cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5106080
title: Trout of Hokkaido data (Rainbow, Brown, Brook, and Sockeye)

description: This is a data set of 928 presents points (and 3 NA locations) for invasive trout species across Hokkaido for the masters thesis "<strong>Perception of fishermen towards invasive salmonids in Hokkaido and the contribution of citizen science to their long-term monitoring</strong>". It is comprised of Scientific studies, gray literature, YouTube videos, social media sites, blogs, word of mouth, email, and iNaturalist data. Please feel free to use this data for research on these species. Columns are broken up into 10 parts. -------------------------------Columns are-------------------------------- <strong>Hits</strong>: Hits on the map one per site. <strong>Paper/source</strong>: where the study or citizen data came from. (Please feel free to check out the blogs as they update with new data often) The key as follows FLK = Fliker / IG = Instagram / TW = Twitter / WEB = website / YTV = youtube <strong>Contributor</strong>: then Name of the citizen sciences contributor. Blogs, Twitter names, Instagram names, Youtube channel names, etc... <strong>Xcoord</strong>: the X coordinate <strong>Ycoord</strong>: the y coordinate <strong>Specie</strong>: one of 4 species denoted as its scientific name <strong>Start Date</strong>: When the study or post started collecting data <strong>End Date</strong>: When the study or post ended collecting data <strong>Science / Citizen</strong>: denotes where the data came from. Broken up into a few groups, <strong>Science</strong>: from scientific source and grey litterture *Science (old) : data was used as a base line for the study <strong>Active</strong>: Citizen data that has been reported during the study period of the study Please go the the iNaturalist link to get that data. <strong> Passive</strong>: Citizen data that came from lots of different places. *Passive(site) : places that are mapped as fishing spots per speceis by fishing shops and tours <strong>Number per hit</strong>: reported or counted species per hit point on the map (accuracy is not 100% ensured) Notes----------------------------------------------------------------------------------------------- River points should be close to actual points caught but lake points should not be used for precise data as much of it was not precisely listed and so guess work on location took place.
DOI: 10.5281/zenodo.5106079
title: Trout of Hokkaido data (Rainbow, Brown, Brook, and Sockeye)

description: This is a data set of 928 presents points (and 3 NA locations) for invasive trout species across Hokkaido for the masters thesis "<strong>Perception of fishermen towards invasive salmonids in Hokkaido and the contribution of citizen science to their long-term monitoring</strong>". It is comprised of Scientific studies, gray literature, YouTube videos, social media sites, blogs, word of mouth, email, and iNaturalist data. Please feel free to use this data for research on these species. Columns are broken up into 10 parts. -------------------------------Columns are-------------------------------- <strong>Hits</strong>: Hits on the map one per site. <strong>Paper/source</strong>: where the study or citizen data came from. (Please feel free to check out the blogs as they update with new data often) The key as follows FLK = Fliker / IG = Instagram / TW = Twitter / WEB = website / YTV = youtube <strong>Contributor</strong>: then Name of the citizen sciences contributor. Blogs, Twitter names, Instagram names, Youtube channel names, etc... <strong>Xcoord</strong>: the X coordinate <strong>Ycoord</strong>: the y coordinate <strong>Specie</strong>: one of 4 species denoted as its scientific name <strong>Start Date</strong>: When the study or post started collecting data <strong>End Date</strong>: When the study or post ended collecting data <strong>Science / Citizen</strong>: denotes where the data came from. Broken up into a few groups, <strong>Science</strong>: from scientific source and grey litterture *Science (old) : data was used as a base line for the study <strong>Active</strong>: Citizen data that has been reported during the study period of the study Please go the the iNaturalist link to get that data. <strong> Passive</strong>: Citizen data that came from lots of different places. *Passive(site) : places that are mapped as fishing spots per speceis by fishing shops and tours <strong>Number per hit</strong>: reported or counted species per hit point on the map (accuracy is not 100% ensured) Notes----------------------------------------------------------------------------------------------- River points should be close to actual points caught but lake points should not be used for precise data as much of it was not precisely listed and so guess work on location took place.
DOI: 10.5281/zenodo.5112257
title: Detection of Neoplasms by Metagenomic Sequencing of Cerebrospinal Fluid

