Di-ammonium fell to 715 USD/T on June 20, 2025, down 0.69% from the previous day. Over the past month, Di-ammonium's price has risen 9.16%, and is up 33.64% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Di-ammonium Phosphate.

Lithium rose to 61,150 CNY/T on June 27, 2025, up 0.91% from the previous day. Over the past month, Lithium's price has fallen 0.57%, and is down 33.17% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Lithium - values, historical data, forecasts and news - updated on June of 2025.

This data set represents the estimated amount of phosphorus and nitrogen fertilizers applied to selected crops for the year 2002, compiled for every catchment of NHDPlus for the conterminous United States. The source data set is based on 2002 fertilizer data (Ruddy and others, 2006) and tabulated by crop type per county (Alexander and others, 2007). The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

BackgroundThe adverse clinical endpoints of cardiovascular and kidney diseases are correlated with increased serum phosphate levels. However, in critically ill patients with coronary heart disease (CHD) accompanied by chronic kidney disease (CKD), the prognostic value of serum phosphate remains unclear.MethodsPatients' medical records from the Medical Information Mart for Intensive Care IV database who had concomitant CKD and CHD were classified into four distinct groups in this large retrospective observational cohort study based on the quartiles of serum phosphate levels. Vital status and the duration of hospital and ICU stays within the short-term follow-up periods of 30 and 90 days constituted the primary outcomes. All-cause mortality in the intensive care unit (ICU) and hospital constituted the secondary outcomes. Further, the Cox proportional hazard and restricted cubic spline (RCS) regression models were employed to ascertain how serum phosphate levels correlated with the primary outcomes. In addition, the occurrence rate of the secondary outcomes across the four quartiles was determined utilizing the Kaplan–Meier method.ResultsAmong the total 3,557 patients (67.6% male) included, the hospital and ICU all-cause mortality rates were 14.6% and 10%, separately. Higher quartiles of serum phosphate concentrations were associated with shorter short-term survival rates, as shown by the Kaplan–Meier curves. Additionally, the Cox proportional hazards analysis illustrated that serum phosphate was independently linked to a higher death risk in the hospital [HR, 1.10 (95% CI: 1.03–1.18), P = 0.007] and ICU [HR, 1.14 (95% CI: 1.07–1.22), P 

'''Short description:'''

You can find here the biogeochemistry non assimilative hindcast simulation GLOBAL_REANALYSIS_BIO_001_018 at 1/4° over period 1998 - 2016. Outputs are delivered as monthly mean files with Netcdf format (CF/COARDS 1.5 convention) on the native tripolar grid (ORCA025) at ¼° resolution with 75 vertical levels. This simulation is based on the PISCES biogeochemical model. It is forced offline at a daily frequency by the equivalent of the GLOBAL-REANALYSIS-PHYS-001-009 physics product but without data assimilation.

'''Detailed description: '''

There are 8 different datasets: * dataset-global-nahindcast-bio-001-018-no3 containing : nitrate concentration * dataset-global-nahindcast-bio-001-018-po4 containing : phosphate concentration * dataset-global-nahindcast-bio-001-018-si containing : silicate concentration * dataset-global-nahindcast-bio-001-018-o2 containing : dissolved oxygen concentration * dataset-global-nahindcast-bio-001-018-fe containing : iron concentration * dataset-global-nahindcast-bio-001-018-chl containing : chlorophyll concentration * dataset-global-nahindcast-bio-001-018-phyc containing : carbon phytoplankton biomass * dataset-global-nahindcast-bio-001-018-pp containing : primary production

