cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10782853
title: Liquid-filled scintillating fiber development

description: In the frame of the EU-funded project "FleX-RAY", an inherently flexible X-ray, imaging detector using single-photon avalanche photodiodes and inorganic scintillating fibres was developed. Silica-based scintillating liquid-filled fibers were developed for constructing the detector hardware.

This archive contains the following data:

RISE_DS01



Evaluation of splice coverings to block stray light Evaluation of splice coverings to block stray light: Different types of painting methods were applied to the splice sections of the silica-scintillating components to block stray light entering the fibers. Light levels were measured and evaluated for different lightness conditions, measured by a photon counter and high-speed oscilloscope at the fiber output. The stray light was measured when the lightness condition was darkness, ambient light, and desk lamp light. The painting methods include splice without paint, black pen marker, black acrylic paint, and black spray paint.


RISE_DS02



Silica Fiber Components Scintillating Signal using UV Illumination: Capillary fiber filled with scintillating liquids (BC505 and Perovskite quantum dots) exposed to UV source (LED- emitting at 275 nm) at specific positions along the fiber length. The scintillation count was then measured by the spectrometer (Ocean Optics Spectrometer-QEB1624) at the fiber output. The data files contain light power measurements which are compressed (filename "DATA - Silica Fiber Components Scintillating Signal UV Illumination.zip") and are presented in the folders named by measurement dates.

Silica Components_splices and setups photos: The pictures in this folder include the experimental setups for loss measurement, UV illumination tests, filling the capillary, images of splicing the liquid-filled capillary (50µm) and MMF for components, and prototypes in this work. 

Silica Fiber Components Scintillating Signal using X-ray illumination: Capillary fiber filled with scintillating liquid (BC505) exposed to the X-ray source (Liquid-metal-jet source, 70kV, 125W). The scintillation count was then measured by the spectrometer at the fiber output. For comparison with silica-based scintillating components, some tests were performed with plastic scintillating fiber (PSF).


RISE_DS03 



Scintillating Silica Fiber_Deliverable Video_and_Conferene-paper: This archive contains a publication and video presentation of the design and fabrication of flexible, liquid-filled scintillating fibers for X-ray detection made from silica fibers and silica capillaries. The scintillating fibers were characterized using ultraviolet light exposure and we also performed an experiment demonstrating X-ray detection.


This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 899634.
DOI: 10.5281/zenodo.10782854
title: Liquid-filled scintillating fiber development

description: In the frame of the EU-funded project "FleX-RAY", an inherently flexible X-ray, imaging detector using single-photon avalanche photodiodes and inorganic scintillating fibres was developed. Silica-based scintillating liquid-filled fibers were developed for constructing the detector hardware.

This archive contains the following data:

RISE_DS01



Evaluation of splice coverings to block stray light Evaluation of splice coverings to block stray light: Different types of painting methods were applied to the splice sections of the silica-scintillating components to block stray light entering the fibers. Light levels were measured and evaluated for different lightness conditions, measured by a photon counter and high-speed oscilloscope at the fiber output. The stray light was measured when the lightness condition was darkness, ambient light, and desk lamp light. The painting methods include splice without paint, black pen marker, black acrylic paint, and black spray paint.


RISE_DS02



Silica Fiber Components Scintillating Signal using UV Illumination: Capillary fiber filled with scintillating liquids (BC505 and Perovskite quantum dots) exposed to UV source (LED- emitting at 275 nm) at specific positions along the fiber length. The scintillation count was then measured by the spectrometer (Ocean Optics Spectrometer-QEB1624) at the fiber output. The data files contain light power measurements which are compressed (filename "DATA - Silica Fiber Components Scintillating Signal UV Illumination.zip") and are presented in the folders named by measurement dates.

Silica Components_splices and setups photos: The pictures in this folder include the experimental setups for loss measurement, UV illumination tests, filling the capillary, images of splicing the liquid-filled capillary (50µm) and MMF for components, and prototypes in this work. 

Silica Fiber Components Scintillating Signal using X-ray illumination: Capillary fiber filled with scintillating liquid (BC505) exposed to the X-ray source (Liquid-metal-jet source, 70kV, 125W). The scintillation count was then measured by the spectrometer at the fiber output. For comparison with silica-based scintillating components, some tests were performed with plastic scintillating fiber (PSF).


RISE_DS03 



Scintillating Silica Fiber_Deliverable Video_and_Conferene-paper: This archive contains a publication and video presentation of the design and fabrication of flexible, liquid-filled scintillating fibers for X-ray detection made from silica fibers and silica capillaries. The scintillating fibers were characterized using ultraviolet light exposure and we also performed an experiment demonstrating X-ray detection.


