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  • 1
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    Seawater trace element data and pre- and post-foraminifer experiment alkalinity and pH (2018)
    PANGAEA
    Details
    Publication Date: 2023-03-14
    Keywords: Alkalinity, total; Autotitrator Metrohm 785 and electrode; Barium/Calcium ratio; Barium/Calcium ratio, standard deviation; Comment; Experiment; ICP-MS, Agilent 7500-ce; Number of observations; pH; Strontium-86/Strontium-88, standard deviation; Strontium-86/Strontium-88 ratio; Strontium-87/Strontium-88, standard deviation; Strontium-87/Strontium-88 ratio; Treatment
    Type: Dataset
    Format: text/tab-separated-values, 98 data points
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  • 2
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    Average barium/calcium ratios of cultured foraminifer specimens of Neogloboquadrina dutertrei, listed by experiment (2018)
    PANGAEA
    Details
    Publication Date: 2023-03-14
    Keywords: Barium/Calcium ratio; Experiment; ICP-MS, Agilent 7500-ce; LA-ICP-MS, Laser-ablation inductively coupled plasma mass spectrometer; Neogloboquadrina dutertrei, Barium/Calcium ratio; pH; Temperature, water
    Type: Dataset
    Format: text/tab-separated-values, 325 data points
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  • 3
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    Bacterial GMGTs in East African lake sediments: Their potential as palaeotemperature indicators (2019)
    PANGAEA
    In:  Supplement to: Baxter, Allix J; Hopmans, Ellen C; Russell, James M; Sinninghe Damsté, Jaap S (2019): Bacterial GMGTs in East African lake sediments: Their potential as palaeotemperature indicators. Geochimica et Cosmochimica Acta, 259, 155-169, https://doi.org/10.1016/j.gca.2019.05.039
    Details
    Publication Date: 2023-03-14
    Description: Glycerol monoalkyl glycerol tetraethers (GMGTs) are a group of membrane spanning lipids produced by some species of archaea and bacteria. They differ from the more commonly studied glycerol dialkyl glycerol tetraethers (GDGTs) in having an additional covalent carbon-carbon bond connecting the two alkyl chain. The relative abundance and distribution of bacterial branched GMGTs (brGMGTs) in surface sediments from a set of East African lakes were studied. The abundance of brGMGTs relative to the brGDGTs is positively correlated to measured mean annual air temperature (MAAT), although with a significant amount of scatter. BrGMGT abundance was not correlated to lake water pH. Seven major brGMGTs that vary in degree of methylation were identified, with m/z 1020, 1034 and 1048. Further, the mass chromatograms of the m/z 1020 and 1034 brGMGTs show an interesting distribution of peaks, which likely relates to the occurrence of distinct brGMGT isomers. This structural complexity is higher than previously observed in peats and marine sediments. Principal component analysis of the fractional abundance of bacterial tetraether lipids revealed the brGMGTs behave similarly to one another but differently from both the 5- or 6-methyl brGDGTs. This suggests the brGMGTs are produced by a common source organism and are methylated at a different position. The distribution of the seven brGMGTs showed considerable correlation with MAAT. This variability was captured in a new proxy index (the brGMGTI), which showed a strong positive linear relationship with MAAT. Lacustrine brGMGTs show potential to be applied to ancient settings to provide information about paleoclimate.
