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2,206 results for “Communication”
Historical Travel and Communications in Finland
<p>This dataset contains a proof-of-concept GIS database of over 29,000 individual historical road polyline segments as a shapefile dataset, covering over 11,000 km<sup>2</sup> in the western Finland from the city of Turku to northern parts of the province of Satakunta. These polylines capture the regional layout of the overland transport infrastructure of late nineteenth and early twentieth century Finland.</p>
PMU measurements altered by wireless communication such as 3G, 4G, 5G
<p><span>Dataset which shows the effect of three types of wireless communication (e.g., 3G, 4G, and 5G, respectively) on data integrity of two real Phasor Measurement Unit (PMU) measurements which are sent to a virtual Phasor Data Concentrator (vPDC) for timestamp synchronization function. Each file contains the values of the two real PMUs installed at each end of a high voltage (HV) transmission line located in a transmission power grid in South Europe. The datasets were collected using an advanced Power-Hardware In the Loop (P-HIL) setup, including in the communication loop between the PMUs and the vPDC a hardware network emulator. The later had the role to realistically emulate the macroscopic behavior of the communication delays and packet data loss of the three wireless communication networks. The delays and data packet loss were imposed only at one of the two PMUs (PMU Lab1) considering a power grid system running in balanced operation conditions. Therefore, only phase A was recorded in the data, and the reason why the current values from phase B and phase C are almost zero. </span></p>
Examples: Bridging Communication Gaps: The Role of Voice-Enabled AI in Medicine
<p><strong>Illustrative examples of potential application cases of advanced voice mode in Clinical Practice. </strong></p>
Dataset supporting the paper "Electronic decoupling of polyacenes from the underlying metal substrate by sp3 carbon atoms. Communications Physics 3, 159 (2020)"
<p>Dataset corresponding to theoretical calculations of the paper "Electronic decoupling of polyacenes from the underlying metal substrate by sp3 carbon atoms". Communications Physics 3, 159 (2020). <a href="https://doi.org/10.1038/s42005-020-00425-y">https://doi.org/10.1038/s42005-020-00425-y</a> </p> <p>Two folders corresponding to pentacene and dihydroheptacene structures on Ag(001):</p> <ul> <li>CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (<a href="https://jp-minerals.org/vesta/en/">https://jp-minerals.org/vesta/en/</a>)</li> <li>.siesta files: STM images in WsXM format (<a href="http://www.wsxm.eu/">http://www.wsxm.eu/</a>) simulated using STMpw (<a href="https://doi.org/10.5281/zenodo.3581159">https://doi.org/10.5281/zenodo.3581159</a>).<br> </li> </ul> <p> </p>
Graphics for implanted brain-computer interfaces for communication and sensorimotor control applications.
<p>Updated information from Nature Reviews Bioengineering, doi: 10.1038/s44222-024-00239-5. Current as of 27 September 2024. Please reference the original publication if using these graphics. As the field moves into the "Translational Era", the original publication reviews the clinical trials up to December 2023. </p>
[review paper] Sustaining the 'Frozen Footprints' of Scholarly Communication through Open Citations_Dataset
<p>This dataset belongs to the review titled <em>Sustaining the ‘Frozen Footprints’ of Scholarly Communication through Open Citations</em>. The review explores the developments in the open citations movement, the OpenCitations infrastructure, and the Initiative for Open Citations (I4OC), providing a comprehensive overview of key milestones and initiatives.</p> <p>The dataset includes bibliographic and citation data for 174 scholarly outputs and 149 blogposts analyzed in the review. These outputs were drawn from a range of sources, including journal articles, conference proceedings, and other scholarly materials. The data has been curated to adhere to open citation principles, ensuring it is structured, separable, and openly accessible. It is provided in accordance with the licenses and terms of use of the original databases.</p> <p>Researchers, practitioners, and policymakers can use this dataset to explore the evolution of open citations and to further their understanding of the connections between scholarly works in the field of open research.</p>
Dataset: Rainbow color map distorts and misleads research in hydrology – guidance for better visualizations and science communication
