cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.10320617
title: Output from simulations

description: Data for figures and simulation studies in main paper, after running 02_summarise_sim_results.R<br>Last updated 11 May 2019.<br><br>For code, see https://github.com/mattsperrin/missing_indicator_sim_paper
DOI: 10.6084/m9.figshare.10320353
title: Wildtype_NPT_production_files.zip

description: Trajectory files for a 20 ns simulation of collagen without crosslinks.
DOI: 10.6084/m9.figshare.10320353.v1
title: Wildtype_NPT_production_files.zip

description: Trajectory files for a 20 ns simulation of collagen without crosslinks.
DOI: 10.6084/m9.figshare.10320347
title: Phylogenetic and morphological infrageneric classification of the genus <i>Dendroceros</i> (Dendrocerotaceae; Anthocerotophyta), with the addition of two new subgenera

description: <i>Dendroceros</i> is one of the most diverse genera of hornworts, with 41 species and a widespread distribution. The genus is highly distinctive by its epiphytic habitat, gametophytes with perforated wings, multicellular spores due to endosporic germination, highly modified pyrenoid, and desiccation tolerance. The genus is monophyletic and, until now, was divided into two subgenera (subg. <i>Apoceros</i> and subg. <i>Dendroceros</i>). This classification has not been tested within a phylogenetic framework. Using published phylogenetic data from four molecular markers (<i>rbcL</i>, <i>trnK</i> including <i>matK</i>, <i>rps4</i>, and <i>nad5</i>) as well as morphological observations of over 80 specimens, we chose 38 morphological characters, which were mapped in the best maximum likelihood tree and optimized for reconstruction of ancestral character. We circumscribe the genus <i>Dendroceros</i> and propose 4 subgenera: subg. <i>Apoceros</i> (eight species), characterized for having schizogenous cavities in the midrib; subg. <i>Dendroceros</i> (14 species) with undulate-crispate gametophyte wings, and spore distal surface covered by aculeate papilla; subg. <i>Nodulosus</i> (five species) with rectangular cells in the capsule with nodular wall thickenings; and the monotypic subg. <i>Cichoraceus</i> with a single species that has granular spore micro-ornamentations, and a macro-ornamentation formed by pustulate papilla.
DOI: 10.6084/m9.figshare.10320344
title: DOGDIC_NPT_production_files.zip

description: Trajectory files for a 20 ns simulation of collagen with a DOGDIC crosslink.
DOI: 10.6084/m9.figshare.10320347.v1
title: Phylogenetic and morphological infrageneric classification of the genus <i>Dendroceros</i> (Dendrocerotaceae; Anthocerotophyta), with the addition of two new subgenera

description: <i>Dendroceros</i> is one of the most diverse genera of hornworts, with 41 species and a widespread distribution. The genus is highly distinctive by its epiphytic habitat, gametophytes with perforated wings, multicellular spores due to endosporic germination, highly modified pyrenoid, and desiccation tolerance. The genus is monophyletic and, until now, was divided into two subgenera (subg. <i>Apoceros</i> and subg. <i>Dendroceros</i>). This classification has not been tested within a phylogenetic framework. Using published phylogenetic data from four molecular markers (<i>rbcL</i>, <i>trnK</i> including <i>matK</i>, <i>rps4</i>, and <i>nad5</i>) as well as morphological observations of over 80 specimens, we chose 38 morphological characters, which were mapped in the best maximum likelihood tree and optimized for reconstruction of ancestral character. We circumscribe the genus <i>Dendroceros</i> and propose 4 subgenera: subg. <i>Apoceros</i> (eight species), characterized for having schizogenous cavities in the midrib; subg. <i>Dendroceros</i> (14 species) with undulate-crispate gametophyte wings, and spore distal surface covered by aculeate papilla; subg. <i>Nodulosus</i> (five species) with rectangular cells in the capsule with nodular wall thickenings; and the monotypic subg. <i>Cichoraceus</i> with a single species that has granular spore micro-ornamentations, and a macro-ornamentation formed by pustulate papilla.
DOI: 10.6084/m9.figshare.10320617.v2
title: Output from simulations

description: Data for figures and simulation studies in main paper, after running 02_summarise_sim_results.R<br>Last updated 11 May 2019.<br><br>For code, see https://github.com/mattsperrin/missing_indicator_sim_paper
DOI: 10.6084/m9.figshare.10320344.v1
title: DOGDIC_NPT_production_files.zip

