cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5997376
title: Novel insight into GLUT1 Deficiency Syndrome: screening for emotional and behavioral problems in youths following ketogenic diet

description: We performed an exploratory study to assess the risk of emotional and behavioral problems and investigated the potential role of influencing factors related to the pathology itself or KD treatment. The database provides data on demographic and neurological data and the scores of CBCL filled in by parents for GLUT1DS patients (cases). It also provides scores of CBCL filled in by parents for migraine patients (controls).
description: Funding: Ricerca Corrente 2017-2019, Ministry of Health (Italy)
DOI: 10.18739/a2b853k0s
title: HELiX Uncrewed Aircraft System data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) campaign, A0 level data

description: A0 level data from HELiX Uncrewed Aircraft System correspond to the raw data in Matlab format collected in the Central Arctic Ocean during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. Synchronized and quality-controlled B1 level data are available in the Arctic Data Center. Users are encouraged to primarily use the B1 level data for analysis (doi:10.18739/A2GH9BB0Q). Raw data are the initial inputs in the processing routines to obtain the B1 and A1 level data (doi:10.18739/A2M90243X). Matlab files include hemispheric irradiance measurements from Kipp and Zonen pyranometers and thermodynamic parameters from Vaisala RSS421 sensors. Autopilot positions and attitudes, along with gimbal attitudes are also provided. Each field of measurements has its own time stamped based on a common clock and associated acquisition frequency. As no Coordinated Universal Time (UTC) time was provided in the FlexLogger acquisition files, the additional A0_PixHawk Matlab files obtained directly from the PixHawk autopilot are used to add UTC time for B1 level data. Please contact the authors if you need to use this dataset. More information on the data and method can be found in de Boer, G. R. Calmer, G. Jozef, J. Cassano, J. Hamilton, D. Lawrence, S. Borenstein, A. Doddi, C. Cox, J. Schmale, A. Preußer and B. Argrow (2022): Observing the Central Arctic Atmosphere and Surface with University of Colorado Uncrewed Aircraft Systems, Nature Scientific Data, in prep.
DOI: 10.6084/m9.figshare.12387383
title: The impact of obesity in rehabilitation: a mismatch between staff perception and hospital outcomes

description: To establish the prevalence of obesity in an inpatient rehabilitation setting, examine its impact on hospital outcomes, and explore staff perceptions of caring for patients with obesity. A retrospective audit of inpatients admitted to a sub-acute rehabilitation hospital over 12 months. Hospital outcomes included length of stay (LOS), Functional Independence measure (FIM), and discharge destination. Linear regression models were used to determine whether obesity was associated with hospital outcomes. Staff working on rehabilitation wards were invited to complete a survey exploring their perceptions on caring for those who are obese. Of 1280 episodes of care, 359 (28%) patients were classified as obese with a body mass index ≥30 kg/m<sup>2</sup>. Obesity was not associated with LOS or functional improvement after controlling for age, gender, and admission FIM. One hundred and twelve hospital staff (response rate 71%) completed the survey. Most rated their bariatric care knowledge as average (45%) or good (36%). The majority (60%) perceived that patients with obesity have longer LOS than those who are non-obese. One-third of patients admitted to inpatient subacute rehabilitation were classified as obese. Whilst obesity was not associated with poorer hospital outcomes, staff perceived that obesity negatively impacts on care requirements and LOS.Implications for rehabilitationA third of patients admitted to a public, inpatient rehabilitation setting may be classified as obese based on their body mass index.Although staff perceived that obesity negatively impacts on length of stay and functional gains, there was no evidence that obesity was associated with poorer hospital outcomes.Patients who are classified as obese were able to achieve comparable hospital outcomes including length of stay in the rehabilitation setting to those who are not obese. A third of patients admitted to a public, inpatient rehabilitation setting may be classified as obese based on their body mass index. Although staff perceived that obesity negatively impacts on length of stay and functional gains, there was no evidence that obesity was associated with poorer hospital outcomes. Patients who are classified as obese were able to achieve comparable hospital outcomes including length of stay in the rehabilitation setting to those who are not obese.
DOI: 10.6084/m9.figshare.12387383.v1
title: The impact of obesity in rehabilitation: a mismatch between staff perception and hospital outcomes

