cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc24qf4g
title: CCDC 1987755: Experimental Crystal Structure Determination

description: Related Article: Brandon R. Barnett, Hayden A. Evans, Gregory M. Su, Henry Z. H. Jiang, Romit Chakraborty, Didier Banyeretse, Tyler J. Hartman, Madison B. Martinez, Benjamin A. Trump, Jacob D. Tarver, Matthew N. Dods, Lena M. Funke, Jonas Börgel, Jeffrey A. Reimer, Walter S. Drisdell, Katherine E. Hurst, Thomas Gennett, Stephen A. FitzGerald, Craig M. Brown, Martin Head-Gordon, Jeffrey R. Long|2021|J.Am.Chem.Soc.|143|14884|doi:10.1021/jacs.1c07223
DOI: 10.5517/ccdc.csd.cc271lkg
title: CCDC 2057115: Experimental Crystal Structure Determination

description: Related Article: Qi-Meige Hasi, Xiao-Tong Mu, Xiao-Hong Su, Yu-Mei Wei|2022|Polyhedron|219|115799|doi:10.1016/j.poly.2022.115799
DOI: 10.5517/ccdc.csd.cc271llh
title: CCDC 2057116: Experimental Crystal Structure Determination

description: Related Article: Qi-Meige Hasi, Xiao-Tong Mu, Xiao-Hong Su, Yu-Mei Wei|2022|Polyhedron|219|115799|doi:10.1016/j.poly.2022.115799
DOI: 10.5517/ccdc.csd.cc27f6zw
title: CCDC 2068288: Experimental Crystal Structure Determination

description: Related Article: Yu Wang, Xiao-He Liu, Rongbin Wang, Beatrice Cula, Zhe-Ning Chen, Qingyun Chen, Norbert Koch, Nicola Pinna|2021|J.Am.Chem.Soc.|143|9595|doi:10.1021/jacs.1c04048
DOI: 10.5517/ccdc.csd.cc288sy9
title: CCDC 2093831: Experimental Crystal Structure Determination

description: Related Article: Luis E. Seijas, Lusbely M. Belandria, Pedro J. Vivas, Marilia Guillén, José L. Burgos, Andrew N. Fitch, Jonathan Wright, Luis Rincón, Gerzon E. Delgado, Asiloé J. Mora|2022|J.Mol.Struct.|1261|132898|doi:10.1016/j.molstruc.2022.132898
description: Related Article: Luis E. Seijas, Lusbely M. Belandria, Pedro J. Vivas, Marilia Guillén, José L. Burgos, Andrew N. Fitch, Jonathan Wright, Luis Rincón, Gerzon E. Delgado, Asiloé J. Mora|2022|J.Mol.Struct.|1261|132898|doi:10.1016/j.molstruc.2022.132898
DOI: 10.5517/ccdc.csd.cc28s56g
title: CCDC 2108595: Experimental Crystal Structure Determination

description: Related Article: Luis E. Seijas, Lusbely M. Belandria, Pedro J. Vivas, Marilia Guillén, José L. Burgos, Andrew N. Fitch, Jonathan Wright, Luis Rincón, Gerzon E. Delgado, Asiloé J. Mora|2022|J.Mol.Struct.|1261|132898|doi:10.1016/j.molstruc.2022.132898
description: Related Article: Luis E. Seijas, Lusbely M. Belandria, Pedro J. Vivas, Marilia Guillén, José L. Burgos, Andrew N. Fitch, Jonathan Wright, Luis Rincón, Gerzon E. Delgado, Asiloé J. Mora|2022|J.Mol.Struct.|1261|132898|doi:10.1016/j.molstruc.2022.132898
DOI: 10.6084/m9.figshare.19419347
title: Supplementary Material for: Incidence, Prevalence and Real-World Treatment Patterns in Chronic Myeloid Leukemia: Results from a Population Representative German Claims Data Analysis