    Keywords: Albert_Lake; Bandara_Lake; Batoda_Lake; Bigata_Lake; Branched glycerol dialkyl glycerol tetraether, Ia; Branched glycerol dialkyl glycerol tetraether, Ia (peak area); Branched glycerol dialkyl glycerol tetraether, Ib; Branched glycerol dialkyl glycerol tetraether, Ib (peak area); Branched glycerol dialkyl glycerol tetraether, Ic; Branched glycerol dialkyl glycerol tetraether, Ic (peak area); Branched glycerol dialkyl glycerol tetraether, IIa; Branched glycerol dialkyl glycerol tetraether, IIa'; Branched glycerol dialkyl glycerol tetraether, IIa' (peak area); Branched glycerol dialkyl glycerol tetraether, IIa (peak area); Branched glycerol dialkyl glycerol tetraether, IIb; Branched glycerol dialkyl glycerol tetraether, IIb'; Branched glycerol dialkyl glycerol tetraether, IIb' (peak area); Branched glycerol dialkyl glycerol tetraether, IIb (peak area); Branched glycerol dialkyl glycerol tetraether, IIIa; Branched glycerol dialkyl glycerol tetraether, IIIa'; Branched glycerol dialkyl glycerol tetraether, IIIa' (peak area); Branched glycerol dialkyl glycerol tetraether, IIIa (peak area); Branched glycerol monoalkyl glycerol tetraethers, H1020a; Branched glycerol monoalkyl glycerol tetraethers, H1020a (peak area); Branched glycerol monoalkyl glycerol tetraethers, H1020b; Branched glycerol monoalkyl glycerol tetraethers, H1020b (peak area); Branched glycerol monoalkyl glycerol tetraethers, H1020c; Branched glycerol monoalkyl glycerol tetraethers, H1020c (peak area); Branched glycerol monoalkyl glycerol tetraethers, H1034a; Branched glycerol monoalkyl glycerol tetraethers, H1034a (peak area); Branched glycerol monoalkyl glycerol tetraethers, H1034b; Branched glycerol monoalkyl glycerol tetraethers, H1034b (peak area); Branched glycerol monoalkyl glycerol tetraethers, H1034c; Branched glycerol monoalkyl glycerol tetraethers, H1034c (peak area); Branched glycerol monoalkyl glycerol tetraethers, H1048; Branched glycerol monoalkyl glycerol tetraethers, H1048 (peak area); Bugwagi_Lake; Bukurungu_East_Lake; Central_Lake; Chibwera_Lake; Country; Crane_Lake; DEPTH, water; Dimtu_Lake; Edward_Lake; Elevation of event; Enchanted_Lake__Lake; Event label; Gallery_Tarn_Lake; Garba_Gurach_Lake; GDGTs; GMGT; Hanging_Tarn_Lake; Hara_Laki_Lake; Hara_Lucas_Lake; Haro_Lakota_Lake; Harris_Tarn_Lake; Hausburg_Tarn_Lake; H-GDGT; Hut_Tarn_Lake; Ibamba_Lake; Kacuba_Lake; Kako_Lake; Kamweru_Lake; Kanyabutetere_Lake; Kanyanchu_Lake; Kasirya_Lake; Katanda_Lake; Katunda_Lake; Kifuruka_Lake; Kisibendi_Lake; Kitere_Lake; Kopello_Lake; Koromi_Lake; Kuware_Lake; Kyasunduka_Lake; Kyerbwato_Lake; Kyogo_Lake; Lake; Lake_Ellis; lakes; Lake surface area; Large_Hall_Tarn_Lake; Latitude of event; Longitude of event; Lower_Kachope_Lake; Lower_Simba_Lake; Mahoma_Lake; Mahuhura_Lake; Mbayo_Lake; membrane lipids; Middle_Kachope_Lake; Mirambi_Lake; MULT; Multiple investigations; Murabio_Lake; Murusi_Lake; Mwengenyi_Lake; Nanyuki_Tarn_Lake; NIOZ_UU; NIOZ Royal Netherlands Institute for Sea Research, and Utrecht University; Njarayabana_Lake; Nkuruba_Lake; Nyamugosani_Lake; Nyamusingere_Lake; Nyantonde_Lake; Oblong_Tarn_Lake; palaeotemperature; pH; Ruhandika_Lake; Rutundu_Lake; sediments; Small_Hall_Tarn_Lake; Square_Tarn_Lake; Sum; Tanganyika_Lake; Teleki_Tarn_Lake; Temperature, air, annual mean; Temperature, water; tetraethers; Thompson_Lake_Lake; Togona_Lake; Veggi_Tarn_Lake; Wandakara_Lake; Wankenzi_Lake
