Find research datasets worth reusing

Maps of ecosystem multifunctionality and ecological connectivity for identifying Green Infrastructure networks in the European Alps

High resolution raster datasets (20 meters) containing the results of an ecological connectivity and an ecosystem multifunctionality assessment for identifying Green Infrastructure networks in 10 pilot regions of the European Alps, modelled as part of the LUIGI Interreg Alpine Space project. Pilot regions include: department of Isère (FR), departments of Savoie and Haute-Savoie (FR), Munich Metropolitan Region (DE), Central Area of Salzburg (AT), South Burgenland (AT), Gori&scaron;ka region (SI), South Tyrol (IT), canton of Grisons (CH), Metropolitan City of Milan (IT), and Metropolitan City of Turin (IT). For a preview of the data and the results available for each pilot region <a href="https://www.alpine-space.org/projects/luigi/en/project-results/d.t1.2.1-pilot-regions-policy-briefs">click here</a> Further information on the LUIGI project is available at: <a href="https://www.alpine-space.org/projects/luigi/en/home">https://www.alpine-space.org/projects/luigi/en/home</a> <a href="https://webassets.eurac.edu/31538/1661510408-luigi-wp1-technical-annex-mapping-a-green-infrastructure-network-in-the-alpine-space.pdf">https://webassets.eurac.edu/31538/1661510408-luigi-wp1-technical-annex-mapping-a-green-infrastructure-network-in-the-alpine-space.pdf </a> The datasets include: <ul> <li>a map for ecosystem service-based multifunctionality calculated out of the average of 11 standardized ecosystem service indicators: water provision, crop potential, timber production, fodder provision, pollination potential, carbon sequestration, nitrogen retention, natural hazard mitigation, runoff retention, outdoor recreation, and landscape aesthetics.</li> <li>a map of the modelled Ecological Network composed of core areas and ecological corridors. Corridors are modelled for medium-large forest mammal species and represent least-cost pathways connecting core areas. Different classes indicate areas with different levels of current ecological connectivity starting from core areas to areas in cities or anthropized land with no connectivity. Modeled corridors are presented in two classes to mirror different levels of prioritization and management actions.</li> <li>a map of the resistance of the landscape to the movement of forest mammal species. The landscape resistance raster has been developed by reclassifying and aggregating a high resolution (5m) land use and land cover map. Resistance values have been determined in relation to the naturalness of different land use and land cover classes. In this context, land use or landscape resistance is intended as the opposite of habitat suitability.</li> </ul>

opencc-by-sa-4.0Jun 2022View details →

Topological Network of the Dutch Fairway Information System

Topological fairway network derived from the <a href="https://www.vaarweginformatie.nl/">Dutch Fairway Information System</a>. The data is processed to be topological connected and usable for transport network analysis.   Files <code>network_digital_twin_v0.3.json</code> This is the json (for web) file for the Rhine corridor extending from Rotterdam (NLD) to Basel (Switzerland). <code>network_digital_twin_v0.3.pickle</code> This is the pickled (for performance) file for the Rhine corridor extending from Rotterdam (NLD) to Basel (Switzerland). <code>network_digital_twin_v0.3.zip</code> This is the shapefile (for gis) file for the Rhine corridor extending from Rotterdam (NLD) to Basel (Switzerland). Methodological information For details about the creation of the network see <a href="https://github.com/Deltares/digitaltwin-waterway/blob/master/notebooks/Build_FIS_network.ipynb">Build_FIS_network.ipynb</a>. Updates in version 0.3.0: - Added information on discharge dependent <a href="https://github.com/Deltares/digitaltwin-waterway/blob/feature/sailing/notebooks/velocities/read-velocities.ipynb">velocities</a> and <a href="https://github.com/Deltares/digitaltwin-waterway/blob/feature/sailing/notebooks/waterlevels/read_waterlevels.ipynb">waterlevels</a> in <code>river_waterlevel.geojson</code> and <code>river_velocity.geojson</code> - Moved information on <a href="https://github.com/Deltares/digitaltwin-waterway/blob/feature/sailing/notebooks/fis-network/generate_bathymetry.ipynb">bathymetry</a> to separate file<code>edges_0.3_with_bathy.geojson</code> - More structures added (fix in source dataset) - Edge and node id's are now always strings - Removed yaml file. It was not efficient enough and reading functionality is removed from networkx   Updates in version 0.2.0: - added bathymetry info: mean, standard deviation , percentiles [0 (min), 5,  10, 50 (median),  90, 95, 100 (max)] - added new output formats: added shapely compatible geometry type Source data for the network is available at <a href="https://www.vaarweginformatie.nl/">https://www.vaarweginformatie.nl/</a>. Sharing and Access information CC BY-SA 4.0 license applies: <a href="https://creativecommons.org/licenses/by-sa/4.0/">https://creativecommons.org/licenses/by-sa/4.0/</a>.

