cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24431/rw1k46c
title: Development and testing of mechanistic fitness-based models to predict habitat choice, behavior, and recruitment of juvenile Chinook salmon in the Arctic-Yukon-Kuskokwim region, 2015-2017

description: These data comprise the laboratory experiments on Arctic Grayling (Thymallus arcticus) and Dolly Varden charr (Salvelinus malma) as part of the larger Drift Model Project fish foraging and behavior study conducted by the Grossman Lab at the University of Georgia. Specifically, these data describe the results of many single- and multi-fish foraging experiments conducted on Arctic Grayling and Dolly Varden charr experimental specimens in an artificial stream flume in Athens, Georgia. The dataset consists of four Microsoft excel workbooks, two for single-fish experiments and two for multi-fish experiments (i.e., one workbook per species per experiment type). The data consists of: 1) individual markers for experimental specimens (or pairs in multi-fish experiments), 2) batch (i.e., experimental specimen groups), 3) predictor variable values (i.e., treatment velocities, fish sizes, days in captivity, and size rank and dominance [for multi-fish experiments]), 4) response variable values (i.e., prey capture success percentages, holding velocities, and reactive distances), and 5) other values of potential interest but not included in analyses (i.e., capture velocity, raw prey capture numbers, and variable measurements in alternate units). Fish used in all experiments were captured via hook and line between fall of 2015 and fall of 2016 from Panguingue Creek in Interior Alaska and immediately shipped to the University of Georgia upon capture. We subjected experimental specimens to a series of increasing water velocity treatment trials in an experimental stream flume to determine how prey capture success, holding velocity, and reactive distance were affected by treatment velocity, fish size, and days kept in captivity with additional categorical predictor variables of size rank (i.e., larger or smaller) and dominance (based on holding position within experimental stream flume) for multi-fish experiments. Treatment velocity and holding velocity measurements were made immediately prior to and following treatment velocity trials with a handheld electronic velocity meter. We made prey capture success measurements in real time immediately following each treatment velocity trial by recording the number of prey captured per fixed number of prey releases. Finally, reactive distance and capture velocity measurements were made after experiments had been completed via trial video analysis using the VidSync (www.vidsync.org) computer software. Dolly Varden charr and Arctic Grayling are economically and ecologically important species in Interior Alaska and understanding how these species utilize and select microhabitats has important implications for their management and overall stream fish-habitat relationship scholarship and conservation. Data are presented as two CSV files: Grayling_Dominance_Experiment_Data.csv Dolly_Dominance_Experiment_Data.csv
DOI: 10.24431/rw1k46d
title: Development and testing of mechanistic fitness-based models to predict habitat choice, behavior, and recruitment of juvenile Chinook salmon in the Arctic-Yukon-Kuskokwim region, 2015-2017

description: These data comprise the laboratory experiments on Arctic Grayling (Thymallus arcticus) and Dolly Varden charr (Salvelinus malma) as part of the larger Drift Model Project fish foraging and behavior study conducted by the Grossman Lab at the University of Georgia. Specifically, these data describe the results of many single- and multi-fish foraging experiments conducted on Arctic Grayling and Dolly Varden charr experimental specimens in an artificial stream flume in Athens, Georgia. The dataset consists of four Microsoft excel workbooks, two for single-fish experiments and two for multi-fish experiments (i.e., one workbook per species per experiment type). The data consists of: 1) individual markers for experimental specimens (or pairs in multi-fish experiments), 2) batch (i.e., experimental specimen groups), 3) predictor variable values (i.e., treatment velocities, fish sizes, days in captivity, and size rank and dominance [for multi-fish experiments]), 4) response variable values (i.e., prey capture success percentages, holding velocities, and reactive distances), and 5) other values of potential interest but not included in analyses (i.e., capture velocity, raw prey capture numbers, and variable measurements in alternate units). Fish used in all experiments were captured via hook and line between fall of 2015 and fall of 2016 from Panguingue Creek in Interior Alaska and immediately shipped to the University of Georgia upon capture. We subjected experimental specimens to a series of increasing water velocity treatment trials in an experimental stream flume to determine how prey capture success, holding velocity, and reactive distance were affected by treatment velocity, fish size, and days kept in captivity with additional categorical predictor variables of size rank (i.e., larger or smaller) and dominance (based on holding position within experimental stream flume) for multi-fish experiments. Treatment velocity and holding velocity measurements were made immediately prior to and following treatment velocity trials with a handheld electronic velocity meter. We made prey capture success measurements in real time immediately following each treatment velocity trial by recording the number of prey captured per fixed number of prey releases. Finally, reactive distance and capture velocity measurements were made after experiments had been completed via trial video analysis using the VidSync (www.vidsync.org) computer software. Dolly Varden charr and Arctic Grayling are economically and ecologically important species in Interior Alaska and understanding how these species utilize and select microhabitats has important implications for their management and overall stream fish-habitat relationship scholarship and conservation. Data are presented as two CSV files: Grayling_SingleFish_Experiment_Data.csv Dolly_SingleFish_Experiment_Data.csv
DOI: 10.24431/rw1k46e
title: Development and testing of mechanistic fitness-based models to predict habitat choice, behavior, and recruitment of juvenile Chinook salmon in the Arctic-Yukon-Kuskokwim region