    Type: Dataset
    Format: text/tab-separated-values, 2991 data points
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  • 4
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    The chemistry of interstitial waters at DSDP Site 45-395
    PANGAEA
    In:  Supplement to: McDuff, Russell E (1984): The chemistry of interstitial waters from the upper ocean crust, Site 395, Deep Sea Drilling Project Leg 78B. In: Hyndman, RD; Salisbury, MH; et al. (eds.), Initial Reports of the Deep Sea Drilling Project (U.S. Govt. Printing Office), 78B, 795-799, https://doi.org/10.2973/dsdp.proc.78b.114.1984
    Details
    Publication Date: 2023-05-12
    Description: Chemical analysis of fluids sampled in situ during Leg 78B has been carried out. The fluids found in Hole 395A upon reoccupation of the site after a five-year hiatus were identical in composition to local bottom seawater to depths of at least 400 meters, suggesting drawdown. No local bottom-water component is present at 543 meters however. The chemistry of this deep sample shows a large depletion in magnesium, with charge balance maintained by sodium, rather than calcium, enrichment. Calcium is lost through calcite precipitation. A sediment interstitial-water sample from the offset Hole 395B has a chemical signature indicative of glacial influence, thereby constraining drawdown through the sediments to a velocity less than 0.5 cm/y.
    Keywords: Deep Sea Drilling Project; DSDP
    Type: Dataset
    Format: application/zip, 2 datasets
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  • 5
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    Chronology and stable carbon isotopic ratios of sediments from the Western Pacific Warm Pool region
    PANGAEA
    In:  Supplement to: Tierney, Jessica E; Oppo, Delia W; Rosenthal, Yair; Russell III, James M; Linsley, Braddock K (2010): Coordinated hydrological regimes in the Indo-Pacific region during the past two millennia. Paleoceanography, 25(1), PA1102, https://doi.org/10.1029/2009PA001871
    Details
    Publication Date: 2023-05-12
    Description: Instrumental data suggest that major shifts in tropical Pacific atmospheric dynamics and hydrology have occurred within the past century, potentially in response to anthropogenic warming. To better understand these trends, we use the hydrogen isotopic ratios of terrestrial higher plant leaf waxes (DDwax) in marine sediments from southwest Sulawesi, Indonesia, to compile a detailed reconstruction of central Indo-Pacific Warm Pool (IPWP) hydrologic variability spanning most of the last two millennia. Our paleodata are highly correlated with a monsoon reconstruction from Southeast Asia, indicating that intervals of strong East Asian summer monsoon (EASM) activity are associated with a weaker Indonesian monsoon (IM). Furthermore, the centennial-scale oscillations in our data follow known changes in Northern Hemisphere climate (e.g., the Little Ice Age and Medieval Warm Period) implying a dynamic link between Northern Hemisphere temperatures and IPWP hydrology. The inverse relationship between the EASM and IM suggests that migrations of the Intertropical Convergence Zone and associated changes in monsoon strength caused synoptic hydrologic shifts in the IPWP throughout most of the past two millennia.
