cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.17971115.v2
title: Progress Tracker: Oscars Best Picture, Actress, Actor, Screenplay Winners for 1928 - 2019.

description: This spreadsheet allows users to track their progress watching films nominated for Best Picture, Best Actress/Actor winners, and Best Original/Adapted Screenplay winners from 1928-2019 (spanning from the 1st to the 92rd Academy Awards ceremonies). <br>Rotten Tomatoes scores are listed for films that won Best Actress, Actor, and/or Screenplay. <br><br>Directions<br>Enter an 'X' in the column to the left of the movie to indicate you have watched the movie. If the movie also won Best Actress/Actor/Screenplay awards, an 'X' will be auto-populated on the corresponding sheets (the associated cell will display a '0' if the movie has not yet been watched). If those awards went to a movie not also nominated for Best Picture, you will need to enter an 'X' in manually (the cell will be blank otherwise). <br>Rows at the bottom calculate the % of nominated films and Best Picture winners watched by year, by decade, and all-time.<br>Statistics<br>74% of Best Actress winning roles were in films that were nominated for Best Picture. 78% of Best Actor winning roles were in films nominated for Best Picture. <br>Only 12% of Best Actress winning roles were in films that won Best Picture vs. 29% for Best Actor winners.<br>The film that won Best Adapted Screenplay also won Best Picture 38 times. The film that won Best Original Screenplay also won Best Picture 18 times (less than half of the total for Best Adapted Screenplay). <br>32% of Best Original Screenplay winners were not nominated for Best Picture. Only 5% of the Best Adapted Screenplay winners did not receive a nomination for Best Picture. <br>The average Rotten Tomatoes score for Best Original Screenplay and Best Adapted Screenplay is the same - 90%. The average Rotten Tomatoes score for movies with Best Actress winning roles is 86% (v. 89% for Best Actor winning roles). <br>
DOI: 10.6084/m9.figshare.17971115.v1
title: Progress Tracker: Oscars Best Picture, Actress, Actor, Screenplay Winners for 1928 - 2019.

description: This spreadsheet allows users to track their progress watching films nominated for Best Picture, Best Actress/Actor winners, and Best Original/Adapted Screenplay winners from 1928-2019 (spanning from the 1st to the 92rd Academy Awards ceremonies). <br>Rotten Tomatoes scores are listed for films that won Best Actress, Actor, and/or Screenplay. <br><br>Directions<br>Enter an 'X' in the column to the left of the movie to indicate you have watched the movie. If the movie also won Best Actress/Actor/Screenplay awards, an 'X' will be auto-populated on the corresponding sheets (the associated cell will display a '0' if the movie has not yet been watched). If those awards went to a movie not also nominated for Best Picture, you will need to enter an 'X' in manually (the cell will be blank otherwise). <br>Rows at the bottom calculate the % of nominated films and Best Picture winners watched by year, by decade, and all-time.<br>Statistics<br>74% of Best Actress winning roles were in films that were nominated for Best Picture. 78% of Best Actor winning roles were in films nominated for Best Picture. <br>Only 12% of Best Actress winning roles were in films that won Best Picture vs. 29% for Best Actor winners.<br>The film that won Best Adapted Screenplay also won Best Picture 38 times. The film that won Best Original Screenplay also won Best Picture 18 times (less than half of the total for Best Adapted Screenplay). <br>32% of Best Original Screenplay winners were not nominated for Best Picture. Only 5% of the Best Adapted Screenplay winners did not receive a nomination for Best Picture. <br>The average Rotten Tomatoes score for Best Original Screenplay and Best Adapted Screenplay is the same - 90%. The average Rotten Tomatoes score for movies with Best Actress winning roles is 86% (v. 89% for Best Actor winning roles). <br>
DOI: 10.5281/zenodo.5829561
title: SCOPe 2.08-stable dataset

description: This is the complete set of data files released with SCOPe 2.08-stable. All SCOPe data are described in the publications, available here: https://scop.berkeley.edu/references/ver=2.08. Full documentation for SCOPe is available on the SCOPe website, https://scop.berkeley.edu. Parseable files are described here: https://scop.berkeley.edu/help/ver=2.08#parseablefiles-2.08. The SQL tables are documented here: https://scop.berkeley.edu/downloads/schema/2.08/
DOI: 10.5281/zenodo.5829560
title: SCOPe 2.08-stable dataset

