cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc26kq6q
title: CCDC 2042813: Experimental Crystal Structure Determination

description: Related Article: Manan Ahmed, Zixi Xie, Shannon Thoonen, Carol Hua, Cameron J. Kepert, Jason R. Price, Suzanne M. Neville|2021|Chem.Commun.|57|85|doi:10.1039/D0CC07326A
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.cc26kq7r
title: CCDC 2042814: Experimental Crystal Structure Determination

description: Related Article: Manan Ahmed, Zixi Xie, Shannon Thoonen, Carol Hua, Cameron J. Kepert, Jason R. Price, Suzanne M. Neville|2021|Chem.Commun.|57|85|doi:10.1039/D0CC07326A
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.cc26kq8s
title: CCDC 2042815: Experimental Crystal Structure Determination

description: Related Article: Manan Ahmed, Zixi Xie, Shannon Thoonen, Carol Hua, Cameron J. Kepert, Jason R. Price, Suzanne M. Neville|2021|Chem.Commun.|57|85|doi:10.1039/D0CC07326A
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.cc26kq9t
title: CCDC 2042816: Experimental Crystal Structure Determination

description: Related Article: Manan Ahmed, Zixi Xie, Shannon Thoonen, Carol Hua, Cameron J. Kepert, Jason R. Price, Suzanne M. Neville|2021|Chem.Commun.|57|85|doi:10.1039/D0CC07326A
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.cc261nyw
title: CCDC 2027398: Experimental Crystal Structure Determination

description: Related Article: Xiaoyu Ma, Bingqing Zhu, Zeen Yang, Yuhang Jiang, Xuefeng Mei|2021|CrystEngComm|23|30|doi:10.1039/D0CE01551B
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.cc261nqn
title: CCDC 2027391: Experimental Crystal Structure Determination

description: Related Article: Xiaoyu Ma, Bingqing Zhu, Zeen Yang, Yuhang Jiang, Xuefeng Mei|2021|CrystEngComm|23|30|doi:10.1039/D0CE01551B
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.cc261nvs
title: CCDC 2027395: Experimental Crystal Structure Determination

description: Related Article: Xiaoyu Ma, Bingqing Zhu, Zeen Yang, Yuhang Jiang, Xuefeng Mei|2021|CrystEngComm|23|30|doi:10.1039/D0CE01551B
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.cc26q4kn
title: CCDC 2047071: Experimental Crystal Structure Determination

description: Related Article: Yuji Liu, Xiujuan Qi, Wenquan Zhang, Ping Yin, Ziwu Cai, Qinghua Zhang|2021|Org.Lett.|23|734|doi:10.1021/acs.orglett.0c03952
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.cc26rhl2
title: CCDC 2048374: Experimental Crystal Structure Determination

description: Related Article: Andreas O. Schüren, Benjamin M. Ridgway, Florencia Di Salvo, Luca M. Carella, Verena K. Gramm, Elisa Metzger, Fabio Doctorovich, Eva Rentschler, Volker Schünemann, Uwe Ruschewitz, Axel Klein|2023|Dalton Trans.|52|1277|doi:10.1039/D2DT03203A
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.cc26s38d
title: CCDC 2048953: Experimental Crystal Structure Determination

description: Related Article: Andreas O. Schüren, Benjamin M. Ridgway, Florencia Di Salvo, Luca M. Carella, Verena K. Gramm, Elisa Metzger, Fabio Doctorovich, Eva Rentschler, Volker Schünemann, Uwe Ruschewitz, Axel Klein|2023|Dalton Trans.|52|1277|doi:10.1039/D2DT03203A
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.cc26s39f
title: CCDC 2048954: Experimental Crystal Structure Determination

description: Related Article: Andreas O. Schüren, Benjamin M. Ridgway, Florencia Di Salvo, Luca M. Carella, Verena K. Gramm, Elisa Metzger, Fabio Doctorovich, Eva Rentschler, Volker Schünemann, Uwe Ruschewitz, Axel Klein|2023|Dalton Trans.|52|1277|doi:10.1039/D2DT03203A
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.cc248g1y
title: CCDC 1974329: Experimental Crystal Structure Determination

