cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7556184
title: Towards a reliable spatial analysis of missing features via spatially-regularized imputation

description: Recent spatial transcriptomic (ST) technologies offer a lens for observing the spatial distribution of RNA transcripts in tissues, yet achieving a whole-genome-level spatial landscape remains technically challenging. Multiple computational methods hence have been proposed to impute missing genes from a single-cell reference dataset, while they lack mechanisms of explicitly encoding spatial patterns in the modeling.<br> To fill the research gaps, we introduce a computational model, TransImp, that leverages a spatial auto-correlation metric as a regularization for imputing missing features in ST. Evaluation results from multiple platforms demonstrate that TransImp remarkably preserves the spatial patterns, hence substantially improving the accuracy of downstream analysis in detecting spatially highly variable genes and spatial interactions. Therefore, TransImp offers a way towards a reliable spatial analysis of missing features for both matched and unseen modalities, e.g., nascent RNAs.
DOI: 10.5281/zenodo.8166268
title: Towards a reliable spatial analysis of missing features via spatially-regularized imputation

description: Recent spatial transcriptomic (ST) technologies offer a lens for observing the spatial distribution of RNA transcripts in tissues, yet achieving a whole-genome-level spatial landscape remains technically challenging. Multiple computational methods hence have been proposed to impute missing genes from a single-cell reference dataset, while they lack mechanisms of explicitly encoding spatial patterns in the modeling.<br> To fill the research gaps, we introduce a computational model, TransImp, that leverages a spatial auto-correlation metric as a regularization for imputing missing features in ST. Evaluation results from multiple platforms demonstrate that TransImp remarkably preserves the spatial patterns, hence substantially improving the accuracy of downstream analysis in detecting spatially highly variable genes and spatial interactions. Therefore, TransImp offers a way towards a reliable spatial analysis of missing features for both matched and unseen modalities, e.g., nascent RNAs.
DOI: 10.5281/zenodo.8172197
title: Towards a reliable spatial analysis of missing features via spatially-regularized imputation

description: Recent spatial transcriptomic (ST) technologies offer a lens for observing the spatial distribution of RNA transcripts in tissues, yet achieving a whole-genome-level spatial landscape remains technically challenging. Multiple computational methods hence have been proposed to impute missing genes from a single-cell reference dataset, while they lack mechanisms of explicitly encoding spatial patterns in the modeling.<br> To fill the research gaps, we introduce a computational model, TransImp, that leverages a spatial auto-correlation metric as a regularization for imputing missing features in ST. Evaluation results from multiple platforms demonstrate that TransImp remarkably preserves the spatial patterns, hence substantially improving the accuracy of downstream analysis in detecting spatially highly variable genes and spatial interactions. Therefore, TransImp offers a way towards a reliable spatial analysis of missing features for both matched and unseen modalities, e.g., nascent RNAs.
DOI: 10.5281/zenodo.8214151
title: Reliable imputation of spatial transcriptome with uncertainty estimation and spatial regularization

description: Imputation of missing features in spatial transcriptomics is urgently demanded due to technology limitations, while most existing computational methods suffer from moderate accuracy and cannot estimate the reliability of the imputation. <br> To fill the research gaps, we introduce a computational model, TransImp, that imputes the missing feature modality in spatial transcriptomics by mapping it from single-cell reference. Uniquely, we derived a set of attributes that can accurately predict imputation uncertainty, hence enabling us to select reliably imputed genes. Also, we introduced a spatial auto-correlation metric as a regularization to avoid overestimating spatial patterns. Multiple datasets from various platforms have demonstrated that our approach significantly improves the reliability of downstream analyses in detecting spatial variable genes and interacting ligand-receptor pairs. Therefore, TransImp offers a way towards a reliable spatial analysis of missing features for both matched and unseen modalities, e.g., nascent RNAs.
DOI: 10.5281/zenodo.8214466
title: Reliable imputation of spatial transcriptome with uncertainty estimation and spatial regularization

