10 *.parquet’ files (representing different batches of simulation) corresponding to each plan file.</span></p> <p>File name: <span>3_events_i.parquet, i = 0, 1, 2, ..., 8, 9. (10 files in total)</span></p> <p> </p> <table> <tbody> <tr> <td> <div> <div> <p><strong>Column </strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Description </strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Data type </strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Unit </strong></p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>time </p> </div> </div> </td> <td> <div> <div> <p>Time in second in a simulation day (0-86399) </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>second </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>type </p> </div> </div> </td> <td> <div> <div> <p>Event type defined by MATSim simulation* </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>person </p> </div> </div> </td> <td> <div> <div> <p>Agent ID </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>link </p> </div> </div> </td> <td> <div> <div> <p>Nearest road link consistent with the road network </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>vehicle </p> </div> </div> </td> <td> <div> <div> <p>Vehicle ID identical to person </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>from_node </p> </div> </div> </td> <td> <div> <div> <p>Start node of the link </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>to_node </p> </div> </div> </td> <td> <div> <div> <p>End node of the link </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> <p>* 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) </p> <h3>(4) Road Network</h3> <p>This dataset contains the road network.</p> <p>File name: 4_network.shp</p> <table> <tbody> <tr> <td> <div> <div> <p><strong>Column </strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Description </strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Data type </strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Unit </strong></p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>length </p> </div> </div> </td> <td> <div> <div> <p>The length of road link </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>metre </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>freespeed </p> </div> </div> </td> <td> <div> <div> <p>Free speed </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>km/h </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>capacity </p> </div> </div> </td> <td> <div> <div> <p>Number of vehicles </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>permlanes </p> </div> </div> </td> <td> <div> <div> <p>Number of lanes </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>oneway </p> </div> </div> </td> <td> <div> <div> <p>Whether the segment is one-way (0=no, 1=yes) </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>modes </p> </div> </div> </td> <td> <div> <div> <p>Transport mode </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>from_node </p> </div> </div> </td> <td> <div> <div> <p>Start node of the link </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>to_node </p> </div> </div> </td> <td> <div> <div> <p>End node of the link </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>geometry </p> </div> </div> </td> <td> <div> <div> <p>LINESTRING (SWEREF99TM) </p> </div> </div> </td> <td> <div> <div> <p>geometry </p> </div> </div> </td> <td> <div> <div> <p>metre </p> </div> </div> </td> </tr> </tbody> </table> <p> </p> <p><strong><span>Additional Notes</span></strong></p> <p><span>This research is funded by the RISE Research Institutes of Sweden, the Swedish Research Council for Sustainable Development (Formas, project number 2018-01768), and Transport Area of Advance, Chalmers.</span></p> <p><strong><span>Contributions</span></strong></p> <p><span>YL designed the simulation, analyzed the simulation data, and, along with CT, executed the simulation. CT, SD, FS, and SY conceptualized the model (SySMo), with CT and SD further developing the model to produce agents and their activity plans. KG wrote the data document. All authors reviewed, edited, and approved the final document.</span></p>
Barchan Swarm Simulations Using the Two-Flank Agent-Based Model v3
