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8,537 results for “Openness”
DIALLS Dataset of evaluations of Open Educational Resources – The Cultural Literacy Learning Programme resources
<p>This dataset was used to support the development of the DIALLS project’s (Dialogue and Argumentation for cultural Literacy Learning in Schools, www.dialls2020.eu/) teaching and learning materials as open access online educational resources (OER). It consists of quantitative data (i) evaluating the three OER (i.e., professional development (PD) materials, lesson plans (LP), Scale of Progression for Cultural Literacy Learning (SPCLL)) and (ii) assessing the discussion forum as a platform for building a DIALLS community of practice. <em> N</em> = 140 teachers from Germany, UK, Portugal, and Israel participated in the surveys over the course of six months (September 2020 to February 2021). </p> <p>For each country, there are four datasets from the surveys. For data collection, the surveys have been translated into the partner countries’ language, open feedback is translated from the respective languages and now in English (i.e., Translation from German, English, Portuguese, Hebrew). </p> <p>Based on Kirkpatrick and Kirkpatrick’s (2006) evaluation model and the Value Creation Framework (VCF) (Wenger et al., 2011), the surveys have been developed and the discussion forum designed. From this, items regarding the materials’ application, content, design, and value were included in the assessment of the quality of PD materials, lesson plans, and SPCLL. The quality of the discussion forum was assessed regarding its motivation, participation, topic, and value. </p> <p>Considering the context, the dataset is relevant to the following areas of research: Communities of Practice, OER, Teachers’ professional development. </p> <p>The dataset is organised in 1 file (merged quantitative data from all four surveys and all four partners). A detailed description of the dataset can be accessed in a PDF file. Further, the provided codebook in the "variable view window" in SPSS explains the variables and values. </p>
Dataset relating the Social activity of Open Research Data on ResearchGate
<p>The potential of Open Research Data (ORD) within the context of open science and digital scholarship can be frustrated if data remains unused. Although current research has investigated the way ORD is being published, researchers’ behaviour of ORD publishing and sharing on academic social networks (ASN) remains insufficiently explored. The research to which this dataset is connected aims to illustrate some parameters of social activity around self-archived ORDs on ResearchGate. The study analyses whether the ORDs publication leads to social activity (reads and citations) around the ORDs and their linked published articles, including eventual associations between the social activity and the researchers’ profile (scientific domain, gender, region, professional position, reputation) as well as the quality of the ORD published.</p> <p>The current dataset is composed by:</p> <p>A- The .csv file, extracted as a random sample of 752 ORD items from ResearchGate. The dataset has been polished and anonymized. The variables relating the researchers' profiles and citations to the ORD lined research were got from the researchers' profiles and published research. However, for the purpose of anonymisation, these variables have been coded and the original information removed.</p> <p>B- The codebook explaining the variables and metrics contained in the file (A)</p> <p>C- The R script. This script contains a number of explorations not reported in the final paper. The quantitative techniques applied include descriptive statistics, logistic regression and K-means cluster analysis.</p> <p>D- Five tables, three figures and two annexes (Logistic Regression I and II) created over the basis of the dataset (A)</p> <p>The results have been interpreted in terms of three main aspects.</p> <ul> <li>Firstly, there is still an underdeveloped social activity around self-archived ORD in ResearchGate (operationalized as reads and citations) overall and in spite of the published ORDs quality.</li> <li>Secondly, it was found an uncovering of the relevance of the moderating effects over ORD, which spots traditional dynamics within the “innovative” practice of engaging with data practices.</li> <li>Thirdly, a rather similar situation of ResearchGate as ASN with regard to other data platforms and repositories in terms of social activity around ORD was detected.</li> </ul> <p>The potential of Open Research Data (ORD) within the context of open science and digital scholarship can be frustrated if data remains unused. </p>
DeepPlastic: An Open Source Image Dataset for Epipelagic Marine Plastic Detection