description: Imputation of missing features in spatial transcriptomics is urgently demanded due to technology limitations, while most existing computational methods suffer from moderate accuracy and cannot estimate the reliability of the imputation. <br> To fill the research gaps, we introduce a computational model, TransImp, that imputes the missing feature modality in spatial transcriptomics by mapping it from single-cell reference. Uniquely, we derived a set of attributes that can accurately predict imputation uncertainty, hence enabling us to select reliably imputed genes. Also, we introduced a spatial auto-correlation metric as a regularization to avoid overestimating spatial patterns. Multiple datasets from various platforms have demonstrated that our approach significantly improves the reliability of downstream analyses in detecting spatial variable genes and interacting ligand-receptor pairs. Therefore, TransImp offers a way towards a reliable spatial analysis of missing features for both matched and unseen modalities, e.g., nascent RNAs.
DOI: 10.5281/zenodo.8214194
title: Introduction to Biomedical Image Analysis (for Beginners)

description: Small dataset of the zebrafish brain vasculature acquired at 3 days post fertilization (dpf) with a Zeiss Z.2 light-sheet microscope. Transgenic reporter line used: blood vessels <em>Tg(kdrl:HRAS-mCherry)</em><sup><em>s916</em></sup> labels EC membrane (Chi et al., 2008). Data were acquired by Elisabeth Kugler at the University of Sheffield conforming to UK Home Office regulations and were performed under Home Office Project Licence 70/8588 held by TJAC. - Data are part of the publication: Kugler et al., 2019 as well as Dr Kugler's thesis. Dataset consists only of 3 samples and is used as a sample dataset to train students in biomedical image analysis. Image Acquisition Details:<br> Datasets in Sheffield were obtained using a Zeiss Z.1 light sheet microscope with a water-dipping detection objective (Plan-Apochromat 20×/1.0 Corr nd = 1.38) and a scientific complementary metal-oxide semiconductor (sCMOS) detection unit. Data were acquired with activated pivot scan, dual-sided illumination and online fusion; properties of acquired data are as follows: 0.7× zoom, 16bit image depth, 1,920 × 1,920px image size and minimum z-stack interval (approx. 0.33 × 0.33× 0.5 μm). Green and red fluorophores were excited using 488 nm and 561 nm laser, respectively. Used filters in sequential tracks for multi-colour images were LP560 for both and BP505-545 and LP585, respectively. Samples were embedded in 1% or 2% LMP-agarose containing 200 mg/l Tricaine (MS-222, Sigma). The image acquisition chamber was filled with E3 plus Tricaine (200 mg/ml) and maintained at 28°C.
DOI: 10.5281/zenodo.8214193
title: Introduction to Biomedical Image Analysis (for Beginners)

description: Small dataset of the zebrafish brain vasculature acquired at 3 days post fertilization (dpf) with a Zeiss Z.2 light-sheet microscope. Transgenic reporter line used: blood vessels <em>Tg(kdrl:HRAS-mCherry)</em><sup><em>s916</em></sup> labels EC membrane (Chi et al., 2008). Data were acquired by Elisabeth Kugler at the University of Sheffield conforming to UK Home Office regulations and were performed under Home Office Project Licence 70/8588 held by TJAC. - Data are part of the publication: Kugler et al., 2019 as well as Dr Kugler's thesis. Dataset consists only of 3 samples and is used as a sample dataset to train students in biomedical image analysis. Image Acquisition Details:<br> Datasets in Sheffield were obtained using a Zeiss Z.1 light sheet microscope with a water-dipping detection objective (Plan-Apochromat 20×/1.0 Corr nd = 1.38) and a scientific complementary metal-oxide semiconductor (sCMOS) detection unit. Data were acquired with activated pivot scan, dual-sided illumination and online fusion; properties of acquired data are as follows: 0.7× zoom, 16bit image depth, 1,920 × 1,920px image size and minimum z-stack interval (approx. 0.33 × 0.33× 0.5 μm). Green and red fluorophores were excited using 488 nm and 561 nm laser, respectively. Used filters in sequential tracks for multi-colour images were LP560 for both and BP505-545 and LP585, respectively. Samples were embedded in 1% or 2% LMP-agarose containing 200 mg/l Tricaine (MS-222, Sigma). The image acquisition chamber was filled with E3 plus Tricaine (200 mg/ml) and maintained at 28°C.
DOI: 10.5281/zenodo.8214457
title: Resolution Enhancement and Deblurring of Porous Media μ-CT Images based on Super Resolution Generative Adversarial Network

