Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
831
datasets available to search
ShareScore release 0.9.0
Dataset results
831 results for “automaticity”
Static Analysis Warnings and Automatic Fixing: A Replication for C# Projects : Dataset
<p>Dataset used in the paper accepted for publication at the REproducibility Studies and NEgative Results (RENE) Track of SANER 2022.</p> <p>Martin Odermatt, Diego Marcilio, Carlo A. Furia. Static Analysis Warnings and Automatic Fixing: A Replication for C# Projects, International Conference on Software Analysis, Evolution, and Reengineering (SANER) 2022.</p>
Automatic Mechanical Self-Replication
<p>Theoretical discussion of the principles of automatic self-replication was initiated by von Neumann in 1951. The conclusion was drawn then that construction of an automation, which could build replicas of itself, was possible. Many different parts of unspecified complexity would be needed, and energy would have to be supplied from outside.</p> <p>An elementary practical solution of the problem was described by L.S. & R. Penrose in 1957. In this model, the material consisted of solid units of only two kinds which were subjected to agitation in a horizontal track. When a linked pair was introduced as a seed, it caused the formation of other linked pairs of the same type.</p> <p>Soon afterwards, systems with more life-like properties were constructed on similar principles. For example, automatic replication, which involved the growth and eventual partition of the original seed, was demonstrated.</p> <p>The progress of these ideas, from very simple designs capable only of mimicking crystal formation, to analogues of mitosis is sketched in the motion picture. Subsequently, models exhibiting recombination have been built. The accurate automatic replication of chains, unlimited in complexity, has been described in papers now in the press. </p> <p>References <br> 1. Von Neumann, J. (1951) Cerebral Mechanisms in Behaviour. New York & London; McGraw Hill. </p> <p>2. Penrose, L.S. 8: Penrose, R. (1957) A Self-reproducing analogue. Nature 179, 1183</p> <p>3. Penrose, L.S. Automatic Mechanical Self-Reproduction. New Biology (in the Press).</p> <p>4. Penrose, S. Mechanics of Self-Reproduction. Annals of Human Genetics (In the Press). <br> </p> <p>(From the "LS Penrose papers" archive, the Wellcome Collection)</p>
Perceiving numerosity does not cause automatic shifts of spatial attention
<p>It is debated whether the representation of numbers is endowed with a directional-spatial component so that perceiving small-magnitude numbers triggers leftward shifts of attention and perceiving large-magnitude numbers rightward shifts. Contrary to initial findings, recent investigations have demonstrated that centrally presented small-magnitude and large-magnitude Arabic numbers do not cause leftward and rightward shifts of attention, respectively. Here we verified whether perceiving small or large non-symbolic numerosities (i.e., clouds of dots) drives attention to the left or the right side of space, respectively. In experiment 1, participants were presented with central small (1, 2) vs large-numerosity (8, 9) clouds of dots followed by an imperative target in the left or right side of space. In experiment 2, a central cloud of dots (i.e., five dots) was followed by the simultaneous presentation of two identical dot-clouds, one on the left and one on the right side of space. Lateral clouds were both lower (1, 2) or higher in numerosity (8, 9) than the central cloud. After a variable delay, one of the two lateral clouds turned red and participants had to signal the colour change through a unimanual response. We found that (a) in Experiment 1, the small vs large numerosity of the central cloud of dots did not speed up the detection of left vs right targets, respectively, (b) in Experiment 2, the detection of colour change was not faster in the left side of space when lateral clouds were smaller in numerosity than the central reference and in the right side when clouds were larger in numerosity. These findings show that perceiving non-symbolic numerosity does not cause automatic shifts of spatial attention and suggests no inherent association between the representation of numerosity and that of directional space. </p>
