cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25422/azu.data.22297177
title: Library Data Services Landscape Scan

description: R code and data for a landscape scan of data services at academic libraries. Original data is licensed CC By 4.0, data obtained from other sources is licensed according to the original licensing terms. R scripts are licensed under the BSD 3-clause license. Summary This work generally focuses on four questions: Which research data services does an academic library provide? For a subset of those services, what form does the support come in? i.e. consulting, instruction, or web resources? Are there differences in support between three categories of services: data management, geospatial, and data science? How does library resourcing (i.e. salaries) affect the number of research data services? Approach Using direct survey of web resources, we investigated the services offered at 25 Research 1 universities in the United States of America. Please refer to the included README.md files for more information. _______________________________________________________________________________ <em>For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu</em>
DOI: 10.25422/azu.data.22297177.v1
title: Library Data Services Landscape Scan

description: R code and data for a landscape scan of data services at academic libraries. Original data is licensed CC By 4.0, data obtained from other sources is licensed according to the original licensing terms. R scripts are licensed under the BSD 3-clause license. Summary This work generally focuses on four questions: Which research data services does an academic library provide? For a subset of those services, what form does the support come in? i.e. consulting, instruction, or web resources? Are there differences in support between three categories of services: data management, geospatial, and data science? How does library resourcing (i.e. salaries) affect the number of research data services? Approach Using direct survey of web resources, we investigated the services offered at 25 Research 1 universities in the United States of America. Please refer to the included README.md files for more information. _______________________________________________________________________________ <em>For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu</em>
DOI: 10.5517/ccdc.csd.cc2937b0
title: CCDC 2118271: Experimental Crystal Structure Determination

description: Related Article: Jakub Grajewski, Mikołaj Zgorzelak, Agnieszka Janiak, Katarzyna Taras-Goślińska|2022|ChemPlusChem|87|e202100510|doi:10.1002/cplu.202100510
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.7967050
title: U-T training data and test data for Sigsbee2A m odel

description: Here are the training and testing data sets involved in the numerical experiments in the article that has been submitted to the journal “Journal of Geophysical Research: Solid Earth”, named “Joint Model and Data-Driven Simultaneous Inversion of Velocity and Density”: SigsbeeA model. Each dataset consists of two parts: a training dataset and a testing dataset. Both training and testing data sets contain three parts: seismic data, velocity model and density model.
description: {"references": ["Guoxin Chen et al., Joint Model and Data-Driven Simultaneous Inversion of Velocity and Density."]}
DOI: 10.5281/zenodo.7967049
title: U-T training data and test data for Sigsbee2A m odel

description: Here are the training and testing data sets involved in the numerical experiments in the article that has been submitted to the journal “Journal of Geophysical Research: Solid Earth”, named “Joint Model and Data-Driven Simultaneous Inversion of Velocity and Density”: SigsbeeA model. Each dataset consists of two parts: a training dataset and a testing dataset. Both training and testing data sets contain three parts: seismic data, velocity model and density model.
description: {"references": ["Guoxin Chen et al., Joint Model and Data-Driven Simultaneous Inversion of Velocity and Density."]}
DOI: 10.34740/kaggle/dsv/5767765
title: Guava Fruit Disease Images
DOI: 10.5281/zenodo.7968565
title: Crosswalk between ISRM grid and 2010 census block

description: This dataset contain two files: (1) a crosswalk between InMAP Source-Receptor Matrix (ISRM) grid and census block (based on 2010 census); and (2) a shapefile of ISRM grid.
DOI: 10.5281/zenodo.7968564
title: Crosswalk between ISRM grid and 2010 census block

description: This dataset contain two files: (1) a crosswalk between InMAP Source-Receptor Matrix (ISRM) grid and census block (based on 2010 census); and (2) a shapefile of ISRM grid.
DOI: 10.5281/zenodo.7460399
title: MultiplexMS: A mass spectrometry-based multiplexing strategy for ultra-high-throughput analysis of complex mixtures

