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Animation of the blast loaded object
<p>An animation of the motion of a blast loaded object. The motion was tracked using a MEMS 6-axis sensor and corrected in the earth frame-of-reference using the method of quaternions.</p>
Dataset for paper "The Weights can be Harmful: Pareto Search versus Weighted Search in Multi-Objective Search-Based Software Engineering"
<p>This contains the dataset matched with the code on the GitHub page: https://github.com/ideas-labo/pareto-vs-weight-for-sbse.</p>
EDOPT: Event-camera 6-DoF Dynamic Object Pose Tracking
<p>The dataset can be used to test your event-based 6-DoF pose tracking algorithm.</p> <p>If you use any of this data, please cite the following publication:</p> <p>@inproceedings{glover2024,<br> title={EDOPT: Event-camera 6-DoF Dynamic Object Pose Tracking },<br> author={Glover, Arren and Gava, Luna and Li, Zhichao and Bartolozzi, Chiara},<br> booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)},<br> year={2024}<br>}</p> <p>The dataset includes event-driven data and ground truth of 5 objects: dragon, jell-o, mustard, soup can, and spam. For each object, six different motions on independent axes are recorded. </p> <p>To import .log files containing events, we suggest <a href="https://github.com/event-driven-robotics/bimvee">bimvee</a> Python library.</p> <p>Specifically, use the functions to import .log files:</p> <p>data = importIitYarp(filePathOrName=input_path)</p> <p>Ground-truth .csv files have 8 columns, each one corresponding to a different measure: </p> <p>timestamp | x | y | z | qx | qy | qz | qw</p> <p>x, y, z refer to the object position in the camera reference frame, while qx, qy, qz and qw refer to the object orientation expressed in quaternions. </p>
Defining objective clusters for rabies virus sequences using affinity propagation clustering
<table> <tbody> <tr> <td>Rabies is caused by lyssaviruses, and is one of the oldest known zoonoses. In recent years, more than 21,000 nucleotide sequences of rabies viruses (RABV), from the prototype species rabies lyssavirus, have been deposited in public databases. Subsequent phylogenetic analyses in combination with metadata suggest geographic distributions of RABV. However, these analyses somewhat experience technical difficulties in defining verifiable criteria for cluster allocations in phylogenetic trees inviting for a more rational approach. Therefore, we applied a relatively new mathematical clustering algorythm named ‘affinity propagation clustering’ (AP) to propose a standardized sub-species classification utilizing full-genome RABV sequences. Because AP has the advantage that it is computationally fast and works for any meaningful measure of similarity between data samples, it has previously been applied successfully in bioinformatics, for analysis of microarray and gene expression data, however, cluster analysis of sequences is still in its infancy. Existing (516) and original (46) full genome RABV sequences were used to demonstrate the application of AP for RABV clustering. On a global scale, AP proposed four clusters, i.e. New World cluster, Arctic/Arctic-like, Cosmopolitan, and Asian as previously assigned by phylogenetic studies. By combining AP with established phylogenetic analyses, it is possible to resolve phylogenetic relationships between verifiably determined clusters and sequences. This workflow will be useful in confirming cluster distributions in a uniform transparent manner, not only for RABV, but also for other comparative sequence analyses.</td> </tr> </tbody> </table>
Visualizations for multiple objects
<p>The visualizations for multiple objects</p>
MILAN project: raw images of deep sky objects captured with a Stellina observation station -- [2022-10]
