cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2cmhl3
title: CCDC 2193485: Experimental Crystal Structure Determination

description: Related Article: Hao Lu, Jiayi Chen, Wenjun Zhou, Lifen Peng, Shuang-Feng Yin, Nobuaki Kambe, Renhua Qiu|2023|Org.Lett.|25|389|doi:10.1021/acs.orglett.2c04101
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: 10.5066/p9naiacl
title: Data used to parameterize and evaluate a bioenergetics model for Redside Shiner (Richardsonius balteatus)

description: This release includes data used to fit mass- and temperature-dependent functions for consumption and respiration of redside shiner based on controlled laboratory experiments of maximum consumption (Cmax) rates and routine metabolic rates using intermittent-flow respirometry, across a range of fish sizes (0.6–27.3 g) and temperatures (5–31° C). Long term growth experiments were conducted to evaluate performance of the resulting bioenergetics model. Initial bioenergetic simulations of long-term growth experiments indicated large model error for predicted consumption and growth, and a growth rate error (GRE) correction was developed as a function of daily consumption rate (J·g-1·d-1) and water temperature, and included in the model framework [...]
DOI: 10.5517/ccdc.csd.cc2czmpp
title: CCDC 2204183: Experimental Crystal Structure Determination

description: Related Article: Yannick T. Boni, Janakiram Vaitla, Huw M. L. Davies|2022|Org.Lett.|25|5|doi:10.1021/acs.orglett.2c03335
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.cc2d22sc
title: CCDC 2206542: Experimental Crystal Structure Determination

description: Related Article: Yannick T. Boni, Janakiram Vaitla, Huw M. L. Davies|2022|Org.Lett.|25|5|doi:10.1021/acs.orglett.2c03335
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.cc2d3wt7
title: CCDC 2208279: Experimental Crystal Structure Determination

description: Related Article: Qi Chen, Ping Lan, Shen Tan, Martin G. Banwell|2023|Org.Lett.|25|384|doi:10.1021/acs.orglett.2c04099
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.21764114
title: DataFile1: Underlying benchmarking data for software live deskewing

description: Data underlying the results shown in the paper 'An open-source software package for on-the-fly deskewing and live viewing of volumetric lightsheet microscopy data' by Jacob R. Lamb, Edward N. Ward and Clemens F. Kaminski
DOI: 10.5517/ccdc.csd.cc2cz77v
title: CCDC 2203797: Experimental Crystal Structure Determination

description: Related Article: Yan Xu, Ke Xu, Lei He, Ti-Jian Yin, Jie Mu, Jin-Tao Men, Wen Zhang, Qiong Ye|2023|Inorg.Chem.|62|1279|doi:10.1021/acs.inorgchem.2c04163
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.cc2cz78w
title: CCDC 2203798: Experimental Crystal Structure Determination

description: Related Article: Yan Xu, Ke Xu, Lei He, Ti-Jian Yin, Jie Mu, Jin-Tao Men, Wen Zhang, Qiong Ye|2023|Inorg.Chem.|62|1279|doi:10.1021/acs.inorgchem.2c04163
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.cc2cpyjk
title: CCDC 2195839: Experimental Crystal Structure Determination

description: Related Article: Soumya Mondal, Subal Mondal, Siba P. Midya, Suman Das, Sahidul Mondal, Pradyut Ghosh|2022|Org.Lett.|25|184|doi:10.1021/acs.orglett.2c04041
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.cc2d5c76
title: CCDC 2209687: Experimental Crystal Structure Determination

description: Related Article: Katelyn B. Bobek, Nameer S. Ezzat, Brandon S. Jones, Yujia Bian, Thomas E. Shaw, Titel Jurca, Hongya Li, Yu Yuan|2022|Org.Lett.|25|31|doi:10.1021/acs.orglett.2c03727
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.cc2d6n3d
title: CCDC 2210923: Experimental Crystal Structure Determination

description: Related Article: Gen Li, Marissa N. Lavagnino, Siraj Z. Ali, Shicheng Hu, Alexander T. Radosevich|2022|J.Am.Chem.Soc.|145|41|doi:10.1021/jacs.2c12450
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.cc2d6n4f
title: CCDC 2210924: Experimental Crystal Structure Determination

description: Related Article: Gen Li, Marissa N. Lavagnino, Siraj Z. Ali, Shicheng Hu, Alexander T. Radosevich|2022|J.Am.Chem.Soc.|145|41|doi:10.1021/jacs.2c12450
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.cc2d84sm
title: CCDC 2212370: Experimental Crystal Structure Determination

