cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2f33b0
title: CCDC 2237311: Experimental Crystal Structure Determination

description: Related Article: G. Murtaza, M. Khan, S. Farooq, M.I. Choudhary, S. Yousuf|2023|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|79|237|doi:10.1107/S2053229623003753
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: 10.15784/601687
title: Information on 2023 collection sites for Belgica antarctica

description: The file associated with this submission contains information about the collection of Belgica antarctica individuals realized during the 2022/2023 Antarctica summer season. Each entry on the table describes the collection locations, how many sites are within each location, how many individuals were collected in each site, the collection dates, the life stage, and the species.
DOI: 10.5281/zenodo.7465860
title: RGB-LWIR Labeled Dataset for Ground-based platforms

description: Most object detection models deployed on unmanned aerial systems (UAS) focus on identifying objects in the visible spectrum, also known as Red-Green-Blue (RGB) imagery, due to availability and cost. Fusing long wave infrared (LWIR) images with RGB imagery can increase the resiliency and overall performance of a machine learning (ML) object detection model in changing environments. Because LWIR based ML models are not commonly utilized or studied there exists a gap in the literature that discusses performance metrics between LWIR, RGB and LWIR-RGB fused ML object detection models from various ground and air collection platforms. Understanding multispectral ML object detection performance from UAS is highly meaningful because of the increasing convergence of ML and UAS technologies. Therefore, the need to establish baseline performance metrics for how certain ML models perform on various ground and air platforms is necessary for effective implementation and deployment of these two technologies. Using object detection results from both ground and air platforms, this research provided a baseline RGB ML model mean average precision (mAP) of 95.1%, a LWIR ML model mAP of 94.5%, and a blended RGB-LWIR ML model mAP of 92.6%. This research contributes to the literature a labelled training dataset of 12,600 images for ground-based and air-based RGB, LWIR, and RGB-LWIR fused imagery, to encourage further multispectral machine-driven object detection research from both air and ground platforms.
DOI: 10.5281/zenodo.7465859
title: RGB-LWIR Labeled Dataset for Ground-based platforms

description: Most object detection models deployed on unmanned aerial systems (UAS) focus on identifying objects in the visible spectrum, also known as Red-Green-Blue (RGB) imagery, due to availability and cost. Fusing long wave infrared (LWIR) images with RGB imagery can increase the resiliency and overall performance of a machine learning (ML) object detection model in changing environments. Because LWIR based ML models are not commonly utilized or studied there exists a gap in the literature that discusses performance metrics between LWIR, RGB and LWIR-RGB fused ML object detection models from various ground and air collection platforms. Understanding multispectral ML object detection performance from UAS is highly meaningful because of the increasing convergence of ML and UAS technologies. Therefore, the need to establish baseline performance metrics for how certain ML models perform on various ground and air platforms is necessary for effective implementation and deployment of these two technologies. Using object detection results from both ground and air platforms, this research provided a baseline RGB ML model mean average precision (mAP) of 95.1%, a LWIR ML model mAP of 94.5%, and a blended RGB-LWIR ML model mAP of 92.6%. This research contributes to the literature a labelled training dataset of 12,600 images for ground-based and air-based RGB, LWIR, and RGB-LWIR fused imagery, to encourage further multispectral machine-driven object detection research from both air and ground platforms.
DOI: 10.5281/zenodo.7465521
title: RGB-LWIR Labeled Dataset for Air-based platforms

description: Most object detection models deployed on unmanned aerial systems (UAS) focus on identifying objects in the visible spectrum, also known as Red-Green-Blue (RGB) imagery, due to availability and cost. Fusing long wave infrared (LWIR) images with RGB imagery can increase the resiliency and overall performance of a machine learning (ML) object detection model in changing environments. Because LWIR based ML models are not commonly utilized or studied there exists a gap in the literature that discusses performance metrics between LWIR, RGB and LWIR-RGB fused ML object detection models from various ground and air collection platforms. Understanding multispectral ML object detection performance from UAS is highly meaningful because of the increasing convergence of ML and UAS technologies. Therefore, the need to establish baseline performance metrics for how certain ML models perform on various ground and air platforms is necessary for effective implementation and deployment of these two technologies. Using object detection results from both ground and air platforms, this research provided a baseline RGB ML model mean average precision (mAP) of 95.1%, a LWIR ML model mAP of 94.5%, and a blended RGB-LWIR ML model mAP of 92.6%. This research contributes to the literature a labelled training dataset of 12,600 images for ground-based and air-based RGB, LWIR, and RGB-LWIR fused imagery, to encourage further multispectral machine-driven object detection research from both air and ground platforms.
DOI: 10.5281/zenodo.7465520
title: RGB-LWIR Labeled Dataset for Air-based platforms