description: Detection of Neoplasms by Metagenomic Sequencing of Cerebrospinal Fluid Wei Gu, MD, PhD<sup>1,2</sup>*, Andreas M. Rauschecker, MD, PhD<sup>3</sup>, Elaine Hsu, BS<sup>1</sup>, Kelsey C. Zorn, BS<sup>4</sup>, Yasemin Sucu, BS<sup>1</sup>, Scot Federman, BS<sup>1</sup>, Allan Gopez, BS<sup>1</sup>, Shaun Arevalo, BS<sup>1</sup>, Hannah A. Sample, BS<sup>4</sup>, Eric Talevich, PhD<sup>5</sup>, Eric D. Nguyen, MD, PhD<sup> 1</sup>, Marc Gottschall, BS<sup>1</sup>, Bardia Nourbakhsh, MD, MAS<sup>6</sup>, Carl A. Gold, MD, MS<sup>7</sup>, Bruce A.C. Cree, MD, PhD, MAS<sup>8</sup>, Vanja Douglas, MD<sup>8</sup>, Megan B. Richie, MD<sup>8</sup>, Maulik P. Shah, MD, MHS<sup>8</sup>, S. Andrew Josephson, MD<sup>8</sup>, Jeffrey M. Gelfand, MD, MAS<sup>8</sup>, Steve Miller, MD, PhD<sup> 1</sup>, Linlin Wang, MD<sup>1</sup>, Tarik Tihan, MD, PhD<sup>9</sup>, Joseph L. DeRisi, PhD<sup>4,10</sup>, Charles Y. Chiu, MD, PhD<sup>11,12</sup>, Michael R. Wilson, MD, MAS<sup>8</sup>* <sup>1</sup>Department of Laboratory Medicine, University of California San Francisco, CA 94107, USA <sup>2</sup>Department of Pathology, Stanford University, CA 94305, USA <sup>3</sup>Department of Radiology and Biomedical Imaging, University of California San Francisco, CA 94107, USA <sup>4</sup>Department of Biochemistry and Biophysics, University of California San Francisco, CA 94107, USA <sup>5</sup>DNANexus, Mountain View, CA 94040, USA <sup>6</sup>Department of Neurology, Johns Hopkins University, MD 21287, USA <sup>7</sup>Department of Neurology, Stanford University, CA 94305, USA <sup>8</sup>Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, CA 94107, USA <sup>9</sup>Department of Pathology, University of California San Francisco, CA 94107, USA <sup>10</sup>Chan Zuckerberg Biohub, San Francisco, CA 94107, USA <sup>11</sup>UCSF-Abbott Viral Diagnostics and Discovery Center, San Francisco, CA 91407, USA <sup>12</sup>Department of Medicine, Division of Infectious Diseases, University of California San Francisco, CA 94107, USA * Corresponding authors: Michael Wilson, MD, MAS UCSF Department of Neurology 675 Nelson Rising Lane, NS212 San Francisco, CA 94158 Michael.Wilson@ucsf.edu Wei Gu, MD, PhD Stanford University, Department of Pathology 3373 Hillview Ave, 218 Palo Alto, CA 94304 650-723-1914 weigu@stanford.edu
description: v2 updates: Added a segment of files that were missed on the first upload. Added Dr. Bardia Nourbakhsh to the author list.
DOI: 10.5281/zenodo.4682169
title: Detection of Neoplasms by Metagenomic Sequencing of Cerebrospinal Fluid

