Find research datasets worth reusing

opencc-by-4.0Sep 2022View details →

Winter Season Spectral Snowpack Albedo Data For the Caldor and Creek Fires

* Description: The file "Caldor_Creek_Fires_Winter_Snow_Albedo_Spectrometer_Dataset.csv" is a comma-delimited file containing the spectral snowpack albedo measurements from the Caldor and Creek Fires in California and the associated burn severities at the location of each measurement.   Data and File Overview ====================== Summary Metrics --------------- * File count: 1 * Total file size: 909 KB  * Range of individual file sizes: 909 KB  * File formats: .csv   Naming Conventions ------------------ * File naming scheme: One file that includes all dates, all burn severities, all wavelengths. * Format(s): Comma-separated value (.csv) file * Size(s): 909 KB * Dimensions: 17,209 rows x 6 columns * Variables:   * Measurement_Number: An index of the measurement number, unitless   * wavelength: The wavelength of light (units in nanometers) for which albedo sample ranging from 350-2500 nm   * Type: Measurement type is albedo in various burn severity environments (_hb is high burn severity, _mb is moderate burn severity, _ub is unburned, with the last letter corresponding to the month (J is January, F is February, A is April). NA corresponds to estimated January unburned data for the Caldor Fire where unburned albedo for April in the Creek Fire was adjusted downwards by 0.04 to account for less grain-size growth (Colbeck 1982, Rev. Geophys., https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/RG020i001p00045).     * Albedo: The albedo (unitless), is a measurement of the solar radiation reflected by the snow surface divided by the radiation incident on its surface. In other words, albedo is the fraction of the incident sunlight reflected by the snow.   * month: The month when the measurement was taken   * burn: Burn severity at measurement location based upon classifications (High, Medium,, Unburned) from the Monitoring Trends in Burn Severity Dataset (https://mtbs.gov/). NA corresponds to the estimated unburned data for the Caldor Fire using April unburned data in the Creek Fire.   * Missing data codes: No missing data is presented.   Dates and Locations ------------------- * Dates of data collection: Surface albedo collected in 27-28 February 2021, 1 April 2021, and 21 January 2022 * Geographic locations of data collection: Data collected within the Caldor Fire perimeter in the central Sierra Nevada, California-Nevada (January 2022) and the Creek Fire perimeter, California (February and April 2021).   Setup ----- * Recommended software/tools to open file: parentage- R Studio; file can be opened using any text editor or programming language (e.g., R Studio, MATLAB, Python, TextMate, Microsoft Excel, etc)

