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Dataset for "Numerical methods for the detection of phase defect structures in excitable media"
<p>This archive contains the numerical methods presented in the publication "Numerical methods for the detection of phase defect structures in excitable media" as well as the data sets these methods have been applied on. The Python module for Ithildin (py_ithildin.zip) contains the actual Python source code of those methods. Additional Python scripts have been used to generate the figures in the paper (scripts-pdl-detection.zip). The optical voltage mapping data (optical_*) has been slightly pre-processed (noise reduction, re-scaling, etc). The second variable for the optical data (optical_20200204114234_v.npy) is a delayed version of the first variable. The other files contain simulation results from several finite differences simulations of the mono-domain model. For details, see our paper.</p> <p><a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0271351"><strong>Numerical methods for the detection of phase defect structures in excitable media</strong></a><br> Kabus D, Arno L, Leenknegt L, Panfilov AV, Dierckx H (2022) Numerical methods for the detection of phase defect structures in excitable media. PLOS ONE 17(7): e0271351. <a href="https://doi.org/10.1371/journal.pone.0271351">https://doi.org/10.1371/journal.pone.0271351</a></p>
Bolaform Surfactant-Induced Au Nanoparticle Assemblies for Reliable Solution-Based Surface-Enhanced Raman Scattering Detection
<p>Related publication: García-Lojo, D; Méndez-Merino, D; Pérez-Juste, I; Acuña, A; García-Río, L; Rodríguez-Patón, A; Pastoriza-Santos, I; Pérez-Juste, J. Bolaform surfactant-induced Au nanoparticle assemblies for reliable solution-based SERS detection. Adv.Mater. Technol. 2022, 2101726. <a href="https://doi.org/10.1002/admt.202101726">https://doi.org/10.1002/admt.202101726</a></p> <p> </p> <p> </p> <p>Abstract:</p> <p>Solution-based surface-enhanced Raman scattering (SERS) detection typically involves the aggregation of citrate-stabilized Au nanoparticles into colloidal assemblies. Although this sensing methodology offers excellent prospects for sensitivity, portability, and speed, it is still challenging to control the assembly process by a salting-out effect, which affects the reproducibility of the assemblies and, therefore, the reliability of the analysis. This work presents an alternative approach that uses a bolaform surfactant, B<sub>20</sub>, to induce the plasmonic assembly. The decrease of the surface charge and the bridging effect, both promoted by the adsorption of B<sub>20</sub>, are hypothesized as the key points governing the assembly. Furthermore, molecular dynamic simulations supported the bridging effect of the B<sub>20</sub> by showing the preferential bridging of surfactant monomers between two adjacent Au(111) slabs. The colloidal assemblies showed excellent SERS capabilities towards the rapid, on-site detection and quantification of beta-blockers and analgesic drugs in the nanomolar regime, with a portable Raman device. Interestingly, the application of state-of-the-art convolutional neural networks, such as ResNet, allows a 100% accuracy in classifying the concentration of different binary mixtures. Finally, the colloidal approach was successfully implemented in a millifluidic chip allowing the automation of the whole process, as well as improving the performance of the sensor in terms of speed, reliability, and reusability without affecting its sensitivity.</p>
The stellar parameters and the quantities of the residual emissions of the detected active stars in the LAMOST-K2 survey
<p>The full Table 1 in <em>Investigation of stellar magnetic activity using variational autoencoder based on low-resolution spectroscopic survey</em> (Xiang, Gu & Cao, 2022, MNRAS, 514, 4781; <a href="https://arxiv.org/abs/2206.07257">arXiv:2206.07257</a>). The columns are LAMOST obsid, K2 ID, Teff, logg, [Fe/H], EW_res_Halpha, EW_res_Ca II 8498, EW_res_Ca II 8542, EW_res_Ca II 8662, log F_Halpha, log F_Ca, log R'_Halpha, log R'_Ca. The chromospheric emissions were detected and measured with the spectral subtraction technique, which removes the inactive template spectra (photospheric contribution) from the observed stellar spectra. In this work, we used the variational autoencoder neural networks to efficiently generate the proper template spectra in a data-driven manner. More details can be found in the associated paper (<a href="https://arxiv.org/abs/2206.07257">https://arxiv.org/abs/2206.07257</a>). The demo code can be found on GitHub (<a href="https://github.com/xylib/vae-for-spectroscopic-survey">https://github.com/xylib/vae-for-spectroscopic-survey</a>).</p>
Detection of Real-World Influence through Social Media
<p><strong>Description. </strong>This dataset corresponds to the resources produced for the following conference paper and its extended version:</p> <ol> <li>J.-V. Cossu, N. Dugué, and V. Labatut, “Detecting Real-World Influence Through Twitter,” in <em>2nd European Network Intelligence Conference (ENIC)</em>, 2015, pp. 83–90. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01164453">hal-01164453</a>⟩ DOI: <a href="http://doi.org/10.1109/ENIC.2015.20">10.1109/ENIC.2015.20</a></li> <li>J.-V. Cossu, V. Labatut, and N. Dugué, “A Review of Features for the Discrimination of Twitter Users: Application to the Prediction of Offline Influence,” <em>Social Network Analysis and Mining </em>6:25, 2016. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01203171">hal-01203171</a>⟩ DOI: <a href="http://doi.org/10.1007/s13278-016-0329-x">10.1007/s13278-016-0329-x</a></li> </ol> <p>Raw data are available through the official RepLab page: <a href="http://nlp.uned.es/replab2014/">http://nlp.uned.es/replab2014/</a> (follow <a href="http://nlp.uned.es/replab2014/replab2014-dataset.tar.gz">http://nlp.uned.es/replab2014/replab2014-dataset.tar.gz</a>)</p> <p><strong>Source code. </strong>The source code used to generate these output is available on GitHub: <a href="https://github.com/CompNet/Influence">https://github.com/CompNet/Influence</a></p> <p><strong>Funding. </strong>This work was partly funded by the French National Research Agency (ANR), through the project <a href="https://anr.fr/Project-ANR-12-CORD-0002">ImagiWeb ANR-12-CORD-0002</a>.