cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.9962144.v2
title: The development of a simulator-based intervention to rehabilitate driving skills in people with acquired brain injury

description: With little to guide researchers and clinicians on how best to develop driving simulator interventions for ABI survivors, we aimed to describe the development process of a driving simulator intervention for ABI survivors in a rehabilitation setting. Intervention mapping methodology was used as a framework for the development of our driving simulator intervention. A qualitative synthesis of theoretical and empirical literature and stakeholder meetings enabled identification of factors affecting return to driving, selection of justifiable intervention goals, and identification of appropriate theoretically-informed techniques to facilitate change. These were used as a basis for design of intervention components and materials. A plan for delivery, implementation and evaluation was then developed. Determinants of driving ability, including knowledge and skills, self-efficacy, self-awareness of driving skills, awareness of risk and compensatory strategies were identified. These were applied to a range of tactical and operational driving behaviours to identify targets for change. Theoretically-informed strategies included direct instruction, repetition, graded difficulty, feedback and tailoring. An eight-session protocol, with a corresponding clinical manual, was developed for brain-injured patients who were referred for occupational therapy driving assessment. Protocols for recruitment, inclusion/exclusion criteria and facilitator training were developed, as well as a plan for evaluating feasibility, acceptability and effectiveness. Intervention mapping was a useful approach to systematically develop an intervention tailored to the rehabilitation hospital context to complement existing driver rehabilitation. The feasibility and effectiveness of the simulator programme developed in this study will be evaluated in future studies.IMPLICATIONS FOR REHABILITATIONWe were able to gather important information and provide recommendations to tailor a new driving simulator intervention for individuals with acquired brain injury within a rehabilitation service.The processes and methods described provide researchers and clinicians with a systematic process for the selection of driving simulator intervention components and delivery.This investigation can be used to educate rehabilitation clinicians and technicians to improve driver training and delivery to acquired brain injury survivors. We were able to gather important information and provide recommendations to tailor a new driving simulator intervention for individuals with acquired brain injury within a rehabilitation service. The processes and methods described provide researchers and clinicians with a systematic process for the selection of driving simulator intervention components and delivery. This investigation can be used to educate rehabilitation clinicians and technicians to improve driver training and delivery to acquired brain injury survivors.
DOI: 10.6084/m9.figshare.9962144.v1
title: The development of a simulator-based intervention to rehabilitate driving skills in people with acquired brain injury

description: <b>Purpose:</b> With little to guide researchers and clinicians on how best to develop driving simulator interventions for ABI survivors, we aimed to describe the development process of a driving simulator intervention for ABI survivors in a rehabilitation setting. <b>Method:</b> Intervention mapping methodology was used as a framework for the development of our driving simulator intervention. A qualitative synthesis of theoretical and empirical literature and stakeholder meetings enabled identification of factors affecting return to driving, selection of justifiable intervention goals, and identification of appropriate theoretically-informed techniques to facilitate change. These were used as a basis for design of intervention components and materials. A plan for delivery, implementation and evaluation was then developed. <b>Results:</b> Determinants of driving ability, including knowledge and skills, self-efficacy, self-awareness of driving skills, awareness of risk and compensatory strategies were identified. These were applied to a range of tactical and operational driving behaviours to identify targets for change. Theoretically-informed strategies included direct instruction, repetition, graded difficulty, feedback and tailoring. An eight-session protocol, with a corresponding clinical manual, was developed for brain-injured patients who were referred for occupational therapy driving assessment. Protocols for recruitment, inclusion/exclusion criteria and facilitator training were developed, as well as a plan for evaluating feasibility, acceptability and effectiveness. <b>Conclusions:</b> Intervention mapping was a useful approach to systematically develop an intervention tailored to the rehabilitation hospital context to complement existing driver rehabilitation. The feasibility and effectiveness of the simulator programme developed in this study will be evaluated in future studies.IMPLICATIONS FOR REHABILITATIONWe were able to gather important information and provide recommendations to tailor a new driving simulator intervention for individuals with acquired brain injury within a rehabilitation service.The processes and methods described provide researchers and clinicians with a systematic process for the selection of driving simulator intervention components and delivery.This investigation can be used to educate rehabilitation clinicians and technicians to improve driver training and delivery to acquired brain injury survivors. We were able to gather important information and provide recommendations to tailor a new driving simulator intervention for individuals with acquired brain injury within a rehabilitation service. The processes and methods described provide researchers and clinicians with a systematic process for the selection of driving simulator intervention components and delivery. This investigation can be used to educate rehabilitation clinicians and technicians to improve driver training and delivery to acquired brain injury survivors.
DOI: 10.5517/ccdc.csd.cc277sgr
title: CCDC 2063064: Experimental Crystal Structure Determination