<p>Deep Plastic</p> <ul> <li>Enhanced Object Detection for Epipelagic Plastic.</li> <li>This repository contains source code for the method developed in <a href="https://arxiv.org/pdf/2105.01882.pdf">DeepPlastic: Identifying Marine Plastic In The Epipelagic Zone using Computer Vision and Deep Learning</a></li> <li> <p>Information:</p> </li> <li>Paper: [Coming Soon]</li> <li>YouTube video of Results: <a href="https://youtu.be/8zBdFxaK4Os">https://youtu.be/8zBdFxaK4Os</a></li> <li> <p>Object Detection Model</p> </li> <li>Four models: YOLOv4, YOLOv5, MobileSSD, Faster RCNN Inception V2</li> <li>Small efficient and high precision models can be used for real-time object detection.</li> <li>Model architecture and implementation details: <a href="https://arxiv.org/">https://arxiv.org/</a></li> <li>Weights for YOLOv4 and YOLOv5 are provided in the model/ <ul> <li>YOLOv4: best. weights; use <a href="https://drive.google.com/file/d/1YOTtZ2cHbqgxHukzLp01OVsUoa2CwwXs/view?usp=sharing">best.weights</a></li> <li>YOLOv5: best.pt; use <a href="https://drive.google.com/file/d/14mBOhtLrE2d3hudqjwBZmawKAvTF4zxS/view?usp=sharing">best.pt</a></li> </ul> </li> <li> <p>Google Colab Links</p> <p>Note: Click on File and Save Copy in Drive. If you try to edit my file it'll ask you for permission and send me an email. Please make your own copy.</p> </li> <li>YOLOv5: <a href="https://colab.research.google.com/drive/1_qzbpBWkNfxQ0ny-DvsKicCeM0aFU4eW?usp=sharing">https://colab.research.google.com/drive/1_qzbpBWkNfxQ0ny-DvsKicCeM0aFU4eW?usp=sharing</a></li> <li> <p>DeepTrash DataSet</p> </li> <li>1900 training images, 637 test images, 637 validation images (60, 20, 20 split)</li> <li>Field images taken from Lake Tahoe, San Francisco Bay and Bodega Bay in CA.</li> <li>Deep Sea images are from JAMSTEK JEDI dataset: <a href="http://www.godac.jamstec.go.jp/">http://www.godac.jamstec.go.jp/</a></li> </ul>
OCTAVA: an open-source toolbox for quantitative analysis of optical coherence tomography angiography images
<p>This is a dataset of OCTA images used in the development of the manuscript <em>OCTAVA: an open-source toolbox for quantitative analysis of optical coherence tomography angiography images</em></p>
Dependency Management Bots in Open-Source Systems - Prevalence and Adoption
<p>This is a re-analysis package for the paper: Dependency Management Bots in Open-Source Systems - Prevalence and Adoption. The package includes scripts and processed data collected from GitHub about five investigated dependency management bots: Dependabot, Depfu, Greenkeeper, Pyup and Renovate.</p>
IPBES Global Assessment in Linked Open Data format
<p>This dataset contains the Thematic Assessment Report on Invasive Alien Species and their Control of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services in linked open data format.</p> <p>The structure of the file follows the IPBES ontology version 06: <a href="https://github.com/IPBES-Data/IPBES_Ontology">https://github.com/IPBES-Data/IPBES_Ontology</a></p> <p>The report is published in 2019 and consists of 6 chapters and a Summary for Policy Makers. For more information about the report, see: <a href="https://www.ipbes.net/global-assessment">https://www.ipbes.net/global-assessment</a></p> <p>For any questions and enquiries, please contact the IPBES Data and Knowledge Unit <a href="mailto:aidin.niamir@senckenberg.de">aidin.niamir@senckenberg.de</a></p>
Reported funding data for open infrastructure
<p>Reported funding data for open infrastructure projects, focused primarily on the services in the <a href="https://investinopen.org/blog/funding-open-infrastructure-a-survey-of-available-data-sources/">pilot for Invest in Open Infrastructure's funding landscape research</a>, as well as other notable providers in the SCOMCat index.</p>
Database of the educational role of Citizen Science in the framework of Open Science from the paradigm of complex thinking
<p>Database for the analysis of the educational role of Citizen Science projects in the framework of Open Science from the paradigm of complex thinking</p>
The versatility of pulses: Are consumption and consumer perceptions in different European countries related to the actual climate impact of different pulse types? Author links open overlay panel