<p>During the MILAN research project (MachIne Learning for AstroNomy), the research team uses Stellina observation stations to collect raw images of deep sky objects. </p> <p>The Stellina observation station has an apochromatic ED doublet with an aperture of 80 mm and a focal length of 400 mm - focal ratio of f/5. It is equipped with a Sony IMX 178 CMOS sensor with a resolution of 6.4 million pixels (3096 x 2080 pixels). An anti-pollution filter is placed in front of the camera sensor. The station also has a fully automated alt-azimuth mount: setup, object tracking and focusing are also automatic. Stellina also has an integrated field rotator that adapts to the target.</p> <p>Thus, the research team built a dataset that faithfully represents what can be obtained during classical Electronically Assisted Astronomy sessions in the Luxembourg Greater Region:<br> - targets: representative set of deep sky objects from the Messier / NGC / IC / Barnard catalogs during the period.<br> - type of targets: nebulaes, planetary nebula, galaxies, globular clusters, open clusters<br> - for each observation session: sub-frames of 10 seconds exposure time and total cumulative duration of ~15/30 minutes (depending of the target) -- bad sub-frames were not filtered.</p> <p>The dataset is composed of ZIP files -- and each zip file contain raw images in FITS format (Flexible Image Transport System): data come directly from the Sony IMX178 sensor of the Stellina observation station (no debayerisation and no post processing).</p> <p>This research was funded by the Luxembourg National Research Fund (FNR), grant reference 15872557.</p> <p>To refer to or to cite this work, please use: "Parisot, O., Hitzelberger, P., Bruneau, P., Krebs, G., Destruel, C., & Vandame, B. (2023). MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope. Data in Brief, 48, 109133."</p> <p> </p> <p>MILAN project team members: Olivier Parisot (LIST), Patrik Hitzelberger (LIST), Pierrick Bruneau (LIST), Gilles Krebs (VAONIS, Christophe Destruel (VAONIS), Benoît Vandame (VAONIS).</p> <p>More information about the MILAN project: https://www.fnr.lu/results-2021-1-bridges-call/.</p> <p>More information about VAONIS instruments: https://vaonis.com</p> <p>More information about Luxembourg of Science and Technology (LIST): https://www.list.lu</p> <p>Data license for files: Attribution-NonCommercial-NoDerivatives 4.0 International</p>
Objective assessment of the relationship between quantified neuron morphological features and convolutional neural network image analysis
<p>Relevant data for the publication titled: Objective assessment of the relationship between quantified neuron morphological features and convolutional neural network image analysis</p>
Mental comparison of 3D objects is based on 2D optical flow computations: human data
<p>Human behavioural data for manuscript:</p> <p><strong>Mental comparison of 3D objects is based on 2D optical flow computations.</strong></p>
MILAN project: raw images of deep sky objects captured with a Stellina observation station -- [2022-11]
<p>During the MILAN research project (MachIne Learning for AstroNomy), the research team uses Stellina observation stations to collect raw images of deep sky objects. </p> <p>The Stellina observation station has an apochromatic ED doublet with an aperture of 80 mm and a focal length of 400 mm - focal ratio of f/5. It is equipped with a Sony IMX 178 CMOS sensor with a resolution of 6.4 million pixels (3096 x 2080 pixels). An anti-pollution filter is placed in front of the camera sensor. The station also has a fully automated alt-azimuth mount: setup, object tracking and focusing are also automatic. Stellina also has an integrated field rotator that adapts to the target.</p> <p>Thus, the research team built a dataset that faithfully represents what can be obtained during classical Electronically Assisted Astronomy sessions in the Luxembourg Greater Region:<br> - targets: representative set of deep sky objects from the Messier / NGC / IC / Barnard catalogs during the period.<br> - type of targets: nebulaes, planetary nebula, galaxies, globular clusters, open clusters<br> - for each observation session: sub-frames of 10 seconds exposure time and total cumulative duration of ~15/30 minutes (depending of the target) -- bad sub-frames were not filtered.</p> <p>The dataset is composed of ZIP files -- and each zip file contain raw images in FITS format (Flexible Image Transport System): data come directly from the Sony IMX178 sensor of the Stellina observation station (no debayerisation and no post processing).</p> <p>This research was funded by the Luxembourg National Research Fund (FNR), grant reference 15872557.