description: Most object detection models deployed on unmanned aerial systems (UAS) focus on identifying objects in the visible spectrum, also known as Red-Green-Blue (RGB) imagery, due to availability and cost. Fusing long wave infrared (LWIR) images with RGB imagery can increase the resiliency and overall performance of a machine learning (ML) object detection model in changing environments. Because LWIR based ML models are not commonly utilized or studied there exists a gap in the literature that discusses performance metrics between LWIR, RGB and LWIR-RGB fused ML object detection models from various ground and air collection platforms. Understanding multispectral ML object detection performance from UAS is highly meaningful because of the increasing convergence of ML and UAS technologies. Therefore, the need to establish baseline performance metrics for how certain ML models perform on various ground and air platforms is necessary for effective implementation and deployment of these two technologies. Using object detection results from both ground and air platforms, this research provided a baseline RGB ML model mean average precision (mAP) of 95.1%, a LWIR ML model mAP of 94.5%, and a blended RGB-LWIR ML model mAP of 92.6%. This research contributes to the literature a labelled training dataset of 12,600 images for ground-based and air-based RGB, LWIR, and RGB-LWIR fused imagery, to encourage further multispectral machine-driven object detection research from both air and ground platforms.
DOI: 10.13012/b2idb-6296964_v1
title: Code and Data for "Carbon Mitigation Payments Can Reduce the Riskiness of Bioenergy Crop Production"

description: A stochastic domination analysis model was developed to examine the effect that emerging carbon markets can have on the spatially varying returns and risk profiles of bioenergy crops relative to conventional crops. The code is written in MATLAB, and includes the calculated output. See the README file for instructions to run the code.
DOI: 10.5281/zenodo.7240927
title: Fibrin bead assay with VEGF-A stimulation.

description: Confocal microscopy images of fibrin bead assay. Related article for citation: https://www.nature.com/articles/s41598-023-33090-6 "SproutAngio: an open-source bioimage informatics tool for quantitative analysis of sprouting angiogenesis and lumen space" Cells: Human umbilical vein endothelial cells (HUVECs) were isolated, maintained in PromoCell Endothelial Cell Growth Medium (Heidelberg, Germany) with 1X endothelial growth kit addition, and used in passage 2. Human lung pulmonary fibroblasts (hPFs) were purchased from PromoCell, maintained in DMEM with 10% FBS, and used in passage 8. Experimental groups with VEGF-A concentrations: Group1 (no VEGFA), Group2 (1 ng/ml VEGFA), Group3 (10 ng/ml VEGFA), Group4 (20 ng/ml VEGFA), Group5 (50 ng/ml VEGFA) We used VEGF-A from R&D Systems, Minneapolis, MN for the stimulation. Media change and VEGF-A stimulation were done on days 0,2,4,6. Assay fixation was on day 7. Staining: DAPI staining for nuclei, phalloidin staining for F-actin.
description: If you use the data, please cite: Beter, M., Abdollahzadeh, A., Pulkkinen, H. H., Huang, H., Orsenigo, F., Magnusson, P. U., ... & Laakkonen, J. P. (2023). SproutAngio: an open-source bioimage informatics tool for quantitative analysis of sprouting angiogenesis and lumen space. Scientific Reports, 13(1), 7279.
DOI: 10.5281/zenodo.7240926
title: Fibrin bead assay with VEGF-A stimulation.

description: Confocal microscopy images of fibrin bead assay. Related article for citation: https://www.nature.com/articles/s41598-023-33090-6 "SproutAngio: an open-source bioimage informatics tool for quantitative analysis of sprouting angiogenesis and lumen space" Cells: Human umbilical vein endothelial cells (HUVECs) were isolated, maintained in PromoCell Endothelial Cell Growth Medium (Heidelberg, Germany) with 1X endothelial growth kit addition, and used in passage 2. Human lung pulmonary fibroblasts (hPFs) were purchased from PromoCell, maintained in DMEM with 10% FBS, and used in passage 8. Experimental groups with VEGF-A concentrations: Group1 (no VEGFA), Group2 (1 ng/ml VEGFA), Group3 (10 ng/ml VEGFA), Group4 (20 ng/ml VEGFA), Group5 (50 ng/ml VEGFA) We used VEGF-A from R&D Systems, Minneapolis, MN for the stimulation. Media change and VEGF-A stimulation were done on days 0,2,4,6. Assay fixation was on day 7. Staining: DAPI staining for nuclei, phalloidin staining for F-actin.
description: If you use the data, please cite: Beter, M., Abdollahzadeh, A., Pulkkinen, H. H., Huang, H., Orsenigo, F., Magnusson, P. U., ... & Laakkonen, J. P. (2023). SproutAngio: an open-source bioimage informatics tool for quantitative analysis of sprouting angiogenesis and lumen space. Scientific Reports, 13(1), 7279.
DOI: 10.5517/ccdc.csd.cc25s000
title: CCDC 2019061: Experimental Crystal Structure Determination

