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2,206 results for “Communications”
Participant survey for the article: More than Formulas - Integrity, Communication, Computing and Reproducibility in Statistics Education
<p>The artcile More than Formulas - Integrity, Communication, Computing and Reproducibility in Statistics Education concerns the introduction of a new course format in the Master Program in Biostatistics at the University of Zurich. This data set contains the results fo a survey among the participants in this new course.</p> <p>Sepcifically it contains the answers of 22 participants to the following questions:</p> <p>1) Did you use the following concepts or tools since you took STA472? <br>Good practice for...</p> <p>... spreadsheets<br>... file and folder organization<br>... version control<br>... dynamic reporting<br>... LaTeX<br>... presentation slide design <br>... oral presentations<br>... designing graphs<br>... designing tables<br>... structure for manuscript<br>... logic of a paragraph<br>... writing style<br>... writing R functions<br>... using unit tests<br>... setting up simulations<br>... code styling<br>... writing vectorized code<br>... writing parallelized code<br>... containerizing code</p> <p>Answers are in the scale: never since, rarely, sometimes, often, frequently, I do not know</p> <p>2) If you used the above concepts at least rarely, did the training of STA472 help you?</p> <p>Good paractice for...</p> <p>... spreadsheets<br>... file and folder organization<br>... version control<br>... dynamic reporting<br>... LaTeX<br>... presentation slide design <br>... oral presentations<br>... designing graphs<br>... designing tables<br>... structure for manuscript<br>... logic of a paragraph<br>... writing style<br>... writing R functions<br>... using unit tests<br>... setting up simulations<br>... code styling<br>... writing vectorized code<br>... writing parallelized code<br>... containerizing code</p> <p>Answers are in the scale: Not really Somewhat Definitively I do not know I do not use this concept</p>
Two-time correlation function based on speckle patterns from x-ray photon correlation spectroscopy associated with "Intermittent cluster dynamics and temporal fractional diffusion in a bulk metallic glass" (scientific article published in Nature Communications, 2024)
<p>This dataset consists of contrast data, i.e., the two-time correlation function, based on speckle patterns measured at the at the 8ID-E beamline of the Advanced Photon Source at Argonne National Laboratory.</p> <p>Experimental details are stated in the paper specified under "related work" and in the accompanying supplementary information.</p> <p>You are welcome to use this dataset in compliance with the CC BY 4.0 licence assigned to this dataset.</p> <p>Any questions regarding the data can be addressed to birte.riechers@bam.de who would also appreciate a note if you find the data useful.</p> <p>____________________________________________________________________</p> <p>The data consists of 32 text files in total, which correspond to the main and lower panel Figure 2 of the main publication. </p> <p>30 of these text files are contrast data, which are named "contrast_DT250s_nn.text" wiith "nn" as the identifier of consecutive data sets going from 1 to 30. Each data set consists of p rows and q columns, DT250s denotes the time resolution of data points, which is 250 s along both row and column values.</p> <p>The data set called "Time_Contrast_1to30s.txt" states the start time in seconds of the first data point of each of the thirty contrast data set.</p> <p>The data set called "ScatteredIntensity.txt" states the scattered intensity at full time resolution, i.e. 2.5 s.</p> <p>The files are plain text files with the data points separated by "space" along rows and "new line" along columns.</p>
PERCEIVE: WP3: Effectiveness of communication strategies of EU projects
