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4,578 results for “Assistance”
Figure 5A-C in New DNA-assisted records of water mites from Sardinia, with the description of a new species (Acari, Hydrachnidia)
Figure 5A-C. Panisus torrenticolus group, ♀ [CCDB_39399_B10], ITALY, Sardinia, Comune di Fluminimaggiore, Sorgente Pubusinu, It 2022-1c: A – photograph of dorsum; B – dorsocentralia; C – genital field. Scale bar = 100 μm.
Figure 4A-C in New DNA-assisted records of water mites from Sardinia, with the description of a new species (Acari, Hydrachnidia)
Figure 4A-C. Photographs of sampling sites. ITALY, Sardinia, Comune di Buggerru, It 2022-4, Spiaggia di San Nicolò, outflow of the contained Sorgente San Nicolò. A – overview of the Spiaggia di San Nicolò, in the center of the image the bridge of the SP 83 is visible (photo 12. August 2022). B – It 2022-4 with provisorily footpath (photo 12. August 2022). C – It 2022-4 aquatic and emersed plants at sample site (photo 12. August 2022).
Figure 3A-B in New DNA-assisted records of water mites from Sardinia, with the description of a new species (Acari, Hydrachnidia)
Figure 3A-B. Photographs of sampling sites. ITALY, Sardinia, Comune di Fluminimaggiore, It 2022-2, outflow of Sorgente Pubusinu. A – It 2022-2a, b spring stream (photo 7. August 2022); B – It 2022-2a detail of the moss carpet (photo 7. August 2022).
Dataset for "Laser-Assisted Etching of EagleXG Glass by Irradiation at Low Pulse-Repetition Rate"
<div>This dataset contains the raw data at the basis of the graphs and pictures reported in the paper:</div> <div>"Laser-assisted etching of EagleXG glass by irradiation at low pulse-repetition rate"</div> <div>by Roberto Memeo, Mattia Bertaso, Roberto Osellame, Francesca Bragheri and Andrea Crespi.</div> <div> </div> <div>This paper is published as:</div> <div>Appl. Sci. 2022, 12(3), 948. https://doi.org/10.3390/app12030948</div> <div> </div> <div>Each folder refers to the corresponding picture in the published paper and contains the .pdf file of the picture itself and the .csv file of the raw data for the graphs, if present.</div>
Supplemental material for: Software System Testing assisted by Large Language Models: An Exploratory Study
<p>This is the supplemental material of the paper titled as “Software System Testing Assisted by Large Language Models: An Exploratory Study” presented at the 36th International Conference on Testing Software and Systems.</p> <p>It contains the raw execution data generated by both models, GPT-4o and GPT-4omini, during the exploratory study. The supplementary material includes the following files:</p> <ul> <li><em>GPT-4ominiRQ1-2ExecutionData.zip</em>: contains the JSON outputs from the OpenAI API for the GPT-4o mini model. Each output is labeled according to the research question number and the corresponding timestamp (for RQ1) or the requested test case (for RQ2), all provided in plain text format.</li> <li><em>GPT-4oRQ1-2ExecutionData.zip</em>: contains the JSON outputs from the OpenAI API for the GPT-4o model. Like the previous file, each output is named in plain text format based on the research question number and timestamp (for RQ1) or the requested test case (for RQ2).</li> </ul> <p>To cite this work: </p> <p>C. Augusto, J. Morán, A. Bertolino, C. de la Riva and J. Tuya, “S<em>oftware System Testing assisted by Large Language Models: An Exploratory Study</em>”, in <em>Testing Software and Systems</em> (pp. 239–255). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-80889-0_17</p>
Figure 4 in Introgression of bacterial leaf blight (BLB) resistant gene, Xa7 into MARDI elite variety, MR219 by marker assisted backcrossing (MABC) approach
Figure 4. PCR-based genetic polymorphism analysis of Xa7 gene closed linked, ID7 marker. Agarose gel profile of backcrossed progenies in 2% (w/v) of 1X TBE agarose gel. Lane L: 100bp DNA ladder. Lanes D and R represent IRBB7 and MR219 respectively. Line 1-22: (1) PB-2-91, (2) PB-2-107, (3) PB-2-156, (4) PB-2-224, (5) PB-2-234, (6) PB-2-238, (7) PB-2-258, (8) PB-2-29, (9) PB-2-77, (10) PB-2-226, (11) PC3-26-2, (12) PC-39-3, (13) PB-2-34, (14) PB-2-35, (15) PB-2-150, (16) PB-2-223, (17) PB-2-252, (18) PC-3-14-3, (19) PC-3-23-1, (20) PB-2-32, (21) PC3-5-2, (22) MR263.
