cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2c235r
title: CCDC 2176763: Experimental Crystal Structure Determination

description: Related Article: Kazuma Sugawara, Toshikazu Ono, Yoshio Yano, Takanori Suzuki, Yusuke Ishigaki|2023|Mater. Chem. Front.|7|1591|doi:10.1039/D2QM01199A
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.cc2c237t
title: CCDC 2176765: Experimental Crystal Structure Determination

description: Related Article: Kazuma Sugawara, Toshikazu Ono, Yoshio Yano, Takanori Suzuki, Yusuke Ishigaki|2023|Mater. Chem. Front.|7|1591|doi:10.1039/D2QM01199A
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.cc2c752w
title: CCDC 2181627: Experimental Crystal Structure Determination

description: Related Article: Yexin Li , Guoyan Li , Xiaofeng Yang , Jinling Miao , Yong Nie, Shuaijun Yang, Wei Liu, Yu Cui, Guoxin Sun|2023|Spectrochim.Acta,Part A|295|122622|doi:10.1016/j.saa.2023.122622
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.cc2d772z
title: CCDC 2211480: Experimental Crystal Structure Determination

description: Related Article: Alexander S. Phearman, Yotam Ardon, Karen I. Goldberg|2024|J.Am.Chem.Soc.|146|4045|doi:10.1021/jacs.3c12285
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.cc2d7730
title: CCDC 2211481: Experimental Crystal Structure Determination

description: Related Article: Alexander S. Phearman, Yotam Ardon, Karen I. Goldberg|2024|J.Am.Chem.Soc.|146|4045|doi:10.1021/jacs.3c12285
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.cc2d7763
title: CCDC 2211484: Experimental Crystal Structure Determination

description: Related Article: Alexander S. Phearman, Yotam Ardon, Karen I. Goldberg|2024|J.Am.Chem.Soc.|146|4045|doi:10.1021/jacs.3c12285
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.cc2d7774
title: CCDC 2211485: Experimental Crystal Structure Determination

description: Related Article: Alexander S. Phearman, Yotam Ardon, Karen I. Goldberg|2024|J.Am.Chem.Soc.|146|4045|doi:10.1021/jacs.3c12285
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.10653707
title: REACH_3rdCall_Shortlist_Anonymised
DOI: 10.5281/zenodo.10653708
title: REACH_3rdCall_Shortlist_Anonymised
DOI: 10.5281/zenodo.10631245
title: The Effect of Substrate Properties on Cellular Behavior and  Nanoparticle Uptake in Human Fibroblasts and Epithelial Cells

description: The delivery of nanomedicines into cells holds enormous therapeutic potential; however little is known regarding how the extracellular matrix (ECM) can influence cell–nanoparticle (NP) interactions. Changes in ECM organization and composition occur in several pathophysiological states, including fibrosis and tumorigenesis, and may contribute to disease progression. We show that the physical characteristics of cellular substrates, that more closely resemble the ECM in vivo, can influence cell behavior and the subsequent uptake of NPs. Electrospinning was used to create two different substrates made of soft polyurethane (PU) with aligned and non-aligned nanofibers to recapitulate the ECM in two different states. To investigate the impact of cell–substrate interaction, A549 lung epithelial cells and MRC-5 lung fibroblasts were cultured on soft PU membranes with different alignments and compared against stiff tissue culture plastic (TCP)/glass. Both cell types could attach and grow on both PU membranes with no signs of cytotoxicity but with increased cytokine release compared with cells on the TCP. The uptake of silica NPs increased more than three-fold in fibroblasts but not in epithelial cells cultured on both membranes. This study demonstrates that cell–matrix interaction is substrate and cell-type dependent and highlights the importance of considering the ECM and tissue mechanical properties when designing NPs for effective cell targeting and treatment.
DOI: 10.5281/zenodo.10631246
title: The Effect of Substrate Properties on Cellular Behavior and  Nanoparticle Uptake in Human Fibroblasts and Epithelial Cells

