Find research datasets worth reusing

opencc-zeroJan 2023View details →

Supplementary files: Social learning of innovations in dynamic predator-prey systems

We investigate social transmission of behavioral innovations between predators in two classic predator-prey models. We assume that innovations increase predator attack rates or conversion efficiencies, or that innovations reduce predator mortality or prey handling time. We find that a common outcome of innovations is the destabilization of the system. Destabilizing effects include increasing oscillations or limit cycles. Particularly, in systems where prey are self-limiting and predators have a Type II functional response, destabilization occurs due to overexploitation of the prey. Whenever instability increases the risk of extinction, innovations that benefit individual predators may not have positive long-term effects on predator populations. An additional consequence of instability is the maintenance of behavioral variability among predators. Interestingly, when predator populations are low despite coexisting with prey populations near their carrying capacity, innovations that could help predators better exploit their prey are least likely to spread. Precisely how unlikely this is depends on whether or not naïve individuals need to observe an informed individual interact with prey to learn the innovation. Our results offer perspective on the potential role of innovation in biological invasions, urban colonization, and the maintenance of behavioral polymorphisms.

Global coastal transect type classification associated to "Global distribution and dynamics of muddy coasts", Nat. Coms 2023

This dataset is associated with the scientific article "Global distribution and dynamics of muddy coasts"  (https://www.nature.com/articles/s41467-023-43819-6) The dataset contains the following items: <ul> <li>VisualizeResults.qgz: QGIS project with the formatted datasets ready for viewing (tested in QGIS v3.28.2.)</li> <li>PredictedClasses_MuddyBeaches.shp (and associated files): Shapefile containing predicted classes of coastal transects differentiating the following types: <ul> <li>0: Sandy coastal transect</li> <li>1: Muddy coastal transect</li> <li>2: Rocky coastal transect</li> <li>3: Vegetated coastal transect</li> <li>4: Other undefined type</li> </ul> </li> <li>MuddyZonesFleming.shp (and associated files): Shapefile containing hand-drawn polygons based on the description of muddy coastline concentrations by (Flemming, B. W. Geographic distribution of muddy coasts, 2002).</li> <li>TransectTrainingDataframe.csv : comma separated value file containing 1867 hand-labelled coastal type transects randomly distributed around the world. Each transect contains the following entries: <ul> <li>transect_id: individual transect ID</li> <li>box_id: tiled ID</li> <li>country_name</li> <li>continent</li> <li>New_Start_lon: longitude transect start</li> <li>New_Start_lat: latitude transect start</li> <li>New_End_lon: longitude transect end</li> <li>New_End_lat: latitude transect end</li> <li>Center_lon</li> <li>Center_lat</li> <li>Length: transect length</li> <li>label: hand-labeled coastal type, Str (Sandy, Muddy, Rocky, Vegetated, Other)</li> <li>abs lat: latitude</li> <li>flag_sandy: bool, classified as a sandy area (Luiijendijk et al, 2018)</li> <li>sand: % of pixels classified as sand from Sentinel 2 Multispectral land-type classification</li> <li>mud: % of pixels classified as muddy from Sentinel 2 Multispectral land-type classification</li> <li>water: % of pixels classified as water from Sentinel 2 Multispectral land-type classification</li> <li>vegetation: % of pixels classified as vegetation from Sentinel 2 Multispectral land-type classification</li> <li>dry: % of pixels classified as dry-vegetation from Sentinel 2 Multispectral land-type classification</li> <li>other: % of pixels classified as other from Sentinel 2 Multispectral land-type classification</li> <li>turbid % of pixels classified as turbid-water from Sentinel 2 Multispectral land-type classification</li> <li>height max: max height in transect (MERIT-DEM)</li> <li>height var: variance height in transect (MERIT-DEM)</li> <li>mangrove: bool, mangrove presence (Global Mangrove Forest Distributions)</li> <li>intertidal: intertidal range Murray et al 2019.</li> <li>gsw: Global Surface Water dataset, Pekel et al 2016</li> <li>maxtemp: max temperature WorldClim V1 Bioclim dataset</li> <li>mintemp: min temperature WorldClim V1 Bioclim dataset</li> <li>mhhw: Tide MHHW Deltares GTSM model</li> <li>mllw: Tide MLLW Deltares GTSM model</li> <li>tidal range: Tidal range Deltares GTSM model</li> </ul> </li> <li>PixelTrainingDataframe.csv : comma separated value file containing 3240 hand-labelled land-type pixel randomly distributed around the world. These are derived from a Sentinel 2 TOA images as a 2020 composite. Bands (BX) refer to the Sentinel-2 MSI Multispectral Instrument, level-2A spectral bands names. Each entry contains: <ul> <li>Aerosols: B1</li> <li>Blue: B2</li> <li>Green: B3</li> <li>Red: B4</li> <li>Red Edge 1: B5</li> <li>Red Edge 2: B6</li> <li>Red Edge 3: B7</li> <li>NIR: B8</li> <li>Red Edge 4: B8A</li> <li>SWIR 1: B11</li> <li>SWIR 2: B12</li> <li>NDWI: Normalized Differential Water Index ((B3-B8)/(B3+B8)</li> <li>NDVI ((B8-B4)/(B8+B4)</li> <li>num: hand-label type differentiating the following types (1) sandy beaches, (2) mudflats, (3) clear water, (4) 111 turbid (brown) water, (5) green vegetation, (6) dry vegetation, and (7) other (containing clouds, snow, buildings, and all other indefinable points)</li> </ul> </li> </ul>

