cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8313531
title: Water-related vertical displacements for all GNET stations studied

description: Water-related vertical displacements for all GNET stations studied. Vertical displacement time-series from the proposed analytic model, based on calibrated buffered water storage estimates.(1 column: Time; 2 column: Vertical displacement; 3 column: error)
DOI: 10.5281/zenodo.8313978
title: Water-related vertical displacements for all GNET stations studied

description: Water-related vertical displacements for all GNET stations studied. Vertical displacement time-series from the proposed analytic model, based on calibrated buffered water storage estimates.(1 column: Time; 2 column: Vertical displacement; 3 column: error)
DOI: 10.4121/a7e63b3f-18f5-4ae4-8750-255528f82178
title: Data supporting the thesis “Exploring Hybrid Intelligence for Topic Interpretation in Colorectal Cancer Research: A Comparative Study of GPT-3.5 and Human Expertise”

description: The research objective of this thesis is to bridge the gap between human and machine intelligence in the interpretation of colorectal cancer patient experiences extracted from patient web forums. This Computer Science thesis was done in collaboration with colorectal cancer human experts from Erasmus MC. To perform this scientific research and make these human experts and GPT-3.5 interpret colorectal cancer patient experiences, nearly 300k patient web forums were scraped from the American platform called Cancer Survivors Network USA (Colorectal Cancer — Cancer Survivors Network). For extracting the patient web forums, the Selenium webdriver was used to extract the page urls for each discussion thread, and BeautifulSoup4 (bs4) was used to access the page urls and parse the html elements from each type of patient forum, including main post, comment and reply, and store them in a local dataset. The patient forum attributes stored in the dataset are: URL – username (i.e. author of the post)– userposts (i.e. number of posts written by the author)– time (i.e. when the post was made)– title – post (i.e. text consisting of unstructured colorectal cancer patient experiences)
DOI: 10.4121/a7e63b3f-18f5-4ae4-8750-255528f82178.v1
title: Data supporting the thesis “Exploring Hybrid Intelligence for Topic Interpretation in Colorectal Cancer Research: A Comparative Study of GPT-3.5 and Human Expertise”

description: The research objective of this thesis is to bridge the gap between human and machine intelligence in the interpretation of colorectal cancer patient experiences extracted from patient web forums. This Computer Science thesis was done in collaboration with colorectal cancer human experts from Erasmus MC. To perform this scientific research and make these human experts and GPT-3.5 interpret colorectal cancer patient experiences, nearly 300k patient web forums were scraped from the American platform called Cancer Survivors Network USA (Colorectal Cancer — Cancer Survivors Network). For extracting the patient web forums, the Selenium webdriver was used to extract the page urls for each discussion thread, and BeautifulSoup4 (bs4) was used to access the page urls and parse the html elements from each type of patient forum, including main post, comment and reply, and store them in a local dataset. The patient forum attributes stored in the dataset are: URL – username (i.e. author of the post)– userposts (i.e. number of posts written by the author)– time (i.e. when the post was made)– title – post (i.e. text consisting of unstructured colorectal cancer patient experiences)
DOI: 10.5281/zenodo.6865829
title: MILAN project: raw images of deep sky objects captured with a Stellina observation station -- [2022-03]

