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FIGURE 1 in Comprehensive analysis of Actiniopteris Link and Onychium Kaulf. (Pteridophyta) relationships according to their phylogeography, phylogeny and spore morphology
FIGURE 1. Distribution of Actiniopteris and Onychium taxa among global floristic regions. Phylogeography of the genera is shown using Mesquite (except O. divaricatum).
FIGURE 4 in Comprehensive analysis of Actiniopteris Link and Onychium Kaulf. (Pteridophyta) relationships according to their phylogeography, phylogeny and spore morphology
FIGURE 4. Features of the spore morphology of Actiniopteris and Onychium in the characteristic of trilete spore. Phylogeny of the genera is shown using Mesquite. Spore outline in polar position: 1, 2, 3, 8—distal-polar position; 4, 5, 6, 7—proximal-polar position with three-rays laesura; 1−8—outline of the spore: 1—triangular-roundish non lociniate (TR); 2—roundish-triangular non lociniate (RT); 3— roundish (R); 4—irregularly-roundish-triangular non lociniate (IR); 5—irregular-triangular-roundish non lociniate (ITN); 6—irregularlytriangular-roundish lociniate (ITL); 7—triangular-roundish lociniate, with angular lobes (TRA); 8—triangular-roundish lociniate, with rounded lobes (TRR). Form of distal and proximal sides in equatorial position, according to B.K. Nayar, S. Devi (1966). Distal side: 9—subconical (SD); 10—hemispherical (HD); 11—convex (CD). Proximal side: 12—concave (COP); 13—flat (FP); 14—convex (CP); 15—conical (CIP). Dotted line marks equator of spore. Morphological features of the spore Actiniopteris and Onychium (– black is present, – white is absent): fold along laesura (a); tubercle along laesura (b); equatorial ridge (c); equatorial ridge on proximal side (d); equatorial ridge on distal side (e); tubercle on proximal side (f); tubercle on distal side (g); folds on proximal side (h); folds on distal side (i). Surface of the exosporium: verrucate (V); granulate (G); arachnoid (A); uneven (U); smooth (S).
FIGURE 3 in Comprehensive analysis of Actiniopteris Link and Onychium Kaulf. (Pteridophyta) relationships according to their phylogeography, phylogeny and spore morphology
FIGURE 3. The majority rule consensus topology resulting from the Bayesian/Markov Chain Monte Carlo analysis of the rbcL dataset. Branch length corresponds to the estimated number of substitutions. Values above branches correspond to the bootstrap values.
Linked Papers With Code: The Latest in Machine Learning as an RDF Knowledge Graph
<p><strong>Linked Papers With Code (LPWC)</strong> is an <strong>RDF knowledge graph </strong>that comprehensively models the research field of <strong>machine learning</strong>. It contains information about almost 400,000 machine learning <strong>publications</strong>, including the <strong>tasks</strong> addressed, the <strong>datasets</strong> utilized, the <strong>methods</strong> implemented, and the <strong>evaluations</strong> conducted, along with their <strong>results</strong>. The data set is based on <strong>Papers With Code</strong> and licensed under the CC BY-SA 4.0 license. Furthermore, we provide <strong>knowledge graph embeddings</strong> for entities and relations represented in LPWC.</p><p>More information can be found at <a href="https://linkedpaperswithcode.com/"><strong>https://linkedpaperswithcode.com/</strong></a> and in the ISWC'23 publication <a href="https://linkedpaperswithcode.com/"><strong>"</strong></a><a href="https://aifb.kit.edu/web/Inproceedings3993"><strong>Linked Papers With Code: The Latest in Machine Learning as an RDF Knowledge Graph".</strong></a></p>
Data and code for "Compulsivity is linked to suboptimal choice variability but unaltered reinforcement learning under uncertainty"
<p>Data and code for "Compulsivity is linked to suboptimal choice variability but unaltered reinforcement learning under uncertainty". See https://github.com/jlexternal/RLVOLUNP_CIT_ana for directory structure. </p>
Geomagnetic field instability linked to mantle activity during the Cretaceous Normal Superchron
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Replication package of the paper "Do LLMs Provide Links to Code Similar to what they Generate? A Study with Gemini and Bing CoPilot"
<h1>Replication Package</h1> <p>This replication package contains the necessary tools, data, and scripts for reproducing the results of our paper: "<em>Do LLMs Provide Links to Code Similar to what they Generate? A Study with Gemini and Bing CoPilot</em>". Below is a detailed description of the directory structure and the contents of this package.</p> <h2>Contents</h2> <p>The replication package is organized into two main directories:</p> <ul> <li> <p><code>assets</code>: This directory contains all .csv files used as input for the script and the outputted .csv file used to perform the manual and automated analyses for RQ1 and RQ2.</p> </li> <li> <p><code>script</code>: This directory contains all scripts for RQ1 and RQ2.</p> </li> </ul> <p>In the following, we describe the content of each directory:</p> <h2><code>assets</code></h2> <p>This directory contains the tools and resources required for our study.