<p>The rainbow color map is scientifically incorrect and hinders people with color vision deficiency to view visualizations in a correct way. Due to perceptual non-uniform color gradients within the rainbow color map the data representation is distorted what can lead to misinterpretation of results and flaws in science communication. Here we present the data of a paper survey of 797 scientific publication in the journal Hydrology and Earth System Sciences. With in the survey all papers were classified according to color issues. Find details about the data below.</p> <ul> <li><code>year</code> = year of publication (YYYY)</li> <li><code>date</code> = date (YYYY-MM-DD) of publication</li> <li><code>title</code> = full paper title from journal website</li> <li><code>authors</code> = list of authors comma-separated</li> <li><code>n_authors</code> = number of authors (integer between 1 and 27)</li> <li><code>col_code</code> = color-issue classification (see below)</li> <li><code>volume</code> = Journal volume</li> <li><code>start_page</code> = first page of paper (consecutive)</li> <li><code>end_page</code> = last page of paper (consecutive)</li> <li><code>base_url</code> = base url to access the PDF of the paper with <code>/volume/start_page/year/</code></li> <li><code>filename</code> = specific file name of the paper PDF (e.g. <code>hess-9-111-2005.pdf</code>)</li> </ul> <p>Color classification is stored in the <code>col_code</code> variable with:</p> <ul> <li><code>0</code> = chromatic and issue-free,</li> <li><code>1</code> = red-green issues,</li> <li><code>2</code>= rainbow issues and</li> <li><code>bw</code>= black and white paper.</li> </ul> <p> </p> <p>See more details (e.g., sample code to analyse the survey data) on https://github.com/modche/rainbow_hydrology</p> <p>Paper: Stoelzle, M. and Stein, L.: Rainbow color map distorts and misleads research in hydrology – guidance for better visualizations and science communication, Hydrol. Earth Syst. Sci., 25, 4549–4565, https://doi.org/10.5194/hess-25-4549-2021, 2021.</p> <p> </p> <p> </p>
The Data Related to Interfacial Shift Keying Allows a High Information Rate in Molecular Communication
<p>This dataset is related to a method for molecular communication in fluids described on "Fluorescent nanoparticles for reliable communication among implantable medical devices," Carbon, vol. 190, pp. 262-275, Apr. 2022, by Federico Calì, Luca Fichera, Giuseppe Trusso Sfrazzetto, Giuseppe Nicotra, Gianfranco Sfuncia, Elena Bruno, Luca Lanzanò, Ignazio Barbagallo, Giovanni Li-Destri, Nunzio Tuccitto; doi: 10.1016/J.CARBON.2022.01.016. <br> The dataset is linked to the manuscript entitled "Interfacial Shift Keying Allows a High Information Rate in Molecular Communication: Methods and Data" by F. Calì, G. Li-Destri, and N. Tuccitto submitted to IEEE Transactions on Molecular, Biological, and Multi-Scale Communications (T-MBMC).<br> The data, including elapsed time (s), starting from the injection and fluorescence intensity (a.u.), is given in tab-separated values format as .txt files. When present, a column includes the intensity subtracted for the baseline and the subtracted and normalized intensity. In all cases, the baseline was obtained by performing a linear fit between 10 and 110 s and subtracting the line obtained from the entire dataset.<br> </p>
The Role of Informal Communication in Building Shared Understanding of Non-Functional Requirements in Remote Continuous Software Engineering
<p><strong>Study Information</strong></p> <p>We conducted an ethnography-informed case study of a remote software organization that adopts CSE practices to explore how the organization builds a shared understanding of NFRs. Our study uses semi-structured interviews with a period of observations to answer the following research questions:</p> <p> </p> <ol> <li> <p>How does a remote software organization that adopts CSE practices reach a shared understanding of NFRs?</p> </li> <li> <p>What are the limitations to the shared understanding of NFRs in a remote software organization that adopts CSE practices?</p> </li> <li> <p>What organizational practices for remote collaboration supported a shared understanding of NFRs?</p> </li> </ol> <p> </p> <p>In our study, we refer to our partner organization as Alpha. We used ethnography-informed methods to study Alpha's practices and processes and how they approach a shared understanding of NFRs in their product development. </p> <p> </p> <p><strong>Data Analysis</strong></p> <p>We performed a qualitative study through semi-structured interviews and observations. We use the open, axial and selective coding approach from grounded theory [1] to create our codebook, which informed the results and discussion of our study. Two independent coders held agreement sessions to discuss the codes, consolidate the codes and calculate the inter-rater reliability using the Cohen Kappa's coefficient for measuring observer agreement for categorical data [2]. </p> <p> </p> <p><strong>Artifact Descriptions</strong></p> <p>Our replication package contains three artifacts:</p> <p>1. Codebook.csv: The codebook contains rows for the list of codes used, including the code name and the description of the codes. The codes are the final set of themes derived during the thematic analysis of the interview responses. For example, 'Gaps in communication' means when interview participants describe miscommunications due to team members making assumptions about a project/process or having unclear expectations for a project.</p> <p>2. kappa-scores.csv: This contains the associated kappa values for each round of inter-rater agreement sessions. For each agreement session, the Cohen Kappa's coefficient was calculated from the number of agreements and disagreements of codes within one or two interview transcripts. The Kappa values represent the level of agreement ranging from 0 to 1, where > 0.6 represents substantial agreement. </p> <p>3. Interview-questions.csv: This contains the interview questions used in the semi-structured interviews. Some of the interview questions varied depending on the interviewee’s role, experience and the flow of the interviews.