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Text data for keyboard layout evaluation
<div> <h1>Text data</h1> <a href="https://github.com/binarybottle/text_data#text-data"></a></div> <table> <tbody><tr> <th>Text source</th> <th>Information</th> </tr> </tbody><tbody> <tr> <td>"Alice in Wonderland"</td> <td>Alice in Wonderland (Ch.1)</td> </tr> <tr> <td>"Romeo and Juliet"</td> <td><a href="https://www.fulltextarchive.com/page/Romeo-and-Juliet1/" rel="nofollow">Romeo and Juliet</a></td> </tr> <tr> <td>"Bhagavad Gita"</td> <td><a href="https://www.gutenberg.org/files/2388/2388-h/2388-h.htm" rel="nofollow">Bhagavad Gita</a></td> </tr> <tr> <td>"Memento screenplay"</td> <td><a href="https://www.dailyscript.com/scripts/memento.html" rel="nofollow">Memento screenplay</a></td> </tr> <tr> <td>"100K tweets"</td> <td>100,000 tweets from: <a href="https://data.world/data-society/twitter-user-data" rel="nofollow">Sentiment140 dataset</a> training data</td> </tr> <tr> <td>"20K tweets"</td> <td>20,000 tweets from <a href="https://www.kaggle.com/crowdflower/twitter-user-gender-classification" rel="nofollow">Gender Classifier Data</a></td> </tr> <tr> <td>"MASC tweets"</td> <td><a href="http://www.anc.org/data/masc/corpus/" rel="nofollow">MASC</a> tweets (cleaned of html markup)</td> </tr> <tr> <td>"MASC spoken"</td> <td><a href="http://www.anc.org/data/masc/corpus/" rel="nofollow">MASC</a> spoken transcripts (phone and face-to-face: 25,783 words)</td> </tr> <tr> <td>"COCA blogs"</td> <td><a href="https://www.english-corpora.org/coca/" rel="nofollow">Corpus of Contemporary American English</a> <a href="https://www.corpusdata.org/" rel="nofollow">blog samples</a></td> </tr> <tr> <td>"Google website"</td> <td><a href="https://google.com/" rel="nofollow">Google homepage</a> (accessed 10/20/2020)</td> </tr> <tr> <td>"Software languages"</td> <td>"Tower of Hanoi" (programming languages A-Z from <a href="https://rosettacode.org/wiki/Towers_of_Hanoi" rel="nofollow">Rosetta Code</a>)</td> </tr> <tr> <td>"Monkey text"</td> <td>Ian Douglas's English-generated <a href="../record/4642460" rel="nofollow">monkey0-7.txt corpus</a></td> </tr> <tr> <td>"Coder text"</td> <td>Ian Douglas's software-generated <a href="../record/4642460" rel="nofollow">coder0-7.txt corpus</a></td> </tr> <tr> <td>"iweb cleaned corpus"</td> <td>First 150,000 lines of Shai Coleman's <a href="https://colemak.com/pub/corpus/iweb-corpus-samples-cleaned.txt.xz" rel="nofollow">iweb-corpus-samples-cleaned.txt</a></td> </tr> </tbody> </table> <p>Reference for Monkey and Coder texts: Douglas, Ian. (2021, March 28). Keyboard Layout Analysis: Creating the Corpus, Bigram Chains, and Shakespeare's Monkeys (Version 1.0.0). Zenodo. <a href="http://doi.org/10.5281/zenodo.4642460" rel="nofollow">http://doi.org/10.5281/zenodo.4642460</a></p>
Fig. 1 in First Japanese Records of the Indo-Pacific Scorpionfish (Scorpaenidae) Scorpaenodes corallinus, with a Re-evaluation of Coronal Spines as a Diagnostic Character
Fig. 1. Fresh specimen of Scorpaenodes corallinus (KAUM–I. 58534, 80.0 mm SL, off Tomori, Yoron Island, Amami Islands, Japan).
Fig. 2 in First Japanese Records of the Indo-Pacific Scorpionfish (Scorpaenidae) Scorpaenodes corallinus, with a Re-evaluation of Coronal Spines as a Diagnostic Character
Fig. 2. Underwater photograph of Scorpaenodes corallinus from Japan (KPM-NR 80574, Unanzaki, Aka Island, Kerama Islands, 18 m depth, 26 October 2001, taken by A. Moriyama).