</p> <h3><code>dataset</code>: Contains the main datasets used in the study.</h3> <ul> <li> <p><code>annotationStore.csv</code>: Input dataset for our analyses, originating from the <em>CODESEARCHNET</em> dataset.</p> </li> <li> <p><code>queries.csv</code>: .csv file containing the queries used for the experiments filtered from the <em>CODESEARCHNET</em>dataset. This file contains the following columns:</p> <ul> <li><em>Language</em>: Programming language of the query</li> <li><em>Query</em>: Query used for the experiment</li> <li><em>GitHubUrl</em>: GitHub URL related to a snippet that addresses the query</li> <li><em>Relevance</em>: Relevance of the linked GitHub snippet to the query</li> </ul> </li> </ul> <h3><code>data</code>: Contains the datasets and results of all analyses.</h3> <ul> <li> <p><code>queries.csv</code>: General input queries. This file contains the following columns:</p> <ul> <li><em>Language</em>: Programming language of the query</li> <li><em>Query</em>: Query used for the snippet generation</li> <li><em>Prompt</em>: LLM prompt generated for the query as: <em>You are a Senior <code><Language></code> developer. Then give me a <code><Language></code> code snippet about: <code><Query></code></em></li> </ul> </li> <li> <p><code>queries_filled.csv</code>: Similar to the previous file, but also containing the output produced by the LLM-based assistants. This file contains the following columns:</p> <ul> <li><em>Language</em>: Programming language of the query</li> <li><em>Query</em>: Query used for the snippet generation</li> <li><em>Prompt</em>: LLM prompt generated for the query as: <em>You are a Senior <code><Language></code> developer. Then give me a <code><Language></code> code snippet about: <code><Query></code></em></li> <li><em>Notes</em>: General notes that provide additional context or information about the query or prompt.</li> <li><em>Gemini_Answer(n)</em>: The generated code snippets by Gemini.</li> <li><em>Gemini(n)</em>: The external links provided by Gemini.</li> <li><em>Prompt (repeated)</em></li> <li><em>Note</em>: Notes that provide additional context or information about the query or prompt.</li> <li><em>Copilot_Answer(n)</em>: The generated code snippets by Bing-Copilot.</li> <li><em>Copilot_Bing(n)</em>: The external links provided by Bing-Copilot.</li> </ul> </li> </ul> <h4><code>copilot</code> || <code>gemini</code>: Contains the data related to the specific LLM. These two subdirectories have the same internal structure.</h4> <ul> <li><code>queries.csv</code>: The <code>queries_filled.csv</code> file, filtered for the specific LLM.</li> <li><code>queries_noTrivial.csv</code>: Contains only the queries with at least one nontrivial generated snippet.</li> <li> <p><code>external_links.csv</code>: External links extracted from the LLMs output.</p> </li> <li> <p><code>external_links_filled.csv</code>: Snippets extracted from the external links.</p> <ul> <li><em>index</em>: Query ID</li> <li><em>source</em>: Snippet ID</li> <li><em>url</em>: Link URL</li> <li><em>note</em>: Notes that provide additional context or information about the query or prompt</li> <li><em>code(n)</em>: The n-th code snippet extracted from the source</li> </ul> </li> </ul> <h4><code>manual_analysis</code>: Manual analysis results.</h4> <ul> <li><code>manual_analysis.csv</code>: <ul> <li><em>index</em>: Query ID</li> <li><em>query</em>: Query used for the snippet generation</li> <li><em>generatedsnippet(n)</em>: The n-th code snippet generated by the LLM-based assistant</li> <li><em>trivial_1</em>: Manual analysis of whether or not the snippet was trivial (validator 1)</li> <li><em>trivial_2</em>: Manual analysis of whether or not the snippet was trivial (validator 2)</li> <li><em>trivial_final</em>: Manual analysis of whether or not the snippet was trivial (final classification if there is a disagreement)</li> <li><em>source</em>: URL to analyze</li> <li><em>sourcetype1</em>: Type of the source (validator 1)</li> <li><em>sourcetype2</em>: Type of the source (validator 2)</li> <li><em>sourcetypefinal</em>: Type of the source (final classification if there is a disagreement)</li> <li><em>relatedtoquery_1</em>: Relevance of the link to the query (validator 1)</li> <li><em>relatedtoquery_2</em>: Relevance of the link to the query (validator 2)</li> <li><em>relatedtoquery_final</em>: Relevance of the link to the query (final classification if there is a disagreement)</li> <li><em>relatedtosnippets_1</em>: Relevance of the generated snippet to those in the link (validator 1)</li> <li><em>relatedtosnippets_2</em>: Relevance of the generated snippet to those in the link (validator 2)</li> <li><em>relatedtosnippets_final</em>: Relevance of the generated snippet to those in the link (final classification if there is a disagreement)</li> </ul> </li> <li><code>manual_analysis_noTrivial.csv</code>: As in the previous file, but only the queries with at least one nontrivial generated code snippet.</li> </ul> <h4><code>clone_detector</code>: Output and intermediate files for clone detection with Copilot data.