</p> <p><strong>Contact. </strong>Jean-Valère Cossu <<a href="mailto:jean-valere.cossu@alumni.univ-avignon.fr">jean-valere.cossu@alumni.univ-avignon.fr</a>></p> <p><strong>Citation. </strong>If you use these data, please cite paper [1] above.</p> <p><br><code>@InProceedings{Cossu2015,</code><br><code> author = {Cossu, Jean-Valère and Dugué, Nicolas and Labatut, Vincent},</code><br><code> title = {Detecting Real-World Influence Through {Twitter}},</code><br><code> booktitle = {2\textsuperscript{nd} European Network Intelligence Conference},</code><br><code> year = {2015},</code><br><code> pages = {83-90},</code><br><code> address = {Karlskrona, SE},</code><br><code> publisher = {IEEE Publishing},</code><br><code> doi = {10.1109/ENIC.2015.20},</code><br><code>}</code></p> <p><strong>Details. </strong>This archive contains all ranking outputs formatted according to the TREC-EVAL tool format. These outputs consist for each domain in a ranked list of user from the most influential to the least influential. For a classification-type evaluation, just consider that users having a score higher than 0.5 are influential.</p> <p>File names correspond to the system (those starting with Cos*, indicate: the method BoT for Bag-of-Tweets, UaD for User-as-Document; the use of the Tweet-Selection strategy files denoted Artex; the learning process with Global or separated models which are noted Multi and last but not least the decision strategy for Bag-of-Tweets: Counting or Sum) or feature name. Files starting with out_* contain the results of logistic regression ranking outputs. Files matrix_auto.dat and matrix_bank.dat contain the data used to feed the PLS model (code: plspm4influence.R).</p> <p>RepLab 2014 uses Twitter data in English and Spanish. The balance between both languages depends on the availability of data for each of the profiles included in the dataset.</p> <p>The training dataset consists of 7,000 Twitter profiles (all with at least 1,000 followers) related to the automotive and banking domains, evaluation is performed separately. Each profile consists of (i) author name; (ii) profile URL and (iii) the last 600 tweets published by the author at crawling time and have been manually labelled by reputation experts either as “opinion maker” (i.e. authors with reputational influence) or “non-opinion maker”. The objective is to find out which authors have more reputational influence (who the opinion makers are) and which profiles are less influential or have no influence at all. </p> <p>Since Twitter ToS do not allow redistribution of tweets, only tweets ids and screen names are provided. Replab organizers provide details about how to download the tweets.</p>
Aerial Multi-Vehicle Detection Dataset
<p><strong>Aerial Multi-Vehicle Detection Dataset</strong>: Efficient road traffic monitoring is playing a fundamental role in successfully resolving traffic congestion in cities. Unmanned Aerial Vehicles (UAVs) or drones equipped with cameras are an attractive proposition to provide flexible and infrastructure-free traffic monitoring. Due to the affordability of such drones, computer vision solutions for traffic monitoring have been widely used. Therefore, this dataset provide images that can be used for either training or evaluating Traffic Monitoring applications. More specifically, it can be used for training an aerial vehicle detection algorithm, benchmark an already trained vehicle detection algorithm, enhance an existing dataset and aid in traffic monitoring and analysis of road segments. </p> <p>The dataset construction involved manually collecting all aerial images of vehicles using UAV drones and manually annotated into three classes 'Car', 'Bus', and ''Truck'.The aerial images were collected through manual flights in road segments in Nicosia or Limassol, Cyprus, during busy hours. The images are in High Quality, Full HD (1080p) to 4k (2160p) but are usually resized before training. All images were manually annotated and inspected afterward with the vehicles that indicate 'Car' for small to medium sized vehicles, 'Bus' for busses, and 'Truck' for large sized vehicles and trucks. All annotations were converted into VOC and COCO formats for training in numerous frameworks. The data collection took part in different periods, covering busy road segments in the cities of Nicosia and Limassol in Cyprus. The altitude of the flights varies between 150 to 250 meters high, with a top view perspective. Some of the images found in this dataset are taken from Harpy Data dataset [1] </p> <p>The dataset includes a total of 9048 images of which 904 are split for validation, 905 for testing, and the rest 7239 for training. </p> <table> <tbody> <tr> <td><strong>Subset</strong></td> <td><strong>Images</strong></td> <td><strong>Car</strong></td> <td><strong>Bus</strong></td> <td><strong>Truck</strong></td> </tr> <tr> <td>Training</td> <td>7239</td> <td>200301</td> <td>1601</td> <td>6247</td> </tr> <tr> <td>Validation</td> <td>904</td> <td>23397 </td> <td>193 </td> <td>727</td> </tr> <tr> <td>Testing</td> <td>905</td> <td>24715</td> <td>208</td> <td>770</td> </tr> </tbody> </table> <p>It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).</p> <p> </p> <p>[1] Makrigiorgis, R., 2021. <em>Harpy Data Dataset</em>. [online] Kios.ucy.ac.cy. Available at: <https://www.kios.ucy.ac.cy/harpydata/> [Accessed 22 September 2022].</p> <p> </p> <p><strong>**NOTE** If you use this dataset in your research/publication please cite us using the following :</strong></p> <blockquote> <p>Rafael Makrigiorgis, Panayiotis Kolios, & Christos Kyrkou. (2022). Aerial Multi-Vehicle Detection Dataset (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7053442</p> </blockquote>
Modelled and Sentinel-1 detected firn aquifers areas in the Antarctic Peninsula