description: The above is the Data2 of Super Resolution Generative Adversarial Network based on High-Resolution Representation Learning.
DOI: 10.5281/zenodo.8208561
title: Resolution Enhancement and Deblurring of Porous Media μ-CT Images based on Super Resolution Generative Adversarial Network

description: The above is the Data2 of Super Resolution Generative Adversarial Network based on High-Resolution Representation Learning.
DOI: 10.5517/ccdc.csd.cc2bxj2w
title: CCDC 2172358: Experimental Crystal Structure Determination

description: Related Article: Jiangyu Zhu, Wenhao Li, Ning Zhang, Dongyue An, Yan Zhao, Xuefeng Lu, Yunqi Liu|2022|Chemical Science|13|11174|doi:10.1039/D2SC03167A
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: 10.5517/ccdc.csd.cc2clryp
title: CCDC 2192783: Experimental Crystal Structure Determination

description: Related Article: Yuquan Feng, Zhiguo Zhong, Zhaohui Meng, Keshuan Ma, Hongwei Wang, Xun Feng, Suhong Wang, Zilong Yue, Zhaoge Zeng, Shuhan Zhang|2022|Dalton Trans.|51|14413|doi:10.1039/D2DT02575B
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: 10.5517/ccdc.csd.cc2bxh1t
title: CCDC 2172326: Experimental Crystal Structure Determination

description: Related Article: Jiangyu Zhu, Wenhao Li, Ning Zhang, Dongyue An, Yan Zhao, Xuefeng Lu, Yunqi Liu|2022|Chemical Science|13|11174|doi:10.1039/D2SC03167A
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: 10.5517/ccdc.csd.cc2fpk6x
title: CCDC 2255039: Experimental Crystal Structure Determination

description: Related Article: Fan-Zhi Bu, Yue-Ming Yu, Min Zhang, Cui-Wei Yan, Zhi-Yong Wu, Yan-Tuan Li|2023|Cryst.Growth Des.|23|5868|doi:10.1021/acs.cgd.3c00489
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: 10.5517/ccdc.csd.cc2g3lqx
title: CCDC 2267579: Experimental Crystal Structure Determination

description: Related Article: Ahmed M. Mansour, Rabaa M. Khaled, Krzysztof Radacki, Zeina Younes, Mariam Gamal, Beatrice Guirguis, Gamal A. E. Mostafa, Essam A. Ali, Ola R. Shehab|2023|Dalton Trans.|52|10286|doi:10.1039/D3DT01798B
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: 10.6084/m9.figshare.23857590
title: XP spectra of used TiO2 photocatalysts in the presence of alkali metal chlorides

description: XPS data in VAMAS format from the used catalysts
DOI: 10.6084/m9.figshare.23857590.v1
title: XP spectra of used TiO2 photocatalysts in the presence of alkali metal chlorides

description: XPS data in VAMAS format from the used catalysts
DOI: 10.23728/b2share.ec4fa718a1a1482cae299268615668f0
title: LTSER Zone Atelier Alpes - France, Annual Phenology, 2019 -2021

description: Phenology metrics, green-up, maturity, senescence was generated using Sentinel-2 Level-2A time series of the years 2019-2021.
description: The phenology product is generated from a time series (1 year) using Sentinel-2A. The algorithm uses Normalised Difference Vegetation Index (NDVI).
description: The final output consists of raster layers in GeoTIFF format with the following values per pixel: SOS : start of the season, expressed as Day Of the Year (DOY) SOSV: Vegetation Index value at SOS POS: peak of the season, expressed as Day Of the Year (DOY) PSOV: Vegetation Index value at POS EOS: end of season, expressed as Day Of the Year (DOY) EOSV: Vegetation Index value at EOS
DOI: 10.5281/zenodo.8208780
title: Combining Proofs for Description Logic and Concrete Domain Reasoning - RuleML+RR23 - Resources

description: <strong>experiment-RuleML+RR-2023.zip</strong> contains the materials used in the experiment described in the paper, and a README file with instructions on how to rerun the experiment.
DOI: 10.5281/zenodo.8208781
title: Combining Proofs for Description Logic and Concrete Domain Reasoning - RuleML23 - Resources

