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831 results for “automaticity”
Exercise Addiction: The Dark Side of Automatic Reactions Toward Exercise?
<p>Dataset related to the project Exercise addiction and automatic reactions toward exercise</p> <p>This dataset includes:</p> <p>- the behavioral data for the study 1 "dpt_behavioral_both_sample.RData"</p> <p>- the exe-tracker data for the study 1 "dpt_both_sample_EyeTrack"</p> <p>- the R script for the analyses of the study 1</p> <p>- the approach-avoidance data for study 2 "Addict_all_MT_long.RData"</p> <p>- the R script for the analyses of the approach-avoidance data for study 2 "Script_analyses_MT_zenodo.R"</p> <p>- the implicit attitudes data for study 2 "Addict_all_IAT_long.RData"</p> <p>- the R script for the analyses of the implicit attitudes data for study 2 "Script_analyses_IAT_zenodo.R"</p>
Chemical Name Extraction Based on Automatic Training Data Generation
<p>The automation of extracting chemical names from text has significant value to biomedical and life science research. A major barrier in this task is the difficulty of getting a sizable and good quality data to train a reliable entity extraction model. Another difficulty is the selection of informative features of chemical names, since comprehensive domain knowledge on chemistry nomenclature is required. Leveraging random text generation techniques, we explore the idea of automatically creating training sets for the task of chemical name extraction. Assuming the availability of an incomplete list of chemical names, called a dictionary, we are able to generate well-controlled, random, yet realistic chemical-like training documents. We statistically analyze the construction of chemical names based on the incomplete dictionary, and propose a series of new features, without relying on any domain knowledge. Compared to state-of-the-art models learned from manually labeled data and domain knowledge, our solution shows better or comparable results in annotating real-world data with less human effort. Moreover, we report an interesting observation about the language for chemical names. That is, both the structural and semantic components of chemical names follow a Zipfian distribution, which resembles many natural languages.</p>
FIGURE 1 in Spreadsheets to expedite taxonomic publications by automatic generation of morphological descriptions and specimen lists
FIGURE 1. Chart exemplifying the use of the three spreadsheets presented here. Inputs in red, outputs in green.
Manuscript data for "Iroki: automatic customization and visualization of phylogenetic trees"
<p>Manuscript data for "Iroki: automatic customization and visualization of phylogenetic trees"</p>
Identifying continuing education resources on new data standards and technologies through an automatic crawling system
<p><span>This presentation discusses part of a project that aims to create a continuing education repository for information professionals. The presentation covers: the current challenges that information professionals face; issues in accessing continuing education resources; the design of the project and in particular its automatic crawler; and the results.</span></p>
Automatically deglitched dataset for "Searching the InSight seismic data for Mars's background free oscillations"
<p>The file Deglitched_sols.zip includes the dataset employed in the article "Searching the InSight seismic data for Mars's background free oscillations", published in<em> Seismological Research Letters.</em></p> <p>It includes 966 ".mseed" files, where each file corresponds to each automatically deglitched (SEISglitch + Twistpy) sol employed in the analysis. </p> <p>This dataset corresponds to the data depicted in Figure 6, and it was employed to produce Figure 8 (final results) of the manuscript. </p>
Automatic Bottom-Up Taxonomy Construction: A Software Application Domain Study - Data
Open the record for dataset details and reuse information.
Dataset for Automatic motion estimation with applications to hiPSC-CMs
<p>Supplementary data for paper Automatic motion estimation with applications to hiPSC-CMs</p>
Data from: StomataCounter: a neural network for automatic stomata identification and counting
Stomata regulate important physiological processes in plants and are often phenotyped by researchers in diverse fields of plant biology. Currently, there are no user friendly, fully-automated methods to perform the task of identifying and counting stomata, and stomata density is generally estimated by manually counting stomata. We introduce StomataCounter, an automated stomata counting system using a deep convolutional neural network to identify stomata in a variety of different microscopic images. We use a human-in-the-loop approach to train and refine a neural network on a taxonomically diverse collection of microscopic images. Our network achieves 98.1% identification accuracy on Ginkgo SEM micrographs, and 94.2% transfer accuracy when tested on untrained species. To facilitate adoption of the method, we provide the method in a publicly available website at http://www.stomata.science/.
Data from: Towards the automatic classification of avian flight calls for bioacoustic monitoring
Automatic classification of animal vocalizations has great potential to enhance the monitoring of species movements and behaviors. This is particularly true for monitoring nocturnal bird migration, where automated classification of migrants' flight calls could yield new biological insights and conservation applications for birds that vocalize during migration. In this paper we investigate the automatic classification of bird species from flight calls, and in particular the relationship between two different problem formulations commonly found in the literature: classifying a short clip containing one of a fixed set of known species (N-class problem) and the continuous monitoring problem, the latter of which is relevant to migration monitoring. We implemented a state-of-the-art audio classification model based on unsupervised feature learning and evaluated it on three novel datasets, one for studying the N-class problem including over 5000 flight calls from 43 different species, and two realistic datasets for studying the monitoring scenario comprising hundreds of thousands of audio clips that were compiled by means of remote acoustic sensors deployed in the field during two migration seasons. We show that the model achieves high accuracy when classifying a clip to one of N known species, even for a large number of species. In contrast, the model does not perform as well in the continuous monitoring case. Through a detailed error analysis (that included full expert review of false positives and negatives) we show the model is confounded by varying background noise conditions and previously unseen vocalizations. We also show that the model needs to be parameterized and benchmarked differently for the continuous monitoring scenario. Finally, we show that despite the reduced performance, given the right conditions the model can still characterize the migration pattern of a specific species. The paper concludes with directions for future research.