opencc-by-4.0Apr 2023View details →

Sea ice core biogeochemical data collected during the 2019 SCALE Winter Cruise

Title: Biogeochemical profiles of sea ice cores sampled during the Southern oCean seAsonal Experiment (SCALE) winter cruise in 2019.   Authors: Riesna R. Audh, Siobhan Johnson, Mark Hambrock, Hazel Little, Joshua Mirkin, Emmanuel Omatuku, Benjamin Hall, Tokoloho Rampai, Keith MacHutchon, Sebastian Skatulla, Sarah E. Fawcett, Marcello Vichi   Data Description:   Abstract Biogeochemical profiles of sea ice cores extracted from in situ sea ice floes and lifted pancakes were measured in the Atlantic sector of the Antarctic Marginal Ice Zone during the Southern oCean seAsonal Experiment (SCALE) winter cruise in 2019 (<a href="http://www.scale.org.za">www.scale.org.za</a>) aboard the SA Agulhas II.   A total of four sea ice cores (cores) were sampled during the cruise. Two cores were collected overboard on a consolidated floe that was accessed via a personnel carrier suspended by the ship’s forward crane. Two cores were collected from a pancake that was lifted aboard the ship via a net that was attached to the ship’s aft crane and placed on the helideck for sampling. Profiles were obtained by cutting the cores using a bandsaw in a cold laboratory at -10 °C. The cores were cut into approximately 0.05 m segments, starting from the bottom of the core. These segments were allowed to melt in the dark in an insulated box. The meltwater was filtered for chlorophyll measurements (Welschmeyer, 1994) and the filtrate was analysed for oxygen isotopes (Walker and others, 2015), ammonium (Holmes et al., 1999), phosphate, nitrate, nitrite and silicate (using a SEAL AA500 segmented flow autoanalyser). These values are reported at the depth of the top of the segment in the core in μM. In order to facilitate comparison with the seawater concentrations below the ice, the in-ice nutrients (including NH4+) were salinity normalised using the equation of Fripiat and others (2017):   Cnorm = CSwS   Where C is the measured bulk concentration, Sw is the salinity of the seawater, and S is the corresponding measured bulk salinity of the ice segment.   Although sampling of the core occurred from the bottom of the core to the top of the core, the data are reported as the top of the core (snow/ice interface) being 0 m (depth=0 m).    This research has been funded by the National Research Foundation of South Africa (NRF)   Cruise: VOY-038 (SCALE2019-WINTER) (URL: https://scale.org.za/) Station(s): VOY-038-MIZ3A VOY-038-MIZ1D Position(s): -58.13783 S; 0.00442 W -56.8017 S; 0.30262 E Date/Time: 2019-07-27/10:38:00 2019-07-28/09:15:00 Method(s): Overboard coring Pancake lifting via aft crane, on deck coring Parameters: Station Number (Station) Date/Time of station (Date/Time) Latitude of station (Latitude) Longitude of station (Longitude) Ice type (Ice Type) Core ID(Core), Pancake identifier A/B/C/D Oxygen isotopes (d18O) Chlorophyll (Chl-a) Ammonium (NH4) Nitrate + Nitrite (NO3+NO2) Nitrite (NO2) Phosphate (PO4) Silicate (Si) Nitrate (NO3) Salinity of the ice segment from physical cores (IceSalinity) Standard deviation of the salinity average from physical cores (IceSalinityStdev) Seawater salinity from CTD (SeawaterSalinity) Salinity normalised nitrate+nitrite (N03+N02_Avg_SalinityNormalised) Salinity normalised ammonium (NH4_SalinityNormalised) Salinity normalised nitrite (NO2_SalinityNormalised) Salinity normalised phosphate (PO4_SalinityNormalised) Salinity normalised silicate (Si_SalinityNormalised) Salinity normalised nitrate (NO3_SalinityNormalised)     Keywords: sea ice cores, Antarctica, pancake ice, sea ice, biogeochemistry, winter   References:   Fripiat, F., Meiners, K.M., Vancoppenolle, M., Papadimitriou, S., Thomas, D.N., Ackley, S.F., Arrigo, K.R., Carnat, G., Cozzi, S., Delille, B. and Dieckmann, G.S., 2017. Macro-nutrient concentrations in Antarctic pack ice: Overall patterns and overlooked processes. Elementa: Science of the Anthropocene, 5.     Holmes, R.M., Aminot, A., Kérouel, R., Hooker, B.A. and Peterson, B.J., 1999. A simple and precise method for measuring ammonium in marine and freshwater ecosystems. Canadian Journal of Fisheries and Aquatic Sciences, 56(10), pp.1801-1808.     Walker, S.A., Azetsu‐Scott, K., Normandeau, C., Kelley, D.E., Friedrich, R., Newton, R., Schlosser, P., McKay, J.L., Abdi, W., Kerrigan, E. and Craig, S.E., 2016. Oxygen isotope measurements of seawater (18O/16O): A comparison of cavity ring‐down spectroscopy (CRDS) and isotope ratio mass spectrometry (IRMS). Limnology and Oceanography: Methods, 14(1), pp.31-38.     Welschmeyer, N., 1994. A method for the determination of chlorophyll a in the presence of chlorophyll b and pheopigments. Limnology and Oceanography, 39, pp.1985-1992.

opencc-by-4.0May 2023View details →

Polar Iridium Surface Velocity Profilers (p-iSVP), and standard Iridium Surface Velocity Profilers (iSVP) during SCALE 2019 Winter and Spring Cruises

