cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.1594/pangaea.987392
title: Pollen and non-pollen palynomorphs counts from Lake Fazilman, Uzbekistan

description: This data table contains the raw pollen and non-pollen palynomorphs counts of a 227 cm-long core core from Lake Fazilman (Fazilman kul, 40.550406 N, 66.589361 E; 1637 m a.s.l.), collected in July 2021 with a 50 cm Russian corer. Pollen was extracted from the sediment samples using standard procedure: (1) carbonate removal with 10% HCl, (2) silicate removal with 10% HF, (3) heavy liquid separation using ZnCl2 and HCl (density 1.1-1.3), (4) filtration after NaOH, and (5) acetolysis to eliminate cellulose and residual organic matter. Samples were stored in glycerin and counted using a BRESSER Science Infinity microscope at 40x and 100x magnification. Full details follow Dugerdil et al., (2025). Absolute concentrations can be calculated using the exotic Lycopodium spores added to each sample (batch 140119321). This information is given is the 4th column of the data table. The sample depth is given in centimeters and the age in year calibrated BP (age-depth model performed with BACON and the IntCal20 calibration curve based on 15 radiocarbon dating; Blaauw et al., 2011; Reimer et al., 2020).
DOI: 10.15156/bio/sh1336262.10fu
title: SH1336262.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.1594/pangaea.987391
title: Branched Glycerol Dialkyl Glycerol Tetraethers (brGDGTs) from Lake Fazilman, Uzbekistan

description: This data table contains the concentration of the major branched Glycerol Dialkyl Glycerol Tetraethers (brGDGTs) of a 227 cm-long core core from Lake Fazilman (Fazilman kul, 40.550406 N, 66.589361 E; 1637 m a.s.l.), collected in July 2021 with a 50 cm Russian corer. Mainly, each sample of one cm³ of sediment, after a 24 hours lyophilization and weighing, was crushed. The total lipid content (TLC) was twice extracted from the sediment with a DCM:MeOH (3:1) solvent extraction catalysed by microwave at 70 °C and under pressure. Then, to separate GDGTs from other lipids, the TLC was filtered on SPE cartridges and concentrated in vials. External C46 GDGTs were added as internal standard in order to estimate the Fazilman GDGTs absolute concentration (Huguet et al., 2006). GDGTs were injected in hexane:iso-propanol (99.8:0.2) solvent for a high performance liquid chromatography mass spectrometry (HPLC-APCI-MS, Agilent 1200) analysis in LGLTPE-ENS de Lyon laboratory. Then each compound was manually integrated using the m/z ratio and relative abundances in order to identify brGDGTs 5-, 6- (De Jonge et al., 2014) and 7-methyls (Ding et al., 2016). Following De Jonge et al. (2014), the Roman numerals represent different GDGT structures. The different 5-, 6- and 7- isomers are given. The data are in fractional abundances. The sample depth is given in centimeters and the age in year calibrated BP (age-depth model performed with BACON and the IntCal20 calibration curve based on 15 radiocarbon dating; Blaauw et al., 2011; Reimer et al., 2020). The data table also contains the climate reconstructions obtains from ensemble modelling based on several linear calibration and Boosted Regression Trees (BRT) machine learning models. For more details on the statistical method, please refer to Dugerdil et al. (in review). The climate parameters correspond to Mean Annual Air Temperature (MAAT, °C), Aridity Index (AI, no unit), Mean Average temperature of months above Freezing (MAF, °C) and the Mean Precipitation of the Colder Quarter (MPCOQ, mm.yr-1).
DOI: 10.1594/pangaea.987393
title: Pollen-based reconstructions from Lake Fazilman, Uzbekistan