<p>FDM results: This dataset contains firn aquifer extent output from IMAU-FDM (Firn Densification Model), version v1.2A, for the Antarctic Peninsula on a 5.5 km grid. The dataset consists of maps of the extent of simulated seasonal aquifers in at least one year (2017-2020), perennial aquifers in at least on year (2017-2020), and perennial aquifers in all years (2018-2020). Further details are described in Buth et al. (2022).<br> Model adjustments and run were performed by Sanne B. M. Velduijsen.</p> <p>S1 detection results: The GeoTIFF image is the result of the Sentinel-1 firn aquifer detection routine which makes use of the typical delayed increase of SAR backscatter after the peak melt season in case of an aquifer. The image has two bands per year (2017-2020), one containing the DOY80 parameter for the whole Antarctic Peninsula (excluding masked areas, see Buth et al, 2022), the other containing DOY80 only for detected aquifer areas, where it exceeds the threshold of DOY80=105. DOY80 here stands for the day of the year at which 80% of the September Sentinel-1 HH backscatter is reached. Further details are described in Buth et al. (2022).<br> S1 aquifer detection was performed by Lena G. Buth, using the Python API of the Google Earth Engine. The associated code is available as a GitLab project: https://gitlab.awi.de/lenbuth/tc-aquifers</p>
Packaging Industry Anomaly DEtection (PIADE) Dataset
<p>PIADE dataset contains data from five industrial packaging machines:</p> <ul> <li>Machine s_1: from 2020-01-01 14:00:00 to 2021-12-31 13:00:00</li> <li>Machine s_2: from 2020-06-17 08:00:00 to 2021-12-31 07:00:00</li> <li>Machine s_3: from 2020-10-07 12:00:00 to 2022-01-01 23:00:00</li> <li>Machine s_4: from 2020-01-01 01:00:00 to 2022-01-01 23:00:00</li> <li>Machine s_5: from 2020-01-20 08:00:00 to 2022-01-01 12:00:00</li> </ul> <p>## Raw Data</p> <p>Each row represents a production interval, with the following schema:</p> <ul> <li>interval_start: start of the production interval </li> <li>equipment_ID: equipment identifier </li> <li>alarm: alarm code of the active stop reason, if it occurred </li> <li>type: idle, production, downtime, performance_loss or scheduled_downtime </li> <li>start: start of the production interval </li> <li>end: end of the production interval </li> <li>elapsed: duration of the production interval </li> <li>pi: input packages </li> <li>po: output packages </li> <li>speed: speed (packages per hour)</li> </ul> <p>There are 133 different types of alerts, and 429394 rows.<br> </p> <p>## Sequences (1h) data</p> <p>For each piece of equipment, we define sequences of length = 1 hour and we aggregate raw interval data as follows:</p> <ul> <li>'equipment_ID': machine identifier</li> <li>'#changes': changes in machine state</li> <li>'%downtime': time spent in 'downtime' state</li> <li>'%idle': time spent in 'idle' state</li> <li>'%performance_loss': time spent in 'performance loss' state</li> <li>'%production': time spent in production</li> <li>'%scheduled_downtime': time spent in scheduled downtime</li> <li>'count_sum': sum of all alarm occurrences</li> <li>'A_<XXX>': counter of alarm <XXX> occurrences</li> <li>'<state1>/<state2>': number of transitions from <state1> to <state2></li> </ul> <p> </p>
ICESat-2 Arctic Sea Ice Surface Topography from the University of Maryland-Ridge Detection Algorithm: April 2019, 2020, and 2021
<p>This dataset is derived from the ICESat-2 (IS-2) Global Geolocated Photon Height Product (ATL03) using the University of Maryland-Ridge Detection Algorithm (UMD-RDA). The UMD-RDA is applied to ATL03 on a per-shot basis, nominally resulting in elevation measurements at IS-2's maximum along-track resolution of ~0.7 m. From these elevation measurements, the UMD-RDA can measure various sea ice parameters including, but not limited to, individual ridge crests and their respective sail heights, the distance between ridges, and sea ice surface roughness.</p> <p><strong>********Changes in Version 2********</strong></p> <p><em>Version 2 includes a column for time (seconds since 2018-01-01) in all parameter files in addition to longitude, latitude, and parameter value.</em></p> <p><em>The full resolution UMD-RDA derived elevation data was too large to host here, but is available upon request. If you need a particular track or segment for your research please contact me with your request by email: kd</em><em>uncan at umd dot edu</em></p>
Non productive EFAs detection over the Lithuanian pilot region (2021)
<p>In the context of the EU-funded project DIONE (No. 870378), the Super-Resolved Sentinel-2 data were further used to identify small-sized areas that can be characterised of great importance in terms of environmental sustainability, which in the Common Agricultural Policy (CAP) are denoted as Ecological Focus Area (EFA). EFAs are characterised as a portion of farmland area that has to be designed for environmental purposes. For the needs of DIONE, the EFAs regions were identified with the Sentinel-2 & Very High Resolution (VHR) data to be acquired from 01-08-2020 until 31-05-2021 providing tailored information for the needs of the Lithuanian Paying Agency (e.g. NPA).</p> <p><strong>Further Details of the provided EFAs dataset</strong></p> <p>There are only three EFA types present in the dataset given by NPA:</p> <ul> <li><strong>mg0:</strong> Tree group, forest</li> <li><strong>gr0:</strong> Ditch, the canal from 1 m wide</li> <li><strong>ku0:</strong> Pond with coastal vegetation</li> </ul> <p>Due to the nature of the data, the majority of EFA polygons do not contain at least one full Sentinel-2 pixel. To circumvent this issue, the 2.5m resolution output of the multi-temporal SR model is used to produce EFA signals. Using 2.5m resolution, around 20% of FOIs belonging to the <em>"Ditch, canal from 1m wide"</em> class do not contain one full super-resolved pixel at 2.5m resolution. Out of "34157" EFA polygons in the sample area, we have signals for "32732" of them. The majority are missing due to pixelization (~1000 EFA FOIs fully contain less than 1 SR pixel), others are missing due to having no cloud-free observation during the observation period.</p> <p><strong>Details for the model </strong><strong>deployed</strong></p> <p>To classify EFAs an LSTM model is used (same as for crop type classification). The model is trained as a binary classifier using:</p> <ul> <li>LSTM FOIs inside the test area as positive examples</li> <li>Agricultural parcels inside the test area as negative examples</li> </ul> <p>Five models are trained using a 5-fold split to ensure that the results are predicted on data the model has not seen.