description: The delivery of nanomedicines into cells holds enormous therapeutic potential; however little is known regarding how the extracellular matrix (ECM) can influence cell–nanoparticle (NP) interactions. Changes in ECM organization and composition occur in several pathophysiological states, including fibrosis and tumorigenesis, and may contribute to disease progression. We show that the physical characteristics of cellular substrates, that more closely resemble the ECM in vivo, can influence cell behavior and the subsequent uptake of NPs. Electrospinning was used to create two different substrates made of soft polyurethane (PU) with aligned and non-aligned nanofibers to recapitulate the ECM in two different states. To investigate the impact of cell–substrate interaction, A549 lung epithelial cells and MRC-5 lung fibroblasts were cultured on soft PU membranes with different alignments and compared against stiff tissue culture plastic (TCP)/glass. Both cell types could attach and grow on both PU membranes with no signs of cytotoxicity but with increased cytokine release compared with cells on the TCP. The uptake of silica NPs increased more than three-fold in fibroblasts but not in epithelial cells cultured on both membranes. This study demonstrates that cell–matrix interaction is substrate and cell-type dependent and highlights the importance of considering the ECM and tissue mechanical properties when designing NPs for effective cell targeting and treatment.
DOI: 10.5281/zenodo.10571133
title: ComplexObjectMoveLinear Cam

description: ComplexObjectMoveLinearCam:

This plenoptic sequence dataset "ComplexObjectMoveLinearCam" is provided by Eline Soetens, Gauthier Lafruit, Mehrdad Teratani members of the LISA department, EPB (Ecole Polytechnique de Bruxelles), ULB (Université Libre de Bruxelles), Belgium and Jonghoon Yim, Byeungwoo Jeon members of the Department of Electrical and Computer Engineering, SKKU (Sungkyunkwan University), Korea.

License:

CC BY-NC-SA

Terms of Use:

Any kind of publication or report using this sequence should refer to the following reference :

Eline Soetens, Jonghoon Yim, Byeungwoo Jeon, Gauthier Lafruit, Mehrdad Teratani, "ComplexObjectMoveLinearCam", 2024.

@misc{soetens_ComplexObjectMoveLinearCam_2024, title = {ComplexObjectMoveLinearCam}}, author = {Soetens, Eline and Yim, Jonghoon and Jeon, Byeungwoo and Lafruit, Gauthier and Teratani, Mehrdad }, month = jan, year = {2024}, doi = {10.5281/zenodo.10571134} }

Content:

This sequence was captured using a Raytrix R8 plenoptic camera [1] equipped with a 35mm lens. The sequence was recorded for 10 seconds at 30 fps with the Raytrix software RxLive 5.0 [2]. Each frame has a 3840 x 2160 resolution. The scene contains blocks with a simple texture, a sphere with a complex texture, two toy cars, and a reflective unicorn statue placed on a moving board. The toy cars and the unicorn statue present a lot of specularities, thus both objects have a non-Lambertian texture. The scene was filmed by moving the camera in a linear motion from left to right.

Please find a detailed description of the content of each file below:



ComplexObjectMoveLinearCam: Contains the 300 frames of the sequences in PNG format.

R8-ComplexObjectMoveLinearCam_Calibration.xml: Contains the calibration parameters of the plenoptic camera given by the Raytrix SDK.


References and links:

[1] https://raytrix.de/products/

[2] https://raytrix.de/downloads/
description: This work was supported in part by the FER 2021 project (1060H000066-FAISAN), Belgium; in part by the Emile DEFAY 2021 project (4R00H000236), Belgium; and in part by the FER 2023 project (1060H000075), Belgium. This work was supported in part by Basic Science Research Program (RS-2023-00208453) through the National Research Foundation of Korea (NRF) and by the ICT Creative Consilience Program (IITP-2023-2020-0-01821) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation), both funded by the Ministry of Science and ICT, Korea.
DOI: 10.5281/zenodo.10571134
title: ComplexObjectMoveLinear Cam

description: ComplexObjectMoveLinearCam:

This plenoptic sequence dataset "ComplexObjectMoveLinearCam" is provided by Eline Soetens, Gauthier Lafruit, Mehrdad Teratani members of the LISA department, EPB (Ecole Polytechnique de Bruxelles), ULB (Université Libre de Bruxelles), Belgium and Jonghoon Yim, Byeungwoo Jeon members of the Department of Electrical and Computer Engineering, SKKU (Sungkyunkwan University), Korea.

License:

CC BY-NC-SA

Terms of Use:

Any kind of publication or report using this sequence should refer to the following reference :

Eline Soetens, Jonghoon Yim, Byeungwoo Jeon, Gauthier Lafruit, Mehrdad Teratani, "ComplexObjectMoveLinearCam", 2024.

