cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/bnpw-bw51
title: ExoNet Database: Wearable Camera Images of Human Locomotion Environments

description: Recent advances in computer vision and artificial intelligence have allowed researchers to develop environment recognition systems for robotic lower-limb exoskeletons and prostheses. However, inadequate and private training datasets have impeded the widespread development and dissemination of image classification algorithms for environment recognition, respectively. To address these shortcomings, we developed “ExoNet” - the first open-source, large-scale hierarchical database of high-resolution wearable camera images of human locomotion environments. Unparalleled in both scale and diversity, ExoNet comprises over 5.6 million images of different indoor and outdoor real-world walking environments, which were collected using a lightweight wearable smartphone camera system throughout the summer, fall, and winter seasons. Approximately 940,000 images in ExoNet were human-annotated using a 12-class hierarchical labelling architecture. Available publicly through the IEEE DataPort repository, ExoNet offers an unprecedented communal platform for training, developing, and comparing image classification algorithms (e.g., convolutional neural networks) for next-generation environment recognition systems. Beyond the control of lower-limb exoskeletons and prostheses, applications of ExoNet extend to humanoid and autonomous legged robotics.Reference: Laschowski B, McNally W, Wong A, and McPhee J. (2020). ExoNet Database: Wearable Camera Images of Human Locomotion Environments. Under Review.
DOI: 10.21227/msdj-9764
title: ExoNet Database: Wearable Camera Images of Human Locomotion Environments

description: Abstract: Recent advances in computer vision and artificial intelligence have allowed researchers to develop environment recognition systems for robotic lower-limb exoskeletons and prostheses. However, inadequate and private training datasets have impeded the widespread development and dissemination of image classification algorithms for environment recognition, respectively. To address these shortcomings, we developed “ExoNet” - the first open-source, large-scale hierarchical database of high-resolution wearable camera images of human locomotion environments. Unparalleled in both scale and diversity, ExoNet comprises over 5.6 million images of different indoor and outdoor real-world walking environments, which were collected using a lightweight wearable smartphone camera system throughout the summer, fall, and winter seasons. Approximately 940,000 images in ExoNet were human-annotated using a 12-class hierarchical labelling architecture. Available publicly through the IEEE DataPort repository, ExoNet offers an unprecedented communal platform for training, developing, and comparing image classification algorithms (e.g., convolutional neural networks) for next-generation environment recognition systems. Beyond the control of lower-limb exoskeletons and prostheses, applications of ExoNet extend to humanoid and autonomous legged robotics.Reference: Laschowski B, McNally W, Wong A, and McPhee J. (2020). ExoNet Database: Wearable Camera Images of Human Locomotion Environments. Under Review.
DOI: 10.5281/zenodo.3750812
title: Effects of thiol substitution in low-transition-temperature mixtures (LTTMs) as solvents for metal oxides

description: This study deals with an investigation of how substitution of an alcohol group by a thiol group in mixtures of choline chloride with a series of bio-sourceable molecules affects the physico-chemical properties of the mixtures and their ability to dissolve metal oxides. All of the thiol mixtures studied showed a higher affinity and selectivity for late transition metals and the physical properties of the mixtures were improved compared to their alcohol analogues (<em>i.e.</em> lower viscosity, wider liquid range). The metal solubility was assessed<em> via </em>determinination of the final concentration of the metal oxides dissolved in thiol mixtures<em> via </em>inductively coupled plasma optical emission spectroscopy (ICP-OES). The thiol function selectively improved the solubilities of the late transition metal oxides (<em>i.e.</em> copper and zinc), which are valuable metals often present as residue in industrial waste. The solubility of iron oxides was much lower than that of the valuable metals, which is a significant benefit in industrial applications. The different solubilization behaviour of metal oxides in the thiol and alcohol mixtures was further investigated<em> via </em>UV-vis absorption and infrared spectroscopy. This study allowed to assess the potential of these low-temperature-transition mixtures for the selective recovery of metals.
description: Raw data in attachment to the article.
DOI: 10.5281/zenodo.3750813
title: Effects of thiol substitution in low-transition-temperature mixtures (LTTMs) as solvents for metal oxides

