cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.4tf8hw
title: Occurrence Download

description: A dataset containing 42 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-123.16863 37.74025,-122.61764 36.65058,-122.16165 35.28204,-120.39468 33.95304,-118.28572 34.39318,-118.83671 35.66886,-119.63469 37.87535,-123.16863 37.74025))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Perideridia gairdneri subsp. gairdneri"
 ]
}

 The dataset includes 42 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003418-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u3quz3
title: Occurrence Download

description: A dataset containing 8480 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((36.17249 2.239145, 37.43042 2.239145, 37.43042 3.512488, 36.17249 3.512488, 36.17249 2.239145))"
 ]
}

 The dataset includes 8480 records from 104 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003419-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11408398
title: Deposition of data for developing deep learning models to assess crack width and self-healing progress in concrete (krkCMd)

description: This is a deposition of data for developing deep learning models to assess crack width and self-healing progress in concrete [1]. It relates to an experimental study on the autogenous self-healing of high-strength concrete [2]. Concrete specimens were prepared, matured, cracked, and exposed to self-healing. High-resolution scanning of the specimen surface and scale-invariant image processing were performed, multiple grid lines crossing cracks were established, and brightness degree profiles were extracted. Then, manual measurements of the crack widths were obtained by an operator.

The dataset comprises 19,098 records of brightness profiles, reference crack width measurements, and benchmark measurements by deep learning and analytic models. The source images, which were stacked and marked with grid lines, are provided. The considerable number of brightness profiles coupled with manual reference measurements make the dataset well suited for developing an image-based deep learning models or analytic algorithms for assessing crack widths in concrete.

The deposited data includes:



krkCMd_table.csv: delimited, comma-separated text file containing a dataset of 19,098 crack brightness degree profiles, reference crack width measurements by operator, and benchmark measurements by a deep CNN metasensor and by an analytic edge detector.

krkCMd_images.zip: archive containing source image files in folders by test series: -   stacked images of cracks in subsequent stages of self-healing (.tif files),-   zip archives assigned to image stacks and containing sets of ImageJ data files .roi,-   ImageJ .roi files specifying the locations of grid lines in the images.

krkCMd_scripts.zip: archive containing custom scripts supporting image preprocessing and computing benchmark variables.


For details please see the data descriptor [1]. When referring to the data in publications please cite [1].

[1] Jakubowski, J., Tomczak, K. Dataset for developing deep learning models to assess crack width and self-healing progress in concrete. Sci Data 12, 165 (2025). https://doi.org/10.1038/s41597-025-04485-z

[2] Jakubowski, J. & Tomczak, K. Deep learning metasensor for crack-width assessment and self-healing evaluation in concrete. Constr. Build. Mater. 422, 135768 (2024). https://doi.org/10.1016/j.conbuildmat.2024.135768
DOI: 10.5281/zenodo.14568863
title: Deposition of data for developing deep learning models to assess crack width and self-healing progress in concrete (krkCMd)

description: This is a deposition of data for developing deep learning models to assess crack width and self-healing progress in concrete [1]. It relates to an experimental study on the autogenous self-healing of high-strength concrete [2]. Concrete specimens were prepared, matured, cracked, and exposed to self-healing. High-resolution scanning of the specimen surface and scale-invariant image processing were performed, multiple grid lines crossing cracks were established, and brightness degree profiles were extracted. Then, manual measurements of the crack widths were obtained by an operator.

The dataset comprises 19,098 records of brightness profiles, reference crack width measurements, and benchmark measurements by deep learning and analytic models. The source images, which were stacked and marked with grid lines, are provided. The considerable number of brightness profiles coupled with manual reference measurements make the dataset well suited for developing an image-based deep learning models or analytic algorithms for assessing crack widths in concrete.

The deposited data includes:



krkCMd_table.csv: delimited, comma-separated text file containing a dataset of 19,098 crack brightness degree profiles, reference crack width measurements by operator, and benchmark measurements by a deep CNN metasensor and by an analytic edge detector.

krkCMd_images.zip: archive containing source image files in folders by test series: -   stacked images of cracks in subsequent stages of self-healing (.tif files),-   zip archives assigned to image stacks and containing sets of ImageJ data files .roi,-   ImageJ .roi files specifying the locations of grid lines in the images.

krkCMd_scripts.zip: archive containing custom scripts supporting image preprocessing and computing benchmark variables.


For details please see the data descriptor [1]. When referring to the data in publications please cite [1].