description: This data is part of the NPRB research project 1424. Specifically, these data describe varying prey capture success rates of juvenile Chinook salmon in response to our main variables of water velocity and dominance hierarchy. The complete dataset consists of six csv files. The data consists of 1. Single fish prey capture data from experiment using that used 2D measurements, 2. Single fish 2D microhabitat measurements from our experimental flume, 3. Single fish prey capture data from experiment using 3D measurements, 4. Dominance experiment data from prey capture trials recorded by an observer, 5. Dominance experiment 3D data extracted from the VidSync software, and 6. The lengths, mass, and days in captivity for our fish used in the dominance experiments. Juvenile fish were captured in the Chena River, Alaska, using minnow traps in 2015 and experiments were conducted beginning in January 2015 and ending in July 2015 when video analysis was completed. Chinook Salmon (Oncorhynchus tshawytscha) are a native salmonid to the region and a very popular fish for anglers. Over the past decade their populations have begun to decline due to a variety of factors including climate change, urbanization, and predation. Data are presented as two CSV files: Chinook_SingleFish_2D_Microhabitat.csv Chinook_SingleFish_2D_Behavior.csv
DOI: 10.24431/rw1k46f
title: Development and testing of mechanistic fitness-based models to predict habitat choice, behavior, and recruitment of juvenile Chinook salmon in the Arctic-Yukon-Kuskokwim region

description: This data is part of the NPRB research project 1424. Specifically, these data describe varying prey capture success rates of juvenile Chinook salmon in response to our main variables of water velocity and dominance hierarchy. The complete dataset consists of six csv files. The data consists of 1. Single fish prey capture data from experiment using that used 2D measurements, 2. Single fish 2D microhabitat measurements from our experimental flume, 3. Single fish prey capture data from experiment using 3D measurements, 4. Dominance experiment data from prey capture trials recorded by an observer, 5. Dominance experiment 3D data extracted from the VidSync software, and 6. The lengths, mass, and days in captivity for our fish used in the dominance experiments. Juvenile fish were captured in the Chena River, Alaska, using minnow traps in 2015 and experiments were conducted beginning in January 2015 and ending in July 2015 when video analysis was completed. Chinook Salmon (Oncorhynchus tshawytscha) are a native salmonid to the region and a very popular fish for anglers. Over the past decade their populations have begun to decline due to a variety of factors including climate change, urbanization, and predation. Data are presented as one CSV file: Chinook_SingleFish_3D_Behavior.csv
DOI: 10.5066/p9zhn5y5
title: Daily Reference (RET) and Potential (PET) Evapotranspiration data over Florida, 2017
DOI: 10.24431/rw1k46g
title: Development and testing of mechanistic fitness-based models to predict habitat choice, behavior, and recruitment of juvenile Chinook salmon in the Arctic-Yukon-Kuskokwim region