description: During the MILAN research project (MachIne Learning for AstroNomy), the research team uses Stellina observation stations to collect raw images of deep sky objects. The Stellina observation station has an apochromatic ED doublet with an aperture of 80 mm and a focal length of 400 mm - focal ratio of f/5. It is equipped with a Sony IMX 178 CMOS sensor with a resolution of 6.4 million pixels (3096 x 2080 pixels). An anti-pollution filter is placed in front of the camera sensor. The station also has a fully automated alt-azimuth mount: setup, object tracking and focusing are also automatic. Stellina also has an integrated field rotator that adapts to the target. Thus, the research team built a dataset that faithfully represents what can be obtained during classical Electronically Assisted Astronomy sessions in the Luxembourg Greater Region:<br> - targets: representative set of deep sky objects from the Messier and NGC catalogs during the period.<br> - type of targets: nebulaes, planetary nebula, galaxies, globular clusters, open clusters<br> - for each observation session: sub-frames of 10 seconds exposure time and total cumulative duration of ~15/30 minutes (depending of the target) -- bad sub-frames were not filtered. The dataset is composed of ZIP files -- and each zip file contain raw images in FITS format (Flexible Image Transport System): data come directly from the Sony IMX178 sensor of the Stellina observation station (no debayerisation and no post processing). This research was funded by the Luxembourg National Research Fund (FNR), grant reference 15872557. To refer to or to cite this work, please use: "Parisot, O., Hitzelberger, P., Bruneau, P., Krebs, G., Destruel, C., & Vandame, B. (2023). MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope. Data in Brief, 48, 109133." MILAN project team members: Olivier Parisot (LIST), Patrik Hitzelberger (LIST), Pierrick Bruneau (LIST), Gilles Krebs (VAONIS, Christophe Destruel (VAONIS). More information about the MILAN project: https://www.fnr.lu/results-2021-1-bridges-call/. More information about VAONIS instruments: https://vaonis.com/ More information about Luxembourg of Science and Technology (LIST): https://www.list.lu/ Data license for files: Attribution-NonCommercial-NoDerivatives 4.0 International
DOI: 10.5281/zenodo.6865830
title: MILAN project: raw images of deep sky objects captured with a Stellina observation station -- [2022-03]

description: During the MILAN research project (MachIne Learning for AstroNomy), the research team uses Stellina observation stations to collect raw images of deep sky objects. The Stellina observation station has an apochromatic ED doublet with an aperture of 80 mm and a focal length of 400 mm - focal ratio of f/5. It is equipped with a Sony IMX 178 CMOS sensor with a resolution of 6.4 million pixels (3096 x 2080 pixels). An anti-pollution filter is placed in front of the camera sensor. The station also has a fully automated alt-azimuth mount: setup, object tracking and focusing are also automatic. Stellina also has an integrated field rotator that adapts to the target. Thus, the research team built a dataset that faithfully represents what can be obtained during classical Electronically Assisted Astronomy sessions in the Luxembourg Greater Region:<br> - targets: representative set of deep sky objects from the Messier and NGC catalogs during the period.<br> - type of targets: nebulaes, planetary nebula, galaxies, globular clusters, open clusters<br> - for each observation session: sub-frames of 10 seconds exposure time and total cumulative duration of ~15/30 minutes (depending of the target) -- bad sub-frames were not filtered. The dataset is composed of ZIP files -- and each zip file contain raw images in FITS format (Flexible Image Transport System): data come directly from the Sony IMX178 sensor of the Stellina observation station (no debayerisation and no post processing). This research was funded by the Luxembourg National Research Fund (FNR), grant reference 15872557. To refer to or to cite this work, please use: "Parisot, O., Hitzelberger, P., Bruneau, P., Krebs, G., Destruel, C., & Vandame, B. (2023). MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope. Data in Brief, 48, 109133." MILAN project team members: Olivier Parisot (LIST), Patrik Hitzelberger (LIST), Pierrick Bruneau (LIST), Gilles Krebs (VAONIS, Christophe Destruel (VAONIS). More information about the MILAN project: https://www.fnr.lu/results-2021-1-bridges-call/. More information about VAONIS instruments: https://vaonis.com/ More information about Luxembourg of Science and Technology (LIST): https://www.list.lu/ Data license for files: Attribution-NonCommercial-NoDerivatives 4.0 International
DOI: 10.5281/zenodo.6979484
title: MILAN project: raw images of deep sky objects captured with a Stellina observation station -- [2022-04]

