Find research datasets worth reusing

FIGURE 5 in Revisiting the description of Atractoscion macrolepis (Perciformes: Sciaenidae): Another argument for comprehensive taxonomy

FIGURE 5. Frequency histogram of observations of individuals of Atractoscion aequidens (grey) and A. macrolepis (black) according to their scores along the canonical root extracted in the Discriminant Functions Analysis using four (of eight) transformed otolith variables.

FIGURE 3 in Revisiting the description of Atractoscion macrolepis (Perciformes: Sciaenidae): Another argument for comprehensive taxonomy

FIGURE 3. Scatterplots of (A) medial caudal fin length (MCL) against head length (HL), (B) anal fin length (AFL) against pelvic fin length (PLFL) and (C) pectoral fin length (PFL) against anal fin length (AFL), all expressed as proportions of standard length (SL), showing the distinction between individuals of Atractoscion aequidens (red circles) and A. macrolepis (blue circles).

FIGURE 2 in Revisiting the description of Atractoscion macrolepis (Perciformes: Sciaenidae): Another argument for comprehensive taxonomy

FIGURE 2. Frequency histogram of observations of individuals of Atractoscion aequidens (light grey) and A. macrolepis (dark grey) according to their scores along the canonical root extracted in the Discriminant Functions Analysis using 23 transformed morphometric variables.

FIGURE 1 in Revisiting the description of Atractoscion macrolepis (Perciformes: Sciaenidae): Another argument for comprehensive taxonomy

FIGURE 1. Scatterplot of individuals of Atractoscion aequidens (red circles) and A. macrolepis (blue circles) along Principal Components 1 (explaining 40.4% of the observed variation) and 2 (17.5%) from the Principal Components Analysis of 23 transformed morphometric variables. 95% confidence ellipses are indicated for each species, overlayed in the respective colours.

openJun 2023View details →

Dataset on "Argument maps as a proxy for critical thinking development: A Lab for undergraduate students"

Argumentative skills are crucial for any individual at the personal and professional levels. In recent decades, there has been an increasing concern about the weak undergraduates' skills and considerable difficulty in reworking and expressing one's own reflection on a topic. In turn, this has implications for being a critical thinker, able to express an original point of view. Tailored interventions in Higher Education could constitute a powerful approach to promote argumentative skills and extend these skills to professional and personal life. In this regard, argument maps (AM) could prove to be a valuable support to the visualization process of arguments. They don’t just create associations between concepts, but trace the logical relationships between different statements, allowing you to track the reasoning chain and understand it better. We conducted an experimental study to investigate how a path with AM could support students in increasing the level of text comprehension (CoT) competence, in terms of identifying the elements of an argumentative text, and critical thinking (CT), in terms of reconstructing meaning and building their own reflection.  Our preliminary descriptive analysis suggested the positivity of the role of AM in increasing students’ CoT and CT proficiency levels   This Zenodo record follows the full analysis process with R (https://cran.r-project.org/bin/windows/base/ ) composed of the following datasets and script: 1. Comprehension of Text and AMs Results - ExpAM.xlsx 2. Critical Thinking Results - CriThink.xlsx 3. Argumentative skills in Forum - ExpForum.xlsx 4. Selfassessment Results - Dataset_Quest.xlsx 5. Data for Correlation and Regression - Dataset_CorRegr.xlsx 6. Descriptive Statistics - Preliminary Analysis.R 7. Inferential Statistics - Correlation and Regression.R   Any comments or improvements are welcome!

opencc-by-4.0Jul 2023View details →

ArgU: A Controllable Factual Argument Generator

Dataset from the paper ArgU: A Controllable Factual Argument Generator Paper: https://aclanthology.org/2023.acl-long.466.pdf Github: https://github.com/sougata-ub/argu-generator <ol> <li> ArgSpan: 1,153 examples from the BASN corpus annotated for argument span and concept. </li> <li> ArgSpanScheme: An annotated corpora of 69,428 arguments spanning six topics and six argument schemes, making it the largest publicly available corpus for identifying argument schemes </li> <li> ArgU data: A formatted version of the ArgSpanScheme dataset conducive for training language models. </li> </ol>

dryad32/100

Data from: Neutralizing misinformation through inoculation: exposing misleading argumentation techniques reduces their influence