This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 899634.
DOI: 10.5281/zenodo.6525021
title: Replication Package: "Simulation-based Test Case Generation for Unmanned Aerial Vehicles in the Neighborhood of Real Flights"

description: Structure:


For each of the experiments, conducted for our Research questions, we include the following:


1. An spreadsheet file containing the aggregated experiment results



	
first tab: aggregated data for all the 10 repetitions, and metrics reported in the paper
	
raw logs for each of 10 repetitions in separate tabs



2. Experiment summary for each of the 10 repetitions



	
plots of the evaluated solutions at each iteration, and their fitness values
	
the plot of the overall progress of the fitness values over iterations
	
the raw log containing the details of computations for each iteration



3. Experiment simulation logs (raw simulation outputs)



	
'.ulg' flight logs of the solutions evaluated at each iteration ( 'n' parallel simulations at each iteration)
	
only the repetition with the best final result is included. We exclude the other repetitions since the total space needed is too large (more than 200 GB).
DOI: 10.5281/zenodo.6525022
title: Flight2Sim Replication Package

description: replication package for the ASE 2022 submission
DOI: 10.5281/zenodo.6525028
title: Replication Package: "Test Case Generation for Unmanned Aerial Vehicles based on Flight Replication"

description: <strong>Structure:</strong> For each of the experiments, conducted for our Research questions, we include the following: 1. An excel file containing the aggregated experiment results first tab: aggregated data for all the 10 repetitions, and metrics reported in the paper raw logs for each of 10 repetitions in separate tabs 2. Experiment summary for each of the 10 repetitions plots of the evaluated solutions at each iteration, and their fitness values the plot of the overall progress of the fitness values over iterations the raw log containing the details of computations for each iteration 3. Experiment simulation logs (raw simulation outputs) '.ulg' flight logs of the solutions evaluated at each iteration ( 'n' parallel simulations at each iteration) only the repetition with the best final result is included. We exclude the other repetitions since the total space needed is too large (more than 200 GB).
DOI: 10.5281/zenodo.6563065
title: Replication Package: "Test Case Generation for Unmanned Aerial Vehicles based on Flight Replication"

description: <strong>Structure:</strong> For each of the experiments, conducted for our Research questions, we include the following: 1. An excel file containing the aggregated experiment results first tab: aggregated data for all the 10 repetitions, and metrics reported in the paper raw logs for each of 10 repetitions in separate tabs 2. Experiment summary for each of the 10 repetitions plots of the evaluated solutions at each iteration, and their fitness values the plot of the overall progress of the fitness values over iterations the raw log containing the details of computations for each iteration 3. Experiment simulation logs (raw simulation outputs) '.ulg' flight logs of the solutions evaluated at each iteration ( 'n' parallel simulations at each iteration) only the repetition with the best final result is included. We exclude the other repetitions since the total space needed is too large (more than 200 GB).
DOI: 10.5281/zenodo.7287620
title: Replication Package: "Simulation-based Test Case Generation for Unmanned Aerial Vehicles in the Neighborhood of Real Flights"

description: Structure:


For each of the experiments, conducted for our Research questions, we include the following:


1. An spreadsheet file containing the aggregated experiment results



	
first tab: aggregated data for all the 10 repetitions, and metrics reported in the paper
	
raw logs for each of 10 repetitions in separate tabs



2. Experiment summary for each of the 10 repetitions



	
plots of the evaluated solutions at each iteration, and their fitness values
	
the plot of the overall progress of the fitness values over iterations
	
the raw log containing the details of computations for each iteration



3. Experiment simulation logs (raw simulation outputs)



	
'.ulg' flight logs of the solutions evaluated at each iteration ( 'n' parallel simulations at each iteration)
	
only the repetition with the best final result is included. We exclude the other repetitions since the total space needed is too large (more than 200 GB).
DOI: 10.5281/zenodo.10118980
title: Dataset - FetMRQC: an open-source machine learning framework for multi-centric fetal brain MRI quality control

description: This dataset contains the data and model used in the paper



Thomas Sanchez, Oscar Esteban, Yvan Gomez, Alexandre Pron, Mériam Koob, Vincent Dunet, Nadine Girard, Andras Jakab, Elisenda Eixarch, Guillaume Auzias, and Meritxell Bach Cuadra. "FetMRQC: an open-source machine learning framework for multi-centric fetal brain MRI quality control." arXiv preprint arXiv:2311.04780 (2023).


If you found this dataset useful or used it in your research, please cite this reference.

This dataset contains manual quality annotations and image quality metrics (IQMs) obtained from 1647 stacks of T2-weighted (T2w) slices of fetal brain magnetic resonance (MR) images collected from 233 subjects at four different institutions Lausanne University Hospital (CHUV) in Switzerland, BCNatal at Hospital Sant Joan de Déu in Barcelona (Spain), University Children's Hospital Zürich (KISPI) in Switzerland and La Timone University Hospital in Marseille, France. The data were acquired on scanners from different vendors (Siemens at CHUV, BCNatal and Marseille, General Electrics at KISPI), MR sequences (Half Fourier Single-shot Turbo spin-Echo –HASTE– for Siemens scanners and Single-Short Fast Spin Echo –SS-FSE– for GE scanners), magnetic field strengths (1.5 T and 3 T), image resolutions, fields of view, repetition times and echo times, with both neurotypical and pathological cases.

