cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5063/377744
title: Pink salmon age, sex, and length data from Prince William Sound, Alaska, 1997-2016

description: Age, sex and length (ASL) data provide population dynamics information that can indicate how population trends occur and may be changing. These data can help researchers estimate population growth rates, age-class distribution, and population demographics. Knowing population demographics, growth rates and trends is particularly valuable to fisheries managers who must perform population assessments to inform management decisions. These data are therefore important in valuable fisheries like the salmon fisheries of Alaska. This dataset includes length data on pink salmon from annual sampling of commercial salmon harvests in Prince William Sound from 1997-2016. Length was measured in millimeters, generally from mid-eye to the fork of the tail. Because pink salmon always return after 2 years at sea, the salt water age was set at 2 for all rows of the dataset to enable easier integration with ASL datasets from other species. This data package includes the original data file (PWS Pink_LenWt_Individual Salmon Data_ALLYEARS copy.xls), a script that reformats the original data file into a consistent format (ASL_formatting_PWS_pink.R), and the reformatted dataset as a .csv file (ASL_formatted_PrinceWilliamSoundpinks.csv).
DOI: 10.5063/zg6qnf
title: Coho salmon brood tables, northeast Pacific, 1967-2015

description: Brood tables, also called run reconstructions, utilize annual estimates of the total run (commercial catch plus escapement), and samples of ages, to estimate the number of recruits per age class. These data are useful for salmon biologists to understand salmon productivity and salmon life histories. These data can come in a number of different formats, but generally follow the pattern of: rows for each brood year, and columns for the estimated number of fish in each age class. This dataset consists of reformatted coho salmon brood tables that have been compiled together. These datasets were gathered as part of the State of Alaska Salmon and People project (https://alaskasalmonandpeople.org/). The file "CohoBroodTables_FW_SW_Age.csv" includes stocks where age classes are in freshwater/saltwater notation (number of winters in freshwater, followed by number of winters in saltwater). The file "CohoBroodTables_TotalAge.csv" includes stocks where age classes are presented as total age (number of winters in freshwater + number of winters at sea + 1). Stocks that have data in freshwater/saltwater age had their total age calculated and were then added to the total age table. The file cohoBroodTableProcessing.Rmd details the data reformatting steps in R Markdown, and cohoBroodTableProcessing.html is the html output of the Rmd file. The file StockInfo_Coho.csv lists the chinook stocks that are included in this package, and SourceInfo_Coho.csv lists the data sources for each Source.ID in the data files.
DOI: 10.5281/zenodo.5172966
title: Barriers to Melt Ascent in the Lithosphere of Io with Applications to Heat Pipe Formation: MELTS Calculations

description: MELTS input and output datafiles for "Barriers to Melt Ascent in the Lithosphere of Io with Applications to Heat Pipe Formation." Each zip file corresponds to one dataset with a set resurfacing rate and a choice of nepheline allowed or nepheline suppressed. Each data set is organized first by lithosphere thickness, then mantle potential temperature. PTpath.txt is the text file read by MELTS to create the geotherm P-T path in the crystallization calculation. Residue.melts is the Residual solid composition after the melting calculation. Liquid.melts is the liquid composition that enters the crystallization calculation. PBRate.txt is a two column text file which is a record of crystallization rate with depth. The output_1 folder contains the MELTS output files for the decompression melting calculation. The output_2 folder contains the MELTS output files for the crystallization in the lithosphere calculation. Associated MATLAB functions for this work can be found here: https://github.com/joeschools/Io_PB (https://doi.org/10.5281/zenodo.5172975)
description: This project was supported by a NASA Earth and Space Sciences Fellowship, Grant 80NSSC17K0486
DOI: 10.5281/zenodo.5172965
title: Barriers to Melt Ascent in the Lithosphere of Io with Applications to Heat Pipe Formation: MELTS Calculations

