Find research datasets worth reusing

Figure 7. The mental spaces set up by the sentence If John buys the car, he will drive to Berlin.-Representing Mental Spaces and Dynamics of Natural Language Semantics

In the sentence (4) there are three proper names which constitute elements of the base space or reality space (R). The CNSB if sets up the hypothetical space (H) with elements identical to those of reality space (Figure 7). Every space’s internal structure is presented in the boxes next to them. In the next part, we will see how MultiNet’s built-in meaning representation mechanisms are capable of representing the basic principles of mental space building outlined above.

Figure 6. The mental spaces set up by the sentence Mary thinks that John smokes.-Representing Mental Spaces and Dynamics of Natural Language Semantics

The proper nouns Mary and John setup a base space (B). By the help of background knowledge and activated frames we know that they are names of female and male humans. Not having access to the previous discourse, we also consider their existence presupposed. The SVSB Mary thinks that sets up a belief space (L) relative to space B (Figure 6). The identity connector maintains the referential link between elements a and a ׳ both referring to the same person.

Figure 5. The mental spaces set up by the second interpretation of the sentence in the film, John is riding a unicorn.-Representing Mental Spaces and Dynamics of Natural Language Semantics

Second interpretation: The proper name John exists only in film space without having a counterpart in base space. Therefore, the second interpretation of the sentence is: John is a film character who is riding a unicorn in the film (Figure 5).

Figure 4. The mental spaces set up by the first interpretation of the sentence in the film, John is riding a unicorn-Representing Mental Spaces and Dynamics of Natural Language Semantics

First interpretation: The reality space (let’s call it R) contains an element a associated with the proper name John. The noun phrase a unicorn introduces an element b ׳ to the film space (call it F). I is the connector linking a in the space B to a ׳ in the space F (Figure 4). Since the elements of both mental spaces are co-referential, this connector is an identity connector. The rectangles represent the internal structure of the spaces next to them. The dashed line indicates that the space F is set up in relation to R and that it is subordinate to R in discourse.

Data set on soil physicochemical parameters, biomass accumulation and carbon credit generation in different management systems in Rio Verde, GO, Brazil

<h1>Description</h1> This repository contains a comprehensive dataset focused on soil organic carbon and its role in mitigating climate change through carbon sequestration on agricultural lands in Rio Verde, GO, Brazil. With the global imperative to reduce anthropogenic CO2 emissions, our data highlights the effectiveness of no-till agricultural practices in both improving soil quality and enhancing carbon storage. This collection represents extensive soil and biomass sampling from five distinct areas within the Cerrado region, utilizing three priority management systems: No-till with soybean and maize in sequence under rainfed conditions. No-till with soybean and maize in sequence with central pivot irrigation. First and second cuts of sugarcane. The samples were meticulously collected post-harvest and used to estimate both soil biomass accumulation and carbon stock indices. A thorough analysis of the soil's physicochemical parameters was conducted for the 0-20 cm soil profile in each area. This dataset not only provides a valuable resource for studying the impact of different no-till practices on carbon sequestration but also serves as a critical input for modeling future contributions of conservation management systems to carbon trading markets. <div> <div> </div> <div> <h2>Data Contents</h2> </div> Soil organic carbon measurements for various no-till systems. Biomass accumulation data post-harvest. Carbon stock indices derived from biomass samples. Detailed physicochemical profiles of soil samples. <div> <h2>Significance</h2> </div> This dataset is pivotal for researchers and policymakers focusing on the potentials of agricultural carbon sequestration and its implications for carbon trading schemes. It offers insights into the current contributions of no-till conservation management systems and aids in the development of future strategies to enhance carbon <h1>Metadata Description and Script</h1> </div> This repository contains two key data files that encapsulate diverse aspects of soil physicochemical parameters, biomass accumulation, and carbon credit generation across different management systems in Rio Verde, GO, Brazil. Below are descriptions of each file's contents and structure. <div> <h2>all.txt</h2> </div> This text file presents aggregated data from various sites under different agricultural management systems. Each row in the dataset represents measurements from distinct sample plots, with the following fields: <ul> <li><code>Sites</code> - Identifier for the plot location.</li> <li><code>SB</code> - Soil bulk density (g/cm³).</li> <li><code>SOC</code> - Soil organic carbon (%).</li> <li><code>Stock</code> - Carbon stock (ton/ha).</li> <li><code>Biomass</code> - Biomass accumulation (ton/ha).</li> <li><code>Credits</code> - Estimated carbon credits (ton CO2 equivalent/ha).</li> </ul> <div> <h2>Quimica.xlsx</h2> </div> This Excel file provides detailed physicochemical analyses of soil samples from different management zones in the study area. The data is structured to support in-depth analysis of soil characteristics influencing carbon sequestration capabilities. Each sheet in the workbook corresponds to a specific area, with columns typically representing: <ul> <li><code>pH</code> - Soil pH, indicating the acidity or alkalinity.</li> <li><code>EC</code> - Electrical conductivity (dS/m).</li> <li><code>Cation Exchange Capacity (CEC):</code> - (meq/100g).</li> <li><code>Organipont c Matter:</code> - (%).</li> <li><code>NPK levels</code> - Concentrations of Nitrogen (N), Phosphorus (P), and Potassium (K).</li> </ul>

