Wolf, Gerhard · Kolbe, Georg

2 files · 14 MB · pdf, tiff

Historical questionnaire/s 1924/1948 and index cards, partly selected enclosures regarding the history of a German pharmacy, catalogued via Kalliope portal (Historischer Fragebogen 1924/1948 und Karteikarten, ggf. gemeinfreie Anlagen zur Apothekengeschichte; als Katalog dient das Nachlassportal Kalliope): https://kalliope-verbund.info/DE-611-BF-70963 [Funktion: Im Findbuch anzeigen] Please note: The Kalliope catalogue entry might indicate related material in the archival folder which cannot be published due to copyright or other legal restrictions (NB: Das Katalogisat bei Kalliope kann auch auf Materialien - teils erheblichen Umfangs - verweisen, die aus archiv- oder urheberrechtlichen Gründen nicht veröffentlicht werden dürfen).

Wang, Yafei

15 files · 129 KB · tiff

A gridded time-series dataset reconstructing population distribution across the Yellow River Basin, China, from 1000 to 2000 AD, with a temporal resolution of one century and a spatial resolution of 10 km.

Wang, Wei · Liu, Cheng · Han, Lianhuan · et al.

10 files · 211 KB · tiff

Cyclic voltammograms (CVs) obtained at the 35-nm radius gold nanoelectrode in 0.5 M H2SO4 solution deoxygenated under argon at different scan rates (from 0.3 to 1 V/s). The potential range is 0-1.75 V vs RHE. The data in the text files is given in the following format: Potential vs. RHE (V) | Current (A) Scan rate is indicated in the text file name. The figure Voltammograms.tif shows all CVs in this dataset.

Paduano, Angelo · Scarano, Francesco · Casalino, Damiano · et al.

200 rows × 10 cols · 39 KB · csv, jpeg

10 numeric

The interaction between acoustic waves and turbulent grazing flow over an acoustic liner is investigated using Lattice-Boltzmann Very-Large-Eddy simulations. A single-degree-of-freedom liner with 11 streamwise-aligned cavities is studied in a grazing flow impedance tube. The conditions replicate reference experiments from the Federal University of Santa Catarina. The influence of grazing flow (with a centerline Mach number of 0.32), acoustic wave amplitude, frequency, and propagation direction relative to the mean flow is analysed. Impedance is computed using both direct (i.e. the in-situ method) and model-fitting inference (i.e. the mode-matching method) methods. The former reveals strong spatial variations; however, averaged values throughout the sample show minimal differences between upstream and downstream propagating waves, in contrast to what is obtained with the latter method. Flow analyses reveal that the orifices displace the flow away from the face sheet, with this effect amplified by acoustic waves and dependent on the wave propagation direction. Consequently, the boundary layer displacement thickness ($\delta^*$) increases along the streamwise direction compared to a smooth wall and exhibits localised humps downstream of each orifice. The growth of $\delta^*$ alters the flow dynamics within the orifices by weakening the shear layer at downstream positions. This influences the acoustic-induced mass flow rate through the orifices at equal Sound Pressure Level, suggesting that acoustic energy is dissipated differently along the liner. The asymmetry of the flow field experienced by the acoustic wave, depending on its propagation direction, highlights the need to consider a spatially evolving turbulent flow when studying the acoustic-flow interaction and measuring impedance.

Habr, Jiří · Novák, Jan · Behalek, Lubos · et al.

30 rows × 11 cols · 1.5 MB · csv, xlsx

11 categorical

Dataset projektu TAČR SS07020411 "Efektivní využití plastového odpadu v automobilovém průmyslu".