description: This is the complete set of data files released with SCOPe 2.08-stable. All SCOPe data are described in the publications, available here: https://scop.berkeley.edu/references/ver=2.08. Full documentation for SCOPe is available on the SCOPe website, https://scop.berkeley.edu. Parseable files are described here: https://scop.berkeley.edu/help/ver=2.08#parseablefiles-2.08. The SQL tables are documented here: https://scop.berkeley.edu/downloads/schema/2.08/
DOI: 10.5281/zenodo.5829593
title: Results of matrix factorization decompositions for a set of 14 colon cancer transcriptomic datasets using different methods

description: These are the results of application of several matrix factorization methods (ICA, StabilizedICA, NMF, PCA) for a set of 14 independent colon cancer transcriptomics datasets. The analysis was done to compare the methods in terms of their reproducibility (ability to generalize). The results of this analysis was published in Cantini L, Kairov U, de Reyniès A, Barillot E, Radvanyi F, Zinovyev A. Assessing reproducibility of matrix factorization methods in independent transcriptomes. Bioinformatics. 2019 Nov 1;35(21):4307-4313. doi: 10.1093/bioinformatics/btz225.
DOI: 10.5281/zenodo.5829592
title: Results of matrix factorization decompositions for a set of 14 colon cancer transcriptomic datasets using different methods

description: These are the results of application of several matrix factorization methods (ICA, StabilizedICA, NMF, PCA) for a set of 14 independent colon cancer transcriptomics datasets. The analysis was done to compare the methods in terms of their reproducibility (ability to generalize). The results of this analysis was published in Cantini L, Kairov U, de Reyniès A, Barillot E, Radvanyi F, Zinovyev A. Assessing reproducibility of matrix factorization methods in independent transcriptomes. Bioinformatics. 2019 Nov 1;35(21):4307-4313. doi: 10.1093/bioinformatics/btz225.
DOI: 10.6084/m9.figshare.16850998
title: minimal dataset

description: It contains the minimal dataset we submitted to PLOS Genetics
DOI: 10.6084/m9.figshare.16850998.v2
title: minimal dataset

description: It contains the minimal dataset we submitted to PLOS Genetics
DOI: 10.5281/zenodo.5829585
title: # Single-cell network biology characterizes cell type gene regulation for drug repurposing and phenotype prediction in Alzheimer's disease

description: Dysregulation of gene expression in Alzheimer’s disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern target gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, it is still challenging to understand how cell type networks work abnormally under AD. To address this, we integrated single-cell multi-omics data and predicted the gene regulatory networks in AD and control for four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes. Importantly, we applied network biology approaches to analyze the changes of network characteristics across these cell types, and between AD and control. For instance, many hub TFs target different genes between AD and control (rewiring). Also, these networks show strong hierarchical structures in which top TFs (master regulators) are largely common across cell types, whereas different TFs operate at the middle levels in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell type-specific TF-TF cooperativities in gene regulation. The cell type networks are highly modular and several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes and putative targets of approved and pending AD drugs, suggesting possible cell-type genomic medicine in AD. Finally, using the cell type gene regulatory networks, we developed machine learning models to classify and prioritize additional AD genes. We found that top prioritized genes predict clinical phenotypes (e.g., cognitive impairment) with reasonable accuracy. Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals dysregulated cell type gene dysregulatory mechanisms in AD.
DOI: 10.5281/zenodo.5829584
title: # Single-cell network biology characterizes cell type gene regulation for drug repurposing and phenotype prediction in Alzheimer's disease

description: Dysregulation of gene expression in Alzheimer’s disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern target gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, it is still challenging to understand how cell type networks work abnormally under AD. To address this, we integrated single-cell multi-omics data and predicted the gene regulatory networks in AD and control for four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes. Importantly, we applied network biology approaches to analyze the changes of network characteristics across these cell types, and between AD and control. For instance, many hub TFs target different genes between AD and control (rewiring). Also, these networks show strong hierarchical structures in which top TFs (master regulators) are largely common across cell types, whereas different TFs operate at the middle levels in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell type-specific TF-TF cooperativities in gene regulation. The cell type networks are highly modular and several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes and putative targets of approved and pending AD drugs, suggesting possible cell-type genomic medicine in AD. Finally, using the cell type gene regulatory networks, we developed machine learning models to classify and prioritize additional AD genes. We found that top prioritized genes predict clinical phenotypes (e.g., cognitive impairment) with reasonable accuracy. Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals dysregulated cell type gene dysregulatory mechanisms in AD.
DOI: 10.3886/e113182v1-106180
title: Replication data for: The Logic of Insurgent Electoral Violence

