cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7865929
title: Effect of cement on the consolidation behavior of marine clay in comparison with clay minerals

description: This is data for paper ""<strong>Effect of cement on the consolidation behavior of marine clay in comparison with clay minerals</strong>
DOI: 10.5281/zenodo.7865928
title: Effect of cement on the consolidation behavior of marine clay in comparison with clay minerals

description: This is data for paper ""<strong>Effect of cement on the consolidation behavior of marine clay in comparison with clay minerals</strong>
DOI: 10.18112/openneuro.ds004560.v1.0.0
title: The Phantom of Bern: repeated scans of two volunteers with eight different combinations of MR sequence parameters
DOI: 10.6084/m9.figshare.22698103
title: Supplementary Material for: Optimal use of serum leucine-rich alpha-2 glycoprotein as a biomarker for small bowel lesions of Crohn's disease

description: Introduction A large proportion of small bowel lesions in Crohn’s disease (CD) may exist beyond the reach of ileocolonoscopy and there is no gold standard imaging modality to screen them, suggesting the need for optimal biomarkers. We aimed to compare the usefulness of C-reactive protein (CRP), faecal calprotectin (FC), and leucine-rich alpha-2 glycoprotein (LRG) in determining small bowel lesions of CD. Methods This was a cross-sectional observational study. CRP, FC, and LRG were prospectively measured in patients with quiescent CD who underwent imaging examinations (capsule or balloon-assisted endoscopy, magnetic resonance enterography, or intestinal ultrasound) selected by the physician in clinical practice. Mucosal healing (MH) of the small bowel was defined as a lack of ulcers. Patients with a Crohn’s disease activity index &gt; 150 and active colonic lesions were excluded. Results A total of 65 patients (27, MH; 38, small bowel inflammation) were analysed. The area under the curve (AUC) of CRP, FC, and LRG was 0.74 (95% confidence interval 0.61-0.87), 0.69 (0.52-0.81), and 0.77 (0.59-0.85), respectively. The AUC of FC and LRG in a subgroup of 61 patients with CRP &lt; 3 mg/l (26, MH; 32, small bowel inflammation) was 0.68 (0.50-0.81), and 0.74 (0.54-0.84), respectively. The cut-off of 16 μg/ml of LRG showed the highest positive predictive value of 1.00 with specificity of 1.00 while negative predictive value was highest (0.71) with sensitivity of 0.89 at the cut-off of 9 μg/ml. Discussion/Conclusion LRG can accurately detect and/or exclude the small bowel lesions by two cut-off values.
DOI: 10.6084/m9.figshare.22698103.v1
title: Supplementary Material for: Optimal use of serum leucine-rich alpha-2 glycoprotein as a biomarker for small bowel lesions of Crohn's disease

description: Introduction A large proportion of small bowel lesions in Crohn’s disease (CD) may exist beyond the reach of ileocolonoscopy and there is no gold standard imaging modality to screen them, suggesting the need for optimal biomarkers. We aimed to compare the usefulness of C-reactive protein (CRP), faecal calprotectin (FC), and leucine-rich alpha-2 glycoprotein (LRG) in determining small bowel lesions of CD. Methods This was a cross-sectional observational study. CRP, FC, and LRG were prospectively measured in patients with quiescent CD who underwent imaging examinations (capsule or balloon-assisted endoscopy, magnetic resonance enterography, or intestinal ultrasound) selected by the physician in clinical practice. Mucosal healing (MH) of the small bowel was defined as a lack of ulcers. Patients with a Crohn’s disease activity index &gt; 150 and active colonic lesions were excluded. Results A total of 65 patients (27, MH; 38, small bowel inflammation) were analysed. The area under the curve (AUC) of CRP, FC, and LRG was 0.74 (95% confidence interval 0.61-0.87), 0.69 (0.52-0.81), and 0.77 (0.59-0.85), respectively. The AUC of FC and LRG in a subgroup of 61 patients with CRP &lt; 3 mg/l (26, MH; 32, small bowel inflammation) was 0.68 (0.50-0.81), and 0.74 (0.54-0.84), respectively. The cut-off of 16 μg/ml of LRG showed the highest positive predictive value of 1.00 with specificity of 1.00 while negative predictive value was highest (0.71) with sensitivity of 0.89 at the cut-off of 9 μg/ml. Discussion/Conclusion LRG can accurately detect and/or exclude the small bowel lesions by two cut-off values.
DOI: 10.57967/hf/0575
title: byznen
DOI: 10.6084/m9.figshare.22219426.v1
title: Supplementary Material for: Current management of patients with RPE65 mutation-associated Inherited Retinal Degenerations (RPE65-IRD) in Europe. Results of a 2 years follow-up multinational survey