[1] Jakubowski, J., Tomczak, K. Dataset for developing deep learning models to assess crack width and self-healing progress in concrete. Sci Data 12, 165 (2025). https://doi.org/10.1038/s41597-025-04485-z

[2] Jakubowski, J. & Tomczak, K. Deep learning metasensor for crack-width assessment and self-healing evaluation in concrete. Constr. Build. Mater. 422, 135768 (2024). https://doi.org/10.1016/j.conbuildmat.2024.135768
DOI: 10.6084/m9.figshare.28245245
title: <b>Data from "Dark nectar pouches are visually similar to colored nectar in bird-pollinated flowers"</b>

description: Data from "Dark nectar pouches are visually similar to colored nectar in bird-pollinated flowers", including visitation observation, nectar properties, reflectance spectra, parameters used in color vision model, and rhododendron floral traits.
DOI: 10.5281/zenodo.14499422
title: Safe Reinforcement Learning for Real-World Engine Control Data and Scripts

description: This dataset supports our publication, "Safe Reinforcement Learning for Real-World Engine Control". It includes data and scripts for training artificial neural networks used as a reference control strategy, as well as datasets collected during reinforcement learning (RL) policy training on a real-world single-cylinder Homogeneous Charge Compression Ignition (HCCI) engine testbench.

The provided resources cover two key RL experiments:



Training the initial control policy for transient load control in direct interaction with the real-world engine.

Adapting the policy to increase ethanol energy shares while maintaining safety constraints.


The RL experiments were conducted by applying the Learning and Experiencing Cyclic Interface (LExCI, versions 2.22.0 and before), a free and open-source tool enabling RL with embedded hardware.

This dataset enables the reproduction of the artificial neural network-based reference strategy and provides a foundation for analyzing the RL agent’s performance in a safety-critical environment. These resources aim to support further research into applying RL and machine learning to real-world combustion engines.

This research was performed as part of the research unit 2401 (FOR2401) “Optimization based Multiscale Control for Low Temperature Combustion Engines” funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 277012063. This support is gratefully acknowledged.
DOI: 10.5281/zenodo.14499423
title: Safe Reinforcement Learning for Real-World Engine Control Data and Scripts

description: This dataset supports our publication, "Safe Reinforcement Learning for Real-World Engine Control". It includes data and scripts for training artificial neural networks used as a reference control strategy, as well as datasets collected during reinforcement learning (RL) policy training on a real-world single-cylinder Homogeneous Charge Compression Ignition (HCCI) engine testbench.

The provided resources cover two key RL experiments:



Training the initial control policy for transient load control in direct interaction with the real-world engine.

Adapting the policy to increase ethanol energy shares while maintaining safety constraints.


The RL experiments were conducted by applying the Learning and Experiencing Cyclic Interface (LExCI, versions 2.22.0 and before), a free and open-source tool enabling RL with embedded hardware.

This dataset enables the reproduction of the artificial neural network-based reference strategy and provides a foundation for analyzing the RL agent’s performance in a safety-critical environment. These resources aim to support further research into applying RL and machine learning to real-world combustion engines.

This research was performed as part of the research unit 2401 (FOR2401) “Optimization based Multiscale Control for Low Temperature Combustion Engines” funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 277012063. This support is gratefully acknowledged.
DOI: 10.15468/dl.tbj8g6
title: Occurrence Download

description: A dataset containing 24317 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((35.99121 1.915385, 38.08411 1.915385, 38.08411 3.935018, 35.99121 3.935018, 35.99121 1.915385))"
 ]
}

 The dataset includes 24317 records from 167 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003421-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2zcs3c
title: Occurrence Download

description: A dataset containing 3898 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Living Specimen, Occurrence evidence, Specimen, Material citation, Material sample, Human Observation)",
 "Continent is Asia",
 "Geometry POLYGON((90.86727 45.12937,79.71455 27.96923,84.98111 18.59267,92.88096 18.73943,112.70803 20.49027,120.45298 24.77798,124.48036 30.53662,128.35283 34.0706,131.29591 37.34005,131.14102 41.75676,127.57834 42.90185,120.91768 42.67451,115.18642 41.75676,108.68066 42.10248,97.60538 41.75675,92.49371 43.35406,90.86727 45.12937))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Castanea mollissima Blume"
 ]
}

 The dataset includes 3898 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003422-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.58032/agh/dwlpj4
title: Transformation of slope microrelief across an unused mountain forest road - replication data