description: Related Article: Erlin Yue, Furong Cao, Jun Zhang, Wenjuan Zhang, Youshu Jiang, Tongling Liang, Wen-Hua Sun|2021|RSC Advances|11|13274|doi:10.1039/D1RA01288F
DOI: 10.5517/ccdc.csd.cc277shs
title: CCDC 2063065: Experimental Crystal Structure Determination

description: Related Article: Erlin Yue, Furong Cao, Jun Zhang, Wenjuan Zhang, Youshu Jiang, Tongling Liang, Wen-Hua Sun|2021|RSC Advances|11|13274|doi:10.1039/D1RA01288F
DOI: 10.5517/ccdc.csd.cc277sjt
title: CCDC 2063066: Experimental Crystal Structure Determination

description: Related Article: Erlin Yue, Furong Cao, Jun Zhang, Wenjuan Zhang, Youshu Jiang, Tongling Liang, Wen-Hua Sun|2021|RSC Advances|11|13274|doi:10.1039/D1RA01288F
DOI: 10.5517/ccdc.csd.cc277x6m
title: CCDC 2063180: Experimental Crystal Structure Determination

description: Related Article: Erlin Yue, Furong Cao, Jun Zhang, Wenjuan Zhang, Youshu Jiang, Tongling Liang, Wen-Hua Sun|2021|RSC Advances|11|13274|doi:10.1039/D1RA01288F
DOI: 10.5517/ccdc.csd.cc26g02s
title: CCDC 2039244: Experimental Crystal Structure Determination

description: Related Article: Kenta Kato, Kiyofumi Takaba, Saori Maki-Yonekura, Nobuhiko Mitoma, Yusuke Nakanishi, Taishi Nishihara, Taito Hatakeyama, Takuma Kawada, Yuh Hijikata, Jenny Pirillo, Lawrence T. Scott, Koji Yonekura, Yasutomo Segawa, Kenichiro Itami|2021|J.Am.Chem.Soc.|143|5465|doi:10.1021/jacs.1c00863
DOI: 10.6084/m9.figshare.14478096
title: Script Data

description: Allotofdata
DOI: 10.5281/zenodo.4096035
title: Body-fit and Body-rgb (NOMO-3d-4K-scans)

description: For details, see <strong>Silhouette Body Measurement Benchmarks</strong> (ICPR2020). <strong>We did not upload the data here, please contact the authors to access the dataset.</strong> The scans are collected by NOMO Technologies (https://nomo3d.com/) Ltd, Finland. We scanned 1474 male and 2675 female 3D body scans (<strong>BODY-fit</strong>), and captured a realistic RGB dataset (<strong>BODY-rgb</strong>) of 86 male and 108 female subjects and manually tape measured ground truth. This <em>database</em> is only used for scientific purpose. Companies need to contact NOMO Technologies for commercial purpose. -----------------------------------<br> <em>The database can be freely donwloaded and used for scientific research purposes. You are not allowed redistribute or modify it. Proper citing of this resource is expected if the database is used in research or other reporting. </em> <em>The database is meant to be useful, but it is distributed WITHOUT ANY WARRANTY, and without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.</em> <em>Inclusion of this database or even parts of it in a proprietary program is not allowed without a written permission from the owners of the copyright. If you wish to obtain such a permission, you should contact:</em> <strong>Sizey Ltd</strong> <strong> Company ID: 2642975-3</strong> <strong> Company address: Metsänneidonkuja 6, 02130 Espoo Finland</strong> <strong> Contact e-mail: sizey (at) sizey.ai</strong> OR: <strong>Song Yan</strong>, <em>song.yan at tuni.fi</em> <strong>Joni-Kristian Kämäräinen</strong>, <em>joni.kamarainen at tuni.fi</em> <strong>Johan Wirta</strong>, <em>johan.wirta at nomo3d.com</em>
DOI: 10.14272/ztnyoevvfzjzrl-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) (3-octoxyquinoxalin-2-amine)
title: InChI=1S/C16H23N3O/c1-2-3-4-5-6-9-12-20-16-15(17)18-13-10-7-8-11-14(13)19-16/h7-8,10-11H,2-6,9,12H2,1H3,(H2,17,18)

