cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/p9bpx9ctcv.1
title: CADICA: a new dataset for coronary artery disease

description: The CADICA dataset is an annotated Invasive Coronary Angiography (ICA) dataset of 42 patients. In ICA imaging, lesion degree assessment is commonly done by visual estimation, which implies a subjective factor and interobserver variability. Accurate recognition of lesions is crucial for a correct diagnosis and treatment. This motivates the development of computer-aided systems that can support specialists in their clinical procedures. This dataset can be used by clinicians to train their skills in angiographic assessment of CAD severity, by computer scientists to create computer-aided diagnostic systems to help in such assessment, and to validate existing methods for CAD detection approaching solutions for clinical settings.

CADICA dataset includes ICA images, manually labeled lesion bounding boxes, and selected clinical features.

The CADICA dataset becomes a directory that contains the "metadata.xlsx" file, which is the file where the clinical data is located, as well as two main folders that differentiate the videos selected by the medical team for each patient: "nonselectedVideos" and "selectedVideos". Inside each folder, there are several sub-directories with the naming convention "pX", where X is the ID of each patient, and "vY", where Y is the ID of the video of that patient. 

The folder "pX" contains the following information: 
 "vY": several sub-directories with the videos selected for that patient. 
 "lesionVideos.txt": includes the IDs of the videos chosen where appears at least one lesion which is labeled.
 "nonlesionVideos.txt": contains the IDs of the selected videos with no visible lesions.

The folder "vY" contains the following information:
 "input": a sub-directory containing a separate PNG file for each video frame.
 "pX_vY_selectedFrames.txt": includes the IDs of the keyframes for the medical team for all the selected videos. 
 "groundtruth": a sub-directory available only if there are lesions in that selected video.

The folder "groundtruth" contains the following information:
 "pX_vY_000ZZ.txt": contains the bounding boxes and their category in each row. There are such files as frames in "pX_vY_selectedFrames.txt". Bounding boxes are specified in the format [x,y,w,h], where (x,y) are the pixel coordinates of the top left corner, w is the width and h is the height of the bounding box.
 "pX_vY_groundTruthTable.mat": contains a table with the ground truth information of that video.
DOI: 10.17632/p9bpx9ctcv.2
title: CADICA: a new dataset for coronary artery disease

description: The CADICA dataset is an annotated Invasive Coronary Angiography (ICA) dataset of 42 patients. In ICA imaging, lesion degree assessment is commonly done by visual estimation, which implies a subjective factor and interobserver variability. Accurate recognition of lesions is crucial for a correct diagnosis and treatment. This motivates the development of computer-aided systems that can support specialists in their clinical procedures. This dataset can be used by clinicians to train their skills in angiographic assessment of CAD severity, by computer scientists to create computer-aided diagnostic systems to help in such assessment, and to validate existing methods for CAD detection approaching solutions for clinical settings.

CADICA dataset includes ICA images, manually labeled lesion bounding boxes, and selected clinical features.

The CADICA dataset becomes a directory that contains the "metadata.xlsx" file, which is the file where the clinical data is located, as well as two main folders that differentiate the videos selected by the medical team for each patient: "nonselectedVideos" and "selectedVideos". Inside each folder, there are several sub-directories with the naming convention "pX", where X is the ID of each patient, and "vY", where Y is the ID of the video of that patient. 

The folder "pX" contains the following information: 
 "vY": several sub-directories with the videos selected for that patient. 
 "lesionVideos.txt": includes the IDs of the videos chosen where appears at least one lesion which is labeled.
 "nonlesionVideos.txt": contains the IDs of the selected videos with no visible lesions.

The folder "vY" contains the following information:
 "input": a sub-directory containing a separate PNG file for each video frame.
 "pX_vY_selectedFrames.txt": includes the IDs of the keyframes for the medical team for all the selected videos. 
 "groundtruth": a sub-directory available only if there are lesions in that selected video.

