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PROCRAFT Final Meeting - Material study on the aircraft of the Deutsches Museum by Marisa Pamplona – Deutsches - Museum

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PROCRAFT Final Meeting - Educational activities on WWII aircraft at the Montreal Aviation Museum by Jean Desbiens – Montreal Museum

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PROCRAFT Final Meeting - Collecting wrecks: approaches, discoveries and valorization by an association by Gilles Collaveri – Aerocherche

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PROCRAFT Final Meeting – Preserving a Rare WWII Bomber by Lauren Horelick, Smithsonian Museum

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PROCRAFT Final Meeting - Case study: conservation of the Dornier 17 wreck (collaboration between institutes and volunteers) by Darren Priday, RAF Museum

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PROCRAFT Final Meeting - Choice of restoration or renovation of WWII aircraft paintwork, illustrated by two case studies Bristol Bolingbroke and Messerschmitt 163B Komet by Thilo Bürgel, National Museum of Flight

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opencc-by-4.0Mar 2023View details →

Dataset: Fano meets Stokes: Four-order-of-magnitude enhancement of asymmetric Brillouin light scattering spectra

Dataset accompanying publication: Rafał Białek, Thomas Vasileiadis, Mikołaj Pochylski, Bartłomiej Graczykowski, Fano meets Stokes: Four-order-of-magnitude enhancement of asymmetric Brillouin light scattering spectra, Photoacoustics, Volume 30, 2023, 100478, ISSN 2213-5979, https://doi.org/10.1016/j.pacs.2023.100478.

opencc-by-4.0Mar 2024View details →

2024.03.13 OCFL Community Meeting

This month's community meeting included: <ul> <li>A demo of <a href="https://github.com/srerickson/chaparral">Chaparral</a> by Seth Erickson.</li> <li>Review and discussion of the V2 prioritized Use Cases</li> </ul> The full notes and the recording can be found at https://github.com/OCFL/spec/wiki/2024.03.13-Community-Meeting. The next community meeting is Wednesday, April 10th, 8pm EDT / 5pm PDT / Thursday, April 11th, 1am GMT / 10am AEDT (<a href="https://dateful.com/convert/australia-sydney?t=10am&d=2024-04-11&tz2=EST-EDT-Eastern-Time" rel="nofollow">Convert to your time zone</a>).

Figure 2. Neodendrina carnelia igen. et isp. n in Large dendrinids meet giant clam: the bioerosion trace fossil Neodendrina carnelia igen. et isp. n. in a Tridacna shell from Pleistocene-Holocene coral reef deposits, Red Sea, Egypt

Figure 2. Neodendrina carnelia igen. et isp. n. on the inner side of a Tridacna maxima bivalve shell from the Pleistocene–Holocene coral reef deposits in the Marsa Alam area, Red Sea, Egypt. (a) Inner side of valve (left; prior to sectioning) with hundreds of N. carnelia specimens, and outer surface (right) intensely bioeroded by the sponge boring Entobia isp. (b) Section of the valve (MB.W 5640) with the holotype (centre) and the paratypes (all other specimens) in various ichnogenetic stages. (c) Close-up of the holotype trace. (d–e) Respective micro-CT scan of the holotype in plan and angular views as seen from inside the substrate.

Figure 1 in Large dendrinids meet giant clam: the bioerosion trace fossil Neodendrina carnelia igen. et isp. n. in a Tridacna shell from Pleistocene-Holocene coral reef deposits, Red Sea, Egypt

Figure 1. The Pleistocene raised coral reef limestones exposed at the type locality of Neodendrina carnelia igen. et isp. n. just south of the Carnelia Beach Resort, located between El Quseir and Marsa Alam, exhibiting scleractinian corals as primary reef builders (a) and giant clams Tridacna spp. weathering from the carbonate–siliciclastic rocks (b) that mix with Holocene and modern Tridacna valves, forming a highly time-averaged assemblage (c).

Figure 4. Neodendrina carnelia igen. et isp. n in Large dendrinids meet giant clam: the bioerosion trace fossil Neodendrina carnelia igen. et isp. n. in a Tridacna shell from Pleistocene-Holocene coral reef deposits, Red Sea, Egypt

Figure 4. Neodendrina carnelia igen. et isp. n. on the outer surface of a large recent Tridacna squamosa valve from Nosy-BØ, northern Madagascar (ZMB/Mol 102671). (a) Shell surface with various encrusters as well as bioerosion traces. (b) Close-up of a cluster of N. carnelia. (c) A large specimen with distinct pitted arrays developed in most of the branches.

