cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5285/cddac04f-ea6e-439f-9e54-36db0d8c843f
title: Medium resolution vector polylines of the Antarctic coastline - VERSION 7.8

description: Coastline for Antarctica created from various mapping and remote sensing sources, consisting of the following coast types: ice coastline, rock coastline, grounding line, ice shelf and front, ice rumple, and rock against ice shelf. Covering all land and ice shelves south of 60degS. Suitable for topographic mapping and analysis. This dataset has been generalised from the high resolution vector polyline. Medium resolution versions of ADD data are suitable for scales smaller than 1:1,000,000, although certain regions will appear more detailed than others due to variable data availability and coastline characteristics. Changes in v7.8 include updates to the Brunt, Wilkins and Stange Ice Shelves, Pine Island and Thwaites Glaciers and James Ross Island ice fronts. Data compiled, managed and distributed by the Mapping and Geographic Information Centre and the UK Polar Data Centre, British Antarctic Survey on behalf of the Scientific Committee on Antarctic Research.
description: Dataset compiled from a variety of Antarctic map and satellite image sources. The dataset was created using ArcGIS and QGIS GIS software programmes and has been checked for basic topography and geometry checks, but does not contain strict topology. Quality varies across the dataset and certain areas where high resolution source data were available are suitable for large scale maps whereas other areas are only suitable for smaller scales. Each line has attributes detailing the source which can give the user further indications of its suitability for specific uses. Attributes also give information including 'surface' (e.g. grounding line, ice coastline, ice shelf front) and revision date. Compiled from sources ranging in time from 1990s-2023 - individual lines contain exact source dates. This medium resolution version has been generalised from the high resolution version. All areas inferior to 0.1 km2; not intersecting anything else were deleted and the 'simplify' tool was used in ArcGIS with the 'point remove' algorithm and a smoothing tolerance of 50 m.
DOI: 10.5285/c7fe759d-e042-479a-9ecf-274255b4f0a1
title: High resolution vector polygons of the Antarctic coastline - VERSION 7.8

description: Coastline for Antarctica created from various mapping and remote sensing sources, provided as polygons with 'land', 'ice shelf', 'ice tongue' or 'rumple' attribute. Covering all land and ice shelves south of 60degS. Suitable for topographic mapping and analysis. High resolution versions of ADD data are suitable for scales larger than 1:1,000,000. The largest suitable scale is changeable and dependent on the region. Changes in v7.8 include updates to the Brunt, Wilkins and Stange Ice Shelves, Pine Island and Thwaites Glaciers and James Ross Island ice fronts. Data compiled, managed and distributed by the Mapping and Geographic Information Centre and the UK Polar Data Centre, British Antarctic Survey on behalf of the Scientific Committee on Antarctic Research.
description: Dataset compiled from a variety of Antarctic map and satellite image sources. The dataset was created using ArcGIS and QGIS GIS software programmes and has been checked for basic topography and geometry checks, but does not contain strict topology. Quality varies across the dataset and certain areas where high resolution source data were available are suitable for large scale maps whereas other areas are only suitable for smaller scales. Each polygon contains a 'surface' attribute with either 'land', 'ice shelf', 'ice tongue' or 'rumple'. Details of when and how each line was created can be found in the attributes of the high resolution polyline coastline dataset. Data sources range in time from 1990s-2023 - individual lines contain exact source dates.
DOI: 10.5285/3ea85903-e068-4242-a63f-46767e16f85c
title: Medium resolution vector polygons of the Antarctic coastline - VERSION 7.8