Supplementary material of "VIGIL: A Python tool for automatized probabilistic VolcanIc Gas dIspersion modeLling"
<p>This dataset includes the Supplementary material of the paper: </p> <p>Dioguardi F, Massaro S, Chiodini G, Costa A, Folch A, Macedonio G, Sandri L, Selva J, Tamburello G. VIGIL: A Python tool for automatized probabilistic VolcanIc Gas dIspersion modeLling. Ann. Geophys. [Internet]. 2022Apr.3 [cited 2022Apr.7];65(1). Available from: https://www.annalsofgeophysics.eu/index.php/annals/article/view/8796</p>
Automatic taxonomic identification based on the Fossil Image Dataset (>415,000 images) and deep convolutional neural networks
<p>The rapid and accurate taxonomic identification of fossils is of great significance in paleontology, biostratigraphy, and other fields. However, taxonomic identification is often labor-intensive and tedious, and the requisition of extensive prior knowledge about a taxonomic group also requires long-term training. Moreover, identification results are often inconsistent across researchers and communities. Accordingly, in this study, we used deep learning to support taxonomic identification. We used web crawlers to collect the Fossil Image Dataset (FID) via the Internet, obtaining 415,339 images belonging to 50 fossil clades. Then we trained three powerful convolutional neural networks on a high-performance workstation. The Inception ResNet v2 architecture achieved an average accuracy of 0.90 in the test dataset when transfer learning was applied. The clades of microfossils and vertebrate fossils exhibited the highest identification accuracies of 0.95 and 0.90, respectively. In contrast, clades of sponges, bryozoans, and trace fossils with various morphologies or with few samples in the dataset exhibited a performance below 0.80. Visual explanation methods further highlighted the discrepancies among different fossil clades and suggested similarities between the identifications made by machine classifiers and taxonomists. Collecting large paleontological datasets from various sources, such as the literature, digitization of dark data, citizen-science data, and public data from the Internet may further enhance deep learning methods and their adoption. Such developments will also possibly lead to image-based systematic taxonomy to be replaced by machine-aided classification in the future. Pioneering studies can include microfossils and some invertebrate fossils. To contribute to this development, we deployed our model on a server for public access at www.ai-fossil.com.</p>
Figure 3 in Automatic identification of bird females using egg phenotype
Figure 3. Correlation between spectral (A), pattern/luminance (B) and shape (C) distances, respectively and genetic distances. Individual phenotypic distances of average eggs laid by nine genotyped common cuckoo females: spectral (D), pattern/luminance (E) and shape (F) distances.
Figure 1 in Automatic identification of bird females using egg phenotype
Figure 1. Values for individual eggs on the two most important PC variables (according to the random forest model), grouped by cuckoo female ID based on the genetic assignment. PCA2 pattern variable indicates egg skew and PC2 spectra variable indicates blueness/greenness of eggs (for details, see Table 2).
Figure 2 in Automatic identification of bird females using egg phenotype
Figure 2. Phenotypic distances of nine average eggs laid by nine genotyped common cuckoo females (A) and their genetic distances (B). Thicker green lines denote higher phenotypic and genetic similarity. Correlation between phenotypic and genetic distances (C).