The horizontal grid is the standard ORCA025 tri-polar grid (1440 x 1021 grid points). The three poles are located over Antarctic, Central Asia and North Canada. The ¼ degree resolution corresponds to the equator. The vertical grid has 75 levels, with a resolution of 1 meter near the surface and 200 meters in the deep ocean.Biogeochemical and physical simulations start at rest (cold start) in December 1991. The spin-up period consists of 5 years of interannual simulation between 1992 and 1997. The simulation period covers the ocean color era (1998 – 2016).The biogeochemical model used is PISCES (Aumont, in prep). It is a model of intermediate complexity designed for global ocean applications (Aumont and Bopp, 2006) and is part of NEMO modeling platform. It has 24 prognostic variables and simulates biogeochemical cycles of oxygen, carbon and the main nutrients controlling phytoplankton growth (nitrate, ammonium, phosphate, silicic acid and iron). The model distinguishes four plankton functional types based on size: two phytoplankton groups and two zooplankton groups Prognostic variables of phytoplankton are total biomass in C, Fe, Si (for diatoms) and chlorophyll and hence the Fe/C, Si/C, Chl/C ratios are variable. For zooplankton, all these ratios are constant and total biomass in C is the only prognostic variable. The bacterial pool is not modeled explicitly. PISCES distinguishes three non-living pools for organic carbon: small particulate organic carbon, big particulate organic carbon and semi-labile dissolved organic carbon. While the C/N/P composition of dissolved and particulate matter is tied to Redfield stoichiometry, the iron, silicon and carbonate contents of the particles are computed prognostically. Next to the three organic detrital pools, carbonate and biogenic siliceous particles are modeled. Besides, the model simulates dissolved inorganic carbon and total alkalinity. In PISCES, phosphate and nitrate + ammonium are linked by constant Redfield ratio , but cycles of phosphorus and nitrogen are decoupled by nitrogen fixation and denitrification. Biogeochemical model PISCES (NEMO3.5) is forced offline by daily fields of the physical model NEMO (OPA module in the NEMO platform) without any assimilation of physical data. The main features of this dynamical ocean are: * NEMO 3.1 * Atmospheric forcings from 3-hourly ERA-Interim reanalysis products, CORE bulk formulation * Vertical diffusivity coefficient is computed by solving the TKE equation * Tidal mixing is parameterized according to the works of Bessières et al. (2008) and Koch-Larrouy et al, (2006). * Sea-Ice model: LIM2 with the Elastic-Viscous-Plastic rheology * Initial conditions: Levitus 98 climatology for temperature and salinity, patched with PHC2.1 for the Arctic regions, and Medatlas for the Mediterranean Sea.

A special treatment is done on vertical diffusivity coefficient (Kz): the daily mean is done on Log10(Kz) after a filtering of enhanced convection (Kz increased artificially to 10 m2.s-1 when the water column is unstable). The purpose of this Log10 is to average the orders of magnitudes and to give more weight to small values of vertical diffusivity.

The atmospheric forcing fields are daily averages from ERA-Interim reanalysis product (CORE bulk formulation). Boundary fluxes account for nutrient supply from three different sources: Atmospheric deposition (Aumont et al., 2008), rivers for nutrients, dissolved inorganic carbon and alkalinity (Ludwig et al., 1996; Mayorga et al., 2010) and inputs of Fe from marine sediments. Nutrient and freshwater inflows by rivers are colocalized. River and dust inputs are balanced with sediment trapping of NO3, Si and Carbon. An annual and global value of atmospheric carbon dioxide is imposed at sea surface.