opencc-by-4.0Jan 2020View details →

Datasets of the article "Femtosecond-to-nanosecond dynamics of flavin mononucleotide monitored by stimulated Raman spectroscopy and simulations"

Experimental time-resolved femtosecond-stimulated Raman spectra (FSRS) and visible transient absorption (visTA) spectra of flavin mononucleotide (FMN) in water, MES pH=6, and TRIS pH=8  buffers (DATA files). The dataset includes also the amplitudes and evolution-associated difference spectra (EADS) arising from global kinetic analysis using a sequential model.  In the DATA files, the first column contains the Stokes shift (in cm-1) or wavelength (in nm), and the first row contains the time delays (in seconds). In the AMPLITUDES files, the first column contains the time delays (in seconds), and the first row contains the lifetimes (in seconds). In the EADS files, the first column contains the wavenumbers (in cm-1) or wavelength (in nm), and the first row contains the lifetimes (in seconds). Note that the FSRS spectra have been normalized to 1. If you use these datasets please cite: Andrikopoulos, P. C.; Liu, Y.; Picchiotti, A.; Lenngren, N.; Kloz, M.; Chaudhari, A. S.; Precek, M.; Rebarz, M.; Andreasson, J.; Hajdu, J.; Schneider, B.; Fuertes, G.* Femtosecond-to-Nanosecond Dynamics of Flavin Mononucleotide Monitored by Stimulated Raman Spectroscopy and Simulations. Phys. Chem. Chem. Phys. PCCP 2020, 22, 6538–6552.

opencc-by-4.0Sep 2022View details →

Data for the manuscript titled "Solute front shear and coalescence control concentration gradient dynamics in porous micromodel"

Concentration images and data for plots for the manuscript "Solute front shear and coalescence control concentration gradient dynamics in porous micromodel" submitted for publication in Geophysical Research Letters. See the README file for explanations on the data file. Movie captions: Movie S1: Concentration field for the Péclet Pe = 33 experiment. Solid pillars are represented by the gray discs, and the colormap corresponds to the rescaled concentration $c/c_\mathrm{max}$. The black line represents the solute front, which is the boundary of the continuous $c/c_\mathrm{max} < 0.5$ region within the porous medium. The x- and y-coordinates of the porous medium length and width (respectively) are rescaled by the average pore diameter $\lambda$. Movie S2: Concentration gradient field for the Pe = 33 experiment. Solid pillars are represented by the gray discs, and the colormap corresponds to the rescaled gradient $ a \nabla c / c_\mathrm{max}$. The x- and y-coordinates of the porous medium length and width (respectively) are rescaled by the average pore diameter $\lambda$. Movie S3: Concentration field for the Pe = 1104 experiment. Solid pillars are represented by the gray discs, and the colormap corresponds to the rescaled concentration $c/c_\mathrm{max}$. The black line represents the solute front, which is the boundary of the continuous $c/c_\mathrm{max} < 0.5$ region within the porous medium. The x- and y-coordinates of the porous medium length and width (respectively) are rescaled by the average pore diameter $\lambda$. Movie S4: Concentration gradients field for the Pe = 1104 experiment. Solid pillars are represented by the gray discs, and the colormap corresponds to the rescaled gradient $ a \nabla c / c_\mathrm{max}$. The x- and y-coordinates of the porous medium length and width (respectively) are rescaled by the average pore diameter $\lambda$.