The folder "groundtruth" contains the following information:
 "pX_vY_000ZZ.txt": contains the bounding boxes and their category in each row. There are such files as frames in "pX_vY_selectedFrames.txt". Bounding boxes are specified in the format [x,y,w,h], where (x,y) are the pixel coordinates of the top left corner, w is the width and h is the height of the bounding box.
 "pX_vY_groundTruthTable.mat": contains a table with the ground truth information of that video.
DOI: 10.17632/p9bpx9ctcv.3
title: CADICA: a new dataset for coronary artery disease

description: The CADICA dataset is an annotated Invasive Coronary Angiography (ICA) dataset of 42 patients. In ICA imaging, lesion degree assessment is commonly done by visual estimation, which implies a subjective factor and interobserver variability. Accurate recognition of lesions is crucial for a correct diagnosis and treatment. This motivates the development of computer-aided systems that can support specialists in their clinical procedures. This dataset can be used by clinicians to train their skills in angiographic assessment of CAD severity, by computer scientists to create computer-aided diagnostic systems to help in such assessment, and to validate existing methods for CAD detection approaching solutions for clinical settings.

CADICA dataset includes ICA images, manually labeled lesion bounding boxes, and selected clinical features.

The CADICA dataset becomes a directory that contains the "metadata.xlsx" file, which is the file where the clinical data is located, as well as two main folders that differentiate the videos selected by the medical team for each patient: "nonselectedVideos" and "selectedVideos". Inside each folder, there are several sub-directories with the naming convention "pX", where X is the ID of each patient, and "vY", where Y is the ID of the video of that patient. 

The folder "pX" contains the following information: 
 "vY": several sub-directories with the videos selected for that patient. 
 "lesionVideos.txt": includes the IDs of the videos chosen where appears at least one lesion which is labeled.
 "nonlesionVideos.txt": contains the IDs of the selected videos with no visible lesions.

The folder "vY" contains the following information:
 "input": a sub-directory containing a separate PNG file for each video frame.
 "pX_vY_selectedFrames.txt": includes the IDs of the keyframes for the medical team for all the selected videos. 
 "groundtruth": a sub-directory available only if there are lesions in that selected video.

The folder "groundtruth" contains the following information:
 "pX_vY_000ZZ.txt": contains the bounding boxes and their category in each row. There are such files as frames in "pX_vY_selectedFrames.txt". Bounding boxes are specified in the format [x,y,w,h], where (x,y) are the pixel coordinates of the top left corner, w is the width and h is the height of the bounding box.
 "pX_vY_groundTruthTable.mat": contains a table with the ground truth information of that video.
DOI: 10.5281/zenodo.15162734
title: Supplementary Dataset for Manuscript: Large-scale photonic chip based pulse interleaver for low- noise microwave generation

description: Containing files:

04/06/2025  05:13      (58,644,097) Cext.oas -> layout file used for fabrication of the device12/21/2023  04:58         (666,711) CoherentInterlever.ipynb -> jupyter notebook for simulation of the interleaver's operation, with full coherent treatmeant.04/06/2025  09:41    <DIR>          Data -> raw data04/06/2025  09:41    <DIR>          DelayLength -> more raw data for the delay length characterization.03/12/2025  13:04         (535,566) IncoherentInterleaverNew.ipynb -> jupyter notebook for simulation of the interleaver's operation -> fast version without consideration of the phase of the pulses.06/06/2024  11:36       (1,089,331) InterleaverDataCleared.ipynb -> jupyter notebook for data analysis04/06/2025  09:41    <DIR>          InterleaverWBPCB -> files used for fabrication of the breakout PCB for the device.12/18/2023  07:27       (1,157,376) interleaver_heater.ipynb -> jupyter notebook for processing the data for the characterization of the heaters.03/01/2025  06:01           (2,649) plotinterleaver.m ->data processing04/06/2025  09:41    <DIR>          preprocessed->preprocessed data (downsampled) for the heaters' characterization04/09/2024  05:17           (4,158) transmissionFFT_cutting.m->data processing for the delay length data10/15/2023  10:16           (2,483) transmission_preproc.m -> downsampling for the heaters' characterization.
DOI: 10.5281/zenodo.15162735
title: Supplementary Dataset for Manuscript: Large-scale photonic chip based pulse interleaver for low- noise microwave generation

description: Containing files:

04/06/2025  05:13      (58,644,097) Cext.oas -> layout file used for fabrication of the device12/21/2023  04:58         (666,711) CoherentInterlever.ipynb -> jupyter notebook for simulation of the interleaver's operation, with full coherent treatmeant.04/06/2025  09:41    <DIR>          Data -> raw data04/06/2025  09:41    <DIR>          DelayLength -> more raw data for the delay length characterization.03/12/2025  13:04         (535,566) IncoherentInterleaverNew.ipynb -> jupyter notebook for simulation of the interleaver's operation -> fast version without consideration of the phase of the pulses.06/06/2024  11:36       (1,089,331) InterleaverDataCleared.ipynb -> jupyter notebook for data analysis04/06/2025  09:41    <DIR>          InterleaverWBPCB -> files used for fabrication of the breakout PCB for the device.12/18/2023  07:27       (1,157,376) interleaver_heater.ipynb -> jupyter notebook for processing the data for the characterization of the heaters.03/01/2025  06:01           (2,649) plotinterleaver.m ->data processing04/06/2025  09:41    <DIR>          preprocessed->preprocessed data (downsampled) for the heaters' characterization04/09/2024  05:17           (4,158) transmissionFFT_cutting.m->data processing for the delay length data10/15/2023  10:16           (2,483) transmission_preproc.m -> downsampling for the heaters' characterization.
DOI: 10.17632/p9bpx9ctcv.4
title: CADICA: a new dataset for coronary artery disease

description: The CADICA dataset is an annotated Invasive Coronary Angiography (ICA) dataset of 42 patients. In ICA imaging, lesion degree assessment is commonly done by visual estimation, which implies a subjective factor and interobserver variability. Accurate recognition of lesions is crucial for a correct diagnosis and treatment. This motivates the development of computer-aided systems that can support specialists in their clinical procedures. This dataset can be used by clinicians to train their skills in angiographic assessment of CAD severity, by computer scientists to create computer-aided diagnostic systems to help in such assessment, and to validate existing methods for CAD detection approaching solutions for clinical settings.

The CADICA dataset includes ICA images, manually labeled lesion bounding boxes, and selected clinical features.

The CADICA dataset becomes a directory that contains the "metadata.xlsx" file, which is the file where the clinical data is located, as well as two main folders that differentiate the videos selected by the medical team for each patient: "nonselectedVideos" and "selectedVideos". Inside each folder, there are several sub-directories with the naming convention "pX", where X is the ID of each patient, and "vY", where Y is the ID of the video of that patient. 

The folder "pX" contains the following information: 
 "vY": several sub-directories with the videos selected for that patient. 
 "lesionVideos.txt": includes the IDs of the videos chosen where appears at least one lesion which is labeled.
 "nonlesionVideos.txt": contains the IDs of the selected videos with no visible lesions.

The folder "vY" contains the following information:
 "input": a sub-directory containing a separate PNG file for each video frame.
 "pX_vY_selectedFrames.txt": includes the IDs of the keyframes for the medical team for all the selected videos. 
 "groundtruth": a sub-directory available only if there are lesions in that selected video.

The folder "groundtruth" contains the following information:
 "pX_vY_000ZZ.txt": contains the bounding boxes and their category in each row. There are such files as frames in "pX_vY_selectedFrames.txt". Bounding boxes are specified in the format [x,y,w,h], where (x,y) are the pixel coordinates of the top left corner, w is the width, and h is the height of the bounding box.
 "pX_vY_groundTruthTable.mat": contains a table with the ground truth information of that video. 

Inside the "selectedVideos" folder, you can find the "CADICAprojections.json" file, containing video projections. Please note that maybe the total videos does not match the total of selected videos, as manual discarding can be done.
DOI: 10.6084/m9.figshare.28937999
title: A frequency tunable low-noise YIG-GGG based oscillator with strong magneto-elastic coupling - Source Data

description: Data underlying the publication: "A frequency tunable low-noise YIG-GGG based oscillator with strong magneto-elastic coupling".
DOI: 10.17632/wt52w9g9p5.1
title: Optimization-based multitarget stacked machine-learning model for estimating mechanical properties of conventional and fiber-reinforced preplaced aggregate concrete

