cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5130737
title: SemEval-2021 Task 12: Learning with Disagreements

description: This repository contains the Post-Evaluation data for SemEval-2021 Task 12: Learning with Disagreement, a shared task on learning to classify with datasets containing disagreements. The aim of this shared task is to provide a unified testing framework for learning from disagreements using the best-known datasets containing information about disagreements for interpreting language and classifying images: 1. LabelMe-IC: Image Classification using a subset of LabelMe images (Russell et al., 2008), is a widely used, community-created image classification dataset where images are assigned to one of 8 categories: highway, inside city, tall building, street, forest, coast, mountain, open country. Rodrigues and Pereira (2017) collected crowd labels for these images using Amazon Mechanical Turk (AMT). 2. CIFAR10-IC: Image Classification using a subset of CIFAR-10 dataset, https://www.cs.toronto.edu/~kriz/cifar.html. The entire dataset consists of colour images in 10 categories (airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck). Crowdsourced labels for this dataset were collected by Peterson et al (2019). 3. PDIS: Information Status Classification using Phrase Detectives Information. Information Status Classification (IS) in Phrase Detectives (Poesio et al., 2019) dataset involves identifying the information status of a noun phrase: whether that noun phrase refers to new information or to old information. 4. Gimpel-POS: Part-of-Speech tagging using the Gimpel dataset (Gimpel et al., 2011) for Twitter posts. Plank et al.(2014b) mapped the Gimpel tags to the universal tag set (Petrov et al., 2011), using these tags as gold, and collected crowdsourced labels. 5. Humour: ranking one-line texts using pairwise funniness judgements (Simpson et al., 2019). Crowdworkers have annotated pairs of puns to indicate which is funniest. A gold standard ranking was produced using a large number of redundant annotations. The goal is to infer the gold standard ranking from a reduced number of crowdsourced judgements. <br> The files contained in this data collection are as follows:<br> starting_kit.zip - Base models used provided for the shared task. <br> practice_phase_data.zip - The training and development data used during the Practice Phase of the competition. <br> test_phase_data.zip - The test data, used during the Evaluation Phase of the competition Details of format of each dataset for each task can be found on Codalab.
description: This research is supported in part by the Independent Research Fund Denmark (DFF) grant 9131-00019B and 9063-00077B.
description: {"references": ["Gimpel et al. (2011) Part-of-speech tagging for Twitter: Annotation, features, and experiments", "Russell et al. (2008) LabelMe: A database and Web-based tool for image annotation.", "Rodrigues and Pereira (2018) Deep learning from crowds", "Krizhevsky (2009) Learning multiple layers of features from tiny images.", "Peterson et al. (2019) Human un-certainty makes classification more robust"]}
DOI: 10.5281/zenodo.5130736
title: SemEval-2021 Task 12: Learning with Disagreements