Figure 3 in Introgression of bacterial leaf blight (BLB) resistant gene, Xa7 into MARDI elite variety, MR219 by marker assisted backcrossing (MABC) approach
Figure 3. Differential response of parents. (A) MR263, (B) CL2, (C) IRBB7 and (D) MR219, respectively after being inoculated with Xanthomonas oryzae pv. oryzae at 14 DAI.
Figure 2 in Introgression of bacterial leaf blight (BLB) resistant gene, Xa7 into MARDI elite variety, MR219 by marker assisted backcrossing (MABC) approach
Figure 2. Bacterial leaf blight disease progression recorded on four potential parents; IRBB7 (blue), MR219 (orange), CL2 (gray) and MR219 (yellow) from 3 to 30 days after inoculation.
Raw data for Microwave-assisted Condensation Approach for Vanadium Silicate Microspheres and Their Catalytic Activity in Cyclohexene Epoxidation and Ethyl Lactate Oxidation
<p><strong>Specification of affiliations:</strong></p> <ul> <li>David Skoda - Centre of Polymer Systems</li> <li>Kamila Kuzelova - Centre of Polymer Systems</li> <li>Rajendran Blessy Pricilla - Centre of Polymer Systems</li> <li>Barbora Hanulikova - Centre of Polymer Systems</li> <li>Michal Urbanek - Centre of Polymer Systems</li> <li>Ales Styskalik - Department of Chemistry, Faculty of Science</li> <li>Tomas Pokorny - Department of Chemistry, Faculty of Science</li> <li>Iaroslav Doroshenko - Department of Chemistry, Faculty of Science</li> <li>Lucie Simonikova - Department of Chemistry, Faculty of Science</li> <li>Ivo Kuritka - Centre of Polymer Systems</li> </ul> <p> </p> <p>Raw data for the research paper. Information on the data collection are described in the manuscript.</p>
Research Data for the Journal Article: Efficient DMF-assisted synthesis of formamides from amines using CO2 catalyzed by heterogeneous metal-free imidazolium-hypercrosslinked polymers
Open the record for dataset details and reuse information.
Datasets for Manuscript: Neural-Network-Assisted Detection of Superconducting Topological Semimetals
<p>This file contains datasets for an original machine-learning-approach that we developed for the identification of superconducting topological semimetals.</p>
Example data for using napari-pyclesperanto-assistant
<p>CalibZAPWfixed_000154_max.tif</p> <p>* Maximum projection of a part of a Drosophila melanogaster embryo showing cell divisions, marked with histone-GFP. Imaged using lightsheet microscopy in Gene Myers lab, MPI-CBG / CSBD Dresden</p> <p>CLIJ_benchmarking_000350.raw.tif</p> <p>* 3D Volume of a Drosophila melanogaster during gastrulation marked histone-GFP. Imaged using lightsheet microscopy in Gene Myers lab, MPI-CBG / CSBD Dresden. This dataset was earlier published <a href="https://bds.mpi-cbg.de/CLIJ_benchmarking_data/">https://bds.mpi-cbg.de/CLIJ_benchmarking_data</a><br> <br> EM_C_6_c0.tif2</p> <p>* Arabidopsis ovule primordium</p> <p>* This file has been resaved to TIF</p> <p>* It originates from https://datadryad.org/stash/dataset/doi:10.5061/dryad.02v6wwq2c It was originally available under CC0 Public Domain by Baroux, Célia, University of Zurich, Mendocilla-Sato, Ethel, University of Zurich, Autran, Daphné, IRD Montpellier<br> <br> Haase_MRT_tfl3d1.tif</p> <p>* This MRI dataset of the author was acquired at University Hospital Carl Gustav Carus of the University of Technology, TU Dresden as part of academic training of students in the Department of Radiology<br> <br> tissue.tif, tissue_measurements.tif</p> <p>* Simulated tissue + measurement. Code provided for reporducibility:</p> <p>import pyclesperanto_prototype as cle<br> import numpy as np<br> import matplotlib<br> from numpy.random import random</p> <p>cle.select_device("RTX")</p> <p># Generate artificial cells as test data<br> tissue = cle.artificial_tissue_2d()</p> <p># fill it with random measurements<br> values = random([int(cle.maximum_of_all_pixels(tissue))])<br> for i, y in enumerate(values):<br> if (i != 95):<br> values[i] = values[i] * 10 + 45<br> else:<br> values[i] = values[i] * 10 + 90</p> <p>measurements = cle.push(np.asarray([values]))</p> <p># visualize measurements in space<br> tissue_measurements = cle.replace_intensities(tissue, measurements)</p>
