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9,113 results for “Sampling”
Chlorophyll and phaeopigments from water column samples, collected at selected depths at Palmer Station Antarctica, during the Palmer LTER field seasons, 1991-2025.
Phytoplankton chlorophyll sampling was led by Smith from the 1991-1992 season through the 2001-2002 season, and then by Vernet from the 2002-2003 season through the 2006-2007 season. Schofield is the third, and current lead, beginning in the 2008-2009 season. Methods have been kept consistent as much as possible over the full time series and different Principal Investigators. Chlorophyll a (Chl a) is the principal photosynthetic pigment of phytoplankton, and is used as a proxy measurement for estimating phytoplankton biomass in water samples. Chl a concentrations reflect the distribution of active phytoplankton spatially and with depth in the water column and their changes over time. Phaeopigments are non-photosynthetic pigments that are degradation products of phytoplankton chlorophylls which form during and after phytoplankton blooms. Water samples are collected throughout the water column at stations within the Palmer LTER region (primarily B and E, to 50m and 65m respectively). Beginning in the 2020-2021 season, Station B is no longer sampled. Chlorophyll and phaeopigment concentrations are determined by filtration, extraction, and fluorometric detection of samples. The primary source of error for phaeopigment measurement is Chlorophyll b. If high amounts of Chlorophyll b are present in the sample, phaeopigments may be overestimated. There was no field season in 2021-2022.
Photosynthetic pigments of water column samples analyzed using High Performance Liquid Chromatography (HPLC), sampled during the Palmer LTER field seasons at Palmer Station, Antarctica, 1991 – 2023.
Phytoplankton pigment sampling was led by Prezelin from the 1991-1992 season through the 1993-1994 season, and then by Vernet from the 1994-1995 season through the 2006-2007 season. Schofield is the third, and current lead, beginning in the 2008-2009 season. Methods have been kept consistent as much as possible over the full time series and different Principal Investigators. Phytoplankton have a suite of accessory pigments in addition to Chlorophyll a, including other Chlorophyll’s (e.g. Chlorophyll b), Xanthophylls, and Carotenes. These accessory pigments can be used as chemotaxonomic markers to assess the composition and distribution of the phytoplankton community. For example, Fucoxanthin is a marker pigment of Diatoms, whereas Alloxanthin is a marker pigment of Cryptophytes. Accessory pigments also assist in photoacclimation and photoprotective processes. Water samples are collected throughout the water column at stations within the Palmer LTER region (primarily B and E, to 50m and 65m respectively). Water samples are filtered onto GF/F filters, and filters kept frozen at -80C until analysis. HPLC analysis is completed following Wright et al (1991). Following the guidelines set by NASA SeaHARRE, we use an internal standard and replicate injects on the HPLC to track recovery and replicability of the pigment extraction methods. Data is unavailable for the Palmer 2009-2010 season due to instrumentation problems and for the Palmer 2011-2012 season due to a freezer failure which resulted in the loss of samples. There is a temporary data gap for the Palmer 2015-2016, Palmer 2016-2017, Palmer 2019-2020, Palmer 2020-2021, and Palmer 2023-2024 seasons because those samples have not been analyzed yet.
Chlorophyll determined by extraction of samples taken approximately weekly from seawater intake starting at Palmer Station by station personnel including during winter-over period, 1991-2024.
Chlorophyll a (Chl a) is the principal photosynthetic pigment of phytoplankton, and is used as a proxy measurement for estimating phytoplankton biomass in water samples. Chl a concentrations reflect the distribution of active phytoplankton spatially and with depth in the water column and their changes over time. Chlorophyll a is determined weekly year-round at the laboratory seawater intake (SWI), from a depth of 6 meters. Concentrations are typically very low (< 1 µg Chl a per liter) in winter (April-October), and higher (1-30 µg/L) following the initiation of the annual spring-summer phytoplankton bloom in November - January.
Chlorophyll and phaeopigments from water column samples, collected at selected depths aboard Palmer LTER annual cruises off the coast of the Western Antarctic Peninsula, 1991-2024.