@misc{soetens_ComplexObjectMoveLinearCam_2024, title = {ComplexObjectMoveLinearCam}}, author = {Soetens, Eline and Yim, Jonghoon and Jeon, Byeungwoo and Lafruit, Gauthier and Teratani, Mehrdad }, month = jan, year = {2024}, doi = {10.5281/zenodo.10571134} }

Content:

This sequence was captured using a Raytrix R8 plenoptic camera [1] equipped with a 35mm lens. The sequence was recorded for 10 seconds at 30 fps with the Raytrix software RxLive 5.0 [2]. Each frame has a 3840 x 2160 resolution. The scene contains blocks with a simple texture, a sphere with a complex texture, two toy cars, and a reflective unicorn statue placed on a moving board. The toy cars and the unicorn statue present a lot of specularities, thus both objects have a non-Lambertian texture. The scene was filmed by moving the camera in a linear motion from left to right.

Please find a detailed description of the content of each file below:



ComplexObjectMoveLinearCam: Contains the 300 frames of the sequences in PNG format.

R8-ComplexObjectMoveLinearCam_Calibration.xml: Contains the calibration parameters of the plenoptic camera given by the Raytrix SDK.


References and links:

[1] https://raytrix.de/products/

[2] https://raytrix.de/downloads/
description: This work was supported in part by the FER 2021 project (1060H000066-FAISAN), Belgium; in part by the Emile DEFAY 2021 project (4R00H000236), Belgium; and in part by the FER 2023 project (1060H000075), Belgium. This work was supported in part by Basic Science Research Program (RS-2023-00208453) through the National Research Foundation of Korea (NRF) and by the ICT Creative Consilience Program (IITP-2023-2020-0-01821) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation), both funded by the Ministry of Science and ICT, Korea.
DOI: 10.34740/kaggle/dsv/7629782
title: biosignal and blood vessel data
DOI: 10.5281/zenodo.10635735
title: Engineering multi-degrading bacterial communities to bioremediate soils contaminated with pesticides residues

description: These data and scripts are related to the article entitled "Engineering multi-degrading bacterialcommunities to bioremediate soils contaminated with pesticides residues".   The objective of this study is to broaden our understanding of how degrading microorganisms must overcome abiotic filters and interact with the au-tochtonous microbial communities are needed in order to efficiently design bioremediation strategies.We designed a protocol aiming at studying the degradation of two herbicides, glyphosate (GLY)and isoproturon (IPU), via experimental modifications of two source bacterial communities. We usedstatistical methods stemming from genomic prediction to link community composition to herbicidesdegradation potentials. Then OTUs significantly associated with the degradation ability, and therefore identified as relevant by the models were confronted to the literature.Next, multi-degrading bacterial communities were obtained by coalescing bacterial communities withhigh GLY or IPU degradation ability based on their community-level properties. Finally, we evaluatedthe efficiency of constructed multi-degrading communities to remove pesticide contamination in a differ-ent soil. While results are less clear in the case of GLY, we showed an efficient transfer of degrading ca-pacities towards the receiving soil even at relatively low inoculation levels in the case of IPU. Altogether,we developed an innovative protocol for building multi-degrading simplified bacterial communities withthe help of genomic prediction tools and coalescence, and proved their efficiency in a contaminated soil. 

Data and codes needed to reproduce the statistical analyses presented in the manuscript are provided.
DOI: 10.5281/zenodo.10635736
title: Engineering multi-degrading bacterial communities to bioremediate soils contaminated with pesticides residues

description: These data and scripts are related to the article entitled "Engineering multi-degrading bacterialcommunities to bioremediate soils contaminated with pesticides residues".   The objective of this study is to broaden our understanding of how degrading microorganisms must overcome abiotic filters and interact with the au-tochtonous microbial communities are needed in order to efficiently design bioremediation strategies.We designed a protocol aiming at studying the degradation of two herbicides, glyphosate (GLY)and isoproturon (IPU), via experimental modifications of two source bacterial communities. We usedstatistical methods stemming from genomic prediction to link community composition to herbicidesdegradation potentials. Then OTUs significantly associated with the degradation ability, and therefore identified as relevant by the models were confronted to the literature.Next, multi-degrading bacterial communities were obtained by coalescing bacterial communities withhigh GLY or IPU degradation ability based on their community-level properties. Finally, we evaluatedthe efficiency of constructed multi-degrading communities to remove pesticide contamination in a differ-ent soil. While results are less clear in the case of GLY, we showed an efficient transfer of degrading ca-pacities towards the receiving soil even at relatively low inoculation levels in the case of IPU. Altogether,we developed an innovative protocol for building multi-degrading simplified bacterial communities withthe help of genomic prediction tools and coalescence, and proved their efficiency in a contaminated soil. 