Brief data description In 2019, winter and spring scientific research expeditions aboard the SA Agulhas II were conducted along the Good-Hope line (0o E) to the Antarctic marginal ice zone (MIZ) in the north-eastern Weddell Sea region as part of the Southern oCean seAsonal Experiment (SCALE; Ryan-Keogh and Vichi, 2022). During the winter expedition, three polar Iridium Surface Velocity Profilers (p-iSVPs; MetOcean model) were deployed by the South African Weather Service (SAWS) between 27 July and 28 July 2019. These buoys were analysed in de Vos et al. (2022). The region of deployment consisted of pancake-ice conditions with an average ice thickness of 40-60 cm. The instruments were deployed by hand by three people, lowered by crane from the ship to the ice on a basket cradle. The first buoy (p-iSVP 1) was deployed in water, in between pancake ice floes, while the other two buoys (p-iSVP 2 and p-iSVP 3) were deployed on roughly circular ice floes > 3 m in diameter. These buoys were expendable devices that recorded GPS position, air and ice temperature, and barometric pressure. The temporal resolution is 30 minutes for p-iSVP 1 and hourly for p-iSVP 2 and p-iSVP 3. The survival of these sensors depended on their battery life, since p-iSVPs can continue to drift in the ocean after ice melting and can be further refrozen in between floes. p-iSVP 1 and p-iSVP 3 continued to transmit data until 15 October 2019. p-iSVP 2 stopped transmitting data on 25 August 2019. During the spring expedition, three standard Iridium Surface Velocity Profilers (iSVPs 4-6; Pacific Gyre model) were deployed by SAWS between 24 October and 28 October 2019 (de Vos et al., 2022). Specifically-designed frames were built around these three iSVPs to allow them to stand securely on the ice, without damaging the non-polar battery, and also to make sure they operated as Lagrangian ice trackers. These buoys were deployed during first-year ice conditions, with an average ice thickness of 80-90 cm. The instruments were deployed with the same protocol as the winter buoys. These buoys recorded GPS position, air temperature and barometric pressure, every hour. Their survival, like the winter p-iSVPs, also depended on their battery life, and therefore it was possible for them to continue to drift after ice melting. The iSVPs transmitted data until 19 December 2019. Buoy names and raw data: p-iSVP 1: 300234067003010-300234067003010-20191015T064320UTC.csv p-iSVP 2: 300234067002060-300234067002060-20191015T064316UTC.csv p-iSVP 3: 300234066992870-300234066992870-20191015T064314UTC.csv iSVP 4: 300234066433050.xlsx iSVP 5: 300234066433051.xlsx iSVP 6: 300234066433052.xlsx Related code: The buoy data has been processed using https://github.com/mvichi/antarctic-buoys/.

opencc-by-4.0Jan 2023View details →

AgriCarbon-EO Winter wheat Net Ecosystem Exchange and Biomass over South-west France at 10 m resolution