description: MELTS input and output datafiles for "Barriers to Melt Ascent in the Lithosphere of Io with Applications to Heat Pipe Formation." Each zip file corresponds to one dataset with a set resurfacing rate and a choice of nepheline allowed or nepheline suppressed. Each data set is organized first by lithosphere thickness, then mantle potential temperature. PTpath.txt is the text file read by MELTS to create the geotherm P-T path in the crystallization calculation. Residue.melts is the Residual solid composition after the melting calculation. Liquid.melts is the liquid composition that enters the crystallization calculation. PBRate.txt is a two column text file which is a record of crystallization rate with depth. The output_1 folder contains the MELTS output files for the decompression melting calculation. The output_2 folder contains the MELTS output files for the crystallization in the lithosphere calculation. Associated MATLAB functions for this work can be found here: https://github.com/joeschools/Io_PB (https://doi.org/10.5281/zenodo.5172975)
description: This project was supported by a NASA Earth and Space Sciences Fellowship, Grant 80NSSC17K0486
DOI: 10.5063/pz5777
title: Salmon age, sex, and length data from Bristol Bay region of Alaska, 2010-2015

description: Age, sex and length data provide population dynamics information that can indicate how populations trends occur and may be changing. These data can help researchers estimate population growth rates, age-class distribution and population demographics. Knowing population demographics, growth rates and trends is particularly valuable to fisheries managers who must perform population assessments to inform management decisions. These data are therefore particularly important in valuable fisheries like the salmon fisheries of Alaska. This dataset includes age, sex and length data compiled from annual sampling of commercial and subsistence salmon harvests and research projects in the Bristol Bay region of Alaska. Age estimates were made by examining scales or bony structures (e.g. otoliths - ear bones). Scales were removed from the side of the fish; usually the left side above the lateral line. Scales or bony structures were then mounted on gummed cards and pressed on acetate to make an impression. The number of freshwater and saltwater annuli (i.e. rings) was counted to estimate age in years. Fish sex was determined by either examining external morphology (eg. head and belly shape) or internal sex organ. Length was measured in millimeters, generally from mid-eye to the fork of the tail. This data package includes the original data files and an R script that reformats the original data files into a single dataset, 'bristol_bay_full.csv'.
DOI: 10.5517/ccdc.csd.cc25v6t2
title: CCDC 2021194: Experimental Crystal Structure Determination

description: Related Article: Nels T. Anderson, Joseph C. Wright, Gregory S. Girolami|2021|Inorg.Chem.|60|11164|doi:10.1021/acs.inorgchem.1c01139
DOI: 10.5517/ccdc.csd.cc25v6v3
title: CCDC 2021195: Experimental Crystal Structure Determination

description: Related Article: Nels T. Anderson, Joseph C. Wright, Gregory S. Girolami|2021|Inorg.Chem.|60|11164|doi:10.1021/acs.inorgchem.1c01139
DOI: 10.5517/ccdc.csd.cc25v6w4
title: CCDC 2021196: Experimental Crystal Structure Determination

description: Related Article: Nels T. Anderson, Joseph C. Wright, Gregory S. Girolami|2021|Inorg.Chem.|60|11164|doi:10.1021/acs.inorgchem.1c01139
DOI: 10.5517/ccdc.csd.cc25v6y6
title: CCDC 2021198: Experimental Crystal Structure Determination

description: Related Article: Nels T. Anderson, Joseph C. Wright, Gregory S. Girolami|2021|Inorg.Chem.|60|11164|doi:10.1021/acs.inorgchem.1c01139
DOI: 10.5517/ccdc.csd.cc26f998
title: CCDC 2038569: Experimental Crystal Structure Determination

description: Related Article: Biplab Keshari Pandia, Chidambaram Gunanathan|2021|J.Org.Chem.|86|9994|doi:10.1021/acs.joc.1c00685
DOI: 10.5517/ccdc.csd.cc26f9b9
title: CCDC 2038570: Experimental Crystal Structure Determination

description: Related Article: Biplab Keshari Pandia, Chidambaram Gunanathan|2021|J.Org.Chem.|86|9994|doi:10.1021/acs.joc.1c00685
DOI: 10.5517/ccdc.csd.cc2539p9
title: CCDC 1999180: Experimental Crystal Structure Determination

description: Related Article: Mahak Vij, Harsh Yadav, Sahil Goel, Nikita Vashistha, Sonia, Debabrata Nayak, Prashant Kumar, K.K. Maurya|2021|J.Mol.Struct.|1246|131177|doi:10.1016/j.molstruc.2021.131177
DOI: 10.3886/e147461
title: Binaural Beats and Memory

