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”
Automatic Diagnosis of Spinal Stenosis on CT
ClinicalTrials.gov study NCT03746561. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
All-Sky Automatic Survey (ASAS) Photometry of ROSAT All-Sky Survey Sources
Photometric data from the All-Sky Automatic Survey (ASAS) - South (Declination less than 29 degrees) Survey have been used for the identification of bright stars located near the sources from the ROSAT All Sky Survey Bright Source Catalog (RASSBSC). In total, 6,028 stars brighter than 12.5 magnitude in the I- or the V-bands have been selected and analyzed for periodicity. Altogether, 2,302 variable stars have been found with periods ranging from 0.137 days to 193 days. Most of these stars have X-ray emission of coronal origin, but there are a few cataclysmic binaries and early type stars with colliding winds. Whenever it was possible, the authors collected data available in the literature so as to verify the periods and to classify variable objects. The catalog includes 1,936 stars (1,233 new) considered to be variable due to presence of spots (rotationally variable), 127 detached eclipsing binary stars (33 new), 124 contact binaries (11 new), 96 eclipsing stars with deformed components (19 new), 13 ellipsoidal variables (4 new), 5 miscellaneous variables and one pulsating RR Lyr type star (blended with an eclipsing binary). More than 70% of the new variable stars have amplitudes smaller than 0.1 magnitudes, but for the star ASAS 063656-0521.0 the authors have found the largest known amplitude of brightness variations due to the presence of spots (up to Delta V = 0.8 magnitudes). This table was created by the HEASARC in May 2018, based on <a href="https://cdsarc.cds.unistra.fr/ftp/cats/J/AcA/62/67">CDS Catalog J/AcA/62/67</a> files catalog.dat and remarks.dat. This is a service provided by NASA HEASARC .
High Mountain Asia Langtang Automatic Weather Station Measurements V001
This data set contains meteorological data, such as air temperature, pressure, rainfall intensity, relative humidity, and wind direction/speed measured by the International Centre for Integrated Mountain Development (ICIMOD).
Comparing gene expression of human cardiac stem cells between automatic cell culture and manual handling culture
GEO Series GSE44032. Homo sapiens. 4 samples. Type: Expression profiling by array.
Dataset for the paper "TestDossier: A Dataset of Tested Values Automatically Extracted from Test Execution"
<p><strong>Dataset for the paper "TestDossier: A Dataset of Tested Values Automatically Extracted from Test Execution".</strong></p><p> </p><p>We store values of the basic build-in Python types: numeric (int, float, and complex), boolean (bool), and text sequence (str). We also store values of the types: sequence (list, tuple, and range), set (frozenset and set), and mapping (dict) that include only basic types. To create the dataset, we monitored the test suites of 15 Python Standard Libraries. TestDossier contains 1,234 distinct argument/variable names and 133,169 dis- tinct values, leading to a total of 12,9M individual values.</p>
Dataset for the paper "TestDossier: A Dataset of Tested Values Automatically Extracted from Test Execution"
<p><strong>Dataset for the paper "TestDossier: A Dataset of Tested Values Automatically Extracted from Test Execution".</strong></p><p>We store values of the basic build-in Python types: numeric (int, float, and complex), boolean (bool), and text sequence (str). We also store values of the types: sequence (list, tuple, and range), set (frozenset and set), and mapping (dict) that include only basic types. To create the dataset, we monitored the test suites of 15 Python Standard Libraries. TestDossier contains 1,234 distinct argument/variable names and 133,169 dis- tinct values, leading to a total of 12,9M individual values.</p>
Dataset related to the article "Comparison between Automatic and Semiautomatic System for the 3D Echocardiographic Multiparametric Evaluation of RV Function and Dimension"
<p>This record contains raw data related to the article “Comparison between Automatic and Semiautomatic System for the 3D Echocardiographic Multiparametric Evaluation of RV Function and Dimension”</p> <p>Background: The right ventricle (RV) plays a pivotal role in cardiovascular diseases and 3-dimensional echocardiography (3DE) has gained acceptance for the evaluation of RV volumes and function. Recently, a new artificial intelligence (AI)–based automated 3DE software for RV evaluation has been proposed and validated against cardiac magnetic resonance. The aims of this study were three-fold: (i) feasibility of the AI-based 3DE RV quantification, (ii) comparison with the semi-automatic 3DE method and (iii) assessment of 2-dimensional echocardiography (2DE) and strain measurements obtained automatically. Methods: A total of 203 subject (122 normal and 81 patients) underwent a 2DE and both the semi-automatic and automatic 3DE methods for Doppler standard, RV volumes and ejection fraction (RVEF) measurements. Results: The automatic 3DE method was highly feasible, faster than 2DE and semi-automatic 3DE and data obtained were comparable with traditional measurements. Both in normal subjects and patients, the RVEF was similar to the two 3DE methods and 2DE and strain measurements obtained by the automated system correlated very well with the standard 2DE and strain ones. Conclusions: results showed that rapid analysis and excellent reproducibility of AI-based 3DE RV analysis supported the routine adoption of this automated method in the daily clinical workflow.</p>
