cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4835668
title: Dynamic X-ray CT of Synthetic magma for Digital Volume Correlation analysis

description: Dataset of synthetic magma subjected to compression, useful for Digital Volume Correlation analysis, ref [1,2]. The data has been acquired at the Diamond Light Source synchrotron, with a bespoke thermo-mechanical rig (“P2R”) on the I12 beamline, ref [3,4,5]. Dataset 0 has no applied compression, while dataset 1 has applied compression. The data was saved with numpy 1.21 with NumPy format version 1.0 as dataset_0.npy and dataset_1.npy, and NumPy can be used to read it back in. Both data files have a header specifying how the data is stored, and following the header comes the array data. In particular the header length is 128 bytes, and the data consists of a 3 dimensional matrix of size (1520, 1257, 1260) stored in unsigned integer 8 bit, Fortran order. The screenshot named import_imagej.png shows how to import the data in with ImageJ. A METAImage header describing the data in text form for each dataset is also provided, i.e. dataset_0.mhd and dataset_1.mhd,
description: {"references": ["Bay, B.K., Smith, T.S., Fyhrie, D.P., Saad, M., 1999. Digital volume correlation: Three-dimensional strain mapping using x-ray tomography. Experimental Mechanics 39, 217\u2013226.", "Bay, B.K., 2008. Methods and applications of digital volume correlation. Journal of Strain Analysis for Engineering Design 43, 745\u2013760. https://doi.org/10.1243/03093247JSA436", "Puncreobutr, C., Lee, P.D., Hamilton, R.W., and Phillion, A.B., \"Quantitative 3D characterization of solidification structure and defect evolution in Al alloys\", JOM: Volume 64, Issue 1 (2012), 89-95. /10.1007/s11837-011-0219-7 (EP/F001452/1, P2R)", "Kareh, K.M., Lee, P.D., Atwood, RC, Connolley, T., and Gourlay, C.M., \"Revealing the micromechanisms behind semi-solid metal deformation with time-resolved X-ray tomography\", Nature Commun., 5:5464, /10.1038/ncomms5464, July 2014.", "Cai, B., Lee, PD, Karagadde, S., Marrow, T.J., Connolley, T., \"Time-resolved synchrotron tomographic quantification of deformation during indentation of an equiaxed semi-solid granular alloy\", Acta Mat. 105 (2016) 338-46, 10.1016/j.actamat.2015.11.028"]}
DOI: 10.5281/zenodo.7363182
title: Weakly-Supervised Learning Significantly Reduces the Number of Labels Required for Intracranial Hemorrhage Detection on Head CT

description: Modern machine learning pipelines, in particular those based on deep learning (DL) models, require large amounts of labeled data. For classification problems, the most common learning paradigm consists of presenting labeled examples during training, thus providing \emph{strong supervision} by directly presenting examples from the different classes, e.g. positive and negative samples. As a result, the adequate training of these models demands the curation of large datasets with high-quality labels. This constitutes a major obstacle for the development of DL models in radiology---in particular for cross-sectional imaging (e.g., computed tomography [CT] scans)---where labels must come from manual annotations by expert radiologists at the image or slice-level (as opposed to the examination level, such as could be obtained using natural language processing of radiology reports). <br> This work studies the question of what kind of labels should be collected for the problem of intracranial hemorrhage detection in brain CT. We investigate whether image-level annotations should be preferred to examination-level ones. By framing this task as a Multiple Instance Learning (MIL) problem, and employing modern attention-based DL architectures, we analyze the degree to which different levels of supervision improve the detection performance. We find that strong supervision (learning with local image-level annotations) and weak supervision (learning with only global examination-level labels) achieve comparable performance in both examination- and image-level hemorrhage detection, as well as in hemorrhage localization at the pixel-level (explainability). Furthermore, we study this behavior as a function of the number of labels available during training. Our results suggest that local labels may not be necessary at all, drastically reducing the time and cost involved in collecting and curating datasets.
DOI: 10.5281/zenodo.7363181
title: Weakly-Supervised Learning Significantly Reduces the Number of Labels Required for Intracranial Hemorrhage Detection on Head CT

