cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24638061
title: Barriers and facilitators to exoskeleton use in persons with spinal cord injury: a systematic review

description: Exoskeleton can assist individuals with spinal cord injuries (SCI) with simple movements and transform their lives by enhancing strength and mobility. Nonetheless, the current utilization outside of rehabilitation contexts is limited. To promote the widespread adoption of exoskeletons, it is crucial to consider the acceptance of these devices for both rehabilitation and functional purposes. This systematic review aims to identify the barriers or facilitators of the use of lower limbs exoskeletons, thereby providing strategies to improve interventions and increase the adoption of these devices. A comprehensive search was conducted in EMBASE, Web of Science, Scopus, Cochrane, and PubMed. Studies reporting barriers and facilitators of exoskeleton use were included. The studies’ quality was assessed using the Mixed Methods Appraisal Tool and undertook a thematic content analysis for papers examining the barriers and facilitators. Fifteen articles met the inclusion criteria. These revealed various factors that impact the utilization of exoskeletons. Factors like age, engagement in an active lifestyle, and motivation were identified as facilitators, while fear of falling and unfulfilled expectations were recognized as barriers. Physical aspects such as fatigue, neuropathic discomfort, and specific health conditions were found to be barriers. This systematic review provides a comprehensive overview of the barriers and facilitators to the use of exoskeleton technology. There are therefore still challenges to be faced, efforts must be made to improve its design, functionality, and accessibility. By addressing these barriers, exoskeletons can significantly improve the quality of life of people with SCI. Optimize the use of this technology to adapt as much as possible to individual needs.Offer an in-depth understanding of the challenges that can arise when adopting exoskeletons.Highlight the critical issues with the device, that prevent me from using it outside of clinical contexts.Offer useful information to therapists to select the most suitable patients for the use of the exoskeleton and to customize rehabilitation programs more effectively. Optimize the use of this technology to adapt as much as possible to individual needs. Offer an in-depth understanding of the challenges that can arise when adopting exoskeletons. Highlight the critical issues with the device, that prevent me from using it outside of clinical contexts. Offer useful information to therapists to select the most suitable patients for the use of the exoskeleton and to customize rehabilitation programs more effectively.
DOI: 10.6084/m9.figshare.24638061.v1
title: Barriers and facilitators to exoskeleton use in persons with spinal cord injury: a systematic review

description: Exoskeleton can assist individuals with spinal cord injuries (SCI) with simple movements and transform their lives by enhancing strength and mobility. Nonetheless, the current utilization outside of rehabilitation contexts is limited. To promote the widespread adoption of exoskeletons, it is crucial to consider the acceptance of these devices for both rehabilitation and functional purposes. This systematic review aims to identify the barriers or facilitators of the use of lower limbs exoskeletons, thereby providing strategies to improve interventions and increase the adoption of these devices. A comprehensive search was conducted in EMBASE, Web of Science, Scopus, Cochrane, and PubMed. Studies reporting barriers and facilitators of exoskeleton use were included. The studies’ quality was assessed using the Mixed Methods Appraisal Tool and undertook a thematic content analysis for papers examining the barriers and facilitators. Fifteen articles met the inclusion criteria. These revealed various factors that impact the utilization of exoskeletons. Factors like age, engagement in an active lifestyle, and motivation were identified as facilitators, while fear of falling and unfulfilled expectations were recognized as barriers. Physical aspects such as fatigue, neuropathic discomfort, and specific health conditions were found to be barriers. This systematic review provides a comprehensive overview of the barriers and facilitators to the use of exoskeleton technology. There are therefore still challenges to be faced, efforts must be made to improve its design, functionality, and accessibility. By addressing these barriers, exoskeletons can significantly improve the quality of life of people with SCI. Optimize the use of this technology to adapt as much as possible to individual needs.Offer an in-depth understanding of the challenges that can arise when adopting exoskeletons.Highlight the critical issues with the device, that prevent me from using it outside of clinical contexts.Offer useful information to therapists to select the most suitable patients for the use of the exoskeleton and to customize rehabilitation programs more effectively. Optimize the use of this technology to adapt as much as possible to individual needs. Offer an in-depth understanding of the challenges that can arise when adopting exoskeletons. Highlight the critical issues with the device, that prevent me from using it outside of clinical contexts. Offer useful information to therapists to select the most suitable patients for the use of the exoskeleton and to customize rehabilitation programs more effectively.
DOI: 10.5281/zenodo.13167793
title: Binary black-hole simulation SXS:BBH:0358

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13167794
title: Binary black-hole simulation SXS:BBH:0358

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13167810
title: Binary black-hole simulation SXS:BBH:0451

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13167811
title: Binary black-hole simulation SXS:BBH:0451

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13167815
title: Binary black-hole simulation SXS:BBH:0371

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13167816
title: Binary black-hole simulation SXS:BBH:0371

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13167847
title: Binary black-hole simulation SXS:BBH:0446

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13167848
title: Binary black-hole simulation SXS:BBH:0446

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13143906
title: AILabs.tw Pop1K7

description: AILabs.tw Pop1K7 is a dataset comprising 1747 transcribed piano performances of Western, Japanese and Korean pop songs. It was compiled in Compound Word Transformer to support research on composing expressive pop piano music at full-song length. The songs average about 4 minutes in duration, totaling 108 hours of music. All pieces are in a 4/4 time signature (four beats per bar). Each song (an audio) was converted into a symbolic sequence following specific instructions. 