Data and codes needed to reproduce the statistical analyses presented in the manuscript are provided.
DOI: 10.5519/qd.rgndrkxp
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (125 records)
DOI: 10.14272/yvhvoazmkudafb-uhfffaoysa-m.1
title: N-methyl-4-(4'-(3-azide) pyridyl)[2.2]paracyclophane chloride
title: InChI=1S/C22H21N4.ClH/c1-26-13-12-20(22(15-26)24-25-23)21-14-18-7-6-16-2-4-17(5-3-16)8-10-19(21)11-9-18;/h2-5,9,11-15H,6-8,10H2,1H3;1H/q+1;/p-1

description: This is a physical chemical entity[CHEBI_24431] associated with a molecule[CHEBI_25367].
 The molecule[CHEBI_25367] can be described by the following structural desciptors[cheminf_000085]:
 InChI descriptor[cheminf_000113]: InChI=1S/C22H21N4.ClH/c1-26-13-12-20(22(15-26)24-25-23)21-14-18-7-6-16-2-4-17(5-3-16)8-10-19(21)11-9-18;/h2-5,9,11-15H,6-8,10H2,1H3;1H/q+1;/p-1, and canonical SMILES descriptor[cheminf_000007]: [N-]=[N+]=Nc1c[n+](C)ccc1c1cc2ccc1CCc1ccc(CC2)cc1.[Cl-], and by the IUPAC name[cheminf_000107]: .
 
 The physical chemical entity[CHEBI_24431] has a component solvent[CHEBI_46787] which is described by the canonical SMILES descriptor[cheminf_000007]:
 
 The physical chemical entity[CHEBI_24431] has the following Sample ID as registered in the research data repository chemotion (www.chemotion-repository.net, https://doi.org/10.25504/FAIRsharing.iagXcR): CRS-37679

 The physical chemical entity[CHEBI_24431] can be described by the physical descriptors [CHEMINF_000025]:
 Melting point descriptor[CHEMINF_000256]: 
 Boiling point descriptor[CHEMINF_000257]: 
 Refractive index descriptor[CHEMINF_000253]:

 The physical chemical entity[CHEBI_24431] can be further described by the following assays[OBI:0000070][CHMO:0001133]:
 
 CHMO:0000593 | 1H nuclear magnetic resonance spectroscopy (1H NMR)
 
 CHMO:0000595 | 13C nuclear magnetic resonance spectroscopy (13C NMR)
 
 CHMO:0000470 | mass spectrometry (MS)
 
 CHMO:0000630 | infrared absorption spectroscopy (IR)
 
 The physical chemical entity[CHEBI_24431] was deposited to the Molecule Archive of the Karlsruhe Insitute of Technology (KIT) with the following Sample ID: 

 Used ontologies:
 CHEBI - Chemical Entities of Biological Interest
 CHEMINF - chemical information ontology (information entities about chemical entities)
 CHMO - Chemical Methods Ontology
 OBI - Ontology for Biomedical Investigations
DOI: 10.14272/yvhvoazmkudafb-uhfffaoysa-m/chmo0000630
title: infrared absorption spectroscopy (IR) ()
title: InChI=1S/C22H21N4.ClH/c1-26-13-12-20(22(15-26)24-25-23)21-14-18-7-6-16-2-4-17(5-3-16)8-10-19(21)11-9-18;/h2-5,9,11-15H,6-8,10H2,1H3;1H/q+1;/p-1

description: dataset for infrared absorption spectroscopy (IR)
 
 IR (ATR, ṽ) = 3408 (w), 3403 (w), 3397 (w), 3370 (w), 2980 (s), 2937 (s), 2922 (vs), 2851 (m), 2122 (vs), 1638 (m), 1517 (m), 1502 (m), 1495 (m), 1472 (w), 1331 (vs), 1290 (w), 851 (w) cm–1.
DOI: 10.14272/yvhvoazmkudafb-uhfffaoysa-m/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C22H21N4.ClH/c1-26-13-12-20(22(15-26)24-25-23)21-14-18-7-6-16-2-4-17(5-3-16)8-10-19(21)11-9-18;/h2-5,9,11-15H,6-8,10H2,1H3;1H/q+1;/p-1

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR)
 