Explanation of Plate I. Figure 1.—Left tibia of Ornithomimus velox, Marsh; A, front view; b, distal end; c, transverse section. Figure 2.—Left metatarsals of same specimen; A, front view; b, proximal ends; c, transverse section; d, distal ends. Figure 3.—Phalanges of second digit of same foot; front view, a, first phalange; b, second phalange; c, third, or terminal phalange. Figure 4.—Left metacarpals of same species, perhaps of smaller individual; front view. Figure 5.—Left tibia of young Ostrich (Struthio camelus, Linn.); a, front view; b, distal end. The separate calcaneum was first observed by the writer's assistant, Dr. G-. Baur, who prepared the specimen. Figure 6.—Left metatarsals of young turkey (Meleagris gallipavo, Linn.); a, front view; b, proximal ends. a, astragalus; as, ascending process of astragalus; c, calcaneum; f, fibula; f' face for fibula; II, second metatarsal; III, third metatarsal; iv, fourth metatarsal. Figures 1-4 are one-third natural size, and figures 5 and 6, one-half natural size. in Description of new dinosaurian reptiles
Explanation of Plate I. Figure 1.—Left tibia of Ornithomimus velox, Marsh; A, front view; b, distal end; c, transverse section. Figure 2.—Left metatarsals of same specimen; A, front view; b, proximal ends; c, transverse section; d, distal ends. Figure 3.—Phalanges of second digit of same foot; front view, a, first phalange; b, second phalange; c, third, or terminal phalange. Figure 4.—Left metacarpals of same species, perhaps of smaller individual; front view. Figure 5.—Left tibia of young Ostrich (Struthio camelus, Linn.); a, front view; b, distal end. The separate calcaneum was first observed by the writer's assistant, Dr. G-. Baur, who prepared the specimen. Figure 6.—Left metatarsals of young turkey (Meleagris gallipavo, Linn.); a, front view; b, proximal ends. a, astragalus; as, ascending process of astragalus; c, calcaneum; f, fibula; f' face for fibula; II, second metatarsal; III, third metatarsal; iv, fourth metatarsal. Figures 1-4 are one-third natural size, and figures 5 and 6, one-half natural size.
Activity of a freshwater turtle varies across a latitudinal gradient: implications for the success of assisted colonisation
<p>The value of assisted colonisation as a response to climate change can only be realised if focal species are well suited to their new habitats. For ectotherms, new habitats must offer microclimates that promote crucial behaviours such as thermoregulation and foraging.</p> <p>The Western Swamp Turtle (Pseudemydura umbrina), a Critically Endangered species from southwestern Australia, serves as a global case-study of assisted colonisation in action. Initial trials where juvenile P. umbrina were released into wetter and cooler climates found that individuals spent considerable time at body temperatures that apparently limited their growth.</p> <p>Using high-resolution biologging data (temperature and depth), here we tested if turtle activity is thermally constrained in cooler latitudes by releasing 48 juveniles into seasonal swamps at three sites. One site was core natural habitat, and the other sites were wetlands 380 km apart that offered either warmer or cooler microclimates. Generalised additive mixed models were used to evaluate behaviours and time spent at optimal temperatures for approximately one month following release, and growth rates were measured and analysed after release until the end of the hydroperiod 4-5 months later.</p> <p>We found that turtles released into the most poleward (southern) wetland spent significantly less time active and basking and grew significantly less compared to turtles released further north. When analysed together, behavioural and growth datasets showed that activity was positively correlated with growth rates.</p> <p>We conclude that poor growth of turtles in the southern wetland was likely a result of lower body temperatures, stemming from a reduced ability to thermoregulate in water. Consequently, for assisted colonisation of P. umbrina to be successful, recipient wetlands must offer aquatic microclimates that are sufficiently warm to promote foraging activity that leads to growth, and ultimately to maturation.</p>
Dataset of Program Source Codes Solving Unique Programming Exercises Generated by Digital Teaching Assistant
<p>The programming exercises were automatically generated by the Digital Teaching Assistant (DTA) system that automates a massive Python programming course at MIREA – Russian Technological University (RTU MIREA). Source codes of the small programs grouped by the type of the solved task can be used for benchmarking source code classification and clustering algorithms. Moreover, the data can be used for training intelligent program synthesizers, or benchmarking mutation testing frameworks, and more applications are yet to be discovered. This dataset is a supplementary material for a paper entitled <a href="https://doi.org/10.3390/data8060109"><strong>Dataset of Program Source Codes Solving Unique Programming Exercises Generated by Digital Teaching Assistant</strong></a> submitted to the <strong>MDPI Data</strong> journal.</p>