Dataset contains the outputs of the AgriCarbon-EO An agronomical modeling tool for the carbon and water flux estimates by Bayesian assimilation of S2 and LandSat8 remote sensing data into the Prosail radiative transfer model and the SAFYE-CO2 crop model. ----------------------- -for TILE : T31TCJ   -for year: 2017 -for Winter wheat crops - at 10 m resolution   Maps: -file: "GLA_statmap.tif"     Description: A raster with 4 bands containing respectively:         *The R2 of the GLAI observed by satellite and simulations from 2016/11/01 until 2017/08/01         *The RMSE of the GLAI observed by satellite and simulations from 2016/11/01 until 2017/08/01         *The Bias of the GLAI observed by satellite and simulations from 2016/11/01 until 2017/08/01         *The number of images that are assimilated into SAFYE-CO2  from 2016/11/01 until 2017/08/01 -file: "emerg_wheat_2017.tif"     Description: A raster with 2 bands containing respectively:         *The mean value of emerg retrieved by the SAFYE-CO2 inversion in days of simulation (the simulation begins the 01/01/2016).         *The standard deviation of emerg retrieved by the SAFYE-CO2 inversion.          -file: "LUEa_wheat_2017.tif"     Description: A raster with 2 bands containing respectively:         *The mean value of LUEa retrieved by the SAFYE-CO2 inversion in g/MJ.         *The standard deviation of LUEa retrieved by the SAFYE-CO2 inversion in g/MJ. -file: "SENa_wheat_2017.tif"     Description: A raster with 2 bands containing respectively:         *The mean value of Sena retrieved by the SAFYE-CO2 inversion in °C.         *The standard deviation of Sena retrieved by the SAFYE-CO2 inversion in °C. -file: "SENb_wheat_2017.tif"     Description: A raster with 2 bands containing respectively:         *The mean value of SENb retrieved by the SAFYE-CO2 inversion.         *The standard deviation of SENb retrieved by the SAFYE-CO2 inversion. -file: "PRT_La_wheat_2017.tif"     Description: A raster with 2 bands containing respectively:         *The mean value of DAM retrieved by the SAFYE-CO2 inversion.         *The standard deviation of DAM retrieved by the SAFYE-CO2 inversion. -file: "DAM_wheat_2017.tif"     Description: A raster with 2 bands containing respectively:         *The mean value of DAM retrieved by the SAFYE-CO2 inversion in g/m2.         *The standard deviation of DAM  retrieved by the SAFYE-CO2 inversion in g/m2. -file: "NEP_wheat_2017.tif"     Description: A raster with 2 bands containing respectively:         *The mean value of NEP retrieved by the SAFYE-CO2 inversion in g/m2.         *The standard deviation of NEP retrieved by the SAFYE-CO2 inversion in g/m2. -file: "NECB_exportG_wheat_2017.tif"     Description: A raster with 2 bands containing respectively:         *The mean value of NECB retrieved by the SAFYE-CO2 inversion in g/m2 , considering an export scénario with grains export only.         *The standard deviation of NECB_exportG retrieved by the SAFYE-CO2 inversion in g/m2. -file: "NECB_exportGLS_wheat_2017.tif"     Description: A raster with 2 bands containing respectively:         *The mean value of NECB retrieved by the SAFYE-CO2 inversion in g/m2, considering an export scénario with grains, stems and leaves.         *The standard deviation of NECB_exportGLS retrieved by the SAFYE-CO2 inversion in g/m2.   Shapefiles a GIS:  -file: "S2_TILE_T31TCJ.shp"     Description: shape file of the contour of the T231 TCJ sentinel2 tile  -file: "FR_AUR.shp"     Description: shape file of the contour of AURADE experimental field  -file: "FR_AUR_TOWER.shp"     Description: shape file of the location of the AURADE eddy covariance flux tower -file: "POI_2017.shp"     Description:  shape file of the location of points of interest that illustrate the ... paper -file: "ESU_DAM.shp"     Description: shape file of the contour of the plots where dry biomass samples were taken. -file: "ESU_DAM_points.shp"     Description: shape file of the location of the points where dry biomass samples were taken. -file: "mapT31TCJ_spamaps.qgz"         QGIS project file for the visualisation of the NEP maps.

opencc-by-4.0Sep 2023View details →

Updated gridded reconstruction of sea level pressure, temperature, and precipitation during winter in the North Atlantic region covering 1241-1970 CE

<ul> <li>This dataset is an updated version of the gridded climate reconstruction by Sjolte et al. 2018 (SEA18): Solar and volcanic forcing of North Atlantic climate inferred from a process-based reconstruction, Climate of the Past, 14, 1179–1194, https://doi.org/10.5194/cp-14-1179-2018. </li> </ul>   <ul> <li>Relevant results of this new version (SEA18v2) are available in our recent paper: Tao, Q. , Sjolte, J. , & Muscheler, R. (2023). Persistent model biases in the spatial variability of winter North Atlantic atmospheric circulation. Geophysical Research Letters, 50, e2023GL105231. https://doi.org/10.1029/2023GL105231</li> </ul>   <ul> <li>This dataset contains the gridded reconstruction of winter sea level pressure (slp), 2m temperature (t2m) and precipitation (precip) for the North Atlantic region over 1241-1970.</li> </ul>   <ul> <li>Methodology: The new reconstruction (SEA18v2), has been optimized for a better representation of the variability of the main modes of sea level pressure. The original reconstruction, SEA18, was an ensemble of 39 model analogues for each year and the reconstruction comprised of the mean of the analogues. For the new version, SEA18v2, a different approach to calculating the ensemble mean of the analogues has been applied. While the overall evaluation and ranking of model analogues are the same as for SEA18, we now apply a weighting function so that poor-fitting model analogues receive less weight and good-fitting analogues receive more weight. Furthermore, we evaluate the main modes of the reconstructed SLP and test the minimum number of ensemble members that can be used and still retain skill for the temporal and spatial variability of the first three modes. Retaining 16 ensemble members gives better performance for the spatial patterns for the first three EOFs of SLP compared to SEA18 and good skill for the temporal variability of the NAO.</li> </ul>