description: Introduction Real-world data on usage of 1st, 2nd and 3rd generation tyrosine kinase inhibitor (TKIs) in chronic myeloid leukemia (CML) is scarce. This study therefore aimed to analyze the use of different TKIs used in 1st- and 2nd-line treatment and the frequency of TKI switches in CML. Methods This observational study was based on the InGef research database, an anonymized representative claims dataset in Germany (n= 4 million). An incidence and prevalence patient CML cohort was followed for 5 and 3 years. Analyses regarding incidence, prevalence and therapy distribution were performed descriptively. Results 151 patients were included in the incidence and 636 patients in the prevalence cohort. This resulted in an incidence of 1.8 (95%-confidence interval [CI]: 1.34 – 2.20) and a prevalence of 14.9 (95%-CI 13.70 – 16.03) per 100,000 inhabitants. For the incidence cohort, data on 1st-line therapy was available for 124 patients and distributed across imatinib (N=52), nilotinib (N=44), dasatinib (N=12), chemotherapies as hydroxycarbamide (N=11) and ponatinib/bosutinib (N=5). 26% of patients switched TKI therapy at least once in three years. In the prevalence cohort, 423 patients (66.5%) had claims on existing or newly emerged cardiovascular diseases (CD). No significant differences (p=0.32) between TKIs in patients with CD were found. Discussion Every fourth patient switched TKI therapy within the first three years after treatment initiation. Switches were more likely when hints of disease progression or intolerability were observable in the database.
DOI: 10.5517/ccdc.csd.cc293n69
title: CCDC 2118670: Experimental Crystal Structure Determination

description: Related Article: Maruthupandi Mannarsamy, Ganesan Prabusankar|2022|Polyhedron|219|115795|doi:10.1016/j.poly.2022.115795
DOI: 10.5517/ccdc.csd.cc293n7b
title: CCDC 2118671: Experimental Crystal Structure Determination

description: Related Article: Maruthupandi Mannarsamy, Ganesan Prabusankar|2022|Polyhedron|219|115795|doi:10.1016/j.poly.2022.115795
DOI: 10.5517/ccdc.csd.cc293n9d
title: CCDC 2118673: Experimental Crystal Structure Determination

description: Related Article: Maruthupandi Mannarsamy, Ganesan Prabusankar|2022|Polyhedron|219|115795|doi:10.1016/j.poly.2022.115795
DOI: 10.5517/ccdc.csd.cc293nbf
title: CCDC 2118674: Experimental Crystal Structure Determination

description: Related Article: Maruthupandi Mannarsamy, Ganesan Prabusankar|2022|Polyhedron|219|115795|doi:10.1016/j.poly.2022.115795
DOI: 10.5517/ccdc.csd.cc293ncg
title: CCDC 2118675: Experimental Crystal Structure Determination

description: Related Article: Maruthupandi Mannarsamy, Ganesan Prabusankar|2022|Polyhedron|219|115795|doi:10.1016/j.poly.2022.115795
DOI: 10.48370/ofd/ndo2gj
title: Chlorophorus cursor (Sama et Rapuzzi, 1999), C-822

description: Coleoptera collection
DOI: 10.5517/ccdc.csd.cc29h3wt
title: CCDC 2129696: Experimental Crystal Structure Determination

description: Related Article: Yuanyuan Li, Haowen Li, Wenhui Jin, Xinqi Xu, Huiying Liu, Yang Ding, Gang Wang, Tingting Zhang, Qiuchen Peng, Juan He, Qinggang Hu, Li Pan, Kai Li|2022|Dyes Pigm.|202|110295|doi:10.1016/j.dyepig.2022.110295
DOI: 10.5517/ccdc.csd.cc29wrts
title: CCDC 2141846: Experimental Crystal Structure Determination

description: Related Article: Peiying Li, Ruige Su, Di Zhang, Minghu Han, Yuling Zhao, Lishan Ma, Hui Zhang, Tianzhi Yu|2022|Synth.Met.|287|117069|doi:10.1016/j.synthmet.2022.117069
DOI: 10.5517/ccdc.csd.cc29yfh6
title: CCDC 2143448: Experimental Crystal Structure Determination

