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3,172 results for “Publications”
Datasets used for the publication: UAV-Lidar reveals that canopy structure mediates the influence of edge effects on forest diversity, function and microclimate
<p>Datasets used for the publication: UAV-Lidar reveals that canopy structure mediates the influence of edge effects on forest diversity, function and microclimate.</p> <p>The file "Blanchard_et_al_JoE_2023_data_plot_trees.csv" contains individual tree indentification data for the 46 plots used in the study.</p> <p>The file "Blanchard_et_al_JoE_2023_data_plot_aggregated.csv" contains plot-level aggregated metrics used for the analyses:</p> <p>- distance to the forest edge</p> <p>- diversity indices : the 20-sp rarefied species richness "rar_sp_richness_20" and the 20-sp rarefied Beta diversity "Beta_div" which corresponds to the plos coordinates on the PCoA first axis.</p> <p>- functional indices: the community weighted mean trait values for the four traits used in this study : ,"WD","SLA","LA","LDMC"; the fonctional divergence index "FD_trans.FDiv"; and the synthetic community weigthed mean trait wich corresponds to the postion on plots on the principal component analysis of species trait values "Functional_composition_trans". Note that the SLA and LA values were log-transformed before computing "FD_trans.FDiv" and "Functional_composition_trans".</p> <p>- UAV-LiDAR-dervived metrics: canopy height, gap fraction ("gap_fraction2"), slope, curvature</p> <p>- The estimated plot above graound biomass "agb_plot", and the mean value of the vapor pressure deficit during the drisest month "max_monthly_VPD".</p> <p>- coordinates of the plots in UTM 58S (Coordinate reference system)</p> <p>Please read the material and method section of the article for more informations on this dataset.</p>
Brush Creek Flux Data for Publication
<p>These files contain the Surface Flux and turbulence data at the Brush Creek site during the deployment of NOAA's Physical Science Laboratory deployment of the atmospheric profile systems in late 2021 and early 2022. </p>
Species distribution data and distribution of private and public protected areas in Cerrado, Brazil
<p>Here we present the models of 103 threatened vertebrates (Anphibia, Reptile, Birds and Mammals) from the Cerrado in Brazil and all information about spatial distribution of private and public conservation protected areas that is a part of an article about the value of private areas to conservation.</p>
Artifacts for the 2023 Trends in Functional Programming Publication: Versatile and Flexible Modelling of the RISC-V Instruction Set Architecture
<p>This dataset contains the artifacts for the performance evaluation conducted in the publication <em>Versatile and Flexible Modelling of the RISC-V Instruction Set Architecture</em> which will be published in the proceedings of the 2023 <em>Trends in Functional Programming</em> conference. The provided artifacts contain a <a href="https://docker.io">Docker</a> container for executing <a href="https://embench.org">Embench</a> benchmarks with LibRISCV, RISC-V VP, Grift and Forvis. A pre-built version of the container is included.</p>
Publications from government weather and climate authorities on the X platform and replies from citizens in five Brazilian cities during a year between 2021 and 2022
<p>This dataset contains messages published and replies received by government weather and climate authorities on the X (former Twitter) social media platforms. The data comprises government weather and climate authorities for the Brazilian cities of São Paulo, Rio de Janeiro, Belo Horizonte, Porto Alegre, and Belém. Government weather and climate authorities are city hall departments or sectors responsible for informing and keeping the population updated about weather events.</p><p>Publications made by the authority and replies published by citizens to these publications are observed. This data supports the study on the interaction dynamics between the climate authority and citizens over time.</p><p><strong>Data Structure</strong></p><p>Two files are available <strong>publications.csv</strong> and <strong>replies.csv</strong>.