cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25505/fiz.icsd.cc1mkvfg
title: CSD 1506706: Experimental Crystal Structure Determination

description: Related Article: Xiao-Fang Fu, Lian-Cai Lei, Jun-Qing Yang, Li-Hua Gao, Yi-Bo Wang, Li-Li Liu, Xiao-Liang Yuan, Hai-Yan Liang, Rui Sun, Yun-Zhu Liu, Chun-Li Qin|2018|Polyhedron|155|129|doi:10.1016/j.poly.2018.08.037
DOI: 10.25505/fiz.icsd.cc1mkvgh
title: CSD 1506707: Experimental Crystal Structure Determination

description: Related Article: Xiao-Fang Fu, Lian-Cai Lei, Jun-Qing Yang, Li-Hua Gao, Yi-Bo Wang, Li-Li Liu, Xiao-Liang Yuan, Hai-Yan Liang, Rui Sun, Yun-Zhu Liu, Chun-Li Qin|2018|Polyhedron|155|129|doi:10.1016/j.poly.2018.08.037
DOI: 10.25505/fiz.icsd.cc1q64ln
title: CSD 1584831: Experimental Crystal Structure Determination

description: Related Article: Lili Liu, Joshua Young, Manuel Smeu, P. Shiv Halasyamani|2018|Inorg.Chem.|57|4771|doi:10.1021/acs.inorgchem.8b00510
DOI: 10.25505/fiz.icsd.cc1q7mq9
title: CSD 1586261: Experimental Crystal Structure Determination

description: Related Article: Leszek M. Malec, Marlena Gryl, Katarzyna M. Stadnicka|2018|Inorg.Chem.|57|4340|doi:10.1021/acs.inorgchem.7b03161
DOI: 10.25505/fiz.icsd.cc1q7mrb
title: CSD 1586262: Experimental Crystal Structure Determination

description: Related Article: Leszek M. Malec, Marlena Gryl, Katarzyna M. Stadnicka|2018|Inorg.Chem.|57|4340|doi:10.1021/acs.inorgchem.7b03161
DOI: 10.25505/fiz.icsd.cc1q7msc
title: CSD 1586263: Experimental Crystal Structure Determination

description: Related Article: Leszek M. Malec, Marlena Gryl, Katarzyna M. Stadnicka|2018|Inorg.Chem.|57|4340|doi:10.1021/acs.inorgchem.7b03161
DOI: 10.25505/fiz.icsd.cc1q7mtd
title: CSD 1586264: Experimental Crystal Structure Determination

description: Related Article: Leszek M. Malec, Marlena Gryl, Katarzyna M. Stadnicka|2018|Inorg.Chem.|57|4340|doi:10.1021/acs.inorgchem.7b03161
DOI: 10.6084/m9.figshare.13139027
title: R0312_20191107a_videos

description: Rat 0312, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.6084/m9.figshare.13136078
title: Influence of Patient Characteristics on Chimeric Antigen Receptor T Cell Therapy in B-cell Acute Lymphoblastic Leukemia

description: This is the Source Data of the paper titled “Influence of Patient Characteristics on Chimeric Antigen Receptor T Cell Therapy in B-cell Acute Lymphoblastic Leukemia”.
DOI: 10.6084/m9.figshare.13139378
title: Genotype data for Brassica napus ddRAD mapping population bioRxiv 2020.06.26.162966

