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119 results for “Argumentation / argumentation”
Database of Annotated Core Arguments: English, Lao and Russian
<p>This database contains corpus examples of transitive clauses with annotated core arguments realized as syntactic subjects and objects (A and P) in English, Lao and Russian. The coding scheme was developed together with Alena Witzlack-Makarevich</p>
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>
Learning to Give a Complete Argument with a Conversational Agent: An Experimental Study in Two Domains of Argumentation
<p>This data is collected to find out how having a conversation with our agent affects argumentation. To model the arguments, we used Toulmin's model of argument. Based on the model, a good argument contains 3 different parts: 1. Claim, 2. Warrant, 3. Evidence. Based on Toulmin's model, these three components are the core components of arguments. This dataset has been collected during a between-subject experiment in which the treatment groups first talked to our agent in Task 1 and received feedback on faulty structural arguments and then did Task 2 and 3 which were answering a question on the same and completely different topic in comparison to Task 1.</p>
Touché22-Image-Retrieval-for-Arguments
<p>Data for the <a href="https://touche.webis.de/clef22/touche22-web/image-retrieval-for-arguments.html">Image Retrieval for Arguments</a> task at Touché 2022.</p> <p>This version is lacking the touche22-image-search-archives.zip and touche22-image-search-screenshots.zip for space restrictions. Please get them from <a href="https://files.webis.de/corpora/corpora-webis/corpus-touche-image-search-22/">https://files.webis.de/corpora/corpora-webis/corpus-touche-image-search-22/</a></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>
Zero marking and word order of core arguments
<p>This material contains the dataset from the <a href="https://version.helsinki.fi/hals/sinnemaki/sinnemaki2010">gitlab repository</a> of the following article, with corrections and some additions. Please cite the article when using the data.</p> <p>Sinnemäki, Kaius 2010. Word order in zero-marking languages. <em>Studies in Language</em> 34(4): 869–912.</p>
Touché23-Image-Retrieval-for-Arguments
<p>Data for the <a href="https://touche.webis.de/clef23/touche23-web/image-retrieval-for-arguments.html">Image Retrieval for Arguments</a> task at Touché 2023.</p> <p>This version is lacking the touche23-image-search-archives.zip and touche23-image-search-screenshots.zip for space restrictions. Please get them from <a href="https://files.webis.de/corpora/corpora-webis/corpus-touche-image-search-23/">https://files.webis.de/corpora/corpora-webis/corpus-touche-image-search-23/</a></p> <p> </p>
Argument Mining Driven Analysis of Peer-Reviews Dataset
<p>Argument Mining in Scientific Reviews (AMSR)</p> <p>We release a new dataset of peer-reviews from different computer science conferences with annotated arguments, called AMSR (<strong>A</strong>rgument <strong>M</strong>ining in <strong>S</strong>cientific <strong>R</strong>eviews).<br> <br> The dataset has been crawled by the OpenReview platform (https://openreview.net/) and the OpenReviewCrawler (https://openreview-py.readthedocs.io/en/latest/getting data.html)<br> <br> From 12,135 collected papers and reviews, we sample 77 for the annotation.<br> We use a simple argumentation scheme, which distinguishes between non-arguments, supporting arguments, and attacking arguments, which we denote as NON/PRO/CON accordingly.</p>
Dataset for the article "When possessor and argument indexes coincide. A typological survey" (Alessandra Barotto, Andrea Sansò)
<p><span>The dataset stored here contains the data underlying the article “When Possessor and Argument Indexes Coincide” (Alessandra Barotto, Andrea Sansò)<em>. </em>The dataset comes with a README file providing an explanation of the decisions made.</span></p>
Touché25-Image-Retrieval-and-Generation-for-Arguments
<p>Data for the <a href="https://touche.webis.de/clef25/touche25-web/image-retrieval-for-arguments.html">Image Retrieval/Generation for Arguments</a> task at Touché 2025.</p> <p> </p> <p>Only the main.zip and nodes.zip are uploaded here due to space restrictions. Find the web page screenshots and web archives here: <a href="https://files.webis.de/corpora/corpora-webis/corpus-touche-image-search-25/version-2025-04-02/">https://files.webis.de/corpora/corpora-webis/corpus-touche-image-search-25/version-2025-04-02/</a></p>
