cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7741430
title: MUSES Leaf Area Index (LAI) Monthly Global 500m SIN Grid in 2016

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 LAI product at 500m spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2016. Please <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2015</strong></em><strong>, </strong>and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2017</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2016 Spatial Resolution: 500m Temporal Resolution: 1 month Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7741429
title: MUSES Leaf Area Index (LAI) Monthly Global 500m SIN Grid in 2016

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 LAI product at 500m spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2016. Please <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2015</strong></em><strong>, </strong>and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2017</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2016 Spatial Resolution: 500m Temporal Resolution: 1 month Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.34740/kaggle/dsv/5180675
title: 🇺🇦 Ukraine Conflict Twitter Dataset
DOI: 10.6084/m9.figshare.22290430
title: 20190902-3 100 Hz 10X ventrolateral medulla respiratory activity image series

description: Zipped image series via GCaMP6F expressed in the germline of respiratory network activity along the ventral respiratory column exposed at the surface of the sagittally-secttioned mouse hindbrain preparation. this dataset (9-2-2019) is the same dataset shown in supplemental video 2, shown there in differenced form. Acquissiition parameters: 10X, 100 Hz
DOI: 10.6084/m9.figshare.22290430.v1
title: 20190902-3 100 Hz 10X ventrolateral medulla respiratory activity image series

description: Zipped image series via GCaMP6F expressed in the germline of respiratory network activity along the ventral respiratory column exposed at the surface of the sagittally-secttioned mouse hindbrain preparation. this dataset (9-2-2019) is the same dataset shown in supplemental video 2, shown there in differenced form. Acquissiition parameters: 10X, 100 Hz
DOI: 10.5517/ccdc.csd.cc2d9899
title: CCDC 2213440: Experimental Crystal Structure Determination

description: Related Article: Subbarayan Vijayapritha, Paramasivan Nithya, Periasamy Viswanathamurthi, Saravanan Raju, Wolfgang Linert|2023|Polyhedron|235|116351|doi:10.1016/j.poly.2023.116351
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.cc2df3zy
title: CCDC 2217150: Experimental Crystal Structure Determination

description: Related Article: Zhijie Liang, Haibing Cheng, Xiaoli Zhang, Qinghui Mao|2023|J.Mol.Liq.|377|121483|doi:10.1016/j.molliq.2023.121483
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.cc2dmpvl
title: CCDC 2223470: Experimental Crystal Structure Determination

description: Related Article: Ebru Keskin, Hakan Arslan|2023|J.Mol.Struct.|1283|135252|doi:10.1016/j.molstruc.2023.135252
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.6590026
title: Dataset Validation Images for the MICCAI-2022-Challenge: Airway Tree Modeling (ATM'22)

description: Dataset for the MICCAI-2022-Challenge: Airway Tree Modeling (ATM'22) This is the Validation Image part. If you use this dataset in your research, you must cite the papers in the References below !!!
description: {"references": ["Zhang M, Wu Y, Zhang H, et al. Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation[J]. arXiv preprint arXiv:2303.05745, 2023.", "Zhang M, Zhang H, Yang G Z, et al. CFDA: Collaborative Feature Disentanglement and Augmentation for Pulmonary Airway Tree Modeling of COVID-19 CTs[C]//Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2022: 25th International Conference, Singapore, September 18\u201322, 2022, Proceedings, Part I. Cham: Springer Nature Switzerland, 2022: 506-516.", "Zheng H, Qin Y, Gu Y, et al. Alleviating class-wise gradient imbalance for pulmonary airway segmentation[J]. IEEE Transactions on Medical Imaging, 2021, 40(9): 2452-2462.", "Yu W, Zheng H, Zhang M, et al. BREAK: Bronchi Reconstruction by gEodesic transformation And sKeleton embedding[C]//2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). IEEE, 2022: 1-5.", "Qin Y, Chen M, Zheng H, et al. Airwaynet: a voxel-connectivity aware approach for accurate airway segmentation using convolutional neural networks[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019: 212-220."]}
DOI: 10.5281/zenodo.6590025
title: Dataset Validation Images for the MICCAI-2022-Challenge: Airway Tree Modeling (ATM'22)