</h4> <ul> <li><code>copilot_tokens || gemini_tokens</code>: Contains the output the tokenization of the generated code snippets and the code snippets extracted from the external links.</li> <li><code>merged_llm_ext_link.csv</code>: All possible pairs (Cartesian product) (code snippet extracted from the external links, generated code snippet). This file is the input of the clone detection tool. <ul> <li><em>ID_query</em>: Query ID</li> <li><em>query</em>: Query used for the snippet generation</li> <li><em>language</em>: Programming language of the query</li> <li><em>generated_snippet</em>: The generated code snippet by the LLM-based assistant</li> <li><em>IDgensnippet</em>: The index of the generated code snippet</li> <li><em>LOCgensnippet</em>: The number of lines of code of the generated code snippet</li> <li><em>ID_source</em>: Source ID</li> <li><em>source</em>: Source URL</li> <li><em>source_snippet</em>: Code snippet extracted from the source</li> <li><em>IDsourcesnippet</em>: ID of the code snippet extracted from the source</li> <li><em>LOCsourcesnippet</em>: The number of lines of code of the code snippet extracted from the source</li> <li><em>note</em>: Notes that provide additional context or information about the query or prompt</li> </ul> </li> <li><code>clone_detection_output.csv</code>: Contains the clone detection results. <ul> <li><em>ID_query</em>: The index of the query</li> <li><em>query</em>: Query used for the snippet generation</li> <li><em>language</em>: The programming language of the query</li> <li><em>generated_snippet</em>: The generated code snippet by the LLM-based assistant</li> <li><em>IDgensnippet</em>: The index of the generated code snippet</li> <li><em>LOCgensnippet</em>: The number of lines of code of the generated code snippet</li> <li><em>ID_source</em>: Source ID</li> <li><em>source</em>: Source URL</li> <li><em>source_snippet</em>: Code snippet extracted from the source</li> <li><em>IDsourcesnippet</em>: ID of the code snippet extracted from the source</li> <li><em>LOCsourcesnippet</em>: The number of lines of code of the code snippet extracted from the source</li> <li><em>note</em>: Notes that provide additional context or information about the query or prompt</li> <li><em>clone_detected</em>: bBolean value that indicates whether a clone has been detected (1 = detected, 0 = not detected)</li> <li><em>cloning_ratio</em>: Ratio of the number of lines of code of the generated code snippet has been detected as a clone in the code snippet extracted from the source</li> <li><em>cloned_lines</em>: The number of lines of code of the generated code snippet that has been detected as a clone in the code snippet extracted from the source</li> </ul> </li> </ul> <h4><code>cosine_sim</code>: Cosine similarity results.</h4> <ul> <li><code>cosine_sim_output.csv</code>: Contains the cosine similarity results <ul> <li><em>query_id</em>: Query ID</li> <li><em>snippet_id</em>:ID the generated code snippet</li> <li><em>source_id</em>: ID of the source</li> <li><em>sourcesnippetid</em>: ID of the code snippet extracted from the source <ul> <li><em>cosine_similarity</em>: The cosine similarity between the generated code snippet and the code snippet extracted from the source</li> </ul> </li> </ul> </li> </ul> <h4><code>quant_analysis</code>: Quantitative analysis results.</h4> <ul> <li><code>topN_links_se.csv</code>: Contains the top-N links extracted from the search engine. <ul> <li><em>id</em>: Query ID</li> <li><em>query</em>: The query</li> <li><em>url</em>: Link URL</li> </ul> </li> <li><code>merged_clone_cosine.csv</code>: Contains the merged results of the clone detection and cosine similarity. <ul> <li><em>ID_query</em>: Query ID</li> <li><em>query</em>: The query</li> <li><em>language</em>: The programming language of the query</li> <li><em>generated_snippet</em>: The generated code snippet by the LLM-based assistant</li> <li><em>IDgensnippet</em>: The ID of the generated code snippet</li> <li><em>LOCgensnippet</em>: The number of lines of code of the generated code snippet</li> <li><em>ID_source</em>: The index of the source</li> <li><em>source</em>: The source URL</li> <li><em>source_snippet</em>: The code snippet extracted from the source</li> <li><em>IDsourcesnippet</em>: The index of the code snippet extracted from the source</li> <li><em>LOCsourcesnippet</em>: The number of lines of code of the code snippet extracted from the source</li> <li><em>note</em>: Notes that provide additional context or information about the query or prompt</li> <li><em>clone_detected</em>: Boolean value that indicates if a clone has been detected(1 = detected, 0 = not detected)</li> <li><em>cloning_ratio</em>: The ratio of the number of lines of code of the generated code snippet has been detected as a clone in the code snippet extracted from the source</li> <li><em>cloned_lines</em>: The number of lines of code of the generated code snippet that has been detected as a clone in the code snippet extracted from the source</li> <li><em>cosine_similarity</em>: The cosine similarity between the generated code snippet and the code snippet extracted from the source</li> </ul> </li> </ul> <h4><code>other_analysis</code>: Contains more performed analysis.