Nutrients, such as nitrogen and phosphorus, are essential for plant and animal growth and nourishment, but the overabundance of bioavailable nitrogen and phosphorus in water can cause adverse health and ecological effects. It is generally accepted that major increases in the primary production of surface-water bodies due to high inputs of nutrients is now the most important polluting effect in surface water in the developed world. The Fish Creek watershed is located along the southwestern margin of the city of Jackson Hole. Fish Creek is an important water body because it is used for irrigation, fishing, recreation, and adds scenic value to the Jackson Hole properties it flows through. Recent U.S. Geological Survey (USGS) studies indicated there is a greater biovolume of aquatic plants in Fish Creek than is typically observed in streams of similar size. Studies by the USGS also indicated that (1) the amount of biovolume in Fish Creek was inversely correlated to nitrate concentration, suggesting that the aquatic vegetation was likely consuming most or all of the nutrients available to the plants and (2) land-use activities in the west bank area of the watershed can affect groundwater quality, which can then affect Fish Creek’s water quality. The Fish Creek watershed has multiple natural and anthropogenic sources of nutrients (nitrogen and phosphorus species) that can eventually migrate into Fish Creek. These sources include (1) atmospheric deposition, (2) fertilizers applied to lawns, trees, and golf courses, (3) wastewater (septic systems and sewage treatment plans), (4) livestock, (5) surface-water diversions entering the watershed, and (6) avalanche explosives. The U.S. Geological Survey, in cooperation with the Teton Conservation District (TCD), completed a study to identify and quantify nitrogen and phosphorus sources and inputs into the Fish Creek watershed. Geospatial datasets, values from literature reviews, water-quality data, and questionnaires distributed by the TCD were used to identify locations of sources and to quantify nitrogen and phosphorus inputs. This study did not address the transformation and uptake of nitrogen species (ammonia, ammonium, nitrite, nitrate, nitrogen gas, organic nitrogen) and phosphorus species (orthophosphate, organic phosphorus), because complex hydrological and chemical modeling are required for this depth of understanding. Human activities in the watershed predominately occur in the valley, on the east-south-eastern part of the watershed, and that area shows the greatest input of nitrogen and phosphorus. To characterize spatial patterns of nutrient inputs to the Fish Creek watershed, a grid of 10-acre cell was created and overlaid on the study area. Nutrient inputs were then aggregated for each 10-acre cells. The largest 10-acre cell input values are generally associated with cells that coincide with sewage treatment plant injection sites, livestock, and distributed land use where septic systems and lawns are located. Annual nitrogen input ranged from 25 to over 4,000 pounds in a 10-acre cell and annual phosphorus input ranged from about 3 to about 1,200 pounds in a 10-acre cell. Atmospheric deposition represented the largest overall source of estimated nitrogen input (46 percent) into the watershed, and represented the second highest percentage (26 percent) of total phosphorus input into the watershed. It is noteworthy that much of nutrient input from atmospheric deposition are likely used by canopy vegetation before they reach Fish Creek. The next largest sources of nitrogen input associated with human activities are cattle and lawns, and the next largest phosphorus inputs are cattle and horses. Although cattle are not in the watershed for the entire year, the large number of cattle grazing on the land produced higher input for both nitrogen and phosphorus than many of the other sources. Lawns had higher application rates of nutrients and had larger acreages than other fertilized areas. Management of human waste in the watershed is accomplished by use of septic systems and water-treatment plants and both methods contribute to the total nutrient inputs into the watershed. Nitrogen contributions from water-treatment plants produced a high-input 10-acre cell; however, when the total input of nitrogen from water-treatment plants (both liquid waste and biosolids) is compared to the total nitrogen input into the watershed and to the input of individual septic systems in the watershed, the water-treatment plants contribute a relatively small amount of nitrogen. Results from this study provide important information regarding sources and quantity of nitrogen and phosphorus inputs to Fish Creek watershed. These data provide valuable insight regarding the relative effects of various human activities and can be used to assist resource managers seeking to improve the water quality of the Fish Creek watershed.

nuts-STeauRY dataset: hydrochemical and catchment characteristics dataset for large sample studies of Carbon, Nitrogen, Phosphorus and Silicon in French watercourses

Antoine Casquin, Marie Silvestre, Vincent Thieu

10.5281/zenodo.10830852

v0.1, 18^th March 2024

Brief overview of data:

· Carbon and nutrients data for 5470 continental French catchments

· Modelled discharge for 5128 of catchments out of 5470

· Geopackages with catchment delineations and outlets

· DEM conditioned to delimit additional catchments

· Land-use and climatic data for 5470 continental French catchments

Citation of this work

A data paper is currently being submitted with details of methods and results. Once published, it will be the preferential source to cite. The data paper will be link to the new version of the dataset that will be updated on doi.org/10.5281/zenodo.10830852. If you use this dataset in your research or report, you must cite it.

Motivations

Data was collected and curated for the nuts-STeauRY project (http://nuts-steaury.cnrs.fr), which deployed a national generic land to sea modelling chain.

Data was primarily used (see related works):

1. To calibrate concentrations of dissolved organic carbon and dissolve silica in headwaters
2. To validate spatially and temporally the modelling chain (DOC, NO3-, NH4+, TP, SRP, DSi)

Hydrochemical large sample datasets have numerous other uses: trends computations elucidate transfer mechanisms, machine learning, retrospective studies etc.

