Find research datasets worth reusing

PM_152153_B_Leuven

File Name: PM_152153_B_Leuven.jpg Sublocation: M Leuven, museum Location: Leuven Province: Vlaams Brabant Country: Belgium Header: Sculptuurgroep, "De graflegging van Christus met zes heilige figuren", Leuven (?), ca 1500-1525, gepolychromeerd eikehout Description: Sculpture group The entombment of Christ with six holy figures Leuven (?) Ca 1500-1525 Oak, polychrome Keywords: Cultural heritage, Museum/private collection, Painting, Techniques Author: Dieric Bouts (c. 1410/1415-1475) Copyright: Paul M.R. Maeyaert

PM_152283_B_Leuven

File Name: PM_152283_B_Leuven.jpg Sublocation: M Leuven, museum Location: Leuven Province: Vlaams Brabant Country: Belgium Header: Schilderij, "Pest in de Sint-Jacobsparochie in Leuven", Brabant, ca 1578; detail Description: Painting Plague in Saint james Parish in Leuven Ca 1578 Detail Keywords: Belgium, Cultural heritage, Europe, Leuven, Museum/private collection, Painting, Techniques, Vlaams-Brabant Author: Anonymous Copyright: Paul M.R. Maeyaert

PM_152141_B_Leuven

File Name: PM_152141_B_Leuven.jpg Sublocation: M Leuven, museum Location: Leuven Province: Vlaams Brabant Country: Belgium Header: Schilderij, Kalenderwijzerplaat, ca 1500; 25,6 x 123,4 cm, olieverf op hout Description: Painting Calendar dial Ca 1500 Oil on panel Keywords: Belgium, Cultural heritage, Europe, Leuven, Museum/private collection, Painting, Techniques, Vlaams-Brabant Author: Paul M.R. Maeyaert Copyright: Paul M.R. Maeyaert

PM_152146_B_Leuven

File Name: PM_152146_B_Leuven.jpg Sublocation: M Leuven, museum Location: Leuven Province: Vlaams Brabant Country: Belgium Header: Schilderij, Gerrit Van Stellingwerf, Vanitasstilleven, 1641 Description: Painting Gerrit van Stellingwerf Still Life Vanitas 1641 Keywords: Belgium, Cultural heritage, Europe, Leuven, Museum/private collection, Painting, Techniques, Vlaams-Brabant Author: Gerrit van Stellingwerf (1614-1659) Copyright: Paul M.R. Maeyaert

PM_152284_B_Leuven

File Name: PM_152284_B_Leuven.jpg Sublocation: M Leuven, museum Location: Leuven Province: Vlaams Brabant Country: Belgium Header: Schilderij, "Pest in de Sint-Jacobsparochie in Leuven", Brabant, ca 1578; detail Description: Painting Plague in Saint james Parish in Leuven Ca 1578 Detail Keywords: Belgium, Cultural heritage, Europe, Leuven, Museum/private collection, Painting, Techniques, Vlaams-Brabant Author: Anonymous Copyright: Paul M.R. Maeyaert

PM_152290_B_Leuven

File Name: PM_152290_B_Leuven.jpg Sublocation: M Leuven, museum Location: Leuven Province: Vlaams Brabant Country: Belgium Header: Schilderij, Mater Dolorosa, Albrecht Bouts, na 1400; privé verzameling Luxemburg, langdurige bruikleen Description: Painting Mater Dolorosa Albrecht Bouts After 1400 Keywords: Belgium, Cultural heritage, Europe, Leuven, Museum/private collection, Painting, Techniques, Vlaams-Brabant Author: Albrecht Bouts (1452/1460-1549) Copyright: Paul M.R. Maeyaert

PM_152170_B_Leuven

File Name: PM_152170_B_Leuven.jpg Sublocation: M Leuven, museum Location: Leuven Province: Vlaams Brabant Country: Belgium Header: Retabel, Description: Altarpiece. Keywords: Belgium, Cultural heritage, Europe, Leuven, Museum/private collection, Sculpture, Techniques, Vlaams-Brabant Author: Photo: Paul M.R. Maeyaert Copyright: Paul M.R. Maeyaert