Figure 3 in Large dendrinids meet giant clam: the bioerosion trace fossil Neodendrina carnelia igen. et isp. n. in a Tridacna shell from Pleistocene-Holocene coral reef deposits, Red Sea, Egypt

Figure 3. SEM images (BSE detector) of Neodendrina carnelia igen. et isp. n. of the inner side of a Tridacna maxima bivalve shell from the Pleistocene–Holocene coral reef deposits in the Marsa Alam area, Red Sea, Egypt. (a–c) Overview and close-ups of the holotype. (d–e) Overview and close-up of an early ichnogenetic stage. (f–g) Overview and close-up of a specimen with authigenic gypsum crystals, calcite spar, and clay minerals within the boring as well as on the host's shell surface. (h) Different morphologies possibly developed in the trace, comprising deep open canals (1), isolated deep pits (2), shallow open canals (3), pits in shallow canals (4) and discontinuities (5). (i) Cross section of a trace showing deep (1) and shallow (2) open canals. (j–k) Overview and detail of an epoxy resin cast of a specimen, illustrating the smooth surface texture and the high degree of microbioerosion in the surrounding (partly mechanically removed to gain a view of the dendrinid).

opencc-by-4.0Jun 2021View details →

CIMPA Kick Off Meeting Press Release

Press release at the end of the CIMPA project Kick Off meeting in June 2021

opencc-by-4.0Jan 2022View details →

Data for Ecology Letters paper: Jack-of-all-trades paradigm meets long-term data: generalist herbivores are more widespread and locally less abundant.

Data, R-code, and meta-data document for a 2022 paper in Ecology Letters that examines assumptions about associations between local abundance and dietary specialization using an 18-year dataset of caterpillar-plant interactions in Ecuador.

opencc-by-4.0Aug 2022View details →

ROADMAP interviews - Meet the Scientifics

<a href="https://www.youtube.com/hashtag/roadmaph2020">#ROADMAPH2020</a> On this series of interviews, you will learn more about LL and ROADMAP project. You can watch the interviews here: https://www.youtube.com/watch?v=-tD3V51w5mE&list=PLW5PYxzlSCfyQCvfv1p0rc9wjfZrWJT27 ROADMAP is a multi-actor project, including multiple institutions and organisations as project partners from different countries. In each country, partners formed working groups so called “Living Labs” composed of stakeholders from different groups, such as vets, farmers, farmer institutions, pharmaceutical companies. There are 12 Living Labs (LL) in total and each LL works differently, focusing on four levels, 1) farming systems, 2) sector, 3) regulatory, and 4) societal – but no LL works on all four levels. Please check out our website to learn more: www.roadmap-2020.eu <a href="https://www.youtube.com/hashtag/antimicrobial">#antimicrobial</a> <a href="https://www.youtube.com/hashtag/antibiotics">#antibiotics</a> <a href="https://www.youtube.com/hashtag/animalbreeding">#animalbreeding</a> <a href="https://www.youtube.com/hashtag/h2020">#H2020</a>

opencc-by-4.0Dec 2021View details →

Fig. 1 in Abstracts of the Immature Beetles Meeting 2021

Fig. 1. Most participants of the Immature Beetles Meeting 2021 during the lunch (Prague, 30th September).

opencc-by-4.0Oct 2022View details →

NFFA-Europe|Pilot proporsal "NANO meets ASTRO: simulating the formation of silicon oxide nanoparticles in the atmosphere of dying stars" (PID: 140).

XPS, IRRAS, QMS and OES data of the nanoparticles synthesized within the NFFA-Europe|Pilot proporsal "NANO meets ASTRO: simulating the formation of silicon oxide nanoparticles in the atmosphere of dying stars" (PID: 140).

opencc-by-4.0Oct 2013View details →

BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 10. Meeting Room Equipped with Different Sensors

To illustrate the basic working principle of a perceptual neuro-symbolic network in a concrete application, a simplified, concrete example is given in the following. In this example, office meeting room is equipped with different sensors (tactile floor sensors, motion detectors, light barriers, a door contact sensor, a camera, and a microphone) as sketched in Figure 10.

opencc-by-4.0Apr 2024View details →

Synthetic Indoor Climate and Occupancy Data from Office and Meeting Room Simulations