</p> <p>To refer to or to cite this work, please use: "Parisot, O., Hitzelberger, P., Bruneau, P., Krebs, G., Destruel, C., & Vandame, B. (2023). MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope. Data in Brief, 48, 109133."</p> <p> </p> <p>MILAN project team members: Olivier Parisot (LIST), Patrik Hitzelberger (LIST), Pierrick Bruneau (LIST), Gilles Krebs (VAONIS, Christophe Destruel (VAONIS), Benoît Vandame (VAONIS).</p> <p>More information about the MILAN project: https://www.fnr.lu/results-2021-1-bridges-call/.</p> <p>More information about VAONIS instruments: https://vaonis.com</p> <p>More information about Luxembourg of Science and Technology (LIST): https://www.list.lu</p> <p>Data license for files: Attribution-NonCommercial-NoDerivatives 4.0 International</p>
MILAN project: raw images of deep sky objects captured with a Stellina observation station -- [2022-12]
<p>During the MILAN research project (MachIne Learning for AstroNomy), the research team uses Stellina observation stations to collect raw images of deep sky objects. </p> <p>The Stellina observation station has an apochromatic ED doublet with an aperture of 80 mm and a focal length of 400 mm - focal ratio of f/5. It is equipped with a Sony IMX 178 CMOS sensor with a resolution of 6.4 million pixels (3096 x 2080 pixels). An anti-pollution filter is placed in front of the camera sensor. The station also has a fully automated alt-azimuth mount: setup, object tracking and focusing are also automatic. Stellina also has an integrated field rotator that adapts to the target.</p> <p>Thus, the research team built a dataset that faithfully represents what can be obtained during classical Electronically Assisted Astronomy sessions in the Luxembourg Greater Region:<br> - targets: representative set of deep sky objects from the Messier / NGC / IC catalogs during the period.<br> - type of targets: nebulaes, planetary nebula, galaxies, globular clusters, open clusters<br> - for each observation session: sub-frames of 10 seconds exposure time and total cumulative duration of ~15/30 minutes (depending of the target) -- bad sub-frames were not filtered.</p> <p>The dataset is composed of ZIP files -- and each zip file contain raw images in FITS format (Flexible Image Transport System): data come directly from the Sony IMX178 sensor of the Stellina observation station (no debayerisation and no post processing).</p> <p>This research was funded by the Luxembourg National Research Fund (FNR), grant reference 15872557.</p> <p>To refer to or to cite this work, please use: "Parisot, O., Hitzelberger, P., Bruneau, P., Krebs, G., Destruel, C., & Vandame, B. (2023). MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope. Data in Brief, 48, 109133."</p> <p> </p> <p>MILAN project team members: Olivier Parisot (LIST), Patrik Hitzelberger (LIST), Pierrick Bruneau (LIST), Gilles Krebs (VAONIS, Christophe Destruel (VAONIS), Benoît Vandame (VAONIS).</p> <p>More information about the MILAN project: https://www.fnr.lu/results-2021-1-bridges-call/.</p> <p>More information about VAONIS instruments: https://vaonis.com</p> <p>More information about Luxembourg of Science and Technology (LIST): https://www.list.lu</p> <p>Data license for files: Attribution-NonCommercial-NoDerivatives 4.0 International</p> <p> </p>
Object 6-1: Video Demonstration of Low C to Low C quarter tone sharp
<p>A brief video demonstration showing the challenges of performing a low C to low C quarter tone sharp on the bass clarinet</p>
MILAN project: raw images of deep sky objects captured with a Stellina observation station -- [2023-01]
<p>During the MILAN research project (MachIne Learning for AstroNomy), the research team uses Stellina observation stations to collect raw images of deep sky objects. </p> <p>The Stellina observation station has an apochromatic ED doublet with an aperture of 80 mm and a focal length of 400 mm - focal ratio of f/5. It is equipped with a Sony IMX 178 CMOS sensor with a resolution of 6.4 million pixels (3096 x 2080 pixels). An anti-pollution filter is placed in front of the camera sensor. The station also has a fully automated alt-azimuth mount: setup, object tracking and focusing are also automatic. Stellina also has an integrated field rotator that adapts to the target.