Supplementary Material—Evaluating tactile feedback in addition to kinesthetic feedback for haptic shape rendering: a pilot study
<p>Supplementary Material of the journal article: "<a href="https://www.frontiersin.org/articles/10.3389/frobt.2024.1298537/abstract">Evaluating tactile feedback in addition to kinesthetic feedback for Haptic Shape rendering: a pilot study</a>": DOI: <a href="https://doi.org/10.3389/frobt.2024.1298537">10.3389/frobt.2024.1298537</a></p> <p> </p>
Train and Evaluation Code, Road Classification Models and Test set of the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification"
<p>This repository contains the Python scripts built for training and evaluation of the implementation, together with the test data and the resulting road classification models corresponding to the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification". The scripts make use of the Tensorflow with Keras framework and the additional required dependencies.</p> <p>The training and validation set is based on the binary SROADEX dataset (https://zenodo.org/records/6482346) that was re-split into tiles that feature the image resolutions (256 x 256, 512 x 512, and 1024 x 1024 pixels) and image overlaps (0% and 12.5%) considered in this study. The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography that represents the axes of the different types of roads (urban, interurban and rural). This binary road data contains information from 16 full orthoimages (28.5 km * 18.5 km) with spatial resolution of 0.5 m/pixel from the insular and peninsular Spanish territory. Due to the size on disk of approximately 546 gigabytes, this training and validation data is only available upon request from the corresponding author. The test set has been generated from a novel area of 28.5 km * 18.5 km and features binary road labels. The test sets are provided in the repository for each resolution (with no overlap), so that additional DL models can be evaluated on the same data and compared with the results achieved in this study.</p> <p>The structure of the information shared in this repository is as follows:<br>The scripts have been grouped by tile resolution (256, 512 and 1024). First, the test set and the evaluation script can be found. For each tile resolution, there are two subfolders (corresponding to the "no overlap" and "12.5% overlap"). In each case, the Python scripts for training the models in the three repetitions are shared, and the trained models (H5 format) are shared in compressed form. Finally, for each resolution we also share the testing dataset which consists of two folders.</p> <p>The material is distributed under a CC-BY 4.0 license.</p>
Evaluation of JavaScript Instrumentation by OpenTelemetry visualized in ExplorViz
<p>This dataset represents the results of a survey done with a small group, consisting mostly of experts in the research tool ExplorViz. The survey focused on evaluating whether the data that stem from automatic instrumentation by OpenTelemetry of JavaScript applications does benefit software comprehension. </p>
OneNet - Polish Demo KPI evaluation
<p>The file contains all datasets and KPI evaluation related to OneNet project for Polish Demonstrator. </p>
Figure 10 in A new Northeast Asian Lynceus (Crustacea: Branchiopoda: Laevicaudata) with uniquely modified thoracopods and an evaluation of DNA barcoding for clam shrimp species identification
Figure 10. Genetic distances and DNA barcoding gaps in clam shrimps. A. Stacked histogram of p-distances (yellow) and K2P genetic distances (blue) between pairs of COX1 sequences available in GenBank (accession date: 11/11/2018) for every suborder of clam shrimp (Laevicaudata, Spinicaudata, and Cyclestherida). The presence of a DNA barcoding gap is highlighted with a dashed vertical line. B. Saturation plot showing the strong correlation between p-distances and corrected genetic distances (Kimura 2-parameter).
Figure 9. Lynceus grossipedia n in A new Northeast Asian Lynceus (Crustacea: Branchiopoda: Laevicaudata) with uniquely modified thoracopods and an evaluation of DNA barcoding for clam shrimp species identification
Figure 9. Lynceus grossipedia n. sp., male, light microscopy of right side thoracopods (unmodified) (paratype, NHMD-616086). A. Thoracopods I (male clasper) and II, seen from posterior. B. Higher magnification of clasper. C–K. Thoracopods III–X, seen from anterior; all of "regular" Lynceus type (= unmodified). Arrows indicate broken/hidden parts.
