cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12894761.v1
title: Transcriptome-wide expression profiling of Sporothrix schenckii yeast and mycelial forms and the establishment of the Sporothrix genome database

description: Supplementary Material for ‘Transcriptome-wide expression profiling of Sporothrix schenckii yeast and mycelial forms and the establishment of the Sporothrix genome database’ as published in Microbial Genomics.<br>
DOI: 10.6084/m9.figshare.12894761
title: Transcriptome-wide expression profiling of Sporothrix schenckii yeast and mycelial forms and the establishment of the Sporothrix genome database

description: Supplementary Material for ‘Transcriptome-wide expression profiling of Sporothrix schenckii yeast and mycelial forms and the establishment of the Sporothrix genome database’ as published in Microbial Genomics.<br>
DOI: 10.17909/t9-1t8f-m374
title: testjwst

description: testjwst test test
DOI: 10.25345/c5qj3p
title: MassIVE MSV000086146 - A Chemical Acetylation based Mass Spectrometry Platform for Histone Profiling
title: Here we present a strategy for the identification and quantitation of histone methylation that utilizes nondeuterated acetic anhydride to fully acetylate all free lysine residues prior to digestion with trypsin and analysis by LC-MS/MS.
DOI: 10.25345/c5kt9g
title: MassIVE MSV000086147 - Proteomic study of in vitro epidermis of reconstructed skins made with cells from African or Caucasian skin type
title: This study aimed to understand the in vitro behaviour of epidermal cells of African and Caucasian skin types in the context of 3D reconstructed skin. Reconstructed skins epidermis made with cells isolated from skin of African or Caucasian skin type exhibited high differences in stratification and differentiation. The objective of this study is a first global approach to identify at the protein level the differences between reconstructed skins.
DOI: 10.5281/zenodo.4030435
title: A2aR Oligomeric assemblies identified from MD simulations using in-vivo mimetic biomembranes

description: GPCR oligomerisation is known to play an important role in the receptor signalling. However, due to the technical challenges, the structural information of GPCR oligomerisation is still very limited, which hinders our understanding of GPCR signalling in a fuller picture. In this deposit, we provide the structural coordinates of various oligomeric assemblies (up to pentamers) of Adenosine A2a receptor that were sampled from unbiased MD simulations. For more information regarding the MD simulation setup and definitions of the various calculated values, please check out our paper on BioRxiv: https://www.biorxiv.org/content/10.1101/2020.06.24.168260v1 <em>** Simulation setup **</em><br> 9 copies of A2aR were randomly inserted into an <em>in-vivo </em>mimetic biomembrane to build the initial configuration of the simulations. 10 such systems were set up for A2aR in the inactive state and 10 for the active state. The systems were represented by MARTINI 2 coarse-grained models and were simulated for 50 micro-seconds. The use of MARTINI coarse-grained force field would freeze the receptor conformation as in the initial configuration, thus decoupled the oligomerization from such processes as ligand-induced conformational change. The more efficient sampling of coarse-grained force field therefore allowed us to explore efficiently the protein-protein associations in the oligomerisation process. Protein-protein associations were identified when any atoms from two protomers were getting closer than 0.75 nm. The oligomerisation process was monitored and the sampled oligomeric assemblies were stored. <em>** Coordinate file explained **</em><br> The coarse-grained oligomeric structures were converted back to atomistic model in CHARMM force field. Oligomeric assemblies of the same oligomeric state (OS) were clustered to identify the different arrangements. 100 oligomeric arrangements (or all of the sampled oligomers if the cluster contains less than 100 samples) were randomly taken from each cluster and stored as seperate models in a pdb coordinate file. The pdb coordinate files were named as {Conf. State}_OS{Oligomeric State}_cl{Cluster id}.pdb, and can be viewed by such visualization tools as PyMol, Chimera or JMol etc. A Excel file containing some statistics of the oligomeric assemblies were also provided.
description: {"references": ["Song et al (2020) GPCR Oligomerisation Modulation by Conformational State and Lipid Interactions Revealed by MD Simulations and Markov Models; bioRxiv 2020.06.24.168260; doi: https://doi.org/10.1101/2020.06.24.168260"]}
DOI: 10.21227/9x6w-dv06
title: Drone Motion Primitive Dataset