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (100 MHz, CDCl3, ppm) δ = 14.2, 22.8, 26.2, 28.8, 29.4, 29.5, 31.9, 67.5, 124.1, 125.4, 126.7, 127.2, 135.9, 136.3, 145.0, 148.6.
DOI: 10.5281/zenodo.4716579
title: Dataset for "A simple criterion and experiments for onset of flocculation in kaolin clay suspensions"

description: This file includes the datasets and MATLAB scripts used to produce the figures presented within "A simple criterion and experiments for onset of flocculation in kaolin clay suspensions". After extracting the .zip file, please run Fig_4.m to reproduce Figure 4, run Figs_5_and_6.m to reproduce Figures 5 and 6, and run Fig_8.m to reproduce Figure 8.
DOI: 10.5281/zenodo.3931090
title: Dataset for "A simple criterion and experiments for onset of flocculation in kaolin clay suspensions"

description: This file includes the datasets and MATLAB scripts used to produce the figures presented within "A simple criterion and experiments for onset of flocculation in kaolin clay suspensions". After extracting the .zip file, please run Fig_4.m to reproduce Figure 4, run Figs_5_and_6.m to reproduce Figures 5 and 6, and run Fig_8.m to reproduce Figure 8.
DOI: 10.6084/m9.figshare.14478096.v1
title: Script Data

description: Allotofdata
DOI: 10.5281/zenodo.4716654
title: Herseninstituut/FYD_Datasets: PersistenActivityV1 added

description: Example data set for testing purposes
DOI: 10.5281/zenodo.4592249
title: On the Role of Images for Analyzing Claims in Social Media

description: This is a multimodal dataset used in the paper "On the Role of Images for Analyzing Claims in Social Media", accepted at CLEOPATRA-2021 (2nd International Workshop on Cross-lingual Event-centric Open Analytics), co-located with The Web Conference 2021.<br> <br> The four datasets are curated for two different tasks that broadly come under fake news detection. Originally, the datasets were released as part of challenges or papers for text-based NLP tasks and are further extended here with corresponding images. 1. <em><strong>clef_en</strong></em> and <em><strong>clef_ar</strong></em> are English and Arabic Twitter datasets for claim check-worthiness detection released in CLEF CheckThat! 2020 <em>Barr</em>ó<em>n-Cedeno et al.</em><sup> [1]</sup>.<br> 2. <strong><em>lesa</em></strong> is an English Twitter dataset for claim detection released by <em>Gupta et al.</em><sup>[2]</sup><br> 3. <strong><em>mediaeval</em></strong><em> </em>is an English Twitter dataset for conspiracy detection released in MediaEval 2020 Workshop by <em>Pogorelov et al.</em><sup>[3]</sup> The dataset details like data curation and annotation process can be found in the cited papers.<br> <br> Datasets released here with corresponding images are relatively smaller than the original text-based tweets. The data statistics are as follows:<br> 1. <strong><em>clef_en</em></strong>: 281<br> 2. <em><strong>clef_ar</strong></em>: 2571<br> 3. <strong><em>lesa</em></strong>: 1395<br> 4. <em><strong>mediaeval</strong></em>: 1724<br> <br> Each folder has two sub-folders and a json file <em>data.json</em> that consists of crawled tweets. Two sub-folders are:<br> 1. <em>images</em>: This Contains crawled images with the same name as tweet-id in <em>data.json</em>.<br> 2. <em>splits</em>: This contains 5-fold splits used for training and evaluation in our paper. Each file in this folder is a csv with two columns <em>&lt;tweet-id, label&gt;.</em> Code for the paper: https://github.com/cleopatra-itn/image_text_claim_detection If you find the dataset and the paper useful, please cite our paper and the corresponding dataset papers<sup>[1,2,3]</sup><br> <strong>Cheema, Gullal S., et al. "On the Role of Images for Analyzing Claims in Social Media" </strong><em>2<sup>nd</sup> International Workshop on Cross-lingual Event-centric Open Analytics (CLEOPATRA) co-located with The Web Conf 2021.</em> [1] Barrón-Cedeno, Alberto, et al. "Overview of CheckThat! 2020: Automatic identification and verification of claims in social media." <em>International Conference of the Cross-Language Evaluation Forum for European Languages</em>. Springer, Cham, 2020.<br> [2] Gupta, Shreya, et al. "LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content." <em>arXiv preprint arXiv:2101.11891</em> (2021).<br> [3] Pogorelov, Konstantin, et al. "FakeNews: Corona Virus and 5G Conspiracy Task at MediaEval 2020." <em>MediaEval 2020 Workshop</em>. 2020.
DOI: 10.5281/zenodo.4592248
title: On the Role of Images for Analyzing Claims in Social Media