description: Related Article: Ishtiyaq Ahmad, Arshid A. Ganie, Shamim Ahmad, Aadil A. Ahangar, C. Malla Reddy, Aijaz A. Dar|2023|CrystEngComm|25|3164|doi:10.1039/D2CE01693A
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.cc2d35h6
title: CCDC 2207587: Experimental Crystal Structure Determination

description: Related Article: Carlos F. R. A. C. Lima, Joel T. Mague, Yuchen Du, Robert A. Pascal, Luís M. N. B. F. Santos|2023|Phys.Chem.Chem.Phys.(PCCP)|25|13359|doi:10.1039/D2CP05085D
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.cc2d35j7
title: CCDC 2207588: Experimental Crystal Structure Determination

description: Related Article: Carlos F. R. A. C. Lima, Joel T. Mague, Yuchen Du, Robert A. Pascal, Luís M. N. B. F. Santos|2023|Phys.Chem.Chem.Phys.(PCCP)|25|13359|doi:10.1039/D2CP05085D
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.cc2d35k8
title: CCDC 2207589: Experimental Crystal Structure Determination

description: Related Article: Carlos F. R. A. C. Lima, Joel T. Mague, Yuchen Du, Robert A. Pascal, Luís M. N. B. F. Santos|2023|Phys.Chem.Chem.Phys.(PCCP)|25|13359|doi:10.1039/D2CP05085D
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.cc2d35l9
title: CCDC 2207590: Experimental Crystal Structure Determination

description: Related Article: Carlos F. R. A. C. Lima, Joel T. Mague, Yuchen Du, Robert A. Pascal, Luís M. N. B. F. Santos|2023|Phys.Chem.Chem.Phys.(PCCP)|25|13359|doi:10.1039/D2CP05085D
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.cc2d8977
title: CCDC 2212508: Experimental Crystal Structure Determination

description: Related Article: Yiliang Chen, Wenwen Pan, Xiaolong Ding, Liang Zhang, Qinfei Xia, Qi Wang, Qian Chen, Qinghe Gao, Jufen Yan, Roman Lesyk, Zilong Tang, Xinya Han|2023|Tetrahedron|138|133393|doi:10.1016/j.tet.2023.133393
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.cc2d9x5t
title: CCDC 2214056: Experimental Crystal Structure Determination

description: Related Article: Wafa Arar, Ridha Ben Ali, Michèle Véronique El May, Abderrahim Khatyr, Isabelle Jourdain, Michael Knorr, Lukas Brieger, Rebecca Scheel, Carsten Strohmann, Asma Chaker, Azaiez Ben Akacha|2023|J.Mol.Struct.|1284|135370|doi:10.1016/j.molstruc.2023.135370
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.cc2d9x7w
title: CCDC 2214058: Experimental Crystal Structure Determination

description: Related Article: Wafa Arar, Ridha Ben Ali, Michèle Véronique El May, Abderrahim Khatyr, Isabelle Jourdain, Michael Knorr, Lukas Brieger, Rebecca Scheel, Carsten Strohmann, Asma Chaker, Azaiez Ben Akacha|2023|J.Mol.Struct.|1284|135370|doi:10.1016/j.molstruc.2023.135370
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.cc2d9xbz
title: CCDC 2214061: Experimental Crystal Structure Determination

description: Related Article: Wafa Arar, Ridha Ben Ali, Michèle Véronique El May, Abderrahim Khatyr, Isabelle Jourdain, Michael Knorr, Lukas Brieger, Rebecca Scheel, Carsten Strohmann, Asma Chaker, Azaiez Ben Akacha|2023|J.Mol.Struct.|1284|135370|doi:10.1016/j.molstruc.2023.135370
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.cc2d9xc0
title: CCDC 2214062: Experimental Crystal Structure Determination

description: Related Article: Wafa Arar, Ridha Ben Ali, Michèle Véronique El May, Abderrahim Khatyr, Isabelle Jourdain, Michael Knorr, Lukas Brieger, Rebecca Scheel, Carsten Strohmann, Asma Chaker, Azaiez Ben Akacha|2023|J.Mol.Struct.|1284|135370|doi:10.1016/j.molstruc.2023.135370
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.cc2d9xh4
title: CCDC 2214066: Experimental Crystal Structure Determination