description: Drone technology is one of the largest tackled fields in today’s world, as it can range from pure enjoyment of drone racing to medical use and fighting crime. Several teams are interested in developing improved human machine interfaces for operating drones. This dataset is a collection of different motion primitives commanded using a PS3 joystick to control an Ardrone on Gazebo. This has been conducted using ROS Melodic on Ubuntu 18.04. The PS3 six-axis joystick was used to collect this data, and it was connected to the Gazebo drone by using the ArDrone Helpers libraries.This dataset contains 4 different motion primitives which are: Line, Helix, Sine, and Arc. The testing was conducted on each primitive alone and using a stop indicator, which is specified for each primitive, at the end of each primitive. The stop indicators are as follows:· X for Sine· Circle for Helix· Square for Line· Triangle for ArcThen a collection of every two primitives consecutively was also conducted (i.e. line to helix, arc to line, helix to sine …) using the same stop indictors at the end of every corresponding primitive. Each test has around 300 to 500 samples. It is also important to note that our data is sample and hold, meaning that if the joystick’s analog stick is in a certain position for a few seconds, then it will not send any new samples for the velocities.The data was extracted into a .YAML file, as continuous commands from the joystick which were used to move the drone in Gazebo. They are then parsed (using python 2.7) to extract the velocities of the drone along the x y z axes as well as the pressed buttons (x or circle or square or triangle) into a .CSV file.This parsed data is further processed (using python3) into another .CSV file containing all the velocities as arrays, a histogram to get the average number of samples for every sequence, and a .RTF file which contains some analysis for each primitive or collection of primitives (number of samples taken, min and max sequence size, mean, median, and deviation).N.B: for the sine primitive, the data has been extracted into two categories: one where the sine was along the x-y axes and another where it was along the x-z or y-z axes. The github link for the two codes used is: https://github.com/justinelhalabi/PS3-Joystick-Command-Parsing-into-CSV
DOI: 10.5281/zenodo.2591038
title: InterTVA. Single-trial beta maps for event-related voice localizer.

description: The InterTVA dataset has been acquired with two main objectives. First, from a neuroscientific perspective, it aims at studying the inter-individual differences observed in people's ability at performing voice perception and voice identification tasks. Secondly, from a methodological perspective, it should allow benchmarking multi-view machine learning methods. Indeed, it includes several MRI modalities: anatomical MRI, diffusion MRI and several sessions of functional MRI -- one resting state run, one event-related voice localizer run (passive listening of vocal and non-vocal sounds), and four runs during which the subject performed a voice identification task. The present dataset contains a pre-processed version of the data acquired during the event-related voice localizer run. A GLM was performed using one regressor for each of the 144 trials, during each of which the participant was passively listening to a vocal or non-vocal stimulus. We therefore provide 144 beta maps for 39 subjects out of the 40 participants (one was excluded for excessive motion). This allows performing group-level MVPA using an inter-subject pattern analysis (ISPA) approach, as described in the following paper: Q. Wang, B. Cagna, T. Chaminade, et S. Takerkart, « Inter-subject pattern analysis: A straightforward and powerful scheme for group-level MVPA », <em>NeuroImage</em>, vol. 204, p. 116205, janv. 2020. https://doi.org/10.1016/j.neuroimage.2019.116205 The source code to perform ISPA is also available: http://www.github.com/SylvainTakerkart/inter_subject_pattern_analysis If you use this data, please cite the following paper which describes it exhaustively: V. Aglieri, B. Cagna, P. Belin, S. Takerkart, "Single-trial fMRI activation maps measured during the InterTVA event-related voice localizer. A data set ready for inter-subject pattern analysis", Data in Brief, vol. 29, p. 105170, April 2020. https://doi.org/10.1016/j.dib.2020.105170
description: {"references": ["Q. Wang, B. Cagna, T. Chaminade, et S. Takerkart, \u00ab Inter-subject pattern analysis: A straightforward and powerful scheme for group-level MVPA \u00bb, NeuroImage, vol. 204, p. 116205, January 2020. https://doi.org/10.1016/j.neuroimage.2019.116205", "V. Aglieri, B. Cagna, P. Belin, S. Takerkart, \"Single-trial fMRI activation maps measured during the InterTVA event-related voice localizer. A data set ready for inter-subject pattern analysis\", Data in Brief, vol. 29, p. 105170, April 2020. https://doi.org/10.1016/j.dib.2020.105170"]}
DOI: 10.5281/zenodo.2591037
title: InterTVA. Single-trial beta maps for event-related voice localizer.