description: To establish the prevalence of obesity in an inpatient rehabilitation setting, examine its impact on hospital outcomes, and explore staff perceptions of caring for patients with obesity. A retrospective audit of inpatients admitted to a sub-acute rehabilitation hospital over 12 months. Hospital outcomes included length of stay (LOS), Functional Independence measure (FIM), and discharge destination. Linear regression models were used to determine whether obesity was associated with hospital outcomes. Staff working on rehabilitation wards were invited to complete a survey exploring their perceptions on caring for those who are obese. Of 1280 episodes of care, 359 (28%) patients were classified as obese with a body mass index ≥30 kg/m<sup>2</sup>. Obesity was not associated with LOS or functional improvement after controlling for age, gender, and admission FIM. One hundred and twelve hospital staff (response rate 71%) completed the survey. Most rated their bariatric care knowledge as average (45%) or good (36%). The majority (60%) perceived that patients with obesity have longer LOS than those who are non-obese. One-third of patients admitted to inpatient subacute rehabilitation were classified as obese. Whilst obesity was not associated with poorer hospital outcomes, staff perceived that obesity negatively impacts on care requirements and LOS.Implications for rehabilitationA third of patients admitted to a public, inpatient rehabilitation setting may be classified as obese based on their body mass index.Although staff perceived that obesity negatively impacts on length of stay and functional gains, there was no evidence that obesity was associated with poorer hospital outcomes.Patients who are classified as obese were able to achieve comparable hospital outcomes including length of stay in the rehabilitation setting to those who are not obese. A third of patients admitted to a public, inpatient rehabilitation setting may be classified as obese based on their body mass index. Although staff perceived that obesity negatively impacts on length of stay and functional gains, there was no evidence that obesity was associated with poorer hospital outcomes. Patients who are classified as obese were able to achieve comparable hospital outcomes including length of stay in the rehabilitation setting to those who are not obese.
DOI: 10.6084/m9.figshare.12465194
title: Clustering of functioning and disability profile based on the WHO disability assessment schedule 2.0 – a nationwide databank study

description: To compare and cluster the health status and disability restrictions associated with eight major physiological functions of body systems, using functioning domains of WHO Disability Assessment Schedule 2.0. Retrospective analyses of a nation-wide disability database. Population-based study. Records from patients &gt;18 years of age with disability were obtained from the Taiwan Data Bank of Persons with Disability (July 2012–November 2017). Disability functioning profile of the following diagnosis were analyzed: stroke, schizophrenia, hearing loss, liver cirrhosis, chronic kidney disease, congestive heart failure, burn, head and neck cancer. Not applicable. Demographic data, severity of impairment, and Disability Assessment Scale scores were obtained and analyzed. Radar charts were constructed using the WHO Disability Assessment Schedule 2.0. functioning domain score. Degree of similarity between any two given diagnosis was assessed by cluster analysis, comparing the Euclidean distances between radar chart data points among the six domains. Based on cluster analysis of similarities between functioning domain profiles, the eight diagnoses were grouped into different disability clusters. Four clusters of disability were named according to the type restriction patterns: global-impact cluster (stroke); interaction-restriction cluster (schizophrenia, hearing loss); physical-limitation cluster, (liver cirrhosis, CKD, and congestive heart failure); and specific-impact cluster (burn, head and neck cancer). The rates of institutionalization and unemployment differed between the four clusters. We converted WHO Disability Assessment Schedule 2.0. functioning domain scores into six-dimensioned radar chart, and demonstrate disability restrictions can be further categorized into clusters according to similarity of functioning impairment. Understanding of disease-related disabilities provides an important basis for designing rehabilitation programs and policies on social welfare and health that reflect the daily-living needs of people according to diagnosis.Implication for RehabilitationThe use of radar charts provided a direct visualization of the scope and severity of disabilities associated with specific diagnoses.Diagnosis-related disabilities can be organized into clusters based on similarities in WHODAS 2.0 disability domain profiles.Knowledge of the characteristics of disability clusters is important to understand disease-related disabilities and provide a basis for designing rehabilitation. The use of radar charts provided a direct visualization of the scope and severity of disabilities associated with specific diagnoses. Diagnosis-related disabilities can be organized into clusters based on similarities in WHODAS 2.0 disability domain profiles. Knowledge of the characteristics of disability clusters is important to understand disease-related disabilities and provide a basis for designing rehabilitation.
DOI: 10.6084/m9.figshare.12387449
title: Developing a stroke-vision care pathway: a consensus study

