cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc27050g
title: CCDC 2055734: Experimental Crystal Structure Determination

description: Related Article: Kotaro Obayashi, Keiichi Imato, Satoshi Aoyama, Toshiaki Enoki, Seiji Akiyama, Mio Ishida, Seiji Suga, Koichi Mitsudo, Yousuke Ooyama|2021|RSC Advances|11|18870|doi:10.1039/D1RA01189H
DOI: 10.5517/ccdc.csd.cc26y7dt
title: CCDC 2053886: Experimental Crystal Structure Determination

description: Related Article: Jiangfei Chen, Jin-Heng Li, Yan-Ping Zhu, Qiu-An Wang|2021|Org.Chem.Front.|8|2532|doi:10.1039/D1QO00186H
DOI: 10.15468/6n5mdw
title: The discovery of two new species of Lithopagurus Provenzano, 1968 (Crustacea, Decapoda, Anomura, Paguroidea, Paguridae) and the first records of the genus in the western Pacific

description: This dataset contains the digitized treatments in Plazi based on the original journal article Mclaughlin, Patsy A., Lemaitre, Rafael (2004): The discovery of two new species of Lithopagurus Provenzano, 1968 (Crustacea, Decapoda, Anomura, Paguroidea, Paguridae) and the first records of the genus in the western Pacific. Zoosystema 26 (3): 483-494, DOI: http://doi.org/10.5281/zenodo.4689539
DOI: 10.6084/m9.figshare.14710638
title: Facial training.xlsx

description: The file Facial training.xlsx separately reports the number of responses Happiness, Fear, Anger, Sadness, of each participant of two groups (Experimental and Control) of patients with schizophrenia to a facial expression categorization test, performed at the beginning (Test1) and at the end (Test2) of the study. Three times a week for five weeks, the participants of the Experimental group (n=12) were submitted to a facial physical training during which transitive actions were proposed as exercises (e.g., to inflate balloons, to hold round objects of various sizes by tightly contracting the muscles around the eyes). During the same period the participants in the Control group (n=12) carried out the activities proposed by the nursing home. The stimuli of the test were the result of a morphed transformation of four original pictures portraying the different emotion expressions. This transformation gave rise to four continua: anger-fear, anger-sadness, fear-happiness and happiness-sadness. For each emotion (e.g., Fear), the data set shows the sum of the relative responses given in trials belonging to the less ambiguous half of the two continua in which the emotion was present (e.g., anger-fear and fear-happiness). For each emotion we calculated the number of responses given in 48 trials.
DOI: 10.6084/m9.figshare.14607960
title: Data_availability.xlsx

description: Description of data sources used.
DOI: 10.6084/m9.figshare.14607960.v1
title: Data_availability.xlsx

description: Description of data sources used.
DOI: 10.6084/m9.figshare.14710770
title: luna16LungPriors.nii.gz

description: lung segmentation weights for u-net
DOI: 10.5522/04/12043224
title: Data supporting the paper "Synchronous subnanosecond clock and data recovery for optically switched data centres using clock phase caching"

description: This data was used to plot the figures in the final accepted version of "Synchronous subnanosecond clock and data recovery for optically switched data centres using clock phase caching" hosted on UCL Discovery. The full published version of this paper may be found in Nature Electronics at https://doi.org/10.1038/s41928-020-0423-y.
DOI: 10.5522/04/12043224.v1
title: Data supporting the paper "Synchronous subnanosecond clock and data recovery for optically switched data centres using clock phase caching"

description: This data was used to plot the figures in the final accepted version of "Synchronous subnanosecond clock and data recovery for optically switched data centres using clock phase caching" hosted on UCL Discovery. The full published version of this paper may be found in Nature Electronics at https://doi.org/10.1038/s41928-020-0423-y.
DOI: 10.6084/m9.figshare.14710782
title: lungCtOctantWithPriorsSegmentationWeights.h5