<p>Pulses support sustainable production and consumption. Their culinary versatility creates a wide range of possibilities for new products, bridging consumers’ preparation barriers. However, this potential is often intangible for consumers who have little knowledge about plant-based foods. Based on an online survey in Denmark, Germany, Poland, Spain, and the United Kingdom (<em>N</em> = 4,226), this study aimed to investigate consumer utilization and perception of pulses as a versatile, low-carbon food relative to objective life cycle assessment (LCA) measures of 12 pulse types. The most popular pulse types, with specific preferences across countries, were lentils, kidney beans, and chickpeas, typically consumed at home and purchased in dried or canned form. Respondents associated pulses with being healthy and natural, but sustainability was not an essential attribute related to the perception of pulses. LCA revealed a low environmental impact caused by pulse production and consumption, with marginal variations between types and produce. Respondents were unaware of the nuances in the environmental impact of different pulse types, generally perceiving uncommon pulses to be relatively more sustainable than others. In conclusion, a low consumption combined with a misconception of pulses’ environmental impact may demand different promotional strategies including clear communication to inform consumers.</p>
Open Data_Remedial trial of sequential anoxic_oxic chemico_biological treatment for decontamination of extreme hexachlorocyclohexane concentrations in polluted soil
<p>Open data sheet used for the preparation of the publication "Remedial trial of sequential anoxic/oxic chemico-biological treatment for decontamination of extreme hexachlorocyclohexane concentrations in polluted soil" containing graphs and other data</p>
Target enrichment of long open reading frames and ultraconserved elements to link microevolution and macroevolution in non-model organisms
<p>Despite the increasing accessibility of high-throughput sequencing, obtaining high-quality genomic data on non-model organisms without proximate well-assembled and annotated genomes remains challenging. Here we describe a workflow that takes advantage of distant genomic resources and ingroup transcriptomes to select and jointly enrich long open reading frames (ORFs) and ultraconserved elements (UCEs) from genomic samples for integrative studies of microevolutionary and macroevolutionary dynamics. This workflow is applied to samples of the African unionid bivalve tribe Coelaturini (Parreysiinae) at basin and continent-wide scales. Our results indicate that ORFs are efficiently captured without prior identification of intron-exon boundaries. The enrichment of UCEs was less successful but nevertheless produced substantial datasets. Exploratory continent-wide phylogenetic analyses with ORF supercontigs (> 515,000 parsimony informative sites) resulted in a fully resolved phylogeny, the backbone of which was also retrieved with UCEs (> 11,000 informative sites). Variant calling on ORFs and UCEs of Coelaturini from the Malawi Basin produced ~2,000 SNPs per population pair. Estimates of nucleotide diversity and population differentiation were similar for ORFs and UCEs. They were low compared to previous estimates in mollusks, but comparable to those in recently diversifying Malawi cichlids and other taxa at an early stage of speciation. Skimming off-target sequence data from the same enriched libraries of Coelaturini from the Malawi Basin, we reconstructed the maternally-inherited mitogenome, which displays the gene order inferred for the most recent common ancestor of Unionidae. Overall, our workflow and results provide exciting perspectives for integrative genomic studies of microevolutionary and macroevolutionary dynamics in non-model organisms.</p>
SMARTEOLE Wind Farm Control open dataset
<p><strong>Introduction</strong></p> <p>This dataset is issued from the third and final field campaign of the French national project SMARTEOLE. It consists in data from 7 wind turbines of a single wind farm (Sole du Moulin Vieux, located in France) for which Wind Farm Control field tests were performed to evaluate the performance of a wake steering strategy for improving the power production.</p> <p>The wind farm consists of 7x Senvion MM82 wind turbines (rotor diameter of 82m, nominal power of 2.05 MW).</p> <p> </p> <p><strong>Description</strong></p> <p>The tests were realized between 17 February – 25 May 2020, with wake steering implemented on turbine SMV6. This dataset covers this full period, and it has been pre-processed to facilitate the analysis of the Wind Farm Control experiment. All timesteps when at least one turbine was stopped were removed, and SCADA nacelle position and wind direction signals have been corrected to remove any north alignment issues. Finally, the time resolution has been standardized at 1-min from the raw data recorded at higher frequencies from the different sensors. For more details about the development of the field campaign and the pre-processing steps followed in the data analysis, please consult the related publication : <a href="https://wes.copernicus.org/articles/6/1427/2021/wes-6-1427-2021.html">https://wes.copernicus.org/articles/6/1427/2021/wes-6-1427-2021.html</a>. Some information can also be found in the related <a href="https://ieawindtask44.tudelft.nl/index.php?title=SMARTEOLE_Field_Test_3">IEA task 44 wiki page</a>.