description: Introduction: To evaluate the current management of RPE65-biallelic mutation-associated inherited retinal degeneration (RPE65-IRD) in Europe since market authorization of Voretigene Neparvovec (VN, LuxturnaTM) in 2018. By July 2022, over 200 patients have been treated outside the USA, of whom about 90% in Europe. We conducted among all centers of the European Vision Institute Clinical Research Network (EVICR.net) and health care providers (HCPs) of the European Reference Network dedicated to Rare Eye Diseases (ERN-Eye) the second multinational survey on management of IRDs in Europe elaborated by EVICR.net with a special focus on RPE65-IRD. Methods: An electronic survey questionnaire with 48 questions specifically addressing RPE65-IRD (2019 survey 35) was developed and sent by June 2021 to 95 EVICR.net centers and 40 ERN-EYE HCPs and affiliated members. Of note, 11 centers are members of both networks. Statistical analysis was performed with Excel and R. Results: The overall response rate was 44% (55/124); 26 centers follow RPE65 biallelic mutation-associated IRD patients. By June 2021, 8/26 centers have treated 57 RPE65-IRD cases (1 – 19/center, median 6), and 43 planned for treatment (range 0 – 10/center, median 6). The overall age range was 3 – 52 years, and on average 22% of the patients did not (yet) qualify for treatment (range 2 – 60%/center, median 15%). Main reasons were too advanced (range 0-100, median 75%) or mild disease (range 0-100, median 0). Eighty-three percent of centers (10/12) that follow RPE65 mutation-associated IRD patients treated with VN participate in the PERCEIVE registry (EUPAS31153, http://www.encepp.eu/encepp/viewResource.htm?id=37005). Quality of life and full field stimulus test (FST) improvements had the highest scores of the survey-reported outcome parameters in VN treatment follow-up. Discussion/Conclusion: This second multinational survey on management of RPE65-IRD by EVICR.net centers and ERN-Eye HCPs in Europe indicates that RPE65-IRD might be diagnosed more reliably in 2021 compared to 2019. By June 2021, 8/26 centers reported detailed results including VN treatment. Main reasons for non-treatment were too advanced or mild disease, followed by absence of two class 4 or 5 mutations on both alleles, or because of a too young age. Patient satisfaction with treatment was estimated to be high by 50% of the centers.
DOI: 10.5281/zenodo.7865517
title: MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 500m SIN Grid in 2000

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FVC product at 500m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2018 (continuously updated). It was generated from the MUSES LAI product at 500m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2000. <em>Please </em><em><strong>click here</strong> to download the MUSES FVC product <strong>in 2001</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2000 Spatial Resolution: 500m Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.004 Valid Range: 0 – 250 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7865516
title: MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 500m SIN Grid in 2000

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FVC product at 500m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2018 (continuously updated). It was generated from the MUSES LAI product at 500m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2000. <em>Please </em><em><strong>click here</strong> to download the MUSES FVC product <strong>in 2001</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2000 Spatial Resolution: 500m Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.004 Valid Range: 0 – 250 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7866010
title: Photoluminescence of Dy3+:Y2SiO5 and luminescence thermometry in the 300-900 K temperature range

description: Data from Dy3+:Y2SiO5 photoluminescence and luminescence thermometry measurements in the 300-900 K temperature range. All data are in Origin files.
DOI: 10.5281/zenodo.7866009
title: Photoluminescence of Dy3+:Y2SiO5 and luminescence thermometry in the 300-900 K temperature range

description: Data from Dy3+:Y2SiO5 photoluminescence and luminescence thermometry measurements in the 300-900 K temperature range. All data are in Origin files.
DOI: 10.6084/m9.figshare.22303000
title: Supplementary Material for: Hospitalizations of chronic dialysis patients: a national study in China