description: u-net weight for ct lung segmentation
DOI: 10.15468/4afhux
title: A new deep water leucosiid genus (Crustacea, Decapoda, Brachyura)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Galil, Bella S. (2004): A new deep water leucosiid genus (Crustacea, Decapoda, Brachyura). Zoosystema 26 (3): 495-502, DOI: http://doi.org/10.5281/zenodo.4689549
DOI: 10.25402/fsg.14710653
title: Supplementary Figures E1-E3. Propensity score matching versus coarsened exact matching in observational comparative effectiveness research

description: • Supplementary materials – figures:o Figure E1a. Selection process for comparison oneo Figure E1b. Selection process for comparison twoo Figure E2a. Distribution of baseline covariates by PSM caliper width for comparison oneo Figure E2b. Distribution of baseline covariates by PSM caliper width for comparison twoo Figure E3a. Distribution of baseline covariates by CEM strategy for comparison oneo Figure E3b. Distribution of baseline covariates by CEM strategy for comparison two<br>Supplementary materials – figuresFigure E1a. Selection process for comparison oneFigure E1b. Selection process for comparison twoFigure E2a. Distribution of baseline covariates by PSM caliper width for comparison oneFigure E2b. Distribution of baseline covariates by PSM caliper width for comparison twoFigure E3a. Distribution of baseline covariates by CEM strategy for comparison oneFigure E3b. Distribution of baseline covariates by CEM strategy for comparison two<br>Supplementary materials – tablesTable E1a. Characteristics of PSM strategies for comparison oneTable E1b. Characteristics of PSM strategies for comparison twoTable E2a. Coarsening of covariates used in CEM for comparison oneTable E2b. Coarsening of covariates used in CEM for comparison twoTable E3a. Characteristics of CEM strategies for comparison oneTable E3b. Characteristics of CEM strategies for comparison two<br>AbstractAims & Methods: We compared propensity score matching (PSM) and coarsened exact matching (CEM) in balancing baseline characteristics between treatment groups using observational data obtained from a pan-Canadian prostate cancer radiotherapy database. Changes in effect estimates were evaluated as a function of improvements in balance, using results from RCTs to guide interpretation. Results: CEM and PSM improved balance between groups in both comparisons, while retaining the majority of original data. Improvements in balance were associated with effect estimates closer to those obtained in RCTs. Conclusions: CEM and PSM led to substantial improvements in balance between comparison groups, while retaining a considerable proportion of original data. This could lead to improved accuracy in effect estimates obtained using observational data in a variety of clinical situations.<br>
DOI: 10.25402/fsg.14710737.v1
title: Supplementary Tables E1-E3. Propensity score matching versus coarsened exact matching in observational comparative effectiveness research

description: Supplementary materials – tables:Table E1a. Characteristics of PSM strategies for comparison oneTable E1b. Characteristics of PSM strategies for comparison twoTable E2a. Coarsening of covariates used in CEM for comparison oneTable E2b. Coarsening of covariates used in CEM for comparison twoTable E3a. Characteristics of CEM strategies for comparison oneTable E3b. Characteristics of CEM strategies for comparison two<br>Supplementary materials – figuresFigure E1a. Selection process for comparison oneFigure E1b. Selection process for comparison twoFigure E2a. Distribution of baseline covariates by PSM caliper width for comparison oneFigure E2b. Distribution of baseline covariates by PSM caliper width for comparison twoFigure E3a. Distribution of baseline covariates by CEM strategy for comparison oneFigure E3b. Distribution of baseline covariates by CEM strategy for comparison two<br>Supplementary materials – tablesTable E1a. Characteristics of PSM strategies for comparison oneTable E1b. Characteristics of PSM strategies for comparison twoTable E2a. Coarsening of covariates used in CEM for comparison oneTable E2b. Coarsening of covariates used in CEM for comparison twoTable E3a. Characteristics of CEM strategies for comparison oneTable E3b. Characteristics of CEM strategies for comparison two<br>AbstractAims & Methods: We compared propensity score matching (PSM) and coarsened exact matching (CEM) in balancing baseline characteristics between treatment groups using observational data obtained from a pan-Canadian prostate cancer radiotherapy database. Changes in effect estimates were evaluated as a function of improvements in balance, using results from RCTs to guide interpretation. Results: CEM and PSM improved balance between groups in both comparisons, while retaining the majority of original data. Improvements in balance were associated with effect estimates closer to those obtained in RCTs. Conclusions: CEM and PSM led to substantial improvements in balance between comparison groups, while retaining a considerable proportion of original data. This could lead to improved accuracy in effect estimates obtained using observational data in a variety of clinical situations.<br>
DOI: 10.25402/fsg.14710653.v1
title: Supplementary Figures E1-E3. Propensity score matching versus coarsened exact matching in observational comparative effectiveness research

