cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.25038548
title: tne_ewas_mec_sex.zip

description: Summary statistics for the male-specific and female-specific epigenome-wide association study (EWAS) of total nicotine equivalents (TNE) in the Multiethnic Cohort Study. Estimates from the models represent the change in methylation β-value per one log-unit increase in TNE.Column descriptions:<b>probe_id: </b>CpG probe ID<b>chr: </b>Chromosome containing the CpG (Build 37)<b>mapinfo:</b> Chromosomal coordinates of the CpG (Build 37)<b>strand:</b> The Forward (F) or Reverse (R) designation of the Design Strand<b>gene: </b>Target gene names, from the UCSC database<b>gene_group:</b> Gene region feature category describing the CpG position, from UCSC. Features are listed in the same order as the target gene transcripts<b>male_est:</b> Parameter estimate for the male-specific association between TNE and DNA methylation<b>male_se:</b> Standard error of the parameter estimate for the male-specific association between TNE and DNA methylation<b>male_p: </b>P-value for the male-specific association between TNE and DNA methylation<b>female_est:</b> Parameter estimate for the female-specific association between TNE and DNA methylation<b>female_se: </b>Standard error of the parameter estimate for the female-specific association between TNE and DNA methylation<b>female_p:</b> P-value for the female-specific association between TNE and DNA methylation<br>
DOI: 10.6084/m9.figshare.25038548.v1
title: tne_ewas_mec_sex.zip

description: Summary statistics for the male-specific and female-specific epigenome-wide association study (EWAS) of total nicotine equivalents (TNE) in the Multiethnic Cohort Study. Estimates from the models represent the change in methylation β-value per one log-unit increase in TNE.Column descriptions:<b>probe_id: </b>CpG probe ID<b>chr: </b>Chromosome containing the CpG (Build 37)<b>mapinfo:</b> Chromosomal coordinates of the CpG (Build 37)<b>strand:</b> The Forward (F) or Reverse (R) designation of the Design Strand<b>gene: </b>Target gene names, from the UCSC database<b>gene_group:</b> Gene region feature category describing the CpG position, from UCSC. Features are listed in the same order as the target gene transcripts<b>male_est:</b> Parameter estimate for the male-specific association between TNE and DNA methylation<b>male_se:</b> Standard error of the parameter estimate for the male-specific association between TNE and DNA methylation<b>male_p: </b>P-value for the male-specific association between TNE and DNA methylation<b>female_est:</b> Parameter estimate for the female-specific association between TNE and DNA methylation<b>female_se: </b>Standard error of the parameter estimate for the female-specific association between TNE and DNA methylation<b>female_p:</b> P-value for the female-specific association between TNE and DNA methylation<br>
DOI: 10.6084/m9.figshare.25039832
title: Pakistan Railways Data 1965-2010

description: Research on railway efficiency was part of my post-doctoral research work at the University of Cambridge
DOI: 10.5281/zenodo.10539933
title: Datasets for "Where the White Continent is blue: deep learning locates bare ice in Antarctica"

description: This archive provides datasets related to the following publication:

V. Tollenaar, H. Zekollari, F. Pattyn, M. Rußwurm, B. Kellenberger, S. Lhermitte, M. Izeboud, D. Tuia, Where the White Continent is blue: deep learning locates bare ice in Antarctica. Geophysical Research Letters, 50, e2023GL106285 (2024). DOI: 10.1029/2023GL106285

Contact: Veronica Tollenaar, Veronica.Tollenaar@ulb.be

Users should cite the original publication when using all or part of the data.

About the datasets: it includes the seven-band MODIS composite ("merged_bands_composite3031080910.tif"), the outlines of our set of training ("train_squares.zip"), validation ("validation_squares.zip"), and testing regions ("test_squares.zip"), the predictions on the presence of blue ice ("BIA_map.nc"), the corresponding uncertainty estimates ("BIA_map.nc"), and the smoothed blue ice area outlines ("smoothed_BIAs.zip"). References to the literature are provided in the corresponding manuscript.