description: The InterTVA dataset has been acquired with two main objectives. First, from a neuroscientific perspective, it aims at studying the inter-individual differences observed in people's ability at performing voice perception and voice identification tasks. Secondly, from a methodological perspective, it should allow benchmarking multi-view machine learning methods. Indeed, it includes several MRI modalities: anatomical MRI, diffusion MRI and several sessions of functional MRI -- one resting state run, one event-related voice localizer run (passive listening of vocal and non-vocal sounds), and four runs during which the subject performed a voice identification task. The present dataset contains a pre-processed version of the data acquired during the event-related voice localizer run. A GLM was performed using one regressor for each of the 144 trials, during each of which the participant was passively listening to a vocal or non-vocal stimulus. We therefore provide 144 beta maps for 39 subjects out of the 40 participants (one was excluded for excessive motion). This allows performing group-level MVPA using an inter-subject pattern analysis (ISPA) approach, as described in the following paper: Q. Wang, B. Cagna, T. Chaminade, et S. Takerkart, « Inter-subject pattern analysis: A straightforward and powerful scheme for group-level MVPA », <em>NeuroImage</em>, vol. 204, p. 116205, janv. 2020. https://doi.org/10.1016/j.neuroimage.2019.116205 The source code to perform ISPA is also available: http://www.github.com/SylvainTakerkart/inter_subject_pattern_analysis If you use this data, please cite the following paper which describes it exhaustively: V. Aglieri, B. Cagna, P. Belin, S. Takerkart, "Single-trial fMRI activation maps measured during the InterTVA event-related voice localizer. A data set ready for inter-subject pattern analysis", Data in Brief, vol. 29, p. 105170, April 2020. https://doi.org/10.1016/j.dib.2020.105170
description: {"references": ["Q. Wang, B. Cagna, T. Chaminade, et S. Takerkart, \u00ab Inter-subject pattern analysis: A straightforward and powerful scheme for group-level MVPA \u00bb, NeuroImage, vol. 204, p. 116205, January 2020. https://doi.org/10.1016/j.neuroimage.2019.116205", "V. Aglieri, B. Cagna, P. Belin, S. Takerkart, \"Single-trial fMRI activation maps measured during the InterTVA event-related voice localizer. A data set ready for inter-subject pattern analysis\", Data in Brief, vol. 29, p. 105170, April 2020. https://doi.org/10.1016/j.dib.2020.105170"]}
DOI: 10.6084/m9.figshare.12173598
title: Atrazine, desethylatrazine (DEA) and desisopropylatrazine (DIA) degradation by <i>Pleurotus ostreatus</i> INCQS 40310

description: Atrazine is the most common herbicide applied in crops of economic relevance, such as sugar cane, soybean, and corn. Atrazine and its derivatives desethylatrazine (DEA) and desisopropylatrazine (DIA) are toxic to the environment, affecting animal and human health. Thus, this study aimed to evaluate the degradation of atrazine and its derivatives by the fungus <i>Pleurotus ostreatus</i> INCQS 40310, as well as the potential of the enzymes involved in this process. <i>P. ostreatus</i> INCQS 40310 was able to degrade atrazine (82%), DEA (71%), and DIA (56%) over 22 days of fungal cultivation. Proteomic analysis indicated the participation of hydrolases and peroxidases during the degradation process. Additionally, resting cells of the fungus were tested to verify the action of intracellular enzymes in the degradation process, suggesting the participation of cytochrome P450 enzymatic complex. Resting cells experiments promoted the degradation of 50% of atrazine, 36% of DIA, 30% of DEA. So far, this is the first work evaluating the biodegradation of DEA and DIA by fungus.
DOI: 10.6084/m9.figshare.12173598.v1
title: Atrazine, desethylatrazine (DEA) and desisopropylatrazine (DIA) degradation by <i>Pleurotus ostreatus</i> INCQS 40310