description: Abstract: Throughput for chemical analysis of natural products mixtures has not kept pace with recent developments in genome sequencing technologies and laboratory automation for high-throughput screening, leading to a disconnect between chemical and biological profiling at the library scale that limits new molecule discovery. Here we report a new strategy for sample multiplexing that can increase mass spectrometry-based profiling up to 30-fold over traditional methods. This strategy involves the analysis of pooled samples and subsequent computational deconvolution to reconstruct peak lists for each sample in the set. We validated this approach using <em>in silico</em> experiments and demonstrated that the method has a high precision (&gt;97%) for large, pooled samples (n = 30), particularly for infrequently occurring metabolites (n &lt; 10) of relevance in drug discovery applications. Finally, we repeated a recently reported biological activity profiling study on 925 natural products extracts, leading to the rediscovery of all previously reported bioactive metabolites using just 5% of the previously required MS acquisition time. This new method is compatible with mass spectrometry data from any instrument vendor and is supported by an open-source software package available at https://github.com/liningtonlab/MultiplexMS.Throughput for chemical analysis of natural products mixtures has not kept pace with recent developments in genome sequencing technologies and laboratory automation for high-throughput screening, leading to a disconnect between chemical and biological profiling at the library scale that limits new molecule discovery. Here we report a new strategy for sample multiplexing that can increase mass spectrometry-based profiling up to 30-fold over traditional methods. This strategy involves the analysis of pooled samples and subsequent computational deconvolution to reconstruct peak lists for each sample in the set. We validated this approach using <em>in silico</em> experiments and demonstrated that the method has a high precision (&gt;97%) for large, pooled samples (n = 30), particularly for infrequently occurring metabolites (n &lt; 10) of relevance in drug discovery applications. Finally, we repeated a recently reported biological activity profiling study on 925 natural products extracts, leading to the rediscovery of all previously reported bioactive metabolites using just 5% of the previously required MS acquisition time. This new method is compatible with mass spectrometry data from any instrument vendor and is supported by an open-source software package available at https://github.com/liningtonlab/MultiplexMS.
DOI: 10.5281/zenodo.7460400
title: MultiplexMS: A mass spectrometry-based multiplexing strategy for ultra-high-throughput analysis of complex mixtures

description: Abstract: Throughput for chemical analysis of natural products mixtures has not kept pace with recent developments in genome sequencing technologies and laboratory automation for high-throughput screening, leading to a disconnect between chemical and biological profiling at the library scale that limits new molecule discovery. Here we report a new strategy for sample multiplexing that can increase mass spectrometry-based profiling up to 30-fold over traditional methods. This strategy involves the analysis of pooled samples and subsequent computational deconvolution to reconstruct peak lists for each sample in the set. We validated this approach using <em>in silico</em> experiments and demonstrated that the method has a high precision (&gt;97%) for large, pooled samples (n = 30), particularly for infrequently occurring metabolites (n &lt; 10) of relevance in drug discovery applications. Finally, we repeated a recently reported biological activity profiling study on 925 natural products extracts, leading to the rediscovery of all previously reported bioactive metabolites using just 5% of the previously required MS acquisition time. This new method is compatible with mass spectrometry data from any instrument vendor and is supported by an open-source software package available at https://github.com/liningtonlab/MultiplexMS.Throughput for chemical analysis of natural products mixtures has not kept pace with recent developments in genome sequencing technologies and laboratory automation for high-throughput screening, leading to a disconnect between chemical and biological profiling at the library scale that limits new molecule discovery. Here we report a new strategy for sample multiplexing that can increase mass spectrometry-based profiling up to 30-fold over traditional methods. This strategy involves the analysis of pooled samples and subsequent computational deconvolution to reconstruct peak lists for each sample in the set. We validated this approach using <em>in silico</em> experiments and demonstrated that the method has a high precision (&gt;97%) for large, pooled samples (n = 30), particularly for infrequently occurring metabolites (n &lt; 10) of relevance in drug discovery applications. Finally, we repeated a recently reported biological activity profiling study on 925 natural products extracts, leading to the rediscovery of all previously reported bioactive metabolites using just 5% of the previously required MS acquisition time. This new method is compatible with mass spectrometry data from any instrument vendor and is supported by an open-source software package available at https://github.com/liningtonlab/MultiplexMS.
DOI: 10.5281/zenodo.7968494
title: MultiplexMS: A mass spectrometry-based multiplexing strategy for ultra-high-throughput analysis of complex mixtures