Open the record for dataset details and reuse information.

publicApr 2018View details →

opencc-by-4.0Dec 2023View details →

Supplementary materials for the article entitled 'Metaphor Forces Argument Overtness', published in Linguistics

see the publication for details

opencc-by-4.0May 2024View details →

Multimodal Argument Mining: A Case Study in Political Debates

MM-USED supplementary files for building the corpus

Figure 4 from: Heger T, Zarrieß S, Algergawy A, Jeschke JM, König-Ries B (2022) INAS: Interactive Argumentation Support for the Scientific Domain of Invasion Biology. Research Ideas and Outcomes 8: e80457. https://doi.org/10.3897/rio.8.e80457

Figure 4 Work plan with full-time tasks (dark colour) and half-time tasks (light color) for the PI Heger (red), the PhD (blue) and student assistants (gray).

Figure 3 from: Heger T, Zarrieß S, Algergawy A, Jeschke JM, König-Ries B (2022) INAS: Interactive Argumentation Support for the Scientific Domain of Invasion Biology. Research Ideas and Outcomes 8: e80457. https://doi.org/10.3897/rio.8.e80457

Figure 3 Interactive hypothesis development, based on a semantic model of hypotheses in the invasion biology domain (left) and a made-up example of a short interaction with an information-state-based dialogue system that iteratively refines a hypothesis introducing domain-specific terms in collaboration with the user (right, resolved questions appear in grey, questions under discussion in yellow).

Figure 5 from: Heger T, Zarrieß S, Algergawy A, Jeschke JM, König-Ries B (2022) INAS: Interactive Argumentation Support for the Scientific Domain of Invasion Biology. Research Ideas and Outcomes 8: e80457. https://doi.org/10.3897/rio.8.e80457

Figure 5 Refining hypotheses as nested chains; data symbols indicate that this part of the chain has been tested with data for the South-African Ragwort; red crosses symbolize that this part of the chain has not been tested yet for this specific species.

Figure 1 from: Heger T, Zarrieß S, Algergawy A, Jeschke JM, König-Ries B (2022) INAS: Interactive Argumentation Support for the Scientific Domain of Invasion Biology. Research Ideas and Outcomes 8: e80457. https://doi.org/10.3897/rio.8.e80457

Figure 1 Screenshot of the website hiknowledge.org, showing a network of twelve major hypotheses on potential causes of biological invasions. The insert shows the hierarchy of hypotheses (HoH) for the disturbance hypothesis which can be retrieved by clicking on the respective dot in the network, with information on the numbers of studies supporting (green), questioning (red) or being undecided (grey) about the respective (sub)-hypotheses.

opencc-by-4.0Sep 2020View details →

Touché20-Argument-Retrieval-for-Controversial-Questions

Data for the <a href="https://touche.webis.de/clef20/touche20-web/argument-retrieval-for-controversial-questions">Argument Retrieval for Controversial Questions task</a> at Touché 2020.

opencc-by-4.0Sep 2020View details →

Touché20-Argument-Retrieval-for-Comparative-Questions

Data for the <a href="https://touche.webis.de/clef20/touche20-web/argument-retrieval-for-comparative-questions">Argument Retrieval for Comparative Questions task</a> at Touché 2020.

opencc-by-4.0May 2019View details →

Modeling Frames in Argumentation

Dataset of arguments from Debatepdia.org with their topics and stances.

opencc-by-4.0Jun 2024View details →

Replication Package: Unboxing Default Argument Breaking Changes in 1 + 2 Data Science Libraries in Python