description: This study explored whether listening to a publicly available beta-frequency binaural beats program while learning new material may have a positive effect on memory for the material. Undergraduate students (46 women, 29 men) first completed measures of verbal learning ability (California Verbal Learning Test II), preexisting knowledge (questions asking for level of knowledge about each sleep disorder in the forthcoming passage), and mood (Profile of Mood States-Brief). Afterward, they spent 20 minutes reading and reviewing a passage about sleep disorders, either in silence or while listening via headphones to either a beta-frequency or theta-frequency binaural beats program from the Brain Wave app developed by Banzai Labs. Finally, participants completed the mood measure a second time, followed by a 15-item multiple-choice test with questions about the passage. The groups did not differ on verbal learning ability, preexisting knowledge, or mood (using difference scores), but they did differ on test score, with the beta-frequencies group having greater test scores than the theta-frequencies group (p = .001) and control group (p = .03); the latter two groups did not differ on test scores (p = .54). These findings suggest that learning may be enhanced with the use of this “Memory Boost” binaural beats program.<br><br><br>
DOI: 10.5517/ccdc.csd.cc26q0gf
title: CCDC 2046944: Experimental Crystal Structure Determination

description: Related Article: Rasoul Vafazadeh, Sahar Aghayani, Anthony C. Willis|2021|J.Mol.Struct.|1246|131192|doi:10.1016/j.molstruc.2021.131192
DOI: 10.3886/e147461v1
title: Binaural Beats and Memory

description: This study explored whether listening to a publicly available beta-frequency binaural beats program while learning new material may have a positive effect on memory for the material. Undergraduate students (46 women, 29 men) first completed measures of verbal learning ability (California Verbal Learning Test II), preexisting knowledge (questions asking for level of knowledge about each sleep disorder in the forthcoming passage), and mood (Profile of Mood States-Brief). Afterward, they spent 20 minutes reading and reviewing a passage about sleep disorders, either in silence or while listening via headphones to either a beta-frequency or theta-frequency binaural beats program from the Brain Wave app developed by Banzai Labs. Finally, participants completed the mood measure a second time, followed by a 15-item multiple-choice test with questions about the passage. The groups did not differ on verbal learning ability, preexisting knowledge, or mood (using difference scores), but they did differ on test score, with the beta-frequencies group having greater test scores than the theta-frequencies group (p = .001) and control group (p = .03); the latter two groups did not differ on test scores (p = .54). These findings suggest that learning may be enhanced with the use of this “Memory Boost” binaural beats program.<br><br><br>
DOI: 10.5517/ccdc.csd.cc26q0hg
title: CCDC 2046945: Experimental Crystal Structure Determination

description: Related Article: Rasoul Vafazadeh, Sahar Aghayani, Anthony C. Willis|2021|J.Mol.Struct.|1246|131192|doi:10.1016/j.molstruc.2021.131192
DOI: 10.5517/ccdc.csd.cc26q0kj
title: CCDC 2046947: Experimental Crystal Structure Determination

description: Related Article: Rasoul Vafazadeh, Sahar Aghayani, Anthony C. Willis|2021|J.Mol.Struct.|1246|131192|doi:10.1016/j.molstruc.2021.131192
DOI: 10.5517/ccdc.csd.cc26r101
title: CCDC 2047922: Experimental Crystal Structure Determination

description: Related Article: Shalu Gupta, Marilyn Daisy Milton|2021|J.Photochem.Photobiol.A:Chem.|419|113444|doi:10.1016/j.jphotochem.2021.113444
DOI: 10.15468/w9xhmt
title: Hygrotus (Coelambus) nubilus (LECONTE, 1855) on Mauna Kea (Hawaii) - first record of the genus from the Pacific zoogeographical region (Coleoptera: Dytiscidae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Fery, Hans, Challet, Gil (2015): Hygrotus (Coelambus) nubilus (LECONTE, 1855) on Mauna Kea (Hawaii) - first record of the genus from the Pacific zoogeographical region (Coleoptera: Dytiscidae). Linzer biologische Beiträge 47 (2): 1303-1309, DOI: http://doi.org/10.5281/zenodo.5191312
DOI: 10.5063/k935zk
title: Salmon escapement data from Arcic-Yukon-Kusokwim Region, 1965-2015