description: Related Article: Liu-Yan Wang, Wen-Yan Li, Hao-Feng Zhou, Xue-Yu Zhao, Xiao-Nian Li, Xing-De Wu, Qin-Shi Zhao|2022|Bioorg.Chem.|123|105749|doi:10.1016/j.bioorg.2022.105749
DOI: 10.5517/ccdc.csd.cc29yfj7
title: CCDC 2143449: Experimental Crystal Structure Determination

description: Related Article: Liu-Yan Wang, Wen-Yan Li, Hao-Feng Zhou, Xue-Yu Zhao, Xiao-Nian Li, Xing-De Wu, Qin-Shi Zhao|2022|Bioorg.Chem.|123|105749|doi:10.1016/j.bioorg.2022.105749
DOI: 10.5517/ccdc.csd.cc2b09h5
title: CCDC 2145246: Experimental Crystal Structure Determination

description: Related Article: Qi-Ping Hu, Hao Zhou, Teng-Yu Huang, Yu-Fei Ao, De-Xian Wang, Qi-Qiang Wang|2022|J.Am.Chem.Soc.|144|6180|doi:10.1021/jacs.2c02040
DOI: 10.5517/ccdc.csd.cc2b0mbb
title: CCDC 2145551: Experimental Crystal Structure Determination

description: Related Article: Manoj Kumar Sahoo, Sourav Pradhan, Dongwook Kim, Jung-Woo Park, Sukbok Chang|2022|Organometallics|41|900|doi:10.1021/acs.organomet.2c00073
DOI: 10.5517/ccdc.csd.cc2b0mcc
title: CCDC 2145552: Experimental Crystal Structure Determination

description: Related Article: Manoj Kumar Sahoo, Sourav Pradhan, Dongwook Kim, Jung-Woo Park, Sukbok Chang|2022|Organometallics|41|900|doi:10.1021/acs.organomet.2c00073
DOI: 10.5517/ccdc.csd.cc2b0mdd
title: CCDC 2145553: Experimental Crystal Structure Determination

description: Related Article: Manoj Kumar Sahoo, Sourav Pradhan, Dongwook Kim, Jung-Woo Park, Sukbok Chang|2022|Organometallics|41|900|doi:10.1021/acs.organomet.2c00073
DOI: 10.5517/ccdc.csd.cc2b0mff
title: CCDC 2145554: Experimental Crystal Structure Determination

description: Related Article: Manoj Kumar Sahoo, Sourav Pradhan, Dongwook Kim, Jung-Woo Park, Sukbok Chang|2022|Organometallics|41|900|doi:10.1021/acs.organomet.2c00073
DOI: 10.5517/ccdc.csd.cc2b0mgg
title: CCDC 2145555: Experimental Crystal Structure Determination

description: Related Article: Manoj Kumar Sahoo, Sourav Pradhan, Dongwook Kim, Jung-Woo Park, Sukbok Chang|2022|Organometallics|41|900|doi:10.1021/acs.organomet.2c00073
DOI: 10.5517/ccdc.csd.cc2b0mhh
title: CCDC 2145556: Experimental Crystal Structure Determination

description: Related Article: Manoj Kumar Sahoo, Sourav Pradhan, Dongwook Kim, Jung-Woo Park, Sukbok Chang|2022|Organometallics|41|900|doi:10.1021/acs.organomet.2c00073
DOI: 10.6084/m9.figshare.19403651
title: Supplementary Material for: Integrin αⅤ mediates the effects of irisin on human mature adipocytes

description: Medicine
DOI: 10.15161/oar.it/75538
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated..
DOI: 10.6084/m9.figshare.19596646
title: raw_data_PARV-D-22-00088.zip

description: Raw data associated with manuscrit PARV-D-22-00088. Data collection described in Material&Methods section and Additional File.
DOI: 10.6084/m9.figshare.19596646.v1
title: raw_data_PARV-D-22-00088.zip