These data and the derived IQMs were used to train and evaluate models for quality assessment and quality control of fetal brain MR images. The code to reproduce the experiments is available on GitHub.

Each entry describe the information for a single stack of T2w slices. It contains information regarding which subject it belongs to, its manual quality rating, scanner-related information and 332 IQMs, starting at the `centroid` column in the file. Further description of the data is available in the materials and methods section of the paper.

The model is a 2D nnUNet [1] segmentation network trained on the super-resolution reconstructed data and manual segmentations available as part of the Fetal Tissue Annotation Challenge (FeTA).

Copyright (c) - All rights reserved. Medical Image Analysis Laboratory - Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland & CIBM Center for Biomedical Imaging. 2023.

[1] Isensee, Fabian, et al. "nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation." Nature methods 18.2 (2021): 203-211.
DOI: 10.5281/zenodo.10118981
title: Dataset - FetMRQC: an open-source machine learning framework for multi-centric fetal brain MRI quality control

description: This dataset contains the data used in the paper

Thomas Sanchez, Oscar Esteban, Yvan Gomez, Alexandre Pron, Mériam Koob, Vincent Dunet, Nadine Girard, Andras Jakab, Elisenda Eixarch, Guillaume Auzias, and Meritxell Bach Cuadra. "FetMRQC: an open-source machine learning framework for multi-centric fetal brain MRI quality control." arXiv preprint arXiv:2311.04780 (2023).
If you found this dataset useful or used it in your research, please cite this reference.
This dataset contains manual quality annotations and image quality metrics (IQMs) obtained from 1647 stacks of T2-weighted (T2w) slices of fetal brain magnetic resonance (MR) images collected from 233 subjects at four different institutions Lausanne University Hospital (CHUV) in Switzerland, BCNatal at Hospital Sant Joan de Déu in Barcelona (Spain), University Children's Hospital Zürich (KISPI) in Switzerland and La Timone University Hospital in Marseille, France. The data were acquired on scanners from different vendors (Siemens at CHUV, BCNatal and Marseille, General Electrics at KISPI), MR sequences (Half Fourier Single-shot Turbo spin-Echo –HASTE– for Siemens scanners and Single-Short Fast Spin Echo –SS-FSE– for GE scanners), magnetic field strengths (1.5 T and 3 T), image resolutions, fields of view, repetition times and echo times, with both neurotypical and pathological cases.
These data and the derived IQMs were used to train and evaluate models for quality assessment and quality control of fetal brain MR images. The code to reproduce the experiments is available on GitHub.
Each entry describe the information for a single stack of T2w slices. It contains information regarding which subject it belongs to, its manual quality rating, scanner-related information and 332 IQMs, starting at the `centroid` column in the file. Further description of the data is available in the materials and methods section of the paper.
Copyright (c) - All rights reserved. Medical Image Analysis Laboratory - Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland & CIBM Center for Biomedical Imaging. 2023.
DOI: 10.5281/zenodo.10785234
title: Dataset - FetMRQC: an open-source machine learning framework for multi-centric fetal brain MRI quality control

description: This dataset contains the data and model used in the paper



Thomas Sanchez, Oscar Esteban, Yvan Gomez, Alexandre Pron, Mériam Koob, Vincent Dunet, Nadine Girard, Andras Jakab, Elisenda Eixarch, Guillaume Auzias, and Meritxell Bach Cuadra. "FetMRQC: an open-source machine learning framework for multi-centric fetal brain MRI quality control." arXiv preprint arXiv:2311.04780 (2023).


If you found this dataset useful or used it in your research, please cite this reference.

This dataset contains manual quality annotations and image quality metrics (IQMs) obtained from 1647 stacks of T2-weighted (T2w) slices of fetal brain magnetic resonance (MR) images collected from 233 subjects at four different institutions Lausanne University Hospital (CHUV) in Switzerland, BCNatal at Hospital Sant Joan de Déu in Barcelona (Spain), University Children's Hospital Zürich (KISPI) in Switzerland and La Timone University Hospital in Marseille, France. The data were acquired on scanners from different vendors (Siemens at CHUV, BCNatal and Marseille, General Electrics at KISPI), MR sequences (Half Fourier Single-shot Turbo spin-Echo –HASTE– for Siemens scanners and Single-Short Fast Spin Echo –SS-FSE– for GE scanners), magnetic field strengths (1.5 T and 3 T), image resolutions, fields of view, repetition times and echo times, with both neurotypical and pathological cases.