description: Related Article: Jiwang Fang|2019|CSD Communication|||
description: Related Article: Ji-Wang Fang, Fang-jie Liao, Yang Qian, Chao-Chen Dong, Li-Jin Xu, Han-Yuan Gong|2021|J.Org.Chem.|86|3648|doi:10.1021/acs.joc.0c02484
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.cc26rvjc
title: CCDC 2048713: Experimental Crystal Structure Determination

description: Related Article: Yanyu Chen, Zhengyuan Xin, Hui Wang, Haibing He, Shuanhu Gao|2022|Org.Chem.Front.|10|651|doi:10.1039/D2QO01834A
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.cc26x9ct
title: CCDC 2052986: Experimental Crystal Structure Determination

description: Related Article: Yanyu Chen, Zhengyuan Xin, Hui Wang, Haibing He, Shuanhu Gao|2022|Org.Chem.Front.|10|651|doi:10.1039/D2QO01834A
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.cc26x9dv
title: CCDC 2052987: Experimental Crystal Structure Determination

description: Related Article: Yanyu Chen, Zhengyuan Xin, Hui Wang, Haibing He, Shuanhu Gao|2022|Org.Chem.Front.|10|651|doi:10.1039/D2QO01834A
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.7592149
title: Studied Projects: An Empirical Study on the Characteristics of Reusable Code Clones

description: This is the excel file describing urls for the studied projects
DOI: 10.5281/zenodo.7065651
title: MCMC chains for demographic fits presented in "NICMOS Kernel-Phase Interferometry II: Demographics of Nearby Brown Dwarfs"

description: These files are the data behind the figure for Figure 3 (and the corresponding Figure Set) as well as other fits presented in Table 5. They are saved in npy format which can be read into python using numpy according to the code snippet below. The files are flattened and trimmed MCMC chains produced by running emcee (Foreman-Mackey et al. 2013) using 64 walkers for 10,000 steps. The first 1,000 steps were trimmed for burn in and the remaining chains were thinned by 40 steps. The files are named according to the following convention: flatSamples&lt;malm cor&gt;&lt;age&gt;&lt;prior&gt;.npy where: &lt;malm cor&gt; is either 'Malm' or '' (nothing) if the model population was or was not corrected for Malmquist bias (before comparing to the observed population while fitting). &lt;age&gt; is '0p9', '1p2', '1p5', '1p9', '2p4', or '3p1' according to that assumed field age (in Gyr). &lt;prior&gt; is 'U' or 'I' for uninformed or informed (incorporating the information from Blake et al. 2010 on the unresolved population). The true underlying population corresponds to the flatSamplesMalm&lt;age&gt;I.npy files while the others are included for context and comparison to populations fit to the observed (not Malmquist corrected) population. The uninformed prior chains are dominated by a significant population of unresolved companions which is not consistent with previous RV studies. The files can be read into python using: <pre><code class="language-python">import numpy as np flat_samples0p9I = np.load('flatSamples0p9I.npy') </code></pre> which produces an array with shape 14400 x 4. The rows are the samples and the four columns are the parameters \(F, \gamma, \overline{\log(\rho)}\), and \(\sigma_{\log(\rho)}\), respectively.
description: {"references": ["Foreman-Mackey et al. (2013)"]}
DOI: 10.5281/zenodo.7065650
title: MCMC chains for demographic fits presented in "NICMOS Kernel-Phase Interferometry II: Demographics of Nearby Brown Dwarfs"

