cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc206sxy
title: CCDC 1853580: Experimental Crystal Structure Determination

description: Related Article: Di-Zhong Zheng, Dong-Hui Li, Huan Liu, Youxiang Shao, Zhuofeng Ke, Feng-Shou Liu|2022|Organometallics|41|948|doi:10.1021/acs.organomet.2c00007
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.cc214hnc
title: CCDC 1881162: Experimental Crystal Structure Determination

description: Related Article: Masashi Ozaki, Yasuhisa Ishikura, Minh Anh Truong, Jiewei Liu, Iku Okada, Taro Tanabe, Shun Sekimoto, Tsutomu Ohtsuki, Yasujiro Murata, Richard Murdey, Atsushi Wakamiya|2019|J.Mater.Chem.A|7|16947|doi:10.1039/C9TA02142F
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.cc260l72
title: CCDC 2026353: Experimental Crystal Structure Determination

description: Related Article: Susana Ibáñez, Dmitry G. Gusev, Eduardo Peris|2020|Organometallics|39|4078|doi:10.1021/acs.organomet.0c00639
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.cc2d8xyk
title: CCDC 2213119: Experimental Crystal Structure Determination

description: Related Article: K.Sh. Khusenov, B.B. Umarov, K.K. Turgunov, T.B. Aliev, B.T. Ibragimov|2023|Dokl.Akad.Nauk Uzbekskoi SSR||72|
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.23834814
title: Sequencing data for reporter assay in Jindal et al Dev Cell 2023 article

description: Heart-specific enhancers drive expression of genes specifically in heart tissues. We find that low-affinity ETS transcription factor binding sites are necessary for the FoxF enhancer in Ciona and the GATA4-G9 enhancer in mice. To determine if higher affinity sites would result in gain-of-function activity, we tested the human GATA4-G9 enhancer and 2 variants with optimized ETS sites in human iPSC-cardiomyocytes, using a reporter assay. We discovered that both variants with optimized ETS sites drove gain-of-function activity and that just a single nucleotide variant within a human GATA4 enhancer increases ETS binding affinity and causes gain-of-function enhancer activity. The prevalence of suboptimal-affinity sites within enhancers creates a vulnerability whereby affinity-optimizing SNVs can lead to gain-of-function gene expression, changes in cellular identity, and organismal-level phenotypes that could contribute to the evolution of novel traits or diseases.
DOI: 10.6084/m9.figshare.24018948
title: Fasta sequences of LSDV from 2022 outbreak in India

description: Repository for the FASTA sequences of the Lumpy Skin Disease Virus isolated from affected cattle during the 2022 outbreak in Rajasthan, Gujarat, Karnataka, Maharashtra in India.
DOI: 10.6084/m9.figshare.24018948.v1
title: Fasta sequences of LSDV from 2022 outbreak in India

description: Repository for the FASTA sequences of the Lumpy Skin Disease Virus isolated from affected cattle during the 2022 outbreak in Rajasthan, Gujarat, Karnataka, Maharashtra in India.
DOI: 10.6084/m9.figshare.24018948.v2
title: Fasta sequences of LSDV from 2022 outbreak in India

description: Repository for the FASTA sequences of the Lumpy Skin Disease Virus isolated from affected cattle during the 2022 outbreak in Rajasthan, Gujarat, Karnataka, Maharashtra in India.
DOI: 10.25625/fg2ex9
title: CM chondrites research data

description: CM data
DOI: 10.25625/fg2ex9/vybqtw
title: DataRepository.tab

description: :unav
DOI: 10.5281/zenodo.7750175
title: Annual time series of global VIIRS nighttime lights for 2000-2021 at 500-m spatial resolution extrapolated using logistic regression