Figure 8. Lynceus grossipedia n in A new Northeast Asian Lynceus (Crustacea: Branchiopoda: Laevicaudata) with uniquely modified thoracopods and an evaluation of DNA barcoding for clam shrimp species identification
Figure 8. Lynceus grossipedia n. sp., male, light microscopy of left side thoracopods, seen from anterior (paratype, NHMD-616086). A. Thoracopod I (male clasper). B. Higher magnification of clasper. C–K. Thoracopods II–X of which III–VI (D–G) are modified, not least with notably enlarged muscular bases. F. Thoracopod V with endopod and distal part of exopod explanate. G. Thoracopod VI with exopod proximal part with ~7 knob-shaped processes and distal part highly setose (seen better on SEM images, Fig. 7H), endites 4–5 and endopod non-visible as folded behind thoracopod. Arrows indicate broken/hidden parts.
Figure 4. Lynceus grossipedia n in A new Northeast Asian Lynceus (Crustacea: Branchiopoda: Laevicaudata) with uniquely modified thoracopods and an evaluation of DNA barcoding for clam shrimp species identification
Figure 4. Lynceus grossipedia n. sp., male, scanning electron microscopy (paratype, NHMD-616086). A. Left lateral view, carapace valve removed. B. Frontal setal fields. C. Left ventrolateral view. D. Left second antenna. E. Head, lateral view. F. Head, anterior view. G. Head, anteroventral view. H. Dorsal organ. I. Telson, dorsal view. J. Telson, ventral view. K. First antennae. L. Left mandible, lateral view. M. Mandible close-up, dorsolateral view. N. Mandible close-up, dorsal view.
Figure 7. Lynceus grossipedia n in A new Northeast Asian Lynceus (Crustacea: Branchiopoda: Laevicaudata) with uniquely modified thoracopods and an evaluation of DNA barcoding for clam shrimp species identification
Figure 7. Lynceus grossipedia n. sp., male, scanning electron microscopy of modified thoracopods, broad muscular bases highlighted by coloring (same specimen as on Figs. 4, 6; paratype, NHMD-616086). A. Dorsolateral view of thoracopods, broad muscular bases of thoracopods III–VI colored. B. Modified exopods of thoracopod V and VI. C–H: all of thoracopod VI. C. Thoracopod VI in posterolateral view, note exopod with ~7 knob-shaped processes. D. Thoracopod VI in posterior view with endites, endopod and exopod labelled. E. Exopod, knob-shaped processes and posterior surface with long setae (magnification of D). F. Endopod, broadly lobiform (magnification of D). G. Setae of endopod (magnification of F). Distal part of exopod densely setose.
Figure 1 in A new Northeast Asian Lynceus (Crustacea: Branchiopoda: Laevicaudata) with uniquely modified thoracopods and an evaluation of DNA barcoding for clam shrimp species identification
Figure 1. Line drawings of Lynceus grossipedia n. sp., A–G: male, H–M: female. A. Head, anterior view. B. Head, left lateral view. C. Carapace, left lateral view. D. Left clasper, posterior view. E. Right clasper, anterior view. F. Opercular lamella, ventral view. G. Telson, left lateral view. H. Telson, ventral view. I. Telson, dorsal view. J. Head, left lateral view. K. Head, anterior view. L. Carapace, left lateral view. M. Lamina abdominalis, left lateral view. Scale bars 1 mm.
Figure 3. Lynceus grossipedia n in A new Northeast Asian Lynceus (Crustacea: Branchiopoda: Laevicaudata) with uniquely modified thoracopods and an evaluation of DNA barcoding for clam shrimp species identification
Figure 3. Lynceus grossipedia n. sp., female, stereo microscopy (allotype, NHMD-616085). A. Right lateral view, intact animal. B. Right lateral view, carapace valve removed. C. Ventral view, carapace valves slightly apart. D. Dorsal view. E. Head close-up (from B). F. Telson close-up (from B). G. Carapace, right valve interior.