Yuan, Guangyuan

72 rows × 13 cols · 6.0 KB · csv, fasta

11 numeric · 2 categorical

This dataset supports the findings of the manuscript "Root anatomical traits modulate the assembly and nitrogen transformation potential of root-associated microbiomes in a temperate steppe" (NPH-MS-2026-55667). It contains root traits data, bacterial 16S rRNA gene absolute abundances, functional genes relative abundances, DNA extraction metadata, and phylogenetic marker sequences for 37 plant species from a temperate steppe ecosystem. The dataset includes the following files: 1. root traits.csv - Root traits including average diameter (AD), specific root length (SRL), specific root area (SRA), root tissue density (RTD), root nitrogen content (RNC), root carbon content (RCC), carbon‑nitrogen ratio (RCN), cortex layer number (CLN), cortex thickness (CT), and the ratio of cortex thickness to root diameter (CTRD). The first column lists plant species names. 2. Absolute abundance of 16S rRNA gene.csv - Quantitative PCR (qPCR) derived absolute abundances of bacterial 16S rRNA gene copies (copies/ng DNA) across different root compartments (rhizosphere, rhizoplane, endosphere), host species, root orders, and cotyledon classes (monocot/dicot). 3. DNA extraction sample weight.csv - Fresh weight (grams) of root material used for DNA extraction for each sample, linked by SampleID to the abundance data. 4. DNA extraction concentration.csv - Qubit‑measured DNA concentrations (ng/μL) and the sample volume (μL) used for quality control, together with sample metadata. 5. 37species.fasta - DNA sequences of two chloroplast markers (matK and rbcL) for the 37 plant species included in the study. The sequences are in FASTA format with headers formatted as ">Species". These were used for host phylogeny construction and Pagel's λ analyses. 6. Quantitative PCR results of functional gene.csv - Quantitative PCR (qPCR) derived relative abundances of bacterial 16S rRNA gene and functional genes across different root compartments (rhizosphere, rhizoplane, endosphere), host species, root orders, and cotyledon classes (monocot/dicot). 7. README.md - A detailed description of each file, column headers, abbreviations, units, and any missing value codings (NA). All data are provided to ensure transparency and reproducibility of the analyses. For methodological details, please refer to the Materials and Methods section of the associated publication. These data are under embargo until the associated research article is published. After that date, they will be freely available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. During the embargo period, the metadata (title, authors, abstract) and the DOI remain publicly visible, but the data files are not accessible. For access requests before the embargo expires, please contact the corresponding author.

Budzinski, Lisa · Beenken, Anne Elisabeth · Sempert, Toni · et al.

9 rows × 1 cols · 743 B · csv, docx, zip

1 categorical

We have investigated an IgG4-RD (IgG4-RD) cohort by our multi-parameter microbiota flow cytometry approach to characterise the microbiota on single-cell level for attributes of the disease. The microbiota is isolated from stool samples and stained according to the published protocol for (a) host immunoglobulins IgA1, IgA2, IgM, IgG and (b) agglutinin binding to mannose, galactose or N-Acetyl-glucosamine surface sugar moieties. For all samples we also determined the microbiome composition by 16S rRNA (V3-V4) sequencing on the illumina MiSeq platform. We provide the raw .fcs and FASTQ files of 40 IgG4-RD patients. For comparison we additionally analysed 36 healthy donors. All .fcs files were generated on BD Influx®. The metadata is collected in the provided meta.csv. The staining parameters are summarized in provided panel.csv.

Gómez Maqueo Anaya, Sofía · Dos Santos Souza, Eduardo José · Fomba, Khanneh Wadinga · et al.

116 rows × 16 cols · 7.0 KB · csv

11 categorical · 5 numeric

The dataset consists of three files: In-situ-NorthAfrica-compilation.csv : This file contains a compilation of in situ datasets used to produce the initial scatterplots comparing modeled and measured mineral mass fractions. All values are given as mass fraction percentages (%). The first column lists the authors associated with each published measurement. Elemental_CM-DUSTRISK-Praia.csv : This file includes the updated elemental mass concentration measurements used for comparison against the DUSTRISK2022 dataset. Measured total mass is reported in μg/m³, while elemental concentrations are given in ng/m³. The COSMO-MUSCAT outputs are identified by the prefix "CM" and are reported in μg/m³. "SD" denotes standard deviation. All variables ending in "_frac" represent mass fraction percentages (%). measurements_and-CM_compilation-JATAC2022.csv : This file contains measured elemental and the updated mineral mass fractions used for the results presented in the subsections 5.1.2 "Comparison with JATAC 2022" and 5.2.1 "JATAC 2022" . Particle size classes correspond to the MUSCAT size bins (see Table 1 of the associated manuscript) and are provided in separate columns following the format "X##", where X denotes the mineral or element and ## corresponds to MUSCAT bins (i.e., 01, 03, 09, or 26). Modeled values are provided in columns starting with "CM". The prefixes "CI025" and "CI975" denote the lower and upper confidence intervals, respectively.