description: Detection of Neoplasms by Metagenomic Sequencing of Cerebrospinal Fluid Wei Gu, MD, PhD<sup>1,2</sup>*, Andreas M. Rauschecker, MD, PhD<sup>3</sup>, Elaine Hsu, BS<sup>1</sup>, Kelsey C. Zorn, BS<sup>4</sup>, Yasemin Sucu, BS<sup>1</sup>, Scot Federman, BS<sup>1</sup>, Allan Gopez, BS<sup>1</sup>, Shaun Arevalo, BS<sup>1</sup>, Hannah A. Sample, BS<sup>4</sup>, Eric Talevich, PhD<sup>5</sup>, Eric D. Nguyen, MD, PhD<sup> 1</sup>, Marc Gottschall, BS<sup>1</sup>, Bardia Nourbakhsh, MD, MAS<sup>6</sup>, Carl A. Gold, MD, MS<sup>7</sup>, Bruce A.C. Cree, MD, PhD, MAS<sup>8</sup>, Vanja Douglas, MD<sup>8</sup>, Megan B. Richie, MD<sup>8</sup>, Maulik P. Shah, MD, MHS<sup>8</sup>, S. Andrew Josephson, MD<sup>8</sup>, Jeffrey M. Gelfand, MD, MAS<sup>8</sup>, Steve Miller, MD, PhD<sup> 1</sup>, Linlin Wang, MD<sup>1</sup>, Tarik Tihan, MD, PhD<sup>9</sup>, Joseph L. DeRisi, PhD<sup>4,10</sup>, Charles Y. Chiu, MD, PhD<sup>11,12</sup>, Michael R. Wilson, MD, MAS<sup>8</sup>* <sup>1</sup>Department of Laboratory Medicine, University of California San Francisco, CA 94107, USA <sup>2</sup>Department of Pathology, Stanford University, CA 94305, USA <sup>3</sup>Department of Radiology and Biomedical Imaging, University of California San Francisco, CA 94107, USA <sup>4</sup>Department of Biochemistry and Biophysics, University of California San Francisco, CA 94107, USA <sup>5</sup>DNANexus, Mountain View, CA 94040, USA <sup>6</sup>Department of Neurology, Johns Hopkins University, MD 21287, USA <sup>7</sup>Department of Neurology, Stanford University, CA 94305, USA <sup>8</sup>Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, CA 94107, USA <sup>9</sup>Department of Pathology, University of California San Francisco, CA 94107, USA <sup>10</sup>Chan Zuckerberg Biohub, San Francisco, CA 94107, USA <sup>11</sup>UCSF-Abbott Viral Diagnostics and Discovery Center, San Francisco, CA 91407, USA <sup>12</sup>Department of Medicine, Division of Infectious Diseases, University of California San Francisco, CA 94107, USA * Corresponding authors: Michael Wilson, MD, MAS UCSF Department of Neurology 675 Nelson Rising Lane, NS212 San Francisco, CA 94158 Michael.Wilson@ucsf.edu Wei Gu, MD, PhD Stanford University, Department of Pathology 3373 Hillview Ave, 218 Palo Alto, CA 94304 650-723-1914 weigu@stanford.edu
description: v2 updates: Added a segment of files that were missed on the first upload. Added Dr. Bardia Nourbakhsh to the author list.
DOI: 10.6084/m9.figshare.15001200
title: Lineage-Specific Core and Niche Differentiating Traits

description: Table of core and niche differentiating traits for each phyla
DOI: 10.6084/m9.figshare.14583213
title: Nonlinear fracture flow datasets

description: Correction coefficient in the equation (9) and Pressure-flux curves
DOI: 10.6084/m9.figshare.14583213.v1
title: Nonlinear fracture flow datasets

description: Correction coefficient in the equation (9) and Pressure-flux curves
DOI: 10.25345/c5kg10
title: MassIVE MSV000087847 - GNPS Crude methanol extract of rapeseed -1
title: This is the crude methanol extract of rapeseed, then the crude methanol extract of rapeseed was submitted to UPLC-ESI-TOF-MS in full scan mode in order to analysis as many of the phenolic compounds as possible therein.
DOI: 10.5281/zenodo.5112581
title: Data referring to submergence stories from NW Europe and Australia

description: In support of a manuscript submitted to 'Royal Society Open Science' about submergence stories from NW Europe, I submit a data set showing the locations of these stories, the present depths (below sea level) to which they refer, and the minimum ages calculated, with a final column listing data sources for sea-level palaeodata. Each column is labelled at the top. Data from Australia (previously published) are given for comparison.
DOI: 10.5281/zenodo.5112580
title: Data referring to submergence stories from NW Europe and Australia

