The depletion of dissolved oxygen in lakes (hypoxia) is an ongoing phenomenon that put under risk ecological systems and impact sedimentary environments. This phenomenon is driven by the increasing anthropogenic pressure on such environments. This dataset contains high-resolution sedimentological, geochemical and biological depth series of selected short sediment cores from lake Tiefer See (NE Germany). Those cores cover the recent transition from homogeneous to laminated sediments (~100 years ago), a transition that reflect the onset of hypoxic conditions in the lake. The cores were taken from different locations and water depths across the lake and allow to trace the spatiotemporal evolution of hypoxia spread in the lake.
The overarching goal of the Drilling Overdeepened Alpine Valleys (DOVE) project will be to date the age and extent of past glaciations. Formerly-glaciated areas are often characterized by deeply incised structures, often filled by Quaternary deposits. These buried troughs and valleys were formed by glacial overdeepening, likely caused by pressurized subglacial meltwater below warm-based glaciers. Results of this drilling campaign, supported by new dating technologies, will further provide critical data on 'how' and 'at which rate' glacial erosion affects such mountain ranges and their foreland. These processes are also of fundamental importance for evaluating the safety of radioactive waste disposal sites, which are planned in areas of former glaciations. Moreover, results of this project will fill gaps in the knowledge of paleoclimate and atmospheric circulation patterns during past glacial epochs and how these patterns affected ice build-up.
The operational data sets include the drill core documentation from the mobile Drilling Information System (mDIS), full round core scans, MSCL data sets, a preliminary core description and the geophysical downhole logging data that were acquired during and subsequent to the drilling operations. All downhole logs and core depth were subject to depth correction to a common depth master (cf. operational report for detailed information). The data are described by two scientific reports, the Operational Report (https://doi.org/10.48440/ICDP.5068.001) and the Explanatory Remarks on the Operational Datasets (https://doi.org/10.48440/ICDP.5068.002).
sandbox is an R-tool for probabilistic numerical modelling of sediment properties. A flexible framework for definition and application of time/depth- based rules for sets of parameters for single grains that can be used to create artificial sediment profiles. Such profiles can be used for virtual sample preparation and synthetic, for instance, luminescence measurements.
This dataset includes paleomagnetic and rock magnetic analyses from four sediment cores collected on continental slope of Storfjorden (EG-02, EG-03, SV-04) and Kveithola (GeoB17603-3) trough‐mouth fans and two cores collected at the crest of the Bellsund (GS191-01PC) and Isfjorden (GS191-02PC) sediment drifts (NW Barents Sea). The dataset gave the opportunity to reconstruct variation of past geomagnetic field at high latitude for the last 22 kya and define the path of the virtual geomagnetic pole (VGP).
Data are presented as two metadata table: one with definitions of the column heads and one with the core details; six tables with the data on the measured rock magnetic and paleomagnetic parameters and 3 tables with the results of data analyses and elaboration.
List of tables is as follows:
1) Metadata: definition of columns heads;
2) Metadata: core details;
3) GS191-01PC: down-core variation of rock magnetic and paleomagnetic parameters [k (10E-05 SI); ARM (A/m); MDF (mT); NRM (A/m); MAD (°); Incl PCA (°); Decl PCA (°)] for Core GS191-01PC;
4) GS191-02PC: down-core variation of rock magnetic and paleomagnetic parameters [k (10E-05 SI); ARM (A/m); MDF (mT); NRM (A/m); MAD (°); Incl PCA (°); Decl PCA (°)] for Core GS191-02PC;
5) EG03: down-core variation of rock magnetic and paleomagnetic parameters [k (10E-05 SI); ARM (A/m); MDF (mT); NRM (A/m); MAD (°); Incl PCA (°); Decl PCA (°)] for Core EG03;
6) EG02: down-core variation of rock magnetic and paleomagnetic parameters [k (10E-05 SI); ARM (A/m); MDF (mT); NRM (A/m); MAD (°); Incl PCA (°); Decl PCA (°)] for Core EG02;
7) SV04: down-core variation of rock magnetic and paleomagnetic parameters [k (10E-05 SI); ARM (A/m); MDF (mT); NRM (A/m); MAD (°); Incl PCA (°); Decl PCA (°)] for Core SV04;
8) GeoB17603-3: down-core variation of rock magnetic and paleomagnetic parameters [k (10E-05 SI); ARM (A/m); MDF (mT); NRM (A/m); MAD (°); Incl PCA (°); Decl PCA (°)] for Core GeoB17603-3;
9) Cores Correlation: GS191-01PC depth (cm) and ARM (A/m) down-core variations for core GS191-01PC (master core); GS191-02PC depth (cm), GS191-02PC depth transferred to GS191-01PC depth (cm), ARM (A/m) down-core for core GS191-02PC and correlation tie points; GeoB17603-3 depth (cm), GeoB17603-3 depth transferred to GS191-01PC depth (cm), ARM (A/m) down-core for core GeoB17603-3 and correlation tie points; EG02 depth (cm), EG02 depth transferred to GS191-01PC depth (cm), ARM (A/m) down-core for core EG02 and correlation tie points; EG03 depth (cm), EG03 depth transferred to GS191-01PC depth (cm), ARM (A/m) down-core and correlation tie points; SV04 depth (cm), SV04 transferred to GS191-01PC (cm), ARM (A/m) down-core for core SV04 and correlation tie points;
10) Age model: age model for Core GS191-01PC; GS191-02PC; EG02; EG03; SV04 and correlation tie points;
11) NBS stack: paleomagnetic inclination, declination and RPI variations for NBS22.2k stack.
In order to define high-resolution correlation between the cores the along-core variation of rock magnetic and paleomagnetic parameters (Sagnotti et al., 2011; Caricchi et al., 2018; Caricchi et al., 2019) have been integrated with the distribution of characteristic lithofacies (Lucchi et al., 2013), and the available age constraints (Sagnotti et al., 2011; Caricchi et al., 2018, Caricchi et al., 2019; Caricchi et al., 2020).
Core to core correlation has been reconstructed by means of the StratFit software (Sagnotti and Caricchi, 2018), which is based on the Excel forecast function and linear regression between subsequent couples of selected tie-points.
The data are presented as one Excel sheet with eleven tables and in tab-delimited ASCII format in the zip folder: 2022-028_Caricchi-et-al_data-txt.zip.