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.

Bar, Ido

9 files · 8.0 MB · gff, gzip

These files are downsampled WGS fastq files (250k paired-end reads each) of a fungal pathogen ( Ascochyta rabiei ), generated on MGI DNSeq-T7. The files are intended to be used directly as test datasets in PopFun - a Nextflow pipeline for variant calling in fungal genomes.

Kovaliov, Michael

1 files · 100 MB · parquet

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.

Madhusudan, Gujral

6 files · 29 MB · parquetdeclared

Large language models (LLMs) are trained on massive, publicly available text datasets comprising trillions of tokens, enabling them to excel at general language tasks like next-token prediction. However, LLMs often struggle with domain-specific prompts, exhibiting reduced accuracy or generating inaccurate information (hallucinations). This is because they lack sufficient subject matter expertise. Two primary approaches exist to address this limitation for augmenting LLMs knowledge: Retrieval-Augmented Generation (RAG) and fine-tuning. This presentation focuses on fine-tuning smaller LLMs with domain-specific instruct datasets using the LoRA (Low-Rank Adaptation) technique on Gaudi hardware. We will leverage publicly available LLMs and datasets from the Hugging Face Hub for this demonstration. Though it is possible to fine tune LLMs with plain text data - sourced from documents, articles, and other materials.

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.