cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4119/unibi/2911537
title: Data for Prenatal social conditions shape offspring adult phenotype and reproductive success

description: The prenatal social environment affects offspring development in most studied taxa with potentially lifelong consequences. To understand the adaptive significance of such maternal influences on offspring development, it is important to study their effects on fitness. In guinea pigs, social instability during pregnancy leads to delayed development of male offspring. This has been interpreted as an adaptation to high social densities, where young males need to queue for reproductive opportunities since they cannot out-compete older dominant males. The consequences for male reproductive success are, however, so far unknown. To study the effects of different prenatal social densities on offspring reproductive performance, we housed females individually or in small groups during late pregnancy. Offspring from both treatments were reared together in large groups until independence and thereafter housed in same-sex pairs of the same treatment.We then observed courtship, aggressive behavior, and reproductive success in a low-density context with one male from each treatment competing over access to two females. Sons born to individually housed females initiated more fights, had more social contacts, courted females more, and had a higher reproductive success than sons of group-housed females. Sons born to mothers experiencing low social densities before birth therefore perform better at low social group sizes, suggesting that male development may be adaptively adjusted to anticipated social densities, although performance under high densities still needs to be compared.
DOI: 10.15787/vtt1/aog1ax
title: Developing and Validating Senior Driver Fitness to Drive Models using SHRP 2 Naturalistic Driving Data

description: Project Description This dataset supported the analysis of several different categories of SHRP 2 data to evaluate and generate models of seniors’ fitness to drive. Data Request Scope The dataset was extracted from the InSight website (doi:10.15787/VTT1/3YVSY4, Version 1). The dataset includes data from participants aged 35 and above related to driver assessment including: CPT II, Trails A, UFoV, Visualizing Missing Information, Clock Drawing, visual ability metrics (acuity, contrast sensitivity, peripheral vision, color vision, and depth perception), grip strength, and rapid pace walk. In addition, the dataset included subsets of the event detail table for all safety-critical events (crashes and near crashes) experienced by the participants in the sample. Data Specification Please refer to the data dictionary in the "Files" section for a list of the variables included. Participants in the dataset were 35 and older at the time of consent (N=1785).
DOI: 10.18739/a2qd63
title: Examining the intersections of place-making and development for fisheries policy, Pribilof Islands, Alaska, 2015.
DOI: 10.18739/a2v681
title: UpTempO Buoys deployed in Arctic Ocean in 2016
DOI: 10.5281/zenodo.581696
title: Total Agriculture Climate Impact Distributions

description: Percentiles of agricultural impacts due to climate change, organized by level of aggregation, climate scenario, and time period. Agricultural impacts are estimated by applying future climate data to dose-response functions of yields for maize, wheat, cotton, and soybeans, and combining them according to land area. The dose-response functions include the effects of moderate and extreme temperatures, precipitation, and CO2 fertilization. The uncertainty represented by these percentiles comes from a combination of statistical uncertainty in the dose-response functions, climate uncertainty across GCMs, and within-month weather realization, as sampled by Monte Carlo runs. The archive contains four folders: county_20a with county-level impacts, state_20yr with state-aggregated impacts, nca_20yr with NCA-region aggregated impacts, and national_20y with nationally aggregated impacts. Aggregation is weighted by planted area within each region The files are labeled as follow: yields-total-&lt;RCP&gt;-&lt;YEAR&gt;&lt;SUFFIX&gt;. The RCP may be RCP 2.6, RCP 4.5, RCP 6.0, or RCP 8.5; the years are "2020", for average impacts during 2020 - 2039, "2040" for impacts 2040 - 2059, and "2080" for impacts 2080 - 2099. Files with the suffix 'b.csv' contain percent changes; those with the suffix '-absolute.csv' contain level changes in MT. The columns of these files specify the region (using the FIPS code for counties), and the quantiles from 1% ("q0.01") to 99% ("q0.99"). <em>This data is provided for non-commercial research and educational purposes.</em>
DOI: 10.5281/zenodo.581697
title: Crime Climate Impact Distributions

