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1,199 results for “aspect”
Speech disfluencies: Neurophysiological aspect in normal population
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Quantitative and qualitative aspects of dissolved organic carbon leached from plant biomass in Taylor Slough, Shark River and Florida Bay (FCE) for samples collected in July 2004
Plant biomass was collected from Taylor Slough, Shark River and Florida Bay in Everglades National Park. Samples were taken to the lab and incubated with Milli-Q water in the dark for a period of 36 days. NaN3 was added to half the bottles to test the role of microbial activity on the leaching rates and composition of leachate. Every three days the water was decanted and replaced with fresh Milli-Q water. The decanted samples were filtered and analyzed for DOC concentration, sugar content, and total phenol content.
Multi-aspect Integrated Migration Indicators (MIMI) dataset
<p>Nowadays, new branches of research are proposing the use of non-traditional data sources for the study of migration trends in order to find an original methodology to answer open questions about cross-border human mobility. The Multi-aspect Integrated Migration Indicators (MIMI) dataset is a new dataset to be exploited in migration studies as a concrete example of this new approach. It includes both official data about bidirectional human migration (traditional flow and stock data) with multidisciplinary variables and original indicators, including economic, demographic, cultural and geographic indicators, together with the Facebook Social Connectedness Index (SCI). It is built by gathering, embedding and integrating traditional and novel variables, resulting in this new multidisciplinary dataset that could significantly contribute to nowcast/forecast bilateral migration trends and migration drivers.</p> <p>Thanks to this variety of knowledge, experts from several research fields (demographers, sociologists, economists) could exploit MIMI to investigate the trends in the various indicators, and the relationship among them. Moreover, it could be possible to develop complex models based on these data, able to assess human migration by evaluating related interdisciplinary drivers, as well as models able to nowcast and predict traditional migration indicators in accordance with original variables, such as the strength of social connectivity. Here, the SCI could have an important role. It measures the relative probability that two individuals across two countries are friends with each other on Facebook, therefore it could be employed as a proxy of social connections across borders, to be studied as a possible driver of migration. </p> <p>All in all, the motivations for building and releasing the MIMI dataset lie in the need of new perspectives, methods and analyses that can no longer prescind from taking into account a variety of new factors. The heterogeneous and multidimensional sets of data present in MIMI offer an all-encompassing overview of the characteristics of human migration, enabling a better understanding and an original potential exploration of the relationship between migration and non-traditional sources of data.</p> <p> </p> <p>The MIMI dataset is made up of one single CSV file that includes 28,821 rows (records/entries) and 876 columns (variables/features/indicators). Each row is identified uniquely by a pairs of countries, built from the joining of the two ISO-3166 alpha-2 codes for the origin and destination country, respectively. The dataset contains as main features the country-to-country bilateral migration flows and stocks, together with multidisciplinary variables measuring cultural, demographic, geographic and economic variables for the two countries, together with the Facebook strength of connectedness of each pair. </p> <p> </p> <p><strong>Related paper: </strong>Goglia, D., Pollacci, L., Sirbu, A. (2022). Dataset of Multi-aspect Integrated Migration Indicators. <a href="https://doi.org/10.5281/zenodo.6500885">https://doi.org/10.5281/zenodo.6500885</a></p>
SOILCARE_database1_WP2_SICS_aspects