opencc-by-4.0May 2024View details →

Structures extracted from the AlphaFold database for the set of sequences used in Rosenberg et al. Nat. Comm. 2022.

Structures extracted from the AlphaFold database for the set of sequences used in Rosenberg, A.A., Marx, A. & Bronstein, A.M. Codon-specific Ramachandran plots show amino acid backbone conformation depends on identity of the translated codon. Nat Commum 13, 2815 (2022) <a href="https://doi.org/10.1038/s41467-022-30390-9">https://doi.org/10.1038/s41467-022-30390-9</a>.

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Cloud_ICA: A deterministic cloud-overlap algorithm for generating a complete set of independent column atmospheres

In calculating solar radiation, climate models make many simplifications, in part to reduce computational cost and enable climate modeling, and in part from lack of understanding of critical atmospheric information. Whether known errors or unknown errors, the community's concern is how these could impact the modeled climate. The simplifications are well known and most have published studies evaluating them, but with individual studies it is difficult to compare. Here, we collect a wide range of such simplifications in either radiative transfer modeling or atmospheric conditions and assess potential errors within a consistent framework on climate‐relevant scales. We build benchmarking capability around a solar heating code (Solar‐J) that doubles as a photolysis code for chemistry and can be readily adapted to consider other errors and uncertainties. The broad classes here include: use of broad wavelength bands to integrate over spectral features; scattering approximations that alter phase function and optical depths for clouds and gases; uncertainty in ice‐cloud optics; treatment of fractional cloud cover including overlap; and variability of ocean surface albedo. We geographically map the errors in W m−2 using a full climate re‐creation for January 2015 from a weather forecasting model. For many approximations assessed here, mean errors are ∼2 W m−2 with greater latitudinal biases and are likely to affect a model's ability to match the current climate state. Combining this work with previous studies, we make priority recommendations for fixing these simplifications based on both the magnitude of error and the ease or computational cost of the fix.

opencc-zeroMay 2024View details →

Data set for "A Magnesium Binding Site And The Anomeric Effect Regulate The Abiotic Redox Chemistry Of Nicotinamide Nucleotides"

Data associated with Sebastianelli L, Kaur H, Chen Z, Krishnamurthy R, Mansy SS (2024) A magnesium binding site and the anomeric effect regulate the abiotic redox chemistry of nicotinamide nucleotides. Chem Eur J. 30, e202400411. DOI: 10.1002/chem.202400411 [<a href="https://chemistry-europe.onlinelibrary.wiley.com/doi/abs/10.1002/chem.202400411">link</a>]