description: • Supplementary materials – figures:o Figure E1a. Selection process for comparison oneo Figure E1b. Selection process for comparison twoo Figure E2a. Distribution of baseline covariates by PSM caliper width for comparison oneo Figure E2b. Distribution of baseline covariates by PSM caliper width for comparison twoo Figure E3a. Distribution of baseline covariates by CEM strategy for comparison oneo Figure E3b. Distribution of baseline covariates by CEM strategy for comparison two<br>Supplementary materials – figuresFigure E1a. Selection process for comparison oneFigure E1b. Selection process for comparison twoFigure E2a. Distribution of baseline covariates by PSM caliper width for comparison oneFigure E2b. Distribution of baseline covariates by PSM caliper width for comparison twoFigure E3a. Distribution of baseline covariates by CEM strategy for comparison oneFigure E3b. Distribution of baseline covariates by CEM strategy for comparison two<br>Supplementary materials – tablesTable E1a. Characteristics of PSM strategies for comparison oneTable E1b. Characteristics of PSM strategies for comparison twoTable E2a. Coarsening of covariates used in CEM for comparison oneTable E2b. Coarsening of covariates used in CEM for comparison twoTable E3a. Characteristics of CEM strategies for comparison oneTable E3b. Characteristics of CEM strategies for comparison two<br>AbstractAims & Methods: We compared propensity score matching (PSM) and coarsened exact matching (CEM) in balancing baseline characteristics between treatment groups using observational data obtained from a pan-Canadian prostate cancer radiotherapy database. Changes in effect estimates were evaluated as a function of improvements in balance, using results from RCTs to guide interpretation. Results: CEM and PSM improved balance between groups in both comparisons, while retaining the majority of original data. Improvements in balance were associated with effect estimates closer to those obtained in RCTs. Conclusions: CEM and PSM led to substantial improvements in balance between comparison groups, while retaining a considerable proportion of original data. This could lead to improved accuracy in effect estimates obtained using observational data in a variety of clinical situations.<br>
DOI: 10.25402/fsg.14710737
title: Supplementary Tables E1-E3. Propensity score matching versus coarsened exact matching in observational comparative effectiveness research

description: Supplementary materials – tables:Table E1a. Characteristics of PSM strategies for comparison oneTable E1b. Characteristics of PSM strategies for comparison twoTable E2a. Coarsening of covariates used in CEM for comparison oneTable E2b. Coarsening of covariates used in CEM for comparison twoTable E3a. Characteristics of CEM strategies for comparison oneTable E3b. Characteristics of CEM strategies for comparison two<br>Supplementary materials – figuresFigure E1a. Selection process for comparison oneFigure E1b. Selection process for comparison twoFigure E2a. Distribution of baseline covariates by PSM caliper width for comparison oneFigure E2b. Distribution of baseline covariates by PSM caliper width for comparison twoFigure E3a. Distribution of baseline covariates by CEM strategy for comparison oneFigure E3b. Distribution of baseline covariates by CEM strategy for comparison two<br>Supplementary materials – tablesTable E1a. Characteristics of PSM strategies for comparison oneTable E1b. Characteristics of PSM strategies for comparison twoTable E2a. Coarsening of covariates used in CEM for comparison oneTable E2b. Coarsening of covariates used in CEM for comparison twoTable E3a. Characteristics of CEM strategies for comparison oneTable E3b. Characteristics of CEM strategies for comparison two<br>AbstractAims & Methods: We compared propensity score matching (PSM) and coarsened exact matching (CEM) in balancing baseline characteristics between treatment groups using observational data obtained from a pan-Canadian prostate cancer radiotherapy database. Changes in effect estimates were evaluated as a function of improvements in balance, using results from RCTs to guide interpretation. Results: CEM and PSM improved balance between groups in both comparisons, while retaining the majority of original data. Improvements in balance were associated with effect estimates closer to those obtained in RCTs. Conclusions: CEM and PSM led to substantial improvements in balance between comparison groups, while retaining a considerable proportion of original data. This could lead to improved accuracy in effect estimates obtained using observational data in a variety of clinical situations.<br>
DOI: 10.5281/zenodo.4603987
title: AN OPEN-SOURCED TIME-FREQUENCY DOMAIN RF CLASSIFICATION FRAMEWORK