description: Dataset for the MICCAI-2022-Challenge: Airway Tree Modeling (ATM'22) This is the Validation Image part. If you use this dataset in your research, you must cite the papers in the References below !!!
description: {"references": ["Zhang M, Wu Y, Zhang H, et al. Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation[J]. arXiv preprint arXiv:2303.05745, 2023.", "Zhang M, Zhang H, Yang G Z, et al. CFDA: Collaborative Feature Disentanglement and Augmentation for Pulmonary Airway Tree Modeling of COVID-19 CTs[C]//Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2022: 25th International Conference, Singapore, September 18\u201322, 2022, Proceedings, Part I. Cham: Springer Nature Switzerland, 2022: 506-516.", "Zheng H, Qin Y, Gu Y, et al. Alleviating class-wise gradient imbalance for pulmonary airway segmentation[J]. IEEE Transactions on Medical Imaging, 2021, 40(9): 2452-2462.", "Yu W, Zheng H, Zhang M, et al. BREAK: Bronchi Reconstruction by gEodesic transformation And sKeleton embedding[C]//2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). IEEE, 2022: 1-5.", "Qin Y, Chen M, Zheng H, et al. Airwaynet: a voxel-connectivity aware approach for accurate airway segmentation using convolutional neural networks[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019: 212-220."]}
DOI: 10.6084/m9.figshare.22290451
title: 2_15_2021-4_06 PM-16.7z

description: zippped raw image series matching supplemental video 4 above. This dataset exemplifies the syncytial rhythm detected along the ventral respiratory column following synaptic blockade. Recording parameters: 10X, 10 Hz
DOI: 10.6084/m9.figshare.22290451.v1
title: 2_15_2021-4_06 PM-16.7z

description: zippped raw image series matching supplemental video 4 above. This dataset exemplifies the syncytial rhythm detected along the ventral respiratory column following synaptic blockade. Recording parameters: 10X, 10 Hz
DOI: 10.48577/jpl.16mnyf
title: Mars 2020 EDL Reconstruction As-Flown Report

description: On February 18, 2021, amidst a global pandemic sweeping the nation and the world, the Perseverance rover safely completed its autonomous landing in Jezero Crater. The largest, heaviest, and most complex planetary rover ever built depended on its seven-minute entry, descent, and landing (EDL) sequence to touch down safely at the most dangerous landing site ever attempted on Mars. Building upon the success of the Mars Science Laboratory (MSL) mission, the Mars 2020 EDL system preserved as much of the MSL EDL architecture as possible while simultaneously fixing issues identified both pre- and post-MSL’s landing, adding mission enabling new EDL capabilities, and accommodating new mission-enhancing payloads. Reconstruction results to date have shown that the Mars 2020 EDL system performed largely as expected. Issues identified with MSL’s landing were successfully addressed during the Mars 2020 landing. The mission’s two new landing capabilities, range trigger and terrain relative navigation, proved critical in identifying and reaching a safe landing target within the hazardous Jezero Crater landing site, a site deemed too hazardous for previous landing systems. Finally, new EDL instrumentation and a new EDL camera suite gave the team greater insight into the landing performance and brought the world along for the ride. This As-Flown Reconstruction Report details the design, development challenges, pre-flight expectations, and flight performance of almost all aspects of the Mars 2020 EDL system. While this report captures the best understanding of the system as of the time of publication, no one knows better than this team that additional time and scrutiny will yield further insights; the team’s scrutiny of the MSL EDL system throughout the development of Mars 2020 led to several fixes and improvements to the EDL design. As a result, this report is a snapshot in time of the team’s best understanding of the performance of the system; expect that understanding to grow and evolve with future efforts. The Mars 2020 EDL team faced numerous challenges, some expected, some not, over the project’s lifecycle. Their engineering prowess, creativity, dedication, and can-do attitude allowed the tight-knit team to face each of these challenges and overcome them. This report documents their story. With Mars 2020’s successful landing, the edge of the Mars landing envelope has been pushed out once again, enabling the first controlled aircraft flights on another planet by the Ingenuity helicopter, the search for evidence of past life by the Perseverance rover, and the beginning of the long-envision Mars sample return campaign.
DOI: 10.5281/zenodo.7262614
title: Energy Consumption Estimation of API-usage in Smartphone Apps via Static Analysis

