cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.7885103.v1
title: Long-term physical activity in people with multiple sclerosis: exploring expert views on facilitators and barriers

description: <b>Purpose:</b> To explore the views of experts on facilitators and barriers of long-term physical activity among people with multiple sclerosis living in different European countries. <b>Methods:</b> We conducted semi-structured telephone or face to face interviews with twelve multiple sclerosis and physical activity experts (scientists, practitioners, patient representatives) from five European countries. Interviews were audio-recorded, transcribed verbatim and analyzed using thematic analysis. <b>Results:</b> We identified 20 themes and categorized them into environmental and personal factors. The most frequently mentioned and intensively discussed themes were environmental factors. The themes were structured according to possible intervention level: organizational, interpersonal and intrapersonal level themes. Themes at the organizational level comprised availability, access and quality of exercise/physical activity options; health system characteristics like services and organization, health professionals and information provision. The interpersonal level comprised social support and peer support. Disease related factors were the most frequently mentioned intrapersonal level theme. In our study, more codes were obtained for environmental factors than for personal factors. <b>Conclusions:</b> The results suggested that environmental factors may need to be addressed in particular to increase long-term physical activity adherence. This study will inform the design of a survey questionnaire assessing possible barriers and facilitators among people with multiple sclerosis. Implications for rehabilitationLong-term physical activity among people with multiple sclerosis is subject to a number of modifiable determinants: personal and environmental factors.Multiple sclerosis exercise experts emphasized the importance of environmental factors on the organizational and interpersonal level.Future physical activity interventions should be guided by a social-ecological perspective.Addressing environmental and personal factors simultaneously to reach optimal long-term outcomes should be considered in future interventions. Long-term physical activity among people with multiple sclerosis is subject to a number of modifiable determinants: personal and environmental factors. Multiple sclerosis exercise experts emphasized the importance of environmental factors on the organizational and interpersonal level. Future physical activity interventions should be guided by a social-ecological perspective. Addressing environmental and personal factors simultaneously to reach optimal long-term outcomes should be considered in future interventions.
DOI: 10.5281/zenodo.4083449
title: Data for "Bayesian inference of mantle viscosity from whole-mantle density models"

description: <strong>Supplementary Data</strong><br> Rudolph, M.L., Moulik, P., and Lekic, V. (2020). Bayesian inference of mantle viscosity from whole-mantle density models. Geochemistry, Geophysics, Geosystems This data archive contains files needed to reproduce the figures from our 2020 G-Cubed paper, including the full ensemble solutions for mantle viscosity structure.
DOI: 10.5281/zenodo.4083448
title: Data for "Bayesian inference of mantle viscosity from whole-mantle density models"

description: <strong>Supplementary Data</strong><br> Rudolph, M.L., Moulik, P., and Lekic, V. (2020). Bayesian inference of mantle viscosity from whole-mantle density models. Geochemistry, Geophysics, Geosystems This data archive contains files needed to reproduce the figures from our 2020 G-Cubed paper, including the full ensemble solutions for mantle viscosity structure.
DOI: 10.6084/m9.figshare.13092926
title: Species richness (raw + estimates)

description: Fish species richness (raw counts) and estimates (accounting for detectability issues) of alpha diversity during pre- and post- predators collapse time periods (pre-1992 and post-1991) on each of the ten offshore banks of the Scotian Shelf within three trophic groups: mesopredators (meso), prey (prey) and large-bodied predators (pred). These data accompany SARs in main text and comparison of estimators in supplementary material.
DOI: 10.6084/m9.figshare.13092926.v1
title: Species richness (raw + estimates)

description: Fish species richness (raw counts) and estimates (accounting for detectability issues) of alpha diversity during pre- and post- predators collapse time periods (pre-1992 and post-1991) on each of the ten offshore banks of the Scotian Shelf within three trophic groups: mesopredators (meso), prey (prey) and large-bodied predators (pred). These data accompany SARs in main text and comparison of estimators in supplementary material.
DOI: 10.6084/m9.figshare.11663748
title: Sediment Balance in Coastal Louisiana from 2006 to 2015