- BIA_map.nc: netcdf in which the band "mean" represents the predictions on the presence of blue ice, and the band "std" the corresponding uncertainty estimates, in EPSG:3031- handlabels_sqXXX.zip: archived .cpg, .bdf, .prj, .shp, and .shx files of the handlabeled blue ice areas (Figures S3 and S4) in the respective squares (indicated in the files "xxxx_squares.zip")- merged_bands_composite3031080910.tif: 7-band tif file of the 7-band MODIS composite (Figure S1, Text S1)- smoothed_BIAs.zip: archived .cpg, .bdf, .prj, .shp, and .shx files of the smoothed outlines of our BIA predictions (Text S5)- xxxx_squares.zip: archived .cpg, .bdf, .prj, .shp, and .shx files of the outlines of training, validation, and testing regions (Figure S2)
DOI: 10.5281/zenodo.8333865
title: Datasets for "Where the White Continent is blue: deep learning locates bare ice in Antarctica"

description: This archive provides datasets related to the following manuscript: V. Tollenaar, H. Zekollari, F. Pattyn, M. Rußwurm, B. Kellenberger, S. Lhermitte, M. Izeboud, D. Tuia, Where the White Continent is blue: deep learning locates bare ice in Antarctica, [Paper # 2023GL106285] Contact: Veronica Tollenaar, Veronica.Tollenaar@ulb.be Data is provided for review and should not be shared. About the datasets: it includes the seven-band MODIS composite ("merged_bands_composite3031080910.tif"), the outlines of our set of training ("train_squares.zip"), validation ("validation_squares.zip"), and testing regions ("test_squares.zip"), the predictions on the presence of blue ice ("final_map.nc"), the corresponding uncertainty estimates ("final_map.nc"), and the smoothed blue ice area outlines ("smoothed_BIAs.zip"). References to the literature are provided in the corresponding manuscript. - final_map.nc: netcdf in which the band "mean" represents the predictions on the presence of blue ice, and the band "std" the corresponding uncertainty estimates<br> - handlabels_sqXXX.zip: archived .cpg, .bdf, .prj, .shp, and .shx files of the handlabeled blue ice areas (Figures S3 and S4) in the respective squares (indicated in the files "xxxx_squares.zip")<br> - merged_bands_composite3031080910.tif: 7-band tif file of the 7-band MODIS composite (Figure S1, Text S1)<br> - smoothed_BIAs.zip: archived .cpg, .bdf, .prj, .shp, and .shx files of the smoothed outlines of our BIA predictions (Text S5)<br> - xxxx_squares.zip: archived .cpg, .bdf, .prj, .shp, and .shx files of the outlines of training, validation, and testing regions (Figure S2)
DOI: 10.5281/zenodo.8337624
title: DSE synthesis x-net and Comba designs

description: Synthesis and simulation results of the polynomial multiplier designs based on the x-net and Comba algorithms when targeting an Xilinx UltraScale+ ZCU106 FPGA.
DOI: 10.5281/zenodo.8337625
title: DSE synthesis x-net and Comba designs

description: Synthesis and simulation results of the polynomial multiplier designs based on the x-net and Comba algorithms when targeting an Xilinx UltraScale+ ZCU106 FPGA.
DOI: 10.5281/zenodo.10548571
title: An integrated single-cell RNA-seq atlas of the mouse hypothalamic paraventricular nucleus links transcriptional and function types

description: The hypothalamic paraventricular nucleus (PVN) is a highly complex brain region that is crucial for homeostatic
regulation through neuroendocrine signalling, outflow of the autonomic nervous system (ANS), and projections
to other brain areas. The past years, single-cell datasets of the hypothalamus have contributed immensely
to the current understanding of the diverse hypothalamic cellular composition. While the PVN has been
adequately classified functionally, its molecular classification is currently still insufficient.


To address this, we created a detailed atlas of PVN transcriptional cell types by integrating various PVN
single-cell datasets into a recently published hypothalamus single-cell transcriptome atlas. Furthermore, we
functionally profiled transcriptional cell types, based on relevant literature, existing retrograde tracing data
and existing single-cell data of a PVN-projection target region.
DOI: 10.5281/zenodo.8160037
title: An integrated single-cell RNA-seq atlas of the mouse hypothalamic paraventricular nucleus links transcriptional and function types

description: The hypothalamic paraventricular nucleus (PVN) is a highly complex brain region that is crucial for homeostatic
regulation through neuroendocrine signalling, outflow of the autonomic nervous system (ANS), and projections
to other brain areas. The past years, single-cell datasets of the hypothalamus have contributed immensely
to the current understanding of the diverse hypothalamic cellular composition. While the PVN has been
adequately classified functionally, its molecular classification is currently still insufficient.


