cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/icpsr38355
title: The Upswing: How America Came Together a Century Ago and How We Can Do It Again, United States, 1890-2020
title: Archival Version

description: The purpose for which these data were collected was to explore aggregate, time-series trends in economics, politics, society, and culture in the US between roughly 1890 and 2020. The sources for these various time series are given in the sources for each series. The results of the project appeared in Robert D. Putnam and Shaylyn Romney Garrett, The Upswing (Simon and Schuster, 2020).
description: Datasets: 
DS1: The Upswing: How America Came Together a Century Ago and How We Can Do It Again, United States, 1890-2020
description: record abstracts
DOI: 10.3886/icpsr38355.v1
title: The Upswing: How America Came Together a Century Ago and How We Can Do It Again, United States, 1890-2020
title: Version 1

description: The purpose for which these data were collected was to explore aggregate, time-series trends in economics, politics, society, and culture in the US between roughly 1890 and 2020. The sources for these various time series are given in the sources for each series. The results of the project appeared in Robert D. Putnam and Shaylyn Romney Garrett, The Upswing (Simon and Schuster, 2020).
description: Datasets: 
DS1: The Upswing: How America Came Together a Century Ago and How We Can Do It Again, United States, 1890-2020
description: record abstracts
DOI: 10.5281/zenodo.10452427
title: Dataset for: Pre-collapse spaceborne deformation monitoring of the Kakhovka dam, Ukraine, from 2017 to 2023

description: Abstract

The Kakhovka Dam, situated on the Dnieper River in Ukraine, suffered a collapse on June 6, 2023, resulting in downstream inundation. In this dataset, we provide Sentinel-1 geocoded images acquired both before and after the incident—specifically, on June 1, 2023, and June 13, 2023, for the ascending geometry, and on May 28, 2023, and June 9, 2023, for the descending geometry. Additionally, we include the 50 cm resolution UMBRA descending geocoded image acquired on June 6, 2023, to identify parts of the dam that were breached. We then used a stack of ascending and descending Sentinel-1 A/B interferometric images spanning from 2015 to 2023 to generate a pre-collapse line of sight time-series of surface deformation of the dam. Our analysis reveals distinct deformation signals indicating pre-existing potential ongoing damage mechanisms on the southern part of the dam. Table 1: Acquisition attributes of both the ascending and descending Sentinel1 images, used for the MT-InSAR process.










Flight direction



IW



Orbit number



Look angle (deg)



Heading angle (deg)



Number of images



First date



Last date





Descending



1



65



43.66



-10.82



226



November 2015



May

2023





Ascending



3



14



33.79



-170.82



180



July

2017



May

2023







 

Description of the data and file structure

This dataset contains the Sentinel-1 and UMBRA geocoded images as well as the Sentinel-1 MT-InSAR results over Kakhovka dam in Ukraine.

GRDs folder contains the Sentinel-1 pre and after-the-dam collapse, geocoded images as well as an UMBRA image acquired after the collapse of the dam.

LOS_DCS.csv contains the MT-InSAR results of processing Sentinel-1 descending images, acquired from 2015 to 2023, deformation is expressed in millimeters. 

LOS_ACS.csv contains the MT-InSAR results of processing Sentinel1 ascending images, acquired from 2017 to 2023, deformation is expressed in millimeters. 

WaterLevel.xlsx contains the water-level data from Hydroweb website in meter.

AllTemp.xlsx contains the weather temperature from Visual Crossing website in celsius.

Funding

The Commercial Smallsat Data Scientific Analysis Program of NASA: NNH22ZDA001N-CSDSA