description: Background Patients receiving chronic dialysis are usually with multiple comorbidities and at high-risk for hospitalization, which lead to tremendous healthcare resources utilization. This study aims to explore the characteristics of hospitalizations among chronic dialysis patients in China. Methods Hospital admissions from January 2013 to December 2015 were extracted from a national inpatient database in China. Chronic dialysis, including hemodialysis and peritoneal dialysis, were identified according to inpatients discharge records and International Classification of Diseases-10 (ICD-10) codes. The primary kidney disease, causes of admissions, modalities of dialysis, and comorbidities were analyzed. Multivariable Logistic regression model was used to assess the association of patient characteristics with multiple hospitalizations per year. Results Altogether, 266,636 hospitalizations from 124,721 chronic dialysis patients were included in the study. The mean age was 54.46±15.63 years and 78.29% of them were receiving hemodialysis. The leading cause of hospitalizations was dialysis access-related, including dialysis access creation (25.06%) and complications of access (21.09%). The following causes were non-access surgery (1.89%), cardiovascular disease (1.66%), and infectious diseases (1.43%). One fourth of the patients were hospitalized more than once per year. Multivariate Logistic regression models indicated that the primary kidney disease of diabetic kidney disease (odds ratio [OR]: 1.16, 95% confidence interval [CI]: 1.11-1.22) or hypertensive nephropathy (OR: 1.33, 95% CI: 1.27-1.40), coronary heart disease (OR: 1.09, 95% CI: 1.05-1.14), cancer (OR: 1.21, 95% CI: 1.13-1.30), or modality of peritoneal dialysis (OR: 2.67, 95% CI: 2.59-2.75) were risk factors for multiple hospitalizations. Conclusion Our study described characteristics and revealed the burden of hospitalizations of chronic dialysis patients in China. These findings highlight the importance of effective and efficient management strategies to reduce the high burden of hospitalization in dialysis population.
DOI: 10.5281/zenodo.7779105
title: Rotational and Dilational Reconstruction in Transition Metal Dichalcogenide Moire Bilayers