description: Coastline for Antarctica created from various mapping and remote sensing sources, provided as polygons with 'land', 'ice shelf', 'ice tongue' or 'rumple' attribute. Covering all land and ice shelves south of 60degS. Suitable for topographic mapping and analysis. This dataset has been generalised from the high resolution vector polygons. Medium resolution versions of ADD data are suitable for scales smaller than 1:1,000,000, although certain regions will appear more detailed than others due to variable data availability and coastline characteristics. Changes in v7.8 include updates to the Brunt, Wilkins and Stange Ice Shelves, Pine Island and Thwaites Glaciers and James Ross Island ice fronts. Data compiled, managed and distributed by the Mapping and Geographic Information Centre and the UK Polar Data Centre, British Antarctic Survey on behalf of the Scientific Committee on Antarctic Research.
description: Dataset compiled from a variety of Antarctic map and satellite image sources. The dataset was created using ArcGIS and QGIS GIS software programmes and has been checked for basic topography and geometry checks, but does not contain strict topology. Quality varies across the dataset and certain areas where high resolution source data were available are suitable for large scale maps whereas other areas are only suitable for smaller scales. Each polygon contains a 'surface' attribute with either 'land', 'ice shelf', 'ice tongue' or 'rumple'. Details of when and how each line was created can be found in the attributes of the high or medium resolution polyline coastline dataset. Data sources range in time from 1990s-2023 - individual lines contain exact source dates. This medium resolution version has been generalised from the high resolution version. All polygons inferior to 0.1 km2; not intersecting anything else were deleted and the 'simplify' tool was used in ArcGIS with the 'point remove' algorithm and a smoothing tolerance of 50 m.
DOI: 10.5285/15ad60c0-9e88-4d9f-988f-de5ab3b733ee
title: Medium resolution vector polygon seamask for areas south of 60S - VERSION 7.8

description: A medium resolution seamask for regions south of 60S, comprised of a roundel polygon with all land and ice shelves erased. Medium resolution versions of ADD data are suitable for scales smaller than 1:1,000,000, although certain regions will appear more detailed than others due to variable data availability and coastline characteristics. Data compiled, managed and distributed by the Mapping and Geographic Information Centre and the UK Polar Data Centre, British Antarctic Survey on behalf of the Scientific Committee on Antarctic Research.
description: This dataset was generated by erasing the medium resolution polygon coastline v7.7 from a circular polygon covering the data extent up to 60S.
DOI: 10.34740/kaggle/dsv/6936794
title: Indian Cities Distance Dataset
DOI: 10.21942/uva.24541708.v1
title: LLM for SCA dataset

description: The dataset contains ChipWhisperer measurements used for analysis with LLMs
DOI: 10.15480/336.2658
title: Benchmark parts for the evaluation of optimized support structures in laser powder bed fusion of metals

description: Laser powder bed fusion (PBF-LB/M) of metals belongs to the advanced additive manufacturing processes on the brink of industrialization. Successful manufacturing often requires the utilization of support structures to support overhangs, dissipate heat, and prevent distortion due to residual stresses. Since the support structures result in increased costs, research, as well as industry, aim at optimizing the application of those or the support structures themselves. New approaches are validated with individual use cases, though, preventing an objective comparison of optimization strategies. This paper contributes to the advance of support structure optimization by providing a benchmark strategy including part geometries, which enables to evaluate technical as well as economical aspects of support structures or support strategies. The benchmark process is demonstrated with the help of the currently most used block and pin support structures.
DOI: 10.15480/336.5207.2
title: Supplementary material (Version 2) to publication with title: Development of a tree-support software module for PBF-LB/M

description: The goal of this work was to develop a tool that allows users with little experience to manufacture components with efficient support structures using PBF-LB/M. Tree shaped structures were used and created via algorithmic, biological growth using botanical methods. TO was used to optimize the support structures. The generated trees were then compared to two commercially available supports using FEA to simulate the whole PBF-LB/M process including a stress relief heat treatment and the removal of the part from build plate and supports. The data is supplementary material to the publication with the title " Development of a tree-support software module for PBF-LB/M " of the conference Laser in Manufacturing (LiM) 2023, 26 - 29 July 2023, Munich/Germany.
DOI: 10.5281/zenodo.10100824
title: deleted

description: The executable jar file of PIPI2 and a simple test data set to check the availability.
DOI: 10.5281/zenodo.10100850
title: null

description: The executable jar file of PIPI2 and a simple test data set to check the availability.
DOI: 10.5281/zenodo.10099991
title: Flame retarded polypropylene formulations - UL94 results
DOI: 10.5281/zenodo.10101045
title: Flame retarded polypropylene formulations - UL94 results
DOI: 10.5281/zenodo.10100635
title: null

description: The executable jar file of PIPI2 and a simple test data set to check the availability.
DOI: 10.5281/zenodo.10100634
title: null

description: The executable jar file of PIPI2 and a simple test data set to check the availability.
DOI: 10.5281/zenodo.10088524
title: A Two Level Neural Approach Combining Off-Chip Prediction with Adaptive Prefetch Filtering
title: Traces - Volume 3