description: Percentiles of crime impacts due to climate change, organized by level of aggregation, climate scenario, and time period. Crime impacts are estimated by applying future climate data to dose-response functions of property and violent crime rates, aggregated by population levels. The uncertainty represented by these percentiles comes from a combination of statistical uncertainty in the dose-response functions, climate uncertainty across GCMs, and within-month weather realization, as sampled by Monte Carlo runs. The archive contains four folders: county_20a with county-level impacts, state_20yr with state-aggregated impacts, nca_20yr with NCA-region aggregated impacts, and national_20y with nationally aggregated impacts. Aggregation is weighted by crimes within each region. The files are labeled as follow: crime-&lt;TYPE&gt;-&lt;RCP&gt;-&lt;YEAR&gt;&lt;SUFFIX&gt;. The TYPE can be property or violent crime. The RCP may be RCP 2.6, RCP 4.5, RCP 6.0, or RCP 8.5; the years are "2020", for average impacts during 2020 - 2039, "2040" for impacts 2040 - 2059, and "2080" for impacts 2080 - 2099. Files with the suffix 'b.csv' contain percent changes; those with the suffix '-absolute.csv' contain level changes in MT. The columns of these files specify the region (using the FIPS code for counties), and the quantiles from 1% ("q0.01") to 99% ("q0.99"). <em>This data is provided for non-commercial research and educational purposes.</em>
DOI: 10.5281/zenodo.581698
title: Labor Productivity Climate Impact Distributions

description: Percentiles of labor productivity impacts due to climate change, organized by level of aggregation, climate scenario, and time period. Labor productivity impacts are estimated by applying future climate data to dose-response functions of absenteeism for low and high-sensitivity sectors. The uncertainty represented by these percentiles comes from a combination of statistical uncertainty in the dose-response functions, climate uncertainty across GCMs, and within-month weather realization, as sampled by Monte Carlo runs. The archive contains four folders: county_20a with county-level impacts, state_20yr with state-aggregated impacts, nca_20yr with NCA-region aggregated impacts, and national_20y with nationally aggregated impacts. Aggregation is weighted by jobs within each region. The files are labeled as follow: labor-&lt;SENSITIVITY&gt;-&lt;RCP&gt;-&lt;YEAR&gt;&lt;SUFFIX&gt;. The SENSITIVITY can be high, for outdoor jobs, or low for indoor tasks. The RCP may be RCP 2.6, RCP 4.5, RCP 6.0, or RCP 8.5; the years are "2020", for average impacts during 2020 - 2039, "2040" for impacts 2040 - 2059, and "2080" for impacts 2080 - 2099. Files with the suffix 'b.csv' contain percent changes; those with the suffix '-absolute.csv' contain level changes in MT. The columns of these files specify the region (using the FIPS code for counties), and the quantiles from 1% ("q0.01") to 99% ("q0.99"). <em>This data is provided for non-commercial research and educational purposes.</em>
DOI: 10.5281/zenodo.581699
title: All-Age Mortality Climate Impact Distributions

description: Percentiles of all-age mortality impacts due to climate change, organized by level of aggregation, climate scenario, and time period. Mortality impacts are estimated by applying future climate data to dose-response functions of mortality rates. The uncertainty represented by these percentiles comes from a combination of statistical uncertainty in the dose-response functions, climate uncertainty across GCMs, and within-month weather realization, as sampled by Monte Carlo runs. The archive contains four folders: county_20a with county-level impacts, state_20yr with state-aggregated impacts, nca_20yr with NCA-region aggregated impacts, and national_20y with nationally aggregated impacts. Aggregation is weighted by population within each region The files are labeled as follow: health-mortality-&lt;RCP&gt;-&lt;YEAR&gt;&lt;SUFFIX&gt;. The RCP may be RCP 2.6, RCP 4.5, RCP 6.0, or RCP 8.5; the years are "2020", for average impacts during 2020 - 2039, "2040" for impacts 2040 - 2059, and "2080" for impacts 2080 - 2099. Files with the suffix 'b.csv' contain percent changes; those with the suffix '-absolute.csv' contain level changes in MT. The columns of these files specify the region (using the FIPS code for counties), and the quantiles from 1% ("q0.01") to 99% ("q0.99"). <em>This data is provided for non-commercial research and educational purposes.</em>
DOI: 10.5281/zenodo.581700
title: Age-Specific Mortality Climate Impact Distributions