description: The dataset employed in the study "Optimization-based multitarget stacked machine-learning model for estimating mechanical properties of conventional and fiber-reinforced preplaced aggregate concrete" consists of 475 data points, with 419 datasets allocated for PAC and 56 datasets designated for FR-PAC. These datasets have been meticulously gathered from both existing literature (389 and 42 for PAC and FR-PAC, respectively) and our experimental work with 30 datasets for PAC and 14 for FR-PAC. To use the dataset, you should refer to the original paper linked below to ensure licensing and credit.
DOI: 10.17632/txrk7jm6x3.1
title: Dataset of Khorezm dialect words of Uzbek language

description: As part of the study, a dataset was formed, which was used by a rule-oriented algorithm to standardize dialect forms into formal equivalents. In particular, the dataset contains 2249 dialect words:
1) The words in this dataset were compiled thanks to the joint work of expert linguists who are well versed not only in the Uzbek (formal) language, but also in the dialect forms of this language.
2) The sources of words in the dataset were not only expert linguists, but also native speakers of the dialect language, who are also co-authors of this article.
The dataset was formed manually, no automation processes were carried out except for cases of transliteration of Cyrillic into Latin.
DOI: 10.15468/dl.68ghfg
title: Occurrence Download

description: A dataset containing 109216 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Corallorhiza trifida Châtel.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation)"
 ]
}

 The dataset includes 109216 records from 410 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030336-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/x3k6xnjg2s
title: Spray Cooling Heat Transfer Enhancement Using Graphene Nanofluids: An Experimental Approach

description: The data set is taken for graphene nanofluid experimentation using spray cooling at fluid inlet temperature of 25 degrees centigrade.
DOI: 10.17632/x3k6xnjg2s.1
title: Spray Cooling Heat Transfer Enhancement Using Graphene Nanofluids: An Experimental Approach

description: The data set is taken for graphene nanofluid experimentation using spray cooling at fluid inlet temperature of 25 degrees centigrade.
DOI: 10.15468/dl.y2pdn3
title: Occurrence Download

description: A dataset containing 267 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Deherainia Decne."
 ]
}

 The dataset includes 267 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030347-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/rtgkgkf99t.1
title: Scattering peculiarities of Weyl semimetal rotationally symmetric spherical particles

description: Data for plotting the graphs in Figs. 1-7.
Figure 1. Real part of Drude permittivity vs. wavelength in um in the file "Fig1_ReEps.dat", imaginary part of Drude permittivity vs. wavelength in um in the file "Fig1_ImEps.dat", axion permittivity vs. wavelength in um in the file "Fig1_epsa.dat".
Figure 2a. Scattering efficiency vs. core permittivity for 100 nm core radius "Fig2a Rc=100.dat" and 10 nm radius "Fig2a Rc=10.dat" .
Figure 2b. Scattering efficiency vs. number of sublayers for size parameter 2 and wabelength 5 um "Fig2b_kR=2_5um.dat", size parameter 2 and wabelength 20 um "Fig2b_kR=2_20um.dat", size parameter 2*pi and wabelength 5 um "Fig2b_kR=2Pi_5um.dat".
Figure 3. Scattering efficiency vs. wavelength in um "Fig3_R=2um5-15.dat". Diferential cross sections at peaks "Fig3dcs12.6.dat", Fig3dcs9.16.dat etc.
Figure 4a. Forward to backward ratio "Fig4a_FtoB_R=2.dat".
Figure 4b. Scattering patterns for RCP "Fig4b7.285RCP.dat" an LCP "Fig4b7.285LCP.dat" polarizations.
Figure 5. Scattering efficiency vs. Core permittivity for ditterent thicknesses 't' of the Weyl semimetal shell: "Fig5_t=0.dat", "Fig5_t=0.1.dat", "Fig5_t=1.dat"
Figure 6. Scattering efficiency vs. Number of layers for different wabelengths 'lambda': "Fig6_lambda=5.dat", "Fig6_lambda=7.dat", "Fig6_lambda=10.dat" and for averaged gyrotropy: "Fig6_lambda=5_homo.dat", "Fig6_lambda=7_homo.dat", "Fig6_lambda=10_homo.dat".
Figure 7. Scattering patterns for two cuts denoted as 0 (polarisation is in plane) and 90 (polarization is orthogonal to the plane): "Fig7_0.dat", "Fig7_90.dat".
DOI: 10.17632/7fx7c3vxd6.1
title: nematadadata