description: Data sets used in "Rotational and Dilational Reconstruction in Transition Metal Dichalcogenide Moire Bilayers" by Van Winkle and Craig et al. File name Appearance in Manuscript Material P/AP Twist Angle (°) Hetstrain (%) Scan Shape (pixels) Pixel Size (nm) Smoothing Sigma DS1 Fig. 2m, 2n MoS2/MoS2 P 0.369 0.451 200x200 0.5 2 DS2 Fig. 2m, 2n MoS2/MoS2 P 0.523 0.138 200x200 0.5 2 DS3 Fig. 2m, 2n MoS2/MoS2 P 1.849 0.546 100x100 0.5 2 DS4 Fig. 2m, 2n MoS2/MoS2 P 1.814 0.453 100x100 0.5 2 DS5 Fig. 2m, 2n, 5a, 5d MoS2/MoS2 P 1.769 0.472 100x100 0.5 2 DS6 Fig. 2m, 2n MoS2/MoS2 P 1.721 0.849 100x100 0.5 2 DS7 Fig. 2a, 2d, 2g, 2m, 2n MoS2/MoS2 P 1.227 0.304 100x100 0.5 2 DS8 Fig. 2m, 2n, 5b, 5e MoS2/MoS2 P 1.644 0.970 100x100 0.5 2 DS9 Fig. 2m, 2n MoS2/MoS2 P 1.353 0.597 100x100 0.5 2 DS10 Fig. 2m, 2n MoS2/MoS2 P 1.597 0.930 100x100 0.5 2 DS11 Fig. 5c, 5f MoS2/MoS2 P 1.919 1.320 100x100 0.5 2 DS12 Fig. 2m, 2n MoS2/MoS2 P 1.193 0.581 100x100 0.5 2 DS13 Fig. 2m, 2n MoS2/MoS2 P 0.856 0.619 200x200 0.5 2 DS14 Fig. 1b, 1c, 2m, 2n MoS2/MoS2 P 0.818 0.319 200x200 0.5 2 DS15 Fig. 2k, 2l MoS2/MoS2 AP 2.562 0.444 100x100 0.5 2 DS16 Fig. 2k, 2l MoS2/MoS2 AP 2.121 0.595 100x100 0.5 2 DS17 Fig. 5h, 5k MoS2/MoS2 AP 1.533 1.353 100x100 0.5 2 DS18 Fig. 2k, 2l MoS2/MoS2 AP 2.697 0.455 100x100 0.5 2 DS19 Fig. 5i, 5l MoS2/MoS2 AP 1.459 1.138 100x100 0.5 2 DS20 Fig. 2k, 2l MoS2/MoS2 AP 0.514 0.434 200x200 0.5 2 DS21 Fig. 2k, 2l MoS2/MoS2 AP 0.652 0.500 200x200 0.5 2 DS22 Fig. 2c, 2f, 2i, 2k, 2l MoS2/MoS2 AP 1.688 0.588 100x100 0.5 2 DS23 Fig. 2k, 2l, 5g, 5j MoS2/MoS2 AP 1.361 0.620 100x100 0.5 2 DS24 Fig. 2k, 2l MoS2/MoS2 AP 1.226 0.472 100x100 0.5 2 DS25 Fig. 1d, 2b, 2e, 2h, 2k, 2l MoS2/MoS2 AP 0.771 0.380 100x100 0.5 2 DS26 Fig. 2k, 2l MoS2/MoS2 AP 1.483 0.568 100x100 0.5 2 DS28 Fig. 1b, 1c, 3c, 3f, 3j WSe2/MoS2 P 0.795 100x100 0.5 2 DS29 Fig. 1d WSe2/MoS2 AP 0.070 100x100 0.5 2 DS30 Fig. 4g, 4j WSe2/MoS2 AP 0.776 100x100 0.5 2 DS31 Fig. 4h, 4k WSe2/MoS2 AP 0.852 100x100 0.5 2 DS32 Fig. 3b, 3e, 3i WSe2/MoS2 AP 1.072 100x100 0.5 2 DS33 Fig. 4i, 4l WSe2/MoS2 AP 0.824 100x100 0.5 2 DS34 Fig. 4a, 4d, 4g, 4j WSe2/MoS2 AP 0.097 100x100 0.5 2 DS35 Fig. 4b, 4e, 4h, 4k WSe2/MoS2 AP 0.096 100x100 0.5 2 DS36 Fig. 3a, 3d, 3h WSe2/MoS2 AP 0.127 100x100 0.5 2 DS37 Fig. 4c, 4f, 4i, 4l WSe2/MoS2 AP 0.167 100x100 0.5 2
DOI: 10.5281/zenodo.7779104
title: Rotational and Dilational Reconstruction in Transition Metal Dichalcogenide Moire Bilayers

