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Golden Agents - Coördinaten Personen/Locaties Notarieel Archief Amsterdam
<p>Dataset met coördinaatgegevens (op een scan) van aktes en de personen en locaties daarin uit het Amsterdamse Notariële archief.</p> <p>Deze dataset bevat een bewerkte versie van de exports van het VeleHanden-indexeringsproject Alle Amsterdamse Akten (<a href="https://alleamsterdamseakten.nl/">https://alleamsterdamseakten.nl/</a>). De data zijn voorzien van URI's op persoonsnamen en records zoals deze in de huidige index op het notarieel archief die beheerd wordt door het Stadsarchief Amsterdam ook kunnen worden aangetroffen (<a href="https://archief.amsterdam/indexen/persons?f=%7B%22search_s_register_type_title%22:%7B%22v%22:%22Notari%C3%ABle%20archieven%22%7D%7D">https://archief.amsterdam/indexen/persons?f=%7B%22search_s_register_type_title%22:%7B%22v%22:%22Notari%C3%ABle%20archieven%22%7D%7D</a>).</p> <p>De exports zijn voor het laatst in het najaar van 2022 geactualiseerd. Belangrijke bewerkingen die in deze dataset kunnen worden aangetroffen en die momenteel niet in de reguliere Stadsarchief-index zijn opgenomen, zijn:</p> <ul> <li>Coördinaten (xy) op de scan van de begin- en eindmarkeringen die de documenteenheid (de akte) aanduiden zijn opgenomen;</li> <li>Locatienamen en hun locatie op de scan (xywh) .</li> </ul> <p>De huidige index levert deze gegevens (xywh kaders) wel mee voor persoonsnamen, maar voor de volledigheid zijn ook zij in deze repository opgenomen.</p> <p>Door opname van scannamen en vooral coördinaten zou deze dataset het bijvoorbeeld gemakkelijker moeten maken om documentherkenning en entiteitsextractie mogelijk te maken op scans van akten waarvan ook HTR beschikbaar is (zie <a href="https://transkribus.eu/r/amsterdam-city-archives">https://transkribus.eu/r/amsterdam-city-archives</a>) en <a href="https://gitlab.com/readcoop/webdev/public-docs/-/blob/master/read-and-search/API-README.md">https://gitlab.com/readcoop/webdev/public-docs/-/blob/master/read-and-search/API-README.md</a>).</p> <p>Voor meer informatie, zie de README.md of bekijk de repository op Github: https://github.com/knaw-huc/golden-agents-notarial-coordinates/</p>
Golden Agents - Amsterdam Corporate Group Portraits
<p>The Amsterdam Corporate Group Portraits dataset contains biographical information on persons depicted on institutional/corporate group portraits in the seventeenth and eighteenth century in Amsterdam. The dataset is part of the <a href="https://goldenagents.org/">Golden Agents</a> project.</p> <p>The original data were collected by Norbert Middelkoop and published as attachment to his dissertation <a href="https://hdl.handle.net/11245.1/509fbcc0-8dc0-44ae-869d-2620f905092e">Schutters, gildebroeders, regenten en regentessen</a> (2019). The Golden Agent project took his data and heavily structured these, so that they could be used in the project's infrastructure infrastructure.</p> <p>We kept the original structure of the dataset, so that we separate information on:</p> <ul> <li>Corporate group portraits (visual works)</li> <li>Poorters [=Burghers] (persons)</li> <li>Regentessen [=Regents (F)] (persons)</li> <li>Regenten [=Regents (M)] (persons)</li> <li>Gildenleden [=Guild members] (persons)</li> </ul> <p>For all the persons, if available, we modelled information on their name, the portrait they are depicted on, when they were a regent or churchmaster, when they were a member of the civic guards (schutterij), when they were a member of a guild, and when they were a member of the city council. Besides this, we added information on their birth and death date, and their marriages.</p> <p>Through linksets (see the <a href="https://github.com/knaw-huc/golden-agents-amsterdam-corporate-group-portraits/blob/master/linksets/README.md">linkset documentation</a>) we linked the persons to Wikidata and to among others the <a href="https://archief.amsterdam/">Amsterdam City Archives</a>. These linksets also serve as a way to link the persons to other datasets in the Golden Agents project.</p> <p>This release marks the stable version of this data at the end of the Golden Agents project (2016-2022).</p>
Golden Agents - Processes of Creativity (case study)