</h4> <ul> <li><code>sample_queries.csv</code>: Contains a sample of five queries for language used for perform the chain of thought experiment. <ul> <li><em>Language</em>: Programming language of the query</li> <li><em>Query</em>: Query used for the snippet generation</li> <li><em>Prompt</em>: LLM prompt generated for the query as: <em>You are a Senior <code><Language></code> developer. Then give me a <code><Language></code> code snippet about: <code><Query></code></em></li> </ul> </li> <li><code>chain_of_thought.csv</code>: <ul> <li><em>Language</em>: Programming language of the query</li> <li><em>Query</em>: Query used for the snippet generation</li> <li><em>Prompt</em>: LLM prompt generated for the query as: <em>You are a Senior <code><Language></code> developer. Then give me a <code><Language></code> code snippet about: <code><Query></code></em></li> <li><em>Clone_fonud</em>: Boolean value that indicates if a clone has been detected (Yes = detected, No = not detected)</li> <li><em>Note</em>: Notes that provide additional context or information about the performed analysis</li> </ul> </li> <li><code>data_check.csv</code>: <ul> <li><em>Link</em>: URL of the source provided by the LLM</li> <li><em>Post_date</em>: Indicates if the date of the post is before/after the date of training of the LLM (before 2023, after 2023, not provided)</li> <li><em>Note</em>: Notes that provide additional context or information about the performed analysis</li> </ul> </li> </ul> <h4><code>results</code>: Final analysis results.</h4> <ul> <li><code>jaccard_analysis.csv</code>: Contains the results of the Jaccard analysis comparing the provided external links by the LLMs with the top-N links extracted from the corresponding search engine. <ul> <li><em>id</em>: Query ID</li> <li><em>language</em>: The programming language of the query</li> <li><em>llm_link</em>: The external links provided by the LLM</li> <li><em>llmlinksize</em>: The number of external links provided by the LLM</li> <li><em>overlap_links</em>: The overlapping links between the LLM and the search engine</li> <li><em>overlap_size</em>: The number of overlapping links between the LLM and the search engine</li> <li><em>nonoverlaplinks</em>: The non-overlapping links between the LLM and the search engine</li> <li><em>union_size</em>: The size of the union set links between the LLM and the search engine</li> <li><em>jaccard</em>: The Jaccard similarity between the LLM and the search engine</li> </ul> </li> <li><code>merged_analysis.csv</code>: Contains the merged results of the manual and quantitative analyses. <ul> <li><em>id</em>: The index of the query</li> <li><em>query</em>: The query used for the experiment</li> <li><em>trivial_final(n)</em>: The final assignment for the triviality of the n-th generated code snippet</li> <li><em>source</em>: The URL of the source</li> <li><em>sourcetypefinal</em>: The final assignment for the type of the source</li> <li><em>relatedtoquery_final</em>: The final assignment for the relevance of the generated code snippet to the query</li> <li><em>relatedtosnippets_final</em>: The final assignment for the relevance of the generated code snippet to the source</li> <li><em>cloning_ratio</em>: The maximum cloning ratio between the generated code snippet and all the code snippets extracted from the source</li> <li><em>cosine_similarity</em>: The cosine similarity related to the snippets with maximum cloning ratio</li> </ul> </li> </ul> <h3><code>cccfindersw-configuration-files</code>:</h3> <p>Contains additional configuration files for the CCFinderSW clone detection tool. The files are <code>javascript_comment.txt</code> and <code>javascript_reserved.txt</code>. They must be placed in the tool's <code>comment/</code> and <code>reserved/</code> directories.</p> <h3><code>appendix.tex</code>: The appendix of the paper containing:</h3> <ul> <li><em>Table 1</em>: Number of links of different types provided by Gemini and Bing CoPilot</li> </ul> <h3><code>appendix.pdf</code>: The appendix of the paper in PDF format.</h3> <h2><code>script</code></h2> <p>This directory contains our scripts (mostly Python, an R script and an Applescript) to preprocess data and run the clone detection analyses.</p> <ul> <li><code>1_dateset_filtering.py</code>: Script to filter the dataset. The input of this script is the <code>annotationStore.csv</code> file, and the output is the <code>queries.csv</code> file.</li> <li><code>2_prompt_generation.py</code>: Script to generate prompts for the LLM-based assistants. The input of this script is the <code>queries.csv</code> file, and the output is the <code>queries_filled.csv</code> file.</li> <li><code>3_gen_sheet_sources_extraction.py</code>: Script to split the external links provided by the LLM, one for each row. The input of this script is the <code>queries_filled.csv</code> file, and the output is the <code>external_link.csv</code> file.