Data from: Plant community stability is associated with a decoupling of prokaryote and fungal soil networks

Data from the manuscript Plant community stability is associated with a decoupling of prokaryote and fungal soil networks: https://doi.org/10.1101/2022.06.21.496867

Data for RAPPPID: Towards Generalisable Protein Interaction Prediction with AWD-LSTM Twin Networks

Data for RAPPPID, a method for the Regularised Automative Prediction of Protein-Protein Interactions using Deep Learning. These datasets are in a format that RAPPPID is ready to read. Comparatives Dataset These datasets were derived from the STRING v11 H. sapiens dataset, according to the C1, C2, and C3 procedures outlined by Park and Marcotte, 2012. Negative samples are sampled randomly from the space of proteins not known to interact. See <a href="https://doi.org/10.1101/2021.08.13.456309">Szymborski & Emad</a> for details. Repeatability Datasets The following datasets are all derived from STRING in the manner as the comparatives dataset, but three different random seeds are used for drawing proteins. References Park,Y. and Marcotte,E.M. (2012) Flaws in evaluation schemes for pair-input computational predictions. Nat Methods, 9, 1134–1136. Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N. T., Morris, J. H., Bork, P., Jensen, L. J., and Mering, C. (2019). String v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research, 47(D1), D607–D613. Szymborski,J. and Emad,A. (2021) RAPPPID: Towards Generalisable Protein Interaction Prediction with AWD-LSTM Twin Networks. bioRxiv https://doi.org/10.1101/2021.08.13.456309

In-network data collection and data processing - Supplementary materials for deliverable D5.1 - EU-H2020 FET project 'Watchplant'

Supplementary material for D5.1 - Watchplant. Contains collected dataset from plant experiments with blue and red light stimuli, classification results based on statistical methods, and plots of the recorded electropotentials.

Neural-network-backed evolutionary search for SrTiO3(110) surface reconstructions

The archive "dataset.tar.gz" contains trained models (neural networks), training-, validation- and test-data and selected structures in POSCAR format, obtained in the neural-network-backed evolutionary search for SrTiO3(110) surface reconstructions. See README for more information on the archive content.

Sprites observed with Global Meteor Network camera DE000C on 2022-06-30

This dataset contains compressed video observations of sprites, made with one low-light video camera of the Global Meteor Network. The camera, DE000C, is located in Sörup, Northern Germany, and has pointing azimuth 217˚ (so South-West), elevation 39˚. The files are 10.24 second videos compressed in the four-frame format. They are just FITS files with four frames, containing per pixel 1) the maximum value over 256 frames 2) the frame nr (between 0 and 255) where the maximum occurred 3) the mean value of all 256 frames and 4) the RMS of the 256 values. The platepar files contain calibration data that can be read with the sofware at https://github.com/CroatianMeteorNetwork/RMS/. The astrometry contained in the fits files is derived from this and may be less accurate. A stack of the maxpixel images is also contained.