description: Related Article: Meiqi Zhang, Weizhe Zhao, Jiamin Ma, Jiaoxiong Li, Qiansen Meng, Chengshuo Shen, Xianghua Zeng|2022|Org.Lett.|25|231|doi:10.1021/acs.orglett.2c04074
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.cc2d84tn
title: CCDC 2212371: Experimental Crystal Structure Determination

description: Related Article: Meiqi Zhang, Weizhe Zhao, Jiamin Ma, Jiaoxiong Li, Qiansen Meng, Chengshuo Shen, Xianghua Zeng|2022|Org.Lett.|25|231|doi:10.1021/acs.orglett.2c04074
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.cc2ddvf3
title: CCDC 2216885: Experimental Crystal Structure Determination

description: Related Article: Hui Yan, Xusheng Shao, Xiaoyong Xu, Zhong Li, Wu-Lin Yang|2023|Org.Lett.|25|325|doi:10.1021/acs.orglett.2c03885
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.cc2dfp8t
title: CCDC 2217686: Experimental Crystal Structure Determination

description: Related Article: Chikkabagilu Nagaraju Shambhavi, Masilamani Jeganmohan|2023|Org.Lett.|25|358|doi:10.1021/acs.orglett.2c04036
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: 10.17608/k6.auckland.21618879.v1
title: Cumulative propagule pressure exerted by escaped pet parrots

description: Data (reported lost bird listings and species reference table) and R code for simulated cumulative propagule pressure for manuscript: Journal of Applied Ecology
DOI: 10.17638/datacat.liverpool.ac.uk/1565
title: Rapid identification of mosquito species, sex and age by mass spectrometric analysis. RAW MS data files

description: readme file privided
DOI: 10.5281/zenodo.7332003
title: Agroforestry datasets and calculations

description: Agroforestry datasets and calculations
DOI: 10.5517/ccdc.csd.cc21mqv8
title: CCDC 1895800: Experimental Crystal Structure Determination

description: Related Article: Jia-Qi Wang,  Zong-Jie Guan,  Wen-Di Liu,  Yang Yang,  and Quan-Ming Wang|2019|J.Am.Chem.Soc.|141|2384|doi:10.1021/jacs.8b11096
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.cc2dly65
title: CCDC 2222737: Experimental Crystal Structure Determination

description: Related Article: Noah J. Sims, Weston C. Bonnet, Danielle M. Lawson, John L. Wood|2022|J.Am.Chem.Soc.|145|37|doi:10.1021/jacs.2c12275
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.21670412.v2
title: Supplement_Material_1 Quality control of Hi-C data

description: Quality control of Hi-C data
DOI: 10.6084/m9.figshare.21946595
title: Supplementary Material for: Non-Invasive Zinc Protoporphyrin Screening Offers Opportunities for Secondary Prevention of Iron Deficiency in Blood Donors

description: <b><i>Background:</i></b> Frequent blood donors are at high risk of developing iron deficiency. Currently, there is no potent screening during blood donation to detect iron deficient erythropoiesis (IDE) before anemia develops and deferral from donation is inevitable. <b><i>Study Design and Methods:</i></b> In addition to capillary and venous hemoglobin, the iron status of 99 frequent blood donors was assessed by various venous blood parameters and zinc protoporphyrin IX (ZnPP). ZnPP was determined by high-performance liquid chromatography (HPLC) and a new prototype fiber-optic device was employed for non-invasive measurements of ZnPP through the blood collection tubing (NI-tubing) and on lip tissue (NI-lip). We aimed to evaluate the feasibility and diagnostic value of the NI-tubing measurement for early detection of severe iron deficiency in blood donors. <b><i>Results:</i></b> NI-tubing and HPLC reference measurements of ZnPP showed narrow limits of agreement of 12.2 μmol ZnPP/mol heme and very high correlation (Spearman’s Rho = 0.938). Using a cutoff of 65 μmol ZnPP/mol heme, NI-tubing measurements (<i>n</i> = 93) identified 100% of donors with iron deficiency anemia (IDA) and an additional 38% of donors with IDE. Accordingly, NI-tubing measurements would allow detection and selective protection of particularly vulnerable donors. <b><i>Conclusion:</i></b> NI-tubing measurements are an accurate and simple method to implement ZnPP determination into the routine blood donation process. ZnPP was able to identify the majority of subjects with IDE and IDA and might therefore be a valuable tool to provide qualified information to donors about dietary measures and adjustments of the donation interval and thereby help to prevent IDA and hemoglobin deferral in the future.
DOI: 10.6084/m9.figshare.21946730
title: Supplementary Material for: Short-Term and Long-Term Morbidity after Radical Cystectomy in Patients with NMIBC and Comparison with MIBC: Identifying Risk Factors for Severe Short-Term Complications