description: This repository contains the Post-Evaluation data for SemEval-2021 Task 12: Learning with Disagreement, a shared task on learning to classify with datasets containing disagreements. The aim of this shared task is to provide a unified testing framework for learning from disagreements using the best-known datasets containing information about disagreements for interpreting language and classifying images: 1. LabelMe-IC: Image Classification using a subset of LabelMe images (Russell et al., 2008), is a widely used, community-created image classification dataset where images are assigned to one of 8 categories: highway, inside city, tall building, street, forest, coast, mountain, open country. Rodrigues and Pereira (2017) collected crowd labels for these images using Amazon Mechanical Turk (AMT). 2. CIFAR10-IC: Image Classification using a subset of CIFAR-10 dataset, https://www.cs.toronto.edu/~kriz/cifar.html. The entire dataset consists of colour images in 10 categories (airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck). Crowdsourced labels for this dataset were collected by Peterson et al (2019). 3. PDIS: Information Status Classification using Phrase Detectives Information. Information Status Classification (IS) in Phrase Detectives (Poesio et al., 2019) dataset involves identifying the information status of a noun phrase: whether that noun phrase refers to new information or to old information. 4. Gimpel-POS: Part-of-Speech tagging using the Gimpel dataset (Gimpel et al., 2011) for Twitter posts. Plank et al.(2014b) mapped the Gimpel tags to the universal tag set (Petrov et al., 2011), using these tags as gold, and collected crowdsourced labels. 5. Humour: ranking one-line texts using pairwise funniness judgements (Simpson et al., 2019). Crowdworkers have annotated pairs of puns to indicate which is funniest. A gold standard ranking was produced using a large number of redundant annotations. The goal is to infer the gold standard ranking from a reduced number of crowdsourced judgements. <br> The files contained in this data collection are as follows:<br> starting_kit.zip - Base models used provided for the shared task. <br> practice_phase_data.zip - The training and development data used during the Practice Phase of the competition. <br> test_phase_data.zip - The test data, used during the Evaluation Phase of the competition Details of format of each dataset for each task can be found on Codalab.
description: This research is supported in part by the Independent Research Fund Denmark (DFF) grant 9131-00019B and 9063-00077B.
description: {"references": ["Gimpel et al. (2011) Part-of-speech tagging for Twitter: Annotation, features, and experiments", "Russell et al. (2008) LabelMe: A database and Web-based tool for image annotation.", "Rodrigues and Pereira (2018) Deep learning from crowds", "Krizhevsky (2009) Learning multiple layers of features from tiny images.", "Peterson et al. (2019) Human un-certainty makes classification more robust"]}
DOI: 10.6084/m9.figshare.15058173
title: elepant grass genome.fasta

description: elephant grass genome data<br>
DOI: 10.5281/zenodo.5139187
title: Benchmark dataset for arby

description: Datasets for the benchmarks performed on the reduce_basis function of the arby project https://arby.readthedocs.io/
DOI: 10.5281/zenodo.5139186
title: Benchmark dataset for arby

description: Datasets for the benchmarks performed on the reduce_basis function of the arby project https://arby.readthedocs.io/
DOI: 10.6084/m9.figshare.15058329.v1
title: Post-IDP17 Nd data

description: TODO: This description is a draft and will be updated soon.<br><br>Marine neodymium and epsilon data taken from the literature after GEOTRACES IDP-17.<br>Compilation of data from the Indian Ocean (Amakawaet al., 2019), the Barents Sea (Laukert et al., 2018; Lauk-ert et al., 2019), the northern Iceland Basin (Morrison et al.,2019), the Northwestern Pacific (Che and Zhang, 2018), the Kerguelen Plateau (Grenier et al., 2018), the southeastern At-lantic Ocean (GA08, Rahlf et al., 2020; Rahlf et al., 2019;Rahlf et al., 2021; Rahlf et al., 2020), the Bay of Biscay (Dausmann et al., 2020; Dausmann et al., 2019), the Western North Atlantic (Stichel et al., 2020; Stichel et al., 2020), the arctic (Laukert et al., 2017; Laukert et al., 2017a, d), and theBermuda Atlantic Time-series Study (BATS; Laukert et al.,2017; Laukert et al., 2017b, c)<br><br>See Pasquier et al. (in prep) for more details<br><br>
DOI: 10.6084/m9.figshare.15058329
title: Post-IDP17 Nd data

description: TODO: This description is a draft and will be updated soon.<br><br>Marine neodymium and epsilon data taken from the literature after GEOTRACES IDP-17.<br>Compilation of data from the Indian Ocean (Amakawaet al., 2019), the Barents Sea (Laukert et al., 2018; Lauk-ert et al., 2019), the northern Iceland Basin (Morrison et al.,2019), the Northwestern Pacific (Che and Zhang, 2018), the Kerguelen Plateau (Grenier et al., 2018), the southeastern At-lantic Ocean (GA08, Rahlf et al., 2020; Rahlf et al., 2019;Rahlf et al., 2021; Rahlf et al., 2020), the Bay of Biscay (Dausmann et al., 2020; Dausmann et al., 2019), the Western North Atlantic (Stichel et al., 2020; Stichel et al., 2020), the arctic (Laukert et al., 2017; Laukert et al., 2017a, d), and theBermuda Atlantic Time-series Study (BATS; Laukert et al.,2017; Laukert et al., 2017b, c)<br><br>See Pasquier et al. (in prep) for more details<br><br>
DOI: 10.5517/ccdc.csd.cc1yxjx9
title: CCDC 1814892: Experimental Crystal Structure Determination