description: This study deals with an investigation of how substitution of an alcohol group by a thiol group in mixtures of choline chloride with a series of bio-sourceable molecules affects the physico-chemical properties of the mixtures and their ability to dissolve metal oxides. All of the thiol mixtures studied showed a higher affinity and selectivity for late transition metals and the physical properties of the mixtures were improved compared to their alcohol analogues (<em>i.e.</em> lower viscosity, wider liquid range). The metal solubility was assessed<em> via </em>determinination of the final concentration of the metal oxides dissolved in thiol mixtures<em> via </em>inductively coupled plasma optical emission spectroscopy (ICP-OES). The thiol function selectively improved the solubilities of the late transition metal oxides (<em>i.e.</em> copper and zinc), which are valuable metals often present as residue in industrial waste. The solubility of iron oxides was much lower than that of the valuable metals, which is a significant benefit in industrial applications. The different solubilization behaviour of metal oxides in the thiol and alcohol mixtures was further investigated<em> via </em>UV-vis absorption and infrared spectroscopy. This study allowed to assess the potential of these low-temperature-transition mixtures for the selective recovery of metals.
description: Raw data in attachment to the article.
DOI: 10.18716/nmrshiftdb2/77290
title: NMR data for 1,2,3,4,5,6-hexahydro-1,6-benzodiazocine
DOI: 10.21227/8x0j-d116
title: Wearable RGB Camera Images of Human Locomotion Environments

description: This RGB image dataset was collected using a wearable camera system and intended for image classification of different walking environments conducive to controlling lower-limb prostheses and exoskeletons. Approximately 34,254 images were collected and individually labelled.Reference: Laschowski B, McNally W, McPhee J, and Wong A. (2019). Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons. IEEE International Conference on Rehabilitation Robotics, pp. 868-873. DOI: 10.1109/ICORR.2019.8779540.
DOI: 10.21227/j838-m694
title: Wearable RGB Camera Images of Human Locomotion Environments

description: Abstract: This RGB image dataset was collected using a wearable camera system and intended for image classification of different walking environments conducive to controlling lower-limb prostheses and exoskeletons. Approximately 34,254 images were collected and individually labelled. Reference: Laschowski B, McNally W, McPhee J, and Wong A. (2019). Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons. IEEE International Conference on Rehabilitation Robotics, pp. 868-873. DOI: 10.1109/ICORR.2019.8779540.
DOI: 10.5281/zenodo.3765462
title: LG-covid19-HOTP: Literature Graph of Scholarly Articles Relevant to COVID-19 Study

description: Parallel to the dataset CORD-19 of scholarly articles, we provide the literature graph LG-covid19-HOTP composed of not only articles (graph nodes) that are relevant to the study of coronavirus, but also in and out citation links (directed graph edges) to base navigation and search among the articles. The article records are related and connected, not isolated. The graph has been updated weekly since March 26, 2020. The current graph includes <strong>9,371</strong> hot-off-the-press (HOTP) articles since January 2020. It contains <strong>129,968</strong> articles and <strong>1,074,305</strong> links. The link-to-node ratio is remarkably higher than some other existing literature graphs. In addition to the dataset we provide more functionalities at lg-covid-19-hotp.cs.duke.edu such as new articles, weekly meta-data analysis in terms of publication growth over time, ranking by citation, and statistical near-neighbor embedding maps by similarity in co-citation, and similarity in co-reference. Since April 11, we have enabled a novel functionality - self-navigated surf-search over the maps. At the site we also take courtesy input of COVID-19 articles that are missing from the current collection.
description: {"references": ["Semantic Scholar Open Research Corpus. 2019. Version 2019-11-01. Retrieved from http://s2-public-api-prod.us-west-2.elasticbeanstalk.com/corpus/download/. Accessed 2019-12-06.", "Elsevier Scopus Citation Overview API. Accessed 2020-03-25.", "COVID-19 Open Research Dataset (CORD-19). 2020. Version 2020-03-20. Retrieved from https://pages.semanticscholar.org/coronavirus-research. Accessed 2020-03-26. 10.5281/zenodo.3727291", "Crossref REST API. Available at www.crossref.org. Accessed 2020-03-25."]}
DOI: 10.7910/dvn/ttmuzk/rtmpq9
title: sumstats.do

description: Stata 16 script producing table S.1 in the supplement (summary demographic and other background information)
DOI: 10.21227/6zsj-5h98
title: Wearable RGB Camera Images of Human Locomotion Environments