description: The <strong>Annual Visible Night Light (VNL) V2</strong> (VIIRS) images at 500-m spatial resolution for the period 2012 to 2021 (Elvidge et al., 2021) have been used to extrapolate the values backwards for years 2000–2011. This was done by fitting a logistic regression (per pixel) and then predicting the values for the previous years (see nightlights_stack_500m.R). After consistent time-series have been produced, I also derived the difference between average of the years 2020/2021 and years 2000/2021 (nightlights.difference_viirs.v21_m_500m_s_2000_2021_go_epsg4326_v20230318.tif): this shows average rate of change for the 22 years period. Use with caution: extrapolation of values can lead to artifacts. For most of the land surface, however, it appears that the growth of night lights follows exponential growth function and hence nights in the past can be represented accurately by fitting decay / logistic regression function. Original values from the Annual VNL V2 product have been converted from 0–200 to 0–2000 scale and are available as Cloud-Optimized GeoTIFFs. To cite the Annual VNL V2, please use: Elvidge, C. D., Zhizhin, M., Ghosh, T., Hsu, F. C., & Taneja, J. (2021). Annual time series of global VIIRS nighttime lights derived from monthly averages: 2012 to 2019. Remote Sensing, 13(5), 922. https://doi.org/10.3390/rs13050922 Historic night light images are also available (but at a much coarser spatial resolution) from: Li, X., Zhou, Y., Zhao, M., & Zhao, X. (2020). A harmonized global nighttime light dataset 1992–2018. Scientific data, 7(1), 168. https://doi.org/10.1038/s41597-020-0510-y
description: {"references": ["Elvidge, C. D., Zhizhin, M., Ghosh, T., Hsu, F. C., & Taneja, J. (2021). Annual time series of global VIIRS nighttime lights derived from monthly averages: 2012 to 2019. Remote Sensing, 13(5), 922. https://doi.org/10.3390/rs13050922", "Li, X., Zhou, Y., Zhao, M., & Zhao, X. (2020). A harmonized global nighttime light dataset 1992\u20132018. Scientific data, 7(1), 168. https://doi.org/10.1038/s41597-020-0510-y"]}
DOI: 10.5281/zenodo.8277198
title: Annual time series of global VIIRS nighttime lights for 2000-2021 at 500-m spatial resolution extrapolated using logistic regression

description: The <strong>Annual Visible Night Light (VNL) V2</strong> (VIIRS) images at 500-m spatial resolution for the period 2012 to 2021 (Elvidge et al., 2021) have been used to extrapolate the values backwards for years 2000–2011. This was done by fitting a logistic regression (per pixel) and then predicting the values for the previous years (see nightlights_stack_500m.R). After consistent time-series have been produced, I also derived the difference between average of the years 2020/2021 and years 2000/2021 (nightlights.difference_viirs.v21_m_500m_s_2000_2021_go_epsg4326_v20230318.tif): this shows average rate of change for the 22 years period. Use with caution: extrapolation of values can lead to artifacts. For most of the land surface, however, it appears that the growth of night lights follows exponential growth function and hence nights in the past can be represented accurately by fitting decay / logistic regression function. Original values from the Annual VNL V2 product have been converted from 0–200 to 0–2000 scale and are available as Cloud-Optimized GeoTIFFs. To cite the Annual VNL V2, please use: Elvidge, C. D., Zhizhin, M., Ghosh, T., Hsu, F. C., & Taneja, J. (2021). Annual time series of global VIIRS nighttime lights derived from monthly averages: 2012 to 2019. Remote Sensing, 13(5), 922. https://doi.org/10.3390/rs13050922 Historic night light images are also available (but at a much coarser spatial resolution) from: Li, X., Zhou, Y., Zhao, M., & Zhao, X. (2020). A harmonized global nighttime light dataset 1992–2018. Scientific data, 7(1), 168. https://doi.org/10.1038/s41597-020-0510-y
description: {"references": ["Elvidge, C. D., Zhizhin, M., Ghosh, T., Hsu, F. C., & Taneja, J. (2021). Annual time series of global VIIRS nighttime lights derived from monthly averages: 2012 to 2019. Remote Sensing, 13(5), 922. https://doi.org/10.3390/rs13050922", "Li, X., Zhou, Y., Zhao, M., & Zhao, X. (2020). A harmonized global nighttime light dataset 1992\u20132018. Scientific data, 7(1), 168. https://doi.org/10.1038/s41597-020-0510-y"]}
DOI: 10.5517/ccdc.csd.cc29f1tm
title: CCDC 2127710: Experimental Crystal Structure Determination