<p>This archive contains several animations of barchan swarms simulated using the Two-Flank Agent-Based model which was introduced in following article: </p> <p> </p> <p>Robson, Dominic T., and Andreas CW Baas. "A Simple Agent‐Based Model That Reproduces All Types of Barchan Interactions." Geophysical Research Letters 50.19 (2023): e2023GL105182. </p> <p> </p> <p>which is Open Access and available at https://doi.org/10.1029/2023GL105182</p> <p> </p> <p>The Two-Flank Agent-Based Model has been developed openly on GitHub by Dominic T Robson and Andreas CW Baas. All the necessary source files together with an example run file can be found at:</p> <p> </p> <p>https://github.com/DTRobson/TwoFlankABModel/releases/tag/TFABM</p> <p> </p> <p>The following model parameters and initial conditions were used for these simulations:</p> <p> </p> <p> simwidth = 9000 or 15000 #(m)</p> <p> simlength = 10000 #(m)</p> <p> fieldwidth = 3000 or 5000 #(m)</p> <p> </p> <p> qsatinit = 79 #(m^2 year^{-1})</p> <p> q0 = 0.25 #(q_sat)</p> <p> dt = 1/8 #(years) </p> <p> collson = True</p> <p> inject = True</p> <p> injectdist = Uniform </p> <p> periodic = False</p> <p> </p> <p> lambda1 = 1.</p> <p> lambda2 = 1.8</p> <p> lambda3 = 1/3</p> <p> alpha = 0.05</p> <p> delta = 4.6 #(m)</p> <p> a = 0.45</p> <p> b = 0.1</p> <p> </p> <p> </p> <p> eqw = 0.5 * delta/(q0 - alpha)</p> <p> injectparams = [2*eqw, 2*eqw]</p> <p> lws = [eqw]</p> <p> rws = [eqw]</p> <p> xs = [fieldwidth*1.5] </p> <p> ys = [simlength - 1]</p> <p> </p> <p> c = 45. </p> <p> </p> <p> w0 = 16.6 #(m)</p> <p> </p> <p> outfluxmode = 'Hersen' or 'Duran' #Hersen for unscaled outflux, Duran for scaled outflux</p> <p> plottinghornflux = False</p> <p> </p> <p> keep_coll_rec = True</p> <p> </p> <p> helpplotting = True</p> <p> </p> <p> </p> <p>The remaining model parameters varied across the different runs and took the values shown in the filenames. Where the filename does not list the angular separation of the secondary wind mode (thetab) simulations were unidirectional. The primary mode was normally distributed with mean 270degrees and standard deviation theta_sd. The list of all values used are shown here:</p> <p> </p> <p> initdensity = 12 or 24 or 37#(km^{-2}) this sets the rate at which dunes are injected into the model</p> <p> qshift = 0. or 0.05 or 0.1 or 0.15#(q_sat)</p> <p> thetab = 22.5 or 45 or 67.5 #(degrees)</p> <p> theta_sd = 3 #(degrees) </p> <p> </p> <p>In bimodal simulations the secondary mode angle was normally distributed with mean 270+thetab and standard deviation theta_sd. Each year (12 iterations) the first 9 iterations had wind direction taken from the primary mode and the final 3 iterations were from the secondary mode i.e. the 3:1 seasons of primary:secondary wind direction. Note that, in the simulations the primary mode 270deg means that dunes migrate in the negative y-direction, to produce the plots and revert to the convention of the primary wind being in the x-direction, the dunes were then rotated.</p> <p>The simulations were performed by Dominic T Robson using a 12th Gen Intel(R) Core(TM) i7-1255U 1.70 GHz processor and 16.0GB of RAM.</p> <p> </p> <p> </p>
An agent-based model of the origins of modern linguistic complexity – supplementary information
<p>A central question in the evolution of human language is whether it emerged as a result of one specific event or from a mosaic-like constellation of different phenomena and their interactions. Three potential processes have been identified by recent research as the potential <em>primum mobile</em> for the origins of modern linguistic complexity: Self-domestication, characterized by a reduction in reactive aggression and often associated with a gracilization of the face; changes in early brain development manifested by globularization of the skull; and demographic expansion of H. sapiens during the Middle Pleistocene. We developed an agent-based model to investigate how these three factors influence transmission of information within a population. Our model shows that there is an optimal degree of both hostility and mental capacity at which the amount of transmitted information is the largest. It also shows that linguistic communi- ties formed within the population are strongest under circumstances where individuals have high levels of cognitive capacity available for information processing and there is at least a certain degree of hos- tility present. In contrast, we find no significant effects related to population size.</p>
Annotation of the the assembled genome of Fusarium oxysporum f. sp. albedinis strain 133, the causal agent of date palm dieback.