description: A decadal (2006 to 2015) sediment mass and volume balance for coastal Louisiana from calculated from the Coastwide Reference Monitoring System (CRMS) data using Bayesian kriging.
DOI: 10.6084/m9.figshare.11663748.v7
title: Sediment Balance in Coastal Louisiana from 2006 to 2015

description: A decadal (2006 to 2015) sediment mass and volume balance for coastal Louisiana from calculated from the Coastwide Reference Monitoring System (CRMS) data using Bayesian kriging.
DOI: 10.6084/m9.figshare.11663748.v6
title: Sediment Balance in Coastal Louisiana from 2006 to 2015

description: A decadal (2006 to 2015) sediment mass and volume balance for coastal Louisiana from calculated from the Coastwide Reference Monitoring System (CRMS) data using Bayesian kriging.
DOI: 10.6084/m9.figshare.12600833
title: Supplemental materiale.docx

description: This is the supplemental material to the article in titled:<b>Molecular markers of skeletal muscle hypertrophy following 10 weeks of resistance training in oral contraceptive users and non-users</b><br>
DOI: 10.6084/m9.figshare.12600833.v1
title: Supplemental materiale.docx

description: This is the supplemental material to the article in titled:<b>Molecular markers of skeletal muscle hypertrophy following 10 weeks of resistance training in oral contraceptive users and non-users</b><br>
DOI: 10.6084/m9.figshare.5998583
title: multiAtlasTT_data

description: Multi-Atlas Transfer Tools for Neuroimaging (maTT)<br><br>This figshare repository contains the data used for the maTT project. The <b>atlas_data</b> folder here contains additional .gcs files that can be used for the maTT2 functionality. When running maTT2, the data here should be organized into each respective atlas folder (here: <i>https://github.com/faskowit/multiAtlasTT/tree/master/atlas_data</i>). When this tar.gz is unzipped in the multiAtlasTT base directory, it should automatically unzip into this structure.<br><br>Please check out the accompanying Github repository for details about using maTT and maTT2. <br><br>These gcs files were created after labeling the Mindboggle 101 individual subjects data with the maTT label transfer scripts, and then creating a GCS with these individual subject annotation files. Note: only light quality control was performed on the FreeSurfer outputs. Unlike the Mindboggle project, the annotation files were not carefully checked. Therefore, please carefully check the outputs of this app to make sure the data quality is sufficient. <br><br>See also app-multiAtlasTT (<i>https://github.com/faskowit/app-multiAtlasTT</i>) for the brainlife.io version of this tool, which will automatically download the info you need from this data repo.<br><sub>Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors(s) and do not necessarily reflect the views of the National Science Foundation.</sub><br><br><br>
DOI: 10.6084/m9.figshare.5998583.v6
title: multiAtlasTT_data

description: Multi-Atlas Transfer Tools for Neuroimaging (maTT)<br><br>This figshare repository contains the data used for the maTT project. The <b>atlas_data</b> folder here contains additional .gcs files that can be used for the maTT2 functionality. When running maTT2, the data here should be organized into each respective atlas folder (here: <i>https://github.com/faskowit/multiAtlasTT/tree/master/atlas_data</i>). When this tar.gz is unzipped in the multiAtlasTT base directory, it should automatically unzip into this structure.<br><br>Please check out the accompanying Github repository for details about using maTT and maTT2. <br><br>These gcs files were created after labeling the Mindboggle 101 individual subjects data with the maTT label transfer scripts, and then creating a GCS with these individual subject annotation files. Note: only light quality control was performed on the FreeSurfer outputs. Unlike the Mindboggle project, the annotation files were not carefully checked.<br>See also app-multiAtlasTT (<i>https://github.com/faskowit/app-multiAtlasTT</i>) for the brainlife.io version of this tool, which will automatically download the info you need from this data repo.<br><sub>Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors(s) and do not necessarily reflect the views of the National Science Foundation.</sub><br><br><br>
DOI: 10.5281/zenodo.4088880
title: Unstructured global to coastal wave modeling for the Energy Exascale Earth System Model - Simuation results and observed data

description: This data set contains the simulation results and observed data at NDBC buoy locations. wave_data.pickle File containing python data objects which store: station ID data, observed data, model data, and model output dates. Requires python 3.8. data_access.py Example python script which reads in a prints the data from wave_data.pickle. It also demonstrates how to access data from the objects stored in the pickle file.
DOI: 10.15482/usda.adc/1520086
title: NALTreeTo351
DOI: 10.6084/m9.figshare.13090655
title: R0190_20170904a_videos