description: This is a multimodal dataset used in the paper "On the Role of Images for Analyzing Claims in Social Media", accepted at CLEOPATRA-2021 (2nd International Workshop on Cross-lingual Event-centric Open Analytics), co-located with The Web Conference 2021.<br> <br> The four datasets are curated for two different tasks that broadly come under fake news detection. Originally, the datasets were released as part of challenges or papers for text-based NLP tasks and are further extended here with corresponding images. 1. <em><strong>clef_en</strong></em> and <em><strong>clef_ar</strong></em> are English and Arabic Twitter datasets for claim check-worthiness detection released in CLEF CheckThat! 2020 <em>Barr</em>ó<em>n-Cedeno et al.</em><sup> [1]</sup>.<br> 2. <strong><em>lesa</em></strong> is an English Twitter dataset for claim detection released by <em>Gupta et al.</em><sup>[2]</sup><br> 3. <strong><em>mediaeval</em></strong><em> </em>is an English Twitter dataset for conspiracy detection released in MediaEval 2020 Workshop by <em>Pogorelov et al.</em><sup>[3]</sup> The dataset details like data curation and annotation process can be found in the cited papers.<br> <br> Datasets released here with corresponding images are relatively smaller than the original text-based tweets. The data statistics are as follows:<br> 1. <strong><em>clef_en</em></strong>: 281<br> 2. <em><strong>clef_ar</strong></em>: 2571<br> 3. <strong><em>lesa</em></strong>: 1395<br> 4. <em><strong>mediaeval</strong></em>: 1724<br> <br> Each folder has two sub-folders and a json file <em>data.json</em> that consists of crawled tweets. Two sub-folders are:<br> 1. <em>images</em>: This Contains crawled images with the same name as tweet-id in <em>data.json</em>.<br> 2. <em>splits</em>: This contains 5-fold splits used for training and evaluation in our paper. Each file in this folder is a csv with two columns <em>&lt;tweet-id, label&gt;.</em> Code for the paper: https://github.com/cleopatra-itn/image_text_claim_detection If you find the dataset and the paper useful, please cite our paper and the corresponding dataset papers<sup>[1,2,3]</sup><br> <strong>Cheema, Gullal S., et al. "On the Role of Images for Analyzing Claims in Social Media" </strong><em>2<sup>nd</sup> International Workshop on Cross-lingual Event-centric Open Analytics (CLEOPATRA) co-located with The Web Conf 2021.</em> [1] Barrón-Cedeno, Alberto, et al. "Overview of CheckThat! 2020: Automatic identification and verification of claims in social media." <em>International Conference of the Cross-Language Evaluation Forum for European Languages</em>. Springer, Cham, 2020.<br> [2] Gupta, Shreya, et al. "LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content." <em>arXiv preprint arXiv:2101.11891</em> (2021).<br> [3] Pogorelov, Konstantin, et al. "FakeNews: Corona Virus and 5G Conspiracy Task at MediaEval 2020." <em>MediaEval 2020 Workshop</em>. 2020.
DOI: 10.3886/e113267v1-84740
title: Replication data for: The Costs of Remoteness: Evidence from German Division and Reunification