Phytoplankton chlorophyll sampling was led by Smith from 1991-2002, and then by Vernet from 2003-2008. Schofield is the third, and current lead, beginning in 2009. Methods have been kept consistent as much as possible over the full time series and different Principal Investigators. Chlorophyll a (Chl a) is the principal photosynthetic pigment of phytoplankton, and is used as a proxy measurement for estimating phytoplankton biomass in water samples. Chl a concentrations reflect the distribution of active phytoplankton spatially and with depth in the water column and their changes over time. Phaeopigments are non-photosynthetic pigments that are degradation products of phytoplankton chlorophylls which form during and after phytoplankton blooms. Water samples are collected throughout the water column along the Western Antarctic Peninsula at regular LTER grid stations where CTD casts are preformed and in surface waters at underway stations, where CTD casts are not done, using the ship's flow-through seawater system. Chlorophyll and phaeopigment concentrations are determined by filtration, extraction, and fluorometric detection of samples. The primary source of error for phaeopigment measurement is Chlorophyll b. If high amounts of Chlorophyll b are present in the sample, phaeopigments may be overestimated.
Skin-blubber biopsy samples and associated demographic data collected from cetaceans encountered along the Western Antarctic Peninsula (WAP), 2010 – 2024
Baleen whale populations in the Southern Ocean are recovering after intense commercial whaling in the 20th century. Along the Western Antarctic Peninsula (WAP), this recovery is occurring in one of the planet's most rapidly changing marine ecosystems. Understanding how climate-driven changes influence the population dynamics of whales in this region is critical for understanding what conservation and management actions must be prioritized to maintain the structure and function of this marine ecosystem. To begin understanding the dynamics of whale recovery under continued environmental change, we need to study these whales' demography and population dynamics. As part of our annual sampling surveys for cetaceans along the WAP through the PAL LTER program, we actively collect remote non-lethal skin-blubber biopsy samples and have developed one of the most extensive tissue archives in the Southern Ocean. With these samples, we conduct a series of demographic and physiological measurements. Using the skin portion of the biopsy sample, we isolate nuclear and mitochondrial DNA (mtDNA) to develop a DNA profile for each sample, including genetic sex, a microsatellite genotype, and a mtDNA haplotype. These profiles are used to compare sex ratios of the population, determine individual recaptures through genotype analysis, and better understand population mixing. Using the blubber portion of the biopsy sample, we isolate endocrine markers (e.g., progesterone and cortisol) to monitor population pregnancy rates and stress levels. This data represents some of the first non-lethal quantitative observations of the demography and population dynamics of recovering whale populations in the Antarctic and provides a critical reference point for future work as the Antarctic climate continues to change and populations continue to recover from whaling.
Marine Mammal Survey, Sightings and Sampling Event Log at Palmer Station, Antarctica, 2020-2024
Seasonal sea ice-influenced marine ecosystems at both poles are characterized by high productivity concentrated in space and time by local, regional, and remote physical forcing. These polar ecosystems are among the most rapidly changing on Earth. The PALmer (PAL) LTER seeks to build on three decades of long-term research along the western side of the Antarctic Peninsula (WAP) to gain new mechanistic and predictive understanding of ecosystem changes in response to disturbances spanning long-term, subdecadal, and higher-frequency “pulses” driven by a range of processes, including long-term climate warming, natural climate variability, and storms. These disturbances alter food-web composition and ecological interactions across time and space scales that are not well understood. We seek to determine the differential effects of disturbance and resilience on krill predators with different life histories, foraging behaviors, and demographic patterns. Specifically, changes in foraging behavior can affect adult fitness, body condition, and reproductive rates, as well as offspring survival. Preliminary analyses suggest mean chick fledgling mass decreases later in the austral summer as storm disturbances increase. If storms are not a factor influencing chick mass, parental effects or ecosystem phenology may play a larger role. For whales, changes in foraging effort and increases in body condition should correlate with increased pregnancy rates. We will test for linkages between whale foraging efficiency related to storms with female pregnancy rates the following year. Our prediction is that in seasons with more storms and poorer foraging conditions, fewer whales will become pregnant. However, as whales are long-lived, we predict this will not have a major effect on the long-term positive population trend. We will contribute fundamental understanding of how population dynamics and physiological processes are responding within a polar marine ecosystem undergoing profound change
Photosynthetic pigments of water column samples and analyzed with High Performance Liquid Chromatography (HPLC), collected aboard Palmer LTER annual cruises off the coast of the Western Antarctica Peninsula, 1991-2024.