<p>This Excel file contains the information gathered from literature searches regarding meta-analysis studies which published results on the impact of soil improving cropping systems. These impacts were considered for 10 crop husbandry management practices and were split in 5 types (see below). Results pertain to the (relative) effect of a specific management practice compared to a reference (conventional) situation; mostly only the main effects were taken. Early results were used in the WP2 deliverable D2.1 (Oenema et al., 2017; see https://www.soilcare-project.eu/resources/deliverables). Later, results were added due to the increase of new information in the literature. All gathered information is available in sheet 'Table', and formed the basis of a scientific paper (in preparation) to be published in a special issue dedicated to SoilCare.</p> <p>Crop Husbandry management practices:<br> 1. Crop type & crop rotations, including intercropping, cover crops and perennial crops<br> 2. Nutrient management<br> 3. Irrigation + fertigation<br> 4. (Controlled) Drainage<br> 5. Tillage<br> 6. Pest management<br> 7. Weed management<br> 8. Crop residue management & mulching<br> 9. Mechanization & technology<br> 10. Landscape management </p> <p>Areas of interest:<br> a) Agronomic effects (typically: yield, crop quality)<br> b) Soil quality & soil health<br> c) Resource use efficiency (mainly: water, nutrients)<br> d) Economic aspects<br> e) Environmental impacts (mainly: losses of greenhouse gases, and leaching)<br> Originally we also had 'Human health' as area of interest, but since hardly any information was available on this subject, this was no longer considered.</p>
Landscape classes of combinations of elevation, slope angle, and aspect, for the Ilirney Lake System Region, Chukotka, Russia
<p>The elevation was accessed for the area of interest in 90 m spatial resolution from the TanDEM-X 90 m digital elevation model (DEM) product (Krieger et al, 2013). Prior to spatial topographical parameters extraction, the DEM was resampled from the 90-m cell spacing to a 30-m resolution. The result was classified into 589 different possible combinations of elevation, slope angle, aspect. For the classification we used the possible combinations of elevation, slope, and aspect which were grouped into the following categories:</p> <p>Elevation:</p> <ul> <li>0-400 m</li> <li>400-450m</li> <li>450-500m</li> <li>500-600m</li> <li>600-650m</li> <li>650-700m</li> <li>700-1000m</li> <li>1000-1500m</li> </ul> <p>Slope:</p> <ul> <li>0-2°</li> <li>2-4°</li> <li>4-6°</li> <li>6-8°</li> <li>8-10°</li> <li>10-12°</li> <li>12-16°</li> <li>16-18°</li> <li>18-20°</li> <li>20-25°</li> <li>25-50°</li> </ul> <p>Aspect:</p> <ul> <li>0-45°</li> <li>45-90°</li> <li>90-135°</li> <li>135-180°</li> <li>180-225°</li> <li>225-270°</li> <li>270-315°</li> <li>315-360°</li> </ul> <p>Format: Geotiff; projection UTM58N and 30x30 m tiles; extent: 642010.1, 654910.1, 7462218, 7492908 m (xmin, xmax, ymin, ymax)</p>
The TREASURE semantic social network data on the circular economy aspect of automotive manufacturing
<p>The <a href="https://www.treasureproject.eu/">TREASURE</a> project looks at industrial innovation to address the problem of making onboard electronics in the automotive industry easier to recycle, increasing the industry's contribution to the circular economy. As part of it, a team of ethnographers generated and coded the corpus contained in this dataset. interviews conducted between January 2022 and June 2023 with car owners and enthusiasts at car industry events. The interviews focus on experiences with car electronics and perspectives on sustainability and the circular economy. The dataset is pseudonymized. TREASURE is supported by the European Union's Horizon 2020 programme, grant n. 101003587.</p>
Higher-Mode Contact Resonance Operation of a High-Aspect- Ratio Piezoresistive Cantilever Microprobe (Data)
<p>Raw data, Ansys Workbench Projects and figures used for the article "Higher-Mode Contact Resonance Operation of a High-Aspect- Ratio Piezoresistive Cantilever Microprobe", published in the proceedings of SMSI 2020, which did not take place because of Covid-19 virus pandemic.</p> <p>The data can be opened by a text editor<br> Ansys projects are compressed using 7-Zip and can be opened by Ansys Workbench.</p>
Radiative transfer modeling in structurally-complex stands: what aspects matter most?: Dataset