description: We aimed to develop a stroke-vision care pathway for stroke survivors with visual impairment. A literature review searched key electronic bibliographic databases for care pathways related to stroke/vision. Two focus group meetings using semi-structured/nominal group technique reached consensus on items relevant for inclusion in a stroke-vision care pathway. Following the development of the pathway, we obtained feedback through consultation with patient and professional groups. The literature review identified two care pathways relevant to acute stroke and generic vision disorders. Outputs from focus groups related to how stroke survivors present with vision problems; the time points at which stroke survivors present with vision symptoms; the relevance of different types of visual condition to different vision services; the importance of support services supplementary to hospital services and; the importance of key resources to promote awareness of vision problems in stroke survivors. Refinement of the pathway considered time duration from stroke onset, reporting of symptoms to services, and signposting/referrals required dependent on visual condition type. This new stroke-vision care pathway is a process pathway describing potential options for stroke survivors with visual impairment to access health care and obtain appropriate referral(s) to vision services relevant to their specific vision problem(s).IMPLICATIONS FOR REHABILITATIONVisual impairment is a common consequence of stroke.It is imperative that those who care for stroke survivors are aware of the visual consequences of stroke and make the appropriate referrals for vision and support services.The stroke-vision care pathway is a process pathway that describes the potential options for stroke survivors with visual impairment to access health care and obtain the appropriate referral(s) to vision services relevant to their specific vision problem(s).The stroke-vision care pathway is available (free to download) from the VISION research unit (www.vision-research.co.uk) website and available as supplemental information with this publication. Visual impairment is a common consequence of stroke. It is imperative that those who care for stroke survivors are aware of the visual consequences of stroke and make the appropriate referrals for vision and support services. The stroke-vision care pathway is a process pathway that describes the potential options for stroke survivors with visual impairment to access health care and obtain the appropriate referral(s) to vision services relevant to their specific vision problem(s). The stroke-vision care pathway is available (free to download) from the VISION research unit (www.vision-research.co.uk) website and available as supplemental information with this publication.
DOI: 10.6084/m9.figshare.12387449.v1
title: Developing a stroke-vision care pathway: a consensus study