Photon bound state dynamics from a single artificial atom: Source Data

Source Data for figures in main manuscript of Tomm, N., Mahmoodian, S., et al., Photon bound state dynamics from a single artificial atom.

Exploring spatiotemporal dynamics of flower visitor association pattern on two Avicennia mangroves: A network approach

All the data sets used in the analyses of this study are provided here along with the R-Script. The datasets for foraging behaviour and Generalized linear mixed models will be provided upon request to the first or corresponding author of this article. Please note that, in this R-script, we have shown the codes only for one dataset of respective analyses. PLEASE NOTE: In this R-script, there are two minor mistakes, as follows: 1) Line no. 46 Present code: nulls <- nullmodel(I_S.network, N=1000, method=3) ##(3=Vaznull) ## file name mistake Correct code: nulls <- nullmodel(AO_Site, N=1000, method=3) ##(3=Vaznull) 2) Line no. 55 Present code: AO_Site_V<-AM_Site[,-1] ##Omitting individual coloumn(species) ## file name mistake Correct code: AO_Site_V<-AO_Site[,-1] ##Omitting individual coloumn(species) <ul> <li>Description of the data set</li> </ul> Data explorers the plant-flower visitor network with spatiotemporal approaches. Here, AM denotes Avicennia marina and AO denotes Avicennia officinalis. For the overall site-visitor network (combining all years and all time frames) datasets are AO_Site and AM_Site. For the overall visiting time-visitor network (combining all years and all sites) the datasets are AO_Time and AM_Time For the site-visitor networks on the yearly scale, the datasets are AO_2016, AO_2017, AO_2018, AM_2016, AM_2017 and AM_2018. For the site-specific visiting time-visitor networks the datasets are AO_Satjelia, AO_Bali, AO_Sagar, AO_Bakkhali, AM_Satjelia, AM_Bali, AM_Sagar and AM_Bakkhali.

Data for "Relaxing Hardware Requirements for Surface Code Circuits using Time-dynamics"

Includes the circuit files for each benchmarked stim circuit (circuits.zip), the raw statistics for each memory experiment (stats.csv), the 'fused' stats combining the X and Z error rates to represent the liklihood that neither logical observable fails (fused_stats.csv), the svg files for the main figures in the manuscript (svg_figures.zip), and a snapshot of the full code repo (midout.zip)

opencc-zeroFeb 2023View details →

Niche dynamics of Memecylon in Sri Lanka: distribution patterns, climate change effects, and conservation priorities

Aim: Recent climate projections have shown that the distribution of organisms in island biotas is highly affected by climate change. Here, we present the results of the analysis of niche dynamics of a plant group, Memecylon on Sri Lanka, an island, using species occurrences and climate data. We aim to determine which climate variables explain current distribution, model how climate change impacts the availability of suitable habitat for Memecylon, and determine conservation priority areas for Sri Lankan Memecylon. Location: Sri Lanka Methods: We used georeferenced occurrence data of Sri Lankan Memecylon to develop ecological niche models and assess both current and future potential distributions under six climate change scenarios in 2041-2060 and 2061-2080. We also overlaid land-cover, and protected area maps and performed a gap analysis to understand the impacts of land-cover changes on Memecylon distributions and propose new areas for conservation. Results: Differences among suitable habitats of Memecylon were found to be related to patterns of endemism. Under varying future climate scenarios, endemic groups were predicted to experience habitat shifts, gains, or losses. The narrow endemic Memecylon restricted to the montane zone were predicted to be the most impacted by climate change. Projections also indicated that changes in species' habitats can be expected as early as 2041-2060. Gap analysis showed that while narrow endemic categories are considerably protected as demonstrated by their overlap with protected areas, more conservation efforts in Sri Lankan forests containing wide endemic and non-endemic Memecylon are needed. Main conclusions: This research helped clarify general patterns of responses of Sri Lankan Memecylon to global climate change. Data from this study are useful for designing measures aimed at filling the gaps in forest conservation on this island.