description: This data is part of the NPRB research project 1424. Specifically, these data describe varying prey capture success rates of juvenile Chinook salmon in response to our main variables of water velocity and dominance hierarchy. The complete dataset consists of six csv files. The data consists of 1. Single fish prey capture data from experiment using that used 2D measurements, 2. Single fish 2D microhabitat measurements from our experimental flume, 3. Single fish prey capture data from experiment using 3D measurements, 4. Dominance experiment data from prey capture trials recorded by an observer, 5. Dominance experiment 3D data extracted from the VidSync software, and 6. The lengths, mass, and days in captivity for our fish used in the dominance experiments. Juvenile fish were captured in the Chena River, Alaska, using minnow traps in 2015 and experiments were conducted beginning in January 2015 and ending in July 2015 when video analysis was completed. Chinook Salmon (Oncorhynchus tshawytscha) are a native salmonid to the region and a very popular fish for anglers. Over the past decade their populations have begun to decline due to a variety of factors including climate change, urbanization, and predation. Data are presented as three CSV files: Chinook_Dominance_Experiment_VidSync_Output.csv Chinook_Dominance_Experiment_LengthMass.csv Chinook_Dominance_Experiment_Behavior.csv
DOI: 10.18112/openneuro.ds003054.v1.0.0
title: P3
DOI: 10.24431/rw1k46h
title: Drift foraging behavior data for Chinook salmon, Dolly Varden, and Grayling in Alaska, 2015-2016

description: This dataset includes processed results on the behavior of drift foraging fish and the context for that behavior (e.g., habitat, prey abundance). It comes from 3-D video analysis of spatial foraging behavior, drift net sampling to measure drift and debris concentrations, and diet sampling for approximately 24 juvenile Chinook Salmon from the Chena River, 32 Dolly Varden from Panguingue Creek, and 29 Arctic Grayling from the Richardson Clearwater, all in interior Alaska. Field observational data are presented in the following file formats: Raw Field Datasheet Scans (Source Data).zip Invertebrate_Drift_and_Diet_Source_Data.zip VidSync_Video_Source_Data_XML_Files.zip PythonDataAnalysis.zip contains six python scripts : Maneuver Paper Calculations.py Bug Size Reference Table Creator.py Python Bug Data Importer.py Temperature Interpolation.py VelocityTracerAveragingMethodsTest.py VidSyncSummaries.py Public_Field_Data.json TimesAndTemperatures.csv contains data collected on stream temperatures
DOI: 10.5517/ccdc.csd.cc252d0p
title: CCDC 1998291: Experimental Crystal Structure Determination

description: Related Article: Anastasiya V. Kuznetcova, Alexander A. Golovanov, Aleksander V. Vasilyev|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc252dzn
title: CCDC 1998321: Experimental Crystal Structure Determination

description: Related Article: Olivier Blacque, Xianghua Yang, Heinz Berke|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc252f5w
title: CCDC 1998327: Experimental Crystal Structure Determination

description: Related Article: Olivier Blacque, Xianghua Yang, Heinz Berke|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc252zjs
title: CCDC 1998865: Experimental Crystal Structure Determination

description: Related Article: Garrett A. Taggart, Gregory R. Lorzing, Michael R. Dworzak, Glenn P. A. Yap, Eric D. Bloch|2020|Chem.Commun.|56|8924|doi:10.1039/D0CC03266B
DOI: 10.5517/ccdc.csd.cc252zgq
title: CCDC 1998863: Experimental Crystal Structure Determination

description: Related Article: Garrett A. Taggart, Gregory R. Lorzing, Michael R. Dworzak, Glenn P. A. Yap, Eric D. Bloch|2020|Chem.Commun.|56|8924|doi:10.1039/D0CC03266B
DOI: 10.5517/ccdc.csd.cc252zhr
title: CCDC 1998864: Experimental Crystal Structure Determination

description: Related Article: Garrett A. Taggart, Gregory R. Lorzing, Michael R. Dworzak, Glenn P. A. Yap, Eric D. Bloch|2020|Chem.Commun.|56|8924|doi:10.1039/D0CC03266B
DOI: 10.5517/ccdc.csd.cc253t04
title: CCDC 1999655: Experimental Crystal Structure Determination