description: <strong>experiment-RuleML-2023.zip</strong> contains the materials used in the experiment described in the paper, and a README file with instructions on how to rerun the experiment.
DOI: 10.5281/zenodo.8214331
title: Combining Proofs for Description Logic and Concrete Domain Reasoning - RuleML+RR23 - Resources

description: <strong>experiment-RuleML-2023.zip</strong> contains the materials used in the experiment described in the paper, and a README file with instructions on how to rerun the experiment.
DOI: 10.5281/zenodo.8214503
title: Combining Proofs for Description Logic and Concrete Domain Reasoning - RuleML+RR23 - Resources

description: <strong>experiment-RuleML+RR-2023.zip</strong> contains the materials used in the experiment described in the paper, and a README file with instructions on how to rerun the experiment.
DOI: 10.23728/b2share.c7ffe7052f2f4f73bc2f1770e6716d7d
title: Whim Bog - United Kingdom, Annual Phenology, 2019 - 2021

description: Phenology metrics, green-up, maturity, senescence generated from Sentinel-2 Level-2A time series of years 2019-2021.
description: The phenology product is generated from a time series (1 year). The algorithm uses Normalized Difference Vegetation Index (NDVI).
description: The final output consists of raster layers in GeoTIFF format with the following values per pixel: SOS : start of the season, expressed as Day Of the Year (DOY) SOSV: Vegetation Index value at SOS POS: peak of the season, expressed as Day Of the Year (DOY) PSOV: Vegetation Index value at POS EOS: end of season, expressed as Day Of the Year (DOY) EOSV: Vegetation Index value at EOS
DOI: 10.34740/kaggle/dsv/6247365
title: Egypt Gold Prices | Daily Updated
DOI: 10.23728/b2share.6de1e903ae0445a487cb219142bbcfb2
title: Lammi LTER - Finland, Annual Phenology, 2019 - 2021

description: Phenology metrics, green-up, maturity, senescence was generated using Sentinel-2 Level-2A time series of the years 2019-2021.
description: The phenology product is generated from a time series (1 year). The algorithm uses Normalised Difference Vegetation Index (NDVI).
description: The final output consists of raster layers in GeoTIFF format with the following values per pixel: SOS : start of the season, expressed as Day Of the Year (DOY) SOSV: Vegetation Index value at SOS POS: peak of the season, expressed as Day Of the Year (DOY) PSOV: Vegetation Index value at POS EOS: end of season, expressed as Day Of the Year (DOY) EOSV: Vegetation Index value at EOS
DOI: 10.6084/m9.figshare.22731666
title: Thirteenth and fourteenth century ceramic decoration and social groups in the central great Plains

description: This paper considers possible social groups and interactions on the thirteenth through fifteenth century central Plains by examining patterns of ceramic rim face and upper body decoration. My analysis tabulates design motifs by site and/or locality, identifying a basic dichotomy between the northern / western area of the central Plains and the southern / eastern area, along with a third distinct pattern in the set of anomalously large sites on Ponca Creek in northeastern Nebraska. In the north and west, potters often used a diverse set of motifs on the faces of paneled rims and sometimes put Oneota designs on vessel upper bodies. In the south and east, they rarely decorated the face of any form of rim and sometimes put a pattern of alternating triangles filled with opposed diagonal lines on vessel upper bodies. This dichotomy maps onto a distinction between ossuary and cemetery burials and likely defines a social boundary between groups with long-standing interaction with different regions to the east of the Plains. The Ponca Creek data show a mixture of central Plains and Oneota styles along with hints of other regional patterns that may suggest the existence of a multi-ethnic community.
DOI: 10.6084/m9.figshare.23267666
title: The best laid plans: Assemblage formation and abandonment at two house sites in the Central Plains

description: Systematic, fine-grained data recovery and spatial analysis facilitate interpretation of household assemblage formation and abandonment at two late prehistoric (AD 1000–1500) lodges in the Central Plains: the Scott and Phil sites in northeastern and north-central Kansas, respectively. This is based on patterns in lodge design, construction, storage, and domestic activities inferred from the distribution of burned wood, burned stone, daub, ceramics, chipped stone tools, and lithic debris. These reflect patterns of cultural material discard, displacement, clean-up, and gendered space utilization, as well as planned vs. unplanned abandonment that may compare to households of other sedentary, low level food producers in the Great Plains and other regions.
DOI: 10.6084/m9.figshare.23267666.v1
title: The best laid plans: Assemblage formation and abandonment at two house sites in the Central Plains