</p> <p><strong> </strong></p> <p><strong>Usefulness</strong></p> <p>We recognize that the value and usefulness of our replication package are yet-to-be-determined. In the interest of transparency of open science, we published our artifacts. We hope that these artifacts are useful to either replicate our findings or to further analyze them to produce other enlightening results.</p> <p><strong> </strong></p> <p><strong>References</strong></p> <p>1. Rashina Hoda, James Noble, and Stuart Marshall. "Grounded theory for geeks". In: Proceedings of the 18th conference on pattern languages of programs. 2011, pp. 1–17.</p> <p>2. J Richard Landis and Gary G Koch. "The measurement of observer agreement for categorical data". In: biometrics (1977), pp. 159–174.</p> <p><strong> </strong></p> <p> </p>
Data used in Machine learning reveals the waggle drift's role in the honey bee dance communication system
<p><strong>Data and metadata used in "Machine learning reveals the waggle drift’s role in the honey bee dance communication system" </strong></p> <p>All timestamps are given in ISO 8601 format.</p> <p><strong>The following files are included:</strong></p> <p><strong>Berlin2019_waggle_phases.csv, Berlin2021_waggle_phases.csv</strong></p> <p>Automatic individual detections of waggle phases during our recording periods in 2019 and 2021.</p> <ul> <li> <p>timestamp: Date and time of the detection.</p> </li> <li> <p>cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).</p> </li> <li> <p>x_median, y_median: Median position of the bee during the waggle phase (for 2019 given in millimeters after applying a homography, for 2021 in the original image coordinates).</p> </li> <li> <p>waggle_angle: Body orientation of the bee during the waggle phase in radians (0: oriented to the right, PI / 4: oriented upwards).</p> </li> </ul> <p><strong>Berlin2019_dances.csv</strong></p> <p>Automatic detections of dance behavior during our recording period in 2019.</p> <ul> <li> <p>dancer_id: Unique ID of the individual bee.</p> </li> <li> <p>dance_id: Unique ID of the dance.</p> </li> <li> <p>ts_from, ts_to: Date and time of the beginning and end of the dance.</p> </li> <li> <p>cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).</p> </li> <li> <p>median_x, median_y: Median position of the individual during the dance.</p> </li> <li> <p>feeder_cam_id: ID of the feeder that the bee was detected at prior to the dance.</p> </li> </ul> <p><strong>Berlin2019_followers.csv</strong></p> <p>Automatic detections of attendance and following behavior, corresponding to the dances in Berlin2019_dances.csv.</p> <ul> <li> <p>dance_id: Unique ID of the dance being attended or followed.</p> </li> <li> <p>follower_id: Unique ID of the individual attending or following the dance.</p> </li> <li> <p>ts_from, ts_to: Date and time of the beginning and end of the interaction.</p> </li> <li> <p>label: “attendance” or “follower”</p> </li> <li> <p>cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).</p> </li> </ul> <p><strong>Berlin2019_dances_with_manually_verified_times.csv</strong></p> <p>A sample of dances from Berlin2019_dances.csv where the exact timestamps have been manually verified to correspond to the beginning of the first and last waggle phase down to a precision of ca. 166 ms (video material was recorded at 6 FPS).</p> <ul> <li> <p>dance_id: Unique ID of the dance.</p> </li> <li> <p>dancer_id: Unique ID of the dancing individual.</p> </li> <li> <p>cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).</p> </li> <li> <p>feeder_cam_id: ID of the feeder that the bee was detected at prior to the dance.</p> </li> <li> <p>dance_start, dance_end: Manually verified date and times of the beginning and end of the dance.</p> </li> </ul> <p><strong>Berlin2019_dance_classifier_labels.csv</strong></p> <p>Manually annotated waggle phases or following behavior for our recording season in 2019 that was used to train the dancing and following classifier. Can be merged with the supplied individual detections.</p> <ul> <li> <p>timestamp: Timestamp of the individual frame the behavior was observed in.</p> </li> <li> <p>frame_id: Unique ID of the video frame the behavior was observed in.</p> </li> <li> <p>bee_id: Unique ID of the individual bee.</p> </li> <li> <p>label: One of “nothing”, “waggle”, “follower”</p> </li> </ul> <p><strong>Berlin2019_dance_classifier_unlabeled.csv</strong></p> <p>Additional unlabeled samples of timestamp and individual ID with the same format as Berlin2019_dance_classifier_labels.csv, but without a label. The data points have been sampled close to detections of our waggle phase classifier, so behaviors related to the waggle dance are likely overrepresented in that sample.</p> <p><strong>Berlin2021_waggle_phase_classifier_labels.csv</strong></p> <p>Manually annotated detections of our waggle phase detector (bb_wdd2) that were used to train the neural network filter (bb_wdd_filter) for the 2021 data.</p> <ul> <li> <p>detection_id: Unique ID of the waggle phase.