description: OPEN CALL FOR COLLECTING ENERGY PROFILES @ https://github.com/AbdulAli/replication-kit-msr-2023 Cite this work as: @inproceedings{bangash2023msr,<br> title={Energy Consumption Estimation of API-usage in Mobile Apps via Static Analysis},<br> author={Bangash, Abdul Ali and Jamal, Qasim and Eng, Kalvin and Ali, Karim and Hindle, Abram},<br> booktitle={2023 20th International Conference on Mining Software Repositories (MSR)},<br> pages={5721--5730},<br> year={2023},<br> organization={IEEE}<br> } This is the replication-kit of the paper published at MSR 2023. It includes: SQLite operations' benchmarks SQLite benchmarks' energy profiles The E-Factor Calculation program
DOI: 10.5281/zenodo.7262615
title: Energy Consumption Estimation of API-usage in Smartphone Apps via Static Analysis

description: OPEN CALL FOR COLLECTING ENERGY PROFILES @ https://github.com/AbdulAli/replication-kit-msr-2023 Cite this work as: @inproceedings{bangash2023msr,<br> title={Energy Consumption Estimation of API-usage in Mobile Apps via Static Analysis},<br> author={Bangash, Abdul Ali and Jamal, Qasim and Eng, Kalvin and Ali, Karim and Hindle, Abram},<br> booktitle={2023 20th International Conference on Mining Software Repositories (MSR)},<br> pages={5721--5730},<br> year={2023},<br> organization={IEEE}<br> } This is the replication-kit of the paper published at MSR 2023. It includes: SQLite operations' benchmarks SQLite benchmarks' energy profiles The E-Factor Calculation program
DOI: 10.3886/e119446v75-134200
title: COVID-19 US State Policy Database

description: For questions or comments about the database please contact:<br>Alexandra Skinner <br>skinnera@bu.edu <br>Research Fellow & Database Manager<br>Department of Health Law, Policy & Management <br>Boston University School of Public Health<br><br>Database of state policies on closures, shelter-in-place orders, housing protections, changes to Medicaid and SNAP, physical distancing closures, reopening, and more created by researchers at the Boston University School of Public Health. Policies included are state-wide directives or mandates, not guidance or recommendations. In order for a policy to be included, it must apply to the entire state. We are working quickly to go through state government websites to make the policy database as complete and accurate as possible in a rapidly changing policy context. If you use data on a given policy, we encourage you to triangulate based on additional sources of policy data and to review the source documentation to consider the coding decisions that are right for your work. Of course, please email us if you note a discrepancy so we can improve the database for everyone. State policy source documentation can be found at: tinyurl.com/statepolicysources.
description: State policies. Smallest Geographic Unit: State
description: other; Contributors: Megan Cole Brahim, Noa Krawczyk, Carolyn Hoffman, Claire Corkish, Elizabeth Ferrara, Elizabeth Long, Emily Baroni, Faith Contador, Hannah Simon, Morgan Simko, Rachel Scheckman, Sarah Brewer, Sue Kulkarni, Felicia Heykoop, Manish Patel, Aishwarya Vidyasagaran, Andrew Chiao, Cara Safon, Samantha Burkhart, Harry Jung, Victor Lee, Marina MacKinnon, Aarohi Rede, Marie Vearling, Sophie Li, Christina Wang, Tim Levengood, María Jordán P. Emma Chistolini
DOI: 10.5281/zenodo.7743107
title: VIOLA JONES ALGORITHM WITH CAPSULE GRAPH NETWORK FOR DEEPFAKE DETECTION