Replication Package This repository contains data and source files needed to replicate our work described in the paper "Unboxing Default Argument Breaking Changes in Scikit Learn". Requirements We recommend the following requirements to replicate our study: <ol> <li>Internet access</li> <li>At least 100GB of space</li> <li>Docker installed</li> <li>Git installed</li> </ol> Package Structure We relied on Docker containers to provide a working environment that is easier to replicate. Specifically, we configure the following containers: <ul> <li><code>data-analysis</code>, an R-based Container we used to run our data analysis.</li> <li><code>data-collection</code>, a Python Container we used to collect Scikit's default arguments and detect them in client applications.</li> <li><code>database</code>, a Postgres Container we used to store clients' data, obtainer from Grotov et al.</li> <li><code>storage</code>, a directory used to store the data processed in <code>data-analysis</code> and <code>data-collection</code>. This directory is shared in both containers.</li> <li><code>docker-compose.yml</code>, the Docker file that configures all containers used in the package.</li> </ul> In the remainder of this document, we describe how to set up each container properly. Using VSCode to Setup the Package We selected VSCode as the IDE of choice because its extensions allow us to implement our scripts directly inside the containers. In this package, we provide configuration parameters for both <code>data-analysis</code> and <code>data-collection</code> containers. This way you can directly access and run each container inside it without any specific configuration. You first need to set up the containers <pre><code>$ cd /replication/package/folder $ docker-compose build $ docker-compose up # Wait docker creating and running all containers </code></pre> Then, you can open them in Visual Studio Code: <ol> <li>Open VSCode in project root folder</li> <li>Access the command palette and select "Dev Container: Reopen in Container" <ol> <li>Select either Data Collection or Data Analysis.</li> </ol> </li> <li>Start working</li> </ol> If you want/need a more customized organization, the remainder of this file describes it in detail. Longest Road: Manual Package Setup Database Setup The database container will automatically restore the dump in <code>dump_matroskin.tar</code> in its first launch. To set up and run the container, you should: Build an image: <pre><code>$ cd ./database $ docker build --tag 'dabc-database' . $ docker image ls REPOSITORY TAG IMAGE ID CREATED SIZE dabc-database latest b6f8af99c90d 50 minutes ago 18.5GB </code></pre> Create and enter inside the container: <pre><code>$ docker run -it --name dabc-database-1 dabc-database $ docker exec -it dabc-database-1 /bin/bash root# psql -U postgres -h localhost -d jupyter-notebooks jupyter-notebooks=# \dt List of relations Schema | Name | Type | Owner --------+-------------------+-------+------- public | Cell | table | root public | Code_cell | table | root public | Md_cell | table | root public | Notebook | table | root public | Notebook_features | table | root public | Notebook_metadata | table | root public | repository | table | root </code></pre> If you got the tables list as above, your database is properly setup. It is important to mention that this database is extended from the one provided by <a href="https://markdowntohtml.com/">Grotov et al.