The first file, Pop1K7.zip, contains these processed MIDI files at each step, along with their REMI and CP representations for unconditional generation tasks. The second file, Pop1K7-emo.zip, includes REMI and functional representations specifically designed for emotion-driven conditional generation tasks, as well as detected key signatures. For general conditional generation tasks, simply remove the <Emotion_*> token in each .pkl file. Please refer to the paper Compound Word Transformer for definitions of unconditional and conditional generation.

Citation

@inproceedings{compoundword2021,    author = {Wen-Yi Hsiao and Jen-Yu Liu and Yin-Cheng Yeh and Yi-Hsuan Yang},    title = {{Compound Word Transformer}: Learning to Compose Full-Song Music over Dynamic Directed Hypergraphs},    booktitle = {Thirty-Fifth {AAAI} Conference on Artificial Intelligence, {AAAI}},    year = {2021}}@inproceedings{emodisentanger2024,
  author = {Jingyue Huang and Ke Chen and Yi-Hsuan Yang},
  title = {Emotion-driven Piano Music Generation via Two-stage Disentanglement and Functional Representation},
  booktitle = {Proceedings of the International Society for Music Information Retrieval Conference, {ISMIR}},
  year = {2024}
}
DOI: 10.5281/zenodo.13143907
title: AILabs.tw Pop1K7

description: AILabs.tw Pop1K7 is a dataset comprising 1747 transcribed piano performances of Western, Japanese and Korean pop songs. It was compiled in Compound Word Transformer to support research on composing expressive pop piano music at full-song length. The songs average about 4 minutes in duration, totaling 108 hours of music. All pieces are in a 4/4 time signature (four beats per bar). Each song (an audio) was converted into a symbolic sequence following specific instructions. 

The first file, Pop1K7.zip, contains these processed MIDI files at each step, along with their REMI and CP representations for unconditional generation tasks. The second file, Pop1K7-emo.zip, includes REMI and functional representations specifically designed for emotion-driven conditional generation tasks, as well as detected key signatures. For general conditional generation tasks, simply remove the <Emotion_*> token in each .pkl file. Please refer to the paper Compound Word Transformer for definitions of unconditional and conditional generation.

Citation

@inproceedings{compoundword2021,    author = {Wen-Yi Hsiao and Jen-Yu Liu and Yin-Cheng Yeh and Yi-Hsuan Yang},    title = {{Compound Word Transformer}: Learning to Compose Full-Song Music over Dynamic Directed Hypergraphs},    booktitle = {Thirty-Fifth {AAAI} Conference on Artificial Intelligence, {AAAI}},    year = {2021}}@inproceedings{emodisentanger2024,
  author = {Jingyue Huang and Ke Chen and Yi-Hsuan Yang},
  title = {Emotion-driven Piano Music Generation via Two-stage Disentanglement and Functional Representation},
  booktitle = {Proceedings of the International Society for Music Information Retrieval Conference, {ISMIR}},
  year = {2024}
}
DOI: 10.5281/zenodo.13167761
title: AILabs.tw Pop1K7

description: AILabs.tw Pop1K7 is a dataset comprising 1747 transcribed piano performances of Western, Japanese and Korean pop songs. It was compiled in Compound Word Transformer to support research on composing expressive pop piano music at full-song length. The songs average about 4 minutes in duration, totaling 108 hours of music. All pieces are in a 4/4 time signature (four beats per bar). Each song (an audio) was converted into a symbolic sequence following specific instructions. 

The first file, Pop1K7.zip, contains these processed MIDI files at each step, along with their REMI and CP representations for unconditional generation tasks. The second file, Pop1K7-emo.zip, includes REMI and functional representations specifically designed for emotion-driven conditional generation tasks, as well as detected key signatures. For general conditional generation tasks, simply remove the <Emotion_*> token in each .pkl file. Please refer to the paper Compound Word Transformer for definitions of unconditional and conditional generation.

