cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7955264
title: Deuterated TEKPol Biradicals and the Spin-Diffusion Barrier in MAS DNP

description: Raw data set for the publication "Deuterated TEKPol Biradicals and the Spin-Diffusion Barrier in MAS DNP". The file contains data from DFT, EPR, NMR and DNP-calculations, and a text file describing the contents of all the folders. DOI of published article: https://doi.org/10.1002/anie.202304844
description: This work was supported by Swiss National Science Foundation Grant No. 200020_212046, EU H2020-INFRAIA Grant No. 101008500, and by an H2020 Marie Skłodowska-Curie Individual fellowship (grant number 101024369) (AV).
DOI: 10.5281/zenodo.7955263
title: Deuterated TEKPol Biradicals and the Spin-Diffusion Barrier in MAS DNP

description: Raw data set for the publication "Deuterated TEKPol Biradicals and the Spin-Diffusion Barrier in MAS DNP". The file contains data from DFT, EPR, NMR and DNP-calculations, and a text file describing the contents of all the folders. DOI of published article: https://doi.org/10.1002/anie.202304844
description: This work was supported by Swiss National Science Foundation Grant No. 200020_212046, EU H2020-INFRAIA Grant No. 101008500, and by an H2020 Marie Skłodowska-Curie Individual fellowship (grant number 101024369) (AV).
DOI: 10.5517/ccdc.csd.cc2dhvqh
title: CCDC 2219777: Experimental Crystal Structure Determination

description: Related Article: Alexander Ahrens, Andreas Bonde, Hongwei Sun, Nina Kølln Wittig, Hans Christian D. Hammershøj, Gabriel Martins Ferreira Batista, Andreas Sommerfeldt, Simon Frølich, Henrik Birkedal, Troels Skrydstrup |2023|Nature (London)|617|730|doi:10.1038/s41586-023-05944-6
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.
DOI: 10.5281/zenodo.7996178
title: IODP Expedition 369 Inorganic carbon (coulometer)

description: Inorganic carbon (carbonate) is determined by coulometry, which uses a photodetection cell to measure carbon dioxide evolved during sample acidification. Report includes percent inorganic carbon and calcium carbonate.
DOI: 10.5281/zenodo.7996177
title: IODP Expedition 369 Inorganic carbon (coulometer)

description: Inorganic carbon (carbonate) is determined by coulometry, which uses a photodetection cell to measure carbon dioxide evolved during sample acidification. Report includes percent inorganic carbon and calcium carbonate.
DOI: 10.5281/zenodo.7996174
title: IODP Expedition 369 Core summary

description: Report includes detailed core data: drilling and coring depths, advancement, recovered core length measured on the catwalk and final curated length, core recovery, and sections cut.
DOI: 10.5281/zenodo.7996173
title: IODP Expedition 369 Core summary

description: Report includes detailed core data: drilling and coring depths, advancement, recovered core length measured on the catwalk and final curated length, core recovery, and sections cut.
DOI: 10.5281/zenodo.7996180
title: IODP Expedition 369 Visual core description

description: Descriptions of samples, generally at the section half and smear slide or thin section scale, were performed by shipboard scientists and recorded in the JRSO description software. Descriptive data for both macroscopic and microscopic examination were collected in a Microscoft Excel workbook by hole. A zip file of the entire expedition's observations is also available.
DOI: 10.5281/zenodo.7996179
title: IODP Expedition 369 Visual core description

description: Descriptions of samples, generally at the section half and smear slide or thin section scale, were performed by shipboard scientists and recorded in the JRSO description software. Descriptive data for both macroscopic and microscopic examination were collected in a Microscoft Excel workbook by hole. A zip file of the entire expedition's observations is also available.
DOI: 10.5281/zenodo.7996188
title: IODP Expedition 369 Core composite images

description: A digital composite image (PNG) is made for each core comprising core sections scanned using a line-scan camera. The composite layout is equivalent to traditional core table photos. Top left is top of core; color and meter rule references are included.
DOI: 10.5281/zenodo.7996187
title: IODP Expedition 369 Core composite images

description: A digital composite image (PNG) is made for each core comprising core sections scanned using a line-scan camera. The composite layout is equivalent to traditional core table photos. Top left is top of core; color and meter rule references are included.
DOI: 10.5281/zenodo.7888462
title: Prediction of Diblock Copolymer Lamellar Morphologies in 3D and Complex Geometry via Machine Learning

description: 3d morphology evolution of diblock copolymer via 3D UNet.
DOI: 10.5281/zenodo.7916838
title: Prediction of Diblock Copolymer Lamellar Morphologies in 3D and Complex Geometry via Machine Learning