description: Abstract: Throughput for chemical analysis of natural products mixtures has not kept pace with recent developments in genome sequencing technologies and laboratory automation for high-throughput screening, leading to a disconnect between chemical and biological profiling at the library scale that limits new molecule discovery. Here we report a new strategy for sample multiplexing that can increase mass spectrometry-based profiling up to 30-fold over traditional methods. This strategy involves the analysis of pooled samples and subsequent computational deconvolution to reconstruct peak lists for each sample in the set. We validated this approach using <em>in silico</em> experiments and demonstrated that the method has a high precision (&gt;97%) for large, pooled samples (n = 30), particularly for infrequently occurring metabolites (n &lt; 10) of relevance in drug discovery applications. Finally, we repeated a recently reported biological activity profiling study on 925 natural products extracts, leading to the rediscovery of all previously reported bioactive metabolites using just 5% of the previously required MS acquisition time. This new method is compatible with mass spectrometry data from any instrument vendor and is supported by an open-source software package available at https://github.com/liningtonlab/MultiplexMS.Throughput for chemical analysis of natural products mixtures has not kept pace with recent developments in genome sequencing technologies and laboratory automation for high-throughput screening, leading to a disconnect between chemical and biological profiling at the library scale that limits new molecule discovery. Here we report a new strategy for sample multiplexing that can increase mass spectrometry-based profiling up to 30-fold over traditional methods. This strategy involves the analysis of pooled samples and subsequent computational deconvolution to reconstruct peak lists for each sample in the set. We validated this approach using <em>in silico</em> experiments and demonstrated that the method has a high precision (&gt;97%) for large, pooled samples (n = 30), particularly for infrequently occurring metabolites (n &lt; 10) of relevance in drug discovery applications. Finally, we repeated a recently reported biological activity profiling study on 925 natural products extracts, leading to the rediscovery of all previously reported bioactive metabolites using just 5% of the previously required MS acquisition time. This new method is compatible with mass spectrometry data from any instrument vendor and is supported by an open-source software package available at https://github.com/liningtonlab/MultiplexMS.
DOI: 10.25505/fiz.icsd.cc23bq5c
title: CSD 1946712: Experimental Crystal Structure Determination

description: Related Article: Xu-Dong Dong, Guo-Ying Yao, Qing-Lu Liu, Qing-Meng Zhao, Zong-Yan Zhao|2019|Inorg.Chem.|58|15344|doi:10.1021/acs.inorgchem.9b02328
DOI: 10.25505/fiz.icsd.cc23bmkn
title: CSD 1946631: Experimental Crystal Structure Determination

description: Related Article: Shuaishuai Hu, Zhi Su|2019|New J.Chem.|43|16127|doi:10.1039/C9NJ04424H
DOI: 10.25505/fiz.icsd.cc23bmlp
title: CSD 1946632: Experimental Crystal Structure Determination

description: Related Article: Shuaishuai Hu, Zhi Su|2019|New J.Chem.|43|16127|doi:10.1039/C9NJ04424H
DOI: 10.5517/ccdc.csd.cc230prl
title: CCDC 1937089: Experimental Crystal Structure Determination

description: Related Article: Julia Bader,  Beate Neumann,  Hans-Georg Stammler,  Berthold Hoge|2019|Eur.J.Inorg.Chem.|2019|3904|doi:10.1002/ejic.201900728
DOI: 10.25505/fiz.icsd.cc23bxhw
title: CSD 1946908: Experimental Crystal Structure Determination

description: Related Article: Arthur Haffner,  Anna-Katharina Hatz,  Constatin Hoch,  Bettina V. Lotsch,  and Dirk Johrendt|2020|Eur.J.Inorg.Chem.|2020|617|doi:10.1002/ejic.201901083
DOI: 10.25505/fiz.icsd.cc23cpx3
title: CSD 1947665: Experimental Crystal Structure Determination