description: <b><i>Introduction:</i></b> RC represents a viable treatment option for certain NMIBC patients. However, studies investigating morbidity in the context of RC for NMIBC are scarce. The goal of the current study was to assess and compare morbidity after RC performed in patients with NMIBC and patients with MIBC and to identify risk factors for severe short-term complications. <b><i>Methods:</i></b> Medical records of 521 patients who underwent RC for bladder cancer were retrospectively reviewed. Patients were divided into patients with NMIBC and patients with MIBC. The groups were compared and risk factors for severe complications were identified. <b><i>Results:</i></b> RC for NMIBC was performed in 123 patients (23.6%). Histological upstaging was seen in 47 NMIBC patients (38.2%) and in 231 MIBC patients (58%, <i>p</i> &lt; 0.001). OS was 29.8% and CSS was 15.5%. Both endpoints were higher for RC for MIBC (<i>p</i> &lt; 0.001). More complications affecting the urinary diversion were seen with RC for NMIBC (<i>p</i> = 0.033) and more continent urinary diversions (<i>p</i> = 0.040) were performed in those patients. Obesity (<i>p</i> = 0.008), a higher ASA score (<i>p</i> = 0.004), and preoperative medical drug anticoagulation (<i>p</i> = 0.025) were risk factors for severe short-term morbidity after both, RC for NMIBC and for MIBC. <b><i>Conclusion:</i></b> Patients who underwent RC for NMIBC are exposed to a comparably high perioperative risk than patients with MIBC. RC seems to be a viable treatment option for certain NMIBC patients with a significant histological upstaging in both groups. In patients with obesity, a high ASA score, and with medical drug anticoagulation, the indication for surgery should be confirmed especially strict and possible treatment alternatives should be considered particularly thorough.
DOI: 10.5281/zenodo.7438920
title: Data for "Understanding density driven errors via reaction barrier heights"

description: Contains gzipped tarballs for BH76 data, inputs, and outputs computed with PySCF and the UTEP-NRLMOL code. Contents: r2SCAN_BH76_FODs.tar.gz : all relaxed Fermi orbital descriptors (FODs) computed self-consistently for r<sup>2</sup>SCAN-FLOSIC All entries are space-separated The first line contains the number of up-spin FODs followed by the number of down-spin FODs The first three values in each ensuing line are the x, y, and z coordinates of the FOD results_aug-cc-pvqz.tar.gz : all PySCF inputs/outputs for BH76 computed using the aug-cc-pVQZ basis set (spherical representation) DFAs = HF/EXOA, LSDA, PBE, BLYP, SCAN, r<sup>2</sup>SCAN, M06-L, MN15-L, B3LYP, LC-wPBE And DFA @ DFA2, where DFA2 = HF, LSDA, PBE, LC-wPBE results_def2-QZVP.tar.gz : all PySCF inputs/outputs for BH76 computed using the def2-QZVP basis set (spherical representation) DFAs = HF/EXOA, LSDA, PBE, SCAN, r<sup>2</sup>SCAN, LC-wPBE And DFA @ DFA2, where DFA2 = HF, LC-wPBE results_NRLMOL_cart.tar.gz : all PySCF inputs/outputs for BH76 computed using the default NRLMOL (density-functional optimized or DFO) basis set (Cartesian representation) DFAs = HF/EXOA, LSDA, PBE, SCAN, r<sup>2</sup>SCAN, LC-wPBE And DFA @ DFA2, where DFA2 = HF, LC-wPBE FLOSIC.tar.gz : data computed using the UTEP-NRLMOL code (NRLMOL basis set, Cartesian representation) DFAs = LSDA, PBE, SCAN, r<sup>2</sup>SCAN And DFA@SCAN-FLOSIC DC_DFT_processed_data.tar.gz : processed density-driven and functional-driven errors, and density sensitivity data r2SCAN_hybids.tar.gz : self-consistent r2 SCAN hybrid calculations (R2X-<em>x</em>) and DFA@R2X-<em>x</em> calculations All PySCF input/output, aug-cc-pVQZ basis (spherical representation) <em>x </em>= 0, 0.1, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.75, 0.8, 0.9, 1.0 DFA = LSDA, PBE, BLYP, SCAN, r2SCAN, B3LYP, LC-wPBE SCAN_hybids.tar.gz : self-consistent SCAN hybrid calculations (SX-<em>x</em>) and DFA@SX-<em>x</em> calculations All PySCF input/output, aug-cc-pVQZ basis (spherical representation) <em>x </em>= 0, 0.1, 0.25, 0.5, 0.75, 1.0 DFA = LSDA, PBE, SCAN, r2SCAN, LC-wPBE
description: Version 2 adds @PBE results using the aug-cc-pVQZ basis set. These are used in the SM
DOI: 10.5517/ccdc.csd.cc2dly76
title: CCDC 2222738: Experimental Crystal Structure Determination