</p> </li> <li> <p>label: One of “waggle”, “activating”, “ventilating”, “trembling”, “other”. Where “waggle” denoted a waggle phase, “activating” is the shaking signal, “ventilating” is a bee fanning her wings. “trembling” denotes a tremble dance, but the distinction from the “other” class was often not clear, so “trembling” was merged into “other” for training.</p> </li> <li> <p>orientation: The body orientation of the bee that triggered the detection in radians (0: facing to the right, PI /4: facing up).</p> </li> <li> <p>metadata_path: Path to the individual detection in the same directory structure as created by the waggle dance detector.</p> </li> </ul> <p><strong>Berlin2021_waggle_phase_classifier_ground_truth.zip</strong></p> <p>The output of the waggle dance detector (bb_wdd2) that corresponds to Berlin2021_waggle_phase_classifier_labels.csv and is used for training. The archive includes a directory structure as output by the bb_wdd2 and each directory includes the original image sequence that triggered the detection in an archive and the corresponding metadata. The training code supplied in bb_wdd_filter directly works with this directory structure.</p> <p><strong>Berlin2019_tracks.zip</strong></p> <p>Detections and tracks from the recording season in 2019 as produced by our tracking system. As the full data is several terabytes in size, we include the subset of our data here that is relevant for our publication which comprises over 46 million detections. We included tracks for all detected behaviors (dancing, following, attending) including one minute before and after the behavior. We also included all tracks that correspond to the labeled and unlabeled data that was used to train the dance classifier including 30 seconds before and after the data used for training.<br> We grouped the exported data by date to make the handling easier, but to efficiently work with the data, we recommend importing it into an indexable database.</p> <p>The individual files contain the following columns:</p> <ul> <li> <p>cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).</p> </li> <li> <p>timestamp: Date and time of the detection.</p> </li> <li> <p>frame_id: Unique ID of the video frame of the recording from which the detection was extracted.</p> </li> <li> <p>track_id: Unique ID of an individual track (short motion path from one individual). For longer tracks, the detections can be linked based on the bee_id.</p> </li> <li> <p>bee_id: Unique ID of the individual bee.</p> </li> <li> <p>bee_id_confidence: Confidence between 0 and 1 that the bee_id is correct as output by our tracking system.</p> </li> <li> <p>x_pos_hive, y_pos_hive: Spatial position of the bee in the hive on the side indicated by cam_id. Given in millimeters after applying a homography on the video material.</p> </li> <li> <p>orientation_hive: Orientation of the bees’ thorax in the hive in radians (0: oriented to the right, PI / 4: oriented upwards).</p> </li> </ul> <p><strong>Berlin2019_feeder_experiment_log.csv</strong></p> <p>Experiment log for our feeder experiments in 2019.</p> <ul> <li> <p>date: Date given in the format year-month-day.</p> </li> <li> <p>feeder_cam_id: Numeric ID of the feeder.</p> </li> <li> <p>coordinates: Longitude and latitude of the feeder. For feeders 1 and 2 this is only given once and held constant. Feeder 3 had varying locations.</p> </li> <li> <p>time_opened, time_closed: Date and time when the feeder was set up or closed again.<br> sucrose_solution: Concentration of the sucrose solution given as sugar:water (in terms of weight). On days where feeder 3 was open, the other two feeders offered water without sugar.</p> </li> </ul> <p> </p> <ul> </ul> <p><strong>Software used to acquire and analyze the data:</strong></p> <ul> <li> <p><a href="https://github.com/BioroboticsLab/bb_pipeline">bb_pipeline: Tag localization and decoding pipeline</a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_pipeline_models">bb_pipeline_models: Pretrained localizer and decoder models for bb_pipeline</a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_binary">bb_binary: Raw detection data storage format</a></p> </li> <li> <p><a href="https://doi.org/10.5281/zenodo.4436419">bb_irflash: IR flash system schematics and arduino code</a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_imgacquisition">bb_imgacquisition: Recording and network storage </a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_behavior">bb_behavior: Database interaction and data (pre)processing, feature extraction</a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_tracking">bb_tracking: Tracking of bee detections over time</a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_wdd2">bb_wdd2: Automatic detection and decoding of honey bee waggle dances</a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_wdd_filter/">bb_wdd_filter: Machine learning model to improve the accuracy of the waggle dance detector</a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_dance_networks/tree/master/bb_dance_networks">bb_dance_networks: Detection of dancing and following behavior from trajectories</a></p> </li> </ul> <p> </p>
Corticothalamic communication under analgesia, sedation and gradual ischemia: a multimodal model of controlled gradual cerebral ischemia in pig
Open the record for dataset details and reuse information.
