cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6073/pasta/e17b351933e981f369a058076f14c328
title: Phytoplankton chlorophyll in Iowa state recreational lakes 2018-2020

description: Chlorophyll a dataset is curated by Phyto-PAM, a multi-wavelength fluorometer that rapidly infers the abundance and composition of phytoplankton communities based on the fluorescence excitation of chlorophyll a and accessory pigments. This dataset categorizes phytoplankton into four taxa: 1) cyanobacteria; 2) ‘Green algae’ (euglenophytes and chlorophytes; 3) ‘Brown algae’ (ochrophytes, dinophytes, haptophytes); and 4) ‘Red organisms’ (phycoerythrin-rich phytoplankton). Sub-surface water samples (n &gt; 450) collected in eutrophic lakes for three summer seasons (2018-2020; 15 weeks per season; from May through September). DNA sequencing data for bacteria and eukaryotes are available at NCBI (16s rRNA: PRJNA764241, PRJNA763651, and 18s rRNA: PRJNA787030).
DOI: 10.6073/pasta/0a0d884667f55ffe551e33dcf7ebe535
title: This data set is six 30-day ocean state estimation products for LTER-CCE cruises. The time periods of data assimilation fits for each LTER-CCE cruise are the following: 2006: 5/8 - 6/6, 2007: 4/1- 4/30, 2008 : 10/1 - 10/30, 2009 : 4/15 - 5/14, 2011 : 6/17 - 7/16, 2012 : 7/27 - 8/25

description: Used the sea surface height, sea surface temperature data measured from the space, and in situ temperature and salinity data from varied observational platforms including CCE-LTER cruises, we constrained the numerical ocean circulation model, Regional Ocean Modeling System (ROMS), so that the model simulation closely follows the observed ocean states. Data files are in netcdf format containing longitude, latitude, bathymetry, sea surface height, ocean current, temperature, salinity, density anomaly and surface fluxes. ROMS has a terrain following vertical coordinate and resolves shallow coastal regions better by construction. As a result, the depth of vertical level changes depending on the depth of bathymetry. This data set is the product of the investigations presented in Miller, A. J., H. Song and A. C. Subramanian, 2015: The physical oceanographic environment during the CCE-LTER years: Changes in climate and concepts. Deep-Sea Research II, 112, 6-17. Song, H., A. J. Miller, S. McClatchie, E. D. Weber, K. M. Nieto and D. M. Checkley, 2012: Application of a data-assimilation model to variability of Pacific sardine spawning and survivor habitats with ENSO in the California Current System. Journal of Geophysical Research-Oceans, 117, C03009, doi:10.1029/2011JC007302. We gratefully acknowledge continued funding from NSF (California Current Ecosystem LTER: OCE-0417616 and OCE-1026607) and NOAA (IOOS: NA17RJ1231)
DOI: 10.6073/pasta/3c607138a88218846cae4d6f201942f6
title: Particulate organic carbon and nitrogen measurements at selected depths in the water column in the CCE region since 2006 - 2017 (ongoing).

description: Water column bottle samples at multiple depths are taken during CCE Process cruises (since 2006, ongoing) at various CTD stations, filtered, and stored at -20°C. Measurements of particulate organic carbon (POC) and nitrogen (PON) are performed onshore in the lab where samples are acidified, dried and analyzed by high-temperature combustion. The sample and tin capsule react with oxygen and combust at 1000°C, and the sample is broken down, thus converting organic carbon to CO2 and reducing nitrogen oxides to N2 gas. Both gases are measured by thermal conductivity. Samples analyzed within the CCE constrain the mean C:N ratio of small particulates and by difference relative to measured living biomass, the biomass of suspended detritus.
DOI: 10.6073/pasta/4c90b0a9fca143c5203ba02027030555
title: Vertically-integrated primary production, determined using an algorithm that incorporates in situ chlorophyll, ammonium, and photosynthetically active radiation, tuned to CCE C-14 NPP data, 2017 - (ongoing).