</p> <p>Thus, the research team built a dataset that faithfully represents what can be obtained during classical Electronically Assisted Astronomy sessions in the Luxembourg Greater Region:<br> - targets: representative set of deep sky objects from the Messier / NGC / IC catalogs during the period.<br> - type of targets: nebulaes, planetary nebula, galaxies, globular clusters, open clusters<br> - for each observation session: sub-frames of 10 seconds exposure time and total cumulative duration of ~15/30 minutes (depending of the target) -- bad sub-frames were not filtered.</p> <p>The dataset is composed of ZIP files -- and each zip file contain raw images in FITS format (Flexible Image Transport System): data come directly from the Sony IMX178 sensor of the Stellina observation station (no debayerisation and no post processing).</p> <p>This research was funded by the Luxembourg National Research Fund (FNR), grant reference 15872557.</p> <p>To refer to or to cite this work, please use: "Parisot, O., Hitzelberger, P., Bruneau, P., Krebs, G., Destruel, C., & Vandame, B. (2023). MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope. Data in Brief, 48, 109133."</p> <p> </p> <p>MILAN project team members: Olivier Parisot (LIST), Patrik Hitzelberger (LIST), Pierrick Bruneau (LIST), Gilles Krebs (VAONIS, Christophe Destruel (VAONIS), Benoît Vandame (VAONIS).</p> <p>More information about the MILAN project: https://www.fnr.lu/results-2021-1-bridges-call/.</p> <p>More information about VAONIS instruments: https://vaonis.com</p> <p>More information about Luxembourg of Science and Technology (LIST): https://www.list.lu</p> <p>Data license for files: Attribution-NonCommercial-NoDerivatives 4.0 International</p>
Dataset for Runaway and Hypervelocity Stars from Compact Object Encounters in Globular Clusters
<p>The dataset used for Cabrera & Rodriguez 2023. If building from the showyourwork-enabled GitHub, the file can be directly unzipped into the <code>src/data</code> folder.</p> <p>The top-level contains folders for <code>CMC</code> model- and Milky Way globular cluster-delineated data (<code>cmc</code> and <code>mwgcs</code>, respectively), and some auxiliary files, including the composite catalogs. Within the two folders are subfolders for each of the <code>CMC</code> models and MWGCs. The <code>CMC</code> model folders contain some of the output files from the <code>CMC Cluster Catalog</code> <a href="https://ui.adsabs.harvard.edu/abs/2020ApJS..247...48K">(Kremer+20</a>) (used in this project to examine GC evolution), and also two files <code>output_N-10.txt</code> (which has data for all <code>Fewbody</code> realizations for the model) and <code>output_N-10_ejections.txt</code> (which has data for all ejections from the model). The columns in the former file are regrettably not labeled, but correspond to the following parameters, many of which are direct <code>Fewbody</code> arguments:</p> <table align="center"> <thead> <tr> <th scope="col">#</th> <th scope="col">Parameter</th> <th scope="col">Description</th> <th scope="col">Units</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>time</td> <td>Physical time of encounter in <code>CMC </code>model</td> <td>Myr</td> </tr> <tr> <td>2</td> <td>b</td> <td>Impact parameter</td> <td>a1</td> </tr> <tr> <td>3</td> <td>vinf</td> <td>Velocity of single object at infinity</td> <td>v_crit</td> </tr> <tr> <td>4</td> <td>a1</td> <td>Initial semi-major axis of binary</td> <td>AU</td> </tr> <tr> <td>5</td> <td>e1</td> <td>Initial binary eccentricity</td> <td>-</td> </tr> <tr> <td>6</td> <td>vesc</td> <td>Local escape velocity in <code>CMC </code>model</td> <td>km/s</td> </tr> <tr> <td>7</td> <td>m10</td> <td>Mass of first binary component</td> <td>Msun</td> </tr> <tr> <td>8</td> <td>m11</td> <td>Mass of second binary component</td> <td>Msun</td> </tr> <tr> <td>9</td> <td>m0</td> <td>Mass of single object</td> <td>Msun</td> </tr> <tr> <td>10</td> <td>r10</td> <td>Radius of first binary component</td> <td>Rsun</td> </tr> <tr> <td>11</td> <td>r11</td> <td>Radius of second binary component</td> <td>Rsun</td> </tr> <tr> <td>12</td> <td>r0</td> <td>Radius of single object</td> <td>Rsun</td> </tr> <tr> <td>13</td> <td>k10</td> <td>BSE k-type of first binary component</td> <td>-</td> </tr> <tr> <td>14</td> <td>k11</td> <td>BSE k-type of second binary