<p>This repository is linked to the paper "Radiative transfer modeling in structurally-complex stands: what aspects matter most?" submitted to Annals of Forest Science and written by Frédéric ANDRÉ (corresponding author), Louis DE WERGIFOSSE, François DE COLIGNY, Nicolas BEUDEZ, Gauthier LIGOT, Vincent GAUTHRAY-GUYÉNET, Benoit COURBAUD and Mathieu JONARD.</p> <p>The repository contains the three following files :</p> <ul> <li>CalibrationResults.csv: Bayes factors and summary statistics of parameter estimates for each calibration run</li> <li>ParameterPosteriorDistributions.csv: median values and 90% credible intervals for the parameter posterior distributions</li> <li>StatisticalComparison.csv: statistics (Fractional bias, Root mean square error, Paired Student test, Pearson correlation coefficient, Parameters of the Deming regression between observed and predicted values) used to compare the 'Best model configurations'</li> </ul> <p>For more information concerning this repository or the study, please do not hesitate to contact Frédéric ANDRÉ (frederic.andre@uclouvain.be) or Mathieu JONARD (mathieu.jonard@uclouvain.be).</p>
MINIATURA 6 Housing decisions, behavioral aspects of choices, price expectations and anchoring effect - Polsh case study
<p>The data was created as a result of a survey conducted in accordance with the guidelines: - the survey questionnaire consisted of approximately 30 questions and a form, - the surveyed population was defined as 1,000 households living in a large Polish city (over 450,000 inhabitants), quota selection based on the number of city inhabitants, - CAWI method (online), - completion date: 1 week. The survey was parameterized. Part of the sample is a control trial, part is an experimental trial.</p><p>Dane powstały w wyniku przeprowadzonej ankiety zgodnie z wytycznymi: - kwestionariusz badania składał się z ok. 30 pytań oraz metryczki, - badana zbiorowość określono na 1000 gospodarstw domowych zamieszkałych w dużym mieście Polski (powyżej 450 tys. ludności), dobór kwotowy na podstawie liczby mieszkańców miast, - badanie metodą CAWI (on-line), - termin realizacji 1 tydzień. Ankieta byłą sparametryzowana. Część próby stanowi próba kontrolna, część próba eksperymentalna. </p>
Investigating the ageing process of polymer modified bitumen using a modified Thin-Film Oven Test in the aspect of recycling purpose within Weave-UNISONO 2021 project, NCN project No 2021/03/Y/ST8/00079
<div><strong>Summary:</strong></div> <div>One polymer-modified bitumen PMB 25/55-60 was tested in two stages: original and after the modified Thin-Film Oven Test (TFOT). The time ranges from 1h-5h, and temperatures from 120°C-200°C were used. The Fourier-Transform Infrared (FTIR) Spectroscopy and Dynamic Shear Rheometer (DSR) with parallel plates were conducted. Test temperatures range from 30–70°C for a 25 mm diameter plate and 0–30°C for an 8 mm plate with 10°C intervals and angular frequency range of 0.1, 1.0, and 10 Hz.</div> <div> </div> <div> </div> <div><strong>The dataset includes:</strong></div> <div>Basic characteristics of bituminous binder (R&B Temperatur, Penetration), CSV raw data:</div> <div> <ul> <li>01 - SP Pen.csv</li> </ul> </div> <div> </div> <div>Dynamic shear rheometer (Temperatures 0-70 °C, Angular Frequency 0.1Hz, 1.0Hz, 10 Hz, Complex Shear Modulus, Phase Angle):</div> <ul> <li>02.1 - DSR Rheology_Unaged.csv</li> <li>02.2 - DSR Rheology_2h_140C.csv</li> <li>02.3 - DSR Rheology_2h_200C.csv</li> <li>02.4 - DSR Rheology_5h_140C.csv</li> <li>02.5 - DSR Rheology_5h_200C.csv</li> </ul> <div> </div> <div>FTIR - Fourier-Transform Infrared Spectroscopy </div> <div> <ul> <li>OPUS Spectroscopy files.zip</li> </ul> </div> <div> </div> <div>--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---</div> <div>to open the OPUS files, please go to the © Bruker webpage and download the free OPUS Viewer.</div> <div>https://www.bruker.com/en/products-and-solutions/infrared-and-raman/opus-spectroscopy-software/downloads.html</div> <div>--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---</div> <div> </div>
Argument Aspect Corpus - Nuclear Energy