description: Modern machine learning pipelines, in particular those based on deep learning (DL) models, require large amounts of labeled data. For classification problems, the most common learning paradigm consists of presenting labeled examples during training, thus providing \emph{strong supervision} by directly presenting examples from the different classes, e.g. positive and negative samples. As a result, the adequate training of these models demands the curation of large datasets with high-quality labels. This constitutes a major obstacle for the development of DL models in radiology---in particular for cross-sectional imaging (e.g., computed tomography [CT] scans)---where labels must come from manual annotations by expert radiologists at the image or slice-level (as opposed to the examination level, such as could be obtained using natural language processing of radiology reports). <br> This work studies the question of what kind of labels should be collected for the problem of intracranial hemorrhage detection in brain CT. We investigate whether image-level annotations should be preferred to examination-level ones. By framing this task as a Multiple Instance Learning (MIL) problem, and employing modern attention-based DL architectures, we analyze the degree to which different levels of supervision improve the detection performance. We find that strong supervision (learning with local image-level annotations) and weak supervision (learning with only global examination-level labels) achieve comparable performance in both examination- and image-level hemorrhage detection, as well as in hemorrhage localization at the pixel-level (explainability). Furthermore, we study this behavior as a function of the number of labels available during training. Our results suggest that local labels may not be necessary at all, drastically reducing the time and cost involved in collecting and curating datasets.
DOI: 10.5281/zenodo.7363345
title: Dynamic X-ray CT of Synthetic magma for Digital Volume Correlation analysis

description: Dataset of synthetic magma subjected to compression, useful for Digital Volume Correlation analysis, ref [1,2]. The data has been acquired at the Diamond Light Source synchrotron, with a bespoke thermo-mechanical rig (“P2R”) on the I12 beamline, ref [3,4,5]. Dataset 0 has no applied compression, while dataset 1 has applied compression. The data was saved with numpy 1.21 with NumPy format version 1.0 as dataset_0.npy and dataset_1.npy, and NumPy can be used to read it back in. Both data files have a header specifying how the data is stored, and following the header comes the array data. In particular the header length is 128 bytes, and the data consists of a 3 dimensional matrix of size (1520, 1257, 1260) stored in unsigned integer 8 bit, Fortran order. The screenshot named import_imagej.png shows how to import the data in with ImageJ. A METAImage header describing the data in text form for each dataset is also provided, i.e. dataset_0.mhd and dataset_1.mhd,
description: {"references": ["Bay, B.K., Smith, T.S., Fyhrie, D.P., Saad, M., 1999. Digital volume correlation: Three-dimensional strain mapping using x-ray tomography. Experimental Mechanics 39, 217\u2013226.", "Bay, B.K., 2008. Methods and applications of digital volume correlation. Journal of Strain Analysis for Engineering Design 43, 745\u2013760. https://doi.org/10.1243/03093247JSA436", "Puncreobutr, C., Lee, P.D., Hamilton, R.W., and Phillion, A.B., \"Quantitative 3D characterization of solidification structure and defect evolution in Al alloys\", JOM: Volume 64, Issue 1 (2012), 89-95. /10.1007/s11837-011-0219-7 (EP/F001452/1, P2R)", "Kareh, K.M., Lee, P.D., Atwood, RC, Connolley, T., and Gourlay, C.M., \"Revealing the micromechanisms behind semi-solid metal deformation with time-resolved X-ray tomography\", Nature Commun., 5:5464, /10.1038/ncomms5464, July 2014.", "Cai, B., Lee, PD, Karagadde, S., Marrow, T.J., Connolley, T., \"Time-resolved synchrotron tomographic quantification of deformation during indentation of an equiaxed semi-solid granular alloy\", Acta Mat. 105 (2016) 338-46, 10.1016/j.actamat.2015.11.028"]}
DOI: 10.5281/zenodo.4835667
title: Dynamic X-ray CT of Synthetic magma for Digital Volume Correlation analysis