description: These datasets accompany the EUSIPCO 2021 paper: AN OPEN-SOURCED TIME-FREQUENCY<br> DOMAIN RF CLASSIFICATION FRAMEWORK Labeled in-phase and quadrature (IQ) datasets captured using software defined radios (SDR). 12 classes comprised of 10 different transceivers plus 'noise' class and class for zero hz artifact from SDR direct conversion receiver. The IQ data has the extension .sigmf-data, and the associated JSON file that contains the labeled metadata is .sigmf-meta. There is a directory called 'data', this has all the extracted features for each class and is used for training. There are also 12 folders named 'data_val1' - 'data_val12'. These contain the extracted features used validation accuracy. There are numerous training, validation and testing datasets used to build the training and validation sets, all comprised of the ten primary RF modulation waveforms: 1) LoRa 125 (lora125) 2) LoRa 250 (lora250) 3) PTT analog transceivers (uv5r, vx8) 4) PTT digital transceivers (gd55, tyt) 5) RF doorbell (vod, sado) 6) misc. RF keyfob (click) 7) RF light switch (light) 8) Yaesu system fusion PTT (ysf) The testing directory: testing_data_1msps conatins all the test data for the auto grader notebooks at the github repo. These datasets go with the public github repo called: https://github.com/rdbadger/open_source_RFML
DOI: 10.5281/zenodo.4603986
title: AN OPEN-SOURCED TIME-FREQUENCY DOMAIN RF CLASSIFICATION FRAMEWORK

description: These datasets accompany the EUSIPCO 2021 paper: AN OPEN-SOURCED TIME-FREQUENCY<br> DOMAIN RF CLASSIFICATION FRAMEWORK Labeled in-phase and quadrature (IQ) datasets captured using software defined radios (SDR). 12 classes comprised of 10 different transceivers plus 'noise' class and class for zero hz artifact from SDR direct conversion receiver. The IQ data has the extension .sigmf-data, and the associated JSON file that contains the labeled metadata is .sigmf-meta. There is a directory called 'data', this has all the extracted features for each class and is used for training. There are also 12 folders named 'data_val1' - 'data_val12'. These contain the extracted features used validation accuracy. There are numerous training, validation and testing datasets used to build the training and validation sets, all comprised of the ten primary RF modulation waveforms: 1) LoRa 125 (lora125) 2) LoRa 250 (lora250) 3) PTT analog transceivers (uv5r, vx8) 4) PTT digital transceivers (gd55, tyt) 5) RF doorbell (vod, sado) 6) misc. RF keyfob (click) 7) RF light switch (light) 8) Yaesu system fusion PTT (ysf) The testing directory: testing_data_1msps conatins all the test data for the auto grader notebooks at the github repo. These datasets go with the public github repo called: https://github.com/rdbadger/open_source_RFML
DOI: 10.25345/c55r8p
title: MassIVE MSV000087551 - Photoredox-catalyzed decarboxylative C-terminal differentiation for bulk and single molecule proteomics
title: Methods for the selective labeling of functional groups on peptides are being developed and used in the workflow of both current and emerging proteomics technologies, such as single-molecule fluorosequencing. Photoredox methods in discriminative targeting of the terminal carboxylic acid from the internal acidic residues have been described but not effectively applied in concert with peptide-centric proteomic technologies. In this work, we describe methods for the use of lumiflavin based photoredox chemistry in peptide mass-spectrometry and single molecule protein sequencing. After optimizing the instrumentation and reaction conditions with the peptide angiotensin, we demonstrate successfully labeling of peptides in complex mixtures generated from enzymatic digests from bovine serum albumin, as well as protein mixtures from yeast and human cell extracts. Using two distinct analytical approaches, we characterize labeling efficiencies of greater than 50% across full proteomes and determine biases due to different C-terminal amino acids, finding histidine to be preferentially disfavored. Finally we demonstrate the ability to fluorescently label an internal acidic residue following C-terminal carboxylic acid derivatization, as suitable for single molecule protein sequencing technologies. The strategies and limitations described in this work provide clear guidance for further improvement and general use of this chemistry for analyzing proteins.
DOI: 10.15482/usda.adc/1522563
title: Data from: Response to selection for parasitism of a sub-optimal, low-preference host in an aphid parasitoid
DOI: 10.5281/zenodo.4890813
title: Arth02 raw sequences