description: Competitive elections are essential to establishing the political legitimacy of democratizing regimes. We argue that insurgents undermine the state's mandate through electoral violence. We study insurgent violence during elections using newly declassified microdata on the conflict in Afghanistan. Our data track insurgent activity by hour to within meters of attack locations. Our results suggest that insurgents carefully calibrate their production of violence during elections to avoid harming civilians. Leveraging a novel instrumental variables approach, we find that violence depresses voting. Collectively, the results suggest insurgents try to depress turnout while avoiding backlash from harming civilians. Counterfactual exercises provide potentially actionable insights for safeguarding at-risk elections and enhancing electoral legitimacy in emerging democracies.
DOI: 10.5281/zenodo.5829602
title: KD8CGH Grape 1 WWV 10 MHz

description: KD8CGH, node N0000022 Grape 1 WWV 10 MHz 143 .csv files from 21-06-28 to 22-01-01
DOI: 10.5281/zenodo.5829601
title: KD8CGH Grape 1 WWV 10 MHz

description: KD8CGH, node N0000022 Grape 1 WWV 10 MHz 143 .csv files from 21-06-28 to 22-01-01
DOI: 10.6084/m9.figshare.18070448.v1
title: Supplementary appendix_Gomes NL_final

description: <b>Supplementary information from "Contribution of clinical and genetic approaches for diagnosing 209 index cases with 46,XY Differences of Sex Development"</b>
DOI: 10.6084/m9.figshare.18070448
title: Supplementary appendix_Gomes NL_final

description: <b>Supplementary information from "Contribution of clinical and genetic approaches for diagnosing 209 index cases with 46,XY Differences of Sex Development"</b>
DOI: 10.34740/kaggle/dsv/3017611
title: Cryptocurrency extra data - Bitcoin Cash
DOI: 10.6084/m9.figshare.18070454
title: Supplementary appendix_Gomes NL_JCEM

description: Supplementary material from: "<b>Contribution of clinical and genetic approaches for diagnosing 209 index cases with 46,XY Differences of Sex Development"</b><b></b>
DOI: 10.6084/m9.figshare.18070454.v1
title: Supplementary appendix_Gomes NL_JCEM

description: Supplementary material from: "<b>Contribution of clinical and genetic approaches for diagnosing 209 index cases with 46,XY Differences of Sex Development"</b><b></b>
DOI: 10.5281/zenodo.5827758
title: TMEM97 is correlated with the clinical features and clinical prognosis, and acts as a potential marker of multiple myeloma

description: <strong>Abstract</strong> <strong>Background</strong>: TMEM97 has been proven to be involved in many biological processes of malignancies. This study was designed to investigate the role of TMEM97 in multiple myeloma progression and clinical features and prognosis. We also predicted the potential mechanism of TMEM97 in MM prognosis. <strong>Methods:</strong> In the present study, five GEO datasets including GSE24080, GSE13591, GSE2113, GSE19784 and GSE2658 were obtained from the National Cancer for Biotechnology Information database for analysis of TMEM97 expression and clinical features and prognosis in MM.We performed GSEA analysis to investigate the potential mechanism of TMEM97 in MM. <strong>Results: </strong>We collected multiple myeloma data based on GEO datasets, and demonstrated the expression level of TMEM97 increased with the progression of multiple myeloma. Also the expression level of TMEM97 was correlated significantly with ISS stage in MM patients.Additionally, TMEM97 was more likely to be associated with important factors of MM prognosis, such as cytogenetic, B2M, CRP, LDH, BMPC, MRI and HGB. The multivariate analysis showed that TMEM97 was an independent factor for OS and EFS. In addition, top positively correlated genes with TMEM97 covered PUS7, GGCT, PFAS, CTPS1, RFC4 and PRMT3. <strong>Conclusion:</strong>The expression of TMEM97 was a factor affecting the survival and prognosis of MM patients. TMEM97 may be an important prognosis biomarker and promising therapeutic target in multiple myeloma.
DOI: 10.5281/zenodo.5827757
title: TMEM97 is correlated with the clinical features and clinical prognosis, and acts as a potential marker of multiple myeloma