description: Trajectory files for a 20 ns simulation of collagen with a DOGDIC crosslink.
DOI: 10.6084/m9.figshare.10320617.v1
title: Output from simulations

description: Data for figures and simulation studies in main paper, after running 02_summarise_sim_results.R<br>For code, see https://github.com/mattsperrin/missing_indicator_sim_paper
DOI: 10.6084/m9.figshare.10320266
title: Supplemental Data from Three-dimensional myocardial strain correlates with murine left ventricular remodelling severity post-infarction

description: Data summarizing the global metrics of LV function obtained from 4DUS data. Strain data measured radially from the apex of the LV are also made available for mice that underwent sham, ischemia–reperfusion (I/R), and permanent ligation (PL) surgeries.
DOI: 10.6084/m9.figshare.10320266.v1
title: Supplemental Data from Three-dimensional myocardial strain correlates with murine left ventricular remodelling severity post-infarction

description: Data summarizing the global metrics of LV function obtained from 4DUS data. Strain data measured radially from the apex of the LV are also made available for mice that underwent sham, ischemia–reperfusion (I/R), and permanent ligation (PL) surgeries.
DOI: 10.6084/m9.figshare.10320227
title: Uniaxial compressive strength data from Microscopic characterization and strength characteristics of cemented backfill under different humidity curing conditions

description: This document indicates that the data used in the paper was tested by the instrument.
DOI: 10.6084/m9.figshare.10320104
title: The raw data of KIT cloning, mRNA expression.

description: The raw data include KIT cloning, mRNA expression of KIT and melanogenesis-related genes, melanocyte proliferation, apoptosis and melanogenesis. The results indicated KIT positively regulated mRNA expression of melanogenesis-related genes, and promoted melanocyte proliferation and melanogenesis.
DOI: 10.6084/m9.figshare.10320227.v1
title: Uniaxial compressive strength data from Microscopic characterization and strength characteristics of cemented backfill under different humidity curing conditions

description: This document indicates that the data used in the paper was tested by the instrument.
DOI: 10.6084/m9.figshare.10320044
title: Integrating Open Science in the Humanities: The Case of Computational History - Mikko Tolonen, Keynote Talk #SciData19

description: Mikko Tolonen delivers the fourth keynote talk at Better Science through Better Data 2019 (#SciData19) - his talk is entitled 'Integrating Open Science in the Humanities: The Case of Computational History'. The video recording, presentation slides and live scribe illustrations are included.<br>Mikko Tolonen is an assistant professor of Digital Humanities at the University of Helsinki. He is the PI of Helsinki Computational History Group (COMHIS). In 2015-17 he also worked in the National Library of Finland on digitized newspapers as professor of research on digital resources. He is the chair of Digital Humanities in the Nordic Countries (DHN). His current main research focus is on an integrated study of early modern public discourse and knowledge production that combines bibliographic metadata and full-text sources. In 2016, he was awarded an Open Science and Research Award by the Finnish Ministry of Education and Culture.<br>
DOI: 10.6084/m9.figshare.10319807
title: Getting Big Data: Social Scientists Must Strive To Be Autonomous From Corporate Charity - David Stillwell, Keynote Talk #SciData19

description: David Stillwell delivers the third keynote talk at Better Science through Better Data 2019 (#SciData19) - his talk is entitled 'Getting Big Data: Social Scientists Must Strive To Be Autonomous From Corporate Charity'. The video recording, presentation slides and live scribe illustrations are included.<br>David is a Lecturer in Big Data Analytics and Quantitative Social Science at Cambridge University’s Judge Business School. David's research uses big data to understand psychology. He has published papers using social media data from millions of consenting individuals to show that the computer can predict a user's personality as accurately as their spouse can. This research has important public policy implications. How should consumers' data be used to target them? Should regulators step in, and if so how? David has spoken at workshops at the EU Parliament and to UK government regulators.<br>David has also published research using various big data sources such as from credit card data and textual data to show that spending money on products that match one's personality leads to greater life satisfaction, that people tend to date others whose personality is similar, and that people who swear seem to be more honest.<br><br>
DOI: 10.6084/m9.figshare.10320104.v2
title: The raw data of KIT cloning, mRNA expression.