description: 3d morphology evolution of diblock copolymer via 3D UNet.
DOI: 10.5281/zenodo.7988241
title: Prediction of Diblock Copolymer Lamellar Morphologies in 3D and Complex Geometry via Machine Learning

description: 3d morphology evolution of diblock copolymer via 3D UNet.
DOI: 10.5281/zenodo.7995819
title: curatedPCaData metadata template (PRAD) tsv-file

description: This tab-separated value table lists the clinical metadata template, which was applied to extract relevant metadata for the cohorts presented in the curatedPCaData R-package.
DOI: 10.5281/zenodo.7996182
title: IODP Expedition 369 Micropaleontology

description: Paleontological data were collected using microscopes and recorded in the JRSO description software. All data for a species group (e.g., diatoms or nannofossils) were collected in a Microsoft Excel worksheet by hole. A zip file of the entire expedition's observations is also available.
DOI: 10.5281/zenodo.7996181
title: IODP Expedition 369 Micropaleontology

description: Paleontological data were collected using microscopes and recorded in the JRSO description software. All data for a species group (e.g., diatoms or nannofossils) were collected in a Microsoft Excel worksheet by hole. A zip file of the entire expedition's observations is also available.
DOI: 10.5281/zenodo.7995818
title: curatedPCaData metadata template (PRAD) tsv-file

description: This tab-separated value table lists the clinical metadata template, which was applied to extract relevant metadata for the cohorts presented in the curatedPCaData R-package.
DOI: 10.5281/zenodo.7595655
title: Data and analysis for orthopaedics literature review.

description: This is a repository of all the data and analysis code to reproduce all the plots and analysis for this paper. To reproduce all plots in the paper, we used Python 3.9.7, install all the requirements in `requirements.txt`,open `Analysis.ipynb` and run every cell in order. The file 'LiteratureTaxonomy.csv' contains information on the 492 papers analysed in the paper.
DOI: 10.5281/zenodo.7595654
title: Data and analysis for orthopaedics literature review.

description: This is a repository of all the data and analysis code to reproduce all the plots and analysis for this paper. To reproduce all plots in the paper, we used Python 3.9.7, install all the requirements in `requirements.txt`,open `Analysis.ipynb` and run every cell in order. The file 'LiteratureTaxonomy.csv' contains information on the 492 papers analysed in the paper.
DOI: 10.5281/zenodo.7995323
title: Supplementary Table 3

description: Study title: <strong><em>SLC6A1</em> variant pathogenicity, molecular function, and phenotype: a genetic and clinical analysis<em>SLC6A1</em></strong><strong> variant pathogenicity, molecular function, and phenotype: a genetic and clinical analysis</strong> Content: Supplementary Table 3 - Patient variants - GnomAD variants - Legend
DOI: 10.5281/zenodo.7995322
title: Supplementary Table 3

description: Study title: <strong><em>SLC6A1</em> variant pathogenicity, molecular function, and phenotype: a genetic and clinical analysis<em>SLC6A1</em></strong><strong> variant pathogenicity, molecular function, and phenotype: a genetic and clinical analysis</strong> Content: Supplementary Table 3 - Patient variants - GnomAD variants - Legend
DOI: 10.5281/zenodo.7996198
title: Prediction of Diblock Copolymer Lamellar Morphologies in 3D and Complex Geometry via Machine Learning

description: 3d morphology evolution of diblock copolymer via 3D UNet. Manuscript: Prediction of Diblock Copolymer Lamellar Morphologies in 3D and Complex Geometry via Machine Learning
DOI: 10.5281/zenodo.7888461
title: Prediction of Diblock Copolymer Lamellar Morphologies in 3D and Complex Geometry via Machine Learning

description: 3d morphology evolution of diblock copolymer via 3D UNet. Manuscript: Prediction of Diblock Copolymer Lamellar Morphologies in 3D and Complex Geometry via Machine Learning
DOI: 10.5281/zenodo.7840163
title: 27 MAGs from the Family of Endozoicomonadaceae derived from Tara Pacific Metagenomes

