cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/evgv-x871
title: Anatomic information from phase data from 72 transcranial MRI-guided focused ultrasound clinical treatments

description: Example axial and coronal phase maps and post-treatment MRI from 68 thalamotomies in essential tremor patients and four pallidotomies in Parkinson's disease patients. From the manuscript "Using phase data from MR temperature imaging to visualize anatomy during MRI-guided focused ultrasound neurosurgery" published in 2020 in IEEE Trans. Med. Imaging.
DOI: 10.7910/dvn/1tbgo7/9psncr
title: GAEZAct2015_Yield_Vegetables_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/1tbgo7/dwyihh
title: GAEZAct2015_Yield_Sugarbeet_Irrigated.tif

description: :unav
DOI: 10.7910/dvn/1tbgo7/hf0dlr
title: GAEZAct2015_Yield_Oilpalmfruit_Irrigated.tif

description: :unav
DOI: 10.7910/dvn/1tbgo7/qpnslc
title: GAEZAct2015_Yield_Wheat_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/1tbgo7/qv3zkx
title: GAEZAct2015_Yield_Groundnut_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/1tbgo7/zsaajm
title: GAEZAct2015_Yield_Sunflower_Rainfed.tif

description: :unav
DOI: 10.5281/zenodo.3887285
title: Dummy CT and Structure Set

description: Dummy CT and Structure Set
DOI: 10.5281/zenodo.3887286
title: Dummy CT and Structure Set

description: Dummy CT and Structure Set
DOI: 10.7910/dvn/1tbgo7/5jh3yk
title: GAEZAct2015_Yield_Rapeseed_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/1tbgo7/9tfrqs
title: GAEZAct2015_Yield_Banana_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/1tbgo7/a4kvep
title: GAEZAct2015_Yield_Oilpalmfruit_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/1tbgo7/byb6qd
title: GAEZAct2015_Yield_Rice_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/1tbgo7/eygjdj
title: GAEZAct2015_Yield_Olives_Irrigated.tif

description: :unav
DOI: 10.7910/dvn/1tbgo7/hwlzkk
title: GAEZAct2015_Yield_Soybean_Irrigated.tif

description: :unav
DOI: 10.7910/dvn/1tbgo7/le6dig
title: GAEZAct2015_Yield_Sugarcane_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/1tbgo7/lvdl3i
title: GAEZAct2015_Yield_Olives_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/1tbgo7/m4x73r
title: GAEZAct2015_Yield_Foddercrops_Rainfed.tif

description: :unav
DOI: 10.5257/iea/co2/2020-1
title: CO2 Emissions from Fuel Combustion (April 2020 Edition)

description: In recognition of the fundamental importance of understanding energy-related environmental issues, the IEA CO2 Emissions from Fuel Combustion provides a full analysis of emissions stemming from energy use. This annual publication has become an essential tool for analysts and policy makers in many international fora. The data in this dataset are designed to assist in understanding the evolution of the emissions of CO2 from 1971 for countries and regions by sector and by fuel. Emissions were calculated using IEA energy databases and the default methods and emission factors from the 2006 IPCC Guidelines for National Greenhouse Gas Inventories.
DOI: 10.7910/dvn/1tbgo7/dxzc7g
title: GAEZAct2015_Yield_Foddercrops_Irrigated.tif

description: :unav
DOI: 10.7910/dvn/1tbgo7/opekcj
title: GAEZAct2015_Yield_Tobacco_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/1tbgo7/qso0uh
title: GAEZAct2015_Yield_PotatoAndSweetpotato_Irrigated.tif

description: :unav
DOI: 10.5281/zenodo.3886561
title: Dynamo in weakly collisional non-magnetized plasmas impeded by Landau damping of magnetic fields