description: The salmon life cycle begins in freshwater streams when adult salmon spawn, leaving fertilized eggs which hatch in the stream. Juvenile salmon migrate downstream to the ocean, where they spend several years until they reach reproductive age. Upon reaching sexual maturity, they return to their natal streams to spawn. The number of mature salmon migrating from the marine environment to freshwater streams is defined as escapement. Escapement data are the enumeration of these migrating fish as they pass upstream, and are a widely used index of spawning salmon abundance. These data are important for fisheries management, since most salmon harvest occurs in freshwater rivers during this migration. Escapement data are collected in a variety of ways. Stationary projects utilize observers stationed along freshwater corridors who count salmon as they pass upriver through weirs or past elevated towers. Sonar equipment placed in the river can also give a stationary escapement count. These counts usually represent a sample, and are expanded to represent a 24h period. Escapement data can also be collected using aerial surveys, where observers in an aircraft provide an index to estimate escapement. In general, escapement counts do not represent total abundance, but instead an index of abundance. Surveys are usually timed to coincide with peak spawning activity, generally in the summer, but in the case of Coho salmon in the fall as well. These data are the result of a multi-year effort by the Alaska Department of Fish and Game, Division of Commercial Fisheries, Arctic-Yukon-Kusokwim (AYK) Region to create a salmon database management system that centralizes AYK salmon data in a standard format, making the data more accessible to management agencies and the general public. The escapement data portion of this database includes data from more than 70 projects conducted by the Alaska Department of Fish and Game, federal agencies, non-governmental organizations, and the Canadian Department of Fisheries and Oceans. Data span the time period of 1965 to present, and were collected on 58 unique rivers in four management areas. These areas are Kotzebue, Kuskokwim, Norton Sound-Port Clarence, Yukon and Yukon-Canada. The five Alaskan salmon species (Chinook, Chum, Coho, Pink, Sockeye) are all represented in this dataset, in addition to two salmonids (Dolly Varden, Arctic Char).
DOI: 10.5063/fj2f78
title: Sockeye salmon brood tables, PWS and Southeast, Alaska, 1958-2016

description: Brood tables, also called run reconstructions, utilize annual estimates of the total run (commercial catch plus escapement), and samples of ages, to estimate the number of recruits per age class. These data are useful for salmon biologists to understand salmon productivity and salmon life histories. This dataset consists of: original brood tables in excel format for sockeye salmon from Copper River, Coghill and Eshamy Lakes of Prince William Sound (PWS), and Chilkoot, Redoubt, and Speel Lakes of Southeast Alaska; R scripts that slightly reformat the dataset into a format consistent with other brood tables collected as part of the State of Alaskan Salmon and People project (https://alaskasalmonandpeople.org/); and the reformatted tables as .csv files. Age classes are given in European Notation, where the first number is the number of winters spent in freshwater before going to sea (1 winter in freshwater = age-1.X), and the second number is the number of winters spent at sea (3 winters at sea = age-X.3).
DOI: 10.5281/zenodo.5191768
title: Neurally Relevant BOLD Response Delay: Intermediary Dataset

description: BOLD fMRI timeseries from V1 voxels, extracted from preprocessed BOLD volume timeseries. Accompanying a scientific article to be submitted to Neuroimage. Code will follow.
DOI: 10.6084/m9.figshare.14245478
title: Data for CC Risk GW Danger.xlsx

description: Data matrices and correspondence analysis factors for study of risk-related framing of climate change and global warming in online US news media.
DOI: 10.6084/m9.figshare.14245478.v1
title: Data for CC Risk GW Danger.xlsx

description: Data matrices and correspondence analysis factors for study of risk-related framing of climate change and global warming in online US news media.
DOI: 10.5063/9s1pg3
title: Sockeye salmon brood tables, PWS and Southeast, Alaska, 1958-2016

description: Brood tables, also called run reconstructions, utilize annual estimates of the total run (commercial catch plus escapement), and samples of ages, to estimate the number of recruits per age class. These data are useful for salmon biologists to understand salmon productivity and salmon life histories. This dataset consists of: original brood tables in excel format for sockeye salmon from Copper River, Coghill and Eshamy Lakes of Prince William Sound (PWS), and Chilkoot, Redoubt, and Speel Lakes of Southeast Alaska; R scripts that slightly reformat the dataset into a format consistent with other brood tables collected as part of the State of Alaskan Salmon and People project (https://alaskasalmonandpeople.org/); and the reformatted tables as .csv files. Age classes are given in European Notation, where the first number is the number of winters spent in freshwater before going to sea (1 winter in freshwater = age-1.X), and the second number is the number of winters spent at sea (3 winters at sea = age-X.3).
DOI: 10.6084/m9.figshare.15093855
title: Workshop: Cancer Molecular Subtypes