Citation

@inproceedings{compoundword2021,    author = {Wen-Yi Hsiao and Jen-Yu Liu and Yin-Cheng Yeh and Yi-Hsuan Yang},    title = {{Compound Word Transformer}: Learning to Compose Full-Song Music over Dynamic Directed Hypergraphs},    booktitle = {Thirty-Fifth {AAAI} Conference on Artificial Intelligence, {AAAI}},    year = {2021}}@inproceedings{emodisentanger2024,
  author = {Jingyue Huang and Ke Chen and Yi-Hsuan Yang},
  title = {Emotion-driven Piano Music Generation via Two-stage Disentanglement and Functional Representation},
  booktitle = {Proceedings of the International Society for Music Information Retrieval Conference, {ISMIR}},
  year = {2024}
}
DOI: 10.5281/zenodo.13167861
title: Binary black-hole simulation SXS:BBH:0456

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13167862
title: Binary black-hole simulation SXS:BBH:0456

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.34740/kaggle/dsv/9089036
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.5281/zenodo.13167869
title: Binary black-hole simulation SXS:BBH:0507

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13167870
title: Binary black-hole simulation SXS:BBH:0507

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13167890
title: Binary black-hole simulation SXS:BBH:0577

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13167891
title: Binary black-hole simulation SXS:BBH:0577

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13167907
title: Binary black-hole simulation SXS:BBH:0606

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13167908
title: Binary black-hole simulation SXS:BBH:0606

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13167919
title: Binary black-hole simulation SXS:BBH:0592

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13167920
title: Binary black-hole simulation SXS:BBH:0592

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13167945
title: Binary black-hole simulation SXS:BBH:0513

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13167946
title: Binary black-hole simulation SXS:BBH:0513

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13167961
title: Binary black-hole simulation SXS:BBH:0331

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13167962
title: Binary black-hole simulation SXS:BBH:0331

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.7715992
title: System Data for Robust Scheduling of Integrated Electricity and Gas Systems: A Cost and Flexibility Assessment

description: This data is published as a supplement to the following reference:

B. Akbari and G. Sansavini, “Robust scheduling of integrated electricity and gas systems: A cost and flexibility assessment,” International Journal of Electrical Power & Energy Systems, vol. 160, p. 110107, Sep. 2024, doi: 10.1016/j.ijepes.2024.110107.
DOI: 10.5281/zenodo.7715993
title: System Data for Robust Scheduling of Integrated Electricity and Gas Systems: A Cost and Flexibility Assessment

description: This data is published as a supplement to the following reference:

B. Akbari and G. Sansavini, “Robust scheduling of integrated electricity and gas systems: A cost and flexibility assessment,” International Journal of Electrical Power & Energy Systems, vol. 160, p. 110107, Sep. 2024, doi: 10.1016/j.ijepes.2024.110107.
DOI: 10.5281/zenodo.13167967
title: Binary black-hole simulation SXS:BBH:0392

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13167968
title: Binary black-hole simulation SXS:BBH:0392

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13135689
title: Causal Effect of the FEMA CRS Program

description: Dataset produced and used in the Cano & Hahn (2023). The data is produced and used by code in the causalCRS github repo:



 zipcode.fema.fsf.acs.rainfall.v2.FSF_REDACTED.csv: Dataset compiled from the FEMA Flood Insurance Mitigation Administration NFIP Redacted Claims data with additional information on community characteristics from: the First Street Foundation data set, US Census Bureau American Community Survey, and PRISM climate group data sets. In total, the data set has 74,288 entries from 14,729. See Cano & Hahn (2023) for details. Note: data from First Street Foundation (FSF) has been redacted (all values set to -999). FSF data was used under license for the study and are not publicly available. Please contact privately for unredacted version. 

 


typology_*.npy: Conditional Average Treatment Effect (CATE) and accompanying measurements for the 27 community typologies defined in the paper. 



typology_cate.npy: Measured CATE for the 27 community typologies

typology_sig_cate.npy: Statistical uncertainty on CATE measurements.  

typology_y_treat.npy: Claim per policy in USD (outcome) of the community typologies for treated sample 

typology_y_control.npy: Claim per policy in USD (outcome) of the community typologies for control sample

typology_covar.npy: Median covariate values for communities that fall within the typology. This is for reference only. 



fema_crs.*.npy: Compiled dataset for training/validation/testing the q_phi(Y|X) of the treated/control samples used in CausalFlow. 

qphi_control.tar.gz / qphi_treat.tar.gz: Compressed tar file that contains all the trained ensemble of flows for the control/treated samples used in CausalFlow. 

qphi_support.v2.log.control.pt / qphi_support.v2.log.treat.pt: NDEs estimating the distribution of the covariates for the control/treated samples. 

fema_cid.almost_only_c300.npy: An array of communities that participated in the CRS but only executed the c300 activity, which corresponds to the outreach effort. This dataset is used to estimate the overall impact of outreach.
DOI: 10.5281/zenodo.13135690
title: Causal Effect of the FEMA CRS Program

description: Dataset produced and used in the Cano & Hahn (2023). The data is produced and used by code in the causalCRS github repo:



 zipcode.fema.fsf.acs.rainfall.v2.csv: Dataset compiled from the FEMA Flood Insurance Mitigation Administration NFIP Redacted Claims data with additional information on community characteristics from: the First Street Foundation data set, US Census Bureau American Community Survey, and PRISM climate group data sets. In total, the data set has 74,288 entries from 14,729. See Cano & Hahn (2023) for details. 

