Helmholtz Institute Freiberg for Resource Technology
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PrecisionCarriers.jl
No full textA julia package to find imprecisions in chains of arithmetic functions
pyMarAI: nnU-Net-based Tumor Spheroids Auto Delineation
No full textCollection of neural network models for automatic image segmentation of microscopic tumor spheroids. Intended to be used with nnU-Net deep-learning framework. Trained and tested on a total of microscopic images of mouse pheochromocytoma (MPC) tumor cells.
In addition to the trained network model, a PyQt5-based graphical user interface tool is provided. This tool provides a complete pipeline for handling microscopic spheroid image data, running deep-learning–based delineation, and curating results for continuous model improvement.
For installation and usage instructions, please visit https://github.com/hzdr-MedImaging/pyMarAI
Please cite nnU-Net and the respective paper when using pyMarAI.
List of available model types:
pyMarAI-1.0.0-ecat.zip: nnUNetv2 ready network (for ECAT7)
pyMarAI-1.0.0-nifti.zip: nnUNetv2 ready network (for NIFTI
Multiphase Python Repository by HZDR
No full textThe python package provides several routines and scripts required to operate the code and cases repositories containing additional code and setups for the open-source software released by the OpenFOAM Foundation. This includes, among other things, utilities for pre- and post-processing of simulation cases, utilities to launch virtual environments containing the source code, and utilities to operate the continuous integration and development environment in a self-hosted Gitlab instance
Data publication: Large contact angle hysteresis enhances post-impact droplet oscillations
No full textExperimental raw data: Large contact angle hysteresis enhances post-impact droplet oscillation
Multiphase Python Repository by HZDR
No full textThe python package provides several routines and scripts required to operate the code and cases repositories containing additional code and setups for the open-source software released by the OpenFOAM Foundation. This includes, among other things, utilities for pre- and post-processing of simulation cases, utilities to launch virtual environments containing the source code, and utilities to operate the continuous integration and development environment in a self-hosted Gitlab instance
Data publication: Elemental data from transpired fluid from Norway spruce needles of the Horizon 2020 project NEXT
No full text1. Overview
This repository contains the **final, cleaned chemistry data** that were produced from the 2019 Raja field campaign of the Horizon 2020 Project NEXT.
The data combine:
Norway spruce needle transpired fluids concentrations for a set of selected elements.
Corresponding soil‑till depth, point location, lithology and basic tree information.
Element‑specific relative‑standard‑deviation (RSD) uncertainties that were calculated from laboratory‑derived uncertainty parameters.
All files are provided in **CSV** format (UTF‑8, `,` separator) and/or the R programming language binary format RData.
2. Column description
**Field Descriptions for Repository Metadata**
#### **Point Identification & Metadata**
1. **PointID** – Unique identifier for each sampling point in the dataset (e.g., `NEXT-2019-193`).
2. **till_depth** – Depth of the till layer (in meters) at the sampling location.
3. **ID_num** – Numeric identifier for the point (e.g., `193`).
4. **grainsize_2mm_pct** – Percentage of grain size >2mm in the soil sample (proxy for rock fragments or coarse material).
5. **OM_thickness** – Thickness of organic matter (cm) in the soil profile.
6. **Soilwetness_by_photo** – Soil wetness category inferred from field photos (e.g., `mesic` = moist, `sub-xeric` = moderately dry).
7. **Soilwetness_cat** – Numeric ranking of soil wetness (likely 1–7, with `1` = driest, `7` = wettest).
8. **notes_by_photos** – Qualitative observations from site photos (e.g., "paludified areas nearby," "boulders present").
#### **Geophysical & Electrical Properties**
9. **conductivity_ph-acid** – Electrical conductivity (EC) measured after acidification (mS/m).
10. **conductivity_ph-initial** – Initial EC of the soil sample (mS/m).
11. **conductivity_pit** – EC measured in the field pit (mS/m).
12. **conductivity** – Conductivity averaged or processed for analysis (units vary).