To address this, we created a detailed atlas of PVN transcriptional cell types by integrating various PVN
single-cell datasets into a recently published hypothalamus single-cell transcriptome atlas. Furthermore, we
functionally profiled transcriptional cell types, based on relevant literature, existing retrograde tracing data
and existing single-cell data of a PVN-projection target region.
DOI: 10.5281/zenodo.8160038
title: An integrated single-cell RNA-seq atlas of the mouse hypothalamic paraventricular nucleus links transcriptional and function types

description: The hypothalamic paraventricular nucleus (PVN) is a highly complex brain region that is crucial for homeostatic<br> regulation through neuroendocrine signalling, outflow of the autonomic nervous system (ANS), and projections<br> to other brain areas. The past years, single-cell datasets of the hypothalamus have contributed immensely<br> to the current understanding of the diverse hypothalamic cellular composition. While the PVN has been<br> adequately classified functionally, its molecular classification is currently still insufficient. To address this, we created a detailed atlas of PVN transcriptional cell types by integrating various PVN<br> single-cell datasets into a recently published hypothalamus single-cell transcriptome atlas. Furthermore, we<br> functionally profiled transcriptional cell types, based on relevant literature, existing retrograde tracing data<br> and existing single-cell data of a PVN-projection target region.
DOI: 10.5281/zenodo.10548561
title: Biotic and abiotic factors and the phylogenetic structure of extinction in the evolution of Tethysuchia
DOI: 10.5281/zenodo.7951043
title: EEG and EMG dataset for the detection of errors introduced by an active orthosis device (IJCAI'23 CC6 Competition)

description: This dataset was a part of the  IJCAI 2023 competition : CC6: IntEr-HRI: Intrinsic Error Evaluation during Human-Robot Interaction (IJCAI'23 Official Website). This dataset repository is divided into 3 versions:



Version 1: Training data + Metadata

Version 2: Test data

Version 3: Complete dataset (EEG + EMG) 


For more detailed information about the competition, please visit our competition webpage.

This dataset contains recordings of the electroencephalogram (EEG) data from eight subjects who were assisted in moving their right arm by an active orthosis. 

The orthosis-supported movements were elbow joint movements, i.e., flexion and extension of the right arm. While the orthosis was actively moving the subject's arm, some errors were deliberately introduced for a short duration of time. During this time, the orthosis moved in the opposite direction. The errors are very simple and easy to detect. EEG and EMG data are provided. The recorded EEG data follows the BrainVision Core Data Format 1.0, consisting of a binary data file (.eeg), a header file (.vhdr), and a marker file (.vmrk) (https://www.brainproducts.com/support-resources/brainvision-core-data-format-1-0/). For ease of use, the data can be exported into the widely adopted BIDS format. Furthermore, for data analysis, processing, and classification, two popular options are available - MNE (Python) and EEGLAB (MATLAB). 

If you use our dataset, cite our paper.

Frontiers in Human Neuroscience DOI: 10.3389/fnhum.2024.1304311

BibTeX citation:



@ARTICLE{10.3389/fnhum.2024.1304311,

AUTHOR={Kueper, Niklas and Chari, Kartik and Bütefür, Judith and Habenicht, Julia and Rossol, Tobias and Kim, Su Kyoung and Tabie, Marc and Kirchner, Frank and Kirchner, Elsa Andrea},

TITLE={EEG and EMG dataset for the detection of errors introduced by an active orthosis device},

JOURNAL={Frontiers in Human Neuroscience},

VOLUME={18},

YEAR={2024},

URL={https://www.frontiersin.org/articles/10.3389/fnhum.2024.1304311},

DOI={10.3389/fnhum.2024.1304311},

ISSN={1662-5161}

}
DOI: 10.5281/zenodo.7951044
title: EEG and EMG dataset for the detection of errors introduced by an active orthosis device