PM_152291_B_Leuven

File Name: PM_152291_B_Leuven.jpg Sublocation: M Leuven, museum Location: Leuven Province: Vlaams Brabant Country: Belgium Header: Schilderij, Mater Dolorosa, Albrecht Bouts, na 1400; privé verzameling Luxemburg, langdurige bruikleen Description: Painting Mater Dolorosa Albrecht Bouts After 1400 Keywords: Belgium, Cultural heritage, Europe, Leuven, Museum/private collection, Painting, Techniques, Vlaams-Brabant Author: Albrecht Bouts (1452/1460-1549) Copyright: Paul M.R. Maeyaert

opencc-by-sa-4.0Apr 2024View details →

zenodo44/100

Audiovisual, Gaze-controlled Auditory Attention Decoding Dataset KU Leuven (AV-GC-AAD)

This dataset is described in detail in the following journal paper [1]: Rotaru, I., Geirnaert, S., Heintz, N., Van de Ryck, I., Bertrand, A., & Francart, T. (2024). What are we really decoding? Unveiling biases in EEG-based decoding of the spatial focus of auditory attention. Journal of Neural Engineering, 21(1), 016017. <a href="https://iopscience.iop.org/article/10.1088/1741-2552/ad2214/meta">https://iopscience.iop.org/article/10.1088/1741-2552/ad2214/meta</a> If using this dataset, please cite the original paper above and the current Zenodo repository. Note from the authors: Recent evaluations reveal that various published AAD (Auditory Attention Decoding) algorithms do not achieve significant above-chance performance on this AV-GC-AAD dataset, and in particular on the two gaze-incongruent conditions 'MovingVideo' and 'MovingTargetNoise'). This suggests that previously reported successes may have been largely influenced by eye gaze confounds present in other datasets, which can be exploited as shortcuts by machine learning algorithms. Despite these findings, poor performance on the AV-GC-AAD dataset is often dismissed, with reasons cited such as insufficient training data, high heterogeneity in audiovisual conditions, or the claim that participants were unable to focus their auditory attention due to the complexity of the instructions. To address these concerns, we provide a supplementary technical report (and accompanying code), showcasing results from a simple linear stimulus reconstruction AAD algorithm applied to this dataset. Our findings demonstrate that high AAD accuracy can be achieved within individual conditions, and that the model generalizes across conditions, new subjects, and even across different datasets. <a title="https://doi.org/10.48550/arxiv.2412.01401" href="https://doi.org/10.48550/arXiv.2412.01401" target="_blank" rel="noreferrer noopener">Report</a> | <a title="https://github.com/alexanderbertrandlab/linear-stimulus-reconstruction-aad-av-gc-aad-dataset" href="https://github.com/AlexanderBertrandLab/linear-stimulus-reconstruction-AAD-AV-GC-AAD-dataset" target="_blank" rel="noreferrer noopener">Matlab Code</a> Through this report, we aim to remove any doubts that the AV-GC-AAD dataset's limitations are the primary cause of AAD algorithms failing to exceed chance-level performance. Additionally, this report and its accompanying code offer a simple baseline evaluation procedure, which can serve as a minimal benchmark for testing more advanced AAD algorithms on this dataset. When reporting results on this data set, it is good practice to show performance for each condition separately, since 2 of the 4 conditions still contain gaze shortcuts, which could be exploited by machine learning algorithms.  ________________________________________________________________________________ Dataset description This work was performed at ExpORL, Dept. Neurosciences, KU Leuven and Dept. Electrical Engineering (ESAT), KU Leuven (Belgium), with the goal of investigating and controlling for the effect of gaze during a competing listening task. The full dataset contains EEG and EOG data collected from 16 normal-hearing subjects, during a competing listening task, where the subjects were instructed to focus on one of two competing speech signals. However, subjects 2, 5 and 6 were excluded from the online repository due to not consenting to sharing their data in a public database (cf. signed informed consents approved by KU Leuven Ethical Committee). EEG recordings were conducted in a soundproof, electromagnetically shielded room at ExpORL, KU Leuven. The BioSemi ActiveTwo system was used to record 64-channel EEG signals at 8196 Hz sample rate. Additionally, the participants' gaze movements were measured via 4 EOG (electrooculography) electrodes placed symmetrically around the eyes.  The audio signals were administered to each subject at 65 dB SPL through a pair of insert phones (Etymotic ER10). In some experimental trials, the video depicting the attended talker was also presented on the screen. The original presented speech and video stimuli (.wav and .mp4 files) are excluded from the dataset due to copyrights. However, the acoustic envelopes of the attended and unattended audio stimuli are calculated and included in the dataset (see below).  