cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14855728
title: Dataset for DiffSMol: Generating 3D Small Binding Molecules Using Shape-Conditioned Diffusion Models with Guidance

description: This is the dataset repository of our paper: Generating 3D Small Binding Molecules Using Shape-Conditioned Di ffusion Models with Guidance.

This paper has been accepted by Nature Machine Intelligence.

All the results in our manuscript can be found in the paper_results.zip. For both SMG and PMG, we provided our generated molecules and their corresponding properties.

MOSES2_training_val_dataset.pkl is the processed training dataset in our model.

The arxiv preprint of our paper is available at https://arxiv.org/abs/2502.06027
DOI: 10.5281/zenodo.14825866
title: Dataset for "RAIBO2: Highly efficient quadruped robot completing full marathon with a single battery charge"
DOI: 10.5281/zenodo.14825865
title: Dataset for "RAIBO2: Highly efficient quadruped robot completing full marathon with a single battery charge"
DOI: 10.5281/zenodo.14862088
title: Dataset for "RAIBO2: Highly efficient quadruped robot completing full marathon with a single battery charge"
DOI: 10.5281/zenodo.14862849
title: Distribution and Morphometry of Halalaimus species
DOI: 10.5281/zenodo.14862850
title: Distribution and Morphometry of Halalaimus species
DOI: 10.5281/zenodo.14862914
title: AMP test

description: This dataset includes aims trajectories and training data
DOI: 10.5281/zenodo.14862915
title: AMP test

description: This dataset includes aims trajectories and training data
DOI: 10.5281/zenodo.14862978
title: Development of a prediction model for urinary tract infection following unilateral pyeloplasty in children
DOI: 10.5281/zenodo.14862979
title: Development of a prediction model for urinary tract infection following unilateral pyeloplasty in children
DOI: 10.57707/fmi-b2share.bac65edfcadb459b89248090d2412240
title: Data for manuscript "Emulator-based calibration of a dynamic grassland model using recurrent neural networks and Hamiltonian Monte Carlo" by Aakula et al.

description: Data and python code for the manuscript "Emulator-based calibration of a dynamic grassland model using recurrent neural networks and Hamiltonian Monte Carlo", for performing emulator hyperparameter optimization and training. Python file optimize_LSTM_emulator.py can be used either for training an LSTM emulator with predefined hyperparameters or to optimize hyperparameters from a given hyperparameter space. The training data for each fold is included in the files of shape training_data_fold_{}.parquet. The data is obtained from model simulations, including model inputs (meteorological forcings obtained from ERA5 data), model parameters (sampled from distributions defined in the manuscript) and model (BASGRA) outputs. Text file examples.txt gives instructions and examples on running the script.
DOI: 10.5281/zenodo.14862990
title: Corn Kernel Images Dataset

description: Description:







The Corn Kernel Images Dataset is a comprehensive collection of 78,881 images of individual corn kernels. These images were captured after the kernels were harvested, processed, and hand-shelled, primarily for agricultural research and data analysis.

 







Download Dataset

 









Image Capture Process:



The kernels were singulated using a seed meter and imaged under both front and back lighting conditions, enhancing visual detail.

This imaging method ensured high clarity and consistency for each kernel, capturing critical aspects like size, shape, and texture.


Applications:



Agriculture: The dataset can be used for machine learning models focused on crop health analysis, yield predictions, and kernel classification.

Automation: This dataset provides valuable input for automated systems in grain sorting and quality control.

Breeding Programs: Plant breeders can utilize the data for phenotypic analysis and crop improvement efforts.


Advanced Techniques and Data Augmentation: In addition to the core dataset, users can apply various data augmentation techniques, such as:



Rotation and Scaling: This enhances the robustness of AI models by providing variability in kernel orientation.

Lighting Adjustments: Simulating different lighting conditions can improve model performance in diverse real-world environments.

Texture and Shape Analysis: The high-resolution images allow for detailed texture mapping, which can be integrated into AI training pipelines for accurate kernel identification.