</a>. Basically, we added three columns in the table <code>Notebook_features</code> (<code>API_functions_calls</code>, <code>defined_functions_calls</code>, and<code>other_functions_calls</code>) containing the function calls performed by each client in the database. Data Collection Setup This container is responsible for collecting the data to answer our research questions. It has the following structure: <ul> <li><code>dabcs.py</code>, extract DABCs from Scikit Learn source code, and export them to a CSV file.</li> <li><code>dabcs-clients.py</code>, extract function calls from clients and export them to a CSV file. We rely on a modified version of <a href="https://markdowntohtml.com/">Matroskin</a> to leverage the function calls. You can find the tool's source code in the `matroskin`` directory.</li> <li><code>Makefile</code>, commands to set up and run both <code>dabcs.py</code> and <code>dabcs-clients.py</code></li> <li><code>matroskin</code>, the directory containing the modified version of matroskin tool. We extended the library to collect the function calls performed on the client notebooks of Grotov's dataset.</li> <li><code>storage</code>, a docker volume where the data-collection should save the exported data. This data will be used later in <a href="https://markdowntohtml.com/#data-analysis-setup">Data Analysis</a>.</li> <li><code>requirements.txt</code>, Python dependencies adopted in this module.</li> </ul> Note that the container will automatically configure this module for you, e.g., install dependencies, configure matroskin, download scikit learn source code, etc. For this, you must run the following commands: <pre><code>$ cd ./data-collection $ docker build --tag "data-collection" . $ docker run -it -d --name data-collection-1 -v $(pwd)/:/data-collection -v $(pwd)/../storage/:/data-collection/storage/ data-collection $ docker exec -it data-collection-1 /bin/bash $ ls Dockerfile Makefile config.yml dabcs-clients.py dabcs.py matroskin storage requirements.txt utils.py </code></pre> If you see project files, it means the container is configured accordingly. Data Analysis Setup We use this container to conduct the analysis over the data produced by the <a href="https://markdowntohtml.com/#data-collection-setup">Data Collection</a> container. It has the following structure: <ul> <li><code>dependencies.R</code>, an R script containing the dependencies used in our data analysis.</li> <li><code>data-analysis.Rmd</code>, the R notebook we used to perform our data analysis</li> <li><code>datasets</code>, a docker volume pointing to the <code>storage</code> directory.</li> </ul> Execute the following commands to run this container: <pre><code>$ cd ./data-analysis $ docker build --tag "data-analysis" . $ docker run -it -d --name data-analysis-1 -v $(pwd)/:/data-analysis -v $(pwd)/../storage/:/data-collection/datasets/ data-analysis $ docker exec -it data-analysis-1 /bin/bash $ ls data-analysis.Rmd datasets dependencies.R Dockerfile figures Makefile </code></pre> If you see project files, it means the container is configured accordingly. A note on <code>storage</code> shared folder As mentioned, the <code>storage</code> folder is mounted as a volume and shared between <code>data-collection</code> and <code>data-analysis</code> containers. We compressed the content of this folder due to space constraints. Therefore, before starting working on <a href="https://markdowntohtml.com/#data-collection-setup">Data Collection</a> or <a href="https://markdowntohtml.com/#data-analysis-setup">Data Analysis</a>, make sure you extracted the compressed files. You can do this by running the <code>Makefile</code> inside <code>storage</code> folder. <pre><code>$ make unzip # extract files $ ls clients-dabcs.csv clients-validation.csv dabcs.csv Makefile scikit-learn-versions.csv versions.csv $ make zip # compress files $ ls csv-files.tar.gz Makefile</code></pre>