description: These files are the data behind the figure for Figure 3 (and the corresponding Figure Set) as well as other fits presented in Table 5. They are saved in npy format which can be read into python using numpy according to the code snippet below. The files are flattened and trimmed MCMC chains produced by running emcee (Foreman-Mackey et al. 2013) using 64 walkers for 10,000 steps. The first 1,000 steps were trimmed for burn in and the remaining chains were thinned by 40 steps. The files are named according to the following convention: flatSamples&lt;malm cor&gt;&lt;age&gt;&lt;prior&gt;.npy where: &lt;malm cor&gt; is either 'Malm' or '' (nothing) if the model population was or was not corrected for Malmquist bias (before comparing to the observed population while fitting). &lt;age&gt; is '0p9', '1p2', '1p5', '1p9', '2p4', or '3p1' according to that assumed field age (in Gyr). &lt;prior&gt; is 'U' or 'I' for uninformed or informed (incorporating the information from Blake et al. 2010 on the unresolved population). The true underlying population corresponds to the flatSamplesMalm&lt;age&gt;I.npy files while the others are included for context and comparison to populations fit to the observed (not Malmquist corrected) population. The uninformed prior chains are dominated by a significant population of unresolved companions which is not consistent with previous RV studies. The files can be read into python using: <pre><code class="language-python">import numpy as np flat_samples0p9I = np.load('flatSamples0p9I.npy') </code></pre> which produces an array with shape 14400 x 4. The rows are the samples and the four columns are the parameters \(F, \gamma, \overline{\log(\rho)}\), and \(\sigma_{\log(\rho)}\), respectively.
description: {"references": ["Foreman-Mackey et al. (2013)"]}
DOI: 10.25625/5yttiq/es9j76
title: Spo7 wt 2 A04.tif

description: :unav
DOI: 10.25625/5yttiq/doltjx
title: YCR007C dget3 1 H02.tif

description: :unav
DOI: 10.25625/5yttiq/fhz7cj
title: Tcb2 dget3 1 F03.tif

description: :unav
DOI: 10.25625/5yttiq/35z38x
title: Prm8 dget3 1 F02.tif

description: :unav
DOI: 10.25625/5yttiq/433r9i
title: Prm8 dget3 2 F05.tif

description: :unav
DOI: 10.25625/5yttiq/kppl1s
title: YAR028W dget3 3 K08.tif

description: :unav
DOI: 10.25625/5yttiq/nfy94t
title: Uip3 dget3 2 L05.tif

description: :unav
DOI: 10.25625/5yttiq/rxpcne
title: Spo7 dget3 3 M08.tif

description: :unav
DOI: 10.25625/5yttiq/4tu7aw
title: Spc1 wt 1 N01.tif

description: :unav
DOI: 10.25625/5yttiq/2yarsl
title: Tcb2 dget3 3 F09.tif

description: :unav
DOI: 10.25625/5yttiq/7921gv
title: Uip3 wt 1 B01.tif

description: :unav
DOI: 10.25625/5yttiq/groxxv
title: YCR007C dget3 2 H05.tif

description: :unav
DOI: 10.25625/5yttiq/uaturi
title: YAR028W wt 3 C07.tif

description: :unav
DOI: 10.25625/5yttiq/okqc4k
title: YAR028W wt 2 C04.tif

description: :unav
DOI: 10.25625/5yttiq/fhrry5
title: Tcb1 wt 2 O05.tif

description: :unav
DOI: 10.25625/5yttiq/y4m2dh
title: Tcb2 dget3 2 F06.tif

description: :unav
DOI: 10.25625/5yttiq/skld4n
title: Tcb1 dget3 1 E03.tif

description: :unav
DOI: 10.25625/5yttiq/a4hzvi
title: Prm8 wt 3 H07.tif

description: :unav
DOI: 10.25625/5yttiq/zkrceu
title: Uip3 wt 3 B07.tif

description: :unav
DOI: 10.25625/5yttiq/pcfqbf
title: Spo7 wt 1 A01.tif

description: :unav
DOI: 10.25625/5yttiq/vjxvvb
title: Sna4 dget3 2 I05.tif

description: :unav
DOI: 10.25625/5yttiq/rx6zqd
title: YCR007C wt 1 F01.tif

description: :unav
DOI: 10.25625/5yttiq/6u22fk
title: Sey1 wt 3 L07.tif

description: :unav
DOI: 10.25625/5yttiq
title: Raw microscopy data of Farkas et al., 2022