Data Set for "Alteration's control on frictional behavior and the depth of the ductile shear zone in geothermal reservoirs in volcanic arcs" II: Cascade Volcanic Arc

Data set for the 48 friction experiments performed for gouge samples (altered andesitic rocks) from the Cascades used in the manuscript, "Alteration's control on frictional behavior and the depth of the ductile shear zone in geothermal reservoirs in volcanic arcs". This data set can be used in combination with the data set for the Lesser Antilles used in the same manuscript (doi:10.5281/zenodo.10912445). This large combined data set (of 108 frictional experiments) represents a unique opportunity to systematically study frictional behaviour in the framework of rate and state. All samples are tested in wet and dry conditions at 10, 30, and 50 MPa with velocity steps and slide-hold-slides. These two data sets have the further advantage of being performed with exactly the same protocol (same run in, same initial gouge thickness, same velocity steps, same hold periods), in the same machine, by the same operator (or by an operator who was trained and supervised by the original operator).

opencc-by-4.0May 2024View details →

DeltaCAN: A new data set of Canadian Arctic and subarctic coastal deltas

Arctic coasts constitute the critical interface between land and sea, and are subject to rapid changes caused by a warming climate. Current trends throughout the Arctic show increasing erosion trends, while other parts of the coast are experiencing prograding trends. Until now, a vast majority of our knowledge of Arctic coastal evolution is confined to site-specific studies with limited geospatial representation. Here, we present DeltaCAN, a novel data set on the locations of Canadian deltas larger than 500 m in width derived by visual interpretation of freely available satellite imagery. DeltaCAN is Canada's first nationwide coastal detection covering 250.000 km of coastline in the Arctic, identifying 2712 deltas. The inventory is based on inspection of remotely-sensed satellite imageries, developed through an expert-based mapping approach where we implemented a quality control mechanism to assess the completeness of the data set. The DeltaCAN data set allows for assessing changes at an unprecedented spatial extent, improving our understanding of delta morphodynamics.

dryad40/100

Data from: How many specimens make a sufficient training set for automated three dimensional feature extraction?

Deep learning has emerged as a robust tool for automating feature extraction from 3D images, offering an efficient alternative to labour-intensive and potentially biased manual image segmentation methods. However, there has been limited exploration into the optimal training set sizes, including assessing whether artificial expansion by data augmentation can achieve consistent results in less time and how consistent these benefits are across different types of traits. In this study, we manually segmented 50 planktonic foraminifera specimens from the genus Menardella to determine the minimum number of training images required to produce accurate volumetric and shape data from internal and external structures. The results reveal unsurprisingly that deep learning models improve with a larger number of training images with eight specimens being required to achieve 95% accuracy. Furthermore, data augmentation can enhance network accuracy by up to 8.0%. Notably, predicting both volumetric and shape measurements for the internal structure poses a greater challenge compared to the external structure, due to low contrast differences between different materials and increased geometric complexity. These results provide novel insight into optimal training set sizes for precise image segmentation of diverse traits and highlight the potential of data augmentation for enhancing multivariate feature extraction from 3D images.

opencc-zeroMay 2024View details →

Pose Selector Workflow - Structure Input Files for Machine Learning (Set 2)

Second set of structure input files for the docking poses of the remaining 2022 protein-ligand complexes. Together with the structure files and absolute binding free energy (ABFE) estimates shared in 10.5281/zenodo.11397017, this data can be used to train a machine-learning (ML) model predicting the ABFE of binding poses of protein-ligand complexes. More background on the workflow generating the structure files and ABFE estimates is provided in 10.5281/zenodo.11397017.