<p>This data set contains all the relevant data referred to PERCEIVE WP3, as tasks within WP3 are logically connected. Data analyzed within WP3, but collected in WP5, are included in the data set “<em>PERCEIVE: WP5: The multiplicity of shared meanings of EU and Cohesion Regional and Urban Policy at different discursive levels</em>” (<a href="http://doi.org/10.5281/zenodo.1038041">http://doi.org/10.5281/zenodo.1038041</a>). Data analyzed in Task3.1 are mostly based on transcripts already included in the data set “<em>PERCEIVE. WP1. Framework for comparative analysis of the perception of Cohesion Policy and identification with the European Union at citizen level in different European countries. Task1.2. Focus group with Cohesion Policy practitioners</em>” (focus groups and other interviews).</p> <p>Task3.2 data are the results of a European wide online survey (at the moment this document is being compiled, the website of the survey is no longer online) targeting policy communicators and focused on three strategic aspects of communicating policy: a) factors of success and barriers, b) support from central institutions and c) communication mix and storytelling.</p> <p>Task3.3 data regard the analysis of social media communication from EU communication offices at both local and European level. Data covers a sentiment analysis performed on the Facebook homepages of Local Management Authorities (LMA) of PERCEIVE case study regions as well as twitter networks and timelines for international accounts and hashtags. </p> <p>Task3.4 data contain elements used in the statistical modeling of communication efforts (see Deliverable 3.4, <a href="http://doi.org/10.6092/unibo/amsacta/6111">http://doi.org/10.6092/unibo/amsacta/6111</a> or <a href="http://doi.org/10.5281/zenodo.1318144">http://doi.org/10.5281/zenodo.1318144</a>). Data cover the algorithmic clustering of topics detected in Task5.3, data derived from the PERCEIVE survey, the code (R programming environment) used to run regression analyses, as well as the results of the analyses themselves.</p> <p>Task3.5 data refer to secondary publicly available data. Namely the collection of the PANORAMA magazine available at INFOREGIO, the Directorate of Regional Policy web portal (<a href="https://ec.europa.eu/regional_policy/en/information/publications/panorama-magazine/">https://ec.europa.eu/regional_policy/en/information/publications/panorama-magazine/</a>), and Eurobarometer data on “awareness” available at the Open Data Portal of the EC (<a href="http://ec.europa.eu/commfrontoffice/publicopinion/index.cfm">http://ec.europa.eu/commfrontoffice/publicopinion/index.cfm</a>). The textual content of PANORAMA magazine has been content analyzed and the results are made available as a .csv table of concepts’ frequencies per magazine issue.</p>
Database of non-communicable disease reports contributing to UN high-level meeting process (2000-2020)
<p>Database of the key non-communicable disease reports, policy papers, strategies and journal series published 2000-2020 that fed into the UN high-level meeting process</p>
Urbanus and Cosgrove et al. Nature Communications (2023) - CellRanger outputs for scRNAseq experiments in Figures 2,5, and 6
<p>Attached are the outputs of the cell ranger pipeline for 10x 3' scRNAseq of HSPCs. For any questions regarding this dataset please contact Leila Perie (leila.perie@curie.fr)</p>
Urbanus and Cosgrove et al. Nature Communications (2023) - 12 months scRNAseq fastq files (mouse 1) for Figures 5 and 6
<p>This dataset contains .fastq files for the 12 month timepoint (mouse 1) scRNAseq dataset used in Figures 5 and 6. For any questions about this dataset please contact Leila Perie (leile.perie@curie.fr)</p>
Urbanus and Cosgrove et al. Nature Communications (2023) - 12 months scRNAseq fastq files (mouse 2) for Figures 5 and 6
<p>This dataset contains .fastq files for the 12 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>
Assessment of non-communicable diseases screening practices among university lecturers in Ghana – a cross sectional single centre study
<p>This section highlights the various methods used for this study. It covered study setting, study design, study approach, study population, sampling techniques, sample size calculation, inclusion and exclusion criteria, ethical consideration, data collection, data management and data analysis<strong>. </strong></p> <p> </p> <p><strong>Study Setting</strong></p> <p>The study was carried out at Kwame Nkrumah University of Science and Technology (KNUST), Kumasi between February to August, 2022. The study covered all the six (6) Colleges in the University.</p> <p> </p> <p><strong>Study Design</strong></p> <p>This was a cross sectional study to ascertain health check practices among university lecturers.</p> <p> </p> <p><strong>Study Approach</strong></p> <p>The study employed quantitative approach in which data was collected using questionnaires with both closed- and open-ended questions.</p> <p><strong>Study Population</strong></p> <p>The study population involved 838 Lecturers across the six Colleges at Kwame Nkrumah University of Science and Technology (KNUST), Kumasi. A study of the lecturer population per college revealed that Colleges of Health Sciences (highest) and Agric /Natural resources (lowest) were the outliers (Quality Assurance and Planning Office, 2020).