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Fig. 1 in Relationship between host searching and wind direction in Ophraella communa (Coleoptera: Chrysomelidae)
Fig. 1. Plot design to study the location of host plants by Ophraella communa adults in relation to wind direction. This diagram displays the planting pattern for 1 m wide concentric plots of the host, common ragweed, separated by 1 m wide concentric barriers of a non-host, sunflower.
Fig. 2 in Relationship between host searching and wind direction in Ophraella communa (Coleoptera: Chrysomelidae)
Fig. 2. Wind roses at different dates observed by Changsha Meteorological Observatory from 5 Jul to 15 Aug 2010. The letters a, b, c, d, and e in each small figure represented general wind roses on 5–10 Jul, 11–20 Jul, 21–31 Jul, 1–10 Aug, and 11–15 Aug 2010, respectively.
Data from: Wasted efforts impair random search efficiency and reduce choosiness in mate-pairing termites. American Naturalist
<div> <h1>README</h1> <a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/tree/main#readme"></a></div> <div> <h2>Article Information</h2> <a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/tree/main#article-information"></a></div> <p>This repository provides access to the data and source code used for the manuscript</p> <div> <h3><strong>Wasted efforts impair random search efficiency and reduce the level of choosiness in mate-pairing termites</strong></h3> <a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/tree/main#wasted-efforts-impair-random-search-efficiency-and-reduce-the-level-of-choosiness-in-mate-pairing-termites"></a></div> <div> <h4>Nobuaki Mizumoto, Naohisa Nataya, Ryusuke Fujisawa</h4> <a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/tree/main#nobuaki-mizumoto-naohisa-nataya-ryusuke-fujisawa"></a></div> <p>Contact, Nobuaki Mizumoto: <a href="mailto:nzm0095@auburn.edu">nzm0095@auburn.edu</a></p> <p>This paper is accepted at The American Naturalist.<br>Preprint is available at <a href="http://img.shields.io/badge/DOI-10.1101/2024.02.01.578198.svg" rel="nofollow">bioRxiv</a>.</p> <p>This study examines how movement patterns of mate searchers of a termite <em>Reticulitermes speratus</em> changes according to time. Then investigated how this change in movement patterns affect random search efficiency and mate choice behavior.<br>This includes tracking data, R codes to analyze it, and Cpp code for simulations.</p> <div> <h2>Table of Contents</h2> <a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/tree/main#table-of-contents"></a></div> <ul> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/README.md">README</a></li> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/analysis/scripts">scripts</a> <ul> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/analysis/scripts/output.R">output.R</a> - output all results</li> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/analysis/scripts/processing.R">processing.R</a> - data processing of coordinates obtained from servosphere</li> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/analysis/scripts/simulations.R">simulations.R</a> - for data-based simulations</li> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/analysis/scripts/onesim.cpp">onesim.cpp</a> - functions for simulations</li> </ul> </li> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/analysis/output">output</a> - all outputs are stored</li> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/analysis/data">data</a> <ul> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/analysis/data/raw">raw</a> - raw data in .csv <ul> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/analysis/data/raw/ANTAM_4day">ANTAM_4day</a> - directory includes raw data obtained from servosphere. Ignore the first four columns. col4: x, col5: y, col6: time</li> <li><strong>colonyfoundation.csv</strong> - data for colony foundation experiments.</li> <li><strong>tandem_sum.csv</strong> - the number of observations of individual units in each experiment. units include solomale (single male), solofemale (single female), heterotandem (female-male tandem), male tandem (male-male tandem), female tandem (female-female tandem), and tandem3 (tandem run with >= 3 individuals).</li> <li><strong>tandem_timedevelopment.csv</strong> - breakdown of tandem_sum.csv. The measurement was not summarized but for each observation bouts (5: 0-5 min, 10: 5-10 min, ...).</li> <li><strong>termite_weight.csv</strong> - Termite weight measurement. fresh: fresh weight in mg.</li> </ul> </li> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/analysis/data/fmt">fmt</a> - formatted data created in processing.R and simulations.R. The formatted data will be used for output.R. The all formatted data are in .rda files. The .csv files with the same contents are also generated for reviewing purpose. <ul> <li><strong>df_all.rda</strong> - The processed data of trajectories for furthur analysis. It has three datafrmaes named df_all (trajectories with traveled distance information for each frame), df_MSD (MSD data for each individual), df_pause (duration of pauses for each pausing events for each individual). The corresponding csv files are df_all.csv, df_MSD.csv, and df_pause.csv</li> <li><strong>df_sum.rda</strong> - The file further processed df_all.rda to summarize all parameters for each individual. df_sum.csv is corresponding.