Phytoplankton pigment sampling was led by Prezelin from 1991-1994, and then by Vernet from 1995-2008. Schofield is the third, and current lead, beginning in 2009. Methods have been kept consistent as much as possible over the full time series and different Principal Investigators. Phytoplankton have a suite of accessory pigments in addition to Chlorophyll a, including other Chlorophyll's (e.g. Chlorophyll b), Xanthophylls, and Carotenes. These accessory pigments can be used as chemotaxonomic markers to assess the composition and distribution of the phytoplankton community. For example, Fucoxanthin is a marker pigment of Diatoms, whereas Alloxanthin is a marker pigment of Cryptophytes. Accessory pigments also assist in photoacclimation and photoprotective processes. Water samples are collected throughout the water column along the Western Antarctic Peninsula at regular LTER grid stations where CTD casts are preformed and in surface waters at underway stations, where CTD casts are not done, using the ship's flow-through seawater system. Water samples are filtered onto GF/F filters, and filters kept frozen at -80C until analysis. HPLC analysis is completed following Wright et al (1991). Following the guidelines set by NASA SeaHARRE, we use an internal standard and replicate injects on the HPLC to track recovery and replicability of the pigment extraction methods and the HPLC. Data is unavailable for the LMG10-01 cruise due to instrumentation problems and for the LMG12-01 cruise due to a freezer failure which resulted in the loss of samples. There is no data for 2021 because there was no LTER cruise.
Dissolved inorganic carbon and alkalinity of discrete water column samples, collected aboard Palmer LTER annual cruises of the Western Antarctic Peninsula, 1993 - 2019.
Dissolved inorganic carbon (DIC or total CO2 – TCO2) and total alkalinity (TALK) are two of the four parameters defining the carbonate system in seawater. DIC is composed of dissolved CO2 gas, which dissociates into carbonate, 〖CO〗_3^(2-), and bicarbonate, 〖HCO〗_3^- in seawater. About 90% of the DIC is in the form of bicarbonate, ~10% is carbonate, and ~1% is CO2. The dissociation of CO2 dissolved in seawater into carbonate and bicarbonate gives seawater its great capacity to absorb CO2 from the atmosphere. Alkalinity (also known as “buffer capacity”) is a measure of the capacity of water to neutralize acids. Alkalinity is a complex product of the concentrations of (in decreasing order of importance) the DIC components, borate, hydroxide, phosphate, silicate and dissolved ammonium. Ocean biology regulates the alkalinity through the uptake and release of the DIC and the macronutrients N, P and Si. We measure surface DIC and ALK to understand the exchange of CO2 across the air-sea interface in our study area. With DIC and dissolved CO2, we can also derive estimates of ocean pH and thus monitor the extent and evolution of ocean acidification. Analytical methods and QC are presented under the Methods and Protocols tab.