description: This dataset comprises genotypes of Brassica napus parents and progeny generated for the study "Linkage mapping and QTL analysis of flowering time using ddRAD sequencing with genotype error correction in <i>Brassica napus" . </i>The study is available via bioRxiv: 2020.06.26.162966.<br>File 1 GATK genotype calls from aligned reads of parents and progeny; minimum depth (minDP), maximum missingness (maxmiss) and minor allele frequency (maf) filters were applied: bnapus_ddrad_gatk_genotypes_minDP5_maxmiss05_maf005.vcf.gzFile 2 Genotype calls converted into a filtered genotype matrix with parent1 (A) , parent2 (B), heterozygous (H) and missing (-) genotypes: bnapus_ddrad_gatk_genotypes_minDP5_maxmiss05_maf005.vcf.gzFile 3 GATK genotype calls from aligned low coverage whole genome sequencing reads of progeny diverging for flowering tome; maximum missingness (maxmiss) and minor allele count (mac) filters were applied: bnapus_wgs_gatk_genotypes_maxmiss25_mac5_ann_C02.vcf.gzFile 4 GFF annotation of the B. napus v81 reference genome used for chromosome C2: bnapus_darmor_v81_maker_annotation_C02.gff.gz
DOI: 10.5281/zenodo.4075461
title: Recalibrating classifiers for interpretable abusive content detection

description: Dataset and code for the paper, 'Recalibrating classifiers for interpretable abusive content detection' by Vidgen et al. (2020) -- to appear at the NLP + CSS workshop at EMNLP 2020. We provide: 1,000 annotated tweets, sampled using the Davidson classifier with 20 0.05 increments (50 from each) from a dataset of tweets directed against MPs in the UK 2017 General Election 1,000 annotated tweets, sampled using the Perspective classifier with 20 0.05 increments (50 from each) from a dataset of tweets directed against MPs in the UK 2017 General Election Code for recalibration in R and STAN. Annotation guidelines for both datasets. <strong>Paper abstract</strong> We investigate the use of machine learning classifiers for detecting online abuse in empirical research. We show that uncalibrated classifiers (i.e. where the 'raw' scores are used) align poorly with human evaluations. This limits their use to understand the dynamics, patterns and prevalence of online abuse. We examine two widely used classifiers (created by Perspective and Davidson et al.) on a dataset of tweets directed against candidates in the UK's 2017 general election.<br> A Bayesian approach is presented to recalibrate the raw scores from the classifiers, using probabilistic programming and newly annotated data. We argue that interpretability evaluation and recalibration is integral to the application of abusive content classifiers.
DOI: 10.5281/zenodo.4075460
title: Recalibrating classifiers for interpretable abusive content detection

description: Dataset and code for the paper, 'Recalibrating classifiers for interpretable abusive content detection' by Vidgen et al. (2020) -- to appear at the NLP + CSS workshop at EMNLP 2020. We provide: 1,000 annotated tweets, sampled using the Davidson classifier with 20 0.05 increments (50 from each) from a dataset of tweets directed against MPs in the UK 2017 General Election 1,000 annotated tweets, sampled using the Perspective classifier with 20 0.05 increments (50 from each) from a dataset of tweets directed against MPs in the UK 2017 General Election Code for recalibration in R and STAN. Annotation guidelines for both datasets. <strong>Paper abstract</strong> We investigate the use of machine learning classifiers for detecting online abuse in empirical research. We show that uncalibrated classifiers (i.e. where the 'raw' scores are used) align poorly with human evaluations. This limits their use to understand the dynamics, patterns and prevalence of online abuse. We examine two widely used classifiers (created by Perspective and Davidson et al.) on a dataset of tweets directed against candidates in the UK's 2017 general election.<br> A Bayesian approach is presented to recalibrate the raw scores from the classifiers, using probabilistic programming and newly annotated data. We argue that interpretability evaluation and recalibration is integral to the application of abusive content classifiers.
DOI: 10.6084/m9.figshare.13139027.v1
title: R0312_20191107a_videos

description: Rat 0312, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.34740/kaggle/dsv/1589580
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5281/zenodo.4058479
title: Comfort and wearability of orthodontic mouthguards during contact sports in adolescent patients undergoing fixed appliance orthodontic treatment: a randomised clincal trial

description: Dataset for all analyses in the paper
DOI: 10.5281/zenodo.4058478
title: Comfort and wearability of orthodontic mouthguards during contact sports in adolescent patients undergoing fixed appliance orthodontic treatment: a randomised clincal trial