</li> <li><strong>df_sim.rda</strong> - The simulation results that record encounter time for each searching attempts. encounter_time = 1501 indicates the failure to encounter. df_sim.csv is corresponding.</li> </ul> </li> </ul> </li> </ul> <div> <h2>Session information</h2> <a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/tree/main#session-information"></a></div> <div> <pre><code>R version 4.3.1 (2023-06-16 ucrt) Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows 11 x64 (build 22621) Matrix products: default locale: [1] LC_COLLATE=English_United States.utf8 [2] LC_CTYPE=English_United States.utf8 [3] LC_MONETARY=English_United States.utf8 [4] LC_NUMERIC=C [5] LC_TIME=English_United States.utf8 time zone: Asia/Tokyo tzcode source: internal attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] CircStats_0.2-6 boot_1.3-28.1 MASS_7.3-60 [4] stringr_1.5.0 survival_3.5-5 survminer_0.4.9 [7] ggpubr_0.6.0 Rcpp_1.0.10 PupillometryR_0.0.5 [10] rlang_1.1.1 dplyr_1.1.2 viridis_0.6.3 [13] viridisLite_0.4.2 ggplot2_3.4.2 Rmisc_1.5.1 [16] plyr_1.8.8 lattice_0.21-8 exactRankTests_0.8-35 [19] car_3.1-2 carData_3.0-5 lme4_1.1-34 [22] Matrix_1.6-1 data.table_1.14.8 </code></pre> </div>
GBM Targeted Search Data Release for EP 240919a /GRB 240919A
<p>The following Files include:</p> <ul> <li>The summed GBM NaI detector lightcurve,</li> <li>The individual GBM NaI detector lightcurves,</li> <li>The BGO detector lightcurves, </li> <li>The GBM NaI detector lightcurves sliced by energy.</li> <li>The waterfall plot (all energy) showing the most significant timescale of the event,</li> <li>The waterfall plot with the three different spectral models (soft, normal and hard, see below).</li> <li>The localization of EP 240919a / GRB 240919A, with the green circle centered on the EP-WXT position with a 1 degree error radius.</li> <li>The healpix map for "Event 1", which is the event found by the Targeted Search and shown in these plots. </li> </ul> <p><br>The definitions for soft, normal and hard spectra are:<br><br>"soft" spectrum (Band function with Epeak = 70 keV, alpha = -1.9, beta = -3.7) for a GRB.<br>"normal" spectrum (Band function with Epeak = 230 keV, alpha = -1.0, beta = -2.3) for a GRB.<br>"hard" spectrum (Comptonized function with Epeak = 1500 keV, alpha = -0.5) for a GRB. </p> <p> </p>
GBM Targeted Search Data Release for GRB 241002D
<p>The following Files include:</p> <ul> <li>The summed GBM NaI detector lightcurve,</li> <li>The individual GBM NaI detector lightcurves,</li> <li>The BGO detector lightcurves, </li> <li>The GBM NaI detector lightcurves sliced by energy.</li> <li>The waterfall plot (all energy) showing the most significant timescale of the event,</li> <li>The waterfall plot with the three different spectral models (soft, normal and hard, see below).</li> <li>The localization of GRB 241002D, showing the 3 and 1-sigma contours.</li> <li>The healpix map for "Event 1", which is the event found by the Targeted Search and shown in these plots. </li> </ul> <p><br>The definitions for soft, normal and hard spectra are:<br><br>"soft" spectrum (Band function with Epeak = 70 keV, alpha = -1.9, beta = -3.7) for a GRB.<br>"normal" spectrum (Band function with Epeak = 230 keV, alpha = -1.0, beta = -2.3) for a GRB.<br>"hard" spectrum (Comptonized function with Epeak = 1500 keV, alpha = -0.5) for a GRB. </p>
Data from "A search using GEO600 for gravitational waves coincident with fast radio bursts from SGR 1935+2154"
<p>This includes the data and scripts used to generate the plots in the paper "A search using GEO600 for gravitational waves coincident with fast radio bursts from SGR 1935+2154."</p>
CLIP Features and Selected Relevance Judgments Subset for TRECVID Ad-hoc Search (2019-2023)
<div> <div> <div> <div> <div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> <p>This repository contains CLIP features and annotations for a subset of V3C images, based on their relevance to selected queries from the TREC Video Retrieval Evaluation (TRECVID) Ad-hoc Video Search (AVS) task. The data includes annotations for AVS queries and judgments conducted in TRECVID from 2019 to 2023 [1], using the V3C1 and V3C2 collections [2]. Specifically, the TRECVID-AVS collection covers 89 queries, with video shots manually labeled as relevant (1), non-relevant (0), or not annotated (-1).