Movement data set for trust assessment (Drapebot robot cell/Profactor)

In the Drapebot project, a worker collaborates with a large industrial manipulator in two tasks: collaborative transport of carbon fibre patches and collaborative draping. To realize data-driven trust assessement, the worker is equipped with a motion tracking suit and the body movement data is labeled with the trust scores from two standard Trust questionnaire (1. Trust perception scale - HRI, Schaefer 2016; 2. Trust in industrial human robo collaboration, Charalambous, et.al. 2016). For this data set, data has been collected for the draping task from 21 participants all familiar with working with large industrial manipulators. For all sessions, body tracking was performed using the Xsens MVN Awinda tracking suit. It consists of a tight-fitting shirt, gloves, headband, and a series of straps used to attach 17 IMUs to the participant. After calibration the system uses inverse kinematics to track and log the movements of the participant at a rate of 60 Hz. The measurements include linear and angular speed, velocity, and acceleration of every skeleton tracking point (see <a href="https://www.xsens.com/hubfs/Downloads/Manuals/MVN_real-time_network_streaming_protocol_specification.pdf">XSENS manual</a> for a detailed description of avaiable measurements). Data organization There are 21 files for 21 participants. The name of the files is PID01, where the number 01 is the participant. Each file contains all the data that was generated from the XSENS motion capture system. The files are xlsx files and for each sheet inside the excel file there are different types of data: <ul> <li>Segment Orientation - Quat</li> <li>Segment Orientation - Euler</li> <li>Segment Position</li> <li>Segment Velocity</li> <li>Segment Acceleration</li> <li>Segment Angular Velocity</li> <li>Segment Angular Acceleration</li> <li>Joint Angles ZXY</li> <li>Joint Angles XZY</li> <li>Ergonomic Joint Angles ZXY</li> <li>Ergonomic Joint Angles XZY</li> <li>Center of Mass</li> <li>Sensor Free Acceleration</li> <li>Sensor Magnetic Field</li> <li>Sensor Orientation - Quat</li> <li>Sensor Orientation - Euler</li> </ul> See also: <a href="https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US">https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US</a> For more information on each specific data and/or sensors please see the xsens manual (Link above) Data Annotation In each .xlsx file the first tab (sheet) is called "Markers". It annotates the starting frame of the individual tasks. The annotations are pickup, draping, return and some files may contain a also a "fail" annotation. Failed attempts should not be taken into consideration for model training. The file trustscores.xlsx includes the results of the trust questionaires for each participant (scores for the individual items as well as the calculated overall trust scores). Items for Trust perception scale - HRI, Schaefer 2016: <ul> <li>Which % of time does the robot <ul> <li>Function successfully</li> <li>Act consistently</li> <li>Communicate with people</li> <li>Provide feedback</li> <li>Malfunction</li> <li>Follow directions</li> <li>Meet the needs of the mission</li> <li>Perform exactly as instructed</li> <li>Have errors</li> </ul> </li> <li>Which % of the time is the robot: <ul> <li>Unresponsive</li> <li>Dependable</li> <li>Reliable</li> <li>Predictable</li> </ul> </li> </ul> Items for Trust in industrial human robo collaboration, Charalambous, et.al. 2016: <ul> <li>The way the robot moved made me uncomfortable</li> <li>I felt I could rely on the robot to do what it was supposed to do  </li> <li>The speed at which the gripper picked up and released the components made me uneasy    </li> <li>I felt safe interacting with the robot</li> <li>I knew the gripper would not drop the components</li> <li>The size of the robot did not intimidate me</li> <li>The robot gripper did not look reliable</li> <li>I was comfortable the robot would not hurt me</li> <li>I trusted that the robot was safe to cooperate with</li> <li>The gripper seemed like it could be trusted</li> </ul>   K. E. Schaefer, Measuring Trust in Human Robot Interactions: Development of the “Trust Perception Scale-HRI”. Boston, MA: Springer US, 2016, pp. 191–218. G. Charalambous, S. Fletcher, and P. Webb, “The development of a scale to evaluate trust in industrial human-robot collaboration,” International Journal of Social Robotics, vol. 8, pp. 193–209, 2016.