component</td> <td>-</td> </tr> <tr> <td>15</td> <td>k0</td> <td>BSE k-type of single object</td> <td>-</td> </tr> <tr> <td>16</td> <td>s</td> <td>Random seed for <code>Fewbody</code></td> <td>-</td> </tr> <tr> <td>17</td> <td>type_i</td> <td> <p>Index classifying initial system (see below)</p> </td> <td>-</td> </tr> <tr> <td>18</td> <td>type_f</td> <td>Index classifying final system (see below)</td> <td>-</td> </tr> <tr> <td>19</td> <td>v_crit</td> <td>Critical velocity of encounter (<a href="https://ui.adsabs.harvard.edu/abs/2004MNRAS.352....1F/abstract">Fregeau+04</a>)</td> <td>km/s</td> </tr> <tr> <td>20</td> <td>a_fin</td> <td>Final semi-major axis of binary (0 if no binary is present)</td> <td>AU</td> </tr> <tr> <td>21</td> <td>e_fin</td> <td>Final binary eccentricity (identically 0 if no binary is present)</td> <td>-</td> </tr> <tr> <td>22</td> <td>Lx</td> <td>x-component of the initial angular momentum of the system</td> <td>code</td> </tr> <tr> <td>23</td> <td>Ly</td> <td>y-component of the initial angular momentum of the system</td> <td>code</td> </tr> <tr> <td>24</td> <td>Lz</td> <td>z-component of the initial angular momentum of the system</td> <td>code</td> </tr> <tr> <td>25</td> <td>Lbinx</td> <td>x-component of the angular momentum of the initial binary</td> <td>code</td> </tr> <tr> <td>26</td> <td>Lbiny</td> <td>y-component of the angular momentum of the initial binary</td> <td>code</td> </tr> <tr> <td>27</td> <td>Lbinz</td> <td>z-component of the angular momentum of the initial binary</td> <td>code</td> </tr> <tr> <td>28</td> <td>Ei</td> <td>Initial energy of the encounter</td> <td>code</td> </tr> <tr> <td>29</td> <td>DeltaEfrac</td> <td>Fractional change in energy by the time of termination</td> <td>-</td> </tr> <tr> <td>30/34/38</td> <td>vfin0/1/2</td> <td>Final velocity of the final top-level object with<code> Fewbody </code>index 0/1/2 (<a href="https://ui.adsabs.harvard.edu/abs/2004MNRAS.352....1F/abstract">Fregeau+04</a>)</td> <td>v_crit</td> </tr> <tr> <td>31/35/39</td> <td>kf0/1/2</td> <td>BSE k-type of the same</td> <td>-</td> </tr> <tr> <td>32/36/40</td> <td>Rmin0/1/2</td> <td>Minimum distance between this object and any other object during the encounter</td> <td>AU</td> </tr> <tr> <td>33/37/41</td> <td>Rmin_j0/1/2</td> <td><code>Fewbody </code>index of the other object at closest passage</td> <td>AU</td> </tr> </tbody> </table> <p>The initial encounter classifying indices are as follows, where "C" denotes a compact object and "S" a star:</p> <table align="center"> <thead> <tr> <th scope="col">type_i</th> <th scope="col">Configuration</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>(C,C)+S</td> </tr> <tr> <td>2</td> <td>(C,S)+C</td> </tr> <tr> <td>3</td> <td>(C,S)+S</td> </tr> <tr> <td>4</td> <td>(S,S)+C</td> </tr> </tbody> </table> <p>The final encounter classifying indices are as follows, using the initial <code>Fewbody </code>object indices to specify if objects end up in a binary ((a,b)) or if they merge (a:b) (note that <code>Fewbody</code> initializes all binary-single encounters in the <code>type_f=3</code> configuration, i.e. the object index 0 is assigned to the single):</p> <table align="center"> <thead> <tr> <th scope="col">type_f</th> <th scope="col">Configuration</th> </tr> </thead> <tbody> <tr> <td>0</td> <td>0+1+2 (Ionization)</td> </tr> <tr> <td>1</td> <td>(0,1)+2</td> </tr> <tr> <td>2</td> <td>(0,2)+1</td> </tr> <tr> <td>3</td> <td>0+(1,2)</td> </tr> <tr> <td>4</td> <td>0:1+2</td> </tr> <tr> <td>5</td> <td>0:2+1</td> </tr> <tr> <td>6</td> <td>1:2+0</td> </tr> <tr> <td>7</td> <td>0:1:2</td> </tr> <tr> <td>-2</td> <td>binary</td> </tr> <tr> <td>-3</td> <td>hierarchical triple</td> </tr> </tbody> </table> <p>The columns in <code>output_N-10_ejections.txt</code> are labeled, and use many of the same headers in the first table; the object indices for fields 30-41 are dropped because each row in this file corresponds to an escaper. The two additions are <code>mf</code> and <code>rf</code>, which indicate the mass and radius of the ejected object.</p> <p>Each of the <code>mwgcs</code> folders contain the FITS files described in Appendix B of the text. There are also <code>output_N-10_ejections.txt</code> files similar to the ones for the <code>CMC</code> models; the MWGC versions contain the additional fields below:</p> <table align="center"> <thead> <tr> <th scope="col">Parameter</th> <th scope="col">Description</th> <th scope="col">Units</th> </tr> </thead> <tbody> <tr> <td>vout</td> <td>Velocity of object at the time of ejection from the representative model</td> <td>km/s</td> </tr> <tr> <td>X/Y/Z</td> <td>Galactocentric X/Y/Z coordinate of object at the present day</td> <td>kpc</td> </tr> <tr> <td>U/V/W</td> <td>Galactocentric U/V/W velocity of object at the present day</td> <td>km/s</td> </tr> </tbody> </table> <p> </p>