</p> <p><strong>Results</strong></p> <table> <caption><strong>Overall performance of the models</strong></caption> <thead> <tr> <th scope="col">Accuracy = 91.1</th> </tr> </thead> <tbody> <tr> <td> </td> <td><strong>Precision </strong></td> <td><strong>Recall</strong></td> <td><strong> F1 </strong></td> <td><strong>Count</strong></td> </tr> <tr> <td><strong>EFA</strong></td> <td>92.3 </td> <td>93.2</td> <td>92.8</td> <td>32732</td> </tr> <tr> <td><strong>NOT EFA</strong></td> <td>89.2</td> <td>87.9</td> <td>88.6</td> <td>21005</td> </tr> </tbody> </table> <p>In reality, we are only interested to detect wrongly / fraudulently claimed EFAs - meaning FOIs that are claimed as EFAs and are in reality some kind of agricultural land. The model is less confident in predicting EFAs as EFAs for the <em>"Ditch/Canal"</em> group. This is probably due to the fact that these polygons are long and narrow and the most susceptible to registration errors and shifts which we know are present in the SR data from which the signals were produced.</p> <p>This dataset is comprised of one geopackage file, the <em>"efas-w-results-v0-binary.gpkg", which</em> was computed for the Lithuanian pilot region<em>. </em>Descriptions are given below.</p> <ul> <li><strong>classification</strong>: Whether the model thinks the FOI is an EFA (`EFA`) or not (`NOT_EFA`)</li> <li><strong>classification_score:</strong> the pseudoprobability of the EFA prediction as assigned by the crop-group model <ul> <li> a score close to 1 indicates that the model is very confident in the prediction</li> <li> a score close to 0 indicates that the model is not confident in the prediction</li> </ul> </li> </ul>
Non productive EFAs detection over the Cypriotic pilot region (2021)
<p>In the context of the EU-funded project DIONE (No. 870378), the Super-Resolved Sentinel-2 data were further used to identify small-sized areas that can be characterised of great importance in terms of environmental sustainability, which in the Common Agricultural Policy (CAP) are denoted as Ecological Focus Area (EFA). EFAs are characterised as a portion of farmland area that has to be designed for environmental purposes. For the needs of DIONE, the EFAs regions were identified with the Sentinel-2 & Very High Resolution (VHR) data to be acquired from 01-08-2020 until 25-06-2021 providing tailored information for the needs of the Cypriotic Paying Agency (e.g. CAPO).</p> <p><strong>Further Details of the provided EFAs dataset</strong></p> <p>The EFA polygons were downloaded from the EFA endpoints provided by CAPO.</p> <p>There are three EFA types present in the dataset:</p> <ul> <li><strong>Points: </strong>Trees</li> <li><strong>Lines</strong>: Ditches</li> <li><strong>Polygons</strong>: Productive EFAs - The current layer will not be exploited in the content of the DIONE project, as it is predominately focused on the detection of *non-productive (stable)* EFA elements.</li> </ul> <p>The points and lines had to be buffered to an extent of 5m so that the signals could be downloaded. Because the elements considered are very small and they would not fit into one full Sentinel-2 pixel, signals were downloaded from the 2.5m resolution output of the multi-temporal Super Resolution model. The observation period for EFAs detection is from 01-08-2020 until 25-06-2021. Results are provided for 52691 point polygons and 454 line polygons located in the test area.</p> <p><strong>Details for the model </strong><strong>deployed</strong></p> <p>To classify EFAs an LSTM model is used. The model is trained as a binary classifier using:</p> <ul> <li>Signals from EFA (buffered point and line geometries) FOIs inside the test area as positive examples</li> <li>Signals from agricultural parcels inside the test area as negative examples</li> </ul> <p>Five models are trained using a 5-fold split to ensure that the results are predicted on data the model has not seen.</p> <p><strong>Results</strong></p> <table> <caption><strong>Overall performance of the models</strong></caption> <thead> <tr> <th scope="col">Accuracy = 82.9</th> </tr> </thead> <tbody> <tr> <td> </td> <td><strong>Precision </strong></td> <td><strong>Recall</strong></td> <td><strong> F1 </strong></td> <td><strong>Count</strong></td> </tr> <tr> <td><strong>EFA</strong></td> <td>84.9</td> <td>92.9</td> <td>88.7</td> <td>53008</td> </tr> <tr> <td><strong>NOT EFA</strong></td> <td>74.9</td> <td>56.4</td> <td>64.3</td> <td>19992</td> </tr> </tbody> </table> <p>In reality, we are only interested in one side of the confusion matrix. We want to detect wrongly / fraudulently claimed EFAs - meaning FOIs that are claimed as EFAs and are in reality some kind of agricultural land. In these cases, 93% of the claimed EFAs were confirmed to be EFAs and for 7% of the EFA FOIs, the model disagrees.</p> <p>This dataset is comprised of one geopackage file, the <em>"efas_summary.gpkg", which</em> was computed for the Cypriotic pilot region<em>. </em>Descriptions are given below.</p> <ul> <li> <p><strong>classification: </strong>Whether the model thinks the FOI is an EFA (`EFA`) or not (`NOT_EFA`)</p> </li> <li> <p><strong>classification_score:</strong><strong> </strong>The pseudoprobability of the EFA prediction as assigned by the crop-group mode</p> <ul> <li> <p>a score close to 1 indicates that the model is very confident in the prediction</p> </li> <li> <p>a score close to 0.5 indicates that the model is not confident in the prediction</p> </li> </ul> </li> </ul>
Aerial Vessels Detection Dataset