</p> <p>The following files can be found in the dataset :</p> <ul> <li>SMARTEOLE_WakeSteering_SCADA_1minData.csv : the Supervisory Control and Data Acquisition (SCADA) data from the 7 turbines.</li> <li>SMARTEOLE_WakeSteering_ControlLog_1minData.csv : logs from the control system located on turbine SMV6, responsible for the application of the wake steering. The applied yaw offset on the turbine at each timestep can be found here.</li> <li>SMARTEOLE_WakeSteering_WindCube_1minData.csv : data from the ground based WindCube profiler lidar, located between SMV2 and SMV3. This can be used to assess the ambient environmental wind conditions at the farm.</li> <li>SMARTEOLE_WakeSteering_Coordinates_staticData.csv : file listing the coordinates of the wind turbines in the farm and WindCube location in traditional Latitude / Longitude system (<a href="https://epsg.io/4326">WGS84</a>) and XY metric system (<a href="https://epsg.io/2154">French Lambert 93</a>).</li> <li>SMARTEOLE_WakeSteering_Map.pdf : the map of the farm showing the location of wind turbines and WindCube. This is the exact same map as the one seen in the paper indicated above.</li> <li>SMARTEOLE_WakeSteering_NTF_SMV6_staticData.csv : the transfer function used in the paper to correct the wind speed measured by SMV6 to better match the freestream wind speed at 150m upstream (i.e. approximately 1.8 diameters), derived using WindCube nacelle lidar installed on top of the turbine.</li> <li>SMARTEOLE_WakeSteering_correction_factors_SMV1237_staticData.csv : the transfer function used in the paper to derive and correct the reference power and wind speed signals —defined as the mean values of the power and wind speeds from SMV1, SMV2, SMV3, and SMV7— to remove biases from the values at SMV6 as a function of wind direction and wind speed. These corrected reference signals are used for quantifying the impact of the wake steering.</li> <li>SMARTEOLE_WakeSteering_GuaranteedPowerCurve_staticData.csv : the warranted power and thrust curves for the standard mode (Mode 0) of the MM82 wind turbine.</li> <li>SMARTEOLE_WakeSteering_ReadMe.xlsx : read me file indicating for each dataset the signification of the different variables.</li> </ul> <p>Unfortunately, the WindCube nacelle lidar data on top of SMV6 could not be shared, instead the transfer functions derived thanks to this sensor can be used to correct the SCADA channels. The Wind Energy Science publication describes how these transfer functions were obtained.</p> <p> </p> <p><strong>Acknowledgement</strong></p> <p>The creation of this dataset was realized in the scope of French national project SMARTEOLE, supported by the <em>Agence Nationale de la Recherche</em> (grant no. ANR-14-CE05-0034).</p> <p>Furthermore, we would like to thank ENGIE Green for allowing us to make this dataset publicly available.</p> <p> </p> <p><strong>How to cite this dataset</strong></p> <p>When using this dataset in future research, please add the following sentence in the Ackowledgement section of your publication :</p> <p>"The dataset used in this research has been obtained by ENGIE Green in the scope of French national project SMARTEOLE (grant no. ANR-14-CE05-0034)".</p> <p>When citing the dataset in the core text of a paper, the reference to <a href="https://wes.copernicus.org/articles/6/1427/2021/">Simley et al.</a> can simply be used.</p> <p> </p> <p><strong>Related datasets and publications</strong></p> <p>Several field test campaigns were realized in the scope of SMARTEOLE project. Although these data are not made publicly available by default, they can be shared in a per-project basis and under the protection of a dedicated NDA. Please refer to the following publications listed below to get an idea of the content of the different datasets.</p> <p><em>SMARTEOLE Field Test 1</em></p> <ul> <li>Ahmad T. et al., Field Implementation and Trial of Coordinated Control of WIND Farms, <em>IEEE Transactions on Sustainable Energy</em>, 9(3), 2018, 10.1109/TSTE.2017.2774508.</li> <li>Duc T., Optimization of wind farm power production using innovative control strategies, Master’s thesis, DTU Wind Energy-M-0161, 2017.</li> <li>Duc T. et al., Local turbulence parameterization improves the Jensen wake model and its implementation for power optimization of an operating wind farm, <em>Wind Energy Science</em>, 4(2), 2019, 10.5194/wes-4-287-2019.</li> <li>Torres Garcia E. et al., Statistical characteristics of interacting wind turbine wakes from a 7-month LiDAR measurement campaign, <em>Renewable Energy</em>, 130, 2019, 10.1016/j.renene.2018.06.030.