</p><p>Each line in the publications' file (<strong>publications.csv</strong>) refers to an authority publication/tweet. For each publication, it is stored the public authority's unique Twitter identifier (<i>AUTHORITY_ID</i>), the tweet unique identifier (<i>TWEET_ID</i>), the Unix timestamp that indicates when it was published (<i>TIMESTAMP</i>), and the text of the publication (<i>TEXT</i>). </p><p>Each line in the replies file (<strong>replies.csv</strong>) is a reply from a citizen to an authority. For each reply, it is stored the authority's unique Twitter identifier (<i>AUTHORITY_ID</i>), the unique identifier of the authority's tweet being replied to (<i>TWEET_ID</i>), the replier masked unique Twitter identifier (<i>AUTHOR_ID</i>), and the reply Unix timestamp (<i>TIMESTAMP</i>) that indicates when it was published.</p><p>All data were collected through the X's application programming interface (API) provided to scientific researchers. Publications and replies were posted by users (authorities and citizens) with public visibility.</p><p><strong>Data Content</strong></p><p>The dataset covers 1-year observation period, starting on July 17, 2021, and ending on June 16, 2022. It contains a total of 10,229 publications and 5,471 replies. The observed authorities are as follows:</p><p> </p><p><strong>City Authority name X handle </strong><i><strong>AUTHORITY_ID</strong></i></p><p>São Paulo Centro de Gerenciamento de Emergências Climáticas da Prefeitura de SP <i>@cge_sp </i>268407434</p><p>Rio de Janeiro Sistema de Alerta localizado no Centro de Operações do Rio (COR) @<i>alertario </i>87487749</p><p>Belo Horizonte Defesa Civil de Belo Horizonte @<i>defesacivilbh </i>837731966</p><p>Porto Alegre Defesa Civil Porto Alegre @<i>defesacivilpoa </i>1037420896473022466</p><p>Belém Defesa Civil de Belém @<i>defesacivilbel </i>1346501728632500225</p><p> </p><p>As weather and climate authorities are government bodies, the whole content of their publications is of public interest according to Brazilian law. Thus, the text messages in their publications on social media are in the public domain and are stored in this dataset. As the data structure describes, text messages of citizens' replies are not stored. According to the terms of use of the X platform, citizen text messages cannot be publicly stored outside the X platform. Such text messages are public on that platform, and, for reproductivity, they can be recollected using the platform web page or API informing the <i>TWEET_ID</i> stored in this dataset.</p>
Comparing Finnish universities' publication profiles using multidimensional field-normalized indicators - dataset
<p>This dataset from the VIRTA Publication Information Service consists of the metadata of 241,575 publications of Finnish universities (publication years 2016–2021) merged from yearly datasets downloaded from <a href="https://wiki.eduuni.fi/display/cscvirtajtp/Vuositasoiset+Excel-tiedostot">https://wiki.eduuni.fi/display/cscvirtajtp/Vuositasoiset+Excel-tiedostot</a>.</p> <p>The dataset contains following information:</p> <ul> <li>Organisation: name of the university</li> <li>Publication year: the year of publication</li> <li>Subfield: one of 66 fields of science based on Statistics Finland field of science classification (in Finnish), see the classification in English: <a href="https://www2.stat.fi/en/luokitukset/tieteenala/">https://www2.stat.fi/en/luokitukset/tieteenala/</a></li> <li>Peer-reviewed: 1=peer-reviewed publications, 0=not peer-reviewed publications</li> <li>Science communication: 1=publications aimed at professional and general audiences, 0=peer-reviewed and not peer-reviewed publications aimed at academic audience.</li> <li>Bibliodiversity: 1=peer-reviewed book publications (chapters, monographs and edited volumes) and conference articles, 0=peer-reviewed journal articles.</li> <li>Multilingualism: share of peer-reviewed publications in languages other than English (Finnish, Swedish and other languages).</li> <li>Domestic publishing: 1=peer-reviewed publications in journals and books published in Finland, 0=peer-reviewed publications in journals and books published outside Finland.