The Decadal Survey Incubation Program: Science and Technology: NNH21ZDA001N-DSI

The Vidi project InStruct, project number 18912, financed by the Dutch Research Council (NWO)
DOI: 10.5281/zenodo.10452426
title: Dataset for: Pre-collapse spaceborne deformation monitoring of the Kakhovka dam, Ukraine, from 2017 to 2023

description: Abstract

The Kakhovka Dam, situated on the Dnieper River in Ukraine, suffered a collapse on June 6, 2023, resulting in downstream inundation. In this dataset, we provide Sentinel-1 geocoded images acquired both before and after the incident—specifically, on June 1, 2023, and June 13, 2023, for the ascending geometry, and on May 28, 2023, and June 9, 2023, for the descending geometry. Additionally, we include the 50 cm resolution UMBRA descending geocoded image acquired on June 6, 2023, to identify parts of the dam that were breached. We then used a stack of ascending and descending Sentinel-1 A/B interferometric images spanning from 2015 to 2023 to generate a pre-collapse line of sight time-series of surface deformation of the dam. Our analysis reveals distinct deformation signals indicating pre-existing potential ongoing damage mechanisms on the southern part of the dam. Table 1: Acquisition attributes of both the ascending and descending Sentinel1 images, used for the MT-InSAR process.










Flight direction



IW



Orbit number



Look angle (deg)



Heading angle (deg)



Number of images



First date



Last date





Descending



1



65



43.66



-10.82



226



November 2015



May

2023





Ascending



3



14



33.79



-170.82



180



July

2017



May

2023







 

Description of the data and file structure

This dataset contains the Sentinel-1 and UMBRA geocoded images as well as the Sentinel-1 MT-InSAR results over Kakhovka dam in Ukraine.

GRDs folder contains the Sentinel-1 pre and after-the-dam collapse, geocoded images as well as an UMBRA image acquired after the collapse of the dam.

LOS_DCS.csv contains the MT-InSAR results of processing Sentinel-1 descending images, acquired from 2015 to 2023, deformation is expressed in millimeters. 

LOS_ACS.csv contains the MT-InSAR results of processing Sentinel1 ascending images, acquired from 2017 to 2023, deformation is expressed in millimeters. 

WaterLevel.xlsx contains the water-level data from Hydroweb website in meter.

AllTemp.xlsx contains the weather temperature from Visual Crossing website in celsius.

Funding

The Commercial Smallsat Data Scientific Analysis Program of NASA: NNH22ZDA001N-CSDSA

The Decadal Survey Incubation Program: Science and Technology: NNH21ZDA001N-DSI

The Vidi project InStruct, project number 18912, financed by the Dutch Research Council (NWO)
DOI: 10.34740/kaggle/dsv/7620457
title: OECD Revenue Statistics
DOI: 10.25444/nhlbi.25152866.v1
title: Data for "Low Dose Post-transplant Cyclophosphamide and Sirolimus Induce Mixed Chimerism with CTLA4-Ig or Lymphocyte Depletion in an MHC-Mismatched Murine Allotransplantation Model"

description: These datasets are the raw Fcs file of the transplantation experiments reported in the article titled "<b>Low Dose Post-transplant Cyclophosphamide and Sirolimus Induce Mixed Chimerism with CTLA4-Ig or Lymphocyte Depletion in an MHC-Mismatched Murine Allotransplantation Model</b>". Each file contains several time point analyses as well as the prism files that were used to create the article figures.
DOI: 10.25444/nhlbi.25152866
title: Data for "Low Dose Post-transplant Cyclophosphamide and Sirolimus Induce Mixed Chimerism with CTLA4-Ig or Lymphocyte Depletion in an MHC-Mismatched Murine Allotransplantation Model"

description: These datasets are the raw Fcs file of the transplantation experiments reported in the article titled "<b>Low Dose Post-transplant Cyclophosphamide and Sirolimus Induce Mixed Chimerism with CTLA4-Ig or Lymphocyte Depletion in an MHC-Mismatched Murine Allotransplantation Model</b>". Each file contains several time point analyses as well as the prism files that were used to create the article figures.
DOI: 10.5281/zenodo.10558317
title: Artifact for the paper "Mutation-based Lifted Repair of Software Product Lines"

description: This work presents a novel lifted repair algorithm for program families (Software Product Lines - SPLs) based on code mutations.The inputs of our tool are an erroneous SPL and a specification given in the form of assertions. We use variability encoding to transform the given SPL into a single program, called family simulator, which is translated into a set of SMT formulas whose conjunction is satisfiable iff the simulator (i.e. the input SPL) violates an assertion. We use a predefined set of mutations applied to feature and program expressions of the given SPL. The tool  repeatedly mutates the erroneous family simulator and checks if it becomes (bounded) correct. The outputs of our tool are all minimal repairs in the form of minimal number of (feature and program) expression replacements such that the repaired SPL is (bounded) correct with respect to a given set of assertions.