description: Data sets used in "Rotational and Dilational Reconstruction in Transition Metal Dichalcogenide Moire Bilayers" by Van Winkle and Craig et al. File name Appearance in Manuscript Material P/AP Twist Angle (°) Hetstrain (%) Scan Shape (pixels) Pixel Size (nm) Smoothing Sigma DS1 Fig. 2m, 2n MoS2/MoS2 P 0.369 0.451 200x200 0.5 2 DS2 Fig. 2m, 2n MoS2/MoS2 P 0.523 0.138 200x200 0.5 2 DS3 Fig. 2m, 2n MoS2/MoS2 P 1.849 0.546 100x100 0.5 2 DS4 Fig. 2m, 2n MoS2/MoS2 P 1.814 0.453 100x100 0.5 2 DS5 Fig. 2m, 2n, 5a, 5d MoS2/MoS2 P 1.769 0.472 100x100 0.5 2 DS6 Fig. 2m, 2n MoS2/MoS2 P 1.721 0.849 100x100 0.5 2 DS7 Fig. 2a, 2d, 2g, 2m, 2n MoS2/MoS2 P 1.227 0.304 100x100 0.5 2 DS8 Fig. 2m, 2n, 5b, 5e MoS2/MoS2 P 1.644 0.970 100x100 0.5 2 DS9 Fig. 2m, 2n MoS2/MoS2 P 1.353 0.597 100x100 0.5 2 DS10 Fig. 2m, 2n MoS2/MoS2 P 1.597 0.930 100x100 0.5 2 DS11 Fig. 5c, 5f MoS2/MoS2 P 1.919 1.320 100x100 0.5 2 DS12 Fig. 2m, 2n MoS2/MoS2 P 1.193 0.581 100x100 0.5 2 DS13 Fig. 2m, 2n MoS2/MoS2 P 0.856 0.619 200x200 0.5 2 DS14 Fig. 1b, 1c, 2m, 2n MoS2/MoS2 P 0.818 0.319 200x200 0.5 2 DS15 Fig. 2k, 2l MoS2/MoS2 AP 2.562 0.444 100x100 0.5 2 DS16 Fig. 2k, 2l MoS2/MoS2 AP 2.121 0.595 100x100 0.5 2 DS17 Fig. 5h, 5k MoS2/MoS2 AP 1.533 1.353 100x100 0.5 2 DS18 Fig. 2k, 2l MoS2/MoS2 AP 2.697 0.455 100x100 0.5 2 DS19 Fig. 5i, 5l MoS2/MoS2 AP 1.459 1.138 100x100 0.5 2 DS20 Fig. 2k, 2l MoS2/MoS2 AP 0.514 0.434 200x200 0.5 2 DS21 Fig. 2k, 2l MoS2/MoS2 AP 0.652 0.500 200x200 0.5 2 DS22 Fig. 2c, 2f, 2i, 2k, 2l MoS2/MoS2 AP 1.688 0.588 100x100 0.5 2 DS23 Fig. 2k, 2l, 5g, 5j MoS2/MoS2 AP 1.361 0.620 100x100 0.5 2 DS24 Fig. 2k, 2l MoS2/MoS2 AP 1.226 0.472 100x100 0.5 2 DS25 Fig. 1d, 2b, 2e, 2h, 2k, 2l MoS2/MoS2 AP 0.771 0.380 100x100 0.5 2 DS26 Fig. 2k, 2l MoS2/MoS2 AP 1.483 0.568 100x100 0.5 2 DS28 Fig. 1b, 1c, 3c, 3f, 3j WSe2/MoS2 P 0.795 100x100 0.5 2 DS29 Fig. 1d WSe2/MoS2 AP 0.070 100x100 0.5 2 DS30 Fig. 4g, 4j WSe2/MoS2 AP 0.776 100x100 0.5 2 DS31 (and raw dm4 data) Fig. 4h, 4k WSe2/MoS2 AP 0.852 100x100 0.5 2 DS32 Fig. 3b, 3e, 3i WSe2/MoS2 AP 1.072 100x100 0.5 2 DS33 Fig. 4i, 4l WSe2/MoS2 AP 0.824 100x100 0.5 2 DS34 Fig. 4a, 4d, 4g, 4j WSe2/MoS2 AP 0.097 100x100 0.5 2 DS35 Fig. 4b, 4e, 4h, 4k WSe2/MoS2 AP 0.096 100x100 0.5 2 DS36 Fig. 3a, 3d, 3h WSe2/MoS2 AP 0.127 100x100 0.5 2 DS37 Fig. 4c, 4f, 4i, 4l WSe2/MoS2 AP 0.167 100x100 0.5 2
DOI: 10.5281/zenodo.7865970
title: Rotational and Dilational Reconstruction in Transition Metal Dichalcogenide Moire Bilayers