 


typology_*.npy: Conditional Average Treatment Effect (CATE) and accompanying measurements for the 27 community typologies defined in the paper. 



typology_cate.npy: Measured CATE for the 27 community typologies

typology_sig_cate.npy: Statistical uncertainty on CATE measurements.  

typology_y_treat.npy: Claim per policy in USD (outcome) of the community typologies for treated sample 

typology_y_control.npy: Claim per policy in USD (outcome) of the community typologies for control sample

typology_covar.npy: Median covariate values for communities that fall within the typology. This is for reference only. 



fema_crs.*.npy: Compiled dataset for training/validation/testing the q_phi(Y|X) of the treated/control samples used in CausalFlow. 

qphi_control.tar.gz / qphi_treat.tar.gz: Compressed tar file that contains all the trained ensemble of flows for the control/treated samples used in CausalFlow. 

qphi_support.v2.log.control.pt / qphi_support.v2.log.treat.pt: NDEs estimating the distribution of the covariates for the control/treated samples. 

fema_cid.almost_only_c300.npy: An array of communities that participated in the CRS but only executed the c300 activity, which corresponds to the outreach effort. This dataset is used to estimate the overall impact of outreach.
DOI: 10.5281/zenodo.13167966
title: Causal Effect of the FEMA CRS Program

description: Dataset produced and used in the Cano & Hahn (2023). The data is produced and used by code in the causalCRS github repo:



 zipcode.fema.fsf.acs.rainfall.v2.FSF_REDACTED.csv: Dataset compiled from the FEMA Flood Insurance Mitigation Administration NFIP Redacted Claims data with additional information on community characteristics from: the First Street Foundation data set, US Census Bureau American Community Survey, and PRISM climate group data sets. In total, the data set has 74,288 entries from 14,729. See Cano & Hahn (2023) for details. Note: data from First Street Foundation (FSF) has been redacted (all values set to -999). FSF data was used under license for the study and are not publicly available. Please contact privately for unredacted version. 

 


typology_*.npy: Conditional Average Treatment Effect (CATE) and accompanying measurements for the 27 community typologies defined in the paper. 



typology_cate.npy: Measured CATE for the 27 community typologies

typology_sig_cate.npy: Statistical uncertainty on CATE measurements.  

typology_y_treat.npy: Claim per policy in USD (outcome) of the community typologies for treated sample 

typology_y_control.npy: Claim per policy in USD (outcome) of the community typologies for control sample

typology_covar.npy: Median covariate values for communities that fall within the typology. This is for reference only. 



fema_crs.*.npy: Compiled dataset for training/validation/testing the q_phi(Y|X) of the treated/control samples used in CausalFlow. 

qphi_control.tar.gz / qphi_treat.tar.gz: Compressed tar file that contains all the trained ensemble of flows for the control/treated samples used in CausalFlow. 

qphi_support.v2.log.control.pt / qphi_support.v2.log.treat.pt: NDEs estimating the distribution of the covariates for the control/treated samples. 

fema_cid.almost_only_c300.npy: An array of communities that participated in the CRS but only executed the c300 activity, which corresponds to the outreach effort. This dataset is used to estimate the overall impact of outreach.
DOI: 10.5281/zenodo.13167989
title: Binary black-hole simulation SXS:BBH:0505

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13167990
title: Binary black-hole simulation SXS:BBH:0505

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.15468/dl.4epucn
title: Occurrence Download

description: A dataset containing 573 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Centre for Plant Medicine Research (CPMR) medicinal plants dataset"
 ]
}

 The dataset includes 573 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045595-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wwjzjs
title: Occurrence Download

description: A dataset containing 46 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Distribución, abundancia y efectos nocivos de tres especies de plantas invasoras (Cenchrus ciliaris_matorrales)"
 ]
}

 The dataset includes 46 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045597-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13168006
title: Binary black-hole simulation SXS:BBH:0485

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13168007
title: Binary black-hole simulation SXS:BBH:0485

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.15468/dl.dkasxn
title: Occurrence Download

description: A dataset containing 70866 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Mikania micrantha Kunth, Chromolaena odorata (L.) R.M.King & H.Rob.)"
 ]
}

 The dataset includes 70866 records from 662 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045596-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13168010
title: Binary black-hole simulation SXS:BBH:0420

description: Simulation of a black-hole binary system evolved by the SpEC code.