tabular
0.00TRASYS Metamodels and M2M/M2T Transformation Specifications for Continuous Traceability Management
Morales Trujillo, Leticia · García García, Julián Alberto · Domínguez Mayo, Francisco José · et al.
hybrid · semantic + lexical · 1183 datasets ranked · 2.22s
Morales Trujillo, Leticia · García García, Julián Alberto · Domínguez Mayo, Francisco José · et al.
3 categorical
This package provides the TRASYS validation and verification metamodel diagrams together with concise specifications of the model-to-model and model-to-text transformations documented in the doctoral thesis Continuous Traceability Management in Assisted Reproduction Processes. The materials describe the derivation of a preliminary verification model from a validation model and the generation of traceability-rule and data-structure code from the verification model. No clinical data, personal data, production code, Enterprise Architect project files, credentials, or proprietary artifacts are included.
Melnik, Elena
13 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
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.
Altenhoff, Adrian
6 files · 100 MB · hdf5
OMAmer - tree-driven and alignment-free protein assignment to subfamilies OMAmer is an alignment-free protein family assignment method designed to avoid overly specific subfamily predictions and to scale efficiently to phylogenomic databases containing thousands of genomes. It relies on an innovative approach that uses evolutionarily informed k-mers for alignment-free mapping to ancestral protein subfamilies. This dataset provides precomputed OMAmer databases derived from the Hierarchical Orthologous Groups in the OMA Browser . We aim to update these databases with every new OMA Browser release. Each OMAmer database is built using the latest version of the OMAmer package available at the time of the corresponding OMA Browser release. The dataset includes databases for different subsets of the species taxonomy. In most cases, we recommend using the LUCA.h5 database, which contains information from all species in the OMA database. The subset-specific databases are mainly useful when disk space is limited. The release May2026 is based on the OMA Browser release May 2026 which comprises 2983 species. We used OMAmer version 2.1.0 to build these databases.
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.