cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2djs91
title: CCDC 2220663: Experimental Crystal Structure Determination

description: Related Article: Philipp Royla, Kai Schwedtmann, Zeyu Han, Jannis Fidelius, Derek P. Gates, Rosa M. Gomila, Antonio Frontera, Jan J. Weigand|2023|J.Am.Chem.Soc.|145|10364|doi:10.1021/jacs.3c02256
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.cc2dp3qy
title: CCDC 2224830: Experimental Crystal Structure Determination

description: Related Article: Jiaquan Li, Yanshan Zhu, Chang Zhang, Dezhi Yang, Yang Lu, Zhengzheng Zhou|2023|J.Mol.Liq.|382|121929|doi:10.1016/j.molliq.2023.121929
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.cc2dp3rz
title: CCDC 2224831: Experimental Crystal Structure Determination

description: Related Article: Jiaquan Li, Yanshan Zhu, Chang Zhang, Dezhi Yang, Yang Lu, Zhengzheng Zhou|2023|J.Mol.Liq.|382|121929|doi:10.1016/j.molliq.2023.121929
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.7041675
title: ANON-OLD-for-review

description: Repository for review only.
DOI: 10.5281/zenodo.7041674
title: ANON-OLD-for-review

description: Repository for review only.
DOI: 10.6084/m9.figshare.23248199
title: Processed SpatialTranscriptomics Data (Goyal et. al)

description: GeoMx spatial transcriptomics processed datasets. All raw data from the GeoMx spatial transcriptomics used in this manuscript can be found at BioProject Accession <strong>PRJNA976929</strong>.
DOI: 10.5517/ccdc.csd.cc2dp3s0
title: CCDC 2224832: Experimental Crystal Structure Determination

description: Related Article: Jiaquan Li, Yanshan Zhu, Chang Zhang, Dezhi Yang, Yang Lu, Zhengzheng Zhou|2023|J.Mol.Liq.|382|121929|doi:10.1016/j.molliq.2023.121929
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.cc2f9kt4
title: CCDC 2243526: Experimental Crystal Structure Determination

description: Related Article: Philipp Royla, Kai Schwedtmann, Zeyu Han, Jannis Fidelius, Derek P. Gates, Rosa M. Gomila, Antonio Frontera, Jan J. Weigand|2023|J.Am.Chem.Soc.|145|10364|doi:10.1021/jacs.3c02256
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.cc2f9kv5
title: CCDC 2243527: Experimental Crystal Structure Determination

description: Related Article: Philipp Royla, Kai Schwedtmann, Zeyu Han, Jannis Fidelius, Derek P. Gates, Rosa M. Gomila, Antonio Frontera, Jan J. Weigand|2023|J.Am.Chem.Soc.|145|10364|doi:10.1021/jacs.3c02256
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.cc2f9kw6
title: CCDC 2243528: Experimental Crystal Structure Determination

description: Related Article: Philipp Royla, Kai Schwedtmann, Zeyu Han, Jannis Fidelius, Derek P. Gates, Rosa M. Gomila, Antonio Frontera, Jan J. Weigand|2023|J.Am.Chem.Soc.|145|10364|doi:10.1021/jacs.3c02256
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.cc2fffcp
title: CCDC 2247232: Experimental Crystal Structure Determination

description: Related Article: Márton A. Kiss, Miroslav Peřina, Laura Bereczki, Ádám Baji, Jakub Bělíček, Radek Jorda, Éva Frank|2023|J.Steroid Biochem.Mol.Biol.|231|106315|doi:10.1016/j.jsbmb.2023.106315
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: 10.3886/e171621v1-139185
title: National Poll on Healthy Aging (NPHA), [United States], December 2019