</p> <p> </p> <p><strong>Sampling Technique </strong></p> <p> </p> <p>Simple probability technique was used to select the name of a college and the day/date to visit. Two sets of papers were folded with names of colleges (set 1) and day/date of visit (set 2). A picker picked one folded paper from each set and the name of the college and the day/date to visit was matched. In this case, the ordering of date and visit gave 1<sup>st</sup> College of Humanities & Social Sciences, 2<sup>nd</sup> College of Agric and Natural Resources, 3<sup>rd</sup> College of Art & Built Environment, 4<sup>th</sup> College of Engineering, 5<sup>th</sup> College of Science and 6<sup>th</sup> College of Health Sciences. We then used the ‘walk in’’ system to select the study participants. Within the days to visit a college, any lecturer we meet in his/ her office was a potential study participant.</p> <p> </p> <p> </p> <p> </p> <p><strong>Sample Size Calculation</strong></p> <p>The sample size was obtained using Yamane, 1967 formulae as shown below:</p> <p> </p> <p> </p> <p>Where n= is the population of Lecturers in at KNUST</p> <p>E= is the level of precision</p> <p>Therefore: n= 838</p> <p> 1+838 (0.0025)</p> <p>n = 838</p> <p>1+ 2.098</p> <p> </p> <p>838</p> <p>3.095</p> <p> </p> <p> n=270 </p> <p>However, due to logistical constrains, 205 participants were contacted across the 6 Colleges at Kwame Nkrumah University of Science and Technology. We then applied simple proportions to get the number of lecturers to be consulted in each college.</p> <p> </p> <p><strong>Inclusion and Exclusions Criteria</strong></p> <p>Inclusion criteria was made up of all Lecturers on KNUST campus who are in active service and consented to participate. All other staff not within this category were excluded from this research.</p> <p> </p> <p><strong>Ethical Considerations</strong></p> <p>Ethical approval was sought from the CHRPE, KNUST with approval reference no: CHRPE/AP/581/21. The aim of the research was explained to participants. Those who consented to participate in the research were given consent forms to sign and date. Again, participants were assured of confidentiality. Participants were told that, they were free to withdraw from the study in the cause of time. In other words, study participants were not coerced into the study.</p> <p> </p> <p><strong>Data Collection Tool</strong></p> <p>Data was collected using structured questionnaires. The questionnaires covered dietary intake, alcohol intake, issues on physical inactivity and tobacco use. Aside these four main risk factors of NCDs, the questionnaire also captured frequency of blood pressure checks, blood pressure outcome anytime it is checked (systolic and diastolic), frequency of general body check-up, frequency of anthropometric measurement checks (weight and height), an assessment of impressions about the outcome of weight and height checks, an assessment of intended measures to be taken depending on the outcomes of weight and health checked. Again, the general observation of the nature of job as a lecturer and health status especially the outcome of blood pressure monitoring were also assessed. The questionnaire also captured the socio-demographic status of Lecturers,</p> <p> </p> <p><strong>Data Management</strong></p> <p>Only the Research Team had access to data. Data was kept confidential. The researchers had planned of disposing data from the storage 5 years after the publication of this research. Collected data was entered and cleaned using Microsoft Excel spread sheet, and then imported into STATA version 14.0 (Stata Corp LP, College Station, Texas, USA) for statistical analysis and results.</p> <p> </p> <p><strong>Data Analysis</strong></p> <p>Descriptive statistics were used to summarize the characteristics of the study population by employing frequencies and percentages for categorical data. In addition, the degree of relatedness (association) was evaluated using Chi-square (χ<sup>2</sup>) or Fisher’s exact tests where appropriate with a p ≤0.05 assumed to be statistically significant. Both bivariate and multivariate logistic regression analyses were performed and adjusted for colleges effect to identify associations among the variables of interest. Variables having significant association in the logistic regression models were set at p≤0.05 with 95% confidence interval (95% CI) for both unadjusted and adjusted odds ratios (OR, AOR).