Conclusion:

This dataset offers extensive opportunities for researchers and professionals in the agriculture and AI domains. By providing high-quality images and comprehensive metadata, it serves as a valuable tool for studying corn kernel morphology, developing AI models, and improving agricultural practices. The potential for further data linkage with the Corn Ear Physical Measurements dataset amplifies its utility, especially for advanced phenotyping studies.

This dataset is sourced from Kaggle.
DOI: 10.5281/zenodo.14862991
title: Corn Kernel Images Dataset

description: Description:







The Corn Kernel Images Dataset is a comprehensive collection of 78,881 images of individual corn kernels. These images were captured after the kernels were harvested, processed, and hand-shelled, primarily for agricultural research and data analysis.

 







Download Dataset

 









Image Capture Process:



The kernels were singulated using a seed meter and imaged under both front and back lighting conditions, enhancing visual detail.

This imaging method ensured high clarity and consistency for each kernel, capturing critical aspects like size, shape, and texture.


Applications:



Agriculture: The dataset can be used for machine learning models focused on crop health analysis, yield predictions, and kernel classification.

Automation: This dataset provides valuable input for automated systems in grain sorting and quality control.

Breeding Programs: Plant breeders can utilize the data for phenotypic analysis and crop improvement efforts.


Advanced Techniques and Data Augmentation: In addition to the core dataset, users can apply various data augmentation techniques, such as:



Rotation and Scaling: This enhances the robustness of AI models by providing variability in kernel orientation.

Lighting Adjustments: Simulating different lighting conditions can improve model performance in diverse real-world environments.

Texture and Shape Analysis: The high-resolution images allow for detailed texture mapping, which can be integrated into AI training pipelines for accurate kernel identification.


Conclusion:

This dataset offers extensive opportunities for researchers and professionals in the agriculture and AI domains. By providing high-quality images and comprehensive metadata, it serves as a valuable tool for studying corn kernel morphology, developing AI models, and improving agricultural practices. The potential for further data linkage with the Corn Ear Physical Measurements dataset amplifies its utility, especially for advanced phenotyping studies.

This dataset is sourced from Kaggle.
DOI: 10.6084/m9.figshare.26143534
title: A Similarity Law-based Approach for Transferability of Urban Expansion Model

description: Abstract: Due to spatial heterogeneity and temporal non-stationarity, urban expansion models are often only applicable locally. This paper proposes a similarity law-based method for transferability of urban expansion model. The method utilizes the geographically optimal similarity (GOS) model to realize the transfer of the transition rules based on the similarity of geographical configurations. The model is applied to the Beijing-Tianjin-Hebei region for empirical analysis, the results show that (1) The proposed method can better capture the shift of urban expansion strategies during validation period. The figure of merit (FoM) of results using the local transition rules is on average 0.010 higher and the fuzzy similarity of different (FSD) is on average 0.025 higher than those using the transition rules transfer from Beijing in calibration period, but in validation period the FoM is on average 0.018 lower and the FSD is on average 0.033 lower. (2) The similarity of urban expansion strategies varies among different cities, selecting appropriate cities for the transfer of transition rule can make the results of proposed method perform better. For example, during validation period, in Shijiazhuang, the transition rule transferred from Beijing is preferable to that transferred from Xingtai, while in Handan and Hengshui, it is opposite.
DOI: 10.6084/m9.figshare.28388111
title: Predicting initiation and failure of static liquefaction landslides in loess sediment: A laboratory data-driven sliding-block model

description: Static liquefaction landslides are particularly prevalent in loose saturated granular soils. Generally, they are catastrophic geological processes due to their high-speed and long-runout mobility. However, here can be challenging to accurately predict the initiation and failure of the static liquefaction landslides. Here, I compiled a database of undrained triaxial compression tests of 152 saturated loess at 15 sampling sites in the Chinese Loess Plateau. Meanwhile, I collected the landslide monitoring data from six flume tests and three field landslides. These data help to build and verify the laboratory data-driven sliding-block model. The model can serve as an effective tool for static liquefaction landslide prediction.
DOI: 10.5281/zenodo.14862535
title: Wind tunnel experiment on rooftop wind environment of an isolated building for Safe Urban-Air-Mobility Operations