Figure 6. Lynceus grossipedia n in A new Northeast Asian Lynceus (Crustacea: Branchiopoda: Laevicaudata) with uniquely modified thoracopods and an evaluation of DNA barcoding for clam shrimp species identification
Figure 6. Lynceus grossipedia n. sp., male, scanning electron microscopy of claspers (same specimen as on Figs. 4, 7; paratype, NHMD-616086). A. Right clasper, anterior view. B. Left clasper, posterior view. C. Movable finger (endopod), large palp (endite 5), and gripping area (part of palm, endite 3), left clasper, apical view. D. Movable finger with band of minute setae, gripping area with type 4 setae, left clasper. E. Small palp (endite 4) and large palp (endite 5), right clasper. F. Palm with scales (encircled area in B), lateral view, right clasper. G. Setation on movable finger (endopod). H. Setation on posterior side of gripping area. I. Magnification of scales on clasper palm.
Figure 2. Lynceus grossipedia n in A new Northeast Asian Lynceus (Crustacea: Branchiopoda: Laevicaudata) with uniquely modified thoracopods and an evaluation of DNA barcoding for clam shrimp species identification
Figure 2. Lynceus grossipedia n. sp., male, stereo microscopy (holotype, NHMD-615874). A. Left lateral view, intact animal. B. Left lateral view, carapace valve removed. C. Ventral view, carapace valves apart. D. Dorsal view, carapace valves slightly apart. E. Head close-up (from B). F. Left side thoracopods, showing enlarged muscular bases. G. Carapace, left valve interior.
Figure 5. Lynceus grossipedia n in A new Northeast Asian Lynceus (Crustacea: Branchiopoda: Laevicaudata) with uniquely modified thoracopods and an evaluation of DNA barcoding for clam shrimp species identification
Figure 5. Lynceus grossipedia n. sp., female, scanning electron microscopy (paratype, NHMD-616086). A. Left lateral view, carapace valve removed. B. Frontal setal fields. C. Left ventrolateral view. D. Left second antenna. E. Head, lateral view. F. Head, anterior view. G. Head, anteroventral view. H. Dorsal organ. I. Telson and lamina abdominalis, dorsolateral view. J. Egg-carrying exopods of thoracopods IX and X. K. Distal part of right first antenna. L. Serration of rostral distal margin, lateral view (gap at edge likely due to drying artifact). M. Left mandible, lateral view. N. Left mandible, dorsal view. O. Mandible close-up (from N).
User participation in digital accessibility evaluations: reviewing methods and objectives
<p><span>Although laws and standardization bodies promote user participation in digital accessibility evaluations, people with disabilities still consider themselves excluded from this process. One reason could be the lack of systematized knowledge about evaluation methods involving users. This article seeks to understand how and for what purpose digital accessibility evaluations with user participation were conducted in the scientific literature from 2018 to 2021. Three types of user participation emerged: 1) user-based usability testing to evaluate task accomplishment, user reactions and interface qualities; 2) interviewing users to assess the local and social factors impacting digital service accessibility; 3) using questionnaires or crowdsourcing to check the compliance of certain interfaces with accessibility standards. Participants are primarily chosen based on their functional impairments and, to a lesser degree, their project-related skills, biographical information, technology habits, among other criteria. The comprehensive user insights gained with these methods are judged to be positive whereas the lack of representativeness of the selected user samples is found to be regrettable. The article finally discusses the definitions of accessibility and disability that underpin these methodologies.</span></p>
Raw data for Figures 2-4 for journal article: "Experimental Evaluation of the Adhear, a Novel Transcutaneous Bone Conduction Hearing Aid""
<p>This is a data set containing the raw data for figures 2-4 from the journal article:</p> <p>"Experimental Evaluation of the Adhear, a Novel Transcutaneous Bone Conduction Hearing Aid"</p> <p>Original article DOI: 10.1055/a-1308-3888</p> <p>Original article link: https://pubmed.ncbi.nlm.nih.gov/33260222/</p> <p> </p> <p>The data is contained within MATLAB figure (.fig) files, all saved with MATLAB version R2020a.</p> <p> </p>
Research Data for Comparative Evaluation of RT-PCR and Antigen-based Rapid Diagnostic Tests (Ag-RDTs) for SARS-CoV-2 Detection: Performance, Variant Specificity, and Clinical Implications
<p>This dataset represents laboratory findings for the comparative evaluation of the diagnostic performance of Ag-RDTs (Flourescence Immunoassay and Lateral Flow Immunoassay) with RT-PCR</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.