Eyebright species maintenance (scripts and data accompanying Becher et al., Plant Communications)
<p><strong>This gzipped TAR ball contains data and scripts related to the study on Fair Isle eyebrights by Hannes Becher, Max R. Brown, Gavin Powell, Chris Metherell, Nick J. Riddiford, and Alex D. Twyford, submitted to Plant Communications.</strong></p> <p>Data: genome assembly of<em> Euphrasia arctica</em>, variant call files of the "tetraploid" and "conserved" sets of scaffolds, per-individual k-mer spectra, mapping depths, etc.</p> <p>Scripts: R scripts for the analysis of plant trait data, heterozygosity, ect.; an ipython notebook for the analysis of variant data, and a Mathematica notebook with the derivation of the formulae used to fit pop gen parameters to k-mer spectra.</p>
Dataset for publication "Efficient magnetic switching in a correlated spin glass", Nature Communications volume 14, Article number: 6127 (2023).
<p>Dataset for publication "Efficient magnetic switching in a correlated spin glass", Nature Communications volume 14, Article number: 6127 (2023), DOI 10.1038/s41467-023-41718-4, include images, data used for generate that images, input files, converged potential files used for the calculations on SPR-KKR package 8.6. and raw data files.</p>
DUCC - Dataset for UAS Cellular Communications
<p><strong>Motivation</strong><br>The Dataset for Unmanned Aircraft System (UAS) Cellular Communications, short DUCC, was created with the aim of advancing communications for Beyond Visual Line of Sight (BVLOS) operations. With this objective in mind, datasets were generated to analyse the behaviour of cellular communications for UAS operations.</p> <p><strong>Measurement</strong><br>A measurement setup was implemented to execute the measurements. Two Sierra Wireless EM9191 modems possessing both LTE and 5G capabilities were utilized in order to establish a connection to the cellular network and measure the physical parameters of the air-link. Every modem was equipped with four Taoglas antennas, two of type TG 35.8113 and two of type TG 45.8113. To capture the measurements a Raspberry Pi 4B is used. All hardware components were integrated into a box and attached to a DJI Matrice 300 RTK. A connection to the drone controller has been established to obtain location, speed and attitude. To measure end-to-end network parameters, dummy data was exchanged bidirectionally between the Raspberry Pi and a server. Both the server as well as the Raspberry Pi are synchronized with the GPS time in order to measure the one-way packet delay. For this purpose, we utilised Iperf3 and customised it to suit our requirements. To ensure precise positioning of the drone a Real Time Kinematik (RTK) station was placed on the ground during the measurements.</p> <p>The measurements were performed at three distinct rural locations. Waypoint flights were undertaken with the points arranged in a cuboid formation maximizing the coverage of the air volume. Thereby, the campaigns were conducted with varying drone speeds. Moreover, for location A, different flight routes with rotated grids were implemented to reduce bias. Finally, a validation dataset is provided for location A, where the waypoints were calculated according to Quality of Service (QoS) based path-planning.</p> <p><strong>Dataset Structure and Usage</strong><br>The dataset's structure consists of:<br>-- Dataset<br> |-- LocationX<br> |-- RouteX (in case different routes at LocationX were created)<br> |-- LocXRouteX.kml (file containing the waypoints in the kml format)<br> |-- SpeedXMeterPerSecond (folder containing the datasets recorded with a specific drone speed)<br> |-- YYYY-MM-DD hh_mm_ss.s.pkl.gz (Dataset file)<br> |-- RouteY<br> |-- ...<br> |-- ...</p> <p>The dataset files can be loaded using the pandas module in python3. The file "load.py" provides a sample script for loading a dataset as well as the corresponding .kml file which contains the predefined waypoints. In the file "Parameter_Description.csv" each parameter measured is further explained.</p> <p><strong>License</strong><br>All datasets are copyright by us and published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International. This means that you must attribute the work in the manner specified by the authors, you may not use this work for commercial purposes and if you alter, transform, or build upon this work, you may distribute the resulting work only under the same license. This dataset is made available for academic use only. However, we take your privacy seriously! If you find yourself or personal belongings in this dataset and feel unwell about it, please contact us at automotive@oth-aw.de and we will immediately remove the respective data from our server.</p> <p><strong>Achnowledgement</strong><br>The authors gratefully acknowledge the following European Union H2020 -- ECSEL Joint Undertaking project for financial support including funding by the German Federal Ministry for Education and Research (BMBF): ADACORSA (Grant Agreement No. 876019, funding code 16MEE0039).</p>