description: This dataset contains a collection of simulation inputs and results used in the paper [I. Pusztai et al (2020) Phys. Rev. Lett., Dynamo in weakly collisional non-magnetized plasmas impeded by Landau damping of magnetic fields, https://arxiv.org/abs/2001.11929]. References to figures below refer to this publication. These simulations are performed using the kinetic-Vlasov solver Gkeyll [version: cd65328c077f+ 2228+ default], for more information on the code visit https://gkyl.readthedocs.io/en/latest/index.html, or consult [J. Juno et al (2018) J. Comp. Phys 353, 110]. The input files are found with .lua extension in each simulation directory Content: * Galloway-Proctor-flow_Fig1-kinetic-and-Fig2 <br> Kinetic simulation of the Galloway-Proctor flow, corresponding to the solid lines in Fig. 1 and Fig. 2. * Cnu-and-k-scan_Fig3-and-Fig4a<br> This is a parameter scan in wavelength of the magnetic perturbations [ranging from L0 ("L0") to L0/8 ("L0per8"), with baseline domain size L0] and collision frequencies [ranging from 0.05 ("Cnu005") to 1 ("Cnu1") times the baseline values]. These results are presented in Fig. 3 and Fig. 4a. * Magnetization-scan_Fig4b<br> Scan in magnetization shown in Fig. 4 b. The magnetic field varies between 1 and 100 T ["B1" and "B100", respectively]. * Roberts-flow_Fig5 <br> Kinetic simulations of the Roberts flow, shown in Fig. 5. The collision frequency is scaled to 0.3 the physical value (dashed lines, "Roberts_Cnu03_Fig5"), and zero (solid lines, "Roberts_Cnu00_Fig5"). * Pencil_Run_12x12x12.tar.gz<br> Input files for PENCIL CODE simulations.
description: {"references": ["Pusztai et al, Dynamo in weakly collisional non-magnetized plasmas impeded by Landau damping of magnetic fields, (2020) https://arxiv.org/abs/2001.11929", "https://gkyl.readthedocs.io/en/latest/index.html", "J. Juno et al, Journal of Computational Physics, 353, 110\u2013147 (2018) https://doi.org/10.1016/j.jcp.2017.10.009", "https://github.com/pencil-code/"]}
DOI: 10.5281/zenodo.3886562
title: Dynamo in weakly collisional non-magnetized plasmas impeded by Landau damping of magnetic fields

description: This dataset contains a collection of simulation inputs and results used in the paper [I. Pusztai et al (2020) Phys. Rev. Lett., Dynamo in weakly collisional non-magnetized plasmas impeded by Landau damping of magnetic fields, https://arxiv.org/abs/2001.11929]. References to figures below refer to this publication. These simulations are performed using the kinetic-Vlasov solver Gkeyll [version: cd65328c077f+ 2228+ default], for more information on the code visit https://gkyl.readthedocs.io/en/latest/index.html, or consult [J. Juno et al (2018) J. Comp. Phys 353, 110]. The input files are found with .lua extension in each simulation directory Content: * Galloway-Proctor-flow_Fig1-kinetic-and-Fig2 <br> Kinetic simulation of the Galloway-Proctor flow, corresponding to the solid lines in Fig. 1 and Fig. 2. * Cnu-and-k-scan_Fig3-and-Fig4a<br> This is a parameter scan in wavelength of the magnetic perturbations [ranging from L0 ("L0") to L0/8 ("L0per8"), with baseline domain size L0] and collision frequencies [ranging from 0.05 ("Cnu005") to 1 ("Cnu1") times the baseline values]. These results are presented in Fig. 3 and Fig. 4a. * Magnetization-scan_Fig4b<br> Scan in magnetization shown in Fig. 4 b. The magnetic field varies between 1 and 100 T ["B1" and "B100", respectively]. * Roberts-flow_Fig5 <br> Kinetic simulations of the Roberts flow, shown in Fig. 5. The collision frequency is scaled to 0.3 the physical value (dashed lines, "Roberts_Cnu03_Fig5"), and zero (solid lines, "Roberts_Cnu00_Fig5"). * Pencil_Run_12x12x12.tar.gz<br> Input files for PENCIL CODE simulations.
description: {"references": ["Pusztai et al, Dynamo in weakly collisional non-magnetized plasmas impeded by Landau damping of magnetic fields, (2020) https://arxiv.org/abs/2001.11929", "https://gkyl.readthedocs.io/en/latest/index.html", "J. Juno et al, Journal of Computational Physics, 353, 110\u2013147 (2018) https://doi.org/10.1016/j.jcp.2017.10.009", "https://github.com/pencil-code/"]}
DOI: 10.7910/dvn/1tbgo7/9bzms1
title: GAEZAct2015_Yield_Stimulants_Irrigated.tif