description: The raw data include KIT cloning, mRNA expression of KIT and melanogenesis-related genes, melanocyte proliferation, apoptosis and melanogenesis. The results indicated KIT positively regulated mRNA expression of melanogenesis-related genes, and promoted melanocyte proliferation and melanogenesis.
DOI: 10.6084/m9.figshare.10319807.v2
title: Getting Big Data: Social Scientists Must Strive To Be Autonomous From Corporate Charity - David Stillwell, Keynote Talk #SciData19

description: David Stillwell delivers the third keynote talk at Better Science through Better Data 2019 (#SciData19) - his talk is entitled 'Getting Big Data: Social Scientists Must Strive To Be Autonomous From Corporate Charity'. The video recording, presentation slides and live scribe illustrations are included.<br>David is a Lecturer in Big Data Analytics and Quantitative Social Science at Cambridge University’s Judge Business School. David's research uses big data to understand psychology. He has published papers using social media data from millions of consenting individuals to show that the computer can predict a user's personality as accurately as their spouse can. This research has important public policy implications. How should consumers' data be used to target them? Should regulators step in, and if so how? David has spoken at workshops at the EU Parliament and to UK government regulators.<br>David has also published research using various big data sources such as from credit card data and textual data to show that spending money on products that match one's personality leads to greater life satisfaction, that people tend to date others whose personality is similar, and that people who swear seem to be more honest.<br><br>
DOI: 10.6084/m9.figshare.10319672.v1
title: Updated knowledge on the Notoungulata (Mammalia) from the late Miocene Cerro Azul Formation, La Pampa Province, Argentina

description: We describe new remains of notoungulates from the Cerro Azul Formation (late Miocene) in La Pampa Province and reassign others previously studied, helping to precise the taxonomic diversity of 25 fossiliferous localities of this formation. Geomorphological context previously described for the area provides data to place some localities in a temporal sequence. The following Notoungulata are recognised: <i>Protypotherium minutum</i> (Interatheriidae), <i>Paedotherium minor, Tremacyllus impressus, Hemihegetotherium achataleptum, H. torresi</i>, and <i>Hemihegetotherium</i> sp. (Hegetotheriidae), <i>Typotheriopsis silveyrai, T. chasicoensis</i>, and <i>Pseudotypotherium subinsigne</i> (Mesotheriidae), and <i>Pisanodon nazari, Hemixotodon chasicoensis</i>, and cf. <i>Stenotephanos plicidens</i> (Toxodontidae). This study allows adding new localities to the biochronological sequence previously proposed based on the evolutionary pattern of the octodontoid rodents. Chasicoan and early Huayquerian (as herein considered) faunal assemblages present higher diversity of notoungulates than those assigned to late Huayquerian. Some localities lack a representation of taxa significant enough to precise their biochronological position within the late Miocene. The difference in the notoungulates representation along the Chasicoan–Huayquerian ages may be due to climatic changes that affected the centre of Argentina from late Miocene to early Pliocene.
DOI: 10.6084/m9.figshare.10319807.v1
title: Getting Big Data: Social Scientists Must Strive To Be Autonomous From Corporate Charity - David Stillwell, #SciData Keynote Talk

description: David Stillwell delivers the third keynote talk at Better Science through Better Data 2019 (#SciData19) - his talk is entitled 'Getting Big Data: Social Scientists Must Strive To Be Autonomous From Corporate Charity'. The video recording, presentation slides and live scribe illustrations are included.<br>David is a Lecturer in Big Data Analytics and Quantitative Social Science at Cambridge University’s Judge Business School. David's research uses big data to understand psychology. He has published papers using social media data from millions of consenting individuals to show that the computer can predict a user's personality as accurately as their spouse can. This research has important public policy implications. How should consumers' data be used to target them? Should regulators step in, and if so how? David has spoken at workshops at the EU Parliament and to UK government regulators.<br>David has also published research using various big data sources such as from credit card data and textual data to show that spending money on products that match one's personality leads to greater life satisfaction, that people tend to date others whose personality is similar, and that people who swear seem to be more honest.<br><br>
DOI: 10.6084/m9.figshare.10320104.v1
title: The raw data of KIT cloning, mRNA expression.