A 3D dental model dataset with pre/post-orthodontic treatment for automatic tooth alignment
<p>This is the first public 3D orthodontic dental dataset, comprising 1060 pairs of pre/post-treatment dental models sourced from 435 patients aged between 8 and 35 years old. The dataset encompasses raw dental models with diverse malocclusion, e.g., tooth crowding, deep overbite, and deep overjet; and comprehensive professional annotations, including tooth segmentation labels, tooth position information, and crown landmarks.</p>
ClearCausal: Cross Layer Causal Analysis for Automatic Microservice Performance Debugging
<p>Dataset for the paper: <em>ClearCausal: Cross Layer Causal Analysis for Automatic Microservice Performance Debugging.</em></p>
Data from Automatic Lidar and Ceilometer (ALC) measurements at Paris – SIRTA Atmospheric Observatory (PASIRS) from 2023-06-16 to 2023-06-16 [RAW]
<p>Original data files from ALC measurements.</p>
Data from Automatic Lidar and Ceilometer (ALC) measurements at Paris – SIRTA Atmospheric Observatory (PASIRS) from 2023-06-15 to 2023-06-15 [RAW]
<p>Original data files from ALC measurements.</p>
Data from Automatic Lidar and Ceilometer (ALC) measurements at Paris – SIRTA Atmospheric Observatory (PASIRS) from 2023-06-16 to 2023-06-16 [RAW]
<p>Original data files from ALC measurements.</p>
Dataset related to article "Automatic Planning of the Lower-Extremities for Total Marrow Irradiation Using Volumetric Modulated Arc Therapy"
<p>This record contains raw data related to article "Automatic Planning of the Lower-Extremities for Total Marrow Irradiation Using Volumetric Modulated Arc Therapy"</p> <p><strong>Abstract</strong></p> <p><strong>Purpose</strong>: Total marrow (and lymphoid) irradiation (TMI-TMLI) is limited by the couch travel range of modern linacs, which forces to split the treatment delivery into two plans with opposite orientations: a head-first supine upper-body plan, and a feet-first supine lower-extremities plan. A specific field junction is thus needed to obtain adequate target coverage in the overlap region of the two plans. In this study, an automatic procedure was developed for field junction creation and lower-extremities plan optimization.</p> <p><strong>Methods</strong>: Ten patients treated with TMI-TMLI at our institution were selected retrospectively. The planning of the lower-extremities was performed automatically. Target volume parameters (CTV_J-V<sub>98%</sub>>98%) at the junction region and several dose statistics (D<sub>98%</sub>, D<sub>mean</sub>, and D<sub>2%</sub>) were compared between automatic and manual plans. The Modulation Complexity Score (MCS) was used to assess plan complexity.</p> <p><strong>Results</strong>: The automatic procedure required 60-90 minutes, depending on the case. All automatic plans achieved clinically acceptable dosimetric results (CTV_J-V<sub>98%</sub>>98%), with significant differences found at the junction region, where D<sub>mean </sub>and D<sub>2% </sub>increased on average by 2.4% (p<0.03) and 3.0% (p<0.02), respectively. Similar plan complexity was observed (median MCS=0.12). Since March 2022 the automatic procedure has been introduced in our clinic, reducing the TMI-TMLI simulation-to-delivery schedule by 2 days.</p> <p><strong>Conclusions</strong>: The developed procedure allowed to streamline the treatment planning of TMI-TMLI, increasing efficiency and standardization, preventing human errors, while maintaining the dosimetric plan quality and complexity of manual plans. Automated strategies can simplify the future adoption and clinical implementation of TMI-TMLI treatments in new centers.</p> <p> </p>
Time series from "Telescope: An Automatic Feature Extraction and Transformation Approach for Time Series Forecasting on a Level-Playing Field"
<p>Time Series used in the evaluation of "Telescope: An Automatic Feature Extraction and Transformation Approach for Time Series Forecasting on a Level-Playing Field"</p>
Replication Package for the Paper: "A Machine Learning Based Ensemble Method for Automatic Classification of Decisions"