description: Related Article: Ikuya Fujii,  Kazuhiko Semba,  Qiao-Zhi Li,  Shigeyoshi Sakaki,  Yoshiaki Nakao|2020|J.Am.Chem.Soc.|142|11647|doi:10.1021/jacs.0c04905
DOI: 10.5517/ccdc.csd.cc253ygq
title: CCDC 1999793: Experimental Crystal Structure Determination

description: Related Article: Jordan J. Dotson,  J. Logan Bachman,  Miguel A. Garcia-Garibay,  Neil K. Garg|2020|J.Am.Chem.Soc.|142|11685|doi:10.1021/jacs.0c04760
DOI: 10.5517/ccdc.csd.cc253yhr
title: CCDC 1999794: Experimental Crystal Structure Determination

description: Related Article: Jordan J. Dotson,  J. Logan Bachman,  Miguel A. Garcia-Garibay,  Neil K. Garg|2020|J.Am.Chem.Soc.|142|11685|doi:10.1021/jacs.0c04760
DOI: 10.5517/ccdc.csd.cc253yjs
title: CCDC 1999795: Experimental Crystal Structure Determination

description: Related Article: Jordan J. Dotson,  J. Logan Bachman,  Miguel A. Garcia-Garibay,  Neil K. Garg|2020|J.Am.Chem.Soc.|142|11685|doi:10.1021/jacs.0c04760
DOI: 10.5066/p9j466jy
title: Eddy covariance measurements of ET, CO2 and Energy Fluxes, and, ancillary data for the Rocky Flats National Wildlife Refuge West site

description: This dataset includes the following measurements made over a xeric tall grass prairie in the southwest portion of Rocky Flats National Wildlife Refuge in 2011 and 2012: 1) half hourly eddy covariance measurements of latent heat flux (evapotranspiration) and fluxes of carbon dioxide and sensible heat, and friction velocity (U star) 2) half hour averages of solar and net radiation; photosynthetically active radiation; air temperature and humidity; soil temperature, moisture, and heat flux; wind speed and direction; and half hour sums of precipitation. 3) approximately weekly measurements of vegetation including leaf area index, height of vegetation, dry and wet weights
DOI: 10.5517/ccdc.csd.cc25433k
title: CCDC 1999937: Experimental Crystal Structure Determination

description: Related Article: Jordan J. Dotson,  J. Logan Bachman,  Miguel A. Garcia-Garibay,  Neil K. Garg|2020|J.Am.Chem.Soc.|142|11685|doi:10.1021/jacs.0c04760
DOI: 10.5517/ccdc.csd.cc254nmm
title: CCDC 2000480: Experimental Crystal Structure Determination

description: Related Article: Ikuya Fujii,  Kazuhiko Semba,  Qiao-Zhi Li,  Shigeyoshi Sakaki,  Yoshiaki Nakao|2020|J.Am.Chem.Soc.|142|11647|doi:10.1021/jacs.0c04905
DOI: 10.5517/ccdc.csd.cc24gtjz
title: CCDC 1980451: Experimental Crystal Structure Determination

description: Related Article: Elena I. Klimova, Carlos Guerrero Rubio, Jesús García-Valdés, Eder Nuñez-Cruz, Elena Martínez-Klimova, Irina P. Beletskaya, Marcos Flores-Álamo, Jessica J. Sánchez García|2020|J.Organomet.Chem.|922|121363|doi:10.1016/j.jorganchem.2020.121363
DOI: 10.5517/ccdc.csd.cc24gtl1
title: CCDC 1980453: Experimental Crystal Structure Determination

description: Related Article: Elena I. Klimova, Carlos Guerrero Rubio, Jesús García-Valdés, Eder Nuñez-Cruz, Elena Martínez-Klimova, Irina P. Beletskaya, Marcos Flores-Álamo, Jessica J. Sánchez García|2020|J.Organomet.Chem.|922|121363|doi:10.1016/j.jorganchem.2020.121363
DOI: 10.5517/ccdc.csd.cc2558qc
title: CCDC 2001072: Experimental Crystal Structure Determination