description: Data sets used in "Rotational and Dilational Reconstruction in Transition Metal Dichalcogenide Moire Bilayers" by Van Winkle and Craig et al. File name Appearance in Manuscript Material P/AP Twist Angle (°) Hetstrain (%) Scan Shape (pixels) Pixel Size (nm) Smoothing Sigma DS1 Fig. 2m, 2n MoS2/MoS2 P 0.369 0.451 200x200 0.5 2 DS2 Fig. 2m, 2n MoS2/MoS2 P 0.523 0.138 200x200 0.5 2 DS3 Fig. 2m, 2n MoS2/MoS2 P 1.849 0.546 100x100 0.5 2 DS4 Fig. 2m, 2n MoS2/MoS2 P 1.814 0.453 100x100 0.5 2 DS5 Fig. 2m, 2n, 5a, 5d MoS2/MoS2 P 1.769 0.472 100x100 0.5 2 DS6 Fig. 2m, 2n MoS2/MoS2 P 1.721 0.849 100x100 0.5 2 DS7 Fig. 2a, 2d, 2g, 2m, 2n MoS2/MoS2 P 1.227 0.304 100x100 0.5 2 DS8 Fig. 2m, 2n, 5b, 5e MoS2/MoS2 P 1.644 0.970 100x100 0.5 2 DS9 Fig. 2m, 2n MoS2/MoS2 P 1.353 0.597 100x100 0.5 2 DS10 Fig. 2m, 2n MoS2/MoS2 P 1.597 0.930 100x100 0.5 2 DS11 Fig. 5c, 5f MoS2/MoS2 P 1.919 1.320 100x100 0.5 2 DS12 Fig. 2m, 2n MoS2/MoS2 P 1.193 0.581 100x100 0.5 2 DS13 Fig. 2m, 2n MoS2/MoS2 P 0.856 0.619 200x200 0.5 2 DS14 Fig. 1b, 1c, 2m, 2n MoS2/MoS2 P 0.818 0.319 200x200 0.5 2 DS15 Fig. 2k, 2l MoS2/MoS2 AP 2.562 0.444 100x100 0.5 2 DS16 Fig. 2k, 2l MoS2/MoS2 AP 2.121 0.595 100x100 0.5 2 DS17 Fig. 5h, 5k MoS2/MoS2 AP 1.533 1.353 100x100 0.5 2 DS18 Fig. 2k, 2l MoS2/MoS2 AP 2.697 0.455 100x100 0.5 2 DS19 Fig. 5i, 5l MoS2/MoS2 AP 1.459 1.138 100x100 0.5 2 DS20 Fig. 2k, 2l MoS2/MoS2 AP 0.514 0.434 200x200 0.5 2 DS21 Fig. 2k, 2l MoS2/MoS2 AP 0.652 0.500 200x200 0.5 2 DS22 Fig. 2c, 2f, 2i, 2k, 2l MoS2/MoS2 AP 1.688 0.588 100x100 0.5 2 DS23 Fig. 2k, 2l, 5g, 5j MoS2/MoS2 AP 1.361 0.620 100x100 0.5 2 DS24 Fig. 2k, 2l MoS2/MoS2 AP 1.226 0.472 100x100 0.5 2 DS25 Fig. 1d, 2b, 2e, 2h, 2k, 2l MoS2/MoS2 AP 0.771 0.380 100x100 0.5 2 DS26 Fig. 2k, 2l MoS2/MoS2 AP 1.483 0.568 100x100 0.5 2 DS28 Fig. 1b, 1c, 3c, 3f, 3j WSe2/MoS2 P 0.795 100x100 0.5 2 DS29 Fig. 1d WSe2/MoS2 AP 0.070 100x100 0.5 2 DS30 Fig. 4g, 4j WSe2/MoS2 AP 0.776 100x100 0.5 2 DS31 (and raw dm4 data) Fig. 4h, 4k WSe2/MoS2 AP 0.852 100x100 0.5 2 DS32 Fig. 3b, 3e, 3i WSe2/MoS2 AP 1.072 100x100 0.5 2 DS33 Fig. 4i, 4l WSe2/MoS2 AP 0.824 100x100 0.5 2 DS34 Fig. 4a, 4d, 4g, 4j WSe2/MoS2 AP 0.097 100x100 0.5 2 DS35 Fig. 4b, 4e, 4h, 4k WSe2/MoS2 AP 0.096 100x100 0.5 2 DS36 Fig. 3a, 3d, 3h WSe2/MoS2 AP 0.127 100x100 0.5 2 DS37 Fig. 4c, 4f, 4i, 4l WSe2/MoS2 AP 0.167 100x100 0.5 2
DOI: 10.5281/zenodo.7861227
title: MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 500m SIN Grid in 2001

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FVC product at 500m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2018 (continuously updated). It was generated from the MUSES LAI product at 500m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2001. <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2000</strong></em>, and <em><strong>click here</strong> to download the MUSES FVC product <strong>in 2002</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2001 Spatial Resolution: 500m Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.004 Valid Range: 0 – 250 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).