description: This RGB image dataset was collected using a wearable camera system and intended for image classification of different walking environments conducive to controlling lower-limb prostheses and exoskeletons. Approximately 34,254 images were collected and individually labelled.Reference: Laschowski B, McNally W, McPhee J, and Wong A. (2019). Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons. IEEE International Conference on Rehabilitation Robotics, pp. 868-873. DOI: 10.1109/ICORR.2019.8779540.
DOI: 10.5281/zenodo.3765215
title: Linked COVID-19 Data: Johns Hopkins University (JHU) and European Centre for Disease Prevention and Control (ECDC)

description: Linked COVID-19 Data derived from <strong>Johns Hopkins University</strong> and <strong>European Centre for Disease Prevention and Control</strong> using the COVID-19 Ontology 10.5281/zenodo.3757828 developed for the Linked COVID-19 Data Dashboard: http://covid19data.link This files include data for covid19.ttl - COVID-19 data collected by the JHU and ECDC This RDF files are based on https://pomber.github.io/covid19/timeseries.json https://opendata.ecdc.europa.eu/covid19/casedistribution/json/
DOI: 10.6073/pasta/e972edd8bdeca67b3c6d54ce62eb847b
title: Lake chloride concentrations and model predictions for 49,432 lakes in the Midwest and Northeast United States.

description: Lakes in the Midwest and Northeast United States are at risk of anthropogenic chloride contamination, but we have little knowledge of the prevalence and spatial distribution of the problem. The majority of salt pollution in north temperate regions stems from road salt application but other chloride sources include water softeners, synthetic fertilizers, and livestock excretion. Although chloride contamination of lakes is well documented, it is unknown how many lakes are at risk of long-term salinization. We used a quantile regression forest to leverage information from 2,773 lakes to predict the chloride concentration of all 49,432 lakes greater than 4 ha in a 17-state area. The QRF used 22 predictor variables, which included lake morphometry characteristics, watershed land use, and distance to the nearest interstate and road. Model predictions had an r2 of 0.94 for all chloride observations, and 0.87 for predictions of the mean chloride concentration observed at each lake.
DOI: 10.7910/dvn/ttmuzk/btustv
title: readme.rtf

description: Read me!
DOI: 10.15784/601294
title: SOAR-WMB Airborne gravity data

description: Free air gravity anomaly data collected along flight tracks of the Western Marie Byrd Land and Ross Sea Boundary
DOI: 10.18716/nmrshiftdb2/88201
title: NMR data for 1-(bromomethyl)-3-nitrobenzene
DOI: 10.18716/nmrshiftdb2/88394
title: NMR data for 1-benzhydryl-1,2,4-triazole
DOI: 10.21227/k008-8p22
title: Wearable RGB Camera Images of Human Locomotion Environments

description: Drawing inspiration from autonomous vehicles, using future environment information could improve the control of wearable biomechatronic devices for assisting human locomotion. To the authors knowledge, this research represents the first documented investigation using machine vision and deep convolutional neural networks for environment recognition to support the predictive control of robotic lower-limb prostheses and exoskeletons. One participant was instrumented with a battery-powered, chest-mounted RGB camera system. Approximately 10 hours of video footage were experimentally collected while ambulating throughout unknown outdoor and indoor environments. The sampled images were preprocessed and individually labelled. A deep convolutional neural network was developed and trained to automatically recognize three walking environments: level-ground, incline staircases, and decline staircases. The environment recognition system achieved 94.85% overall image classification accuracy. Extending these preliminary findings, future research should incorporate other environment classes (e.g., incline ramps) and integrate the environment recognition system with electromechanical sensors and/or surface electromyography for automated locomotion mode recognition. The challenges associated with implementing deep learning on wearable biomechatronic devices are discussed.Reference: Laschowski B, McNally W, McPhee J, and Wong A. (2019). Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons. IEEE International Conference on Rehabilitation Robotics, pp. 868-873. DOI: 10.1109/ICORR.2019.8779540.
DOI: 10.21227/y1m4-3695
title: Wearable RGB Camera Images of Human Locomotion Environments