description: This dataset is a part of the training data for the IJCAI 2023 competition : CC6: IntEr-HRI: Intrinsic Error Evaluation during Human-Robot Interaction (IJCAI'23 Official Website). This dataset repository is divided into 2 versions: <strong><em>Version 1: </em>Training data + Metadata</strong> <strong><em>Version 2: </em>Test data </strong> <strong>N.B.: </strong> <strong>After conducting a small survey to determine the willingness of the participating teams to travel to Macao, it became evident that a significant number of them preferred not to travel. With this in mind, we have decided to modify the initial plan for the online stage of the competition wherein the participating teams can participate from anywhere on Earth. Hope that this motivates more teams to participate. For more detailed information, please visit our competition webpage.</strong> <strong>Although the registration for the offline stage is officially closed, if you still wish to participate, please reach out to us via the contact form available on our webpage.</strong> This dataset contains recordings of the electroencephalogram (EEG) data from eight subjects who were assisted in moving their right arm by an active orthosis. This is only a part of the complete dataset which also contains electromyogram (EMG) data and the complete dataset will be made public after the end of the competition. The orthosis-supported movements were elbow joint movements, i.e., flexion and extension of the right arm. While the orthosis was actively moving the subject's arm, some errors were deliberately introduced for a short duration of time. During this time, the orthosis moved in the opposite direction. The errors are very simple and easy to detect. EEG and EMG data are provided. The recorded EEG data follows the BrainVision Core Data Format 1.0, consisting of a binary data file (.eeg), a header file (.vhdr), and a marker file (.vmrk) (https://www.brainproducts.com/support-resources/brainvision-core-data-format-1-0/). For ease of use, the data can be exported into the widely adopted BIDS format. Furthermore, for data analysis, processing, and classification, two popular options are available - MNE (Python) and EEGLAB (MATLAB). <strong>If you use our dataset, cite our paper.</strong> arXiv-issued DOI: https://doi.org/10.48550/arXiv.2305.11996 BibTeX citation: @misc{kueper2023eeg,<br> title={EEG and EMG dataset for the detection of errors introduced by an active orthosis device}, <br> author={Niklas Kueper and Kartik Chari and Judith Bütefür and Julia Habenicht and Su Kyoung Kim and Tobias Rossol and Marc Tabie and Frank Kirchner and Elsa Andrea Kirchner},<br> year={2023},<br> eprint={2305.11996},<br> archivePrefix={arXiv},<br> primaryClass={cs.HC}<br> }
DOI: 10.5281/zenodo.7966275
title: EEG and EMG dataset for the detection of errors introduced by an active orthosis device (IJCAI Competition)

description: This dataset is a part of the  IJCAI 2023 competition : CC6: IntEr-HRI: Intrinsic Error Evaluation during Human-Robot Interaction (IJCAI'23 Official Website). This dataset repository is divided into 2 versions:



	
Version 1: Training data + Metadata
	
Version 2: Test data 



N.B.: After conducting a small survey to determine the willingness of the participating teams to travel to Macao, it became evident that a significant number of them preferred not to travel. With this in mind, we have decided to modify the initial plan for the online stage of the competition wherein the participating teams can participate from anywhere on Earth. Hope that this motivates more teams to participate. For more detailed information, please visit our competition webpage.


Although the registration for the offline stage is officially closed, if you still wish to participate, please reach out to us via the contact form available on our webpage.


This dataset contains recordings of the electroencephalogram (EEG) data from eight subjects who were assisted in moving their right arm by an active orthosis. This is only a part of the complete dataset which also contains electromyogram (EMG) data and the complete dataset will be made public after the end of the competition.


The orthosis-supported movements were elbow joint movements, i.e., flexion and extension of the right arm. While the orthosis was actively moving the subject's arm, some errors were deliberately introduced for a short duration of time. During this time, the orthosis moved in the opposite direction. The errors are very simple and easy to detect. EEG and EMG data are provided. The recorded EEG data follows the BrainVision Core Data Format 1.0, consisting of a binary data file (.eeg), a header file (.vhdr), and a marker file (.vmrk) (https://www.brainproducts.com/support-resources/brainvision-core-data-format-1-0/). For ease of use, the data can be exported into the widely adopted BIDS format. Furthermore, for data analysis, processing, and classification, two popular options are available - MNE (Python) and EEGLAB (MATLAB). 


If you use our dataset, cite our paper.


arXiv-issued DOI: https://doi.org/10.48550/arXiv.2305.11996


BibTeX citation:


@misc{kueper2023eeg,
      title={EEG and EMG dataset for the detection of errors introduced by an active orthosis device}, 
      author={Niklas Kueper and Kartik Chari and Judith Bütefür and Julia Habenicht and Su Kyoung Kim and Tobias Rossol and Marc Tabie and Frank Kirchner and Elsa Andrea Kirchner},
      year={2023},
      eprint={2305.11996},
      archivePrefix={arXiv},
      primaryClass={cs.HC}
}
DOI: 10.5281/zenodo.8345429
title: EEG and EMG dataset for the detection of errors introduced by an active orthosis device (IJCAI'23 CC6 Competition)

description: This dataset was a part of the  IJCAI 2023 competition : CC6: IntEr-HRI: Intrinsic Error Evaluation during Human-Robot Interaction (IJCAI'23 Official Website). This dataset repository is divided into 3 versions:



Version 1: Training data + Metadata

Version 2: Test data

Version 3: Complete dataset (EEG + EMG) 


For more detailed information about the competition, please visit our competition webpage.

This dataset contains recordings of the electroencephalogram (EEG) data from eight subjects who were assisted in moving their right arm by an active orthosis. 