Symbolic Automatic Relations and Their Applications to SMT and CHC Solving
<p>This is a replication package for the paper "Symbolic Automatic Relations and Their Applications to SMT and CHC Solving," published in the proceedings of the 28th Static Analysis Symposium (SAS 2021).</p> <p>This package contains all the tools discussed in the paper, and all the benchmarks the evaluation was conducted on, along with README.md and benchmark scripts.</p>
FIGURE 4 in Ultra-small RFID p-Chips on the heads of entomological pins provide an automatic and durable means to track and label insect specimens
FIGURE 4. Schematic of a cap for tagging specimen-bearing pins with p-Chips attached to a piece of ferrite to amplify the transponder response. A: Cross-section; B: Overall view; C: Mounting of the cap on a pin.
FIGURE 1 in Ultra-small RFID p-Chips on the heads of entomological pins provide an automatic and durable means to track and label insect specimens
FIGURE 1. Examples of how labels may be stacked below entomological specimens. Important information may be hidden and difficult to access.
FIGURE 3. a in Ultra-small RFID p-Chips on the heads of entomological pins provide an automatic and durable means to track and label insect specimens
FIGURE 3. a)-c) Schematic representation of p-Chips embedded in an epoxy dome and mounted onto entomological pins. d)- e) Photographs of p-Chip-tagged pins; a view from above the pin head (d), and from the side (e).
A Mechanism for Automatically Extracting Reusable and Maintainable Code Idioms from Software Repositories
<p>The provided dataset contains the data used by "A Mechanism for Automatically Extracting Reusable and Maintainable Code Idioms from Software Repositories", in order to extract maintainable and reusable code idioms from the most popular GitHub repositories. The dataset includes the code snippets, the Abstract Syntax Trees and repositories information about Control Flow Statements (if-blocks, for-blocks, enhanced-for-blocks, try-blocks, while-blocks, switch-blocks and do-blocks) data coming from the top GitHub projects.</p>
Modules for Experiments in Stellar Astrophysics (MESA): Time-Dependent Convection, Energy Conservation, Automatic Differentiation, and Infrastructure
<p>We update the capabilities of the open-knowledge software instrument Modules for Experiments in Stellar Astrophysics (MESA). The new auto_diff module implements automatic differentiation in MESA, an enabling capability that alleviates the need for hard-coded analytic expressions or finite difference approximations. We significantly enhance the treatment of the growth and decay of convection in MESA with a new model for time-dependent convection, which is particularly important during late-stage nuclear burning in massive stars and electron degenerate ignition events. We strengthen MESA's implementation of the equation of state, and we quantify continued improvements to energy accounting and solver accuracy through a discussion of different energy equation features and enhancements. To improve the modeling of stars in MESA we describe key updates to the treatment of stellar atmospheres, molecular opacities, Compton opacities, conductive opacities, element diffusion coefficients, and nuclear reaction rates. We introduce treatments of starspots, an important consideration for low-mass stars, and modifications for superadiabatic convection in radiation-dominated regions. We describe new approaches for increasing the efficiency of calculating monochromatic opacities and radiative levitation, and for increasing the efficiency of evolving the late stages of massive stars with a new operator split nuclear burning mode. We close by discussing major updates to MESA's software infrastructure that enhance source code development and community engagement.</p>
A Multimodal Dataset for Automatic Edge-AI Cough Detection
<p>Counting the number of times a patient coughs per day is an essential biomarker in determining treatment efficacy for novel antitussive therapies and personalizing patient care. There is a need for wearable devices that employ multimodal sensors to perform accurate, privacy-preserving, automatic cough counting algorithms directly on the device in an edge-AI fashion. To advance this research field, we contribute the first publicly accessible cough counting dataset of multimodal biosignals. The database contains nearly 4 hours of biosignal data, with both acoustic and kinematic modalities, covering 4,300 annotated cough events. Furthermore, several non-cough sounds (i.e. breathing, laughing, and throat clearing), background noises (i.e. music, traffic, bystander coughing) and motion scenarios (i.e. sitting, walking) mimicking daily life activities are also present, which the research community can use to accelerate ML algorithm development.</p> <p>For detailed information about using this dataset to train edge-AI models and example code, please refer to our public Git repository: https://github.com/esl-epfl/edge-ai-cough-count/</p>
Dataset: Automatic Derivation of Vulnerability Models for Software Architectures
<p>Dataset for our publication "<em>Automatic Derivation of Vulnerability Models for Software Architectures</em>"</p>
Explainable Deep Learning for Automatic Rock Classification: High Accuracy Does Not Mean Great Model Performance <Dataset>
<p>This is the dataset of manuscript entitled "Explainable Deep Learning for Automatic Rock Classification: High Accuracy Does Not Mean Great Model Performance". The manuscript is currently under review. Full access of this dataset will be released once the manuscript is accepted.</p>
Resulting Pseudonymized Classification Data for "Automatic Core-Developer Identification on GitHub: A Validation Study"
<p>Resulting pseudonymized classification data of the study "Automatic Core-Developer Identification on GitHub: A Validation Study". The corresponding input data, from which the output data have been derived, can be found here: https://zenodo.org/record/7775078</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.