description: This data table contains all the output from the modelling approaches based on pollen assemblages retrieved from the Lake Fazilman, Nuratau range, Uzbekistan: (1) climate reconstructions based on an ensemble averaging of quantitative climate modelling using Boosted Regression Trees (BRT; Salonen et al., 2012), Modern Analogue Technique (MAT; Guiot et al., 1990) and Weighted Averaging Partial Least Squares (WAPLS; Ter Braak et al., 1993) from calibration dataset presented in Dugerdil et al. (2025b); Community Weighted Mean (CWM) trait values following the method developped in Dugerdil et al. (2025a); and biomization-scores following the method from Prentice et al. (1996). Pollen types were assigned to Plant Functional Types (PFTs) using classical pollen to PFTs assignation matrices (Peyron et al., 1998; Tarasov et al., 1998), which were then aggregated into biome scores. The events corresponds to the samples retrieved from the Lake Fazilman composit sediment core. The sample depth is given in centimeters and the age in year calibrated BP (age-depth model performed with BACON and the IntCal20 calibration curve based on 15 radiocarbon dating; Blaauw et al., 2011; Reimer et al., 2020). The climate parameters correspond to Mean Annual Air Temperature (MAAT, °C), Aridity Index (AI, no unit), Mean Average temperature of months above Freezing (MAF, °C) and the Mean Precipitation of the Colder Quarter (MPCOQ, mm.yr-1). The reconstructed plant traits are the plant height (average adult plant height in metre), the leaf area (mm2), the N_Leaf (N content per unit leaf mass, mg.g-1), the seed mass (diaspore mass in mg), specific leaf area (SLA, the ratio of leaf area to oven-dry mass, in mm2.mg-1) and the stem specific density (SSD in mg.mm-3). The biomes predicted from the biomization approach are Cold Deciduous Forest (CLDE), Cold Mixed Forest (CLMX), Cold Conifer Forest (COCO), Cold Desert (CODE), Cool Mixed Forest (COMX), Cold Steppe (COST), Hot Desert (HODE), Taiga forest (TAIG), Temperate Deciduous Forest (TEDE), Tundra (TUND), Warm Mixed Forest (WAMX), Warm Steppe (WAST) and Xerophytic Shrubland (XERO).
DOI: 10.15156/bio/sh1336263.10fu
title: SH1336263.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.1594/pangaea.987390
title: Isoprenoid Glycerol Dialkyl Glycerol Tetraethers (isoGDGTs) from Lake Fazilman, Uzbekistan

description: This data table contains the concentration of the major isoprenoid Glycerol Dialkyl Glycerol Tetraethers (isoGDGTs) of a 227 cm-long core core from Lake Fazilman (Fazilman kul, 40.550406 N, 66.589361 E; 1637 m a.s.l.), collected in July 2021 with a 50 cm Russian corer. Mainly, each sample of one cm³ of sediment, after a 24 hours lyophilization and weighing, was crushed. The total lipid content (TLC) was twice extracted from the sediment with a DCM:MeOH (3:1) solvent extraction catalysed by microwave at 70 °C and under pressure. Then, to separate GDGTs from other lipids, the TLC was filtered on SPE cartridges and concentrated in vials. External C46 GDGTs were added as internal standard in order to estimate the Fazilman GDGTs absolute concentration (Huguet et al., 2006). GDGTs were injected in hexane:iso-propanol (99.8:0.2) solvent for a high performance liquid chromatography mass spectrometry (HPLC-APCI-MS, Agilent 1200) analysis in LGLTPE-ENS de Lyon laboratory. Then each compound was manually integrated using the m/z ratio and relative abundances in order to identify isoGDGTs (Liu et al., 2012). The external standard (C46) and the concentration of the different isoGDGTs is given in nanogrammes per one gramme of sediment. The sample depth is given in centimeters and the age in year calibrated BP (age-depth model performed with BACON and the IntCal20 calibration curve based on 15 radiocarbon dating; Blaauw et al., 2011; Reimer et al., 2020).
DOI: 10.15156/bio/sh1336267.10fu
title: SH1336267.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.1594/pangaea.987373
title: XRF analyses from Lake Fazilman, Uzbekistan