description: Arth02 raw sequences
DOI: 10.5281/zenodo.4890812
title: Arth02 raw sequences

description: Arth02 raw sequences
DOI: 10.5517/ccdc.csd.cc272dzq
title: CCDC 2057903: Experimental Crystal Structure Determination

description: Related Article: Yong Hu, Xiao-Jun Wang, Wen-Shuai Dong, Yu-Fan Bi, Zu-Jia Lu, Wen-Li Cao, Jian-Guo Zhang, Qi Zhang, Dong Chen|2021|Org.Chem.Front.|8|2420|doi:10.1039/D1QO00123J
DOI: 10.5517/ccdc.csd.cc272g71
title: CCDC 2057942: Experimental Crystal Structure Determination

description: Related Article: Jun Shu, Jian Yue, Xin Qiu, Xiaoqiang Liu, Wang Ren, Qianli Li, Yulong Li, Bin Xu, Kaiming Zhang, Weidong Jiang|2021|Inorg.Chem.Front.|8|2684|doi:10.1039/D1QI00108F
DOI: 10.5517/ccdc.csd.cc272g82
title: CCDC 2057943: Experimental Crystal Structure Determination

description: Related Article: Jun Shu, Jian Yue, Xin Qiu, Xiaoqiang Liu, Wang Ren, Qianli Li, Yulong Li, Bin Xu, Kaiming Zhang, Weidong Jiang|2021|Inorg.Chem.Front.|8|2684|doi:10.1039/D1QI00108F
DOI: 10.5517/ccdc.csd.cc272vw2
title: CCDC 2058334: Experimental Crystal Structure Determination

description: Related Article: Jaime Ponce-de-León, Rebeca Infante, Pablo Espinet|2021|Chem.Commun.|57|5458|doi:10.1039/D1CC00976A
DOI: 10.5517/ccdc.csd.cc272vx3
title: CCDC 2058335: Experimental Crystal Structure Determination

description: Related Article: Jaime Ponce-de-León, Rebeca Infante, Pablo Espinet|2021|Chem.Commun.|57|5458|doi:10.1039/D1CC00976A
DOI: 10.5517/ccdc.csd.cc272vy4
title: CCDC 2058336: Experimental Crystal Structure Determination

description: Related Article: Jaime Ponce-de-León, Rebeca Infante, Pablo Espinet|2021|Chem.Commun.|57|5458|doi:10.1039/D1CC00976A
DOI: 10.5517/ccdc.csd.cc26n5wy
title: CCDC 2045190: Experimental Crystal Structure Determination

description: Related Article: Angelina Z. Petrović, Dušan C. Ćoćić, Dirk Bockfeld, Marko Živanović, Nevena Milivojević, Katarina Virijević, Nenad Janković, Andreas Scheurer, Milan Vraneš, Jovana V. Bogojeski|2021|Inorg.Chem.Front.|8|2749|doi:10.1039/D0QI01540G
DOI: 10.5517/ccdc.csd.cc27727t
title: CCDC 2062375: Experimental Crystal Structure Determination

description: Related Article: Debasmita Mondal, Gargi Pal, Chinmay Chowdhury|2021|Chem.Commun.|57|5462|doi:10.1039/D1CC00793A
DOI: 10.5517/ccdc.csd.cc27728v
title: CCDC 2062376: Experimental Crystal Structure Determination