description: We investigated the processes driving variability in primary productivity in the California Current Ecosystem (CCE) in order to develop an algorithm for predicting primary productivity from in situ irradiance, nutrient, and chlorophyll (chl) measurements. Primary productivity data from seven process cruises of the CCE Long-Term Ecological Research (CCE LTER) program were used to parameterize the algorithm. An initial algorithm was developed using only irradiance to predict chl-specific productivity was found to have model-data misfit that was correlated with NH4+ concentrations. We thus found that the best estimates of primary productivity were obtained using an equation including NH4+ and irradiance: PP/Chl = V0m×(1-exp(-α×PAR/V0m)×NH4/(NH4+KS), where PP/Chl is chlorophyll-specific primary production in units of mg C d-1 / mg Chl, PAR is photosynthetically active radiation (units of µEi m-2 s-1) , NH4+ is in units of μmol L-1, V0m = 66.5 mg C d-1 / mg Chl , α = 1.5, and KS = 0.025 μmol L-1. We then used this algorithm to compute primary productivity rates for the CCE-P1706 cruise on which in situ primary productivity samples were not available. We compared these estimates to independent productivity estimates derived from protistan grazing dilution experiments and found excellent agreement. For additional details, see Stukel et al. 2019 (doi: 10.1101/590240).
DOI: 10.6073/pasta/d19f13b361177f7d10135d48498fa7c0
title: Primary production estimates from 14C uptake (in situ), determined by the incorporation of inorganic carbon into particulate organic carbon (POC) due to photosynthesis at selected light levels from CCE LTER process cruises in the California Current System, 2006 - 2019 (ongoing).

description: Primary productivity samples of seawater are taken each day shortly before noon on the CTD rosette up-cast during the CCE Process crusies (since 2006, ongoing). Light penetration below the surface is estimated from the Secchi disk depth. Niskin bottles from depths with ambient light intensities corresponding to light levels simulated by on-deck incubators are identified and sampled. Primary production is estimated from 14C uptake using this simulated in situ technique (followed by filtering) by which the assimilation of dissolved inorganic carbon by phytoplankton yields a measure (in µg/L/day) of the rate of photosynthetic primary production (particulate organic carbon, POC) at selected light levels in the euphotic zone within the CCE study area.
DOI: 10.5281/zenodo.7837518
title: Data for "Tracking C-H activation with orbital resolution"

description: These are the data displayed in the figures in the main text and in the Supplementary Materials of the manuscript entitled "Tracking C-H activation with orbital resolution".
DOI: 10.5281/zenodo.7837517
title: Data for "Tracking C-H activation with orbital resolution"

description: These are the data displayed in the figures in the main text and in the Supplementary Materials of the manuscript entitled "Tracking C-H activation with orbital resolution".
DOI: 10.6084/m9.figshare.21824569
title: Specifying Non-White Light Sources in Outdoor Applications to Reduce Light Pollution

description: Anthropogenic (“human-generated”) light at night (ALAN) from outdoor lighting produces light pollution and as a result causes a range of deleterious responses in humans, plants, and animals. An emerging strategy to combat light pollution, especially sky glow, is to use “amber” LEDs in lieu of phosphor-converted white LEDs with high correlated color temperatures. Importantly, however, there is no standardized terminology or chromaticity designation that is applicable to “amber” LED lighting products for outdoor illumination. In this work, we propose a specification structure for light sources with nonstandard chromaticities to increase the precision of language used in the architectural lighting industry and demonstrate that light sources with well-defined chromaticity ranges outside of the ANSI C78.377–2017 quadrangles may be useful for reducing the portion of relative sky glow related to light source spectral power distribution. We encourage lighting standards development organizations to consider standardizing such a system.
DOI: 10.6084/m9.figshare.21824569.v2
title: Specifying Non-White Light Sources in Outdoor Applications to Reduce Light Pollution

description: Anthropogenic (“human-generated”) light at night (ALAN) from outdoor lighting produces light pollution and as a result causes a range of deleterious responses in humans, plants, and animals. An emerging strategy to combat light pollution, especially sky glow, is to use “amber” LEDs in lieu of phosphor-converted white LEDs with high correlated color temperatures. Importantly, however, there is no standardized terminology or chromaticity designation that is applicable to “amber” LED lighting products for outdoor illumination. In this work, we propose a specification structure for light sources with nonstandard chromaticities to increase the precision of language used in the architectural lighting industry and demonstrate that light sources with well-defined chromaticity ranges outside of the ANSI C78.377–2017 quadrangles may be useful for reducing the portion of relative sky glow related to light source spectral power distribution. We encourage lighting standards development organizations to consider standardizing such a system.
DOI: 10.6084/m9.figshare.20346295
title: Visual Comfort and Acute Alerting Effects of Diurnal Intermittent Bright Light