description: :unav
DOI: 10.7910/dvn/1tbgo7/nrecli
title: GAEZAct2015_Yield_Pulses_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/1tbgo7/q479ex
title: GAEZAct2015_Yield_Yamsandotherroots_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/1tbgo7/y0iyzr
title: GAEZAct2015_Yield_Banana_Irrigated.tif

description: :unav
DOI: 10.7910/dvn/1tbgo7/ykt63v
title: GAEZAct2015_Yield_Othercereals_Irrigated.tif

description: :unav
DOI: 10.7910/dvn/1tbgo7/4yblzm
title: GAEZ+_2015 metadata.pdf

description: MetadataPDF
DOI: 10.7910/dvn/1tbgo7/g5vonh
title: GAEZAct2015_Yield_Vegetables_Irrigated.tif

description: :unav
DOI: 10.7910/dvn/1tbgo7/rwenpy
title: GAEZAct2015_Yield_Pulses_Irrigated.tif

description: :unav
DOI: 10.7910/dvn/1tbgo7/te91ax
title: GAEZAct2015_Yield_Cotton_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/1tbgo7/wtvrfe
title: GAEZAct2015_Yield_Sugarcane_Irrigated.tif

description: :unav
DOI: 10.7910/dvn/1tbgo7
title: GAEZ+_2015 Crop Yield

description: FAOSTAT yield data for all countries and crops, 2009-2100 and 2014-2016, are calculated from production and area, not directly reported yield values (http://www.fao.org/faostat/en/#data/QA). We computed the 2015 crop yield grids by doing a grid cell by grid cell division of crop production by crop harvested area, for all crops, both irrigated and rainfed. For each crop, m, and grid cell, k, crop yield in 2015 was computed as the ratio of crop production in 2015 to crop harvested area in 2015. Note that for many grid cells and many crops, the GAEZ+_2015 harvested area is zero. In these cases, the GAEZ+_2015 yield is reported as -3.39999999999999996e+38.
DOI: 10.21227/q50t-2h56
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets were captured by an on-going project deployed at https://live.rlamsal.com.np. The geolocation data was extracted from the tweets which mentioned anything about “corona”, "coronavirus", "covid" and possible variants of "sarscov2", "nCov", "covid-19", "ncov2019". Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location. Please note that this dataset should be solely used for non-commercial research purposes (ignore every other LICENSE category given on this page).Note: I started sharing the IDs of the tweets that had exact 'point' location information, only since April 28, 2020, with some genuine requests coming in from academic researchers who did not want to hydrate the whole lists of IDs (above 160+ million tweets) shared in the Coronavirus (COVID-19) Tweets Dataset.If you need the geolocation-based data starting March 20, 2020, then use the Coronavirus (COVID-19) Tweets Dataset and hydrate the IDs while adding the following condition:data = json.loads(data)if data["coordinates"]: longitude, latitude = data["coordinates"]["coordinates"]The data is available in two formats: CSV and JSON. I'll be sharing new files every day, and the files will be named period-wise. For example, april28-june5.zip will contain tweet ID and sentiment score of the tweets (in CSV and JSON formats) that were created between April 28, 2020, and June 05, 2020.Why are only tweet IDs being shared? It is Twitter's content redistribution policy that restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers to always pull fresh data using their API. Why? Here's my opinion. Maybe, some user might want to delete a particular tweet after a couple of minute(s)/hour(s)/day(s), and if the same tweet has already been pulled and is shared on a public domain, then it might make the user/community vulnerable to many inferences coming out of the shared data.
DOI: 10.5519/qd.94dkbsv2
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (11937 records)
DOI: 10.25387/g3.12448676
title: Supplemental Material for Katz et al., 2020