Zamora, Esteban · Perez de Lara, David · Guidi, Luca · et al.

164 rows × 70 cols · 94 KB · csv, xlsx

70 numeric

Dataset corresponding to the figures of the article: Esteban Zamora-Amo, et al. "Ultra-Conformal memristors for on-skin electronics".

Putri Lenggo Genni · Tavasya Alia Anjani · Muhammad Fasha Asshofa

3 files · 2.8 MB · csv, pdf, xlsx

Xiong, Mo · Xue, Ming

501 rows × 2 cols · 16 KB · csv

2 numeric

Drerup, Christian

8,000 rows × 9 cols · 511 KB · csv

5 numeric · 4 categorical

LI, Chuan

200 rows × 5 cols · 28 KB · csv

4 categorical · 1 text

1 files · 29 KB · netcdf

NOAA Coral Reef Watch (v3.1 CoralTemp) Daily Global 5km Satellite Sea Surface Temperature Anomaly. This is a product of NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite cdm_data_type = Grid VARIABLES (all of which use the dimensions [time][latitude][longitude]): sea_surface_temperature_anomaly (degree_C) mask (pixel characteristics flag array, pixel_classification)

1 files · 15 KB · netcdf

7-day average sea surface temperature (SST) from the AVHRR and VIIRS instruments aboard the NOAA and MetOp satellites, 2006-present, is generated by NOAA/NESDIS/STAR using the Advanced Clear-Sky Processor for Oceans (ACSPO) processing system. Only nighttime overpasses are used and composited into daily mean grids (~830 m), then the daily composite grids are averaged into gridded 7-day temperature averages. More info: https://eastcoast.coastwatch.noaa.gov/cw_avhrr-viirs_sst.php cdm_data_type = Grid VARIABLES (all of which use the dimensions [time][level][latitude][longitude]): sst (sea_surface_subskin_temperature, degree_C)

1 files · 16 KB · netcdf

Climatological 7-day average sea surface temperature (SST) from 2007 to 2025, i.e. there are 52 7-day periods each representing a 2007-2025 mean SST for that period. The 18-year period for averaging begins May 21, 2007 and ends May 20, 2025. Data are merged from the AVHRR and VIIRS instruments aboard the NOAA and MetOp satellites. SST is generated by NOAA/NESDIS/STAR using the Advanced Clear-Sky Processor for Oceans (ACSPO) processing system. Only nighttime overpasses are used and composited into daily mean grids (~830 m), then the daily composite grids are averaged into gridded 7-day temperature averages. Finally, the 18-year climatological average for each 7-day period is averaged from the 7-day files. More info: https://eastcoast.coastwatch.noaa.gov/cw_avhrr-viirs_sst.php cdm_data_type = Grid VARIABLES (all of which use the dimensions [sevenDayPeriodOfYear][level][latitude][longitude]): sst (sea_surface_subskin_temperature, degree_C)

1 files · 16 KB · netcdf

Jet Propulsion Laboratory data from a local source. cdm_data_type = Grid VARIABLES (all of which use the dimensions [time][level][latitude][longitude]): sst (Analysed SST with land and ice masks applied, degree_C)

1 files · 15 KB · netcdf

Jet Propulsion Laboratory SST MUR data from a local source. cdm_data_type = Grid VARIABLES (all of which use the dimensions [time][level][latitude][longitude]): sst (Analysed SST with land and ice masks applied, degree_C)

1 files · 15 KB · netcdf

Jet Propulsion Laboratory data from a local source. cdm_data_type = Grid VARIABLES (all of which use the dimensions [time][level][latitude][longitude]): sst (Analysed SST with land and ice masks applied, degree_C)