</p> <p> </p> <p><strong>Variables</strong></p> <p>BP was selected as the dependent variable, and in turn define as Normal: ≤ 120/80 mmHg; Elevated: Systolic between 120-129 and diastolic ≤ 80; Hypertension: Systolic ≥ 130 or diastolic ≥ 80. Then dichotomized into Normal blood pressure: ≤ 120/80 mmHg and high blood pressure (Hypertension): ≥ 130/90 mmHg for logistic regression analyses. Independent variables were socio-demographics; gender, age, marital status, staff rank and lecturer’s colleges (categorized into binary variable; COHS /COS/COE and CABE/CANR/COHSS), family history of NCDs and health check status. In this study, the variable “very often” denotes (doing the activity in question more than 4 times a month), “often” denotes (doing the activity in question at least twice a month), and “not often” denotes (doing the activity in question once a month).</p> <p> </p> <p> </p>
Assessment of non-communicable diseases screening practices among university lecturers in Ghana – a cross sectional single centre study
<p><strong>Data Collection Tool</strong></p> <p>Data were collected using structured questionnaires. The questionnaires covered dietary intake, alcohol intake, issues with physical inactivity, and tobacco use. Aside from these four main risk factors of NCDs, the questionnaire also captured the frequency of blood pressure checks, blood pressure outcome anytime it is checked (systolic and diastolic), frequency of general body check-ups, frequency of anthropometric measurement checks (weight and height), an assessment of impressions about the outcome of weight and height checks, an assessment of intended measures to be taken depending on the outcomes of weight and health checked. Again, the general observation of the nature of the job as a lecturer and health status especially the outcome of blood pressure monitoring were also assessed. The questionnaire also captured the socio-demographic status of Lecturers,</p> <p> </p> <p><strong>Data Management</strong></p> <p>Only the Research Team had access to data. Data was kept confidential. The researchers had planned of disposing data from the storage 5 years after the publication of this research. Collected data was entered and cleaned using Microsoft Excel spread sheet, and then imported into STATA version 14.0 (Stata Corp LP, College Station, Texas, USA) for statistical analysis and results.</p> <p> </p> <p><strong>Data Analysis</strong></p> <p>Descriptive statistics were used to summarize the characteristics of the study population by employing frequencies and percentages for categorical data. In addition, the degree of relatedness (association) was evaluated using Chi-square (χ<sup>2</sup>) or Fisher’s exact tests where appropriate with a p ≤0.05 assumed to be statistically significant. Both bivariate and multivariate logistic regression analyses were performed and adjusted for colleges' effect to identify associations among the variables of interest. Variables having significant association in the logistic regression models were set at p≤0.05 with 95% confidence interval (95% CI) for both unadjusted and adjusted odds ratios (OR, AOR).</p> <p> </p> <p><strong>Variables</strong></p> <p>BP was selected as the dependent variable, and in turn define as Normal: ≤ 120/80 mmHg; Elevated: Systolic between 120-129 and diastolic ≤ 80; Hypertension: Systolic ≥ 130 or diastolic ≥ 80. Then dichotomized into Normal blood pressure: ≤ 120/80 mmHg and high blood pressure (Hypertension): ≥ 130/90 mmHg for logistic regression analyses. Independent variables were socio-demographics; gender, age, marital status, staff rank, and lecturer’s colleges (categorized into binary variables; Colleges, family history of NCDs, and health check status. In this study, the variable “very often” denotes (doing the activity in question more than 4 times a month), “often” denotes (doing the activity in question at least twice a month), and “not often” denotes (doing the activity in question once a month).</p> <p> </p>
Original data for "Imaging and quantifying the chemical communication between single particles in metal alloy" article
