cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.22723243
title: Supplemental Data (RNA-seq Data)

description: Full data of RNAseq-based gene expression profiling
DOI: 10.5523/bris.2elwx7gwh5of1cean5iht89cm
title: A projector embedding approach for multiscale coupled-cluster calculations applied to citrate synthase.

description: Projector-based embedding has recently emerged as a robust and accurate method for the calculation of various electronic molecular properties. We present the coupling of projector embedding with quantum mechanical / molecular mechanical (QM/MM) modeling and apply it for the first time to an enzyme-catalyzed reaction. Using projector-based embedding, we combine coupled-cluster theory, density-functional theory (DFT) and molecular mechanics to compute energies for the proton abstraction from acetyl-coenzyme A by citrate synthase. By embedding correlated ab initio methods in DFT we eliminate functional sensitivity and obtain high-accuracy profiles in a procedure that is straightforward to apply.
DOI: 10.5281/zenodo.7879937
title: PASS - A SCORING SYSTEM TO EVALUATE PERSISTENT ACUTE KIDNEY INJURY IN CRITICALLY ILL ADULT ICU PATIENTS

description: Acute Kidney Injury (AKI) and sepsis is highly prevalent in Intensive Care Units (ICU) and has direct effect on mortality and morbidity. We aimed to evaluate if course and recovery of sepsis-AKI can be predicted using variables collected at the time of admission, and to develop a formula - Persistent AKI Scoring System (PASS) to predict recovery. 63 consecutive patients admitted for sepsis-AKI in our ICUs were evaluated over a period of 8 months. Baseline demographic and clinical/laboratory parameters ,including serum creatinine (SCr), Base Excess (BE), Plethysmographic Variability Index (PVI), Caval Index, R wave Variability (RVI), Mean Arterial Pressure (MAP) and Renal Resistivity Index (RI) using renal doppler and need for inotropes were assessed on admission. Patients were managed with standard protocols with fluid resuscitation and other specific therapy. After 6 of hours of fluid resuscitation, hemodynamic status and volume status of the patient was assessed. Patients were classified as Volume Responders or Non-responders depending on hemodynamic stabilization using parameters like MAP. Re- assessment of all the variables was done at 24 hours and 72 hours after admission. The primary outcome was persistent AKI after 72hrs. The secondary outcome was initiation of dialysis and death within 15 days of admission. 34 subjects recovered from AKI. 32 patients showed volume response with respect to hemodynamic parameters and 31 patients were non-responders. Response to fluid, MAP at admission, MAP after 6 hours, BE at admission, inotrope requirement, and PVI at admission did not correlate with recovery of AKI. RI, RVI and S Cr at admission significantly correlated with recovery from AKI These 3 parameters were evaluated further to develop the model for predicting recovery from AKI.• Score&gt; 7.8 predicted recovery from AKI with sensitivity of 79.4%, specificity of 72.4%. PPV 81.8% and NPV 76.7%. PASS Score&gt;7.8 had AUROC of 0.85. Multiple logistic regression showed that SCr&lt; 2.36 mg%, RVI &gt; 14.45 and RI &lt; 0.8 on admission correlated with recovery. PASS SCORE Formula was created using the same. CONCLUSION: PASS score can be a simple bed side method to reliably identify salvageable cases of sepsis-AKI, guiding fluid resuscitation and also aiding early referrals from rural to higher centers for better management.
DOI: 10.34740/kaggle/dsv/5564124
title: AutoXGB
DOI: 10.17632/j8wd9dj86m.1
title: Late Neoarchean magmatism and crustal growth in northern Liaoning: evidence from zircon U-Pb geochronology and petro-geochemistry of the Qingyuan trondhjemites

description: Data accompanying " Late Neoarchean magmatism and crustal growth in northern Liaoning: evidence from zircon U-Pb geochronology and petro-geochemistry of the Qingyuan trondhjemites" by LL Yuan, J Liu, XH Zhang, ZL Yang, H Zhang, HB Li. Petrologica Sinica, 2020, 36(2).
DOI: 10.6084/m9.figshare.22723405
title: Data for Ortiz et al. 2023. AJB. Florivory in the Pantanal