Pre-built Symphonypy reference objects that can be downloaded and used to map new query datasets
<p>Pre-built Symphonypy reference objects that can be downloaded and used to map new query datasets. The same data as in <a href="https://zenodo.org/record/5090425#.Y9EmLC8w3tg">https://zenodo.org/record/5090425</a>, but for Python port for Symphony.</p> <p>The Symphony algorithm is used to perform reference mapping to these atlases. </p> <ul> <li>Paper: <a href="https://www.nature.com/articles/s41467-021-25957-x">https://www.nature.com/articles/s41467-021-25957-x</a></li> <li>Usage: <a href="https://github.com/potulabe/symphonypy">https://github.com/potulabe/symphonypy</a></li> </ul> <p><strong>References available for download:</strong></p> <ol> <li>10x PBMCs Atlas (pbmcs_10x_reference.h5ad)</li> <li>Pancreatic Islet Cells Atlas (pancreas_plate-based_reference.h5ad)</li> <li>Fetal Liver Hematopoiesis Atlas (fetal_liver_reference_3p.h5ad)</li> <li>Healthy Fetal Kidney Atlas (kidney_healthy_fetal_reference.h5ad)</li> <li>T cell CITE-seq atlas (tbru_ref.h5ad)</li> <li>Cross-tissue Inflammatory Immune Atlas (zhang_reference.h5ad)</li> <li>Tabula Muris Senis (FACS) Atlas (TMS_facs_reference.h5ad)</li> </ol>
Behavioral responses in an object segmentation task and neural responses during passive viewing
<p>The rodent visual system has attracted great interest in recent years due to its experimental tractability, but the fundamental mechanisms used by the mouse to represent the visual world remain unclear. In the primate, researchers have argued from both behavioral and neural evidence that a key step in visual representation is "figure-ground segmentation," the delineation of figures as distinct from backgrounds [<a href="https://www.biorxiv.org/content/10.1101/2021.07.04.451059v1#ref-1">1</a>–<a href="https://www.biorxiv.org/content/10.1101/2021.07.04.451059v1#ref-4">4</a>]. To determine if mice also show behavioral and neural signatures of figure-ground segmentation, we trained mice on a figure-ground segmentation task where figures were defined by gratings and naturalistic textures moving counterphase to the background. Unlike primates, mice were severely limited in their ability to segment figure from ground using the opponent motion cue, with segmentation behavior strongly dependent on the specific carrier pattern. Remarkably, when mice were forced to localize naturalistic patterns defined by opponent motion, they adopted a strategy of brute force memorization of texture patterns. In contrast, primates, including humans, macaques, and mouse lemurs, could readily segment figures independent of carrier pattern using the opponent motion cue. Consistent with mouse behavior, neural responses to the same stimuli recorded in mouse visual areas V1, RL, and LM also did not support texture-invariant segmentation of figures using opponent motion. Modeling revealed that the texture dependence of both the mouse's behavior and neural responses could be explained by a feedforward neural network lacking explicit segmentation capabilities. These findings reveal a fundamental limitation in the ability of mice to segment visual objects compared to primates.</p>
Dataset of WiFi-based Environment-independent In-baggage Object Identification System