Adelie penguin diet composition, preliminary analyses of whole samples, 1991-2024
The fundamental long-term objective of the seabird component of the Palmer LTER (PAL) has been to identify and understand the mechanistic processes that regulate the mean fitness (population growth rate) of regional penguin populations. Since the inception of PAL, Adélie penguin populations have effectively collapsed, gentoo penguin populations have increased dramatically and chinstrap penguin populations have remained relatively stable. These trends are spatially and temporally coherent with regional warming and decreasing sea ice duration. Adélie penguins are an ice-obligate polar species whose life history is intimately linked to the presence of sea ice, while chinstrap and gentoo penguins are ice-intolerant species whose life histories evolved in the sub-Antarctic, where sea ice is a less permanent feature of the marine ecosystem. The PAL study region includes five main islands on which Adélie penguin colonies have historically occurred, with each island containing a different number of spatially segregated sub-colonies. These colonies are censused to determine the total number of nests and chicks produced each year, and breeding success. Diet samples are acquired to understand diet composition (e.g., krill, fish) and krill length-frequencies. In general, krill constitute the most important component of the summer diets by mass of these three penguin species, but changes in PAL krill abundances have exhibited no long-term trends and thus far, have failed to explain the divergent patterns in penguin populations evident in our time series. Chick fledging masses are recorded as a cumulative measure of climate, weather, diet, and parental influences on chick health at the end of the breeding season. These data have provided valuable insights into the marine and terrestrial factors that influence Adélie penguin population fitness. No data were collected during the 2021-2022 season due to the Palmer Station pier rebuild.
PIE LTER nutrient samples collected by Sigma Autosampler between 2001 and 2017 in three headwater sites of contrasting land use, and at the Parker and Ipswich River Dams as they enter into the Plum Island Sound estuary, Massachusetts.
Total organic nitrogen, total organic phosphorus, and nitrate concentrations collected frequently by Sigma autosampler (or volunteers in winter) from 5 sites. Sites include three headwater sites of contrasting land use (CC= Forest, SB = suburban, CS = wetland) and at the mouth of the Ipswich and Parker Rivers where they flow into the estuary.
PIE LTER time series of nutrient grab samples from Ipswich River and Parker River watershed catchments, Masachusetts, with frequency ranging from weekly to monthly between 2001 and 2019.
Time series of nutrient grab samples collected by hand (i.e. not with the Sigma autosampler) with frequency ranging from weekly to monthly between 2001 and 2016. Sites include three headwater catchments of contrasting land use (forest, urban, wetland), and the mouth of the two main watersheds draining to the Plum Island Estuary (Ipswich and Parker R.). An additional time series was collected for a site in the Upper Ipswich at North Reading and at Fish Br. in Boxford – this sampling was ended in 2002. All Samples were analysed for NO3, NO2, NH4, PO4, TDN and DOC. Si was analyzed until 2002. Particulates, anions and TSS have been analyzed since 2006.
Mean - Variance Experiment sample archives
We designed novel field experimental infrastructure to resolve the relative importance and interactions among changes in precipitation mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean x Variance Experiment (MVE) adds three novel elements to prior designs (Gherardi & Sala 2013) that have manipulated interannual variance in climate in the field by (i) determining interactive effects of mean and variance in a factorial design that crosses a drier mean with increased (more) variance, (ii) studying multiple dryland ecosystem types to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. We collected samples of soils, biological soil crusts, leaves of the foundation plant species, and roots of the two dominant grass species each year during peak productivity (September-October). These samples enable us to address the question: How do interactions between the mean and variance of precipitation alter the biogeochemistry and microbiomes of plants and soils. This data package includes accession numbers for all samples collected from the Mean x Variance Experiment at the Sevilleta National Wildlife Refuge, Socorro, NM.