We present a prototype tool for repairing #ifdef-based C programs (i.e., annotative SPLs). The experimental results show that our approach is able to successfully repair various interesting SPLs.
DOI: 10.5281/zenodo.10655665
title: Artifact for the paper "Mutation-based Lifted Repair of Software Product Lines"

description: This work presents a novel lifted repair algorithm for program families (Software Product Lines - SPLs) based on code mutations.The inputs of our tool are an erroneous SPL and a specification given in the form of assertions. We use variability encoding to transform the given SPL into a single program, called family simulator, which is translated into a set of SMT formulas whose conjunction is satisfiable iff the simulator (i.e. the input SPL) violates an assertion. We use a predefined set of mutations applied to feature and program expressions of the given SPL. The tool  repeatedly mutates the erroneous family simulator and checks if it becomes (bounded) correct. The outputs of our tool are all minimal repairs in the form of minimal number of (feature and program) expression replacements such that the repaired SPL is (bounded) correct with respect to a given set of assertions.

We present a prototype tool for repairing #ifdef-based C programs (i.e., annotative SPLs). The experimental results show that our approach is able to successfully repair various interesting SPLs.
DOI: 10.60520/ieda/113128
title: One mafic whole rock lava sample from south-central Vietnam: 40Ar/39Ar geochronology

description: One mafic whole rock lava sample was dated using 40Ar/39Ar geochronology to determine the maximum age of faulting in the area, based on the observation that the lava flow is cross-cut by a fault.
DOI: 10.6084/m9.figshare.25215173
title: 2024 results

description: preliminary results
DOI: 10.6084/m9.figshare.25215173.v1
title: 2024 results

description: preliminary results
DOI: 10.5281/zenodo.10606167
title: Dataset for the work "The impact of CP-violating phases on DM observables in the cpMSSM"

description: All data in .csv format for the paper "The impact of CP-violating phases on DM observables in the cpMSSM". All data is sorted for individual pMSSM seed points, with excluded and non-excluded model points being saved in a separate csv file. The relevant pMSSM seed is included in each folder
DOI: 10.5281/zenodo.10606168
title: Dataset for the work "The impact of CP-violating phases on DM observables in the cpMSSM"

description: All data in .csv format for the paper "The impact of CP-violating phases on DM observables in the cpMSSM". All data is sorted for individual pMSSM seed points, with excluded and non-excluded model points being saved in a separate csv file. The relevant pMSSM seed is included in each folder
DOI: 10.5281/zenodo.5775674
title: eSEEd: emotional State Estimation based on Eye-tracking dataset

description: We present eSEEd- emotional State Estimation based on Eye-tracking database. Eye movements of 49 participants were recorded as they watched 10 emotion evoking videos each of them followed by a neutral video. Participants rated five emotions (tenderness, anger, disgust, sadness, neutral) on a scale from 0 to 10, later translated in terms of emotional arousal and valence levels. Furthermore, each participant filled 3 self-assessment questionnaires. An extensive analysis of the participants' answers to the questionnaires self-assessment scores as well as their ratings during the experiments is presented. Moreover, eye and gaze features were extracted from the low level eye recorded metrics and their correlations with the participants' ratings are investigated. Finally, analysis and results are presented for machine learning approaches, for the classification of various arousal and valence levels based solely on eye and gaze features. The dataset is made publicly available and we encourage other researchers to use it for testing new methods and analytic pipelines for the estimation of an individual's affective state.<br> <br> Important note: Version 0 contains only video files and readme. The eye-tracking data are going to be uploaded in the next version.
description: This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 826429 (Project: SeeFar). This paper reflects only the author's view and the Commission is not responsible for any use that may be made of the information it contains. Please cite:
DOI: 10.5281/zenodo.5913566
title: eSEEd: emotional State Estimation based on Eye-tracking dataset