Dataset for manuscript "During haptic communication, the central nervous system compensates distinctly for delay and noise"
<p>Data relating to the manuscript "Dataset for manuscript "During haptic communication, the central nervous system compensates distinctly for delay and noise". This includes the experiment dataset (in file experiment_dataset.csv) as well as the MATLAB functions used for the development of the simulation model (with main function main_delay.m)</p>
Understanding the Publish-Review-Curate (PRC) Model of Scholarly Communication - Data and Code
<p>Summary data for the number of articles submitted to publish-review-curate platforms as of August 2024 (Figure 1) [Update 14 Nov 2024: Added JMIRx. Data still from August 2024]</p> <p>Summary data for the number of articles reviewed by review platforms (Figure 2)</p> <p>Analysis code to produce Figures 1 and 2</p> <p>Code to extract articles for inclusion in data</p>
LADDER. Learners' digital communication: a corpus for pragmatic competences in Italian L1/L2
<p> </p> <p><strong>Ladder</strong>. A Corpus of Computer-Mediated Communication for the Analysis of the Acquisition of Pragmalinguistic Competences by German-Speaking Learners of Italian.</p> <p> </p> <p> </p> <p> </p> <p>Project description:</p> <p>Many recent research projects (Artoni, Benigni, & Nuzzo, 2020; Cortés Velásquez & Nuzzo, 2017; Nuzzo & Cortés Velásquez, 2020) have underlined the usefulness of creating and analyzing corpora for teaching pragmatics, which, unlike other linguistic levels such as syntax, cannot be explained by rules but only by reference to tendential values or more or less appropriate choices in a given context. This is even more true for interactions via digital media, such as email and instant-messaging services, which have little place in manuals or L2 courses and for which learners have few reference models (Brocca, 2021; Trubnikova & Garofolin, 2020).</p> <p>Data collection:</p> <p>Data were collected from April 2020 to April 2021 with the help of a discourse completion task (DCT). The data consists of emails and instant messages. The informants are (i) German learners of Italian between A2-C1 level according to the CEFR and most of them are students living in Tyrol (Austria) and (ii) native speakers of Italian most of whom are students from Rome (Italy). The data of the learners were collected by students of the undergraduate seminar “Insegnare la pragmatica” which is part of the compulsory module 2b for student teachers at the Institute of Didactics of the University of Innsbruck. The data of the native speakers were collected in large part from students in foreign languages at the University RomaTre thanks to the collaboration with Prof. Elena Nuzzo.</p> <p>The DCTs have been conducted with online questionnaires. Along with the texts, metadata were also registered with the help of an online questionnaire giving sociolinguistic information about the informant (age, self-assessed language level, place of residence, native language, etc.). The DCTs aim to elicit linguistic acts of request and refusal in increasing levels of social distance and different media (Taguchi & Roever, 2017, pp. 85, 231; Hinger et al. 2018: 148). The DCTs elicit different speech acts (requests and refusals) with different degrees of formality (study/work or free time), directed at different people (lecturer, friend, boss) and in different media (mail or instant messaging). The scenarios represent authentic circumstances for the students. The following table shows the situations that were studied:</p> <p> </p> <p><strong>Email</strong></p> <p>high level of social distance between sender and recipient</p> <p>Scenario 1: Sender is asking for something that he/she is not entitled to</p> <p>Scenario 2: Sender is asking for something that he/she is entitled to</p> <p><strong><em>WhatsApp</em></strong><strong> messages</strong></p> <p>a) low level of social distance between sender and recipient</p> <p>Scenario 1: Request</p> <p>Scenario 2: Rejecting a request</p> <p>Scenario 3: Short-notice cancellation of an invitation</p> <p>b) medium level of social distance between sender and recipient</p> <p>Scenario 4: Request</p> <p>Scenario 5: Rejecting a request</p> <p>Scenario 6: Short-term rejection of an invitation</p> <p> </p> <p> </p> <p>The <em>WhatsApp</em> messages, which are exemplary of the text type instant messaging, were produced directly with the cell phone. The metadata were subsequently associated with the respective messages in an Excel spreadsheet. All personal data were anonymized.