TABLE 5 in Revisiting the description of Atractoscion macrolepis (Perciformes: Sciaenidae): Another argument for comprehensive taxonomy

TABLE 5. Frequency distribution of the number of second dorsal fin rays in Atractoscion aequidens and A. macrolepis.<table><tbody><tr><th>Species</th><th></th><th></th><th>2nd Dorsal fin rays (2DR)</th><th></th><th></th></tr></tbody><tbody><tr><th></th><td>24</td><td>25</td><td>26</td><td>27</td><td>28</td><td>29</td></tr><tr><th>Atractoscion aequidens</th><td></td><td></td><td>6</td><td>15</td><td>9</td><td>3</td></tr><tr><th>Atractoscion macrolepis</th><td>1</td><td>6</td><td>17</td><td>27</td><td>1</td><td></td></tr></tbody></table>

TABLE 2 in Revisiting the description of Atractoscion macrolepis (Perciformes: Sciaenidae): Another argument for comprehensive taxonomy

TABLE 2. Factor loadings, eigenvalues and the variation (individually and cumulatively) explained by the first five principal components in the Principal Components Analysis of 23 transformed morphometric variables examining morphological variation among Atractoscion aequidens and A. macrolepis. Only Principal Components with eigenvalues> 1 are reported.<table><tbody><tr><th>Variable</th><th></th><th></th><th>Principal Components</th><th></th><th></th></tr></tbody><tbody><tr><th></th><td>PC1</td><td>PC2</td><td>PC3</td><td>PC4</td><td>PC5</td></tr><tr><th>TL’</th><td>0.667</td><td>-0.213</td><td>-0.057</td><td>0.184</td><td>0.186</td></tr><tr><th>FL’</th><td>0.510</td><td>-0.118</td><td>-0.098</td><td>-0.073</td><td>0.401</td></tr><tr><th>BD’</th><td>0.590</td><td>-0.046</td><td>0.420</td><td>0.198</td><td>0.008</td></tr><tr><th>1DFL’</th><td>0.554</td><td>-0.079</td><td>0.155</td><td>0.353</td><td>-0.190</td></tr><tr><th>2DFL’</th><td>-0.036</td><td>0.203</td><td>0.749</td><td>0.444</td><td>-0.031</td></tr><tr><th>CPL’</th><td>-0.504</td><td>0.217</td><td>-0.107</td><td>0.215</td><td>0.377</td></tr><tr><th>UCLL’</th><td>0.791</td><td>-0.055</td><td>-0.206</td><td>0.077</td><td>-0.042</td></tr><tr><th>MCL’</th><td>0.931</td><td>-0.194</td><td>-0.049</td><td>0.054</td><td>-0.043</td></tr><tr><th>LCLL’</th><td>0.475</td><td>-0.073</td><td>-0.287</td><td>0.331</td><td>0.518</td></tr><tr><th>AFB’</th><td>0.416</td><td>0.568</td><td>-0.193</td><td>-0.099</td><td>-0.380</td></tr><tr><th>AFL’</th><td>0.926</td><td>-0.036</td><td>-0.059</td><td>-0.056</td><td>-0.078</td></tr><tr><th>PFB’</th><td>0.444</td><td>0.722</td><td>-0.152</td><td>0.144</td><td>-0.179</td></tr><tr><th>PFL’</th><td>0.871</td><td>0.008</td><td>-0.003</td><td>0.055</td><td>0.092</td></tr><tr><th>PLFB’</th><td>0.047</td><td>0.820</td><td>-0.137</td><td>0.218</td><td>-0.214</td></tr><tr><th>PLFL’</th><td>0.877</td><td>-0.201</td><td>-0.027</td><td>-0.019</td><td>-0.125</td></tr><tr><th>PPL’</th><td>0.707</td><td>0.025</td><td>0.394</td><td>-0.169</td><td>0.182</td></tr><tr><th>HL’</th><td>0.934</td><td>-0.072</td><td>0.071</td><td>-0.216</td><td>0.012</td></tr><tr><th>LDL’</th><td>0.895</td><td>0.054</td><td>0.063</td><td>-0.290</td><td>0.026</td></tr><tr><th>IOW’</th><td>0.173</td><td>0.695</td><td>0.215</td><td>-0.483</td><td>0.146</td></tr><tr><th>OD’</th><td>0.854</td><td>0.175</td><td>-0.175</td><td>0.086</td><td>-0.030</td></tr><tr><th>SNL’</th><td>-0.033</td><td>0.792</td><td>0.177</td><td>-0.311</td><td>0.280</td></tr><tr><th>GFL’</th><td>0.392</td><td>0.715</td><td>-0.031</td><td>0.232</td><td>0.096</td></tr><tr><th>PSF’</th><td>-0.344</td><td>0.777</td><td>-0.107</td><td>0.184</td><td>0.159</td></tr><tr><th>Eigenvalue</th><td>9.296</td><td>4.024</td><td>1.283</td><td>1.232</td><td>1.068</td></tr><tr><th>% Total variation</th><td>40.42</td><td>17.50</td><td>5.58</td><td>5.36</td><td>4.64</td></tr><tr><th>Cumulative %</th><td>40.42</td><td>57.91</td><td>63.49</td><td>68.85</td><td>73.49</td></tr></tbody></table>

TABLE 1 in Revisiting the description of Atractoscion macrolepis (Perciformes: Sciaenidae): Another argument for comprehensive taxonomy