<p>The Argument Aspect Corpus–Nuclear Energy (AAC-NE) contains English-language sentences with aspect annotations describing the content of arguments on the topic of nuclear energy.</p> <p>It was introduced in this paper:</p> <blockquote> <p>Jurkschat, L., Wiedemann, G., Heinrich, M., Ruckdeschel, M., & Torge, S. (2022). Few-Shot Learning for Argument Aspects of the Nuclear Energy Debate. In Proceedings of the 13th International Conference on Language Resources and Evaluation (LREC 2022). European Language Resources Association (ELRA).</p> </blockquote> <p>The AAC-NE corpus is based on a subset of all argumentative sentences contained in the UKP SAM dataset [1] for which a majority vote of three annotators could be achieved during the annotation of the main argument aspect of each sentence.</p> <p>The CSV files contain one of nine aspect labels per argumentative sentence split into training, dev, and test set.</p> <table> <thead> <tr> <th><strong>aspect</strong></th> <th><strong>train</strong></th> <th><strong>dev</strong></th> <th><strong>test</strong></th> <th><strong>Sum</strong></th> <th><strong>Kripp. Alpha</strong></th> </tr> </thead> <tbody> <tr> <td>alternatives</td> <td>100</td> <td>16</td> <td>21</td> <td>137</td> <td>0.69</td> </tr> <tr> <td>costs</td> <td>98</td> <td>17</td> <td>29</td> <td>144</td> <td>0.72</td> </tr> <tr> <td>environment</td> <td>209</td> <td>27</td> <td>64</td> <td>300</td> <td>0.74</td> </tr> <tr> <td>innovation</td> <td>33</td> <td>2</td> <td>8</td> <td>43</td> <td>0.38</td> </tr> <tr> <td>reactor safety</td> <td>112</td> <td>17</td> <td>43</td> <td>172</td> <td>0.59</td> </tr> <tr> <td>reliability</td> <td>47</td> <td>5</td> <td>10</td> <td>62</td> <td>0.36</td> </tr> <tr> <td>waste</td> <td>87</td> <td>5</td> <td>26</td> <td>118</td> <td>0.80</td> </tr> <tr> <td>weapons</td> <td>52</td> <td>11</td> <td>15</td> <td>78</td> <td>0.77</td> </tr> <tr> <td>other</td> <td>120</td> <td>23</td> <td>29</td> <td>172</td> <td>0.49</td> </tr> <tr> <td><strong>all</strong></td> <td><strong>858</strong></td> <td><strong>123</strong></td> <td><strong>245</strong></td> <td><strong>1226</strong></td> <td><strong>0.62</strong></td> </tr> <tr> <td>pro</td> <td> </td> <td> </td> <td> </td> <td>706</td> <td> </td> </tr> <tr> <td>cons</td> <td> </td> <td> </td> <td> </td> <td>520</td> <td> </td> </tr> </tbody> </table> <p>Additionally, it contains 2000 unlabeled sentences with presumably argumentative content sampled from the newspaper “The Guardian”.</p> <p>[1] Stab, C., Miller, T., Schiller, B., Rai, P., & Gurevych, I. Cross-topic Argument Mining from Heterogeneous Sources. In E. Riloff, D. Chiang, J. Hockenmaier, & J. Tsujii (Eds.), Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp. 3664–3674). Association for Computational Linguistics. <a href="https://doi.org/10.18653/v1/D18-1402">https://doi.org/10.18653/v1/D18-1402 </a></p> <p> </p>
Prioritization of semantic over visuo- perceptual aspects in multi-item working memory
<p>All data and code supporting Prioritization of semantic over visuo- perceptual aspects in multi-item working memory</p>
Dataset and additional figures for: decomposing geographical and universal aspects of human mobility
<p>Data and additional figures for: <em>Decomposing geographical and universal aspects of human mobility, </em><a href="https://arxiv.org/pdf/2405.08746">https://arxiv.org/pdf/2405.08746</a></p>
Regional Aspects of a Climate and Energy Tax Reform in Norway—Exploring Double and Multiple Dividends
<p>Results for the different scenarios described in Table 4.</p>
Dataset for Method for Delivery Planning in Urban Areas with Environmental Aspects
<p>The purpose of this dataset is to enable the replication of the research results presented in the article: Michał Lasota, Aleksandra Zabielska, Marianna Jacyna, Piotr Gołębiowski, Renata Żochowska, Mariusz Wasiak. Method for Delivery Planning in Urban Areas with Environmental Aspects. Sustainability 2024, 16(4), 1571. https://doi.org/10.3390/su16041571 - published online: 2024-02-13, which a method of large-criteria decision-making support was developed in the field of urban supply planning, taking into account the minimization of harmful compound emissions.</p> <p>Dataset contains:</p> <ul> <li>Readme.txt: description of the dataset</li> <li>InputData.xlsx: Contains the input data in the model. The data is presented in three tables.</li> <li>OutputOptimization.xlsx: Contains the output optimization data. The data is prsented in four tables.</li> <li>OutputSummary.xlsx: Contains contains the final results of the aggregated variable.</li> </ul> <p>The dataset was created as part of the E-Laas project (Energy optimal urban logistics As A Service).