<p>Annotation of the the assembled genome of <em>Fusarium oxysporum f. sp. albedinis</em> strain 133 (Khayi et al., 2020). Gene prediction and annotation were carried out using funnotate pipeline v1.8.1 (Stajich, 2020), which includes masking, ab initio gene-prediction training, using Augustus and Genmark, with the EST dataset reported to the Ganoderma mycocosm repository, gene prediction, and the assignment of functional annotation to protein-coding gene models.</p>
Learning to Give a Complete Argument with a Conversational Agent: An Experimental Study in Two Domains of Argumentation
<p>This data is collected to find out how having a conversation with our agent affects argumentation. To model the arguments, we used Toulmin's model of argument. Based on the model, a good argument contains 3 different parts: 1. Claim, 2. Warrant, 3. Evidence. Based on Toulmin's model, these three components are the core components of arguments. This dataset has been collected during a between-subject experiment in which the treatment groups first talked to our agent in Task 1 and received feedback on faulty structural arguments and then did Task 2 and 3 which were answering a question on the same and completely different topic in comparison to Task 1.</p>
Comprender la vida social urbana una propuesta a partir de las estrategias y tácticas de los agente
<p>Coordinador del Seminario: Carlos A. Navarrete Ulloa.<br> Expositor: Hiram Eduardo Urias Barrera.</p> <p>Comité Ejecutivo PRONACE-Vivienda</p> <ul> <li>Fernando Córdova Canela, Centro Universitario de Arte, Arquitectura y Diseño, Universidad de Guadalajara (UdeG).</li> <li>Francisco Javier Porras Sánchez, Instituto de Investigaciones Dr. José María Luis Mora.</li> <li>Gabriel Castañeda Nolasco, Universidad Autónoma de Chiapas (UNACH).</li> </ul> <p>Carlos A. Navarrete Ulloa, Centro Universitario de Tonalá, (UdeG).</p> <p>Exposición realizada en el marco del PRONACE Vivienda en el cual se comenta la lectura:</p> <p>Lange, C. (2013). Comprender la vida social urbana: una propuesta a partir de las estrategias y tácticas de los agentes sociales urbanos.En: A. Garrido y G. Gándara (coords.), Nuestras ciudades del futuro. ¿Cómo hacer sostenibles los espacios urbanos, 155-168.</p> <p><a href="https://repositorio.uchile.cl/handle/2250/133111">https://repositorio.uchile.cl/handle/2250/133111</a></p> <p> </p>
PhysiCell Studio: a graphical tool to make agent-based modeling more accessible. Supplemental material.
<p>Defining a multicellular model can be challenging. There may be hundreds of parameters that specify the attributes and behaviors of objects. In the best case, the model will be defined using some format specification, i.e., a markup language, that will provide easy model sharing (and a minimal step toward reproducibility). PhysiCell is an open source, physics-based multicellular simulation framework with an active and growing user community. It uses XML to define a model and, traditionally, users needed to manually edit the XML to modify the model. PhysiCell Studio is a tool to make this task easier. It provides a graphical user interface that allows editing the XML model definition, including the creation and deletion of fundamental objects: cell types and substrates in the microenvironment. It also lets users build their model by defining initial conditions and biological rules, run simulations, and view results interactively. PhysiCell Studio has evolved over multiple workshops and academic courses in recent years which has led to many improvements. There is both a desktop and cloud version. Its design and development has benefited from an active undergraduate and graduate research program. Like PhysiCell, the Studio is open source software and contributions from the community are encouraged. This dataset provides Supplemental material for the PhysiCell Studio publication.</p>
EVO-NANO modified PhysiCell simulation run with 10K vasculature agents
<p>PhysiCell modifications:</p> <p>- added CSC</p> <p>-added vasculature</p> <p> </p> <p>this run:</p> <p>Cell cycle speed increased to 1/10 (default is 0.0432/60 = 0,00072)<br> snapshot is taken each 180 minutes (of simulation time)<br> There are 10000 vasculature agents with 100 branches<br> oxygen secretion rate for each fasculature agent is: phenotype.secretion.secretion_rates[0] = 10<br> </p>
EVO-NANO modified PhysiCell simulation run with 50K vasculature agents
<p>PhysiCell modifications:</p> <p>- added CSC</p> <p>-added vasculature</p> <p> </p> <p>this run:</p> <p>Cell cycle speed increased to 1/10 (default is 0.0432/60 = 0,00072)<br> snapshot is taken each 180 minutes (of simulation time)<br> There are 50000 vasculature agents with 500 branches<br> oxygen secretion rate for each fasculature agent is: phenotype.secretion.secretion_rates[0] = 10</p>
Dataset for the article "Capping agent control over the physicochemical and antibacterial properties of ZnO nanoparticles".