description: <em>Premises of the study</em>: Floral damage caused by florivores often has negative consequences for plant reproduction. However, the factors affecting plant-florivore interactions are still poorly understood, especially the role of abiotic factors and interspecific variation in florivory within ecosystems. Thus, it is largely required to investigate the patterns of florivory levels and its consequences for plant communities. <em>Methods</em>: We assessed the influence of abiotic factors related to climatic seasonality, of phylogenetic relationships among plants, and of functional attributes associated with attractiveness to pollinators on florivory levels (incidence and intensity) in the Pantanal, the world’s largest tropical wetland. Between December 2020 and November 2021, the percentage of both flowers attacked (incidence) and area removed from petals (intensity) by florivores were examined in 51 species from 25 families, considering flowering season, the substrate where the plants occur, and floral attributes as potentially determining factors on florivory levels. <em>Key results</em>: Phylogeny and environmental factors did not have a significant influence on florivory. The only determinant of interspecific variation in florivory incidence and intensity was flower size, where larger flowers experienced higher florivory levels regardless of season and substrate, while flower arrangement and color were not significant factors. <em>Conclusions</em>: Our study is one of the first to estimate the community-wide effects of biotic and abiotic factors on both the incidence and the intensity of florivory. The magnitude of this plant-florivore interaction may reduce reproductive success and entail selective pressures on plant attractiveness to pollinators.
DOI: 10.6084/m9.figshare.22723432.v1
title: Extended Data 1: Biodiversity RIs & Projects

description: A non-exhaustive list of biodiversity research infrastructures, collected as part of the development of the ELIXIR Biodiversity Community white paper 2022-2023.
DOI: 10.5519/qd.bdfzr138
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (10 records)
DOI: 10.34740/kaggle/dsv/5564680
title: YouTube Daily
DOI: 10.48366/r585679
title: Review on ICT for education of children with special needs: relevant features and expert evaluation design

description: Contributions were analyzed on features of ICTs used in education of students with special needs. The analysis had two directions: (1) ICT relevant features and (2) review design for evaluation of the ICT.
DOI: 10.6084/m9.figshare.19636617
title: Data from: Bombardiers and assassins: mimetic interactions between unequally defended insects

description: Sugiura S, Hayashi M (2023) Bombardiers and assassins: mimetic interactions between unequally defended insects. Peerj (https://doi.org/10.7717/peerj.15380)
DOI: 10.5281/zenodo.7880095
title: Graph-Pan vitis

description: Vitis genome and annotation
DOI: 10.5281/zenodo.7880094
title: Graph-Pan vitis

description: Vitis genome and annotation
DOI: 10.6084/m9.figshare.22721086
title: Data_and_codes.zip

description: Data and codes for HMSC and Zonation.
DOI: 10.6084/m9.figshare.22721086.v1
title: Data_and_codes.zip

description: Data and codes for HMSC and Zonation.
DOI: 10.5519/qd.lv0xrzu7
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1001 records)
DOI: 10.34740/kaggle/dsv/5565372
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.34740/kaggle/dsv/5565382
title: UNET Lung Segmentation Weights for Chest X Rays
DOI: 10.34740/kaggle/dsv/5565417
title: Egypt Gold Prices | Daily Updated
DOI: 10.34740/kaggle/dsv/5565524
title: 2021 World Population (updated daily)
DOI: 10.6084/m9.figshare.22723465.v1
title: Book Chapter_Data

description: This data is intended for the purpose of the book chapter tutorial.
DOI: 10.34740/kaggle/dsv/5565598
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5281/zenodo.6354727
title: Designing single-cell experiments to harvest fluctuation information while rejecting measurement noise

description: Numerical simulation outputs and model schematic figures for the Python portion (main text 3.1 and Supporting Information) in the preprint <em>Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise</em> Huy D. Vo, Linda Forero, Luis Aguilera, Brian Munsky bioRxiv doi: https://doi.org/10.1101/2021.05.11.443611
DOI: 10.5281/zenodo.6354728
title: Designing single-cell experiments to harvest fluctuation information while rejecting measurement noise

description: Numerical simulation outputs in the preprint <em>Designing single-cell experiments to harvest fluctuation information while rejecting measurement noise</em> Huy D. Vo, Brian Munsky bioRxiv 2021.05.11.443611.
DOI: 10.5281/zenodo.7880494
title: Designing single-cell experiments to harvest fluctuation information while rejecting measurement noise

description: Numerical simulation outputs and model schematic figures for the Python portion (main text 3.1 and Supporting Information) in the preprint <em>Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise</em> Huy D. Vo, Linda Forero, Luis Aguilera, Brian Munsky bioRxiv doi: https://doi.org/10.1101/2021.05.11.443611
DOI: 10.6084/m9.figshare.14935255
title: Adjusting for Measurement Noninvariance with Alignment in Growth Modeling