<p>We can observe a 'rise' of or 'renewed interest' in the illustrated book in the second half of 17th century and the beginning of the 18th century. New genres and techniques and an increasing competition in the book market lead to new collaborations between authors, printers, and illustrators. It would be interesting to track these producers who collaborated on illustrated book projects and trace whether these commercial collaborations were grounded or resulted in social and/or religious interactions. For this, we are bringing together several datasets in the Golden Agents infrastructure that each contain information on the production of books, or on social relations between actors involved in the production process.</p> <p>Aim of case study:</p> <ul> <li>Linking & querying different datasets to find overarching patterns on creative industries in the Dutch Republic</li> </ul> <p>Overarching Research Question:</p> <ul> <li>How can we map innovation in cultural production?</li> <li>What is the relation between different sorts (bonding & bridging) of creative and social interactions?</li> </ul> <p>This release (v0.9) marks the state of the data and case study at the end of the Golden Agents project (2016-2022).</p>
Attack of the clones: population genetics reveals clonality of Colletotrichum lupini, the causal agent of lupin anthracnose
<p><em>Colletotrichum lupini</em>, causing lupin anthracnose, is one of the worst pathogens to lupin cultivation worldwide. Understanding its population structure and evolutionary potential is crucial to design successful disease management strategies. The objective of this study was to employ population genetics to investigate the diversity, evolutionary dynamics and molecular basis of host interaction of this notorious lupin pathogen. A collection of globally representative <em>C. lupini </em>isolates was genotyped through triple digest restriction-site associated DNA sequencing (3D-RADseq), resulting in a dataset of unparalleled resolution. Phylogenetic and structural analysis could distinguish four (I – IV) independent lineages. The strong population structure, low recombination and slow linkage decay strongly suggests that <em>C. lupini</em> reproduces clonally. Different morphologies and virulence patterns on white (<em>Lupinus albus</em>) and Andean lupin (<em>L. mutabilis</em>) were observed between and within clonal lineages. Isolates belonging to lineage II were shown to have a mini-chromosome which was also partly present in lineage III and IV, but not in lineage I isolates. Variation in the presence of this mini-chromosome could indicate a role in host interaction. All four lineages were present in the South American Andes region, which is concluded to be the center of origin of this species. Only members of lineage II have been found outside South America since the 1990s, indicating it as the current pandemic population. As a seed-borne pathogen, <em>C. lupini</em> has mainly spread through infected but symptomless seeds, stressing the importance of phytosanitary measures to prevent future outbreaks of strains that are yet confined to South America.</p>
Open synthetic data on travel and charging demand of battery electric cars: An agent-based simulation on three charging behavior archetypes
<p><strong>Background</strong></p> <p>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.</p> <p>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).</p> <p><a href="https://doi.org/10.1016/j.trd.2023.103645">https://doi.org/10.1016/j.trd.2023.103645</a></p> <p><strong>Methodology</strong></p> <p>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.</p> <p>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).</p> <p><strong>Data description</strong></p> <p>The current data product covers seven data files.</p> <p><strong>(1) Agents' experienced activity plans</strong></p> <p>File name: 1_activity_plans.csv</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>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</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>deso</p> </td> <td> <p>Zone code of Demographic statistical areas (DeSO)<sup>1</sup></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>meter</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>meter</p> </td> </tr> <tr> <td> <p>act_purpose</p> </td> <td> <p>Activity purpose (work, home, other)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>mode</p> </td> <td> <p>Transport mode to reach the activity location (car)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>dep_time</p> </td> <td> <p>Departure time in decimal hour (0-23.99)</p> </td> <td> <p>Float</p> </td> <td> <p>hour</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:second</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>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> <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>act_start</p> </td> <td> <p>Start