description: Related Article: Hong-Xin Tang, Chao Zhuo, Yu-Xiao Zhang, Qi-Rui Shui, Rui-Biao Fu, Zu-Ju Ma, Xin-Tao Wu|2020|Dalton Trans.|49|3672|doi:10.1039/C9DT03628H
DOI: 10.25505/fiz.icsd.cc23cpy4
title: CSD 1947666: Experimental Crystal Structure Determination

description: Related Article: Hong-Xin Tang, Chao Zhuo, Yu-Xiao Zhang, Qi-Rui Shui, Rui-Biao Fu, Zu-Ju Ma, Xin-Tao Wu|2020|Dalton Trans.|49|3672|doi:10.1039/C9DT03628H
DOI: 10.25505/fiz.icsd.cc23csbm
title: CSD 1947740: Experimental Crystal Structure Determination

description: Related Article: Jianhua Jiang, Bei Zhu, Tianyu Zhu, Haoming Yang, Yong Jin, Minfeng Lü|2020|Dalton Trans.|49|841|doi:10.1039/C9DT03370J
DOI: 10.25505/fiz.icsd.cc23cscn
title: CSD 1947741: Experimental Crystal Structure Determination

description: Related Article: Jianhua Jiang, Bei Zhu, Tianyu Zhu, Haoming Yang, Yong Jin, Minfeng Lü|2020|Dalton Trans.|49|841|doi:10.1039/C9DT03370J
DOI: 10.25318/3510019801-eng
title: Federally appointed judges by gender

description: Number and proportion of federally appointed judges in federal, provincial and territorial courts by gender, Canada, provinces and territories.
DOI: 10.6073/pasta/001e2d144c2da15fd97b10643a656939
title: Effects of chronic nutrient enrichment on plant diversity and ecosystem productivity, 2008-2019

description: Human activities are enriching many of Earth's ecosystems with biologically limiting mineral nutrients such as nitrogen (N) and phosphorus (P). In grasslands, this enrichment generally reduces plant diversity and increases productivity. The widely demonstrated positive effect of diversity on productivity suggests a potential negative feedback, whereby nutrient induced declines in diversity reduce the initial gains in productivity arising from nutrient enrichment. In addition, plant productivity and diversity can be inhibited by accumulations of dead biomass, which may be altered by nutrient enrichment. Over longer timeframes, nutrient addition may increase soil fertility by increasing soil organic matter and nutrient pools. We examined the effects of 5-11 years of nutrient addition at 47 grasslands in twelve countries. Nutrient enrichment increased aboveground live biomass and reduced plant diversity at nearly all sites, and these effects became stronger over time. We did not find evidence that nutrient induced losses of diversity reduced the positive effects of nutrients on biomass, however nutrient effects on live biomass increased more slowly at sites where litter was also increasing, regardless of plant diversity. This work suggests that short-term experiments may underestimate the long term nutrient enrichment effects on global, grassland ecosystems.
DOI: 10.25318/3510019801-fra
title: Juges nommés par le gouvernement fédéral, selon le genre

description: Nombre et proportion de juges nommés par le gouvernement fédéral dans les tribunaux fédéraux, provinciaux et territoriaux, selon le genre, Canada, provinces et territoires.
DOI: 10.6073/pasta/14410e386e00420277f2ccbc570c30c5
title: Jornada Basin LTER: Wireless meteorological station at NPP G-BASN site: 1-hour summary data: 2013 - ongoing

description: Hourly summary data at NPP G-BASN met station. Average/maximum/minimum air temperature; average relative humidity and wind direction; average/maximum wind speed. These are measured and calculated based on 1-second scan rate of all sensors located at an automated meteorological station installed at Jornada LTER NPP G-BASN site. Wind speed is measured at 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximate 2.5m. This climate station is operated by the Jornada LTER Program. This is an ONGOING dataset.
DOI: 10.25318/1310083301-eng
title: Death counts, age-standardized mortality rate per 100,000 people, and rate ratios for all-causes and selected causes of death by neighbourhood income quintile, Canada (excluding territories) and selected regions, 2020

description: This table provides Canadians and researchers with 2020 mortality data for all-causes and selected causes of death by neighbourhood income quintile. The data are available for Canada (excluding territories) and for selected regions.
DOI: 10.25318/1310083301-fra
title: Nombre de décès, taux de mortalité normalisé selon l'âge pour 100 000 habitants et ratio des taux pour toutes les causes et certaines causes de décès selon le quintile de revenu du quartier, Canada (à l'exclusion des territoires) et certaines régions, 2020