VivesDebate: A New Annotated Multilingual Corpus of Argumentation in a Debate Tournament
<p>The application of the latest Natural Language Processing breakthroughs in computational argumentation has shown promising results which have raised the interest in this area of research. However, the available corpora with argumentative annotations are often limited to a very specific purpose or are not of adequate size to take advantage of state-of-the-art deep learning techniques (e.g., deep neural networks). In this paper, we present VivesDebate, a large, richly annotated, and versatile professional debate corpus for computational argumentation research. The corpus has been created from 29 transcripts of a debate tournament in Catalan and has been machine-translated into Spanish and English. The annotation contains argumentative propositions, argumentative relations, debate interactions, and professional evaluations of the arguments and argumentation. The presented corpus can be useful for research on a heterogeneous set of computational argumentation underlying tasks such as argument mining, argument analysis, argument evaluation, or argument generation among others. All this makes VivesDebate a valuable resource for computational argumentation research within the context of massive corpora aimed at Natural Language Processing tasks.</p>
Touché24-Image-Retrieval-and-Generation-for-Arguments
<div> <p>Data for the <a href="https://touche.webis.de/clef24/touche24-web/image-retrieval-for-arguments.html">Image Retrieval/Generation for Arguments</a> task at Touché 2024.</p> <p>Only the main.zip and nodes.zip are uploaded here due to space restrictions. Find the web page screenshots and web archives here: <a href="https://files.webis.de/corpora/corpora-webis/corpus-touche-image-retrieval-and-generation-24/">https://files.webis.de/corpora/corpora-webis/corpus-touche-image-retrieval-and-generation-24/</a></p> </div>
Fig. 14. Titanoeca spominima, A–C in Spiders (Araneae) Of Subotica Sandland (Serbia): Additional Arguments In Environmental Protection
Fig. 14. Titanoeca spominima, A–C = male left palp, A = retrolateral, B = prolateral, C = dorsal, D = male habitus. Scale bars: 1 mm for A, 0.5 mm for B–C, 1 mm D
Fig. 7 in Spiders (Araneae) Of Subotica Sandland (Serbia): Additional Arguments In Environmental Protection
Fig. 7. Halpodrassus moderatus: A–D = male left palp, A = retrolateral, B = ventral, C = bulbus, ventral, D = dorsal; E = epigyne, ventral. Scale bars: 1 mm
Fig. 1 in Spiders (Araneae) Of Subotica Sandland (Serbia): Additional Arguments In Environmental Protection
Fig. 1. Geographical position of the Subotica Sandland (Serbia) at the southern part of the Danube-Tisza Interfluve (area marked in black with arrow pointer). The northern part belongs to the Kiskunság Sand which spreads northwards to Budapest. The Serbian – Hun-
Fig. 16 in Spiders (Araneae) Of Subotica Sandland (Serbia): Additional Arguments In Environmental Protection
Fig. 16. Dendrogram (UPGMA-clustering) of the Renkonen indexes for the habitats at Subotica Sandland investigated in 2014. Locality 1 (habitats M1,W1-4), Locality 2 (M2, F, W5), Locality 3 (M3, W6)
Fig. 10 in Spiders (Araneae) Of Subotica Sandland (Serbia): Additional Arguments In Environmental Protection
Fig. 10. Gylphesis taoplesius: A = left male palp, retrolateral, B = epigyne in situ. Scale bars: 0.1 mm for A, 0.5 mm for B
Fig. 15 in Spiders (Araneae) Of Subotica Sandland (Serbia): Additional Arguments In Environmental Protection
Fig. 15. Diversity indexes for the habitats at Subotica Sandland investigated in 2014. Shannon-Weaver's Diversity Index (H'), Shannon's Equality Index of Species (E), the complement value of Simpson's Diversity Index (1-D); Locality 1 (habitats M1,W1-4), Locality 2
Fig. 6 in Spiders (Araneae) Of Subotica Sandland (Serbia): Additional Arguments In Environmental Protection
Fig. 6. Haplodrassus bohemicus: A–B = male left palp, A = ventral, B = tibial apophysis, C–D = epigyne in clove oil, C = ventral, D = dorsal. Scale bars: 0.5 mm for A–B, 1 mm for C–D
Fig. 8 in Spiders (Araneae) Of Subotica Sandland (Serbia): Additional Arguments In Environmental Protection
Fig. 8. Pardosa maisa, male, A, B = mal left palp, A = ventral, B = lateral, C = male, habitus. Scale bars: 1 mm for A–B, 3 mm for C
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.