description: The raw data include KIT cloning, mRNA expression of KIT and melanogenesis-related genes, melanocyte proliferation, apoptosis and melanogenesis. The results indicated KIT positively regulated mRNA expression of melanogenesis-related genes, and promoted melanocyte proliferation and melanogenesis.
DOI: 10.6084/m9.figshare.10319567
title: Supplemental table for IOL&RS

description: This is the supplemental figure for IOL&RS (a journal in Japanese language)
DOI: 10.6084/m9.figshare.10319534
title: Lightning talks: 7 data sharing stories #SciData19

description: Seven data sharing stories, in seven minutes or less. The video recordings, presentation slides and live scribe illustrations are included.<br>Featuring:<br>The citation advantage of linking publications to research data - Barbara McGillivray, The Alan Turing Institute and University of Cambridge<br>Bibliographic Data Science: Open Ecosystems for Scalable Collaboration - Leo Lahti, University of Turku <br>Reproducible Research - Why and How? -Yasemin Turkyilmaz-van der Velden, TU Delft<br>Research Data Management Using Open Source Off The Shelf Tools -Graham Addis, Nuffield Department of Experimental Medicine<br>X chromosome genetic data in a Spanish children cohort, dataset description and analysis pipeline - Augusto Anguita-Ruiz, University of Granada<br>Participatory Science to Empower: Building a Citizen Science Platform - Georgia Aitkenhead, The Alan Turing Institute<br>‘Open Science’ opens doors: How #Scidata18 helped me unlock career opportunities - Connie Clare, University of Nottingham
DOI: 10.6084/m9.figshare.10319567.v1
title: Supplemental table for IOL&RS

description: This is the supplemental figure for IOL&RS (a journal in Japanese language)
DOI: 10.6084/m9.figshare.10319534.v1
title: Lightning talks: 7 data sharing stories #SciData19

description: Seven data sharing stories, in seven minutes or less. The video recordings, presentation slides and live scribe illustrations are included.<br>Featuring:<br>The citation advantage of linking publications to research data - Barbara McGillivray, The Alan Turing Institute and University of Cambridge<br>Bibliographic Data Science: Open Ecosystems for Scalable Collaboration - Leo Lahti, University of Turku <br>Reproducible Research - Why and How? -Yasemin Turkyilmaz-van der Velden, TU Delft<br>Research Data Management Using Open Source Off The Shelf Tools -Graham Addis, Nuffield Department of Experimental Medicine<br>X chromosome genetic data in a Spanish children cohort, dataset description and analysis pipeline - Augusto Anguita-Ruiz, University of Granada<br>Participatory Science to Empower: Building a Citizen Science Platform - Georgia Aitkenhead, The Alan Turing Institute<br>‘Open Science’ opens doors: How #Scidata18 helped me unlock career opportunities - Connie Clare, University of Nottingham
DOI: 10.6084/m9.figshare.10318838
title: Simulated Logs

description: Event logs in xes format with 250 instances each.
DOI: 10.6084/m9.figshare.10318835
title: Models as Petrinets

description: Petri net models
DOI: 10.6084/m9.figshare.10318838.v1
title: Simulated Logs

description: Event logs in xes format with 250 instances each.
DOI: 10.6084/m9.figshare.10318832
title: Model as BPMN

description: Business process models in BPMN.
DOI: 10.6084/m9.figshare.10318985.v1
title: VTKEL* dataset

description: <b>VTKEL </b>dataset with 1000 documents.
DOI: 10.6084/m9.figshare.10318823
title: Panel Discussion: Who's afraid of data misuse? #SciData19

description: Grace Baynes chairs the panel discussion entitled 'Who's afraid of data misuse?' at Better Science through Better Data 2019 (#SciData19). <br>The panelists are:<br>- Yves-Alexandre de Montjoye, Imperial College London- Sabina Leonelli, University of Exeter- Shelley Stall, American Geophysical Union<br>The video recording, live scribe illustrations plus Grace and Sabina's presentation slides are included.<br>
DOI: 10.6084/m9.figshare.10318832.v1
title: Model as BPMN

description: Business process models in BPMN.
DOI: 10.6084/m9.figshare.10318835.v1
title: Models as Petrinets

description: Petri net models
DOI: 10.6084/m9.figshare.10318985.v2
title: VTKEL* dataset

description: <b>VTKEL* dataset with 1000 documents.</b>VTKL* dataset, contains documents composed of pictures with five corresponding textual captions for each image. The VTKL* dataset is obtained from 30K VTKEL dataset designed for visual-textual mention alignment, with links to YAGO ontology, one of the largest <em>web knowledge base</em>. The links are checked manually and fixed the wrong by processing each image caption with PIKES, an <em>NLP</em> tool for <em>entity recognition</em> and <em>linking</em>.<b></b><i></i><sub></sub><sup></sup><br>
DOI: 10.6084/m9.figshare.10318649
title: Putting our money where our mouth is: How funders are backing up data policy with practical support - #SciData19, Funder Talks

