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832 results for “automatic”
PLDI2024 Artifact: A HAT Trick: Automatically Verifying Representation Invariants Using Symbolic Finite Automata
<p>This artifact contains:</p> <ol> <li>README.md : the artifact guide.</li> <li>marple-original-submission.pdf: the original submitted paper.</li> <li>marple:pldi-2024.tar.gz: the docker image (optional, we recommend to pull from the docker hub, see README.md).</li> <li>Dockerfile: the docker file that can reproduce the docker image (optional, we recommend to pull from the docker hub, see README.md).</li> </ol>
LLMs for Automatic Financial Statement Analysis
<p>Financial Accounting provides standards for the recording and valuation of economic elements, facilitating the representation of financial activity in standardized models called Financial Statements, such as the Balance Sheet or the Income Statement. Financial Statements are analyzed using different techniques, including the use of ratios or indicators that reveal vital information for understanding, controlling, and planning the financial management of an organization. While it is feasible to achieve a certain degree of automation in Financial Statement Analysis, especially in the computation of financial ratios, human intervention remains essential to extract conclusions that serve as the basis for decision-making. This exploratory study aims to automate the analysis process through the employment of Large Language Models. Leveraging Financial Statements and indicators calculated automatically as the base knowledge, this study applies these language models to autonomously generate an understandable Financial Statement Analysis.</p>
Instrument in pills: Automatic lidars and ceilometers (ALC)
<p>In this 10 minutes video, Simone Kotthaus tells everything about automatic lidars and ceilometers, often named with the acronym ALC. </p>
Towards automatic derivation of geometry-based descriptors as surrogates for complex structural approaches in enzyme-substrate prediction
<p>Dataset produced for the Project PRELUDIUM19 2020/37/N/NZ2/00967 entitled: "Towards automatic derivation of geometry-based descriptors as surrogates for complex structural approaches in enzyme-substrate prediction"</p> <p>The dataset counts with the three families of enzymes used: dehalogenase, aldehyde reductase and nitrilase.</p> <p>For each enzyme, the docked structures, docked parameters and scripts to analyze them further are present. Moreover, the protocol that derives geometric descriptors from docked structures is also present.</p> <p>This work was supported by the National Science Centre, Poland (grant no. 2020/37/N/NZ2/00967)</p>
Data Package for "A Platform-Agnostic Approach for Automatically Identifying Real-Life Performance Issue Reports with Heuristic Linguistic Patterns"
<p>This Zenodo repository contains the data supporting the findings of the journal paper, titled "A Platform-Agnostic Approach for Automatically Identifying Real-Life Performance Issue Reports with Heuristic Linguistic Patterns", published on IEEE Transactions on Software Engineering, including:</p> <ol> <li><strong>Heuristic Linguistic Pattern Set</strong>: <span>we listed the 80 HLP we derived from </span><span>Apache's JIRA issue tracking system</span><span>. Column "</span><span>Category" </span><span>lists the type of each pattern. Namely, LEX represents lexical pattern, STR represents structural pattern, SEM represents semantic pattern, and PRF represents profiling pattern. Column "Name" is a descriptive name we give to each pattern. Column "Definition" defines the detailed content in each pattern.</span></li> <li><strong>Manual Tagging Results</strong>: manual_tagging.xlsx spreadsheet <span>comprises both sentence-level and issue-level manually tagging results for three datasets: 'Dataset-1: Apache Jira's Homologous Evaluation', '</span><span>Dataset-</span><span>2: Apache Jira's Heterologous Evaluation', and '</span><span>Dataset-</span><span>3: Other Platform's Evaluation'. The tagging results are segmented into sentence-level tabs ("Dataset-1 Sen", "Dataset-2 Sen", "Dataset-3 Sen") and issue-level tabs ("Dataset-1 Issue", "Dataset-2 Issue", "Dataset-3 Issue").</span></li> <li><span><strong>RQ Findings</strong>: </span> <p><span>This section contains detailed data findings from six research questions (RQ1 to RQ6).</span></p> <ul> <li> <p><span>The RQ1 tab provides an evaluation of our HLP-based approach, showing the precision, recall, and F1-Score of eight classifiers. These results are juxtaposed with the corresponding values from baseline methods, at both sentence and issue levels for automatic tagging.