description: Ce tableau fournit aux Canadiens et aux chercheurs des données sur la mortalité en 2020 pour toutes les causes et certaines causes de décès selon le quintile de revenu du quartier. Les données sont disponibles pour le Canada (à l'exclusion des territoires) et pour certaines régions.
DOI: 10.6073/pasta/7e73f812f1cbf173c504408ad7f94eba
title: Jornada Basin LTER: Wireless meteorological station at NPP G-BASN site: 5-minute summary wind and air temperature data: 2013 - ongoing

description: 5-minute summary data at NPP G-BASN met station. Average air temperature, wind speed and wind direction are measured and calculated based on 1-second scan rate of all sensors located at an automated meteorological station installed at Jornada LTER NPP G-BASN site. Wind speed is measured at 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature at approximate 2.5m. This climate station is operated by the Jornada LTER Program. This is an ONGOING dataset.
DOI: 10.6073/pasta/add04fbff95a1cff1c913beb445870ea
title: Jornada Basin LTER: Wireless meteorological station at NPP G-BASN site: 30-minute summary data: 2013 - ongoing

description: 30-minute summary data at NPP G-BASN met station. Average air temperature, relative humidity, wind speed and wind direction are measured and calculated based on 1-second scan rate of all sensors located at an automated meteorological station installed at Jornada LTER NPP G-BASN site. Wind speed is measured at 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximate 2.5m. This climate station is operated by the Jornada LTER Program. This is an ONGOING dataset.
DOI: 10.6073/pasta/a3fa0a6770d89271d2fea71e0570967f
title: Jornada Basin LTER: Wireless meteorological station at NPP G-BASN site: Daily summary data: 2013 - ongoing

description: Daily summary data at NPP G-BASN met station. Average/maximum/minimum air temperature; average/maximum relative humidity and wind speed and average wind direction. These are measured and calculated based on 1-second scan rate of all sensors located at an automated meteorological station installed at Jornada LTER NPP G-BASN site. Wind speed is measured at 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximate 2.5m. This climate station is operated by the Jornada LTER Program. This is an ONGOING dataset.
DOI: 10.6073/pasta/34dedfbbcb133d1bbf72cc08e0d93c31
title: Jornada Basin LTER: Wireless meteorological station at NPP G-BASN site: 1-second summary precipitation data: 2013 - ongoing

description: 1-second summary precipitation data at NPP G-BASN met station. Total precipitation per second is recorded only if there is measurable precipitation. This is recorded at an automated meteorological station installed at Jornada LTER NPP G-BASN site.. This climate station is operated by the Jornada LTER Program. IMPORTANT: Rainfall is only recorded when a bucket tip occurs on the tipping bucket rain gauge. If there is missing data due to equipment or other problems, there will be no indication of this within this dataset. The user needs to review precipitation values in the 30 minute or hourly summary datasets to determine if there are missing data in this 1 second detailed precipitation dataset. Reference the package i.d.'s knb-lter-jrn.210437019, knb-lter-jrn.210437034, and knb-lter-jrn.210437049 for the NPP G-BASN met station 30 minute, hourly, and daily summary datasets, respectively. This is an ONGOING dataset.
DOI: 10.6073/pasta/99e2d05a6cee282cc2b44f9c349b23b4
title: Jornada Basin LTER: Wireless meteorological station at NPP G-BASN site: 30-minute soil volumetric water content data: 2013 - ongoing

description: 30-minute Soil Moisture data at NPP G-BASN met station. Collection of soil volumetric water content data at Jornada LTER NPP sites, New Mexico, supports the environmental monitoring objectives of the Jornada LTER monitoring program that look at plant-soil water dynamics. Volumetric water content, bulk electrical conductivity, soil temperature, and bulk dielectric permittivity are measured every 30 minutes at an automated meteorological station installed at Jornada LTER NPP G-BASN site. Measurements are made every 30 minutes at 10 cm, 20 cm, and 30 cm soil depths. This is an ONGOING dataset.