    Type: Dataset
    Format: application/zip, 3 datasets
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  • 6
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    Stable isotopic record of planktonic foraminifera of the Pacific Ocean
    PANGAEA
    In:  Supplement to: Russell, Ann D; Spero, Howard J (2000): Field examination of the oceanic carbonate ion effect on stable isotopes in planktonic foraminifera. Paleoceanography, 15(1), 43-52, https://doi.org/10.1029/1998PA000312
    Details
    Publication Date: 2023-05-12
    Description: We determined the d18O and d13C of individual Globigerinoides ruber and Pulleniatina obliquiloculata from sediment traps located from 5°N to 12°S along 140°W in the Pacific Ocean to evaluate the effects of varying [CO3=] on shell d18O and d13C. Variations in the offset between shell d13C and d13CDIC (Dd13Cs-DIC) are attributed to differences in [CO3]2-, temperature, and shell size between sample sites. When Dd13Cs-DIC of G. ruber was corrected for variations in [CO3]2- using the experimental slope of Bijma et al. (1998), the residual Dd13Cs-DIC was correlated with mixed layer temperature (+0.10±0.04 per mil °C**-1). The slope of this temperature effect is consistent with experimental results. In P. obliquiloculata, Dd13Cs-DIC and temperature were strongly anticorrelated (−0.14±0.03 per mil C**-1). We are unable to separate the influences of [CO3]2- and temperature in this species without independent experimental data. Correcting for [CO3]2- variability on d18Os of G. ruber improves the accuracy of estimated sea surface temperatures.
    Type: Dataset
    Format: application/zip, 2 datasets
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  • 7
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    Foraminiferal magnesium in Globeriginoides sacculifer
    PANGAEA
    In:  Supplement to: Hastings, D; Russell, Ann D; Emerson, Steven R (1998): Foraminiferal magnesium in Globeriginoides sacculifer as a paleotemperature proxy. Paleoceanography, 13(2), 161-169, https://doi.org/10.1029/97PA03147
    Details
    Publication Date: 2023-05-12
    Description: Foraminiferal magnesium shows increasing promise as a paleothermometer, but the accuracy and precision are limited by biases introduced by partial dissolution, salinity variations, Mg-rich gametogenic calcite, and contaminant phases. We improved cleaning methods and reduced errors introduced by partial dissolution by sampling from well-preserved cores in the equatorial Atlantic and the Caribbean Sea with different dissolution histories. All cores reveal a synchronous 25% increase in Mg/Ca from the stage 2/3 boundary to the Holocene core top, indicating that dissolution is not a controlling factor. Modern temperatures estimated from core top Mg/Ca are 24.5°-25.0°C, equal to mean annual water temperatures at 50-100 m. We estimate that sea surface temperature increased by 2.6°C (±1.3) from the last glacial maximum to the Holocene. Holocene values were comparable to those during isotope stage 5e. Our data indicate that biases from contaminant phases and partial dissolution can be reduced. This paleothermometer holds promise if uncertainties introduced by salinity variations and gametogenic calcite can be constrained.
    Type: Dataset
    Format: application/zip, 3 datasets
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  • 8
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    Microfossil, stable isotope, and facies of a near-continuous core from the Gippsland Basin (2013)
    PANGAEA
    In:  Supplement to: Gallagher, Stephen John; Villa, Giuliana; Drysdale, Russell N; Wade, Bridget S; Scher, Howie D; Li, Qianyu; Wallace, Malcolm W; Holdgate, Guy R (2013): A near-field sea level record of East Antarctic Ice Sheet instability from 32 to 27 Myr. Paleoceanography, 28(1), 1-13, https://doi.org/10.1029/2012PA002326
    Details
    Publication Date: 2023-01-13
    Description: Fossil, facies, and isotope analyses of an early high-paleolatitude (55°S) section suggests a highly unstable East Antarctic Ice Sheet from 32 to 27 Myr. The waxing and waning of this ice sheet from 140% to 40% of its present volume caused sea level changes of ±25 m (ranging from -30 to +50 m) related to periodic glacial (100,000 to 200,000 years) and shorter interglacial events. The near-field Gippsland sea level (GSL) curve shares many similarities to the far-field New Jersey sea level (NJSL) estimates. However, there are possible resolution errors due to biochronology, taphonomy, and paleodepth estimates and the relative lack of lowstand deposits (in NJSL) that prevent detailed correlations with GSL. Nevertheless, the lateral variations in sea level between the GSL section and NJSL record that suggest ocean siphoning and antisiphoning may have propagated synchronous yet variable sea levels.