description: Intermittent bright light during the night has shown to be able to generate circadian phase-shifting effects, suppress melatonin and induce alertness, but little attention has been devoted to the effects of diurnal intermittent bright light. Following a night of sleep restriction, forty participants were exposed in a counterbalanced within-subject design to an intermittent (100 lux – 1000 lux), a continuous dim (100 lux) and a continuous bright light condition (1000 lux) each lasting 90 min. Repeated assessments of self-reported sleepiness, cognitive performance and physiological arousal as well as subjective visual comfort were taken during each light condition. Results showed that alertness-related parameters were not significantly affected by the light conditions: neither the intermittent nor the bright condition improved alertness compared to the dim condition. Visual comfort was highest in the dim condition, followed by the intermittent and bright conditions respectively, even though the visualizations showed marked decreases in visual comfort during the bright light phases in the intermittent condition. The results illustrate the diversity in mechanisms underlying these visual experiences and neurobehavioral responses.
DOI: 10.6084/m9.figshare.20346295.v1
title: Visual Comfort and Acute Alerting Effects of Diurnal Intermittent Bright Light

description: Intermittent bright light during the night has shown to be able to generate circadian phase-shifting effects, suppress melatonin and induce alertness, but little attention has been devoted to the effects of diurnal intermittent bright light. Following a night of sleep restriction, forty participants were exposed in a counterbalanced within-subject design to an intermittent (100 lux – 1000 lux), a continuous dim (100 lux) and a continuous bright light condition (1000 lux) each lasting 90 min. Repeated assessments of self-reported sleepiness, cognitive performance and physiological arousal as well as subjective visual comfort were taken during each light condition. Results showed that alertness-related parameters were not significantly affected by the light conditions: neither the intermittent nor the bright condition improved alertness compared to the dim condition. Visual comfort was highest in the dim condition, followed by the intermittent and bright conditions respectively, even though the visualizations showed marked decreases in visual comfort during the bright light phases in the intermittent condition. The results illustrate the diversity in mechanisms underlying these visual experiences and neurobehavioral responses.
DOI: 10.6084/m9.figshare.21824569.v1
title: Specifying Non-White Light Sources in Outdoor Applications to Reduce Light Pollution

description: Anthropogenic (“human-generated”) light at night (ALAN) from outdoor lighting produces light pollution and as a result causes a range of deleterious responses in humans, plants, and animals. An emerging strategy to combat light pollution, especially sky glow, is to use “amber” LEDs in lieu of phosphor-converted white LEDs with high correlated color temperatures. Importantly, however, there is no standardized terminology or chromaticity designation that is applicable to “amber” LED lighting products for outdoor illumination. In this work, we propose a specification structure for light sources with nonstandard chromaticities to increase the precision of language used in the architectural lighting industry and demonstrate that light sources with well-defined chromaticity ranges outside of the ANSI C78.377–2017 quadrangles may be useful for reducing the portion of relative sky glow related to light source spectral power distribution. We encourage lighting standards development organizations to consider standardizing such a system.
DOI: 10.5517/ccdc.csd.cc20wrxl
title: CCDC 1873730: Experimental Crystal Structure Determination

description: Related Article: Marcos Marçal Ferreira Queiroz, Robin Huber, Laurence Marcourt, Laure Guénée, Pierre-Marie Allard, Adriano Rutz, Louis-Félix Nothias, Concetta Carlotta De Ruvo, Jonathan Kissling, Mahabir Prashad Gupta, Emerson Ferreira Queiroz, Jean-Luc Wolfender|2023|Front. Nat. Prod.|2|1147195|doi:10.3389/fntpr.2023.1147195
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.cc257jqp
title: CCDC 2003242: Experimental Crystal Structure Determination