<p><strong>Aerial Vessels Detection Dataset</strong>: The dataset construction involved manually collecting all aerial images of vessels using UAV drones and manually annotated into three classes 'Person', 'Ship', and ''Boat'. The aerial images were collected through manual flights above Cyprus Coasts in Limassol, Famagusta and Larnaca areas. The main purpose of this dataset is to be used for marine monitoring. Capturing footage over large areas and localizing any unwanted vessels entering an area of interest, can aid in localizing refugees that illegally enter a country or manage marine traffic for commercial use.</p> <p>The images are collected in 720p and Full HD (1080p) but are usually resized before training.</p> <p>All images were manually annotated and inspected afterward with the vessels that indicate 'Person' for people detection, 'Boat' for small to medium-sized boats, and 'Ship' for large ships or commercial ships. All annotations were converted into VOC and COCO formats and initially labeled in YOLO, for training in numerous frameworks. The data collection took part in different periods.</p> <p>The dataset includes a total of 10252 images of which 1024 are split for validation, 1025 for testing, and the rest 8203 for training. </p> <table> <tbody> <tr> <td><strong>Subset</strong></td> <td><strong>Images</strong></td> <td><strong>Person</strong></td> <td><strong>Boat</strong></td> <td><strong>Ship</strong></td> </tr> <tr> <td>Training</td> <td>8203</td> <td>219</td> <td>48550</td> <td>920</td> </tr> <tr> <td>Validation</td> <td>1024</td> <td>7</td> <td>5890</td> <td>143</td> </tr> <tr> <td>Testing</td> <td>1025</td> <td>13</td> <td>5247</td> <td>109</td> </tr> </tbody> </table> <p>It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).</p> <p> </p> <p><strong>**NOTE** If you use this dataset in your research/publication please cite us using the following :</strong></p> <blockquote> <p>Rafael Makrigiorgis, Panayiotis Kolios, & Christos Kyrkou. (2022). Aerial Vessels Detection Dataset (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7076145</p> </blockquote>
The Object Detection for Olfactory References (ODOR) Dataset
<p><strong>The Object Detection for Olfactory References (ODOR) Dataset</strong></p> <p>Real-world applications of computer vision in the humanities require algorithms to be robust against artistic abstraction, peripheral objects, and subtle differences between fine-grained target classes. </p> <p>Existing datasets provide instance-level annotations on artworks but are generally biased towards the image centre and limited with regard to detailed object classes. The ODOR dataset fills this gap, offering 38,116 object-level annotations across 4,712 images, spanning an extensive set of 139 fine-grained categories. </p> <p>It has challenging dataset properties, such as a detailed set of categories, dense and overlapping objects, and spatial distribution over the whole image canvas. </p> <p>Inspiring further research on artwork object detection and broader visual cultural heritage studies, the dataset challenges researchers to explore the intersection of object recognition and smell perception.</p> <p><strong>How to use</strong></p> <p>The annotations are provided in COCO JSON format. To represent the two-level hierarchy of the object classes, we make use of the supercategory field in the categories array as defined by COCO. In addition to the object-level annotations, we provide an additional CSV file with image-level metadata, which includes content-related fields, such as Iconclass codes or image descriptions, as well as formal annotations, such as artist, license, or creation year. </p> <p>In addition to a zip containing the dataset images, we provide links to their source collections in the metadata file and a Python script to conveniently download the artwork images (`download_imgs.py`).</p> <p>The mapping between the `images` array of the `annotations.json` and the `metadata.csv` file can be accomplished via the `file_name` attribute of the elements of the `images` array and the unique `File Name` column of the `metadata.csv` file, respectively.</p>
CVoiceFake (crafted by SafeEar: Content Privacy-Preserving Audio Deepfake Detection)
<h1><strong>Introduction:</strong></h1> <p>CVoiceFake (small) is a dataset that features a random selection of 10% of samples from the entire collection. This dataset encompasses <strong>five common languages (English, Chinese, German, French, and Italian)</strong> and utilizes <strong>multi-advanced and classical voice cloning techniques</strong> (Parallel WaveGAN, Multi-band MelGAN, Style MelGAN, Griffin-Lim, WORLD, and DiffWave) to produce audio samples that bear a high resemblance to authentic audio.</p> <ol> <li><strong>Parallel WaveGAN</strong>: As a non-autoregressive vocoder-based model, Parallel WaveGAN produces high-fidelity audio rapidly, ideal for efficient and quality deepfake generation.</li> <li><strong>Multi-band MelGAN</strong>: Multi-band MelGAN is a variant of MelGAN that divides the frequency spectrum into sub-bands for faster and more stable multi-lingual vocoder training, enhancing the robustness and scalability of the dataset.</li> <li><strong>Style MelGAN</strong>: Style MelGAN is designed to capture fine prosodic and stylistic nuances of speech, making it particularly compelling for deepfake applications that require high levels of expressivity and variation in speech synthesis.</li> <li><strong>Griffin-Lim</strong>: This algorithm reconstructs waveforms from spectrograms using an iterative phase estimation method. Though less high-fidelity than neural vocoders, it serves as a traditional baseline for comparing deepfake generation.</li> <li><strong>WORLD</strong>: WORLD is a statistical parameter-based voice synthesis system that offers fine control over the spectral and prosodic features of the synthesized audio. Its fine manipulation is useful for crafting the nuanced variations needed in deepfake datasets.</li> <li>We have also built the SOTA diffusion-based deepfake audio (DiffWave); please contact the author at <code>xinfengli@zju.edu.cn</code> if you are interested in the dataset, particularly the DiffWave portion. Furthermore, any additional discussions are welcomed.<br><strong>DiffWave</strong>: DiffWave is a diffusion probability model for waveform generation. It converts the white noise signal into structured waveform through a Markov chain, capable of both conditional and unconditional generation tasks. DiffWave represents the advanced synthesis method for its fast synthesis speed and high synthesis quality.