Water stages in a tidal marsh measured using images, visually and automatically, along with stages measured using pressure transducer and Doppler sensors
<p>This data was acquired to evaluate the performance of an image based system to measure water stages in streams and rivers.</p> <p>It contains measurements performed visually and stored (NR_Visual_Data_120201_120724.csv)</p> <p>It contains the measurements performed automatically by the system studied corresponding to those done visually (NR_Gaugecam_Data_120201_120724.csv)</p> <p>It contains the measurements done automatically (GC) and those measured by ISCO, HOBO, and Sontek instruments (NR-GC-vs-ISCO.csv)</p>
Data for domains without generator for the ICAPS 2021 paper "Automatic Instance Generation for Classical Planning"'
<p>This dataset contains raw data (log files) and parsed data (JSON files) of all planners used in the paper run on planning domains for which there is no generator that could directly be used for the Autoscale training process. This dataset was used to select a subset of tasks as described in the paper, for all Autoscale versions up to this point (Autoscale 21.08 and 21.11). As such, it complements the original Zenodo entry https://zenodo.org/record/4586397.</p> <p>domains-without-generator.zip contains the raw experimental data, distributed over a subdirectory for each experiment. Each of these contain a subdirectory tree structure "runs-*" where each planner run has its own directory. For each run, there are symbolic links to the input PDDL files domain.pddl and problem.pddl (can be resolved by putting the benchmarks directory to the right place), the run log file "run.log" (stdout), possibly also a run error file "run.err" (stderr), the run script "run" used to start the experiment, and a "properties" file that contains data parsed from the log file(s).</p> <p>domains-without-generator-eval.zip contains the parsed data, again distributed over a subdirectory for each experiment. Each contains a "properties" file, which is a JSON file with combined data of all runs of the corresponding experiment. In essence, the properties file is the union over all properties files generated for each individual planner run.</p>
Eyetracking and keystroke logging metrics for measuring automaticity in translation
<p>This dataset contains the behavioural data from 35 novice translators and 30 experienced translators (65 participants in total) during their translation process. The data are specifically generated eyetracking and keystroke logging metrics for measuring automaticity in the complex activity of translation, which includes four aspects, namely, speed, degree of parallel processing, effortlessness, and the pattern of attention allocated to the source text and target text areas. The dataset is provided for the paper Automaticity in Translation: Effects of Time Pressure and Work Experience.</p>
Data used in the paper Automatically Detecting Visual Bugs in HTML5 <canvas> Games
<p>This repository contains the snapshots (i.e., screenshot, <canvas> object representation pairs) and game assets collected from our test <canvas> game for use in the paper <code>Automatically Detecting Visual Bugs in HTML5 <canvas> Games</code>, accepted at ASE 2022.</p> <p>The data in this repository can be used to benchmark new visual bug detection approaches for HTML5 <canvas> games. The source code for our test <canvas> game and our 24 synthetic visual bugs can be found at the following <a href="https://github.com/asgaardlab/canvas-visual-bugs-testbed">link</a>.</p> <p><a href="https://asgaardlab.github.io/canvas-visual-bugs-testbed/">Project page</a><br> <br> <strong>Directory structure</strong></p> <pre> data/ | - assets/ | - exp_0/ | | - a/ | | | - 0.png | | | - 0.json | | | ... | | | - 9.png | | | - 9.json | | ... | | - j/ | - exp_appearance_1 | ... | - exp_state_6 </pre> <p><br> <strong>Data description</strong></p> <table> <tbody> <tr> <td>assets/</td> <td>Source images from the test <canvas> game</td> </tr> <tr> <td>exp_0/</td> <td>Snapshots with no visual bugs injected (okay/non-buggy experiment)</td> </tr> <tr> <td>exp_*_{1,2,3,4,5,6}/</td> <td>Snapshots for each of the injected visual bugs (buggy experiments)</td> </tr> <tr> <td>*/{a,b,c,d,e,f,g,h,i,j}/</td> <td>10 runs of data for each experiment</td> </tr> <tr> <td>*/{0,1,2,3,4,5,6,7,8,9}.png</td> <td>10 snapshots per experiment, each snapshot has a screenshot (png)</td> </tr> <tr> <td>*/{0,1,2,3,4,5,6,7,8,9}.json</td> <td>10 snapshots per experiment, each snapshot has a COR<sup>1</sup> (json)</td> </tr> </tbody> </table> <p><sup>1</sup>COR = <canvas> objects representation</p>
