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2,206 results for “Communications”
MD trajectories for "Communication Breakdown: Dissecting the COM Interfaces between the Subunits of Nonribosomal Peptide Synthetases"
<p>This dataset contains Amber MD trajectories for the MD simulations described in the manuscript "Communication Breakdown: Dissecting the COM Interfaces between the Subunits of Nonribosomal Peptide Synthetases" by Christopher D. Fage, Simone Kosol, Matthew Jenner, Carl Öster, Angelo Gallo, Milda Kaniusaite, Roman Steinbach, Michael Staniforth, Vasilios G. Stavros, Mohamed A. Marahiel, Max J. Cryle, and Józef R. Lewandowski published in ACS Catalysis (<a href="https://doi.org/10.1021/acscatal.1c02113">https://doi.org/10.1021/acscatal.1c02113</a>). If you use these data please cite the original manuscript (follow the manuscript DOI for the final citation, which was not available at the time of publishing this data set). </p> <p>To reduce their size the trajectories were stripped of water and ions. Only frames every 1 ns or 5 ns were saved. Please see the Supporting Information of the source manuscript for the conditions for the simulations. </p>
Figure 1 in Visual communication in Brazilian species of anurans from the Atlantic forest
Figure 1. Visual signalization of two anuran species from the Atlantic forest, Parque Estadual da Serra do Mar, Núcleo Picinguaba, Municipality of Ubatuba, São Paulo state, Brazil. Hyla albomarginata (nocturnal species that call from temporary ponds near the forest edges): (A) limb lifting (see Table I); (B) leg kicking. Hylodes phyllodes (diurnal species that call near rivulets in the forest): (C) mouth opening and leg stretching. See Table I for definitions of the behaviours.
Fig. 1 in Short Communication: The Javan Rhinoceros Rhinoceros Sondaicus In Borneo
Fig. 1. Comparison of the terminal phalanx fragment from Niah Cave West mouth Trench E/D 8, 0–12 ins with: a, the forefoot of Rhinoceros sondaicus (BMNH 1861.3.11.1); b, hindfoot of R. sondaicus (BMNH 1861.3.11.1); c, forefoot of Dicerorhinus sumatrensis (BMNH 1879.6.14.2)
Open Chat Series: How-to-Advocacy "Why advocate for open scholarly communication in the social sciences and humanities?"
<p>On the 21.10.2022 Alíz Horváth, researcher, expert in East Asian studies, creator of “Humanista” podcast - and member of OPERAS's Special Interest Group "Advocacy" moderated an Open Chat with our guest speaker Per Pippin Aspaas, Head of Library Research and Publishing Support at the UiT in Tromsø, discussing the question "Why advocate for scholarly communication in the social sciences and humanities?"</p>
Reputation Communication from an Information Perspective
<p>Here, the data underlying the article "Reputation Communication from an Information Perspective" is provided.<br> <br> There are two example simulations, one with 3 ordinary agents and one with a dominant agent among two ordinary agents. Each simulation is represented by a .json file in which all events that happened during the simulation are collected. Generally, there are three types of events: communications, self-updates (information that the speaker gained about itself is processed) and updates (information that the receiver gained about the speaker and the topic is processed). Additionally, the first line specifies the parameters of each simulation, and the last few lines summarize the final status of the simulation. In the following all important abbreviations are explained:</p> <ul> <li>parameters <ul> <li>decpeting: whether or not agents in generally make dishonest statements</li> <li>listening: whether or not agents in listen to their communication partners</li> <li>disturbing: whether or not agents are particularly risk-taking when making dishonest statements</li> <li>x_est: intrinsic honesties of the agents</li> <li>RSeed: the used random seed</li> <li>NA: number of agents</li> <li>NR: number of rounds</li> </ul> </li> <li>communication <ul> <li>a: speaker</li> <li>b: receiver</li> <li>c: topic</li> <li>J: transmitted message in the form of</li> </ul> </li> <li>self_update <ul> <li>id: number of agent who is updating knowledge about itself</li> <li>Nl, Nt: number of dishonest/honest statements the agent has observed from itself so far</li> <li>I_<id>: knowledge that the agents has about itself after the update in the form of</li> </ul> </li> <li>update <ul> <li>id: number of agent who is updating its knowledge</li> <li>I_<id1>: knowledge that the updating agent has about agent <id1> in the form of</li> <li>Jothers_<id1>_<id2>: last statement that the updating agent heared agent <id1> make about agent <id2></li> <li>Iothers_<id1>_<id2>: what the updating agent believes that agent <id1> thinks about agent <id2> after the update</li> <li>Cothers_<id1>_<id2>: what the updating agent believes after the update that agent <id1> wants it to think about agent <id2></li> <li>new_friends/enemies: id of the agent, the updating agent after the update considers a friend/enemy</li> <li>new_K: normalized surprise the updating agent experienced in the last communication (used to calculate kappa)</li> <li>kappa: median of the last ten normalized surprises the updating agent experienced</li> </ul> </li> <li>final_status <ul> <li>id/name: number if the described agent</li> <li>x: the agent's honesty</li> <li>I: the agent's knowledge about all others</li> <li>Nc/Nt/Nl: total number of conversations/honest statements/dishonest statements the agent has made</li> <li>K: the last 10 normalized surprises the agent experienced</li> <li>kappa: the median of K</li> <li>friends/enemies: list of the agent's friends/enemies</li> <li>Jothers/Iothers/Cothers: same as above, now as full array, i.e. the combined information about all others</li> <li>openess/mind/decepting/strategic/egocentric/deceptive/flattering/aggressive/shameless/disturbing: the agent's character traits</li> </ul> </li> </ul>
Open Chat Series: How-to-Advocacy "How to Advocate for Innovation in Scholarly Communication?"