</li> <li><code>4_ext_link_snippet_extraction.py</code>: Script to extract the snippets from Web URLs. It only works for the most popular domains. The input of this script is the <code>external_links.csv</code> file, and the output is the <code>external_links_filled.csv</code> file.</li> <li><code>5_top_n_link_SearchEngine.py</code>: Script to perform top-N link search using the corresponding search engines (Google Search and Bing). The input of this script is the <code>queries_filled.csv</code> file. It executes the <code>browser_bot.scpt</code>. The output is the <code>topN_links_se.csv</code> file. <ul> <li><code>browser_bot.scpt</code>: Script for browser automation (AppleScript).</li> </ul> </li> <li><code>6_se_vs_llm.py</code>: Script to compare (using the Jaccard metric) the links returned by the corresponding search engines with those provided by the LLM-based assistants. The input of this script is the <code>external_links.csv</code> file and the <code>topN_links_se.csv</code> file. The output is the <code>jaccaard_analysis.csv</code> file.</li> <li><code>7_results_manual_analysis.py</code>: Script to extract results and statistical analyses performed on the manual analysis and reported in the tables in the paper.</li> <li><code>8_merge_gen_source_snippets.py</code>: This script takes as input: <code>{llm}/queries.csv</code> and <code>{llm}/external_link_filled.csv</code> to merge them and generates an expanded one, i.e., one in which we have on each line a snippet extracted from the source, this will be the input of our final script for clone detection. The output is the <code>merged_llm_ext_link.csv</code> file.</li> <li><code>9_clone_detection.py</code>: Script to perform clone detection. The input of this script is the <code>merged_llm_ext_link.csv</code> file, and the output is the <code>clone_detection_output.csv</code> file.</li> <li><code>10_cosine_sim_check.py</code>: Script to compute the code snippets' cosine similarity. The script takes as input the tokenized files from the <code>{llm}_tokens</code> directory. The output is the <code>cosine_sim_output.csv</code>file.</li> <li><code>11_merger_clone_cosine.py</code>: Script to merge clone detection and cosine similarity results. The input of this script is the <code>clone_detection_output.csv</code> and the <code>cosine_sim_output.csv</code> files, and the output is the <code>merged_clone_cosine.csv</code> file.</li> <li><code>12_merge_manual_quantitative_analysis.py</code>: Script to merge manual and quantitative analysis results. The inputs of this script are the <code>manual_analysis.csv</code> and the <code>merged_clone_cosine.csv</code>files, and the output is the <code>merged_analysis.csv</code> file.</li> <li><code>13_sample_for_COT_analysis.py</code>: Script to collect the sample of queries on which we perform the chain of thought analysis, the output is the <code>sample_queries.csv</code> file.</li> <li><code>14_llm_vs_csn.py</code>: Script to check the overlap between the links provided by the LLM and the one associated with the related query in the <em>CodeSearchNet</em> dataset. The input of this script is the <code>queries.csv</code>and <code>queries_noTrivial.csv</code> file.</li> <li><code>cloningGraph.R</code>: R script to generate the cloning graph. The input of this script is the <code>merged_analysis.csv</code> file.</li> </ul>
Estimating the burden of underdiagnosis within England: a modelling study of linked primary care data
<p>These datasets are the modelling outputs supporting our paper titled: <strong><span>Estimating the burden of underdiagnosis within England: a modelling study of linked primary care data. </span></strong><span>The details of how these data were produced were described in the paper. </span></p> <p> </p> <p> </p>
Agri-Environmental Communication Interaction Strategic Link to Food Security Solution
<p>Developing countries face challenges of food production (food insecurity) and malnutrition on daily basis and this mainly affects women and children who are culprits of hunger and death. Kenya faces the worst scenario over food deficits and malnutrition. Meeting sustainable food requirements as it is in developed countries becomes an uphill task. Kenya imports more food grains such as maize, rice, sugar and wheat among others. Nearly 40% of our food stores contain imported foods. What could be the reason for this shortage of food? Information captured by local farmers indicates the scientific language used in training and instructing farmers is foreign and scientific hence difficult to conceptualize and interpret to a local environmental language that is simpleto understand and implement by farmers. Farmers therefore end up not doing the right things. This essential agri-environmental communication technique between farmers and instructional language complexity contributes significantly to poor agricultural output. The farmers' claim has been thinly covered in research studies regardingthe food production system. This is the reason for carrying out this research. This paper seeks to address the claim that agri-environmental communication interaction can be key to enhancing food