<p>Dataset for the article "Capping agent control over the physicochemical and antibacterial properties of ZnO nanoparticles".</p> <p>David Rutherford1, Markéta Šlapal Bařinková1, Thaiskang Jamatia2, Pavol Šuly2, Martin Cvek2, Bohuslav Rezek1</p> <p><br>1 Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, 16227 Prague, Czech Republic<br>2 Centre of Polymer systems, Tomas Bata University in Zlin, Trida T. Bati 5678, 760 01 Zlín, Czech Republic</p> <p><br>Dataset description:</p> <p>240909 UV-vis_capped_ZnO.xlsx UV-vis spectroscopy<br>220623 ZnO Zlin Zn ion.xlsx Zinc ion measurement<br>230511 dls_zeta_data.xlsx DLS & zeta potential measurement<br>230221 ZnO_Zlin_MIC_MASTER.xlsx Minimum inhibitory concentration</p>
Supplementary material: Experimental acoustic characterisation of an endoskeletal antibubble contrast agent: first results
<p>Raw data connected to the journal article: </p> <p>Anastasiia Panfilova, Peiran Chen, Ruud J.G. van Sloun, Hessel Wijkstra, Michiel Postema, Albert T. Poortinga, Massimo Mischi. Experimental acoustic characterisation of an endoskeletal antibubble contrast agent: first results. <em>Medical Physics</em>, American Association of Physicists in Medicine. Volume 48, Issue 11, November 2021, Pages 6765-6780.</p>
Simulated data from abmAnimalMovement: An R package for simulating animal movement using an agent-based model
<p>Contained here are the data simulated as part of the manuscript: "abmAnimalMovement: An R package for simulating animal movement using an agent-based model" that can be found at: https://github.com/BenMMarshall/abmAnimalMovement (and archived at: https://doi.org/10.5281/zenodo.6951937).</p> <p>- BADGER_locations.csv: A csv file that contains the realised locations of the example badger simulation, where each row is equal to a timestep. Columns include: timestep, the timestep as an integer; x, the x coordinate of the animal; y, the y coordinate of the animal; sl, the step length between locations used during the simulation; sl_rescale the rescale factor required to return step lengths back to the input scale; ta, turning angle between locations in degrees; behave, the behavioural mode the animal was in at a given timestep; chosen, the location chosen out of the number of options available; destination_x and destination_y the point the animal was attracted to at that time (note exploratory behaviour is not subject attraction).</p> <p>- BADGER_options.csv: A csv file that contains the options available to the example badger simulation over the entire simulation duration, where each row is equal to an option repeated for each timestep. Columns include: timestep, the timestep as an integer; oall_x, and oall_y show the x and y coordinates of all the options available to an animal at a timestep; oall_steplengths are the step lengths from the current location compared to all the options.</p> <p>- completelist.RDS: This RDS file contains a list object of length three, where the full simulation outputs from each three examples are stored. Each species slot contains the “locations” dataframe (see description of locations.csv), the "options" dataframe (see description of options.csv), and a nested list containing all the "inputs" used to generate the simulated results (split into subsections: inputs_basic that contains inputs linked to simulation duration and intensity, inputs_destination that contains inputs linked to destination and attraction aspects, inputs_movement that contains inputs linked to movement capacity and behavioural switching, inputs_cycle that contains inputs linked to activity cycling, inputs_layerSeed that contains the environmental matrices and seed). A fourth object is returned called "others" that captures all other outputs, mainly used internally for debugging and checking.</p> <p>- KINGCOBRA_locations.csv: A csv file that contains the realised locations of the example king cobra simulation, where each row is equal to a timestep. The file structure follows the same as the BADGER_options.csv file.<br>KINGCOBRA_options.csv: A csv file that contains the options available to the example king cobra simulation over the entire simulation duration, where each row is equal to an option repeated for each timestep. The file structure follows the same as the BADGER_options.csv file.</p> <p>- VULTURE_locations.csv: A csv file that contains the realised locations of the example vulture simulation, where each row is equal to a timestep. The file structure follows the same as the BADGER_locations.csv file.<br>VULTURE_options.csv: A csv file that contains the options available to the example vulture simulation over the entire simulation duration, where each row is equal to an option repeated for each timestep. The file structure follows the same as the BADGER_locations.csv file.</p> <p>- eg_landscapedata_completelist.RDS: This RDS file contains the landscape matrices required for recreating the simulated outputs described in the manuscript named above. It is a list of three objects ("shelter", "forage", "movement"), each a numeric matrix of equal size, with values describing the quality of each landscape characteristic.</p> <p>- argument_table.csv: Descriptive table of the simulation inputs used in the walk-through manuscript.</p>
