cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25442/hku.29107037
title: Supporting Data For 'Disease-Associated Amyloid-β Filaments in Biliary Atresia, an Incurable Liver Disease in Infants'

description: Biliary atresia (BA) is an obstructive bile duct disease in newborns with a complex and diverse etiology, with most cases lacking a clear genetic basis. Infants with BA show altered regulation of genes involved in amyloid-β (Aβ) biology and exhibit abnormal intracellular inclusions within hepatic bile ducts that are immunoreactive to amyloid-β precursor protein (APP). However, the identity, structure, and pathogenic role of these inclusions remain unclear. In this study, we employed cryogenic electron microscopy (cryo-EM) to resolve the structures of protein aggregates isolated from the livers of four infants diagnosed with BA. Our analysis revealed that these aggregates are composed of amyloid-β 42 (Aβ42) peptides. Specifically, we identified two structurally related M-shaped Aβ42 protofilament folds, which form two distinct filament types: left-handed twisted type BA I dimeric filaments (residues V12–A42) with extensive protofilament interfaces, and right-handed twisted type BA II trimeric filaments, consisting of the type BA I dimeric core (residues V12–A42 and G9–A42) along with a third distinctively folded protofilament representing the C-terminal portion of Aβ42 (residues G29–A42). The Aβ42 filaments found in BA livers are structurally identical across all cases but differ from those in extracellular neural plaques characteristic of Alzheimer’s disease or Down syndrome. Furthermore, Aβ42 filaments derived from BA livers induce morphological and transcriptional changes associated with BA in healthy liver organoids. This study provides evidence of amyloid-β aggregation outside the brain and offers new insights into the pathobiology of BA. Understanding the structural properties of Aβ42 filaments in BA could aid in developing improved model systems, structure-based inhibitors targeting amyloid-β deposition, and diagnostic tools for this currently incurable condition.<br>
DOI: 10.15468/dl.jzm8pk
title: Occurrence Download

description: A dataset containing 12753 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Lindera obtusiloba Blume, Lindera obtusiloba var. heterophylla (Meisn.) H.P.Tsui, Lindera obtusiloba var. obtusiloba, Lindera obtusiloba f. quinqueloba (Uyeki) T.B.Lee, Lindera obtusiloba var. praetermissa (Grierson & Long) H.P.Tsui, Lindera obtusiloba f. quinqueloba (Uyeki) C.M.Pak, Lindera obtusiloba var. heterophylla (Meisn.) H.B.Cui, Lindera obtusiloba var. praetermissa (Grierson & D.G.Long) H.B.Cui, Lindera obtusiloba var. tomentosa Miq., Lindera obtusiloba f. villosa (Blume) Kosterm., Lindera obtusiloba f. villosa (Blume) Kitag., Lindera obtusiloba var. villosa Blume, Lindera obtusiloba f. ovata (Nakai) T.B.Lee, Lindera obtusiloba f. villosa (Blume) T.B.Lee, Lindera obtusiloba f. ovata (Nakai) C.M.Pak, Lindera obtusiloba f. velutina T.B.Lee)"
 ]
}

 The dataset includes 12753 records from 192 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0077833-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11636942
title: Carbon dioxide (CO2) variations across India: Synthesis of observations and model simulations

description: India is the 3rd largest emitter of fossil fuel carbon dioxide (CO2), making it essential to understand CO2 dynamics to manage carbon emissions. This study examines CO2 variability and its dynamics based on observations at eleven observational sites in India, satellite observations and model simulations. Results reveal distinct diurnal and seasonal patterns, along with an overall increasing trend in CO2. Deeper (shallower) seasonal cycle amplitudes (SCA) observed over northern (southern) part of India are due to the influence of the monsoon system, seasonal climate and terrestrial biosphere. The lowest SCA is observed over the high-altitude site, Hanle in north India (7.4 ppm), followed by the coastal sites, Pondicherry (8.0 ppm) and Thumba (8.4 ppm) in south India. Deepest SCA, 26.7 ppm, is observed at Mohali with one of the peaks observed in November attributed to crop residue burning activities in the Indo-Gangetic Plain. We have used an Atmospheric Chemistry Transport Model (ACTM) to simulate spatial and temporal variations in CO2. While the ACTM generally reproduces diurnal variability in January, it fails to capture the CO2 minima in July. The model simulates seasonal patterns at Thumba reasonably well, whereas underestimates the SCA at Gadanki. Satellite observations of column CO2 (XCO2) show higher values (410–414 ppm) during the pre-monsoon season, while they remain lower (407–410 ppm) during winter and post-monsoon seasons during 2014–2024. Mean XCO2 trend (2.41–2.46 ppm yr-1) and growth rate variations are similar to Mauna Loa observations.
DOI: 10.5281/zenodo.11636943
title: Carbon dioxide (CO2) variations across India: Synthesis of observations and model simulations