</li> <li>Domestic collaboration: 1=peer-reviewed publications with co-authors from more than one Finnish university, 0=peer-reviewed publications without co-authors from more than one Finnish university.</li> <li>International collaboration: 1=share of peer-reviewed publications with co-authors affiliated with foreign institutions, 0=share of peer-reviewed publications without co-authors affiliated with foreign institutions.</li> <li>Research performance: 1=peer-reviewed outputs in JUFO levels 2 (“leading”) and 3 (“top”) publication channels, 0=peer-reviewed outputs in JUFO levels 1 (“basic”) and 0 (“other”) publication channels.</li> <li>Open access: 1=peer-reviewed open access publications, including gold, hybrid and green OA, 0=peer-reviewed closed publications.</li> </ul>
Reporting quality of abstracts and inconsistencies with full text articles in pediatric orthopedic publications
<p>Abstracts should provide a brief yet comprehensive reporting of all components of a manuscript. Inaccurate reporting may mislead readers and impact citation practices. The primary objective of this study was to investigate the reporting quality of abstracts of observational studies in three major pediatric orthopedic journals against an itemized checklist. The secondary objective was to analyze any reporting inconsistencies between the abstracts and their corresponding full-text articles. Herein, we present the raw data with explanatory comments for the above-titled research. </p>
Publication Database on Recent Trends and Perspectives of Collaborative Robotics in Work 4.0
<p>This publication database is part of a literature review project aimed at providing a comprehensive and up-to-date (31st of January 2022) overview of recent trends and perspectives of robotics in the working world 4.0. The related journal article can be found here: DOI (https://doi.org/10.3390/robotics12030084). The database contains bibliographic information of all the publications that were included in the literature review.</p> <p> </p> <p>This publication database (including data and complementary documents) is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) by INSTITUTE OF MECHANISM THEORY, MACHINE DYNAMICS AND ROBOTICS - RWTH AACHEN UNIVERSITY.</p>
MEMS-cochlea: Dataset for publication
<p>This is the dataset to the publication " Neuromorphic acoustic sensing using an adaptive microelectromechanical cochlea with integrated feedback" by Lenk et al. (DOI will follow soon).</p> <p>Explanation of data:</p> <p>1.) 'MEMS cochlea response to natural sound' (dataset for fig 2 in publication): In this dataset, the file "natural sound dateset" was used to drive a loudspeaker. Its given in wav-format. File named timeseries..." give the data of the response of two different sensors as well as a measurement microphone (named "input") to the wav-file. First column time in sec, second column sensor signal in V. Files named "powerspectra..." give the power spectra data of the three time series, first column frequency in Hz, second column power in absolute values not dB.</p> <p>2.) 'Sensor response in dependence of feedback' (dataset for fig 3 in publication): The dataset includes the sensor signal amplitudes in mV (2nd column) as function of sound pressure amplitudes in Pa (1st column) in files with name starting "fig3a..." for different feedback strengths a_f given by the filename. Files, whose names start with "fig3b+c", give the gain (sensor amplitude active, i.e. a_f>0, divided by sensor amplitude passive, i.e. a_f=0) in the 2nd column as a function of the feedback strength a_f (1st column). In files named "fig3e_sensamp...", the sensor signal amplitude in V (2nd column) is given in dependence of the feedback strength a_f (1st column) for different driving voltages of the loudpseaker, given by the number after "loud" in the filename. If the filename says "negafnegDC", the feedback strength a_f is negativ. If it says "posafnegDC", the feedback strength was positive. The DC voltage of the feedback was always -200mV. From the dependence of sensor signal amplitude on the driving signal amplitude (both in mV), the sensitivity is extracted as the slope of the curve in mV/mV. The sensitvity is given in files, named "fig3e_sensitvity..." in the 2nd column as function of feedback strength a_f (first column).