description: This paper exploits the division of Germany after the Second World War and the reunification of East and West Germany in 1990 as a natural experiment to provide evidence for the importance of market access for economic development. In line with a standard new economic geography model, we find that, following division, cities in West Germany close to the East-West German border experienced a substantial decline in population growth relative to other West German cities. We show that the model can account for the quantitative magnitude of our findings and provide additional evidence against alternative possible explanations. (JEL F15, N94, R12, R23)
DOI: 10.5519/qd.y0q3gpzg
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (10 records)
DOI: 10.17632/rfs4drbzn8.1
title: de Vries et.al. (2021) - New Phytologist

description: Model code and data published in: de Vries J, Evers JB, Kuyper TW, Ruijven J, Mommer L. 2021. Mycorrhizal associations change root functionality: a 3D modelling study on competitive interactions between plants for light and nutrients. New Phytologist. The model required GroImp version 1.5, for further information please contact the corresponding author.
DOI: 10.17632/k59j8jxskh.1
title: Source code and demo for pyCLAMs.py

description: Supplemental Materials for the article "pyCLAMs: an integrated python toolkit for classifiability analysis" File list: pyCLAMs.py - the main module other py files - dependent modules pyCLAMs demo.pdf - API demo report
DOI: 10.17632/c8h23xms5w.1
title: Fluvial flood risk assessment in rural settlements along the Sirba River in Niger

description: This dataset contains the key information to estimate the fluvial flood risk in the 4 main settlements along the River Sirba, Niger. The hypothesis is that fluvial flood hazard has increased in recent years and that many exposed assets remain in the riparian communities. The data concern: 1) the maximum annual discharge between 1956 and 2020 as measured at the Garbey Kourou hydrometer 2) the location of Garbey Kourou, Larba Birno, Tallé, and Touré 3) the flood hazard map showing three flood probability scenarios with return periods of 10, 30, and 100 years and the River Niger backwater flood zones with return period of 20 years 4) the detailed list of all assets in the three flood zones (houses, barns, latrines, boreholes, wells) with their geographic coordinates and the fenced fields as surveyed in 2018-19 5) the replacement value of exposed assets by settlement and exposed zone 6) the risk level by settlement and zone. These data confirm that catastrophic fluvial flood hazard has greatly increased in the last decade and that several exposed assets persist, despite repeated flooding. This dataset can be used to monitor the dynamics of fluvial flood risk along the Sirba River and to inspire other assessments in similar contexts.
DOI: 10.17632/m6jx255stv.1
title: Data for: Same, but different? A state-level chronology of the 2014-2016 Brazilian economic recession and comparisons with the GFC and (early data on) COVID-19.

description: This is the final dataset to replicate results of the following paper: Colombo, J. A., & Lazzari, M. R. (2020). Same, but different? A state-level chronology of the 2014-2016 Brazilian economic recession and comparisons with the GFC and (early data on) COVID-19''. Economics Bulletin, 40(3), 2445-2456.
DOI: 10.17632/4ysv5stvps.1
title: In vitro detoxification of aflatoxin B1 by aqueous extracts of medicinal herbs

description: Detoxification of aflatoxin B1 by aqueous extracts of medicinal herbs
DOI: 10.17632/9psknxmgfj.1
title: Three novel pathogenic NEK9 variants in patients with nevus comedonicus: a case series

description: Three novel pathogenic NEK9 variants in patients with nevus comedonicus: a case series (Poof version)
DOI: 10.17632/68cdfyssgs.1
title: CESM2-CLM5 Framework for Hindcasting Tropical Mountain Glaciation: Examples and Pre-Industrial Validation Analysis

description: This data archive contains data and code related to simulating tropical mountain glaciation within high-resolution standalone simulations by the Community Land Model version 5 (CLM5) with a data atmosphere created by the Community Earth System Model version 2. The information here is intended allow one to perform simulations of this type as well as evaluate the performance of the CESM2-CLM5 simulation framework for example tropical mountain glaciers under pre-industrial climate conditions. This work was funded by the Sedimentary Geology and Paleobiology program of the National Science Foundation (EAR-1849754).
DOI: 10.25345/c56236
title: MassIVE MSV000087294 - Metabolomics of Phyllidiidae from North Sulawesi, Indonesia
title: Metabolomic investigation of individually extracted Phyllidiidae.
DOI: 10.6084/m9.figshare.3761562.v1848
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.