The experiments were conducted using custom-made Python scripts. The experimental trials were split into 2 blocks. Each block consisted of the following sequence of conditions: MovingVideo, MovingTargetNoise, NoVisuals, StaticVideo. The auditory task was the same for all conditions: the subjects had to attend to one of the two presented talkers, as indicated by an arrow on the screen. The visual task differed across conditions: <ul> <li>MovingVideo: the subjects had to follow the moving video of the to-be-attended speaker presented on a randomized horizontal trajectory on the screen.</li> <li>MovingTargetNoise: the subjects had to follow a moving cross-hair presented on a randomized horizontal trajectory on the screen.</li> <li>NoVisuals: a black screen was presented and the subjects had to fixate on an imaginary point in the center of the screen while minimizing the eye movements.</li> <li>StaticVideo: the subjects had to fixate the static video of the to-be-attended speaker presented on the same side with the audio stimulus of the attended speaker.</li> </ul> The full description of all experimental conditions can be consulted in [1]. Each trial/condition lasted for 10 minutes, with a spatial switch in attention after 5 minutes (i.e., the  presented speech stimuli  were programmed to swap sides - from L to R or vice versa, such that after the switch the subjects kept listening to the same speaker, but coming from the opposite spatial location). This means that the participant kept attending to the same speaker throughout an entire trial. To keep the subjects motivated, they had to answer one comprehension question related to the attended acoustic stimulus after each trial. For each subject, there is a .mat file containing the following variables: conditionID: the condition ID for each trial  data: the preprocessed EEG and EOG data for each trial (first 64 channels are EEG, last 4 are EOG) fs: the sampling rate of the EEG, EOG and stimuli envelopes initAttention: the initial spatial location of the attended stimulus for each trial metadata: the original metadata (e.g. channel names, triggers) saved in the raw .bdf files for each trial params: the filtering parameters used for each trial randomization: the randomization parameters (e.g. presented stimuli, attention switch times etc.) for each trial stimulus: the precalculated envelopes for the attended and unattended stimuli for each trial subjID: the anonymised ID of the current subject Preprocessing EEG and EOG All the following preprocessing steps were applied per trial. The EEG was initially downsampled using an antialiasing filter from 8192 Hz to 256 Hz. The data was then filtered between 1–40 Hz using a zero-phase Chebyshev filter (type II, with 80 dB attenuation at 10% outside the passband). Finally, downsampling to 128 Hz was performed to speed up computation. Speech envelopes extraction The original speech signals at 44100 Hz were downsampled to 8192 Hz (to match the EEG sampling rate). They were then passed through a gammatone filterbank, which roughly approximates the spectral decomposition as performed by the human auditory system. Per subband, the audio envelopes were extracted, and their dynamic range was compressed using a power-law operation with exponent 0.6 (as proposed in [2]). Each subband was then bandpass-filtered with the same filter used for the EEG data. The resulting subband envelopes were then summed to construct a single broadband envelope. Finally, the envelope signals were downsampled to 128 Hz to match the sampling rate of the preprocessed EEG. Notes <ol> <li>For subjects 1-3, 6 trials corresponding to 3 conditions (MovingVideo, NoVisuals, StaticVideo) were measured.</li> <li>For subjects 4-16, 8 trials corresponding to 4 conditions (MovingVideo, MovingTargetNoise, NoVisuals, StaticVideo) were measured.</li> <li>For subject 14, trial 2 from the StaticVideo condition was not recorded due to some technical problems.</li> <li>In the dataset, 'FixedVideo' is the alias name for the 'StaticVideo' condition described in [1].</li> <li>The EEG/EOG data was not referenced. Before further analysis, rereferencing the data (e.g., to an arbitrary EEG channel, or the common-average of all channels) is necessary to achieve a better common-mode rejection and thus increase the SNR of recorded data. (for details, see https://www.biosemi.com/faq/cms&drl.htm)</li> </ol> References [1] Rotaru, Iustina, et al. "What are we really decoding? Unveiling biases in EEG-based decoding of the spatial focus of auditory attention." Journal of Neural Engineering 21.1 (2024): 016017. [2] Biesmans, Wouter, et al. "Auditory-inspired speech envelope extraction methods for improved EEG-based auditory attention detection in a cocktail party scenario." IEEE Transactions on neural systems and rehabilitation engineering 25.5 (2016): 402-412.