description: Percentiles of age-specific mortality impacts due to climate change, organized by level of aggregation, climate scenario, and time period. Mortality impacts are estimated by applying future climate data to dose-response functions of mortality rates for specific age bands. The uncertainty represented by these percentiles comes from a combination of statistical uncertainty in the dose-response functions, climate uncertainty across GCMs, and within-month weather realization, as sampled by Monte Carlo runs. The archive contains four folders: county_20a with county-level impacts, state_20yr with state-aggregated impacts, nca_20yr with NCA-region aggregated impacts, and national_20y with nationally aggregated impacts. Aggregation is weighted by population within each region The files are labeled as follow: health-mortage-&lt;BAND&gt;-&lt;RCP&gt;-&lt;YEAR&gt;&lt;SUFFIX&gt;. The age BAND is one of 0-0 (newborns), ages 1 - 44, 45 - 64, and 65-inf (older than 64). The RCP may be RCP 2.6, RCP 4.5, RCP 6.0, or RCP 8.5; the years are "2020", for average impacts during 2020 - 2039, "2040" for impacts 2040 - 2059, and "2080" for impacts 2080 - 2099. Files with the suffix 'b.csv' contain percent changes; those with the suffix '-absolute.csv' contain level changes in MT. The columns of these files specify the region (using the FIPS code for counties), and the quantiles from 1% ("q0.01") to 99% ("q0.99"). <em>This data is provided for non-commercial research and educational purposes.</em>
DOI: 10.14457/cu.the.2007.1382
title: การเตรียมและการใช้กากตะกอนเคลือบด้วยเหล็กออกไซด์ สำหรับกำจัดโลหะจากน้ำ
DOI: 10.5281/zenodo.582286
title: Distributions of Uncertainty by Climate Impact

description: These summary tables describe the distributions of results produced by allowing only one of statistical, model, and weather uncertainty to vary, to determine the contributions of each. These files are extracted from the general ACP Monte Carlo results using scripts from https://github.com/ClimateImpactLab/acp-impacts/tree/master/extract as defined below: The contents of variation-split are produced by uncertain.py. These results are organized into files with the filename template &lt;VARIATION&gt;-&lt;IMPACT&gt;--&lt;RCP&gt;-&lt;YEAR&gt;.csv. VARIATION may be "impact", for the variation driven by statistical uncertainty; "model", for the uncertainty driven by GCM choice; "weather", for the variation driven by weather realization; or "total", allowing all of these to vary simultaneously. IMPACT may be any of the estimated impacts, RCP can be RCP 2.6, RCP 4.5, RCP 6.0, or RCP 8.5, and YEAR is 2020, for an average from 2020 - 2039; 2040 for an average of 2040 - 2059; or 2080 for an average of 2080 - 2099. The contents of baseline-testing are produced by uncertain-test.py. These results are organized into files with the filename template &lt;IMPACT&gt;-&lt;VARIATION&gt;-&lt;GCM&gt;-&lt;RCP&gt;-&lt;YEAR&gt;.csv. The filename parts are as described above, with the addition of the name of the GCM that is held fixed when the GCM is not allowed to change. variation-split.tsv is produced by calcvar.py and is a summary of the results in variation-split/. baseline-testing.tsv is produced by calcvar-test.py and is a summary of the results in baseline-testing/. <em>This data is provided for non-commercial research and educational purposes.</em>
DOI: 10.18739/a2fx0h
title: Glaciomarine Oceanographic and Suspended Sediment Dynamics, Kongsbreen system, Svalbard
DOI: 10.18739/a2kq3t
title: Ice-Proximal 2011 CTD Oceanographic and Suspended Sediment Data from Kongsfjorden, Svalbard Research Experience for Undergraduates
DOI: 10.14457/cu.the.2007.1441
title: การพัฒนาเทคนิคการสำรวจระยะไกลภาคพื้นดิน เพื่อประเมินและติดตามผลผลิตของข้าวนาปี /
DOI: 10.15121/1357916
title: Newberry Volcano EGS Demonstration Well 55-29 Stimulation Data