description: India is the 3rd largest emitter of fossil fuel carbon dioxide (CO2), making it essential to understand CO2 dynamics to manage carbon emissions. This study examines CO2 variability and its dynamics based on observations at eleven observational sites in India, satellite observations and model simulations. Results reveal distinct diurnal and seasonal patterns, along with an overall increasing trend in CO2. Deeper (shallower) seasonal cycle amplitudes (SCA) observed over northern (southern) part of India are due to the influence of the monsoon system, seasonal climate and terrestrial biosphere. The lowest SCA is observed over the high-altitude site, Hanle in north India (7.4 ppm), followed by the coastal sites, Pondicherry (8.0 ppm) and Thumba (8.4 ppm) in south India. Deepest SCA, 26.7 ppm, is observed at Mohali with one of the peaks observed in November attributed to crop residue burning activities in the Indo-Gangetic Plain. We have used an Atmospheric Chemistry Transport Model (ACTM) to simulate spatial and temporal variations in CO2. While the ACTM generally reproduces diurnal variability in January, it fails to capture the CO2 minima in July. The model simulates seasonal patterns at Thumba reasonably well, whereas underestimates the SCA at Gadanki. Satellite observations of column CO2 (XCO2) show higher values (410–414 ppm) during the pre-monsoon season, while they remain lower (407–410 ppm) during winter and post-monsoon seasons during 2014–2024. Mean XCO2 trend (2.41–2.46 ppm yr-1) and growth rate variations are similar to Mauna Loa observations.
DOI: 10.48366/r700389
title: Graphene-Based fillers in polymer composites

description: This comparison table focuses on graphene-polymer composites, detailing their mechanical properties such as Young’s modulus, tensile strength, and fracture strain. It is crucial in materials science for optimizing composite performance in various applications, from structural materials to advanced functional materials. Understanding these properties aids in developing stronger, more durable, and versatile materials with tailored properties for specific industrial and technological uses.
DOI: 10.15468/dl.2zgfr4
title: Occurrence Download

description: A dataset containing 326 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Linum arboreum L., Linum arboreum Pall., Linum arboreum Pall. ex M.Bieb., Linum arboreum var. doerfleri (Rech.) Critop.)"
 ]
}

 The dataset includes 326 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0077834-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.m4ywtd
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is one of (Carex kucyniakii Raymond)",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 }
 ]
}

 The dataset includes 15 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0077835-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18167/dvn1/2qxnvu
title: NIRS Database on Fresh Yam tuber for Dry Matter Starch Calibrations at NRCRI, Nigeria

description: The dataset contains spectra data of intact yam tuber from the On-farm Verification Trial, harvested in Igbariam and Abakiliki locations in Nigeria obtained using QualitySpec Trek: S-10016 used to predict starch and dry matter of fresh yam
DOI: 10.15468/dl.eju5n7
title: Occurrence Download

description: A dataset containing 1566 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Meriones unguiculatus (Milne-Edwards, 1867)"
 ]
}

 The dataset includes 1566 records from 65 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0077837-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18167/dvn1/iwjagu
title: NIRS Database on Dried Yam for DM, Protein, Starch, Sugar, Hardness, Cohesiveness, Adhesiveness, Springiness, Extensibility Calibrations at INRAe-CIRAD, Guadeloupe

description: The dataset contains 174 spectra from ground dried yam samples cultivated in CIRAD, Roujol, Year 2016-2017 obtained using FOSS 6500 for DM, Protein, Starch, Sugar, Hardness, Cohesiveness, Adhesiveness, Springiness, Extensibility Calibrations
DOI: 10.15468/dl.9ghy87
title: Occurrence Download