description: During the MILAN research project (MachIne Learning for AstroNomy), the research team uses Stellina observation stations to collect raw images of deep sky objects. The Stellina observation station has an apochromatic ED doublet with an aperture of 80 mm and a focal length of 400 mm - focal ratio of f/5. It is equipped with a Sony IMX 178 CMOS sensor with a resolution of 6.4 million pixels (3096 x 2080 pixels). An anti-pollution filter is placed in front of the camera sensor. The station also has a fully automated alt-azimuth mount: setup, object tracking and focusing are also automatic. Stellina also has an integrated field rotator that adapts to the target. Thus, the research team built a dataset that faithfully represents what can be obtained during classical Electronically Assisted Astronomy sessions in the Luxembourg Greater Region:<br> - targets: representative set of deep sky objects from the Messier and NGC catalogs during the period.<br> - type of targets: nebulaes, planetary nebula, galaxies, globular clusters, open clusters<br> - for each observation session: sub-frames of 10 seconds exposure time and total cumulative duration of ~15/30 minutes (depending of the target) -- bad sub-frames were not filtered. The dataset is composed of ZIP files -- and each zip file contain raw images in FITS format (Flexible Image Transport System): data come directly from the Sony IMX178 sensor of the Stellina observation station (no debayerisation and no post processing). This research was funded by the Luxembourg National Research Fund (FNR), grant reference 15872557. To refer to or to cite this work, please use: "Parisot, O., Hitzelberger, P., Bruneau, P., Krebs, G., Destruel, C., & Vandame, B. (2023). MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope. Data in Brief, 48, 109133." MILAN project team members: Olivier Parisot (LIST), Patrik Hitzelberger (LIST), Pierrick Bruneau (LIST), Gilles Krebs (VAONIS, Christophe Destruel (VAONIS). More information about the MILAN project: https://www.fnr.lu/results-2021-1-bridges-call/. More information about VAONIS instruments: https://vaonis.com/ More information about Luxembourg of Science and Technology (LIST): https://www.list.lu/ Data license for files: Attribution-NonCommercial-NoDerivatives 4.0 International
DOI: 10.5281/zenodo.6866880
title: MILAN project: raw images of deep sky objects captured with a Stellina observation station -- [2022-04]

description: During the MILAN research project (MachIne Learning for AstroNomy), the research team uses Stellina observation stations to collect raw images of deep sky objects. The Stellina observation station has an apochromatic ED doublet with an aperture of 80 mm and a focal length of 400 mm - focal ratio of f/5. It is equipped with a Sony IMX 178 CMOS sensor with a resolution of 6.4 million pixels (3096 x 2080 pixels). An anti-pollution filter is placed in front of the camera sensor. The station also has a fully automated alt-azimuth mount: setup, object tracking and focusing are also automatic. Stellina also has an integrated field rotator that adapts to the target. Thus, the research team built a dataset that faithfully represents what can be obtained during classical Electronically Assisted Astronomy sessions in the Luxembourg Greater Region:<br> - targets: representative set of deep sky objects from the Messier and NGC catalogs during the period.<br> - type of targets: nebulaes, planetary nebula, galaxies, globular clusters, open clusters<br> - for each observation session: sub-frames of 10 seconds exposure time and total cumulative duration of ~15/30 minutes (depending of the target) -- bad sub-frames were not filtered. The dataset is composed of ZIP files -- and each zip file contain raw images in FITS format (Flexible Image Transport System): data come directly from the Sony IMX178 sensor of the Stellina observation station (no debayerisation and no post processing). This research was funded by the Luxembourg National Research Fund (FNR), grant reference 15872557. To refer to or to cite this work, please use: "Parisot, O., Hitzelberger, P., Bruneau, P., Krebs, G., Destruel, C., & Vandame, B. (2023). MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope. Data in Brief, 48, 109133." MILAN project team members: Olivier Parisot (LIST), Patrik Hitzelberger (LIST), Pierrick Bruneau (LIST), Gilles Krebs (VAONIS, Christophe Destruel (VAONIS). More information about the MILAN project: https://www.fnr.lu/results-2021-1-bridges-call/. More information about VAONIS instruments: https://vaonis.com/ More information about Luxembourg of Science and Technology (LIST): https://www.list.lu/ Data license for files: Attribution-NonCommercial-NoDerivatives 4.0 International
DOI: 10.5281/zenodo.5814909
title: Bioimpedance phase angle as a prognostic tool in late-onset pompe disease: a single-centre prospective study with a 15-year follow-up