description: Dataset for all analyses in the paper
DOI: 10.5517/ccdc.csd.cc2371s6
title: CCDC 1943197: Experimental Crystal Structure Determination

description: Related Article: Vitaly A. Osyanin, Dmitry V. Osipov, Irina A. Semenova, Kirill S. Korzhenko, A. V. Lukashenko, Oleg P. Demidov, Yuri N. Klimochkin|2020|RSC Advances|10|34344|doi:10.1039/D0RA06450E
DOI: 10.5517/ccdc.csd.cc23ydk1
title: CCDC 1964673: Experimental Crystal Structure Determination

description: Related Article: Xiaonan Wang, Yuan Gao, Zhonglin Wei, Jungang Cao, Dapeng Liang, Yingjie Lin, Haifeng Duan|2020|Org.Biomol.Chem.|18|7431|doi:10.1039/D0OB01725F
DOI: 10.5517/ccdc.csd.cc251kmf
title: CCDC 1997504: Experimental Crystal Structure Determination

description: Related Article: Chuan-Ping Li, Wei-Wei Long, Zhen Lei, Ling Guo, Ming-Jun Xie, Jian Lü, Xian-Dong Zhu|2020|Chem.Commun.|56|12403|doi:10.1039/D0CC05175F
DOI: 10.5281/zenodo.4126984
title: BreakingVane/Defining-resilience-empirically_UCM: Defining resilience empirically_UCM

description: Database with our main variables to perform multinomial logistic regression analyses used to determine which health-related, psychological, interpersonal and socio-demographic variables predicted the four psychological response patterns over time of resilience. Health-related variables (i.e., pre-existing health condition, pregnancy, SARS-CoV-2 infection and history of mental health difficulties), psychological variables (i.e. anxiety about the COVID-19 pandemic, economic threat due to COVID-19, increased substance use during confinement, intolerance to uncertainty, death anxiety, well-being, openness to the future and self-reported resilience), interpersonal variables (i.e. loneliness, perception of belonging, suspiciousness of others, religious identity, goodness of the world beliefs and identification with humanity) and demographic and living conditions variables (i.e. sex, age, education, employment, income, urbanicity, living with children and housing conditions).
DOI: 10.5281/zenodo.4126983
title: BreakingVane/Defining-resilience-empirically_UCM: Defining resilience empirically_UCM

description: Database with our main variables to perform multinomial logistic regression analyses used to determine which health-related, psychological, interpersonal and socio-demographic variables predicted the four psychological response patterns over time of resilience. Health-related variables (i.e., pre-existing health condition, pregnancy, SARS-CoV-2 infection and history of mental health difficulties), psychological variables (i.e. anxiety about the COVID-19 pandemic, economic threat due to COVID-19, increased substance use during confinement, intolerance to uncertainty, death anxiety, well-being, openness to the future and self-reported resilience), interpersonal variables (i.e. loneliness, perception of belonging, suspiciousness of others, religious identity, goodness of the world beliefs and identification with humanity) and demographic and living conditions variables (i.e. sex, age, education, employment, income, urbanicity, living with children and housing conditions).
DOI: 10.5519/qd.iadi885i
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.6084/m9.figshare.13139033.v1
title: R0311_20191121a_videos

description: Rat 0311, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.6084/m9.figshare.13139033
title: R0311_20191121a_videos

description: Rat 0311, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.25919/5f946786d3554
title: Parkes observations for project PX501 semester 2020APRS_07

description: This proposal relates to time purchased by the Chinese Academy of Sciences for Parkes follow-up confirmation and timing of FAST discoveries. As the observing time has been purchased this proposal (and the related PX500) does not require TAC grading. The primary reason for submitting a proposal is to ensure an outreach statement and that the proposal is entered into OPAL for the data archive to access. The Chinese team have two time requests. The time scheduled for the first (PX500) will be allocated in blocks of 24 hours and the data will become available in the data archive with an 18 month embargo period. The second type of time (relating to this proposal) will be scheduled in shorter blocks and the data archived with a 10 year embargo.
DOI: 10.15468/stv4m5
title: FAMILY SYRINGOGASTRIDAE

