cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e184425
title: Making Up Is Hard To Do

description: The issue of rapprochement between former adversaries has received scant scholarly attention and there is little understanding of why some foes reconcile quickly while others remain hostile for decades. This study advances research on enduring rivalries while proposing a novel theory of reconciliation involving shared democracy and economic incentives. Specifically, the paper proposes that more democratic and trade dependent dyads with larger markets are more likely to reconcile quickly after a conflict. This theory is tested on a new dataset that includes all potential cases of reconciliation after warfare since World War II. The data includes all countries involved in interstate conflict between 1945 and 2010. It is coded dyad-year, with 60 dyads and 1,342 observations. The data was analyzed using a hazard models. Survival analysis shows that shared democracy, trade dependence, and market size are significantly related to faster reconciliation.
DOI: 10.3886/e184425v1
title: Making Up Is Hard To Do

description: The issue of rapprochement between former adversaries has received scant scholarly attention and there is little understanding of why some foes reconcile quickly while others remain hostile for decades. This study advances research on enduring rivalries while proposing a novel theory of reconciliation involving shared democracy and economic incentives. Specifically, the paper proposes that more democratic and trade dependent dyads with larger markets are more likely to reconcile quickly after a conflict. This theory is tested on a new dataset that includes all potential cases of reconciliation after warfare since World War II. The data includes all countries involved in interstate conflict between 1945 and 2010. It is coded dyad-year, with 60 dyads and 1,342 observations. The data was analyzed using a hazard models. Survival analysis shows that shared democracy, trade dependence, and market size are significantly related to faster reconciliation.
DOI: 10.18112/openneuro.ds004456.v1.0.0
title: Bilingual speech production with functional localizers
DOI: 10.15482/usda.adc/1528484
title: Data on Pregnancy rates, calf weaning weights, and sale prices on the University of Nevada Agricultural Experiment Station’s Gund Ranch from 2008-2020

description: The University of Nevada Agricultural Experiment Station’s Gund Ranch near Austin, Nevada is approximately 100,000 acres with grazing by 325 cow-calf pairs.  Cows are mostly Angus (80%) and Hereford (20%).  Bulls are Angus X Hereford.  Calves are mostly Angus with some black baldies. Cattle graze a Bureau of Land Management (BLM) grazing permit from April through July. Calving starts about mid-April. Branding is planned for 90-100 days after birth with bull calves castrated as this time. Privately-owned meadows are grazed July-September with weaning starting on September 15. Cows are grazed on BLM low elevation shrublands and weaned calves are kept in meadows with supplemental hay feeding on the Gund Ranch from October 1st through January 1st.  The calves not kept for replacements are gathered and weighed for a total herd weight and shipped approximately the 1st of November. The larger end of the steers and heifers typically range from 475-490 pounds.  Around 100 head are loaded onto a semi to achieve a full truckload of 48,000-50,0000 pounds.  The smaller end of the steers and heifers are shipped on a second semi load.  Weights are decreased with a 2% shrink calculation.  In Nevada, there are only two sale barns, so many ranches receive more money by selling semi loads shipped to grazinglands of buyers’ choice.  Calves are primarily sold on the internet go through Roundup Cattle Company.  In years with drought conditions, calves may not reach the 48,000-50,000 weight limit for a second truck. Thus, the smaller calves can be marketed through a local sale barn at a one of their special calf sales.
Supported/funded by the University of Nevada, Reno, the Nevada Agricultural Experiment Station, and USDA.
DOI: 10.5519/qd.fa7e6n3b
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (4151 records)
DOI: 10.3886/e127081
title: COVID Isolation on Sleep and Health in Healthcare Workers

description: <b>Brief Summary Statement </b> <b>Current Knowledge/Study Rationale: </b>Previous studies conducted across the globe have shown higher stress, worsening mental health, and worsening sleep patterns in front line healthcare workers during the COVID-19 pandemic. The rationale for this study was to examine whether sleep, mood, and health related behaviors might differ between healthcare workers who transitioned to conducting care from home and those who continued to report in-person to their respective hospitals or healthcare facilities. <b></b> <b>Study Impact: </b> This study shows worsening mood across healthcare workers, regardless of whether they were working from home or continuing to report to work. Our findings highlight the need for support programs to monitor mood and health during the during the COVID-19 pandemic.
DOI: 10.3886/e127081v2
title: COVID Isolation on Sleep and Health in Healthcare Workers