<p>On 25. November 2022 Magdalena Wnuk moderated the Open Chat with Maciej Maryl from IBL PAN and talked about how to advocate for innovation in scholarly communication. Together with our guest speaker and the audience we discussed the advocacy goals and challenges for the SSH community while advocating for innovation in open science. </p>
Vocal communication is tied to interpersonal arousal coupling in caregiver-infant dyads
<p>It has been argued that a necessary condition for the emergence of speech in humans is the ability to vocalize irrespectively of underlying affective states, but when and how this happens during development remains unclear. To examine this, we used wearable microphones and autonomic sensors to collect multimodal naturalistic datasets from 12-month-olds and their caregivers. We observed that, across the day, clusters of vocalisations occur during elevated infant and caregiver arousal. This relationship is stronger in infants than caregivers: caregivers' vocalizations show greater decoupling with their own states of arousal, and their vocal production is more influenced by the infant's arousal than their own. Different types of vocalisation elicit different patterns of change across the dyad. Cries occur following reduced infant arousal stability and lead to increased child-caregiver arousal coupling, and decreased infant arousal. Speech-like vocalisations also occur at elevated arousal, but lead to longer-lasting increases in arousal, and elicit more parental verbal responses. Our results suggest that: 12-month-old infants' vocalisations are strongly contingent on their arousal state (for both cries and speech-like vocalisations), whereas adults' vocalisations are more flexibly tied to their own arousal; that cries and speech-like vocalisations alter the intra-dyadic dynamics of arousal in different ways, which may be an important factor driving speech development; and that this selection mechanism which drives vocal development is anchored in our stress physiology.</p>
Urbanus and Cosgrove Nature Communications (2023) - scRNAseq fastq files GFP positive sample in Figure 2
<p>This dataset contains .fastq files for the GFP negative sample in the scRNAseq dataset used in Figures 2. For any questions about this dataset please contact Leila Perie (leile.perie@curie.fr)</p>
Urbanus and Cosgrove et al. Nature Communications (2023) - 18 months scRNAseq fastq files (mouse 2) for Figures 5 and 6
<p>This dataset contains .fastq files for the 18 month timepoint (mouse 2) scRNAseq dataset used in Figures 5 and 6. For any questions about this dataset please contact Leila Perie (leile.perie@curie.fr)</p> <p> </p> <p> </p>
Urbanus and Cosgrove et al. Nature Communications (2023) - 6 months scRNAseq fastq files (mouse 1 and 2) for figures 5 and 6
<p>This dataset contains .fastq files for the 6 month timepoint (mouse 1 & 2) scRNAseq dataset used in Figures 5 and 6. For any questions about this dataset please contact Leila Perie (leile.perie@curie.fr)</p>
A Dataset of UN Agencies' Public Communication about Climate Change on Twitter
<p>The present dataset contains the Twitter communication of eight international organizations (IOs) in different policy areas that are known to be central in communicating about climate change. The IOs are comparable in their communication, all being parts of the United Nations (UN). The IOs under consideration are:</p> <ul> <li>Food and Agriculture Organization (FAO),</li> <li>Office for the Coordination of Humanitarian Affairs (UNOCHA),</li> <li>UN Development Programme (UNDP),</li> <li>UN Office for Disaster Risk Reduction (UNDRR),</li> <li>UN Environmental Program (UNEP),</li> <li>UN International Children’s Emergency Fund (UNICEF),</li> <li>UN High Commissioner for Refugees (UNHCR),</li> <li>World Health Organization (WHO).</li> </ul> <p>The tweets were downloaded and parsed via the Twitter Academic Research API (<a href="https://developer.twitter.com/en/products/twitter-api/academic-research">link</a>). In total, the dataset contains 222,191 tweet IDs of the tweets posted by the above 8 UN organizations from their official accounts. This number represents the total number of tweets posted by these selected UN organizations since the beginning of their tweeting history until the end of 2019. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter and the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.</p> <p>The dataset consists of two parts:</p> <ul> <li>Unlabeled tweet IDs of the considered IOs (8 txt-files),</li> <li>Labeled dataset of tweet IDs with labels indicating whether tweets are about climate change or not (1 csv-file).</li> </ul> <p><strong>Unlabeled tweet IDs</strong></p> <p>The corresponding 8 txt-files contain tweet IDs of the corresponding tweets posted by the UN organizations. The files are summarised in Table 1 below. </p> <p> </p> <table align="center"> <caption><strong>Table 1</strong>. Summary of the collected dataset files.</caption> <tbody> <tr> <td><strong>File</strong></td> <td><strong>Organization</strong></td> <td><strong>Account</strong></td> <td><strong>Start date</strong></td> <td><strong>End date</strong></td> <td><strong>Tweet IDs</strong></td> </tr> <tr> <td><strong>tweet_ids_FAO_2009_2019.txt</strong></td> <td>FAO</td> <td>@FAO</td> <td>Jan. 2009</td> <td>Dec. 2019</td> <td>28,630</td> </tr> <tr> <td> <p><strong>tweet_ids_UNDP_2009_2019.txt</strong></p> </td> <td>UNDP</td> <td>@UNDP</td> <td>Jul. 2009</td> <td>Dec. 2019</td> <td>47,960</td> </tr> <tr> <td> <p><strong>tweet_ids_UNDRR_2009_2019.txt</strong></p> </td> <td>UNDRR</td> <td>@UNDRR</td> <td>Oct. 2010</td> <td>Dec. 2019</td> <td>9,735</td> </tr> <tr> <td> <p><strong>tweet_ids_UNEP_2009_2019.txt</strong></p> </td> <td>UNEP</td> <td>@UNEP</td> <td>May 2009</td> <td>Dec. 2019</td> <td>21,615</td> </tr> <tr> <td> <p><strong>tweet_ids_Refugees_2008_2019.txt</strong></p> </td> <td>UNHCR</td> <td>@Refugees</td> <td>Jun. 2008</td> <td>Dec. 2019</td> <td>42,882</td> </tr> <tr> <td> <p><strong>ttweet_ids_UNICEF_2009_2019.txt</strong></p> </td> <td>UNICEF</td> <td>@UNICEF</td> <td>Jul. 2009</td> <td>Nov. 2019</td> <td>34,288</td> </tr> <tr> <td> <p><strong>tweet_ids_UNOCHA_2011_2019.txt</strong></p> </td> <td>UNOCHA</td> <td>@UNOCHA</td> <td>Jul. 2011</td> <td>Jul. 2019</td> <td>12,521</td> </tr> <tr> <td> <p><strong>tweet_ids_WHO_2008_2019.txt</strong></p> </td> <td>WHO</td> <td>@WHO</td> <td>May 2008</td> <td>Dec. 2019</td> <td>24,560</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td><strong>Total</strong></td> <td>222,191</td> </tr> </tbody> </table> <p>The dataset contains only tweet IDs to ensure compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The tweet IDs need to be hydrated to be used. For hydrating the present dataset, the Hydrator application (<a href="https://github.com/DocNow/hydrator/releases">link</a>) may be used; see a step-by-step tutorial on how to use Hydrator (<a href="http://towardsdatascience.com/learn-how-to-easily-hydrate-tweets-a0f393ed340e#:~:text=Hydrating%20Tweets">link</a>).</p> <p><strong>Labeled dataset related to climate change</strong></p> <p>This is a subset of the entire dataset described above. Namely, 5,750 tweets are randomly selected from the entire dataset and labeled manually as either "climate change-related" or "not climate change-related". The dataset is available in the file <strong>dataset_UN_climate_change_labeled.csv</strong> and is summarised in Table 2 below. </p> <table align="center"> <caption><strong>Table 2</strong>. Summary of the labeled dataset.</caption> <tbody> <tr> <td><strong>Organization</strong></td> <td><strong>Tweets</strong></td> </tr> <tr> <td>FAO</td> <td>753</td> </tr> <tr> <td>UNDP</td> <td>1,199</td> </tr> <tr> <td>UNDRR</td> <td>256</td> </tr> <tr> <td>UNEP</td> <td>540</td> </tr> <tr> <td>UNHCR</td> <td>1,114</td> </tr> <tr> <td>UNICEF</td> <td>910</td> </tr> <tr> <td>UNOCHA</td> <td>366</td> </tr> <tr> <td>WHO</td> <td>612</td> </tr> <tr> <td><strong>Total</strong></td> <td>5,750</td> </tr> </tbody> </table> <p> </p> <p> </p>
Using Social Virtual Reality in Teaching Intercultural Communication
<p>This data represents the supplementary material for the journal article with the above title submitted to Technology, Knowledge and Learning.</p> <p> </p> <p>The supplementary material contains:</p> <ol> <li>the interview guideline for the group of the class that was taught in the online remote study using video conferencing tools only</li> <li>the interview guideline for the group of the class that was taught in the online remote study using video conferencing tools and additional social virtual reality sessions</li> <li>the quantitative survey questionnaire that was sent to both groups</li> </ol>