13. **dielectric permittivity_pit** – Dielectric constant measured in the pit (proxy for soil moisture).
14. **dielectric permittivity** – Processed or averaged dielectric permittivity value.
15. **pH_initial** – Initial pH of the soil sample.
16. **pH_with acid** – pH after acidification (indicates buffer capacity).
17. **pore water conductivity** – EC of extracted pore water (mS/m).
#### **Geospatial & Environmental Context**
18. **VTEM, TMI, APR** – Geophysical survey metrics (VTEM = Vertical Electromagnetic, TMI = Total Magnetic Intensity, APR = Airborne Radiometrics).
19. **Soilwetness** – Categorical soil wetness classification (e.g., `mesic`, `sub-xeric`).
20. **Soiltype** – Predominant soil type (e.g., `mineral soil`, `peat`).
21. **Naturetype** – Ecological classification (e.g., `Boreaaliset luonnonmetsät` = "Boreal natural forests").
22. **TMI_class** – Categorization of Total Magnetic Intensity (e.g., `low`, `middle`, `high`).
23. **Lithology** – Original rock type classification (e.g., `Calcsilicate rocks`, `Mafic rocks`).
24. **Lithology_updated** – Updated lithological classification (refining earlier interpretations).
25. **Lithology_updated_Sol** – Lithology-specific to soil horizons (e.g., `Mafic rocks + quartzite`).
26. **Mineralization** – Qualitative assessment of mineralized zones (e.g., `barren`, `potential`, `min`).
27. **Deposit** – Type of geological deposit (e.g., `Till`, `hardpan layer`).
28. **TMI_cat, VTEM_cat, APPRES_cat** – Categorized geophysical survey results (e.g., `high` intensity).
29. **vegetation_class_EFTAS** – Vegetation classification (e.g., `mesic heath forest`).
30. **i.ID_num** – Redundant numeric ID (same as `ID_num`).
31. **x, y** – Cartesian coordinates for the sampling point.
32. **Fotos** – Reference to associated field photos (e.g., `104-0905`).
#### **Site & Sampling Details**
33. **GeneralSiteDescription** – Textual description of terrain (e.g., "flat, paludified area starts 1m below").
34. **ForestType** – Dominant forest type (e.g., `VMT` = "Välimetsä" [forest type code]).
35. **Soil_paludification** – Degree of peatland/wetland influence on soil (e.g., `not paludified`).
36. **Soil_nutrient_status** – Nutrient availability assessment (e.g., `normal`).
37. **Observations2019** – Anomalies or notes from 2019 fieldwork (e.g., "eggs of gnomes in upper podsol layer").
38. **SedimentType** – Type of sediment observed (e.g., `Till`, `Sand`).
39. **SedimentGenesis** – Origin of sediment (e.g., `glacial`, `alluvial`).
40. **Pointtype** – Classification of sampling point (e.g., `replicate`).
41. **TMI_intensity, VTEM_intensity, APPRES_intensity** – Quantitative geophysical survey intensities.
42. **GPS-Easting, GPS-Northing** – Precise GPS coordinates for the point.
43. **SampleID** – Unique identifier for lab samples (e.g., `TF-NEXT-2019-193-NrS-tf-0A`).
44. **Date_full** – Timestamp of sample collection/analysis.
#### **Geochemical Data**
45. **Al, B, Ba, Bi, ... Zn** – Concentrations of elements (likely in **ppm/mg/g**) measured via lab analysis (e.g., ICP-MS).
*Note: These columns represent a full suite of geochemical elements (e.g., Aluminum, Barium, Lead, Zinc) critical for mineral exploration, environmental studies, or soil health assessments.*
---
### **Key Notes for Repository Users**
- **Units**: Conductivity (mS/m), pH (unitless), dielectric permittivity (dimensionless), GPS coordinates (meters, local system).
- **Categorical Fields**: Wetness, lithology, and mineralization use standardized codes (e.g., `mesic` vs. `xeric`).