description: We reported the long-term follow-up of a population of Late onset Pompe disease treated with enzyme replacement therapy. Among predictors of treatment effectiveness and prognosis, we include body composition assessment by bioelectrical vectorial impedance. <strong>Methods. </strong>In this single Centre, prospective study, we evaluate the response to enzyme replacement therapy in 15 patients (7 males) with LOPD in different stages of disease, aged 49.4+16.1, followed-up for 15 years. Treatment response was measured by the six-minute walking test, vital capacity in supine and upright position, respiratory muscle strength, muscle MRI, manual muscle testing. We investigated the usefulness of Body Impedance Vectorial Analysis for serial body composition assessment. <strong>Results (in brief). </strong>Although most patients with LOPD benefit from long-term treatment, some secondary decline may occur after the first 3-5 years. Some nutritional (lower body mass index, higher fat free mass, higher phase angle) and disease parameters (higher creatinine and shorter disease duration at the beginning of treatment) seem to predict a better motor outcome. Lower Phase Angle may reflect loss of integrity of skeletal muscle membranes, resulting from lysosomal and autophagosomal dysregulation; this leads to treatment mis-targeting, and thus to worse treatment response, and ultimately to whole cell damage. Indeed, the muscle is one of the tissue with the highest rate of autophagosome formation and degradation.
description: Funding: ricerca corrente Ministry of Health (Italy)
DOI: 10.5281/zenodo.5814908
title: Bioimpedance phase angle as a prognostic tool in late-onset pompe disease: a single-centre prospective study with a 15-year follow-up

description: We reported the long-term follow-up of a population of Late onset Pompe disease treated with enzyme replacement therapy. Among predictors of treatment effectiveness and prognosis, we include body composition assessment by bioelectrical vectorial impedance. <strong>Methods. </strong>In this single Centre, prospective study, we evaluate the response to enzyme replacement therapy in 15 patients (7 males) with LOPD in different stages of disease, aged 49.4+16.1, followed-up for 15 years. Treatment response was measured by the six-minute walking test, vital capacity in supine and upright position, respiratory muscle strength, muscle MRI, manual muscle testing. We investigated the usefulness of Body Impedance Vectorial Analysis for serial body composition assessment. <strong>Results (in brief). </strong>Although most patients with LOPD benefit from long-term treatment, some secondary decline may occur after the first 3-5 years. Some nutritional (lower body mass index, higher fat free mass, higher phase angle) and disease parameters (higher creatinine and shorter disease duration at the beginning of treatment) seem to predict a better motor outcome. Lower Phase Angle may reflect loss of integrity of skeletal muscle membranes, resulting from lysosomal and autophagosomal dysregulation; this leads to treatment mis-targeting, and thus to worse treatment response, and ultimately to whole cell damage. Indeed, the muscle is one of the tissue with the highest rate of autophagosome formation and degradation.
description: Funding: ricerca corrente Ministry of Health (Italy)
DOI: 10.5281/zenodo.8314259
title: Continuous verification - platooning case

description: <pre>This repository provides raw data used in the paper "Continuous Verification with Acknowledged MAPE-K Pattern and Time Logic-based Slicing: A platooning System of Systems Case Study". Related tools include the PRISM model checker (https://github.com/prismmodelchecker/prism.git) and the platooning simulator (https://github.com/KAIST-SE-Lab/StarPlateS). The Original folder is the data obtained from the existing simulator. The CVplatooning folder is data obtained by improving the existing simulator. Finally, the slicing folder provides the existing model verification results and model slicing verification results.</pre>
DOI: 10.5281/zenodo.8314258
title: Continuous verification - platooning case