</p> <p>3.) 'Comparison experiment vs model' (dataset for fig 4 in publication): These files give the values plotted in the graphs. The files, named "acrit..." contain the values of a_crit (feedback strength at bifurcation, 2nd column of file) as function of bias voltage u_DC in mV (first column of file) obtained either from experiment or from the formula (last equation in methods part). The file, named "sensitivity...", has the feedback strength a_f in the 1st column and the sensitivity in nm/Pa, obtained frome xperiments, in the 2nd column. The files, named "effective_Q_factor...", have the feedback strength a_f in 1st column and the effective Q factor, obtained from simulations, in the 2nd column. </p> <p>4.) 'Two coupled sensors' (dataset for fig 5 in publication): The files contain the frequency response, i.e. power spectral density in dB (2nd column) as function of frequency in kHz (1st column), of two different sensors for different values of the coupling strength, given by the value after "b" in the filename.</p> <p>5.) 'Dynamic_adaptation_with_code' (dataset for fig 6 in publication): This dataset contains files, names starting with "timeseries...", which give the time series (sensor signal in mV vs. time in sec) for two different driving voltages of the loudspeaker (given by the value after "sound" in the filename), which are shown in fig. 6b in the publication. Files, named "envelope...", give the extracted envelope of the modelled sensor signal in V (2nd column) as function of time in sec (1st column) for different modelled sound inputs, as shown in fig 6c. The envelope was extracted with the program "env.m", written in Matlab. The program "spice_sim" is used to start the LTSpice simulations for adaptation with different parameters. The files in the zip-archive "adapt_,8_,5_natelec" incorporates the necessary files for the LTSPice simulation of the adaptation process. </p> <p> </p>
Dataset of scoping reviews on climate-disease publications for Lyme disease and cryptosporidiosis
<p>The climate-disease relationship is complex, where multiple driver-pressure factors and interactions combine with climate change in determining the occurrence geographies and timings of various infectious diseases. However, studies often focus on just some selected factor(s) and interaction(s). Such focus choices may limit and bias our understanding and predictive capability of disease sensitivity to climate and other driver-pressure changes. To assess these research choices and identify possible remaining key gaps and biases, a scoping review is applied to climate-disease related publications for Lyme disease and cryptosporidiosis. </p> <p>This dataset includes three excel files for Lyme disease (“literature_search_LD_raw.xls”), cryptosporidiosis (“literature_search_CY_raw.xls”), and categories of driver-pressure factors ("Table_1.xlsx") respectively, covering all the relevant information for further analysis. The first two excel files contain three sheets named “Search”, “Filter”, and “Quantitative”. Contents of each sheet is explained as follows.</p> <p>1. “Search”</p> <p>Sheet “Search” listed all the publications found from the literature searches, including the information of publication year, title, DOI, and Authors. The searches were performed in Web of Science™ (WoS) and considered publications from 1 January 2000 to 10 February 2022. Search terms were (('borreliosis' OR 'Lyme disease') AND ('climate' OR 'climate change' OR 'climate variability')) for Lyme disease, ((‘cryptosporidiosis’ OR ‘cryptosporidium’ OR ‘crypto.’) AND (‘climate’ OR ‘climate change’ OR ‘climate variability’)) for cryptosporidiosis. The search yielded 555 publication results for Lyme disease and 185 for cryptosporidiosis.