description: This dataset contains the digitized treatments in Plazi based on the original journal article Rung, Alessandra (2016): FAMILY SYRINGOGASTRIDAE. Zootaxa 4122 (1): 558-560, DOI: http://dx.doi.org/10.11646/zootaxa.4122.1.43
DOI: 10.5519/qd.ci6kxlsz
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (4663279 records)
DOI: 10.25505/fiz.icsd.cc1q7mvf
title: CSD 1586265: Experimental Crystal Structure Determination

description: Related Article: Leszek M. Malec, Marlena Gryl, Katarzyna M. Stadnicka|2018|Inorg.Chem.|57|4340|doi:10.1021/acs.inorgchem.7b03161
DOI: 10.25505/fiz.icsd.cc1q7mwg
title: CSD 1586266: Experimental Crystal Structure Determination

description: Related Article: Leszek M. Malec, Marlena Gryl, Katarzyna M. Stadnicka|2018|Inorg.Chem.|57|4340|doi:10.1021/acs.inorgchem.7b03161
DOI: 10.25505/fiz.icsd.cc1q7mxh
title: CSD 1586267: Experimental Crystal Structure Determination

description: Related Article: Leszek M. Malec, Marlena Gryl, Katarzyna M. Stadnicka|2018|Inorg.Chem.|57|4340|doi:10.1021/acs.inorgchem.7b03161
DOI: 10.25505/fiz.icsd.cc1q7myj
title: CSD 1586268: Experimental Crystal Structure Determination

description: Related Article: Leszek M. Malec, Marlena Gryl, Katarzyna M. Stadnicka|2018|Inorg.Chem.|57|4340|doi:10.1021/acs.inorgchem.7b03161
DOI: 10.25505/fiz.icsd.cc1q7mzk
title: CSD 1586269: Experimental Crystal Structure Determination

description: Related Article: Leszek M. Malec, Marlena Gryl, Katarzyna M. Stadnicka|2018|Inorg.Chem.|57|4340|doi:10.1021/acs.inorgchem.7b03161
DOI: 10.25505/fiz.icsd.cc1q7n0m
title: CSD 1586270: Experimental Crystal Structure Determination

description: Related Article: Leszek M. Malec, Marlena Gryl, Katarzyna M. Stadnicka|2018|Inorg.Chem.|57|4340|doi:10.1021/acs.inorgchem.7b03161
DOI: 10.25505/fiz.icsd.cc1q7n1n
title: CSD 1586271: Experimental Crystal Structure Determination

description: Related Article: Leszek M. Malec, Marlena Gryl, Katarzyna M. Stadnicka|2018|Inorg.Chem.|57|4340|doi:10.1021/acs.inorgchem.7b03161
DOI: 10.25505/fiz.icsd.cc1q43cb
title: CSD 1582871: Experimental Crystal Structure Determination

description: Related Article: Weiwei Cao, Xiaoyan Yang, Fengqi Lu, Wenfeng Zhu, Laijun Liu, Xiaojun Kuang, Mathieu Allix|2018|Inorg.Chem.|57|4117|doi:10.1021/acs.inorgchem.8b00296
DOI: 10.25919/5f9472f40ddff
title: Parkes observations for project PX500 semester 2020APRS_14