</span></p> </li> <li> <p><span>The RQ2 tab illustrates the precision, recall, and F1-Score of eight classifiers under two training conditions: a balanced training dataset (BT+HLP) and an imbalanced training dataset (UBT+HLP). These outcomes are contrasted with the equivalent values from baseline methods, also trained under balanced (BT+BLM) and imbalanced (UBT+BLM) conditions. The results are shown at both sentence and issue levels for automatic tagging.</span></p> </li> <li> <p><span>The RQ3 tab evaluates the dataset transferability of our HLP-based approach in comparison to baseline methods. It achieves this by analyzing the precision, recall, and F1-Score metrics for eight classifiers under two different "training/testing" dataset conditions, i.e., 'D1/D1' and 'D1/D3'. These conditions allow for a direct comparison of performance when applied to the same dataset ('D1/D1') versus when transferred to a different dataset ('D1/D3'). Additionally, the tab includes an 'Avg Change' and 'p-value' section, summarizing the statistical change in performance metrics between the two dataset conditions. </span></p> </li> <li> <p><span>The RQ4 tab presents a direct comparison between strict and fuzzy HLP matching approaches, assessed through precision, recall, and F1-Score metrics across eight issue classifiers.</span></p> </li> <li> <p><span>The RQ5 tab examines the influence of sentence order on the accuracy of eight classifiers within our approach. It shows the change in precision, recall, and F1-Score when the sentence order feature is taken into consideration versus when it is not.</span></p> </li> <li> <p><span>The RQ6 tab explores the impact of feature selection algorithms on both issue and sentence-level tagging accuracy. This tab presents the average precision, recall, and F1-Score for three experiments: Boruta, Recursive Feature Elimination (RFE), and the usage of all 80 features. </span></p> </li> </ul> </li> <li><strong>Qualitative Analysis</strong>: <p><span>This spreadsheet offers a comprehensive examination of the data supporting Section 6.1, which focuses on Qualitative Analysis. It is organized into several tabs, each dedicated to specific research questions (RQs) as outlined below:</span></p> <ul> <li> <p><span>Tab "RQ-1" showcases performance issue reports accurately detected by our High-Level Performance (HLP) approach's top model, XGBoost, which were not identified by the benchmark method's leading model, BERT. This highlights the comparative advantage of our approach in identifying nuanced performance issues.</span></p> </li> <li> <p><span>Tab "RQ-2" continues the exploration of performance issue reports, presenting cases with specific details (to be added).</span></p> </li> <li> <p><span>Tab "RQ-3" delves into the unique capabilities of XGBoost, the leading model in our HLP approach, showcasing its ability to detect performance issues missed by the baseline's top model, BERT. This comparison is drawn under distinct conditions: with pre-training (Dataset 1) and without pre-training (Dataset 3), illustrating the robustness and adaptability of our model.</span></p> </li> <li> <p><span>Tab "RQ-4" focuses on performance issue reports uniquely identified through the implementation of Fuzzy HLP Matching within our HLP approach. This method underscores the innovative matching techniques that enhance issue detection.</span></p> </li> <li> <p><span>Tab "RQ-5" presents performance issue reports pinpointed exclusively by applying the Issue HLP Matrix within our approach. This tab demonstrates the effectiveness of our matrix-based analysis in isolating and identifying specific performance concerns.</span></p> </li> <li> <p><span>Tab "RQ-6" is dedicated to performance issue reports uniquely detected by incorporating feature selection techniques into our HLP approach. This illustrates the value of advanced feature selection in improving the precision of performance issue identification.</span></p> </li> </ul> </li> <li><strong>LLM Experiment Data</strong>: presents the tagging outcomes of Large Language Models (LLMs), specifically ChatGPT-3.5 and ChatGPT-4, across three distinct datasets: 'Dataset-1: Apache Jira's Homologous Evaluation', 'Dataset-2: Apache Jira's Heterologous Evaluation', and 'Dataset-3: Evaluation on Other Platforms'. The results are organized into three separate tabs: 'Dataset-1 Issue', 'Dataset-2 Issue', and 'Dataset-3 Issue'.</li> <li><strong>ChatGPT Operation Python Script</strong>: crafted for automating the evaluation and tagging of issue reports in Excel using Large Language Models (LLMs) like ChatGPT-3.5 and ChatGPT-4. It underscores the importance of administrative rights for file modifications and outlines procedures for reading from and writing responses to Excel files. Key functions include querying LLMs with issue descriptions, processing their responses, and updating the spreadsheet with 'Yes' or 'No' labels and explanatory reasons, thereby facilitating an organized review of LLM performance across different datasets.</li> </ol>