description: <b>Brief Summary Statement </b> <b>Current Knowledge/Study Rationale: </b>Previous studies conducted across the globe have shown higher stress, worsening mental health, and worsening sleep patterns in front line healthcare workers during the COVID-19 pandemic. The rationale for this study was to examine whether sleep, mood, and health related behaviors might differ between healthcare workers who transitioned to conducting care from home and those who continued to report in-person to their respective hospitals or healthcare facilities. <b></b> <b>Study Impact: </b> This study shows worsening mood across healthcare workers, regardless of whether they were working from home or continuing to report to work. Our findings highlight the need for support programs to monitor mood and health during the during the COVID-19 pandemic.
DOI: 10.6084/m9.figshare.20483691
title: Dengue and chikungunya virus loads in the mosquito Aedes aegypti are determined by distinct genetic architectures

description: We include several data sets 1. 'viruscopiesperfamilyforheritability' is the calculated virus counts based on a standard curve per each mosquito in the family design. 2. 'CHIKDENVcandidategeneexpression' is the raw CT values for both the genes of interest and RPS17 as well as the calcuations. 3. 'raw_counts.csv'. Raw read counts for the RNAseq 4. All the individual BAM files for RNAseq. High and Low files are named by family number.
DOI: 10.6084/m9.figshare.20483691.v1
title: Dengue and chikungunya virus loads in the mosquito Aedes aegypti are determined by distinct genetic architectures

description: We include several data sets 1. 'viruscopiesperfamilyforheritability' is the calculated virus counts based on a standard curve per each mosquito in the family design. 2. 'CHIKDENVcandidategeneexpression' is the raw CT values for both the genes of interest and RPS17 as well as the calcuations. 3. 'raw_counts.csv'. Raw read counts for the RNAseq 4. All the individual BAM files for RNAseq. High and Low files are named by family number.
DOI: 10.6073/pasta/99364188b6408dc6c1e59cf482a08ebf
title: Interagency Ecological Program: Phytoplankton monitoring in the Sacramento-San Joaquin Bay-Delta, collected by the Environmental Monitoring Program, 2008-2021

description: The State Water Resources Control Board (SWRCB) sets water quality objectives to protect beneficial uses of water in the Sacramento-San Joaquin Delta and Suisun Bay. These objectives are met by establishing standards mandated in water right permits issued to the Department of Water Resources and U.S. Bureau of Reclamation by the SWRCB. The standards include minimum Delta outflows, limits to Delta water export by the State Water Project (SWP) and the Central Valley Project (CVP), and maximum allowable salinity levels. In 1971, the State Water Resources Control Board (SWRCB) established Water Right Decision 1379 (D-1379). This Decision contained new water quality requirements for the San Francisco Bay-Delta Estuary. D-1379 was also the first water right decision to provide terms and conditions for a comprehensive monitoring program to routinely determine water quality conditions and changes in environmental conditions within the estuary. The monitoring program described in D-1379 was developed by the Stanford Research Institute through a contract with the SWRCB. Implementation of the monitoring program began in 1972, as SWRCB, DWR, and USBR met to define their individual responsibilities for various elements of the monitoring program. In 1978, amendments to water quality standards were implemented and resulted in Water Right Decision 1485 (D-1485). More recently these standards were again amended under the 1995 Water Quality Control Plan and Water Right Decision 1641 (D-1641) established in 1999. The SWP and CVP are currently operated to comply with the monitoring and reporting requirements described in D-1641. D-1641 requires DWR and USBR to conduct a comprehensive environmental monitoring program to determine compliance with the water quality standards and also to submit an annual report to SWRCB discussing data collected. The phytoplankton monitoring program is one element of DWR’s and USBR’s Environmental Monitoring Program (EMP) conducted under the Interagency Ecological Program (IEP) umbrella. The EMP also includes monitoring of water quality, zooplankton, and benthic organisms. The overall objective of the phytoplankton monitoring program is to determine the impacts of water project operations on the estuary. These impacts are interpreted by changes in phytoplankton diversity, abundance, and distribution associated with physical and other biological factors in the estuary. Complimentary EMP Datasets: Water Quality: https://portal.edirepository.org/nis/mapbrowse?scope=edi&identifier=458&revision=7 Benthic: https://portal.edirepository.org/nis/mapbrowse?packageid=edi.1036.2
DOI: 10.34740/kaggle/dsv/4927743
title: GOAT Tweets
DOI: 10.5281/zenodo.7575579
title: Curated Dataset of Association Constants Between a Cyclodextrin and a Guest for Machine Learning: Raw Data and Generation Script