description: To alleviate the performance and energy overheads of contemporary applications with large data footprints, we propose the Two Level Perceptron (TLP) predictor, a neural mechanism that effectively combines predicting whether an access will be off-chip with adaptive prefetch filtering at the first-level data cache (L1D). TLP is composed of two connected microarchitectural perception predictors, named First Level Predictor (FLP) and Second Level Predictor (SLP). FLP performs accurate off-chip prediction by using several program features based on virtual addresses and a novel selective delay component. The novelty of SLP relies on leveraging off-chip prediction to drive L1D prefetch filtering by using physical addresses and the FLP prediction as features. TLP constitutes the first hardware proposal targeting both off-chip prediction and prefetch filtering using a multi-level perception hardware approach. TLP only requires 7KB of storage. To demonstrate the benefits of TLP we compare its performance with state-of-the-art approaches using off-chip prediction and prefetch filtering on a wide range of single-core and multi-core workloads. Our experiments show that TLP reduces the average DRAM transactions by 30.7% and 17.7%, as compared to a baseline using state-of-the-art cache prefetchers but no off-chip prediction mechanism, across the single-core and multi-core workloads, respectively, while recent work significantly increases DRAM transactions. As a result, TLP achieves geometric mean performance speedups of 6.2% and 11.8% across single-core and multi-core workloads, respectively. In addition, our evaluation demonstrates that TLP is effective independently of the L1D prefetching logic.
DOI: 10.5281/zenodo.10088525
title: A Two Level Neural Approach Combining Off-Chip Prediction with Adaptive Prefetch Filtering
title: Traces - Volume 3

description: To alleviate the performance and energy overheads of contemporary applications with large data footprints, we propose the Two Level Perceptron (TLP) predictor, a neural mechanism that effectively combines predicting whether an access will be off-chip with adaptive prefetch filtering at the first-level data cache (L1D). TLP is composed of two connected microarchitectural perception predictors, named First Level Predictor (FLP) and Second Level Predictor (SLP). FLP performs accurate off-chip prediction by using several program features based on virtual addresses and a novel selective delay component. The novelty of SLP relies on leveraging off-chip prediction to drive L1D prefetch filtering by using physical addresses and the FLP prediction as features. TLP constitutes the first hardware proposal targeting both off-chip prediction and prefetch filtering using a multi-level perception hardware approach. TLP only requires 7KB of storage. To demonstrate the benefits of TLP we compare its performance with state-of-the-art approaches using off-chip prediction and prefetch filtering on a wide range of single-core and multi-core workloads. Our experiments show that TLP reduces the average DRAM transactions by 30.7% and 17.7%, as compared to a baseline using state-of-the-art cache prefetchers but no off-chip prediction mechanism, across the single-core and multi-core workloads, respectively, while recent work significantly increases DRAM transactions. As a result, TLP achieves geometric mean performance speedups of 6.2% and 11.8% across single-core and multi-core workloads, respectively. In addition, our evaluation demonstrates that TLP is effective independently of the L1D prefetching logic.
DOI: 10.5281/zenodo.10089529
title: Projekt938_scRNAseq_TSd5_d7_d14_NextSeq_demultiplex_143_AAC2JFMHV_1
DOI: 10.5281/zenodo.10089528
title: Projekt938_scRNAseq_TSd5_d7_d14_NextSeq_demultiplex_143_AAC2JFMHV_1
DOI: 10.5281/zenodo.10083541
title: A Two Level Neural Approach Combining Off-Chip Prediction with Adaptive Prefetch Filtering
title: Traces - Volume 1

description: To alleviate the performance and energy overheads of contemporary applications with large data footprints, we propose the Two Level Perceptron (TLP) predictor, a neural mechanism that effectively combines predicting whether an access will be off-chip with adaptive prefetch filtering at the first-level data cache (L1D). TLP is composed of two connected microarchitectural perception predictors, named First Level Predictor (FLP) and Second Level Predictor (SLP). FLP performs accurate off-chip prediction by using several program features based on virtual addresses and a novel selective delay component. The novelty of SLP relies on leveraging off-chip prediction to drive L1D prefetch filtering by using physical addresses and the FLP prediction as features. TLP constitutes the first hardware proposal targeting both off-chip prediction and prefetch filtering using a multi-level perception hardware approach. TLP only requires 7KB of storage. To demonstrate the benefits of TLP we compare its performance with state-of-the-art approaches using off-chip prediction and prefetch filtering on a wide range of single-core and multi-core workloads. Our experiments show that TLP reduces the average DRAM transactions by 30.7% and 17.7%, as compared to a baseline using state-of-the-art cache prefetchers but no off-chip prediction mechanism, across the single-core and multi-core workloads, respectively, while recent work significantly increases DRAM transactions. As a result, TLP achieves geometric mean performance speedups of 6.2% and 11.8% across single-core and multi-core workloads, respectively. In addition, our evaluation demonstrates that TLP is effective independently of the L1D prefetching logic.
DOI: 10.5281/zenodo.10083542
title: A Two Level Neural Approach Combining Off-Chip Prediction with Adaptive Prefetch Filtering
title: Traces - Volume 1