</li> <li>Hegazy A. et al., LiDAR and SCADA data processing for interacting wind turbine wakes with comparison to analytical wake models, <em>Renewable Energy</em>, 181, 2022, 10.1016/j.renene.2021.09.019.</li> </ul> <p><em>SMARTEOLE Field Test 2</em></p> <ul> <li>Tagliatti F., Investigation of Wind Turbine Fatigue Loads under Wind Farm Control: Analysis of Field Measurements, Master’s thesis, DTU Wind Energy-M-0302, 2019.</li> <li>Göçmen T. et al., FarmConners wind farm flow control benchmark – Part 1: Blind test results, <em>Wind Energy Science</em>, 7(5), 2022, 10.5194/wes-7-1791-2022.</li> </ul> <p><em>SMARTEOLE Field Test 3</em></p> <ul> <li>Simley E. et al., Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance, <em>Wind Energy Science</em>, 6(6) 2021, 10.5194/wes-6-1427-2021.</li> </ul> <p> </p> <p><strong>Release Notes</strong></p> <ul> <li>v1.0 (2022-11-24) : first version of the dataset.</li> </ul>
POIs from Open Nature Innovation Arena (Open311 standard)
<p>This dataset has been generated from the Open Nature Innovation Arena according to the Open311 standard. It contains all the point of interests registered from the users into the Open Nature Innovation Arena to publish problems and challenges.</p>
Published ideas in Open Nature Innovation Arena
<p>This dataset has been generated from the Open Nature Innovation Arena. It contains all the ideas published from the users into the Open Nature Innovation Arena.</p>
Literature review of Design in Open Source Agriculture - Images
<p>Literature review of Design in Open Source Agriculture - Images</p> <ol> <li>Fig. 1. Publications by subject areas</li> <li>Fig. 2. Publications by country</li> <li>Fig. 3. Yearly output of publications</li> <li>Fig. 4. Network of co-authorship (generated with VOSviewer) <em>(Extra image not included in the article)</em></li> <li>Fig. 5. Network of co-citation (generated with VOSviewer) <em>(Extra image not included in the article)</em></li> <li>Fig. 6. Network of bibliographic coupling(generated with VOSviewer) <em>(Extra image not included in the article)</em></li> <li>Fig. 7. Co-word analysis: network of terms from title and abstract (generated with VOSviewer) <em>(Extra image not included in the article)</em></li> <li>Fig. 8. Co-word analysis: network of keywords (generated with VOSviewer) <em>(Extra image not included in the article)</em></li> <li>Fig. 9. Thematic Map based on Authors’ keywords (generated with bibliometrix) <em>(Extra image not included in the article)</em></li> <li>Fig. 10. Thematic Map based on Titles (just single words - unigrams) (generated with bibliometrix) <em>(Extra image not included in the article)</em></li> <li>Fig. 11. Thematic Map based on Abstracts (just single words - unigrams) (generated with bibliometrix) <em>(Extra image not included in the article)</em></li> <li>Fig. 12. Trend Topics (generated with bibliometrix) <em>(Extra image not included in the article)</em></li> </ol>
SOMOS: The Samsung Open MOS Dataset for the Evaluation of Neural Text-to-Speech Synthesis
<p>This is the public release of the Samsung Open Mean Opinion Scores (SOMOS) dataset for the evaluation of neural text-to-speech (TTS) synthesis, which consists of audio files generated with a public domain voice from trained TTS models based on bibliography, and numbers assigned to each audio as quality (naturalness) evaluations by several crowdsourced listeners.<br><br><strong>Description</strong><br><br>The SOMOS dataset contains 20,000 synthetic utterances (wavs), 100 natural utterances and 374,955 naturalness evaluations (human-assigned scores in the range 1-5). The synthetic utterances are single-speaker, generated by training several Tacotron-like acoustic models and an LPCNet vocoder on the LJ Speech voice public dataset. 2,000 text sentences were synthesized, selected from Blizzard Challenge texts of years 2007-2016, the LJ Speech corpus as well as Wikipedia and general domain data from the Internet.<br>Naturalness evaluations were collected via crowdsourcing a listening test on Amazon Mechanical Turk in the US, GB and CA locales. The records of listening test participants (workers) are fully anonymized. Statistics on the reliability of the scores assigned by the workers are also included, generated through processing the scores and validation controls per submission page.</p> <p>To listen to audio samples of the dataset, please see <a href="https://innoetics.github.io/publications/somos-dataset/index.html">our Github page</a>.</p> <p>The dataset release comes with a carefully designed train-validation-test split (70%-15%-15%) with unseen systems, listeners and texts, which can be used for experimentation on MOS prediction.</p> <p><em>This version also contains the necessary resources to obtain the transcripts corresponding to all dataset audios.