description: Rat 0190, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.6084/m9.figshare.13090655.v1
title: R0190_20170904a_videos

description: Rat 0190, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.6084/m9.figshare.13050386.v1
title: Code and data for estimating fishing effort from spatially extensive data across a lake-rich landscape

description: This repository contains code and data used to answer the following questions: 1. When fit to spatially extensive data, can models detect annual, seasonal, and daily changes in fishing effort? 2. How do fishing effort estimates derived from extensive observations compare to those derived from intensive observation? 3. How well can models fit to extensive data predict total fishing effort on unobserved lakes? 4. How can these model-based methods be applied to predict fishing effort across a fisheries landscape? <br>All subfolders of code and data can be found in "code data output subfolders.zip"<br>This code pulls from the 'csvs' folder as well as from the 9/30/2020 version of the MFE database found here: https://caryinstitute.figshare.com/articles/MFE_database_Data_from_ecosystem_ecology_research_by_Jones_Solomon_and_collaborators_on_the_ecology_and_biogeochemistry_of_lakes_and_lake_organisms_in_the_Upper_Midwest_USA/7438598 <br>See the "ReadMe.txt" file for code order and metadata. <br>
DOI: 10.5281/zenodo.4088833
title: Replication data for Convolutional neural networks with hierarchical context transfer for high-resolution spatiotemporal predictions

description: Data represents number of posts from Instagram in area for six large cities (New York, London, Moscow, Vienna, Tokyo, and Saint Petersburg) covering 2017 and 2018. We aggregated data by hours and split each city using hierarchical area split by 10x10 grid with three levels. The size of the cell on the micro-level equals to 50 meters. Thus, we get 8760 examples for each year. Cells with zero number of posts in an hour are not listed. Data stored in JSON files with the following format:<br> {"2018-12-13 04:00:00":<br> {"(4, 4)":<br> {"sum":1,<br> "data":<br> {"(9, 6)":<br> {"sum":1,<br> "data":{"(2, 4)":1}}}}}}
DOI: 10.5281/zenodo.4088832
title: Replication data for Convolutional neural networks with hierarchical context transfer for high-resolution spatiotemporal predictions

description: Data represents number of posts from Instagram in area for six large cities (New York, London, Moscow, Vienna, Tokyo, and Saint Petersburg) covering 2017 and 2018. We aggregated data by hours and split each city using hierarchical area split by 10x10 grid with three levels. The size of the cell on the micro-level equals to 50 meters. Thus, we get 8760 examples for each year. Cells with zero number of posts in an hour are not listed. Data stored in JSON files with the following format:<br> {"2018-12-13 04:00:00":<br> {"(4, 4)":<br> {"sum":1,<br> "data":<br> {"(9, 6)":<br> {"sum":1,<br> "data":{"(2, 4)":1}}}}}}
DOI: 10.25345/c5kb54
title: MassIVE MSV000086286 - GNPS_Synthetic cannabbinoids in OF_02
title: untargeted approach synthetic cannabinoids in oral fluid
DOI: 10.25573/data.13093070
title: Asteroid COI data

description: This is the fasta file of the COI sequences used to generate Figure 1 and used the the 2020 Invertebrate Biology paper:<br><b>How I wonder what you are: Can DNA barcoding identify the larval asteroids of Panama?</b>
DOI: 10.25573/data.13093052
title: Luidia 16S alignment

description: This is the fasta file of the Luidia 16S alignment from the 2020 paper in Invertebrate Biology entitled <b>How I wonder what you are: Can DNA barcoding identify the larval asteroids of Panama?</b><b></b>
DOI: 10.25573/data.13093052.v1
title: Luidia 16S alignment

description: This is the fasta file of the Luidia 16S alignment from the 2020 paper in Invertebrate Biology entitled <b>How I wonder what you are: Can DNA barcoding identify the larval asteroids of Panama?</b><b></b>
DOI: 10.25573/data.13093082
title: Asteroid 16S alignment