zenodo40/100

The development of the Leuven Embedded Figures Test (L-EFT)

The Embedded Figures task has a long history as an important clinical and psychological test. There is however some ambiguity as to what exactly the test measures. This ambiguity is brought into clear focus by the fact that some researchers use the test as a measure of a local or global perceptual bias while others regard the test as a good measure of a much broader cognitive capacity related to intelligence or executive function. Given the importance of this test, particularly in clinical domains such as Autism, we have set out to develop a new version of the embedded figures test that more systematically manipulates the perceptual factors that contribute to the effective embedding of a target in a complex context. The result from two experiments will be presented, in which a range of factors, including continuity, complexity, closure and symmetry are revealed as potentially important. Based on these two experiments, a new set of stimuli will be presented which will form the basis of our new version of the Embedded Figures Test, which we plan to launch as an online test using the format of the Leuven Perceptual Organization Screening Test (L-POST). By more systematically manipulating the perceptual factors that contribute to effective embedding, we hope to offer a much more sensitive test, and a test that is better able to differentiate between genuine perceptual, as appose to executive, contributions to performance on this test.

opencc-by-4.0Jan 2014View details →

zenodo40/100

PLD results with Archives of the Old University of Leuven: 22

Combination of screenshots with 'color' and 'sketch1' shader of Archives of the Old University of Leuven: 22 (KU Leuven University Archives), i.e. the front of the pendant seal of Emperor Charles V, rendered in PLDviewer 7.0.05.

opencc-by-4.0May 2018View details →

zenodo40/100

The Portraits Collection Dataset of KU Leuven Libraries, Special Collections

Full dataset from the Portraits Collection offered as open data for digital humanities research and other creative engagement