description: Dataset of synthetic magma subjected to compression, useful for Digital Volume Correlation analysis, ref [1,2]. The data has been acquired at the Diamond Light Source synchrotron, with a bespoke thermo-mechanical rig (“P2R”) on the I12 beamline, ref [3,4,5]. Dataset 0 has no applied compression, while dataset 1 has applied compression. The data was saved with numpy 1.21 with NumPy format version 1.0 as dataset_0.npy and dataset_1.npy, and NumPy can be used to read it back in. Both data files have a header specifying how the data is stored, and following the header comes the array data. In particular the header length is 128 bytes, and the data consists of a 3 dimensional matrix of size (1520, 1257, 1260) stored in unsigned integer 8 bit, Fortran order. The screenshot named import_imagej.png shows how to import the data in with ImageJ. A METAImage header describing the data in text form for each dataset is also provided, i.e. dataset_0.mhd and dataset_1.mhd,
description: {"references": ["Bay, B.K., Smith, T.S., Fyhrie, D.P., Saad, M., 1999. Digital volume correlation: Three-dimensional strain mapping using x-ray tomography. Experimental Mechanics 39, 217\u2013226.", "Bay, B.K., 2008. Methods and applications of digital volume correlation. Journal of Strain Analysis for Engineering Design 43, 745\u2013760. https://doi.org/10.1243/03093247JSA436", "Puncreobutr, C., Lee, P.D., Hamilton, R.W., and Phillion, A.B., \"Quantitative 3D characterization of solidification structure and defect evolution in Al alloys\", JOM: Volume 64, Issue 1 (2012), 89-95. /10.1007/s11837-011-0219-7 (EP/F001452/1, P2R)", "Kareh, K.M., Lee, P.D., Atwood, RC, Connolley, T., and Gourlay, C.M., \"Revealing the micromechanisms behind semi-solid metal deformation with time-resolved X-ray tomography\", Nature Commun., 5:5464, /10.1038/ncomms5464, July 2014.", "Cai, B., Lee, PD, Karagadde, S., Marrow, T.J., Connolley, T., \"Time-resolved synchrotron tomographic quantification of deformation during indentation of an equiaxed semi-solid granular alloy\", Acta Mat. 105 (2016) 338-46, 10.1016/j.actamat.2015.11.028"]}
DOI: 10.5281/zenodo.7363402
title: KASUS PENGENDALIAN BIAYA TENAGA KERJA_ AKUNTANSI BIAYA_JAM 20.00-21.30_UNIVERSITAS GRESIK

description: <strong>Capaian Pembelajaran Mata Kuliah (CPMK)</strong> Untuk mengevaluasi atau mengukur kemampuan penyerapan materi <strong>Indikator Penilaian</strong> Ujian Lisan Ujian Tulis
description: KASUS PENGENDALIAN BIAYA TENAGA KERJA_ AKUNTANSI BIAYA_JAM 20.00-21.30_UNIVERSITAS GRESIK
DOI: 10.5281/zenodo.7363401
title: KASUS PENGENDALIAN BIAYA TENAGA KERJA_ AKUNTANSI BIAYA_JAM 20.00-21.30_UNIVERSITAS GRESIK

description: <strong>Capaian Pembelajaran Mata Kuliah (CPMK)</strong> Untuk mengevaluasi atau mengukur kemampuan penyerapan materi <strong>Indikator Penilaian</strong> Ujian Lisan Ujian Tulis
description: KASUS PENGENDALIAN BIAYA TENAGA KERJA_ AKUNTANSI BIAYA_JAM 20.00-21.30_UNIVERSITAS GRESIK
DOI: 10.5281/zenodo.5914342
title: Fast Hierarchical Games for Image Explanations

description: As modern complex neural networks keep breaking records and solving harder problems, their predictions also become less and less intelligible. The current lack of interpretability often undermines the deployment of accurate machine learning tools in sensitive settings. In this work, we present a model-agnostic explanation method for image classification based on a hierarchical extension of Shapley coefficients–Hierarchical Shap (h-Shap)–that resolves some of the limitations of current approaches. Unlike other Shapley-based explanation methods, h-Shap is scalable and can be computed without the need of approximation. Under certain distributional assumptions, such as those common in multiple instance learning, h-Shap retrieves the exact Shapley coefficients with an exponential improvement in computational complexity. We compare our hierarchical approach with popular Shapley-based and non-Shapley-based methods on a synthetic dataset, a medical imaging scenario, and a general computer vision problem, showing that h-Shap outperforms the state of the art in both accuracy and runtime. Code and experiments are made publicly available.
DOI: 10.5281/zenodo.5895224
title: Fast Hierarchical Games for Image Explanations

description: As modern complex neural networks keep breaking records and solving harder problems, their predictions also become less and less intelligible. The current lack of interpretability often undermines the deployment of accurate machine learning tools in sensitive settings. In this work, we present a model-agnostic explanation method for image classification based on a hierarchical extension of Shapley coefficients–Hierarchical Shap (h-Shap)–that resolves some of the limitations of current approaches. Unlike other Shapley-based explanation methods, h-Shap is scalable and can be computed without the need of approximation. Under certain distributional assumptions, such as those common in multiple instance learning, h-Shap retrieves the exact Shapley coefficients with an exponential improvement in computational complexity. We compare our hierarchical approach with popular Shapley-based and non-Shapley-based methods on a synthetic dataset, a medical imaging scenario, and a general computer vision problem, showing that h-Shap outperforms the state of the art in both accuracy and runtime. Code and experiments are made publicly available.
DOI: 10.5281/zenodo.7362495
title: Cerveteri_Tomba-Campana_rendering, Storytelling, Back end scientifico