<p><strong>Description:</strong></p> <p>The dataset of environment-independent in-baggage object identification system leveraging low-cost WiFi. The dataset contains the extracted CSI features from 14 representative in-baggage objects of 4 different materials. The experiments are conducted in 3 different office environments with different sizes. We hope this dataset will help researchers to reproduce the former work of in-baggage object identification through WiFi sensing. </p> <p> </p> <p><strong>Dataset Format: </strong></p> <p>.mat files</p> <p> </p> <p><strong>Section 1: Device Configuration: </strong></p> <ul> <li> <p><strong>Transmitter: </strong>Aaronia HyperLOG 7060 direction antenna with a Dell Inspiron 3910 desktop for control. </p> </li> <li> <p><strong>Receiver: </strong>Hawking HD9DP orthogonal antennas with a Dell Inspiron 3910 desktop for control</p> </li> <li> <p><strong>NIC:</strong> Atheros QCA9590. The configuration and installation guide of CSI tool can be found at <a href="https://wands.sg/research/wifi/AtherosCSI/">https://wands.sg/research/wifi/AtherosCSI/</a></p> </li> <li> <p><strong>WiFi Packet Rate: </strong>1000 pkts/s </p> </li> </ul> <p> </p> <p><strong>Section 2: Data Format</strong></p> <p>We provide the CSI features through .mat files. The details are shown in the following:</p> <ul> <li> <p>14 different objects made of 4 different materials are included in 3 different environments and 3 different days.</p> </li> <li> <p>Each object is tested for 60 seconds and repeated for 3 times. </p> </li> <li> <p>The dataset file name is presented as "Object_Number". The detailed information are:</p> <ul> <li> <p>Object: The object we involved in the experiment (e.g., book, laptop)</p> </li> <li> <p>Number: The number of repeats. </p> </li> </ul> </li> </ul> <p> </p> <p><strong>Section 3: Experimental Setups</strong></p> <p>There are 3 different office experiment setups for our data collection. The detailed setups are shown in the paper. For the objects, we involve 14 types of objects made of 4 different materials. </p> <ul> <li> <p><strong>Environments: </strong></p> <ul> <li> <p>3 different environments are involved, including 3 office environments with the size of 15 ft × 13 ft, 16 ft × 12 ft, 28 ft × 23 ft, respectively. </p> </li> <li> <p>For each room environment, data is collected on different days and with different furniture settings (i.e., 2 desks and 2 chairs are moved at least 3 ft. )</p> </li> </ul> </li> <li> <p><strong>Representative objects: </strong></p> <ul> <li> <p>Data is collected using 14 representative objects of 4 different materials including fiber: book, magazine, newspaper; metal: thermal cup, laptop; cotton/polyester: cotton T-shirts (×2), cotton T-shirts (×4), hoodie, polyester T-shirts, polyester pants; water: 1L bottle with 1L water, 1L bottle with 500ml water, 500ml bottle with 500ml water. </p> </li> </ul> </li> </ul> <p> </p> <p><strong>Section 4: Data Description</strong></p> <p>For our data organization, we separate the data files into different folders based on different days and different environments. Under these folders, data are further distributed in terms of different objects and repeat times. All the files are .mat files, which can be directly read for further applications. </p> <ul> <li> <p><strong>Features of CSI amplitude: </strong>We calculate 7 different types of statistical features, including mean, variance, median, skewness, kurtosis, interquartile range and range, and polarization feature from CSI amplitude. Particularly, we calculate the features for all 56 subcarriers with different operating frequencies and responses to the target object. </p> </li> <li> <p><strong>Features of CSI phase: </strong>For the features of CSI phase, the same features with CSI amplitude are extracted and stored in the dataset. </p> </li> </ul> <p> </p> <p><strong>Section 6: Citations</strong></p> <p>If your work is related to our work, please cite our papers as follows. </p> <p><a href="https://ieeexplore.ieee.org/document/9637801">https://ieeexplore.ieee.org/document/9637801</a></p> <p>Shi, Cong, Tianming Zhao, Yucheng Xie, Tianfang Zhang, Yan Wang, Xiaonan Guo, and Yingying Chen. "Environment-independent in-baggage object identification using wifi signals." In 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS), pp. 71-79. IEEE, 2021.</p> <p> </p>
MILAN project: raw images of deep sky objects captured with a Stellina observation station -- [2023-02]