</p> <p>The prompts were presented in Italian, as follows:</p> <p>Mail</p> <p><strong>Mail a)</strong> Immagina di star facendo un corso con il Dr. Nicola Brocca. Domani devi fare una presentazione in classe. Non hai avuto tempo per studiare perché dovevi prepararti a un esame di inglese e ti accorgi che il materiale da presentare è più di quello che avevi previsto. Scrivi una mail al professore: la tua speranza è spostare la presentazione.</p> <p>Engl: Imagine you are taking a course with Dr. Nicola Brocca. Tomorrow you have to give a presentation in class. You had no time to study because you had to prepare for an English exam, and you realize that there is more material to present than you had imagined. You write an email to the professor: your hope is to reschedule the presentation.</p> <p><strong>Mail b)</strong> Hai fatto un corso con il Dr. Brocca. Hai consegnato il tuo portfolio il 01.02.2020 adesso è il 01.03.2020 e non hai ancora ricevuto il voto. Ti serve il voto per registrarti per una borsa di studio. Manda una mail al prof.: il tuo obiettivo è ricevere il voto al più presto</p> <p>Engl: You have taken a course with Dr. Brocca. You turned in your portfolio on 02/01/2020, it is now 03/01/2020 and you have not received the grade yet. You need the grade to register for a scholarship. Send an email to the professor: your goal is to receive the grade as soon as possible.</p> <p> </p> <p><em>WhatsApp</em> messages</p> <p><strong>1.</strong> Sei in Erasmus in Italia. Avete creato una chat con 10 compagni di corso. Hai perso la tua tessera della biblioteca a vuoi chiedere se qualcuno ti può aiutare perché ti serve un libro entro domani...per esempio prestandoti la sua. Cosa scrivi?</p> <p>Engl: You are taking part in the Erasmus program in Italy. You have created a chat with 10 classmates. You lost your library card and want to ask if someone can help you because you need a book by tomorrow.... E.g. by lending you their card. What do you write?</p> <p><strong>2.</strong> Ricevi questo messaggio da un amico/a che fa un seminario con te: "Ciao, sono a corto di tempo. Ho visto che hai preso 30 all'esame. Potresti darmi una mano e restare con me in biblioteca oggi?" Non vuoi aiutare il tuo amico. Come reagisci?</p> <p>Engl: You receive this message from a friend who is attending a seminar with you: "Hello, I'm running out of time. I saw that you got a 30 on the exam. Could you help me and stay with me in the library today?" You don't want to help the friend. How do you respond?</p> <p><strong>3.</strong> Cinque giorni fa hai promesso ad un/a amico/a che questa sera sareste andati al cinema assieme. Però hai cambiato idea. Cosa fai? Cosa scrivi?</p> <p> Engl: Five days ago, you promised a friend that tonight you would go to the movies together. But you changed your mind. What would you do? What do you write?</p> <p> </p> <p><strong>4.</strong> Sei al lavoro e hai smarrito il documento elettronico per entrare nel parcheggio. Sei nuovo in questo gruppo di lavoro e hai solo il numero del tuo diretto superiore. Gli mandi un messaggio per chiedergli se ti può aiutare.</p> <p>Engl: You are at work and have lost your electronic badge to enter the parking lot. You are new to this work group and only have the number of your direct supervisor. You send him/her a message and ask if he/she can help you.</p> <p> </p> <p><strong>5.</strong> Ricevi questo messaggio dal/la tuo/a superiore. "Gentile collega, domani c'è una scadenza importante. Per caso sarebbe in grado di restare oggi in ufficio oltre l'orario?" Non vuoi restare in ufficio oltre il normale. Come reagisci?</p> <p>Engl: You receive this message from your supervisor. "Dear colleague, tomorrow is an important appointment. Would you be able to stay in the office after hours today?" You don't want to stay in the office beyond normal working hours. How do you respond?</p> <p> </p> <p><strong>6.</strong> Cinque giorni fa hai promesso al/la tuo/a superiore che oggi saresti andato a una cena di lavoro. Però devi disdire. Cosa fai?</p> <p>Engl: Five days ago, you promised your superior that you would go to a business dinner today. However, you have to cancel. What do you do?</p> <p> </p> <p>The corpus, which was first collected in .xlsx format, was exported to XML format and CSV format in cooperation with Joseph Wang-Kathrein (Brenner Archive Research Center). It was ensured that the emoticons and special characters were also transferred unchanged in the conversion process. These formats allow long-term archiving and significantly facilitate data exchange.