Combined network file for "FAVA: High-quality functional association networks inferred from scRNA-seq and proteomics data"

Combined network from scRNA-seq and proteomics data Given the complementary nature of the networks based on scRNA-seq and proteomics data individually, we decided to combine them into a single network. As the Pearson Correlation Coefficient scores from FAVA cannot be assumed to be directly comparable across the two networks, we converted them to probabilistic scores based on the KEGG benchmarks. These calibrated scores were then combined to produce a single network based on scRNA-seq as well as proteomics data. As should be expected, this network outperforms the individual networks, combining the best aspects of both.

The effect of co-location on human communication networks

Representative dataset for "The effect of co-location on human communication networks." The files are serialized python objects pickled using python 3.8. dist_dict_* contains data on the pairwise distance between researchers, while undir_semiactive_* contains networks representing daily email counts between researchers.

opencc-by-4.0Apr 2016View details →

Two Dynamic Attributed Networks: Enron & Jazz LastFM

Description. This repository contains two dynamic and attributed social networks extracted from the well-known Enron email dataset, and from the LastFM online music platform. We used both networks in the following papers: <ol> <li>G. K. Orman, V. Labatut, M. Plantevit, and J.-F. Boulicaut, “A Method for Characterizing Communities in Dynamic Attributed Complex Networks,” in IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM), 2014, pp. 481–484. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01011913">hal-01011913</a>⟩ DOI: <a href="http://doi.org/10.1109/ASONAM.2014.6921629">10.1109/ASONAM.2014.6921629</a></li> <li>G. K. Orman, V. Labatut, M. Plantevit, and J.-F. Boulicaut, “Interpreting communities based on the evolution of a dynamic attributed network,” Social Network Analysis and Mining, vol. 5, p. 20, 2015. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01163778">hal-01163778</a>⟩ DOI: <a href="http://doi.org/10.1007/s13278-015-0262-4">10.1007/s13278-015-0262-4</a></li> </ol> Citation. If you use these data, please cite the paper [1]. <code>@InProceedings{Orman2014,</code> <code>  author    = {Orman, Günce Keziban and Labatut, Vincent and Plantevit, Marc and Boulicaut, Jean-François},</code> <code>  title     = {A Method for Characterizing Communities in Dynamic Attributed Complex Networks},</code> <code>  booktitle = {IEEE/ACM International Conference on Advances in Social Network Analysis and Mining},</code> <code>  year      = {2014},</code> <code>  pages     = {481-484},</code> <code>  address   = {Beijing, CN},</code> <code>  publisher = {IEEE Publishing},</code> <code>  doi       = {10.1109/ASONAM.2014.6921629},</code> <code>}</code> ---------------------------------------- Enron dataset. Enron is a well-known dataset in network science and text mining. It has been widely studied in academia. In network science, several different static networks appear in the literature. However, up to now, no dynamic network has been published, even though the email conversations have timestamps. We processed the original dataset to extract a dynamic network. There are 158 nodes representing Enron employees between 1997 and 2002. All the addresses in the From and To fields of each email are considered, resulting in a network of 28,802 nodes representing a distinct email addresses. A time span of one month is chosen for the time slices, generating 46 time slices. Two nodes are connected if the corresponding persons emailed each other during the given time slice. We did not make any distinction between sender and receiver, and thus produced an undirected dynamic network.  ---------------------------------------- LastFM dataset. LastFM is a music website that allows its members to register and listen to music online. It is also a social network platform, because its members can declare friendship relationships. In LastFM, members can join a predefined group related to their music tastes, and  participate in music-related events such as concerts. Using the LastFM API, One can retrieve the information of the artist and track a user has listened to, with the exact timestamp. Moreover, it is also possible to get some information regarding the music-related events the users joined, including the exact timestamps. We extracted a network by focusing on the members of the Jazz group, which is supposed to include users appreciating this type of music. We took advantage of the LastFM API to retrieve the members of this group and the existing friendship connection between them. In the end, our network contains 1,702 nodes representing the Jazz users. The friendship relationships between them is static, though, in the sense that the LastFM API does not give access to any temporal information regarding their beginning or end. So, we decided to take advantage of some additional information to get a dynamic structure. We put a link between two nodes if two conditions were simultaneously true: 1) both considered users listened to at least one common artist for a specific period of time, and 2) they are friends on the LastFM platform. For the mentioned period of time, we decided to use 3 months with 1 month overlap, after having analyzed the dynamics of the platform. In other words, we extracted a dynamic network in which each time slice represents three months of LastFM usage for our 1,702 users of interest. There are one month overlap between two consecutive time slices.