description: Join representatives from the top funders of open data for '<em>Putting our money where our mouth is: How funders are backing up data policy with practical support' </em><br>Featuring: Maximising the value of research data - Current priorities for Wellcome - David Carr, Wellcome TrustFAIR and open data at the European Commission- Konstantinos Repanas, European CommissionEnabling a national environment for improved data sharing - Initiatives from the HRB- Aileen Sheehy, Irish Health Research BoardBuilding a data sharing culture at Cancer Research UK - Paola Quattroni, CRUKPolicy into practice - Paul Stokes, JISCThe video recording, presentation slides and live scribe illustrations are included.<br>
DOI: 10.6084/m9.figshare.10318985.v3
title: VTKEL* dataset

description: <b>VTKEL* dataset with 1000 documents.</b>VTKL* dataset, contains documents composed of pictures with five corresponding textual captions for each image. The VTKL* dataset is obtained from 30K VTKEL dataset designed for visual-textual mention alignment, with links to YAGO ontology, one of the largest <em>web knowledge base</em>. The links are checked manually and fixed the wrong by processing each image caption with PIKES, an <em>NLP</em> tool for <em>entity recognition</em> and <em>linking</em>.<b></b><i></i><sub></sub><sup></sup><br>
DOI: 10.17045/sthlmuni.10318610
title: pciSeq files in csv format

description: This figshare data set contains more easily accessible format of files included in figshare dataset https://doi.org/10.6084/m9.figshare.7150760<br><i></i>In situ sequencing (ISS) is a technology to sequence RNA directly inside tissue sections, and probabilistic cell typing by ISS (pciSeq) method is a method to spatially map single-cell RNA-sequencing defined cell types. (See Associated Publication)<br> <br>* <b>spots_whole_section</b> files have three columns: genes, X and Y position<br>* <b>mask </b>files contain polygon coordinates of CA1 regions<br>* <b>cells </b>files have cell ID, X and Y position of cells in CA1, and sum of probabilities of all assigned reads. <br><br>* <b>spots_w_segmentation </b>files contain spot assignment results from pciSeq algorithm.<br>* <b>celltype </b>files contain cell type assignment results from pciSeq algorithm.<br><br>
DOI: 10.6084/m9.figshare.10318592
title: Metadata

description: Metadata file that provides units and explanation of all variables.
DOI: 10.6084/m9.figshare.10318595
title: Tree characteristics

description: Files that contains tree characteristics: DBH, height, individual coordinates, and wood density.
DOI: 10.6084/m9.figshare.10318595.v1
title: Tree characteristics

description: Files that contains tree characteristics: DBH, height, individual coordinates, and wood density.
DOI: 10.6084/m9.figshare.10318592.v1
title: Metadata

description: Metadata file that provides units and explanation of all variables.
DOI: 10.6084/m9.figshare.10318172
title: How I Learned to Stop Worrying and Love Open Science - Tomas Knapen, Keynote Talk #SciData19

description: Tomas Knapen gives the second of the keynote talks at Better Science through Better Data 2019 (#SciData19) - his talk is entitled 'How I learned to stop worrying and love open science'. The video recording, presentation slides and live scribe illustrations are included.<br>Tomas is a cognitive neuroscientist whose research focuses on the role sensory topographies (visual retinotopy, auditory tonotopy and bodily somatotopy) play in the detailed organization of the human brain and cognition. For this work, Tomas uses state of the art 7-Tesla MRI techniques. Early-career experiences where he ‘failed to replicate’ previous findings have impressed upon him the need to make research reproducible from top to bottom. Because of this, his lab uses only open methods and puts all their data and methods online. Having invested in these methods, Tomas is convinced that, in the end, it is not a burden to perform open science, rather it provides researchers with great opportunities for ground-breaking science.
DOI: 10.6084/m9.figshare.10318097
title: Metadata_tumour_volume_350mm3

description: Metadata when the tumour had reached 350 mm3
DOI: 10.6084/m9.figshare.10318091
title: Metadata_tumour_volume_50mm3

description: Metadata when the tumours had reached 50 mm3
DOI: 10.6084/m9.figshare.10318091.v1
title: Metadata_tumour_volume_50mm3

description: Metadata when the tumours had reached 50 mm3
DOI: 10.6084/m9.figshare.10318073
title: CL_dataset_v1(20190415)_reduce(0913)_upload(1117).xlsx

description: The factors influencing the choice of maternity care and satisfaction rating compare the obstetrics service provided by a regional public hospital and that by the private sector in Hong Kong