</p> <p>2. “Filter”</p> <p>Sheet “Filter” listed the inclusions and exclusions for searched publications, and categories each included publication belonging to. Excluded articles (marked as blank in column “Included”) are ones that: (i) do not consider both climate and disease; (ii) are for Lyme disease, but not about the <em>Ixodes</em> transmission of the <em>Borrelia </em>pathogen; (iii) are not written in English; and (iv) are not full-text open access. Included articles (marked as “x” in column “Included”) are further classified into following categories based on their focus: for Lyme disease, categories of publications include reviews (mentioning), reviews (specifically discussing), public awareness, mitigation, survey (implications), investigations, projections, and others; for cryptosporidiosis, categories include reviews (mentioning), reviews (specifically discussing), survey (implications), investigations, projections, and others. Articles belonging to any categories are marked as “x”.</p> <p>3. “Quantitative”</p> <p>Sheet “Quantitative” lists further information extracted from quantitative studies which are articles under category of investigations and projections in sheet “filter”. Further extracted information is from the methods section of each study or from the full text if necessary. The information includes transmission components, study region (if applicable), category of investigations or projections, included driver-pressure factors, and methods. Transmission components are reproduction host, transmission host, vector, and human for Lyme disease; animal reservoir, environmental reservoir, and human for cryptosporidiosis. Study region considers specific countries as the smallest scale for spatial resolution, so that smaller than whole-country study sites were counted as studies of the associated countries. Included driver-pressure factors are categorized as shown in Table 1 in the file of "Table_1.xlsx", including main categories and their covered variables. Methods include laboratory/field experimentation/observation, statistical analysis, mechanistic modelling, and synthesis/meta-analysis.</p> Enable Ginger<em>Cannot connect to Ginger</em> Check your internet connection<br> or reload the browserDisable in this text fieldRephraseRephrase current sentenceEdit in Ginger×
deividrvale/nijn-coq-script-generation: First Release of public API
<p>This release describes the first version of the public API for ONijn.</p> <p>Please check <a href="url">https://deividrvale.github.io/nijn-coq-script-generation/onijn/index.html</a> for more details on this release. This version is attached, as software artifact, to the paper "Certifying Higher-Order Polynomial Interpretation", by Niels van der Weide, Deivid Vale, and Cynthia Kop.</p>
Data supporting the publication "Quadrature nonreciprocity in bosonic networks without breaking time-reversal symmetry"
<p>Data supporting the publication "Quadrature nonreciprocity in bosonic networks without breaking time-reversal symmetry"</p>
User-Item interactions dataset from a Public Service Media
<p>Dataset from a Public Service Media (PSM) of user and item interactions.</p>
Data corresponding to "Disparities in disruptions to public drinking water service in Texas communities during Winter Storm Uri 2021"
<p><strong>Contents:</strong></p> <p><strong>TX_pws_data_uri_zenodo.csv</strong>: This dataset was generated by integrating information on community public water systems (PWSs) from the Texas Commission of Environmental Quality (TCEQ) and Texas Water Development Board's water service boundary viewer, weather data from the National Oceanic and Atmospheric Administration (NOAA), and demographic information from the US Census Bureau's 2010 Decennial Census. More information is available in:<br> Tomko, Brianna, 2022, Public drinking water access in Texas communities during Winter Storm Uri 2021, Bachelor Thesis, The Ohio State University, Columbus. </p> <p><strong>readme_for_TX_pws_data_uri_zenodo.txt</strong>: Explains contents of TX_pws_data_uri_zenodo.csv.</p> <p><strong>WaterOutageSurvey-PeopleOnPublicWaterSystems.xlsx</strong>: This dataset was generated as part of a cross-sectionally survey of people living in Texas during Winter Storm Uri.</p> <p>More information on all datasets is available in:</p> <p>Tomko, B., C.L. Nittrouer, X. Sanchez-Vila, and A.H. Sawyer. (2023) "Disparities in disruptions to public drinking water services in Texas communities during Winter Storm Uri 2021," provisionally accepted at PLOS Water, doi: 10.1371/journal.pwat.0000137.</p>
Source data for VALIS: Virtual Alignment of pathoLogy Image Series for multi-gigapixel whole slide images publication