time of activity in minute (0-1439)</p> </td> <td> <p>Integer</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>act_end</p> </td> <td> <p>End time of activity in decimal hour (0-23.99)</p> </td> <td> <p>Float</p> </td> <td> <p>hour</p> </td> </tr> <tr> <td> <p>score</p> </td> <td> <p>Utility score of the simulation day given by MATSim</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </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> <p> </p> <p><strong>(2) Travel trajectories</strong></p> <p>File name: 2_input_zip</p> <p>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.</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>time</p> </td> <td> <p>Time in second in a simulation day (0-86399)</p> </td> <td> <p>Integer</p> </td> <td> <p>Second</p> </td> </tr> <tr> <td> <p>type</p> </td> <td> <p>Event type defined by MATSim simulation<sup>2</sup></p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>link</p> </td> <td> <p>Nearest road link consistent with (5)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>vehicle</p> </td> <td> <p>Vehicle ID identical to person</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p><sup>2 </sup>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> <p> </p> <p><strong>(3) Overnight charger access</strong></p> <p>File name: 3_home_charger_access.csv</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>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>home_charger</p> </td> <td> <p>Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>(4) Car fleet composition</strong></p> <p>File name: 4_car_fleet.csv</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>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>income_class</p> </td> <td> <p>Income group (0=None, 1=below 180K, 2=180K-300K, 3=300K-420K, 4=above 420K)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>car</p> </td> <td> <p>Car model class (B=40 kWh, C=60 kWh, D=100 kWh)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p> </p> <p>(<strong>5) Road network with slope information</strong></p> <p>File name: 5_road_network_with_slope.shp (5 files in total)</p> <table> <tbody> <tr> <td> <p>Column</p> </td> <td> <p>Description</p> </td> <td> <p>Data type</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>length</p> </td> <td> <p>The length of road link</p> </td> <td> <p>Float</p> </td> <td> <p>meter</p> </td> </tr> <tr> <td> <p>freespeed</p> </td> <td> <p>Free speed</p> </td> <td> <p>Float</p> </td> <td> <p>km/h</p> </td> </tr> <tr> <td> <p>capacity</p> </td> <td> <p>Number of vehicles</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>permlanes</p> </td> <td> <p>Number of lanes</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>oneway</p> </td> <td> <p>Whether the segment is one-way (0=no, 1=yes)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>modes</p> </td> <td> <p>Transport mode (car)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>link_id</p> </td> <td> <p>Link ID</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>from_node</p> </td> <td> <p>Start node of the link</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>to_node</p> </td> <td> <p>End node of the link</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>count</p> </td> <td> <p>Aggregated traffic (number of cars travelled per day)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>slope</p> </td> <td> <p>Slope in percent from -6% to 6%</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>geometry</p> </td> <td> <p>LINESTRING (SWEREF99TM)</p> </td> <td> <p>geometry</p> </td> <td> <p>meter</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>(6) Simulation scenarios specifying the parameter sets</strong></p> <p>File name: 6_scenarios.txt</p> <table> <tbody> <tr> <td> <p><strong>Parameter set</strong></p> <p><strong>(paraset)</strong></p> </td> <td> <p><strong>Strategy 1</strong></p> </td> <td> <p><strong>Strategy 2</strong></p> </td> <td> <p><strong>Strategy 3</strong></p> </td> <td> <p><strong>Fast charging power (kW)</strong></p> </td> <td> <p><strong>Minimum parking time for charging (min)</strong></p> </td> <td> <p><strong>Intermediate charging power (kW)</strong></p> </td> </tr> <tr> <td> <p>0</p> </td> <td> <p>0.2</p> </td> <td> <p>0.2</p> </td> <td> <p>0.9</p> </td> <td> <p>150</p> </td> <td> <p>5</p> </td> <td> <p>22</p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>0.2</p> </td> <td> <p>0.2</p> </td> <td> <p>0.9</p> </td> <td> <p>50</p> </td> <td> <p>5</p> </td> <td> <p>22</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>0.3</p> </td> <td> <p>0.3</p> </td> <td> <p>0.9</p> </td> <td> <p>150</p> </td> <td> <p>5</p> </td> <td> <p>22</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>0.3</p> </td> <td> <p>0.3</p> </td> <td> <p>0.9</p> </td> <td> <p>50</p> </td> <td> <p>5</p> </td> <td> <p>22</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>(7) Time history of travel trajectories and charging of the simulated BEVs</strong></p> <p>File name: 7_output.zip</p> <p>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.