description: The dataset includes Digital Elevation Models (DEMs) derived from UAV-photogrammetry data collected during three measurement series on November 12, 2022, July 8, 2023, and October 19, 2023, for a forest road located in the Lejowa Valley in southern Poland. Data collection was carried out using a DJI Phantom 4RTK UAV equipped with a 1-inch CMOS camera (20MP) and a 24 mm equivalent focal length lens. The UAV was supported by an RTK GNSS module to ensure precise trajectory determination. The UAV flights were meticulously planned to achieve 80% forward and side image overlap, with a target Ground Sampling Distance (GSD) of 7 mm. Georeferencing of the UAV-photogrammetry data was ensured by utilizing a network of 25 Ground Control Points (GCPs) and 17 Check Points (CPs), whose coordinates were measured using the RTK GNSS method. The UAV-photogrammetry data processing was carried out using Agisoft Metashape Professional, which included image alignment, tie point filtration, optimization of image alignment with camera calibration parameters, and rigid transformation of the image block using GCP coordinates. Dense point clouds were generated, and DEMs were subsequently extracted. The accuracy of UAV-photogrammetry products, assessed using the CPs, is ±5-10 mm in the horizontal plane and ±5-15 mm in the vertical plane. The dataset includes DEMs for two sections of the road: the lower road section with a DEM resolution of 17 mm/px and the upper road section with a DEM resolution of 15 mm/px.
DOI: 10.15468/dl.7zufxg
title: Occurrence Download

description: A dataset containing 38743 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Insect specimens of Himeji City Science Museum"
 ]
}

 The dataset includes 38743 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003423-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.27175534
title: Effectiveness of remdesivir in patients with COVID-19 and severe renal insufficiency: a nationwide cohort study in Japan

description: The effectiveness of remdesivir in patients with coronavirus disease 2019 (COVID-19) and severe renal insufficiency remains underexplored. To evaluate whether remdesivir reduces the risk of mortality or invasive mechanical ventilation/extracorporeal membrane oxygenation (IMV/ECMO) in this population. This retrospective observational study utilising the COVID-19 Registry Japan (COVIREGI-JP) included noncritical patients with COVID-19 and severe renal insufficiency (defined as serum creatinine levels ≥3 mg/dL, on maintenance dialysis, or kidney transplant recipients) admitted to Japanese hospitals within 7 days of symptom onset between January 1, 2020 and May 8, 2023. Patients were classified into the remdesivir group if remdesivir was initiated within the first 2 days of admission. We estimated the multivariable-adjusted hazard ratio (HR) for mortality and initiation of IMV/ECMO using landmark analysis to address immortal time bias. Among the 1,449 patients included in the landmark analysis (median age, 74 years [interquartile range 62–84 years]; 992 [68.5%] were male), 272 initiated remdesivir within the first 2 days of admission. During the 28 days from the landmark timepoint, 19 (7.0%) and 136 (11.6%) patients in the remdesivir and control groups, respectively, had an outcome. The remdesivir group had a lower risk of mortality or IMV/ECMO initiation than the control group (adjusted HR, 0.44; 95% confidence interval, 0.23–0.83). In noncritical patients with COVID-19 and severe renal insufficiency at admission, initiating remdesivir early after disease onset, within the first 2 days of admission, led to a lower risk of mortality or IMV/ECMO initiation, compared with non-initiation of remdesivir.
DOI: 10.6084/m9.figshare.27004922.v1
title: Clinical evaluation of a highly multiplexed CRISPR-based diagnostic assay for diagnosing lower respiratory tract infection: a prospective cohort study

description: Accurate and rapid identification of causative pathogens is essential to guide the clinical management of lower respiratory tract infections (LRTIs). Here we conducted a single-centre prospective study in 284 patients suspected of lower respiratory tract infections to evaluate the utility of a nucleic acid test based on highly multiplexed polymerase chain reaction (PCR) and CRISPR-Cas12a. We determined the analytical and diagnostic performance of the CRISPR assay using a combination of reference standards, including conventional microbiological tests (CMTs), metagenomic Next-Generation Sequencing (mNGS), and clinical adjudication by a panel of experts on infectious diseases and microbiology. The CRISPR assay showed a higher detection rate (63.0%) than conventional microbiological tests (38.4%) and was lower than metagenomic Next-Generation Sequencing (72.9%). In detecting polymicrobial infections, the positivity rate of the CRISPR assay (19.4%) was higher than conventional microbiological tests (3.5%) and lower than metagenomic Next-Generation Sequencing (28.9%). The overall diagnostic sensitivity of the CRISPR assay (67.8%) was higher than conventional microbiological tests (41.8%), and lower than metagenomic Next-Generation Sequencing (93.2%). Considering the low cost, ease of operation, short turnaround time, and broad range of pathogens detected in a single test, the CRISPR assay has the potential to be implemented as a screening tool for the aetiological diagnosis of lower respiratory tract infections patients, especially in cases where atypical bacteria or coinfections are suspected.
DOI: 10.6084/m9.figshare.27175534.v2
title: Effectiveness of remdesivir in patients with COVID-19 and severe renal insufficiency: a nationwide cohort study in Japan