The orthosis-supported movements were elbow joint movements, i.e., flexion and extension of the right arm. While the orthosis was actively moving the subject's arm, some errors were deliberately introduced for a short duration of time. During this time, the orthosis moved in the opposite direction. The errors are very simple and easy to detect. EEG and EMG data are provided. The recorded EEG data follows the BrainVision Core Data Format 1.0, consisting of a binary data file (.eeg), a header file (.vhdr), and a marker file (.vmrk) (https://www.brainproducts.com/support-resources/brainvision-core-data-format-1-0/). For ease of use, the data can be exported into the widely adopted BIDS format. Furthermore, for data analysis, processing, and classification, two popular options are available - MNE (Python) and EEGLAB (MATLAB). 

If you use our dataset, cite our paper.

Frontiers in Human Neuroscience DOI: 10.3389/fnhum.2024.1304311

BibTeX citation:



@ARTICLE{10.3389/fnhum.2024.1304311,

AUTHOR={Kueper, Niklas and Chari, Kartik and Bütefür, Judith and Habenicht, Julia and Rossol, Tobias and Kim, Su Kyoung and Tabie, Marc and Kirchner, Frank and Kirchner, Elsa Andrea},

TITLE={EEG and EMG dataset for the detection of errors introduced by an active orthosis device},

JOURNAL={Frontiers in Human Neuroscience},

VOLUME={18},

YEAR={2024},

URL={https://www.frontiersin.org/articles/10.3389/fnhum.2024.1304311},

DOI={10.3389/fnhum.2024.1304311},

ISSN={1662-5161}

}
DOI: 10.15151/esrf-es-1424923825
title: Element-specific proof for the formation of magnetic MXenes by intercalation of Fe in surface activated Ti3C2Tx

description: The goal of the study is the characterization of magnetic Ti3C2Tx-based MXenes produced via partial 
desorption of Tx functional groups and subsequent Fe intercalation. Such magnetic MXene flakes show an 
ordering temperature of about 500 K as our laboratory-based magnetometry studies suggest. However, we 
could not directly correlate the structure, the chemical state and the magnetic response on the identical 
sample, yet, since X-ray diffraction (XRD), electron microscopy (TEM), X-ray photoelectron spectroscopy
(XPS) and magnetometry have different demands. Here, we aim to collect combined information by 
diffraction, XANES and XMCD in the identical sample states using the new and unique ULMAG end station
at ID12. While many theoretical papers predict magnetic MXenes, there is no experimental realization with 
a clear spectroscopic proof and element-specific magnetic data to date.
DOI: 10.5281/zenodo.10034611
title: GitBug-Actions Go Benchmark January 2023

description: The files are organized as follows:



collect_repos



Contains the execution files of the scanned repositories (one file per repo, with execution results)



collect_bugs 



Contains files generated by the "collect_bugs" step, which include all identifed bug-fix commits (one file per repo, one line per bug-fix commit).



export_bugs 



Contains files generated by executing  GHAs locally for each bug-fix commit. Required for offline execution.



filter_repos



Contains the list of bug-fix commits considered as flaky, non-flaky, and high-quality.



repos



Contains the cloned repositories from which the non-flaky bug-fix commits were collected.
DOI: 10.5281/zenodo.10034612
title: GitBug-Actions Go Benchmark January 2023
DOI: 10.5281/zenodo.10463557
title: GitBug-Actions Go Benchmark January 2023

description: The files are organized as follows:



collect_repos



Contains the execution files of the scanned repositories (one file per repo, with execution results)



collect_bugs 



Contains files generated by the "collect_bugs" step, which include all identifed bug-fix commits (one file per repo, one line per bug-fix commit).



export_bugs 



Contains files generated by executing  GHAs locally for each bug-fix commit. Required for offline execution.



filter_repos



Contains the list of bug-fix commits considered as flaky, non-flaky, and high-quality.



repos



Contains the cloned repositories from which the non-flaky bug-fix commits were collected.
DOI: 10.5281/zenodo.10537093
title: GitBug-Actions Go Benchmark January 2023

description: The files are organized as follows:



collect_repos



Contains the execution files of the scanned repositories (one file per repo, with execution results)



collect_bugs 



Contains files generated by the "collect_bugs" step, which include all identifed bug-fix commits (one file per repo, one line per bug-fix commit).



export_bugs 



Contains files generated by executing  GHAs locally for each bug-fix commit. Required for offline execution.



filter_repos



Contains the list of bug-fix commits considered as flaky, non-flaky, and high-quality.



repos



Contains the cloned repositories from which the non-flaky bug-fix commits were collected.