description: This is a dataset for the measurement results of a wind tunnel experiment on rooftop wind environment of an isolated building for Safe Urban-Air-Mobility Operations. The experiments were conducted in the boundary layer wind tunnel at Institute of Industrial Science, the University of Tokyo. The test section had dimensions of 1.8 × 2.2 × 16.47 m3 (height × width × length). The wind tunnel can provide adjustable inflow wind speed from 0.2 m/s to 20 m/s. Three vertical triangular spires and a large number of wooden cubes with dimensions of 3, 6 and 9 cm were placed upstream of the test section to generate and reproduce turbulence and the atmospheric boundary layer (ABL) profile. A 1:1:2 single block model with a block height of H=0.32 m was set in the turbulent boundary layer. 

The time-averaged velocity and turbulence intensity of inflow were measured in the wind tunnel using a constant temperature anemometer with an X wire probe (55P61, Dantec). The vertical profile of the time-averaged velocity can be fitted by the power law, where the power-law exponent is 0.22. In this study, the wind velocity at building height UH was set as 2.85 m/s. Three wind directions were considered: perpendicular, 22.5-degree and 45-degree to the building's frontal edge.

The velocity measurements included the velocity distribution in the rooftop region of the building. The measurement heights are in the horizontal planes of 1.05H, 1.125H and 1.25H. Wind velocity was measured using a constant-temperature hot wire anemometer (CT-HWA) with a split-fiber probe (SFP), (55R55, DANTEC). The SFP can measure the adverse velocity for a wind direction. The CT-HWA can measure the wind velocity from 0.02 to 300 m/s. The measuring accuracy depends on the calibration accuracy, which is typically around 0.5% and will not exceed 2%. The response time is short enough and can be negligible. Each measurement's period time was 90 s at a frequency of 1000 Hz. The time-averaged velocity, standard deviation, skewness, kurtosis, 5% percentile and 95% percentile of the velocity time series are provided.
DOI: 10.5281/zenodo.14862534
title: Wind tunnel experiment on rooftop wind environment of an isolated building for Safe Urban-Air-Mobility Operations

description: This is a dataset for the measurement results of a wind tunnel experiment on rooftop wind environment of an isolated building for Safe Urban-Air-Mobility Operations. The experiments were conducted in the boundary layer wind tunnel at Institute of Industrial Science, the University of Tokyo. The test section had dimensions of 1.8 × 2.2 × 16.47 m3 (height × width × length). The wind tunnel can provide adjustable inflow wind speed from 0.2 m/s to 20 m/s. Three vertical triangular spires and a large number of wooden cubes with dimensions of 3, 6 and 9 cm were placed upstream of the test section to generate and reproduce turbulence and the atmospheric boundary layer (ABL) profile. A 1:1:2 single block model with a block height of H=0.32 m was set in the turbulent boundary layer. 

The time-averaged velocity and turbulence intensity of inflow were measured in the wind tunnel using a constant temperature anemometer with an X wire probe (55P61, Dantec). The vertical profile of the time-averaged velocity can be fitted by the power law, where the power-law exponent is 0.22. In this study, the wind velocity at building height UH was set as 2.85 m/s. Three wind directions were considered: perpendicular, 22.5-degree and 45-degree to the building's frontal edge.

The velocity measurements included the velocity distribution in the rooftop region of the building. The measurement heights are in the horizontal planes of 1.05H, 1.125H and 1.25H. Wind velocity was measured using a constant-temperature hot wire anemometer (CT-HWA) with a split-fiber probe (SFP), (55R55, DANTEC). The SFP can measure the adverse velocity for a wind direction. The CT-HWA can measure the wind velocity from 0.02 to 300 m/s. The measuring accuracy depends on the calibration accuracy, which is typically around 0.5% and will not exceed 2%. The response time is short enough and can be negligible. Each measurement's period time was 90 s at a frequency of 1000 Hz. The time-averaged velocity, standard deviation, skewness, kurtosis, 5% percentile and 95% percentile of the velocity time series are provided.
DOI: 10.5281/zenodo.13731968
title: benchmark scripts/data used in skalo manuscript