Automatic ESG Assessment of Companies by Mining and Evaluating Media Coverage Data: NLP Approach and Tool
<p><strong>Replication package for our paper </strong><a href="https://arxiv.org/abs/2212.06540">Automatic ESG Assessment of Companies by Mining and Evaluating Media Coverage Data: NLP Approach and Tool</a></p> <p>It contains the following files:</p> <ol> <li>Our data set of 432,411 news headlines annotated as being environmental-, governance-, or social-related. We encourage fellow researchers to use the corpus as a benchmark for other ESG-relevant NLP tasks.</li> <li>Code/Notebooks that we used for the training and evaluation of our company detection, ESG classification, and sentiment models</li> <li>Full tables detailing the results of all experiments performed </li> </ol>
Automatic unsupported electromembrane extraction followed by on-line electrochemical detection of diclofenac in urine
<p>Raw voltammograms (including calibration) of data presented in Table 1 of the article entitled "Programmable millifluidic platform integrating automatic electromembrane extraction clean-up and in-line electrochemical detection: A proof of concept"<strong> </strong>published in ACS Sensors (<a href="https://doi.org/10.1021/acssensors.2c01648">10.1021/acssensors.2c01648</a>)</p>
Dataset for the paper pyATM "An Automatic Differentiation Method for Surface Carbon Flux Inversion"
<p>Dataset for the paper pyATM "An Automatic Differentiation Method for Surface Carbon Flux Inversion"</p>
Supplementary material 2 from: Senderov V, Georgiev T, Penev L (2016) Online direct import of specimen records into manuscripts and automatic creation of data papers from biological databases . Research Ideas and Outcomes 2: e10617. https://doi.org/10.3897/rio.2.e10617
This spreadsheet contains the information about the specimen API's of GBIF, BOLD Systems, iDigBio, and PlutoF. It lists the endpoints and the documentation URLs in the sheet named "APIs". In the sheet named "Mappings" it lists how to map the non-DwC compliant APIs (BOLD and PlutoF) to DwC-terms.
Supplementary material 3 from: Senderov V, Georgiev T, Penev L (2016) Online direct import of specimen records into manuscripts and automatic creation of data papers from biological databases . Research Ideas and Outcomes 2: e10617. https://doi.org/10.3897/rio.2.e10617
This archive contains XSLT transformations from EML v. 2.1.1 and v. 2.1.0 to Pensoft data paper format.
Dataset of RCQE (Automatic Code Quality Estimation in multi-round Code Review)
<p>A multi-modal dataset , including source code, code diffs, and natural language comments as three types of metadata. The format for each instance in the dataset is as follows: code source file (in source code format), current commit code (in diff format), current commit comment (in natural language format, empty if none), previous commit (in diff format), previous submission commit comment (in natural language format).</p>
Impact of an Automatic Judge in Database Learning (dataset)
<p>This is a anonymized dataset for the assesment of the impact of using an online automatic judge for free practice in a database<br>course over four academic years.</p> <ul> <li><code>raw_data</code> contains one directory for each group (CSE or CSE/M) of the DB course, which in turn contains one directory for each academic year, which finally contains two CSV files with the anonymized gradebook and the online judge submission submission logs for that academic year and group. There is no submission logs for the year 2019/20 where the online judge was not available. User identifiers have been converted to sequential numbers after random shuffling the original dataset to preserve the anonymity of students.</li> <li><code>megatable.csv</code> summarizes in a single table the data contained in the separate tables for each group. Most information for the analyses have been obtained from this table, which can be generated with an attached script. Student identifiers consist of the corresponding identifier in the original table prefixed by the academic year.</li> <li><code>src</code> contains several scripts that have been used to process the data, compute statistics on them, and generate the graph and tables included in the submitted paper. They have been included to facilitate the reproducibility of the results.</li> </ul>
Data used in the paper "Machine-learning-enhanced automatic spectral characterization of x-ray pulses from a free-electron laser".
<p>Data recorded for the experiment at the European XFEL are available at <strong>doi:10.22003/XFEL.EU-DATA-900331-00</strong> and <strong>doi:10.22003/XFEL.EU-DATA-900383-00.</strong></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.