<p>Source data used to create figures in <em>VALIS: Virtual Alignment of pathoLogy Image Series for multi-gigapixel whole slide images</em> (Nature Communications, 2023)</p>
Dataset associated to the publication NATASTRON-22125961A arXiv:2212.10924
<p># Dataset description</p> <p>1. Single stellar model grids at Zsun</p> <p>All single stellar model grids are labeled as "single_star_grid_Zsun_*.h5".<br> To access the dataset you should use the POSYDON v1 code and refer to the<br> code documentation, see https://github.com/POSYDON-code/POSYDON/releases/tag/v1.0.0.<br> The data can be accessed, e.g., with POSYDON as</p> <p>```py<br> from posydon.grids.psygrid import PSyGrid<br> grid = PSyGrid()<br> grid.load(path_to_grid)<br> print(grid.initial_values)<br> print(grid.final_values)<br> print(grid[0].history1['log_L'])<br> print(grid[0].history1['log_Teff'])<br> ```</p> <p>2. Binary black hole population at Zsun</p> <p>We provide the binary black hole population in the file labeled<br> "BBH_population_Zsun.h5". This file contains all binary systems forming<br> binary black holes as simulated from a population synthesis model of 50 million<br> binaries generated with POSYDON v1 at solar metallicity. The data can be<br> accessed, e.g., with Pandas as</p> <p>```py<br> import pandas as pd<br> df = pd.read_hdf('./data_release/source_data/BBH_population_Zsun.h5',<br> key='history')<br> df.head(10)<br> ```</p> <p>For convenience we also provide a dataset labeled "arXiv_2212.10924.csv.gz"<br> that contains the subset of merging binary black holes obtained from<br> "BBH_population_Zsun.h5" at the time of double compact object formation used to<br> generate the Figure 4 of the main manuscript and compute the binary black hole<br> rates using the software released on https://github.com/ssbvr/BBH_merging_rates.<br> The data can be accessed, e.g., with Pandas as</p> <p>```py<br> import pandas as pd<br> df = pd.read_csv('./data_release/source_data/arXiv_2212.10924.csv.gz',<br> compression='gzip')<br> df.head(10)<br> ```</p> <p>Please refer to the following notebook on how use the data to compute the<br> detectable and intrinsic merging population as well as rates, see<br> https://github.com/ssbvr/BBH_merging_rates/blob/main/arXiv_221210924_POSYDON_BBH_Zsun.ipynb</p>
Illustration of the academic publication process
<p>Illustration of the academic publication process, created for Open Educational Ressources provided for the Center for Reproducible Science at the University of Zurich.</p>
Datasets for publication: FREEDA: an automated computational pipeline guides experimental testing of protein innovation
<p>Supplementary materials related to the article: Dudka D, Akins RB, Lampson MA (2023) FREEDA: an automated computational pipeline guides experimental testing of protein innovation. Journal of Cell Biology.</p> <p> </p> <p>The file includes FREEDA pipeline validation results: orthologue detection, comparison with previously published datasets, analysis of rodent centromere proteins and additional analyses of KIF4A, KIF4B, histone H4, MICA, MICB, NUP73 and HERC5. Manually aligned structural prediction models for MIS18A, MIS18B, AURKC, CENP-O and CENP-P are also included.</p>
Supplementary Material to the Publication: Clonal relation between Salmonella enterica subspecies enterica serovar Dublin strains of bovine and food origin in Germany
<p>OHEJP Project: BeOne</p> <p><em>Salmonella enterica </em>serovar Dublin (<em>S</em>. Dublin) is a host-adapted serovar that causes enteritis and/or systemic diseases in cattle. Because the serovar is not host-specific, it can infect other species, including human beings, causing severe disease and a higher mortality rate than other non-typhoidal serovars. Given that human illnesses are primarily caused by contaminated milk, milk products, and beef, data on the genetic connection between <em>S</em>. Dublin strains from livestock and food should be analyzed. </p> <p>Whole genome sequencing (WGS) was performed on 144 <em>S</em>. Dublin strains from cattle and 30 strains from food. Multilocus sequence typing (MLST) found that the majority of livestock and food isolates were of the sequence type ST-10. As discovered by core-genome Single-Nucleotide Polymorphisms Typing and