description: Systematic, fine-grained data recovery and spatial analysis facilitate interpretation of household assemblage formation and abandonment at two late prehistoric (AD 1000–1500) lodges in the Central Plains: the Scott and Phil sites in northeastern and north-central Kansas, respectively. This is based on patterns in lodge design, construction, storage, and domestic activities inferred from the distribution of burned wood, burned stone, daub, ceramics, chipped stone tools, and lithic debris. These reflect patterns of cultural material discard, displacement, clean-up, and gendered space utilization, as well as planned vs. unplanned abandonment that may compare to households of other sedentary, low level food producers in the Great Plains and other regions.
DOI: 10.6084/m9.figshare.22731666.v1
title: Thirteenth and fourteenth century ceramic decoration and social groups in the central great Plains

description: This paper considers possible social groups and interactions on the thirteenth through fifteenth century central Plains by examining patterns of ceramic rim face and upper body decoration. My analysis tabulates design motifs by site and/or locality, identifying a basic dichotomy between the northern / western area of the central Plains and the southern / eastern area, along with a third distinct pattern in the set of anomalously large sites on Ponca Creek in northeastern Nebraska. In the north and west, potters often used a diverse set of motifs on the faces of paneled rims and sometimes put Oneota designs on vessel upper bodies. In the south and east, they rarely decorated the face of any form of rim and sometimes put a pattern of alternating triangles filled with opposed diagonal lines on vessel upper bodies. This dichotomy maps onto a distinction between ossuary and cemetery burials and likely defines a social boundary between groups with long-standing interaction with different regions to the east of the Plains. The Ponca Creek data show a mixture of central Plains and Oneota styles along with hints of other regional patterns that may suggest the existence of a multi-ethnic community.
DOI: 10.23728/b2share.2f3df5f7baf04288853f24121d0810cd
title: DoñanaLTSERPlatform, Annual Phenology, 2019 - 2021

description: Phenology metrics, green-up, maturity, senescence generated from Sentinel-2 Level-2A time series of the years 2019 - 2021.
description: The phenology product is generated from a time series (1 year). The algorithm uses Normalised Difference Vegetation Index (NDVI).
description: The final output consists of raster layers in GeoTIFF format with the following values per pixel: SOS : start of the season, expressed as Day Of the Year (DOY) SOSV: Vegetation Index value at SOS POS: peak of the season, expressed as Day Of the Year (DOY) PSOV: Vegetation Index value at POS EOS: end of season, expressed as Day Of the Year (DOY) EOSV: Vegetation Index value at EOS
DOI: 10.23728/b2share.9a664abd7ebb4db6b26d9d955147490e
title: Svartberget Research Station - Sweden , Annual Phenology, 2019 - 2021

description: Phenology metrics, green-up, maturity, senescence was generated using Sentinel-2 Level-2A time series of the year 2019.
description: The phenology product is generated from a time series (1 year). The algorithm uses Normalised Difference Vegetation Index (NDVI).
description: The final output consists of raster layers in GeoTIFF format with the following values per pixel: SOS : start of the season, expressed as Day Of the Year (DOY) SOSV: Vegetation Index value at SOS POS: peak of the season, expressed as Day Of the Year (DOY) PSOV: Vegetation Index value at POS EOS: end of season, expressed as Day Of the Year (DOY) EOSV: Vegetation Index value at EOS
DOI: 10.23728/b2share.d147da761fdd47cea6833178ba0597cd
title: LTER Zöbelboden, Austria, Forest Inventory and Tree biomass data 1993-2019

description: Long-term forest inventory and aboveground tree biomass data from LTER Zöbelboden Austria (1993-2019). The dataset contains the forest inventory parameter (tree species, tree vitality status, diameter at breast height, tree height) as well as stem, branch, foliage/needle and total aboveground biomass of the trees within the permanent inventory plots covering the LTER Zöbelboden. The forest inventories were conducted in 1993, 2005, 2010, 2014 and 2019.