These data and the derived IQMs were used to train and evaluate models for quality assessment and quality control of fetal brain MR images. The code to reproduce the experiments is available on GitHub.

Each entry describe the information for a single stack of T2w slices. It contains information regarding which subject it belongs to, its manual quality rating, scanner-related information and 332 IQMs, starting at the `centroid` column in the file. Further description of the data is available in the materials and methods section of the paper.

The model is a 2D nnUNet [1] segmentation network trained on the super-resolution reconstructed data and manual segmentations available as part of the Fetal Tissue Annotation Challenge (FeTA).

Copyright (c) - All rights reserved. Medical Image Analysis Laboratory - Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland & CIBM Center for Biomedical Imaging. 2023.

[1] Isensee, Fabian, et al. "nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation." Nature methods 18.2 (2021): 203-211.
DOI: 10.48597/2dpv-9j44
title: Measurement of Ozone at Aspvreten

description: Measurement of Ozone at Aspvreten
DOI: 10.48597/2epq-7amd
title: Measurement of Ozone at Gerlitzen

description: Measurement of Ozone at Gerlitzen
DOI: 10.48597/2tzt-4tzz
title: Measurement of Ozone at Masenberg

description: Measurement of Ozone at Masenberg
DOI: 10.48597/2wqd-7sc4
title: Measurement of Ozone at Vorhegg

description: Measurement of Ozone at Vorhegg
DOI: 10.15468/dl.25heew
title: Occurrence Download

description: A dataset containing 86 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Dotilla fenestrata"
 ]
}

 The dataset includes 86 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027604-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ku96xp
title: Occurrence Download

description: A dataset containing 295 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Brucea antidysenterica",
 "Geometry POLYGON ((-17.62504 -34.81917, 51.13387 -34.81917, 51.13387 37.34999, -17.62504 37.34999, -17.62504 -34.81917))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 295 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027602-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18738/t8/7ureyt
title: Literary Criticism: Its Present Status in Latin America

description: Part 1: The afternoon session on literature issues begins with introductory remarks by Allen Phillips and Rodolfo Cardona, who introduces the panelists. Tomas Segovia is the first to begin discussing the the present status of literary criticism in Latin America.
Part 2: The panel focuses on a comprehensive discourse on the nature, evolution, and challenges of literary criticism and ideology in Latin America, with particular focus on Brazil and broader Hispanic America. Jaime adds a historical perspective on Latin American literary criticism, highlighting its social and political ties from the 19th century to the present, the institutionalization of criticism in universities, and the fragmentation of literary works detached from their social contexts.
Part 3: Octavio Paz and other panelists address the unique relationship between literary criticism and philosophical and scientific thought in Europe. The discussion expands to the current role of literary criticism in Latin America, addressing distinctions between university and non-university criticism. It is proposed that criticism should be a dialogical and therapeutic space, beyond ideological monologue, with the journal Plural as an attempt to create this plural and rational space. Debates among academics reflect divergences on the function, scope, and ideological nature of literary criticism and theory, as well as the difficulties of constructing authentic critical thinking in contexts where criticism itself can become a form of ideology.
description: Speak Out! Symposium
description: AVAnnotate
description: ChatGPT, GPT-4o
description: DeepL
description: Rebekah Ramos and Ash Catalan used ChatGPT and DeepL to generate item-level metadata for the radio program recording, then reviewed and corrected the output. To create the radio program annotation dataset, they created a spreadsheet containing columns for the start and end timestamps that encapsulated each speaker segment and a corresponding transcription of the audio. They then parsed out the corrected auto-generated metadata accordingly into additional columns/categories (people, places, organizations, subjects, art topics, and communities). Once complete, Ramos and Catalan published the annotation dataset with the program's recording using AVAnnotate (see related publication). The metadata generation and annotation of these audio recordings took place between 2025 May 1 and 2025 August 8.
DOI: 10.15468/dl.7xu6yh
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Dotilla intermedia"
 ]
}

 The dataset includes 7 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027605-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cfzy6t
title: Occurrence Download

description: A dataset containing 602 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Phytolacca dioica",
 "Country is South Africa",
 "HasCoordinate is true"
 ]
}

 The dataset includes 602 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027607-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17058383
title: Raman spectra of environmental snow samples from the Statistical Classification of Biosignature Information (SCOBI) project

description: This data set consists of Raman spectra of several local snow samples taken with a scanning Raman microscope at the NASA Ames Research Center's ICEE (In Situ Carbon Evolution Experiments) Facility.  The spectra were taken as part of a larger effort, the Statistical Classification of Biosignature Information (SCOBI) project, to collect measurements of biogenic and abiogenic matter as if they were unknown samples encountered during planetary exploration.