description: Related Article: Debasmita Mondal, Gargi Pal, Chinmay Chowdhury|2021|Chem.Commun.|57|5462|doi:10.1039/D1CC00793A
DOI: 10.5517/ccdc.csd.cc27729w
title: CCDC 2062377: Experimental Crystal Structure Determination

description: Related Article: Debasmita Mondal, Gargi Pal, Chinmay Chowdhury|2021|Chem.Commun.|57|5462|doi:10.1039/D1CC00793A
DOI: 10.5517/ccdc.csd.cc2772bx
title: CCDC 2062378: Experimental Crystal Structure Determination

description: Related Article: Debasmita Mondal, Gargi Pal, Chinmay Chowdhury|2021|Chem.Commun.|57|5462|doi:10.1039/D1CC00793A
DOI: 10.5517/ccdc.csd.cc279kvz
title: CCDC 2064781: Experimental Crystal Structure Determination

description: Related Article: Ryosuke Kobayashi, Takashi Yumura, Hiroaki Imoto, Kensuke Naka|2021|Chem.Commun.|57|5382|doi:10.1039/D1CC01316E
DOI: 10.5517/ccdc.csd.cc279kw0
title: CCDC 2064782: Experimental Crystal Structure Determination

description: Related Article: Ryosuke Kobayashi, Takashi Yumura, Hiroaki Imoto, Kensuke Naka|2021|Chem.Commun.|57|5382|doi:10.1039/D1CC01316E
DOI: 10.5517/ccdc.csd.cc279kx1
title: CCDC 2064783: Experimental Crystal Structure Determination

description: Related Article: Ryosuke Kobayashi, Takashi Yumura, Hiroaki Imoto, Kensuke Naka|2021|Chem.Commun.|57|5382|doi:10.1039/D1CC01316E
DOI: 10.5517/ccdc.csd.cc279ky2
title: CCDC 2064784: Experimental Crystal Structure Determination

description: Related Article: Ryosuke Kobayashi, Takashi Yumura, Hiroaki Imoto, Kensuke Naka|2021|Chem.Commun.|57|5382|doi:10.1039/D1CC01316E
DOI: 10.5517/ccdc.csd.cc279kz3
title: CCDC 2064785: Experimental Crystal Structure Determination

description: Related Article: Ryosuke Kobayashi, Takashi Yumura, Hiroaki Imoto, Kensuke Naka|2021|Chem.Commun.|57|5382|doi:10.1039/D1CC01316E
DOI: 10.5517/ccdc.csd.cc279l05
title: CCDC 2064786: Experimental Crystal Structure Determination

description: Related Article: Ryosuke Kobayashi, Takashi Yumura, Hiroaki Imoto, Kensuke Naka|2021|Chem.Commun.|57|5382|doi:10.1039/D1CC01316E
DOI: 10.5517/ccdc.csd.cc27fb01
title: CCDC 2068382: Experimental Crystal Structure Determination

description: Related Article: Solhye Choe, Hyosun Lee, Saira Nayab|2021|RSC Advances|11|18840|doi:10.1039/D1RA02365A
DOI: 10.5517/ccdc.csd.cc27fbdf
title: CCDC 2068394: Experimental Crystal Structure Determination

description: Related Article: Leila G. Ranis, Jacqueline Gianino, Justin M. Hoffman, Seth N. Brown|2021|Inorg.Chem.Front.|8|2865|doi:10.1039/D1QI00308A
DOI: 10.5517/ccdc.csd.cc27gl2d
title: CCDC 2069593: Experimental Crystal Structure Determination

description: Related Article: Toby J. Blundell, Michael Brannan, Hiroshi Nishimoto, Tomofumi Kadoya, Jun-ichi Yamada, Hiroki Akutsu, Yasuhiro Nakazawa, Lee Martin|2021|Chem.Commun.|57|5406|doi:10.1039/D1CC01441B
DOI: 10.5517/ccdc.csd.cc27hhgq
title: CCDC 2070473: Experimental Crystal Structure Determination

description: Related Article: Xue-Wen Yi, Juan He, Li-Tang Sun, Ji-Kai Liu, Guo-Kai Wang, Tao Feng|2021|RSC Advances|11|18827|doi:10.1039/D1RA02120F