description: To alleviate the performance and energy overheads of contemporary applications with large data footprints, we propose the Two Level Perceptron (TLP) predictor, a neural mechanism that effectively combines predicting whether an access will be off-chip with adaptive prefetch filtering at the first-level data cache (L1D). TLP is composed of two connected microarchitectural perception predictors, named First Level Predictor (FLP) and Second Level Predictor (SLP). FLP performs accurate off-chip prediction by using several program features based on virtual addresses and a novel selective delay component. The novelty of SLP relies on leveraging off-chip prediction to drive L1D prefetch filtering by using physical addresses and the FLP prediction as features. TLP constitutes the first hardware proposal targeting both off-chip prediction and prefetch filtering using a multi-level perception hardware approach. TLP only requires 7KB of storage. To demonstrate the benefits of TLP we compare its performance with state-of-the-art approaches using off-chip prediction and prefetch filtering on a wide range of single-core and multi-core workloads. Our experiments show that TLP reduces the average DRAM transactions by 30.7% and 17.7%, as compared to a baseline using state-of-the-art cache prefetchers but no off-chip prediction mechanism, across the single-core and multi-core workloads, respectively, while recent work significantly increases DRAM transactions. As a result, TLP achieves geometric mean performance speedups of 6.2% and 11.8% across single-core and multi-core workloads, respectively. In addition, our evaluation demonstrates that TLP is effective independently of the L1D prefetching logic.
DOI: 10.5281/zenodo.10090487
title: Projekt938_scRNAseq_TSd5_d7_d14_NextSeq_demultiplex_143_AAC2JFMHV_2
DOI: 10.5281/zenodo.10090488
title: Projekt938_scRNAseq_TSd5_d7_d14_NextSeq_demultiplex_143_AAC2JFMHV_2
DOI: 10.5281/zenodo.7936344
title: Backup L. Vornholz CVRL

description: Backu Lilly Vornholz CVRL 2023
DOI: 10.5281/zenodo.7936345
title: Backup L. Vornholz CVRL

description: Backu Lilly Vornholz CVRL 2023
DOI: 10.5281/zenodo.10101058
title: Properties of recovered polyols by glycolysis of polyurethane waste foams
DOI: 10.5281/zenodo.10101059
title: Properties of recovered polyols by glycolysis of polyurethane waste foams
DOI: 10.5281/zenodo.10101177
title: ISOMORPHIC AND ALLOMORPHIC FEATURES OF COMPLEX SENTENCES WITH ADVERBIAL CLAUSES OF PLACE IN ENGLISH AND UZBEK LANGUAGES.
DOI: 10.5281/zenodo.10101178
title: ISOMORPHIC AND ALLOMORPHIC FEATURES OF COMPLEX SENTENCES WITH ADVERBIAL CLAUSES OF PLACE IN ENGLISH AND UZBEK LANGUAGES.
DOI: 10.5281/zenodo.10101238
title: PHRASEOLOGISMS DENOTING PHYSICAL AND MENTAL STATE
DOI: 10.5281/zenodo.10101239
title: PHRASEOLOGISMS DENOTING PHYSICAL AND MENTAL STATE
DOI: 10.5281/zenodo.6541836
title: Reflection Ultrasound Computed Tomography (RUCT) Data

description: Data for Reflection Ultrasound Computed Tomography (RUCT) Delay and Sum Algorithm


Data is shared for "pyruct" package tests and as supporting files of the research article indicated below.


"pyruct" package can be found in "https://github.com/berkanlafci/pyruct"


If you use this data in your research, please cite the following paper:


B. Lafci, J. Robin, X. L. Deán-Ben and D. Razansky, "Expediting Image Acquisition in Reflection Ultrasound Computed Tomography," in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, doi: 10.1109/TUFFC.2022.3172713.