The objective here is to provide a large sample curated dataset of carbon and nutrients concentrations along with modelled discharges, catchment characteristics and delimitations for the continental France. Such large sample dataset aims at easing the large sample studies over France and/or Europe. Although part of the data gathered here is obtainable via public sources, the catchments delineations, their characteristics and modelled hydrology were note not publicly available yet. Moreover, a unification of units and detection and removal of outliers was performed on carbon and nutrients data.

Data sources & processing

Sampling points where snapped on the CCM database v2.1 (http://data.europa.eu/89h/fe1878e8-7541-4c66-8453-afdae7469221)(Vogt et al., 2007) and catchments were delineated using a 100m resolution Digital Elevation Model (DEM) conditioned by the hydrographic network and elementary catchments’ delineations of the CCM data v2.1. More than 6000 catchments were delineated and screened manually to check consistency: 5470 were retained.

Nutrient data was collected mainly through the Naiades portal (https://naiades.eaufrance.fr/), a database collecting water quality data produced by different water related actors across France. Nutrient data was also collected directly with regional water agencies (https://www.eau-seine-normandie.fr/, https://eau-grandsudouest.fr/, https://www.eaurmc.fr/, https://www.eau-artois-picardie.fr/, https://www.eau-rhin-meuse.fr/ and https://agence.eau-loire-bretagne.fr/home.html), and pre-processed using a database management system relying on PostgreSQL with PostGIS extension (Thieu & Silvestre, 2015). A three-pass strategy was used to curate raw carbon and nutrients data: 1. Removal of “obvious outliers”, 2. Detection of baseline change and correction if possible (or removal of data) 3. Removal of outliers using a quantile based approach by element and temporal series.

Hydrological time series are interpolation trough hydrograph transfer (de Lavenne et al., 2023) of 1664 time series of discharge completed with GR4J model (Pelletier & Andréassian, 2020; Pelletier 2021).

Land cover data was extracted from Corine Land Cover dataset for years 2000, 2006, 2012, and 2018 (EEA, 2020). Raw CLC typology contains 44 classes. Results of percent cover per year per class were computed for each catchment. An aggregated typology of 8 classes is also proposed.

Climatological data was extracted from daily reconstruction at 5 arcmin for temperatures and 1 arcmin for precipitation over Europe (Thiemig et al., 2022). Mean by catchment for min&max daily temperature and precipitation were computed for each catchment for the 1990-2019 period.

Nuts-STeauRY dataset

Carbon and nutrients time series

Time series of carbon and nutrients within the 1962-2019 period on 5470 stations: Dissolved Organic Carbon (DOC), Total Organic Carbon (TOC) Nitrates (NO3-), Nitrites (NO2-), Ammonia (NH4+), Soluble Reactive Phosphorus (SRP), Total Phosphorus (TP) and Dissolved Silica (DSi).

|var | n_unique_station| n_total_meas| mean_duration_y| mean_frequency_y|

|:---|----------------:|------------:|---------------:|----------------:|

|DOC | 4 992| 658 147| 14.3| 9.0|

|DSi | 3 299| 333 866| 12.9| 8.3|

|NH4 | 5 318| 907 343| 19.3| 8.7|

|NO2 | 5 264| 891 886| 19.2| 8.6|

|NO3 | 5 465| 939 279| 19.0| 9.0|

|SRP | 5 361| 910 107| 19.1| 8.7|

|TOC | 935| 111 993| 13.6| 9.6|

|TP | 5 199| 802 841| 17.1| 8.8|

Note that some SRP and DSi measurements were declared as realized on raw water. A thorough analysis of time series show no evidence of difference on baselines. For more accuracy, it is advised to filter out those analyses using the “fraction” attribute of each measurement.

Discharge modelled daily time series

Modelled naturalized discharge through hydrograph transfer and interpolated measured discharges when available for the 1980-2019 period.