TABLE 1. The morphometric, meristic and otolith variables considered in the comparison of Atractoscion aequidens and A. macrolepis in the current study. Means and ranges (in parenthesis) of both raw measurements and proportions (prop.), with body variables expressed as a proportion of standard length (SL), head variables (*) as a proportion of head length (HL) and otolith variables as a proportion of fork length (FL), are presented for the morphometric and otolith variables. Modes and ranges are presented for the meristic counts and counts of otolith processes. Samples sizes (n) are indicated for each variable.<table><tbody><tr><th>Variable</th><th></th><th>Atractoscion aequidens</th><th></th><th>Atractoscion macrolepis</th></tr></tbody><tbody><tr><th></th><td>n</td><td>Mean (min.–max.)</td><td>Mean prop. (min.–max.)</td><td>n</td><td>Mean (min.–max.)</td><td>Mean prop. (min.–max.)</td></tr><tr><th>Morphometrics</th></tr><tr><th>SL</th><td>33</td><td>522.2 (323.0–825.0)</td><td>–</td><td>52</td><td>334.0 (229.9–556.0)</td><td>–</td></tr><tr><th>TL</th><td>31</td><td>603.7 (381.0–958.0)</td><td>1.18 (1.16–1.30)</td><td>52</td><td>401.3 (279.6–663.0)</td><td>1.20 (1.16–1.24)</td></tr><tr><th>FL</th><td>33</td><td>580.8 (364.0–913.0)</td><td>1.11 (1.05–1.14)</td><td>52</td><td>381.5 (268.8–629.0)</td><td>1.14 (1.01–1.19)</td></tr><tr><th>BD</th><td>33</td><td>105.6 (68.4–165.7)</td><td>0.20 (0.17–0.23)</td><td>52</td><td>73.4 (48.7–123.6)</td><td>0.22 (0.17–0.24)</td></tr><tr><th>1DFL</th><td>33</td><td>73.4 (46.8–115.8)</td><td>0.14 (0.11–0.16)</td><td>52</td><td>52.6 (35.0–87.1)</td><td>0.16 (0.13–0.26)</td></tr><tr><th>2DFL</th><td>33</td><td>175.2 (75.4–298.6)</td><td>0.33 (0.14–0.38)</td><td>52</td><td>111.9 (68.8–205.1)</td><td>0.34 (0.19–0.37)</td></tr><tr><th>CPL</th><td>33</td><td>79.8 (50.3–125.7)</td><td>0.15 (0.13–0.17)</td><td>52</td><td>47.4 (29.6–77.8)</td><td>0.14 (0.12–0.19)</td></tr><tr><th>UCLL</th><td>32</td><td>100.0 (62.4–158.7)</td><td>0.19 (0.15–0.21)</td><td>52</td><td>72.9 (47.2–122.1)</td><td>0.22 (0.19–0.25)</td></tr><tr><th>MCL</th><td>32</td><td>60.8 (41.5–87.7)</td><td>0.12 (0.10–0.13)</td><td>52</td><td>48.5 (33.2–75.8)</td><td>0.15 (0.13–0.17)</td></tr><tr><th>LCLL</th><td>32</td><td>94.8 (63.4–145.9)</td><td>0.19 (0.11–0.21)</td><td>51</td><td>66.2 (42.7–111.0)</td><td>0.20 (0.13–0.22)</td></tr><tr><th>AFB</th><td>33</td><td>43.9 (29.3–71.7)</td><td>0.08 (0.08–0.09)</td><td>52</td><td>29.3 (14.6–47.6)</td><td>0.09 (0.06–0.10)</td></tr><tr><th>AFL</th><td>33</td><td>59.9 (38.6–90.1)</td><td>0.12 (0.10–0.13)</td><td>52</td><td>44.2 (28.7–71.0)</td><td>0.13 (0.12–0.15)</td></tr><tr><th>PFB</th><td>33</td><td>17.8 (12.6–27.7)</td><td>0.03 (0.03–0.04)</td><td>52</td><td>12.7 (4.2–21.2)</td><td>0.04 (0.02–0.05)</td></tr><tr><th>PFL</th><td>33</td><td>82.7 (52.3–125.2)</td><td>0.16 (0.15–0.17)</td><td>52</td><td>61.7 (37.6–103.9)</td><td>0.19 (0.14–0.20)</td></tr><tr><th>PLFB</th><td>33</td><td>18.4 (12.5–30.0)</td><td>0.04 (0.03–0.04)</td><td>52</td><td>12.1 (3.1–22.9)</td><td>0.04 (0.01–0.04)</td></tr><tr><th>PLFL</th><td>33</td><td>73.1 (47.8–111.8)</td><td>0.14 (0.12–0.16)</td><td>52</td><td>53.0 (37.5–82.1)</td><td>0.16 (0.14–0.18)</td></tr><tr><th>PPL</th><td>33</td><td>168.7 (104.9–271.4)</td><td>0.32 (0.29–0.34)</td><td>52</td><td>113.3 (76.8–186.5)</td><td>0.34 (0.32–0.36)</td></tr><tr><th>HL</th><td>33</td><td>146.4 (94.5–229.0)</td><td>0.28 (0.26–0.30)</td><td>52</td><td>103.4 (70.5–166.9)</td><td>0.31 (0.29–0.33)</td></tr><tr><th>LDL*</th><td>33</td><td>81.7 (52.3–128.0)</td><td>0.56 (0.53–0.59)</td><td>52</td><td>56.9 (37.3–91.8)</td><td>0.55 (0.53–0.58)</td></tr><tr><th>IOW*</th><td>33</td><td>32.1 (17.9–52.2)</td><td>0.22 (0.16–0.24)</td><td>52</td><td>20.8 (11.3–34.0)</td><td>0.20 (0.16–0.23)</td></tr><tr><th>OD*</th><td>33</td><td>20.0 (14.9–28.3)</td><td>0.14 (0.09–0.17)</td><td>52</td><td>15.9 (7.5–23.0)</td><td>0.16 (0.11–0.18)</td></tr><tr><th>SNL*</th><td>33</td><td>43.2 (26.2–69.5)</td><td>0.29 (0.23–0.31)</td><td>52</td><td>27.0 (13.9–46.2)</td><td>0.26 (0.20–0.28)</td></tr><tr><th>GFL*</th><td>32</td><td>15.9 (10.0–23.8)</td><td>0.11 (0.08–0.13)</td><td>52</td><td>11.8 (3.2–18.8)</td><td>0.11 (0.04–0.14)</td></tr><tr><th>PSF*</th><td>32</td><td>23.9 (12.4–38.4)</td><td>0.16 (0.13–0.20)</td><td>52</td><td>13.7 (3.6–28.2)</td><td>0.13 (0.05–0.17)</td></tr><tr><th>Meristics</th></tr><tr><th>1DS</th><td>33</td><td>10</td><td>–</td><td>52</td><td>10</td><td>–</td></tr><tr><th>2DS</th><td>33</td><td>1</td><td>–</td><td>52</td><td>1</td><td>–</td></tr><tr><th>2DR</th><td>33</td><td>27 (26–29)</td><td>–</td><td>52</td><td>27 (24–28)</td><td>–</td></tr><tr><th>AFS</th><td>33</td><td>2</td><td>–</td><td>52</td><td>2</td><td>–</td></tr><tr><th>AFR</th><td>33</td><td>9</td><td>–</td><td>52</td><td>9 (9–10)</td><td>–</td></tr><tr><th>P</th><td>33</td><td>17 (16–18)</td><td>–</td><td>52</td><td>18 (16–19)</td><td>–</td></tr><tr><th>LL</th><td>15</td><td>72, 73, 74 (71–75)</td><td>–</td><td>31</td><td>70 (68–74)</td><td>–</td></tr><tr><th>Otoliths</th></tr><tr><th>OH</th><td>16</td><td>6.8 (5.1–8.7)</td><td>0.014 (0.011–0.016)</td><td>27</td><td>7.5 (5.6–9.7)</td><td>0.015 (0.013–0.036)</td></tr><tr><th>OL</th><td>17</td><td>14.3 (10.9–7.7)</td><td>0.030 (0.024–0.041)</td><td>27</td><td>14.7 (11.0–18.1)</td><td>0.030 (0.025–0.071)</td></tr><tr><th>OCD</th><td>17</td><td>3.9 (2.6–6.3)</td><td>0.008 (0.006–0.013)</td><td>27</td><td>3.5 (2.3–4.8)</td><td>0.007 (0.005–0.016)</td></tr><tr><th>OrCD</th><td>17</td><td>6.3 (4.9–7.7)</td><td>0.013 (0.010–0.018)</td><td>27</td><td>6.4 (4.3–8.3)</td><td>0.013 (0.010–0.028)</td></tr><tr><th>TOTC</th><td>17</td><td>6.3 (4.5–8.0)</td><td>0.013 (0.011–0.018)</td><td>27</td><td>6.8 (4.8–8.2)</td><td>0.014 (0.011–0.035)</td></tr><tr><th>TOBC</th><td>17</td><td>13.2 (9.7–16.8)</td><td>0.028 (0.023–0.039)</td><td>27</td><td>13.5 (9.8–16.9)</td><td>0.028 (0.023–0.062)</td></tr><tr><th>WC</th><td>17</td><td>1.9 (1.1–2.6)</td><td>0.004 (0.003–0.006)</td><td>27</td><td>2.0 (1.3–2.7)</td><td>0.004 (0.003–0.008)</td></tr><tr><th>OW</th><td>17</td><td>6.1 (4.3–7.4)</td><td>0.013 (0.010–0.022)</td><td>27</td><td>6.1 (1.1–8.6)</td><td>0.013 (0.002–0.028)</td></tr><tr><th>Processes</th><td>17</td><td>5 (3–10)</td><td></td><td>27</td><td>2, 3, 4 (0–8)</td><td></td></tr></tbody></table>