description: Related Article: K. Anitha, M. Subha, M. Tamil Elakkiya|2021|J.Mol.Struct.|1244|130850|doi:10.1016/j.molstruc.2021.130850
DOI: 10.5517/ccdc.csd.cc23f8vp
title: CCDC 1949182: Experimental Crystal Structure Determination

description: Related Article: Mathias Glatz, Nikolaus Gorgas, Berthold Stöger, Ernst Pittenauer, Liliana Ferreira, Luis F. Veiros, Maria José Calhorda, Karl Kirchner|2021|Z.Anorg.Allg.Chem.|647|1429|doi:10.1002/zaac.202100015
DOI: 10.5517/ccdc.csd.cc23f8wq
title: CCDC 1949183: Experimental Crystal Structure Determination

description: Related Article: Mathias Glatz, Nikolaus Gorgas, Berthold Stöger, Ernst Pittenauer, Liliana Ferreira, Luis F. Veiros, Maria José Calhorda, Karl Kirchner|2021|Z.Anorg.Allg.Chem.|647|1429|doi:10.1002/zaac.202100015
DOI: 10.5517/ccdc.csd.cc26wh5s
title: CCDC 2052205: Experimental Crystal Structure Determination

description: Related Article: Ehud Gazit, Yu Chen, Yuqin Yang, Asuka A. Orr, Pandeeswar Makam, Boris Redko, Elvira Haimov, Yannan Wang, Linda J. W. Shimon, Sigal Rencus-Lazar, Meiting Ju, Phanourios Tamamis, Hao Dong|2021|Angew.Chem.,Int.Ed.|60|17164|doi:10.1002/anie.202105830
DOI: 10.5517/ccdc.csd.cc26z30b
title: CCDC 2054711: Experimental Crystal Structure Determination

description: Related Article: Saad Shaaban, Hela Ferjani, Ismail Althagafi, Tarek Yousef|2021|J.Mol.Struct.|1245|131072|doi:10.1016/j.molstruc.2021.131072
DOI: 10.5517/ccdc.csd.cc2712v7
title: CCDC 2056628: Experimental Crystal Structure Determination

description: Related Article: Chunhui Zhou, Jinsong Hu, Priyanka Chakraborty, Kuo‐Wei Huang|2021|Z.Anorg.Allg.Chem.|647|1399|doi:10.1002/zaac.202100050
DOI: 10.48342/drss-8q87
title: Uzbek Speech Corpus

description: The Uzbek speech corpus (USC) has been developed in academic collaboration between ISSAI and TUIT. The USC comprises 958 different speakers with a total of 105 hours of transcribed audio recordings. To ensure high quality, the USC has been manually checked by native speakers. The USC is primarily designed for the ASR task, however, it can also be used to aid other speech-related tasks, such as speech synthesis and speech translation. To the best of our knowledge, the USC is the first open-source Uzbek speech corpus available for both academic and commercial use under the Creative Commons Attribution 4.0 International License. We expect that the USC will be a valuable resource for the general speech research community and become the baseline dataset for Uzbek ASR research.
DOI: 10.6084/m9.figshare.14152016
title: Interpretable machine learning for analysing heterogeneous drivers of geographic events in space-time