A daily discharge was computed for 5128 catchments. For small catchments (< 1000 km², n = 4530), hydrograph transfer was used, while for big catchments, a direct interpolation of measured/completed discharges was performed. The direct interpolation was only possible for 598 catchments > 1000 km². The criteria retained for a direct interpolation is 0.8*area_discharge_station < area_quality < 1.2*area_discharge_station when discharge and quality stations were nested.

Hydrological time series uncertainties varies a lot depending on: quality of data source, distance from pseudo-gauged outlets, land cover of the catchments, natural spatial and temporal variability of discharge, size of the catchment (de Lavenne et al., 2016). We advise a cautious use of those modelled discharges as uncertainties could not be computed.

Catchments, outlets and conditioned DEM

5470 catchments and outlets are delivered as geopackages (EPSG: 3035).

The DEM, conditioned by CCM 2.1 is also delivered as a GeoTIFF (EPSG: 3035) as way to delimit new catchment for the area that are consistent with the dataset.

Catchments characteristics and climate

Refer to Data sources & processing and File descriptions.

File and attributes descriptions:

The key “sta_code” is present across all files. For time varying records, “date” can be a secondary key.

Description of CNPSi.csv data attributes

Each line is a couple measurement/parameter/station

· sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)

· sta_name: Name of the station in the Sandre referentiel (public french "dataverse" for water data)

· var: Abbreviation of parameter name

· fraction: "water_filtrated" or "water_raw"

· date: date of sampling

· hour: hour of sampling

· value: analytical result (concentration)

· provider: provider of the data

· producer: producer of the data

· from_db: "Naiades2022" (https://naiades.eaufrance.fr/france-entiere#/ dump from 2022) or "DoNuts" (Thieu, V., Silvestre, M., 2015. DoNuts: un système d’information sur les observations environnementales. Présentation Séminaire UMR Métis)

· n_meas: number of observations for a given parameter / station

· unit: unit of concentration

· element: "C" "N" "P" or "Si"

· year: year of observation

· month: month of observation

· day: day of observation

· julian_day: julian day observation (1-366)

· decade: decade of observation (one of "1961-1970", "1971-1980", "1981-1990", "1991-2000", "2001-2010", "2011-2020")

Description of CNPSi_stats.csv data attributes

Each line is a couple parameter / station

· sta_code: Code of the station in the Sandre

Polyphosphate is a ubiquitous linear homopolymer of phosphate residues linked by high-energy bonds similar to those found in ATP. It has been associated with many processes including pathogenicity, DNA uptake and multiple stress responses across all domains. Bacteria have also been shown to use polyphosphate as a way to store phosphate when transferred from phosphate-limited to phosphate-rich media – a process exploited in wastewater treatment and other environmental contaminant remediation. Despite this, there has, to date, been little research into the role of polyphosphate in the survival of marine bacterioplankton in oligotrophic environments. The three main proteins involved in polyphosphate metabolism, Ppk1, Ppk2 and Ppx are multi-domain and have differential inter-domain and inter-gene conservation, making unbiased analysis of relative abundance in metagenomic datasets difficult. This paper describes the development of a novel Isofunctional Homolog Annotation Tool (IHAT) to detect homologs of genes with a broad range of conservation without bias of traditional expect-value cutoffs. IHAT analysis of the Global Ocean Sampling (GOS) dataset revealed that genes associated with polyphosphate metabolism are more abundant in environments where available phosphate is limited, suggesting an important role for polyphosphate metabolism in marine oligotrophs.

This dataset reports alkaline phosphatase activities (APA) for 3 incubation runs and 33 in situ samples collected on RV/Atlantic Explorer cruise AE1812 in May 2018. The samples were collected between Bermuda and Rhode Island. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson acquisition_description=For APA analysis, triplicate biological samples (250 mL) from in situ and incubation samples were filtered onto 47-mm polycarbonate membranes (0.2 \u03bcm). Stored at \u221220\u00b0C until analysis.