description: These are the 16S data used for ABGD analysis mentioned in the methods of the 2020 paper in Invertebrate Biology: <b>How I wonder what you are: Can DNA barcoding identify the larval asteroids of Panama?</b><b></b>
DOI: 10.25573/data.13093070.v1
title: Asteroid COI data

description: This is the fasta file of the COI sequences used to generate Figure 1 and used the the 2020 Invertebrate Biology paper:<br><b>How I wonder what you are: Can DNA barcoding identify the larval asteroids of Panama?</b>
DOI: 10.25573/data.13093082.v1
title: Asteroid 16S alignment

description: These are the 16S data used for ABGD analysis mentioned in the methods of the 2020 paper in Invertebrate Biology: <b>How I wonder what you are: Can DNA barcoding identify the larval asteroids of Panama?</b><b></b>
DOI: 10.21227/xkzq-9603
title: Supplementary Material To: A Safety Benchmark for Capacitive Proximity Sensing Robotic Skins –Safe Transient Contact in Compliance to ISO TS 15066: Power andForce Limiting (PFL)

description: Supplementary Data for Publication "A Safety Benchmark for Capacitive Proximity Sensing Robotic Skins - Safe Transient Contact in Compliance to ISO TS 15066: Power and Force Limiting (PFL) Disclaimer: This publication was submitted and is currently under review to be published for IEEE RA-L with ICRA 2021 option. Fundings: The research leading to this results has received fundingfrom the ”Kärntner Wirtschaftsförderung Fonds” (KWF) and the ”European Regional Development Fund” (EFRE) within the CapSize project 26616/30969/44253 Preliminary version of the abstract: During co-presence of human workers and robotsin the same workspace, Power and Force Limiting (PFL)according to ISO TS 15066 is required to avoid injuries duringundesired contacts. Capacitive Proximity Sensors (CPSs) offer acost-effective solution to cover the entire robot manipulator withfast close-range perception for Human Robot Collaboration(HRC) tasks.Compared to pure tactile or force sensing, the sensors allowan increase of operating speeds of cobots while still maintainingPFL. The robot reacts before impacts occur and does not sufferfrom occlusion, as the sensors close the perception gap left bytactile detection and mid range distance sensors. However, sincecapacitive coupling to obstacles varies not only with distance,but also with shape and material properties, the projectionfrom capacitance to actual distances is a general problem. Areference benchmark is required for the evaluation to ensuresafe PFL.We derive the requirements described in ISO TS 15066for PFL of critical body regions and propose a method fordetermining the pre-detection operation speed to comply withPFL. Researchers from four different institutes present threedifferent designs of capacitive sensors and their applicabilityon different robots. We propose a universal benchmark testprocedure for fellow researchers to evaluate their capacitivesensors for safe PFL
DOI: 10.15482/usda.adc/1520088
title: NALTreeTo351
DOI: 10.6084/m9.figshare.13090142
title: Recreational drug use and clinical outcomes in people with HIV

description: This dataset is part of a study that assessed the factors associated with drug use and impact on clinical outcomes among people living with HIV (PLHIV) in Spain. We conducted a multicenter retrospective cohort study in which 275 PLHIV participated. Data were collected in four University Hospitals from three of the most populated Spanish regions (Madrid, Barcelona, and Alicante) We collected some retrospective last-12-month clinical data from clinical records and participants responded to a cross-sectional online survey containing the patient-reported outcomes measures. It measured use of illicit drugs and other substances, treatment adherence and other health-related variables. This dataset contains 151 of these variables that were used for the analysis. It is on Excel format. The file contains three sheets: Sheet . It contains the dataset. The first row of the table corresponds to variable names. Sheet . It contains a description of each variable. Some of the variables correspond to items from the questionnaire (S_) and others variables correspond to variables from clinical data (H_). Sheet . It contains the labels of the values in the dataset, for each categorical codified variable. Variables not listed in this sheet are numeric, meaning that the numbers on those variables are the real values of the variables, and not a code.<br>
DOI: 10.6084/m9.figshare.13090142.v2
title: Recreational drug use and clinical outcomes in people with HIV