description: All sequence whole exome sequences variants unique to a cat with neuronal ceroid lipofuscinosis relative to cat exome Sequence Read Archive (accession number PRJNA627536).
DOI: 10.5519/qd.6nbvwa7n
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (4559602 records)
DOI: 10.25345/c5h13n
title: MassIVE MSV000085567 - Proteomic portraits of human and porcine spinal cord injury define cross-species biomarkers of injury severity and recovery
title: Despite the emergence of promising therapeutic approaches in preclinical studies, the failure of large-scale clinical trials leaves clinicians without effective treatments for acute spinal cord injury (SCI). These trials are hindered by their reliance on detailed neurological examinations to establish outcomes, which inflate the time and resources required for completion. Moreover, therapeutic development takes place in animal models whose relevance to human injury remains unclear. Here, we address these challenges through targeted proteomic analyses of CSF and serum samples from 111 acute SCI patients and, in parallel, a large animal (porcine) model of SCI. We develop and validate protein biomarkers of injury severity and recovery, including a prognostic model of neurological improvement at six months with an AUC of 0.91. Through comparative proteomic analyses, we dissect conserved and divergent aspects of the SCI response, and establish the CSF abundance of glial fibrillary acidic protein (GFAP) as a biochemical outcome measure in both human and pig. Our work defines new resources to catalyze translation by facilitating the evaluation of novel SCI therapies, while also providing a resource from which to direct future preclinical efforts.
DOI: 10.25345/c5mq6m
title: MassIVE MSV000085566 - Residue Analysis with Exclusion List Analysis -- 10.25.19
title: Second run of this project, first to use an exclusion list to filter out blank-derived features. Analysis performed on 10/25/2019. Samples were from fragmented potsherds from an NM archaeological site. Each sherd had 8 subsamples: 4 surface swabs, 2 fragments collected beneath the exterior surface, and 2 fragments broken off from whole sherd.
DOI: 10.21227/w00t-mn85
title: Robot Dynamics Identification

description: This matlab code allows to reproduce some of the constrained and unconstrained dynamic identification techniques for open-chain robots. Below you can find an summary of the underlying research."Identification of dynamic parameters of robots is a long standing topic in robot control. Recently, the research witnessed a renewal of the activity due to some interesting results concerning issues involving the physical consistency of the obtained parameters leading to constraints, for instance, on mass, first moment of mass and inertia matrix of each rigid link of the robot. This is motivated by high performance required by model-based control of, for example, legged or surgical robots and to obtain realistic simulations of the same systems. The objective of this article is twofold. First, we review and experimentally compare on a Jaco 7 Degrees of Freedom anthropomorphic arm the latest results on the identification of the dynamic parameters which range from Least Mean Squares to constrained non-linear optimisation and Linear-Matrix-Inequality Semi-Definite-Programming methods. Second, motivated by the increasing attention paid to measurable robotics and for the sake of reproducibility, data and code (which can be easily adapted to consider any open-chain manipulator) are made available to the community for improvement and to run further comparisons. As indirect but not less important contribution, this paper provides a first physical fully consistent identified rigid dynamic model of theJaco robot publicly made available to the robotic community."
DOI: 10.20375/0000-000c-d91f-e
title: Sphaera Paratexts Data

description: This collection contains raw data used in the publication "Paratexts, Printers, and Publishers", published in the scope of the The Sphere-Project of the Max-Planck-Institute for the History of Science The project investigates the knowledge tradition that is interwoven with the history of one book: De sphaera of Johannes de Sacrobosco
DOI: 10.20375/0000-000c-d920-b
title: persons.csv
DOI: 10.21227/t3f7-4e32
title: Measurement and Evaluation of Human Sitting and Standing Movement Biomechanics