core-genome MLST, 14 of 30 strains from food origin were clonally related to at least one strain from cattle. Without outliers, the remaining 16 food-borne strains fit into the genomic structure of <em>S</em>. Dublin in Germany. WGS demonstrated to be an effective method not only for learning about the epidemiology of Salmonella strains, but also for detecting clonal relationships between organisms isolated at different stages of production. This study discovered a strong genetic link between <em>S</em>. Dublin strains from cattle and food, and thus the potential to cause human infections. <em>S</em>. Dublin strains from both origins have a nearly comparable collection of virulence factors, emphasizing their ability to produce severe clinical symptoms in animals as well as humans, emphasizing the importance of effective <em>S</em>. Dublin management in a farm to fork strategy.</p>
802.11 Managemement frames from a public location
<p><strong>About</strong></p> <p>The following datasets were captured at a busy Belgian train station between 9pm and 10pm, it contains all 802.11 management frames that were captured. both datasets were captured with approximately 20 minutes between then.</p> <p>Both datasets are represented by a pcap and CSV file. The CSV file contains the frame type, timestamps, signal strength, SSID and MAC addresses for every frame. In the pcap file, all generic 802.11 elements were removed for anonymization purposes.</p> <p> </p> <p><strong>Anonymization</strong></p> <p>All frames were anonymized by removing identifying information or renaming identifiers. Concretely, the following transformations were applied to both datasets:</p> <ul> <li>All MAC addresses were renamed (e.g. 00:00:00:00:00:01)</li> <li>All SSID's were renamed (e.g. NETWORK_1)</li> <li>All generec 802.11 elements were removed from the pcap</li> </ul> <p>In the pcap file, anonymization actions could lead to "corrupted" frames because length tags do not correspond with the actual data. However, the file and its frames are still readable in packet analyzing tools such as Wireshark or Scapy.</p> <p>The script which was used to anonymize is available in the dataset.</p> <p> </p> <p><strong>Data</strong></p> <table> <caption>Specifications for the datasets</caption> <thead> <tr> <th scope="col">N/o</th> <th scope="col">Dataset 1</th> <th scope="col">dataset 2</th> </tr> </thead> <tbody> <tr> <td>Frames</td> <td>36306</td> <td>60984</td> </tr> <tr> <td>Beacon frames</td> <td>19693</td> <td>27983</td> </tr> <tr> <td>Request frames</td> <td>798</td> <td>1580</td> </tr> <tr> <td>Response frames</td> <td>15815</td> <td>31421</td> </tr> <tr> <td>Identified Wi-Fi Networks</td> <td>54</td> <td>70</td> </tr> <tr> <td>Identified MAC addresses</td> <td>2092</td> <td>2705</td> </tr> <tr> <td>Identified Wireless devices</td> <td>128</td> <td>186</td> </tr> <tr> <td>Capturetime</td> <td>480s</td> <td>422s</td> </tr> </tbody> </table> <p> </p> <p><strong>Dataset contents</strong></p> <p>The two datasets are stored in the directories `1/` and `2/`. Each directory contains:</p> <ul> <li>`capture-X.pcap`: an anonymized version of the original capture</li> <li>`capture-X.csv`: content of each captured frame (timestamp, MAC address...) saved as a CSV file</li> </ul> <p>`anonymization.py` is the script which was used to remove identifiers.</p> <p>`README.md` contains the documentation about the datasets</p> <p> </p> <p><strong>License</strong></p> <p>Copyright 2022-2023 Benjamin Vermunicht, Beat Signer, Maxim Van de Wynckel, Vrije Universiteit Brussel</p> <p>Permission is hereby granted, free of charge, to any person obtaining a copy of this dataset and associated documentation files (the “Dataset”), to deal in the Dataset without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Dataset, and to permit persons to whom the Dataset is furnished to do so, subject to the following conditions:</p> <p>The above copyright notice and this permission notice shall be included in all copies or substantial portions that make use of the Dataset.</p> <p>THE DATASET IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE DATASET OR THE USE OR OTHER DEALINGS IN THE DATASET.</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.