Reference data set used to validate the hybrid cropland map at 500m (Fritz, S. 2024)

This is a reference data set for validation of the hybrid cropland map at 500m resolution for the year 2019 (Fritz, 2024, map <a title="Hybrid cropland map (GLAD/WorldCereal)" href="../doi/10.5281/zenodo.10818823" target="_blank" rel="noopener">available here</a>) Sampling design: random whithin areas of improvement, where the WorldCereal map is performing better (less errors) than the GLAD cropland map 2019.  Number of sample sites: 500 Method of data collection: visual interpreation of various sources of information, including very high resolution images and photos.  Tool for data collection: <a href="https://www.geo-wiki.org" target="_blank" rel="noopener">Geo-Wiki</a>

Train and Evaluation Code, Road Classification Models and Test set of the paper "Insights into the Effects of Image Overlap and Image Size on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography"

This repository contains the Python scripts built for training and evaluation of the implementation, together with the test data and the resulting road segmentation models corresponding to the paper "Insights into the Effects of Image Overlap and Image Size on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography". The scripts make use of the Tensorflow with Keras framework and their additional required dependencies. The training and validation set is based on the binary SROADEX dataset (<a href="../records/6482346">https://zenodo.org/records/6482346</a>) that was re-split into tiles that feature the image resolutions (256 x 256, 512 x 512, and 1024 x 1024 pixels) and image overlaps (0% and 12.5%) considered in this study. The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography that represents the axes of the different types of roads (urban, interurban and rural). This binary road data contains information from 16 full orthoimages (28.5 km * 18.5 km) with spatial resolution of 0.5 m/pixel from the insular and peninsular Spanish territory. Due to the size on disk of approximately 492 gigabytes, this training and validation data is only available upon request from the corresponding author. The test set has been generated from a novel area from Palencia (Spain) and features 18 million pixels labelled with the positive "Road" class. The test sets are provided in the repository for each resolution (with no overlap), so that additional DL models can be evaluated on the same data and compared with the results achieved in this study. The structure of the information shared in this repository is as follows: The scripts have been grouped by tile resolution (256, 512 and 1024). First, the test set and the evaluation script can be found. For each tile resolution, there are two subfolders (corresponding to the "no overlap" and "12.5% overlap"). In each case, the Python scripts for training the models in the three repetitions are shared, and the trained models (H5 format) are shared in compressed form. Finally, for each resolution we also share the testing dataset which consists of two folders. The material is distributed under a CC-BY 4.0 license.

Movement data set for trust assessment (Drapebot robot cell/Dallara)