</p> <p>We used approximately 2.6 million keyframes extracted from these video shots, mapping the annotations to the corresponding keyframes (note that there may not be a one-to-one correspondence between TRECVID shotID since multiple frames might be extracted from a single shot). Image representations are based on CLIP ViT-H/14 - LAION-2B features [3]. The timestamps of the keyframes and their CLIP features are sourced from the VISIONE repository [4].</p> <p>Given the incomplete nature of the TRECVID ground truth (where only a subset of video segments were judged per query), we focused on queries with at least 200 positive and 1400 negative annotations. This resulted in 80 datasets, each containing 1500 images—10% labeled as relevant and 90% as non-relevant.</p> <h3>Contents of the Repository:</h3> <ol> <li> <p><strong>Query-specific CSV Files:</strong> For each of the 80 selected AVS query (e.g., <code>1591</code>), the corresponding CSV file (e.g., <code>1591.csv</code>) contains a column for each image, where:</p> <ul> <li><strong>VISIONE image ID</strong> is in the first row.</li> <li><strong>CLIP features</strong> are in the subsequent rows.</li> <li><strong>Relevance annotations</strong> are in the last row: <code>1</code> for relevant, <code>0</code> for non-relevant.</li> </ul> </li> <li> <p><strong>Post-processed Datasets:</strong></p> <ul> <li><code>dataset_normalized.zip</code>: L2-normalized CLIP features.</li> <li><code>dataset_softmax.zip</code>: CLIP features converted into probabilities using a softmax function.</li> <li><code>dataset_logistic.zip</code>: CLIP features converted into probabilities using a logistic function followed by L1 normalization.</li> </ul> </li> <li> <p><strong>Text Feature Data:</strong> <code>clip_laion_text_features.csv</code> contains additional details for each query, including the query ID, query text, and L2 normalized CLIP features extracted from the query text.</p> </li> </ol> <h3>Citation and Usage:</h3> <p>This data was used in the experiments described in:</p> <p>Lucia Vadicamo, Francesca Scotti, Alan Dearle, Richard Connor, <em>Comparative Analysis of Relevance Feedback Techniques for Image Retrieval</em>, in Proceedings of the 31st International Conference on Multimedia Modeling (MMM 2025).</p> <p>The data is released under a Creative Commons Attribution license. If you use it in your research, please cite the above work. </p> <h3>References:</h3> <p>[1]TRECVID Data: <a href="https://www-nlpir.nist.gov/projects/trecvid/trecvid.data.html">https://www-nlpir.nist.gov/projects/trecvid/trecvid.data.html</a><br>[2] Rossetto, L., Schuldt, H., Awad, G., Butt, A.A.: V3C - A research video collection. <em>In: International Conference on Multimedia Modeling</em>, pp. 349–360. Springer (2019).<br>[3] https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K<br>[4] VISIONE Repository: <a href="https://zenodo.org/records/8188570">https://zenodo.org/records/8188570</a></p> </div> </div> </div> </div> </div> </div> <p> </p> <p> </p> <p> </p>
Learning How to Search: Generating Effective Test Cases Through Adaptive Fitness Function Selection
<p>Data Package for "Learning How to Search: Generating Effective Test Cases Through Adaptive Fitness Function Selection"</p> <p>This package contains data generated as part of our experiments on adaptive fitness function selection as part of unit test generation for Java systems.</p> <p>This paper is currently under submission. A draft of the paper is included in the data package.</p> <p>This package contains experimental data (in folder "experiment_data"), including goal attainment, fault detection, time per generation, and choices made by the reinforcement learning algorithm. In the folder "test_suites", the suites generated by each technique are included for each project. </p> <p>If you have questions, please contact Gregory Gay at greg@greggay.com.</p> <p>NOTE: A small number of items are currently missing from this data package and will be added shortly. Please make sure you have the latest version of this package.</p>
Dataset from: "Which search are you on? Adapting to color while searching for shape"
<p>Dataset for Bergmann, N., Tünnermann, J., & Schubö, A. (2020). Which search are you on? Adapting to color while searching for shape. <em>Attention, Perception, & Psychophysics, 82</em>, 457–477. https://doi.org/10.3758/s13414-019-01858-6</p>
FIG. 3 in On the distinction of Atractocarpus heterophyllus (Montrouz.) Guillaumin & Beauvis. and A. bracteatus Schltr. & K.Krause (Rubiaceae): in search of the correct name for the type species of Atractocarpus Schltr. & K.Krause
FIG. 3. — Atractocarpus heterophyllus (Montrouz.) Guillaumin & Beauvis.: A, view of the apex of a female individual in fruit; B, detail of stipules at branch apex; C, transversal section of a fruit; D, longitudinal section of a fruit; E, developed placenta at fruit stage, in dorsal and ventral views; F, longitudinal section of a seed; G, embryo. Scale bars: A-E, 3 cm; F, G, 2 mm. Based on MacKee 26464 (P) and unpublished N. Hallé's drawings of New Caledonian Rubiaceae in sched (P).