description: A dataset containing 6605 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Linum perenne L., Linum perenne Lam., Linum perenne Guss., Linum perenne Vill., Linum perenne subsp. ockendonii (Greuter & Burdet) Seybold, Linum perenne f. albiflorum Cockerell, Linum perenne var. austriacum (L.) Schiede, Linum perenne subsp. austriacum (L.) O.Bolòs & Vigo, Linum perenne var. austriacum (L.) Jess., Linum perenne subsp. extraaxillare (Kit.) Nyman, Linum perenne f. baldwinii B.Boivin, Linum perenne subsp. anglicum (Mill.) Ockendon, Linum perenne var. collinum (Guss. ex Boiss.) O.Bolòs & Vigo, Linum perenne subsp. boreale (Juz.) Serg., Linum perenne subsp. lepagei (B.Boivin) Lepage ex Dut. et al., Linum perenne var. boreale (Juz.) Serg., Linum perenne var. procumbens Gray, Linum perenne subsp. lepagei (B.Boivin) Lepage, Linum perenne var. lepagei (B.Boivin) B.Boivin, Linum perenne subsp. alpinum (Jacq.) Stoj. & Stef., Linum perenne subsp. alpinum (Jacq.) Ockendon, Linum perenne var. alpinum (Jacq.) Schiede, Linum perenne subsp. petryi (Beyer) Graebn., Linum perenne var. saxosum (Maguire & A.H.Holmgren) Reveal, Linum perenne subsp. montanum (W.D.J.Koch) Ockendon, Linum perenne subsp. perenne, Linum perenne subsp. lewisii (Pursh) Hultén, Linum perenne var. perenne, Linum perenne var. pyrenaicum (Pourr.) Planch., Linum perenne var. stocksianum (Boiss.) Boiss., Linum perenne var. lewisii (Pursh) Eaton & C.Wright, Linum perenne var. pauciflorum Regel, Linum perenne subsp. roseum Barr & Sugden)"
 ]
}

 The dataset includes 6605 records from 387 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0077836-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pmfk2h
title: Occurrence Download

description: A dataset containing 4251 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Liriope spicata Lour., Liriope spicata var. densifolia (Maxim. ex Baker) C.H.Wright, Liriope spicata subsp. densiflora (Maxim. ex Baker) C.H.Wright, Liriope spicata var. densiflora (Maxim. ex Baker) C.H.Wright, Liriope spicata subsp. latifolia Franch., Liriope spicata var. latifolia Franch., Liriope spicata subsp. prolifera Y.T.Ma, Liriope spicata var. prolifera Y.T.Ma, Liriope spicata f. koreana (Palib.) H.Hara, Liriope spicata subsp. koreana (Palib.) H.Hara, Liriope spicata subsp. humilis F.Z.Li, Liriope spicata var. humilis F.Z.Li, Liriope spicata var. minor (Maxim.) C.H.Wright, Liriope spicata subsp. minor (Maxim.) C.H.Wright)"
 ]
}

 The dataset includes 4251 records from 162 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0077838-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6cubh5
title: Occurrence Download

description: A dataset containing 99 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cycas pectinata Buch.-Ham.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 99 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0077840-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u89xhn
title: Occurrence Download

description: A dataset containing 393725 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Ireland, Svalbard and Jan Mayen, Norway, Sweden, Finland, Estonia, Latvia, Denmark, Portugal, Spain, France, Germany, Netherlands, Switzerland, Austria, Italy, Greece, Türkiye, Russian Federation, Belarus, Ukraine, Poland, Czechia, Slovakia, Hungary, Romania, Bulgaria, Albania, Serbia, Slovenia, Bosnia and Herzegovina)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Stellaria media (L.) Vill."
 ]
}

 The dataset includes 393725 records from 2044 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0077808-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pj9gty
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"GADM_LEVEL_2_GID","value":"USA.4.64_1","matchCase":false},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 6 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007424-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.k23m6s
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "CatalogNumber is V236880",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007423-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57760/sciencedb.10078
title: Diffraction data for crystal of mouse SNX25 PX domain
title: Diffraction data for crystal of mouse SNX25 PX domain

description: "The diffraction data was collected in BL19U1 Beamlines at Shanghai Synchrotron Radiation Facilityof China."
description: "The diffraction data was collected in BL19U1 Beamlines at Shanghai Synchrotron Radiation Facilityof China."
DOI: 10.57760/sciencedb.j00207.00009
title: Appendix of "Kriging Model Averaging Based on Leave-One-Out Cross-Validation Method"
title: Appendix of "Kriging Model Averaging Based on Leave-One-Out Cross-Validation Method"

description: This appendix contains all supplementary materials for the accepted manuscript JSSC-2023-0150, including detailed proofs of all theorems presented in the paper as well as additional simulation results.
description: This appendix contains all supplementary materials for the accepted manuscript JSSC-2023-0150, including detailed proofs of all theorems presented in the paper as well as additional simulation results.
DOI: 10.57760/sciencedb.10107
title: The illegal trade in pangolins in China (2011 to 2022)
title: The illegal trade in pangolins in China (2011 to 2022)