description: Raw data associated with manuscrit PARV-D-22-00088. Data collection described in Material&Methods section and Additional File.
DOI: 10.48370/ofd/5ggizn
title: Chlorophorus simillimus (Kraatz, 1879), C-823

description: Coleoptera collection
DOI: 10.48370/ofd/2folje
title: Raphuma diminuta (Bates, 1873), C-824

description: Coleoptera collection
DOI: 10.48370/ofd/ptnhfs
title: Raphuma gracillipes (Faldrmann, 1835) C-825

description: Coleoptera collection
DOI: 10.5281/zenodo.6460815
title: Data for: From pattern to process? Dual travelling waves, with contrasting propagation speeds, best describe a self-organised spatio-temporal pattern in population growth of a cyclic rodent

description: Centroid data used for the analysis in Roos et al. Eco Lett. Transects, up to 99 m in length (dependent on the field's length), were surveyed in linear stable landscape features (field, track or ditch margins) to estimate vole abundance from November 2011 until September 2017. Each transect was divided into 3 m sections (33 in total) and the presence or absence of one or more signs of vole activity (i.e., latrines by burrows, fresh vegetation clippings, and recent burrow excavations) in each section was noted. The proportion of sections with signs of vole presence per transect was then used as the abundance index. The number of surveys carried out at any time varied adaptively with the perceived risk of an outbreak (according to changes in estimated abundance in previous monitoring surveys). The response variable typically used in all models is proportional growth rate (r_{t,i}, where is the abundance index for site at time (Royama 1992; Berryman 2002). A benefit of using r_{t,i}, rather than ln(N_{t,i}), is that any multiplicative effects of site quality are cancelled out, provided they are constant over time. To calculate r_{t,i}, vole abundance indices are required at the same location in successive time periods (i.e., N_{t,i} and N_{t+1,i}). Given that exact transect locations were rarely reused in successive months, and all transect measurements took place throughout the year rather than discrete seasons, the data had to be aggregated to consistent locations and times to allow growth rate to be calculated. As such, transects were temporally aggregated into a respective yearly quarter (e.g., January to March 2014). Transects were spatially aggregated by sequentially selecting an unassigned transect as a reference point for the ith centroid and assigning all unassigned transects within a 5 km radius to the ith centroid, and repeating until all transects had been allocated (see Figure 2 for a summary of the number of transects assigned to each centroid, centroid locations, and time series of growth rate of each centroid). Once complete, the mean Julian day, X and Y UTM (Universal Transverse Mercator) and the mean index was calculated for all transects assigned to each centroid for each time period. Where a centroid had successive values of N_{t,i} and N_{t+1,i} available, the corresponding proportional growth rate was calculated. A constant of 3.03 was added to N_{t,i} to avoid zero entries (3.03 was the lowest non-zero value of <em>N</em> observed). The final dataset consisted of 3,751 observations.
description: See README for details of data formatting.
DOI: 10.5281/zenodo.6460814
title: Data for: From pattern to process? Dual travelling waves, with contrasting propagation speeds, best describe a self-organised spatio-temporal pattern in population growth of a cyclic rodent