Automatic User Story Generation: A Comprehensive Systematic Literature Review - Data Extraction
<p>This document presents the data extraction performed for the Systematic Literature Review in Automatic User Story Generation.</p>
Automatic message sequence chart creation from simulation run of the proposed parametric colored Petri net model of the Chandy-Lamport algorithm
<p><span>These videos show the creation of two message sequence charts from simulation runs of the proposed parametric colored Petri net model of the Chandy-Lamport algorithm using the CPN tool with three constituting processes. </span></p> <p><strong><span>Message Sequence Chart of <span> </span>Parametric Model With 3 Processes via Automatic Simulation Run_SuppInfo.mp4</span></strong><span>: This video shows the automatic generation of a message sequence chart of the proposed parametric colored Petri net model of the Chandy-Lamport distributed global snapshot algorithm using the CPN tool version 4.0.0. The number of constituting processes is parametric in the model and was set to three. The video was generated using the authors' updated CPN tool extension server. The automatic simulation run of the model has been used to create this video. The CPN tool randomly selects the enabled transition at each step in an automatic simulation run.</span></p> <p><strong><span>Message Sequence Chart of Parametric Model With 3 Processes via Step-By-Step Simulation Run_SuppInfo.mp4:</span></strong><span> This video shows the automatic generation of a message sequence chart of the proposed parametric colored Petri net model of the Chandy-Lamport algorithm in a step-by-step simulation run with our updated extension server of the CPN tools version 4.0.0. The number of constituting processes is parametric in the model and was set to three. We manually fired our selected enabled transition of the model to create this video. </span></p>
Anomalous Cognition, Dissociation, and Motor Automatism
<p>The research that created this data is described in:</p> <ul> <li>Palmer, J. (2017). Anomalous Cognition, Dissociation, and Motor Automatism. <em>Journal of Parapsychology, 81</em>(1), 46-62.</li> </ul> <p>Eight files are included:</p> <ul> <li>trials1.csv - Trial data</li> <li>subjects1_2.csv - Subject Data</li> <li>autom-codes.odt - Code book (subjects)</li> <li>Raw data (SPSS).zip - Raw data (SPSS)</li> <li>Convert SPSS to CSV.zip -Conversion script for transforming from SPSS to CSV format.</li> <li>autom_interview_ec.odt - Post-task questionnaire - eyes-closed</li> <li>autom_interview_quotes.odt - Post-task questionnaire - quotations</li> <li>PIF_rev0411.odt - Participant Information Form</li> </ul>
Automatic Generation of Explanations in Autonomous Systems: Enhancing Human Interaction in Smart Home Environments
<p>The file named “Dataset” is the generation of scenarios and explanations used for the proposal.<br>The file named “Questionnaire answers & data analysis” corresponds to the application of a questionnaire addressed to 118 people.</p>
AI4PROFHEALTH - Automatic Silver Gazetteer for Named Entity Recognition and Normalization
<p>This dataset comprises a professions gazetteer generated with automatically extracted terminology from the Mesinesp2 corpus, a manually annotated corpus in which domain experts have labeled a set of scientific literature, clinical trials, and patent abstracts, as well as clinical case reports. </p> <p>A silver gazetteer for mention classification and normalization is created combining the predictions of automatic Named Entity Recognition models and normalization using Entity Linking to three controlled vocabularies SNOMED CT, NCBI and ESCO. The sources are 265,025 different documents, where 249,538 correspond to <a href="https://zenodo.org/records/4707104">MESINESP2 Corpora</a> and 15,487 to clinical cases from open clinical journals. From them, 5,682,000 mentions are extracted and 4,909,966 (86.42%) are normalized to any of the ontologies: SNOMED CT (4,909,966) for diseases, symptoms, drugs, locations, occupations, procedures and species; ESCO (215,140) for occupations; and NCBI (1,469,256) for species.