description: We present eSEEd- emotional State Estimation based on Eye-tracking database. Eye movements of 48 participants were recorded as they watched 10 emotion evoking videos each of them followed by a neutral video. Participants rated five emotions (tenderness, anger, disgust, sadness, neutral) on a scale from 0 to 10, later translated in terms of emotional arousal and valence levels. Furthermore, each participant filled 3 self-assessment questionnaires. An extensive analysis of the participants' answers to the questionnaires self-assessment scores as well as their ratings during the experiments is presented. Moreover, eye and gaze features were extracted from the low level eye recorded metrics and their correlations with the participants' ratings are investigated. Finally, analysis and results are presented for machine learning approaches, for the classification of various arousal and valence levels based solely on eye and gaze features. The dataset is made publicly available and we encourage other researchers to use it for testing new methods and analytic pipelines for the estimation of an individual's affective state.<br> <br> Important note: Version 0 contains only video files and readme. The eye-tracking data are going to be uploaded in the next version.
description: This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 826429 (Project: SeeFar). This paper reflects only the author's view and the Commission is not responsible for any use that may be made of the information it contains. Please cite:
DOI: 10.5281/zenodo.7786485
title: eSEEd: emotional State Estimation based on Eye-tracking dataset

description: We present eSEEd- emotional State Estimation based on Eye-tracking database. Eye movements of 48 participants were recorded as they watched 10 emotion evoking videos each of them followed by a neutral video. Participants rated five emotions (tenderness, anger, disgust, sadness, neutral) on a scale from 0 to 10, later translated in terms of emotional arousal and valence levels. Furthermore, each participant filled 3 self-assessment questionnaires. An extensive analysis of the participants' answers to the questionnaires self-assessment scores as well as their ratings during the experiments is presented. Moreover, eye and gaze features were extracted from the low level eye recorded metrics and their correlations with the participants' ratings are investigated. Finally, analysis and results are presented for machine learning approaches, for the classification of various arousal and valence levels based solely on eye and gaze features. The dataset is made publicly available and we encourage other researchers to use it for testing new methods and analytic pipelines for the estimation of an individual's affective state.<br> <br> TO USE THIS DATASET PLEASE CITE:<br> Skaramagkas, V.; Ktistakis, E.; Manousos, D.; Kazantzaki, E.; Tachos, N.S.; Tripoliti, E.; Fotiadis, D.I.; Tsiknakis, M. eSEE-d: Emotional State Estimation Based on Eye-Tracking Dataset. <em>Brain Sci.</em> 2023, <em>13</em>, 589. https://doi.org/10.3390/brainsci13040589
description: This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 826429 (Project: SeeFar). This paper reflects only the author's view and the Commission is not responsible for any use that may be made of the information it contains. Please cite:
DOI: 10.5281/zenodo.5775673
title: eSEEd: emotional State Estimation based on Eye-tracking dataset

description: We present eSEEd- emotional State Estimation based on Eye-tracking database. Eye movements of 48 participants were recorded as they watched 10 emotion evoking videos each of them followed by a neutral video. Participants rated five emotions (tenderness, anger, disgust, sadness, neutral) on a scale from 0 to 10, later translated in terms of emotional arousal and valence levels. Furthermore, each participant filled 3 self-assessment questionnaires. An extensive analysis of the participants' answers to the questionnaires self-assessment scores as well as their ratings during the experiments is presented. Moreover, eye and gaze features were extracted from the low level eye recorded metrics and their correlations with the participants' ratings are investigated. Finally, analysis and results are presented for machine learning approaches, for the classification of various arousal and valence levels based solely on eye and gaze features. The dataset is made publicly available and we encourage other researchers to use it for testing new methods and analytic pipelines for the estimation of an individual's affective state.TO USE THIS DATASET PLEASE CITE:Skaramagkas, V.; Ktistakis, E.; Manousos, D.; Kazantzaki, E.; Tachos, N.S.; Tripoliti, E.; Fotiadis, D.I.; Tsiknakis, M. eSEE-d: Emotional State Estimation Based on Eye-Tracking Dataset. Brain Sci. 2023, 13, 589. https://doi.org/10.3390/brainsci13040589
description: This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 826429 (Project: SeeFar). This paper reflects only the author's view and the Commission is not responsible for any use that may be made of the information it contains. Please cite: Skaramagkas, V.; Ktistakis, E.; Manousos, D.; Kazantzaki, E.; Tachos, N.S.; Tripoliti, E.; Fotiadis, D.I.; Tsiknakis, M. eSEE-d: Emotional State Estimation Based on Eye-Tracking Dataset. Brain Sci. 2023, 13, 589. https://doi.org/10.3390/brainsci13040589
DOI: 10.5281/zenodo.7794625
title: eSEEd: emotional State Estimation based on Eye-tracking dataset