description: This proposal relates to time purchased by the Chinese Academy of Sciences for Parkes follow-up confirmation and timing of FAST discoveries. As the observing time has been purchased this proposal (and the related PX501) does not require TAC grading. The primary reason for submitting a proposal is to ensure an outreach statement and that the proposal is entered into OPAL for the data archive to access. The Chinese team have two time requests. The time scheduled for the first, relating to this proposal, will be allocated in blocks of 24 hours and the data will become available in the data archive with an 18 month embargo period. The second type of time will be scheduled in shorter blocks and the data archived with a 10 year embargo.
DOI: 10.25345/c5wb7p
title: MassIVE MSV000086364 - Eosinophil activation by IL5 and IL33: Similarities and differences
title: Eosinophils (EOS) play a crucial role in various aspects of human immunity and defense. The activation of these leukocytes by cytokines contributes to their functionality and unique phenotypes marked by a dramatic morphological change. The IL5/1-family cytokines IL5 and IL33 are potent activators of eosinophils and lead to distinct morphologies. The discrete pathways that IL5 and IL33 activate and how this contributes to downstream upregulation and subsequent proteomic changes is not well understood. To characterize these pathways, we quantified protein abundance and phosphorylation by multiplexed isobaric labeling combined with high-resolution mass spectroscopy.
DOI: 10.6084/m9.figshare.13139348
title: B cell and CD4 cell

description: bigwig
DOI: 10.6084/m9.figshare.13139348.v1
title: B cell and CD4 cell

description: bigwig
DOI: 10.5281/zenodo.4127118
title: The Little Things that Run the City dataset

description: Plant-insect metanetwork dataset collected across 15 greenspaces within a densely urbanised inner-city municipality (Melbourne, Australia).
DOI: 10.25505/fiz.icsd.cc21lsp4
title: CSD 1894896: Experimental Crystal Structure Determination

description: Related Article: Simon D. Kloß, Wolfgang Schnick|2018|Inorg.Chem.|57|4189|doi:10.1021/acs.inorgchem.8b00455
DOI: 10.5281/zenodo.4127235
title: Updated dataset for the manuscript - "Pore-scale Water Vapor Condensation Behaviors in Shales: An Experimental Study"

description: Updated dataset for the manuscript entitled "Pore-scale Water Vapor Condensation Behaviors in Shales: An Experimental Study".
DOI: 10.5281/zenodo.3711064
title: Updated dataset for the manuscript - "Pore-scale Water Vapor Condensation Behaviors in Shales: An Experimental Study"

description: Updated dataset for the manuscript entitled "Pore-scale Water Vapor Condensation Behaviors in Shales: An Experimental Study".
DOI: 10.5281/zenodo.4127023
title: China Brazil Joint Na-K LIDAR data

description: These data are photon counting of Na-K Dual-beam LIDAR operation from 2017 to 2019 in São José dos Campos under the Cooperative Agreement between the National Space Science Center-CAS, China, and the National Institute for Space Research - MCTI, Brazil. That agreement was signed in São José dos Campos on August 6th, 2014. More information about the system and data acquisition can be found at Adrioli et al. (2020) (https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019JA027164)
DOI: 10.5281/zenodo.4127022
title: China Brazil Joint Na-K LIDAR data

description: These data are photon counting of Na-K Dual-beam LIDAR operation from 2017 to 2019 in São José dos Campos under the Cooperative Agreement between the National Space Science Center-CAS, China, and the National Institute for Space Research - MCTI, Brazil. That agreement was signed in São José dos Campos on August 6th, 2014. More information about the system and data acquisition can be found at Adrioli et al. (2020) (https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019JA027164)
DOI: 10.5519/qd.onikmmwo
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (159 records)
DOI: 10.13012/b2idb-3968226_v1
title: Dataset for "Why are Drainage Networks Dendritic?"