</p> <p>The repository contains a .tsv file with the following columns:</p> <ul> <li><strong><code>filenameid</code></strong>: A unique identifier combining the file name and mention span within the text. This ensures each extracted mention is uniquely traceable. Example: biblio-1000005#239#256 refers to a mention spanning characters 239–256 in the file with the name biblio-1000005.</li> <li> <p><strong><code>span</code></strong>: The specific text span (mention) extracted from the document, representing a term or phrase identified in the dataset. Example: centro oncológico.</p> </li> <li> <p><strong><code>source</code></strong>: The origin of the document, indicating the corpus from which the mention was extracted. Possible values: mesinesp2, clinical_cases.</p> </li> <li> <p><strong><code>filename</code></strong>: The name of the file from which the mention was extracted. Example: biblio-1000005.</p> </li> <li> <p><strong><code>mention_class</code></strong>: Categories or semantic tags assigned to the mention, describing its type or context in the text. Example: ['ENFERMEDAD', 'SINTOMA'].</p> </li> <li> <p><strong><code>codes_esco</code></strong>: The normalized ontology codes from the European Skills, Competences, Qualifications, and Occupations (ESCO) vocabulary for the identified mention (if applicable). This field may be empty if no ESCO mapping exists. Example: 30629002.</p> </li> <li> <p><strong><code>terms_esco</code></strong>: The human-readable terms from the ESCO ontology corresponding to the <code>codes_esco</code>. Example: ['responsable de recursos', 'director de recursos', 'directora de recursos'].</p> </li> <li> <p><strong><code>codes_ncbi</code></strong>: The normalized ontology codes from the NCBI Taxonomy vocabulary for species (if applicable). This field may be empty if no NCBI mapping exists.</p> </li> <li> <p><strong><code>terms_ncbi</code></strong>: The human-readable terms from the NCBI Taxonomy vocabulary corresponding to the <code>codes_ncbi</code>. Example: ['Lacandoniaceae', 'Pandanaceae R.Br., 1810', 'Pandanaceae', 'Familia'].</p> </li> <li> <p><strong><code>codes_sct</code></strong>: The normalized ontology codes from SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) vocabulary for diseases, symptoms, drugs, locations, occupations, procedures, and species (if applicable). Example: 22232009.</p> </li> <li> <p><strong><code>terms_sct</code></strong>: The human-readable terms from the SNOMED CT ontology corresponding to the <code>codes_sct</code>. Example: ['adjudicador de regulaciones del seguro nacional'].</p> </li> <li> <p><strong><code>sct_sem_tag</code></strong>: The semantic category tag assigned by SNOMED CT to describe the general classification of the mention. Example: environment.</p> </li> </ul> <p> </p> <p><strong>Suggestion</strong>: If you load the dataset using python, it is recommended to read the columns containing lists as follows</p> <div> <blockquote> <div>import ast</div> <div>df["mention_class"] = df["mention_class"].apply(lambda x: ast.literal_eval(x) if isinstance(x, str) else x)</div> </blockquote> </div> <p> </p> <p><strong>License</strong></p> <p>This dataset is licensed under <strong>Creative Commons Attribution 4.0 International (CC BY 4.0)</strong>. This means you are free to:</p> <ul> <li>Share: Copy and redistribute the material in any medium or format.</li> <li>Adapt: Remix, transform, and build upon the material for any purpose, even commercially.</li> </ul> <p><strong>Attribution Requirement</strong>: Please credit the dataset creators appropriately, provide a link to the license, and indicate if changes were made.</p> <p><strong>Contact</strong></p> <p>If you have any questions or suggestions, please contact us at:</p> <p>Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p> <p><strong>Additional resources and corpora</strong></p> <p>If you are interested, you might want to check out these corpora and resources:</p> <ul> <li><a href="https://zenodo.org/records/5602914">MESINESP-2</a> (Corpus of manually indexed records with DeCS /MeSH terms comprising scientific literature abstracts, clinical trials, and patent abstracts, different document collection)</li> <li><a href="10.5281/zenodo.5070540" target="_blank" rel="noopener">MEDDOPROF corpus </a></li> <li> <p><a href="https://zenodo.org/record/4722741">Codes Reference List</a> (for MEDDOPROF-NORM)</p> </li> <li> <p><a href="https://zenodo.org/record/4720833">Annotation Guidelines</a></p> </li> <li> <p><a href="https://doi.org/10.5281/zenodo.4524658">Occupations Gazetteer</a></p> </li> </ul>
Testing nectar price effect on bumblebee feeding by automatized computer-controlled laboratory platform
Open the record for dataset details and reuse information.