description: Raw microscopy data of Farkas et al., 2022
DOI: 10.6084/m9.figshare.19503907
title: Supplementary Material for: Effect of Normalizing eGFR to Standard Body Surface Area on Rates of Obesity-Related Hyperfiltration among Diverse Female Adolescents

description: <b><i>Introduction:</i></b> Obesity is more prevalent among African American individuals, increasing the risk for cardiorenal morbidity. We explored interactions between race, BMI, and the risk of hyperfiltration associated with obesity-related glomerulopathy (ORG). <b><i>Methods:</i></b> We created a cohort of female adolescents from electronic health records. Glomerular filtration rate (GFR) was estimated in two ways: (A) using standard age recommended formulae and (B) absolute eGFR – adjusted to individual body surface area (BSA). Multivariate logistic regression was used to analyze the contribution of risk factors for ORG-associated hyperfiltration defined as 135 mL/min/1.73 m<sup>2</sup> or 135 mL/min, according to BMI group. Pearson’s coefficient was used to assess correlation with creatinine clearance (CrCl). <b><i>Results:</i></b> The final cohort included 7,315 African American and 15,102 non-African American adolescent females, with CrCl available for internal validation in 207 non-African American and 107 African American individuals. Compared with non-African American ethnicity, African American ethnicity was independently associated with a lower risk of hyperfiltration with standard eGFR calculations (odds ratio [OR] = 0.57, 95% confidence intervals [CIs] 0.45–0.71), associations were enhanced for absolute eGFR (OR = 0.81, 95% CI 0.69–0.95). Absolute eGFR values agreed better with CrCl (<i>r</i> = 0.63), compared to standard indexed eGFR formulae. Proportions classified as hyperfiltration changed with standard versus absolute eGFR; they were similar across BMI groups with the first and reflected obesity with the later. <b><i>Conclusion:</i></b> Adjusting to individual BSA improves estimation of GFR and identification of obesity-related hyperfiltration. More accurate and earlier ascertainment of obesity-related hyperfiltration may have important consequences for preservation of kidney function.
DOI: 10.6084/m9.figshare.19085444
title: Supplementary Material for: A hybrid machine learning model based on semantic information can optimize treatment decision for naïve single 3-5cm HCC patients

description: Abstract: Background: Tumor recurrence is an abomination for hepatocellular carcinoma (HCC) patients receiving local treatment. Purpose: To build a hybrid machine learning model to recommend optimized first treatment (Laparoscopic hepatectomy (LH) or Microwave ablation (MWA)) for naïve single 3-5cm HCC patients based on early recurrence (ER, ≤2 years) probability. Methods: This retrospective study collected 20 semantic variables of 582 patients (LH:300, MWA:282) from 13 hospitals with at least 24 months follow-up. Both groups were divided into training, validation and test set, respectively. Five algorithms (Logistics Regression, Random Forest, Neural Network, Stochastic Gradient Boosting (SGB) and eXtreme Gradient Boosting (XGB)) were used for model building. Model with highest AUC in validation set of LH and MWA was selected to connect as a hybrid model which made decision based on ER probability. Model testing was performed in a comprehensive set composing of LH and MWA test set. Results: Four variables in each group were selected to build LH and MWA model, respectively. LH-XGB model (AUC=0.744) and MWA-SGM (AUC=0.750) model were selected for model building. In comprehensive set, a treatment confusion matrix was established based on recommended and actual treatment. The predicted ER probabilities were comparable with the actual ER rates for various types of patients in matrix (p&gt;0.05). ER rate of patients whose actual treatment consistent with recommendation was lower than that of inconsistent patients (LH:21.2%vs46.2%, p=0.042; MWA:26.3%vs54.1%, p=0.048). By recommending optimal treatment, hybrid model can significantly reduce ER probability from 38.2% to 25.6% for overall patients (p&lt;0.001). Conclusions: The hybrid model can accurately predict ER probability of different treatments, and thereby provide a reliable evidence to make optimal treatment decision for patients with single 3-5cm HCC.
DOI: 10.6084/m9.figshare.19085444.v1
title: Supplementary Material for: A hybrid machine learning model based on semantic information can optimize treatment decision for naïve single 3-5cm HCC patients