opencc-by-4.0Dec 2020View details →

Multiple Partitioning of Multiplex Signed Networks: Application to European Parliament Votes

Presentation. For more than a decade, graphs have been used to model the voting behavior taking place in parliaments. However, the methods described in the literature suffer from several limitations. The two main ones are that 1) they rely on some temporal integration of the raw data, which causes some information loss; and/or 2) they identify groups of antagonistic voters, but not the context associated with their occurrence. In this article, we propose a novel method taking advantage of multiplex signed graphs to solve both these issues. It consists in first partitioning separately each layer, before grouping these partitions by similarity. We show the interest of our approach by applying it to a European Parliament dataset. Particularly, we study the voting behavior of French and Italian MEPs on "Agriculture and Rural Development" (AGRI) during the 2012-13 legislative year. These are the data used in the following paper: <ul> <li>N. Arınık, R. Figueiredo, and V. Labatut, “Multiple partitioning of multiplex signed networks: Application to European Parliament votes,” Social Networks, vol. 60, pp. 83–102, 2020. DOI: <a href="http://doi.org/10.1016/j.socnet.2019.02.001">10.1016/j.socnet.2019.02.001</a> ⟨<a href="https://hal.archives-ouvertes.fr/hal-02082574">hal-02082574</a>⟩</li> </ul> Source code. The code source is accessible on GitHub: <a href="https://github.com/CompNet/MultiNetVotes">https://github.com/CompNet/MultiNetVotes</a> Citation. If you use these data our this source code, please cite the above paper. <code>@Article{Arinik2020,</code> <code>  author    = {Arınık, Nejat and Figueiredo, Rosa and Labatut, Vincent},</code> <code>  title     = {Multiple Partitioning of Multiplex Signed Networks: Application to {E}uropean {P}arliament Votes},</code> <code>  journal   = {Social Networks},</code> <code>  year      = {2020},</code> <code>  volume    = {60},</code> <code>  pages     = {83-102},</code> <code>  doi       = {10.1016/j.socnet.2019.02.001},</code> <code>}</code> ---------------------------------------------- Details. # RAW INPUT FILES The 'itsyourparliament' folder contains all raw input files for further data processing. This is the same raw data that can be found in our previous Figshare repository: https://doi.org/10.6084/m9.figshare.5785833 The folder structure is as follows: * itsyourparliament/ ** domains: There are 28 domain files. Each file corresponds to a domain (such as Agriculture, Economy, etc.) and contains corresponding vote identifiers and their "itsyourparliament.eu" links. ** meps: There are 870 Members of Parliament (MEP) files. Each file contains the MEP information (such as name, country, address, etc.) ** votes: There are 7513 vote files. Each file contains the votes expressed by MEPs # ROLLCALL NETWORKS This folder contains two separate zip files regarding rollcall networks: - rollcall-networks: This folder contains only the rollcall networks that are used in the article. - all-rollcall-networks: For those who are interested in other countries or domains, we make available all rollcall networks that we can extract from raw data. Note that these rollcall networks constitute the layers of the input signed multplex network, as illustrated in Figure 1 of the article. Note also that we consider three vote types in our network extraction process: FOR, AGAINST and ABSTAIN. # ROLLCALL PARTITIONS Note that MEPs who voted similarly are connected together by positive links, and are connected by negative links to MEPs that voted differently from them. MEPs who did not vote at all (ABSENT) are isolates (nodes without any neighbor). We identify the factions of similarly voting MEPs in the graph by solving the Correlation Clustering problem (CC). The rollcall partitions correspond to voting patterns, as illustrated in Figure 1 of the article. # ROLLCALL CLUSTERING This folder contains the results of Steps 3 and 4 of our workflow (see Figure 1 in the article). The structure of this folder is as follows: |__ votetypes=FAA/: 'FAA' means we consider three vote types in our analysis: FOR, AGAINST and ABSTAIN. |__ F.purity-k=2-sil=SILHOUETTE_SCORE |__ clu=CLUSTER_NO/ |__ network: It corresponds to the network created through the similarity network-based approach, as explained in Section 4.4 of the article. |__ partition: It corresponds to the characteristic voting pattern, as explained in Section 4.4 of the article. ---------------------------------------------- Funding: this research benefited from the support of the Agorantic FR 3621, as well as the FMJH Program PGMO and from the support to this program from EDF-THALES-ORANGE-CRITEO.