openCC (other)Mar 2023View details →

Winter sampling microclimatic data using handheld devices at a shrub gradient across Californian drylands.

During a two-week sampling period in February 2023, we collected microclimatic data, including temperature and relative humidity, at shrubs and in the open across 9 sites in California. We collected ambient and ground temperatures.

openCustomJan 2014View details →

Stream nutrient sampling during winter baseflow conditions in the Andrews Forest and Willamette River Basin, February 2009

To better understand the impact of land use on stream nutrient export, a synoptic sampling of streams draining 57 sub-basins within the Willamette River basin was conducted during winter baseflow conditions. The objective of the study was to assess how streamwater values of stream dissolved organic carbon (DOC), NO3- and Cl- and specific ultra-violet absorbance (SUVA) were related with the proportion of land in the watershed in urban areas, agriculture, and forest. Stream water samples were collected during baseflow conditions in February 2009 throughout the Willamette River basin, including the experimental watersheds (1,2,6,7,8,9,10), Lookout and Mack Creeks at the HJ Andrews Forest. Land use for each point-delineated watershed were determined from published values from the PNWERC dataset (http://www.fsl.orst.edu/pnwerc/wrb/access.html).

openCC (other)Mar 2021View details →

Hubbard Brook Experimental Forest: In-situ Nitrogen Mineralization and Nitrification measurements for 4 winter climate change projects

These data are from four separate projects undertaken between 1997 and 2017. The first of these are two snow manipulation (freeze) projects: 1) In 1997, as part of a study of the relationships between snow depth, soil freezing and nutrient cycling, we established eight 10 x 10-m plots located within four stands; two dominated (80%) by sugar maple (SM1 and SM2) and two dominated by yellow birch(YB1 and YB2), with one snow reduction (shoveling) and one reference plot in each stand. 2) In 2001, we established eight new 10-m x 10-m plots (4 treatment, 4 reference) in four new sites; two high elevation, north facing and (East Kineo and West Kineo) two low elevation, south facing (Upper Valley and Lower Valley) maple-beech-birch stands. To establish plots, we cleared minor amounts of understory vegetation from all (both treatment and reference) plots (to facilitate shoveling). Treatments (keeping plots snow free by shoveling through the end of January) were applied in the winters of 1997/98, 1998/99, 2002/2003 and 2003/2004. The Climate Gradient Project was established in October 2010. Here we evaluated relationships between snow depth, soil freezing and nutrient cycling along an elevation/aspect gradient that created variation in climate with little variation in soils or vegetation. We established 6 20 x 20-m plots (intensive plots) and 14 10 x 10-m plots (extensive plots), with eight of the plots facing north and twelve facing south. The Ice Storm project was designed to evaluate the damage and changes ice storms cause to northern hardwood forests in forest structure, nutrient cycling and carbon storage. Ten 20x30 meter plots were established in a predominately sugar maple stand, with 4 icing treatments and 2 control plots. The treatments are as follows: Low (0.25"), Mid (0.5"), Midx2 (0.5") 2 Years in a row, High: (0.75"), Control. The icing treatment was conducted in the winter of 2015-2016, with a second year of icing on the Midx2 treatments plots in the winter of 20

openCC (other)Jun 2025View details →

Climate Change Across Seasons Experiment (CCASE) at the Hubbard Brook Experimental Forest: Tree Growth Data in support of "Declining Winter Snowpack Offsets Carbon Storage Enhancement from Growing Season Warming in Northern Temperate Forest Ecosystems", Conrad-Rooney et al. PNAS 2025