Stochastic motion and transcriptional dynamics of pairs of distal DNA loci on a compacted chromosome

This repository contains the trajectory data from "Stochastic motion and transcriptional dynamics of pairs of distal DNA loci on a compacted chromosome". Data sets We provide data sets for the following constructs and imaging conditions, where we give the name of the construct and the MS2-parS genomic separation in kb: <ul> <li>data_line0.csv: parS-homie-evePr-PP7, 58 kb</li> <li>data_line1.csv: parS-homie-evePr-PP7, 82 kb</li> <li>data_line2.csv: parS-homie-evePr-PP7, 88 kb</li> <li>data_line3.csv: parS-homie-evePr-PP7, 149 kb</li> <li>data_line4.csv: parS-homie-evePr-PP7, 190 kb</li> <li>data_line5.csv: parS-homie-evePr-PP7, 595 kb</li> <li>data_line6.csv: parS-homie-evePr-PP7, 3.3 Mb</li> <li>data_line3_5s.csv: parS-homie-evePr-PP7, 149 kb, 5 second time interval</li> <li>data_line0_nohomie.csv: parS-lambda-evePr-PP7, 58 kb</li> <li>data_line3_nohomie.csv: parS-lambda-evePr-PP7, 149 kb</li> </ul> Structure of Data The trajectory data are provided as csv files consisting of 13 columns. The column headers are: <ul> <li>cell_id: a unique cell index</li> <li>time_point: the time frame</li> <li>x_blue: x-coordinate of the locus in the blue channel (units in nm)</li> <li>y_blue: y-coordinate of the locus in the blue channel (units in nm)</li> <li>z_blue: z-coordinate of the locus in the blue channel (units in nm)</li> <li>x_green: x-coordinate of the locus in the green channel (units in nm)  </li> <li>y_green: y-coordinate of the locus in the green channel (units in nm)</li> <li>z_green: z-coordinate of the locus in the green channel (units in nm)  </li> <li>x_Rij: x-component of the aberration corrected 3D distance vector connecting the blue and green loci (units in nm)</li> <li>y_Rij: y-component of the aberration corrected 3D distance vector connecting the blue and green loci (units in nm)</li> <li>z_Rij: z-component of the aberration corrected 3D distance vector connecting the blue and green loci (units in nm)</li> <li>red: intensity in the red channel (a.u.)</li> <li>state: inferred state using a 3-state HMM, with entries 0 (O_off), 1 (P_off), 2 (P_on)</li> </ul>

opencc-zeroFeb 2023View details →

Aligned and curated mtDNA sequences from: Ancient DNA of narrow-headed voles reveals common features of the Late Pleistocene population dynamics in cold-adapted small mammals

Narrow-headed vole, together with collared lemming and common vole, was the most abundant small mammal species across Eurasian Late Pleistocene steppe-tundra environments. Previous ancient DNA studies of the latter two revealed dynamic past population histories shaped by climatic fluctuations. To investigate the extent to which species with similar adaptations share common evolutionary histories, we generated a dataset comprising mitochondrial genomes of 139 ancient and 6 modern narrow-headed voles from multiple sites across Europe and north-western Asia and covering the last ca. 100 thousand years (ka). We inferred Bayesian time-aware phylogenies using 11 radiocarbon-dated samples for calibration of the molecular clock. We found that across the three species, divergence of the main mtDNA lineages occurred during Marine Isotope Stages (MIS) 7 and MIS 5, suggesting a common response of species adapted to open habitat to the interglacial environments. In European narrow-headed voles, we identified multiple time-structured mtDNA lineages, implying lineage turnovers. Timing of some of these turnovers was synchronous across all three species, allowing us to identify the main drivers of the Late Pleistocene dynamics of steppe- and cold-adapted species.