<p>This is the replication package for the paper: "A Machine Learning Based Ensemble Method for Automatic Classification of Decisions". It contains the source code and dataset of our experiment for the replication by other researchers. In the meanwhile, we provide brief description of the files in the replication package in the following.</p> <p><strong>1. code folder</strong></p> <ul> <li><em>experiment.py </em>contains the source code for our experiment, which is conducted on Windows 10 and Python 3.7.0. <strong>Note that you may get slightly</strong> <strong>different experiment results when conducting the experiments on different environment configurations.</strong></li> <li><em>requirements.txt</em> records all the installation packages and their version numbers needed for the current program to run. You can use "<em>pip install -r requirements.txt</em>" to rebuild the project and install all dependencies. <strong>Note that you may get slightly different experiment results when using different packages or versions. </strong></li> </ul> <p><strong>2. dataset folder</strong></p> <ul> <li><em>decisions.xlsx </em>contains 848 labelled sentence-level decisions from the Hibernate developer mailing list.</li> </ul>
Database for Automatic Risk Tuning in Short-Term Electricity Market Models Using a Machine Learning Proxy
<p>Database (2014-2018) used in the paper entitled "Automatic Risk Tuning in Short-Term Electricity Market Models Using a Machine Learning Proxy".</p> <p>This database includes :</p> <ul> <li>the inputs and outputs of the probabilistic forecaster which predicts the Belgian system imbalance.</li> <li>the market data related to the construction of the balancing market.</li> </ul> <p>These data are obtained from the Belgian transmission system operator (Elia) and the European Network of Transmission system Operators (ENTSO-E).</p> <p>If you use these data, please refer to the following paper:</p> <p>J. Bottieau, K. Bruninx, A. Sanjab, Z. De Grève, F. Vallée and J-F. Toubeau, “Automatic Risk Tuning in Short-Term Electricity Market Models Using a Machine Learning Proxy,”.</p>
Replication Package for the Paper: "A Machine Learning Based Ensemble Method for Automatic Classification of Decisions: A Study of the Hibernate Developer Mailing List"
<p>This is the replication package for the paper: "A Machine Learning Based Ensemble Method for Automatic Classification of Decisions: A Study of the Hibernate Developer Mailing List". It contains the source code and dataset of our experiment for the replication by other researchers. In the meanwhile, we provide brief description of the files in the replication package below.</p> <p><strong>1. code folder</strong></p> <ul> <li><em>experiment.py </em>contains the source code for our experiment, which is conducted on Windows 10 and Python 3.7.0. <strong>Note that you may get slightly</strong> <strong>different experiment results when conducting the experiments on different environment configurations.</strong></li> <li><em>requirement.txt</em> records all the installation packages and their version numbers needed for the current program to run. You can use "<em>pip install -r requirement.txt</em>" to rebuild the project and install all dependencies. <strong>Note that you may get slightly different experiment results when using different packages or versions. </strong></li> </ul> <p><strong>2. dataset folder</strong></p> <ul> <li><em>decisions.xlsx </em>contains 844 labelled sentence-level decisions from the Hibernate developer mailing list.</li> </ul>
Replication Package for the Paper: "Will Data Influence the Experiment Results?: A Replication Study of Automatic Identification of Decisions"
<p>This is the replication package for the paper: "Will Data Influence the Experiment Results?: A Replication Study of Automatic Identification of Decisions". It contains the source code and dataset of our experiment for the replication by other researchers. In the meanwhile, we provide brief description of the files in the replication package below.</p> <p><strong>1. main_code folder</strong></p> <ul> <li><em>automatic_approach.py </em>contains the main source code of the automatic approach for identifying decisions in our experiment, which is conducted on MacOs and Python 3.7.9. <strong>Note that you may get slightly</strong> <strong>different experiment results when conducting the experiments on different environment configurations.</strong></li> <li><em>requirement.txt</em> records all the installation packages and their version numbers needed for the current program to run. You can use "<em>pip install -r requirement.txt</em>" to rebuild the project and install all dependencies. <strong>Note that you may get slightly different experiment results when using different packages or versions. </strong></li> </ul> <p><strong>2. dataset folder</strong></p> <ul> <li><em>EASE2020 - 650 decisions.xlsx </em>contains 650 decision sentences from our previous work (EASE2020)</li> <li><em>EASE2020 - 650 non decisions.xlsx </em>contains 650 non-decision sentences from our previous work (EASE2020)</li> <li><em>Our 844 relabeled decisions.xlsx</em> contains 844 relabeled decisions in this work.</li> <li><em>Our 750 assumptions.xlsx</em> contains 750 assumptions from our previous work (APSEC2019)</li> </ul> <p><strong>3. RQ1 folder</strong></p> <ul> <li><em>experiment_RQ1.py</em> contains the main source code of the experiment for answering RQ1, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em></li> </ul> <p><strong>4. RQ2 folder</strong></p> <ul> <li><em>experiment_RQ2.py</em> contains the main source code of the experiment for answering RQ2, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em></li> </ul> <p> </p> <p><strong>5. RQ3 folder</strong></p> <ul> <li><em>experiment_RQ3.py</em> contains the main source code of the experiment for answering RQ3, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em></li> </ul> <p> </p>