Dataset for An Automatized Rebalancing System to Address Faradaic Imbalance and Prolong Cycle Life in Alkaline Ferrocyanide – Anthraquinone Redox Flow Batteries
<p>Dataset for the results shown in the publications "An Automatized Rebalancing System to Address Faradaic Imbalance and Prolong Cycle Life in Alkaline Ferrocyanide – Anthraquinone Redox Flow Batteries"</p>
An enhanced single Gaussian point continuum finite element formulation using automatic differentiation: Source code and data
<p>This dataset contains the source code and the data with an example of uniaxial strain of an enhanced single Gaussian point continuum finite elemnet formulation using automatic differentiation.</p> <p> </p> <p>This contribution presents a low-order 3D finite element formulation with hourglass stabilization using automatic differentiation. Here, the former Q1STc element formulation is enhanced by an approximation-free computation of the inverse of the Jacobian. The improved version is termed "Q1STc+."</p> <p> </p> <p>The corresponding publication is:</p> <p><br>Pacolli, N., Awad, A., Kehls, J., Sauren, B., Klinkel, S., Reese, S., Holthusen, H.<br><em>An enhanced single Gaussian point continuum finite elemnet formulation using automatic differentiation.</em></p> <p>Standalone_Elementroutine: <em>Q1STc+_Codes</em> contains:</p> <ul> <li><strong>main.f90</strong>: Standalone routine for local uniaxial strain test</li> <li><strong>Makefile</strong>: Makefile to create executable "Q1STc+"</li> <li><strong>elem40.f90</strong>: Element routine "Q1STc+" with elem_sub.f90 as the subroutine written in AceGen</li> <li><strong>mat52.f90</strong>: Elasto-plastic material routine with all subroutines written in AceGen</li> <li><strong>elem_mat_select.f90</strong>: The selected material routine (Here: mat52)</li> <li><strong>elem_subs.f90</strong>: Subroutines for elem40.f90</li> <li><strong>mat_subs.f90</strong>: Subroutines for mat52.f90</li> </ul>
Automatic bayesian procedure code and reply figures
<p>Bayesian automatic procedure based on an Dirichlet Multinomial Distribution.</p>
Automatic Detection of Photovoltaic from Sentinel-2 observations by an Enhanced U-Net method - DataSet
<p>Data Availability for <strong>Enhanced U-Net(E-UNET)</strong></p> <p>Thank you for your interest in our dataset.<br> <strong>Repository contents:</strong><br> <em>Sentinel-2 L2A product</em>: The tiles that we selected containing photovoltaic.<br> <em>ImageFusion_Result</em>: The image fusion results of ROI including photovoltaic which we intercepted from the Sentinel-2 data. It contains 10m resolution, 10m and 20m resolution, 10m, 20m and 60m resolution fusion results, and RGB 3-channels fusion results.<br> <em>Image</em>: The RGB images for the ROI containing photovoltaic.<br> <em>Label</em>: The manual annotations for the ROI containing photovoltaic.</p>
Dataset of ""Statistical atlases and automatic labelling strategies to accelerate the analysis of social insect brain evolution"
<p>Dataset of <em>Statistical atlases and automatic labelling strategies to accelerate the analysis of social insect brain evolution</em> by Sara Arganda, Ignacio Arganda-Carreras, Darcy G. Gordon, Andrew P. Hoadley, Alfonso Pérez-Escudero, Martin Giurfa and James F. A. Traniello.</p> <p>In this dataset, we are presenting:</p> <ul> <li>10 confocal brain images from <em>Pheidole spadonia </em>minors (in the original confocal TIFF format and in the open NRRD format), with manually segmented labels of 8 subregions (Optic Lobes, OL; Antennal Lobes, AL; Mushroom Body Medial Calyx, MB-MC; Mushroom Body Lateral Calyx, MB-LC; Mushroom Body Peduncle, MB-P; Central Complex, CX; Subesophageal zone, SEZ; and Rest of Central Brain, ROCB – in NRRD format) from one expert annotator.