<p>This upload contains original data and supplementary data for the publication titled "Imaging and Quantifying the Chemical Communication Between Single Particles in Metal Alloy" by L. Godeffroy, A. Makogon, S. G. Derouich, F. Kanoufi, V. Shkirskiy in the ACS Analytical Chemistry journal. The preprint of the paper is included in this upload and is also available on ChemRxiv (<a href="https://doi.org/10.26434/chemrxiv-2022-rn77b-v2">https://doi.org/10.26434/chemrxiv-2022-rn77b-v2</a>). </p> <p>The file "OpticalData.zip" contains the original optical images. Each pixel in the images has a size of 180 nm, and the time interval between frames is 1.87 seconds.</p> <p>The file "sem_identical.png" contains a scanning electron microscopy (SEM) image of the same surface area as the optical images, captured using secondary electrons.</p> <p>The file "WorkingWithData.ipynbb" is a Jupyter Lab file that demonstrates a few examples of how data can be loaded and processed in a Python environment. Python 3 was used for this purpose.</p> <p>There are also two supplementary movies:</p> <p>Movie S1: This movie provides an enlarged view (marked with a square in Movie S2) of the surface film transformation on Al6061 during immersion in 10 mM H2SO4. It includes snapshots at 17 s, 28 s, 37 s, and 84 s, along with a related SEM image of the same location.</p> <p>Movie S2: This movie presents a wide-field view of the surface film transformation on Al6061 during immersion in 10 mM H2SO4, along with a related SEM image of the same location. The square highlights the area around which the narrative in the manuscript is focused.</p>
Unveiling metastable ensembles of GRB2 and the relevance of interdomain communication during folding - available data.
<p>The folding process of multidomain proteins is a highly intricate phenomenon involving the assembly of distinct domains into a functional three-dimensional structure. During this process, each domain may fold independently while interacting with other domains to form a functional protein. The folding of multidomain proteins can be influenced by various factors, including the composition and structure of each domain or the presence of disordered linker regions, as well as the surrounding environment. Misfolding of multidomain proteins can lead to the formation of non-functional structures associated with a range of diseases, including cancers and neurodegenerative disorders. Understanding this process is an essential step for many biophysical analyzes, such as stability, interaction, malfunctioning, and rational drug design. One such multidomain protein is the growth factor receptor-bound protein 2 (GRB2), an adaptor protein essential in regulating cell survival. GRB2 consists of one central Src Homology 2 (SH2) domain flanked by two Src Homology 3 (SH3) domains. The SH2 domain interacts with phosphotyrosine regions in other proteins, while the SH3 domains recognize proline-rich regions on protein partners during cell signaling. In this study, we combined computational and experimental techniques to investigate the folding process of GRB2. We sampled the conformational space through computational simulations and mapped the mechanisms involved by calculating free energy profiles, indicating possible intermediate states. From the molecular dynamics and trajectories, we used the Energy Landscape Visualization Method (ELViM), which allowed us to visualize a three-dimensional representation of the overall energy surface. We identified two possible parallel folding routes that cannot be seen in a one-dimensional analysis, with one occurring more frequently during folding. Supporting these results, we used DSC and fluorescence spectroscopy techniques to confirm these intermediate states in vitro. Finally, we analyzed the deletion of domains to compare our model outputs with previously published results, supporting the presence of interdomain modulation. Overall, our study highlights the significance of interdomain communication within the GRB2 protein and its impact on the formation, stability, and structural plasticity, which are crucial for its interaction with other proteins in key signaling pathways.</p> <p> </p>
Model Weights for "Watch This Space: Securing Satellite Communication through Resilient Transmitter Fingerprinting"
<p>Model weights for use with the SatIQ fingerprinting models used in the paper “Watch This Space: Securing Satellite Communication through Resilient Transmitter Fingerprinting”. The models are used to authenticate Iridium satellites from high sample rate message headers.</p> <p>The data collection and model code can be found at the following URL: <a href="https://github.com/ssloxford/SatIQ">https://github.com/ssloxford/SatIQ</a></p> <p>The preprint is available on arXiv at the following URL: <a href="https://arxiv.org/abs/2305.06947">https://arxiv.org/abs/2305.06947</a></p> <p>The final trained model is <code>ae-triplet-final.h5</code>. The others are from the additional experiments and analyses described in the paper, and are included for completeness.