<br>Project implemented as part of the call ERA-NET Cofund Urban Accessibility and Connectivity (ENUAC China Call) organized by JPI Urban Europe and the National Natural Science Foundation of China (NSFC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875022.<br> E-Laas project is carried out in an international consortium. Project coordinator in Europe: Chalmers University of Technology (Sweden), project coordinator in China: Shanghai University (China), consortium members: Tsinghua University (China), Warsaw University of Technology (Poland), cooperation partners: Stockholms stad, Trafikkontoret (Sweden), ParkUnload (Spain), Metropolis GZM (Poland), Shanghai Urban-Rural Construction and Transportation Department (China), Volvo Group Trucks Technology and Operations (Sweden).<br>- The Chinese part of the project is funded by National Natural Science Foundation of China.<br>- The Swedish part of the project is funded by Swedish Energy Agency.<br>- The Polish part of the project is funded by the National Science Centre, Poland (project no. 2022/04/Y/ST8/00134). The value of the co-financing is PLN 878,107.00. Project duration 27/04/2023 - 26/04/2026 (36 months).</p>
Insights into the Cyst Organisation and Selected Morpho-Physiological Aspects of Encystment in Thulinius ruffoi
<p>This dataset is related with studies on morpho-physiological aspects of encystment in Thulinius ruffoi (Parachela, Isohypsibioidea: Doryphoribiidae). Data gathered to elucidate the adaptations of these microinvertebrates to environmental changes. Encystment is an adaptive response in tardigrades triggered by environmental cues and potentially by internal factors. The dataset provides insights into cellular organization, morphology, and anatomy during cyst formation in selected tardigrade species. This dataset is supplemented by two others available at <a target="_new" rel="noopener">10.5281/zenodo.10008352</a> and <a target="_new" rel="noopener">10.5281/zenodo.11213808</a>.</p>
AWARE: Dataset for Aspect-Based Sentiment Analysis of Apps Reviews
<p> </p> <p><em><strong>The peer-reviewed paper of AWARE dataset is published in ASEW 2021, and can be accessed through: <a href="http://doi.org/10.1109/ASEW52652.2021.00049">http://doi.org/10.1109/ASEW52652.2021.00049</a>. Kindly cite this paper when using AWARE dataset.</strong></em></p> <p> </p> <p>Aspect-Based Sentiment Analysis (ABSA) aims to identify the opinion (sentiment) with respect to a specific aspect. Since there is a lack of <em>smartphone apps reviews</em> dataset that is annotated to support the ABSA task, we present AWARE: <strong>A</strong>BSA <strong>W</strong>arehouse of <strong>A</strong>pps <strong>RE</strong>views.</p> <p>AWARE contains apps reviews from three different domains (Productivity, Social Networking, and Games), as each domain has its distinct functionalities and audience. Each sentence is annotated with three labels, as follows: </p> <ul> <li><strong>Aspect Term: </strong>a term that exists in the sentence and describes an aspect of the app that is expressed by the sentiment. A term value of “N/A” means that the term is not explicitly mentioned in the sentence.</li> <li><strong>Aspect Category:</strong> one of the pre-defined set of domain-specific categories that represent an aspect of the app (e.g., security, usability, etc.).</li> <li><strong>Sentiment:</strong> positive or negative.</li> </ul> <p><em>Note: games domain does not contain aspect terms.</em></p> <p>We provide a comprehensive dataset of 11323 sentences from the three domains, where each sentence is additionally annotated with a Boolean value indicating whether the sentence expresses a positive/negative opinion. In addition, we provide three separate datasets, one for each domain, containing only sentences that express opinions. The file named “AWARE_metadata.csv” contains a description of the dataset’s columns.</p> <p><strong>How AWARE can be used?</strong></p> <p>We designed AWARE such that it can be used to serve various tasks. The tasks can be, but are not limited to:</p> <ul> <li>Sentiment Analysis.</li> <li>Aspect Term Extraction.</li> <li>Aspect Category Classification.</li> <li>Aspect Sentiment Analysis.</li> <li>Explicit/Implicit Aspect Term Classification.</li> <li>Opinion/Not-Opinion Classification.</li> </ul> <p>Furthermore, researchers can experiment with and investigate the effects of different domains on users' feedback.</p>
Argument Aspect Corpus