description: By tapping into the perspectives of older adults and their caregivers, the University of Michigan National Poll on Healthy Aging (NPHA) helps inform the public, health care providers, policymakers, and advocates on issues related to health, health care and health policy affecting Americans 50 years of age and older. The poll is designed as a recurring, nationally representative household survey of U.S. adults, which allows assessment of issues in a timely fashion. Launched in spring 2017, the NPHA is modeled after the highly successful University of Michigan C.S. Mott Children's Hospital National Poll on Children's Health. The NPHA grew out of a strong interest in aging-related issues among many members of the University of Michigan Institute for Healthcare Policy and Innovation (IHPI), which brings together more than 600 faculty who study health, health care and the impacts of health policy. IHPI directs the poll which is sponsored by AARP and Michigan Medicine, the University of Michigan academic medical center.<br>
description: Three survey weights are included with the dataset, one for the full population of 50-80 year old respondents, one for those 50-64, and one for those ages 65-80. Weights should be applied to interpret population-level rates. See user guide for additional information about the weights.
description: Response Rates: The main survey completion rate for respondents ages 50-64 was 76%. The main survey completion rate for respondents ages 65-80 was 78%. Overall completion rate was 77%
description: Survey respondents were sampled from a nationally representative panel (KnowledgePanel from Ipsos, formerly GfK). See user guide for details about respondent selection and sampling.
description: Adults ages 50-80 living in non-institutionalized settings in the United States of America.<br>. Smallest Geographic Unit: Smallest geographic unit: State
description: The data files listed in this study for NPHA Wave 6 represent the same variables/content, but have been converted to a variety of file formats for user convenience. <br><br>NPHA Wave 1 can be found by visiting the study page, National Poll on Healthy Aging (NPHA), [United States], April 2017 (ICPSR 37305): https://doi.org/10.3886/ICPSR37305<br><br>NPHA Wave 2 can be found by visiting the study page, National Poll on Healthy Aging (NPHA), [United States], October 2017 (Open ICPSR): https://doi.org/10.3886/E116171<br><br>NPHA Wave 3 can be found by visiting the study page, National Poll on Healthy Aging (NPHA), [United States], March 2018 (Open ICPSR): https://doi.org/10.3886/E130461<br><br>NPHA Wave 4 can be found by visiting the study page, National Poll on Healthy Aging (NPHA), [United States], October 2018 (Open ICPSR): https://doi.org/10.3886/E142542<br><br>NPHA Wave 5 can be found by visiting the study page, National Poll on Healthy Aging (NPHA), [United States], May 2019 (Open ICPSR): https://doi.org/10.3886/E142661V2<br>
DOI: 10.3886/e185802v2
title: ECIN Replication Package for "Anticipating the Honeymoon: Event Study Estimation of New Stadium Effects in Major League Baseball Using the Imputation Method"

description: This paper estimates the impact of new stadiums on attendance and revenues of Major League Baseball teams between 1970 and 2019. Recent studies have shown that conventional two-way fixed effects (TWFE) models may produce biased estimate and propose an “imputation” method instead. This paper uses the imputation method to generate a counterfactual estimate, based on untreated observations, so that the average treatment effect on the treated (ATT) equals the difference between actual and counterfactual estimates. Additionally, almost ideal unconditional estimates of the ATT are calculated, using a subset of the control group consisting of games played in the same season against treated teams. These approaches reveal that there were significant anticipation effects associated with new stadiums, up to three seasons before opening. They suggest that previous estimates may significantly understate the revenue gains from new stadiums.
description: Major League Baseball teams.
description: web scraping;
DOI: 10.3886/e185923v2
title: ECIN Replication Package for "Adams and Eves: High School Math and the Gender Gap in Economics Majors"

description: Why so few women graduate in Economics? We investigate the gender gap amongItalian university graduates in Economics between 2010 and 2019. With women'sprobability of graduating in Economics being 27 percent lower than men's, the gapis larger than in Business and even STEM. The association between the gender gapand the mathematical content of high school curricula is especially strong in Economics. A triple difference analysis shows that a reform raising the mathematicalcontent of traditionally low math curricula caused an increase in the gender gap,with women's probability of graduating in Economics decreasing by 12 percentagepoints.
DOI: 10.3886/e187383v1
title: COEP Replication Package for "Methods in Open Policy Analysis: An Application to California's Building Energy Codes"