</p> <p>When using this data, please cite the following paper: “Watch This Space: Securing Satellite Communication through Resilient Transmitter Fingerprinting”. The BibTeX entry is given below:</p> <pre><code>@inproceedings{smailesWatch2023, author = {Smailes, Joshua and K{\"o}hler, Sebastian and Birnbach, Simon and Strohmeier, Martin and Martinovic, Ivan}, title = {{Watch This Space}: {Securing Satellite Communication through Resilient Transmitter Fingerprinting}}, year = {2023}, publisher = {Association for Computing Machinery}, booktitle = {Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security}, location = {Copenhagen, Denmark}, series = {CCS '23} }</code></pre> <p> </p>
InnORBIT dissemination and communication plan and outcomes
<p>The present dataset is generated in the frame of the Horizon 2020 project "InnORBIT: Empowering innovation intermediaries to generate sustainable initiatives to accelerate the commercialisation of space innovation" (<a href="https://innorbit.eu">innorbit.eu</a>). </p> <p>This dataset describes the InnORBIT project's dissemination and communication plan and also includes the data collected from dissemination and communication activities to measure the progress against the project's targets for outreach during project implementation (January 1st, 2021 - July 31st, 2023). The current (second) version of the dataset includes the following files:</p> <ul> <li><strong>InnORBIT Dissemination and Communication Plan: </strong>Final version of the deliverable, updated on 30/06/2023.</li> <li><strong>InnORBIT Dissemination Activities:</strong> Spreadsheet with detailed dissemination data and calculations for the estimation of progress against KPIs.</li> <li><strong>InnORBIT website analytics (2 files): </strong>Google Analytics reports for the https://innorbit.eu website (file #1: 30/04/2021 - 05/07/2023; file #2: 06/07/2023 - 31/07/2023)</li> <li><strong>InnORBIT Monitoring and Evalution: </strong>Analytics related to the e-learning platform, including usage and performance of the e-learning content. </li> </ul>
Primary somatosensory cortical processing in tactile communication
<p>Touch is an essential form of non-verbal communication. While language and its neural basis are widely studied, tactile communication is less well understood. We used fMRI and multivariate pattern analyses in pairs of emotionally close adults to examine the neural basis of human-to-human tactile communication. In each pair, a participant was designated either as sender or as receiver. The sender was instructed to communicate specific messages by touching only the arm of the receiver, who was inside the scanner. The receiver then identified the message based on the touch gesture alone. We designed two multivariate decoders – one based on the sender's intent (sender-decoder), and another based on the receiver's response (receiver-decoder). Both were able to differentiate accurately the messages using signals from the receiver's primary somatosensory cortex (S1). The receiver-decoder, which is based on receivers' interpretations of the touch gestures, outperformed the sender-decoder, which is more indicative of sensory input. Our results support the notion of non-sensory factors being represented in S1. </p><p>Content: anonymized T1, functional EPI from run1 and run 2 per subject, analysis script (ECOC_s1.mat)</p><p>Log files can be found here: https://zenodo.org/uploads/10012490</p>
Raw EEG-EOG data used in the publication "Auditory Electrooculogram-based Communication System for ALS Patients in Transition from Locked-in to Complete Locked-in State"
<p>The dataset includes raw EEG and EOG recordings during BCI experiments for three patients: p11, p13, p15, and p16. The structure of the dataset is the following: patient/visit/day.</p> <p>The experiment is described in detail in the publication "Auditory Electrooculogram-based Communication System for ALS Patients in Transition from Locked-in to Complete Locked-in State". The correspondence between raw file and BCI session is reported in the attached pdf file "Supplementary Table S5 Session to Raw File Recordings Correspondence".</p> <p>The datasets include EEG and EOG channels. The data are raw (i.e. non filtered and non processed). Data have been acquired with a sampling rate of 500Hz using active electrodes and the amplifier V-Amp DC (Brain Products, Germany). EOG channels are labeled EOGU, EOGD, EOGR, EOGL namely for EOG up, down, right, left; the location in the 10-20 system are respectively SO1, IO1, LO1, LO2.