description: Judgement records on pangolin seizures on the China Judgements Online website (http://wenshu.court.gov.cn/) from 2011 to 2022.
description: Judgement records on pangolin seizures on the China Judgements Online website (http://wenshu.court.gov.cn/) from 2011 to 2022.
DOI: 10.57760/sciencedb.10108
title: digital transformation and strategic risk taking
title: digital transformation and strategic risk taking

description: digital transformation and strategic risk taking data set
description: digital transformation and strategic risk taking data set
DOI: 10.57760/sciencedb.j00133.00302
title: The dataset after matching segmentation of Sohu events
title: 搜狐事件匹配划分后的数据集

description: The dataset is sourced from the event matching part of the 2021 Sohu Campus Text Matching Algorithm Competition（https://www.biendata.xyz/competition/sohu_2021/). The event matching datasets released in the preliminary and final rounds were merged, and the event matching parts of short text and short text, short text and long text, and long text and long text were selected. 20% were used as the test set, the remaining 20% were used as the validation set, and the rest were used as the training set.
description: The dataset is sourced from the event matching part of the 2021 Sohu Campus Text Matching Algorithm Competition（https://www.biendata.xyz/competition/sohu_2021/). The event matching datasets released in the preliminary and final rounds were merged, and the event matching parts of short text and short text, short text and long text, and long text and long text were selected. 20% were used as the test set, the remaining 20% were used as the validation set, and the rest were used as the training set.
DOI: 10.57760/sciencedb.10115
title: Scalable preparation of macroporous collagen microgels by air bubble-induced breakup and ice templating
title: Scalable preparation of macroporous collagen microgels by air bubble-induced breakup and ice templating

description: Data in support of a manuscript
description: Data in support of a manuscript
DOI: 10.57760/sciencedb.10123
title: origin date (Sprint SkiMo)
title: 原始数据（短距离滑雪登山）

description: Stage-by-stage results from the Ski Mountaineering World Championships in Cortina d'Ampezzo, Boitau and Schladming
description: Stage-by-stage results from the Ski Mountaineering World Championships in Cortina d'Ampezzo, Boitau and Schladming
DOI: 10.57760/sciencedb.10127
title: Data of microbiota
title: 肠道菌群数据

description: raw data
description: raw data
DOI: 10.57760/sciencedb.hjs.00027
title: Brightness Fusion and Multi-Scale Optimized Enhancement Algorithm for Fuel Rod DR Images
title: 燃料棒DR图像亮度融合与多尺度优化增强算法

description: [Background] A fuel rod is a fundamental unit of a fuel assembly, and it directly impacts the safe operation of nuclear reactors. [Purpose] To efficiently detect internal defects in fuel rods, a high-resolution visual nondestructive testing method, X-ray imaging, is employed. To address the issue of low contrast in fuel rod X-ray DR images, a brightness fusion and multiscale optimized enhancement algorithm for fuel rod DR images is proposed. [Methods] First, the image brightness is corrected using logarithmic and gamma transformations and further refined by incorporating local information fusion. Subsequently, a wavelet function is applied for multiscale decomposition, enhancing and sharpening low-frequency components with Retinex, and filtering high-frequency components using NL_Means. Finally, image enhancement is realized via wavelet reconstruction. [Results] To evaluate the performance of the algorithm, experiments are conducted using the DR images of fuel rods as test subjects. Four representative image quality assessment metrics are employed for quantitative analysis. [Conclusions] The experimental results demonstrate that image brightness fusion and multiscale
description: [Background] A fuel rod is a fundamental unit of a fuel assembly, and it directly impacts the safe operation of nuclear reactors. [Purpose] To efficiently detect internal defects in fuel rods, a high-resolution visual nondestructive testing method, X-ray imaging, is employed. To address the issue of low contrast in fuel rod X-ray DR images, a brightness fusion and multiscale optimized enhancement algorithm for fuel rod DR images is proposed. [Methods] First, the image brightness is corrected using logarithmic and gamma transformations and further refined by incorporating local information fusion. Subsequently, a wavelet function is applied for multiscale decomposition, enhancing and sharpening low-frequency components with Retinex, and filtering high-frequency components using NL_Means. Finally, image enhancement is realized via wavelet reconstruction. [Results] To evaluate the performance of the algorithm, experiments are conducted using the DR images of fuel rods as test subjects. Four representative image quality assessment metrics are employed for quantitative analysis. [Conclusions] The experimental results demonstrate that image brightness fusion and multiscale