description: benchmark scripts and datasets used in "From split k-mers to graph variant groups: an efficient reference-free variant caller for pathogen genomics (skalo)"
DOI: 10.5281/zenodo.13731969
title: scripts/data used in skalo manuscript

description: scripts and data used in the manuscript describing the Rust program skalo (https://github.com/rderelle/skalo): "

skalo: using SKA split k-mers with coloured de Brujin graphs to genotype indels"
DOI: 10.5281/zenodo.14863014
title: benchmark scripts/data used in skalo manuscript

description: benchmark scripts and datasets used in "From split k-mers to graph variant groups: an efficient reference-free variant caller for pathogen genomics (skalo)"
DOI: 10.6084/m9.figshare.28406402
title: JabREF Data– Accepted, Rejected & All Questions

description: This dataset encompasses a comprehensive collection of questions extracted from the JabRef project's forums, specifically Gitter and GitHub. It categorizes questions based on various attributes, offering insights into the types of queries posed by users and their acceptance within the community. Each entry in the dataset is tagged with multiple attributes, including the nature of the question, context requirements, user type, and intended purpose. This data is pivotal for analyzing community engagement, identifying common challenges, and optimizing the implementation of conversational agents to facilitate more effective communication and problem-solving within the OSS community.
DOI: 10.6084/m9.figshare.28406402.v2
title: JabREF Data– Accepted, Rejected & All Questions

description: This dataset encompasses a comprehensive collection of questions extracted from the JabRef project's forums, specifically Gitter and GitHub. It categorizes questions based on various attributes, offering insights into the types of queries posed by users and their acceptance within the community. Each entry in the dataset is tagged with multiple attributes, including the nature of the question, context requirements, user type, and intended purpose. This data is pivotal for analyzing community engagement, identifying common challenges, and optimizing the implementation of conversational agents to facilitate more effective communication and problem-solving within the OSS community.
DOI: 10.6084/m9.figshare.28406402.v1
title: JabREF Data– Accepted, Rejected & All Questions

description: This dataset encompasses a comprehensive collection of questions extracted from the JabRef project's forums, specifically Gitter and GitHub. It categorizes questions based on various attributes, offering insights into the types of queries posed by users and their acceptance within the community. Each entry in the dataset is tagged with multiple attributes, including the nature of the question, context requirements, user type, and intended purpose. This data is pivotal for analyzing community engagement, identifying common challenges, and optimizing the implementation of conversational agents to facilitate more effective communication and problem-solving within the OSS community.
DOI: 10.5281/zenodo.14326332
title: Potential impact of the Sagittarius dwarf galaxy on the formation of young O-rich stars

description: The dataset used in the paper "Young O-rich Stars Migrated from the Outer Disc: Imprints of Sagittarius Accretion Event".
DOI: 10.5281/zenodo.14326331
title: Potential impact of the Sagittarius dwarf galaxy on the formation of young O-rich stars

description: The dataset used in the paper "Young O-rich Stars Migrated from the Outer Disc: Imprints of Sagittarius Accretion Event".
DOI: 10.6084/m9.figshare.23282627.v3
title: Supplementary file for " Dynamic simulations of feeding and respiration of the early Cambrian periderm-bearing cnidarian polyps"

description:  Dynamic simulations of feeding and respiration of the early Cambrian periderm-bearing cnidarian polyps
DOI: 10.18150/tzygqr
title: Tourist Transport Behavior in European Capitals (2022)

description: The data for this study were collected in the summer of 2022 in ten European capitals: Amsterdam, Berlin, Brussels, London, Madrid, Paris, Prague, Rome, Stockholm, and Warsaw. The data collection process was based on conducting questionnaire surveys among domestic and foreign tourists using the Computer-Assisted Personal Interviewing (CAPI) technique. The survey questionnaire was developed based on a review of the relevant literature and included a research instrument specifically designed and validated for this study. In particular, it contained items related to tourists' motivations and constraints in choosing public transportation during their stay in European capitals. A five-point Likert scale was applied. The questionnaire was originally designed in English and then translated into local languages. The surveys were conducted in strategically selected locations with high tourist traffic, such as areas near major tourist attractions and transportation hubs. A total of 5,220 respondents participated in the study, with nearly equal numbers of domestic (2,603) and foreign (2,617) tourists. To ensure high data quality and minimize errors associated with self-completed surveys, the data collection was carried out by trained interviewers. After the data collection was completed, the dataset was subjected to statistical analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM) and multi-group analysis.
DOI: 10.18150/hir8ov
title: Tourists’ Mobility Experiences in European Capitals (2022)