description: We aimed to develop a stroke-vision care pathway for stroke survivors with visual impairment. A literature review searched key electronic bibliographic databases for care pathways related to stroke/vision. Two focus group meetings using semi-structured/nominal group technique reached consensus on items relevant for inclusion in a stroke-vision care pathway. Following the development of the pathway, we obtained feedback through consultation with patient and professional groups. The literature review identified two care pathways relevant to acute stroke and generic vision disorders. Outputs from focus groups related to how stroke survivors present with vision problems; the time points at which stroke survivors present with vision symptoms; the relevance of different types of visual condition to different vision services; the importance of support services supplementary to hospital services and; the importance of key resources to promote awareness of vision problems in stroke survivors. Refinement of the pathway considered time duration from stroke onset, reporting of symptoms to services, and signposting/referrals required dependent on visual condition type. This new stroke-vision care pathway is a process pathway describing potential options for stroke survivors with visual impairment to access health care and obtain appropriate referral(s) to vision services relevant to their specific vision problem(s).IMPLICATIONS FOR REHABILITATIONVisual impairment is a common consequence of stroke.It is imperative that those who care for stroke survivors are aware of the visual consequences of stroke and make the appropriate referrals for vision and support services.The stroke-vision care pathway is a process pathway that describes the potential options for stroke survivors with visual impairment to access health care and obtain the appropriate referral(s) to vision services relevant to their specific vision problem(s).The stroke-vision care pathway is available (free to download) from the VISION research unit (www.vision-research.co.uk) website and available as supplemental information with this publication. Visual impairment is a common consequence of stroke. It is imperative that those who care for stroke survivors are aware of the visual consequences of stroke and make the appropriate referrals for vision and support services. The stroke-vision care pathway is a process pathway that describes the potential options for stroke survivors with visual impairment to access health care and obtain the appropriate referral(s) to vision services relevant to their specific vision problem(s). The stroke-vision care pathway is available (free to download) from the VISION research unit (www.vision-research.co.uk) website and available as supplemental information with this publication.
DOI: 10.6084/m9.figshare.12465194.v1
title: Clustering of functioning and disability profile based on the WHO disability assessment schedule 2.0 – a nationwide databank study

description: To compare and cluster the health status and disability restrictions associated with eight major physiological functions of body systems, using functioning domains of WHO Disability Assessment Schedule 2.0. Retrospective analyses of a nation-wide disability database. Population-based study. Records from patients &gt;18 years of age with disability were obtained from the Taiwan Data Bank of Persons with Disability (July 2012–November 2017). Disability functioning profile of the following diagnosis were analyzed: stroke, schizophrenia, hearing loss, liver cirrhosis, chronic kidney disease, congestive heart failure, burn, head and neck cancer. Not applicable. Demographic data, severity of impairment, and Disability Assessment Scale scores were obtained and analyzed. Radar charts were constructed using the WHO Disability Assessment Schedule 2.0. functioning domain score. Degree of similarity between any two given diagnosis was assessed by cluster analysis, comparing the Euclidean distances between radar chart data points among the six domains. Based on cluster analysis of similarities between functioning domain profiles, the eight diagnoses were grouped into different disability clusters. Four clusters of disability were named according to the type restriction patterns: global-impact cluster (stroke); interaction-restriction cluster (schizophrenia, hearing loss); physical-limitation cluster, (liver cirrhosis, CKD, and congestive heart failure); and specific-impact cluster (burn, head and neck cancer). The rates of institutionalization and unemployment differed between the four clusters. We converted WHO Disability Assessment Schedule 2.0. functioning domain scores into six-dimensioned radar chart, and demonstrate disability restrictions can be further categorized into clusters according to similarity of functioning impairment. Understanding of disease-related disabilities provides an important basis for designing rehabilitation programs and policies on social welfare and health that reflect the daily-living needs of people according to diagnosis.Implication for RehabilitationThe use of radar charts provided a direct visualization of the scope and severity of disabilities associated with specific diagnoses.Diagnosis-related disabilities can be organized into clusters based on similarities in WHODAS 2.0 disability domain profiles.Knowledge of the characteristics of disability clusters is important to understand disease-related disabilities and provide a basis for designing rehabilitation. The use of radar charts provided a direct visualization of the scope and severity of disabilities associated with specific diagnoses. Diagnosis-related disabilities can be organized into clusters based on similarities in WHODAS 2.0 disability domain profiles. Knowledge of the characteristics of disability clusters is important to understand disease-related disabilities and provide a basis for designing rehabilitation.
DOI: 10.6084/m9.figshare.19131827
title: Raw data

description: Raw data of the cross-cultural adaptation and psychometric validation of the MovAd scale in Spain
DOI: 10.6084/m9.figshare.19131827.v1
title: Raw data