</li> </ol> <h1><strong>🔥</strong><strong>News:</strong></h1> <p>Please note that we recently released our DiffWave subset in Version 2 in comparison to Version 1, which is available on <a href="../records/14062964" target="_blank" rel="noopener">CVoiceFake Full</a>. You can download the file named CVoiceFake_Large_diffwave_update.tar.gz.xx, and after unzipping it, you will find it retains the same file structure as before.<br> <strong>| CVoiceFake_Large_diffwave_update.tar.gz.00 |<br> | CVoiceFake_Large_diffwave_update.tar.gz.01 |</strong></p> <p> </p> <h1><strong>Full Dataset & Project Page:</strong></h1> <p>The whole dataset is available on <a href="../records/14062964" target="_blank" rel="noopener">CVoiceFake Full</a> as well. Please kindly also refer to the project page: <a title="SafeEar Website" href="https://safeearweb.github.io/Project/" target="_blank" rel="noopener">SafeEar Website</a>.</p> <p> </p> <h1><strong>Citation:</strong></h1> <p>If you find our paper/code/benchmark helpful, please kindly consider citing this work with the following reference:</p> <pre><code>@inproceedings{li2024safeear,<br> author = {Li, Xinfeng and Li, Kai and Zheng, Yifan and Yan, Chen and Ji, Xiaoyu, and Xu, Wenyuan},<br> title = {{SafeEar: Content Privacy-Preserving Audio Deepfake Detection}},<br> booktitle = {Proceedings of the 2024 {ACM} {SIGSAC} Conference on Computer and Communications Security (CCS)}<br> year = {2024},<br>} </code></pre> <div> <div> </div> </div>
BirdVox-full-night: a dataset for avian flight call detection in continuous recordings
<p>BirdVox-full-night: a dataset for avian flight call detection in continuous recordings<br> ======================================================================================<br> Version 3.0, March 2018.</p> <p><br> Created By<br> ----------</p> <p>Vincent Lostanlen (1, 2, 3), Justin Salamon (2, 3), Andrew Farnsworth (1), Steve Kelling (1), and Juan Pablo Bello (2, 3).</p> <p>(1): Cornell Lab of Ornithology (CLO)<br> (2): Center for Urban Science and Progress, New York University<br> (3): Music and Audio Research Lab, New York University</p> <p>https://wp.nyu.edu/birdvox</p> <p> </p> <p>Description<br> -----------</p> <p>The BirdVox-full-night dataset contains 6 audio recordings, each about ten hours in duration. These recordings come from ROBIN autonomous recording units, placed near Ithaca, NY, USA during the fall 2015. They were captured on the night of September 23rd, 2015, by six different sensors, originally numbered 1, 2, 3, 5, 7, and 10.</p> <p>Andrew Farnsworth used the Raven software to pinpoint every avian flight call in time and frequency. He found 35402 flight calls in total. He estimates that about 25 different species of passerines (thrushes, warblers, and sparrows) are present in this recording. Species are not labeled in BirdVox-full-night, but it is possible to tell apart thrushes from warblers and sparrrows by looking at the center frequencies of their calls. The annotation process took 102 hours.</p> <p>The dataset can be used, among other things, for the research,<br> development and testing of bioacoustic classification models, including the reproduction of the results reported in [1].</p> <p>For details on the hardware of ROBIN recording units, we refer the reader to [2].</p> <p>[1] V. Lostanlen, J. Salamon, A. Farnsworth, S. Kelling, J. Bello. BirdVox-full-night: a dataset and benchmark for avian flight call detection. Proc. IEEE ICASSP, 2018.</p> <p>[2] J. Salamon, J. P. Bello, A. Farnsworth, M. Robbins, S. Keen, H. Klinck, and S. Kelling. Towards the Automatic Classification of Avian Flight Calls for Bioacoustic Monitoring. PLoS One, 2016.</p> <p>@inproceedings{lostanlen2018icassp,<br> title = {BirdVox-full-night: a dataset and benchmark for avian flight call detection},<br> author = {Lostanlen, Vincent and Salamon, Justin and Farnsworth, Andrew and Kelling, Steve and Bello, Juan Pablo},<br> booktitle = {Proc. IEEE ICASSP},<br> year = {2018},<br> published = {IEEE},<br> venue = {Calgary, Canada},<br> month = {April},<br> }</p> <p> </p> <p>Data Files<br> ------------</p> <p>The BirdVox-full-night_flac-audio folder contains the recordings as FLAC files, sampled at 24 kHz, with a single channel (mono).</p> <p> </p> <p>Metadata Files<br> --------------</p> <p>The BirdVox-full-night_csv-annotations folder contains JAMS files, where each row correspond to a different location in the time frequency domain (columns "Time (s)" and "Freq (Hz)").</p> <p>The approximate GPS coordinates of the sensors (latitudes and longitudes rounded to 2 decimal points) and UTC timestamps corresponding to the start of the recording for each sensor are included as CSV files in the main directory.</p> <p> </p> <p>Please acknowledge BirdVox-full-night in academic research<br> ----------------------------------------------------------</p> <p>When BirdVox-full-night is used for academic research, we would highly appreciate it if scientific publications of works partly based on this dataset cite the following publication:</p> <p>V. Lostanlen, J. Salamon, A. Farnsworth, S. Kelling, J. Bello. BirdVox-full-night: a dataset and benchmark for avian flight call detection, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2018.</p> <p>The creation of this dataset was supported by NSF grants 1125098 (BIRDCAST) and 1633259 (BIRDVOX), a Google Faculty Award, the Leon Levy Foundation, and two anonymous donors.</p> <p> </p> <p>Conditions of Use<br> -----------------</p> <p>Dataset created by Vincent Lostanlen, Justin Salamon, Andrew Farnsworth, Steve Kelling, and Juan Pablo Bello.</p> <p>The BirdVox-full-night dataset is offered free of charge under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license:<br> https://creativecommons.org/licenses/by/4.0/</p> <p>The dataset and its contents are made available on an "as is" basis and without warranties of any kind, including without limitation satisfactory quality and conformity, merchantability, fitness for a particular purpose, accuracy or completeness, or absence of errors. Subject to any liability that may not be excluded or limited by law, Cornell Lab of Ornithology is not liable for, and expressly excludes all liability for, loss or damage however and whenever caused to anyone by any use of the BirdVox-full-night dataset or any part of it.</p> <p> </p> <p>Feedback<br> -----------</p> <p>Please help us improve BirdVox-full-night by sending your feedback to:<br> vincent.lostanlen@gmail.com and af27@cornell.edu</p> <p>In case of a problem, please include as many details as possible.</p> <p> </p> <p>Acknowledgements<br> ----------------</p> <p>Jessie Barry, Ian Davies, Tom Fredericks, Jeff Gerbracht, Sara Keen, Holger Klinck, Anne Klingensmith, Ray Mack, Peter Marchetto, Ed Moore, Matt Robbins, Ken Rosenberg, and Chris Tessaglia-Hymes.</p> <p>We acknowledge that the land on which the data was collected is the unceded territory of the Cayuga nation, which is part of the Haudenosaunee (Iroquois) confederacy.</p>