FIG. 2 in On the distinction of Atractocarpus heterophyllus (Montrouz.) Guillaumin & Beauvis. and A. bracteatus Schltr. & K.Krause (Rubiaceae): in search of the correct name for the type species of Atractocarpus Schltr. & K.Krause
FIG. 2. — Atractocarpus bracteatus Schltr. & K.Krause: A, female flower; B, longitudinal section of a female flower (corolla and stamens detached from hypanthium); C, detail of stipules at branch apex; D, entire fruit and longitudinal section of a fruit; E, transversal section of a fruit; F, entire seed and its longitudinal section; G, embryo. Scale bars: A-C, 1.5 cm; D, 6 cm; E, 1.5 cm; F, 5 mm; G, 2 mm. Based on Mouly 149 (P).
FIG. 1 in On the distinction of Atractocarpus heterophyllus (Montrouz.) Guillaumin & Beauvis. and A. bracteatus Schltr. & K.Krause (Rubiaceae): in search of the correct name for the type species of Atractocarpus Schltr. & K.Krause
FIG. 1. — Atractocarpus bracteatus Schltr. & K.Krause: A, view of a female individual in fruit; B, male inflorescence, and detail of a flower with ant; C, mature fruit. Scale bars: A, 10 cm; B, C, 1 cm. Based on pictures by Daniel & Irène Létocart (www.endemia.nc).
Figure 6 in In search of the true Tydeus (Acari, Tydeidae)
Figure 6. Outline of the coxal organ in Tydeus croceus (A), T. spathulatus (B), T. nieuwkerkeni (C), and T. cruciatus (D). Depending on the orientation or state of specimens, the outline may be deformed due to perspective (C) or difficult to see (D).
Figure 4 in In search of the true Tydeus (Acari, Tydeidae)
Figure 4. Striae density on the 3rd intercoxal area in Tydeus spathulatus (A), T. cruciatus (B) and T. spathulatus (C). NS(ep)536, 18, 32, respectively. Scale bar: 10 Mm.
Figure 2 in In search of the true Tydeus (Acari, Tydeidae)
Figure 2. The two types of genital striation pattern, extended (A, B) and compact (C, D). (A) Tydeus croceus; (B) T. cruciatus; (C) T. spathulatus; (D) T. nieuwkerkeni. Scale bar: 10 Mm.
Figure 1 in In search of the true Tydeus (Acari, Tydeidae)
Figure 1. Prodorsum of two specimens of Tydeus goetzi showing the two types of striae, simple (A) and double (B). In both specimens, the number of striae between setae (vi) amounts to 23. Note that the circular bases around the setae are not taken into account to estimate NS(vi).
Figure 12 in In search of the true Tydeus (Acari, Tydeidae)
Figure 12. Scanning electron micrograph of the apotele in a Tydeinae (species undetermined). Courtesy of University of Basel.
Figure 5 in In search of the true Tydeus (Acari, Tydeidae)
Figure 5. Shape of terminal dorsal setae, serrate (A), smooth (B), spatulate (C), and lanceolate (D, E). (A) Tydeus croceus; (B) T. cruciatus; (C) T. nieuwkerkeni; (D, E) T. spathulatus. Scale bar: 10 Mm.
Figure 3 in In search of the true Tydeus (Acari, Tydeidae)
Figure 3. Striae density on prodorsum in Tydeus croceus (A), T. cruciatus (B), T. nieuwkerkeni (C) and T. spathulatus (D). NS(vi)528, 22, 27 and 26, respectively. Scale bar: 10 Mm.
Figure 11. Tydeus nieuwkerkeni n in In search of the true Tydeus (Acari, Tydeidae)
Figure 11. Tydeus nieuwkerkeni n. sp. The three spatulate terminal dorsal setae (A), serrate seta of the opisthosoma (B), dorsal setae of genu II (C) and III (D). Scale bar: 10 Mm.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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