description: Raw data of the cross-cultural adaptation and psychometric validation of the MovAd scale in Spain
DOI: 10.6084/m9.figshare.19131863
title: Lick sites in herbivores

description: Licking sites for herbivores in northern Iran
DOI: 10.6084/m9.figshare.19131863.v1
title: Lick sites in herbivores

description: Licking sites for herbivores in northern Iran
DOI: 10.5281/zenodo.5996774
title: Data in support of "Self-adaptive multi-objective climate policies align mitigation and adaptation strategies"

description: The optimization results used for the paper "Self-adaptive multi-objective climate policies align mitigation and adaptation strategies" are reported in this folder. The content should be used together with the GitHub repository hosting the scripts for replication of the results discussed in the paper. In particular, it should be included in the CleanADCDICE folder before running the scripts to produce the figures.
DOI: 10.5517/ccdc.csd.cc24rnwg
title: CCDC 1988956: Experimental Crystal Structure Determination

description: Related Article: Xin-Xue Yang, Wei-Dong Yu, Xiao-Yi Yi, Chao Liu|2020|Inorg.Chem.|59|7512|doi:10.1021/acs.inorgchem.0c00330
DOI: 10.5517/ccdc.csd.cc26cdyy
title: CCDC 2036760: Experimental Crystal Structure Determination

description: Related Article: Mu-Xue He, Yan Yao, Chun-Zhi Ai, Zu-Yu Mo, Yu-Zheng Wu, Qi Zhou, Ying-Ming Pan, Hao-Tao Tang|2022|Org.Chem.Front.|9|781|doi:10.1039/D1QO01458G
DOI: 10.5517/ccdc.csd.cc2780j2
title: CCDC 2063283: Experimental Crystal Structure Determination

description: Related Article: Tomohiko Nishiuchi|2021|CSD Communication|||
description: Related Article: Tomohiko Nishiuchi, Hikaru Sotome, Risa Fukuuchi, Kenji Kamada, Hiroshi Miyasaka, Takashi Kubo|2021|Aggregate|2|e126|doi:10.1002/agt2.126
DOI: 10.34740/kaggle/dsv/3153321
title: Covid-19 in Singapore : Latest Detailed Data
DOI: 10.18739/a26m3349r
title: Shelfbreak Upwelling in the Western Beaufort Sea, Zooplankton Abundance data, September 2017

description: Atmospherically-forced wind-induced upwelling along the shelf break leads to enhanced feeding opportunities for intermediate links in the pelagic ecosystem that in turn sustain the exploitation of this environment by animals such as beluga, seabirds, and seals. The Beaufort Sea shelf break is a hotspot for upper trophic level animals because elevated numbers and biomass of large, high-energy zooplankton (e.g., lipid-rich copepods, euphausiids) are regularly upwelled from deeper water onto the shelf during winds from the east, retained there by frontal features when upwelling relaxes, and exploited by bowhead whales, seabirds, and forage fish that in turn are prey for piscivorous marine mammals, such as beluga whales. The zooplankton abundance data were collected to measure upwelling impacts to the Beaufort shelf break community structure. Data were acquired as part of the National Science Foundation (NSF)-funded project "The Importance of Shelf Break Upwelling to Upper Trophic Level Ecology in the Western Beaufort Sea". Data from net tows within the box 165 West (W) - 145 W, 69 North (N) - 72N between 30 August and 14 September 2017.
DOI: 10.6084/m9.figshare.19131920
title: Data File for "Institutional Convergence: Exit or Voice?"

description: Data File for article:<br>Joshua Hall, “Institutional Convergence: Exit or Voice?” <i>Journal of Economics and Finance </i>40(4) 2016: 829-840.
DOI: 10.6084/m9.figshare.19131920.v1
title: Data File for "Institutional Convergence: Exit or Voice?"

description: Data File for article:<br>Joshua Hall, “Institutional Convergence: Exit or Voice?” <i>Journal of Economics and Finance </i>40(4) 2016: 829-840.