description: Ricostruzione virtuale della Tomba Campana nella necropoli di Monte Abatone a Cerveteri, in particolare la camera principale e la camera laterale destra. E' stato incluso anche un potenziale corredo e una simulazione delle deposizioni .Al modello 3D si accompagnano alcuni rendering 360 della tomba attuale (come è) e ricostruita (come era, da due punti di vista all'interno della tomba). Una mappa indica il loro posizionamento in pianta. A ciascun rendering si associano contenuti narrativi riferiti alla vista generale e ad alcuni elementi di approfondimento (identificati da maschere semantiche). Inoltre sui rendering è mappato anche il back end scientifico: il livello di affidabilità della ricostruzione è indicato con colori simbolici (rosso: elementi archeologici documentati in situ; azzurro: elementi soggetti a ricostruzione o restauro virtuale di un risarcimento derivante da evidenza fisica; verde: elementi ricostruiti in mancanza di concrete evidenze attraverso processi comparativi o deduttivi; giallo chiaro: anastilosi tipo 1: riposizionamento di frammenti esistenti ma rimossi o ritrovati in giacitura secondaria e attualmente conservati in altro contesto; giallo scuro: anastilosi tipo 2: ricostruzione o restauro virtuale del frammento riposizionato in anastilosi virtuale). Infine a completamento del back end scientifico ai vari elementi ricostruiti virtualmente sono associate le informazioni relative alle fonti e ai processi interpretatativi che hanno supporttao l'ipotesi ricostruttiva. Il gruppo di lavoro è multidisciplinare ed include istituzioni di ricerca e industrie creative.
DOI: 10.5281/zenodo.7362494
title: Cerveteri_Tomba-Campana_rendering, Storytelling, Back end scientifico

description: Ricostruzione virtuale della Tomba Campana nella necropoli di Monte Abatone a Cerveteri, in particolare la camera principale e la camera laterale destra. E' stato incluso anche un potenziale corredo e una simulazione delle deposizioni .Al modello 3D si accompagnano alcuni rendering 360 della tomba attuale (come è) e ricostruita (come era, da due punti di vista all'interno della tomba). Una mappa indica il loro posizionamento in pianta. A ciascun rendering si associano contenuti narrativi riferiti alla vista generale e ad alcuni elementi di approfondimento (identificati da maschere semantiche). Inoltre sui rendering è mappato anche il back end scientifico: il livello di affidabilità della ricostruzione è indicato con colori simbolici (rosso: elementi archeologici documentati in situ; azzurro: elementi soggetti a ricostruzione o restauro virtuale di un risarcimento derivante da evidenza fisica; verde: elementi ricostruiti in mancanza di concrete evidenze attraverso processi comparativi o deduttivi; giallo chiaro: anastilosi tipo 1: riposizionamento di frammenti esistenti ma rimossi o ritrovati in giacitura secondaria e attualmente conservati in altro contesto; giallo scuro: anastilosi tipo 2: ricostruzione o restauro virtuale del frammento riposizionato in anastilosi virtuale). Infine a completamento del back end scientifico ai vari elementi ricostruiti virtualmente sono associate le informazioni relative alle fonti e ai processi interpretatativi che hanno supporttao l'ipotesi ricostruttiva. Il gruppo di lavoro è multidisciplinare ed include istituzioni di ricerca e industrie creative.
DOI: 10.3886/e112788v1-130862
title: Replication data for: Bundling Health Insurance and Microfinance in India: There Cannot Be Adverse Selection If There Is No Demand

description: Microfinance institutions have started to bundle their basic loans with other financial services, such as health insurance. Using a randomized control trial in Karnataka, India, we evaluate the impact on loan renewal from mandating the purchase of actuarially-fair health insurance covering hospitalization and maternity expenses. Bundling loans with insurance led to a 16 percentage points (23 percent) increase in drop-out from microfinance, as many clients preferred to give up microfinance than pay higher interest rates and receive insurance. In a Pyrrhic victory, the total absence of demand for health insurance led to there being no adverse selection in insurance enrollment.
DOI: 10.34740/kaggle/dsv/4588601
title: BBC News
DOI: 10.5281/zenodo.6407406
title: Not the same: phylogenetic relationships and ecological niche comparisons between two different forms of Aglaoctenus lagotis from Argentina and Uruguay