description: The Newberry Volcano EGS Demonstration in central Oregon, a 3 year project started in 2010, tests recent technological advances designed to reduce the cost of power generated by EGS in a hot, dry well (NWG 55-29) drilled in 2008. First, the stimulation pumps used were designed to run for weeks and deliver large volumes of water at moderate well-head pressure. Second, to stimulate multiple zones, AltaRock developed thermo-degradable zonal isolation materials (TZIMs) to seal off fractures in a geothermal well to stimulate secondary and tertiary fracture zones. The TZIMs degrade within weeks, resulting in an optimized injection/ production profile of the entire well. Third, the project followed a project-specific Induced Seismicity Mitigation Plan (ISMP) to evaluate, monitor for, and mitigate felt induced seismicity. Stimulation started October 17, 2012 and continued for 7 weeks, with over 41,000 m3 of water injected. Two TZIM treatments successfully shifted the depth of stimulation. Injectivity, DTS, and seismic analysis indicate that fracture permeability in well NWG 55-29 was enhanced by two orders of magnitude. This submission includes all of the files and reports associated with the geophysical exploration, stimulation, and monitoring included in the scope of the project.
DOI: 10.5066/f71r6nq2
title: Bridge Scour Countermeasure Assessment Data for Select Bridges in the United States

description: Scouring of streambed material surrounding bridge structures is a leading cause of bridge failure in the United States. Damages resulting from bridge failure oftentimes lead to financial burdens and loss of life. To date, there has been no comprehensive evaluation of the current (2016) effectiveness of the guidance or overall long-term performance of bridge-scour countermeasures provided in the Federal Highway Administration, Hydraulic Engineering Circular No. 23, Bridge Scour and Stream Instability Countermeasures. To that end, the U.S. Geological Survey, in cooperation with the Federal Highway Administration, obtained bathymetric, topographical, and other data at 14 of the surveyed sites across the United States to begin an evaluation of the effectiveness of bridge-scour countermeasures. This report presents survey site selection criteria, site-specific details, the bathymetric and topographical surveying methods used to collect data, and the compilation of the acquired data.
DOI: 10.5066/f7474820
title: Delineation of marsh types and marsh type-change in Coastal Louisiana for 2007 and 2013

description: The Bureau of Ocean Energy Management (BOEM) researchers often require detailed information regarding emergent marsh vegetation types (i.e., fresh, intermediate, brackish, and saline) for modeling habitat capacities and mitigation. In response, the U.S. Geological Survey, in collaboration with the Bureau of Ocean Energy Management produced a detailed change classification of emergent marsh vegetation types in coastal Louisiana from 2007 and 2013. This study incorporates decision-tree analyses to classify emergent marsh vegetation types using two existing vegetation surveys and independent variables such as Landsat and high-resolution airborne imagery from 2007 and 2013, bare-earth digital elevation models based on airborne light detection and ranging (lidar), alternative contemporary land cover classifications, and other spatially explicit variables. Image objects were created from 2007 and 2013 National Agriculture Imagery Program (NAIP) color-infrared aerial photography. The final classification consists of three 10-m raster datasets that were produced by using a majority filter to classify image objects according to the marsh vegetation type covering the majority of each image object. The classifications are dated 2007 and 2013 because the dates of the two vegetation surveys and of the high-resolution airborne imagery that was used to develop image objects. The seamless classification produced through this work can be used to help develop and refine conservation efforts for priority natural resources.
DOI: 10.5066/f7hx19vw
title: Thermoelectric power plant water withdrawals and associated attributes for three Federal datasets in the United States, 2010

description: This dataset combines three Federal datasets of thermoelectric, non-industrial, power plant water withdrawals and associated plant information for the United States in 2010, excluding Puerto Rico and the U.S. Virgin Islands. Historically, thermoelectric water withdrawal has been estimated by the Department of Energy's Energy Information Administration (EIA) through surveys of plant operator-reported data, and the Department of Interior's U.S. Geological Survey's (USGS) 5-year water-use reports including data compiled from state water agencies, plant operators, and the EIA. Recently, the USGS developed models for estimating withdrawal at thermoelectric plants to provide independent estimates from plant operator-reported data. The three Federal datasets include plant-level data for 1,349 plants derived from EIA's 2010 Form EIA-860 and Form EIA-923 databases, USGS 2010 compilation-reported data (hereafter referred to as the USGS-compilation dataset), and USGS 2010 model-estimated data (hereafter referred to as the USGS-model dataset). The plant-level USGS-compilation data provided in this dataset were disaggregated from county-level data published in USGS Circular 1405 (Maupin and others, 2014). The USGS-model data and the EIA data presented in this dataset were previously published in USGS Scientific Investigations Report 2014-5184 (Diehl and Harris, 2014). The year 2010 was chosen because it is the most recent year the USGS 5-year compilation report was published and the only year for which the USGS model estimates have been calculated. Diehl, T.H., and Harris, M.A., 2014, Withdrawal and consumption of water by thermoelectric power plants in the United States, 2010: U.S. Geological Survey Scientific Investigations Report 20145184, 28 p., http://dx.doi.org/10.3133/sir20145184. Maupin, M.A., Kenny, J.F., Hutson, S.S., Lovelace, J.K., Barber, N.L., and Linsey, K.S., 2014, Estimated use of water in the United States in 2010: U.S. Geological Survey Circular 1405, 56 p., http://dx.doi.org/10.3133/cir1405.
DOI: 10.14469/hpc/2580
title: Cubyl cation_1iso (opt modredundant)