Electronic supplemental material for Mann et al. "Multi-decadal records of shoreline erosion reveal the roles of river delta dynamics, hard protection measures, and other interventions in Eastern Ghana"

Electronic supplemental material for Mann et al. "Multi-decadal records of shoreline erosion reveal the roles of river delta dynamics, hard protection measures, and other interventions in Eastern Ghana" The supplemental material comprises the database "Geodatabase Ghana.mdb" and the MSc thesis by Aleksandra Serwa (Serwa 2017), the results of which built the foundation for the paper. The database contains one layer with all digitized shorelines (HWL_END) as indicated in table 1 of the manuscript, and additional baseline and transect layers that are necessary for the shoreline change analyses with DSAS (Thieler & Danforth 1994). Further information can be found in Serwa (2017). References: Serwa, A., 2017. Shoreline changes in the Keta Municipal District (Ghana): Evidence from multi-decadal records. Master Thesis, Department of Geosciences, University of Bremen, 88 p. Thieler, E.R. & Danforth, W.W., 1994. Historical Shoreline Mapping ( II ): Application of the Digital Shoreline Mapping and Analysis Systems ( DSMS / DSAS ) to Shoreline Change Mapping in Puerto Rico. Journal of Coastal Research, 10(3), pp.600–620.

opencc-zeroFeb 2023View details →

Revealing the drivers of parasite community assembly: using avian haemosporidians to model global dynamics of parasite species turnover

Why do some regions share more or fewer species than others? Community assembly relies on the ability of individuals to disperse, colonize, and thrive in new regions. Therefore, many distinct factors, such as geographic distance and environmental features, can determine the odds of a species colonizing a new environment. For parasites, host community composition (i.e., resources) also plays a key role in their ability to colonize a new environment as they rely on their hosts to complete their life cycle. Thus, variation in host community composition and environmental conditions should determine parasite turnover among regions. Here, we explored the global drivers of parasite turnover using avian malaria and malaria-like (haemosporidian) parasites. We compiled global databases on avian haemosporidian lineages distributions, environmental conditions, avian species distributions and functional traits and ran generalized dissimilarity models to uncover the main drivers of parasite turnover. We demonstrated that haemosporidian parasite turnover is mainly driven by geographic distance followed by host functional traits, environmental conditions, and host distributions. The main host functional traits associated with high parasite turnover were the predominance of resident (i.e., non-migratory) species and strong territoriality while the most important climatic drivers of haemosporidian turnover were mean temperature and temperature seasonality. Overall, we establish the importance of geographic distance as a key predictor of ecological dissimilarity and show that host resources influence parasite turnover more strongly than environmental conditions. We also evidenced that parasite turnover is most pronounced among tropical and less interconnected regions (i.e., regions with mostly territorial and non-migratory hosts). Our findings provide a robust foundation for the prediction of avian pathogen spread and the emergence of infectious diseases.

ConforMine Molecular Dynamics Data: Conformational Variability, Secondary Structure Propensities and Molecular Dynamics Simulations

<pre>This dataset contains all the data used to calculate Conformational Variability (ConVa) and Conformational Propensities as well as to train ConforMine. Each directory one level below this document contains another readme for further explanation on the contained data. The following information can be found in this dataset: </pre> <ul> <li>ConforMine_MD_training_sequences.fasta: FASTA file with the amino acid sequences of all used proteins.</li> <li>simulations (directory): Contains all the raw data derived from the MD simulations.</li> <li>ConforMine_training_MD_dihedrals (directory): Contains .xvg files with the dihedral angles of each amino acid at each step of the MD simulation.</li> <li>ConforMine_training_data_conformational_variability (directory): Contains the Conformational Variability values for all amino acids. Each file contains all ConVa values for a whole protein. The data is provided in .csv and .npy format.</li> <li>ConforMine_training_data_conformational_propensities (directory): Contains the Conformational Propensities values for all amino acids. Each file contains all propensities for a whole protein. The data is provided in .csv and .npy format.</li> </ul>

Supporting data for: Condensed-phase molecular representation to link structure and thermodynamics in molecular dynamics

This repository contains supporting data and code for the paper titled "Condensed-phase molecular representation to link structure and thermodynamics in molecular dynamics" by Bernadette Mohr, Diego van der Mast, and Tristan Bereau.