</em></p> <p><strong>Terms of use</strong></p> <ul> <li>The dataset may be used for <strong>research</strong> purposes only, for <strong>non-commercial</strong> purposes only, and may be distributed with the same terms.</li> <li>Every time you produce research that has used this dataset, please <strong>cite </strong>the dataset appropriately.</li> </ul> <p>Cite as:</p> <pre><code>@inproceedings{maniati22_interspeech, author={Georgia Maniati and Alexandra Vioni and Nikolaos Ellinas and Karolos Nikitaras and Konstantinos Klapsas and June Sig Sung and Gunu Jho and Aimilios Chalamandaris and Pirros Tsiakoulis}, title={{SOMOS: The Samsung Open MOS Dataset for the Evaluation of Neural Text-to-Speech Synthesis}}, year=2022, booktitle={Proc. Interspeech 2022}, pages={2388--2392}, doi={10.21437/Interspeech.2022-10922} } </code></pre> <p><br><strong>References of resources & models used</strong></p> <p>Voice & synthesized texts:<br>K. Ito and L. Johnson, “The LJ Speech Dataset,” https://keithito.com/LJ-Speech-Dataset/, 2017.</p> <p>Vocoder:<br>J.-M. Valin and J. Skoglund, “LPCNet: Improving neural speech synthesis through linear prediction,” in Proc. ICASSP, 2019.<br>R. Vipperla, S. Park, K. Choo, S. Ishtiaq, K. Min, S. Bhattacharya, A. Mehrotra, A. G. C. P. Ramos, and N. D. Lane, “Bunched lpcnet: Vocoder for low-cost neural text-to-speech systems,” in Proc. Interspeech, 2020.</p> <p>Acoustic models:<br>N. Ellinas, G. Vamvoukakis, K. Markopoulos, A. Chalamandaris, G. Maniati, P. Kakoulidis, S. Raptis, J. S. Sung, H. Park, and P. Tsiakoulis, “High quality streaming speech synthesis with low, sentence-length-independent latency,” in Proc. Interspeech, 2020.<br>Y. Wang, R. Skerry-Ryan, D. Stanton, Y. Wu, R. J. Weiss, N. Jaitly, Z. Yang, Y. Xiao, Z. Chen, S. Bengio et al., “Tacotron: Towards End-to-End Speech Synthesis,” in Proc. Interspeech, 2017.<br>J. Shen, R. Pang, R. J. Weiss, M. Schuster, N. Jaitly, Z. Yang, Z. Chen, Y. Zhang, Y. Wang, R. Skerrv-Ryan et al., “Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions,” in Proc. ICASSP, 2018.<br>J. Shen, Y. Jia, M. Chrzanowski, Y. Zhang, I. Elias, H. Zen, and Y. Wu, “Non-Attentive Tacotron: Robust and Controllable Neural TTS Synthesis Including Unsupervised Duration Modeling,” arXiv preprint arXiv:2010.04301, 2020.<br>M. Honnibal and M. Johnson, “An Improved Non-monotonic Transition System for Dependency Parsing,” in Proc. EMNLP, 2015.<br>M. Dominguez, P. L. Rohrer, and J. Soler-Company, “PyToBI: A Toolkit for ToBI Labeling Under Python,” in Proc. Interspeech, 2019.<br>Y. Zou, S. Liu, X. Yin, H. Lin, C. Wang, H. Zhang, and Z. Ma, “Fine-grained prosody modeling in neural speech synthesis using ToBI representation,” in Proc. Interspeech, 2021.<br>K. Klapsas, N. Ellinas, J. S. Sung, H. Park, and S. Raptis, “WordLevel Style Control for Expressive, Non-attentive Speech Synthesis,” in Proc. SPECOM, 2021.<br>T. Raitio, R. Rasipuram, and D. Castellani, “Controllable neural text-to-speech synthesis using intuitive prosodic features,” in Proc. Interspeech, 2020.</p> <p>Synthesized texts from the Blizzard Challenges 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2016:<br>M. Fraser and S. King, "The Blizzard Challenge 2007," in Proc. SSW6, 2007.<br>V. Karaiskos, S. King, R. A. Clark, and C. Mayo, "The Blizzard Challenge 2008," in Proc. Blizzard Challenge Workshop, 2008.<br>A. W. Black, S. King, and K. Tokuda, "The Blizzard Challenge 2009," in Proc. Blizzard Challenge, 2009.<br>S. King and V. Karaiskos, "The Blizzard Challenge 2010," 2010.<br>S. King and V. Karaiskos, "The Blizzard Challenge 2011," 2011.<br>S. King and V. Karaiskos, "The Blizzard Challenge 2012," 2012.<br>S. King and V. Karaiskos, "The Blizzard Challenge 2013," 2013.<br>S. King and V. Karaiskos, "The Blizzard Challenge 2016," 2016.</p> <p><strong>Contact</strong></p> <p>Alexandra Vioni - a.vioni@samsung.com</p> <ul> <li>If you have any questions or comments about the dataset, please feel free to write to us.</li> <li>We are interested in knowing if you find our dataset useful! If you use our dataset, please email us and tell us about your research.</li> </ul>
Research Workflows and Open Science - Data Set