Time-Resolved Plasmon-Assisted Generation of Arbitrary Optical-Vortex Pulses - Supporting Information for Trajectories
<p>We provide videos of trajectories for a test charge, bound by Lennard-Jones potential. First video, titled "PW Trajectory - Point 2 " refers to the particle under the effect of a plane wave pulse. The other video , titled "Emitter Trajectory - Point 2" refers to the particle affected by an orbital angular momentum carrying pulse.</p> <p>These videos are supplementary materials for the article titled "Time-Resolved Plasmon-Assisted Generation of Arbitrary Optical-Vortex Pulses".</p>
Thermal evaporation as sample preparation for silver‐assisted laser desorption/ionization mass spectrometry imaging of cholesterol in amyloid tissues
<p><strong>Thermal evaporation as sample preparation </strong><strong>for </strong><strong>silver‐assisted laser desorption/ionization mass spectrometry imaging of cholesterol in amyloid tissues</strong></p> <p>MSI datasets in SCiLS Lab SL File (*.sl) or as flexImaging sequence (*.mis)</p>
Dataset for "Computer vision assisted decomposition analysis of atom probe tomography data"
<p>Dataset for the article "Computer vision assisted decomposition analysis of atom probe tomography data". APT measurements were performed by Marcus Hans at Materials Chemistry (RWTH Aachen University) using a CAMECA LEAP 4000X HR. Training data was created by Janis A. Sälker.</p> <p>Content:</p> <p>- 13 (V,Al)N and 3 (Ti,Al)N APT reconstructions (.epos file format) and the corresponding range file (.rrng file format).</p> <p>- Training data (images & masks) for 9 labeled (V,Al)N APT samples (h5 file format). Image data with key "image" of shape (2, number_of_slices, 608, 192), where 2 corresponds to the V- and Al-contribution/channel and 608/192 to the height/width of the images. Masks/labels with key "label" of shape (number_of_slices, 608, 192)</p> <p> </p>
TAM survey questionnaire responses of a head-mounted assistive mouse controller for people with upper limb disability
<p>This dataset contains TAM survey questionnaire and the corresponding responses of a head-mounted assistive mouse controller for people with upper limb disability.</p>
ASSIST-IoT Multimodal Fall Detection Dataset
<p>Multimodal dataset for fall detection. Includes acceleration data collected from a tag and two smartwatches, and location reported by the tag. More details about the data collection procedure can be found in <code>notes.md</code>.</p> <p><strong>Contents</strong></p> <p>The repository contains:</p> <ul> <li><code>data/location_data.csv</code> and <code>data/full_acceleration</code> – preprocessed acceleration and location data from 10 participants and mannequin simulated falls with target variable identified</li> <li><code>data/subsampled_acceleration_data.csv</code> – subsampled acceleration dataset used for training the AI model</li> <li><code>notes.md</code> – description of activities performed and notes from data collection</li> <li><code>videos</code> – reference videos for performed activities</li> </ul> <p><strong>Authors</strong></p> <ul> <li><a href="https://orcid.org/0000-0002-2543-9461">Piotr Sowiński</a> – research methodology, data collection and processing</li> <li><a href="https://orcid.org/0000-0003-3217-1050">Monika Kobus</a> – research methodology, data collection</li> <li><a href="https://orcid.org/0000-0003-4295-3005">Anna Dąbrowska</a> – research methodology, methodological supervision</li> <li><a href="https://orcid.org/0000-0003-1524-7877">Kajetan Rachwał</a> – data collection</li> <li><a href="https://orcid.org/0000-0002-7109-891X">Karolina Bogacka</a> – research methodology</li> <li><a href="https://orcid.org/0000-0002-9572-2705">Krzysztof Baszczyński</a> – research methodology, data collection</li> <li><a href="https://orcid.org/0000-0002-3080-0303">Anastasiya Danilenka</a> – research methodology, data collection and processing</li> </ul> <p><strong>Acknowledgements</strong></p> <p>This work is part of the <a href="https://assist-iot.eu/">ASSIST-IoT project</a> that has received funding from the EU’s Horizon 2020 research and innovation programme under grant agreement No 957258.</p> <p>The <a href="https://www.ciop.pl/en">Central Institute for Labour Protection – National Research Institute</a> provided facilities and equipment for data collection.</p> <p><strong>License</strong></p> <p>The dataset is licensed under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p>
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.