description: Related Article: Sonia Moreno, Nicola Casati, María Rodríguez-Castillo, Miguel Monge, M. Elena Olmos, José M. López-de-Luzuriaga|2023|Inorg.Chem.|62|10307|doi:10.1021/acs.inorgchem.3c01130
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.cc29f1vn
title: CCDC 2127711: Experimental Crystal Structure Determination

description: Related Article: Sonia Moreno, Nicola Casati, María Rodríguez-Castillo, Miguel Monge, M. Elena Olmos, José M. López-de-Luzuriaga|2023|Inorg.Chem.|62|10307|doi:10.1021/acs.inorgchem.3c01130
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.cc29f1wp
title: CCDC 2127712: Experimental Crystal Structure Determination

description: Related Article: Sonia Moreno, Nicola Casati, María Rodríguez-Castillo, Miguel Monge, M. Elena Olmos, José M. López-de-Luzuriaga|2023|Inorg.Chem.|62|10307|doi:10.1021/acs.inorgchem.3c01130
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.7773762
title: Repeated mapped tree inventory data in a tree plantation for pedunculate oak (Quercus robur) genetic conservation

description: We performed replicated, repeated mapped tree inventory measures (x,y, height, diameter, vitality, etc.) to allow analysis of the spatial and temporal structure in 1 ha planted pedunculate oak (Quercus robur) stand established in 2003 to resemble the oak-hornnbeam forests for genetic conservation purposes in Po Valley (Foresta Carpaneta). Two inventories were carried out in 2009 and 2019. The use of replicated, repeated, and mapped tree measures allows the examination of true changes in spatial pattern processes through time in this forest type.
DOI: 10.5281/zenodo.7773761
title: Repeated mapped tree inventory data in a tree plantation for pedunculate oak (Quercus robur) genetic conservation

description: We performed replicated, repeated mapped tree inventory measures (x,y, height, diameter, vitality, etc.) to allow analysis of the spatial and temporal structure in 1 ha planted pedunculate oak (Quercus robur) stand established in 2003 to resemble the oak-hornnbeam forests for genetic conservation purposes in Po Valley (Foresta Carpaneta). Two inventories were carried out in 2009 and 2019. The use of replicated, repeated, and mapped tree measures allows the examination of true changes in spatial pattern processes through time in this forest type.
DOI: 10.5281/zenodo.8099407
title: Repeated mapped tree inventory data in a tree plantation for pedunculate oak (Quercus robur) genetic conservation

description: We performed replicated, repeated mapped tree inventory measures (x,y, height, diameter, vitality, etc.) to allow analysis of the spatial and temporal structure in 1 ha planted pedunculate oak (Quercus robur) stand established in 2003 to resemble the oak-hornnbeam forests for genetic conservation purposes in Po Valley (Foresta Carpaneta). Two inventories were carried out in 2009 and 2019. The use of replicated, repeated, and mapped tree measures allows the examination of true changes in spatial pattern processes through time in this forest type.
DOI: 10.5281/zenodo.8099408
title: Repeated mapped tree inventory data in a tree plantation for pedunculate oak (Quercus robur) genetic conservation

description: We performed replicated, repeated mapped tree inventory measures (x,y, height, diameter, vitality, etc.) to allow analysis of the spatial and temporal structure in 1 ha planted pedunculate oak (Quercus robur) stand established in 2003 to resemble the oak-hornnbeam forests for genetic conservation purposes in Po Valley (Foresta Carpaneta). Two inventories were carried out in 2009 and 2019. The use of replicated, repeated, and mapped tree measures allows the examination of true changes in spatial pattern processes through time in this forest type.
DOI: 10.5281/zenodo.8277228
title: Repeated mapped tree inventory data in a tree plantation for pedunculate oak (Quercus robur) genetic conservation

description: We performed replicated, repeated mapped tree inventory measures (x,y, height, diameter, vitality, etc.) to allow analysis of the spatial and temporal structure in 1 ha planted pedunculate oak (Quercus robur) stand established in 2003 to resemble the oak-hornnbeam forests for genetic conservation purposes in Po Valley (Foresta Carpaneta). Two inventories were carried out in 2009 and 2019. The use of replicated, repeated, and mapped tree measures allows the examination of true changes in spatial pattern processes through time in this forest type.
DOI: 10.5281/zenodo.8276053
title: VIO-GNSS Dataset: Benchmarking Dataset for Sensor Fusion of Visual Inertial Odometry and GNSS Positioning