description: This dataset contains data from 10 simulations using a landscape evolution model. A landscape evolution model simulates how uplift and rock incision shape the Earth's (or other planets) surface. To date, most landscape evolution models exhibit "extreme memory" (paper: https://doi.org/10.1029/2019GL083305 and dataset: https://doi.org/10.13012/B2IDB-4484338_V1). Extreme memory in landscape evolution models causes initial conditions to be unrealistically preserved. This dataset contains simulations from a new landscape evolution model that incorporates a sub-model that allows bedrock channels to erode laterally. With this addition, the landscapes no longer exhibit extreme memory. Initial conditions are erased over time, and the landscapes tend towards a dynamic steady state instead of a static one. There are 2 animations for each simulation, which totals for 20 animations (10 simulations x 2). For each simulation, there is 1 animation that shows elevation and 1 animation that shows drainage area over time. Elevation depicts the height of the landscape, and drainage area represents a contributing area that is upslope. There are 20 folders in the dataset, and each folder contains: &gt;1000 .asc raster files &gt;1 .wmv formatted movie &gt;1 .mp4 formatted movie The associated publication for this dataset has not yet been published, and we will update this description with a link when it is.
DOI: 10.7910/dvn/aebefe
title: Replication Data for: Russian Holidays Predict Troll Activity 2015-2017

description: Datasets and programs for ``Russian Holidays Predict Troll Activity 2015-2017''
DOI: 10.25505/fiz.icsd.cc23s3kl
title: CSD 1959589: Experimental Crystal Structure Determination

description: Related Article: Jian Lin, Zenghui Yue, Mark A. Silver, Meiying Qie, Xiaomei Wang, Wei Liu, Xiao Lin, Hong-Liang Bao, Lin-Juan Zhang, Shuao Wang, Jian-Qiang Wang|2018|Inorg.Chem.|57|6753|doi:10.1021/acs.inorgchem.8b01098
DOI: 10.25505/fiz.icsd.cc24r3mn
title: CSD 1988421: Experimental Crystal Structure Determination

description: Related Article: P.C. Quayle|2020|Acta Crystallogr.,Sect.A: Found. Adv.|76|410|doi:10.1107/S2053273320003095
DOI: 10.25505/fiz.icsd.cc24r3np
title: CSD 1988422: Experimental Crystal Structure Determination

description: Related Article: P.C. Quayle|2020|Acta Crystallogr.,Sect.A: Found. Adv.|76|410|doi:10.1107/S2053273320003095
DOI: 10.25505/fiz.icsd.cc24r3pq
title: CSD 1988423: Experimental Crystal Structure Determination

description: Related Article: P.C. Quayle|2020|Acta Crystallogr.,Sect.A: Found. Adv.|76|410|doi:10.1107/S2053273320003095
DOI: 10.25505/fiz.icsd.cc24r3qr
title: CSD 1988424: Experimental Crystal Structure Determination

description: Related Article: P.C. Quayle|2020|Acta Crystallogr.,Sect.A: Found. Adv.|76|410|doi:10.1107/S2053273320003095
DOI: 10.6084/m9.figshare.13139420.v1
title: R0311_20191122a_videos

description: Rat 0311, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.6084/m9.figshare.13139420
title: R0311_20191122a_videos

description: Rat 0311, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.6084/m9.figshare.13139390.v1
title: R0312_20191108a_videos

description: Rat 0312, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.6084/m9.figshare.13139390
title: R0312_20191108a_videos

description: Rat 0312, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.5281/zenodo.4122270
title: Dataset for "Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control"

description: Dataset to reproduce results of "Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control" <strong>Abstract</strong>: Recent approaches for modelling dynamics of physical systems with neural networks enforce Lagrangian or Hamiltonian structure to improve prediction and generalization. However, when coordinates are embedded in high-dimensional data such as images, these approaches either lose interpretability or can only be applied to one particular example. We introduce a new unsupervised neural network model that learns Lagrangian dynamics from images, with interpretability that benefits prediction and control. The model infers Lagrangian dynamics on generalized coordinates that are simultaneously learned with a coordinate-aware variational autoencoder (VAE). The VAE is designed to account for the geometry of physical systems composed of multiple rigid bodies in the plane. By inferring interpretable Lagrangian dynamics, the model learns physical system properties, such as kinetic and potential energy, which enables long-term prediction of dynamics in the image space and synthesis of energy-based controllers.