description: Research data and code files for "Methods in Open Policy Analysis: A Case Study of California's Building Energy Codes," to be published by Contemporary Economic Policy.<br>
DOI: 10.34740/kaggle/dsv/5793330
title: Airbnb Listing Data 2023
DOI: 10.34740/kaggle/dsv/5793564
title: arXiv Dataset
DOI: 10.34740/kaggle/dsv/5793697
title: Youtube Trending Video - Taiwan & Hong Kong
DOI: 10.34740/kaggle/dsv/5793796
title: US Accidents (2016 - 2021)
DOI: 10.17909/x16g-g718
title: NIRSpec IFU: Deuterated PAHs, PAH-nitriles, and PAH Overtone and Combination Bands

description: STScI reduced data set for the observations of JWST Cycle 1 program 1591.
DOI: 10.25582/data.2022-05.2524023/1910311
title: Data for EMSL Project 51134 from May 2022
DOI: 10.25582/data.2022-05.2523947/1910319
title: Data for EMSL Project 51134 from May 2022
DOI: 10.25582/data.2022-05.2523948/1910330
title: Data for EMSL Project 51134 from May 2022
DOI: 10.25582/data.2022-05.2523896/1910332
title: Data for EMSL Project 51134 from May 2022
DOI: 10.25582/data.2022-05.2523915/1910337
title: Data for EMSL Project 51134 from May 2022
DOI: 10.25582/data.2022-05.2524003/1910360
title: Data for EMSL Project 51134 from May 2022
DOI: 10.25582/data.2022-05.2523913/1910374
title: Data for EMSL Project 51134 from May 2022
DOI: 10.25582/data.2022-05.2523935/1910381
title: Data for EMSL Project 51134 from May 2022
DOI: 10.25582/data.2022-05.2523972/1910383
title: Data for EMSL Project 51134 from May 2022
DOI: 10.25582/data.2022-05.2524009/1910384
title: Data for EMSL Project 51134 from May 2022
DOI: 10.25582/data.2022-05.2523895/1910392
title: Data for EMSL Project 51134 from May 2022
DOI: 10.25582/data.2022-05.2523956/1910407
title: Data for EMSL Project 51134 from May 2022
DOI: 10.25582/data.2022-05.2524010/1910410
title: Data for EMSL Project 51134 from May 2022
DOI: 10.25582/data.2022-05.2524025/1910418
title: Data for EMSL Project 51134 from May 2022
DOI: 10.25582/data.2022-05.2523986/1910422
title: Data for EMSL Project 51134 from May 2022
DOI: 10.25582/data.2022-05.2523996/1910443
title: Data for EMSL Project 51134 from May 2022
DOI: 10.25582/data.2022-05.2523925/1910444
title: Data for EMSL Project 51134 from May 2022
DOI: 10.25582/data.2022-05.2523971/1910454
title: Data for EMSL Project 51134 from May 2022
DOI: 10.25582/data.2022-05.2523936/1910458
title: Data for EMSL Project 51134 from May 2022
DOI: 10.25582/data.2022-05.2523923/1910460
title: Data for EMSL Project 51134 from May 2022
DOI: 10.25582/data.2022-05.2523987/1910464
title: Data for EMSL Project 51134 from May 2022
DOI: 10.25582/data.2022-05.2523960/1910468
title: Data for EMSL Project 51134 from May 2022
DOI: 10.25582/data.2022-05.2523880/1932694
title: Data for EMSL Project 51575 from May 2022
DOI: 10.25582/data.2022-05.2523889/1932835
title: Data for EMSL Project 51533 from May 2022
DOI: 10.25582/data.2022-05.2523917/1932845
title: Data for EMSL Project 51533 from May 2022
DOI: 10.6084/m9.figshare.23250641.v1
title: Frequency of occurrence of keywords in journal articles of biological sciences

description: Keywords are commonly used to describe the main concepts of a journal article. Hence, do they follow a statistical distribution in terms of frequency of occurrence in the article? This work sets out to elucidate if frequency of occurrence of keywords follows a statistical distribution as well as whether the authors draw on the statistical occurrence of the concept to propose keywords. Using an in-house MATLAB machine reading software, the frequency of occurrence of keywords in the main text of 30 journal articles was tabulated. Results reveal that at least one of the authors’ proposed keywords is of high frequency of occurrence. However, occurrence frequency of other keywords largely depends on the scholastic style of the author where there is a substantial number of articles with keywords of low frequency of occurrence. These are likely important concepts of low frequency of occurrence.
DOI: 10.6084/m9.figshare.23250641
title: Frequency of occurrence of keywords in journal articles of biological sciences