<p>The Argument Aspect Corpus (AAC) contains argumentative English-language sentences from four different topics with aspect annotations on a token level. It was introduced in this paper:</p> <blockquote> <p>Mattes Ruckdeschel and Gregor Wiedemann. 2022. Boundary Detection and Categorization of Argument Aspects via Supervised Learning. In Proceedings of the 9th Workshop on Argument Mining, pages 126–136, Online and in Gyeongju, Republic of Korea. International Conference on Computational Linguistics.</p> </blockquote> <p>The Corpus is based on the argumentative sentences in the UKP SAM[1] dataset for four highly debated topics: nuclear energy, minimum wage, abortion, and marijuana legalization. The corpus contains one conll-formatted file per topic, containing the gold standard annotation. Further the coding guidelines used for annotation are uploaded. of all sentences from that topic. For the reproduction of paper results, check out the corresponding <a href="https://github.com/Leibniz-HBI/argument-aspect-corpus-v1">GitHub repository</a>. The gold standard annotation was obtained by <em>chunk-normalization</em> of a token-level gold standard. Using the default chunker from flair[2], sentences were split into chunks, and all tokens of a chunk were labeled with an aspect if at least one token in the chunk was labeled. Any conflicts were resolved by an additional coder.</p> <p>Coding was done by two trained expert coders with a background in Social science. Conflicts were resolved by a third trained coder with a background in Computer Science.</p> <p><strong>Topic information</strong></p> <p>The following tables shows statistics for the different topics. <span class="math-tex">\(\alpha_k\)</span> gives the intercoder-agreement as Krippendorff’s alpha. <em>Arg Occurrences</em> gives the number of arguments containig a specific aspect, while <em>Chunk Occurrences</em> gives the number chunks that have been labeled with a specific aspect.</p> <p><strong>General Statistics</strong></p> <p><span class="math-tex">\(N_{args}\)</span> describes the number of arguments for a topic and <span class="math-tex">\(N_{singles}\)</span> the amount of arguments with only one aspect.</p> <table> <tbody> <tr> <td> <p><strong>Topic</strong></p> </td> <td> <p><span class="math-tex">\(N_{args}\)</span></p> </td> <td> <p><span class="math-tex">\(N_{singles}\)</span></p> </td> </tr> </tbody> <tbody> <tr> <td> <p>Minimum Wage (MW)</p> </td> <td> <p>1118</p> </td> <td> <p>938</p> </td> </tr> <tr> <td> <p>Nuclear Energy (NE)</p> </td> <td> <p>1261</p> </td> <td> <p>992</p> </td> </tr> <tr> <td> <p>Marijuana Legalization (MJ)</p> </td> <td> <p>1213</p> </td> <td> <p>1006</p> </td> </tr> <tr> <td> <p>Abortion (AB)</p> </td> <td> <p>1502</p> </td> <td> <p>1305</p> </td> </tr> </tbody> </table> <p><strong>Minimum Wage</strong></p> <table> <tbody> <tr> <td> <p><strong>Aspect</strong></p> </td> <td> <p><span class="math-tex">\(\alpha_k\)</span></p> </td> <td> <p>Arg Occurrences</p> </td> <td> <p>Chunk Occurrences</p> </td> </tr> </tbody> <tbody> <tr> <td> <p>Un/employment rate</p> </td> <td> <p>0.80</p> </td> <td> <p>259</p> </td> <td> <p>287</p> </td> </tr> <tr> <td> <p>Motivation/chances</p> </td> <td> <p>0.67</p> </td> <td> <p>86</p> </td> <td> <p>107</p> </td> </tr> <tr> <td> <p>Competition/business challenges</p> </td> <td> <p>0.58</p> </td> <td> <p>104</p> </td> <td> <p>129</p> </td> </tr> <tr> <td> <p>Prices</p> </td> <td> <p>0.88</p> </td> <td> <p>93</p> </td> <td> <p>104</p> </td> </tr> <tr> <td> <p>Social justice/injustice</p> </td> <td> <p>0.70</p> </td> <td> <p>305</p> </td> <td> <p>353</p> </td> </tr> <tr> <td> <p>Welfare</p> </td> <td> <p>0.76</p> </td> <td> <p>49</p> </td> <td> <p>57</p> </td> </tr> <tr> <td> <p>Economic impact</p> </td> <td> <p>0.80</p> </td> <td> <p>81</p> </td> <td> <p>99</p> </td> </tr> <tr> <td> <p>Turnover</p> </td> <td> <p>0.96</p> </td> <td> <p>22</p> </td> <td> <p>32</p> </td> </tr> <tr> <td> <p>Capital vs labour</p> </td> <td> <p>0.51</p> </td> <td> <p>25</p> </td> <td> <p>32</p> </td> </tr> <tr> <td> <p>Government</p> </td> <td> <p>0.65</p> </td> <td> <p>38</p> </td> <td> <p>71</p> </td> </tr> <tr> <td> <p>Low-skilled</p> </td> <td> <p>0.69</p> </td> <td> <p>85</p> </td> <td> <p>100</p> </td> </tr> <tr> <td> <p>Youth and secondary wage earners</p> </td> <td> <p>0.58</p> </td> <td> <p>24</p> </td> <td> <p>37</p> </td> </tr> <tr> <td> <p>Other</p> </td> <td> <p>0.56</p> </td> <td> <p>160</p> </td> <td> <p>160</p> </td> </tr> <tr> <td> <p>all