Data associated with the publication: Conrad-Rooney E, AB Reinmann, PH Templer. Declining Winter Snowpack Offsets Carbon Storage Enhancement from Growing Season Warming in Northern Temperate Forest Ecosystems. Proceedings of the National Academy of Sciences, 2025. This dataset includes cumulative stem biomass carbon data (from pre-treatment in 2012 until 2022) and annual stem biomass growth rates (not cumulative) for 2015-2022 for the red maple trees at the Climate Change Across Seasons Experiment. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.

openCC (other)Jun 2025View details →

Climate Change Across Seasons Experiment (CCASE) at the Hubbard Brook Experimental Forest: Soil Temperature, Soil Frost, and Snow Depth Data in support of "Declining Winter Snowpack Offsets Carbon Storage Enhancement from Growing Season Warming in Northern Temperate Forest Ecosystems", Conrad-Rooney et al. PNAS 2025

Data associated with the publication: Conrad-Rooney E, AB Reinmann, PH Templer. Declining Winter Snowpack Offsets Carbon Storage Enhancement from Growing Season Warming in Northern Temperate Forest Ecosystems. Proceedings of the National Academy of Sciences, 2025. This dataset includes soil temperature (winter 2021-2022) and snow depth and frost depth (winter 2022-2023) at the Climate Change Across Seasons Experiment. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.

openCC (other)May 2020View details →

Abundance and biovolume of taxonomically-resolved phytoplankton and microzooplankton imaged continuously underway with an Imaging FlowCytobot along the NES-LTER Transect in winter 2018

These data represent the abundance and biovolume of taxonomically-resolved phytoplankton and microzooplankton imaged continuously underway along the NES-LTER Transect during cruise EN608 in winter 2018. Images were obtained with an Imaging FlowCytobot (IFCB) sampling at approximately 20-min intervals from seawater supplied from 5 meters water depth. Data are provided for the subset of images during the cruise that were on the north-south transect along longitude 70 53’ W. Sizes for individuals were determined automatically by image processing, while identifications to morphological categories were done manually. Data are provided by taxon with names and machine-readable identifiers matched to the lowest taxonomic level to the World Register of Marine Species. Two data tables are provided: the level 1b for each occurrence and the level 2 that summarizes the occurrences by taxon per sample. Individual and sample identifiers are linked to images served by an external repository. This data package provides 144,281 machine-readable occurrences for incorporation into the Ocean Biogeographic Information System and the Global Ocean Observing System Essential Ocean Variables Phytoplankton biomass and diversity and the microplankton size class of Zooplankton biomass and diversity.

opencc-by-4.0Dec 2009View details →

Figure 4 in Winter species composition, diversity and abundance of macrozoobenthos in Kuwait's waters, Arabian Gulf

Figure 4. Percentage composition of the abundance of the main taxonomic groups in the sublittoral zone of the three sampled areas of Kuwait Bay, Bubiyan Island and Failaka Island.

opencc-by-4.0Dec 2009View details →

Figure 7 in Winter species composition, diversity and abundance of macrozoobenthos in Kuwait's waters, Arabian Gulf

Figure 7. The variation in a Pielou index of evenness (J`) and b Simpson index of dominance (λ`) for the three sampled areas of Failaka, Kuwait Bay and Bubiyan.

opencc-by-4.0Dec 2009View details →

Figure 3 in Winter species composition, diversity and abundance of macrozoobenthos in Kuwait's waters, Arabian Gulf

Figure 3. The abundance (ind/m2) of different taxonomic groups of macrozoobenthos in the sublittoral zone: a Average distribution and b Distribution at station 12 in Khor Al-Sabiyah.

opencc-by-4.0Apr 2014View details →

Figure 1 in Winter-active wolf spiders (Araneae: Lycosidae) in thermal habitats from western Romania