description: Longitudinal measurement invariance—the consistency of measurement in data collected over time—is a prerequisite for any meaningful inferences of growth patterns. When one or more items measuring the construct of interest show noninvariant measurement properties over time, it leads to biased parameter estimates and inferences on the growth parameters. In this paper, I extend the recently developed alignment-within-confirmatory factor analysis (AwC) technique to adjust for measurement biases for growth models. The proposed AwC method does not require a priori knowledge of noninvariant items and the iterative searching of noninvariant items in typical longitudinal measurement invariance research. Results of a Monte Carlo simulation study comparing AwC with the partial invariance modeling method show that AwC largely reduces biases in growth parameter estimates and gives good control of Type I error rates, especially when the sample size is at least 1,000. It also outperforms the partial invariance method in conditions when all items are noninvariant. However, all methods give biased growth parameter estimates when the proportion of noninvariant parameters is over 25%. Based on the simulation results, I conclude that AO is a viable alternative to the partial invariance method in growth modeling when it is not clear whether longitudinal measurement invariance holds. The current paper also demonstrates AwC in an example modeling neuroticism over three time points using a public data set, which shows how researchers can compute effect size indices for noninvariance in AwC to assess to what degree invariance holds and whether AwC results are trustworthy.
DOI: 10.6084/m9.figshare.14935255.v1
title: Adjusting for Measurement Noninvariance with Alignment in Growth Modeling

description: Longitudinal measurement invariance—the consistency of measurement in data collected over time—is a prerequisite for any meaningful inferences of growth patterns. When one or more items measuring the construct of interest show noninvariant measurement properties over time, it leads to biased parameter estimates and inferences on the growth parameters. In this paper, I extend the recently developed alignment-within-confirmatory factor analysis (AwC) technique to adjust for measurement biases for growth models. The proposed AwC method does not require a priori knowledge of noninvariant items and the iterative searching of noninvariant items in typical longitudinal measurement invariance research. Results of a Monte Carlo simulation study comparing AwC with the partial invariance modeling method show that AwC largely reduces biases in growth parameter estimates and gives good control of Type I error rates, especially when the sample size is at least 1,000. It also outperforms the partial invariance method in conditions when all items are noninvariant. However, all methods give biased growth parameter estimates when the proportion of noninvariant parameters is over 25%. Based on the simulation results, I conclude that AO is a viable alternative to the partial invariance method in growth modeling when it is not clear whether longitudinal measurement invariance holds. The current paper also demonstrates AwC in an example modeling neuroticism over three time points using a public data set, which shows how researchers can compute effect size indices for noninvariance in AwC to assess to what degree invariance holds and whether AwC results are trustworthy.
DOI: 10.5281/zenodo.7880574
title: cephus_rjabovi_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7880573
title: cephus_rjabovi_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7880592
title: desmognathus_monticola_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7880591
title: desmognathus_monticola_zanfona_1.fasta.gz

description: .
DOI: 10.5517/ccdc.csd.cc29wt23
title: CCDC 2141885: Experimental Crystal Structure Determination

description: Related Article: Yu-Xuan Chen, Tao Huo, Quan Yin, Ling-Feng Jiang, Xuan Cheng, Hong-Xiang Ma, Yu-Xuan Jiang, Mei-Zhi Sun, Qing-Hai Deng|2023|Org.Lett.|25|2739|doi:10.1021/acs.orglett.3c01005
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.cc2bcjpy
title: CCDC 2156040: Experimental Crystal Structure Determination

description: Related Article: Xiushang Xu, Satoshi Takebayashi, Hiroki Hanayama, Serhii Vasylevskyi, Takatsugu Onishi, Tatsuhiko Ohto, Hirokazu Tada, Akimitsu Narita|2023|J.Am.Chem.Soc.|145|3891|doi:10.1021/jacs.2c13890
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.cc2bjpq9
title: CCDC 2161001: Experimental Crystal Structure Determination

description: Related Article: Arthur H. G. David, Amine Garci, Seifallah Abid, Xuesong Li, Ryan M. Young, James S. W. Seale, Jessica E. Hornick, Chandra S. Azad, Yang Jiao, Indranil Roy, Isil Akpinar, Tanay Kesharwani, Charlotte L. Stern, Michael R. Wasielewski, J. Fraser Stoddart|2023|J.Am.Chem.Soc.|145|9182|doi:10.1021/jacs.3c01244
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.cc2bjzxs
title: CCDC 2161286: Experimental Crystal Structure Determination

description: Related Article: Tobias Theiss, Stefan Buss, Iván Maisuls, Rafael López-Arteaga, Dana Brünink, Jutta Kösters, Alexander Hepp, Nikos L. Doltsinis, Emily A. Weiss, Cristian A. Strassert|2023|J.Am.Chem.Soc.|145|3937|doi:10.1021/jacs.2c09775
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.cc2cmk5r
title: CCDC 2193534: Experimental Crystal Structure Determination