Handling Dynamic Environment Changes for Behavior-Based User Authentication

Description: This environment-independent user authentication dataset is from our MASS 2020 paper: Towards Environment-independent Behavior-based User Authentication Using WiFi. This dataset contains the physiological characteristics captured by WiFi from 10 participants for 10 different activities. Each participant performs 20 rounds for each activity. The experiments are conducted in two different environments, the campus office, and the home apartment. The system performance is tested on the cross-environment scenarios (training in one environment and testing in another environment). Note: The MASS 2020 paper is based on our MobiHoc 2017 paper, Smart User Authentication through Actuation of Daily Activities Leveraging WiFi-enabled IoT. The MobiHoc 2017 work focused on user authentication using CSI extracted from human activity while the MASS 2020 work focused on the domain adaptation of user authentication using activity CSI. The dataset of our MobiHoc 2017 work is also published: <a href="https://zenodo.org/record/7750976#.ZBfTZ3bMKUk">https://zenodo.org/record/7750976#.ZBfTZ3bMKUk</a>   Format: .dat format Section 1: Device Configuration <ul> <li>Two commercial laptops, Dell E6430, as transmitter and receiver. Run with a Linux 14.04 operating system with 4.2.0 kernel. Equipped with 3 MINI PCI-E internal antennas. </li> <li>Intel 5300 network interface card (NIC) for CSI collection. The detail information regarding the CSI tool can be found at <a href="https://dhalperi.github.io/linux-80211n-csitool/faq.html">https://dhalperi.github.io/linux-80211n-csitool/faq.html</a>.</li> <li>WiFi packet transmission is set to 1000 pkts/s</li> </ul> Section 2: Data Format We provide raw data received by the CSI tool. The data files are saved in the dat format. The details are shown in the following: <ol> <li>10 participants are included in two different experiments.</li> <li>Each participant performed 20 rounds for each activity.</li> <li>The dataset file name is presented as "User_Day_Action_Location". The detailed information as: <ul> <li>User: The participants that CSI was collected from.</li> <li>Day: The date this data was collected. </li> <li>Action: The specific activity performed.</li> <li>Location: The specific location the experiment was conducted.</li> </ul> </li> </ol> Section 3: Experimental Setups There are two experiment setups for our data collection. An image of the experimental setup and the illustration of activities from two different environments is included in the dataset. Each activity was performed in a designated location. In each activity location, the specific activity was conducted in 4 different proximate locations at least one foot away from each other.  <ol> <li>Residential Apartment <ul> <li>Environment: The experiments are conducted in a residential apartment with a size 33ft × 17ft.</li> <li>Participant: 10 users are students from Rutgers University (aged from 20 to 30).</li> <li>Activity: 7 activities were performed. <table> <caption>Detailed Activities Performed in Apartment</caption> <tbody> <tr> <td>Code</td> <td>Activity</td> </tr> <tr> <td>      A→B</td> <td>Walking (trajectory 1)</td> </tr> <tr> <td>      B→C</td> <td>Walking (trajectory 2)</td> </tr> <tr> <td>         B</td> <td>Picking up a remote control</td> </tr> <tr> <td>         C</td> <td>Sitting in a chair </td> </tr> <tr> <td>         D</td> <td>Exercising</td> </tr> <tr> <td>         E</td> <td>Operating on the oven</td> </tr> <tr> <td>         F</td> <td>Using the stove</td> </tr> </tbody> </table>   </li> </ul> </li> <li>Office  <ul> <li>Environment: The experiments are conducted in an office with a size 21ft × 12ft.</li> <li>Participant: 5 users are students from Rutgers University (aged from 20 to 30).</li> <li>Activity: 3 activities were performed. <table> <caption>Detailed Activities Performed in Office</caption> <tbody> <tr> <td>Code</td> <td>Activity</td> </tr> <tr> <td>      G</td> <td>Sitting in a seat</td> </tr> <tr> <td>      H</td> <td>Stretching the body</td> </tr> <tr> <td>       I</td> <td>Typing on a keyboard</td> </tr> </tbody> </table> </li> </ul> </li> </ol>   Section 4: Data Description We separate our raw data into different folders based on different environment types. In each environment type, data are further distributed in terms of date. Each file includes all data from three internal antennas. All data files are in .dat format. We also provide Matlab scripts for CSI analysis and visualization. The following variables can be revealed from the codes: <ol> <li>CSI: This is the Channel State Information (CSI) received from one receiver antenna. It describes the signal propagation from the transmitter to the receiver, and it is very sensitive to the impact of environmental changes. Each data reveals CSI from 30 subcarriers. </li> <li>Relative Phase: Relative Phase is a measurement to describe the degree of synchronization between data received from different antennas. It can be used to determine the phase offset for further signal preprocessing.</li> <li>Time: This is the time interval in which the data file contains. It measures time by the number of seconds. It can be used to determine how long the signal has been received.</li> </ol> Section 5: Codes <ul> <li>analysis_spectrogram.m: load a .dat file and extract all data by Data description(I.e, CSI, and Relative Phase).</li> </ul> Section 6: Citations If your paper is related to our works, please cite our papers as follows. <a href="https://ieeexplore.ieee.org/document/9356038">https://ieeexplore.ieee.org/document/9356038</a> C. Shi, J. Liu, N. Borodinov, B. Leao and Y. Chen, "Towards Environment-independent Behavior-based User Authentication Using WiFi," 2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), Delhi, India, 2020, pp. 666-674, doi: 10.1109/MASS50613.2020.00086 Bibtex: @INPROCEEDINGS{9356038,   author={Shi, Cong and Liu, Jian and Borodinov, Nick and Leao, Bruno and Chen, Yingying},   booktitle={2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)},    title={Towards Environment-independent Behavior-based User Authentication Using WiFi},    year={2020},   volume={},   number={},   pages={666-674},   doi={10.1109/MASS50613.2020.00086}}   The current version of the dataset is shrunk due to its size. If you wish to acquire the full version or you have any questions regarding the dataset, contact us by email:  cl1361@scarletmail.rutgers.edu.