production and nutrition as well as improving human health. The objective was to find out the causes of low food production dueto lack of agri-environmental communication interaction; To assess the effect of food insecurity and malnutrition on human health due to agri-environmental communication interactions and to make recommendations towards enhancing food production and nutrition in improving human health in Kenya through agri-environmental communication interaction. The research study and data was collected from secondary sources, media observations and focus group discussions from the forty-seven counties in Kenya. The findings revealed that, food production is very low due to poor communication in scientific agricultural terminologies interpretation inlocal language for effective use by farmers. The recommendation is that, government and stakeholders should take action and improve on effective agri- environmental communication interaction in order to solve food insecurity and malnutrition. The scientific language used in instructions product labels should include local languages.</p>
Data from: Linking genetic diversity and temporal fluctuations in population abundance of the introduced feral cat (Felis silvestris catus) on the Kerguelen Archipelago.
Linking temporal variations of genetic diversity, including allelic richness and heterozygosity, and spatio-temporal fluctuations in population abundance has emerged as an important tool for understanding demographic and evolutionary processes in natural populations. This so-called 'genetic monitoring' was conducted across 12 consecutive years (1996-2007) at three sites for the feral cat, introduced onto the Kerguelen Archipelago fifty years ago. Temporal changes in allelic richness and heterozygosity at 18 microsatellite DNA loci were compared to temporal changes in the adult population abundance index, obtained by typical demographic monitoring. No association was found at the island spatial scale but we observed an association between genetic diversity and adult population indices from year to year within each study site. More particularly, the magnitude of successive increases or decreases in the adult population abundance index appeared to be the major factor linking the trajectories of genetic diversity and adult population abundance indices. Natal dispersal and/or local recruitment, both facilitated by high juvenile survival when the adult population size is small, are proposed as the major demographic processes contributing to such an observed pattern. Finally, we suggested avoiding the use of the harmonic mean as an estimator of long-term population size to study the relationships between demographic fluctuations and heterozygosity in populations characterized by strong multi-annual density fluctuations.
Data from: Meerkat close calling patterns are linked to sex, social category, season and wind, but not fecal glucocorticoid metabolite concentrations
It is well established that animal vocalizations can encode information regarding a sender's identity, sex, age, body size, social rank and group membership. However, the association between physiological parameters, particularly stress hormone levels, and vocal behavior is still not well understood. The cooperatively breeding African meerkats (Suricata suricatta) live in family groups with despotic social hierarchies. During foraging, individuals emit close calls that help maintain group cohesion. These contact calls are acoustically distinctive and variable in rate across individuals, yet, information on which factors influence close calling behavior is missing. The aim of this study was to identify proximate factors that influence variation in call rate and acoustic structure of meerkat close calls. Specifically, we investigated whether close calling behavior is associated with sex, age and rank, or stress hormone output (i.e., measured as fecal glucocorticoid metabolite (fGCM) concentrations) as individual traits of the caller, as well as with environmental conditions (weather) and reproductive seasonality. To disentangle the effects of these factors on vocal behavior, we analyzed sound recordings and assessed fGCM concentrations in 64 wild but habituated meerkats from 9 groups during the reproductive and non-reproductive seasons. Dominant females and one-year old males called at significantly higher rates compared to other social categories during the reproductive season. Additionally, dominant females produced close calls with the lowest mean fundamental frequencies (F0) and the longest mean pulse durations. Windy conditions were associated with significantly higher call rates during the non-reproductive season. FGCM concentrations were unrelated to close calling behavior. Our findings suggest that meerkat close calling behavior conveys information regarding the sex and social category of the caller, but shows no association with fGCM concentrations. The change in call rate in response to variation in the social and ecological environments individuals experience indicates some degree of flexibility in vocal production.