Golden Agents / RKD - Bredius Notes
<p>Data from the RKD's Bredius Notes project, enriched with information from and links to the Golden Agents project (https://www.goldenagents.org/).</p>
Data for: Assets in Periphery, Agents in the Core: Mapping the Micro Structures of International Tax Planning
<p>Data for: Assets in Periphery, Agents in the Core: Mapping the Micro Structures of International Tax Planning</p> <p>See paper at: https://osf.io/preprints/socarxiv/hyc4p</p> <p>In the last two decades, tax avoidance has risen to the top of the agenda of policy makers and international organizations. The majority of political action and academic research has focused on the macro-level of states, pointing towards the responsibility of ‘tax havens’ or ‘offshore financial centers’. Research on the micro-level has demonstrated the importance of non-state actors who facilitate tax planning, but tax advisors have never been studied systematically with global data. In this paper, we connect the micro and macro levels. We map tax advisors geographically using a novel empirical approach based on LinkedIn. We show that tax advisors generally locate in large cities in the EU and OECD, rather than in places targeted as ‘tax havens’. We further consider what determines the locations of tax advisors. Using multiple regression analysis, we find that locations of tax advisors does not correlate with the location of corporate profits, financial secrecy, or economic activity. Rather, it correlates with the managerial and financial activity. Our results underscore the core-periphery structure in offshore finance. Effective regulation of tax avoidance should focus on tax advisors, not only on the destination of money flows, since the active facilitation does not occur in those places.</p>
ITACA agent-based simulation model results
<p>This files downloaded present the raw results and derived indicators per scenario considered obtained with the agent-based model developed for policy assessment in the scope of the ITACA project (<a href="https://www.itaca-h2020.eu/">https://www.itaca-h2020.eu/</a>).</p> <p>The files are structured in three different case studies:</p> <ul> <li>CS01 - Past unsuccessful and delayed solutions</li> <li>CS02 - Past successful solutions</li> <li>CS03 - Future solutions</li> </ul> <p>In each case study there are included a set of scenarios, each of them testing one or several policy measures. Each scenario file includes the short name of the policy under study. For example, "Results_visualisation_CS01_Mandates.xlsm" includes the results when testing mandates as policy measure in the CS01 case study. The corresponding raw outputs of the simulator are included in the folder "yyyymmdd_hhmmss_CS01_Mandates".</p> <p>Each excel file contains the following tabs:</p> <ul> <li>Files - Files loaded to create the file. The loading interactive buttons have been disabled to avoid errors.</li> <li>H0 - Tab containing the visualisation of different raw outputs for the adopters agents. The user can select all agents or one specific agent to look at the results by selecting its ID in the slicers on top of each agent type (enable editing in excel to do so).</li> <li>H0_KPI_tech - Shows the number of adoptions per technology and agent type</li> <li>H0_KPI_eco - Shows the surpluses per agent type and total social welfare</li> <li>H0_KPI_eco_metrics - Same results, disaggregated in a tabular format.</li> <li>H0_KPI_op - Shows the total fuel burnt, throughput and Determined Unitary Cost (DUC) for enroute and terminal ANS.</li> <li>H1 - Configuration file used as input for the simulation.</li> <li>H2 - Raw results for airlines.</li> <li>H3 - Raw results for airports.</li> <li>H4 - Raw results for enroute ANSPs.</li> <li>H5 - Raw results for terminal ANSPs.</li> <li>H6 - Raw results per route.</li> <li>H7 - Raw results for regulators.</li> </ul> <p>The raw results folders include, apart from the data shown in the visualisation excel files, the input data per stakeholder, policy parameters, technology parameters, exogenous variables that affect the agents and the charging zones defined in the European airspace. </p> <p>The complete description of the case studies and scenarios tested is included in ITACA's deliverable <a href="http://www.nommon-files.es/itaca/ITACA-D5.1_Impact_assessment_of_policies_and_regulations_to_boost_ATM_technology_adoption_v01.00.00.pdf">D5.1 Impact Assessment of Policies and Regulations to Boost ATM Technology Adoption</a>.</p> <p>Thank you for downloading the dataset! It would be very helpful if you share your view on the data show with us. We have created the following short <a href="https://forms.gle/EgVAW57siXizsEn8A">questionnaire</a> to facilitate the task. </p>
ScienceDex guides
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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.