description: Keywords are commonly used to describe the main concepts of a journal article. Hence, do they follow a statistical distribution in terms of frequency of occurrence in the article? This work sets out to elucidate if frequency of occurrence of keywords follows a statistical distribution as well as whether the authors draw on the statistical occurrence of the concept to propose keywords. Using an in-house MATLAB machine reading software, the frequency of occurrence of keywords in the main text of 30 journal articles was tabulated. Results reveal that at least one of the authors’ proposed keywords is of high frequency of occurrence. However, occurrence frequency of other keywords largely depends on the scholastic style of the author where there is a substantial number of articles with keywords of low frequency of occurrence. These are likely important concepts of low frequency of occurrence.
DOI: 10.6084/m9.figshare.23250629
title: OCPall

description: OCP IS2RE all dataset https://github.com/Open-Catalyst-Project/ocp/blob/main/MODELS.md#is2re-models
DOI: 10.18112/openneuro.ds004408.v1.0.2
title: Invariant encoding of phonemes in neural responses to continuous speech
DOI: 10.6084/m9.figshare.23250714
title: Additional file 4 of Compliance with national snakebite treatment guidelines in rural Sri Lankan hospitals: a cluster randomized controlled trial of a brief educational intervention

description: Additional file 4: Dataset_RCTscore.
DOI: 10.6084/m9.figshare.23250714.v1
title: Additional file 4 of Compliance with national snakebite treatment guidelines in rural Sri Lankan hospitals: a cluster randomized controlled trial of a brief educational intervention

description: Additional file 4: Dataset_RCTscore.
DOI: 10.6084/m9.figshare.23250717
title: Additional file 5 of Compliance with national snakebite treatment guidelines in rural Sri Lankan hospitals: a cluster randomized controlled trial of a brief educational intervention

description: Additional file 5: Dataset_Transfer.
DOI: 10.6084/m9.figshare.23250717.v1
title: Additional file 5 of Compliance with national snakebite treatment guidelines in rural Sri Lankan hospitals: a cluster randomized controlled trial of a brief educational intervention

description: Additional file 5: Dataset_Transfer.
DOI: 10.6084/m9.figshare.23250720
title: Additional file 6 of Compliance with national snakebite treatment guidelines in rural Sri Lankan hospitals: a cluster randomized controlled trial of a brief educational intervention

description: Additional file 6: Dataset_ Managed.
DOI: 10.6084/m9.figshare.23250720.v1
title: Additional file 6 of Compliance with national snakebite treatment guidelines in rural Sri Lankan hospitals: a cluster randomized controlled trial of a brief educational intervention

description: Additional file 6: Dataset_ Managed.
DOI: 10.6084/m9.figshare.23250744
title: Additional file 1 of Genome-wide DNA methylation profiling of HPV-negative leukoplakia and gingivobuccal complex cancers

description: Additional file 1. Table S1: Literature of genome wide DNA methylation analysis in OSCC. Table-S2: Detailed demographic and clinicopathological characteristics of the leukoplakia samples. Table S3: Detailed demographic clinicopathological and characteristics of the OSCC samples. Table S4: Summary of genomewide differentially methylated regions. Table S5: Top 20 differentially methylated CpG sites identified across the leukoplakia samples. Table S6: Top 20 differentially methylated CpG sites identified across the OSCC samples. Table S7: Top 20 differentially methylated CpG islands identified across the leukoplakia samples. Table S8: Top 20 differentially methylated CpG islands identified across the OSCC samples. Table S9: Top 20 differentially hypermethylated and hypomethylated promoters identified across the leukoplakia samples. Table S10: Top 20 differentially hypermethylated and hypomethylated promoters identified across the OSCC samples. Table S11: Top 20 differentially hypermethylated and hypomethylated genes identified across the leukoplakia samples. Table S12: Top 20 differentially hypermethylated and hypomethylated genes identified across the OSCC samples. Table S13: Gene enrichment analysis of top 100 differentially methylated promoters in leukoplakia based on combined rank. Table S14: Gene enrichment analysis of top 100 differentially methylated promoters in OSCC based on combined rank. Table S15: List of differentially methylated promoters common between leukoplakia and OSCC. Table S16: Candidate target gene list obtained from integrative analysis of copy number, gene expression, and DNA methylation data using CNAmet. Table S17: Comparison of our data with TCGA_HNSC cohort. Table S18: Details for pyrosequencing primers. Table S19: Copy number alteration distribution among candidate genes. Table S20: Association between biomarkers and clinicopathological parameters. Table S21: Correlation among different targets. Table S22: Univariate Cox analysis of the association between markers and clinical outcome. Table S23: Univariate Cox analysis of the association between markers and clinical outcome in N0 and N+ group. Table S24: Univariate Cox analysis of the association between markers and clinical outcome in early-stage and advanced-stage groups. Table S25: Prognostic significance of biomarkers based on nodal status and stage of the disease. Table S26: Summary of genomewide differentially methylated regions as a function of deltathreshold. Table S27: Details for TaqMan qPCR CNV assays.
DOI: 10.6084/m9.figshare.23250744.v1
title: Additional file 1 of Genome-wide DNA methylation profiling of HPV-negative leukoplakia and gingivobuccal complex cancers