description: Centroid data used for the analysis in Roos et al. Eco Lett. Transects, up to 99 m in length (dependent on the field's length), were surveyed in linear stable landscape features (field, track or ditch margins) to estimate vole abundance from November 2011 until September 2017. Each transect was divided into 3 m sections (33 in total) and the presence or absence of one or more signs of vole activity (i.e., latrines by burrows, fresh vegetation clippings, and recent burrow excavations) in each section was noted. The proportion of sections with signs of vole presence per transect was then used as the abundance index. The number of surveys carried out at any time varied adaptively with the perceived risk of an outbreak (according to changes in estimated abundance in previous monitoring surveys). The response variable typically used in all models is proportional growth rate (r_{t,i}, where is the abundance index for site at time (Royama 1992; Berryman 2002). A benefit of using r_{t,i}, rather than ln(N_{t,i}), is that any multiplicative effects of site quality are cancelled out, provided they are constant over time. To calculate r_{t,i}, vole abundance indices are required at the same location in successive time periods (i.e., N_{t,i} and N_{t+1,i}). Given that exact transect locations were rarely reused in successive months, and all transect measurements took place throughout the year rather than discrete seasons, the data had to be aggregated to consistent locations and times to allow growth rate to be calculated. As such, transects were temporally aggregated into a respective yearly quarter (e.g., January to March 2014). Transects were spatially aggregated by sequentially selecting an unassigned transect as a reference point for the ith centroid and assigning all unassigned transects within a 5 km radius to the ith centroid, and repeating until all transects had been allocated (see Figure 2 for a summary of the number of transects assigned to each centroid, centroid locations, and time series of growth rate of each centroid). Once complete, the mean Julian day, X and Y UTM (Universal Transverse Mercator) and the mean index was calculated for all transects assigned to each centroid for each time period. Where a centroid had successive values of N_{t,i} and N_{t+1,i} available, the corresponding proportional growth rate was calculated. A constant of 3.03 was added to N_{t,i} to avoid zero entries (3.03 was the lowest non-zero value of <em>N</em> observed). The final dataset consisted of 3,751 observations.
description: See README for details of data formatting.
DOI: 10.34740/kaggle/ds/2083628
title: Aliexpress exchange rate
DOI: 10.48370/ofd/h7lbn2
title: Chlorophorus motschulskyi (Ganglbauer, 1887), C-826

description: Coleoptera collection
DOI: 10.5281/zenodo.6460867
title: DeepBacs – Escherichia coli MreB denoising dataset and CARE model

description: Training and test images of MreB-sfGFP<sup>sw</sup> expressing <em>E. coli </em>cells for image denoising, as well as a trained CARE model. Additional information can be found on our github wiki. The example images show confocal images of labelled <em>E. coli</em> MreB filaments at low and high SNR. <strong>Training and test dataset:</strong> <strong>Data type</strong>: Paired microscopy images (fluorescence) <strong>Microscopy data type</strong>: Confocal fluorescence images <strong>Microscope</strong>: Leica SP8 confocal microscope with a 1.40 NA 63x oil immersion objective <strong>Cell type</strong>: <em>E. coli</em> strain NO34 expressing MreB-sfGFPsw fusion protein (kindly provided by Zemer Gitai) <strong>File format</strong>: .tif (16-bit) <strong>Image size</strong>: 512x512 (Pixel size: 45 nm) The CARE 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 100 epochs (600 steps/epoch) on 1500 paired image patches (image dimensions: (512 x 512 px²), patch size: (64 x 64 px²), 50 patches/image) with a batch size of 8 and a laplace loss function, using the CARE 2D ZeroCostDL4Mic notebook (v 1). Key python packages used include tensorflow (v 0.1.12), Keras (v2.3.1), csbdeep (v 0.6.3), numpy (v 1.21.5), cuda (v 11.1.105). The training was accelerated using a Tesla K80 GPU and data was augmented by a factor of 4 using rotation and flipping. The model weights can be used with the ZeroCostDL4Mic CARE 2D notebook and the CSBDeep Fiji plugin. <strong>Author(s)</strong>: Christoph Spahn<sup>1,2</sup>, Mike Heilemann<sup>1,3</sup> <strong>Contact email</strong>: christoph.spahn@mpi-marburg.mpg.de <strong>Affiliation(s)</strong>: 1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany 2) ORCID: 0000-0001-9886-2263 3) ORCID: 0000-0002-9821-3578
DOI: 10.5281/zenodo.6460866
title: DeepBacs – Escherichia coli MreB denoising dataset and CARE model