description: Although regenerative actuators can extend the operating durations of robotic lower-limb exoskeletons and prostheses, these energy-efficient powertrains have been exclusively designed and evaluated for continuous level-ground walking. Building on previous research, this work analyzed the lower-limb joint mechanical power during stand-to-sit movements using inverse dynamics modelling and simulation to estimate the biomechanical energy available for electrical regeneration. Nine subjects performed 20 sitting and standing movements while lower-limb kinematics and ground reaction forces were measured. Subject-specific body segment parameters were estimated using parameter identification, whereby differences in ground reaction forces and moments between the experimental measurements and inverse dynamic simulations were minimized. Joint mechanical power was calculated from net joint torques and rotational velocities and numerically integrated over time to determine joint biomechanical energy. The hip generated the largest peak negative mechanical power (1.8 ± 0.5 W/kg), followed by the knee joint (0.8 ± 0.3 W/kg) and ankle joint (0.2 ± 0.1 W/kg). Negative mechanical work from the hip, knee, and ankle joints per stand-to-sit movement were 0.36 ± 0.06 J/kg, 0.16 ± 0.08 J/kg, and 0.03 ± 0.01 J/kg, respectively. Assuming 80-kg bodyweight and previous regenerative actuator efficiencies (i.e., maximum 63%), robotic lower-limb exoskeletons and prostheses could theoretically regenerate ~28 Joules of total electrical energy while sitting down, compared to ~19 Joules from level-ground walking. Future research should focus on the biomechanical analyses and movement tracking of seniors and rehabilitation patients to better estimate the biomechanical energy available for electrical regeneration among individuals with mobility impairments.Reference:Laschowski B, Razavian RS, and McPhee J. (2020). Simulation of Stand-to-Sit Biomechanics for Design of Lower-Limb Exoskeletons and Prostheses with Energy Regeneration. Under Review.
DOI: 10.20375/0000-000c-d921-a
title: parts.csv
DOI: 10.20375/0000-000c-d922-9
title: partsAndAuthors.csv
DOI: 10.20375/0000-000c-d923-8
title: partContentTypes.csv
DOI: 10.20375/0000-000c-d924-7
title: adaptions.csv
DOI: 10.21227/54kn-9r42
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/q4xq-gf12
title: Measurement and Evaluation of Human Sitting and Standing Movement Biomechanics

description: Although regenerative actuators can extend the operating durations of robotic lower-limb exoskeletons and prostheses, these energy-efficient powertrains have been exclusively designed and evaluated for continuous level-ground walking. Building on previous research, this work analyzed the lower-limb joint mechanical power during stand-to-sit movements using inverse dynamics modelling and simulation to estimate the biomechanical energy available for electrical regeneration. Nine subjects performed 20 sitting and standing movements while lower-limb kinematics and ground reaction forces were measured. Subject-specific body segment parameters were estimated using parameter identification, whereby differences in ground reaction forces and moments between the experimental measurements and inverse dynamic simulations were minimized. Joint mechanical power was calculated from net joint torques and rotational velocities and numerically integrated over time to determine joint biomechanical energy. The hip generated the largest peak negative mechanical power (1.8 ± 0.5 W/kg), followed by the knee joint (0.8 ± 0.3 W/kg) and ankle joint (0.2 ± 0.1 W/kg). Negative mechanical work from the hip, knee, and ankle joints per stand-to-sit movement were 0.36 ± 0.06 J/kg, 0.16 ± 0.08 J/kg, and 0.03 ± 0.01 J/kg, respectively. Assuming 80-kg bodyweight and previous regenerative actuator efficiencies (i.e., maximum 63%), robotic lower-limb exoskeletons and prostheses could theoretically regenerate ~28 Joules of total electrical energy while sitting down, compared to ~19 Joules from level-ground walking. Future research should focus on the biomechanical analyses and movement tracking of seniors and rehabilitation patients to better estimate the biomechanical energy available for electrical regeneration among individuals with mobility impairments.Reference:Laschowski B, Razavian RS, and McPhee J. (2020). Simulation of Stand-to-Sit Biomechanics for Design of Lower-Limb Exoskeletons and Prostheses with Energy Regeneration. Under Review.
DOI: 10.5281/zenodo.3887112
title: Machine learning for predicting the average length of vertically aligned TiO2 nanotubes. Dataset Description

description: Machine learning for predicting the average length of vertically aligned TiO2 nanotubes. Dataset Description. Dataset is found in two versions: MATLAB mat file and CSV format. Rows contains samples and Columns Features. Last Column represents the Response variable.
DOI: 10.20375/0000-000c-d925-6
title: books.csv
DOI: 10.20375/0000-000c-d926-5
title: edges.csv
DOI: 10.21227/p1ta-xs47
title: PV Array Reconfiguration Based Converter-less MPPT Method for Rural Area Applications

description: Description of the proposed method is presented with the support of experimental videos.
DOI: 10.25346/s6/ujojhe/l8l3b0
title: unit08.gdb.zip

description: geodatabase test file