description: ABSTRACT<br> Machine learning (ML) interpretability has become increasingly crucial for identifying accurate and relevant structural relationships between spatial events and factors that explain them. Methodologically aspatial ML algorithms with an apparent high predictive power ignore non-stationary domain relationships in spatio-temporal data (e.g., dependence, heterogeneity), leading to incorrect interpretations and poor management decisions. This study addresses this critical methodological issue of ‘interpretability’ in ML-based modeling of structural relationships using the example of heterogeneous drivers of wildfires across the United States. Specifically, we present and evaluate a spatio-temporally interpretable random forest (iST-RF) that uses spatio-temporal sampling-based training and weighted prediction. Although the ultimate scientific objective is to derive interpretation in space-time, experiments show that iST-RF can improve predictive accuracy (76%) compared to the aspatial RF approach (70%), while enhancing interpretations of the trained model’s spatio-temporal relevance for its ensemble prediction. This novel approach can help balance prediction and interpretation with fidelity in a spatial data science life cycle. However, challenges exist for predictive modeling when dataset is very small, because in such cases locally optimized sub-model’s prediction performance can be suboptimal. With that caveat, our proposed approach is an ideal choice for identifying drivers of spatio-temporal events at country or regional-scale studies. <br><br><i>Author contributions</i> A.M. conceived and designed the study, coded and performed data processing, modeling and interpretations, and wrote the manuscript. M.Y., P.M., D.P., and A.T. contributed to the refinement of the proposed methodology, experiments, and write-up. All authors reviewed the manuscript.<br>
DOI: 10.6084/m9.figshare.14152016.v1
title: Interpretable machine learning for analysing heterogeneous drivers of geographic events in space-time

description: ABSTRACT<br> Machine learning (ML) interpretability has become increasingly crucial for identifying accurate and relevant structural relationships between spatial events and factors that explain them. Methodologically aspatial ML algorithms with an apparent high predictive power ignore non-stationary domain relationships in spatio-temporal data (e.g., dependence, heterogeneity), leading to incorrect interpretations and poor management decisions. This study addresses this critical methodological issue of ‘interpretability’ in ML-based modeling of structural relationships using the example of heterogeneous drivers of wildfires across the United States. Specifically, we present and evaluate a spatio-temporally interpretable random forest (iST-RF) that uses spatio-temporal sampling-based training and weighted prediction. Although the ultimate scientific objective is to derive interpretation in space-time, experiments show that iST-RF can improve predictive accuracy (76%) compared to the aspatial RF approach (70%), while enhancing interpretations of the trained model’s spatio-temporal relevance for its ensemble prediction. This novel approach can help balance prediction and interpretation with fidelity in a spatial data science life cycle. However, challenges exist for predictive modeling when dataset is very small, because in such cases locally optimized sub-model’s prediction performance can be suboptimal. With that caveat, our proposed approach is an ideal choice for identifying drivers of spatio-temporal events at country or regional-scale studies. <br><br><i>Author contributions</i> A.M. conceived and designed the study, coded and performed data processing, modeling and interpretations, and wrote the manuscript. M.Y., P.M., D.P., and A.T. contributed to the refinement of the proposed methodology, experiments, and write-up. All authors reviewed the manuscript.<br>
DOI: 10.5281/zenodo.5121857
title: EDI_OpenCalls_RP3Export

description: Export of EDI_OPENCALLS_DB at the end of the third reporting period (M42), i.e. June 2021, including aggregated and anonymized results of 1st, 2nd and 3rd Open Call. This dataset corresponds to the one generated with information about those applying to EDI’s three Open Calls. The contents of such dataset will be the result of aggregating (taking measures to prevent identification of personal data) participants’ attributes, e.g. country of origin, challenge targeted and so on, for statistical purposes and analysis of the performance of the Open Call
DOI: 10.5281/zenodo.5121856
title: EDI_OpenCalls_RP3Export