description: In support of a manuscript submitted to 'Royal Society Open Science' about submergence stories from NW Europe, I submit a data set showing the locations of these stories, the present depths (below sea level) to which they refer, and the minimum ages calculated, with a final column listing data sources for sea-level palaeodata. Each column is labelled at the top. Data from Australia (previously published) are given for comparison.
DOI: 10.6084/m9.figshare.14999991
title: Seagrass Occurrences Master Data

description: This is a master dataset containing spatial occurrences of all seagrass species as well as pseudo-points randomly generated using seagrass range polygons.<br>
DOI: 10.6084/m9.figshare.14999991.v2
title: Seagrass Occurrences Master Data

description: This is a master dataset containing spatial occurrences of all seagrass species as well as pseudo-points randomly generated using seagrass range polygons.<br>
DOI: 10.6084/m9.figshare.14999991.v1
title: Seagrass Occurrences Master Data

description: This is a master dataset containing spatial occurrences of all seagrass species as well as pseudo-points randomly generated using seagrass range polygons.<br>
DOI: 10.6084/m9.figshare.13273283.v2
title: Geochemical and isotopic data for the Longladuo mafic intrusions in the western Indus–Tsangpo suture zone, southwest Tibet

description: (1) Analytical methods for the Longladuo mafic intrusions; (2) Whole-rock compositions and Sr-Nd-Pb isotopic data for the Longladuo diabases; (3) Zircon LA-ICP-MS U-Pb isotopic data and Lu-Hf isotopic compositions for the Longladuo diabases ;(4) Geochemical plots for the Longladuo diabases.
DOI: 10.5281/zenodo.5112599
title: Does Disease Activity Influence the Levels of Uric Acid in Psoriatic Arthritis?

description: Background: Hyperuricemia is not only associated with the development of gout but also with renal and vascular dysfunction. The prevalence of this condition has already been studied in psoriasis, but there are a few studies that have been carried out in psoriatic arthritis (PsA). Some studies have shown an association with metabolic syndrome, while others with the extent of cutaneous involvement, but there are no studies that have evaluated the disease activity with compound indexes.<br> Objective: The aim of the study was to determine if disease activity, measured by different composite scores, influences the levels of uric acid.<br> Method: This was a cross-sectional, observational study, which included 52 PsA patients. Clinical assessments included dactylitis, tender and swollen joint counts, Psoriasis Area and Severity Index, Leeds Enthesis Index, Minimal Disease Activity and Disease Activity for Psoriatic Arthritis. Hyperuricemia was defined as serum uric acid levels ≥ 6mg/dL in females and ≥ 7mg/dL in males.<br> Results: Among the 52 included patients, 55.76% were female. The mean age was 54.9 ± 11.6 years. Hyperuricemia occurred in 26.92%. Demographic data, diet, comorbidities and medication were similar between patients with and without hyperuricemia. Patients with hyperuricemia had higher waist circumference (p &lt;0.0046). There was no difference in disease activity between groups, either in the isolated items or in the composite indexes. There was a significant difference in uric acid levels according to the classification of chronic kidney disease by estimated glomerular filtration rate (p=0.0016). Individuals using leflunomide had significantly lower levels of uric acid than those who were not using (p=0.0071).<br> Conclusion: This study supports the notion that, in PsA, hyperuricemia is more related to metabolic factors than to disease activity.
DOI: 10.5281/zenodo.5112598
title: Does Disease Activity Influence the Levels of Uric Acid in Psoriatic Arthritis?