description: This data table contains the X-Ray Fluorescence results for a 227 cm-long core core from Lake Fazilman (Fazilman kul, 40.550406 N, 66.589361 E; 1637 m a.s.l.), collected in July 2021 with a 50 cm Russian corer. The XRF are expressed in counts per second (cps) and were obtained with a XRF Core Scanner 3 in the EDYTEM laboratory, Chambéry, France. A Rhodium X-ray source and Ultralene film were used, with measurements at 10 kV (light elements) and 30 kV (heavy elements). The samples are retrieved from the Lake Fazilman composit sediment core. The results for the Br, Ca, Fe, K, Mn, P, Pb, Rb, S, Sr, Ti, Zr, Zn, as well as the Incoherent (Inc) and Coherent (Coh) scatter signals are provided. The first (PC1) and second (PC2) components of the Principal Component Analysis performed on the XRF data are also provided. The sample depth is given in centimeters and the age in year calibrated BP (age-depth model performed with BACON and the IntCal20 calibration curve based on 15 radiocarbon dating; Blaauw et al., 2011; Reimer et al., 2020).
DOI: 10.1594/pangaea.987389
title: Inorganic geochemistry data from Lake Fazilman, Uzbekistan

description: This data table contains the inorganic geochemistry analyses results for a 227 cm-long core core from Lake Fazilman (Fazilman kul, 40.550406 N, 66.589361 E; 1637 m a.s.l.), collected in July 2021 with a 50 cm Russian corer. The data were otbained with a Geotek Multi Sensor Core Logger at the Chronoenvironnement laboratory in Besançon, France. Particularly, data contain the sediment density, the Magnetic Suseptibility (MS, 10.e-5 SI), the optical lightness (L*, %), the ratio 700/400 nm wavelenght and the Relative Absorption-Band Depth between 660 and 670 nm (RADB660-670) calculated following the method of Rein and Sirocko (2002). The sample depth is given in centimeters and the age in year calibrated BP (age-depth model performed with BACON and the IntCal20 calibration curve based on 15 radiocarbon dating; Blaauw et al., 2011; Reimer et al., 2020).
DOI: 10.15156/bio/sh1336273.10fu
title: SH1336273.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.1594/pangaea.977618
title: Coastal hydrographical time series at monitoring site TEPAST in the Southeastern Indian Ocean, Indonesia (2021-2024)

description: In 2021, the TEPAST monitoring site was established to monitor the coastal conditions on the southeastern coast of Java, Indonesia. Six key variables - pH, temperature, chlorophyll-a, salinity, dissolved oxygen, and water transparency - are measured monthly using advanced scientific instruments. Over 36 months of measurements, 1488 samples were collected. The measured parameters were validated through monthly calibrations and statistical checks, with the distribution of each parameter assessed using histograms of the sampling data. Sampling at the TEPAST study site is conducted monthly, typically between the 15th and 20th of each month, from 7:00 to 10:00 local time (UTC+7). Morning hours are chosen to take advantage of generally calm water conditions, minimizing the influence of wind and waves on the data collection process. The TEPAST COD dataset includes two types of observations: (1) in-situ measurements obtained from three fixed locations over 36 months (September 2021 to August 2024) with measurements continuing, and (2) laboratory analysis of water samples to determine salinity and chlorophyll-a concentrations. In-situ measurements are performed at three depth layers: surface (0 m), 10 m, and 20 m. All instruments adhere to standard measurement protocols and are regularly recalibrated before use. The same individual conducted all measurements throughout the study to ensure consistency and minimize uncertainties. Seawater samples (1.5 L) were collected from each station and specific depth layer using a 3 L Nansen bottle for Chlorophyll-a analysis. The samples were immediately placed in cold storage on the boat and transferred to a dark -20°C freezer at the laboratory within one hour of collection.
DOI: 10.15156/bio/sh1336276.10fu
title: SH1336276.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.1594/pangaea.977628
title: Transparency (secchi depth) time series at monitoring site TEPAST in the Southeastern Indian Ocean, Indonesia (2021-2024)