description: Export of EDI_OPENCALLS_DB at the end of the third reporting period (M42), i.e. June 2021, including aggregated and anonymized results of 1st, 2nd and 3rd Open Call. This dataset corresponds to the one generated with information about those applying to EDI’s three Open Calls. The contents of such dataset will be the result of aggregating (taking measures to prevent identification of personal data) participants’ attributes, e.g. country of origin, challenge targeted and so on, for statistical purposes and analysis of the performance of the Open Call
DOI: 10.6084/m9.figshare.15058638
title: Supplemental File 1

description: This data set is the RNA sequencing results of slow transit constipation tissues and control tissues.
DOI: 10.6084/m9.figshare.15058638.v1
title: Supplemental File 1

description: This data set is the RNA sequencing results of slow transit constipation tissues and control tissues.
DOI: 10.5517/ccdc.csd.cc27gjhr
title: CCDC 2069544: Experimental Crystal Structure Determination

description: Related Article: Oluwatosin Yemisi Audu, Jessica Jooste, Frederick P. Malan, Olayinka O. Ajani, Natasha October|2021|J.Mol.Struct.|1245|131077|doi:10.1016/j.molstruc.2021.131077
DOI: 10.5517/ccdc.csd.cc27gjjs
title: CCDC 2069545: Experimental Crystal Structure Determination

description: Related Article: Oluwatosin Yemisi Audu, Jessica Jooste, Frederick P. Malan, Olayinka O. Ajani, Natasha October|2021|J.Mol.Struct.|1245|131077|doi:10.1016/j.molstruc.2021.131077
DOI: 10.5517/ccdc.csd.cc27xhf3
title: CCDC 2082965: Experimental Crystal Structure Determination

description: Related Article: Julia A. Eremina, Ksenia S. Smirnova, Lyubov S. Klyushova, Alexey S. Berezin, Elizaveta V. Lider|2021|J.Mol.Struct.|1245|131024|doi:10.1016/j.molstruc.2021.131024
DOI: 10.5517/ccdc.csd.cc27xhd2
title: CCDC 2082964: Experimental Crystal Structure Determination

description: Related Article: Julia A. Eremina, Ksenia S. Smirnova, Lyubov S. Klyushova, Alexey S. Berezin, Elizaveta V. Lider|2021|J.Mol.Struct.|1245|131024|doi:10.1016/j.molstruc.2021.131024
DOI: 10.6084/m9.figshare.15059325
title: Supplementary Material for: Sarcopenia Is Associated with Mortality in Adults: A Systematic Review and Meta-Analysis

description: <b><i>Background:</i></b> Sarcopenia can predispose individuals to falls, fractures, hospitalization, and mortality. The prevalence of sarcopenia depends on the population studied and the definition used for the diagnosis. <b><i>Objective:</i></b> This systematic review and meta-analysis aimed to investigate the association between sarcopenia and mortality and if it is dependent on the population and sarcopenia definition. <b><i>Methods:</i></b> A systematic search was conducted in MEDLINE, EMBASE, and Cochrane from 1 January 2010 to 6 April 2020 for articles relating to sarcopenia and mortality. Articles were included if they met the following criteria – cohorts with a mean or median age ≥18 years and either of the following sarcopenia definitions: Asian Working Group for Sarcopenia (AWGS and AWGS2019), European Working Group on Sarcopenia in Older People (EWGSOP and EWGSOP2), Foundation for the National Institutes of Health (FNIH), International Working Group for Sarcopenia (IWGS), or Sarcopenia Definition and Outcomes Consortium (SDOC). Hazard ratios (HR) and odds ratios (OR) were pooled separately in meta-analyses using a random-effects model, stratified by population (community-dwelling adults, outpatients, inpatients, and nursing home residents). Subgroup analyses were performed for sarcopenia definition and follow-up period. <b><i>Results:</i></b> Out of 3,025 articles, 57 articles were included in the systematic review and 56 in the meta-analysis (42,108 participants, mean age of 49.4 ± 11.7 to 86.6 ± 1.0 years, 40.3% females). Overall, sarcopenia was associated with a significantly higher risk of mortality (HR: 2.00 [95% CI: 1.71, 2.34]; OR: 2.35 [95% CI: 1.64, 3.37]), which was independent of population, sarcopenia definition, and follow-up period in subgroup analyses. <b><i>Conclusions:</i></b> Sarcopenia is associated with a significantly higher risk of mortality, independent of population and sarcopenia definition, which highlights the need for screening and early diagnosis in all populations.
DOI: 10.6084/m9.figshare.15059328
title: Supplementary Material for: Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging for the Analysis of Microvascular Permeability in Peritumor Brain Edema of Fibrous Meningiomas