description: Determining the association constant between a cyclodextrin and a guest molecule is an important task for various applications in various industrial and academical fields. However, such a task is time consuming, tedious and requires samples of both molecules. A significant number of association constants and relevant data is available from the literature. The availability of data makes the use of machine learning techniques to predict association constants possible. However, such data is mainly available from tables in articles or appendices. It is necessary to make them available in a computer friendly format and to curate them. Furthermore, the raw data need to be enriched with physicochemical information about each molecule and when such information does not allow to discriminate molecules, some additional data is needed. We present a dataset built from data gathered from the literature. The dataset contains both the original raw data from the articles and the enriched ones. We also provide the scripts used to curate and enrich the raw data.
description: {"references": ["10.5281/zenodo.7575539"]}
DOI: 10.5281/zenodo.7575539
title: Curated Dataset of Association Constants Between a Cyclodextrin and a Guest for Machine Learning

description: Determining the association constant between a cyclodextrin and a guest molecule is an important task for various applications in various industrial and academical fields. However, such a task is time consuming, tedious and requires samples of both molecules. A significant number of association constants and relevant data is available from the literature. The availability of data makes the use of machine learning techniques to predict association constants possible. However, such data is mainly available from tables in articles or appendices. It is necessary to make them available in a computer friendly format and to curate them. Furthermore, the raw data need to be enriched with physicochemical information about each molecule and when such information does not allow to discriminate molecules, some additional data is needed. We present a dataset built from data gathered from the literature.
DOI: 10.5281/zenodo.7575578
title: Curated Dataset of Association Constants Between a Cyclodextrin and a Guest for Machine Learning: Raw Data and Generation Script

description: Determining the association constant between a cyclodextrin and a guest molecule is an important task for various applications in various industrial and academical fields. However, such a task is time consuming, tedious and requires samples of both molecules. A significant number of association constants and relevant data is available from the literature. The availability of data makes the use of machine learning techniques to predict association constants possible. However, such data is mainly available from tables in articles or appendices. It is necessary to make them available in a computer friendly format and to curate them. Furthermore, the raw data need to be enriched with physicochemical information about each molecule and when such information does not allow to discriminate molecules, some additional data is needed. We present a dataset built from data gathered from the literature. The dataset contains both the original raw data from the articles and the enriched ones. We also provide the scripts used to curate and enrich the raw data.
description: {"references": ["10.5281/zenodo.7575539"]}
DOI: 10.5281/zenodo.7575538
title: Curated Dataset of Association Constants Between a Cyclodextrin and a Guest for Machine Learning

description: Determining the association constant between a cyclodextrin and a guest molecule is an important task for various applications in various industrial and academical fields. However, such a task is time consuming, tedious and requires samples of both molecules. A significant number of association constants and relevant data is available from the literature. The availability of data makes the use of machine learning techniques to predict association constants possible. However, such data is mainly available from tables in articles or appendices. It is necessary to make them available in a computer friendly format and to curate them. Furthermore, the raw data need to be enriched with physicochemical information about each molecule and when such information does not allow to discriminate molecules, some additional data is needed. We present a dataset built from data gathered from the literature.
DOI: 10.5517/ccdc.csd.cc261nrp
title: CCDC 2027392: Experimental Crystal Structure Determination

description: Related Article: Xiaoyu Ma, Bingqing Zhu, Zeen Yang, Yuhang Jiang, Xuefeng Mei|2021|CrystEngComm|23|30|doi:10.1039/D0CE01551B
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.cc261nsq
title: CCDC 2027393: Experimental Crystal Structure Determination