description: Including: 
1) Schedule 1 nematoda raw data: Family Name; Genus Name; Trophic Group ; C-P Value; Nematode Count and Proportion Central urban, Plain area Ecological conservation area
2) Schedule 2 Distribution of sample points and raw nematode data in different functional areas
3) Schedule 3 Raw data on soil physical and chemical properties in different functional areas
4) Schedule 4 Soil nematode community diversity index in different functional areas
DOI: 10.17632/zyhn8498rv.1
title: Large Language Models in Middle School Education_Research_Data

description: Quantitative data regarding students' programming performance, learning interest, and self-efficacy.
DOI: 10.17632/2pxd8wc64y.1
title: Rare earth element signatures of the Yarlung Zangbo basin aeolian sediments

description: Rare earth element (REE) characteristics of the mainstream and major tributaries in the Yarlung Zangbo River Basin
DOI: 10.17632/gjw57h2spf.1
title: Data for: Measurement and Monte Carlo simulation of steel and copper activation at the CHARM and CSBF facilities at CERN

description: Activation experiments in selected materials commonly used in particle accelerator elements and high-energy physics detector components were conducted at the CERN High-energy AcceleRator Mixed-field (CHARM) facility. Emphasis was placed on materials that are particularly challenging in terms of activation, such as copper and steel alloys, and on medium- to long-lived radionuclides produced within them (e.g., Mn-54, Co-57, Co-58, and Co-60) that often play a crucial role in determining the radiological procedures to be applied to components of these materials. Dedicated simulations with the FLUKA Monte Carlo code (Ahdida et al., 2022; Battistoni et al., 2015) were performed, and comparisons with experimental data showed a satisfactory agreement.
DOI: 10.17632/gjw57h2spf.2
title: Data for: Measurement and Monte Carlo simulation of steel and copper activation at the CHARM and CSBF facilities at CERN

description: Activation experiments in selected materials commonly used in particle accelerator elements and high-energy physics detector components were conducted at the CERN High-energy AcceleRator Mixed-field (CHARM) facility. Emphasis was placed on materials that are particularly challenging in terms of activation, such as copper and steel alloys, and on medium- to long-lived radionuclides produced within them (e.g., Mn-54, Co-57, Co-58, and Co-60) that often play a crucial role in determining the radiological procedures to be applied to components of these materials. Dedicated simulations with the FLUKA Monte Carlo code (Ahdida et al., 2022; Battistoni et al., 2015) were performed, and comparisons with experimental data showed a satisfactory agreement. The description of the experiments, the analysis of the measurements and the comparison with FLUKA simulations are presented in the related publication: https://doi.org/10.1016/j.radphyschem.2025.112879
DOI: 10.15468/dl.swnrwf
title: Occurrence Download

description: A dataset containing 311652 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Epipactis helleborine (L.) Crantz",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation)"
 ]
}

 The dataset includes 311652 records from 1275 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030337-250426092105405/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.29123063
title: ADR process

description: The diagram represents the AWS ADR process adapted to make use of decision drivers and LLM to draft the document.
DOI: 10.6084/m9.figshare.29123063.v1
title: ADR process

description: The diagram represents the AWS ADR process adapted to make use of decision drivers and LLM to draft the document.
DOI: 10.15468/dl.t5wh2t
title: Occurrence Download

description: A dataset containing 4927117 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Polypodiales)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1981",
 "Geometry POLYGON((-30 25,50 25,50 75,-30 75,-30 25))"
 ]
}

 The dataset includes 4927117 records from 3453 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002777-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w5ctzd
title: Occurrence Download

description: A dataset containing 90 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Polyclads (Platyhelminthes) in the southern Gulf of Mexico: unveiling biodiversity and descriptions of two new species"
 ]
}

 The dataset includes 90 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012046-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.