</li> <li>12 confocal brain images from <em>P. spadonia</em>, <em>P. rhea</em>, <em>P. tepicana</em> and <em>P. obtusospinosa</em> minors, with manually segmented labels of the same 8 subregions (OL; AL; MB-MC; MB-LC; MB-P; CX; SEZ; and ROCB) from one expert annotator.</li> <li>5 confocal brain images from <em>Pheidole spadonia </em>minors (“test brains”), with five sets of manually segmented labels of the same 8 subregions (OL; AL; MB-MC; MB-LC; MB-P; CX; SEZ; and ROCB) from three expert annotators (one set from annotator 1, one set from annotator 2 and three sets from annotator 3, to evaluate inter and intra person differences).</li> <li>1 group-wise template generated from the 10 confocal brain images from <em>Pheidole spadonia </em>minors, with three sets of manually segmented labels of the same 8 subregions (OL; AL; MB-MC; MB-LC; MB-P; CX; SEZ; and ROCB).</li> <li>5 group-wise templates generated from the 9 confocal brain images from <em>Pheidole spadonia </em>minors, with consensus labels of the same 8 subregions (OL; AL; MB-MC; MB-LC; MB-P; CX; SEZ; and ROCB).</li> <li>1 group-wise template generated from 12 confocal brain images from <em>P. spadonia</em>, <em>P. rhea</em>, <em>P. tepicana</em> and <em>P. obtusospinosa</em> minors, with consensus labels of the same 8 subregions (OL; AL; MB-MC; MB-LC; MB-P; CX; SEZ; and ROCB).</li> <li>7 sets of automatic labels for the 5 “test brains”: 3 sets of “Direct Labels”, 3 sets of “Consensus Labels”, 1 set of “Multispecies Template Labels”.</li> </ul> <p>Brain of minor workers were dissected from the ant head capsule in ice cold HEPES-buffered saline and were fixed and immunohistochemically stained using SYNORF1 (a monoclonal <em>Drosophila</em> synapsin I antibody obtained from the Developmental Studies Hybridoma Bank, catalog 3C11) and secondarily stained using Alexa Fluor 488 for visualization of neuropil (slightly modified from Ott, 2008). Later, brains were mounted in methyl salicylate and imaged on an Olympus Fluoview BX50 laser scanning confocal microscope with a ×20 objective at a resolution of ~0.7 × 0.7 × 5µm/voxel. All brain tissue manipulation, staining and recording was performed by Darcy G. Gordon. Brain images were obtained in TIFF format by the confocal microscope and then opened and saved as Amira Mesh (.am) stack images in Amira (version 6.0). Manual segmentation of each brain was done using Amira (version 6.0 or 2019.2). Labels were traced on eight compartments in only one brain hemisphere, except for the CX, SEZ and ROCB, which lack a clear subdivision between hemispheres. Brain grey image stacks and labels were transformed to NRRD format for template construction using the Fiji plugin SaveAsGzipNrrd<a href="#_ftn1">[1]</a>. Volume and volume similarity of labels were calculated using the Fiji toolbox MorphoLibJ<a href="#_ftn2">[2]</a>.</p> <p><strong>Acknowledgements: </strong>We thank Ming Huang (from Dr. Diana Wheeler’s laboratory) who kindly provided access to colonies from four species of the hyperdiverse ant genus <em>Pheidole</em> (<em>P. spadonia</em>, <em>P. rhea</em>, <em>P. tepicana </em>and <em>P. obtusospinosa</em>). This research was supported by National Science Foundation grants IOS 1354291 and IOS 1953393 to JT, a Marie Skłodowska-Curie Individual Fellowship BrainiAnts-660976 and Ayudas destinadas a la atracción de talento investigador a la Comunidad de Madrid en centros de I+D. This work is supported in part by the University of the <a href="https://www.sciencedirect.com/topics/engineering/basque-country">Basque Country</a> UPV/EHU grant GIU19/027.</p> <p> </p> <p><a href="#_ftnref1">[1]</a> https://github.com/iarganda/tefor</p> <p><a href="#_ftnref2">[2]</a> https://imagej.net/plugins/morpholibj</p>