description: This dataset is part of a study that assessed the factors associated with drug use and impact on clinical outcomes among people living with HIV (PLHIV) in Spain. We conducted a multicenter retrospective cohort study in which 275 PLHIV participated. Data were collected in four University Hospitals from three of the most populated Spanish regions (Madrid, Barcelona, and Alicante) We collected some retrospective last-12-month clinical data from clinical records and participants responded to a cross-sectional online survey containing the patient-reported outcomes measures. It measured use of illicit drugs and other substances, treatment adherence and other health-related variables. This dataset contains 151 of these variables that were used for the analysis. It is on Excel format. The file contains three sheets: Sheet . It contains the dataset. The first row of the table corresponds to variable names. Sheet . It contains a description of each variable. Some of the variables correspond to items from the questionnaire (S_) and others variables correspond to variables from clinical data (H_). Sheet . It contains the labels of the values in the dataset, for each categorical codified variable. Variables not listed in this sheet are numeric, meaning that the numbers on those variables are the real values of the variables, and not a code.<br>
DOI: 10.6084/m9.figshare.13090142.v1
title: Recreational drug use and clinical outcomes in people with HIV

description: This dataset is part of a study that assessed the factors associated with drug use and impact on clinical outcomes among people living with HIV (PLHIV) in Spain. We conducted a multicenter retrospective cohort study in which 275 PLHIV participated. Data were collected in four University Hospitals from three of the most populated Spanish regions (Madrid, Barcelona, and Alicante) We collected some retrospective last-12-month clinical data from clinical records and participants responded to a cross-sectional online survey containing the patient-reported outcomes measures. It measured use of illicit drugs and other substances, treatment adherence and other health-related variables. This dataset contains 129 of these variables that were used for the analysis. It is on Excel format. The file contains three sheets: Sheet . It contains the dataset. The first row of the table corresponds to variable names. Sheet . It contains a description of each variable. Some of the variables correspond to items from the questionnaire (S_) and others variables correspond to variables from clinical data (H_). Sheet . It contains the labels of the values in the dataset, for each categorical codified variable. Variables not listed in this sheet are numeric, meaning that the numbers on those variables are the real values of the variables, and not a code.<br>
DOI: 10.21227/3q5d-6t25
title: Supplementary Material To: A Safety Benchmark for Capacitive Proximity Sensing Robotic Skins –Safe Transient Contact in Compliance to ISO TS 15066: Power and Force Limiting (PFL)

description: Supplementary Data for Publication "A Safety Benchmark for Capacitive Proximity Sensing Robotic Skins - Safe Transient Contact in Compliance to ISO TS 15066: Power and Force Limiting (PFL) Disclaimer: This publication was submitted and is currently under review to be published for IEEE RA-L with ICRA 2021 option. Fundings: The research leading to this results has received fundingfrom the ”Kärntner Wirtschaftsförderung Fonds” (KWF) and the ”European Regional Development Fund” (EFRE) within the CapSize project 26616/30969/44253 Preliminary version of the abstract: During co-presence of human workers and robotsin the same workspace, Power and Force Limiting (PFL)according to ISO TS 15066 is required to avoid injuries duringundesired contacts. Capacitive Proximity Sensors (CPSs) offer acost-effective solution to cover the entire robot manipulator withfast close-range perception for Human Robot Collaboration(HRC) tasks.Compared to pure tactile or force sensing, the sensors allowan increase of operating speeds of cobots while still maintainingPFL. The robot reacts before impacts occur and does not sufferfrom occlusion, as the sensors close the perception gap left bytactile detection and mid range distance sensors. However, sincecapacitive coupling to obstacles varies not only with distance,but also with shape and material properties, the projectionfrom capacitance to actual distances is a general problem. Areference benchmark is required for the evaluation to ensuresafe PFL.We derive the requirements described in ISO TS 15066for PFL of critical body regions and propose a method fordetermining the pre-detection operation speed to comply withPFL. Researchers from four different institutes present threedifferent designs of capacitive sensors and their applicabilityon different robots. We propose a universal benchmark testprocedure for fellow researchers to evaluate their capacitivesensors for safe PFL
DOI: 10.6084/m9.figshare.13093091.v1
title: PyMC3 PROBPROG example

description: InferenceData containing results of the radon model generated with PyMC3 and ArviZ
DOI: 10.6084/m9.figshare.13093091
title: PyMC3 PROBPROG example

description: InferenceData containing results of the radon model generated with PyMC3 and ArviZ