description: Related Article: Bo-Sun Yun, So-Yoen Kim, Jin-Hyoung Kim, Sunghan Choi, Sanghun Lee, Ho-Jin Son, Sang Ook Kang|2022|ACS Appl. Elect. Mater.|4|2699|doi:10.1021/acsaelm.2c00235
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: 10.6073/pasta/24107bfde25b116686e01dd30227cfd2
title: Urban Heat and Desert Wildlife: Rodent Body Condition Across a Gradient of Surface Temperatures in the greater Phoenix, Arizona (USA) metropolitan area (2019-2020)

description: We live-trapped wild rodents from seven field sites spanning three strata of land-surface temperatures in the Phoenix, Arizona (USA) metropolitan area. We captured 116 adult pocket mice (Chaetodipus spp. and Perognathus spp.) and Merriam’s kangaroo rats (Dipodomys merriami) during 2019 and 2020 from mountainous urban parks and open spaces. Animal body condition was quantified as percent body fat (i.e., fat mass divided by body mass). We used a noninvasive quantitative magnetic resonance instrument to measure body condition.
DOI: 10.6084/m9.figshare.22644796
title: Malesian flower traits

description: Flower trait data for Malesian species with references.
DOI: 10.23729/86f6b329-c4b3-4ada-a4a3-4407552bab61
title: Data for: "Combination of peracetic acid dosing with diffused aeration in municipal wastewater treatment"

description: This dataset contains the raw data of the publication "Combination of peracetic acid dosing with diffused aeration in municipal wastewater treatment".
DOI: 10.5066/p90l47ti
title: Hydrogeologic Framework data and models for the Eel River groundwater basin, Humboldt County, California

description: This data release contains a geospatial database related to a digital 3D hydrogeologic framework model (3D HFM) of the Eel River Valley groundwater basin, California. The geospatial database contains two main data elements: (1) input data to the 3D HFM; (2) interpolated elevations and thicknesses of hydrogeologic units as a cellular array, which are a spatial representation of the 3D HFM results. Three hydrogeologic framework models of the same extent and similar conceptualization but successively increasing complexity were developed to characterize the structure, thickness, and extent of the materials within the basin; data from each phase of model-building are contained in this data release. The Phase 1 3D HFM has three hydrogeologic units (HGUs) and is based on a state-scale geologic map and generalized geologic cross sections, with no faults. The Phase 2 3D HFM has four HGUs and refines the elevation and thickness of the HGUs using data from detailed geologic maps and on structure contour and isopach maps that were based on formation tops picked from oil and gas well logs; the model includes faults. The Phase 3 3D HFM has seven HGUs and further refines the elevation and thickness of the HGUs using additional detailed geologic map data and stratigraphic interpretation of lithology data from water wells; the model includes faults. Each 3D HFM is a digital representation of the subsurface configuration of the HGUs that can be visualized in three dimensions and directly incorporated into numerical flow models. Input data to the 3D HFMs included in this data release include surface map data polygons of the hydrogeologic units, fault traces, and structure contour and isopach data that were used in the Phase 2 and 3 HFMs to define the extent and thickness of the deepest basin-filling hydrogeologic unit. Textural (grain size) data were compiled from driller?s lithologic logs from water wells. Well locations are included as point data within the geospatial database; the lithologic data from downhole intervals in each borehole are tabulated in spreadsheet form. The interpolated elevations and thickness of stratigraphic units are presented as a cellular array: essentially a ?flattened?, two-dimensional representation of the digital 3D geologic framework, which defines the elevation and thickness of the HGUs within the 3D HFMs from each phase of model construction. The elevation and thickness of the geologic units are contained within a single polygon feature class ModelCells, which contains a mesh of polygons that represent model cells that have multiple attributes including XY location, elevation and thickness of each hydrogeologic unit. The spatial data are accompanied by non-spatial tables that describe the sources of geologic information, a glossary of terms, a description of model units, and a Data Dictionary that duplicates the Entity and Attribute information contained in the metadata file. Spatial data are also presented as shapefiles.
DOI: 10.6084/m9.figshare.22644760
title: Different insects mortality rates.xlsx

description: The concentration of fungus in all fungal treatments is 2×108 spores/ml. The mortality caused by the same fungus isolate was compared by one-way analysis of variance (ANOVA) followed by post hoc Tukey tests with insect species as the factor, the data were checked for normality by Shapiro-Wilk tests before analysis. The data represent the mean of five replicates with standard error. Data with different letters indicate significant differences among different insect species with the same fungal treatment.
DOI: 10.6084/m9.figshare.22644760.v1
title: Different insects mortality rates.xlsx