description: Related Article: Zhanchao Meng,  Alois Fürstner|2020|J.Am.Chem.Soc.|142|11703|doi:10.1021/jacs.0c05347
DOI: 10.5517/ccdc.csd.cc2558rd
title: CCDC 2001073: Experimental Crystal Structure Determination

description: Related Article: Zhanchao Meng,  Alois Fürstner|2020|J.Am.Chem.Soc.|142|11703|doi:10.1021/jacs.0c05347
DOI: 10.5517/ccdc.csd.cc240qws
title: CCDC 1966915: Experimental Crystal Structure Determination

description: Related Article: Zhanchao Meng,  Alois Fürstner|2020|J.Am.Chem.Soc.|142|11703|doi:10.1021/jacs.0c05347
DOI: 10.5517/ccdc.csd.cc258cjc
title: CCDC 2004042: Experimental Crystal Structure Determination

description: Related Article: Zhanchao Meng,  Alois Fürstner|2020|J.Am.Chem.Soc.|142|11703|doi:10.1021/jacs.0c05347
DOI: 10.5517/ccdc.csd.cc24bcc7
title: CCDC 1976168: Experimental Crystal Structure Determination

description: Related Article: Jian Wu, Wen-Xia Gao, Xiao-Bo Huang, Yun-Bing Zhou, Miao-Chang Liu, Hua-Yue Wu|2020|Org.Lett.|22|5555|doi:10.1021/acs.orglett.0c01914
DOI: 10.5517/ccdc.csd.cc24cr8k
title: CCDC 1977498: Experimental Crystal Structure Determination

description: Related Article: Jian Wu, Wen-Xia Gao, Xiao-Bo Huang, Yun-Bing Zhou, Miao-Chang Liu, Hua-Yue Wu|2020|Org.Lett.|22|5555|doi:10.1021/acs.orglett.0c01914
DOI: 10.5517/ccdc.csd.cc24b04n
title: CCDC 1975820: Experimental Crystal Structure Determination

description: Related Article: Jian Wu, Wen-Xia Gao, Xiao-Bo Huang, Yun-Bing Zhou, Miao-Chang Liu, Hua-Yue Wu|2020|Org.Lett.|22|5555|doi:10.1021/acs.orglett.0c01914
DOI: 10.5517/ccdc.csd.cc248mvx
title: CCDC 1974509: Experimental Crystal Structure Determination

description: Related Article: Jian Wu, Wen-Xia Gao, Xiao-Bo Huang, Yun-Bing Zhou, Miao-Chang Liu, Hua-Yue Wu|2020|Org.Lett.|22|5555|doi:10.1021/acs.orglett.0c01914
DOI: 10.5517/ccdc.csd.cc248msv
title: CCDC 1974507: Experimental Crystal Structure Determination

description: Related Article: Jian Wu, Wen-Xia Gao, Xiao-Bo Huang, Yun-Bing Zhou, Miao-Chang Liu, Hua-Yue Wu|2020|Org.Lett.|22|5555|doi:10.1021/acs.orglett.0c01914
DOI: 10.5517/ccdc.csd.cc252zkt
title: CCDC 1998866: Experimental Crystal Structure Determination

description: Related Article: Garrett A. Taggart, Gregory R. Lorzing, Michael R. Dworzak, Glenn P. A. Yap, Eric D. Bloch|2020|Chem.Commun.|56|8924|doi:10.1039/D0CC03266B
DOI: 10.5517/ccdc.csd.cc25fwl3
title: CCDC 2009345: Experimental Crystal Structure Determination

description: Related Article: James T. Moore,  Connie C. Lu|2020|J.Am.Chem.Soc.|142|11641|doi:10.1021/jacs.0c04937
DOI: 10.5517/ccdc.csd.cc25fwm4
title: CCDC 2009346: Experimental Crystal Structure Determination

description: Related Article: James T. Moore,  Connie C. Lu|2020|J.Am.Chem.Soc.|142|11641|doi:10.1021/jacs.0c04937
DOI: 10.5517/ccdc.csd.cc25fwn5
title: CCDC 2009347: Experimental Crystal Structure Determination

description: Related Article: James T. Moore,  Connie C. Lu|2020|J.Am.Chem.Soc.|142|11641|doi:10.1021/jacs.0c04937
DOI: 10.5517/ccdc.csd.cc25fwp6
title: CCDC 2009348: Experimental Crystal Structure Determination