description: The effectiveness of remdesivir in patients with coronavirus disease 2019 (COVID-19) and severe renal insufficiency remains underexplored. To evaluate whether remdesivir reduces the risk of mortality or invasive mechanical ventilation/extracorporeal membrane oxygenation (IMV/ECMO) in this population. This retrospective observational study utilising the COVID-19 Registry Japan (COVIREGI-JP) included noncritical patients with COVID-19 and severe renal insufficiency (defined as serum creatinine levels ≥3 mg/dL, on maintenance dialysis, or kidney transplant recipients) admitted to Japanese hospitals within 7 days of symptom onset between January 1, 2020 and May 8, 2023. Patients were classified into the remdesivir group if remdesivir was initiated within the first 2 days of admission. We estimated the multivariable-adjusted hazard ratio (HR) for mortality and initiation of IMV/ECMO using landmark analysis to address immortal time bias. Among the 1,449 patients included in the landmark analysis (median age, 74 years [interquartile range 62–84 years]; 992 [68.5%] were male), 272 initiated remdesivir within the first 2 days of admission. During the 28 days from the landmark timepoint, 19 (7.0%) and 136 (11.6%) patients in the remdesivir and control groups, respectively, had an outcome. The remdesivir group had a lower risk of mortality or IMV/ECMO initiation than the control group (adjusted HR, 0.44; 95% confidence interval, 0.23–0.83). In noncritical patients with COVID-19 and severe renal insufficiency at admission, initiating remdesivir early after disease onset, within the first 2 days of admission, led to a lower risk of mortality or IMV/ECMO initiation, compared with non-initiation of remdesivir.
DOI: 10.6084/m9.figshare.27004922
title: Clinical evaluation of a highly multiplexed CRISPR-based diagnostic assay for diagnosing lower respiratory tract infection: a prospective cohort study

description: Accurate and rapid identification of causative pathogens is essential to guide the clinical management of lower respiratory tract infections (LRTIs). Here we conducted a single-centre prospective study in 284 patients suspected of lower respiratory tract infections to evaluate the utility of a nucleic acid test based on highly multiplexed polymerase chain reaction (PCR) and CRISPR-Cas12a. We determined the analytical and diagnostic performance of the CRISPR assay using a combination of reference standards, including conventional microbiological tests (CMTs), metagenomic Next-Generation Sequencing (mNGS), and clinical adjudication by a panel of experts on infectious diseases and microbiology. The CRISPR assay showed a higher detection rate (63.0%) than conventional microbiological tests (38.4%) and was lower than metagenomic Next-Generation Sequencing (72.9%). In detecting polymicrobial infections, the positivity rate of the CRISPR assay (19.4%) was higher than conventional microbiological tests (3.5%) and lower than metagenomic Next-Generation Sequencing (28.9%). The overall diagnostic sensitivity of the CRISPR assay (67.8%) was higher than conventional microbiological tests (41.8%), and lower than metagenomic Next-Generation Sequencing (93.2%). Considering the low cost, ease of operation, short turnaround time, and broad range of pathogens detected in a single test, the CRISPR assay has the potential to be implemented as a screening tool for the aetiological diagnosis of lower respiratory tract infections patients, especially in cases where atypical bacteria or coinfections are suspected.
DOI: 10.6084/m9.figshare.27175534.v1
title: Effectiveness of remdesivir in patients with COVID-19 and severe renal insufficiency: a nationwide cohort study in Japan

description: The effectiveness of remdesivir in patients with coronavirus disease 2019 (COVID-19) and severe renal insufficiency remains underexplored. To evaluate whether remdesivir reduces the risk of mortality or invasive mechanical ventilation/extracorporeal membrane oxygenation (IMV/ECMO) in this population. This retrospective observational study utilising the COVID-19 Registry Japan (COVIREGI-JP) included noncritical patients with COVID-19 and severe renal insufficiency (defined as serum creatinine levels ≥3 mg/dL, on maintenance dialysis, or kidney transplant recipients) admitted to Japanese hospitals within 7 days of symptom onset between January 1, 2020 and May 8, 2023. Patients were classified into the remdesivir group if remdesivir was initiated within the first 2 days of admission. We estimated the multivariable-adjusted hazard ratio (HR) for mortality and initiation of IMV/ECMO using landmark analysis to address immortal time bias. Among the 1,449 patients included in the landmark analysis (median age, 74 years [interquartile range 62–84 years]; 992 [68.5%] were male), 272 initiated remdesivir within the first 2 days of admission. During the 28 days from the landmark timepoint, 19 (7.0%) and 136 (11.6%) patients in the remdesivir and control groups, respectively, had an outcome. The remdesivir group had a lower risk of mortality or IMV/ECMO initiation than the control group (adjusted HR, 0.44; 95% confidence interval, 0.23–0.83). In noncritical patients with COVID-19 and severe renal insufficiency at admission, initiating remdesivir early after disease onset, within the first 2 days of admission, led to a lower risk of mortality or IMV/ECMO initiation, compared with non-initiation of remdesivir.