APA was assayed after Dyhrman and Ruttenberg (2006) using the fluorogenic phosphatase substrate 6,8-difluoro-4-methylumbelliferyl phosphate. Values were normalized to both volume and chl a. Reagents/Abs/Em used:

D-6567 6,8-difluoro-4-methylumbelliferyl phosphate (DiFMUP):
- Storage upon receipt: \u2264 20\u00b0C; Desiccate
- Abs/Em = 358/455
- Molecular Formula: C10H7F2O6P
- Molecular Weight: 292.1
- CAS Name/Number: 2H-1-Benzopyran-2-one, 6,8-difluoro-4-methyl-7-(phosphonooxy)-/ 214491-43-7

D-6566 6,8-difluoro-7-Hydroxy-4-Methylcoumarin (DiFMU) - Reference Standard:
- Storage upon receipt: Room temp.; protect from light
- Molecular Formula: C10H6F2O3
- Molecular Weight: 212.15
- CAS Name/Number: 2H-1-Benzopyran-2-one, 6,8-difluoro-7-hydroxy-4-methyl-/ 215868-23-8

Incubation key:
Control = no addition of nutrients or deep water
DSW = deep seawater addition (added 20% deep seawater (700 m))
+P = Added phosphate only (0.5 \u00b5M final for incubations 1 and 2, 1 \u00b5M final for incubation 3)
+N = Added nitrate only (6 \u00b5M final for incubations 1 and 2, 12 \u00b5M final for incubation 3)
phi_P = All but P added (N, Si, Fe, B12)
\u00a0phi_N = All but N added (P, Si, Fe, B12)
-1, -2, -3 = biological replicates