Replication Package for the Paper: "Will Data Influence the Experiment Results?: A Replication Study of Automatic Identification of Decisions"
<p>This is the replication package for the paper: "Will Data Influence the Experiment Results?: A Replication Study of Automatic Identification of Decisions". It contains the source code and dataset of our experiment for the replication by other researchers. In the meanwhile, we provide brief description of the files in the replication package below.</p> <p><strong>1. main_code folder</strong></p> <ul> <li><em>automatic_approach.py </em>contains the main source code of the automatic approach for identifying decisions in our experiment, which is conducted on MacOs and Python 3.7.9. <strong>Note that you may get slightly</strong> <strong>different experiment results when conducting the experiments on different environment configurations.</strong></li> <li><em>requirement.txt</em> records all the installation packages and their version numbers needed for the current program to run. You can use "<em>pip install -r requirement.txt</em>" to rebuild the project and install all dependencies. <strong>Note that you may get slightly different experiment results when using different packages or versions. </strong></li> </ul> <p><strong>2. dataset folder</strong></p> <ul> <li><em>EASE2020 - 650 decisions.xlsx </em>contains 650 decision sentences from our previous work (EASE2020)</li> <li><em>EASE2020 - 650 non decisions.xlsx </em>contains 650 non-decision sentences from our previous work (EASE2020)</li> <li><em>Our 844 relabeled decisions.xlsx</em> contains 844 relabeled decisions in this work.</li> <li><em>Our 750 assumptions.xlsx</em> contains 750 assumptions from our previous work (APSEC2019)</li> </ul> <p><strong>3. RQ1 folder</strong></p> <ul> <li><em>experiment_RQ1.py</em> contains the main source code of the experiment for answering RQ1, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em></li> </ul> <p><strong>4. RQ2 folder</strong></p> <ul> <li><em>experiment_RQ2.py</em> contains the main source code of the experiment for answering RQ2, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em></li> </ul> <p><strong>5. RQ3 folder</strong></p> <ul> <li><em>experiment_RQ3.py</em> contains the main source code of the experiment for answering RQ3, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em></li> </ul>
Replication Package for the Paper: "Will Data Influence the Experiment Results?: A Replication Study of Automatic Identification of Decisions"
<p>This is the replication package for the paper: "Will Data Influence the Experiment Results?: A Replication Study of Automatic Identification of Decisions". It contains the source code and dataset of our experiment for the replication by other researchers. In the meanwhile, we provide brief description of the files in the replication package below.</p> <p><strong>1. main_code folder</strong></p> <ul> <li><em>automatic_approach.py </em>contains the main source code of the automatic approach for identifying decisions in our experiment, which is conducted on MacOs and Python 3.7.9. <strong>Note that you may get slightly</strong> <strong>different experiment results when conducting the experiments on different environment configurations.</strong></li> <li><em>requirement.txt</em> records all the installation packages and their version numbers needed for the current program to run. You can use "<em>pip install -r requirement.txt</em>" to rebuild the project and install all dependencies. <strong>Note that you may get slightly different experiment results when using different packages or versions. </strong></li> </ul> <p><strong>2. dataset folder</strong></p> <ul> <li><em>EASE2020 - 650 decisions.xlsx </em>contains 650 decision sentences from our previous work (EASE2020)</li> <li><em>EASE2020 - 650 non decisions.xlsx </em>contains 650 non-decision sentences from our previous work (EASE2020)</li> <li><em>Our 844 relabeled decisions.xlsx</em> contains 844 relabeled decisions in this work.</li> <li><em>Our 750 assumptions.xlsx</em> contains 750 assumptions from our previous work (APSEC2019)</li> </ul> <p><strong>3. RQ1 folder</strong></p> <ul> <li><em>experiment_RQ1.py</em> contains the main source code of the experiment for answering RQ1, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em></li> </ul> <p><strong>4. RQ2 folder</strong></p> <ul> <li><em>experiment_RQ2.py</em> contains the main source code of the experiment for answering RQ2, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em></li> </ul> <p><strong>5. RQ3 folder</strong></p> <ul> <li><em>experiment_RQ3.py</em> contains the main source code of the experiment for answering RQ3, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em></li> </ul>
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.