opencc-by-4.0Dec 2018View details →

Extraction and Analysis of Fictional Character Networks: A Survey

Description. Resources used in our survey of character network extraction and analysis methods. This version of the resources does not correspond to the published paper, but to a later update used in the (longer) arXiv/HAL version of the paper. The resources used in the original published paper are provided in v1.0.0. Updates. The latest version of the data (especially the bibliographic table) is available on a GitHub page, which is easier to update: <a href="https://compnet.github.io/CharNetReview/">https://compnet.github.io/CharNetReview/</a>. This Zenodo repository is no longer updated. Citation. If you use these data or figures, please cite the following paper: <ul> <li>V. Labatut and X. Bost, “Extraction and Analysis of Fictional Character Networks: A Survey,” ACM Computing Surveys 52(5):89, 2019. ⟨<a href="https://hal.archives-ouvertes.fr/hal-02173918">hal-02173918</a>⟩ DOI: <a href="http://doi.org/10.1145/3344548">10.1145/3344548</a></li> </ul> <code>@Article{Labatut2019,</code> <code>  author    = {Labatut, Vincent and Bost, Xavier},</code> <code>  title     = {Extraction and Analysis of Fictional Character Networks: A Survey},</code> <code>  journal   = {ACM Computing Surveys},</code> <code>  year      = {2019},</code> <code>  volume    = {52},</code> <code>  number    = {5},</code> <code>  pages     = {89},</code> <code>  doi       = {10.1145/3344548},</code> <code>}</code>

Satellite-derived chlorophyll-a concentrations for Lake Hume (Australia) using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery

This dataset contains satellite-derived chlorophyll-a data of Lake Hume (Australia) for the period 21 Mar. 2013 - 01 Feb. 2021. Chlorophyll-a concentrations have been calculated using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery. Mixture Density Networks are a class of neural networks that tackle the inverse problem by modelling the multimodal distribution of target variables using a mixture of Gaussians. For more information, please refer to the following: <ul> <li>Pahlevan, N., Smith, B., Alikas, K., Anstee, J., et al. (2022). Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3. Remote Sensing of Environment, 270, 112860</li> <li>Smith, B., Pahlevan, N., Schalles, J., et al. (2021). A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks. Frontiers in Remote Sensing, 1</li> <li>Pahlevan, N., Smith, B., Schalles, J., et al. (2020). Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach. Remote Sensing of Environment, 240, 111604</li> </ul>

Satellite-derived chlorophyll-a concentrations for Western Water Treatment Plant (Melbourne, Australia) using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery

This dataset contains satellite-derived chlorophyll-a data of the Western Water Treatment Plant (Melbourne, Australia) for the period 21 Mar. 2013 - 01 Feb. 2021. Chlorophyll-a concentrations have been calculated using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery. Mixture Density Networks are a class of neural networks that tackle the inverse problem by modelling the multimodal distribution of target variables using a mixture of Gaussians. For more information, please refer to the following: <ul> <li>Pahlevan, N., Smith, B., Alikas, K., Anstee, J., et al. (2022). Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3. Remote Sensing of Environment, 270, 112860</li> <li>Smith, B., Pahlevan, N., Schalles, J., et al. (2021). A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks. Frontiers in Remote Sensing, 1</li> <li>Pahlevan, N., Smith, B., Schalles, J., et al. (2020). Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach. Remote Sensing of Environment, 240, 111604</li> </ul>

Predicting Shallow Water Dynamics using Echo-State Networks with Transfer Learning

This is the source code and data for the publication "Predicting Shallow Water Dynamics using Echo-State Networks with Transfer Learning". Preprint - https://arxiv.org/abs/2112.09182

opencc-by-4.0Nov 2020View details →

A Dataset for Exploring Wi-Fi Network Diversity in Vehicle-to-Infrastructure Communication

Introduction: This dataset contains space and time-indexed performance data for Wi-Fi communication between a moving vehicle and a set of stationary Access Points (APs). In order to allow comparisons between technologies, 3 different types of Wi-Fi are used in parallel: 800.11n, ac, and ad. For more information, please consult the following article: <a href="https://www.cs.vassar.edu/~rpachecomeireles/research/papers/vnc-2020.pdf">Exploring Wi-Fi Network Diversity for Vehicle-to-Infrastructure Communication</a>, Rui Meireles, António Rodrigues, Andrei Stanciu, Ana Aguiar, Peter Steenkiste, in the 2020 IEEE Vehicular Networking Conference (VNC 2020), December 2020, <a href="https://doi.org/10.1109/VNC51378.2020.9318407">doi:10.1109/VNC51378.2020.9318407</a>. Video presentation available <a href="https://youtu.be/IREMIGV4XLc">here</a>. Experiment description: <ul> <li> The AP was placed at the corner of a residential area intersection while the mobile client drove a circuit around it. The mobility pattern is shown in the animated file <code>vehicle-movement.gif</code>. </li> <li> The data is divided into traces, gathered on different dates, using different vehicles to support the AP on the roof, as shown below: </li> </ul> <table> <tbody><tr> <th>trace nr</th> <th>date</th> <th>start time</th> <th>n(n)</th> <th>n(ac)</th> <th>n(ad)</th> <th>AP vehicle</th> <th>n(clients)</th> </tr> </tbody><tbody> <tr> <td>302</td> <td>2019-08-20</td> <td>10:28:45</td> <td>3262</td> <td>2787</td> <td>423</td> <td>2001 Honda Civic sedan</td> <td>1</td> </tr> <tr> <td>303</td> <td>2019-08-20</td> <td>11:26:23</td> <td>3374</td> <td>3027</td> <td>312</td> <td>-</td> <td>2</td> </tr> <tr> <td>304</td> <td>2019-08-20</td> <td>12:39:46</td> <td>1711</td> <td>216</td> <td>14</td> <td>-</td> <td>3 (n & ac) 2 (ad)</td> </tr> <tr> <td>401</td> <td>2019-08-22</td> <td>10:19:24</td> <td>1685</td> <td>1681</td> <td>545</td> <td>2003 Peugeot Partner</td> <td>1</td> </tr> <tr> <td>402</td> <td>2019-08-22</td> <td>10:48:26</td> <td>2859</td> <td>2827</td> <td>764</td> <td>-</td> <td>2</td> </tr> <tr> <td>403</td> <td>2019-08-22</td> <td>11:39:36</td> <td>135</td> <td>135</td> <td>116</td> <td>-</td> <td>2</td> </tr> <tr> <td>404</td> <td>2019-08-22</td> <td>11:42:50</td> <td>114</td> <td>114</td> <td>53</td> <td>-</td> <td>2</td> </tr> <tr> <td>405</td> <td>2019-08-22</td> <td>11:45:07</td> <td>2019</td> <td>2019</td> <td>507</td> <td>-</td> <td>2</td> </tr> </tbody> </table> <ul> <li>APs: all positioned at coordinates {lat : 41.111879, lon : -8.631146}</li> </ul> <table> <tbody><tr> <th>ap</th> <th>device</th> <th>802.11 type</th> <th>channel</th> <th>cntr. freq (MHz)</th> <th>bw (MHz)</th> </tr> </tbody><tbody> <tr> <td>unifi-003</td> <td>ubiquiti ac lite</td> <td>n</td> <td>6</td> <td>2437</td> <td>20</td> </tr> <tr> <td>unifi-001</td> <td>-</td> <td>ac</td> <td>40</td> <td>5200</td> <td>40</td> </tr> <tr> <td>tp-01</td> <td>tp-link talon ad7200*</td> <td>ad</td> <td>1</td> <td>60480</td> <td>2160</td> </tr> </tbody> </table> *running tp-link's original firmware, not OpenWrt <ul> <li>Main clients: all positioned in the moving vehicle's roof, a vw golf mk3</li> </ul> <table> <tbody><tr> <th>802.11 type</th> <th>radio</th> <th>nr. antennas</th> <th>laptop</th> </tr> </tbody><tbody> <tr> <td>n</td> <td>csl usb 2.0 wlan Adapter 300 Mbps</td> <td>2</td> <td>m1</td> </tr> <tr> <td>ac</td> <td>tp-link archer t4uh</td> <td>2</td> <td>w4</td> </tr> <tr> <td>ad</td> <td>tp-link talon ad7200 (tp-03)</td> <td>-</td> <td>w4</td> </tr> </tbody> </table> <ul> <li>Background clients: the purpose is to increase channel util.</li> </ul> <table> <tbody><tr> <th>802.11 type</th> <th>radio</th> <th>nr. antennas</th> <th>laptop</th> <th>position</th> </tr> </tbody><tbody> <tr> <td>n</td> <td>tp-link wn722n</td> <td>1</td> <td>w2</td> <td>fixed, ~2m away from AP</td> </tr> <tr> <td>n</td> <td>tp-link wn722n</td> <td>1</td> <td>w3</td> <td>''</td> </tr> <tr> <td>ac</td> <td>csl usb 2.0 wlan Adapter 300 Mbps</td> <td>2</td> <td>w2</td> <td>''</td> </tr> <tr> <td>ac</td> <td>csl usb 2.0 wlan Adapter 300 Mbps</td> <td>2</td> <td>w3</td> <td>''</td> </tr> <tr> <td>ad</td> <td>tp-link talon ad7200 (tp-04)</td> <td>-</td> <td>macbook</td> <td>stopped vehicle's roof</td> </tr> </tbody> </table> <ul> <li>Monitor nodes: all positioned in the moving vehicle's roof</li> </ul> <table> <tbody><tr> <th>802.11 type</th> <th>radio</th> <th>nr. antennas</th> <th>laptop</th> </tr> </tbody><tbody> <tr> <td>n</td> <td>csl usb 2.0 wlan Adapter 300 Mbps</td> <td>2</td> <td>m1</td> </tr> <tr> <td>ac</td> <td>tp-link talon ad7200 (tp-02)</td> <td>8</td> <td>w4</td> </tr> <tr> <td>ad</td> <td>tp-link talon ad7200 (tp-02)</td> <td>-</td> <td>w4</td> </tr> </tbody> </table> Dataset structure: All the packet captures are already digested and ready to use in the file <code>wifi-exp-log-summary.csv</code>. An explanation of the fields below: <ul> <li>systime : system time (1 Hz resolution) that this row refers to. All node clocks were synchronized through NTP.</li> <li>traceNr : nr. of the trace the row belongs to.</li> <li>lon : longitude (in degrees) reported by the receiver's GPS at <code>systime</code></li> <li>lat : latitude reported by the receiver's GPS at <code>systime</code></li> <li>receiverAlt : altitude (in meters) reported by the receiver's GPS at <code>systime</code></li> <li>receiverX : x coordinate of the receiver's position when space is discretized as a Cartesian plane and the sender is set to be the origin of the coordinate system. The x axis corresponds to east-west (positive values are east, negative values are west). Unit is meters.</li> <li>receiverY : y coordinate of the receiver's position when space is discretized as a Cartesian plane</li> <li>receiverDist : distance (in meters) of receiver to ap(s)</li> <li>receiverSpeed : speed (in m/s) reported by the receiver's GPS at <code>systime</code></li> <li>receiverId : system-specific id for the client (in the vehicle)</li> <li>senderId : system-specific id for the ap serving the client (side of the road)</li> <li>isIperfOn : 1 if row's <code>systime</code> corresponds to a period where our UDP packet consumer application is known to have been running on the receiver side.