description: Training and test images of MreB-sfGFP<sup>sw</sup> expressing <em>E. coli </em>cells for image denoising, as well as a trained CARE model. Additional information can be found on our github wiki. The example images show confocal images of labelled <em>E. coli</em> MreB filaments at low and high SNR. <strong>Training and test dataset:</strong> <strong>Data type</strong>: Paired microscopy images (fluorescence) <strong>Microscopy data type</strong>: Confocal fluorescence images <strong>Microscope</strong>: Leica SP8 confocal microscope with a 1.40 NA 63x oil immersion objective <strong>Cell type</strong>: <em>E. coli</em> strain NO34 expressing MreB-sfGFPsw fusion protein (kindly provided by Zemer Gitai) <strong>File format</strong>: .tif (16-bit) <strong>Image size</strong>: 512x512 (Pixel size: 45 nm) The CARE 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 100 epochs (600 steps/epoch) on 1500 paired image patches (image dimensions: (512 x 512 px²), patch size: (64 x 64 px²), 50 patches/image) with a batch size of 8 and a laplace loss function, using the CARE 2D ZeroCostDL4Mic notebook (v 1). Key python packages used include tensorflow (v 0.1.12), Keras (v2.3.1), csbdeep (v 0.6.3), numpy (v 1.21.5), cuda (v 11.1.105). The training was accelerated using a Tesla K80 GPU and data was augmented by a factor of 4 using rotation and flipping. The model weights can be used with the ZeroCostDL4Mic CARE 2D notebook and the CSBDeep Fiji plugin. <strong>Author(s)</strong>: Christoph Spahn<sup>1,2</sup>, Mike Heilemann<sup>1,3</sup> <strong>Contact email</strong>: christoph.spahn@mpi-marburg.mpg.de <strong>Affiliation(s)</strong>: 1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany 2) ORCID: 0000-0001-9886-2263 3) ORCID: 0000-0002-9821-3578
DOI: 10.25405/data.ncl.19228272
title: A mobile laboratory for fecal pollution source tracking with onsite qPCR assays

description: This folder contains data sets underpinning the figure, tables and statistical analysis of the articleA mobile laboratory for fecal pollution source tracking with onsite qPCR assays<br>
DOI: 10.25405/data.ncl.19228272.v1
title: A mobile laboratory for fecal pollution source tracking with onsite qPCR assays

description: This folder contains data sets underpinning the figure, tables and statistical analysis of the articleA mobile laboratory for fecal pollution source tracking with onsite qPCR assays<br>
DOI: 10.5281/zenodo.6460920
title: The link between non-aureus staphylococci in bulk milk and in milk samples of dairy cows

description: Non-<em>aureus</em> staphylococci (NAS) have become the most common causes of bovine subclinical mastitis on modern dairy farms. Identifying and understanding the ecological nature and epidemiological behavior of the causative mastitis agents circulating in a dairy herd is integral to the implementation of herd-specific mastitis prevention plans and the improvement of mastitis management. Bulk milk sampling for bacterial culturing is readily available as part of (regulatory) milk quality screening programs worldwide and includes the milk of all lactating and milked animals in the herd. In a first phase of a larger project we aim (1) to study the presence and distribution of NAS species in bulk milk samples and composite cow milk samples simultaneously and (2) to perform strain-typing to determine which proportion of NAS isolates from bulk milk in dairy herds originate from the mammary quarters of cows.Non-<em>aureus</em> staphylococci (NAS) have become the most common causes of bovine subclinical mastitis on modern dairy farms Identifying and understanding the ecological nature and epidemiological behavior of the causative mastitis agents circulating in a dairy herd is integral to the implementation of herd-specific mastitis prevention plans and the improvement of mastitis management . Bulk milk sampling for bacterial culturing is readily available as part of (regulatory) milk quality screening programs worldwide and includes the milk of all lactating and milked animals in the herd. In a first phase of a larger project we aim (1) to study the presence and distribution of NAS species in bulk milk samples and composite cow milk samples simultaneously and (2) to perform strain-typing to determine which proportion of NAS isolates from bulk milk in dairy herds originate from the mammary quarters of cows.