description: The data for this study were collected in the summer of 2022 in ten European capitals: Amsterdam, Berlin, Brussels, London, Madrid, Paris, Prague, Rome, Stockholm, and Warsaw. The service safari method was used, which involved an immersive experience of urban transport by trained observers. Each participant conducted three separate journeys, using public transport (e.g., metro, tram, bus), motorized individual transport (e.g., taxis, rental cars), and active transport (e.g., walking, cycling, electric scooters). Each journey was thoroughly documented through reports, photographs, and GPS tracking. Observers, who were visiting the cities for the first time, recorded their experiences, paying particular attention to aspects such as the decision-making process for choosing a mode of transport, ease of navigation and ticket purchasing, travel time, comfort, safety, and environmental factors influencing mobility. The collected qualitative data were then organized, coded, and integrated with fuzzy set analysis, allowing for the identification of key factors affecting tourists' satisfaction with urban mobility. By applying this methodology, it was possible to capture both objective infrastructure and transport conditions as well as the subjective experiences of travelers.
DOI: 10.18150/mytn8o
title: Tourist Perceptions of Crowding and Satisfaction (2023)

description: The data for this study were collected in the summer of 2023 in three European capitals: London, Paris, and Rome. The data collection process was based on the Computer-Assisted Personal Interviewing (CAPI) method, which involves computer-assisted direct interviews. Surveys were conducted in key tourist locations in each city to obtain a representative sample of tourists. A total of 1,859 respondents participated in the study, with half being domestic tourists and the other half international tourists. The questionnaire was prepared in English and translated into four other languages. It was then verified by members of the research team and native speakers to ensure maximum clarity and comprehensibility of the statements. The choice of the CAPI technique enabled the collection of high-quality data by increasing the response rate, minimizing errors resulting from misinterpretation of questions, and eliminating missing responses. The collected data covered a wide range of variables, including perception of crowding in tourist spaces, destination attractiveness, travel satisfaction, and tourist loyalty. Additionally, various forms of mobility were considered, such as public transport, active mobility (walking, cycling), and micromobility. The data were subjected to statistical analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM) and multi-group analysis, allowing for the identification of key factors influencing tourists' perceptions in different urban conditions.
DOI: 10.5281/zenodo.13319018
title: BioPedia

description: Bio*pedia is a communal repository of descriptions of organisms. Bio*pedia works in conjunction with the STAR biodiversity web sites, such as micro*scope, and with the Encyclopedia of Life. Text descriptions will be immediately be visible in the STAR sites. If you would like to be able to add to Bio*pedia, please contact us or register. We accept content on the understanding it can be made available under a Creative Commons attribution license.   http://biopedia.org/index.php/Main_Page
DOI: 10.5281/zenodo.13319019
title: BioPedia

description: Bio*pedia is a communal repository of descriptions of organisms. Bio*pedia works in conjunction with the STAR biodiversity web sites, such as micro*scope, and with the Encyclopedia of Life. Text descriptions will be immediately be visible in the STAR sites. If you would like to be able to add to Bio*pedia, please contact us or register. We accept content on the understanding it can be made available under a Creative Commons attribution license.   http://biopedia.org/index.php/Main_Page
DOI: 10.5281/zenodo.13322336
title: PlanetScott: Partner supplied EOL XML

description: This is where EOL hosts source datasets (archives, dumps, etc.) from EOL content partners (especially partners without a web presence of their own).

This organization will also include the content partner utility files EOL connectors use to generate a particular content partner__s resource EOL archive or XML.

For questions or suggestions please visit the EOL Services forum at http://discuss.eol.org/c/eol-services