MACREL software benchmark data set: Simulated metagenomes with sequencing quality, errors profile and abundance distributions derived from real samples
<p>These metagenomes were used in the benchmarking of FACS pipeline, and were designed after NGLess benchmark dataset (doi.org/10.5281/zenodo.2560288). Metagenomes were simulated with <a href="https://www.niehs.nih.gov/research/resources/software/biostatistics/art/index.cfm">ART-bin-MountRainier-2016.06.05</a> using real abundance profiles (.abund files) available <a href="https://doi.org/10.5281/zenodo.2560288">elsewhere</a>, and <a href="http://progenomes1.embl.de/data/repGenomes/representatives.contigs.fasta.gz">proGenomes' representative contigs</a> as reference genomes. There are available metagenomes with 40, 60 and 80 M (million of reads) based in the reference genomes and abundances of the following samples:</p> <pre><code>SAMEA2466916 SAMEA2466953 SAMEA2466965 SAMEA2621107 SAMEA2621229 SAMEA2621247</code></pre> <p>To convert them from the CRAM format back to fastq files:</p> <pre><code> ## 1. converting from cram to bam format: samtools view -b -T refgenome.fa -o file.bam file.cram ## 2. sorting the bam file: samtools sort -n file.bam -o input_sorted.bam # sort reads by identifier-name (-n) ## 3. converting from bam to fastq format: bedtools bamtofastq -i input_sorted.bam -fq output_r1.fastq -fq2 output_r2.fastq </code></pre> <p> </p>
Sample of facial mask N95 and FFP2 pricing on retail webs and time evolution per country
<p>We've gathered - for a Data Science educational project - the pricing of several face mask for breathing protection in a given period of time.</p> <p>Countries : Spain', 'USA', 'France', 'UK', 'Germany', 'Italy', 'Netherlands', 'Australia'</p> <p> </p> <p>'asin' type: STRING "Código de identifícación único de product equivalente de AMAZON"</p> <p>'description' type: STRING 'Texto descriptivo del producto'</p> <p>'dateTime' type: TIMESTAMP 'Cadena de carateres que contiene fecha y hora GMT'</p> <p>'date' Type: DATETIME ' Formato diferente de la misma fecha / hora de captura '</p> <p>'country' type: STRING 'Pais al que pertenece a distribución del producto 'Valores posibles: '</p>
BAMBI ITS - Analysis of the fungal component (via ITS amplicon sequencing) of stool samples from preterm babies
<p>Amplicon analysis of ITS amplicons from preterm babies.</p> <p>Associated GitHub repository: <a href="https://github.com/quadram-institute-bioscience/bambi-its">https://github.com/quadram-institute-bioscience/bambi-its</a></p>
ds-uct-001: Cast Iron GGG40: X-Ray micro-CT of a nodular cast iron sample class GGG40.
<p><strong>Summary</strong>:<br> .X-Ray micro-computed tomography (micro-CT) of a nodular cast iron sample class GGG40, including both raw projection data and the final reconstructions, for three different resolutions (voxel sizes of 1 μm, 3 μm and 11 μm).<br> .The 3D image was generated with an X-Ray micro-CT Scanner version Xradia Versa 510 from Zeiss performed by A Pereira at the UFF micro-CT Facility.<br> .For use of these data, please remember to cite the DOI of the Zenodo repository and relevant papers.</p> <p><strong>Details</strong>:<br> .Tomo1 (1024) - Voxel size: 1 μm; Sample-source: 26 mm; Sample-detector: 150 mm; Optical magnification: 4.0X; Filter: HE#6; Beam energy: 160 kV; Power: 10 W; Exposure time: 60.0 sec; Projections: 1600.<br> .Tomo2 (1024) - Voxel size: 3 μm; Sample-source: 28 mm; Sample-detector: 35 mm; Optical magnification: 4.0X; Filter: HE#4; Beam energy: 160 kV; Power: 10 W; Exposure time: 10.0 sec; Projections: 3200.<br> .Tomo3 (1024) - Voxel size: 11 μm; Sample-source: 30 mm; Sample-detector: 158 mm; Optical magnification: 0.4X; Filter: HE#4; Beam energy: 160 kV; Power: 10 W; Exposure time: 3.0 sec; Projections: 3200.</p> <p><strong>Contents</strong>:<br> ._info_ds-uct-001.txt<br> .ds-uct-001_cast_iron_ggg40_01um_8bits.zip<br> .ds-uct-001_cast_iron_ggg40_03um_8bits.zip<br> .ds-uct-001_cast_iron_ggg40_11um_8bits.zip<br> .ds-uct-001_cast_iron_ggg40_01um_1600p.txrm<br> .ds-uct-001_cast_iron_ggg40_01um_1600p_Drift.txrm<br> .ds-uct-001_cast_iron_ggg40_01um_1600p_recon.txm<br> .ds-uct-001_cast_iron_ggg40_03um_3200p.txrm<br> .ds-uct-001_cast_iron_ggg40_03um_3200p_Drift.txrm<br> .ds-uct-001_cast_iron_ggg40_03um_3200p_recon.txm<br> .ds-uct-001_cast_iron_ggg40_11um_3200p.txrm<br> .ds-uct-001_cast_iron_ggg40_11um_3200p_Drift.txrm<br> .ds-uct-001_cast_iron_ggg40_11um_3200p_recon.txm</p>
ds-uct-007: Asphalt Concrete: X-Ray micro-CT of an asphalt concrete sample.