description: This dataset contains 27 MAGs from the family of Endozoicomonadaceae generated from a subset of Tara Pacific metagenomes. Contextual information of the MAGs can be found in the associated publication: <strong>Ecology of Endozoicomonadaceae in three coral species across the Pacific Ocean</strong>, Hochart et al, submitted
description: Special thanks to the Tara Ocean Foundation, the R/V Tara crew and the Tara Pacific Expedition Participants (https://doi.org/10.5281/zenodo.3777760). We are keen to thank the commitment of the following institutions for their financial and scientific support that made this unique Tara Pacific Expedition possible: CNRS, PSL, CSM, EPHE, Genoscope, CEA, Inserm, Université Côte d'Azur, ANR, agnès b., UNESCO-IOC, the Veolia Foundation, the Prince Albert II de Monaco Foundation, Région Bretagne, Billerudkorsnas, AmerisourceBergen Company, Lorient Agglomération, Oceans by Disney, L'Oréal, Biotherm, France Collectivités, Fonds Français pour l'Environnement Mondial (FFEM), Etienne Bourgois, and the Tara Ocean Foundation teams. Tara Pacific would not exist without the continuous support of the participating institutes. The authors also particularly thank Serge Planes, Denis Allemand, and the Tara Pacific consortium.
DOI: 10.5281/zenodo.7839795
title: Assemblies of 269 Metagenomic Tara Pacific Sequencing Samples - part 1

description: This data is the result of the metagenomic assembly of 269 sequencing samples reflecting a first subset of the Tara Pacific metagenomes. Assemblies are used in - Preprint: Endogenous viral elements reveal associations between a non-retroviral RNA virus and symbiotic dinoflagellate genomes - Part 2
description: Special thanks to the Tara Ocean Foundation, the R/V Tara crew and the Tara Pacific Expedition Participants (https://doi.org/10.5281/zenodo.3777760). We are keen to thank the commitment of the following institutions for their financial and scientific support that made this unique Tara Pacific Expedition possible: CNRS, PSL, CSM, EPHE, Genoscope, CEA, Inserm, Université Côte d'Azur, ANR, agnès b., UNESCO-IOC, the Veolia Foundation, the Prince Albert II de Monaco Foundation, Région Bretagne, Billerudkorsnas, AmerisourceBergen Company, Lorient Agglomération, Oceans by Disney, L'Oréal, Biotherm, France Collectivités, Fonds Français pour l'Environnement Mondial (FFEM), Etienne Bourgois, and the Tara Ocean Foundation teams. Tara Pacific would not exist without the continuous support of the participating institutes. The authors also particularly thank Serge Planes, Denis Allemand, and the Tara Pacific consortium.
DOI: 10.5281/zenodo.7901148
title: Landsat Quality Assessment (LTQA)

description: We developed a suite of quality metrics that characterize the annual and year-to-year frequency of satellite observations, their year-to-year recurrence, and their within-year distribution. These equate the quality of individual observations as reported by the data providers, and limitations to the usability of these data caused by cloud cover. This dataset includes a zip file for each of the following metrics: <strong>nrTiles -</strong> Number of Landsat tiles used to compute quality metric. <strong>imageFrequency - </strong>Number of collected images. <strong>monthFrequency - </strong>Number of months with collected images. <strong>maxQuality - </strong>Maximum image quality. <strong>totalQuality - </strong>Number of collected images, weighted by the quality of each image. <strong>lastYear - </strong>Closest year with usable data<strong>, </strong>from the start of the time-series to the reference year. <strong>distributionBalance - </strong>Average of the maximum monthly image quality. <strong>distributionQuality - </strong>Ratio between the distribution balance of the first and second half of the year. We calculated these quality metrics for each descending tile as drawn in the World Reference System 2 (WRS-2), and for each year. Then, for each year, we combined the tile-specific metrics by averaging them into global grids with a 1-km resolution using the script "map_landsat_quality.py". This was executed in python 3.10. and the associated module requirements are recorded in the file "requirements.txt". All tile-specific metrics, which is an input for the python script, are provided through the file "LTQA_metadata.csv". The calculation of quality metrics is informed by metadata of all unique acquisitions obtained through the Landsat’s bulk metadata service. This considers images acquired with Landsat 4, 5, 7, 8, and 9, but disregards those from Landsat’s Multispectral Scanner System (MSS). The original metadata is provided through the file "LTQA_metadata.zip", which also includes the R code used in the calculation of quality metrics, and a data structure that can be updated to generate new values. We calculated these metrics are calculated annually discounting those unusable due to 100% cloud cover or advanced image degradation.
DOI: 10.5281/zenodo.7839794
title: Assemblies of 269 Metagenomic Tara Pacific Sequencing Samples - part 1