description: In 2021, the TEPAST monitoring site was established to monitor the coastal conditions on the southeastern coast of Java, Indonesia. Six key variables - pH, temperature, chlorophyll-a, salinity, dissolved oxygen, and water transparency - are measured monthly using advanced scientific instruments. Over 36 months of measurements, 1488 samples were collected. The measured parameters were validated through monthly calibrations and statistical checks, with the distribution of each parameter assessed using histograms of the sampling data. Sampling at the TEPAST study site is conducted monthly, typically between the 15th and 20th of each month, from 7:00 to 10:00 local time (UTC+7). Morning hours are chosen to take advantage of generally calm water conditions, minimizing the influence of wind and waves on the data collection process. The TEPAST COD dataset includes two types of observations: (1) in-situ measurements obtained from three fixed locations over 36 months (September 2021 to August 2024) with measurements continuing, and (2) laboratory analysis of water samples to determine salinity and chlorophyll-a concentrations. In-situ measurements are performed at three depth layers: surface (0 m), 10 m, and 20 m. All instruments adhere to standard measurement protocols and are regularly recalibrated before use. The same individual conducted all measurements throughout the study to ensure consistency and minimize uncertainties. Seawater samples (1.5 L) were collected from each station and specific depth layer using a 3 L Nansen bottle for Chlorophyll-a analysis. The samples were immediately placed in cold storage on the boat and transferred to a dark -20°C freezer at the laboratory within one hour of collection.
DOI: 10.15156/bio/sh1336279.10fu
title: SH1336279.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.1594/pangaea.982658
title: Multibeam bathymetry raw data (EM 1002 echosounder entire dataset) of RV MARIA S. MERIAN during cruise MSM20/3

description: Raw bathymetry data were collected using the ship's own Kongsberg EM1002 multibeam echosounder during the RV MARIA S. MERIAN cruise MSM20/3 (Response of Amazon sedimentation to deforestation, land use and climate variability). The cruise took place between 2012-02-19 and 2012-03-11 (Recife, Brazil – Bridgetown, Barbados). The primary goal of the cruise was to reconstruct the Holocene and Pleistocene climate history of the Amazon River basin and the adjacent ocean.Bathymetry data were collected in three working ares on the continental slope and shelf off northeastern Brazil (working areas A and B) and French Guiana (working area C). The deep-water system EM120 was used for mapping from the shelf break to deep waters in working areas A and C, while the shallow-water system EM1002 was run in areas B and C. The data are archived at the Federal Maritime and Hydrographic Agency of Germany (Bundesamt für Seeschifffahrt und Hydrographie, BSH) and provided to PANGAEA database for data curation and publication. No sound velocity profiles (SVP) files from the cruise are archived at the BSH, thus no SVP files are added to this dataset. Data are unprocessed and therefore contain incorrect depth measurements (artifacts). Data can be processed e.g. with the open source software package MB-System (Caress, D. W., and D. N. Chayes, MB-System: Mapping the Seafloor, http://www.mbari.org/products/research-software/mb-system/, 2022).
description: These data should not be used for navigational purposes.This data submission was prepared by Evgenia Bazhenova (MARUM).
DOI: 10.15156/bio/sh1336281.10fu
title: SH1336281.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.1594/pangaea.984670
title: Continuous thermosalinograph oceanography along RV ALKOR cruise track AL633

description: Underway temperature and salinity data was collected along the cruise track with a thermosalinograph (TSG) together with a SBE38 Thermometer. Both systems worked throughout the cruise. While temperature is taken at the water inlet in about 4 m depth, salinity is calculated within the interior TSG from conductivity and interior temperature. No temperature validation was performed. Salinity was validated with independent water samples taken at the water inlet. For details to all processing steps see Data Processing Report.
description: Salinity was validated independently with water samples from 4m depth (tim is the time in days since Expocode of this cruise (06AK20250524), i.e. since 2025-05-24T00:00:00): Value added to salinity data = 0.0001132*tim+0.00063.