description: Related Article: Wafa Arar, Ridha Ben Ali, Michèle Véronique El May, Abderrahim Khatyr, Isabelle Jourdain, Michael Knorr, Lukas Brieger, Rebecca Scheel, Carsten Strohmann, Asma Chaker, Azaiez Ben Akacha|2023|J.Mol.Struct.|1284|135370|doi:10.1016/j.molstruc.2023.135370
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.cc2dh4nq
title: CCDC 2219093: Experimental Crystal Structure Determination

description: Related Article: Amita Mahapatra, Aneeya K. Samantara, Sahadev Barik, Malaya K. Sahoo, J. N. Behera, Moloy Sarkar|2023|Soft Matter|19|3510|doi:10.1039/D3SM00040K
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.21823455.v53
title: The ReptileEvolution.com Project

description: Taxon exclusion is the number one problem currently vexing paleontology. The search for pterosaur, turtle, bat, snake, whale, reptile and tetrapod ancestors continues to unnecessarily befuddle paleontologists who continue to work from cherry-picked, textbook and traditional taxon lists that overlook and omit pertinent taxa. That’s why the large reptile tree (LRT), the nearly completely resolved cladogram at the heart of www.ReptileEvolution.com, includes 2212 taxa. Larger taxon numbers minimize taxon exclusion issues. Results indicate the simple act of adding taxa resolves all phylogenetic issues.
DOI: 10.5066/p9prva0m
title: Antioch Dunes evening primrose (Oenothera deltoides subsp. howellii) juvenile and adult abundance across the known range, California, USA (2019)

description: These datasets provide information on total plant cover, Normalized Difference Vegetation Index, and Antioch Dunes evening primrose (Oenothera deltoides subsp. howellii) abundance within grids across the subspecies' extant range in California. These data support the following publication: Jones, S.F., Kennedy, A., Freeman, C.M. et al. Intensity of grass invasion negatively correlated with population density and age structure of an endangered dune plant across its range. Biol Invasions (2021). https://doi.org/10.1007/s10530-021-02516-5
DOI: 10.6084/m9.figshare.9616169
title: Dysfunction of iPSC-derived endothelial cells in human Hutchinson–Gilford progeria syndrome

description: Children with Hutchinson–Gilford progeria syndrome (HGPS) succumb to myocardial infarction and stroke in their teen years. Endothelial dysfunction is an early event in more common forms of atherosclerosis. Endothelial pathobiology may contribute to HGPS, but a comprehensive characterization of endothelial function in HGPS has not been performed. iPSCs derived from fibroblasts of HGPS patients or unaffected relatives were differentiated into endothelial cells (ECs). Immunofluorescent signal of the pluripotent stem cell markers SSEA4, Oct4, Sox2 and TRAI-60 was similar in HGPS or control iPSCs. Following the differentiation, FACS analysis and immunocytochemistry for CD31 and CD144 revealed a smaller percentage of ECs from HGPS iPSCs. Immunostaining for Lamin A revealed nuclear dysmorphology in HGPS iPSC-ECs. Furthermore, these cells were significantly larger and rounded, and they proliferated less, features which are typical of senescent endothelial cells. HGPS iPSC-ECs manifested less Dil-Ac-LDL uptake; less DAF-2DA staining for nitric oxide generation and formed fewer networks in matrigel <i>in vitro</i>. In immunodeficient mice injected with iPSC-ECs, HGPS iPSC-ECs generated a sparser vascular network compared to the control, with reduced capillary number. Telomere length (T/S ratio) of HGPS iPSC-EC was reduced as assessed by mmqPCR. iPSC-ECs derived from HGPS patients have dysmorphic appearance, abnormal nuclear morphology, shortened telomeres, reduced replicative capacity and impaired functions in vitro and in vivo. Targeting the endothelial abnormality in patients with HGPS may provide a new therapeutic avenue for the treatment of this condition. <b>Abbreviations:</b> HGPS: Hutchinson–Gilford progeria syndrome; ZMPSTE24: Zinc metallopeptidase STE24; FTI: Farnesyltransferase inhibitors; VSMCs: Vascular smooth muscle cells; iPSC: Induced pluripotent stem cells; EC: Endothelial cells; hTERT: Human telomerase reverse transcriptase; VEGF: vascular endothelial growth factor; DAF-FM DA: 3-Amino, 4-aminomethyl-2’,7’-difluorofluorescein diacetate; BMP4: Bone Morphogenetic Protein 4; mmqPCR: mono chrome multiplex PCR; SCG: single-copy gene; CSI: Cell shape index.