Figure 1. Map of the surveyed localities with thermal habitats in western Romania (1, Moneasa; 2, Ciocaia; 3, Roşiori; 4, Roşiori/Tămăşeu; 5, Săcuieni I; 6, Săcuieni II; 7, Curtici; 8, Socodor; 9, Chiribiş; 10, Chişlaz; 11, Livada de Bihor; 12, Mădăras; 13, Răbăgani; 14, Sânnicolau de Munte; 15, Tărian; 16, Acâş; 17, Beltiug; 18, Mihăieni; 19, Chiraleu; 20, Oradea; 21, Săcuieni III; 22, Tămăşeu; AR = Arad county, BH = Bihor county, SM = Satu Mare county).

dryad40/100

Genetic diversity and connectivity of southern right whales (Eubalaena australis) found in the Brazil and Chile–Peru wintering grounds and the South Georgia (Islas Georgias del Sur) feeding ground

As species recover from exploitation, continued assessments of connectivity and population structure are warranted to provide information for conservation and management. This is particularly true in species with high dispersal capacity, such as migratory whales, where patterns of connectivity could change rapidly. Here we build on a previous long-term, large-scale collaboration on southern right whales (Eubalaena australis) to combine new (nnew) and published (npub) mitochondrial (mtDNA) and microsatellite genetic data from all major wintering grounds and, uniquely, the South Georgia (Islas Georgias del Sur: SG) feeding grounds. Specifically, we include data from Argentina (npub mtDNA/microsatellite = 208/46), Brazil (nnew mtDNA/microsatellite = 50/50), South Africa (nnew mtDNA/microsatellite = 66/77, npub mtDNA/microsatellite = 350/47), Chile–Peru (nnew mtDNA/microsatellite = 1/1), the Indo-Pacific (npub mtDNA/microsatellite = 769/126), and SG (npub mtDNA/microsatellite = 8/0, nnew mtDNA/microsatellite = 3/11) to investigate the position of previously unstudied habitats in the migratory network: Brazil, SG, and Chile–Peru. These new genetic data show connectivity between Brazil and Argentina, exemplified by weak genetic differentiation and the movement of 1 genetically identified individual between the South American grounds. The single sample from Chile–Peru had an mtDNA haplotype previously only observed in the Indo-Pacific and had a nuclear genotype that appeared admixed between the Indo-Pacific and South Atlantic, based on genetic clustering and assignment algorithms. The SG samples were clearly South Atlantic and were more similar to the South American than the South African wintering grounds. This study highlights how international collaborations are critical to provide context for emerging or recovering regions, like the SG feeding ground, as well as those that remain critically endangered, such as Chile–Peru.

opencc-zeroSep 2020View details →

opencc-by-4.0Nov 2019View details →

Chromosome-scale assembly of winter oilseed rape Brassica napus

The files correspond to data and results referenced in research article titled "Chromosome-scale assembly of winter oilseed rape Brassica napus". Data files below were used in the scaffolding process of genome assembly: <ol> <li>Genetic maps (csv) <ul> <li>ExR53-DH_60kSNPmap</li> <li>ExV8-DH_60kSNPmap</li> </ul> </li> </ol> Result files below are assembled sequences of the genome and the predicted annotation: <ol> <li>Genome assembly (Express617_v1.fa.gz)</li> <li>Predicted coding sequences (Express617_v1_cds.fa.gz)</li> <li>Predicted coding sequences (Express617_v1_gene.gff3.gz)</li> <li>Predicted protein sequences (Express617_v1_protein.fa.gz)</li> <li>Predicted repetitive elements (Express617_v1_repeats.gff.gz)</li> </ol>

opencc-by-4.0Nov 2020View details →

Data archive for the peer-reviewed journal article "Variability in the mass absorption cross-section of black carbon (BC) aerosols is driven by BC internal mixing state at a central European background site (Melpitz, Germany) in winter""

Data archive for figures accompanying the peer-reviewed journal article "Variability in the mass absorption cross-section of black carbon (BC) aerosols is driven by BC internal mixing state at a central European background site (Melpitz, Germany) in winter". In 2020 this article was accepted for publication in the journal Atmospheric Chemistry and Physics. Data are uploaded in the form of Igor Pro experiment files (.pxp).

opencc-by-4.0Feb 2019View details →

AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL winter wheat simulations

This data set contains output data from simulations with the model LPJmL for winter wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season'). Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.