In situ key:
IS = in situ
-1, -2, -3 = biological replicates

Lost = sample was lost awards_0_award_nid=704773 awards_0_award_number=OCE-1558506 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1558506 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=David L. Garrison awards_0_program_manager_nid=50534 cdm_data_type=Other comment=AE1812 Alkaline phosphatase activity in situs and incubation samples from AE1812 cruise transect from Bermuda to Rhode Island in May 2018 PI: S. Dyhrman (LDEO) version: 2018-07-17 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 dataset_current_state=Final and no updates defaultDataQuery=&time<now doi=10.26008/1912/bco-dmo.739973.1 Easternmost_Easting=-56.56 geospatial_lat_max=40.42 geospatial_lat_min=31.42 geospatial_lat_units=degrees_north geospatial_lon_max=-56.56 geospatial_lon_min=-70.58 geospatial_lon_units=degrees_east infoUrl=https://www.bco-dmo.org/dataset/739973 institution=BCO-DMO instruments_0_dataset_instrument_description=Samples were run on a Biotek Synergy fluorescent plate reader using black plates instruments_0_dataset_instrument_nid=739977 instruments_0_description=Plate readers (also known as microplate readers) are laboratory instruments designed to detect biological, chemical or physical events of samples in microtiter plates. They are widely used in research, drug discovery, bioassay validation, quality control and manufacturing processes in the pharmaceutical and biotechnological industry and academic organizations. Sample reactions can be assayed in 6-1536 well format microtiter plates. The most common microplate format used in academic research laboratories or clinical diagnostic laboratories is 96-well (8 by 12 matrix) with a typical reaction volume between 100 and 200 uL per well. Higher density microplates (384- or 1536-well microplates) are typically used for screening applications, when throughput (number of samples per day processed) and assay cost per sample become critical parameters, with a typical assay volume between 5 and 50 µL per well. Common detection modes for microplate assays are absorbance, fluorescence intensity, luminescence, time-resolved fluorescence, and fluorescence polarization. From: https://en.wikipedia.org/wiki/Plate_reader, 2014-09-0-23. instruments_0_instrument_name=plate reader instruments_0_instrument_nid=528693 instruments_0_supplied_name=Biotek Synergy fluorescent plate reader metadata_source=https://www.bco-dmo.org/api/dataset/739973 Northernmost_Northing=40.42 param_mapping={'739973': {'lat': 'flag - latitude', 'lon': 'flag - longitude'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/739973/parameters people_0_affiliation=Lamont-Doherty Earth Observatory people_0_affiliation_acronym=LDEO people_0_person_name=Sonya T. Dyhrman people_0_person_nid=51101 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=University of Rhode Island people_1_affiliation_acronym=URI-GSO people_1_person_name=Bethany D. Jenkins people_1_person_nid=558172 people_1_role=Co-Principal Investigator people_1_role_type=originator people_2_affiliation=University of Rhode Island people_2_affiliation_acronym=URI-GSO people_2_person_name=Tatiana Rynearson people_2_person_nid=511706 people_2_role=Co-Principal Investigator people_2_role_type=originator people_3_affiliation=Woods Hole Oceanographic Institution people_3_affiliation_acronym=WHOI BCO-DMO people_3_person_name=Nancy Copley people_3_person_nid=50396 people_3_role=BCO-DMO Data Manager people_3_role_type=related project=North Atlantic Diatoms projects_0_acronym=North Atlantic Diatoms projects_0_description=NSF abstract: About half of photosynthesis on earth is generated by marine phytoplankton, single celled organisms that drift with tides and currents. Within the phytoplankton, the diatoms conduct nearly half of this photosynthesis, exerting profound control over global carbon cycling. Despite their importance, there are surprisingly fundamental gaps in understanding how diatoms function in their natural environment, in part because methods to assess in situ physiology are lacking. This project focuses on the application of a powerful new approach, called Quantitative Metabolic Fingerprinting (QMF), to address this knowledge gap and examine species-specific physiology in the field. The project will provide transformative insights into how ocean geochemistry controls the distribution of diatoms, the metabolic responses of individual diatom species, and how metabolic potential is partitioned between diatom species, thus providing new insights into the structure and function of marine systems. The overarching goal is to examine how diatom species respond to changes in biogeochemistry across marine provinces, from the coast to the open ocean, by following shifts in diatom physiology using QMF. This research is critical to understand future changes in oceanic phytoplankton in response to climate and environmental change. Furthermore, activities on this project will include supporting a graduate student and postdoctoral fellow and delivering the Artistic Oceanographer Program (AOP) to diverse middle school age children and teachers in the NYC metropolitan area and to middle-school girls in the Girl Scouts of RI, reaching an anticipated 60 children and 30 teachers annually. The programs will foster multidisciplinary hands-on learning and will directly impact STEM education at a critical point in the pipeline by targeting diverse middle-school aged groups in both NY and RI. In laboratory studies with cultured isolates, there are profound differences among diatom species' responses to nutrient limitation. Thus, it is likely that different species contribute differently to nutrient uptake, carbon flux and burial. However, marine ecosystem models often rely on physiological attributes drawn from just one species and apply those attributes globally (e.g. coastal species used to model open ocean dynamics) or choose a single average value to represent all species across the world's oceans. In part, this is due to a relatively poor understanding of diatom physiological ecology and a limited tool set for assessing in situ diatom physiological ecology. This research project will address this specific challenge by explicitly tracking metabolic pathways, measuring their regulation and determining their taxonomic distribution in a suite of environmentally significant diatoms using a state of the art, species-specific approach. A research expedition is set in the North Atlantic, a system that plays a major role in carbon cycling. Starting with a New England coastal shelf site, samples will be collected from the coast where diatoms thrive, to the open ocean and a site of a long term ocean time series station (the Bermuda Atlantic Time Series) where diatom growth is muted by nutrient limitation. This research takes advantage of new ocean observatories initiative (OOI) and time series information. Through the research expedition and downstream laboratory experiments, the molecular pathways of nutrient metabolism and related gene expression in a suite of environmentally significant diatoms will be identified. Data will be combined to predict major limiting factors and potentially important substrates for diatoms across marine provinces. Importantly, this integrated approach takes advantage of new advances in molecular and bioinformatics tools to examine in situ physiological ecology at the species-specific level, a key knowledge gap in the field. projects_0_end_date=2019-08 projects_0_geolocation=North Atlantic projects_0_name=Collaborative Research: Defining the biogeochemical drivers of diatom physiological ecology in the North Atlantic projects_0_project_nid=704768 projects_0_start_date=2016-09 sourceUrl=(local files) Southernmost_Northing=31.42 standard_name_vocabulary=CF Standard Name Table v55 version=1 Westernmost_Easting=-70.58 xml_source=osprey2erddap.update_xml() v1.5