This is the dataset used for the publication "Coddora: CO2-based Occupancy Detection model trained via DOmain RAndomization". The goal is to provide training data for occupancy detection. The dataset contains one million days of data including 10 occupied days for each of 100,000 randomized room models (50,000 rooms considering office activity and 50,000 meeting room activity). Data were generated in EnergyPlus simulations according to the methodology described in the paper. When using the dataset, please cite: <blockquote> Manuel Weber, Farzan Banihashemi, Davor Stjelja, Peter Mandl, Ruben Mayer, and Hans-Arno Jacobsen. 2024. Coddora: CO2-Based Occupancy Detection Model Trained via Domain Randomization. In International Joint Conference on Neural Networks (IJCNN). June 30 - July 5, 2024, Yokohama, Japan. </blockquote> <h2>Dataset Structure</h2> The following files are provided:     1. dataset_office_rooms.h5   (provided as zip file)     2. dataset_meeting_rooms.h5   (provided as zip file)     3. simulated_occupancy_office_rooms.csv     4. simulated_occupancy_meeting_rooms.csv Please use an archiving tool such as 7zip to unzip the hdf5 files. Both hdf5 files contain two datasets with the following keys:     1. "data": contains the simulated indoor climate and occupancy data     2. "metadata": contains the metadata that were used for each simulation The csv files contain the time series of occupancy that were used for the simulations. <h2>Data</h2> Data includes the following fields: Datetime: day of the year (may be relevant due to seasonal differences) and time of the day Zone Air CO2 Concentration: CO2 level in ppm Zone Mean Air Temperature: temperature in °C Zone Air Relative Humidity: relative humidity in % Occupancy: level of occupancy relative to the maximum capacity of the room (in the range [0-1]) Ventilation: fraction of window opening in the range [0.01, 1] SimID: foreign key to reference the room properties the simulation was based on BinaryOccupancy: 0 or 1 denoting absence or presence (for binary classification)   Example row: <table> <tbody> <tr> <th>Datetime</th> <th>Zone Air CO2 Concentration</th> <th>Zone Mean Air Temperature</th> <th>Zone Air Relative Humidity</th> <th>Occupancy</th> <th>Ventilation</th> <th>simID</th> <th>BinaryOccupancy</th> </tr> <tr> <td> 10/09 11:21:00 </td> <td> 1084.5624647371608 </td> <td> 24.545635909907148 </td> <td> 41.18393114737054 </td> <td> 0.7 </td> <td> 0.0 </td> <td>99</td> <td>1</td> </tr> </tbody> </table> <pre> </pre> <h2>Metadata</h2> Metadata includes the following fields. Underscores denote that the field was not selected during randomization but calculated from the other values. width: room width in m length: room length in m height: hoom height in m infiltration:  infiltration per exterior area in m³/m²s outdoor_co2: co2 concentration in the outdoor air in ppm (set to a random value between [300, 500]) orientation: angle between the room's facade orientation and the north direction in degrees maxOccupants: room occupation limit, i.e. the maximum number of occupants _floorArea: floor area in m² (calculated from room dimensions) _volume: room volume in m³ (calculated from room dimensions) _exteriorSurfaceArea: surface area of the facade wall (calculated from room dimensions) _winToFloorRatio: ratio between total window area and floor area (calculated from room model) firstDayUsedOfOccupancySequence: selected starting day in the sequence of occupancy data for rooms with the respective maxOccupants value simID: unique identifier of the simulation to relate between simulation metadata and resulting simulated data   Example row: <table> <tbody> <tr> <th>width</th> <th>length</th> <th>height</th> <th>infiltration</th> <th>outdoor_co2</th> <th>orientation</th> <th>maxOccupants</th> <th>_floorArea</th> <th>_volume</th> <th>_exteriorSurfaceArea</th> <th>_winToFloorRatio</th> <th>firstDayOfUsedOccupancySequence</th> <th>simID</th> </tr> <tr> <td>5.481</td> <td>5.190</td> <td>3.264</td> <td>0.000214</td> <td>438.0</td> <td>316.0</td> <td>4.0</td> <td>28.446</td> <td>92.849</td> <td>16.940</td> <td>0.216</td> <td>192</td> <td>0</td> </tr> </tbody> </table>   <h2>Occupancy Data</h2> The occupancy data provided through the separate csv files contain the data from the upfront occupancy simulations that the climate simulation was based on. For each level of considered room occupancy limit (maxOccupants), the datasets provide minute values of occupancy throughout 1000 days. Datetime, Date, Timestamp: fictive time of simulated occupancy record (sequences are in 1-minute resolution) Occupants: number of present occupants Occupancy: binary occupancy state (0=unoccupied, 1=occupied) WindowState: binary state of ventilation (0=windows closed, 1=room is ventilated) maxOccupants: maximum number of occupants considered for the simulated sequence WindowOpeningFraction: fractional extent to which windows are opened, within the interval [0.01, 1] Example row: <table> <tbody> <tr> <th>Datetime</th> <th>Date</th> <th>Timestamp</th> <th>Occupants</th> <th>Occupancy</th> <th>WindowState</th> <th>maxOccupants</th> <th>WindowOpeningFraction</th> </tr> <tr> <td>2023-01-01 00:00:00</td> <td>2023-01-01</td> <td>1.672531e+09</td> <td>0</td> <td>0</td> <td>0</td> <td>1</td> <td>0.0</td> </tr> </tbody> </table>