description: Background: Hyperuricemia is not only associated with the development of gout but also with renal and vascular dysfunction. The prevalence of this condition has already been studied in psoriasis, but there are a few studies that have been carried out in psoriatic arthritis (PsA). Some studies have shown an association with metabolic syndrome, while others with the extent of cutaneous involvement, but there are no studies that have evaluated the disease activity with compound indexes.<br> Objective: The aim of the study was to determine if disease activity, measured by different composite scores, influences the levels of uric acid.<br> Method: This was a cross-sectional, observational study, which included 52 PsA patients. Clinical assessments included dactylitis, tender and swollen joint counts, Psoriasis Area and Severity Index, Leeds Enthesis Index, Minimal Disease Activity and Disease Activity for Psoriatic Arthritis. Hyperuricemia was defined as serum uric acid levels ≥ 6mg/dL in females and ≥ 7mg/dL in males.<br> Results: Among the 52 included patients, 55.76% were female. The mean age was 54.9 ± 11.6 years. Hyperuricemia occurred in 26.92%. Demographic data, diet, comorbidities and medication were similar between patients with and without hyperuricemia. Patients with hyperuricemia had higher waist circumference (p &lt;0.0046). There was no difference in disease activity between groups, either in the isolated items or in the composite indexes. There was a significant difference in uric acid levels according to the classification of chronic kidney disease by estimated glomerular filtration rate (p=0.0016). Individuals using leflunomide had significantly lower levels of uric acid than those who were not using (p=0.0071).<br> Conclusion: This study supports the notion that, in PsA, hyperuricemia is more related to metabolic factors than to disease activity.
DOI: 10.6084/m9.figshare.15001329
title: Additional file 3 of Need of care in interpreting Google Trends-based COVID-19 infodemiological study results: potential risk of false-positivity

description: Additional file 3. P-value/FDR results of all keywords in each country (excluding Japan). P-value/FDR results of all English symptom keywords as listed in the left-most row, for each country (excluding Japan) and each statistical condition (lag order 4-8, Spearman’s rank correlation or granger-causality, and p-value or FDR). Abbreviations: AU, Australia; CA, Canada; GB, Great Britain; IE, Ireland; IN, India; JP, Japan; SG, Singapore; US, United States; ZA, South Africa; FDR, false discovery rate.
DOI: 10.6084/m9.figshare.15001329.v1
title: Additional file 3 of Need of care in interpreting Google Trends-based COVID-19 infodemiological study results: potential risk of false-positivity

description: Additional file 3. P-value/FDR results of all keywords in each country (excluding Japan). P-value/FDR results of all English symptom keywords as listed in the left-most row, for each country (excluding Japan) and each statistical condition (lag order 4-8, Spearman’s rank correlation or granger-causality, and p-value or FDR). Abbreviations: AU, Australia; CA, Canada; GB, Great Britain; IE, Ireland; IN, India; JP, Japan; SG, Singapore; US, United States; ZA, South Africa; FDR, false discovery rate.
DOI: 10.6084/m9.figshare.15001332.v1
title: Additional file 4 of Need of care in interpreting Google Trends-based COVID-19 infodemiological study results: potential risk of false-positivity

description: Additional file 4. P-value/FDR results of all keywords in Japan. P-value/FDR results of all Japanese symptom keywords in Japan as listed in the left-most row, for each statistical condition (lag order 4-8, Spearman’s rank correlation or granger-causality, and p-value or FDR). Abbreviations: AU, Australia; CA, Canada; GB, Great Britain; IE, Ireland; IN, India; JP, Japan; SG, Singapore; US, United States; ZA, South Africa; FDR, false discovery rate.
DOI: 10.6084/m9.figshare.15001332
title: Additional file 4 of Need of care in interpreting Google Trends-based COVID-19 infodemiological study results: potential risk of false-positivity

description: Additional file 4. P-value/FDR results of all keywords in Japan. P-value/FDR results of all Japanese symptom keywords in Japan as listed in the left-most row, for each statistical condition (lag order 4-8, Spearman’s rank correlation or granger-causality, and p-value or FDR). Abbreviations: AU, Australia; CA, Canada; GB, Great Britain; IE, Ireland; IN, India; JP, Japan; SG, Singapore; US, United States; ZA, South Africa; FDR, false discovery rate.
DOI: 10.6084/m9.figshare.15001245
title: Global distribution and trends of ground-level O3 (4,245 stations, 2014-2019)