description: Related Article: Noah J. Sims, Weston C. Bonnet, Danielle M. Lawson, John L. Wood|2022|J.Am.Chem.Soc.|145|37|doi:10.1021/jacs.2c12275
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: 10.6073/pasta/949164a64de132ff3bbb7b92d2e1c729
title: State-of-the-art review of near-term freshwater forecasting literature published between 2017 and 2022

description: This data publication includes code and results from a systematic literature review on the current state of near-term forecasting of freshwater quality. The review aimed to address the following questions: (1) Freshwater variables, scales, models, and skill: Which freshwater variables and temporal scales are most commonly targeted for near-term forecasts, and what modeling methods are most commonly employed to develop these forecasts? How is the accuracy of freshwater quality forecasts assessed, and how accurate are they? How is uncertainty typically incorporated into water quality forecast output? (2) Forecast infrastructure and workflows: Are iterative, automated workflows commonly employed in near-term freshwater quality forecasting? How are forecasts validated and archived? (3) Human dimensions: What is the stated motivation for development of most near-term freshwater quality forecasts, and who are the most common end users (if any)? How are end users engaged in forecast development? An initial search was conducted for published papers presenting freshwater quality forecasts from 1 January 2017 to 17 February 2022 in the Web of Science Core Collection. Results were subsequently analyzed in three stages. First, paper titles were screened for relevance. Second, an initial screen was conducted to assess whether each paper presented a near-term freshwater quality forecast. Third, papers that passed the initial screen were analyzed using a standardized matrix to assess the state of near-term freshwater quality forecasting and identify areas of recent progress and ongoing challenges. Additional details regarding the systematic literature search and review are presented in the Methods section of the metadata.
DOI: 10.6084/m9.figshare.21640343
title: Supplementary Material for: Loss of Pten in renal tubular cells leads to water retention by upregulating AQP2

description: Introduction: Phosphatase and tensin (PTEN) is a multifunctional gene associated with the normal development and physiological function of various tissues including the kidney. However, its role in renal tubular reabsorption function has not been well elucidated. Methods: We generated a renal tubule-specific Pten knockout mouse model by crossing Ptenfl/fl mice with Ksp-Cre transgenic mice, evaluated the effect of Pten loss on renal tubular function, and investigated the underlying mechanisms. Results: Pten loss resulted in abnormal renal structure and function and water retention in multiple organs. Our results also demonstrated that aquaporin-2 AQP2, an important water channel protein, was upregulated and concentrated on the apical plasma membrane of collecting duct cells, which could be responsible for the impaired water balance in Pten loss mice. The regulation of Pten loss on AQP2 was mediated by Protein kinase B (AKT) activation. Discussion/Conclusions: Our results reveal a connection between PTEN gene inactivation and water retention, suggesting the importance of PTEN in normal kidney development and function.
DOI: 10.3886/icpsr37056.v1
title: Uniform Crime Reporting Program Data: Arrests by Age, Sex, and Race, United States, 2016
title: Version 1

description: These data provide information on the number of arrests reported to the Federal Bureau of Investigation's (FBI) Uniform Crime Reporting (UCR) Program each month by police agencies in the United States. Although not as well known as the "Crimes Known to the Police" data drawn from the Uniform Crime Report's Return A form, the arrest reports by age, sex, and race provide valuable data on 43 offenses including violent, drug, gambling, and larceny crimes. The data received by ICPSR were structured as a hierarchical file containing (per reporting police agency) an agency header record, and 1 to 12 monthly header reports, and 1 to 43 detail offense records containing the counts of arrests by age, sex, and race for a particular offense. ICPSR restructured the original data to a rectangular format.
description: The data for the Uniform Crime Reports are submitted voluntarily by city, county, and state law enforcement agencies. Some agencies receive forms directly from the FBI and return them directly to the FBI. Many others receive forms from state collecting programs and return the reports to those programs. The state programs, in turn, forward the reports to the FBI. Reports are sent out and collected on a monthly basis.
description: Datasets: DS1: Uniform Crime Reporting Program Data: Arrests by Age, Sex, and Race, United States, 2016
description: Arrests reported by law enforcement agencies to the Federal Bureau of Investigation's Uniform Crime Reporting program. Smallest Geographic Unit: states, counties, cities
description: mail questionnaire; on-site questionnaire
DOI: 10.48567/key7-ch19
title:  AntAWS Dataset (2022)