description: Abstract: Background: Tumor recurrence is an abomination for hepatocellular carcinoma (HCC) patients receiving local treatment. Purpose: To build a hybrid machine learning model to recommend optimized first treatment (Laparoscopic hepatectomy (LH) or Microwave ablation (MWA)) for naïve single 3-5cm HCC patients based on early recurrence (ER, ≤2 years) probability. Methods: This retrospective study collected 20 semantic variables of 582 patients (LH:300, MWA:282) from 13 hospitals with at least 24 months follow-up. Both groups were divided into training, validation and test set, respectively. Five algorithms (Logistics Regression, Random Forest, Neural Network, Stochastic Gradient Boosting (SGB) and eXtreme Gradient Boosting (XGB)) were used for model building. Model with highest AUC in validation set of LH and MWA was selected to connect as a hybrid model which made decision based on ER probability. Model testing was performed in a comprehensive set composing of LH and MWA test set. Results: Four variables in each group were selected to build LH and MWA model, respectively. LH-XGB model (AUC=0.744) and MWA-SGM (AUC=0.750) model were selected for model building. In comprehensive set, a treatment confusion matrix was established based on recommended and actual treatment. The predicted ER probabilities were comparable with the actual ER rates for various types of patients in matrix (p&gt;0.05). ER rate of patients whose actual treatment consistent with recommendation was lower than that of inconsistent patients (LH:21.2%vs46.2%, p=0.042; MWA:26.3%vs54.1%, p=0.048). By recommending optimal treatment, hybrid model can significantly reduce ER probability from 38.2% to 25.6% for overall patients (p&lt;0.001). Conclusions: The hybrid model can accurately predict ER probability of different treatments, and thereby provide a reliable evidence to make optimal treatment decision for patients with single 3-5cm HCC.
DOI: 10.34740/kaggle/dsv/3404679
title: Data Science on Reddit
DOI: 10.25375/uct.19336586
title: Nominal CPUE for the catches taken by the Edinburgh off Tristan da Cunha island

description: The vessel Edinburgh (owned by Ovenstone Agencies) has been utilised to catch outstanding TAC at the Tristan da Cunha island since the 20073 season. Powerboats manned by islanders catch the bulk of the TAC each year, but increasingly due to weather constraints and falling catch rates, the Edinburgh has been catching the outstanding TAC at the island. The Edinburgh has had certain constraints imposed since the 2011 season, for example fishing must occur in waters deeper than 50 fathoms with no fishing to take place between Anchorstock Point and Big Point. The catch is landed using Monster traps.<br>A simple nominal CPUE is reported in Table 1 below, where the CPUE is the average catch per day each season by the Edinburgh.
DOI: 10.25375/uct.19336586.v1
title: Nominal CPUE for the catches taken by the Edinburgh off Tristan da Cunha island

description: The vessel Edinburgh (owned by Ovenstone Agencies) has been utilised to catch outstanding TAC at the Tristan da Cunha island since the 20073 season. Powerboats manned by islanders catch the bulk of the TAC each year, but increasingly due to weather constraints and falling catch rates, the Edinburgh has been catching the outstanding TAC at the island. The Edinburgh has had certain constraints imposed since the 2011 season, for example fishing must occur in waters deeper than 50 fathoms with no fishing to take place between Anchorstock Point and Big Point. The catch is landed using Monster traps.<br>A simple nominal CPUE is reported in Table 1 below, where the CPUE is the average catch per day each season by the Edinburgh.