    Keywords: Australia; Groper-1; Sampling Well; WELL
    Type: Dataset
    Format: application/zip, 4 datasets
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  • 9
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    NanoSIMS data, 13C assimilation in the symbiotic dinoflagellates and their foraminiferal host (Orbulina universa) (2018)
    PANGAEA
    In:  Supplement to: LeKieffre, Charlotte; Spero, Howard J; Russell, Ann D; Fehrenbacher, Jennifer; Geslin, Emmanuelle; Meibom, Anders (2018): Assimilation, translocation, and utilization of carbon between photosynthetic symbiotic dinoflagellates and their planktic foraminifera host. Marine Biology, 165(6), https://doi.org/10.1007/s00227-018-3362-7
    Details
    Publication Date: 2023-01-13
    Description: Here we performed pulse-chase experiments with 13C-enriched dissolved inorganic carbon, followed by TEM and quantitative NanoSIMS isotopic imaging to visualize photosynthetic C assimilation by individual symbiotic dinoflagellates and subsequent translocation to their Orbulina universa host. NanoSIMS image processing was carried out as described in LeKieffre et al. (2017) and Nomaki et al. (2018). Briefly, TEM images were aligned with corresponding NanoSIMS 12C14N- images (Online Resource 1) using the software Look@NanoSIMS (Polerecky et al. 2012), which allows a user to hand-draw regions of interest (ROIs) corresponding to different organelles (e.g., dinoflagellate starch grains, foraminiferal lipid droplets, and fibrillar bodies). For each type of organelle and each time point, the average 13C-enrichment and its standard deviation were calculated based on 3 replicate foraminifera (except for the 6 h and 30 h time points, where only 2 replicates were available). The ROIs drawn on TEM images were also used to assess the relative abundance (in %) of lipid droplets in the foraminiferal endoplasm and starch grains in the dinoflagellate cytoplasm, respectively. Lipid droplet abundance was determined as the number of pixels occupied by lipid droplets divided by the total number of pixels of foraminiferal endoplasm. Starch grain abundance was determined as the number of pixels of occupied by starch grains divided by the total number of pixels covering dinoflagellate cytoplasm.
    Type: Dataset
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet, 277.1 kBytes
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  • 10
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    Thiamethoxam insecticide detections in high-capacity irrigation well water, Wisconsin, USA (2018)
    PANGAEA
    In:  Supplement to: Bradford, Benjamin Z; Huseth, Anders S; Groves, Russell L (2018): Widespread detections of neonicotinoid contaminants in central Wisconsin groundwater. PLoS ONE, 13(10), e0201753, https://doi.org/10.1371/journal.pone.0201753
    Details
    Publication Date: 2023-01-13
    Description: Neonicotinoids are a popular and widely-used class of insecticides whose heavy usage rates and purported negative impacts on bees and other beneficial insects has led to questions about their mobility and accumulation in the environment. Neonicotinoid compounds are currently registered for over 140 different crop uses in the United States, with commercial growers continuing to rely heavily on neonicotinoid insecticides for the control of key insect pests through a combination of in-ground and foliar applications. In 2008, the Wisconsin Department of Agriculture, Trade and Consumer Protection (DATCP) began testing for neonicotinoids in groundwater test wells in the state, reporting detections of one or more neonicotinoids in dozens of shallow groundwater test wells. In 2011, similar detection levels were confirmed in several high-capacity overhead center-pivot irrigation systems in central Wisconsin. The current study was initiated to investigate the spatial extent and magnitude of neonicotinoid contamination in groundwater in and around areas of irrigated commercial agriculture in central Wisconsin. From 2013-2015 a total of 317 samples were collected from 91 unique high-capacity irrigation wells and tested for the presence of thiamethoxam (TMX), a neonicotinoid, using enzyme-linked immunosorbent assays. 67% of all samples were positive for TMX at a concentration above the analytical limit of quantification (0.05 µg/L) and 78% of all wells tested positive at least once. Mean detection was 0.28 µg/L, with a maximum detection of 1.67 µg/L. Five wells had at least one detection exceeding 1.00 µg/L. Furthermore, an analysis of the spatial structure of these well detects suggests that contamination profiles vary across the landscape, with differences in mean detection levels observed from landscape (25 km), to farm (5 km), to individual well (500 m) scales.