description: The concentration of fungus in all fungal treatments is 2×108 spores/ml. The mortality caused by the same fungus isolate was compared by one-way analysis of variance (ANOVA) followed by post hoc Tukey tests with insect species as the factor, the data were checked for normality by Shapiro-Wilk tests before analysis. The data represent the mean of five replicates with standard error. Data with different letters indicate significant differences among different insect species with the same fungal treatment.
DOI: 10.25625/hwtbw5/03n9r3
title: Area4_zq80_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/bekskw
title: Area2_zq35_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/hjafkg
title: Area4_zq40_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/pdgbjb
title: Area1_enl_50m.tif

description: :unav
DOI: 10.25625/hwtbw5/sugbxw
title: Area3_zkurt_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/zu5nrt
title: Area4_zmax_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/70br7x
title: Area2_zq5_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/7l5dox
title: Area4_lai_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/oer069
title: Area2_enl_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/ooxxaa
title: Area1_zq50_50m.tif

description: :unav
DOI: 10.25625/hwtbw5/pdm2nt
title: Area2_num_gaps_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/ybh60m
title: Area1_zq95_50m.tif

description: :unav
DOI: 10.25625/hwtbw5/5eom3p
title: Area3_zq65_10m.tif

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DOI: 10.25625/hwtbw5/6qtqoi
title: Area3_zq10_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/7r5xlj
title: Area4_zentropy_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/g400uq
title: Area4_pzabovezmean_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/jprynb
title: Area4_zq30_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/trhhpl
title: Area1_rumple_50m.tif

description: :unav
DOI: 10.25625/hwtbw5/56xxsy
title: Area2_pzabovemean_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/edd75p
title: Area2_zq25_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/fvton4
title: Area1_zq85_50m.tif

description: :unav
DOI: 10.25625/hwtbw5/jijcyy
title: Area4_zq15_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/p367zt
title: Area4_zq95_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/eeemjy
title: Area4_zq25_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/keujuy
title: Area2_zq30_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/rb3is7
title: Area4_zq55_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/rsovou
title: Area4_fhd_shan_div_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/snv7aq
title: Area1_zkurt_50m.tif

description: :unav
DOI: 10.25625/hwtbw5/zwihx2
title: Area3_pzabovezmean_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/2oagy9
title: Area3_zq90_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/co7iac
title: Area3_lai_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/ofwsyx
title: Area3_zq85_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/qsj2bx
title: Area3_zq15_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/whfwnm
title: Area2_sum_gapArea_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/xlrv2c
title: Area1_zskew_50m.tif

description: :unav
DOI: 10.25625/hwtbw5/8rzook
title: Area1_zmean_50m.tif

description: :unav
DOI: 10.25625/hwtbw5/hzbemk
title: Area3_sum_gapArea_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/k9erji
title: Area4_zq10_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/qwctlu
title: Area2_zsd_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/tkr20m
title: Area4_max_gapArea_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/wr27eh
title: Area1_num_gaps_50m.tif

description: :unav
DOI: 10.25625/hwtbw5/354ef3
title: Area1_zq30_50m.tif

description: :unav
DOI: 10.25625/hwtbw5/38uz3w
title: Area1_veg_cover_50m.tif

description: :unav
DOI: 10.25625/hwtbw5/7yfweb
title: Area1_max_gapArea_50m.tif

description: :unav
DOI: 10.25625/hwtbw5/gn3k17
title: Area1_zq45_50m.tif

description: :unav
DOI: 10.25625/hwtbw5/hikmec
title: Area3_zmax_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/qq8ige
title: Area3_veg_cover_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/0zdvvg
title: Area1_pzabove2_50m.tif

description: :unav
DOI: 10.25625/hwtbw5/46vprb
title: Area3_zq75_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/af6cbv
title: Area1_sum_gapArea_50m.tif

description: :unav
DOI: 10.25625/hwtbw5/cxkdqk
title: Area2_rumple_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/diy4wq
title: Area3_fhd_shan_eve_10m.tif

description: :unav
DOI: 10.25625/hwtbw5/44cnb0
title: Area4_num_gaps_10m.tif

description: :unav