</p> <p>The size of the corpus (as of May 2021, version Ladder 1.0):</p> <p>The LADDER corpus includes emails and instant-messaging messages amounting to 18,935 tokens and 33,966 tokens respectively. The corpus of <em>WhatsApp</em> messages consists of a total of 1,204 messages from 80 native speakers and 114 learners. The corpus of emails consists of a total of 235 emails from 78 native-speaker informants and 38 learners. The amount of data allows a qualitatively relevant comparison in sub-corpora e.g. language levels.</p> <p>The size of the corpus is necessarily limited quantitatively, as data collection must be done manually through individual DCT management and metadata checking. The major bottleneck is currently the annotation of socio-pragmatic aspects, a process that is difficult to automate and that needs to be conducted through cross-annotation by multiple annotators.</p> <p>Some students' works on the corpus have been collected and are accessible via the following link: https://ladder.hypotheses.org/</p> <p> </p> <p>Bibliography:</p> <p>Artoni, D., Benigni, V., & Nuzzo, E. (2020), "Pragmatic instruction in L2-Russian: a study on requests and advice" in <em>Instructed Second Language Acquisition, 4</em>(1), 62-95. doi:10.1558/isla.39864</p> <p>Brocca, N. (2021), "LADDER: La costruzione e analisi di un corpus di scritture digitali per l’insegnamento della pragmatica in L2" in <em>Italiano Lingua Due, 13</em>(1 (2021)).</p> <p>Cortés Velásquez, D., & Nuzzo, E. (2017), "Disdire un appuntamento: spunti per la didattica dell'italiano L2 a partire da un corpus di parlanti nativi" in <em>Italiano Lingua Due, 1</em>, 17-36.</p> <p>Hinger, B., Stadler, W., Schmiderer, K., Bauer, M., (Hrg.) (2018). Testen und Bewerten fremdsprachlicher Kompetenzen. Tübingen: Narr Francke Attempto Verlag.</p> <p>Nuzzo, E., & Cortés Velásquez, D. (2020), "Canceling Last Minute in Italian and Colombian Spanish: A Cross-Cultural Account of Pragmalinguistic Strategies" in <em>Corpus Pragmatics, 4</em>, 1-26. doi:10.1007/s41701-020-00084-y</p> <p>Taguchi, N., & Roever, C. (2017), <em>Second language pragmatics</em>: Oxford: Oxford University Press.</p> <p>Trubnikova, V., & Garofolin, B. (2020), <em>Lingua e interazione. Insegnare la pragmatica a scuola</em>. Pisa: ETS.</p> <p> </p>
Communication score lexicon
<p>This lexicon was developed in the context of measuring <em>“openness of communication” in the paper named </em> ''Predicting Openness of Communication in Families with Hereditary Breast and Ovarian Cancer Syndrome: Natural Language Processing Analysis''. To develop an <em>“openness of communication”</em> score we built a lexicon containing words and phrases linked to communication and we classified them as positive or negative.</p> <p>The lexicon contains 532 items (132 unigrams, 215 bigrams, 185 trigrams). Two people independently created the scoring of N-grams in the lexicon as positive or negative. More specifically, they evaluated each item on a 7-point scale on how favorable the items measure <em>“openness of communication”</em>. Scoring values ranged from -3 (extremely strong negative word related to communication) to +3 (extremely strong positive word related to communication).</p>
Data for manuscript "Creating boundaries along a synthetic frequency dimension" in Nature Communications
<p>Data for manuscript "Creating boundaries along a synthetic frequency dimension"</p> <p>https://www.nature.com/articles/s41467-022-31140-7</p> <p>https://arxiv.org/abs/2203.11296</p>
Dataset for Local Communication in Small-Scale PV Systems: Study on Inverter - Smart Meter PLC Communication
<p>This study investigates communication technologies and protocols for small-scale photovoltaic (PV) systems, focusing on the interaction between inverters and smart meters. The research evaluates the performance of Power Line Communication (PLC) technologies, comparing both narrowband (NB-PLC) and broadband (BB-PLC) options. The analysis identifies MODBUS protocol limitations and highlights the benefits of advanced protocols like DLMS/COSEM and DNP3 for enhanced efficiency and reliability. Field tests demonstrate the viability of PLC for residential PV systems, with narrowband PLC showing better performance over longer distances. Future work aims to optimize PLC communication, digitize ripple control signals, and develop a Multi-Radio and Cable Access Technology (Multi-RCAT) module. This module will integrate various communication technologies, enabling flexible and redundant local communication behind utility sub-meters. These advancements will support real-time production and consumption control, contributing to the efficient and sustainable operation of decentralized energy systems.</p>
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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.