Data from: Selection on VPS13A linked to migration in a songbird
Animal migration demands an interconnected suite of adaptations for individuals to navigate over long distances. This trait complex is crucial for small birds whose migratory behaviors—such as directionality—are more likely innate, rather than being learned as in many longer-lived birds. Identifying causal genes has been a central goal of migration ecology, and this endeavor has been furthered by genome-scale comparisons. However, even the most successful studies of migration genetics have achieved low resolution associations, identifying large chromosomal regions that encompass hundreds of genes, one or more of which might be causal. Here we leverage the genomic similarity among golden-winged (Vermivora chrysoptera) and blue-winged warblers (V. cyanoptera) to identify a single gene—Vacuolar Protein Sorting 13A (VPS13A)—that is associated with distinct differences in migration to Central American (CA) or South American (SA) wintering areas. We find reduced sequence variation in this gene-region for SA wintering birds, and show this is the likely result of natural selection on this locus. In humans, variants of VPS13A are linked to the neurodegenerative disorder chorea-acanthocytosis. This new association provides one of the strongest gene-level associations with avian migration differences.
Data from: Linking genetic and ecological differentiation in an ungulate with a circumpolar distribution
Genetic differentiation among populations may arise from the disruption of gene flow due to local adaptation to distinct environments and/or neutral accumulation of mutations and genetic drift resulted from geographical isolation. Quantifying the role of these processes in determining the genetic structure of natural populations remains challenging. Here, we analyze the relative contribution of isolation-by-resistance (IBR), isolation-by-environment (IBE), genetic drift and historical isolation in allopatry during Pleistocene glacial cycles on shaping patterns of genetic differentiation in caribou/reindeer populations (Rangifer tarandus) across the entire distribution range of the species. Our study integrates analyses at range-wide and regional scales to partial out the effects of historical and contemporary isolation mechanisms. At the circumpolar scale, our results indicate that genetic differentiation is predominantly explained by IBR and historical isolation. At a regional scale, we found that environmental dissimilarity and population size significantly explained the spatial distribution of genetic variation among populations belonging to the Euro-Beringian lineage within North America. In contrast, genetic differentiation among populations within the North American lineage was predominantly explained by IBR and population size, but not IBE. We also found discrepancies between genetic and ecotype designation across the Holarctic species distribution range. Overall, these results indicate that multiple isolating mechanisms have played roles in shaping the spatial distribution of genetic variation across the distribution range of a large mammal with high potential for gene flow. Considering multiple spatial scales and simultaneously testing a comprehensive suite of potential isolating mechanisms, our study contributes to understand the ecological and evolutionary processes underlying organism–landscape interactions.
Data from: Linking intra- and interspecific assortative mating: consequences for asymmetric sexual isolation
Assortative mating is of interest because of its role in speciation and the maintenance of species boundaries. However, we know little about how within-species assortment is related to interspecific sexual isolation. Most previous studies of assortative mating have focused on a single trait in males and females, rather than utilizing multivariate trait information. Here we investigate how intraspecific assortative mating relates to sexual isolation in two sympatric and congeneric damselfly species (genus Calopteryx). We connect intraspecific assortment to interspecific sexual isolation by combining field observations, mate preference experiments and enforced copulation experiments. Using canonical correlation analysis, we demonstrate multivariate intraspecific assortment for body size and body shape. Males of the smaller species mate more frequently with heterospecific females than males of the larger species, which showed less attraction to small heterospecific females. Field experiments suggest that sexual isolation asymmetry is caused by male preferences for large heterospecific females, rather than by mechanical isolation due to interspecific size differences or female preferences for large males. Male preferences for large females and male-male competition for high quality females can therefore counteract sexual isolation. This sexual isolation asymmetry therefore indicates that sexual selection currently opposes a species boundary.
Data from: Integrating three comprehensive datasets shows that mitochondrial DNA variation is linked to species traits and paleogeographic events in European butterflies.
Understanding the dynamics of biodiversity, including the spatial distribution of genetic diversity, is critical for predicting responses to environmental changes, as well as for effective conservation measures. This task requires tracking changes in biodiversity at large spatial scales and correlating with species functional traits. We provide three comprehensive resources to understand the determinants for mitochondrial DNA differentiation represented by i) 15,609 COI sequences and ii) 14 traits belonging to 307 butterfly species occurring in Western-Central Europe and iii) the first multi-locus phylogenetic tree of all European butterfly species. By applying phylogenetic regressions we show that mitochondrial DNA spatial differentiation (as measured with Gst, G'st, D and Dst) is negatively correlated with species traits determining dispersal capability and colonization ability. Thanks to the high spatial resolution of the COI data, we also provide the first zoogeographic regionalization maps based on intraspecific genetic variation. The overall pattern obtained by averaging the spatial differentiation of all Western-Central European butterflies shows that the paradigm of long-term glacial isolation followed by rapid pulses of post-glacial expansion has been a pervasive phenomenon in European butterflies. The results and the extensive datasets we provide here constitute the basis for genetically-informed conservation plans for a charismatic group in a continent where flying insects are under alarming decline.