description: The dataset is composed of average mixing ratios of DMA8 O3 and the trends during 2014-2019 warm seasons at 4,245 stations over the world. The site names, geographic locations and data sources are also indicated.
DOI: 10.6084/m9.figshare.15001362.v1
title: STS_fatigue_data.zip

description: Data related to execution of the Sit-to-Stand (STS) excerice and physical fatigue.
DOI: 10.5517/ccdc.csd.cc1zf5rb
title: CCDC 1829922: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc25j4pk
title: CCDC 2011518: Experimental Crystal Structure Determination

description: Related Article: Ilse Barnard, Klaus R Koch, Wilhelmus J Gerber|2021|J.Mol.Struct.|1244|131009|doi:10.1016/j.molstruc.2021.131009
DOI: 10.5517/ccdc.csd.cc25j4ql
title: CCDC 2011519: Experimental Crystal Structure Determination

description: Related Article: Ilse Barnard, Klaus R Koch, Wilhelmus J Gerber|2021|J.Mol.Struct.|1244|131009|doi:10.1016/j.molstruc.2021.131009
DOI: 10.5517/ccdc.csd.cc25j4rm
title: CCDC 2011520: Experimental Crystal Structure Determination

description: Related Article: Ilse Barnard, Klaus R Koch, Wilhelmus J Gerber|2021|J.Mol.Struct.|1244|131009|doi:10.1016/j.molstruc.2021.131009
DOI: 10.5517/ccdc.csd.cc26pcx6
title: CCDC 2046338: Experimental Crystal Structure Determination

description: Related Article: Emrah Karaer, Dursun Ali Köse, Zarife Sibel Şahin, Onur Şahin|2021|J.Mol.Struct.|1244|130970|doi:10.1016/j.molstruc.2021.130970
DOI: 10.5517/ccdc.csd.cc26pcy7
title: CCDC 2046339: Experimental Crystal Structure Determination

description: Related Article: Emrah Karaer, Dursun Ali Köse, Zarife Sibel Şahin, Onur Şahin|2021|J.Mol.Struct.|1244|130970|doi:10.1016/j.molstruc.2021.130970
DOI: 10.5517/ccdc.csd.cc26r2rt
title: CCDC 2047976: Experimental Crystal Structure Determination

description: Related Article: Xiao-Fen Guan, Hao-Jun Zhao, Ya-Jie Hao, Xiao-Rong Guo, Zhong-Ping Yang, Feng-Ying Zhang, Wen-Min Wang|2021|J.Mol.Struct.|1245|131010|doi:10.1016/j.molstruc.2021.131010
DOI: 10.5517/ccdc.csd.cc26r2sv
title: CCDC 2047977: Experimental Crystal Structure Determination

description: Related Article: Xiao-Fen Guan, Hao-Jun Zhao, Ya-Jie Hao, Xiao-Rong Guo, Zhong-Ping Yang, Feng-Ying Zhang, Wen-Min Wang|2021|J.Mol.Struct.|1245|131010|doi:10.1016/j.molstruc.2021.131010
DOI: 10.5517/ccdc.csd.cc26r303
title: CCDC 2047984: Experimental Crystal Structure Determination

description: Related Article: Xiao-Fen Guan, Hao-Jun Zhao, Ya-Jie Hao, Xiao-Rong Guo, Zhong-Ping Yang, Feng-Ying Zhang, Wen-Min Wang|2021|J.Mol.Struct.|1245|131010|doi:10.1016/j.molstruc.2021.131010
DOI: 10.5517/ccdc.csd.cc26r314
title: CCDC 2047985: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc26x3cm
title: CCDC 2052800: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc26x3dn
title: CCDC 2052801: Experimental Crystal Structure Determination

description: Related Article: Shao-Liang Zhang, Xiu-Fang Fan, Rui-Li Du, Bao-Wei Shen, Xiao-Dong Song, Xiao-Qin Wei, Shan-Shan Li|2021|Polyhedron|206|115336|doi:10.1016/j.poly.2021.115336
DOI: 10.5517/ccdc.csd.cc26x3fp
title: CCDC 2052802: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc27h10t
title: CCDC 2070025: Experimental Crystal Structure Determination

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