</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>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>home_charger</p> </td> <td> <p>Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>car</p> </td> <td> <p>Car model class (B=40 kWh, C=60 kWh, D=100 kWh)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>seq</p> </td> <td> <p>Sequence ID of time history by agent</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>time</p> </td> <td> <p>Time (0-86399)</p> </td> <td> <p>Integer</p> </td> <td> <p>Second</p> </td> </tr> <tr> <td> <p>purpose</p> </td> <td> <p>Valid for activities (home, work, school, other)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>type</p> </td> <td> <p>Event type defined by MATSim simulation</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>link</p> </td> <td> <p>Link ID (link_id in File 5)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>distance_driven</p> </td> <td> <p>Cumulative driven distance in the simulation day</p> </td> <td> <p>Float</p> </td> <td> <p>km</p> </td> </tr> <tr> <td> <p>energy_1</p> </td> <td> <p>Energy consumed while driving (-) or charging (+) (Strategy 1)</p> </td> <td> <p>Float</p> </td> <td> <p>kWh</p> </td> </tr> <tr> <td> <p>energy_2</p> </td> <td> <p>Energy consumed while driving (-) or charging (+) (Strategy 2)</p> </td> <td> <p>Float</p> </td> <td> <p>kWh</p> </td> </tr> <tr> <td> <p>energy_3</p> </td> <td> <p>Energy consumed while driving (-) or charging (+) (Strategy 3)</p> </td> <td> <p>Float</p> </td> <td> <p>kWh</p> </td> </tr> <tr> <td> <p>charger_1</p> </td> <td> <p>Power rating of the charger (Strategy 1)</p> </td> <td> <p>Float</p> </td> <td> <p>kW</p> </td> </tr> <tr> <td> <p>charger_2</p> </td> <td> <p>Power rating of the charger (Strategy 2)</p> </td> <td> <p>Float</p> </td> <td> <p>kW</p> </td> </tr> <tr> <td> <p>charger_3</p> </td> <td> <p>Power rating of the charger (Strategy 3)</p> </td> <td> <p>Float</p> </td> <td> <p>kW</p> </td> </tr> <tr> <td> <p>soc_1</p> </td> <td> <p>State of charge (0-1, Strategy 1)</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>soc_2</p> </td> <td> <p>State of charge (0-1, Strategy 2)</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>soc_3</p> </td> <td> <p>State of charge (0-1, Strategy 3)</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p> </p>
Golden Agents - SAA Pipeline
<p>The Golden Agents - SAA Pipeline is a set of data and scripts designed to generate linked open data in RDF format for several datasets for the Golden Agents project. The purpose of this pipeline is to make available as open data various sources of historical information about Amsterdam, such as notary archives, baptism and marriage registers, and burial registries, among others.</p> <p>This repository contains the scripts and data-(pointers) for the Golden Agents project's SAA (StadsArchief Amsterdam) pipeline that converts historical archival data from the Amsterdam City Archives into Linked Open Data (LOD/RDF) in the Golden Agents Archival Ontology ROAR++. The data is part of the Golden Agents research infrastructure.</p> <p>The materials in this repository are for three datasets:</p> <ol> <li>City Archives of Amsterdam (inventories + indices),</li> <li>City Archives of Amsterdam (Burial Registries, old index data), and</li> <li>Ja, ik wil!</li> </ol> <p>The pipeline processes EAD files of relevant archive's collections and data downloaded from the archives' open data endpoint (in A2A-format), or a custom format. The result is a collection of RDF TriG files that can be loaded into a triplestore.</p> <p>Addendum v1.1:</p> <p>This newer release also contains the HTR Annotation files (`ga_saa_htr_2022.zip`) as processed by https://github.com/knaw-huc/golden-agents-htr. Among others this includes (Web) Annotations that tag/classify probate inventory items in the HTR'ed material by the SAA. </p>