description: Related Article: Xiaoyu Ma, Bingqing Zhu, Zeen Yang, Yuhang Jiang, Xuefeng Mei|2021|CrystEngComm|23|30|doi:10.1039/D0CE01551B
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.cc261ntr
title: CCDC 2027394: Experimental Crystal Structure Determination

description: Related Article: Xiaoyu Ma, Bingqing Zhu, Zeen Yang, Yuhang Jiang, Xuefeng Mei|2021|CrystEngComm|23|30|doi:10.1039/D0CE01551B
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.cc261nwt
title: CCDC 2027396: Experimental Crystal Structure Determination

description: Related Article: Xiaoyu Ma, Bingqing Zhu, Zeen Yang, Yuhang Jiang, Xuefeng Mei|2021|CrystEngComm|23|30|doi:10.1039/D0CE01551B
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.cc261nxv
title: CCDC 2027397: Experimental Crystal Structure Determination

description: Related Article: Xiaoyu Ma, Bingqing Zhu, Zeen Yang, Yuhang Jiang, Xuefeng Mei|2021|CrystEngComm|23|30|doi:10.1039/D0CE01551B
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.cc2659k8
title: CCDC 2030889: Experimental Crystal Structure Determination

description: Related Article: Jia Zhou, Zi-Hao Li, Le Wang, Jun-Chen Kang, Xun-Hui Wang, Shu-Yu Zhang|2021|Org.Lett.|23|9353|doi:10.1021/acs.orglett.1c03399
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.cc265c3w
title: CCDC 2030937: Experimental Crystal Structure Determination

description: Related Article: Elisabeth Sitte, Brendan Twamley, Nitika Grover, Mathias O. Senge|2021|J.Org.Chem.|86|1238|doi:10.1021/acs.joc.0c02432
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.cc265c4x
title: CCDC 2030938: Experimental Crystal Structure Determination

description: Related Article: Elisabeth Sitte, Brendan Twamley, Nitika Grover, Mathias O. Senge|2021|J.Org.Chem.|86|1238|doi:10.1021/acs.joc.0c02432
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.cc265c5y
title: CCDC 2030939: Experimental Crystal Structure Determination

description: Related Article: Elisabeth Sitte, Brendan Twamley, Nitika Grover, Mathias O. Senge|2021|J.Org.Chem.|86|1238|doi:10.1021/acs.joc.0c02432
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.cc265c6z
title: CCDC 2030940: Experimental Crystal Structure Determination

description: Related Article: Elisabeth Sitte, Brendan Twamley, Nitika Grover, Mathias O. Senge|2021|J.Org.Chem.|86|1238|doi:10.1021/acs.joc.0c02432
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.cc265c70
title: CCDC 2030941: Experimental Crystal Structure Determination

description: Related Article: Elisabeth Sitte, Brendan Twamley, Nitika Grover, Mathias O. Senge|2021|J.Org.Chem.|86|1238|doi:10.1021/acs.joc.0c02432
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.cc265c81
title: CCDC 2030942: Experimental Crystal Structure Determination

description: Related Article: Elisabeth Sitte, Brendan Twamley, Nitika Grover, Mathias O. Senge|2021|J.Org.Chem.|86|1238|doi:10.1021/acs.joc.0c02432
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.cc265c92
title: CCDC 2030943: Experimental Crystal Structure Determination

description: Related Article: Elisabeth Sitte, Brendan Twamley, Nitika Grover, Mathias O. Senge|2021|J.Org.Chem.|86|1238|doi:10.1021/acs.joc.0c02432
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.cc25p7mr
title: CCDC 2016414: Experimental Crystal Structure Determination

description: Related Article: Krishanu Samanta, Jayanta Samanta, Ramalingam Natarajan|2021|Cryst.Growth Des.|21|166|doi:10.1021/acs.cgd.0c00978
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.cc265cb3
title: CCDC 2030944: Experimental Crystal Structure Determination

description: Related Article: Elisabeth Sitte, Brendan Twamley, Nitika Grover, Mathias O. Senge|2021|J.Org.Chem.|86|1238|doi:10.1021/acs.joc.0c02432
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.