description: Gaussian Calculation
DOI: 10.14469/hpc/2581
title: ENE_eeProduct_1 (opt freq)

description: Gaussian Calculation
DOI: 10.14469/hpc/2582
title: ENE_IM_e2nd_1 (opt freq)

description: Gaussian Calculation
DOI: 10.14469/hpc/2583
title: Cubyl cation_1 (opt modred)

description: Gaussian Calculation
DOI: 10.14469/hpc/2584
title: IRC_ENE_ee2nd_1 (IRC)

description: Gaussian Calculation
DOI: 10.14469/hpc/2585
title: ENE_eeTS_1 (opt ts freq)

description: Gaussian Calculation
DOI: 10.14469/hpc/2586
title: ENE_Product_e2nd_1 (opt freq)

description: Gaussian Calculation
DOI: 10.14469/hpc/2587
title: Cubyl cation_TS_1iso (opt ts freq)

description: Gaussian Calculation
DOI: 10.14469/hpc/2589
title: Cubyl cation_TS_2iso (opt ts freq)

description: Gaussian Calculation
DOI: 10.18739/a21q2g
title: Examining the intersections of place-making and development for fisheries policy, Pribilof Islands, Alaska, 2015
DOI: 10.18739/a2b69r
title: Beaufort Sea methane data, 2015
DOI: 10.4227/11/592631edbd9d5
title: Pharmaceutical industry-funded events for Australian health professionals (Oct 2011-Sept 2015)

description: The dataset contains 301 publicly available company transparency reports covering the period from October 2011 to September 2015. The PDF reports were downloaded from the website of Medicines Australia and converted in Excel files. The reports contain information on the location, the type of event and all “transfers of value” to professionals who attended the events, including hospitality and travel for individual attendees, as well as event costs such as room rentals and speaker honoraria.
DOI: 10.5066/f7cf9nmv
title: Bioelectrical impedance analysis for an endangered desert fishData

description: These data were used to examine the effectiveness of a non-lethal tool (Bioelectrical Impedance Analysis, "BIA") to estimate the physiological condition of endangered and threatened fishes in the Colorado River Basin. We conducted laboratory trials using hatchery-raised Humpback Chub and Bonytail and wild-captured Roundtail Chub, where fish were subjected to different feeding trials to elucidate a response in physiological condition and different temperature treatments to approximate field conditions. At the end of each 6-week trial fish were removed from tanks, lateral and dorsal measurements of BIA were taken, and fish were sacrificed for proximate composition analysis (lipid, protein, water, ash, dry mass, energy density). We developed models that evaluated the effectiveness of BIA alone vs. standard morphological condition-based measurements taken in the field (e.g., using total length and wet mass). We conducted a simulation experiment to increase the observation error around morphological wet mass measurements to determine the crititcal error at which BIA becomes a more important predictor of proximate composition than condition factor. In addition, we tested the influence of different BIA temperature conversion factors (20 C, 15 C, none) for use in Humpback Chub GLMM models. We also conducted an experiment to determine mortality rates when standard field methods or BIA is used to estimate fish condition. Detailed methods and results are reported in the manuscript associated with this data publication. These data are associated with the journal manuscript:Dibble, K.L., Yard, M.D., Ward, D.L., and Yackulic, C.B., 2017, Does bioelectrical impedance analysis accurately estimate the physiological condition of threatened and endangered desert fish species?: Transactions of the American Fisheries Society v. (online), https://doi.org/10.1080/00028487.2017.1302993..