<p>Data set accompanying the report "Research Workflows and Open Science", a systematic study of open science research workflows.</p> <p>The data set summarises the open science characteristics exhibited by the analysed workflows. The first two columns ‘<strong>workflow ID</strong>’ and ‘<strong>URL</strong>’ are dedicated to the ID we used to identify each workflow and to the publications related to the workflows respectively.</p> <p>The remaining columns are dedicated to the characteristics exhibited by the analysed workflows and are named The remaining columns are dedicated to the characteristics exhibited by the analysed workflows and are named following the different categories identified:</p> <ul> <li> <p>'<strong>used/open science infrastructure/virtual</strong>'</p> <ul> <li> <p>If a workflow relies on a virtual open infrastructure (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>used/open science infrastructure/physical</strong>'</p> <ul> <li> <p>If a workflow relies on a physical open infrastructure (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>used/open scientific knowledge/open source software</strong>'</p> <ul> <li> <p>If a workflow relies on open source software (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>used/open scientific knowledge/open hardware</strong>'</p> <ul> <li> <p>If a workflow relies on open hardware (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>used/open scientific knowledge/open research data</strong>'</p> <ul> <li> <p>If a workflow (re)uses open research data (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>used/open scientific knowledge/open educational resources</strong>'</p> <ul> <li> <p>If a workflow (re)uses open educational resources (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>produced/open scientific knowledge/(open access) scientific publication</strong>'</p> <ul> <li> <p>If a workflow envisages the release of a scientific publication (e.g. papers, reports, data management plans, preprints, study designs) under an open access licence (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>produced/open scientific knowledge/open source software</strong>'</p> <ul> <li> <p>If a workflow envisages the release of software (e.g. code, analysis scripts) under an open access licence (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>produced/open scientific knowledge/open research data</strong>'</p> <ul> <li> <p>If a workflow envisages the release of open research data (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>produced/open scientific knowledge/open educational resources</strong>'</p> <ul> <li> <p>If a workflow envisages the release of open educational resources (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>transparency/transparency type</strong>'</p> <ul> <li> <p>degree of transparency of a workflow, defined in terms of which research products are openly shared and when in order to document the research processes (‘built-in’ if transparent, ‘enabled’ if capable of being transparent, ‘opaque’ otherwise)</p> </li> </ul> </li> <li> <p>'<strong>transparency/sharing type</strong>'</p> <ul> <li> <p>workflow categories based on when the research products are shared (‘end’ for sharing at the end of the workflow, mixed for sharing part of the research products during the workflow and the rest at the end of it, ‘iterative’ for sharing iteratively during or at the end of the related workflow phase, and ‘user-dependent’, where it is ultimately up to the researcher to decide when to share the research products since the workflow offers different paths to follow while imposing no sharing constraint.)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/collaboration implementation</strong>'</p> <ul> <li> <p>If a workflow implements collaborative practices (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open engagement of societal actors/crowdfunding</strong>'</p> <ul> <li> <p>If a workflow envisages crowdfunding (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open engagement of societal actors/crowdsourcing</strong>'</p> <ul> <li> <p>If a workflow envisages crowdsourcing (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open engagement of societal actors/scientific volunteering</strong>'</p> <ul> <li> <p>If a workflow envisages scientific volunteering (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open engagement of societal actors/citizen and participatory science</strong>'</p> <ul> <li> <p>If a workflow envisages citizen and participatory science (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open dialogue with other knowledge systems/indigenous peoples</strong>'</p> <ul> <li> <p>If a workflow envisages the establishment of a dialogue with indigenous peoples (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open dialogue with other knowledge systems/marginalised scholars</strong>'</p> <ul> <li> <p>If a workflow envisages the establishment of a dialogue with marginalised scholars (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open dialogue with other knowledge systems/local communities</strong>'</p> <ul> <li> <p>If a workflow envisages the establishment of a dialogue with local communities (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>assessment</strong>'</p> <ul> <li> <p>If a workflow implements assessment processes for the evaluation of the research products created (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>automation</strong>'</p> <ul> <li> <p>If a workflow includes automated processes (yes/no)</p> </li> </ul> </li> </ul>