</li> <li>isInLap : 1 if this row's systime has been marked as being part of a time period where clients were doing laps around the APs, 0 otherwise.</li> <li>rssiMean : the mean of the RSSI (Received Signal Strength Indicator) values of frames received by the client from the ap during the 1-second period systime period the row refers to.</li> <li>snrMean : SNR (signal to noise ratio) retrieved from 802.11ad sectore sweep frames.</li> <li>channelFreq : center frequency of the WiFi channel used, in MHz.</li> <li>channelBw : bandwidth of the WiFi channel used, in MHz.</li> <li>channelUtil : percentage of time the wireless medium was sensed to be busy during the 1-second period systime period the row refers to. In traces 40x, the 802.11n and ac routers didn't log channel busy time, and as such we had to approximate channel util. based on x,y coordinates and nr. of active clients.</li> <li>wifiType : 802.11 type (e.g., n, ac or ad).</li> <li>nrClients : nr. of parallel clients operating in <code>wifiType</code> mode, on the same channel and bandwidth as <code>receiverId</code>.</li> <li>dataRateMedian : the median of the bitrate values of frames received by the client from the ap during the 1-second period systime period the row refers to.</li> <li>dataRateMean : the mean of the bitrate values of frames received by the client from the ap during the 1-second period systime period the row refers to.</li> <li>nBytesReceived : total number of bytes received by the client from the ap during the 1-second period systime period the row refers to.</li> <li>tghptConsumer : throughput reported by the UDP packet consumer application, during the 1-second period systime period the row refers to.</li> <li>nRetries : nr. of WLAN-level re-transmissions on 1 second period</li> <li>meanBeaconRssi : mean RSSI measured from beacons in 1 second period. nan values are filled with -100 dBm.</li> <li>meanInterBeaconTime : mean interval between consecutive beacons, within 1 second period. nan values are filled with 1 sec.</li> <li>nBeacons : total nr. of beacons received by client within 1 second period.</li> </ul>

Image sensing with multilayer, nonlinear optical neural networks

This data repository contains the information necessary to reproduce the main results of the paper “Image sensing with multiplayer, nonlinear optical neural networks”. This repository contains the data and the code for generating the figures in the manuscript "Image sensing with multilayer, nonlinear optical neural networks", including figures in the main text and in supplementary materials. The repository also contains the code for controling the experiment setup and running the experiments conducted in the paper: <ul> <li>Folder 'Data_Collection_Example' and 'Data_Extraction_Example' contain example scripts for instrument control and data collection using the multilayer optical-neural-network sensor.</li> <li>Other folders are organized according to the figure panels in the main text, each containing the data and the code required to reproduce the plots in a main figure panel and its associated supplementary figures. In each of these folders, there is a README.txt file that summarizes the role of each file in the folder. </li> </ul>

Training material for Genome assembly quality control (Galaxy Training Network tutorial)

This Zenodo repository includes the required datasets for following the GTN: Genome assembly quality control.