description: Related Article: James T. Moore,  Connie C. Lu|2020|J.Am.Chem.Soc.|142|11641|doi:10.1021/jacs.0c04937
DOI: 10.5517/ccdc.csd.cc25fwq7
title: CCDC 2009349: Experimental Crystal Structure Determination

description: Related Article: James T. Moore,  Connie C. Lu|2020|J.Am.Chem.Soc.|142|11641|doi:10.1021/jacs.0c04937
DOI: 10.5517/ccdc.csd.cc25fwr8
title: CCDC 2009350: Experimental Crystal Structure Determination

description: Related Article: James T. Moore,  Connie C. Lu|2020|J.Am.Chem.Soc.|142|11641|doi:10.1021/jacs.0c04937
DOI: 10.5517/ccdc.csd.cc25g32v
title: CCDC 2009546: Experimental Crystal Structure Determination

description: Related Article: Jake R. Jagannathan,  James C. Fettinger,  Jared T. Shaw,  Annaliese K. Franz|2020|J.Am.Chem.Soc.|142|11674|doi:10.1021/jacs.0c04533
DOI: 10.5066/p9b70dc6
title: Eddy covariance measurements of ET, CO2 and Energy Fluxes, and, ancillary data for the Ft. Logan-Denver urban lawn site

description: This dataset includes the following measurements made over a managed lawn at Fort Logan Cemetery in Denver Colorado in 2011 and 2012. (No measurements were made in winter months: December, January, February). 1) half hourly eddy covariance measurements of evapotranspiration and fluxes of carbon dioxide and sensible heat 2) half hour averages of solar and net radiation, air temperature and humidity, soil temperature, moisture, and heat flux, and half hour sums of precipitation. 3) approximately weekly measurements of vegetation: leaf area index, height of vegetation, dry and wet weights
DOI: 10.25573/data.10059461.v7
title: Monthly summary_AVA Tower

description: Barro Colorado Island, AVA tower.Two Sensors: North and South<br>Parameters: air temperature, relative humidity, wind speed and direction, precipitation, solar radiation (pyranometer, photosynthetically active radiation)<br>Established in Mar, 2011 as part of the ForestGEO Tropical Ecology, Assessment and Monitoring (TEAM) project.<br>The original tower was Located near the center of the island at the north west corner of the 50ha plot (9.1568°, -79.8486°).<br>In 2018 this tower was struck by a tree fall and rendered unusable.<br>In Feb. 2019 a new 45m tower was build close by (9.1568°, -79.8486°). <br><br><br>
DOI: 10.21429/knyb-2353
title: Hawaii statewide drought frequency maps based on the Standardized Precipitation Index maps for each decade 1920-2012

description: Hawaii statewide drought frequency maps based on the Standardized Precipitation Index (SPI) maps for each decade from 1920-2012 (last decade includes 2000-2012) for all 10 SPI periods. For each decade, sum the number of months with SPI < 0, SPI < -1, SPI < -1.5, and SPI < -2 and divide by the total number of months to create "frequency" grids statewide based on these four thresholds. Values range from 0 to 1. Outputs are raster grid files (GeoTIFF format).
DOI: 10.25573/data.10304303.v3
title: Monthly Rain_ACP_Horizontal.xlsx

description: Monthly summaries of the Panama Canal Authority meteorological monitoring program. These data were collected from approximately 50 stations located in the Panama Canal watershed. Information about station locations can be found in the metadata in the file.<br><br>This file has been formatted horizontally so that monthly and seasonal patterns can be easily analyzed.<br><br>Published with permission from the Panama Canal Authority.
DOI: 10.25573/data.10304306.v3
title: Monthly Rain_ACP_Vertical.xlsx

description: Monthly summaries of the Panama Canal Authority meteorological monitoring program. These data were collected from approximately 50 stations located in the Panama Canal watershed. Information about station locations can be found in the metadata in the file.<br><br>This file has been formatted vertically so that long-term trends can be easily analyzed.<br><br>Published with permission from the Panama Canal Authority.