A Systematic Review on the Visualization of Avatars and Agents in AR & VR displayed using Head-Mounted Displays
<p>This repository contains the data for the article "Systematic Review on the Visualization of Avatars and Agents in<br> AR & VR displayed using Head-Mounted Displays" including a BibTex file and supporting figures.</p>
Carnauba Wax and Beeswax as Structuring Agents for Water-in-Oleogel Emulsions without Added Emulsifiers
<p>This dataset contains the data used in the publication: <br> <strong><em>"Carnauba Wax and Beeswax as Structuring Agents for Water-in-Oleogel Emulsions without Added Emulsifiers</em>" </strong> (https://doi.org/10.3390/foods12091850)</p> <p> </p> <p> </p> <p> </p> <p>Important abbreviations: </p> <ul> <li>#CRW: Indicates the concentration of carnauba wax in the fat phase (e.g., 5CRW - indicates 5%w/w of carnauba wax in the fat phase)</li> <li>#BZW: Indicates the concentration of beeswax in the fat phase (e.g., 5BZW - indicates 5%w/w of beeswax in the fat phase)</li> <li>#W: The number preceding indicates the percentage of water in the sample (e.g., 40W indicates 40%w/w of water in the emulsion)</li> <li>t#: Indicates the number of days in storage (e.g. t1 indicates the sample was measured after 1 day of storage time)</li> <li>LS: Indicates that the sample was prepared on a lab scale</li> <li>PS: Indicates that the sample was prepared on a pilot scale</li> </ul> <p>This dataset contains: </p> <ul> <li>Microscopies <ul> <li>PLM <ul> <li><strong>Figure 6A</strong> - Pilot scale emulsion - 5% carnauba wax - 40% water </li> <li><strong>Figure 6D</strong> - Pilot scale oleogel - 5% carnauba wax<br> </li> <li><strong>Figure 7A</strong> - Pilot scale emulsion - 5% beeswax - 20% water</li> <li><strong>Figure 7D </strong>- Pilot scale oleogel - 5% beeswax<br> </li> <li><strong>Figure 8A </strong>- CRW-S - Lab scale oleogel - 5% carnauba wax</li> <li><strong>Figure 8B - </strong>CRW-D<strong> - </strong>Pilot scale oleogel - 5% carnauba wax</li> <li><strong>Figure 8C - </strong>BZW-S<strong>- </strong>Lab scale oleogel - 5% beeswax</li> <li><strong>Figure 8D</strong> - BZW-D - Pilot scale oleogel - 5% beeswax<br> </li> </ul> </li> <li>Cryo-SEM <ul> <li><strong>Figure 6B </strong>- Pilot scale emulsion - 5% carnauba wax - 40% water </li> <li><strong>Figure 6E</strong> - Pilot scale oleogel - 5% carnauba wax<br> </li> <li><strong>Figure 7B</strong> - Pilot scale emulsion - 5% beeswax - 20% water </li> <li><strong>Figure 7E</strong> - Pilot scale oleogel - 5% beeswax<br> </li> </ul> </li> <li>CLSM <ul> <li><strong>Figure 6C</strong> - Lab scale emulsion - 5% carnauba wax - 40% water </li> <li><strong>Figure 6F</strong> - Lab scale oleogel - 5% carnauba wax<br> </li> <li><strong>Figure 7C</strong> - Lab scale emulsion - 5% beeswax - 40% water</li> <li><strong>Figure 7F </strong>- Lab scale oleogel - 5% beeswax <ul> </ul> </li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li>Rheology data <ul> <li>Strain sweeps <ul> <li><strong>LS_2.5CRW_20W_t1.txt</strong> - Lab scale emulsion - 2.5% carnauba wax - 20% water - day 1</li> <li><strong>LS_5CRW_0W_t1.txt</strong> - Lab scale oleogel - 5% carnauba wax - day 1</li> <li><strong>LS_5CRW_20W_t1.txt</strong> - Lab scale emulsion - 5% carnauba wax - 20% water - day 1</li> <li><strong>LS_5CRW_30W_t1.txt</strong> - Lab scale emulsion - 5% carnauba wax - 30% water - day 1</li> <li><strong>LS_5CRW_40W_t1.txt</strong> - Lab scale emulsion - 5% carnauba wax - 40% water - day 1</li> <li><strong>LS_7.5CRW_20W_t1.txt</strong> - Lab scale emulsion- 7.5% carnauba wax - 20% water - day 1<br> </li> <li><strong>LS_1.5BZW_20W_t1.txt</strong> - Lab scale emulsion - 1.5% beeswax - 20% water - day 1</li> <li><strong>LS_2.25BZW_20W_t1.txt</strong> - Lab scale emulsion - 2.25% beeswax - 20% water - day 1 </li> <li><strong>LS_5BZW_0W_t1.txt</strong> - Lab scale oleogel - 5% beeswax - day 1</li> <li><strong>LS_5BZW_20W_t1.txt</strong> - Lab scale emulsion - 5% beeswax - 20% water - day 1</li> <li><strong>LS_5BZW_30W_t1.txt</strong> - Lab scale emulsion - 5% beeswax - 30% water - day 1</li> <li><strong>LS_5BZW_40W_t1.txt</strong> - Lab scale emulsion - 5% beeswax - 40% water - day 1<br> </li> <li><strong>PS_5BZW_0W_t1.txt</strong> - Pilot scale oleogel - 5% beeswax - day 1</li> <li><strong>PS_5BZW_20W_t1.txt</strong> - Pilot scale emulsion - 5% beeswax - 20% water - day 1</li> <li><strong>PS_5BZW_40W_t1.txt</strong> - Pilot scale emulsion - 5% beeswax - 40% water - day 1</li> <li><strong>PS_5CRW_0W_t1.txt</strong> - Pilot scale oleogel - 5% carnauba