    Keywords: Date/Time of event; Event label; Latitude of event; Longitude of event; Sample ID; Sampling Well; Thiamethoxam; WELL; Wisconsin_Well-01_20130830; Wisconsin_Well-02_20130830; Wisconsin_Well-02_20140626; Wisconsin_Well-02_20150519; Wisconsin_Well-02_20150804; Wisconsin_Well-02_20150902; Wisconsin_Well-02_20151017; Wisconsin_Well-03_20130830; Wisconsin_Well-03_20140709; Wisconsin_Well-03_20150519; Wisconsin_Well-03_20150725; Wisconsin_Well-03_20150902; Wisconsin_Well-04_20130830; Wisconsin_Well-04_20140709; Wisconsin_Well-04_20150519; Wisconsin_Well-04_20150725; Wisconsin_Well-04_20150902; Wisconsin_Well-05_20130830; Wisconsin_Well-05_20140709; Wisconsin_Well-05_20150618; Wisconsin_Well-05_20150727; Wisconsin_Well-05_20151021; Wisconsin_Well-06_20130830; Wisconsin_Well-06_20140706; Wisconsin_Well-06_20150710; Wisconsin_Well-06_20150727; Wisconsin_Well-07_20140626; Wisconsin_Well-07_20150511; Wisconsin_Well-07_20150519; Wisconsin_Well-07_20150725; Wisconsin_Well-08_20130830; Wisconsin_Well-08_20140626; Wisconsin_Well-08_20150511; Wisconsin_Well-08_20150519; Wisconsin_Well-08_20150725; Wisconsin_Well-08_20151019; Wisconsin_Well-09_20150518; Wisconsin_Well-09_20150529; Wisconsin_Well-09_20150615; Wisconsin_Well-09_20150623; Wisconsin_Well-09_20150702; Wisconsin_Well-09_20150710; Wisconsin_Well-09_20150717; Wisconsin_Well-09_20150725; Wisconsin_Well-09_20150805; Wisconsin_Well-09_20150813; Wisconsin_Well-09_20150826; Wisconsin_Well-09_20150902; Wisconsin_Well-09_20150917; Wisconsin_Well-09_20151019; Wisconsin_Well-10_20130830; Wisconsin_Well-10_20140701; Wisconsin_Well-10_20150519; Wisconsin_Well-10_20150725; Wisconsin_Well-10_20151017; Wisconsin_Well-11_20130830; Wisconsin_Well-11_20140726; Wisconsin_Well-11_20150702; Wisconsin_Well-11_20150727; Wisconsin_Well-12_20130830; Wisconsin_Well-12_20140701; Wisconsin_Well-12_20150512; Wisconsin_Well-12_20150519; Wisconsin_Well-12_20150814; Wisconsin_Well-13_20150504; Wisconsin_Well-13_20150618; Wisconsin_Well-13_20150721; Wisconsin_Well-13_20150916; Wisconsin_Well-14_20150514; Wisconsin_Well-14_20150618; Wisconsin_Well-14_20150723; Wisconsin_Well-14_20150915; Wisconsin_Well-15_20150506; Wisconsin_Well-15_20150618; Wisconsin_Well-15_20150721; Wisconsin_Well-15_20150916; Wisconsin_Well-16_20150506; Wisconsin_Well-16_20150618; Wisconsin_Well-16_20150723; Wisconsin_Well-16_20150916; Wisconsin_Well-17_20150506; Wisconsin_Well-17_20150618; Wisconsin_Well-17_20150721; Wisconsin_Well-17_20150916; Wisconsin_Well-18_20150506; Wisconsin_Well-18_20150618; Wisconsin_Well-18_20150721; Wisconsin_Well-19_20140722; Wisconsin_Well-19_20140909; Wisconsin_Well-19_20150520; Wisconsin_Well-20_20140722; Wisconsin_Well-20_20140909; Wisconsin_Well-21_20130911; Wisconsin_Well-21_20150505; Wisconsin_Well-21_20150602; Wisconsin_Well-21_20150702; Wisconsin_Well-21_20150905; Wisconsin_Well-22_20130911; Wisconsin_Well-22_20150501; Wisconsin_Well-22_20150522; Wisconsin_Well-22_20150701; Wisconsin_Well-22_20150905; Wisconsin_Well-23_20130911; Wisconsin_Well-23_20140722; Wisconsin_Well-23_20140909; Wisconsin_Well-23_20150522; Wisconsin_Well-23_20150602; Wisconsin_Well-23_20150701; Wisconsin_Well-24_20150527; Wisconsin_Well-24_20150603; Wisconsin_Well-24_20150610; Wisconsin_Well-24_20150617; Wisconsin_Well-24_20150624; Wisconsin_Well-24_20150701; Wisconsin_Well-24_20150708; Wisconsin_Well-24_20150715; Wisconsin_Well-24_20150722; Wisconsin_Well-24_20150729; Wisconsin_Well-24_20150909; Wisconsin_Well-24_20150916; Wisconsin_Well-25_20140721; Wisconsin_Well-25_20140909; Wisconsin_Well-26_20130911; Wisconsin_Well-26_20140721; Wisconsin_Well-26_20140909; Wisconsin_Well-26_20150507; Wisconsin_Well-26_20150523; Wisconsin_Well-26_20150704; Wisconsin_Well-27_20130911; Wisconsin_Well-27_20140721; Wisconsin_Well-27_20140909; Wisconsin_Well-28_20130911; Wisconsin_Well-28_20150511; Wisconsin_Well-28_20150602; Wisconsin_Well-28_20150701; Wisconsin_Well-29_20130911; Wisconsin_Well-29_20140722; Wisconsin_Well-29_20140909; Wisconsin_Well-30_20130911; Wisconsin_Well-30_20150522; Wisconsin_Well-30_20150702; Wisconsin_Well-30_20150917; Wisconsin_Well-31_20130911; Wisconsin_Well-31_20140710; Wisconsin_Well-32_20130911; Wisconsin_Well-32_20140710; Wisconsin_Well-33_20130911; Wisconsin_Well-33_20140710; Wisconsin_Well-34_20130911; Wisconsin_Well-34_20140710; Wisconsin_Well-35_20130911; Wisconsin_Well-35_20140710; Wisconsin_Well-36_20140722; Wisconsin_Well-36_20140909; Wisconsin_Well-36_20150501; Wisconsin_Well-36_20150523; Wisconsin_Well-36_20150704; Wisconsin_Well-37_20130911; Wisconsin_Well-37_20140710; Wisconsin_Well-38_20140721; Wisconsin_Well-38_20140909; Wisconsin_Well-38_20150508; Wisconsin_Well-38_20150522; Wisconsin_Well-38_20150706; Wisconsin_Well-39_20130911; Wisconsin_Well-39_20150501; Wisconsin_Well-39_20150522; Wisconsin_Well-39_20150702; Wisconsin_Well-40_20130911; Wisconsin_Well-40_20140722; Wisconsin_Well-40_20140909; Wisconsin_Well-40_20150508; Wisconsin_Well-40_20150522; Wisconsin_Well-40_20150702; Wisconsin_Well-41_20140807; Wisconsin_Well-41_20150518; Wisconsin_Well-41_20150703; Wisconsin_Well-41_20150922; Wisconsin_Well-42_20130910; Wisconsin_Well-42_20140801; Wisconsin_Well-42_20140909; Wisconsin_Well-43_20150511; Wisconsin_Well-43_20150703; Wisconsin_Well-43_20150922; Wisconsin_Well-44_20140807; Wisconsin_Well-44_20140909; Wisconsin_Well-44_20150703; Wisconsin_Well-44_20150922; Wisconsin_Well-45_20140