description: Atrazine is the most common herbicide applied in crops of economic relevance, such as sugar cane, soybean, and corn. Atrazine and its derivatives desethylatrazine (DEA) and desisopropylatrazine (DIA) are toxic to the environment, affecting animal and human health. Thus, this study aimed to evaluate the degradation of atrazine and its derivatives by the fungus <i>Pleurotus ostreatus</i> INCQS 40310, as well as the potential of the enzymes involved in this process. <i>P. ostreatus</i> INCQS 40310 was able to degrade atrazine (82%), DEA (71%), and DIA (56%) over 22 days of fungal cultivation. Proteomic analysis indicated the participation of hydrolases and peroxidases during the degradation process. Additionally, resting cells of the fungus were tested to verify the action of intracellular enzymes in the degradation process, suggesting the participation of cytochrome P450 enzymatic complex. Resting cells experiments promoted the degradation of 50% of atrazine, 36% of DIA, 30% of DEA. So far, this is the first work evaluating the biodegradation of DEA and DIA by fungus.
DOI: 10.6084/m9.figshare.12957722
title: Figure8

description: Attenuation doses from aircraft skin<br>
DOI: 10.6084/m9.figshare.12957722.v2
title: Figure8

description: Attenuation doses from aircraft skin<br>
DOI: 10.6084/m9.figshare.12957722.v3
title: Figure8

description: Attenuation doses from aircraft skin<br>
DOI: 10.6084/m9.figshare.12957992
title: Dataset for Hypertension

description: Dataset for Risk Factors of Hypertension in Rural Indonesia - A Cross Sectional Study from a Rural Village in Indonesia
DOI: 10.6084/m9.figshare.12957716
title: Figure7

description: 2D cross section of the photon doses<br>
DOI: 10.6084/m9.figshare.12957716.v1
title: Figure7

description: 2D cross section of the photon doses<br>
DOI: 10.6084/m9.figshare.12957716.v2
title: Figure7

description: 2D cross section of the photon doses<br>
DOI: 10.6084/m9.figshare.12957629
title: Figure6b

description: 2D cross section of the homogeneous field model for electron doses with E = 500 kV/m
DOI: 10.6084/m9.figshare.12957629.v1
title: Figure6b

description: 2D cross section of the homogeneous field model for electron doses with E = 500 kV/m
DOI: 10.6084/m9.figshare.12957629.v2
title: Figure6b

description: 2D cross section of the homogeneous field model for electron doses with E = 500 kV/m
DOI: 10.6084/m9.figshare.12957440
title: Figure6a

description: 2D cross section of the homogeneous field model for electron doses with E = 400 kV/m<br>
DOI: 10.6084/m9.figshare.12957440.v2
title: Figure6a

description: 2D cross section of the homogeneous field model for electron doses with E = 400 kV/m<br>
DOI: 10.6084/m9.figshare.12957440.v1
title: Figure6a

description: 2D cross section of the homogeneous field model for electron doses with E = 400 kV/m<br>
DOI: 10.6084/m9.figshare.12957431
title: Figure5b

description: Homogeneous field model electron doses with E=500 kV/m<br>
DOI: 10.6084/m9.figshare.12957431.v2
title: Figure5b

description: Homogeneous field model electron doses with E=500 kV/m<br>
DOI: 10.6084/m9.figshare.12957431.v1
title: Figure5b

description: Homogeneous field model electron doses with E=500 kV/m<br>
DOI: 10.6084/m9.figshare.12957419
title: Figure5a

description: Homogeneous field model electron doses with E=400 kV/m<br>
DOI: 10.6084/m9.figshare.12957419.v2
title: Figure5a

description: Homogeneous field model electron doses with E=400 kV/m<br>
DOI: 10.6084/m9.figshare.12957419.v1
title: Figure5a

description: Homogeneous field model electron doses with E=400 kV/m<br>
DOI: 10.6084/m9.figshare.12957413
title: Figure4