- **Geophysical Data**: VTEM/TMI/APR are airborne survey metrics linked to subsurface mineralization.
- **Geochemical Data**: Elemental concentrations are essential for understanding soil fertility, contamination, or ore potential.
---
### **Suggested Use Cases**
- **Mineral Exploration**: Cross-reference `Mineralization` + `TMI_cat` with elemental data (e.g., high `Fe`, `Cr` for mafic rocks).
- **Soil Science**: Analyze `Soilwetness` vs. `dielectric permittivity` to model moisture regimes.
- **Ecological Studies**: Correlate `vegetation_class_EFTAS` with `Soil_nutrient_status`
Data publication: Catalytic Activity of Cobalt Ferrites in Water Oxidation Reactions and Its Defect Dependency
No full textCoFe2O4 (CFO) nanoparticles were synthesized via controlled co-precipitation with subsequent calcination at 400 °C, 500 °C, and 600 °C to systematically investigate the influence of thermal treatment on mesostructure, catalytic performance, and especially defect landscape. Structural characterization revealed enhanced crystallinity, sintering, and reduced defect concentration with increasing calcination temperature. Mössbauer spectroscopy and magnetometry indicated increased inversion parameters, improved magnetic alignment, and reduced spin canting, which is consistent with enhanced atomic diffusion during calcination and structural ordering. Positron annihilation lifetime spectroscopy confirmed a calcination-dependent decrease in vacancy-type defects. Catalytic testing showed diverging trends: chemical water oxidation (CAN test) activity increased with calcination temperature, but electrochemical oxygen evolution (OER) activity decreased. The opposing behavior is attributed to distinct differences in mechanism: CAN test reactivity is dominated by surface site availability, whereas OER benefits from defect mediated conductivity and charge-transfer. These results underline the pivotal role of defect engineering while tailing spinel catalysts and highlight that optimal mesostructures depend strongly on the target reactio
Multiphase Python Repository by HZDR
No full textThe python package provides several routines and scripts required to operate the code and cases repositories containing additional code and setups for the open-source software released by the OpenFOAM Foundation. This includes, among other things, utilities for pre- and post-processing of simulation cases, utilities to launch virtual environments containing the source code, and utilities to operate the continuous integration and development environment in a self-hosted Gitlab instance
Data publication: Van-der-Waals exchange-correlation functionals and their high pressure and warm dense matter applications
No full textQMC and DFT-MD data for all the figures and additional (unused) DFT-MD data. All input and output files
Research Data: Recovery of rare earth elements by peptide-induced Ln3+ precipitation
No full textElectronic waste and wastewater from mining, industry, etc. are valuable secondary sources of strategic high-tech metals like rare earth elements (REEs). Due to low concentrations of REEs, their recovery is challenging. Current separation processes have high energy consumption and use large amounts of toxic or expensive reagents, resulting in contaminated water and its costly reprocessing. Biomolecules, as environmentally friendly alternatives, are able to overcome these economic and ecological issues. Metal-binding peptides are convincing not only because of their high selectivity and stability under various conditions. In case of biobased production, they are also “renewable” resources and are neither toxic nor difficult to degrade at the process end. Here, we successfully utilized phage surface display (PSD) to screen for peptides with high affinity for REEs. The selected peptide GC22 (CEPDLWIDRFWC), identified by PSD in combination with next-generation sequencing, revealed the ability to precipitate lanthanide and yttrium ions from aqueous solutions in large quantities (> 60 %). It largely favors all REE ions over other commonly occurring metal ions in wastewater. The amorphous REE-GC22-precipitate is characterized by curled and spherical structures. Nuclear magnetic resonance spectroscopy revealed that in dimethyl sulfoxide Arg9 and Cys12 are most likely involved in metal binding. Reversibility of binding and thus regeneration of the peptide was demonstrated, enabling its potential use for multiple extraction cycles. GC22 thus offers a sustainable, cost-effective, and environmentally friendly alternative for future REE-recovery from low-REE-concentration wastewaters and e-waste leachates