description: We present eSEEd- emotional State Estimation based on Eye-tracking database. Eye movements of 48 participants were recorded as they watched 10 emotion evoking videos each of them followed by a neutral video. Participants rated five emotions (tenderness, anger, disgust, sadness, neutral) on a scale from 0 to 10, later translated in terms of emotional arousal and valence levels. Furthermore, each participant filled 3 self-assessment questionnaires. An extensive analysis of the participants' answers to the questionnaires self-assessment scores as well as their ratings during the experiments is presented. Moreover, eye and gaze features were extracted from the low level eye recorded metrics and their correlations with the participants' ratings are investigated. Finally, analysis and results are presented for machine learning approaches, for the classification of various arousal and valence levels based solely on eye and gaze features. The dataset is made publicly available and we encourage other researchers to use it for testing new methods and analytic pipelines for the estimation of an individual's affective state.TO USE THIS DATASET PLEASE CITE:Skaramagkas, V.; Ktistakis, E.; Manousos, D.; Kazantzaki, E.; Tachos, N.S.; Tripoliti, E.; Fotiadis, D.I.; Tsiknakis, M. eSEE-d: Emotional State Estimation Based on Eye-Tracking Dataset. Brain Sci. 2023, 13, 589. https://doi.org/10.3390/brainsci13040589
description: This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 826429 (Project: SeeFar). This paper reflects only the author's view and the Commission is not responsible for any use that may be made of the information it contains. Please cite: Skaramagkas, V.; Ktistakis, E.; Manousos, D.; Kazantzaki, E.; Tachos, N.S.; Tripoliti, E.; Fotiadis, D.I.; Tsiknakis, M. eSEE-d: Emotional State Estimation Based on Eye-Tracking Dataset. Brain Sci. 2023, 13, 589. https://doi.org/10.3390/brainsci13040589
DOI: 10.5517/ccdc.csd.cc2g1k47
title: CCDC 2265608: Experimental Crystal Structure Determination

description: Related Article: Meiqi Geng, Jinqiang Kuang, Weiwei Fang, Xuqiong Xiao, Yongmin Ma|2024|Org.Chem.Front.|11|1198|doi:10.1039/D3QO02053C
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2g3v6n
title: CCDC 2267811: Experimental Crystal Structure Determination

description: Related Article: Si-Yuan Chen, Ying-Shan Wang, Xian Han, Zhen-Dong Zhang, Zhi-Kai Li, Dong-Liang Lu, Shaoyu Li|2024|Org.Chem.Front.|11|1169|doi:10.1039/D3QO01974H
description: Related Article: shaoyu li|2023|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2g4h8d
title: CCDC 2268433: Experimental Crystal Structure Determination

description: Related Article: Xin Li, Yan-Yan Ren, Can Chen, Li-Ying Sun, Ying-Feng Han|2024|Org.Chem.Front.|11|1076|doi:10.1039/D3QO01644G
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2gp6r4
title: CCDC 2284506: Experimental Crystal Structure Determination

description: Related Article: Wenyi Luo, Qiaolong Chen, Xiaoyu Mo, Jun Jiang, Peng Xie|2024|Org.Chem.Front.|11|1112|doi:10.1039/D3QO01753B
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fm6ny
title: CCDC 2252790: Experimental Crystal Structure Determination

description: Related Article: Qinglin Wang, Chunyang Guo, Ming-Hui Qi, Bin Zhu, Guo-Bin Ren, Minghuang Hong|2024|Cryst.Growth Des.|24|977|doi:10.1021/acs.cgd.3c00948
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.