Melnik, Elena

7 files · 4.5 MB · tiff

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Melnik, Elena

4 files · 4.5 MB · tiff

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Melnik, Elena

25 files · 4.5 MB · tiff

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Melnik, Elena

40 files · 4.5 MB · tiff

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Melnik, Elena

19 files · 4.5 MB · tiff

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Melnik, Elena

25 files · 4.5 MB · tiff

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Melnik, Elena

25 files · 4.5 MB · tiff

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Melnik, Elena

25 files · 4.5 MB · tiff

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Melnik, Elena

37 files · 4.5 MB · tiff

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Gennady, Gusev

6 files · 453 KB · xls

Table 1. DC current and magnetotransport of the small Corbino device. The table contains dc current ( I dc ) data measured in a small Corbino device under a constant bias voltage of V dc = 25 mV. The first column gives the perpendicular magnetic field ( B ) in Tesla. The remaining columns provide the measured current ( I dc ) in Amperes. Each current column corresponds to a different temperature ( T ), with the temperature value given in the column header in Kelvin. The sample resistance can be calculated using R = V dc / I dc . Table 2. DC current and magnetotransport of the medium Corbino device. The table contains dc current ( I dc ) data measured in a medium Corbino device under a constant bias voltage of V dc = 15 mV. The first column gives the perpendicular magnetic field ( B ) in Tesla. The remaining columns provide the measured current ( I dc ) in Amperes. Each current column corresponds to a different temperature ( T ), with the temperature value given in the column header in Kelvin. The corresponding sample resistance can be extracted using R = V dc / I dc . Table 3. DC current and magnetotransport of the large Corbino device. The table contains dc current ( I dc ) data measured in a large Corbino device under a constant bias voltage of V dc = 15 mV. The first column gives the perpendicular magnetic field ( B ) in Tesla. The remaining columns provide the measured current ( I dc ) in Amperes. Each current column corresponds to a different temperature ( T ), with the temperature value given in the column header in Kelvin. The corresponding sample resistance can be extracted using R = V dc / I dc . Table 4. DC current and magnetotransport of the Corbino device with R b = 11 μ m . The table contains dc current ( I dc ) data measured in a Corbino device with an outer radius R b = 11 μ m under a constant bias voltage of V dc = 20 mV. The first column gives the perpendicular magnetic field ( B ) in Tesla. The remaining columns provide the measured current ( I dc ) in Amperes. Each current column corresponds to a different temperature ( T ), with the temperature value given in the column header in Kelvin. The corresponding sample resistance can be extracted using R = V dc / I dc . Table 5. DC current and magnetotransport of the Corbino device with R b = 12 μ m . The table contains dc current ( I dc ) data measured in a Corbino device with an outer radius R b = 12 μ m under a constant bias voltage of V dc = 20 mV. The first column gives the perpendicular magnetic field ( B ) in Tesla. The remaining columns provide the measured current ( I dc ) in Amperes. Each current column corresponds to a different temperature ( T ), with the temperature value given in the column header in Kelvin. The corresponding sample resistance can be extracted using R = V dc / I dc . Table 6. DC current and magnetotransport of the Corbino device with R b = 18 μ m . The table contains dc current ( I dc ) data measured in a Corbino device with an outer radius R b = 18 μ m under a constant bias voltage of V dc = 15 mV. The first column gives the perpendicular magnetic field ( B ) in Tesla. The remaining columns provide the measured current ( I dc ) in Amperes. Each current column corresponds to a different temperature ( T ), with the temperature value given in the column header in Kelvin. The corresponding sample resistance can be extracted using R = V dc / I dc .

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.

Melnik, Elena

37 files · 444 MB · tiffdeclared

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Melnik, Elena

49 files · 591 MB · tiffdeclared

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Mauro, Francisco · González-Mesquida, José Bernardo

12 files · 492 MB · tiffdeclared

Thirthy meter resolution maps of canopy fuel attributes for pure maritime pine polygons of the Spanish Forest Map in the Autonomous comunities of Aragon, Castilla y León, La Rioja, Madrid, Castilla la Mancha and Extremadura, Spain, in EPSG3035 and EPSG25830. Mapped attributes are canopy base height (CBH, m), canopy bulk density (kg/m3), canopy fuel load (CFL, Mg/ha) and canopy height (CH, m) Maps were obtained using random forest models relating canopy fuel attributes with airborne laser scanning data, landsat time series imagery, topographic and climate metrics. Models were trained using data from the fourth National Forest Inventory cycle in Spain. Coefficients of determination were, 88.44% for CH, 81.28% for CFL, 76.45% for CBD and 58.23% for CBH. The process to create this dataset is described in "Estimating crown fuels in Pinus pinaster Aiton forests of interior Spain using remote sensing and national forest inventory data". Accepted version. For fire spread simulation use the ETRS89 UTM30N (EPSG25830) versions.

Hackl, Jürgen

6 files · 132 MB · parquet, tiffdeclared

EuroFlood is an open, cloud-native index over the JRC/Copernicus CEMS-EFAS Satellite-Derived Flood Depth Maps for Europe (Betterle & Salamon, 2025; CC-BY-4.0) - ~3,280 satellite-derived observed flood-depth maps across Europe, 2015-2024. The bundle is a sparse Cloud-Optimized GeoTIFF encoding, per pixel, the set of flood events that inundated it, plus a combo_id -sorted GeoParquet dictionary and a small events table. Query by region and time via HTTP range reads (GDAL /vsicurl + DuckDB) to retrieve matching events, then fetch only the source depth rasters needed. Built with the open-source EuroFlood Python package ( pip install euroflood ).

Vera, Lourdes

31 files · 4.6 MB · csv, geojson, pngdeclared

Data, analysis scripts, and derived outputs reproducing the setback analysis between occupied buildings and active oil and gas wells in Karnes County, Texas, using public data (Texas Railroad Commission well locations; FEMA/ORNL USA Structures building footprints; and U.S. Census TIGER/Line block groups and 2020-2024 American Community Survey). The pipeline runs offline in Python (geopandas) and reproduces every reported distance, summary statistic, table, and figure in the associated article. This package reproduces and updates Chapter 3 of the author's doctoral dissertation: Vera, Lourdes (2022), "Environmental Data Justice in Action: Civically Valid Air Monitoring Near Oil and Gas Extraction in the Eagle Ford Shale Play," Ph.D. dissertation, Northeastern University, Boston, MA.

Tiskus, Edvinas · Tiškuvienė, Rūta · Bučas, Martynas · et al.

37 files · 1.6 GB · hdf5, tiff, torchdeclared

This record contains the labeled data, trained models, and analysis code supporting the article "Comparing a Vision Foundation Model (DINOv3) and a Task-Specific U-Net for Mapping Emergent Aquatic Vegetation from Fused UAV Multispectral and LiDAR Data" (Remote Sensing in Ecology and Conservation). Contents: - masks/ : georeferenced ground-truth segmentation masks (five classes: aquatic vegetation, water, sand, other objects, background), aligned to the fused UAV orthomosaics and spanning 13 sites across nine Lithuanian waterbodies surveyed between May and August 2024. - models/ : the two final trained segmentation models, a Keras/HDF5 U-Net and a PyTorch DINOv3 model. - code/ : Python scripts for training, evaluation, the label-efficiency experiment, and full-scene prediction. The fused 9-band orthomosaics (five-band multispectral, RGB, and a LiDAR canopy height model; approximately 62 GB) are archived separately because of their size and are available from the corresponding author on request. The DINOv3 SAT-493M pretrained backbone is distributed by Meta under its own license and is not redistributed here; obtain it from the official DINOv3 release.