 1H NMR (500 MHz, D2O, ppm) δ = 8.81 (s, 1H, HAr), 8.68 (dd, J = 6.3, 1.5 Hz, 1H, HAr), 8.23 (d, J = 6.3 Hz, 1H, HAr), 6.86–6.68 (m, 6H, HAr), 6.55 (dd, J = 7.9, 1.9 Hz, 1H, HAr), 4.43 (s, 3H, CH3), 3.26–3.07 (m, 5H, CH2), 2.94 (m, 2H, CH2), 2.73 (m, 1H, CH2).
DOI: 10.14272/yvhvoazmkudafb-uhfffaoysa-m/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C22H21N4.ClH/c1-26-13-12-20(22(15-26)24-25-23)21-14-18-7-6-16-2-4-17(5-3-16)8-10-19(21)11-9-18;/h2-5,9,11-15H,6-8,10H2,1H3;1H/q+1;/p-1

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR)
 
 13C NMR (126 MHz, D2O, ppm) δ = 147.7 (1C, CAr), 141.3 (1C, CAr), 140.8 (1C, CAr), 140.7 (1C, CAr), 140.3 (1C, CAr), 140.1 (1C, CAr), 139.5 (1C, CAr), 136.5 (1C, CAr), 136.3 (1C, CAr), 135.5 (1C, CAr), 133.8 (1C, CAr), 132.9 (1C, CAr), 132.4 (1C, CAr), 132.0 (1C, CAr), 131.1 (1C, CAr), 129.2 (1C, CAr), 128.8 (1C, CAr), 47.7 (1C, CH3), 34.6 (1C, CH2), 34.5 (1C, CH2), 34.4 (1C, CH2), 33.8 (1C, CH2).
DOI: 10.14272/yvhvoazmkudafb-uhfffaoysa-m/chmo0000470
title: mass spectrometry (MS) ()
title: InChI=1S/C22H21N4.ClH/c1-26-13-12-20(22(15-26)24-25-23)21-14-18-7-6-16-2-4-17(5-3-16)8-10-19(21)11-9-18;/h2-5,9,11-15H,6-8,10H2,1H3;1H/q+1;/p-1

description: dataset for mass spectrometry (MS)
 
 HRESI-MS for [C22H21N4]+: Calc. m/z = 341.1761, found m/z = 341.1758.ESI-MS, m/z (%): 341 (100) [M]+
DOI: 10.6084/m9.figshare.25117925
title: The energy of multi-valued neutrosophic matrix and neutrosophic hesitant matrix and relationship between them in multi-criteria decision-making

description: The concept of energy in graphs and matrices is frequently used in all application fields. The energy of a matrix is an extended version of the energy of a graph. The neutrosophic matrix energy concept needs to be more noticeable in multi-criteria decision-making environments. This study presents the concept of the energy of a multi-valued neutrosophic matrix. The upper and lower bounds of the proposed energy were determined. The connection between the neutrosophic hesitant matrix and the multi-valued neutrosophic matrix is provided. Since the parameters of the two matrices differ in truth, indeterminacy, and false entries, the matrix is converted into a single-valued form, and then the energy was calculated. The MCDM problem was addressed with the proposed energy, which is solved by a novel decision-making method. The task is to select a machine from a group of repaired machines in a particular industry that is in good condition. The final ranking values are calculated by the ranking energy formula that decides which is preferred. Then the comparative results were given to demonstrate the accuracy of the proposed energy outcomes.
DOI: 10.6084/m9.figshare.25117925.v1
title: The energy of multi-valued neutrosophic matrix and neutrosophic hesitant matrix and relationship between them in multi-criteria decision-making

description: The concept of energy in graphs and matrices is frequently used in all application fields. The energy of a matrix is an extended version of the energy of a graph. The neutrosophic matrix energy concept needs to be more noticeable in multi-criteria decision-making environments. This study presents the concept of the energy of a multi-valued neutrosophic matrix. The upper and lower bounds of the proposed energy were determined. The connection between the neutrosophic hesitant matrix and the multi-valued neutrosophic matrix is provided. Since the parameters of the two matrices differ in truth, indeterminacy, and false entries, the matrix is converted into a single-valued form, and then the energy was calculated. The MCDM problem was addressed with the proposed energy, which is solved by a novel decision-making method. The task is to select a machine from a group of repaired machines in a particular industry that is in good condition. The final ranking values are calculated by the ranking energy formula that decides which is preferred. Then the comparative results were given to demonstrate the accuracy of the proposed energy outcomes.