REASSURE (H2020 731591) Datasets AES128 for Understanding Leakage Detection - Section 2
<p>Datasets collection for the Section 2 of "Understanding Leakage Detection" tutorial (presented also at CARDIS 2018). These datasets have been collected adopting the ChipWhisperer Lite board, running the popular AES Furious implementation. The code and the additional material of the tutorial can be found at http://reassure.eu/leakage-detection-tutorial/.</p> <p> </p>
Data set for anomaly detection on a HPC system
<p>This data set contains the data collected on the DAVIDE HPC system (CINECA & E4 & University of Bologna, Bologna, Italy) in the period March-May 2018.</p> <p>The data set has been used to train a autoencoder-based model to automatically detect anomalies in a semi-supervised fashion, on a real HPC system.</p> <p>This work is described in:</p> <p>1) "Anomaly Detection using Autoencoders in High Performance Computing Systems", <a href="https://arxiv.org/search/cs?searchtype=author&query=Borghesi%2C+A">Andrea Borghesi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bartolini%2C+A">Andrea Bartolini</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lombardi%2C+M">Michele Lombardi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Milano%2C+M">Michela Milano</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Benini%2C+L">Luca Benini,</a> IAAI19 (proceedings in process) -- https://arxiv.org/abs/1902.08447</p> <p>2) "Online Anomaly Detection in HPC Systems", <a href="https://arxiv.org/search/cs?searchtype=author&query=Borghesi%2C+A">Andrea Borghesi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Libri%2C+A">Antonio Libri</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Benini%2C+L">Luca Benini</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bartolini%2C+A">Andrea Bartolini, </a>AICAS19 (proceedings in process) -- https://arxiv.org/abs/1811.05269</p> <p>See the git repository for usage examples & details --> https://github.com/AndreaBorghesi/anomaly_detection_HPC</p>
Hybrid Approaches to Detect Comments Violating Macro Norms on Reddit
<p>[<strong>Content warning: </strong><em>Files may contain instances of highly inflammatory and offensive content.]</em></p> <p><br> This dataset was generated as an extension of our <a href="https://www.cc.gatech.edu/~eshwar3/uploads/3/8/0/4/38043045/eshwar-norms-cscw2018.pdf">CSCW 2018 paper</a>:</p> <p><em>Eshwar Chandrasekharan, Mattia Samory, Shagun Jhaver, Hunter Charvat, Amy Bruckman, Cliff Lampe, Jacob Eisenstein, and Eric Gilbert. 2018. The Internet’s Hidden Rules: An Empirical Study of Reddit Norm Violations at Micro, Meso, and Macro Scales. Proceedings of the ACM on Human-Computer Interaction 2, CSCW (2018), 32.</em></p> <p><strong>Description:</strong></p> <p>Working with over 2M removed comments collected from 100 different communities on Reddit (subreddit names listed in data/study-subreddits.csv), we identified <strong>8 macro norms</strong>, i.e., norms that are widely enforced on most parts of Reddit. We extracted these macro norms by employing a hybrid approach—classification, topic modeling, and open-coding—on comments identified to be norm violations within at least 85 out of the 100 study subreddits. Finally, we labelled over 40K Reddit comments removed by moderators according to the specific type of macro norm being violated, and make this dataset publicly available (also available on <a href="https://github.com/ceshwar/reddit-norm-violations">Github</a>).</p> <p>For each of the labeled topics, we identified the top 5000 removed comments that were best fit by the LDA topic model. In this way, we identified over 5000 removed comments that are examples of each type of macro norm violation described in the paper. The removed comments were sorted by their topic fit, stored into respective files based on the type of norm violation they represent, and are made available on this repo.</p> <p>Here we make the following datasets publicly available:</p> <p>* <strong>1 file</strong> containing the log of over 2M removed comments obtained from the top 100 subreddits between May 2016 to March 2017, after filtering out the following comments: 1) comments by u/AutoModerator, 2) replies to removed comments (i.e., children of the poisoned tree - refer to the paper for more information), and 3) non-readable comments (not utf-8 encoded).</p> <p>* <strong>8 files</strong>, each containing 5000+ removed comments obtained from Reddit, are stored in: data/macro-norm-violations/ , and they are split into different files based on the macro norm they violated. Each new line in the files represent a comment that was posted on Reddit between May 2016 to March 2017, and subsequently removed by subreddit moderators for violating community norms. All comments were preprocessed using the script in code/preprocessing-reddit-comments.py , in order to do the following: 1. remove new lines, 2. convert text to lowercase, and 3. strip numbers and punctuations from comments.</p> <p><strong>Description of 1 file</strong> containing over<em> 2M removed comments </em>from <em>100 subreddits.</em></p> <ul> <li>"reddit-removal-log.csv" - all comments that were removed from the 100 study subreddits during the study period described above (post-filtering).