Information-optimal local features automatically attract covert and overt attention
<p><strong>COVERT ATTENTION FOLDER </strong></p> <p>The <strong>"COVERT ATTENTION" folder</strong> contains <strong>one hundred .mat files</strong> (eight files for sixteen participants). </p> <p>The file name for each subject (from S01 to S16) describes:</p> <ul> <li>Condition: Experimental or Control</li> <li>Cue Validity: 50% or 80%</li> <li>Type of trials: Neutral or Non-Neutral</li> </ul> <p>Each matrix of Neutral trials and Non-Neutral Control trials contains 8 columns:</p> <ul> <li>trial number</li> <li>side of presentation of the gabor: 1=SX; 2=DX </li> <li>gabor orientation: 1 = -20°; 2 = +20°</li> <li>gabor contrast</li> <li>subject's response: 1 = -20°; 2 = +20°</li> <li>correct response: 1= right; 0=wrong</li> <li>trial type: 1= Valid trial; 2 = Invalid trial</li> <li>Reaction time</li> </ul> <p>Each matrix of Non-Neutral Experimental trials contains 10 columns:</p> <ul> <li>trial number</li> <li>side of presentation of the gabor: 1=SX; 2=DX </li> <li>gabor orientation: 1 = -20°; 2 = +20°</li> <li>gabor contrast</li> <li>subject's response: 1 = -20°; 2 = +20°</li> <li>correct response: 1= right; 0=wrong</li> <li>trial type: 1= Valid trial; 2 = Invalid trial</li> <li>Identification code of the specific <em>optimal feature</em> presented</li> <li>Identification code of the specific <em>non-optimal</em> feature presented</li> <li>Reaction time</li> </ul> <p> </p> <p><strong>OVERT ATTENTION FOLDER</strong></p> <p>The file name for each subject (from S01 to S16) describes:</p> <ul> <li>Condition: Experimental or Control</li> <li>Cue Validity: 50% or 80%</li> </ul> <p>The <strong>"OVERT ATTENTION" folder</strong> contains <strong>sixty-four .EDF files</strong> (four files for sixteen participants). The EyeLink EDF* files contain many types of data, including eye movement events (BLINKS, FIXATIONS, SACCADES), messages (including the “default” ones by Eyelink, and the custom made ones, defining the single trial’s specific properties: this type of messages starts with “Trialinfo”), button presses, and eye-data samples. Here we have used the (calibrated) x and y position data of the right eye. Custom-made matlab scripts (available upon request to the authors) have been used to parse the data into trials, filter eye position and estimate eye velocity, select exploitable trials, detect and analyze saccades, etc. The EDF file format is a highly compressed binary format, intended for use with SR Research EyeLink viewers and applications. EDF files are not readable, they need to be transformed into a readable format (typically ASCII). A tool to transform edf files into ascii is freely available on the SR Research Support online forum site: https://download.sr-support.com/dispdoc/page25.html.</p> <p>The folder also contains <strong>sixty-four .mat files</strong> (four files for sixteen participants), containing the relevant information of each trial.</p> <p>Each matrix of the Control condition contains 5 columns:</p> <ul> <li>trial number</li> <li>side of presentation of the place holder: 1=SX; 2=DX </li> <li>Type of trial: 0 = Neutral, 1 = Valid, 2 = Invalid</li> <li>Saccade to target: 1 correct saccade 0 no saccade</li> <li>Saccade latency</li> </ul> <p>Each matrix of Experimental condition contains 8 columns:</p> <ul> <li>trial number</li> <li>side of presentation of the place holder: 1=SX; 2=DX </li> <li>Identification code of the specific <em>optimal feature</em> presented</li> <li>Identification code of the specific <em>non-optimal</em> feature presented</li> <li>Type of trial: 3 = neutral, 4 = valid, 5 = invalid</li> <li>Saccade to target: 1 correct saccade 0 no saccade</li> <li>Saccade latency</li> </ul>