"nct" means number of consecutive transducer elements used in transmission event. Please use the files with "nct_1" tags for the full acquisition and reconstruction.
description: This project is supported by Swiss Data Science Center (SDSC) grant C19-04.
DOI: 10.5281/zenodo.6541837
title: Reflection Ultrasound Computed Tomography (RUCT) Data

description: Data for Reflection Ultrasound Computed Tomography (RUCT) Delay and Sum Algorithm


Data is shared for "pyruct" package tests and as supporting files of the research article indicated below.


"pyruct" package can be found in "https://github.com/berkanlafci/pyruct"


If you use this data in your research, please cite the following paper:


B. Lafci, J. Robin, X. L. Deán-Ben and D. Razansky, "Expediting Image Acquisition in Reflection Ultrasound Computed Tomography," in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, doi: 10.1109/TUFFC.2022.3172713.


"nct" means number of consecutive transducer elements used in transmission event. Please use the files with "nct_1" tags for the full acquisition and reconstruction.
description: This project is supported by Swiss Data Science Center (SDSC) grant C19-04.
DOI: 10.5281/zenodo.7713299
title: Orthophoto and DSM Rutor Glacier

description: Orthophoto and Digital Surface Model (DSM) obtained from the photogrammetric flight over Rutor Glacier in September 2021. Ground Sample Distance (GSD) = 0.5 m
description: The presented dataset is the result of the multidisciplinary work of the cc-glacier-lab of the Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino. The Italian Ministry for Education and Research (MIUR), Dipartimento di Eccellenza, funded this research.
DOI: 10.5281/zenodo.10100968
title: Orthophoto and DSM Rutor Glacier 2021

description: Orthophoto and Digital Surface Model (DSM) obtained from the photogrammetric flight over Rutor Glacier in September 2021. Ground Sample Distance (GSD) = 0.5 m (resampled form the 0.2 m original GSD)
description: The presented dataset is the result of the multidisciplinary work of the cc-glacier-lab of the Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino.

The Italian Ministry for Education and Research (MIUR), Dipartimento di Eccellenza, funded this research.
DOI: 10.5281/zenodo.7713298
title: Orthophoto and DSM Rutor Glacier 2021

description: Orthophoto and Digital Surface Model (DSM) obtained from the photogrammetric flight over Rutor Glacier in September 2021. Ground Sample Distance (GSD) = 0.5 m (resampled form the 0.2 m original GSD)
description: The presented dataset is the result of the multidisciplinary work of the cc-glacier-lab of the Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino.

The Italian Ministry for Education and Research (MIUR), Dipartimento di Eccellenza, funded this research.
DOI: 10.57745/qvp30o
title: 3.LBV-acetone-398K, 0.990bar.tab

description: Speed Flame vs Equivalence ratio - 398K - 0990 bar
DOI: 10.5281/zenodo.8089498
title: Orthophoto and DSM Rutor Glacier 2020

description: Orthophoto and Digital Surface Model (DSM) obtained from the photogrammetric flight over Rutor Glacier in September 2020. Ground Sample Distance (GSD) = 0.5 m (resampled form the 0.2 m original GSD)
description: The presented dataset is the result of the multidisciplinary work of the cc-glacier-lab of the Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino.

The Italian Ministry for Education and Research (MIUR), Dipartimento di Eccellenza, funded this research.
DOI: 10.5281/zenodo.8089499
title: Orthophoto and DSM Rutor Glacier 2020

description: Orthophoto and Digital Surface Model (DSM) obtained from the photogrammetric flight over Rutor Glacier in September 2020. Ground Sample Distance (GSD) = 0.5 m (resampled form the 0.2 m original GSD)
description: The presented dataset is the result of the multidisciplinary work of the cc-glacier-lab of the Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino.

The Italian Ministry for Education and Research (MIUR), Dipartimento di Eccellenza, funded this research.
DOI: 10.57745/enjy6h
title: 1.LBV-acetone-306K, 0.978 bar.tab

description: Speed Flame vs Equivalence ratio - 306K - 0978 bar
DOI: 10.57745/meblp1
title: 2.LBV-acetone-358K, 0.992 bar.tab

description: Speed Flame vs Equivalence ratio - 358K - 0992 bar
DOI: 10.57745/bjfosz
title: ReadMe.txt

description: ReadMe - gives some information