description: <strong>dataset of this paper VIOLA JONES ALGORITHM WITH CAPSULE GRAPH NETWORK FOR DEEPFAKE DETECTION</strong>
DOI: 10.5281/zenodo.7743106
title: VIOLA JONES ALGORITHM WITH CAPSULE GRAPH NETWORK FOR DEEPFAKE DETECTION

description: <strong>dataset of this paper VIOLA JONES ALGORITHM WITH CAPSULE GRAPH NETWORK FOR DEEPFAKE DETECTION</strong>
DOI: 10.6084/m9.figshare.22186879.v3
title: People Living with HIV Who Has Becomes an Influencer on Social

description: Contents of the interview transcripts PLHIV on social media
DOI: 10.34740/kaggle/dsv/5181146
title: Nifty-50 & Nifty Future 50 Dataset
DOI: 10.5281/zenodo.7297085
title: Correlation of viral loads in disease transmission chains could bias early estimates of the reproduction number

description: This dataset accompanies the an article titled, 'Correlation of viral loads in disease transmission chains could bias early estimates of the reproduction number', by T. Harris et al. which can be found at https://arxiv.org/abs/2211.08673. Abstract: Early estimation of the transmission properties of a newly emerged pathogen is critical to inform an effective public health response. Early estimates are based on limited data, meaning methods for estimating pathogen characteristics based on limited data are crucial. Here, we investigate a potential source of bias arising from correlations between the viral load of cases in transmission chains. We show that this mechanism can affect estimates of common transmission properties characterising the spread of a virus. To model these correlations we simulate a disease transmission mechanism in which the viral load of the infector at the time of transmission influences the infectiousness of the infectee. These correlations in transmission pairs produce a population-level decoherence process during which the distributions of initial viral loads in each subsequent generation converge to a steady state. Through our simulation study, we find that outbreaks arising from index cases with low initial viral loads give rise to early estimates of transmission properties that are subject to large biases. These findings demonstrate the potential for bias arising from transmission mechanics to affect estimates of the transmission properties of newly emerged viruses. Contents of this dataset include: Model code - including experiments (python3) Estimation code (R) Code used for figure production (python3 & R) Raw data produced from Experiments 1-3 and presented in the text (.csv & .Rdata) See 'README.md' in main upload folder for more information
DOI: 10.5281/zenodo.7297084
title: Correlation of viral loads in disease transmission could affect early estimates of the reproduction number

description: This dataset accompanies the an article titled, 'Correlation of viral loads in disease transmission could affect early estimates of the reproduction number', by T. Harris et al. which can be found at https://arxiv.org/abs/2211.08673. Abstract: Early estimates of the transmission properties of a newly emerged pathogen are critical to an effective public health response, and are often based on limited outbreak data. Here, we use simulations to investigate how correlations between the viral load of cases in transmission chains can affect estimates of these fundamental transmission properties. Our computational model simulates a disease transmission mechanism in which the viral load of the infector at the time of transmission influences the infectiousness of the infectee. These correlations in transmission pairs produce a population-level convergence process during which the distributions of initial viral loads in each subsequent generation converge to a steady state. We find that outbreaks arising from index cases with low initial viral loads give rise to early estimates of transmission properties that could be misleading. These findings demonstrate the potential for transmission mechanisms to affect estimates of the transmission properties of newly emerged viruses in ways that could be operationally significant to a public health response. Contents of this dataset include: Model code - including experiments (python3) Estimation code (R) Code used for figure production (python3 & R) Raw data produced from Experiments 1-3 and presented in the text (.csv & .Rdata) See 'README.md' in main upload folder for more information