wax - day 1</li> <li><strong>PS_5CRW_20W_t1.txt</strong> - Pilot scale emulsion - 5% carnauba wax - 20% water - day 1</li> <li><strong>PS_5CRW_40W_t1.txt</strong> - Pilot scale emulsion - 5% carnauba wax - 40% water - day 1<br> </li> </ul> </li> <li>Data compilation: <ul> <li><strong>Rheology - Average.csv</strong><br> Please consider the following abbreviations in the file <ul> <li>CGC = Critical gelling concentration</li> <li>G' = Storage modulus</li> <li>G'' = Loss modulus</li> <li>G* = Complex modulus</li> <li>Strain LVE = Yield strain </li> <li>COP = Crossover point</li> <li>NaN = indicates that the system was not stable and thus not measured</li> <li>Data is presented as Avgerave ± Standard Deviation (of the three repetitions)</li> </ul> </li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> </ul>
The virus and socioeconomic inequality: An agent-based model to simulate and assess the impact of interventions to reduce the spread of COVID-19 in Rio de Janeiro, Brazil
<p>This video shows de simulation of scenarios presented in the article "The virus and socioeconomic inequality: An agent-based model to simulate and assess the impact of interventions to reduce the spread of COVID-19 in Rio de Janeiro, Brazil"</p>
DATASET OF "Impact of short-acting vs. standard anaesthetic agents on obstructive sleep apnoea: a randomised, controlled, triple-blind trial"
<p>Sleep apnoea is associated with negative outcomes following general anaesthesia. Current recommendations suggest using short-acting anaesthetic agents in preference to standard agents to reduce this risk, but there is currently no evidence to support this. This randomised controlled triple-blind trial tested the hypothesis that a combination of short-acting agents (desflurane-remifentanil) would reduce the postoperative impact of general anaesthesia on sleep apnoea severity compared with standard agents (sevoflurane-fentanyl). Sixty patients undergoing hip arthroplasty under general anaesthesia were randomised to anaesthesia with desflurane-remifentanil or sevoflurane-fentanyl. Respiratory polygraphy was performed before surgery and on the first and third postoperative nights. The primary outcome was the supine apnoea-hypopnoea index on the first postoperative night. Secondary outcomes were the supine apnoea-hypopnoea index on the third postoperative night, and the oxygen desaturation index on the first and third postoperative nights. Additional outcomes included intravenous morphine equivalent consumption and pain scores on postoperative days 1, 2 and 3. Pre-operative sleep study data were similar between groups. Mean (95%CI) values for the supine apnoea-hypopnoea index on the first postoperative night were 18.9 (12.7–25.0) and 21.4 (14.2–28.7) events.h<sup>-1</sup>, respectively, in the short-acting and standard anaesthesia groups (p=0.64). Corresponding values on the third postoperative night were 28.1 (15.8–40.3) and 38.0 (18.3–57.6) events.h<sup>-1</sup> (p=0.34). Secondary sleep- and pain-related outcomes were generally similar in the two groups. In conclusion, short-acting anaesthetic agents did not reduce the impact of general anaesthesia on sleep apnoea severity compared with standard agents. These data should prompt an update of current recommendations.</p>
Synthetic COVID-19 Case Reporting Data Generated from an Agent-Based Simulation Model
<p>This is a synthetic case reporting data set for the SARS-CoV-2 epidemic in Austria. The data set statistically reproduces and synthetically augments data on reported cases and was generated with an agent-based simulation model. References to descriptions of the model and the parameterization used to generate the data set is included in the attached PDF file. The data format is described in the README file.</p>
Benchmark maps and assignments for multi-agent path finding
<p><strong>The dataset</strong></p> <p>The dataset is produced within the SafeLog project and it is used for benchmarking of multi-agent path planning algorithms. Specifically, the dataset consists of a set of 21 maps with increasing density and a set of 500 random assignments, each for a group of 100 agents for planning on each of the maps.