description: <pre>This repository provides raw data used in the paper "Continuous Verification with Acknowledged MAPE-K Pattern and Time Logic-based Slicing: A platooning System of Systems Case Study". Related tools include the PRISM model checker (https://github.com/prismmodelchecker/prism.git) and the platooning simulator (https://github.com/KAIST-SE-Lab/StarPlateS). The Original folder is the data obtained from the existing simulator. The CVplatooning folder is data obtained by improving the existing simulator. Finally, the slicing folder provides the existing model verification results and model slicing verification results.</pre>
DOI: 10.4121/6cfdc8d8-67a1-48f0-ac77-87fb12bf316f
title: Analytical Model for Pre-Alignment Filtering in Memory

description: Analytical Model for Pre-Alignment Filtering in Memory used for the evaluation of the paper titled RattlesnakeJake: A Fast and Accurate Pre-Alignment Filter Suitable for Computation-in-Memory and the M.Sc. thesis of Michael Miao at QCE department. This analytical model is used to calculate the resource requirements (tile sizes, etc.) for the architectures in the research presented in the paper and the thesis given a genomic dataset.
DOI: 10.4121/6cfdc8d8-67a1-48f0-ac77-87fb12bf316f.v1
title: Analytical Model for Pre-Alignment Filtering in Memory

description: Analytical Model for Pre-Alignment Filtering in Memory used for the evaluation of the paper titled RattlesnakeJake: A Fast and Accurate Pre-Alignment Filter Suitable for Computation-in-Memory and the M.Sc. thesis of Michael Miao at QCE department. This analytical model is used to calculate the resource requirements (tile sizes, etc.) for the architectures in the research presented in the paper and the thesis given a genomic dataset.
DOI: 10.7910/dvn/whyp5g
title: Base de données SIGMENT

description: The « Sites du Gravettien Moyen et récENT » (SIGMENT) database aims to reliably estimate the geographic distribution of Middle and Recent Gravettian lithic technical traditions in France and neighboring regions.
DOI: 10.5281/zenodo.6874896
title: MILAN project: raw images of deep sky objects captured with a Stellina observation station -- [2022-05]

description: During the MILAN research project (MachIne Learning for AstroNomy), the research team uses Stellina observation stations to collect raw images of deep sky objects. The Stellina observation station has an apochromatic ED doublet with an aperture of 80 mm and a focal length of 400 mm - focal ratio of f/5. It is equipped with a Sony IMX 178 CMOS sensor with a resolution of 6.4 million pixels (3096 x 2080 pixels). An anti-pollution filter is placed in front of the camera sensor. The station also has a fully automated alt-azimuth mount: setup, object tracking and focusing are also automatic. Stellina also has an integrated field rotator that adapts to the target. Thus, the research team built a dataset that faithfully represents what can be obtained during classical Electronically Assisted Astronomy sessions in the Luxembourg Greater Region:<br> - targets: representative set of deep sky objects from the Messier and NGC catalogs during the period.<br> - type of targets: nebulaes, planetary nebula, galaxies, globular clusters, open clusters<br> - for each observation session: sub-frames of 10 seconds exposure time and total cumulative duration of ~15/30 minutes (depending of the target) -- bad sub-frames were not filtered. The dataset is composed of ZIP files -- and each zip file contain raw images in FITS format (Flexible Image Transport System): data come directly from the Sony IMX178 sensor of the Stellina observation station (no debayerisation and no post processing). This research was funded by the Luxembourg National Research Fund (FNR), grant reference 15872557. To refer to or to cite this work, please use: "Parisot, O., Hitzelberger, P., Bruneau, P., Krebs, G., Destruel, C., & Vandame, B. (2023). MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope. Data in Brief, 48, 109133." MILAN project team members: Olivier Parisot (LIST), Patrik Hitzelberger (LIST), Pierrick Bruneau (LIST), Gilles Krebs (VAONIS, Christophe Destruel (VAONIS). More information about the MILAN project: https://www.fnr.lu/results-2021-1-bridges-call/. More information about VAONIS instruments: https://vaonis.com/ More information about Luxembourg of Science and Technology (LIST): https://www.list.lu/ Data license for files: Attribution-NonCommercial-NoDerivatives 4.0 International