description: This upload contains datasets for benchmarking and improving different Sensor Fusion implementations/algorithms. The documentation for these datasets can be found on GitHub. The upload contains two datasets (version 1.0.0): urban_with_gnss_dead_zones (7.0 GB, ~16 minutes) City streets A building is passed through on two occasions which makes the GNSS location signal unavailable at times. RTK Fix is acquired at times suburban_nature (10.6 GB, ~19 minutes) The route begins on a suburban street but quickly turns into a nature trail. Lots of vegetation The RTK solution is only Float or None most of the route. Details on collecting the data: Software The data was collected using this open-source recorder. Can be easily replayed using SpectacularAI's SDK (sdk-examples/python/oak/vio_replay.py) Each dataset contains a map of the travelled route in Otaniemi, Espoo, Finland. <strong>Necessary files to implement SLAM are included</strong> in the dataset. Use of NTRIP and the high precision GNSS antenna enables global positioning accuracy of only few centimeters. Hardware OAK-D stereo depth + color camera (Luxonis) C099-F9P GNSS module (u-blox) ANN-MB-00 high precision GNSS antenna (u-blox)
DOI: 10.5281/zenodo.8276054
title: VIO-GNSS Dataset: Benchmarking Dataset for Sensor Fusion of Visual Inertial Odometry and GNSS Positioning

description: This upload contains datasets for benchmarking and improving different Sensor Fusion implementations/algorithms. The documentation for these datasets can be found on GitHub. The upload contains two datasets (version 1.0.0): urban_with_gnss_dead_zones (7.0 GB, ~16 minutes) City streets A building is passed through on two occasions which makes the GNSS location signal unavailable at times. RTK Fix is acquired at times suburban_nature (10.6 GB, ~19 minutes) The route begins on a suburban street but quickly turns into a nature trail. Lots of vegetation The RTK solution is only Float or None most of the route. Details on collecting the data: Software The data was collected using this open-source recorder. Can be easily replayed using SpectacularAI's SDK (sdk-examples/python/oak/vio_replay.py) Each dataset contains a map of the travelled route in Otaniemi, Espoo, Finland. <strong>Necessary files to implement SLAM are included</strong> in the dataset. Use of NTRIP and the high precision GNSS antenna enables global positioning accuracy of only few centimeters. Hardware OAK-D stereo depth + color camera (Luxonis) C099-F9P GNSS module (u-blox) ANN-MB-00 high precision GNSS antenna (u-blox)
DOI: 10.5281/zenodo.8248168
title: Remondis Contamination Dataset (RCD)

description: The Remondis Contamination Dataset (RCD) is a novel and challenging collection of images designed to facilitate the development and evaluation of computer vision models for the detection of plastic-bag contamination in waste collection scenarios. This dataset was meticulously curated from historical records of Remondis, a waste management company, and open source online resources which provides real-world visuals captured by cameras installed on waste collection trucks. The images cover various scenarios including waste bins, truck hoppers, different camera zooms, angles, and lighting conditions, accurately reflecting the complexities of waste collection environments. <strong>Dataset Details:</strong> <strong>Format:</strong> JPEG images, RGB color scheme <strong>Dimensions:</strong> 640 x 480 pixels <strong>Resolution:</strong> 72 pixels-per-inch <strong>Annotations:</strong> Bounding box annotations for plastic-bag candidates <strong>Annotation Format:</strong> KITTI <strong>Contaminant Types:</strong> Plastic bags including coles bags, woolie bags, color bags, white bags, black bags, and packaging material <strong>Total Images:</strong> 1125 samples (968 for training, 157 for validation) <strong>Total Bounding Box Annotations:</strong> 1851 (1588 for training, 263 for validation) <strong>Key Challenges:</strong> The RCD dataset poses unique challenges due to visual similarities between plastic bags and non-contaminants, such as white paper or dark portions in the image. The diverse range of plastic bag types, along with the intricacies of waste collection environments, make the task of plastic-bag contamination detection particularly demanding. <strong>Applications:</strong> The RCD dataset serves as a benchmark for practical waste segregation tasks, including the detection of various waste contaminants, characterization of waste contents, and counting occurrences of specific waste types. The dataset is a valuable resource for researchers, practitioners, and enthusiasts working on computer vision applications in waste management and environmental monitoring. <strong>Citation:</strong> If you use the RCD dataset in your research, please consider citing it using the following reference: U. Iqbal, J. Barthelemy, P. Perez, and T. Davies, “Edge-Computing Video Analytics Solution for Automated Plastic-Bag Contamination Detection: A Case from Remondis,” <em>Sensors</em>, vol. 22, no. 20, p. 7821, Oct. 2022, doi: 10.3390/s22207821.
DOI: 10.5281/zenodo.8248169
title: Remondis Contamination Dataset (RCD)