In the Drapebot project, a worker collaborates with a large industrial manipulator in two tasks: collaborative transport of carbon fibre patches and collaborative draping. To realize data-driven trust assessement, the worker is equipped with a motion tracking suit and the body movement data is labeled with the trust scores from two standard Trust questionnaire (1. Trust perception scale - HRI, Schaefer 2016; 2. Trust in industrial human robo collaboration, Charalambous, et.al. 2016). For this data set, data has been collected for the draping task in a near-production setting from 5 participants all familiar with carbon-fibre draping. For all sessions, body tracking was performed using the Xsens MVN Awinda tracking suit. It consists of a tight-fitting shirt, gloves, headband, and a series of straps used to attach 17 IMUs to the participant. After calibration the system uses inverse kinematics to track and log the movements of the participant at a rate of 60 Hz. The measurements include linear and angular speed, velocity, and acceleration of every skeleton tracking point (see <a href="https://www.xsens.com/hubfs/Downloads/Manuals/MVN_real-time_network_streaming_protocol_specification.pdf">XSENS manual</a> for a detailed description of avaiable measurements). Data organization There are 5 files for 5 participants. The name of the files is PID01, where the number 01 is the participant. Each file contains all the data that was generated from the XSENS motion capture system. The files are xlsx files and for each sheet inside the excel file there are different types of data: <ul> <li>Segment Orientation - Quat</li> <li>Segment Orientation - Euler</li> <li>Segment Position</li> <li>Segment Velocity</li> <li>Segment Acceleration</li> <li>Segment Angular Velocity</li> <li>Segment Angular Acceleration</li> <li>Joint Angles ZXY</li> <li>Joint Angles XZY</li> <li>Ergonomic Joint Angles ZXY</li> <li>Ergonomic Joint Angles XZY</li> <li>Center of Mass</li> <li>Sensor Free Acceleration</li> <li>Sensor Magnetic Field</li> <li>Sensor Orientation - Quat</li> <li>Sensor Orientation - Euler</li> </ul> See also: <a href="https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US">https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US</a> For more information on each specific data and/or sensors please see the xsens manual (Link above) Data Annotation In each .xlsx file the first tab (sheet) is called "Markers". It annotates the starting frame of the individual tasks. The annotations are pickup, draping, return and some files may contain a also a "fail" annotation. Failed attempts should not be taken into consideration for model training. The file trustscores.xlsx includes the results of the trust questionaires for each participant (scores for the individual items as well as the calculated overall trust scores). Items for Trust perception scale - HRI, Schaefer 2016: <ul> <li>Which % of time does the robot <ul> <li>Function successfully</li> <li>Act consistently</li> <li>Communicate with people</li> <li>Provide feedback</li> <li>Malfunction</li> <li>Follow directions</li> <li>Meet the needs of the mission</li> <li>Perform exactly as instructed</li> <li>Have errors</li> </ul> </li> <li>Which % of the time is the robot: <ul> <li>Unresponsive</li> <li>Dependable</li> <li>Reliable</li> <li>Predictable</li> </ul> </li> </ul> Items for Trust in industrial human robo collaboration, Charalambous, et.al. 2016: <ul> <li>The way the robot moved made me uncomfortable</li> <li>I felt I could rely on the robot to do what it was supposed to do  </li> <li>The speed at which the gripper picked up and released the components made me uneasy    </li> <li>I felt safe interacting with the robot</li> <li>I knew the gripper would not drop the components</li> <li>The size of the robot did not intimidate me</li> <li>The robot gripper did not look reliable</li> <li>I was comfortable the robot would not hurt me</li> <li>I trusted that the robot was safe to cooperate with</li> <li>The gripper seemed like it could be trusted</li> </ul>   K. E. Schaefer, Measuring Trust in Human Robot Interactions: Development of the “Trust Perception Scale-HRI”. Boston, MA: Springer US, 2016, pp. 191–218. G. Charalambous, S. Fletcher, and P. Webb, “The development of a scale to evaluate trust in industrial human-robot collaboration,” International Journal of Social Robotics, vol. 8, pp. 193–209, 2016.

Movement data set for trust assessment (Drapebot robot cell/DLR)