description: Additional file 1. Table S1: Literature of genome wide DNA methylation analysis in OSCC. Table-S2: Detailed demographic and clinicopathological characteristics of the leukoplakia samples. Table S3: Detailed demographic clinicopathological and characteristics of the OSCC samples. Table S4: Summary of genomewide differentially methylated regions. Table S5: Top 20 differentially methylated CpG sites identified across the leukoplakia samples. Table S6: Top 20 differentially methylated CpG sites identified across the OSCC samples. Table S7: Top 20 differentially methylated CpG islands identified across the leukoplakia samples. Table S8: Top 20 differentially methylated CpG islands identified across the OSCC samples. Table S9: Top 20 differentially hypermethylated and hypomethylated promoters identified across the leukoplakia samples. Table S10: Top 20 differentially hypermethylated and hypomethylated promoters identified across the OSCC samples. Table S11: Top 20 differentially hypermethylated and hypomethylated genes identified across the leukoplakia samples. Table S12: Top 20 differentially hypermethylated and hypomethylated genes identified across the OSCC samples. Table S13: Gene enrichment analysis of top 100 differentially methylated promoters in leukoplakia based on combined rank. Table S14: Gene enrichment analysis of top 100 differentially methylated promoters in OSCC based on combined rank. Table S15: List of differentially methylated promoters common between leukoplakia and OSCC. Table S16: Candidate target gene list obtained from integrative analysis of copy number, gene expression, and DNA methylation data using CNAmet. Table S17: Comparison of our data with TCGA_HNSC cohort. Table S18: Details for pyrosequencing primers. Table S19: Copy number alteration distribution among candidate genes. Table S20: Association between biomarkers and clinicopathological parameters. Table S21: Correlation among different targets. Table S22: Univariate Cox analysis of the association between markers and clinical outcome. Table S23: Univariate Cox analysis of the association between markers and clinical outcome in N0 and N+ group. Table S24: Univariate Cox analysis of the association between markers and clinical outcome in early-stage and advanced-stage groups. Table S25: Prognostic significance of biomarkers based on nodal status and stage of the disease. Table S26: Summary of genomewide differentially methylated regions as a function of deltathreshold. Table S27: Details for TaqMan qPCR CNV assays.
DOI: 10.6084/m9.figshare.22147283.v4
title: Dataset for paper "Overestimated nitrogen loss from denitrification for natural terrestrial ecosystems in CMIP6 Earth System Models" authored by Feng et al. (2023) (https://doi.org/10.1038/s41467-023-38803-z)

description: This is the dataset for paper "Overestimated nitrogen loss from denitrification for natural terrestrial ecosystems in CMIP6 Earth System Models" authored by Feng et al. <br> The dataset contains six .nc files, and each file contains soil_N15, fgas, and N loss and their quantiles derived with different combinations of N weathering, N depostion and BNF. <br> Figure1_data.nc is the data for Figure 1 in the main.
DOI: 10.6084/m9.figshare.23171333
title: cell type-specific marker genes

description: This is the set of marker genes for the example using SPADE algorithm.