description: The Remondis Contamination Dataset (RCD) is a novel and challenging collection of images designed to facilitate the development and evaluation of computer vision models for the detection of plastic-bag contamination in waste collection scenarios. This dataset was meticulously curated from historical records of Remondis, a waste management company, and open source online resources which provides real-world visuals captured by cameras installed on waste collection trucks. The images cover various scenarios including waste bins, truck hoppers, different camera zooms, angles, and lighting conditions, accurately reflecting the complexities of waste collection environments. <strong>Dataset Details:</strong> <strong>Format:</strong> JPEG images, RGB color scheme <strong>Dimensions:</strong> 640 x 480 pixels <strong>Resolution:</strong> 72 pixels-per-inch <strong>Annotations:</strong> Bounding box annotations for plastic-bag candidates <strong>Annotation Format:</strong> KITTI <strong>Contaminant Types:</strong> Plastic bags including coles bags, woolie bags, color bags, white bags, black bags, and packaging material <strong>Total Images:</strong> 1125 samples (968 for training, 157 for validation) <strong>Total Bounding Box Annotations:</strong> 1851 (1588 for training, 263 for validation) <strong>Key Challenges:</strong> The RCD dataset poses unique challenges due to visual similarities between plastic bags and non-contaminants, such as white paper or dark portions in the image. The diverse range of plastic bag types, along with the intricacies of waste collection environments, make the task of plastic-bag contamination detection particularly demanding. <strong>Applications:</strong> The RCD dataset serves as a benchmark for practical waste segregation tasks, including the detection of various waste contaminants, characterization of waste contents, and counting occurrences of specific waste types. The dataset is a valuable resource for researchers, practitioners, and enthusiasts working on computer vision applications in waste management and environmental monitoring. <strong>Citation:</strong> If you use the RCD dataset in your research, please consider citing it using the following reference: U. Iqbal, J. Barthelemy, P. Perez, and T. Davies, “Edge-Computing Video Analytics Solution for Automated Plastic-Bag Contamination Detection: A Case from Remondis,” <em>Sensors</em>, vol. 22, no. 20, p. 7821, Oct. 2022, doi: 10.3390/s22207821.
DOI: 10.17909/1cab-7p42
title: Data for TOI 519, TOI 3629, TOI 3714, and TOI 5344 used in paper

description: TESS QLP and SPOC light curves used in paper on these objects.
DOI: 10.5281/zenodo.7800196
title: Dataset for paper Pavel Perezhogin, Andrey Glazunov "Subgrid parameterizations of ocean mesoscale eddies based on Germano decomposition" submitted to JAMES.

description: The directory structure is : <pre><code>├── barotropic │ ├── data │ ├── figures │ └── code ├── pyqg │ ├── data │ ├── figures │ └── code ├── NEMO │ ├── data │ ├── figures │ └── code </code></pre> Where <strong>barotropic</strong> - experiments in barotropic fluid, <strong>pyqg</strong> - experiments in QG model, <strong>NEMO</strong> - experiments in primitive equation model. Subfolder <strong>data</strong> contains numerical simulations, <strong>figures</strong> contains plotters of Figures for Publication, and <strong>code</strong> is source code with parameterized barotropic/QG/primitive equation ocean models.