description: This data is the result of the metagenomic assembly of 269 sequencing samples reflecting a first subset of the Tara Pacific metagenomes. Assemblies are used in - Preprint: Endogenous viral elements reveal associations between a non-retroviral RNA virus and symbiotic dinoflagellate genomes - Part 2
description: Special thanks to the Tara Ocean Foundation, the R/V Tara crew and the Tara Pacific Expedition Participants (https://doi.org/10.5281/zenodo.3777760). We are keen to thank the commitment of the following institutions for their financial and scientific support that made this unique Tara Pacific Expedition possible: CNRS, PSL, CSM, EPHE, Genoscope, CEA, Inserm, Université Côte d'Azur, ANR, agnès b., UNESCO-IOC, the Veolia Foundation, the Prince Albert II de Monaco Foundation, Région Bretagne, Billerudkorsnas, AmerisourceBergen Company, Lorient Agglomération, Oceans by Disney, L'Oréal, Biotherm, France Collectivités, Fonds Français pour l'Environnement Mondial (FFEM), Etienne Bourgois, and the Tara Ocean Foundation teams. Tara Pacific would not exist without the continuous support of the participating institutes. The authors also particularly thank Serge Planes, Denis Allemand, and the Tara Pacific consortium.
DOI: 10.5281/zenodo.7840162
title: 27 MAGs from the Family of Endozoicomonadaceae derived from Tara Pacific Metagenomes

description: This dataset contains 27 MAGs from the family of Endozoicomonadaceae generated from a subset of Tara Pacific metagenomes. Contextual information of the MAGs can be found in the associated publication: <strong>Ecology of Endozoicomonadaceae in three coral species across the Pacific Ocean</strong>, Hochart et al, submitted
description: Special thanks to the Tara Ocean Foundation, the R/V Tara crew and the Tara Pacific Expedition Participants (https://doi.org/10.5281/zenodo.3777760). We are keen to thank the commitment of the following institutions for their financial and scientific support that made this unique Tara Pacific Expedition possible: CNRS, PSL, CSM, EPHE, Genoscope, CEA, Inserm, Université Côte d'Azur, ANR, agnès b., UNESCO-IOC, the Veolia Foundation, the Prince Albert II de Monaco Foundation, Région Bretagne, Billerudkorsnas, AmerisourceBergen Company, Lorient Agglomération, Oceans by Disney, L'Oréal, Biotherm, France Collectivités, Fonds Français pour l'Environnement Mondial (FFEM), Etienne Bourgois, and the Tara Ocean Foundation teams. Tara Pacific would not exist without the continuous support of the participating institutes. The authors also particularly thank Serge Planes, Denis Allemand, and the Tara Pacific consortium.
DOI: 10.5281/zenodo.7840045
title: Assemblies of 269 Metagenomic Tara Pacific Sequencing Samples - part 2

description: This data is the result of the metagenomic assembly of 269 sequencing samples reflecting a first subset of the Tara Pacific metagenomes. Assemblies are used in - Preprint: Endogenous viral elements reveal associations between a non-retroviral RNA virus and symbiotic dinoflagellate genomes - Part 1
description: Special thanks to the Tara Ocean Foundation, the R/V Tara crew and the Tara Pacific Expedition Participants (https://doi.org/10.5281/zenodo.3777760). We are keen to thank the commitment of the following institutions for their financial and scientific support that made this unique Tara Pacific Expedition possible: CNRS, PSL, CSM, EPHE, Genoscope, CEA, Inserm, Université Côte d'Azur, ANR, agnès b., UNESCO-IOC, the Veolia Foundation, the Prince Albert II de Monaco Foundation, Région Bretagne, Billerudkorsnas, AmerisourceBergen Company, Lorient Agglomération, Oceans by Disney, L'Oréal, Biotherm, France Collectivités, Fonds Français pour l'Environnement Mondial (FFEM), Etienne Bourgois, and the Tara Ocean Foundation teams. Tara Pacific would not exist without the continuous support of the participating institutes. The authors also particularly thank Serge Planes, Denis Allemand, and the Tara Pacific consortium.
DOI: 10.5281/zenodo.7901147
title: Landsat Quality Assessment (LTQA)