description: Electron spectrum 50 m above the source<br>
DOI: 10.6084/m9.figshare.12957413.v2
title: Figure4

description: Electron spectrum 50 m above the source<br>
DOI: 10.6084/m9.figshare.12957413.v1
title: Figure4

description: Electron spectrum 50 m above the source<br>
DOI: 10.6084/m9.figshare.12957401
title: Figure3

description: Pure leader model electron doses<br>
DOI: 10.6084/m9.figshare.12957401.v2
title: Figure3

description: Pure leader model electron doses<br>
DOI: 10.6084/m9.figshare.12957401.v1
title: Figure3

description: Pure leader model electron doses<br>
DOI: 10.6084/m9.figshare.12957341
title: Figure2

description: Fluence to Ambient dose equivalent conversion coefficients from the Fluka database<br>
DOI: 10.6084/m9.figshare.12957341.v3
title: Figure2

description: Fluence to Ambient dose equivalent conversion coefficients from the Fluka database<br>
DOI: 10.6084/m9.figshare.12957341.v2
title: Figure2

description: Fluence to Ambient dose equivalent conversion coefficients from the Fluka database<br>
DOI: 10.18112/openneuro.ds003146.v1.0.1
title: Vocal task
DOI: 10.5281/zenodo.4029589
title: Multiple sequence alignment of DNA-A sequences from ACMBFV, ACMV, CMMGV, EACMCV, EACMKV, EACMMV, EACMV, EACMZV, SACMV, ICMV, SLCMV (11 species)

description: All full-length DNA-A sequences available in GenBank as of July 2019 were downloaded via the Taxonomy Browser interface. Sequence names were normalized/simplified and orientations of these circular sequences were standardized to begin at the replication origin nick site. Sequences were aligned with MUSCLE and alignments were manually adjusted with SeAl (A. Rambaut) and AliView (A. Larsson).
DOI: 10.5281/zenodo.4029588
title: Multiple sequence alignment of DNA-A sequences from ACMBFV, ACMV, CMMGV, EACMCV, EACMKV, EACMMV, EACMV, EACMZV, SACMV, ICMV, SLCMV (11 species)

description: All full-length DNA-A sequences available in GenBank as of July 2019 were downloaded via the Taxonomy Browser interface. Sequence names were normalized/simplified and orientations of these circular sequences were standardized to begin at the replication origin nick site. Sequences were aligned with MUSCLE and alignments were manually adjusted with SeAl (A. Rambaut) and AliView (A. Larsson).
DOI: 10.15468/vkga6t
title: New species of Ferreola L, 1845 and Eoferreola A, 1935 with key to Ferreola species from the Palaearctic region (Hymenoptera, Pompilidae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Schmid-Egger, Rnold Christian (2018): New species of Ferreola L, 1845 and Eoferreola A, 1935 with key to Ferreola species from the Palaearctic region (Hymenoptera, Pompilidae). Linzer biologische Beiträge 50 (1): 389-412, DOI: http://doi.org/10.5281/zenodo.4030909
DOI: 10.25919/5f60d65c504e3
title: Parkes observations for project P885 semester 2020APRS_12

description: We propose to observe the magnetars 1E 1547.0-5408 and PSR J1622-4950 approximately weekly, to track their rotation, flux density, and polarisation, as we have long been doing through this project. The revolutionary UWL capabilities promise new opportunities to disentangle still mysterious radio emission mechanisms in magnetars. This promise is supported by the recent follow up of XTE J1810-197, again a radio magnetar after 10 years dormant, for which our observations with the UWL receiver are providing unique and tantalizing measurements of its radio spectrum and polarisation.
DOI: 10.6084/m9.figshare.12338585.v1
title: Guay J and Kopp SL PNBs for Hip Fractures in Adults RoB2 20 May 2020.pdf

description: Risks of Bias 2 applied to Cochrane Review Peripheral Nerve Blocks for Hip Fractures in Adults by Joanne Guay and Sandra L Kopp
DOI: 10.6084/m9.figshare.12958166
title: Albrich_et_al_2020_GEB