</p> <p>All of the maps, in the form of a graph G = {V, E}, are built on the same set of 400 vertices V. The sets of edges Ej, where j ∈ (0; 20), in the maps then form a set ranging from a spanning tree to a mostly 4-connected graph. These maps were created by generating a complete square graph with the size of 20*20 vertices. The graph was then simplified to a spanning tree, and, finally, approximately 50 random edges from the complete graph were added 20 times, to create the set of 21 maps of density ranging from 800 to 1500 edges in the graph. <br> </p> <p><strong>Content and format</strong></p> <p>The following files are included in the dataset</p> <p><em>test_nodes.txt </em>- 400 nodes of a 20*20 square map in the form "id x y"<br> <em>testAssignment.txt</em> - 50499 random pairs of nodes ids from test_nodes.txt<br> <em>test_edgesX.txt</em> - pairs of adjacent nodes ids from test_nodes.txt forming edges<br> - X = 0 - tree<br> - X = 20 - full graph<br> - created starting at a full graph and repeatedly erasing edges until a tree remains</p> <p>To illustrate the maps in the dataset, we provide three images (1008.png, 1190.png, and 1350.png) showing maps with 1008 (1190, 1350) edges. </p> <p><strong>Citation</strong></p> <p>If you use the dataset, please cite:</p> <p>[1] Hvězda, J., Rybecký, T., Kulich, M., and Přeučil, L. (2018). <a href="https://doi.org/10.1109/ITSC.2018.8569712"><em>Context-Aware Route Planning for Automated Warehouses</em></a>. Proceedings of 2018 21st International Conference on Intelligent Transportation Systems (ITSC).</p> <p><em>@inproceedings{Hvezda18itsc,<br> author = {Hvězda, Jakub and Rybecký, Tomáš and Kulich, Miroslav and Přeučil, Libor},<br> title = {Context-Aware Route Planning for Automated Warehouses},<br> booktitle = {Proceedings of 2018 21st International Conference on Intelligent Transportation Systems (ITSC)},<br> publisher = {IEEE Intelligent Transportation Systems Society},<br> address = {Maui},<br> year = {2018},<br> doi = {10.1109/ITSC.2018.8569712},<br> }</em></p> <p> </p> <p>[2] Hvězda, J., Kulich, M., and Přeučil, L. (2019). <a href="https://link.springer.com/chapter/10.1007/978-3-030-31993-9_18">On Randomized Searching for Multi-robot Coordination</a>. In: Gusikhin O., Madani K. (eds) Informatics in Control, Automation and Robotics. ICINCO 2018. Lecture Notes in Electrical Engineering, vol 613. Springer, Cham.</p> <p><em>@inbook{Hvezda19springer,<br> author = {Hvězda, Jakub and Kulich, Miroslav and Přeučil, Libor},<br> title = {On Randomized Searching for Multi-robot Coordination},<br> booktitle = {Informatics in Control, Automation and Robotics},<br> publisher = {Springer},<br> address = {Cham, CH},<br> year = {2019},<br> series = {Lecture Notes in Electrical Engineering},<br> language = {English},<br> url = {https://link.springer.com/chapter/10.1007/978-3-030-31993-9_18},<br> doi = {10.1007/978-3-030-31993-9},<br> }</em></p> <p><br> [3] Hvězda, J., Kulich, M., and Přeučil, L. (2018). <a href="http://www.scitepress.org/PublicationsDetail.aspx?ID=ppwUqsGaX18=\&t=1">Improved Discrete RRT for Coordinated Multi-robot Planning</a>. Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - (Volume 2).</p> <p><em>@inproceedings{Hvezda18icinco,<br> author = {Hvězda, Jakub and Kulich, Miroslav and Přeučil, Libor},<br> title = {Improved Discrete RRT for Coordinated Multi-robot Planning},<br> booktitle = {Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - (Volume 2)},<br> publisher = {SciTePress},<br> address = {Madeira, PT},<br> year = {2018},<br> language = {English},<br> url = {http://www.scitepress.org/PublicationsDetail.aspx?ID=ppwUqsGaX18=\&t=1},<br> doi = {10.5220/0006865901710179},<br> access = {full}<br> }</em></p> <p> </p> <p> </p> <p> </p>
An Agent-Based Model to Predict Pedestrians Trajectories with an Autonomous Vehicle in Shared Spaces: Video Results
<p>A video illustrating the results presented in the paper: <em>"Prédhumeau M., Mancheva L., Dugdale J., and Spalanzani A. 2021. An Agent-Based Model to Predict Pedestrians Trajectories with an Autonomous Vehicle in Shared Spaces. In the Proc. of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021). IFAAMAS, Online."</em></p> <p> </p>
The Missing Link: A New Skeleton for Evolutionary Multi-Agent Systems in Erlang
<p>Evolutionary multi-agent systems (EMAS) play a critical role in many artificial intelligence applications that are in use today. In this paper, we present a new generic skeleton for parallel EMAS computations, written in Erlang. The skeleton enables us to capture a wide variety of concrete evolu- tionary computations that can exploit the same underlying parallel implemen- tation. We demonstrate the use of our skeleton on two different evolutionary computing applications: i) computing the minimum of the Rastrigin function; and ii) solving an urban traffic optimisation problem. We show that we can ob- tain very good speedups (up to 142.44× the sequential performance using 244 threads on a 61-core accelerator) on a variety of different parallel hardware, while requiring very little parallelisation effort.</p>