description: <strong>Abstract</strong> <strong>Background</strong>: TMEM97 has been proven to be involved in many biological processes of malignancies. This study was designed to investigate the role of TMEM97 in multiple myeloma progression and clinical features and prognosis. We also predicted the potential mechanism of TMEM97 in MM prognosis. <strong>Methods:</strong> In the present study, five GEO datasets including GSE24080, GSE13591, GSE2113, GSE19784 and GSE2658 were obtained from the National Cancer for Biotechnology Information database for analysis of TMEM97 expression and clinical features and prognosis in MM.We performed GSEA analysis to investigate the potential mechanism of TMEM97 in MM. <strong>Results: </strong>We collected multiple myeloma data based on GEO datasets, and demonstrated the expression level of TMEM97 increased with the progression of multiple myeloma. Also the expression level of TMEM97 was correlated significantly with ISS stage in MM patients.Additionally, TMEM97 was more likely to be associated with important factors of MM prognosis, such as cytogenetic, B2M, CRP, LDH, BMPC, MRI and HGB. The multivariate analysis showed that TMEM97 was an independent factor for OS and EFS. In addition, top positively correlated genes with TMEM97 covered PUS7, GGCT, PFAS, CTPS1, RFC4 and PRMT3. <strong>Conclusion:</strong>The expression of TMEM97 was a factor affecting the survival and prognosis of MM patients. TMEM97 may be an important prognosis biomarker and promising therapeutic target in multiple myeloma.
DOI: 10.25625/ls2sq8/hfw66g
title: Methyl glycolate_20C_benzyl alcohol_50C_1p0bar_70C_653p0nm_1mm_20W_6x600s.dat

description: Raman (methyl glycolate + benzyl alcohol)
DOI: 10.25625/ls2sq8/hocsfk
title: Methyl lactate_15C_benzyl alcohol_50C_1p0bar_60C_653p0nm_1p5mm_20W_6x240s.dat

description: Raman (methyl lactate + benzyl alcohol)
DOI: 10.25625/ls2sq8/hjhkga
title: Methyl glycolate_20C_4Chlorobenzyl alcohol_70C_1p0bar_80C_653p0nm_1mm_25W_11x240s.dat

description: Raman (methyl glycolate + 4-chlorobenzyl alcohol)
DOI: 10.25625/ls2sq8/9zfufv
title: Methyl lactate_15C_4Chlorobenzyl alcohol_70C_1p0bar_80C_653p0nm_1mm_25W_4x240s.dat

description: Raman (methyl lactate + 4-chlorobenzyl alcohol)
DOI: 10.25625/ls2sq8/ngjd7x
title: Methyl glycolate_10C_4Chlorobenzyl alcohol_75C_95C_412pulses

description: FTIR (methyl glycolate + 4-chlorobenzyl alcohol)
DOI: 10.25625/ls2sq8/qncp3z
title: Methyl glycolate_10C_benzyl alcohol_40C_60C_523pulses

description: FTIR (methyl glycolate + benzyl alcohol)
DOI: 10.25625/ls2sq8/ycgorm
title: Methyl lactate_0C_benzyl alcohol_50C_70C_356pulses

description: FTIR (methyl lactate + benzyl alcohol)
DOI: 10.25625/ls2sq8/v5yy9o
title: Methyl lactate_m5C_4Chlorobenzyl alcohol_75C_95C_344pulses

description: FTIR (methyl lactate + 4-chlorobenzyl alcohol)
DOI: 10.25625/ls2sq8
title: Experimental FTIR and Raman spectra of dimers of transiently chiral alcohols and (a)chiral hydroxy esters in the range of 3700 and 3300 cm-1

description: We provide experimental FTIR and Raman spectra of supersonic jet expansions for four systems (all in the range of 3700 and 3300 cm-1): benzyl alcohol + methyl (S)-(-)-lactate, benzyl alcohol + methyl glycolate, 4-chlorobenzyl alcohol + methyl (S)-(-)-lactate, 4-chlorobenzyl alcohol + methyl glycolate. Nomenclature for FTIR spectra: volatile compound_saturator temperature(in °C, "m" stands for "minus")_low volatile compound_temperature_nozzle temperature_number of pulses. Nomenclature for Raman spectra: volatile compound_temperature of coolable saturator_low volatile compound_temperature of heatable saturator_backing pressure_nozzle temperature_monochromator wavelength_distance nozzle-laser_laser power_number of exposures x time of single exposure The spectra are provided as data point tables (FTIR) / in the ASCII format (Raman). Columns are separated by commas (FTIR) / tabulators (Raman). The first column corresponds to the wavenumber in cm−1 and the second to the absorbance (FTIR) / intensity in arbitrary units(Raman).
DOI: 10.25625/nfiknj/aiq4ql
title: benzyl alcohol_methyl lactate_inserted_hom.xyz

description: :unav
DOI: 10.25625/nfiknj/rphg25
title: 4chlorobenzyl alcohol_methyl glycolate_inserted.xyz

description: :unav
DOI: 10.25625/nfiknj/bxw1tg
title: benzyl alcohol_methyl lactate_inserted_het.xyz

description: :unav
DOI: 10.25625/nfiknj/tcx17u
title: benzyl alcohol_methyl lactate_associated_het.xyz

description: :unav
DOI: 10.25625/nfiknj/cvzhss
title: benzyl alcohol_methyl lactate_associated_hom.xyz