807; Wisconsin_Well-45_20140909; Wisconsin_Well-46_20150703; Wisconsin_Well-47_20150512; Wisconsin_Well-47_20150703; Wisconsin_Well-47_20150922; Wisconsin_Well-48_20130910; Wisconsin_Well-48_20150703; Wisconsin_Well-48_20150709; Wisconsin_Well-48_20150922; Wisconsin_Well-49_20150703; Wisconsin_Well-50_20130910; Wisconsin_Well-50_20140807; Wisconsin_Well-50_20140909; Wisconsin_Well-51_20140807; Wisconsin_Well-51_20140909; Wisconsin_Well-51_20150703; Wisconsin_Well-51_20150709; Wisconsin_Well-51_20150922; Wisconsin_Well-52_20140807; Wisconsin_Well-52_20140909; Wisconsin_Well-52_20150511; Wisconsin_Well-52_20150703; Wisconsin_Well-52_20150709; Wisconsin_Well-52_20150922; Wisconsin_Well-53_20130910; Wisconsin_Well-53_20140801; Wisconsin_Well-53_20140909; Wisconsin_Well-54_20130910; Wisconsin_Well-54_20140807; Wisconsin_Well-54_20140909; Wisconsin_Well-55_20130830; Wisconsin_Well-55_20140724; Wisconsin_Well-55_20140828; Wisconsin_Well-55_20150716; Wisconsin_Well-55_20150923; Wisconsin_Well-56_20130830; Wisconsin_Well-56_20140724; Wisconsin_Well-56_20140828; Wisconsin_Well-56_20150716; Wisconsin_Well-56_20150923; Wisconsin_Well-57_20130830; Wisconsin_Well-57_20140724; Wisconsin_Well-57_20140828; Wisconsin_Well-57_20150716; Wisconsin_Well-58_20130830; Wisconsin_Well-58_20140724; Wisconsin_Well-58_20140828; Wisconsin_Well-58_20150716; Wisconsin_Well-58_20150923; Wisconsin_Well-59_20130830; Wisconsin_Well-59_20140724; Wisconsin_Well-59_20140828; Wisconsin_Well-59_20150716; Wisconsin_Well-59_20150923; Wisconsin_Well-60_20130830; Wisconsin_Well-60_20140724; Wisconsin_Well-60_20140828; Wisconsin_Well-60_20150716; Wisconsin_Well-60_20150923; Wisconsin_Well-61_20130830; Wisconsin_Well-61_20140724; Wisconsin_Well-61_20140828; Wisconsin_Well-61_20150716; Wisconsin_Well-61_20150923; Wisconsin_Well-62_20130830; Wisconsin_Well-62_20140724; Wisconsin_Well-62_20140828; Wisconsin_Well-62_20150716; Wisconsin_Well-62_20150923; Wisconsin_Well-63_20150519; Wisconsin_Well-63_20150723; Wisconsin_Well-63_20151005; Wisconsin_Well-64_20130910; Wisconsin_Well-65_20150522; Wisconsin_Well-65_20150617; Wisconsin_Well-65_20150723; Wisconsin_Well-65_20150914; Wisconsin_Well-66_20140708; Wisconsin_Well-67_20140626; Wisconsin_Well-68_20150523; Wisconsin_Well-68_20150617; Wisconsin_Well-68_20150721; Wisconsin_Well-69_20130910; Wisconsin_Well-70_20130910; Wisconsin_Well-71_20130910; Wisconsin_Well-72_20140708; Wisconsin_Well-73_20140626; Wisconsin_Well-74_20140626; Wisconsin_Well-75_20140708; Wisconsin_Well-76_20150519; Wisconsin_Well-76_20150617; Wisconsin_Well-76_20150721;
    Type: Dataset
    Format: text/tab-separated-values, 951 data points
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