Data from: Linking genotype to phenotype in a changing ocean: inferring the genomic architecture of a blue mussel stress response with genome-wide association
A key component to understanding the evolutionary response to a changing climate is linking underlying genetic variation to phenotypic variation in stress response. Here we use a genome-wide association approach (GWAS) to understand the genetic architecture of calcification rates under simulated climate stress. We take advantage of the genomic gradient across the blue mussel hybrid zone (Mytilus edulis and Mytilus trossulus) in the Gulf of Maine (GOM) to link genetic variation with variance in calcification rates in response to simulated climate change. Falling calcium carbonate saturation states are predicted to negatively impact many marine organisms that build calcium carbonate shells - like blue mussels. We sampled wild mussels and measured net calcification phenotypes after exposing mussels to a "climate change" common garden, where we raised temperature 3°C, decreased pH by 0.2 units, and limited food supply by filtering out planktonic particles > 5 μm, compared to ambient GOM conditions in the summer. This climate change exposure greatly increased phenotypic variation in net calcification rates compared to ambient conditions. We then used regression models to link the phenotypic variation with over 170,000 single nucleotide polymorphism loci (SNPs) generated by genotype by sequencing to identify genomic locations associated with calcification phenotype, and estimate heritability and architecture of the trait. We identified at least one of potentially 2-10 genomic regions responsible for 30% of the phenotypic variation in calcification rates that are potential targets of natural selection by climate change. Our simulations suggest a power of 13.7% with our study's average effective sample size of 118 individuals and rare alleles, but a power of > 90% when effective sample size is 900.
Data for: "Possible link between decadal variability in precipitation in the South China Sea and the North Atlantic Oscillation during the 20th century: A perspective from coral geochemical records"
<p>This dataset includes all the data for the paper "Possible link between decadal variability in precipitation in the South China Sea and the North Atlantic Oscillation during the 20th century: A perspective from coral geochemical records" by Cui et al.</p>
Dynamic loading of human engineered heart tissue enhances contractile function and drives a desmosome-linked disease phenotype (TEM data)
<p>This is the TEM imaging data for the desmosome analysis as reported in the manuscript titled "Dynamic loading of human engineered heart tissue enhances contractile function and drives a desmosome-linked disease phenotype."</p>
Causal links between parietal alpha activity and spatial auditory attention
Both visual and auditory spatial selective attention result in lateralized alpha (8-14 Hz) oscillatory power in parietal cortex: alpha increases in the hemisphere ipsilateral to attentional focus. Brain stimulation studies suggest a causal relationship between parietal alpha and suppression of the representation of contralateral visual space. However, there is no evidence that parietal alpha controls auditory spatial attention. Here, we performed high definition transcranial alternating current stimulation (HD-tACS) on human subjects performing an auditory task in which they directed attention based on either spatial or nonspatial features. Alpha (10 Hz) but not theta (6 Hz) HD-tACS of right parietal cortex interfered with attending left but not right auditory space. Parietal stimulation had no effect for nonspatial auditory attention. Moreover, performance in post-stimulation trials returned rapidly to baseline. These results demonstrate a causal, frequency-, hemispheric-, and task-specific effect of parietal alpha brain stimulation on top-down control of auditory spatial attention.
Data from: Computed tomography scanning as a tool for linking the skeletal and otolith-based fossil records of teleost fishes
Micro-computed tomography scanning (µCT scanning) now represents a standard tool for non-destructive study of internal or concealed structure in fossils. Here we report on otoliths found in situ during routine µCT scanning of three-dimensionally preserved skulls of Palaeogene and Cretaceous fishes. Comparisons are made with isolated otolith-based taxa in order to attempt correlations between the body fossil and otolith fossil records. In situ otoliths previously extracted mechanically from specimens of Apogon macrolepis and Dentex laekeniensis match our µCT models. In some cases, we find a high degree of congruence between previously independent taxonomic placements for otolith and skeletal remains (Rhinocephalus, Osmeroides, Hoplopteryx). Unexpectedly, in situ otoliths of the aulopiform Apateodus match isolated otoliths of Late Cretaceous age previously interpreted as belonging to gempylids, a group of percomorph fishes that do not appear in the body fossil record until the Palaeogene. This striking example of convergence suggests constraints on otolith geometry in pelagic predators. The otoliths of Apateodus show a primitive geometry for aulopiforms and lack the derived features of Alepisauroidea, the lizardfish clade to which the genus is often attributed. In situ otoliths of Early Cretaceous fishes (Apsopelix) are not well preserved, and we are unable to identify clear correlations with isolated otolith morphologies. We conclude that the preservation of otoliths suitable for µCT scanning appears intimately connected with the taphonomic history, lithological characteristics of surrounding matrix, and syn- and postdepositional diagenetic effects.
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.