description: Related Article: Simon Suhr, Nicolai Schröter, Merlin Kleoff, Nicolas Neuman, David Hunger, Robert Walter, Clemens Lücke, Felix Stein, Serhiy Demeshko, Hang Liu, Hans-Ulrich Reissig, Joris van Slageren, Biprajit Sarkar|2023|Inorg.Chem.|62|6375|doi:10.1021/acs.inorgchem.3c00253
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.cc2cp68k
title: CCDC 2195118: Experimental Crystal Structure Determination

description: Related Article: Simon Suhr, Nicolai Schröter, Merlin Kleoff, Nicolas Neuman, David Hunger, Robert Walter, Clemens Lücke, Felix Stein, Serhiy Demeshko, Hang Liu, Hans-Ulrich Reissig, Joris van Slageren, Biprajit Sarkar|2023|Inorg.Chem.|62|6375|doi:10.1021/acs.inorgchem.3c00253
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.cc2cp6bm
title: CCDC 2195120: Experimental Crystal Structure Determination

description: Related Article: Simon Suhr, Nicolai Schröter, Merlin Kleoff, Nicolas Neuman, David Hunger, Robert Walter, Clemens Lücke, Felix Stein, Serhiy Demeshko, Hang Liu, Hans-Ulrich Reissig, Joris van Slageren, Biprajit Sarkar|2023|Inorg.Chem.|62|6375|doi:10.1021/acs.inorgchem.3c00253
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.cc2cp6dp
title: CCDC 2195122: Experimental Crystal Structure Determination

description: Related Article: Simon Suhr, Nicolai Schröter, Merlin Kleoff, Nicolas Neuman, David Hunger, Robert Walter, Clemens Lücke, Felix Stein, Serhiy Demeshko, Hang Liu, Hans-Ulrich Reissig, Joris van Slageren, Biprajit Sarkar|2023|Inorg.Chem.|62|6375|doi:10.1021/acs.inorgchem.3c00253
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.cc2cws9c
title: CCDC 2201443: Experimental Crystal Structure Determination

description: Related Article: Runze Lin, P. K. Hashim, Saugata Sahu, Ammathnadu S. Amrutha, Nusaiba Madappuram Cheruthu, Shakkeeb Thazhathethil, Kiyonori Takahashi, Takayoshi Nakamura, Takashi Kikukawa, Nobuyuki Tamaoki|2023|J.Am.Chem.Soc.|145|9072|doi:10.1021/jacs.3c00609
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.cc2cwsbd
title: CCDC 2201444: Experimental Crystal Structure Determination

description: Related Article: Runze Lin, P. K. Hashim, Saugata Sahu, Ammathnadu S. Amrutha, Nusaiba Madappuram Cheruthu, Shakkeeb Thazhathethil, Kiyonori Takahashi, Takayoshi Nakamura, Takashi Kikukawa, Nobuyuki Tamaoki|2023|J.Am.Chem.Soc.|145|9072|doi:10.1021/jacs.3c00609
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.cc2cwscf
title: CCDC 2201445: Experimental Crystal Structure Determination

description: Related Article: Runze Lin, P. K. Hashim, Saugata Sahu, Ammathnadu S. Amrutha, Nusaiba Madappuram Cheruthu, Shakkeeb Thazhathethil, Kiyonori Takahashi, Takayoshi Nakamura, Takashi Kikukawa, Nobuyuki Tamaoki|2023|J.Am.Chem.Soc.|145|9072|doi:10.1021/jacs.3c00609
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.cc2cwsdg
title: CCDC 2201446: Experimental Crystal Structure Determination

description: Related Article: Runze Lin, P. K. Hashim, Saugata Sahu, Ammathnadu S. Amrutha, Nusaiba Madappuram Cheruthu, Shakkeeb Thazhathethil, Kiyonori Takahashi, Takayoshi Nakamura, Takashi Kikukawa, Nobuyuki Tamaoki|2023|J.Am.Chem.Soc.|145|9072|doi:10.1021/jacs.3c00609
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.cc2df87c
title: CCDC 2217282: Experimental Crystal Structure Determination

description: Related Article: Yingwei Li, Hong Ki Kim, Ryan D. McGillicuddy, Shao-Liang Zheng, Kevin J. Anderton, Grant J. Stec, Jaehyeong Lee, Dongtao Cui, Jarad A. Mason|2023|J.Am.Chem.Soc.|145|9304|doi:10.1021/jacs.3c02458
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.cc2df88d
title: CCDC 2217283: Experimental Crystal Structure Determination

description: Related Article: Yingwei Li, Hong Ki Kim, Ryan D. McGillicuddy, Shao-Liang Zheng, Kevin J. Anderton, Grant J. Stec, Jaehyeong Lee, Dongtao Cui, Jarad A. Mason|2023|J.Am.Chem.Soc.|145|9304|doi:10.1021/jacs.3c02458
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.