description: We extracted genomic DNA from three species of <em>Aglaoctenus:</em> <em>A. lagotis, </em>and<em> A. castaneus </em>from Uruguay and Argentina. From those we obtained three mitochondrial markers including the 5’ half of cytochrome c oxidase subunit I (<em>cox1</em>), the 3’ half of the 16S rRNA ribosomal subunit plus the complete tRNA-Leu plus 5’ half of the NADH dehydrogenase subunit I (16S+L1+nad1), and a partial fragment of the small ribosomal unit (<em>12S</em>). Additionally, we sequenced the nuclear intron of the gene encoding translation initiation factor 5A (<em>tif5A</em>). The sequences of the <em>12S</em>, <em>16S</em>+<em>L1+nad1 </em>and <em>tif5A </em>gene fragments were aligned using the online version of MAFFT v7 using the Q-ins-i algorithm. The alignment of the partial fragment of <em>cox1 </em>sequences was trivial since no insertions/deletions (indels) were observed. The alleles in heterozygous individuals for the <em>tif5A</em> intron were separated using the PHASE algorithm, as implemented in DnaSP v6. 12.03. With this data we inferred gene trees with Maximum Likelihood, Bayesian and statistical parsimony analyses. We perform molecular species delimitation analyses conducted with STACEY, and the species tree and divergence times were co-estimated with *BEAST. Additionally, we build a haplotype network of the nuclear intron <em>tif5A</em>, the concatenated mitochondrial genes <em>cox1</em>+<em>12S</em>+<em>16S</em>+<em>L1</em>+<em>nad1</em>, and a partial fragment of <em>cox1 </em>gene were estimated using statistical parsimony in TCS and implemented in PopART v1.7.
DOI: 10.5281/zenodo.6407405
title: Not the same: phylogenetic relationships and ecological niche comparisons between two different forms of Aglaoctenus lagotis from Argentina and Uruguay

description: We extracted genomic DNA from three species of <em>Aglaoctenus:</em> <em>A. lagotis, </em>and<em> A. castaneus </em>from Uruguay and Argentina. From those we obtained three mitochondrial markers including the 5’ half of cytochrome c oxidase subunit I (<em>cox1</em>), the 3’ half of the 16S rRNA ribosomal subunit plus the complete tRNA-Leu plus 5’ half of the NADH dehydrogenase subunit I (16S+L1+nad1), and a partial fragment of the small ribosomal unit (<em>12S</em>). Additionally, we sequenced the nuclear intron of the gene encoding translation initiation factor 5A (<em>tif5A</em>). The sequences of the <em>12S</em>, <em>16S</em>+<em>L1+nad1 </em>and <em>tif5A </em>gene fragments were aligned using the online version of MAFFT v7 using the Q-ins-i algorithm. The alignment of the partial fragment of <em>cox1 </em>sequences was trivial since no insertions/deletions (indels) were observed. The alleles in heterozygous individuals for the <em>tif5A</em> intron were separated using the PHASE algorithm, as implemented in DnaSP v6. 12.03. With this data we inferred gene trees with Maximum Likelihood, Bayesian and statistical parsimony analyses. We perform molecular species delimitation analyses conducted with STACEY, and the species tree and divergence times were co-estimated with *BEAST. Additionally, we build a haplotype network of the nuclear intron <em>tif5A</em>, the concatenated mitochondrial genes <em>cox1</em>+<em>12S</em>+<em>16S</em>+<em>L1</em>+<em>nad1</em>, and a partial fragment of <em>cox1 </em>gene were estimated using statistical parsimony in TCS and implemented in PopART v1.7.
DOI: 10.6084/m9.figshare.21624129
title: Supplementary Tables 1–5

description: Supplementary Tables 1–5
DOI: 10.6084/m9.figshare.21624129.v1
title: Supplementary Tables 1–5

description: Supplementary Tables 1–5
DOI: 10.18112/openneuro.ds004313.v2.0.0
title: Evaluating methods for measuring background connectivity in slow event-related fMRI designs
DOI: 10.34740/kaggle/dsv/4588708
title: OntoUML/UFO Catalog
DOI: 10.5517/ccdc.csd.cc27vxtv
title: CCDC 2081458: Experimental Crystal Structure Determination

description: Related Article: Panpan Si, Jiaxing Chen, Shijiang Yu, Xiajuan Huang, Qianqian Li, Wenxin Lin|2022|Inorg.Chem.|61|18504|doi:10.1021/acs.inorgchem.2c02742
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.