topics</p> </td> <td> <p>0.65</p> </td> <td> <p>1331</p> </td> <td> <p>1568</p> </td> </tr> </tbody> </table> <p><strong>Nuclear Energy</strong></p> <table> <tbody> <tr> <td> <p><strong>Aspect</strong></p> </td> <td> <p><span class="math-tex">\(\alpha_k\)</span></p> </td> <td> <p>Arg Occurrences</p> </td> <td> <p>Chunk Occurrences</p> </td> </tr> </tbody> <tbody> <tr> <td> <p>Waste</p> </td> <td> <p>0.80</p> </td> <td> <p>121</p> </td> <td> <p>152</p> </td> </tr> <tr> <td> <p>Health effects</p> </td> <td> <p>0.67</p> </td> <td> <p>100</p> </td> <td> <p>128</p> </td> </tr> <tr> <td> <p>Environmental impact</p> </td> <td> <p>0.58</p> </td> <td> <p>236</p> </td> <td> <p>313</p> </td> </tr> <tr> <td> <p>Costs</p> </td> <td> <p>0.88</p> </td> <td> <p>131</p> </td> <td> <p>170</p> </td> </tr> <tr> <td> <p>Weapons</p> </td> <td> <p>0.70</p> </td> <td> <p>60</p> </td> <td> <p>66</p> </td> </tr> <tr> <td> <p>Reliability</p> </td> <td> <p>0.76</p> </td> <td> <p>106</p> </td> <td> <p>134</p> </td> </tr> <tr> <td> <p>Technological innovation</p> </td> <td> <p>0.80</p> </td> <td> <p>59</p> </td> <td> <p>79</p> </td> </tr> <tr> <td> <p>Energy policy</p> </td> <td> <p>0.96</p> </td> <td> <p>99</p> </td> <td> <p>135</p> </td> </tr> <tr> <td> <p>Renewables</p> </td> <td> <p>0.51</p> </td> <td> <p>121</p> </td> <td> <p>143</p> </td> </tr> <tr> <td> <p>Fossil fuels</p> </td> <td> <p>0.65</p> </td> <td> <p>99</p> </td> <td> <p>120</p> </td> </tr> <tr> <td> <p>Accidents/security</p> </td> <td> <p>0.69</p> </td> <td> <p>270</p> </td> <td> <p>365</p> </td> </tr> <tr> <td> <p>Public debate</p> </td> <td> <p>0.58</p> </td> <td> <p>47</p> </td> <td> <p>75</p> </td> </tr> <tr> <td> <p>Other</p> </td> <td> <p>0.56</p> </td> <td> <p>139</p> </td> <td> <p>139</p> </td> </tr> <tr> <td> <p>all topics</p> </td> <td> <p>0.65</p> </td> <td> <p>1585</p> </td> <td> <p>2017</p> </td> </tr> </tbody> </table> <p><strong>Marijuana Legalization</strong></p> <table> <tbody> <tr> <td> <p><strong>Aspect</strong></p> </td> <td> <p><span class="math-tex">\(\alpha_k\)</span></p> </td> <td> <p>Arg Occurrences</p> </td> <td> <p>Chunk Occurrences</p> </td> </tr> </tbody> <tbody> <tr> <td> <p>Illegal trade</p> </td> <td> <p>0.87</p> </td> <td> <p>100</p> </td> <td> <p>130</p> </td> </tr> <tr> <td> <p>Child and teen safety</p> </td> <td> <p>0.89</p> </td> <td> <p>124</p> </td> <td> <p>149</p> </td> </tr> <tr> <td> <p>Community/Societal effects</p> </td> <td> <p>0.54</p> </td> <td> <p>153</p> </td> <td> <p>196</p> </td> </tr> <tr> <td> <p>Health/Psychological effects</p> </td> <td> <p>0.78</p> </td> <td> <p>188</p> </td> <td> <p>302</p> </td> </tr> <tr> <td> <p>Medical Marijuana</p> </td> <td> <p>0.92</p> </td> <td> <p>134</p> </td> <td> <p>183</p> </td> </tr> <tr> <td> <p>Drug abuse</p> </td> <td> <p>0.78</p> </td> <td> <p>66</p> </td> <td> <p>78</p> </td> </tr> <tr> <td> <p>Addiction</p> </td> <td> <p>0.95</p> </td> <td> <p>59</p> </td> <td> <p>72</p> </td> </tr> <tr> <td> <p>Personal freedom</p> </td> <td> <p>0.79</p> </td> <td> <p>41</p> </td> <td> <p>54</p> </td> </tr> <tr> <td> <p>National budget</p> </td> <td> <p>0.77</p> </td> <td> <p>114</p> </td> <td> <p>154</p> </td> </tr> <tr> <td> <p>Gateway drug</p> </td> <td> <p>0.90</p> </td> <td> <p>47</p> </td> <td> <p>60</p> </td> </tr> <tr> <td> <p>Legal drugs</p> </td> <td> <p>0.91</p> </td> <td> <p>108</p> </td> <td> <p>130</p> </td> </tr> <tr> <td> <p>Drug policy</p> </td> <td> <p>0.50</p> </td> <td> <p>104</p> </td> <td> <p>137</p> </td> </tr> <tr> <td> <p>Harm</p> </td> <td> <p>0.53</p> </td> <td> <p>77</p> </td> <td> <p>94</p> </td> </tr> <tr> <td> <p>Other</p> </td> <td> <p>0.49</p> </td> <td> <p>139</p> </td> <td> <p>139</p> </td> </tr> <tr> <td> <p>all topics</p> </td> <td> <p>0.64</p> </td> <td> <p>1454</p> </td> <td> <p>1879</p> </td> </tr> </tbody> </table> <p><strong>Abortion</strong></p> <table> <tbody> <tr> <td> <p><strong>Aspect</strong></p> </td> <td> <p><span class="math-tex">\(\alpha_k\)</span></p> </td> <td> <p>Arg Occurrencens</p> </td> <td> <p>Chunk Occurrences</p> </td> </tr> </tbody> <tbody> <tr> <td> <p>Bodily autonomy/Women’s rights</p> </td> <td> <p>0.57</p> </td> <td> <p>267</p> </td> <td> <p>385</p> </td> </tr> <tr> <td> <p>Fetal/newborn