openother-openAug 2019View details →

opencc-by-4.0Jan 2024View details →

Video-EEG Encoding-Decoding Dataset KU Leuven

If using this dataset, please cite the following paper and the current Zenodo repository. This dataset is described in detail in the following paper: <a href="https://iopscience.iop.org/article/10.1088/1741-2552/ad2333/meta">[1] Yao, Y., Stebner, A., Tuytelaars, T., Geirnaert, S., & Bertrand, A. (2024). Identifying temporal correlations between natural single-shot videos and EEG signals. Journal of Neural Engineering, 21(1), 016018. doi:10.1088/1741-2552/ad2333</a> The associated code is available at: <a href="https://github.com/YYao-42/Identifying-Temporal-Correlations-Between-Natural-Single-shot-Videos-and-EEG-Signals?tab=readme-ov-file">https://github.com/YYao-42/Identifying-Temporal-Correlations-Between-Natural-Single-shot-Videos-and-EEG-Signals?tab=readme-ov-file</a> <h2>Introduction</h2> The research work leading to this dataset was conducted at the Department of Electrical Engineering (ESAT), KU Leuven. This dataset contains electroencephalogram (EEG) data collected from 19 young participants with normal or corrected-to-normal eyesight when they were watching a series of carefully selected YouTube videos. The videos were muted to avoid the confounds introduced by audio. For synchronization, a square box was encoded outside of the original frames and flashed every 30 seconds in the top right corner of the screen. A photosensor, detecting the light changes from this flashing box, was affixed to that region using black tape to ensure that the box did not distract participants. The EEG data was recorded using a BioSemi ActiveTwo system at a sample rate of 2048 Hz. Participants wore a 64-channel EEG cap, and 4 electrooculogram (EOG) sensors were positioned around the eyes to track eye movements. The dataset includes a total of (19 subjects x 63 min + 9 subjects x 24 min) of data. Further details can be found in the following section. <h2>Content</h2> <ul> <li>YouTube Videos: Due to copyright constraints, the dataset includes links to the original YouTube videos along with precise timestamps for the segments used in the experiments. The features proposed in [1] (Object Flow) have been extracted and can be downloaded here: <a href="https://drive.google.com/file/d/1J1tYrxVizrl1xP-W1imvlA_v-DPzZ2Qh/view?usp=sharing">https://drive.google.com/file/d/1J1tYrxVizrl1xP-W1imvlA_v-DPzZ2Qh/view?usp=sharing</a>.</li> <li>Raw EEG Data: Organized by subject ID, the dataset contains EEG segments corresponding to the presented videos. Both EEGLAB .set files (containing metadata) and .fdt files (containing raw data) are provided, which can also be read by popular EEG analysis Python packages such as MNE. <ul> <li>The naming convention links each EEG segment to its corresponding video. E.g., the EEG segment 01_eeg corresponds to video 01_Dance_1, 03_eeg corresponds to video 03_Acrob_1, Mr_eeg corresponds to video Mr_Bean, etc.</li> <li>The raw data have 68 channels. The first 64 channels are EEG data, and the last 4 channels are EOG data. The position coordinates of the standard BioSemi headcaps can be downloaded here: <a href="https://www.biosemi.com/download/Cap_coords_all.xls">https://www.biosemi.com/download/Cap_coords_all.xls</a>.</li> <li>Due to minor synchronization ambiguities, different clocks in the PC and EEG recorder, and missing or extra video frames during video playback (rarely occurred), the length of the EEG data may not perfectly match the corresponding video data. The difference, typically within a few milliseconds, can be resolved by truncating the modality with the excess samples.</li> </ul> </li> <li>Signal Quality Information: A supplementary .txt file detailing potential bad channels. Users can opt to create their own criteria for identifying and handling bad channels.</li> </ul> The dataset is divided into two subsets: Single-shot and MrBean, based on the characteristics of the video stimuli. <h3>Single-shot Dataset</h3> The stimuli of this dataset consist of 13 single-shot videos (63 min in total), each depicting a single individual engaging in various activities such as dancing, mime, acrobatics, and magic shows. All the participants watched this video collection. <table> <tbody> <tr> <th>Video ID</th> <th>Link</th> <th>Start time (s)</th> <th>End time (s)</th> </tr> </tbody> <tbody> <tr> <td>01_Dance_1</td> <td><a href="https://youtu.be/uOUVE5rGmhM">https://youtu.be/uOUVE5rGmhM</a></td> <td>8.54</td> <td>231.20</td> </tr> <tr> <td>03_Acrob_1</td> <td><a href="https://youtu.be/DjihbYg6F2Y">https://youtu.be/DjihbYg6F2Y</a></td> <td>4.24</td> <td>231.91</td> </tr> <tr> <td>04_Magic_1</td> <td><a href="https://youtu.be/CvzMqIQLiXE">https://youtu.be/CvzMqIQLiXE</a></td> <td>3.68</td> <td>348.17</td> </tr> <tr> <td>05_Dance_2</td> <td><a href="https://youtu.be/f4DZp0OEkK4">https://youtu.be/f4DZp0OEkK4</a></td> <td>5.05</td> <td>227.99</td> </tr> <tr> <td>06_Mime_2</td> <td><a href="https://youtu.be/u9wJUTnBdrs">https://youtu.be/u9wJUTnBdrs</a></td> <td>5.79</td> <td>347.05</td> </tr> <tr> <td>07_Acrob_2</td> <td><a href="https://youtu.be/kRqdxGPLajs">https://youtu.be/kRqdxGPLajs</a></td> <td>183.61</td> <td>519.27</td> </tr> <tr> <td>08_Magic_2</td> <td><a href="https://youtu.be/FUv-Q6EgEFI">https://youtu.be/FUv-Q6EgEFI</a></td> <td>3.36</td> <td>270.62</td> </tr> <tr> <td>09_Dance_3</td> <td><a href="https://youtu.be/LXO-jKksQkM">https://youtu.be/LXO-jKksQkM</a></td> <td>5.61</td> <td>294.17</td> </tr> <tr> <td>12_Magic_3</td> <td><a href="https://youtu.be/S84AoWdTq3E">https://youtu.be/S84AoWdTq3E</a></td> <td>1.76</td> <td>426.36</td> </tr> <tr> <td>13_Dance_4</td> <td><a href="https://youtu.be/0wc60tA1klw">https://youtu.be/0wc60tA1klw</a></td> <td>14.28</td> <td>217.18</td> </tr> <tr> <td>14_Mime_3</td> <td><a href="https://youtu.be/0Ala3ypPM3M">https://youtu.be/0Ala3ypPM3M</a></td> <td>21.87</td> <td>386.84</td> </tr> <tr> <td>15_Dance_5</td> <td><a href="https://youtu.be/mg6-SnUl0A0">https://youtu.be/mg6-SnUl0A0</a></td> <td>15.14</td> <td>233.85</td> </tr> <tr> <td>16_Mime_6</td> <td><a href="https://youtu.be/8V7rhAJF6Gc">https://youtu.be/8V7rhAJF6Gc</a></td> <td>31.64</td> <td>388.61</td> </tr> </tbody> </table> <h3>MrBean Dataset</h3> Additionally, 9 participants watched an extra 24-minute clip from the first episode of Mr. Bean, where multiple (moving) objects may exist and interact, and the camera viewpoint may change. The subject IDs and the signal quality files are inherited from the single-shot dataset. <table> <tbody> <tr> <th>Video ID</th> <th>Link</th> <th>Start time (s)</th> <th>End time (s)</th> </tr> </tbody> <tbody> <tr> <td>Mr_Bean</td> <td><a href="https://www.youtube.com/watch?v=7Im2I6STbms">https://www.youtube.com/watch?v=7Im2I6STbms</a></td> <td>39.77</td> <td>1495.00</td> </tr> </tbody> </table> <h2>Acknowledgement</h2> This research is funded by the Research Foundation - Flanders (FWO) project No G081722N, junior postdoctoral fellowship fundamental research of the FWO (for S. Geirnaert, No. 1242524N), the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No 802895), the Flemish Government (AI Research Program), and the PDM mandate from KU Leuven (for S. Geirnaert, No PDMT1/22/009). We also thank the participants for their time and effort in the experiments. <h2>Contact Information</h2> Executive researcher: Yuanyuan Yao, <a href="mailto:yuanyuan.yao@kuleuven.be">yuanyuan.yao@kuleuven.be</a> Led by: Prof. Alexander Bertrand, <a href="mailto:alexander.bertrand@kuleuven.be">alexander.bertrand@kuleuven.be</a>

Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Leuven City centre, Belgium

Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Leuven City Centre (50°52'48"N 4°42'0" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016).  The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).

Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Leuven Casa Blanca, Belgium

Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Casa Blanca neighbourhood Leuven (50°52'48"N, 4°43'48"E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016).  The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).

Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Leuven Casa Blanca, Belgium

Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of the Casa Blanca Neighbourhood Leuven (50°52'48"N 4°43'48"E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016).  The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).

Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Leuven City centre, Belgium

Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of city centre of Leuven (50°52'48"N, 4°42'0"E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016).  The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).

opencc-by-4.0Jan 2018View details →

WP2 T2.4 MEDIA T07 Live pilot 2 raw files, Cyclocross Soudal Leuven

Two zip files - Directive and Omnidirectional. Inside the first one there is a proxy file of the TV broadcast of the filmed cyclocross men’s elite race. The zip file Omni has the 4 cameras and their respective raw files, that would require further editing to create a product in the form of a simulated live cyclocross experience, using either the ImmersiaTV plug-in or Cinegy Live.

PM_122340_B_Leuven

File Name: PM_122340_B_Leuven.jpg Sublocation: Parochiekerk Sint-Pieter Location: Leuven Province: Vlaams-Brabant Country: Belgium Header: Parochiekerk Sint-Pieter, buitenzicht Description: Parish church ("Parochiekerk Sint-Pieter") Exterior Keywords: Belgium, Cultural heritage, Europe, Gothic, Leuven, Styles, Vlaams-Brabant Author: Photo: Paul M.R. Maeyaert Copyright: Paul M.R. Maeyaert

PM_122355_B_Leuven

File Name: PM_122355_B_Leuven.jpg Sublocation: Oude Markt Location: Leuven Province: Vlaams-Brabant Country: Belgium Header: Eclectische huisgevels Description: House facades Eclecticism Keywords: Belgium, Cultural heritage, Europe, Leuven, Vlaams-Brabant Author: Photo: Paul M.R. Maeyaert Copyright: Paul M.R. Maeyaert