description: <b><i>Introduction:</i></b> This study aims to analyze the permeability of intra- and peri-meningiomas regions and compare the microvascular permeability between peritumoral brain edema (PTBE) and non-PTBE using DCE-MRI. <b><i>Methods:</i></b> This was a retrospective of patients with meningioma who underwent surgery. The patients were grouped as PTBE and non-PTBE. The DCE-MRI quantitative parameters, including volume transfer constant (<i>K</i><sup>trans</sup>), rate constant (<i>K</i><sub>ep</sub>), extracellular volume (<i>V</i><sub>e</sub>), and mean plasma volume (<i>V</i><sub>p</sub>), obtained using the extended Tofts-Kety 2-compartment model. Logistic regression analysis was conducted to explore the risk factor of PTBE. <b><i>Results:</i></b> Sixty-three patients, diagnosed as fibrous meningioma, were included in this study. They were 17 males and 46 females, aged from 32 to 88 years old. <i>K</i><sub>ep</sub> and <i>V</i><sub>p</sub> were significantly lower in patients with PTBE compared with those without (<i>K</i><sub>ep</sub>: 0.1852 ± 0.0369 vs. 0.5087 ± 0.1590, <i>p</i> = 0.010; <i>V</i><sub>p</sub>: 0.0090 ± 0.0020 vs. 0.0521 ± 0.0262, <i>p</i> = 0.007), while there were no differences regarding <i>K</i><sup>trans</sup> and <i>V</i><sub>e</sub> (both <i>p</i> &gt; 0.05). The multivariable analysis showed that tumor size ≥10 cm<sup>3</sup> (OR = 4.457, 95% CI: 1.322–15.031, <i>p</i> = 0.016) and <i>V</i><sub>p</sub> (OR = 0.572, 95%CI: 0.333–0.981, <i>p</i> = 0.044) were independently associated with PTBE in patients with meningiomas. <b><i>Conclusion:</i></b> DCE-magnetic resonance imaging·Meningioma·Blood vessel MRI can be used to quantify the microvascular permeability of PTBE in patients with meningioma.
DOI: 10.48370/ofd/36vl7v/0ssl2v
title: HMW_Buphthalmum_salicifolium_L._H_03367.jpg

description: :unav
DOI: 10.48370/ofd/36vl7v
title: Kołotocznik wierzbolistny (Buphthalmum salicifolium L.), 3367

description: Herbarium
DOI: 10.15151/esrf-es-514223675
title: industrial tomography tests

description: Tomography of metallic objects and dead plants to test for industrial tomography
DOI: 10.15151/esrf-es-466486585
title: Using nano-tomography to understand polarisation vision in insects

description: Many insects rely heavily on polarisation vision navigate through their world but we understand little about the features of their visual systems that underlie this behaviour, partly due to the lack of methods for resolving photoreceptor structures in 3D. The aim of this project is to take advantage of the recently developed nano-tomography technique, well established at ESRF, to create high resolution 3D models of the fine structures of insect eyes. With this beamtime we will describe, for the first time, the 3D structure and orientation of the dorsal rim area (DRA) which is part of the eye used primarily for navigation by sensing polarized light. The work will provide the first nanoscale 3D reconstructions of insect eyes and provide new insights into how this important compass cue is sensed by insects. If successful, our results will also lead to the first publications using nano-tomography to describe the fine detail of animal eyes.