In the Drapebot project, a worker collaborates with a large industrial manipulator in two tasks: collaborative transport of carbon fibre patches and collaborative draping. To realize data-driven trust assessement, the worker is equipped with a motion tracking suit and the body movement data is labeled with the trust scores from two standard Trust questionnaire (1. Trust perception scale - HRI, Schaefer 2016; 2. Trust in industrial human robo collaboration, Charalambous, et.al. 2016). For this data set, data has been collected for the draping task from 21 participants all familiar with working with large industrial manipulators. For all sessions, body tracking was performed using the Xsens MVN Awinda tracking suit. It consists of a tight-fitting shirt, gloves, headband, and a series of straps used to attach 17 IMUs to the participant. After calibration the system uses inverse kinematics to track and log the movements of the participant at a rate of 60 Hz. The measurements include linear and angular speed, velocity, and acceleration of every skeleton tracking point (see <a href="https://www.xsens.com/hubfs/Downloads/Manuals/MVN_real-time_network_streaming_protocol_specification.pdf">XSENS manual</a> for a detailed description of avaiable measurements). Data organization There are 21 files for 21 participants. The name of the files is PID01, where the number 01 is the participant. Each file contains all the data that was generated from the XSENS motion capture system. The files are xlsx files and for each sheet inside the excel file there are different types of data: <ul> <li>Segment Orientation - Quat</li> <li>Segment Orientation - Euler</li> <li>Segment Position</li> <li>Segment Velocity</li> <li>Segment Acceleration</li> <li>Segment Angular Velocity</li> <li>Segment Angular Acceleration</li> <li>Joint Angles ZXY</li> <li>Joint Angles XZY</li> <li>Ergonomic Joint Angles ZXY</li> <li>Ergonomic Joint Angles XZY</li> <li>Center of Mass</li> <li>Sensor Free Acceleration</li> <li>Sensor Magnetic Field</li> <li>Sensor Orientation - Quat</li> <li>Sensor Orientation - Euler</li> </ul> See also: <a href="https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US">https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US</a> For more information on each specific data and/or sensors please see the xsens manual (Link above) Data Annotation In each .xlsx file the first tab (sheet) is called "Markers". It annotates the starting frame of the individual tasks. The annotations are pickup, draping, return and some files may contain a also a "fail" annotation. Failed attempts should not be taken into consideration for model training. The file trustscores.xlsx includes the results of the trust questionaires for each participant (scores for the individual items as well as the calculated overall trust scores). Items for Trust perception scale - HRI, Schaefer 2016: <ul> <li>Which % of time does the robot <ul> <li>Function successfully</li> <li>Act consistently</li> <li>Communicate with people</li> <li>Provide feedback</li> <li>Malfunction</li> <li>Follow directions</li> <li>Meet the needs of the mission</li> <li>Perform exactly as instructed</li> <li>Have errors</li> </ul> </li> <li>Which % of the time is the robot: <ul> <li>Unresponsive</li> <li>Dependable</li> <li>Reliable</li> <li>Predictable</li> </ul> </li> </ul> Items for Trust in industrial human robo collaboration, Charalambous, et.al. 2016: <ul> <li>The way the robot moved made me uncomfortable</li> <li>I felt I could rely on the robot to do what it was supposed to do  </li> <li>The speed at which the gripper picked up and released the components made me uneasy    </li> <li>I felt safe interacting with the robot</li> <li>I knew the gripper would not drop the components</li> <li>The size of the robot did not intimidate me</li> <li>The robot gripper did not look reliable</li> <li>I was comfortable the robot would not hurt me</li> <li>I trusted that the robot was safe to cooperate with</li> <li>The gripper seemed like it could be trusted</li> </ul>   K. E. Schaefer, Measuring Trust in Human Robot Interactions: Development of the “Trust Perception Scale-HRI”. Boston, MA: Springer US, 2016, pp. 191–218. G. Charalambous, S. Fletcher, and P. Webb, “The development of a scale to evaluate trust in industrial human-robot collaboration,” International Journal of Social Robotics, vol. 8, pp. 193–209, 2016.

Data Set for the Journal Article "Heron: Visualizing and Controlling Chemical Reaction Explorations and Networks"

This data archive contains all data newly created in the following publication: Charlotte H. Müller, Miguel Steiner, Jan P. Unsleber, Thomas Weymuth, Moritz Bensberg, Katja- Sophia Csizi, Maximilian Mörchen, Paul L. Türtscher, and Markus Reiher, "Heron: Visualizing and Controlling Chemical Reaction Explorations and Networks", in preparation. The directory contents are as follows: <ul> <li>steered_eschenmoser.tar.xz: Dump of the database created during the steered exploration</li> <li>steered_exploration_protocol_chemoton_3.1.json: Protocol used for the steered exploration</li> </ul>

opencc-by-nc-4.0May 2024View details →

AIR-DERSL-Earthquake Feature Set

The dataset covers the data and characteristics of houses damaged by five mega earthquakes. Among them, some of the pre-disaster data was disturbed by clouds and fog, and the file name was stored in the PreCloud file.