</p> <p>The data are marked with triggers: for each session two markers indicate start and end of the session; for each trial markers indicate start of baseline, start of presentation of question, start of response time, start of feedback. Each trial was marked in a different way if it was a yes trial belonging to a training or feedback session, a no trial belonging to a training or feedback session, or a trial belonging to a speller session. The markers that have been used are the following:<br> <strong>start</strong> 9<br> <em> yes no speller</em><br> <strong>baseline</strong> 10 11 12<br> <strong>presentation</strong> 5 6 7<br> <strong>response</strong> 4 8 13<br> <strong>feedback</strong> 1 2 3</p> <p><strong>end</strong><strong> </strong> 15</p>
Science Communication Terminology
<p>You may have already encountered the terms “public engagement”, “knowledge transfer” or “outreach”. Watch this video with Dr. Luiza Bengtsson to get a better understanding of what these terms mean and what the difference between science communication and public engagement is.</p>
Brain-Computer Interfaces for communication: preferences of individuals with locked-in syndrome, caregives and researchers
<p>Nine animation videos used in the questionnaire described in the articles "<strong>Brain-Computer Interfaces for communication: preferences of individuals with locked-in syndrome</strong>" (<a href="https://doi.org/10.1177%2F1545968321989331">https://doi.org/10.1177/1545968321989331</a>) and "<strong>Brain-Computer Interfaces for communication: preferences of individuals with locked-in syndrome, caregivers and researchers</strong>" (<a href="https://doi.org/10.1080/17483107.2021.1958932">https://doi.org/10.1080/17483107.2021.1958932</a>). <em>Video animations were designed and produced by Merel Horsmeier.</em></p>
Communicating Animal Research Part 1
<p><strong>Episode Summary:</strong></p> <p>In this episode we are discussing the issues connected to communicating animal research. Open Science is based on making science more transparent and accessible, but what does that mean for those who do more controversial research? We will cover what fears scientists might have, and the arguments for and against animal research that scientists often hear. In Part 1, Luiza gives her perspective as a former lab scientist and Emma talks about her ideological change from animal rights activist to science communicator. </p> <p><strong>Links:</strong></p> <ul> <li><a href="http://eara.eu/en/">European Animal Research Association (EARA)</a></li> <li><a href="http://concordatopenness.org.uk/about-the-concordat-on-openness/openness-in-animal-research-public-dialogue">Openness in Animal Research Public Dialogue</a></li> </ul> <p><strong>Quotes:</strong></p> <p>'If one scientist feels they can talk about this a bit more then that's good, right?' </p>
Dataset for "Brief communication: On calculating the sea-level contribution in marine ice-sheet models"
<p>This archive provides the data in Figures 3 and S1 of the following publication:</p> <p>Goelzer, H., Coulon, V., Pattyn, F., de Boer, B., and van de Wal, R.: Brief communication: On calculating the sea-level contribution in marine ice-sheet models , The Cryosphere, 14, 833–840, https://doi.org/10.5194/tc-14-833-2020, 2020.</p>
An Ignoble Pursuit: Laughing and Thinking about Science Communication and the Ig Nobel Prize
<p><strong>Episode Summary:</strong></p> <p>In this episode we are discussing the relationship between science and being funny. Science communication relies on grabbing attention, making science relatable and exciting, and humanising scientists, we talked to Marc Abrahams founder and emcee of the Ig Nobel Prize and editor of Annals of Improbable Research about the role humour plays in this.</p> <p><strong>Resources and Links:</strong></p> <ul> <li><a href="https://improbable.com/about/people/MarcAbrahams.html">Marc Abrahams</a></li> <li><a href="https://www.improbable.com/ig-about/">Ig Nobel Prize</a></li> <li><a href="https://drive.google.com/file/d/19N4X5aNWEBLyjHl54EmlVm1w2SqPlKRK/view?usp=sharing">Public engagement factsheet</a></li> </ul> <p><strong>Episode Quotes:</strong></p> <p>‘If you spend five seconds really looking at it, there’s something funny about it, and that’s what makes it interesting, and once you're interested, if only for five seconds, now you’re paying attention’</p> <p>‘All of us were looking at each other and thinking: at any moment now some ‘grown up’ person is going to stop us’</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.