<p>During the MILAN research project (MachIne Learning for AstroNomy), the research team uses Stellina observation stations to collect raw images of deep sky objects. </p> <p>The Stellina observation station has an apochromatic ED doublet with an aperture of 80 mm and a focal length of 400 mm - focal ratio of f/5. It is equipped with a Sony IMX 178 CMOS sensor with a resolution of 6.4 million pixels (3096 x 2080 pixels). An anti-pollution filter is placed in front of the camera sensor. The station also has a fully automated alt-azimuth mount: setup, object tracking and focusing are also automatic. Stellina also has an integrated field rotator that adapts to the target.</p> <p>Thus, the research team built a dataset that faithfully represents what can be obtained during classical Electronically Assisted Astronomy sessions in the Luxembourg Greater Region:<br> - targets: representative set of deep sky objects from the Messier / NGC / IC catalogs during the period.<br> - type of targets: nebulaes, planetary nebula, galaxies, globular clusters, open clusters<br> - for each observation session: sub-frames of 10 seconds exposure time and total cumulative duration of ~15/30 minutes (depending of the target) -- bad sub-frames were not filtered.</p> <p>The dataset is composed of ZIP files -- and each zip file contain raw images in FITS format (Flexible Image Transport System): data come directly from the Sony IMX178 sensor of the Stellina observation station (no debayerisation and no post processing).</p> <p>This research was funded by the Luxembourg National Research Fund (FNR), grant reference 15872557.</p> <p>To refer to or to cite this work, please use: "Parisot, O., Hitzelberger, P., Bruneau, P., Krebs, G., Destruel, C., & Vandame, B. (2023). MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope. Data in Brief, 48, 109133."</p> <p> </p> <p>MILAN project team members: Olivier Parisot (LIST), Patrik Hitzelberger (LIST), Pierrick Bruneau (LIST), Gilles Krebs (VAONIS, Christophe Destruel (VAONIS), Benoît Vandame (VAONIS).</p> <p>More information about the MILAN project: https://www.fnr.lu/results-2021-1-bridges-call/.</p> <p>More information about VAONIS instruments: https://vaonis.com</p> <p>More information about Luxembourg of Science and Technology (LIST): https://www.list.lu</p> <p>Data license for files: Attribution-NonCommercial-NoDerivatives 4.0 International</p>
Multi-objective near-optimal necessary conditions for multi-sectoral planning - Dataset
<p>Dataset (input and output) of the paper 'Multi-objective near-optimal necessary conditions for multi-sectoral planning'</p>
MATERIALS OF ARCHAEOLOGICAL RESEARCH OF THE ARCHAEOLOGICAL HERITAGE OBJECT CULTURAL LAYER OF VORONEZH ON THE TERRITORY OF THE LAND PLOT AT 1, KLUBNAYA STREET AND 30D, 20-LETIYA OKTYABRYA STREET
<p>Archaeological research on the land plot at the address: Voronezh, 1, Klubnaya Street, and 30d, 20-letiya Oktyabrya Street, was carried out as part of the implementation of the section of project documentation to ensure the safety of the identified cultural heritage site "Cultural layer of the city of Voronezh". As a result of the work, additional information was obtained on the history of the coastal part of the city in the XIX-th-XX-th centuries.</p>
Data for: Effects of domestication and captive breeding on reaction to moving objects: Implications for avoidance behaviors of obstacles and predators by masu salmon Oncorhynchus masou
<p>Domestication and captive breeding can compromise obstacle- and predator-avoidance of animals in the wild. Whereas previous studies only examined these effects in combination, here we examine them individually by comparing the abilities of wild, F1 (offspring of wild parents), and captive-bred (approx. F15) masu salmon (<em>Oncorhynchus</em> <em>masou</em>) to avoid a falling object under experimental conditions. Rates of avoidance failure were low (wild, 12.5%; F1, 10.7%; captive-bred, 8%) under light conditions but increased under dark conditions (wild, 11.1%; F1, 32.1%; captive-bred, 60.0%). We attribute the elevated avoidance-failure rate among F1 fish to the lack of learning opportunities in hatchery environments, and the further elevation of avoidance-failure rate among captive-bred fish to the degradation of sensory organ function. These results imply reduced survival rates for F1 and captive-bred fish in the wild and are consistent with the low stocking efficiencies reported for captive-bred masu salmon.</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.