<p><strong>Summary</strong>:<br> .X-Ray micro-computed tomography (micro-CT) of an asphalt concrete sample, including both raw projection data and the final reconstructions, for one single resolution (voxel size of 7 μm).<br> .The 3D image was generated with an X-Ray micro-CT Scanner version Xradia Versa 510 from Zeiss performed by A Pereira at the UFF micro-CT Facility.<br> .Image data segmented with four different segmentation techniques: DL (Deep Learning), ML (Machine Learning), TH (Thresholding) and WS (Watershed).<br> .For use of these data, please remember to cite the DOI of the Zenodo repository and relevant papers.<br> <strong>Details</strong>:<br> .Tomo - Voxel size: 7 μm; Sample-source: 31 mm; Sample-detector: 274.25 mm; Optical magnification: 0.4X; Filter: LE#6; Beam energy: 100 kV; Power: 9 W; Exposure time: 4.0 sec; Projections: 1600.</p> <p><strong>Contents</strong>:<br> ._info_ds-uct-007.txt<br> .ds-uct-007_asphalt_concrete_07um_16bits.zip<br> .ds-uct-007_asphalt_concrete_07um_1600p.txrm<br> .ds-uct-007_asphalt_concrete_07um_1600p_Drift.txrm<br> .ds-uct-007_asphalt_concrete_07um_1600p_recon.txm<br> .ds-uct-007_asphalt_concrete_07um_DL.zip<br> .ds-uct-007_asphalt_concrete_07um_ML.zip<br> .ds-uct-007_asphalt_concrete_07um_TH32.zip<br> .ds-uct-007_asphalt_concrete_07um_WS.zip</p>
A Dataset of European Union Land Cover Validation Samples
<p>A dataset of European Union land cover validation samples in 2015 and 2010 based on the LUCAS micro dataset ( publicly available at <a href="https://ec.europa.eu/eurostat/web/lucas/data/lucas-grid">https://ec.europa.eu/eurostat/web/lucas/data/lucas-grid</a> ) . The dataset provides 9 land cover types of land cover including cropland, forest, grassland, shrubland, wetland, water, bareland, impervious surface and permanent snow/ice. The dataset is provided in .csv format.</p>
Stratigraphy and genesis of the Biogenic Reefs in the Venice offshore: Tegnùa Chioggia, Site 2, Rock samples.
<p>Rock samples</p> <p>Research Activity: Geology of the Northern Adriatic Biogenic Reefs</p> <p>Project: Stratigraphy and genesis of the Biogenic Reefs in the Venice offshore </p> <p>Scientific coordinators: Sandra Donnici (CNR) and Luigi Tosi (CNR)</p> <p>Scientific Divers: Andrea Bergamasco (CNR), Luigi Tosi (CNR)</p> <p>Surface coordinator: Sandra Donnici (CNR)</p> <p>Sampling Date: 2013.10.18</p> <p>Sampling Site: Tegnùa Chioggia</p> <p>Site Coordinates: 45.230503 N; 12.489984 E (DEG WGS84)</p> <p>Seabed Depth: 22.2 m</p> <p>Biogenic reef elevation: 1.5 m</p>
PROGRAMS project. PRM workflows from IDEKO S. Coop. on 2020-09 sample 1
<p>These data is the PRM workflow elaboration of the ones collected from FIDIA machine tool controller during milling operation.</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.