Dataset and additional files/softwares required for the paper "LeSICiN: A Heterogeneous Graph-based Approach for Automatic Legal Statute Identification from Indian Legal Documents"
<p>This dump contains all files and softwares required for running the codes for the paper "LeSICiN: A Heterogeneous Graph-based Approach for Automatic Legal Statute Identification from Indian Legal Documents". Specifically, these codes are available at https://github.com/Law-AI/LeSICiN.</p> <p>LeSICiN is a deep neural network for the task of Legal Statute Identification which also uses graphical properties of the document-statute citation network for training and predictions.</p> <p>We have three datasets --- train, dev and test. These are all .jsonl files with each instance dict per line; each instance dict contains the unique id, list of sentences and cited labels of the particular instance. Also, there is a fourth file --- secs.jsonl, which stores the text of all the statutes in similar format.</p> <p>schemas.json list out the metapath schemas for fact and section type nodes, while type_map.json maps the id of each node to its type (Act/Chapter/Topic/Section/Fact). </p> <p>label_tree.json and citation_network.json list out the edges for the two parts of the network in the format of a 3-tuple ('source id', 'relationship type', 'target id')</p> <p>"ils2v.bin" is the pretrained sent2vec vectorizer that can generate a 200-dim vector for each sentence</p>
Vikidia En/Fr bilingual dataset for Automatic Readability Assessment
<p>Vikidia.org is a children's encyclopedia, with content targeting 8-13 year old children, in several European languages. Our dataset contains 24660 texts distributed across 6165 articles in 2 reading levels, for English and French respectively i.e., each text in the corpus has four versions: en, en-simple, fr and fr-simple, and there are 6165 slugs in total. The uniqueness of the current dataset is that these are parallel, document level aligned texts in four versions - en, en-simple, fr, fr-simple. While we did not create paragraph/sentence level alignments on the corpus, we hope that this will be a useful dataset for future English and French research on ARA and Automatic Text Simplification. This is the first such dataset in ARA, and perhaps the first readily available French readability dataset.</p> <p>This dataset is used in the paper "A neural pairwise ranking model for automatic readability assessment" by Justin Lee and Sowmya Vajjala, to appear in Findings of ACL 2022. </p>
Dvoice : An open source dataset for Automatic Speech Recognition on African Languages and Dialects
<p>DVoice is a community initiative that aims to provide African languages and dialects with data and models to facilitate their use of voice technologies. The lack of data on these languages makes it necessary to collect data using methods that are specific to each language. Two different approaches are currently used: the DVoice platform, which is based on Mozilla Common Voice, for collecting authentic recordings from the community, and transfer learning techniques for automatically labeling the recordings. The DVoice platform currently manages 7 languages including Darija (Moroccan Arabic dialect) whose dataset appears on this version, Wolof, Mandingo, Serere, Pular, Diola and Soninke. The Swahili-labeled data present in this version was obtained after automatic labeling via the learning transfer of the Voxlingua107 dataset. For a first time, we also advocate for the increase of data given their small size that we currently have. Thus this version of the dataset contains easily identifiable augmented data.</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.