description: Data set and analysis script accompanying the study "Simulating forest resilience - a review", published in Global Ecology and Biogeography<br>
DOI: 10.5517/ccdc.csd.cc253hyr
title: CCDC 1999374: Experimental Crystal Structure Determination

description: Related Article: Anton V. Rozhkov, Anastasiya A. Eliseeva, Sergey V. Baykov, Bartomeu Galmés, Antonio Frontera, Vadim Yu. Kukushkin|2020|Cryst.Growth Des.|20|5908|doi:10.1021/acs.cgd.0c00606
DOI: 10.5517/ccdc.csd.cc253j1w
title: CCDC 1999377: Experimental Crystal Structure Determination

description: Related Article: Anton V. Rozhkov, Anastasiya A. Eliseeva, Sergey V. Baykov, Bartomeu Galmés, Antonio Frontera, Vadim Yu. Kukushkin|2020|Cryst.Growth Des.|20|5908|doi:10.1021/acs.cgd.0c00606
DOI: 10.5517/ccdc.csd.cc253jc6
title: CCDC 1999387: Experimental Crystal Structure Determination

description: Related Article: Anton V. Rozhkov, Anastasiya A. Eliseeva, Sergey V. Baykov, Bartomeu Galmés, Antonio Frontera, Vadim Yu. Kukushkin|2020|Cryst.Growth Des.|20|5908|doi:10.1021/acs.cgd.0c00606
DOI: 10.5517/ccdc.csd.cc253jf8
title: CCDC 1999389: Experimental Crystal Structure Determination

description: Related Article: Anton V. Rozhkov, Anastasiya A. Eliseeva, Sergey V. Baykov, Bartomeu Galmés, Antonio Frontera, Vadim Yu. Kukushkin|2020|Cryst.Growth Des.|20|5908|doi:10.1021/acs.cgd.0c00606
DOI: 10.5517/ccdc.csd.cc253jg9
title: CCDC 1999390: Experimental Crystal Structure Determination

description: Related Article: Anton V. Rozhkov, Anastasiya A. Eliseeva, Sergey V. Baykov, Bartomeu Galmés, Antonio Frontera, Vadim Yu. Kukushkin|2020|Cryst.Growth Des.|20|5908|doi:10.1021/acs.cgd.0c00606
DOI: 10.5517/ccdc.csd.cc253j3y
title: CCDC 1999379: Experimental Crystal Structure Determination

description: Related Article: Anton V. Rozhkov, Anastasiya A. Eliseeva, Sergey V. Baykov, Bartomeu Galmés, Antonio Frontera, Vadim Yu. Kukushkin|2020|Cryst.Growth Des.|20|5908|doi:10.1021/acs.cgd.0c00606
DOI: 10.5517/ccdc.csd.cc253jkd
title: CCDC 1999393: Experimental Crystal Structure Determination

description: Related Article: Anton V. Rozhkov, Anastasiya A. Eliseeva, Sergey V. Baykov, Bartomeu Galmés, Antonio Frontera, Vadim Yu. Kukushkin|2020|Cryst.Growth Des.|20|5908|doi:10.1021/acs.cgd.0c00606
DOI: 10.5517/ccdc.csd.cc253jb5
title: CCDC 1999386: Experimental Crystal Structure Determination

description: Related Article: Anton V. Rozhkov, Anastasiya A. Eliseeva, Sergey V. Baykov, Bartomeu Galmés, Antonio Frontera, Vadim Yu. Kukushkin|2020|Cryst.Growth Des.|20|5908|doi:10.1021/acs.cgd.0c00606
DOI: 10.5517/ccdc.csd.cc25g7lh
title: CCDC 2009686: Experimental Crystal Structure Determination

description: Related Article: Hina Goyal, Sanya Pachisia, Rajeev Gupta|2020|Cryst.Growth Des.|20|6117|doi:10.1021/acs.cgd.0c00820
DOI: 10.5517/ccdc.csd.cc25g7mj
title: CCDC 2009687: Experimental Crystal Structure Determination

description: Related Article: Hina Goyal, Sanya Pachisia, Rajeev Gupta|2020|Cryst.Growth Des.|20|6117|doi:10.1021/acs.cgd.0c00820