Data used in "Morphosedimentary Response of an Urban Beach System to Interannual Variability in Shorefast Ice Dynamics"

This dataset includes digital surface models (DSM) and orthomosaics of Tibbits Cove beach located along the St. Lawrence Fluvial Estuary (Québec, Canada). Surveys were conducted using a UAV before, during and after the 2021 and 2022 winter season. It also includes videos acquired with fixed cameras for these periods. For questions, please contact jean-francois.bernier@ggr.ulaval.ca.

Dynamic Knowledge Graphs for Continual Learning of Embeddings

These datasets are generated from real world usecases. They are treated as Knowledge graphs and include 20 snapshots, where between two snapshots there are 10% added links and 10% deleted links, making the first and last snapshot non-overlapping.

opencc-by-4.0Oct 2022View details →

Dynamic interactome of the MHC I peptide loading complex in human dendritic cells - Source II

Source data underlying MS data set (SFig.2). Datafile comprises MS raw files + MaxQuant output files.

opencc-by-4.0Oct 2022View details →

Dynamic interactome of the MHC I peptide loading complex in human dendritic cells - Source III

Source data underlying Raji cell data set (SFig.3). Datafile comprises MS raw data + MaxQuant output files.

Input data for Reversible Unwrapping Algorithm for Constant-Pressure Molecular Dynamics Simulations

As described in the main text, here is the input data used for simulation, as well as analysis directories. The archive was generated in my project folder with "tar --exclude=*trr --exclude=pbctools --exclude=qtwrap --exclude=old* --exclude=*npz --exclude=*pdf --exclude=*png --exclude=*ppm --exclude=*dcd* --exclude=*xtc --exclude=*slurm* --exclude=core* --exclude=*sh --exclude=*xvg --exclude=*out --exclude=*git* --exclude=*edr --exclude=*log --dereference -zcvf kulke-$(date +"%F").tar.gz data figures scripts Simulations". Big data and trajectory files were excluded to keep the archive size small. The archive includes all necessary files to reproduce the simulations, analysis and figures for the publication.