description: :unav
DOI: 10.25625/nfiknj/yd3j79
title: 4chlorobenzyl alcohol_methyl lactate_associated_het.xyz

description: :unav
DOI: 10.25625/nfiknj/qaaqwd
title: 4chlorobenzyl alcohol_methyl lactate_inserted_het.xyz

description: :unav
DOI: 10.25625/nfiknj/skr0iu
title: 4chlorobenzyl alcohol_methyl lactate_associated_hom.xyz

description: :unav
DOI: 10.25625/nfiknj/bcz57a
title: benzyl alcohol_methyl glycolate_inserted.xyz

description: :unav
DOI: 10.25625/nfiknj/3a08rl
title: benzyl alcohol_methyl glycolate_associated.xyz

description: :unav
DOI: 10.25625/nfiknj/4o8kla
title: 4chlorobenzyl alcohol_methyl glycolate_associated.xyz

description: :unav
DOI: 10.25625/nfiknj/weykpd
title: 4chlorobenzyl alcohol_methyl lactate_inserted_hom.xyz

description: :unav
DOI: 10.25625/nfiknj
title: xyz structure files of dimers of transiently chiral alcohols and (a)chiral hydroxy esters

description: We provide xyz structure files at the B3LYP-D3(BJ,abc)/def2-TZVP level of computation for the following dimers: - benzyl alcohol + methyl (S)-(-)-lactate: most stable homochiral (gauche(-) benzyl alchohol) / heterochiral (gauche(+) benzyl alcohol) inserted and most stable homochiral / heterochiral associated - benzyl alcohol + methyl glycolate: most stable inserted and associated - 4-chlorobenzyl alcohol + methyl (S)-(-)-lactate: most stable homochiral / heterochiral inserted and most stable homochiral / heterochiral associated - 4-chlorobenzyl alcohol + methyl glycolate: most stable inserted and associated
DOI: 10.34740/kaggle/dsv/3017709
title: Cryptocurrency extra data - Binance Coin
DOI: 10.34740/kaggle/dsv/3017723
title: Omicron daily cases by country (COVID-19 variant)
DOI: 10.3886/e119446v137-106200
title: COVID-19 US State Policy Database

description: For questions or comments about the database please contact:<br>Alexandra Skinner <br>skinnera@bu.edu <br>Research Fellow & Database Manager<br>Department of Health Law, Policy & Management <br>Boston University School of Public Health<br><br>Database of state policies on closures, shelter-in-place orders, housing protections, changes to Medicaid and SNAP, physical distancing closures, reopening, and more created by researchers at the Boston University School of Public Health. Policies included are state-wide directives or mandates, not guidance or recommendations. In order for a policy to be included, it must apply to the entire state. We are working quickly to go through state government websites to make the policy database as complete and accurate as possible in a rapidly changing policy context. If you use data on a given policy, we encourage you to triangulate based on additional sources of policy data and to review the source documentation to consider the coding decisions that are right for your work. Of course, please email us if you note a discrepancy so we can improve the database for everyone. State policy source documentation can be found at: tinyurl.com/statepolicysources.
description: State policies. Smallest Geographic Unit: State
description: other; Contributors: Megan Cole Brahim, Noa Krawczyk, Carolyn Hoffman, Claire Corkish, Elizabeth Ferrara, Elizabeth Long, Emily Baroni, Faith Contador, Hannah Simon, Morgan Simko, Rachel Scheckman, Sarah Brewer, Sue Kulkarni, Felicia Heykoop, Manish Patel, Aishwarya Vidyasagaran, Andrew Chiao, Cara Safon, Samantha Burkhart, Harry Jung, Victor Lee, Marina MacKinnon, Aarohi Rede, Marie Vearling, Sophie Li, Christina Wang, Tim Levengood, María Jordán P. Emma Chistolini
DOI: 10.5517/ccdc.csd.cc28jyg7
title: CCDC 2101659: Experimental Crystal Structure Determination

description: Related Article: Laura Lamkowski, Daniel Komisarek, Klaus Merz|2021|Cryst.Growth Des.|22|356|doi:10.1021/acs.cgd.1c00991
DOI: 10.5281/zenodo.5829718
title: A new Insight into the Sources of the 1733DC-M7.8 Earthquake on the Xiaojiang Fault zone, southeastern Tibet

description: The observed near-fault GPS data of northern Xiaojiang fault.
DOI: 10.5281/zenodo.5829717
title: A new Insight into the Sources of the 1733DC-M7.8 Earthquake on the Xiaojiang Fault zone, southeastern Tibet

description: The observed near-fault GPS data of northern Xiaojiang fault.