DataSet-Neural signatures of linguistic predictions and listener's attention to speaker's communication intention
<p>Researchers can find the raw EEG data with scripts of EEG data analyses, false alarms data, and sentence materials related to he project entitled "Neural signatures of linguistic predictions and listener's attention to speaker's communication intention". Corrections due to the available article form: "I-" was replaced with "E-" and "I+" was replaced with "E+" in the online article form. In the same manner, "INTENTION" should be replaced with "PROSODIC EMPHASIS" in the scripts.</p>
Social signal learning of referential communication in a social insect
<p>This is the dataset for a paper showing that honey bees can use social learning to improve their waggle dancing.</p>
Teaching storytelling in business communication
<p>Dataset for a descriptive survey of how storytelling is taught in business communication courses at universities around the world.</p>
FIG. 3 in Chemical communication in the symbiotic interaction between the anemone Exaiptasia diaphana (ex Aiptasia pallida) Rapp and the dinoflagellate Symbiodinium spp.
FIG. 3. — Iron (A), Manganese (B), Magnesium (C), Copper (D), Zinc (E) mean contents in bleached Exaiptasia diaphana Rapp samples. The error bars represent the standard deviation (n ≥ 3). Abbreviations: C, Aposymbiotic Exaiptasia samples exposed to an empty dialysis tube (control group); E, Aposymbiotic Exaiptasia samples exposed to a dialysis tube containing the holobionts; S, Aposymbiotic Exaiptasia samples exposed to a dialysis tube containing Symbiodinium cells.
FIG. 2 in Chemical communication in the symbiotic interaction between the anemone Exaiptasia diaphana (ex Aiptasia pallida) Rapp and the dinoflagellate Symbiodinium spp.
FIG. 2. — Carbon (A), Nitrogen (B), Phosphorus (C), Sulphur (D) mean contents in bleached Exaiptasia diaphana Rapp samples. The error bars represent the standard deviation (n ≥ 3). Abbreviations: C, Aposymbiotic Exaiptasia samples exposed to an empty dialysis tube (control group); E, Aposymbiotic Exaiptasia samples exposed to a dialysis tube containing the holobionts; S, Aposymbiotic Exaiptasia samples exposed to a dialysis tube containing Symbiodinium cells.
FIG. 1 in Chemical communication in the symbiotic interaction between the anemone Exaiptasia diaphana (ex Aiptasia pallida) Rapp and the dinoflagellate Symbiodinium spp.
FIG. 1. — Organic composition in bleached Exaiptasia diaphana Rapp samples. The error bars represent the standard deviation (n ≥ 3). Abbreviations: C, Aposymbiotic Exaiptasia samples exposed to an empty dialysis tube (control group); E, Aposymbiotic Exaiptasia samples exposed to a dialysis tube containing the holobionts; S, Aposymbiotic Exaiptasia samples exposed to a dialysis tube containing Symbiodinium cells.
Questionnaire: Co-occurrence of behavioural risk factors for non-communicable diseases among 40-year and above aged community members in three regions of Myanmar
<p>This questionnaire was applied as data collection tool for the dataset of "Co-occurrence of behavioural risk factors for non-communicable diseases among 40-year and above aged community members in three regions of Myanmar".</p>
Short communication: Synchrotron-based elemental mapping of single grains to investigate variable infrared-radiofluorescence emissions [Data set]
<p>This dataset accompanies a research paper in the journal Geochronology (<span><a href="https://doi.org/10.5194/gchron-6-77-2024"><span>https://doi.org/10.5194/gchron-6-77-2024</span></a></span>). It contains the raw output of micro-XRF measurements carried out at the 5-ID SRX beamline at the National Synchrotron Light Source II (NSLS-II) at Brookhaven National Laboratory, USA, on coarse K-feldspar grains. Additionally, the XRF intensity attributed to each element after spectral fitting is provided for elemental mapping. The dataset also includes the output from scanning electron microscope energy-dispersive X-ray spectroscopy (SEM-EDS) measurements on coarse K-feldspar grains of two samples taken at Archéosciences Bordeaux, France.</p>
ScienceDex guides
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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.