description: Drawing inspiration from autonomous vehicles, using future environment information could improve the control of wearable biomechatronic devices for assisting human locomotion. To the authors knowledge, this research represents the first documented investigation using machine vision and deep convolutional neural networks for environment recognition to support the predictive control of robotic lower-limb prostheses and exoskeletons. One participant was instrumented with a battery-powered, chest-mounted RGB camera system. Approximately 10 hours of video footage were experimentally collected while ambulating throughout unknown outdoor and indoor environments. The sampled images were preprocessed and individually labelled. A deep convolutional neural network was developed and trained to automatically recognize three walking environments: level-ground, incline staircases, and decline staircases. The environment recognition system achieved 94.85% overall image classification accuracy. Extending these preliminary findings, future research should incorporate other environment classes (e.g., incline ramps) and integrate the environment recognition system with electromechanical sensors and/or surface electromyography for automated locomotion mode recognition. The challenges associated with implementing deep learning on wearable biomechatronic devices are discussed.Reference: Laschowski B, McNally W, McPhee J, and Wong A. (2019). Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons. IEEE International Conference on Rehabilitation Robotics, pp. 868-873. DOI: 10.1109/ICORR.2019.8779540.
DOI: 10.5281/zenodo.3765219
title: Linked COVID-19 Data: Robert Koch-Institut (RKI)

description: Linked COVID-19 Data derived from <strong>Robert Koch Institute</strong> using the COVID-19 Ontology 10.5281/zenodo.3757828 developed for the Linked COVID-19 Data Dashboard: http://covid19data.link This files include data for covid19_rki*.ttl - single COVID-19 cases per day, collected by the RKI https://www.arcgis.com/home/item.html?id=dd4580c810204019a7b8eb3e0b329dd6 "confirmed" is calcaulated by the column "Refdatum" covid19_rki_cum.ttl - COVID-19 cases per day for Germany, collected by the RKI covid19_rki_fs.ttl - COVID-19 cases per day for the federal states in Germany, collected by the RKI This RDF files are based on the NPGEO Corona Hub 2020 https://opendata.arcgis.com/datasets/dd4580c810204019a7b8eb3e0b329dd6_0.geojson https://npgeo-corona-npgeo-de.hub.arcgis.com/datasets/dd4580c810204019a7b8eb3e0b329dd6_0/data
DOI: 10.18716/nmrshiftdb2/88200
title: NMR data for 1-(chloromethyl)-3-nitrobenzene
DOI: 10.5281/zenodo.3765196
title: Diamond Throne image sequence [chapter 01] 23/04/2020

description: <em>Precious Treasures from the Diamond Throne</em> (London: British Museum Press, 2020), image sequence 23/04/2020.
DOI: 10.5281/zenodo.3765208
title: Data set | Geoelectrical and transient electromagnetic surveys at Viveros de Netzahualcóyotl in Xochimilco, Mexico City, Mexico

description: This repository contains geophysical raw data collected during two field campaigns in 2016 and 2017 at Viveros de Netzahualcóyotl in Xochimilco, Mexico City, Mexico. The data set includes raw data analysed for the first case study discussed in the following publication Bücker, M., Lozano-Garcia. S., Ortega-Guerrero, B., Caballero-Miranda, M., Pérez, L., Caballero, L., Pita de la Paz, C., Sánchez-Galindo, A., Jesús Villegas, F., Flores Orozco, A., Brown, E., Werne, J., Valero Garcés, B., Schwalb, A., Kemna, A., Sánchez-Alvaro, E., Launizar-Martínez, N., Valverde-Placencia, A., Garay-Jiménez, F. (2017). <em><strong>Geoelectrical and Electromagnetic Methods Applied to Paleolimnological Studies: Two Examples from Desiccated Lakes in the Basin of Mexico</strong></em>. Boletín de la Sociedad Geológica Mexicana, 69(2), 279-298, http://dx.doi.org/10.18268/bsgm2017v69n2a1. If you find our data useful in your own research, please mention this data set and/or the manuscript. The data are provided under the Creative Commons Attribution 4.0 International license.
DOI: 10.5281/zenodo.3765209
title: Data set | Geoelectrical and transient electromagnetic surveys at Viveros de Netzahualcóyotl in Xochimilco, Mexico City, Mexico

description: This repository contains geophysical raw data collected during two field campaigns in 2016 and 2017 at Viveros de Netzahualcóyotl in Xochimilco, Mexico City, Mexico. The data set includes raw data analysed for the first case study discussed in the following publication Bücker, M., Lozano-Garcia. S., Ortega-Guerrero, B., Caballero-Miranda, M., Pérez, L., Caballero, L., Pita de la Paz, C., Sánchez-Galindo, A., Jesús Villegas, F., Flores Orozco, A., Brown, E., Werne, J., Valero Garcés, B., Schwalb, A., Kemna, A., Sánchez-Alvaro, E., Launizar-Martínez, N., Valverde-Placencia, A., Garay-Jiménez, F. (2017). <em><strong>Geoelectrical and Electromagnetic Methods Applied to Paleolimnological Studies: Two Examples from Desiccated Lakes in the Basin of Mexico</strong></em>. Boletín de la Sociedad Geológica Mexicana, 69(2), 279-298, http://dx.doi.org/10.18268/bsgm2017v69n2a1. If you find our data useful in your own research, please mention this data set and/or the manuscript. The data are provided under the Creative Commons Attribution 4.0 International license.
DOI: 10.5281/zenodo.3765864
title: SullivanKokKatraYizhaq_MarsLargeRipplesArchive