description: Includes gene expression values which differentiate breast cancer subtypes.
DOI: 10.6084/m9.figshare.15093855.v2
title: Workshop: Cancer Molecular Subtypes

description: Includes gene expression values which differentiate breast cancer subtypes.
DOI: 10.6084/m9.figshare.15093855.v1
title: Workshop: Cancer Molecular Subtypes

description: Includes gene expression values which differentiate breast cancer subtypes.
DOI: 10.5281/zenodo.5191136
title: LM_MM_Inverse_Rig

description: The shared data directory that accompanies the paper...
DOI: 10.5063/610xrp
title: Compiled annual statewide Alaskan salmon escapement counts, 1921-2017

description: As salmon migrate from the marine environment to freshwater streams to spawn, a certain number are allowed to pass through, or '/escape/' the salmon fishery (frequently located near the mouth of the freshwater stream) as a management strategy to preserve the species. Escapement data are the enumeration of these migrating fish as they pass upstream, and represent an index of the total abundance of spawning salmon in that particular stream. These data are important for fisheries management, since most salmon harvest occurs in freshwater rivers during this migration. Escapement data are collected in a variety of ways. Stationary projects utilize observers stationed along freshwater corridors who count salmon as they pass upriver through weirs or past elevated towers. Sonar equipment placed in the river can also give a stationary escapement count. These counts usually represent a sample, and are expanded to represent a 24h period. Escapement data can also be collected using aerial surveys, where observers in an aircraft provide an index to estimate escapement. In general, escapement counts do not represent total abundance, but instead an index of abundance. Surveys are usually timed to coincide with peak spawning activity, generally in the summer, but in the case of Coho salmon in the fall as well. Some data about non-salmon species are also included. This dataset contains compiled annual data from multiple sources. The .Rmd merges all datasets, attaches location information from the escapement_locations.csv file, identifies and flags duplicate records, and performs quality assurance checks by filtering and graphing results.
DOI: 10.5517/ccdc.csd.cc27dpr3
title: CCDC 2067785: Experimental Crystal Structure Determination

description: Related Article: Tanausú Santos, David S. Rivero, Yaiza Pérez-Pérez, Endika Martín-Encinas, Jorge Pasán, Antonio Hernández Daranas, Romen Carrillo|2021|Angew.Chem.,Int.Ed.|60|18783|doi:10.1002/anie.202106230
DOI: 10.5517/ccdc.csd.cc27r6hq
title: CCDC 2077883: Experimental Crystal Structure Determination

description: Related Article: Shen Li, Lu Gan, Yi-Jing Tian, Yang Tian, Run-Zhu Fan, Dong Huang, Fang-Yu Yuan, Xu Zhang, Yan Lin, Qin-Feng Zhu, Gui-Hua Tang, Xue-Long Yan, Sheng Yin|2021|Bioorg.Chem.|114|105222|doi:10.1016/j.bioorg.2021.105222
DOI: 10.5517/ccdc.csd.cc27r6t1
title: CCDC 2077893: Experimental Crystal Structure Determination

description: Related Article: Shen Li, Lu Gan, Yi-Jing Tian, Yang Tian, Run-Zhu Fan, Dong Huang, Fang-Yu Yuan, Xu Zhang, Yan Lin, Qin-Feng Zhu, Gui-Hua Tang, Xue-Long Yan, Sheng Yin|2021|Bioorg.Chem.|114|105222|doi:10.1016/j.bioorg.2021.105222
DOI: 10.5517/ccdc.csd.cc27r5sz
title: CCDC 2077861: Experimental Crystal Structure Determination

description: Related Article: Shen Li, Lu Gan, Yi-Jing Tian, Yang Tian, Run-Zhu Fan, Dong Huang, Fang-Yu Yuan, Xu Zhang, Yan Lin, Qin-Feng Zhu, Gui-Hua Tang, Xue-Long Yan, Sheng Yin|2021|Bioorg.Chem.|114|105222|doi:10.1016/j.bioorg.2021.105222