rights</p> </td> <td> <p>0.83</p> </td> <td> <p>507</p> </td> <td> <p>719</p> </td> </tr> <tr> <td> <p>Rape</p> </td> <td> <p>0.96</p> </td> <td> <p>49</p> </td> <td> <p>59</p> </td> </tr> <tr> <td> <p>Abortion industry</p> </td> <td> <p>0.84</p> </td> <td> <p>15</p> </td> <td> <p>18</p> </td> </tr> <tr> <td> <p>Moral/ethical values</p> </td> <td> <p>0.67</p> </td> <td> <p>139</p> </td> <td> <p>173</p> </td> </tr> <tr> <td> <p>Safety/health effects of legal abortion</p> </td> <td> <p>0.81</p> </td> <td> <p>88</p> </td> <td> <p>113</p> </td> </tr> <tr> <td> <p>Psychological effects of abortion</p> </td> <td> <p>0.84</p> </td> <td> <p>60</p> </td> <td> <p>78</p> </td> </tr> <tr> <td> <p>Health effects of pregnancy/childbirth</p> </td> <td> <p>0.75</p> </td> <td> <p>95</p> </td> <td> <p>116</p> </td> </tr> <tr> <td> <p>Illegal abortions</p> </td> <td> <p>0.83</p> </td> <td> <p>54</p> </td> <td> <p>75</p> </td> </tr> <tr> <td> <p>Responsibility</p> </td> <td> <p>0.64</p> </td> <td> <p>59</p> </td> <td> <p>81</p> </td> </tr> <tr> <td> <p>Adoption</p> </td> <td> <p>0.93</p> </td> <td> <p>39</p> </td> <td> <p>44</p> </td> </tr> <tr> <td> <p>Consequences of childbirth</p> </td> <td> <p>0.66</p> </td> <td> <p>96</p> </td> <td> <p>130</p> </td> </tr> <tr> <td> <p>Fetal defects/disabilities</p> </td> <td> <p>0.90</p> </td> <td> <p>47</p> </td> <td> <p>60</p> </td> </tr> <tr> <td> <p>Parental consent</p> </td> <td> <p>0.80</p> </td> <td> <p>16</p> </td> <td> <p>25</p> </td> </tr> <tr> <td> <p>Funding of abortions</p> </td> <td> <p>0.70</p> </td> <td> <p>20</p> </td> <td> <p>25</p> </td> </tr> <tr> <td> <p>Other</p> </td> <td> <p>0.48</p> </td> <td> <p>172</p> </td> <td> <p>172</p> </td> </tr> <tr> <td> <p>all topics</p> </td> <td> <p>0.66</p> </td> <td> <p>1723</p> </td> <td> <p>2273</p> </td> </tr> </tbody> </table> <p>[1] Stab, C., Miller, T., Schiller, B., Rai, P., & Gurevych, I. Cross-topic Argument Mining from Heterogeneous Sources. In E. Riloff, D. Chiang, J. Hockenmaier, & J. Tsujii (Eds.), Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp. 3664–3674). Association for Computational Linguistics. https://doi.org/10.18653/v1/D18-1402</p> <p>[2] Alan Akbik, Tanja Bergmann, Duncan Blythe, Kashif Rasul, Stefan Schweter, and Roland Vollgraf. 2019. FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations), pages 54–59, Minneapolis, Minnesota. Association for Computational Linguistics.</p>
EnergyPROSPECTS Energy Citizenship Factsheet Series, Part 5: Aspects of ENCI I.: Hybridity, private/public, passive/active forms
<p>This document is Part 5 of the EnergyPROSPECTS Factsheet Series. We have created the Series to publish the results of a mapping of energy citizenship in Europe, along with the first stage of our analysis of the respective data. The EnergyPROSPECTS consortium mapped 596 cases of energy citizenship (ENCI) between November 2020 and May 2021 using desk research, collecting data on many aspects of the cases. Although the analysis is a work in progress, we believe it is important to share our data and, through doing this, contribute to the understanding of energy citizenship in Europe.</p> <p>EnergyPROSPECTS (PROactive Strategies and Policies for Energy Citizenship Transformation), a H2020 project between 2021-2024, works with a critical understanding of energy citizenship that is grounded in state-of-the-art social sciences and humanities (SSH) insights.</p>
EnergyPROSPECTS Energy Citizenship Factsheet Series, Part 7: Aspects of ENCI III.: Towards social sustainability
<p>This document is Part 7 of the EnergyPROSPECTS Factsheet Series. We have created the Series to publish the results of a mapping of energy citizenship in Europe, along with the first stage of our analysis of the respective data. The EnergyPROSPECTS consortium mapped 596 cases of energy citizenship (ENCI) between November 2020 and May 2021 using desk research, collecting data on many aspects of the cases. Although the analysis is a work in progress, we believe it is important to share our data and, through doing this, contribute to the understanding of energy citizenship in Europe.</p> <p>EnergyPROSPECTS (PROactive Strategies and Policies for Energy Citizenship Transformation), a H2020 project between 2021-2024, works with a critical understanding of energy citizenship that is grounded in state-of-the-art social sciences and humanities (SSH) insights.</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.