A New Open-source Geomagnetosphere Propagation Tool (OTSO) and its Applications - Data
<p>Data files for the computations done with OTSO for the asymptotic cones and effective cut-off rigidities for several neutron monitor stations during three ground-level enhancement events (GLE 66, 70, 71). The computations for GLE 66 and 71 were done using three external geomagnetic field models (TSY89, TSY96, TSY01). Data for the global map of effective cut-off rigidities during GLE70 is also included. Data is in CSV format.</p>
C and N stable isotope ratios in fishes from marine protected areas and areas open to fishing
<p><span>Here, we assess </span><span>whether fishery exploitation affects the trophic structure of carnivorous fishes</span><span>. We censused fishes and analysed the stable isotope ratios of C and N of species targeted by fishermen in areas open to fishing and marine protected areas in the Mediterranean Sea and the north-eastern Atlantic Ocean. Results demonstrated a major impact of fishing on the biomass and the size structure of nektobenthic carnivorous fishes. However, those changes did not modify the diversity of the trophic resources used by the assemblage, </span><span>the pattern of resource partitioning between species or the degree of trophic redundancy</span><span>. These results add to recent evidence suggesting that marine protected areas implemented in fished seascapes may fail to restore the original structure of the food webs that once existed in pre-fished ecosystems, because regional decimation and extinction of highly mobile predators prevents recovering the original diversity of predators at local scales, even at no-take areas. If so, more strict local fishing regulations are unlikely to restore the original diversity of high trophic level carnivores and restoration goals should be reframed </span><span>in terms of an objective that is less unrealistic than restoring the pre-fished condition while still recovering aspects of the historical trophic structure</span><span>.</span></p>
Data for study "'Conditional Acceptance' (additional experiments required): A scoping review of recent evidence on key aspects of Open Peer Review"
<p>Dataset for study "‘Conditional Acceptance’ (additional experiments required): A scoping review of recent evidence on key aspects of Open Peer Review", 2022 preprint by Tony Ross-Hellauer and Serge Horbach.</p> <p>Dataset includes excel file with 10 sheets showing systematic literature search (per PRISMA-SCR protocol) of academic databases (Web of Science, Scopus), snowballing and web-search to identify 52 studies on key aspects of Open Peer Review published from Jan 2017 until May 2022.</p> <p><strong>Study Abstract: </strong>Diverse efforts are underway to reform the journal peer review system. Combined with growing interest in Open Science practices, Open Peer Review (OPR) has become of central concern to the scholarly community. However, what Open Peer Review is understood to encompass and how effective some of its elements are in meeting the expectations of the peer review system, are uncertain. This scoping review updates previous efforts to summarise research on OPR to date. Following the PRISMA methodological framework, it addresses the question: “What evidence has been reported in the scientific literature from 2017 to date regarding uptake, attitudes, and efficacy of two key aspects of Open Peer Review (Open Identities and Open Reports)?” The review identifies, analyses and synthesises 52 studies matching inclusion criteria, finding that OPR is growing, but still far from common practice. Our findings indicate positive attitudes towards Open Reports and more sceptical approaches to Open Identities. Changes in reviewer behaviour seem limited. and no evidence for lower acceptance rates of review invitations or slower turnaround times is reported. Concerns about power dynamics and potential backfiring on critical reviews are in need of further experimentation. We conclude that elements of OPR seem to be gaining acceptance, but more experimentation is needed. Evidence still mainly consists of either survey data or case studies of individual or few journals, not allowing for generalisability across fields and journals, and revealing no studies which compare the quality of review under Open Identities or Open Reports versus other modes of peer review.</p>
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Allen Brain Atlas
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International Brain Laboratory public data
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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.