description: We developed a suite of quality metrics that characterize the annual and year-to-year frequency of satellite observations, their year-to-year recurrence, and their within-year distribution. These equate the quality of individual observations as reported by the data providers, and limitations to the usability of these data caused by cloud cover. This dataset includes a zip file for each of the following metrics: <strong>nrTiles -</strong> Number of Landsat tiles used to compute quality metric. <strong>imageFrequency - </strong>Number of collected images. <strong>monthFrequency - </strong>Number of months with collected images. <strong>maxQuality - </strong>Maximum image quality. <strong>totalQuality - </strong>Number of collected images, weighted by the quality of each image. <strong>lastYear - </strong>Closest year with usable data<strong>, </strong>from the start of the time-series to the reference year. <strong>distributionBalance - </strong>Average of the maximum monthly image quality. <strong>distributionQuality - </strong>Ratio between the distribution balance of the first and second half of the year. We calculated these quality metrics for each descending tile as drawn in the World Reference System 2 (WRS-2), and for each year. Then, for each year, we combined the tile-specific metrics by averaging them into global grids with a 1-km resolution using the script "map_landsat_quality.py". This was executed in python 3.10. and the associated module requirements are recorded in the file "requirements.txt". All tile-specific metrics, which is an input for the python script, are provided through the file "LTQA_metadata.csv". The calculation of quality metrics is informed by metadata of all unique acquisitions obtained through the Landsat’s bulk metadata service. This considers images acquired with Landsat 4, 5, 7, 8, and 9, but disregards those from Landsat’s Multispectral Scanner System (MSS). The original metadata is provided through the file "LTQA_metadata.zip", which also includes the R code used in the calculation of quality metrics, and a data structure that can be updated to generate new values. We calculated these metrics are calculated annually discounting those unusable due to 100% cloud cover or advanced image degradation.
DOI: 10.5281/zenodo.7928545
title: Partial liquid metal dealloying to synthesize nickel-containing porous and composite ferrous and high-entropy alloys

description: Raw data for the graphs in the article.
DOI: 10.5281/zenodo.7928544
title: Partial liquid metal dealloying to synthesize nickel-containing porous and composite ferrous and high-entropy alloys

description: Raw data for the graphs in the article.
DOI: 10.5281/zenodo.7840044
title: Assemblies of 269 Metagenomic Tara Pacific Sequencing Samples - part 2

description: This data is the result of the metagenomic assembly of 269 sequencing samples reflecting a first subset of the Tara Pacific metagenomes. Assemblies are used in - Preprint: Endogenous viral elements reveal associations between a non-retroviral RNA virus and symbiotic dinoflagellate genomes - Part 1
description: Special thanks to the Tara Ocean Foundation, the R/V Tara crew and the Tara Pacific Expedition Participants (https://doi.org/10.5281/zenodo.3777760). We are keen to thank the commitment of the following institutions for their financial and scientific support that made this unique Tara Pacific Expedition possible: CNRS, PSL, CSM, EPHE, Genoscope, CEA, Inserm, Université Côte d'Azur, ANR, agnès b., UNESCO-IOC, the Veolia Foundation, the Prince Albert II de Monaco Foundation, Région Bretagne, Billerudkorsnas, AmerisourceBergen Company, Lorient Agglomération, Oceans by Disney, L'Oréal, Biotherm, France Collectivités, Fonds Français pour l'Environnement Mondial (FFEM), Etienne Bourgois, and the Tara Ocean Foundation teams. Tara Pacific would not exist without the continuous support of the participating institutes. The authors also particularly thank Serge Planes, Denis Allemand, and the Tara Pacific consortium.
DOI: 10.5281/zenodo.7993375
title: MEWL: Few-shot multimodal word learning with referential uncertainty

description: <strong>Dataset Release for MEWL: Few-shot multimodal word learning with referential uncertainty (ICML 2023) </strong> <strong>GitHub:</strong> https://github.com/jianggy/MEWL <strong>Abstract: </strong>Without explicit feedback, humans can rapidly learn the meaning of words. Children can acquire a new word after just a few passive exposures, a process known as fast mapping. This word learning capability is believed to be the most fundamental building block of multimodal understanding and reasoning. Despite recent advancements in multimodal learning, a systematic and rigorous evaluation is still missing for human-like word learning in machines. To fill in this gap, we introduce the MachinE Word Learning (MEWL) benchmark to assess how machines learn word meaning in grounded visual scenes. MEWL covers human's core cognitive toolkits in word learning: cross-situational reasoning, bootstrapping, and pragmatic learning. Specifically, MEWL is a few-shot benchmark suite consisting of nine tasks for probing various word learning capabilities. These tasks are carefully designed to be aligned with the children's core abilities in word learning and echo the theories in the developmental literature. By evaluating multimodal and unimodal agents' performance with a comparative analysis of human performance, we notice a sharp divergence in human and machine word learning. We further discuss these differences between humans and machines and call for human-like few-shot word learning in machines.
description: {"references": ["Jiang, G., Xu, M., Xin, S., Liang, W., Peng, Y., Zhang, C., & Zhu, Y. (2023). MEWL: Few-shot multimodal word learning with referential uncertainty. International Conference on Machine Learning (ICML)."]}