description: This archive supports reviewer evaluation of Journal of Geophysical Research-Planets manuscript 2020JE006485 entitled "A broad continuum of aeolian impact ripple morphologies on Mars is enabled by low wind dynamic pressures" by authors R. Sullivan, J. F. Kok, I. Katra, and H. Yizhaq. After review and before publication, any revisions recommended by JGR-Planets will be incorporated into this archive as needed, then posted in final form without restriction (possibly to a fresh DOI, to avoid confusion).
DOI: 10.21227/7491-gr45
title: Machine Learning for RSS-Based Visible Light Positioning

description: Visible Light Positioning is an indoor localization technology that uses wireless transmission of visible light signals to obtain a location estimate of a mobile receiver.This dataset can be used to validate supervised machine learning approaches in the context of Received Signal Strength Based Visible Light Positioning.The set is acquired in an experimental setup that consists of 4 LED transmitter beacons and a photodiode as receiving element that can move in 2D.The dataset contains, as features, the demultiplexed received light intensities of the 4 beacons and, as target vectors, the associated measurement locations.
DOI: 10.21227/f28n-6292
title: Machine Learning for RSS-Based Visible Light Positioning

description: Visible Light Positioning is an indoor localization technology that uses wireless transmission of visible light signals to obtain a location estimate of a mobile receiver.This dataset can be used to validate supervised machine learning approaches in the context of Received Signal Strength Based Visible Light Positioning.The set is acquired in an experimental setup that consists of 4 LED transmitter beacons and a photodiode as receiving element that can move in 2D.The dataset contains, as features, the demultiplexed received light intensities of the 4 beacons and, as target vectors, the associated measurement locations.
DOI: 10.5281/zenodo.3766276
title: Realistic modeling of mesoscopic ephaptic coupling in the human brain

description: Comsol models with E-field distributions generated by dipole sources in a realistic head model and a stylized 'toy' model representing a sulcus.
DOI: 10.5281/zenodo.3766277
title: Realistic modeling of mesoscopic ephaptic coupling in the human brain

description: Comsol models with E-field distributions generated by dipole sources in a realistic head model and a stylized 'toy' model representing a sulcus.
DOI: 10.18716/nmrshiftdb2/77287
title: NMR data for (2R,3S)-2-ethyl-3-methyl-2,3-dihydro-1H-1-benzazepine
DOI: 10.5281/zenodo.3764734
title: Fractal analysis of muscle activity patterns during locomotion: pitfalls and how to avoid them

description: Despite the lack of consensus on how to perform fractal analysis of physiological time series, many studies rely on this technique. Here, we shed light on the potential pitfalls of using the Higuchi’s fractal dimension (HFD) and the Hurst exponent (H). We expose and suggest how to solve the drawbacks of such methods when applied to data from normal and perturbed locomotion by combining <em>in vivo</em> recordings and computational approaches. In this supplementary data set we made available: a) the metadata file “metadata.dat”; b) the baseline signals file “baseline_data.RData”; c) the R script “fractal_analysis.R” to calculate the H and HFD of the baseline data and produce the log-log plots. Explanatory comments are profusely present throughout the script and in the metadata file.
DOI: 10.15468/6dugp2
title: Squamarina (lichenised fungi) species described from China belong to at least three unrelated genera

description: This dataset contains the digitized treatments in Plazi based on the original journal article Zhang, Yan-Yun, Wang, Xin-Yu, Li, Li-Juan, Printzen, Christian, Timdal, Einar, Niu, Dong-Ling, Yin, An-Cheng, Wang, Shi-Qiong, Wang, Li-Song (2020): Squamarina (lichenised fungi) species described from China belong to at least three unrelated genera. MycoKeys 66: 135-157, DOI: http://dx.doi.org/10.3897/mycokeys.66.39057, URL: http://dx.doi.org/10.3897/mycokeys.66.39057