Microbiological agents as notified to EFSA
<p>This is the list of “Microbiological agents as notified to EFSA” from 2007, in the context of technical dossiers to EFSA Units, for intentional use in feed and/or food or as sources of food and feed additives, enzymes and plant protection products (PPPs) for safety assessment.</p> <p>This is supplementing the QPS statements part 11, part 12, part 13, part 14, part 15, part 16, part 17, part 18, part 19, part 20, part 21, part 22 and the 2022 Scientific Opinion of the BIOHAZ Panel of EFSA - see related identifiers.</p> <p>EFSA topic on QPS: <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.efsa.europa.eu%2Fen%2Ftopics%2Ftopic%2Fqualified-presumption-safety-qps&data=02%7C01%7C%7Ca3e4d3a851c84defc2a008d7aa44f9e5%7C406a174be31548bdaa0acdaddc44250b%7C1%7C0%7C637165085527366985&sdata=TlcglouTWhBY24K5ApAoawNpADKgyl%2FefxVUEbxjQo0%3D&reserved=0">https://www.efsa.europa.eu/en/topics/topic/qualified-presumption-safety-qps</a></p>
Collection agents affiliated with the Museum für Naturkunde Berlin
<p>Open dataset of collection agents affiliated with the Museum für Naturkunde Berlin, static snapshot related to project output, dynamic dataset available via Wikidata SPARQL queries. Related article submitted to journal RIO.</p>
NoVAGraphS FSA User-Agent Corpus
<ul> <li><strong>Paper:</strong> Di Nuovo E., Sanguinetti M., Balestrucci P.F,Anselma L., Bernareggi C., Mazzei A. (2024),Educational Dialogue Systems for Visually Impaired Students: Introducing a Task-Oriented User-Agent Corpus. Accepted paper at the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)</li> <li><strong>Contact person: </strong>Elisa Di Nuovo,<strong> </strong>elisa.dinuovo@gmail.com</li> </ul> <h2>Dataset Summary</h2> <p>Collection of user-agent interactions revolving around the description of Finite State Automata.</p> <h2>Daset Description</h2> <p>The corpus consists of a CSV file encoded in UTF-8 comprising the following columns:</p> <ul> <li><strong>CODE_ID</strong>: the id of the interaction</li> <li><strong>Turn</strong>: the turn number within the interaction</li> <li><strong>Participant</strong>: it identifies the sender (<code>U</code> for the user, <code>S</code> for the agent)</li> <li><strong>Text</strong>: the utterance content</li> <li><strong>VIP</strong>: it determines whether the user is a Visually-Impaired Person</li> <li><strong>Token count</strong>: the number of tokens in the utterance (counted using <code>Spacy</code> tokenizer)</li> <li><strong>DAs_GOLD</strong> and <strong>Errors_GOLD</strong>: the columns including the assigned labels for Dialog Acts and Errors, respectively</li> <li><strong>FSA_ID</strong>: the id of the Finite State Automaton that is being referred to within the conversation (it corresponds to the PNG and HTML file names containing the relevant information on the FSA)</li> </ul> <h2>Additional Data</h2> <ul> <li>Two PNG files with the graphical representation of the automata</li> <li>Two HTML files containing the state tables of the automata</li> <li>RASA configuration files used to train the DIET classifier on the DAs</li> </ul> <h2>Access Request</h2> <p>To access the data users need to fill out the following Google <a href="https://docs.google.com/forms/u/0/d/e/1FAIpQLSeZJnUMj8tb-KtNK5nx1SkZMCjlCZIjuCFP7CYToaCila3QbA/formResponse" target="_blank" rel="noopener">form</a></p>
Figure 2 in Aculus taihangensis (Acari: Prostigmata: Eriophyidae), a potential biological control agent identified from the highly invasive pest plant, tree of heaven, in Türkiye
Figure 2. Aculus taihangensis. Prodorsal shield and part of dorsal opisthosoma: A. Protogyne, B. Deutogyne.
Figure 4 in Aculus taihangensis (Acari: Prostigmata: Eriophyidae), a potential biological control agent identified from the highly invasive pest plant, tree of heaven, in Türkiye
Figure 4. Aculus taihangensis – Male: A. Prodorsal shield and part of dorsal opisthosoma, B. Coxigenital region.
Figure 5 in Aculus taihangensis (Acari: Prostigmata: Eriophyidae), a potential biological control agent identified from the highly invasive pest plant, tree of heaven, in Türkiye
Figure 5. Dense aggregation of Aculus taihangensis along the midrib of a leaflet of the tree of heaven.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.