</li> </ul> <p><strong>Descriptions of each file</strong> containing <em>5059 comments</em> (that were removed from Reddit, and preprocessed)<strong> violating macro norms </strong>present in data/macro-norm-violations/:</p> <ul> <li>"macro-norm-violations-n10-t0-misogynistic-slurs.csv" - Comments that use misogynistic slurs.</li> <li>"macro-norm-violations-n15-t2-hatespeech-racist-homophobic.csv" - Comments containing hate speech that is racist or homophobic.</li> <li>"macro-norm-violations-n10-t3-opposing-political-views-trump.csv", "macro-norm-violations-n15-t10-opposing-political-views-trump.csv" - Comments with opposing political views around Trump (depends on originating sub).</li> <li>"macro-norm-violations-n10-t4-verbal-attacks-on-Reddit.csv" - Comments containing verbal attacks on Reddit or specific subreddits.</li> <li>"macro-norm-violations-n10-t5-porno-links.csv" - Comments with pornographic links.</li> <li>"macro-norm-violations-n10-t8-personal-attacks.csv", "macro-norm-violations-n10-t9-personal-attacks.csv"- Comments containing personal attacks.</li> <li>"macro-norm-violations-n15-t3-abusing-and-criticisizing-mods.csv" - Comments abusing and criticisizng moderators.</li> <li>"macro-norm-violations-n15-t9-namecalling-claiming-other-too-sensitive.csv" - Comments with name-calling, or claiming that the other person is too sensitive.</li> </ul> <p>More details about the dataset can be found on arXiv: <a href="https://arxiv.org/abs/1904.03596">https://arxiv.org/abs/1904.03596</a></p>
RSOI: Sea ice properties collected during the detection of oil on-in-and-under ice experiment
<p>Data collected during the detection of oil on-in-and-under ice oil experiment lead at CRREL in 2014/2015.<br> - Sea ice core properties (salinity and temperature)</p> <p>- Sea ice porosity and permeability field, derived from salinity and temperature</p> <p>- Oil volumes, in the lens derived from underwater acoustic measurement, are included in the RSOI-data-*.xlsx spreadsheet.</p>
Ramularia detection dates by country
<p>Dark and light themed maps showing the detection rate of ramularia by country. Detection rates are taken from the following two publications.</p> <pre>Walters, D. R., Havis, N. D., and Oxley, S. J. P. <strong>2008</strong>. Ramularia collo-cygni: the biology of an emerging pathogen of barley. FEMS Microbiology Letters 279:1–7. https://doi.org/10.1111/j.1574-6968.2007.00986.x </pre> <pre>Havis N. D., Brown J. K. M., Clemente G., Frei P., Jedryczka M., Kaczmarek J., Kaczmarek M., Matusinsky P., McGrann GRD., Pereyra S., Piotrowska M., Sghyer H., Tellier A., and Hess M., <strong>2015</strong>. Ramularia collo-cygni - an emerging pathogen of barley crops. Phytopathology 105: 895-904. https://doi.org/10.1094/PHYTO-11-14-0337-FI </pre> <p>Country outlines were taken from Natural Earth: http://www.naturalearthdata.com/downloads/50m-cultural-vectors/</p>
Table S27: Target and identified unknown organic micropollutants detected in surface water samples taken during heavy rain events
<p>In the following table, peak intensities of detected organic micropollutants in water samples are displayed.</p> <p>This data table is part of the appendix of Chapter 4 of the PhD thesis “Novel approaches to identify drivers of chemical stress in small rivers” by Liza-Marie Beckers prepared at RWTH Aachen University and at the Helmholtz Centre for Environmental Research-UFZ. In Chapter 4, precipitation-related pollutant patterns and indicator compounds during heavy rain events were identified in the Holtemme River by nontarget screening and cluster analysis. The table contains peak heights of organic micropollutants detected in water samples taken during heavy rain events in the Holtemme River (Saxony – Anhalt, Germany). The table is structured into the following columns: Compound name, use class of compound (e.g., pharmaceutical or pesticide), distinction between target or identified unknown compounds, mass-to-charge ratio (m/z), retention time (RT), assignment to a pattern identified by cluster analysis (i.e., “Base” or “Quick”), the probability of belonging to the assigned pattern as number between 0 and 1 as well as the peak height of the compound in each sample. The samples are indicated by "B" for "bottle" and a number from 1-16. The use class “NA” indicates that now major use class for this compound could be identified.</p> <p>The sampling was triggered by combined sewer overflow at a wastewater treatment plant upstream of the sampling point. Samples were taken by an automated sampler in 30-min composite samples for 8 hours resulting in 16 samples per rain event. In total, 6 heavy rain events from May to September 2016 were sampled during this study. The table is divided into 6 subtables (i.e., Table S27 A-F). Each subtable displays compounds and their peak heights detected in samples from one heavy rain event. The different rain events are abbreviated by the sampling date:</p> <p>Table S27A displays results from the rain event samples May 29<sup>th</sup> 2016 : E2905</p> <p>Table S27B displays results from the rain event samples June 01<sup>st</sup> 2016 : E0106</p> <p>Table S27C displays results from the rain event samples June 24<sup>th</sup> 2016 : E1306</p> <p>Table S27D displays results from the rain event samples June 13<sup>th</sup> 2016 : E2406</p> <p>Table S27E displays results from the rain event samples July 13<sup>th</sup> 2016 : E1307</p> <p>Table S27F displays results from the rain event samples September 17<sup>th</sup> 2016 : E1709</p> <p>Chemical analysis of the water samples was performed by liquid chromatography (UltiMate 3000 LC system (Thermo Scientific)) coupled to high resolution mass spectrometry (Q Exactive Plus, Thermo Scientific) with a heated electrospray ionization (HESI) source. Nontarget screening was performed as it allows for a comprehensive characterization of the chemical exposure during heavy rain events. However, only annotated target compounds and unknown compounds identified by structure elucidation are presented in the table. Details on data evaluation methods are described in Chapter 4 of the PhD thesis.</p> <p>Beckers, L.M. (2019): Novel approaches to identify drivers of chemical stress in small rivers. RWTH Aachen University, Aachen.</p>
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.